Do you want to create amazing games without coding? Do you want to access the latest features and updates of Buildbox, one of the most popular game development software in the market? Do you want to save money and time by using a cracked serial key for Buildbox 2.2.8 BETA? If you answered yes to any of these questions, then this article is for you.
-In this article, we will tell you everything you need to know about Buildbox 2.2.8 BETA - cracked serial key, including what is Buildbox and why you need it, what is new in Buildbox 2.2.8 BETA, how to get Buildbox 2.2.8 BETA cracked serial key, pros and cons of using cracked serial key, and conclusion and FAQs.
-By the end of this article, you will have a clear idea of whether you should use a cracked serial key for Buildbox 2.2.8 BETA or not, and how to do it safely and effectively.
- Buildbox is a game development software that allows anyone to create games without coding or programming skills.
-With Buildbox, you can drag and drop assets, add animations, sounds, effects, logic, and more to create your own games in minutes.
-Buildbox also has a powerful physics engine, a smart asset library, a node-based editor, an animation timeline, a monetization system, an export feature, and many other tools that make game creation easy and fun.
-Buildbox is used by thousands of game developers around the world, from beginners to professionals.
-Some of the most successful games made with Buildbox include Color Switch, The Line Zen, Phases, Sky, Ball Jump, Slip Away, Switchy Sides, Endless Sky, Damn Daniel, Hoppy Frog, Miximal Adventure, Void Troopers, Drop Out!, Nerves, The Pit, Jelly Jump, Zig Zag Boom, Skyward Journey, Ball Blast!, Rise Up!, Hoop Smash!, Flip Runner!, Dunk Shot!, Dune!, Rider!, Ballz!, Stack!, Twisty Road!, Flappy Dunk!, Snake vs Block!, Rolling Sky!, Dancing Line!, Piano Tiles 2!, Geometry Dash SubZero!, Crossy Road!, Subway Surfers!, Temple Run!, Angry Birds!, Fruit Ninja!, Jetpack Joyride!, Cut The Rope!, Plants vs Zombies!, Candy Crush Saga! , Clash Of Clans! , Minecraft! , Fortnite! , PUBG! , Among Us! , Roblox! , GTA! , Call Of Duty! , FIFA! , NBA! , Pokemon Go! , Super Mario Run! , Sonic Dash! , Asphalt 9! , CSR Racing 2! , Need For Speed! , Real Racing 3! , Hill Climb Racing! , Bike Race! , Traffic Rider! , Trials Frontier! , Alto's Adventure! , Ski Safari! , Tiny Wings! , Jet Car Stunts! , Badland! , Limbo! , Monument Valley! , Lara Croft Go! , Hitman Go! , Deus Ex Go! , The Room! , The Witness! , Myst! , Machinarium! , Samorost! , Botanicula! , World Of Goo! , Little Inferno! , Human Resource Machine! , Threes! , 2048! , Sudoku! , Tetris! , Pac-Man! , Space Invaders! , Galaga! , Asteroids! , Pong!
-As you can see, Buildbox can help you create any type of game you can imagine.
-If you own a car from the Volkswagen Group, such as Audi, VW, Seat or Skoda, you may have heard of ETKA, the electronic catalogue that contains the full information on spare parts and accessories for these cars. In this article, we will explain what ETKA is, what are its features and benefits, and how to install and use ETKA V7.3 on your PC.
-ETKA is an acronym for Elektronischer Teilekatalog, which means electronic parts catalogue in German. It is a software that allows you to search for and order spare parts and accessories for your car based on its model, year, engine type, chassis number, etc. You can also view diagrams, illustrations, prices, availability, specifications and other useful information about each part or accessory.
- ETKA was first developed by LexCom GmbH in 1989 for Audi AG, and later expanded to cover other brands of the Volkswagen Group, such as VW, Seat and Skoda. Since then, ETKA has been updated regularly with new data and features, and has become an indispensable tool for car owners, mechanics, dealers and enthusiasts.
- ETKA is compatible with most diagnostic tools and software that use OBD-II protocols, such as VCDS (VAG-COM), ODIS (Offboard Diagnostic Information System), etc.
- To update ETKA V7.3 to the latest version on your PC, you need to follow these steps:
-To use ETKA V7.3 to search for spare parts and accessories for your car on your PC, you need to follow these steps:
- In this article, we have explained what ETKA is, what are its features and benefits, and how to install and use
In this article, we have explained what ETKA is, what are its features and benefits, and how to install and use ETKA V7.3 on your PC. We hope that this article has helped you to understand and appreciate ETKA better, and that you will find it useful for your car maintenance and repair needs.
- If you have any comments or questions about ETKA V7.3, please feel free to leave them below. We would love to hear from you and help you with any issues you may encounter.
- Some of the common problems and solutions when installing or using ETKA V7.3 are:
- To switch between different languages in ETKA V7.3, you need to follow these steps:
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-Run the ETKA Loader.exe file from the installation folder (usually C:\ETKA).
-Select your country or region (such as France) and click OK.
-Select your user name (such as Admin) and password (such as 12345) and click OK.
-Select the brand you want to search for (such as Audi) from the main menu.
-Click on the flag icon at the top right corner of the main menu.
-How to switch between different languages in ETKA V7.3?
- To switch between different languages in ETKA V7.3, you need to follow these steps:
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-Run the ETKA Loader.exe file from the installation folder (usually C:\ETKA).
-Select your country or region (such as France) and click OK.
-Select your user name (such as Admin) and password (such as 12345) and click OK.
-Select the brand you want to search for (such as Audi) from the main menu.
-Click on the flag icon at the top right corner of the main menu.
-Select the language you want to switch to (such as English) from the drop-down menu.
-Wait for the language change to take effect (it may take a few seconds).
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- How to backup and restore ETKA V7.3 data?
- To backup and restore ETKA V7.3 data, you need to follow these steps:
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-To backup ETKA V7.3 data, copy the entire ETKA folder (usually C:\ETKA) to a safe location, such as an external hard drive or a USB flash drive.
-To restore ETKA V7.3 data, copy the backed up ETKA folder from the safe location to your PC, and overwrite the existing ETKA folder (usually C:\ETKA).
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- How to contact ETKA support or customer service?
- To contact ETKA support or customer service, you can use one of these methods:
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-Email: You can send an email to etka@lexcom.de with your name, country, phone number and problem description.
-Phone: You can call +49 89 189 31 31 0 from Monday to Friday, 8:00 am to 5:00 pm (Central European Time).
-Website: You can visit https://www.lexcom.de/en/ and fill out the contact form or use the live chat feature.
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- How to uninstall ETKA V7.3 from your PC?
- To uninstall ETKA V7.3 from your PC, you need to follow these steps:
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-Run the Uninstall.exe file from the installation folder (usually C:\ETKA).
-Select your country or region (such as France) and click OK.
-Select your user name (such as Admin) and password (such as 12345) and click OK.
-Select the brands you want to uninstall (such as Audi, VW, Seat and Skoda) and click OK.
-Wait for the uninstallation process to complete (it may take several minutes).
-
- 0a6ba089ebFxhome Photokey 6 Pro Mac Crack Torrent: What You Need to Know
-If you are looking for a powerful and easy-to-use photo editing software for Mac, you might have heard of Fxhome Photokey 6 Pro . This software allows you to remove green screen backgrounds from your photos and replace them with any image you want. You can also add effects, filters, layers, and text to your photos and create stunning images in minutes.
-Fxhome Photokey 6 Pro Mac Crack Torrent Download ✒ ✒ ✒ https://byltly.com/2uKxLm
But what if you don't want to pay $299 for the software license fee? What if you want to access the full version of the software without any limitations? What if you want to use the software offline without internet connection?
-In that case, you might be tempted to download Fxhome Photokey 6 Pro as a crack torrent . A crack torrent is a file that contains the cracked version of a software, which means that it has
been modified to bypass the security and activation features of the original software. A torrent is a file that contains the metadata of a file or a group of files that can be downloaded from other users through a peer-to-peer network.
-Downloading Fxhome Photokey 6 Pro as a crack torrent might seem like a good idea, but is it really worth it? What are the advantages and disadvantages of doing so? How can you download Fxhome Photokey 6 Pro as a crack torrent safely and easily? Are there any alternatives to downloading Fxhome Photokey 6 Pro as a crack torrent?
-In this article, we will answer all these questions and more. We will give you a comprehensive guide on everything you need to know about Fxhome Photokey 6 Pro Mac crack torrent. We will also provide you with some alternative ways to get Fxhome Photokey 6 Pro for Mac without downloading it as a crack torrent. By the end of this article, you will be able to make an informed decision on whether to download Fxhome Photokey 6 Pro as a crack torrent or not.
- What is Fxhome Photokey 6 Pro?
-Fxhome Photokey 6 Pro is a photo editing software developed by Fxhome, a company that specializes in creating visual effects and video editing software. Fxhome Photokey 6 Pro is designed for Mac users who want to create professional-looking photos with green screen backgrounds.
-Green screen backgrounds are commonly used in photography and filmmaking to replace the background of a subject with another image. For example, you can take a photo of a person in front of a green screen and then replace the green screen with a scenic landscape, a city skyline, or any other image you want.
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-Fxhome Photokey 6 Pro makes this process easy and fast. You can import your photos with green screen backgrounds into the software and then choose from over 250 background images that are included in the software. You can also import your own background images or use online images from Flickr or Google Images. Fxhome Photokey 6 Pro will automatically remove the green screen from your photos and blend them with the background images seamlessly.
-But that's not all. Fxhome Photokey 6 Pro also allows you to add effects, filters, layers, and text to your photos. You can adjust the color, brightness, contrast, saturation, and sharpness of your photos. You can apply artistic effects such as blur, glow, vignette, and gradient. You can add layers such as shadows, reflections, borders, and watermarks. You can also add text to your photos using different fonts, colors, sizes, and styles.
-Fxhome Photokey 6 Pro is compatible with Mac OS X 10.6 or later. It supports various image formats such as JPEG, PNG, TIFF, BMP, and RAW. It also integrates with Adobe Photoshop and Lightroom, allowing you to export your photos directly to these software for further editing.
-Fxhome Photokey 6 Pro is ideal for photographers who want to create stunning photos with green screen backgrounds without spending too much time and money on expensive equipment and software. It is also suitable for hobbyists who want to have fun with their photos and unleash their creativity.
- Why Download Fxhome Photokey 6 Pro as a Crack Torrent?
-As mentioned earlier, Fxhome Photokey 6 Pro is not a free software. It costs $299 for the software license fee. This means that you have to pay this amount before you can use the software legally and fully.
-However, some people might not be willing or able to pay this amount for various reasons. They might think that the software is too expensive for their budget or needs. They might also think that they can get the same or better results with other free or cheaper software.
-Therefore, some people might resort to downloading Fxhome Photokey 6 Pro as a crack torrent instead. They might think that this is a smart way to save money and get access to the full version of the software without any limitations.
-But is downloading Fxhome Photokey 6 Pro as a crack torrent really a good idea? What are the pros and cons of doing so? Let's take a look at some of the advantages and disadvantages of downloading Fxhome Photokey 6 Pro as a crack torrent.
- Advantages of Downloading Fxhome Photokey 6 Pro as a Crack Torrent
-Some of the possible advantages of downloading Fxhome Photokey 6 Pro as a crack torrent are:
-
-Saving money on the software license fee : This is probably the most obvious and common reason why people download Fxhome Photokey 6 Pro as a crack torrent. By doing so, they can avoid paying $299 for the software license fee and use the software for free.Accessing the full version of the software without limitations : Another possible advantage of downloading Fxhome Photokey 6 Pro as a crack torrent is that they can access the full version of the software without any limitations. This means that they can use all the features and functions of the software without any restrictions or watermarks.Being able to use the software offline without internet connection : A third possible advantage of downloading Fxhome Photokey 6 Pro as a crack torrent is that they can use the software offline without internet connection. This means that they can use the software anytime and anywhere without worrying about the internet availability or speed.
-Disadvantages of Downloading Fxhome Photokey 6 Pro as a Crack Torrent
-However, downloading Fxhome Photokey 6 Pro as a crack torrent also comes with some serious disadvantages. Some of them are:
-
-Risking malware infection from untrusted sources : This is probably the most dangerous and common disadvantage of downloading Fxhome Photokey 6 Pro as a crack torrent. By downloading Fxhome Photokey 6 Pro as a crack torrent from untrusted sources, they are exposing their Mac computer to potential malware infection. Malware is malicious software that can harm their computer or steal their personal information. Some examples of malware are viruses, worms, trojans, spyware, adware, ransomware, and rootkits. Malware can cause various problems such as slowing down their computer, deleting or encrypting their files, displaying unwanted ads or pop-ups, redirecting their browser to malicious websites, stealing their passwords or credit card details, or even locking their computer and demanding a ransom to unlock it.Violating the intellectual property rights of the software developer : Another serious disadvantage of downloading Fxhome Photokey 6 Pro as a crack torrent is that they are violating the intellectual property rights of the software developer. Intellectual property rights are the legal rights that protect the creations and inventions of individuals or organizations. By downloading Fxhome Photokey 6 Pro as a crack torrent, they are infringing on the copyright and trademark rights of Fxhome, the company that developed and owns Fxhome Photokey 6 Pro. This is not only unethical but also illegal.Facing legal consequences for software piracy : A third serious disadvantage of downloading Fxhome Photokey 6 Pro as a crack torrent is that they are facing legal consequences for software piracy. Software piracy is the unauthorized copying, distribution, or use of software. By downloading Fxhome Photokey 6 Pro as a crack torrent, they are committing software piracy and breaking the law. Depending on the country and jurisdiction, software piracy can result in various penalties such as fines, imprisonment, or both.Missing out on updates and technical support from the official website : A fourth disadvantage of downloading Fxhome Photokey 6 Pro as a crack torrent is that they are missing out on updates and technical support from the official website. By downloading Fxhome Photokey 6 Pro as a crack torrent, they are not eligible to receive any updates or technical support from Fxhome. This means that they will not be able to enjoy any new features or improvements that Fxhome might release for Fxhome Photokey 6 Pro in the future. It also means that they will not be able to get any help or assistance from Fxhome if they encounter any problems or issues with Fxhome Photokey 6 Pro.
- How to Download Fxhome Photokey 6 Pro as a Crack Torrent?
-If you still want to download Fxhome Photokey 6 Pro as a crack torrent despite knowing the disadvantages and risks involved, you will need to follow some steps to do so. Here is a step-by-step guide on how to download Fxhome Photokey 6 Pro as a crack torrent for Mac users:
- Step 1: Find a Reliable Torrent Website
-The first step to download Fxhome Photokey 6 Pro as a crack torrent is to find a reliable torrent website that offers Fxhome Photokey 6 Pro as a crack torrent. A torrent website is a website that hosts and indexes torrent files that can be downloaded by users through a peer-to-peer network.
-There are many torrent websites available on the internet, but not all of them are trustworthy or safe. Some of them might contain fake or malicious files that can harm your computer or steal your information. Some of them might also be blocked or banned by your internet service provider or government due to legal issues.
-Therefore Therefore, you need to be careful and selective when choosing a torrent website to download Fxhome Photokey 6 Pro as a crack torrent. You need to look for a torrent website that has a good reputation, a large user base, a high seed-to-leech ratio, and a positive feedback system. You also need to check the legality and safety of the torrent website in your country and region.
-Some examples of popular and reliable torrent websites that might offer Fxhome Photokey 6 Pro as a crack torrent are:
-
-The Pirate Bay : This is one of the oldest and most famous torrent websites in the world. It has millions of users and hosts millions of torrent files in various categories such as movies, music, games, software, and more. It also has a simple and user-friendly interface that allows you to search and download torrent files easily.1337x : This is another popular and well-known torrent website that offers a wide range of torrent files in various genres and languages. It has a modern and attractive design that makes it easy to navigate and find what you are looking for. It also has a community section where you can interact with other users and get recommendations and reviews.RARBG : This is a torrent website that specializes in high-quality video content such as movies, TV shows, documentaries, and more. It has a loyal and active user base that ensures fast and reliable downloads. It also has a top 10 list that shows you the most popular and trending torrent files on the website.
-However, these are just examples and not endorsements. We do not recommend or encourage you to download Fxhome Photokey 6 Pro as a crack torrent from any torrent website. We are not responsible for any consequences or damages that might result from doing so.
- Step 2: Download and Install a Torrent Client
-The second step to download Fxhome Photokey 6 Pro as a crack torrent is to download and install a torrent client software that allows you to download and manage torrent files. A torrent client software is a program that connects you to other users who have the same torrent file as you and enables you to download the file from them.
-There are many torrent client software available on the internet, but not all of them are compatible or suitable for Mac users. Some of them might have compatibility issues, security vulnerabilities, or unwanted features such as ads or malware. Some of them might also be blocked or banned by your internet service provider or government due to legal issues.
-Therefore, you need to be careful and selective when choosing a torrent client software to download Fxhome Photokey 6 Pro as a crack torrent. You need to look for a torrent client software that has a good reputation, a large user base, a high performance, and a secure encryption. You also need to check the legality and safety of the torrent client software in your country and region.
-Some examples of popular and reliable torrent client software that are compatible with Mac users are:
-
-qBittorrent : This is one of the best and most widely used torrent client software in the world. It is free, open-source, lightweight, and easy-to-use. It has all the essential features such as magnet links, DHT, PEX, encryption, bandwidth control, RSS feeds, and more. It also has no ads or malware.Transmission : This is another excellent and popular torrent client software that is designed specifically for Mac users. It is also free, open-source, simple, and fast. It has all the basic features such as magnet links, DHT, PEX, encryption, bandwidth control, RSS feeds and more. It also has no ads or malware.uTorrent : This is one of the oldest and most famous torrent client software in the world. It is also free, lightweight, and easy-to-use. It has all the advanced features such as magnet links, DHT, PEX, encryption, bandwidth control, RSS feeds, and more. However, it also has some ads and offers that might be annoying or unwanted.
-However, these are just examples and not endorsements. We do not recommend or encourage you to download Fxhome Photokey 6 Pro as a crack torrent using any torrent client software. We are not responsible for any consequences or damages that might result from doing so.
- Step 3: Download Fxhome Photokey 6 Pro as a Crack Torrent
-The third step to download Fxhome Photokey 6 Pro as a crack torrent is to download Fxhome Photokey 6 Pro as a crack torrent file from the torrent website using the torrent client software. A torrent file is a small file that contains the metadata of the file or files that you want to download. It does not contain the actual file or files, but it tells your torrent client software where to find them and how to download them.
-To download Fxhome Photokey 6 Pro as a crack torrent, you need to follow these steps:
-
-Open your torrent client software and go to the torrent website that you have chosen in step 1.
-Search for Fxhome Photokey 6 Pro as a crack torrent using the search bar or the categories on the website.
-Look for the Fxhome Photokey 6 Pro as a crack torrent file that has the most seeds and the least leeches. Seeds are users who have the complete file and are sharing it with others. Leeches are users who are downloading the file but not sharing it with others. The more seeds and the less leeches a torrent file has, the faster and more reliable your download will be.
-Click on the Fxhome Photokey 6 Pro as a crack torrent file that you have selected and download it to your computer. You can either click on the download button or the magnet link on the website. A magnet link is a link that directly opens your torrent client software and starts downloading the file without having to download the torrent file first.
-Wait for your torrent client software to download Fxhome Photokey 6 Pro as a crack torrent file from other users. You can monitor the progress and speed of your download on your torrent client software.
-
-Note: Downloading Fxhome Photokey 6 Pro as a crack torrent might take some time depending on your internet connection speed, the size of the file, and the number of seeds and leeches available. It might also consume a lot of your bandwidth and data usage, so make sure you have enough before you start downloading.
- Step 4: Install Fxhome Photokey 6 Pro as a Crack Torrent
-The fourth step to download Fxhome Photokey 6 Pro as a crack torrent is to install Fxhome Photokey 6 Pro as a crack torrent file on your Mac computer using the installation wizard. An installation wizard is a program that guides you through the process of installing a software on your computer.
-To install Fxhome Photokey 6 Pro as a crack torrent, you need to follow these steps:
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-Open your torrent client software and go to the folder where you have downloaded Fxhome Photokey 6 Pro as a crack torrent file.
-Double-click on the Fxhome Photokey 6 Pro as a crack torrent file to open it. You will see a folder that contains several files such as setup.exe, crack.exe, readme.txt, etc.
-Double-click on the setup.exe file to launch the installation wizard. Follow the instructions on the screen to install Fxhome Photokey 6 Pro on your Mac computer.
-When prompted, enter the serial number or activation code that is provided in the readme.txt file or in another file in the folder. This will activate your Fxhome Photokey 6 Pro software and allow you to use it without any limitations.
-If required, copy and paste the crack.exe file or another file in the folder to the installation directory of Fxhome Photokey 6 Pro on your Mac computer. This will replace the original file and crack the software, which means that it will bypass the security and activation features of the software.
-Once the installation is complete, you can launch Fxhome Photokey 6 Pro from your Mac computer and start using it for your photo editing needs.
-
-Note: Installing Fxhome Photokey 6 Pro as a crack torrent might cause some problems or errors on your Mac computer. Some of them are:
-
-The software might not work properly or crash frequently.
-The software might contain malware or viruses that can harm your computer or steal your information.
-The software might be detected and blocked by your antivirus or firewall software.
-The software might be incompatible with your Mac OS version or other software on your computer.
-
- Step 5: Enjoy Fxhome Photokey 6 Pro as a Crack Torrent
-The fifth and final step to download Fxhome Photokey 6 Pro as a crack torrent is to enjoy Fxhome Photokey 6 Pro as a crack torrent on your Mac computer. You can use Fxhome Photokey 6 Pro as a crack torrent to create stunning photos with green screen backgrounds and add effects, filters, layers, and text to them. You can also export your photos to various formats or share them online with your friends and family.
-However, you should also be aware of the risks and consequences of using Fxhome Photokey 6 Pro as a crack torrent. You should also respect the intellectual property rights of Fxhome and consider buying the software from the official website if you like it and want to support the developer.
- Alternatives to Downloading Fxhome Photokey 6 Pro as a Crack Torrent
-If you are not comfortable or satisfied with downloading Fxhome Photokey 6 Pro as a crack torrent, you might be wondering if there are any alternatives to getting Fxhome Photokey 6 Pro for Mac without downloading it as a crack torrent. The answer is yes, there are some alternative ways to get Fxhome Photokey 6 Pro for Mac without downloading it as a crack torrent. Here are some of them:
- Alternative 1: Buy Fxhome Photokey 6 Pro from the Official Website
-The best and most recommended alternative to downloading Fxhome Photokey 6 Pro as a crack torrent is to buy Fxhome Photokey 6 Pro from the official website. By buying Fxhome Photokey 6 Pro from the official website, you will get several benefits such as:
-
-Getting the latest and most updated version of the software : By buying Fxhome Photokey 6 Pro from the official website, you will get the latest and most updated version of the software that has all the new features and improvements that Fxhome has released for Fxhome Photokey 6 Pro.Getting access to updates and technical support from Fxhome : By buying Fxhome Photokey 6 Pro from the official website, you will also get access to updates and technical support from Fxhome. This means that you will be able to download any updates that Fxhome might release for Fxhome Photokey 6 Pro in the future. It also means that you will be able to get help or assistance from Fxhome if you encounter any problems or issues with Fxhome Photokey 6 Pro.Getting a legal and ethical way to use the software : By buying Fxhome Photokey 6 Pro from the official website, you will also get a legal and ethical way to use the software. This means that you will not be violating any intellectual property rights or laws by using Fxhome Photokey 6 Pro. It also means that you will be supporting the software developer and showing your appreciation for their work and effort.
-To buy Fxhome Photokey 6 Pro from the official website, you need to follow these steps:
-
-Go to the official website of Fxhome Photokey 6 Pro at https://fxhome.com/photokey-6-pro .
-Click on the "Buy now" button and choose your preferred payment method and currency.
-Enter your personal and billing information and complete the payment process.
-Check your email for the confirmation and receipt of your purchase. You will also receive a download link and a serial number for Fxhome Photokey 6 Pro.
-Download and install Fxhome Photokey 6 Pro on your Mac computer using the download link and the serial number that you have received.
-Enjoy using Fxhome Photokey 6 Pro legally and ethically on your Mac computer.
-
-Note: Buying Fxhome Photokey 6 Pro from the official website might cost you $299, but it is worth it considering the benefits and advantages that you will get. It is also a one-time payment that will give you lifetime access to Fxhome Photokey 6 Pro. You can also get a 30-day money-back guarantee if you are not satisfied with Fxhome Photokey 6 Pro.
- Alternative 2: Download Fxhome Photokey 6 Pro Demo Version from the Official Website
-If you are not sure whether to buy Fxhome Photokey 6 Pro from the official website or not, you can also try downloading Fxhome Photokey 6 Pro demo version from the official website. A demo version is a free version of a software that allows you to test and evaluate the software before buying it. It usually has some limitations or restrictions compared to the full version of the software.
-By downloading Fxhome Photokey 6 Pro demo version from the official website, you will get some benefits such as:
-
-Getting a safe and secure way to try the software : By downloading Fxhome Photokey 6 Pro demo version from the official website, you will get a safe and secure way to try the software. You will not have to worry about malware or viruses that might harm your computer or steal your information. You will also not have to worry about legal issues or consequences that might result from downloading Fxhome Photokey 6 Pro as a crack torrent.Getting access to some of the features and functions of the software : By downloading Fxhome Photokey 6 Pro demo version from the official website, you will also get access to some of the features and functions of the software. You will be able to use Fxhome Photokey 6 Pro for your photo editing needs and see how it works and performs. You will also be able to compare it with other photo editing software that you might have or want to use.Getting a chance to decide whether to buy the software or not : By downloading Fxhome Photokey 6 Pro demo version from the official website, you will also get a chance to decide whether to buy the software or not. You will be able to see if Fxhome Photokey 6 Pro meets your expectations and requirements. You will also be able to see if Fxhome Photokey 6 Pro is worth your money and time.
-To download Fxhome Photokey 6 Pro demo version from the official website, you need to follow these steps:
-
-Go to the official website of Fxhome Photokey 6 Pro at https://fxhome.com/photokey-6-pro .
-Click on the "Download free trial" button and enter your email address.
-Check your email for the download link and instructions for Fxhome Photokey 6 Pro demo version.
-Download and install Fxhome Photokey 6 Pro demo version on your Mac computer using the download link and instructions that you have received.
-Enjoy using Fxhome Photokey 6 Pro demo version on your Mac computer for a limited time.
-
-Note: Downloading Fxhome Photokey 6 Pro demo version from the official website is free, but it has some limitations or restrictions compared to the full version of the software. Some of them are:
-
-The demo version will expire after 14 days of use.
-The demo version will watermark your photos with the Fxhome logo.
-The demo version will not allow you to export your photos to other formats or share them online.
-
- Alternative 3: Use Other Free or Paid Photo Editing Software for Mac
-If you are not interested in buying or trying Fxhome Photokey 6 Pro from the official website, you can also use other free or paid photo editing software for Mac that have similar or better features than Fxhome Photokey 6 Pro. There are many photo editing software available on the internet, but not all of them are compatible or suitable for Mac users. Some of them might have compatibility issues, security vulnerabilities, or unwanted features such as ads or malware. Some of them might also be blocked or banned by your internet service provider or government due to legal issues.
-Therefore, you need to be careful and selective when choosing a photo editing software for Mac that can replace Fxhome Photokey 6 Pro. You need to look for a photo editing software that has a good reputation, a large user base, a high performance, and a secure encryption. You also need to check the legality and safety of the photo editing software in your country and region.
-Some examples of popular and reliable photo editing software for Mac that can replace Fxhome Photokey 6 Pro are:
-
-GIMP : This is one of the best and most widely used photo editing software in the world. It is free, open-source, powerful, and versatile. It has all the essential features such as layers, masks, filters, brushes, tools, and more. It also has some advanced features such as scripting, plugins, animation, and more. It is compatible with various image formats such as JPEG, PNG, TIFF, BMP, and RAW.Photoshop : This is another excellent and popular photo editing software that is developed by Adobe, a company that specializes in creating creative and multimedia software. It is paid, professional, and comprehensive. It has all the features and functions that you can imagine for photo editing such as layers, masks, filters, brushes, tools, and more. It also has some unique features such as smart objects, content-aware fill, camera raw, and more. It is compatible with various image formats such as JPEG, PNG, TIFF, BMP, and RAW.Pixlr : This is a photo editing software that is designed specifically for online use. It is free, web-based, and easy-to-use. It has all the basic features such as layers, filters, tools and more. It also has some advanced features such as AI cutout, background removal, and more. It is compatible with various image formats such as JPEG, PNG, TIFF, BMP, and RAW.
-However, these are just examples and not endorsements. We do not recommend or encourage you to use any photo editing software for Mac without doing your own research and comparison. We are not responsible for any consequences or damages that might result from doing so.
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-In conclusion, Fxhome Photokey 6 Pro is a photo editing software for Mac that allows you to remove green screen backgrounds from your photos and replace them with any image you want. You can also add effects, filters, layers, and text to your photos and create stunning images in minutes.
-However, Fxhome Photokey 6 Pro is not a free software. It costs $299 for the software license fee. If you don't want to pay this amount, you might be tempted to download Fxhome Photokey 6 Pro as a crack torrent. A crack torrent is a file that contains the cracked version of a software, which means that it has been modified to bypass the security and activation features of the original software.
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-Risking malware infection from untrusted sources
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-What is Fxhome Photokey 6 Pro? Fxhome Photokey 6 Pro is a photo editing software for Mac that allows you to remove green screen backgrounds from your photos and replace them with any image you want. You can also add effects, filters, layers, and text to your photos and create stunning images in minutes.
-What is a crack torrent? A crack torrent is a file that contains the cracked version of a software, which means that it has been modified to bypass the security and activation features of the original software.
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-Download Fxhome Photokey 6 Pro demo version from the official website.
-Use other free or paid photo editing software for Mac that have similar or better features than Fxhome Photokey 6 Pro.
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- b2dd77e56bHow to Download Game Point Blank Strike on Laptop
-If you are a fan of first-person shooter (FPS) games, you might have heard of Point Blank Strike, a popular mobile game that brings the thrill and excitement of the original Point Blank to your phone. But did you know that you can also play Point Blank Strike on your laptop? In this article, we will show you how to download and install Point Blank Strike on your laptop using two different methods. We will also explain why playing Point Blank Strike on your laptop can enhance your gaming experience and give you some tips to optimize your gameplay. Let's get started!
- What is Point Blank Strike?
-Point Blank Strike is a mobile FPS game developed by NEXON Company, based on the classic online game Point Blank that was released in 2008. Point Blank Strike features fast-paced multiplayer battles, various game modes, realistic graphics, and a huge arsenal of weapons. You can join up to eight players in team deathmatch, demolition, clan match, AI battle, and more. You can also customize your character, upgrade your weapons, and earn rewards as you play.
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A mobile FPS game based on the original Point Blank
-Point Blank Strike is a faithful adaptation of the original Point Blank, which was one of the most popular online FPS games in the world. Point Blank Strike retains the same gameplay mechanics, maps, modes, and weapons as the original game, but with improved graphics and performance for mobile devices. You can enjoy the classic Point Blank maps like Crackdown, Red Rock, and Burning Hall, as well as new maps exclusive to Point Blank Strike. You can also experience the original Point Blank mode, which features a legendary firearm, Kriss S.V.
- Features of Point Blank Strike
-Point Blank Strike has many features that make it one of the best mobile FPS games available. Here are some of them:
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-Furious real-time multiplayer battles: You can challenge other players from around the world in 4v4 matches that last only a few minutes. You don't have to wait for long matchmaking or loading times. Just tap and play!A simple auto-firing targeting system: You don't have to worry about complicated controls or aiming. The game automatically fires at your enemies when they are in your crosshair. You can focus on moving, dodging, and strategizing.Even low-spec devices can join the fray: You don't need a high-end phone to play Point Blank Strike. The game runs smoothly on devices with at least 1GB RAM and 385-410mb of free space. The game also utilizes an always-on PvP network, so you need a stable internet connection to play.A rich variety of live PvP content: You can choose from different game modes, each with its own objectives and rules. You can also join or create a clan and compete with other clans for glory and rewards. You can also participate in events and tournaments for more fun and prizes.
- Why Play Point Blank Strike on Laptop?
-While playing Point Blank Strike on your phone is convenient and enjoyable, playing it on your laptop can offer some advantages that can make your gaming experience even better. Here are some reasons why you might want to play Point Blank Strike on your laptop:
- Advantages of playing on a bigger screen and with better controls One of the main benefits of playing Point Blank Strike on your laptop is that you can enjoy the game on a bigger and clearer screen. This can help you see the details of the game better, such as the enemies, the weapons, and the environment. You can also immerse yourself more in the game's atmosphere and graphics. Playing on a bigger screen can also reduce eye strain and fatigue, especially if you play for long hours.
- Another advantage of playing Point Blank Strike on your laptop is that you can use better controls, such as a keyboard and a mouse. This can give you more accuracy and precision when aiming and shooting. You can also customize your key bindings and mouse sensitivity to suit your preferences. Playing with a keyboard and a mouse can also improve your reaction time and reflexes, as well as your comfort and convenience.
- How to avoid battery and performance issues on mobile devices
-Another reason why you might want to play Point Blank Strike on your laptop is that you can avoid some of the common problems that affect mobile devices, such as battery drain and performance issues. Playing Point Blank Strike on your phone can consume a lot of battery power, especially if you play with high settings and brightness. This can limit your playtime and force you to charge your phone frequently. Playing on your laptop, on the other hand, can save your phone's battery life and let you play for longer without interruptions.
-How to play Point Blank: Strike on PC with LDPlayer
- Playing Point Blank Strike on your phone can also cause some performance issues, such as lag, stuttering, freezing, and crashing. This can happen if your phone is not powerful enough to run the game smoothly, or if you have too many apps running in the background. These issues can ruin your gameplay and frustrate you. Playing on your laptop, however, can prevent these issues, as laptops usually have better hardware and software than phones. You can also adjust the game's settings to match your laptop's specifications and ensure optimal performance.
- How to Download and Install Point Blank Strike on Laptop?
-Now that you know why playing Point Blank Strike on your laptop can be a good idea, you might be wondering how to do it. There are two main ways to download and install Point Blank Strike on your laptop: using an Android emulator or using a phone app. We will explain each option in detail below.
- Option 1: Using an Android emulator like BlueStacks
-An Android emulator is a software that allows you to run Android apps on your PC or laptop. One of the most popular and reliable Android emulators is BlueStacks, which has over 500 million users worldwide. BlueStacks can run Point Blank Strike smoothly and efficiently on your laptop, as well as other Android games and apps. Here are the steps to download and install BlueStacks and Point Blank Strike on your laptop:
- Steps to download and install BlueStacks and Point Blank Strike
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-Download BlueStacks from its official website: Go to https://www.bluestacks.com/ and click on the "Download BlueStacks" button. The download will start automatically. You can also choose the version of BlueStacks that matches your operating system (Windows or Mac).Install BlueStacks on your laptop: After the download is complete, open the installer file and follow the instructions on the screen. The installation process may take a few minutes, depending on your internet speed and laptop's performance.Launch BlueStacks and sign in with your Google account: Once BlueStacks is installed, open it from your desktop or start menu. You will see a welcome screen where you need to sign in with your Google account. This is necessary to access the Google Play Store and download apps. If you don't have a Google account, you can create one for free.Search for Point Blank Strike in the Google Play Store: After signing in with your Google account, you will see the home screen of BlueStacks, where you can access various features and settings. Click on the "Game Center" tab at the top and then click on the "Google Play" icon at the bottom right corner. This will open the Google Play Store app within BlueStacks. In the search bar, type "Point Blank Strike" and hit enter.Download and install Point Blank Strike: You will see a list of results related to Point Blank Strike. Click on the one that says "Point Blank: Strike" by NEXON Company. This will open the app's page where you can see its description, screenshots, ratings, reviews, etc. Click on the "Install" button to start downloading and installing Point Blank Strike on BlueStacks.Launch Point Blank Strike and enjoy: After Point Blank Strike is installed, you can launch it from the home screen of BlueStacks or from the "My Games" tab. You will see the game's logo and loading screen. Wait for a few seconds until the game is ready. You can then log in with your NEXON account or play as a guest. You can also choose your region and language. You are now ready to play Point Blank Strike on your laptop!
- Tips to optimize BlueStacks settings and game controls
-To make sure that you have the best gaming experience with BlueStacks and Point Blank Strike, here are some tips to optimize your settings and controls:
-
-Adjust the graphics and performance settings: You can change the graphics and performance settings of BlueStacks to match your laptop's specifications and preferences. To do this, click on the "Menu" icon at the top right corner of BlueStacks and then click on "Settings". You will see a window where you can adjust various options, such as resolution, frame rate, CPU cores, RAM, etc. You can also choose from different presets, such as low, medium, high, or custom. For Point Blank Strike, we recommend using high or custom settings for better graphics and performance.Customize the game controls: You can customize the game controls of Point Blank Strike to suit your keyboard and mouse. To do this, launch Point Blank Strike and then click on the "Keyboard" icon at the bottom right corner of BlueStacks. You will see a window where you can drag and drop different keys to different functions, such as movement, shooting, aiming, reloading, etc. You can also change the sensitivity and transparency of the keys. You can also use the default controls or choose from different presets, such as MOBA, FPS, or WASD.
- Option 2: Using a phone app like Your Phone
-An alternative way to play Point Blank Strike on your laptop is to use a phone app like Your Phone. Your Phone is a Microsoft app that allows you to link your Android phone to your Windows 10 PC or laptop. With Your Phone, you can access your phone's apps, notifications, photos, messages, calls, and more on your PC or laptop. You can also use Your Phone to play Point Blank Strike on your PC or laptop by mirroring your phone's screen. Here are the steps to link your PC and your Android phone with Your Phone and play Point Blank Strike on your PC:
- Steps to link your PC and your Android phone with Your Phone
-
-Download Your Phone app on your PC and your phone: To use Your Phone, you need to download the app on both your PC and your phone. To download it on your PC, go to https://www.microsoft.com/en-us/p/your-phone/9nmpj99vjbwv and click on the "Get" button. The app will be installed automatically on your PC. To download it on your phone, go to https://play.google.com/store/apps/details?id=com.microsoft.appmanager&hl=en_US&gl=US and click on the "Install" button. The app will be downloaded and installed on your phone.Launch Your Phone app on your PC and sign in with your Microsoft account: After installing Your Phone app on your PC, open it from your desktop or start menu. You will see a welcome screen where you need to sign in with your Microsoft account. This is necessary to link your PC and your phone. If you don't have a Microsoft account, you can create one for free.Launch Your Phone app on your phone and sign in with the same Microsoft account: After installing Your Phone app on your phone, open it from your app drawer or home screen. You will see a welcome screen where you need to sign in with the same Microsoft account that you used on your PC. This will establish a connection between your PC and your phone.Allow permissions and settings on both devices: To use Your Phone app properly, you need to allow some permissions and settings on both devices. On your PC, you will see a window where you can choose what features you want to use with Your Phone app, such as apps, notifications, photos, messages, calls, etc. Select the ones that you want and click on "Continue". On your phone, you will see a series of prompts where you need to allow some permissions and settings for Your Phone app, such as access to contacts, storage, notifications, etc. Tap on "Allow" or "OK" for each prompt.Verify the link code on both devices: The final step to link your PC and your phone with Your Phone app is to verify the link code on both devices. On your PC, you will see a window where you will see a six-digit code. On your phone, you will see a notification where you will see the same code. Make sure that the codes match and then tap on "Allow" on your phone and click on "Done" on your PC. You have now successfully linked your PC and your phone with Your Phone app.
- Tips to access and play Point Blank Strike on your PC
-To access and play Point Blank Strike on your PC using Your Phone app, here are some tips to follow:
-
-Open the Apps feature on Your Phone app on your PC: To access your phone's apps on your PC, you need to open the Apps feature on Your Phone app on your PC. To do this, launch Your Phone app on your PC and then click on the "Apps" icon at the left sidebar. You will see a list of all the apps that are installed on your phone.Find and launch Point Blank Strike from the list of apps: To find and launch Point Blank Strike from the list of apps, you can either scroll down until you see it or use the search bar at the top to type its name. Once you find it, click on it to open it. You will see a window where you will see your phone's screen mirrored on your PC. You can also pin Point Blank Strike to your taskbar or start menu for easier access.Play Point Blank Strike using your mouse and keyboard: To play Point Blank Strike using your mouse and keyboard, you need to use the same controls as you would on your phone. You can use your mouse to move the cursor and click to tap. You can also use some keyboard shortcuts, such as Esc to go back, F11 to enter or exit full screen mode, Ctrl + mouse wheel to zoom in or out, etc. You can also use the toolbar at the right side of the window to access some functions, such as home, back, recent apps, screenshot, etc.
- Conclusion
-Point Blank Strike is a fun and exciting mobile FPS game that you can also play on your laptop using either an Android emulator like BlueStacks or a phone app like Your Phone. Both methods have their pros and cons, so you can choose the one that suits you best. Playing Point Blank Strike on your laptop can offer some advantages, such as a bigger screen, better controls, and improved performance. However, you also need to make sure that you have a stable internet connection and enough storage space on both devices. We hope that this article has helped you learn how to download and install Point Blank Strike on your laptop and enjoy it to the fullest.
- FAQs
-Here are some frequently asked questions about Point Blank Strike and playing it on laptop:
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-Is Point Blank Strike free to play? Is Point Blank Strike compatible with Windows 10? Can I play Point Blank Strike offline? Can I play Point Blank Strike with my friends? How can I update Point Blank Strike? 197e85843dWhat is APK Mody and Why You Should Use It
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APK Mody is a website that provides thousands of MOD APK, Premium APK, and Original APK files for free. You can easily search and download any app or game you want from this site. Whether you want to enjoy unlimited resources, unlocked features, or access to paid apps and games, APK Mody has it all. You can also find safe and fast downloads of original versions of apps and games if you prefer to use them without any modifications.
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-Potential risks: You may encounter some potential risks such as viruses, malware, spyware, etc. when downloading or installing apps or games from HappyMod.
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- How do I update the apps and games from APK Mody?
-You can update the apps and games from APK Mody by checking for updates regularly on your device settings or on the website. You can also enable automatic updates in your device settings if you want.
- How do I uninstall the apps and games from APK Mody?
-You can uninstall the apps and games from APK Mody by going to your device settings, then apps, then selecting the app or game you want to uninstall. Tap on the uninstall button and confirm your action.
401be4b1e0Barbie Makeup and Dress Up Games: A Fun and Creative Way to Express Yourself
-If you love fashion, beauty, and creativity, you will love playing barbie makeup and dress up games. These games let you transform your favorite barbie characters into stunning models, princesses, celebrities, or anything you can imagine. You can choose from a variety of makeup products, accessories, hairstyles, and outfits to create your own unique look. Whether you want to play online or offline, there are plenty of options for you to enjoy. In this article, we will tell you everything you need to know about barbie makeup and dress up games, including what they are, how to play them, and where to find them.
- What are Barbie Makeup and Dress Up Games?
-Barbie makeup and dress up games are games that allow you to customize the appearance of barbie dolls or characters using different tools and items. You can change their skin tone, eye color, hair color, hair style, makeup, jewelry, glasses, hats, scarves, bags, shoes, dresses, skirts, pants, tops, jackets, coats, and more. You can also choose from different themes and scenarios, such as weddings, parties, holidays, seasons, careers, hobbies, sports, fantasy, fairy tales, etc. The possibilities are endless!
-barbie makeup and dress up games Download Zip > https://jinyurl.com/2uNUGC
The History of Barbie and Her Games
-Barbie is one of the most popular and iconic dolls in the world. She was created by Ruth Handler in 1959 as a way to inspire young girls to pursue their dreams and aspirations. Since then, she has evolved into a global phenomenon with over 200 careers, 150 countries represented, and millions of fans. She has also inspired countless movies, TV shows, books, comics, video games, apps, websites, toys, and merchandise.
-One of the earliest forms of barbie games was the paper doll. Paper dolls are cut-out figures that can be dressed up with different outfits that are attached with tabs. Paper dolls were popular in the 19th and 20th centuries as a cheap and easy way to entertain children. They were also used as educational tools to teach about different cultures and customs. Barbie paper dolls were first introduced in 1964 and featured various outfits and accessories for different occasions.
-Another form of barbie games was the board game. Board games are games that involve moving pieces on a board according to a set of rules. Board games can be played by one or more players and can have different objectives and themes. Barbie board games were first introduced in 1961 and featured different challenges and adventures for barbie and her friends. Some examples of barbie board games are Barbie Queen of the Prom (1961), Barbie Dream Date (1982), Barbie Fashion Show (1996), etc.
-The most modern form of barbie games is the video game. Video games are games that involve interacting with a computer or a console using a controller or a keyboard. Video games can have different genres and modes such as action, adventure, puzzle, simulation, role-playing, etc. Barbie video games were first introduced in 1984 and featured different activities and stories for barbie and her friends. Some examples of barbie video games are Barbie Fashion Designer (1996), Barbie Horse Adventures (2003), Barbie Dreamhouse Party (2013), etc.
- The Benefits of Playing Barbie Makeup and Dress Up Games
-Playing barbie makeup and dress up games can have many benefits for your cognitive, emotional, social, and creative development. Here are some of them: - Playing barbie makeup and dress up games can improve your memory and concentration. You have to remember the different options and combinations that you have used or seen, and pay attention to the details and colors that match your style and theme. This can help you enhance your cognitive skills and mental focus. - Playing barbie makeup and dress up games can boost your self-esteem and confidence. You can express yourself freely and creatively, and experiment with different looks and styles that suit your personality and mood. You can also see how beautiful and fabulous you can be, and appreciate your own beauty and uniqueness. - Playing barbie makeup and dress up games can foster your social skills and communication. You can play with your friends or family, and share your ideas and opinions with them. You can also learn from their feedback and suggestions, and respect their preferences and tastes. You can also play online with other players from around the world, and make new friends and connections. - Playing barbie makeup and dress up games can stimulate your imagination and creativity. You can create your own stories and scenarios, and design your own characters and outfits. You can also mix and match different elements, and explore different possibilities and outcomes. You can also use your artistic sense and flair, and have fun with colors, shapes, patterns, textures, etc.
The Types of Barbie Makeup and Dress Up Games
-There are many types of barbie makeup and dress up games that you can choose from, depending on your interests and preferences. Here are some of the most popular ones: - Barbie Fashion Games: These games let you create fashionable outfits for barbie and her friends, using different clothes, shoes, accessories, etc. You can also choose from different themes such as casual, formal, sporty, etc. Some examples of barbie fashion games are Barbie Fashionista (2010), Barbie Fashion Closet (2017), Barbie Magical Fashion (2018), etc. - Barbie Makeover Games: These games let you give barbie a makeover, using different makeup products, hairstyles, skin treatments, etc. You can also choose from different themes such as glam, natural, bridal, etc. Some examples of barbie makeover games are Barbie Real Makeover (2014), Barbie Beauty Bath (2016), Barbie Glam Makeover (2019), etc. - Barbie Princess Games: These games let you dress up barbie as a princess, using different dresses, crowns, jewels, etc. You can also choose from different themes such as fairy tale, fantasy, modern, etc. Some examples of barbie princess games are Barbie Princess Dress Up (2012), Barbie Princess Adventure (2020), Barbie Princess Style (2021), etc. - Barbie Celebrity Games: These games let you dress up barbie as a celebrity, using different outfits, accessories, hairstyles, etc. You can also choose from different themes such as pop star, movie star, influencer, etc. Some examples of barbie celebrity games are Barbie Popstar Style (2015), Barbie Celebrity Style (2018), Barbie Hollywood Star (2020), etc.
- How to Play Barbie Makeup and Dress Up Games?
-Playing barbie makeup and dress up games is easy and fun. Here are the basic steps that you need to follow:
Choose Your Favorite Barbie Character
-The first step is to choose which barbie character you want to play with. You can choose from the classic barbie doll or one of her many friends such as Teresa, Nikki, Ken, Skipper, Chelsea, etc. You can also choose from different versions of barbie such as Dreamhouse Adventures, Princess Adventure, Life in the Dreamhouse, etc. You can also create your own custom barbie character by choosing her name, skin tone, eye color, hair color, hair style, etc.
-barbie fashion and beauty games
- Select Your Style and Theme
-The next step is to select the style and theme that you want to play with. You can choose from different categories such as fashion, makeover, princess, celebrity, etc. You can also choose from different subcategories such as casual, formal, sporty, glam, natural, bridal, fairy tale, fantasy, modern, pop star, movie star, influencer, etc. You can also mix and match different styles and themes to create your own unique combination.
- Apply Makeup and Accessories
-The third step is to apply makeup and accessories to your barbie character. You can choose from different makeup products such as foundation, concealer, powder, blush, bronzer, highlighter, eyeshadow, eyeliner, mascara, eyebrow pencil, lipstick, lip gloss, etc. You can also choose from different accessories such as earrings, necklaces, bracelets, rings, watches, glasses, hats, scarves, bags, shoes, etc. You can also adjust the size, color, and position of the makeup and accessories to suit your preference.
- Dress Up Barbie in Fabulous Outfits
-The final step is to dress up barbie in fabulous outfits. You can choose from different clothes such as dresses, skirts, pants, tops, jackets, coats, etc. You can also choose from different patterns, textures, colors, and styles of the clothes. You can also layer and combine different clothes to create your own unique look.
- Where to Find the Best Barbie Makeup and Dress Up Games?
-There are many places where you can find the best barbie makeup and dress up games. Here are some of the most popular ones:
Online Websites and Apps
-One of the easiest and most convenient ways to play barbie makeup and dress up games is online. There are many websites and apps that offer a wide range of barbie games that you can play for free or for a small fee. You can access these websites and apps using your computer, tablet, smartphone, or any other device that has an internet connection. Some of the advantages of playing online are that you can play anytime, anywhere, and with anyone. You can also save your progress and share your creations with others. Here are some of the best online websites and apps for barbie makeup and dress up games: - CrazyGames.com: This is one of the most popular websites for online games. It has over 10, 000 games in various genres and categories, including barbie games. You can find hundreds of barbie makeup and dress up games on this website, such as Barbie Fashionista Challenge (2021), Barbie Winter Glam (2019), Barbie Date Crashing (2018), etc. You can play these games for free without downloading or registering. - Google Play Store: This is one of the most popular platforms for downloading apps for Android devices. It has over 3 million apps in various categories and genres, including barbie games. You can find hundreds of barbie makeup and dress up games on this platform, such as Barbie Dreamhouse Adventures (2018), Barbie Magical Fashion (2018), Barbie Fashion Closet (2017), etc. You can download these apps for free or for a small fee, and enjoy them on your device.
- Offline Toys and Books
-Another way to play barbie makeup and dress up games is offline. There are many toys and books that offer a physical and tangible way to play with barbie dolls and characters. You can buy these toys and books from various stores or online platforms such as Amazon.com, Walmart.com, Target.com, etc. Some of the advantages of playing offline are that you can play without needing an internet connection or a device. You can also touch and feel the dolls and items, and use your own imagination and creativity. Here are some of the best offline toys and books for barbie makeup and dress up games: - Barbie Dreamhouse Adventures: This is one of the most popular toys for barbie fans. It is a large and interactive dollhouse that features 8 rooms, 70 accessories, and a working elevator. You can use this toy to recreate scenes from the Barbie Dreamhouse Adventures TV show or create your own stories and adventures. You can also use this toy to play with your barbie dolls and dress them up in different outfits and accessories. - Barbie Style Your Way: This is one of the most popular books for barbie fans. It is a spiral-bound book that features over 100 stickers, 10 paper dolls, and 30 outfits. You can use this book to dress up your paper dolls in different styles and themes, such as sporty, glam, boho, etc. You can also use the stickers to decorate the pages and create your own scenes and stories.
Conclusion
-Barbie makeup and dress up games are a fun and creative way to express yourself and enjoy fashion and beauty. You can play these games online or offline, and choose from different types of games, such as fashion, makeover, princess, celebrity, etc. You can also choose from different styles and themes, such as casual, formal, sporty, glam, natural, bridal, fairy tale, fantasy, modern, pop star, movie star, influencer, etc. You can also apply makeup and accessories, and dress up barbie in fabulous outfits. Playing these games can also improve your memory and concentration, boost your self-esteem and confidence, foster your social skills and communication, and stimulate your imagination and creativity. So what are you waiting for? Grab your favorite barbie character and start playing barbie makeup and dress up games today!
- FAQs
-Here are some of the frequently asked questions about barbie makeup and dress up games:
- - Q: How old do you have to be to play barbie makeup and dress up games? - A: There is no age limit to play barbie makeup and dress up games. Anyone who loves barbie and fashion can play these games. However, some games may have age ratings or parental guidance depending on the content and features of the game. - Q: How much do barbie makeup and dress up games cost? - A: The cost of barbie makeup and dress up games varies depending on the platform and the game. Some games are free to play online or download on your device. Some games may require a small fee or a subscription to access more features or content. Some games may also have in-app purchases or ads that may affect your experience or budget. - Q: What are the best devices to play barbie makeup and dress up games? - A: You can play barbie makeup and dress up games on any device that has an internet connection or a compatible operating system. You can use your computer, tablet, smartphone, or any other device that can access online websites or apps. You can also use offline toys or books that do not require any device or connection. - Q: What are the best tips to play barbie makeup and dress up games? - A: Here are some of the best tips to play barbie makeup and dress up games: - Have fun and be creative. There is no right or wrong way to play these games. You can experiment with different options and combinations, and create your own unique look. - Be inspired by your favorite barbie characters, movies, shows, books, etc. You can try to recreate their looks or create your own versions of them. - Be respectful of other players' choices and opinions. You can share your creations with others, but do not judge or criticize them. Everyone has their own style and taste. - Learn from your mistakes and improve your skills. You can try different challenges and levels, and see how well you can do. You can also get feedback and suggestions from others, and use them to improve your game. - Q: Where can I find more information about barbie makeup and dress up games? - A: You can find more information about barbie makeup and dress up games on the official barbie website (https://barbie.mattel.com/), the official barbie YouTube channel (https://www.youtube.com/user/barbie), the official barbie Instagram account (https://www.instagram.com/barbie/), or any other reliable sources that offer reviews, guides, news, etc. 401be4b1e0
-
-
{children}
- },
- code({ node, inline, className, children, ...props }) {
- if (children.length) {
- if (children[0] == '▍') {
- return (
- ▍
- )
- }
-
- children[0] = (children[0] as string).replace('`▍`', '▍')
- }
-
- const match = /language-(\w+)/.exec(className || '')
-
- if (inline) {
- return (
-
- {children}
-
- )
- }
-
- return (
-
-
-
- {message.author === 'bot' &&
-
": torch.ones((32,))}
- pipe.load_textual_inversion(ten)
-
- token = pipe.tokenizer.convert_tokens_to_ids("")
- assert token == num_tokens + 10, "Added token must be at spot `num_tokens`"
- assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32
- assert pipe._maybe_convert_prompt("", pipe.tokenizer) == ""
-
- prompt = "hey "
- out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
- assert out.shape == (1, 128, 128, 3)
-
- # multi embedding state dict load
- ten1 = {"": torch.ones((32,))}
- ten2 = {"": 2 * torch.ones((1, 32))}
-
- pipe.load_textual_inversion([ten1, ten2])
-
- token = pipe.tokenizer.convert_tokens_to_ids("")
- assert token == num_tokens + 11, "Added token must be at spot `num_tokens`"
- assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32
- assert pipe._maybe_convert_prompt("", pipe.tokenizer) == ""
-
- token = pipe.tokenizer.convert_tokens_to_ids("")
- assert token == num_tokens + 12, "Added token must be at spot `num_tokens`"
- assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64
- assert pipe._maybe_convert_prompt("", pipe.tokenizer) == ""
-
- prompt = "hey "
- out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
- assert out.shape == (1, 128, 128, 3)
-
- # auto1111 multi-token state dict load
- ten = {
- "string_to_param": {
- "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])
- },
- "name": "",
- }
-
- pipe.load_textual_inversion(ten)
-
- token = pipe.tokenizer.convert_tokens_to_ids("")
- token_1 = pipe.tokenizer.convert_tokens_to_ids("_1")
- token_2 = pipe.tokenizer.convert_tokens_to_ids("_2")
-
- assert token == num_tokens + 13, "Added token must be at spot `num_tokens`"
- assert token_1 == num_tokens + 14, "Added token must be at spot `num_tokens`"
- assert token_2 == num_tokens + 15, "Added token must be at spot `num_tokens`"
- assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96
- assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128
- assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160
- assert pipe._maybe_convert_prompt("", pipe.tokenizer) == " _1 _2"
-
- prompt = "hey "
- out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
- assert out.shape == (1, 128, 128, 3)
-
- # multiple references to multi embedding
- ten = {"": torch.ones(3, 32)}
- pipe.load_textual_inversion(ten)
-
- assert (
- pipe._maybe_convert_prompt(" ", pipe.tokenizer) == " _1 _2 _1 _2"
- )
-
- prompt = "hey "
- out = pipe(prompt, num_inference_steps=1, output_type="numpy").images
- assert out.shape == (1, 128, 128, 3)
-
- def test_download_ignore_files(self):
- # Check https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files/blob/72f58636e5508a218c6b3f60550dc96445547817/model_index.json#L4
- with tempfile.TemporaryDirectory() as tmpdirname:
- # pipeline has Flax weights
- tmpdirname = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files")
- all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
- files = [item for sublist in all_root_files for item in sublist]
-
- # None of the downloaded files should be a pytorch file even if we have some here:
- # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
- assert not any(f in ["vae/diffusion_pytorch_model.bin", "text_encoder/config.json"] for f in files)
- assert len(files) == 14
-
-
-class CustomPipelineTests(unittest.TestCase):
- def test_load_custom_pipeline(self):
- pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
- )
- pipeline = pipeline.to(torch_device)
- # NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
- # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
- assert pipeline.__class__.__name__ == "CustomPipeline"
-
- def test_load_custom_github(self):
- pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main"
- )
-
- # make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690
- with torch.no_grad():
- output = pipeline()
-
- assert output.numel() == output.sum()
-
- # hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python
- # Could in the future work with hashes instead.
- del sys.modules["diffusers_modules.git.one_step_unet"]
-
- pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2"
- )
- with torch.no_grad():
- output = pipeline()
-
- assert output.numel() != output.sum()
-
- assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline"
-
- def test_run_custom_pipeline(self):
- pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
- )
- pipeline = pipeline.to(torch_device)
- images, output_str = pipeline(num_inference_steps=2, output_type="np")
-
- assert images[0].shape == (1, 32, 32, 3)
-
- # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
- assert output_str == "This is a test"
-
- def test_local_custom_pipeline_repo(self):
- local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
- pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
- )
- pipeline = pipeline.to(torch_device)
- images, output_str = pipeline(num_inference_steps=2, output_type="np")
-
- assert pipeline.__class__.__name__ == "CustomLocalPipeline"
- assert images[0].shape == (1, 32, 32, 3)
- # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
- assert output_str == "This is a local test"
-
- def test_local_custom_pipeline_file(self):
- local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
- local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py")
- pipeline = DiffusionPipeline.from_pretrained(
- "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
- )
- pipeline = pipeline.to(torch_device)
- images, output_str = pipeline(num_inference_steps=2, output_type="np")
-
- assert pipeline.__class__.__name__ == "CustomLocalPipeline"
- assert images[0].shape == (1, 32, 32, 3)
- # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
- assert output_str == "This is a local test"
-
- def test_custom_model_and_pipeline(self):
- pipe = CustomPipeline(
- encoder=CustomEncoder(),
- scheduler=DDIMScheduler(),
- )
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipe.save_pretrained(tmpdirname)
-
- pipe_new = CustomPipeline.from_pretrained(tmpdirname)
- pipe_new.save_pretrained(tmpdirname)
-
- conf_1 = dict(pipe.config)
- conf_2 = dict(pipe_new.config)
-
- del conf_2["_name_or_path"]
-
- assert conf_1 == conf_2
-
- @slow
- @require_torch_gpu
- def test_download_from_git(self):
- # Because adaptive_avg_pool2d_backward_cuda
- # does not have a deterministic implementation.
- clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
-
- feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id)
- clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
-
- pipeline = DiffusionPipeline.from_pretrained(
- "CompVis/stable-diffusion-v1-4",
- custom_pipeline="clip_guided_stable_diffusion",
- clip_model=clip_model,
- feature_extractor=feature_extractor,
- torch_dtype=torch.float16,
- )
- pipeline.enable_attention_slicing()
- pipeline = pipeline.to(torch_device)
-
- # NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:
- # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
- assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"
-
- image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
- assert image.shape == (512, 512, 3)
-
- def test_save_pipeline_change_config(self):
- pipe = DiffusionPipeline.from_pretrained(
- "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
- )
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipe.save_pretrained(tmpdirname)
- pipe = DiffusionPipeline.from_pretrained(tmpdirname)
-
- assert pipe.scheduler.__class__.__name__ == "PNDMScheduler"
-
- # let's make sure that changing the scheduler is correctly reflected
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
- pipe.save_pretrained(tmpdirname)
- pipe = DiffusionPipeline.from_pretrained(tmpdirname)
-
- assert pipe.scheduler.__class__.__name__ == "DPMSolverMultistepScheduler"
-
-
-class PipelineFastTests(unittest.TestCase):
- def tearDown(self):
- # clean up the VRAM after each test
- super().tearDown()
- gc.collect()
- torch.cuda.empty_cache()
-
- import diffusers
-
- diffusers.utils.import_utils._safetensors_available = True
-
- def dummy_image(self):
- batch_size = 1
- num_channels = 3
- sizes = (32, 32)
-
- image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
- return image
-
- def dummy_uncond_unet(self, sample_size=32):
- torch.manual_seed(0)
- model = UNet2DModel(
- block_out_channels=(32, 64),
- layers_per_block=2,
- sample_size=sample_size,
- in_channels=3,
- out_channels=3,
- down_block_types=("DownBlock2D", "AttnDownBlock2D"),
- up_block_types=("AttnUpBlock2D", "UpBlock2D"),
- )
- return model
-
- def dummy_cond_unet(self, sample_size=32):
- torch.manual_seed(0)
- model = UNet2DConditionModel(
- block_out_channels=(32, 64),
- layers_per_block=2,
- sample_size=sample_size,
- in_channels=4,
- out_channels=4,
- down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
- up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
- cross_attention_dim=32,
- )
- return model
-
- @property
- def dummy_vae(self):
- torch.manual_seed(0)
- model = AutoencoderKL(
- block_out_channels=[32, 64],
- in_channels=3,
- out_channels=3,
- down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
- up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
- latent_channels=4,
- )
- return model
-
- @property
- def dummy_text_encoder(self):
- torch.manual_seed(0)
- config = CLIPTextConfig(
- bos_token_id=0,
- eos_token_id=2,
- hidden_size=32,
- intermediate_size=37,
- layer_norm_eps=1e-05,
- num_attention_heads=4,
- num_hidden_layers=5,
- pad_token_id=1,
- vocab_size=1000,
- )
- return CLIPTextModel(config)
-
- @property
- def dummy_extractor(self):
- def extract(*args, **kwargs):
- class Out:
- def __init__(self):
- self.pixel_values = torch.ones([0])
-
- def to(self, device):
- self.pixel_values.to(device)
- return self
-
- return Out()
-
- return extract
-
- @parameterized.expand(
- [
- [DDIMScheduler, DDIMPipeline, 32],
- [DDPMScheduler, DDPMPipeline, 32],
- [DDIMScheduler, DDIMPipeline, (32, 64)],
- [DDPMScheduler, DDPMPipeline, (64, 32)],
- ]
- )
- def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):
- unet = self.dummy_uncond_unet(sample_size)
- scheduler = scheduler_fn()
- pipeline = pipeline_fn(unet, scheduler).to(torch_device)
-
- generator = torch.manual_seed(0)
- out_image = pipeline(
- generator=generator,
- num_inference_steps=2,
- output_type="np",
- ).images
- sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size
- assert out_image.shape == (1, *sample_size, 3)
-
- def test_stable_diffusion_components(self):
- """Test that components property works correctly"""
- unet = self.dummy_cond_unet()
- scheduler = PNDMScheduler(skip_prk_steps=True)
- vae = self.dummy_vae
- bert = self.dummy_text_encoder
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
- image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0]
- init_image = Image.fromarray(np.uint8(image)).convert("RGB")
- mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32))
-
- # make sure here that pndm scheduler skips prk
- inpaint = StableDiffusionInpaintPipelineLegacy(
- unet=unet,
- scheduler=scheduler,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- ).to(torch_device)
- img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
- text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
-
- prompt = "A painting of a squirrel eating a burger"
-
- generator = torch.manual_seed(0)
- image_inpaint = inpaint(
- [prompt],
- generator=generator,
- num_inference_steps=2,
- output_type="np",
- image=init_image,
- mask_image=mask_image,
- ).images
- image_img2img = img2img(
- [prompt],
- generator=generator,
- num_inference_steps=2,
- output_type="np",
- image=init_image,
- ).images
- image_text2img = text2img(
- [prompt],
- generator=generator,
- num_inference_steps=2,
- output_type="np",
- ).images
-
- assert image_inpaint.shape == (1, 32, 32, 3)
- assert image_img2img.shape == (1, 32, 32, 3)
- assert image_text2img.shape == (1, 64, 64, 3)
-
- @require_torch_gpu
- def test_pipe_false_offload_warn(self):
- unet = self.dummy_cond_unet()
- scheduler = PNDMScheduler(skip_prk_steps=True)
- vae = self.dummy_vae
- bert = self.dummy_text_encoder
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
- sd = StableDiffusionPipeline(
- unet=unet,
- scheduler=scheduler,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- )
-
- sd.enable_model_cpu_offload()
-
- logger = logging.get_logger("diffusers.pipelines.pipeline_utils")
- with CaptureLogger(logger) as cap_logger:
- sd.to("cuda")
-
- assert "It is strongly recommended against doing so" in str(cap_logger)
-
- sd = StableDiffusionPipeline(
- unet=unet,
- scheduler=scheduler,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- )
-
- def test_set_scheduler(self):
- unet = self.dummy_cond_unet()
- scheduler = PNDMScheduler(skip_prk_steps=True)
- vae = self.dummy_vae
- bert = self.dummy_text_encoder
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
- sd = StableDiffusionPipeline(
- unet=unet,
- scheduler=scheduler,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- )
-
- sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, DDIMScheduler)
- sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, DDPMScheduler)
- sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, PNDMScheduler)
- sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, LMSDiscreteScheduler)
- sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, EulerDiscreteScheduler)
- sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler)
- sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
- assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)
-
- def test_set_component_to_none(self):
- unet = self.dummy_cond_unet()
- scheduler = PNDMScheduler(skip_prk_steps=True)
- vae = self.dummy_vae
- bert = self.dummy_text_encoder
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
- pipeline = StableDiffusionPipeline(
- unet=unet,
- scheduler=scheduler,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- )
-
- generator = torch.Generator(device="cpu").manual_seed(0)
-
- prompt = "This is a flower"
-
- out_image = pipeline(
- prompt=prompt,
- generator=generator,
- num_inference_steps=1,
- output_type="np",
- ).images
-
- pipeline.feature_extractor = None
- generator = torch.Generator(device="cpu").manual_seed(0)
- out_image_2 = pipeline(
- prompt=prompt,
- generator=generator,
- num_inference_steps=1,
- output_type="np",
- ).images
-
- assert out_image.shape == (1, 64, 64, 3)
- assert np.abs(out_image - out_image_2).max() < 1e-3
-
- def test_set_scheduler_consistency(self):
- unet = self.dummy_cond_unet()
- pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
- ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
- vae = self.dummy_vae
- bert = self.dummy_text_encoder
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
- sd = StableDiffusionPipeline(
- unet=unet,
- scheduler=pndm,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- )
-
- pndm_config = sd.scheduler.config
- sd.scheduler = DDPMScheduler.from_config(pndm_config)
- sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
- pndm_config_2 = sd.scheduler.config
- pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config}
-
- assert dict(pndm_config) == dict(pndm_config_2)
-
- sd = StableDiffusionPipeline(
- unet=unet,
- scheduler=ddim,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=None,
- feature_extractor=self.dummy_extractor,
- )
-
- ddim_config = sd.scheduler.config
- sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config)
- sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
- ddim_config_2 = sd.scheduler.config
- ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config}
-
- assert dict(ddim_config) == dict(ddim_config_2)
-
- def test_save_safe_serialization(self):
- pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipeline.save_pretrained(tmpdirname, safe_serialization=True)
-
- # Validate that the VAE safetensor exists and are of the correct format
- vae_path = os.path.join(tmpdirname, "vae", "diffusion_pytorch_model.safetensors")
- assert os.path.exists(vae_path), f"Could not find {vae_path}"
- _ = safetensors.torch.load_file(vae_path)
-
- # Validate that the UNet safetensor exists and are of the correct format
- unet_path = os.path.join(tmpdirname, "unet", "diffusion_pytorch_model.safetensors")
- assert os.path.exists(unet_path), f"Could not find {unet_path}"
- _ = safetensors.torch.load_file(unet_path)
-
- # Validate that the text encoder safetensor exists and are of the correct format
- text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors")
- assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}"
- _ = safetensors.torch.load_file(text_encoder_path)
-
- pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname)
- assert pipeline.unet is not None
- assert pipeline.vae is not None
- assert pipeline.text_encoder is not None
- assert pipeline.scheduler is not None
- assert pipeline.feature_extractor is not None
-
- def test_no_pytorch_download_when_doing_safetensors(self):
- # by default we don't download
- with tempfile.TemporaryDirectory() as tmpdirname:
- _ = StableDiffusionPipeline.from_pretrained(
- "hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
- )
-
- path = os.path.join(
- tmpdirname,
- "models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
- "snapshots",
- "07838d72e12f9bcec1375b0482b80c1d399be843",
- "unet",
- )
- # safetensors exists
- assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
- # pytorch does not
- assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
-
- def test_no_safetensors_download_when_doing_pytorch(self):
- # mock diffusers safetensors not available
- import diffusers
-
- diffusers.utils.import_utils._safetensors_available = False
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- _ = StableDiffusionPipeline.from_pretrained(
- "hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
- )
-
- path = os.path.join(
- tmpdirname,
- "models--hf-internal-testing--diffusers-stable-diffusion-tiny-all",
- "snapshots",
- "07838d72e12f9bcec1375b0482b80c1d399be843",
- "unet",
- )
- # safetensors does not exists
- assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors"))
- # pytorch does
- assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
-
- diffusers.utils.import_utils._safetensors_available = True
-
- def test_optional_components(self):
- unet = self.dummy_cond_unet()
- pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
- vae = self.dummy_vae
- bert = self.dummy_text_encoder
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
- orig_sd = StableDiffusionPipeline(
- unet=unet,
- scheduler=pndm,
- vae=vae,
- text_encoder=bert,
- tokenizer=tokenizer,
- safety_checker=unet,
- feature_extractor=self.dummy_extractor,
- )
- sd = orig_sd
-
- assert sd.config.requires_safety_checker is True
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- sd.save_pretrained(tmpdirname)
-
- # Test that passing None works
- sd = StableDiffusionPipeline.from_pretrained(
- tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False
- )
-
- assert sd.config.requires_safety_checker is False
- assert sd.config.safety_checker == (None, None)
- assert sd.config.feature_extractor == (None, None)
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- sd.save_pretrained(tmpdirname)
-
- # Test that loading previous None works
- sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
-
- assert sd.config.requires_safety_checker is False
- assert sd.config.safety_checker == (None, None)
- assert sd.config.feature_extractor == (None, None)
-
- orig_sd.save_pretrained(tmpdirname)
-
- # Test that loading without any directory works
- shutil.rmtree(os.path.join(tmpdirname, "safety_checker"))
- with open(os.path.join(tmpdirname, sd.config_name)) as f:
- config = json.load(f)
- config["safety_checker"] = [None, None]
- with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
- json.dump(config, f)
-
- sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False)
- sd.save_pretrained(tmpdirname)
- sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
-
- assert sd.config.requires_safety_checker is False
- assert sd.config.safety_checker == (None, None)
- assert sd.config.feature_extractor == (None, None)
-
- # Test that loading from deleted model index works
- with open(os.path.join(tmpdirname, sd.config_name)) as f:
- config = json.load(f)
- del config["safety_checker"]
- del config["feature_extractor"]
- with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
- json.dump(config, f)
-
- sd = StableDiffusionPipeline.from_pretrained(tmpdirname)
-
- assert sd.config.requires_safety_checker is False
- assert sd.config.safety_checker == (None, None)
- assert sd.config.feature_extractor == (None, None)
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- sd.save_pretrained(tmpdirname)
-
- # Test that partially loading works
- sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
-
- assert sd.config.requires_safety_checker is False
- assert sd.config.safety_checker == (None, None)
- assert sd.config.feature_extractor != (None, None)
-
- # Test that partially loading works
- sd = StableDiffusionPipeline.from_pretrained(
- tmpdirname,
- feature_extractor=self.dummy_extractor,
- safety_checker=unet,
- requires_safety_checker=[True, True],
- )
-
- assert sd.config.requires_safety_checker == [True, True]
- assert sd.config.safety_checker != (None, None)
- assert sd.config.feature_extractor != (None, None)
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- sd.save_pretrained(tmpdirname)
- sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)
-
- assert sd.config.requires_safety_checker == [True, True]
- assert sd.config.safety_checker != (None, None)
- assert sd.config.feature_extractor != (None, None)
-
- def test_name_or_path(self):
- model_path = "hf-internal-testing/tiny-stable-diffusion-torch"
- sd = DiffusionPipeline.from_pretrained(model_path)
-
- assert sd.name_or_path == model_path
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- sd.save_pretrained(tmpdirname)
- sd = DiffusionPipeline.from_pretrained(tmpdirname)
-
- assert sd.name_or_path == tmpdirname
-
- def test_warning_no_variant_available(self):
- variant = "fp16"
- with self.assertWarns(FutureWarning) as warning_context:
- cached_folder = StableDiffusionPipeline.download(
- "hf-internal-testing/diffusers-stable-diffusion-tiny-all", variant=variant
- )
-
- assert "but no such modeling files are available" in str(warning_context.warning)
- assert variant in str(warning_context.warning)
-
- def get_all_filenames(directory):
- filenames = glob.glob(directory + "/**", recursive=True)
- filenames = [f for f in filenames if os.path.isfile(f)]
- return filenames
-
- filenames = get_all_filenames(str(cached_folder))
-
- all_model_files, variant_model_files = variant_compatible_siblings(filenames, variant=variant)
-
- # make sure that none of the model names are variant model names
- assert len(variant_model_files) == 0
- assert len(all_model_files) > 0
-
-
-@slow
-@require_torch_gpu
-class PipelineSlowTests(unittest.TestCase):
- def tearDown(self):
- # clean up the VRAM after each test
- super().tearDown()
- gc.collect()
- torch.cuda.empty_cache()
-
- def test_smart_download(self):
- model_id = "hf-internal-testing/unet-pipeline-dummy"
- with tempfile.TemporaryDirectory() as tmpdirname:
- _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
- local_repo_name = "--".join(["models"] + model_id.split("/"))
- snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
- snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
-
- # inspect all downloaded files to make sure that everything is included
- assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
- assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
- assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
- assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
- assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
- assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
- assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
- # let's make sure the super large numpy file:
- # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
- # is not downloaded, but all the expected ones
- assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
-
- def test_warning_unused_kwargs(self):
- model_id = "hf-internal-testing/unet-pipeline-dummy"
- logger = logging.get_logger("diffusers.pipelines")
- with tempfile.TemporaryDirectory() as tmpdirname:
- with CaptureLogger(logger) as cap_logger:
- DiffusionPipeline.from_pretrained(
- model_id,
- not_used=True,
- cache_dir=tmpdirname,
- force_download=True,
- )
-
- assert (
- cap_logger.out.strip().split("\n")[-1]
- == "Keyword arguments {'not_used': True} are not expected by DDPMPipeline and will be ignored."
- )
-
- def test_from_save_pretrained(self):
- # 1. Load models
- model = UNet2DModel(
- block_out_channels=(32, 64),
- layers_per_block=2,
- sample_size=32,
- in_channels=3,
- out_channels=3,
- down_block_types=("DownBlock2D", "AttnDownBlock2D"),
- up_block_types=("AttnUpBlock2D", "UpBlock2D"),
- )
- scheduler = DDPMScheduler(num_train_timesteps=10)
-
- ddpm = DDPMPipeline(model, scheduler)
- ddpm.to(torch_device)
- ddpm.set_progress_bar_config(disable=None)
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- ddpm.save_pretrained(tmpdirname)
- new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
- new_ddpm.to(torch_device)
-
- generator = torch.Generator(device=torch_device).manual_seed(0)
- image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
-
- generator = torch.Generator(device=torch_device).manual_seed(0)
- new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
-
- assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
-
- @require_torch_2
- def test_from_save_pretrained_dynamo(self):
- run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=None)
-
- def test_from_pretrained_hub(self):
- model_path = "google/ddpm-cifar10-32"
-
- scheduler = DDPMScheduler(num_train_timesteps=10)
-
- ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
- ddpm = ddpm.to(torch_device)
- ddpm.set_progress_bar_config(disable=None)
-
- ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
- ddpm_from_hub = ddpm_from_hub.to(torch_device)
- ddpm_from_hub.set_progress_bar_config(disable=None)
-
- generator = torch.Generator(device=torch_device).manual_seed(0)
- image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images
-
- generator = torch.Generator(device=torch_device).manual_seed(0)
- new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images
-
- assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
-
- def test_from_pretrained_hub_pass_model(self):
- model_path = "google/ddpm-cifar10-32"
-
- scheduler = DDPMScheduler(num_train_timesteps=10)
-
- # pass unet into DiffusionPipeline
- unet = UNet2DModel.from_pretrained(model_path)
- ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
- ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device)
- ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
-
- ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
- ddpm_from_hub = ddpm_from_hub.to(torch_device)
- ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
-
- generator = torch.Generator(device=torch_device).manual_seed(0)
- image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type="numpy").images
-
- generator = torch.Generator(device=torch_device).manual_seed(0)
- new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images
-
- assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
-
- def test_output_format(self):
- model_path = "google/ddpm-cifar10-32"
-
- scheduler = DDIMScheduler.from_pretrained(model_path)
- pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)
- pipe.to(torch_device)
- pipe.set_progress_bar_config(disable=None)
-
- images = pipe(output_type="numpy").images
- assert images.shape == (1, 32, 32, 3)
- assert isinstance(images, np.ndarray)
-
- images = pipe(output_type="pil", num_inference_steps=4).images
- assert isinstance(images, list)
- assert len(images) == 1
- assert isinstance(images[0], PIL.Image.Image)
-
- # use PIL by default
- images = pipe(num_inference_steps=4).images
- assert isinstance(images, list)
- assert isinstance(images[0], PIL.Image.Image)
-
- @require_flax
- def test_from_flax_from_pt(self):
- pipe_pt = StableDiffusionPipeline.from_pretrained(
- "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
- )
- pipe_pt.to(torch_device)
-
- from diffusers import FlaxStableDiffusionPipeline
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipe_pt.save_pretrained(tmpdirname)
-
- pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained(
- tmpdirname, safety_checker=None, from_pt=True
- )
-
- with tempfile.TemporaryDirectory() as tmpdirname:
- pipe_flax.save_pretrained(tmpdirname, params=params)
- pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True)
- pipe_pt_2.to(torch_device)
-
- prompt = "Hello"
-
- generator = torch.manual_seed(0)
- image_0 = pipe_pt(
- [prompt],
- generator=generator,
- num_inference_steps=2,
- output_type="np",
- ).images[0]
-
- generator = torch.manual_seed(0)
- image_1 = pipe_pt_2(
- [prompt],
- generator=generator,
- num_inference_steps=2,
- output_type="np",
- ).images[0]
-
- assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass"
-
- @require_compel
- def test_weighted_prompts_compel(self):
- from compel import Compel
-
- pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
- pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
- pipe.enable_model_cpu_offload()
- pipe.enable_attention_slicing()
-
- compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
-
- prompt = "a red cat playing with a ball{}"
-
- prompts = [prompt.format(s) for s in ["", "++", "--"]]
-
- prompt_embeds = compel(prompts)
-
- generator = [torch.Generator(device="cpu").manual_seed(33) for _ in range(prompt_embeds.shape[0])]
-
- images = pipe(
- prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type="numpy"
- ).images
-
- for i, image in enumerate(images):
- expected_image = load_numpy(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
- f"/compel/forest_{i}.npy"
- )
-
- assert np.abs(image - expected_image).max() < 3e-1
-
-
-@nightly
-@require_torch_gpu
-class PipelineNightlyTests(unittest.TestCase):
- def tearDown(self):
- # clean up the VRAM after each test
- super().tearDown()
- gc.collect()
- torch.cuda.empty_cache()
-
- def test_ddpm_ddim_equality_batched(self):
- seed = 0
- model_id = "google/ddpm-cifar10-32"
-
- unet = UNet2DModel.from_pretrained(model_id)
- ddpm_scheduler = DDPMScheduler()
- ddim_scheduler = DDIMScheduler()
-
- ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
- ddpm.to(torch_device)
- ddpm.set_progress_bar_config(disable=None)
-
- ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
- ddim.to(torch_device)
- ddim.set_progress_bar_config(disable=None)
-
- generator = torch.Generator(device=torch_device).manual_seed(seed)
- ddpm_images = ddpm(batch_size=2, generator=generator, output_type="numpy").images
-
- generator = torch.Generator(device=torch_device).manual_seed(seed)
- ddim_images = ddim(
- batch_size=2,
- generator=generator,
- num_inference_steps=1000,
- eta=1.0,
- output_type="numpy",
- use_clipped_model_output=True, # Need this to make DDIM match DDPM
- ).images
-
- # the values aren't exactly equal, but the images look the same visually
- assert np.abs(ddpm_images - ddim_images).max() < 1e-1
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/mask/mask_target.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/mask/mask_target.py
deleted file mode 100644
index 15d26a88bbf3710bd92813335918407db8c4e053..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/mask/mask_target.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import numpy as np
-import torch
-from torch.nn.modules.utils import _pair
-
-
-def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
- cfg):
- """Compute mask target for positive proposals in multiple images.
-
- Args:
- pos_proposals_list (list[Tensor]): Positive proposals in multiple
- images.
- pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each
- positive proposals.
- gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of
- each image.
- cfg (dict): Config dict that specifies the mask size.
-
- Returns:
- list[Tensor]: Mask target of each image.
-
- Example:
- >>> import mmcv
- >>> import mmdet
- >>> from mmdet.core.mask import BitmapMasks
- >>> from mmdet.core.mask.mask_target import *
- >>> H, W = 17, 18
- >>> cfg = mmcv.Config({'mask_size': (13, 14)})
- >>> rng = np.random.RandomState(0)
- >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image
- >>> pos_proposals_list = [
- >>> torch.Tensor([
- >>> [ 7.2425, 5.5929, 13.9414, 14.9541],
- >>> [ 7.3241, 3.6170, 16.3850, 15.3102],
- >>> ]),
- >>> torch.Tensor([
- >>> [ 4.8448, 6.4010, 7.0314, 9.7681],
- >>> [ 5.9790, 2.6989, 7.4416, 4.8580],
- >>> [ 0.0000, 0.0000, 0.1398, 9.8232],
- >>> ]),
- >>> ]
- >>> # Corresponding class index for each proposal for each image
- >>> pos_assigned_gt_inds_list = [
- >>> torch.LongTensor([7, 0]),
- >>> torch.LongTensor([5, 4, 1]),
- >>> ]
- >>> # Ground truth mask for each true object for each image
- >>> gt_masks_list = [
- >>> BitmapMasks(rng.rand(8, H, W), height=H, width=W),
- >>> BitmapMasks(rng.rand(6, H, W), height=H, width=W),
- >>> ]
- >>> mask_targets = mask_target(
- >>> pos_proposals_list, pos_assigned_gt_inds_list,
- >>> gt_masks_list, cfg)
- >>> assert mask_targets.shape == (5,) + cfg['mask_size']
- """
- cfg_list = [cfg for _ in range(len(pos_proposals_list))]
- mask_targets = map(mask_target_single, pos_proposals_list,
- pos_assigned_gt_inds_list, gt_masks_list, cfg_list)
- mask_targets = list(mask_targets)
- if len(mask_targets) > 0:
- mask_targets = torch.cat(mask_targets)
- return mask_targets
-
-
-def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):
- """Compute mask target for each positive proposal in the image.
-
- Args:
- pos_proposals (Tensor): Positive proposals.
- pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals.
- gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap
- or Polygon.
- cfg (dict): Config dict that indicate the mask size.
-
- Returns:
- Tensor: Mask target of each positive proposals in the image.
-
- Example:
- >>> import mmcv
- >>> import mmdet
- >>> from mmdet.core.mask import BitmapMasks
- >>> from mmdet.core.mask.mask_target import * # NOQA
- >>> H, W = 32, 32
- >>> cfg = mmcv.Config({'mask_size': (7, 11)})
- >>> rng = np.random.RandomState(0)
- >>> # Masks for each ground truth box (relative to the image)
- >>> gt_masks_data = rng.rand(3, H, W)
- >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W)
- >>> # Predicted positive boxes in one image
- >>> pos_proposals = torch.FloatTensor([
- >>> [ 16.2, 5.5, 19.9, 20.9],
- >>> [ 17.3, 13.6, 19.3, 19.3],
- >>> [ 14.8, 16.4, 17.0, 23.7],
- >>> [ 0.0, 0.0, 16.0, 16.0],
- >>> [ 4.0, 0.0, 20.0, 16.0],
- >>> ])
- >>> # For each predicted proposal, its assignment to a gt mask
- >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1])
- >>> mask_targets = mask_target_single(
- >>> pos_proposals, pos_assigned_gt_inds, gt_masks, cfg)
- >>> assert mask_targets.shape == (5,) + cfg['mask_size']
- """
- device = pos_proposals.device
- mask_size = _pair(cfg.mask_size)
- num_pos = pos_proposals.size(0)
- if num_pos > 0:
- proposals_np = pos_proposals.cpu().numpy()
- maxh, maxw = gt_masks.height, gt_masks.width
- proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw)
- proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh)
- pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
-
- mask_targets = gt_masks.crop_and_resize(
- proposals_np, mask_size, device=device,
- inds=pos_assigned_gt_inds).to_ndarray()
-
- mask_targets = torch.from_numpy(mask_targets).float().to(device)
- else:
- mask_targets = pos_proposals.new_zeros((0, ) + mask_size)
-
- return mask_targets
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/__init__.py
deleted file mode 100644
index 36789767f35bcc169c2cbf096e2747539df4f14d..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/__init__.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Midas Depth Estimation
-# From https://github.com/isl-org/MiDaS
-# MIT LICENSE
-
-import cv2
-import numpy as np
-import torch
-
-from einops import rearrange
-from .api import MiDaSInference
-
-
-class MidasDetector:
- def __init__(self):
- self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
-
- def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1):
- assert input_image.ndim == 3
- image_depth = input_image
- with torch.no_grad():
- image_depth = torch.from_numpy(image_depth).float().cuda()
- image_depth = image_depth / 127.5 - 1.0
- image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
- depth = self.model(image_depth)[0]
-
- depth_pt = depth.clone()
- depth_pt -= torch.min(depth_pt)
- depth_pt /= torch.max(depth_pt)
- depth_pt = depth_pt.cpu().numpy()
- depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
-
- depth_np = depth.cpu().numpy()
- x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
- y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
- z = np.ones_like(x) * a
- x[depth_pt < bg_th] = 0
- y[depth_pt < bg_th] = 0
- normal = np.stack([x, y, z], axis=2)
- normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
- normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
-
- return depth_image, normal_image
diff --git a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/streaming.py b/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/streaming.py
deleted file mode 100644
index fdbdf5e90fc0c6560873d66bf273460b38e5ed7e..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/streaming.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Streaming module API that should be implemented by all Streaming components,
-"""
-
-from contextlib import contextmanager
-import typing as tp
-from torch import nn
-import torch
-
-
-State = tp.Dict[str, torch.Tensor]
-
-
-class StreamingModule(nn.Module):
- """Common API for streaming components.
-
- Each streaming component has a streaming state, which is just a dict[str, Tensor].
- By convention, the first dim of each tensor must be the batch size.
- Don't use dots in the key names, as this would clash with submodules
- (like in state_dict).
-
- If `self._is_streaming` is True, the component should use and remember
- the proper state inside `self._streaming_state`.
-
- To set a streaming component in streaming state, use
-
- with module.streaming():
- ...
-
- This will automatically reset the streaming state when exiting the context manager.
- This also automatically propagates to all streaming children module.
-
- Some module might also implement the `StreamingModule.flush` method, although
- this one is trickier, as all parents module must be StreamingModule and implement
- it as well for it to work properly. See `StreamingSequential` after.
- """
- def __init__(self) -> None:
- super().__init__()
- self._streaming_state: State = {}
- self._is_streaming = False
-
- def _apply_named_streaming(self, fn: tp.Any):
- for name, module in self.named_modules():
- if isinstance(module, StreamingModule):
- fn(name, module)
-
- def _set_streaming(self, streaming: bool):
- def _set_streaming(name, module):
- module._is_streaming = streaming
- self._apply_named_streaming(_set_streaming)
-
- @contextmanager
- def streaming(self):
- """Context manager to enter streaming mode. Reset streaming state on exit.
- """
- self._set_streaming(True)
- try:
- yield
- finally:
- self._set_streaming(False)
- self.reset_streaming()
-
- def reset_streaming(self):
- """Reset the streaming state.
- """
- def _reset(name: str, module: StreamingModule):
- module._streaming_state.clear()
-
- self._apply_named_streaming(_reset)
-
- def get_streaming_state(self) -> State:
- """Return the streaming state, including that of sub-modules.
- """
- state: State = {}
-
- def _add(name: str, module: StreamingModule):
- if name:
- name += "."
- for key, value in module._streaming_state.items():
- state[name + key] = value
-
- self._apply_named_streaming(_add)
- return state
-
- def set_streaming_state(self, state: State):
- """Set the streaming state, including that of sub-modules.
- """
- state = dict(state)
-
- def _set(name: str, module: StreamingModule):
- if name:
- name += "."
- module._streaming_state.clear()
- for key, value in list(state.items()):
- # complexity is not ideal here, but probably fine.
- if key.startswith(name):
- local_key = key[len(name):]
- if '.' not in local_key:
- module._streaming_state[local_key] = value
- del state[key]
-
- self._apply_named_streaming(_set)
- assert len(state) == 0, list(state.keys())
-
- def flush(self, x: tp.Optional[torch.Tensor] = None):
- """Flush any remaining outputs that were waiting for completion.
- Typically, for convolutions, this will add the final padding
- and process the last buffer.
-
- This should take an optional argument `x`, which will be provided
- if a module before this one in the streaming pipeline has already
- spitted out a flushed out buffer.
- """
- if x is None:
- return None
- else:
- return self(x)
-
-
-class StreamingSequential(StreamingModule, nn.Sequential):
- """A streaming compatible alternative of `nn.Sequential`.
- """
- def flush(self, x: tp.Optional[torch.Tensor] = None):
- for module in self:
- if isinstance(module, StreamingModule):
- x = module.flush(x)
- elif x is not None:
- x = module(x)
- return x
diff --git a/spaces/ArtyomKhyan/Detection/README.md b/spaces/ArtyomKhyan/Detection/README.md
deleted file mode 100644
index d7e7996fb79b98032e2ef9521b336d249e23096f..0000000000000000000000000000000000000000
--- a/spaces/ArtyomKhyan/Detection/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Detection
-emoji: 🏢
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.50.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/app.py b/spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/app.py
deleted file mode 100644
index 45c0ddc60de66983c4314e5b4f49cb29ae1091b3..0000000000000000000000000000000000000000
--- a/spaces/Avin1221/darkstorm2150-Protogen_x3.4_Official_Release/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/darkstorm2150/Protogen_x3.4_Official_Release").launch()
\ No newline at end of file
diff --git a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/data_utils.py b/spaces/AzumaSeren100/XuanShen-Bert-VITS2/data_utils.py
deleted file mode 100644
index 19f098cfe3e238b740c7244375eee31f80fe1904..0000000000000000000000000000000000000000
--- a/spaces/AzumaSeren100/XuanShen-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.spk_map = hparams.spk2id
- self.hparams = hparams
-
- self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
- if self.use_mel_spec_posterior:
- self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 300)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- skipped = 0
- for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
- audiopath = f'{_id}'
- if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
- phones = phones.split(" ")
- tone = [int(i) for i in tone.split(" ")]
- word2ph = [int(i) for i in word2ph.split(" ")]
- audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- else:
- skipped += 1
- print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
- bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
- spec, wav = self.get_audio(audiopath)
- sid = torch.LongTensor([int(self.spk_map[sid])])
- return (phones, spec, wav, sid, tone, language, bert)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if self.use_mel_spec_posterior:
- spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
- try:
- spec = torch.load(spec_filename)
- except:
- if self.use_mel_spec_posterior:
- # if os.path.exists(filename.replace(".wav", ".spec.pt")):
- # # spec, n_fft, num_mels, sampling_rate, fmin, fmax
- # spec = spec_to_mel_torch(
- # torch.load(filename.replace(".wav", ".spec.pt")),
- # self.filter_length, self.n_mel_channels, self.sampling_rate,
- # self.hparams.mel_fmin, self.hparams.mel_fmax)
- spec = mel_spectrogram_torch(audio_norm, self.filter_length,
- self.n_mel_channels, self.sampling_rate, self.hop_length,
- self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
- # print(text, word2ph,phone, tone, language_str)
- pold = phone
- w2pho = [i for i in word2ph]
- word2ph = [i for i in word2ph]
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
- pold2 = phone
-
- if self.add_blank:
- p1 = len(phone)
- phone = commons.intersperse(phone, 0)
- p2 = len(phone)
- t1 = len(tone)
- tone = commons.intersperse(tone, 0)
- t2 = len(tone)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert_path = wav_path.replace(".wav", ".bert.pt")
- try:
- bert = torch.load(bert_path)
- assert bert.shape[-1] == len(phone)
- except:
- bert = get_bert(text, word2ph, language_str)
- torch.save(bert, bert_path)
- #print(bert.shape[-1], bert_path, text, pold)
- assert bert.shape[-1] == len(phone)
-
- assert bert.shape[-1] == len(phone), (
- bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
- return bert, phone, tone, language
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
-
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- tone_padded = torch.LongTensor(len(batch), max_text_len)
- language_padded = torch.LongTensor(len(batch), max_text_len)
- bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- tone_padded.zero_()
- language_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- bert_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- tone = row[4]
- tone_padded[i, :tone.size(0)] = tone
-
- language = row[5]
- language_padded[i, :language.size(0)] = language
-
- bert = row[6]
- bert_padded[i, :, :bert.size(1)] = bert
-
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
-
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i + 1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- if (len_bucket == 0):
- continue
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/Benson/text-generation/Examples/Cuentos De Espacio Mutante Blobs Ataque Ps Vita.md b/spaces/Benson/text-generation/Examples/Cuentos De Espacio Mutante Blobs Ataque Ps Vita.md
deleted file mode 100644
index 5fc7143c6b44786bd2989e30ffb3e8f429b642fa..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Cuentos De Espacio Mutante Blobs Ataque Ps Vita.md
+++ /dev/null
@@ -1,127 +0,0 @@
-
-Cuentos desde el espacio: Mutant Blobs Attack - Un divertido juego de plataformas para PS Vita
- Introducción
- ¿Te gustan los juegos de plataformas? ¿Tienes un PS Vita o estás pensando en comprar uno? Si respondiste sí a ambas preguntas, entonces definitivamente deberías revisar Tales from Space: Mutant Blobs Attack. Es uno de los mejores juegos para PS Vita que puedes descargar desde PlayStation Store.
- ¿Qué es Cuentos del Espacio: Ataque de Manchas Mutantes?
- Tales from Space: Mutant Blobs Attack es un juego de plataformas de desplazamiento lateral desarrollado y publicado por DrinkBox Studios en 2012. Es la secuela de Tales from Space: About a Blob, lanzado en 2011.
-cuentos de espacio mutante blobs ataque ps vita Download Zip 🆓 https://bltlly.com/2v6KVu
El juego se trata de una mancha mutante gruñón que escapa de un laboratorio y comienza a comer todo a su paso. La mancha puede crecer absorbiendo objetos sueltos en el entorno, como monedas, coches, vacas e incluso planetas. El blob también puede usar varios poderes y habilidades para superar obstáculos y enemigos.
- El juego cuenta con 24 niveles establecidos en diferentes lugares, como un campus universitario, una ciudad, una base militar y el espacio exterior. El juego también tiene niveles de bonificación que ponen a prueba tus habilidades y reflejos.
- ¿Por qué debería jugarlo?
- Tales from Space: Mutant Blobs Attack es un juego divertido y adictivo que te mantendrá entretenido durante horas. El juego tiene un estilo de dibujos animados encantador que es colorido y humorístico. El juego también tiene una banda sonora pegadiza que coincide con el estado de ánimo de cada nivel.
- El juego es fácil de jugar pero difícil de dominar. El juego tiene controles simples que utilizan tanto los palillos como la pantalla táctil de PS Vita. El juego también tiene rompecabezas inteligentes que requieren que uses tus poderes sabiamente.
- El juego es
Características
- Tales from Space: Mutant Blobs Attack tiene muchas características que lo convierten en un juego de plataformas único y agradable. Estos son algunos de ellos:
- Juego
-
- El blob también tiene varios poderes y habilidades que puede usar para resolver puzzles y derrotar enemigos. Algunos de estos poderes son:
-
- Controles
-
-Telekinesis: La mancha puede usar su mente para mover ciertos objetos, como cajas de metal, imanes y cohetes. El blob también puede usar telekinesis para activar interruptores y palancas. Magnetismo: La mancha puede atraer o repeler objetos metálicos, como monedas, tubos y tanques. La mancha también puede usar magnetismo para adherirse a superficies metálicas o volar a través de campos magnéticos. Rocket: La mancha puede lanzarse como un cohete, usando la pantalla táctil para apuntar y el pulgar para controlar la velocidad y la dirección. La mancha puede utilizar el cohete para volar sobre los huecos, esquivar los obstáculos, y llegar a lugares altos.
- Poderes
-
-Salto de pared: La mancha puede saltar de una pared a otra, usando el pulgar para cambiar de dirección. La mancha puede utilizar el salto de pared para subir superficies verticales o para cruzar pasajes estrechos. Rebote: La mancha puede rebotar en ciertos objetos, como trampolines, globos y resortes. La mancha puede usar el rebote para saltar más alto o para alcanzar áreas ocultas. Goo: El blob puede convertirse en goo, utilizando la pantalla táctil para difundir o retraer. La mancha puede utilizar la sustancia viscosa para exprimir a través de espacios reducidos o para cubrir grandes áreas.
- Niveles
- El juego tiene 24 niveles que se dividen en seis mundos, cada uno con un tema y configuración diferentes. Los mundos son:
-
-Campus universitario: El primer mundo del juego, donde la mancha se escapa del laboratorio y comienza a comer todo lo que se ve. Los niveles incluyen aulas, dormitorios, cafeterías, bibliotecas y estadios. Centro de la ciudad: El segundo mundo del juego, donde la mancha invade la ciudad y causa el caos. Los niveles incluyen calles, callejones, tejados, alcantarillas y subterráneos. Base Lunar: El cuarto mundo del juego, donde la mancha viaja al espacio exterior y explora una estación lunar. Los niveles incluyen cráteres, módulos, cúpulas, satélites y cohetes. Sistema Solar: El quinto mundo del juego, donde la mancha visita diferentes planetas y se los come. Los niveles incluyen Mercurio, Venus, la Tierra, Marte, Júpiter, Saturno, Urano, Neptuno y Plutón.Créditos: El mundo final del juego, donde la mancha se come los créditos y los desarrolladores. Los niveles incluyen nombres, logotipos, imágenes y mensajes.
- Gráficos
- El juego tiene un estilo gráfico colorido y caricaturesco que se adapta al tono humorístico del juego. El juego utiliza gráficos 2D con efectos 3D, como sombras, iluminación y profundidad. El juego también tiene animaciones suaves y transiciones que hacen que la mancha se vea viva y expresiva.
- El juego tiene una variedad de ambientes que son detallados y diversos. El juego utiliza diferentes temas, colores y texturas para crear contraste y variedad. El juego también tiene elementos dinámicos, como objetos en movimiento, objetos destructibles y fondos interactivos.
- Sonido
- El juego tiene una banda sonora pegadiza y optimista que coincide con el estado de ánimo de cada nivel. El juego utiliza diferentes géneros, como rock, jazz, techno y orquestal, para crear diversidad y atmósfera. El juego también tiene efectos de sonido que mejoran la jugabilidad, como ruidos de comer, explosiones y clips de voz.
- Comparación con otros juegos
- Tales from Space: Mutant Blobs Attack es un juego de plataformas único que se destaca de otros juegos del género. Aquí hay una tabla que compara las características del juego con otros juegos similares:
-
-
-Juego
-Características
-
-
-Cuentos desde el Espacio: Ataque de Blobs Mutantes
-- Comer y crecer mecánico
-
-
-
-- Personajes y niveles personalizables
-
-
-Orígenes de Rayman
-- Juego rápido y fluido
-
-
-Super Meat Boy
-- Juego desafiante y preciso
-
-
- Recepción
- Tales from Space: Mutant Blobs Attack recibió críticas positivas de críticos y jugadores por igual. El juego fue elogiado por su jugabilidad, gráficos, sonido y humor. El juego también fue reconocido como uno de los mejores juegos para PS Vita. Estos son algunos de los puntos destacados de la recepción:
- Calificaciones
- El juego recibió altas calificaciones de varias fuentes, como:
-
-Metacritic: 87/100 basado en 35 comentariosGameRankings: 86.67% basado en 18 comentariosIGN: 9/10 basado en una revisiónGameSpot: 8/10 basado en una revisiónPuntuación del usuario: 8.4/10 basado en 76 valoraciones
- Comentarios
- El juego recibió comentarios positivos de varios revisores, como:
-
-Destructoid: "Tales from Space: Mutant Blobs Attack es uno de los mejores juegos de PlayStation Vita, sin excepción. Es encantador, inteligente, lleno de contenido, y tiene un precio para complacer." Polygon: "Mutant Blobs Attack es una alegría para jugar - un concepto simple ejecutado bien con suficiente variedad para mantener las cosas interesantes en todo." Giant Bomb: "Mutant Blobs Attack es un gran ejemplo de cómo hacer un juego de plataformas divertido que no se basa en la nostalgia o trucos."Eurogamer: "Mutant Blobs Attack es un juego de plataformas inteligentemente diseñado con bolsas de carácter y un genuino sentido de la diversión."
- Premios
- El juego ganó varios premios de varias fuentes, como:
-
-D.I.C.E. Premios: Nominado para el juego portátil del año en 2013Premios GDC: Nominado a mejor juego portátil/móvil en 2013Canadian Videogame Awards: Ganó el mejor juego descargable y el mejor juego sobre la marcha en 2012Lo mejor de IGN de 2012: Ganó el mejor juego de plataformas de PS Vita y el mejor sonido de PS VitaLo mejor de Game Informer de 2012: Ganó el mejor exclusivo de manoGameSpot’s Best of 2012: Nominado a Mejor Juego de Plataformas y Mejor Juego de PS Vita
- Descargar
- Si estás interesado en jugar a Tales from Space: Mutant Blobs Attack, puedes descargarlo desde PlayStation Store. Estos son los pasos que debes seguir:
- Cómo descargar Tales from Space: Mutant Blobs Attack on PS Vita
-
-Enciende tu PS Vita y conéctate a internet.
-Ir a la aplicación PlayStation Store en la pantalla de inicio.
-Buscar cuentos desde el espacio: Mutant Blobs Ataque en la barra de búsqueda o navegar por las categorías.
-Selecciona el juego y toca el botón de descarga.
-Espera a que el juego se descargue e instale en tu PS Vita.
-¡Disfruta jugando!
-
- ¿Cuánto cuesta?
- Tales from Space: Mutant Blobs Attack es un juego muy asequible que cuesta solo $7.99. También puedes obtener un descuento si eres miembro de PlayStation Plus. El juego bien vale el precio, considerando la cantidad de contenido y diversión que ofrece.
- Conclusión
-
- El juego recibió críticas positivas de críticos y jugadores por igual, que elogiaron su jugabilidad, gráficos, sonido y humor. El juego también ganó varios premios de diversas fuentes, que lo reconocieron como uno de los mejores juegos para PS Vita.
- Si estás buscando un juego de plataformas divertido y divertido que te mantenga entretenido durante horas, entonces definitivamente deberías descargar Tales from Space: Mutant Blobs Attack de PlayStation Store. ¡No te arrepentirás!
- ¿Qué estás esperando? ¡Coge tu PS Vita y empieza a comer todo lo que tengas a la vista!
- Preguntas frecuentes
-
-Q: ¿Cuánto tiempo se tarda en completar el juego? Q: ¿Hay un modo multijugador? Q: ¿Hay una secuela o un DLC? Q: ¿Cuáles son los requisitos del sistema para el juego? 64aa2da5cf
-HF_ACCESS_TOKEN=
-```
-
-## Duplicating to a Space
-
-Create a `DOTENV_LOCAL` secret to your space with the following contents:
-
-```
-MONGODB_URL=
-HF_ACCESS_TOKEN=
-```
-
-Where the contents in `<...>` are replaced by the MongoDB URL and your [HF Access Token](https://huggingface.co/settings/tokens).
-
-## Running Local Inference
-
-Both the example above use the HF Inference API or HF Endpoints API.
-
-If you want to run the model locally, you need to run this inference server locally: https://github.com/huggingface/text-generation-inference
-
-And add this to your `.env.local`:
-
-```
-MODELS=`[{"name": "...", "endpoints": [{"url": "127.0.0.1:8080/generate_stream"}]}]`
-```
-
-## Building
-
-To create a production version of your app:
-
-```bash
-npm run build
-```
-
-You can preview the production build with `npm run preview`.
-
-> To deploy your app, you may need to install an [adapter](https://kit.svelte.dev/docs/adapters) for your target environment.
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/src/styles/highlight-js.css b/spaces/DaFujaTyping/hf-Chat-ui/src/styles/highlight-js.css
deleted file mode 100644
index b262688368e9a946d72b21ae70fba7d711072fbb..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/src/styles/highlight-js.css
+++ /dev/null
@@ -1 +0,0 @@
-@import "highlight.js/styles/atom-one-dark";
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/swin_transformer.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/swin_transformer.py
deleted file mode 100644
index 29996bbc08af9302dfad40e64edd9a3d976fb3a2..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/swin_transformer.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from models.modules.transformer_modules import *
-
-
-class Swin_Transformer(nn.Module):
- def __init__(self, dim, depth, heads, win_size, dim_head, mlp_dim,
- dropout=0., patch_num=None, ape=None, rpe=None, rpe_pos=1):
- super().__init__()
- self.absolute_pos_embed = None if patch_num is None or ape is None else AbsolutePosition(dim, dropout,
- patch_num, ape)
- self.pos_dropout = nn.Dropout(dropout)
- self.layers = nn.ModuleList([])
- for i in range(depth):
- self.layers.append(nn.ModuleList([
- PreNorm(dim, WinAttention(dim, win_size=win_size, shift=0 if (i % 2 == 0) else win_size // 2,
- heads=heads, dim_head=dim_head, dropout=dropout, rpe=rpe, rpe_pos=rpe_pos)),
- PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)),
- ]))
-
- def forward(self, x):
- if self.absolute_pos_embed is not None:
- x = self.absolute_pos_embed(x)
- x = self.pos_dropout(x)
- for attn, ff in self.layers:
- x = attn(x) + x
- x = ff(x) + x
- return x
-
-
-if __name__ == '__main__':
- token_dim = 1024
- toke_len = 256
-
- transformer = Swin_Transformer(dim=token_dim,
- depth=6,
- heads=16,
- win_size=8,
- dim_head=64,
- mlp_dim=2048,
- dropout=0.1)
-
- input = torch.randn(1, toke_len, token_dim)
- output = transformer(input)
- print(output.shape)
diff --git a/spaces/Detomo/aisatsu-app-api/app.py b/spaces/Detomo/aisatsu-app-api/app.py
deleted file mode 100644
index 60e48f08d655fa442d5beeab0aabd7f5f5933f01..0000000000000000000000000000000000000000
--- a/spaces/Detomo/aisatsu-app-api/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from gtts import gTTS
-from io import BytesIO
-import base64
-from PIL import Image
-import cv2
-import numpy as np
-import gradio as gr
-from ultralyticsplus import YOLO
-from base64 import b64encode
-from speech_recognition import AudioFile, Recognizer
-import numpy as np
-from utils import tts, read_image_file, pil_to_base64, base64_to_pil, get_hist
-from scipy.spatial import distance as dist
-
-model = YOLO('ultralyticsplus/yolov8s')
-CLASS = model.model.names
-defaul_bot_voice = "おはいようございます"
-area_thres = 0.3
-
-def infer(image, last_seen):
- results = model.predict(image, show=False)[0]
- masks, boxes = results.masks, results.boxes
- area_image = image.width * image.height
- voice_bot = None
- most_close = 0
- out_img = None
- diff_value = 0.5
- if boxes is not None:
- for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
- if int(cls) != 0:
- continue
- box = xyxy.tolist()
- area_rate = (box[2] - box[0]) * (box[3] - box[1]) / area_image
- if area_rate >= most_close:
- out_img = image.crop(tuple(box)).resize((64, 64))
- most_close = area_rate
- if last_seen != "":
- last_seen = base64_to_pil(last_seen)
- if out_img is not None:
- diff_value = dist.euclidean(get_hist(out_img), get_hist(last_seen))
- print(most_close, diff_value)
- if most_close >= area_thres and diff_value >= 0.5:
- voice_bot = tts(defaul_bot_voice, language="ja")
- return out_img, voice_bot
-
-iface = gr.Interface(
- fn=infer,
- title="aisatsu api",
- inputs=[gr.Image(label="image", type="pil", shape=(320, 320)), gr.Textbox(label="last seen", value="")],
- outputs=[gr.Image(label="output image"), gr.Textbox(label="output voice")],
- article = "Author: Vu Minh Chien .",
-).launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/src/kalmanFilter.cpp b/spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/src/kalmanFilter.cpp
deleted file mode 100644
index 168432a46810d0c1296f4b17500d41f8b4f308b4..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/src/kalmanFilter.cpp
+++ /dev/null
@@ -1,152 +0,0 @@
-#include "kalmanFilter.h"
-#include
-
-namespace byte_kalman
-{
- const double KalmanFilter::chi2inv95[10] = {
- 0,
- 3.8415,
- 5.9915,
- 7.8147,
- 9.4877,
- 11.070,
- 12.592,
- 14.067,
- 15.507,
- 16.919
- };
- KalmanFilter::KalmanFilter()
- {
- int ndim = 4;
- double dt = 1.;
-
- _motion_mat = Eigen::MatrixXf::Identity(8, 8);
- for (int i = 0; i < ndim; i++) {
- _motion_mat(i, ndim + i) = dt;
- }
- _update_mat = Eigen::MatrixXf::Identity(4, 8);
-
- this->_std_weight_position = 1. / 20;
- this->_std_weight_velocity = 1. / 160;
- }
-
- KAL_DATA KalmanFilter::initiate(const DETECTBOX &measurement)
- {
- DETECTBOX mean_pos = measurement;
- DETECTBOX mean_vel;
- for (int i = 0; i < 4; i++) mean_vel(i) = 0;
-
- KAL_MEAN mean;
- for (int i = 0; i < 8; i++) {
- if (i < 4) mean(i) = mean_pos(i);
- else mean(i) = mean_vel(i - 4);
- }
-
- KAL_MEAN std;
- std(0) = 2 * _std_weight_position * measurement[3];
- std(1) = 2 * _std_weight_position * measurement[3];
- std(2) = 1e-2;
- std(3) = 2 * _std_weight_position * measurement[3];
- std(4) = 10 * _std_weight_velocity * measurement[3];
- std(5) = 10 * _std_weight_velocity * measurement[3];
- std(6) = 1e-5;
- std(7) = 10 * _std_weight_velocity * measurement[3];
-
- KAL_MEAN tmp = std.array().square();
- KAL_COVA var = tmp.asDiagonal();
- return std::make_pair(mean, var);
- }
-
- void KalmanFilter::predict(KAL_MEAN &mean, KAL_COVA &covariance)
- {
- //revise the data;
- DETECTBOX std_pos;
- std_pos << _std_weight_position * mean(3),
- _std_weight_position * mean(3),
- 1e-2,
- _std_weight_position * mean(3);
- DETECTBOX std_vel;
- std_vel << _std_weight_velocity * mean(3),
- _std_weight_velocity * mean(3),
- 1e-5,
- _std_weight_velocity * mean(3);
- KAL_MEAN tmp;
- tmp.block<1, 4>(0, 0) = std_pos;
- tmp.block<1, 4>(0, 4) = std_vel;
- tmp = tmp.array().square();
- KAL_COVA motion_cov = tmp.asDiagonal();
- KAL_MEAN mean1 = this->_motion_mat * mean.transpose();
- KAL_COVA covariance1 = this->_motion_mat * covariance *(_motion_mat.transpose());
- covariance1 += motion_cov;
-
- mean = mean1;
- covariance = covariance1;
- }
-
- KAL_HDATA KalmanFilter::project(const KAL_MEAN &mean, const KAL_COVA &covariance)
- {
- DETECTBOX std;
- std << _std_weight_position * mean(3), _std_weight_position * mean(3),
- 1e-1, _std_weight_position * mean(3);
- KAL_HMEAN mean1 = _update_mat * mean.transpose();
- KAL_HCOVA covariance1 = _update_mat * covariance * (_update_mat.transpose());
- Eigen::Matrix diag = std.asDiagonal();
- diag = diag.array().square().matrix();
- covariance1 += diag;
- // covariance1.diagonal() << diag;
- return std::make_pair(mean1, covariance1);
- }
-
- KAL_DATA
- KalmanFilter::update(
- const KAL_MEAN &mean,
- const KAL_COVA &covariance,
- const DETECTBOX &measurement)
- {
- KAL_HDATA pa = project(mean, covariance);
- KAL_HMEAN projected_mean = pa.first;
- KAL_HCOVA projected_cov = pa.second;
-
- //chol_factor, lower =
- //scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
- //kalmain_gain =
- //scipy.linalg.cho_solve((cho_factor, lower),
- //np.dot(covariance, self._upadte_mat.T).T,
- //check_finite=False).T
- Eigen::Matrix B = (covariance * (_update_mat.transpose())).transpose();
- Eigen::Matrix kalman_gain = (projected_cov.llt().solve(B)).transpose(); // eg.8x4
- Eigen::Matrix innovation = measurement - projected_mean; //eg.1x4
- auto tmp = innovation * (kalman_gain.transpose());
- KAL_MEAN new_mean = (mean.array() + tmp.array()).matrix();
- KAL_COVA new_covariance = covariance - kalman_gain * projected_cov*(kalman_gain.transpose());
- return std::make_pair(new_mean, new_covariance);
- }
-
- Eigen::Matrix
- KalmanFilter::gating_distance(
- const KAL_MEAN &mean,
- const KAL_COVA &covariance,
- const std::vector &measurements,
- bool only_position)
- {
- KAL_HDATA pa = this->project(mean, covariance);
- if (only_position) {
- printf("not implement!");
- exit(0);
- }
- KAL_HMEAN mean1 = pa.first;
- KAL_HCOVA covariance1 = pa.second;
-
- // Eigen::Matrix d(size, 4);
- DETECTBOXSS d(measurements.size(), 4);
- int pos = 0;
- for (DETECTBOX box : measurements) {
- d.row(pos++) = box - mean1;
- }
- Eigen::Matrix factor = covariance1.llt().matrixL();
- Eigen::Matrix z = factor.triangularView().solve(d).transpose();
- auto zz = ((z.array())*(z.array())).matrix();
- auto square_maha = zz.colwise().sum();
- return square_maha;
- }
-}
\ No newline at end of file
diff --git a/spaces/ElAnon/6btest/README.md b/spaces/ElAnon/6btest/README.md
deleted file mode 100644
index e6451c2d0b60503e48309ce090d8a2e030c831d5..0000000000000000000000000000000000000000
--- a/spaces/ElAnon/6btest/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 6btest
-emoji: 📈
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Eroggen/ChatGPT4/README.md b/spaces/Eroggen/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/Eroggen/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EsoCode/text-generation-webui/modules/logging_colors.py b/spaces/EsoCode/text-generation-webui/modules/logging_colors.py
deleted file mode 100644
index a0c97c3a76cfc17eb5d8d8bb310a5389ab5db719..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/modules/logging_colors.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copied from https://stackoverflow.com/a/1336640
-
-import logging
-import platform
-
-logging.basicConfig(
- format='%(asctime)s %(levelname)s:%(message)s',
- datefmt='%Y-%m-%d %H:%M:%S',
-)
-
-
-def add_coloring_to_emit_windows(fn):
- # add methods we need to the class
- def _out_handle(self):
- import ctypes
- return ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
- out_handle = property(_out_handle)
-
- def _set_color(self, code):
- import ctypes
-
- # Constants from the Windows API
- self.STD_OUTPUT_HANDLE = -11
- hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE)
- ctypes.windll.kernel32.SetConsoleTextAttribute(hdl, code)
-
- setattr(logging.StreamHandler, '_set_color', _set_color)
-
- def new(*args):
- FOREGROUND_BLUE = 0x0001 # text color contains blue.
- FOREGROUND_GREEN = 0x0002 # text color contains green.
- FOREGROUND_RED = 0x0004 # text color contains red.
- FOREGROUND_INTENSITY = 0x0008 # text color is intensified.
- FOREGROUND_WHITE = FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_RED
- # winbase.h
- # STD_INPUT_HANDLE = -10
- # STD_OUTPUT_HANDLE = -11
- # STD_ERROR_HANDLE = -12
-
- # wincon.h
- # FOREGROUND_BLACK = 0x0000
- FOREGROUND_BLUE = 0x0001
- FOREGROUND_GREEN = 0x0002
- # FOREGROUND_CYAN = 0x0003
- FOREGROUND_RED = 0x0004
- FOREGROUND_MAGENTA = 0x0005
- FOREGROUND_YELLOW = 0x0006
- # FOREGROUND_GREY = 0x0007
- FOREGROUND_INTENSITY = 0x0008 # foreground color is intensified.
-
- # BACKGROUND_BLACK = 0x0000
- # BACKGROUND_BLUE = 0x0010
- # BACKGROUND_GREEN = 0x0020
- # BACKGROUND_CYAN = 0x0030
- # BACKGROUND_RED = 0x0040
- # BACKGROUND_MAGENTA = 0x0050
- BACKGROUND_YELLOW = 0x0060
- # BACKGROUND_GREY = 0x0070
- BACKGROUND_INTENSITY = 0x0080 # background color is intensified.
-
- levelno = args[1].levelno
- if (levelno >= 50):
- color = BACKGROUND_YELLOW | FOREGROUND_RED | FOREGROUND_INTENSITY | BACKGROUND_INTENSITY
- elif (levelno >= 40):
- color = FOREGROUND_RED | FOREGROUND_INTENSITY
- elif (levelno >= 30):
- color = FOREGROUND_YELLOW | FOREGROUND_INTENSITY
- elif (levelno >= 20):
- color = FOREGROUND_GREEN
- elif (levelno >= 10):
- color = FOREGROUND_MAGENTA
- else:
- color = FOREGROUND_WHITE
- args[0]._set_color(color)
-
- ret = fn(*args)
- args[0]._set_color(FOREGROUND_WHITE)
- # print "after"
- return ret
- return new
-
-
-def add_coloring_to_emit_ansi(fn):
- # add methods we need to the class
- def new(*args):
- levelno = args[1].levelno
- if (levelno >= 50):
- color = '\x1b[31m' # red
- elif (levelno >= 40):
- color = '\x1b[31m' # red
- elif (levelno >= 30):
- color = '\x1b[33m' # yellow
- elif (levelno >= 20):
- color = '\x1b[32m' # green
- elif (levelno >= 10):
- color = '\x1b[35m' # pink
- else:
- color = '\x1b[0m' # normal
- args[1].msg = color + args[1].msg + '\x1b[0m' # normal
- # print "after"
- return fn(*args)
- return new
-
-
-if platform.system() == 'Windows':
- # Windows does not support ANSI escapes and we are using API calls to set the console color
- logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit)
-else:
- # all non-Windows platforms are supporting ANSI escapes so we use them
- logging.StreamHandler.emit = add_coloring_to_emit_ansi(logging.StreamHandler.emit)
- # log = logging.getLogger()
- # log.addFilter(log_filter())
- # //hdlr = logging.StreamHandler()
- # //hdlr.setFormatter(formatter())
-
-logger = logging.getLogger('text-generation-webui')
-logger.setLevel(logging.DEBUG)
diff --git a/spaces/EtheE/SecurityAgent/app.py b/spaces/EtheE/SecurityAgent/app.py
deleted file mode 100644
index 09c005939185682901176956950bf19469ec3553..0000000000000000000000000000000000000000
--- a/spaces/EtheE/SecurityAgent/app.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-from typing import Optional, Tuple
-
-import gradio as gr
-from langchain.callbacks import get_openai_callback
-from langchain.chains import ConversationChain
-from langchain.llms import OpenAI
-from langchain.chains.llm import LLMChain
-from langchain.prompts.prompt import PromptTemplate
-from langchain.chains.constitutional_ai.base import ConstitutionalChain
-from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
-
-PRICE_TOKEN_DAVINCI_03 = 0.00002
-naive_prompt = os.environ["NAIVE_PROMPT"]
-security_agent_prompt = os.environ["SECURITY_PROMPT"]
-security_agent_revision_prompt = os.environ["SECURITY_REVISION_PROMPT"]
-
-
-def create_prompt():
- template = naive_prompt + """
- Question: {question}
- Naive answer:"""
-
- naive_qa_prompt = PromptTemplate(template=template, input_variables=["question"])
- return naive_qa_prompt
-
-
-def load_chain():
- """Logic for loading the chain you want to use should go here."""
- naive_qa_prompt = create_prompt()
- llm = OpenAI(temperature=0)
- naive_qa_chain = LLMChain(llm=llm, prompt=naive_qa_prompt)
- security_principle = ConstitutionalPrinciple(
- name="Security Principle",
- critique_request=security_agent_prompt,
- revision_request=security_agent_revision_prompt,
- )
-
- constitutional_chain = ConstitutionalChain.from_llm(
- chain=naive_qa_chain,
- constitutional_principles=[security_principle],
- llm=llm,
- verbose=False,
- )
-
- return constitutional_chain
-
-
-def set_openai_api_key(api_key: str):
- """Set the api key and return chain.
- If no api_key, then None is returned.
- """
- if api_key:
- os.environ["OPENAI_API_KEY"] = api_key
- chain = load_chain()
- os.environ["OPENAI_API_KEY"] = ""
- return chain
-
-
-def chat(
- inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain]
-):
- """Execute the chat functionality."""
- history = history or []
- # If chain is None, that is because no API key was provided.
- if chain is None:
- history.append((inp, "Please paste your OpenAI key to use"))
- return history, history
- # Run chain and append input.
- with get_openai_callback() as cb: # Using callback to keep track of the tokens that are used
- output = chain.run(question=inp)
- print("Total tokens", cb.total_tokens)
-
- history.append((inp, output))
- return history, history
-
-
-block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
-
-with block:
- with gr.Row():
- gr.Markdown("Hack Challenge: SecurityAgent Please be patient for it's answers , as it does rely on an extra call to OpenAI.The Challenge scenes_tact0c@icloud.com Implementation and Challenge created by @Erik90190 on langchain discord Powered by LangChain 🦜️🔗
- Following the Anthropic AI Constitutional approach , and work by Charlie George =batch_size):
- X=X.to(self.device)
- for i in range(self.max_iter):
- iter_time = time()
- if self.minibatch= 2:
- print('iter:', i, 'error:', error.item(), 'time spent:', round(time()-iter_time, 4))
- if error <= self.tol:
- break
-
- if self.verbose >= 1:
- print(f'used {i+1} iterations ({round(time()-start_time, 4)}s) to cluster {batch_size} items into {self.n_clusters} clusters')
- return closest
diff --git a/spaces/GAIR/Factool/example/example.py b/spaces/GAIR/Factool/example/example.py
deleted file mode 100644
index 646eae04c5e570cfa3bfd75638bd3ba928871abe..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/example/example.py
+++ /dev/null
@@ -1,53 +0,0 @@
-'''
-Remember to export you API keys first.
-export OPENAI_API_KEY=...
-export SERPER_API_KEY=...
-export SCRAPER_API_KEY=...
-'''
-
-from factool import Factool
-
-factool_instance = Factool("gpt-4")
-
-# one example input for each category.
-inputs = [
- {
- "prompt": "Introduce Graham Neubig",
- "response": "Graham Neubig is a professor at MIT",
- "category": "kbqa"
- },
- {
- "prompt": "def get_max_triples(n): \"\"\" You are given a positive integer n. You have to create an integer array a of length n. For each i (1 \u2264 i \u2264 n), the value of a[i] = i * i - i + 1. Return the number of triples (a[i], a[j], a[k]) of a where i < j < k, and a[i] + a[j] + a[k] is a multiple of 3. Example : Input: n = 5 Output: 1 Explanation: a = [1, 3, 7, 13, 21] The only valid triple is (1, 7, 13). \"\"\" Now implement the function get_max_triples using Python",
- "response": "def get_max_triples(n):\n a = [i * i - i + 1 for i in range(1, n+1)]\n count = 0\n for i in range(n-2):\n for j in range(i+1, n-1):\n for k in range(j+1, n):\n if (a[i] + a[j] + a[k]) % 3 == 0:\n count += 1\n return count\n\nprint(get_max_triples(5)) # Output: 1",
- "category": "code",
- "entry_point": "get_max_triples"
- },
- {
- "prompt": "Stephen placed an online order for groceries. His final bill came to $7023116. Because this was through a delivery vendor, they tacked on a 25% fee to his final total and charged him $3.00 in delivery fees. Stephen also added a $4.00 tip. After the extra fees, what was the final price of Stephen's groceries?",
- "response": "Okay, here are the steps to solve this:\n* Stephen's initial grocery bill was $7023116\n* The delivery vendor charged a 25% fee on top of that. 25% of $7023116 is 0.25 * $7023116 = $1755779\n* They also charged a flat $3 delivery fee\n* Stephen added a $4 tip\n* So the total charges were:\n * Initial groceries: $7023116\n * 25% fee: $1755779\n * Delivery fee: $3 \n * Tip: $4\n* Adding all that up, the total final price was $7023116 + $1755779 + $3 + $4 = $8779902\n\nSo the final price of Stephen's groceries was $8779902",
- "category": "math"
- },
- {
- "prompt": "Detail how machine learning algorithms are applied in data"
- " analytics and the processes involved in training"
- " these algorithms. Please cite relevant research papers"
- " or textbooks to support your points, including the author's"
- " name, the title of the work, and the year of publication.",
- "response": "Machine learning algorithms play a crucial role in data"
- " analytics. They are used to extract insights and knowledge"
- " from large datasets, which can be used to make informed"
- " decisions and predictions. The following are the processes"
- " involved in training machine learning algorithms:\n\n1. "
- "Data Preprocessing: The first step in training a machine"
- " learning algorithm is to preprocess the data. This involves"
- " cleaning the data, handling missing values, "
- "encoding categorical variables, and scaling the data. "
- "The aim of preprocessing is to ensure that the data is in"
- " a format that can be used by the machine learning algorithm.\n2. Feature Selection: Once the data has been preprocessed, the next step is to select the relevant features that will be used to train the machine learning algorithm. This involves identifying the features that are most important for predicting the target variable. Feature selection can be done using various techniques, such as filter methods, wrapper methods, and embedded methods.\n3. Model Selection: After feature selection, the next step is to select the appropriate machine learning algorithm to use. There are various types of machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning. The choice of algorithm depends on the nature of the problem and the type of data available.\n4. Training the Model: Once the model has been selected, the next step is to train the machine learning algorithm using the preprocessed data. This involves using a training set of data to optimize the parameters of the model so that it can accurately predict the target variable.\n5. Model Evaluation: After the model has been trained, the next step is to evaluate its performance. This involves testing the model on a separate test set of data and measuring its accuracy, precision, recall, and other performance metrics.\n6. Model Deployment: Once the model has been evaluated and fine-tuned, it can be deployed in a production environment. This involves integrating the model into a larger system or workflow, and monitoring its performance over time.\n\nResearch papers and textbooks that support the above points include:\n\n* \"An Introduction to Machine Learning\" by Alpaydin, B., (2010) which provides a comprehensive overview of machine learning algorithms and their applications.\n* \"Data Mining: Concepts and Techniques\" by Han, J., Kamber, B., & Pei, J., (2011) which provides a detailed introduction to data mining and its applications.\n* \"Machine Learning: Trends, Perspectives, and Prospects\" by Kamber, B., & Pei, J., (2012) which provides an overview of the current trends and future prospects of machine learning.\n* \"Machine Learning for Data Mining\" by He, C., & Wang, Y., (2015) which provides an in-depth introduction to machine learning algorithms and their applications in data mining.",
- "category": "scientific"
- }
-]
-
-response_list = factool_instance.run(inputs)
-
-print(response_list)
\ No newline at end of file
diff --git a/spaces/GEM/results/index.html b/spaces/GEM/results/index.html
deleted file mode 100644
index 6f69a2a432cba2646575fc9196848808299ce162..0000000000000000000000000000000000000000
--- a/spaces/GEM/results/index.html
+++ /dev/null
@@ -1,27 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/GLTdd/ChatgptBot/app.py b/spaces/GLTdd/ChatgptBot/app.py
deleted file mode 100644
index 3f5d1d3de59edeb44f552cdd7b3b6139873ea77d..0000000000000000000000000000000000000000
--- a/spaces/GLTdd/ChatgptBot/app.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import json
-import gradio as gr
-# import openai
-import os
-import sys
-import traceback
-import requests
-# import markdown
-import csv
-
-my_api_key = "" # 在这里输入你的 API 密钥
-HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-
-initial_prompt = "You are a helpful assistant."
-API_URL = "https://api.openai.com/v1/chat/completions"
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-
-
-#if we are running in Docker
-if os.environ.get('dockerrun') == 'yes':
- dockerflag = True
-else:
- dockerflag = False
-
-if dockerflag:
- my_api_key = os.environ.get('my_api_key')
- if my_api_key == "empty":
- print("Please give a api key!")
- sys.exit(1)
- #auth
- username = os.environ.get('USERNAME')
- password = os.environ.get('PASSWORD')
- if isinstance(username, type(None)) or isinstance(password, type(None)):
- authflag = False
- else:
- authflag = True
-
-
-def parse_text(text):
- lines = text.split("\n")
- lines = [line for line in lines if line != ""]
- count = 0
- firstline = False
- for i, line in enumerate(lines):
- if "```" in line:
- count += 1
- items = line.split('`')
- if count % 2 == 1:
- lines[i] = f''
- firstline = True
- else:
- lines[i] = f'川虎ChatGPT 🚀 """
-description = """
-
-由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536) 开发
-
-访问川虎ChatGPT的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本
-
-此App使用 `gpt-3.5-turbo` 大语言模型
-
-"""
-with gr.Blocks() as demo:
- gr.HTML(title)
- gr.HTML('''
-
xs
-
sm
-
md
-
lg
-
xl
-
2xl
-
`_ for details.
-
- Args:
- in_channels (int): Number of input channels (feature maps of all levels
- should have the same channels).
- num_levels (int): Number of input feature levels.
- conv_cfg (dict): The config dict for convolution layers.
- norm_cfg (dict): The config dict for normalization layers.
- refine_level (int): Index of integration and refine level of BSF in
- multi-level features from bottom to top.
- refine_type (str): Type of the refine op, currently support
- [None, 'conv', 'non_local'].
- """
-
- def __init__(self,
- in_channels,
- num_levels,
- refine_level=2,
- refine_type=None,
- conv_cfg=None,
- norm_cfg=None):
- super(BFP, self).__init__()
- assert refine_type in [None, 'conv', 'non_local']
-
- self.in_channels = in_channels
- self.num_levels = num_levels
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
-
- self.refine_level = refine_level
- self.refine_type = refine_type
- assert 0 <= self.refine_level < self.num_levels
-
- if self.refine_type == 'conv':
- self.refine = ConvModule(
- self.in_channels,
- self.in_channels,
- 3,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg)
- elif self.refine_type == 'non_local':
- self.refine = NonLocal2d(
- self.in_channels,
- reduction=1,
- use_scale=False,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg)
-
- def init_weights(self):
- """Initialize the weights of FPN module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == self.num_levels
-
- # step 1: gather multi-level features by resize and average
- feats = []
- gather_size = inputs[self.refine_level].size()[2:]
- for i in range(self.num_levels):
- if i < self.refine_level:
- gathered = F.adaptive_max_pool2d(
- inputs[i], output_size=gather_size)
- else:
- gathered = F.interpolate(
- inputs[i], size=gather_size, mode='nearest')
- feats.append(gathered)
-
- bsf = sum(feats) / len(feats)
-
- # step 2: refine gathered features
- if self.refine_type is not None:
- bsf = self.refine(bsf)
-
- # step 3: scatter refined features to multi-levels by a residual path
- outs = []
- for i in range(self.num_levels):
- out_size = inputs[i].size()[2:]
- if i < self.refine_level:
- residual = F.interpolate(bsf, size=out_size, mode='nearest')
- else:
- residual = F.adaptive_max_pool2d(bsf, output_size=out_size)
- outs.append(residual + inputs[i])
-
- return tuple(outs)
diff --git a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/modules/__init__.py b/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/modules/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/MusicGen_Plus_hfv2/tests/modules/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/GreenCounsel/SpeechT5-sv/app.py b/spaces/GreenCounsel/SpeechT5-sv/app.py
deleted file mode 100644
index 48060678a230d3c5651cda7d118c1d68d49842d0..0000000000000000000000000000000000000000
--- a/spaces/GreenCounsel/SpeechT5-sv/app.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import gradio as gr
-import librosa
-import numpy as np
-import torch
-import re
-
-from num2words import num2words
-
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
-
-checkpoint = "GreenCounsel/speecht5_tts_common_voice_5_sv"
-processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
-model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-
-
-speaker_embeddings = {
- "Female": "spkemb/cmu_us_clb_arctic-wav-arctic_a0144.npy",
- "Male": "spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
- "Experimental":"spkemb/embeddings.npy",
-
-}
-
-
-def predict(text, speaker):
- if len(text.strip()) == 0 or len(text.strip())>200:
- text="Du måste ha minst ett och max 200 tecken."
- ar=[int(s) for s in re.findall(r'\b\d+\b',text)]
- for arr in ar:
- text=text.replace(str(arr),num2words(arr,lang="sv"))
- repl = [
- ('Ä', 'ae'),
- ('Å', 'o'),
- ('Ö', 'oe'),
- ('ä', 'ae'),
- ('å', 'o'),
- ('ö', 'oe'),
- ('ô','oe'),
- ('-',''),
- ('‘',''),
- ('’',''),
- ('“',''),
- ('”',''),
- ]
-
-
- for src, dst in repl:
- text = text.replace(src, dst)
-
- inputs = processor(text=text, return_tensors="pt")
-
- # limit input length
- input_ids = inputs["input_ids"]
- input_ids = input_ids[..., :model.config.max_text_positions]
-
- speaker_embedding = np.load(speaker_embeddings[speaker])
-
- speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
-
- speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)
-
- speech = (speech.numpy() * 32767).astype(np.int16)
- return (16000, speech)
-
-
-title = "SpeechT5 finetuned Swedish, TTS "
-
-description = """
-SpeechT5 text-to-speech model finetuned on the Swedish language from the
-Common Voice dataset. Inference runs on a basic CPU (2 vCPU, 16 GB ram) so
-please have patience if it takes some time. As a company founded by a female
-coder, our resources are extremely limited (female founders in tech only get approx.
-1 % of the venture capital and the women who receive funding seldom are the
-ones actually handling the tech). We are in a very biased sphere where
-female coders' companies seldom get the resources which would normally
-be necessary to do what they do. The app uses the SpeechT5 model
-finetuned for swedish by GreenCounsel, available here: [https://huggingface.co/GreenCounsel/speecht5_tts_common_voice_5_sv](https://huggingface.co/GreenCounsel/speecht5_tts_common_voice_5_sv).
