Silkroad Online is a massively multiplayer online role-playing game that takes place in a historical fantasy world inspired by the ancient Silk Road trade route. Players can choose from three races: Chinese, European, and Arabian, and explore a vast open world full of quests, dungeons, and PvP battles.
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-Agbot is a useful tool for players who want to level up faster, earn more gold, or enjoy other aspects of Silkroad Online without spending too much time on grinding. However, players should also be aware of the risks involved in using a bot, such as getting banned by the game developers or losing their account information to hackers. Therefore, players should always use Agbot at their own discretion and responsibility.
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7b8c122e87Bhaag Johnny is a 2015 Bollywood thriller movie starring Kunal Khemu, Zoa Morani and Mandana Karimi. The movie revolves around Johnny, a casanova who is blackmailed by his boss to kill a girl or lose his job. However, he gets a chance to live two lives: one where he commits the crime and another where he refuses and goes on the run. The movie explores the consequences of his choices and how they affect his love life and destiny.
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7b8c122e87If you are a fan of soccer games, you may have played or heard of FIFA 08, a football simulation video game developed by EA Canada and published by Electronic Arts under the EA Sports label. FIFA 08 was released on all popular gaming formats in September 2007 in Europe, Australia and Asia, and in October 2007 in North America. The PlayStation 3 and Xbox 360 versions of the game feature an improved game engine with superior graphics and different commentators and are dubbed "next-generation" by EA. In this article, we will focus on the Xbox 360 version of FIFA 08 and review its features, gameplay, and pros and cons.
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899543212bHow to Download Cars Fast as Lightning on Android
-Do you love the Cars movie franchise and want to experience the thrill of racing with your favorite characters? If so, you might want to check out Cars Fast as Lightning, a fun and exciting racing game that lets you play as 20 different characters from the movies, customize your cars and tracks, build your own Radiator Springs, and enjoy animated cutscenes and voice acting. In this article, we will show you how to download and install this game on your android device, as well as some tips and tricks for playing it.
- What is Cars Fast as Lightning?
-Cars Fast as Lightning is a racing game based on the Cars movie franchise, developed by Gameloft. The game features a variety of characters from the movies, such as Lightning McQueen, Mater, Francesco Bernoulli, Sally, Doc Hudson, and more. You can choose your favorite character and race against other characters in different locations, such as Radiator Springs, Tokyo, Porto Corsa, London, Paris, and more. You can also customize your cars and tracks with different paint jobs, stickers, accessories, jumps, loops, and props.
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Features of the game
-The game has many features that make it fun and engaging for players of all ages. Some of these features are:
-Play as 20 different characters
-You can play as 20 different characters from the Cars movies, each with their own personality, voice, and style. You can also unlock new characters by winning races and cups. Some of the characters you can play as are:
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-Lightning McQueen
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- Customize your cars and tracks
-You can customize your cars and tracks with different paint jobs, stickers, accessories, jumps, loops, and props. You can make your cars look cool and unique with different colors, patterns, decals, spoilers, wheels, exhausts, lights, horns, and more. You can also make your tracks more fun and challenging with different ramps, bridges, tunnels, cacti, dinosaurs, rockets, fireworks, and more.
- Build your own Radiator Springs You can build your own Radiator Springs by placing different buildings, landmarks, and decorations. You can create your own version of the town from the movies, or make your own original design. You can also visit other players' towns and see how they have built their own Radiator Springs.
- Enjoy animated cutscenes and voice acting
-The game has animated cutscenes and voice acting that make it feel like you are watching a Cars movie. You can see your favorite characters interact with each other, crack jokes, and show their emotions. You can also hear their original voices from the movies, such as Owen Wilson as Lightning McQueen, Larry the Cable Guy as Mater, John Turturro as Francesco Bernoulli, and more.
- How to download and install the game on your android device
-If you are interested in playing Cars Fast as Lightning on your android device, you will need to download and install the game from the Google Play Store. Here are the requirements and steps for doing so.
- Requirements for the game
-Before you download and install the game, you will need to make sure that your device meets the following requirements:
-Android version 4.0 or higher
-The game requires Android version 4.0 or higher to run smoothly. You can check your device's Android version by going to Settings > About phone > Software information.
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-At least 1 GB of free storage space
-The game takes up about 1 GB of storage space on your device. You will need to have at least that much free space available to download and install the game. You can check your device's storage space by going to Settings > Storage.
-A stable internet connection
-The game requires a stable internet connection to download and play. You will need to have a Wi-Fi or mobile data connection that is fast and reliable. You can check your device's internet connection by going to Settings > Network & internet.
- Steps to download and install the game
-Once you have made sure that your device meets the requirements, you can follow these steps to download and install the game:
-Go to the Google Play Store app on your device
-The Google Play Store app is where you can find and download apps and games for your android device. You can access it by tapping on its icon on your home screen or app drawer.
-Search for Cars Fast as Lightning or use this link
-You can search for Cars Fast as Lightning by typing its name in the search bar at the top of the app. Alternatively, you can use this link to go directly to the game's page on the Google Play Store.
-Tap on the Install button and wait for the download to finish Tap on the Install button and wait for the download to finish
-Once you have found the game on the Google Play Store, you can tap on the Install button to start the download process. You will see a progress bar that shows how much of the game has been downloaded. You will need to wait for the download to finish before you can install and play the game.
-Tap on the Open button and enjoy the game
-After the download is complete, you will see an Open button that lets you launch the game. You can tap on it to start the game and enjoy racing with your favorite Cars characters. You can also find the game's icon on your home screen or app drawer and tap on it to open the game anytime.
- Tips and tricks for playing the game
-Now that you have downloaded and installed Cars Fast as Lightning on your android device, you might want to know some tips and tricks for playing the game. Here are some of them:
- How to win races and earn coins
-Races are the main mode of gameplay in Cars Fast as Lightning. You can race against other characters in different locations and try to beat them to the finish line. You can also earn coins by winning races, which you can use to customize your cars and tracks. Here are some tips for winning races and earning coins:
-Tap on the screen to accelerate and release to drift
-The game has a simple control scheme that lets you control your car's speed and direction. You can tap on the screen to accelerate and release to drift. Drifting helps you turn corners faster and avoid obstacles. You can also use drifting to perform stunts and tricks, which we will discuss later.
-Collect lightning bolts and use them to boost your speed Collect lightning bolts and use them to boost your speed
-As you race, you will see lightning bolts on the track. These are power-ups that can help you boost your speed and gain an advantage over your opponents. You can collect them by driving over them or by performing stunts and tricks. You can use them by tapping on the lightning icon on the bottom right corner of the screen. You can also save them for later by tapping on the pause icon next to the lightning icon.
-Perform stunts and tricks to fill up your turbo meter
-Another way to boost your speed is by filling up your turbo meter. You can do this by performing stunts and tricks on the track, such as jumping, flipping, spinning, and drifting. You will see a blue bar on the top left corner of the screen that shows how much turbo you have. When it is full, you can tap on it to activate turbo mode, which makes your car go faster and glow with sparks. You can also use turbo mode to smash through obstacles and opponents.
-Upgrade your cars and tracks to improve your performance
-You can use the coins you earn from winning races to upgrade your cars and tracks. You can improve your car's speed, acceleration, handling, and nitro by buying new parts and accessories. You can also improve your track's difficulty, length, and fun factor by buying new props and decorations. Upgrading your cars and tracks can help you win more races and earn more coins.
- How to unlock new characters and locations
-The game has a lot of characters and locations to unlock and explore. You can unlock new characters by winning races and cups, and new locations by completing missions and challenges. Here are some tips for unlocking new characters and locations:
-Complete missions and challenges to earn stars Complete missions and challenges to earn stars
-Missions and challenges are tasks that you can complete to earn stars. Stars are used to unlock new cups and tournaments, which in turn unlock new characters and tracks. You can see your missions and challenges by tapping on the map icon on the bottom left corner of the screen. You can also see how many stars you have and how many you need to unlock the next cup or tournament by tapping on the trophy icon on the top right corner of the screen.
-Use stars to unlock new cups and tournaments
-Cups and tournaments are series of races that you can compete in to win prizes and unlock new characters and tracks. You can access them by tapping on the cup icon on the bottom right corner of the screen. You will see different cups and tournaments with different themes, such as Radiator Springs Cup, Tokyo Cup, World Grand Prix, and more. You will need a certain number of stars to unlock each cup or tournament. You will also need to have a specific character to enter each cup or tournament.
-Win races and cups to unlock new characters and tracks
-Winning races and cups is the main way to unlock new characters and tracks. You will see a lock icon on the characters and tracks that you have not unlocked yet. You will need to win a specific race or cup to unlock them. For example, you will need to win the Radiator Springs Cup to unlock Mater, or the Tokyo Cup to unlock Shu Todoroki. You will also see a star icon on the characters and tracks that you have unlocked but not played yet. You can tap on them to play as them or race on them.
-Visit other players' towns and race against them
-You can also unlock new characters and tracks by visiting other players' towns and racing against them. You can access this feature by tapping on the social icon on the top left corner of the screen. You will see a list of your friends who play the game, as well as random players from around the world. You can tap on their names to visit their towns and see how they have built their own Radiator Springs. You can also tap on the race icon next to their names to challenge them to a race. You can win coins, stars, and sometimes new characters and tracks by racing against other players.
- Conclusion and FAQs
-Cars Fast as Lightning is a fun and exciting racing game that lets you play as 20 different characters from the Cars movie franchise, customize your cars and tracks, build your own Radiator Springs, and enjoy animated cutscenes and voice acting. You can download and install this game on your android device by following the steps we have shown you in this article. You can also improve your skills and experience by following the tips and tricks we have shared with you. We hope you enjoy playing this game as much as we do.
-If you have any questions or doubts about this game, you might find the answers in these FAQs:
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-Q: How do I save my progress in the game? A: The game automatically saves your progress every time you finish a race or make a change in your town. You can also manually save your progress by tapping on the settings icon on the top right corner of the screen and then tapping on the save icon. Q: How do I restore my progress if I lose it or change my device? A: You can restore your progress by connecting your game to your Facebook account. You can do this by tapping on the settings icon on the top right corner of the screen and then tapping on the connect icon. This will allow you to sync your progress across different devices and recover it if you lose it. Q: How do I get more coins without spending real money? A: You can get more coins by winning races, completing missions and challenges, visiting other players' towns, and watching ads. You can also get free coins by tapping on the gift icon on the top right corner of the screen and claiming your daily reward.
-Q: How do I change the language of the game? A: You can change the language of the game by tapping on the settings icon on the top right corner of the screen and then tapping on the language icon. You will see a list of available languages that you can choose from, such as English, Spanish, French, German, Italian, Portuguese, Russian, Turkish, Arabic, Chinese, Japanese, Korean, and more. Q: How do I contact the support team if I have a problem with the game? A: You can contact the support team by tapping on the settings icon on the top right corner of the screen and then tapping on the help icon. You will see a list of frequently asked questions and answers that might solve your problem. If you still need help, you can tap on the contact us icon and fill out a form with your name, email, device model, game version, and description of your problem. You can also attach a screenshot or a video of your problem if you have one. The support team will get back to you as soon as possible.
197e85843dHow to Download Euro Truck Simulator 2
-Have you ever dreamed of becoming a truck driver and traveling across Europe? If so, you might want to check out Euro Truck Simulator 2, a popular simulation game that lets you do just that. Euro Truck Simulator 2 is a game that gives you the chance to become a real truck driver from the comfort of your home. Featuring licensed trucks with countless customization options and advanced driving physics, the game delivers an unparalleled driving experience which has put it in the spot of the most popular truck driving simulator on the market. In this article, we will show you how to download Euro Truck Simulator 2 and enjoy its amazing features.
- Why You Should Play Euro Truck Simulator 2
-Euro Truck Simulator 2 is not only about driving - it's also about exploring, managing, and customizing. Here are some of the reasons why you should play this game:
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-You can transport a vast variety of cargo across more than 60 European cities, from London to Lisbon, from Berlin to Bucharest.
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-Create a Steam account if you don't have one already.
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-To install Euro Truck Simulator 2 from the official website, you need to follow these steps:
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-Go to the official website of Euro Truck Simulator 2.
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-Launch the game and enjoy!
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- Other , or just have fun with them. You can also join online forums and communities where you can discuss the game, share your experiences, ask for help, give feedback, and more. You can find multiplayer servers and online forums on websites such as TruckersMP, ETS2MP, SCS Software Forum, and more. Connecting with other players is a great way to make new friends and enjoy the game together.
- Conclusion
- Euro Truck Simulator 2 is a game that offers you a unique and immersive experience of driving a truck across Europe. You can explore different countries and cultures, run your own business, customize your trucks, and connect with other players. You can download the game from various platforms and sources, such as Steam, the official website, or other sources. However, you should be careful when choosing your source, as some of them may be unsafe or illegal. You can also use mods and community content to enhance your game in many ways. You can find them on websites such as Steam Workshop, ETS2 Mods, ETS2 World, and more. However, you should also backup your game files before using them, as they may not be compatible with your game version or other mods. Euro Truck Simulator 2 is a game that will keep you entertained for hours and hours. If you are looking for a realistic and fun simulation game, you should definitely give it a try.
- FAQs
-Here are some of the frequently asked questions about Euro Truck Simulator 2:
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-Q: How much does Euro Truck Simulator 2 cost?
-Q: What are the system requirements for Euro Truck Simulator 2?
-OS: Windows 7 or higher
-Processor: Dual core CPU 2.4 GHz
-Memory: 4 GB RAM
-Graphics: GeForce GTS 450-class (Intel HD 4000)
-Storage: 5 GB available space
-
-The recommended system requirements for Euro Truck Simulator 2 are:
-OS: Windows 7 or higher
-Processor: Quad core CPU 3.0 GHz
-Memory: 6 GB RAM
-Graphics: GeForce GTX 760-class (2 GB)
-Storage: 5 GB available space
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-Q: How many trucks are there in Euro Truck Simulator 2?
-Q: How many countries are there in Euro Truck Simulator 2?
-Q: How do I update Euro Truck Simulator 2?
- 401be4b1e0Download Nebulous IO Mod APK Unlimited Plasma - A Fun and Addictive Mobile Game
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- How to download and install Nebulous IO Mod APK Unlimited Plasma
- If you want to download and install Nebulous IO Mod APK Unlimited Plasma on your Android device, you need to follow these steps:
-
-Click on this link to download the mod Allow your device to install apps from unknown sources by going to Settings > Security > Unknown Sources and enable it
-Locate the downloaded file in your file manager and tap on it to install it
-Launch the game and enjoy unlimited plasma and other features
-
- Conclusion
- Nebulous IO is a fun and addictive mobile game that you can play online or offline with millions of players around the world. You can customize your blob with hundreds of skins and items, and compete in various game modes and maps. If you want to have more fun and advantages, you should download Nebulous IO Mod APK Unlimited Plasma, which gives you unlimited plasma and access to all the features of the game. Download it now and have a blast!
- FAQs
- Here are some frequently asked questions about Nebulous IO Mod APK Unlimited Plasma:
-
-Q: Is Nebulous IO Mod APK Unlimited Plasma safe to download and install?
-A: Yes, it is safe and virus-free. However, you should always download it from a trusted source and scan it before installing it.
-Q: Do I need to root my device to use Nebulous IO Mod APK Unlimited Plasma?
-A: No, you do not need to root your device to use this mod. It works on both rooted and non-rooted devices.
-Q: Will I get banned from the game if I use Nebulous IO Mod APK Unlimited Plasma?
-A: No, you will not get banned from the game if you use this mod. However, you should not abuse it or use it to cheat or harass other players.
-Q: Can I play online with other players who do not have Nebulous IO Mod APK Unlimited Plasma?
-A: Yes, you can play online with other players who do not have this mod. However, they will not have the same features and advantages as you.
-Q: Can I update Nebulous IO Mod APK Unlimited Plasma when a new version of the game is released?
-A: Yes, you can update this mod when a new version of the game is released. However, you may need to wait for a few days until the mod is updated as well.
-
-How to get nebulous io mod apk with unlimited plasma and skins
197e85843dEA SPORTS™ FIFA 23 Companion Download: How to Manage Your FUT Club on the Go
- If you are a fan of FIFA Ultimate Team (FUT), you might want to download the official EA SPORTS™ FIFA 23 Companion App on your mobile device. This app allows you to access and manage your FUT Club from anywhere, anytime, without having to log into your console or PC. In this article, we will explain what the FIFA 23 Companion App is, why you should use it, and how to download and use it.
- What is the FIFA 23 Companion App?
- The FIFA 23 Companion App is a mobile extension of FIFA Ultimate Team, the most popular mode in FIFA 23. It lets you build your dream squad from thousands of players past and present, customize your FUT Stadium, participate in FUT Events, trade on the Transfer Market, complete Squad Building Challenges, claim rewards, and more.
-ea sportstm fifa 23 companion download Download »»» https://jinyurl.com/2uNLDl
A mobile extension of FIFA Ultimate Team
- The FIFA 23 Companion App gives you access to all the features and functions of FIFA Ultimate Team on your mobile device. You can create and edit your squads, buy and sell players, open packs, check your progress, and much more. You can also sync your app with your console or PC version of FIFA 23, so you can switch between devices seamlessly.
- Compatible with Android and iOS devices
- The FIFA 23 Companion App is available for both Android and iOS mobile devices and tablets. You can download it for free from Google Play or the App Store. The app requires an Internet connection (network fees may apply) and a compatible device. You can check the minimum requirements on the app's page before downloading it.
- Requires FIFA 23 and an EA account to use
- To use the FIFA 23 Companion App, you need to have FIFA 23 for PlayStation 4, PlayStation 5, Xbox One, Xbox Series X|S, PC, or Stadia (sold separately) and an EA account. You also need to create a FUT Club and a FUT Security Question and Answer on your console or PC. Then, you can log in to your EA account from the app and connect to your FUT Club.
- Why should you use the FIFA 23 Companion App?
- The FIFA 23 Companion App offers many benefits for FUT enthusiasts. Here are some of the main reasons why you should use it:
- FUT Stadium Customisation
- The FIFA 23 Companion App allows you to customize every aspect of your FUT Stadium on the go. You can change your walkout music, goal celebrations, pyrotechnics, Tifos, banners, pitch patterns, seat colors, net shapes, and more. You can also flaunt your achievements and show off your style to your opponents.
- FUT Events
- The FIFA 23 Companion App lets you compete or collaborate in all new FUT Events to unlock rewards for your Club and the wider FUT Community. You can choose a side in Team Events and compete against other players in various challenges. Or you can join forces with other players in Community Events and track the collective progress towards a common goal.
-ea sportstm fifa 23 companion app android
- Transfer Market
- The FIFA 23 Companion App enables you to buy and sell players with the global FUT Community in the Transfer Market. You can search for players by name, rating, position, league, club, nationality, chemistry style, or price range. You can also bid on auctions, list your own players, and monitor your transactions. The Transfer Market is the best way to improve your squad and make some coins.
- Squad Building Challenges
- The FIFA 23 Companion App allows you to complete Squad Building Challenges (SBCs) on your mobile device. SBCs are puzzles that require you to build a squad that meets certain criteria, such as chemistry, rating, or nationality. You can exchange your squad for rewards, such as packs, coins, or special players. You can also browse and track the progress of all the available SBCs, including the ones that are exclusive to the app.
- Rewards and Objectives
- The FIFA 23 Companion App lets you claim your rewards and track your objectives on the go. You can collect your rewards from Division Rivals, FUT Champions, FUT Events, SBCs, and more. You can also view your active and completed objectives, such as Season Objectives, Milestones, Foundations, and Daily Objectives. You can earn XP, coins, packs, players, and other items by completing objectives.
- How to download and use the FIFA 23 Companion App?
- Downloading and using the FIFA 23 Companion App is easy and convenient. Here are the steps you need to follow:
- Download from Google Play or App Store
- The first step is to download the FIFA 23 Companion App from Google Play or the App Store on your mobile device. The app is free to download and use, but it may require some storage space and data usage. You can check the app's page for the minimum requirements and ratings before downloading it.
- Log in with your EA account
- The next step is to log in with your EA account on the app. If you don't have an EA account, you can create one for free on the app or on the EA website. You need to use the same EA account that you use for FIFA 23 on your console or PC. You also need to accept the User Agreement and Privacy Policy of EA.
- Connect to your FUT Club
- The final step is to connect to your FUT Club on the app. You need to have FIFA 23 for PlayStation 4, PlayStation 5, Xbox One, Xbox Series X|S, PC, or Stadia (sold separately) and a FUT Club created on your console or PC. You also need to set up a FUT Security Question and Answer on your console or PC. Then, you can select your platform and FUT Club on the app and start managing it.
- Enjoy the features and benefits
- Once you have connected to your FUT Club on the app, you can enjoy all the features and benefits that we have mentioned above. You can build your squad, customize your stadium, participate in events, trade on the market, complete challenges, claim rewards, and more. You can also sync your app with your console or PC version of FIFA 23, so you can switch between devices without losing any progress.
- Conclusion
- The FIFA 23 Companion App is a must-have for any FUT fan who wants to manage their FUT Club on the go. It offers many features and benefits that enhance your FUT experience and help you achieve your goals. You can download it for free from Google Play or the App Store and connect it to your EA account and FUT Club. Then, you can enjoy all the aspects of FIFA Ultimate Team on your mobile device.
- FAQs
- Here are some of the frequently asked questions about the FIFA 23 Companion App:
- - Q: Is the FIFA 23 Companion App safe to use? - A: Yes, the FIFA 23 Companion App is safe to use as long as you download it from official sources (Google Play or App Store) and log in with a secure EA account. The app uses encryption and authentication methods to protect your data and transactions. - Q: Can I use the FIFA 23 Companion App without FIFA 23? - A: No, you cannot use the FIFA 23 Companion App without FIFA 23. You need to have FIFA 23 for PlayStation 4, PlayStation 5, Xbox One, Xbox Series X|S, PC, or Stadia (sold separately) and a FUT Club created on your console or PC to use the app. You also need to log in with the same EA account that you use for FIFA 23. - Q: Can I play matches on the FIFA 23 Companion App? - A: No, you cannot play matches on the FIFA 23 Companion App. The app is designed to help you manage your FUT Club, not to play the game itself. You can only play matches on your console or PC version of FIFA 23. - Q: How can I contact EA support if I have any issues with the FIFA 23 Companion App? - A: If you have any issues with the FIFA 23 Companion App, you can contact EA support through the app itself or through the EA website. You can also check the EA Help Center for FAQs, guides, and troubleshooting tips. - Q: How can I update the FIFA 23 Companion App? - A: The FIFA 23 Companion App will automatically update itself when there is a new version available. You can also check for updates manually on Google Play or the App Store. You should always keep your app updated to enjoy the latest features and improvements. 197e85843d
-
-
-
-
-
- {messages.length ? (
- <>
-
-
-
-
-
- {generating ? (
-
-
- 停止响应
-
-
- ) : null}
-
- ) : null}
-
-
-
-
-
- {children}
-
-
-)
-DialogPortal.displayName = DialogPrimitive.Portal.displayName
-
-const DialogOverlay = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
- {children}
-
- Close
-
-
-
-))
-DialogContent.displayName = DialogPrimitive.Content.displayName
-
-const DialogHeader = ({
- className,
- ...props
-}: React.HTMLAttributes) => (
-
-)
-DialogHeader.displayName = 'DialogHeader'
-
-const DialogFooter = ({
- className,
- ...props
-}: React.HTMLAttributes) => (
-
-)
-DialogFooter.displayName = 'DialogFooter'
-
-const DialogTitle = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ', ['']])
- # txt_struct.append(['', ['']])
- return txt_struct, txt
-
-
-if __name__ == '__main__':
- # t = 'simon演唱过后，simon还进行了simon精彩的文艺演出simon.'
- t = '你当我傻啊？脑子那么大怎么塞进去？?？'
- phs, txt = TxtProcessor.process(t, {'use_tone': True})
- print(phs, txt)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/tf_layers.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/tf_layers.py
deleted file mode 100644
index c0f46bd755c161cda2ac904fe37f3f3c6357a88d..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/tf_layers.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 MINH ANH (@dathudeptrai)
-# MIT License (https://opensource.org/licenses/MIT)
-
-"""Tensorflow Layer modules complatible with pytorch."""
-
-import tensorflow as tf
-
-
-class TFReflectionPad1d(tf.keras.layers.Layer):
- """Tensorflow ReflectionPad1d module."""
-
- def __init__(self, padding_size):
- """Initialize TFReflectionPad1d module.
-
- Args:
- padding_size (int): Padding size.
-
- """
- super(TFReflectionPad1d, self).__init__()
- self.padding_size = padding_size
-
- @tf.function
- def call(self, x):
- """Calculate forward propagation.
-
- Args:
- x (Tensor): Input tensor (B, T, 1, C).
-
- Returns:
- Tensor: Padded tensor (B, T + 2 * padding_size, 1, C).
-
- """
- return tf.pad(x, [[0, 0], [self.padding_size, self.padding_size], [0, 0], [0, 0]], "REFLECT")
-
-
-class TFConvTranspose1d(tf.keras.layers.Layer):
- """Tensorflow ConvTranspose1d module."""
-
- def __init__(self, channels, kernel_size, stride, padding):
- """Initialize TFConvTranspose1d( module.
-
- Args:
- channels (int): Number of channels.
- kernel_size (int): kernel size.
- strides (int): Stride width.
- padding (str): Padding type ("same" or "valid").
-
- """
- super(TFConvTranspose1d, self).__init__()
- self.conv1d_transpose = tf.keras.layers.Conv2DTranspose(
- filters=channels,
- kernel_size=(kernel_size, 1),
- strides=(stride, 1),
- padding=padding,
- )
-
- @tf.function
- def call(self, x):
- """Calculate forward propagation.
-
- Args:
- x (Tensor): Input tensor (B, T, 1, C).
-
- Returns:
- Tensors: Output tensor (B, T', 1, C').
-
- """
- x = self.conv1d_transpose(x)
- return x
-
-
-class TFResidualStack(tf.keras.layers.Layer):
- """Tensorflow ResidualStack module."""
-
- def __init__(self,
- kernel_size,
- channels,
- dilation,
- bias,
- nonlinear_activation,
- nonlinear_activation_params,
- padding,
- ):
- """Initialize TFResidualStack module.
-
- Args:
- kernel_size (int): Kernel size.
- channles (int): Number of channels.
- dilation (int): Dilation ine.
- bias (bool): Whether to add bias parameter in convolution layers.
- nonlinear_activation (str): Activation function module name.
- nonlinear_activation_params (dict): Hyperparameters for activation function.
- padding (str): Padding type ("same" or "valid").
-
- """
- super(TFResidualStack, self).__init__()
- self.block = [
- getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params),
- TFReflectionPad1d(dilation),
- tf.keras.layers.Conv2D(
- filters=channels,
- kernel_size=(kernel_size, 1),
- dilation_rate=(dilation, 1),
- use_bias=bias,
- padding="valid",
- ),
- getattr(tf.keras.layers, nonlinear_activation)(**nonlinear_activation_params),
- tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias)
- ]
- self.shortcut = tf.keras.layers.Conv2D(filters=channels, kernel_size=1, use_bias=bias)
-
- @tf.function
- def call(self, x):
- """Calculate forward propagation.
-
- Args:
- x (Tensor): Input tensor (B, T, 1, C).
-
- Returns:
- Tensor: Output tensor (B, T, 1, C).
-
- """
- _x = tf.identity(x)
- for i, layer in enumerate(self.block):
- _x = layer(_x)
- shortcut = self.shortcut(x)
- return shortcut + _x
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/data/extract_mel_spectrogram.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/data/extract_mel_spectrogram.py
deleted file mode 100644
index 42cade483a576f7166011a25d7e4d4bb0ae0f55c..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/data/extract_mel_spectrogram.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import argparse
-import os
-import os.path as P
-from copy import deepcopy
-from functools import partial
-from glob import glob
-from multiprocessing import Pool
-from pathlib import Path
-
-import librosa
-import numpy as np
-import torchvision
-
-
-class MelSpectrogram(object):
- def __init__(self, sr, nfft, fmin, fmax, nmels, hoplen, spec_power, inverse=False):
- self.sr = sr
- self.nfft = nfft
- self.fmin = fmin
- self.fmax = fmax
- self.nmels = nmels
- self.hoplen = hoplen
- self.spec_power = spec_power
- self.inverse = inverse
-
- self.mel_basis = librosa.filters.mel(sr=sr, n_fft=nfft, fmin=fmin, fmax=fmax, n_mels=nmels)
-
- def __call__(self, x):
- if self.inverse:
- spec = librosa.feature.inverse.mel_to_stft(
- x, sr=self.sr, n_fft=self.nfft, fmin=self.fmin, fmax=self.fmax, power=self.spec_power
- )
- wav = librosa.griffinlim(spec, hop_length=self.hoplen)
- return wav
- else:
- spec = np.abs(librosa.stft(x, n_fft=self.nfft, hop_length=self.hoplen)) ** self.spec_power
- mel_spec = np.dot(self.mel_basis, spec)
- return mel_spec
-
-class LowerThresh(object):
- def __init__(self, min_val, inverse=False):
- self.min_val = min_val
- self.inverse = inverse
-
- def __call__(self, x):
- if self.inverse:
- return x
- else:
- return np.maximum(self.min_val, x)
-
-class Add(object):
- def __init__(self, val, inverse=False):
- self.inverse = inverse
- self.val = val
-
- def __call__(self, x):
- if self.inverse:
- return x - self.val
- else:
- return x + self.val
-
-class Subtract(Add):
- def __init__(self, val, inverse=False):
- self.inverse = inverse
- self.val = val
-
- def __call__(self, x):
- if self.inverse:
- return x + self.val
- else:
- return x - self.val
-
-class Multiply(object):
- def __init__(self, val, inverse=False) -> None:
- self.val = val
- self.inverse = inverse
-
- def __call__(self, x):
- if self.inverse:
- return x / self.val
- else:
- return x * self.val
-
-class Divide(Multiply):
- def __init__(self, val, inverse=False):
- self.inverse = inverse
- self.val = val
-
- def __call__(self, x):
- if self.inverse:
- return x * self.val
- else:
- return x / self.val
-
-class Log10(object):
- def __init__(self, inverse=False):
- self.inverse = inverse
-
- def __call__(self, x):
- if self.inverse:
- return 10 ** x
- else:
- return np.log10(x)
-
-class Clip(object):
- def __init__(self, min_val, max_val, inverse=False):
- self.min_val = min_val
- self.max_val = max_val
- self.inverse = inverse
-
- def __call__(self, x):
- if self.inverse:
- return x
- else:
- return np.clip(x, self.min_val, self.max_val)
-
-class TrimSpec(object):
- def __init__(self, max_len, inverse=False):
- self.max_len = max_len
- self.inverse = inverse
-
- def __call__(self, x):
- if self.inverse:
- return x
- else:
- return x[:, :self.max_len]
-
-class MaxNorm(object):
- def __init__(self, inverse=False):
- self.inverse = inverse
- self.eps = 1e-10
-
- def __call__(self, x):
- if self.inverse:
- return x
- else:
- return x / (x.max() + self.eps)
-
-
-TRANSFORMS_16000 = torchvision.transforms.Compose([
- MelSpectrogram(sr=16000, nfft=1024, fmin=125, fmax=7600, nmels=80, hoplen=1024//4, spec_power=1),
- LowerThresh(1e-5),
- Log10(),
- Multiply(20),
- Subtract(20),
- Add(100),
- Divide(100),
- Clip(0, 1.0)
- # TrimSpec(860)
-])
-
diff --git a/spaces/AILab-CVC/SEED-LLaMA/scripts/start_frontend_8b.sh b/spaces/AILab-CVC/SEED-LLaMA/scripts/start_frontend_8b.sh
deleted file mode 100644
index 3ce38fc0515bdb61391254aab2a6b5ec73479c51..0000000000000000000000000000000000000000
--- a/spaces/AILab-CVC/SEED-LLaMA/scripts/start_frontend_8b.sh
+++ /dev/null
@@ -1 +0,0 @@
-python3 gradio_demo/seed_llama_gradio.py --server_port 80 --request_address http://127.0.0.1:7890/generate --model_type seed-llama-8b
\ No newline at end of file
diff --git a/spaces/AIWaves/SOP_Generation-single/gradio_backend.py b/spaces/AIWaves/SOP_Generation-single/gradio_backend.py
deleted file mode 100644
index 8f278debf0df6b7e71b20421c6aeef32ecb5edf0..0000000000000000000000000000000000000000
--- a/spaces/AIWaves/SOP_Generation-single/gradio_backend.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import json
-import os
-import argparse
-import sys
-sys.path.append("Gradio_Config")
-from SOP import SOP
-from Agent import Agent
-from Environment import Environment
-from Memory import Memory
-from gradio_base import Client, convert2list4agentname
-
-# add ===================
-def process(action):
- response = action.response
- send_name = action.name
- send_role = action.role
- if not action.is_user:
- print(f"{send_name}({send_role}):{response}")
- memory = Memory(send_role, send_name, response)
- return memory
-
-def gradio_process(action,current_state):
- response = action.response
- all = ""
- for i,res in enumerate(response):
- all+=res
- state = 10
- if action.is_user:
- state = 30
- elif action.state_begin:
- state = 12
- action.state_begin = False
- elif i>0:
- state = 11
- send_name = f"{action.name}({action.role})"
- Client.send_server(str([state, send_name, res, current_state.name]))
- if state == 30:
- # print("client: waiting for server")
- data: list = next(Client.receive_server)
- content = ""
- for item in data:
- if item.startswith(""):
- content = item.split("")[1]
- break
- # print(f"client: received `{content}` from server.")
- action.response = content
- break
- else:
- action.response = all
-
-def prepare(agents, sop, environment):
- client = Client()
- Client.send_server = client.send_message
-
- client.send_message(
- {
- "agents_name": convert2list4agentname(sop)[0],
- "api_key": os.environ["API_KEY"]
- }
- )
- print(f"client: {list(agents.keys())}")
- client.listening_for_start_()
- client.mode = Client.mode = client.cache["mode"]
- os.environ["API_KEY"] = client.cache["api_key"]
- uploaded_sop = Client.cache['uploaded_sop']
- agents,sop,environment = init(uploaded_sop)
- run(agents,sop,environment)
-
-def block_when_next(current_agent, current_state):
- if Client.LAST_USER:
- assert not current_agent.is_user
- Client.LAST_USER = False
- return
- if current_agent.is_user:
- # if next turn is user, we don't handle it here
- Client.LAST_USER = True
- return
- if Client.FIRST_RUN:
- Client.FIRST_RUN = False
- else:
- # block current process
- if Client.mode == Client.SINGLE_MODE:
- Client.send_server(str([98, f"{current_agent.name}({current_agent.state_roles[current_state.name]})", " ", current_state.name]))
- data: list = next(Client.receive_server)
-
-# =======================
-
-def init(config):
- if not os.path.exists("logs"):
- os.mkdir("logs")
- sop = SOP.from_config(config)
- agents,roles_to_names,names_to_roles = Agent.from_config(config)
- environment = Environment.from_config(config)
- environment.agents = agents
- environment.roles_to_names,environment.names_to_roles = roles_to_names,names_to_roles
- sop.roles_to_names,sop.names_to_roles = roles_to_names,names_to_roles
- for name,agent in agents.items():
- agent.environment = environment
- return agents,sop,environment
-
-def run(agents,sop,environment):
- while True:
- current_state,current_agent= sop.next(environment,agents)
- if sop.finished:
- print("finished!")
- Client.send_server(str([99, " ", " ", "done"]))
- os.environ.clear()
- break
- block_when_next(current_agent, current_state)
- action = current_agent.step(current_state) #component_dict = current_state[self.role[current_node.name]] current_agent.compile(component_dict)
- gradio_process(action,current_state)
- memory = process(action)
- environment.update_memory(memory,current_state)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='A demo of chatbot')
- parser.add_argument('--agent', type=str, help='path to SOP json',default="config.json")
- args = parser.parse_args()
-
- agents,sop,environment = init(args.agent)
- prepare(agents, sop, environment)
- # run(agents,sop,environment)
diff --git a/spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/README.md b/spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/README.md
deleted file mode 100644
index 3e0168a9785c658bd05d494b537f58c060fc6dd3..0000000000000000000000000000000000000000
--- a/spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 02 Transformers Sentence2Paragraph
-emoji: 🐨
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.1.5
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ANILYADAV/mygenaichatbot/README.md b/spaces/ANILYADAV/mygenaichatbot/README.md
deleted file mode 100644
index f2beebedb64623ff4f84babae2e0a11bfbe5c920..0000000000000000000000000000000000000000
--- a/spaces/ANILYADAV/mygenaichatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Mygenaichatbot
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-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/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/__init__.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/After-the-Dark/paragraph-similarity/app.py b/spaces/After-the-Dark/paragraph-similarity/app.py
deleted file mode 100644
index eb32f094cf24fafa5e74351b94ef1e3a737854a9..0000000000000000000000000000000000000000
--- a/spaces/After-the-Dark/paragraph-similarity/app.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-model_sentence = SentenceTransformer('all-MiniLM-L6-v2')
-
-para_1 ="""
-Natural language processing (NLP) is a field of computer science that studies how computers can understand and process human language. NLP is a subfield of artificial intelligence (AI) that deals with the interaction between computers and human (natural) languages.
-
-NLP has a wide range of applications, including:
-
-Machine translation: translating text from one language to another
-Text summarization: extracting the main points of a text
-Question answering: answering questions posed in natural language
-Text classification: classifying text into categories, such as spam or ham
-Sentiment analysis: determining the sentiment of a text, such as positive, negative, or neutral
-Natural language generation: generating text that is similar to human-written text
-NLP is a challenging field, as human language is complex and nuanced. However, NLP has made significant progress in recent years, and it is now a powerful tool that can be used to solve a wide range of problems.