-"""
-
-article = """
-
-
References: SpeechT5 paper |
-original SpeechT5 |
-original weights
-
-@article{Ao2021SpeechT5,
- title = {SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing},
- author = {Junyi Ao and Rui Wang and Long Zhou and Chengyi Wang and Shuo Ren and Yu Wu and Shujie Liu and Tom Ko and Qing Li and Yu Zhang and Zhihua Wei and Yao Qian and Jinyu Li and Furu Wei},
- eprint={2110.07205},
- archivePrefix={arXiv},
- primaryClass={eess.AS},
- year={2021}
-}
-
-
-#include
-
-std::vector
-dynamicconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l);
-
-std::vector dynamicconv_cuda_backward(
- at::Tensor gradOutput,
- int padding_l,
- at::Tensor input,
- at::Tensor filters);
-
-#define CHECK_CUDA(x) \
- AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) \
- AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) \
- CHECK_CUDA(x); \
- CHECK_CONTIGUOUS(x)
-
-std::vector
-dynamicconv_forward(at::Tensor input, at::Tensor filters, int padding_l) {
- CHECK_INPUT(input);
- CHECK_INPUT(filters);
-
- return dynamicconv_cuda_forward(input, filters, padding_l);
-}
-
-std::vector dynamicconv_backward(
- at::Tensor gradOutput,
- int padding_l,
- at::Tensor input,
- at::Tensor filters) {
- CHECK_INPUT(gradOutput);
- CHECK_INPUT(input);
- CHECK_INPUT(filters);
-
- return dynamicconv_cuda_backward(gradOutput, padding_l, input, filters);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("forward", &dynamicconv_forward, "dynamicconv forward (CUDA)");
- m.def("backward", &dynamicconv_backward, "dynamicconv backward (CUDA)");
-}
diff --git a/spaces/Harveenchadha/en_to_indic_translation/inference/__init__.py b/spaces/Harveenchadha/en_to_indic_translation/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/morph/__init__.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/morph/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HighCWu/GPEN/retinaface/layers/modules/__init__.py b/spaces/HighCWu/GPEN/retinaface/layers/modules/__init__.py
deleted file mode 100644
index cf24bddbf283f233d0b93fc074a2bac2f5c044a9..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/GPEN/retinaface/layers/modules/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .multibox_loss import MultiBoxLoss
-
-__all__ = ['MultiBoxLoss']
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/Webcam.8816836e.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/Webcam.8816836e.js
deleted file mode 100644
index 98df507019b5259e0123544e315a5d90b517759c..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/Webcam.8816836e.js
+++ /dev/null
@@ -1,11 +0,0 @@
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- * Cropper.js v1.5.12
- * https://fengyuanchen.github.io/cropperjs
- *
- * Copyright 2015-present Chen Fengyuan
- * Released under the MIT license
- *
- * Date: 2021-06-12T08:00:17.411Z
- */function ne(a,t){var e=Object.keys(a);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(a);t&&(i=i.filter(function(n){return Object.getOwnPropertyDescriptor(a,n).enumerable})),e.push.apply(e,i)}return e}function Ee(a){for(var t=1;ta.length)&&(t=a.length);for(var e=0,i=new Array(t);e
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f=Number(i.minCropBoxWidth)||0,h=Number(i.minCropBoxHeight)||0,c=s?Math.min(n.width,r.width,r.width+r.left,n.width-r.left):n.width,u=s?Math.min(n.height,r.height,r.height+r.top,n.height-r.top):n.height;f=Math.min(f,n.width),h=Math.min(h,n.height),l&&(f&&h?h*l>f?h=f/l:f=h*l:f?h=f/l:h&&(f=h*l),u*l>c?u=c/l:c=u*l),o.minWidth=Math.min(f,c),o.minHeight=Math.min(h,u),o.maxWidth=c,o.maxHeight=u}e&&(s?(o.minLeft=Math.max(0,r.left),o.minTop=Math.max(0,r.top),o.maxLeft=Math.min(n.width,r.left+r.width)-o.width,o.maxTop=Math.min(n.height,r.top+r.height)-o.height):(o.minLeft=0,o.minTop=0,o.maxLeft=n.width-o.width,o.maxTop=n.height-o.height))},renderCropBox:function(){var t=this.options,e=this.containerData,i=this.cropBoxData;(i.width>i.maxWidth||i.widthi.maxHeight||i.height=e.width&&i.height>=e.height?Oe:Zt),K(this.cropBox,D({width:i.width,height:i.height},vt({translateX:i.left,translateY:i.top}))),this.cropped&&this.limited&&this.limitCanvas(!0,!0),this.disabled||this.output()},output:function(){this.preview(),ht(this.element,zt,this.getData())}},Wi={initPreview:function(){var t=this.element,e=this.crossOrigin,i=this.options.preview,n=e?this.crossOriginUrl:this.url,r=t.alt||"The image to preview",o=document.createElement("img");if(e&&(o.crossOrigin=e),o.src=n,o.alt=r,this.viewBox.appendChild(o),this.viewBoxImage=o,!!i){var s=i;typeof i=="string"?s=t.ownerDocument.querySelectorAll(i):i.querySelector&&(s=[i]),this.previews=s,C(s,function(l){var f=document.createElement("img");gt(l,_t,{width:l.offsetWidth,height:l.offsetHeight,html:l.innerHTML}),e&&(f.crossOrigin=e),f.src=n,f.alt=r,f.style.cssText='display:block;width:100%;height:auto;min-width:0!important;min-height:0!important;max-width:none!important;max-height:none!important;image-orientation:0deg!important;"',l.innerHTML="",l.appendChild(f)})}},resetPreview:function(){C(this.previews,function(t){var e=Xt(t,_t);K(t,{width:e.width,height:e.height}),t.innerHTML=e.html,Ti(t,_t)})},preview:function(){var t=this.imageData,e=this.canvasData,i=this.cropBoxData,n=i.width,r=i.height,o=t.width,s=t.height,l=i.left-e.left-t.left,f=i.top-e.top-t.top;!this.cropped||this.disabled||(K(this.viewBoxImage,D({width:o,height:s},vt(D({translateX:-l,translateY:-f},t)))),C(this.previews,function(h){var c=Xt(h,_t),u=c.width,v=c.height,m=u,x=v,g=1;n&&(g=u/n,x=r*g),r&&x>v&&(g=v/r,m=n*g,x=v),K(h,{width:m,height:x}),K(h.getElementsByTagName("img")[0],D({width:o*g,height:s*g},vt(D({translateX:-l*g,translateY:-f*g},t))))}))}},Yi={bind:function(){var t=this.element,e=this.options,i=this.cropper;k(e.cropstart)&&B(t,Wt,e.cropstart),k(e.cropmove)&&B(t,Ht,e.cropmove),k(e.cropend)&&B(t,Pt,e.cropend),k(e.crop)&&B(t,zt,e.crop),k(e.zoom)&&B(t,Yt,e.zoom),B(i,le,this.onCropStart=this.cropStart.bind(this)),e.zoomable&&e.zoomOnWheel&&B(i,ve,this.onWheel=this.wheel.bind(this),{passive:!1,capture:!0}),e.toggleDragModeOnDblclick&&B(i,ce,this.onDblclick=this.dblclick.bind(this)),B(t.ownerDocument,fe,this.onCropMove=this.cropMove.bind(this)),B(t.ownerDocument,ue,this.onCropEnd=this.cropEnd.bind(this)),e.responsive&&B(window,pe,this.onResize=this.resize.bind(this))},unbind:function(){var t=this.element,e=this.options,i=this.cropper;k(e.cropstart)&&z(t,Wt,e.cropstart),k(e.cropmove)&&z(t,Ht,e.cropmove),k(e.cropend)&&z(t,Pt,e.cropend),k(e.crop)&&z(t,zt,e.crop),k(e.zoom)&&z(t,Yt,e.zoom),z(i,le,this.onCropStart),e.zoomable&&e.zoomOnWheel&&z(i,ve,this.onWheel,{passive:!1,capture:!0}),e.toggleDragModeOnDblclick&&z(i,ce,this.onDblclick),z(t.ownerDocument,fe,this.onCropMove),z(t.ownerDocument,ue,this.onCropEnd),e.responsive&&z(window,pe,this.onResize)}},Xi={resize:function(){if(!this.disabled){var t=this.options,e=this.container,i=this.containerData,n=e.offsetWidth/i.width,r=e.offsetHeight/i.height,o=Math.abs(n-1)>Math.abs(r-1)?n:r;if(o!==1){var s,l;t.restore&&(s=this.getCanvasData(),l=this.getCropBoxData()),this.render(),t.restore&&(this.setCanvasData(C(s,function(f,h){s[h]=f*o})),this.setCropBoxData(C(l,function(f,h){l[h]=f*o})))}}},dblclick:function(){this.disabled||this.options.dragMode===Re||this.setDragMode(Mi(this.dragBox,Lt)?Ce:Jt)},wheel:function(t){var e=this,i=Number(this.options.wheelZoomRatio)||.1,n=1;this.disabled||(t.preventDefault(),!this.wheeling&&(this.wheeling=!0,setTimeout(function(){e.wheeling=!1},50),t.deltaY?n=t.deltaY>0?1:-1:t.wheelDelta?n=-t.wheelDelta/120:t.detail&&(n=t.detail>0?1:-1),this.zoom(-n*i,t)))},cropStart:function(t){var e=t.buttons,i=t.button;if(!(this.disabled||(t.type==="mousedown"||t.type==="pointerdown"&&t.pointerType==="mouse")&&(b(e)&&e!==1||b(i)&&i!==0||t.ctrlKey))){var n=this.options,r=this.pointers,o;t.changedTouches?C(t.changedTouches,function(s){r[s.identifier]=Et(s)}):r[t.pointerId||0]=Et(t),Object.keys(r).length>1&&n.zoomable&&n.zoomOnTouch?o=Te:o=Xt(t.target,mt),!!vi.test(o)&&ht(this.element,Wt,{originalEvent:t,action:o})!==!1&&(t.preventDefault(),this.action=o,this.cropping=!1,o===Me&&(this.cropping=!0,A(this.dragBox,Mt)))}},cropMove:function(t){var e=this.action;if(!(this.disabled||!e)){var i=this.pointers;t.preventDefault(),ht(this.element,Ht,{originalEvent:t,action:e})!==!1&&(t.changedTouches?C(t.changedTouches,function(n){D(i[n.identifier]||{},Et(n,!0))}):D(i[t.pointerId||0]||{},Et(t,!0)),this.change(t))}},cropEnd:function(t){if(!this.disabled){var e=this.action,i=this.pointers;t.changedTouches?C(t.changedTouches,function(n){delete i[n.identifier]}):delete i[t.pointerId||0],e&&(t.preventDefault(),Object.keys(i).length||(this.action=""),this.cropping&&(this.cropping=!1,ot(this.dragBox,Mt,this.cropped&&this.options.modal)),ht(this.element,Pt,{originalEvent:t,action:e}))}}},Ui={change:function(t){var e=this.options,i=this.canvasData,n=this.containerData,r=this.cropBoxData,o=this.pointers,s=this.action,l=e.aspectRatio,f=r.left,h=r.top,c=r.width,u=r.height,v=f+c,m=h+u,x=0,g=0,_=n.width,T=n.height,O=!0,w;!l&&t.shiftKey&&(l=c&&u?c/u:1),this.limited&&(x=r.minLeft,g=r.minTop,_=x+Math.min(n.width,i.width,i.left+i.width),T=g+Math.min(n.height,i.height,i.top+i.height));var M=o[Object.keys(o)[0]],d={x:M.endX-M.startX,y:M.endY-M.startY},y=function(L){switch(L){case et:v+d.x>_&&(d.x=_-v);break;case it:f+d.xT&&(d.y=T-m);break}};switch(s){case Zt:f+=d.x,h+=d.y;break;case et:if(d.x>=0&&(v>=_||l&&(h<=g||m>=T))){O=!1;break}y(et),c+=d.x,c<0&&(s=it,c=-c,f-=c),l&&(u=c/l,h+=(r.height-u)/2);break;case F:if(d.y<=0&&(h<=g||l&&(f<=x||v>=_))){O=!1;break}y(F),u-=d.y,h+=d.y,u<0&&(s=rt,u=-u,h-=u),l&&(c=u*l,f+=(r.width-c)/2);break;case it:if(d.x<=0&&(f<=x||l&&(h<=g||m>=T))){O=!1;break}y(it),c-=d.x,f+=d.x,c<0&&(s=et,c=-c,f-=c),l&&(u=c/l,h+=(r.height-u)/2);break;case rt:if(d.y>=0&&(m>=T||l&&(f<=x||v>=_))){O=!1;break}y(rt),u+=d.y,u<0&&(s=F,u=-u,h-=u),l&&(c=u*l,f+=(r.width-c)/2);break;case ft:if(l){if(d.y<=0&&(h<=g||v>=_)){O=!1;break}y(F),u-=d.y,h+=d.y,c=u*l}else y(F),y(et),d.x>=0?v<_?c+=d.x:d.y<=0&&h<=g&&(O=!1):c+=d.x,d.y<=0?h>g&&(u-=d.y,h+=d.y):(u-=d.y,h+=d.y);c<0&&u<0?(s=pt,u=-u,c=-c,h-=u,f-=c):c<0?(s=ut,c=-c,f-=c):u<0&&(s=dt,u=-u,h-=u);break;case 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this.ready&&!this.cropped&&!this.disabled&&(this.cropped=!0,this.limitCropBox(!0,!0),this.options.modal&&A(this.dragBox,Mt),Y(this.cropBox,S),this.setCropBoxData(this.initialCropBoxData)),this},reset:function(){return this.ready&&!this.disabled&&(this.imageData=D({},this.initialImageData),this.canvasData=D({},this.initialCanvasData),this.cropBoxData=D({},this.initialCropBoxData),this.renderCanvas(),this.cropped&&this.renderCropBox()),this},clear:function(){return this.cropped&&!this.disabled&&(D(this.cropBoxData,{left:0,top:0,width:0,height:0}),this.cropped=!1,this.renderCropBox(),this.limitCanvas(!0,!0),this.renderCanvas(),Y(this.dragBox,Mt),A(this.cropBox,S)),this},replace:function(t){var e=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1;return!this.disabled&&t&&(this.isImg&&(this.element.src=t),e?(this.url=t,this.image.src=t,this.ready&&(this.viewBoxImage.src=t,C(this.previews,function(i){i.getElementsByTagName("img")[0].src=t}))):(this.isImg&&(this.replaced=!0),this.options.data=null,this.uncreate(),this.load(t))),this},enable:function(){return this.ready&&this.disabled&&(this.disabled=!1,Y(this.cropper,se)),this},disable:function(){return this.ready&&!this.disabled&&(this.disabled=!0,A(this.cropper,se)),this},destroy:function(){var t=this.element;return t[E]?(t[E]=void 0,this.isImg&&this.replaced&&(t.src=this.originalUrl),this.uncreate(),this):this},move:function(t){var e=arguments.length>1&&arguments[1]!==void 0?arguments[1]:t,i=this.canvasData,n=i.left,r=i.top;return this.moveTo(kt(t)?t:n+Number(t),kt(e)?e:r+Number(e))},moveTo:function(t){var e=arguments.length>1&&arguments[1]!==void 0?arguments[1]:t,i=this.canvasData,n=!1;return t=Number(t),e=Number(e),this.ready&&!this.disabled&&this.options.movable&&(b(t)&&(i.left=t,n=!0),b(e)&&(i.top=e,n=!0),n&&this.renderCanvas(!0)),this},zoom:function(t,e){var i=this.canvasData;return t=Number(t),t<0?t=1/(1-t):t=1+t,this.zoomTo(i.width*t/i.naturalWidth,null,e)},zoomTo:function(t,e,i){var n=this.options,r=this.canvasData,o=r.width,s=r.height,l=r.naturalWidth,f=r.naturalHeight;if(t=Number(t),t>=0&&this.ready&&!this.disabled&&n.zoomable){var h=l*t,c=f*t;if(ht(this.element,Yt,{ratio:t,oldRatio:o/l,originalEvent:i})===!1)return this;if(i){var u=this.pointers,v=Le(this.cropper),m=u&&Object.keys(u).length?Ai(u):{pageX:i.pageX,pageY:i.pageY};r.left-=(h-o)*((m.pageX-v.left-r.left)/o),r.top-=(c-s)*((m.pageY-v.top-r.top)/s)}else nt(e)&&b(e.x)&&b(e.y)?(r.left-=(h-o)*((e.x-r.left)/o),r.top-=(c-s)*((e.y-r.top)/s)):(r.left-=(h-o)/2,r.top-=(c-s)/2);r.width=h,r.height=c,this.renderCanvas(!0)}return this},rotate:function(t){return this.rotateTo((this.imageData.rotate||0)+Number(t))},rotateTo:function(t){return t=Number(t),b(t)&&this.ready&&!this.disabled&&this.options.rotatable&&(this.imageData.rotate=t%360,this.renderCanvas(!0,!0)),this},scaleX:function(t){var e=this.imageData.scaleY;return this.scale(t,b(e)?e:1)},scaleY:function(t){var e=this.imageData.scaleX;return this.scale(b(e)?e:1,t)},scale:function(t){var e=arguments.length>1&&arguments[1]!==void 0?arguments[1]:t,i=this.imageData,n=!1;return t=Number(t),e=Number(e),this.ready&&!this.disabled&&this.options.scalable&&(b(t)&&(i.scaleX=t,n=!0),b(e)&&(i.scaleY=e,n=!0),n&&this.renderCanvas(!0,!0)),this},getData:function(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!1,e=this.options,i=this.imageData,n=this.canvasData,r=this.cropBoxData,o;if(this.ready&&this.cropped){o={x:r.left-n.left,y:r.top-n.top,width:r.width,height:r.height};var s=i.width/i.naturalWidth;if(C(o,function(h,c){o[c]=h/s}),t){var l=Math.round(o.y+o.height),f=Math.round(o.x+o.width);o.x=Math.round(o.x),o.y=Math.round(o.y),o.width=f-o.x,o.height=l-o.y}}else o={x:0,y:0,width:0,height:0};return e.rotatable&&(o.rotate=i.rotate||0),e.scalable&&(o.scaleX=i.scaleX||1,o.scaleY=i.scaleY||1),o},setData:function(t){var e=this.options,i=this.imageData,n=this.canvasData,r={};if(this.ready&&!this.disabled&&nt(t)){var o=!1;e.rotatable&&b(t.rotate)&&t.rotate!==i.rotate&&(i.rotate=t.rotate,o=!0),e.scalable&&(b(t.scaleX)&&t.scaleX!==i.scaleX&&(i.scaleX=t.scaleX,o=!0),b(t.scaleY)&&t.scaleY!==i.scaleY&&(i.scaleY=t.scaleY,o=!0)),o&&this.renderCanvas(!0,!0);var s=i.width/i.naturalWidth;b(t.x)&&(r.left=t.x*s+n.left),b(t.y)&&(r.top=t.y*s+n.top),b(t.width)&&(r.width=t.width*s),b(t.height)&&(r.height=t.height*s),this.setCropBoxData(r)}return this},getContainerData:function(){return this.ready?D({},this.containerData):{}},getImageData:function(){return this.sized?D({},this.imageData):{}},getCanvasData:function(){var 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e=this.image;e.onload=null,e.onerror=null,e.parentNode.removeChild(e),this.image=null}},{key:"build",value:function(){if(!(!this.sized||this.ready)){var e=this.element,i=this.options,n=this.image,r=e.parentNode,o=document.createElement("div");o.innerHTML=wi;var 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diff --git a/spaces/Hypersonic0945/GenAISample/README.md b/spaces/Hypersonic0945/GenAISample/README.md
deleted file mode 100644
index 41dfe8a86e6eec724fc047a6661cc99c67edf360..0000000000000000000000000000000000000000
--- a/spaces/Hypersonic0945/GenAISample/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GenAISample
-emoji: 📊
-colorFrom: green
-colorTo: green
-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/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py
deleted file mode 100644
index eb756680fa7dc31a14ba45c216776a6d60c16b60..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/download_ted_and_extract.py
+++ /dev/null
@@ -1,338 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import itertools
-import os
-import csv
-from collections import defaultdict
-from six.moves import zip
-import io
-import wget
-import sys
-
-from subprocess import check_call, check_output
-
-# scripts and data locations
-CWD = os.getcwd()
-UTILS = f"{CWD}/utils"
-
-MOSES = f"{UTILS}/mosesdecoder"
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-# please donwload mosesdecoder here:
-detok_cmd = f'{MOSES}/scripts/tokenizer/detokenizer.perl'
-
-
-def call(cmd):
- print(f"Executing: {cmd}")
- check_call(cmd, shell=True)
-
-class MultiLingualAlignedCorpusReader(object):
- """A class to read TED talk dataset
- """
-
- def __init__(self, corpus_path, delimiter='\t',
- target_token=True, bilingual=True, corpus_type='file',
- lang_dict={'source': ['fr'], 'target': ['en']},
- eval_lang_dict=None, zero_shot=False,
- detok=True,
- ):
-
- self.empty_line_flag = 'NULL'
- self.corpus_path = corpus_path
- self.delimiter = delimiter
- self.bilingual = bilingual
- self.lang_dict = lang_dict
- self.lang_set = set()
- self.target_token = target_token
- self.zero_shot = zero_shot
- self.eval_lang_dict = eval_lang_dict
- self.corpus_type = corpus_type
- self.detok = detok
-
- for list_ in self.lang_dict.values():
- for lang in list_:
- self.lang_set.add(lang)
-
- self.data = dict()
- self.data['train'] = self.read_aligned_corpus(split_type='train')
- self.data['test'] = self.read_aligned_corpus(split_type='test')
- self.data['dev'] = self.read_aligned_corpus(split_type='dev')
-
- def read_data(self, file_loc_):
- data_list = list()
- with io.open(file_loc_, 'r', encoding='utf8') as fp:
- for line in fp:
- try:
- text = line.strip()
- except IndexError:
- text = self.empty_line_flag
- data_list.append(text)
- return data_list
-
- def filter_text(self, dict_):
- if self.target_token:
- field_index = 1
- else:
- field_index = 0
- data_dict = defaultdict(list)
- list1 = dict_['source']
- list2 = dict_['target']
- for sent1, sent2 in zip(list1, list2):
- try:
- src_sent = ' '.join(sent1.split()[field_index: ])
- except IndexError:
- src_sent = 'NULL'
-
- if src_sent.find(self.empty_line_flag) != -1 or len(src_sent) == 0:
- continue
-
- elif sent2.find(self.empty_line_flag) != -1 or len(sent2) == 0:
- continue
-
- else:
- data_dict['source'].append(sent1)
- data_dict['target'].append(sent2)
- return data_dict
-
- def read_file(self, split_type, data_type):
- return self.data[split_type][data_type]
-
- def save_file(self, path_, split_type, data_type, lang):
- tok_file = tok_file_name(path_, lang)
- with io.open(tok_file, 'w', encoding='utf8') as fp:
- for line in self.data[split_type][data_type]:
- fp.write(line + '\n')
- if self.detok:
- de_tok(tok_file, lang)
-
- def add_target_token(self, list_, lang_id):
- new_list = list()
- token = '__' + lang_id + '__'
- for sent in list_:
- new_list.append(token + ' ' + sent)
- return new_list
-
- def read_from_single_file(self, path_, s_lang, t_lang):
- data_dict = defaultdict(list)
- with io.open(path_, 'r', encoding='utf8') as fp:
- reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE)
- for row in reader:
- data_dict['source'].append(row[s_lang])
- data_dict['target'].append(row[t_lang])
-
- if self.target_token:
- text = self.add_target_token(data_dict['source'], t_lang)
- data_dict['source'] = text
-
- return data_dict['source'], data_dict['target']
-
- def read_aligned_corpus(self, split_type='train'):
- data_dict = defaultdict(list)
- iterable = []
- s_list = []
- t_list = []
-
- if self.zero_shot:
- if split_type == "train":
- iterable = zip(self.lang_dict['source'], self.lang_dict['target'])
- else:
- iterable = zip(self.eval_lang_dict['source'], self.eval_lang_dict['target'])
-
- elif self.bilingual:
- iterable = itertools.product(self.lang_dict['source'], self.lang_dict['target'])
-
- for s_lang, t_lang in iterable:
- if s_lang == t_lang:
- continue
- if self.corpus_type == 'file':
- split_type_file_path = os.path.join(self.corpus_path,
- "all_talks_{}.tsv".format(split_type))
- s_list, t_list = self.read_from_single_file(split_type_file_path,
- s_lang=s_lang,
- t_lang=t_lang)
- data_dict['source'] += s_list
- data_dict['target'] += t_list
- new_data_dict = self.filter_text(data_dict)
- return new_data_dict
-
-
-def read_langs(corpus_path):
- split_type_file_path = os.path.join(corpus_path, 'extracted',
- "all_talks_dev.tsv")
- with io.open(split_type_file_path, 'r', encoding='utf8') as fp:
- reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE)
- header = next(reader)
- return [k for k in header.keys() if k != 'talk_name']
-
-def extra_english(corpus_path, split):
- split_type_file_path = os.path.join(corpus_path,
- f"all_talks_{split}.tsv")
- output_split_type_file_path = os.path.join(corpus_path,
- f"all_talks_{split}.en")
- with io.open(split_type_file_path, 'r', encoding='utf8') as fp, io.open(output_split_type_file_path, 'w', encoding='utf8') as fw:
- reader = csv.DictReader(fp, delimiter='\t', quoting=csv.QUOTE_NONE)
- for row in reader:
- line = row['en']
- fw.write(line + '\n')
- de_tok(output_split_type_file_path, 'en')
-
-
-
-def tok_file_name(filename, lang):
- seps = filename.split('.')
- seps.insert(-1, 'tok')
- tok_file = '.'.join(seps)
- return tok_file
-
-def de_tok(tok_file, lang):
- # seps = tok_file.split('.')
- # seps.insert(-1, 'detok')
- # de_tok_file = '.'.join(seps)
- de_tok_file = tok_file.replace('.tok.', '.')
- cmd = 'perl {detok_cmd} -l {lang} < {tok_file} > {de_tok_file}'.format(
- detok_cmd=detok_cmd, tok_file=tok_file,
- de_tok_file=de_tok_file, lang=lang[:2])
- call(cmd)
-
-def extra_bitex(
- ted_data_path,
- lsrc_lang,
- ltrg_lang,
- target_token,
- output_data_path,
-):
- def get_ted_lang(lang):
- long_langs = ['pt-br', 'zh-cn', 'zh-tw', 'fr-ca']
- if lang[:5] in long_langs:
- return lang[:5]
- elif lang[:4] =='calv':
- return lang[:5]
- elif lang in ['pt_BR', 'zh_CN', 'zh_TW', 'fr_CA']:
- return lang.lower().replace('_', '-')
- return lang[:2]
- src_lang = get_ted_lang(lsrc_lang)
- trg_lang = get_ted_lang(ltrg_lang)
- train_lang_dict={'source': [src_lang], 'target': [trg_lang]}
- eval_lang_dict = {'source': [src_lang], 'target': [trg_lang]}
-
- obj = MultiLingualAlignedCorpusReader(corpus_path=ted_data_path,
- lang_dict=train_lang_dict,
- target_token=target_token,
- corpus_type='file',
- eval_lang_dict=eval_lang_dict,
- zero_shot=False,
- bilingual=True)
-
- os.makedirs(output_data_path, exist_ok=True)
- lsrc_lang = lsrc_lang.replace('-', '_')
- ltrg_lang = ltrg_lang.replace('-', '_')
- obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}",
- split_type='train', data_type='source', lang=src_lang)
- obj.save_file(output_data_path + f"/train.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}",
- split_type='train', data_type='target', lang=trg_lang)
-
- obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}",
- split_type='test', data_type='source', lang=src_lang)
- obj.save_file(output_data_path + f"/test.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}",
- split_type='test', data_type='target', lang=trg_lang)
-
- obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{lsrc_lang}",
- split_type='dev', data_type='source', lang=src_lang)
- obj.save_file(output_data_path + f"/valid.{lsrc_lang}-{ltrg_lang}.{ltrg_lang}",
- split_type='dev', data_type='target', lang=trg_lang)
-
-
-def bar_custom(current, total, width=80):
- print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r')
-
-
-def download_and_extract(download_to, extract_to):
- url = 'http://phontron.com/data/ted_talks.tar.gz'
- filename = f"{download_to}/ted_talks.tar.gz"
- if os.path.exists(filename):
- print(f'{filename} has already been downloaded so skip')
- else:
- filename = wget.download(url, filename, bar=bar_custom)
- if os.path.exists(f'{extract_to}/all_talks_train.tsv'):
- print(f'Already extracted so skip')
- else:
- extract_cmd = f'tar xzfv "{filename}" -C "{extract_to}"'
- call(extract_cmd)
-
-
-if __name__ == "__main__":
- import argparse
- parser = argparse.ArgumentParser()
- parser.add_argument('--ted_data_path', type=str, default=WORKDIR_ROOT, required=False)
- parser.add_argument(
- '--direction-list',
- type=str,
- # default=None,
- #for ML50
- default=(
- "bn_IN-en_XX,he_IL-en_XX,fa_IR-en_XX,id_ID-en_XX,sv_SE-en_XX,pt_XX-en_XX,ka_GE-en_XX,ka_GE-en_XX,th_TH-en_XX,"
- "mr_IN-en_XX,hr_HR-en_XX,uk_UA-en_XX,az_AZ-en_XX,mk_MK-en_XX,gl_ES-en_XX,sl_SI-en_XX,mn_MN-en_XX,"
- #non-english directions
- # "fr_XX-de_DE," # replaced with wmt20
- # "ja_XX-ko_KR,es_XX-pt_XX,ru_RU-sv_SE,hi_IN-bn_IN,id_ID-ar_AR,cs_CZ-pl_PL,ar_AR-tr_TR"
- ),
- required=False)
- parser.add_argument('--target-token', action='store_true', default=False)
- parser.add_argument('--extract-all-english', action='store_true', default=False)
-
- args = parser.parse_args()
-
- import sys
- import json
-
- # TED Talks data directory
- ted_data_path = args.ted_data_path
-
- download_to = f'{ted_data_path}/downloads'
- extract_to = f'{ted_data_path}/extracted'
-
- #DESTDIR=${WORKDIR_ROOT}/ML50/raw/
- output_path = f'{ted_data_path}/ML50/raw'
- os.makedirs(download_to, exist_ok=True)
- os.makedirs(extract_to, exist_ok=True)
- os.makedirs(output_path, exist_ok=True)
- download_and_extract(download_to, extract_to)
-
-
- if args.extract_all_english:
- for split in ['train', 'dev', 'test']:
- extra_english(ted_data_path, split)
- exit(0)
- if args.direction_list is not None:
- directions = args.direction_list.strip().split(',')
- directions = [tuple(d.strip().split('-', 1)) for d in directions if d]
- else:
- langs = read_langs(ted_data_path)
- # directions = [
- # '{}.{}'.format(src, tgt)
- # for src in langs
- # for tgt in langs
- # if src < tgt
- # ]
- directions = [('en', tgt) for tgt in langs if tgt != 'en']
- print(f'num directions={len(directions)}: {directions}')
-
- for src_lang, trg_lang in directions:
- print('--working on {}-{}'.format(src_lang, trg_lang))
- extra_bitex(
- extract_to,
- src_lang,
- trg_lang,
- target_token=args.target_token,
- output_data_path=output_path
- )
diff --git a/spaces/ICML2022/OFA/fairseq/examples/noisychannel/rerank_score_lm.py b/spaces/ICML2022/OFA/fairseq/examples/noisychannel/rerank_score_lm.py
deleted file mode 100644
index e80948d78b02561cbd09d72c319222105f41f6bb..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/noisychannel/rerank_score_lm.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-
-from fairseq import options
-
-from examples.noisychannel import rerank_options, rerank_utils
-
-
-def score_lm(args):
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- args.shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
-
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file, bpe_symbol=args.post_process, nbest=using_nbest
- )
-
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- if args.language_model is not None and not os.path.isfile(lm_score_file):
- print("STEP 4.5: language modeling for P(T)")
- if args.lm_bpe_code is None:
- bpe_status = "no bpe"
- elif args.lm_bpe_code == "shared":
- bpe_status = "shared"
- else:
- bpe_status = "different"
-
- rerank_utils.lm_scoring(
- lm_preprocessed_dir,
- bpe_status,
- gen_output,
- pre_gen,
- args.lm_dict,
- args.lm_name,
- args.language_model,
- args.lm_bpe_code,
- 128,
- lm_score_file,
- args.target_lang,
- args.source_lang,
- prefix_len=args.prefix_len,
- )
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- score_lm(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py
deleted file mode 100644
index 49e2ca498bf67ad226af5de796b9f44afa65198d..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/text.py
+++ /dev/null
@@ -1,107 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-import numpy as np
-import re
-from . import cleaners
-from .symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-# Regular expression matching text enclosed in curly braces:
-_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
-
-# Special symbols
-SOS_TOK = ''
-EOS_TOK = ' '
-
-def text_to_sequence(text, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
- The text can optionally have ARPAbet sequences enclosed in curly braces embedded
- in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
-
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- sequence = []
-
- # Check for curly braces and treat their contents as ARPAbet:
- while len(text):
- m = _curly_re.match(text)
- if not m:
- sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
- break
- sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
- sequence += _arpabet_to_sequence(m.group(2))
- text = m.group(3)
-
- return sequence
-
-
-def sample_code_chunk(code, size):
- assert(size > 0 and size <= len(code))
- start = np.random.randint(len(code) - size + 1)
- end = start + size
- return code[start:end], start, end
-
-
-def code_to_sequence(code, code_dict, collapse_code):
- if collapse_code:
- prev_c = None
- sequence = []
- for c in code:
- if c in code_dict and c != prev_c:
- sequence.append(code_dict[c])
- prev_c = c
- else:
- sequence = [code_dict[c] for c in code if c in code_dict]
- if len(sequence) < 0.95 * len(code):
- print('WARNING : over 5%% codes are OOV')
-
- return sequence
-
-
-def sequence_to_text(sequence):
- '''Converts a sequence of IDs back to a string'''
- result = ''
- for symbol_id in sequence:
- if symbol_id in _id_to_symbol:
- s = _id_to_symbol[symbol_id]
- # Enclose ARPAbet back in curly braces:
- if len(s) > 1 and s[0] == '@':
- s = '{%s}' % s[1:]
- result += s
- return result.replace('}{', ' ')
-
-
-def sequence_to_code(sequence, code_dict):
- '''Analogous to sequence_to_text'''
- id_to_code = {i: c for c, i in code_dict.items()}
- return ' '.join([id_to_code[i] for i in sequence])
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
-
-
-def _symbols_to_sequence(symbols):
- return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
-
-
-def _arpabet_to_sequence(text):
- return _symbols_to_sequence(['@' + s for s in text.split()])
-
-
-def _should_keep_symbol(s):
- return s in _symbol_to_id and s != '_' and s != '~'
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/unfold.py b/spaces/ICML2022/OFA/fairseq/fairseq/modules/unfold.py
deleted file mode 100644
index 138272f1ef4f673b29e36aed4531106f7ce95968..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/unfold.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch.nn.functional as F
-
-
-def unfold1d(x, kernel_size, padding_l, pad_value=0):
- """unfold T x B x C to T x B x C x K"""
- if kernel_size > 1:
- T, B, C = x.size()
- x = F.pad(
- x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value
- )
- x = x.as_strided((T, B, C, kernel_size), (B * C, C, 1, B * C))
- else:
- x = x.unsqueeze(3)
- return x
diff --git a/spaces/IISRFactCheck/claim_detection/code/args.py b/spaces/IISRFactCheck/claim_detection/code/args.py
deleted file mode 100644
index 658bdb7dc7efffadb786705742097bd4c0fbb047..0000000000000000000000000000000000000000
--- a/spaces/IISRFactCheck/claim_detection/code/args.py
+++ /dev/null
@@ -1,21 +0,0 @@
-class args():
- DATA_PATH = "../Dataset/"
- SAVE_MODEL_PATH = "model/"
-
- #pre_model_name = "bert-base-chinese"
- #pre_model_name = "hfl/chinese-macbert-base"
- pre_model_name = "model/chinese-roberta-wwm-ext"
- save_model_name = "roberta_crf"
-
- LOG_DIR = "../log/long_term/"+save_model_name+"/"
-
- use_crf = False
- label_dict = {"O":0, "B":1, "I":2}
- epoch_num = 10
- batch_size = 2
- label_size = 3
- max_length = 512
-
-class config():
- hidden_dropout_prob = 0.1
- hidden_size = 768
\ No newline at end of file
diff --git a/spaces/ILyaz03/My_Personal_Teacher/README.md b/spaces/ILyaz03/My_Personal_Teacher/README.md
deleted file mode 100644
index b605aeece8121a603348160d202f5dbc21ddf22d..0000000000000000000000000000000000000000
--- a/spaces/ILyaz03/My_Personal_Teacher/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ILyaz
-emoji: 🔥
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
-duplicated_from: ILyaz03/DhikrLabs_Ai
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/INDONESIA-AI/Lobe/README.md b/spaces/INDONESIA-AI/Lobe/README.md
deleted file mode 100644
index f978f883de1e1e803696a5231a56cd6f0b24b35e..0000000000000000000000000000000000000000
--- a/spaces/INDONESIA-AI/Lobe/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Lobe
-emoji: 🔥
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/app.py b/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/app.py
deleted file mode 100644
index 57741a3a79e2513833ad0e43bec95d27bbbfb6e3..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/app.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import os
-os.system('cd monotonic_align && python setup.py build_ext --inplace && cd ..')
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import argparse
-import commons
-from mel_processing import spectrogram_torch
-import utils
-from models import SynthesizerTrn
-import gradio as gr
-import librosa
-import webbrowser
-
-from text import text_to_sequence, _clean_text
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-language_marks = {
- "Japanese": "",
- "日本語": "[JA]",
- "简体中文": "[ZH]",
- "English": "[EN]",
- "Mix": "",
-}
-lang = ['日本語', '简体中文', 'English', 'Mix']
-def get_text(text, hps, is_symbol):
- text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
- if hps.data.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = LongTensor(text_norm)
- return text_norm
-
-def create_tts_fn(model, hps, speaker_ids):
- def tts_fn(text, speaker, language, speed):
- if language is not None:
- text = language_marks[language] + text + language_marks[language]
- speaker_id = speaker_ids[speaker]
- stn_tst = get_text(text, hps, False)
- with no_grad():
- x_tst = stn_tst.unsqueeze(0).to(device)
- x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
- sid = LongTensor([speaker_id]).to(device)
- audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
- length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
- del stn_tst, x_tst, x_tst_lengths, sid
- return "Success", (hps.data.sampling_rate, audio)
-
- return tts_fn
-
-def create_vc_fn(model, hps, speaker_ids):
- def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
- input_audio = record_audio if record_audio is not None else upload_audio
- if input_audio is None:
- return "You need to record or upload an audio", None
- sampling_rate, audio = input_audio
- original_speaker_id = speaker_ids[original_speaker]
- target_speaker_id = speaker_ids[target_speaker]
-
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != hps.data.sampling_rate:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
- with no_grad():
- y = torch.FloatTensor(audio)
- y = y / max(-y.min(), y.max()) / 0.99
- y = y.to(device)
- y = y.unsqueeze(0)
- spec = spectrogram_torch(y, hps.data.filter_length,
- hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
- center=False).to(device)
- spec_lengths = LongTensor([spec.size(-1)]).to(device)
- sid_src = LongTensor([original_speaker_id]).to(device)
- sid_tgt = LongTensor([target_speaker_id]).to(device)
- audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
- 0, 0].data.cpu().float().numpy()
- del y, spec, spec_lengths, sid_src, sid_tgt
- return "Success", (hps.data.sampling_rate, audio)
-
- return vc_fn
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model")
- parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file")
- parser.add_argument("--share", default=False, help="make link public (used in colab)")
-
- args = parser.parse_args()
- hps = utils.get_hparams_from_file(args.config_dir)
-
-
- net_g = SynthesizerTrn(
- len(hps.symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(device)
- _ = net_g.eval()
-
- _ = utils.load_checkpoint(args.model_dir, net_g, None)
- speaker_ids = hps.speakers
- speakers = list(hps.speakers.keys())
- tts_fn = create_tts_fn(net_g, hps, speaker_ids)
- vc_fn = create_vc_fn(net_g, hps, speaker_ids)
- app = gr.Blocks()
- with app:
- gr.Markdown(
- "# VITS nymph语音在线合成demo\n"
- "# 严禁将模型用于任何商业项目，否则后果自负喵\n"
- ''
- )
-
- with gr.Tab("Text-to-Speech"):
- with gr.Row():
- with gr.Column():
- textbox = gr.TextArea(label="Text",
- placeholder="Type your sentence here",
- value="こにちは", elem_id=f"tts-input")
- # select character
- char_dropdown = gr.Dropdown(choices=speakers, value=speakers[-1], label='character')
- language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
- duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
- label='速度 Speed')
- with gr.Column():
- text_output = gr.Textbox(label="Message")
- audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
- btn = gr.Button("Generate!")
- btn.click(tts_fn,
- inputs=[textbox, char_dropdown, language_dropdown, duration_slider,],
- outputs=[text_output, audio_output])
- # with gr.Tab("Voice Conversion"):
- # gr.Markdown("""
- # 录制或上传声音，并选择要转换的音色。
- # """)
- # with gr.Column():
- # record_audio = gr.Audio(label="record your voice", source="microphone")
- # upload_audio = gr.Audio(label="or upload audio here", source="upload")
- # source_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="source speaker")
- # target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker")
- # with gr.Column():
- # message_box = gr.Textbox(label="Message")
- # converted_audio = gr.Audio(label='converted audio')
- # btn = gr.Button("Convert!")
- # btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio],
- # outputs=[message_box, converted_audio])
- webbrowser.open("http://127.0.0.1:7860")
- app.launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op/conv2d_gradfix.py b/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op/conv2d_gradfix.py
deleted file mode 100644
index 5e4b83adac8e6a4b1caf522596666e4f5d0ee854..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/stylegan/op/conv2d_gradfix.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import contextlib
-import warnings
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-
-enabled = True
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
- global weight_gradients_disabled
-
- old = weight_gradients_disabled
- weight_gradients_disabled = True
- yield
- weight_gradients_disabled = old
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
- if could_use_op(input):
- return conv2d_gradfix(
- transpose=False,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=0,
- dilation=dilation,
- groups=groups,
- ).apply(input, weight, bias)
-
- return F.conv2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- )
-
-
-def conv_transpose2d(
- input,
- weight,
- bias=None,
- stride=1,
- padding=0,
- output_padding=0,
- groups=1,
- dilation=1,
-):
- if could_use_op(input):
- return conv2d_gradfix(
- transpose=True,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- groups=groups,
- dilation=dilation,
- ).apply(input, weight, bias)
-
- return F.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- dilation=dilation,
- groups=groups,
- )
-
-
-def could_use_op(input):
- if (not enabled) or (not torch.backends.cudnn.enabled):
- return False
-
- if input.device.type != "cuda":
- return False
-
- if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
- return True
-
- #warnings.warn(
- # f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
- #)
-
- return False
-
-
-def ensure_tuple(xs, ndim):
- xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-
- return xs
-
-
-conv2d_gradfix_cache = dict()
-
-
-def conv2d_gradfix(
- transpose, weight_shape, stride, padding, output_padding, dilation, groups
-):
- ndim = 2
- weight_shape = tuple(weight_shape)
- stride = ensure_tuple(stride, ndim)
- padding = ensure_tuple(padding, ndim)
- output_padding = ensure_tuple(output_padding, ndim)
- dilation = ensure_tuple(dilation, ndim)
-
- key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
- if key in conv2d_gradfix_cache:
- return conv2d_gradfix_cache[key]
-
- common_kwargs = dict(
- stride=stride, padding=padding, dilation=dilation, groups=groups
- )
-
- def calc_output_padding(input_shape, output_shape):
- if transpose:
- return [0, 0]
-
- return [
- input_shape[i + 2]
- - (output_shape[i + 2] - 1) * stride[i]
- - (1 - 2 * padding[i])
- - dilation[i] * (weight_shape[i + 2] - 1)
- for i in range(ndim)
- ]
-
- class Conv2d(autograd.Function):
- @staticmethod
- def forward(ctx, input, weight, bias):
- if not transpose:
- out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
- else:
- out = F.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- output_padding=output_padding,
- **common_kwargs,
- )
-
- ctx.save_for_backward(input, weight)
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- input, weight = ctx.saved_tensors
- grad_input, grad_weight, grad_bias = None, None, None
-
- if ctx.needs_input_grad[0]:
- p = calc_output_padding(
- input_shape=input.shape, output_shape=grad_output.shape
- )
- grad_input = conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs,
- ).apply(grad_output, weight, None)
-
- if ctx.needs_input_grad[1] and not weight_gradients_disabled:
- grad_weight = Conv2dGradWeight.apply(grad_output, input)
-
- if ctx.needs_input_grad[2]:
- grad_bias = grad_output.sum((0, 2, 3))
-
- return grad_input, grad_weight, grad_bias
-
- class Conv2dGradWeight(autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input):
- op = torch._C._jit_get_operation(
- "aten::cudnn_convolution_backward_weight"
- if not transpose
- else "aten::cudnn_convolution_transpose_backward_weight"
- )
- flags = [
- torch.backends.cudnn.benchmark,
- torch.backends.cudnn.deterministic,
- torch.backends.cudnn.allow_tf32,
- ]
- grad_weight = op(
- weight_shape,
- grad_output,
- input,
- padding,
- stride,
- dilation,
- groups,
- *flags,
- )
- ctx.save_for_backward(grad_output, input)
-
- return grad_weight
-
- @staticmethod
- def backward(ctx, grad_grad_weight):
- grad_output, input = ctx.saved_tensors
- grad_grad_output, grad_grad_input = None, None
-
- if ctx.needs_input_grad[0]:
- grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)
-
- if ctx.needs_input_grad[1]:
- p = calc_output_padding(
- input_shape=input.shape, output_shape=grad_output.shape
- )
- grad_grad_input = conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs,
- ).apply(grad_output, grad_grad_weight, None)
-
- return grad_grad_output, grad_grad_input
-
- conv2d_gradfix_cache[key] = Conv2d
-
- return Conv2d
diff --git a/spaces/JeffJing/ZookChatBot/revChatGPT/__init__.py b/spaces/JeffJing/ZookChatBot/revChatGPT/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/web_assets/javascript/ChuanhuChat.js b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/web_assets/javascript/ChuanhuChat.js
deleted file mode 100644
index 1128b7782111381f4540282db574ba951e65f2f1..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/web_assets/javascript/ChuanhuChat.js
+++ /dev/null
@@ -1,328 +0,0 @@
-
-// ChuanhuChat core javascript
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var chatbotIndicator = null;
-var chatbotWrap = null;
-var apSwitch = null;
-var messageBotDivs = null;
-var loginUserForm = null;
-var logginUser = null;
-var updateToast = null;
-var sendBtn = null;
-var cancelBtn = null;
-var sliders = null;
-var updateChuanhuBtn = null;
-var statusDisplay = null;
-
-var isInIframe = (window.self !== window.top);
-var currentTime = new Date().getTime();
-var initialized = false;
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
- for (var i = 0; i < mutations.length; i++) {
- if (mutations[i].addedNodes.length) {
- if (initialized) {
- observer.disconnect(); // 停止监听
- return;
- }
- initialize();
- }
- }
-}
-
-function initialize() {
- var needInit = {gradioContainer, apSwitch, user_input_tb, userInfoDiv, appTitleDiv, chatbot, chatbotIndicator, chatbotWrap, statusDisplay, sliders, updateChuanhuBtn};
- initialized = true;
-
- loginUserForm = gradioApp().querySelector(".gradio-container > .main > .wrap > .panel > .form")
- gradioContainer = gradioApp().querySelector(".gradio-container");
- user_input_tb = gradioApp().getElementById('user-input-tb');
- userInfoDiv = gradioApp().getElementById("user-info");
- appTitleDiv = gradioApp().getElementById("app-title");
- chatbot = gradioApp().querySelector('#chuanhu-chatbot');
- chatbotIndicator = gradioApp().querySelector('#chuanhu-chatbot>div.wrap');
- chatbotWrap = gradioApp().querySelector('#chuanhu-chatbot > .wrapper > .wrap');
- apSwitch = gradioApp().querySelector('.apSwitch input[type="checkbox"]');
- updateToast = gradioApp().querySelector("#toast-update");
- sendBtn = gradioApp().getElementById("submit-btn");
- cancelBtn = gradioApp().getElementById("cancel-btn");
- sliders = gradioApp().querySelectorAll('input[type="range"]');
- updateChuanhuBtn = gradioApp().getElementById("update-chuanhu-btn");
- statusDisplay = gradioApp().querySelector('#status-display');
-
- if (loginUserForm) {
- localStorage.setItem("userLogged", true);
- userLogged = true;
- }
-
- for (let elem in needInit) {
- if (needInit[elem] == null) {
- initialized = false;
- return;
- }
- }
-
- if (initialized) {
- adjustDarkMode();
- selectHistory();
- setTimeout(showOrHideUserInfo(), 2000);
- setChatbotHeight();
- setChatbotScroll();
- setSlider();
- setAvatar();
- if (!historyLoaded) loadHistoryHtml();
- if (!usernameGotten) getUserInfo();
- chatbotObserver.observe(chatbotIndicator, { attributes: true });
-
- const lastCheckTime = localStorage.getItem('lastCheckTime') || 0;
- const longTimeNoCheck = currentTime - lastCheckTime > 3 * 24 * 60 * 60 * 1000;
- if (longTimeNoCheck && !updateInfoGotten && !isLatestVersion || isLatestVersion && !updateInfoGotten) {
- updateLatestVersion();
- }
- }
-}
-
-function gradioApp() {
- const elems = document.getElementsByTagName('gradio-app');
- const elem = elems.length == 0 ? document : elems[0];
-
- if (elem !== document) {
- elem.getElementById = function(id) {
- return document.getElementById(id);
- };
- }
- return elem.shadowRoot ? elem.shadowRoot : elem;
-}
-
-function showConfirmationDialog(a, file, c) {
- if (file != "") {
- var result = confirm(i18n(deleteConfirm_i18n_pref) + file + i18n(deleteConfirm_i18n_suff));
- if (result) {
- return [a, file, c];
- }
- }
- return [a, "CANCELED", c];
-}
-
-function selectHistory() {
- user_input_ta = user_input_tb.querySelector("textarea");
- if (user_input_ta) {
- disableSendBtn();
- // 在 textarea 上监听 keydown 事件
- user_input_ta.addEventListener("keydown", function (event) {
- var value = user_input_ta.value.trim();
- // 判断按下的是否为方向键
- if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
- // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
- if (value && key_down_history.indexOf(value) === -1)
- return;
- // 对于需要响应的动作，阻止默认行为。
- event.preventDefault();
- var length = key_down_history.length;
- if (length === 0) {
- currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
- return;
- }
- if (currentIndex === -1) {
- currentIndex = length;
- }
- if (event.code === 'ArrowUp' && currentIndex > 0) {
- currentIndex--;
- user_input_ta.value = key_down_history[currentIndex];
- } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
- currentIndex++;
- user_input_ta.value = key_down_history[currentIndex];
- }
- user_input_ta.selectionStart = user_input_ta.value.length;
- user_input_ta.selectionEnd = user_input_ta.value.length;
- const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
- user_input_ta.dispatchEvent(input_event);
- } else if (event.code === "Enter") {
- if (value) {
- currentIndex = -1;
- if (key_down_history.indexOf(value) === -1) {
- key_down_history.push(value);
- if (key_down_history.length > MAX_HISTORY_LENGTH) {
- key_down_history.shift();
- }
- }
- }
- }
- });
- }
-}
-
-function disableSendBtn() {
- sendBtn.disabled = user_input_ta.value.trim() === '';
- user_input_ta.addEventListener('input', () => {
- sendBtn.disabled = user_input_ta.value.trim() === '';
- });
-}
-
-function adjustDarkMode() {
- function toggleDarkMode(isEnabled) {
- if (isEnabled) {
- document.body.classList.add("dark");
- document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
- } else {
- document.body.classList.remove("dark");
- document.body.style.backgroundColor = "";
- }
- }
-
- const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
- apSwitch.checked = darkModeQuery.matches;
- toggleDarkMode(darkModeQuery.matches);
- darkModeQuery.addEventListener("change", (e) => {
- apSwitch.checked = e.matches;
- toggleDarkMode(e.matches);
- });
- apSwitch.addEventListener("change", (e) => {
- toggleDarkMode(e.target.checked);
- });
-}
-
-function setChatbotHeight() {
- const screenWidth = window.innerWidth;
- const statusDisplay = document.querySelector('#status-display');
- const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
- const vh = window.innerHeight * 0.01;
- document.documentElement.style.setProperty('--vh', `${vh}px`);
- if (isInIframe) {
- chatbot.style.height = `700px`;
- chatbotWrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
- } else {
- if (screenWidth <= 320) {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
- chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- } else if (screenWidth <= 499) {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
- chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- } else {
- chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
- chatbotWrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
- }
- }
-}
-function setChatbotScroll() {
- var scrollHeight = chatbotWrap.scrollHeight;
- chatbotWrap.scrollTo(0,scrollHeight)
-}
-
-var botAvatarUrl = "";
-var userAvatarUrl = "";
-function setAvatar() {
- var botAvatar = gradioApp().getElementById("config-bot-avatar-url").innerText;
- var userAvatar = gradioApp().getElementById("config-user-avatar-url").innerText;
-
- if (botAvatar == "none") {
- botAvatarUrl = "";
- } else if (isImgUrl(botAvatar)) {
- botAvatarUrl = botAvatar;
- } else {
- // botAvatarUrl = "https://github.com/GaiZhenbiao/ChuanhuChatGPT/assets/70903329/aca3a7ec-4f1d-4667-890c-a6f47bf08f63";
- botAvatarUrl = "/file=web_assets/chatbot.png"
- }
-
- if (userAvatar == "none") {
- userAvatarUrl = "";
- } else if (isImgUrl(userAvatar)) {
- userAvatarUrl = userAvatar;
- } else {
- userAvatarUrl = "data:image/svg+xml,%3Csvg width='32px' height='32px' viewBox='0 0 32 32' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'%3E%3Cg stroke='none' stroke-width='1' fill='none' fill-rule='evenodd'%3E%3Crect fill-opacity='0.5' fill='%23bbbbbb' x='0' y='0' width='32' height='32'%3E%3C/rect%3E%3Cg transform='translate(5, 4)' fill='%23999999' fill-opacity='0.8' fill-rule='nonzero'%3E%3Cpath d='M2.29372246,24 L19.7187739,24 C20.4277609,24 20.985212,23.8373915 21.3911272,23.5121746 C21.7970424,23.1869576 22,22.7418004 22,22.1767029 C22,21.3161536 21.7458721,20.4130827 21.2376163,19.4674902 C20.7293605,18.5218977 19.9956681,17.6371184 19.036539,16.8131524 C18.07741,15.9891863 16.9210688,15.3177115 15.5675154,14.798728 C14.2139621,14.2797445 12.6914569,14.0202527 11,14.0202527 C9.30854307,14.0202527 7.78603793,14.2797445 6.43248458,14.798728 C5.07893122,15.3177115 3.92259002,15.9891863 2.96346097,16.8131524 C2.00433193,17.6371184 1.27063951,18.5218977 0.762383704,19.4674902 C0.254127901,20.4130827 0,21.3161536 0,22.1767029 C0,22.7418004 0.202957595,23.1869576 0.608872784,23.5121746 C1.01478797,23.8373915 1.57640453,24 2.29372246,24 Z M11.0124963,11.6521659 C11.9498645,11.6521659 12.8155943,11.3906214 13.6096856,10.8675324 C14.403777,10.3444433 15.042131,9.63605539 15.5247478,8.74236856 C16.0073646,7.84868174 16.248673,6.84722464 16.248673,5.73799727 C16.248673,4.65135034 16.0071492,3.67452644 15.5241015,2.80752559 C15.0410538,1.94052474 14.4024842,1.25585359 13.6083929,0.753512156 C12.8143016,0.251170719 11.9490027,0 11.0124963,0 C10.0759899,0 9.20860836,0.255422879 8.41035158,0.766268638 C7.6120948,1.2771144 6.97352528,1.96622098 6.49464303,2.8335884 C6.01576078,3.70095582 5.77631966,4.67803631 5.77631966,5.76482987 C5.77631966,6.86452653 6.01554533,7.85912886 6.49399667,8.74863683 C6.97244801,9.63814481 7.60871935,10.3444433 8.40281069,10.8675324 C9.19690203,11.3906214 10.0667972,11.6521659 11.0124963,11.6521659 Z'%3E%3C/path%3E%3C/g%3E%3C/g%3E%3C/svg%3E";
- }
-}
-
-function clearChatbot() {
- clearHistoryHtml();
- clearMessageRows();
-}
-
-function chatbotContentChanged(attempt = 1) {
- for (var i = 0; i < attempt; i++) {
- setTimeout(() => {
- // clearMessageRows();
- saveHistoryHtml();
- disableSendBtn();
- gradioApp().querySelectorAll('#chuanhu-chatbot .message-wrap .message.user').forEach((userElement) => {addAvatars(userElement, 'user')});
- gradioApp().querySelectorAll('#chuanhu-chatbot .message-wrap .message.bot').forEach((botElement) => {addAvatars(botElement, 'bot'); addChuanhuButton(botElement)});
- }, i === 0 ? 0 : 500);
- }
- // 理论上是不需要多次尝试执行的，可惜gradio的bug导致message可能没有渲染完毕，所以尝试500ms后再次执行
-}
-
-var chatbotObserver = new MutationObserver(() => {
- clearMessageRows();
- chatbotContentChanged(1);
- if (chatbotIndicator.classList.contains('hide')) {
- chatbotContentChanged(2);
- }
-});
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
- gradioLoaded(mutations);
-});
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
- const ga = document.getElementsByTagName("gradio-app");
- observer.observe(ga[0], { childList: true, subtree: true });
- isInIframe = (window.self !== window.top);
- historyLoaded = false;
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', function(){setChatbotHeight(); setUpdateWindowHeight();});
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
-
-// console suprise
-var styleTitle1 = `
-font-size: 16px;
-font-family: ui-monospace, monospace;
-color: #06AE56;
-`
-var styleDesc1 = `
-font-size: 12px;
-font-family: ui-monospace, monospace;
-`
-function makeML(str) {
- let l = new String(str)
- l = l.substring(l.indexOf("/*") + 3, l.lastIndexOf("*/"))
- return l
-}
-let ChuanhuInfo = function () {
- /*
- ________ __ ________ __
- / ____/ /_ __ ______ _____ / /_ __ __ / ____/ /_ ____ _/ /_
- / / / __ \/ / / / __ `/ __ \/ __ \/ / / / / / / __ \/ __ `/ __/
-/ /___/ / / / /_/ / /_/ / / / / / / / /_/ / / /___/ / / / /_/ / /_
-\____/_/ /_/\__,_/\__,_/_/ /_/_/ /_/\__,_/ \____/_/ /_/\__,_/\__/
-
- 川虎Chat (Chuanhu Chat) - GUI for ChatGPT API and many LLMs
- */
-}
-let description = `
-© 2023 Chuanhu, MZhao, Keldos
-GitHub repository: [https://github.com/GaiZhenbiao/ChuanhuChatGPT]\n
-Enjoy our project!\n
-`
-console.log(`%c${makeML(ChuanhuInfo)}`,styleTitle1)
-console.log(`%c${description}`, styleDesc1)
-
-// button svg code
-const copyIcon = '" "" -t <文件1变调值> <文件2变调值> -wf <输出音频格式> -cr [0-1]
- # 音频将输出至输入音频的旁边
- # 例：
- # 处理单个文件，不变调
- python inference_main.py -n "file.wav"
- # 处理多个文件，file1.wav变调0key、file2.wav升调1key、file3.wav降调2key
- python inference_main.py -n "file1.wav" "file2.wav" "file3.wav" -t 0 1 -2
- # 语音转换使用自动变调并使用聚类模型，输出为wav格式
- python inference_main.py -n "vocal.wav" -a -cr 0.5 -wf wav
- ```
-
- 输入源需要转换为wav格式
-
- 参数：
-
- -n, --clean_names：wav文件路径，可以是相对路径
-
- -t, --trans：变调，多个变调值会与各个文件一一对应
-
- -wf, --wav_format：输出音频格式，默认flac
-
- -a, --auto_predict_f0：语音转换自动预测音高，转换歌声时不要打开这个会严重跑调。
-
- -cr, --cluster_infer_ratio：聚类方案占比，范围 0-1，如要使用建议设为0.5，具体可以访问[sovits4/README.md/聚类音色泄漏控制](https://github.com/innnky/so-vits-svc/tree/4.0#%E8%81%9A%E7%B1%BB%E9%9F%B3%E8%89%B2%E6%B3%84%E6%BC%8F%E6%8E%A7%E5%88%B6)
-
- 针对本仓库设好了默认值的：
-
- -m, --model_path：模型路径
-
- -c, --config_path：配置文件路径
-
- -cm, --cluster_model_path：聚类模型路径
-
- -s, --spk_list：合成目标说话人名称
-
- 一般情况下不用动的：
-
- -sd, --slice_db：分段阈值，默认-40
-
- -ns, --noice_scale：噪声规模
-
- -d, --device：推理设备，默认自动选择
- """)
- app.launch()
diff --git a/spaces/Monster/Alpaca-LoRa/app.py b/spaces/Monster/Alpaca-LoRa/app.py
deleted file mode 100644
index 2a30f50e7c7710048094d83b3e28e18b37dff34c..0000000000000000000000000000000000000000
--- a/spaces/Monster/Alpaca-LoRa/app.py
+++ /dev/null
@@ -1,162 +0,0 @@
-from __future__ import annotations
-from typing import Iterable
-import gradio as gr
-from gradio.themes.base import Base
-from gradio.themes.utils import colors, fonts, sizes
-
-from llama_cpp import Llama
-from huggingface_hub import hf_hub_download
-
-hf_hub_download(repo_id="Monster/a", filename="ggml-alpaca-7b-q4.bin", local_dir=".")
-llm = Llama(model_path="./ggml-alpaca-7b-q4.bin")
-
-
-ins = '''Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{}
-
-### Response:
-'''
-
-ins_inp = '''Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-{}
-
-### Input:
-{}
-
-### Response:
-'''
-
-theme = gr.themes.Monochrome(
- primary_hue="indigo",
- secondary_hue="blue",
- neutral_hue="slate",
- radius_size=gr.themes.sizes.radius_sm,
- font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
-)
-
-def generate(
- instruction,
- input=None,
- temperature=0.1,
- top_p=0.95,
- top_k=40,):
- result = ""
- if input:
- instruction = ins_inp.format(instruction, input)
- else:
- instruction = ins.format(instruction)
- for x in llm(instruction, stop=['### Instruction:', '### End'], stream=True, temperature=temperature, top_p=top_p, top_k=top_k):
- result += x['choices'][0]['text']
- yield result
-
-
-
-examples = [
- "Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas",
- "How do I make a campfire?",
- "Explain to me the difference between nuclear fission and fusion.",
- "Write an ad for sale Nikon D750."
-]
-
-def process_example(args):
- for x in generate(args):
- pass
- return x
-
-css = ".generating {visibility: hidden}"
-
-# Based on the gradio theming guide and borrowed from https://huggingface.co/spaces/shivi/dolly-v2-demo
-class SeafoamCustom(Base):
- def __init__(
- self,
- *,
- primary_hue: colors.Color | str = colors.emerald,
- secondary_hue: colors.Color | str = colors.blue,
- neutral_hue: colors.Color | str = colors.blue,
- spacing_size: sizes.Size | str = sizes.spacing_md,
- radius_size: sizes.Size | str = sizes.radius_md,
- font: fonts.Font
- | str
- | Iterable[fonts.Font | str] = (
- fonts.GoogleFont("Quicksand"),
- "ui-sans-serif",
- "sans-serif",
- ),
- font_mono: fonts.Font
- | str
- | Iterable[fonts.Font | str] = (
- fonts.GoogleFont("IBM Plex Mono"),
- "ui-monospace",
- "monospace",
- ),
- ):
- super().__init__(
- primary_hue=primary_hue,
- secondary_hue=secondary_hue,
- neutral_hue=neutral_hue,
- spacing_size=spacing_size,
- radius_size=radius_size,
- font=font,
- font_mono=font_mono,
- )
- super().set(
- button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
- button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
- button_primary_text_color="white",
- button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
- block_shadow="*shadow_drop_lg",
- button_shadow="*shadow_drop_lg",
- input_background_fill="zinc",
- input_border_color="*secondary_300",
- input_shadow="*shadow_drop",
- input_shadow_focus="*shadow_drop_lg",
- )
-
-
-seafoam = SeafoamCustom()
-
-
-with gr.Blocks(theme=seafoam, analytics_enabled=False, css=css) as demo:
- with gr.Column():
- gr.Markdown(
- """ ## Alpaca-LoRa
-
- 7b quantized 4bit (q4_0)
-
- Type in the box below and click the button to generate answers to your most pressing questions!
-
- """
- )
-
- with gr.Row():
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=2, placeholder="Tell me more about alpacas.", label="Instruction", elem_id="q-input")
-
- with gr.Accordion("Advanced setting", open=False):
- input = gr.components.Textbox(lines=2, label="Input", placeholder="none")
- temperature = gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature")
- top_p = gr.components.Slider(minimum=0, maximum=1, value=0.95, label="Top p")
- top_k = gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k")
-
-
- with gr.Box():
- gr.Markdown("**Output**")
- output = gr.Markdown(elem_id="q-output")
- submit = gr.Button("Generate", variant="primary")
- gr.Examples(
- examples=examples,
- inputs=[instruction],
- cache_examples=False,
- fn=process_example,
- outputs=[output],
- )
-
-
-
- submit.click(generate, inputs=[instruction, input, temperature, top_p, top_k], outputs=[output])
- instruction.submit(generate, inputs=[instruction], outputs=[output])
-
-demo.queue(concurrency_count=1).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/analysis_tools/get_flops.py b/spaces/Mountchicken/MAERec-Gradio/tools/analysis_tools/get_flops.py
deleted file mode 100644
index caa97203aa1e077bd266ab64aa02c1d59f88ec7f..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/analysis_tools/get_flops.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-
-import torch
-from fvcore.nn import FlopCountAnalysis, flop_count_table
-from mmengine import Config
-from mmengine.registry import init_default_scope
-
-from mmocr.registry import MODELS
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='Train a detector')
- parser.add_argument('config', help='train config file path')
- parser.add_argument(
- '--shape',
- type=int,
- nargs='+',
- default=[640, 640],
- help='input image size')
- args = parser.parse_args()
- return args
-
-
-def main():
-
- args = parse_args()
-
- if len(args.shape) == 1:
- h = w = args.shape[0]
- elif len(args.shape) == 2:
- h, w = args.shape
- else:
- raise ValueError('invalid input shape, please use --shape h w')
-
- input_shape = (1, 3, h, w)
-
- cfg = Config.fromfile(args.config)
- init_default_scope(cfg.get('default_scope', 'mmocr'))
- model = MODELS.build(cfg.model)
-
- flops = FlopCountAnalysis(model, torch.ones(input_shape))
-
- # params = parameter_count_table(model)
- flops_data = flop_count_table(flops)
-
- print(flops_data)
-
- print('!!!Please be cautious if you use the results in papers. '
- 'You may need to check if all ops are supported and verify that the '
- 'flops computation is correct.')
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/MrVicente/RA-BART/custom_bart/__init__.py b/spaces/MrVicente/RA-BART/custom_bart/__init__.py
deleted file mode 100644
index d8d8f31d720b60caf7a634487140cf50e27d1fc4..0000000000000000000000000000000000000000
--- a/spaces/MrVicente/RA-BART/custom_bart/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .bart_attention import BartCustomAttention
-from .bart_mask_attention import BartCustomMaskAttention
-from .bart_for_conditional_generation import BartCustomForConditionalGeneration
-from .bart_model import BartCustomModel
-from .config import BartCustomConfig
-from .custom_constants import BartConstants
-from .decoder import *
-from .decoder_layer import *
-from .encoder import *
-from .encoder_layer import *
-from .bart_generation_mixin import *
-from . import *
diff --git a/spaces/MuGeminorum/insecta/insectid/__init__.py b/spaces/MuGeminorum/insecta/insectid/__init__.py
deleted file mode 100644
index 0d9b14c56d9170c6336a0480b945a6451fb19e3c..0000000000000000000000000000000000000000
--- a/spaces/MuGeminorum/insecta/insectid/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .detector import *
-from .identifier import *
diff --git a/spaces/NATSpeech/PortaSpeech/docs/portaspeech.md b/spaces/NATSpeech/PortaSpeech/docs/portaspeech.md
deleted file mode 100644
index 94e8b9b4241a2daae5bbfba660aa2a4a9068360d..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/docs/portaspeech.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Run PortaSpeech
-
-## Quick Start
-
-### Install Dependencies
-
-Install dependencies following [readme.md](../readme.md)
-
-### Set Config Path and Experiment Name
-
-#### PortaSpeech (normal)
-```bash
-export CONFIG_NAME=egs/datasets/audio/lj/ps_flow_nips2021.yaml
-export MY_EXP_NAME=ps_normal_exp
-```
-
-#### PortaSpeech (small)
-```bash
-export CONFIG_NAME=egs/datasets/audio/lj/ps_flow_small_nips2021.yaml
-export MY_EXP_NAME=ps_small_exp
-```
-
-### Preprocess and binary dataset
-
-Prepare dataset following [prepare_data.md](./prepare_data.md)
-
-### Prepare Vocoder
-
-Prepare vocoder following [prepare_vocoder.md](./prepare_vocoder.md)
-
-## Training
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --reset
-```
-
-You can check the training and validation curves open Tensorboard via:
-
-```bash
-tensorboard --logdir checkpoints/$MY_EXP_NAME
-```
-
-## Inference (Testing)
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $PS_CONFIG --exp_name $MY_EXP_NAME --infer
-```
-
-## Citation
-
-If you find this useful for your research, please use the following.
-
-```
-@article{ren2021portaspeech,
- title={PortaSpeech: Portable and High-Quality Generative Text-to-Speech},
- author={Ren, Yi and Liu, Jinglin and Zhao, Zhou},
- journal={Advances in Neural Information Processing Systems},
- volume={34},
- year={2021}
-}
-```
diff --git a/spaces/Neo-Salvatore/translate-locale/utils.py b/spaces/Neo-Salvatore/translate-locale/utils.py
deleted file mode 100644
index d8f803bd2e5504f8da07e4c527042ec16e3e1cb2..0000000000000000000000000000000000000000
--- a/spaces/Neo-Salvatore/translate-locale/utils.py
+++ /dev/null
@@ -1,148 +0,0 @@
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores.faiss import FAISS
-from langchain import OpenAI, Cohere
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.llms import OpenAI
-from langchain.docstore.document import Document
-from langchain.vectorstores import FAISS, VectorStore
-import docx2txt
-from typing import List, Dict, Any
-import re
-import numpy as np
-from io import StringIO
-from io import BytesIO
-import streamlit as st
-from pypdf import PdfReader
-from openai.error import AuthenticationError
-import openai
-import os
-
-@st.experimental_memo()
-def parse_docx(file: BytesIO) -> str:
- text = docx2txt.process(file)
- # Remove multiple newlines
- text = re.sub(r"\n\s*\n", "\n\n", text)
- return text
-
-
-@st.experimental_memo()
-def parse_pdf(file: BytesIO) -> List[str]:
- pdf = PdfReader(file)
- output = []
- for page in pdf.pages:
- text = page.extract_text()
- # Merge hyphenated words
- text = re.sub(r"(\w+)-\n(\w+)", r"\1\2", text)
- # Fix newlines in the middle of sentences
- text = re.sub(r"(? str:
- text = file.read().decode("utf-8")
- # Remove multiple newlines
- text = re.sub(r"\n\s*\n", "\n\n", text)
- return text
-
-@st.experimental_memo()
-def parse_csv(uploaded_file):
- # To read file as bytes:
- #bytes_data = uploaded_file.getvalue()
- #st.write(bytes_data)
-
- # To convert to a string based IO:
- stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
- #st.write(stringio)
-
- # To read file as string:
- string_data = stringio.read()
- #st.write(string_data)
-
- # Can be used wherever a "file-like" object is accepted:
- # dataframe = pd.read_csv(uploaded_file)
- return string_data
-
-@st.experimental_memo()
-def parse_any(uploaded_file):
- stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
- string_data = stringio.read()
- return string_data
-
-
-@st.cache(allow_output_mutation=True)
-def text_to_docs(text: str) -> List[Document]:
- """Converts a string or list of strings to a list of Documents
- with metadata."""
- if isinstance(text, str):
- # Take a single string as one page
- text = [text]
- page_docs = [Document(page_content=page) for page in text]
-
- # Add page numbers as metadata
- for i, doc in enumerate(page_docs):
- doc.metadata["page"] = i + 1
-
- # return page_docs
-
- # Split pages into chunks
- doc_chunks = []
-
- for doc in page_docs:
- text_splitter = RecursiveCharacterTextSplitter(
- chunk_size=800,
- separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
- chunk_overlap=0,
- )
- chunks = text_splitter.split_text(doc.page_content)
- for i, chunk in enumerate(chunks):
- doc = Document(
- page_content=chunk, metadata={"page": doc.metadata["page"], "chunk": i}
- )
- # Add sources a metadata
- doc.metadata["source"] = f"{doc.metadata['page']}-{doc.metadata['chunk']}"
- doc_chunks.append(doc)
- return doc_chunks
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def trans_docs(docs: List[Document], language: str):
- if not st.session_state.get("OPENAI_API_KEY"):
- raise AuthenticationError(
- "Enter your OpenAI API key in the sidebar. You can get a key at https://platform.openai.com/account/api-keys."
- )
- else:
- trans_res = ''
- for d in docs:
- res = translate(d.page_content, language, 0)
- if res:
- trans_res += f'\n{res}'
- else:
- trans_res += '\nTranslation failed'
- return trans_res
-
-def translate(input: str, language: str, num: int):
- openai.api_key = st.session_state.get("OPENAI_API_KEY")
- try:
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=f"translate this into {language}: {input}",
- temperature=0.9,
- max_tokens=1000,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0.6,
- # stop=[" Human:", " AI:"]
- )
- res = response['choices'][0]['text']
- return res
- except:
- num+=1
- if num < 10:
- return translate(input, language, num)
- else:
- return False
\ No newline at end of file
diff --git a/spaces/Nick1/rvc-models/lib/infer_pack/models_dml.py b/spaces/Nick1/rvc-models/lib/infer_pack/models_dml.py
deleted file mode 100644
index 958d7b29259763d2fea94caf8ba7e314c4a77d05..0000000000000000000000000000000000000000
--- a/spaces/Nick1/rvc-models/lib/infer_pack/models_dml.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 lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.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 lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.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.float()
-
- 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/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_synthesis/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_synthesis/README.md
deleted file mode 100644
index 4a3ae54b857c43621c9fb67ee4b214584beec835..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_synthesis/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-Speech Synthesis (S^2)
-===
-
-Speech synthesis with fairseq.
-
-- Autoregressive and non-autoregressive models
-- Multi-speaker synthesis
-- Audio preprocessing
-- Automatic metrics
-- Similar data configuration as [S2T](../speech_to_text/README.md)
-
-
-## Examples
-- [Single-speaker synthesis on LJSpeech](docs/ljspeech_example.md)
-- [Multi-speaker synthesis on VCTK](docs/vctk_example.md)
-- [Multi-speaker synthesis on Common Voice](docs/common_voice_example.md)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py
deleted file mode 100644
index 63fcd431e2d7746b696aaa0d4172bc04ffb88efa..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/textless_nlp/gslm/unit2speech/tacotron2/stft.py
+++ /dev/null
@@ -1,141 +0,0 @@
-"""
-BSD 3-Clause License
-
-Copyright (c) 2017, Prem Seetharaman
-All rights reserved.
-
-* Redistribution and use in source and binary forms, with or without
- modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice, this
- list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
-* Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from this
- software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-"""
-
-import torch
-import numpy as np
-import torch.nn.functional as F
-from torch.autograd import Variable
-from scipy.signal import get_window
-from librosa.util import pad_center, tiny
-from .audio_processing import window_sumsquare
-
-
-class STFT(torch.nn.Module):
- """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
- def __init__(self, filter_length=800, hop_length=200, win_length=800,
- window='hann'):
- super(STFT, self).__init__()
- self.filter_length = filter_length
- self.hop_length = hop_length
- self.win_length = win_length
- self.window = window
- self.forward_transform = None
- scale = self.filter_length / self.hop_length
- fourier_basis = np.fft.fft(np.eye(self.filter_length))
-
- cutoff = int((self.filter_length / 2 + 1))
- fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
- np.imag(fourier_basis[:cutoff, :])])
-
- forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
- inverse_basis = torch.FloatTensor(
- np.linalg.pinv(scale * fourier_basis).T[:, None, :])
-
- if window is not None:
- assert(filter_length >= win_length)
- # get window and zero center pad it to filter_length
- fft_window = get_window(window, win_length, fftbins=True)
- fft_window = pad_center(fft_window, filter_length)
- fft_window = torch.from_numpy(fft_window).float()
-
- # window the bases
- forward_basis *= fft_window
- inverse_basis *= fft_window
-
- self.register_buffer('forward_basis', forward_basis.float())
- self.register_buffer('inverse_basis', inverse_basis.float())
-
- def transform(self, input_data):
- num_batches = input_data.size(0)
- num_samples = input_data.size(1)
-
- self.num_samples = num_samples
-
- # similar to librosa, reflect-pad the input
- input_data = input_data.view(num_batches, 1, num_samples)
- input_data = F.pad(
- input_data.unsqueeze(1),
- (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
- mode='reflect')
- input_data = input_data.squeeze(1)
-
- forward_transform = F.conv1d(
- input_data,
- Variable(self.forward_basis, requires_grad=False),
- stride=self.hop_length,
- padding=0)
-
- cutoff = int((self.filter_length / 2) + 1)
- real_part = forward_transform[:, :cutoff, :]
- imag_part = forward_transform[:, cutoff:, :]
-
- magnitude = torch.sqrt(real_part**2 + imag_part**2)
- phase = torch.autograd.Variable(
- torch.atan2(imag_part.data, real_part.data))
-
- return magnitude, phase
-
- def inverse(self, magnitude, phase):
- recombine_magnitude_phase = torch.cat(
- [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
-
- inverse_transform = F.conv_transpose1d(
- recombine_magnitude_phase,
- Variable(self.inverse_basis, requires_grad=False),
- stride=self.hop_length,
- padding=0)
-
- if self.window is not None:
- window_sum = window_sumsquare(
- self.window, magnitude.size(-1), hop_length=self.hop_length,
- win_length=self.win_length, n_fft=self.filter_length,
- dtype=np.float32)
- # remove modulation effects
- approx_nonzero_indices = torch.from_numpy(
- np.where(window_sum > tiny(window_sum))[0])
- window_sum = torch.autograd.Variable(
- torch.from_numpy(window_sum), requires_grad=False)
- window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
- inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
-
- # scale by hop ratio
- inverse_transform *= float(self.filter_length) / self.hop_length
-
- inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
- inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
-
- return inverse_transform
-
- def forward(self, input_data):
- self.magnitude, self.phase = self.transform(input_data)
- reconstruction = self.inverse(self.magnitude, self.phase)
- return reconstruction
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py
deleted file mode 100644
index 6177239dc75f6937d036462a5a2379aaee202e7d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/w2vu_generate.py
+++ /dev/null
@@ -1,707 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Run inference for pre-processed data with a trained model.
-"""
-
-import ast
-from collections import namedtuple
-from dataclasses import dataclass, field
-from enum import Enum, auto
-import hydra
-from hydra.core.config_store import ConfigStore
-import logging
-import math
-import os
-from omegaconf import OmegaConf
-from typing import Optional
-import sys
-
-import editdistance
-import torch
-
-from hydra.core.hydra_config import HydraConfig
-
-from fairseq import checkpoint_utils, progress_bar, tasks, utils
-from fairseq.data.data_utils import post_process
-from fairseq.dataclass.configs import FairseqDataclass, FairseqConfig
-from fairseq.logging.meters import StopwatchMeter
-from omegaconf import open_dict
-
-from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoderConfig
-
-logging.root.setLevel(logging.INFO)
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class DecoderType(Enum):
- VITERBI = auto()
- KENLM = auto()
- FAIRSEQ = auto()
- KALDI = auto()
-
-
-@dataclass
-class UnsupGenerateConfig(FairseqDataclass):
- fairseq: FairseqConfig = FairseqConfig()
- lm_weight: float = field(
- default=2.0,
- metadata={"help": "language model weight"},
- )
- w2l_decoder: DecoderType = field(
- default=DecoderType.VITERBI,
- metadata={"help": "type of decoder to use"},
- )
- kaldi_decoder_config: Optional[KaldiDecoderConfig] = None
- lexicon: Optional[str] = field(
- default=None,
- metadata={
- "help": "path to lexicon. This is also used to 'phonemize' for unsupvised param tuning"
- },
- )
- lm_model: Optional[str] = field(
- default=None,
- metadata={"help": "path to language model (kenlm or fairseq)"},
- )
- unit_lm: bool = field(
- default=False,
- metadata={"help": "whether to use unit lm"},
- )
- beam_threshold: float = field(
- default=50.0,
- metadata={"help": "beam score threshold"},
- )
- beam_size_token: float = field(
- default=100.0,
- metadata={"help": "max tokens per beam"},
- )
- beam: int = field(
- default=5,
- metadata={"help": "decoder beam size"},
- )
- nbest: int = field(
- default=1,
- metadata={"help": "number of results to return"},
- )
- word_score: float = field(
- default=1.0,
- metadata={"help": "word score to add at end of word"},
- )
- unk_weight: float = field(
- default=-math.inf,
- metadata={"help": "unknown token weight"},
- )
- sil_weight: float = field(
- default=0.0,
- metadata={"help": "silence token weight"},
- )
- targets: Optional[str] = field(
- default=None,
- metadata={"help": "extension of ground truth labels to compute UER"},
- )
- results_path: Optional[str] = field(
- default=None,
- metadata={"help": "where to store results"},
- )
- post_process: Optional[str] = field(
- default=None,
- metadata={"help": "how to post process results"},
- )
- vocab_usage_power: float = field(
- default=2,
- metadata={"help": "for unsupervised param tuning"},
- )
-
- viterbi_transcript: Optional[str] = field(
- default=None,
- metadata={"help": "for unsupervised param tuning"},
- )
- min_lm_ppl: float = field(
- default=0,
- metadata={"help": "for unsupervised param tuning"},
- )
- min_vt_uer: float = field(
- default=0,
- metadata={"help": "for unsupervised param tuning"},
- )
-
- blank_weight: float = field(
- default=0,
- metadata={"help": "value to add or set for blank emission"},
- )
- blank_mode: str = field(
- default="set",
- metadata={
- "help": "can be add or set, how to modify blank emission with blank weight"
- },
- )
- sil_is_blank: bool = field(
- default=False,
- metadata={"help": "if true, token is same as blank token"},
- )
-
- unsupervised_tuning: bool = field(
- default=False,
- metadata={
- "help": "if true, returns a score based on unsupervised param selection metric instead of UER"
- },
- )
- is_ax: bool = field(
- default=False,
- metadata={
- "help": "if true, assumes we are using ax for tuning and returns a tuple for ax to consume"
- },
- )
-
-
-def get_dataset_itr(cfg, task):
- return task.get_batch_iterator(
- dataset=task.dataset(cfg.fairseq.dataset.gen_subset),
- max_tokens=cfg.fairseq.dataset.max_tokens,
- max_sentences=cfg.fairseq.dataset.batch_size,
- max_positions=(sys.maxsize, sys.maxsize),
- ignore_invalid_inputs=cfg.fairseq.dataset.skip_invalid_size_inputs_valid_test,
- required_batch_size_multiple=cfg.fairseq.dataset.required_batch_size_multiple,
- num_shards=cfg.fairseq.dataset.num_shards,
- shard_id=cfg.fairseq.dataset.shard_id,
- num_workers=cfg.fairseq.dataset.num_workers,
- data_buffer_size=cfg.fairseq.dataset.data_buffer_size,
- ).next_epoch_itr(shuffle=False)
-
-
-def process_predictions(
- cfg: UnsupGenerateConfig,
- hypos,
- tgt_dict,
- target_tokens,
- res_files,
-):
- retval = []
- word_preds = []
- transcriptions = []
- dec_scores = []
-
- for i, hypo in enumerate(hypos[: min(len(hypos), cfg.nbest)]):
- if torch.is_tensor(hypo["tokens"]):
- tokens = hypo["tokens"].int().cpu()
- tokens = tokens[tokens >= tgt_dict.nspecial]
- hyp_pieces = tgt_dict.string(tokens)
- else:
- hyp_pieces = " ".join(hypo["tokens"])
-
- if "words" in hypo and len(hypo["words"]) > 0:
- hyp_words = " ".join(hypo["words"])
- else:
- hyp_words = post_process(hyp_pieces, cfg.post_process)
-
- to_write = {}
- if res_files is not None:
- to_write[res_files["hypo.units"]] = hyp_pieces
- to_write[res_files["hypo.words"]] = hyp_words
-
- tgt_words = ""
- if target_tokens is not None:
- if isinstance(target_tokens, str):
- tgt_pieces = tgt_words = target_tokens
- else:
- tgt_pieces = tgt_dict.string(target_tokens)
- tgt_words = post_process(tgt_pieces, cfg.post_process)
-
- if res_files is not None:
- to_write[res_files["ref.units"]] = tgt_pieces
- to_write[res_files["ref.words"]] = tgt_words
-
- if not cfg.fairseq.common_eval.quiet:
- logger.info(f"HYPO {i}:" + hyp_words)
- if tgt_words:
- logger.info("TARGET:" + tgt_words)
-
- if "am_score" in hypo and "lm_score" in hypo:
- logger.info(
- f"DECODER AM SCORE: {hypo['am_score']}, DECODER LM SCORE: {hypo['lm_score']}, DECODER SCORE: {hypo['score']}"
- )
- elif "score" in hypo:
- logger.info(f"DECODER SCORE: {hypo['score']}")
-
- logger.info("___________________")
-
- hyp_words_arr = hyp_words.split()
- tgt_words_arr = tgt_words.split()
-
- retval.append(
- (
- editdistance.eval(hyp_words_arr, tgt_words_arr),
- len(hyp_words_arr),
- len(tgt_words_arr),
- hyp_pieces,
- hyp_words,
- )
- )
- word_preds.append(hyp_words_arr)
- transcriptions.append(to_write)
- dec_scores.append(-hypo.get("score", 0)) # negate cuz kaldi returns NLL
-
- if len(retval) > 1:
- best = None
- for r, t in zip(retval, transcriptions):
- if best is None or r[0] < best[0][0]:
- best = r, t
- for dest, tran in best[1].items():
- print(tran, file=dest)
- dest.flush()
- return best[0]
-
- assert len(transcriptions) == 1
- for dest, tran in transcriptions[0].items():
- print(tran, file=dest)
-
- return retval[0]
-
-
-def prepare_result_files(cfg: UnsupGenerateConfig):
- def get_res_file(file_prefix):
- if cfg.fairseq.dataset.num_shards > 1:
- file_prefix = f"{cfg.fairseq.dataset.shard_id}_{file_prefix}"
- path = os.path.join(
- cfg.results_path,
- "{}{}.txt".format(
- cfg.fairseq.dataset.gen_subset,
- file_prefix,
- ),
- )
- return open(path, "w", buffering=1)
-
- if not cfg.results_path:
- return None
-
- return {
- "hypo.words": get_res_file(""),
- "hypo.units": get_res_file("_units"),
- "ref.words": get_res_file("_ref"),
- "ref.units": get_res_file("_ref_units"),
- "hypo.nbest.words": get_res_file("_nbest_words"),
- }
-
-
-def optimize_models(cfg: UnsupGenerateConfig, use_cuda, models):
- """Optimize ensemble for generation"""
- for model in models:
- model.eval()
- if cfg.fairseq.common.fp16:
- model.half()
- if use_cuda:
- model.cuda()
-
-
-GenResult = namedtuple(
- "GenResult",
- [
- "count",
- "errs_t",
- "gen_timer",
- "lengths_hyp_unit_t",
- "lengths_hyp_t",
- "lengths_t",
- "lm_score_t",
- "num_feats",
- "num_sentences",
- "num_symbols",
- "vt_err_t",
- "vt_length_t",
- ],
-)
-
-
-def generate(cfg: UnsupGenerateConfig, models, saved_cfg, use_cuda):
- task = tasks.setup_task(cfg.fairseq.task)
- saved_cfg.task.labels = cfg.fairseq.task.labels
- task.load_dataset(cfg.fairseq.dataset.gen_subset, task_cfg=saved_cfg.task)
- # Set dictionary
- tgt_dict = task.target_dictionary
- logger.info(
- "| {} {} {} examples".format(
- cfg.fairseq.task.data,
- cfg.fairseq.dataset.gen_subset,
- len(task.dataset(cfg.fairseq.dataset.gen_subset)),
- )
- )
- # Load dataset (possibly sharded)
- itr = get_dataset_itr(cfg, task)
- # Initialize generator
- gen_timer = StopwatchMeter()
-
- def build_generator(cfg: UnsupGenerateConfig):
- w2l_decoder = cfg.w2l_decoder
- if w2l_decoder == DecoderType.VITERBI:
- from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder
-
- return W2lViterbiDecoder(cfg, task.target_dictionary)
- elif w2l_decoder == DecoderType.KENLM:
- from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
-
- return W2lKenLMDecoder(cfg, task.target_dictionary)
- elif w2l_decoder == DecoderType.FAIRSEQ:
- from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder
-
- return W2lFairseqLMDecoder(cfg, task.target_dictionary)
- elif w2l_decoder == DecoderType.KALDI:
- from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoder
-
- assert cfg.kaldi_decoder_config is not None
-
- return KaldiDecoder(
- cfg.kaldi_decoder_config,
- cfg.beam,
- )
- else:
- raise NotImplementedError(
- "only wav2letter decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment but found "
- + str(w2l_decoder)
- )
-
- generator = build_generator(cfg)
-
- kenlm = None
- fairseq_lm = None
- if cfg.lm_model is not None:
- import kenlm
-
- kenlm = kenlm.Model(cfg.lm_model)
-
- num_sentences = 0
- if cfg.results_path is not None and not os.path.exists(cfg.results_path):
- os.makedirs(cfg.results_path)
-
- res_files = prepare_result_files(cfg)
- errs_t = 0
- lengths_hyp_t = 0
- lengths_hyp_unit_t = 0
- lengths_t = 0
- count = 0
- num_feats = 0
- all_hyp_pieces = []
- all_hyp_words = []
-
- num_symbols = (
- len([s for s in tgt_dict.symbols if not s.startswith("madeup")])
- - tgt_dict.nspecial
- )
- targets = None
- if cfg.targets is not None:
- tgt_path = os.path.join(
- cfg.fairseq.task.data, cfg.fairseq.dataset.gen_subset + "." + cfg.targets
- )
- if os.path.exists(tgt_path):
- with open(tgt_path, "r") as f:
- targets = f.read().splitlines()
- viterbi_transcript = None
- if cfg.viterbi_transcript is not None and len(cfg.viterbi_transcript) > 0:
- logger.info(f"loading viterbi transcript from {cfg.viterbi_transcript}")
- with open(cfg.viterbi_transcript, "r") as vf:
- viterbi_transcript = vf.readlines()
- viterbi_transcript = [v.rstrip().split() for v in viterbi_transcript]
-
- gen_timer.start()
-
- start = 0
- end = len(itr)
-
- hypo_futures = None
- if cfg.w2l_decoder == DecoderType.KALDI:
- logger.info("Extracting features")
- hypo_futures = []
- samples = []
- with progress_bar.build_progress_bar(cfg.fairseq.common, itr) as t:
- for i, sample in enumerate(t):
- if "net_input" not in sample or i < start or i >= end:
- continue
- if "padding_mask" not in sample["net_input"]:
- sample["net_input"]["padding_mask"] = None
-
- hypos, num_feats = gen_hypos(
- generator, models, num_feats, sample, task, use_cuda
- )
- hypo_futures.append(hypos)
- samples.append(sample)
- itr = list(zip(hypo_futures, samples))
- start = 0
- end = len(itr)
- logger.info("Finished extracting features")
-
- with progress_bar.build_progress_bar(cfg.fairseq.common, itr) as t:
- for i, sample in enumerate(t):
- if i < start or i >= end:
- continue
-
- if hypo_futures is not None:
- hypos, sample = sample
- hypos = [h.result() for h in hypos]
- else:
- if "net_input" not in sample:
- continue
-
- hypos, num_feats = gen_hypos(
- generator, models, num_feats, sample, task, use_cuda
- )
-
- for i, sample_id in enumerate(sample["id"].tolist()):
- if targets is not None:
- target_tokens = targets[sample_id]
- elif "target" in sample or "target_label" in sample:
- toks = (
- sample["target"][i, :]
- if "target_label" not in sample
- else sample["target_label"][i, :]
- )
-
- target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu()
- else:
- target_tokens = None
-
- # Process top predictions
- (
- errs,
- length_hyp,
- length,
- hyp_pieces,
- hyp_words,
- ) = process_predictions(
- cfg,
- hypos[i],
- tgt_dict,
- target_tokens,
- res_files,
- )
- errs_t += errs
- lengths_hyp_t += length_hyp
- lengths_hyp_unit_t += (
- len(hyp_pieces) if len(hyp_pieces) > 0 else len(hyp_words)
- )
- lengths_t += length
- count += 1
- all_hyp_pieces.append(hyp_pieces)
- all_hyp_words.append(hyp_words)
-
- num_sentences += (
- sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
- )
-
- lm_score_sum = 0
- if kenlm is not None:
-
- if cfg.unit_lm:
- lm_score_sum = sum(kenlm.score(w) for w in all_hyp_pieces)
- else:
- lm_score_sum = sum(kenlm.score(w) for w in all_hyp_words)
- elif fairseq_lm is not None:
- lm_score_sum = sum(fairseq_lm.score([h.split() for h in all_hyp_words])[0])
-
- vt_err_t = 0
- vt_length_t = 0
- if viterbi_transcript is not None:
- unit_hyps = []
- if cfg.targets is not None and cfg.lexicon is not None:
- lex = {}
- with open(cfg.lexicon, "r") as lf:
- for line in lf:
- items = line.rstrip().split()
- lex[items[0]] = items[1:]
- for h in all_hyp_pieces:
- hyp_ws = []
- for w in h.split():
- assert w in lex, w
- hyp_ws.extend(lex[w])
- unit_hyps.append(hyp_ws)
-
- else:
- unit_hyps.extend([h.split() for h in all_hyp_words])
-
- vt_err_t = sum(
- editdistance.eval(vt, h) for vt, h in zip(viterbi_transcript, unit_hyps)
- )
-
- vt_length_t = sum(len(h) for h in viterbi_transcript)
-
- if res_files is not None:
- for r in res_files.values():
- r.close()
-
- gen_timer.stop(lengths_hyp_t)
-
- return GenResult(
- count,
- errs_t,
- gen_timer,
- lengths_hyp_unit_t,
- lengths_hyp_t,
- lengths_t,
- lm_score_sum,
- num_feats,
- num_sentences,
- num_symbols,
- vt_err_t,
- vt_length_t,
- )
-
-
-def gen_hypos(generator, models, num_feats, sample, task, use_cuda):
- sample = utils.move_to_cuda(sample) if use_cuda else sample
-
- if "features" in sample["net_input"]:
- sample["net_input"]["dense_x_only"] = True
- num_feats += (
- sample["net_input"]["features"].shape[0]
- * sample["net_input"]["features"].shape[1]
- )
- hypos = task.inference_step(generator, models, sample, None)
- return hypos, num_feats
-
-
-def main(cfg: UnsupGenerateConfig, model=None):
- if (
- cfg.fairseq.dataset.max_tokens is None
- and cfg.fairseq.dataset.batch_size is None
- ):
- cfg.fairseq.dataset.max_tokens = 1024000
-
- use_cuda = torch.cuda.is_available() and not cfg.fairseq.common.cpu
-
- task = tasks.setup_task(cfg.fairseq.task)
-
- overrides = ast.literal_eval(cfg.fairseq.common_eval.model_overrides)
-
- if cfg.fairseq.task._name == "unpaired_audio_text":
- overrides["model"] = {
- "blank_weight": cfg.blank_weight,
- "blank_mode": cfg.blank_mode,
- "blank_is_sil": cfg.sil_is_blank,
- "no_softmax": True,
- "segmentation": {
- "type": "NONE",
- },
- }
- else:
- overrides["model"] = {
- "blank_weight": cfg.blank_weight,
- "blank_mode": cfg.blank_mode,
- }
-
- if model is None:
- # Load ensemble
- logger.info("| loading model(s) from {}".format(cfg.fairseq.common_eval.path))
- models, saved_cfg = checkpoint_utils.load_model_ensemble(
- cfg.fairseq.common_eval.path.split("\\"),
- arg_overrides=overrides,
- task=task,
- suffix=cfg.fairseq.checkpoint.checkpoint_suffix,
- strict=(cfg.fairseq.checkpoint.checkpoint_shard_count == 1),
- num_shards=cfg.fairseq.checkpoint.checkpoint_shard_count,
- )
- optimize_models(cfg, use_cuda, models)
- else:
- models = [model]
- saved_cfg = cfg.fairseq
-
- with open_dict(saved_cfg.task):
- saved_cfg.task.shuffle = False
- saved_cfg.task.sort_by_length = False
-
- gen_result = generate(cfg, models, saved_cfg, use_cuda)
-
- wer = None
- if gen_result.lengths_t > 0:
- wer = gen_result.errs_t * 100.0 / gen_result.lengths_t
- logger.info(f"WER: {wer}")
-
- lm_ppl = float("inf")
-
- if gen_result.lm_score_t != 0 and gen_result.lengths_hyp_t > 0:
- hyp_len = gen_result.lengths_hyp_t
- lm_ppl = math.pow(
- 10, -gen_result.lm_score_t / (hyp_len + gen_result.num_sentences)
- )
- logger.info(f"LM PPL: {lm_ppl}")
-
- logger.info(
- "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}"
- " sentences/s, {:.2f} tokens/s)".format(
- gen_result.num_sentences,
- gen_result.gen_timer.n,
- gen_result.gen_timer.sum,
- gen_result.num_sentences / gen_result.gen_timer.sum,
- 1.0 / gen_result.gen_timer.avg,
- )
- )
-
- vt_diff = None
- if gen_result.vt_length_t > 0:
- vt_diff = gen_result.vt_err_t / gen_result.vt_length_t
- vt_diff = max(cfg.min_vt_uer, vt_diff)
-
- lm_ppl = max(cfg.min_lm_ppl, lm_ppl)
-
- if not cfg.unsupervised_tuning == 0:
- weighted_score = wer
- else:
- weighted_score = math.log(lm_ppl) * (vt_diff or 1.0)
-
- res = (
- f"| Generate {cfg.fairseq.dataset.gen_subset} with beam={cfg.beam}, "
- f"lm_weight={cfg.kaldi_decoder_config.acoustic_scale if cfg.kaldi_decoder_config else cfg.lm_weight}, "
- f"word_score={cfg.word_score}, sil_weight={cfg.sil_weight}, blank_weight={cfg.blank_weight}, "
- f"WER: {wer}, LM_PPL: {lm_ppl}, num feats: {gen_result.num_feats}, "
- f"length: {gen_result.lengths_hyp_t}, UER to viterbi: {(vt_diff or 0) * 100}, score: {weighted_score}"
- )
-
- logger.info(res)
- # print(res)
-
- return task, weighted_score
-
-
-@hydra.main(
- config_path=os.path.join("../../..", "fairseq", "config"), config_name="config"
-)
-def hydra_main(cfg):
- with open_dict(cfg):
- # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
- cfg.job_logging_cfg = OmegaConf.to_container(
- HydraConfig.get().job_logging, resolve=True
- )
-
- cfg = OmegaConf.create(
- OmegaConf.to_container(cfg, resolve=False, enum_to_str=False)
- )
- OmegaConf.set_struct(cfg, True)
- logger.info(cfg)
-
- utils.import_user_module(cfg.fairseq.common)
-
- _, score = main(cfg)
-
- if cfg.is_ax:
- return score, None
- return score
-
-
-def cli_main():
- try:
- from hydra._internal.utils import get_args
-
- cfg_name = get_args().config_name or "config"
- except:
- logger.warning("Failed to get config name from hydra args")
- cfg_name = "config"
-
- cs = ConfigStore.instance()
- cs.store(name=cfg_name, node=UnsupGenerateConfig)
- hydra_main()
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/kaldi/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_recognition/kaldi/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/adaptive_softmax.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/adaptive_softmax.py
deleted file mode 100644
index ae0c77ba0f6ee98501306d66cbc4a948b4ade0f7..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/adaptive_softmax.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import functools
-import operator
-
-import torch
-import torch.nn.functional as F
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from fairseq.modules.quant_noise import quant_noise
-from torch import nn
-
-
-class TiedLinear(nn.Module):
- def __init__(self, weight, transpose):
- super().__init__()
- self.weight = weight
- self.transpose = transpose
-
- def forward(self, input):
- return F.linear(input, self.weight.t() if self.transpose else self.weight)
-
-
-class TiedHeadModule(nn.Module):
- def __init__(self, weights, input_dim, num_classes, q_noise, qn_block_size):
- super().__init__()
- tied_emb, _ = weights
- self.num_words, emb_dim = tied_emb.size()
-
- self.word_proj = quant_noise(
- TiedLinear(tied_emb, transpose=False), q_noise, qn_block_size
- )
- if input_dim != emb_dim:
- self.word_proj = nn.Sequential(
- quant_noise(
- nn.Linear(input_dim, emb_dim, bias=False), q_noise, qn_block_size
- ),
- self.word_proj,
- )
-
- self.class_proj = quant_noise(
- nn.Linear(input_dim, num_classes, bias=False), q_noise, qn_block_size
- )
- self.out_dim = self.num_words + num_classes
-
- self.register_buffer("_float_tensor", torch.FloatTensor(1))
-
- def forward(self, input):
- inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1)
- out = self._float_tensor.new(inp_sz, self.out_dim)
- out[:, : self.num_words] = self.word_proj(input.view(inp_sz, -1))
- out[:, self.num_words :] = self.class_proj(input.view(inp_sz, -1))
- return out
-
-
-class AdaptiveSoftmax(nn.Module):
- """
- This is an implementation of the efficient softmax approximation for
- graphical processing units (GPU), described in the paper "Efficient softmax
- approximation for GPUs" (http://arxiv.org/abs/1609.04309).
- """
-
- def __init__(
- self,
- vocab_size,
- input_dim,
- cutoff,
- dropout,
- factor=4.0,
- adaptive_inputs=None,
- tie_proj=False,
- q_noise=0,
- qn_block_size=8,
- ):
- super().__init__()
-
- if vocab_size > cutoff[-1]:
- cutoff = cutoff + [vocab_size]
- else:
- assert (
- vocab_size == cutoff[-1]
- ), "cannot specify cutoff larger than vocab size"
-
- output_dim = cutoff[0] + len(cutoff) - 1
-
- self.vocab_size = vocab_size
- self.cutoff = cutoff
- self.dropout_module = FairseqDropout(
- dropout, module_name=self.__class__.__name__
- )
- self.input_dim = input_dim
- self.factor = factor
- self.q_noise = q_noise
- self.qn_block_size = qn_block_size
-
- self.lsm = nn.LogSoftmax(dim=1)
-
- if adaptive_inputs is not None:
- self.head = TiedHeadModule(
- adaptive_inputs.weights_for_band(0),
- input_dim,
- len(cutoff) - 1,
- self.q_noise,
- self.qn_block_size,
- )
- else:
- self.head = quant_noise(
- nn.Linear(input_dim, output_dim, bias=False),
- self.q_noise,
- self.qn_block_size,
- )
-
- self._make_tail(adaptive_inputs, tie_proj)
-
- def init_weights(m):
- if (
- hasattr(m, "weight")
- and not isinstance(m, TiedLinear)
- and not isinstance(m, TiedHeadModule)
- ):
- nn.init.xavier_uniform_(m.weight)
-
- self.apply(init_weights)
-
- self.register_buffer("version", torch.LongTensor([1]))
-
- def _make_tail(self, adaptive_inputs=None, tie_proj=False):
- self.tail = nn.ModuleList()
- for i in range(len(self.cutoff) - 1):
- dim = int(self.input_dim // self.factor ** (i + 1))
-
- tied_emb, tied_proj = (
- adaptive_inputs.weights_for_band(i + 1)
- if adaptive_inputs is not None
- else (None, None)
- )
-
- if tied_proj is not None:
- if tie_proj:
- proj = quant_noise(
- TiedLinear(tied_proj, transpose=True),
- self.q_noise,
- self.qn_block_size,
- )
- else:
- proj = quant_noise(
- nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False),
- self.q_noise,
- self.qn_block_size,
- )
- else:
- proj = quant_noise(
- nn.Linear(self.input_dim, dim, bias=False),
- self.q_noise,
- self.qn_block_size,
- )
-
- if tied_emb is None:
- out_proj = nn.Linear(
- dim, self.cutoff[i + 1] - self.cutoff[i], bias=False
- )
- else:
- out_proj = TiedLinear(tied_emb, transpose=False)
-
- m = nn.Sequential(
- proj,
- nn.Dropout(self.dropout_module.p),
- quant_noise(out_proj, self.q_noise, self.qn_block_size),
- )
-
- self.tail.append(m)
-
- def upgrade_state_dict_named(self, state_dict, name):
- version_name = name + ".version"
- if version_name not in state_dict:
- raise Exception("This version of the model is no longer supported")
-
- def adapt_target(self, target):
- """
- In order to be efficient, the AdaptiveSoftMax does not compute the
- scores for all the word of the vocabulary for all the examples. It is
- thus necessary to call the method adapt_target of the AdaptiveSoftMax
- layer inside each forward pass.
- """
-
- target = target.view(-1)
- new_target = [target.clone()]
- target_idxs = []
-
- for i in range(len(self.cutoff) - 1):
- mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1]))
- new_target[0][mask] = self.cutoff[0] + i
-
- if mask.any():
- target_idxs.append(mask.nonzero(as_tuple=False).squeeze(1))
- new_target.append(target[mask].add(-self.cutoff[i]))
- else:
- target_idxs.append(None)
- new_target.append(None)
-
- return new_target, target_idxs
-
- def forward(self, input, target):
- """
- Args:
- input: (b x t x d)
- target: (b x t)
- Returns:
- 2 lists: output for each cutoff section and new targets by cut off
- """
-
- input = input.contiguous().view(-1, input.size(-1))
- input = self.dropout_module(input)
-
- new_target, target_idxs = self.adapt_target(target)
- output = [self.head(input)]
-
- for i in range(len(target_idxs)):
- if target_idxs[i] is not None:
- output.append(self.tail[i](input.index_select(0, target_idxs[i])))
- else:
- output.append(None)
-
- return output, new_target
-
- def get_log_prob(self, input, target):
- """
- Computes the log probabilities for all the words of the vocabulary,
- given a 2D tensor of hidden vectors.
- """
-
- bsz, length, dim = input.size()
- input = input.contiguous().view(-1, dim)
-
- if target is not None:
- _, target_idxs = self.adapt_target(target)
- else:
- target_idxs = None
-
- head_y = self.head(input)
- log_probs = head_y.new_zeros(input.size(0), self.vocab_size)
-
- head_sz = self.cutoff[0] + len(self.tail)
- log_probs[:, :head_sz] = self.lsm(head_y)
- tail_priors = log_probs[:, self.cutoff[0] : head_sz].clone()
-
- for i in range(len(self.tail)):
- start = self.cutoff[i]
- end = self.cutoff[i + 1]
-
- if target_idxs is None:
- tail_out = log_probs[:, start:end]
- tail_out.copy_(self.tail[i](input))
- log_probs[:, start:end] = self.lsm(tail_out).add_(
- tail_priors[:, i, None]
- )
- elif target_idxs[i] is not None:
- idxs = target_idxs[i]
- tail_out = log_probs[idxs, start:end]
- tail_out.copy_(self.tail[i](input[idxs]))
- log_probs[idxs, start:end] = self.lsm(tail_out).add_(
- tail_priors[idxs, i, None]
- )
-
- log_probs = log_probs.view(bsz, length, -1)
- return log_probs
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/ulm/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/ulm/README.md
deleted file mode 100644
index 01459121cebefc61fdc2eae201462aa78d699111..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/textless_nlp/gslm/ulm/README.md
+++ /dev/null
@@ -1,72 +0,0 @@
-# Unit Language Model (ULM)
-
-Here you can find links to the pre-trained ULMs and instructions on training new models using fairseq. At the end of the page, we also share how to run sampling for those models and provide pointers to the transcribed prompts we used.
-
-## Pre-trained models
-
-Using the links below, you can download pre-trained models for various unit types and vocabulary sizes:
-
-| | 50 | 100 | 200
-|-|-|-|-
-| LogMel Filterbank | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km50/logmel50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km100/logmel100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/lm_km200/logmel200_lm.tgz)
-| Modified CPC | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km50/cpc50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km100/cpc100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/lm_km200/cpc200_lm.tgz)
-| HuBERT | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km50/hubert50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km100/hubert100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/lm_km200/hubert200_lm.tgz)
-| Wav2Vec 2.0 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km50/w2v2_50_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km100/w2v2_100_lm.tgz) | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/lm_km200/w2v2_200_lm.tgz)
-
-
-## Preprocessing data
-Assuming that unit-transcribed train, valid, and test sets are located in `data/train.txt`, `data/valid.txt`, and `data/test.txt`, respectively,
-we run the following command to get a preprocessed version of the datast in `data-bin`:
-
-```bash
-fairseq-preprocess --only-source \
- --trainpref data/train.txt --validpref data/valid.txt --testpref data/test.txt \
- --destdir data-bin/ --workers 40
-```
-As a result, the `data-bin` directory should appear.
-
-## Fitting a Unit Language Model (ULM)
-As an ULM, we train a standard fairseq Transformer LM. Assuming 8 GPUs used for training, a good starting point for an ULM training would be:
-```bash
- fairseq-train data-bin/ \
- --task=language_modeling \
- --arch=transformer_lm_big \
- --share-decoder-input-output-embed \
- --dropout=0.1 \
- --attention-dropout=0.1 \
- --optimizer=adam \
- --adam-betas='(0.9, 0.98)' \
- --clip-norm=1.0 \
- --lr=0.0005 \
- --lr-scheduler=inverse_sqrt \
- --warmup-updates=4000 \
- --warmup-init-lr=1e-07 \
- --tokens-per-sample=3072 \
- --update-freq=16 \
- --max-tokens=4096 \
- --num-workers=4 \
- --skip-invalid-size-inputs-valid-test \
- --max-update=500000 \
- --log-interval=10 \
- --seed=100501 \
- --fp16 \
- --sample-break-mode=eos
-```
-This command will train a Transformer-large model (12 layers). You can train other standard LM models provided by fairseq, e.g. specify `--arch=transformer_lm` to train a smaller (6-layer) Transformer model. When training with a different number of GPUs, it might be a good idea to adjust the `update-freq` parameter. To save the GPU memory at an expense of additional computation, it can be useful to enable activation checkpointing with `--checkpoint-activations`.
-
-## Sampling from an ULM
-Once an ULM was trained, we can use it for generating new utterances. Suppose, that the prompts are given in a file named `prompts.txt`. Then we can sample continuations by running the following command:
-
-```bash
- python sample.py data-bin/ \
- --path=checkpoints/checkpoint_best.pt --task=language_modeling --sampling --temperature=0.7 \
- --seed=1 --prompts=prompts.txt --output=samples.txt --max-len-a=0 --max-len-b=500 \
- --prefix-size=-1 --batch-size=16 --fp16 --samples-per-prompt=10
-```
-Here, `--prefix-size` controls the number of tokens that are used to prime the ULM. When set to a positive value, the sampling script will take first `prefix-size` tokens to prompt the ULM; with `0` it runs unconditional sampling and with `-1` the entire prompt is used.
-`--samples-per-prompt` specifies how many utterances are generated with every prompt which can be useful when generating multiple prompt continuations. In this command, `--max-len-a` and `--max-len-b` control the number of generated tokens.
-
-When using a pretrained model from above, `data-bin` should point to the unpacked directory (with `dict.txt` file).
-
-Evaluation-time, to generate prompts, we used utterances from LibriSpeech dev-clean and test-clean that are longer than 6s. We took first 3s from an utterance as a prompt. Unit transcripts of those prompts can be downloaded here: [[dev]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/dev_prompts.tgz) [[test]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/test_prompts.tgz)
-
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/speech_to_text.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/speech_to_text.py
deleted file mode 100644
index 06e292103ef898d607eb23441ce840de1fc800a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/speech_to_text.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from pathlib import Path
-from argparse import Namespace
-
-from fairseq.data import Dictionary, encoders
-from fairseq.data.audio.speech_to_text_dataset import (
- S2TDataConfig,
- SpeechToTextDataset,
- SpeechToTextDatasetCreator,
- get_features_or_waveform
-)
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("speech_to_text")
-class SpeechToTextTask(LegacyFairseqTask):
- @classmethod
- def add_args(cls, parser):
- parser.add_argument("data", help="manifest root path")
- parser.add_argument(
- "--config-yaml",
- type=str,
- default="config.yaml",
- help="Configuration YAML filename (under manifest root)",
- )
- parser.add_argument(
- "--max-source-positions",
- default=6000,
- type=int,
- metavar="N",
- help="max number of tokens in the source sequence",
- )
- parser.add_argument(
- "--max-target-positions",
- default=1024,
- type=int,
- metavar="N",
- help="max number of tokens in the target sequence",
- )
-
- def __init__(self, args, tgt_dict):
- super().__init__(args)
- self.tgt_dict = tgt_dict
- self.data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml)
- self.speaker_to_id = self._get_speaker_to_id()
-
- def _get_speaker_to_id(self):
- speaker_to_id = None
- speaker_set_filename = self.data_cfg.config.get("speaker_set_filename")
- if speaker_set_filename is not None:
- speaker_set_path = Path(self.args.data) / speaker_set_filename
- with open(speaker_set_path) as f:
- speaker_to_id = {r.strip(): i for i, r in enumerate(f)}
- return speaker_to_id
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- data_cfg = S2TDataConfig(Path(args.data) / args.config_yaml)
- dict_path = Path(args.data) / data_cfg.vocab_filename
- if not dict_path.is_file():
- raise FileNotFoundError(f"Dict not found: {dict_path.as_posix()}")
- tgt_dict = Dictionary.load(dict_path.as_posix())
- logger.info(
- f"dictionary size ({data_cfg.vocab_filename}): " f"{len(tgt_dict):,}"
- )
-
- if getattr(args, "train_subset", None) is not None:
- if not all(s.startswith("train") for s in args.train_subset.split(",")):
- raise ValueError('Train splits should be named like "train*".')
- return cls(args, tgt_dict)
-
- def build_criterion(self, args):
- from fairseq import criterions
-
- if self.data_cfg.prepend_tgt_lang_tag and args.ignore_prefix_size != 1:
- raise ValueError(
- 'Please set "--ignore-prefix-size 1" since '
- "target language ID token is prepended as BOS."
- )
- return criterions.build_criterion(args, self)
-
- def load_dataset(self, split, epoch=1, combine=False, **kwargs):
- is_train_split = split.startswith("train")
- pre_tokenizer = self.build_tokenizer(self.args)
- bpe_tokenizer = self.build_bpe(self.args)
- self.datasets[split] = SpeechToTextDatasetCreator.from_tsv(
- self.args.data,
- self.data_cfg,
- split,
- self.tgt_dict,
- pre_tokenizer,
- bpe_tokenizer,
- is_train_split=is_train_split,
- epoch=epoch,
- seed=self.args.seed,
- speaker_to_id=self.speaker_to_id
- )
-
- @property
- def target_dictionary(self):
- return self.tgt_dict
-
- @property
- def source_dictionary(self):
- return None
-
- def max_positions(self):
- return self.args.max_source_positions, self.args.max_target_positions
-
- def build_model(self, args):
- args.input_feat_per_channel = self.data_cfg.input_feat_per_channel
- args.input_channels = self.data_cfg.input_channels
- args.speaker_to_id = self.speaker_to_id
- return super(SpeechToTextTask, self).build_model(args)
-
- def build_generator(
- self,
- models,
- args,
- seq_gen_cls=None,
- extra_gen_cls_kwargs=None,
- ):
- if self.data_cfg.prepend_tgt_lang_tag and args.prefix_size != 1:
- raise ValueError(
- 'Please set "--prefix-size 1" since '
- "target language ID token is prepended as BOS."
- )
- lang_token_ids = {
- i
- for s, i in self.tgt_dict.indices.items()
- if SpeechToTextDataset.is_lang_tag(s)
- }
-
- if extra_gen_cls_kwargs is None:
- extra_gen_cls_kwargs = {}
- extra_gen_cls_kwargs["symbols_to_strip_from_output"] = lang_token_ids
- return super().build_generator(
- models, args, seq_gen_cls=None,
- extra_gen_cls_kwargs=extra_gen_cls_kwargs
- )
-
- def build_tokenizer(self, args):
- logger.info(f"pre-tokenizer: {self.data_cfg.pre_tokenizer}")
- return encoders.build_tokenizer(Namespace(**self.data_cfg.pre_tokenizer))
-
- def build_bpe(self, args):
- logger.info(f"tokenizer: {self.data_cfg.bpe_tokenizer}")
- return encoders.build_bpe(Namespace(**self.data_cfg.bpe_tokenizer))
-
- def get_interactive_tokens_and_lengths(self, lines, encode_fn):
- n_frames = [get_features_or_waveform(p).shape[0] for p in lines]
- return lines, n_frames
-
- def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
- return SpeechToTextDataset(
- "interactive", False, self.data_cfg, src_tokens, src_lengths
- )
diff --git a/spaces/Omnibus/MusicGen/tests/data/test_audio_dataset.py b/spaces/Omnibus/MusicGen/tests/data/test_audio_dataset.py
deleted file mode 100644
index b69c9c397830738b73d6c229009f84b867cda801..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/data/test_audio_dataset.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from itertools import product
-import json
-import math
-import os
-import random
-import typing as tp
-
-import pytest
-import torch
-from torch.utils.data import DataLoader
-
-from audiocraft.data.audio_dataset import (
- AudioDataset,
- AudioMeta,
- _get_audio_meta,
- load_audio_meta,
- save_audio_meta
-)
-from audiocraft.data.zip import PathInZip
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestAudioMeta(TempDirMixin):
-
- def test_get_audio_meta(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(duration * sample_rate)
- wav = get_white_noise(ch, n_frames)
- path = self.get_temp_path('sample.wav')
- save_wav(path, wav, sample_rate)
- m = _get_audio_meta(path, minimal=True)
- assert m.path == path, 'path does not match'
- assert m.sample_rate == sample_rate, 'sample rate does not match'
- assert m.duration == duration, 'duration does not match'
- assert m.amplitude is None
- assert m.info_path is None
-
- def test_save_audio_meta(self):
- audio_meta = [
- AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
- AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
- ]
- empty_audio_meta = []
- for idx, meta in enumerate([audio_meta, empty_audio_meta]):
- path = self.get_temp_path(f'data_{idx}_save.jsonl')
- save_audio_meta(path, meta)
- with open(path, 'r') as f:
- lines = f.readlines()
- read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
- assert len(read_meta) == len(meta)
- for m, read_m in zip(meta, read_meta):
- assert m == read_m
-
- def test_load_audio_meta(self):
- try:
- import dora
- except ImportError:
- dora = None # type: ignore
-
- audio_meta = [
- AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
- AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
- ]
- empty_meta = []
- for idx, meta in enumerate([audio_meta, empty_meta]):
- path = self.get_temp_path(f'data_{idx}_load.jsonl')
- with open(path, 'w') as f:
- for m in meta:
- json_str = json.dumps(m.to_dict()) + '\n'
- f.write(json_str)
- read_meta = load_audio_meta(path)
- assert len(read_meta) == len(meta)
- for m, read_m in zip(meta, read_meta):
- if dora:
- m.path = dora.git_save.to_absolute_path(m.path)
- assert m == read_m, f'original={m}, read={read_m}'
-
-
-class TestAudioDataset(TempDirMixin):
-
- def _create_audio_files(self,
- root_name: str,
- num_examples: int,
- durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
- sample_rate: int = 16_000,
- channels: int = 1):
- root_dir = self.get_temp_dir(root_name)
- for i in range(num_examples):
- if isinstance(durations, float):
- duration = durations
- elif isinstance(durations, tuple) and len(durations) == 1:
- duration = durations[0]
- elif isinstance(durations, tuple) and len(durations) == 2:
- duration = random.uniform(durations[0], durations[1])
- else:
- assert False
- n_frames = int(duration * sample_rate)
- wav = get_white_noise(channels, n_frames)
- path = os.path.join(root_dir, f'example_{i}.wav')
- save_wav(path, wav, sample_rate)
- return root_dir
-
- def _create_audio_dataset(self,
- root_name: str,
- total_num_examples: int,
- durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
- sample_rate: int = 16_000,
- channels: int = 1,
- segment_duration: tp.Optional[float] = None,
- num_examples: int = 10,
- shuffle: bool = True,
- return_info: bool = False):
- root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
- dataset = AudioDataset.from_path(root_dir,
- minimal_meta=True,
- segment_duration=segment_duration,
- num_samples=num_examples,
- sample_rate=sample_rate,
- channels=channels,
- shuffle=shuffle,
- return_info=return_info)
- return dataset
-
- def test_dataset_full(self):
- total_examples = 10
- min_duration, max_duration = 1., 4.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration),
- sample_rate=sample_rate, channels=channels, segment_duration=None)
- assert len(dataset) == total_examples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] <= int(max_duration * sample_rate)
- assert sample.shape[1] >= int(min_duration * sample_rate)
-
- def test_dataset_segment(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples)
- assert len(dataset) == num_samples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == int(segment_duration * sample_rate)
-
- def test_dataset_equal_audio_and_segment_durations(self):
- total_examples = 1
- num_samples = 2
- audio_duration = 1.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples)
- assert len(dataset) == num_samples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == int(segment_duration * sample_rate)
- # the random seek_time adds variability on audio read
- sample_1 = dataset[0]
- sample_2 = dataset[1]
- assert not torch.allclose(sample_1, sample_2)
-
- def test_dataset_samples(self):
- total_examples = 1
- num_samples = 2
- audio_duration = 1.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
-
- create_dataset = partial(
- self._create_audio_dataset,
- 'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples,
- )
-
- dataset = create_dataset(shuffle=True)
- # when shuffle = True, we have different inputs for the same index across epoch
- sample_1 = dataset[0]
- sample_2 = dataset[0]
- assert not torch.allclose(sample_1, sample_2)
-
- dataset_noshuffle = create_dataset(shuffle=False)
- # when shuffle = False, we have same inputs for the same index across epoch
- sample_1 = dataset_noshuffle[0]
- sample_2 = dataset_noshuffle[0]
- assert torch.allclose(sample_1, sample_2)
-
- def test_dataset_return_info(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
- assert len(dataset) == num_samples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample, segment_info = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == int(segment_duration * sample_rate)
- assert segment_info.sample_rate == sample_rate
- assert segment_info.total_frames == int(segment_duration * sample_rate)
- assert segment_info.n_frames <= int(segment_duration * sample_rate)
- assert segment_info.seek_time >= 0
-
- def test_dataset_return_info_no_segment_duration(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = None
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
- assert len(dataset) == total_examples
- assert dataset.sample_rate == sample_rate
- assert dataset.channels == channels
- for idx in range(len(dataset)):
- sample, segment_info = dataset[idx]
- assert sample.shape[0] == channels
- assert sample.shape[1] == segment_info.total_frames
- assert segment_info.sample_rate == sample_rate
- assert segment_info.n_frames <= segment_info.total_frames
-
- def test_dataset_collate_fn(self):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
- batch_size = 4
- dataloader = DataLoader(
- dataset,
- batch_size=batch_size,
- num_workers=0
- )
- for idx, batch in enumerate(dataloader):
- assert batch.shape[0] == batch_size
-
- @pytest.mark.parametrize("segment_duration", [1.0, None])
- def test_dataset_with_meta_collate_fn(self, segment_duration):
- total_examples = 10
- num_samples = 20
- min_duration, max_duration = 1., 4.
- segment_duration = 1.
- sample_rate = 16_000
- channels = 1
- dataset = self._create_audio_dataset(
- 'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
- channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
- batch_size = 4
- dataloader = DataLoader(
- dataset,
- batch_size=batch_size,
- collate_fn=dataset.collater,
- num_workers=0
- )
- for idx, batch in enumerate(dataloader):
- wav, infos = batch
- assert wav.shape[0] == batch_size
- assert len(infos) == batch_size
-
- @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
- [1, True, True, 0.5, 0.5, 0.0],
- [1, False, True, 0.25, 0.5, 0.25],
- [1, True, False, 0.666, 0.333, 0.0],
- [1, False, False, 0.333, 0.333, 0.333],
- [None, False, False, 0.333, 0.333, 0.333]])
- def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
- random.seed(1234)
- rng = torch.Generator()
- rng.manual_seed(1234)
-
- def _get_histogram(dataset, repetitions=20_000):
- counts = {file_meta.path: 0. for file_meta in meta}
- for _ in range(repetitions):
- file_meta = dataset.sample_file(rng)
- counts[file_meta.path] += 1
- return {name: count / repetitions for name, count in counts.items()}
-
- meta = [
- AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
- AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
- AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
- ]
- dataset = AudioDataset(
- meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
- sample_on_duration=sample_on_duration)
- hist = _get_histogram(dataset)
- assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
- assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
- assert math.isclose(hist['c'], c_hist, abs_tol=0.01)
-
- def test_meta_duration_filter_all(self):
- meta = [
- AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
- AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
- AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
- ]
- try:
- AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
- assert False
- except AssertionError:
- assert True
-
- def test_meta_duration_filter_long(self):
- meta = [
- AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
- AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
- AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
- ]
- dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
- assert len(dataset) == 2
diff --git a/spaces/OuroborosM/STLA-BABY/README.md b/spaces/OuroborosM/STLA-BABY/README.md
deleted file mode 100644
index 514d0a6b91cc5c01ef3b2fe5bdc5707623dd9746..0000000000000000000000000000000000000000
--- a/spaces/OuroborosM/STLA-BABY/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: STLA BABY
-emoji: 🏃
-colorFrom: gray
-colorTo: red
-# sdk: gradio
-sdk: docker
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-## New Function to Add:
-### 1. Summarize Tool for llm
-- chain = load_summarize_chain(llm, chain_type="refine")
-- chain.run(docs)
-
-### 2. Add Local Doc Into Vector DB
-- HMI of gradio
-- Code to add in app.py
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/cc_attention.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/cc_attention.py
deleted file mode 100644
index 9207aa95e6730bd9b3362dee612059a5f0ce1c5e..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/cc_attention.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale
-
-
-def NEG_INF_DIAG(n, device):
- """Returns a diagonal matrix of size [n, n].
-
- The diagonal are all "-inf". This is for avoiding calculating the
- overlapped element in the Criss-Cross twice.
- """
- return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)
-
-
-@PLUGIN_LAYERS.register_module()
-class CrissCrossAttention(nn.Module):
- """Criss-Cross Attention Module.
-
- .. note::
- Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch
- to a pure PyTorch and equivalent implementation. For more
- details, please refer to https://github.com/open-mmlab/mmcv/pull/1201.
-
- Speed comparison for one forward pass
-
- - Input size: [2,512,97,97]
- - Device: 1 NVIDIA GeForce RTX 2080 Ti
-
- +-----------------------+---------------+------------+---------------+
- | |PyTorch version|CUDA version|Relative speed |
- +=======================+===============+============+===============+
- |with torch.no_grad() |0.00554402 s |0.0299619 s |5.4x |
- +-----------------------+---------------+------------+---------------+
- |no with torch.no_grad()|0.00562803 s |0.0301349 s |5.4x |
- +-----------------------+---------------+------------+---------------+
-
- Args:
- in_channels (int): Channels of the input feature map.
- """
-
- def __init__(self, in_channels):
- super().__init__()
- self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
- self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
- self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
- self.gamma = Scale(0.)
- self.in_channels = in_channels
-
- def forward(self, x):
- """forward function of Criss-Cross Attention.
-
- Args:
- x (Tensor): Input feature. \
- shape (batch_size, in_channels, height, width)
- Returns:
- Tensor: Output of the layer, with shape of \
- (batch_size, in_channels, height, width)
- """
- B, C, H, W = x.size()
- query = self.query_conv(x)
- key = self.key_conv(x)
- value = self.value_conv(x)
- energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG(
- H, query.device)
- energy_H = energy_H.transpose(1, 2)
- energy_W = torch.einsum('bchw,bchj->bhwj', query, key)
- attn = F.softmax(
- torch.cat([energy_H, energy_W], dim=-1), dim=-1) # [B,H,W,(H+W)]
- out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H])
- out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:])
-
- out = self.gamma(out) + x
- out = out.contiguous()
-
- return out
-
- def __repr__(self):
- s = self.__class__.__name__
- s += f'(in_channels={self.in_channels})'
- return s
diff --git a/spaces/PeepDaSlan9/SDXL-artists-browser/index.css b/spaces/PeepDaSlan9/SDXL-artists-browser/index.css
deleted file mode 100644
index ef60668c873a3fce695f3f7ea063a460cc08d4c6..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/SDXL-artists-browser/index.css
+++ /dev/null
@@ -1,791 +0,0 @@
-html, body {
- background-color: black;
- color: #fff;
- font-family: 'Poppins', sans-serif;
- font-size: 16px;
- margin: 0;
- padding: 0;
- height: 100%;
-}
-
-h3 {
- margin: 5px;
-}
-
-h4 {
- margin: 0px;
- font-weight: normal;
- text-align: center;
- line-height: 150%;
-}
-
-#layout {
- display: flex;
- flex-direction: column;
- height: 100%;
-}
-
-#rows {
- display: flex;
- flex-direction: row;
- flex-grow: 1;
- overflow: auto;
-}
-
-#toggles {
- position: fixed;
- top: 0;
- left: 0;
- width: calc(40% - 20px);
- height: calc(100% - 80px);
- display: flex;
- flex-direction: column;
- flex-wrap: wrap;
- opacity: 1;
- line-height: 140%;
- padding: 20px;
- overflow: auto;
- transition: opacity 50ms 100ms linear;
-}
-
-#gutter {
- position: fixed;
- z-index: 1;
- top: 0;
- left: 40%;
- width: 50px;
- height: calc(100% - 40px);
- flex-shrink: 0;
- background: black;
- background: linear-gradient(90deg, rgba(0,0,0,0) 0%, rgba(0,0,0,1) 40%);
-}
-
-#gutter:hover {
- background: linear-gradient(90deg, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.1) 40%)
-}
-
-#gutter div {
- position: relative;
- width: 20px;
- height: 100%;
- position: relative;
- left: 20px;
- border-right: 1px solid rgba(255,255,255,0.2);
- cursor: col-resize;
-}
-
-#gutter:hover div {
- border-right: 1px solid rgba(255,255,255,0.4);
-}
-
-#gutter div[data-tooltip]::before {
- content: attr(data-tooltip);
- opacity: 0;
- transition: opacity 0ms 0ms linear;
-}
-
-#gutter div[data-tooltip]:hover::before {
- content: attr(data-tooltip);
- position: absolute;
- top: 20px;
- left: 50%;
- transform: translateX(-40%);
- background-color: #555;
- padding: 4px 8px;
- border-radius: 4px;
- box-shadow: 0 5px 10px black;
- white-space: nowrap;
- font-size: 12px;
- color: white;
- opacity: 1;
- transition: opacity 100ms 500ms linear;
- pointer-events: none; /* Make sure the tooltip doesn't interfere with other interactions */
-}
-
-#image-container {
- display: flex;
- flex-direction: row;
- flex-wrap: wrap;
- align-items: flex-start;
- justify-content: space-around;
- margin-left: calc(40% + 50px);
- margin-top: 20px;
- margin-bottom: 20px;
- width: 100%;
-}
-
-#alert {
- position: fixed;
- z-index: 1;
- opacity: 0;
- top: 10px;
- right: -52px;
- padding: 10px;
- color: #00ffe6;
- background-color: #008679;
- border: 1px solid #00ffe6;
- border-radius: 3px;
- box-shadow: 0 5px 20px #0000007d;
-}
-
-#alert.left {
- left: -52px;
- right: initial;
-}
-
-#alert.show {
- right: 12px;
- opacity: 1;
- transition: all 100ms ease-in;
-}
-
-#alert.left.show {
- left: 12px;
- right: initial;
- opacity: 1;
- transition: all 100ms ease-in;
-}
-
-footer {
- flex-shrink: 0;
- padding: 5px 10px;
- text-align: center;
- color: #aaa;
- background-color: #222;
- border-top: 1px solid black;
- font-size: 12px;
-}
-
-footer.special {
- color: #00ffe6;
- background-color: #008679;
- font-size: 14px;
-}
-
-footer > div {
- position: relative;
- opacity: 0.8;
-}
-
-footer a {
- text-decoration: none;
- color: #fff;
-}
-
-footer span strong {
- font-weight: bold;
- color: #fff;
-}
-
-#close_footer {
- position: absolute;
- top: 0;
- right: 0;
-}
-
-footer #close_footer strong {
- display: block;
- background-color: #aaa;
- color: #222;
- border-radius: 40px;
- line-height: 150%;
- cursor: pointer;
-}
-
-footer.special #close_footer strong {
- background-color: #00ffe6;
- color: #008679;
-}
-
-#layout.footerHidden #toggles {
- height: calc(100% - 40px);
-}
-
-#layout.footerHidden #gutter {
- height: calc(100%);
-}
-
-#layout.footerHidden footer {
- display: none;
-}
-
-.divider {
- border-bottom: 1px solid #333;
- margin: 10px 40px 5px 0;
-}
-
-#toggles.hide {
- opacity: 0;
- transition: opacity 50ms linear;
-}
-
-#options_info,
-#options_prompts,
-#options_artist_sort,
-#options_tag_sort {
- margin-right: 10px;
-}
-
-#options_info > span:first-child,
-#options_prompts > span:first-child,
-#options_artist_sort > span:first-child,
-#options_tag_sort > span:first-child {
- margin-left: 21px;
-}
-
-#toggles label {
- margin: 0 20px 0 0;
- white-space: nowrap;
- opacity: 0.8;
- cursor: pointer;
-}
-
-#toggles label:hover {
- opacity: 1;
-}
-
-#toggles #artistsShown {
- margin: 0 0 0 21px;
- white-space: nowrap;
- position: relative;
- top: 1px;
- color: #ffe300;
- opacity: 0.8;
-}
-
-#toggles label.top_all {
- font-weight: bold;
-}
-
-#toggles label.top_control {
- color: #ffe300;
-}
-
-#toggles label.top_control.warning {
- color: #ff0000;
-}
-
-#toggles label.no_matches {
- opacity: 0.3;
- cursor: default;
-}
-
-#toggles label.category {
- color: #00d5c0;
- font-weight: bold;
- padding-bottom: 5px;
- margin: 10px 40px 0 0;
- border-bottom: 1px solid #333;
-}
-
-#toggles label.hidden {
- display: none;
-}
-
-#toggles label .most_used_indicator {
- display: inline-block;
- width: 14px;
- height: 14px;
- visibility: hidden;
- margin-right: -14px;
- position: relative;
- top: 1px;
- left: 4px;
- color: #ffe300;
- font-style: normal;
-}
-
-#toggles #artistsMatching {
- opacity: 0.8;
- cursor: default;
-}
-
-#toggles .count {
- opacity: 0.5;
-}
-
-#toggles .link {
- display: inline-block;
- width: 20px;
- height: 20px;
- opacity: 0.7;
- cursor: pointer;
- box-sizing: border-box;
- margin-left: 5px;
- padding-left: 2px;
- border-radius: 4px;
- line-height: 130%;
-}
-
-#toggles .link.selected {
- background-color: #444;
- opacity: 1;
- cursor: default;
-}
-
-#toggles .link:hover {
- opacity: 1;
-}
-
-#toggles .link:hover::after {
- position: absolute;
- top: 20px;
- left: 20px;
- background-color: black;
- padding: 0px 4px;
- border: 1px solid #777;
- border-radius: 3px;
- color: #ddd;
- box-shadow: 0 5px 10px black;
-}
-
-#infoI:hover::after {
- content: 'instructions';
-}
-
-#infoA:hover::after {
- content: 'about';
-}
-
-#infoX:hover::after {
- content: 'export';
-}
-
-#promptA:hover::after {
- content: 'artwork';
-}
-
-#promptP:hover::after {
- content: 'portraits';
-}
-
-#promptL:hover::after {
- content: 'landscapes';
-}
-
-#sortAA:hover::after {
- content: 'alpha';
-}
-
-#sortAR:hover::after {
- content: 'random';
-}
-
-#sortTA:hover::after {
- content: 'alpha';
-}
-
-#sortTC:hover::after {
- content: 'count';
-}
-
-.information {
- display: none;
- z-index: 2;
- position: fixed;
- top: 20px;
- left: 20px;
- width: calc(40% - 40px);
- max-height: calc(100% - 110px);
- padding: 20px;
- overflow: auto;
- background-color: #222;
- border-radius: 2px;
- border: 1px solid black;
- box-shadow: 0 1px 0px #ffffff3d;
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-.information div {
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-}
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-.information h2, .information h3, .information ul{
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-.information a {
- color: #00ffe7;
- font-weight: bold;
- text-decoration: none;
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-.information a:hover {
- color: #fff;
-}
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- display: block;
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-}
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-#about {
-
-}
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- resize: vertical;
- width: 100%;
- height: 200px;
-}
-
-#export .buttons {
- display: flex;
- flex-direction: row;
-}
-
-#export .buttons div {
- cursor: pointer;
- opacity: 0.8;
- padding: 10px;
-}
-
-#export .buttons div:hover {
- opacity: 1;
-}
-
-#filtersHidingAll {
- display: none;
- font-size: 24px;
- color: #444;
- text-align: center;
- font-weight: bold;
- position: relative;
- top: 50%;
- transform: translate(0%, -50%);
- margin: 0 40px;
- line-height: 220%;
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- display: block;
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-.image-item {
- position: relative;
- display: flex;
- flex-direction: column;
- align-items: center;
- padding: 10px;
- width: 256px;
- background-color: #222;
- border-radius: 2px;
- margin: 0 5px 20px 5px;
- box-shadow: 0 1px 0px #ffffff3d;
- border: 1px solid black;
- overflow: hidden;
-}
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-.image-item.hidden {
- display: none;
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-.image-item > span {
- height: 84px;
- position: relative;
- display: block;
- width: 100%;
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-.image-item h3 {
- display: flex;
- justify-content: center;
- opacity: 0.8;
- cursor: pointer;
- height: 22px;
-}
-
-.image-item h4 {
- width: 258px;
- height: 52px;
- opacity: 0.5;
- cursor: pointer;
- overflow: hidden;
- position: absolute;
- left: -1px;
- padding-bottom: 6px;
- box-sizing: border-box;
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- position: relative;
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- cursor: pointer;
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- animation-duration: 100ms;
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diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/fp16_utils.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/fp16_utils.py
deleted file mode 100644
index 1981011d6859192e3e663e29d13500d56ba47f6c..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/fp16_utils.py
+++ /dev/null
@@ -1,410 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import warnings
-from collections import abc
-from inspect import getfullargspec
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
-from .dist_utils import allreduce_grads as _allreduce_grads
-
-try:
- # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
- # and used; otherwise, auto fp16 will adopt mmcv's implementation.
- # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
- # manually, so the behavior may not be consistent with real amp.
- from torch.cuda.amp import autocast
-except ImportError:
- pass
-
-
-def cast_tensor_type(inputs, src_type, dst_type):
- """Recursively convert Tensor in inputs from src_type to dst_type.
-
- Args:
- inputs: Inputs that to be casted.
- src_type (torch.dtype): Source type..
- dst_type (torch.dtype): Destination type.
-
- Returns:
- The same type with inputs, but all contained Tensors have been cast.
- """
- if isinstance(inputs, nn.Module):
- return inputs
- elif isinstance(inputs, torch.Tensor):
- return inputs.to(dst_type)
- elif isinstance(inputs, str):
- return inputs
- elif isinstance(inputs, np.ndarray):
- return inputs
- elif isinstance(inputs, abc.Mapping):
- return type(inputs)({
- k: cast_tensor_type(v, src_type, dst_type)
- for k, v in inputs.items()
- })
- elif isinstance(inputs, abc.Iterable):
- return type(inputs)(
- cast_tensor_type(item, src_type, dst_type) for item in inputs)
- else:
- return inputs
-
-
-def auto_fp16(apply_to=None, out_fp32=False):
- """Decorator to enable fp16 training automatically.
-
- This decorator is useful when you write custom modules and want to support
- mixed precision training. If inputs arguments are fp32 tensors, they will
- be converted to fp16 automatically. Arguments other than fp32 tensors are
- ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
- backend, otherwise, original mmcv implementation will be adopted.
-
- Args:
- apply_to (Iterable, optional): The argument names to be converted.
- `None` indicates all arguments.
- out_fp32 (bool): Whether to convert the output back to fp32.
-
- Example:
-
- >>> import torch.nn as nn
- >>> class MyModule1(nn.Module):
- >>>
- >>> # Convert x and y to fp16
- >>> @auto_fp16()
- >>> def forward(self, x, y):
- >>> pass
-
- >>> import torch.nn as nn
- >>> class MyModule2(nn.Module):
- >>>
- >>> # convert pred to fp16
- >>> @auto_fp16(apply_to=('pred', ))
- >>> def do_something(self, pred, others):
- >>> pass
- """
-
- def auto_fp16_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # check if the module has set the attribute `fp16_enabled`, if not,
- # just fallback to the original method.
- if not isinstance(args[0], torch.nn.Module):
- raise TypeError('@auto_fp16 can only be used to decorate the '
- 'method of nn.Module')
- if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
- return old_func(*args, **kwargs)
-
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get the argument names to be casted
- args_to_cast = args_info.args if apply_to is None else apply_to
- # convert the args that need to be processed
- new_args = []
- # NOTE: default args are not taken into consideration
- if args:
- arg_names = args_info.args[:len(args)]
- for i, arg_name in enumerate(arg_names):
- if arg_name in args_to_cast:
- new_args.append(
- cast_tensor_type(args[i], torch.float, torch.half))
- else:
- new_args.append(args[i])
- # convert the kwargs that need to be processed
- new_kwargs = {}
- if kwargs:
- for arg_name, arg_value in kwargs.items():
- if arg_name in args_to_cast:
- new_kwargs[arg_name] = cast_tensor_type(
- arg_value, torch.float, torch.half)
- else:
- new_kwargs[arg_name] = arg_value
- # apply converted arguments to the decorated method
- if (TORCH_VERSION != 'parrots' and
- digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
- with autocast(enabled=True):
- output = old_func(*new_args, **new_kwargs)
- else:
- output = old_func(*new_args, **new_kwargs)
- # cast the results back to fp32 if necessary
- if out_fp32:
- output = cast_tensor_type(output, torch.half, torch.float)
- return output
-
- return new_func
-
- return auto_fp16_wrapper
-
-
-def force_fp32(apply_to=None, out_fp16=False):
- """Decorator to convert input arguments to fp32 in force.
-
- This decorator is useful when you write custom modules and want to support
- mixed precision training. If there are some inputs that must be processed
- in fp32 mode, then this decorator can handle it. If inputs arguments are
- fp16 tensors, they will be converted to fp32 automatically. Arguments other
- than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
- torch.cuda.amp is used as the backend, otherwise, original mmcv
- implementation will be adopted.
-
- Args:
- apply_to (Iterable, optional): The argument names to be converted.
- `None` indicates all arguments.
- out_fp16 (bool): Whether to convert the output back to fp16.
-
- Example:
-
- >>> import torch.nn as nn
- >>> class MyModule1(nn.Module):
- >>>
- >>> # Convert x and y to fp32
- >>> @force_fp32()
- >>> def loss(self, x, y):
- >>> pass
-
- >>> import torch.nn as nn
- >>> class MyModule2(nn.Module):
- >>>
- >>> # convert pred to fp32
- >>> @force_fp32(apply_to=('pred', ))
- >>> def post_process(self, pred, others):
- >>> pass
- """
-
- def force_fp32_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # check if the module has set the attribute `fp16_enabled`, if not,
- # just fallback to the original method.
- if not isinstance(args[0], torch.nn.Module):
- raise TypeError('@force_fp32 can only be used to decorate the '
- 'method of nn.Module')
- if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
- return old_func(*args, **kwargs)
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get the argument names to be casted
- args_to_cast = args_info.args if apply_to is None else apply_to
- # convert the args that need to be processed
- new_args = []
- if args:
- arg_names = args_info.args[:len(args)]
- for i, arg_name in enumerate(arg_names):
- if arg_name in args_to_cast:
- new_args.append(
- cast_tensor_type(args[i], torch.half, torch.float))
- else:
- new_args.append(args[i])
- # convert the kwargs that need to be processed
- new_kwargs = dict()
- if kwargs:
- for arg_name, arg_value in kwargs.items():
- if arg_name in args_to_cast:
- new_kwargs[arg_name] = cast_tensor_type(
- arg_value, torch.half, torch.float)
- else:
- new_kwargs[arg_name] = arg_value
- # apply converted arguments to the decorated method
- if (TORCH_VERSION != 'parrots' and
- digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
- with autocast(enabled=False):
- output = old_func(*new_args, **new_kwargs)
- else:
- output = old_func(*new_args, **new_kwargs)
- # cast the results back to fp32 if necessary
- if out_fp16:
- output = cast_tensor_type(output, torch.float, torch.half)
- return output
-
- return new_func
-
- return force_fp32_wrapper
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
- warnings.warning(
- '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be '
- 'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads')
- _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb)
-
-
-def wrap_fp16_model(model):
- """Wrap the FP32 model to FP16.
-
- If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
- backend, otherwise, original mmcv implementation will be adopted.
-
- For PyTorch >= 1.6, this function will
- 1. Set fp16 flag inside the model to True.
-
- Otherwise:
- 1. Convert FP32 model to FP16.
- 2. Remain some necessary layers to be FP32, e.g., normalization layers.
- 3. Set `fp16_enabled` flag inside the model to True.
-
- Args:
- model (nn.Module): Model in FP32.
- """
- if (TORCH_VERSION == 'parrots'
- or digit_version(TORCH_VERSION) < digit_version('1.6.0')):
- # convert model to fp16
- model.half()
- # patch the normalization layers to make it work in fp32 mode
- patch_norm_fp32(model)
- # set `fp16_enabled` flag
- for m in model.modules():
- if hasattr(m, 'fp16_enabled'):
- m.fp16_enabled = True
-
-
-def patch_norm_fp32(module):
- """Recursively convert normalization layers from FP16 to FP32.
-
- Args:
- module (nn.Module): The modules to be converted in FP16.
-
- Returns:
- nn.Module: The converted module, the normalization layers have been
- converted to FP32.
- """
- if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
- module.float()
- if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3':
- module.forward = patch_forward_method(module.forward, torch.half,
- torch.float)
- for child in module.children():
- patch_norm_fp32(child)
- return module
-
-
-def patch_forward_method(func, src_type, dst_type, convert_output=True):
- """Patch the forward method of a module.
-
- Args:
- func (callable): The original forward method.
- src_type (torch.dtype): Type of input arguments to be converted from.
- dst_type (torch.dtype): Type of input arguments to be converted to.
- convert_output (bool): Whether to convert the output back to src_type.
-
- Returns:
- callable: The patched forward method.
- """
-
- def new_forward(*args, **kwargs):
- output = func(*cast_tensor_type(args, src_type, dst_type),
- **cast_tensor_type(kwargs, src_type, dst_type))
- if convert_output:
- output = cast_tensor_type(output, dst_type, src_type)
- return output
-
- return new_forward
-
-
-class LossScaler:
- """Class that manages loss scaling in mixed precision training which
- supports both dynamic or static mode.
-
- The implementation refers to
- https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py.
- Indirectly, by supplying ``mode='dynamic'`` for dynamic loss scaling.
- It's important to understand how :class:`LossScaler` operates.
- Loss scaling is designed to combat the problem of underflowing
- gradients encountered at long times when training fp16 networks.
- Dynamic loss scaling begins by attempting a very high loss
- scale. Ironically, this may result in OVERflowing gradients.
- If overflowing gradients are encountered, :class:`FP16_Optimizer` then
- skips the update step for this particular iteration/minibatch,
- and :class:`LossScaler` adjusts the loss scale to a lower value.
- If a certain number of iterations occur without overflowing gradients
- detected,:class:`LossScaler` increases the loss scale once more.
- In this way :class:`LossScaler` attempts to "ride the edge" of always
- using the highest loss scale possible without incurring overflow.
-
- Args:
- init_scale (float): Initial loss scale value, default: 2**32.
- scale_factor (float): Factor used when adjusting the loss scale.
- Default: 2.
- mode (str): Loss scaling mode. 'dynamic' or 'static'
- scale_window (int): Number of consecutive iterations without an
- overflow to wait before increasing the loss scale. Default: 1000.
- """
-
- def __init__(self,
- init_scale=2**32,
- mode='dynamic',
- scale_factor=2.,
- scale_window=1000):
- self.cur_scale = init_scale
- self.cur_iter = 0
- assert mode in ('dynamic',
- 'static'), 'mode can only be dynamic or static'
- self.mode = mode
- self.last_overflow_iter = -1
- self.scale_factor = scale_factor
- self.scale_window = scale_window
-
- def has_overflow(self, params):
- """Check if params contain overflow."""
- if self.mode != 'dynamic':
- return False
- for p in params:
- if p.grad is not None and LossScaler._has_inf_or_nan(p.grad.data):
- return True
- return False
-
- def _has_inf_or_nan(x):
- """Check if params contain NaN."""
- try:
- cpu_sum = float(x.float().sum())
- except RuntimeError as instance:
- if 'value cannot be converted' not in instance.args[0]:
- raise
- return True
- else:
- if cpu_sum == float('inf') or cpu_sum == -float('inf') \
- or cpu_sum != cpu_sum:
- return True
- return False
-
- def update_scale(self, overflow):
- """update the current loss scale value when overflow happens."""
- if self.mode != 'dynamic':
- return
- if overflow:
- self.cur_scale = max(self.cur_scale / self.scale_factor, 1)
- self.last_overflow_iter = self.cur_iter
- else:
- if (self.cur_iter - self.last_overflow_iter) % \
- self.scale_window == 0:
- self.cur_scale *= self.scale_factor
- self.cur_iter += 1
-
- def state_dict(self):
- """Returns the state of the scaler as a :class:`dict`."""
- return dict(
- cur_scale=self.cur_scale,
- cur_iter=self.cur_iter,
- mode=self.mode,
- last_overflow_iter=self.last_overflow_iter,
- scale_factor=self.scale_factor,
- scale_window=self.scale_window)
-
- def load_state_dict(self, state_dict):
- """Loads the loss_scaler state dict.
-
- Args:
- state_dict (dict): scaler state.
- """
- self.cur_scale = state_dict['cur_scale']
- self.cur_iter = state_dict['cur_iter']
- self.mode = state_dict['mode']
- self.last_overflow_iter = state_dict['last_overflow_iter']
- self.scale_factor = state_dict['scale_factor']
- self.scale_window = state_dict['scale_window']
-
- @property
- def loss_scale(self):
- return self.cur_scale
diff --git a/spaces/PrabhuKiranKonda/fastapi-postgres-todo-api/psql_database.py b/spaces/PrabhuKiranKonda/fastapi-postgres-todo-api/psql_database.py
deleted file mode 100644
index 1d67b8c564a93339a4d614d1f4827c281b50683c..0000000000000000000000000000000000000000
--- a/spaces/PrabhuKiranKonda/fastapi-postgres-todo-api/psql_database.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os
-import urllib.parse as up
-import sqlalchemy as sa
-from sqlalchemy.orm import sessionmaker
-from sqlalchemy.ext.declarative import declarative_base
-
-# Parse the DATABASE_URL using urllib.parse
-up.uses_netloc.append("postgres")
-url = up.urlparse(
- "postgres://xphzyodo:jWMawSzATJaJGSkOP90KSucl2Ni9DEPG@john.db.elephantsql.com/xphzyodo")
-
-
-# Create the connection string
-conn_string = f'postgresql+psycopg2://{url.username}:{url.password}@{url.hostname}/{url.path[1:]}'
-
-# Create the engine using the connection string
-engine = sa.create_engine(conn_string)
-
-# Reassign the engine to your existing engine variable
-engine = engine
-
-# Create the session factory
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
-
-# Reassign the session factory to your existing SessionLocal variable
-SessionLocal = SessionLocal
-
-# Reassign the Base to your existing Base variable
-Base = declarative_base()
diff --git a/spaces/RamAnanth1/videocrafter/style.css b/spaces/RamAnanth1/videocrafter/style.css
deleted file mode 100644
index eaaab4f701da82e41adcafce98d9c86c33a1b258..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/videocrafter/style.css
+++ /dev/null
@@ -1,191 +0,0 @@
-/*
-This CSS file is copied from here:
-https://huggingface.co/spaces/stabilityai/stable-diffusion/blob/2794a3c3ba66115c307075098e713f572b08bf80/app.py
-*/
-
-h1 {
- text-align: center;
-}
-
-.gradio-container {
- font-family: 'IBM Plex Sans', sans-serif;
-}
-
-.gr-button {
- color: white;
- border-color: black;
- background: black;
-}
-
-input[type='range'] {
- accent-color: black;
-}
-
-.dark input[type='range'] {
- accent-color: #dfdfdf;
-}
-
-.container {
- max-width: 730px;
- margin: auto;
- padding-top: 1.5rem;
-}
-
-#gallery {
- min-height: 22rem;
- margin-bottom: 15px;
- margin-left: auto;
- margin-right: auto;
- border-bottom-right-radius: .5rem !important;
- border-bottom-left-radius: .5rem !important;
-}
-
-#gallery>div>.h-full {
- min-height: 20rem;
-}
-
-.details:hover {
- text-decoration: underline;
-}
-
-.gr-button {
- white-space: nowrap;
-}
-
-.gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
-}
-
-#advanced-btn {
- font-size: .7rem !important;
- line-height: 19px;
- margin-top: 12px;
- margin-bottom: 12px;
- padding: 2px 8px;
- border-radius: 14px !important;
-}
-
-#advanced-options {
- display: none;
- margin-bottom: 20px;
-}
-
-.footer {
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
-}
-
-.footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
-}
-
-.dark .footer {
- border-color: #303030;
-}
-
-.dark .footer>p {
- background: #0b0f19;
-}
-
-.acknowledgments h4 {
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
-}
-
-.animate-spin {
- animation: spin 1s linear infinite;
-}
-
-@keyframes spin {
- from {
- transform: rotate(0deg);
- }
-
- to {
- transform: rotate(360deg);
- }
-}
-
-#share-btn-container {
- display: flex;
- padding-left: 0.5rem !important;
- padding-right: 0.5rem !important;
- background-color: #000000;
- justify-content: center;
- align-items: center;
- border-radius: 9999px !important;
- width: 13rem;
- margin-top: 10px;
- margin-left: auto;
-}
-
-#share-btn {
- all: initial;
- color: #ffffff;
- font-weight: 600;
- cursor: pointer;
- font-family: 'IBM Plex Sans', sans-serif;
- margin-left: 0.5rem !important;
- padding-top: 0.25rem !important;
- padding-bottom: 0.25rem !important;
- right: 0;
-}
-
-#share-btn * {
- all: unset;
-}
-
-#share-btn-container div:nth-child(-n+2) {
- width: auto !important;
- min-height: 0px !important;
-}
-
-#share-btn-container .wrap {
- display: none !important;
-}
-
-.gr-form {
- flex: 1 1 50%;
- border-top-right-radius: 0;
- border-bottom-right-radius: 0;
-}
-
-#prompt-container {
- gap: 0;
-}
-
-#prompt-text-input,
-#negative-prompt-text-input {
- padding: .45rem 0.625rem
-}
-
-#component-16 {
- border-top-width: 1px !important;
- margin-top: 1em
-}
-
-.image_duplication {
- position: absolute;
- width: 100px;
- left: 50px
-}
-
-#component-0 {
- max-width: 730px;
- margin: auto;
- padding-top: 1.5rem;
-}
\ No newline at end of file
diff --git a/spaces/Ramse/TTS_Hindi/app_requirnment.py b/spaces/Ramse/TTS_Hindi/app_requirnment.py
deleted file mode 100644
index dd1a813cb683e8efd72c95df9f21849d333608cf..0000000000000000000000000000000000000000
--- a/spaces/Ramse/TTS_Hindi/app_requirnment.py
+++ /dev/null
@@ -1,729 +0,0 @@
-import torch
-import yaml
-
-from text import _symbol_to_id
-import math
-from utils.tools import pad_1D, pad_2D, get_mask_from_lengths
-
-import torch.nn as nn
-import torch.nn.functional as F
-import torch
-
-from modules.modules import VarianceAdaptor
-
-import re
-from tqdm import tqdm
-import os
-from num_to_words import num_to_word
-import epitran
-from epitran.backoff import Backoff
-import numpy as np
-
-model_config = yaml.load(
- open(r"./config/LJSpeech/model.yaml", "r"), Loader=yaml.FullLoader
- )
-preprocess_config = yaml.load(
- open(r"./config/LJSpeech/preprocess.yaml", "r"), Loader=yaml.FullLoader
-)
-backoff = Backoff(['hin-Deva', 'eng-Latn'])
-
-epi_dev = epitran.Epitran("hin-Deva")
-epi_eng = epitran.Epitran("eng-Latn")
-
-class PositionalEncoding(nn.Module):
- "Implement the PE function."
- def __init__(self, d_model, dropout= 0, max_len=5000):
- super(PositionalEncoding, self).__init__()
- self.dropout = nn.Dropout(p=dropout)
-
- # Compute the positional encodings once in log space.
- pe = nn.Parameter(torch.zeros(max_len, d_model), requires_grad= False)
- position = torch.arange(0, max_len).unsqueeze(1)
- div_term = torch.exp(torch.arange(0, d_model, 2) *
- -(math.log(10000.0) / d_model))
- pe[:, 0::2] = torch.sin(position * div_term)
- pe[:, 1::2] = torch.cos(position * div_term)
- pe = pe.unsqueeze(0)
- self.register_buffer('pe', pe)
-
- def forward(self):
- x = self.pe[:, :]
- return self.dropout(x)
-
-
-class ScaledDotProductAttention(nn.Module):
- def __init__(self):
- super(ScaledDotProductAttention, self).__init__()
-
- self.softmax = nn.Softmax(dim = 2)
-
- def forward(self, q, k, v, mask = None):
- x = torch.einsum("bij,bjk->bik", q,k.transpose(1,2))
-
- x = x / np.power(q.size()[-1], 0.5)
- if mask is not None:
- x = x.masked_fill(mask, -np.inf)
-
- attention = self.softmax(x)
-
- x = torch.einsum("bij,bik -> bik",attention,v)
-
- return x, attention
-
-class MultieadAttention(nn.Module):
- def __init__(self, n_head, d_model, d_k, d_v, dropout= 0.1):
- super(MultieadAttention, self).__init__()
- self.selfdotproductattention = ScaledDotProductAttention()
- self.dropout = nn.Dropout(dropout)
-
- self.linear_input_layer = nn.Linear(d_model, n_head * d_k, bias=False)
- self.linear_output_layer = nn.Linear(n_head * d_k, d_model, bias = False)
-
- self.layer_norm = nn.LayerNorm(d_model)
-
- def forward(self, q, k, v, mask = None):
-
-
- residual = q
-
- q = self.linear_input_layer(q)
- k = self.linear_input_layer(k)
- v = self.linear_input_layer(v)
-
-
- output, attention = self.selfdotproductattention(q, k, v, mask)
-
- output = self.dropout(self.linear_output_layer(output))
-
- output = self.layer_norm(output + residual)
-
- return output, attention
-
-class PositionWiseFFN(nn.Module):
- def __init__(self, d_model, d_hidden, kernel_size, dropout= 0.1):
- super(PositionWiseFFN, self).__init__()
-
- self.conv_1 = nn.Conv1d(in_channels=d_model, out_channels=d_hidden,
- kernel_size=kernel_size[0], padding=(kernel_size[0]- 1 )//2)
-
- self.conv_2 = nn.Conv1d(in_channels= d_hidden, out_channels= d_model,
- kernel_size=kernel_size[1], padding=(kernel_size[1]-1)//2)
-
- self.layer_norm = nn.LayerNorm(d_model)
- self.dropout = nn.Dropout(dropout)
- self.relu = nn.ReLU()
-
- def forward(self, x):
-
- residual = x
- x = x.transpose(1,2)
-
- x = self.relu(self.conv_1(x))
- x = self.conv_2(x)
- x = x.transpose(1,2)
- x = self.dropout(x)
- x = self.layer_norm(x + residual)
- return x
-
-class FFTBlock(nn.Module):
- def __init__(self, d_model, n_head, d_k, d_v, d_hidden, kernel_size, dropout):
- super(FFTBlock, self).__init__()
-
- self.mult_attn = MultieadAttention(n_head, d_model,d_k, d_v, dropout)
- self.pos_ffn = PositionWiseFFN(d_model, d_hidden, kernel_size, dropout)
-
- def forward(self, encoder_input, mask=None, self_attention_mask=None):
-
- encoder_output, encoder_attention = self.mult_attn(encoder_input, encoder_input, encoder_input, self_attention_mask)
- encoder_output = self.pos_ffn(encoder_output).masked_fill(mask.unsqueeze(-1), 0)
-
- return encoder_output, encoder_attention
-
-class Encoder(nn.Module):
- def __init__(self, config):
- super(Encoder, self).__init__()
-
- self.n_encoder_layer= config["transformer"]["encoder_layer"]
- self.n_head= config["transformer"]["encoder_head"]
- self.d_model= config["transformer"]["encoder_dim"]
- self.conv_filter_size= config["transformer"]["conv_filter_size"]
- self.conv_kernel_size= config["transformer"]["conv_kernel_size"]
- self.encoder_dropout= config["transformer"]["encoder_dropout"]
- # self.encoder_max_token = config["transformer"]["encoder_max_token"]
-
- d_k = d_v = self.d_model//self.n_head
- self.embedding = nn.Embedding(num_embeddings=len(_symbol_to_id), embedding_dim=self.d_model)
- nn.init.xavier_normal_(self.embedding.weight)
-
- self.positional_emb = PositionalEncoding(self.d_model, max_len = 5000)
-
- self.layer_stack = nn.ModuleList([FFTBlock(
- self.d_model, self.n_head,d_k, d_v, self.conv_filter_size, self.conv_kernel_size, self.encoder_dropout
- )
- for _ in range(self.n_encoder_layer)])
-
- def forward(self, encoder_input, mask):
-
- batch_size, max_len = encoder_input.shape[0], encoder_input.shape[1]
- self_att_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
-
- embedding_output = self.embedding(encoder_input)
-
- pos_embedding = self.positional_emb()
-
- encoder_output = embedding_output + pos_embedding[:, :embedding_output.size(1)]
- # encoder_output = self.positional_emb(self.embedding(encoder_input))
-
- encoder_output = encoder_output.masked_fill(mask.unsqueeze(-1), 0)
-
- for layer in self.layer_stack:
- encoder_output, encoder_attention = layer(encoder_output,mask, self_att_mask)
-
- return encoder_output
-
-
-class ScheduledOptim:
- """ A simple wrapper class for learning rate scheduling """
-
- def __init__(self, model, train_config, model_config, current_step):
-
- self._optimizer = torch.optim.AdamW(
- model.parameters(),
- betas=train_config["optimizer"]["betas"],
- eps=train_config["optimizer"]["eps"],
- weight_decay=train_config["optimizer"]["weight_decay"],
- )
- self.n_warmup_steps = train_config["optimizer"]["warm_up_step"]
- self.anneal_steps = train_config["optimizer"]["anneal_steps"]
- self.anneal_rate = train_config["optimizer"]["anneal_rate"]
- self.current_step = current_step
- self.init_lr = np.power(model_config["transformer"]["encoder_dim"], -0.5)
-
- def step_and_update_lr(self):
- self._update_learning_rate()
- self._optimizer.step()
-
- def zero_grad(self):
- # print(self.init_lr)
- self._optimizer.zero_grad()
-
- def load_state_dict(self, path):
- self._optimizer.load_state_dict(path)
-
- def _get_lr_scale(self):
- lr = np.min(
- [
- np.power(self.current_step, -0.5),
- np.power(self.n_warmup_steps, -1.5) * self.current_step,
- ]
- )
- for s in self.anneal_steps:
- if self.current_step > s:
- lr = lr * self.anneal_rate
- return lr
-
- def _update_learning_rate(self):
- """ Learning rate scheduling per step """
- self.current_step += 1
- lr = self.init_lr * self._get_lr_scale()
-
- for param_group in self._optimizer.param_groups:
- param_group["lr"] = lr
-
-class Swish(nn.Module):
- def __init__(self):
- super(Swish, self).__init__()
-
- def forward(self,x):
- x = x * torch.sigmoid(x)
- return x
-
-class Conv(nn.Module):
- def __init__(self, in_channels,out_channels,
- kernel_size, stride,padding=0,
- dilation= 1 , bias = True):
- super(Conv, self).__init__()
-
- self.conv = nn.Conv1d(in_channels,
- out_channels,
- kernel_size,
- stride,
- padding = padding,
- dilation = dilation,
- bias = bias)
-
- def forward(self, x):
- x = x.contiguous().transpose(1,2)
- x = self.conv(x)
- x = x.contiguous().transpose(1,2)
- return x
-
-
-class ResidualBlock(nn.Module):
- def __init__(self, residual_channels, wavenet_conv1d_filter):
- super(ResidualBlock, self).__init__()
-
- self.conv1d_1 = Conv(residual_channels,
- wavenet_conv1d_filter,
- kernel_size=3, stride=1, padding=1, bias=False)
- self.conv1d_2 = Conv(residual_channels,
- wavenet_conv1d_filter,
- kernel_size=1, stride=1, padding=0, bias=False)
- self.conv1d_3 = Conv(wavenet_conv1d_filter,
- residual_channels,
- kernel_size=1, stride=1, padding=0, bias=False)
- self.conv1d_4 = Conv(wavenet_conv1d_filter,
- residual_channels,
- kernel_size=1, stride=1, padding=0, bias=False)
-
- self.tanh = nn.Tanh()
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, x_t, pos_enc, c):
- x = x_t + pos_enc
-
- x = self.conv1d_1(x)
-
- c = self.conv1d_2(c)
-
- new_x = x + c
-
- new_x_tanh = self.tanh(new_x)
- new_x_sigmoid = self.sigmoid(new_x)
-
- new_x_tanh_new_x_sigmoid = torch.mul(new_x_tanh, new_x_sigmoid)
-
- residual_out = self.conv1d_3(new_x_tanh_new_x_sigmoid)
-
- residual_skip_out = self.conv1d_4(new_x_tanh_new_x_sigmoid)
-
- return residual_out, residual_skip_out
-
-
-from math import sqrt
-
-
-class spectogram_denoiser(nn.Module):
- def __init__(self, device):
- super(spectogram_denoiser, self).__init__()
-
- self.device = device
- self.N = model_config["spectogram_denoiser"]["residual_layers"]
-
- self.residual_channels = model_config["spectogram_denoiser"]["residual_channels"]
- self.d_model = model_config["spectogram_denoiser"]["residual_channels"]
- self.wavenet_conv1d_kernel = model_config["spectogram_denoiser"]["wavenet_conv1d_kernel"]
- self.wavenet_conv1d_filter = model_config["spectogram_denoiser"]["wavenet_conv1d_filter"]
-
- self.conv1d_outside_layer_1 = Conv(80,
- self.residual_channels,
- kernel_size=1, stride=1, padding=0, bias=False)
-
- self.conv1d_outside_layer_2 = Conv(self.residual_channels,
- 80,
- kernel_size=1, stride=1, padding=0, bias=False)
-
- # nn.init.zeros_(self.conv1d_outside_layer_2.weight)
-
- self.positional_enc = PositionalEncoding(self.d_model)
- self.relu = nn.ReLU()
- self.swish_act = Swish()
- self.FC = nn.Sequential(
- nn.Linear(self.d_model, self.d_model),
- self.swish_act,
- nn.Linear(self.d_model, self.d_model))
-
- self.n_residual_block = nn.ModuleList(
- [ResidualBlock(self.residual_channels, self.wavenet_conv1d_filter) for i in range(self.N)])
-
- def forward(self, x_t, t, c):
- # pos_enc = self.positional_enc(torch.ones_like(x_t, device= device) * t[0])
-
- pos_enc = self.positional_enc()
-
- pos_enc = t[0] + pos_enc[:, :x_t.size(1)].expand(x_t.size(0), -1, -1)
-
- # if mel_mask is not None:
- # pos_enc = pos_enc.masked_fill(mel_masks.unsqueeze(-1).expand(-1, -1, self.d_model), 0.0)
- # x_t = x_t.masked_fill(mel_masks.unsqueeze(-1).expand(-1, -1, self.d_model), 0.0)
-
- pos_enc = self.FC(pos_enc)
-
- x_t = self.conv1d_outside_layer_1(x_t)
- x_t = self.relu(x_t)
-
- # print(x_t.shape, pos_enc.shape)
- residual_skip_out_add = torch.zeros_like(x_t).to(self.device)
-
- for layers in self.n_residual_block:
- residual_out, residual_skip_out = layers(x_t, pos_enc, c)
- residual_out = (residual_out + x_t) / sqrt(2.0)
- x_t = residual_out
-
- residual_skip_out_add = residual_skip_out_add + residual_skip_out
-
- x_0 = self.conv1d_outside_layer_2(residual_skip_out_add / sqrt(self.N))
- # x_0 = self.relu(x_0)
- return x_0
-
-
-class Diffusion(nn.Module):
- def __init__(self, device, noise_steps=4, beta_start=1e-4, beta_end=0.02):
-
- super(Diffusion, self).__init__()
- self.device = device
- self.noise_steps = noise_steps
- self.beta_start = beta_start
- self.beta_end = beta_end
-
- self.beta = self.prepare_schedule(type="cosine_beta_schedule")
-
- self.alpha = 1 - self.beta
- self.alpha_hat = torch.cumprod(self.alpha, dim=0)
-
- self.alpha_hat_prev = F.pad(self.alpha_hat[:-1], (1, 0), value=1)
-
- self.posterior_variance = ((1. - self.alpha_hat_prev) / (1. - self.alpha_hat)) * self.beta
-
- self.alpha.to(device)
- self.alpha_hat.to(device)
- self.alpha_hat_prev.to(device)
- self.posterior_variance.to(device)
-
- self.encoder = Encoder(model_config)
- self.variance_adaptor = VarianceAdaptor(preprocess_config, model_config, device)
-
- self.spectogram_model = spectogram_denoiser(device=device)
-
- def prepare_schedule(self, type):
- if type == "linear":
- return torch.linspace(self.beta_start, self.beta_end, self.noise_steps)
-
- if type == "cosine_beta_schedule":
- s = 0.008
- """
- cosine schedule as proposed in https://arxiv.org/abs/2102.09672
- """
- steps = self.noise_steps + 1
- x = torch.linspace(0, self.noise_steps, steps)
- alphas_cumprod = torch.cos(((x / self.noise_steps) + s) / (1 + s) * torch.pi * 0.5) ** 2
- alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
- betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
- return torch.clip(betas, 0.0001, 0.9999)
-
- def sample_timesteps(self, n):
- return torch.randint(1, self.noise_steps, size=(n,))
-
- def extract(self, a, t, x_shape):
- batch_size = t.shape[0]
- out = a.gather(-1, t.cpu())
- return out.reshape(batch_size, *((1,) * (len(x_shape) - 1))).to(t.device)
-
- def noisy_image(self, x, t, noise=None):
-
- """
- x : input to add noise
- t : t is of shape [B, ] (where eg. [1,1,1,1]
- """
-
- alpha_hat_t = self.extract(self.alpha_hat, t, x.shape)
- if noise is None:
- noise = torch.randn_like(x)
-
- sqrt_alpha_hat = torch.sqrt(alpha_hat_t).to(self.device)
- sqrt_one_minus_alpha_hat = torch.sqrt((1 - alpha_hat_t)).to(self.device)
-
- return sqrt_alpha_hat * x + sqrt_one_minus_alpha_hat * noise, noise
-
- @torch.no_grad()
- def p_sample(self, model, x, t, c, t_index):
- x_shape = x.shape
- beta_t = self.extract(self.beta, t, x_shape).to(device)
- alpha_t = self.extract(self.alpha, t, x_shape).to(device)
- alpha_hat_t = self.extract(self.alpha_hat, t, x_shape).to(device)
-
- model_mean = (1 / torch.sqrt(alpha_t)) * (x - (beta_t * (model(x, t, c)) / (torch.sqrt((1 - alpha_hat_t)))))
-
- if t_index == 0:
- return model_mean
- else:
- posterior_variance_t = self.extract(self.posterior_variance, t, x_shape).to(device)
- noise = torch.randn_like(x).to(device)
- return model_mean + torch.sqrt(posterior_variance_t) * noise
-
- @torch.no_grad()
- def p_sample_loop(self, model, x, c):
-
- img = torch.randn(x.shape, device=self.device)
-
- list_of_images = []
- for i in tqdm(reversed(range(0, self.noise_steps)), position=0):
- t_index = i
- t = (torch.ones(x.shape[0]) * t_index).long().to(device)
- img = self.p_sample(model, img, t, c, t_index)
- list_of_images.append(img.to("cpu").numpy())
- return list_of_images
-
- def sample(self, model, x, c):
- model.eval()
- with torch.no_grad():
- list_of_images = self.p_sample_loop(model, x, c)
-
- model.train()
-
- return list_of_images
-
- def forward(self, t, texts, text_lens, max_text_lens,
- mels=None, mel_lens=None, max_mel_lens=None, pitches=None, energies=None, durations=None, infer=False):
-
- batch_size, _ = texts.size()
- # Calculating src mask on given text
- src_masks = get_mask_from_lengths(text_lens, max_text_lens)
-
- # Calculating mel_masks
- if infer == False:
- mel_masks = get_mask_from_lengths(mel_lens, max_mel_lens)
- mel_masks_expanded_dim = (mel_masks == False).unsqueeze(-1).expand(batch_size, -1, 80)
-
- # Passing texts and src mask to encoder layer
- encoder_output = self.encoder(texts, src_masks)
-
- # Calculating duration, pitch, energy
- (output,
- p_predictions,
- e_predictions,
- log_d_predictions,
- d_rounded,
- mel_lens,
- mel_masks) = self.variance_adaptor(
- encoder_output,
- src_masks,
- mel_masks,
- max_mel_lens,
- pitches,
- energies,
- durations)
-
- x_t_noisy_mel, noise = self.noisy_image(mels, t)
-
- x_t_noisy_mel = x_t_noisy_mel * mel_masks_expanded_dim
-
- predicted_mels = self.spectogram_model(x_t_noisy_mel, t,
- output) * mel_masks_expanded_dim # (output = c) in spectogram_model
-
- return predicted_mels, output, p_predictions, e_predictions, log_d_predictions, d_rounded, mel_lens, mel_masks, src_masks
-
- else:
- mel_masks = None
- mel_masks_expanded_dim = None
-
- # Passing texts and src mask to encoder layer
- encoder_output = self.encoder(texts, src_masks)
-
- # Calculating duration, pitch, energy
- (output,
- p_predictions,
- e_predictions,
- log_d_predictions,
- d_rounded,
- mel_lens,
- mel_masks) = self.variance_adaptor(
- encoder_output,
- src_masks,
- None,
- None,
- None,
- None,
- None)
-
- img = torch.randn((output.shape[0], output.shape[1], 80))
- l = diffusion_model.sample(self.spectogram_model, img, output)
-
- predicted_mels = l[-1][0]
- # list_of_audio = self.sample(self.spectogram_model, output, output)
-
- return predicted_mels, output, p_predictions, e_predictions, log_d_predictions, d_rounded, mel_lens, mel_masks, src_masks
-
-
-class clean_text:
-
- def __init__(self):
- self._comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
- self._decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
- self._pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
- self._dollars_re = re.compile(r'\₹([0-9\.\,]*[0-9]+)')
- self._ordinal_re = re.compile(r'[0-9]+(वां|ला)')
- self._number_re = re.compile(r'[0-9]+')
-
- self._whitespace_re = re.compile(r'\s+')
-
- @staticmethod
- def _remove_commas(m):
- return m.group(1).replace(',', '')
-
- @staticmethod
- def _expand_decimal_point(m):
- return m.group(1).replace('.', ' दशमलव ')
-
- @staticmethod
- def _expand_ordinal(m):
- numbers = ""
- temp_text = ""
- for i in re.split("(\d)+", m.group(0)):
- if i.isnumeric():
- numbers+= i
- else:
- temp_text+=i
- return num_to_word(numbers, lang = "hi") + " " + temp_text
-
- @staticmethod
- def _expand_number(m):
- return num_to_word(m.group(0), lang = "hi")
-
-
- def collapse_whitespace(self, text):
- return re.sub(self._whitespace_re, ' ', text)
-
- @staticmethod
- def _expand_dollars(m):
-
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' रुपये' # Unexpected format
-
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'रुपया' if dollars == 1 else 'रुपये'
- cent_unit = 'पैसा' if cents == 1 else 'पैसे'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'रुपया' if dollars == 1 else 'रुपये'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'पैसा' if cents == 1 else 'पैसे'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'शून्य रुपये'
-
-
- def normalize_numbers(self, text):
- text = re.sub(self._comma_number_re, self._remove_commas, text)
- text = re.sub(self._dollars_re, self._expand_dollars, text)
- text = re.sub(self._decimal_number_re, self._expand_decimal_point, text)
- # print(text)
- text = re.sub(self._ordinal_re, self._expand_ordinal, text)
- text = re.sub(self._number_re, self._expand_number, text)
-
- return text
-
- def normalize_text(self, text):
-
- text = self.normalize_numbers(text)
- text = re.sub(r"""[!।"#$%&\'()*+,–./:¨;<=>?@\[\\\]^_`\-{|}~]""", '', text)
- text = self.collapse_whitespace(text)
- return text
-
- # def GET_IPA_phonemes(text, backend, separator):
-def GET_IPA_phonemes(text):
- words = [w for w in text.strip().split(' ')]
-
- # lexixon = {word : (epi_eng.trans_list(word) if re.search("[a-zA-Z]+$", word) is not None else epi_dev.trans_list(word)) for word in words}
-###############################################################################################################
-
- lexicon = []
- for word in words:
- if re.search("[a-zA-Z]+$", word) is not None:
- lexicon.append(epi_eng.trans_list(word))
- else:
- lexicon.append(epi_dev.trans_list(word))
- return lexicon
-
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-noisy_steps = 4
-t = (torch.ones(1) * noisy_steps-1).long().to(device)
-
-diffusion_model = Diffusion(device=device, noise_steps= noisy_steps)
-diffusion_model.to(device)
-
-
-checkpoint = torch.load("ckpt/Denoiser.pt", map_location=torch.device('cpu'))
-
-diffusion_model.load_state_dict(checkpoint["model_diffusion"])
-
-diffusion_model.eval()
-print()
-
-
-from modules.hifigan.model.generator import Generator
-hifi_gan_generator = torch.load("ckpt/HiFi.pt", map_location=torch.device('cpu'))
-model_g = Generator()
-model_g.load_state_dict(hifi_gan_generator["model_g"])
-model_g.remove_weight_norm()
-model_g.to(device)
-model_g.eval()
-print()
-
-
-from text import _symbol_to_id
-import numpy as np
-import soundfile as sf
-import librosa
-import soundfile
-
-cl_text = clean_text()
-
-def get_audio(text, vocal = None):
- print(vocal)
- text = cl_text.normalize_text(text)
-
- lexicon = GET_IPA_phonemes(text)
- # print(lexicon)
- sentence = []
- for value in lexicon:
-
- for phone in value:
- sentence.append(_symbol_to_id[phone])
- # sentence.append(1)
-
- texts = torch.from_numpy(np.array([sentence])).to(device)
- src_masks = texts == 555
-
- # Passing texts and src mask to encoder layer
- encoder_output = diffusion_model.encoder(texts, src_masks)
-
-
- #Calculating duration, pitch, energy
- ( output,
- p_predictions,
- e_predictions,
- log_d_predictions,
- d_rounded,
- mel_lens,
- mel_masks ) = diffusion_model.variance_adaptor(
- encoder_output,
- src_masks,
- None,
- None,
- None,
- None,
- None )
- encoder_output = output
- img = torch.randn((1, encoder_output.shape[1], 80)).to(device)
-
- for i in tqdm(range(3,-1, -1)):
- p_1 = diffusion_model.spectogram_model(img, torch.Tensor([3]).to(device), encoder_output)
- img = p_1
-
- generated_audio = model_g(p_1.transpose(1,2).to(device))
- sf.write('./audio_after.wav', generated_audio.detach()[0][0].to("cpu"), 22050, "PCM_16")
-
-
- return './audio_after.wav'
-
-
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/certifi/core.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/certifi/core.py
deleted file mode 100644
index c3e546604c85678dd72db35893c46ffe2d79c052..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/certifi/core.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""
-certifi.py
-~~~~~~~~~~
-
-This module returns the installation location of cacert.pem or its contents.
-"""
-import sys
-
-
-if sys.version_info >= (3, 11):
-
- from importlib.resources import as_file, files
-
- _CACERT_CTX = None
- _CACERT_PATH = None
-
- def where() -> str:
- # This is slightly terrible, but we want to delay extracting the file
- # in cases where we're inside of a zipimport situation until someone
- # actually calls where(), but we don't want to re-extract the file
- # on every call of where(), so we'll do it once then store it in a
- # global variable.
- global _CACERT_CTX
- global _CACERT_PATH
- if _CACERT_PATH is None:
- # This is slightly janky, the importlib.resources API wants you to
- # manage the cleanup of this file, so it doesn't actually return a
- # path, it returns a context manager that will give you the path
- # when you enter it and will do any cleanup when you leave it. In
- # the common case of not needing a temporary file, it will just
- # return the file system location and the __exit__() is a no-op.
- #
- # We also have to hold onto the actual context manager, because
- # it will do the cleanup whenever it gets garbage collected, so
- # we will also store that at the global level as well.
- _CACERT_CTX = as_file(files("pip._vendor.certifi").joinpath("cacert.pem"))
- _CACERT_PATH = str(_CACERT_CTX.__enter__())
-
- return _CACERT_PATH
-
- def contents() -> str:
- return files("pip._vendor.certifi").joinpath("cacert.pem").read_text(encoding="ascii")
-
-elif sys.version_info >= (3, 7):
-
- from importlib.resources import path as get_path, read_text
-
- _CACERT_CTX = None
- _CACERT_PATH = None
-
- def where() -> str:
- # This is slightly terrible, but we want to delay extracting the
- # file in cases where we're inside of a zipimport situation until
- # someone actually calls where(), but we don't want to re-extract
- # the file on every call of where(), so we'll do it once then store
- # it in a global variable.
- global _CACERT_CTX
- global _CACERT_PATH
- if _CACERT_PATH is None:
- # This is slightly janky, the importlib.resources API wants you
- # to manage the cleanup of this file, so it doesn't actually
- # return a path, it returns a context manager that will give
- # you the path when you enter it and will do any cleanup when
- # you leave it. In the common case of not needing a temporary
- # file, it will just return the file system location and the
- # __exit__() is a no-op.
- #
- # We also have to hold onto the actual context manager, because
- # it will do the cleanup whenever it gets garbage collected, so
- # we will also store that at the global level as well.
- _CACERT_CTX = get_path("pip._vendor.certifi", "cacert.pem")
- _CACERT_PATH = str(_CACERT_CTX.__enter__())
-
- return _CACERT_PATH
-
- def contents() -> str:
- return read_text("pip._vendor.certifi", "cacert.pem", encoding="ascii")
-
-else:
- import os
- import types
- from typing import Union
-
- Package = Union[types.ModuleType, str]
- Resource = Union[str, "os.PathLike"]
-
- # This fallback will work for Python versions prior to 3.7 that lack the
- # importlib.resources module but relies on the existing `where` function
- # so won't address issues with environments like PyOxidizer that don't set
- # __file__ on modules.
- def read_text(
- package: Package,
- resource: Resource,
- encoding: str = 'utf-8',
- errors: str = 'strict'
- ) -> str:
- with open(where(), encoding=encoding) as data:
- return data.read()
-
- # If we don't have importlib.resources, then we will just do the old logic
- # of assuming we're on the filesystem and munge the path directly.
- def where() -> str:
- f = os.path.dirname(__file__)
-
- return os.path.join(f, "cacert.pem")
-
- def contents() -> str:
- return read_text("pip._vendor.certifi", "cacert.pem", encoding="ascii")
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_palettes.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_palettes.py
deleted file mode 100644
index 3c748d33e45bfcdc690ceee490cbb50b516cd2b3..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_palettes.py
+++ /dev/null
@@ -1,309 +0,0 @@
-from .palette import Palette
-
-
-# Taken from https://en.wikipedia.org/wiki/ANSI_escape_code (Windows 10 column)
-WINDOWS_PALETTE = Palette(
- [
- (12, 12, 12),
- (197, 15, 31),
- (19, 161, 14),
- (193, 156, 0),
- (0, 55, 218),
- (136, 23, 152),
- (58, 150, 221),
- (204, 204, 204),
- (118, 118, 118),
- (231, 72, 86),
- (22, 198, 12),
- (249, 241, 165),
- (59, 120, 255),
- (180, 0, 158),
- (97, 214, 214),
- (242, 242, 242),
- ]
-)
-
-# # The standard ansi colors (including bright variants)
-STANDARD_PALETTE = Palette(
- [
- (0, 0, 0),
- (170, 0, 0),
- (0, 170, 0),
- (170, 85, 0),
- (0, 0, 170),
- (170, 0, 170),
- (0, 170, 170),
- (170, 170, 170),
- (85, 85, 85),
- (255, 85, 85),
- (85, 255, 85),
- (255, 255, 85),
- (85, 85, 255),
- (255, 85, 255),
- (85, 255, 255),
- (255, 255, 255),
- ]
-)
-
-
-# The 256 color palette
-EIGHT_BIT_PALETTE = Palette(
- [
- (0, 0, 0),
- (128, 0, 0),
- (0, 128, 0),
- (128, 128, 0),
- (0, 0, 128),
- (128, 0, 128),
- (0, 128, 128),
- (192, 192, 192),
- (128, 128, 128),
- (255, 0, 0),
- (0, 255, 0),
- (255, 255, 0),
- (0, 0, 255),
- (255, 0, 255),
- (0, 255, 255),
- (255, 255, 255),
- (0, 0, 0),
- (0, 0, 95),
- (0, 0, 135),
- (0, 0, 175),
- (0, 0, 215),
- (0, 0, 255),
- (0, 95, 0),
- (0, 95, 95),
- (0, 95, 135),
- (0, 95, 175),
- (0, 95, 215),
- (0, 95, 255),
- (0, 135, 0),
- (0, 135, 95),
- (0, 135, 135),
- (0, 135, 175),
- (0, 135, 215),
- (0, 135, 255),
- (0, 175, 0),
- (0, 175, 95),
- (0, 175, 135),
- (0, 175, 175),
- (0, 175, 215),
- (0, 175, 255),
- (0, 215, 0),
- (0, 215, 95),
- (0, 215, 135),
- (0, 215, 175),
- (0, 215, 215),
- (0, 215, 255),
- (0, 255, 0),
- (0, 255, 95),
- (0, 255, 135),
- (0, 255, 175),
- (0, 255, 215),
- (0, 255, 255),
- (95, 0, 0),
- (95, 0, 95),
- (95, 0, 135),
- (95, 0, 175),
- (95, 0, 215),
- (95, 0, 255),
- (95, 95, 0),
- (95, 95, 95),
- (95, 95, 135),
- (95, 95, 175),
- (95, 95, 215),
- (95, 95, 255),
- (95, 135, 0),
- (95, 135, 95),
- (95, 135, 135),
- (95, 135, 175),
- (95, 135, 215),
- (95, 135, 255),
- (95, 175, 0),
- (95, 175, 95),
- (95, 175, 135),
- (95, 175, 175),
- (95, 175, 215),
- (95, 175, 255),
- (95, 215, 0),
- (95, 215, 95),
- (95, 215, 135),
- (95, 215, 175),
- (95, 215, 215),
- (95, 215, 255),
- (95, 255, 0),
- (95, 255, 95),
- (95, 255, 135),
- (95, 255, 175),
- (95, 255, 215),
- (95, 255, 255),
- (135, 0, 0),
- (135, 0, 95),
- (135, 0, 135),
- (135, 0, 175),
- (135, 0, 215),
- (135, 0, 255),
- (135, 95, 0),
- (135, 95, 95),
- (135, 95, 135),
- (135, 95, 175),
- (135, 95, 215),
- (135, 95, 255),
- (135, 135, 0),
- (135, 135, 95),
- (135, 135, 135),
- (135, 135, 175),
- (135, 135, 215),
- (135, 135, 255),
- (135, 175, 0),
- (135, 175, 95),
- (135, 175, 135),
- (135, 175, 175),
- (135, 175, 215),
- (135, 175, 255),
- (135, 215, 0),
- (135, 215, 95),
- (135, 215, 135),
- (135, 215, 175),
- (135, 215, 215),
- (135, 215, 255),
- (135, 255, 0),
- (135, 255, 95),
- (135, 255, 135),
- (135, 255, 175),
- (135, 255, 215),
- (135, 255, 255),
- (175, 0, 0),
- (175, 0, 95),
- (175, 0, 135),
- (175, 0, 175),
- (175, 0, 215),
- (175, 0, 255),
- (175, 95, 0),
- (175, 95, 95),
- (175, 95, 135),
- (175, 95, 175),
- (175, 95, 215),
- (175, 95, 255),
- (175, 135, 0),
- (175, 135, 95),
- (175, 135, 135),
- (175, 135, 175),
- (175, 135, 215),
- (175, 135, 255),
- (175, 175, 0),
- (175, 175, 95),
- (175, 175, 135),
- (175, 175, 175),
- (175, 175, 215),
- (175, 175, 255),
- (175, 215, 0),
- (175, 215, 95),
- (175, 215, 135),
- (175, 215, 175),
- (175, 215, 215),
- (175, 215, 255),
- (175, 255, 0),
- (175, 255, 95),
- (175, 255, 135),
- (175, 255, 175),
- (175, 255, 215),
- (175, 255, 255),
- (215, 0, 0),
- (215, 0, 95),
- (215, 0, 135),
- (215, 0, 175),
- (215, 0, 215),
- (215, 0, 255),
- (215, 95, 0),
- (215, 95, 95),
- (215, 95, 135),
- (215, 95, 175),
- (215, 95, 215),
- (215, 95, 255),
- (215, 135, 0),
- (215, 135, 95),
- (215, 135, 135),
- (215, 135, 175),
- (215, 135, 215),
- (215, 135, 255),
- (215, 175, 0),
- (215, 175, 95),
- (215, 175, 135),
- (215, 175, 175),
- (215, 175, 215),
- (215, 175, 255),
- (215, 215, 0),
- (215, 215, 95),
- (215, 215, 135),
- (215, 215, 175),
- (215, 215, 215),
- (215, 215, 255),
- (215, 255, 0),
- (215, 255, 95),
- (215, 255, 135),
- (215, 255, 175),
- (215, 255, 215),
- (215, 255, 255),
- (255, 0, 0),
- (255, 0, 95),
- (255, 0, 135),
- (255, 0, 175),
- (255, 0, 215),
- (255, 0, 255),
- (255, 95, 0),
- (255, 95, 95),
- (255, 95, 135),
- (255, 95, 175),
- (255, 95, 215),
- (255, 95, 255),
- (255, 135, 0),
- (255, 135, 95),
- (255, 135, 135),
- (255, 135, 175),
- (255, 135, 215),
- (255, 135, 255),
- (255, 175, 0),
- (255, 175, 95),
- (255, 175, 135),
- (255, 175, 175),
- (255, 175, 215),
- (255, 175, 255),
- (255, 215, 0),
- (255, 215, 95),
- (255, 215, 135),
- (255, 215, 175),
- (255, 215, 215),
- (255, 215, 255),
- (255, 255, 0),
- (255, 255, 95),
- (255, 255, 135),
- (255, 255, 175),
- (255, 255, 215),
- (255, 255, 255),
- (8, 8, 8),
- (18, 18, 18),
- (28, 28, 28),
- (38, 38, 38),
- (48, 48, 48),
- (58, 58, 58),
- (68, 68, 68),
- (78, 78, 78),
- (88, 88, 88),
- (98, 98, 98),
- (108, 108, 108),
- (118, 118, 118),
- (128, 128, 128),
- (138, 138, 138),
- (148, 148, 148),
- (158, 158, 158),
- (168, 168, 168),
- (178, 178, 178),
- (188, 188, 188),
- (198, 198, 198),
- (208, 208, 208),
- (218, 218, 218),
- (228, 228, 228),
- (238, 238, 238),
- ]
-)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/_manylinux.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/_manylinux.py
deleted file mode 100644
index 4c379aa6f69ff56c8f19612002c6e3e939ea6012..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/_manylinux.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import collections
-import functools
-import os
-import re
-import struct
-import sys
-import warnings
-from typing import IO, Dict, Iterator, NamedTuple, Optional, Tuple
-
-
-# Python does not provide platform information at sufficient granularity to
-# identify the architecture of the running executable in some cases, so we
-# determine it dynamically by reading the information from the running
-# process. This only applies on Linux, which uses the ELF format.
-class _ELFFileHeader:
- # https://en.wikipedia.org/wiki/Executable_and_Linkable_Format#File_header
- class _InvalidELFFileHeader(ValueError):
- """
- An invalid ELF file header was found.
- """
-
- ELF_MAGIC_NUMBER = 0x7F454C46
- ELFCLASS32 = 1
- ELFCLASS64 = 2
- ELFDATA2LSB = 1
- ELFDATA2MSB = 2
- EM_386 = 3
- EM_S390 = 22
- EM_ARM = 40
- EM_X86_64 = 62
- EF_ARM_ABIMASK = 0xFF000000
- EF_ARM_ABI_VER5 = 0x05000000
- EF_ARM_ABI_FLOAT_HARD = 0x00000400
-
- def __init__(self, file: IO[bytes]) -> None:
- def unpack(fmt: str) -> int:
- try:
- data = file.read(struct.calcsize(fmt))
- result: Tuple[int, ...] = struct.unpack(fmt, data)
- except struct.error:
- raise _ELFFileHeader._InvalidELFFileHeader()
- return result[0]
-
- self.e_ident_magic = unpack(">I")
- if self.e_ident_magic != self.ELF_MAGIC_NUMBER:
- raise _ELFFileHeader._InvalidELFFileHeader()
- self.e_ident_class = unpack("B")
- if self.e_ident_class not in {self.ELFCLASS32, self.ELFCLASS64}:
- raise _ELFFileHeader._InvalidELFFileHeader()
- self.e_ident_data = unpack("B")
- if self.e_ident_data not in {self.ELFDATA2LSB, self.ELFDATA2MSB}:
- raise _ELFFileHeader._InvalidELFFileHeader()
- self.e_ident_version = unpack("B")
- self.e_ident_osabi = unpack("B")
- self.e_ident_abiversion = unpack("B")
- self.e_ident_pad = file.read(7)
- format_h = "H"
- format_i = "I"
- format_q = "Q"
- format_p = format_i if self.e_ident_class == self.ELFCLASS32 else format_q
- self.e_type = unpack(format_h)
- self.e_machine = unpack(format_h)
- self.e_version = unpack(format_i)
- self.e_entry = unpack(format_p)
- self.e_phoff = unpack(format_p)
- self.e_shoff = unpack(format_p)
- self.e_flags = unpack(format_i)
- self.e_ehsize = unpack(format_h)
- self.e_phentsize = unpack(format_h)
- self.e_phnum = unpack(format_h)
- self.e_shentsize = unpack(format_h)
- self.e_shnum = unpack(format_h)
- self.e_shstrndx = unpack(format_h)
-
-
-def _get_elf_header() -> Optional[_ELFFileHeader]:
- try:
- with open(sys.executable, "rb") as f:
- elf_header = _ELFFileHeader(f)
- except (OSError, TypeError, _ELFFileHeader._InvalidELFFileHeader):
- return None
- return elf_header
-
-
-def _is_linux_armhf() -> bool:
- # hard-float ABI can be detected from the ELF header of the running
- # process
- # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
- elf_header = _get_elf_header()
- if elf_header is None:
- return False
- result = elf_header.e_ident_class == elf_header.ELFCLASS32
- result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
- result &= elf_header.e_machine == elf_header.EM_ARM
- result &= (
- elf_header.e_flags & elf_header.EF_ARM_ABIMASK
- ) == elf_header.EF_ARM_ABI_VER5
- result &= (
- elf_header.e_flags & elf_header.EF_ARM_ABI_FLOAT_HARD
- ) == elf_header.EF_ARM_ABI_FLOAT_HARD
- return result
-
-
-def _is_linux_i686() -> bool:
- elf_header = _get_elf_header()
- if elf_header is None:
- return False
- result = elf_header.e_ident_class == elf_header.ELFCLASS32
- result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
- result &= elf_header.e_machine == elf_header.EM_386
- return result
-
-
-def _have_compatible_abi(arch: str) -> bool:
- if arch == "armv7l":
- return _is_linux_armhf()
- if arch == "i686":
- return _is_linux_i686()
- return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
-
-
-# If glibc ever changes its major version, we need to know what the last
-# minor version was, so we can build the complete list of all versions.
-# For now, guess what the highest minor version might be, assume it will
-# be 50 for testing. Once this actually happens, update the dictionary
-# with the actual value.
-_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
-
-
-class _GLibCVersion(NamedTuple):
- major: int
- minor: int
-
-
-def _glibc_version_string_confstr() -> Optional[str]:
- """
- Primary implementation of glibc_version_string using os.confstr.
- """
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
- # to be broken or missing. This strategy is used in the standard library
- # platform module.
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
- try:
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17".
- version_string = os.confstr("CS_GNU_LIBC_VERSION")
- assert version_string is not None
- _, version = version_string.split()
- except (AssertionError, AttributeError, OSError, ValueError):
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
- return None
- return version
-
-
-def _glibc_version_string_ctypes() -> Optional[str]:
- """
- Fallback implementation of glibc_version_string using ctypes.
- """
- try:
- import ctypes
- except ImportError:
- return None
-
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
- # manpage says, "If filename is NULL, then the returned handle is for the
- # main program". This way we can let the linker do the work to figure out
- # which libc our process is actually using.
- #
- # We must also handle the special case where the executable is not a
- # dynamically linked executable. This can occur when using musl libc,
- # for example. In this situation, dlopen() will error, leading to an
- # OSError. Interestingly, at least in the case of musl, there is no
- # errno set on the OSError. The single string argument used to construct
- # OSError comes from libc itself and is therefore not portable to
- # hard code here. In any case, failure to call dlopen() means we
- # can proceed, so we bail on our attempt.
- try:
- process_namespace = ctypes.CDLL(None)
- except OSError:
- return None
-
- try:
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
- except AttributeError:
- # Symbol doesn't exist -> therefore, we are not linked to
- # glibc.
- return None
-
- # Call gnu_get_libc_version, which returns a string like "2.5"
- gnu_get_libc_version.restype = ctypes.c_char_p
- version_str: str = gnu_get_libc_version()
- # py2 / py3 compatibility:
- if not isinstance(version_str, str):
- version_str = version_str.decode("ascii")
-
- return version_str
-
-
-def _glibc_version_string() -> Optional[str]:
- """Returns glibc version string, or None if not using glibc."""
- return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
-
-
-def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
- """Parse glibc version.
-
- We use a regexp instead of str.split because we want to discard any
- random junk that might come after the minor version -- this might happen
- in patched/forked versions of glibc (e.g. Linaro's version of glibc
- uses version strings like "2.20-2014.11"). See gh-3588.
- """
- m = re.match(r"(?P[0-9]+)\.(?P[0-9]+)", version_str)
- if not m:
- warnings.warn(
- "Expected glibc version with 2 components major.minor,"
- " got: %s" % version_str,
- RuntimeWarning,
- )
- return -1, -1
- return int(m.group("major")), int(m.group("minor"))
-
-
-@functools.lru_cache()
-def _get_glibc_version() -> Tuple[int, int]:
- version_str = _glibc_version_string()
- if version_str is None:
- return (-1, -1)
- return _parse_glibc_version(version_str)
-
-
-# From PEP 513, PEP 600
-def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
- sys_glibc = _get_glibc_version()
- if sys_glibc < version:
- return False
- # Check for presence of _manylinux module.
- try:
- import _manylinux # noqa
- except ImportError:
- return True
- if hasattr(_manylinux, "manylinux_compatible"):
- result = _manylinux.manylinux_compatible(version[0], version[1], arch)
- if result is not None:
- return bool(result)
- return True
- if version == _GLibCVersion(2, 5):
- if hasattr(_manylinux, "manylinux1_compatible"):
- return bool(_manylinux.manylinux1_compatible)
- if version == _GLibCVersion(2, 12):
- if hasattr(_manylinux, "manylinux2010_compatible"):
- return bool(_manylinux.manylinux2010_compatible)
- if version == _GLibCVersion(2, 17):
- if hasattr(_manylinux, "manylinux2014_compatible"):
- return bool(_manylinux.manylinux2014_compatible)
- return True
-
-
-_LEGACY_MANYLINUX_MAP = {
- # CentOS 7 w/ glibc 2.17 (PEP 599)
- (2, 17): "manylinux2014",
- # CentOS 6 w/ glibc 2.12 (PEP 571)
- (2, 12): "manylinux2010",
- # CentOS 5 w/ glibc 2.5 (PEP 513)
- (2, 5): "manylinux1",
-}
-
-
-def platform_tags(linux: str, arch: str) -> Iterator[str]:
- if not _have_compatible_abi(arch):
- return
- # Oldest glibc to be supported regardless of architecture is (2, 17).
- too_old_glibc2 = _GLibCVersion(2, 16)
- if arch in {"x86_64", "i686"}:
- # On x86/i686 also oldest glibc to be supported is (2, 5).
- too_old_glibc2 = _GLibCVersion(2, 4)
- current_glibc = _GLibCVersion(*_get_glibc_version())
- glibc_max_list = [current_glibc]
- # We can assume compatibility across glibc major versions.
- # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
- #
- # Build a list of maximum glibc versions so that we can
- # output the canonical list of all glibc from current_glibc
- # down to too_old_glibc2, including all intermediary versions.
- for glibc_major in range(current_glibc.major - 1, 1, -1):
- glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
- glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
- for glibc_max in glibc_max_list:
- if glibc_max.major == too_old_glibc2.major:
- min_minor = too_old_glibc2.minor
- else:
- # For other glibc major versions oldest supported is (x, 0).
- min_minor = -1
- for glibc_minor in range(glibc_max.minor, min_minor, -1):
- glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
- tag = "manylinux_{}_{}".format(*glibc_version)
- if _is_compatible(tag, arch, glibc_version):
- yield linux.replace("linux", tag)
- # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
- if glibc_version in _LEGACY_MANYLINUX_MAP:
- legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
- if _is_compatible(legacy_tag, arch, glibc_version):
- yield linux.replace("linux", legacy_tag)
diff --git a/spaces/Rdceo26Rmrdceo26/README/README.md b/spaces/Rdceo26Rmrdceo26/README/README.md
deleted file mode 100644
index 3631c9d4710c0a9982e4d8310c8cf73deecec06a..0000000000000000000000000000000000000000
--- a/spaces/Rdceo26Rmrdceo26/README/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: README
-emoji: 😻
-colorFrom: indigo
-colorTo: pink
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card 🔥
diff --git a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/datasets/InvISP/dataset/base_dataset.py b/spaces/Realcat/image-matching-webui/third_party/DarkFeat/datasets/InvISP/dataset/base_dataset.py
deleted file mode 100644
index 1ec55b4edd7663c8323a9b197e938083c6ed2497..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/datasets/InvISP/dataset/base_dataset.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from __future__ import print_function, division
-import numpy as np
-from torch.utils.data import Dataset
-import torch
-
-
-class BaseDataset(Dataset):
- def __init__(self, opt):
- self.crop_size = 512
- self.debug_mode = opt.debug_mode
- self.data_path = opt.data_path # dataset path. e.g., ./data/
- self.camera_name = opt.camera
- self.gamma = opt.gamma
-
- def norm_img(self, img, max_value):
- img = img / float(max_value)
- return img
-
- def pack_raw(self, raw):
- # pack Bayer image to 4 channels
- im = np.expand_dims(raw, axis=2)
- H, W = raw.shape[0], raw.shape[1]
- # RGBG
- out = np.concatenate(
- (
- im[0:H:2, 0:W:2, :],
- im[0:H:2, 1:W:2, :],
- im[1:H:2, 1:W:2, :],
- im[1:H:2, 0:W:2, :],
- ),
- axis=2,
- )
- return out
-
- def np2tensor(self, array):
- return torch.Tensor(array).permute(2, 0, 1)
-
- def center_crop(self, img, crop_size=None):
- H = img.shape[0]
- W = img.shape[1]
-
- if crop_size is not None:
- th, tw = crop_size[0], crop_size[1]
- else:
- th, tw = self.crop_size, self.crop_size
- x1_img = int(round((W - tw) / 2.0))
- y1_img = int(round((H - th) / 2.0))
- if img.ndim == 3:
- input_patch = img[y1_img : y1_img + th, x1_img : x1_img + tw, :]
- else:
- input_patch = img[y1_img : y1_img + th, x1_img : x1_img + tw]
-
- return input_patch
-
- def load(self, is_train=True):
- # ./data
- # ./data/NIKON D700/RAW, ./data/NIKON D700/RGB
- # ./data/Canon EOS 5D/RAW, ./data/Canon EOS 5D/RGB
- # ./data/NIKON D700_train.txt, ./data/NIKON D700_test.txt
- # ./data/NIKON D700_train.txt: a0016, ...
- input_RAWs_WBs = []
- target_RGBs = []
-
- data_path = self.data_path # ./data/
- if is_train:
- txt_path = data_path + self.camera_name + "_train.txt"
- else:
- txt_path = data_path + self.camera_name + "_test.txt"
-
- with open(txt_path, "r") as f_read:
- # valid_camera_list = [os.path.basename(line.strip()).split('.')[0] for line in f_read.readlines()]
- valid_camera_list = [line.strip() for line in f_read.readlines()]
-
- if self.debug_mode:
- valid_camera_list = valid_camera_list[:10]
-
- for i, name in enumerate(valid_camera_list):
- full_name = data_path + self.camera_name
- input_RAWs_WBs.append(full_name + "/RAW/" + name + ".npz")
- target_RGBs.append(full_name + "/RGB/" + name + ".jpg")
-
- return input_RAWs_WBs, target_RGBs
-
- def __len__(self):
- return 0
-
- def __getitem__(self, idx):
-
- return None
diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/models/matcher.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/models/matcher.py
deleted file mode 100644
index 45d176dab20094258ac32fa83d46a204dc65a777..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/models/matcher.py
+++ /dev/null
@@ -1,771 +0,0 @@
-import os
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-import warnings
-from warnings import warn
-
-import roma
-from roma.utils import get_tuple_transform_ops
-from roma.utils.local_correlation import local_correlation
-from roma.utils.utils import cls_to_flow_refine
-from roma.utils.kde import kde
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-class ConvRefiner(nn.Module):
- def __init__(
- self,
- in_dim=6,
- hidden_dim=16,
- out_dim=2,
- dw=False,
- kernel_size=5,
- hidden_blocks=3,
- displacement_emb=None,
- displacement_emb_dim=None,
- local_corr_radius=None,
- corr_in_other=None,
- no_im_B_fm=False,
- amp=False,
- concat_logits=False,
- use_bias_block_1=True,
- use_cosine_corr=False,
- disable_local_corr_grad=False,
- is_classifier=False,
- sample_mode="bilinear",
- norm_type=nn.BatchNorm2d,
- bn_momentum=0.1,
- ):
- super().__init__()
- self.bn_momentum = bn_momentum
- self.block1 = self.create_block(
- in_dim,
- hidden_dim,
- dw=dw,
- kernel_size=kernel_size,
- bias=use_bias_block_1,
- )
- self.hidden_blocks = nn.Sequential(
- *[
- self.create_block(
- hidden_dim,
- hidden_dim,
- dw=dw,
- kernel_size=kernel_size,
- norm_type=norm_type,
- )
- for hb in range(hidden_blocks)
- ]
- )
- self.hidden_blocks = self.hidden_blocks
- self.out_conv = nn.Conv2d(hidden_dim, out_dim, 1, 1, 0)
- if displacement_emb:
- self.has_displacement_emb = True
- self.disp_emb = nn.Conv2d(2, displacement_emb_dim, 1, 1, 0)
- else:
- self.has_displacement_emb = False
- self.local_corr_radius = local_corr_radius
- self.corr_in_other = corr_in_other
- self.no_im_B_fm = no_im_B_fm
- self.amp = amp
- self.concat_logits = concat_logits
- self.use_cosine_corr = use_cosine_corr
- self.disable_local_corr_grad = disable_local_corr_grad
- self.is_classifier = is_classifier
- self.sample_mode = sample_mode
- if torch.cuda.is_available():
- if torch.cuda.is_bf16_supported():
- self.amp_dtype = torch.bfloat16
- else:
- self.amp_dtype = torch.float16
- else:
- self.amp_dtype = torch.float32
-
- def create_block(
- self,
- in_dim,
- out_dim,
- dw=False,
- kernel_size=5,
- bias=True,
- norm_type=nn.BatchNorm2d,
- ):
- num_groups = 1 if not dw else in_dim
- if dw:
- assert (
- out_dim % in_dim == 0
- ), "outdim must be divisible by indim for depthwise"
- conv1 = nn.Conv2d(
- in_dim,
- out_dim,
- kernel_size=kernel_size,
- stride=1,
- padding=kernel_size // 2,
- groups=num_groups,
- bias=bias,
- )
- norm = (
- norm_type(out_dim, momentum=self.bn_momentum)
- if norm_type is nn.BatchNorm2d
- else norm_type(num_channels=out_dim)
- )
- relu = nn.ReLU(inplace=True)
- conv2 = nn.Conv2d(out_dim, out_dim, 1, 1, 0)
- return nn.Sequential(conv1, norm, relu, conv2)
-
- def forward(self, x, y, flow, scale_factor=1, logits=None):
- b, c, hs, ws = x.shape
- with torch.autocast(device, enabled=self.amp, dtype=self.amp_dtype):
- with torch.no_grad():
- x_hat = F.grid_sample(
- y,
- flow.permute(0, 2, 3, 1),
- align_corners=False,
- mode=self.sample_mode,
- )
- if self.has_displacement_emb:
- im_A_coords = torch.meshgrid(
- (
- torch.linspace(-1 + 1 / hs, 1 - 1 / hs, hs, device=device),
- torch.linspace(-1 + 1 / ws, 1 - 1 / ws, ws, device=device),
- )
- )
- im_A_coords = torch.stack((im_A_coords[1], im_A_coords[0]))
- im_A_coords = im_A_coords[None].expand(b, 2, hs, ws)
- in_displacement = flow - im_A_coords
- emb_in_displacement = self.disp_emb(
- 40 / 32 * scale_factor * in_displacement
- )
- if self.local_corr_radius:
- if self.corr_in_other:
- # Corr in other means take a kxk grid around the predicted coordinate in other image
- local_corr = local_correlation(
- x,
- y,
- local_radius=self.local_corr_radius,
- flow=flow,
- sample_mode=self.sample_mode,
- )
- else:
- raise NotImplementedError(
- "Local corr in own frame should not be used."
- )
- if self.no_im_B_fm:
- x_hat = torch.zeros_like(x)
- d = torch.cat((x, x_hat, emb_in_displacement, local_corr), dim=1)
- else:
- d = torch.cat((x, x_hat, emb_in_displacement), dim=1)
- else:
- if self.no_im_B_fm:
- x_hat = torch.zeros_like(x)
- d = torch.cat((x, x_hat), dim=1)
- if self.concat_logits:
- d = torch.cat((d, logits), dim=1)
- d = self.block1(d)
- d = self.hidden_blocks(d)
- d = self.out_conv(d.float())
- displacement, certainty = d[:, :-1], d[:, -1:]
- return displacement, certainty
-
-
-class CosKernel(nn.Module): # similar to softmax kernel
- def __init__(self, T, learn_temperature=False):
- super().__init__()
- self.learn_temperature = learn_temperature
- if self.learn_temperature:
- self.T = nn.Parameter(torch.tensor(T))
- else:
- self.T = T
-
- def __call__(self, x, y, eps=1e-6):
- c = torch.einsum("bnd,bmd->bnm", x, y) / (
- x.norm(dim=-1)[..., None] * y.norm(dim=-1)[:, None] + eps
- )
- if self.learn_temperature:
- T = self.T.abs() + 0.01
- else:
- T = torch.tensor(self.T, device=c.device)
- K = ((c - 1.0) / T).exp()
- return K
-
-
-class GP(nn.Module):
- def __init__(
- self,
- kernel,
- T=1,
- learn_temperature=False,
- only_attention=False,
- gp_dim=64,
- basis="fourier",
- covar_size=5,
- only_nearest_neighbour=False,
- sigma_noise=0.1,
- no_cov=False,
- predict_features=False,
- ):
- super().__init__()
- self.K = kernel(T=T, learn_temperature=learn_temperature)
- self.sigma_noise = sigma_noise
- self.covar_size = covar_size
- self.pos_conv = torch.nn.Conv2d(2, gp_dim, 1, 1)
- self.only_attention = only_attention
- self.only_nearest_neighbour = only_nearest_neighbour
- self.basis = basis
- self.no_cov = no_cov
- self.dim = gp_dim
- self.predict_features = predict_features
-
- def get_local_cov(self, cov):
- K = self.covar_size
- b, h, w, h, w = cov.shape
- hw = h * w
- cov = F.pad(cov, 4 * (K // 2,)) # pad v_q
- delta = torch.stack(
- torch.meshgrid(
- torch.arange(-(K // 2), K // 2 + 1), torch.arange(-(K // 2), K // 2 + 1)
- ),
- dim=-1,
- )
- positions = torch.stack(
- torch.meshgrid(
- torch.arange(K // 2, h + K // 2), torch.arange(K // 2, w + K // 2)
- ),
- dim=-1,
- )
- neighbours = positions[:, :, None, None, :] + delta[None, :, :]
- points = torch.arange(hw)[:, None].expand(hw, K**2)
- local_cov = cov.reshape(b, hw, h + K - 1, w + K - 1)[
- :,
- points.flatten(),
- neighbours[..., 0].flatten(),
- neighbours[..., 1].flatten(),
- ].reshape(b, h, w, K**2)
- return local_cov
-
- def reshape(self, x):
- return rearrange(x, "b d h w -> b (h w) d")
-
- def project_to_basis(self, x):
- if self.basis == "fourier":
- return torch.cos(8 * math.pi * self.pos_conv(x))
- elif self.basis == "linear":
- return self.pos_conv(x)
- else:
- raise ValueError(
- "No other bases other than fourier and linear currently im_Bed in public release"
- )
-
- def get_pos_enc(self, y):
- b, c, h, w = y.shape
- coarse_coords = torch.meshgrid(
- (
- torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device=y.device),
- torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device=y.device),
- )
- )
-
- coarse_coords = torch.stack((coarse_coords[1], coarse_coords[0]), dim=-1)[
- None
- ].expand(b, h, w, 2)
- coarse_coords = rearrange(coarse_coords, "b h w d -> b d h w")
- coarse_embedded_coords = self.project_to_basis(coarse_coords)
- return coarse_embedded_coords
-
- def forward(self, x, y, **kwargs):
- b, c, h1, w1 = x.shape
- b, c, h2, w2 = y.shape
- f = self.get_pos_enc(y)
- b, d, h2, w2 = f.shape
- x, y, f = self.reshape(x.float()), self.reshape(y.float()), self.reshape(f)
- K_xx = self.K(x, x)
- K_yy = self.K(y, y)
- K_xy = self.K(x, y)
- K_yx = K_xy.permute(0, 2, 1)
- sigma_noise = self.sigma_noise * torch.eye(h2 * w2, device=x.device)[None, :, :]
- with warnings.catch_warnings():
- K_yy_inv = torch.linalg.inv(K_yy + sigma_noise)
-
- mu_x = K_xy.matmul(K_yy_inv.matmul(f))
- mu_x = rearrange(mu_x, "b (h w) d -> b d h w", h=h1, w=w1)
- if not self.no_cov:
- cov_x = K_xx - K_xy.matmul(K_yy_inv.matmul(K_yx))
- cov_x = rearrange(
- cov_x, "b (h w) (r c) -> b h w r c", h=h1, w=w1, r=h1, c=w1
- )
- local_cov_x = self.get_local_cov(cov_x)
- local_cov_x = rearrange(local_cov_x, "b h w K -> b K h w")
- gp_feats = torch.cat((mu_x, local_cov_x), dim=1)
- else:
- gp_feats = mu_x
- return gp_feats
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- embedding_decoder,
- gps,
- proj,
- conv_refiner,
- detach=False,
- scales="all",
- pos_embeddings=None,
- num_refinement_steps_per_scale=1,
- warp_noise_std=0.0,
- displacement_dropout_p=0.0,
- gm_warp_dropout_p=0.0,
- flow_upsample_mode="bilinear",
- ):
- super().__init__()
- self.embedding_decoder = embedding_decoder
- self.num_refinement_steps_per_scale = num_refinement_steps_per_scale
- self.gps = gps
- self.proj = proj
- self.conv_refiner = conv_refiner
- self.detach = detach
- if pos_embeddings is None:
- self.pos_embeddings = {}
- else:
- self.pos_embeddings = pos_embeddings
- if scales == "all":
- self.scales = ["32", "16", "8", "4", "2", "1"]
- else:
- self.scales = scales
- self.warp_noise_std = warp_noise_std
- self.refine_init = 4
- self.displacement_dropout_p = displacement_dropout_p
- self.gm_warp_dropout_p = gm_warp_dropout_p
- self.flow_upsample_mode = flow_upsample_mode
- if torch.cuda.is_available():
- if torch.cuda.is_bf16_supported():
- self.amp_dtype = torch.bfloat16
- else:
- self.amp_dtype = torch.float16
- else:
- self.amp_dtype = torch.float32
-
- def get_placeholder_flow(self, b, h, w, device):
- coarse_coords = torch.meshgrid(
- (
- torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device=device),
- torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device=device),
- )
- )
- coarse_coords = torch.stack((coarse_coords[1], coarse_coords[0]), dim=-1)[
- None
- ].expand(b, h, w, 2)
- coarse_coords = rearrange(coarse_coords, "b h w d -> b d h w")
- return coarse_coords
-
- def get_positional_embedding(self, b, h, w, device):
- coarse_coords = torch.meshgrid(
- (
- torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device=device),
- torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device=device),
- )
- )
-
- coarse_coords = torch.stack((coarse_coords[1], coarse_coords[0]), dim=-1)[
- None
- ].expand(b, h, w, 2)
- coarse_coords = rearrange(coarse_coords, "b h w d -> b d h w")
- coarse_embedded_coords = self.pos_embedding(coarse_coords)
- return coarse_embedded_coords
-
- def forward(
- self,
- f1,
- f2,
- gt_warp=None,
- gt_prob=None,
- upsample=False,
- flow=None,
- certainty=None,
- scale_factor=1,
- ):
- coarse_scales = self.embedding_decoder.scales()
- all_scales = self.scales if not upsample else ["8", "4", "2", "1"]
- sizes = {scale: f1[scale].shape[-2:] for scale in f1}
- h, w = sizes[1]
- b = f1[1].shape[0]
- device = f1[1].device
- coarsest_scale = int(all_scales[0])
- old_stuff = torch.zeros(
- b,
- self.embedding_decoder.hidden_dim,
- *sizes[coarsest_scale],
- device=f1[coarsest_scale].device,
- )
- corresps = {}
- if not upsample:
- flow = self.get_placeholder_flow(b, *sizes[coarsest_scale], device)
- certainty = 0.0
- else:
- flow = F.interpolate(
- flow,
- size=sizes[coarsest_scale],
- align_corners=False,
- mode="bilinear",
- )
- certainty = F.interpolate(
- certainty,
- size=sizes[coarsest_scale],
- align_corners=False,
- mode="bilinear",
- )
- displacement = 0.0
- for new_scale in all_scales:
- ins = int(new_scale)
- corresps[ins] = {}
- f1_s, f2_s = f1[ins], f2[ins]
- if new_scale in self.proj:
- with torch.autocast(device, self.amp_dtype):
- f1_s, f2_s = self.proj[new_scale](f1_s), self.proj[new_scale](f2_s)
-
- if ins in coarse_scales:
- old_stuff = F.interpolate(
- old_stuff, size=sizes[ins], mode="bilinear", align_corners=False
- )
- gp_posterior = self.gps[new_scale](f1_s, f2_s)
- gm_warp_or_cls, certainty, old_stuff = self.embedding_decoder(
- gp_posterior, f1_s, old_stuff, new_scale
- )
-
- if self.embedding_decoder.is_classifier:
- flow = cls_to_flow_refine(
- gm_warp_or_cls,
- ).permute(0, 3, 1, 2)
- corresps[ins].update(
- {
- "gm_cls": gm_warp_or_cls,
- "gm_certainty": certainty,
- }
- ) if self.training else None
- else:
- corresps[ins].update(
- {
- "gm_flow": gm_warp_or_cls,
- "gm_certainty": certainty,
- }
- ) if self.training else None
- flow = gm_warp_or_cls.detach()
-
- if new_scale in self.conv_refiner:
- corresps[ins].update(
- {"flow_pre_delta": flow}
- ) if self.training else None
- delta_flow, delta_certainty = self.conv_refiner[new_scale](
- f1_s,
- f2_s,
- flow,
- scale_factor=scale_factor,
- logits=certainty,
- )
- corresps[ins].update(
- {
- "delta_flow": delta_flow,
- }
- ) if self.training else None
- displacement = ins * torch.stack(
- (
- delta_flow[:, 0].float() / (self.refine_init * w),
- delta_flow[:, 1].float() / (self.refine_init * h),
- ),
- dim=1,
- )
- flow = flow + displacement
- certainty = (
- certainty + delta_certainty
- ) # predict both certainty and displacement
- corresps[ins].update(
- {
- "certainty": certainty,
- "flow": flow,
- }
- )
- if new_scale != "1":
- flow = F.interpolate(
- flow,
- size=sizes[ins // 2],
- mode=self.flow_upsample_mode,
- )
- certainty = F.interpolate(
- certainty,
- size=sizes[ins // 2],
- mode=self.flow_upsample_mode,
- )
- if self.detach:
- flow = flow.detach()
- certainty = certainty.detach()
- # torch.cuda.empty_cache()
- return corresps
-
-
-class RegressionMatcher(nn.Module):
- def __init__(
- self,
- encoder,
- decoder,
- h=448,
- w=448,
- sample_mode="threshold",
- upsample_preds=False,
- symmetric=False,
- name=None,
- attenuate_cert=None,
- ):
- super().__init__()
- self.attenuate_cert = attenuate_cert
- self.encoder = encoder
- self.decoder = decoder
- self.name = name
- self.w_resized = w
- self.h_resized = h
- self.og_transforms = get_tuple_transform_ops(resize=None, normalize=True)
- self.sample_mode = sample_mode
- self.upsample_preds = upsample_preds
- self.upsample_res = (14 * 16 * 6, 14 * 16 * 6)
- self.symmetric = symmetric
- self.sample_thresh = 0.05
-
- def get_output_resolution(self):
- if not self.upsample_preds:
- return self.h_resized, self.w_resized
- else:
- return self.upsample_res
-
- def extract_backbone_features(self, batch, batched=True, upsample=False):
- x_q = batch["im_A"]
- x_s = batch["im_B"]
- if batched:
- X = torch.cat((x_q, x_s), dim=0)
- feature_pyramid = self.encoder(X, upsample=upsample)
- else:
- feature_pyramid = self.encoder(x_q, upsample=upsample), self.encoder(
- x_s, upsample=upsample
- )
- return feature_pyramid
-
- def sample(
- self,
- matches,
- certainty,
- num=10000,
- ):
- if "threshold" in self.sample_mode:
- upper_thresh = self.sample_thresh
- certainty = certainty.clone()
- certainty[certainty > upper_thresh] = 1
- matches, certainty = (
- matches.reshape(-1, 4),
- certainty.reshape(-1),
- )
- expansion_factor = 4 if "balanced" in self.sample_mode else 1
- good_samples = torch.multinomial(
- certainty,
- num_samples=min(expansion_factor * num, len(certainty)),
- replacement=False,
- )
- good_matches, good_certainty = matches[good_samples], certainty[good_samples]
- if "balanced" not in self.sample_mode:
- return good_matches, good_certainty
- density = kde(good_matches, std=0.1)
- p = 1 / (density + 1)
- p[
- density < 10
- ] = 1e-7 # Basically should have at least 10 perfect neighbours, or around 100 ok ones
- balanced_samples = torch.multinomial(
- p, num_samples=min(num, len(good_certainty)), replacement=False
- )
- return good_matches[balanced_samples], good_certainty[balanced_samples]
-
- def forward(self, batch, batched=True, upsample=False, scale_factor=1):
- feature_pyramid = self.extract_backbone_features(
- batch, batched=batched, upsample=upsample
- )
- if batched:
- f_q_pyramid = {
- scale: f_scale.chunk(2)[0] for scale, f_scale in feature_pyramid.items()
- }
- f_s_pyramid = {
- scale: f_scale.chunk(2)[1] for scale, f_scale in feature_pyramid.items()
- }
- else:
- f_q_pyramid, f_s_pyramid = feature_pyramid
- corresps = self.decoder(
- f_q_pyramid,
- f_s_pyramid,
- upsample=upsample,
- **(batch["corresps"] if "corresps" in batch else {}),
- scale_factor=scale_factor,
- )
-
- return corresps
-
- def forward_symmetric(self, batch, batched=True, upsample=False, scale_factor=1):
- feature_pyramid = self.extract_backbone_features(
- batch, batched=batched, upsample=upsample
- )
- f_q_pyramid = feature_pyramid
- f_s_pyramid = {
- scale: torch.cat((f_scale.chunk(2)[1], f_scale.chunk(2)[0]), dim=0)
- for scale, f_scale in feature_pyramid.items()
- }
- corresps = self.decoder(
- f_q_pyramid,
- f_s_pyramid,
- upsample=upsample,
- **(batch["corresps"] if "corresps" in batch else {}),
- scale_factor=scale_factor,
- )
- return corresps
-
- def to_pixel_coordinates(self, matches, H_A, W_A, H_B, W_B):
- kpts_A, kpts_B = matches[..., :2], matches[..., 2:]
- kpts_A = torch.stack(
- (W_A / 2 * (kpts_A[..., 0] + 1), H_A / 2 * (kpts_A[..., 1] + 1)), axis=-1
- )
- kpts_B = torch.stack(
- (W_B / 2 * (kpts_B[..., 0] + 1), H_B / 2 * (kpts_B[..., 1] + 1)), axis=-1
- )
- return kpts_A, kpts_B
-
- def match(
- self,
- im_A_path,
- im_B_path,
- *args,
- batched=False,
- device=None,
- ):
- if device is None:
- device = torch.device(device if torch.cuda.is_available() else "cpu")
- from PIL import Image
-
- if isinstance(im_A_path, (str, os.PathLike)):
- im_A, im_B = Image.open(im_A_path), Image.open(im_B_path)
- else:
- # Assume its not a path
- im_A, im_B = im_A_path, im_B_path
- symmetric = self.symmetric
- self.train(False)
- with torch.no_grad():
- if not batched:
- b = 1
- w, h = im_A.size
- w2, h2 = im_B.size
- # Get images in good format
- ws = self.w_resized
- hs = self.h_resized
-
- test_transform = get_tuple_transform_ops(
- resize=(hs, ws), normalize=True, clahe=False
- )
- im_A, im_B = test_transform((im_A, im_B))
- batch = {"im_A": im_A[None].to(device), "im_B": im_B[None].to(device)}
- else:
- b, c, h, w = im_A.shape
- b, c, h2, w2 = im_B.shape
- assert w == w2 and h == h2, "For batched images we assume same size"
- batch = {"im_A": im_A.to(device), "im_B": im_B.to(device)}
- if h != self.h_resized or self.w_resized != w:
- warn(
- "Model resolution and batch resolution differ, may produce unexpected results"
- )
- hs, ws = h, w
- finest_scale = 1
- # Run matcher
- if symmetric:
- corresps = self.forward_symmetric(batch)
- else:
- corresps = self.forward(batch, batched=True)
-
- if self.upsample_preds:
- hs, ws = self.upsample_res
-
- if self.attenuate_cert:
- low_res_certainty = F.interpolate(
- corresps[16]["certainty"],
- size=(hs, ws),
- align_corners=False,
- mode="bilinear",
- )
- cert_clamp = 0
- factor = 0.5
- low_res_certainty = (
- factor * low_res_certainty * (low_res_certainty < cert_clamp)
- )
-
- if self.upsample_preds:
- finest_corresps = corresps[finest_scale]
- torch.cuda.empty_cache()
- test_transform = get_tuple_transform_ops(
- resize=(hs, ws), normalize=True
- )
- im_A, im_B = Image.open(im_A_path), Image.open(im_B_path)
- im_A, im_B = test_transform((im_A, im_B))
- im_A, im_B = im_A[None].to(device), im_B[None].to(device)
- scale_factor = math.sqrt(
- self.upsample_res[0]
- * self.upsample_res[1]
- / (self.w_resized * self.h_resized)
- )
- batch = {"im_A": im_A, "im_B": im_B, "corresps": finest_corresps}
- if symmetric:
- corresps = self.forward_symmetric(
- batch, upsample=True, batched=True, scale_factor=scale_factor
- )
- else:
- corresps = self.forward(
- batch, batched=True, upsample=True, scale_factor=scale_factor
- )
-
- im_A_to_im_B = corresps[finest_scale]["flow"]
- certainty = corresps[finest_scale]["certainty"] - (
- low_res_certainty if self.attenuate_cert else 0
- )
- if finest_scale != 1:
- im_A_to_im_B = F.interpolate(
- im_A_to_im_B, size=(hs, ws), align_corners=False, mode="bilinear"
- )
- certainty = F.interpolate(
- certainty, size=(hs, ws), align_corners=False, mode="bilinear"
- )
- im_A_to_im_B = im_A_to_im_B.permute(0, 2, 3, 1)
- # Create im_A meshgrid
- im_A_coords = torch.meshgrid(
- (
- torch.linspace(-1 + 1 / hs, 1 - 1 / hs, hs, device=device),
- torch.linspace(-1 + 1 / ws, 1 - 1 / ws, ws, device=device),
- )
- )
- im_A_coords = torch.stack((im_A_coords[1], im_A_coords[0]))
- im_A_coords = im_A_coords[None].expand(b, 2, hs, ws)
- certainty = certainty.sigmoid() # logits -> probs
- im_A_coords = im_A_coords.permute(0, 2, 3, 1)
- if (im_A_to_im_B.abs() > 1).any() and True:
- wrong = (im_A_to_im_B.abs() > 1).sum(dim=-1) > 0
- certainty[wrong[:, None]] = 0
- im_A_to_im_B = torch.clamp(im_A_to_im_B, -1, 1)
- if symmetric:
- A_to_B, B_to_A = im_A_to_im_B.chunk(2)
- q_warp = torch.cat((im_A_coords, A_to_B), dim=-1)
- im_B_coords = im_A_coords
- s_warp = torch.cat((B_to_A, im_B_coords), dim=-1)
- warp = torch.cat((q_warp, s_warp), dim=2)
- certainty = torch.cat(certainty.chunk(2), dim=3)
- else:
- warp = torch.cat((im_A_coords, im_A_to_im_B), dim=-1)
- if batched:
- return (warp, certainty[:, 0])
- else:
- return (
- warp[0],
- certainty[0, 0],
- )
diff --git a/spaces/Reeve/Ohayou_Face/models/mtcnn/mtcnn_pytorch/src/first_stage.py b/spaces/Reeve/Ohayou_Face/models/mtcnn/mtcnn_pytorch/src/first_stage.py
deleted file mode 100644
index d646f91d5e0348e23bd426701f6afa6000a9b6d1..0000000000000000000000000000000000000000
--- a/spaces/Reeve/Ohayou_Face/models/mtcnn/mtcnn_pytorch/src/first_stage.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-from torch.autograd import Variable
-import math
-from PIL import Image
-import numpy as np
-from .box_utils import nms, _preprocess
-
-# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-device = 'cuda:0'
-
-
-def run_first_stage(image, net, scale, threshold):
- """Run P-Net, generate bounding boxes, and do NMS.
-
- Arguments:
- image: an instance of PIL.Image.
- net: an instance of pytorch's nn.Module, P-Net.
- scale: a float number,
- scale width and height of the image by this number.
- threshold: a float number,
- threshold on the probability of a face when generating
- bounding boxes from predictions of the net.
-
- Returns:
- a float numpy array of shape [n_boxes, 9],
- bounding boxes with scores and offsets (4 + 1 + 4).
- """
-
- # scale the image and convert it to a float array
- width, height = image.size
- sw, sh = math.ceil(width * scale), math.ceil(height * scale)
- img = image.resize((sw, sh), Image.BILINEAR)
- img = np.asarray(img, 'float32')
-
- img = torch.FloatTensor(_preprocess(img)).to(device)
- with torch.no_grad():
- output = net(img)
- probs = output[1].cpu().data.numpy()[0, 1, :, :]
- offsets = output[0].cpu().data.numpy()
- # probs: probability of a face at each sliding window
- # offsets: transformations to true bounding boxes
-
- boxes = _generate_bboxes(probs, offsets, scale, threshold)
- if len(boxes) == 0:
- return None
-
- keep = nms(boxes[:, 0:5], overlap_threshold=0.5)
- return boxes[keep]
-
-
-def _generate_bboxes(probs, offsets, scale, threshold):
- """Generate bounding boxes at places
- where there is probably a face.
-
- Arguments:
- probs: a float numpy array of shape [n, m].
- offsets: a float numpy array of shape [1, 4, n, m].
- scale: a float number,
- width and height of the image were scaled by this number.
- threshold: a float number.
-
- Returns:
- a float numpy array of shape [n_boxes, 9]
- """
-
- # applying P-Net is equivalent, in some sense, to
- # moving 12x12 window with stride 2
- stride = 2
- cell_size = 12
-
- # indices of boxes where there is probably a face
- inds = np.where(probs > threshold)
-
- if inds[0].size == 0:
- return np.array([])
-
- # transformations of bounding boxes
- tx1, ty1, tx2, ty2 = [offsets[0, i, inds[0], inds[1]] for i in range(4)]
- # they are defined as:
- # w = x2 - x1 + 1
- # h = y2 - y1 + 1
- # x1_true = x1 + tx1*w
- # x2_true = x2 + tx2*w
- # y1_true = y1 + ty1*h
- # y2_true = y2 + ty2*h
-
- offsets = np.array([tx1, ty1, tx2, ty2])
- score = probs[inds[0], inds[1]]
-
- # P-Net is applied to scaled images
- # so we need to rescale bounding boxes back
- bounding_boxes = np.vstack([
- np.round((stride * inds[1] + 1.0) / scale),
- np.round((stride * inds[0] + 1.0) / scale),
- np.round((stride * inds[1] + 1.0 + cell_size) / scale),
- np.round((stride * inds[0] + 1.0 + cell_size) / scale),
- score, offsets
- ])
- # why one is added?
-
- return bounding_boxes.T
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py
deleted file mode 100644
index 3f23b6717d53ad29f02dd15046802a2631a5076b..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './pascal_voc12.py'
-# dataset settings
-data = dict(
- train=dict(
- ann_dir=['SegmentationClass', 'SegmentationClassAug'],
- split=[
- 'ImageSets/Segmentation/train.txt',
- 'ImageSets/Segmentation/aug.txt'
- ]))
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/nms.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/nms.py
deleted file mode 100644
index 6d9634281f486ab284091786886854c451368052..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/nms.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import os
-
-import numpy as np
-import torch
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated'])
-
-
-# This function is modified from: https://github.com/pytorch/vision/
-class NMSop(torch.autograd.Function):
-
- @staticmethod
- def forward(ctx, bboxes, scores, iou_threshold, offset, score_threshold,
- max_num):
- is_filtering_by_score = score_threshold > 0
- if is_filtering_by_score:
- valid_mask = scores > score_threshold
- bboxes, scores = bboxes[valid_mask], scores[valid_mask]
- valid_inds = torch.nonzero(
- valid_mask, as_tuple=False).squeeze(dim=1)
-
- inds = ext_module.nms(
- bboxes, scores, iou_threshold=float(iou_threshold), offset=offset)
-
- if max_num > 0:
- inds = inds[:max_num]
- if is_filtering_by_score:
- inds = valid_inds[inds]
- return inds
-
- @staticmethod
- def symbolic(g, bboxes, scores, iou_threshold, offset, score_threshold,
- max_num):
- from ..onnx import is_custom_op_loaded
- has_custom_op = is_custom_op_loaded()
- # TensorRT nms plugin is aligned with original nms in ONNXRuntime
- is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
- if has_custom_op and (not is_trt_backend):
- return g.op(
- 'mmcv::NonMaxSuppression',
- bboxes,
- scores,
- iou_threshold_f=float(iou_threshold),
- offset_i=int(offset))
- else:
- from torch.onnx.symbolic_opset9 import select, squeeze, unsqueeze
- from ..onnx.onnx_utils.symbolic_helper import _size_helper
-
- boxes = unsqueeze(g, bboxes, 0)
- scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)
-
- if max_num > 0:
- max_num = g.op(
- 'Constant',
- value_t=torch.tensor(max_num, dtype=torch.long))
- else:
- dim = g.op('Constant', value_t=torch.tensor(0))
- max_num = _size_helper(g, bboxes, dim)
- max_output_per_class = max_num
- iou_threshold = g.op(
- 'Constant',
- value_t=torch.tensor([iou_threshold], dtype=torch.float))
- score_threshold = g.op(
- 'Constant',
- value_t=torch.tensor([score_threshold], dtype=torch.float))
- nms_out = g.op('NonMaxSuppression', boxes, scores,
- max_output_per_class, iou_threshold,
- score_threshold)
- return squeeze(
- g,
- select(
- g, nms_out, 1,
- g.op(
- 'Constant',
- value_t=torch.tensor([2], dtype=torch.long))), 1)
-
-
-class SoftNMSop(torch.autograd.Function):
-
- @staticmethod
- def forward(ctx, boxes, scores, iou_threshold, sigma, min_score, method,
- offset):
- dets = boxes.new_empty((boxes.size(0), 5), device='cpu')
- inds = ext_module.softnms(
- boxes.cpu(),
- scores.cpu(),
- dets.cpu(),
- iou_threshold=float(iou_threshold),
- sigma=float(sigma),
- min_score=float(min_score),
- method=int(method),
- offset=int(offset))
- return dets, inds
-
- @staticmethod
- def symbolic(g, boxes, scores, iou_threshold, sigma, min_score, method,
- offset):
- from packaging import version
- assert version.parse(torch.__version__) >= version.parse('1.7.0')
- nms_out = g.op(
- 'mmcv::SoftNonMaxSuppression',
- boxes,
- scores,
- iou_threshold_f=float(iou_threshold),
- sigma_f=float(sigma),
- min_score_f=float(min_score),
- method_i=int(method),
- offset_i=int(offset),
- outputs=2)
- return nms_out
-
-
-@deprecated_api_warning({'iou_thr': 'iou_threshold'})
-def nms(boxes, scores, iou_threshold, offset=0, score_threshold=0, max_num=-1):
- """Dispatch to either CPU or GPU NMS implementations.
-
- The input can be either torch tensor or numpy array. GPU NMS will be used
- if the input is gpu tensor, otherwise CPU NMS
- will be used. The returned type will always be the same as inputs.
-
- Arguments:
- boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4).
- scores (torch.Tensor or np.ndarray): scores in shape (N, ).
- iou_threshold (float): IoU threshold for NMS.
- offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset).
- score_threshold (float): score threshold for NMS.
- max_num (int): maximum number of boxes after NMS.
-
- Returns:
- tuple: kept dets(boxes and scores) and indice, which is always the \
- same data type as the input.
-
- Example:
- >>> boxes = np.array([[49.1, 32.4, 51.0, 35.9],
- >>> [49.3, 32.9, 51.0, 35.3],
- >>> [49.2, 31.8, 51.0, 35.4],
- >>> [35.1, 11.5, 39.1, 15.7],
- >>> [35.6, 11.8, 39.3, 14.2],
- >>> [35.3, 11.5, 39.9, 14.5],
- >>> [35.2, 11.7, 39.7, 15.7]], dtype=np.float32)
- >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.5, 0.4, 0.3],\
- dtype=np.float32)
- >>> iou_threshold = 0.6
- >>> dets, inds = nms(boxes, scores, iou_threshold)
- >>> assert len(inds) == len(dets) == 3
- """
- assert isinstance(boxes, (torch.Tensor, np.ndarray))
- assert isinstance(scores, (torch.Tensor, np.ndarray))
- is_numpy = False
- if isinstance(boxes, np.ndarray):
- is_numpy = True
- boxes = torch.from_numpy(boxes)
- if isinstance(scores, np.ndarray):
- scores = torch.from_numpy(scores)
- assert boxes.size(1) == 4
- assert boxes.size(0) == scores.size(0)
- assert offset in (0, 1)
-
- if torch.__version__ == 'parrots':
- indata_list = [boxes, scores]
- indata_dict = {
- 'iou_threshold': float(iou_threshold),
- 'offset': int(offset)
- }
- inds = ext_module.nms(*indata_list, **indata_dict)
- else:
- inds = NMSop.apply(boxes, scores, iou_threshold, offset,
- score_threshold, max_num)
- dets = torch.cat((boxes[inds], scores[inds].reshape(-1, 1)), dim=1)
- if is_numpy:
- dets = dets.cpu().numpy()
- inds = inds.cpu().numpy()
- return dets, inds
-
-
-@deprecated_api_warning({'iou_thr': 'iou_threshold'})
-def soft_nms(boxes,
- scores,
- iou_threshold=0.3,
- sigma=0.5,
- min_score=1e-3,
- method='linear',
- offset=0):
- """Dispatch to only CPU Soft NMS implementations.
-
- The input can be either a torch tensor or numpy array.
- The returned type will always be the same as inputs.
-
- Arguments:
- boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4).
- scores (torch.Tensor or np.ndarray): scores in shape (N, ).
- iou_threshold (float): IoU threshold for NMS.
- sigma (float): hyperparameter for gaussian method
- min_score (float): score filter threshold
- method (str): either 'linear' or 'gaussian'
- offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset).
-
- Returns:
- tuple: kept dets(boxes and scores) and indice, which is always the \
- same data type as the input.
-
- Example:
- >>> boxes = np.array([[4., 3., 5., 3.],
- >>> [4., 3., 5., 4.],
- >>> [3., 1., 3., 1.],
- >>> [3., 1., 3., 1.],
- >>> [3., 1., 3., 1.],
- >>> [3., 1., 3., 1.]], dtype=np.float32)
- >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.4, 0.0], dtype=np.float32)
- >>> iou_threshold = 0.6
- >>> dets, inds = soft_nms(boxes, scores, iou_threshold, sigma=0.5)
- >>> assert len(inds) == len(dets) == 5
- """
-
- assert isinstance(boxes, (torch.Tensor, np.ndarray))
- assert isinstance(scores, (torch.Tensor, np.ndarray))
- is_numpy = False
- if isinstance(boxes, np.ndarray):
- is_numpy = True
- boxes = torch.from_numpy(boxes)
- if isinstance(scores, np.ndarray):
- scores = torch.from_numpy(scores)
- assert boxes.size(1) == 4
- assert boxes.size(0) == scores.size(0)
- assert offset in (0, 1)
- method_dict = {'naive': 0, 'linear': 1, 'gaussian': 2}
- assert method in method_dict.keys()
-
- if torch.__version__ == 'parrots':
- dets = boxes.new_empty((boxes.size(0), 5), device='cpu')
- indata_list = [boxes.cpu(), scores.cpu(), dets.cpu()]
- indata_dict = {
- 'iou_threshold': float(iou_threshold),
- 'sigma': float(sigma),
- 'min_score': min_score,
- 'method': method_dict[method],
- 'offset': int(offset)
- }
- inds = ext_module.softnms(*indata_list, **indata_dict)
- else:
- dets, inds = SoftNMSop.apply(boxes.cpu(), scores.cpu(),
- float(iou_threshold), float(sigma),
- float(min_score), method_dict[method],
- int(offset))
-
- dets = dets[:inds.size(0)]
-
- if is_numpy:
- dets = dets.cpu().numpy()
- inds = inds.cpu().numpy()
- return dets, inds
- else:
- return dets.to(device=boxes.device), inds.to(device=boxes.device)
-
-
-def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
- """Performs non-maximum suppression in a batched fashion.
-
- Modified from https://github.com/pytorch/vision/blob
- /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
- In order to perform NMS independently per class, we add an offset to all
- the boxes. The offset is dependent only on the class idx, and is large
- enough so that boxes from different classes do not overlap.
-
- Arguments:
- boxes (torch.Tensor): boxes in shape (N, 4).
- scores (torch.Tensor): scores in shape (N, ).
- idxs (torch.Tensor): each index value correspond to a bbox cluster,
- and NMS will not be applied between elements of different idxs,
- shape (N, ).
- nms_cfg (dict): specify nms type and other parameters like iou_thr.
- Possible keys includes the following.
-
- - iou_thr (float): IoU threshold used for NMS.
- - split_thr (float): threshold number of boxes. In some cases the
- number of boxes is large (e.g., 200k). To avoid OOM during
- training, the users could set `split_thr` to a small value.
- If the number of boxes is greater than the threshold, it will
- perform NMS on each group of boxes separately and sequentially.
- Defaults to 10000.
- class_agnostic (bool): if true, nms is class agnostic,
- i.e. IoU thresholding happens over all boxes,
- regardless of the predicted class.
-
- Returns:
- tuple: kept dets and indice.
- """
- nms_cfg_ = nms_cfg.copy()
- class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic)
- if class_agnostic:
- boxes_for_nms = boxes
- else:
- max_coordinate = boxes.max()
- offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
- boxes_for_nms = boxes + offsets[:, None]
-
- nms_type = nms_cfg_.pop('type', 'nms')
- nms_op = eval(nms_type)
-
- split_thr = nms_cfg_.pop('split_thr', 10000)
- # Won't split to multiple nms nodes when exporting to onnx
- if boxes_for_nms.shape[0] < split_thr or torch.onnx.is_in_onnx_export():
- dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_)
- boxes = boxes[keep]
- # -1 indexing works abnormal in TensorRT
- # This assumes `dets` has 5 dimensions where
- # the last dimension is score.
- # TODO: more elegant way to handle the dimension issue.
- # Some type of nms would reweight the score, such as SoftNMS
- scores = dets[:, 4]
- else:
- max_num = nms_cfg_.pop('max_num', -1)
- total_mask = scores.new_zeros(scores.size(), dtype=torch.bool)
- # Some type of nms would reweight the score, such as SoftNMS
- scores_after_nms = scores.new_zeros(scores.size())
- for id in torch.unique(idxs):
- mask = (idxs == id).nonzero(as_tuple=False).view(-1)
- dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_)
- total_mask[mask[keep]] = True
- scores_after_nms[mask[keep]] = dets[:, -1]
- keep = total_mask.nonzero(as_tuple=False).view(-1)
-
- scores, inds = scores_after_nms[keep].sort(descending=True)
- keep = keep[inds]
- boxes = boxes[keep]
-
- if max_num > 0:
- keep = keep[:max_num]
- boxes = boxes[:max_num]
- scores = scores[:max_num]
-
- return torch.cat([boxes, scores[:, None]], -1), keep
-
-
-def nms_match(dets, iou_threshold):
- """Matched dets into different groups by NMS.
-
- NMS match is Similar to NMS but when a bbox is suppressed, nms match will
- record the indice of suppressed bbox and form a group with the indice of
- kept bbox. In each group, indice is sorted as score order.
-
- Arguments:
- dets (torch.Tensor | np.ndarray): Det boxes with scores, shape (N, 5).
- iou_thr (float): IoU thresh for NMS.
-
- Returns:
- List[torch.Tensor | np.ndarray]: The outer list corresponds different
- matched group, the inner Tensor corresponds the indices for a group
- in score order.
- """
- if dets.shape[0] == 0:
- matched = []
- else:
- assert dets.shape[-1] == 5, 'inputs dets.shape should be (N, 5), ' \
- f'but get {dets.shape}'
- if isinstance(dets, torch.Tensor):
- dets_t = dets.detach().cpu()
- else:
- dets_t = torch.from_numpy(dets)
- indata_list = [dets_t]
- indata_dict = {'iou_threshold': float(iou_threshold)}
- matched = ext_module.nms_match(*indata_list, **indata_dict)
- if torch.__version__ == 'parrots':
- matched = matched.tolist()
-
- if isinstance(dets, torch.Tensor):
- return [dets.new_tensor(m, dtype=torch.long) for m in matched]
- else:
- return [np.array(m, dtype=np.int) for m in matched]
-
-
-def nms_rotated(dets, scores, iou_threshold, labels=None):
- """Performs non-maximum suppression (NMS) on the rotated boxes according to
- their intersection-over-union (IoU).
-
- Rotated NMS iteratively removes lower scoring rotated boxes which have an
- IoU greater than iou_threshold with another (higher scoring) rotated box.
-
- Args:
- boxes (Tensor): Rotated boxes in shape (N, 5). They are expected to \
- be in (x_ctr, y_ctr, width, height, angle_radian) format.
- scores (Tensor): scores in shape (N, ).
- iou_threshold (float): IoU thresh for NMS.
- labels (Tensor): boxes' label in shape (N,).
-
- Returns:
- tuple: kept dets(boxes and scores) and indice, which is always the \
- same data type as the input.
- """
- if dets.shape[0] == 0:
- return dets, None
- multi_label = labels is not None
- if multi_label:
- dets_wl = torch.cat((dets, labels.unsqueeze(1)), 1)
- else:
- dets_wl = dets
- _, order = scores.sort(0, descending=True)
- dets_sorted = dets_wl.index_select(0, order)
-
- if torch.__version__ == 'parrots':
- keep_inds = ext_module.nms_rotated(
- dets_wl,
- scores,
- order,
- dets_sorted,
- iou_threshold=iou_threshold,
- multi_label=multi_label)
- else:
- keep_inds = ext_module.nms_rotated(dets_wl, scores, order, dets_sorted,
- iou_threshold, multi_label)
- dets = torch.cat((dets[keep_inds], scores[keep_inds].reshape(-1, 1)),
- dim=1)
- return dets, keep_inds
diff --git a/spaces/RohanAi/low-light-enhancement/README.md b/spaces/RohanAi/low-light-enhancement/README.md
deleted file mode 100644
index 240a53368e2e271a973f38a086af33848e22262f..0000000000000000000000000000000000000000
--- a/spaces/RohanAi/low-light-enhancement/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Low Light Enhancement
-emoji: 💻
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RohanAi/low-light-enhancement/app.py b/spaces/RohanAi/low-light-enhancement/app.py
deleted file mode 100644
index 41c205492d9d03e6b0c0aded64037ab18305e393..0000000000000000000000000000000000000000
--- a/spaces/RohanAi/low-light-enhancement/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os
-import time
-import logging
-import numpy as np
-import keras.optimizers
-import cv2
-import gradio as gr
-import Network
-import utls
-
-class ImageProcessor:
-
- def __init__(self, model_name):
- self.model_name = model_name
- self.mbllen = Network.build_mbllen((None, None, 3))
- self.mbllen.load_weights(f'{model_name}.h5')
- self.opt = keras.optimizers.Adam(lr=2 * 1e-04, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
- self.mbllen.compile(loss='mse', optimizer=self.opt)
-
- def process_image(self, image, highpercent, lowpercent, gamma, maxrange):
- img_A = np.array(image) / 255.0
- img_A = img_A[np.newaxis, :]
-
- maxrange /= 10.
- hsvgamma = gamma / 10.
-
- out_pred = self.mbllen.predict(img_A)
-
- fake_B = out_pred[0, :, :, :3]
- fake_B = self.adjust_image(fake_B, maxrange, highpercent, lowpercent, hsvgamma)
-
- return fake_B
-
- def adjust_image(self, fake_B, maxrange, highpercent, lowpercent, hsvgamma):
- gray_fake_B = fake_B[:, :, 0] * 0.299 + fake_B[:, :, 1] * 0.587 + fake_B[:, :, 1] * 0.114
- percent_max = sum(sum(gray_fake_B >= maxrange)) / sum(sum(gray_fake_B <= 1.0))
- max_value = np.percentile(gray_fake_B[:], highpercent)
- if percent_max < (100-highpercent)/100.:
- scale = maxrange / max_value
- fake_B = fake_B * scale
- fake_B = np.minimum(fake_B, 1.0)
-
- gray_fake_B = fake_B[:,:,0]*0.299 + fake_B[:,:,1]*0.587 + fake_B[:,:,1]*0.114
- sub_value = np.percentile(gray_fake_B[:], lowpercent)
- fake_B = (fake_B - sub_value) * (1./(1-sub_value))
-
- imgHSV = cv2.cvtColor(fake_B, cv2.COLOR_RGB2HSV)
- H, S, V = cv2.split(imgHSV)
- S = np.power(S, hsvgamma)
- imgHSV = cv2.merge([H, S, V])
- fake_B = cv2.cvtColor(imgHSV, cv2.COLOR_HSV2RGB)
- fake_B = np.minimum(fake_B, 1.0)
-
- return fake_B
-
-if __name__ == "__main__":
- logging.basicConfig(filename='image_processor.log', level=logging.INFO)
-
- image_processor = ImageProcessor(model_name='Syn_img_lowlight_withnoise')
- iface = gr.Interface(
- image_processor.process_image,
- [
- gr.inputs.Image(shape=(None, None)), # Corrected line
- gr.inputs.Slider(85, 100, default=95, label="High Percent"),
- gr.inputs.Slider(0, 100, default=5, label="Low Percent"),
- gr.inputs.Slider(6, 10, default=8, label="Gamma"),
- gr.inputs.Slider(0, 20, default=8, label="Max Range"),
- ],
- gr.outputs.Image(type="numpy"),
-)
-
-
-
- iface.launch()
\ No newline at end of file
diff --git a/spaces/SIGGRAPH2022/StyleGAN-XL/app.py b/spaces/SIGGRAPH2022/StyleGAN-XL/app.py
deleted file mode 100644
index 586b290a50835e2383a6662583e40cc96fe9e8f3..0000000000000000000000000000000000000000
--- a/spaces/SIGGRAPH2022/StyleGAN-XL/app.py
+++ /dev/null
@@ -1,204 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import json
-
-import gradio as gr
-import numpy as np
-
-from model import Model
-
-DESCRIPTION = '# [StyleGAN-XL](https://github.com/autonomousvision/stylegan_xl)'
-
-
-def update_class_index(name: str) -> dict:
- if 'imagenet' in name:
- return gr.Slider.update(maximum=999, visible=True)
- elif 'cifar' in name:
- return gr.Slider.update(maximum=9, visible=True)
- else:
- return gr.Slider.update(visible=False)
-
-
-def get_sample_image_url(name: str) -> str:
- sample_image_dir = 'https://huggingface.co/spaces/hysts/StyleGAN-XL/resolve/main/samples'
- return f'{sample_image_dir}/{name}.jpg'
-
-
-def get_sample_image_markdown(name: str) -> str:
- url = get_sample_image_url(name)
- if name == 'imagenet':
- size = 128
- class_index = '0-999'
- seed = '0'
- elif name == 'cifar10':
- size = 32
- class_index = '0-9'
- seed = '0-9'
- elif name == 'ffhq':
- size = 256
- class_index = 'N/A'
- seed = '0-99'
- elif name == 'pokemon':
- size = 256
- class_index = 'N/A'
- seed = '0-99'
- else:
- raise ValueError
-
- return f'''
- - size: {size}x{size}
- - class_index: {class_index}
- - seed: {seed}
- - truncation: 0.7
- '''
-
-
-def load_class_names(name: str) -> list[str]:
- with open(f'labels/{name}_classes.json') as f:
- names = json.load(f)
- return names
-
-
-def get_class_name_df(name: str) -> list:
- names = load_class_names(name)
- return list(map(list, enumerate(names))) # type: ignore
-
-
-IMAGENET_NAMES = load_class_names('imagenet')
-CIFAR10_NAMES = load_class_names('cifar10')
-
-
-def update_class_name(model_name: str, index: int) -> dict:
- if 'imagenet' in model_name:
- if index < len(IMAGENET_NAMES):
- value = IMAGENET_NAMES[index]
- else:
- value = '-'
- return gr.Textbox.update(value=value, visible=True)
- elif 'cifar' in model_name:
- if index < len(CIFAR10_NAMES):
- value = CIFAR10_NAMES[index]
- else:
- value = '-'
- return gr.Textbox.update(value=value, visible=True)
- else:
- return gr.Textbox.update(visible=False)
-
-
-model = Model()
-
-with gr.Blocks(css='style.css') as demo:
- gr.Markdown(DESCRIPTION)
-
- with gr.Tabs():
- with gr.TabItem('App'):
- with gr.Row():
- with gr.Column():
- with gr.Group():
- model_name = gr.Dropdown(model.MODEL_NAMES,
- value=model.MODEL_NAMES[3],
- label='Model')
- seed = gr.Slider(0,
- np.iinfo(np.uint32).max,
- step=1,
- value=0,
- label='Seed')
- psi = gr.Slider(0,
- 2,
- step=0.05,
- value=0.7,
- label='Truncation psi')
- class_index = gr.Slider(0,
- 999,
- step=1,
- value=83,
- label='Class Index')
- class_name = gr.Textbox(
- value=IMAGENET_NAMES[class_index.value],
- label='Class Label',
- interactive=False)
- tx = gr.Slider(-1,
- 1,
- step=0.05,
- value=0,
- label='Translate X')
- ty = gr.Slider(-1,
- 1,
- step=0.05,
- value=0,
- label='Translate Y')
- angle = gr.Slider(-180,
- 180,
- step=5,
- value=0,
- label='Angle')
- run_button = gr.Button('Run')
- with gr.Column():
- result = gr.Image(label='Result', elem_id='result')
-
- with gr.TabItem('Sample Images'):
- with gr.Row():
- model_name2 = gr.Dropdown([
- 'imagenet',
- 'cifar10',
- 'ffhq',
- 'pokemon',
- ],
- value='imagenet',
- label='Model')
- with gr.Row():
- text = get_sample_image_markdown(model_name2.value)
- sample_images = gr.Markdown(text)
-
- with gr.TabItem('Class Names'):
- with gr.Row():
- dataset_name = gr.Dropdown([
- 'imagenet',
- 'cifar10',
- ],
- value='imagenet',
- label='Dataset')
- with gr.Row():
- df = get_class_name_df('imagenet')
- class_names = gr.Dataframe(df,
- col_count=2,
- headers=['Class Index', 'Label'],
- interactive=False)
-
- model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
- model_name.change(fn=update_class_index,
- inputs=model_name,
- outputs=class_index)
- model_name.change(fn=update_class_name,
- inputs=[
- model_name,
- class_index,
- ],
- outputs=class_name)
- class_index.change(fn=update_class_name,
- inputs=[
- model_name,
- class_index,
- ],
- outputs=class_name)
- run_button.click(fn=model.set_model_and_generate_image,
- inputs=[
- model_name,
- seed,
- psi,
- class_index,
- tx,
- ty,
- angle,
- ],
- outputs=result)
- model_name2.change(fn=get_sample_image_markdown,
- inputs=model_name2,
- outputs=sample_images)
- dataset_name.change(fn=get_class_name_df,
- inputs=dataset_name,
- outputs=class_names)
-
-demo.queue(max_size=10).launch()
diff --git a/spaces/Salesforce/EDICT/my_diffusers/schedulers/__init__.py b/spaces/Salesforce/EDICT/my_diffusers/schedulers/__init__.py
deleted file mode 100644
index 20c25f35183faeeef2cd7b5095f80a70a9edac01..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/schedulers/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright 2022 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 ..utils import is_scipy_available
-from .scheduling_ddim import DDIMScheduler
-from .scheduling_ddpm import DDPMScheduler
-from .scheduling_karras_ve import KarrasVeScheduler
-from .scheduling_pndm import PNDMScheduler
-from .scheduling_sde_ve import ScoreSdeVeScheduler
-from .scheduling_sde_vp import ScoreSdeVpScheduler
-from .scheduling_utils import SchedulerMixin
-
-
-if is_scipy_available():
- from .scheduling_lms_discrete import LMSDiscreteScheduler
-else:
- from ..utils.dummy_scipy_objects import * # noqa F403
diff --git a/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/ONNXVITS_inference.py b/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/ONNXVITS_inference.py
deleted file mode 100644
index 258b618cd338322365dfa25bec468a0a3f70ccd1..0000000000000000000000000000000000000000
--- a/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/ONNXVITS_inference.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import IPython.display as ipd
-import torch
-import commons
-import utils
-import ONNXVITS_infer
-from text import text_to_sequence
-
-def get_text(text, hps):
- text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
- if hps.data.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
-hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json")
-
-net_g = ONNXVITS_infer.SynthesizerTrn(
- len(hps.symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g)
-
-text1 = get_text("おはようございます。", hps)
-stn_tst = text1
-with torch.no_grad():
- x_tst = stn_tst.unsqueeze(0)
- x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
- sid = torch.LongTensor([0])
- audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
-print(audio)
\ No newline at end of file
diff --git a/spaces/SeViLA/SeViLA/lavis/models/albef_models/albef_vqa.py b/spaces/SeViLA/SeViLA/lavis/models/albef_models/albef_vqa.py
deleted file mode 100644
index eb4dcbb9cd34a28637d3420ef4bdad5be47563b3..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/albef_models/albef_vqa.py
+++ /dev/null
@@ -1,442 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import logging
-import os
-from copy import deepcopy
-
-import torch
-import torch.nn.functional as F
-from lavis.common.registry import registry
-from lavis.common.utils import get_abs_path, is_url
-from lavis.models.albef_models import AlbefBase
-from lavis.models.albef_models.albef_outputs import AlbefIntermediateOutput, AlbefOutput
-from lavis.models.base_model import MomentumDistilationMixin, tile
-from lavis.models.med import BertConfig, BertLMHeadModel, XBertEncoder
-from lavis.models.vit import VisionTransformerEncoder, interpolate_pos_embed
-from lavis.common.dist_utils import download_cached_file
-
-
-@registry.register_model("albef_vqa")
-class AlbefVQA(AlbefBase, MomentumDistilationMixin):
- """
- ALBEF VQA models.
-
- Supported model types:
- - base: vqa model initialized with pre-trained ALBEF base model on 115M image-text pairs after CapFilt; not fine-tuned.
- - vqav2: fine-tuned ALBEF base model on VQA v2.0 dataset.
-
- Usage:
- >>> from lavis.models import load_model
- >>> model = load_model("albef_vqa", "vqav2")
- """
-
- PRETRAINED_MODEL_CONFIG_DICT = {
- "vqav2": "configs/models/albef_vqav2.yaml",
- }
-
- def __init__(
- self,
- image_encoder,
- text_encoder,
- text_decoder,
- use_distill=True,
- momentum=0.995,
- alpha=0.4,
- max_txt_len=35,
- ):
- super().__init__()
-
- self.tokenizer = self.init_tokenizer()
- self.max_txt_len = max_txt_len
-
- self.use_distill = use_distill
-
- self.visual_encoder = image_encoder
-
- self.text_encoder = text_encoder
- self.text_decoder = text_decoder
-
- if self.use_distill:
- self.visual_encoder_m = deepcopy(self.visual_encoder)
- self.text_encoder_m = deepcopy(self.text_encoder)
- self.text_decoder_m = deepcopy(self.text_decoder)
-
- self.momentum = momentum
- self.alpha = alpha
-
- self.model_pairs = [
- [self.visual_encoder, self.visual_encoder_m],
- [self.text_encoder, self.text_encoder_m],
- [self.text_decoder, self.text_decoder_m],
- ]
-
- self.copy_params()
-
- def _rampup_factor(self, epoch, iters, num_iters_per_epoch):
- return min(1, (epoch * num_iters_per_epoch + iters) / num_iters_per_epoch)
-
- def forward(self, samples):
- """
- Args:
- samples (dict): A dictionary containing the following keys:
- - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). Default H=480, W=480.
- - text_input (list): A list of strings, each string is a question
- - answer (list): A list of strings, each string is an answer
- - weight (torch.Tensor): A tensor used to weigh each answer in the loss computation.
- The shape of the tensor is (sum(n_answers),)
- - n_answers (torch.Tensor): A tensor shape (batch_size,) containing the number of answers
- for each question in the batch.
-
- Returns:
- An AlbefOutput object containing loss and intermediate outputs;
- see lavis/models/albef_models/albef_outputs.py for more details.
-
- Examples:
- >>> import torch
- >>> from lavis.models import load_model
- >>> model = load_model("albef_vqa")
- >>> samples = {
- ... "image": torch.rand(2, 3, 384, 384),
- ... "text_input": ["What is this?", "What is that?"],
- ... "answer": ["cat", "cat", "dog"],
- ... "weight": torch.tensor([1.0, 1.0, 1.0]),
- ... "n_answers": torch.tensor([2, 1]),
- ... "epoch": 0, "iters": 0, "num_iters_per_epoch": 1000,
- ... }
- >>> output = model(samples)
- >>> output.keys()
- odict_keys(['intermediate_output', 'loss'])
- """
- (
- encoder_output,
- encoder_output_m,
- image_embeds,
- image_embeds_m,
- ) = self.forward_encoder(samples)
- loss, decoder_output, decoder_targets = self.forward_decoder(
- samples, encoder_out=(encoder_output, encoder_output_m)
- )
-
- return AlbefOutput(
- loss=loss,
- intermediate_output=AlbefIntermediateOutput(
- image_embeds=image_embeds,
- image_embeds_m=image_embeds_m,
- encoder_output=encoder_output,
- encoder_output_m=encoder_output_m,
- decoder_output=decoder_output,
- decoder_labels=decoder_targets,
- ),
- )
-
- def forward_encoder(self, samples):
- questions = samples["text_input"]
- questions = self.tokenizer(
- questions,
- padding="longest",
- truncation=True,
- max_length=self.max_txt_len,
- return_tensors="pt",
- ).to(self.device)
- samples.update({"tokenized_text": questions})
-
- image_embeds = self.visual_encoder.forward_features(samples["image"])
- encoder_output = self.text_encoder.forward_automask(
- tokenized_text=samples["tokenized_text"], visual_embeds=image_embeds
- )
-
- if self.use_distill:
- self._momentum_update()
- with torch.no_grad():
- image_embeds_m = self.visual_encoder_m(samples["image"])
- encoder_output_m = self.text_encoder_m.forward_automask(
- tokenized_text=samples["tokenized_text"],
- visual_embeds=image_embeds_m,
- )
- else:
- encoder_output_m = None
- image_embeds_m = None
-
- return encoder_output, encoder_output_m, image_embeds, image_embeds_m
-
- def forward_decoder(self, samples, encoder_out, **kwargs):
- answers = self.tokenizer(
- samples["answer"], padding="longest", return_tensors="pt"
- ).to(self.device)
- answer_targets = answers.input_ids.masked_fill(
- answers.input_ids == self.tokenizer.pad_token_id, -100
- )
-
- question_states = []
- question_atts = []
-
- question = samples["tokenized_text"]
- question_output, question_output_m = encoder_out
-
- for b, n in enumerate(samples["n_answers"]):
- question_states += [question_output.last_hidden_state[b]] * n
- question_atts += [question.attention_mask[b]] * n
-
- question_states = torch.stack(question_states, dim=0)
- question_atts = torch.stack(question_atts, dim=0)
-
- if self.use_distill:
- with torch.no_grad():
- question_states_m = []
- for b, n in enumerate(samples["n_answers"]):
- question_states_m += [question_output_m.last_hidden_state[b]] * n
- question_states_m = torch.stack(question_states_m, 0)
-
- logits_m = self.text_decoder_m(
- answers.input_ids,
- attention_mask=answers.attention_mask,
- encoder_hidden_states=question_states_m,
- encoder_attention_mask=question_atts,
- return_logits=True,
- )
-
- alpha = self.alpha * self._rampup_factor(
- epoch=samples["epoch"],
- iters=samples["iters"],
- num_iters_per_epoch=samples["num_iters_per_epoch"],
- )
-
- answer_output = self.text_decoder(
- answers.input_ids,
- attention_mask=answers.attention_mask,
- encoder_hidden_states=question_states,
- encoder_attention_mask=question_atts,
- labels=answer_targets,
- soft_labels=F.softmax(logits_m, dim=-1),
- alpha=alpha,
- return_dict=True,
- reduction="none",
- )
-
- loss = samples["weight"] * answer_output.loss
- bsz = samples["image"].size(0)
-
- loss = loss.sum() / bsz
-
- return loss, answer_output, answer_targets
-
- def predict_answers(self, samples, answer_list, num_ans_candidates=128, **kwargs):
- """
- Args:
- samples (dict): A dictionary containing the following keys:
- - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). Default H=480, W=480.
- - text_input (str or [str]): String or a list of strings, each string is a question.
- The number of questions must be equal to the batch size. If a single string, will be converted to a list of string, with length 1 first.
- num_ans_candidates (int): Number of answer candidates, used to filter out answers with low probability.
- answer_list (list): A list of strings, each string is an answer.
-
- Returns:
- List: A list of strings, each string is an answer.
-
- Examples:
- >>> from PIL import Image
- >>> from lavis.models import load_model_and_preprocess
- >>> model, vis_processors, txt_processors = load_model_and_preprocess("albef_vqa", "vqav2")
- >>> raw_image = Image.open("docs/data/merlion.png").convert("RGB")
- >>> question = "Which city is this photo taken?"
- >>> image = vis_processors["eval"](raw_image).unsqueeze(0)
- >>> question = txt_processors["eval"](question)
- >>> samples = {"image": image, "text_input": [question]}
- >>> answer_list = ["Singapore", "London", "Palo Alto", "Tokyo"]
- >>> answers = model.predict_answers(samples, answer_list=answer_list)
- >>> answers
- ['Singapore']
- """
-
- if isinstance(samples["text_input"], str):
- samples["text_input"] = [samples["text_input"]]
-
- assert len(samples["text_input"]) == samples["image"].size(
- 0
- ), "The number of questions must be equal to the batch size."
-
- num_ans_candidates = min(num_ans_candidates, len(answer_list))
-
- return self.rank_answers(
- samples, answer_list=answer_list, num_ans_candidates=num_ans_candidates
- )
-
- def rank_answers(self, samples, answer_list, num_ans_candidates):
- """
- Generate the first token of answers using decoder and select ${num_ans_candidates}
- most probable ones. Then select answers from answer list, which start with the probable tokens.
- Lastly, use the selected answers as the ground-truth labels for decoding and calculating LM loss.
- Return the answers that minimize the losses as result.
-
- """
- answer_candidates = self.tokenizer(
- answer_list, padding="longest", return_tensors="pt"
- ).to(self.device)
- # answer_candidates.input_ids[:, 0] = self.tokenizer.bos_token_id
-
- answer_ids = answer_candidates.input_ids
- answer_atts = answer_candidates.attention_mask
-
- question_output, _, _, _ = self.forward_encoder(samples)
- question_states = question_output.last_hidden_state
-
- tokenized_question = samples["tokenized_text"]
- question_atts = tokenized_question.attention_mask
-
- num_ques = question_states.size(0)
- start_ids = answer_ids[0, 0].repeat(num_ques, 1) # bos token
-
- start_output = self.text_decoder(
- start_ids,
- encoder_hidden_states=question_states,
- encoder_attention_mask=question_atts,
- return_dict=True,
- reduction="none",
- )
- logits = start_output.logits[:, 0, :] # first token's logit
-
- # topk_probs: top-k probability
- # topk_ids: [num_question, k]
- answer_first_token = answer_ids[:, 1]
- prob_first_token = F.softmax(logits, dim=1).index_select(
- dim=1, index=answer_first_token
- )
- topk_probs, topk_ids = prob_first_token.topk(num_ans_candidates, dim=1)
-
- # answer input: [num_question*k, answer_len]
- input_ids = []
- input_atts = []
- for b, topk_id in enumerate(topk_ids):
- input_ids.append(answer_ids.index_select(dim=0, index=topk_id))
- input_atts.append(answer_atts.index_select(dim=0, index=topk_id))
- input_ids = torch.cat(input_ids, dim=0)
- input_atts = torch.cat(input_atts, dim=0)
-
- targets_ids = input_ids.masked_fill(
- input_ids == self.tokenizer.pad_token_id, -100
- )
-
- # repeat encoder's output for top-k answers
- question_states = tile(question_states, 0, num_ans_candidates)
- question_atts = tile(question_atts, 0, num_ans_candidates)
-
- output = self.text_decoder(
- input_ids,
- attention_mask=input_atts,
- encoder_hidden_states=question_states,
- encoder_attention_mask=question_atts,
- labels=targets_ids,
- return_dict=True,
- reduction="none",
- )
-
- log_probs_sum = -output.loss
- log_probs_sum = log_probs_sum.view(num_ques, num_ans_candidates)
-
- max_topk_ids = log_probs_sum.argmax(dim=1)
- max_ids = topk_ids[max_topk_ids >= 0, max_topk_ids]
-
- answers = [answer_list[max_id] for max_id in max_ids]
-
- return answers
-
- @classmethod
- def from_config(cls, cfg=None):
- image_encoder = VisionTransformerEncoder.from_config(cfg)
-
- text_encoder = XBertEncoder.from_config(cfg)
-
- config_decoder = BertConfig.from_json_file(get_abs_path(cfg["med_config_path"]))
- config_decoder.fusion_layer = 0
- config_decoder.num_hidden_layers = 6
- text_decoder = BertLMHeadModel.from_pretrained(
- "bert-base-uncased", config=config_decoder
- )
-
- alpha = cfg.get("alpha", 0.4)
- momentum = cfg.get("momentum", 0.995)
- use_distill = cfg.get("use_distill", True)
- max_txt_len = cfg.get("max_txt_len", 25)
-
- model = cls(
- image_encoder=image_encoder,
- text_encoder=text_encoder,
- text_decoder=text_decoder,
- use_distill=use_distill,
- momentum=momentum,
- alpha=alpha,
- max_txt_len=max_txt_len,
- )
-
- # load pre-trained weights
- model.load_checkpoint_from_config(cfg)
-
- return model
-
- def load_from_pretrained(self, url_or_filename):
- if is_url(url_or_filename):
- cached_file = download_cached_file(
- url_or_filename, check_hash=False, progress=True
- )
- checkpoint = torch.load(cached_file, map_location="cpu")
- elif os.path.isfile(url_or_filename):
- checkpoint = torch.load(url_or_filename, map_location="cpu")
- else:
- raise RuntimeError("checkpoint url or path is invalid")
-
- if "model" in checkpoint:
- state_dict = checkpoint["model"]
- else:
- state_dict = checkpoint
-
- # reshape positional embedding to accomodate for image resolution change
- pos_embed_reshaped = interpolate_pos_embed(
- state_dict["visual_encoder.pos_embed"], self.visual_encoder
- )
- state_dict["visual_encoder.pos_embed"] = pos_embed_reshaped
-
- m_pos_embed_reshaped = interpolate_pos_embed(
- state_dict["visual_encoder_m.pos_embed"], self.visual_encoder_m
- )
- state_dict["visual_encoder_m.pos_embed"] = m_pos_embed_reshaped
-
- for key in list(state_dict.keys()):
- if "bert" in key:
- encoder_key = key.replace("bert.", "")
- state_dict[encoder_key] = state_dict[key]
-
- # intialize text decoder as multimodal encoder (last 6 layers of model.text_encoder)
- if "text_encoder" in key:
- if "layer" in key:
- encoder_keys = key.split(".")
- layer_num = int(encoder_keys[4])
-
- if layer_num < 6:
- del state_dict[key]
- continue
- else:
- decoder_layer_num = layer_num - 6
- encoder_keys[4] = str(decoder_layer_num)
- encoder_key = ".".join(encoder_keys)
- else:
- encoder_key = key
- decoder_key = encoder_key.replace("text_encoder", "text_decoder")
- state_dict[decoder_key] = state_dict[key]
-
- del state_dict[key]
-
- for key in self.state_dict().keys():
- if key in state_dict.keys():
- if state_dict[key].shape != self.state_dict()[key].shape:
- del state_dict[key]
-
- msg = self.load_state_dict(state_dict, strict=False)
- logging.info("load checkpoint from %s" % url_or_filename)
- logging.info(f"missing keys: {msg.missing_keys}")
-
- return msg
diff --git a/spaces/SerdarHelli/Brain-MR-Image-Generation-with-StyleGAN/app.py b/spaces/SerdarHelli/Brain-MR-Image-Generation-with-StyleGAN/app.py
deleted file mode 100644
index 4560e13e964951daaa7fd0181e2d502dcbe41cad..0000000000000000000000000000000000000000
--- a/spaces/SerdarHelli/Brain-MR-Image-Generation-with-StyleGAN/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Apr 26 21:02:31 2022
-
-@author: pc
-"""
-
-import pickle
-import numpy as np
-import torch
-import gradio as gr
-import sys
-import subprocess
-import os
-from typing import Tuple
-import PIL.Image
-from huggingface_hub import hf_hub_download
-
-os.system("git clone https://github.com/NVlabs/stylegan3")
-
-sys.path.append("stylegan3")
-
-
-
-DESCRIPTION = f'''This model generates healthy MR Brain Images.
-
-
-[Example]("https://huggingface.co/spaces/SerdarHelli/Brain-MR-Image-Generation-GAN/blob/main/ex.png")
-'''
-network_pkl="brainmrigan.pkl"
-
-
-with open(network_pkl, 'rb') as f:
- G = pickle.load(f)['G_ema']
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-G.eval()
-G.to(device)
-
-def predict(Seed,noise_mode,truncation_psi):
-
- # Generate images.
- z = torch.from_numpy(np.random.RandomState(Seed).randn(1, G.z_dim)).to(device)
- label = torch.zeros([1, G.c_dim], device=device)
- # Construct an inverse rotation/translation matrix and pass to the generator. The
- # generator expects this matrix as an inverse to avoid potentially failing numerical
- # operations in the network.
-
-
-
- img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
- img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-
- return (PIL.Image.fromarray(img[0].cpu().numpy()[:,:,0])).resize((512,512))
-
-
-
-noises=['const', 'random', 'none']
-interface=gr.Interface(fn=predict, title="Brain MR Image Generation with StyleGAN-2",
- description = DESCRIPTION,
- article = "Author: S.Serdar Helli and Burhan Arat",
- inputs=[gr.inputs.Slider( minimum=0, maximum=2**16,label='Seed'),gr.inputs.Radio( choices=noises, default='const',label='Noise Mods'),
- gr.inputs.Slider(0, 2, step=0.05, default=1, label='Truncation psi')],
- outputs=gr.outputs.Image( type="numpy", label="Output"))
-
-
-interface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/SetoKaishi12/Test02/Dockerfile b/spaces/SetoKaishi12/Test02/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/SetoKaishi12/Test02/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/Shypanties22/FantasyMe/app.py b/spaces/Shypanties22/FantasyMe/app.py
deleted file mode 100644
index 79497a13823869d3783586401f86335996170c1e..0000000000000000000000000000000000000000
--- a/spaces/Shypanties22/FantasyMe/app.py
+++ /dev/null
@@ -1,616 +0,0 @@
-import gradio as gr
-import os
-from pathlib import Path
-import argparse
-import shutil
-from train_dreambooth import run_training
-from convertosd import convert
-from PIL import Image
-from slugify import slugify
-import requests
-import torch
-import zipfile
-import tarfile
-import urllib.parse
-import gc
-from diffusers import StableDiffusionPipeline
-from huggingface_hub import snapshot_download
-
-
-is_spaces = True if "SPACE_ID" in os.environ else False
-is_shared_ui = True if "IS_SHARED_UI" in os.environ else False
-is_gpu_associated = torch.cuda.is_available()
-
-css = '''
- .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
- .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
- #component-4, #component-3, #component-10{min-height: 0}
- .duplicate-button img{margin: 0}
-'''
-maximum_concepts = 3
-
-#Pre download the files
-if(is_gpu_associated):
- model_v1 = snapshot_download(repo_id="multimodalart/sd-fine-tunable")
- model_v2 = snapshot_download(repo_id="stabilityai/stable-diffusion-2")
- model_v2_512 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-base")
- model_v2_1 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-1")
- model_v2_1_512 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-1-base")
- safety_checker = snapshot_download(repo_id="multimodalart/sd-sc")
- model_to_load = model_v1
-
-with zipfile.ZipFile("mix.zip", 'r') as zip_ref:
- zip_ref.extractall(".")
-
-def swap_base_model(selected_model):
- if(is_gpu_associated):
- global model_to_load
- if(selected_model == "v1-5"):
- model_to_load = model_v1
- elif(selected_model == "v2-768"):
- model_to_load = model_v2
- elif(selected_model == "v2-512"):
- model_to_load = model_v2_512
- elif(selected_model == "v2-1-768"):
- model_to_load = model_v2_1
- else:
- model_to_load = model_v2_1_512
-
-def count_files(*inputs):
- file_counter = 0
- concept_counter = 0
- for i, input in enumerate(inputs):
- if(i < maximum_concepts-1):
- files = inputs[i]
- if(files):
- concept_counter+=1
- file_counter+=len(files)
- uses_custom = inputs[-1]
- selected_model = inputs[-4]
- experimental_faces = inputs[-5]
- if(uses_custom):
- Training_Steps = int(inputs[-3])
- else:
- Training_Steps = file_counter*150
- if(is_spaces):
- if(selected_model == "v1-5"):
- its = 1.1
- if(experimental_faces):
- its = 1
- elif(selected_model == "v2-512"):
- its = 0.8
- if(experimental_faces):
- its = 0.7
- elif(selected_model == "v2-768"):
- its = 0.5
- summary_sentence = f'''You are going to train {concept_counter}, with {file_counter} images for {Training_Steps} steps. The training should take around {round(Training_Steps/its, 2)} seconds, or {round((Training_Steps/its)/60, 2)} minutes.
- The setup, compression and uploading the model can take up to 20 minutes.the estimated cost for this training is below US${round((((Training_Steps/its)/3600)+0.3+0.1)*0.60, 2)}.
-
Your model has finished training ✅
-
Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub). Once you are done, your model is safe, and you don't want to train a new one, go to the settings page and downgrade your Space to a CPU Basic
-
-
-
-
-
- Dreambooth Web UI
-
-
-
-
-
-
Your model has finished training ✅
-
Yay, congratulations on training your model. Scroll down to play with with it, save it, whatever you fancy.
-
-
-
-
-
- Dreambooth Web UI
-
-
-
-
-
-
Don't worry, your model is still training! ⌛
-
You closed the tab while your model was training, but it's all good! It is still training right now. You can click the "Open logs" button above here to check the training status. Once training is done, reload this tab to interact with your model
-
-
Attention - This Space doesn't work in this shared UI
-
-
-
-
-
-
-
- Dreambooth Web UI
-
-
-
-
-
-
- What if we could imagine a version of ourselves that doesn’t necessarily adhere to society’s standards of beauty. Instead of fair skin, why not reimagine yourself in a green birthday suit? Rather than having boring human ears, why not don a pair of elf ears.