-
-
-"""
-para_2 ="""
-Generative adversarial networks (GANs) are a type of machine learning model that can be used to generate realistic and creative content. GANs were first introduced in 2014 by Ian Goodfellow, and they have since been used to generate a wide range of content, including images, text, and music.
-
-GANs work by pitting two neural networks against each other in a game-like setting. One network, the generator, is responsible for creating new content. The other network, the discriminator, is responsible for determining whether the content created by the generator is real or fake.
-
-The generator is trained to create content that is as realistic as possible, while the discriminator is trained to distinguish between real and fake content. As the two networks compete against each other, they both become better at their respective tasks.
-
-GANs have been used to generate a wide range of content, including:
-
-Images: GANs have been used to generate realistic images of people, animals, and objects.
-Text: GANs have been used to generate realistic text, such as news articles, blog posts, and even poetry.
-Music: GANs have been used to generate realistic music, such as songs, symphonies, and even jazz improvisations.
-GANs are a powerful tool that can be used to generate realistic and creative content. As GANs continue to develop, they are likely to be used to create even more amazing and impressive content in the future.
-
-
-"""
-def paragraph_similar(text1, text2):
- sentences = []
- sentences.append(text1)
- sentences.append(text2)
- paraphrases = util.paraphrase_mining(model_sentence, sentences, corpus_chunk_size=len(sentences))
- return {"Similarity": [round(paraphrases[0][0], 2)]}
-
-
-with gr.Blocks(title="Paragraph",css="footer {visibility: hidden}") as demo:
- with gr.Row():
- with gr.Column():
- gr.Markdown("## Paragraph Compare")
- with gr.Row():
- with gr.Column():
- inputs_1 = gr.TextArea(label="Paragraph 1",value=para_1,interactive=True)
- inputs_2 = gr.TextArea(label="Paragraph 2",value=para_2,interactive=True)
- with gr.Column():
- btn = gr.Button(value="RUN")
- output = gr.Label(label="output")
- btn.click(fn=paragraph_similar,inputs=[inputs_1,inputs_2],outputs=[output])
-demo.launch()
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/LayoutBackgrounds.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/LayoutBackgrounds.js
deleted file mode 100644
index 3cfa39b558496f3cc5759b679bf0513c9494eb73..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/LayoutBackgrounds.js
+++ /dev/null
@@ -1,41 +0,0 @@
-import ResizeGameObject from '../../../plugins/utils/size/ResizeGameObject.js';
-import PreLayoutChild from './utils/PreLayoutChild.js';
-import LayoutChild from './utils/LayoutChild.js';
-
-const ALIGN_CENTER = Phaser.Display.Align.CENTER;
-
-var LayoutBackgrounds = function () {
- if (this.backgroundChildren === undefined) {
- return;
- }
- var backgrounds = this.backgroundChildren;
-
- var startX = this.left,
- startY = this.top;
- var parentWidth = this.width,
- parentHeight = this.height;
- var child, childConfig, padding,
- x, y, width, height;
- for (var i = 0, cnt = backgrounds.length; i < cnt; i++) {
- child = backgrounds[i];
- childConfig = child.rexSizer;
- if (childConfig.hidden) {
- continue;
- }
-
- padding = childConfig.padding;
-
- PreLayoutChild.call(this, child);
-
- x = startX + padding.left;
- y = startY + padding.top;
- width = parentWidth - padding.left - padding.right;
- height = parentHeight - padding.top - padding.bottom;
-
- ResizeGameObject(child, width, height);
-
- LayoutChild.call(this, child, x, y, width, height, ALIGN_CENTER);
- }
-}
-
-export default LayoutBackgrounds;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/IsLocalPointInKnob.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/IsLocalPointInKnob.js
deleted file mode 100644
index 8ae388e261c7914ab6f379bdff60ebfb839cb68c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/input/IsLocalPointInKnob.js
+++ /dev/null
@@ -1,8 +0,0 @@
-var GetDistance = Phaser.Math.Distance.Between;
-
-var IsLocalPointInKnob = function (knob, localX, localY) {
- var centerX = knob.width / 2;
- return GetDistance(centerX, centerX, localX, localY) <= centerX;
-}
-
-export default IsLocalPointInKnob;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/NameValueLabel.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/NameValueLabel.d.ts
deleted file mode 100644
index c662d95c3bda525c3668c98b04cf7f463e3fe17e..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/NameValueLabel.d.ts
+++ /dev/null
@@ -1,94 +0,0 @@
-import LineProgressCanvas from '../lineprogresscanvas/LineProgressCanvas';
-
-// import * as Phaser from 'phaser';
-import Sizer from '../sizer/Sizer';
-
-export default NameValueLabel;
-
-declare namespace NameValueLabel {
-
- interface IConfig extends Sizer.IConfig {
- space?: {
- left?: number, right?: number, top?: number, bottom?: number,
-
- icon?: number, iconTop?: number, iconBottom?: number, iconLeft?: number, iconRight?: number,
-
- name?: number,
- value?: number,
-
- bar?: number, barBottom?: number, barLeft?: number, barRight?: number,
-
- action?: number, actionTop?: number, actionBottom?: number, actionLeft?: number, actionRight?: number,
- },
-
- background?: Phaser.GameObjects.GameObject,
-
- icon?: Phaser.GameObjects.GameObject,
- iconMask?: boolean,
-
- nameText?: Phaser.GameObjects.GameObject,
- valueText?: Phaser.GameObjects.GameObject,
- bar?: Phaser.GameObjects.GameObject | LineProgressCanvas.IConfig,
-
- action?: Phaser.GameObjects.GameObject,
- actionMask?: boolean,
-
- valueTextFormatCallback?: (
- value: number,
- min: number,
- max: number
- ) => string,
-
- align?: {
- text?: 'left' | 'right' | 'center' | number,
- title?: 'left' | 'right' | 'center' | number,
- },
-
- proportion?: {
- title?: number,
- separator?: number,
- text?: number,
- }
- }
-}
-
-declare class NameValueLabel extends Sizer {
- constructor(
- scene: Phaser.Scene,
- config?: NameValueLabel.IConfig
- );
-
- nameText: string;
- setNameText(value?:string):this;
-
- valueText: string;
- setValueText(value?:string):this;
-
- barValue: number;
- setBarValue(
- value: number,
- min?: number,
- max?: number
- ): this;
- easeBarValueTo(
- value: number,
- min?: number,
- max?: number
- ): this;
-
- setTexture(
- key: string | Phaser.Textures.Texture,
- frame?: string | number
- ): this;
- readonly texture: Phaser.Textures.Texture | Phaser.Textures.CanvasTexture;
- readonly frame: Phaser.Textures.Frame;
-
- setValue(
- value: number,
- min: number,
- max: number
- ): this;
- value: number;
- minValue: number;
- maxValue: number;
-}
\ No newline at end of file
diff --git a/spaces/Ajay07pandey/Netfilx_Movie_Recommendation_System/app.py b/spaces/Ajay07pandey/Netfilx_Movie_Recommendation_System/app.py
deleted file mode 100644
index 9840f1a38dabf4be94d2fbf89033e52c0b681df3..0000000000000000000000000000000000000000
--- a/spaces/Ajay07pandey/Netfilx_Movie_Recommendation_System/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import streamlit as st
-import pickle
-import pandas as pd
-
-
-st.image("Netflix.png")
-
-movies_list = pickle.load(open("content_dict.pkl",'br'))
-movies = pd.DataFrame(movies_list)
-
-similarity= pickle.load(open('cosine_similarity.pkl','rb'))
-
-def recommend(title, cosine_sim=similarity, data=movies):
- recommended_content=[]
- # Get the index of the input title in the programme_list
- programme_list = data['title'].to_list()
- index = programme_list.index(title)
-
-
- # Create a list of tuples containing the similarity score and index
- # between the input title and all other programs in the dataset
- sim_scores = list(enumerate(cosine_sim[index]))
-
-
- # Sort the list of tuples by similarity score in descending order
- sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)[1:11]
-
-
- # Get the recommended movie titles and their similarity scores
- recommend_index = [i[0] for i in sim_scores]
- rec_movie = data['title'].iloc[recommend_index]
- rec_score = [round(i[1], 4) for i in sim_scores]
-
-
- # Create a pandas DataFrame to display the recommendations
- rec_table = pd.DataFrame(list(zip(rec_movie, rec_score)), columns=['Recommendation', 'Similarity_score(0-1)'])
- # recommended_content.append(rec_table['Recommendation'].values)
-
-
- return rec_table['Recommendation'].values
-
-
-# Displaying title
-st.title(" Movie Recommender System ")
-
-
-movie_list = movies['title'].values
-selected_movie = st.selectbox(
- "Type or select a movie from the dropdown",
- movie_list
-)
-
-# Setting a button
-if st.button('Show Recommendation'):
- recommended_movie_names = recommend(selected_movie)
- st.balloons()
- for j in recommended_movie_names:
- st.write(j)
\ No newline at end of file
diff --git a/spaces/Akmyradov/TurkmenTTSweSTT/uroman/bin/string-distance.pl b/spaces/Akmyradov/TurkmenTTSweSTT/uroman/bin/string-distance.pl
deleted file mode 100644
index 9870fdf9c0ddaf4928da5fe4c11632facefbaa38..0000000000000000000000000000000000000000
--- a/spaces/Akmyradov/TurkmenTTSweSTT/uroman/bin/string-distance.pl
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/perl -w
-
-# Author: Ulf Hermjakob
-# Release date: October 13, 2019
-
-# Usage: string-distance.pl {-lc1 } {-lc2 } < STDIN > STDOUT
-# Example: string-distance.pl -lc1 rus -lc2 ukr < STDIN > STDOUT
-# Example: string-distance.pl < ../test/string-similarity-test-input.txt
-# Input format: two strings per line (tab-separated, in Latin script)
-# Strings in non-Latin scripts should first be romanized. (Recommended script: uroman.pl)
-# Output format: repetition of the two input strings, plus the string distance between them (tab-separated).
-# Additional output meta info lines at the top are marked with an initial #.
-#
-# The script uses data from a string-distance-cost-rules file that lists costs,
-# where the default cost is "1" with lower costs for differences in vowels,
-# duplicate consonants, "f" vs. "ph" etc.
-# Language cost rules can be language-specific and context-sensitive.
-
-$|=1;
-
-use FindBin;
-use Cwd "abs_path";
-use File::Basename qw(dirname);
-use File::Spec;
-
-my $bin_dir = abs_path(dirname($0));
-my $root_dir = File::Spec->catfile($bin_dir, File::Spec->updir());
-my $data_dir = File::Spec->catfile($root_dir, "data");
-my $lib_dir = File::Spec->catfile($root_dir, "lib");
-
-use lib "$FindBin::Bin/../lib";
-use List::Util qw(min max);
-use NLP::utilities;
-use NLP::stringDistance;
-$util = NLP::utilities;
-$sd = NLP::stringDistance;
-$verbose = 0;
-$separator = "\t";
-
-$cost_rule_filename = File::Spec->catfile($data_dir, "string-distance-cost-rules.txt");
-
-$lang_code1 = "eng";
-$lang_code2 = "eng";
-%ht = ();
-
-while (@ARGV) {
- $arg = shift @ARGV;
- if ($arg =~ /^-+lc1$/) {
- $lang_code_candidate = shift @ARGV;
- $lang_code1 = $lang_code_candidate if $lang_code_candidate =~ /^[a-z]{3,3}$/;
- } elsif ($arg =~ /^-+lc2$/) {
- $lang_code_candidate = shift @ARGV;
- $lang_code2 = $lang_code_candidate if $lang_code_candidate =~ /^[a-z]{3,3}$/;
- } elsif ($arg =~ /^-+(v|verbose)$/) {
- $verbose = shift @ARGV;
- } else {
- print STDERR "Ignoring unrecognized arg $arg\n";
- }
-}
-
-$sd->load_string_distance_data($cost_rule_filename, *ht, $verbose);
-print STDERR "Loaded resources.\n" if $verbose;
-
-my $chart_id = 0;
-my $line_number = 0;
-print "# Lang-code-1: $lang_code1 Lang-code-2: $lang_code2\n";
-while (<>) {
- $line_number++;
- if ($verbose) {
- if ($line_number =~ /000$/) {
- if ($line_number =~ /0000$/) {
- print STDERR $line_number;
- } else {
- print STDERR ".";
- }
- }
- }
- my $line = $_;
- $line =~ s/^\xEF\xBB\xBF//;
- next if $line =~ /^\s*(\#.*)?$/;
- my $s1;
- my $s2;
- if (($s1, $s2) = ($line =~ /^("(?:\\"|[^"])*"|\S+)$separator("(?:\\"|[^"])*"|\S+)\s*$/)) {
- $s1 = $util->dequote_string($s1);
- $s2 = $util->dequote_string($s2);
- } elsif ($line =~ /^\s*(#.*)$/) {
- } else {
- print STDERR "Could not process line $line_number: $line" if $verbose;
- print "\n";
- next;
- }
-
- $cost = $sd->quick_romanized_string_distance_by_chart($s1, $s2, *ht, "", $lang_code1, $lang_code2);
- print "$s1\t$s2\t$cost\n";
-}
-print STDERR "\n" if $verbose;
-
-exit 0;
-
diff --git a/spaces/AlawnCN/webui-docker/README.md b/spaces/AlawnCN/webui-docker/README.md
deleted file mode 100644
index c0814227cf1eed6f0ce20a7312bb2ae91d8b3b6b..0000000000000000000000000000000000000000
--- a/spaces/AlawnCN/webui-docker/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: Stable Diffusion Web UI Docker
-emoji: 🐳
-colorFrom: blue
-colorTo: blue
-sdk: docker
-sdk_version: 3.9
-app_file: oh-no.py
-pinned: false
----
-
-## Stable Diffusion Web UI
-https://github.com/AUTOMATIC1111/stable-diffusion-webui
-
-## Documentation
-https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki
-
-## Models License
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/facerender/modules/util.py b/spaces/Alpaca233/SadTalker/src/facerender/modules/util.py
deleted file mode 100644
index b916deefbb8b957ad6ab3cd7403c28513e5ae18e..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/facerender/modules/util.py
+++ /dev/null
@@ -1,564 +0,0 @@
-from torch import nn
-
-import torch.nn.functional as F
-import torch
-
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
-
-import torch.nn.utils.spectral_norm as spectral_norm
-
-
-def kp2gaussian(kp, spatial_size, kp_variance):
- """
- Transform a keypoint into gaussian like representation
- """
- mean = kp['value']
-
- coordinate_grid = make_coordinate_grid(spatial_size, mean.type())
- number_of_leading_dimensions = len(mean.shape) - 1
- shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
- coordinate_grid = coordinate_grid.view(*shape)
- repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1)
- coordinate_grid = coordinate_grid.repeat(*repeats)
-
- # Preprocess kp shape
- shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3)
- mean = mean.view(*shape)
-
- mean_sub = (coordinate_grid - mean)
-
- out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
-
- return out
-
-def make_coordinate_grid_2d(spatial_size, type):
- """
- Create a meshgrid [-1,1] x [-1,1] of given spatial_size.
- """
- h, w = spatial_size
- x = torch.arange(w).type(type)
- y = torch.arange(h).type(type)
-
- x = (2 * (x / (w - 1)) - 1)
- y = (2 * (y / (h - 1)) - 1)
-
- yy = y.view(-1, 1).repeat(1, w)
- xx = x.view(1, -1).repeat(h, 1)
-
- meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
-
- return meshed
-
-
-def make_coordinate_grid(spatial_size, type):
- d, h, w = spatial_size
- x = torch.arange(w).type(type)
- y = torch.arange(h).type(type)
- z = torch.arange(d).type(type)
-
- x = (2 * (x / (w - 1)) - 1)
- y = (2 * (y / (h - 1)) - 1)
- z = (2 * (z / (d - 1)) - 1)
-
- yy = y.view(1, -1, 1).repeat(d, 1, w)
- xx = x.view(1, 1, -1).repeat(d, h, 1)
- zz = z.view(-1, 1, 1).repeat(1, h, w)
-
- meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3)
-
- return meshed
-
-
-class ResBottleneck(nn.Module):
- def __init__(self, in_features, stride):
- super(ResBottleneck, self).__init__()
- self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features//4, kernel_size=1)
- self.conv2 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features//4, kernel_size=3, padding=1, stride=stride)
- self.conv3 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features, kernel_size=1)
- self.norm1 = BatchNorm2d(in_features//4, affine=True)
- self.norm2 = BatchNorm2d(in_features//4, affine=True)
- self.norm3 = BatchNorm2d(in_features, affine=True)
-
- self.stride = stride
- if self.stride != 1:
- self.skip = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=1, stride=stride)
- self.norm4 = BatchNorm2d(in_features, affine=True)
-
- def forward(self, x):
- out = self.conv1(x)
- out = self.norm1(out)
- out = F.relu(out)
- out = self.conv2(out)
- out = self.norm2(out)
- out = F.relu(out)
- out = self.conv3(out)
- out = self.norm3(out)
- if self.stride != 1:
- x = self.skip(x)
- x = self.norm4(x)
- out += x
- out = F.relu(out)
- return out
-
-
-class ResBlock2d(nn.Module):
- """
- Res block, preserve spatial resolution.
- """
-
- def __init__(self, in_features, kernel_size, padding):
- super(ResBlock2d, self).__init__()
- self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
- padding=padding)
- self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
- padding=padding)
- self.norm1 = BatchNorm2d(in_features, affine=True)
- self.norm2 = BatchNorm2d(in_features, affine=True)
-
- def forward(self, x):
- out = self.norm1(x)
- out = F.relu(out)
- out = self.conv1(out)
- out = self.norm2(out)
- out = F.relu(out)
- out = self.conv2(out)
- out += x
- return out
-
-
-class ResBlock3d(nn.Module):
- """
- Res block, preserve spatial resolution.
- """
-
- def __init__(self, in_features, kernel_size, padding):
- super(ResBlock3d, self).__init__()
- self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
- padding=padding)
- self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
- padding=padding)
- self.norm1 = BatchNorm3d(in_features, affine=True)
- self.norm2 = BatchNorm3d(in_features, affine=True)
-
- def forward(self, x):
- out = self.norm1(x)
- out = F.relu(out)
- out = self.conv1(out)
- out = self.norm2(out)
- out = F.relu(out)
- out = self.conv2(out)
- out += x
- return out
-
-
-class UpBlock2d(nn.Module):
- """
- Upsampling block for use in decoder.
- """
-
- def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
- super(UpBlock2d, self).__init__()
-
- self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
- padding=padding, groups=groups)
- self.norm = BatchNorm2d(out_features, affine=True)
-
- def forward(self, x):
- out = F.interpolate(x, scale_factor=2)
- out = self.conv(out)
- out = self.norm(out)
- out = F.relu(out)
- return out
-
-class UpBlock3d(nn.Module):
- """
- Upsampling block for use in decoder.
- """
-
- def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
- super(UpBlock3d, self).__init__()
-
- self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
- padding=padding, groups=groups)
- self.norm = BatchNorm3d(out_features, affine=True)
-
- def forward(self, x):
- # out = F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear')
- out = F.interpolate(x, scale_factor=(1, 2, 2))
- out = self.conv(out)
- out = self.norm(out)
- out = F.relu(out)
- return out
-
-
-class DownBlock2d(nn.Module):
- """
- Downsampling block for use in encoder.
- """
-
- def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
- super(DownBlock2d, self).__init__()
- self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
- padding=padding, groups=groups)
- self.norm = BatchNorm2d(out_features, affine=True)
- self.pool = nn.AvgPool2d(kernel_size=(2, 2))
-
- def forward(self, x):
- out = self.conv(x)
- out = self.norm(out)
- out = F.relu(out)
- out = self.pool(out)
- return out
-
-
-class DownBlock3d(nn.Module):
- """
- Downsampling block for use in encoder.
- """
-
- def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
- super(DownBlock3d, self).__init__()
- '''
- self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
- padding=padding, groups=groups, stride=(1, 2, 2))
- '''
- self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
- padding=padding, groups=groups)
- self.norm = BatchNorm3d(out_features, affine=True)
- self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2))
-
- def forward(self, x):
- out = self.conv(x)
- out = self.norm(out)
- out = F.relu(out)
- out = self.pool(out)
- return out
-
-
-class SameBlock2d(nn.Module):
- """
- Simple block, preserve spatial resolution.
- """
-
- def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False):
- super(SameBlock2d, self).__init__()
- self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,
- kernel_size=kernel_size, padding=padding, groups=groups)
- self.norm = BatchNorm2d(out_features, affine=True)
- if lrelu:
- self.ac = nn.LeakyReLU()
- else:
- self.ac = nn.ReLU()
-
- def forward(self, x):
- out = self.conv(x)
- out = self.norm(out)
- out = self.ac(out)
- return out
-
-
-class Encoder(nn.Module):
- """
- Hourglass Encoder
- """
-
- def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
- super(Encoder, self).__init__()
-
- down_blocks = []
- for i in range(num_blocks):
- down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
- min(max_features, block_expansion * (2 ** (i + 1))),
- kernel_size=3, padding=1))
- self.down_blocks = nn.ModuleList(down_blocks)
-
- def forward(self, x):
- outs = [x]
- for down_block in self.down_blocks:
- outs.append(down_block(outs[-1]))
- return outs
-
-
-class Decoder(nn.Module):
- """
- Hourglass Decoder
- """
-
- def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
- super(Decoder, self).__init__()
-
- up_blocks = []
-
- for i in range(num_blocks)[::-1]:
- in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
- out_filters = min(max_features, block_expansion * (2 ** i))
- up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
-
- self.up_blocks = nn.ModuleList(up_blocks)
- # self.out_filters = block_expansion
- self.out_filters = block_expansion + in_features
-
- self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1)
- self.norm = BatchNorm3d(self.out_filters, affine=True)
-
- def forward(self, x):
- out = x.pop()
- # for up_block in self.up_blocks[:-1]:
- for up_block in self.up_blocks:
- out = up_block(out)
- skip = x.pop()
- out = torch.cat([out, skip], dim=1)
- # out = self.up_blocks[-1](out)
- out = self.conv(out)
- out = self.norm(out)
- out = F.relu(out)
- return out
-
-
-class Hourglass(nn.Module):
- """
- Hourglass architecture.
- """
-
- def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
- super(Hourglass, self).__init__()
- self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
- self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
- self.out_filters = self.decoder.out_filters
-
- def forward(self, x):
- return self.decoder(self.encoder(x))
-
-
-class KPHourglass(nn.Module):
- """
- Hourglass architecture.
- """
-
- def __init__(self, block_expansion, in_features, reshape_features, reshape_depth, num_blocks=3, max_features=256):
- super(KPHourglass, self).__init__()
-
- self.down_blocks = nn.Sequential()
- for i in range(num_blocks):
- self.down_blocks.add_module('down'+ str(i), DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
- min(max_features, block_expansion * (2 ** (i + 1))),
- kernel_size=3, padding=1))
-
- in_filters = min(max_features, block_expansion * (2 ** num_blocks))
- self.conv = nn.Conv2d(in_channels=in_filters, out_channels=reshape_features, kernel_size=1)
-
- self.up_blocks = nn.Sequential()
- for i in range(num_blocks):
- in_filters = min(max_features, block_expansion * (2 ** (num_blocks - i)))
- out_filters = min(max_features, block_expansion * (2 ** (num_blocks - i - 1)))
- self.up_blocks.add_module('up'+ str(i), UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
-
- self.reshape_depth = reshape_depth
- self.out_filters = out_filters
-
- def forward(self, x):
- out = self.down_blocks(x)
- out = self.conv(out)
- bs, c, h, w = out.shape
- out = out.view(bs, c//self.reshape_depth, self.reshape_depth, h, w)
- out = self.up_blocks(out)
-
- return out
-
-
-
-class AntiAliasInterpolation2d(nn.Module):
- """
- Band-limited downsampling, for better preservation of the input signal.
- """
- def __init__(self, channels, scale):
- super(AntiAliasInterpolation2d, self).__init__()
- sigma = (1 / scale - 1) / 2
- kernel_size = 2 * round(sigma * 4) + 1
- self.ka = kernel_size // 2
- self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
-
- kernel_size = [kernel_size, kernel_size]
- sigma = [sigma, sigma]
- # The gaussian kernel is the product of the
- # gaussian function of each dimension.
- kernel = 1
- meshgrids = torch.meshgrid(
- [
- torch.arange(size, dtype=torch.float32)
- for size in kernel_size
- ]
- )
- for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
- mean = (size - 1) / 2
- kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))
-
- # Make sure sum of values in gaussian kernel equals 1.
- kernel = kernel / torch.sum(kernel)
- # Reshape to depthwise convolutional weight
- kernel = kernel.view(1, 1, *kernel.size())
- kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
-
- self.register_buffer('weight', kernel)
- self.groups = channels
- self.scale = scale
- inv_scale = 1 / scale
- self.int_inv_scale = int(inv_scale)
-
- def forward(self, input):
- if self.scale == 1.0:
- return input
-
- out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
- out = F.conv2d(out, weight=self.weight, groups=self.groups)
- out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale]
-
- return out
-
-
-class SPADE(nn.Module):
- def __init__(self, norm_nc, label_nc):
- super().__init__()
-
- self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
- nhidden = 128
-
- self.mlp_shared = nn.Sequential(
- nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1),
- nn.ReLU())
- self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
- self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
-
- def forward(self, x, segmap):
- normalized = self.param_free_norm(x)
- segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
- actv = self.mlp_shared(segmap)
- gamma = self.mlp_gamma(actv)
- beta = self.mlp_beta(actv)
- out = normalized * (1 + gamma) + beta
- return out
-
-
-class SPADEResnetBlock(nn.Module):
- def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1):
- super().__init__()
- # Attributes
- self.learned_shortcut = (fin != fout)
- fmiddle = min(fin, fout)
- self.use_se = use_se
- # create conv layers
- self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation)
- self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation)
- if self.learned_shortcut:
- self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
- # apply spectral norm if specified
- if 'spectral' in norm_G:
- self.conv_0 = spectral_norm(self.conv_0)
- self.conv_1 = spectral_norm(self.conv_1)
- if self.learned_shortcut:
- self.conv_s = spectral_norm(self.conv_s)
- # define normalization layers
- self.norm_0 = SPADE(fin, label_nc)
- self.norm_1 = SPADE(fmiddle, label_nc)
- if self.learned_shortcut:
- self.norm_s = SPADE(fin, label_nc)
-
- def forward(self, x, seg1):
- x_s = self.shortcut(x, seg1)
- dx = self.conv_0(self.actvn(self.norm_0(x, seg1)))
- dx = self.conv_1(self.actvn(self.norm_1(dx, seg1)))
- out = x_s + dx
- return out
-
- def shortcut(self, x, seg1):
- if self.learned_shortcut:
- x_s = self.conv_s(self.norm_s(x, seg1))
- else:
- x_s = x
- return x_s
-
- def actvn(self, x):
- return F.leaky_relu(x, 2e-1)
-
-class audio2image(nn.Module):
- def __init__(self, generator, kp_extractor, he_estimator_video, he_estimator_audio, train_params):
- super().__init__()
- # Attributes
- self.generator = generator
- self.kp_extractor = kp_extractor
- self.he_estimator_video = he_estimator_video
- self.he_estimator_audio = he_estimator_audio
- self.train_params = train_params
-
- def headpose_pred_to_degree(self, pred):
- device = pred.device
- idx_tensor = [idx for idx in range(66)]
- idx_tensor = torch.FloatTensor(idx_tensor).to(device)
- pred = F.softmax(pred)
- degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
-
- return degree
-
- def get_rotation_matrix(self, yaw, pitch, roll):
- yaw = yaw / 180 * 3.14
- pitch = pitch / 180 * 3.14
- roll = roll / 180 * 3.14
-
- roll = roll.unsqueeze(1)
- pitch = pitch.unsqueeze(1)
- yaw = yaw.unsqueeze(1)
-
- roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll),
- torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll),
- torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1)
- roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
-
- pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch),
- torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch),
- -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1)
- pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
-
- yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw),
- torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw),
- torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1)
- yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
-
- rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat)
-
- return rot_mat
-
- def keypoint_transformation(self, kp_canonical, he):
- kp = kp_canonical['value'] # (bs, k, 3)
- yaw, pitch, roll = he['yaw'], he['pitch'], he['roll']
- t, exp = he['t'], he['exp']
-
- yaw = self.headpose_pred_to_degree(yaw)
- pitch = self.headpose_pred_to_degree(pitch)
- roll = self.headpose_pred_to_degree(roll)
-
- rot_mat = self.get_rotation_matrix(yaw, pitch, roll) # (bs, 3, 3)
-
- # keypoint rotation
- kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
-
-
-
- # keypoint translation
- t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
- kp_t = kp_rotated + t
-
- # add expression deviation
- exp = exp.view(exp.shape[0], -1, 3)
- kp_transformed = kp_t + exp
-
- return {'value': kp_transformed}
-
- def forward(self, source_image, target_audio):
- pose_source = self.he_estimator_video(source_image)
- pose_generated = self.he_estimator_audio(target_audio)
- kp_canonical = self.kp_extractor(source_image)
- kp_source = self.keypoint_transformation(kp_canonical, pose_source)
- kp_transformed_generated = self.keypoint_transformation(kp_canonical, pose_generated)
- generated = self.generator(source_image, kp_source=kp_source, kp_driving=kp_transformed_generated)
- return generated
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/controlnet_flax.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/controlnet_flax.py
deleted file mode 100644
index a826df48e41a632454c513877ec55be7f86089f9..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/controlnet_flax.py
+++ /dev/null
@@ -1,394 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import Optional, Tuple, Union
-
-import flax
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict
-
-from ..configuration_utils import ConfigMixin, flax_register_to_config
-from ..utils import BaseOutput
-from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
-from .modeling_flax_utils import FlaxModelMixin
-from .unet_2d_blocks_flax import (
- FlaxCrossAttnDownBlock2D,
- FlaxDownBlock2D,
- FlaxUNetMidBlock2DCrossAttn,
-)
-
-
-@flax.struct.dataclass
-class FlaxControlNetOutput(BaseOutput):
- """
- The output of [`FlaxControlNetModel`].
-
- Args:
- down_block_res_samples (`jnp.ndarray`):
- mid_block_res_sample (`jnp.ndarray`):
- """
-
- down_block_res_samples: jnp.ndarray
- mid_block_res_sample: jnp.ndarray
-
-
-class FlaxControlNetConditioningEmbedding(nn.Module):
- conditioning_embedding_channels: int
- block_out_channels: Tuple[int] = (16, 32, 96, 256)
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- self.conv_in = nn.Conv(
- self.block_out_channels[0],
- kernel_size=(3, 3),
- padding=((1, 1), (1, 1)),
- dtype=self.dtype,
- )
-
- blocks = []
- for i in range(len(self.block_out_channels) - 1):
- channel_in = self.block_out_channels[i]
- channel_out = self.block_out_channels[i + 1]
- conv1 = nn.Conv(
- channel_in,
- kernel_size=(3, 3),
- padding=((1, 1), (1, 1)),
- dtype=self.dtype,
- )
- blocks.append(conv1)
- conv2 = nn.Conv(
- channel_out,
- kernel_size=(3, 3),
- strides=(2, 2),
- padding=((1, 1), (1, 1)),
- dtype=self.dtype,
- )
- blocks.append(conv2)
- self.blocks = blocks
-
- self.conv_out = nn.Conv(
- self.conditioning_embedding_channels,
- kernel_size=(3, 3),
- padding=((1, 1), (1, 1)),
- kernel_init=nn.initializers.zeros_init(),
- bias_init=nn.initializers.zeros_init(),
- dtype=self.dtype,
- )
-
- def __call__(self, conditioning):
- embedding = self.conv_in(conditioning)
- embedding = nn.silu(embedding)
-
- for block in self.blocks:
- embedding = block(embedding)
- embedding = nn.silu(embedding)
-
- embedding = self.conv_out(embedding)
-
- return embedding
-
-
-@flax_register_to_config
-class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):
- r"""
- A ControlNet model.
-
- This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for it’s generic methods
- implemented for all models (such as downloading or saving).
-
- This model is also a Flax Linen [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
- subclass. Use it as a regular Flax Linen module and refer to the Flax documentation for all matters related to its
- general usage and behavior.
-
- Inherent JAX features such as the following are supported:
-
- - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
- Parameters:
- sample_size (`int`, *optional*):
- The size of the input sample.
- in_channels (`int`, *optional*, defaults to 4):
- The number of channels in the input sample.
- down_block_types (`Tuple[str]`, *optional*, defaults to `("FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D")`):
- The tuple of downsample blocks to use.
- block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
- The tuple of output channels for each block.
- layers_per_block (`int`, *optional*, defaults to 2):
- The number of layers per block.
- attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
- The dimension of the attention heads.
- num_attention_heads (`int` or `Tuple[int]`, *optional*):
- The number of attention heads.
- cross_attention_dim (`int`, *optional*, defaults to 768):
- The dimension of the cross attention features.
- dropout (`float`, *optional*, defaults to 0):
- Dropout probability for down, up and bottleneck blocks.
- flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
- Whether to flip the sin to cos in the time embedding.
- freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
- controlnet_conditioning_channel_order (`str`, *optional*, defaults to `rgb`):
- The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
- conditioning_embedding_out_channels (`tuple`, *optional*, defaults to `(16, 32, 96, 256)`):
- The tuple of output channel for each block in the `conditioning_embedding` layer.
- """
- sample_size: int = 32
- in_channels: int = 4
- down_block_types: Tuple[str] = (
- "CrossAttnDownBlock2D",
- "CrossAttnDownBlock2D",
- "CrossAttnDownBlock2D",
- "DownBlock2D",
- )
- only_cross_attention: Union[bool, Tuple[bool]] = False
- block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
- layers_per_block: int = 2
- attention_head_dim: Union[int, Tuple[int]] = 8
- num_attention_heads: Optional[Union[int, Tuple[int]]] = None
- cross_attention_dim: int = 1280
- dropout: float = 0.0
- use_linear_projection: bool = False
- dtype: jnp.dtype = jnp.float32
- flip_sin_to_cos: bool = True
- freq_shift: int = 0
- controlnet_conditioning_channel_order: str = "rgb"
- conditioning_embedding_out_channels: Tuple[int] = (16, 32, 96, 256)
-
- def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
- # init input tensors
- sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
- sample = jnp.zeros(sample_shape, dtype=jnp.float32)
- timesteps = jnp.ones((1,), dtype=jnp.int32)
- encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)
- controlnet_cond_shape = (1, 3, self.sample_size * 8, self.sample_size * 8)
- controlnet_cond = jnp.zeros(controlnet_cond_shape, dtype=jnp.float32)
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.init(rngs, sample, timesteps, encoder_hidden_states, controlnet_cond)["params"]
-
- def setup(self):
- block_out_channels = self.block_out_channels
- time_embed_dim = block_out_channels[0] * 4
-
- # If `num_attention_heads` is not defined (which is the case for most models)
- # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
- # The reason for this behavior is to correct for incorrectly named variables that were introduced
- # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
- # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
- # which is why we correct for the naming here.
- num_attention_heads = self.num_attention_heads or self.attention_head_dim
-
- # input
- self.conv_in = nn.Conv(
- block_out_channels[0],
- kernel_size=(3, 3),
- strides=(1, 1),
- padding=((1, 1), (1, 1)),
- dtype=self.dtype,
- )
-
- # time
- self.time_proj = FlaxTimesteps(
- block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
- )
- self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
-
- self.controlnet_cond_embedding = FlaxControlNetConditioningEmbedding(
- conditioning_embedding_channels=block_out_channels[0],
- block_out_channels=self.conditioning_embedding_out_channels,
- )
-
- only_cross_attention = self.only_cross_attention
- if isinstance(only_cross_attention, bool):
- only_cross_attention = (only_cross_attention,) * len(self.down_block_types)
-
- if isinstance(num_attention_heads, int):
- num_attention_heads = (num_attention_heads,) * len(self.down_block_types)
-
- # down
- down_blocks = []
- controlnet_down_blocks = []
-
- output_channel = block_out_channels[0]
-
- controlnet_block = nn.Conv(
- output_channel,
- kernel_size=(1, 1),
- padding="VALID",
- kernel_init=nn.initializers.zeros_init(),
- bias_init=nn.initializers.zeros_init(),
- dtype=self.dtype,
- )
- controlnet_down_blocks.append(controlnet_block)
-
- for i, down_block_type in enumerate(self.down_block_types):
- input_channel = output_channel
- output_channel = block_out_channels[i]
- is_final_block = i == len(block_out_channels) - 1
-
- if down_block_type == "CrossAttnDownBlock2D":
- down_block = FlaxCrossAttnDownBlock2D(
- in_channels=input_channel,
- out_channels=output_channel,
- dropout=self.dropout,
- num_layers=self.layers_per_block,
- num_attention_heads=num_attention_heads[i],
- add_downsample=not is_final_block,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=only_cross_attention[i],
- dtype=self.dtype,
- )
- else:
- down_block = FlaxDownBlock2D(
- in_channels=input_channel,
- out_channels=output_channel,
- dropout=self.dropout,
- num_layers=self.layers_per_block,
- add_downsample=not is_final_block,
- dtype=self.dtype,
- )
-
- down_blocks.append(down_block)
-
- for _ in range(self.layers_per_block):
- controlnet_block = nn.Conv(
- output_channel,
- kernel_size=(1, 1),
- padding="VALID",
- kernel_init=nn.initializers.zeros_init(),
- bias_init=nn.initializers.zeros_init(),
- dtype=self.dtype,
- )
- controlnet_down_blocks.append(controlnet_block)
-
- if not is_final_block:
- controlnet_block = nn.Conv(
- output_channel,
- kernel_size=(1, 1),
- padding="VALID",
- kernel_init=nn.initializers.zeros_init(),
- bias_init=nn.initializers.zeros_init(),
- dtype=self.dtype,
- )
- controlnet_down_blocks.append(controlnet_block)
-
- self.down_blocks = down_blocks
- self.controlnet_down_blocks = controlnet_down_blocks
-
- # mid
- mid_block_channel = block_out_channels[-1]
- self.mid_block = FlaxUNetMidBlock2DCrossAttn(
- in_channels=mid_block_channel,
- dropout=self.dropout,
- num_attention_heads=num_attention_heads[-1],
- use_linear_projection=self.use_linear_projection,
- dtype=self.dtype,
- )
-
- self.controlnet_mid_block = nn.Conv(
- mid_block_channel,
- kernel_size=(1, 1),
- padding="VALID",
- kernel_init=nn.initializers.zeros_init(),
- bias_init=nn.initializers.zeros_init(),
- dtype=self.dtype,
- )
-
- def __call__(
- self,
- sample,
- timesteps,
- encoder_hidden_states,
- controlnet_cond,
- conditioning_scale: float = 1.0,
- return_dict: bool = True,
- train: bool = False,
- ) -> Union[FlaxControlNetOutput, Tuple]:
- r"""
- Args:
- sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor
- timestep (`jnp.ndarray` or `float` or `int`): timesteps
- encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states
- controlnet_cond (`jnp.ndarray`): (batch, channel, height, width) the conditional input tensor
- conditioning_scale: (`float`) the scale factor for controlnet outputs
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a
- plain tuple.