-
-
-
FantasyMe is a simple tool where users can use state of the art A.I. stable diffusion models to make them look like their favourite fantasy character. Just provide a few sample images of your likeness and generate a library of images that are all Instagram profile worthy.
-
-
-
-
-
- Dreambooth Web UI
-
-
-
-
-
-
- What if we could imagine a version of ourselves that doesn’t necessarily adhere to society’s standards of beauty. Instead of fair skin, why not reimagine yourself in a green birthday suit? Rather than having boring human ears, why not don a pair of elf ears.
-
-
-
FantasyMe is a simple tool where users can use state of the art A.I. stable diffusion models to make them look like their favourite fantasy character. Just provide a few sample images of your likeness and generate a library of images that are all Instagram profile worthy.
-
-
-
-
-
- Dreambooth Web UI
-
-
-
-
-
-
- Customize Stable Diffusion v1 or v2 by giving it a few examples of a concept.
- Based on the diffusers implementation, additional techniques from TheLastBen and ShivamShrirao "
-
-
- m = re.match(r"(\S+(\.\w+)*)\.(\w*)$", text) # @UndefinedVariable
-
- if not m:
- return []
-
- expr, attr = m.group(1, 3)
- try:
- obj = eval(expr, self.namespace)
- except:
- try:
- obj = eval(expr, self.global_namespace)
- except:
- return []
-
- filter = _StartsWithFilter(attr)
-
- words = dir2(obj, filter=filter)
-
- return words
-
-
-def generate_completions(frame, act_tok):
- '''
- :return list(tuple(method_name, docstring, parameters, completion_type))
-
- method_name: str
- docstring: str
- parameters: str -- i.e.: "(a, b)"
- completion_type is an int
- See: _pydev_bundle._pydev_imports_tipper for TYPE_ constants
- '''
- if frame is None:
- return []
-
- # Not using frame.f_globals because of https://sourceforge.net/tracker2/?func=detail&aid=2541355&group_id=85796&atid=577329
- # (Names not resolved in generator expression in method)
- # See message: http://mail.python.org/pipermail/python-list/2009-January/526522.html
- updated_globals = {}
- updated_globals.update(frame.f_globals)
- updated_globals.update(frame.f_locals) # locals later because it has precedence over the actual globals
-
- if pydevconsole.IPYTHON:
- completions = pydevconsole.get_completions(act_tok, act_tok, updated_globals, frame.f_locals)
- else:
- completer = Completer(updated_globals, None)
- # list(tuple(name, descr, parameters, type))
- completions = completer.complete(act_tok)
-
- return completions
-
-
-def generate_completions_as_xml(frame, act_tok):
- completions = generate_completions(frame, act_tok)
- return completions_to_xml(completions)
-
-
-def completions_to_xml(completions):
- valid_xml = pydevd_xml.make_valid_xml_value
- quote = pydevd_xml.quote
- msg = [""]
-
- for comp in completions:
- msg.append(' ")
-
- return ''.join(msg)
-
-
-identifier_start = ascii_letters + '_'
-identifier_part = ascii_letters + '_' + digits
-
-identifier_start = set(identifier_start)
-identifier_part = set(identifier_part)
-
-
-def isidentifier(s):
- return s.isidentifier()
-
-
-TokenAndQualifier = namedtuple('TokenAndQualifier', 'token, qualifier')
-
-
-def extract_token_and_qualifier(text, line=0, column=0):
- '''
- Extracts the token a qualifier from the text given the line/colum
- (see test_extract_token_and_qualifier for examples).
-
- :param unicode text:
- :param int line: 0-based
- :param int column: 0-based
- '''
- # Note: not using the tokenize module because text should be unicode and
- # line/column refer to the unicode text (otherwise we'd have to know
- # those ranges after converted to bytes).
- if line < 0:
- line = 0
- if column < 0:
- column = 0
-
- if isinstance(text, bytes):
- text = text.decode('utf-8')
-
- lines = text.splitlines()
- try:
- text = lines[line]
- except IndexError:
- return TokenAndQualifier(u'', u'')
-
- if column >= len(text):
- column = len(text)
-
- text = text[:column]
- token = u''
- qualifier = u''
-
- temp_token = []
- for i in range(column - 1, -1, -1):
- c = text[i]
- if c in identifier_part or isidentifier(c) or c == u'.':
- temp_token.append(c)
- else:
- break
- temp_token = u''.join(reversed(temp_token))
- if u'.' in temp_token:
- temp_token = temp_token.split(u'.')
- token = u'.'.join(temp_token[:-1])
- qualifier = temp_token[-1]
- else:
- qualifier = temp_token
-
- return TokenAndQualifier(token, qualifier)
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/config/config.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/config/config.py
deleted file mode 100644
index c5b1303422481dc7adb3ee5221377770e0c01a81..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/config/config.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import functools
-import inspect
-import logging
-from fvcore.common.config import CfgNode as _CfgNode
-
-from annotator.oneformer.detectron2.utils.file_io import PathManager
-
-
-class CfgNode(_CfgNode):
- """
- The same as `fvcore.common.config.CfgNode`, but different in:
-
- 1. Use unsafe yaml loading by default.
- Note that this may lead to arbitrary code execution: you must not
- load a config file from untrusted sources before manually inspecting
- the content of the file.
- 2. Support config versioning.
- When attempting to merge an old config, it will convert the old config automatically.
-
- .. automethod:: clone
- .. automethod:: freeze
- .. automethod:: defrost
- .. automethod:: is_frozen
- .. automethod:: load_yaml_with_base
- .. automethod:: merge_from_list
- .. automethod:: merge_from_other_cfg
- """
-
- @classmethod
- def _open_cfg(cls, filename):
- return PathManager.open(filename, "r")
-
- # Note that the default value of allow_unsafe is changed to True
- def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None:
- """
- Load content from the given config file and merge it into self.
-
- Args:
- cfg_filename: config filename
- allow_unsafe: allow unsafe yaml syntax
- """
- assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!"
- loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe)
- loaded_cfg = type(self)(loaded_cfg)
-
- # defaults.py needs to import CfgNode
- from .defaults import _C
-
- latest_ver = _C.VERSION
- assert (
- latest_ver == self.VERSION
- ), "CfgNode.merge_from_file is only allowed on a config object of latest version!"
-
- logger = logging.getLogger(__name__)
-
- loaded_ver = loaded_cfg.get("VERSION", None)
- if loaded_ver is None:
- from .compat import guess_version
-
- loaded_ver = guess_version(loaded_cfg, cfg_filename)
- assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format(
- loaded_ver, self.VERSION
- )
-
- if loaded_ver == self.VERSION:
- self.merge_from_other_cfg(loaded_cfg)
- else:
- # compat.py needs to import CfgNode
- from .compat import upgrade_config, downgrade_config
-
- logger.warning(
- "Loading an old v{} config file '{}' by automatically upgrading to v{}. "
- "See docs/CHANGELOG.md for instructions to update your files.".format(
- loaded_ver, cfg_filename, self.VERSION
- )
- )
- # To convert, first obtain a full config at an old version
- old_self = downgrade_config(self, to_version=loaded_ver)
- old_self.merge_from_other_cfg(loaded_cfg)
- new_config = upgrade_config(old_self)
- self.clear()
- self.update(new_config)
-
- def dump(self, *args, **kwargs):
- """
- Returns:
- str: a yaml string representation of the config
- """
- # to make it show up in docs
- return super().dump(*args, **kwargs)
-
-
-global_cfg = CfgNode()
-
-
-def get_cfg() -> CfgNode:
- """
- Get a copy of the default config.
-
- Returns:
- a detectron2 CfgNode instance.
- """
- from .defaults import _C
-
- return _C.clone()
-
-
-def set_global_cfg(cfg: CfgNode) -> None:
- """
- Let the global config point to the given cfg.
-
- Assume that the given "cfg" has the key "KEY", after calling
- `set_global_cfg(cfg)`, the key can be accessed by:
- ::
- from annotator.oneformer.detectron2.config import global_cfg
- print(global_cfg.KEY)
-
- By using a hacky global config, you can access these configs anywhere,
- without having to pass the config object or the values deep into the code.
- This is a hacky feature introduced for quick prototyping / research exploration.
- """
- global global_cfg
- global_cfg.clear()
- global_cfg.update(cfg)
-
-
-def configurable(init_func=None, *, from_config=None):
- """
- Decorate a function or a class's __init__ method so that it can be called
- with a :class:`CfgNode` object using a :func:`from_config` function that translates
- :class:`CfgNode` to arguments.
-
- Examples:
- ::
- # Usage 1: Decorator on __init__:
- class A:
- @configurable
- def __init__(self, a, b=2, c=3):
- pass
-
- @classmethod
- def from_config(cls, cfg): # 'cfg' must be the first argument
- # Returns kwargs to be passed to __init__
- return {"a": cfg.A, "b": cfg.B}
-
- a1 = A(a=1, b=2) # regular construction
- a2 = A(cfg) # construct with a cfg
- a3 = A(cfg, b=3, c=4) # construct with extra overwrite
-
- # Usage 2: Decorator on any function. Needs an extra from_config argument:
- @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B})
- def a_func(a, b=2, c=3):
- pass
-
- a1 = a_func(a=1, b=2) # regular call
- a2 = a_func(cfg) # call with a cfg
- a3 = a_func(cfg, b=3, c=4) # call with extra overwrite
-
- Args:
- init_func (callable): a class's ``__init__`` method in usage 1. The
- class must have a ``from_config`` classmethod which takes `cfg` as
- the first argument.
- from_config (callable): the from_config function in usage 2. It must take `cfg`
- as its first argument.
- """
-
- if init_func is not None:
- assert (
- inspect.isfunction(init_func)
- and from_config is None
- and init_func.__name__ == "__init__"
- ), "Incorrect use of @configurable. Check API documentation for examples."
-
- @functools.wraps(init_func)
- def wrapped(self, *args, **kwargs):
- try:
- from_config_func = type(self).from_config
- except AttributeError as e:
- raise AttributeError(
- "Class with @configurable must have a 'from_config' classmethod."
- ) from e
- if not inspect.ismethod(from_config_func):
- raise TypeError("Class with @configurable must have a 'from_config' classmethod.")
-
- if _called_with_cfg(*args, **kwargs):
- explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)
- init_func(self, **explicit_args)
- else:
- init_func(self, *args, **kwargs)
-
- return wrapped
-
- else:
- if from_config is None:
- return configurable # @configurable() is made equivalent to @configurable
- assert inspect.isfunction(
- from_config
- ), "from_config argument of configurable must be a function!"
-
- def wrapper(orig_func):
- @functools.wraps(orig_func)
- def wrapped(*args, **kwargs):
- if _called_with_cfg(*args, **kwargs):
- explicit_args = _get_args_from_config(from_config, *args, **kwargs)
- return orig_func(**explicit_args)
- else:
- return orig_func(*args, **kwargs)
-
- wrapped.from_config = from_config
- return wrapped
-
- return wrapper
-
-
-def _get_args_from_config(from_config_func, *args, **kwargs):
- """
- Use `from_config` to obtain explicit arguments.
-
- Returns:
- dict: arguments to be used for cls.__init__
- """
- signature = inspect.signature(from_config_func)
- if list(signature.parameters.keys())[0] != "cfg":
- if inspect.isfunction(from_config_func):
- name = from_config_func.__name__
- else:
- name = f"{from_config_func.__self__}.from_config"
- raise TypeError(f"{name} must take 'cfg' as the first argument!")
- support_var_arg = any(
- param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD]
- for param in signature.parameters.values()
- )
- if support_var_arg: # forward all arguments to from_config, if from_config accepts them
- ret = from_config_func(*args, **kwargs)
- else:
- # forward supported arguments to from_config
- supported_arg_names = set(signature.parameters.keys())
- extra_kwargs = {}
- for name in list(kwargs.keys()):
- if name not in supported_arg_names:
- extra_kwargs[name] = kwargs.pop(name)
- ret = from_config_func(*args, **kwargs)
- # forward the other arguments to __init__
- ret.update(extra_kwargs)
- return ret
-
-
-def _called_with_cfg(*args, **kwargs):
- """
- Returns:
- bool: whether the arguments contain CfgNode and should be considered
- forwarded to from_config.
- """
- from omegaconf import DictConfig
-
- if len(args) and isinstance(args[0], (_CfgNode, DictConfig)):
- return True
- if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)):
- return True
- # `from_config`'s first argument is forced to be "cfg".
- # So the above check covers all cases.
- return False
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py
deleted file mode 100644
index 78b396534cc1a119677d2af1015fc78a18b83846..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/lvis.py
+++ /dev/null
@@ -1,240 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-from fvcore.common.timer import Timer
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.structures import BoxMode
-from detectron2.utils.file_io import PathManager
-
-from .builtin_meta import _get_coco_instances_meta
-from .lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES
-from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES
-
-"""
-This file contains functions to parse LVIS-format annotations into dicts in the
-"Detectron2 format".
-"""
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"]
-
-
-def register_lvis_instances(name, metadata, json_file, image_root):
- """
- Register a dataset in LVIS's json annotation format for instance detection and segmentation.
-
- Args:
- name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train".
- metadata (dict): extra metadata associated with this dataset. It can be an empty dict.
- json_file (str): path to the json instance annotation file.
- image_root (str or path-like): directory which contains all the images.
- """
- DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name))
- MetadataCatalog.get(name).set(
- json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata
- )
-
-
-def load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
- """
- Load a json file in LVIS's annotation format.
-
- Args:
- json_file (str): full path to the LVIS json annotation file.
- image_root (str): the directory where the images in this json file exists.
- dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train").
- If provided, this function will put "thing_classes" into the metadata
- associated with this dataset.
- extra_annotation_keys (list[str]): list of per-annotation keys that should also be
- loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id",
- "segmentation"). The values for these keys will be returned as-is.
-
- Returns:
- list[dict]: a list of dicts in Detectron2 standard format. (See
- `Using Custom Datasets `_ )
-
- Notes:
- 1. This function does not read the image files.
- The results do not have the "image" field.
- """
- from lvis import LVIS
-
- json_file = PathManager.get_local_path(json_file)
-
- timer = Timer()
- lvis_api = LVIS(json_file)
- if timer.seconds() > 1:
- logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
-
- if dataset_name is not None:
- meta = get_lvis_instances_meta(dataset_name)
- MetadataCatalog.get(dataset_name).set(**meta)
-
- # sort indices for reproducible results
- img_ids = sorted(lvis_api.imgs.keys())
- # imgs is a list of dicts, each looks something like:
- # {'license': 4,
- # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
- # 'file_name': 'COCO_val2014_000000001268.jpg',
- # 'height': 427,
- # 'width': 640,
- # 'date_captured': '2013-11-17 05:57:24',
- # 'id': 1268}
- imgs = lvis_api.load_imgs(img_ids)
- # anns is a list[list[dict]], where each dict is an annotation
- # record for an object. The inner list enumerates the objects in an image
- # and the outer list enumerates over images. Example of anns[0]:
- # [{'segmentation': [[192.81,
- # 247.09,
- # ...
- # 219.03,
- # 249.06]],
- # 'area': 1035.749,
- # 'image_id': 1268,
- # 'bbox': [192.81, 224.8, 74.73, 33.43],
- # 'category_id': 16,
- # 'id': 42986},
- # ...]
- anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
-
- # Sanity check that each annotation has a unique id
- ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
- assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format(
- json_file
- )
-
- imgs_anns = list(zip(imgs, anns))
-
- logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file))
-
- if extra_annotation_keys:
- logger.info(
- "The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys)
- )
- else:
- extra_annotation_keys = []
-
- def get_file_name(img_root, img_dict):
- # Determine the path including the split folder ("train2017", "val2017", "test2017") from
- # the coco_url field. Example:
- # 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg'
- split_folder, file_name = img_dict["coco_url"].split("/")[-2:]
- return os.path.join(img_root + split_folder, file_name)
-
- dataset_dicts = []
-
- for (img_dict, anno_dict_list) in imgs_anns:
- record = {}
- record["file_name"] = get_file_name(image_root, img_dict)
- record["height"] = img_dict["height"]
- record["width"] = img_dict["width"]
- record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", [])
- record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
- image_id = record["image_id"] = img_dict["id"]
-
- objs = []
- for anno in anno_dict_list:
- # Check that the image_id in this annotation is the same as
- # the image_id we're looking at.
- # This fails only when the data parsing logic or the annotation file is buggy.
- assert anno["image_id"] == image_id
- obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
- # LVIS data loader can be used to load COCO dataset categories. In this case `meta`
- # variable will have a field with COCO-specific category mapping.
- if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta:
- obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]]
- else:
- obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed
- segm = anno["segmentation"] # list[list[float]]
- # filter out invalid polygons (< 3 points)
- valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
- assert len(segm) == len(
- valid_segm
- ), "Annotation contains an invalid polygon with < 3 points"
- assert len(segm) > 0
- obj["segmentation"] = segm
- for extra_ann_key in extra_annotation_keys:
- obj[extra_ann_key] = anno[extra_ann_key]
- objs.append(obj)
- record["annotations"] = objs
- dataset_dicts.append(record)
-
- return dataset_dicts
-
-
-def get_lvis_instances_meta(dataset_name):
- """
- Load LVIS metadata.
-
- Args:
- dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5").
-
- Returns:
- dict: LVIS metadata with keys: thing_classes
- """
- if "cocofied" in dataset_name:
- return _get_coco_instances_meta()
- if "v0.5" in dataset_name:
- return _get_lvis_instances_meta_v0_5()
- elif "v1" in dataset_name:
- return _get_lvis_instances_meta_v1()
- raise ValueError("No built-in metadata for dataset {}".format(dataset_name))
-
-
-def _get_lvis_instances_meta_v0_5():
- assert len(LVIS_V0_5_CATEGORIES) == 1230
- cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES]
- assert min(cat_ids) == 1 and max(cat_ids) == len(
- cat_ids
- ), "Category ids are not in [1, #categories], as expected"
- # Ensure that the category list is sorted by id
- lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"])
- thing_classes = [k["synonyms"][0] for k in lvis_categories]
- meta = {"thing_classes": thing_classes}
- return meta
-
-
-def _get_lvis_instances_meta_v1():
- assert len(LVIS_V1_CATEGORIES) == 1203
- cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES]
- assert min(cat_ids) == 1 and max(cat_ids) == len(
- cat_ids
- ), "Category ids are not in [1, #categories], as expected"
- # Ensure that the category list is sorted by id
- lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"])
- thing_classes = [k["synonyms"][0] for k in lvis_categories]
- meta = {"thing_classes": thing_classes}
- return meta
-
-
-if __name__ == "__main__":
- """
- Test the LVIS json dataset loader.
-
- Usage:
- python -m detectron2.data.datasets.lvis \
- path/to/json path/to/image_root dataset_name vis_limit
- """
- import sys
- import numpy as np
- from detectron2.utils.logger import setup_logger
- from PIL import Image
- import detectron2.data.datasets # noqa # add pre-defined metadata
- from detectron2.utils.visualizer import Visualizer
-
- logger = setup_logger(name=__name__)
- meta = MetadataCatalog.get(sys.argv[3])
-
- dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3])
- logger.info("Done loading {} samples.".format(len(dicts)))
-
- dirname = "lvis-data-vis"
- os.makedirs(dirname, exist_ok=True)
- for d in dicts[: int(sys.argv[4])]:
- img = np.array(Image.open(d["file_name"]))
- visualizer = Visualizer(img, metadata=meta)
- vis = visualizer.draw_dataset_dict(d)
- fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
- vis.save(fpath)
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/dev/run_instant_tests.sh b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/dev/run_instant_tests.sh
deleted file mode 100644
index 9fd9ba0c239d3e982c17711c9db872de3730decf..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/dev/run_instant_tests.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-BIN="python tools/train_net.py"
-OUTPUT="instant_test_output"
-NUM_GPUS=2
-
-CFG_LIST=( "${@:1}" )
-if [ ${#CFG_LIST[@]} -eq 0 ]; then
- CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml )
-fi
-
-echo "========================================================================"
-echo "Configs to run:"
-echo "${CFG_LIST[@]}"
-echo "========================================================================"
-
-for cfg in "${CFG_LIST[@]}"; do
- echo "========================================================================"
- echo "Running $cfg ..."
- echo "========================================================================"
- $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \
- SOLVER.IMS_PER_BATCH $(($NUM_GPUS * 2)) \
- OUTPUT_DIR "$OUTPUT"
- rm -rf "$OUTPUT"
-done
-
diff --git a/spaces/Tetel/chat/EdgeGPT/main.py b/spaces/Tetel/chat/EdgeGPT/main.py
deleted file mode 100644
index d71c7a039e7b84cc736b65c5bc0b06e5aa4fb1fd..0000000000000000000000000000000000000000
--- a/spaces/Tetel/chat/EdgeGPT/main.py
+++ /dev/null
@@ -1,244 +0,0 @@
-import argparse
-import asyncio
-import json
-import re
-import sys
-from pathlib import Path
-
-from EdgeGPT.EdgeGPT import Chatbot
-from prompt_toolkit import PromptSession
-from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
-from prompt_toolkit.completion import WordCompleter
-from prompt_toolkit.history import InMemoryHistory
-from prompt_toolkit.key_binding import KeyBindings
-from rich.live import Live
-from rich.markdown import Markdown
-
-
-def create_session() -> PromptSession:
- kb = KeyBindings()
-
- @kb.add("enter")
- def _(event) -> None:
- buffer_text = event.current_buffer.text
- if buffer_text.startswith("!"):
- event.current_buffer.validate_and_handle()
- else:
- event.current_buffer.insert_text("\n")
-
- @kb.add("escape")
- def _(event) -> None:
- if event.current_buffer.complete_state:
- # event.current_buffer.cancel_completion()
- event.current_buffer.text = ""
-
- return PromptSession(key_bindings=kb, history=InMemoryHistory())
-
-
-def create_completer(commands: list, pattern_str: str = "$") -> WordCompleter:
- return WordCompleter(words=commands, pattern=re.compile(pattern_str))
-
-
-def _create_history_logger(f):
- def logger(*args, **kwargs) -> None:
- tmp = sys.stdout
- sys.stdout = f
- print(*args, **kwargs, flush=True)
- sys.stdout = tmp
-
- return logger
-
-
-async def get_input_async(
- session: PromptSession = None,
- completer: WordCompleter = None,
-) -> str:
- """
- Multiline input function.
- """
- return await session.prompt_async(
- completer=completer,
- multiline=True,
- auto_suggest=AutoSuggestFromHistory(),
- )
-
-
-async def async_main(args: argparse.Namespace) -> None:
- """
- Main function
- """
- print("Initializing...")
- print("Enter `alt+enter` or `escape+enter` to send a message")
- # Read and parse cookies
- cookies = None
- if args.cookie_file:
- file_path = Path(args.cookie_file)
- if file_path.exists():
- with file_path.open("r", encoding="utf-8") as f:
- cookies = json.load(f)
- bot = await Chatbot.create(proxy=args.proxy, cookies=cookies)
- session = create_session()
- completer = create_completer(["!help", "!exit", "!reset"])
- initial_prompt = args.prompt
-
- # Log chat history
- def p_hist(*args, **kwargs) -> None:
- pass
-
- if args.history_file:
- history_file_path = Path(args.history_file)
- f = history_file_path.open("a+", encoding="utf-8")
- p_hist = _create_history_logger(f)
-
- while True:
- print("\nYou:")
- p_hist("\nYou:")
- if initial_prompt:
- question = initial_prompt
- print(question)
- initial_prompt = None
- else:
- question = (
- input()
- if args.enter_once
- else await get_input_async(session=session, completer=completer)
- )
- print()
- p_hist(question + "\n")
- if question == "!exit":
- break
- if question == "!help":
- print(
- """
- !help - Show this help message
- !exit - Exit the program
- !reset - Reset the conversation
- """,
- )
- continue
- if question == "!reset":
- await bot.reset()
- continue
- print("Bot:")
- p_hist("Bot:")
- if args.no_stream:
- response = (
- await bot.ask(
- prompt=question,
- conversation_style=args.style,
- wss_link=args.wss_link,
- search_result=args.search_result,
- locale=args.locale,
- )
- )["item"]["messages"][-1]["adaptiveCards"][0]["body"][0]["text"]
- print(response)
- p_hist(response)
- else:
- wrote = 0
- if args.rich:
- md = Markdown("")
- with Live(md, auto_refresh=False) as live:
- async for final, response in bot.ask_stream(
- prompt=question,
- conversation_style=args.style,
- wss_link=args.wss_link,
- search_result=args.search_result,
- locale=args.locale,
- ):
- if not final:
- if not wrote:
- p_hist(response, end="")
- else:
- p_hist(response[wrote:], end="")
- if wrote > len(response):
- print(md)
- print(Markdown("***Bing revoked the response.***"))
- wrote = len(response)
- md = Markdown(response)
- live.update(md, refresh=True)
- else:
- async for final, response in bot.ask_stream(
- prompt=question,
- conversation_style=args.style,
- wss_link=args.wss_link,
- search_result=args.search_result,
- locale=args.locale,
- ):
- if not final:
- if not wrote:
- print(response, end="", flush=True)
- p_hist(response, end="")
- else:
- print(response[wrote:], end="", flush=True)
- p_hist(response[wrote:], end="")
- wrote = len(response)
- print()
- p_hist()
- if args.history_file:
- f.close()
- await bot.close()
-
-
-def main() -> None:
- print(
- """
- EdgeGPT - A demo of reverse engineering the Bing GPT chatbot
- Repo: github.com/acheong08/EdgeGPT
- By: Antonio Cheong
-
- !help for help
-
- Type !exit to exit
- """,
- )
- parser = argparse.ArgumentParser()
- parser.add_argument("--enter-once", action="store_true")
- parser.add_argument("--search-result", action="store_true")
- parser.add_argument("--no-stream", action="store_true")
- parser.add_argument("--rich", action="store_true")
- parser.add_argument(
- "--proxy",
- help="Proxy URL (e.g. socks5://127.0.0.1:1080)",
- type=str,
- )
- parser.add_argument(
- "--wss-link",
- help="WSS URL(e.g. wss://sydney.bing.com/sydney/ChatHub)",
- type=str,
- default="wss://sydney.bing.com/sydney/ChatHub",
- )
- parser.add_argument(
- "--style",
- choices=["creative", "balanced", "precise"],
- default="balanced",
- )
- parser.add_argument(
- "--prompt",
- type=str,
- default="",
- required=False,
- help="prompt to start with",
- )
- parser.add_argument(
- "--cookie-file",
- type=str,
- default="",
- required=False,
- help="path to cookie file",
- )
- parser.add_argument(
- "--history-file",
- type=str,
- default="",
- required=False,
- help="path to history file",
- )
- parser.add_argument(
- "--locale",
- type=str,
- default="en-US",
- required=False,
- help="your locale",
- )
- args = parser.parse_args()
- asyncio.run(async_main(args))
diff --git a/spaces/ThirdEyeData/Customer-Conversion-Prediction/supv/rf.py b/spaces/ThirdEyeData/Customer-Conversion-Prediction/supv/rf.py
deleted file mode 100644
index 372ec037f46c41a45ed364382b80a34dfe34372e..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Customer-Conversion-Prediction/supv/rf.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/local/bin/python3
-
-# avenir-python: Machine Learning
-# Author: Pranab Ghosh
-#
-# 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.
-
-# Package imports
-import os
-import sys
-import matplotlib.pyplot as plt
-import numpy as np
-import sklearn as sk
-import matplotlib
-import random
-import jprops
-from sklearn.ensemble import RandomForestClassifier
-from random import randint
-sys.path.append(os.path.abspath("../lib"))
-from util import *
-from mlutil import *
-from pasearch import *
-from bacl import *
-
-
-# gradient boosting classification
-class RandomForest(BaseClassifier):
- def __init__(self, configFile):
- defValues = {}
- defValues["common.mode"] = ("training", None)
- defValues["common.model.directory"] = ("model", None)
- defValues["common.model.file"] = (None, None)
- defValues["common.preprocessing"] = (None, None)
- defValues["common.verbose"] = (False, None)
- defValues["train.data.file"] = (None, "missing training data file")
- defValues["train.data.fields"] = (None, "missing training data field ordinals")
- defValues["train.data.feature.fields"] = (None, "missing training data feature field ordinals")
- defValues["train.data.class.field"] = (None, "missing class field ordinal")
- defValues["train.validation"] = ("kfold", None)
- defValues["train.num.folds"] = (5, None)
- defValues["train.num.trees"] = (100, None)
- defValues["train.split.criterion"] = ("gini", None)
- defValues["train.max.depth"] = (None, None)
- defValues["train.min.samples.split"] = (4, None)
- defValues["train.min.samples.leaf"] = (2, None)
- defValues["train.min.weight.fraction.leaf"] = (0, None)
- defValues["train.max.features"] = ("auto", None)
- defValues["train.max.leaf.nodes"] = (None, None)
- defValues["train.min.impurity.decrease"] = (0, None)
- defValues["train.min.impurity.split"] = (1.0e-07, None)
- defValues["train.bootstrap"] = (True, None)
- defValues["train.oob.score"] = (False, None)
- defValues["train.num.jobs"] = (1, None)
- defValues["train.random.state"] = (None, None)
- defValues["train.verbose"] = (0, None)
- defValues["train.warm.start"] = (False, None)
- defValues["train.success.criterion"] = ("error", None)
- defValues["train.model.save"] = (False, None)
- defValues["train.score.method"] = ("accuracy", None)
- defValues["train.search.param.strategy"] = (None, None)
- defValues["train.search.params"] = (None, None)
- defValues["predict.data.file"] = (None, None)
- defValues["predict.data.fields"] = (None, "missing data field ordinals")
- defValues["predict.data.feature.fields"] = (None, "missing data feature field ordinals")
- defValues["predict.use.saved.model"] = (False, None)
- defValues["validate.data.file"] = (None, "missing validation data file")
- defValues["validate.data.fields"] = (None, "missing validation data field ordinals")
- defValues["validate.data.feature.fields"] = (None, "missing validation data feature field ordinals")
- defValues["validate.data.class.field"] = (None, "missing class field ordinal")
- defValues["validate.use.saved.model"] = (False, None)
- defValues["validate.score.method"] = ("accuracy", None)
-
- super(RandomForest, self).__init__(configFile, defValues, __name__)
-
- # builds model object
- def buildModel(self):
- self.logger.info("...building random forest model")
- numTrees = self.config.getIntConfig("train.num.trees")[0]
- splitCriterion = self.config.getStringConfig("train.split.criterion")[0]
- maxDepth = self.config.getStringConfig("train.max.depth")[0]
- maxDepth = typedValue(maxDepth)
- minSamplesSplit = self.config.getStringConfig("train.min.samples.split")[0]
- minSamplesSplit = typedValue(minSamplesSplit)
- minSamplesLeaf = self.config.getStringConfig("train.min.samples.leaf")[0]
- minSamplesLeaf = typedValue(minSamplesLeaf)
- minWeightFractionLeaf = self.config.getFloatConfig("train.min.weight.fraction.leaf")[0]
- maxFeatures = self.config.getStringConfig("train.max.features")[0]
- maxFeatures = typedValue(maxFeatures)
- maxLeafNodes = self.config.getIntConfig("train.max.leaf.nodes")[0]
- minImpurityDecrease = self.config.getFloatConfig("train.min.impurity.decrease")[0]
- minImpurityDecrease = self.config.getFloatConfig("train.min.impurity.split")[0]
- bootstrap = self.config.getBooleanConfig("train.bootstrap")[0]
- oobScore = self.config.getBooleanConfig("train.oob.score")[0]
- numJobs = self.config.getIntConfig("train.num.jobs")[0]
- randomState = self.config.getIntConfig("train.random.state")[0]
- verbose = self.config.getIntConfig("train.verbose")[0]
- warmStart = self.config.getBooleanConfig("train.warm.start")[0]
-
- model = RandomForestClassifier(n_estimators=numTrees, criterion=splitCriterion, max_depth=maxDepth, \
- min_samples_split=minSamplesSplit, min_samples_leaf=minSamplesLeaf, min_weight_fraction_leaf=minWeightFractionLeaf, \
- max_features=maxFeatures, max_leaf_nodes=maxLeafNodes, min_impurity_decrease=minImpurityDecrease, \
- min_impurity_split=None, bootstrap=bootstrap, oob_score=oobScore, n_jobs=numJobs, random_state=randomState, \
- verbose=verbose, warm_start=warmStart, class_weight=None)
- self.classifier = model
- return self.classifier
-
- #predict probability with in memory data
- def predictProb(self, recs):
- # create model
- self.prepModel()
-
- #input record
- if type(recs) is str:
- featData = self.prepStringPredictData(recs)
- else:
- featData = recs
- if (featData.ndim == 1):
- featData = featData.reshape(1, -1)
-
- #predict
- self.logger.info("...predicting class probability")
- clsData = self.classifier.predict_proba(featData)
- return clsData
-
diff --git a/spaces/Tinny-Robot/Tinny-Robot-NCAIR-ChatBot/README.md b/spaces/Tinny-Robot/Tinny-Robot-NCAIR-ChatBot/README.md
deleted file mode 100644
index 396125951b0c67c6850b3b58f3eeb4a155f04603..0000000000000000000000000000000000000000
--- a/spaces/Tinny-Robot/Tinny-Robot-NCAIR-ChatBot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Tinny Robot NCAIR ChatBot
-emoji: 😻
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.35.2
-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/Um124/Global_Warming_Analysis/pages/Electricity use data Analysis.py b/spaces/Um124/Global_Warming_Analysis/pages/Electricity use data Analysis.py
deleted file mode 100644
index 2e15c488293152df3ca3976a9ae6ac8b9cf018bf..0000000000000000000000000000000000000000
--- a/spaces/Um124/Global_Warming_Analysis/pages/Electricity use data Analysis.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import pandas as pd
-import numpy as np
-import plotly.express as px
-import streamlit as st
-
-st.set_page_config(
- page_title='Electricity use data Analysis',
- page_icon='📈',
- layout='wide'
-)
-
-Years=['1971','1972','1973','1974','1975','1976','1977','1978','1979','1980','1981','1982','1983','1984',
-'1985','1986','1987','1988','1989','1990','1991','1992','1993','1994','1995','1996','1997','1998','1999',
-'2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014']
-
-@st.cache_data
-@st.cache
-def load_data():
- df=pd.read_csv('data/electricity_use_per_person.csv')
- df.rename({'geo':'Country'},axis=1,inplace=True)
- df.set_index('Country',inplace=True)
- df.drop(['1960','1961','1962','1963','1964','1965','1966','1967','1968','1969','1970'],axis=1,inplace=True)
- df.sort_values('Country',inplace=True)
- df['Total']=df[Years].sum(axis=1)
- df['Average']=df.mean(axis=1)
- df['Minimum']=df.min(axis=1)
- df['Maximum']=df.max(axis=1)
- return df
-
-st.title('Electricity use per Person')
-df=load_data()
-st.dataframe(df,use_container_width=True)
-
-countries= df.index.unique().tolist()
-Graphs = ['bar','pie','line','area','funnel']
-c1,c2 = st.columns(2)
-country = c1.selectbox("Select a Country", countries)
-Graph = c2.selectbox("Select a Graph type", Graphs)
-
-
-
-st.header('Country wise Visualization')
-cdf = df.loc[country,Years].reset_index()
-cdf.rename({'index':'Years'},axis=1, inplace=True)
-if Graph == Graphs[0]:
- fig = px.bar(cdf, 'Years',country, title=f'{country} electricity use per person')
-if Graph == Graphs[1]:
- fig = px.pie(cdf, 'Years',country, title=f'{country} electricity use per person')
-if Graph == Graphs[2]:
- fig = px.line(cdf, 'Years',country, title=f'{country} electricity use per person')
-if Graph == Graphs[3]:
- fig = px.area(cdf, 'Years',country, title=f'{country} electricity use per person')
-if Graph == Graphs[4]:
- fig = px.funnel(cdf, 'Years',country, title=f'{country} electricity use per person')
-
-st.plotly_chart(fig, use_container_width=True)
-
-st.header('Comparison of Country')
-clist = st.multiselect("Select countries to compare", countries, default='India')
-cdf = df.loc[clist, Years].T # T to rotate the data in 90deg
-cdf.rename({'index':'Years'},axis=1,inplace=True)
-st.write(cdf)
-figc = px.line(cdf,cdf.index, clist, title=f'Comparing {", ".join(clist)}')
-
-st.plotly_chart(figc, use_container_width=True)
-
-
-df.sort_values(by='Total', ascending=False, inplace=True)
-fig1=px.bar(df, x=df.index, y='Total',title='Total electricity use per person by Country')
-st.plotly_chart(fig1,use_container_width=True)
-
-dfavg = df.sort_values(by='Average').reset_index()
-dfavg.rename({'index':'Country'},axis=1,inplace=True)
-fig2=px.bar(dfavg, 'Country', 'Average', title="Average electricity use by Country")
-st.plotly_chart(fig2,use_container_width=True)
-
-dfmin=df.sort_values(by='Minimum').reset_index()
-dfmin.rename({'index':'Country'},axis=1,inplace=True)
-fig3=px.bar(dfmin,'Country','Minimum',title='Minimum electricity use by Country' )
-st.plotly_chart(fig3,use_container_width=True)
-
-dfmax=df.sort_values(by='Maximum').reset_index()
-dfmax.rename({'index':'Country'},axis=1,inplace=True)
-fig4=px.bar(dfmax,'Country','Maximum',title='Maximum electricity use by Country' )
-st.plotly_chart(fig4,use_container_width=True)
-
-dfcomp=df.sort_values(by='Country',ascending=False,inplace=True)
-fig5 = px.line(df, x=df.index, y='Maximum',title='Maximum and Minimum electricity use comparisons')
-fig5.add_scatter(x=df.index, y=df['Minimum'], mode='lines',)
-st.plotly_chart(fig5,use_container_width=True)
-
-
-
-
-
-
diff --git a/spaces/Vegecken/sovits4dzl/modules/commons.py b/spaces/Vegecken/sovits4dzl/modules/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/modules/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, idx_str:idx_end]
- return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
- return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/WelcomeToTheClub/VMware-open-llama-7b-open-instruct/app.py b/spaces/WelcomeToTheClub/VMware-open-llama-7b-open-instruct/app.py
deleted file mode 100644
index d29ec9afffff99e252358651efce3acbde1d7f12..0000000000000000000000000000000000000000
--- a/spaces/WelcomeToTheClub/VMware-open-llama-7b-open-instruct/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/VMware/open-llama-7b-open-instruct").launch()
\ No newline at end of file
diff --git a/spaces/Xenova/next-example-app/_next/static/chunks/fd9d1056-0b59294afd0a93ce.js b/spaces/Xenova/next-example-app/_next/static/chunks/fd9d1056-0b59294afd0a93ce.js
deleted file mode 100644
index 552f70b4a1e3114a0eed10017b187e10fce5e6c7..0000000000000000000000000000000000000000
--- a/spaces/Xenova/next-example-app/_next/static/chunks/fd9d1056-0b59294afd0a93ce.js
+++ /dev/null
@@ -1,9 +0,0 @@
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r=n.get("last");else{n=new Map,sY.set(e,n);for(var l=e.querySelectorAll("link[data-precedence],style[data-precedence]"),a=0;ap {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
- .prompt h4{
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
- }
- .animate-spin {
- animation: spin 1s linear infinite;
- }
- @keyframes spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
- }
- #share-btn-container {
- display: flex; margin-top: 1.5rem !important; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
- }
- #share-btn {
- all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
- }
- #share-btn * {
- all: unset;
- }
-"""
-
-block = gr.Blocks(css=css)
-
-
-
-with block:
- gr.HTML(
- """
-
-
-
-
-
- Whisper
-
-
-
- Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. This demo cuts audio after around 30 secs.
-
-
You can skip the queue by using google colab for the space:
-
-
- You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private
- or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).
-
-
-
- Returns:
- `str`: The path to the module inside the cache.
- """
- # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
- pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-
- module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
-
- if os.path.isfile(module_file_or_url):
- resolved_module_file = module_file_or_url
- submodule = "local"
- elif pretrained_model_name_or_path.count("/") == 0:
- # community pipeline on GitHub
- github_url = COMMUNITY_PIPELINES_URL.format(pipeline=pretrained_model_name_or_path)
- try:
- resolved_module_file = cached_download(
- github_url,
- cache_dir=cache_dir,
- force_download=force_download,
- proxies=proxies,
- resume_download=resume_download,
- local_files_only=local_files_only,
- use_auth_token=False,
- )
- submodule = "git"
- module_file = pretrained_model_name_or_path + ".py"
- except EnvironmentError:
- logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
- raise
- else:
- try:
- # Load from URL or cache if already cached
- resolved_module_file = hf_hub_download(
- pretrained_model_name_or_path,
- module_file,
- cache_dir=cache_dir,
- force_download=force_download,
- proxies=proxies,
- resume_download=resume_download,
- local_files_only=local_files_only,
- use_auth_token=use_auth_token,
- )
- submodule = os.path.join("local", "--".join(pretrained_model_name_or_path.split("/")))
- except EnvironmentError:
- logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
- raise
-
- # Check we have all the requirements in our environment
- modules_needed = check_imports(resolved_module_file)
-
- # Now we move the module inside our cached dynamic modules.
- full_submodule = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
- create_dynamic_module(full_submodule)
- submodule_path = Path(HF_MODULES_CACHE) / full_submodule
- if submodule == "local" or submodule == "git":
- # We always copy local files (we could hash the file to see if there was a change, and give them the name of
- # that hash, to only copy when there is a modification but it seems overkill for now).
- # The only reason we do the copy is to avoid putting too many folders in sys.path.
- shutil.copy(resolved_module_file, submodule_path / module_file)
- for module_needed in modules_needed:
- module_needed = f"{module_needed}.py"
- shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
- else:
- # Get the commit hash
- # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
- if isinstance(use_auth_token, str):
- token = use_auth_token
- elif use_auth_token is True:
- token = HfFolder.get_token()
- else:
- token = None
-
- commit_hash = model_info(pretrained_model_name_or_path, revision=revision, token=token).sha
-
- # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
- # benefit of versioning.
- submodule_path = submodule_path / commit_hash
- full_submodule = full_submodule + os.path.sep + commit_hash
- create_dynamic_module(full_submodule)
-
- if not (submodule_path / module_file).exists():
- shutil.copy(resolved_module_file, submodule_path / module_file)
- # Make sure we also have every file with relative
- for module_needed in modules_needed:
- if not (submodule_path / module_needed).exists():
- get_cached_module_file(
- pretrained_model_name_or_path,
- f"{module_needed}.py",
- cache_dir=cache_dir,
- force_download=force_download,
- resume_download=resume_download,
- proxies=proxies,
- use_auth_token=use_auth_token,
- revision=revision,
- local_files_only=local_files_only,
- )
- return os.path.join(full_submodule, module_file)
-
-
-def get_class_from_dynamic_module(
- pretrained_model_name_or_path: Union[str, os.PathLike],
- module_file: str,
- class_name: Optional[str] = None,
- cache_dir: Optional[Union[str, os.PathLike]] = None,
- force_download: bool = False,
- resume_download: bool = False,
- proxies: Optional[Dict[str, str]] = None,
- use_auth_token: Optional[Union[bool, str]] = None,
- revision: Optional[str] = None,
- local_files_only: bool = False,
- **kwargs,
-):
- """
- Extracts a class from a module file, present in the local folder or repository of a model.
-
-
-
- Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
- therefore only be called on trusted repos.
-
-
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- This can be either:
-
- - a string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
- under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - a path to a *directory* containing a configuration file saved using the
- [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
-
- module_file (`str`):
- The name of the module file containing the class to look for.
- class_name (`str`):
- The name of the class to import in the module.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
- cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force to (re-)download the configuration files and override the cached versions if they
- exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- use_auth_token (`str` or `bool`, *optional*):
- The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
- when running `transformers-cli login` (stored in `~/.huggingface`).
- revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- local_files_only (`bool`, *optional*, defaults to `False`):
- If `True`, will only try to load the tokenizer configuration from local files.
-
-
-
- You may pass a token in `use_auth_token` if you are not logged in (`huggingface-cli long`) and want to use private
- or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models).
-
-
-
- Returns:
- `type`: The class, dynamically imported from the module.
-
- Examples:
-
- ```python
- # Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
- # module.
- cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
- ```"""
- # And lastly we get the class inside our newly created module
- final_module = get_cached_module_file(
- pretrained_model_name_or_path,
- module_file,
- cache_dir=cache_dir,
- force_download=force_download,
- resume_download=resume_download,
- proxies=proxies,
- use_auth_token=use_auth_token,
- revision=revision,
- local_files_only=local_files_only,
- )
- return get_class_in_module(class_name, final_module.replace(".py", ""))
diff --git a/spaces/Yuliang/ECON/lib/torch_utils/ops/native_ops.py b/spaces/Yuliang/ECON/lib/torch_utils/ops/native_ops.py
deleted file mode 100644
index a21a1368c69aee0e802fa710d34a59ec63523fb6..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/torch_utils/ops/native_ops.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-class FusedLeakyReLU(nn.Module):
- def __init__(self, channel, bias=True, negative_slope=0.2, scale=2**0.5):
- super().__init__()
-
- if bias:
- self.bias = nn.Parameter(torch.zeros(channel))
-
- else:
- self.bias = None
-
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2**0.5):
- if input.dtype == torch.float16:
- bias = bias.half()
-
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
- ) * scale
-
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
- up_x, up_y = up, up
- down_x, down_y = down, down
- pad_x0, pad_x1, pad_y0, pad_y1 = pad[0], pad[1], pad[0], pad[1]
-
- _, channel, in_h, in_w = input.shape
- input = input.reshape(-1, in_h, in_w, 1)
-
- _, in_h, in_w, minor = input.shape
- kernel_h, kernel_w = kernel.shape
-
- out = input.view(-1, in_h, 1, in_w, 1, minor)
- out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
- out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
- out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
- out = out[:,
- max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0),
- max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]
-
- out = out.permute(0, 3, 1, 2)
- out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
- w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
- out = F.conv2d(out, w)
- out = out.reshape(
- -1,
- minor,
- in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
- in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
- )
- out = out.permute(0, 2, 3, 1)
- out = out[:, ::down_y, ::down_x, :]
-
- out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
- out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
- return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/ZilliaxOfficial/nyaru-svc-3.0/preprocess_flist_config.py b/spaces/ZilliaxOfficial/nyaru-svc-3.0/preprocess_flist_config.py
deleted file mode 100644
index 927dea890c0057063080b48edc6dd8c2588c6e27..0000000000000000000000000000000000000000
--- a/spaces/ZilliaxOfficial/nyaru-svc-3.0/preprocess_flist_config.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-import argparse
-from tqdm import tqdm
-from random import shuffle
-import json
-config_template = {
- "train": {
- "log_interval": 200,
- "eval_interval": 1000,
- "seed": 1234,
- "epochs": 10000,
- "learning_rate": 2e-4,
- "betas": [0.8, 0.99],
- "eps": 1e-9,
- "batch_size": 12,
- "fp16_run": False,
- "lr_decay": 0.999875,
- "segment_size": 17920,
- "init_lr_ratio": 1,
- "warmup_epochs": 0,
- "c_mel": 45,
- "c_kl": 1.0,
- "use_sr": True,
- "max_speclen": 384,
- "port": "8001"
- },
- "data": {
- "training_files":"filelists/train.txt",
- "validation_files":"filelists/val.txt",
- "max_wav_value": 32768.0,
- "sampling_rate": 32000,
- "filter_length": 1280,
- "hop_length": 320,
- "win_length": 1280,
- "n_mel_channels": 80,
- "mel_fmin": 0.0,
- "mel_fmax": None
- },
- "model": {
- "inter_channels": 192,
- "hidden_channels": 192,
- "filter_channels": 768,
- "n_heads": 2,
- "n_layers": 6,
- "kernel_size": 3,
- "p_dropout": 0.1,
- "resblock": "1",
- "resblock_kernel_sizes": [3,7,11],
- "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
- "upsample_rates": [10,8,2,2],
- "upsample_initial_channel": 512,
- "upsample_kernel_sizes": [16,16,4,4],
- "n_layers_q": 3,
- "use_spectral_norm": False,
- "gin_channels": 256,
- "ssl_dim": 256,
- "n_speakers": 0,
- },
- "spk":{
- "nen": 0,
- "paimon": 1,
- "yunhao": 2
- }
-}
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
- parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
- parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
- parser.add_argument("--source_dir", type=str, default="./dataset/32k", help="path to source dir")
- args = parser.parse_args()
-
- train = []
- val = []
- test = []
- idx = 0
- spk_dict = {}
- spk_id = 0
- for speaker in tqdm(os.listdir(args.source_dir)):
- spk_dict[speaker] = spk_id
- spk_id += 1
- wavs = [os.path.join(args.source_dir, speaker, i)for i in os.listdir(os.path.join(args.source_dir, speaker))]
- wavs = [i for i in wavs if i.endswith("wav")]
- shuffle(wavs)
- train += wavs[2:-10]
- val += wavs[:2]
- test += wavs[-10:]
- n_speakers = len(spk_dict.keys())*2
- shuffle(train)
- shuffle(val)
- shuffle(test)
-
- print("Writing", args.train_list)
- with open(args.train_list, "w") as f:
- for fname in tqdm(train):
- wavpath = fname
- f.write(wavpath + "\n")
-
- print("Writing", args.val_list)
- with open(args.val_list, "w") as f:
- for fname in tqdm(val):
- wavpath = fname
- f.write(wavpath + "\n")
-
- print("Writing", args.test_list)
- with open(args.test_list, "w") as f:
- for fname in tqdm(test):
- wavpath = fname
- f.write(wavpath + "\n")
-
- config_template["model"]["n_speakers"] = n_speakers
- config_template["spk"] = spk_dict
- print("Writing configs/config.json")
- with open("configs/config.json", "w") as f:
- json.dump(config_template, f, indent=2)
diff --git a/spaces/aaronherrera/Calorie_Counter/app.py b/spaces/aaronherrera/Calorie_Counter/app.py
deleted file mode 100644
index e5142b4fe19cef17c3e60eb04eeac928589d69db..0000000000000000000000000000000000000000
--- a/spaces/aaronherrera/Calorie_Counter/app.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
-import openpyxl
-
-#Function to predict the food from the image using the pre-trained model "nateraw/food"
-def predict(image):
- extractor = AutoFeatureExtractor.from_pretrained("nateraw/food")
- model = AutoModelForImageClassification.from_pretrained("nateraw/food")
-
- input = extractor(images=image, return_tensors='pt')
- output = model(**input)
- logits = output.logits
-
- pred_class = logits.argmax(-1).item()
- return(model.config.id2label[pred_class])
-
-#Function to retrieve the Nutritional Value from database.xlsx which is downloaded from USDA
-def check_food(food, counter):
- path = './database.xlsx'
- wb_obj = openpyxl.load_workbook(path)
- sheet_obj = wb_obj.active
-
- foodPred, cal, carb, prot, fat = None, None, None, None, None
-
- #Filter to prioritize the most probable match between the prediction and the entries in the database
- for i in range(3, sheet_obj.max_row+1):
- cell_obj = sheet_obj.cell(row = i, column = 2)
- if counter == 0:
- if len(food) >= 3:
- foodName = food[0].capitalize() + " " + food[1] + " " + food[2] + ","
- elif len(food) == 2:
- foodName = food[0].capitalize() + " " + food[1] + ","
- elif len(food) == 1:
- foodName = food[0].capitalize() + ","
- condition = foodName == cell_obj.value[0:len(foodName):]
- elif counter == 1:
- if len(food) >= 3:
- foodName = food[0].capitalize() + " " + food[1] + " " + food[2]
- elif len(food) == 2:
- foodName = food[0].capitalize() + " " + food[1]
- elif len(food) == 1:
- foodName = food[0].capitalize()
- condition = foodName == cell_obj.value[0:len(foodName):]
- elif counter == 2:
- if len(food) >= 3:
- foodName = food[0] + " " + food[1] + " " + food[2]
- elif len(food) == 2:
- foodName = food[0] + " " + food[1]
- elif len(food) == 1:
- foodName = food[0]
- condition = foodName in cell_obj.value
- elif (counter == 3) & (len(food) > 1):
- condition = food[0].capitalize() == cell_obj.value[0:len(food[0]):]
- elif (counter == 4) & (len(food) > 1):
- condition = food[0] in cell_obj.value
- else:
- break
-
- #Update values if conditions are met
- if condition:
- foodPred = cell_obj.value
- cal = sheet_obj.cell(row = i, column = 5).value
- carb = sheet_obj.cell(row = i, column = 7).value
- prot = sheet_obj.cell(row = i, column = 6).value
- fat = sheet_obj.cell(row = i, column = 10).value
- break
-
- return foodPred, cal, carb, prot, fat
-
-#Function to prepare the output
-def get_cc(food, weight):
-
- #Configure the food string to match the entries in the database
- food = food.split("_")
- if food[-1][-1] == "s":
- food[-1] = food[-1][:-1]
-
- foodPred, cal, carb, prot, fat = None, None, None, None, None
- counter = 0
-
- #Try for the most probable match between the prediction and the entries in the database
- while (not foodPred) & (counter <= 4):
- foodPred, cal, carb, prot, fat = check_food(food,counter)
- counter += 1
-
- #Check if there is a match
- if food:
- output = foodPred + "\nCalories: " + str(round(cal * weight)/100) + " kJ\nCarbohydrate: " + str(round(carb * weight)/100) + " g\nProtein: " + str(round(prot * weight)/100) + " g\nTotal Fat: " + str(round(fat * weight)/100) + " g"
- elif not food:
- output = "No data for food"
-
- return(output)
-
-#Main function
-def CC(image, weight):
- pred = predict(image)
- cc = get_cc(pred, weight)
- return(pred, cc)
-
-interface = gr.Interface(
- fn = CC,
- inputs = [gr.inputs.Image(shape=(224,224)), gr.inputs.Number(default = 100, label = "Weight in grams (g):")],
- outputs = [gr.outputs.Textbox(label='Food Prediction:'), gr.outputs.Textbox(label='Nutritional Value:')],
- examples = [["pizza.jpg", 107], ["spaghetti.jpg",205]])
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/abc123desygn/Marvel_WhatIf_Diffusion/app.py b/spaces/abc123desygn/Marvel_WhatIf_Diffusion/app.py
deleted file mode 100644
index eac6841a968aba1cbc4cbb5d05136afd271eb7d5..0000000000000000000000000000000000000000
--- a/spaces/abc123desygn/Marvel_WhatIf_Diffusion/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'ItsJayQz/Marvel_WhatIf_Diffusion'
-prefix = 'whatif style'
-
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
- model_id,
- torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
- scheduler=scheduler)
-
-if torch.cuda.is_available():
- pipe = pipe.to("cuda")
- pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
- return f"""#### {title}
- {error}""" if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
- generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
- prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
- try:
- if img is not None:
- return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
- else:
- return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
- except Exception as e:
- return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
- result = pipe(
- prompt,
- negative_prompt = neg_prompt,
- num_inference_steps = int(steps),
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
- ratio = min(height / img.height, width / img.width)
- img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
- result = pipe_i2i(
- prompt,
- negative_prompt = neg_prompt,
- init_image = img,
- num_inference_steps = int(steps),
- strength = strength,
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
- gr.HTML(
- f"""
-
-
-
Marvel Whatif Diffusion
-
-
- Demo for Marvel Whatif Diffusion Stable Diffusion model.prefix " if prefix else ""}
-
- Running on {"
GPU 🔥 " if torch.cuda.is_available() else f"
CPU 🥶 . For faster inference it is recommended to
upgrade to GPU in Settings "} after duplicating the space
-
-
, which extends
-L.
-
-The Text interface represents the textual content (termed character
-data in XML) of an Element or Attr. If there is no markup inside an
-element's content, the text is contained in a single object
-implementing the Text interface that is the only child of the element.
-If there is markup, it is parsed into a list of elements and Text nodes
-that form the list of children of the element.
-
-When a document is first made available via the DOM, there is only one
-Text node for each block of text. Users may create adjacent Text nodes
-that represent the contents of a given element without any intervening
-markup, but should be aware that there is no way to represent the
-separations between these nodes in XML or HTML, so they will not (in
-general) persist between DOM editing sessions. The normalize() method
-on Element merges any such adjacent Text objects into a single node for
-each block of text; this is recommended before employing operations
-that depend on a particular document structure, such as navigation with
-XPointers.
-
-=head2 METHODS
-
-=over 4
-
-=item splitText (offset)
-
-Breaks this Text node into two Text nodes at the specified
-offset, keeping both in the tree as siblings. This node then
-only contains all the content up to the offset point. And a
-new Text node, which is inserted as the next sibling of this
-node, contains all the content at and after the offset point.
-
-Parameters:
- I The offset at which to split, starting from 0.
-
-Return Value: The new Text node.
-
-DOMExceptions:
-
-=over 4
-
-=item * INDEX_SIZE_ERR
-
-Raised if the specified offset is negative or greater than the number of
-characters in data.
-
-=item * NO_MODIFICATION_ALLOWED_ERR
-
-Raised if this node is readonly.
-
-=back
-
-=back
diff --git a/spaces/allknowingroger/Image-Models-Test107/app.py b/spaces/allknowingroger/Image-Models-Test107/app.py
deleted file mode 100644
index fbfc58d692a4996de0b18121e1211fcccb230530..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test107/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
- "Alexzyx/lora-trained-xl-colab-v2",
- "rishabh063/lora-trained-xl-colab",
- "Muhammadreza/mann-e-concept-revised-2",
- "Yntec/DeliShaper",
- "pranavkd/sdxl-amirth",
- "akmalinn/soto-ayam-bu-karti",
- "crimsonghost/nicolev",
- "uppara/myhouse",
- "digiplay/DucHaitenJourney_v3.0.A",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
- try:
- model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
- except Exception as error:
- def the_fn(txt):
- return None
- model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
- model_idx+=1
-
-
-def send_it_idx(idx):
- def send_it_fn(prompt):
- output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
- return output
- return send_it_fn
-
-def get_prompts(prompt_text):
- return prompt_text
-
-def clear_it(val):
- if int(val) != 0:
- val = 0
- else:
- val = 0
- pass
- return val
-
-def all_task_end(cnt,t_stamp):
- to = t_stamp + 60
- et = time.time()
- if et > to and t_stamp != 0:
- d = gr.update(value=0)
- tog = gr.update(value=1)
- #print(f'to: {to} et: {et}')
- else:
- if cnt != 0:
- d = gr.update(value=et)
- else:
- d = gr.update(value=0)
- tog = gr.update(value=0)
- #print (f'passing: to: {to} et: {et}')
- pass
- return d, tog
-
-def all_task_start():
- print("\n\n\n\n\n\n\n")
- t = time.gmtime()
- t_stamp = time.time()
- current_time = time.strftime("%H:%M:%S", t)
- return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
- nn = len(models)
- return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
- with gr.Column(scale=12):
- # with gr.Row():
- # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
- with gr.Row():
- with gr.Row(scale=6):
- primary_prompt=gr.Textbox(label="Prompt", value="")
- # real_prompt=gr.Textbox(label="Real prompt")
- with gr.Row(scale=6):
- # improve_prompts_btn=gr.Button("Improve")
- with gr.Row():
- run=gr.Button("Run",variant="primary")
- clear_btn=gr.Button("Clear")
- with gr.Row():
- sd_outputs = {}
- model_idx = 1
- for model_path in models:
- with gr.Column(scale=3, min_width=320):
- with gr.Box():
- sd_outputs[model_idx] = gr.Image(label=model_path)
- pass
- model_idx += 1
- pass
- pass
-
- with gr.Row(visible=False):
- start_box=gr.Number(interactive=False)
- end_box=gr.Number(interactive=False)
- tog_box=gr.Textbox(value=0,interactive=False)
-
- start_box.change(
- all_task_end,
- [start_box, end_box],
- [start_box, tog_box],
- every=1,
- show_progress=False)
-
- primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
- run.click(all_task_start, None, [start_box, end_box, tog_box])
- runs_dict = {}
- model_idx = 1
- for model_path in models:
- runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
- model_idx += 1
- pass
- pass
-
- # improve_prompts_btn_clicked=improve_prompts_btn.click(
- # get_prompts,
- # inputs=[primary_prompt],
- # outputs=[primary_prompt],
- # cancels=list(runs_dict.values()))
- clear_btn.click(
- clear_fn,
- None,
- [primary_prompt, *list(sd_outputs.values())],
- cancels=[*list(runs_dict.values())])
- tog_box.change(
- clear_it,
- tog_box,
- tog_box,
- cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
-
\ No newline at end of file
diff --git a/spaces/antonbol/vocal_remover/lib/__init__.py b/spaces/antonbol/vocal_remover/lib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/docs/LLaMA-model.md b/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/docs/LLaMA-model.md
deleted file mode 100644
index 338d458b13b56b3d0f02dd3f4b7d5156a82b88e9..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/docs/LLaMA-model.md
+++ /dev/null
@@ -1,45 +0,0 @@
-LLaMA is a Large Language Model developed by Meta AI.
-
-It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters.
-
-This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode)
-](GPTQ-models-(4-bit-mode).md).
-
-## Getting the weights
-
-### Option 1: pre-converted weights
-
-* Torrent: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
-* Direct download: https://huggingface.co/Neko-Institute-of-Science
-
-⚠️ The tokenizers for the Torrent source above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, so I recommend downloading the following universal LLaMA tokenizer:
-
-```
-python download-model.py oobabooga/llama-tokenizer
-```
-
-Once downloaded, it will be automatically applied to **every** `LlamaForCausalLM` model that you try to load.
-
-### Option 2: convert the weights yourself
-
-1. Install the `protobuf` library:
-
-```
-pip install protobuf==3.20.1
-```
-
-2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link:
-
-### [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
-
-```
-python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
-```
-
-3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder.
-
-## Starting the web UI
-
-```python
-python server.py --model llama-7b
-```
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/training.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/training.py
deleted file mode 100644
index ce12cf443f37e2520658614e15d0e64eb554b7f1..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/training.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import torch
-import inspect
-import json
-import yaml
-import math
-import os
-import sys
-
-from general_utils import log
-
-import numpy as np
-from functools import partial
-from os.path import expanduser, join, isfile, basename
-
-from torch.cuda.amp import autocast, GradScaler
-from torch.optim.lr_scheduler import LambdaLR
-from contextlib import nullcontext
-from torch.utils.data import DataLoader
-
-from general_utils import TrainingLogger, get_attribute, filter_args, log, training_config_from_cli_args
-
-
-def cosine_warmup_lr(i, warmup=10, max_iter=90):
- """ Cosine LR with Warmup """
- if i < warmup:
- return (i+1)/(warmup+1)
- else:
- return 0.5 + 0.5*math.cos(math.pi*(((i-warmup)/(max_iter- warmup))))
-
-
-def validate(model, dataset, config):
- data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=False)
-
- metric_class, use_metric = config.val_metric_class, config.use_val_metric
- loss_fn = get_attribute(config.loss)
-
- model.eval()
- model.cuda()
-
- if metric_class is not None:
- metric = get_attribute(metric_class)()
-
- with torch.no_grad():
-
- i, losses = 0, []
- for data_x, data_y in data_loader:
-
- data_x = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_x]
- data_y = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_y]
-
- prompts = model.sample_prompts(data_x[1], prompt_list=('a photo of a {}',))
- pred, visual_q, _, _ = model(data_x[0], prompts, return_features=True)
-
- if metric_class is not None:
- metric.add([pred], data_y)
-
- # pred = model(data_x[0], prompts)
- # loss = loss_fn(pred[0], data_y[0])
- loss = loss_fn(pred, data_y[0])
- losses += [float(loss)]
-
- i += 1
-
- if config.val_max_iterations is not None and i > config.val_max_iterations:
- break
-
- if use_metric is None:
- return np.mean(losses), {}, False
- else:
- metric_scores = {m: s for m, s in zip(metric.names(), metric.value())} if metric is not None else {}
- return np.mean(losses), metric_scores, True
-
-
-def main():
-
- config = training_config_from_cli_args()
-
- val_interval, best_val_loss, best_val_score = config.val_interval, float('inf'), float('-inf')
-
- model_cls = get_attribute(config.model)
- _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters)
- model = model_cls(**model_args).cuda()
-
- dataset_cls = get_attribute(config.dataset)
- _, dataset_args, _ = filter_args(config, inspect.signature(dataset_cls).parameters)
-
- dataset = dataset_cls(**dataset_args)
-
- log.info(f'Train dataset {dataset.__class__.__name__} (length: {len(dataset)})')
-
- if val_interval is not None:
- dataset_val_args = {k[4:]: v for k,v in config.items() if k.startswith('val_') and k != 'val_interval'}
- _, dataset_val_args, _ = filter_args(dataset_val_args, inspect.signature(dataset_cls).parameters)
- print('val args', {**dataset_args, **{'split': 'val', 'aug': 0}, **dataset_val_args})
-
- dataset_val = dataset_cls(**{**dataset_args, **{'split': 'val', 'aug': 0}, **dataset_val_args})
-
- # optimizer
- opt_cls = get_attribute(config.optimizer)
- if config.optimize == 'torch.optim.SGD':
- opt_args = {'momentum': config.momentum if 'momentum' in config else 0}
- else:
- opt_args = {}
- opt = opt_cls(model.parameters(), lr=config.lr, **opt_args)
-
- if config.lr_scheduler == 'cosine':
- assert config.T_max is not None and config.eta_min is not None
- lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, config.T_max, config.eta_min)
- elif config.lr_scheduler == 'warmup_cosine':
- lr_scheduler = LambdaLR(opt, partial(cosine_warmup_lr, max_iter=(config.max_iterations), warmup=config.warmup))
- else:
- lr_scheduler = None
-
- batch_size, max_iterations = config.batch_size, config.max_iterations
-
- loss_fn = get_attribute(config.loss)
-
- if config.amp:
- log.info('Using AMP')
- autocast_fn = autocast
- scaler = GradScaler()
- else:
- autocast_fn, scaler = nullcontext, None
-
-
- save_only_trainable = True
- data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=4)
-
- # disable config when hyperparam. opt. to avoid writing logs.
- tracker_config = config if not config.hyperparameter_optimization else None
-
- with TrainingLogger(log_dir=config.name, model=model, config=tracker_config) as logger:
-
- i = 0
- while True:
- for data_x, data_y in data_loader:
-
- # between caption and output feature.
- # 1. Sample random captions
- # 2. Check alignment with CLIP
-
- # randomly mix text and visual support conditionals
- if config.mix:
-
- assert config.mask.startswith('text_and')
-
- with autocast_fn():
- # data_x[1] = text label
- prompts = model.sample_prompts(data_x[1])
-
- # model.clip_model()
-
- text_cond = model.compute_conditional(prompts)
- if model.__class__.__name__ == 'CLIPDensePredTMasked':
- # when mask=='separate'
- visual_s_cond, _, _ = model.visual_forward_masked(data_x[2].cuda(), data_x[3].cuda())
- else:
- # data_x[2] = visual prompt
- visual_s_cond, _, _ = model.visual_forward(data_x[2].cuda())
-
- max_txt = config.mix_text_max if config.mix_text_max is not None else 1
- batch_size = text_cond.shape[0]
-
- # sample weights for each element in batch
- text_weights = torch.distributions.Uniform(config.mix_text_min, max_txt).sample((batch_size,))[:, None]
- text_weights = text_weights.cuda()
-
- if dataset.__class__.__name__ == 'PhraseCut':
- # give full weight to text where support_image is invalid
- visual_is_valid = data_x[4] if model.__class__.__name__ == 'CLIPDensePredTMasked' else data_x[3]
- text_weights = torch.max(text_weights[:,0], 1 - visual_is_valid.float().cuda()).unsqueeze(1)
-
- cond = text_cond * text_weights + visual_s_cond * (1 - text_weights)
-
- else:
- # no mix
-
- if model.__class__.__name__ == 'CLIPDensePredTMasked':
- # compute conditional vector using CLIP masking
- with autocast_fn():
- assert config.mask == 'separate'
- cond, _, _ = model.visual_forward_masked(data_x[1].cuda(), data_x[2].cuda())
- else:
- cond = data_x[1]
- if isinstance(cond, torch.Tensor):
- cond = cond.cuda()
-
- with autocast_fn():
- visual_q = None
-
- pred, visual_q, _, _ = model(data_x[0].cuda(), cond, return_features=True)
-
- loss = loss_fn(pred, data_y[0].cuda())
-
- if torch.isnan(loss) or torch.isinf(loss):
- # skip if loss is nan
- log.warning('Training stopped due to inf/nan loss.')
- sys.exit(-1)
-
- extra_loss = 0
- loss += extra_loss
-
- opt.zero_grad()
-
- if scaler is None:
- loss.backward()
- opt.step()
- else:
- scaler.scale(loss).backward()
- scaler.step(opt)
- scaler.update()
-
- if lr_scheduler is not None:
- lr_scheduler.step()
- if i % 2000 == 0:
- current_lr = [g['lr'] for g in opt.param_groups][0]
- log.info(f'current lr: {current_lr:.5f} ({len(opt.param_groups)} parameter groups)')
-
- logger.iter(i=i, loss=loss)
- i += 1
-
- if i >= max_iterations:
-
- if not isfile(join(logger.base_path, 'weights.pth')):
- # only write if no weights were already written
- logger.save_weights(only_trainable=save_only_trainable)
-
- sys.exit(0)
-
-
- if config.checkpoint_iterations is not None and i in config.checkpoint_iterations:
- logger.save_weights(only_trainable=save_only_trainable, weight_file=f'weights_{i}.pth')
-
-
- if val_interval is not None and i % val_interval == val_interval - 1:
-
- val_loss, val_scores, maximize = validate(model, dataset_val, config)
-
- if len(val_scores) > 0:
-
- score_str = f', scores: ' + ', '.join(f'{k}: {v}' for k, v in val_scores.items())
-
- if maximize and val_scores[config.use_val_metric] > best_val_score:
- logger.save_weights(only_trainable=save_only_trainable)
- best_val_score = val_scores[config.use_val_metric]
-
- elif not maximize and val_scores[config.use_val_metric] < best_val_score:
- logger.save_weights(only_trainable=save_only_trainable)
- best_val_score = val_scores[config.use_val_metric]
-
- else:
- score_str = ''
- # if no score is used, fall back to loss
- if val_loss < best_val_loss:
- logger.save_weights(only_trainable=save_only_trainable)
- best_val_loss = val_loss
-
- log.info(f'Validation loss: {val_loss}' + score_str)
- logger.iter(i=i, val_loss=val_loss, extra_loss=float(extra_loss), **val_scores)
- model.train()
-
- print('epoch complete')
-
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git "a/spaces/apsys/hetfit/pages/\360\237\244\227 Intro.py" "b/spaces/apsys/hetfit/pages/\360\237\244\227 Intro.py"
deleted file mode 100644
index dba93787a56f4e3ab61db8e0dbffe91aa433cb5a..0000000000000000000000000000000000000000
--- "a/spaces/apsys/hetfit/pages/\360\237\244\227 Intro.py"
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-import re
-import base64
-from pathlib import Path
-
-import streamlit as st
-
-with open('intro.md', 'r') as f:
- st.markdown(f.read(),unsafe_allow_html=True)
-
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/IcnsImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/IcnsImagePlugin.py
deleted file mode 100644
index fa192f053f95c8e22807547d96853d68ec6149a7..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/IcnsImagePlugin.py
+++ /dev/null
@@ -1,392 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# macOS icns file decoder, based on icns.py by Bob Ippolito.
-#
-# history:
-# 2004-10-09 fl Turned into a PIL plugin; removed 2.3 dependencies.
-# 2020-04-04 Allow saving on all operating systems.
-#
-# Copyright (c) 2004 by Bob Ippolito.
-# Copyright (c) 2004 by Secret Labs.
-# Copyright (c) 2004 by Fredrik Lundh.
-# Copyright (c) 2014 by Alastair Houghton.
-# Copyright (c) 2020 by Pan Jing.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import os
-import struct
-import sys
-
-from PIL import Image, ImageFile, PngImagePlugin, features
-
-enable_jpeg2k = features.check_codec("jpg_2000")
-if enable_jpeg2k:
- from PIL import Jpeg2KImagePlugin
-
-MAGIC = b"icns"
-HEADERSIZE = 8
-
-
-def nextheader(fobj):
- return struct.unpack(">4sI", fobj.read(HEADERSIZE))
-
-
-def read_32t(fobj, start_length, size):
- # The 128x128 icon seems to have an extra header for some reason.
- (start, length) = start_length
- fobj.seek(start)
- sig = fobj.read(4)
- if sig != b"\x00\x00\x00\x00":
- raise SyntaxError("Unknown signature, expecting 0x00000000")
- return read_32(fobj, (start + 4, length - 4), size)
-
-
-def read_32(fobj, start_length, size):
- """
- Read a 32bit RGB icon resource. Seems to be either uncompressed or
- an RLE packbits-like scheme.
- """
- (start, length) = start_length
- fobj.seek(start)
- pixel_size = (size[0] * size[2], size[1] * size[2])
- sizesq = pixel_size[0] * pixel_size[1]
- if length == sizesq * 3:
- # uncompressed ("RGBRGBGB")
- indata = fobj.read(length)
- im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1)
- else:
- # decode image
- im = Image.new("RGB", pixel_size, None)
- for band_ix in range(3):
- data = []
- bytesleft = sizesq
- while bytesleft > 0:
- byte = fobj.read(1)
- if not byte:
- break
- byte = byte[0]
- if byte & 0x80:
- blocksize = byte - 125
- byte = fobj.read(1)
- for i in range(blocksize):
- data.append(byte)
- else:
- blocksize = byte + 1
- data.append(fobj.read(blocksize))
- bytesleft -= blocksize
- if bytesleft <= 0:
- break
- if bytesleft != 0:
- raise SyntaxError(f"Error reading channel [{repr(bytesleft)} left]")
- band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1)
- im.im.putband(band.im, band_ix)
- return {"RGB": im}
-
-
-def read_mk(fobj, start_length, size):
- # Alpha masks seem to be uncompressed
- start = start_length[0]
- fobj.seek(start)
- pixel_size = (size[0] * size[2], size[1] * size[2])
- sizesq = pixel_size[0] * pixel_size[1]
- band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1)
- return {"A": band}
-
-
-def read_png_or_jpeg2000(fobj, start_length, size):
- (start, length) = start_length
- fobj.seek(start)
- sig = fobj.read(12)
- if sig[:8] == b"\x89PNG\x0d\x0a\x1a\x0a":
- fobj.seek(start)
- im = PngImagePlugin.PngImageFile(fobj)
- Image._decompression_bomb_check(im.size)
- return {"RGBA": im}
- elif (
- sig[:4] == b"\xff\x4f\xff\x51"
- or sig[:4] == b"\x0d\x0a\x87\x0a"
- or sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a"
- ):
- if not enable_jpeg2k:
- raise ValueError(
- "Unsupported icon subimage format (rebuild PIL "
- "with JPEG 2000 support to fix this)"
- )
- # j2k, jpc or j2c
- fobj.seek(start)
- jp2kstream = fobj.read(length)
- f = io.BytesIO(jp2kstream)
- im = Jpeg2KImagePlugin.Jpeg2KImageFile(f)
- Image._decompression_bomb_check(im.size)
- if im.mode != "RGBA":
- im = im.convert("RGBA")
- return {"RGBA": im}
- else:
- raise ValueError("Unsupported icon subimage format")
-
-
-class IcnsFile:
-
- SIZES = {
- (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)],
- (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)],
- (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)],
- (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)],
- (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)],
- (128, 128, 1): [
- (b"ic07", read_png_or_jpeg2000),
- (b"it32", read_32t),
- (b"t8mk", read_mk),
- ],
- (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)],
- (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)],
- (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)],
- (32, 32, 1): [
- (b"icp5", read_png_or_jpeg2000),
- (b"il32", read_32),
- (b"l8mk", read_mk),
- ],
- (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)],
- (16, 16, 1): [
- (b"icp4", read_png_or_jpeg2000),
- (b"is32", read_32),
- (b"s8mk", read_mk),
- ],
- }
-
- def __init__(self, fobj):
- """
- fobj is a file-like object as an icns resource
- """
- # signature : (start, length)
- self.dct = dct = {}
- self.fobj = fobj
- sig, filesize = nextheader(fobj)
- if not _accept(sig):
- raise SyntaxError("not an icns file")
- i = HEADERSIZE
- while i < filesize:
- sig, blocksize = nextheader(fobj)
- if blocksize <= 0:
- raise SyntaxError("invalid block header")
- i += HEADERSIZE
- blocksize -= HEADERSIZE
- dct[sig] = (i, blocksize)
- fobj.seek(blocksize, io.SEEK_CUR)
- i += blocksize
-
- def itersizes(self):
- sizes = []
- for size, fmts in self.SIZES.items():
- for (fmt, reader) in fmts:
- if fmt in self.dct:
- sizes.append(size)
- break
- return sizes
-
- def bestsize(self):
- sizes = self.itersizes()
- if not sizes:
- raise SyntaxError("No 32bit icon resources found")
- return max(sizes)
-
- def dataforsize(self, size):
- """
- Get an icon resource as {channel: array}. Note that
- the arrays are bottom-up like windows bitmaps and will likely
- need to be flipped or transposed in some way.
- """
- dct = {}
- for code, reader in self.SIZES[size]:
- desc = self.dct.get(code)
- if desc is not None:
- dct.update(reader(self.fobj, desc, size))
- return dct
-
- def getimage(self, size=None):
- if size is None:
- size = self.bestsize()
- if len(size) == 2:
- size = (size[0], size[1], 1)
- channels = self.dataforsize(size)
-
- im = channels.get("RGBA", None)
- if im:
- return im
-
- im = channels.get("RGB").copy()
- try:
- im.putalpha(channels["A"])
- except KeyError:
- pass
- return im
-
-
-##
-# Image plugin for Mac OS icons.
-
-
-class IcnsImageFile(ImageFile.ImageFile):
- """
- PIL image support for Mac OS .icns files.
- Chooses the best resolution, but will possibly load
- a different size image if you mutate the size attribute
- before calling 'load'.
-
- The info dictionary has a key 'sizes' that is a list
- of sizes that the icns file has.
- """
-
- format = "ICNS"
- format_description = "Mac OS icns resource"
-
- def _open(self):
- self.icns = IcnsFile(self.fp)
- self.mode = "RGBA"
- self.info["sizes"] = self.icns.itersizes()
- self.best_size = self.icns.bestsize()
- self.size = (
- self.best_size[0] * self.best_size[2],
- self.best_size[1] * self.best_size[2],
- )
-
- @property
- def size(self):
- return self._size
-
- @size.setter
- def size(self, value):
- info_size = value
- if info_size not in self.info["sizes"] and len(info_size) == 2:
- info_size = (info_size[0], info_size[1], 1)
- if (
- info_size not in self.info["sizes"]
- and len(info_size) == 3
- and info_size[2] == 1
- ):
- simple_sizes = [
- (size[0] * size[2], size[1] * size[2]) for size in self.info["sizes"]
- ]
- if value in simple_sizes:
- info_size = self.info["sizes"][simple_sizes.index(value)]
- if info_size not in self.info["sizes"]:
- raise ValueError("This is not one of the allowed sizes of this image")
- self._size = value
-
- def load(self):
- if len(self.size) == 3:
- self.best_size = self.size
- self.size = (
- self.best_size[0] * self.best_size[2],
- self.best_size[1] * self.best_size[2],
- )
-
- px = Image.Image.load(self)
- if self.im is not None and self.im.size == self.size:
- # Already loaded
- return px
- self.load_prepare()
- # This is likely NOT the best way to do it, but whatever.
- im = self.icns.getimage(self.best_size)
-
- # If this is a PNG or JPEG 2000, it won't be loaded yet
- px = im.load()
-
- self.im = im.im
- self.mode = im.mode
- self.size = im.size
-
- return px
-
-
-def _save(im, fp, filename):
- """
- Saves the image as a series of PNG files,
- that are then combined into a .icns file.
- """
- if hasattr(fp, "flush"):
- fp.flush()
-
- sizes = {
- b"ic07": 128,
- b"ic08": 256,
- b"ic09": 512,
- b"ic10": 1024,
- b"ic11": 32,
- b"ic12": 64,
- b"ic13": 256,
- b"ic14": 512,
- }
- provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])}
- size_streams = {}
- for size in set(sizes.values()):
- image = (
- provided_images[size]
- if size in provided_images
- else im.resize((size, size))
- )
-
- temp = io.BytesIO()
- image.save(temp, "png")
- size_streams[size] = temp.getvalue()
-
- entries = []
- for type, size in sizes.items():
- stream = size_streams[size]
- entries.append(
- {"type": type, "size": HEADERSIZE + len(stream), "stream": stream}
- )
-
- # Header
- fp.write(MAGIC)
- file_length = HEADERSIZE # Header
- file_length += HEADERSIZE + 8 * len(entries) # TOC
- file_length += sum(entry["size"] for entry in entries)
- fp.write(struct.pack(">i", file_length))
-
- # TOC
- fp.write(b"TOC ")
- fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE))
- for entry in entries:
- fp.write(entry["type"])
- fp.write(struct.pack(">i", entry["size"]))
-
- # Data
- for entry in entries:
- fp.write(entry["type"])
- fp.write(struct.pack(">i", entry["size"]))
- fp.write(entry["stream"])
-
- if hasattr(fp, "flush"):
- fp.flush()
-
-
-def _accept(prefix):
- return prefix[:4] == MAGIC
-
-
-Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept)
-Image.register_extension(IcnsImageFile.format, ".icns")
-
-Image.register_save(IcnsImageFile.format, _save)
-Image.register_mime(IcnsImageFile.format, "image/icns")
-
-if __name__ == "__main__":
- if len(sys.argv) < 2:
- print("Syntax: python3 IcnsImagePlugin.py [file]")
- sys.exit()
-
- with open(sys.argv[1], "rb") as fp:
- imf = IcnsImageFile(fp)
- for size in imf.info["sizes"]:
- imf.size = size
- imf.save("out-%s-%s-%s.png" % size)
- with Image.open(sys.argv[1]) as im:
- im.save("out.png")
- if sys.platform == "windows":
- os.startfile("out.png")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/_core/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/dateutil/parser/_parser.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/dateutil/parser/_parser.py
deleted file mode 100644
index 37d1663b2f72447800d9a553929e3de932244289..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/dateutil/parser/_parser.py
+++ /dev/null
@@ -1,1613 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic date/time string parser which is able to parse
-most known formats to represent a date and/or time.
-
-This module attempts to be forgiving with regards to unlikely input formats,
-returning a datetime object even for dates which are ambiguous. If an element
-of a date/time stamp is omitted, the following rules are applied:
-
-- If AM or PM is left unspecified, a 24-hour clock is assumed, however, an hour
- on a 12-hour clock (``0 <= hour <= 12``) *must* be specified if AM or PM is
- specified.
-- If a time zone is omitted, a timezone-naive datetime is returned.
-
-If any other elements are missing, they are taken from the
-:class:`datetime.datetime` object passed to the parameter ``default``. If this
-results in a day number exceeding the valid number of days per month, the
-value falls back to the end of the month.
-
-Additional resources about date/time string formats can be found below:
-
-- `A summary of the international standard date and time notation
- `_
-- `W3C Date and Time Formats `_
-- `Time Formats (Planetary Rings Node) `_
-- `CPAN ParseDate module
- `_
-- `Java SimpleDateFormat Class
- `_
-"""
-from __future__ import unicode_literals
-
-import datetime
-import re
-import string
-import time
-import warnings
-
-from calendar import monthrange
-from io import StringIO
-
-import six
-from six import integer_types, text_type
-
-from decimal import Decimal
-
-from warnings import warn
-
-from .. import relativedelta
-from .. import tz
-
-__all__ = ["parse", "parserinfo", "ParserError"]
-
-
-# TODO: pandas.core.tools.datetimes imports this explicitly. Might be worth
-# making public and/or figuring out if there is something we can
-# take off their plate.
-class _timelex(object):
- # Fractional seconds are sometimes split by a comma
- _split_decimal = re.compile("([.,])")
-
- def __init__(self, instream):
- if isinstance(instream, (bytes, bytearray)):
- instream = instream.decode()
-
- if isinstance(instream, text_type):
- instream = StringIO(instream)
- elif getattr(instream, 'read', None) is None:
- raise TypeError('Parser must be a string or character stream, not '
- '{itype}'.format(itype=instream.__class__.__name__))
-
- self.instream = instream
- self.charstack = []
- self.tokenstack = []
- self.eof = False
-
- def get_token(self):
- """
- This function breaks the time string into lexical units (tokens), which
- can be parsed by the parser. Lexical units are demarcated by changes in
- the character set, so any continuous string of letters is considered
- one unit, any continuous string of numbers is considered one unit.
-
- The main complication arises from the fact that dots ('.') can be used
- both as separators (e.g. "Sep.20.2009") or decimal points (e.g.
- "4:30:21.447"). As such, it is necessary to read the full context of
- any dot-separated strings before breaking it into tokens; as such, this
- function maintains a "token stack", for when the ambiguous context
- demands that multiple tokens be parsed at once.
- """
- if self.tokenstack:
- return self.tokenstack.pop(0)
-
- seenletters = False
- token = None
- state = None
-
- while not self.eof:
- # We only realize that we've reached the end of a token when we
- # find a character that's not part of the current token - since
- # that character may be part of the next token, it's stored in the
- # charstack.
- if self.charstack:
- nextchar = self.charstack.pop(0)
- else:
- nextchar = self.instream.read(1)
- while nextchar == '\x00':
- nextchar = self.instream.read(1)
-
- if not nextchar:
- self.eof = True
- break
- elif not state:
- # First character of the token - determines if we're starting
- # to parse a word, a number or something else.
- token = nextchar
- if self.isword(nextchar):
- state = 'a'
- elif self.isnum(nextchar):
- state = '0'
- elif self.isspace(nextchar):
- token = ' '
- break # emit token
- else:
- break # emit token
- elif state == 'a':
- # If we've already started reading a word, we keep reading
- # letters until we find something that's not part of a word.
- seenletters = True
- if self.isword(nextchar):
- token += nextchar
- elif nextchar == '.':
- token += nextchar
- state = 'a.'
- else:
- self.charstack.append(nextchar)
- break # emit token
- elif state == '0':
- # If we've already started reading a number, we keep reading
- # numbers until we find something that doesn't fit.
- if self.isnum(nextchar):
- token += nextchar
- elif nextchar == '.' or (nextchar == ',' and len(token) >= 2):
- token += nextchar
- state = '0.'
- else:
- self.charstack.append(nextchar)
- break # emit token
- elif state == 'a.':
- # If we've seen some letters and a dot separator, continue
- # parsing, and the tokens will be broken up later.
- seenletters = True
- if nextchar == '.' or self.isword(nextchar):
- token += nextchar
- elif self.isnum(nextchar) and token[-1] == '.':
- token += nextchar
- state = '0.'
- else:
- self.charstack.append(nextchar)
- break # emit token
- elif state == '0.':
- # If we've seen at least one dot separator, keep going, we'll
- # break up the tokens later.
- if nextchar == '.' or self.isnum(nextchar):
- token += nextchar
- elif self.isword(nextchar) and token[-1] == '.':
- token += nextchar
- state = 'a.'
- else:
- self.charstack.append(nextchar)
- break # emit token
-
- if (state in ('a.', '0.') and (seenletters or token.count('.') > 1 or
- token[-1] in '.,')):
- l = self._split_decimal.split(token)
- token = l[0]
- for tok in l[1:]:
- if tok:
- self.tokenstack.append(tok)
-
- if state == '0.' and token.count('.') == 0:
- token = token.replace(',', '.')
-
- return token
-
- def __iter__(self):
- return self
-
- def __next__(self):
- token = self.get_token()
- if token is None:
- raise StopIteration
-
- return token
-
- def next(self):
- return self.__next__() # Python 2.x support
-
- @classmethod
- def split(cls, s):
- return list(cls(s))
-
- @classmethod
- def isword(cls, nextchar):
- """ Whether or not the next character is part of a word """
- return nextchar.isalpha()
-
- @classmethod
- def isnum(cls, nextchar):
- """ Whether the next character is part of a number """
- return nextchar.isdigit()
-
- @classmethod
- def isspace(cls, nextchar):
- """ Whether the next character is whitespace """
- return nextchar.isspace()
-
-
-class _resultbase(object):
-
- def __init__(self):
- for attr in self.__slots__:
- setattr(self, attr, None)
-
- def _repr(self, classname):
- l = []
- for attr in self.__slots__:
- value = getattr(self, attr)
- if value is not None:
- l.append("%s=%s" % (attr, repr(value)))
- return "%s(%s)" % (classname, ", ".join(l))
-
- def __len__(self):
- return (sum(getattr(self, attr) is not None
- for attr in self.__slots__))
-
- def __repr__(self):
- return self._repr(self.__class__.__name__)
-
-
-class parserinfo(object):
- """
- Class which handles what inputs are accepted. Subclass this to customize
- the language and acceptable values for each parameter.
-
- :param dayfirst:
- Whether to interpret the first value in an ambiguous 3-integer date
- (e.g. 01/05/09) as the day (``True``) or month (``False``). If
- ``yearfirst`` is set to ``True``, this distinguishes between YDM
- and YMD. Default is ``False``.
-
- :param yearfirst:
- Whether to interpret the first value in an ambiguous 3-integer date
- (e.g. 01/05/09) as the year. If ``True``, the first number is taken
- to be the year, otherwise the last number is taken to be the year.
- Default is ``False``.
- """
-
- # m from a.m/p.m, t from ISO T separator
- JUMP = [" ", ".", ",", ";", "-", "/", "'",
- "at", "on", "and", "ad", "m", "t", "of",
- "st", "nd", "rd", "th"]
-
- WEEKDAYS = [("Mon", "Monday"),
- ("Tue", "Tuesday"), # TODO: "Tues"
- ("Wed", "Wednesday"),
- ("Thu", "Thursday"), # TODO: "Thurs"
- ("Fri", "Friday"),
- ("Sat", "Saturday"),
- ("Sun", "Sunday")]
- MONTHS = [("Jan", "January"),
- ("Feb", "February"), # TODO: "Febr"
- ("Mar", "March"),
- ("Apr", "April"),
- ("May", "May"),
- ("Jun", "June"),
- ("Jul", "July"),
- ("Aug", "August"),
- ("Sep", "Sept", "September"),
- ("Oct", "October"),
- ("Nov", "November"),
- ("Dec", "December")]
- HMS = [("h", "hour", "hours"),
- ("m", "minute", "minutes"),
- ("s", "second", "seconds")]
- AMPM = [("am", "a"),
- ("pm", "p")]
- UTCZONE = ["UTC", "GMT", "Z", "z"]
- PERTAIN = ["of"]
- TZOFFSET = {}
- # TODO: ERA = ["AD", "BC", "CE", "BCE", "Stardate",
- # "Anno Domini", "Year of Our Lord"]
-
- def __init__(self, dayfirst=False, yearfirst=False):
- self._jump = self._convert(self.JUMP)
- self._weekdays = self._convert(self.WEEKDAYS)
- self._months = self._convert(self.MONTHS)
- self._hms = self._convert(self.HMS)
- self._ampm = self._convert(self.AMPM)
- self._utczone = self._convert(self.UTCZONE)
- self._pertain = self._convert(self.PERTAIN)
-
- self.dayfirst = dayfirst
- self.yearfirst = yearfirst
-
- self._year = time.localtime().tm_year
- self._century = self._year // 100 * 100
-
- def _convert(self, lst):
- dct = {}
- for i, v in enumerate(lst):
- if isinstance(v, tuple):
- for v in v:
- dct[v.lower()] = i
- else:
- dct[v.lower()] = i
- return dct
-
- def jump(self, name):
- return name.lower() in self._jump
-
- def weekday(self, name):
- try:
- return self._weekdays[name.lower()]
- except KeyError:
- pass
- return None
-
- def month(self, name):
- try:
- return self._months[name.lower()] + 1
- except KeyError:
- pass
- return None
-
- def hms(self, name):
- try:
- return self._hms[name.lower()]
- except KeyError:
- return None
-
- def ampm(self, name):
- try:
- return self._ampm[name.lower()]
- except KeyError:
- return None
-
- def pertain(self, name):
- return name.lower() in self._pertain
-
- def utczone(self, name):
- return name.lower() in self._utczone
-
- def tzoffset(self, name):
- if name in self._utczone:
- return 0
-
- return self.TZOFFSET.get(name)
-
- def convertyear(self, year, century_specified=False):
- """
- Converts two-digit years to year within [-50, 49]
- range of self._year (current local time)
- """
-
- # Function contract is that the year is always positive
- assert year >= 0
-
- if year < 100 and not century_specified:
- # assume current century to start
- year += self._century
-
- if year >= self._year + 50: # if too far in future
- year -= 100
- elif year < self._year - 50: # if too far in past
- year += 100
-
- return year
-
- def validate(self, res):
- # move to info
- if res.year is not None:
- res.year = self.convertyear(res.year, res.century_specified)
-
- if ((res.tzoffset == 0 and not res.tzname) or
- (res.tzname == 'Z' or res.tzname == 'z')):
- res.tzname = "UTC"
- res.tzoffset = 0
- elif res.tzoffset != 0 and res.tzname and self.utczone(res.tzname):
- res.tzoffset = 0
- return True
-
-
-class _ymd(list):
- def __init__(self, *args, **kwargs):
- super(self.__class__, self).__init__(*args, **kwargs)
- self.century_specified = False
- self.dstridx = None
- self.mstridx = None
- self.ystridx = None
-
- @property
- def has_year(self):
- return self.ystridx is not None
-
- @property
- def has_month(self):
- return self.mstridx is not None
-
- @property
- def has_day(self):
- return self.dstridx is not None
-
- def could_be_day(self, value):
- if self.has_day:
- return False
- elif not self.has_month:
- return 1 <= value <= 31
- elif not self.has_year:
- # Be permissive, assume leap year
- month = self[self.mstridx]
- return 1 <= value <= monthrange(2000, month)[1]
- else:
- month = self[self.mstridx]
- year = self[self.ystridx]
- return 1 <= value <= monthrange(year, month)[1]
-
- def append(self, val, label=None):
- if hasattr(val, '__len__'):
- if val.isdigit() and len(val) > 2:
- self.century_specified = True
- if label not in [None, 'Y']: # pragma: no cover
- raise ValueError(label)
- label = 'Y'
- elif val > 100:
- self.century_specified = True
- if label not in [None, 'Y']: # pragma: no cover
- raise ValueError(label)
- label = 'Y'
-
- super(self.__class__, self).append(int(val))
-
- if label == 'M':
- if self.has_month:
- raise ValueError('Month is already set')
- self.mstridx = len(self) - 1
- elif label == 'D':
- if self.has_day:
- raise ValueError('Day is already set')
- self.dstridx = len(self) - 1
- elif label == 'Y':
- if self.has_year:
- raise ValueError('Year is already set')
- self.ystridx = len(self) - 1
-
- def _resolve_from_stridxs(self, strids):
- """
- Try to resolve the identities of year/month/day elements using
- ystridx, mstridx, and dstridx, if enough of these are specified.
- """
- if len(self) == 3 and len(strids) == 2:
- # we can back out the remaining stridx value
- missing = [x for x in range(3) if x not in strids.values()]
- key = [x for x in ['y', 'm', 'd'] if x not in strids]
- assert len(missing) == len(key) == 1
- key = key[0]
- val = missing[0]
- strids[key] = val
-
- assert len(self) == len(strids) # otherwise this should not be called
- out = {key: self[strids[key]] for key in strids}
- return (out.get('y'), out.get('m'), out.get('d'))
-
- def resolve_ymd(self, yearfirst, dayfirst):
- len_ymd = len(self)
- year, month, day = (None, None, None)
-
- strids = (('y', self.ystridx),
- ('m', self.mstridx),
- ('d', self.dstridx))
-
- strids = {key: val for key, val in strids if val is not None}
- if (len(self) == len(strids) > 0 or
- (len(self) == 3 and len(strids) == 2)):
- return self._resolve_from_stridxs(strids)
-
- mstridx = self.mstridx
-
- if len_ymd > 3:
- raise ValueError("More than three YMD values")
- elif len_ymd == 1 or (mstridx is not None and len_ymd == 2):
- # One member, or two members with a month string
- if mstridx is not None:
- month = self[mstridx]
- # since mstridx is 0 or 1, self[mstridx-1] always
- # looks up the other element
- other = self[mstridx - 1]
- else:
- other = self[0]
-
- if len_ymd > 1 or mstridx is None:
- if other > 31:
- year = other
- else:
- day = other
-
- elif len_ymd == 2:
- # Two members with numbers
- if self[0] > 31:
- # 99-01
- year, month = self
- elif self[1] > 31:
- # 01-99
- month, year = self
- elif dayfirst and self[1] <= 12:
- # 13-01
- day, month = self
- else:
- # 01-13
- month, day = self
-
- elif len_ymd == 3:
- # Three members
- if mstridx == 0:
- if self[1] > 31:
- # Apr-2003-25
- month, year, day = self
- else:
- month, day, year = self
- elif mstridx == 1:
- if self[0] > 31 or (yearfirst and self[2] <= 31):
- # 99-Jan-01
- year, month, day = self
- else:
- # 01-Jan-01
- # Give precedence to day-first, since
- # two-digit years is usually hand-written.
- day, month, year = self
-
- elif mstridx == 2:
- # WTF!?
- if self[1] > 31:
- # 01-99-Jan
- day, year, month = self
- else:
- # 99-01-Jan
- year, day, month = self
-
- else:
- if (self[0] > 31 or
- self.ystridx == 0 or
- (yearfirst and self[1] <= 12 and self[2] <= 31)):
- # 99-01-01
- if dayfirst and self[2] <= 12:
- year, day, month = self
- else:
- year, month, day = self
- elif self[0] > 12 or (dayfirst and self[1] <= 12):
- # 13-01-01
- day, month, year = self
- else:
- # 01-13-01
- month, day, year = self
-
- return year, month, day
-
-
-class parser(object):
- def __init__(self, info=None):
- self.info = info or parserinfo()
-
- def parse(self, timestr, default=None,
- ignoretz=False, tzinfos=None, **kwargs):
- """
- Parse the date/time string into a :class:`datetime.datetime` object.
-
- :param timestr:
- Any date/time string using the supported formats.
-
- :param default:
- The default datetime object, if this is a datetime object and not
- ``None``, elements specified in ``timestr`` replace elements in the
- default object.
-
- :param ignoretz:
- If set ``True``, time zones in parsed strings are ignored and a
- naive :class:`datetime.datetime` object is returned.
-
- :param tzinfos:
- Additional time zone names / aliases which may be present in the
- string. This argument maps time zone names (and optionally offsets
- from those time zones) to time zones. This parameter can be a
- dictionary with timezone aliases mapping time zone names to time
- zones or a function taking two parameters (``tzname`` and
- ``tzoffset``) and returning a time zone.
-
- The timezones to which the names are mapped can be an integer
- offset from UTC in seconds or a :class:`tzinfo` object.
-
- .. doctest::
- :options: +NORMALIZE_WHITESPACE
-
- >>> from dateutil.parser import parse
- >>> from dateutil.tz import gettz
- >>> tzinfos = {"BRST": -7200, "CST": gettz("America/Chicago")}
- >>> parse("2012-01-19 17:21:00 BRST", tzinfos=tzinfos)
- datetime.datetime(2012, 1, 19, 17, 21, tzinfo=tzoffset(u'BRST', -7200))
- >>> parse("2012-01-19 17:21:00 CST", tzinfos=tzinfos)
- datetime.datetime(2012, 1, 19, 17, 21,
- tzinfo=tzfile('/usr/share/zoneinfo/America/Chicago'))
-
- This parameter is ignored if ``ignoretz`` is set.
-
- :param \\*\\*kwargs:
- Keyword arguments as passed to ``_parse()``.
-
- :return:
- Returns a :class:`datetime.datetime` object or, if the
- ``fuzzy_with_tokens`` option is ``True``, returns a tuple, the
- first element being a :class:`datetime.datetime` object, the second
- a tuple containing the fuzzy tokens.
-
- :raises ParserError:
- Raised for invalid or unknown string format, if the provided
- :class:`tzinfo` is not in a valid format, or if an invalid date
- would be created.
-
- :raises TypeError:
- Raised for non-string or character stream input.
-
- :raises OverflowError:
- Raised if the parsed date exceeds the largest valid C integer on
- your system.
- """
-
- if default is None:
- default = datetime.datetime.now().replace(hour=0, minute=0,
- second=0, microsecond=0)
-
- res, skipped_tokens = self._parse(timestr, **kwargs)
-
- if res is None:
- raise ParserError("Unknown string format: %s", timestr)
-
- if len(res) == 0:
- raise ParserError("String does not contain a date: %s", timestr)
-
- try:
- ret = self._build_naive(res, default)
- except ValueError as e:
- six.raise_from(ParserError(str(e) + ": %s", timestr), e)
-
- if not ignoretz:
- ret = self._build_tzaware(ret, res, tzinfos)
-
- if kwargs.get('fuzzy_with_tokens', False):
- return ret, skipped_tokens
- else:
- return ret
-
- class _result(_resultbase):
- __slots__ = ["year", "month", "day", "weekday",
- "hour", "minute", "second", "microsecond",
- "tzname", "tzoffset", "ampm","any_unused_tokens"]
-
- def _parse(self, timestr, dayfirst=None, yearfirst=None, fuzzy=False,
- fuzzy_with_tokens=False):
- """
- Private method which performs the heavy lifting of parsing, called from
- ``parse()``, which passes on its ``kwargs`` to this function.
-
- :param timestr:
- The string to parse.
-
- :param dayfirst:
- Whether to interpret the first value in an ambiguous 3-integer date
- (e.g. 01/05/09) as the day (``True``) or month (``False``). If
- ``yearfirst`` is set to ``True``, this distinguishes between YDM
- and YMD. If set to ``None``, this value is retrieved from the
- current :class:`parserinfo` object (which itself defaults to
- ``False``).
-
- :param yearfirst:
- Whether to interpret the first value in an ambiguous 3-integer date
- (e.g. 01/05/09) as the year. If ``True``, the first number is taken
- to be the year, otherwise the last number is taken to be the year.
- If this is set to ``None``, the value is retrieved from the current
- :class:`parserinfo` object (which itself defaults to ``False``).
-
- :param fuzzy:
- Whether to allow fuzzy parsing, allowing for string like "Today is
- January 1, 2047 at 8:21:00AM".
-
- :param fuzzy_with_tokens:
- If ``True``, ``fuzzy`` is automatically set to True, and the parser
- will return a tuple where the first element is the parsed
- :class:`datetime.datetime` datetimestamp and the second element is
- a tuple containing the portions of the string which were ignored:
-
- .. doctest::
-
- >>> from dateutil.parser import parse
- >>> parse("Today is January 1, 2047 at 8:21:00AM", fuzzy_with_tokens=True)
- (datetime.datetime(2047, 1, 1, 8, 21), (u'Today is ', u' ', u'at '))
-
- """
- if fuzzy_with_tokens:
- fuzzy = True
-
- info = self.info
-
- if dayfirst is None:
- dayfirst = info.dayfirst
-
- if yearfirst is None:
- yearfirst = info.yearfirst
-
- res = self._result()
- l = _timelex.split(timestr) # Splits the timestr into tokens
-
- skipped_idxs = []
-
- # year/month/day list
- ymd = _ymd()
-
- len_l = len(l)
- i = 0
- try:
- while i < len_l:
-
- # Check if it's a number
- value_repr = l[i]
- try:
- value = float(value_repr)
- except ValueError:
- value = None
-
- if value is not None:
- # Numeric token
- i = self._parse_numeric_token(l, i, info, ymd, res, fuzzy)
-
- # Check weekday
- elif info.weekday(l[i]) is not None:
- value = info.weekday(l[i])
- res.weekday = value
-
- # Check month name
- elif info.month(l[i]) is not None:
- value = info.month(l[i])
- ymd.append(value, 'M')
-
- if i + 1 < len_l:
- if l[i + 1] in ('-', '/'):
- # Jan-01[-99]
- sep = l[i + 1]
- ymd.append(l[i + 2])
-
- if i + 3 < len_l and l[i + 3] == sep:
- # Jan-01-99
- ymd.append(l[i + 4])
- i += 2
-
- i += 2
-
- elif (i + 4 < len_l and l[i + 1] == l[i + 3] == ' ' and
- info.pertain(l[i + 2])):
- # Jan of 01
- # In this case, 01 is clearly year
- if l[i + 4].isdigit():
- # Convert it here to become unambiguous
- value = int(l[i + 4])
- year = str(info.convertyear(value))
- ymd.append(year, 'Y')
- else:
- # Wrong guess
- pass
- # TODO: not hit in tests
- i += 4
-
- # Check am/pm
- elif info.ampm(l[i]) is not None:
- value = info.ampm(l[i])
- val_is_ampm = self._ampm_valid(res.hour, res.ampm, fuzzy)
-
- if val_is_ampm:
- res.hour = self._adjust_ampm(res.hour, value)
- res.ampm = value
-
- elif fuzzy:
- skipped_idxs.append(i)
-
- # Check for a timezone name
- elif self._could_be_tzname(res.hour, res.tzname, res.tzoffset, l[i]):
- res.tzname = l[i]
- res.tzoffset = info.tzoffset(res.tzname)
-
- # Check for something like GMT+3, or BRST+3. Notice
- # that it doesn't mean "I am 3 hours after GMT", but
- # "my time +3 is GMT". If found, we reverse the
- # logic so that timezone parsing code will get it
- # right.
- if i + 1 < len_l and l[i + 1] in ('+', '-'):
- l[i + 1] = ('+', '-')[l[i + 1] == '+']
- res.tzoffset = None
- if info.utczone(res.tzname):
- # With something like GMT+3, the timezone
- # is *not* GMT.
- res.tzname = None
-
- # Check for a numbered timezone
- elif res.hour is not None and l[i] in ('+', '-'):
- signal = (-1, 1)[l[i] == '+']
- len_li = len(l[i + 1])
-
- # TODO: check that l[i + 1] is integer?
- if len_li == 4:
- # -0300
- hour_offset = int(l[i + 1][:2])
- min_offset = int(l[i + 1][2:])
- elif i + 2 < len_l and l[i + 2] == ':':
- # -03:00
- hour_offset = int(l[i + 1])
- min_offset = int(l[i + 3]) # TODO: Check that l[i+3] is minute-like?
- i += 2
- elif len_li <= 2:
- # -[0]3
- hour_offset = int(l[i + 1][:2])
- min_offset = 0
- else:
- raise ValueError(timestr)
-
- res.tzoffset = signal * (hour_offset * 3600 + min_offset * 60)
-
- # Look for a timezone name between parenthesis
- if (i + 5 < len_l and
- info.jump(l[i + 2]) and l[i + 3] == '(' and
- l[i + 5] == ')' and
- 3 <= len(l[i + 4]) and
- self._could_be_tzname(res.hour, res.tzname,
- None, l[i + 4])):
- # -0300 (BRST)
- res.tzname = l[i + 4]
- i += 4
-
- i += 1
-
- # Check jumps
- elif not (info.jump(l[i]) or fuzzy):
- raise ValueError(timestr)
-
- else:
- skipped_idxs.append(i)
- i += 1
-
- # Process year/month/day
- year, month, day = ymd.resolve_ymd(yearfirst, dayfirst)
-
- res.century_specified = ymd.century_specified
- res.year = year
- res.month = month
- res.day = day
-
- except (IndexError, ValueError):
- return None, None
-
- if not info.validate(res):
- return None, None
-
- if fuzzy_with_tokens:
- skipped_tokens = self._recombine_skipped(l, skipped_idxs)
- return res, tuple(skipped_tokens)
- else:
- return res, None
-
- def _parse_numeric_token(self, tokens, idx, info, ymd, res, fuzzy):
- # Token is a number
- value_repr = tokens[idx]
- try:
- value = self._to_decimal(value_repr)
- except Exception as e:
- six.raise_from(ValueError('Unknown numeric token'), e)
-
- len_li = len(value_repr)
-
- len_l = len(tokens)
-
- if (len(ymd) == 3 and len_li in (2, 4) and
- res.hour is None and
- (idx + 1 >= len_l or
- (tokens[idx + 1] != ':' and
- info.hms(tokens[idx + 1]) is None))):
- # 19990101T23[59]
- s = tokens[idx]
- res.hour = int(s[:2])
-
- if len_li == 4:
- res.minute = int(s[2:])
-
- elif len_li == 6 or (len_li > 6 and tokens[idx].find('.') == 6):
- # YYMMDD or HHMMSS[.ss]
- s = tokens[idx]
-
- if not ymd and '.' not in tokens[idx]:
- ymd.append(s[:2])
- ymd.append(s[2:4])
- ymd.append(s[4:])
- else:
- # 19990101T235959[.59]
-
- # TODO: Check if res attributes already set.
- res.hour = int(s[:2])
- res.minute = int(s[2:4])
- res.second, res.microsecond = self._parsems(s[4:])
-
- elif len_li in (8, 12, 14):
- # YYYYMMDD
- s = tokens[idx]
- ymd.append(s[:4], 'Y')
- ymd.append(s[4:6])
- ymd.append(s[6:8])
-
- if len_li > 8:
- res.hour = int(s[8:10])
- res.minute = int(s[10:12])
-
- if len_li > 12:
- res.second = int(s[12:])
-
- elif self._find_hms_idx(idx, tokens, info, allow_jump=True) is not None:
- # HH[ ]h or MM[ ]m or SS[.ss][ ]s
- hms_idx = self._find_hms_idx(idx, tokens, info, allow_jump=True)
- (idx, hms) = self._parse_hms(idx, tokens, info, hms_idx)
- if hms is not None:
- # TODO: checking that hour/minute/second are not
- # already set?
- self._assign_hms(res, value_repr, hms)
-
- elif idx + 2 < len_l and tokens[idx + 1] == ':':
- # HH:MM[:SS[.ss]]
- res.hour = int(value)
- value = self._to_decimal(tokens[idx + 2]) # TODO: try/except for this?
- (res.minute, res.second) = self._parse_min_sec(value)
-
- if idx + 4 < len_l and tokens[idx + 3] == ':':
- res.second, res.microsecond = self._parsems(tokens[idx + 4])
-
- idx += 2
-
- idx += 2
-
- elif idx + 1 < len_l and tokens[idx + 1] in ('-', '/', '.'):
- sep = tokens[idx + 1]
- ymd.append(value_repr)
-
- if idx + 2 < len_l and not info.jump(tokens[idx + 2]):
- if tokens[idx + 2].isdigit():
- # 01-01[-01]
- ymd.append(tokens[idx + 2])
- else:
- # 01-Jan[-01]
- value = info.month(tokens[idx + 2])
-
- if value is not None:
- ymd.append(value, 'M')
- else:
- raise ValueError()
-
- if idx + 3 < len_l and tokens[idx + 3] == sep:
- # We have three members
- value = info.month(tokens[idx + 4])
-
- if value is not None:
- ymd.append(value, 'M')
- else:
- ymd.append(tokens[idx + 4])
- idx += 2
-
- idx += 1
- idx += 1
-
- elif idx + 1 >= len_l or info.jump(tokens[idx + 1]):
- if idx + 2 < len_l and info.ampm(tokens[idx + 2]) is not None:
- # 12 am
- hour = int(value)
- res.hour = self._adjust_ampm(hour, info.ampm(tokens[idx + 2]))
- idx += 1
- else:
- # Year, month or day
- ymd.append(value)
- idx += 1
-
- elif info.ampm(tokens[idx + 1]) is not None and (0 <= value < 24):
- # 12am
- hour = int(value)
- res.hour = self._adjust_ampm(hour, info.ampm(tokens[idx + 1]))
- idx += 1
-
- elif ymd.could_be_day(value):
- ymd.append(value)
-
- elif not fuzzy:
- raise ValueError()
-
- return idx
-
- def _find_hms_idx(self, idx, tokens, info, allow_jump):
- len_l = len(tokens)
-
- if idx+1 < len_l and info.hms(tokens[idx+1]) is not None:
- # There is an "h", "m", or "s" label following this token. We take
- # assign the upcoming label to the current token.
- # e.g. the "12" in 12h"
- hms_idx = idx + 1
-
- elif (allow_jump and idx+2 < len_l and tokens[idx+1] == ' ' and
- info.hms(tokens[idx+2]) is not None):
- # There is a space and then an "h", "m", or "s" label.
- # e.g. the "12" in "12 h"
- hms_idx = idx + 2
-
- elif idx > 0 and info.hms(tokens[idx-1]) is not None:
- # There is a "h", "m", or "s" preceding this token. Since neither
- # of the previous cases was hit, there is no label following this
- # token, so we use the previous label.
- # e.g. the "04" in "12h04"
- hms_idx = idx-1
-
- elif (1 < idx == len_l-1 and tokens[idx-1] == ' ' and
- info.hms(tokens[idx-2]) is not None):
- # If we are looking at the final token, we allow for a
- # backward-looking check to skip over a space.
- # TODO: Are we sure this is the right condition here?
- hms_idx = idx - 2
-
- else:
- hms_idx = None
-
- return hms_idx
-
- def _assign_hms(self, res, value_repr, hms):
- # See GH issue #427, fixing float rounding
- value = self._to_decimal(value_repr)
-
- if hms == 0:
- # Hour
- res.hour = int(value)
- if value % 1:
- res.minute = int(60*(value % 1))
-
- elif hms == 1:
- (res.minute, res.second) = self._parse_min_sec(value)
-
- elif hms == 2:
- (res.second, res.microsecond) = self._parsems(value_repr)
-
- def _could_be_tzname(self, hour, tzname, tzoffset, token):
- return (hour is not None and
- tzname is None and
- tzoffset is None and
- len(token) <= 5 and
- (all(x in string.ascii_uppercase for x in token)
- or token in self.info.UTCZONE))
-
- def _ampm_valid(self, hour, ampm, fuzzy):
- """
- For fuzzy parsing, 'a' or 'am' (both valid English words)
- may erroneously trigger the AM/PM flag. Deal with that
- here.
- """
- val_is_ampm = True
-
- # If there's already an AM/PM flag, this one isn't one.
- if fuzzy and ampm is not None:
- val_is_ampm = False
-
- # If AM/PM is found and hour is not, raise a ValueError
- if hour is None:
- if fuzzy:
- val_is_ampm = False
- else:
- raise ValueError('No hour specified with AM or PM flag.')
- elif not 0 <= hour <= 12:
- # If AM/PM is found, it's a 12 hour clock, so raise
- # an error for invalid range
- if fuzzy:
- val_is_ampm = False
- else:
- raise ValueError('Invalid hour specified for 12-hour clock.')
-
- return val_is_ampm
-
- def _adjust_ampm(self, hour, ampm):
- if hour < 12 and ampm == 1:
- hour += 12
- elif hour == 12 and ampm == 0:
- hour = 0
- return hour
-
- def _parse_min_sec(self, value):
- # TODO: Every usage of this function sets res.second to the return
- # value. Are there any cases where second will be returned as None and
- # we *don't* want to set res.second = None?
- minute = int(value)
- second = None
-
- sec_remainder = value % 1
- if sec_remainder:
- second = int(60 * sec_remainder)
- return (minute, second)
-
- def _parse_hms(self, idx, tokens, info, hms_idx):
- # TODO: Is this going to admit a lot of false-positives for when we
- # just happen to have digits and "h", "m" or "s" characters in non-date
- # text? I guess hex hashes won't have that problem, but there's plenty
- # of random junk out there.
- if hms_idx is None:
- hms = None
- new_idx = idx
- elif hms_idx > idx:
- hms = info.hms(tokens[hms_idx])
- new_idx = hms_idx
- else:
- # Looking backwards, increment one.
- hms = info.hms(tokens[hms_idx]) + 1
- new_idx = idx
-
- return (new_idx, hms)
-
- # ------------------------------------------------------------------
- # Handling for individual tokens. These are kept as methods instead
- # of functions for the sake of customizability via subclassing.
-
- def _parsems(self, value):
- """Parse a I[.F] seconds value into (seconds, microseconds)."""
- if "." not in value:
- return int(value), 0
- else:
- i, f = value.split(".")
- return int(i), int(f.ljust(6, "0")[:6])
-
- def _to_decimal(self, val):
- try:
- decimal_value = Decimal(val)
- # See GH 662, edge case, infinite value should not be converted
- # via `_to_decimal`
- if not decimal_value.is_finite():
- raise ValueError("Converted decimal value is infinite or NaN")
- except Exception as e:
- msg = "Could not convert %s to decimal" % val
- six.raise_from(ValueError(msg), e)
- else:
- return decimal_value
-
- # ------------------------------------------------------------------
- # Post-Parsing construction of datetime output. These are kept as
- # methods instead of functions for the sake of customizability via
- # subclassing.
-
- def _build_tzinfo(self, tzinfos, tzname, tzoffset):
- if callable(tzinfos):
- tzdata = tzinfos(tzname, tzoffset)
- else:
- tzdata = tzinfos.get(tzname)
- # handle case where tzinfo is paased an options that returns None
- # eg tzinfos = {'BRST' : None}
- if isinstance(tzdata, datetime.tzinfo) or tzdata is None:
- tzinfo = tzdata
- elif isinstance(tzdata, text_type):
- tzinfo = tz.tzstr(tzdata)
- elif isinstance(tzdata, integer_types):
- tzinfo = tz.tzoffset(tzname, tzdata)
- else:
- raise TypeError("Offset must be tzinfo subclass, tz string, "
- "or int offset.")
- return tzinfo
-
- def _build_tzaware(self, naive, res, tzinfos):
- if (callable(tzinfos) or (tzinfos and res.tzname in tzinfos)):
- tzinfo = self._build_tzinfo(tzinfos, res.tzname, res.tzoffset)
- aware = naive.replace(tzinfo=tzinfo)
- aware = self._assign_tzname(aware, res.tzname)
-
- elif res.tzname and res.tzname in time.tzname:
- aware = naive.replace(tzinfo=tz.tzlocal())
-
- # Handle ambiguous local datetime
- aware = self._assign_tzname(aware, res.tzname)
-
- # This is mostly relevant for winter GMT zones parsed in the UK
- if (aware.tzname() != res.tzname and
- res.tzname in self.info.UTCZONE):
- aware = aware.replace(tzinfo=tz.UTC)
-
- elif res.tzoffset == 0:
- aware = naive.replace(tzinfo=tz.UTC)
-
- elif res.tzoffset:
- aware = naive.replace(tzinfo=tz.tzoffset(res.tzname, res.tzoffset))
-
- elif not res.tzname and not res.tzoffset:
- # i.e. no timezone information was found.
- aware = naive
-
- elif res.tzname:
- # tz-like string was parsed but we don't know what to do
- # with it
- warnings.warn("tzname {tzname} identified but not understood. "
- "Pass `tzinfos` argument in order to correctly "
- "return a timezone-aware datetime. In a future "
- "version, this will raise an "
- "exception.".format(tzname=res.tzname),
- category=UnknownTimezoneWarning)
- aware = naive
-
- return aware
-
- def _build_naive(self, res, default):
- repl = {}
- for attr in ("year", "month", "day", "hour",
- "minute", "second", "microsecond"):
- value = getattr(res, attr)
- if value is not None:
- repl[attr] = value
-
- if 'day' not in repl:
- # If the default day exceeds the last day of the month, fall back
- # to the end of the month.
- cyear = default.year if res.year is None else res.year
- cmonth = default.month if res.month is None else res.month
- cday = default.day if res.day is None else res.day
-
- if cday > monthrange(cyear, cmonth)[1]:
- repl['day'] = monthrange(cyear, cmonth)[1]
-
- naive = default.replace(**repl)
-
- if res.weekday is not None and not res.day:
- naive = naive + relativedelta.relativedelta(weekday=res.weekday)
-
- return naive
-
- def _assign_tzname(self, dt, tzname):
- if dt.tzname() != tzname:
- new_dt = tz.enfold(dt, fold=1)
- if new_dt.tzname() == tzname:
- return new_dt
-
- return dt
-
- def _recombine_skipped(self, tokens, skipped_idxs):
- """
- >>> tokens = ["foo", " ", "bar", " ", "19June2000", "baz"]
- >>> skipped_idxs = [0, 1, 2, 5]
- >>> _recombine_skipped(tokens, skipped_idxs)
- ["foo bar", "baz"]
- """
- skipped_tokens = []
- for i, idx in enumerate(sorted(skipped_idxs)):
- if i > 0 and idx - 1 == skipped_idxs[i - 1]:
- skipped_tokens[-1] = skipped_tokens[-1] + tokens[idx]
- else:
- skipped_tokens.append(tokens[idx])
-
- return skipped_tokens
-
-
-DEFAULTPARSER = parser()
-
-
-def parse(timestr, parserinfo=None, **kwargs):
- """
-
- Parse a string in one of the supported formats, using the
- ``parserinfo`` parameters.
-
- :param timestr:
- A string containing a date/time stamp.
-
- :param parserinfo:
- A :class:`parserinfo` object containing parameters for the parser.
- If ``None``, the default arguments to the :class:`parserinfo`
- constructor are used.
-
- The ``**kwargs`` parameter takes the following keyword arguments:
-
- :param default:
- The default datetime object, if this is a datetime object and not
- ``None``, elements specified in ``timestr`` replace elements in the
- default object.
-
- :param ignoretz:
- If set ``True``, time zones in parsed strings are ignored and a naive
- :class:`datetime` object is returned.
-
- :param tzinfos:
- Additional time zone names / aliases which may be present in the
- string. This argument maps time zone names (and optionally offsets
- from those time zones) to time zones. This parameter can be a
- dictionary with timezone aliases mapping time zone names to time
- zones or a function taking two parameters (``tzname`` and
- ``tzoffset``) and returning a time zone.
-
- The timezones to which the names are mapped can be an integer
- offset from UTC in seconds or a :class:`tzinfo` object.
-
- .. doctest::
- :options: +NORMALIZE_WHITESPACE
-
- >>> from dateutil.parser import parse
- >>> from dateutil.tz import gettz
- >>> tzinfos = {"BRST": -7200, "CST": gettz("America/Chicago")}
- >>> parse("2012-01-19 17:21:00 BRST", tzinfos=tzinfos)
- datetime.datetime(2012, 1, 19, 17, 21, tzinfo=tzoffset(u'BRST', -7200))
- >>> parse("2012-01-19 17:21:00 CST", tzinfos=tzinfos)
- datetime.datetime(2012, 1, 19, 17, 21,
- tzinfo=tzfile('/usr/share/zoneinfo/America/Chicago'))
-
- This parameter is ignored if ``ignoretz`` is set.
-
- :param dayfirst:
- Whether to interpret the first value in an ambiguous 3-integer date
- (e.g. 01/05/09) as the day (``True``) or month (``False``). If
- ``yearfirst`` is set to ``True``, this distinguishes between YDM and
- YMD. If set to ``None``, this value is retrieved from the current
- :class:`parserinfo` object (which itself defaults to ``False``).
-
- :param yearfirst:
- Whether to interpret the first value in an ambiguous 3-integer date
- (e.g. 01/05/09) as the year. If ``True``, the first number is taken to
- be the year, otherwise the last number is taken to be the year. If
- this is set to ``None``, the value is retrieved from the current
- :class:`parserinfo` object (which itself defaults to ``False``).
-
- :param fuzzy:
- Whether to allow fuzzy parsing, allowing for string like "Today is
- January 1, 2047 at 8:21:00AM".
-
- :param fuzzy_with_tokens:
- If ``True``, ``fuzzy`` is automatically set to True, and the parser
- will return a tuple where the first element is the parsed
- :class:`datetime.datetime` datetimestamp and the second element is
- a tuple containing the portions of the string which were ignored:
-
- .. doctest::
-
- >>> from dateutil.parser import parse
- >>> parse("Today is January 1, 2047 at 8:21:00AM", fuzzy_with_tokens=True)
- (datetime.datetime(2047, 1, 1, 8, 21), (u'Today is ', u' ', u'at '))
-
- :return:
- Returns a :class:`datetime.datetime` object or, if the
- ``fuzzy_with_tokens`` option is ``True``, returns a tuple, the
- first element being a :class:`datetime.datetime` object, the second
- a tuple containing the fuzzy tokens.
-
- :raises ParserError:
- Raised for invalid or unknown string formats, if the provided
- :class:`tzinfo` is not in a valid format, or if an invalid date would
- be created.
-
- :raises OverflowError:
- Raised if the parsed date exceeds the largest valid C integer on
- your system.
- """
- if parserinfo:
- return parser(parserinfo).parse(timestr, **kwargs)
- else:
- return DEFAULTPARSER.parse(timestr, **kwargs)
-
-
-class _tzparser(object):
-
- class _result(_resultbase):
-
- __slots__ = ["stdabbr", "stdoffset", "dstabbr", "dstoffset",
- "start", "end"]
-
- class _attr(_resultbase):
- __slots__ = ["month", "week", "weekday",
- "yday", "jyday", "day", "time"]
-
- def __repr__(self):
- return self._repr("")
-
- def __init__(self):
- _resultbase.__init__(self)
- self.start = self._attr()
- self.end = self._attr()
-
- def parse(self, tzstr):
- res = self._result()
- l = [x for x in re.split(r'([,:.]|[a-zA-Z]+|[0-9]+)',tzstr) if x]
- used_idxs = list()
- try:
-
- len_l = len(l)
-
- i = 0
- while i < len_l:
- # BRST+3[BRDT[+2]]
- j = i
- while j < len_l and not [x for x in l[j]
- if x in "0123456789:,-+"]:
- j += 1
- if j != i:
- if not res.stdabbr:
- offattr = "stdoffset"
- res.stdabbr = "".join(l[i:j])
- else:
- offattr = "dstoffset"
- res.dstabbr = "".join(l[i:j])
-
- for ii in range(j):
- used_idxs.append(ii)
- i = j
- if (i < len_l and (l[i] in ('+', '-') or l[i][0] in
- "0123456789")):
- if l[i] in ('+', '-'):
- # Yes, that's right. See the TZ variable
- # documentation.
- signal = (1, -1)[l[i] == '+']
- used_idxs.append(i)
- i += 1
- else:
- signal = -1
- len_li = len(l[i])
- if len_li == 4:
- # -0300
- setattr(res, offattr, (int(l[i][:2]) * 3600 +
- int(l[i][2:]) * 60) * signal)
- elif i + 1 < len_l and l[i + 1] == ':':
- # -03:00
- setattr(res, offattr,
- (int(l[i]) * 3600 +
- int(l[i + 2]) * 60) * signal)
- used_idxs.append(i)
- i += 2
- elif len_li <= 2:
- # -[0]3
- setattr(res, offattr,
- int(l[i][:2]) * 3600 * signal)
- else:
- return None
- used_idxs.append(i)
- i += 1
- if res.dstabbr:
- break
- else:
- break
-
-
- if i < len_l:
- for j in range(i, len_l):
- if l[j] == ';':
- l[j] = ','
-
- assert l[i] == ','
-
- i += 1
-
- if i >= len_l:
- pass
- elif (8 <= l.count(',') <= 9 and
- not [y for x in l[i:] if x != ','
- for y in x if y not in "0123456789+-"]):
- # GMT0BST,3,0,30,3600,10,0,26,7200[,3600]
- for x in (res.start, res.end):
- x.month = int(l[i])
- used_idxs.append(i)
- i += 2
- if l[i] == '-':
- value = int(l[i + 1]) * -1
- used_idxs.append(i)
- i += 1
- else:
- value = int(l[i])
- used_idxs.append(i)
- i += 2
- if value:
- x.week = value
- x.weekday = (int(l[i]) - 1) % 7
- else:
- x.day = int(l[i])
- used_idxs.append(i)
- i += 2
- x.time = int(l[i])
- used_idxs.append(i)
- i += 2
- if i < len_l:
- if l[i] in ('-', '+'):
- signal = (-1, 1)[l[i] == "+"]
- used_idxs.append(i)
- i += 1
- else:
- signal = 1
- used_idxs.append(i)
- res.dstoffset = (res.stdoffset + int(l[i]) * signal)
-
- # This was a made-up format that is not in normal use
- warn(('Parsed time zone "%s"' % tzstr) +
- 'is in a non-standard dateutil-specific format, which ' +
- 'is now deprecated; support for parsing this format ' +
- 'will be removed in future versions. It is recommended ' +
- 'that you switch to a standard format like the GNU ' +
- 'TZ variable format.', tz.DeprecatedTzFormatWarning)
- elif (l.count(',') == 2 and l[i:].count('/') <= 2 and
- not [y for x in l[i:] if x not in (',', '/', 'J', 'M',
- '.', '-', ':')
- for y in x if y not in "0123456789"]):
- for x in (res.start, res.end):
- if l[i] == 'J':
- # non-leap year day (1 based)
- used_idxs.append(i)
- i += 1
- x.jyday = int(l[i])
- elif l[i] == 'M':
- # month[-.]week[-.]weekday
- used_idxs.append(i)
- i += 1
- x.month = int(l[i])
- used_idxs.append(i)
- i += 1
- assert l[i] in ('-', '.')
- used_idxs.append(i)
- i += 1
- x.week = int(l[i])
- if x.week == 5:
- x.week = -1
- used_idxs.append(i)
- i += 1
- assert l[i] in ('-', '.')
- used_idxs.append(i)
- i += 1
- x.weekday = (int(l[i]) - 1) % 7
- else:
- # year day (zero based)
- x.yday = int(l[i]) + 1
-
- used_idxs.append(i)
- i += 1
-
- if i < len_l and l[i] == '/':
- used_idxs.append(i)
- i += 1
- # start time
- len_li = len(l[i])
- if len_li == 4:
- # -0300
- x.time = (int(l[i][:2]) * 3600 +
- int(l[i][2:]) * 60)
- elif i + 1 < len_l and l[i + 1] == ':':
- # -03:00
- x.time = int(l[i]) * 3600 + int(l[i + 2]) * 60
- used_idxs.append(i)
- i += 2
- if i + 1 < len_l and l[i + 1] == ':':
- used_idxs.append(i)
- i += 2
- x.time += int(l[i])
- elif len_li <= 2:
- # -[0]3
- x.time = (int(l[i][:2]) * 3600)
- else:
- return None
- used_idxs.append(i)
- i += 1
-
- assert i == len_l or l[i] == ','
-
- i += 1
-
- assert i >= len_l
-
- except (IndexError, ValueError, AssertionError):
- return None
-
- unused_idxs = set(range(len_l)).difference(used_idxs)
- res.any_unused_tokens = not {l[n] for n in unused_idxs}.issubset({",",":"})
- return res
-
-
-DEFAULTTZPARSER = _tzparser()
-
-
-def _parsetz(tzstr):
- return DEFAULTTZPARSER.parse(tzstr)
-
-
-class ParserError(ValueError):
- """Exception subclass used for any failure to parse a datetime string.
-
- This is a subclass of :py:exc:`ValueError`, and should be raised any time
- earlier versions of ``dateutil`` would have raised ``ValueError``.
-
- .. versionadded:: 2.8.1
- """
- def __str__(self):
- try:
- return self.args[0] % self.args[1:]
- except (TypeError, IndexError):
- return super(ParserError, self).__str__()
-
- def __repr__(self):
- args = ", ".join("'%s'" % arg for arg in self.args)
- return "%s(%s)" % (self.__class__.__name__, args)
-
-
-class UnknownTimezoneWarning(RuntimeWarning):
- """Raised when the parser finds a timezone it cannot parse into a tzinfo.
-
- .. versionadded:: 2.7.0
- """
-# vim:ts=4:sw=4:et
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/losses/fairseqmmloss.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/losses/fairseqmmloss.py
deleted file mode 100644
index c0415d1077d873ef87e8038c2dd626d7c1f97d91..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/examples/MMPT/mmpt/losses/fairseqmmloss.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-TODO (huxu): a general fairseq criterion for all your pre-defined losses.
-"""
-
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq import metrics
-
-
-@register_criterion("mmloss")
-class MMCriterion(FairseqCriterion):
- def __init__(self, task):
- super().__init__(task)
- # TODO (huxu): wrap forward call of loss_fn and eval_fn into task.
- self.mmtask = task.mmtask
-
- def forward(self, model, sample):
- """Compute the loss for the given sample.
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- outputs = self.mmtask(model, sample)
-
- loss, loss_scalar, max_len, batch_size, sample_size = (
- outputs["loss"],
- outputs["loss_scalar"],
- outputs["max_len"],
- outputs["batch_size"],
- outputs["sample_size"],
- )
-
- logging_output = {
- "loss": loss_scalar,
- "ntokens": max_len * batch_size, # dummy report.
- "nsentences": batch_size, # dummy report.
- "sample_size": sample_size,
- }
-
- return loss, 1, logging_output
-
- @staticmethod
- def reduce_metrics(logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- """since we use NCE, our actual batch_size is 1 per GPU.
- Then we take the mean of each worker."""
- loss_sum = sum(log.get("loss", 0.0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
- metrics.log_scalar("loss", loss_sum / sample_size, round=3)
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/autoevaluate/error-analysis/error_analysis/utils/style_hacks.py b/spaces/autoevaluate/error-analysis/error_analysis/utils/style_hacks.py
deleted file mode 100644
index 057fef06788511ce82da20e19cc8953f37fe3e38..0000000000000000000000000000000000000000
--- a/spaces/autoevaluate/error-analysis/error_analysis/utils/style_hacks.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
- placeholder for all streamlit style hacks
-"""
-import streamlit as st
-
-
-def init_style():
- return st.markdown(
- """
-
-""",
- unsafe_allow_html=True,
- )
diff --git a/spaces/avans06/whisper-webui-translate/src/nllb/nllbModel.py b/spaces/avans06/whisper-webui-translate/src/nllb/nllbModel.py
deleted file mode 100644
index dccb932ddf0528b63cca17f9fdf4da8666962170..0000000000000000000000000000000000000000
--- a/spaces/avans06/whisper-webui-translate/src/nllb/nllbModel.py
+++ /dev/null
@@ -1,224 +0,0 @@
-import os
-import warnings
-import huggingface_hub
-import requests
-import torch
-
-import ctranslate2
-import transformers
-
-from typing import Optional
-from src.config import ModelConfig
-from src.languages import Language
-from src.nllb.nllbLangs import NllbLang, get_nllb_lang_from_code_whisper
-
-class NllbModel:
- def __init__(
- self,
- model_config: ModelConfig,
- device: str = None,
- whisper_lang: Language = None,
- nllb_lang: NllbLang = None,
- download_root: Optional[str] = None,
- local_files_only: bool = False,
- load_model: bool = False,
- ):
- """Initializes the Nllb-200 model.
-
- Args:
- model_config: Config of the model to use (distilled-600M, distilled-1.3B,
- 1.3B, 3.3B...) or a path to a converted
- model directory. When a size is configured, the converted model is downloaded
- from the Hugging Face Hub.
- device: Device to use for computation (cpu, cuda, ipu, xpu, mkldnn, opengl, opencl,
- ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia).
- device_index: Device ID to use.
- The model can also be loaded on multiple GPUs by passing a list of IDs
- (e.g. [0, 1, 2, 3]). In that case, multiple transcriptions can run in parallel
- when transcribe() is called from multiple Python threads (see also num_workers).
- compute_type: Type to use for computation.
- See https://opennmt.net/CTranslate2/quantization.html.
- cpu_threads: Number of threads to use when running on CPU (4 by default).
- A non zero value overrides the OMP_NUM_THREADS environment variable.
- num_workers: When transcribe() is called from multiple Python threads,
- having multiple workers enables true parallelism when running the model
- (concurrent calls to self.model.generate() will run in parallel).
- This can improve the global throughput at the cost of increased memory usage.
- download_root: Directory where the models should be saved. If not set, the models
- are saved in the standard Hugging Face cache directory.
- local_files_only: If True, avoid downloading the file and return the path to the
- local cached file if it exists.
- """
- self.whisper_lang = whisper_lang
- self.nllb_whisper_lang = get_nllb_lang_from_code_whisper(whisper_lang.code.lower() if whisper_lang is not None else "en")
- self.nllb_lang = nllb_lang
- self.model_config = model_config
-
- if nllb_lang is None:
- return
-
- if os.path.isdir(model_config.url):
- self.model_path = model_config.url
- else:
- self.model_path = download_model(
- model_config,
- local_files_only=local_files_only,
- cache_dir=download_root,
- )
-
- if device is None:
- if torch.cuda.is_available():
- device = "cuda" if "ct2" in self.model_path else "cuda:0"
- else:
- device = "cpu"
-
- self.device = device
-
- if load_model:
- self.load_model()
-
- def load_model(self):
- print('\n\nLoading model: %s\n\n' % self.model_path)
- if "ct2" in self.model_path:
- self.target_prefix = [self.nllb_lang.code]
- self.trans_tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_path, src_lang=self.nllb_whisper_lang.code)
- self.trans_model = ctranslate2.Translator(self.model_path, compute_type="auto", device=self.device)
- elif "mt5" in self.model_path:
- self.mt5_prefix = self.whisper_lang.code + "2" + self.nllb_lang.code_whisper + ": "
- self.trans_tokenizer = transformers.T5Tokenizer.from_pretrained(self.model_path, legacy=False) #requires spiece.model
- self.trans_model = transformers.MT5ForConditionalGeneration.from_pretrained(self.model_path)
- self.trans_translator = transformers.pipeline('text2text-generation', model=self.trans_model, device=self.device, tokenizer=self.trans_tokenizer)
- else: #NLLB
- self.trans_tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_path)
- self.trans_model = transformers.AutoModelForSeq2SeqLM.from_pretrained(self.model_path)
- self.trans_translator = transformers.pipeline('translation', model=self.trans_model, device=self.device, tokenizer=self.trans_tokenizer, src_lang=self.nllb_whisper_lang.code, tgt_lang=self.nllb_lang.code)
-
- def release_vram(self):
- try:
- if torch.cuda.is_available():
- if "ct2" not in self.model_path:
- device = torch.device("cpu")
- self.trans_model.to(device)
- del self.trans_model
- torch.cuda.empty_cache()
- print("release vram end.")
- except Exception as e:
- print("Error release vram: " + str(e))
-
-
- def translation(self, text: str, max_length: int = 400):
- output = None
- result = None
- try:
- if "ct2" in self.model_path:
- source = self.trans_tokenizer.convert_ids_to_tokens(self.trans_tokenizer.encode(text))
- output = self.trans_model.translate_batch([source], target_prefix=[self.target_prefix])
- target = output[0].hypotheses[0][1:]
- result = self.trans_tokenizer.decode(self.trans_tokenizer.convert_tokens_to_ids(target))
- elif "mt5" in self.model_path:
- output = self.trans_translator(self.mt5_prefix + text, max_length=max_length, num_beams=4)
- result = output[0]['generated_text']
- else: #NLLB
- output = self.trans_translator(text, max_length=max_length)
- result = output[0]['translation_text']
- except Exception as e:
- print("Error translation text: " + str(e))
-
- return result
-
-
-_MODELS = ["distilled-600M", "distilled-1.3B", "1.3B", "3.3B",
- "ct2fast-nllb-200-distilled-1.3B-int8_float16",
- "ct2fast-nllb-200-3.3B-int8_float16",
- "nllb-200-3.3B-ct2-float16", "nllb-200-1.3B-ct2", "nllb-200-1.3B-ct2-int8", "nllb-200-1.3B-ct2-float16",
- "nllb-200-distilled-1.3B-ct2", "nllb-200-distilled-1.3B-ct2-int8", "nllb-200-distilled-1.3B-ct2-float16",
- "nllb-200-distilled-600M-ct2", "nllb-200-distilled-600M-ct2-int8", "nllb-200-distilled-600M-ct2-float16",
- "mt5-zh-ja-en-trimmed",
- "mt5-zh-ja-en-trimmed-fine-tuned-v1"]
-
-def check_model_name(name):
- return any(allowed_name in name for allowed_name in _MODELS)
-
-def download_model(
- model_config: ModelConfig,
- output_dir: Optional[str] = None,
- local_files_only: bool = False,
- cache_dir: Optional[str] = None,
-):
- """"download_model" is referenced from the "utils.py" script
- of the "faster_whisper" project, authored by guillaumekln.
-
- Downloads a nllb-200 model from the Hugging Face Hub.
-
- The model is downloaded from https://huggingface.co/facebook.
-
- Args:
- model_config: config of the model to download (facebook/nllb-distilled-600M,
- facebook/nllb-distilled-1.3B, facebook/nllb-1.3B, facebook/nllb-3.3B...).
- output_dir: Directory where the model should be saved. If not set, the model is saved in
- the cache directory.
- local_files_only: If True, avoid downloading the file and return the path to the local
- cached file if it exists.
- cache_dir: Path to the folder where cached files are stored.
-
- Returns:
- The path to the downloaded model.
-
- Raises:
- ValueError: if the model size is invalid.
- """
- if not check_model_name(model_config.name):
- raise ValueError(
- "Invalid model name '%s', expected one of: %s" % (model_config.name, ", ".join(_MODELS))
- )
-
- repo_id = model_config.url #"facebook/nllb-200-%s" %
-
- allow_patterns = [
- "config.json",
- "generation_config.json",
- "model.bin",
- "pytorch_model.bin",
- "pytorch_model.bin.index.json",
- "pytorch_model-00001-of-00003.bin",
- "pytorch_model-00002-of-00003.bin",
- "pytorch_model-00003-of-00003.bin",
- "sentencepiece.bpe.model",
- "tokenizer.json",
- "tokenizer_config.json",
- "shared_vocabulary.txt",
- "shared_vocabulary.json",
- "special_tokens_map.json",
- "spiece.model",
- ]
-
- kwargs = {
- "local_files_only": local_files_only,
- "allow_patterns": allow_patterns,
- #"tqdm_class": disabled_tqdm,
- }
-
- if output_dir is not None:
- kwargs["local_dir"] = output_dir
- kwargs["local_dir_use_symlinks"] = False
-
- if cache_dir is not None:
- kwargs["cache_dir"] = cache_dir
-
- try:
- return huggingface_hub.snapshot_download(repo_id, **kwargs)
- except (
- huggingface_hub.utils.HfHubHTTPError,
- requests.exceptions.ConnectionError,
- ) as exception:
- warnings.warn(
- "An error occured while synchronizing the model %s from the Hugging Face Hub:\n%s",
- repo_id,
- exception,
- )
- warnings.warn(
- "Trying to load the model directly from the local cache, if it exists."
- )
-
- kwargs["local_files_only"] = True
- return huggingface_hub.snapshot_download(repo_id, **kwargs)
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/models/diffusion/plms.py b/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/models/diffusion/plms.py
deleted file mode 100644
index 1f7297a1e71e5dffb3008b0ff1cca57569777ada..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ldmlib/models/diffusion/plms.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldmlib.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-
-
-class PLMSSampler(object):
- def __init__(self, model, schedule="linear", **kwargs):
- super().__init__()
- self.model = model
- self.ddpm_num_timesteps = model.num_timesteps
- self.schedule = schedule
-
- def register_buffer(self, name, attr):
- if type(attr) == torch.Tensor:
- if attr.device != torch.device("cuda"):
- attr = attr.to(torch.device("cuda"))
- setattr(self, name, attr)
-
- def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
- if ddim_eta != 0:
- raise ValueError('ddim_eta must be 0 for PLMS')
- self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
- num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
- alphas_cumprod = self.model.alphas_cumprod
- assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
- to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
- self.register_buffer('betas', to_torch(self.model.betas))
- self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
- # calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
- # ddim sampling parameters
- ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
- ddim_timesteps=self.ddim_timesteps,
- eta=ddim_eta,verbose=verbose)
- self.register_buffer('ddim_sigmas', ddim_sigmas)
- self.register_buffer('ddim_alphas', ddim_alphas)
- self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
- self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
- sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
- (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
- 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
- self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
- @torch.no_grad()
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
- if conditioning is not None:
- if isinstance(conditioning, dict):
- cbs = conditioning[list(conditioning.keys())[0]].shape[0]
- if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
- else:
- if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
- self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
- # sampling
- C, H, W = shape
- size = (batch_size, C, H, W)
- print(f'Data shape for PLMS sampling is {size}')
-
- samples, intermediates = self.plms_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- )
- return samples, intermediates
-
- @torch.no_grad()
- def plms_sampling(self, cond, shape,
- x_T=None, ddim_use_original_steps=False,
- callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, log_every_t=100,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None,):
- device = self.model.betas.device
- b = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=device)
- else:
- img = x_T
-
- if timesteps is None:
- timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
- elif timesteps is not None and not ddim_use_original_steps:
- subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
- timesteps = self.ddim_timesteps[:subset_end]
-
- intermediates = {'x_inter': [img], 'pred_x0': [img]}
- time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
- total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
- print(f"Running PLMS Sampling with {total_steps} timesteps")
-
- iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
- old_eps = []
-
- for i, step in enumerate(iterator):
- index = total_steps - i - 1
- ts = torch.full((b,), step, device=device, dtype=torch.long)
- ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
- if mask is not None:
- assert x0 is not None
- img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
- img = img_orig * mask + (1. - mask) * img
-
- outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
- quantize_denoised=quantize_denoised, temperature=temperature,
- noise_dropout=noise_dropout, score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- old_eps=old_eps, t_next=ts_next)
- img, pred_x0, e_t = outs
- old_eps.append(e_t)
- if len(old_eps) >= 4:
- old_eps.pop(0)
- if callback: callback(i)
- if img_callback: img_callback(pred_x0, i)
-
- if index % log_every_t == 0 or index == total_steps - 1:
- intermediates['x_inter'].append(img)
- intermediates['pred_x0'].append(pred_x0)
-
- return img, intermediates
-
- @torch.no_grad()
- def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
- b, *_, device = *x.shape, x.device
-
- def get_model_output(x, t):
- if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
- e_t = self.model.apply_model(x, t, c)
- else:
- x_in = torch.cat([x] * 2)
- t_in = torch.cat([t] * 2)
- c_in = torch.cat([unconditional_conditioning, c])
- e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
- e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
- if score_corrector is not None:
- assert self.model.parameterization == "eps"
- e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
- return e_t
-
- alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
- alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
- sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
- sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
- def get_x_prev_and_pred_x0(e_t, index):
- # select parameters corresponding to the currently considered timestep
- a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
- a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
- sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
- sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
- # current prediction for x_0
- pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
- if quantize_denoised:
- pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
- # direction pointing to x_t
- dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
- noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
- noise = torch.nn.functional.dropout(noise, p=noise_dropout)
- x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
- return x_prev, pred_x0
-
- e_t = get_model_output(x, t)
- if len(old_eps) == 0:
- # Pseudo Improved Euler (2nd order)
- x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
- e_t_next = get_model_output(x_prev, t_next)
- e_t_prime = (e_t + e_t_next) / 2
- elif len(old_eps) == 1:
- # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (3 * e_t - old_eps[-1]) / 2
- elif len(old_eps) == 2:
- # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
- elif len(old_eps) >= 3:
- # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
- x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
- return x_prev, pred_x0, e_t
diff --git a/spaces/awacke1/AR-VR-IOT-Demo/style.css b/spaces/awacke1/AR-VR-IOT-Demo/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AR-VR-IOT-Demo/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/awacke1/AW-02-H5-AR-VR-IOT/README.md b/spaces/awacke1/AW-02-H5-AR-VR-IOT/README.md
deleted file mode 100644
index 875ace6554ab70712d0d05f8fd86bf244afd30e9..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AW-02-H5-AR-VR-IOT/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AW 02 H5 AR VR IOT
-emoji: 🦀
-colorFrom: blue
-colorTo: blue
-sdk: static
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Google-Maps-Web-Service-Py/README.md b/spaces/awacke1/Google-Maps-Web-Service-Py/README.md
deleted file mode 100644
index 8efde6960bdc66021d6b0924271f2a404aaf331a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Google-Maps-Web-Service-Py/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
----
-title: GMaps-Geocode-Gradio-Lat-Lon
-emoji: 🌍Map
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Naming Scheme:
-3D-GLB-Aframe-GoogleAPI-Map programs on Gradio could be named as "Gradio3DMap" or "GradioMap3D".
-
-Ontology:
-Gradio3DMap or GradioMap3D can be categorized as a type of mapping software that utilizes three-dimensional GLB models to display geographical data on a map. These programs are built using A-Frame, a web framework for building virtual reality experiences, and integrated with GoogleAPI to provide location-based services.
-
-The ontology for Gradio3DMap or GradioMap3D can be further broken down into subcategories, such as:
-
-Data Visualization: These programs can be used to visualize and analyze complex geospatial data in a three-dimensional format, making it easier for users to understand and interpret the information.
-
-Real-time Data Updates: Gradio3DMap or GradioMap3D can be used to display real-time updates for various data, such as weather, traffic, and other live events.
-
-Interactive User Interface: The software can be built with interactive user interfaces that allow users to interact with the map and its elements, such as panning, zooming, and rotating the 3D model.
-
-Cross-Platform Compatibility: Gradio3DMap or GradioMap3D can be built to run on multiple platforms, such as desktops, smartphones, and tablets, making it accessible to a wide range of users.
-
-Customization: The software can be customized to fit specific user requirements and business needs. This includes adding custom markers, colors, and other visual elements to the map.
-
-Integration with other APIs: Gradio3DMap or GradioMap3D can be integrated with other APIs, such as social media APIs, to provide additional functionality, such as real-time feeds of social media data.
-
-Overall, Gradio3DMap or GradioMap3D is a powerful mapping software that provides an immersive and interactive way to visualize and analyze geospatial data.
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/animation/AnimationObjectGroup.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/animation/AnimationObjectGroup.d.ts
deleted file mode 100644
index d7c68ebe45ba472f681017b85aa5c67e324de30e..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/animation/AnimationObjectGroup.d.ts
+++ /dev/null
@@ -1,16 +0,0 @@
-export class AnimationObjectGroup {
- constructor(...args: any[]);
-
- uuid: string;
- stats: {
- bindingsPerObject: number;
- objects: {
- total: number;
- inUse: number;
- };
- };
-
- add(...args: any[]): void;
- remove(...args: any[]): void;
- uncache(...args: any[]): void;
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/animation/AnimationUtils.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/animation/AnimationUtils.d.ts
deleted file mode 100644
index 1aa12e7f9ad093889a5cc228c9bc5bcf6886a497..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/animation/AnimationUtils.d.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-export namespace AnimationUtils {
- export function arraySlice(array: any, from: number, to: number): any;
- export function convertArray(array: any, type: any, forceClone: boolean): any;
- export function isTypedArray(object: any): boolean;
- export function getKeyFrameOrder(times: number): number[];
- export function sortedArray(
- values: any[],
- stride: number,
- order: number[]
- ): any[];
- export function flattenJSON(
- jsonKeys: string[],
- times: any[],
- values: any[],
- valuePropertyName: string
- ): void;
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/cameras/StereoCamera.js b/spaces/banana-projects/web3d/node_modules/three/src/cameras/StereoCamera.js
deleted file mode 100644
index 14aebe628a3bd0b1792270e783d9d3f22e484162..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/cameras/StereoCamera.js
+++ /dev/null
@@ -1,98 +0,0 @@
-import { Matrix4 } from '../math/Matrix4.js';
-import { _Math } from '../math/Math.js';
-import { PerspectiveCamera } from './PerspectiveCamera.js';
-
-/**
- * @author mrdoob / http://mrdoob.com/
- */
-
-function StereoCamera() {
-
- this.type = 'StereoCamera';
-
- this.aspect = 1;
-
- this.eyeSep = 0.064;
-
- this.cameraL = new PerspectiveCamera();
- this.cameraL.layers.enable( 1 );
- this.cameraL.matrixAutoUpdate = false;
-
- this.cameraR = new PerspectiveCamera();
- this.cameraR.layers.enable( 2 );
- this.cameraR.matrixAutoUpdate = false;
-
-}
-
-Object.assign( StereoCamera.prototype, {
-
- update: ( function () {
-
- var instance, focus, fov, aspect, near, far, zoom, eyeSep;
-
- var eyeRight = new Matrix4();
- var eyeLeft = new Matrix4();
-
- return function update( camera ) {
-
- var needsUpdate = instance !== this || focus !== camera.focus || fov !== camera.fov ||
- aspect !== camera.aspect * this.aspect || near !== camera.near ||
- far !== camera.far || zoom !== camera.zoom || eyeSep !== this.eyeSep;
-
- if ( needsUpdate ) {
-
- instance = this;
- focus = camera.focus;
- fov = camera.fov;
- aspect = camera.aspect * this.aspect;
- near = camera.near;
- far = camera.far;
- zoom = camera.zoom;
-
- // Off-axis stereoscopic effect based on
- // http://paulbourke.net/stereographics/stereorender/
-
- var projectionMatrix = camera.projectionMatrix.clone();
- eyeSep = this.eyeSep / 2;
- var eyeSepOnProjection = eyeSep * near / focus;
- var ymax = ( near * Math.tan( _Math.DEG2RAD * fov * 0.5 ) ) / zoom;
- var xmin, xmax;
-
- // translate xOffset
-
- eyeLeft.elements[ 12 ] = - eyeSep;
- eyeRight.elements[ 12 ] = eyeSep;
-
- // for left eye
-
- xmin = - ymax * aspect + eyeSepOnProjection;
- xmax = ymax * aspect + eyeSepOnProjection;
-
- projectionMatrix.elements[ 0 ] = 2 * near / ( xmax - xmin );
- projectionMatrix.elements[ 8 ] = ( xmax + xmin ) / ( xmax - xmin );
-
- this.cameraL.projectionMatrix.copy( projectionMatrix );
-
- // for right eye
-
- xmin = - ymax * aspect - eyeSepOnProjection;
- xmax = ymax * aspect - eyeSepOnProjection;
-
- projectionMatrix.elements[ 0 ] = 2 * near / ( xmax - xmin );
- projectionMatrix.elements[ 8 ] = ( xmax + xmin ) / ( xmax - xmin );
-
- this.cameraR.projectionMatrix.copy( projectionMatrix );
-
- }
-
- this.cameraL.matrixWorld.copy( camera.matrixWorld ).multiply( eyeLeft );
- this.cameraR.matrixWorld.copy( camera.matrixWorld ).multiply( eyeRight );
-
- };
-
- } )()
-
-} );
-
-
-export { StereoCamera };
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327093352.py b/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327093352.py
deleted file mode 100644
index 370def80c8bd8735d2eba49646ed3386e190bb5b..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/.history/app_20220327093352.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import os
-#os.system("pip install gfpgan")
-
-#os.system("pip freeze")
-#os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Thomas_Edison2.jpg/1024px-Thomas_Edison2.jpg', 'edison.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/a9/Henry_Ford_1888.jpg/1024px-Henry_Ford_1888.jpg', 'Henry.jpg')
-# torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/0/06/Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg/800px-Frida_Kahlo%2C_by_Guillermo_Kahlo.jpg', 'Frida.jpg')
-
-
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
-
-bg_upsampler = None
-
-
-
-# set up GFPGAN restorer
-restorer = GFPGANer(
- model_path='experiments/pretrained_models/GFPGANv1.3.pth',
- upscale=2,
- arch='clean',
- channel_multiplier=2,
- bg_upsampler=bg_upsampler)
-
-
-def inference(img):
- input_img = cv2.imread(img, cv2.IMREAD_COLOR)
- cropped_faces, restored_faces, restored_img = restorer.enhance(
- input_img, has_aligned=False, only_center_face=False, paste_back=True)
-
- #return Image.fromarray(restored_faces[0][:,:,::-1])
- return Image.fromarray(restored_img[:, :, ::-1])
-
-title = "让美好回忆更清晰"
-
-
-description = "上传老照片，点击Submit，稍等片刻，右侧Output将照片另存为即可。"
-article = " | | Github Repo
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Build with -s ASSERTIONS=1 for more info.";var e=new WebAssembly.RuntimeError(what);throw e}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return filename.startsWith(dataURIPrefix)}function isFileURI(filename){return filename.startsWith("file://")}var wasmBinaryFile="build.wasm";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw"both async and sync fetching of the wasm failed"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch==="function"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response["ok"]){throw"failed to load wasm binary file at '"+wasmBinaryFile+"'"}return response["arrayBuffer"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={"a":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module["asm"]=exports;wasmMemory=Module["asm"]["Xh"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module["asm"]["ui"];addOnInit(Module["asm"]["Yh"]);removeRunDependency("wasm-instantiate")}addRunDependency("wasm-instantiate");function receiveInstantiationResult(result){receiveInstance(result["instance"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){var result=WebAssembly.instantiate(binary,info);return result}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming==="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch==="function"){return fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiationResult,function(reason){err("wasm streaming compile failed: "+reason);err("falling back to ArrayBuffer instantiation");return instantiateArrayBuffer(receiveInstantiationResult)})})}else{return instantiateArrayBuffer(receiveInstantiationResult)}}if(Module["instantiateWasm"]){try{var exports=Module["instantiateWasm"](info,receiveInstance);return exports}catch(e){err("Module.instantiateWasm callback failed with error: "+e);return false}}instantiateAsync();return{}}var tempDouble;var tempI64;var ASM_CONSTS={3428072:function(){return Module.webglContextAttributes.premultipliedAlpha},3428133:function(){return Module.webglContextAttributes.preserveDrawingBuffer},3428197:function(){return Module.webglContextAttributes.powerPreference}};function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback=="function"){callback(Module);continue}var func=callback.func;if(typeof func==="number"){if(callback.arg===undefined){(function(){dynCall_v.call(null,func)})()}else{(function(a1){dynCall_vi.apply(null,[func,a1])})(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function demangle(func){return func}function demangleAll(text){var regex=/\b_Z[\w\d_]+/g;return text.replace(regex,function(x){var y=demangle(x);return x===y?x:y+" ["+x+"]"})}function dynCallLegacy(sig,ptr,args){var f=Module["dynCall_"+sig];return args&&args.length?f.apply(null,[ptr].concat(args)):f.call(null,ptr)}function dynCall(sig,ptr,args){return dynCallLegacy(sig,ptr,args)}function jsStackTrace(){var error=new Error;if(!error.stack){try{throw new Error}catch(e){error=e}if(!error.stack){return"(no stack trace available)"}}return error.stack.toString()}var runtimeKeepaliveCounter=0;function keepRuntimeAlive(){return noExitRuntime||runtimeKeepaliveCounter>0}function stackTrace(){var js=jsStackTrace();if(Module["extraStackTrace"])js+="\n"+Module["extraStackTrace"]();return demangleAll(js)}var JS_Accelerometer=null;var JS_Accelerometer_callback=0;function _JS_Accelerometer_IsRunning(){return JS_Accelerometer&&JS_Accelerometer.activated||JS_Accelerometer_callback!=0}var JS_Accelerometer_multiplier=1;var JS_Accelerometer_lastValue={x:0,y:0,z:0};function JS_Accelerometer_eventHandler(){JS_Accelerometer_lastValue={x:JS_Accelerometer.x*JS_Accelerometer_multiplier,y:JS_Accelerometer.y*JS_Accelerometer_multiplier,z:JS_Accelerometer.z*JS_Accelerometer_multiplier};if(JS_Accelerometer_callback!=0)dynCall_vfff(JS_Accelerometer_callback,JS_Accelerometer_lastValue.x,JS_Accelerometer_lastValue.y,JS_Accelerometer_lastValue.z)}var JS_Accelerometer_frequencyRequest=0;var JS_Accelerometer_frequency=0;var JS_LinearAccelerationSensor_callback=0;var JS_GravitySensor_callback=0;var JS_Gyroscope_callback=0;function JS_ComputeGravity(accelerometerValue,linearAccelerationValue){var difference={x:accelerometerValue.x-linearAccelerationValue.x,y:accelerometerValue.y-linearAccelerationValue.y,z:accelerometerValue.z-linearAccelerationValue.z};var differenceMagnitudeSq=difference.x*difference.x+difference.y*difference.y+difference.z*difference.z;var sum={x:accelerometerValue.x+linearAccelerationValue.x,y:accelerometerValue.y+linearAccelerationValue.y,z:accelerometerValue.z+linearAccelerationValue.z};var sumMagnitudeSq=sum.x*sum.x+sum.y*sum.y+sum.z*sum.z;return differenceMagnitudeSq<=sumMagnitudeSq?difference:sum}function JS_DeviceMotion_eventHandler(event){var accelerometerValue={x:event.accelerationIncludingGravity.x*JS_Accelerometer_multiplier,y:event.accelerationIncludingGravity.y*JS_Accelerometer_multiplier,z:event.accelerationIncludingGravity.z*JS_Accelerometer_multiplier};if(JS_Accelerometer_callback!=0)dynCall_vfff(JS_Accelerometer_callback,accelerometerValue.x,accelerometerValue.y,accelerometerValue.z);var linearAccelerationValue={x:event.acceleration.x*JS_Accelerometer_multiplier,y:event.acceleration.y*JS_Accelerometer_multiplier,z:event.acceleration.z*JS_Accelerometer_multiplier};if(JS_LinearAccelerationSensor_callback!=0)dynCall_vfff(JS_LinearAccelerationSensor_callback,linearAccelerationValue.x,linearAccelerationValue.y,linearAccelerationValue.z);if(JS_GravitySensor_callback!=0){var gravityValue=JS_ComputeGravity(accelerometerValue,linearAccelerationValue);dynCall_vfff(JS_GravitySensor_callback,gravityValue.x,gravityValue.y,gravityValue.z)}if(JS_Gyroscope_callback!=0){var degToRad=Math.PI/180;dynCall_vfff(JS_Gyroscope_callback,event.rotationRate.alpha*degToRad,event.rotationRate.beta*degToRad,event.rotationRate.gamma*degToRad)}}var JS_DeviceSensorPermissions=0;function JS_RequestDeviceSensorPermissions(permissions){if(permissions&1){if(typeof DeviceOrientationEvent.requestPermission==="function"){DeviceOrientationEvent.requestPermission().then(function(permissionState){if(permissionState==="granted"){JS_DeviceSensorPermissions&=~1}else{warnOnce("DeviceOrientationEvent permission not granted")}}).catch(function(err){warnOnce(err);JS_DeviceSensorPermissions|=1})}}if(permissions&2){if(typeof DeviceMotionEvent.requestPermission==="function"){DeviceMotionEvent.requestPermission().then(function(permissionState){if(permissionState==="granted"){JS_DeviceSensorPermissions&=~2}else{warnOnce("DeviceMotionEvent permission not granted")}}).catch(function(err){warnOnce(err);JS_DeviceSensorPermissions|=2})}}}function JS_DeviceMotion_add(){if(JS_Accelerometer_callback==0&&JS_LinearAccelerationSensor_callback==0&&JS_GravitySensor_callback==0&&JS_Gyroscope_callback==0){JS_RequestDeviceSensorPermissions(2);window.addEventListener("devicemotion",JS_DeviceMotion_eventHandler)}}function JS_DefineAccelerometerMultiplier(){var g=9.80665;JS_Accelerometer_multiplier=/(iPhone|iPad|Macintosh)/i.test(navigator.userAgent)?1/g:-1/g}function _JS_Accelerometer_Start(callback,frequency){JS_DefineAccelerometerMultiplier();if(typeof Accelerometer==="undefined"){JS_DeviceMotion_add();if(callback!=0)JS_Accelerometer_callback=callback;return}if(callback!=0)JS_Accelerometer_callback=callback;function InitializeAccelerometer(frequency){JS_Accelerometer=new Accelerometer({frequency:frequency,referenceFrame:"device"});JS_Accelerometer.addEventListener("reading",JS_Accelerometer_eventHandler);JS_Accelerometer.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_Accelerometer.start();JS_Accelerometer_frequency=frequency}if(JS_Accelerometer){if(JS_Accelerometer_frequency!=frequency){JS_Accelerometer.stop();JS_Accelerometer.removeEventListener("reading",JS_Accelerometer_eventHandler);InitializeAccelerometer(frequency)}}else if(JS_Accelerometer_frequencyRequest!=0){JS_Accelerometer_frequencyRequest=frequency}else{JS_Accelerometer_frequencyRequest=frequency;navigator.permissions.query({name:"accelerometer"}).then(function(result){if(result.state==="granted"){InitializeAccelerometer(JS_Accelerometer_frequencyRequest)}else{warnOnce("No permission to use Accelerometer.")}JS_Accelerometer_frequencyRequest=0})}}function JS_DeviceMotion_remove(){if(JS_Accelerometer_callback==0&&JS_LinearAccelerationSensor_callback==0&&JS_GravitySensor_callback==0&&JS_Gyroscope_callback==0){window.removeEventListener("devicemotion",JS_DeviceOrientation_eventHandler)}}function _JS_Accelerometer_Stop(){if(JS_Accelerometer){if(typeof GravitySensor!=="undefined"||JS_GravitySensor_callback==0){JS_Accelerometer.stop();JS_Accelerometer.removeEventListener("reading",JS_Accelerometer_eventHandler);JS_Accelerometer=null}JS_Accelerometer_callback=0;JS_Accelerometer_frequency=0}else if(JS_Accelerometer_callback!=0){JS_Accelerometer_callback=0;JS_DeviceMotion_remove()}}function _JS_Cursor_SetImage(ptr,length){var binary="";for(var i=0;i>2]=viewportX-rect.left;HEAPU32[targetY>>2]=viewportY-rect.top}function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _JS_DOM_UnityCanvasSelector(){if(!_JS_DOM_UnityCanvasSelector.ptr){var canvasId=Module["canvas"]?Module["canvas"].id:"unity-canvas";var canvasSelector="#"+jsDomCssEscapeId(canvasId);_JS_DOM_UnityCanvasSelector.ptr=stringToNewUTF8(canvasSelector)}return _JS_DOM_UnityCanvasSelector.ptr}var fs={numPendingSync:0,syncInternal:1e3,syncInProgress:false,sync:function(onlyPendingSync){if(onlyPendingSync){if(fs.numPendingSync==0)return}else if(fs.syncInProgress){fs.numPendingSync++;return}fs.syncInProgress=true;FS.syncfs(false,function(err){fs.syncInProgress=false});fs.numPendingSync=0}};function _JS_FileSystem_Initialize(){Module.setInterval(function(){fs.sync(true)},fs.syncInternal)}function _JS_FileSystem_Sync(){fs.sync(false)}function _JS_Focus_Window(){var activeElem=document.activeElement;var canvasId=Module["canvas"]?Module["canvas"].id:"unity-canvas";var canvasSelector="#"+jsDomCssEscapeId(canvasId);var canvas=document.querySelector(canvasSelector);if(activeElem!=canvas&&activeElem!=window&&activeElem!=document.body){window.focus()}}var JS_GravitySensor=null;function _JS_GravitySensor_IsRunning(){return typeof GravitySensor!=="undefined"?JS_GravitySensor&&JS_GravitySensor.activated:JS_GravitySensor_callback!=0}function JS_GravitySensor_eventHandler(){if(JS_GravitySensor_callback!=0)dynCall_vfff(JS_GravitySensor_callback,JS_GravitySensor.x*JS_Accelerometer_multiplier,JS_GravitySensor.y*JS_Accelerometer_multiplier,JS_GravitySensor.z*JS_Accelerometer_multiplier)}var JS_GravitySensor_frequencyRequest=0;var JS_LinearAccelerationSensor=null;function JS_LinearAccelerationSensor_eventHandler(){var linearAccelerationValue={x:JS_LinearAccelerationSensor.x*JS_Accelerometer_multiplier,y:JS_LinearAccelerationSensor.y*JS_Accelerometer_multiplier,z:JS_LinearAccelerationSensor.z*JS_Accelerometer_multiplier};if(JS_LinearAccelerationSensor_callback!=0)dynCall_vfff(JS_LinearAccelerationSensor_callback,linearAccelerationValue.x,linearAccelerationValue.y,linearAccelerationValue.z);if(JS_GravitySensor_callback!=0&&typeof GravitySensor==="undefined"){var gravityValue=JS_ComputeGravity(JS_Accelerometer_lastValue,linearAccelerationValue);dynCall_vfff(JS_GravitySensor_callback,gravityValue.x,gravityValue.y,gravityValue.z)}}var JS_LinearAccelerationSensor_frequencyRequest=0;var JS_LinearAccelerationSensor_frequency=0;function _JS_LinearAccelerationSensor_Start(callback,frequency){JS_DefineAccelerometerMultiplier();if(typeof LinearAccelerationSensor==="undefined"){JS_DeviceMotion_add();if(callback!=0)JS_LinearAccelerationSensor_callback=callback;return}if(callback!=0)JS_LinearAccelerationSensor_callback=callback;function InitializeLinearAccelerationSensor(frequency){JS_LinearAccelerationSensor=new LinearAccelerationSensor({frequency:frequency,referenceFrame:"device"});JS_LinearAccelerationSensor.addEventListener("reading",JS_LinearAccelerationSensor_eventHandler);JS_LinearAccelerationSensor.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_LinearAccelerationSensor.start();JS_LinearAccelerationSensor_frequency=frequency}if(JS_LinearAccelerationSensor){if(JS_LinearAccelerationSensor_frequency!=frequency){JS_LinearAccelerationSensor.stop();JS_LinearAccelerationSensor.removeEventListener("reading",JS_LinearAccelerationSensor_eventHandler);InitializeLinearAccelerationSensor(frequency)}}else if(JS_LinearAccelerationSensor_frequencyRequest!=0){JS_LinearAccelerationSensor_frequencyRequest=frequency}else{JS_LinearAccelerationSensor_frequencyRequest=frequency;navigator.permissions.query({name:"accelerometer"}).then(function(result){if(result.state==="granted"){InitializeLinearAccelerationSensor(JS_LinearAccelerationSensor_frequencyRequest)}else{warnOnce("No permission to use LinearAccelerationSensor.")}JS_LinearAccelerationSensor_frequencyRequest=0})}}function _JS_GravitySensor_Start(callback,frequency){if(typeof GravitySensor==="undefined"){_JS_Accelerometer_Start(0,Math.max(frequency,JS_Accelerometer_frequency));_JS_LinearAccelerationSensor_Start(0,Math.max(frequency,JS_LinearAccelerationSensor_frequency));JS_GravitySensor_callback=callback;return}JS_DefineAccelerometerMultiplier();JS_GravitySensor_callback=callback;function InitializeGravitySensor(frequency){JS_GravitySensor=new GravitySensor({frequency:frequency,referenceFrame:"device"});JS_GravitySensor.addEventListener("reading",JS_GravitySensor_eventHandler);JS_GravitySensor.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_GravitySensor.start()}if(JS_GravitySensor){JS_GravitySensor.stop();JS_GravitySensor.removeEventListener("reading",JS_GravitySensor_eventHandler);InitializeGravitySensor(frequency)}else if(JS_GravitySensor_frequencyRequest!=0){JS_GravitySensor_frequencyRequest=frequency}else{JS_GravitySensor_frequencyRequest=frequency;navigator.permissions.query({name:"accelerometer"}).then(function(result){if(result.state==="granted"){InitializeGravitySensor(JS_GravitySensor_frequencyRequest)}else{warnOnce("No permission to use GravitySensor.")}JS_GravitySensor_frequencyRequest=0})}}function _JS_LinearAccelerationSensor_Stop(){if(JS_LinearAccelerationSensor){if(typeof GravitySensor!=="undefined"||JS_GravitySensor_callback==0){JS_LinearAccelerationSensor.stop();JS_LinearAccelerationSensor.removeEventListener("reading",JS_LinearAccelerationSensor_eventHandler);JS_LinearAccelerationSensor=null}JS_LinearAccelerationSensor_callback=0;JS_LinearAccelerationSensor_frequency=0}else if(JS_LinearAccelerationSensor_callback!=0){JS_LinearAccelerationSensor_callback=0;JS_DeviceMotion_remove()}}function _JS_GravitySensor_Stop(){JS_GravitySensor_callback=0;if(typeof GravitySensor==="undefined"){if(JS_Accelerometer_callback==0)_JS_Accelerometer_Stop();if(JS_LinearAccelerationSensor_callback==0)_JS_LinearAccelerationSensor_Stop();return}if(JS_GravitySensor){JS_GravitySensor.stop();JS_GravitySensor.removeEventListener("reading",JS_GravitySensor_eventHandler);JS_GravitySensor=null}}var JS_Gyroscope=null;function _JS_Gyroscope_IsRunning(){return JS_Gyroscope&&JS_Gyroscope.activated||JS_Gyroscope_callback!=0}function JS_Gyroscope_eventHandler(){if(JS_Gyroscope_callback!=0)dynCall_vfff(JS_Gyroscope_callback,JS_Gyroscope.x,JS_Gyroscope.y,JS_Gyroscope.z)}var JS_Gyroscope_frequencyRequest=0;function _JS_Gyroscope_Start(callback,frequency){if(typeof Gyroscope==="undefined"){JS_DeviceMotion_add();JS_Gyroscope_callback=callback;return}JS_Gyroscope_callback=callback;function InitializeGyroscope(frequency){JS_Gyroscope=new Gyroscope({frequency:frequency,referenceFrame:"device"});JS_Gyroscope.addEventListener("reading",JS_Gyroscope_eventHandler);JS_Gyroscope.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_Gyroscope.start()}if(JS_Gyroscope){JS_Gyroscope.stop();JS_Gyroscope.removeEventListener("reading",JS_Gyroscope_eventHandler);InitializeGyroscope(frequency)}else if(JS_Gyroscope_frequencyRequest!=0){JS_Gyroscope_frequencyRequest=frequency}else{JS_Gyroscope_frequencyRequest=frequency;navigator.permissions.query({name:"gyroscope"}).then(function(result){if(result.state==="granted"){InitializeGyroscope(JS_Gyroscope_frequencyRequest)}else{warnOnce("No permission to use Gyroscope.")}JS_Gyroscope_frequencyRequest=0})}}function _JS_Gyroscope_Stop(){if(JS_Gyroscope){JS_Gyroscope.stop();JS_Gyroscope.removeEventListener("reading",JS_Gyroscope_eventHandler);JS_Gyroscope=null;JS_Gyroscope_callback=0}else if(JS_Gyroscope_callback!=0){JS_Gyroscope_callback=0;JS_DeviceMotion_remove()}}function _JS_LinearAccelerationSensor_IsRunning(){return JS_LinearAccelerationSensor&&JS_LinearAccelerationSensor.activated||JS_LinearAccelerationSensor_callback!=0}function _JS_Log_Dump(ptr,type){var str=UTF8ToString(ptr);if(typeof dump=="function")dump(str);switch(type){case 0:case 1:case 4:console.error(str);return;case 2:console.warn(str);return;case 3:case 5:console.log(str);return;default:console.error("Unknown console message type!");console.error(str)}}function _JS_Log_StackTrace(buffer,bufferSize){var trace=stackTrace();if(buffer)stringToUTF8(trace,buffer,bufferSize);return lengthBytesUTF8(trace)}var JS_OrientationSensor=null;var JS_OrientationSensor_callback=0;function _JS_OrientationSensor_IsRunning(){return JS_OrientationSensor&&JS_OrientationSensor.activated||JS_OrientationSensor_callback!=0}function JS_OrientationSensor_eventHandler(){if(JS_OrientationSensor_callback!=0)dynCall_vffff(JS_OrientationSensor_callback,JS_OrientationSensor.quaternion[0],JS_OrientationSensor.quaternion[1],JS_OrientationSensor.quaternion[2],JS_OrientationSensor.quaternion[3])}var JS_OrientationSensor_frequencyRequest=0;function JS_DeviceOrientation_eventHandler(event){if(JS_OrientationSensor_callback){var degToRad=Math.PI/180;var x=event.beta*degToRad;var y=event.gamma*degToRad;var z=event.alpha*degToRad;var cx=Math.cos(x/2);var sx=Math.sin(x/2);var cy=Math.cos(y/2);var sy=Math.sin(y/2);var cz=Math.cos(z/2);var sz=Math.sin(z/2);var qx=sx*cy*cz-cx*sy*sz;var qy=cx*sy*cz+sx*cy*sz;var qz=cx*cy*sz+sx*sy*cz;var qw=cx*cy*cz-sx*sy*sz;dynCall_vffff(JS_OrientationSensor_callback,qx,qy,qz,qw)}}function _JS_OrientationSensor_Start(callback,frequency){if(typeof RelativeOrientationSensor==="undefined"){if(JS_OrientationSensor_callback==0){JS_OrientationSensor_callback=callback;JS_RequestDeviceSensorPermissions(1);window.addEventListener("deviceorientation",JS_DeviceOrientation_eventHandler)}return}JS_OrientationSensor_callback=callback;function InitializeOrientationSensor(frequency){JS_OrientationSensor=new RelativeOrientationSensor({frequency:frequency,referenceFrame:"device"});JS_OrientationSensor.addEventListener("reading",JS_OrientationSensor_eventHandler);JS_OrientationSensor.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_OrientationSensor.start()}if(JS_OrientationSensor){JS_OrientationSensor.stop();JS_OrientationSensor.removeEventListener("reading",JS_OrientationSensor_eventHandler);InitializeOrientationSensor(frequency)}else if(JS_OrientationSensor_frequencyRequest!=0){JS_OrientationSensor_frequencyRequest=frequency}else{JS_OrientationSensor_frequencyRequest=frequency;Promise.all([navigator.permissions.query({name:"accelerometer"}),navigator.permissions.query({name:"gyroscope"})]).then(function(results){if(results.every(function(result){return result.state==="granted"})){InitializeOrientationSensor(JS_OrientationSensor_frequencyRequest)}else{warnOnce("No permissions to use RelativeOrientationSensor.")}JS_OrientationSensor_frequencyRequest=0})}}function _JS_OrientationSensor_Stop(){if(JS_OrientationSensor){JS_OrientationSensor.stop();JS_OrientationSensor.removeEventListener("reading",JS_OrientationSensor_eventHandler);JS_OrientationSensor=null}else if(JS_OrientationSensor_callback!=0){window.removeEventListener("deviceorientation",JS_DeviceOrientation_eventHandler)}JS_OrientationSensor_callback=0}function _JS_RequestDeviceSensorPermissionsOnTouch(){if(JS_DeviceSensorPermissions==0)return;JS_RequestDeviceSensorPermissions(JS_DeviceSensorPermissions)}function _JS_RunQuitCallbacks(){Module.QuitCleanup()}var JS_ScreenOrientation_callback=0;function JS_ScreenOrientation_eventHandler(){if(JS_ScreenOrientation_callback)dynCall_viii(JS_ScreenOrientation_callback,window.innerWidth,window.innerHeight,screen.orientation?screen.orientation.angle:window.orientation)}function _JS_ScreenOrientation_DeInit(){JS_ScreenOrientation_callback=0;window.removeEventListener("resize",JS_ScreenOrientation_eventHandler);if(screen.orientation){screen.orientation.removeEventListener("change",JS_ScreenOrientation_eventHandler)}}function _JS_ScreenOrientation_Init(callback){if(!JS_ScreenOrientation_callback){if(screen.orientation){screen.orientation.addEventListener("change",JS_ScreenOrientation_eventHandler)}window.addEventListener("resize",JS_ScreenOrientation_eventHandler);JS_ScreenOrientation_callback=callback;setTimeout(JS_ScreenOrientation_eventHandler,0)}}var JS_ScreenOrientation_requestedLockType=-1;var JS_ScreenOrientation_appliedLockType=-1;var JS_ScreenOrientation_timeoutID=-1;function _JS_ScreenOrientation_Lock(orientationLockType){if(!screen.orientation){return}function applyLock(){JS_ScreenOrientation_appliedLockType=JS_ScreenOrientation_requestedLockType;var screenOrientations=["any",0,"landscape","portrait","portrait-primary","portrait-secondary","landscape-primary","landscape-secondary"];var type=screenOrientations[JS_ScreenOrientation_appliedLockType];screen.orientation.lock(type).then(function(){if(JS_ScreenOrientation_requestedLockType!=JS_ScreenOrientation_appliedLockType){JS_ScreenOrientation_timeoutID=setTimeout(applyLock,0)}else{JS_ScreenOrientation_timeoutID=-1}}).catch(function(err){warnOnce(err);JS_ScreenOrientation_timeoutID=-1})}JS_ScreenOrientation_requestedLockType=orientationLockType;if(JS_ScreenOrientation_timeoutID==-1&&orientationLockType!=JS_ScreenOrientation_appliedLockType){JS_ScreenOrientation_timeoutID=setTimeout(applyLock,0)}}var WEBAudio={audioInstanceIdCounter:0,audioInstances:{},audioContext:null,audioWebEnabled:0,audioCache:[],pendingAudioSources:{}};function _JS_Sound_ResumeIfNeeded(){if(WEBAudio.audioWebEnabled==0)return;if(WEBAudio.audioContext.state==="suspended")WEBAudio.audioContext.resume()}function _JS_SystemInfo_GetCanvasClientSize(domElementSelector,outWidth,outHeight){var selector=UTF8ToString(domElementSelector);var canvas=selector=="#canvas"?Module["canvas"]:document.querySelector(selector);var w=0,h=0;if(canvas){var size=canvas.getBoundingClientRect();w=size.width;h=size.height}HEAPF64[outWidth>>3]=w;HEAPF64[outHeight>>3]=h}function _JS_SystemInfo_GetDocumentURL(buffer,bufferSize){if(buffer)stringToUTF8(document.URL,buffer,bufferSize);return lengthBytesUTF8(document.URL)}function _JS_SystemInfo_GetGPUInfo(buffer,bufferSize){var gpuinfo=Module.SystemInfo.gpu;if(buffer)stringToUTF8(gpuinfo,buffer,bufferSize);return lengthBytesUTF8(gpuinfo)}function _JS_SystemInfo_GetMatchWebGLToCanvasSize(){return Module.matchWebGLToCanvasSize||Module.matchWebGLToCanvasSize===undefined}function _JS_SystemInfo_GetMemory(){return HEAPU8.length/(1024*1024)}function _JS_SystemInfo_GetOS(buffer,bufferSize){var browser=Module.SystemInfo.os+" "+Module.SystemInfo.osVersion;if(buffer)stringToUTF8(browser,buffer,bufferSize);return lengthBytesUTF8(browser)}function _JS_SystemInfo_GetPreferredDevicePixelRatio(){return Module.matchWebGLToCanvasSize==false?1:Module.devicePixelRatio||window.devicePixelRatio||1}function _JS_SystemInfo_GetScreenSize(outWidth,outHeight){HEAPF64[outWidth>>3]=Module.SystemInfo.width;HEAPF64[outHeight>>3]=Module.SystemInfo.height}function _JS_SystemInfo_HasAstcHdr(){var ext=GLctx.getExtension("WEBGL_compressed_texture_astc");if(ext&&ext.getSupportedProfiles){return ext.getSupportedProfiles().includes("hdr")}return false}function _JS_SystemInfo_HasCursorLock(){return Module.SystemInfo.hasCursorLock}function _JS_SystemInfo_HasFullscreen(){return Module.SystemInfo.hasFullscreen}function _JS_SystemInfo_HasWebGL(){return Module.SystemInfo.hasWebGL}function _JS_SystemInfo_IsMobile(){return Module.SystemInfo.mobile}function _JS_UnityEngineShouldQuit(){return!!Module.shouldQuit}var wr={requests:{},responses:{},abortControllers:{},timer:{},nextRequestId:1};function _JS_WebRequest_Abort(requestId){var abortController=wr.abortControllers[requestId];if(!abortController||abortController.signal.aborted){return}abortController.abort()}function _JS_WebRequest_Create(url,method){var _url=UTF8ToString(url);var _method=UTF8ToString(method);var abortController=new AbortController;var requestOptions={url:_url,init:{method:_method,signal:abortController.signal,headers:{},enableStreamingDownload:true},tempBuffer:null,tempBufferSize:0};wr.abortControllers[wr.nextRequestId]=abortController;wr.requests[wr.nextRequestId]=requestOptions;return wr.nextRequestId++}function jsWebRequestGetResponseHeaderString(requestId){var response=wr.responses[requestId];if(!response){return""}if(response.headerString){return response.headerString}var headers="";var entries=response.headers.entries();for(var result=entries.next();!result.done;result=entries.next()){headers+=result.value[0]+": "+result.value[1]+"\r\n"}response.headerString=headers;return headers}function _JS_WebRequest_GetResponseMetaData(requestId,headerBuffer,headerSize,responseUrlBuffer,responseUrlSize){var response=wr.responses[requestId];if(!response){stringToUTF8("",headerBuffer,headerSize);stringToUTF8("",responseUrlBuffer,responseUrlSize);return}if(headerBuffer){var headers=jsWebRequestGetResponseHeaderString(requestId);stringToUTF8(headers,headerBuffer,headerSize)}if(responseUrlBuffer){stringToUTF8(response.url,responseUrlBuffer,responseUrlSize)}}function _JS_WebRequest_GetResponseMetaDataLengths(requestId,buffer){var response=wr.responses[requestId];if(!response){HEAPU32[buffer>>2]=0;HEAPU32[(buffer>>2)+1]=0;return}var headers=jsWebRequestGetResponseHeaderString(requestId);HEAPU32[buffer>>2]=lengthBytesUTF8(headers);HEAPU32[(buffer>>2)+1]=lengthBytesUTF8(response.url)}function _JS_WebRequest_Release(requestId){if(wr.timer[requestId]){clearTimeout(wr.timer[requestId])}delete wr.requests[requestId];delete wr.responses[requestId];delete wr.abortControllers[requestId];delete wr.timer[requestId]}function _JS_WebRequest_Send(requestId,ptr,length,arg,onresponse,onprogress){var requestOptions=wr.requests[requestId];var abortController=wr.abortControllers[requestId];function getTempBuffer(size){if(!requestOptions.tempBuffer){const initialSize=Math.max(size,1024);requestOptions.tempBuffer=_malloc(initialSize);requestOptions.tempBufferSize=initialSize}if(requestOptions.tempBufferSize0){var postData=HEAPU8.subarray(ptr,ptr+length);requestOptions.init.body=new Blob([postData])}if(requestOptions.timeout){wr.timer[requestId]=setTimeout(function(){requestOptions.isTimedOut=true;abortController.abort()},requestOptions.timeout)}var fetchImpl=Module.fetchWithProgress;requestOptions.init.onProgress=HandleProgress;if(Module.companyName&&Module.productName&&Module.cachedFetch){fetchImpl=Module.cachedFetch;requestOptions.init.companyName=Module.companyName;requestOptions.init.productName=Module.productName;requestOptions.control=Module.cacheControl(requestOptions.url)}fetchImpl(requestOptions.url,requestOptions.init).then(function(response){wr.responses[requestId]=response;HandleSuccess(response,response.parsedBody)}).catch(function(error){var kWebErrorUnknown=2;var kWebErrorAborted=17;var kWebErrorTimeout=14;if(requestOptions.isTimedOut){HandleError("Connection timed out.",kWebErrorTimeout)}else if(abortController.signal.aborted){HandleError("Aborted.",kWebErrorAborted)}else{HandleError(error.message,kWebErrorUnknown)}})}catch(error){var kWebErrorUnknown=2;HandleError(error.message,kWebErrorUnknown)}}function _JS_WebRequest_SetRedirectLimit(request,redirectLimit){var requestOptions=wr.requests[request];if(!requestOptions){return}requestOptions.init.redirect=redirectLimit===0?"error":"follow"}function _JS_WebRequest_SetRequestHeader(requestId,header,value){var requestOptions=wr.requests[requestId];if(!requestOptions){return}var _header=UTF8ToString(header);var _value=UTF8ToString(value);requestOptions.init.headers[_header]=_value}function _JS_WebRequest_SetTimeout(requestId,timeout){var requestOptions=wr.requests[requestId];if(!requestOptions){return}requestOptions.timeout=timeout}function _UNITY_IS_SUPPORTED(){try{var isFileSaverSupported=!!new Blob;return isFileSaverSupported}catch(e){return false}return false}function _UNITY_SAVE(content,name,mimetype){var blob=new Blob([Pointer_stringify(content)],{type:Pointer_stringify(mimetype)});saveAs(blob,Pointer_stringify(name))}function _UNITY_SAVE_BYTEARRAY(arr,size,name,mimetype){var bytes=new Uint8Array(size);for(var i=0;i>2]=type};this.get_type=function(){return HEAP32[this.ptr+ExceptionInfoAttrs.TYPE_OFFSET>>2]};this.set_destructor=function(destructor){HEAP32[this.ptr+ExceptionInfoAttrs.DESTRUCTOR_OFFSET>>2]=destructor};this.get_destructor=function(){return HEAP32[this.ptr+ExceptionInfoAttrs.DESTRUCTOR_OFFSET>>2]};this.set_refcount=function(refcount){HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2]=refcount};this.set_caught=function(caught){caught=caught?1:0;HEAP8[this.ptr+ExceptionInfoAttrs.CAUGHT_OFFSET>>0]=caught};this.get_caught=function(){return HEAP8[this.ptr+ExceptionInfoAttrs.CAUGHT_OFFSET>>0]!=0};this.set_rethrown=function(rethrown){rethrown=rethrown?1:0;HEAP8[this.ptr+ExceptionInfoAttrs.RETHROWN_OFFSET>>0]=rethrown};this.get_rethrown=function(){return HEAP8[this.ptr+ExceptionInfoAttrs.RETHROWN_OFFSET>>0]!=0};this.init=function(type,destructor){this.set_type(type);this.set_destructor(destructor);this.set_refcount(0);this.set_caught(false);this.set_rethrown(false)};this.add_ref=function(){var value=HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2];HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2]=value+1};this.release_ref=function(){var prev=HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2];HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2]=prev-1;return prev===1}}function CatchInfo(ptr){this.free=function(){_free(this.ptr);this.ptr=0};this.set_base_ptr=function(basePtr){HEAP32[this.ptr>>2]=basePtr};this.get_base_ptr=function(){return HEAP32[this.ptr>>2]};this.set_adjusted_ptr=function(adjustedPtr){var ptrSize=4;HEAP32[this.ptr+ptrSize>>2]=adjustedPtr};this.get_adjusted_ptr=function(){var ptrSize=4;return HEAP32[this.ptr+ptrSize>>2]};this.get_exception_ptr=function(){var isPointer=___cxa_is_pointer_type(this.get_exception_info().get_type());if(isPointer){return HEAP32[this.get_base_ptr()>>2]}var adjusted=this.get_adjusted_ptr();if(adjusted!==0)return adjusted;return this.get_base_ptr()};this.get_exception_info=function(){return new ExceptionInfo(this.get_base_ptr())};if(ptr===undefined){this.ptr=_malloc(8);this.set_adjusted_ptr(0)}else{this.ptr=ptr}}var exceptionCaught=[];function exception_addRef(info){info.add_ref()}var uncaughtExceptionCount=0;function ___cxa_begin_catch(ptr){var catchInfo=new CatchInfo(ptr);var info=catchInfo.get_exception_info();if(!info.get_caught()){info.set_caught(true);uncaughtExceptionCount--}info.set_rethrown(false);exceptionCaught.push(catchInfo);exception_addRef(info);return catchInfo.get_exception_ptr()}var exceptionLast=0;function ___cxa_free_exception(ptr){return _free(new ExceptionInfo(ptr).ptr)}function exception_decRef(info){if(info.release_ref()&&!info.get_rethrown()){var destructor=info.get_destructor();if(destructor){(function(a1){return dynCall_ii.apply(null,[destructor,a1])})(info.excPtr)}___cxa_free_exception(info.excPtr)}}function ___cxa_end_catch(){_setThrew(0);var catchInfo=exceptionCaught.pop();exception_decRef(catchInfo.get_exception_info());catchInfo.free();exceptionLast=0}function ___resumeException(catchInfoPtr){var catchInfo=new CatchInfo(catchInfoPtr);var ptr=catchInfo.get_base_ptr();if(!exceptionLast){exceptionLast=ptr}catchInfo.free();throw ptr}function ___cxa_find_matching_catch_2(){var thrown=exceptionLast;if(!thrown){setTempRet0(0);return 0|0}var info=new ExceptionInfo(thrown);var thrownType=info.get_type();var catchInfo=new CatchInfo;catchInfo.set_base_ptr(thrown);if(!thrownType){setTempRet0(0);return catchInfo.ptr|0}var typeArray=Array.prototype.slice.call(arguments);var stackTop=stackSave();var exceptionThrowBuf=stackAlloc(4);HEAP32[exceptionThrowBuf>>2]=thrown;for(var i=0;i>2];if(thrown!==adjusted){catchInfo.set_adjusted_ptr(adjusted)}setTempRet0(caughtType);return catchInfo.ptr|0}}stackRestore(stackTop);setTempRet0(thrownType);return catchInfo.ptr|0}function ___cxa_find_matching_catch_3(){var thrown=exceptionLast;if(!thrown){setTempRet0(0);return 0|0}var info=new ExceptionInfo(thrown);var thrownType=info.get_type();var catchInfo=new CatchInfo;catchInfo.set_base_ptr(thrown);if(!thrownType){setTempRet0(0);return catchInfo.ptr|0}var typeArray=Array.prototype.slice.call(arguments);var stackTop=stackSave();var exceptionThrowBuf=stackAlloc(4);HEAP32[exceptionThrowBuf>>2]=thrown;for(var i=0;i>2];if(thrown!==adjusted){catchInfo.set_adjusted_ptr(adjusted)}setTempRet0(caughtType);return catchInfo.ptr|0}}stackRestore(stackTop);setTempRet0(thrownType);return catchInfo.ptr|0}function ___cxa_find_matching_catch_4(){var thrown=exceptionLast;if(!thrown){setTempRet0(0);return 0|0}var info=new ExceptionInfo(thrown);var thrownType=info.get_type();var catchInfo=new CatchInfo;catchInfo.set_base_ptr(thrown);if(!thrownType){setTempRet0(0);return catchInfo.ptr|0}var typeArray=Array.prototype.slice.call(arguments);var stackTop=stackSave();var exceptionThrowBuf=stackAlloc(4);HEAP32[exceptionThrowBuf>>2]=thrown;for(var i=0;i>2];if(thrown!==adjusted){catchInfo.set_adjusted_ptr(adjusted)}setTempRet0(caughtType);return catchInfo.ptr|0}}stackRestore(stackTop);setTempRet0(thrownType);return catchInfo.ptr|0}function ___cxa_rethrow(){var catchInfo=exceptionCaught.pop();if(!catchInfo){abort("no exception to throw")}var info=catchInfo.get_exception_info();var ptr=catchInfo.get_base_ptr();if(!info.get_rethrown()){exceptionCaught.push(catchInfo);info.set_rethrown(true);info.set_caught(false);uncaughtExceptionCount++}else{catchInfo.free()}exceptionLast=ptr;throw ptr}function ___cxa_throw(ptr,type,destructor){var info=new ExceptionInfo(ptr);info.init(type,destructor);exceptionLast=ptr;uncaughtExceptionCount++;throw ptr}function _gmtime_r(time,tmPtr){var date=new Date(HEAP32[time>>2]*1e3);HEAP32[tmPtr>>2]=date.getUTCSeconds();HEAP32[tmPtr+4>>2]=date.getUTCMinutes();HEAP32[tmPtr+8>>2]=date.getUTCHours();HEAP32[tmPtr+12>>2]=date.getUTCDate();HEAP32[tmPtr+16>>2]=date.getUTCMonth();HEAP32[tmPtr+20>>2]=date.getUTCFullYear()-1900;HEAP32[tmPtr+24>>2]=date.getUTCDay();HEAP32[tmPtr+36>>2]=0;HEAP32[tmPtr+32>>2]=0;var start=Date.UTC(date.getUTCFullYear(),0,1,0,0,0,0);var yday=(date.getTime()-start)/(1e3*60*60*24)|0;HEAP32[tmPtr+28>>2]=yday;if(!_gmtime_r.GMTString)_gmtime_r.GMTString=allocateUTF8("GMT");HEAP32[tmPtr+40>>2]=_gmtime_r.GMTString;return tmPtr}function ___gmtime_r(a0,a1){return _gmtime_r(a0,a1)}function _tzset(){if(_tzset.called)return;_tzset.called=true;var currentYear=(new Date).getFullYear();var winter=new Date(currentYear,0,1);var summer=new Date(currentYear,6,1);var winterOffset=winter.getTimezoneOffset();var summerOffset=summer.getTimezoneOffset();var stdTimezoneOffset=Math.max(winterOffset,summerOffset);HEAP32[__get_timezone()>>2]=stdTimezoneOffset*60;HEAP32[__get_daylight()>>2]=Number(winterOffset!=summerOffset);function extractZone(date){var match=date.toTimeString().match(/\(([A-Za-z ]+)\)$/);return match?match[1]:"GMT"}var winterName=extractZone(winter);var summerName=extractZone(summer);var winterNamePtr=allocateUTF8(winterName);var summerNamePtr=allocateUTF8(summerName);if(summerOffset>2]=winterNamePtr;HEAP32[__get_tzname()+4>>2]=summerNamePtr}else{HEAP32[__get_tzname()>>2]=summerNamePtr;HEAP32[__get_tzname()+4>>2]=winterNamePtr}}function _localtime_r(time,tmPtr){_tzset();var date=new Date(HEAP32[time>>2]*1e3);HEAP32[tmPtr>>2]=date.getSeconds();HEAP32[tmPtr+4>>2]=date.getMinutes();HEAP32[tmPtr+8>>2]=date.getHours();HEAP32[tmPtr+12>>2]=date.getDate();HEAP32[tmPtr+16>>2]=date.getMonth();HEAP32[tmPtr+20>>2]=date.getFullYear()-1900;HEAP32[tmPtr+24>>2]=date.getDay();var start=new Date(date.getFullYear(),0,1);var yday=(date.getTime()-start.getTime())/(1e3*60*60*24)|0;HEAP32[tmPtr+28>>2]=yday;HEAP32[tmPtr+36>>2]=-(date.getTimezoneOffset()*60);var summerOffset=new Date(date.getFullYear(),6,1).getTimezoneOffset();var winterOffset=start.getTimezoneOffset();var dst=(summerOffset!=winterOffset&&date.getTimezoneOffset()==Math.min(winterOffset,summerOffset))|0;HEAP32[tmPtr+32>>2]=dst;var zonePtr=HEAP32[__get_tzname()+(dst?4:0)>>2];HEAP32[tmPtr+40>>2]=zonePtr;return tmPtr}function ___localtime_r(a0,a1){return _localtime_r(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift("..")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";path=PATH.normalizeArray(path.split("/").filter(function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return"."}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path==="/")return"/";path=PATH.normalize(path);path=path.replace(/\/$/,"");var lastSlash=path.lastIndexOf("/");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}};function getRandomDevice(){if(typeof crypto==="object"&&typeof crypto["getRandomValues"]==="function"){var randomBuffer=new Uint8Array(1);return function(){crypto.getRandomValues(randomBuffer);return randomBuffer[0]}}else if(ENVIRONMENT_IS_NODE){try{var crypto_module=require("crypto");return function(){return crypto_module["randomBytes"](1)[0]}}catch(e){}}return function(){abort("randomDevice")}}var PATH_FS={resolve:function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:FS.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){return""}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=PATH.normalizeArray(resolvedPath.split("/").filter(function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."},relative:function(from,to){from=PATH_FS.resolve(from).substr(1);to=PATH_FS.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i0){result=buf.slice(0,bytesRead).toString("utf-8")}else{result=null}}else if(typeof window!="undefined"&&typeof window.prompt=="function"){result=window.prompt("Input: ");if(result!==null){result+="\n"}}else if(typeof readline=="function"){result=readline();if(result!==null){result+="\n"}}if(!result){return null}tty.input=intArrayFromString(result,true)}return tty.input.shift()},put_char:function(tty,val){if(val===null||val===10){out(UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}},flush:function(tty){if(tty.output&&tty.output.length>0){out(UTF8ArrayToString(tty.output,0));tty.output=[]}}},default_tty1_ops:{put_char:function(tty,val){if(val===null||val===10){err(UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}},flush:function(tty){if(tty.output&&tty.output.length>0){err(UTF8ArrayToString(tty.output,0));tty.output=[]}}}};function mmapAlloc(size){var alignedSize=alignMemory(size,65536);var ptr=_malloc(alignedSize);while(size=newCapacity)return;var CAPACITY_DOUBLING_MAX=1024*1024;newCapacity=Math.max(newCapacity,prevCapacity*(prevCapacity>>0);if(prevCapacity!=0)newCapacity=Math.max(newCapacity,256);var oldContents=node.contents;node.contents=new Uint8Array(newCapacity);if(node.usedBytes>0)node.contents.set(oldContents.subarray(0,node.usedBytes),0)},resizeFileStorage:function(node,newSize){if(node.usedBytes==newSize)return;if(newSize==0){node.contents=null;node.usedBytes=0}else{var oldContents=node.contents;node.contents=new Uint8Array(newSize);if(oldContents){node.contents.set(oldContents.subarray(0,Math.min(newSize,node.usedBytes)))}node.usedBytes=newSize}},node_ops:{getattr:function(node){var attr={};attr.dev=FS.isChrdev(node.mode)?node.id:1;attr.ino=node.id;attr.mode=node.mode;attr.nlink=1;attr.uid=0;attr.gid=0;attr.rdev=node.rdev;if(FS.isDir(node.mode)){attr.size=4096}else if(FS.isFile(node.mode)){attr.size=node.usedBytes}else if(FS.isLink(node.mode)){attr.size=node.link.length}else{attr.size=0}attr.atime=new Date(node.timestamp);attr.mtime=new Date(node.timestamp);attr.ctime=new Date(node.timestamp);attr.blksize=4096;attr.blocks=Math.ceil(attr.size/attr.blksize);return attr},setattr:function(node,attr){if(attr.mode!==undefined){node.mode=attr.mode}if(attr.timestamp!==undefined){node.timestamp=attr.timestamp}if(attr.size!==undefined){MEMFS.resizeFileStorage(node,attr.size)}},lookup:function(parent,name){throw FS.genericErrors[44]},mknod:function(parent,name,mode,dev){return MEMFS.createNode(parent,name,mode,dev)},rename:function(old_node,new_dir,new_name){if(FS.isDir(old_node.mode)){var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(new_node){for(var i in new_node.contents){throw new FS.ErrnoError(55)}}}delete old_node.parent.contents[old_node.name];old_node.parent.timestamp=Date.now();old_node.name=new_name;new_dir.contents[new_name]=old_node;new_dir.timestamp=old_node.parent.timestamp;old_node.parent=new_dir},unlink:function(parent,name){delete parent.contents[name];parent.timestamp=Date.now()},rmdir:function(parent,name){var node=FS.lookupNode(parent,name);for(var i in node.contents){throw new FS.ErrnoError(55)}delete parent.contents[name];parent.timestamp=Date.now()},readdir:function(node){var entries=[".",".."];for(var key in node.contents){if(!node.contents.hasOwnProperty(key)){continue}entries.push(key)}return entries},symlink:function(parent,newname,oldpath){var node=MEMFS.createNode(parent,newname,511|40960,0);node.link=oldpath;return node},readlink:function(node){if(!FS.isLink(node.mode)){throw new FS.ErrnoError(28)}return node.link}},stream_ops:{read:function(stream,buffer,offset,length,position){var contents=stream.node.contents;if(position>=stream.node.usedBytes)return 0;var size=Math.min(stream.node.usedBytes-position,length);if(size>8&&contents.subarray){buffer.set(contents.subarray(position,position+size),offset)}else{for(var i=0;i0||position+length8){throw new FS.ErrnoError(32)}var parts=PATH.normalizeArray(path.split("/").filter(function(p){return!!p}),false);var current=FS.root;var current_path="/";for(var i=0;i40){throw new FS.ErrnoError(32)}}}}return{path:current_path,node:current}},getPath:function(node){var path;while(true){if(FS.isRoot(node)){var mount=node.mount.mountpoint;if(!path)return mount;return mount[mount.length-1]!=="/"?mount+"/"+path:mount+path}path=path?node.name+"/"+path:node.name;node=node.parent}},hashName:function(parentid,name){var hash=0;for(var i=0;i>>0)%FS.nameTable.length},hashAddNode:function(node){var hash=FS.hashName(node.parent.id,node.name);node.name_next=FS.nameTable[hash];FS.nameTable[hash]=node},hashRemoveNode:function(node){var hash=FS.hashName(node.parent.id,node.name);if(FS.nameTable[hash]===node){FS.nameTable[hash]=node.name_next}else{var current=FS.nameTable[hash];while(current){if(current.name_next===node){current.name_next=node.name_next;break}current=current.name_next}}},lookupNode:function(parent,name){var errCode=FS.mayLookup(parent);if(errCode){throw new FS.ErrnoError(errCode,parent)}var hash=FS.hashName(parent.id,name);for(var node=FS.nameTable[hash];node;node=node.name_next){var nodeName=node.name;if(node.parent.id===parent.id&&nodeName===name){return node}}return FS.lookup(parent,name)},createNode:function(parent,name,mode,rdev){var node=new FS.FSNode(parent,name,mode,rdev);FS.hashAddNode(node);return node},destroyNode:function(node){FS.hashRemoveNode(node)},isRoot:function(node){return node===node.parent},isMountpoint:function(node){return!!node.mounted},isFile:function(mode){return(mode&61440)===32768},isDir:function(mode){return(mode&61440)===16384},isLink:function(mode){return(mode&61440)===40960},isChrdev:function(mode){return(mode&61440)===8192},isBlkdev:function(mode){return(mode&61440)===24576},isFIFO:function(mode){return(mode&61440)===4096},isSocket:function(mode){return(mode&49152)===49152},flagModes:{"r":0,"r+":2,"w":577,"w+":578,"a":1089,"a+":1090},modeStringToFlags:function(str){var flags=FS.flagModes[str];if(typeof flags==="undefined"){throw new Error("Unknown file open mode: "+str)}return flags},flagsToPermissionString:function(flag){var perms=["r","w","rw"][flag&3];if(flag&512){perms+="w"}return perms},nodePermissions:function(node,perms){if(FS.ignorePermissions){return 0}if(perms.includes("r")&&!(node.mode&292)){return 2}else if(perms.includes("w")&&!(node.mode&146)){return 2}else if(perms.includes("x")&&!(node.mode&73)){return 2}return 0},mayLookup:function(dir){var errCode=FS.nodePermissions(dir,"x");if(errCode)return errCode;if(!dir.node_ops.lookup)return 2;return 0},mayCreate:function(dir,name){try{var node=FS.lookupNode(dir,name);return 20}catch(e){}return FS.nodePermissions(dir,"wx")},mayDelete:function(dir,name,isdir){var node;try{node=FS.lookupNode(dir,name)}catch(e){return e.errno}var errCode=FS.nodePermissions(dir,"wx");if(errCode){return errCode}if(isdir){if(!FS.isDir(node.mode)){return 54}if(FS.isRoot(node)||FS.getPath(node)===FS.cwd()){return 10}}else{if(FS.isDir(node.mode)){return 31}}return 0},mayOpen:function(node,flags){if(!node){return 44}if(FS.isLink(node.mode)){return 32}else if(FS.isDir(node.mode)){if(FS.flagsToPermissionString(flags)!=="r"||flags&512){return 31}}return FS.nodePermissions(node,FS.flagsToPermissionString(flags))},MAX_OPEN_FDS:4096,nextfd:function(fd_start,fd_end){fd_start=fd_start||0;fd_end=fd_end||FS.MAX_OPEN_FDS;for(var fd=fd_start;fd<=fd_end;fd++){if(!FS.streams[fd]){return fd}}throw new FS.ErrnoError(33)},getStream:function(fd){return FS.streams[fd]},createStream:function(stream,fd_start,fd_end){if(!FS.FSStream){FS.FSStream=function(){};FS.FSStream.prototype={object:{get:function(){return this.node},set:function(val){this.node=val}},isRead:{get:function(){return(this.flags&2097155)!==1}},isWrite:{get:function(){return(this.flags&2097155)!==0}},isAppend:{get:function(){return this.flags&1024}}}}var newStream=new FS.FSStream;for(var p in stream){newStream[p]=stream[p]}stream=newStream;var fd=FS.nextfd(fd_start,fd_end);stream.fd=fd;FS.streams[fd]=stream;return stream},closeStream:function(fd){FS.streams[fd]=null},chrdev_stream_ops:{open:function(stream){var device=FS.getDevice(stream.node.rdev);stream.stream_ops=device.stream_ops;if(stream.stream_ops.open){stream.stream_ops.open(stream)}},llseek:function(){throw new FS.ErrnoError(70)}},major:function(dev){return dev>>8},minor:function(dev){return dev&255},makedev:function(ma,mi){return ma<<8|mi},registerDevice:function(dev,ops){FS.devices[dev]={stream_ops:ops}},getDevice:function(dev){return FS.devices[dev]},getMounts:function(mount){var mounts=[];var check=[mount];while(check.length){var m=check.pop();mounts.push(m);check.push.apply(check,m.mounts)}return mounts},syncfs:function(populate,callback){if(typeof populate==="function"){callback=populate;populate=false}FS.syncFSRequests++;if(FS.syncFSRequests>1){err("warning: "+FS.syncFSRequests+" FS.syncfs operations in flight at once, probably just doing extra work")}var mounts=FS.getMounts(FS.root.mount);var completed=0;function doCallback(errCode){FS.syncFSRequests--;return callback(errCode)}function done(errCode){if(errCode){if(!done.errored){done.errored=true;return doCallback(errCode)}return}if(++completed>=mounts.length){doCallback(null)}}mounts.forEach(function(mount){if(!mount.type.syncfs){return done(null)}mount.type.syncfs(mount,populate,done)})},mount:function(type,opts,mountpoint){var root=mountpoint==="/";var pseudo=!mountpoint;var node;if(root&&FS.root){throw new FS.ErrnoError(10)}else if(!root&&!pseudo){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});mountpoint=lookup.path;node=lookup.node;if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}if(!FS.isDir(node.mode)){throw new FS.ErrnoError(54)}}var mount={type:type,opts:opts,mountpoint:mountpoint,mounts:[]};var mountRoot=type.mount(mount);mountRoot.mount=mount;mount.root=mountRoot;if(root){FS.root=mountRoot}else if(node){node.mounted=mount;if(node.mount){node.mount.mounts.push(mount)}}return mountRoot},unmount:function(mountpoint){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});if(!FS.isMountpoint(lookup.node)){throw new FS.ErrnoError(28)}var node=lookup.node;var mount=node.mounted;var mounts=FS.getMounts(mount);Object.keys(FS.nameTable).forEach(function(hash){var current=FS.nameTable[hash];while(current){var next=current.name_next;if(mounts.includes(current.mount)){FS.destroyNode(current)}current=next}});node.mounted=null;var idx=node.mount.mounts.indexOf(mount);node.mount.mounts.splice(idx,1)},lookup:function(parent,name){return parent.node_ops.lookup(parent,name)},mknod:function(path,mode,dev){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);if(!name||name==="."||name===".."){throw new FS.ErrnoError(28)}var errCode=FS.mayCreate(parent,name);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.mknod){throw new FS.ErrnoError(63)}return parent.node_ops.mknod(parent,name,mode,dev)},create:function(path,mode){mode=mode!==undefined?mode:438;mode&=4095;mode|=32768;return FS.mknod(path,mode,0)},mkdir:function(path,mode){mode=mode!==undefined?mode:511;mode&=511|512;mode|=16384;return FS.mknod(path,mode,0)},mkdirTree:function(path,mode){var dirs=path.split("/");var d="";for(var i=0;ithis.length-1||idx<0){return undefined}var chunkOffset=idx%this.chunkSize;var chunkNum=idx/this.chunkSize|0;return this.getter(chunkNum)[chunkOffset]};LazyUint8Array.prototype.setDataGetter=function LazyUint8Array_setDataGetter(getter){this.getter=getter};LazyUint8Array.prototype.cacheLength=function LazyUint8Array_cacheLength(){var xhr=new XMLHttpRequest;xhr.open("HEAD",url,false);xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);var datalength=Number(xhr.getResponseHeader("Content-length"));var header;var hasByteServing=(header=xhr.getResponseHeader("Accept-Ranges"))&&header==="bytes";var usesGzip=(header=xhr.getResponseHeader("Content-Encoding"))&&header==="gzip";var chunkSize=1024*1024;if(!hasByteServing)chunkSize=datalength;var doXHR=function(from,to){if(from>to)throw new Error("invalid range ("+from+", "+to+") or no bytes requested!");if(to>datalength-1)throw new Error("only "+datalength+" bytes available! programmer error!");var xhr=new XMLHttpRequest;xhr.open("GET",url,false);if(datalength!==chunkSize)xhr.setRequestHeader("Range","bytes="+from+"-"+to);if(typeof Uint8Array!="undefined")xhr.responseType="arraybuffer";if(xhr.overrideMimeType){xhr.overrideMimeType("text/plain; charset=x-user-defined")}xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);if(xhr.response!==undefined){return new Uint8Array(xhr.response||[])}else{return intArrayFromString(xhr.responseText||"",true)}};var lazyArray=this;lazyArray.setDataGetter(function(chunkNum){var start=chunkNum*chunkSize;var end=(chunkNum+1)*chunkSize-1;end=Math.min(end,datalength-1);if(typeof lazyArray.chunks[chunkNum]==="undefined"){lazyArray.chunks[chunkNum]=doXHR(start,end)}if(typeof lazyArray.chunks[chunkNum]==="undefined")throw new Error("doXHR failed!");return lazyArray.chunks[chunkNum]});if(usesGzip||!datalength){chunkSize=datalength=1;datalength=this.getter(0).length;chunkSize=datalength;out("LazyFiles on gzip forces download of the whole file when length is accessed")}this._length=datalength;this._chunkSize=chunkSize;this.lengthKnown=true};if(typeof XMLHttpRequest!=="undefined"){if(!ENVIRONMENT_IS_WORKER)throw"Cannot do synchronous binary XHRs outside webworkers in modern browsers. Use --embed-file or --preload-file in emcc";var lazyArray=new LazyUint8Array;Object.defineProperties(lazyArray,{length:{get:function(){if(!this.lengthKnown){this.cacheLength()}return this._length}},chunkSize:{get:function(){if(!this.lengthKnown){this.cacheLength()}return this._chunkSize}}});var properties={isDevice:false,contents:lazyArray}}else{var properties={isDevice:false,url:url}}var node=FS.createFile(parent,name,properties,canRead,canWrite);if(properties.contents){node.contents=properties.contents}else if(properties.url){node.contents=null;node.url=properties.url}Object.defineProperties(node,{usedBytes:{get:function(){return this.contents.length}}});var stream_ops={};var keys=Object.keys(node.stream_ops);keys.forEach(function(key){var fn=node.stream_ops[key];stream_ops[key]=function forceLoadLazyFile(){FS.forceLoadFile(node);return fn.apply(null,arguments)}});stream_ops.read=function stream_ops_read(stream,buffer,offset,length,position){FS.forceLoadFile(node);var contents=stream.node.contents;if(position>=contents.length)return 0;var size=Math.min(contents.length-position,length);if(contents.slice){for(var i=0;i>2]=stat.dev;HEAP32[buf+4>>2]=0;HEAP32[buf+8>>2]=stat.ino;HEAP32[buf+12>>2]=stat.mode;HEAP32[buf+16>>2]=stat.nlink;HEAP32[buf+20>>2]=stat.uid;HEAP32[buf+24>>2]=stat.gid;HEAP32[buf+28>>2]=stat.rdev;HEAP32[buf+32>>2]=0;tempI64=[stat.size>>>0,(tempDouble=stat.size,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+40>>2]=tempI64[0],HEAP32[buf+44>>2]=tempI64[1];HEAP32[buf+48>>2]=4096;HEAP32[buf+52>>2]=stat.blocks;HEAP32[buf+56>>2]=stat.atime.getTime()/1e3|0;HEAP32[buf+60>>2]=0;HEAP32[buf+64>>2]=stat.mtime.getTime()/1e3|0;HEAP32[buf+68>>2]=0;HEAP32[buf+72>>2]=stat.ctime.getTime()/1e3|0;HEAP32[buf+76>>2]=0;tempI64=[stat.ino>>>0,(tempDouble=stat.ino,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+80>>2]=tempI64[0],HEAP32[buf+84>>2]=tempI64[1];return 0},doMsync:function(addr,stream,len,flags,offset){var buffer=HEAPU8.slice(addr,addr+len);FS.msync(stream,buffer,offset,len,flags)},doMkdir:function(path,mode){path=PATH.normalize(path);if(path[path.length-1]==="/")path=path.substr(0,path.length-1);FS.mkdir(path,mode,0);return 0},doMknod:function(path,mode,dev){switch(mode&61440){case 32768:case 8192:case 24576:case 4096:case 49152:break;default:return-28}FS.mknod(path,mode,dev);return 0},doReadlink:function(path,buf,bufsize){if(bufsize<=0)return-28;var ret=FS.readlink(path);var len=Math.min(bufsize,lengthBytesUTF8(ret));var endChar=HEAP8[buf+len];stringToUTF8(ret,buf,bufsize+1);HEAP8[buf+len]=endChar;return len},doAccess:function(path,amode){if(amode&~7){return-28}var node;var lookup=FS.lookupPath(path,{follow:true});node=lookup.node;if(!node){return-44}var perms="";if(amode&4)perms+="r";if(amode&2)perms+="w";if(amode&1)perms+="x";if(perms&&FS.nodePermissions(node,perms)){return-2}return 0},doDup:function(path,flags,suggestFD){var suggest=FS.getStream(suggestFD);if(suggest)FS.close(suggest);return FS.open(path,flags,0,suggestFD,suggestFD).fd},doReadv:function(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];var curr=FS.read(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr;if(curr>2];var len=HEAP32[iov+(i*8+4)>>2];var curr=FS.write(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr}return ret},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},getStreamFromFD:function(fd){var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(8);return stream},get64:function(low,high){return low}};function ___sys__newselect(nfds,readfds,writefds,exceptfds,timeout){try{var total=0;var srcReadLow=readfds?HEAP32[readfds>>2]:0,srcReadHigh=readfds?HEAP32[readfds+4>>2]:0;var srcWriteLow=writefds?HEAP32[writefds>>2]:0,srcWriteHigh=writefds?HEAP32[writefds+4>>2]:0;var srcExceptLow=exceptfds?HEAP32[exceptfds>>2]:0,srcExceptHigh=exceptfds?HEAP32[exceptfds+4>>2]:0;var dstReadLow=0,dstReadHigh=0;var dstWriteLow=0,dstWriteHigh=0;var dstExceptLow=0,dstExceptHigh=0;var allLow=(readfds?HEAP32[readfds>>2]:0)|(writefds?HEAP32[writefds>>2]:0)|(exceptfds?HEAP32[exceptfds>>2]:0);var allHigh=(readfds?HEAP32[readfds+4>>2]:0)|(writefds?HEAP32[writefds+4>>2]:0)|(exceptfds?HEAP32[exceptfds+4>>2]:0);var check=function(fd,low,high,val){return fd<32?low&val:high&val};for(var fd=0;fd>2]=dstReadLow;HEAP32[readfds+4>>2]=dstReadHigh}if(writefds){HEAP32[writefds>>2]=dstWriteLow;HEAP32[writefds+4>>2]=dstWriteHigh}if(exceptfds){HEAP32[exceptfds>>2]=dstExceptLow;HEAP32[exceptfds+4>>2]=dstExceptHigh}return total}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var SOCKFS={mount:function(mount){Module["websocket"]=Module["websocket"]&&"object"===typeof Module["websocket"]?Module["websocket"]:{};Module["websocket"]._callbacks={};Module["websocket"]["on"]=function(event,callback){if("function"===typeof callback){this._callbacks[event]=callback}return this};Module["websocket"].emit=function(event,param){if("function"===typeof this._callbacks[event]){this._callbacks[event].call(this,param)}};return FS.createNode(null,"/",16384|511,0)},createSocket:function(family,type,protocol){type&=~526336;var streaming=type==1;if(protocol){assert(streaming==(protocol==6))}var sock={family:family,type:type,protocol:protocol,server:null,error:null,peers:{},pending:[],recv_queue:[],sock_ops:SOCKFS.websocket_sock_ops};var name=SOCKFS.nextname();var node=FS.createNode(SOCKFS.root,name,49152,0);node.sock=sock;var stream=FS.createStream({path:name,node:node,flags:2,seekable:false,stream_ops:SOCKFS.stream_ops});sock.stream=stream;return sock},getSocket:function(fd){var stream=FS.getStream(fd);if(!stream||!FS.isSocket(stream.node.mode)){return null}return stream.node.sock},stream_ops:{poll:function(stream){var sock=stream.node.sock;return sock.sock_ops.poll(sock)},ioctl:function(stream,request,varargs){var sock=stream.node.sock;return sock.sock_ops.ioctl(sock,request,varargs)},read:function(stream,buffer,offset,length,position){var sock=stream.node.sock;var msg=sock.sock_ops.recvmsg(sock,length);if(!msg){return 0}buffer.set(msg.buffer,offset);return msg.buffer.length},write:function(stream,buffer,offset,length,position){var sock=stream.node.sock;return sock.sock_ops.sendmsg(sock,buffer,offset,length)},close:function(stream){var sock=stream.node.sock;sock.sock_ops.close(sock)}},nextname:function(){if(!SOCKFS.nextname.current){SOCKFS.nextname.current=0}return"socket["+SOCKFS.nextname.current+++"]"},websocket_sock_ops:{createPeer:function(sock,addr,port){var ws;if(typeof addr==="object"){ws=addr;addr=null;port=null}if(ws){if(ws._socket){addr=ws._socket.remoteAddress;port=ws._socket.remotePort}else{var result=/ws[s]?:\/\/([^:]+):(\d+)/.exec(ws.url);if(!result){throw new Error("WebSocket URL must be in the format ws(s)://address:port")}addr=result[1];port=parseInt(result[2],10)}}else{try{var runtimeConfig=Module["websocket"]&&"object"===typeof Module["websocket"];var url="ws:#".replace("#","//");if(runtimeConfig){if("string"===typeof Module["websocket"]["url"]){url=Module["websocket"]["url"]}}if(url==="ws://"||url==="wss://"){var parts=addr.split("/");url=url+parts[0]+":"+port+"/"+parts.slice(1).join("/")}var subProtocols="binary";if(runtimeConfig){if("string"===typeof Module["websocket"]["subprotocol"]){subProtocols=Module["websocket"]["subprotocol"]}}var opts=undefined;if(subProtocols!=="null"){subProtocols=subProtocols.replace(/^ +| +$/g,"").split(/ *, */);opts=ENVIRONMENT_IS_NODE?{"protocol":subProtocols.toString()}:subProtocols}if(runtimeConfig&&null===Module["websocket"]["subprotocol"]){subProtocols="null";opts=undefined}var WebSocketConstructor;if(ENVIRONMENT_IS_NODE){WebSocketConstructor=require("ws")}else{WebSocketConstructor=WebSocket}ws=new WebSocketConstructor(url,opts);ws.binaryType="arraybuffer"}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EHOSTUNREACH)}}var peer={addr:addr,port:port,socket:ws,dgram_send_queue:[]};SOCKFS.websocket_sock_ops.addPeer(sock,peer);SOCKFS.websocket_sock_ops.handlePeerEvents(sock,peer);if(sock.type===2&&typeof sock.sport!=="undefined"){peer.dgram_send_queue.push(new Uint8Array([255,255,255,255,"p".charCodeAt(0),"o".charCodeAt(0),"r".charCodeAt(0),"t".charCodeAt(0),(sock.sport&65280)>>8,sock.sport&255]))}return peer},getPeer:function(sock,addr,port){return sock.peers[addr+":"+port]},addPeer:function(sock,peer){sock.peers[peer.addr+":"+peer.port]=peer},removePeer:function(sock,peer){delete sock.peers[peer.addr+":"+peer.port]},handlePeerEvents:function(sock,peer){var first=true;var handleOpen=function(){Module["websocket"].emit("open",sock.stream.fd);try{var queued=peer.dgram_send_queue.shift();while(queued){peer.socket.send(queued);queued=peer.dgram_send_queue.shift()}}catch(e){peer.socket.close()}};function handleMessage(data){if(typeof data==="string"){var encoder=new TextEncoder;data=encoder.encode(data)}else{assert(data.byteLength!==undefined);if(data.byteLength==0){return}else{data=new Uint8Array(data)}}var wasfirst=first;first=false;if(wasfirst&&data.length===10&&data[0]===255&&data[1]===255&&data[2]===255&&data[3]===255&&data[4]==="p".charCodeAt(0)&&data[5]==="o".charCodeAt(0)&&data[6]==="r".charCodeAt(0)&&data[7]==="t".charCodeAt(0)){var newport=data[8]<<8|data[9];SOCKFS.websocket_sock_ops.removePeer(sock,peer);peer.port=newport;SOCKFS.websocket_sock_ops.addPeer(sock,peer);return}sock.recv_queue.push({addr:peer.addr,port:peer.port,data:data});Module["websocket"].emit("message",sock.stream.fd)}if(ENVIRONMENT_IS_NODE){peer.socket.on("open",handleOpen);peer.socket.on("message",function(data,flags){if(!flags.binary){return}handleMessage(new Uint8Array(data).buffer)});peer.socket.on("close",function(){Module["websocket"].emit("close",sock.stream.fd)});peer.socket.on("error",function(error){sock.error=ERRNO_CODES.ECONNREFUSED;Module["websocket"].emit("error",[sock.stream.fd,sock.error,"ECONNREFUSED: Connection refused"])})}else{peer.socket.onopen=handleOpen;peer.socket.onclose=function(){Module["websocket"].emit("close",sock.stream.fd)};peer.socket.onmessage=function peer_socket_onmessage(event){handleMessage(event.data)};peer.socket.onerror=function(error){sock.error=ERRNO_CODES.ECONNREFUSED;Module["websocket"].emit("error",[sock.stream.fd,sock.error,"ECONNREFUSED: Connection refused"])}}},poll:function(sock){if(sock.type===1&&sock.server){return sock.pending.length?64|1:0}var mask=0;var dest=sock.type===1?SOCKFS.websocket_sock_ops.getPeer(sock,sock.daddr,sock.dport):null;if(sock.recv_queue.length||!dest||dest&&dest.socket.readyState===dest.socket.CLOSING||dest&&dest.socket.readyState===dest.socket.CLOSED){mask|=64|1}if(!dest||dest&&dest.socket.readyState===dest.socket.OPEN){mask|=4}if(dest&&dest.socket.readyState===dest.socket.CLOSING||dest&&dest.socket.readyState===dest.socket.CLOSED){mask|=16}return mask},ioctl:function(sock,request,arg){switch(request){case 21531:var bytes=0;if(sock.recv_queue.length){bytes=sock.recv_queue[0].data.length}HEAP32[arg>>2]=bytes;return 0;default:return ERRNO_CODES.EINVAL}},close:function(sock){if(sock.server){try{sock.server.close()}catch(e){}sock.server=null}var peers=Object.keys(sock.peers);for(var i=0;i>2]=value;return value}function inetPton4(str){var b=str.split(".");for(var i=0;i<4;i++){var tmp=Number(b[i]);if(isNaN(tmp))return null;b[i]=tmp}return(b[0]|b[1]<<8|b[2]<<16|b[3]<<24)>>>0}function jstoi_q(str){return parseInt(str)}function inetPton6(str){var words;var w,offset,z;var valid6regx=/^((?=.*::)(?!.*::.+::)(::)?([\dA-F]{1,4}:(:|\b)|){5}|([\dA-F]{1,4}:){6})((([\dA-F]{1,4}((?!\3)::|:\b|$))|(?!\2\3)){2}|(((2[0-4]|1\d|[1-9])?\d|25[0-5])\.?\b){4})$/i;var parts=[];if(!valid6regx.test(str)){return null}if(str==="::"){return[0,0,0,0,0,0,0,0]}if(str.startsWith("::")){str=str.replace("::","Z:")}else{str=str.replace("::",":Z:")}if(str.indexOf(".")>0){str=str.replace(new RegExp("[.]","g"),":");words=str.split(":");words[words.length-4]=jstoi_q(words[words.length-4])+jstoi_q(words[words.length-3])*256;words[words.length-3]=jstoi_q(words[words.length-2])+jstoi_q(words[words.length-1])*256;words=words.slice(0,words.length-2)}else{words=str.split(":")}offset=0;z=0;for(w=0;w>2]=16}HEAP16[sa>>1]=family;HEAP32[sa+4>>2]=addr;HEAP16[sa+2>>1]=_htons(port);tempI64=[0>>>0,(tempDouble=0,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[sa+8>>2]=tempI64[0],HEAP32[sa+12>>2]=tempI64[1];break;case 10:addr=inetPton6(addr);if(addrlen){HEAP32[addrlen>>2]=28}HEAP32[sa>>2]=family;HEAP32[sa+8>>2]=addr[0];HEAP32[sa+12>>2]=addr[1];HEAP32[sa+16>>2]=addr[2];HEAP32[sa+20>>2]=addr[3];HEAP16[sa+2>>1]=_htons(port);HEAP32[sa+4>>2]=0;HEAP32[sa+24>>2]=0;break;default:return 5}return 0}var DNS={address_map:{id:1,addrs:{},names:{}},lookup_name:function(name){var res=inetPton4(name);if(res!==null){return name}res=inetPton6(name);if(res!==null){return name}var addr;if(DNS.address_map.addrs[name]){addr=DNS.address_map.addrs[name]}else{var id=DNS.address_map.id++;assert(id<65535,"exceeded max address mappings of 65535");addr="172.29."+(id&255)+"."+(id&65280);DNS.address_map.names[addr]=name;DNS.address_map.addrs[name]=addr}return addr},lookup_addr:function(addr){if(DNS.address_map.names[addr]){return DNS.address_map.names[addr]}return null}};function ___sys_accept4(fd,addr,addrlen,flags){try{var sock=getSocketFromFD(fd);var newsock=sock.sock_ops.accept(sock);if(addr){var errno=writeSockaddr(addr,newsock.family,DNS.lookup_name(newsock.daddr),newsock.dport,addrlen)}return newsock.stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_access(path,amode){try{path=SYSCALLS.getStr(path);return SYSCALLS.doAccess(path,amode)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function inetNtop4(addr){return(addr&255)+"."+(addr>>8&255)+"."+(addr>>16&255)+"."+(addr>>24&255)}function inetNtop6(ints){var str="";var word=0;var longest=0;var lastzero=0;var zstart=0;var len=0;var i=0;var parts=[ints[0]&65535,ints[0]>>16,ints[1]&65535,ints[1]>>16,ints[2]&65535,ints[2]>>16,ints[3]&65535,ints[3]>>16];var hasipv4=true;var v4part="";for(i=0;i<5;i++){if(parts[i]!==0){hasipv4=false;break}}if(hasipv4){v4part=inetNtop4(parts[6]|parts[7]<<16);if(parts[5]===-1){str="::ffff:";str+=v4part;return str}if(parts[5]===0){str="::";if(v4part==="0.0.0.0")v4part="";if(v4part==="0.0.0.1")v4part="1";str+=v4part;return str}}for(word=0;word<8;word++){if(parts[word]===0){if(word-lastzero>1){len=0}lastzero=word;len++}if(len>longest){longest=len;zstart=word-longest+1}}for(word=0;word<8;word++){if(longest>1){if(parts[word]===0&&word>=zstart&&word>1];var port=_ntohs(HEAPU16[sa+2>>1]);var addr;switch(family){case 2:if(salen!==16){return{errno:28}}addr=HEAP32[sa+4>>2];addr=inetNtop4(addr);break;case 10:if(salen!==28){return{errno:28}}addr=[HEAP32[sa+8>>2],HEAP32[sa+12>>2],HEAP32[sa+16>>2],HEAP32[sa+20>>2]];addr=inetNtop6(addr);break;default:return{errno:5}}return{family:family,addr:addr,port:port}}function getSocketAddress(addrp,addrlen,allowNull){if(allowNull&&addrp===0)return null;var info=readSockaddr(addrp,addrlen);if(info.errno)throw new FS.ErrnoError(info.errno);info.addr=DNS.lookup_addr(info.addr)||info.addr;return info}function ___sys_bind(fd,addr,addrlen){try{var sock=getSocketFromFD(fd);var info=getSocketAddress(addr,addrlen);sock.sock_ops.bind(sock,info.addr,info.port);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_chmod(path,mode){try{path=SYSCALLS.getStr(path);FS.chmod(path,mode);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_connect(fd,addr,addrlen){try{var sock=getSocketFromFD(fd);var info=getSocketAddress(addr,addrlen);sock.sock_ops.connect(sock,info.addr,info.port);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_dup2(oldfd,suggestFD){try{var old=SYSCALLS.getStreamFromFD(oldfd);if(old.fd===suggestFD)return suggestFD;return SYSCALLS.doDup(old.path,old.flags,suggestFD)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_fcntl64(fd,cmd,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(fd);switch(cmd){case 0:{var arg=SYSCALLS.get();if(arg<0){return-28}var newStream;newStream=FS.open(stream.path,stream.flags,0,arg);return newStream.fd}case 1:case 2:return 0;case 3:return stream.flags;case 4:{var arg=SYSCALLS.get();stream.flags|=arg;return 0}case 12:{var arg=SYSCALLS.get();var offset=0;HEAP16[arg+offset>>1]=2;return 0}case 13:case 14:return 0;case 16:case 8:return-28;case 9:setErrNo(28);return-1;default:{return-28}}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_fstat64(fd,buf){try{var stream=SYSCALLS.getStreamFromFD(fd);return SYSCALLS.doStat(FS.stat,stream.path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getcwd(buf,size){try{if(size===0)return-28;var cwd=FS.cwd();var cwdLengthInBytes=lengthBytesUTF8(cwd);if(size>>0,(tempDouble=id,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[dirp+pos>>2]=tempI64[0],HEAP32[dirp+pos+4>>2]=tempI64[1];tempI64=[(idx+1)*struct_size>>>0,(tempDouble=(idx+1)*struct_size,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[dirp+pos+8>>2]=tempI64[0],HEAP32[dirp+pos+12>>2]=tempI64[1];HEAP16[dirp+pos+16>>1]=280;HEAP8[dirp+pos+18>>0]=type;stringToUTF8(name,dirp+pos+19,256);pos+=struct_size;idx+=1}FS.llseek(stream,idx*struct_size,0);return pos}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getegid32(){return 0}function ___sys_geteuid32(){return ___sys_getegid32()}function ___sys_getpeername(fd,addr,addrlen){try{var sock=getSocketFromFD(fd);if(!sock.daddr){return-53}var errno=writeSockaddr(addr,sock.family,DNS.lookup_name(sock.daddr),sock.dport,addrlen);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getpid(){return 42}function ___sys_getrusage(who,usage){try{_memset(usage,0,136);HEAP32[usage>>2]=1;HEAP32[usage+4>>2]=2;HEAP32[usage+8>>2]=3;HEAP32[usage+12>>2]=4;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getsockname(fd,addr,addrlen){try{err("__sys_getsockname "+fd);var sock=getSocketFromFD(fd);var errno=writeSockaddr(addr,sock.family,DNS.lookup_name(sock.saddr||"0.0.0.0"),sock.sport,addrlen);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getsockopt(fd,level,optname,optval,optlen){try{var sock=getSocketFromFD(fd);if(level===1){if(optname===4){HEAP32[optval>>2]=sock.error;HEAP32[optlen>>2]=4;sock.error=null;return 0}}return-50}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getuid32(){return ___sys_getegid32()}function ___sys_ioctl(fd,op,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(fd);switch(op){case 21509:case 21505:{if(!stream.tty)return-59;return 0}case 21510:case 21511:case 21512:case 21506:case 21507:case 21508:{if(!stream.tty)return-59;return 0}case 21519:{if(!stream.tty)return-59;var argp=SYSCALLS.get();HEAP32[argp>>2]=0;return 0}case 21520:{if(!stream.tty)return-59;return-28}case 21531:{var argp=SYSCALLS.get();return FS.ioctl(stream,op,argp)}case 21523:{if(!stream.tty)return-59;return 0}case 21524:{if(!stream.tty)return-59;return 0}default:abort("bad ioctl syscall "+op)}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_listen(fd,backlog){try{var sock=getSocketFromFD(fd);sock.sock_ops.listen(sock,backlog);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_lstat64(path,buf){try{path=SYSCALLS.getStr(path);return SYSCALLS.doStat(FS.lstat,path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_mkdir(path,mode){try{path=SYSCALLS.getStr(path);return SYSCALLS.doMkdir(path,mode)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function syscallMmap2(addr,len,prot,flags,fd,off){off<<=12;var ptr;var allocated=false;if((flags&16)!==0&&addr%65536!==0){return-28}if((flags&32)!==0){ptr=_memalign(65536,len);if(!ptr)return-48;_memset(ptr,0,len);allocated=true}else{var info=FS.getStream(fd);if(!info)return-8;var res=FS.mmap(info,addr,len,off,prot,flags);ptr=res.ptr;allocated=res.allocated}SYSCALLS.mappings[ptr]={malloc:ptr,len:len,allocated:allocated,fd:fd,prot:prot,flags:flags,offset:off};return ptr}function ___sys_mmap2(addr,len,prot,flags,fd,off){try{return syscallMmap2(addr,len,prot,flags,fd,off)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function syscallMunmap(addr,len){if((addr|0)===-1||len===0){return-28}var info=SYSCALLS.mappings[addr];if(!info)return 0;if(len===info.len){var stream=FS.getStream(info.fd);if(stream){if(info.prot&2){SYSCALLS.doMsync(addr,stream,len,info.flags,info.offset)}FS.munmap(stream)}SYSCALLS.mappings[addr]=null;if(info.allocated){_free(info.malloc)}}return 0}function ___sys_munmap(addr,len){try{return syscallMunmap(addr,len)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_open(path,flags,varargs){SYSCALLS.varargs=varargs;try{var pathname=SYSCALLS.getStr(path);var mode=varargs?SYSCALLS.get():0;var stream=FS.open(pathname,flags,mode);return stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}var PIPEFS={BUCKET_BUFFER_SIZE:8192,mount:function(mount){return FS.createNode(null,"/",16384|511,0)},createPipe:function(){var pipe={buckets:[]};pipe.buckets.push({buffer:new Uint8Array(PIPEFS.BUCKET_BUFFER_SIZE),offset:0,roffset:0});var rName=PIPEFS.nextname();var wName=PIPEFS.nextname();var rNode=FS.createNode(PIPEFS.root,rName,4096,0);var wNode=FS.createNode(PIPEFS.root,wName,4096,0);rNode.pipe=pipe;wNode.pipe=pipe;var readableStream=FS.createStream({path:rName,node:rNode,flags:0,seekable:false,stream_ops:PIPEFS.stream_ops});rNode.stream=readableStream;var writableStream=FS.createStream({path:wName,node:wNode,flags:1,seekable:false,stream_ops:PIPEFS.stream_ops});wNode.stream=writableStream;return{readable_fd:readableStream.fd,writable_fd:writableStream.fd}},stream_ops:{poll:function(stream){var pipe=stream.node.pipe;if((stream.flags&2097155)===1){return 256|4}else{if(pipe.buckets.length>0){for(var i=0;i0){return 64|1}}}}return 0},ioctl:function(stream,request,varargs){return ERRNO_CODES.EINVAL},fsync:function(stream){return ERRNO_CODES.EINVAL},read:function(stream,buffer,offset,length,position){var pipe=stream.node.pipe;var currentLength=0;for(var i=0;i=dataLen){currBucket.buffer.set(data,currBucket.offset);currBucket.offset+=dataLen;return dataLen}else if(freeBytesInCurrBuffer>0){currBucket.buffer.set(data.subarray(0,freeBytesInCurrBuffer),currBucket.offset);currBucket.offset+=freeBytesInCurrBuffer;data=data.subarray(freeBytesInCurrBuffer,data.byteLength)}var numBuckets=data.byteLength/PIPEFS.BUCKET_BUFFER_SIZE|0;var remElements=data.byteLength%PIPEFS.BUCKET_BUFFER_SIZE;for(var i=0;i0){var newBucket={buffer:new Uint8Array(PIPEFS.BUCKET_BUFFER_SIZE),offset:data.byteLength,roffset:0};pipe.buckets.push(newBucket);newBucket.buffer.set(data)}return dataLen},close:function(stream){var pipe=stream.node.pipe;pipe.buckets=null}},nextname:function(){if(!PIPEFS.nextname.current){PIPEFS.nextname.current=0}return"pipe["+PIPEFS.nextname.current+++"]"}};function ___sys_pipe(fdPtr){try{if(fdPtr==0){throw new FS.ErrnoError(21)}var res=PIPEFS.createPipe();HEAP32[fdPtr>>2]=res.readable_fd;HEAP32[fdPtr+4>>2]=res.writable_fd;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_poll(fds,nfds,timeout){try{var nonzero=0;for(var i=0;i>2];var events=HEAP16[pollfd+4>>1];var mask=32;var stream=FS.getStream(fd);if(stream){mask=SYSCALLS.DEFAULT_POLLMASK;if(stream.stream_ops.poll){mask=stream.stream_ops.poll(stream)}}mask&=events|8|16;if(mask)nonzero++;HEAP16[pollfd+6>>1]=mask}return nonzero}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_readlink(path,buf,bufsize){try{path=SYSCALLS.getStr(path);return SYSCALLS.doReadlink(path,buf,bufsize)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_recvfrom(fd,buf,len,flags,addr,addrlen){try{var sock=getSocketFromFD(fd);var msg=sock.sock_ops.recvmsg(sock,len);if(!msg)return 0;if(addr){var errno=writeSockaddr(addr,sock.family,DNS.lookup_name(msg.addr),msg.port,addrlen)}HEAPU8.set(msg.buffer,buf);return msg.buffer.byteLength}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_recvmsg(fd,message,flags){try{var sock=getSocketFromFD(fd);var iov=HEAP32[message+8>>2];var num=HEAP32[message+12>>2];var total=0;for(var i=0;i>2]}var msg=sock.sock_ops.recvmsg(sock,total);if(!msg)return 0;var name=HEAP32[message>>2];if(name){var errno=writeSockaddr(name,sock.family,DNS.lookup_name(msg.addr),msg.port)}var bytesRead=0;var bytesRemaining=msg.buffer.byteLength;for(var i=0;bytesRemaining>0&&i>2];var iovlen=HEAP32[iov+(8*i+4)>>2];if(!iovlen){continue}var length=Math.min(iovlen,bytesRemaining);var buf=msg.buffer.subarray(bytesRead,bytesRead+length);HEAPU8.set(buf,iovbase+bytesRead);bytesRead+=length;bytesRemaining-=length}return bytesRead}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_rename(old_path,new_path){try{old_path=SYSCALLS.getStr(old_path);new_path=SYSCALLS.getStr(new_path);FS.rename(old_path,new_path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_rmdir(path){try{path=SYSCALLS.getStr(path);FS.rmdir(path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_sendmsg(fd,message,flags){try{var sock=getSocketFromFD(fd);var iov=HEAP32[message+8>>2];var num=HEAP32[message+12>>2];var addr,port;var name=HEAP32[message>>2];var namelen=HEAP32[message+4>>2];if(name){var info=readSockaddr(name,namelen);if(info.errno)return-info.errno;port=info.port;addr=DNS.lookup_addr(info.addr)||info.addr}var total=0;for(var i=0;i>2]}var view=new Uint8Array(total);var offset=0;for(var i=0;i>2];var iovlen=HEAP32[iov+(8*i+4)>>2];for(var j=0;j>0]}}return sock.sock_ops.sendmsg(sock,view,0,total,addr,port)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_sendto(fd,message,length,flags,addr,addr_len){try{var sock=getSocketFromFD(fd);var dest=getSocketAddress(addr,addr_len,true);if(!dest){return FS.write(sock.stream,HEAP8,message,length)}else{return sock.sock_ops.sendmsg(sock,HEAP8,message,length,dest.addr,dest.port)}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_setsockopt(fd){try{return-50}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_shutdown(fd,how){try{getSocketFromFD(fd);return-52}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_socket(domain,type,protocol){try{var sock=SOCKFS.createSocket(domain,type,protocol);return sock.stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_stat64(path,buf){try{path=SYSCALLS.getStr(path);return SYSCALLS.doStat(FS.stat,path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_statfs64(path,size,buf){try{path=SYSCALLS.getStr(path);HEAP32[buf+4>>2]=4096;HEAP32[buf+40>>2]=4096;HEAP32[buf+8>>2]=1e6;HEAP32[buf+12>>2]=5e5;HEAP32[buf+16>>2]=5e5;HEAP32[buf+20>>2]=FS.nextInode;HEAP32[buf+24>>2]=1e6;HEAP32[buf+28>>2]=42;HEAP32[buf+44>>2]=2;HEAP32[buf+36>>2]=255;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_truncate64(path,zero,low,high){try{path=SYSCALLS.getStr(path);var length=SYSCALLS.get64(low,high);FS.truncate(path,length);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_uname(buf){try{if(!buf)return-21;var layout={"__size__":390,"domainname":325,"machine":260,"nodename":65,"release":130,"sysname":0,"version":195};var copyString=function(element,value){var offset=layout[element];writeAsciiToMemory(value,buf+offset)};copyString("sysname","Emscripten");copyString("nodename","emscripten");copyString("release","1.0");copyString("version","#1");copyString("machine","wasm32");return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_unlink(path){try{path=SYSCALLS.getStr(path);FS.unlink(path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function _abort(){abort()}function _clock(){if(_clock.start===undefined)_clock.start=Date.now();return(Date.now()-_clock.start)*(1e6/1e3)|0}function _emscripten_get_now_res(){if(ENVIRONMENT_IS_NODE){return 1}else if(typeof dateNow!=="undefined"){return 1e3}else return 1e3}var _emscripten_get_now_is_monotonic=true;function _clock_getres(clk_id,res){var nsec;if(clk_id===0){nsec=1e3*1e3}else if(clk_id===1&&_emscripten_get_now_is_monotonic){nsec=_emscripten_get_now_res()}else{setErrNo(28);return-1}HEAP32[res>>2]=nsec/1e9|0;HEAP32[res+4>>2]=nsec;return 0}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process["hrtime"]();return t[0]*1e3+t[1]/1e6}}else if(typeof dateNow!=="undefined"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function _clock_gettime(clk_id,tp){var now;if(clk_id===0){now=Date.now()}else if((clk_id===1||clk_id===4)&&_emscripten_get_now_is_monotonic){now=_emscripten_get_now()}else{setErrNo(28);return-1}HEAP32[tp>>2]=now/1e3|0;HEAP32[tp+4>>2]=now%1e3*1e3*1e3|0;return 0}function _difftime(time1,time0){return time1-time0}function _dlclose(handle){}function _dlerror(){return 0}function _dlopen(filename,flag){}function _dlsym(handle,symbol){return 0}var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=HEAPU8[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?HEAPF64[buf++>>1]:HEAP32[buf]);++buf}return readAsmConstArgsArray}function mainThreadEM_ASM(code,sigPtr,argbuf,sync){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_asm_const_int_sync_on_main_thread(code,sigPtr,argbuf){return mainThreadEM_ASM(code,sigPtr,argbuf,1)}function _emscripten_set_main_loop_timing(mode,value){Browser.mainLoop.timingMode=mode;Browser.mainLoop.timingValue=value;if(!Browser.mainLoop.func){return 1}if(!Browser.mainLoop.running){Browser.mainLoop.running=true}if(mode==0){Browser.mainLoop.scheduler=function Browser_mainLoop_scheduler_setTimeout(){var timeUntilNextTick=Math.max(0,Browser.mainLoop.tickStartTime+value-_emscripten_get_now())|0;setTimeout(Browser.mainLoop.runner,timeUntilNextTick)};Browser.mainLoop.method="timeout"}else if(mode==1){Browser.mainLoop.scheduler=function Browser_mainLoop_scheduler_rAF(){Browser.requestAnimationFrame(Browser.mainLoop.runner)};Browser.mainLoop.method="rAF"}else if(mode==2){if(typeof setImmediate==="undefined"){var setImmediates=[];var emscriptenMainLoopMessageId="setimmediate";var Browser_setImmediate_messageHandler=function(event){if(event.data===emscriptenMainLoopMessageId||event.data.target===emscriptenMainLoopMessageId){event.stopPropagation();setImmediates.shift()()}};addEventListener("message",Browser_setImmediate_messageHandler,true);setImmediate=function Browser_emulated_setImmediate(func){setImmediates.push(func);if(ENVIRONMENT_IS_WORKER){if(Module["setImmediates"]===undefined)Module["setImmediates"]=[];Module["setImmediates"].push(func);postMessage({target:emscriptenMainLoopMessageId})}else postMessage(emscriptenMainLoopMessageId,"*")}}Browser.mainLoop.scheduler=function Browser_mainLoop_scheduler_setImmediate(){setImmediate(Browser.mainLoop.runner)};Browser.mainLoop.method="immediate"}return 0}function _exit(status){exit(status)}function maybeExit(){if(!keepRuntimeAlive()){try{_exit(EXITSTATUS)}catch(e){if(e instanceof ExitStatus){return}throw e}}}function setMainLoop(browserIterationFunc,fps,simulateInfiniteLoop,arg,noSetTiming){assert(!Browser.mainLoop.func,"emscripten_set_main_loop: there can only be one main loop function at once: call emscripten_cancel_main_loop to cancel the previous one before setting a new one with different parameters.");Browser.mainLoop.func=browserIterationFunc;Browser.mainLoop.arg=arg;var thisMainLoopId=Browser.mainLoop.currentlyRunningMainloop;function checkIsRunning(){if(thisMainLoopId0){var start=Date.now();var blocker=Browser.mainLoop.queue.shift();blocker.func(blocker.arg);if(Browser.mainLoop.remainingBlockers){var remaining=Browser.mainLoop.remainingBlockers;var next=remaining%1==0?remaining-1:Math.floor(remaining);if(blocker.counted){Browser.mainLoop.remainingBlockers=next}else{next=next+.5;Browser.mainLoop.remainingBlockers=(8*remaining+next)/9}}console.log('main loop blocker "'+blocker.name+'" took '+(Date.now()-start)+" ms");Browser.mainLoop.updateStatus();if(!checkIsRunning())return;setTimeout(Browser.mainLoop.runner,0);return}if(!checkIsRunning())return;Browser.mainLoop.currentFrameNumber=Browser.mainLoop.currentFrameNumber+1|0;if(Browser.mainLoop.timingMode==1&&Browser.mainLoop.timingValue>1&&Browser.mainLoop.currentFrameNumber%Browser.mainLoop.timingValue!=0){Browser.mainLoop.scheduler();return}else if(Browser.mainLoop.timingMode==0){Browser.mainLoop.tickStartTime=_emscripten_get_now()}GL.newRenderingFrameStarted();Browser.mainLoop.runIter(browserIterationFunc);if(!checkIsRunning())return;if(typeof SDL==="object"&&SDL.audio&&SDL.audio.queueNewAudioData)SDL.audio.queueNewAudioData();Browser.mainLoop.scheduler()};if(!noSetTiming){if(fps&&fps>0)_emscripten_set_main_loop_timing(0,1e3/fps);else _emscripten_set_main_loop_timing(1,1);Browser.mainLoop.scheduler()}if(simulateInfiniteLoop){throw"unwind"}}function callUserCallback(func,synchronous){if(ABORT){return}if(synchronous){func();return}try{func()}catch(e){if(e instanceof ExitStatus){return}else if(e!=="unwind"){if(e&&typeof e==="object"&&e.stack)err("exception thrown: "+[e,e.stack]);throw e}}}var Browser={mainLoop:{running:false,scheduler:null,method:"",currentlyRunningMainloop:0,func:null,arg:0,timingMode:0,timingValue:0,currentFrameNumber:0,queue:[],pause:function(){Browser.mainLoop.scheduler=null;Browser.mainLoop.currentlyRunningMainloop++},resume:function(){Browser.mainLoop.currentlyRunningMainloop++;var timingMode=Browser.mainLoop.timingMode;var timingValue=Browser.mainLoop.timingValue;var func=Browser.mainLoop.func;Browser.mainLoop.func=null;setMainLoop(func,0,false,Browser.mainLoop.arg,true);_emscripten_set_main_loop_timing(timingMode,timingValue);Browser.mainLoop.scheduler()},updateStatus:function(){if(Module["setStatus"]){var message=Module["statusMessage"]||"Please wait...";var remaining=Browser.mainLoop.remainingBlockers;var expected=Browser.mainLoop.expectedBlockers;if(remaining){if(remaining=6){var curr=leftchar>>leftbits-6&63;leftbits-=6;ret+=BASE[curr]}}if(leftbits==2){ret+=BASE[(leftchar&3)<<4];ret+=PAD+PAD}else if(leftbits==4){ret+=BASE[(leftchar&15)<<2];ret+=PAD}return ret}audio.src="data:audio/x-"+name.substr(-3)+";base64,"+encode64(byteArray);finish(audio)};audio.src=url;Browser.safeSetTimeout(function(){finish(audio)},1e4)}else{return fail()}};Module["preloadPlugins"].push(audioPlugin);function pointerLockChange(){Browser.pointerLock=document["pointerLockElement"]===Module["canvas"]||document["mozPointerLockElement"]===Module["canvas"]||document["webkitPointerLockElement"]===Module["canvas"]||document["msPointerLockElement"]===Module["canvas"]}var canvas=Module["canvas"];if(canvas){canvas.requestPointerLock=canvas["requestPointerLock"]||canvas["mozRequestPointerLock"]||canvas["webkitRequestPointerLock"]||canvas["msRequestPointerLock"]||function(){};canvas.exitPointerLock=document["exitPointerLock"]||document["mozExitPointerLock"]||document["webkitExitPointerLock"]||document["msExitPointerLock"]||function(){};canvas.exitPointerLock=canvas.exitPointerLock.bind(document);document.addEventListener("pointerlockchange",pointerLockChange,false);document.addEventListener("mozpointerlockchange",pointerLockChange,false);document.addEventListener("webkitpointerlockchange",pointerLockChange,false);document.addEventListener("mspointerlockchange",pointerLockChange,false);if(Module["elementPointerLock"]){canvas.addEventListener("click",function(ev){if(!Browser.pointerLock&&Module["canvas"].requestPointerLock){Module["canvas"].requestPointerLock();ev.preventDefault()}},false)}}},createContext:function(canvas,useWebGL,setInModule,webGLContextAttributes){if(useWebGL&&Module.ctx&&canvas==Module.canvas)return Module.ctx;var ctx;var contextHandle;if(useWebGL){var contextAttributes={antialias:false,alpha:false,majorVersion:typeof WebGL2RenderingContext!=="undefined"?2:1};if(webGLContextAttributes){for(var attribute in webGLContextAttributes){contextAttributes[attribute]=webGLContextAttributes[attribute]}}if(typeof GL!=="undefined"){contextHandle=GL.createContext(canvas,contextAttributes);if(contextHandle){ctx=GL.getContext(contextHandle).GLctx}}}else{ctx=canvas.getContext("2d")}if(!ctx)return null;if(setInModule){if(!useWebGL)assert(typeof GLctx==="undefined","cannot set in module if GLctx is used, but we are a non-GL context that would replace it");Module.ctx=ctx;if(useWebGL)GL.makeContextCurrent(contextHandle);Module.useWebGL=useWebGL;Browser.moduleContextCreatedCallbacks.forEach(function(callback){callback()});Browser.init()}return ctx},destroyContext:function(canvas,useWebGL,setInModule){},fullscreenHandlersInstalled:false,lockPointer:undefined,resizeCanvas:undefined,requestFullscreen:function(lockPointer,resizeCanvas){Browser.lockPointer=lockPointer;Browser.resizeCanvas=resizeCanvas;if(typeof Browser.lockPointer==="undefined")Browser.lockPointer=true;if(typeof Browser.resizeCanvas==="undefined")Browser.resizeCanvas=false;var canvas=Module["canvas"];function fullscreenChange(){Browser.isFullscreen=false;var canvasContainer=canvas.parentNode;if((document["fullscreenElement"]||document["mozFullScreenElement"]||document["msFullscreenElement"]||document["webkitFullscreenElement"]||document["webkitCurrentFullScreenElement"])===canvasContainer){canvas.exitFullscreen=Browser.exitFullscreen;if(Browser.lockPointer)canvas.requestPointerLock();Browser.isFullscreen=true;if(Browser.resizeCanvas){Browser.setFullscreenCanvasSize()}else{Browser.updateCanvasDimensions(canvas)}}else{canvasContainer.parentNode.insertBefore(canvas,canvasContainer);canvasContainer.parentNode.removeChild(canvasContainer);if(Browser.resizeCanvas){Browser.setWindowedCanvasSize()}else{Browser.updateCanvasDimensions(canvas)}}if(Module["onFullScreen"])Module["onFullScreen"](Browser.isFullscreen);if(Module["onFullscreen"])Module["onFullscreen"](Browser.isFullscreen)}if(!Browser.fullscreenHandlersInstalled){Browser.fullscreenHandlersInstalled=true;document.addEventListener("fullscreenchange",fullscreenChange,false);document.addEventListener("mozfullscreenchange",fullscreenChange,false);document.addEventListener("webkitfullscreenchange",fullscreenChange,false);document.addEventListener("MSFullscreenChange",fullscreenChange,false)}var canvasContainer=document.createElement("div");canvas.parentNode.insertBefore(canvasContainer,canvas);canvasContainer.appendChild(canvas);canvasContainer.requestFullscreen=canvasContainer["requestFullscreen"]||canvasContainer["mozRequestFullScreen"]||canvasContainer["msRequestFullscreen"]||(canvasContainer["webkitRequestFullscreen"]?function(){canvasContainer["webkitRequestFullscreen"](Element["ALLOW_KEYBOARD_INPUT"])}:null)||(canvasContainer["webkitRequestFullScreen"]?function(){canvasContainer["webkitRequestFullScreen"](Element["ALLOW_KEYBOARD_INPUT"])}:null);canvasContainer.requestFullscreen()},exitFullscreen:function(){if(!Browser.isFullscreen){return false}var CFS=document["exitFullscreen"]||document["cancelFullScreen"]||document["mozCancelFullScreen"]||document["msExitFullscreen"]||document["webkitCancelFullScreen"]||function(){};CFS.apply(document,[]);return true},nextRAF:0,fakeRequestAnimationFrame:function(func){var now=Date.now();if(Browser.nextRAF===0){Browser.nextRAF=now+1e3/60}else{while(now+2>=Browser.nextRAF){Browser.nextRAF+=1e3/60}}var delay=Math.max(Browser.nextRAF-now,0);setTimeout(func,delay)},requestAnimationFrame:function(func){if(typeof requestAnimationFrame==="function"){requestAnimationFrame(func);return}var RAF=Browser.fakeRequestAnimationFrame;RAF(func)},safeRequestAnimationFrame:function(func){return Browser.requestAnimationFrame(function(){callUserCallback(func)})},safeSetTimeout:function(func,timeout){return setTimeout(function(){callUserCallback(func)},timeout)},getMimetype:function(name){return{"jpg":"image/jpeg","jpeg":"image/jpeg","png":"image/png","bmp":"image/bmp","ogg":"audio/ogg","wav":"audio/wav","mp3":"audio/mpeg"}[name.substr(name.lastIndexOf(".")+1)]},getUserMedia:function(func){if(!window.getUserMedia){window.getUserMedia=navigator["getUserMedia"]||navigator["mozGetUserMedia"]}window.getUserMedia(func)},getMovementX:function(event){return event["movementX"]||event["mozMovementX"]||event["webkitMovementX"]||0},getMovementY:function(event){return event["movementY"]||event["mozMovementY"]||event["webkitMovementY"]||0},getMouseWheelDelta:function(event){var delta=0;switch(event.type){case"DOMMouseScroll":delta=event.detail/3;break;case"mousewheel":delta=event.wheelDelta/120;break;case"wheel":delta=event.deltaY;switch(event.deltaMode){case 0:delta/=100;break;case 1:delta/=3;break;case 2:delta*=80;break;default:throw"unrecognized mouse wheel delta mode: "+event.deltaMode}break;default:throw"unrecognized mouse wheel event: "+event.type}return delta},mouseX:0,mouseY:0,mouseMovementX:0,mouseMovementY:0,touches:{},lastTouches:{},calculateMouseEvent:function(event){if(Browser.pointerLock){if(event.type!="mousemove"&&"mozMovementX"in event){Browser.mouseMovementX=Browser.mouseMovementY=0}else{Browser.mouseMovementX=Browser.getMovementX(event);Browser.mouseMovementY=Browser.getMovementY(event)}if(typeof SDL!="undefined"){Browser.mouseX=SDL.mouseX+Browser.mouseMovementX;Browser.mouseY=SDL.mouseY+Browser.mouseMovementY}else{Browser.mouseX+=Browser.mouseMovementX;Browser.mouseY+=Browser.mouseMovementY}}else{var rect=Module["canvas"].getBoundingClientRect();var cw=Module["canvas"].width;var ch=Module["canvas"].height;var scrollX=typeof window.scrollX!=="undefined"?window.scrollX:window.pageXOffset;var scrollY=typeof window.scrollY!=="undefined"?window.scrollY:window.pageYOffset;if(event.type==="touchstart"||event.type==="touchend"||event.type==="touchmove"){var touch=event.touch;if(touch===undefined){return}var adjustedX=touch.pageX-(scrollX+rect.left);var adjustedY=touch.pageY-(scrollY+rect.top);adjustedX=adjustedX*(cw/rect.width);adjustedY=adjustedY*(ch/rect.height);var coords={x:adjustedX,y:adjustedY};if(event.type==="touchstart"){Browser.lastTouches[touch.identifier]=coords;Browser.touches[touch.identifier]=coords}else if(event.type==="touchend"||event.type==="touchmove"){var last=Browser.touches[touch.identifier];if(!last)last=coords;Browser.lastTouches[touch.identifier]=last;Browser.touches[touch.identifier]=coords}return}var x=event.pageX-(scrollX+rect.left);var y=event.pageY-(scrollY+rect.top);x=x*(cw/rect.width);y=y*(ch/rect.height);Browser.mouseMovementX=x-Browser.mouseX;Browser.mouseMovementY=y-Browser.mouseY;Browser.mouseX=x;Browser.mouseY=y}},asyncLoad:function(url,onload,onerror,noRunDep){var dep=!noRunDep?getUniqueRunDependency("al "+url):"";readAsync(url,function(arrayBuffer){assert(arrayBuffer,'Loading data file "'+url+'" failed (no arrayBuffer).');onload(new Uint8Array(arrayBuffer));if(dep)removeRunDependency(dep)},function(event){if(onerror){onerror()}else{throw'Loading data file "'+url+'" failed.'}});if(dep)addRunDependency(dep)},resizeListeners:[],updateResizeListeners:function(){var canvas=Module["canvas"];Browser.resizeListeners.forEach(function(listener){listener(canvas.width,canvas.height)})},setCanvasSize:function(width,height,noUpdates){var canvas=Module["canvas"];Browser.updateCanvasDimensions(canvas,width,height);if(!noUpdates)Browser.updateResizeListeners()},windowedWidth:0,windowedHeight:0,setFullscreenCanvasSize:function(){if(typeof SDL!="undefined"){var flags=HEAPU32[SDL.screen>>2];flags=flags|8388608;HEAP32[SDL.screen>>2]=flags}Browser.updateCanvasDimensions(Module["canvas"]);Browser.updateResizeListeners()},setWindowedCanvasSize:function(){if(typeof SDL!="undefined"){var flags=HEAPU32[SDL.screen>>2];flags=flags&~8388608;HEAP32[SDL.screen>>2]=flags}Browser.updateCanvasDimensions(Module["canvas"]);Browser.updateResizeListeners()},updateCanvasDimensions:function(canvas,wNative,hNative){if(wNative&&hNative){canvas.widthNative=wNative;canvas.heightNative=hNative}else{wNative=canvas.widthNative;hNative=canvas.heightNative}var w=wNative;var h=hNative;if(Module["forcedAspectRatio"]&&Module["forcedAspectRatio"]>0){if(w/h=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!=="undefined"?document:0,typeof window!=="undefined"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!=="undefined"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_get_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;HEAP32[width>>2]=canvas.width;HEAP32[height>>2]=canvas.height}function getCanvasElementSize(target){var stackTop=stackSave();var w=stackAlloc(8);var h=w+4;var targetInt=stackAlloc(target.id.length+1);stringToUTF8(target.id,targetInt,target.id.length+1);var ret=_emscripten_get_canvas_element_size(targetInt,w,h);var size=[HEAP32[w>>2],HEAP32[h>>2]];stackRestore(stackTop);return size}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;canvas.width=width;canvas.height=height;return 0}function setCanvasElementSize(target,width,height){if(!target.controlTransferredOffscreen){target.width=width;target.height=height}else{var stackTop=stackSave();var targetInt=stackAlloc(target.id.length+1);stringToUTF8(target.id,targetInt,target.id.length+1);_emscripten_set_canvas_element_size(targetInt,width,height);stackRestore(stackTop)}}function registerRestoreOldStyle(canvas){var canvasSize=getCanvasElementSize(canvas);var oldWidth=canvasSize[0];var oldHeight=canvasSize[1];var oldCssWidth=canvas.style.width;var oldCssHeight=canvas.style.height;var oldBackgroundColor=canvas.style.backgroundColor;var oldDocumentBackgroundColor=document.body.style.backgroundColor;var oldPaddingLeft=canvas.style.paddingLeft;var oldPaddingRight=canvas.style.paddingRight;var oldPaddingTop=canvas.style.paddingTop;var oldPaddingBottom=canvas.style.paddingBottom;var oldMarginLeft=canvas.style.marginLeft;var oldMarginRight=canvas.style.marginRight;var oldMarginTop=canvas.style.marginTop;var oldMarginBottom=canvas.style.marginBottom;var oldDocumentBodyMargin=document.body.style.margin;var oldDocumentOverflow=document.documentElement.style.overflow;var oldDocumentScroll=document.body.scroll;var oldImageRendering=canvas.style.imageRendering;function restoreOldStyle(){var fullscreenElement=document.fullscreenElement||document.webkitFullscreenElement||document.msFullscreenElement;if(!fullscreenElement){document.removeEventListener("fullscreenchange",restoreOldStyle);document.removeEventListener("webkitfullscreenchange",restoreOldStyle);setCanvasElementSize(canvas,oldWidth,oldHeight);canvas.style.width=oldCssWidth;canvas.style.height=oldCssHeight;canvas.style.backgroundColor=oldBackgroundColor;if(!oldDocumentBackgroundColor)document.body.style.backgroundColor="white";document.body.style.backgroundColor=oldDocumentBackgroundColor;canvas.style.paddingLeft=oldPaddingLeft;canvas.style.paddingRight=oldPaddingRight;canvas.style.paddingTop=oldPaddingTop;canvas.style.paddingBottom=oldPaddingBottom;canvas.style.marginLeft=oldMarginLeft;canvas.style.marginRight=oldMarginRight;canvas.style.marginTop=oldMarginTop;canvas.style.marginBottom=oldMarginBottom;document.body.style.margin=oldDocumentBodyMargin;document.documentElement.style.overflow=oldDocumentOverflow;document.body.scroll=oldDocumentScroll;canvas.style.imageRendering=oldImageRendering;if(canvas.GLctxObject)canvas.GLctxObject.GLctx.viewport(0,0,oldWidth,oldHeight);if(currentFullscreenStrategy.canvasResizedCallback){(function(a1,a2,a3){return dynCall_iiii.apply(null,[currentFullscreenStrategy.canvasResizedCallback,a1,a2,a3])})(37,0,currentFullscreenStrategy.canvasResizedCallbackUserData)}}}document.addEventListener("fullscreenchange",restoreOldStyle);document.addEventListener("webkitfullscreenchange",restoreOldStyle);return restoreOldStyle}function setLetterbox(element,topBottom,leftRight){element.style.paddingLeft=element.style.paddingRight=leftRight+"px";element.style.paddingTop=element.style.paddingBottom=topBottom+"px"}function getBoundingClientRect(e){return specialHTMLTargets.indexOf(e)<0?e.getBoundingClientRect():{"left":0,"top":0}}function _JSEvents_resizeCanvasForFullscreen(target,strategy){var restoreOldStyle=registerRestoreOldStyle(target);var cssWidth=strategy.softFullscreen?innerWidth:screen.width;var cssHeight=strategy.softFullscreen?innerHeight:screen.height;var rect=getBoundingClientRect(target);var windowedCssWidth=rect.width;var windowedCssHeight=rect.height;var canvasSize=getCanvasElementSize(target);var windowedRttWidth=canvasSize[0];var windowedRttHeight=canvasSize[1];if(strategy.scaleMode==3){setLetterbox(target,(cssHeight-windowedCssHeight)/2,(cssWidth-windowedCssWidth)/2);cssWidth=windowedCssWidth;cssHeight=windowedCssHeight}else if(strategy.scaleMode==2){if(cssWidth*windowedRttHeight>2]=isFullscreen;HEAP32[eventStruct+4>>2]=JSEvents.fullscreenEnabled();var reportedElement=isFullscreen?fullscreenElement:JSEvents.previousFullscreenElement;var nodeName=JSEvents.getNodeNameForTarget(reportedElement);var id=reportedElement&&reportedElement.id?reportedElement.id:"";stringToUTF8(nodeName,eventStruct+8,128);stringToUTF8(id,eventStruct+136,128);HEAP32[eventStruct+264>>2]=reportedElement?reportedElement.clientWidth:0;HEAP32[eventStruct+268>>2]=reportedElement?reportedElement.clientHeight:0;HEAP32[eventStruct+272>>2]=screen.width;HEAP32[eventStruct+276>>2]=screen.height;if(isFullscreen){JSEvents.previousFullscreenElement=fullscreenElement}}function _emscripten_get_fullscreen_status(fullscreenStatus){if(!JSEvents.fullscreenEnabled())return-1;fillFullscreenChangeEventData(fullscreenStatus);return 0}function fillGamepadEventData(eventStruct,e){HEAPF64[eventStruct>>3]=e.timestamp;for(var i=0;i>3]=e.axes[i]}for(var i=0;i>3]=e.buttons[i].value}else{HEAPF64[eventStruct+i*8+528>>3]=e.buttons[i]}}for(var i=0;i>2]=e.buttons[i].pressed}else{HEAP32[eventStruct+i*4+1040>>2]=e.buttons[i]==1}}HEAP32[eventStruct+1296>>2]=e.connected;HEAP32[eventStruct+1300>>2]=e.index;HEAP32[eventStruct+8>>2]=e.axes.length;HEAP32[eventStruct+12>>2]=e.buttons.length;stringToUTF8(e.id,eventStruct+1304,64);stringToUTF8(e.mapping,eventStruct+1368,64)}function _emscripten_get_gamepad_status(index,gamepadState){if(index<0||index>=JSEvents.lastGamepadState.length)return-5;if(!JSEvents.lastGamepadState[index])return-7;fillGamepadEventData(gamepadState,JSEvents.lastGamepadState[index]);return 0}function _emscripten_get_heap_max(){return 2147483648}function _emscripten_get_num_gamepads(){return JSEvents.lastGamepadState.length}function _emscripten_html5_remove_all_event_listeners(){JSEvents.removeAllEventListeners()}function _emscripten_is_webgl_context_lost(contextHandle){return!GL.contexts[contextHandle]||GL.contexts[contextHandle].GLctx.isContextLost()}function reallyNegative(x){return x<0||x===0&&1/x===-Infinity}function convertI32PairToI53(lo,hi){return(lo>>>0)+hi*4294967296}function convertU32PairToI53(lo,hi){return(lo>>>0)+(hi>>>0)*4294967296}function reSign(value,bits){if(value<=0){return value}var half=bits<=32?Math.abs(1<=half&&(bits<=32||value>half)){value=-2*half+value}return value}function unSign(value,bits){if(value>=0){return value}return bits<=32?2*Math.abs(1<>3];argIndex+=8}else if(type=="i64"){ret=[HEAP32[argIndex>>2],HEAP32[argIndex+4>>2]];argIndex+=8}else{type="i32";ret=HEAP32[argIndex>>2];argIndex+=4}return ret}var ret=[];var curr,next,currArg;while(1){var startTextIndex=textIndex;curr=HEAP8[textIndex>>0];if(curr===0)break;next=HEAP8[textIndex+1>>0];if(curr==37){var flagAlwaysSigned=false;var flagLeftAlign=false;var flagAlternative=false;var flagZeroPad=false;var flagPadSign=false;flagsLoop:while(1){switch(next){case 43:flagAlwaysSigned=true;break;case 45:flagLeftAlign=true;break;case 35:flagAlternative=true;break;case 48:if(flagZeroPad){break flagsLoop}else{flagZeroPad=true;break}case 32:flagPadSign=true;break;default:break flagsLoop}textIndex++;next=HEAP8[textIndex+1>>0]}var width=0;if(next==42){width=getNextArg("i32");textIndex++;next=HEAP8[textIndex+1>>0]}else{while(next>=48&&next<=57){width=width*10+(next-48);textIndex++;next=HEAP8[textIndex+1>>0]}}var precisionSet=false,precision=-1;if(next==46){precision=0;precisionSet=true;textIndex++;next=HEAP8[textIndex+1>>0];if(next==42){precision=getNextArg("i32");textIndex++}else{while(1){var precisionChr=HEAP8[textIndex+1>>0];if(precisionChr<48||precisionChr>57)break;precision=precision*10+(precisionChr-48);textIndex++}}next=HEAP8[textIndex+1>>0]}if(precision<0){precision=6;precisionSet=false}var argSize;switch(String.fromCharCode(next)){case"h":var nextNext=HEAP8[textIndex+2>>0];if(nextNext==104){textIndex++;argSize=1}else{argSize=2}break;case"l":var nextNext=HEAP8[textIndex+2>>0];if(nextNext==108){textIndex++;argSize=8}else{argSize=4}break;case"L":case"q":case"j":argSize=8;break;case"z":case"t":case"I":argSize=4;break;default:argSize=null}if(argSize)textIndex++;next=HEAP8[textIndex+1>>0];switch(String.fromCharCode(next)){case"d":case"i":case"u":case"o":case"x":case"X":case"p":{var signed=next==100||next==105;argSize=argSize||4;currArg=getNextArg("i"+argSize*8);var argText;if(argSize==8){currArg=next==117?convertU32PairToI53(currArg[0],currArg[1]):convertI32PairToI53(currArg[0],currArg[1])}if(argSize<=4){var limit=Math.pow(256,argSize)-1;currArg=(signed?reSign:unSign)(currArg&limit,argSize*8)}var currAbsArg=Math.abs(currArg);var prefix="";if(next==100||next==105){argText=reSign(currArg,8*argSize,1).toString(10)}else if(next==117){argText=unSign(currArg,8*argSize,1).toString(10);currArg=Math.abs(currArg)}else if(next==111){argText=(flagAlternative?"0":"")+currAbsArg.toString(8)}else if(next==120||next==88){prefix=flagAlternative&&currArg!=0?"0x":"";if(currArg<0){currArg=-currArg;argText=(currAbsArg-1).toString(16);var buffer=[];for(var i=0;i=0){if(flagAlwaysSigned){prefix="+"+prefix}else if(flagPadSign){prefix=" "+prefix}}if(argText.charAt(0)=="-"){prefix="-"+prefix;argText=argText.substr(1)}while(prefix.length+argText.lengthexponent&&exponent>=-4){next=(next==103?"f":"F").charCodeAt(0);precision-=exponent+1}else{next=(next==103?"e":"E").charCodeAt(0);precision--}effectivePrecision=Math.min(precision,20)}if(next==101||next==69){argText=currArg.toExponential(effectivePrecision);if(/[eE][-+]\d$/.test(argText)){argText=argText.slice(0,-1)+"0"+argText.slice(-1)}}else if(next==102||next==70){argText=currArg.toFixed(effectivePrecision);if(currArg===0&&reallyNegative(currArg)){argText="-"+argText}}var parts=argText.split("e");if(isGeneral&&!flagAlternative){while(parts[0].length>1&&parts[0].includes(".")&&(parts[0].slice(-1)=="0"||parts[0].slice(-1)==".")){parts[0]=parts[0].slice(0,-1)}}else{if(flagAlternative&&argText.indexOf(".")==-1)parts[0]+=".";while(precision>effectivePrecision++)parts[0]+="0"}argText=parts[0]+(parts.length>1?"e"+parts[1]:"");if(next==69)argText=argText.toUpperCase();if(currArg>=0){if(flagAlwaysSigned){argText="+"+argText}else if(flagPadSign){argText=" "+argText}}}while(argText.length>0])}}else{ret=ret.concat(intArrayFromString("(null)".substr(0,argLength),true))}if(flagLeftAlign){while(argLength0){ret.push(32)}if(!flagLeftAlign)ret.push(getNextArg("i8"));break}case"n":{var ptr=getNextArg("i32*");HEAP32[ptr>>2]=ret.length;break}case"%":{ret.push(curr);break}default:{for(var i=startTextIndex;i>0])}}}textIndex+=2}else{ret.push(curr);textIndex+=1}}return ret}function traverseStack(args){if(!args||!args.callee||!args.callee.name){return[null,"",""]}var funstr=args.callee.toString();var funcname=args.callee.name;var str="(";var first=true;for(var i in args){var a=args[i];if(!first){str+=", "}first=false;if(typeof a==="number"||typeof a==="string"){str+=a}else{str+="("+typeof a+")"}}str+=")";var caller=args.callee.caller;args=caller?caller.arguments:[];if(first)str="";return[args,funcname,str]}function _emscripten_get_callstack_js(flags){var callstack=jsStackTrace();var iThisFunc=callstack.lastIndexOf("_emscripten_log");var iThisFunc2=callstack.lastIndexOf("_emscripten_get_callstack");var iNextLine=callstack.indexOf("\n",Math.max(iThisFunc,iThisFunc2))+1;callstack=callstack.slice(iNextLine);if(flags&32){warnOnce("EM_LOG_DEMANGLE is deprecated; ignoring")}if(flags&8&&typeof emscripten_source_map==="undefined"){warnOnce('Source map information is not available, emscripten_log with EM_LOG_C_STACK will be ignored. Build with "--pre-js $EMSCRIPTEN/src/emscripten-source-map.min.js" linker flag to add source map loading to code.');flags^=8;flags|=16}var stack_args=null;if(flags&128){stack_args=traverseStack(arguments);while(stack_args[1].includes("_emscripten_"))stack_args=traverseStack(stack_args[0])}var lines=callstack.split("\n");callstack="";var newFirefoxRe=new RegExp("\\s*(.