- train (`bool`, *optional*, defaults to `False`):
- Use deterministic functions and disable dropout when not training.
-
- Returns:
- [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
- [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`.
- When returning a tuple, the first element is the sample tensor.
- """
- channel_order = self.controlnet_conditioning_channel_order
- if channel_order == "bgr":
- controlnet_cond = jnp.flip(controlnet_cond, axis=1)
-
- # 1. time
- if not isinstance(timesteps, jnp.ndarray):
- timesteps = jnp.array([timesteps], dtype=jnp.int32)
- elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:
- timesteps = timesteps.astype(dtype=jnp.float32)
- timesteps = jnp.expand_dims(timesteps, 0)
-
- t_emb = self.time_proj(timesteps)
- t_emb = self.time_embedding(t_emb)
-
- # 2. pre-process
- sample = jnp.transpose(sample, (0, 2, 3, 1))
- sample = self.conv_in(sample)
-
- controlnet_cond = jnp.transpose(controlnet_cond, (0, 2, 3, 1))
- controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
- sample += controlnet_cond
-
- # 3. down
- down_block_res_samples = (sample,)
- for down_block in self.down_blocks:
- if isinstance(down_block, FlaxCrossAttnDownBlock2D):
- sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
- else:
- sample, res_samples = down_block(sample, t_emb, deterministic=not train)
- down_block_res_samples += res_samples
-
- # 4. mid
- sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
-
- # 5. contronet blocks
- controlnet_down_block_res_samples = ()
- for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
- down_block_res_sample = controlnet_block(down_block_res_sample)
- controlnet_down_block_res_samples += (down_block_res_sample,)
-
- down_block_res_samples = controlnet_down_block_res_samples
-
- mid_block_res_sample = self.controlnet_mid_block(sample)
-
- # 6. scaling
- down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
- mid_block_res_sample *= conditioning_scale
-
- if not return_dict:
- return (down_block_res_samples, mid_block_res_sample)
-
- return FlaxControlNetOutput(
- down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
- )
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint_legacy.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint_legacy.py
deleted file mode 100644
index 235aa32f7338579210520c675b3776b830cbe3da..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint_legacy.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-
-from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
-from diffusers.utils.testing_utils import (
- is_onnx_available,
- load_image,
- load_numpy,
- nightly,
- require_onnxruntime,
- require_torch_gpu,
-)
-
-
-if is_onnx_available():
- import onnxruntime as ort
-
-
-@nightly
-@require_onnxruntime
-@require_torch_gpu
-class StableDiffusionOnnxInpaintLegacyPipelineIntegrationTests(unittest.TestCase):
- @property
- def gpu_provider(self):
- return (
- "CUDAExecutionProvider",
- {
- "gpu_mem_limit": "15000000000", # 15GB
- "arena_extend_strategy": "kSameAsRequested",
- },
- )
-
- @property
- def gpu_options(self):
- options = ort.SessionOptions()
- options.enable_mem_pattern = False
- return options
-
- def test_inference(self):
- init_image = load_image(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
- "/in_paint/overture-creations-5sI6fQgYIuo.png"
- )
- mask_image = load_image(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
- "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
- )
- expected_image = load_numpy(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
- "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"
- )
-
- # using the PNDM scheduler by default
- pipe = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
- "CompVis/stable-diffusion-v1-4",
- revision="onnx",
- safety_checker=None,
- feature_extractor=None,
- provider=self.gpu_provider,
- sess_options=self.gpu_options,
- )
- pipe.set_progress_bar_config(disable=None)
-
- prompt = "A red cat sitting on a park bench"
-
- generator = np.random.RandomState(0)
- output = pipe(
- prompt=prompt,
- image=init_image,
- mask_image=mask_image,
- strength=0.75,
- guidance_scale=7.5,
- num_inference_steps=15,
- generator=generator,
- output_type="np",
- )
-
- image = output.images[0]
-
- assert image.shape == (512, 512, 3)
- assert np.abs(expected_image - image).max() < 1e-2
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/check_table.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/check_table.py
deleted file mode 100644
index e9f290988916c29b270523897454b21172d91839..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/check_table.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import collections
-import importlib.util
-import os
-import re
-
-
-# All paths are set with the intent you should run this script from the root of the repo with the command
-# python utils/check_table.py
-TRANSFORMERS_PATH = "src/diffusers"
-PATH_TO_DOCS = "docs/source/en"
-REPO_PATH = "."
-
-
-def _find_text_in_file(filename, start_prompt, end_prompt):
- """
- Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
- lines.
- """
- with open(filename, "r", encoding="utf-8", newline="\n") as f:
- lines = f.readlines()
- # Find the start prompt.
- start_index = 0
- while not lines[start_index].startswith(start_prompt):
- start_index += 1
- start_index += 1
-
- end_index = start_index
- while not lines[end_index].startswith(end_prompt):
- end_index += 1
- end_index -= 1
-
- while len(lines[start_index]) <= 1:
- start_index += 1
- while len(lines[end_index]) <= 1:
- end_index -= 1
- end_index += 1
- return "".join(lines[start_index:end_index]), start_index, end_index, lines
-
-
-# Add here suffixes that are used to identify models, separated by |
-ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration"
-# Regexes that match TF/Flax/PT model names.
-_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
-_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
-# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
-_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
-
-
-# This is to make sure the diffusers module imported is the one in the repo.
-spec = importlib.util.spec_from_file_location(
- "diffusers",
- os.path.join(TRANSFORMERS_PATH, "__init__.py"),
- submodule_search_locations=[TRANSFORMERS_PATH],
-)
-diffusers_module = spec.loader.load_module()
-
-
-# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
-def camel_case_split(identifier):
- "Split a camelcased `identifier` into words."
- matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
- return [m.group(0) for m in matches]
-
-
-def _center_text(text, width):
- text_length = 2 if text == "✅" or text == "❌" else len(text)
- left_indent = (width - text_length) // 2
- right_indent = width - text_length - left_indent
- return " " * left_indent + text + " " * right_indent
-
-
-def get_model_table_from_auto_modules():
- """Generates an up-to-date model table from the content of the auto modules."""
- # Dictionary model names to config.
- config_mapping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
- model_name_to_config = {
- name: config_mapping_names[code]
- for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
- if code in config_mapping_names
- }
- model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()}
-
- # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
- slow_tokenizers = collections.defaultdict(bool)
- fast_tokenizers = collections.defaultdict(bool)
- pt_models = collections.defaultdict(bool)
- tf_models = collections.defaultdict(bool)
- flax_models = collections.defaultdict(bool)
-
- # Let's lookup through all diffusers object (once).
- for attr_name in dir(diffusers_module):
- lookup_dict = None
- if attr_name.endswith("Tokenizer"):
- lookup_dict = slow_tokenizers
- attr_name = attr_name[:-9]
- elif attr_name.endswith("TokenizerFast"):
- lookup_dict = fast_tokenizers
- attr_name = attr_name[:-13]
- elif _re_tf_models.match(attr_name) is not None:
- lookup_dict = tf_models
- attr_name = _re_tf_models.match(attr_name).groups()[0]
- elif _re_flax_models.match(attr_name) is not None:
- lookup_dict = flax_models
- attr_name = _re_flax_models.match(attr_name).groups()[0]
- elif _re_pt_models.match(attr_name) is not None:
- lookup_dict = pt_models
- attr_name = _re_pt_models.match(attr_name).groups()[0]
-
- if lookup_dict is not None:
- while len(attr_name) > 0:
- if attr_name in model_name_to_prefix.values():
- lookup_dict[attr_name] = True
- break
- # Try again after removing the last word in the name
- attr_name = "".join(camel_case_split(attr_name)[:-1])
-
- # Let's build that table!
- model_names = list(model_name_to_config.keys())
- model_names.sort(key=str.lower)
- columns = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
- # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
- widths = [len(c) + 2 for c in columns]
- widths[0] = max([len(name) for name in model_names]) + 2
-
- # Build the table per se
- table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n"
- # Use ":-----:" format to center-aligned table cell texts
- table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n"
-
- check = {True: "✅", False: "❌"}
- for name in model_names:
- prefix = model_name_to_prefix[name]
- line = [
- name,
- check[slow_tokenizers[prefix]],
- check[fast_tokenizers[prefix]],
- check[pt_models[prefix]],
- check[tf_models[prefix]],
- check[flax_models[prefix]],
- ]
- table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n"
- return table
-
-
-def check_model_table(overwrite=False):
- """Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`."""
- current_table, start_index, end_index, lines = _find_text_in_file(
- filename=os.path.join(PATH_TO_DOCS, "index.md"),
- start_prompt="",
- )
- new_table = get_model_table_from_auto_modules()
-
- if current_table != new_table:
- if overwrite:
- with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f:
- f.writelines(lines[:start_index] + [new_table] + lines[end_index:])
- else:
- raise ValueError(
- "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."
- )
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
- args = parser.parse_args()
-
- check_model_table(args.fix_and_overwrite)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/cascade_rcnn.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/cascade_rcnn.py
deleted file mode 100644
index d873dceb7e4efdf8d1e7d282badfe9b7118426b9..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/cascade_rcnn.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class CascadeRCNN(TwoStageDetector):
- r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
- Detection `_"""
-
- def __init__(self,
- backbone,
- neck=None,
- rpn_head=None,
- roi_head=None,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super(CascadeRCNN, self).__init__(
- backbone=backbone,
- neck=neck,
- rpn_head=rpn_head,
- roi_head=roi_head,
- train_cfg=train_cfg,
- test_cfg=test_cfg,
- pretrained=pretrained)
-
- def show_result(self, data, result, **kwargs):
- """Show prediction results of the detector.
-
- Args:
- data (str or np.ndarray): Image filename or loaded image.
- result (Tensor or tuple): The results to draw over `img`
- bbox_result or (bbox_result, segm_result).
-
- Returns:
- np.ndarray: The image with bboxes drawn on it.
- """
- if self.with_mask:
- ms_bbox_result, ms_segm_result = result
- if isinstance(ms_bbox_result, dict):
- result = (ms_bbox_result['ensemble'],
- ms_segm_result['ensemble'])
- else:
- if isinstance(result, dict):
- result = result['ensemble']
- return super(CascadeRCNN, self).show_result(data, result, **kwargs)
diff --git a/spaces/Anew1007/extras/README.md b/spaces/Anew1007/extras/README.md
deleted file mode 100644
index 26145498355bebc5337e5f04940e5073fad22978..0000000000000000000000000000000000000000
--- a/spaces/Anew1007/extras/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: extras
-emoji: 🧊
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
-duplicated_from: doctord98/extras
----
-Fixed Server.JS Latest 2023/08/16
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/share.py b/spaces/Anonymous-sub/Rerender/ControlNet/share.py
deleted file mode 100644
index 463af08fb936d650b5dd2e66183661181c34a3d6..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/share.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import config
-from cldm.hack import disable_verbosity, enable_sliced_attention
-
-
-disable_verbosity()
-
-if config.save_memory:
- enable_sliced_attention()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/ansitowin32.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/ansitowin32.py
deleted file mode 100644
index abf209e60c7c4a9b1ae57452e36b383969848c2e..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/ansitowin32.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import re
-import sys
-import os
-
-from .ansi import AnsiFore, AnsiBack, AnsiStyle, Style, BEL
-from .winterm import enable_vt_processing, WinTerm, WinColor, WinStyle
-from .win32 import windll, winapi_test
-
-
-winterm = None
-if windll is not None:
- winterm = WinTerm()
-
-
-class StreamWrapper(object):
- '''
- Wraps a stream (such as stdout), acting as a transparent proxy for all
- attribute access apart from method 'write()', which is delegated to our
- Converter instance.
- '''
- def __init__(self, wrapped, converter):
- # double-underscore everything to prevent clashes with names of
- # attributes on the wrapped stream object.
- self.__wrapped = wrapped
- self.__convertor = converter
-
- def __getattr__(self, name):
- return getattr(self.__wrapped, name)
-
- def __enter__(self, *args, **kwargs):
- # special method lookup bypasses __getattr__/__getattribute__, see
- # https://stackoverflow.com/questions/12632894/why-doesnt-getattr-work-with-exit
- # thus, contextlib magic methods are not proxied via __getattr__
- return self.__wrapped.__enter__(*args, **kwargs)
-
- def __exit__(self, *args, **kwargs):
- return self.__wrapped.__exit__(*args, **kwargs)
-
- def __setstate__(self, state):
- self.__dict__ = state
-
- def __getstate__(self):
- return self.__dict__
-
- def write(self, text):
- self.__convertor.write(text)
-
- def isatty(self):
- stream = self.__wrapped
- if 'PYCHARM_HOSTED' in os.environ:
- if stream is not None and (stream is sys.__stdout__ or stream is sys.__stderr__):
- return True
- try:
- stream_isatty = stream.isatty
- except AttributeError:
- return False
- else:
- return stream_isatty()
-
- @property
- def closed(self):
- stream = self.__wrapped
- try:
- return stream.closed
- # AttributeError in the case that the stream doesn't support being closed
- # ValueError for the case that the stream has already been detached when atexit runs
- except (AttributeError, ValueError):
- return True
-
-
-class AnsiToWin32(object):
- '''
- Implements a 'write()' method which, on Windows, will strip ANSI character
- sequences from the text, and if outputting to a tty, will convert them into
- win32 function calls.
- '''
- ANSI_CSI_RE = re.compile('\001?\033\\[((?:\\d|;)*)([a-zA-Z])\002?') # Control Sequence Introducer
- ANSI_OSC_RE = re.compile('\001?\033\\]([^\a]*)(\a)\002?') # Operating System Command
-
- def __init__(self, wrapped, convert=None, strip=None, autoreset=False):
- # The wrapped stream (normally sys.stdout or sys.stderr)
- self.wrapped = wrapped
-
- # should we reset colors to defaults after every .write()
- self.autoreset = autoreset
-
- # create the proxy wrapping our output stream
- self.stream = StreamWrapper(wrapped, self)
-
- on_windows = os.name == 'nt'
- # We test if the WinAPI works, because even if we are on Windows
- # we may be using a terminal that doesn't support the WinAPI
- # (e.g. Cygwin Terminal). In this case it's up to the terminal
- # to support the ANSI codes.
- conversion_supported = on_windows and winapi_test()
- try:
- fd = wrapped.fileno()
- except Exception:
- fd = -1
- system_has_native_ansi = not on_windows or enable_vt_processing(fd)
- have_tty = not self.stream.closed and self.stream.isatty()
- need_conversion = conversion_supported and not system_has_native_ansi
-
- # should we strip ANSI sequences from our output?
- if strip is None:
- strip = need_conversion or not have_tty
- self.strip = strip
-
- # should we should convert ANSI sequences into win32 calls?
- if convert is None:
- convert = need_conversion and have_tty
- self.convert = convert
-
- # dict of ansi codes to win32 functions and parameters
- self.win32_calls = self.get_win32_calls()
-
- # are we wrapping stderr?
- self.on_stderr = self.wrapped is sys.stderr
-
- def should_wrap(self):
- '''
- True if this class is actually needed. If false, then the output
- stream will not be affected, nor will win32 calls be issued, so
- wrapping stdout is not actually required. This will generally be
- False on non-Windows platforms, unless optional functionality like
- autoreset has been requested using kwargs to init()
- '''
- return self.convert or self.strip or self.autoreset
-
- def get_win32_calls(self):
- if self.convert and winterm:
- return {
- AnsiStyle.RESET_ALL: (winterm.reset_all, ),
- AnsiStyle.BRIGHT: (winterm.style, WinStyle.BRIGHT),
- AnsiStyle.DIM: (winterm.style, WinStyle.NORMAL),
- AnsiStyle.NORMAL: (winterm.style, WinStyle.NORMAL),
- AnsiFore.BLACK: (winterm.fore, WinColor.BLACK),
- AnsiFore.RED: (winterm.fore, WinColor.RED),
- AnsiFore.GREEN: (winterm.fore, WinColor.GREEN),
- AnsiFore.YELLOW: (winterm.fore, WinColor.YELLOW),
- AnsiFore.BLUE: (winterm.fore, WinColor.BLUE),
- AnsiFore.MAGENTA: (winterm.fore, WinColor.MAGENTA),
- AnsiFore.CYAN: (winterm.fore, WinColor.CYAN),
- AnsiFore.WHITE: (winterm.fore, WinColor.GREY),
- AnsiFore.RESET: (winterm.fore, ),
- AnsiFore.LIGHTBLACK_EX: (winterm.fore, WinColor.BLACK, True),
- AnsiFore.LIGHTRED_EX: (winterm.fore, WinColor.RED, True),
- AnsiFore.LIGHTGREEN_EX: (winterm.fore, WinColor.GREEN, True),
- AnsiFore.LIGHTYELLOW_EX: (winterm.fore, WinColor.YELLOW, True),
- AnsiFore.LIGHTBLUE_EX: (winterm.fore, WinColor.BLUE, True),
- AnsiFore.LIGHTMAGENTA_EX: (winterm.fore, WinColor.MAGENTA, True),
- AnsiFore.LIGHTCYAN_EX: (winterm.fore, WinColor.CYAN, True),
- AnsiFore.LIGHTWHITE_EX: (winterm.fore, WinColor.GREY, True),
- AnsiBack.BLACK: (winterm.back, WinColor.BLACK),
- AnsiBack.RED: (winterm.back, WinColor.RED),
- AnsiBack.GREEN: (winterm.back, WinColor.GREEN),
- AnsiBack.YELLOW: (winterm.back, WinColor.YELLOW),
- AnsiBack.BLUE: (winterm.back, WinColor.BLUE),
- AnsiBack.MAGENTA: (winterm.back, WinColor.MAGENTA),
- AnsiBack.CYAN: (winterm.back, WinColor.CYAN),
- AnsiBack.WHITE: (winterm.back, WinColor.GREY),
- AnsiBack.RESET: (winterm.back, ),
- AnsiBack.LIGHTBLACK_EX: (winterm.back, WinColor.BLACK, True),
- AnsiBack.LIGHTRED_EX: (winterm.back, WinColor.RED, True),
- AnsiBack.LIGHTGREEN_EX: (winterm.back, WinColor.GREEN, True),
- AnsiBack.LIGHTYELLOW_EX: (winterm.back, WinColor.YELLOW, True),
- AnsiBack.LIGHTBLUE_EX: (winterm.back, WinColor.BLUE, True),
- AnsiBack.LIGHTMAGENTA_EX: (winterm.back, WinColor.MAGENTA, True),
- AnsiBack.LIGHTCYAN_EX: (winterm.back, WinColor.CYAN, True),
- AnsiBack.LIGHTWHITE_EX: (winterm.back, WinColor.GREY, True),
- }
- return dict()
-
- def write(self, text):
- if self.strip or self.convert:
- self.write_and_convert(text)
- else:
- self.wrapped.write(text)
- self.wrapped.flush()
- if self.autoreset:
- self.reset_all()
-
-
- def reset_all(self):
- if self.convert:
- self.call_win32('m', (0,))
- elif not self.strip and not self.stream.closed:
- self.wrapped.write(Style.RESET_ALL)
-
-
- def write_and_convert(self, text):
- '''
- Write the given text to our wrapped stream, stripping any ANSI
- sequences from the text, and optionally converting them into win32
- calls.
- '''
- cursor = 0
- text = self.convert_osc(text)
- for match in self.ANSI_CSI_RE.finditer(text):
- start, end = match.span()
- self.write_plain_text(text, cursor, start)
- self.convert_ansi(*match.groups())
- cursor = end
- self.write_plain_text(text, cursor, len(text))
-
-
- def write_plain_text(self, text, start, end):
- if start < end:
- self.wrapped.write(text[start:end])
- self.wrapped.flush()
-
-
- def convert_ansi(self, paramstring, command):
- if self.convert:
- params = self.extract_params(command, paramstring)
- self.call_win32(command, params)
-
-
- def extract_params(self, command, paramstring):
- if command in 'Hf':
- params = tuple(int(p) if len(p) != 0 else 1 for p in paramstring.split(';'))
- while len(params) < 2:
- # defaults:
- params = params + (1,)
- else:
- params = tuple(int(p) for p in paramstring.split(';') if len(p) != 0)
- if len(params) == 0:
- # defaults:
- if command in 'JKm':
- params = (0,)
- elif command in 'ABCD':
- params = (1,)
-
- return params
-
-
- def call_win32(self, command, params):
- if command == 'm':
- for param in params:
- if param in self.win32_calls:
- func_args = self.win32_calls[param]
- func = func_args[0]
- args = func_args[1:]
- kwargs = dict(on_stderr=self.on_stderr)
- func(*args, **kwargs)
- elif command in 'J':
- winterm.erase_screen(params[0], on_stderr=self.on_stderr)
- elif command in 'K':
- winterm.erase_line(params[0], on_stderr=self.on_stderr)
- elif command in 'Hf': # cursor position - absolute
- winterm.set_cursor_position(params, on_stderr=self.on_stderr)
- elif command in 'ABCD': # cursor position - relative
- n = params[0]
- # A - up, B - down, C - forward, D - back
- x, y = {'A': (0, -n), 'B': (0, n), 'C': (n, 0), 'D': (-n, 0)}[command]
- winterm.cursor_adjust(x, y, on_stderr=self.on_stderr)
-
-
- def convert_osc(self, text):
- for match in self.ANSI_OSC_RE.finditer(text):
- start, end = match.span()
- text = text[:start] + text[end:]
- paramstring, command = match.groups()
- if command == BEL:
- if paramstring.count(";") == 1:
- params = paramstring.split(";")
- # 0 - change title and icon (we will only change title)
- # 1 - change icon (we don't support this)
- # 2 - change title
- if params[0] in '02':
- winterm.set_title(params[1])
- return text
-
-
- def flush(self):
- self.wrapped.flush()
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/c2_model_loading.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/c2_model_loading.py
deleted file mode 100644
index 8c8d181bd7200bd3fd38446e743f8f16780d6e76..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/c2_model_loading.py
+++ /dev/null
@@ -1,407 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import re
-from typing import Dict, List
-import torch
-from tabulate import tabulate
-
-
-def convert_basic_c2_names(original_keys):
- """
- Apply some basic name conversion to names in C2 weights.
- It only deals with typical backbone models.
-
- Args:
- original_keys (list[str]):
- Returns:
- list[str]: The same number of strings matching those in original_keys.
- """
- layer_keys = copy.deepcopy(original_keys)
- layer_keys = [
- {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys
- ] # some hard-coded mappings
-
- layer_keys = [k.replace("_", ".") for k in layer_keys]
- layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys]
- layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys]
- # Uniform both bn and gn names to "norm"
- layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys]
- layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys]
- layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys]
-
- # stem
- layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys]
- # to avoid mis-matching with "conv1" in other components (e.g. detection head)
- layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys]
-
- # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5)
- # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys]
- # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys]
- # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys]
- # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys]
-
- # blocks
- layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys]
- layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
- layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
- layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
-
- # DensePose substitutions
- layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys]
- layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys]
- layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys]
- layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys]
- layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys]
- return layer_keys
-
-
-def convert_c2_detectron_names(weights):
- """
- Map Caffe2 Detectron weight names to Detectron2 names.
-
- Args:
- weights (dict): name -> tensor
-
- Returns:
- dict: detectron2 names -> tensor
- dict: detectron2 names -> C2 names
- """
- logger = logging.getLogger(__name__)
- logger.info("Renaming Caffe2 weights ......")
- original_keys = sorted(weights.keys())
- layer_keys = copy.deepcopy(original_keys)
-
- layer_keys = convert_basic_c2_names(layer_keys)
-
- # --------------------------------------------------------------------------
- # RPN hidden representation conv
- # --------------------------------------------------------------------------
- # FPN case
- # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then
- # shared for all other levels, hence the appearance of "fpn2"
- layer_keys = [
- k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys
- ]
- # Non-FPN case
- layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # RPN box transformation conv
- # --------------------------------------------------------------------------
- # FPN case (see note above about "fpn2")
- layer_keys = [
- k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas")
- for k in layer_keys
- ]
- layer_keys = [
- k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits")
- for k in layer_keys
- ]
- # Non-FPN case
- layer_keys = [
- k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys
- ]
- layer_keys = [
- k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits")
- for k in layer_keys
- ]
-
- # --------------------------------------------------------------------------
- # Fast R-CNN box head
- # --------------------------------------------------------------------------
- layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys]
- layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys]
- layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys]
- layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys]
- # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s
- layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # FPN lateral and output convolutions
- # --------------------------------------------------------------------------
- def fpn_map(name):
- """
- Look for keys with the following patterns:
- 1) Starts with "fpn.inner."
- Example: "fpn.inner.res2.2.sum.lateral.weight"
- Meaning: These are lateral pathway convolutions
- 2) Starts with "fpn.res"
- Example: "fpn.res2.2.sum.weight"
- Meaning: These are FPN output convolutions
- """
- splits = name.split(".")
- norm = ".norm" if "norm" in splits else ""
- if name.startswith("fpn.inner."):
- # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight']
- stage = int(splits[2][len("res") :])
- return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1])
- elif name.startswith("fpn.res"):
- # splits example: ['fpn', 'res2', '2', 'sum', 'weight']
- stage = int(splits[1][len("res") :])
- return "fpn_output{}{}.{}".format(stage, norm, splits[-1])
- return name
-
- layer_keys = [fpn_map(k) for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # Mask R-CNN mask head
- # --------------------------------------------------------------------------
- # roi_heads.StandardROIHeads case
- layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys]
- layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys]
- layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys]
- # roi_heads.Res5ROIHeads case
- layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # Keypoint R-CNN head
- # --------------------------------------------------------------------------
- # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX"
- layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys]
- layer_keys = [
- k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys
- ]
- layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # Done with replacements
- # --------------------------------------------------------------------------
- assert len(set(layer_keys)) == len(layer_keys)
- assert len(original_keys) == len(layer_keys)
-
- new_weights = {}
- new_keys_to_original_keys = {}
- for orig, renamed in zip(original_keys, layer_keys):
- new_keys_to_original_keys[renamed] = orig
- if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."):
- # remove the meaningless prediction weight for background class
- new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1
- new_weights[renamed] = weights[orig][new_start_idx:]
- logger.info(
- "Remove prediction weight for background class in {}. The shape changes from "
- "{} to {}.".format(
- renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape)
- )
- )
- elif renamed.startswith("cls_score."):
- # move weights of bg class from original index 0 to last index
- logger.info(
- "Move classification weights for background class in {} from index 0 to "
- "index {}.".format(renamed, weights[orig].shape[0] - 1)
- )
- new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]])
- else:
- new_weights[renamed] = weights[orig]
-
- return new_weights, new_keys_to_original_keys
-
-
-# Note the current matching is not symmetric.
-# it assumes model_state_dict will have longer names.
-def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True):
- """
- Match names between the two state-dict, and returns a new chkpt_state_dict with names
- converted to match model_state_dict with heuristics. The returned dict can be later
- loaded with fvcore checkpointer.
- If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2
- model and will be renamed at first.
-
- Strategy: suppose that the models that we will create will have prefixes appended
- to each of its keys, for example due to an extra level of nesting that the original
- pre-trained weights from ImageNet won't contain. For example, model.state_dict()
- might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
- res2.conv1.weight. We thus want to match both parameters together.
- For that, we look for each model weight, look among all loaded keys if there is one
- that is a suffix of the current weight name, and use it if that's the case.
- If multiple matches exist, take the one with longest size
- of the corresponding name. For example, for the same model as before, the pretrained
- weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
- we want to match backbone[0].body.conv1.weight to conv1.weight, and
- backbone[0].body.res2.conv1.weight to res2.conv1.weight.
- """
- model_keys = sorted(model_state_dict.keys())
- if c2_conversion:
- ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict)
- # original_keys: the name in the original dict (before renaming)
- else:
- original_keys = {x: x for x in ckpt_state_dict.keys()}
- ckpt_keys = sorted(ckpt_state_dict.keys())
-
- def match(a, b):
- # Matched ckpt_key should be a complete (starts with '.') suffix.
- # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
- # but matches whatever_conv1 or mesh_head.whatever_conv1.
- return a == b or a.endswith("." + b)
-
- # get a matrix of string matches, where each (i, j) entry correspond to the size of the
- # ckpt_key string, if it matches
- match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys]
- match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys))
- # use the matched one with longest size in case of multiple matches
- max_match_size, idxs = match_matrix.max(1)
- # remove indices that correspond to no-match
- idxs[max_match_size == 0] = -1
-
- logger = logging.getLogger(__name__)
- # matched_pairs (matched checkpoint key --> matched model key)
- matched_keys = {}
- result_state_dict = {}
- for idx_model, idx_ckpt in enumerate(idxs.tolist()):
- if idx_ckpt == -1:
- continue
- key_model = model_keys[idx_model]
- key_ckpt = ckpt_keys[idx_ckpt]
- value_ckpt = ckpt_state_dict[key_ckpt]
- shape_in_model = model_state_dict[key_model].shape
-
- if shape_in_model != value_ckpt.shape:
- logger.warning(
- "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format(
- key_ckpt, value_ckpt.shape, key_model, shape_in_model
- )
- )
- logger.warning(
- "{} will not be loaded. Please double check and see if this is desired.".format(
- key_ckpt
- )
- )
- continue
-
- assert key_model not in result_state_dict
- result_state_dict[key_model] = value_ckpt
- if key_ckpt in matched_keys: # already added to matched_keys
- logger.error(
- "Ambiguity found for {} in checkpoint!"
- "It matches at least two keys in the model ({} and {}).".format(
- key_ckpt, key_model, matched_keys[key_ckpt]
- )
- )
- raise ValueError("Cannot match one checkpoint key to multiple keys in the model.")
-
- matched_keys[key_ckpt] = key_model
-
- # logging:
- matched_model_keys = sorted(matched_keys.values())
- if len(matched_model_keys) == 0:
- logger.warning("No weights in checkpoint matched with model.")
- return ckpt_state_dict
- common_prefix = _longest_common_prefix(matched_model_keys)
- rev_matched_keys = {v: k for k, v in matched_keys.items()}
- original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys}
-
- model_key_groups = _group_keys_by_module(matched_model_keys, original_keys)
- table = []
- memo = set()
- for key_model in matched_model_keys:
- if key_model in memo:
- continue
- if key_model in model_key_groups:
- group = model_key_groups[key_model]
- memo |= set(group)
- shapes = [tuple(model_state_dict[k].shape) for k in group]
- table.append(
- (
- _longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*",
- _group_str([original_keys[k] for k in group]),
- " ".join([str(x).replace(" ", "") for x in shapes]),
- )
- )
- else:
- key_checkpoint = original_keys[key_model]
- shape = str(tuple(model_state_dict[key_model].shape))
- table.append((key_model[len(common_prefix) :], key_checkpoint, shape))
- table_str = tabulate(
- table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"]
- )
- logger.info(
- "Following weights matched with "
- + (f"submodule {common_prefix[:-1]}" if common_prefix else "model")
- + ":\n"
- + table_str
- )
-
- unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())]
- for k in unmatched_ckpt_keys:
- result_state_dict[k] = ckpt_state_dict[k]
- return result_state_dict
-
-
-def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]):
- """
- Params in the same submodule are grouped together.
-
- Args:
- keys: names of all parameters
- original_names: mapping from parameter name to their name in the checkpoint
-
- Returns:
- dict[name -> all other names in the same group]
- """
-
- def _submodule_name(key):
- pos = key.rfind(".")
- if pos < 0:
- return None
- prefix = key[: pos + 1]
- return prefix
-
- all_submodules = [_submodule_name(k) for k in keys]
- all_submodules = [x for x in all_submodules if x]
- all_submodules = sorted(all_submodules, key=len)
-
- ret = {}
- for prefix in all_submodules:
- group = [k for k in keys if k.startswith(prefix)]
- if len(group) <= 1:
- continue
- original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group])
- if len(original_name_lcp) == 0:
- # don't group weights if original names don't share prefix
- continue
-
- for k in group:
- if k in ret:
- continue
- ret[k] = group
- return ret
-
-
-def _longest_common_prefix(names: List[str]) -> str:
- """
- ["abc.zfg", "abc.zef"] -> "abc."