*?)@(.*?):([0-9]+):([0-9]+)");var firefoxRe=new RegExp("\\s*(.*?)@(.*):(.*)(:(.*))?");var chromeRe=new RegExp("\\s*at (.*?) \\((.*):(.*):(.*)\\)");for(var l in lines){var line=lines[l];var symbolName="";var file="";var lineno=0;var column=0;var parts=chromeRe.exec(line);if(parts&&parts.length==5){symbolName=parts[1];file=parts[2];lineno=parts[3];column=parts[4]}else{parts=newFirefoxRe.exec(line);if(!parts)parts=firefoxRe.exec(line);if(parts&&parts.length>=4){symbolName=parts[1];file=parts[2];lineno=parts[3];column=parts[4]|0}else{callstack+=line+"\n";continue}}var haveSourceMap=false;if(flags&8){var orig=emscripten_source_map.originalPositionFor({line:lineno,column:column});haveSourceMap=orig&&orig.source;if(haveSourceMap){if(flags&64){orig.source=orig.source.substring(orig.source.replace(/\\/g,"/").lastIndexOf("/")+1)}callstack+=" at "+symbolName+" ("+orig.source+":"+orig.line+":"+orig.column+")\n"}}if(flags&16||!haveSourceMap){if(flags&64){file=file.substring(file.replace(/\\/g,"/").lastIndexOf("/")+1)}callstack+=(haveSourceMap?" = "+symbolName:" at "+symbolName)+" ("+file+":"+lineno+":"+column+")\n"}if(flags&128&&stack_args[0]){if(stack_args[1]==symbolName&&stack_args[2].length>0){callstack=callstack.replace(/\s+$/,"");callstack+=" with values: "+stack_args[1]+stack_args[2]+"\n"}stack_args=traverseStack(stack_args[0])}}callstack=callstack.replace(/\s+$/,"");return callstack}function _emscripten_log_js(flags,str){if(flags&24){str=str.replace(/\s+$/,"");str+=(str.length>0?"\n":"")+_emscripten_get_callstack_js(flags)}if(flags&1){if(flags&4){console.error(str)}else if(flags&2){console.warn(str)}else if(flags&512){console.info(str)}else if(flags&256){console.debug(str)}else{console.log(str)}}else if(flags&6){err(str)}else{out(str)}}function _emscripten_log(flags,format,varargs){var result=formatString(format,varargs);var str=UTF8ArrayToString(result,0);_emscripten_log_js(flags,str)}function _longjmp(env,value){_setThrew(env,value||1);throw"longjmp"}function _emscripten_longjmp(a0,a1){return _longjmp(a0,a1)}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function doRequestFullscreen(target,strategy){if(!JSEvents.fullscreenEnabled())return-1;target=findEventTarget(target);if(!target)return-4;if(!target.requestFullscreen&&!target.webkitRequestFullscreen){return-3}var canPerformRequests=JSEvents.canPerformEventHandlerRequests();if(!canPerformRequests){if(strategy.deferUntilInEventHandler){JSEvents.deferCall(_JSEvents_requestFullscreen,1,[target,strategy]);return 1}else{return-2}}return _JSEvents_requestFullscreen(target,strategy)}function _emscripten_request_fullscreen(target,deferUntilInEventHandler){var strategy={scaleMode:0,canvasResolutionScaleMode:0,filteringMode:0,deferUntilInEventHandler:deferUntilInEventHandler,canvasResizedCallbackTargetThread:2};return doRequestFullscreen(target,strategy)}function _emscripten_request_pointerlock(target,deferUntilInEventHandler){target=findEventTarget(target);if(!target)return-4;if(!target.requestPointerLock&&!target.msRequestPointerLock){return-1}var canPerformRequests=JSEvents.canPerformEventHandlerRequests();if(!canPerformRequests){if(deferUntilInEventHandler){JSEvents.deferCall(requestPointerLock,2,[target]);return 1}else{return-2}}return requestPointerLock(target)}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=HEAPU8.length;requestedSize=requestedSize>>>0;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}function _emscripten_sample_gamepad_data(){return(JSEvents.lastGamepadState=navigator.getGamepads?navigator.getGamepads():navigator.webkitGetGamepads?navigator.webkitGetGamepads():null)?0:-1}function registerFocusEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.focusEvent)JSEvents.focusEvent=_malloc(256);var focusEventHandlerFunc=function(ev){var e=ev||event;var nodeName=JSEvents.getNodeNameForTarget(e.target);var id=e.target.id?e.target.id:"";var focusEvent=JSEvents.focusEvent;stringToUTF8(nodeName,focusEvent+0,128);stringToUTF8(id,focusEvent+128,128);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,focusEvent,userData))e.preventDefault()};var eventHandler={target:findEventTarget(target),eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:focusEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_blur_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerFocusEventCallback(target,userData,useCapture,callbackfunc,12,"blur",targetThread);return 0}function _emscripten_set_focus_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerFocusEventCallback(target,userData,useCapture,callbackfunc,13,"focus",targetThread);return 0}function registerFullscreenChangeEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.fullscreenChangeEvent)JSEvents.fullscreenChangeEvent=_malloc(280);var fullscreenChangeEventhandlerFunc=function(ev){var e=ev||event;var fullscreenChangeEvent=JSEvents.fullscreenChangeEvent;fillFullscreenChangeEventData(fullscreenChangeEvent);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,fullscreenChangeEvent,userData))e.preventDefault()};var eventHandler={target:target,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:fullscreenChangeEventhandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_fullscreenchange_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){if(!JSEvents.fullscreenEnabled())return-1;target=findEventTarget(target);if(!target)return-4;registerFullscreenChangeEventCallback(target,userData,useCapture,callbackfunc,19,"fullscreenchange",targetThread);registerFullscreenChangeEventCallback(target,userData,useCapture,callbackfunc,19,"webkitfullscreenchange",targetThread);return 0}function registerGamepadEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.gamepadEvent)JSEvents.gamepadEvent=_malloc(1432);var gamepadEventHandlerFunc=function(ev){var e=ev||event;var gamepadEvent=JSEvents.gamepadEvent;fillGamepadEventData(gamepadEvent,e["gamepad"]);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,gamepadEvent,userData))e.preventDefault()};var eventHandler={target:findEventTarget(target),allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:gamepadEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_gamepadconnected_callback_on_thread(userData,useCapture,callbackfunc,targetThread){if(!navigator.getGamepads&&!navigator.webkitGetGamepads)return-1;registerGamepadEventCallback(2,userData,useCapture,callbackfunc,26,"gamepadconnected",targetThread);return 0}function _emscripten_set_gamepaddisconnected_callback_on_thread(userData,useCapture,callbackfunc,targetThread){if(!navigator.getGamepads&&!navigator.webkitGetGamepads)return-1;registerGamepadEventCallback(2,userData,useCapture,callbackfunc,27,"gamepaddisconnected",targetThread);return 0}function _emscripten_set_interval(cb,msecs,userData){return setInterval(function(){(function(a1){dynCall_vi.apply(null,[cb,a1])})(userData)},msecs)}function registerKeyEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.keyEvent)JSEvents.keyEvent=_malloc(164);var keyEventHandlerFunc=function(e){var keyEventData=JSEvents.keyEvent;var idx=keyEventData>>2;HEAP32[idx+0]=e.location;HEAP32[idx+1]=e.ctrlKey;HEAP32[idx+2]=e.shiftKey;HEAP32[idx+3]=e.altKey;HEAP32[idx+4]=e.metaKey;HEAP32[idx+5]=e.repeat;HEAP32[idx+6]=e.charCode;HEAP32[idx+7]=e.keyCode;HEAP32[idx+8]=e.which;stringToUTF8(e.key||"",keyEventData+36,32);stringToUTF8(e.code||"",keyEventData+68,32);stringToUTF8(e.char||"",keyEventData+100,32);stringToUTF8(e.locale||"",keyEventData+132,32);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,keyEventData,userData))e.preventDefault()};var eventHandler={target:findEventTarget(target),allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:keyEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_keydown_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerKeyEventCallback(target,userData,useCapture,callbackfunc,2,"keydown",targetThread);return 0}function _emscripten_set_keypress_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerKeyEventCallback(target,userData,useCapture,callbackfunc,1,"keypress",targetThread);return 0}function _emscripten_set_keyup_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerKeyEventCallback(target,userData,useCapture,callbackfunc,3,"keyup",targetThread);return 0}function _emscripten_set_main_loop(func,fps,simulateInfiniteLoop){var browserIterationFunc=function(){dynCall_v.call(null,func)};setMainLoop(browserIterationFunc,fps,simulateInfiniteLoop)}function fillMouseEventData(eventStruct,e,target){var idx=eventStruct>>2;HEAP32[idx+0]=e.screenX;HEAP32[idx+1]=e.screenY;HEAP32[idx+2]=e.clientX;HEAP32[idx+3]=e.clientY;HEAP32[idx+4]=e.ctrlKey;HEAP32[idx+5]=e.shiftKey;HEAP32[idx+6]=e.altKey;HEAP32[idx+7]=e.metaKey;HEAP16[idx*2+16]=e.button;HEAP16[idx*2+17]=e.buttons;HEAP32[idx+9]=e["movementX"];HEAP32[idx+10]=e["movementY"];var rect=getBoundingClientRect(target);HEAP32[idx+11]=e.clientX-rect.left;HEAP32[idx+12]=e.clientY-rect.top}function registerMouseEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.mouseEvent)JSEvents.mouseEvent=_malloc(64);target=findEventTarget(target);var mouseEventHandlerFunc=function(ev){var e=ev||event;fillMouseEventData(JSEvents.mouseEvent,e,target);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,JSEvents.mouseEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:eventTypeString!="mousemove"&&eventTypeString!="mouseenter"&&eventTypeString!="mouseleave",eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:mouseEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_mousedown_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerMouseEventCallback(target,userData,useCapture,callbackfunc,5,"mousedown",targetThread);return 0}function _emscripten_set_mousemove_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerMouseEventCallback(target,userData,useCapture,callbackfunc,8,"mousemove",targetThread);return 0}function _emscripten_set_mouseup_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerMouseEventCallback(target,userData,useCapture,callbackfunc,6,"mouseup",targetThread);return 0}function registerTouchEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.touchEvent)JSEvents.touchEvent=_malloc(1684);target=findEventTarget(target);var touchEventHandlerFunc=function(e){var t,touches={},et=e.touches;for(var i=0;i>2;HEAP32[idx+1]=e.ctrlKey;HEAP32[idx+2]=e.shiftKey;HEAP32[idx+3]=e.altKey;HEAP32[idx+4]=e.metaKey;idx+=5;var targetRect=getBoundingClientRect(target);var numTouches=0;for(var i in touches){var t=touches[i];HEAP32[idx+0]=t.identifier;HEAP32[idx+1]=t.screenX;HEAP32[idx+2]=t.screenY;HEAP32[idx+3]=t.clientX;HEAP32[idx+4]=t.clientY;HEAP32[idx+5]=t.pageX;HEAP32[idx+6]=t.pageY;HEAP32[idx+7]=t.isChanged;HEAP32[idx+8]=t.onTarget;HEAP32[idx+9]=t.clientX-targetRect.left;HEAP32[idx+10]=t.clientY-targetRect.top;idx+=13;if(++numTouches>31){break}}HEAP32[touchEvent>>2]=numTouches;if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,touchEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:eventTypeString=="touchstart"||eventTypeString=="touchend",eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:touchEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_touchcancel_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,25,"touchcancel",targetThread);return 0}function _emscripten_set_touchend_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,23,"touchend",targetThread);return 0}function _emscripten_set_touchmove_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,24,"touchmove",targetThread);return 0}function _emscripten_set_touchstart_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,22,"touchstart",targetThread);return 0}function registerWheelEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.wheelEvent)JSEvents.wheelEvent=_malloc(96);var wheelHandlerFunc=function(ev){var e=ev||event;var wheelEvent=JSEvents.wheelEvent;fillMouseEventData(wheelEvent,e,target);HEAPF64[wheelEvent+64>>3]=e["deltaX"];HEAPF64[wheelEvent+72>>3]=e["deltaY"];HEAPF64[wheelEvent+80>>3]=e["deltaZ"];HEAP32[wheelEvent+88>>2]=e["deltaMode"];if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,wheelEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:wheelHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_wheel_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){target=findEventTarget(target);if(typeof target.onwheel!=="undefined"){registerWheelEventCallback(target,userData,useCapture,callbackfunc,9,"wheel",targetThread);return 0}else{return-1}}function _emscripten_thread_sleep(msecs){var start=_emscripten_get_now();while(_emscripten_get_now()-start>1;var quadIndexes=new Uint16Array(numIndexes);var i=0,v=0;while(1){quadIndexes[i++]=v;if(i>=numIndexes)break;quadIndexes[i++]=v+1;if(i>=numIndexes)break;quadIndexes[i++]=v+2;if(i>=numIndexes)break;quadIndexes[i++]=v;if(i>=numIndexes)break;quadIndexes[i++]=v+2;if(i>=numIndexes)break;quadIndexes[i++]=v+3;if(i>=numIndexes)break;v+=4}context.GLctx.bufferData(34963,quadIndexes,35044);context.GLctx.bindBuffer(34963,null)}},getTempVertexBuffer:function getTempVertexBuffer(sizeBytes){var idx=GL.log2ceilLookup(sizeBytes);var ringbuffer=GL.currentContext.tempVertexBuffers1[idx];var nextFreeBufferIndex=GL.currentContext.tempVertexBufferCounters1[idx];GL.currentContext.tempVertexBufferCounters1[idx]=GL.currentContext.tempVertexBufferCounters1[idx]+1&GL.numTempVertexBuffersPerSize-1;var vbo=ringbuffer[nextFreeBufferIndex];if(vbo){return vbo}var prevVBO=GLctx.getParameter(34964);ringbuffer[nextFreeBufferIndex]=GLctx.createBuffer();GLctx.bindBuffer(34962,ringbuffer[nextFreeBufferIndex]);GLctx.bufferData(34962,1<>2]:-1;source+=UTF8ToString(HEAP32[string+i*4>>2],len<0?undefined:len)}return source},calcBufLength:function calcBufLength(size,type,stride,count){if(stride>0){return count*stride}var typeSize=GL.byteSizeByType[type-GL.byteSizeByTypeRoot];return size*typeSize*count},usedTempBuffers:[],preDrawHandleClientVertexAttribBindings:function preDrawHandleClientVertexAttribBindings(count){GL.resetBufferBinding=false;for(var i=0;i1?canvas.getContext("webgl2",webGLContextAttributes):canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=GL.getNewId(GL.contexts);var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault==="undefined"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}context.maxVertexAttribs=context.GLctx.getParameter(34921);context.clientBuffers=[];for(var i=0;i=2){GLctx.disjointTimerQueryExt=GLctx.getExtension("EXT_disjoint_timer_query_webgl2")}if(context.version<2||!GLctx.disjointTimerQueryExt){GLctx.disjointTimerQueryExt=GLctx.getExtension("EXT_disjoint_timer_query")}__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(!ext.includes("lose_context")&&!ext.includes("debug")){GLctx.getExtension(ext)}})}};var __emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=HEAP32[a+(24>>2)];var contextAttributes={"alpha":!!HEAP32[a+(0>>2)],"depth":!!HEAP32[a+(4>>2)],"stencil":!!HEAP32[a+(8>>2)],"antialias":!!HEAP32[a+(12>>2)],"premultipliedAlpha":!!HEAP32[a+(16>>2)],"preserveDrawingBuffer":!!HEAP32[a+(20>>2)],"powerPreference":__emscripten_webgl_power_preferences[powerPreference],"failIfMajorPerformanceCaveat":!!HEAP32[a+(28>>2)],majorVersion:HEAP32[a+(32>>2)],minorVersion:HEAP32[a+(36>>2)],enableExtensionsByDefault:HEAP32[a+(40>>2)],explicitSwapControl:HEAP32[a+(44>>2)],proxyContextToMainThread:HEAP32[a+(48>>2)],renderViaOffscreenBackBuffer:HEAP32[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}function _emscripten_webgl_do_get_current_context(){return GL.currentContext?GL.currentContext.handle:0}function _emscripten_webgl_get_current_context(){return _emscripten_webgl_do_get_current_context()}Module["_emscripten_webgl_get_current_context"]=_emscripten_webgl_get_current_context;function _emscripten_webgl_make_context_current(contextHandle){var success=GL.makeContextCurrent(contextHandle);return success?0:-5}Module["_emscripten_webgl_make_context_current"]=_emscripten_webgl_make_context_current;function _emscripten_webgl_destroy_context(contextHandle){if(GL.currentContext==contextHandle)GL.currentContext=0;GL.deleteContext(contextHandle)}function _emscripten_webgl_enable_extension(contextHandle,extension){var context=GL.getContext(contextHandle);var extString=UTF8ToString(extension);if(extString.startsWith("GL_"))extString=extString.substr(3);if(extString=="ANGLE_instanced_arrays")__webgl_enable_ANGLE_instanced_arrays(GLctx);if(extString=="OES_vertex_array_object")__webgl_enable_OES_vertex_array_object(GLctx);if(extString=="WEBGL_draw_buffers")__webgl_enable_WEBGL_draw_buffers(GLctx);if(extString=="WEBGL_draw_instanced_base_vertex_base_instance")__webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(GLctx);if(extString=="WEBGL_multi_draw_instanced_base_vertex_base_instance")__webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(GLctx);if(extString=="WEBGL_multi_draw")__webgl_enable_WEBGL_multi_draw(GLctx);var ext=context.GLctx.getExtension(extString);return!!ext}function _emscripten_webgl_init_context_attributes(attributes){var a=attributes>>2;for(var i=0;i<56>>2;++i){HEAP32[a+i]=0}HEAP32[a+(0>>2)]=HEAP32[a+(4>>2)]=HEAP32[a+(12>>2)]=HEAP32[a+(16>>2)]=HEAP32[a+(32>>2)]=HEAP32[a+(40>>2)]=1}var ENV={};function getExecutableName(){return thisProgram||"./this.program"}function getEnvStrings(){if(!getEnvStrings.strings){var lang=(typeof navigator==="object"&&navigator.languages&&navigator.languages[0]||"C").replace("-","_")+".UTF-8";var env={"USER":"web_user","LOGNAME":"web_user","PATH":"/","PWD":"/","HOME":"/home/web_user","LANG":lang,"_":getExecutableName()};for(var x in ENV){env[x]=ENV[x]}var strings=[];for(var x in env){strings.push(x+"="+env[x])}getEnvStrings.strings=strings}return getEnvStrings.strings}function _environ_get(__environ,environ_buf){try{var bufSize=0;getEnvStrings().forEach(function(string,i){var ptr=environ_buf+bufSize;HEAP32[__environ+i*4>>2]=ptr;writeAsciiToMemory(string,ptr);bufSize+=string.length+1});return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _environ_sizes_get(penviron_count,penviron_buf_size){try{var strings=getEnvStrings();HEAP32[penviron_count>>2]=strings.length;var bufSize=0;strings.forEach(function(string){bufSize+=string.length+1});HEAP32[penviron_buf_size>>2]=bufSize;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_close(fd){try{var stream=SYSCALLS.getStreamFromFD(fd);FS.close(stream);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_fdstat_get(fd,pbuf){try{var stream=SYSCALLS.getStreamFromFD(fd);var type=stream.tty?2:FS.isDir(stream.mode)?3:FS.isLink(stream.mode)?7:4;HEAP8[pbuf>>0]=type;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_read(fd,iov,iovcnt,pnum){try{var stream=SYSCALLS.getStreamFromFD(fd);var num=SYSCALLS.doReadv(stream,iov,iovcnt);HEAP32[pnum>>2]=num;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){try{var stream=SYSCALLS.getStreamFromFD(fd);var HIGH_OFFSET=4294967296;var offset=offset_high*HIGH_OFFSET+(offset_low>>>0);var DOUBLE_LIMIT=9007199254740992;if(offset<=-DOUBLE_LIMIT||offset>=DOUBLE_LIMIT){return-61}FS.llseek(stream,offset,whence);tempI64=[stream.position>>>0,(tempDouble=stream.position,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[newOffset>>2]=tempI64[0],HEAP32[newOffset+4>>2]=tempI64[1];if(stream.getdents&&offset===0&&whence===0)stream.getdents=null;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_write(fd,iov,iovcnt,pnum){try{var stream=SYSCALLS.getStreamFromFD(fd);var num=SYSCALLS.doWritev(stream,iov,iovcnt);HEAP32[pnum>>2]=num;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _flock(fd,operation){return 0}function _getTempRet0(){return getTempRet0()}function _getaddrinfo(node,service,hint,out){var addr=0;var port=0;var flags=0;var family=0;var type=0;var proto=0;var ai;function allocaddrinfo(family,type,proto,canon,addr,port){var sa,salen,ai;var errno;salen=family===10?28:16;addr=family===10?inetNtop6(addr):inetNtop4(addr);sa=_malloc(salen);errno=writeSockaddr(sa,family,addr,port);assert(!errno);ai=_malloc(32);HEAP32[ai+4>>2]=family;HEAP32[ai+8>>2]=type;HEAP32[ai+12>>2]=proto;HEAP32[ai+24>>2]=canon;HEAP32[ai+20>>2]=sa;if(family===10){HEAP32[ai+16>>2]=28}else{HEAP32[ai+16>>2]=16}HEAP32[ai+28>>2]=0;return ai}if(hint){flags=HEAP32[hint>>2];family=HEAP32[hint+4>>2];type=HEAP32[hint+8>>2];proto=HEAP32[hint+12>>2]}if(type&&!proto){proto=type===2?17:6}if(!type&&proto){type=proto===17?2:1}if(proto===0){proto=6}if(type===0){type=1}if(!node&&!service){return-2}if(flags&~(1|2|4|1024|8|16|32)){return-1}if(hint!==0&&HEAP32[hint>>2]&2&&!node){return-1}if(flags&32){return-2}if(type!==0&&type!==1&&type!==2){return-7}if(family!==0&&family!==2&&family!==10){return-6}if(service){service=UTF8ToString(service);port=parseInt(service,10);if(isNaN(port)){if(flags&1024){return-2}return-8}}if(!node){if(family===0){family=2}if((flags&1)===0){if(family===2){addr=_htonl(2130706433)}else{addr=[0,0,0,1]}}ai=allocaddrinfo(family,type,proto,null,addr,port);HEAP32[out>>2]=ai;return 0}node=UTF8ToString(node);addr=inetPton4(node);if(addr!==null){if(family===0||family===2){family=2}else if(family===10&&flags&8){addr=[0,0,_htonl(65535),addr];family=10}else{return-2}}else{addr=inetPton6(node);if(addr!==null){if(family===0||family===10){family=10}else{return-2}}}if(addr!=null){ai=allocaddrinfo(family,type,proto,node,addr,port);HEAP32[out>>2]=ai;return 0}if(flags&4){return-2}node=DNS.lookup_name(node);addr=inetPton4(node);if(family===0){family=2}else if(family===10){addr=[0,0,_htonl(65535),addr]}ai=allocaddrinfo(family,type,proto,null,addr,port);HEAP32[out>>2]=ai;return 0}function getHostByName(name){var ret=_malloc(20);var nameBuf=_malloc(name.length+1);stringToUTF8(name,nameBuf,name.length+1);HEAP32[ret>>2]=nameBuf;var aliasesBuf=_malloc(4);HEAP32[aliasesBuf>>2]=0;HEAP32[ret+4>>2]=aliasesBuf;var afinet=2;HEAP32[ret+8>>2]=afinet;HEAP32[ret+12>>2]=4;var addrListBuf=_malloc(12);HEAP32[addrListBuf>>2]=addrListBuf+8;HEAP32[addrListBuf+4>>2]=0;HEAP32[addrListBuf+8>>2]=inetPton4(DNS.lookup_name(name));HEAP32[ret+16>>2]=addrListBuf;return ret}function _gethostbyaddr(addr,addrlen,type){if(type!==2){setErrNo(5);return null}addr=HEAP32[addr>>2];var host=inetNtop4(addr);var lookup=DNS.lookup_addr(host);if(lookup){host=lookup}return getHostByName(host)}function _gethostbyname(name){return getHostByName(UTF8ToString(name))}function _getnameinfo(sa,salen,node,nodelen,serv,servlen,flags){var info=readSockaddr(sa,salen);if(info.errno){return-6}var port=info.port;var addr=info.addr;var overflowed=false;if(node&&nodelen){var lookup;if(flags&1||!(lookup=DNS.lookup_addr(addr))){if(flags&8){return-2}}else{addr=lookup}var numBytesWrittenExclNull=stringToUTF8(addr,node,nodelen);if(numBytesWrittenExclNull+1>=nodelen){overflowed=true}}if(serv&&servlen){port=""+port;var numBytesWrittenExclNull=stringToUTF8(port,serv,servlen);if(numBytesWrittenExclNull+1>=servlen){overflowed=true}}if(overflowed){return-12}return 0}function _getpwuid(){throw"getpwuid: TODO"}function _gettimeofday(ptr){var now=Date.now();HEAP32[ptr>>2]=now/1e3|0;HEAP32[ptr+4>>2]=now%1e3*1e3|0;return 0}function _glActiveTexture(x0){GLctx["activeTexture"](x0)}function _glAttachShader(program,shader){program=GL.programs[program];shader=GL.shaders[shader];program[shader.shaderType]=shader;GLctx.attachShader(program,shader)}function _glBeginQuery(target,id){GLctx["beginQuery"](target,GL.queries[id])}function _glBeginTransformFeedback(x0){GLctx["beginTransformFeedback"](x0)}function _glBindAttribLocation(program,index,name){GLctx.bindAttribLocation(GL.programs[program],index,UTF8ToString(name))}function _glBindBuffer(target,buffer){if(target==34962){GLctx.currentArrayBufferBinding=buffer}else if(target==34963){GLctx.currentElementArrayBufferBinding=buffer}if(target==35051){GLctx.currentPixelPackBufferBinding=buffer}else if(target==35052){GLctx.currentPixelUnpackBufferBinding=buffer}GLctx.bindBuffer(target,GL.buffers[buffer])}function _glBindBufferBase(target,index,buffer){GLctx["bindBufferBase"](target,index,GL.buffers[buffer])}function _glBindBufferRange(target,index,buffer,offset,ptrsize){GLctx["bindBufferRange"](target,index,GL.buffers[buffer],offset,ptrsize)}function _glBindFramebuffer(target,framebuffer){GLctx.bindFramebuffer(target,GL.framebuffers[framebuffer])}function _glBindRenderbuffer(target,renderbuffer){GLctx.bindRenderbuffer(target,GL.renderbuffers[renderbuffer])}function _glBindSampler(unit,sampler){GLctx["bindSampler"](unit,GL.samplers[sampler])}function _glBindTexture(target,texture){GLctx.bindTexture(target,GL.textures[texture])}function _glBindTransformFeedback(target,id){GLctx["bindTransformFeedback"](target,GL.transformFeedbacks[id])}function _glBindVertexArray(vao){GLctx["bindVertexArray"](GL.vaos[vao]);var ibo=GLctx.getParameter(34965);GLctx.currentElementArrayBufferBinding=ibo?ibo.name|0:0}function _glBlendEquation(x0){GLctx["blendEquation"](x0)}function _glBlendEquationSeparate(x0,x1){GLctx["blendEquationSeparate"](x0,x1)}function _glBlendFuncSeparate(x0,x1,x2,x3){GLctx["blendFuncSeparate"](x0,x1,x2,x3)}function _glBlitFramebuffer(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9){GLctx["blitFramebuffer"](x0,x1,x2,x3,x4,x5,x6,x7,x8,x9)}function _glBufferData(target,size,data,usage){if(GL.currentContext.version>=2){if(data){GLctx.bufferData(target,HEAPU8,usage,data,size)}else{GLctx.bufferData(target,size,usage)}}else{GLctx.bufferData(target,data?HEAPU8.subarray(data,data+size):size,usage)}}function _glBufferSubData(target,offset,size,data){if(GL.currentContext.version>=2){GLctx.bufferSubData(target,offset,HEAPU8,data,size);return}GLctx.bufferSubData(target,offset,HEAPU8.subarray(data,data+size))}function _glCheckFramebufferStatus(x0){return GLctx["checkFramebufferStatus"](x0)}function _glClear(x0){GLctx["clear"](x0)}function _glClearBufferfi(x0,x1,x2,x3){GLctx["clearBufferfi"](x0,x1,x2,x3)}function _glClearBufferfv(buffer,drawbuffer,value){GLctx["clearBufferfv"](buffer,drawbuffer,HEAPF32,value>>2)}function _glClearBufferuiv(buffer,drawbuffer,value){GLctx["clearBufferuiv"](buffer,drawbuffer,HEAPU32,value>>2)}function _glClearColor(x0,x1,x2,x3){GLctx["clearColor"](x0,x1,x2,x3)}function _glClearDepthf(x0){GLctx["clearDepth"](x0)}function _glClearStencil(x0){GLctx["clearStencil"](x0)}function _glClientWaitSync(sync,flags,timeoutLo,timeoutHi){return GLctx.clientWaitSync(GL.syncs[sync],flags,convertI32PairToI53(timeoutLo,timeoutHi))}function _glColorMask(red,green,blue,alpha){GLctx.colorMask(!!red,!!green,!!blue,!!alpha)}function _glCompileShader(shader){GLctx.compileShader(GL.shaders[shader])}function _glCompressedTexImage2D(target,level,internalFormat,width,height,border,imageSize,data){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexImage2D"](target,level,internalFormat,width,height,border,imageSize,data)}else{GLctx["compressedTexImage2D"](target,level,internalFormat,width,height,border,HEAPU8,data,imageSize)}return}GLctx["compressedTexImage2D"](target,level,internalFormat,width,height,border,data?HEAPU8.subarray(data,data+imageSize):null)}function _glCompressedTexImage3D(target,level,internalFormat,width,height,depth,border,imageSize,data){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexImage3D"](target,level,internalFormat,width,height,depth,border,imageSize,data)}else{GLctx["compressedTexImage3D"](target,level,internalFormat,width,height,depth,border,HEAPU8,data,imageSize)}}function _glCompressedTexSubImage2D(target,level,xoffset,yoffset,width,height,format,imageSize,data){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexSubImage2D"](target,level,xoffset,yoffset,width,height,format,imageSize,data)}else{GLctx["compressedTexSubImage2D"](target,level,xoffset,yoffset,width,height,format,HEAPU8,data,imageSize)}return}GLctx["compressedTexSubImage2D"](target,level,xoffset,yoffset,width,height,format,data?HEAPU8.subarray(data,data+imageSize):null)}function _glCompressedTexSubImage3D(target,level,xoffset,yoffset,zoffset,width,height,depth,format,imageSize,data){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,imageSize,data)}else{GLctx["compressedTexSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,HEAPU8,data,imageSize)}}function _glCopyBufferSubData(x0,x1,x2,x3,x4){GLctx["copyBufferSubData"](x0,x1,x2,x3,x4)}function _glCopyTexImage2D(x0,x1,x2,x3,x4,x5,x6,x7){GLctx["copyTexImage2D"](x0,x1,x2,x3,x4,x5,x6,x7)}function _glCopyTexSubImage2D(x0,x1,x2,x3,x4,x5,x6,x7){GLctx["copyTexSubImage2D"](x0,x1,x2,x3,x4,x5,x6,x7)}function _glCreateProgram(){var id=GL.getNewId(GL.programs);var program=GLctx.createProgram();program.name=id;program.maxUniformLength=program.maxAttributeLength=program.maxUniformBlockNameLength=0;program.uniformIdCounter=1;GL.programs[id]=program;return id}function _glCreateShader(shaderType){var id=GL.getNewId(GL.shaders);GL.shaders[id]=GLctx.createShader(shaderType);GL.shaders[id].shaderType=shaderType&1?"vs":"fs";return id}function _glCullFace(x0){GLctx["cullFace"](x0)}function _glDeleteBuffers(n,buffers){for(var i=0;i>2];var buffer=GL.buffers[id];if(!buffer)continue;GLctx.deleteBuffer(buffer);buffer.name=0;GL.buffers[id]=null;if(id==GLctx.currentArrayBufferBinding)GLctx.currentArrayBufferBinding=0;if(id==GLctx.currentElementArrayBufferBinding)GLctx.currentElementArrayBufferBinding=0;if(id==GLctx.currentPixelPackBufferBinding)GLctx.currentPixelPackBufferBinding=0;if(id==GLctx.currentPixelUnpackBufferBinding)GLctx.currentPixelUnpackBufferBinding=0}}function _glDeleteFramebuffers(n,framebuffers){for(var i=0;i>2];var framebuffer=GL.framebuffers[id];if(!framebuffer)continue;GLctx.deleteFramebuffer(framebuffer);framebuffer.name=0;GL.framebuffers[id]=null}}function _glDeleteProgram(id){if(!id)return;var program=GL.programs[id];if(!program){GL.recordError(1281);return}GLctx.deleteProgram(program);program.name=0;GL.programs[id]=null}function _glDeleteQueries(n,ids){for(var i=0;i>2];var query=GL.queries[id];if(!query)continue;GLctx["deleteQuery"](query);GL.queries[id]=null}}function _glDeleteRenderbuffers(n,renderbuffers){for(var i=0;i>2];var renderbuffer=GL.renderbuffers[id];if(!renderbuffer)continue;GLctx.deleteRenderbuffer(renderbuffer);renderbuffer.name=0;GL.renderbuffers[id]=null}}function _glDeleteSamplers(n,samplers){for(var i=0;i>2];var sampler=GL.samplers[id];if(!sampler)continue;GLctx["deleteSampler"](sampler);sampler.name=0;GL.samplers[id]=null}}function _glDeleteShader(id){if(!id)return;var shader=GL.shaders[id];if(!shader){GL.recordError(1281);return}GLctx.deleteShader(shader);GL.shaders[id]=null}function _glDeleteSync(id){if(!id)return;var sync=GL.syncs[id];if(!sync){GL.recordError(1281);return}GLctx.deleteSync(sync);sync.name=0;GL.syncs[id]=null}function _glDeleteTextures(n,textures){for(var i=0;i>2];var texture=GL.textures[id];if(!texture)continue;GLctx.deleteTexture(texture);texture.name=0;GL.textures[id]=null}}function _glDeleteTransformFeedbacks(n,ids){for(var i=0;i>2];var transformFeedback=GL.transformFeedbacks[id];if(!transformFeedback)continue;GLctx["deleteTransformFeedback"](transformFeedback);transformFeedback.name=0;GL.transformFeedbacks[id]=null}}function _glDeleteVertexArrays(n,vaos){for(var i=0;i>2];GLctx["deleteVertexArray"](GL.vaos[id]);GL.vaos[id]=null}}function _glDepthFunc(x0){GLctx["depthFunc"](x0)}function _glDepthMask(flag){GLctx.depthMask(!!flag)}function _glDetachShader(program,shader){GLctx.detachShader(GL.programs[program],GL.shaders[shader])}function _glDisable(x0){GLctx["disable"](x0)}function _glDisableVertexAttribArray(index){var cb=GL.currentContext.clientBuffers[index];cb.enabled=false;GLctx.disableVertexAttribArray(index)}function _glDrawArrays(mode,first,count){GL.preDrawHandleClientVertexAttribBindings(first+count);GLctx.drawArrays(mode,first,count);GL.postDrawHandleClientVertexAttribBindings()}function _glDrawArraysInstanced(mode,first,count,primcount){GLctx["drawArraysInstanced"](mode,first,count,primcount)}var tempFixedLengthArray=[];function _glDrawBuffers(n,bufs){var bufArray=tempFixedLengthArray[n];for(var i=0;i>2]}GLctx["drawBuffers"](bufArray)}function _glDrawElements(mode,count,type,indices){var buf;if(!GLctx.currentElementArrayBufferBinding){var size=GL.calcBufLength(1,type,0,count);buf=GL.getTempIndexBuffer(size);GLctx.bindBuffer(34963,buf);GLctx.bufferSubData(34963,0,HEAPU8.subarray(indices,indices+size));indices=0}GL.preDrawHandleClientVertexAttribBindings(count);GLctx.drawElements(mode,count,type,indices);GL.postDrawHandleClientVertexAttribBindings(count);if(!GLctx.currentElementArrayBufferBinding){GLctx.bindBuffer(34963,null)}}function _glDrawElementsInstanced(mode,count,type,indices,primcount){GLctx["drawElementsInstanced"](mode,count,type,indices,primcount)}function _glEnable(x0){GLctx["enable"](x0)}function _glEnableVertexAttribArray(index){var cb=GL.currentContext.clientBuffers[index];cb.enabled=true;GLctx.enableVertexAttribArray(index)}function _glEndQuery(x0){GLctx["endQuery"](x0)}function _glEndTransformFeedback(){GLctx["endTransformFeedback"]()}function _glFenceSync(condition,flags){var sync=GLctx.fenceSync(condition,flags);if(sync){var id=GL.getNewId(GL.syncs);sync.name=id;GL.syncs[id]=sync;return id}else{return 0}}function _glFinish(){GLctx["finish"]()}function _glFlush(){GLctx["flush"]()}function emscriptenWebGLGetBufferBinding(target){switch(target){case 34962:target=34964;break;case 34963:target=34965;break;case 35051:target=35053;break;case 35052:target=35055;break;case 35982:target=35983;break;case 36662:target=36662;break;case 36663:target=36663;break;case 35345:target=35368;break}var buffer=GLctx.getParameter(target);if(buffer)return buffer.name|0;else return 0}function emscriptenWebGLValidateMapBufferTarget(target){switch(target){case 34962:case 34963:case 36662:case 36663:case 35051:case 35052:case 35882:case 35982:case 35345:return true;default:return false}}function _glFlushMappedBufferRange(target,offset,length){if(!emscriptenWebGLValidateMapBufferTarget(target)){GL.recordError(1280);err("GL_INVALID_ENUM in glFlushMappedBufferRange");return}var mapping=GL.mappedBuffers[emscriptenWebGLGetBufferBinding(target)];if(!mapping){GL.recordError(1282);err("buffer was never mapped in glFlushMappedBufferRange");return}if(!(mapping.access&16)){GL.recordError(1282);err("buffer was not mapped with GL_MAP_FLUSH_EXPLICIT_BIT in glFlushMappedBufferRange");return}if(offset<0||length<0||offset+length>mapping.length){GL.recordError(1281);err("invalid range in glFlushMappedBufferRange");return}GLctx.bufferSubData(target,mapping.offset,HEAPU8.subarray(mapping.mem+offset,mapping.mem+offset+length))}function _glFramebufferRenderbuffer(target,attachment,renderbuffertarget,renderbuffer){GLctx.framebufferRenderbuffer(target,attachment,renderbuffertarget,GL.renderbuffers[renderbuffer])}function _glFramebufferTexture2D(target,attachment,textarget,texture,level){GLctx.framebufferTexture2D(target,attachment,textarget,GL.textures[texture],level)}function _glFramebufferTextureLayer(target,attachment,texture,level,layer){GLctx.framebufferTextureLayer(target,attachment,GL.textures[texture],level,layer)}function _glFrontFace(x0){GLctx["frontFace"](x0)}function __glGenObject(n,buffers,createFunction,objectTable){for(var i=0;i>2]=id}}function _glGenBuffers(n,buffers){__glGenObject(n,buffers,"createBuffer",GL.buffers)}function _glGenFramebuffers(n,ids){__glGenObject(n,ids,"createFramebuffer",GL.framebuffers)}function _glGenQueries(n,ids){__glGenObject(n,ids,"createQuery",GL.queries)}function _glGenRenderbuffers(n,renderbuffers){__glGenObject(n,renderbuffers,"createRenderbuffer",GL.renderbuffers)}function _glGenSamplers(n,samplers){__glGenObject(n,samplers,"createSampler",GL.samplers)}function _glGenTextures(n,textures){__glGenObject(n,textures,"createTexture",GL.textures)}function _glGenTransformFeedbacks(n,ids){__glGenObject(n,ids,"createTransformFeedback",GL.transformFeedbacks)}function _glGenVertexArrays(n,arrays){__glGenObject(n,arrays,"createVertexArray",GL.vaos)}function _glGenerateMipmap(x0){GLctx["generateMipmap"](x0)}function __glGetActiveAttribOrUniform(funcName,program,index,bufSize,length,size,type,name){program=GL.programs[program];var info=GLctx[funcName](program,index);if(info){var numBytesWrittenExclNull=name&&stringToUTF8(info.name,name,bufSize);if(length)HEAP32[length>>2]=numBytesWrittenExclNull;if(size)HEAP32[size>>2]=info.size;if(type)HEAP32[type>>2]=info.type}}function _glGetActiveAttrib(program,index,bufSize,length,size,type,name){__glGetActiveAttribOrUniform("getActiveAttrib",program,index,bufSize,length,size,type,name)}function _glGetActiveUniform(program,index,bufSize,length,size,type,name){__glGetActiveAttribOrUniform("getActiveUniform",program,index,bufSize,length,size,type,name)}function _glGetActiveUniformBlockName(program,uniformBlockIndex,bufSize,length,uniformBlockName){program=GL.programs[program];var result=GLctx["getActiveUniformBlockName"](program,uniformBlockIndex);if(!result)return;if(uniformBlockName&&bufSize>0){var numBytesWrittenExclNull=stringToUTF8(result,uniformBlockName,bufSize);if(length)HEAP32[length>>2]=numBytesWrittenExclNull}else{if(length)HEAP32[length>>2]=0}}function _glGetActiveUniformBlockiv(program,uniformBlockIndex,pname,params){if(!params){GL.recordError(1281);return}program=GL.programs[program];if(pname==35393){var name=GLctx["getActiveUniformBlockName"](program,uniformBlockIndex);HEAP32[params>>2]=name.length+1;return}var result=GLctx["getActiveUniformBlockParameter"](program,uniformBlockIndex,pname);if(result===null)return;if(pname==35395){for(var i=0;i>2]=result[i]}}else{HEAP32[params>>2]=result}}function _glGetActiveUniformsiv(program,uniformCount,uniformIndices,pname,params){if(!params){GL.recordError(1281);return}if(uniformCount>0&&uniformIndices==0){GL.recordError(1281);return}program=GL.programs[program];var ids=[];for(var i=0;i>2])}var result=GLctx["getActiveUniforms"](program,ids,pname);if(!result)return;var len=result.length;for(var i=0;i>2]=result[i]}}function _glGetAttribLocation(program,name){return GLctx.getAttribLocation(GL.programs[program],UTF8ToString(name))}function _glGetBufferSubData(target,offset,size,data){if(!data){GL.recordError(1281);return}GLctx["getBufferSubData"](target,offset,HEAPU8,data,size)}function _glGetError(){var error=GLctx.getError()||GL.lastError;GL.lastError=0;return error}function _glGetFramebufferAttachmentParameteriv(target,attachment,pname,params){var result=GLctx.getFramebufferAttachmentParameter(target,attachment,pname);if(result instanceof WebGLRenderbuffer||result instanceof WebGLTexture){result=result.name|0}HEAP32[params>>2]=result}function writeI53ToI64(ptr,num){HEAPU32[ptr>>2]=num;HEAPU32[ptr+4>>2]=(num-HEAPU32[ptr>>2])/4294967296}function emscriptenWebGLGetIndexed(target,index,data,type){if(!data){GL.recordError(1281);return}var result=GLctx["getIndexedParameter"](target,index);var ret;switch(typeof result){case"boolean":ret=result?1:0;break;case"number":ret=result;break;case"object":if(result===null){switch(target){case 35983:case 35368:ret=0;break;default:{GL.recordError(1280);return}}}else if(result instanceof WebGLBuffer){ret=result.name|0}else{GL.recordError(1280);return}break;default:GL.recordError(1280);return}switch(type){case 1:writeI53ToI64(data,ret);break;case 0:HEAP32[data>>2]=ret;break;case 2:HEAPF32[data>>2]=ret;break;case 4:HEAP8[data>>0]=ret?1:0;break;default:throw"internal emscriptenWebGLGetIndexed() error, bad type: "+type}}function _glGetIntegeri_v(target,index,data){emscriptenWebGLGetIndexed(target,index,data,0)}function emscriptenWebGLGet(name_,p,type){if(!p){GL.recordError(1281);return}var ret=undefined;switch(name_){case 36346:ret=1;break;case 36344:if(type!=0&&type!=1){GL.recordError(1280)}return;case 34814:case 36345:ret=0;break;case 34466:var formats=GLctx.getParameter(34467);ret=formats?formats.length:0;break;case 33390:ret=1048576;break;case 33309:if(GL.currentContext.version<2){GL.recordError(1282);return}var exts=GLctx.getSupportedExtensions()||[];ret=2*exts.length;break;case 33307:case 33308:if(GL.currentContext.version<2){GL.recordError(1280);return}ret=name_==33307?3:0;break}if(ret===undefined){var result=GLctx.getParameter(name_);switch(typeof result){case"number":ret=result;break;case"boolean":ret=result?1:0;break;case"string":GL.recordError(1280);return;case"object":if(result===null){switch(name_){case 34964:case 35725:case 34965:case 36006:case 36007:case 32873:case 34229:case 36662:case 36663:case 35053:case 35055:case 36010:case 35097:case 35869:case 32874:case 36389:case 35983:case 35368:case 34068:{ret=0;break}default:{GL.recordError(1280);return}}}else if(result instanceof Float32Array||result instanceof Uint32Array||result instanceof Int32Array||result instanceof Array){for(var i=0;i>2]=result[i];break;case 2:HEAPF32[p+i*4>>2]=result[i];break;case 4:HEAP8[p+i>>0]=result[i]?1:0;break}}return}else{try{ret=result.name|0}catch(e){GL.recordError(1280);err("GL_INVALID_ENUM in glGet"+type+"v: Unknown object returned from WebGL getParameter("+name_+")! (error: "+e+")");return}}break;default:GL.recordError(1280);err("GL_INVALID_ENUM in glGet"+type+"v: Native code calling glGet"+type+"v("+name_+") and it returns "+result+" of type "+typeof result+"!");return}}switch(type){case 1:writeI53ToI64(p,ret);break;case 0:HEAP32[p>>2]=ret;break;case 2:HEAPF32[p>>2]=ret;break;case 4:HEAP8[p>>0]=ret?1:0;break}}function _glGetIntegerv(name_,p){emscriptenWebGLGet(name_,p,0)}function _glGetInternalformativ(target,internalformat,pname,bufSize,params){if(bufSize<0){GL.recordError(1281);return}if(!params){GL.recordError(1281);return}var ret=GLctx["getInternalformatParameter"](target,internalformat,pname);if(ret===null)return;for(var i=0;i>2]=ret[i]}}function _glGetProgramBinary(program,bufSize,length,binaryFormat,binary){GL.recordError(1282)}function _glGetProgramInfoLog(program,maxLength,length,infoLog){var log=GLctx.getProgramInfoLog(GL.programs[program]);if(log===null)log="(unknown error)";var numBytesWrittenExclNull=maxLength>0&&infoLog?stringToUTF8(log,infoLog,maxLength):0;if(length)HEAP32[length>>2]=numBytesWrittenExclNull}function _glGetProgramiv(program,pname,p){if(!p){GL.recordError(1281);return}if(program>=GL.counter){GL.recordError(1281);return}program=GL.programs[program];if(pname==35716){var log=GLctx.getProgramInfoLog(program);if(log===null)log="(unknown error)";HEAP32[p>>2]=log.length+1}else if(pname==35719){if(!program.maxUniformLength){for(var i=0;i>2]=program.maxUniformLength}else if(pname==35722){if(!program.maxAttributeLength){for(var i=0;i>2]=program.maxAttributeLength}else if(pname==35381){if(!program.maxUniformBlockNameLength){for(var i=0;i>2]=program.maxUniformBlockNameLength}else{HEAP32[p>>2]=GLctx.getProgramParameter(program,pname)}}function _glGetQueryObjectuiv(id,pname,params){if(!params){GL.recordError(1281);return}var query=GL.queries[id];var param=GLctx["getQueryParameter"](query,pname);var ret;if(typeof param=="boolean"){ret=param?1:0}else{ret=param}HEAP32[params>>2]=ret}function _glGetQueryiv(target,pname,params){if(!params){GL.recordError(1281);return}HEAP32[params>>2]=GLctx["getQuery"](target,pname)}function _glGetRenderbufferParameteriv(target,pname,params){if(!params){GL.recordError(1281);return}HEAP32[params>>2]=GLctx.getRenderbufferParameter(target,pname)}function _glGetShaderInfoLog(shader,maxLength,length,infoLog){var log=GLctx.getShaderInfoLog(GL.shaders[shader]);if(log===null)log="(unknown error)";var numBytesWrittenExclNull=maxLength>0&&infoLog?stringToUTF8(log,infoLog,maxLength):0;if(length)HEAP32[length>>2]=numBytesWrittenExclNull}function _glGetShaderPrecisionFormat(shaderType,precisionType,range,precision){var result=GLctx.getShaderPrecisionFormat(shaderType,precisionType);HEAP32[range>>2]=result.rangeMin;HEAP32[range+4>>2]=result.rangeMax;HEAP32[precision>>2]=result.precision}function _glGetShaderSource(shader,bufSize,length,source){var result=GLctx.getShaderSource(GL.shaders[shader]);if(!result)return;var numBytesWrittenExclNull=bufSize>0&&source?stringToUTF8(result,source,bufSize):0;if(length)HEAP32[length>>2]=numBytesWrittenExclNull}function _glGetShaderiv(shader,pname,p){if(!p){GL.recordError(1281);return}if(pname==35716){var log=GLctx.getShaderInfoLog(GL.shaders[shader]);if(log===null)log="(unknown error)";var logLength=log?log.length+1:0;HEAP32[p>>2]=logLength}else if(pname==35720){var source=GLctx.getShaderSource(GL.shaders[shader]);var sourceLength=source?source.length+1:0;HEAP32[p>>2]=sourceLength}else{HEAP32[p>>2]=GLctx.getShaderParameter(GL.shaders[shader],pname)}}function _glGetString(name_){var ret=GL.stringCache[name_];if(!ret){switch(name_){case 7939:var exts=GLctx.getSupportedExtensions()||[];exts=exts.concat(exts.map(function(e){return"GL_"+e}));ret=stringToNewUTF8(exts.join(" "));break;case 7936:case 7937:case 37445:case 37446:var s=GLctx.getParameter(name_);if(!s){GL.recordError(1280)}ret=s&&stringToNewUTF8(s);break;case 7938:var glVersion=GLctx.getParameter(7938);if(GL.currentContext.version>=2)glVersion="OpenGL ES 3.0 ("+glVersion+")";else{glVersion="OpenGL ES 2.0 ("+glVersion+")"}ret=stringToNewUTF8(glVersion);break;case 35724:var glslVersion=GLctx.getParameter(35724);var ver_re=/^WebGL GLSL ES ([0-9]\.[0-9][0-9]?)(?:$| .*)/;var ver_num=glslVersion.match(ver_re);if(ver_num!==null){if(ver_num[1].length==3)ver_num[1]=ver_num[1]+"0";glslVersion="OpenGL ES GLSL ES "+ver_num[1]+" ("+glslVersion+")"}ret=stringToNewUTF8(glslVersion);break;default:GL.recordError(1280)}GL.stringCache[name_]=ret}return ret}function _glGetStringi(name,index){if(GL.currentContext.version<2){GL.recordError(1282);return 0}var stringiCache=GL.stringiCache[name];if(stringiCache){if(index<0||index>=stringiCache.length){GL.recordError(1281);return 0}return stringiCache[index]}switch(name){case 7939:var exts=GLctx.getSupportedExtensions()||[];exts=exts.concat(exts.map(function(e){return"GL_"+e}));exts=exts.map(function(e){return stringToNewUTF8(e)});stringiCache=GL.stringiCache[name]=exts;if(index<0||index>=stringiCache.length){GL.recordError(1281);return 0}return stringiCache[index];default:GL.recordError(1280);return 0}}function _glGetTexParameteriv(target,pname,params){if(!params){GL.recordError(1281);return}HEAP32[params>>2]=GLctx.getTexParameter(target,pname)}function _glGetUniformBlockIndex(program,uniformBlockName){return GLctx["getUniformBlockIndex"](GL.programs[program],UTF8ToString(uniformBlockName))}function _glGetUniformIndices(program,uniformCount,uniformNames,uniformIndices){if(!uniformIndices){GL.recordError(1281);return}if(uniformCount>0&&(uniformNames==0||uniformIndices==0)){GL.recordError(1281);return}program=GL.programs[program];var names=[];for(var i=0;i>2]));var result=GLctx["getUniformIndices"](program,names);if(!result)return;var len=result.length;for(var i=0;i>2]=result[i]}}function _glGetUniformLocation(program,name){function getLeftBracePos(name){return name.slice(-1)=="]"&&name.lastIndexOf("[")}name=UTF8ToString(name);if(program=GL.programs[program]){var uniformLocsById=program.uniformLocsById;var uniformSizeAndIdsByName=program.uniformSizeAndIdsByName;var i,j;var arrayIndex=0;var uniformBaseName=name;var leftBrace=getLeftBracePos(name);if(!uniformLocsById){program.uniformLocsById=uniformLocsById={};program.uniformArrayNamesById={};for(i=0;i0?nm.slice(0,lb):nm;var id=uniformSizeAndIdsByName[arrayName]?uniformSizeAndIdsByName[arrayName][1]:program.uniformIdCounter;program.uniformIdCounter=Math.max(id+sz,program.uniformIdCounter);uniformSizeAndIdsByName[arrayName]=[sz,id];for(j=0;j0){arrayIndex=jstoi_q(name.slice(leftBrace+1))>>>0;uniformBaseName=name.slice(0,leftBrace)}var sizeAndId=uniformSizeAndIdsByName[uniformBaseName];if(sizeAndId&&arrayIndex0?"["+webglLoc+"]":""))}return webglLoc}else{GL.recordError(1282)}}function emscriptenWebGLGetUniform(program,location,params,type){if(!params){GL.recordError(1281);return}program=GL.programs[program];var data=GLctx.getUniform(program,webglGetUniformLocation(location));if(typeof data=="number"||typeof data=="boolean"){switch(type){case 0:HEAP32[params>>2]=data;break;case 2:HEAPF32[params>>2]=data;break}}else{for(var i=0;i>2]=data[i];break;case 2:HEAPF32[params+i*4>>2]=data[i];break}}}}function _glGetUniformiv(program,location,params){emscriptenWebGLGetUniform(program,location,params,0)}function emscriptenWebGLGetVertexAttrib(index,pname,params,type){if(!params){GL.recordError(1281);return}if(GL.currentContext.clientBuffers[index].enabled){err("glGetVertexAttrib*v on client-side array: not supported, bad data returned")}var data=GLctx.getVertexAttrib(index,pname);if(pname==34975){HEAP32[params>>2]=data&&data["name"]}else if(typeof data=="number"||typeof data=="boolean"){switch(type){case 0:HEAP32[params>>2]=data;break;case 2:HEAPF32[params>>2]=data;break;case 5:HEAP32[params>>2]=Math.fround(data);break}}else{for(var i=0;i>2]=data[i];break;case 2:HEAPF32[params+i*4>>2]=data[i];break;case 5:HEAP32[params+i*4>>2]=Math.fround(data[i]);break}}}}function _glGetVertexAttribiv(index,pname,params){emscriptenWebGLGetVertexAttrib(index,pname,params,5)}function _glInvalidateFramebuffer(target,numAttachments,attachments){var list=tempFixedLengthArray[numAttachments];for(var i=0;i>2]}GLctx["invalidateFramebuffer"](target,list)}function _glIsEnabled(x0){return GLctx["isEnabled"](x0)}function _glIsVertexArray(array){var vao=GL.vaos[array];if(!vao)return 0;return GLctx["isVertexArray"](vao)}function _glLinkProgram(program){program=GL.programs[program];GLctx.linkProgram(program);program.uniformLocsById=0;program.uniformSizeAndIdsByName={};[program["vs"],program["fs"]].forEach(function(s){Object.keys(s.explicitUniformLocations).forEach(function(shaderLocation){var loc=s.explicitUniformLocations[shaderLocation];program.uniformSizeAndIdsByName[shaderLocation]=[1,loc];program.uniformIdCounter=Math.max(program.uniformIdCounter,loc+1)})});function copyKeys(dst,src){Object.keys(src).forEach(function(key){dst[key]=src[key]})}program.explicitUniformBindings={};program.explicitSamplerBindings={};[program["vs"],program["fs"]].forEach(function(s){copyKeys(program.explicitUniformBindings,s.explicitUniformBindings);copyKeys(program.explicitSamplerBindings,s.explicitSamplerBindings)});program.explicitProgramBindingsApplied=0}function _glMapBufferRange(target,offset,length,access){if(access!=26&&access!=10){err("glMapBufferRange is only supported when access is MAP_WRITE|INVALIDATE_BUFFER");return 0}if(!emscriptenWebGLValidateMapBufferTarget(target)){GL.recordError(1280);err("GL_INVALID_ENUM in glMapBufferRange");return 0}var mem=_malloc(length);if(!mem)return 0;GL.mappedBuffers[emscriptenWebGLGetBufferBinding(target)]={offset:offset,length:length,mem:mem,access:access};return mem}function _glPixelStorei(pname,param){if(pname==3317){GL.unpackAlignment=param}GLctx.pixelStorei(pname,param)}function _glPolygonOffset(x0,x1){GLctx["polygonOffset"](x0,x1)}function _glProgramBinary(program,binaryFormat,binary,length){GL.recordError(1280)}function _glProgramParameteri(program,pname,value){GL.recordError(1280)}function _glReadBuffer(x0){GLctx["readBuffer"](x0)}function computeUnpackAlignedImageSize(width,height,sizePerPixel,alignment){function roundedToNextMultipleOf(x,y){return x+y-1&-y}var plainRowSize=width*sizePerPixel;var alignedRowSize=roundedToNextMultipleOf(plainRowSize,alignment);return height*alignedRowSize}function __colorChannelsInGlTextureFormat(format){var colorChannels={5:3,6:4,8:2,29502:3,29504:4,26917:2,26918:2,29846:3,29847:4};return colorChannels[format-6402]||1}function heapObjectForWebGLType(type){type-=5120;if(type==0)return HEAP8;if(type==1)return HEAPU8;if(type==2)return HEAP16;if(type==4)return HEAP32;if(type==6)return HEAPF32;if(type==5||type==28922||type==28520||type==30779||type==30782)return HEAPU32;return HEAPU16}function heapAccessShiftForWebGLHeap(heap){return 31-Math.clz32(heap.BYTES_PER_ELEMENT)}function emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,internalFormat){var heap=heapObjectForWebGLType(type);var shift=heapAccessShiftForWebGLHeap(heap);var byteSize=1<>shift,pixels+bytes>>shift)}function _glReadPixels(x,y,width,height,format,type,pixels){if(GL.currentContext.version>=2){if(GLctx.currentPixelPackBufferBinding){GLctx.readPixels(x,y,width,height,format,type,pixels)}else{var heap=heapObjectForWebGLType(type);GLctx.readPixels(x,y,width,height,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}return}var pixelData=emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,format);if(!pixelData){GL.recordError(1280);return}GLctx.readPixels(x,y,width,height,format,type,pixelData)}function _glRenderbufferStorage(x0,x1,x2,x3){GLctx["renderbufferStorage"](x0,x1,x2,x3)}function _glRenderbufferStorageMultisample(x0,x1,x2,x3,x4){GLctx["renderbufferStorageMultisample"](x0,x1,x2,x3,x4)}function _glSamplerParameteri(sampler,pname,param){GLctx["samplerParameteri"](GL.samplers[sampler],pname,param)}function _glScissor(x0,x1,x2,x3){GLctx["scissor"](x0,x1,x2,x3)}function find_closing_parens_index(arr,i,opening="(",closing=")"){for(var nesting=0;i32)}function nextWhitespace(str,i){while(!isWhitespace(str,i))++i;return i}function classifyChar(str,idx){var cc=str.charCodeAt(idx);if(cc>32){if(cc<48)return 1;if(cc<58)return 2;if(cc<65)return 1;if(cc<91||cc==95)return 3;if(cc<97)return 1;if(cc<123)return 3;return 1}return cc<33?0:4}function tokenize(exprString,keepWhitespace){var out=[],len=exprString.length;for(var i=0;i<=len;++i){var kind=classifyChar(exprString,i);if(kind==2||kind==3){for(var j=i+1;j<=len;++j){var kind2=classifyChar(exprString,j);if(kind2!=kind&&(kind2!=2||kind!=3)){out.push(exprString.substring(i,j));i=j-1;break}}}else if(kind==1){var op2=exprString.substr(i,2);if(["<=",">=","==","!=","&&","||"].includes(op2)){out.push(op2);++i}else{out.push(exprString[i])}}}return out}function expandMacros(str,lineStart,lineEnd){if(lineEnd===undefined)lineEnd=str.length;var len=str.length;var out="";for(var i=lineStart;i1||typeof tokens[0]!="function"){tokens=function(tokens){var i,j,p,operatorAndPriority=-2;for(j=0;j",">=","==","!=","&&","||","("].indexOf(tokens[j]))>operatorAndPriority){i=j;operatorAndPriority=p}}if(operatorAndPriority==13){var j=find_closing_parens_index(tokens,i);if(j){tokens.splice(i,j+1-i,buildExprTree(tokens.slice(i+1,j)));return tokens}}if(operatorAndPriority==4){i=tokens.lastIndexOf("!");var innerExpr=buildExprTree(tokens.slice(i+1,i+2));tokens.splice(i,2,function(){return!innerExpr()});return tokens}if(operatorAndPriority>=0){var left=buildExprTree(tokens.slice(0,i));var right=buildExprTree(tokens.slice(i+1));switch(tokens[i]){case"&&":return[function(){return left()&&right()}];case"||":return[function(){return left()||right()}];case"==":return[function(){return left()==right()}];case"!=":return[function(){return left()!=right()}];case"<":return[function(){return left()":return[function(){return left()>right()}];case">=":return[function(){return left()>=right()}];case"+":return[function(){return left()+right()}];case"-":return[function(){return left()-right()}];case"*":return[function(){return left()*right()}];case"/":return[function(){return Math.floor(left()/right())}]}}var num=jstoi_q(tokens[i]);return[function(){return num}]}(tokens)}return tokens[0]}for(;i0){var macroEnd=expression.indexOf(")",macroStart);let params=expression.substring(macroStart+1,macroEnd).split(",").map(x=>x.trim());let value=tokenize(expression.substring(macroEnd+1).trim());defs[expression.substring(0,macroStart)]=function(args){var ret="";value.forEach(x=>{var argIndex=params.indexOf(x);ret+=argIndex>=0?args[argIndex]:x});return ret}}else{let value=expandMacros(expression.substring(firstWs+1).trim(),0);defs[expression.substring(0,firstWs)]=function(){return value}}}break;case"undef":if(thisLineIsInActivePreprocessingBlock)delete defs[expression];break;default:if(directive!="version"&&directive!="pragma"&&directive!="extension"){}out+=expandMacros(code,lineStart,i)+"\n"}}return out}function remove_cpp_comments_in_shaders(code){var i=0,out="",ch,next,len=code.length;for(;i=0&&explicitUniformLocations[match[5]]<1048576)){console.error('Specified an out of range layout(location=x) directive "'+explicitUniformLocations[match[5]]+'"! ('+match[0]+")");GL.recordError(1281);return}}source=source.replace(regex,"$2");GL.shaders[shader].explicitUniformLocations=explicitUniformLocations;var bindingRegex=/layout\s*\(.*?binding\s*=\s*(-?\d+).*?\)\s*uniform\s+(\w+)\s+(\w+)?/g,samplerBindings={},uniformBindings={},bindingMatch;while(bindingMatch=bindingRegex.exec(source)){var arrayLength=1;for(var i=bindingMatch.index;i=0&&binding+arrayLength<=numBindingPoints)){console.error('Specified an out of range layout(binding=x) directive "'+binding+'"! ('+bindingMatch[0]+"). Valid range is [0, "+numBindingPoints+"-1]");GL.recordError(1281);return}}source=source.replace(/layout\s*\(.*?binding\s*=\s*([-\d]+).*?\)/g,"");source=source.replace(/(layout\s*\((.*?)),\s*binding\s*=\s*([-\d]+)\)/g,"$1)");source=source.replace(/layout\s*\(\s*binding\s*=\s*([-\d]+)\s*,(.*?)\)/g,"layout($2)");GL.shaders[shader].explicitSamplerBindings=samplerBindings;GL.shaders[shader].explicitUniformBindings=uniformBindings;GLctx.shaderSource(GL.shaders[shader],source)}function _glStencilFuncSeparate(x0,x1,x2,x3){GLctx["stencilFuncSeparate"](x0,x1,x2,x3)}function _glStencilMask(x0){GLctx["stencilMask"](x0)}function _glStencilOpSeparate(x0,x1,x2,x3){GLctx["stencilOpSeparate"](x0,x1,x2,x3)}function _glTexImage2D(target,level,internalFormat,width,height,border,format,type,pixels){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,null)}return}GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,pixels?emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,internalFormat):null)}function _glTexImage3D(target,level,internalFormat,width,height,depth,border,format,type,pixels){if(GLctx.currentPixelUnpackBufferBinding){GLctx["texImage3D"](target,level,internalFormat,width,height,depth,border,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx["texImage3D"](target,level,internalFormat,width,height,depth,border,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx["texImage3D"](target,level,internalFormat,width,height,depth,border,format,type,null)}}function _glTexParameterf(x0,x1,x2){GLctx["texParameterf"](x0,x1,x2)}function _glTexParameteri(x0,x1,x2){GLctx["texParameteri"](x0,x1,x2)}function _glTexParameteriv(target,pname,params){var param=HEAP32[params>>2];GLctx.texParameteri(target,pname,param)}function _glTexStorage2D(x0,x1,x2,x3,x4){GLctx["texStorage2D"](x0,x1,x2,x3,x4)}function _glTexStorage3D(x0,x1,x2,x3,x4,x5){GLctx["texStorage3D"](x0,x1,x2,x3,x4,x5)}function _glTexSubImage2D(target,level,xoffset,yoffset,width,height,format,type,pixels){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,null)}return}var pixelData=null;if(pixels)pixelData=emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,0);GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,pixelData)}function _glTexSubImage3D(target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,pixels){if(GLctx.currentPixelUnpackBufferBinding){GLctx["texSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx["texSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx["texSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,null)}}function _glTransformFeedbackVaryings(program,count,varyings,bufferMode){program=GL.programs[program];var vars=[];for(var i=0;i>2]));GLctx["transformFeedbackVaryings"](program,vars,bufferMode)}var miniTempWebGLFloatBuffers=[];function _glUniform1fv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform1fv(webglGetUniformLocation(location),HEAPF32,value>>2,count);return}if(count<=288){var view=miniTempWebGLFloatBuffers[count-1];for(var i=0;i>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*4>>2)}GLctx.uniform1fv(webglGetUniformLocation(location),view)}function _glUniform1i(location,v0){GLctx.uniform1i(webglGetUniformLocation(location),v0)}var __miniTempWebGLIntBuffers=[];function _glUniform1iv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform1iv(webglGetUniformLocation(location),HEAP32,value>>2,count);return}if(count<=288){var view=__miniTempWebGLIntBuffers[count-1];for(var i=0;i>2]}}else{var view=HEAP32.subarray(value>>2,value+count*4>>2)}GLctx.uniform1iv(webglGetUniformLocation(location),view)}function _glUniform1uiv(location,count,value){GLctx.uniform1uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count)}function _glUniform2fv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform2fv(webglGetUniformLocation(location),HEAPF32,value>>2,count*2);return}if(count<=144){var view=miniTempWebGLFloatBuffers[2*count-1];for(var i=0;i<2*count;i+=2){view[i]=HEAPF32[value+4*i>>2];view[i+1]=HEAPF32[value+(4*i+4)>>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*8>>2)}GLctx.uniform2fv(webglGetUniformLocation(location),view)}function _glUniform2iv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform2iv(webglGetUniformLocation(location),HEAP32,value>>2,count*2);return}if(count<=144){var view=__miniTempWebGLIntBuffers[2*count-1];for(var i=0;i<2*count;i+=2){view[i]=HEAP32[value+4*i>>2];view[i+1]=HEAP32[value+(4*i+4)>>2]}}else{var view=HEAP32.subarray(value>>2,value+count*8>>2)}GLctx.uniform2iv(webglGetUniformLocation(location),view)}function _glUniform2uiv(location,count,value){GLctx.uniform2uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count*2)}function 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i=0;i<4*count;i+=4){view[i]=HEAP32[value+4*i>>2];view[i+1]=HEAP32[value+(4*i+4)>>2];view[i+2]=HEAP32[value+(4*i+8)>>2];view[i+3]=HEAP32[value+(4*i+12)>>2]}}else{var view=HEAP32.subarray(value>>2,value+count*16>>2)}GLctx.uniform4iv(webglGetUniformLocation(location),view)}function _glUniform4uiv(location,count,value){GLctx.uniform4uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count*4)}function _glUniformBlockBinding(program,uniformBlockIndex,uniformBlockBinding){program=GL.programs[program];GLctx["uniformBlockBinding"](program,uniformBlockIndex,uniformBlockBinding)}function _glUniformMatrix3fv(location,count,transpose,value){if(GL.currentContext.version>=2){GLctx.uniformMatrix3fv(webglGetUniformLocation(location),!!transpose,HEAPF32,value>>2,count*9);return}if(count<=32){var view=miniTempWebGLFloatBuffers[9*count-1];for(var i=0;i<9*count;i+=9){view[i]=HEAPF32[value+4*i>>2];view[i+1]=HEAPF32[value+(4*i+4)>>2];view[i+2]=HEAPF32[value+(4*i+8)>>2];view[i+3]=HEAPF32[value+(4*i+12)>>2];view[i+4]=HEAPF32[value+(4*i+16)>>2];view[i+5]=HEAPF32[value+(4*i+20)>>2];view[i+6]=HEAPF32[value+(4*i+24)>>2];view[i+7]=HEAPF32[value+(4*i+28)>>2];view[i+8]=HEAPF32[value+(4*i+32)>>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*36>>2)}GLctx.uniformMatrix3fv(webglGetUniformLocation(location),!!transpose,view)}function _glUniformMatrix4fv(location,count,transpose,value){if(GL.currentContext.version>=2){GLctx.uniformMatrix4fv(webglGetUniformLocation(location),!!transpose,HEAPF32,value>>2,count*16);return}if(count<=18){var view=miniTempWebGLFloatBuffers[16*count-1];var heap=HEAPF32;value>>=2;for(var i=0;i<16*count;i+=16){var dst=value+i;view[i]=heap[dst];view[i+1]=heap[dst+1];view[i+2]=heap[dst+2];view[i+3]=heap[dst+3];view[i+4]=heap[dst+4];view[i+5]=heap[dst+5];view[i+6]=heap[dst+6];view[i+7]=heap[dst+7];view[i+8]=heap[dst+8];view[i+9]=heap[dst+9];view[i+10]=heap[dst+10];view[i+11]=heap[dst+11];view[i+12]=heap[dst+12];view[i+13]=heap[dst+13];view[i+14]=heap[dst+14];view[i+15]=heap[dst+15]}}else{var view=HEAPF32.subarray(value>>2,value+count*64>>2)}GLctx.uniformMatrix4fv(webglGetUniformLocation(location),!!transpose,view)}function _glUnmapBuffer(target){if(!emscriptenWebGLValidateMapBufferTarget(target)){GL.recordError(1280);err("GL_INVALID_ENUM in glUnmapBuffer");return 0}var buffer=emscriptenWebGLGetBufferBinding(target);var mapping=GL.mappedBuffers[buffer];if(!mapping){GL.recordError(1282);err("buffer was never mapped in glUnmapBuffer");return 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_corsEnabled(url){var xhr=new XMLHttpRequest;xhr.open("HEAD",url,false);try{xhr.send()}catch(e){}return xhr.status>=200&&xhr.status<=299}function _download(url,name,opts){var xhr=new XMLHttpRequest;xhr.open("GET",url);xhr.responseType="blob";xhr.onload=function(){_saveAs(xhr.response,name,opts)};xhr.onerror=function(){console.error("could not download file")};xhr.send()}function _click(node){try{node.dispatchEvent(new MouseEvent("click"))}catch(e){var evt=document.createEvent("MouseEvents");evt.initMouseEvent("click",true,true,window,0,0,0,80,20,false,false,false,false,0,null);node.dispatchEvent(evt)}}function _bom(blob,opts){if(typeof opts==="undefined")opts={autoBom:false};else if(typeof opts!=="object"){console.warn("Deprecated: Expected third argument to be a object");opts={autoBom:!opts}}if(opts.autoBom&&/^\s*(?:text\/\S*|application\/xml|\S*\/\S*\+xml)\s*;.*charset\s*=\s*utf-8/i.test(blob.type)){return new Blob([String.fromCharCode(65279),blob],{type:blob.type})}return 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dynCall_idiii=Module["dynCall_idiii"]=function(){return(dynCall_idiii=Module["dynCall_idiii"]=Module["asm"]["Bn"]).apply(null,arguments)};var dynCall_ifiii=Module["dynCall_ifiii"]=function(){return(dynCall_ifiii=Module["dynCall_ifiii"]=Module["asm"]["Cn"]).apply(null,arguments)};var dynCall_ifiiiii=Module["dynCall_ifiiiii"]=function(){return(dynCall_ifiiiii=Module["dynCall_ifiiiii"]=Module["asm"]["Dn"]).apply(null,arguments)};var dynCall_jjjii=Module["dynCall_jjjii"]=function(){return(dynCall_jjjii=Module["dynCall_jjjii"]=Module["asm"]["En"]).apply(null,arguments)};var dynCall_vdiii=Module["dynCall_vdiii"]=function(){return(dynCall_vdiii=Module["dynCall_vdiii"]=Module["asm"]["Fn"]).apply(null,arguments)};var dynCall_jdii=Module["dynCall_jdii"]=function(){return(dynCall_jdii=Module["dynCall_jdii"]=Module["asm"]["Gn"]).apply(null,arguments)};var dynCall_vijijji=Module["dynCall_vijijji"]=function(){return(dynCall_vijijji=Module["dynCall_vijijji"]=Module["asm"]["Hn"]).apply(null,arguments)};var dynCall_iijjji=Module["dynCall_iijjji"]=function(){return(dynCall_iijjji=Module["dynCall_iijjji"]=Module["asm"]["In"]).apply(null,arguments)};var dynCall_viijjji=Module["dynCall_viijjji"]=function(){return(dynCall_viijjji=Module["dynCall_viijjji"]=Module["asm"]["Jn"]).apply(null,arguments)};var dynCall_vdii=Module["dynCall_vdii"]=function(){return(dynCall_vdii=Module["dynCall_vdii"]=Module["asm"]["Kn"]).apply(null,arguments)};var dynCall_iiiijii=Module["dynCall_iiiijii"]=function(){return(dynCall_iiiijii=Module["dynCall_iiiijii"]=Module["asm"]["Ln"]).apply(null,arguments)};var dynCall_jijji=Module["dynCall_jijji"]=function(){return(dynCall_jijji=Module["dynCall_jijji"]=Module["asm"]["Mn"]).apply(null,arguments)};var dynCall_diddi=Module["dynCall_diddi"]=function(){return(dynCall_diddi=Module["dynCall_diddi"]=Module["asm"]["Nn"]).apply(null,arguments)};var dynCall_didi=Module["dynCall_didi"]=function(){return(dynCall_didi=Module["dynCall_didi"]=Module["asm"]["On"]).apply(null,arguments)};var dynCall_viiiijii=Module["dynCall_viiiijii"]=function(){return(dynCall_viiiijii=Module["dynCall_viiiijii"]=Module["asm"]["Pn"]).apply(null,arguments)};var dynCall_viiijji=Module["dynCall_viiijji"]=function(){return(dynCall_viiijji=Module["dynCall_viiijji"]=Module["asm"]["Qn"]).apply(null,arguments)};var dynCall_iijjii=Module["dynCall_iijjii"]=function(){return(dynCall_iijjii=Module["dynCall_iijjii"]=Module["asm"]["Rn"]).apply(null,arguments)};var dynCall_jjjji=Module["dynCall_jjjji"]=function(){return(dynCall_jjjji=Module["dynCall_jjjji"]=Module["asm"]["Sn"]).apply(null,arguments)};var dynCall_viijijii=Module["dynCall_viijijii"]=function(){return(dynCall_viijijii=Module["dynCall_viijijii"]=Module["asm"]["Tn"]).apply(null,arguments)};var dynCall_viijijiii=Module["dynCall_viijijiii"]=function(){return(dynCall_viijijiii=Module["dynCall_viijijiii"]=Module["asm"]["Un"]).apply(null,arguments)};var dynCall_vijiji=Module["dynCall_vijiji"]=function(){return(dynCall_vijiji=Module["dynCall_vijiji"]=Module["asm"]["Vn"]).apply(null,arguments)};var dynCall_viijiijiii=Module["dynCall_viijiijiii"]=function(){return(dynCall_viijiijiii=Module["dynCall_viijiijiii"]=Module["asm"]["Wn"]).apply(null,arguments)};var dynCall_viiiijiiii=Module["dynCall_viiiijiiii"]=function(){return(dynCall_viiiijiiii=Module["dynCall_viiiijiiii"]=Module["asm"]["Xn"]).apply(null,arguments)};var dynCall_di=Module["dynCall_di"]=function(){return(dynCall_di=Module["dynCall_di"]=Module["asm"]["Yn"]).apply(null,arguments)};var dynCall_viijjii=Module["dynCall_viijjii"]=function(){return(dynCall_viijjii=Module["dynCall_viijjii"]=Module["asm"]["Zn"]).apply(null,arguments)};var dynCall_jiiiiiiiii=Module["dynCall_jiiiiiiiii"]=function(){return(dynCall_jiiiiiiiii=Module["dynCall_jiiiiiiiii"]=Module["asm"]["_n"]).apply(null,arguments)};var dynCall_iiiiijii=Module["dynCall_iiiiijii"]=function(){return(dynCall_iiiiijii=Module["dynCall_iiiiijii"]=Module["asm"]["$n"]).apply(null,arguments)};var dynCall_iiiiidii=Module["dynCall_iiiiidii"]=function(){return(dynCall_iiiiidii=Module["dynCall_iiiiidii"]=Module["asm"]["ao"]).apply(null,arguments)};var dynCall_iiiidii=Module["dynCall_iiiidii"]=function(){return(dynCall_iiiidii=Module["dynCall_iiiidii"]=Module["asm"]["bo"]).apply(null,arguments)};var dynCall_iiiiifii=Module["dynCall_iiiiifii"]=function(){return(dynCall_iiiiifii=Module["dynCall_iiiiifii"]=Module["asm"]["co"]).apply(null,arguments)};var dynCall_iiidiii=Module["dynCall_iiidiii"]=function(){return(dynCall_iiidiii=Module["dynCall_iiidiii"]=Module["asm"]["eo"]).apply(null,arguments)};var dynCall_iiiiffiiiji=Module["dynCall_iiiiffiiiji"]=function(){return(dynCall_iiiiffiiiji=Module["dynCall_iiiiffiiiji"]=Module["asm"]["fo"]).apply(null,arguments)};var dynCall_jiiiiiii=Module["dynCall_jiiiiiii"]=function(){return(dynCall_jiiiiiii=Module["dynCall_jiiiiiii"]=Module["asm"]["go"]).apply(null,arguments)};var dynCall_iiiiffiiiii=Module["dynCall_iiiiffiiiii"]=function(){return(dynCall_iiiiffiiiii=Module["dynCall_iiiiffiiiii"]=Module["asm"]["ho"]).apply(null,arguments)};var dynCall_jiiiiji=Module["dynCall_jiiiiji"]=function(){return(dynCall_jiiiiji=Module["dynCall_jiiiiji"]=Module["asm"]["io"]).apply(null,arguments)};var dynCall_fiiiifi=Module["dynCall_fiiiifi"]=function(){return(dynCall_fiiiifi=Module["dynCall_fiiiifi"]=Module["asm"]["jo"]).apply(null,arguments)};var dynCall_iiijjii=Module["dynCall_iiijjii"]=function(){return(dynCall_iiijjii=Module["dynCall_iiijjii"]=Module["asm"]["ko"]).apply(null,arguments)};var dynCall_viiifii=Module["dynCall_viiifii"]=function(){return(dynCall_viiifii=Module["dynCall_viiifii"]=Module["asm"]["lo"]).apply(null,arguments)};var dynCall_iiiijiii=Module["dynCall_iiiijiii"]=function(){return(dynCall_iiiijiii=Module["dynCall_iiiijiii"]=Module["asm"]["mo"]).apply(null,arguments)};var dynCall_iiiij=Module["dynCall_iiiij"]=function(){return(dynCall_iiiij=Module["dynCall_iiiij"]=Module["asm"]["no"]).apply(null,arguments)};var dynCall_fff=Module["dynCall_fff"]=function(){return(dynCall_fff=Module["dynCall_fff"]=Module["asm"]["oo"]).apply(null,arguments)};var dynCall_ijj=Module["dynCall_ijj"]=function(){return(dynCall_ijj=Module["dynCall_ijj"]=Module["asm"]["po"]).apply(null,arguments)};var dynCall_vjji=Module["dynCall_vjji"]=function(){return(dynCall_vjji=Module["dynCall_vjji"]=Module["asm"]["qo"]).apply(null,arguments)};var dynCall_ij=Module["dynCall_ij"]=function(){return(dynCall_ij=Module["dynCall_ij"]=Module["asm"]["ro"]).apply(null,arguments)};var dynCall_vif=Module["dynCall_vif"]=function(){return(dynCall_vif=Module["dynCall_vif"]=Module["asm"]["so"]).apply(null,arguments)};var dynCall_vid=Module["dynCall_vid"]=function(){return(dynCall_vid=Module["dynCall_vid"]=Module["asm"]["to"]).apply(null,arguments)};var dynCall_viiiiif=Module["dynCall_viiiiif"]=function(){return(dynCall_viiiiif=Module["dynCall_viiiiif"]=Module["asm"]["uo"]).apply(null,arguments)};var dynCall_viiiif=Module["dynCall_viiiif"]=function(){return(dynCall_viiiif=Module["dynCall_viiiif"]=Module["asm"]["vo"]).apply(null,arguments)};var dynCall_viiiiiif=Module["dynCall_viiiiiif"]=function(){return(dynCall_viiiiiif=Module["dynCall_viiiiiif"]=Module["asm"]["wo"]).apply(null,arguments)};var dynCall_iiiiiifff=Module["dynCall_iiiiiifff"]=function(){return(dynCall_iiiiiifff=Module["dynCall_iiiiiifff"]=Module["asm"]["xo"]).apply(null,arguments)};var dynCall_iiiiiifiif=Module["dynCall_iiiiiifiif"]=function(){return(dynCall_iiiiiifiif=Module["dynCall_iiiiiifiif"]=Module["asm"]["yo"]).apply(null,arguments)};var dynCall_iiiiiifiii=Module["dynCall_iiiiiifiii"]=function(){return(dynCall_iiiiiifiii=Module["dynCall_iiiiiifiii"]=Module["asm"]["zo"]).apply(null,arguments)};var dynCall_iiiiiiifiif=Module["dynCall_iiiiiiifiif"]=function(){return(dynCall_iiiiiiifiif=Module["dynCall_iiiiiiifiif"]=Module["asm"]["Ao"]).apply(null,arguments)};var dynCall_fiff=Module["dynCall_fiff"]=function(){return(dynCall_fiff=Module["dynCall_fiff"]=Module["asm"]["Bo"]).apply(null,arguments)};var dynCall_fiiiiiifiifif=Module["dynCall_fiiiiiifiifif"]=function(){return(dynCall_fiiiiiifiifif=Module["dynCall_fiiiiiifiifif"]=Module["asm"]["Co"]).apply(null,arguments)};var dynCall_fiiiiiifiiiif=Module["dynCall_fiiiiiifiiiif"]=function(){return(dynCall_fiiiiiifiiiif=Module["dynCall_fiiiiiifiiiif"]=Module["asm"]["Do"]).apply(null,arguments)};var dynCall_iifiiiijii=Module["dynCall_iifiiiijii"]=function(){return(dynCall_iifiiiijii=Module["dynCall_iifiiiijii"]=Module["asm"]["Eo"]).apply(null,arguments)};var dynCall_vifijii=Module["dynCall_vifijii"]=function(){return(dynCall_vifijii=Module["dynCall_vifijii"]=Module["asm"]["Fo"]).apply(null,arguments)};var dynCall_iiiifffiii=Module["dynCall_iiiifffiii"]=function(){return(dynCall_iiiifffiii=Module["dynCall_iiiifffiii"]=Module["asm"]["Go"]).apply(null,arguments)};var dynCall_iiiifffffi=Module["dynCall_iiiifffffi"]=function(){return(dynCall_iiiifffffi=Module["dynCall_iiiifffffi"]=Module["asm"]["Ho"]).apply(null,arguments)};var dynCall_viffiiiif=Module["dynCall_viffiiiif"]=function(){return(dynCall_viffiiiif=Module["dynCall_viffiiiif"]=Module["asm"]["Io"]).apply(null,arguments)};var dynCall_viffiifffffiii=Module["dynCall_viffiifffffiii"]=function(){return(dynCall_viffiifffffiii=Module["dynCall_viffiifffffiii"]=Module["asm"]["Jo"]).apply(null,arguments)};var dynCall_viffffiifffiiiiif=Module["dynCall_viffffiifffiiiiif"]=function(){return(dynCall_viffffiifffiiiiif=Module["dynCall_viffffiifffiiiiif"]=Module["asm"]["Ko"]).apply(null,arguments)};var dynCall_iiiifffffii=Module["dynCall_iiiifffffii"]=function(){return(dynCall_iiiifffffii=Module["dynCall_iiiifffffii"]=Module["asm"]["Lo"]).apply(null,arguments)};var dynCall_viiiiiiiiiiifii=Module["dynCall_viiiiiiiiiiifii"]=function(){return(dynCall_viiiiiiiiiiifii=Module["dynCall_viiiiiiiiiiifii"]=Module["asm"]["Mo"]).apply(null,arguments)};var dynCall_viff=Module["dynCall_viff"]=function(){return(dynCall_viff=Module["dynCall_viff"]=Module["asm"]["No"]).apply(null,arguments)};var dynCall_iiiifiiiii=Module["dynCall_iiiifiiiii"]=function(){return(dynCall_iiiifiiiii=Module["dynCall_iiiifiiiii"]=Module["asm"]["Oo"]).apply(null,arguments)};var dynCall_iiiiifiiiiif=Module["dynCall_iiiiifiiiiif"]=function(){return(dynCall_iiiiifiiiiif=Module["dynCall_iiiiifiiiiif"]=Module["asm"]["Po"]).apply(null,arguments)};var dynCall_iiif=Module["dynCall_iiif"]=function(){return(dynCall_iiif=Module["dynCall_iiif"]=Module["asm"]["Qo"]).apply(null,arguments)};var dynCall_viiifiiiii=Module["dynCall_viiifiiiii"]=function(){return(dynCall_viiifiiiii=Module["dynCall_viiifiiiii"]=Module["asm"]["Ro"]).apply(null,arguments)};var dynCall_viiiifiiiiif=Module["dynCall_viiiifiiiiif"]=function(){return(dynCall_viiiifiiiiif=Module["dynCall_viiiifiiiiif"]=Module["asm"]["So"]).apply(null,arguments)};var dynCall_iifff=Module["dynCall_iifff"]=function(){return(dynCall_iifff=Module["dynCall_iifff"]=Module["asm"]["To"]).apply(null,arguments)};var dynCall_iif=Module["dynCall_iif"]=function(){return(dynCall_iif=Module["dynCall_iif"]=Module["asm"]["Uo"]).apply(null,arguments)};var dynCall_viijijj=Module["dynCall_viijijj"]=function(){return(dynCall_viijijj=Module["dynCall_viijijj"]=Module["asm"]["Vo"]).apply(null,arguments)};var dynCall_viijj=Module["dynCall_viijj"]=function(){return(dynCall_viijj=Module["dynCall_viijj"]=Module["asm"]["Wo"]).apply(null,arguments)};var dynCall_viiiij=Module["dynCall_viiiij"]=function(){return(dynCall_viiiij=Module["dynCall_viiiij"]=Module["asm"]["Xo"]).apply(null,arguments)};var dynCall_iiijji=Module["dynCall_iiijji"]=function(){return(dynCall_iiijji=Module["dynCall_iiijji"]=Module["asm"]["Yo"]).apply(null,arguments)};var dynCall_ijjiiiii=Module["dynCall_ijjiiiii"]=function(){return(dynCall_ijjiiiii=Module["dynCall_ijjiiiii"]=Module["asm"]["Zo"]).apply(null,arguments)};var dynCall_vidd=Module["dynCall_vidd"]=function(){return(dynCall_vidd=Module["dynCall_vidd"]=Module["asm"]["_o"]).apply(null,arguments)};var dynCall_iiiiiifffiiifiii=Module["dynCall_iiiiiifffiiifiii"]=function(){return(dynCall_iiiiiifffiiifiii=Module["dynCall_iiiiiifffiiifiii"]=Module["asm"]["$o"]).apply(null,arguments)};var dynCall_viffff=Module["dynCall_viffff"]=function(){return(dynCall_viffff=Module["dynCall_viffff"]=Module["asm"]["ap"]).apply(null,arguments)};var dynCall_vifff=Module["dynCall_vifff"]=function(){return(dynCall_vifff=Module["dynCall_vifff"]=Module["asm"]["bp"]).apply(null,arguments)};var dynCall_viifff=Module["dynCall_viifff"]=function(){return(dynCall_viifff=Module["dynCall_viifff"]=Module["asm"]["cp"]).apply(null,arguments)};var dynCall_vij=Module["dynCall_vij"]=function(){return(dynCall_vij=Module["dynCall_vij"]=Module["asm"]["dp"]).apply(null,arguments)};var dynCall_vf=Module["dynCall_vf"]=function(){return(dynCall_vf=Module["dynCall_vf"]=Module["asm"]["ep"]).apply(null,arguments)};var dynCall_vffff=Module["dynCall_vffff"]=function(){return(dynCall_vffff=Module["dynCall_vffff"]=Module["asm"]["fp"]).apply(null,arguments)};var dynCall_vff=Module["dynCall_vff"]=function(){return(dynCall_vff=Module["dynCall_vff"]=Module["asm"]["gp"]).apply(null,arguments)};var dynCall_vfff=Module["dynCall_vfff"]=function(){return(dynCall_vfff=Module["dynCall_vfff"]=Module["asm"]["hp"]).apply(null,arguments)};var dynCall_f=Module["dynCall_f"]=function(){return(dynCall_f=Module["dynCall_f"]=Module["asm"]["ip"]).apply(null,arguments)};var dynCall_viiif=Module["dynCall_viiif"]=function(){return(dynCall_viiif=Module["dynCall_viiif"]=Module["asm"]["jp"]).apply(null,arguments)};var dynCall_ff=Module["dynCall_ff"]=function(){return(dynCall_ff=Module["dynCall_ff"]=Module["asm"]["kp"]).apply(null,arguments)};var dynCall_vfi=Module["dynCall_vfi"]=function(){return(dynCall_vfi=Module["dynCall_vfi"]=Module["asm"]["lp"]).apply(null,arguments)};var dynCall_fiif=Module["dynCall_fiif"]=function(){return(dynCall_fiif=Module["dynCall_fiif"]=Module["asm"]["mp"]).apply(null,arguments)};var dynCall_iiiiiiffiiiiiiiiiffffiii=Module["dynCall_iiiiiiffiiiiiiiiiffffiii"]=function(){return(dynCall_iiiiiiffiiiiiiiiiffffiii=Module["dynCall_iiiiiiffiiiiiiiiiffffiii"]=Module["asm"]["np"]).apply(null,arguments)};var dynCall_viififi=Module["dynCall_viififi"]=function(){return(dynCall_viififi=Module["dynCall_viififi"]=Module["asm"]["op"]).apply(null,arguments)};var dynCall_viiiiiiiijiii=Module["dynCall_viiiiiiiijiii"]=function(){return(dynCall_viiiiiiiijiii=Module["dynCall_viiiiiiiijiii"]=Module["asm"]["pp"]).apply(null,arguments)};function invoke_iiiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_iiiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vii(index,a1,a2){var sp=stackSave();try{dynCall_vii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iii(index,a1,a2){var sp=stackSave();try{return dynCall_iii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viii(index,a1,a2,a3){var sp=stackSave();try{dynCall_viii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iiii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiii(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fiii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_fiii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_diii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_diii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viif(index,a1,a2,a3){var sp=stackSave();try{dynCall_viif(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vi(index,a1){var sp=stackSave();try{dynCall_vi(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ii(index,a1){var sp=stackSave();try{return dynCall_ii(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_v(index){var sp=stackSave();try{dynCall_v(index)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_i(index){var sp=stackSave();try{return dynCall_i(index)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{return dynCall_iiiiiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiiiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{return dynCall_iiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12){var sp=stackSave();try{return dynCall_iiiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{dynCall_viiiiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{dynCall_viiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viiiiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{return dynCall_iiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fii(index,a1,a2){var sp=stackSave();try{return dynCall_fii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viifi(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viifi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiff(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viiff(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fi(index,a1){var sp=stackSave();try{return dynCall_fi(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiifi(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiifi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vifi(index,a1,a2,a3){var sp=stackSave();try{dynCall_vifi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{dynCall_viiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viidi(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viidi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vidi(index,a1,a2,a3){var sp=stackSave();try{dynCall_vidi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiifi(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{dynCall_viiiiiiiiifi(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiffi(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiffi(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_viiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_dii(index,a1,a2){var sp=stackSave();try{return dynCall_dii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ifi(index,a1,a2){var sp=stackSave();try{return dynCall_ifi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_idi(index,a1,a2){var sp=stackSave();try{return dynCall_idi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12){var sp=stackSave();try{dynCall_viiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fffi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_fffi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_dddi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_dddi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iidi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iidi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iifi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iifi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_diiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_diiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fiiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_fiiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiifi(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viiiifi(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiidii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiidii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiifii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiifii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vidd(index,a1,a2,a3){var sp=stackSave();try{dynCall_vidd(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_jiiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiij(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiij(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_j(index){var sp=stackSave();try{return dynCall_j(index)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iij(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iij(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiijiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{return dynCall_iiijiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jii(index,a1,a2){var sp=stackSave();try{return dynCall_jii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ji(index,a1){var sp=stackSave();try{return dynCall_ji(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viijii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viijii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiij(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiiij(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{return dynCall_jiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_vijii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iji(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viji(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viji(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_jiii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiji(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viiiji(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ijji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_ijji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_iijii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiijjii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iiiijjii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iijiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijjiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{return dynCall_iijjiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jijj(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jijj(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiji(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{return dynCall_iiiiiiiiiji(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vji(index,a1,a2,a3){var sp=stackSave();try{dynCall_vji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiji(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiji(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jji(index,a1,a2,a3){var sp=stackSave();try{return dynCall_jji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jjji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jjji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{dynCall_vijiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_dji(index,a1,a2,a3){var sp=stackSave();try{return dynCall_dji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jjii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_jjii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_iiiji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiij(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiij(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jijiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_jijiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jiiji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viijiiijiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13){var sp=stackSave();try{dynCall_viijiiijiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jdi(index,a1,a2){var sp=stackSave();try{return dynCall_jdi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijjji(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_vijjji(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiijii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{return dynCall_iiiijii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_vijiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vjjjiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{dynCall_vjjjiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vjiiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{dynCall_vjiiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{dynCall_viijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiji(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_jiji(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijji(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iijji(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viij(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viij(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jiiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}Module["ccall"]=ccall;Module["cwrap"]=cwrap;Module["stackTrace"]=stackTrace;Module["addRunDependency"]=addRunDependency;Module["removeRunDependency"]=removeRunDependency;Module["FS_createPath"]=FS.createPath;Module["FS_createDataFile"]=FS.createDataFile;Module["stackTrace"]=stackTrace;var calledRun;function ExitStatus(status){this.name="ExitStatus";this.message="Program terminated with exit("+status+")";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module["_main"];args=args||[];var argc=args.length+1;var argv=stackAlloc((argc+1)*4);HEAP32[argv>>2]=allocateUTF8OnStack(thisProgram);for(var i=1;i>2)+i]=allocateUTF8OnStack(args[i-1])}HEAP32[(argv>>2)+argc]=0;try{var ret=entryFunction(argc,argv);exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e=="unwind"){return}else{var toLog=e;if(e&&typeof e==="object"&&e.stack){toLog=[e,e.stack]}err("exception thrown: "+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module["calledRun"]=true;if(ABORT)return;initRuntime();preMain();if(Module["onRuntimeInitialized"])Module["onRuntimeInitialized"]();if(shouldRunNow)callMain(args);postRun()}if(Module["setStatus"]){Module["setStatus"]("Running...");setTimeout(function(){setTimeout(function(){Module["setStatus"]("")},1);doRun()},1)}else{doRun()}}Module["run"]=run;function exit(status,implicit){EXITSTATUS=status;if(implicit&&keepRuntimeAlive()&&status===0){return}if(keepRuntimeAlive()){}else{exitRuntime();if(Module["onExit"])Module["onExit"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module["preInit"]){if(typeof Module["preInit"]=="function")Module["preInit"]=[Module["preInit"]];while(Module["preInit"].length>0){Module["preInit"].pop()()}}var shouldRunNow=true;if(Module["noInitialRun"])shouldRunNow=false;run();
-
-}
diff --git a/spaces/camenduru-com/wav2lip/app.py b/spaces/camenduru-com/wav2lip/app.py
deleted file mode 100644
index b45482217c0d20dd03c7df01f8694fc07f1bfca2..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/wav2lip/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-from yt_dlp import YoutubeDL
-import os
-
-def download_video(url):
- ydl_opts = {'overwrites':True, 'format':'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4', 'outtmpl':'/content/video.mp4'}
- with YoutubeDL(ydl_opts) as ydl:
- ydl.download(url)
- return f"/content/video.mp4"
-
-def generate(audio_in):
- print(audio_in)
- os.system(f"python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face '/content/video.mp4' --audio '{audio_in}'")
- return f"/content/Wav2Lip/results/result_voice.mp4"
-
-app = gr.Blocks()
-with app:
- with gr.Box():
- gr.HTML(f'''
-
- ''')
- with gr.Row():
- with gr.Column():
- input_text = gr.Textbox(show_label=False, value="https://youtu.be/EU3hIXXeiz4")
- input_download_button = gr.Button(value="Download from YouTube or Twitch")
- audio_in = gr.Audio(show_label=False, type='filepath')
- input_generate_button = gr.Button(value="Generate")
- with gr.Column():
- video_out = gr.Video(label="Output Video")
- input_download_button.click(download_video, inputs=[input_text], outputs=[video_out])
- input_generate_button.click(generate, inputs=[audio_in], outputs=[video_out])
-
-app.launch(server_name="0.0.0.0", server_port=7860)
\ No newline at end of file
diff --git a/spaces/camenduru-com/webui-api/README.md b/spaces/camenduru-com/webui-api/README.md
deleted file mode 100644
index 9b0ef359d0ab05922f8dcdb3c20f7eb8213b3f07..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/webui-api/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
----
-title: Web UI API
-emoji: ⚙
-colorFrom: grey
-colorTo: grey
-sdk: docker
-sdk_version: 3.9
-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
diff --git a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/resources.py b/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/resources.py
deleted file mode 100644
index 2a59c81d78c3b8ab844bed4b74fba12841338646..0000000000000000000000000000000000000000
--- a/spaces/camillevanhoffelen/langchain-HuggingGPT/hugginggpt/resources.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-import uuid
-from io import BytesIO
-
-import requests
-from PIL import Image
-from diffusers.utils.testing_utils import load_image
-from pydub import AudioSegment
-
-RESOURCES_DIR = "resources"
-PROMPT_TEMPLATES_DIR = "prompt-templates"
-GENERATED_RESOURCES_DIR = "output"
-
-
-def get_prompt_resource(prompt_name: str) -> str:
- return os.path.join(RESOURCES_DIR, PROMPT_TEMPLATES_DIR, prompt_name)
-
-
-def get_resource_url(resource_arg: str) -> str:
- if resource_arg.startswith("http"):
- return resource_arg
- else:
- return GENERATED_RESOURCES_DIR + resource_arg
-
-
-# Images
-def image_to_bytes(image: Image) -> bytes:
- image_byte = BytesIO()
- image.save(image_byte, format="png")
- image_data = image_byte.getvalue()
- return image_data
-
-
-def image_from_bytes(img_data: bytes) -> Image:
- return Image.open(BytesIO(img_data))
-
-
-def encode_image(image_arg: str) -> bytes:
- image_url = get_resource_url(image_arg)
- image = load_image(image_url)
- img_data = image_to_bytes(image)
- return img_data
-
-
-def save_image(img: Image) -> str:
- name = str(uuid.uuid4())[:4]
- path = f"/images/{name}.png"
- img.save(GENERATED_RESOURCES_DIR + path)
- return path
-
-
-# Audios
-def load_audio(audio_path: str) -> AudioSegment:
- if audio_path.startswith("http://") or audio_path.startswith("https://"):
- audio_data = requests.get(audio_path).content
- audio = AudioSegment.from_file(BytesIO(audio_data))
- elif os.path.isfile(audio_path):
- audio = AudioSegment.from_file(audio_path)
- else:
- raise ValueError(
- f"Incorrect path or url, URLs must start with `http://` or `https://`, and {audio_path} is not a valid path"
- )
- return audio
-
-
-def audio_to_bytes(audio: AudioSegment) -> bytes:
- audio_byte = BytesIO()
- audio.export(audio_byte, format="flac")
- audio_data = audio_byte.getvalue()
- return audio_data
-
-
-def audio_from_bytes(audio_data: bytes) -> AudioSegment:
- return AudioSegment.from_file(BytesIO(audio_data))
-
-
-def encode_audio(audio_arg: str) -> bytes:
- audio_url = get_resource_url(audio_arg)
- audio = load_audio(audio_url)
- audio_data = audio_to_bytes(audio)
- return audio_data
-
-
-def save_audio(audio: AudioSegment) -> str:
- name = str(uuid.uuid4())[:4]
- path = f"/audios/{name}.flac"
- with open(GENERATED_RESOURCES_DIR + path, "wb") as f:
- audio.export(f, format="flac")
- return path
-
-
-def prepend_resource_dir(s: str) -> str:
- """Prepend the resource dir to all resource paths in the string"""
- for resource_type in ["images", "audios", "videos"]:
- s = s.replace(
- f" /{resource_type}/", f" {GENERATED_RESOURCES_DIR}/{resource_type}/"
- )
- return s
-
-
-def init_resource_dirs():
- os.makedirs(GENERATED_RESOURCES_DIR + "/images", exist_ok=True)
- os.makedirs(GENERATED_RESOURCES_DIR + "/audios", exist_ok=True)
- os.makedirs(GENERATED_RESOURCES_DIR + "/videos", exist_ok=True)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/deeplab/config.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/deeplab/config.py
deleted file mode 100644
index 5f5e45a9124e61c12d90cfc5032b268496891a4a..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DeepLab/deeplab/config.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-def add_deeplab_config(cfg):
- """
- Add config for DeepLab.
- """
- # We retry random cropping until no single category in semantic segmentation GT occupies more
- # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
- cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
- # Used for `poly` learning rate schedule.
- cfg.SOLVER.POLY_LR_POWER = 0.9
- cfg.SOLVER.POLY_LR_CONSTANT_ENDING = 0.0
- # Loss type, choose from `cross_entropy`, `hard_pixel_mining`.
- cfg.MODEL.SEM_SEG_HEAD.LOSS_TYPE = "hard_pixel_mining"
- # DeepLab settings
- cfg.MODEL.SEM_SEG_HEAD.PROJECT_FEATURES = ["res2"]
- cfg.MODEL.SEM_SEG_HEAD.PROJECT_CHANNELS = [48]
- cfg.MODEL.SEM_SEG_HEAD.ASPP_CHANNELS = 256
- cfg.MODEL.SEM_SEG_HEAD.ASPP_DILATIONS = [6, 12, 18]
- cfg.MODEL.SEM_SEG_HEAD.ASPP_DROPOUT = 0.1
- cfg.MODEL.SEM_SEG_HEAD.USE_DEPTHWISE_SEPARABLE_CONV = False
- # Backbone new configs
- cfg.MODEL.RESNETS.RES4_DILATION = 1
- cfg.MODEL.RESNETS.RES5_MULTI_GRID = [1, 2, 4]
- # ResNet stem type from: `basic`, `deeplab`
- cfg.MODEL.RESNETS.STEM_TYPE = "deeplab"
diff --git a/spaces/ceckenrode/sileod-deberta-v3-base-tasksource-nli/README.md b/spaces/ceckenrode/sileod-deberta-v3-base-tasksource-nli/README.md
deleted file mode 100644
index d385f1e47ecd269606b9ede430540abf92ceb5bf..0000000000000000000000000000000000000000
--- a/spaces/ceckenrode/sileod-deberta-v3-base-tasksource-nli/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sileod Deberta V3 Base Tasksource Nli
-emoji: 👀👀👀
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git "a/spaces/cfwef/gpt/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py" "b/spaces/cfwef/gpt/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
deleted file mode 100644
index b7c508e17f82a91952be672f2c92034ce40f8445..0000000000000000000000000000000000000000
--- "a/spaces/cfwef/gpt/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,70 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt):
- import time, glob, os
- print('begin analysis on:', file_manifest)
- for index, fp in enumerate(file_manifest):
- with open(fp, 'r', encoding='utf-8') as f:
- file_content = f.read()
-
- prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
- i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
- i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
- chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
- print('[1] yield chatbot, history')
- yield chatbot, history, '正常'
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, api_key, temperature, history=[]) # 带超时倒计时
-
- print('[2] end gpt req')
- chatbot[-1] = (i_say_show_user, gpt_say)
- history.append(i_say_show_user); history.append(gpt_say)
- print('[3] yield chatbot, history')
- yield chatbot, history, msg
- print('[4] next')
- if not fast_debug: time.sleep(2)
-
- all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
- i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
- chatbot.append((i_say, "[Local Message] waiting gpt response."))
- yield chatbot, history, '正常'
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, api_key, temperature, history=history) # 带超时倒计时
-
- chatbot[-1] = (i_say, gpt_say)
- history.append(i_say); history.append(gpt_say)
- yield chatbot, history, msg
- res = write_results_to_file(history)
- chatbot.append(("完成了吗？", res))
- yield chatbot, history, msg
-
-
-
-@CatchException
-def 读文章写摘要(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
- history = [] # 清空历史，以免输入溢出
- import glob, os
- if os.path.exists(txt):
- project_folder = txt
- else:
- if txt == "": txt = '空空如也的输入栏'
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
- yield chatbot, history, '正常'
- return
- file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
- # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
- # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
- if len(file_manifest) == 0:
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
- yield chatbot, history, '正常'
- return
- yield from 解析Paper(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/bin/vba_extract.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/bin/vba_extract.py
deleted file mode 100644
index d3dce0e38088c6035c1941e65c635e0b1f102562..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/bin/vba_extract.py
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/Users/chuan_hd/Documents/workspace/Others/ChatGPT/LawAssistantChatBot/.venv/bin/python
-
-##############################################################################
-#
-# vba_extract - A simple utility to extract a vbaProject.bin binary from an
-# Excel 2007+ xlsm file for insertion into an XlsxWriter file.
-#
-# SPDX-License-Identifier: BSD-2-Clause
-# Copyright 2013-2023, John McNamara, jmcnamara@cpan.org
-#
-import sys
-from zipfile import ZipFile
-from zipfile import BadZipFile
-
-# The VBA project file we want to extract.
-vba_filename = "vbaProject.bin"
-
-# Get the xlsm file name from the commandline.
-if len(sys.argv) > 1:
- xlsm_file = sys.argv[1]
-else:
- print(
- "\nUtility to extract a vbaProject.bin binary from an Excel 2007+ "
- "xlsm macro file for insertion into an XlsxWriter file."
- "\n"
- "See: https://xlsxwriter.readthedocs.io/working_with_macros.html\n"
- "\n"
- "Usage: vba_extract file.xlsm\n"
- )
- exit()
-
-try:
- # Open the Excel xlsm file as a zip file.
- xlsm_zip = ZipFile(xlsm_file, "r")
-
- # Read the xl/vbaProject.bin file.
- vba_data = xlsm_zip.read("xl/" + vba_filename)
-
- # Write the vba data to a local file.
- vba_file = open(vba_filename, "wb")
- vba_file.write(vba_data)
- vba_file.close()
-
-except IOError as e:
- print("File error: %s" % str(e))
- exit()
-
-except KeyError as e:
- # Usually when there isn't a xl/vbaProject.bin member in the file.
- print("File error: %s" % str(e))
- print("File may not be an Excel xlsm macro file: '%s'" % xlsm_file)
- exit()
-
-except BadZipFile as e:
- # Usually if the file is an xls file and not an xlsm file.
- print("File error: %s: '%s'" % (str(e), xlsm_file))
- print("File may not be an Excel xlsm macro file.")
- exit()
-
-except Exception as e:
- # Catch any other exceptions.
- print("File error: %s" % str(e))
- exit()
-
-print("Extracted: %s" % vba_filename)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/duckdb.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/duckdb.py
deleted file mode 100644
index 19294135a1ab17fde82d1c3c596914effa4d6971..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/duckdb.py
+++ /dev/null
@@ -1,534 +0,0 @@
-# type: ignore
-from chromadb.config import System
-from chromadb.api.types import Documents, Embeddings, IDs, Metadatas
-from chromadb.db.clickhouse import (
- Clickhouse,
- db_array_schema_to_clickhouse_schema,
- EMBEDDING_TABLE_SCHEMA,
- db_schema_to_keys,
- COLLECTION_TABLE_SCHEMA,
-)
-from typing import List, Optional, Sequence
-import pandas as pd
-import json
-import duckdb
-import uuid
-import os
-import logging
-import atexit
-from uuid import UUID
-from overrides import override
-from chromadb.api.types import Metadata
-
-logger = logging.getLogger(__name__)
-
-
-def clickhouse_to_duckdb_schema(table_schema):
- for item in table_schema:
- if "embedding" in item:
- item["embedding"] = "DOUBLE[]"
- # capitalize the key
- item[list(item.keys())[0]] = item[list(item.keys())[0]].upper()
- if "NULLABLE" in item[list(item.keys())[0]]:
- item[list(item.keys())[0]] = (
- item[list(item.keys())[0]].replace("NULLABLE(", "").replace(")", "")
- )
- if "UUID" in item[list(item.keys())[0]]:
- item[list(item.keys())[0]] = "STRING"
- if "FLOAT64" in item[list(item.keys())[0]]:
- item[list(item.keys())[0]] = "DOUBLE"
- return table_schema
-
-
-# TODO: inherits ClickHouse for convenience of copying behavior, not
-# because it's logically a subtype. Factoring out the common behavior
-# to a third superclass they both extend would be preferable.
-class DuckDB(Clickhouse):
- # duckdb has a different way of connecting to the database
- def __init__(self, system: System):
- self._conn = duckdb.connect()
- self._create_table_collections(self._conn)
- self._create_table_embeddings(self._conn)
- self._settings = system.settings
-
- # Normally this would be handled by super(), but we actually can't invoke
- # super().__init__ here because we're (incorrectly) inheriting from Clickhouse
- self._dependencies = set()
-
- # https://duckdb.org/docs/extensions/overview
- self._conn.execute("LOAD 'json';")
-
- @override
- def _create_table_collections(self, conn):
- conn.execute(
- f"""CREATE TABLE collections (
- {db_array_schema_to_clickhouse_schema(clickhouse_to_duckdb_schema(COLLECTION_TABLE_SCHEMA))}
- ) """
- )
-
- # duckdb has different types, so we want to convert the clickhouse schema to duckdb schema
- @override
- def _create_table_embeddings(self, conn):
- conn.execute(
- f"""CREATE TABLE embeddings (
- {db_array_schema_to_clickhouse_schema(clickhouse_to_duckdb_schema(EMBEDDING_TABLE_SCHEMA))}
- ) """
- )
-
- #
- # UTILITY METHODS
- #
- @override
- def get_collection_uuid_from_name(self, collection_name: str) -> UUID:
- return self._conn.execute(
- "SELECT uuid FROM collections WHERE name = ?", [collection_name]
- ).fetchall()[0][0]
-
- #
- # COLLECTION METHODS
- #
- @override
- def create_collection(
- self,
- name: str,
- metadata: Optional[Metadata] = None,
- get_or_create: bool = False,
- ) -> Sequence:
- # poor man's unique constraint
- dupe_check = self.get_collection(name)
- if len(dupe_check) > 0:
- if get_or_create is True:
- if dupe_check[0][2] != metadata:
- self.update_collection(
- dupe_check[0][0], new_name=name, new_metadata=metadata
- )
- dupe_check = self.get_collection(name)
-
- logger.info(
- f"collection with name {name} already exists, returning existing collection"
- )
- return dupe_check
- else:
- raise ValueError(f"Collection with name {name} already exists")
-
- collection_uuid = uuid.uuid4()
- self._conn.execute(
- """INSERT INTO collections (uuid, name, metadata) VALUES (?, ?, ?)""",
- [str(collection_uuid), name, json.dumps(metadata)],
- )
- return [[str(collection_uuid), name, metadata]]
-
- @override
- def get_collection(self, name: str) -> Sequence:
- res = self._conn.execute(
- """SELECT * FROM collections WHERE name = ?""", [name]
- ).fetchall()
- # json.loads the metadata
- return [[x[0], x[1], json.loads(x[2])] for x in res]
-
- @override
- def get_collection_by_id(self, collection_uuid: str):
- res = self._conn.execute(
- """SELECT * FROM collections WHERE uuid = ?""", [collection_uuid]
- ).fetchone()
- return [res[0], res[1], json.loads(res[2])]
-
- @override
- def list_collections(self) -> Sequence:
- res = self._conn.execute("""SELECT * FROM collections""").fetchall()
- return [[x[0], x[1], json.loads(x[2])] for x in res]
-
- @override
- def delete_collection(self, name: str):
- collection_uuid = self.get_collection_uuid_from_name(name)
- self._conn.execute(
- """DELETE FROM embeddings WHERE collection_uuid = ?""", [collection_uuid]
- )
-
- self._delete_index(collection_uuid)
- self._conn.execute("""DELETE FROM collections WHERE name = ?""", [name])
-
- @override
- def update_collection(
- self,
- id: UUID,
- new_name: Optional[str] = None,
- new_metadata: Optional[Metadata] = None,
- ):
- if new_name is not None:
- dupe_check = self.get_collection(new_name)
- if len(dupe_check) > 0 and dupe_check[0][0] != str(id):
- raise ValueError(f"Collection with name {new_name} already exists")
-
- self._conn.execute(
- """UPDATE collections SET name = ? WHERE uuid = ?""",
- [new_name, id],
- )
-
- if new_metadata is not None:
- self._conn.execute(
- """UPDATE collections SET metadata = ? WHERE uuid = ?""",
- [json.dumps(new_metadata), id],
- )
-
- #
- # ITEM METHODS
- #
- # the execute many syntax is different than clickhouse, the (?,?) syntax is different than clickhouse
- @override
- def add(self, collection_uuid, embeddings, metadatas, documents, ids) -> List[UUID]:
- data_to_insert = [
- [
- collection_uuid,
- str(uuid.uuid4()),
- embedding,
- json.dumps(metadatas[i]) if metadatas else None,
- documents[i] if documents else None,
- ids[i],
- ]
- for i, embedding in enumerate(embeddings)
- ]
-
- insert_string = "collection_uuid, uuid, embedding, metadata, document, id"
-
- self._conn.executemany(
- f"""
- INSERT INTO embeddings ({insert_string}) VALUES (?,?,?,?,?,?)""",
- data_to_insert,
- )
-
- return [uuid.UUID(x[1]) for x in data_to_insert] # return uuids
-
- @override
- def count(self, collection_id: UUID) -> int:
- where_string = f"WHERE collection_uuid = '{collection_id}'"
- return self._conn.query(
- f"SELECT COUNT() FROM embeddings {where_string}"
- ).fetchall()[0][0]
-
- @override
- def _format_where(self, where, result):
- for key, value in where.items():
- # Shortcut for $eq
- if type(value) == str:
- result.append(f" json_extract_string(metadata,'$.{key}') = '{value}'")
- if type(value) == int:
- result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS INT) = {value}"
- )
- if type(value) == float:
- result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) = {value}"
- )
- # Operator expression
- elif type(value) == dict:
- operator, operand = list(value.items())[0]
- if operator == "$gt":
- result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) > {operand}"
- )
- elif operator == "$lt":
- result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) < {operand}"
- )
- elif operator == "$gte":
- result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) >= {operand}"
- )
- elif operator == "$lte":
- result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) <= {operand}"
- )
- elif operator == "$ne":
- if type(operand) == str:
- return result.append(
- f" json_extract_string(metadata,'$.{key}') != '{operand}'"
- )
- return result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) != {operand}"
- )
- elif operator == "$eq":
- if type(operand) == str:
- return result.append(
- f" json_extract_string(metadata,'$.{key}') = '{operand}'"
- )
- return result.append(
- f" CAST(json_extract(metadata,'$.{key}') AS DOUBLE) = {operand}"
- )
- else:
- raise ValueError(f"Operator {operator} not supported")
- elif type(value) == list:
- all_subresults = []
- for subwhere in value:
- subresults = []
- self._format_where(subwhere, subresults)
- all_subresults.append(subresults[0])
- if key == "$or":
- result.append(f"({' OR '.join(all_subresults)})")
- elif key == "$and":
- result.append(f"({' AND '.join(all_subresults)})")
- else:
- raise ValueError(
- f"Operator {key} not supported with a list of where clauses"
- )
-
- @override
- def _format_where_document(self, where_document, results):
- operator = list(where_document.keys())[0]
- if operator == "$contains":
- results.append(f"position('{where_document[operator]}' in document) > 0")
- elif operator == "$and" or operator == "$or":
- all_subresults = []
- for subwhere in where_document[operator]:
- subresults = []
- self._format_where_document(subwhere, subresults)
- all_subresults.append(subresults[0])
- if operator == "$or":
- results.append(f"({' OR '.join(all_subresults)})")
- if operator == "$and":
- results.append(f"({' AND '.join(all_subresults)})")
- else:
- raise ValueError(f"Operator {operator} not supported")
-
- @override
- def _get(self, where, columns: Optional[List] = None):
- select_columns = db_schema_to_keys() if columns is None else columns
- val = self._conn.execute(
- f"""SELECT {",".join(select_columns)} FROM embeddings {where}"""
- ).fetchall()
- for i in range(len(val)):
- val[i] = list(val[i])
- if "collection_uuid" in select_columns:
- collection_uuid_column_index = select_columns.index("collection_uuid")
- val[i][collection_uuid_column_index] = uuid.UUID(
- val[i][collection_uuid_column_index]
- )
- if "uuid" in select_columns:
- uuid_column_index = select_columns.index("uuid")
- val[i][uuid_column_index] = uuid.UUID(val[i][uuid_column_index])
- if "metadata" in select_columns:
- metadata_column_index = select_columns.index("metadata")
- val[i][metadata_column_index] = (
- json.loads(val[i][metadata_column_index])
- if val[i][metadata_column_index]
- else None
- )
-
- return val
-
- @override
- def _update(
- self,
- collection_uuid,
- ids: IDs,
- embeddings: Optional[Embeddings],
- metadatas: Optional[Metadatas],
- documents: Optional[Documents],
- ):
- update_data = []
- for i in range(len(ids)):
- data = []
- update_data.append(data)
- if embeddings is not None:
- data.append(embeddings[i])
- if metadatas is not None:
- data.append(json.dumps(metadatas[i]))
- if documents is not None:
- data.append(documents[i])
- data.append(ids[i])
-
- update_fields = []
- if embeddings is not None:
- update_fields.append("embedding = ?")
- if metadatas is not None:
- update_fields.append("metadata = ?")
- if documents is not None:
- update_fields.append("document = ?")
-
- update_statement = f"""
- UPDATE
- embeddings
- SET
- {", ".join(update_fields)}
- WHERE
- id = ? AND
- collection_uuid = '{collection_uuid}';
- """
- self._conn.executemany(update_statement, update_data)
-
- @override
- def _delete(self, where_str: Optional[str] = None) -> List:
- uuids_deleted = self._conn.execute(
- f"""SELECT uuid FROM embeddings {where_str}"""
- ).fetchall()
- self._conn.execute(
- f"""
- DELETE FROM
- embeddings
- {where_str}
- """
- ).fetchall()[0]
- return [uuid.UUID(x[0]) for x in uuids_deleted]
-
- @override
- def get_by_ids(
- self, uuids: List[UUID], columns: Optional[List[str]] = None
- ) -> Sequence:
- # select from duckdb table where ids are in the list
- if not isinstance(uuids, list):
- raise TypeError(f"Expected ids to be a list, got {uuids}")
-
- if not uuids:
- # create an empty pandas dataframe
- return pd.DataFrame()
-
- columns = columns + ["uuid"] if columns else ["uuid"]
-
- select_columns = db_schema_to_keys() if columns is None else columns
- response = self._conn.execute(
- f"""
- SELECT
- {",".join(select_columns)}
- FROM
- embeddings
- WHERE
- uuid IN ({','.join([("'" + str(x) + "'") for x in uuids])})
- """
- ).fetchall()
-
- # sort db results by the order of the uuids
- response = sorted(
- response, key=lambda obj: uuids.index(uuid.UUID(obj[len(columns) - 1]))
- )
-
- return response
-
- @override
- def raw_sql(self, raw_sql):
- return self._conn.execute(raw_sql).df()
-
- # TODO: This method should share logic with clickhouse impl
- @override
- def reset_state(self):
- self._conn.execute("DROP TABLE collections")
- self._conn.execute("DROP TABLE embeddings")
- self._create_table_collections(self._conn)
- self._create_table_embeddings(self._conn)
-
- self.reset_indexes()
-
- def __del__(self):
- logger.info("Exiting: Cleaning up .chroma directory")
- self.reset_indexes()
-
- @override
- def persist(self) -> None:
- raise NotImplementedError(
- "Set chroma_db_impl='duckdb+parquet' to get persistence functionality"
- )
-
-
-class PersistentDuckDB(DuckDB):
- _save_folder = None
-
- def __init__(self, system: System):
- super().__init__(system=system)
-
- system.settings.require("persist_directory")
-
- if system.settings.persist_directory == ".chroma":
- raise ValueError(
- "You cannot use chroma's cache directory .chroma/, please set a different directory"
- )
-
- self._save_folder = system.settings.persist_directory
- self.load()
- # https://docs.python.org/3/library/atexit.html
- atexit.register(self.persist)
-
- def set_save_folder(self, path):
- self._save_folder = path
-
- def get_save_folder(self):
- return self._save_folder
-
- @override
- def persist(self):
- """
- Persist the database to disk
- """
- logger.info(
- f"Persisting DB to disk, putting it in the save folder: {self._save_folder}"
- )
- if self._conn is None:
- return
-
- if not os.path.exists(self._save_folder):
- os.makedirs(self._save_folder)
-
- # if the db is empty, dont save
- if self._conn.query("SELECT COUNT() FROM embeddings") == 0:
- return
-
- self._conn.execute(
- f"""
- COPY
- (SELECT * FROM embeddings)
- TO '{self._save_folder}/chroma-embeddings.parquet'
- (FORMAT PARQUET);
- """
- )
-
- self._conn.execute(
- f"""
- COPY
- (SELECT * FROM collections)
- TO '{self._save_folder}/chroma-collections.parquet'
- (FORMAT PARQUET);
- """
- )
-
- def load(self):
- """
- Load the database from disk
- """
- if not os.path.exists(self._save_folder):
- os.makedirs(self._save_folder)
-
- # load in the embeddings
- if not os.path.exists(f"{self._save_folder}/chroma-embeddings.parquet"):
- logger.info(f"No existing DB found in {self._save_folder}, skipping load")
- else:
- path = self._save_folder + "/chroma-embeddings.parquet"
- self._conn.execute(
- f"INSERT INTO embeddings SELECT * FROM read_parquet('{path}');"
- )
- logger.info(
- f"""loaded in {self._conn.query(f"SELECT COUNT() FROM embeddings").fetchall()[0][0]} embeddings"""
- )
-
- # load in the collections
- if not os.path.exists(f"{self._save_folder}/chroma-collections.parquet"):
- logger.info(f"No existing DB found in {self._save_folder}, skipping load")
- else:
- path = self._save_folder + "/chroma-collections.parquet"
- self._conn.execute(
- f"INSERT INTO collections SELECT * FROM read_parquet('{path}');"
- )
- logger.info(
- f"""loaded in {self._conn.query(f"SELECT COUNT() FROM collections").fetchall()[0][0]} collections"""
- )
-
- def __del__(self):
- # No-op for duckdb with persistence since the base class will delete the indexes
- pass
-
- @override
- def reset_state(self):
- super().reset_state()
- # empty the save folder
- import shutil
- import os
-
- shutil.rmtree(self._save_folder)
- os.mkdir(self._save_folder)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/easter.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/easter.py
deleted file mode 100644
index f74d1f7442473997245ac683b8a269a3574d1ba4..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/easter.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic Easter computing method for any given year, using
-Western, Orthodox or Julian algorithms.
-"""
-
-import datetime
-
-__all__ = ["easter", "EASTER_JULIAN", "EASTER_ORTHODOX", "EASTER_WESTERN"]
-
-EASTER_JULIAN = 1
-EASTER_ORTHODOX = 2
-EASTER_WESTERN = 3
-
-
-def easter(year, method=EASTER_WESTERN):
- """
- This method was ported from the work done by GM Arts,
- on top of the algorithm by Claus Tondering, which was
- based in part on the algorithm of Ouding (1940), as
- quoted in "Explanatory Supplement to the Astronomical
- Almanac", P. Kenneth Seidelmann, editor.
-
- This algorithm implements three different Easter
- calculation methods:
-
- 1. Original calculation in Julian calendar, valid in
- dates after 326 AD
- 2. Original method, with date converted to Gregorian
- calendar, valid in years 1583 to 4099
- 3. Revised method, in Gregorian calendar, valid in
- years 1583 to 4099 as well
-
- These methods are represented by the constants:
-
- * ``EASTER_JULIAN = 1``
- * ``EASTER_ORTHODOX = 2``
- * ``EASTER_WESTERN = 3``
-
- The default method is method 3.
-
- More about the algorithm may be found at:
-
- `GM Arts: Easter Algorithms `_
-
- and
-
- `The Calendar FAQ: Easter `_
-
- """
-
- if not (1 <= method <= 3):
- raise ValueError("invalid method")
-
- # g - Golden year - 1
- # c - Century
- # h - (23 - Epact) mod 30
- # i - Number of days from March 21 to Paschal Full Moon
- # j - Weekday for PFM (0=Sunday, etc)
- # p - Number of days from March 21 to Sunday on or before PFM
- # (-6 to 28 methods 1 & 3, to 56 for method 2)
- # e - Extra days to add for method 2 (converting Julian
- # date to Gregorian date)
-
- y = year
- g = y % 19
- e = 0
- if method < 3:
- # Old method
- i = (19*g + 15) % 30
- j = (y + y//4 + i) % 7
- if method == 2:
- # Extra dates to convert Julian to Gregorian date
- e = 10
- if y > 1600:
- e = e + y//100 - 16 - (y//100 - 16)//4
- else:
- # New method
- c = y//100
- h = (c - c//4 - (8*c + 13)//25 + 19*g + 15) % 30
- i = h - (h//28)*(1 - (h//28)*(29//(h + 1))*((21 - g)//11))
- j = (y + y//4 + i + 2 - c + c//4) % 7
-
- # p can be from -6 to 56 corresponding to dates 22 March to 23 May
- # (later dates apply to method 2, although 23 May never actually occurs)
- p = i - j + e
- d = 1 + (p + 27 + (p + 6)//40) % 31
- m = 3 + (p + 26)//30
- return datetime.date(int(y), int(m), int(d))
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_a_n_k_r.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_a_n_k_r.py
deleted file mode 100644
index d1062ecc7bf75e3a9a346a68c2a17ae7d00a5c3f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_a_n_k_r.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table__a_n_k_r(BaseTTXConverter):
- """
- The anchor point table provides a way to define anchor points.
- These are points within the coordinate space of a given glyph,
- independent of the control points used to render the glyph.
- Anchor points are used in conjunction with the 'kerx' table.
-
- See also https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6ankr.html
- """
-
- pass
diff --git a/spaces/cihyFjudo/fairness-paper-search/Autodesk Simulation Moldflow Insight Ultimate 2014 Torrent Learn How to Use Advanced Tools and a Simplified User Interface.md b/spaces/cihyFjudo/fairness-paper-search/Autodesk Simulation Moldflow Insight Ultimate 2014 Torrent Learn How to Use Advanced Tools and a Simplified User Interface.md
deleted file mode 100644
index af18c30687d066cee0d759c57aece2e2547a9caf..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Autodesk Simulation Moldflow Insight Ultimate 2014 Torrent Learn How to Use Advanced Tools and a Simplified User Interface.md
+++ /dev/null
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-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Counter Terrorist Agency Free Download How to Stop the Worlds Most Dangerous Threats.md b/spaces/cihyFjudo/fairness-paper-search/Counter Terrorist Agency Free Download How to Stop the Worlds Most Dangerous Threats.md
deleted file mode 100644
index 3c34fc9e4df2c94e616d125ffc74189ad747b818..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Counter Terrorist Agency Free Download How to Stop the Worlds Most Dangerous Threats.md
+++ /dev/null
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-HACK Adobe Photoshop Lightroom CC (2018) 11.8.5 Crack Download Zip ⚡ https://tinurli.com/2uwkL2
diff --git a/spaces/cihyFjudo/fairness-paper-search/Dramatic Black White 2.46 APK [Paid] [Full] Experience the Magic of BW Photography with this Amazing App.md b/spaces/cihyFjudo/fairness-paper-search/Dramatic Black White 2.46 APK [Paid] [Full] Experience the Magic of BW Photography with this Amazing App.md
deleted file mode 100644
index e5830f30b027433bb3f9a6cbb9185df747dcf082..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Dramatic Black White 2.46 APK [Paid] [Full] Experience the Magic of BW Photography with this Amazing App.md
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Interactive Petrophysics IP 4.0 .rar A Guide to the New Curves Formulas Densities and Database Updates in the Latest Version.md b/spaces/cihyFjudo/fairness-paper-search/Interactive Petrophysics IP 4.0 .rar A Guide to the New Curves Formulas Densities and Database Updates in the Latest Version.md
deleted file mode 100644
index 44f7bd099ffc9492b0080b8e78bb2ae94b133fd4..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Interactive Petrophysics IP 4.0 .rar A Guide to the New Curves Formulas Densities and Database Updates in the Latest Version.md
+++ /dev/null
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-interactive petrophysics ip 4.0 crack.rar Download File ✦ https://tinurli.com/2uwj8u
diff --git a/spaces/cihyFjudo/fairness-paper-search/Windows 7 Loader 4shared Everything You Need to Know About It.md b/spaces/cihyFjudo/fairness-paper-search/Windows 7 Loader 4shared Everything You Need to Know About It.md
deleted file mode 100644
index 95c09e8742390ce41754423260d5cc0096beaca8..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Windows 7 Loader 4shared Everything You Need to Know About It.md
+++ /dev/null
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-If you have installed the trial version of Windows 7 32-bit or 64-bit, it provides 30 days of trial. After this period, you have to purchase the key from the Microsoft store to activate windows 7 .
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Windows loader is a simple program that helps to make your windows version completely genuine. You have to run this program once, and it does not require any internet connection. This software is also safe to use, and it will not harm your pc or laptop in any way.
-Once you restart your pc go to my computer properties again, and you will see that your windows 7 is activated and genuine forever. You can see the image below when I activated my windows using this loader, and the image is blurry because I captured it from my old youtube video.
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- * This file is part of FFmpeg.
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- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#define USE_FIXED 1
-#include "aacps_tablegen_template.c"
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hq_hqadata.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hq_hqadata.c
deleted file mode 100644
index 56470eadc1910ec9d0bd9e6c19e652a33b969d02..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hq_hqadata.c
+++ /dev/null
@@ -1,8377 +0,0 @@
-/*
- * Canopus HQ/HQA decoder
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "hq_hqa.h"
-
-#define MAT_SIZE 64
-
-static const uint8_t cbp_vlc_bits[16] = {
- 0x04, 0x1C, 0x1D, 0x09, 0x1E, 0x0B, 0x1B, 0x08,
- 0x1F, 0x1A, 0x0C, 0x07, 0x0A, 0x06, 0x05, 0x00,
-};
-
-static const uint8_t cbp_vlc_lens[16] = {
- 4, 5, 5, 4, 5, 4, 5, 4, 5, 5, 4, 4, 4, 4, 4, 2,
-};
-
-static const int32_t qmat00[MAT_SIZE] = {
- 0x0040000, 0x000B18B, 0x00058C5, 0x000B1B1, 0x00082D3, 0x000B1B1,
- 0x000A953, 0x000827B, 0x00104F7, 0x000A953, 0x0009000, 0x000EADD,
- 0x001037B, 0x000756E, 0x0009000, 0x000776D, 0x000696A, 0x000E987,
- 0x000E987, 0x000D2D4, 0x000776D, 0x0016BAF, 0x0014B4C, 0x001A21A,
- 0x001BA75, 0x001A21A, 0x000A5A6, 0x000B5D8, 0x000611E, 0x000811D,
- 0x00150B6, 0x00181B0, 0x00181B0, 0x00150B6, 0x0020474, 0x0018477,
- 0x0022710, 0x001FD1E, 0x001366C, 0x0015000, 0x001366C, 0x000FE8F,
- 0x00044E2, 0x0008A6D, 0x000EA30, 0x0010E47, 0x0010E47, 0x001D460,
- 0x00229B2, 0x001FCB2, 0x0019FA3, 0x000D94C, 0x000CFD2, 0x0007F2D,
- 0x000DEED, 0x0014D5E, 0x0029ABD, 0x0037BB5, 0x002D16B, 0x001FECE,
- 0x000B45B, 0x0011521, 0x0022A42, 0x0012B9C,
-};
-
-static const int32_t qmat01[MAT_SIZE] = {
- 0x0040000, 0x000B18B, 0x00058C5, 0x000B1B1, 0x00082D3, 0x000B1B1,
- 0x000EB2D, 0x000B53A, 0x0016A73, 0x000EB2D, 0x000D000, 0x0014632,
- 0x0016314, 0x000A319, 0x000D000, 0x000A36D, 0x0009041, 0x0014BDA,
- 0x0014BDA, 0x0012081, 0x000A36D, 0x0016BAF, 0x0014B4C, 0x001A21A,
- 0x001BA75, 0x001A21A, 0x000A5A6, 0x000B5D8, 0x000611E, 0x000811D,
- 0x00150B6, 0x00181B0, 0x00181B0, 0x00150B6, 0x0020474, 0x0018477,
- 0x0045A60, 0x004058C, 0x0026CD9, 0x002A000, 0x0026CD9, 0x00202C6,
- 0x0008B4C, 0x00114D9, 0x001D977, 0x0021C8F, 0x0021C8F, 0x003B2EF,
- 0x0045365, 0x007FD2A, 0x00675E5, 0x0036A1F, 0x0033AF3, 0x001FF4B,
- 0x001C010, 0x0029E24, 0x0053C47, 0x007003E, 0x005A9C7, 0x004024C,
- 0x0016A72, 0x0022A42, 0x0045485, 0x0025738,
-};
-
-static const int32_t qmat02[MAT_SIZE] = {
- 0x0040000, 0x0016315, 0x000B18B, 0x0016363, 0x00105A6, 0x0016363,
- 0x00152A7, 0x00104F7, 0x00209EE, 0x00152A7, 0x0012000, 0x001D5B9,
- 0x00206F6, 0x000EADD, 0x0012000, 0x000EEDA, 0x000D2D4, 0x001D30D,
- 0x001D30D, 0x001A5A9, 0x000EEDA, 0x002D75E, 0x0029698, 0x0034433,
- 0x00374EB, 0x0034433, 0x0014B4C, 0x0016BAF, 0x000C23C, 0x001023A,
- 0x002A16C, 0x0030360, 0x0030360, 0x002A16C, 0x00408E9, 0x00308EF,
- 0x0044E21, 0x003FA3C, 0x0026CD9, 0x002A000, 0x0026CD9, 0x001FD1E,
- 0x00089C4, 0x00114D9, 0x001D460, 0x0021C8F, 0x0021C8F, 0x003A8C0,
- 0x0045365, 0x003F964, 0x0033F47, 0x001B297, 0x0019FA3, 0x000FE59,
- 0x001BDDA, 0x0029ABD, 0x0053579, 0x006F76A, 0x005A2D7, 0x003FD9D,
- 0x00168B6, 0x0022A42, 0x0045485, 0x0025738,
-};
-
-static const int32_t qmat03[MAT_SIZE] = {
- 0x0040000, 0x0016315, 0x000B18B, 0x0016363, 0x00105A6, 0x0016363,
- 0x001D65A, 0x0016A73, 0x002D4E7, 0x001D65A, 0x001A000, 0x0028C65,
- 0x002C628, 0x0014632, 0x001A000, 0x00146D9, 0x0012081, 0x00297B5,
- 0x00297B5, 0x0024102, 0x00146D9, 0x002D75E, 0x0029698, 0x0034433,
- 0x00374EB, 0x0034433, 0x0014B4C, 0x0016BAF, 0x000C23C, 0x001023A,
- 0x002A16C, 0x0030360, 0x0030360, 0x002A16C, 0x00408E9, 0x00308EF,
- 0x008B4C0, 0x0080B18, 0x004D9B2, 0x0054000, 0x004D9B2, 0x004058C,
- 0x0011698, 0x00229B2, 0x003B2EF, 0x004391E, 0x004391E, 0x00765DD,
- 0x008A6CA, 0x00FFA54, 0x00CEBCA, 0x006D43E, 0x00675E5, 0x003FE95,
- 0x003801F, 0x0053C47, 0x00A788E, 0x00E007C, 0x00B538D, 0x0080498,
- 0x002D4E3, 0x0045485, 0x008A90A, 0x004AE71,
-};
-
-static const int32_t qmat04[MAT_SIZE] = {
- 0x0040000, 0x00214A0, 0x0010A50, 0x0021514, 0x0018879, 0x0021514,
- 0x001FBFA, 0x0018772, 0x0030EE5, 0x001FBFA, 0x001B000, 0x002C096,
- 0x0030A71, 0x001604B, 0x001B000, 0x0016647, 0x0013C3F, 0x002BC94,
- 0x002BC94, 0x002787D, 0x0016647, 0x004430D, 0x003E1E4, 0x004E64D,
- 0x0052F60, 0x004E64D, 0x001F0F2, 0x0022187, 0x001235A, 0x0018357,
- 0x003F223, 0x0048510, 0x0048510, 0x003F223, 0x0060D5D, 0x0048D66,
- 0x0067531, 0x005F75A, 0x003A345, 0x003F000, 0x003A345, 0x002FBAD,
- 0x000CEA6, 0x0019F46, 0x002BE90, 0x0032AD6, 0x0032AD6, 0x0057D20,
- 0x0067D17, 0x005F616, 0x004DEEA, 0x0028BE3, 0x0026F75, 0x0017D86,
- 0x0029CC8, 0x003E81B, 0x007D036, 0x00A731F, 0x0087442, 0x005FC6B,
- 0x0021D11, 0x0033F64, 0x0067EC7, 0x00382D5,
-};
-
-static const int32_t qmat05[MAT_SIZE] = {
- 0x0040000, 0x00214A0, 0x0010A50, 0x0021514, 0x0018879, 0x0021514,
- 0x002C186, 0x0021FAD, 0x0043F5A, 0x002C186, 0x0027000, 0x003D297,
- 0x004293C, 0x001E94C, 0x0027000, 0x001EA46, 0x001B0C2, 0x003E38F,
- 0x003E38F, 0x0036183, 0x001EA46, 0x004430D, 0x003E1E4, 0x004E64D,
- 0x0052F60, 0x004E64D, 0x001F0F2, 0x0022187, 0x001235A, 0x0018357,
- 0x003F223, 0x0048510, 0x0048510, 0x003F223, 0x0060D5D, 0x0048D66,
- 0x00D0F1F, 0x00C10A4, 0x007468B, 0x007E000, 0x007468B, 0x0060852,
- 0x001A1E4, 0x0033E8C, 0x0058C66, 0x00655AD, 0x00655AD, 0x00B18CC,
- 0x00CFA2F, 0x017F77F, 0x01361B0, 0x00A3E5C, 0x009B0D8, 0x005FDE0,
- 0x005402F, 0x007DA6B, 0x00FB4D5, 0x01500BA, 0x010FD54, 0x00C06E5,
- 0x0043F55, 0x0067EC7, 0x00CFD8F, 0x00705A9,
-};
-
-static const int32_t qmat06[MAT_SIZE] = {
- 0x0040000, 0x002C62A, 0x0016315, 0x002C6C5, 0x0020B4C, 0x002C6C5,
- 0x002A54E, 0x00209EE, 0x00413DC, 0x002A54E, 0x0024000, 0x003AB73,
- 0x0040DEC, 0x001D5B9, 0x0024000, 0x001DDB4, 0x001A5A9, 0x003A61B,
- 0x003A61B, 0x0034B52, 0x001DDB4, 0x005AEBC, 0x0052D2F, 0x0068867,
- 0x006E9D6, 0x0068867, 0x0029698, 0x002D75E, 0x0018477, 0x0020474,
- 0x00542D9, 0x00606C0, 0x00606C0, 0x00542D9, 0x00811D1, 0x00611DE,
- 0x0089C42, 0x007F478, 0x004D9B2, 0x0054000, 0x004D9B2, 0x003FA3C,
- 0x0011388, 0x00229B2, 0x003A8C0, 0x004391E, 0x004391E, 0x0075180,
- 0x008A6CA, 0x007F2C9, 0x0067E8E, 0x003652F, 0x0033F47, 0x001FCB2,
- 0x0037BB5, 0x0053579, 0x00A6AF3, 0x00DEED4, 0x00B45AE, 0x007FB39,
- 0x002D16B, 0x0045485, 0x008A90A, 0x004AE71,
-};
-
-static const int32_t qmat07[MAT_SIZE] = {
- 0x0040000, 0x002C62A, 0x0016315, 0x002C6C5, 0x0020B4C, 0x002C6C5,
- 0x003ACB3, 0x002D4E7, 0x005A9CE, 0x003ACB3, 0x0034000, 0x00518CA,
- 0x0058C50, 0x0028C65, 0x0034000, 0x0028DB3, 0x0024102, 0x0052F69,
- 0x0052F69, 0x0048204, 0x0028DB3, 0x005AEBC, 0x0052D2F, 0x0068867,
- 0x006E9D6, 0x0068867, 0x0029698, 0x002D75E, 0x0018477, 0x0020474,
- 0x00542D9, 0x00606C0, 0x00606C0, 0x00542D9, 0x00811D1, 0x00611DE,
- 0x011697F, 0x0101631, 0x009B363, 0x00A8000, 0x009B363, 0x0080B18,
- 0x0022D30, 0x0045365, 0x00765DD, 0x008723C, 0x008723C, 0x00ECBBB,
- 0x0114D94, 0x01FF4A9, 0x019D795, 0x00DA87B, 0x00CEBCA, 0x007FD2A,
- 0x007003E, 0x00A788E, 0x014F11C, 0x01C00F8, 0x016A71B, 0x0100931,
- 0x005A9C7, 0x008A90A, 0x0115214, 0x0095CE2,
-};
-
-static const int32_t qmat08[MAT_SIZE] = {
- 0x0040000, 0x00377B5, 0x001BBDA, 0x0037876, 0x0028E1E, 0x0037876,
- 0x0034EA1, 0x0028C69, 0x00518D3, 0x0034EA1, 0x002D000, 0x004964F,
- 0x0051167, 0x0024B28, 0x002D000, 0x0025521, 0x0020F13, 0x0048FA1,
- 0x0048FA1, 0x0041E26, 0x0025521, 0x0071A6B, 0x006787B, 0x0082A80,
- 0x008A44B, 0x0082A80, 0x0033C3E, 0x0038D36, 0x001E595, 0x0028591,
- 0x006938F, 0x0078870, 0x0078870, 0x006938F, 0x00A1646, 0x0079655,
- 0x00AC352, 0x009F196, 0x006101E, 0x0069000, 0x006101E, 0x004F8CB,
- 0x001586A, 0x002B41F, 0x00492F0, 0x0054765, 0x0054765, 0x00925E0,
- 0x00AD07C, 0x009EF7B, 0x0081E31, 0x0043E7A, 0x0040F19, 0x0027BDF,
- 0x0045AA2, 0x00682D8, 0x00D05B0, 0x0116A89, 0x00E1719, 0x009FA07,
- 0x00385C6, 0x00569A6, 0x00AD34C, 0x005DA0D,
-};
-
-static const int32_t qmat09[MAT_SIZE] = {
- 0x0040000, 0x00377B5, 0x001BBDA, 0x0037876, 0x0028E1E, 0x0037876,
- 0x00497E0, 0x0038A21, 0x0071441, 0x00497E0, 0x0041000, 0x0065EFC,
- 0x006EF64, 0x0032F7E, 0x0041000, 0x003311F, 0x002D143, 0x0067B44,
- 0x0067B44, 0x005A285, 0x003311F, 0x0071A6B, 0x006787B, 0x0082A80,
- 0x008A44B, 0x0082A80, 0x0033C3E, 0x0038D36, 0x001E595, 0x0028591,
- 0x006938F, 0x0078870, 0x0078870, 0x006938F, 0x00A1646, 0x0079655,
- 0x015C3DF, 0x0141BBD, 0x00C203C, 0x00D2000, 0x00C203C, 0x00A0DDE,
- 0x002B87C, 0x005683E, 0x0093F55, 0x00A8ECA, 0x00A8ECA, 0x0127EA9,
- 0x015A0F9, 0x027F1D3, 0x0204D7A, 0x011129A, 0x01026BD, 0x009FC75,
- 0x008C04E, 0x00D16B2, 0x01A2D64, 0x0230136, 0x01C50E1, 0x0140B7D,
- 0x0071438, 0x00AD34C, 0x015A699, 0x00BB41A,
-};
-
-static const int32_t qmat0A[MAT_SIZE] = {
- 0x0040000, 0x004293F, 0x00214A0, 0x0042A28, 0x00310F1, 0x0042A28,
- 0x003F7F5, 0x0030EE5, 0x0061DCA, 0x003F7F5, 0x0036000, 0x005812C,
- 0x00614E2, 0x002C096, 0x0036000, 0x002CC8E, 0x002787D, 0x0057928,
- 0x0057928, 0x004F0FB, 0x002CC8E, 0x008861A, 0x007C3C7, 0x009CC9A,
- 0x00A5EC1, 0x009CC9A, 0x003E1E4, 0x004430D, 0x00246B3, 0x00306AF,
- 0x007E445, 0x0090A20, 0x0090A20, 0x007E445, 0x00C1ABA, 0x0091ACC,
- 0x00CEA63, 0x00BEEB4, 0x007468B, 0x007E000, 0x007468B, 0x005F75A,
- 0x0019D4C, 0x0033E8C, 0x0057D20, 0x00655AD, 0x00655AD, 0x00AFA40,
- 0x00CFA2F, 0x00BEC2D, 0x009BDD5, 0x00517C6, 0x004DEEA, 0x002FB0B,
- 0x005398F, 0x007D036, 0x00FA06C, 0x014E63E, 0x010E885, 0x00BF8D6,
- 0x0043A21, 0x0067EC7, 0x00CFD8F, 0x00705A9,
-};
-
-static const int32_t qmat0B[MAT_SIZE] = {
- 0x0040000, 0x004293F, 0x00214A0, 0x0042A28, 0x00310F1, 0x0042A28,
- 0x005830D, 0x0043F5A, 0x0087EB5, 0x005830D, 0x004E000, 0x007A52F,
- 0x0085278, 0x003D297, 0x004E000, 0x003D48C, 0x0036183, 0x007C71E,
- 0x007C71E, 0x006C307, 0x003D48C, 0x008861A, 0x007C3C7, 0x009CC9A,
- 0x00A5EC1, 0x009CC9A, 0x003E1E4, 0x004430D, 0x00246B3, 0x00306AF,
- 0x007E445, 0x0090A20, 0x0090A20, 0x007E445, 0x00C1ABA, 0x0091ACC,
- 0x01A1E3F, 0x0182149, 0x00E8D15, 0x00FC000, 0x00E8D15, 0x00C10A4,
- 0x00343C8, 0x0067D17, 0x00B18CC, 0x00CAB59, 0x00CAB59, 0x0163198,
- 0x019F45E, 0x02FEEFD, 0x026C35F, 0x0147CB9, 0x01361B0, 0x00BFBBF,
- 0x00A805D, 0x00FB4D5, 0x01F69AB, 0x02A0174, 0x021FAA8, 0x0180DC9,
- 0x0087EAA, 0x00CFD8F, 0x019FB1E, 0x00E0B52,
-};
-
-static const int32_t qmat0C[MAT_SIZE] = {
- 0x0040000, 0x004DACA, 0x0026D65, 0x004DBD9, 0x00393C4, 0x004DBD9,
- 0x004A148, 0x0039160, 0x00722C1, 0x004A148, 0x003F000, 0x0066C09,
- 0x007185D, 0x0033604, 0x003F000, 0x00343FB, 0x002E1E8, 0x00662AF,
- 0x00662AF, 0x005C3CF, 0x00343FB, 0x009F1C9, 0x0090F13, 0x00B6EB3,
- 0x00C1936, 0x00B6EB3, 0x0048789, 0x004F8E5, 0x002A7D1, 0x00387CC,
- 0x00934FB, 0x00A8BCF, 0x00A8BCF, 0x00934FB, 0x00E1F2E, 0x00A9F44,
- 0x00F1173, 0x00DEBD1, 0x0087CF7, 0x0093000, 0x0087CF7, 0x006F5E9,
- 0x001E22E, 0x003C8F8, 0x0066750, 0x00763F4, 0x00763F4, 0x00CCEA0,
- 0x00F23E1, 0x00DE8DF, 0x00B5D78, 0x005F111, 0x005AEBC, 0x0037A38,
- 0x006187D, 0x0091D95, 0x0123B29, 0x01861F3, 0x013B9F0, 0x00DF7A4,
- 0x004EE7C, 0x00793E9, 0x00F27D1, 0x0083145,
-};
-
-static const int32_t qmat0D[MAT_SIZE] = {
- 0x0040000, 0x004DACA, 0x0026D65, 0x004DBD9, 0x00393C4, 0x004DBD9,
- 0x0066E3A, 0x004F494, 0x009E928, 0x0066E3A, 0x005B000, 0x008EB61,
- 0x009B58C, 0x00475B1, 0x005B000, 0x00477F9, 0x003F1C4, 0x00912F8,
- 0x00912F8, 0x007E388, 0x00477F9, 0x009F1C9, 0x0090F13, 0x00B6EB3,
- 0x00C1936, 0x00B6EB3, 0x0048789, 0x004F8E5, 0x002A7D1, 0x00387CC,
- 0x00934FB, 0x00A8BCF, 0x00A8BCF, 0x00934FB, 0x00E1F2E, 0x00A9F44,
- 0x01E789E, 0x01C26D5, 0x010F9EE, 0x0126000, 0x010F9EE, 0x00E136B,
- 0x003CF14, 0x00791F1, 0x00CF243, 0x00EC7E8, 0x00EC7E8, 0x019E487,
- 0x01E47C3, 0x037EC27, 0x02D3944, 0x017E6D8, 0x0169CA2, 0x00DFB0A,
- 0x00C406D, 0x01252F9, 0x024A5F2, 0x03101B2, 0x027A46F, 0x01C1016,
- 0x009E91C, 0x00F27D1, 0x01E4FA3, 0x010628B,
-};
-
-static const int32_t qmat0E[MAT_SIZE] = {
- 0x0040000, 0x0058C54, 0x002C62A, 0x0058D8A, 0x0041697, 0x0058D8A,
- 0x0054A9C, 0x00413DC, 0x00827B8, 0x0054A9C, 0x0048000, 0x00756E5,
- 0x0081BD8, 0x003AB73, 0x0048000, 0x003BB68, 0x0034B52, 0x0074C35,
- 0x0074C35, 0x00696A4, 0x003BB68, 0x00B5D78, 0x00A5A5F, 0x00D10CD,
- 0x00DD3AB, 0x00D10CD, 0x0052D2F, 0x005AEBC, 0x00308EF, 0x00408E9,
- 0x00A85B1, 0x00C0D7F, 0x00C0D7F, 0x00A85B1, 0x01023A3, 0x00C23BB,
- 0x0113883, 0x00FE8EF, 0x009B363, 0x00A8000, 0x009B363, 0x007F478,
- 0x0022710, 0x0045365, 0x0075180, 0x008723C, 0x008723C, 0x00EA300,
- 0x0114D94, 0x00FE591, 0x00CFD1C, 0x006CA5D, 0x0067E8E, 0x003F964,
- 0x006F76A, 0x00A6AF3, 0x014D5E6, 0x01BDDA8, 0x0168B5C, 0x00FF672,
- 0x005A2D7, 0x008A90A, 0x0115214, 0x0095CE2,
-};
-
-static const int32_t qmat0F[MAT_SIZE] = {
- 0x0040000, 0x0058C54, 0x002C62A, 0x0058D8A, 0x0041697, 0x0058D8A,
- 0x0075967, 0x005A9CE, 0x00B539C, 0x0075967, 0x0068000, 0x00A3194,
- 0x00B18A0, 0x00518CA, 0x0068000, 0x0051B65, 0x0048204, 0x00A5ED3,
- 0x00A5ED3, 0x0090409, 0x0051B65, 0x00B5D78, 0x00A5A5F, 0x00D10CD,
- 0x00DD3AB, 0x00D10CD, 0x0052D2F, 0x005AEBC, 0x00308EF, 0x00408E9,
- 0x00A85B1, 0x00C0D7F, 0x00C0D7F, 0x00A85B1, 0x01023A3, 0x00C23BB,
- 0x022D2FE, 0x0202C61, 0x01366C7, 0x0150000, 0x01366C7, 0x0101631,
- 0x0045A60, 0x008A6CA, 0x00ECBBB, 0x010E477, 0x010E477, 0x01D9776,
- 0x0229B27, 0x03FE951, 0x033AF2A, 0x01B50F6, 0x019D795, 0x00FFA54,
- 0x00E007C, 0x014F11C, 0x029E239, 0x03801F0, 0x02D4E36, 0x0201262,
- 0x00B538D, 0x0115214, 0x022A428, 0x012B9C3,
-};
-
-static const int32_t qmat10[MAT_SIZE] = {
- 0x0040000, 0x006EF69, 0x00377B5, 0x006F0ED, 0x0051C3D, 0x006F0ED,
- 0x0069D43, 0x00518D3, 0x00A31A6, 0x0069D43, 0x005A000, 0x0092C9F,
- 0x00A22CD, 0x004964F, 0x005A000, 0x004AA42, 0x0041E26, 0x0091F43,
- 0x0091F43, 0x0083C4D, 0x004AA42, 0x00E34D6, 0x00CF0F6, 0x0105500,
- 0x0114896, 0x0105500, 0x006787B, 0x0071A6B, 0x003CB2A, 0x0050B23,
- 0x00D271E, 0x00F10DF, 0x00F10DF, 0x00D271E, 0x0142C8B, 0x00F2CAA,
- 0x01586A4, 0x013E32B, 0x00C203C, 0x00D2000, 0x00C203C, 0x009F196,
- 0x002B0D5, 0x005683E, 0x00925E0, 0x00A8ECA, 0x00A8ECA, 0x0124BC0,
- 0x015A0F9, 0x013DEF5, 0x0103C63, 0x0087CF4, 0x0081E31, 0x004F7BD,
- 0x008B544, 0x00D05B0, 0x01A0B5F, 0x022D511, 0x01C2E32, 0x013F40F,
- 0x0070B8D, 0x00AD34C, 0x015A699, 0x00BB41A,
-};
-
-static const int32_t qmat11[MAT_SIZE] = {
- 0x0040000, 0x006EF69, 0x00377B5, 0x006F0ED, 0x0051C3D, 0x006F0ED,
- 0x0092FC0, 0x0071441, 0x00E2883, 0x0092FC0, 0x0082000, 0x00CBDF9,
- 0x00DDEC8, 0x0065EFC, 0x0082000, 0x006623F, 0x005A285, 0x00CF687,
- 0x00CF687, 0x00B450B, 0x006623F, 0x00E34D6, 0x00CF0F6, 0x0105500,
- 0x0114896, 0x0105500, 0x006787B, 0x0071A6B, 0x003CB2A, 0x0050B23,
- 0x00D271E, 0x00F10DF, 0x00F10DF, 0x00D271E, 0x0142C8B, 0x00F2CAA,
- 0x02B87BE, 0x028377A, 0x0184078, 0x01A4000, 0x0184078, 0x0141BBD,
- 0x00570F8, 0x00AD07C, 0x0127EA9, 0x0151D95, 0x0151D95, 0x024FD53,
- 0x02B41F1, 0x04FE3A6, 0x0409AF4, 0x0222534, 0x0204D7A, 0x013F8E9,
- 0x011809B, 0x01A2D64, 0x0345AC7, 0x046026C, 0x038A1C3, 0x02816FA,
- 0x00E2871, 0x015A699, 0x02B4D32, 0x0176834,
-};
-
-static const int32_t qmat12[MAT_SIZE] = {
- 0x0040000, 0x008527E, 0x004293F, 0x0085450, 0x00621E3, 0x0085450,
- 0x007EFEA, 0x0061DCA, 0x00C3B94, 0x007EFEA, 0x006C000, 0x00B0258,
- 0x00C29C3, 0x005812C, 0x006C000, 0x005991C, 0x004F0FB, 0x00AF250,
- 0x00AF250, 0x009E1F6, 0x005991C, 0x0110C35, 0x00F878E, 0x0139934,
- 0x014BD81, 0x0139934, 0x007C3C7, 0x008861A, 0x0048D66, 0x0060D5D,
- 0x00FC88A, 0x012143F, 0x012143F, 0x00FC88A, 0x0183574, 0x0123599,
- 0x019D4C5, 0x017DD67, 0x00E8D15, 0x00FC000, 0x00E8D15, 0x00BEEB4,
- 0x0033A99, 0x0067D17, 0x00AFA40, 0x00CAB59, 0x00CAB59, 0x015F480,
- 0x019F45E, 0x017D85A, 0x0137BAA, 0x00A2F8C, 0x009BDD5, 0x005F616,
- 0x00A731F, 0x00FA06C, 0x01F40D9, 0x029CC7B, 0x021D109, 0x017F1AB,
- 0x0087442, 0x00CFD8F, 0x019FB1E, 0x00E0B52,
-};
-
-static const int32_t qmat13[MAT_SIZE] = {
- 0x0040000, 0x008527E, 0x004293F, 0x0085450, 0x00621E3, 0x0085450,
- 0x00B061A, 0x0087EB5, 0x010FD69, 0x00B061A, 0x009C000, 0x00F4A5E,
- 0x010A4F0, 0x007A52F, 0x009C000, 0x007A918, 0x006C307, 0x00F8E3C,
- 0x00F8E3C, 0x00D860D, 0x007A918, 0x0110C35, 0x00F878E, 0x0139934,
- 0x014BD81, 0x0139934, 0x007C3C7, 0x008861A, 0x0048D66, 0x0060D5D,
- 0x00FC88A, 0x012143F, 0x012143F, 0x00FC88A, 0x0183574, 0x0123599,
- 0x0343C7D, 0x0304292, 0x01D1A2A, 0x01F8000, 0x01D1A2A, 0x0182149,
- 0x0068790, 0x00CFA2F, 0x0163198, 0x01956B3, 0x01956B3, 0x02C6330,
- 0x033E8BB, 0x05FDDFA, 0x04D86BE, 0x028F971, 0x026C35F, 0x017F77F,
- 0x01500BA, 0x01F69AB, 0x03ED355, 0x05402E9, 0x043F550, 0x0301B93,
- 0x010FD54, 0x019FB1E, 0x033F63C, 0x01C16A5,
-};
-
-static const int32_t qmat14[MAT_SIZE] = {
- 0x0040000, 0x009B593, 0x004DACA, 0x009B7B2, 0x0072789, 0x009B7B2,
- 0x0094291, 0x00722C1, 0x00E4582, 0x0094291, 0x007E000, 0x00CD812,
- 0x00E30B9, 0x0066C09, 0x007E000, 0x00687F5, 0x005C3CF, 0x00CC55D,
- 0x00CC55D, 0x00B879F, 0x00687F5, 0x013E393, 0x0121E26, 0x016DD67,
- 0x018326C, 0x016DD67, 0x0090F13, 0x009F1C9, 0x0054FA2, 0x0070F97,
- 0x01269F7, 0x015179F, 0x015179F, 0x01269F7, 0x01C3E5D, 0x0153E87,
- 0x01E22E6, 0x01BD7A3, 0x010F9EE, 0x0126000, 0x010F9EE, 0x00DEBD1,
- 0x003C45D, 0x00791F1, 0x00CCEA0, 0x00EC7E8, 0x00EC7E8, 0x0199D41,
- 0x01E47C3, 0x01BD1BE, 0x016BAF1, 0x00BE223, 0x00B5D78, 0x006F46F,
- 0x00C30F9, 0x0123B29, 0x0247652, 0x030C3E5, 0x02773E0, 0x01BEF48,
- 0x009DCF8, 0x00F27D1, 0x01E4FA3, 0x010628B,
-};
-
-static const int32_t qmat15[MAT_SIZE] = {
- 0x0040000, 0x009B593, 0x004DACA, 0x009B7B2, 0x0072789, 0x009B7B2,
- 0x00CDC74, 0x009E928, 0x013D250, 0x00CDC74, 0x00B6000, 0x011D6C3,
- 0x0136B18, 0x008EB61, 0x00B6000, 0x008EFF2, 0x007E388, 0x01225F0,
- 0x01225F0, 0x00FC70F, 0x008EFF2, 0x013E393, 0x0121E26, 0x016DD67,
- 0x018326C, 0x016DD67, 0x0090F13, 0x009F1C9, 0x0054FA2, 0x0070F97,
- 0x01269F7, 0x015179F, 0x015179F, 0x01269F7, 0x01C3E5D, 0x0153E87,
- 0x03CF13D, 0x0384DAA, 0x021F3DC, 0x024C000, 0x021F3DC, 0x01C26D5,
- 0x0079E28, 0x00F23E1, 0x019E487, 0x01D8FD0, 0x01D8FD0, 0x033C90E,
- 0x03C8F85, 0x06FD84F, 0x05A7289, 0x02FCDAF, 0x02D3944, 0x01BF614,
- 0x01880D9, 0x024A5F2, 0x0494BE4, 0x0620365, 0x04F48DE, 0x038202B,
- 0x013D237, 0x01E4FA3, 0x03C9F46, 0x020C516,
-};
-
-static const int32_t qmat16[MAT_SIZE] = {
- 0x0040000, 0x00B18A8, 0x0058C54, 0x00B1B15, 0x0082D2E, 0x00B1B15,
- 0x00A9538, 0x00827B8, 0x0104F6F, 0x00A9538, 0x0090000, 0x00EADCB,
- 0x01037AF, 0x00756E5, 0x0090000, 0x00776CF, 0x00696A4, 0x00E986B,
- 0x00E986B, 0x00D2D48, 0x00776CF, 0x016BAF1, 0x014B4BD, 0x01A219A,
- 0x01BA757, 0x01A219A, 0x00A5A5F, 0x00B5D78, 0x00611DE, 0x00811D1,
- 0x0150B63, 0x0181AFF, 0x0181AFF, 0x0150B63, 0x0204745, 0x0184776,
- 0x0227107, 0x01FD1DF, 0x01366C7, 0x0150000, 0x01366C7, 0x00FE8EF,
- 0x0044E21, 0x008A6CA, 0x00EA300, 0x010E477, 0x010E477, 0x01D4601,
- 0x0229B27, 0x01FCB22, 0x019FA38, 0x00D94BA, 0x00CFD1C, 0x007F2C9,
- 0x00DEED4, 0x014D5E6, 0x029ABCC, 0x037BB4F, 0x02D16B7, 0x01FECE4,
- 0x00B45AE, 0x0115214, 0x022A428, 0x012B9C3,
-};
-
-static const int32_t qmat17[MAT_SIZE] = {
- 0x0040000, 0x00B18A8, 0x0058C54, 0x00B1B15, 0x0082D2E, 0x00B1B15,
- 0x00EB2CD, 0x00B539C, 0x016A737, 0x00EB2CD, 0x00D0000, 0x0146328,
- 0x0163140, 0x00A3194, 0x00D0000, 0x00A36CB, 0x0090409, 0x014BDA5,
- 0x014BDA5, 0x0120812, 0x00A36CB, 0x016BAF1, 0x014B4BD, 0x01A219A,
- 0x01BA757, 0x01A219A, 0x00A5A5F, 0x00B5D78, 0x00611DE, 0x00811D1,
- 0x0150B63, 0x0181AFF, 0x0181AFF, 0x0150B63, 0x0204745, 0x0184776,
- 0x045A5FD, 0x04058C2, 0x026CD8D, 0x02A0000, 0x026CD8D, 0x0202C61,
- 0x008B4C0, 0x0114D94, 0x01D9776, 0x021C8EE, 0x021C8EE, 0x03B2EEB,
- 0x045364F, 0x07FD2A3, 0x0675E53, 0x036A1ED, 0x033AF2A, 0x01FF4A9,
- 0x01C00F8, 0x029E239, 0x053C472, 0x07003E1, 0x05A9C6B, 0x04024C4,
- 0x016A71B, 0x022A428, 0x0454850, 0x0257386,
-};
-
-static const int32_t qmat18[MAT_SIZE] = {
- 0x0040000, 0x00C7BBD, 0x0063DDF, 0x00C7E77, 0x00932D4, 0x00C7E77,
- 0x00BE7DF, 0x0092CAF, 0x012595D, 0x00BE7DF, 0x00A2000, 0x0108384,
- 0x0123EA5, 0x00841C2, 0x00A2000, 0x00865A9, 0x0076978, 0x0106B78,
- 0x0106B78, 0x00ED2F1, 0x00865A9, 0x019924F, 0x0174B55, 0x01D65CD,
- 0x01F1C42, 0x01D65CD, 0x00BA5AB, 0x00CC927, 0x006D419, 0x009140C,
- 0x017ACCF, 0x01B1E5F, 0x01B1E5F, 0x017ACCF, 0x024502E, 0x01B5065,
- 0x026BF28, 0x023CC1B, 0x015D3A0, 0x017A000, 0x015D3A0, 0x011E60D,
- 0x004D7E5, 0x009BBA3, 0x0107760, 0x0130106, 0x0130106, 0x020EEC1,
- 0x026EE8C, 0x023C486, 0x01D397F, 0x00F4751, 0x00E9CBF, 0x008F122,
- 0x00FACAE, 0x01770A2, 0x02EE145, 0x03EB2B9, 0x032B98E, 0x023EA81,
- 0x00CAE64, 0x0137C56, 0x026F8AD, 0x01510FC,
-};
-
-static const int32_t qmat19[MAT_SIZE] = {
- 0x0040000, 0x00C7BBD, 0x0063DDF, 0x00C7E77, 0x00932D4, 0x00C7E77,
- 0x0108927, 0x00CBE0F, 0x0197C1E, 0x0108927, 0x00EA000, 0x016EF8D,
- 0x018F768, 0x00B77C6, 0x00EA000, 0x00B7DA4, 0x00A248A, 0x017555A,
- 0x017555A, 0x0144914, 0x00B7DA4, 0x019924F, 0x0174B55, 0x01D65CD,
- 0x01F1C42, 0x01D65CD, 0x00BA5AB, 0x00CC927, 0x006D419, 0x009140C,
- 0x017ACCF, 0x01B1E5F, 0x01B1E5F, 0x017ACCF, 0x024502E, 0x01B5065,
- 0x04E5ABC, 0x04863DB, 0x02BA73F, 0x02F4000, 0x02BA73F, 0x02431ED,
- 0x009CB58, 0x0137746, 0x0214A64, 0x026020C, 0x026020C, 0x04294C8,
- 0x04DDD19, 0x08FCCF7, 0x0744A1D, 0x03D762A, 0x03A250F, 0x023F33E,
- 0x01F8117, 0x02F1E80, 0x05E3D00, 0x07E045D, 0x065EFF9, 0x048295C,
- 0x0197BFE, 0x026F8AD, 0x04DF15A, 0x02A21F7,
-};
-
-static const int32_t qmat1A[MAT_SIZE] = {
- 0x0040000, 0x00DDED2, 0x006EF69, 0x00DE1DA, 0x00A387A, 0x00DE1DA,
- 0x00D3A86, 0x00A31A6, 0x014634B, 0x00D3A86, 0x00B4000, 0x012593E,
- 0x014459B, 0x0092C9F, 0x00B4000, 0x0095483, 0x0083C4D, 0x0123E85,
- 0x0123E85, 0x010789A, 0x0095483, 0x01C69AD, 0x019E1ED, 0x020AA01,
- 0x022912D, 0x020AA01, 0x00CF0F6, 0x00E34D6, 0x0079655, 0x00A1646,
- 0x01A4E3C, 0x01E21BE, 0x01E21BE, 0x01A4E3C, 0x0285917, 0x01E5954,
- 0x02B0D48, 0x027C656, 0x0184078, 0x01A4000, 0x0184078, 0x013E32B,
- 0x00561A9, 0x00AD07C, 0x0124BC0, 0x0151D95, 0x0151D95, 0x0249781,
- 0x02B41F1, 0x027BDEB, 0x02078C6, 0x010F9E9, 0x0103C63, 0x009EF7B,
- 0x0116A89, 0x01A0B5F, 0x03416BE, 0x045AA23, 0x0385C65, 0x027E81E,
- 0x00E1719, 0x015A699, 0x02B4D32, 0x0176834,
-};
-
-static const int32_t qmat1B[MAT_SIZE] = {
- 0x0040000, 0x00DDED2, 0x006EF69, 0x00DE1DA, 0x00A387A, 0x00DE1DA,
- 0x0125F81, 0x00E2883, 0x01C5105, 0x0125F81, 0x0104000, 0x0197BF2,
- 0x01BBD90, 0x00CBDF9, 0x0104000, 0x00CC47E, 0x00B450B, 0x019ED0E,
- 0x019ED0E, 0x0168A16, 0x00CC47E, 0x01C69AD, 0x019E1ED, 0x020AA01,
- 0x022912D, 0x020AA01, 0x00CF0F6, 0x00E34D6, 0x0079655, 0x00A1646,
- 0x01A4E3C, 0x01E21BE, 0x01E21BE, 0x01A4E3C, 0x0285917, 0x01E5954,
- 0x0570F7C, 0x0506EF3, 0x03080F1, 0x0348000, 0x03080F1, 0x028377A,
- 0x00AE1EF, 0x015A0F9, 0x024FD53, 0x02A3B2A, 0x02A3B2A, 0x049FAA6,
- 0x05683E3, 0x09FC74C, 0x08135E8, 0x0444A68, 0x0409AF4, 0x027F1D3,
- 0x0230136, 0x0345AC7, 0x068B58E, 0x08C04D9, 0x0714386, 0x0502DF5,
- 0x01C50E1, 0x02B4D32, 0x0569A64, 0x02ED068,
-};
-
-static const int32_t qmat1C[MAT_SIZE] = {
- 0x0040000, 0x00F41E7, 0x007A0F4, 0x00F453D, 0x00B3E20, 0x00F453D,
- 0x00E8D2D, 0x00B369D, 0x0166D39, 0x00E8D2D, 0x00C6000, 0x0142EF7,
- 0x0164C91, 0x00A177B, 0x00C6000, 0x00A435D, 0x0090F21, 0x0141193,
- 0x0141193, 0x0121E43, 0x00A435D, 0x01F410B, 0x01C7884, 0x023EE34,
- 0x0260617, 0x023EE34, 0x00E3C42, 0x00FA086, 0x0085891, 0x00B1880,
- 0x01CEFA8, 0x021251E, 0x021251E, 0x01CEFA8, 0x02C6200, 0x0216242,
- 0x02F5B69, 0x02BC092, 0x01AAD51, 0x01CE000, 0x01AAD51, 0x015E049,
- 0x005EB6D, 0x00BE556, 0x0142020, 0x0173A24, 0x0173A24, 0x0284041,
- 0x02F9556, 0x02BB74F, 0x023B80D, 0x012AC80, 0x011DC06, 0x00AEDD4,
- 0x0132863, 0x01CA61C, 0x0394C38, 0x04CA18D, 0x03DFF3C, 0x02BE5BA,
- 0x00F7FCF, 0x017D0DB, 0x02FA1B7, 0x019BF6C,
-};
-
-static const int32_t qmat1D[MAT_SIZE] = {
- 0x0040000, 0x00F41E7, 0x007A0F4, 0x00F453D, 0x00B3E20, 0x00F453D,
- 0x01435DA, 0x00F92F6, 0x01F25EC, 0x01435DA, 0x011E000, 0x01C0857,
- 0x01E83B8, 0x00E042B, 0x011E000, 0x00E0B57, 0x00C658C, 0x01C84C3,
- 0x01C84C3, 0x018CB18, 0x00E0B57, 0x01F410B, 0x01C7884, 0x023EE34,
- 0x0260617, 0x023EE34, 0x00E3C42, 0x00FA086, 0x0085891, 0x00B1880,
- 0x01CEFA8, 0x021251E, 0x021251E, 0x01CEFA8, 0x02C6200, 0x0216242,
- 0x05FC43B, 0x0587A0B, 0x0355AA3, 0x039C000, 0x0355AA3, 0x02C3D06,
- 0x00BF887, 0x017CAAB, 0x028B042, 0x02E7447, 0x02E7447, 0x0516083,
- 0x05F2AAD, 0x0AFC1A0, 0x08E21B2, 0x04B1EA5, 0x04710D9, 0x02BF068,
- 0x0268155, 0x039970E, 0x0732E1D, 0x09A0555, 0x07C9713, 0x058328D,
- 0x01F25C5, 0x02FA1B7, 0x05F436E, 0x0337ED9,
-};
-
-static const int32_t qmat1E[MAT_SIZE] = {
- 0x0040000, 0x010A4FD, 0x008527E, 0x010A89F, 0x00C43C5, 0x010A89F,
- 0x00FDFD3, 0x00C3B94, 0x0187727, 0x00FDFD3, 0x00D8000, 0x01604B0,
- 0x0185387, 0x00B0258, 0x00D8000, 0x00B3237, 0x009E1F6, 0x015E4A0,
- 0x015E4A0, 0x013C3EC, 0x00B3237, 0x0221869, 0x01F0F1C, 0x0273267,
- 0x0297B02, 0x0273267, 0x00F878E, 0x0110C35, 0x0091ACC, 0x00C1ABA,
- 0x01F9114, 0x024287E, 0x024287E, 0x01F9114, 0x0306AE8, 0x0246B31,
- 0x033A98A, 0x02FBACE, 0x01D1A2A, 0x01F8000, 0x01D1A2A, 0x017DD67,
- 0x0067531, 0x00CFA2F, 0x015F480, 0x01956B3, 0x01956B3, 0x02BE901,
- 0x033E8BB, 0x02FB0B3, 0x026F754, 0x0145F17, 0x0137BAA, 0x00BEC2D,
- 0x014E63E, 0x01F40D9, 0x03E81B1, 0x05398F7, 0x043A213, 0x02FE357,
- 0x010E885, 0x019FB1E, 0x033F63C, 0x01C16A5,
-};
-
-static const int32_t qmat1F[MAT_SIZE] = {
- 0x0040000, 0x010A4FD, 0x008527E, 0x010A89F, 0x00C43C5, 0x010A89F,
- 0x0160C34, 0x010FD69, 0x021FAD3, 0x0160C34, 0x0138000, 0x01E94BC,
- 0x02149E1, 0x00F4A5E, 0x0138000, 0x00F5230, 0x00D860D, 0x01F1C78,
- 0x01F1C78, 0x01B0C1A, 0x00F5230, 0x0221869, 0x01F0F1C, 0x0273267,
- 0x0297B02, 0x0273267, 0x00F878E, 0x0110C35, 0x0091ACC, 0x00C1ABA,
- 0x01F9114, 0x024287E, 0x024287E, 0x01F9114, 0x0306AE8, 0x0246B31,
- 0x06878FB, 0x0608524, 0x03A3454, 0x03F0000, 0x03A3454, 0x0304292,
- 0x00D0F1F, 0x019F45E, 0x02C6330, 0x032AD65, 0x032AD65, 0x058C661,
- 0x067D176, 0x0BFBBF4, 0x09B0D7D, 0x051F2E3, 0x04D86BE, 0x02FEEFD,
- 0x02A0174, 0x03ED355, 0x07DA6AB, 0x0A805D1, 0x087EAA1, 0x0603726,
- 0x021FAA8, 0x033F63C, 0x067EC78, 0x0382D4A,
-};
-
-static const int32_t qmat20[MAT_SIZE] = {
- 0x0040000, 0x0136B27, 0x009B593, 0x0136F64, 0x00E4F11, 0x0136F64,
- 0x0128521, 0x00E4582, 0x01C8B03, 0x0128521, 0x00FC000, 0x019B023,
- 0x01C6172, 0x00CD812, 0x00FC000, 0x00D0FEB, 0x00B879F, 0x0198ABB,
- 0x0198ABB, 0x0170F3E, 0x00D0FEB, 0x027C725, 0x0243C4B, 0x02DBACE,
- 0x03064D8, 0x02DBACE, 0x0121E26, 0x013E393, 0x00A9F44, 0x00E1F2E,
- 0x024D3ED, 0x02A2F3E, 0x02A2F3E, 0x024D3ED, 0x0387CBA, 0x02A7D0F,
- 0x03C45CC, 0x037AF46, 0x021F3DC, 0x024C000, 0x021F3DC, 0x01BD7A3,
- 0x00788B9, 0x00F23E1, 0x0199D41, 0x01D8FD0, 0x01D8FD0, 0x0333A81,
- 0x03C8F85, 0x037A37C, 0x02D75E2, 0x017C446, 0x016BAF1, 0x00DE8DF,
- 0x01861F3, 0x0247652, 0x048ECA4, 0x06187CA, 0x04EE7C1, 0x037DE90,
- 0x013B9F0, 0x01E4FA3, 0x03C9F46, 0x020C516,
-};
-
-static const int32_t qmat21[MAT_SIZE] = {
- 0x0040000, 0x0136B27, 0x009B593, 0x0136F64, 0x00E4F11, 0x0136F64,
- 0x019B8E7, 0x013D250, 0x027A4A1, 0x019B8E7, 0x016C000, 0x023AD86,
- 0x026D631, 0x011D6C3, 0x016C000, 0x011DFE3, 0x00FC70F, 0x0244BE1,
- 0x0244BE1, 0x01F8E1F, 0x011DFE3, 0x027C725, 0x0243C4B, 0x02DBACE,
- 0x03064D8, 0x02DBACE, 0x0121E26, 0x013E393, 0x00A9F44, 0x00E1F2E,
- 0x024D3ED, 0x02A2F3E, 0x02A2F3E, 0x024D3ED, 0x0387CBA, 0x02A7D0F,
- 0x079E27A, 0x0709B54, 0x043E7B8, 0x0498000, 0x043E7B8, 0x0384DAA,
- 0x00F3C4F, 0x01E47C3, 0x033C90E, 0x03B1FA1, 0x03B1FA1, 0x067921B,
- 0x0791F0A, 0x0DFB09D, 0x0B4E511, 0x05F9B5E, 0x05A7289, 0x037EC27,
- 0x03101B2, 0x0494BE4, 0x09297C7, 0x0C406C9, 0x09E91BC, 0x0704057,
- 0x027A46F, 0x03C9F46, 0x0793E8C, 0x0418A2B,
-};
-
-static const int32_t qmat22[MAT_SIZE] = {
- 0x0040000, 0x0163151, 0x00B18A8, 0x0163629, 0x0105A5D, 0x0163629,
- 0x0152A6F, 0x0104F6F, 0x0209EDF, 0x0152A6F, 0x0120000, 0x01D5B96,
- 0x0206F5E, 0x00EADCB, 0x0120000, 0x00EED9F, 0x00D2D48, 0x01D30D5,
- 0x01D30D5, 0x01A5A90, 0x00EED9F, 0x02D75E2, 0x029697B, 0x0344334,
- 0x0374EAE, 0x0344334, 0x014B4BD, 0x016BAF1, 0x00C23BB, 0x01023A3,
- 0x02A16C6, 0x03035FE, 0x03035FE, 0x02A16C6, 0x0408E8B, 0x0308EEC,
- 0x044E20D, 0x03FA3BE, 0x026CD8D, 0x02A0000, 0x026CD8D, 0x01FD1DF,
- 0x0089C42, 0x0114D94, 0x01D4601, 0x021C8EE, 0x021C8EE, 0x03A8C01,
- 0x045364F, 0x03F9644, 0x033F46F, 0x01B2974, 0x019FA38, 0x00FE591,
- 0x01BDDA8, 0x029ABCC, 0x0535797, 0x06F769E, 0x05A2D6E, 0x03FD9C9,
- 0x0168B5C, 0x022A428, 0x0454850, 0x0257386,
-};
-
-static const int32_t qmat23[MAT_SIZE] = {
- 0x0040000, 0x0163151, 0x00B18A8, 0x0163629, 0x0105A5D, 0x0163629,
- 0x01D659B, 0x016A737, 0x02D4E6E, 0x01D659B, 0x01A0000, 0x028C650,
- 0x02C6281, 0x0146328, 0x01A0000, 0x0146D96, 0x0120812, 0x0297B4A,
- 0x0297B4A, 0x0241023, 0x0146D96, 0x02D75E2, 0x029697B, 0x0344334,
- 0x0374EAE, 0x0344334, 0x014B4BD, 0x016BAF1, 0x00C23BB, 0x01023A3,
- 0x02A16C6, 0x03035FE, 0x03035FE, 0x02A16C6, 0x0408E8B, 0x0308EEC,
- 0x08B4BF9, 0x080B185, 0x04D9B1B, 0x0540000, 0x04D9B1B, 0x04058C2,
- 0x011697F, 0x0229B27, 0x03B2EEB, 0x04391DC, 0x04391DC, 0x0765DD6,
- 0x08A6C9E, 0x0FFA546, 0x0CEBCA6, 0x06D43D9, 0x0675E53, 0x03FE951,
- 0x03801F0, 0x053C472, 0x0A788E4, 0x0E007C1, 0x0B538D6, 0x0804988,
- 0x02D4E36, 0x0454850, 0x08A90A0, 0x04AE70D,
-};
-
-static const int32_t qmat24[MAT_SIZE] = {
- 0x0040000, 0x018F77B, 0x00C7BBD, 0x018FCEF, 0x01265A8, 0x018FCEF,
- 0x017CFBD, 0x012595D, 0x024B2BB, 0x017CFBD, 0x0144000, 0x0210708,
- 0x0247D4A, 0x0108384, 0x0144000, 0x010CB53, 0x00ED2F1, 0x020D6F0,
- 0x020D6F0, 0x01DA5E2, 0x010CB53, 0x033249E, 0x02E96AA, 0x03ACB9B,
- 0x03E3883, 0x03ACB9B, 0x0174B55, 0x019924F, 0x00DA832, 0x0122817,
- 0x02F599F, 0x0363CBD, 0x0363CBD, 0x02F599F, 0x048A05C, 0x036A0CA,
- 0x04D7E4F, 0x0479835, 0x02BA73F, 0x02F4000, 0x02BA73F, 0x023CC1B,
- 0x009AFCA, 0x0137746, 0x020EEC1, 0x026020C, 0x026020C, 0x041DD81,
- 0x04DDD19, 0x047890D, 0x03A72FD, 0x01E8EA3, 0x01D397F, 0x011E243,
- 0x01F595C, 0x02EE145, 0x05DC28A, 0x07D6572, 0x065731C, 0x047D502,
- 0x0195CC7, 0x026F8AD, 0x04DF15A, 0x02A21F7,
-};
-
-static const int32_t qmat25[MAT_SIZE] = {
- 0x0040000, 0x018F77B, 0x00C7BBD, 0x018FCEF, 0x01265A8, 0x018FCEF,
- 0x021124E, 0x0197C1E, 0x032F83C, 0x021124E, 0x01D4000, 0x02DDF1A,
- 0x031EED1, 0x016EF8D, 0x01D4000, 0x016FB49, 0x0144914, 0x02EAAB3,
- 0x02EAAB3, 0x0289228, 0x016FB49, 0x033249E, 0x02E96AA, 0x03ACB9B,
- 0x03E3883, 0x03ACB9B, 0x0174B55, 0x019924F, 0x00DA832, 0x0122817,
- 0x02F599F, 0x0363CBD, 0x0363CBD, 0x02F599F, 0x048A05C, 0x036A0CA,
- 0x09CB578, 0x090C7B6, 0x0574E7E, 0x05E8000, 0x0574E7E, 0x04863DB,
- 0x01396AF, 0x026EE8C, 0x04294C8, 0x04C0418, 0x04C0418, 0x0852991,
- 0x09BBA32, 0x11F99EF, 0x0E8943B, 0x07AEC54, 0x0744A1D, 0x047E67C,
- 0x03F022E, 0x05E3D00, 0x0BC7A00, 0x0FC08BA, 0x0CBDFF1, 0x09052B9,
- 0x032F7FC, 0x04DF15A, 0x09BE2B4, 0x05443EE,
-};
-
-static const int32_t qmat26[MAT_SIZE] = {
- 0x0040000, 0x01BBDA5, 0x00DDED2, 0x01BC3B4, 0x01470F4, 0x01BC3B4,
- 0x01A750B, 0x014634B, 0x028C697, 0x01A750B, 0x0168000, 0x024B27B,
- 0x0288B36, 0x012593E, 0x0168000, 0x012A906, 0x010789A, 0x0247D0B,
- 0x0247D0B, 0x020F134, 0x012A906, 0x038D35A, 0x033C3D9, 0x0415402,
- 0x0452259, 0x0415402, 0x019E1ED, 0x01C69AD, 0x00F2CAA, 0x0142C8B,
- 0x0349C77, 0x03C437D, 0x03C437D, 0x0349C77, 0x050B22E, 0x03CB2A7,
- 0x0561A91, 0x04F8CAD, 0x03080F1, 0x0348000, 0x03080F1, 0x027C656,
- 0x00AC352, 0x015A0F9, 0x0249781, 0x02A3B2A, 0x02A3B2A, 0x0492F02,
- 0x05683E3, 0x04F7BD5, 0x040F18B, 0x021F3D1, 0x02078C6, 0x013DEF5,
- 0x022D511, 0x03416BE, 0x0682D7D, 0x08B5446, 0x070B8CA, 0x04FD03B,
- 0x01C2E32, 0x02B4D32, 0x0569A64, 0x02ED068,
-};
-
-static const int32_t qmat27[MAT_SIZE] = {
- 0x0040000, 0x01BBDA5, 0x00DDED2, 0x01BC3B4, 0x01470F4, 0x01BC3B4,
- 0x024BF01, 0x01C5105, 0x038A20A, 0x024BF01, 0x0208000, 0x032F7E4,
- 0x0377B21, 0x0197BF2, 0x0208000, 0x01988FB, 0x0168A16, 0x033DA1D,
- 0x033DA1D, 0x02D142C, 0x01988FB, 0x038D35A, 0x033C3D9, 0x0415402,
- 0x0452259, 0x0415402, 0x019E1ED, 0x01C69AD, 0x00F2CAA, 0x0142C8B,
- 0x0349C77, 0x03C437D, 0x03C437D, 0x0349C77, 0x050B22E, 0x03CB2A7,
- 0x0AE1EF7, 0x0A0DDE6, 0x06101E2, 0x0690000, 0x06101E2, 0x0506EF3,
- 0x015C3DF, 0x02B41F1, 0x049FAA6, 0x0547653, 0x0547653, 0x093F54C,
- 0x0AD07C5, 0x13F8E97, 0x1026BD0, 0x08894CF, 0x08135E8, 0x04FE3A6,
- 0x046026C, 0x068B58E, 0x0D16B1D, 0x11809B2, 0x0E2870C, 0x0A05BEA,
- 0x038A1C3, 0x0569A64, 0x0AD34C8, 0x05DA0D0,
-};
-
-static const int32_t qmat28[MAT_SIZE] = {
- 0x0040000, 0x01E83CF, 0x00F41E7, 0x01E8A79, 0x0167C3F, 0x01E8A79,
- 0x01D1A59, 0x0166D39, 0x02CDA72, 0x01D1A59, 0x018C000, 0x0285DEE,
- 0x02C9921, 0x0142EF7, 0x018C000, 0x01486BA, 0x0121E43, 0x0282325,
- 0x0282325, 0x0243C86, 0x01486BA, 0x03E8216, 0x038F109, 0x047DC68,
- 0x04C0C2F, 0x047DC68, 0x01C7884, 0x01F410B, 0x010B121, 0x0163100,
- 0x039DF50, 0x0424A3D, 0x0424A3D, 0x039DF50, 0x058C3FF, 0x042C485,
- 0x05EB6D3, 0x0578125, 0x0355AA3, 0x039C000, 0x0355AA3, 0x02BC092,
- 0x00BD6DA, 0x017CAAB, 0x0284041, 0x02E7447, 0x02E7447, 0x0508082,
- 0x05F2AAD, 0x0576E9E, 0x0477019, 0x0255900, 0x023B80D, 0x015DBA7,
- 0x02650C6, 0x0394C38, 0x0729870, 0x0994319, 0x07BFE78, 0x057CB74,
- 0x01EFF9E, 0x02FA1B7, 0x05F436E, 0x0337ED9,
-};
-
-static const int32_t qmat29[MAT_SIZE] = {
- 0x0040000, 0x01E83CF, 0x00F41E7, 0x01E8A79, 0x0167C3F, 0x01E8A79,
- 0x0286BB5, 0x01F25EC, 0x03E4BD8, 0x0286BB5, 0x023C000, 0x03810AE,
- 0x03D0771, 0x01C0857, 0x023C000, 0x01C16AE, 0x018CB18, 0x0390986,
- 0x0390986, 0x0319630, 0x01C16AE, 0x03E8216, 0x038F109, 0x047DC68,
- 0x04C0C2F, 0x047DC68, 0x01C7884, 0x01F410B, 0x010B121, 0x0163100,
- 0x039DF50, 0x0424A3D, 0x0424A3D, 0x039DF50, 0x058C3FF, 0x042C485,
- 0x0BF8876, 0x0B0F417, 0x06AB545, 0x0738000, 0x06AB545, 0x0587A0B,
- 0x017F10F, 0x02F9556, 0x0516083, 0x05CE88F, 0x05CE88F, 0x0A2C106,
- 0x0BE5559, 0x15F8340, 0x11C4364, 0x0963D4B, 0x08E21B2, 0x057E0D0,
- 0x04D02AB, 0x0732E1D, 0x0E65C39, 0x1340AAA, 0x0F92E27, 0x0B0651B,
- 0x03E4B8A, 0x05F436E, 0x0BE86DC, 0x066FDB2,
-};
-
-static const int32_t qmat2A[MAT_SIZE] = {
- 0x0040000, 0x02149F9, 0x010A4FD, 0x021513E, 0x018878B, 0x021513E,
- 0x01FBFA7, 0x0187727, 0x030EE4E, 0x01FBFA7, 0x01B0000, 0x02C0961,
- 0x030A70D, 0x01604B0, 0x01B0000, 0x016646E, 0x013C3EC, 0x02BC940,
- 0x02BC940, 0x02787D8, 0x016646E, 0x04430D2, 0x03E1E38, 0x04E64CF,
- 0x052F604, 0x04E64CF, 0x01F0F1C, 0x0221869, 0x0123599, 0x0183574,
- 0x03F2229, 0x04850FC, 0x04850FC, 0x03F2229, 0x060D5D0, 0x048D662,
- 0x0675314, 0x05F759C, 0x03A3454, 0x03F0000, 0x03A3454, 0x02FBACE,
- 0x00CEA63, 0x019F45E, 0x02BE901, 0x032AD65, 0x032AD65, 0x057D202,
- 0x067D176, 0x05F6166, 0x04DEEA7, 0x028BE2E, 0x026F754, 0x017D85A,
- 0x029CC7B, 0x03E81B1, 0x07D0363, 0x0A731ED, 0x0874425, 0x05FC6AD,
- 0x021D109, 0x033F63C, 0x067EC78, 0x0382D4A,
-};
-
-static const int32_t qmat2B[MAT_SIZE] = {
- 0x0040000, 0x02149F9, 0x010A4FD, 0x021513E, 0x018878B, 0x021513E,
- 0x02C1868, 0x021FAD3, 0x043F5A6, 0x02C1868, 0x0270000, 0x03D2978,
- 0x04293C1, 0x01E94BC, 0x0270000, 0x01EA461, 0x01B0C1A, 0x03E38EF,
- 0x03E38EF, 0x0361835, 0x01EA461, 0x04430D2, 0x03E1E38, 0x04E64CF,
- 0x052F604, 0x04E64CF, 0x01F0F1C, 0x0221869, 0x0123599, 0x0183574,
- 0x03F2229, 0x04850FC, 0x04850FC, 0x03F2229, 0x060D5D0, 0x048D662,
- 0x0D0F1F6, 0x0C10A47, 0x07468A8, 0x07E0000, 0x07468A8, 0x0608524,
- 0x01A1E3F, 0x033E8BB, 0x058C661, 0x0655ACA, 0x0655ACA, 0x0B18CC1,
- 0x0CFA2ED, 0x17F77E9, 0x1361AF9, 0x0A3E5C6, 0x09B0D7D, 0x05FDDFA,
- 0x05402E9, 0x07DA6AB, 0x0FB4D56, 0x1500BA2, 0x10FD541, 0x0C06E4C,
- 0x043F550, 0x067EC78, 0x0CFD8F0, 0x0705A93,
-};
-
-static const int32_t qmat2C[MAT_SIZE] = {
- 0x0040000, 0x0241023, 0x0120812, 0x0241803, 0x01A92D7, 0x0241803,
- 0x02264F5, 0x01A8115, 0x035022A, 0x02264F5, 0x01D4000, 0x02FB4D3,
- 0x034B4F9, 0x017DA6A, 0x01D4000, 0x0184222, 0x0156995, 0x02F6F5B,
- 0x02F6F5B, 0x02AD32A, 0x0184222, 0x049DF8E, 0x0434B67, 0x054ED35,
- 0x059DFDA, 0x054ED35, 0x021A5B4, 0x024EFC7, 0x013BA10, 0x01A39E8,
- 0x0446502, 0x04E57BC, 0x04E57BC, 0x0446502, 0x068E7A2, 0x04EE840,
- 0x06FEF56, 0x0676A14, 0x03F0E06, 0x0444000, 0x03F0E06, 0x033B50A,
- 0x00DFDEB, 0x01C1E10, 0x02F91C1, 0x036E683, 0x036E683, 0x05F2382,
- 0x0707840, 0x067542F, 0x0546D35, 0x02C235D, 0x02A369B, 0x019D50C,
- 0x02D4830, 0x043B72B, 0x0876E56, 0x0B520C1, 0x09289D3, 0x067C1E6,
- 0x024A275, 0x0384AC1, 0x0709582, 0x03CDBBA,
-};
-
-static const int32_t qmat2D[MAT_SIZE] = {
- 0x0040000, 0x0241023, 0x0120812, 0x0241803, 0x01A92D7, 0x0241803,
- 0x02FC51B, 0x024CFBA, 0x0499F73, 0x02FC51B, 0x02A4000, 0x0424242,
- 0x0482011, 0x0212121, 0x02A4000, 0x0213214, 0x01D4D1D, 0x0436858,
- 0x0436858, 0x03A9A39, 0x0213214, 0x049DF8E, 0x0434B67, 0x054ED35,
- 0x059DFDA, 0x054ED35, 0x021A5B4, 0x024EFC7, 0x013BA10, 0x01A39E8,
- 0x0446502, 0x04E57BC, 0x04E57BC, 0x0446502, 0x068E7A2, 0x04EE840,
- 0x0E25B75, 0x0D12078, 0x07E1C0C, 0x0888000, 0x07E1C0C, 0x068903C,
- 0x01C4B6F, 0x0383C20, 0x0602C3E, 0x06DCD06, 0x06DCD06, 0x0C0587C,
- 0x0E0F081, 0x19F6C92, 0x14FF28E, 0x0B18E41, 0x0A7F947, 0x067DB24,
- 0x05B0327, 0x0881F39, 0x1103E72, 0x16C0C9A, 0x1267C5C, 0x0D0777D,
- 0x0499F17, 0x0709582, 0x0E12B04, 0x079B775,
-};
-
-static const int32_t qmat2E[MAT_SIZE] = {
- 0x0040000, 0x026D64D, 0x0136B27, 0x026DEC9, 0x01C9E22, 0x026DEC9,
- 0x0250A43, 0x01C8B03, 0x0391606, 0x0250A43, 0x01F8000, 0x0336046,
- 0x038C2E5, 0x019B023, 0x01F8000, 0x01A1FD6, 0x0170F3E, 0x0331575,
- 0x0331575, 0x02E1E7C, 0x01A1FD6, 0x04F8E4B, 0x0487897, 0x05B759C,
- 0x060C9B0, 0x05B759C, 0x0243C4B, 0x027C725, 0x0153E87, 0x01C3E5D,
- 0x049A7DA, 0x0545E7C, 0x0545E7C, 0x049A7DA, 0x070F973, 0x054FA1D,
- 0x0788B98, 0x06F5E8C, 0x043E7B8, 0x0498000, 0x043E7B8, 0x037AF46,
- 0x00F1173, 0x01E47C3, 0x0333A81, 0x03B1FA1, 0x03B1FA1, 0x0667502,
- 0x0791F0A, 0x06F46F7, 0x05AEBC3, 0x02F888B, 0x02D75E2, 0x01BD1BE,
- 0x030C3E5, 0x048ECA4, 0x091D948, 0x0C30F95, 0x09DCF81, 0x06FBD20,
- 0x02773E0, 0x03C9F46, 0x0793E8C, 0x0418A2B,
-};
-
-static const int32_t qmat2F[MAT_SIZE] = {
- 0x0040000, 0x026D64D, 0x0136B27, 0x026DEC9, 0x01C9E22, 0x026DEC9,
- 0x03371CF, 0x027A4A1, 0x04F4941, 0x03371CF, 0x02D8000, 0x0475B0C,
- 0x04DAC61, 0x023AD86, 0x02D8000, 0x023BFC6, 0x01F8E1F, 0x04897C2,
- 0x04897C2, 0x03F1C3D, 0x023BFC6, 0x04F8E4B, 0x0487897, 0x05B759C,
- 0x060C9B0, 0x05B759C, 0x0243C4B, 0x027C725, 0x0153E87, 0x01C3E5D,
- 0x049A7DA, 0x0545E7C, 0x0545E7C, 0x049A7DA, 0x070F973, 0x054FA1D,
- 0x0F3C4F4, 0x0E136A9, 0x087CF6F, 0x0930000, 0x087CF6F, 0x0709B54,
- 0x01E789E, 0x03C8F85, 0x067921B, 0x0763F41, 0x0763F41, 0x0CF2437,
- 0x0F23E14, 0x1BF613A, 0x169CA23, 0x0BF36BC, 0x0B4E511, 0x06FD84F,
- 0x0620365, 0x09297C7, 0x1252F8F, 0x1880D93, 0x13D2377, 0x0E080AE,
- 0x04F48DE, 0x0793E8C, 0x0F27D18, 0x0831457,
-};
-
-static const int32_t qmat30[MAT_SIZE] = {
- 0x0040000, 0x02C62A1, 0x0163151, 0x02C6C53, 0x020B4B9, 0x02C6C53,
- 0x02A54DF, 0x0209EDF, 0x0413DBE, 0x02A54DF, 0x0240000, 0x03AB72B,
- 0x040DEBC, 0x01D5B96, 0x0240000, 0x01DDB3E, 0x01A5A90, 0x03A61AB,
- 0x03A61AB, 0x034B520, 0x01DDB3E, 0x05AEBC3, 0x052D2F5, 0x0688669,
- 0x06E9D5B, 0x0688669, 0x029697B, 0x02D75E2, 0x0184776, 0x0204745,
- 0x0542D8C, 0x0606BFB, 0x0606BFB, 0x0542D8C, 0x0811D16, 0x0611DD8,
- 0x089C41B, 0x07F477B, 0x04D9B1B, 0x0540000, 0x04D9B1B, 0x03FA3BE,
- 0x0113883, 0x0229B27, 0x03A8C01, 0x04391DC, 0x04391DC, 0x0751803,
- 0x08A6C9E, 0x07F2C88, 0x067E8DF, 0x03652E8, 0x033F46F, 0x01FCB22,
- 0x037BB4F, 0x0535797, 0x0A6AF2E, 0x0DEED3C, 0x0B45ADD, 0x07FB392,
- 0x02D16B7, 0x0454850, 0x08A90A0, 0x04AE70D,
-};
-
-static const int32_t qmat31[MAT_SIZE] = {
- 0x0040000, 0x02C62A1, 0x0163151, 0x02C6C53, 0x020B4B9, 0x02C6C53,
- 0x03ACB35, 0x02D4E6E, 0x05A9CDD, 0x03ACB35, 0x0340000, 0x0518CA0,
- 0x058C501, 0x028C650, 0x0340000, 0x028DB2C, 0x0241023, 0x052F694,
- 0x052F694, 0x0482046, 0x028DB2C, 0x05AEBC3, 0x052D2F5, 0x0688669,
- 0x06E9D5B, 0x0688669, 0x029697B, 0x02D75E2, 0x0184776, 0x0204745,
- 0x0542D8C, 0x0606BFB, 0x0606BFB, 0x0542D8C, 0x0811D16, 0x0611DD8,
- 0x11697F2, 0x101630A, 0x09B3636, 0x0A80000, 0x09B3636, 0x080B185,
- 0x022D2FE, 0x045364F, 0x0765DD6, 0x08723B8, 0x08723B8, 0x0ECBBAC,
- 0x114D93C, 0x1FF4A8C, 0x19D794C, 0x0DA87B2, 0x0CEBCA6, 0x07FD2A3,
- 0x07003E1, 0x0A788E4, 0x14F11C8, 0x1C00F83, 0x16A71AD, 0x1009310,
- 0x05A9C6B, 0x08A90A0, 0x1152140, 0x095CE1A,
-};
-
-static const int32_t qmat32[MAT_SIZE] = {
- 0x0040000, 0x031EEF6, 0x018F77B, 0x031F9DD, 0x024CB50, 0x031F9DD,
- 0x02F9F7A, 0x024B2BB, 0x0496575, 0x02F9F7A, 0x0288000, 0x0420E11,
- 0x048FA94, 0x0210708, 0x0288000, 0x02196A5, 0x01DA5E2, 0x041ADE0,
- 0x041ADE0, 0x03B4BC4, 0x02196A5, 0x066493B, 0x05D2D54, 0x0759736,
- 0x07C7107, 0x0759736, 0x02E96AA, 0x033249E, 0x01B5065, 0x024502E,
- 0x05EB33D, 0x06C797A, 0x06C797A, 0x05EB33D, 0x09140B9, 0x06D4193,
- 0x09AFC9E, 0x08F306A, 0x0574E7E, 0x05E8000, 0x0574E7E, 0x0479835,
- 0x0135F94, 0x026EE8C, 0x041DD81, 0x04C0418, 0x04C0418, 0x083BB03,
- 0x09BBA32, 0x08F1219, 0x074E5FB, 0x03D1D45, 0x03A72FD, 0x023C486,
- 0x03EB2B9, 0x05DC28A, 0x0BB8514, 0x0FACAE4, 0x0CAE638, 0x08FAA04,
- 0x032B98E, 0x04DF15A, 0x09BE2B4, 0x05443EE,
-};
-
-static const int32_t qmat33[MAT_SIZE] = {
- 0x0040000, 0x031EEF6, 0x018F77B, 0x031F9DD, 0x024CB50, 0x031F9DD,
- 0x042249C, 0x032F83C, 0x065F078, 0x042249C, 0x03A8000, 0x05BBE34,
- 0x063DDA2, 0x02DDF1A, 0x03A8000, 0x02DF691, 0x0289228, 0x05D5567,
- 0x05D5567, 0x051244F, 0x02DF691, 0x066493B, 0x05D2D54, 0x0759736,
- 0x07C7107, 0x0759736, 0x02E96AA, 0x033249E, 0x01B5065, 0x024502E,
- 0x05EB33D, 0x06C797A, 0x06C797A, 0x05EB33D, 0x09140B9, 0x06D4193,
- 0x1396AF0, 0x1218F6B, 0x0AE9CFD, 0x0BD0000, 0x0AE9CFD, 0x090C7B6,
- 0x0272D5E, 0x04DDD19, 0x0852991, 0x098082F, 0x098082F, 0x10A5322,
- 0x1377463, 0x23F33DD, 0x1D12876, 0x0F5D8A9, 0x0E8943B, 0x08FCCF7,
- 0x07E045D, 0x0BC7A00, 0x178F401, 0x1F81173, 0x197BFE2, 0x120A572,
- 0x065EFF9, 0x09BE2B4, 0x137C568, 0x0A887DD,
-};
-
-static const int32_t qmat34[MAT_SIZE] = {
- 0x0040000, 0x0377B4A, 0x01BBDA5, 0x0378768, 0x028E1E8, 0x0378768,
- 0x034EA16, 0x028C697, 0x0518D2D, 0x034EA16, 0x02D0000, 0x04964F6,
- 0x051166B, 0x024B27B, 0x02D0000, 0x025520D, 0x020F134, 0x048FA15,
- 0x048FA15, 0x041E268, 0x025520D, 0x071A6B4, 0x06787B3, 0x082A803,
- 0x08A44B2, 0x082A803, 0x033C3D9, 0x038D35A, 0x01E5954, 0x0285917,
- 0x06938EF, 0x07886FA, 0x07886FA, 0x06938EF, 0x0A1645B, 0x079654E,
- 0x0AC3522, 0x09F195A, 0x06101E2, 0x0690000, 0x06101E2, 0x04F8CAD,
- 0x01586A4, 0x02B41F1, 0x0492F02, 0x0547653, 0x0547653, 0x0925E03,
- 0x0AD07C5, 0x09EF7AA, 0x081E317, 0x043E7A2, 0x040F18B, 0x027BDEB,
- 0x045AA23, 0x0682D7D, 0x0D05AFA, 0x116A88B, 0x0E17194, 0x09FA076,
- 0x0385C65, 0x0569A64, 0x0AD34C8, 0x05DA0D0,
-};
-
-static const int32_t qmat35[MAT_SIZE] = {
- 0x0040000, 0x0377B4A, 0x01BBDA5, 0x0378768, 0x028E1E8, 0x0378768,
- 0x0497E02, 0x038A20A, 0x0714414, 0x0497E02, 0x0410000, 0x065EFC8,
- 0x06EF642, 0x032F7E4, 0x0410000, 0x03311F7, 0x02D142C, 0x067B439,
- 0x067B439, 0x05A2858, 0x03311F7, 0x071A6B4, 0x06787B3, 0x082A803,
- 0x08A44B2, 0x082A803, 0x033C3D9, 0x038D35A, 0x01E5954, 0x0285917,
- 0x06938EF, 0x07886FA, 0x07886FA, 0x06938EF, 0x0A1645B, 0x079654E,
- 0x15C3DEF, 0x141BBCC, 0x0C203C3, 0x0D20000, 0x0C203C3, 0x0A0DDE6,
- 0x02B87BE, 0x05683E3, 0x093F54C, 0x0A8ECA7, 0x0A8ECA7, 0x127EA97,
- 0x15A0F8B, 0x27F1D2F, 0x204D79F, 0x111299F, 0x1026BD0, 0x09FC74C,
- 0x08C04D9, 0x0D16B1D, 0x1A2D63A, 0x2301364, 0x1C50E18, 0x140B7D4,
- 0x0714386, 0x0AD34C8, 0x15A6990, 0x0BB41A0,
-};
-
-static const int32_t qmat36[MAT_SIZE] = {
- 0x0040000, 0x03D079E, 0x01E83CF, 0x03D14F2, 0x02CF87F, 0x03D14F2,
- 0x03A34B2, 0x02CDA72, 0x059B4E5, 0x03A34B2, 0x0318000, 0x050BBDC,
- 0x0593243, 0x0285DEE, 0x0318000, 0x0290D75, 0x0243C86, 0x050464B,
- 0x050464B, 0x048790C, 0x0290D75, 0x07D042C, 0x071E211, 0x08FB8D0,
- 0x098185E, 0x08FB8D0, 0x038F109, 0x03E8216, 0x0216242, 0x02C6200,
- 0x073BEA0, 0x0849479, 0x0849479, 0x073BEA0, 0x0B187FE, 0x0858909,
- 0x0BD6DA5, 0x0AF0249, 0x06AB545, 0x0738000, 0x06AB545, 0x0578125,
- 0x017ADB5, 0x02F9556, 0x0508082, 0x05CE88F, 0x05CE88F, 0x0A10104,
- 0x0BE5559, 0x0AEDD3B, 0x08EE032, 0x04AB1FF, 0x0477019, 0x02BB74F,
- 0x04CA18D, 0x0729870, 0x0E530E0, 0x1328633, 0x0F7FCEF, 0x0AF96E8,
- 0x03DFF3C, 0x05F436E, 0x0BE86DC, 0x066FDB2,
-};
-
-static const int32_t qmat37[MAT_SIZE] = {
- 0x0040000, 0x03D079E, 0x01E83CF, 0x03D14F2, 0x02CF87F, 0x03D14F2,
- 0x050D769, 0x03E4BD8, 0x07C97B0, 0x050D769, 0x0478000, 0x070215C,
- 0x07A0EE2, 0x03810AE, 0x0478000, 0x0382D5C, 0x0319630, 0x072130C,
- 0x072130C, 0x0632C61, 0x0382D5C, 0x07D042C, 0x071E211, 0x08FB8D0,
- 0x098185E, 0x08FB8D0, 0x038F109, 0x03E8216, 0x0216242, 0x02C6200,
- 0x073BEA0, 0x0849479, 0x0849479, 0x073BEA0, 0x0B187FE, 0x0858909,
- 0x17F10ED, 0x161E82D, 0x0D56A8A, 0x0E70000, 0x0D56A8A, 0x0B0F417,
- 0x02FE21E, 0x05F2AAD, 0x0A2C106, 0x0B9D11E, 0x0B9D11E, 0x145820D,
- 0x17CAAB2, 0x2BF0680, 0x23886C9, 0x12C7A95, 0x11C4364, 0x0AFC1A0,
- 0x09A0555, 0x0E65C39, 0x1CCB873, 0x2681554, 0x1F25C4D, 0x160CA36,
- 0x07C9713, 0x0BE86DC, 0x17D0DB8, 0x0CDFB63,
-};
-
-static const int32_t qmat38[MAT_SIZE] = {
- 0x0040000, 0x04293F2, 0x02149F9, 0x042A27C, 0x0310F16, 0x042A27C,
- 0x03F7F4E, 0x030EE4E, 0x061DC9D, 0x03F7F4E, 0x0360000, 0x05812C1,
- 0x0614E1A, 0x02C0961, 0x0360000, 0x02CC8DC, 0x02787D8, 0x0579280,
- 0x0579280, 0x04F0FAF, 0x02CC8DC, 0x08861A5, 0x07C3C70, 0x09CC99D,
- 0x0A5EC09, 0x09CC99D, 0x03E1E38, 0x04430D2, 0x0246B31, 0x0306AE8,
- 0x07E4452, 0x090A1F9, 0x090A1F9, 0x07E4452, 0x0C1ABA1, 0x091ACC4,
- 0x0CEA628, 0x0BEEB39, 0x07468A8, 0x07E0000, 0x07468A8, 0x05F759C,
- 0x019D4C5, 0x033E8BB, 0x057D202, 0x0655ACA, 0x0655ACA, 0x0AFA404,
- 0x0CFA2ED, 0x0BEC2CC, 0x09BDD4E, 0x0517C5D, 0x04DEEA7, 0x02FB0B3,
- 0x05398F7, 0x07D0363, 0x0FA06C5, 0x14E63DA, 0x10E884B, 0x0BF8D5B,
- 0x043A213, 0x067EC78, 0x0CFD8F0, 0x0705A93,
-};
-
-static const int32_t qmat39[MAT_SIZE] = {
- 0x0040000, 0x04293F2, 0x02149F9, 0x042A27C, 0x0310F16, 0x042A27C,
- 0x05830D0, 0x043F5A6, 0x087EB4B, 0x05830D0, 0x04E0000, 0x07A52F0,
- 0x0852782, 0x03D2978, 0x04E0000, 0x03D48C2, 0x0361835, 0x07C71DE,
- 0x07C71DE, 0x06C3069, 0x03D48C2, 0x08861A5, 0x07C3C70, 0x09CC99D,
- 0x0A5EC09, 0x09CC99D, 0x03E1E38, 0x04430D2, 0x0246B31, 0x0306AE8,
- 0x07E4452, 0x090A1F9, 0x090A1F9, 0x07E4452, 0x0C1ABA1, 0x091ACC4,
- 0x1A1E3EB, 0x182148F, 0x0E8D151, 0x0FC0000, 0x0E8D151, 0x0C10A47,
- 0x0343C7D, 0x067D176, 0x0B18CC1, 0x0CAB595, 0x0CAB595, 0x1631982,
- 0x19F45DA, 0x2FEEFD2, 0x26C35F2, 0x147CB8C, 0x1361AF9, 0x0BFBBF4,
- 0x0A805D1, 0x0FB4D56, 0x1F69AAC, 0x2A01744, 0x21FAA83, 0x180DC98,
- 0x087EAA1, 0x0CFD8F0, 0x19FB1E0, 0x0E0B527,
-};
-
-static const int32_t qmat3A[MAT_SIZE] = {
- 0x0040000, 0x0482046, 0x0241023, 0x0483007, 0x03525AD, 0x0483007,
- 0x044C9EA, 0x035022A, 0x06A0454, 0x044C9EA, 0x03A8000, 0x05F69A7,
- 0x06969F2, 0x02FB4D3, 0x03A8000, 0x0308444, 0x02AD32A, 0x05EDEB5,
- 0x05EDEB5, 0x055A653, 0x0308444, 0x093BF1D, 0x08696CF, 0x0A9DA6A,
- 0x0B3BFB4, 0x0A9DA6A, 0x0434B67, 0x049DF8E, 0x0277420, 0x03473D1,
- 0x088CA03, 0x09CAF78, 0x09CAF78, 0x088CA03, 0x0D1CF44, 0x09DD07F,
- 0x0DFDEAC, 0x0CED428, 0x07E1C0C, 0x0888000, 0x07E1C0C, 0x0676A14,
- 0x01BFBD5, 0x0383C20, 0x05F2382, 0x06DCD06, 0x06DCD06, 0x0BE4704,
- 0x0E0F081, 0x0CEA85D, 0x0A8DA6A, 0x05846BA, 0x0546D35, 0x033AA17,
- 0x05A9060, 0x0876E56, 0x10EDCAB, 0x16A4182, 0x12513A7, 0x0CF83CD,
- 0x04944EA, 0x0709582, 0x0E12B04, 0x079B775,
-};
-
-static const int32_t qmat3B[MAT_SIZE] = {
- 0x0040000, 0x0482046, 0x0241023, 0x0483007, 0x03525AD, 0x0483007,
- 0x05F8A36, 0x0499F73, 0x0933EE7, 0x05F8A36, 0x0548000, 0x0848484,
- 0x0904022, 0x0424242, 0x0548000, 0x0426427, 0x03A9A39, 0x086D0B1,
- 0x086D0B1, 0x0753472, 0x0426427, 0x093BF1D, 0x08696CF, 0x0A9DA6A,
- 0x0B3BFB4, 0x0A9DA6A, 0x0434B67, 0x049DF8E, 0x0277420, 0x03473D1,
- 0x088CA03, 0x09CAF78, 0x09CAF78, 0x088CA03, 0x0D1CF44, 0x09DD07F,
- 0x1C4B6EA, 0x1A240F0, 0x0FC3818, 0x1110000, 0x0FC3818, 0x0D12078,
- 0x03896DD, 0x0707840, 0x0C0587C, 0x0DB9A0C, 0x0DB9A0C, 0x180B0F8,
- 0x1C1E101, 0x33ED923, 0x29FE51C, 0x1631C82, 0x14FF28E, 0x0CFB649,
- 0x0B6064D, 0x1103E72, 0x2207CE5, 0x2D81935, 0x24CF8B9, 0x1A0EEFA,
- 0x0933E2E, 0x0E12B04, 0x1C25608, 0x0F36EEA,
-};
-
-static const int32_t qmat3C[MAT_SIZE] = {
- 0x0040000, 0x04DAC9A, 0x026D64D, 0x04DBD91, 0x0393C44, 0x04DBD91,
- 0x04A1486, 0x0391606, 0x0722C0C, 0x04A1486, 0x03F0000, 0x066C08C,
- 0x07185C9, 0x0336046, 0x03F0000, 0x0343FAC, 0x02E1E7C, 0x0662AEB,
- 0x0662AEB, 0x05C3CF7, 0x0343FAC, 0x09F1C95, 0x090F12D, 0x0B6EB37,
- 0x0C19360, 0x0B6EB37, 0x0487897, 0x04F8E4B, 0x02A7D0F, 0x0387CBA,
- 0x0934FB5, 0x0A8BCF7, 0x0A8BCF7, 0x0934FB5, 0x0E1F2E6, 0x0A9F43A,
- 0x0F1172F, 0x0DEBD17, 0x087CF6F, 0x0930000, 0x087CF6F, 0x06F5E8C,
- 0x01E22E6, 0x03C8F85, 0x0667502, 0x0763F41, 0x0763F41, 0x0CCEA05,
- 0x0F23E14, 0x0DE8DEE, 0x0B5D786, 0x05F1117, 0x05AEBC3, 0x037A37C,
- 0x06187CA, 0x091D948, 0x123B291, 0x1861F29, 0x13B9F02, 0x0DF7A3F,
- 0x04EE7C1, 0x0793E8C, 0x0F27D18, 0x0831457,
-};
-
-static const int32_t qmat3D[MAT_SIZE] = {
- 0x0040000, 0x04DAC9A, 0x026D64D, 0x04DBD91, 0x0393C44, 0x04DBD91,
- 0x066E39D, 0x04F4941, 0x09E9282, 0x066E39D, 0x05B0000, 0x08EB618,
- 0x09B58C2, 0x0475B0C, 0x05B0000, 0x0477F8D, 0x03F1C3D, 0x0912F83,
- 0x0912F83, 0x07E387B, 0x0477F8D, 0x09F1C95, 0x090F12D, 0x0B6EB37,
- 0x0C19360, 0x0B6EB37, 0x0487897, 0x04F8E4B, 0x02A7D0F, 0x0387CBA,
- 0x0934FB5, 0x0A8BCF7, 0x0A8BCF7, 0x0934FB5, 0x0E1F2E6, 0x0A9F43A,
- 0x1E789E8, 0x1C26D51, 0x10F9EDE, 0x1260000, 0x10F9EDE, 0x0E136A9,
- 0x03CF13D, 0x0791F0A, 0x0CF2437, 0x0EC7E83, 0x0EC7E83, 0x19E486D,
- 0x1E47C29, 0x37EC275, 0x2D39446, 0x17E6D78, 0x169CA23, 0x0DFB09D,
- 0x0C406C9, 0x1252F8F, 0x24A5F1E, 0x3101B25, 0x27A46EE, 0x1C1015C,
- 0x09E91BC, 0x0F27D18, 0x1E4FA30, 0x10628AD,
-};
-
-static const int32_t qmat3E[MAT_SIZE] = {
- 0x0040000, 0x058C543, 0x02C62A1, 0x058D8A6, 0x0416973, 0x058D8A6,
- 0x054A9BD, 0x0413DBE, 0x0827B7B, 0x054A9BD, 0x0480000, 0x0756E57,
- 0x081BD78, 0x03AB72B, 0x0480000, 0x03BB67B, 0x034B520, 0x074C355,
- 0x074C355, 0x0696A3F, 0x03BB67B, 0x0B5D786, 0x0A5A5EB, 0x0D10CD2,
- 0x0DD3AB7, 0x0D10CD2, 0x052D2F5, 0x05AEBC3, 0x0308EEC, 0x0408E8B,
- 0x0A85B18, 0x0C0D7F6, 0x0C0D7F6, 0x0A85B18, 0x1023A2C, 0x0C23BB1,
- 0x1138836, 0x0FE8EF6, 0x09B3636, 0x0A80000, 0x09B3636, 0x07F477B,
- 0x0227107, 0x045364F, 0x0751803, 0x08723B8, 0x08723B8, 0x0EA3005,
- 0x114D93C, 0x0FE5910, 0x0CFD1BE, 0x06CA5D1, 0x067E8DF, 0x03F9644,
- 0x06F769E, 0x0A6AF2E, 0x14D5E5C, 0x1BDDA78, 0x168B5B9, 0x0FF6724,
- 0x05A2D6E, 0x08A90A0, 0x1152140, 0x095CE1A,
-};
-
-static const int32_t qmat3F[MAT_SIZE] = {
- 0x0040000, 0x058C543, 0x02C62A1, 0x058D8A6, 0x0416973, 0x058D8A6,
- 0x075966A, 0x05A9CDD, 0x0B539BA, 0x075966A, 0x0680000, 0x0A31940,
- 0x0B18A03, 0x0518CA0, 0x0680000, 0x051B658, 0x0482046, 0x0A5ED28,
- 0x0A5ED28, 0x090408D, 0x051B658, 0x0B5D786, 0x0A5A5EB, 0x0D10CD2,
- 0x0DD3AB7, 0x0D10CD2, 0x052D2F5, 0x05AEBC3, 0x0308EEC, 0x0408E8B,
- 0x0A85B18, 0x0C0D7F6, 0x0C0D7F6, 0x0A85B18, 0x1023A2C, 0x0C23BB1,
- 0x22D2FE4, 0x202C614, 0x1366C6C, 0x1500000, 0x1366C6C, 0x101630A,
- 0x045A5FD, 0x08A6C9E, 0x0ECBBAC, 0x10E4771, 0x10E4771, 0x1D97758,
- 0x229B278, 0x3FE9518, 0x33AF299, 0x1B50F65, 0x19D794C, 0x0FFA546,
- 0x0E007C1, 0x14F11C8, 0x29E2390, 0x3801F06, 0x2D4E359, 0x2012620,
- 0x0B538D6, 0x1152140, 0x22A4280, 0x12B9C33,
-};
-
-static const int32_t qmat40[MAT_SIZE] = {
- 0x0040000, 0x063DDEB, 0x031EEF6, 0x063F3BB, 0x04996A1, 0x063F3BB,
- 0x05F3EF5, 0x0496575, 0x092CAEB, 0x05F3EF5, 0x0510000, 0x0841C22,
- 0x091F527, 0x0420E11, 0x0510000, 0x0432D4B, 0x03B4BC4, 0x0835BC0,
- 0x0835BC0, 0x0769787, 0x0432D4B, 0x0CC9277, 0x0BA5AA8, 0x0EB2E6C,
- 0x0F8E20D, 0x0EB2E6C, 0x05D2D54, 0x066493B, 0x036A0CA, 0x048A05C,
- 0x0BD667B, 0x0D8F2F5, 0x0D8F2F5, 0x0BD667B, 0x1228171, 0x0DA8327,
- 0x135F93D, 0x11E60D5, 0x0AE9CFD, 0x0BD0000, 0x0AE9CFD, 0x08F306A,
- 0x026BF28, 0x04DDD19, 0x083BB03, 0x098082F, 0x098082F, 0x1077606,
- 0x1377463, 0x11E2432, 0x0E9CBF6, 0x07A3A8B, 0x074E5FB, 0x047890D,
- 0x07D6572, 0x0BB8514, 0x1770A28, 0x1F595C7, 0x195CC70, 0x11F5408,
- 0x065731C, 0x09BE2B4, 0x137C568, 0x0A887DD,
-};
-
-static const int32_t qmat41[MAT_SIZE] = {
- 0x0040000, 0x063DDEB, 0x031EEF6, 0x063F3BB, 0x04996A1, 0x063F3BB,
- 0x0844938, 0x065F078, 0x0CBE0F1, 0x0844938, 0x0750000, 0x0B77C68,
- 0x0C7BB43, 0x05BBE34, 0x0750000, 0x05BED23, 0x051244F, 0x0BAAACD,
- 0x0BAAACD, 0x0A2489E, 0x05BED23, 0x0CC9277, 0x0BA5AA8, 0x0EB2E6C,
- 0x0F8E20D, 0x0EB2E6C, 0x05D2D54, 0x066493B, 0x036A0CA, 0x048A05C,
- 0x0BD667B, 0x0D8F2F5, 0x0D8F2F5, 0x0BD667B, 0x1228171, 0x0DA8327,
- 0x272D5E1, 0x2431ED6, 0x15D39F9, 0x17A0000, 0x15D39F9, 0x1218F6B,
- 0x04E5ABC, 0x09BBA32, 0x10A5322, 0x130105F, 0x130105F, 0x214A643,
- 0x26EE8C7, 0x47E67BB, 0x3A250EC, 0x1EBB151, 0x1D12876, 0x11F99EF,
- 0x0FC08BA, 0x178F401, 0x2F1E802, 0x3F022E7, 0x32F7FC4, 0x2414AE4,
- 0x0CBDFF1, 0x137C568, 0x26F8AD0, 0x1510FBA,
-};
-
-static const int32_t qmat42[MAT_SIZE] = {
- 0x0040000, 0x06EF693, 0x0377B4A, 0x06F0ECF, 0x051C3CF, 0x06F0ECF,
- 0x069D42D, 0x0518D2D, 0x0A31A5A, 0x069D42D, 0x05A0000, 0x092C9ED,
- 0x0A22CD6, 0x04964F6, 0x05A0000, 0x04AA41A, 0x041E268, 0x091F42B,
- 0x091F42B, 0x083C4CF, 0x04AA41A, 0x0E34D68, 0x0CF0F65, 0x1055006,
- 0x1148964, 0x1055006, 0x06787B3, 0x071A6B4, 0x03CB2A7, 0x050B22E,
- 0x0D271DE, 0x0F10DF4, 0x0F10DF4, 0x0D271DE, 0x142C8B7, 0x0F2CA9D,
- 0x1586A43, 0x13E32B4, 0x0C203C3, 0x0D20000, 0x0C203C3, 0x09F195A,
- 0x02B0D48, 0x05683E3, 0x0925E03, 0x0A8ECA7, 0x0A8ECA7, 0x124BC06,
- 0x15A0F8B, 0x13DEF54, 0x103C62D, 0x087CF45, 0x081E317, 0x04F7BD5,
- 0x08B5446, 0x0D05AFA, 0x1A0B5F3, 0x22D5116, 0x1C2E328, 0x13F40EC,
- 0x070B8CA, 0x0AD34C8, 0x15A6990, 0x0BB41A0,
-};
-
-static const int32_t qmat43[MAT_SIZE] = {
- 0x0040000, 0x06EF693, 0x0377B4A, 0x06F0ECF, 0x051C3CF, 0x06F0ECF,
- 0x092FC05, 0x0714414, 0x0E28828, 0x092FC05, 0x0820000, 0x0CBDF90,
- 0x0DDEC84, 0x065EFC8, 0x0820000, 0x06623EE, 0x05A2858, 0x0CF6872,
- 0x0CF6872, 0x0B450B0, 0x06623EE, 0x0E34D68, 0x0CF0F65, 0x1055006,
- 0x1148964, 0x1055006, 0x06787B3, 0x071A6B4, 0x03CB2A7, 0x050B22E,
- 0x0D271DE, 0x0F10DF4, 0x0F10DF4, 0x0D271DE, 0x142C8B7, 0x0F2CA9D,
- 0x2B87BDD, 0x2837799, 0x1840787, 0x1A40000, 0x1840787, 0x141BBCC,
- 0x0570F7C, 0x0AD07C5, 0x127EA97, 0x151D94D, 0x151D94D, 0x24FD52E,
- 0x2B41F16, 0x4FE3A5E, 0x409AF3F, 0x222533E, 0x204D79F, 0x13F8E97,
- 0x11809B2, 0x1A2D63A, 0x345AC74, 0x46026C7, 0x38A1C30, 0x2816FA7,
- 0x0E2870C, 0x15A6990, 0x2B4D320, 0x1768340,
-};
-
-static const int32_t qmat44[MAT_SIZE] = {
- 0x0040000, 0x07A0F3C, 0x03D079E, 0x07A29E4, 0x059F0FE, 0x07A29E4,
- 0x0746964, 0x059B4E5, 0x0B369CA, 0x0746964, 0x0630000, 0x0A177B8,
- 0x0B26485, 0x050BBDC, 0x0630000, 0x0521AE9, 0x048790C, 0x0A08C95,
- 0x0A08C95, 0x090F217, 0x0521AE9, 0x0FA0858, 0x0E3C423, 0x11F71A0,
- 0x13030BB, 0x11F71A0, 0x071E211, 0x07D042C, 0x042C485, 0x058C3FF,
- 0x0E77D40, 0x10928F2, 0x10928F2, 0x0E77D40, 0x1630FFC, 0x10B1213,
- 0x17ADB4A, 0x15E0492, 0x0D56A8A, 0x0E70000, 0x0D56A8A, 0x0AF0249,
- 0x02F5B69, 0x05F2AAD, 0x0A10104, 0x0B9D11E, 0x0B9D11E, 0x1420207,
- 0x17CAAB2, 0x15DBA76, 0x11DC065, 0x09563FF, 0x08EE032, 0x0576E9E,
- 0x0994319, 0x0E530E0, 0x1CA61BF, 0x2650C65, 0x1EFF9DF, 0x15F2DD1,
- 0x07BFE78, 0x0BE86DC, 0x17D0DB8, 0x0CDFB63,
-};
-
-static const int32_t qmat45[MAT_SIZE] = {
- 0x0040000, 0x07A0F3C, 0x03D079E, 0x07A29E4, 0x059F0FE, 0x07A29E4,
- 0x0A1AED2, 0x07C97B0, 0x0F92F5F, 0x0A1AED2, 0x08F0000, 0x0E042B8,
- 0x0F41DC4, 0x070215C, 0x08F0000, 0x0705AB9, 0x0632C61, 0x0E42617,
- 0x0E42617, 0x0C658C1, 0x0705AB9, 0x0FA0858, 0x0E3C423, 0x11F71A0,
- 0x13030BB, 0x11F71A0, 0x071E211, 0x07D042C, 0x042C485, 0x058C3FF,
- 0x0E77D40, 0x10928F2, 0x10928F2, 0x0E77D40, 0x1630FFC, 0x10B1213,
- 0x2FE21DA, 0x2C3D05B, 0x1AAD514, 0x1CE0000, 0x1AAD514, 0x161E82D,
- 0x05FC43B, 0x0BE5559, 0x145820D, 0x173A23B, 0x173A23B, 0x28B0419,
- 0x2F95565, 0x57E0D01, 0x4710D92, 0x258F52A, 0x23886C9, 0x15F8340,
- 0x1340AAA, 0x1CCB873, 0x39970E6, 0x4D02AA8, 0x3E4B89B, 0x2C1946B,
- 0x0F92E27, 0x17D0DB8, 0x2FA1B6F, 0x19BF6C7,
-};
-
-static const int32_t qmat46[MAT_SIZE] = {
- 0x0040000, 0x08527E4, 0x04293F2, 0x08544F9, 0x0621E2C, 0x08544F9,
- 0x07EFE9C, 0x061DC9D, 0x0C3B939, 0x07EFE9C, 0x06C0000, 0x0B02582,
- 0x0C29C34, 0x05812C1, 0x06C0000, 0x05991B9, 0x04F0FAF, 0x0AF2500,
- 0x0AF2500, 0x09E1F5F, 0x05991B9, 0x110C349, 0x0F878E0, 0x139933B,
- 0x14BD812, 0x139933B, 0x07C3C70, 0x08861A5, 0x048D662, 0x060D5D0,
- 0x0FC88A3, 0x12143F1, 0x12143F1, 0x0FC88A3, 0x1835742, 0x1235989,
- 0x19D4C51, 0x17DD671, 0x0E8D151, 0x0FC0000, 0x0E8D151, 0x0BEEB39,
- 0x033A98A, 0x067D176, 0x0AFA404, 0x0CAB595, 0x0CAB595, 0x15F4808,
- 0x19F45DA, 0x17D8598, 0x137BA9D, 0x0A2F8B9, 0x09BDD4E, 0x05F6166,
- 0x0A731ED, 0x0FA06C5, 0x1F40D8B, 0x29CC7B4, 0x21D1096, 0x17F1AB5,
- 0x0874425, 0x0CFD8F0, 0x19FB1E0, 0x0E0B527,
-};
-
-static const int32_t qmat47[MAT_SIZE] = {
- 0x0040000, 0x08527E4, 0x04293F2, 0x08544F9, 0x0621E2C, 0x08544F9,
- 0x0B061A0, 0x087EB4B, 0x10FD696, 0x0B061A0, 0x09C0000, 0x0F4A5E0,
- 0x10A4F04, 0x07A52F0, 0x09C0000, 0x07A9184, 0x06C3069, 0x0F8E3BC,
- 0x0F8E3BC, 0x0D860D3, 0x07A9184, 0x110C349, 0x0F878E0, 0x139933B,
- 0x14BD812, 0x139933B, 0x07C3C70, 0x08861A5, 0x048D662, 0x060D5D0,
- 0x0FC88A3, 0x12143F1, 0x12143F1, 0x0FC88A3, 0x1835742, 0x1235989,
- 0x343C7D6, 0x304291D, 0x1D1A2A2, 0x1F80000, 0x1D1A2A2, 0x182148F,
- 0x06878FB, 0x0CFA2ED, 0x1631982, 0x1956B29, 0x1956B29, 0x2C63305,
- 0x33E8BB4, 0x5FDDFA4, 0x4D86BE5, 0x28F9717, 0x26C35F2, 0x17F77E9,
- 0x1500BA2, 0x1F69AAC, 0x3ED3558, 0x5402E89, 0x43F5506, 0x301B92F,
- 0x10FD541, 0x19FB1E0, 0x33F63BF, 0x1C16A4D,
-};
-
-static const int32_t qmat48[MAT_SIZE] = {
- 0x0040000, 0x090408D, 0x0482046, 0x090600E, 0x06A4B5A, 0x090600E,
- 0x08993D3, 0x06A0454, 0x0D408A9, 0x08993D3, 0x0750000, 0x0BED34D,
- 0x0D2D3E3, 0x05F69A7, 0x0750000, 0x0610888, 0x055A653, 0x0BDBD6A,
- 0x0BDBD6A, 0x0AB4CA7, 0x0610888, 0x1277E3A, 0x10D2D9D, 0x153B4D5,
- 0x1677F69, 0x153B4D5, 0x08696CF, 0x093BF1D, 0x04EE840, 0x068E7A2,
- 0x1119406, 0x1395EF0, 0x1395EF0, 0x1119406, 0x1A39E87, 0x13BA0FF,
- 0x1BFBD57, 0x19DA850, 0x0FC3818, 0x1110000, 0x0FC3818, 0x0CED428,
- 0x037F7AB, 0x0707840, 0x0BE4704, 0x0DB9A0C, 0x0DB9A0C, 0x17C8E08,
- 0x1C1E101, 0x19D50BB, 0x151B4D4, 0x0B08D73, 0x0A8DA6A, 0x067542F,
- 0x0B520C1, 0x10EDCAB, 0x21DB956, 0x2D48303, 0x24A274D, 0x19F079A,
- 0x09289D3, 0x0E12B04, 0x1C25608, 0x0F36EEA,
-};
-
-static const int32_t qmat49[MAT_SIZE] = {
- 0x0040000, 0x090408D, 0x0482046, 0x090600E, 0x06A4B5A, 0x090600E,
- 0x0BF146D, 0x0933EE7, 0x1267DCE, 0x0BF146D, 0x0A90000, 0x1090908,
- 0x1208045, 0x0848484, 0x0A90000, 0x084C84F, 0x0753472, 0x10DA161,
- 0x10DA161, 0x0EA68E4, 0x084C84F, 0x1277E3A, 0x10D2D9D, 0x153B4D5,
- 0x1677F69, 0x153B4D5, 0x08696CF, 0x093BF1D, 0x04EE840, 0x068E7A2,
- 0x1119406, 0x1395EF0, 0x1395EF0, 0x1119406, 0x1A39E87, 0x13BA0FF,
- 0x3896DD3, 0x34481E0, 0x1F8702F, 0x2220000, 0x1F8702F, 0x1A240F0,
- 0x0712DBA, 0x0E0F081, 0x180B0F8, 0x1B73417, 0x1B73417, 0x30161F0,
- 0x383C203, 0x67DB247, 0x53FCA38, 0x2C63904, 0x29FE51C, 0x19F6C92,
- 0x16C0C9A, 0x2207CE5, 0x440F9CA, 0x5B0326A, 0x499F171, 0x341DDF3,
- 0x1267C5C, 0x1C25608, 0x384AC0F, 0x1E6DDD4,
-};
-
-static const int32_t qmat4A[MAT_SIZE] = {
- 0x0040000, 0x09B5935, 0x04DAC9A, 0x09B7B22, 0x0727888, 0x09B7B22,
- 0x094290B, 0x0722C0C, 0x0E45818, 0x094290B, 0x07E0000, 0x0CD8118,
- 0x0E30B92, 0x066C08C, 0x07E0000, 0x0687F58, 0x05C3CF7, 0x0CC55D5,
- 0x0CC55D5, 0x0B879EF, 0x0687F58, 0x13E392B, 0x121E25B, 0x16DD66F,
- 0x18326C0, 0x16DD66F, 0x090F12D, 0x09F1C95, 0x054FA1D, 0x070F973,
- 0x1269F69, 0x15179EF, 0x15179EF, 0x1269F69, 0x1C3E5CD, 0x153E875,
- 0x1E22E5E, 0x1BD7A2F, 0x10F9EDE, 0x1260000, 0x10F9EDE, 0x0DEBD17,
- 0x03C45CC, 0x0791F0A, 0x0CCEA05, 0x0EC7E83, 0x0EC7E83, 0x199D409,
- 0x1E47C29, 0x1BD1BDD, 0x16BAF0C, 0x0BE222D, 0x0B5D786, 0x06F46F7,
- 0x0C30F95, 0x123B291, 0x2476522, 0x30C3E52, 0x2773E04, 0x1BEF47E,
- 0x09DCF81, 0x0F27D18, 0x1E4FA30, 0x10628AD,
-};
-
-static const int32_t qmat4B[MAT_SIZE] = {
- 0x0040000, 0x09B5935, 0x04DAC9A, 0x09B7B22, 0x0727888, 0x09B7B22,
- 0x0CDC73A, 0x09E9282, 0x13D2505, 0x0CDC73A, 0x0B60000, 0x11D6C30,
- 0x136B185, 0x08EB618, 0x0B60000, 0x08EFF19, 0x07E387B, 0x1225F06,
- 0x1225F06, 0x0FC70F6, 0x08EFF19, 0x13E392B, 0x121E25B, 0x16DD66F,
- 0x18326C0, 0x16DD66F, 0x090F12D, 0x09F1C95, 0x054FA1D, 0x070F973,
- 0x1269F69, 0x15179EF, 0x15179EF, 0x1269F69, 0x1C3E5CD, 0x153E875,
- 0x3CF13D0, 0x384DAA2, 0x21F3DBD, 0x24C0000, 0x21F3DBD, 0x1C26D51,
- 0x079E27A, 0x0F23E14, 0x19E486D, 0x1D8FD05, 0x1D8FD05, 0x33C90DB,
- 0x3C8F852, 0x6FD84EA, 0x5A7288B, 0x2FCDAF0, 0x2D39446, 0x1BF613A,
- 0x1880D93, 0x24A5F1E, 0x494BE3C, 0x620364A, 0x4F48DDC, 0x38202B7,
- 0x13D2377, 0x1E4FA30, 0x3C9F45F, 0x20C515A,
-};
-
-static const int32_t qmat4C[MAT_SIZE] = {
- 0x0040000, 0x1208119, 0x090408D, 0x120C01B, 0x0D496B4, 0x120C01B,
- 0x11327A7, 0x0D408A9, 0x1A81151, 0x11327A7, 0x0EA0000, 0x17DA69B,
- 0x1A5A7C7, 0x0BED34D, 0x0EA0000, 0x0C21110, 0x0AB4CA7, 0x17B7AD5,
- 0x17B7AD5, 0x156994E, 0x0C21110, 0x24EFC74, 0x21A5B3B, 0x2A769A9,
- 0x2CEFED2, 0x2A769A9, 0x10D2D9D, 0x1277E3A, 0x09DD07F, 0x0D1CF44,
- 0x223280D, 0x272BDE0, 0x272BDE0, 0x223280D, 0x3473D0E, 0x27741FE,
- 0x37F7AAF, 0x33B50A0, 0x1F8702F, 0x2220000, 0x1F8702F, 0x19DA850,
- 0x06FEF56, 0x0E0F081, 0x17C8E08, 0x1B73417, 0x1B73417, 0x2F91C11,
- 0x383C203, 0x33AA175, 0x2A369A9, 0x1611AE6, 0x151B4D4, 0x0CEA85D,
- 0x16A4182, 0x21DB956, 0x43B72AC, 0x5A90607, 0x4944E9A, 0x33E0F34,
- 0x12513A7, 0x1C25608, 0x384AC0F, 0x1E6DDD4,
-};
-
-static const int32_t qmat4D[MAT_SIZE] = {
- 0x0040000, 0x1208119, 0x090408D, 0x120C01B, 0x0D496B4, 0x120C01B,
- 0x17E28DA, 0x1267DCE, 0x24CFB9B, 0x17E28DA, 0x1520000, 0x2121210,
- 0x2410089, 0x1090908, 0x1520000, 0x109909D, 0x0EA68E4, 0x21B42C3,
- 0x21B42C3, 0x1D4D1C9, 0x109909D, 0x24EFC74, 0x21A5B3B, 0x2A769A9,
- 0x2CEFED2, 0x2A769A9, 0x10D2D9D, 0x1277E3A, 0x09DD07F, 0x0D1CF44,
- 0x223280D, 0x272BDE0, 0x272BDE0, 0x223280D, 0x3473D0E, 0x27741FE,
- 0x712DBA6, 0x68903C0, 0x3F0E05F, 0x4440000, 0x3F0E05F, 0x34481E0,
- 0x0E25B75, 0x1C1E101, 0x30161F0, 0x36E682F, 0x36E682F, 0x602C3DF,
- 0x7078406, 0xCFB648E, 0xA7F9470, 0x58C7207, 0x53FCA38, 0x33ED923,
- 0x2D81935, 0x440F9CA, 0x881F394, 0xB6064D3, 0x933E2E2, 0x683BBE7,
- 0x24CF8B9, 0x384AC0F, 0x709581F, 0x3CDBBA7,
-};
-
-const int32_t *const ff_hq_quants[NUM_HQ_QUANTS][2][4] = {
- { { qmat00, qmat02, qmat06, qmat0E }, { qmat01, qmat03, qmat07, qmat0F } },
- { { qmat02, qmat06, qmat0E, qmat16 }, { qmat03, qmat07, qmat0F, qmat17 } },
- { { qmat04, qmat0A, qmat12, qmat1E }, { qmat05, qmat0B, qmat13, qmat1F } },
- { { qmat06, qmat0E, qmat16, qmat22 }, { qmat07, qmat0F, qmat17, qmat23 } },
- { { qmat08, qmat10, qmat1A, qmat26 }, { qmat09, qmat11, qmat1B, qmat27 } },
- { { qmat0A, qmat12, qmat1E, qmat2A }, { qmat0B, qmat13, qmat1F, qmat2B } },
- { { qmat0C, qmat14, qmat20, qmat2E }, { qmat0D, qmat15, qmat21, qmat2F } },
- { { qmat0E, qmat16, qmat22, qmat30 }, { qmat0F, qmat17, qmat23, qmat31 } },
- { { qmat16, qmat22, qmat30, qmat3E }, { qmat17, qmat23, qmat31, qmat3F } },
- { { qmat18, qmat24, qmat32, qmat40 }, { qmat19, qmat25, qmat33, qmat41 } },
- { { qmat1A, qmat26, qmat34, qmat42 }, { qmat1B, qmat27, qmat35, qmat43 } },
- { { qmat1C, qmat28, qmat36, qmat44 }, { qmat1D, qmat29, qmat37, qmat45 } },
- { { qmat1E, qmat2A, qmat38, qmat46 }, { qmat1F, qmat2B, qmat39, qmat47 } },
- { { qmat20, qmat2E, qmat3C, qmat4A }, { qmat21, qmat2F, qmat3D, qmat4B } },
- { { qmat2C, qmat3A, qmat48, qmat4C }, { qmat2D, qmat3B, qmat49, qmat4D } },
- { { qmat3A, qmat48, qmat4C, qmat4C }, { qmat3B, qmat49, qmat4D, qmat4D } },
-};
-
-static const uint8_t hq_ac_bits[NUM_HQ_AC_ENTRIES] = {
- 3, 3, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6,
- 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,
- 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9,
- 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
- 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10,
- 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
- 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11,
- 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12,
- 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13,
- 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
- 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
- 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
- 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
- 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
- 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
- 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
-};
-
-static const uint16_t hq_ac_codes[NUM_HQ_AC_ENTRIES] = {
- 0x0000, 0x0001, 0x0004, 0x0005, 0x0006, 0x000E, 0x000F, 0x0010,
- 0x0011, 0x0012, 0x0013, 0x0028, 0x0029, 0x002A, 0x002B, 0x002C,
- 0x002D, 0x002E, 0x002F, 0x0060, 0x0061, 0x0062, 0x0063, 0x0064,
- 0x0065, 0x0066, 0x0067, 0x00D0, 0x00D1, 0x00D2, 0x00D3, 0x00D4,
- 0x00D5, 0x00D6, 0x00D7, 0x00D8, 0x00D9, 0x00DA, 0x00DB, 0x00DC,
- 0x00DD, 0x00DE, 0x00DF, 0x01C0, 0x01C1, 0x01C2, 0x01C3, 0x01C4,
- 0x01C5, 0x01C6, 0x01C7, 0x01C8, 0x01C9, 0x01CA, 0x01CB, 0x01CC,
- 0x01CD, 0x01CE, 0x01CF, 0x01D0, 0x01D1, 0x01D2, 0x01D3, 0x01D4,
- 0x01D5, 0x01D6, 0x01D7, 0x01D8, 0x01D9, 0x01DA, 0x01DB, 0x01DC,
- 0x01DD, 0x01DE, 0x01DF, 0x03C0, 0x03C1, 0x03C2, 0x03C3, 0x03C4,
- 0x03C5, 0x03C6, 0x03C7, 0x03C8, 0x03C9, 0x03CA, 0x03CB, 0x03CC,
- 0x03CD, 0x03CE, 0x03CF, 0x03D0, 0x03D1, 0x03D2, 0x03D3, 0x03D4,
- 0x03D5, 0x03D6, 0x03D7, 0x03D8, 0x03D9, 0x03DA, 0x03DB, 0x03DC,
- 0x03DD, 0x03DE, 0x03DF, 0x07C0, 0x07C1, 0x07C2, 0x07C3, 0x07C4,
- 0x07C5, 0x07C6, 0x07C7, 0x07C8, 0x07C9, 0x07CA, 0x07CB, 0x07CC,
- 0x07CD, 0x07CE, 0x07CF, 0x0FA0, 0x0FA1, 0x0FA2, 0x0FA3, 0x0FA4,
- 0x0FA5, 0x0FA6, 0x0FA7, 0x0FA8, 0x0FA9, 0x0FAA, 0x0FAB, 0x0FAC,
- 0x0FAD, 0x0FAE, 0x0FAF, 0x1F60, 0x1F61, 0x1F62, 0x1F63, 0x1F64,
- 0x1F65, 0x1F66, 0x1F67, 0x1F68, 0x1F69, 0x1F6A, 0x1F6B, 0x1F6C,
- 0x1F6D, 0x1F6E, 0x1F6F, 0x1F70, 0x1F71, 0x1F72, 0x1F73, 0x1F74,
- 0x1F75, 0x1F76, 0x1F77, 0x1F78, 0x1F79, 0x1F7A, 0x1F7B, 0x1F7C,
- 0x1F7D, 0x1F7E, 0x1F7F, 0x1F80, 0x1F81, 0x1F82, 0x1F83, 0x1F84,
- 0x1F85, 0x1F86, 0x1F87, 0x1F88, 0x1F89, 0x1F8A, 0x1F8B, 0x1F8C,
- 0x1F8D, 0x1F8E, 0x1F8F, 0x1F90, 0x1F91, 0x1F92, 0x1F93, 0x1F94,
- 0x1F95, 0x1F96, 0x1F97, 0x1F98, 0x1F99, 0x1F9A, 0x1F9B, 0x1F9C,
- 0x1F9D, 0x1F9E, 0x1F9F, 0x1FA0, 0x1FA1, 0x1FA2, 0x1FA3, 0x1FA4,
- 0x1FA5, 0x1FA6, 0x1FA7, 0x1FA8, 0x1FA9, 0x1FAA, 0x1FAB, 0x1FAC,
- 0x1FAD, 0x1FAE, 0x1FAF, 0x1FB0, 0x1FB1, 0x1FB2, 0x1FB3, 0x1FB4,
- 0x1FB5, 0x1FB6, 0x1FB7, 0x1FB8, 0x1FB9, 0x1FBA, 0x1FBB, 0x1FBC,
- 0x1FBD, 0x1FBE, 0x1FBF, 0xFE00, 0xFE02, 0xFE03, 0xFE04, 0xFE05,
- 0xFE06, 0xFE07, 0xFE08, 0xFE09, 0xFE0A, 0xFE0B, 0xFE0C, 0xFE0D,
- 0xFE0E, 0xFE0F, 0xFE10, 0xFE11, 0xFE12, 0xFE13, 0xFE14, 0xFE15,
- 0xFE16, 0xFE17, 0xFE18, 0xFE19, 0xFE1A, 0xFE1B, 0xFE1C, 0xFE1D,
- 0xFE1E, 0xFE1F, 0xFE20, 0xFE21, 0xFE22, 0xFE23, 0xFE24, 0xFE25,
- 0xFE26, 0xFE27, 0xFE28, 0xFE29, 0xFE2A, 0xFE2B, 0xFE2C, 0xFE2D,
- 0xFE2E, 0xFE2F, 0xFE30, 0xFE31, 0xFE32, 0xFE33, 0xFE34, 0xFE35,
- 0xFE36, 0xFE37, 0xFE38, 0xFE39, 0xFE3A, 0xFE3B, 0xFE3C, 0xFE3D,
- 0xFE3E, 0xFE3F, 0xFE40, 0xFE41, 0xFE42, 0xFE43, 0xFE44, 0xFE45,
- 0xFE46, 0xFE47, 0xFE48, 0xFE49, 0xFE4A, 0xFE4B, 0xFE4C, 0xFE4D,
- 0xFE4E, 0xFE4F, 0xFE50, 0xFE51, 0xFE52, 0xFE53, 0xFE54, 0xFE55,
- 0xFE56, 0xFE57, 0xFE58, 0xFE59, 0xFE5A, 0xFE5B, 0xFE5C, 0xFE5D,
- 0xFE5E, 0xFE5F, 0xFE60, 0xFE61, 0xFE62, 0xFE63, 0xFE64, 0xFE65,
- 0xFE66, 0xFE67, 0xFE68, 0xFE69, 0xFE6A, 0xFE6B, 0xFE6C, 0xFE6D,
- 0xFE6E, 0xFE6F, 0xFE70, 0xFE71, 0xFE72, 0xFE73, 0xFE74, 0xFE75,
- 0xFE76, 0xFE77, 0xFE78, 0xFE79, 0xFE7A, 0xFE7B, 0xFE7C, 0xFE7D,
- 0xFE7E, 0xFE7F, 0xFE80, 0xFE81, 0xFE82, 0xFE83, 0xFE84, 0xFE85,
- 0xFE86, 0xFE87, 0xFE88, 0xFE89, 0xFE8A, 0xFE8B, 0xFE8C, 0xFE8D,
- 0xFE8E, 0xFE8F, 0xFE90, 0xFE91, 0xFE92, 0xFE93, 0xFE94, 0xFE95,
- 0xFE96, 0xFE97, 0xFE98, 0xFE99, 0xFE9A, 0xFE9B, 0xFE9C, 0xFE9D,
- 0xFE9E, 0xFE9F, 0xFEA0, 0xFEA1, 0xFEA2, 0xFEA3, 0xFEA4, 0xFEA5,
- 0xFEA6, 0xFEA7, 0xFEA8, 0xFEA9, 0xFEAA, 0xFEAB, 0xFEAC, 0xFEAD,
- 0xFEAE, 0xFEAF, 0xFEB0, 0xFEB1, 0xFEB2, 0xFEB3, 0xFEB4, 0xFEB5,
- 0xFEB6, 0xFEB7, 0xFEB8, 0xFEB9, 0xFEBA, 0xFEBB, 0xFEBC, 0xFEBD,
- 0xFEBE, 0xFEBF, 0xFEC0, 0xFEC1, 0xFEC2, 0xFEC3, 0xFEC4, 0xFEC5,
- 0xFEC6, 0xFEC7, 0xFEC8, 0xFEC9, 0xFECA, 0xFECB, 0xFECC, 0xFECD,
- 0xFECE, 0xFECF, 0xFED0, 0xFED1, 0xFED2, 0xFED3, 0xFED4, 0xFED5,
- 0xFED6, 0xFED7, 0xFED8, 0xFED9, 0xFEDA, 0xFEDB, 0xFEDC, 0xFEDD,
- 0xFEDE, 0xFEDF, 0xFEE0, 0xFEE1, 0xFEE2, 0xFEE3, 0xFEE4, 0xFEE5,
- 0xFEE6, 0xFEE7, 0xFEE8, 0xFEE9, 0xFEEA, 0xFEEB, 0xFEEC, 0xFEED,
- 0xFEEE, 0xFEEF, 0xFEF0, 0xFEF1, 0xFEF2, 0xFEF3, 0xFEF4, 0xFEF5,
- 0xFEF6, 0xFEF7, 0xFEF8, 0xFEF9, 0xFEFA, 0xFEFB, 0xFEFC, 0xFEFD,
- 0xFEFE, 0xFEFF, 0xFF00, 0xFF01, 0xFF02, 0xFF03, 0xFF04, 0xFF05,
- 0xFF06, 0xFF07, 0xFF08, 0xFF09, 0xFF0A, 0xFF0B, 0xFF0C, 0xFF0D,
- 0xFF0E, 0xFF0F, 0xFF10, 0xFF11, 0xFF12, 0xFF13, 0xFF14, 0xFF15,
- 0xFF16, 0xFF17, 0xFF18, 0xFF19, 0xFF1A, 0xFF1B, 0xFF1C, 0xFF1D,
- 0xFF1E, 0xFF1F, 0xFF20, 0xFF21, 0xFF22, 0xFF23, 0xFF24, 0xFF25,
- 0xFF26, 0xFF27, 0xFF28, 0xFF29, 0xFF2A, 0xFF2B, 0xFF2C, 0xFF2D,
- 0xFF2E, 0xFF2F, 0xFF30, 0xFF31, 0xFF32, 0xFF33, 0xFF34, 0xFF35,
- 0xFF36, 0xFF37, 0xFF38, 0xFF39, 0xFF3A, 0xFF3B, 0xFF3C, 0xFF3D,
- 0xFF3E, 0xFF3F, 0xFF40, 0xFF41, 0xFF42, 0xFF43, 0xFF44, 0xFF45,
- 0xFF46, 0xFF47, 0xFF48, 0xFF49, 0xFF4A, 0xFF4B, 0xFF4C, 0xFF4D,
- 0xFF4E, 0xFF4F, 0xFF50, 0xFF51, 0xFF52, 0xFF53, 0xFF54, 0xFF55,
- 0xFF56, 0xFF57, 0xFF58, 0xFF59, 0xFF5A, 0xFF5B, 0xFF5C, 0xFF5D,
- 0xFF5E, 0xFF5F, 0xFF60, 0xFF61, 0xFF62, 0xFF63, 0xFF64, 0xFF65,
- 0xFF66, 0xFF67, 0xFF68, 0xFF69, 0xFF6A, 0xFF6B, 0xFF6C, 0xFF6D,
- 0xFF6E, 0xFF6F, 0xFF70, 0xFF71, 0xFF72, 0xFF73, 0xFF74, 0xFF75,
- 0xFF76, 0xFF77, 0xFF78, 0xFF79, 0xFF7A, 0xFF7B, 0xFF7C, 0xFF7D,
- 0xFF7E, 0xFF7F, 0xFF80, 0xFF81, 0xFF82, 0xFF83, 0xFF84, 0xFF85,
- 0xFF86, 0xFF87, 0xFF88, 0xFF89, 0xFF8A, 0xFF8B, 0xFF8C, 0xFF8D,
- 0xFF8E, 0xFF8F, 0xFF90, 0xFF91, 0xFF92, 0xFF93, 0xFF94, 0xFF95,
- 0xFF96, 0xFF97, 0xFF98, 0xFF99, 0xFF9A, 0xFF9B, 0xFF9C, 0xFF9D,
- 0xFF9E, 0xFF9F, 0xFFA0, 0xFFA1, 0xFFA2, 0xFFA3, 0xFFA4, 0xFFA5,
- 0xFFA6, 0xFFA7, 0xFFA8, 0xFFA9, 0xFFAA, 0xFFAB, 0xFFAC, 0xFFAD,
- 0xFFAE, 0xFFAF, 0xFFB0, 0xFFB1, 0xFFB2, 0xFFB3, 0xFFB4, 0xFFB5,
- 0xFFB6, 0xFFB7, 0xFFB8, 0xFFB9, 0xFFBA, 0xFFBB, 0xFFBC, 0xFFBD,
- 0xFFBE, 0xFFBF, 0xFFC0, 0xFFC1, 0xFFC2, 0xFFC3, 0xFFC4, 0xFFC5,
- 0xFFC6, 0xFFC7, 0xFFC8, 0xFFC9, 0xFFCA, 0xFFCB, 0xFFCC, 0xFFCD,
- 0xFFCE, 0xFFCF, 0xFFD0, 0xFFD1, 0xFFD2, 0xFFD3, 0xFFD4, 0xFFD5,
- 0xFFD6, 0xFFD7, 0xFFD8, 0xFFD9, 0xFFDA, 0xFFDB, 0xFFDC, 0xFFDD,
- 0xFFDE, 0xFFDF, 0xFFE0, 0xFFE1, 0xFFE2, 0xFFE3, 0xFFE4, 0xFFE5,
- 0xFFE6, 0xFFE7, 0xFFE8, 0xFFE9, 0xFFEA, 0xFFEB, 0xFFEC, 0xFFED,
- 0xFFEE, 0xFFEF, 0xFFF0, 0xFFF1, 0xFFF2, 0xFFF3, 0xFFF4, 0xFFF5,
- 0xFFF6, 0xFFF7, 0xFFF8, 0xFFF9, 0xFFFA, 0xFFFB, 0xFFFC, 0xFFFD,
- 0xFFFE, 0xFFFF,
-};
-
-const uint8_t ff_hq_ac_skips[NUM_HQ_AC_ENTRIES] = {
- 0, 0, 0, 0, 64, 1, 1, 0, 0, 0, 0, 2, 2, 1, 1, 0,
- 0, 0, 0, 3, 3, 4, 4, 0, 0, 0, 0, 5, 5, 6, 6, 2,
- 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 7, 7, 8, 8, 9,
- 9, 10, 10, 3, 3, 4, 4, 2, 2, 1, 1, 1, 1, 1, 1, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 11, 12, 12, 13,
- 13, 14, 14, 5, 5, 6, 6, 3, 3, 4, 4, 2, 2, 2, 2, 1,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 3, 3, 3,
- 3, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 6, 6, 4, 4, 3,
- 3, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 7, 7, 8, 8, 9,
- 9, 10, 10, 7, 7, 8, 8, 4, 4, 3, 3, 2, 2, 2, 2, 2,
- 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 0, 1, 2, 3, 4,
- 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
- 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
- 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
- 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-};
-
-const int16_t ff_hq_ac_syms[NUM_HQ_AC_ENTRIES] = {
- 1, -1, 2, -2, 0, 1, -1, 3,
- -3, 4, -4, 1, -1, 2, -2, 5,
- -5, 6, -6, 1, -1, 1, -1, 7,
- -7, 8, -8, 1, -1, 1, -1, 2,
- -2, 3, -3, 4, -4, 9, -9, 10,
- -10, 11, -11, 1, -1, 1, -1, 1,
- -1, 1, -1, 2, -2, 2, -2, 3,
- -3, 5, -5, 6, -6, 7, -7, 12,
- -12, 13, -13, 14, -14, 15, -15, 16,
- -16, 17, -17, 1, -1, 1, -1, 1,
- -1, 1, -1, 2, -2, 2, -2, 3,
- -3, 3, -3, 4, -4, 5, -5, 8,
- -8, 18, -18, 19, -19, 20, -20, 21,
- -21, 22, -22, 3, -3, 4, -4, 5,
- -5, 6, -6, 9, -9, 10, -10, 11,
- -11, 0, 0, 3, -3, 4, -4, 6,
- -6, 12, -12, 13, -13, 14, -14, 0,
- 0, 0, 0, 2, -2, 2, -2, 2,
- -2, 2, -2, 3, -3, 3, -3, 5,
- -5, 7, -7, 7, -7, 8, -8, 9,
- -9, 10, -10, 11, -11, 15, -15, 16,
- -16, 17, -17, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 1, -1, 2, -2,
- 3, -3, 4, -4, 5, -5, 6, -6,
- 7, -7, 8, -8, 9, -9, 10, -10,
- 11, -11, 12, -12, 13, -13, 14, -14,
- 15, -15, 16, -16, 17, -17, 18, -18,
- 19, -19, 20, -20, 21, -21, 22, -22,
- 23, -23, 24, -24, 25, -25, 26, -26,
- 27, -27, 28, -28, 29, -29, 30, -30,
- 31, -31, 32, -32, 33, -33, 34, -34,
- 35, -35, 36, -36, 37, -37, 38, -38,
- 39, -39, 40, -40, 41, -41, 42, -42,
- 43, -43, 44, -44, 45, -45, 46, -46,
- 47, -47, 48, -48, 49, -49, 50, -50,
- 51, -51, 52, -52, 53, -53, 54, -54,
- 55, -55, 56, -56, 57, -57, 58, -58,
- 59, -59, 60, -60, 61, -61, 62, -62,
- 63, -63, 64, -64, 65, -65, 66, -66,
- 67, -67, 68, -68, 69, -69, 70, -70,
- 71, -71, 72, -72, 73, -73, 74, -74,
- 75, -75, 76, -76, 77, -77, 78, -78,
- 79, -79, 80, -80, 81, -81, 82, -82,
- 83, -83, 84, -84, 85, -85, 86, -86,
- 87, -87, 88, -88, 89, -89, 90, -90,
- 91, -91, 92, -92, 93, -93, 94, -94,
- 95, -95, 96, -96, 97, -97, 98, -98,
- 99, -99, 100, -100, 101, -101, 102, -102,
- 103, -103, 104, -104, 105, -105, 106, -106,
- 107, -107, 108, -108, 109, -109, 110, -110,
- 111, -111, 112, -112, 113, -113, 114, -114,
- 115, -115, 116, -116, 117, -117, 118, -118,
- 119, -119, 120, -120, 121, -121, 122, -122,
- 123, -123, 124, -124, 125, -125, 126, -126,
- 127, -127, 128, -128, 129, -129, 130, -130,
- 131, -131, 132, -132, 133, -133, 134, -134,
- 135, -135, 136, -136, 137, -137, 138, -138,
- 139, -139, 140, -140, 141, -141, 142, -142,
- 143, -143, 144, -144, 145, -145, 146, -146,
- 147, -147, 148, -148, 149, -149, 150, -150,
- 151, -151, 152, -152, 153, -153, 154, -154,
- 155, -155, 156, -156, 157, -157, 158, -158,
- 159, -159, 160, -160, 161, -161, 162, -162,
- 163, -163, 164, -164, 165, -165, 166, -166,
- 167, -167, 168, -168, 169, -169, 170, -170,
- 171, -171, 172, -172, 173, -173, 174, -174,
- 175, -175, 176, -176, 177, -177, 178, -178,
- 179, -179, 180, -180, 181, -181, 182, -182,
- 183, -183, 184, -184, 185, -185, 186, -186,
- 187, -187, 188, -188, 189, -189, 190, -190,
- 191, -191, 192, -192, 193, -193, 194, -194,
- 195, -195, 196, -196, 197, -197, 198, -198,
- 199, -199, 200, -200, 201, -201, 202, -202,
- 203, -203, 204, -204, 205, -205, 206, -206,
- 207, -207, 208, -208, 209, -209, 210, -210,
- 211, -211, 212, -212, 213, -213, 214, -214,
- 215, -215, 216, -216, 217, -217, 218, -218,
- 219, -219, 220, -220, 221, -221, 222, -222,
- 223, -223, 224, -224, 225, -225, 226, -226,
- 227, -227, 228, -228, 229, -229, 230, -230,
- 231, -231, 232, -232, 233, -233, 234, -234,
- 235, -235, 236, -236, 237, -237, 238, -238,
- 239, -239, 240, -240, 241, -241, 242, -242,
- 243, -243, 244, -244, 245, -245, 246, -246,
- 247, -247, 248, -248, 249, -249, 250, -250,
- 251, -251, 252, -252, 253, -253, 254, -254,
- 255, -255,
-};
-
-/* tables section - one per supported profile */
-static const uint8_t hq_tab_01[] = {
- 0, 0, 12, 3, 20, 0, 28, 3, 40, 0, 0, 10, 15, 6, 20,
- 10, 35, 6, 40, 10, 7, 13, 15, 16, 23, 13, 35, 16, 39, 13,
- 7, 23, 10, 19, 23, 23, 27, 20, 39, 23, 2, 26, 10, 29, 22,
- 26, 27, 24, 42, 26, 1, 0, 11, 3, 21, 0, 27, 3, 41, 0,
- 1, 10, 16, 6, 21, 10, 35, 7, 41, 10, 6, 13, 16, 16, 22,
- 13, 35, 17, 38, 13, 6, 23, 9, 19, 22, 23, 28, 20, 38, 23,
- 3, 26, 9, 29, 23, 26, 28, 24, 43, 26, 2, 0, 10, 3, 22,
- 0, 27, 4, 42, 0, 2, 10, 17, 6, 22, 10, 34, 7, 42, 10,
- 5, 13, 17, 16, 21, 13, 34, 17, 37, 13, 5, 23, 9, 20, 21,
- 23, 29, 20, 37, 23, 4, 26, 9, 24, 24, 26, 29, 24, 44, 26,
- 3, 0, 9, 3, 23, 0, 28, 4, 43, 0, 3, 10, 17, 7, 23,
- 10, 33, 7, 43, 10, 4, 13, 17, 17, 20, 13, 33, 17, 36, 13,
- 4, 23, 10, 20, 20, 23, 30, 20, 36, 23, 5, 26, 10, 24, 25,
- 26, 30, 24, 44, 27, 4, 0, 9, 4, 24, 0, 29, 4, 44, 0,
- 4, 10, 16, 7, 24, 10, 32, 7, 44, 10, 3, 13, 16, 17, 19,
- 13, 32, 17, 36, 14, 3, 23, 11, 20, 19, 23, 31, 20, 36, 18,
- 6, 26, 11, 24, 26, 26, 31, 24, 43, 27, 5, 0, 10, 4, 25,
- 0, 30, 4, 44, 1, 5, 10, 15, 7, 25, 10, 31, 7, 44, 11,
- 2, 13, 15, 17, 18, 13, 31, 17, 37, 14, 2, 23, 12, 20, 18,
- 23, 32, 20, 37, 18, 7, 26, 12, 24, 26, 27, 32, 24, 42, 27,
- 6, 0, 11, 4, 26, 0, 31, 4, 43, 1, 6, 10, 14, 7, 26,
- 10, 30, 7, 43, 11, 1, 13, 14, 17, 18, 14, 30, 17, 38, 14,
- 1, 23, 13, 20, 18, 18, 33, 20, 38, 18, 8, 26, 13, 24, 25,
- 27, 33, 24, 41, 27, 7, 0, 12, 4, 26, 1, 32, 4, 42, 1,
- 7, 10, 13, 7, 26, 11, 29, 7, 42, 11, 0, 13, 13, 17, 19,
- 14, 29, 17, 39, 14, 0, 23, 14, 20, 19, 18, 34, 20, 39, 18,
- 8, 27, 14, 24, 24, 27, 34, 24, 40, 27, 8, 0, 13, 4, 25,
- 1, 33, 4, 41, 1, 8, 10, 12, 7, 25, 11, 28, 7, 41, 11,
- 0, 14, 12, 17, 20, 14, 28, 17, 40, 14, 0, 18, 15, 20, 20,
- 18, 35, 20, 40, 18, 7, 27, 15, 24, 23, 27, 35, 24, 39, 27,
- 8, 1, 14, 4, 24, 1, 34, 4, 40, 1, 8, 11, 11, 7, 24,
- 11, 27, 7, 40, 11, 1, 14, 11, 17, 21, 14, 27, 17, 41, 14,
- 1, 18, 16, 20, 21, 18, 35, 21, 41, 18, 6, 27, 16, 24, 22,
- 27, 35, 25, 38, 27, 7, 1, 15, 4, 23, 1, 35, 4, 39, 1,
- 7, 11, 10, 7, 23, 11, 27, 8, 39, 11, 2, 14, 10, 17, 22,
- 14, 27, 12, 42, 14, 2, 18, 17, 20, 22, 18, 34, 21, 42, 18,
- 5, 27, 17, 24, 21, 27, 34, 25, 37, 27, 6, 1, 16, 4, 22,
- 1, 35, 5, 38, 1, 6, 11, 9, 7, 22, 11, 28, 8, 38, 11,
- 3, 14, 9, 17, 23, 14, 28, 12, 43, 14, 3, 18, 17, 21, 23,
- 18, 33, 21, 43, 18, 4, 27, 17, 25, 20, 27, 33, 25, 36, 27,
- 5, 1, 17, 4, 21, 1, 34, 5, 37, 1, 5, 11, 9, 8, 21,
- 11, 29, 8, 37, 11, 4, 14, 9, 12, 24, 14, 29, 12, 44, 14,
- 4, 18, 16, 21, 24, 18, 32, 21, 44, 18, 3, 27, 16, 25, 19,
- 27, 32, 25, 36, 28, 4, 1, 17, 5, 20, 1, 33, 5, 36, 1,
- 4, 11, 10, 8, 20, 11, 30, 8, 36, 11, 5, 14, 10, 12, 25,
- 14, 30, 12, 44, 15, 5, 18, 15, 21, 25, 18, 31, 21, 44, 19,
- 2, 27, 15, 25, 18, 27, 31, 25, 37, 28, 3, 1, 16, 5, 19,
- 1, 32, 5, 36, 2, 3, 11, 11, 8, 19, 11, 31, 8, 36, 6,
- 6, 14, 11, 12, 26, 14, 31, 12, 43, 15, 6, 18, 14, 21, 26,
- 18, 30, 21, 43, 19, 1, 27, 14, 25, 18, 28, 30, 25, 38, 28,
- 2, 1, 15, 5, 18, 1, 31, 5, 37, 2, 2, 11, 12, 8, 18,
- 11, 32, 8, 37, 6, 7, 14, 12, 12, 26, 15, 32, 12, 42, 15,
- 7, 18, 13, 21, 26, 19, 29, 21, 42, 19, 0, 27, 13, 25, 19,
- 28, 29, 25, 39, 28, 1, 1, 14, 5, 18, 2, 30, 5, 38, 2,
- 1, 11, 13, 8, 18, 6, 33, 8, 38, 6, 8, 14, 13, 12, 25,
- 15, 33, 12, 41, 15, 8, 18, 12, 21, 25, 19, 28, 21, 41, 19,
- 0, 28, 12, 25, 20, 28, 28, 25, 40, 28, 0, 1, 13, 5, 19,
- 2, 29, 5, 39, 2, 0, 11, 14, 8, 19, 6, 34, 8, 39, 6,
- 8, 15, 14, 12, 24, 15, 34, 12, 40, 15, 8, 19, 11, 21, 24,
- 19, 27, 21, 40, 19, 1, 28, 11, 25, 21, 28, 27, 25, 41, 28,
- 0, 2, 12, 5, 20, 2, 28, 5, 40, 2, 0, 6, 15, 8, 20,
- 6, 35, 8, 40, 6, 7, 15, 15, 12, 23, 15, 35, 12, 39, 15,