- """
- names = [n.split(".") for n in names]
- m1, m2 = min(names), max(names)
- ret = [a for a, b in zip(m1, m2) if a == b]
- ret = ".".join(ret) + "." if len(ret) else ""
- return ret
-
-
-def _longest_common_prefix_str(names: List[str]) -> str:
- m1, m2 = min(names), max(names)
- lcp = [a for a, b in zip(m1, m2) if a == b]
- lcp = "".join(lcp)
- return lcp
-
-
-def _group_str(names: List[str]) -> str:
- """
- Turn "common1", "common2", "common3" into "common{1,2,3}"
- """
- lcp = _longest_common_prefix_str(names)
- rest = [x[len(lcp) :] for x in names]
- rest = "{" + ",".join(rest) + "}"
- ret = lcp + rest
-
- # add some simplification for BN specifically
- ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*")
- ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*")
- return ret
diff --git a/spaces/BAAI/AltDiffusion-m9/style.css b/spaces/BAAI/AltDiffusion-m9/style.css
deleted file mode 100644
index d954ce678fed7d0f33bdc6af6764b73e06d6e78a..0000000000000000000000000000000000000000
--- a/spaces/BAAI/AltDiffusion-m9/style.css
+++ /dev/null
@@ -1,81 +0,0 @@
-.gradio-container {
- font-family: 'IBM Plex Sans', sans-serif;
-}
-.gr-button {
- color: white;
- /* border-color: black; */
- /* background: black; */
- background: rgb(60, 145, 238);
-}
-/* input[type='range'] {
- accent-color: rgb(60, 145, 238);
-}
-.dark input[type='range'] {
- accent-color: #dfdfdf;
-} */
-.container {
- max-width: 900px;
- margin: auto;
- padding-top: 1.5rem;
-}
-#gallery {
- min-height: 22rem;
- margin-bottom: 15px;
- margin-left: auto;
- margin-right: auto;
- border-bottom-right-radius: .5rem !important;
- border-bottom-left-radius: .5rem !important;
-}
-#gallery>div>.h-full {
- min-height: 20rem;
-}
-.details:hover {
- text-decoration: underline;
-}
-.gr-button {
- white-space: nowrap;
-}
-/* .gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
-} */
-.footer {
- margin-bottom: 45px;
- margin-top: 20px;
- /* text-align: center; */
- border-bottom: 1px solid #e5e5e5;
-}
-.footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
-}
-.footer>p>h4 {
- font-size: .20rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- font-weight: bold;
-}
-.dark .footer {
- /* border-color: #303030; */
- border-color: rgb(60, 145, 238);
-}
-.dark .footer>p {
- /* background: #0b0f19; */
- background: rgb(60, 145, 238);
-}
-.prompt h4{
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
-}
\ No newline at end of file
diff --git a/spaces/BAAI/vid2vid-zero/vid2vid_zero/models/resnet_2d.py b/spaces/BAAI/vid2vid-zero/vid2vid_zero/models/resnet_2d.py
deleted file mode 100644
index c6ed4dc106a18f6b243284f63ca455a8d4524c3d..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/vid2vid_zero/models/resnet_2d.py
+++ /dev/null
@@ -1,209 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from einops import rearrange
-
-
-class InflatedConv3d(nn.Conv2d):
- def forward(self, x):
- video_length = x.shape[2]
-
- x = rearrange(x, "b c f h w -> (b f) c h w")
- x = super().forward(x)
- x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
-
- return x
-
-
-class Upsample2D(nn.Module):
- def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_conv_transpose = use_conv_transpose
- self.name = name
-
- conv = None
- if use_conv_transpose:
- raise NotImplementedError
- elif use_conv:
- conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
-
- if name == "conv":
- self.conv = conv
- else:
- self.Conv2d_0 = conv
-
- def forward(self, hidden_states, output_size=None):
- assert hidden_states.shape[1] == self.channels
-
- if self.use_conv_transpose:
- raise NotImplementedError
-
- # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
- dtype = hidden_states.dtype
- if dtype == torch.bfloat16:
- hidden_states = hidden_states.to(torch.float32)
-
- # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
- if hidden_states.shape[0] >= 64:
- hidden_states = hidden_states.contiguous()
-
- # if `output_size` is passed we force the interpolation output
- # size and do not make use of `scale_factor=2`
- if output_size is None:
- hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
- else:
- hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
-
- # If the input is bfloat16, we cast back to bfloat16
- if dtype == torch.bfloat16:
- hidden_states = hidden_states.to(dtype)
-
- if self.use_conv:
- if self.name == "conv":
- hidden_states = self.conv(hidden_states)
- else:
- hidden_states = self.Conv2d_0(hidden_states)
-
- return hidden_states
-
-
-class Downsample2D(nn.Module):
- def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.padding = padding
- stride = 2
- self.name = name
-
- if use_conv:
- conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
- else:
- raise NotImplementedError
-
- if name == "conv":
- self.Conv2d_0 = conv
- self.conv = conv
- elif name == "Conv2d_0":
- self.conv = conv
- else:
- self.conv = conv
-
- def forward(self, hidden_states):
- assert hidden_states.shape[1] == self.channels
- if self.use_conv and self.padding == 0:
- raise NotImplementedError
-
- assert hidden_states.shape[1] == self.channels
- hidden_states = self.conv(hidden_states)
-
- return hidden_states
-
-
-class ResnetBlock2D(nn.Module):
- def __init__(
- self,
- *,
- in_channels,
- out_channels=None,
- conv_shortcut=False,
- dropout=0.0,
- temb_channels=512,
- groups=32,
- groups_out=None,
- pre_norm=True,
- eps=1e-6,
- non_linearity="swish",
- time_embedding_norm="default",
- output_scale_factor=1.0,
- use_in_shortcut=None,
- ):
- super().__init__()
- self.pre_norm = pre_norm
- self.pre_norm = True
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
- self.time_embedding_norm = time_embedding_norm
- self.output_scale_factor = output_scale_factor
-
- if groups_out is None:
- groups_out = groups
-
- self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
-
- self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
- if temb_channels is not None:
- if self.time_embedding_norm == "default":
- time_emb_proj_out_channels = out_channels
- elif self.time_embedding_norm == "scale_shift":
- time_emb_proj_out_channels = out_channels * 2
- else:
- raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
-
- self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
- else:
- self.time_emb_proj = None
-
- self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
- self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
- if non_linearity == "swish":
- self.nonlinearity = lambda x: F.silu(x)
- elif non_linearity == "mish":
- self.nonlinearity = Mish()
- elif non_linearity == "silu":
- self.nonlinearity = nn.SiLU()
-
- self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
-
- self.conv_shortcut = None
- if self.use_in_shortcut:
- self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
- def forward(self, input_tensor, temb):
- hidden_states = input_tensor
-
- hidden_states = self.norm1(hidden_states)
- hidden_states = self.nonlinearity(hidden_states)
-
- hidden_states = self.conv1(hidden_states)
-
- if temb is not None:
- temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
-
- if temb is not None and self.time_embedding_norm == "default":
- hidden_states = hidden_states + temb
-
- hidden_states = self.norm2(hidden_states)
-
- if temb is not None and self.time_embedding_norm == "scale_shift":
- scale, shift = torch.chunk(temb, 2, dim=1)
- hidden_states = hidden_states * (1 + scale) + shift
-
- hidden_states = self.nonlinearity(hidden_states)
-
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.conv2(hidden_states)
-
- if self.conv_shortcut is not None:
- input_tensor = self.conv_shortcut(input_tensor)
-
- output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
-
- return output_tensor
-
-
-class Mish(torch.nn.Module):
- def forward(self, hidden_states):
- return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
diff --git a/spaces/Badaleeloveashley/badaleeloveashley/README.md b/spaces/Badaleeloveashley/badaleeloveashley/README.md
deleted file mode 100644
index 4fd16a8695a9bb6eee4e8b2bf3154ede7d271a2a..0000000000000000000000000000000000000000
--- a/spaces/Badaleeloveashley/badaleeloveashley/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Badaleeloveashley
-emoji: 🚀
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Banbri/zcvzcv/postcss.config.js b/spaces/Banbri/zcvzcv/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/Banbri/zcvzcv/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
- plugins: {
- tailwindcss: {},
- autoprefixer: {},
- },
-}
diff --git a/spaces/Banbri/zcvzcv/src/app/interface/grid/index.tsx b/spaces/Banbri/zcvzcv/src/app/interface/grid/index.tsx
deleted file mode 100644
index 83bdf555fc742405b59e5e15d9052e918c0e9713..0000000000000000000000000000000000000000
--- a/spaces/Banbri/zcvzcv/src/app/interface/grid/index.tsx
+++ /dev/null
@@ -1,26 +0,0 @@
-"use client"
-
-import { ReactNode } from "react"
-
-import { cn } from "@/lib/utils"
-import { useStore } from "@/app/store"
-
-export function Grid({ children, className }: { children: ReactNode; className: string }) {
- const zoomLevel = useStore(state => state.zoomLevel)
-
- return (
-
- {children}
-
- )
-}
-
diff --git a/spaces/BartPoint/VoiceChange_Beta/infer_pack/models.py b/spaces/BartPoint/VoiceChange_Beta/infer_pack/models.py
deleted file mode 100644
index 5e4b2e72383efaee1fae4f5c42e3db2c627e4190..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange_Beta/infer_pack/models.py
+++ /dev/null
@@ -1,1124 +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 TextEncoder768(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(768, 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 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, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, 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) * torch.randn_like(m_p) * 0.66666) * 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, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(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 = TextEncoder768(
- 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, pitchf, y, y_lengths, ds
- ): # 这里ds是id，[bs,1]
- # print(1,pitch.shape)#[bs,t]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
- pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
- # print(-2,pitchf.shape,z_slice.shape)
- o = self.dec(z_slice, pitchf, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, pitch, nsff0, sid, 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) * torch.randn_like(m_p) * 0.66666) * 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, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(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=None,
- **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 = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- 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, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(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=None,
- **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 = TextEncoder768(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=False,
- )
- self.dec = Generator(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- )
- 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, y, y_lengths, ds): # 这里ds是id，[bs,1]
- g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
- z_slice, ids_slice = commons.rand_slice_segments(
- z, y_lengths, self.segment_size
- )
- o = self.dec(z_slice, g=g)
- return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, phone, phone_lengths, sid, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], g=g)
- return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-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 MultiPeriodDiscriminatorV2(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminatorV2, self).__init__()
- # periods = [2, 3, 5, 7, 11, 17]
- periods = [2, 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/BartPoint/VoiceChange_Beta/infer_pack/models_onnx.py b/spaces/BartPoint/VoiceChange_Beta/infer_pack/models_onnx.py
deleted file mode 100644
index b945eac8e59aac38fbd166da49eda01e2b8f4bd4..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange_Beta/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,818 +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 TextEncoder768(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(768, 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 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 SynthesizerTrnMsNSFsidM(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
- if self.gin_channels == 256:
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- else:
- self.enc_p = TextEncoder768(
- 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)
- self.speaker_map = None
- 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 construct_spkmixmap(self, n_speaker):
- self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
- for i in range(n_speaker):
- self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
- self.speaker_map = self.speaker_map.unsqueeze(0)
-
- def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
- if self.speaker_map is not None: # [N, S] * [S, B, 1, H]
- g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
- g = g * self.speaker_map # [N, S, B, 1, H]
- g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
- g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
- else:
- g = g.unsqueeze(0)
- g = self.emb_g(g).transpose(1, 2)
-
- 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 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 MultiPeriodDiscriminatorV2(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminatorV2, self).__init__()
- # periods = [2, 3, 5, 7, 11, 17]
- periods = [2, 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/BeeMon/dreambooth-training/README.md b/spaces/BeeMon/dreambooth-training/README.md
deleted file mode 100644
index 29f12037c9f880e438297e73306cd2b02f027eb2..0000000000000000000000000000000000000000
--- a/spaces/BeeMon/dreambooth-training/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Dreambooth Training
-emoji: ☁️
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: multimodalart/dreambooth-training
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Brawlhalla Steamunlocked.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Brawlhalla Steamunlocked.md
deleted file mode 100644
index db419f6bd01b12daba6fac463fa1907f0a3f5beb..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Gratis Brawlhalla Steamunlocked.md
+++ /dev/null
@@ -1,64 +0,0 @@
-
-Descarga gratuita de Brawlhalla Steamunlocked: Una guía para los fans de Platform Fighter
-Si usted está buscando un juego de lucha divertido y emocionante que se puede jugar de forma gratuita en su PC, es posible que desee echa un vistazo a Brawlhalla . Este juego es un luchador de plataformas que soporta el juego cruzado en varios dispositivos, incluyendo PC, PlayStation, Xbox, Nintendo Switch, iOS y Android. En este artículo, te diremos qué es Brawlhalla, cómo descargarlo gratis en Steam, cómo desbloquear más contenido en el juego y cómo mejorar tus habilidades como luchador.
-descargar gratis brawlhalla steamunlocked Download Zip ––– https://bltlly.com/2v6K1g
¿Qué es Brawlhalla?
-Brawlhalla es un juego de lucha de plataformas 2D que fue desarrollado por Blue Mammoth Games y publicado por Ubisoft. Fue lanzado en 2017 después de un período beta que comenzó en 2015. El juego ha sido elogiado por su mecánica de juego simple pero profunda, su colorido y diverso elenco de personajes, sus frecuentes actualizaciones y eventos, y su falta de ventajas de pago para ganar.
-Un luchador de plataforma de juego libre con soporte de juego cruzado
-Uno de los principales puntos de venta de Brawlhalla es que es libre de jugar. Usted no necesita pagar nada para descargar y jugar el juego en cualquier plataforma. Tampoco necesitas un servicio de suscripción online como PlayStation Plus o Nintendo Switch Online para jugar online con otros jugadores. El juego también soporta cross-play en todas las plataformas, lo que significa que puedes jugar con o contra cualquier persona que tenga el juego en cualquier dispositivo.
-Una lista de más de 50 leyendas y actualizaciones frecuentes
-
-Una variedad de modos de juego y características
-Brawlhalla también ofrece una variedad de modos de juego y características para mantenerte entretenido y desafiado. Puedes jugar online o localmente con hasta 8 jugadores en varios modos como Free-for-All, 1v1, 2v2, Brawlball, Kung Foot, Capture the Flag, Horde y más. También puedes personalizar tus partidos con diferentes configuraciones, mapas y modificadores. También puede unirse a los partidos clasificados y subir las tablas de clasificación, o participar en torneos y eventos para recompensas y gloria. El juego también tiene un modo para un solo jugador donde puedes luchar contra bots, completar misiones o jugar el modo historia.
-Cómo descargar Brawlhalla gratis en Steam?
-Si quieres jugar Brawlhalla en tu PC, puedes descargarlo gratis en Steam. Estos son los pasos para hacerlo:
-Visita la página de la tienda de Steam y haz clic en "Jugar"
-Primero, necesitas tener una cuenta de Steam y el cliente de Steam instalado en tu PC. Si aún no los tiene, puede crear una cuenta y descargar el cliente desde https://store.steampowered.com/ . Una vez que los tengas, abre el cliente de Steam y busca Brawlhalla en la tienda. Alternativamente, puedes visitar la página de la tienda del juego directamente desde este enlace: https://store.steampowered.com/app/291550/Brawlhalla/ . En la página de la tienda, verá un botón que dice "Jugar". Haga clic en él para comenzar a descargar el juego.
-Instalar el juego y lanzarlo desde su biblioteca
-Después de hacer clic en el botón "Jugar juego", verá una ventana emergente que le pide que confirme la instalación. Haga clic en "Siguiente" y siga las instrucciones para elegir la carpeta de instalación y aceptar los términos del servicio. El juego comenzará a descargarse e instalarse en su PC. El proceso puede tardar unos minutos dependiendo de su velocidad de Internet y espacio en disco. Una vez completada la instalación, puedes iniciar el juego desde tu biblioteca o desde el acceso directo del escritorio.
-
-
-Cuando inicie el juego por primera vez, se le pedirá que cree una cuenta o enlace a la existente. Puedes usar tu dirección de correo electrónico o tus cuentas de redes sociales como Facebook, Twitter, Google o Apple para registrarte o iniciar sesión. Crear una cuenta te permitirá guardar tu progreso, personalizar tu perfil, acceder a funciones en línea y sincronizar tus datos entre dispositivos. Si ya tienes una cuenta en otra plataforma, puedes vincularla a tu cuenta de Steam y mantener tu progreso y tus compras.
-¿Cómo desbloquear más contenido en Brawlhalla?
-Brawlhalla es gratis para jugar, pero también tiene mucho contenido que puedes desbloquear jugando o gastando dinero real. Aquí hay algunas maneras de desbloquear más contenido en Brawlhalla:
-Gana monedas de oro y mamut jugando partidos y completando misiones
-La moneda principal en Brawlhalla es gold , que puedes ganar jugando partidas y completando misiones. Puedes usar oro para comprar leyendas, colores y avatares en la tienda del juego. También puede ganar monedas mammoth , que son moneda premium que puede comprar con dinero real o obtener de eventos especiales. Puedes usar monedas gigantescas para comprar pieles, burlas, compañeros y pases de batalla de la tienda.
-Usa oro para comprar leyendas, colores y avatares
-Una de las cosas más importantes para desbloquear en Brawlhalla es Legends , que son los personajes que puedes jugar. Hay más de 50 leyendas en el juego, cada una con sus propias habilidades, armas, estadísticas y personalidades. Puedes comprar Leyendas con monedas de oro o de mamut en la tienda. Cada Leyenda cuesta 5400 monedas de oro o 100 monedas de mamut. También puede probar cualquier leyenda de forma gratuita en el modo de entrenamiento o durante rotaciones semanales.
-
-Usa monedas gigantescas para comprar pieles, burlas, compañeros y pases de batalla
-Si quieres personalizar tus leyendas aún más, puedes usar monedas gigantescas para comprar skins , burlas , , compañeros , y pases de batalla desde la tienda. Las pieles son opciones cosméticas que cambian la apariencia del atuendo, las armas y los efectos de tu leyenda. Puedes comprar pieles con monedas gigantescas en la tienda o desbloquearlas comprando pases de batalla o durante eventos especiales. Las burlas son emotes que puedes usar para expresarte en el juego. Puedes comprar burlas con monedas gigantescas en la tienda o desbloquearlas comprando pases de batalla o durante eventos especiales. Los compañeros son mascotas que te acompañan en el juego. Puedes comprar compañeros con monedas de mamut en la tienda o desbloquearlos comprando pases de batalla o durante eventos especiales. Los pases de batalla son pases de temporada que te dan acceso a recompensas exclusivas como pieles, burlas, compañeros, colores, avatares y más. Puedes comprar pases de batalla con monedas gigantescas en la tienda o conseguirlos gratis jugando el juego.
-¿Cómo mejorar tus habilidades en Brawlhalla?
-Brawlhalla es un juego que es fácil de aprender pero difícil de dominar. Si quieres mejorar tus habilidades como luchador, aquí hay algunos consejos y trucos que puedes seguir:
-Aprende los conceptos básicos de movimiento, recuperación, esquivar y atacar
-
-tecla de ataque pesado o el botón X mientras se mantiene pulsada la tecla de flecha hacia abajo o el botón B, que realiza un ataque especial y potente que es único para cada leyenda y arma.
-Experimenta con diferentes leyendas y armas
-Otra cosa que necesitas aprender en Brawlhalla es cómo usar diferentes leyendas y armas. Cada leyenda tiene sus propias habilidades, armas, estadísticas y personalidades. Puedes elegir una leyenda que se adapte a tu estilo de juego, preferencias y objetivos. También puedes cambiar entre leyendas para adaptarte a diferentes situaciones, oponentes y modos. Cada leyenda tiene dos armas que pueden usar en batalla. Puede recoger un arma pulsando la tecla de recogida o el botón Z cuando vea un arma en el escenario. También puedes lanzar tu arma presionando la tecla de recogida o el botón Z mientras sostienes un arma, lo que puede ser útil para golpear a tu oponente desde la distancia o desarmarlo. Cada arma tiene su propio movimiento, rango, velocidad y daño. Puedes usar diferentes armas para lidiar con diferentes escenarios, enemigos y estrategias. También puedes combinar tus armas con tus ataques desarmados, tu esquivar y tus ataques característicos para crear combos y cadenas.
-Practica en modo entrenamiento y mira tutoriales
-Lo último que tienes que hacer para mejorar tus habilidades en Brawlhalla es practicar y aprender de los demás. Puedes practicar en el modo de entrenamiento, que es un modo donde puedes probar tus habilidades contra un maniquí o un bot. Puedes personalizar los ajustes del modo de entrenamiento, como la leyenda, el arma, el mapa, el daño, la velocidad, los hitboxes y más. Puedes usar el modo de entrenamiento para aprender nuevos movimientos, practicar combos, probar daños y experimentar con diferentes opciones. También puedes ver tutoriales de otros jugadores o de los desarrolladores, que pueden enseñarte consejos, trucos, técnicas y estrategias para jugar a Brawlhalla. Puedes encontrar tutoriales en YouTube, Twitch, Reddit, Discord, o el sitio web oficial de Brawlhalla.
-
-Brawlhalla es un divertido y accesible luchador de plataformas que puedes jugar gratis en Steam. Tiene una gran y diversa lista de leyendas, una variedad de modos de juego y características, y un sistema de juego simple pero profundo. Puede descargarlo ahora y unirse a millones de jugadores en línea o localmente. También puedes desbloquear más contenido en el juego jugando partidos y completando misiones, o gastando dinero real si quieres. También puedes mejorar tus habilidades en Brawlhalla aprendiendo los conceptos básicos de movimiento, recuperación, esquivar y atacar, experimentando con diferentes leyendas y armas, y practicando en modo entrenamiento y viendo tutoriales. Brawlhalla es un juego que te mantendrá entretenido y retado durante horas. ¡Disfruta del juego y conviértete en el mejor luchador del Valhalla!
-Preguntas frecuentes
-¿Es Brawlhalla pago a ganar?
-No, Brawlhalla no es pagar para ganar. Todo el contenido que afecta al juego, como leyendas y armas, se puede desbloquear jugando el juego o gastando oro, que se gana jugando el juego. El único contenido que requiere dinero real para comprar es cosmético, como pieles, burlas, compañeros y pases de batalla. Estos elementos no dan ninguna ventaja de juego y son solo para personalización y expresión.
-¿Brawlhalla es multiplataforma?
-Sí, Brawlhalla es multiplataforma. Puedes jugar con o contra cualquier persona que tenga el juego en cualquier dispositivo, incluyendo PC, PlayStation, Xbox, Nintendo Switch, iOS y Android. También puedes sincronizar tu progreso y tus compras entre dispositivos mediante la vinculación de tu cuenta. Para habilitar el cross-play, necesita tener una conexión en línea y activar la opción de cross-play en la configuración.
-¿Cuántos jugadores pueden jugar Brawlhalla online o localmente?
-
-¿Cuáles son los requisitos del sistema para Brawlhalla en PC?
-Brawlhalla es un juego que no requiere muchos recursos para ejecutarse en PC. Aquí están los requisitos mínimos y recomendados del sistema para Brawlhalla en PC:
-
-Mínimo Recomendado OS: Windows XP/Vista/7/8/10 OS: Windows 7/8/10 Procesador: 2.4 GHz Dual Core Procesador: 2.8 GHz Quad Core Memoria: 1 GB de RAM Memoria: 4 GB de RAM Gráficos: 512 MB VRAM Gráficos: 1 GB VRAM DirectX: Versión 9.0c DirectX: Versión 9.0c Red: Conexión a Internet de banda ancha Red: Conexión a Internet de banda ancha Almacenamiento: 350 MB de espacio disponible Almacenamiento: 350 MB de espacio disponible
-¿Dónde puedo encontrar más información sobre Brawlhalla?
-Si quieres saber más sobre Brawlhalla, puedes visitar el sitio web oficial del juego en https://www.brawlhalla.com/ . Allí puedes encontrar noticias, actualizaciones, eventos, torneos, guías, videos y más. También puedes seguir el juego en plataformas de redes sociales como Facebook, Twitter, Instagram, YouTube, Twitch, Reddit, Discord y Steam. También puede ponerse en contacto con los desarrolladores o los administradores de la comunidad si tiene alguna pregunta, comentario o sugerencia.
64aa2da5cf` while the other
- exports `std::vector` via `py:bind_vector` and `py::module_local`"""
- import pybind11_cross_module_tests as cm
-
- v1 = cm.VectorInt([1, 2, 3])
- assert m.load_vector_via_caster(v1) == 6
- assert cm.load_vector_via_binding(v1) == 6
-
- v2 = [1, 2, 3]
- assert m.load_vector_via_caster(v2) == 6
- with pytest.raises(TypeError) as excinfo:
- cm.load_vector_via_binding(v2) == 6
- assert msg(excinfo.value) == """
- load_vector_via_binding(): incompatible function arguments. The following argument types are supported:
- 1. (arg0: pybind11_cross_module_tests.VectorInt) -> int
-
- Invoked with: [1, 2, 3]
- """ # noqa: E501 line too long
-
-
-def test_cross_module_calls():
- import pybind11_cross_module_tests as cm
-
- v1 = m.LocalVec()
- v1.append(m.LocalType(1))
- v2 = cm.LocalVec()
- v2.append(cm.LocalType(2))
-
- # Returning the self pointer should get picked up as returning an existing
- # instance (even when that instance is of a foreign, non-local type).
- assert m.return_self(v1) is v1
- assert cm.return_self(v2) is v2
- assert m.return_self(v2) is v2
- assert cm.return_self(v1) is v1
-
- assert m.LocalVec is not cm.LocalVec
- # Returning a copy, on the other hand, always goes to the local type,
- # regardless of where the source type came from.
- assert type(m.return_copy(v1)) is m.LocalVec
- assert type(m.return_copy(v2)) is m.LocalVec
- assert type(cm.return_copy(v1)) is cm.LocalVec
- assert type(cm.return_copy(v2)) is cm.LocalVec
-
- # Test the example given in the documentation (which also tests inheritance casting):
- mycat = m.Cat("Fluffy")
- mydog = cm.Dog("Rover")
- assert mycat.get_name() == "Fluffy"
- assert mydog.name() == "Rover"
- assert m.Cat.__base__.__name__ == "Pet"
- assert cm.Dog.__base__.__name__ == "Pet"
- assert m.Cat.__base__ is not cm.Dog.__base__
- assert m.pet_name(mycat) == "Fluffy"
- assert m.pet_name(mydog) == "Rover"
- assert cm.pet_name(mycat) == "Fluffy"
- assert cm.pet_name(mydog) == "Rover"
-
- assert m.MixGL is not cm.MixGL
- a = m.MixGL(1)
- b = cm.MixGL(2)
- assert m.get_gl_value(a) == 11
- assert m.get_gl_value(b) == 12
- assert cm.get_gl_value(a) == 101
- assert cm.get_gl_value(b) == 102
-
- c, d = m.MixGL2(3), cm.MixGL2(4)
- with pytest.raises(TypeError) as excinfo:
- m.get_gl_value(c)
- assert "incompatible function arguments" in str(excinfo.value)
- with pytest.raises(TypeError) as excinfo:
- m.get_gl_value(d)
- assert "incompatible function arguments" in str(excinfo.value)
diff --git a/spaces/CarlDennis/HYTTS/modules.py b/spaces/CarlDennis/HYTTS/modules.py
deleted file mode 100644
index f5af1fd9a20dc03707889f360a39bb4b784a6df3..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/modules.py
+++ /dev/null
@@ -1,387 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- 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.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- 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.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/analyze_code.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/analyze_code.py
deleted file mode 100644
index e02ea4c5b4ba53530e559d1cab7a07b8e3c7c638..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/analyze_code.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""Code evaluation module."""
-from __future__ import annotations
-
-from autogpt.llm_utils import call_ai_function
-
-
-def analyze_code(code: str) -> list[str]:
- """
- A function that takes in a string and returns a response from create chat
- completion api call.
-
- Parameters:
- code (str): Code to be evaluated.
- Returns:
- A result string from create chat completion. A list of suggestions to
- improve the code.
- """
-
- function_string = "def analyze_code(code: str) -> List[str]:"
- args = [code]
- description_string = (
- "Analyzes the given code and returns a list of suggestions" " for improvements."
- )
-
- return call_ai_function(function_string, args, description_string)
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/CHANGELOG.md b/spaces/CikeyQI/Yunzai/Yunzai/CHANGELOG.md
deleted file mode 100644
index e215f2d78d015ca94b7d81f8d8662b9bcaa6c9d0..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/CHANGELOG.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# 3.1.1
-
-* 支持协议端：米游社大别野Bot
-* 初步适配原神4.0版本，增加对应资源及信息展示，感谢**Ca(HCO₃)₂**、**@touchscale**、**@teriri7**
-* 升级`#探索`内容，支持更多内容展示 **@bangbanbab**
-* 增加 `#全部抽卡记录` **@story-x**
-
-# 3.1.0
-
-* 支持协议端：GSUIDCore、微信
-* 重构CK与UID管理逻辑
- * 支持多UID绑定，可绑定多个UID并进行切换
- * 支持原神与星铁UID共存，可针对查询命令分配对应UID
- * 新增`#删除uid1`命令，可对`#uid`列表内的绑定UID进行删除
- * 使用sqlite进行ck与uid存储
-* 底层对星铁查询进行支持 **@cvs**
-
-# 3.0.2
-
-* 支持协议端：ComWeChat、ICQQ、QQ频道、KOOK、Telegram、Discord
-* 3.6卡池以及图像武器别名等数据更新 **@cvs**
-* 将渲染逻辑独立，支持扩展渲染器 **@ikuaki**
-
-# 3.0.1
-
-* 支持多账号，支持协议端：go-cqhttp
-
-# 3.0.0
-
-* 从 Miao-Yunzai 分支
-
-# 3.0.0
\ No newline at end of file
diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.0d945c8f.js b/spaces/DEEMOSTECH/ChatAvatar/static/js/main.0d945c8f.js
deleted file mode 100644
index 82b940cb9fe24c9e00826420a8b73f5c2ca88890..0000000000000000000000000000000000000000
--- a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.0d945c8f.js
+++ /dev/null
@@ -1,3 +0,0 @@
-/*! For license information please see main.0d945c8f.js.LICENSE.txt */
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n.retention.referenceCounts.get(e)})))a.add(h);else{var p=i.nodeToNodeSubscriptions.get(h);p&&gi(p,(function(e){return a.has(e)}))?a.add(h):(A.add(h),l.add(h))}else a.add(h)}}catch(f){c.e(f)}finally{c.f()}var g,m=new Set,v=k(l);try{for(v.s();!(g=v.n()).done;){var y,w=g.value,B=k(null!==(_=i.nodeDeps.get(w))&&void 0!==_?_:Bi);try{for(B.s();!(y=B.n()).done;){var _,b=y.value;A.has(b)||m.add(b)}}catch(f){B.e(f)}finally{B.f()}}}catch(f){v.e(f)}finally{v.f()}m.size&&o(m)}}(e,A),h=k(d);try{for(h.s();!(c=h.n()).done;){bi(e,r,c.value)}}catch(f){h.e(f)}finally{h.f()}}}function bi(e,t,n){if(wn("recoil_memory_managament_2020")){mi(e,n);var r=e.getState();r.knownAtoms.delete(n),r.knownSelectors.delete(n),r.nodeTransactionSubscriptions.delete(n),r.retention.referenceCounts.delete(n);var i,A=k(Ci(n));try{for(A.s();!(i=A.n()).done;){var a,o=i.value;null===(a=r.retention.nodesRetainedByZone.get(o))||void 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function(){t||(t=!0,e._release())}}},{key:"autoRelease_INTERNAL",value:function(){var e=this;rA||window.setTimeout((function(){return e._release()}),10)}},{key:"_release",value:function(){if(this._refCount--,0===this._refCount){if(this._store.getState().nodeCleanupFunctions.forEach((function(e){return e()})),this._store.getState().nodeCleanupFunctions.clear(),!wn("recoil_memory_managament_2020"))return}else this._refCount}},{key:"isRetained",value:function(){return this._refCount>0}},{key:"checkRefCount_INTERNAL",value:function(){wn("recoil_memory_managament_2020")&&this._refCount}},{key:"getStore_INTERNAL",value:function(){return this.checkRefCount_INTERNAL(),this._store}},{key:"getID",value:function(){return this.checkRefCount_INTERNAL(),this._store.getState().currentTree.stateID}},{key:"getStoreID",value:function(){return this.checkRefCount_INTERNAL(),this._store.storeID}}]),e}();function aA(e,t){var n=arguments.length>2&&void 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new AA(aA(e,i),e.storeID)}),(function(e,t){var n,r;return String(t)+String(e.storeID)+String(null===(n=e.getState().nextTree)||void 0===n?void 0:n.version)+String(e.getState().currentTree.version)+String(null===(r=e.getState().previousTree)||void 0===r?void 0:r.version)})),2),sA=oA[0],lA=oA[1];eA(lA);var uA=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),Yt(C(i=t.call(this,aA(e.getStore_INTERNAL(),e.getStore_INTERNAL().getState().currentTree,!0),e.getStoreID())),"_batch",void 0),Yt(C(i),"set",(function(e,t){i.checkRefCount_INTERNAL();var n=i.getStore_INTERNAL();i._batch((function(){$i(n,e.key,1),Ji(i.getStore_INTERNAL(),e,t)}))})),Yt(C(i),"reset",(function(e){i.checkRefCount_INTERNAL();var t=i.getStore_INTERNAL();i._batch((function(){$i(t,e.key,1),Ji(i.getStore_INTERNAL(),e,Wi)}))})),Yt(C(i),"setUnvalidatedAtomValues_DEPRECATED",(function(e){i.checkRefCount_INTERNAL();var t=i.getStore_INTERNAL();Oi((function(){var 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State updater functions provided to Recoil must be pure functions.");var t=e.getState();if(null===t.nextTree){wn("recoil_memory_managament_2020")&&wn("recoil_release_on_cascading_update_killswitch_2021")&&t.commitDepth>0&&QA(e);var n=t.currentTree.version,r=wA();t.nextTree=u(u({},t.currentTree),{},{version:r,stateID:r,dirtyAtoms:new Set,transactionMetadata:{}}),t.graphsByVersion.set(r,UA(qt(t.graphsByVersion.get(n))))}}var KA=r.createContext({current:VA}),WA=function(){return IA(KA)},jA=r.createContext(null);function XA(e,t,n){var r,i=k(bA(e,n,n.dirtyAtoms));try{for(i.s();!(r=i.n()).done;){var A=r.value,a=t.nodeToComponentSubscriptions.get(A);if(a){var o,s=k(a);try{for(s.s();!(o=s.n()).done;){var l=p(o.value,2),u=(l[0],p(l[1],2));u[0];(0,u[1])(n)}}catch(c){s.e(c)}finally{s.f()}}}}catch(c){i.e(c)}finally{i.f()}}function qA(e){var t=e.getState(),n=t.currentTree,r=n.dirtyAtoms;if(r.size){var i,A=k(t.nodeTransactionSubscriptions);try{for(A.s();!(i=A.n()).done;){var 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n=t.nextTree;if(null==n)return;t.previousTree=t.currentTree,t.currentTree=n,t.nextTree=null,qA(e),null!=t.previousTree?t.graphsByVersion.delete(t.previousTree.version):Cn("Ended batch with no previous state, which is unexpected","recoil"),t.previousTree=null,wn("recoil_memory_managament_2020")&&null==n&&QA(e)}finally{t.commitDepth--}}(n.current)}))})),null}var JA=0;function ZA(e){var t,n=e.initializeState_DEPRECATED,i=e.initializeState,A=e.store_INTERNAL,a=e.children,o=function(e){var n=t.current.graphsByVersion;if(n.has(e))return qt(n.get(e));var r=EA();return n.set(e,r),r},s=function(e,t){if(null==t){var n=f.current.getState().transactionSubscriptions,r=JA++;return n.set(r,e),{release:function(){n.delete(r)}}}var i=f.current.getState().nodeTransactionSubscriptions;i.has(t)||i.set(t,new Map);var A=JA++;return qt(i.get(t)).set(A,e),{release:function(){var e=i.get(t);e&&(e.delete(A),0===e.size&&i.delete(t))}}},l=function(e){GA(f.current);for(var t=0,n=Object.keys(e);t. must be an 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i.get?i.get.call(arguments.length<3?e:n):i.value}},Xc.apply(this,arguments)}var qc=Object.create(null);qc.open="0",qc.close="1",qc.ping="2",qc.pong="3",qc.message="4",qc.upgrade="5",qc.noop="6";var Yc=Object.create(null);Object.keys(qc).forEach((function(e){Yc[qc[e]]=e}));for(var Jc={type:"error",data:"parser error"},Zc="function"===typeof Blob||"undefined"!==typeof Blob&&"[object BlobConstructor]"===Object.prototype.toString.call(Blob),$c="function"===typeof ArrayBuffer,ed=function(e,t){var n=new FileReader;return n.onload=function(){var e=n.result.split(",")[1];t("b"+(e||""))},n.readAsDataURL(e)},td=function(e,t,n){var r,i=e.type,A=e.data;return Zc&&A instanceof Blob?t?n(A):ed(A,n):$c&&(A instanceof ArrayBuffer||(r=A,"function"===typeof ArrayBuffer.isView?ArrayBuffer.isView(r):r&&r.buffer instanceof ArrayBuffer))?t?n(A):ed(new Blob([A]),n):n(qc[i]+(A||""))},nd="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/",rd="undefined"===typeof Uint8Array?[]:new 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n.fn=t,this.on(e,n),this},ud.prototype.off=ud.prototype.removeListener=ud.prototype.removeAllListeners=ud.prototype.removeEventListener=function(e,t){if(this._callbacks=this._callbacks||{},0==arguments.length)return this._callbacks={},this;var n,r=this._callbacks["$"+e];if(!r)return this;if(1==arguments.length)return delete this._callbacks["$"+e],this;for(var i=0;i1?t-1:0),r=1;r0);return t}function Sd(){var e=Cd(+new Date);return e!==md?(bd=0,md=e):e+"."+Cd(bd++)}for(;xd0&&void 0!==arguments[0]?arguments[0]:{};return Object.assign(e,{xd:this.xd,xs:this.xs},this.opts),new Ld(this.uri(),e)}},{key:"doWrite",value:function(e,t){var n=this,r=this.request({method:"POST",data:e});r.on("success",t),r.on("error",(function(e,t){n.onError("xhr post error",e,t)}))}},{key:"doPoll",value:function(){var e=this,t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(function(t,n){e.onError("xhr poll error",t,n)})),this.pollXhr=t}}]),n}(yd),Ld=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),pd(C(i=t.call(this)),r),i.opts=r,i.method=r.method||"GET",i.uri=e,i.async=!1!==r.async,i.data=void 0!==r.data?r.data:null,i.create(),i}return w(n,[{key:"create",value:function(){var e=this,t=dd(this.opts,"agent","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","autoUnref");t.xdomain=!!this.opts.xd,t.xscheme=!!this.opts.xs;var r=this.xhr=new Fd(t);try{r.open(this.method,this.uri,this.async);try{if(this.opts.extraHeaders)for(var i in r.setDisableHeaderCheck&&r.setDisableHeaderCheck(!0),this.opts.extraHeaders)this.opts.extraHeaders.hasOwnProperty(i)&&r.setRequestHeader(i,this.opts.extraHeaders[i])}catch(A){}if("POST"===this.method)try{r.setRequestHeader("Content-type","text/plain;charset=UTF-8")}catch(A){}try{r.setRequestHeader("Accept","*/*")}catch(A){}"withCredentials"in r&&(r.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(r.timeout=this.opts.requestTimeout),r.onreadystatechange=function(){4===r.readyState&&(200===r.status||1223===r.status?e.onLoad():e.setTimeoutFn((function(){e.onError("number"===typeof r.status?r.status:0)}),0))},r.send(this.data)}catch(A){return void this.setTimeoutFn((function(){e.onError(A)}),0)}"undefined"!==typeof document&&(this.index=n.requestsCount++,n.requests[this.index]=this)}},{key:"onError",value:function(e){this.emitReserved("error",e,this.xhr),this.cleanup(!0)}},{key:"cleanup",value:function(e){if("undefined"!==typeof this.xhr&&null!==this.xhr){if(this.xhr.onreadystatechange=Td,e)try{this.xhr.abort()}catch(t){}"undefined"!==typeof document&&delete n.requests[this.index],this.xhr=null}}},{key:"onLoad",value:function(){var e=this.xhr.responseText;null!==e&&(this.emitReserved("data",e),this.emitReserved("success"),this.cleanup())}},{key:"abort",value:function(){this.cleanup()}}]),n}(ud);if(Ld.requestsCount=0,Ld.requests={},"undefined"!==typeof document)if("function"===typeof attachEvent)attachEvent("onunload",Dd);else if("function"===typeof addEventListener){addEventListener("onpagehide"in cd?"pagehide":"unload",Dd,!1)}function Dd(){for(var e in Ld.requests)Ld.requests.hasOwnProperty(e)&&Ld.requests[e].abort()}var Id="function"===typeof Promise&&"function"===typeof Promise.resolve?function(e){return Promise.resolve().then(e)}:function(e,t){return t(e,0)},Rd=cd.WebSocket||cd.MozWebSocket,Pd="undefined"!==typeof navigator&&"string"===typeof navigator.product&&"reactnative"===navigator.product.toLowerCase(),Hd=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this,e)).supportsBinary=!e.forceBase64,r}return w(n,[{key:"name",get:function(){return"websocket"}},{key:"doOpen",value:function(){if(this.check()){var e=this.uri(),t=this.opts.protocols,n=Pd?{}:dd(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(n.headers=this.opts.extraHeaders);try{this.ws=Pd?new Rd(e,t,n):t?new Rd(e,t):new Rd(e)}catch(eC){return this.emitReserved("error",eC)}this.ws.binaryType=this.socket.binaryType||"arraybuffer",this.addEventListeners()}}},{key:"addEventListeners",value:function(){var e=this;this.ws.onopen=function(){e.opts.autoUnref&&e.ws._socket.unref(),e.onOpen()},this.ws.onclose=function(t){return e.onClose({description:"websocket connection closed",context:t})},this.ws.onmessage=function(t){return e.onData(t.data)},this.ws.onerror=function(t){return e.onError("websocket error",t)}}},{key:"write",value:function(e){var t=this;this.writable=!1;for(var n=function(){var n=e[r],i=r===e.length-1;td(n,t.supportsBinary,(function(e){try{t.ws.send(e)}catch(n){}i&&Id((function(){t.writable=!0,t.emitReserved("drain")}),t.setTimeoutFn)}))},r=0;r1&&void 0!==arguments[1]?arguments[1]:{};return v(this,n),(r=t.call(this)).writeBuffer=[],e&&"object"===typeof e&&(i=e,e=null),e?(e=zd(e),i.hostname=e.host,i.secure="https"===e.protocol||"wss"===e.protocol,i.port=e.port,e.query&&(i.query=e.query)):i.host&&(i.hostname=zd(i.host).host),pd(C(r),i),r.secure=null!=i.secure?i.secure:"undefined"!==typeof location&&"https:"===location.protocol,i.hostname&&!i.port&&(i.port=r.secure?"443":"80"),r.hostname=i.hostname||("undefined"!==typeof location?location.hostname:"localhost"),r.port=i.port||("undefined"!==typeof location&&location.port?location.port:r.secure?"443":"80"),r.transports=i.transports||["polling","websocket"],r.writeBuffer=[],r.prevBufferLen=0,r.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},i),r.opts.path=r.opts.path.replace(/\/$/,"")+(r.opts.addTrailingSlash?"/":""),"string"===typeof r.opts.query&&(r.opts.query=function(e){for(var t={},n=e.split("&"),r=0,i=n.length;r1))return this.writeBuffer;for(var e,t=1,n=0;n=57344?n+=3:(r++,n+=4);return n}(e):Math.ceil((e.byteLength||e.size)*gd)),n>0&&t>this.maxPayload)return this.writeBuffer.slice(0,n);t+=2}return this.writeBuffer}},{key:"write",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"send",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"sendPacket",value:function(e,t,n,r){if("function"===typeof t&&(r=t,t=void 0),"function"===typeof n&&(r=n,n=null),"closing"!==this.readyState&&"closed"!==this.readyState){(n=n||{}).compress=!1!==n.compress;var i={type:e,data:t,options:n};this.emitReserved("packetCreate",i),this.writeBuffer.push(i),r&&this.once("flush",r),this.flush()}}},{key:"close",value:function(){var e=this,t=function(){e.onClose("forced close"),e.transport.close()},n=function n(){e.off("upgrade",n),e.off("upgradeError",n),t()},r=function(){e.once("upgrade",n),e.once("upgradeError",n)};return"opening"!==this.readyState&&"open"!==this.readyState||(this.readyState="closing",this.writeBuffer.length?this.once("drain",(function(){e.upgrading?r():t()})):this.upgrading?r():t()),this}},{key:"onError",value:function(e){n.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}},{key:"onClose",value:function(e,t){"opening"!==this.readyState&&"open"!==this.readyState&&"closing"!==this.readyState||(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),"function"===typeof removeEventListener&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,t),this.writeBuffer=[],this.prevBufferLen=0)}},{key:"filterUpgrades",value:function(e){for(var t=[],n=0,r=e.length;n=0&&e.num0;case nh.ACK:case nh.BINARY_ACK:return Array.isArray(t)}}}]),n}(ud),ah=function(){function e(t){v(this,e),this.packet=t,this.buffers=[],this.reconPack=t}return w(e,[{key:"takeBinaryData",value:function(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){var t=eh(this.reconPack,this.buffers);return this.finishedReconstruction(),t}return null}},{key:"finishedReconstruction",value:function(){this.reconPack=null,this.buffers=[]}}]),e}();function oh(e,t,n){return e.on(t,n),function(){e.off(t,n)}}var sh=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1}),lh=function(e){_(n,e);var t=S(n);function n(e,r,i){var A;return v(this,n),(A=t.call(this)).connected=!1,A.recovered=!1,A.receiveBuffer=[],A.sendBuffer=[],A._queue=[],A._queueSeq=0,A.ids=0,A.acks={},A.flags={},A.io=e,A.nsp=r,i&&i.auth&&(A.auth=i.auth),A._opts=Object.assign({},i),A.io._autoConnect&&A.open(),A}return w(n,[{key:"disconnected",get:function(){return!this.connected}},{key:"subEvents",value:function(){if(!this.subs){var e=this.io;this.subs=[oh(e,"open",this.onopen.bind(this)),oh(e,"packet",this.onpacket.bind(this)),oh(e,"error",this.onerror.bind(this)),oh(e,"close",this.onclose.bind(this))]}}},{key:"active",get:function(){return!!this.subs}},{key:"connect",value:function(){return this.connected||(this.subEvents(),this.io._reconnecting||this.io.open(),"open"===this.io._readyState&&this.onopen()),this}},{key:"open",value:function(){return this.connect()}},{key:"send",value:function(){for(var e=arguments.length,t=new Array(e),n=0;n1?t-1:0),r=1;r1?n-1:0),i=1;in._opts.retries&&(n._queue.shift(),t&&t(e));else if(n._queue.shift(),t){for(var i=arguments.length,A=new Array(i>1?i-1:0),a=1;a0&&void 0!==arguments[0]&&arguments[0];if(this.connected&&0!==this._queue.length){var t=this._queue[0];t.pending&&!e||(t.pending=!0,t.tryCount++,this.flags=t.flags,this.emit.apply(this,t.args))}}},{key:"packet",value:function(e){e.nsp=this.nsp,this.io._packet(e)}},{key:"onopen",value:function(){var e=this;"function"==typeof this.auth?this.auth((function(t){e._sendConnectPacket(t)})):this._sendConnectPacket(this.auth)}},{key:"_sendConnectPacket",value:function(e){this.packet({type:nh.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},e):e})}},{key:"onerror",value:function(e){this.connected||this.emitReserved("connect_error",e)}},{key:"onclose",value:function(e,t){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,t)}},{key:"onpacket",value:function(e){if(e.nsp===this.nsp)switch(e.type){case nh.CONNECT:e.data&&e.data.sid?this.onconnect(e.data.sid,e.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case nh.EVENT:case nh.BINARY_EVENT:this.onevent(e);break;case nh.ACK:case nh.BINARY_ACK:this.onack(e);break;case nh.DISCONNECT:this.ondisconnect();break;case nh.CONNECT_ERROR:this.destroy();var t=new Error(e.data.message);t.data=e.data.data,this.emitReserved("connect_error",t)}}},{key:"onevent",value:function(e){var t=e.data||[];null!=e.id&&t.push(this.ack(e.id)),this.connected?this.emitEvent(t):this.receiveBuffer.push(Object.freeze(t))}},{key:"emitEvent",value:function(e){if(this._anyListeners&&this._anyListeners.length){var t,r=k(this._anyListeners.slice());try{for(r.s();!(t=r.n()).done;){t.value.apply(this,e)}}catch(eC){r.e(eC)}finally{r.f()}}Xc(b(n.prototype),"emit",this).apply(this,e),this._pid&&e.length&&"string"===typeof e[e.length-1]&&(this._lastOffset=e[e.length-1])}},{key:"ack",value:function(e){var t=this,n=!1;return function(){if(!n){n=!0;for(var r=arguments.length,i=new Array(r),A=0;A0&&e.jitter<=1?e.jitter:0,this.attempts=0}uh.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=0==(1&Math.floor(10*t))?e-n:e+n}return 0|Math.min(e,this.max)},uh.prototype.reset=function(){this.attempts=0},uh.prototype.setMin=function(e){this.ms=e},uh.prototype.setMax=function(e){this.max=e},uh.prototype.setJitter=function(e){this.jitter=e};var ch=function(t){_(r,t);var n=S(r);function r(t,i){var A,a;v(this,r),(A=n.call(this)).nsps={},A.subs=[],t&&"object"===typeof t&&(i=t,t=void 0),(i=i||{}).path=i.path||"/socket.io",A.opts=i,pd(C(A),i),A.reconnection(!1!==i.reconnection),A.reconnectionAttempts(i.reconnectionAttempts||1/0),A.reconnectionDelay(i.reconnectionDelay||1e3),A.reconnectionDelayMax(i.reconnectionDelayMax||5e3),A.randomizationFactor(null!==(a=i.randomizationFactor)&&void 0!==a?a:.5),A.backoff=new uh({min:A.reconnectionDelay(),max:A.reconnectionDelayMax(),jitter:A.randomizationFactor()}),A.timeout(null==i.timeout?2e4:i.timeout),A._readyState="closed",A.uri=t;var o=i.parser||e;return A.encoder=new o.Encoder,A.decoder=new o.Decoder,A._autoConnect=!1!==i.autoConnect,A._autoConnect&&A.open(),A}return w(r,[{key:"reconnection",value:function(e){return arguments.length?(this._reconnection=!!e,this):this._reconnection}},{key:"reconnectionAttempts",value:function(e){return void 0===e?this._reconnectionAttempts:(this._reconnectionAttempts=e,this)}},{key:"reconnectionDelay",value:function(e){var t;return void 0===e?this._reconnectionDelay:(this._reconnectionDelay=e,null===(t=this.backoff)||void 0===t||t.setMin(e),this)}},{key:"randomizationFactor",value:function(e){var t;return void 0===e?this._randomizationFactor:(this._randomizationFactor=e,null===(t=this.backoff)||void 0===t||t.setJitter(e),this)}},{key:"reconnectionDelayMax",value:function(e){var t;return void 0===e?this._reconnectionDelayMax:(this._reconnectionDelayMax=e,null===(t=this.backoff)||void 0===t||t.setMax(e),this)}},{key:"timeout",value:function(e){return arguments.length?(this._timeout=e,this):this._timeout}},{key:"maybeReconnectOnOpen",value:function(){!this._reconnecting&&this._reconnection&&0===this.backoff.attempts&&this.reconnect()}},{key:"open",value:function(e){var t=this;if(~this._readyState.indexOf("open"))return this;this.engine=new Gd(this.uri,this.opts);var n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;var i=oh(n,"open",(function(){r.onopen(),e&&e()})),A=oh(n,"error",(function(n){r.cleanup(),r._readyState="closed",t.emitReserved("error",n),e?e(n):r.maybeReconnectOnOpen()}));if(!1!==this._timeout){var a=this._timeout;0===a&&i();var o=this.setTimeoutFn((function(){i(),n.close(),n.emit("error",new Error("timeout"))}),a);this.opts.autoUnref&&o.unref(),this.subs.push((function(){clearTimeout(o)}))}return this.subs.push(i),this.subs.push(A),this}},{key:"connect",value:function(e){return this.open(e)}},{key:"onopen",value:function(){this.cleanup(),this._readyState="open",this.emitReserved("open");var 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r=this.setTimeoutFn((function(){t.skipReconnect||(e.emitReserved("reconnect_attempt",t.backoff.attempts),t.skipReconnect||t.open((function(n){n?(t._reconnecting=!1,t.reconnect(),e.emitReserved("reconnect_error",n)):t.onreconnect()})))}),n);this.opts.autoUnref&&r.unref(),this.subs.push((function(){clearTimeout(r)}))}}},{key:"onreconnect",value:function(){var e=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",e)}}]),r}(ud),dh={};function hh(e,t){"object"===typeof e&&(t=e,e=void 0);var n,r=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"",n=arguments.length>2?arguments[2]:void 0,r=e;n=n||"undefined"!==typeof location&&location,null==e&&(e=n.protocol+"//"+n.host),"string"===typeof e&&("/"===e.charAt(0)&&(e="/"===e.charAt(1)?n.protocol+e:n.host+e),/^(https?|wss?):\/\//.test(e)||(e="undefined"!==typeof 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this.x+=e.x*t,this.y+=e.y*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this}},{key:"divide",value:function(e){return this.x/=e.x,this.y/=e.y,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"applyMatrix3",value:function(e){var t=this.x,n=this.y,r=e.elements;return this.x=r[0]*t+r[3]*n+r[6],this.y=r[1]*t+r[4]*n+r[7],this}},{key:"min",value:function(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this}},{key:"max",value:function(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this}},{key:"clamp",value:function(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this}},{key:"clampScalar",value:function(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this}},{key:"clampLength",value:function(e,t){var n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}},{key:"floor",value:function(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}},{key:"ceil",value:function(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}},{key:"round",value:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}},{key:"roundToZero",value:function(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}},{key:"negate",value:function(){return this.x=-this.x,this.y=-this.y,this}},{key:"dot",value:function(e){return this.x*e.x+this.y*e.y}},{key:"cross",value:function(e){return this.x*e.y-this.y*e.x}},{key:"lengthSq",value:function(){return this.x*this.x+this.y*this.y}},{key:"length",value:function(){return Math.sqrt(this.x*this.x+this.y*this.y)}},{key:"manhattanLength",value:function(){return Math.abs(this.x)+Math.abs(this.y)}},{key:"normalize",value:function(){return this.divideScalar(this.length()||1)}},{key:"angle",value:function(){var e=Math.atan2(-this.y,-this.x)+Math.PI;return e}},{key:"distanceTo",value:function(e){return Math.sqrt(this.distanceToSquared(e))}},{key:"distanceToSquared",value:function(e){var t=this.x-e.x,n=this.y-e.y;return t*t+n*n}},{key:"manhattanDistanceTo",value:function(e){return Math.abs(this.x-e.x)+Math.abs(this.y-e.y)}},{key:"setLength",value:function(e){return this.normalize().multiplyScalar(e)}},{key:"lerp",value:function(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this}},{key:"lerpVectors",value:function(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this}},{key:"equals",value:function(e){return e.x===this.x&&e.y===this.y}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this}},{key:"rotateAround",value:function(e,t){var n=Math.cos(t),r=Math.sin(t),i=this.x-e.x,A=this.y-e.y;return this.x=i*n-A*r+e.x,this.y=i*r+A*n+e.y,this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:case"end":return e.stop()}}),e,this)}))}]),t}(),Wg=function(){function e(){v(this,e),e.prototype.isMatrix3=!0,this.elements=[1,0,0,0,1,0,0,0,1]}return w(e,[{key:"set",value:function(e,t,n,r,i,A,a,o,s){var l=this.elements;return l[0]=e,l[1]=r,l[2]=a,l[3]=t,l[4]=i,l[5]=o,l[6]=n,l[7]=A,l[8]=s,this}},{key:"identity",value:function(){return this.set(1,0,0,0,1,0,0,0,1),this}},{key:"copy",value:function(e){var t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],this}},{key:"extractBasis",value:function(e,t,n){return e.setFromMatrix3Column(this,0),t.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}},{key:"setFromMatrix4",value:function(e){var t=e.elements;return this.set(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10]),this}},{key:"multiply",value:function(e){return this.multiplyMatrices(this,e)}},{key:"premultiply",value:function(e){return this.multiplyMatrices(e,this)}},{key:"multiplyMatrices",value:function(e,t){var n=e.elements,r=t.elements,i=this.elements,A=n[0],a=n[3],o=n[6],s=n[1],l=n[4],u=n[7],c=n[2],d=n[5],h=n[8],f=r[0],p=r[3],g=r[6],m=r[1],v=r[4],y=r[7],w=r[2],B=r[5],_=r[8];return i[0]=A*f+a*m+o*w,i[3]=A*p+a*v+o*B,i[6]=A*g+a*y+o*_,i[1]=s*f+l*m+u*w,i[4]=s*p+l*v+u*B,i[7]=s*g+l*y+u*_,i[2]=c*f+d*m+h*w,i[5]=c*p+d*v+h*B,i[8]=c*g+d*y+h*_,this}},{key:"multiplyScalar",value:function(e){var t=this.elements;return t[0]*=e,t[3]*=e,t[6]*=e,t[1]*=e,t[4]*=e,t[7]*=e,t[2]*=e,t[5]*=e,t[8]*=e,this}},{key:"determinant",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8];return t*A*l-t*a*s-n*i*l+n*a*o+r*i*s-r*A*o}},{key:"invert",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8],u=l*A-a*s,c=a*o-l*i,d=s*i-A*o,h=t*u+n*c+r*d;if(0===h)return this.set(0,0,0,0,0,0,0,0,0);var f=1/h;return e[0]=u*f,e[1]=(r*s-l*n)*f,e[2]=(a*n-r*A)*f,e[3]=c*f,e[4]=(l*t-r*o)*f,e[5]=(r*i-a*t)*f,e[6]=d*f,e[7]=(n*o-s*t)*f,e[8]=(A*t-n*i)*f,this}},{key:"transpose",value:function(){var e,t=this.elements;return e=t[1],t[1]=t[3],t[3]=e,e=t[2],t[2]=t[6],t[6]=e,e=t[5],t[5]=t[7],t[7]=e,this}},{key:"getNormalMatrix",value:function(e){return this.setFromMatrix4(e).invert().transpose()}},{key:"transposeIntoArray",value:function(e){var t=this.elements;return e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8],this}},{key:"setUvTransform",value:function(e,t,n,r,i,A,a){var o=Math.cos(i),s=Math.sin(i);return this.set(n*o,n*s,-n*(o*A+s*a)+A+e,-r*s,r*o,-r*(-s*A+o*a)+a+t,0,0,1),this}},{key:"scale",value:function(e,t){return this.premultiply(jg.makeScale(e,t)),this}},{key:"rotate",value:function(e){return this.premultiply(jg.makeRotation(-e)),this}},{key:"translate",value:function(e,t){return this.premultiply(jg.makeTranslation(e,t)),this}},{key:"makeTranslation",value:function(e,t){return this.set(1,0,e,0,1,t,0,0,1),this}},{key:"makeRotation",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,n,t,0,0,0,1),this}},{key:"makeScale",value:function(e,t){return this.set(e,0,0,0,t,0,0,0,1),this}},{key:"equals",value:function(e){for(var t=this.elements,n=e.elements,r=0;r<9;r++)if(t[r]!==n[r])return!1;return!0}},{key:"fromArray",value:function(e){for(var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=0;n<9;n++)this.elements[n]=e[n+t];return this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e}},{key:"clone",value:function(){return(new this.constructor).fromArray(this.elements)}}]),e}(),jg=new Wg;function Xg(e){for(var t=e.length-1;t>=0;--t)if(e[t]>=65535)return!0;return!1}Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function qg(e){return document.createElementNS("http://www.w3.org/1999/xhtml",e)}function Yg(e){return e<.04045?.0773993808*e:Math.pow(.9478672986*e+.0521327014,2.4)}function Jg(e){return e<.0031308?12.92*e:1.055*Math.pow(e,.41666)-.055}var Zg=(s(Yh={},bg,s({},xg,Yg)),s(Yh,xg,s({},bg,Jg)),Yh),$g={legacyMode:!0,get workingColorSpace(){return xg},set workingColorSpace(e){console.warn("THREE.ColorManagement: .workingColorSpace is readonly.")},convert:function(e,t,n){if(this.legacyMode||t===n||!t||!n)return e;if(Zg[t]&&void 0!==Zg[t][n]){var r=Zg[t][n];return e.r=r(e.r),e.g=r(e.g),e.b=r(e.b),e}throw new Error("Unsupported color space conversion.")},fromWorkingColorSpace:function(e,t){return this.convert(e,this.workingColorSpace,t)},toWorkingColorSpace:function(e,t){return this.convert(e,t,this.workingColorSpace)}},em={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},tm={r:0,g:0,b:0},nm={h:0,s:0,l:0},rm={h:0,s:0,l:0};function im(e,t,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?e+6*(t-e)*n:n<.5?t:n<2/3?e+6*(t-e)*(2/3-n):e}function Am(e,t){return t.r=e.r,t.g=e.g,t.b=e.b,t}var am,om=function(e){function t(e,n,r){return v(this,t),this.isColor=!0,this.r=1,this.g=1,this.b=1,void 0===n&&void 0===r?this.set(e):this.setRGB(e,n,r)}return w(t,[{key:"set",value:function(e){return e&&e.isColor?this.copy(e):"number"===typeof e?this.setHex(e):"string"===typeof e&&this.setStyle(e),this}},{key:"setScalar",value:function(e){return this.r=e,this.g=e,this.b=e,this}},{key:"setHex",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:bg;return e=Math.floor(e),this.r=(e>>16&255)/255,this.g=(e>>8&255)/255,this.b=(255&e)/255,$g.toWorkingColorSpace(this,t),this}},{key:"setRGB",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:$g.workingColorSpace;return this.r=e,this.g=t,this.b=n,$g.toWorkingColorSpace(this,r),this}},{key:"setHSL",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:$g.workingColorSpace;if(e=Rg(e,1),t=Ig(t,0,1),n=Ig(n,0,1),0===t)this.r=this.g=this.b=n;else{var i=n<=.5?n*(1+t):n+t-n*t,A=2*n-i;this.r=im(A,i,e+1/3),this.g=im(A,i,e),this.b=im(A,i,e-1/3)}return $g.toWorkingColorSpace(this,r),this}},{key:"setStyle",value:function(e){var t,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:bg;function r(t){void 0!==t&&parseFloat(t)<1&&console.warn("THREE.Color: Alpha component of "+e+" will be ignored.")}if(t=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(e)){var i,A=t[1],a=t[2];switch(A){case"rgb":case"rgba":if(i=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return this.r=Math.min(255,parseInt(i[1],10))/255,this.g=Math.min(255,parseInt(i[2],10))/255,this.b=Math.min(255,parseInt(i[3],10))/255,$g.toWorkingColorSpace(this,n),r(i[4]),this;if(i=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return this.r=Math.min(100,parseInt(i[1],10))/100,this.g=Math.min(100,parseInt(i[2],10))/100,this.b=Math.min(100,parseInt(i[3],10))/100,$g.toWorkingColorSpace(this,n),r(i[4]),this;break;case"hsl":case"hsla":if(i=/^\s*(\d*\.?\d+)\s*,\s*(\d*\.?\d+)\%\s*,\s*(\d*\.?\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a)){var o=parseFloat(i[1])/360,s=parseFloat(i[2])/100,l=parseFloat(i[3])/100;return r(i[4]),this.setHSL(o,s,l,n)}}}else if(t=/^\#([A-Fa-f\d]+)$/.exec(e)){var u=t[1],c=u.length;if(3===c)return this.r=parseInt(u.charAt(0)+u.charAt(0),16)/255,this.g=parseInt(u.charAt(1)+u.charAt(1),16)/255,this.b=parseInt(u.charAt(2)+u.charAt(2),16)/255,$g.toWorkingColorSpace(this,n),this;if(6===c)return this.r=parseInt(u.charAt(0)+u.charAt(1),16)/255,this.g=parseInt(u.charAt(2)+u.charAt(3),16)/255,this.b=parseInt(u.charAt(4)+u.charAt(5),16)/255,$g.toWorkingColorSpace(this,n),this}return e&&e.length>0?this.setColorName(e,n):this}},{key:"setColorName",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:bg,n=em[e.toLowerCase()];return void 0!==n?this.setHex(n,t):console.warn("THREE.Color: Unknown color "+e),this}},{key:"clone",value:function(){return new this.constructor(this.r,this.g,this.b)}},{key:"copy",value:function(e){return this.r=e.r,this.g=e.g,this.b=e.b,this}},{key:"copySRGBToLinear",value:function(e){return this.r=Yg(e.r),this.g=Yg(e.g),this.b=Yg(e.b),this}},{key:"copyLinearToSRGB",value:function(e){return this.r=Jg(e.r),this.g=Jg(e.g),this.b=Jg(e.b),this}},{key:"convertSRGBToLinear",value:function(){return this.copySRGBToLinear(this),this}},{key:"convertLinearToSRGB",value:function(){return this.copyLinearToSRGB(this),this}},{key:"getHex",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:bg;return $g.fromWorkingColorSpace(Am(this,tm),e),Ig(255*tm.r,0,255)<<16^Ig(255*tm.g,0,255)<<8^Ig(255*tm.b,0,255)<<0}},{key:"getHexString",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:bg;return("000000"+this.getHex(e).toString(16)).slice(-6)}},{key:"getHSL",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:$g.workingColorSpace;$g.fromWorkingColorSpace(Am(this,tm),t);var n,r,i=tm.r,A=tm.g,a=tm.b,o=Math.max(i,A,a),s=Math.min(i,A,a),l=(s+o)/2;if(s===o)n=0,r=0;else{var u=o-s;switch(r=l<=.5?u/(o+s):u/(2-o-s),o){case i:n=(A-a)/u+(A1&&void 0!==arguments[1]?arguments[1]:$g.workingColorSpace;return $g.fromWorkingColorSpace(Am(this,tm),t),e.r=tm.r,e.g=tm.g,e.b=tm.b,e}},{key:"getStyle",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:bg;return $g.fromWorkingColorSpace(Am(this,tm),e),e!==bg?"color(".concat(e," ").concat(tm.r," ").concat(tm.g," ").concat(tm.b,")"):"rgb(".concat(255*tm.r|0,",").concat(255*tm.g|0,",").concat(255*tm.b|0,")")}},{key:"offsetHSL",value:function(e,t,n){return this.getHSL(nm),nm.h+=e,nm.s+=t,nm.l+=n,this.setHSL(nm.h,nm.s,nm.l),this}},{key:"add",value:function(e){return this.r+=e.r,this.g+=e.g,this.b+=e.b,this}},{key:"addColors",value:function(e,t){return this.r=e.r+t.r,this.g=e.g+t.g,this.b=e.b+t.b,this}},{key:"addScalar",value:function(e){return this.r+=e,this.g+=e,this.b+=e,this}},{key:"sub",value:function(e){return this.r=Math.max(0,this.r-e.r),this.g=Math.max(0,this.g-e.g),this.b=Math.max(0,this.b-e.b),this}},{key:"multiply",value:function(e){return this.r*=e.r,this.g*=e.g,this.b*=e.b,this}},{key:"multiplyScalar",value:function(e){return this.r*=e,this.g*=e,this.b*=e,this}},{key:"lerp",value:function(e,t){return this.r+=(e.r-this.r)*t,this.g+=(e.g-this.g)*t,this.b+=(e.b-this.b)*t,this}},{key:"lerpColors",value:function(e,t,n){return this.r=e.r+(t.r-e.r)*n,this.g=e.g+(t.g-e.g)*n,this.b=e.b+(t.b-e.b)*n,this}},{key:"lerpHSL",value:function(e,t){this.getHSL(nm),e.getHSL(rm);var n=Pg(nm.h,rm.h,t),r=Pg(nm.s,rm.s,t),i=Pg(nm.l,rm.l,t);return this.setHSL(n,r,i),this}},{key:"equals",value:function(e){return e.r===this.r&&e.g===this.g&&e.b===this.b}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.r=e[t],this.g=e[t+1],this.b=e[t+2],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.r,e[t+1]=this.g,e[t+2]=this.b,e}},{key:"fromBufferAttribute",value:function(e,t){return this.r=e.getX(t),this.g=e.getY(t),this.b=e.getZ(t),this}},{key:"toJSON",value:function(){return this.getHex()}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.r;case 2:return e.next=4,this.g;case 4:return e.next=6,this.b;case 6:case"end":return e.stop()}}),e,this)}))}]),t}();om.NAMES=em;var sm=function(){function e(){v(this,e)}return w(e,null,[{key:"getDataURL",value:function(e){if(/^data:/i.test(e.src))return e.src;if("undefined"==typeof HTMLCanvasElement)return e.src;var t;if(e instanceof HTMLCanvasElement)t=e;else{void 0===am&&(am=qg("canvas")),am.width=e.width,am.height=e.height;var n=am.getContext("2d");e instanceof ImageData?n.putImageData(e,0,0):n.drawImage(e,0,0,e.width,e.height),t=am}return t.width>2048||t.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",e),t.toDataURL("image/jpeg",.6)):t.toDataURL("image/png")}},{key:"sRGBToLinear",value:function(e){if("undefined"!==typeof HTMLImageElement&&e instanceof HTMLImageElement||"undefined"!==typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||"undefined"!==typeof ImageBitmap&&e instanceof ImageBitmap){var t=qg("canvas");t.width=e.width,t.height=e.height;var n=t.getContext("2d");n.drawImage(e,0,0,e.width,e.height);for(var r=n.getImageData(0,0,e.width,e.height),i=r.data,A=0;A0&&void 0!==arguments[0]?arguments[0]:null;v(this,e),this.isSource=!0,this.uuid=Dg(),this.data=t,this.version=0}return w(e,[{key:"needsUpdate",set:function(e){!0===e&&this.version++}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;if(!t&&void 0!==e.images[this.uuid])return e.images[this.uuid];var n={uuid:this.uuid,url:""},r=this.data;if(null!==r){var i;if(Array.isArray(r)){i=[];for(var A=0,a=r.length;A0&&void 0!==arguments[0]?arguments[0]:n.DEFAULT_IMAGE,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:n.DEFAULT_MAPPING,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:Ap,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:Ap,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:up,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:dp,l=arguments.length>6&&void 0!==arguments[6]?arguments[6]:Cp,u=arguments.length>7&&void 0!==arguments[7]?arguments[7]:hp,c=arguments.length>8&&void 0!==arguments[8]?arguments[8]:n.DEFAULT_ANISOTROPY,d=arguments.length>9&&void 0!==arguments[9]?arguments[9]:mg;return v(this,n),(e=t.call(this)).isTexture=!0,Object.defineProperty(C(e),"id",{value:cm++}),e.uuid=Dg(),e.name="",e.source=new lm(r),e.mipmaps=[],e.mapping=i,e.wrapS=A,e.wrapT=a,e.magFilter=o,e.minFilter=s,e.anisotropy=c,e.format=l,e.internalFormat=null,e.type=u,e.offset=new Kg(0,0),e.repeat=new Kg(1,1),e.center=new Kg(0,0),e.rotation=0,e.matrixAutoUpdate=!0,e.matrix=new Wg,e.generateMipmaps=!0,e.premultiplyAlpha=!1,e.flipY=!0,e.unpackAlignment=4,e.encoding=d,e.userData={},e.version=0,e.onUpdate=null,e.isRenderTargetTexture=!1,e.needsPMREMUpdate=!1,e}return w(n,[{key:"image",get:function(){return this.source.data},set:function(e){this.source.data=e}},{key:"updateMatrix",value:function(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.name=e.name,this.source=e.source,this.mipmaps=e.mipmaps.slice(0),this.mapping=e.mapping,this.wrapS=e.wrapS,this.wrapT=e.wrapT,this.magFilter=e.magFilter,this.minFilter=e.minFilter,this.anisotropy=e.anisotropy,this.format=e.format,this.internalFormat=e.internalFormat,this.type=e.type,this.offset.copy(e.offset),this.repeat.copy(e.repeat),this.center.copy(e.center),this.rotation=e.rotation,this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrix.copy(e.matrix),this.generateMipmaps=e.generateMipmaps,this.premultiplyAlpha=e.premultiplyAlpha,this.flipY=e.flipY,this.unpackAlignment=e.unpackAlignment,this.encoding=e.encoding,this.userData=JSON.parse(JSON.stringify(e.userData)),this.needsUpdate=!0,this}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;if(!t&&void 0!==e.textures[this.uuid])return e.textures[this.uuid];var n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,image:this.source.toJSON(e).uuid,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,generateMipmaps:this.generateMipmaps,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};return Object.keys(this.userData).length>0&&(n.userData=this.userData),t||(e.textures[this.uuid]=n),n}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}},{key:"transformUv",value:function(e){if(300!==this.mapping)return e;if(e.applyMatrix3(this.matrix),e.x<0||e.x>1)switch(this.wrapS){case ip:e.x=e.x-Math.floor(e.x);break;case Ap:e.x=e.x<0?0:1;break;case ap:1===Math.abs(Math.floor(e.x)%2)?e.x=Math.ceil(e.x)-e.x:e.x=e.x-Math.floor(e.x)}if(e.y<0||e.y>1)switch(this.wrapT){case ip:e.y=e.y-Math.floor(e.y);break;case Ap:e.y=e.y<0?0:1;break;case ap:1===Math.abs(Math.floor(e.y)%2)?e.y=Math.ceil(e.y)-e.y:e.y=e.y-Math.floor(e.y)}return this.flipY&&(e.y=1-e.y),e}},{key:"needsUpdate",set:function(e){!0===e&&(this.version++,this.source.needsUpdate=!0)}}]),n}(Fg);dm.DEFAULT_IMAGE=null,dm.DEFAULT_MAPPING=300,dm.DEFAULT_ANISOTROPY=1;var hm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;v(this,t),t.prototype.isVector4=!0,this.x=e,this.y=n,this.z=r,this.w=i}return w(t,[{key:"width",get:function(){return this.z},set:function(e){this.z=e}},{key:"height",get:function(){return this.w},set:function(e){this.w=e}},{key:"set",value:function(e,t,n,r){return this.x=e,this.y=t,this.z=n,this.w=r,this}},{key:"setScalar",value:function(e){return this.x=e,this.y=e,this.z=e,this.w=e,this}},{key:"setX",value:function(e){return this.x=e,this}},{key:"setY",value:function(e){return this.y=e,this}},{key:"setZ",value:function(e){return this.z=e,this}},{key:"setW",value:function(e){return this.w=e,this}},{key:"setComponent",value:function(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;case 3:this.w=t;break;default:throw new Error("index is out of range: "+e)}return this}},{key:"getComponent",value:function(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+e)}}},{key:"clone",value:function(){return new this.constructor(this.x,this.y,this.z,this.w)}},{key:"copy",value:function(e){return this.x=e.x,this.y=e.y,this.z=e.z,this.w=void 0!==e.w?e.w:1,this}},{key:"add",value:function(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this.w+=e.w,this}},{key:"addScalar",value:function(e){return this.x+=e,this.y+=e,this.z+=e,this.w+=e,this}},{key:"addVectors",value:function(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this.w=e.w+t.w,this}},{key:"addScaledVector",value:function(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this.w+=e.w*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this.w-=e.w,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this.z-=e,this.w-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this.w=e.w-t.w,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this.w*=e.w,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this.z*=e,this.w*=e,this}},{key:"applyMatrix4",value:function(e){var t=this.x,n=this.y,r=this.z,i=this.w,A=e.elements;return this.x=A[0]*t+A[4]*n+A[8]*r+A[12]*i,this.y=A[1]*t+A[5]*n+A[9]*r+A[13]*i,this.z=A[2]*t+A[6]*n+A[10]*r+A[14]*i,this.w=A[3]*t+A[7]*n+A[11]*r+A[15]*i,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"setAxisAngleFromQuaternion",value:function(e){this.w=2*Math.acos(e.w);var t=Math.sqrt(1-e.w*e.w);return t<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=e.x/t,this.y=e.y/t,this.z=e.z/t),this}},{key:"setAxisAngleFromRotationMatrix",value:function(e){var t,n,r,i,A=.01,a=.1,o=e.elements,s=o[0],l=o[4],u=o[8],c=o[1],d=o[5],h=o[9],f=o[2],p=o[6],g=o[10];if(Math.abs(l-c)v&&m>y?my?v1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this.w=e[t+3],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e[t+3]=this.w,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this.w=e.getW(t),this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:return e.next=6,this.z;case 6:return e.next=8,this.w;case 8:case"end":return e.stop()}}),e,this)}))}]),t}(),fm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};v(this,n),(e=t.call(this)).isWebGLRenderTarget=!0,e.width=r,e.height=i,e.depth=1,e.scissor=new hm(0,0,r,i),e.scissorTest=!1,e.viewport=new hm(0,0,r,i);var a={width:r,height:i,depth:1};return e.texture=new dm(a,A.mapping,A.wrapS,A.wrapT,A.magFilter,A.minFilter,A.format,A.type,A.anisotropy,A.encoding),e.texture.isRenderTargetTexture=!0,e.texture.flipY=!1,e.texture.generateMipmaps=void 0!==A.generateMipmaps&&A.generateMipmaps,e.texture.internalFormat=void 0!==A.internalFormat?A.internalFormat:null,e.texture.minFilter=void 0!==A.minFilter?A.minFilter:up,e.depthBuffer=void 0===A.depthBuffer||A.depthBuffer,e.stencilBuffer=void 0!==A.stencilBuffer&&A.stencilBuffer,e.depthTexture=void 0!==A.depthTexture?A.depthTexture:null,e.samples=void 0!==A.samples?A.samples:0,e}return w(n,[{key:"setSize",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;this.width===e&&this.height===t&&this.depth===n||(this.width=e,this.height=t,this.depth=n,this.texture.image.width=e,this.texture.image.height=t,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,e,t),this.scissor.set(0,0,e,t)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.width=e.width,this.height=e.height,this.depth=e.depth,this.viewport.copy(e.viewport),this.texture=e.texture.clone(),this.texture.isRenderTargetTexture=!0;var t=Object.assign({},e.texture.image);return this.texture.source=new lm(t),this.depthBuffer=e.depthBuffer,this.stencilBuffer=e.stencilBuffer,null!==e.depthTexture&&(this.depthTexture=e.depthTexture.clone()),this.samples=e.samples,this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}}]),n}(Fg),pm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;return v(this,n),(e=t.call(this,null)).isDataArrayTexture=!0,e.image={data:r,width:i,height:A,depth:a},e.magFilter=op,e.minFilter=op,e.wrapR=Ap,e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return w(n)}(dm),gm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;return v(this,n),(e=t.call(this,null)).isData3DTexture=!0,e.image={data:r,width:i,height:A,depth:a},e.magFilter=op,e.minFilter=op,e.wrapR=Ap,e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return w(n)}(dm),mm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;v(this,t),this.isQuaternion=!0,this._x=e,this._y=n,this._z=r,this._w=i}return w(t,[{key:"x",get:function(){return this._x},set:function(e){this._x=e,this._onChangeCallback()}},{key:"y",get:function(){return this._y},set:function(e){this._y=e,this._onChangeCallback()}},{key:"z",get:function(){return this._z},set:function(e){this._z=e,this._onChangeCallback()}},{key:"w",get:function(){return this._w},set:function(e){this._w=e,this._onChangeCallback()}},{key:"set",value:function(e,t,n,r){return this._x=e,this._y=t,this._z=n,this._w=r,this._onChangeCallback(),this}},{key:"clone",value:function(){return new this.constructor(this._x,this._y,this._z,this._w)}},{key:"copy",value:function(e){return this._x=e.x,this._y=e.y,this._z=e.z,this._w=e.w,this._onChangeCallback(),this}},{key:"setFromEuler",value:function(e,t){var n=e._x,r=e._y,i=e._z,A=e._order,a=Math.cos,o=Math.sin,s=a(n/2),l=a(r/2),u=a(i/2),c=o(n/2),d=o(r/2),h=o(i/2);switch(A){case"XYZ":this._x=c*l*u+s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u-c*d*h;break;case"YXZ":this._x=c*l*u+s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u+c*d*h;break;case"ZXY":this._x=c*l*u-s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u-c*d*h;break;case"ZYX":this._x=c*l*u-s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u+c*d*h;break;case"YZX":this._x=c*l*u+s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u-c*d*h;break;case"XZY":this._x=c*l*u-s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u+c*d*h;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+A)}return!1!==t&&this._onChangeCallback(),this}},{key:"setFromAxisAngle",value:function(e,t){var n=t/2,r=Math.sin(n);return this._x=e.x*r,this._y=e.y*r,this._z=e.z*r,this._w=Math.cos(n),this._onChangeCallback(),this}},{key:"setFromRotationMatrix",value:function(e){var t=e.elements,n=t[0],r=t[4],i=t[8],A=t[1],a=t[5],o=t[9],s=t[2],l=t[6],u=t[10],c=n+a+u;if(c>0){var d=.5/Math.sqrt(c+1);this._w=.25/d,this._x=(l-o)*d,this._y=(i-s)*d,this._z=(A-r)*d}else if(n>a&&n>u){var h=2*Math.sqrt(1+n-a-u);this._w=(l-o)/h,this._x=.25*h,this._y=(r+A)/h,this._z=(i+s)/h}else if(a>u){var f=2*Math.sqrt(1+a-n-u);this._w=(i-s)/f,this._x=(r+A)/f,this._y=.25*f,this._z=(o+l)/f}else{var p=2*Math.sqrt(1+u-n-a);this._w=(A-r)/p,this._x=(i+s)/p,this._y=(o+l)/p,this._z=.25*p}return this._onChangeCallback(),this}},{key:"setFromUnitVectors",value:function(e,t){var n=e.dot(t)+1;return nMath.abs(e.z)?(this._x=-e.y,this._y=e.x,this._z=0,this._w=n):(this._x=0,this._y=-e.z,this._z=e.y,this._w=n)):(this._x=e.y*t.z-e.z*t.y,this._y=e.z*t.x-e.x*t.z,this._z=e.x*t.y-e.y*t.x,this._w=n),this.normalize()}},{key:"angleTo",value:function(e){return 2*Math.acos(Math.abs(Ig(this.dot(e),-1,1)))}},{key:"rotateTowards",value:function(e,t){var n=this.angleTo(e);if(0===n)return this;var r=Math.min(1,t/n);return this.slerp(e,r),this}},{key:"identity",value:function(){return this.set(0,0,0,1)}},{key:"invert",value:function(){return this.conjugate()}},{key:"conjugate",value:function(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}},{key:"dot",value:function(e){return this._x*e._x+this._y*e._y+this._z*e._z+this._w*e._w}},{key:"lengthSq",value:function(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}},{key:"length",value:function(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}},{key:"normalize",value:function(){var e=this.length();return 0===e?(this._x=0,this._y=0,this._z=0,this._w=1):(e=1/e,this._x=this._x*e,this._y=this._y*e,this._z=this._z*e,this._w=this._w*e),this._onChangeCallback(),this}},{key:"multiply",value:function(e){return this.multiplyQuaternions(this,e)}},{key:"premultiply",value:function(e){return this.multiplyQuaternions(e,this)}},{key:"multiplyQuaternions",value:function(e,t){var n=e._x,r=e._y,i=e._z,A=e._w,a=t._x,o=t._y,s=t._z,l=t._w;return this._x=n*l+A*a+r*s-i*o,this._y=r*l+A*o+i*a-n*s,this._z=i*l+A*s+n*o-r*a,this._w=A*l-n*a-r*o-i*s,this._onChangeCallback(),this}},{key:"slerp",value:function(e,t){if(0===t)return this;if(1===t)return this.copy(e);var n=this._x,r=this._y,i=this._z,A=this._w,a=A*e._w+n*e._x+r*e._y+i*e._z;if(a<0?(this._w=-e._w,this._x=-e._x,this._y=-e._y,this._z=-e._z,a=-a):this.copy(e),a>=1)return this._w=A,this._x=n,this._y=r,this._z=i,this;var o=1-a*a;if(o<=Number.EPSILON){var s=1-t;return this._w=s*A+t*this._w,this._x=s*n+t*this._x,this._y=s*r+t*this._y,this._z=s*i+t*this._z,this.normalize(),this._onChangeCallback(),this}var l=Math.sqrt(o),u=Math.atan2(l,a),c=Math.sin((1-t)*u)/l,d=Math.sin(t*u)/l;return this._w=A*c+this._w*d,this._x=n*c+this._x*d,this._y=r*c+this._y*d,this._z=i*c+this._z*d,this._onChangeCallback(),this}},{key:"slerpQuaternions",value:function(e,t,n){return this.copy(e).slerp(t,n)}},{key:"random",value:function(){var e=Math.random(),t=Math.sqrt(1-e),n=Math.sqrt(e),r=2*Math.PI*Math.random(),i=2*Math.PI*Math.random();return this.set(t*Math.cos(r),n*Math.sin(i),n*Math.cos(i),t*Math.sin(r))}},{key:"equals",value:function(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._w===this._w}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this._x=e[t],this._y=e[t+1],this._z=e[t+2],this._w=e[t+3],this._onChangeCallback(),this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._w,e}},{key:"fromBufferAttribute",value:function(e,t){return this._x=e.getX(t),this._y=e.getY(t),this._z=e.getZ(t),this._w=e.getW(t),this}},{key:"_onChange",value:function(e){return this._onChangeCallback=e,this}},{key:"_onChangeCallback",value:function(){}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this._x;case 2:return e.next=4,this._y;case 4:return e.next=6,this._z;case 6:return e.next=8,this._w;case 8:case"end":return e.stop()}}),e,this)}))}],[{key:"slerpFlat",value:function(e,t,n,r,i,A,a){var o=n[r+0],s=n[r+1],l=n[r+2],u=n[r+3],c=i[A+0],d=i[A+1],h=i[A+2],f=i[A+3];if(0===a)return e[t+0]=o,e[t+1]=s,e[t+2]=l,void(e[t+3]=u);if(1===a)return e[t+0]=c,e[t+1]=d,e[t+2]=h,void(e[t+3]=f);if(u!==f||o!==c||s!==d||l!==h){var p=1-a,g=o*c+s*d+l*h+u*f,m=g>=0?1:-1,v=1-g*g;if(v>Number.EPSILON){var y=Math.sqrt(v),w=Math.atan2(y,g*m);p=Math.sin(p*w)/y,a=Math.sin(a*w)/y}var B=a*m;if(o=o*p+c*B,s=s*p+d*B,l=l*p+h*B,u=u*p+f*B,p===1-a){var _=1/Math.sqrt(o*o+s*s+l*l+u*u);o*=_,s*=_,l*=_,u*=_}}e[t]=o,e[t+1]=s,e[t+2]=l,e[t+3]=u}},{key:"multiplyQuaternionsFlat",value:function(e,t,n,r,i,A){var a=n[r],o=n[r+1],s=n[r+2],l=n[r+3],u=i[A],c=i[A+1],d=i[A+2],h=i[A+3];return e[t]=a*h+l*u+o*d-s*c,e[t+1]=o*h+l*c+s*u-a*d,e[t+2]=s*h+l*d+a*c-o*u,e[t+3]=l*h-a*u-o*c-s*d,e}}]),t}(),vm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;v(this,t),t.prototype.isVector3=!0,this.x=e,this.y=n,this.z=r}return w(t,[{key:"set",value:function(e,t,n){return void 0===n&&(n=this.z),this.x=e,this.y=t,this.z=n,this}},{key:"setScalar",value:function(e){return this.x=e,this.y=e,this.z=e,this}},{key:"setX",value:function(e){return this.x=e,this}},{key:"setY",value:function(e){return this.y=e,this}},{key:"setZ",value:function(e){return this.z=e,this}},{key:"setComponent",value:function(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;default:throw new Error("index is out of range: "+e)}return this}},{key:"getComponent",value:function(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+e)}}},{key:"clone",value:function(){return new this.constructor(this.x,this.y,this.z)}},{key:"copy",value:function(e){return this.x=e.x,this.y=e.y,this.z=e.z,this}},{key:"add",value:function(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this}},{key:"addScalar",value:function(e){return this.x+=e,this.y+=e,this.z+=e,this}},{key:"addVectors",value:function(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this}},{key:"addScaledVector",value:function(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this.z-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this.z*=e,this}},{key:"multiplyVectors",value:function(e,t){return this.x=e.x*t.x,this.y=e.y*t.y,this.z=e.z*t.z,this}},{key:"applyEuler",value:function(e){return this.applyQuaternion(wm.setFromEuler(e))}},{key:"applyAxisAngle",value:function(e,t){return this.applyQuaternion(wm.setFromAxisAngle(e,t))}},{key:"applyMatrix3",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements;return this.x=i[0]*t+i[3]*n+i[6]*r,this.y=i[1]*t+i[4]*n+i[7]*r,this.z=i[2]*t+i[5]*n+i[8]*r,this}},{key:"applyNormalMatrix",value:function(e){return this.applyMatrix3(e).normalize()}},{key:"applyMatrix4",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements,A=1/(i[3]*t+i[7]*n+i[11]*r+i[15]);return this.x=(i[0]*t+i[4]*n+i[8]*r+i[12])*A,this.y=(i[1]*t+i[5]*n+i[9]*r+i[13])*A,this.z=(i[2]*t+i[6]*n+i[10]*r+i[14])*A,this}},{key:"applyQuaternion",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.x,A=e.y,a=e.z,o=e.w,s=o*t+A*r-a*n,l=o*n+a*t-i*r,u=o*r+i*n-A*t,c=-i*t-A*n-a*r;return this.x=s*o+c*-i+l*-a-u*-A,this.y=l*o+c*-A+u*-i-s*-a,this.z=u*o+c*-a+s*-A-l*-i,this}},{key:"project",value:function(e){return this.applyMatrix4(e.matrixWorldInverse).applyMatrix4(e.projectionMatrix)}},{key:"unproject",value:function(e){return this.applyMatrix4(e.projectionMatrixInverse).applyMatrix4(e.matrixWorld)}},{key:"transformDirection",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements;return this.x=i[0]*t+i[4]*n+i[8]*r,this.y=i[1]*t+i[5]*n+i[9]*r,this.z=i[2]*t+i[6]*n+i[10]*r,this.normalize()}},{key:"divide",value:function(e){return this.x/=e.x,this.y/=e.y,this.z/=e.z,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"min",value:function(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this}},{key:"max",value:function(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this}},{key:"clamp",value:function(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this}},{key:"clampScalar",value:function(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this}},{key:"clampLength",value:function(e,t){var n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}},{key:"floor",value:function(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}},{key:"ceil",value:function(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}},{key:"round",value:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}},{key:"roundToZero",value:function(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}},{key:"negate",value:function(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}},{key:"dot",value:function(e){return this.x*e.x+this.y*e.y+this.z*e.z}},{key:"lengthSq",value:function(){return this.x*this.x+this.y*this.y+this.z*this.z}},{key:"length",value:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}},{key:"manhattanLength",value:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}},{key:"normalize",value:function(){return this.divideScalar(this.length()||1)}},{key:"setLength",value:function(e){return this.normalize().multiplyScalar(e)}},{key:"lerp",value:function(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this}},{key:"lerpVectors",value:function(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this}},{key:"cross",value:function(e){return this.crossVectors(this,e)}},{key:"crossVectors",value:function(e,t){var n=e.x,r=e.y,i=e.z,A=t.x,a=t.y,o=t.z;return this.x=r*o-i*a,this.y=i*A-n*o,this.z=n*a-r*A,this}},{key:"projectOnVector",value:function(e){var t=e.lengthSq();if(0===t)return this.set(0,0,0);var n=e.dot(this)/t;return this.copy(e).multiplyScalar(n)}},{key:"projectOnPlane",value:function(e){return ym.copy(this).projectOnVector(e),this.sub(ym)}},{key:"reflect",value:function(e){return this.sub(ym.copy(e).multiplyScalar(2*this.dot(e)))}},{key:"angleTo",value:function(e){var t=Math.sqrt(this.lengthSq()*e.lengthSq());if(0===t)return Math.PI/2;var n=this.dot(e)/t;return Math.acos(Ig(n,-1,1))}},{key:"distanceTo",value:function(e){return Math.sqrt(this.distanceToSquared(e))}},{key:"distanceToSquared",value:function(e){var t=this.x-e.x,n=this.y-e.y,r=this.z-e.z;return t*t+n*n+r*r}},{key:"manhattanDistanceTo",value:function(e){return Math.abs(this.x-e.x)+Math.abs(this.y-e.y)+Math.abs(this.z-e.z)}},{key:"setFromSpherical",value:function(e){return this.setFromSphericalCoords(e.radius,e.phi,e.theta)}},{key:"setFromSphericalCoords",value:function(e,t,n){var r=Math.sin(t)*e;return this.x=r*Math.sin(n),this.y=Math.cos(t)*e,this.z=r*Math.cos(n),this}},{key:"setFromCylindrical",value:function(e){return this.setFromCylindricalCoords(e.radius,e.theta,e.y)}},{key:"setFromCylindricalCoords",value:function(e,t,n){return this.x=e*Math.sin(t),this.y=n,this.z=e*Math.cos(t),this}},{key:"setFromMatrixPosition",value:function(e){var t=e.elements;return this.x=t[12],this.y=t[13],this.z=t[14],this}},{key:"setFromMatrixScale",value:function(e){var t=this.setFromMatrixColumn(e,0).length(),n=this.setFromMatrixColumn(e,1).length(),r=this.setFromMatrixColumn(e,2).length();return this.x=t,this.y=n,this.z=r,this}},{key:"setFromMatrixColumn",value:function(e,t){return this.fromArray(e.elements,4*t)}},{key:"setFromMatrix3Column",value:function(e,t){return this.fromArray(e.elements,3*t)}},{key:"setFromEuler",value:function(e){return this.x=e._x,this.y=e._y,this.z=e._z,this}},{key:"equals",value:function(e){return e.x===this.x&&e.y===this.y&&e.z===this.z}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}},{key:"randomDirection",value:function(){var e=2*(Math.random()-.5),t=Math.random()*Math.PI*2,n=Math.sqrt(1-Math.pow(e,2));return this.x=n*Math.cos(t),this.y=n*Math.sin(t),this.z=e,this}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:return e.next=6,this.z;case 6:case"end":return e.stop()}}),e,this)}))}]),t}(),ym=new vm,wm=new mm,Bm=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new vm(1/0,1/0,1/0),n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new vm(-1/0,-1/0,-1/0);v(this,e),this.isBox3=!0,this.min=t,this.max=n}return w(e,[{key:"set",value:function(e,t){return this.min.copy(e),this.max.copy(t),this}},{key:"setFromArray",value:function(e){for(var t=1/0,n=1/0,r=1/0,i=-1/0,A=-1/0,a=-1/0,o=0,s=e.length;oi&&(i=l),u>A&&(A=u),c>a&&(a=c)}return this.min.set(t,n,r),this.max.set(i,A,a),this}},{key:"setFromBufferAttribute",value:function(e){for(var t=1/0,n=1/0,r=1/0,i=-1/0,A=-1/0,a=-1/0,o=0,s=e.count;oi&&(i=l),u>A&&(A=u),c>a&&(a=c)}return this.min.set(t,n,r),this.max.set(i,A,a),this}},{key:"setFromPoints",value:function(e){this.makeEmpty();for(var t=0,n=e.length;t1&&void 0!==arguments[1]&&arguments[1];return this.makeEmpty(),this.expandByObject(e,t)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.min.copy(e.min),this.max.copy(e.max),this}},{key:"makeEmpty",value:function(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}},{key:"isEmpty",value:function(){return this.max.x1&&void 0!==arguments[1]&&arguments[1];e.updateWorldMatrix(!1,!1);var n=e.geometry;if(void 0!==n)if(t&&void 0!=n.attributes&&void 0!==n.attributes.position)for(var r=n.attributes.position,i=0,A=r.count;ithis.max.x||e.ythis.max.y||e.zthis.max.z)}},{key:"containsBox",value:function(e){return this.min.x<=e.min.x&&e.max.x<=this.max.x&&this.min.y<=e.min.y&&e.max.y<=this.max.y&&this.min.z<=e.min.z&&e.max.z<=this.max.z}},{key:"getParameter",value:function(e,t){return t.set((e.x-this.min.x)/(this.max.x-this.min.x),(e.y-this.min.y)/(this.max.y-this.min.y),(e.z-this.min.z)/(this.max.z-this.min.z))}},{key:"intersectsBox",value:function(e){return!(e.max.xthis.max.x||e.max.ythis.max.y||e.max.zthis.max.z)}},{key:"intersectsSphere",value:function(e){return this.clampPoint(e.center,bm),bm.distanceToSquared(e.center)<=e.radius*e.radius}},{key:"intersectsPlane",value:function(e){var t,n;return e.normal.x>0?(t=e.normal.x*this.min.x,n=e.normal.x*this.max.x):(t=e.normal.x*this.max.x,n=e.normal.x*this.min.x),e.normal.y>0?(t+=e.normal.y*this.min.y,n+=e.normal.y*this.max.y):(t+=e.normal.y*this.max.y,n+=e.normal.y*this.min.y),e.normal.z>0?(t+=e.normal.z*this.min.z,n+=e.normal.z*this.max.z):(t+=e.normal.z*this.max.z,n+=e.normal.z*this.min.z),t<=-e.constant&&n>=-e.constant}},{key:"intersectsTriangle",value:function(e){if(this.isEmpty())return!1;this.getCenter(Tm),km.subVectors(this.max,Tm),Cm.subVectors(e.a,Tm),Sm.subVectors(e.b,Tm),Em.subVectors(e.c,Tm),Um.subVectors(Sm,Cm),Mm.subVectors(Em,Sm),Fm.subVectors(Cm,Em);var t=[0,-Um.z,Um.y,0,-Mm.z,Mm.y,0,-Fm.z,Fm.y,Um.z,0,-Um.x,Mm.z,0,-Mm.x,Fm.z,0,-Fm.x,-Um.y,Um.x,0,-Mm.y,Mm.x,0,-Fm.y,Fm.x,0];return!!Dm(t,Cm,Sm,Em,km)&&(!!Dm(t=[1,0,0,0,1,0,0,0,1],Cm,Sm,Em,km)&&(Qm.crossVectors(Um,Mm),Dm(t=[Qm.x,Qm.y,Qm.z],Cm,Sm,Em,km)))}},{key:"clampPoint",value:function(e,t){return t.copy(e).clamp(this.min,this.max)}},{key:"distanceToPoint",value:function(e){return bm.copy(e).clamp(this.min,this.max).sub(e).length()}},{key:"getBoundingSphere",value:function(e){return this.getCenter(e.center),e.radius=.5*this.getSize(bm).length(),e}},{key:"intersect",value:function(e){return this.min.max(e.min),this.max.min(e.max),this.isEmpty()&&this.makeEmpty(),this}},{key:"union",value:function(e){return this.min.min(e.min),this.max.max(e.max),this}},{key:"applyMatrix4",value:function(e){return this.isEmpty()||(_m[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(e),_m[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(e),_m[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(e),_m[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(e),_m[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(e),_m[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(e),_m[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(e),_m[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(e),this.setFromPoints(_m)),this}},{key:"translate",value:function(e){return this.min.add(e),this.max.add(e),this}},{key:"equals",value:function(e){return e.min.equals(this.min)&&e.max.equals(this.max)}}]),e}(),_m=[new vm,new vm,new vm,new vm,new vm,new vm,new vm,new vm],bm=new vm,xm=new Bm,Cm=new vm,Sm=new vm,Em=new vm,Um=new vm,Mm=new vm,Fm=new vm,Tm=new vm,km=new vm,Qm=new vm,Lm=new vm;function Dm(e,t,n,r,i){for(var A=0,a=e.length-3;A<=a;A+=3){Lm.fromArray(e,A);var o=i.x*Math.abs(Lm.x)+i.y*Math.abs(Lm.y)+i.z*Math.abs(Lm.z),s=t.dot(Lm),l=n.dot(Lm),u=r.dot(Lm);if(Math.max(-Math.max(s,l,u),Math.min(s,l,u))>o)return!1}return!0}var Im=new Bm,Rm=new vm,Pm=new vm,Hm=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new vm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;v(this,e),this.center=t,this.radius=n}return w(e,[{key:"set",value:function(e,t){return this.center.copy(e),this.radius=t,this}},{key:"setFromPoints",value:function(e,t){var n=this.center;void 0!==t?n.copy(t):Im.setFromPoints(e).getCenter(n);for(var r=0,i=0,A=e.length;ithis.radius*this.radius&&(t.sub(this.center).normalize(),t.multiplyScalar(this.radius).add(this.center)),t}},{key:"getBoundingBox",value:function(e){return this.isEmpty()?(e.makeEmpty(),e):(e.set(this.center,this.center),e.expandByScalar(this.radius),e)}},{key:"applyMatrix4",value:function(e){return this.center.applyMatrix4(e),this.radius=this.radius*e.getMaxScaleOnAxis(),this}},{key:"translate",value:function(e){return this.center.add(e),this}},{key:"expandByPoint",value:function(e){if(this.isEmpty())return this.center.copy(e),this.radius=0,this;Rm.subVectors(e,this.center);var t=Rm.lengthSq();if(t>this.radius*this.radius){var n=Math.sqrt(t),r=.5*(n-this.radius);this.center.addScaledVector(Rm,r/n),this.radius+=r}return this}},{key:"union",value:function(e){return e.isEmpty()?this:this.isEmpty()?(this.copy(e),this):(!0===this.center.equals(e.center)?this.radius=Math.max(this.radius,e.radius):(Pm.subVectors(e.center,this.center).setLength(e.radius),this.expandByPoint(Rm.copy(e.center).add(Pm)),this.expandByPoint(Rm.copy(e.center).sub(Pm))),this)}},{key:"equals",value:function(e){return e.center.equals(this.center)&&e.radius===this.radius}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Nm=new vm,Om=new vm,Vm=new vm,zm=new vm,Gm=new vm,Km=new vm,Wm=new vm,jm=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new vm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new vm(0,0,-1);v(this,e),this.origin=t,this.direction=n}return w(e,[{key:"set",value:function(e,t){return this.origin.copy(e),this.direction.copy(t),this}},{key:"copy",value:function(e){return this.origin.copy(e.origin),this.direction.copy(e.direction),this}},{key:"at",value:function(e,t){return t.copy(this.direction).multiplyScalar(e).add(this.origin)}},{key:"lookAt",value:function(e){return this.direction.copy(e).sub(this.origin).normalize(),this}},{key:"recast",value:function(e){return this.origin.copy(this.at(e,Nm)),this}},{key:"closestPointToPoint",value:function(e,t){t.subVectors(e,this.origin);var n=t.dot(this.direction);return n<0?t.copy(this.origin):t.copy(this.direction).multiplyScalar(n).add(this.origin)}},{key:"distanceToPoint",value:function(e){return Math.sqrt(this.distanceSqToPoint(e))}},{key:"distanceSqToPoint",value:function(e){var t=Nm.subVectors(e,this.origin).dot(this.direction);return t<0?this.origin.distanceToSquared(e):(Nm.copy(this.direction).multiplyScalar(t).add(this.origin),Nm.distanceToSquared(e))}},{key:"distanceSqToSegment",value:function(e,t,n,r){Om.copy(e).add(t).multiplyScalar(.5),Vm.copy(t).sub(e).normalize(),zm.copy(this.origin).sub(Om);var i,A,a,o,s=.5*e.distanceTo(t),l=-this.direction.dot(Vm),u=zm.dot(this.direction),c=-zm.dot(Vm),d=zm.lengthSq(),h=Math.abs(1-l*l);if(h>0)if(A=l*u-c,o=s*h,(i=l*c-u)>=0)if(A>=-o)if(A<=o){var f=1/h;a=(i*=f)*(i+l*(A*=f)+2*u)+A*(l*i+A+2*c)+d}else A=s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;else A=-s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;else A<=-o?a=-(i=Math.max(0,-(-l*s+u)))*i+(A=i>0?-s:Math.min(Math.max(-s,-c),s))*(A+2*c)+d:A<=o?(i=0,a=(A=Math.min(Math.max(-s,-c),s))*(A+2*c)+d):a=-(i=Math.max(0,-(l*s+u)))*i+(A=i>0?s:Math.min(Math.max(-s,-c),s))*(A+2*c)+d;else A=l>0?-s:s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;return n&&n.copy(this.direction).multiplyScalar(i).add(this.origin),r&&r.copy(Vm).multiplyScalar(A).add(Om),a}},{key:"intersectSphere",value:function(e,t){Nm.subVectors(e.center,this.origin);var n=Nm.dot(this.direction),r=Nm.dot(Nm)-n*n,i=e.radius*e.radius;if(r>i)return null;var A=Math.sqrt(i-r),a=n-A,o=n+A;return a<0&&o<0?null:a<0?this.at(o,t):this.at(a,t)}},{key:"intersectsSphere",value:function(e){return this.distanceSqToPoint(e.center)<=e.radius*e.radius}},{key:"distanceToPlane",value:function(e){var t=e.normal.dot(this.direction);if(0===t)return 0===e.distanceToPoint(this.origin)?0:null;var n=-(this.origin.dot(e.normal)+e.constant)/t;return n>=0?n:null}},{key:"intersectPlane",value:function(e,t){var n=this.distanceToPlane(e);return null===n?null:this.at(n,t)}},{key:"intersectsPlane",value:function(e){var t=e.distanceToPoint(this.origin);return 0===t||e.normal.dot(this.direction)*t<0}},{key:"intersectBox",value:function(e,t){var n,r,i,A,a,o,s=1/this.direction.x,l=1/this.direction.y,u=1/this.direction.z,c=this.origin;return s>=0?(n=(e.min.x-c.x)*s,r=(e.max.x-c.x)*s):(n=(e.max.x-c.x)*s,r=(e.min.x-c.x)*s),l>=0?(i=(e.min.y-c.y)*l,A=(e.max.y-c.y)*l):(i=(e.max.y-c.y)*l,A=(e.min.y-c.y)*l),n>A||i>r?null:((i>n||isNaN(n))&&(n=i),(A=0?(a=(e.min.z-c.z)*u,o=(e.max.z-c.z)*u):(a=(e.max.z-c.z)*u,o=(e.min.z-c.z)*u),n>o||a>r?null:((a>n||n!==n)&&(n=a),(o=0?n:r,t)))}},{key:"intersectsBox",value:function(e){return null!==this.intersectBox(e,Nm)}},{key:"intersectTriangle",value:function(e,t,n,r,i){Gm.subVectors(t,e),Km.subVectors(n,e),Wm.crossVectors(Gm,Km);var A,a=this.direction.dot(Wm);if(a>0){if(r)return null;A=1}else{if(!(a<0))return null;A=-1,a=-a}zm.subVectors(this.origin,e);var o=A*this.direction.dot(Km.crossVectors(zm,Km));if(o<0)return null;var s=A*this.direction.dot(Gm.cross(zm));if(s<0)return null;if(o+s>a)return null;var l=-A*zm.dot(Wm);return l<0?null:this.at(l/a,i)}},{key:"applyMatrix4",value:function(e){return this.origin.applyMatrix4(e),this.direction.transformDirection(e),this}},{key:"equals",value:function(e){return e.origin.equals(this.origin)&&e.direction.equals(this.direction)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Xm=function(){function e(){v(this,e),e.prototype.isMatrix4=!0,this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}return w(e,[{key:"set",value:function(e,t,n,r,i,A,a,o,s,l,u,c,d,h,f,p){var g=this.elements;return g[0]=e,g[4]=t,g[8]=n,g[12]=r,g[1]=i,g[5]=A,g[9]=a,g[13]=o,g[2]=s,g[6]=l,g[10]=u,g[14]=c,g[3]=d,g[7]=h,g[11]=f,g[15]=p,this}},{key:"identity",value:function(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}},{key:"clone",value:function(){return(new e).fromArray(this.elements)}},{key:"copy",value:function(e){var t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t[9]=n[9],t[10]=n[10],t[11]=n[11],t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],this}},{key:"copyPosition",value:function(e){var t=this.elements,n=e.elements;return t[12]=n[12],t[13]=n[13],t[14]=n[14],this}},{key:"setFromMatrix3",value:function(e){var t=e.elements;return this.set(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0,0,0,0,1),this}},{key:"extractBasis",value:function(e,t,n){return e.setFromMatrixColumn(this,0),t.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}},{key:"makeBasis",value:function(e,t,n){return this.set(e.x,t.x,n.x,0,e.y,t.y,n.y,0,e.z,t.z,n.z,0,0,0,0,1),this}},{key:"extractRotation",value:function(e){var t=this.elements,n=e.elements,r=1/qm.setFromMatrixColumn(e,0).length(),i=1/qm.setFromMatrixColumn(e,1).length(),A=1/qm.setFromMatrixColumn(e,2).length();return t[0]=n[0]*r,t[1]=n[1]*r,t[2]=n[2]*r,t[3]=0,t[4]=n[4]*i,t[5]=n[5]*i,t[6]=n[6]*i,t[7]=0,t[8]=n[8]*A,t[9]=n[9]*A,t[10]=n[10]*A,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}},{key:"makeRotationFromEuler",value:function(e){var t=this.elements,n=e.x,r=e.y,i=e.z,A=Math.cos(n),a=Math.sin(n),o=Math.cos(r),s=Math.sin(r),l=Math.cos(i),u=Math.sin(i);if("XYZ"===e.order){var c=A*l,d=A*u,h=a*l,f=a*u;t[0]=o*l,t[4]=-o*u,t[8]=s,t[1]=d+h*s,t[5]=c-f*s,t[9]=-a*o,t[2]=f-c*s,t[6]=h+d*s,t[10]=A*o}else if("YXZ"===e.order){var p=o*l,g=o*u,m=s*l,v=s*u;t[0]=p+v*a,t[4]=m*a-g,t[8]=A*s,t[1]=A*u,t[5]=A*l,t[9]=-a,t[2]=g*a-m,t[6]=v+p*a,t[10]=A*o}else if("ZXY"===e.order){var y=o*l,w=o*u,B=s*l,_=s*u;t[0]=y-_*a,t[4]=-A*u,t[8]=B+w*a,t[1]=w+B*a,t[5]=A*l,t[9]=_-y*a,t[2]=-A*s,t[6]=a,t[10]=A*o}else if("ZYX"===e.order){var b=A*l,x=A*u,C=a*l,S=a*u;t[0]=o*l,t[4]=C*s-x,t[8]=b*s+S,t[1]=o*u,t[5]=S*s+b,t[9]=x*s-C,t[2]=-s,t[6]=a*o,t[10]=A*o}else if("YZX"===e.order){var E=A*o,U=A*s,M=a*o,F=a*s;t[0]=o*l,t[4]=F-E*u,t[8]=M*u+U,t[1]=u,t[5]=A*l,t[9]=-a*l,t[2]=-s*l,t[6]=U*u+M,t[10]=E-F*u}else if("XZY"===e.order){var T=A*o,k=A*s,Q=a*o,L=a*s;t[0]=o*l,t[4]=-u,t[8]=s*l,t[1]=T*u+L,t[5]=A*l,t[9]=k*u-Q,t[2]=Q*u-k,t[6]=a*l,t[10]=L*u+T}return t[3]=0,t[7]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}},{key:"makeRotationFromQuaternion",value:function(e){return this.compose(Jm,e,Zm)}},{key:"lookAt",value:function(e,t,n){var r=this.elements;return tv.subVectors(e,t),0===tv.lengthSq()&&(tv.z=1),tv.normalize(),$m.crossVectors(n,tv),0===$m.lengthSq()&&(1===Math.abs(n.z)?tv.x+=1e-4:tv.z+=1e-4,tv.normalize(),$m.crossVectors(n,tv)),$m.normalize(),ev.crossVectors(tv,$m),r[0]=$m.x,r[4]=ev.x,r[8]=tv.x,r[1]=$m.y,r[5]=ev.y,r[9]=tv.y,r[2]=$m.z,r[6]=ev.z,r[10]=tv.z,this}},{key:"multiply",value:function(e){return this.multiplyMatrices(this,e)}},{key:"premultiply",value:function(e){return this.multiplyMatrices(e,this)}},{key:"multiplyMatrices",value:function(e,t){var n=e.elements,r=t.elements,i=this.elements,A=n[0],a=n[4],o=n[8],s=n[12],l=n[1],u=n[5],c=n[9],d=n[13],h=n[2],f=n[6],p=n[10],g=n[14],m=n[3],v=n[7],y=n[11],w=n[15],B=r[0],_=r[4],b=r[8],x=r[12],C=r[1],S=r[5],E=r[9],U=r[13],M=r[2],F=r[6],T=r[10],k=r[14],Q=r[3],L=r[7],D=r[11],I=r[15];return i[0]=A*B+a*C+o*M+s*Q,i[4]=A*_+a*S+o*F+s*L,i[8]=A*b+a*E+o*T+s*D,i[12]=A*x+a*U+o*k+s*I,i[1]=l*B+u*C+c*M+d*Q,i[5]=l*_+u*S+c*F+d*L,i[9]=l*b+u*E+c*T+d*D,i[13]=l*x+u*U+c*k+d*I,i[2]=h*B+f*C+p*M+g*Q,i[6]=h*_+f*S+p*F+g*L,i[10]=h*b+f*E+p*T+g*D,i[14]=h*x+f*U+p*k+g*I,i[3]=m*B+v*C+y*M+w*Q,i[7]=m*_+v*S+y*F+w*L,i[11]=m*b+v*E+y*T+w*D,i[15]=m*x+v*U+y*k+w*I,this}},{key:"multiplyScalar",value:function(e){var t=this.elements;return t[0]*=e,t[4]*=e,t[8]*=e,t[12]*=e,t[1]*=e,t[5]*=e,t[9]*=e,t[13]*=e,t[2]*=e,t[6]*=e,t[10]*=e,t[14]*=e,t[3]*=e,t[7]*=e,t[11]*=e,t[15]*=e,this}},{key:"determinant",value:function(){var e=this.elements,t=e[0],n=e[4],r=e[8],i=e[12],A=e[1],a=e[5],o=e[9],s=e[13],l=e[2],u=e[6],c=e[10],d=e[14];return e[3]*(+i*o*u-r*s*u-i*a*c+n*s*c+r*a*d-n*o*d)+e[7]*(+t*o*d-t*s*c+i*A*c-r*A*d+r*s*l-i*o*l)+e[11]*(+t*s*u-t*a*d-i*A*u+n*A*d+i*a*l-n*s*l)+e[15]*(-r*a*l-t*o*u+t*a*c+r*A*u-n*A*c+n*o*l)}},{key:"transpose",value:function(){var e,t=this.elements;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}},{key:"setPosition",value:function(e,t,n){var r=this.elements;return e.isVector3?(r[12]=e.x,r[13]=e.y,r[14]=e.z):(r[12]=e,r[13]=t,r[14]=n),this}},{key:"invert",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8],u=e[9],c=e[10],d=e[11],h=e[12],f=e[13],p=e[14],g=e[15],m=u*p*s-f*c*s+f*o*d-a*p*d-u*o*g+a*c*g,v=h*c*s-l*p*s-h*o*d+A*p*d+l*o*g-A*c*g,y=l*f*s-h*u*s+h*a*d-A*f*d-l*a*g+A*u*g,w=h*u*o-l*f*o-h*a*c+A*f*c+l*a*p-A*u*p,B=t*m+n*v+r*y+i*w;if(0===B)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);var _=1/B;return e[0]=m*_,e[1]=(f*c*i-u*p*i-f*r*d+n*p*d+u*r*g-n*c*g)*_,e[2]=(a*p*i-f*o*i+f*r*s-n*p*s-a*r*g+n*o*g)*_,e[3]=(u*o*i-a*c*i-u*r*s+n*c*s+a*r*d-n*o*d)*_,e[4]=v*_,e[5]=(l*p*i-h*c*i+h*r*d-t*p*d-l*r*g+t*c*g)*_,e[6]=(h*o*i-A*p*i-h*r*s+t*p*s+A*r*g-t*o*g)*_,e[7]=(A*c*i-l*o*i+l*r*s-t*c*s-A*r*d+t*o*d)*_,e[8]=y*_,e[9]=(h*u*i-l*f*i-h*n*d+t*f*d+l*n*g-t*u*g)*_,e[10]=(A*f*i-h*a*i+h*n*s-t*f*s-A*n*g+t*a*g)*_,e[11]=(l*a*i-A*u*i-l*n*s+t*u*s+A*n*d-t*a*d)*_,e[12]=w*_,e[13]=(l*f*r-h*u*r+h*n*c-t*f*c-l*n*p+t*u*p)*_,e[14]=(h*a*r-A*f*r-h*n*o+t*f*o+A*n*p-t*a*p)*_,e[15]=(A*u*r-l*a*r+l*n*o-t*u*o-A*n*c+t*a*c)*_,this}},{key:"scale",value:function(e){var t=this.elements,n=e.x,r=e.y,i=e.z;return t[0]*=n,t[4]*=r,t[8]*=i,t[1]*=n,t[5]*=r,t[9]*=i,t[2]*=n,t[6]*=r,t[10]*=i,t[3]*=n,t[7]*=r,t[11]*=i,this}},{key:"getMaxScaleOnAxis",value:function(){var e=this.elements,t=e[0]*e[0]+e[1]*e[1]+e[2]*e[2],n=e[4]*e[4]+e[5]*e[5]+e[6]*e[6],r=e[8]*e[8]+e[9]*e[9]+e[10]*e[10];return Math.sqrt(Math.max(t,n,r))}},{key:"makeTranslation",value:function(e,t,n){return this.set(1,0,0,e,0,1,0,t,0,0,1,n,0,0,0,1),this}},{key:"makeRotationX",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(1,0,0,0,0,t,-n,0,0,n,t,0,0,0,0,1),this}},{key:"makeRotationY",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,0,n,0,0,1,0,0,-n,0,t,0,0,0,0,1),this}},{key:"makeRotationZ",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,0,n,t,0,0,0,0,1,0,0,0,0,1),this}},{key:"makeRotationAxis",value:function(e,t){var n=Math.cos(t),r=Math.sin(t),i=1-n,A=e.x,a=e.y,o=e.z,s=i*A,l=i*a;return this.set(s*A+n,s*a-r*o,s*o+r*a,0,s*a+r*o,l*a+n,l*o-r*A,0,s*o-r*a,l*o+r*A,i*o*o+n,0,0,0,0,1),this}},{key:"makeScale",value:function(e,t,n){return this.set(e,0,0,0,0,t,0,0,0,0,n,0,0,0,0,1),this}},{key:"makeShear",value:function(e,t,n,r,i,A){return this.set(1,n,i,0,e,1,A,0,t,r,1,0,0,0,0,1),this}},{key:"compose",value:function(e,t,n){var r=this.elements,i=t._x,A=t._y,a=t._z,o=t._w,s=i+i,l=A+A,u=a+a,c=i*s,d=i*l,h=i*u,f=A*l,p=A*u,g=a*u,m=o*s,v=o*l,y=o*u,w=n.x,B=n.y,_=n.z;return r[0]=(1-(f+g))*w,r[1]=(d+y)*w,r[2]=(h-v)*w,r[3]=0,r[4]=(d-y)*B,r[5]=(1-(c+g))*B,r[6]=(p+m)*B,r[7]=0,r[8]=(h+v)*_,r[9]=(p-m)*_,r[10]=(1-(c+f))*_,r[11]=0,r[12]=e.x,r[13]=e.y,r[14]=e.z,r[15]=1,this}},{key:"decompose",value:function(e,t,n){var r=this.elements,i=qm.set(r[0],r[1],r[2]).length(),A=qm.set(r[4],r[5],r[6]).length(),a=qm.set(r[8],r[9],r[10]).length();this.determinant()<0&&(i=-i),e.x=r[12],e.y=r[13],e.z=r[14],Ym.copy(this);var o=1/i,s=1/A,l=1/a;return Ym.elements[0]*=o,Ym.elements[1]*=o,Ym.elements[2]*=o,Ym.elements[4]*=s,Ym.elements[5]*=s,Ym.elements[6]*=s,Ym.elements[8]*=l,Ym.elements[9]*=l,Ym.elements[10]*=l,t.setFromRotationMatrix(Ym),n.x=i,n.y=A,n.z=a,this}},{key:"makePerspective",value:function(e,t,n,r,i,A){var a=this.elements,o=2*i/(t-e),s=2*i/(n-r),l=(t+e)/(t-e),u=(n+r)/(n-r),c=-(A+i)/(A-i),d=-2*A*i/(A-i);return a[0]=o,a[4]=0,a[8]=l,a[12]=0,a[1]=0,a[5]=s,a[9]=u,a[13]=0,a[2]=0,a[6]=0,a[10]=c,a[14]=d,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}},{key:"makeOrthographic",value:function(e,t,n,r,i,A){var a=this.elements,o=1/(t-e),s=1/(n-r),l=1/(A-i),u=(t+e)*o,c=(n+r)*s,d=(A+i)*l;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-u,a[1]=0,a[5]=2*s,a[9]=0,a[13]=-c,a[2]=0,a[6]=0,a[10]=-2*l,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}},{key:"equals",value:function(e){for(var t=this.elements,n=e.elements,r=0;r<16;r++)if(t[r]!==n[r])return!1;return!0}},{key:"fromArray",value:function(e){for(var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=0;n<16;n++)this.elements[n]=e[n+t];return this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e[t+9]=n[9],e[t+10]=n[10],e[t+11]=n[11],e[t+12]=n[12],e[t+13]=n[13],e[t+14]=n[14],e[t+15]=n[15],e}}]),e}(),qm=new vm,Ym=new Xm,Jm=new vm(0,0,0),Zm=new vm(1,1,1),$m=new vm,ev=new vm,tv=new vm,nv=new Xm,rv=new mm,iv=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t.DEFAULT_ORDER;v(this,t),this.isEuler=!0,this._x=e,this._y=n,this._z=r,this._order=i}return w(t,[{key:"x",get:function(){return this._x},set:function(e){this._x=e,this._onChangeCallback()}},{key:"y",get:function(){return this._y},set:function(e){this._y=e,this._onChangeCallback()}},{key:"z",get:function(){return this._z},set:function(e){this._z=e,this._onChangeCallback()}},{key:"order",get:function(){return this._order},set:function(e){this._order=e,this._onChangeCallback()}},{key:"set",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:this._order;return this._x=e,this._y=t,this._z=n,this._order=r,this._onChangeCallback(),this}},{key:"clone",value:function(){return new this.constructor(this._x,this._y,this._z,this._order)}},{key:"copy",value:function(e){return this._x=e._x,this._y=e._y,this._z=e._z,this._order=e._order,this._onChangeCallback(),this}},{key:"setFromRotationMatrix",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._order,n=!(arguments.length>2&&void 0!==arguments[2])||arguments[2],r=e.elements,i=r[0],A=r[4],a=r[8],o=r[1],s=r[5],l=r[9],u=r[2],c=r[6],d=r[10];switch(t){case"XYZ":this._y=Math.asin(Ig(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-l,d),this._z=Math.atan2(-A,i)):(this._x=Math.atan2(c,s),this._z=0);break;case"YXZ":this._x=Math.asin(-Ig(l,-1,1)),Math.abs(l)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(o,s)):(this._y=Math.atan2(-u,i),this._z=0);break;case"ZXY":this._x=Math.asin(Ig(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-A,s)):(this._y=0,this._z=Math.atan2(o,i));break;case"ZYX":this._y=Math.asin(-Ig(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(c,d),this._z=Math.atan2(o,i)):(this._x=0,this._z=Math.atan2(-A,s));break;case"YZX":this._z=Math.asin(Ig(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-l,s),this._y=Math.atan2(-u,i)):(this._x=0,this._y=Math.atan2(a,d));break;case"XZY":this._z=Math.asin(-Ig(A,-1,1)),Math.abs(A)<.9999999?(this._x=Math.atan2(c,s),this._y=Math.atan2(a,i)):(this._x=Math.atan2(-l,d),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+t)}return this._order=t,!0===n&&this._onChangeCallback(),this}},{key:"setFromQuaternion",value:function(e,t,n){return nv.makeRotationFromQuaternion(e),this.setFromRotationMatrix(nv,t,n)}},{key:"setFromVector3",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._order;return this.set(e.x,e.y,e.z,t)}},{key:"reorder",value:function(e){return rv.setFromEuler(this),this.setFromQuaternion(rv,e)}},{key:"equals",value:function(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._order===this._order}},{key:"fromArray",value:function(e){return this._x=e[0],this._y=e[1],this._z=e[2],void 0!==e[3]&&(this._order=e[3]),this._onChangeCallback(),this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._order,e}},{key:"_onChange",value:function(e){return this._onChangeCallback=e,this}},{key:"_onChangeCallback",value:function(){}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this._x;case 2:return e.next=4,this._y;case 4:return e.next=6,this._z;case 6:return e.next=8,this._order;case 8:case"end":return e.stop()}}),e,this)}))}]),t}();iv.DEFAULT_ORDER="XYZ";var Av=function(){function e(){v(this,e),this.mask=1}return w(e,[{key:"set",value:function(e){this.mask=(1<>>0}},{key:"enable",value:function(e){this.mask|=1<1){for(var t=0;t1){for(var t=0;t0&&(n=n.concat(A))}return n}},{key:"getWorldPosition",value:function(e){return this.updateWorldMatrix(!0,!1),e.setFromMatrixPosition(this.matrixWorld)}},{key:"getWorldQuaternion",value:function(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(cv,e,dv),e}},{key:"getWorldScale",value:function(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(cv,hv,e),e}},{key:"getWorldDirection",value:function(e){this.updateWorldMatrix(!0,!1);var t=this.matrixWorld.elements;return e.set(t[8],t[9],t[10]).normalize()}},{key:"raycast",value:function(){}},{key:"traverse",value:function(e){e(this);for(var t=this.children,n=0,r=t.length;n0&&(r.userData=this.userData),r.layers=this.layers.mask,r.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(r.matrixAutoUpdate=!1),this.isInstancedMesh&&(r.type="InstancedMesh",r.count=this.count,r.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(r.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?r.background=this.background.toJSON():this.background.isTexture&&(r.background=this.background.toJSON(e).uuid)),this.environment&&this.environment.isTexture&&!0!==this.environment.isRenderTargetTexture&&(r.environment=this.environment.toJSON(e).uuid);else if(this.isMesh||this.isLine||this.isPoints){r.geometry=i(e.geometries,this.geometry);var A=this.geometry.parameters;if(void 0!==A&&void 0!==A.shapes){var a=A.shapes;if(Array.isArray(a))for(var o=0,s=a.length;o0){r.children=[];for(var h=0;h0){r.animations=[];for(var f=0;f0&&(n.geometries=g),m.length>0&&(n.materials=m),v.length>0&&(n.textures=v),y.length>0&&(n.images=y),w.length>0&&(n.shapes=w),B.length>0&&(n.skeletons=B),_.length>0&&(n.animations=_),b.length>0&&(n.nodes=b)}return n.object=r,n;function x(e){var t=[];for(var n in e){var r=e[n];delete r.metadata,t.push(r)}return t}}},{key:"clone",value:function(e){return(new this.constructor).copy(this,e)}},{key:"copy",value:function(e){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];if(this.name=e.name,this.up.copy(e.up),this.position.copy(e.position),this.rotation.order=e.rotation.order,this.quaternion.copy(e.quaternion),this.scale.copy(e.scale),this.matrix.copy(e.matrix),this.matrixWorld.copy(e.matrixWorld),this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrixWorldNeedsUpdate=e.matrixWorldNeedsUpdate,this.matrixWorldAutoUpdate=e.matrixWorldAutoUpdate,this.layers.mask=e.layers.mask,this.visible=e.visible,this.castShadow=e.castShadow,this.receiveShadow=e.receiveShadow,this.frustumCulled=e.frustumCulled,this.renderOrder=e.renderOrder,this.userData=JSON.parse(JSON.stringify(e.userData)),!0===t)for(var n=0;n0&&void 0!==arguments[0]?arguments[0]:new vm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new vm,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new vm;v(this,e),this.a=t,this.b=n,this.c=r}return w(e,[{key:"set",value:function(e,t,n){return this.a.copy(e),this.b.copy(t),this.c.copy(n),this}},{key:"setFromPointsAndIndices",value:function(e,t,n,r){return this.a.copy(e[t]),this.b.copy(e[n]),this.c.copy(e[r]),this}},{key:"setFromAttributeAndIndices",value:function(e,t,n,r){return this.a.fromBufferAttribute(e,t),this.b.fromBufferAttribute(e,n),this.c.fromBufferAttribute(e,r),this}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.a.copy(e.a),this.b.copy(e.b),this.c.copy(e.c),this}},{key:"getArea",value:function(){return wv.subVectors(this.c,this.b),Bv.subVectors(this.a,this.b),.5*wv.cross(Bv).length()}},{key:"getMidpoint",value:function(e){return e.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}},{key:"getNormal",value:function(t){return e.getNormal(this.a,this.b,this.c,t)}},{key:"getPlane",value:function(e){return e.setFromCoplanarPoints(this.a,this.b,this.c)}},{key:"getBarycoord",value:function(t,n){return e.getBarycoord(t,this.a,this.b,this.c,n)}},{key:"getUV",value:function(t,n,r,i,A){return e.getUV(t,this.a,this.b,this.c,n,r,i,A)}},{key:"containsPoint",value:function(t){return e.containsPoint(t,this.a,this.b,this.c)}},{key:"isFrontFacing",value:function(t){return e.isFrontFacing(this.a,this.b,this.c,t)}},{key:"intersectsBox",value:function(e){return e.intersectsTriangle(this)}},{key:"closestPointToPoint",value:function(e,t){var n,r,i=this.a,A=this.b,a=this.c;xv.subVectors(A,i),Cv.subVectors(a,i),Ev.subVectors(e,i);var o=xv.dot(Ev),s=Cv.dot(Ev);if(o<=0&&s<=0)return t.copy(i);Uv.subVectors(e,A);var l=xv.dot(Uv),u=Cv.dot(Uv);if(l>=0&&u<=l)return t.copy(A);var c=o*u-l*s;if(c<=0&&o>=0&&l<=0)return n=o/(o-l),t.copy(i).addScaledVector(xv,n);Mv.subVectors(e,a);var d=xv.dot(Mv),h=Cv.dot(Mv);if(h>=0&&d<=h)return t.copy(a);var f=d*s-o*h;if(f<=0&&s>=0&&h<=0)return r=s/(s-h),t.copy(i).addScaledVector(Cv,r);var p=l*h-d*u;if(p<=0&&u-l>=0&&d-h>=0)return Sv.subVectors(a,A),r=(u-l)/(u-l+(d-h)),t.copy(A).addScaledVector(Sv,r);var g=1/(p+f+c);return n=f*g,r=c*g,t.copy(i).addScaledVector(xv,n).addScaledVector(Cv,r)}},{key:"equals",value:function(e){return e.a.equals(this.a)&&e.b.equals(this.b)&&e.c.equals(this.c)}}],[{key:"getNormal",value:function(e,t,n,r){r.subVectors(n,t),wv.subVectors(e,t),r.cross(wv);var i=r.lengthSq();return i>0?r.multiplyScalar(1/Math.sqrt(i)):r.set(0,0,0)}},{key:"getBarycoord",value:function(e,t,n,r,i){wv.subVectors(r,t),Bv.subVectors(n,t),_v.subVectors(e,t);var A=wv.dot(wv),a=wv.dot(Bv),o=wv.dot(_v),s=Bv.dot(Bv),l=Bv.dot(_v),u=A*s-a*a;if(0===u)return i.set(-2,-1,-1);var c=1/u,d=(s*o-a*l)*c,h=(A*l-a*o)*c;return i.set(1-d-h,h,d)}},{key:"containsPoint",value:function(e,t,n,r){return this.getBarycoord(e,t,n,r,bv),bv.x>=0&&bv.y>=0&&bv.x+bv.y<=1}},{key:"getUV",value:function(e,t,n,r,i,A,a,o){return this.getBarycoord(e,t,n,r,bv),o.set(0,0),o.addScaledVector(i,bv.x),o.addScaledVector(A,bv.y),o.addScaledVector(a,bv.z),o}},{key:"isFrontFacing",value:function(e,t,n,r){return wv.subVectors(n,t),Bv.subVectors(e,t),wv.cross(Bv).dot(r)<0}}]),e}(),Tv=0,kv=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isMaterial=!0,Object.defineProperty(C(e),"id",{value:Tv++}),e.uuid=Dg(),e.name="",e.type="Material",e.blending=pf,e.side=cf,e.vertexColors=!1,e.opacity=1,e.transparent=!1,e.blendSrc=Mf,e.blendDst=Ff,e.blendEquation=wf,e.blendSrcAlpha=null,e.blendDstAlpha=null,e.blendEquationAlpha=null,e.depthFunc=Hf,e.depthTest=!0,e.depthWrite=!0,e.stencilWriteMask=255,e.stencilFunc=Sg,e.stencilRef=0,e.stencilFuncMask=255,e.stencilFail=Cg,e.stencilZFail=Cg,e.stencilZPass=Cg,e.stencilWrite=!1,e.clippingPlanes=null,e.clipIntersection=!1,e.clipShadows=!1,e.shadowSide=null,e.colorWrite=!0,e.precision=null,e.polygonOffset=!1,e.polygonOffsetFactor=0,e.polygonOffsetUnits=0,e.dithering=!1,e.alphaToCoverage=!1,e.premultipliedAlpha=!1,e.forceSinglePass=!1,e.visible=!0,e.toneMapped=!0,e.userData={},e.version=0,e._alphaTest=0,e}return w(n,[{key:"alphaTest",get:function(){return this._alphaTest},set:function(e){this._alphaTest>0!==e>0&&this.version++,this._alphaTest=e}},{key:"onBuild",value:function(){}},{key:"onBeforeRender",value:function(){}},{key:"onBeforeCompile",value:function(){}},{key:"customProgramCacheKey",value:function(){return this.onBeforeCompile.toString()}},{key:"setValues",value:function(e){if(void 0!==e)for(var t in e){var n=e[t];if(void 0!==n){var r=this[t];void 0!==r?r&&r.isColor?r.set(n):r&&r.isVector3&&n&&n.isVector3?r.copy(n):this[t]=n:console.warn("THREE."+this.type+": '"+t+"' is not a property of this material.")}else console.warn("THREE.Material: '"+t+"' parameter is undefined.")}}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;t&&(e={textures:{},images:{}});var n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function r(e){var t=[];for(var n in e){var r=e[n];delete r.metadata,t.push(r)}return t}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenColor&&this.sheenColor.isColor&&(n.sheenColor=this.sheenColor.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularColor&&this.specularColor.isColor&&(n.specularColor=this.specularColor.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(e).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(e).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(e).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),void 0!==this.iridescence&&(n.iridescence=this.iridescence),void 0!==this.iridescenceIOR&&(n.iridescenceIOR=this.iridescenceIOR),void 0!==this.iridescenceThicknessRange&&(n.iridescenceThicknessRange=this.iridescenceThicknessRange),this.iridescenceMap&&this.iridescenceMap.isTexture&&(n.iridescenceMap=this.iridescenceMap.toJSON(e).uuid),this.iridescenceThicknessMap&&this.iridescenceThicknessMap.isTexture&&(n.iridescenceThicknessMap=this.iridescenceThicknessMap.toJSON(e).uuid),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(e).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(e).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(e).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(e).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(e).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(e).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(e).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(e).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(e).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(e).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(e).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(e).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(e).uuid),this.specularColorMap&&this.specularColorMap.isTexture&&(n.specularColorMap=this.specularColorMap.toJSON(e).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(e).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(e).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(e).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(e).uuid),void 0!==this.attenuationDistance&&this.attenuationDistance!==1/0&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationColor&&(n.attenuationColor=this.attenuationColor.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==pf&&(n.blending=this.blending),this.side!==cf&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,void 0!==this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),void 0!==this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.forceSinglePass&&(n.forceSinglePass=this.forceSinglePass),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),!1===this.fog&&(n.fog=!1),Object.keys(this.userData).length>0&&(n.userData=this.userData),t){var i=r(e.textures),A=r(e.images);i.length>0&&(n.textures=i),A.length>0&&(n.images=A)}return n}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.name=e.name,this.blending=e.blending,this.side=e.side,this.vertexColors=e.vertexColors,this.opacity=e.opacity,this.transparent=e.transparent,this.blendSrc=e.blendSrc,this.blendDst=e.blendDst,this.blendEquation=e.blendEquation,this.blendSrcAlpha=e.blendSrcAlpha,this.blendDstAlpha=e.blendDstAlpha,this.blendEquationAlpha=e.blendEquationAlpha,this.depthFunc=e.depthFunc,this.depthTest=e.depthTest,this.depthWrite=e.depthWrite,this.stencilWriteMask=e.stencilWriteMask,this.stencilFunc=e.stencilFunc,this.stencilRef=e.stencilRef,this.stencilFuncMask=e.stencilFuncMask,this.stencilFail=e.stencilFail,this.stencilZFail=e.stencilZFail,this.stencilZPass=e.stencilZPass,this.stencilWrite=e.stencilWrite;var t=e.clippingPlanes,n=null;if(null!==t){var r=t.length;n=new Array(r);for(var i=0;i!==r;++i)n[i]=t[i].clone()}return this.clippingPlanes=n,this.clipIntersection=e.clipIntersection,this.clipShadows=e.clipShadows,this.shadowSide=e.shadowSide,this.colorWrite=e.colorWrite,this.precision=e.precision,this.polygonOffset=e.polygonOffset,this.polygonOffsetFactor=e.polygonOffsetFactor,this.polygonOffsetUnits=e.polygonOffsetUnits,this.dithering=e.dithering,this.alphaTest=e.alphaTest,this.alphaToCoverage=e.alphaToCoverage,this.premultipliedAlpha=e.premultipliedAlpha,this.forceSinglePass=e.forceSinglePass,this.visible=e.visible,this.toneMapped=e.toneMapped,this.userData=JSON.parse(JSON.stringify(e.userData)),this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}},{key:"needsUpdate",set:function(e){!0===e&&this.version++}}]),n}(Fg),Qv=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isMeshBasicMaterial=!0,r.type="MeshBasicMaterial",r.color=new om(16777215),r.map=null,r.lightMap=null,r.lightMapIntensity=1,r.aoMap=null,r.aoMapIntensity=1,r.specularMap=null,r.alphaMap=null,r.envMap=null,r.combine=Gf,r.reflectivity=1,r.refractionRatio=.98,r.wireframe=!1,r.wireframeLinewidth=1,r.wireframeLinecap="round",r.wireframeLinejoin="round",r.fog=!0,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return Xc(b(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.fog=e.fog,this}}]),n}(kv),Lv=new vm,Dv=new Kg,Iv=function(){function e(t,n){var r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if(v(this,e),Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.isBufferAttribute=!0,this.name="",this.array=t,this.itemSize=n,this.count=void 0!==t?t.length/n:0,this.normalized=r,this.usage=Eg,this.updateRange={offset:0,count:-1},this.version=0}return w(e,[{key:"onUploadCallback",value:function(){}},{key:"needsUpdate",set:function(e){!0===e&&this.version++}},{key:"setUsage",value:function(e){return this.usage=e,this}},{key:"copy",value:function(e){return this.name=e.name,this.array=new e.array.constructor(e.array),this.itemSize=e.itemSize,this.count=e.count,this.normalized=e.normalized,this.usage=e.usage,this}},{key:"copyAt",value:function(e,t,n){e*=this.itemSize,n*=t.itemSize;for(var r=0,i=this.itemSize;r1&&void 0!==arguments[1]?arguments[1]:0;return this.array.set(e,t),this}},{key:"getX",value:function(e){var t=this.array[e*this.itemSize];return this.normalized&&(t=Vg(t,this.array)),t}},{key:"setX",value:function(e,t){return this.normalized&&(t=zg(t,this.array)),this.array[e*this.itemSize]=t,this}},{key:"getY",value:function(e){var t=this.array[e*this.itemSize+1];return this.normalized&&(t=Vg(t,this.array)),t}},{key:"setY",value:function(e,t){return this.normalized&&(t=zg(t,this.array)),this.array[e*this.itemSize+1]=t,this}},{key:"getZ",value:function(e){var t=this.array[e*this.itemSize+2];return this.normalized&&(t=Vg(t,this.array)),t}},{key:"setZ",value:function(e,t){return this.normalized&&(t=zg(t,this.array)),this.array[e*this.itemSize+2]=t,this}},{key:"getW",value:function(e){var t=this.array[e*this.itemSize+3];return this.normalized&&(t=Vg(t,this.array)),t}},{key:"setW",value:function(e,t){return this.normalized&&(t=zg(t,this.array)),this.array[e*this.itemSize+3]=t,this}},{key:"setXY",value:function(e,t,n){return e*=this.itemSize,this.normalized&&(t=zg(t,this.array),n=zg(n,this.array)),this.array[e+0]=t,this.array[e+1]=n,this}},{key:"setXYZ",value:function(e,t,n,r){return e*=this.itemSize,this.normalized&&(t=zg(t,this.array),n=zg(n,this.array),r=zg(r,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=r,this}},{key:"setXYZW",value:function(e,t,n,r,i){return e*=this.itemSize,this.normalized&&(t=zg(t,this.array),n=zg(n,this.array),r=zg(r,this.array),i=zg(i,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=r,this.array[e+3]=i,this}},{key:"onUpload",value:function(e){return this.onUploadCallback=e,this}},{key:"clone",value:function(){return new this.constructor(this.array,this.itemSize).copy(this)}},{key:"toJSON",value:function(){var e={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.from(this.array),normalized:this.normalized};return""!==this.name&&(e.name=this.name),this.usage!==Eg&&(e.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(e.updateRange=this.updateRange),e}},{key:"copyColorsArray",value:function(){console.error("THREE.BufferAttribute: copyColorsArray() was removed in r144.")}},{key:"copyVector2sArray",value:function(){console.error("THREE.BufferAttribute: copyVector2sArray() was removed in r144.")}},{key:"copyVector3sArray",value:function(){console.error("THREE.BufferAttribute: copyVector3sArray() was removed in r144.")}},{key:"copyVector4sArray",value:function(){console.error("THREE.BufferAttribute: copyVector4sArray() was removed in r144.")}}]),e}(),Rv=function(e){_(n,e);var t=S(n);function n(e,r,i){return v(this,n),t.call(this,new Uint16Array(e),r,i)}return w(n)}(Iv),Pv=function(e){_(n,e);var t=S(n);function n(e,r,i){return v(this,n),t.call(this,new Uint32Array(e),r,i)}return w(n)}(Iv),Hv=function(e){_(n,e);var t=S(n);function n(e,r,i){return v(this,n),t.call(this,new Float32Array(e),r,i)}return w(n)}(Iv),Nv=0,Ov=new Xm,Vv=new yv,zv=new vm,Gv=new Bm,Kv=new Bm,Wv=new vm,jv=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isBufferGeometry=!0,Object.defineProperty(C(e),"id",{value:Nv++}),e.uuid=Dg(),e.name="",e.type="BufferGeometry",e.index=null,e.attributes={},e.morphAttributes={},e.morphTargetsRelative=!1,e.groups=[],e.boundingBox=null,e.boundingSphere=null,e.drawRange={start:0,count:1/0},e.userData={},e}return w(n,[{key:"getIndex",value:function(){return this.index}},{key:"setIndex",value:function(e){return Array.isArray(e)?this.index=new(Xg(e)?Pv:Rv)(e,1):this.index=e,this}},{key:"getAttribute",value:function(e){return this.attributes[e]}},{key:"setAttribute",value:function(e,t){return this.attributes[e]=t,this}},{key:"deleteAttribute",value:function(e){return delete this.attributes[e],this}},{key:"hasAttribute",value:function(e){return void 0!==this.attributes[e]}},{key:"addGroup",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;this.groups.push({start:e,count:t,materialIndex:n})}},{key:"clearGroups",value:function(){this.groups=[]}},{key:"setDrawRange",value:function(e,t){this.drawRange.start=e,this.drawRange.count=t}},{key:"applyMatrix4",value:function(e){var t=this.attributes.position;void 0!==t&&(t.applyMatrix4(e),t.needsUpdate=!0);var n=this.attributes.normal;if(void 0!==n){var r=(new Wg).getNormalMatrix(e);n.applyNormalMatrix(r),n.needsUpdate=!0}var i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(e),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}},{key:"applyQuaternion",value:function(e){return Ov.makeRotationFromQuaternion(e),this.applyMatrix4(Ov),this}},{key:"rotateX",value:function(e){return Ov.makeRotationX(e),this.applyMatrix4(Ov),this}},{key:"rotateY",value:function(e){return Ov.makeRotationY(e),this.applyMatrix4(Ov),this}},{key:"rotateZ",value:function(e){return Ov.makeRotationZ(e),this.applyMatrix4(Ov),this}},{key:"translate",value:function(e,t,n){return Ov.makeTranslation(e,t,n),this.applyMatrix4(Ov),this}},{key:"scale",value:function(e,t,n){return Ov.makeScale(e,t,n),this.applyMatrix4(Ov),this}},{key:"lookAt",value:function(e){return Vv.lookAt(e),Vv.updateMatrix(),this.applyMatrix4(Vv.matrix),this}},{key:"center",value:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(zv).negate(),this.translate(zv.x,zv.y,zv.z),this}},{key:"setFromPoints",value:function(e){for(var t=[],n=0,r=e.length;n0&&(e.userData=this.userData),void 0!==this.parameters){var t=this.parameters;for(var n in t)void 0!==t[n]&&(e[n]=t[n]);return e}e.data={attributes:{}};var r=this.index;null!==r&&(e.data.index={type:r.array.constructor.name,array:Array.prototype.slice.call(r.array)});var i=this.attributes;for(var A in i){var a=i[A];e.data.attributes[A]=a.toJSON(e.data)}var o={},s=!1;for(var l in this.morphAttributes){for(var u=this.morphAttributes[l],c=[],d=0,h=u.length;d0&&(o[l]=c,s=!0)}s&&(e.data.morphAttributes=o,e.data.morphTargetsRelative=this.morphTargetsRelative);var p=this.groups;p.length>0&&(e.data.groups=JSON.parse(JSON.stringify(p)));var g=this.boundingSphere;return null!==g&&(e.data.boundingSphere={center:g.center.toArray(),radius:g.radius}),e}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;var t={};this.name=e.name;var n=e.index;null!==n&&this.setIndex(n.clone(t));var r=e.attributes;for(var i in r){var A=r[i];this.setAttribute(i,A.clone(t))}var a=e.morphAttributes;for(var o in a){for(var s=[],l=a[o],u=0,c=l.length;u0&&void 0!==arguments[0]?arguments[0]:new jv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new Qv;return v(this,n),(e=t.call(this)).isMesh=!0,e.type="Mesh",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return w(n,[{key:"copy",value:function(e,t){return Xc(b(n.prototype),"copy",this).call(this,e,t),void 0!==e.morphTargetInfluences&&(this.morphTargetInfluences=e.morphTargetInfluences.slice()),void 0!==e.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},e.morphTargetDictionary)),this.material=e.material,this.geometry=e.geometry,this}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;rn.far?null:{distance:s,point:ay.clone(),object:e}}(e,t,n,r,Jv,Zv,$v,Ay);if(l){i&&(ny.fromBufferAttribute(i,a),ry.fromBufferAttribute(i,o),iy.fromBufferAttribute(i,s),l.uv=Fv.getUV(Ay,Jv,Zv,$v,ny,ry,iy,new Kg)),A&&(ny.fromBufferAttribute(A,a),ry.fromBufferAttribute(A,o),iy.fromBufferAttribute(A,s),l.uv2=Fv.getUV(Ay,Jv,Zv,$v,ny,ry,iy,new Kg));var u={a:a,b:o,c:s,normal:new vm,materialIndex:0};Fv.getNormal(Jv,Zv,$v,u.normal),l.face=u}return l}var ly=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:1;v(this,n),(e=t.call(this)).type="BoxGeometry",e.parameters={width:r,height:i,depth:A,widthSegments:a,heightSegments:o,depthSegments:s};var l=C(e);a=Math.floor(a),o=Math.floor(o),s=Math.floor(s);var u=[],c=[],d=[],h=[],f=0,p=0;function g(e,t,n,r,i,A,a,o,s,g,m){for(var v=A/s,y=a/g,w=A/2,B=a/2,_=o/2,b=s+1,x=g+1,C=0,S=0,E=new vm,U=0;U0?1:-1,d.push(E.x,E.y,E.z),h.push(F/s),h.push(1-U/g),C+=1}for(var k=0;k0&&(t.defines=this.defines),t.vertexShader=this.vertexShader,t.fragmentShader=this.fragmentShader;var A={};for(var a in this.extensions)!0===this.extensions[a]&&(A[a]=!0);return Object.keys(A).length>0&&(t.extensions=A),t}}]),n}(kv),my=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isCamera=!0,e.type="Camera",e.matrixWorldInverse=new Xm,e.projectionMatrix=new Xm,e.projectionMatrixInverse=new Xm,e}return w(n,[{key:"copy",value:function(e,t){return Xc(b(n.prototype),"copy",this).call(this,e,t),this.matrixWorldInverse.copy(e.matrixWorldInverse),this.projectionMatrix.copy(e.projectionMatrix),this.projectionMatrixInverse.copy(e.projectionMatrixInverse),this}},{key:"getWorldDirection",value:function(e){this.updateWorldMatrix(!0,!1);var t=this.matrixWorld.elements;return e.set(-t[8],-t[9],-t[10]).normalize()}},{key:"updateMatrixWorld",value:function(e){Xc(b(n.prototype),"updateMatrixWorld",this).call(this,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}},{key:"updateWorldMatrix",value:function(e,t){Xc(b(n.prototype),"updateWorldMatrix",this).call(this,e,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),n}(yv),vy=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:50,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:2e3;return v(this,n),(e=t.call(this)).isPerspectiveCamera=!0,e.type="PerspectiveCamera",e.fov=r,e.zoom=1,e.near=A,e.far=a,e.focus=10,e.aspect=i,e.view=null,e.filmGauge=35,e.filmOffset=0,e.updateProjectionMatrix(),e}return w(n,[{key:"copy",value:function(e,t){return Xc(b(n.prototype),"copy",this).call(this,e,t),this.fov=e.fov,this.zoom=e.zoom,this.near=e.near,this.far=e.far,this.focus=e.focus,this.aspect=e.aspect,this.view=null===e.view?null:Object.assign({},e.view),this.filmGauge=e.filmGauge,this.filmOffset=e.filmOffset,this}},{key:"setFocalLength",value:function(e){var t=.5*this.getFilmHeight()/e;this.fov=2*Lg*Math.atan(t),this.updateProjectionMatrix()}},{key:"getFocalLength",value:function(){var e=Math.tan(.5*Qg*this.fov);return.5*this.getFilmHeight()/e}},{key:"getEffectiveFOV",value:function(){return 2*Lg*Math.atan(Math.tan(.5*Qg*this.fov)/this.zoom)}},{key:"getFilmWidth",value:function(){return this.filmGauge*Math.min(this.aspect,1)}},{key:"getFilmHeight",value:function(){return this.filmGauge/Math.max(this.aspect,1)}},{key:"setViewOffset",value:function(e,t,n,r,i,A){this.aspect=e/t,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=r,this.view.width=i,this.view.height=A,this.updateProjectionMatrix()}},{key:"clearViewOffset",value:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}},{key:"updateProjectionMatrix",value:function(){var e=this.near,t=e*Math.tan(.5*Qg*this.fov)/this.zoom,n=2*t,r=this.aspect*n,i=-.5*r,A=this.view;if(null!==this.view&&this.view.enabled){var a=A.fullWidth,o=A.fullHeight;i+=A.offsetX*r/a,t-=A.offsetY*n/o,r*=A.width/a,n*=A.height/o}var s=this.filmOffset;0!==s&&(i+=e*s/this.getFilmWidth()),this.projectionMatrix.makePerspective(i,i+r,t,t-n,e,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}},{key:"toJSON",value:function(e){var t=Xc(b(n.prototype),"toJSON",this).call(this,e);return t.object.fov=this.fov,t.object.zoom=this.zoom,t.object.near=this.near,t.object.far=this.far,t.object.focus=this.focus,t.object.aspect=this.aspect,null!==this.view&&(t.object.view=Object.assign({},this.view)),t.object.filmGauge=this.filmGauge,t.object.filmOffset=this.filmOffset,t}}]),n}(my),yy=-90,wy=1,By=function(e){_(n,e);var t=S(n);function n(e,r,i){var A;v(this,n),(A=t.call(this)).type="CubeCamera",A.renderTarget=i;var a=new vy(yy,wy,e,r);a.layers=A.layers,a.up.set(0,1,0),a.lookAt(1,0,0),A.add(a);var o=new vy(yy,wy,e,r);o.layers=A.layers,o.up.set(0,1,0),o.lookAt(-1,0,0),A.add(o);var s=new vy(yy,wy,e,r);s.layers=A.layers,s.up.set(0,0,-1),s.lookAt(0,1,0),A.add(s);var l=new vy(yy,wy,e,r);l.layers=A.layers,l.up.set(0,0,1),l.lookAt(0,-1,0),A.add(l);var u=new vy(yy,wy,e,r);u.layers=A.layers,u.up.set(0,1,0),u.lookAt(0,0,1),A.add(u);var c=new vy(yy,wy,e,r);return c.layers=A.layers,c.up.set(0,1,0),c.lookAt(0,0,-1),A.add(c),A}return w(n,[{key:"update",value:function(e,t){null===this.parent&&this.updateMatrixWorld();var n=this.renderTarget,r=p(this.children,6),i=r[0],A=r[1],a=r[2],o=r[3],s=r[4],l=r[5],u=e.getRenderTarget(),c=e.toneMapping,d=e.xr.enabled;e.toneMapping=jf,e.xr.enabled=!1;var h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,e.setRenderTarget(n,0),e.render(t,i),e.setRenderTarget(n,1),e.render(t,A),e.setRenderTarget(n,2),e.render(t,a),e.setRenderTarget(n,3),e.render(t,o),e.setRenderTarget(n,4),e.render(t,s),n.texture.generateMipmaps=h,e.setRenderTarget(n,5),e.render(t,l),e.setRenderTarget(u),e.toneMapping=c,e.xr.enabled=d,n.texture.needsPMREMUpdate=!0}}]),n}(yv),_y=function(e){_(n,e);var t=S(n);function n(e,r,i,A,a,o,s,l,u,c){var d;return v(this,n),e=void 0!==e?e:[],r=void 0!==r?r:$f,(d=t.call(this,e,r,i,A,a,o,s,l,u,c)).isCubeTexture=!0,d.flipY=!1,d}return w(n,[{key:"images",get:function(){return this.image},set:function(e){this.image=e}}]),n}(dm),by=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};v(this,n),(e=t.call(this,r,r,i)).isWebGLCubeRenderTarget=!0;var A={width:r,height:r,depth:1},a=[A,A,A,A,A,A];return e.texture=new _y(a,i.mapping,i.wrapS,i.wrapT,i.magFilter,i.minFilter,i.format,i.type,i.anisotropy,i.encoding),e.texture.isRenderTargetTexture=!0,e.texture.generateMipmaps=void 0!==i.generateMipmaps&&i.generateMipmaps,e.texture.minFilter=void 0!==i.minFilter?i.minFilter:up,e}return w(n,[{key:"fromEquirectangularTexture",value:function(e,t){this.texture.type=t.type,this.texture.encoding=t.encoding,this.texture.generateMipmaps=t.generateMipmaps,this.texture.minFilter=t.minFilter,this.texture.magFilter=t.magFilter;var n={tEquirect:{value:null}},r="\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include \n\t\t\t\t\t#include \n\n\t\t\t\t}\n\t\t\t",i="\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include \n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",A=new ly(5,5,5),a=new gy({name:"CubemapFromEquirect",uniforms:uy(n),vertexShader:r,fragmentShader:i,side:df,blending:ff});a.uniforms.tEquirect.value=t;var o=new oy(A,a),s=t.minFilter;return t.minFilter===dp&&(t.minFilter=up),new By(1,10,this).update(e,o),t.minFilter=s,o.geometry.dispose(),o.material.dispose(),this}},{key:"clear",value:function(e,t,n,r){for(var i=e.getRenderTarget(),A=0;A<6;A++)e.setRenderTarget(this,A),e.clear(t,n,r);e.setRenderTarget(i)}}]),n}(fm),xy=new vm,Cy=new vm,Sy=new Wg,Ey=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new vm(1,0,0),n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;v(this,e),this.isPlane=!0,this.normal=t,this.constant=n}return w(e,[{key:"set",value:function(e,t){return this.normal.copy(e),this.constant=t,this}},{key:"setComponents",value:function(e,t,n,r){return this.normal.set(e,t,n),this.constant=r,this}},{key:"setFromNormalAndCoplanarPoint",value:function(e,t){return this.normal.copy(e),this.constant=-t.dot(this.normal),this}},{key:"setFromCoplanarPoints",value:function(e,t,n){var r=xy.subVectors(n,t).cross(Cy.subVectors(e,t)).normalize();return this.setFromNormalAndCoplanarPoint(r,e),this}},{key:"copy",value:function(e){return this.normal.copy(e.normal),this.constant=e.constant,this}},{key:"normalize",value:function(){var e=1/this.normal.length();return this.normal.multiplyScalar(e),this.constant*=e,this}},{key:"negate",value:function(){return this.constant*=-1,this.normal.negate(),this}},{key:"distanceToPoint",value:function(e){return this.normal.dot(e)+this.constant}},{key:"distanceToSphere",value:function(e){return this.distanceToPoint(e.center)-e.radius}},{key:"projectPoint",value:function(e,t){return t.copy(this.normal).multiplyScalar(-this.distanceToPoint(e)).add(e)}},{key:"intersectLine",value:function(e,t){var n=e.delta(xy),r=this.normal.dot(n);if(0===r)return 0===this.distanceToPoint(e.start)?t.copy(e.start):null;var i=-(e.start.dot(this.normal)+this.constant)/r;return i<0||i>1?null:t.copy(n).multiplyScalar(i).add(e.start)}},{key:"intersectsLine",value:function(e){var t=this.distanceToPoint(e.start),n=this.distanceToPoint(e.end);return t<0&&n>0||n<0&&t>0}},{key:"intersectsBox",value:function(e){return e.intersectsPlane(this)}},{key:"intersectsSphere",value:function(e){return e.intersectsPlane(this)}},{key:"coplanarPoint",value:function(e){return e.copy(this.normal).multiplyScalar(-this.constant)}},{key:"applyMatrix4",value:function(e,t){var n=t||Sy.getNormalMatrix(e),r=this.coplanarPoint(xy).applyMatrix4(e),i=this.normal.applyMatrix3(n).normalize();return this.constant=-r.dot(i),this}},{key:"translate",value:function(e){return this.constant-=e.dot(this.normal),this}},{key:"equals",value:function(e){return e.normal.equals(this.normal)&&e.constant===this.constant}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Uy=new Hm,My=new vm,Fy=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Ey,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new Ey,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new Ey,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:new Ey,A=arguments.length>4&&void 0!==arguments[4]?arguments[4]:new Ey,a=arguments.length>5&&void 0!==arguments[5]?arguments[5]:new Ey;v(this,e),this.planes=[t,n,r,i,A,a]}return w(e,[{key:"set",value:function(e,t,n,r,i,A){var a=this.planes;return a[0].copy(e),a[1].copy(t),a[2].copy(n),a[3].copy(r),a[4].copy(i),a[5].copy(A),this}},{key:"copy",value:function(e){for(var t=this.planes,n=0;n<6;n++)t[n].copy(e.planes[n]);return this}},{key:"setFromProjectionMatrix",value:function(e){var t=this.planes,n=e.elements,r=n[0],i=n[1],A=n[2],a=n[3],o=n[4],s=n[5],l=n[6],u=n[7],c=n[8],d=n[9],h=n[10],f=n[11],p=n[12],g=n[13],m=n[14],v=n[15];return t[0].setComponents(a-r,u-o,f-c,v-p).normalize(),t[1].setComponents(a+r,u+o,f+c,v+p).normalize(),t[2].setComponents(a+i,u+s,f+d,v+g).normalize(),t[3].setComponents(a-i,u-s,f-d,v-g).normalize(),t[4].setComponents(a-A,u-l,f-h,v-m).normalize(),t[5].setComponents(a+A,u+l,f+h,v+m).normalize(),this}},{key:"intersectsObject",value:function(e){var t=e.geometry;return null===t.boundingSphere&&t.computeBoundingSphere(),Uy.copy(t.boundingSphere).applyMatrix4(e.matrixWorld),this.intersectsSphere(Uy)}},{key:"intersectsSprite",value:function(e){return Uy.center.set(0,0,0),Uy.radius=.7071067811865476,Uy.applyMatrix4(e.matrixWorld),this.intersectsSphere(Uy)}},{key:"intersectsSphere",value:function(e){for(var t=this.planes,n=e.center,r=-e.radius,i=0;i<6;i++){if(t[i].distanceToPoint(n)0?e.max.x:e.min.x,My.y=r.normal.y>0?e.max.y:e.min.y,My.z=r.normal.z>0?e.max.z:e.min.z,r.distanceToPoint(My)<0)return!1}return!0}},{key:"containsPoint",value:function(e){for(var t=this.planes,n=0;n<6;n++)if(t[n].distanceToPoint(e)<0)return!1;return!0}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}();function Ty(){var e=null,t=!1,n=null,r=null;function i(t,A){n(t,A),r=e.requestAnimationFrame(i)}return{start:function(){!0!==t&&null!==n&&(r=e.requestAnimationFrame(i),t=!0)},stop:function(){e.cancelAnimationFrame(r),t=!1},setAnimationLoop:function(e){n=e},setContext:function(t){e=t}}}function ky(e,t){var n=t.isWebGL2,r=new WeakMap;return{get:function(e){return e.isInterleavedBufferAttribute&&(e=e.data),r.get(e)},remove:function(t){t.isInterleavedBufferAttribute&&(t=t.data);var n=r.get(t);n&&(e.deleteBuffer(n.buffer),r.delete(t))},update:function(t,i){if(t.isGLBufferAttribute){var A=r.get(t);(!A||A.version0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;v(this,n),(e=t.call(this)).type="PlaneGeometry",e.parameters={width:r,height:i,widthSegments:A,heightSegments:a};for(var o=r/2,s=i/2,l=Math.floor(A),u=Math.floor(a),c=l+1,d=u+1,h=r/l,f=i/u,p=[],g=[],m=[],y=[],w=0;w 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie( float roughness, float dotNH ) {\n\tfloat alpha = pow2( roughness );\n\tfloat invAlpha = 1.0 / alpha;\n\tfloat cos2h = dotNH * dotNH;\n\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\n\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\n}\nfloat V_Neubelt( float dotNV, float dotNL ) {\n\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\n}\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenColor, const in float sheenRoughness ) {\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat D = D_Charlie( sheenRoughness, dotNH );\n\tfloat V = V_Neubelt( dotNV, dotNL );\n\treturn sheenColor * ( D * V );\n}\n#endif",iridescence_fragment:"#ifdef USE_IRIDESCENCE\n\tconst mat3 XYZ_TO_REC709 = mat3(\n\t\t 3.2404542, -0.9692660, 0.0556434,\n\t\t-1.5371385, 1.8760108, -0.2040259,\n\t\t-0.4985314, 0.0415560, 1.0572252\n\t);\n\tvec3 Fresnel0ToIor( vec3 fresnel0 ) {\n\t\tvec3 sqrtF0 = sqrt( fresnel0 );\n\t\treturn ( vec3( 1.0 ) + sqrtF0 ) / ( vec3( 1.0 ) - sqrtF0 );\n\t}\n\tvec3 IorToFresnel0( vec3 transmittedIor, float incidentIor ) {\n\t\treturn pow2( ( transmittedIor - vec3( incidentIor ) ) / ( transmittedIor + vec3( incidentIor ) ) );\n\t}\n\tfloat IorToFresnel0( float transmittedIor, float incidentIor ) {\n\t\treturn pow2( ( transmittedIor - incidentIor ) / ( transmittedIor + incidentIor ));\n\t}\n\tvec3 evalSensitivity( float OPD, vec3 shift ) {\n\t\tfloat phase = 2.0 * PI * OPD * 1.0e-9;\n\t\tvec3 val = vec3( 5.4856e-13, 4.4201e-13, 5.2481e-13 );\n\t\tvec3 pos = vec3( 1.6810e+06, 1.7953e+06, 2.2084e+06 );\n\t\tvec3 var = vec3( 4.3278e+09, 9.3046e+09, 6.6121e+09 );\n\t\tvec3 xyz = val * sqrt( 2.0 * PI * var ) * cos( pos * phase + shift ) * exp( - pow2( phase ) * var );\n\t\txyz.x += 9.7470e-14 * sqrt( 2.0 * PI * 4.5282e+09 ) * cos( 2.2399e+06 * phase + shift[ 0 ] ) * exp( - 4.5282e+09 * pow2( phase ) );\n\t\txyz /= 1.0685e-7;\n\t\tvec3 rgb = XYZ_TO_REC709 * xyz;\n\t\treturn rgb;\n\t}\n\tvec3 evalIridescence( float outsideIOR, float eta2, float cosTheta1, float thinFilmThickness, vec3 baseF0 ) {\n\t\tvec3 I;\n\t\tfloat iridescenceIOR = mix( outsideIOR, eta2, smoothstep( 0.0, 0.03, thinFilmThickness ) );\n\t\tfloat sinTheta2Sq = pow2( outsideIOR / iridescenceIOR ) * ( 1.0 - pow2( cosTheta1 ) );\n\t\tfloat cosTheta2Sq = 1.0 - sinTheta2Sq;\n\t\tif ( cosTheta2Sq < 0.0 ) {\n\t\t\t return vec3( 1.0 );\n\t\t}\n\t\tfloat cosTheta2 = sqrt( cosTheta2Sq );\n\t\tfloat R0 = IorToFresnel0( iridescenceIOR, outsideIOR );\n\t\tfloat R12 = F_Schlick( R0, 1.0, cosTheta1 );\n\t\tfloat R21 = R12;\n\t\tfloat T121 = 1.0 - R12;\n\t\tfloat phi12 = 0.0;\n\t\tif ( iridescenceIOR < outsideIOR ) phi12 = PI;\n\t\tfloat phi21 = PI - phi12;\n\t\tvec3 baseIOR = Fresnel0ToIor( clamp( baseF0, 0.0, 0.9999 ) );\t\tvec3 R1 = IorToFresnel0( baseIOR, iridescenceIOR );\n\t\tvec3 R23 = F_Schlick( R1, 1.0, cosTheta2 );\n\t\tvec3 phi23 = vec3( 0.0 );\n\t\tif ( baseIOR[ 0 ] < iridescenceIOR ) phi23[ 0 ] = PI;\n\t\tif ( baseIOR[ 1 ] < iridescenceIOR ) phi23[ 1 ] = PI;\n\t\tif ( baseIOR[ 2 ] < iridescenceIOR ) phi23[ 2 ] = PI;\n\t\tfloat OPD = 2.0 * iridescenceIOR * thinFilmThickness * cosTheta2;\n\t\tvec3 phi = vec3( phi21 ) + phi23;\n\t\tvec3 R123 = clamp( R12 * R23, 1e-5, 0.9999 );\n\t\tvec3 r123 = sqrt( R123 );\n\t\tvec3 Rs = pow2( T121 ) * R23 / ( vec3( 1.0 ) - R123 );\n\t\tvec3 C0 = R12 + Rs;\n\t\tI = C0;\n\t\tvec3 Cm = Rs - T121;\n\t\tfor ( int m = 1; m <= 2; ++ m ) {\n\t\t\tCm *= r123;\n\t\t\tvec3 Sm = 2.0 * evalSensitivity( float( m ) * OPD, float( m ) * phi );\n\t\t\tI += Cm * Sm;\n\t\t}\n\t\treturn max( I, vec3( 0.0 ) );\n\t}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = dFdx( surf_pos.xyz );\n\t\tvec3 vSigmaY = dFdy( surf_pos.xyz );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nvec3 pow2( const in vec3 x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\nfloat average( const in vec3 v ) { return dot( v, vec3( 0.3333333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract( sin( sn ) * c );\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef USE_CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat luminance( const in vec3 rgb ) {\n\tconst vec3 weights = vec3( 0.2126729, 0.7151522, 0.0721750 );\n\treturn dot( weights, rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\thighp vec2 uv = getUV( direction, face ) * ( faceSize - 2.0 ) + 1.0;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tuv.x += filterInt * 3.0 * cubeUV_minTileSize;\n\t\tuv.y += 4.0 * ( exp2( CUBEUV_MAX_MIP ) - faceSize );\n\t\tuv.x *= CUBEUV_TEXEL_WIDTH;\n\t\tuv.y *= CUBEUV_TEXEL_HEIGHT;\n\t\t#ifdef texture2DGradEXT\n\t\t\treturn texture2DGradEXT( envMap, uv, vec2( 0.0 ), vec2( 0.0 ) ).rgb;\n\t\t#else\n\t\t\treturn texture2D( envMap, uv ).rgb;\n\t\t#endif\n\t}\n\t#define cubeUV_r0 1.0\n\t#define cubeUV_v0 0.339\n\t#define cubeUV_m0 - 2.0\n\t#define cubeUV_r1 0.8\n\t#define cubeUV_v1 0.276\n\t#define cubeUV_m1 - 1.0\n\t#define cubeUV_r4 0.4\n\t#define cubeUV_v4 0.046\n\t#define cubeUV_m4 2.0\n\t#define cubeUV_r5 0.305\n\t#define cubeUV_v5 0.016\n\t#define cubeUV_m5 3.0\n\t#define cubeUV_r6 0.21\n\t#define cubeUV_v6 0.0038\n\t#define cubeUV_m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= cubeUV_r1 ) {\n\t\t\tmip = ( cubeUV_r0 - roughness ) * ( cubeUV_m1 - cubeUV_m0 ) / ( cubeUV_r0 - cubeUV_r1 ) + cubeUV_m0;\n\t\t} else if ( roughness >= cubeUV_r4 ) {\n\t\t\tmip = ( cubeUV_r1 - roughness ) * ( cubeUV_m4 - cubeUV_m1 ) / ( cubeUV_r1 - cubeUV_r4 ) + cubeUV_m1;\n\t\t} else if ( roughness >= cubeUV_r5 ) {\n\t\t\tmip = ( cubeUV_r4 - roughness ) * ( cubeUV_m5 - cubeUV_m4 ) / ( cubeUV_r4 - cubeUV_r5 ) + cubeUV_m4;\n\t\t} else if ( roughness >= cubeUV_r6 ) {\n\t\t\tmip = ( cubeUV_r5 - roughness ) * ( cubeUV_m6 - cubeUV_m5 ) / ( cubeUV_r5 - cubeUV_r6 ) + cubeUV_m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), cubeUV_m0, CUBEUV_MAX_MIP );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"vec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\tvec3 getIBLIrradiance( const in vec3 normal ) {\n\t\t#if defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t\t#else\n\t\t\treturn vec3( 0.0 );\n\t\t#endif\n\t}\n\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\n\t\t#if defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 reflectVec = reflect( - viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t\treturn envMapColor.rgb * envMapIntensity;\n\t\t#else\n\t\t\treturn vec3( 0.0 );\n\t\t#endif\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tvFogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float vFogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float vFogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn vec3( texture2D( gradientMap, coord ).r );\n\t#else\n\t\tvec2 fw = fwidth( coord ) * 0.5;\n\t\treturn mix( vec3( 0.7 ), vec3( 1.0 ), smoothstep( 0.7 - fw.x, 0.7 + fw.x, coord.x ) );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\tvec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n\treflectedLight.indirectDiffuse += lightMapIrradiance;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_fragment:"LambertMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularStrength = specularStrength;",lights_lambert_pars_fragment:"varying vec3 vViewPosition;\nstruct LambertMaterial {\n\tvec3 diffuseColor;\n\tfloat specularStrength;\n};\nvoid RE_Direct_Lambert( const in IncidentLight directLight, const in GeometricContext geometry, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Lambert( const in vec3 irradiance, const in GeometricContext geometry, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Lambert\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Lambert",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\n\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\treturn irradiance;\n}\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\t\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\t\tif ( cutoffDistance > 0.0 ) {\n\t\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t\t}\n\t\treturn distanceFalloff;\n\t#else\n\t\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\t\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t\t}\n\t\treturn 1.0;\n\t#endif\n}\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\n\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tlight.color = directionalLight.color;\n\t\tlight.direction = directionalLight.direction;\n\t\tlight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tlight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tlight.color = pointLight.color;\n\t\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\n\t\tlight.visible = ( light.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tlight.direction = normalize( lVector );\n\t\tfloat angleCos = dot( light.direction, spotLight.direction );\n\t\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\tif ( spotAttenuation > 0.0 ) {\n\t\t\tfloat lightDistance = length( lVector );\n\t\t\tlight.color = spotLight.color * spotAttenuation;\n\t\t\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tlight.visible = ( light.color != vec3( 0.0 ) );\n\t\t} else {\n\t\t\tlight.color = vec3( 0.0 );\n\t\t\tlight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\n\t\tfloat dotNL = dot( normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\nmaterial.roughness = min( material.roughness, 1.0 );\n#ifdef IOR\n\tmaterial.ior = ior;\n\t#ifdef SPECULAR\n\t\tfloat specularIntensityFactor = specularIntensity;\n\t\tvec3 specularColorFactor = specularColor;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARCOLORMAP\n\t\t\tspecularColorFactor *= texture2D( specularColorMap, vUv ).rgb;\n\t\t#endif\n\t\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\n\t#else\n\t\tfloat specularIntensityFactor = 1.0;\n\t\tvec3 specularColorFactor = vec3( 1.0 );\n\t\tmaterial.specularF90 = 1.0;\n\t#endif\n\tmaterial.specularColor = mix( min( pow2( ( material.ior - 1.0 ) / ( material.ior + 1.0 ) ) * specularColorFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularF90 = 1.0;\n#endif\n#ifdef USE_CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\tmaterial.clearcoatF0 = vec3( 0.04 );\n\tmaterial.clearcoatF90 = 1.0;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_IRIDESCENCE\n\tmaterial.iridescence = iridescence;\n\tmaterial.iridescenceIOR = iridescenceIOR;\n\t#ifdef USE_IRIDESCENCEMAP\n\t\tmaterial.iridescence *= texture2D( iridescenceMap, vUv ).r;\n\t#endif\n\t#ifdef USE_IRIDESCENCE_THICKNESSMAP\n\t\tmaterial.iridescenceThickness = (iridescenceThicknessMaximum - iridescenceThicknessMinimum) * texture2D( iridescenceThicknessMap, vUv ).g + iridescenceThicknessMinimum;\n\t#else\n\t\tmaterial.iridescenceThickness = iridescenceThicknessMaximum;\n\t#endif\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheenColor;\n\t#ifdef USE_SHEENCOLORMAP\n\t\tmaterial.sheenColor *= texture2D( sheenColorMap, vUv ).rgb;\n\t#endif\n\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\n\t#ifdef USE_SHEENROUGHNESSMAP\n\t\tmaterial.sheenRoughness *= texture2D( sheenRoughnessMap, vUv ).a;\n\t#endif\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat roughness;\n\tvec3 specularColor;\n\tfloat specularF90;\n\t#ifdef USE_CLEARCOAT\n\t\tfloat clearcoat;\n\t\tfloat clearcoatRoughness;\n\t\tvec3 clearcoatF0;\n\t\tfloat clearcoatF90;\n\t#endif\n\t#ifdef USE_IRIDESCENCE\n\t\tfloat iridescence;\n\t\tfloat iridescenceIOR;\n\t\tfloat iridescenceThickness;\n\t\tvec3 iridescenceFresnel;\n\t\tvec3 iridescenceF0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tvec3 sheenColor;\n\t\tfloat sheenRoughness;\n\t#endif\n\t#ifdef IOR\n\t\tfloat ior;\n\t#endif\n\t#ifdef USE_TRANSMISSION\n\t\tfloat transmission;\n\t\tfloat transmissionAlpha;\n\t\tfloat thickness;\n\t\tfloat attenuationDistance;\n\t\tvec3 attenuationColor;\n\t#endif\n};\nvec3 clearcoatSpecular = vec3( 0.0 );\nvec3 sheenSpecular = vec3( 0.0 );\nfloat IBLSheenBRDF( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat r2 = roughness * roughness;\n\tfloat a = roughness < 0.25 ? -339.2 * r2 + 161.4 * roughness - 25.9 : -8.48 * r2 + 14.3 * roughness - 9.95;\n\tfloat b = roughness < 0.25 ? 44.0 * r2 - 23.7 * roughness + 3.26 : 1.97 * r2 - 3.27 * roughness + 0.72;\n\tfloat DG = exp( a * dotNV + b ) + ( roughness < 0.25 ? 0.0 : 0.1 * ( roughness - 0.25 ) );\n\treturn saturate( DG * RECIPROCAL_PI );\n}\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\n\treturn fab;\n}\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\n\tvec2 fab = DFGApprox( normal, viewDir, roughness );\n\treturn specularColor * fab.x + specularF90 * fab.y;\n}\n#ifdef USE_IRIDESCENCE\nvoid computeMultiscatteringIridescence( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float iridescence, const in vec3 iridescenceF0, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#else\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#endif\n\tvec2 fab = DFGApprox( normal, viewDir, roughness );\n\t#ifdef USE_IRIDESCENCE\n\t\tvec3 Fr = mix( specularColor, iridescenceF0, iridescence );\n\t#else\n\t\tvec3 Fr = specularColor;\n\t#endif\n\tvec3 FssEss = Fr * fab.x + specularF90 * fab.y;\n\tfloat Ess = fab.x + fab.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = Fr + ( 1.0 - Fr ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.roughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3( 0, 1, 0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifdef USE_CLEARCOAT\n\t\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = dotNLcc * directLight.color;\n\t\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tsheenSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenColor, material.sheenRoughness );\n\t#endif\n\t#ifdef USE_IRIDESCENCE\n\t\treflectedLight.directSpecular += irradiance * BRDF_GGX_Iridescence( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness );\n\t#else\n\t\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef USE_CLEARCOAT\n\t\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tsheenSpecular += irradiance * material.sheenColor * IBLSheenBRDF( geometry.normal, geometry.viewDir, material.sheenRoughness );\n\t#endif\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\t#ifdef USE_IRIDESCENCE\n\t\tcomputeMultiscatteringIridescence( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness, singleScattering, multiScattering );\n\t#else\n\t\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\n\t#endif\n\tvec3 totalScattering = singleScattering + multiScattering;\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - max( max( totalScattering.r, totalScattering.g ), totalScattering.b ) );\n\treflectedLight.indirectSpecular += radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef USE_CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\n#ifdef USE_IRIDESCENCE\n\tfloat dotNVi = saturate( dot( normal, geometry.viewDir ) );\n\tif ( material.iridescenceThickness == 0.0 ) {\n\t\tmaterial.iridescence = 0.0;\n\t} else {\n\t\tmaterial.iridescence = saturate( material.iridescence );\n\t}\n\tif ( material.iridescence > 0.0 ) {\n\t\tmaterial.iridescenceFresnel = evalIridescence( 1.0, material.iridescenceIOR, dotNVi, material.iridescenceThickness, material.specularColor );\n\t\tmaterial.iridescenceF0 = Schlick_to_F0( material.iridescenceFresnel, 1.0, dotNVi );\n\t}\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointLightInfo( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\tvec4 spotColor;\n\tvec3 spotLightCoord;\n\tbool inSpotLightMap;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotLightInfo( spotLight, geometry, directLight );\n\t\t#if ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n\t\t#define SPOT_LIGHT_MAP_INDEX UNROLLED_LOOP_INDEX\n\t\t#elif ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\t#define SPOT_LIGHT_MAP_INDEX NUM_SPOT_LIGHT_MAPS\n\t\t#else\n\t\t#define SPOT_LIGHT_MAP_INDEX ( UNROLLED_LOOP_INDEX - NUM_SPOT_LIGHT_SHADOWS + NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n\t\t#endif\n\t\t#if ( SPOT_LIGHT_MAP_INDEX < NUM_SPOT_LIGHT_MAPS )\n\t\t\tspotLightCoord = vSpotLightCoord[ i ].xyz / vSpotLightCoord[ i ].w;\n\t\t\tinSpotLightMap = all( lessThan( abs( spotLightCoord * 2. - 1. ), vec3( 1.0 ) ) );\n\t\t\tspotColor = texture2D( spotLightMap[ SPOT_LIGHT_MAP_INDEX ], spotLightCoord.xy );\n\t\t\tdirectLight.color = inSpotLightMap ? directLight.color * spotColor.rgb : directLight.color;\n\t\t#endif\n\t\t#undef SPOT_LIGHT_MAP_INDEX\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getIBLIrradiance( geometry.normal );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\n\t#ifdef USE_CLEARCOAT\n\t\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 sampledDiffuseColor = texture2D( map, vUv );\n\t#ifdef DECODE_VIDEO_TEXTURE\n\t\tsampledDiffuseColor = vec4( mix( pow( sampledDiffuseColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), sampledDiffuseColor.rgb * 0.0773993808, vec3( lessThanEqual( sampledDiffuseColor.rgb, vec3( 0.04045 ) ) ) ), sampledDiffuseColor.w );\n\t#endif\n\tdiffuseColor *= sampledDiffuseColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tdiffuseColor *= texture2D( map, uv );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphcolor_vertex:"#if defined( USE_MORPHCOLORS ) && defined( MORPHTARGETS_TEXTURE )\n\tvColor *= morphTargetBaseInfluence;\n\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t#if defined( USE_COLOR_ALPHA )\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ) * morphTargetInfluences[ i ];\n\t\t#elif defined( USE_COLOR )\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ).rgb * morphTargetInfluences[ i ];\n\t\t#endif\n\t}\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1 ).xyz * morphTargetInfluences[ i ];\n\t\t}\n\t#else\n\t\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\t\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\t\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\t\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n\t#endif\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\n\t\tuniform sampler2DArray morphTargetsTexture;\n\t\tuniform ivec2 morphTargetsTextureSize;\n\t\tvec4 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset ) {\n\t\t\tint texelIndex = vertexIndex * MORPHTARGETS_TEXTURE_STRIDE + offset;\n\t\t\tint y = texelIndex / morphTargetsTextureSize.x;\n\t\t\tint x = texelIndex - y * morphTargetsTextureSize.x;\n\t\t\tivec3 morphUV = ivec3( x, y, morphTargetIndex );\n\t\t\treturn texelFetch( morphTargetsTexture, morphUV, 0 );\n\t\t}\n\t#else\n\t\t#ifndef USE_MORPHNORMALS\n\t\t\tuniform float morphTargetInfluences[ 8 ];\n\t\t#else\n\t\t\tuniform float morphTargetInfluences[ 4 ];\n\t\t#endif\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) transformed += getMorph( gl_VertexID, i, 0 ).xyz * morphTargetInfluences[ i ];\n\t\t}\n\t#else\n\t\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\t\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\t\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\t\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t\t#ifndef USE_MORPHNORMALS\n\t\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t\t#endif\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = dFdx( vViewPosition );\n\tvec3 fdy = dFdy( vViewPosition );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normal_pars_fragment:"#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif",normal_pars_vertex:"#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif",normal_vertex:"#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = dFdx( eye_pos.xyz );\n\t\tvec3 q1 = dFdy( eye_pos.xyz );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef USE_CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",iridescence_pars_fragment:"#ifdef USE_IRIDESCENCEMAP\n\tuniform sampler2D iridescenceMap;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n\tuniform sampler2D iridescenceThicknessMap;\n#endif",output_fragment:"#ifdef OPAQUE\ndiffuseColor.a = 1.0;\n#endif\n#ifdef USE_TRANSMISSION\ndiffuseColor.a *= material.transmissionAlpha + 0.1;\n#endif\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec2 packDepthToRG( in highp float v ) {\n\treturn packDepthToRGBA( v ).yx;\n}\nfloat unpackRGToDepth( const in highp vec2 v ) {\n\treturn unpackRGBAToDepth( vec4( v.xy, 0.0, 0.0 ) );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#if NUM_SPOT_LIGHT_COORDS > 0\n varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#if NUM_SPOT_LIGHT_MAPS > 0\n uniform sampler2D spotLightMap[ NUM_SPOT_LIGHT_MAPS ];\n#endif\n#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbool inFrustum = shadowCoord.x >= 0.0 && shadowCoord.x <= 1.0 && shadowCoord.y >= 0.0 && shadowCoord.y <= 1.0;\n\t\tbool frustumTest = inFrustum && shadowCoord.z <= 1.0;\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#if NUM_SPOT_LIGHT_COORDS > 0\n uniform mat4 spotLightMatrix[ NUM_SPOT_LIGHT_COORDS ];\n varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#if ( defined( USE_SHADOWMAP ) && ( NUM_DIR_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0 ) ) || ( NUM_SPOT_LIGHT_COORDS > 0 )\n\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\tvec4 shadowWorldPosition;\n#endif\n#if defined( USE_SHADOWMAP )\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if NUM_SPOT_LIGHT_COORDS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_COORDS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition;\n\t\t#if ( defined( USE_SHADOWMAP ) && UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\t\tshadowWorldPosition.xyz += shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias;\n\t\t#endif\n\t\tvSpotLightCoord[ i ] = spotLightMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\tuniform highp sampler2D boneTexture;\n\tuniform int boneTextureSize;\n\tmat4 getBoneMatrix( const in float i ) {\n\t\tfloat j = i * 4.0;\n\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\ty = dy * ( y + 0.5 );\n\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\treturn bone;\n\t}\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3( 1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108, 1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605, 1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tmaterial.transmission = transmission;\n\tmaterial.transmissionAlpha = 1.0;\n\tmaterial.thickness = thickness;\n\tmaterial.attenuationDistance = attenuationDistance;\n\tmaterial.attenuationColor = attenuationColor;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tmaterial.transmission *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tmaterial.thickness *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tvec4 transmission = getIBLVolumeRefraction(\n\t\tn, v, material.roughness, material.diffuseColor, material.specularColor, material.specularF90,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, material.ior, material.thickness,\n\t\tmaterial.attenuationColor, material.attenuationDistance );\n\tmaterial.transmissionAlpha = mix( material.transmissionAlpha, transmission.a, material.transmission );\n\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, material.transmission );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationColor;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec3 vWorldPosition;\n\tvec3 getVolumeTransmissionRay( const in vec3 n, const in vec3 v, const in float thickness, const in float ior, const in mat4 modelMatrix ) {\n\t\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\n\t\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\n\t\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\n\t\treturn normalize( refractionVector ) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness( const in float roughness, const in float ior ) {\n\t\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\n\t}\n\tvec4 getTransmissionSample( const in vec2 fragCoord, const in float roughness, const in float ior ) {\n\t\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\n\t\t#ifdef texture2DLodEXT\n\t\t\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\n\t\t#else\n\t\t\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\n\t\t#endif\n\t}\n\tvec3 applyVolumeAttenuation( const in vec3 radiance, const in float transmissionDistance, const in vec3 attenuationColor, const in float attenuationDistance ) {\n\t\tif ( isinf( attenuationDistance ) ) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\n\t\t\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec4 getIBLVolumeRefraction( const in vec3 n, const in vec3 v, const in float roughness, const in vec3 diffuseColor,\n\t\tconst in vec3 specularColor, const in float specularF90, const in vec3 position, const in mat4 modelMatrix,\n\t\tconst in mat4 viewMatrix, const in mat4 projMatrix, const in float ior, const in float thickness,\n\t\tconst in vec3 attenuationColor, const in float attenuationDistance ) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\n\t\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\n\t\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\n\t\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION ) || NUM_SPOT_LIGHT_COORDS > 0\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",background_frag:"uniform sampler2D t2D;\nuniform float backgroundIntensity;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\t#ifdef DECODE_VIDEO_TEXTURE\n\t\ttexColor = vec4( mix( pow( texColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), texColor.rgb * 0.0773993808, vec3( lessThanEqual( texColor.rgb, vec3( 0.04045 ) ) ) ), texColor.w );\n\t#endif\n\ttexColor.rgb *= backgroundIntensity;\n\tgl_FragColor = texColor;\n\t#include \n\t#include \n}",backgroundCube_vert:"varying vec3 vWorldDirection;\n#include \nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include \n\t#include \n\tgl_Position.z = gl_Position.w;\n}",backgroundCube_frag:"#ifdef ENVMAP_TYPE_CUBE\n\tuniform samplerCube envMap;\n#elif defined( ENVMAP_TYPE_CUBE_UV )\n\tuniform sampler2D envMap;\n#endif\nuniform float flipEnvMap;\nuniform float backgroundBlurriness;\nuniform float backgroundIntensity;\nvarying vec3 vWorldDirection;\n#include \nvoid main() {\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 texColor = textureCube( envMap, vec3( flipEnvMap * vWorldDirection.x, vWorldDirection.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 texColor = textureCubeUV( envMap, vWorldDirection, backgroundBlurriness );\n\t#else\n\t\tvec4 texColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t#endif\n\ttexColor.rgb *= backgroundIntensity;\n\tgl_FragColor = texColor;\n\t#include \n\t#include \n}",cube_vert:"varying vec3 vWorldDirection;\n#include \nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include \n\t#include \n\tgl_Position.z = gl_Position.w;\n}",cube_frag:"uniform samplerCube tCube;\nuniform float tFlip;\nuniform float opacity;\nvarying vec3 vWorldDirection;\nvoid main() {\n\tvec4 texColor = textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\n\tgl_FragColor = texColor;\n\tgl_FragColor.a *= opacity;\n\t#include \n\t#include \n}",depth_vert:"#include \n#include \n#include \n#include \n#include \n#include \n#include \nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include \n\t#include \n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include \n\t\t#include \n\t\t#include \n\t#endif\n\t#include \n\t#include \n\t#include \n\t#include \n\t#include \n\t#include \n\t#include \n\tvHighPrecisionZW = gl_Position.zw;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include \n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include \n\t#include \n\t#include \n\t#include \n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n\t#include \n\t#include \n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include \n\t\t#include \n\t\t#include \n\t#endif\n\t#include \n\t#include \n\t#include \n\t#include \n\t#include \n\t#include \n\t#include \n\tvWorldPosition = worldPosition.xyz;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main () {\n\t#include \n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include \n\t#include \n\t#include \n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include \nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include \n\t#include \n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include \nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\t#include \n\t#include \n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include \n\t#include \n\t#include \n\t#include