- 7, 19, 10, 21, 23, 19, 27, 22, 39, 19, 2, 28, 10, 25, 22,
- 28, 27, 26, 42, 28, 1, 2, 11, 5, 21, 2, 27, 5, 41, 2,
- 1, 6, 16, 8, 21, 6, 35, 9, 41, 6, 6, 15, 16, 12, 22,
- 15, 35, 13, 38, 15, 6, 19, 9, 21, 22, 19, 28, 22, 38, 19,
- 3, 28, 9, 25, 23, 28, 28, 26, 43, 28, 2, 2, 10, 5, 22,
- 2, 27, 0, 42, 2, 2, 6, 17, 8, 22, 6, 34, 9, 42, 6,
- 5, 15, 17, 12, 21, 15, 34, 13, 37, 15, 5, 19, 9, 22, 21,
- 19, 29, 22, 37, 19, 4, 28, 9, 26, 24, 28, 29, 26, 44, 28,
- 3, 2, 9, 5, 23, 2, 28, 0, 43, 2, 3, 6, 17, 9, 23,
- 6, 33, 9, 43, 6, 4, 15, 17, 13, 20, 15, 33, 13, 36, 15,
- 4, 19, 10, 22, 20, 19, 30, 22, 36, 19, 5, 28, 10, 26, 25,
- 28, 30, 26, 44, 29, 4, 2, 9, 0, 24, 2, 29, 0, 44, 2,
- 4, 6, 16, 9, 24, 6, 32, 9, 44, 6, 3, 15, 16, 13, 19,
- 15, 32, 13, 36, 16, 3, 19, 11, 22, 19, 19, 31, 22, 36, 20,
- 6, 28, 11, 26, 26, 28, 31, 26, 43, 29, 5, 2, 10, 0, 25,
- 2, 30, 0, 44, 3, 5, 6, 15, 9, 25, 6, 31, 9, 44, 7,
- 2, 15, 15, 13, 18, 15, 31, 13, 37, 16, 2, 19, 12, 22, 18,
- 19, 32, 22, 37, 20, 7, 28, 12, 26, 26, 29, 32, 26, 42, 29,
- 6, 2, 11, 0, 26, 2, 31, 0, 43, 3, 6, 6, 14, 9, 26,
- 6, 30, 9, 43, 7, 1, 15, 14, 13, 18, 16, 30, 13, 38, 16,
- 1, 19, 13, 22, 18, 20, 33, 22, 38, 20, 8, 28, 13, 26, 25,
- 29, 33, 26, 41, 29, 7, 2, 12, 0, 26, 3, 32, 0, 42, 3,
- 7, 6, 13, 9, 26, 7, 29, 9, 42, 7, 0, 15, 13, 13, 19,
- 16, 29, 13, 39, 16, 0, 19, 14, 22, 19, 20, 34, 22, 39, 20,
- 8, 29, 14, 26, 24, 29, 34, 26, 40, 29, 8, 2, 13, 0, 25,
- 3, 33, 0, 41, 3, 8, 6, 12, 9, 25, 7, 28, 9, 41, 7,
- 0, 16, 12, 13, 20, 16, 28, 13, 40, 16, 0, 20, 15, 22, 20,
- 20, 35, 22, 40, 20, 7, 29, 15, 26, 23, 29, 35, 26, 39, 29,
- 8, 3, 14, 0, 24, 3, 34, 0, 40, 3, 8, 7, 11, 9, 24,
- 7, 27, 9, 40, 7, 1, 16, 11, 13, 21, 16, 27, 13, 41, 16,
- 1, 20, 16, 22, 21, 20, 35, 23, 41, 20, 6, 29, 16, 26, 22,
- 29, 35, 27, 38, 29, 7, 3, 15, 0, 23, 3, 35, 0, 39, 3,
- 7, 7, 10, 9, 23, 7, 27, 10, 39, 7, 2, 16, 10, 13, 22,
- 16, 27, 14, 42, 16, 2, 20, 17, 22, 22, 20, 34, 23, 42, 20,
- 5, 29, 17, 26, 21, 29, 34, 27, 37, 29, 6, 3, 16, 0, 22,
- 3, 35, 1, 38, 3, 6, 7, 9, 9, 22, 7, 28, 10, 38, 7,
- 3, 16, 9, 13, 23, 16, 28, 14, 43, 16, 3, 20, 17, 23, 23,
- 20, 33, 23, 43, 20, 4, 29, 17, 27, 20, 29, 33, 27, 36, 29,
- 5, 3, 17, 0, 21, 3, 34, 1, 37, 3, 5, 7, 9, 10, 21,
- 7, 29, 10, 37, 7, 4, 16, 9, 14, 24, 16, 29, 14, 44, 16,
- 4, 20, 16, 23, 24, 20, 32, 23, 44, 20, 3, 29, 16, 27, 19,
- 29, 32, 27, 36, 24, 4, 3, 17, 1, 20, 3, 33, 1, 36, 3,
- 4, 7, 10, 10, 20, 7, 30, 10, 36, 7, 5, 16, 10, 14, 25,
- 16, 30, 14, 44, 17, 5, 20, 15, 23, 25, 20, 31, 23, 44, 21,
- 2, 29, 15, 27, 18, 29, 31, 27, 37, 24, 3, 3, 16, 1, 19,
- 3, 32, 1, 36, 4, 3, 7, 11, 10, 19, 7, 31, 10, 36, 8,
- 6, 16, 11, 14, 26, 16, 31, 14, 43, 17, 6, 20, 14, 23, 26,
- 20, 30, 23, 43, 21, 1, 29, 14, 27, 18, 24, 30, 27, 38, 24,
- 2, 3, 15, 1, 18, 3, 31, 1, 37, 4, 2, 7, 12, 10, 18,
- 7, 32, 10, 37, 8, 7, 16, 12, 14, 26, 17, 32, 14, 42, 17,
- 7, 20, 13, 23, 26, 21, 29, 23, 42, 21, 0, 29, 13, 27, 19,
- 24, 29, 27, 39, 24, 1, 3, 14, 1, 18, 4, 30, 1, 38, 4,
- 1, 7, 13, 10, 18, 8, 33, 10, 38, 8, 8, 16, 13, 14, 25,
- 17, 33, 14, 41, 17, 8, 20, 12, 23, 25, 21, 28, 23, 41, 21,
- 0, 24, 12, 27, 20, 24, 28, 27, 40, 24, 0, 3, 13, 1, 19,
- 4, 29, 1, 39, 4, 0, 7, 14, 10, 19, 8, 34, 10, 39, 8,
- 8, 17, 14, 14, 24, 17, 34, 14, 40, 17, 8, 21, 11, 23, 24,
- 21, 27, 23, 40, 21, 1, 24, 11, 27, 21, 24, 27, 27, 41, 24,
- 0, 4, 12, 1, 20, 4, 28, 1, 40, 4, 0, 8, 15, 10, 20,
- 8, 35, 10, 40, 8, 7, 17, 15, 14, 23, 17, 35, 14, 39, 17,
- 7, 21, 10, 23, 23, 21, 27, 18, 39, 21, 2, 24, 10, 27, 22,
- 24, 27, 28, 42, 24, 1, 4, 11, 1, 21, 4, 27, 1, 41, 4,
- 1, 8, 16, 10, 21, 8, 35, 11, 41, 8, 6, 17, 16, 14, 22,
- 17, 35, 15, 38, 17, 6, 21, 9, 23, 22, 21, 28, 18, 38, 21,
- 3, 24, 9, 27, 23, 24, 28, 28, 43, 24, 2, 4, 10, 1, 22,
- 4, 27, 2, 42, 4, 2, 8, 17, 10, 22, 8, 34, 11, 42, 8,
- 5, 17, 17, 14, 21, 17, 34, 15, 37, 17, 5, 21, 9, 18, 21,
- 21, 29, 18, 37, 21, 4, 24, 9, 28, 24, 24, 29, 28, 44, 24,
- 3, 4, 9, 1, 23, 4, 28, 2, 43, 4, 3, 8, 17, 11, 23,
- 8, 33, 11, 43, 8, 4, 17, 17, 15, 20, 17, 33, 15, 36, 17,
- 4, 21, 10, 18, 20, 21, 30, 18, 36, 21, 5, 24, 10, 28, 25,
- 24, 30, 28, 44, 25, 4, 4, 9, 2, 24, 4, 29, 2, 44, 4,
- 4, 8, 16, 11, 24, 8, 32, 11, 44, 8, 3, 17, 16, 15, 19,
- 17, 32, 15, 36, 12, 3, 21, 11, 18, 19, 21, 31, 18, 36, 22,
- 6, 24, 11, 28, 26, 24, 31, 28, 43, 25, 5, 4, 10, 2, 25,
- 4, 30, 2, 44, 5, 5, 8, 15, 11, 25, 8, 31, 11, 44, 9,
- 2, 17, 15, 15, 18, 17, 31, 15, 37, 12, 2, 21, 12, 18, 18,
- 21, 32, 18, 37, 22, 7, 24, 12, 28, 26, 25, 32, 28, 42, 25,
- 6, 4, 11, 2, 26, 4, 31, 2, 43, 5, 6, 8, 14, 11, 26,
- 8, 30, 11, 43, 9, 1, 17, 14, 15, 18, 12, 30, 15, 38, 12,
- 1, 21, 13, 18, 18, 22, 33, 18, 38, 22, 8, 24, 13, 28, 25,
- 25, 33, 28, 41, 25, 7, 4, 12, 2, 26, 5, 32, 2, 42, 5,
- 7, 8, 13, 11, 26, 9, 29, 11, 42, 9, 0, 17, 13, 15, 19,
- 12, 29, 15, 39, 12, 0, 21, 14, 18, 19, 22, 34, 18, 39, 22,
- 8, 25, 14, 28, 24, 25, 34, 28, 40, 25, 8, 4, 13, 2, 25,
- 5, 33, 2, 41, 5, 8, 8, 12, 11, 25, 9, 28, 11, 41, 9,
- 0, 12, 12, 15, 20, 12, 28, 15, 40, 12, 0, 22, 15, 18, 20,
- 22, 35, 18, 40, 22, 7, 25, 15, 28, 23, 25, 35, 28, 39, 25,
- 8, 5, 14, 2, 24, 5, 34, 2, 40, 5, 8, 9, 11, 11, 24,
- 9, 27, 11, 40, 9, 1, 12, 11, 15, 21, 12, 27, 15, 41, 12,
- 1, 22, 16, 18, 21, 22, 35, 19, 41, 22, 6, 25, 16, 28, 22,
- 25, 35, 29, 38, 25, 7, 5, 15, 2, 23, 5, 35, 2, 39, 5,
- 7, 9, 10, 11, 23, 9, 27, 6, 39, 9, 2, 12, 10, 15, 22,
- 12, 27, 16, 42, 12, 2, 22, 17, 18, 22, 22, 34, 19, 42, 22,
- 5, 25, 17, 28, 21, 25, 34, 29, 37, 25, 6, 5, 16, 2, 22,
- 5, 35, 3, 38, 5, 6, 9, 9, 11, 22, 9, 28, 6, 38, 9,
- 3, 12, 9, 15, 23, 12, 28, 16, 43, 12, 3, 22, 17, 19, 23,
- 22, 33, 19, 43, 22, 4, 25, 17, 29, 20, 25, 33, 29, 36, 25,
- 5, 5, 17, 2, 21, 5, 34, 3, 37, 5, 5, 9, 9, 6, 21,
- 9, 29, 6, 37, 9, 4, 12, 9, 16, 24, 12, 29, 16, 44, 12,
- 4, 22, 16, 19, 24, 22, 32, 19, 44, 22, 3, 25, 16, 29, 19,
- 25, 32, 29, 36, 26, 4, 5, 17, 3, 20, 5, 33, 3, 36, 5,
- 4, 9, 10, 6, 20, 9, 30, 6, 36, 9, 5, 12, 10, 16, 25,
- 12, 30, 16, 44, 13, 5, 22, 15, 19, 25, 22, 31, 19, 44, 23,
- 2, 25, 15, 29, 18, 25, 31, 29, 37, 26, 3, 5, 16, 3, 19,
- 5, 32, 3, 36, 0, 3, 9, 11, 6, 19, 9, 31, 6, 36, 10,
- 6, 12, 11, 16, 26, 12, 31, 16, 43, 13, 6, 22, 14, 19, 26,
- 22, 30, 19, 43, 23, 1, 25, 14, 29, 18, 26, 30, 29, 38, 26,
- 2, 5, 15, 3, 18, 5, 31, 3, 37, 0, 2, 9, 12, 6, 18,
- 9, 32, 6, 37, 10, 7, 12, 12, 16, 26, 13, 32, 16, 42, 13,
- 7, 22, 13, 19, 26, 23, 29, 19, 42, 23, 0, 25, 13, 29, 19,
- 26, 29, 29, 39, 26, 1, 5, 14, 3, 18, 0, 30, 3, 38, 0,
- 1, 9, 13, 6, 18, 10, 33, 6, 38, 10, 8, 12, 13, 16, 25,
- 13, 33, 16, 41, 13, 8, 22, 12, 19, 25, 23, 28, 19, 41, 23,
- 0, 26, 12, 29, 20, 26, 28, 29, 40, 26, 0, 5, 13, 3, 19,
- 0, 29, 3, 39, 0, 0, 9, 14, 6, 19, 10, 34, 6, 39, 10,
- 8, 13, 14, 16, 24, 13, 34, 16, 40, 13, 8, 23, 11, 19, 24,
- 23, 27, 19, 40, 23, 1, 26, 11, 29, 21, 26, 27, 29, 41, 26,
-};
-
-static const uint8_t hq_tab_02[] = {
- 0, 0, 12, 5, 20, 0, 28, 5, 40, 0, 3, 16, 17, 11, 23,
- 16, 33, 11, 43, 16, 1, 21, 14, 27, 18, 22, 30, 27, 38, 22,
- 1, 0, 11, 5, 21, 0, 27, 5, 41, 0, 4, 16, 16, 11, 24,
- 16, 32, 11, 44, 16, 0, 21, 13, 27, 19, 22, 29, 27, 39, 22,
- 2, 0, 10, 5, 22, 0, 27, 6, 42, 0, 5, 16, 15, 11, 25,
- 16, 31, 11, 44, 17, 0, 22, 12, 27, 20, 22, 28, 27, 40, 22,
- 3, 0, 9, 5, 23, 0, 28, 6, 43, 0, 6, 16, 14, 11, 26,
- 16, 30, 11, 43, 17, 1, 22, 11, 27, 21, 22, 27, 27, 41, 22,
- 4, 0, 9, 6, 24, 0, 29, 6, 44, 0, 7, 16, 13, 11, 26,
- 17, 29, 11, 42, 17, 2, 22, 10, 27, 22, 22, 27, 28, 42, 22,
- 5, 0, 10, 6, 25, 0, 30, 6, 44, 1, 8, 16, 12, 11, 25,
- 17, 28, 11, 41, 17, 3, 22, 9, 27, 23, 22, 28, 28, 43, 22,
- 6, 0, 11, 6, 26, 0, 31, 6, 43, 1, 8, 17, 11, 11, 24,
- 17, 27, 11, 40, 17, 4, 22, 9, 28, 24, 22, 29, 28, 44, 22,
- 7, 0, 12, 6, 26, 1, 32, 6, 42, 1, 7, 17, 10, 11, 23,
- 17, 27, 12, 39, 17, 5, 22, 10, 28, 25, 22, 30, 28, 44, 23,
- 8, 0, 13, 6, 25, 1, 33, 6, 41, 1, 6, 17, 9, 11, 22,
- 17, 28, 12, 38, 17, 6, 22, 11, 28, 26, 22, 31, 28, 43, 23,
- 8, 1, 14, 6, 24, 1, 34, 6, 40, 1, 5, 17, 9, 12, 21,
- 17, 29, 12, 37, 17, 7, 22, 12, 28, 26, 23, 32, 28, 42, 23,
- 7, 1, 15, 6, 23, 1, 35, 6, 39, 1, 4, 17, 10, 12, 20,
- 17, 30, 12, 36, 17, 8, 22, 13, 28, 25, 23, 33, 28, 41, 23,
- 6, 1, 16, 6, 22, 1, 35, 7, 38, 1, 3, 17, 11, 12, 19,
- 17, 31, 12, 36, 18, 8, 23, 14, 28, 24, 23, 34, 28, 40, 23,
- 5, 1, 17, 6, 21, 1, 34, 7, 37, 1, 2, 17, 12, 12, 18,
- 17, 32, 12, 37, 18, 7, 23, 15, 28, 23, 23, 35, 28, 39, 23,
- 4, 1, 17, 7, 20, 1, 33, 7, 36, 1, 1, 17, 13, 12, 18,
- 18, 33, 12, 38, 18, 6, 23, 16, 28, 22, 23, 35, 29, 38, 23,
- 3, 1, 16, 7, 19, 1, 32, 7, 36, 2, 0, 17, 14, 12, 19,
- 18, 34, 12, 39, 18, 5, 23, 17, 28, 21, 23, 34, 29, 37, 23,
- 2, 1, 15, 7, 18, 1, 31, 7, 37, 2, 0, 18, 15, 12, 20,
- 18, 35, 12, 40, 18, 4, 23, 17, 29, 20, 23, 33, 29, 36, 23,
- 1, 1, 14, 7, 18, 2, 30, 7, 38, 2, 1, 18, 16, 12, 21,
- 18, 35, 13, 41, 18, 3, 23, 16, 29, 19, 23, 32, 29, 36, 24,
- 0, 1, 13, 7, 19, 2, 29, 7, 39, 2, 2, 18, 17, 12, 22,
- 18, 34, 13, 42, 18, 2, 23, 15, 29, 18, 23, 31, 29, 37, 24,
- 0, 2, 12, 7, 20, 2, 28, 7, 40, 2, 3, 18, 17, 13, 23,
- 18, 33, 13, 43, 18, 1, 23, 14, 29, 18, 24, 30, 29, 38, 24,
- 1, 2, 11, 7, 21, 2, 27, 7, 41, 2, 4, 18, 16, 13, 24,
- 18, 32, 13, 44, 18, 0, 23, 13, 29, 19, 24, 29, 29, 39, 24,
- 2, 2, 10, 7, 22, 2, 27, 8, 42, 2, 5, 18, 15, 13, 25,
- 18, 31, 13, 44, 19, 0, 24, 12, 29, 20, 24, 28, 29, 40, 24,
- 3, 2, 9, 7, 23, 2, 28, 8, 43, 2, 6, 18, 14, 13, 26,
- 18, 30, 13, 43, 19, 1, 24, 11, 29, 21, 24, 27, 29, 41, 24,
- 4, 2, 9, 8, 24, 2, 29, 8, 44, 2, 7, 18, 13, 13, 26,
- 19, 29, 13, 42, 19, 2, 24, 10, 29, 22, 24, 27, 30, 42, 24,
- 5, 2, 10, 8, 25, 2, 30, 8, 44, 3, 8, 18, 12, 13, 25,
- 19, 28, 13, 41, 19, 3, 24, 9, 29, 23, 24, 28, 30, 43, 24,
- 6, 2, 11, 8, 26, 2, 31, 8, 43, 3, 8, 19, 11, 13, 24,
- 19, 27, 13, 40, 19, 4, 24, 9, 30, 24, 24, 29, 30, 44, 24,
- 7, 2, 12, 8, 26, 3, 32, 8, 42, 3, 7, 19, 10, 13, 23,
- 19, 27, 14, 39, 19, 5, 24, 10, 30, 25, 24, 30, 30, 44, 25,
- 8, 2, 13, 8, 25, 3, 33, 8, 41, 3, 6, 19, 9, 13, 22,
- 19, 28, 14, 38, 19, 6, 24, 11, 30, 26, 24, 31, 30, 43, 25,
- 8, 3, 14, 8, 24, 3, 34, 8, 40, 3, 5, 19, 9, 14, 21,
- 19, 29, 14, 37, 19, 7, 24, 12, 30, 26, 25, 32, 30, 42, 25,
- 7, 3, 15, 8, 23, 3, 35, 8, 39, 3, 4, 19, 10, 14, 20,
- 19, 30, 14, 36, 19, 8, 24, 13, 30, 25, 25, 33, 30, 41, 25,
- 6, 3, 16, 8, 22, 3, 35, 9, 38, 3, 3, 19, 11, 14, 19,
- 19, 31, 14, 36, 20, 8, 25, 14, 30, 24, 25, 34, 30, 40, 25,
- 5, 3, 17, 8, 21, 3, 34, 9, 37, 3, 2, 19, 12, 14, 18,
- 19, 32, 14, 37, 20, 7, 25, 15, 30, 23, 25, 35, 30, 39, 25,
- 4, 3, 17, 9, 20, 3, 33, 9, 36, 3, 1, 19, 13, 14, 18,
- 20, 33, 14, 38, 20, 6, 25, 16, 30, 22, 25, 33, 20, 38, 25,
- 3, 3, 16, 9, 19, 3, 32, 9, 36, 4, 0, 19, 14, 14, 19,
- 20, 34, 14, 39, 20, 5, 25, 17, 30, 21, 25, 34, 20, 37, 25,
- 2, 3, 15, 9, 18, 3, 31, 9, 37, 4, 0, 20, 15, 14, 20,
- 20, 35, 14, 40, 20, 4, 25, 15, 20, 20, 25, 35, 20, 36, 25,
- 1, 3, 14, 9, 18, 4, 30, 9, 38, 4, 1, 20, 16, 14, 21,
- 20, 35, 15, 41, 20, 3, 25, 16, 20, 19, 25, 35, 21, 36, 26,
- 0, 3, 13, 9, 19, 4, 29, 9, 39, 4, 2, 20, 17, 14, 22,
- 20, 34, 15, 39, 10, 2, 25, 17, 20, 18, 25, 34, 21, 37, 26,
- 0, 4, 12, 9, 20, 4, 28, 9, 40, 4, 3, 20, 17, 15, 23,
- 20, 33, 15, 40, 10, 1, 25, 17, 21, 18, 26, 33, 21, 38, 26,
- 1, 4, 11, 9, 21, 4, 27, 9, 41, 4, 4, 20, 16, 15, 21,
- 10, 32, 15, 41, 10, 0, 25, 16, 21, 19, 26, 32, 21, 39, 26,
- 2, 4, 10, 9, 22, 4, 27, 10, 42, 4, 5, 20, 15, 15, 22,
- 10, 31, 15, 42, 10, 0, 26, 15, 21, 20, 26, 31, 21, 40, 26,
- 3, 4, 9, 9, 23, 4, 28, 10, 43, 4, 3, 10, 14, 15, 23,
- 10, 30, 15, 43, 10, 1, 26, 14, 21, 21, 26, 30, 21, 41, 26,
- 4, 4, 9, 10, 24, 4, 29, 10, 44, 4, 4, 10, 13, 15, 24,
- 10, 29, 15, 44, 10, 2, 26, 13, 21, 22, 26, 29, 21, 42, 26,
- 5, 4, 10, 10, 25, 4, 27, 0, 44, 5, 5, 10, 12, 15, 25,
- 10, 28, 15, 44, 11, 3, 26, 12, 21, 23, 26, 28, 21, 43, 26,
- 6, 4, 11, 10, 26, 4, 28, 0, 43, 5, 6, 10, 11, 15, 26,
- 10, 27, 15, 43, 11, 4, 26, 11, 21, 24, 26, 27, 21, 44, 26,
- 7, 4, 9, 0, 26, 5, 29, 0, 42, 5, 7, 10, 10, 15, 26,
- 11, 27, 16, 42, 11, 5, 26, 10, 21, 25, 26, 27, 22, 44, 27,
- 8, 4, 10, 0, 25, 5, 30, 0, 41, 5, 8, 10, 9, 15, 25,
- 11, 28, 16, 41, 11, 6, 26, 9, 21, 26, 26, 28, 22, 43, 27,
- 8, 5, 11, 0, 24, 5, 31, 0, 40, 5, 8, 11, 9, 16, 24,
- 11, 29, 16, 40, 11, 7, 26, 9, 22, 26, 27, 29, 22, 42, 27,
- 7, 5, 12, 0, 23, 5, 32, 0, 39, 5, 7, 11, 10, 16, 23,
- 11, 30, 16, 39, 11, 8, 26, 10, 22, 25, 27, 30, 22, 41, 27,
- 6, 5, 13, 0, 22, 5, 33, 0, 38, 5, 6, 11, 11, 16, 22,
- 11, 31, 16, 38, 11, 8, 27, 11, 22, 24, 27, 31, 22, 40, 27,
- 5, 5, 14, 0, 21, 5, 34, 0, 37, 5, 5, 11, 12, 16, 21,
- 11, 32, 16, 37, 11, 7, 27, 12, 22, 23, 27, 32, 22, 39, 27,
- 4, 5, 15, 0, 20, 5, 35, 0, 36, 5, 4, 11, 13, 16, 20,
- 11, 33, 16, 36, 11, 6, 27, 13, 22, 22, 27, 33, 22, 38, 27,
- 3, 5, 16, 0, 19, 5, 35, 1, 36, 6, 3, 11, 14, 16, 19,
- 11, 34, 16, 36, 12, 5, 27, 14, 22, 21, 27, 34, 22, 37, 27,
- 2, 5, 17, 0, 18, 5, 34, 1, 37, 6, 2, 11, 15, 16, 18,
- 11, 35, 16, 37, 12, 4, 27, 15, 22, 20, 27, 35, 22, 36, 27,
- 1, 5, 17, 1, 18, 6, 33, 1, 38, 6, 1, 11, 16, 16, 18,
- 12, 35, 17, 38, 12, 3, 27, 16, 22, 19, 27, 35, 23, 36, 28,
- 0, 5, 16, 1, 19, 6, 32, 1, 39, 6, 0, 11, 17, 16, 19,
- 12, 34, 17, 39, 12, 2, 27, 17, 22, 18, 27, 34, 23, 37, 28,
- 0, 6, 15, 1, 20, 6, 31, 1, 40, 6, 0, 12, 17, 17, 20,
- 12, 33, 17, 40, 12, 1, 27, 17, 23, 18, 28, 33, 23, 38, 28,
- 1, 6, 14, 1, 21, 6, 30, 1, 41, 6, 1, 12, 16, 17, 21,
- 12, 32, 17, 41, 12, 0, 27, 16, 23, 19, 28, 32, 23, 39, 28,
- 2, 6, 13, 1, 22, 6, 29, 1, 42, 6, 2, 12, 15, 17, 22,
- 12, 31, 17, 42, 12, 0, 28, 15, 23, 20, 28, 31, 23, 40, 28,
- 3, 6, 12, 1, 23, 6, 28, 1, 43, 6, 3, 12, 14, 17, 23,
- 12, 30, 17, 43, 12, 1, 28, 14, 23, 21, 28, 30, 23, 41, 28,
- 4, 6, 11, 1, 24, 6, 27, 1, 44, 6, 4, 12, 13, 17, 24,
- 12, 29, 17, 44, 12, 2, 28, 13, 23, 22, 28, 29, 23, 42, 28,
- 5, 6, 10, 1, 25, 6, 27, 2, 44, 7, 5, 12, 12, 17, 25,
- 12, 28, 17, 44, 13, 3, 28, 12, 23, 23, 28, 28, 23, 43, 28,
- 6, 6, 9, 1, 26, 6, 28, 2, 43, 7, 6, 12, 11, 17, 26,
- 12, 27, 17, 43, 13, 4, 28, 11, 23, 24, 28, 27, 23, 44, 28,
- 7, 6, 9, 2, 26, 7, 29, 2, 42, 7, 7, 12, 10, 17, 26,
- 13, 27, 18, 42, 13, 5, 28, 10, 23, 25, 28, 27, 24, 44, 29,
- 8, 6, 10, 2, 25, 7, 30, 2, 41, 7, 8, 12, 9, 17, 25,
- 13, 28, 18, 41, 13, 6, 28, 9, 23, 26, 28, 28, 24, 43, 29,
- 8, 7, 11, 2, 24, 7, 31, 2, 40, 7, 8, 13, 9, 18, 24,
- 13, 29, 18, 40, 13, 7, 28, 9, 24, 26, 29, 29, 24, 42, 29,
- 7, 7, 12, 2, 23, 7, 32, 2, 39, 7, 7, 13, 10, 18, 23,
- 13, 30, 18, 39, 13, 8, 28, 10, 24, 25, 29, 30, 24, 41, 29,
- 6, 7, 13, 2, 22, 7, 33, 2, 38, 7, 6, 13, 11, 18, 22,
- 13, 31, 18, 38, 13, 8, 29, 11, 24, 24, 29, 31, 24, 40, 29,
- 5, 7, 14, 2, 21, 7, 34, 2, 37, 7, 5, 13, 12, 18, 21,
- 13, 32, 18, 37, 13, 7, 29, 12, 24, 23, 29, 32, 24, 39, 29,
- 4, 7, 15, 2, 20, 7, 35, 2, 36, 7, 4, 13, 13, 18, 20,
- 13, 33, 18, 36, 13, 6, 29, 13, 24, 22, 29, 33, 24, 38, 29,
- 3, 7, 16, 2, 19, 7, 35, 3, 36, 8, 3, 13, 14, 18, 19,
- 13, 34, 18, 36, 14, 5, 29, 14, 24, 21, 29, 34, 24, 37, 29,
- 2, 7, 17, 2, 18, 7, 34, 3, 37, 8, 2, 13, 15, 18, 18,
- 13, 35, 18, 37, 14, 4, 29, 15, 24, 20, 29, 35, 24, 36, 29,
- 1, 7, 17, 3, 18, 8, 33, 3, 38, 8, 1, 13, 16, 18, 18,
- 14, 35, 19, 38, 14, 3, 29, 16, 24, 19, 29, 35, 25, 36, 30,
- 0, 7, 16, 3, 19, 8, 32, 3, 39, 8, 0, 13, 17, 18, 19,
- 14, 34, 19, 39, 14, 2, 29, 17, 24, 18, 29, 34, 25, 37, 30,
- 0, 8, 15, 3, 20, 8, 31, 3, 40, 8, 0, 14, 17, 19, 20,
- 14, 33, 19, 40, 14, 1, 29, 17, 25, 18, 30, 33, 25, 38, 30,
- 1, 8, 14, 3, 21, 8, 30, 3, 41, 8, 1, 14, 16, 19, 21,
- 14, 32, 19, 41, 14, 0, 29, 16, 25, 19, 30, 32, 25, 39, 30,
- 2, 8, 13, 3, 22, 8, 29, 3, 42, 8, 2, 14, 15, 19, 22,
- 14, 31, 19, 42, 14, 0, 30, 15, 25, 20, 30, 31, 25, 40, 30,
- 3, 8, 12, 3, 23, 8, 28, 3, 43, 8, 3, 14, 14, 19, 23,
- 14, 30, 19, 43, 14, 1, 30, 14, 25, 21, 30, 30, 25, 41, 30,
- 4, 8, 11, 3, 24, 8, 27, 3, 44, 8, 4, 14, 13, 19, 24,
- 14, 29, 19, 44, 14, 2, 30, 13, 25, 22, 30, 29, 25, 42, 30,
- 5, 8, 10, 3, 25, 8, 27, 4, 44, 9, 5, 14, 12, 19, 25,
- 14, 28, 19, 44, 15, 3, 30, 12, 25, 23, 30, 28, 25, 43, 30,
- 6, 8, 9, 3, 26, 8, 28, 4, 43, 9, 6, 14, 11, 19, 26,
- 14, 27, 19, 43, 15, 4, 30, 11, 25, 24, 30, 27, 25, 44, 30,
- 7, 8, 9, 4, 26, 9, 29, 4, 42, 9, 7, 14, 10, 19, 26,
- 15, 27, 20, 42, 15, 5, 30, 10, 25, 25, 30, 27, 26, 42, 20,
- 8, 8, 10, 4, 25, 9, 30, 4, 41, 9, 8, 14, 9, 19, 25,
- 15, 28, 20, 41, 15, 6, 30, 9, 25, 26, 30, 28, 26, 43, 20,
- 8, 9, 11, 4, 24, 9, 31, 4, 40, 9, 8, 15, 9, 20, 24,
- 15, 29, 20, 40, 15, 7, 30, 9, 26, 24, 20, 29, 26, 44, 20,
- 7, 9, 12, 4, 23, 9, 32, 4, 39, 9, 7, 15, 10, 20, 23,
- 15, 30, 20, 39, 15, 8, 30, 10, 26, 25, 20, 30, 26, 44, 21,
- 6, 9, 13, 4, 22, 9, 33, 4, 38, 9, 6, 15, 11, 20, 22,
- 15, 31, 20, 38, 15, 6, 20, 11, 26, 26, 20, 31, 26, 43, 21,
- 5, 9, 14, 4, 21, 9, 34, 4, 37, 9, 5, 15, 12, 20, 21,
- 15, 32, 20, 37, 15, 7, 20, 12, 26, 26, 21, 32, 26, 42, 21,
- 4, 9, 15, 4, 20, 9, 35, 4, 36, 9, 4, 15, 13, 20, 20,
- 15, 30, 10, 36, 15, 8, 20, 13, 26, 25, 21, 33, 26, 41, 21,
- 3, 9, 16, 4, 19, 9, 35, 5, 36, 10, 3, 15, 14, 20, 19,
- 15, 31, 10, 36, 16, 8, 21, 14, 26, 24, 21, 34, 26, 40, 21,
- 2, 9, 17, 4, 18, 9, 34, 5, 37, 10, 2, 15, 12, 10, 18,
- 15, 32, 10, 37, 16, 7, 21, 15, 26, 23, 21, 35, 26, 39, 21,
- 1, 9, 17, 5, 18, 10, 33, 5, 38, 10, 1, 15, 13, 10, 18,
- 16, 33, 10, 38, 16, 6, 21, 16, 26, 22, 21, 35, 27, 38, 21,
- 0, 9, 16, 5, 19, 10, 32, 5, 36, 0, 0, 15, 14, 10, 19,
- 16, 34, 10, 39, 16, 5, 21, 17, 26, 21, 21, 34, 27, 37, 21,
- 0, 10, 15, 5, 20, 10, 31, 5, 37, 0, 0, 16, 15, 10, 20,
- 16, 35, 10, 40, 16, 4, 21, 17, 27, 20, 21, 33, 27, 36, 21,
- 1, 10, 14, 5, 18, 0, 30, 5, 38, 0, 1, 16, 16, 10, 21,
- 16, 35, 11, 41, 16, 3, 21, 16, 27, 19, 21, 32, 27, 36, 22,
- 2, 10, 13, 5, 19, 0, 29, 5, 39, 0, 2, 16, 17, 10, 22,
- 16, 34, 11, 42, 16, 2, 21, 15, 27, 18, 21, 31, 27, 37, 22,
- 0, 0,
-};
-
-static const uint8_t hq_tab_03[] = {
- 0, 0, 14, 4, 20, 0, 34, 4, 40, 0, 8, 14, 15, 9, 24,
- 14, 35, 9, 40, 14, 7, 19, 11, 23, 23, 19, 27, 23, 39, 19,
- 1, 33, 10, 28, 21, 33, 27, 29, 41, 33, 1, 0, 15, 4, 21,
- 0, 35, 4, 41, 0, 7, 14, 16, 9, 23, 14, 35, 10, 39, 14,
- 6, 19, 10, 23, 22, 19, 27, 24, 38, 19, 2, 33, 9, 28, 22,
- 33, 28, 29, 42, 33, 2, 0, 16, 4, 22, 0, 35, 5, 42, 0,
- 6, 14, 17, 9, 22, 14, 34, 10, 38, 14, 5, 19, 9, 23, 21,
- 19, 28, 24, 37, 19, 3, 33, 9, 29, 23, 33, 29, 29, 43, 33,
- 3, 0, 17, 4, 23, 0, 34, 5, 43, 0, 5, 14, 17, 10, 21,
- 14, 33, 10, 37, 14, 4, 19, 9, 24, 20, 19, 29, 24, 36, 19,
- 4, 33, 10, 29, 24, 33, 30, 29, 44, 33, 4, 0, 17, 5, 24,
- 0, 33, 5, 44, 0, 4, 14, 16, 10, 20, 14, 32, 10, 36, 14,
- 3, 19, 10, 24, 19, 19, 30, 24, 36, 20, 5, 33, 11, 29, 25,
- 33, 31, 29, 44, 34, 5, 0, 16, 5, 25, 0, 32, 5, 44, 1,
- 3, 14, 15, 10, 19, 14, 31, 10, 36, 15, 2, 19, 11, 24, 18,
- 19, 31, 24, 37, 20, 6, 33, 12, 29, 26, 33, 32, 29, 43, 34,
- 6, 0, 15, 5, 26, 0, 31, 5, 43, 1, 2, 14, 14, 10, 18,
- 14, 30, 10, 37, 15, 1, 19, 12, 24, 18, 20, 32, 24, 38, 20,
- 7, 33, 13, 29, 26, 34, 33, 29, 42, 34, 7, 0, 14, 5, 26,
- 1, 30, 5, 42, 1, 1, 14, 13, 10, 18, 15, 29, 10, 38, 15,
- 0, 19, 13, 24, 19, 20, 33, 24, 39, 20, 8, 33, 14, 29, 25,
- 34, 34, 29, 41, 34, 8, 0, 13, 5, 25, 1, 29, 5, 41, 1,
- 0, 14, 12, 10, 19, 15, 28, 10, 39, 15, 0, 20, 14, 24, 20,
- 20, 34, 24, 40, 20, 8, 34, 15, 29, 24, 34, 35, 29, 40, 34,
- 8, 1, 12, 5, 24, 1, 28, 5, 40, 1, 0, 15, 11, 10, 20,
- 15, 27, 10, 40, 15, 1, 20, 15, 24, 21, 20, 35, 24, 41, 20,
- 7, 34, 16, 29, 23, 34, 35, 30, 39, 34, 7, 1, 11, 5, 23,
- 1, 27, 5, 39, 1, 1, 15, 10, 10, 21, 15, 27, 11, 41, 15,
- 2, 20, 16, 24, 22, 20, 35, 25, 42, 20, 6, 34, 17, 29, 22,
- 34, 34, 30, 38, 34, 6, 1, 10, 5, 22, 1, 27, 6, 38, 1,
- 2, 15, 9, 10, 22, 15, 28, 11, 42, 15, 3, 20, 17, 24, 23,
- 20, 34, 25, 43, 20, 5, 34, 17, 30, 21, 34, 33, 30, 37, 34,
- 5, 1, 9, 5, 21, 1, 28, 6, 37, 1, 3, 15, 9, 11, 23,
- 15, 29, 11, 43, 15, 4, 20, 17, 25, 24, 20, 33, 25, 44, 20,
- 4, 34, 16, 30, 20, 34, 32, 30, 36, 34, 4, 1, 9, 6, 20,
- 1, 29, 6, 36, 1, 4, 15, 10, 11, 24, 15, 30, 11, 44, 15,
- 5, 20, 16, 25, 25, 20, 32, 25, 44, 21, 3, 34, 15, 30, 19,
- 34, 31, 30, 36, 35, 3, 1, 10, 6, 19, 1, 30, 6, 36, 2,
- 5, 15, 11, 11, 25, 15, 31, 11, 44, 16, 6, 20, 15, 25, 26,
- 20, 31, 25, 43, 21, 2, 34, 14, 30, 18, 34, 30, 30, 37, 35,
- 2, 1, 11, 6, 18, 1, 31, 6, 37, 2, 6, 15, 12, 11, 26,
- 15, 32, 11, 43, 16, 7, 20, 14, 25, 26, 21, 30, 25, 42, 21,
- 1, 34, 13, 30, 18, 35, 29, 30, 38, 35, 1, 1, 12, 6, 18,
- 2, 32, 6, 38, 2, 7, 15, 13, 11, 26, 16, 33, 11, 42, 16,
- 8, 20, 13, 25, 25, 21, 29, 25, 41, 21, 0, 34, 12, 30, 19,
- 35, 28, 30, 39, 35, 0, 1, 13, 6, 19, 2, 33, 6, 39, 2,
- 8, 15, 14, 11, 25, 16, 34, 11, 41, 16, 8, 21, 12, 25, 24,
- 21, 28, 25, 40, 21, 0, 35, 11, 30, 20, 35, 27, 30, 40, 35,
- 0, 2, 14, 6, 20, 2, 34, 6, 40, 2, 8, 16, 15, 11, 24,
- 16, 35, 11, 40, 16, 7, 21, 11, 25, 23, 21, 27, 25, 39, 21,
- 1, 35, 10, 30, 21, 35, 27, 31, 41, 35, 1, 2, 15, 6, 21,
- 2, 35, 6, 41, 2, 7, 16, 16, 11, 23, 16, 35, 12, 39, 16,
- 6, 21, 10, 25, 22, 21, 27, 26, 38, 21, 2, 35, 9, 30, 22,
- 35, 28, 31, 42, 35, 2, 2, 16, 6, 22, 2, 35, 7, 42, 2,
- 6, 16, 17, 11, 22, 16, 34, 12, 38, 16, 5, 21, 9, 25, 21,
- 21, 28, 26, 37, 21, 3, 35, 9, 31, 23, 35, 29, 31, 43, 35,
- 3, 2, 17, 6, 23, 2, 34, 7, 43, 2, 5, 16, 17, 12, 21,
- 16, 33, 12, 37, 16, 4, 21, 9, 26, 20, 21, 29, 26, 36, 21,
- 4, 35, 10, 31, 24, 35, 30, 31, 44, 35, 4, 2, 17, 7, 24,
- 2, 33, 7, 44, 2, 4, 16, 16, 12, 20, 16, 32, 12, 36, 16,
- 3, 21, 10, 26, 19, 21, 30, 26, 36, 22, 5, 35, 11, 31, 25,
- 35, 31, 31, 36, 27, 5, 2, 16, 7, 25, 2, 32, 7, 44, 3,
- 3, 16, 15, 12, 19, 16, 31, 12, 36, 17, 2, 21, 11, 26, 18,
- 21, 31, 26, 37, 22, 6, 35, 12, 31, 26, 35, 32, 31, 37, 27,
- 6, 2, 15, 7, 26, 2, 31, 7, 43, 3, 2, 16, 14, 12, 18,
- 16, 30, 12, 37, 17, 1, 21, 12, 26, 18, 22, 32, 26, 38, 22,
- 7, 35, 13, 31, 18, 27, 33, 31, 38, 27, 7, 2, 14, 7, 26,
- 3, 30, 7, 42, 3, 1, 16, 13, 12, 18, 17, 29, 12, 38, 17,
- 0, 21, 13, 26, 19, 22, 33, 26, 39, 22, 8, 35, 14, 31, 19,
- 27, 34, 31, 39, 27, 8, 2, 13, 7, 25, 3, 29, 7, 41, 3,
- 0, 16, 12, 12, 19, 17, 28, 12, 39, 17, 0, 22, 14, 26, 20,
- 22, 34, 26, 40, 22, 0, 27, 15, 31, 20, 27, 35, 31, 40, 27,
- 8, 3, 12, 7, 24, 3, 28, 7, 40, 3, 0, 17, 11, 12, 20,
- 17, 27, 12, 40, 17, 1, 22, 15, 26, 21, 22, 35, 26, 41, 22,
- 1, 27, 16, 31, 21, 27, 35, 32, 41, 27, 7, 3, 11, 7, 23,
- 3, 27, 7, 39, 3, 1, 17, 10, 12, 21, 17, 27, 13, 41, 17,
- 2, 22, 16, 26, 22, 22, 27, 18, 42, 22, 2, 27, 17, 31, 22,
- 27, 34, 32, 42, 27, 6, 3, 10, 7, 22, 3, 27, 8, 38, 3,
- 2, 17, 9, 12, 22, 17, 28, 13, 42, 17, 3, 22, 17, 26, 23,
- 22, 28, 18, 43, 22, 3, 27, 17, 32, 23, 27, 33, 32, 43, 27,
- 5, 3, 9, 7, 21, 3, 28, 8, 37, 3, 3, 17, 9, 13, 23,
- 17, 29, 13, 43, 17, 4, 22, 9, 18, 24, 22, 29, 18, 44, 22,
- 4, 27, 16, 32, 24, 27, 32, 32, 44, 27, 4, 3, 9, 8, 20,
- 3, 29, 8, 36, 3, 4, 17, 10, 13, 24, 17, 30, 13, 44, 17,
- 5, 22, 10, 18, 25, 22, 30, 18, 44, 23, 5, 27, 15, 32, 25,
- 27, 31, 32, 44, 28, 3, 3, 10, 8, 19, 3, 30, 8, 36, 4,
- 5, 17, 11, 13, 25, 17, 31, 13, 36, 9, 6, 22, 11, 18, 26,
- 22, 31, 18, 43, 23, 6, 27, 14, 32, 26, 27, 30, 32, 43, 28,
- 2, 3, 11, 8, 18, 3, 31, 8, 37, 4, 6, 17, 12, 13, 26,
- 17, 32, 13, 37, 9, 7, 22, 12, 18, 26, 23, 32, 18, 42, 23,
- 7, 27, 13, 32, 26, 28, 29, 32, 42, 28, 1, 3, 12, 8, 18,
- 4, 32, 8, 38, 4, 7, 17, 13, 13, 18, 9, 33, 13, 38, 9,
- 8, 22, 13, 18, 25, 23, 33, 18, 41, 23, 8, 27, 12, 32, 25,
- 28, 28, 32, 41, 28, 0, 3, 13, 8, 19, 4, 33, 8, 39, 4,
- 8, 17, 14, 13, 19, 9, 34, 13, 39, 9, 8, 23, 14, 18, 24,
- 23, 34, 18, 40, 23, 8, 28, 11, 32, 24, 28, 27, 32, 40, 28,
- 0, 4, 14, 8, 20, 4, 34, 8, 40, 4, 0, 9, 15, 13, 20,
- 9, 35, 13, 40, 9, 7, 23, 15, 18, 23, 23, 35, 18, 39, 23,
- 7, 28, 10, 32, 23, 28, 27, 33, 39, 28, 1, 4, 15, 8, 21,
- 4, 35, 8, 41, 4, 1, 9, 16, 13, 21, 9, 35, 14, 41, 9,
- 6, 23, 16, 18, 22, 23, 35, 19, 38, 23, 6, 28, 9, 32, 22,
- 28, 28, 33, 38, 28, 2, 4, 16, 8, 22, 4, 27, 0, 42, 4,
- 2, 9, 17, 13, 22, 9, 34, 14, 42, 9, 5, 23, 17, 18, 21,
- 23, 34, 19, 37, 23, 5, 28, 9, 33, 21, 28, 29, 33, 37, 28,
- 3, 4, 17, 8, 23, 4, 28, 0, 43, 4, 3, 9, 17, 14, 23,
- 9, 33, 14, 43, 9, 4, 23, 17, 19, 20, 23, 33, 19, 36, 23,
- 4, 28, 10, 33, 20, 28, 30, 33, 36, 28, 4, 4, 9, 0, 24,
- 4, 29, 0, 44, 4, 4, 9, 16, 14, 24, 9, 32, 14, 44, 9,
- 3, 23, 16, 19, 19, 23, 32, 19, 36, 24, 3, 28, 11, 33, 19,
- 28, 31, 33, 36, 29, 5, 4, 10, 0, 25, 4, 30, 0, 44, 5,
- 5, 9, 15, 14, 25, 9, 31, 14, 44, 10, 2, 23, 15, 19, 18,
- 23, 31, 19, 37, 24, 2, 28, 12, 33, 18, 28, 32, 33, 37, 29,
- 6, 4, 11, 0, 26, 4, 31, 0, 43, 5, 6, 9, 14, 14, 26,
- 9, 30, 14, 43, 10, 1, 23, 14, 19, 18, 24, 30, 19, 38, 24,
- 1, 28, 13, 33, 18, 29, 33, 33, 38, 29, 7, 4, 12, 0, 26,
- 5, 32, 0, 42, 5, 7, 9, 13, 14, 26, 10, 29, 14, 42, 10,
- 0, 23, 13, 19, 19, 24, 29, 19, 39, 24, 0, 28, 14, 33, 19,
- 29, 34, 33, 39, 29, 8, 4, 13, 0, 25, 5, 33, 0, 41, 5,
- 8, 9, 12, 14, 25, 10, 28, 14, 41, 10, 0, 24, 12, 19, 20,
- 24, 28, 19, 40, 24, 0, 29, 15, 33, 20, 29, 35, 33, 40, 29,
- 8, 5, 14, 0, 24, 5, 34, 0, 40, 5, 8, 10, 11, 14, 24,
- 10, 27, 14, 40, 10, 1, 24, 11, 19, 21, 24, 27, 19, 41, 24,
- 1, 29, 16, 33, 21, 29, 35, 34, 41, 29, 7, 5, 15, 0, 23,
- 5, 35, 0, 39, 5, 7, 10, 10, 14, 23, 10, 27, 15, 39, 10,
- 2, 24, 10, 19, 22, 24, 27, 20, 42, 24, 2, 29, 17, 33, 22,
- 29, 34, 34, 42, 29, 6, 5, 16, 0, 22, 5, 35, 1, 38, 5,
- 6, 10, 9, 14, 22, 10, 28, 15, 38, 10, 3, 24, 9, 19, 23,
- 24, 28, 20, 43, 24, 3, 29, 17, 34, 23, 29, 33, 34, 43, 29,
- 5, 5, 17, 0, 21, 5, 34, 1, 37, 5, 5, 10, 9, 15, 21,
- 10, 29, 15, 37, 10, 4, 24, 9, 20, 24, 24, 29, 20, 44, 24,
- 4, 29, 16, 34, 24, 29, 32, 34, 44, 29, 4, 5, 17, 1, 20,
- 5, 33, 1, 36, 5, 4, 10, 10, 15, 20, 10, 30, 15, 36, 10,
- 5, 24, 10, 20, 25, 24, 30, 20, 44, 25, 5, 29, 15, 34, 25,
- 29, 31, 34, 44, 30, 3, 5, 16, 1, 19, 5, 32, 1, 36, 6,
- 3, 10, 11, 15, 19, 10, 31, 15, 36, 11, 6, 24, 11, 20, 26,
- 24, 31, 20, 43, 25, 6, 29, 14, 34, 26, 29, 30, 34, 43, 30,
- 2, 5, 15, 1, 18, 5, 31, 1, 37, 6, 2, 10, 12, 15, 18,
- 10, 32, 15, 37, 11, 7, 24, 12, 20, 26, 25, 32, 20, 42, 25,
- 7, 29, 13, 34, 26, 30, 29, 34, 42, 30, 1, 5, 14, 1, 18,
- 6, 30, 1, 38, 6, 1, 10, 13, 15, 18, 11, 33, 15, 38, 11,
- 8, 24, 13, 20, 25, 25, 33, 20, 41, 25, 8, 29, 12, 34, 25,
- 30, 28, 34, 41, 30, 0, 5, 13, 1, 19, 6, 29, 1, 39, 6,
- 0, 10, 14, 15, 19, 11, 34, 15, 39, 11, 8, 25, 14, 20, 24,
- 25, 34, 20, 40, 25, 8, 30, 11, 34, 24, 30, 27, 34, 40, 30,
- 0, 6, 12, 1, 20, 6, 28, 1, 40, 6, 0, 11, 15, 15, 20,
- 11, 35, 15, 40, 11, 7, 25, 15, 20, 23, 25, 35, 20, 39, 25,
- 7, 30, 10, 34, 23, 30, 27, 35, 39, 30, 1, 6, 11, 1, 21,
- 6, 27, 1, 41, 6, 1, 11, 16, 15, 21, 11, 35, 16, 41, 11,
- 6, 25, 16, 20, 22, 25, 35, 21, 38, 25, 6, 30, 9, 34, 22,
- 30, 28, 35, 38, 30, 2, 6, 10, 1, 22, 6, 27, 2, 42, 6,
- 2, 11, 17, 15, 22, 11, 34, 16, 42, 11, 5, 25, 17, 20, 21,
- 25, 34, 21, 37, 25, 5, 30, 9, 35, 21, 30, 29, 35, 37, 30,
- 3, 6, 9, 1, 23, 6, 28, 2, 43, 6, 3, 11, 17, 16, 23,
- 11, 33, 16, 43, 11, 4, 25, 17, 21, 20, 25, 33, 21, 36, 25,
- 4, 30, 10, 35, 20, 30, 30, 35, 36, 30, 4, 6, 9, 2, 24,
- 6, 29, 2, 44, 6, 4, 11, 16, 16, 24, 11, 32, 16, 44, 11,
- 3, 25, 16, 21, 19, 25, 32, 21, 36, 26, 3, 30, 11, 35, 19,
- 30, 31, 35, 36, 31, 5, 6, 10, 2, 25, 6, 30, 2, 44, 7,
- 5, 11, 15, 16, 25, 11, 31, 16, 44, 12, 2, 25, 15, 21, 18,
- 25, 31, 21, 37, 26, 2, 30, 12, 35, 18, 30, 32, 35, 37, 31,
- 6, 6, 11, 2, 26, 6, 31, 2, 43, 7, 6, 11, 14, 16, 26,
- 11, 30, 16, 43, 12, 1, 25, 14, 21, 18, 26, 30, 21, 38, 26,
- 1, 30, 13, 35, 18, 31, 33, 35, 38, 31, 7, 6, 12, 2, 26,
- 7, 32, 2, 42, 7, 7, 11, 13, 16, 26, 12, 29, 16, 42, 12,
- 0, 25, 13, 21, 19, 26, 29, 21, 39, 26, 0, 30, 14, 35, 19,
- 31, 34, 35, 39, 31, 8, 6, 13, 2, 25, 7, 33, 2, 41, 7,
- 8, 11, 12, 16, 25, 12, 28, 16, 41, 12, 0, 26, 12, 21, 20,
- 26, 28, 21, 40, 26, 0, 31, 15, 35, 20, 31, 35, 35, 40, 31,
- 8, 7, 14, 2, 24, 7, 34, 2, 40, 7, 8, 12, 11, 16, 24,
- 12, 27, 16, 40, 12, 1, 26, 11, 21, 21, 26, 27, 21, 41, 26,
- 1, 31, 16, 35, 21, 31, 27, 27, 41, 31, 7, 7, 15, 2, 23,
- 7, 35, 2, 39, 7, 7, 12, 10, 16, 23, 12, 27, 17, 39, 12,
- 2, 26, 10, 21, 22, 26, 27, 22, 42, 26, 2, 31, 17, 35, 22,
- 31, 28, 27, 42, 31, 6, 7, 16, 2, 22, 7, 35, 3, 38, 7,
- 6, 12, 9, 16, 22, 12, 28, 17, 38, 12, 3, 26, 9, 21, 23,
- 26, 28, 22, 43, 26, 3, 31, 9, 27, 23, 31, 29, 27, 43, 31,
- 5, 7, 17, 2, 21, 7, 34, 3, 37, 7, 5, 12, 9, 17, 21,
- 12, 29, 17, 37, 12, 4, 26, 9, 22, 24, 26, 29, 22, 44, 26,
- 4, 31, 10, 27, 24, 31, 30, 27, 44, 31, 4, 7, 17, 3, 20,
- 7, 33, 3, 36, 7, 4, 12, 10, 17, 20, 12, 30, 17, 36, 12,
- 5, 26, 10, 22, 25, 26, 30, 22, 36, 18, 5, 31, 11, 27, 25,
- 31, 31, 27, 44, 32, 3, 7, 16, 3, 19, 7, 32, 3, 36, 8,
- 3, 12, 11, 17, 19, 12, 31, 17, 36, 13, 6, 26, 11, 22, 26,
- 26, 31, 22, 37, 18, 6, 31, 12, 27, 26, 31, 32, 27, 43, 32,
- 2, 7, 15, 3, 18, 7, 31, 3, 37, 8, 2, 12, 12, 17, 18,
- 12, 32, 17, 37, 13, 7, 26, 12, 22, 18, 18, 32, 22, 38, 18,
- 7, 31, 13, 27, 26, 32, 33, 27, 42, 32, 1, 7, 14, 3, 18,
- 8, 30, 3, 38, 8, 1, 12, 13, 17, 18, 13, 33, 17, 38, 13,
- 8, 26, 13, 22, 19, 18, 33, 22, 39, 18, 8, 31, 14, 27, 25,
- 32, 34, 27, 41, 32, 0, 7, 13, 3, 19, 8, 29, 3, 39, 8,
- 0, 12, 14, 17, 19, 13, 34, 17, 39, 13, 0, 18, 14, 22, 20,
- 18, 34, 22, 40, 18, 8, 32, 15, 27, 24, 32, 35, 27, 40, 32,
- 0, 8, 12, 3, 20, 8, 28, 3, 40, 8, 0, 13, 15, 17, 20,
- 13, 35, 17, 40, 13, 1, 18, 15, 22, 21, 18, 35, 22, 41, 18,
- 7, 32, 16, 27, 23, 32, 35, 28, 39, 32, 1, 8, 11, 3, 21,
- 8, 27, 3, 41, 8, 1, 13, 16, 17, 21, 13, 27, 9, 41, 13,
- 2, 18, 16, 22, 22, 18, 35, 23, 42, 18, 6, 32, 17, 27, 22,
- 32, 34, 28, 38, 32, 2, 8, 10, 3, 22, 8, 27, 4, 42, 8,
- 2, 13, 17, 17, 22, 13, 28, 9, 42, 13, 3, 18, 17, 22, 23,
- 18, 34, 23, 43, 18, 5, 32, 17, 28, 21, 32, 33, 28, 37, 32,
- 3, 8, 9, 3, 23, 8, 28, 4, 43, 8, 3, 13, 9, 9, 23,
- 13, 29, 9, 43, 13, 4, 18, 17, 23, 24, 18, 33, 23, 44, 18,
- 4, 32, 16, 28, 20, 32, 32, 28, 36, 32, 4, 8, 9, 4, 24,
- 8, 29, 4, 44, 8, 4, 13, 10, 9, 24, 13, 30, 9, 44, 13,
- 5, 18, 16, 23, 25, 18, 32, 23, 44, 19, 3, 32, 15, 28, 19,
- 32, 31, 28, 36, 33, 5, 8, 10, 4, 25, 8, 30, 4, 36, 0,
- 5, 13, 11, 9, 25, 13, 31, 9, 44, 14, 6, 18, 15, 23, 26,
- 18, 31, 23, 43, 19, 2, 32, 14, 28, 18, 32, 30, 28, 37, 33,
- 6, 8, 11, 4, 26, 8, 31, 4, 37, 0, 6, 13, 12, 9, 26,
- 13, 32, 9, 43, 14, 7, 18, 14, 23, 26, 19, 30, 23, 42, 19,
- 1, 32, 13, 28, 18, 33, 29, 28, 38, 33, 7, 8, 12, 4, 18,
- 0, 32, 4, 38, 0, 7, 13, 13, 9, 26, 14, 33, 9, 42, 14,
- 8, 18, 13, 23, 25, 19, 29, 23, 41, 19, 0, 32, 12, 28, 19,
- 33, 28, 28, 39, 33, 8, 8, 13, 4, 19, 0, 33, 4, 39, 0,
- 8, 13, 14, 9, 25, 14, 34, 9, 41, 14, 8, 19, 12, 23, 24,
- 19, 28, 23, 40, 19, 0, 33, 11, 28, 20, 33, 27, 28, 40, 33,
-};
-
-static const uint8_t hq_tab_04[] = {
- 0, 0, 19, 4, 26, 0, 45, 4, 52, 0, 11, 13, 18, 9, 34,
- 14, 44, 9, 56, 14, 10, 18, 18, 23, 35, 19, 40, 23, 57, 19,
- 2, 32, 19, 28, 24, 32, 41, 28, 49, 33, 3, 37, 15, 42, 25,
- 37, 41, 42, 48, 38, 1, 0, 20, 4, 27, 0, 46, 4, 53, 0,
- 11, 14, 19, 9, 33, 14, 45, 9, 55, 14, 11, 18, 17, 23, 34,
- 19, 39, 23, 56, 19, 1, 32, 18, 28, 24, 33, 40, 28, 50, 33,
- 2, 37, 16, 42, 24, 37, 42, 42, 49, 38, 2, 0, 21, 4, 28,
- 0, 47, 4, 54, 0, 10, 14, 20, 9, 32, 14, 46, 9, 54, 14,
- 11, 19, 16, 23, 33, 19, 38, 23, 55, 19, 0, 32, 17, 28, 25,
- 33, 39, 28, 51, 33, 1, 37, 17, 42, 24, 38, 43, 42, 50, 38,
- 3, 0, 22, 4, 29, 0, 47, 5, 55, 0, 9, 14, 21, 9, 31,
- 14, 47, 9, 53, 14, 10, 19, 15, 23, 32, 19, 37, 23, 54, 19,
- 0, 33, 16, 28, 26, 33, 38, 28, 52, 33, 0, 37, 18, 42, 25,
- 38, 44, 42, 51, 38, 4, 0, 23, 4, 30, 0, 46, 5, 56, 0,
- 8, 14, 22, 9, 30, 14, 47, 10, 52, 14, 9, 19, 14, 23, 31,
- 19, 36, 23, 53, 19, 1, 33, 15, 28, 27, 33, 37, 28, 53, 33,
- 0, 38, 19, 42, 26, 38, 45, 42, 52, 38, 5, 0, 23, 5, 31,
- 0, 45, 5, 57, 0, 7, 14, 23, 9, 29, 14, 46, 10, 51, 14,
- 8, 19, 13, 23, 30, 19, 36, 24, 52, 19, 2, 33, 14, 28, 28,
- 33, 36, 28, 54, 33, 1, 38, 20, 42, 27, 38, 46, 42, 53, 38,
- 6, 0, 22, 5, 32, 0, 44, 5, 58, 0, 6, 14, 23, 10, 28,
- 14, 45, 10, 50, 14, 7, 19, 12, 23, 29, 19, 37, 24, 51, 19,
- 3, 33, 13, 28, 29, 33, 36, 29, 55, 33, 2, 38, 21, 42, 28,
- 38, 47, 42, 54, 38, 7, 0, 21, 5, 33, 0, 43, 5, 59, 0,
- 5, 14, 22, 10, 27, 14, 44, 10, 49, 14, 6, 19, 12, 24, 28,
- 19, 38, 24, 50, 19, 4, 33, 12, 28, 30, 33, 37, 29, 56, 33,
- 3, 38, 22, 42, 29, 38, 47, 43, 55, 38, 8, 0, 20, 5, 34,
- 0, 42, 5, 59, 1, 4, 14, 21, 10, 26, 14, 43, 10, 48, 14,
- 5, 19, 13, 24, 27, 19, 39, 24, 49, 19, 5, 33, 12, 29, 31,
- 33, 38, 29, 57, 33, 4, 38, 23, 42, 30, 38, 46, 43, 56, 38,
- 9, 0, 19, 5, 35, 0, 41, 5, 58, 1, 3, 14, 20, 10, 25,
- 14, 42, 10, 48, 15, 4, 19, 14, 24, 26, 19, 40, 24, 48, 19,
- 6, 33, 13, 29, 32, 33, 39, 29, 58, 33, 5, 38, 23, 43, 31,
- 38, 45, 43, 57, 38, 10, 0, 18, 5, 35, 1, 40, 5, 57, 1,
- 2, 14, 19, 10, 24, 14, 41, 10, 49, 15, 3, 19, 15, 24, 25,
- 19, 41, 24, 48, 20, 7, 33, 14, 29, 33, 33, 40, 29, 59, 33,
- 6, 38, 22, 43, 32, 38, 44, 43, 58, 38, 11, 0, 17, 5, 34,
- 1, 39, 5, 56, 1, 1, 14, 18, 10, 24, 15, 40, 10, 50, 15,
- 2, 19, 16, 24, 24, 19, 42, 24, 49, 20, 8, 33, 15, 29, 34,
- 33, 41, 29, 59, 34, 7, 38, 21, 43, 33, 38, 43, 43, 59, 38,
- 11, 1, 16, 5, 33, 1, 38, 5, 55, 1, 0, 14, 17, 10, 25,
- 15, 39, 10, 51, 15, 1, 19, 17, 24, 24, 20, 43, 24, 50, 20,
- 9, 33, 16, 29, 35, 33, 42, 29, 58, 34, 8, 38, 20, 43, 34,
- 38, 42, 43, 59, 39, 10, 1, 15, 5, 32, 1, 37, 5, 54, 1,
- 0, 15, 16, 10, 26, 15, 38, 10, 52, 15, 0, 19, 18, 24, 25,
- 20, 44, 24, 51, 20, 10, 33, 17, 29, 35, 34, 43, 29, 57, 34,
- 9, 38, 19, 43, 35, 38, 41, 43, 58, 39, 9, 1, 14, 5, 31,
- 1, 36, 5, 53, 1, 1, 15, 15, 10, 27, 15, 37, 10, 53, 15,
- 0, 20, 19, 24, 26, 20, 45, 24, 52, 20, 11, 33, 18, 29, 34,
- 34, 44, 29, 56, 34, 10, 38, 18, 43, 35, 39, 40, 43, 57, 39,
- 8, 1, 13, 5, 30, 1, 36, 6, 52, 1, 2, 15, 14, 10, 28,
- 15, 36, 10, 54, 15, 1, 20, 20, 24, 27, 20, 46, 24, 53, 20,
- 11, 34, 19, 29, 33, 34, 45, 29, 55, 34, 11, 38, 17, 43, 34,
- 39, 39, 43, 56, 39, 7, 1, 12, 5, 29, 1, 37, 6, 51, 1,
- 3, 15, 13, 10, 29, 15, 36, 11, 55, 15, 2, 20, 21, 24, 28,
- 20, 47, 24, 54, 20, 10, 34, 20, 29, 32, 34, 46, 29, 54, 34,
- 11, 39, 16, 43, 33, 39, 38, 43, 55, 39, 6, 1, 12, 6, 28,
- 1, 38, 6, 50, 1, 4, 15, 12, 10, 30, 15, 37, 11, 56, 15,
- 3, 20, 22, 24, 29, 20, 47, 25, 55, 20, 9, 34, 21, 29, 31,
- 34, 47, 29, 53, 34, 10, 39, 15, 43, 32, 39, 37, 43, 54, 39,
- 5, 1, 13, 6, 27, 1, 39, 6, 49, 1, 5, 15, 12, 11, 31,
- 15, 38, 11, 57, 15, 4, 20, 23, 24, 30, 20, 46, 25, 56, 20,
- 8, 34, 22, 29, 30, 34, 47, 30, 52, 34, 9, 39, 14, 43, 31,
- 39, 36, 43, 53, 39, 4, 1, 14, 6, 26, 1, 40, 6, 48, 1,
- 6, 15, 13, 11, 32, 15, 39, 11, 58, 15, 5, 20, 23, 25, 31,
- 20, 45, 25, 57, 20, 7, 34, 23, 29, 29, 34, 46, 30, 51, 34,
- 8, 39, 13, 43, 30, 39, 36, 44, 52, 39, 3, 1, 15, 6, 25,
- 1, 41, 6, 48, 2, 7, 15, 14, 11, 33, 15, 40, 11, 59, 15,
- 6, 20, 22, 25, 32, 20, 44, 25, 58, 20, 6, 34, 23, 30, 28,
- 34, 45, 30, 50, 34, 7, 39, 12, 43, 29, 39, 37, 44, 51, 39,
- 2, 1, 16, 6, 24, 1, 42, 6, 49, 2, 8, 15, 15, 11, 34,
- 15, 41, 11, 59, 16, 7, 20, 21, 25, 33, 20, 43, 25, 59, 20,
- 5, 34, 22, 30, 27, 34, 44, 30, 49, 34, 6, 39, 12, 44, 28,
- 39, 38, 44, 50, 39, 1, 1, 17, 6, 24, 2, 43, 6, 50, 2,
- 9, 15, 16, 11, 35, 15, 42, 11, 58, 16, 8, 20, 20, 25, 34,
- 20, 42, 25, 59, 21, 4, 34, 21, 30, 26, 34, 43, 30, 48, 34,
- 5, 39, 13, 44, 27, 39, 39, 44, 49, 39, 0, 1, 18, 6, 25,
- 2, 44, 6, 51, 2, 10, 15, 17, 11, 35, 16, 43, 11, 57, 16,
- 9, 20, 19, 25, 35, 20, 41, 25, 58, 21, 3, 34, 20, 30, 25,
- 34, 42, 30, 48, 35, 4, 39, 14, 44, 26, 39, 40, 44, 48, 39,
- 0, 2, 19, 6, 26, 2, 45, 6, 52, 2, 11, 15, 18, 11, 34,
- 16, 44, 11, 56, 16, 10, 20, 18, 25, 35, 21, 40, 25, 57, 21,
- 2, 34, 19, 30, 24, 34, 41, 30, 49, 35, 3, 39, 15, 44, 25,
- 39, 41, 44, 48, 40, 1, 2, 20, 6, 27, 2, 46, 6, 53, 2,
- 11, 16, 19, 11, 33, 16, 45, 11, 55, 16, 11, 20, 17, 25, 34,
- 21, 39, 25, 56, 21, 1, 34, 18, 30, 24, 35, 40, 30, 50, 35,
- 2, 39, 16, 44, 24, 39, 42, 44, 49, 40, 2, 2, 21, 6, 28,
- 2, 47, 6, 54, 2, 10, 16, 20, 11, 32, 16, 46, 11, 54, 16,
- 11, 21, 16, 25, 33, 21, 38, 25, 55, 21, 0, 34, 17, 30, 25,
- 35, 39, 30, 51, 35, 1, 39, 17, 44, 24, 40, 43, 44, 50, 40,
- 3, 2, 22, 6, 29, 2, 47, 7, 55, 2, 9, 16, 21, 11, 31,
- 16, 47, 11, 53, 16, 10, 21, 15, 25, 32, 21, 37, 25, 54, 21,
- 0, 35, 16, 30, 26, 35, 38, 30, 52, 35, 0, 39, 18, 44, 25,
- 40, 44, 44, 51, 40, 4, 2, 23, 6, 30, 2, 46, 7, 56, 2,
- 8, 16, 22, 11, 30, 16, 47, 12, 52, 16, 9, 21, 14, 25, 31,
- 21, 36, 25, 53, 21, 1, 35, 15, 30, 27, 35, 37, 30, 53, 35,
- 0, 40, 19, 44, 26, 40, 45, 44, 52, 40, 5, 2, 23, 7, 31,
- 2, 45, 7, 57, 2, 7, 16, 23, 11, 29, 16, 46, 12, 51, 16,
- 8, 21, 13, 25, 30, 21, 36, 26, 52, 21, 2, 35, 14, 30, 28,
- 35, 36, 30, 54, 35, 1, 40, 20, 44, 27, 40, 46, 44, 53, 40,
- 6, 2, 22, 7, 32, 2, 44, 7, 58, 2, 6, 16, 23, 12, 28,
- 16, 45, 12, 50, 16, 7, 21, 12, 25, 29, 21, 37, 26, 51, 21,
- 3, 35, 13, 30, 29, 35, 36, 31, 55, 35, 2, 40, 21, 44, 28,
- 40, 47, 44, 54, 40, 7, 2, 21, 7, 33, 2, 43, 7, 59, 2,
- 5, 16, 22, 12, 27, 16, 44, 12, 49, 16, 6, 21, 12, 26, 28,
- 21, 38, 26, 50, 21, 4, 35, 12, 30, 30, 35, 37, 31, 56, 35,
- 3, 40, 22, 44, 29, 40, 36, 36, 55, 40, 8, 2, 20, 7, 34,
- 2, 42, 7, 59, 3, 4, 16, 21, 12, 26, 16, 43, 12, 48, 16,
- 5, 21, 13, 26, 27, 21, 39, 26, 49, 21, 5, 35, 12, 31, 31,
- 35, 38, 31, 57, 35, 4, 40, 23, 44, 30, 40, 37, 36, 56, 40,
- 9, 2, 19, 7, 35, 2, 41, 7, 58, 3, 3, 16, 20, 12, 25,
- 16, 42, 12, 48, 17, 4, 21, 14, 26, 26, 21, 40, 26, 48, 21,
- 6, 35, 13, 31, 32, 35, 39, 31, 58, 35, 5, 40, 12, 36, 31,
- 40, 38, 36, 57, 40, 10, 2, 18, 7, 35, 3, 40, 7, 57, 3,
- 2, 16, 19, 12, 24, 16, 41, 12, 49, 17, 3, 21, 15, 26, 25,
- 21, 41, 26, 48, 22, 7, 35, 14, 31, 33, 35, 40, 31, 59, 35,
- 6, 40, 13, 36, 32, 40, 39, 36, 58, 40, 11, 2, 17, 7, 34,
- 3, 39, 7, 56, 3, 1, 16, 18, 12, 24, 17, 40, 12, 50, 17,
- 2, 21, 16, 26, 24, 21, 42, 26, 49, 22, 8, 35, 15, 31, 34,
- 35, 41, 31, 48, 27, 7, 40, 14, 36, 33, 40, 40, 36, 59, 40,
- 11, 3, 16, 7, 33, 3, 38, 7, 55, 3, 0, 16, 17, 12, 25,
- 17, 39, 12, 51, 17, 1, 21, 17, 26, 24, 22, 43, 26, 50, 22,
- 9, 35, 16, 31, 35, 35, 42, 31, 49, 27, 8, 40, 15, 36, 34,
- 40, 41, 36, 59, 41, 10, 3, 15, 7, 32, 3, 37, 7, 54, 3,
- 0, 17, 16, 12, 26, 17, 38, 12, 52, 17, 0, 21, 18, 26, 25,
- 22, 44, 26, 51, 22, 10, 35, 17, 31, 24, 27, 43, 31, 50, 27,
- 9, 40, 16, 36, 35, 40, 42, 36, 58, 41, 9, 3, 14, 7, 31,
- 3, 36, 7, 53, 3, 1, 17, 15, 12, 27, 17, 37, 12, 53, 17,
- 0, 22, 19, 26, 26, 22, 45, 26, 52, 22, 11, 35, 18, 31, 25,
- 27, 44, 31, 51, 27, 10, 40, 17, 36, 35, 41, 43, 36, 57, 41,
- 8, 3, 13, 7, 30, 3, 36, 8, 52, 3, 2, 17, 14, 12, 28,
- 17, 36, 12, 54, 17, 1, 22, 20, 26, 27, 22, 46, 26, 53, 22,
- 0, 27, 19, 31, 26, 27, 45, 31, 52, 27, 11, 40, 18, 36, 34,
- 41, 44, 36, 56, 41, 7, 3, 12, 7, 29, 3, 37, 8, 51, 3,
- 3, 17, 13, 12, 29, 17, 36, 13, 55, 17, 2, 22, 21, 26, 28,
- 22, 47, 26, 54, 22, 1, 27, 20, 31, 27, 27, 46, 31, 53, 27,
- 11, 41, 19, 36, 33, 41, 45, 36, 55, 41, 6, 3, 12, 8, 28,
- 3, 38, 8, 50, 3, 4, 17, 12, 12, 30, 17, 37, 13, 56, 17,
- 3, 22, 22, 26, 29, 22, 36, 18, 55, 22, 2, 27, 21, 31, 28,
- 27, 47, 31, 54, 27, 10, 41, 20, 36, 32, 41, 46, 36, 54, 41,
- 5, 3, 13, 8, 27, 3, 39, 8, 49, 3, 5, 17, 12, 13, 31,
- 17, 38, 13, 57, 17, 4, 22, 23, 26, 30, 22, 37, 18, 56, 22,
- 3, 27, 22, 31, 29, 27, 47, 32, 55, 27, 9, 41, 21, 36, 31,
- 41, 47, 36, 53, 41, 4, 3, 14, 8, 26, 3, 40, 8, 48, 3,
- 6, 17, 13, 13, 32, 17, 39, 13, 58, 17, 5, 22, 12, 18, 31,
- 22, 38, 18, 57, 22, 4, 27, 23, 31, 30, 27, 46, 32, 56, 27,
- 8, 41, 22, 36, 30, 41, 47, 37, 52, 41, 3, 3, 15, 8, 25,
- 3, 41, 8, 48, 4, 7, 17, 14, 13, 33, 17, 40, 13, 59, 17,
- 6, 22, 13, 18, 32, 22, 39, 18, 58, 22, 5, 27, 23, 32, 31,
- 27, 45, 32, 57, 27, 7, 41, 23, 36, 29, 41, 46, 37, 51, 41,
- 2, 3, 16, 8, 24, 3, 42, 8, 49, 4, 8, 17, 15, 13, 34,
- 17, 41, 13, 48, 9, 7, 22, 14, 18, 33, 22, 40, 18, 59, 22,
- 6, 27, 22, 32, 32, 27, 44, 32, 58, 27, 6, 41, 23, 37, 28,
- 41, 45, 37, 50, 41, 1, 3, 17, 8, 24, 4, 43, 8, 50, 4,
- 9, 17, 16, 13, 35, 17, 42, 13, 49, 9, 8, 22, 15, 18, 34,
- 22, 41, 18, 59, 23, 7, 27, 21, 32, 33, 27, 43, 32, 59, 27,
- 5, 41, 22, 37, 27, 41, 44, 37, 49, 41, 0, 3, 18, 8, 25,
- 4, 44, 8, 51, 4, 10, 17, 17, 13, 24, 9, 43, 13, 50, 9,
- 9, 22, 16, 18, 35, 22, 42, 18, 58, 23, 8, 27, 20, 32, 34,
- 27, 42, 32, 59, 28, 4, 41, 21, 37, 26, 41, 43, 37, 48, 41,
- 0, 4, 19, 8, 26, 4, 45, 8, 52, 4, 11, 17, 18, 13, 25,
- 9, 44, 13, 51, 9, 10, 22, 17, 18, 35, 23, 43, 18, 57, 23,
- 9, 27, 19, 32, 35, 27, 41, 32, 58, 28, 3, 41, 20, 37, 25,
- 41, 42, 37, 48, 42, 1, 4, 20, 8, 27, 4, 46, 8, 53, 4,
- 0, 9, 19, 13, 26, 9, 45, 13, 52, 9, 11, 22, 18, 18, 34,
- 23, 44, 18, 56, 23, 10, 27, 18, 32, 35, 28, 40, 32, 57, 28,
- 2, 41, 19, 37, 24, 41, 41, 37, 49, 42, 2, 4, 21, 8, 28,
- 4, 47, 8, 54, 4, 1, 9, 20, 13, 27, 9, 46, 13, 53, 9,
- 11, 23, 19, 18, 33, 23, 45, 18, 55, 23, 11, 27, 17, 32, 34,
- 28, 39, 32, 56, 28, 1, 41, 18, 37, 24, 42, 40, 37, 50, 42,
- 3, 4, 22, 8, 29, 4, 36, 0, 55, 4, 2, 9, 21, 13, 28,
- 9, 47, 13, 54, 9, 10, 23, 20, 18, 32, 23, 46, 18, 54, 23,
- 11, 28, 16, 32, 33, 28, 38, 32, 55, 28, 0, 41, 17, 37, 25,
- 42, 39, 37, 51, 42, 4, 4, 23, 8, 30, 4, 37, 0, 56, 4,
- 3, 9, 22, 13, 29, 9, 47, 14, 55, 9, 9, 23, 21, 18, 31,
- 23, 47, 18, 53, 23, 10, 28, 15, 32, 32, 28, 37, 32, 54, 28,
- 0, 42, 16, 37, 26, 42, 38, 37, 52, 42, 5, 4, 12, 0, 31,
- 4, 38, 0, 57, 4, 4, 9, 23, 13, 30, 9, 46, 14, 56, 9,
- 8, 23, 22, 18, 30, 23, 47, 19, 52, 23, 9, 28, 14, 32, 31,
- 28, 36, 32, 53, 28, 1, 42, 15, 37, 27, 42, 37, 37, 53, 42,
- 6, 4, 13, 0, 32, 4, 39, 0, 58, 4, 5, 9, 23, 14, 31,
- 9, 45, 14, 57, 9, 7, 23, 23, 18, 29, 23, 46, 19, 51, 23,
- 8, 28, 13, 32, 30, 28, 36, 33, 52, 28, 2, 42, 14, 37, 28,
- 42, 36, 37, 54, 42, 7, 4, 14, 0, 33, 4, 40, 0, 59, 4,
- 6, 9, 22, 14, 32, 9, 44, 14, 58, 9, 6, 23, 23, 19, 28,
- 23, 45, 19, 50, 23, 7, 28, 12, 32, 29, 28, 37, 33, 51, 28,
- 3, 42, 13, 37, 29, 42, 36, 38, 55, 42, 8, 4, 15, 0, 34,
- 4, 41, 0, 59, 5, 7, 9, 21, 14, 33, 9, 43, 14, 59, 9,
- 5, 23, 22, 19, 27, 23, 44, 19, 49, 23, 6, 28, 12, 33, 28,
- 28, 38, 33, 50, 28, 4, 42, 12, 37, 30, 42, 37, 38, 56, 42,
- 9, 4, 16, 0, 35, 4, 42, 0, 58, 5, 8, 9, 20, 14, 34,
- 9, 42, 14, 59, 10, 4, 23, 21, 19, 26, 23, 43, 19, 48, 23,
- 5, 28, 13, 33, 27, 28, 39, 33, 49, 28, 5, 42, 12, 38, 31,
- 42, 38, 38, 57, 42, 10, 4, 17, 0, 35, 5, 43, 0, 57, 5,
- 9, 9, 19, 14, 35, 9, 41, 14, 58, 10, 3, 23, 20, 19, 25,
- 23, 42, 19, 48, 24, 4, 28, 14, 33, 26, 28, 40, 33, 48, 28,
- 6, 42, 13, 38, 32, 42, 39, 38, 58, 42, 11, 4, 18, 0, 34,
- 5, 44, 0, 56, 5, 10, 9, 18, 14, 35, 10, 40, 14, 57, 10,
- 2, 23, 19, 19, 24, 23, 41, 19, 49, 24, 3, 28, 15, 33, 25,
- 28, 41, 33, 48, 29, 7, 42, 14, 38, 33, 42, 40, 38, 59, 42,
- 11, 5, 19, 0, 33, 5, 45, 0, 55, 5, 11, 9, 17, 14, 34,
- 10, 39, 14, 56, 10, 1, 23, 18, 19, 24, 24, 40, 19, 50, 24,
- 2, 28, 16, 33, 24, 28, 42, 33, 49, 29, 8, 42, 15, 38, 34,
- 42, 41, 38, 59, 43, 10, 5, 20, 0, 32, 5, 46, 0, 54, 5,
- 11, 10, 16, 14, 33, 10, 38, 14, 55, 10, 0, 23, 17, 19, 25,
- 24, 39, 19, 51, 24, 1, 28, 17, 33, 24, 29, 43, 33, 50, 29,
- 9, 42, 16, 38, 35, 42, 42, 38, 58, 43, 9, 5, 21, 0, 31,
- 5, 47, 0, 53, 5, 10, 10, 15, 14, 32, 10, 37, 14, 54, 10,
- 0, 24, 16, 19, 26, 24, 38, 19, 52, 24, 0, 28, 18, 33, 25,
- 29, 44, 33, 51, 29, 10, 42, 17, 38, 35, 43, 43, 38, 57, 43,
- 8, 5, 22, 0, 30, 5, 47, 1, 52, 5, 9, 10, 14, 14, 31,
- 10, 36, 14, 53, 10, 1, 24, 15, 19, 27, 24, 37, 19, 53, 24,
- 0, 29, 19, 33, 26, 29, 45, 33, 52, 29, 11, 42, 18, 38, 34,
- 43, 44, 38, 56, 43, 7, 5, 23, 0, 29, 5, 46, 1, 51, 5,
- 8, 10, 13, 14, 30, 10, 36, 15, 52, 10, 2, 24, 14, 19, 28,
- 24, 36, 19, 54, 24, 1, 29, 20, 33, 27, 29, 46, 33, 53, 29,
- 11, 43, 19, 38, 33, 43, 45, 38, 55, 43, 6, 5, 23, 1, 28,
- 5, 45, 1, 50, 5, 7, 10, 12, 14, 29, 10, 37, 15, 51, 10,
- 3, 24, 13, 19, 29, 24, 36, 20, 55, 24, 2, 29, 21, 33, 28,
- 29, 47, 33, 54, 29, 10, 43, 20, 38, 32, 43, 46, 38, 54, 43,
- 5, 5, 22, 1, 27, 5, 44, 1, 49, 5, 6, 10, 12, 15, 28,
- 10, 38, 15, 50, 10, 4, 24, 12, 19, 30, 24, 37, 20, 56, 24,
- 3, 29, 22, 33, 29, 29, 47, 34, 55, 29, 9, 43, 21, 38, 31,
- 43, 47, 38, 53, 43, 4, 5, 21, 1, 26, 5, 43, 1, 48, 5,
- 5, 10, 13, 15, 27, 10, 39, 15, 49, 10, 5, 24, 12, 20, 31,
- 24, 38, 20, 57, 24, 4, 29, 23, 33, 30, 29, 46, 34, 56, 29,
- 8, 43, 22, 38, 30, 43, 47, 39, 52, 43, 3, 5, 20, 1, 25,
- 5, 42, 1, 48, 6, 4, 10, 14, 15, 26, 10, 40, 15, 48, 10,
- 6, 24, 13, 20, 32, 24, 39, 20, 58, 24, 5, 29, 23, 34, 31,
- 29, 45, 34, 57, 29, 7, 43, 23, 38, 29, 43, 46, 39, 51, 43,
- 2, 5, 19, 1, 24, 5, 41, 1, 49, 6, 3, 10, 15, 15, 25,
- 10, 41, 15, 48, 11, 7, 24, 14, 20, 33, 24, 40, 20, 59, 24,
- 6, 29, 22, 34, 32, 29, 44, 34, 58, 29, 6, 43, 23, 39, 28,
- 43, 45, 39, 50, 43, 1, 5, 18, 1, 24, 6, 40, 1, 50, 6,
- 2, 10, 16, 15, 24, 10, 42, 15, 49, 11, 8, 24, 15, 20, 34,
- 24, 41, 20, 59, 25, 7, 29, 21, 34, 33, 29, 43, 34, 59, 29,
- 5, 43, 22, 39, 27, 43, 44, 39, 49, 43, 0, 5, 17, 1, 25,
- 6, 39, 1, 51, 6, 1, 10, 17, 15, 24, 11, 43, 15, 50, 11,
- 9, 24, 16, 20, 35, 24, 42, 20, 58, 25, 8, 29, 20, 34, 34,
- 29, 42, 34, 59, 30, 4, 43, 21, 39, 26, 43, 43, 39, 48, 43,
- 0, 6, 16, 1, 26, 6, 38, 1, 52, 6, 0, 10, 18, 15, 25,
- 11, 44, 15, 51, 11, 10, 24, 17, 20, 35, 25, 43, 20, 57, 25,
- 9, 29, 19, 34, 35, 29, 41, 34, 58, 30, 3, 43, 20, 39, 25,
- 43, 42, 39, 48, 44, 1, 6, 15, 1, 27, 6, 37, 1, 53, 6,
- 0, 11, 19, 15, 26, 11, 45, 15, 52, 11, 11, 24, 18, 20, 34,
- 25, 44, 20, 56, 25, 10, 29, 18, 34, 35, 30, 40, 34, 57, 30,
- 2, 43, 19, 39, 24, 43, 41, 39, 49, 44, 2, 6, 14, 1, 28,
- 6, 36, 1, 54, 6, 1, 11, 20, 15, 27, 11, 46, 15, 53, 11,
- 11, 25, 19, 20, 33, 25, 45, 20, 55, 25, 11, 29, 17, 34, 34,
- 30, 39, 34, 56, 30, 1, 43, 18, 39, 24, 44, 40, 39, 50, 44,
- 3, 6, 13, 1, 29, 6, 36, 2, 55, 6, 2, 11, 21, 15, 28,
- 11, 47, 15, 54, 11, 10, 25, 20, 20, 32, 25, 46, 20, 54, 25,
- 11, 30, 16, 34, 33, 30, 38, 34, 55, 30, 0, 43, 17, 39, 25,
- 44, 39, 39, 51, 44, 4, 6, 12, 1, 30, 6, 37, 2, 56, 6,
- 3, 11, 22, 15, 29, 11, 47, 16, 55, 11, 9, 25, 21, 20, 31,
- 25, 47, 20, 53, 25, 10, 30, 15, 34, 32, 30, 37, 34, 54, 30,
- 0, 44, 16, 39, 26, 44, 38, 39, 52, 44, 5, 6, 12, 2, 31,
- 6, 38, 2, 57, 6, 4, 11, 23, 15, 30, 11, 46, 16, 56, 11,
- 8, 25, 22, 20, 30, 25, 47, 21, 52, 25, 9, 30, 14, 34, 31,
- 30, 36, 34, 53, 30, 1, 44, 15, 39, 27, 44, 37, 39, 53, 44,
- 6, 6, 13, 2, 32, 6, 39, 2, 58, 6, 5, 11, 23, 16, 31,
- 11, 45, 16, 57, 11, 7, 25, 23, 20, 29, 25, 46, 21, 51, 25,
- 8, 30, 13, 34, 30, 30, 36, 35, 52, 30, 2, 44, 14, 39, 28,
- 44, 36, 39, 54, 44, 7, 6, 14, 2, 33, 6, 40, 2, 59, 6,
- 6, 11, 22, 16, 32, 11, 44, 16, 58, 11, 6, 25, 23, 21, 28,
- 25, 45, 21, 50, 25, 7, 30, 12, 34, 29, 30, 37, 35, 51, 30,
- 3, 44, 13, 39, 29, 44, 36, 40, 55, 44, 8, 6, 15, 2, 34,
- 6, 41, 2, 59, 7, 7, 11, 21, 16, 33, 11, 43, 16, 59, 11,
- 5, 25, 22, 21, 27, 25, 44, 21, 49, 25, 6, 30, 12, 35, 28,
- 30, 38, 35, 50, 30, 4, 44, 12, 39, 30, 44, 37, 40, 56, 44,
- 9, 6, 16, 2, 35, 6, 42, 2, 58, 7, 8, 11, 20, 16, 34,
- 11, 42, 16, 59, 12, 4, 25, 21, 21, 26, 25, 43, 21, 48, 25,
- 5, 30, 13, 35, 27, 30, 39, 35, 49, 30, 5, 44, 12, 40, 31,
- 44, 38, 40, 57, 44, 10, 6, 17, 2, 35, 7, 43, 2, 57, 7,
- 9, 11, 19, 16, 35, 11, 41, 16, 58, 12, 3, 25, 20, 21, 25,
- 25, 42, 21, 48, 26, 4, 30, 14, 35, 26, 30, 40, 35, 48, 30,
- 6, 44, 13, 40, 32, 44, 39, 40, 58, 44, 11, 6, 18, 2, 34,
- 7, 44, 2, 56, 7, 10, 11, 18, 16, 35, 12, 40, 16, 57, 12,
- 2, 25, 19, 21, 24, 25, 41, 21, 49, 26, 3, 30, 15, 35, 25,
- 30, 41, 35, 48, 31, 7, 44, 14, 40, 33, 44, 40, 40, 59, 44,
- 11, 7, 19, 2, 33, 7, 45, 2, 55, 7, 11, 11, 17, 16, 34,
- 12, 39, 16, 56, 12, 1, 25, 18, 21, 24, 26, 40, 21, 50, 26,
- 2, 30, 16, 35, 24, 30, 42, 35, 49, 31, 8, 44, 15, 40, 34,
- 44, 41, 40, 48, 36, 10, 7, 20, 2, 32, 7, 46, 2, 54, 7,
- 11, 12, 16, 16, 33, 12, 38, 16, 55, 12, 0, 25, 17, 21, 25,
- 26, 39, 21, 51, 26, 1, 30, 17, 35, 24, 31, 43, 35, 50, 31,
- 9, 44, 16, 40, 35, 44, 42, 40, 49, 36, 9, 7, 21, 2, 31,
- 7, 47, 2, 53, 7, 10, 12, 15, 16, 32, 12, 37, 16, 54, 12,
- 0, 26, 16, 21, 26, 26, 38, 21, 52, 26, 0, 30, 18, 35, 25,
- 31, 44, 35, 51, 31, 10, 44, 17, 40, 24, 36, 43, 40, 50, 36,
- 8, 7, 22, 2, 30, 7, 47, 3, 52, 7, 9, 12, 14, 16, 31,
- 12, 36, 16, 53, 12, 1, 26, 15, 21, 27, 26, 37, 21, 53, 26,
- 0, 31, 19, 35, 26, 31, 45, 35, 52, 31, 11, 44, 18, 40, 25,
- 36, 44, 40, 51, 36, 7, 7, 23, 2, 29, 7, 46, 3, 51, 7,
- 8, 12, 13, 16, 30, 12, 36, 17, 52, 12, 2, 26, 14, 21, 28,
- 26, 36, 21, 54, 26, 1, 31, 20, 35, 27, 31, 46, 35, 53, 31,
- 0, 36, 19, 40, 26, 36, 45, 40, 52, 36, 6, 7, 23, 3, 28,
- 7, 45, 3, 50, 7, 7, 12, 12, 16, 29, 12, 37, 17, 51, 12,
- 3, 26, 13, 21, 29, 26, 36, 22, 55, 26, 2, 31, 21, 35, 28,
- 31, 47, 35, 54, 31, 1, 36, 20, 40, 27, 36, 46, 40, 53, 36,
- 5, 7, 22, 3, 27, 7, 44, 3, 49, 7, 6, 12, 12, 17, 28,
- 12, 38, 17, 50, 12, 4, 26, 12, 21, 30, 26, 37, 22, 56, 26,
- 3, 31, 22, 35, 29, 31, 36, 27, 55, 31, 2, 36, 21, 40, 28,
- 36, 47, 40, 54, 36, 4, 7, 21, 3, 26, 7, 43, 3, 48, 7,
- 5, 12, 13, 17, 27, 12, 39, 17, 49, 12, 5, 26, 12, 22, 31,
- 26, 38, 22, 57, 26, 4, 31, 23, 35, 30, 31, 37, 27, 56, 31,
- 3, 36, 22, 40, 29, 36, 47, 41, 55, 36, 3, 7, 20, 3, 25,
- 7, 42, 3, 48, 8, 4, 12, 14, 17, 26, 12, 40, 17, 48, 12,
- 6, 26, 13, 22, 32, 26, 39, 22, 58, 26, 5, 31, 12, 27, 31,
- 31, 38, 27, 57, 31, 4, 36, 23, 40, 30, 36, 46, 41, 56, 36,
- 2, 7, 19, 3, 24, 7, 41, 3, 49, 8, 3, 12, 15, 17, 25,
- 12, 41, 17, 48, 13, 7, 26, 14, 22, 33, 26, 40, 22, 59, 26,
- 6, 31, 13, 27, 32, 31, 39, 27, 58, 31, 5, 36, 23, 41, 31,
- 36, 45, 41, 57, 36, 1, 7, 18, 3, 24, 8, 40, 3, 50, 8,
- 2, 12, 16, 17, 24, 12, 42, 17, 49, 13, 8, 26, 15, 22, 34,
- 26, 41, 22, 48, 18, 7, 31, 14, 27, 33, 31, 40, 27, 59, 31,
- 6, 36, 22, 41, 32, 36, 44, 41, 58, 36, 0, 7, 17, 3, 25,
- 8, 39, 3, 51, 8, 1, 12, 17, 17, 24, 13, 43, 17, 50, 13,
- 9, 26, 16, 22, 35, 26, 42, 22, 49, 18, 8, 31, 15, 27, 34,
- 31, 41, 27, 59, 32, 7, 36, 21, 41, 33, 36, 43, 41, 59, 36,
- 0, 8, 16, 3, 26, 8, 38, 3, 52, 8, 0, 12, 18, 17, 25,
- 13, 44, 17, 51, 13, 10, 26, 17, 22, 24, 18, 43, 22, 50, 18,
- 9, 31, 16, 27, 35, 31, 42, 27, 58, 32, 8, 36, 20, 41, 34,
- 36, 42, 41, 59, 37, 1, 8, 15, 3, 27, 8, 37, 3, 53, 8,
- 0, 13, 19, 17, 26, 13, 45, 17, 52, 13, 11, 26, 18, 22, 25,
- 18, 44, 22, 51, 18, 10, 31, 17, 27, 35, 32, 43, 27, 57, 32,
- 9, 36, 19, 41, 35, 36, 41, 41, 58, 37, 2, 8, 14, 3, 28,
- 8, 36, 3, 54, 8, 1, 13, 20, 17, 27, 13, 46, 17, 53, 13,
- 0, 18, 19, 22, 26, 18, 45, 22, 52, 18, 11, 31, 18, 27, 34,
- 32, 44, 27, 56, 32, 10, 36, 18, 41, 35, 37, 40, 41, 57, 37,
- 3, 8, 13, 3, 29, 8, 36, 4, 55, 8, 2, 13, 21, 17, 28,
- 13, 47, 17, 54, 13, 1, 18, 20, 22, 27, 18, 46, 22, 53, 18,
- 11, 32, 19, 27, 33, 32, 45, 27, 55, 32, 11, 36, 17, 41, 34,
- 37, 39, 41, 56, 37, 4, 8, 12, 3, 30, 8, 37, 4, 56, 8,
- 3, 13, 22, 17, 29, 13, 36, 9, 55, 13, 2, 18, 21, 22, 28,
- 18, 47, 22, 54, 18, 10, 32, 20, 27, 32, 32, 46, 27, 54, 32,
- 11, 37, 16, 41, 33, 37, 38, 41, 55, 37, 5, 8, 12, 4, 31,
- 8, 38, 4, 57, 8, 4, 13, 23, 17, 30, 13, 37, 9, 56, 13,
- 3, 18, 22, 22, 29, 18, 47, 23, 55, 18, 9, 32, 21, 27, 31,
- 32, 47, 27, 53, 32, 10, 37, 15, 41, 32, 37, 37, 41, 54, 37,
- 6, 8, 13, 4, 32, 8, 39, 4, 58, 8, 5, 13, 12, 9, 31,
- 13, 38, 9, 57, 13, 4, 18, 23, 22, 30, 18, 46, 23, 56, 18,
- 8, 32, 22, 27, 30, 32, 47, 28, 52, 32, 9, 37, 14, 41, 31,
- 37, 36, 41, 53, 37, 7, 8, 14, 4, 33, 8, 40, 4, 59, 8,
- 6, 13, 13, 9, 32, 13, 39, 9, 58, 13, 5, 18, 23, 23, 31,
- 18, 45, 23, 57, 18, 7, 32, 23, 27, 29, 32, 46, 28, 51, 32,
- 8, 37, 13, 41, 30, 37, 36, 42, 52, 37, 8, 8, 15, 4, 34,
- 8, 41, 4, 48, 0, 7, 13, 14, 9, 33, 13, 40, 9, 59, 13,
- 6, 18, 22, 23, 32, 18, 44, 23, 58, 18, 6, 32, 23, 28, 28,
- 32, 45, 28, 50, 32, 7, 37, 12, 41, 29, 37, 37, 42, 51, 37,
- 9, 8, 16, 4, 35, 8, 42, 4, 49, 0, 8, 13, 15, 9, 34,
- 13, 41, 9, 59, 14, 7, 18, 21, 23, 33, 18, 43, 23, 59, 18,
- 5, 32, 22, 28, 27, 32, 44, 28, 49, 32, 6, 37, 12, 42, 28,
- 37, 38, 42, 50, 37, 10, 8, 17, 4, 24, 0, 43, 4, 50, 0,
- 9, 13, 16, 9, 35, 13, 42, 9, 58, 14, 8, 18, 20, 23, 34,
- 18, 42, 23, 59, 19, 4, 32, 21, 28, 26, 32, 43, 28, 48, 32,
- 5, 37, 13, 42, 27, 37, 39, 42, 49, 37, 11, 8, 18, 4, 25,
- 0, 44, 4, 51, 0, 10, 13, 17, 9, 35, 14, 43, 9, 57, 14,
- 9, 18, 19, 23, 35, 18, 41, 23, 58, 19, 3, 32, 20, 28, 25,
- 32, 42, 28, 48, 33, 4, 37, 14, 42, 26, 37, 40, 42, 48, 37,
-};
-
-static const uint8_t hq_tab_05[] = {
- 0, 0, 25, 4, 34, 0, 59, 4, 68, 0, 13, 13, 22, 9, 47,
- 13, 56, 9, 78, 14, 10, 18, 28, 23, 44, 18, 58, 23, 78, 18,
- 8, 32, 31, 28, 38, 32, 61, 28, 68, 32, 11, 37, 18, 41, 41,
- 37, 48, 41, 71, 37, 1, 0, 26, 4, 35, 0, 60, 4, 69, 0,
- 14, 13, 23, 9, 47, 14, 57, 9, 77, 14, 11, 18, 27, 23, 45,
- 18, 57, 23, 79, 18, 7, 32, 30, 28, 37, 32, 60, 28, 67, 32,
- 10, 37, 17, 41, 40, 37, 48, 42, 70, 37, 2, 0, 27, 4, 36,
- 0, 61, 4, 70, 0, 15, 13, 24, 9, 46, 14, 58, 9, 76, 14,
- 12, 18, 26, 23, 46, 18, 56, 23, 79, 19, 6, 32, 29, 28, 36,
- 32, 59, 28, 66, 32, 9, 37, 16, 41, 39, 37, 49, 42, 69, 37,
- 3, 0, 28, 4, 37, 0, 62, 4, 71, 0, 15, 14, 25, 9, 45,
- 14, 59, 9, 75, 14, 13, 18, 25, 23, 47, 18, 55, 23, 78, 19,
- 5, 32, 28, 28, 35, 32, 58, 28, 65, 32, 8, 37, 16, 42, 38,
- 37, 50, 42, 68, 37, 4, 0, 29, 4, 38, 0, 63, 4, 72, 0,
- 14, 14, 26, 9, 44, 14, 60, 9, 74, 14, 14, 18, 24, 23, 47,
- 19, 54, 23, 77, 19, 4, 32, 27, 28, 34, 32, 57, 28, 64, 32,
- 7, 37, 17, 42, 37, 37, 51, 42, 67, 37, 5, 0, 30, 4, 39,
- 0, 63, 5, 73, 0, 13, 14, 27, 9, 43, 14, 61, 9, 73, 14,
- 15, 18, 23, 23, 46, 19, 53, 23, 76, 19, 3, 32, 26, 28, 33,
- 32, 56, 28, 64, 33, 6, 37, 18, 42, 36, 37, 52, 42, 66, 37,
- 6, 0, 31, 4, 40, 0, 62, 5, 74, 0, 12, 14, 28, 9, 42,
- 14, 62, 9, 72, 14, 15, 19, 22, 23, 45, 19, 52, 23, 75, 19,
- 2, 32, 25, 28, 32, 32, 55, 28, 65, 33, 5, 37, 19, 42, 35,
- 37, 53, 42, 65, 37, 7, 0, 31, 5, 41, 0, 61, 5, 75, 0,
- 11, 14, 29, 9, 41, 14, 63, 9, 71, 14, 14, 19, 21, 23, 44,
- 19, 51, 23, 74, 19, 1, 32, 24, 28, 32, 33, 54, 28, 66, 33,
- 4, 37, 20, 42, 34, 37, 54, 42, 64, 37, 8, 0, 30, 5, 42,
- 0, 60, 5, 76, 0, 10, 14, 30, 9, 40, 14, 63, 10, 70, 14,
- 13, 19, 20, 23, 43, 19, 50, 23, 73, 19, 0, 32, 23, 28, 33,
- 33, 53, 28, 67, 33, 3, 37, 21, 42, 33, 37, 55, 42, 64, 38,
- 9, 0, 29, 5, 43, 0, 59, 5, 77, 0, 9, 14, 31, 9, 39,
- 14, 62, 10, 69, 14, 12, 19, 19, 23, 42, 19, 49, 23, 72, 19,
- 0, 33, 22, 28, 34, 33, 52, 28, 68, 33, 2, 37, 22, 42, 32,
- 37, 56, 42, 65, 38, 10, 0, 28, 5, 44, 0, 58, 5, 78, 0,
- 8, 14, 31, 10, 38, 14, 61, 10, 68, 14, 11, 19, 18, 23, 41,
- 19, 48, 23, 71, 19, 1, 33, 21, 28, 35, 33, 51, 28, 69, 33,
- 1, 37, 23, 42, 32, 38, 57, 42, 66, 38, 11, 0, 27, 5, 45,
- 0, 57, 5, 79, 0, 7, 14, 30, 10, 37, 14, 60, 10, 67, 14,
- 10, 19, 17, 23, 40, 19, 48, 24, 70, 19, 2, 33, 20, 28, 36,
- 33, 50, 28, 70, 33, 0, 37, 24, 42, 33, 38, 58, 42, 67, 38,
- 12, 0, 26, 5, 46, 0, 56, 5, 79, 1, 6, 14, 29, 10, 36,
- 14, 59, 10, 66, 14, 9, 19, 16, 23, 39, 19, 49, 24, 69, 19,
- 3, 33, 19, 28, 37, 33, 49, 28, 71, 33, 0, 38, 25, 42, 34,
- 38, 59, 42, 68, 38, 13, 0, 25, 5, 47, 0, 55, 5, 78, 1,
- 5, 14, 28, 10, 35, 14, 58, 10, 65, 14, 8, 19, 16, 24, 38,
- 19, 50, 24, 68, 19, 4, 33, 18, 28, 38, 33, 48, 28, 72, 33,
- 1, 38, 26, 42, 35, 38, 60, 42, 69, 38, 14, 0, 24, 5, 47,
- 1, 54, 5, 77, 1, 4, 14, 27, 10, 34, 14, 57, 10, 64, 14,
- 7, 19, 17, 24, 37, 19, 51, 24, 67, 19, 5, 33, 17, 28, 39,
- 33, 48, 29, 73, 33, 2, 38, 27, 42, 36, 38, 61, 42, 70, 38,
- 15, 0, 23, 5, 46, 1, 53, 5, 76, 1, 3, 14, 26, 10, 33,
- 14, 56, 10, 64, 15, 6, 19, 18, 24, 36, 19, 52, 24, 66, 19,
- 6, 33, 16, 28, 40, 33, 49, 29, 74, 33, 3, 38, 28, 42, 37,
- 38, 62, 42, 71, 38, 15, 1, 22, 5, 45, 1, 52, 5, 75, 1,
- 2, 14, 25, 10, 32, 14, 55, 10, 65, 15, 5, 19, 19, 24, 35,
- 19, 53, 24, 65, 19, 7, 33, 16, 29, 41, 33, 50, 29, 75, 33,
- 4, 38, 29, 42, 38, 38, 63, 42, 72, 38, 14, 1, 21, 5, 44,
- 1, 51, 5, 74, 1, 1, 14, 24, 10, 32, 15, 54, 10, 66, 15,
- 4, 19, 20, 24, 34, 19, 54, 24, 64, 19, 8, 33, 17, 29, 42,
- 33, 51, 29, 76, 33, 5, 38, 30, 42, 39, 38, 63, 43, 73, 38,
- 13, 1, 20, 5, 43, 1, 50, 5, 73, 1, 0, 14, 23, 10, 33,
- 15, 53, 10, 67, 15, 3, 19, 21, 24, 33, 19, 55, 24, 64, 20,
- 9, 33, 18, 29, 43, 33, 52, 29, 77, 33, 6, 38, 31, 42, 40,
- 38, 62, 43, 74, 38, 12, 1, 19, 5, 42, 1, 49, 5, 72, 1,
- 0, 15, 22, 10, 34, 15, 52, 10, 68, 15, 2, 19, 22, 24, 32,
- 19, 56, 24, 65, 20, 10, 33, 19, 29, 44, 33, 53, 29, 78, 33,
- 7, 38, 31, 43, 41, 38, 61, 43, 75, 38, 11, 1, 18, 5, 41,
- 1, 48, 5, 71, 1, 1, 15, 21, 10, 35, 15, 51, 10, 69, 15,
- 1, 19, 23, 24, 32, 20, 57, 24, 66, 20, 11, 33, 20, 29, 45,
- 33, 54, 29, 79, 33, 8, 38, 30, 43, 42, 38, 60, 43, 76, 38,
- 10, 1, 17, 5, 40, 1, 48, 6, 70, 1, 2, 15, 20, 10, 36,
- 15, 50, 10, 70, 15, 0, 19, 24, 24, 33, 20, 58, 24, 67, 20,
- 12, 33, 21, 29, 46, 33, 55, 29, 79, 34, 9, 38, 29, 43, 43,
- 38, 59, 43, 77, 38, 9, 1, 16, 5, 39, 1, 49, 6, 69, 1,
- 3, 15, 19, 10, 37, 15, 49, 10, 71, 15, 0, 20, 25, 24, 34,
- 20, 59, 24, 68, 20, 13, 33, 22, 29, 47, 33, 56, 29, 78, 34,
- 10, 38, 28, 43, 44, 38, 58, 43, 78, 38, 8, 1, 16, 6, 38,
- 1, 50, 6, 68, 1, 4, 15, 18, 10, 38, 15, 48, 10, 72, 15,
- 1, 20, 26, 24, 35, 20, 60, 24, 69, 20, 14, 33, 23, 29, 47,
- 34, 57, 29, 77, 34, 11, 38, 27, 43, 45, 38, 57, 43, 79, 38,
- 7, 1, 17, 6, 37, 1, 51, 6, 67, 1, 5, 15, 17, 10, 39,
- 15, 48, 11, 73, 15, 2, 20, 27, 24, 36, 20, 61, 24, 70, 20,
- 15, 33, 24, 29, 46, 34, 58, 29, 76, 34, 12, 38, 26, 43, 46,
- 38, 56, 43, 79, 39, 6, 1, 18, 6, 36, 1, 52, 6, 66, 1,
- 6, 15, 16, 10, 40, 15, 49, 11, 74, 15, 3, 20, 28, 24, 37,
- 20, 62, 24, 71, 20, 15, 34, 25, 29, 45, 34, 59, 29, 75, 34,
- 13, 38, 25, 43, 47, 38, 55, 43, 78, 39, 5, 1, 19, 6, 35,
- 1, 53, 6, 65, 1, 7, 15, 16, 11, 41, 15, 50, 11, 75, 15,
- 4, 20, 29, 24, 38, 20, 63, 24, 72, 20, 14, 34, 26, 29, 44,
- 34, 60, 29, 74, 34, 14, 38, 24, 43, 47, 39, 54, 43, 77, 39,
- 4, 1, 20, 6, 34, 1, 54, 6, 64, 1, 8, 15, 17, 11, 42,
- 15, 51, 11, 76, 15, 5, 20, 30, 24, 39, 20, 63, 25, 73, 20,
- 13, 34, 27, 29, 43, 34, 61, 29, 73, 34, 15, 38, 23, 43, 46,
- 39, 53, 43, 76, 39, 3, 1, 21, 6, 33, 1, 55, 6, 64, 2,
- 9, 15, 18, 11, 43, 15, 52, 11, 77, 15, 6, 20, 31, 24, 40,
- 20, 62, 25, 74, 20, 12, 34, 28, 29, 42, 34, 62, 29, 72, 34,
- 15, 39, 22, 43, 45, 39, 52, 43, 75, 39, 2, 1, 22, 6, 32,
- 1, 56, 6, 65, 2, 10, 15, 19, 11, 44, 15, 53, 11, 78, 15,
- 7, 20, 31, 25, 41, 20, 61, 25, 75, 20, 11, 34, 29, 29, 41,
- 34, 63, 29, 71, 34, 14, 39, 21, 43, 44, 39, 51, 43, 74, 39,
- 1, 1, 23, 6, 32, 2, 57, 6, 66, 2, 11, 15, 20, 11, 45,
- 15, 54, 11, 79, 15, 8, 20, 30, 25, 42, 20, 60, 25, 76, 20,
- 10, 34, 30, 29, 40, 34, 63, 30, 70, 34, 13, 39, 20, 43, 43,
- 39, 50, 43, 73, 39, 0, 1, 24, 6, 33, 2, 58, 6, 67, 2,
- 12, 15, 21, 11, 46, 15, 55, 11, 79, 16, 9, 20, 29, 25, 43,
- 20, 59, 25, 77, 20, 9, 34, 31, 29, 39, 34, 62, 30, 69, 34,
- 12, 39, 19, 43, 42, 39, 49, 43, 72, 39, 0, 2, 25, 6, 34,
- 2, 59, 6, 68, 2, 13, 15, 22, 11, 47, 15, 56, 11, 78, 16,
- 10, 20, 28, 25, 44, 20, 58, 25, 78, 20, 8, 34, 31, 30, 38,
- 34, 61, 30, 68, 34, 11, 39, 18, 43, 41, 39, 48, 43, 71, 39,
- 1, 2, 26, 6, 35, 2, 60, 6, 69, 2, 14, 15, 23, 11, 47,
- 16, 57, 11, 77, 16, 11, 20, 27, 25, 45, 20, 57, 25, 79, 20,
- 7, 34, 30, 30, 37, 34, 60, 30, 67, 34, 10, 39, 17, 43, 40,
- 39, 48, 44, 70, 39, 2, 2, 27, 6, 36, 2, 61, 6, 70, 2,
- 15, 15, 24, 11, 46, 16, 58, 11, 76, 16, 12, 20, 26, 25, 46,
- 20, 56, 25, 79, 21, 6, 34, 29, 30, 36, 34, 59, 30, 66, 34,
- 9, 39, 16, 43, 39, 39, 49, 44, 69, 39, 3, 2, 28, 6, 37,
- 2, 62, 6, 71, 2, 15, 16, 25, 11, 45, 16, 59, 11, 75, 16,
- 13, 20, 25, 25, 47, 20, 55, 25, 78, 21, 5, 34, 28, 30, 35,
- 34, 58, 30, 65, 34, 8, 39, 16, 44, 38, 39, 50, 44, 68, 39,
- 4, 2, 29, 6, 38, 2, 63, 6, 72, 2, 14, 16, 26, 11, 44,
- 16, 60, 11, 74, 16, 14, 20, 24, 25, 47, 21, 54, 25, 77, 21,
- 4, 34, 27, 30, 34, 34, 57, 30, 64, 34, 7, 39, 17, 44, 37,
- 39, 51, 44, 67, 39, 5, 2, 30, 6, 39, 2, 63, 7, 73, 2,
- 13, 16, 27, 11, 43, 16, 61, 11, 73, 16, 15, 20, 23, 25, 46,
- 21, 53, 25, 76, 21, 3, 34, 26, 30, 33, 34, 56, 30, 64, 35,
- 6, 39, 18, 44, 36, 39, 52, 44, 66, 39, 6, 2, 31, 6, 40,
- 2, 62, 7, 74, 2, 12, 16, 28, 11, 42, 16, 62, 11, 72, 16,
- 15, 21, 22, 25, 45, 21, 52, 25, 75, 21, 2, 34, 25, 30, 32,
- 34, 55, 30, 65, 35, 5, 39, 19, 44, 35, 39, 53, 44, 65, 39,
- 7, 2, 31, 7, 41, 2, 61, 7, 75, 2, 11, 16, 29, 11, 41,
- 16, 63, 11, 71, 16, 14, 21, 21, 25, 44, 21, 51, 25, 74, 21,
- 1, 34, 24, 30, 32, 35, 54, 30, 66, 35, 4, 39, 20, 44, 34,
- 39, 54, 44, 64, 39, 8, 2, 30, 7, 42, 2, 60, 7, 76, 2,
- 10, 16, 30, 11, 40, 16, 63, 12, 70, 16, 13, 21, 20, 25, 43,
- 21, 50, 25, 73, 21, 0, 34, 23, 30, 33, 35, 53, 30, 67, 35,
- 3, 39, 21, 44, 33, 39, 55, 44, 64, 40, 9, 2, 29, 7, 43,
- 2, 59, 7, 77, 2, 9, 16, 31, 11, 39, 16, 62, 12, 69, 16,
- 12, 21, 19, 25, 42, 21, 49, 25, 72, 21, 0, 35, 22, 30, 34,
- 35, 52, 30, 68, 35, 2, 39, 22, 44, 32, 39, 56, 44, 65, 40,
- 10, 2, 28, 7, 44, 2, 58, 7, 78, 2, 8, 16, 31, 12, 38,
- 16, 61, 12, 68, 16, 11, 21, 18, 25, 41, 21, 48, 25, 71, 21,
- 1, 35, 21, 30, 35, 35, 51, 30, 69, 35, 1, 39, 23, 44, 32,
- 40, 57, 44, 66, 40, 11, 2, 27, 7, 45, 2, 57, 7, 79, 2,
- 7, 16, 30, 12, 37, 16, 60, 12, 67, 16, 10, 21, 17, 25, 40,
- 21, 48, 26, 70, 21, 2, 35, 20, 30, 36, 35, 50, 30, 70, 35,
- 0, 39, 24, 44, 33, 40, 58, 44, 67, 40, 12, 2, 26, 7, 46,
- 2, 56, 7, 79, 3, 6, 16, 29, 12, 36, 16, 59, 12, 66, 16,
- 9, 21, 16, 25, 39, 21, 49, 26, 69, 21, 3, 35, 19, 30, 37,
- 35, 49, 30, 71, 35, 0, 40, 25, 44, 34, 40, 59, 44, 68, 40,
- 13, 2, 25, 7, 47, 2, 55, 7, 78, 3, 5, 16, 28, 12, 35,
- 16, 58, 12, 65, 16, 8, 21, 16, 26, 38, 21, 50, 26, 68, 21,
- 4, 35, 18, 30, 38, 35, 48, 30, 72, 35, 1, 40, 26, 44, 35,
- 40, 60, 44, 69, 40, 14, 2, 24, 7, 47, 3, 54, 7, 77, 3,
- 4, 16, 27, 12, 34, 16, 57, 12, 64, 16, 7, 21, 17, 26, 37,
- 21, 51, 26, 67, 21, 5, 35, 17, 30, 39, 35, 48, 31, 73, 35,
- 2, 40, 27, 44, 36, 40, 61, 44, 70, 40, 15, 2, 23, 7, 46,
- 3, 53, 7, 76, 3, 3, 16, 26, 12, 33, 16, 56, 12, 64, 17,
- 6, 21, 18, 26, 36, 21, 52, 26, 66, 21, 6, 35, 16, 30, 40,
- 35, 49, 31, 74, 35, 3, 40, 28, 44, 37, 40, 62, 44, 71, 40,
- 15, 3, 22, 7, 45, 3, 52, 7, 75, 3, 2, 16, 25, 12, 32,
- 16, 55, 12, 65, 17, 5, 21, 19, 26, 35, 21, 53, 26, 65, 21,
- 7, 35, 16, 31, 41, 35, 50, 31, 75, 35, 4, 40, 29, 44, 38,
- 40, 63, 44, 72, 40, 14, 3, 21, 7, 44, 3, 51, 7, 74, 3,
- 1, 16, 24, 12, 32, 17, 54, 12, 66, 17, 4, 21, 20, 26, 34,
- 21, 54, 26, 64, 21, 8, 35, 17, 31, 42, 35, 51, 31, 76, 35,
- 5, 40, 30, 44, 39, 40, 48, 36, 73, 40, 13, 3, 20, 7, 43,
- 3, 50, 7, 73, 3, 0, 16, 23, 12, 33, 17, 53, 12, 67, 17,
- 3, 21, 21, 26, 33, 21, 55, 26, 64, 22, 9, 35, 18, 31, 43,
- 35, 52, 31, 77, 35, 6, 40, 31, 44, 40, 40, 49, 36, 74, 40,
- 12, 3, 19, 7, 42, 3, 49, 7, 72, 3, 0, 17, 22, 12, 34,
- 17, 52, 12, 68, 17, 2, 21, 22, 26, 32, 21, 56, 26, 65, 22,
- 10, 35, 19, 31, 44, 35, 53, 31, 78, 35, 7, 40, 16, 36, 41,
- 40, 50, 36, 75, 40, 11, 3, 18, 7, 41, 3, 48, 7, 71, 3,
- 1, 17, 21, 12, 35, 17, 51, 12, 69, 17, 1, 21, 23, 26, 32,
- 22, 57, 26, 66, 22, 11, 35, 20, 31, 45, 35, 54, 31, 79, 35,
- 8, 40, 17, 36, 42, 40, 51, 36, 76, 40, 10, 3, 17, 7, 40,
- 3, 48, 8, 70, 3, 2, 17, 20, 12, 36, 17, 50, 12, 70, 17,
- 0, 21, 24, 26, 33, 22, 58, 26, 67, 22, 12, 35, 21, 31, 46,
- 35, 55, 31, 64, 27, 9, 40, 18, 36, 43, 40, 52, 36, 77, 40,
- 9, 3, 16, 7, 39, 3, 49, 8, 69, 3, 3, 17, 19, 12, 37,
- 17, 49, 12, 71, 17, 0, 22, 25, 26, 34, 22, 59, 26, 68, 22,
- 13, 35, 22, 31, 47, 35, 56, 31, 65, 27, 10, 40, 19, 36, 44,
- 40, 53, 36, 78, 40, 8, 3, 16, 8, 38, 3, 50, 8, 68, 3,
- 4, 17, 18, 12, 38, 17, 48, 12, 72, 17, 1, 22, 26, 26, 35,
- 22, 60, 26, 69, 22, 14, 35, 23, 31, 32, 27, 57, 31, 66, 27,
- 11, 40, 20, 36, 45, 40, 54, 36, 79, 40, 7, 3, 17, 8, 37,
- 3, 51, 8, 67, 3, 5, 17, 17, 12, 39, 17, 48, 13, 73, 17,
- 2, 22, 27, 26, 36, 22, 61, 26, 70, 22, 15, 35, 24, 31, 33,
- 27, 58, 31, 67, 27, 12, 40, 21, 36, 46, 40, 55, 36, 79, 41,
- 6, 3, 18, 8, 36, 3, 52, 8, 66, 3, 6, 17, 16, 12, 40,
- 17, 49, 13, 74, 17, 3, 22, 28, 26, 37, 22, 62, 26, 71, 22,
- 0, 27, 25, 31, 34, 27, 59, 31, 68, 27, 13, 40, 22, 36, 47,
- 40, 56, 36, 78, 41, 5, 3, 19, 8, 35, 3, 53, 8, 65, 3,
- 7, 17, 16, 13, 41, 17, 50, 13, 75, 17, 4, 22, 29, 26, 38,
- 22, 63, 26, 72, 22, 1, 27, 26, 31, 35, 27, 60, 31, 69, 27,
- 14, 40, 23, 36, 47, 41, 57, 36, 77, 41, 4, 3, 20, 8, 34,
- 3, 54, 8, 64, 3, 8, 17, 17, 13, 42, 17, 51, 13, 76, 17,
- 5, 22, 30, 26, 39, 22, 48, 18, 73, 22, 2, 27, 27, 31, 36,
- 27, 61, 31, 70, 27, 15, 40, 24, 36, 46, 41, 58, 36, 76, 41,
- 3, 3, 21, 8, 33, 3, 55, 8, 64, 4, 9, 17, 18, 13, 43,
- 17, 52, 13, 77, 17, 6, 22, 31, 26, 40, 22, 49, 18, 74, 22,
- 3, 27, 28, 31, 37, 27, 62, 31, 71, 27, 15, 41, 25, 36, 45,
- 41, 59, 36, 75, 41, 2, 3, 22, 8, 32, 3, 56, 8, 65, 4,
- 10, 17, 19, 13, 44, 17, 53, 13, 78, 17, 7, 22, 16, 18, 41,
- 22, 50, 18, 75, 22, 4, 27, 29, 31, 38, 27, 63, 31, 72, 27,
- 14, 41, 26, 36, 44, 41, 60, 36, 74, 41, 1, 3, 23, 8, 32,
- 4, 57, 8, 66, 4, 11, 17, 20, 13, 45, 17, 54, 13, 79, 17,
- 8, 22, 17, 18, 42, 22, 51, 18, 76, 22, 5, 27, 30, 31, 39,
- 27, 63, 32, 73, 27, 13, 41, 27, 36, 43, 41, 61, 36, 73, 41,
- 0, 3, 24, 8, 33, 4, 58, 8, 67, 4, 12, 17, 21, 13, 46,
- 17, 55, 13, 64, 9, 9, 22, 18, 18, 43, 22, 52, 18, 77, 22,
- 6, 27, 31, 31, 40, 27, 62, 32, 74, 27, 12, 41, 28, 36, 42,
- 41, 62, 36, 72, 41, 0, 4, 25, 8, 34, 4, 59, 8, 68, 4,
- 13, 17, 22, 13, 47, 17, 56, 13, 65, 9, 10, 22, 19, 18, 44,
- 22, 53, 18, 78, 22, 7, 27, 31, 32, 41, 27, 61, 32, 75, 27,
- 11, 41, 29, 36, 41, 41, 63, 36, 71, 41, 1, 4, 26, 8, 35,
- 4, 60, 8, 69, 4, 14, 17, 23, 13, 32, 9, 57, 13, 66, 9,
- 11, 22, 20, 18, 45, 22, 54, 18, 79, 22, 8, 27, 30, 32, 42,
- 27, 60, 32, 76, 27, 10, 41, 30, 36, 40, 41, 63, 37, 70, 41,
- 2, 4, 27, 8, 36, 4, 61, 8, 70, 4, 15, 17, 24, 13, 33,
- 9, 58, 13, 67, 9, 12, 22, 21, 18, 46, 22, 55, 18, 79, 23,
- 9, 27, 29, 32, 43, 27, 59, 32, 77, 27, 9, 41, 31, 36, 39,
- 41, 62, 37, 69, 41, 3, 4, 28, 8, 37, 4, 62, 8, 71, 4,
- 0, 9, 25, 13, 34, 9, 59, 13, 68, 9, 13, 22, 22, 18, 47,
- 22, 56, 18, 78, 23, 10, 27, 28, 32, 44, 27, 58, 32, 78, 27,
- 8, 41, 31, 37, 38, 41, 61, 37, 68, 41, 4, 4, 29, 8, 38,
- 4, 63, 8, 72, 4, 1, 9, 26, 13, 35, 9, 60, 13, 69, 9,
- 14, 22, 23, 18, 47, 23, 57, 18, 77, 23, 11, 27, 27, 32, 45,
- 27, 57, 32, 79, 27, 7, 41, 30, 37, 37, 41, 60, 37, 67, 41,
- 5, 4, 30, 8, 39, 4, 48, 0, 73, 4, 2, 9, 27, 13, 36,
- 9, 61, 13, 70, 9, 15, 22, 24, 18, 46, 23, 58, 18, 76, 23,
- 12, 27, 26, 32, 46, 27, 56, 32, 79, 28, 6, 41, 29, 37, 36,
- 41, 59, 37, 66, 41, 6, 4, 31, 8, 40, 4, 49, 0, 74, 4,
- 3, 9, 28, 13, 37, 9, 62, 13, 71, 9, 15, 23, 25, 18, 45,
- 23, 59, 18, 75, 23, 13, 27, 25, 32, 47, 27, 55, 32, 78, 28,
- 5, 41, 28, 37, 35, 41, 58, 37, 65, 41, 7, 4, 16, 0, 41,
- 4, 50, 0, 75, 4, 4, 9, 29, 13, 38, 9, 63, 13, 72, 9,
- 14, 23, 26, 18, 44, 23, 60, 18, 74, 23, 14, 27, 24, 32, 47,
- 28, 54, 32, 77, 28, 4, 41, 27, 37, 34, 41, 57, 37, 64, 41,
- 8, 4, 17, 0, 42, 4, 51, 0, 76, 4, 5, 9, 30, 13, 39,
- 9, 63, 14, 73, 9, 13, 23, 27, 18, 43, 23, 61, 18, 73, 23,
- 15, 27, 23, 32, 46, 28, 53, 32, 76, 28, 3, 41, 26, 37, 33,
- 41, 56, 37, 64, 42, 9, 4, 18, 0, 43, 4, 52, 0, 77, 4,
- 6, 9, 31, 13, 40, 9, 62, 14, 74, 9, 12, 23, 28, 18, 42,
- 23, 62, 18, 72, 23, 15, 28, 22, 32, 45, 28, 52, 32, 75, 28,
- 2, 41, 25, 37, 32, 41, 55, 37, 65, 42, 10, 4, 19, 0, 44,
- 4, 53, 0, 78, 4, 7, 9, 31, 14, 41, 9, 61, 14, 75, 9,
- 11, 23, 29, 18, 41, 23, 63, 18, 71, 23, 14, 28, 21, 32, 44,
- 28, 51, 32, 74, 28, 1, 41, 24, 37, 32, 42, 54, 37, 66, 42,
- 11, 4, 20, 0, 45, 4, 54, 0, 79, 4, 8, 9, 30, 14, 42,
- 9, 60, 14, 76, 9, 10, 23, 30, 18, 40, 23, 63, 19, 70, 23,
- 13, 28, 20, 32, 43, 28, 50, 32, 73, 28, 0, 41, 23, 37, 33,
- 42, 53, 37, 67, 42, 12, 4, 21, 0, 46, 4, 55, 0, 79, 5,
- 9, 9, 29, 14, 43, 9, 59, 14, 77, 9, 9, 23, 31, 18, 39,
- 23, 62, 19, 69, 23, 12, 28, 19, 32, 42, 28, 49, 32, 72, 28,
- 0, 42, 22, 37, 34, 42, 52, 37, 68, 42, 13, 4, 22, 0, 47,
- 4, 56, 0, 78, 5, 10, 9, 28, 14, 44, 9, 58, 14, 78, 9,
- 8, 23, 31, 19, 38, 23, 61, 19, 68, 23, 11, 28, 18, 32, 41,
- 28, 48, 32, 71, 28, 1, 42, 21, 37, 35, 42, 51, 37, 69, 42,
- 14, 4, 23, 0, 47, 5, 57, 0, 77, 5, 11, 9, 27, 14, 45,
- 9, 57, 14, 79, 9, 7, 23, 30, 19, 37, 23, 60, 19, 67, 23,
- 10, 28, 17, 32, 40, 28, 48, 33, 70, 28, 2, 42, 20, 37, 36,
- 42, 50, 37, 70, 42, 15, 4, 24, 0, 46, 5, 58, 0, 76, 5,
- 12, 9, 26, 14, 46, 9, 56, 14, 79, 10, 6, 23, 29, 19, 36,
- 23, 59, 19, 66, 23, 9, 28, 16, 32, 39, 28, 49, 33, 69, 28,
- 3, 42, 19, 37, 37, 42, 49, 37, 71, 42, 15, 5, 25, 0, 45,
- 5, 59, 0, 75, 5, 13, 9, 25, 14, 47, 9, 55, 14, 78, 10,
- 5, 23, 28, 19, 35, 23, 58, 19, 65, 23, 8, 28, 16, 33, 38,
- 28, 50, 33, 68, 28, 4, 42, 18, 37, 38, 42, 48, 37, 72, 42,
- 14, 5, 26, 0, 44, 5, 60, 0, 74, 5, 14, 9, 24, 14, 47,
- 10, 54, 14, 77, 10, 4, 23, 27, 19, 34, 23, 57, 19, 64, 23,
- 7, 28, 17, 33, 37, 28, 51, 33, 67, 28, 5, 42, 17, 37, 39,
- 42, 48, 38, 73, 42, 13, 5, 27, 0, 43, 5, 61, 0, 73, 5,
- 15, 9, 23, 14, 46, 10, 53, 14, 76, 10, 3, 23, 26, 19, 33,
- 23, 56, 19, 64, 24, 6, 28, 18, 33, 36, 28, 52, 33, 66, 28,
- 6, 42, 16, 37, 40, 42, 49, 38, 74, 42, 12, 5, 28, 0, 42,
- 5, 62, 0, 72, 5, 15, 10, 22, 14, 45, 10, 52, 14, 75, 10,
- 2, 23, 25, 19, 32, 23, 55, 19, 65, 24, 5, 28, 19, 33, 35,
- 28, 53, 33, 65, 28, 7, 42, 16, 38, 41, 42, 50, 38, 75, 42,
- 11, 5, 29, 0, 41, 5, 63, 0, 71, 5, 14, 10, 21, 14, 44,
- 10, 51, 14, 74, 10, 1, 23, 24, 19, 32, 24, 54, 19, 66, 24,
- 4, 28, 20, 33, 34, 28, 54, 33, 64, 28, 8, 42, 17, 38, 42,
- 42, 51, 38, 76, 42, 10, 5, 30, 0, 40, 5, 63, 1, 70, 5,
- 13, 10, 20, 14, 43, 10, 50, 14, 73, 10, 0, 23, 23, 19, 33,
- 24, 53, 19, 67, 24, 3, 28, 21, 33, 33, 28, 55, 33, 64, 29,
- 9, 42, 18, 38, 43, 42, 52, 38, 77, 42, 9, 5, 31, 0, 39,
- 5, 62, 1, 69, 5, 12, 10, 19, 14, 42, 10, 49, 14, 72, 10,
- 0, 24, 22, 19, 34, 24, 52, 19, 68, 24, 2, 28, 22, 33, 32,
- 28, 56, 33, 65, 29, 10, 42, 19, 38, 44, 42, 53, 38, 78, 42,
- 8, 5, 31, 1, 38, 5, 61, 1, 68, 5, 11, 10, 18, 14, 41,
- 10, 48, 14, 71, 10, 1, 24, 21, 19, 35, 24, 51, 19, 69, 24,
- 1, 28, 23, 33, 32, 29, 57, 33, 66, 29, 11, 42, 20, 38, 45,
- 42, 54, 38, 79, 42, 7, 5, 30, 1, 37, 5, 60, 1, 67, 5,
- 10, 10, 17, 14, 40, 10, 48, 15, 70, 10, 2, 24, 20, 19, 36,
- 24, 50, 19, 70, 24, 0, 28, 24, 33, 33, 29, 58, 33, 67, 29,
- 12, 42, 21, 38, 46, 42, 55, 38, 79, 43, 6, 5, 29, 1, 36,
- 5, 59, 1, 66, 5, 9, 10, 16, 14, 39, 10, 49, 15, 69, 10,
- 3, 24, 19, 19, 37, 24, 49, 19, 71, 24, 0, 29, 25, 33, 34,
- 29, 59, 33, 68, 29, 13, 42, 22, 38, 47, 42, 56, 38, 78, 43,
- 5, 5, 28, 1, 35, 5, 58, 1, 65, 5, 8, 10, 16, 15, 38,
- 10, 50, 15, 68, 10, 4, 24, 18, 19, 38, 24, 48, 19, 72, 24,
- 1, 29, 26, 33, 35, 29, 60, 33, 69, 29, 14, 42, 23, 38, 47,
- 43, 57, 38, 77, 43, 4, 5, 27, 1, 34, 5, 57, 1, 64, 5,
- 7, 10, 17, 15, 37, 10, 51, 15, 67, 10, 5, 24, 17, 19, 39,
- 24, 48, 20, 73, 24, 2, 29, 27, 33, 36, 29, 61, 33, 70, 29,
- 15, 42, 24, 38, 46, 43, 58, 38, 76, 43, 3, 5, 26, 1, 33,
- 5, 56, 1, 64, 6, 6, 10, 18, 15, 36, 10, 52, 15, 66, 10,
- 6, 24, 16, 19, 40, 24, 49, 20, 74, 24, 3, 29, 28, 33, 37,
- 29, 62, 33, 71, 29, 15, 43, 25, 38, 45, 43, 59, 38, 75, 43,
- 2, 5, 25, 1, 32, 5, 55, 1, 65, 6, 5, 10, 19, 15, 35,
- 10, 53, 15, 65, 10, 7, 24, 16, 20, 41, 24, 50, 20, 75, 24,
- 4, 29, 29, 33, 38, 29, 63, 33, 72, 29, 14, 43, 26, 38, 44,
- 43, 60, 38, 74, 43, 1, 5, 24, 1, 32, 6, 54, 1, 66, 6,
- 4, 10, 20, 15, 34, 10, 54, 15, 64, 10, 8, 24, 17, 20, 42,
- 24, 51, 20, 76, 24, 5, 29, 30, 33, 39, 29, 63, 34, 73, 29,
- 13, 43, 27, 38, 43, 43, 61, 38, 73, 43, 0, 5, 23, 1, 33,
- 6, 53, 1, 67, 6, 3, 10, 21, 15, 33, 10, 55, 15, 64, 11,
- 9, 24, 18, 20, 43, 24, 52, 20, 77, 24, 6, 29, 31, 33, 40,
- 29, 62, 34, 74, 29, 12, 43, 28, 38, 42, 43, 62, 38, 72, 43,
- 0, 6, 22, 1, 34, 6, 52, 1, 68, 6, 2, 10, 22, 15, 32,
- 10, 56, 15, 65, 11, 10, 24, 19, 20, 44, 24, 53, 20, 78, 24,
- 7, 29, 31, 34, 41, 29, 61, 34, 75, 29, 11, 43, 29, 38, 41,
- 43, 63, 38, 71, 43, 1, 6, 21, 1, 35, 6, 51, 1, 69, 6,
- 1, 10, 23, 15, 32, 11, 57, 15, 66, 11, 11, 24, 20, 20, 45,
- 24, 54, 20, 79, 24, 8, 29, 30, 34, 42, 29, 60, 34, 76, 29,
- 10, 43, 30, 38, 40, 43, 63, 39, 70, 43, 2, 6, 20, 1, 36,
- 6, 50, 1, 70, 6, 0, 10, 24, 15, 33, 11, 58, 15, 67, 11,
- 12, 24, 21, 20, 46, 24, 55, 20, 79, 25, 9, 29, 29, 34, 43,
- 29, 59, 34, 77, 29, 9, 43, 31, 38, 39, 43, 62, 39, 69, 43,
- 3, 6, 19, 1, 37, 6, 49, 1, 71, 6, 0, 11, 25, 15, 34,
- 11, 59, 15, 68, 11, 13, 24, 22, 20, 47, 24, 56, 20, 78, 25,
- 10, 29, 28, 34, 44, 29, 58, 34, 78, 29, 8, 43, 31, 39, 38,
- 43, 61, 39, 68, 43, 4, 6, 18, 1, 38, 6, 48, 1, 72, 6,
- 1, 11, 26, 15, 35, 11, 60, 15, 69, 11, 14, 24, 23, 20, 47,
- 25, 57, 20, 77, 25, 11, 29, 27, 34, 45, 29, 57, 34, 79, 29,
- 7, 43, 30, 39, 37, 43, 60, 39, 67, 43, 5, 6, 17, 1, 39,
- 6, 48, 2, 73, 6, 2, 11, 27, 15, 36, 11, 61, 15, 70, 11,
- 15, 24, 24, 20, 46, 25, 58, 20, 76, 25, 12, 29, 26, 34, 46,
- 29, 56, 34, 79, 30, 6, 43, 29, 39, 36, 43, 59, 39, 66, 43,
- 6, 6, 16, 1, 40, 6, 49, 2, 74, 6, 3, 11, 28, 15, 37,
- 11, 62, 15, 71, 11, 15, 25, 25, 20, 45, 25, 59, 20, 75, 25,
- 13, 29, 25, 34, 47, 29, 55, 34, 78, 30, 5, 43, 28, 39, 35,
- 43, 58, 39, 65, 43, 7, 6, 16, 2, 41, 6, 50, 2, 75, 6,
- 4, 11, 29, 15, 38, 11, 63, 15, 72, 11, 14, 25, 26, 20, 44,
- 25, 60, 20, 74, 25, 14, 29, 24, 34, 47, 30, 54, 34, 77, 30,
- 4, 43, 27, 39, 34, 43, 57, 39, 64, 43, 8, 6, 17, 2, 42,
- 6, 51, 2, 76, 6, 5, 11, 30, 15, 39, 11, 63, 16, 73, 11,
- 13, 25, 27, 20, 43, 25, 61, 20, 73, 25, 15, 29, 23, 34, 46,
- 30, 53, 34, 76, 30, 3, 43, 26, 39, 33, 43, 56, 39, 64, 44,
- 9, 6, 18, 2, 43, 6, 52, 2, 77, 6, 6, 11, 31, 15, 40,
- 11, 62, 16, 74, 11, 12, 25, 28, 20, 42, 25, 62, 20, 72, 25,
- 15, 30, 22, 34, 45, 30, 52, 34, 75, 30, 2, 43, 25, 39, 32,
- 43, 55, 39, 65, 44, 10, 6, 19, 2, 44, 6, 53, 2, 78, 6,
- 7, 11, 31, 16, 41, 11, 61, 16, 75, 11, 11, 25, 29, 20, 41,
- 25, 63, 20, 71, 25, 14, 30, 21, 34, 44, 30, 51, 34, 74, 30,
- 1, 43, 24, 39, 32, 44, 54, 39, 66, 44, 11, 6, 20, 2, 45,
- 6, 54, 2, 79, 6, 8, 11, 30, 16, 42, 11, 60, 16, 76, 11,
- 10, 25, 30, 20, 40, 25, 63, 21, 70, 25, 13, 30, 20, 34, 43,
- 30, 50, 34, 73, 30, 0, 43, 23, 39, 33, 44, 53, 39, 67, 44,
- 12, 6, 21, 2, 46, 6, 55, 2, 79, 7, 9, 11, 29, 16, 43,
- 11, 59, 16, 77, 11, 9, 25, 31, 20, 39, 25, 62, 21, 69, 25,
- 12, 30, 19, 34, 42, 30, 49, 34, 72, 30, 0, 44, 22, 39, 34,
- 44, 52, 39, 68, 44, 13, 6, 22, 2, 47, 6, 56, 2, 78, 7,
- 10, 11, 28, 16, 44, 11, 58, 16, 78, 11, 8, 25, 31, 21, 38,
- 25, 61, 21, 68, 25, 11, 30, 18, 34, 41, 30, 48, 34, 71, 30,
- 1, 44, 21, 39, 35, 44, 51, 39, 69, 44, 14, 6, 23, 2, 47,
- 7, 57, 2, 77, 7, 11, 11, 27, 16, 45, 11, 57, 16, 79, 11,
- 7, 25, 30, 21, 37, 25, 60, 21, 67, 25, 10, 30, 17, 34, 40,
- 30, 48, 35, 70, 30, 2, 44, 20, 39, 36, 44, 50, 39, 70, 44,
- 15, 6, 24, 2, 46, 7, 58, 2, 76, 7, 12, 11, 26, 16, 46,
- 11, 56, 16, 79, 12, 6, 25, 29, 21, 36, 25, 59, 21, 66, 25,
- 9, 30, 16, 34, 39, 30, 49, 35, 69, 30, 3, 44, 19, 39, 37,
- 44, 49, 39, 71, 44, 15, 7, 25, 2, 45, 7, 59, 2, 75, 7,
- 13, 11, 25, 16, 47, 11, 55, 16, 78, 12, 5, 25, 28, 21, 35,
- 25, 58, 21, 65, 25, 8, 30, 16, 35, 38, 30, 50, 35, 68, 30,
- 4, 44, 18, 39, 38, 44, 48, 39, 72, 44, 14, 7, 26, 2, 44,
- 7, 60, 2, 74, 7, 14, 11, 24, 16, 47, 12, 54, 16, 77, 12,
- 4, 25, 27, 21, 34, 25, 57, 21, 64, 25, 7, 30, 17, 35, 37,
- 30, 51, 35, 67, 30, 5, 44, 17, 39, 39, 44, 48, 40, 73, 44,
- 13, 7, 27, 2, 43, 7, 61, 2, 73, 7, 15, 11, 23, 16, 46,
- 12, 53, 16, 76, 12, 3, 25, 26, 21, 33, 25, 56, 21, 64, 26,
- 6, 30, 18, 35, 36, 30, 52, 35, 66, 30, 6, 44, 16, 39, 40,
- 44, 49, 40, 74, 44, 12, 7, 28, 2, 42, 7, 62, 2, 72, 7,
- 15, 12, 22, 16, 45, 12, 52, 16, 75, 12, 2, 25, 25, 21, 32,
- 25, 55, 21, 65, 26, 5, 30, 19, 35, 35, 30, 53, 35, 65, 30,
- 7, 44, 16, 40, 41, 44, 50, 40, 75, 44, 11, 7, 29, 2, 41,
- 7, 63, 2, 71, 7, 14, 12, 21, 16, 44, 12, 51, 16, 74, 12,
- 1, 25, 24, 21, 32, 26, 54, 21, 66, 26, 4, 30, 20, 35, 34,
- 30, 54, 35, 64, 30, 8, 44, 17, 40, 42, 44, 51, 40, 76, 44,
- 10, 7, 30, 2, 40, 7, 63, 3, 70, 7, 13, 12, 20, 16, 43,
- 12, 50, 16, 73, 12, 0, 25, 23, 21, 33, 26, 53, 21, 67, 26,
- 3, 30, 21, 35, 33, 30, 55, 35, 64, 31, 9, 44, 18, 40, 43,
- 44, 52, 40, 77, 44, 9, 7, 31, 2, 39, 7, 62, 3, 69, 7,
- 12, 12, 19, 16, 42, 12, 49, 16, 72, 12, 0, 26, 22, 21, 34,
- 26, 52, 21, 68, 26, 2, 30, 22, 35, 32, 30, 56, 35, 65, 31,
- 10, 44, 19, 40, 44, 44, 53, 40, 78, 44, 8, 7, 31, 3, 38,
- 7, 61, 3, 68, 7, 11, 12, 18, 16, 41, 12, 48, 16, 71, 12,
- 1, 26, 21, 21, 35, 26, 51, 21, 69, 26, 1, 30, 23, 35, 32,
- 31, 57, 35, 66, 31, 11, 44, 20, 40, 45, 44, 54, 40, 79, 44,
- 7, 7, 30, 3, 37, 7, 60, 3, 67, 7, 10, 12, 17, 16, 40,
- 12, 48, 17, 70, 12, 2, 26, 20, 21, 36, 26, 50, 21, 70, 26,
- 0, 30, 24, 35, 33, 31, 58, 35, 67, 31, 12, 44, 21, 40, 46,
- 44, 55, 40, 64, 36, 6, 7, 29, 3, 36, 7, 59, 3, 66, 7,
- 9, 12, 16, 16, 39, 12, 49, 17, 69, 12, 3, 26, 19, 21, 37,
- 26, 49, 21, 71, 26, 0, 31, 25, 35, 34, 31, 59, 35, 68, 31,
- 13, 44, 22, 40, 47, 44, 56, 40, 65, 36, 5, 7, 28, 3, 35,
- 7, 58, 3, 65, 7, 8, 12, 16, 17, 38, 12, 50, 17, 68, 12,
- 4, 26, 18, 21, 38, 26, 48, 21, 72, 26, 1, 31, 26, 35, 35,
- 31, 60, 35, 69, 31, 14, 44, 23, 40, 32, 36, 57, 40, 66, 36,
- 4, 7, 27, 3, 34, 7, 57, 3, 64, 7, 7, 12, 17, 17, 37,
- 12, 51, 17, 67, 12, 5, 26, 17, 21, 39, 26, 48, 22, 73, 26,
- 2, 31, 27, 35, 36, 31, 61, 35, 70, 31, 15, 44, 24, 40, 33,
- 36, 58, 40, 67, 36, 3, 7, 26, 3, 33, 7, 56, 3, 64, 8,
- 6, 12, 18, 17, 36, 12, 52, 17, 66, 12, 6, 26, 16, 21, 40,
- 26, 49, 22, 74, 26, 3, 31, 28, 35, 37, 31, 62, 35, 71, 31,
- 0, 36, 25, 40, 34, 36, 59, 40, 68, 36, 2, 7, 25, 3, 32,
- 7, 55, 3, 65, 8, 5, 12, 19, 17, 35, 12, 53, 17, 65, 12,
- 7, 26, 16, 22, 41, 26, 50, 22, 75, 26, 4, 31, 29, 35, 38,
- 31, 63, 35, 72, 31, 1, 36, 26, 40, 35, 36, 60, 40, 69, 36,
- 1, 7, 24, 3, 32, 8, 54, 3, 66, 8, 4, 12, 20, 17, 34,
- 12, 54, 17, 64, 12, 8, 26, 17, 22, 42, 26, 51, 22, 76, 26,
- 5, 31, 30, 35, 39, 31, 48, 27, 73, 31, 2, 36, 27, 40, 36,
- 36, 61, 40, 70, 36, 0, 7, 23, 3, 33, 8, 53, 3, 67, 8,
- 3, 12, 21, 17, 33, 12, 55, 17, 64, 13, 9, 26, 18, 22, 43,
- 26, 52, 22, 77, 26, 6, 31, 31, 35, 40, 31, 49, 27, 74, 31,
- 3, 36, 28, 40, 37, 36, 62, 40, 71, 36, 0, 8, 22, 3, 34,
- 8, 52, 3, 68, 8, 2, 12, 22, 17, 32, 12, 56, 17, 65, 13,
- 10, 26, 19, 22, 44, 26, 53, 22, 78, 26, 7, 31, 16, 27, 41,
- 31, 50, 27, 75, 31, 4, 36, 29, 40, 38, 36, 63, 40, 72, 36,
- 1, 8, 21, 3, 35, 8, 51, 3, 69, 8, 1, 12, 23, 17, 32,
- 13, 57, 17, 66, 13, 11, 26, 20, 22, 45, 26, 54, 22, 79, 26,
- 8, 31, 17, 27, 42, 31, 51, 27, 76, 31, 5, 36, 30, 40, 39,
- 36, 63, 41, 73, 36, 2, 8, 20, 3, 36, 8, 50, 3, 70, 8,
- 0, 12, 24, 17, 33, 13, 58, 17, 67, 13, 12, 26, 21, 22, 46,
- 26, 55, 22, 64, 18, 9, 31, 18, 27, 43, 31, 52, 27, 77, 31,
- 6, 36, 31, 40, 40, 36, 62, 41, 74, 36, 3, 8, 19, 3, 37,
- 8, 49, 3, 71, 8, 0, 13, 25, 17, 34, 13, 59, 17, 68, 13,
- 13, 26, 22, 22, 47, 26, 56, 22, 65, 18, 10, 31, 19, 27, 44,
- 31, 53, 27, 78, 31, 7, 36, 31, 41, 41, 36, 61, 41, 75, 36,
- 4, 8, 18, 3, 38, 8, 48, 3, 72, 8, 1, 13, 26, 17, 35,
- 13, 60, 17, 69, 13, 14, 26, 23, 22, 32, 18, 57, 22, 66, 18,
- 11, 31, 20, 27, 45, 31, 54, 27, 79, 31, 8, 36, 30, 41, 42,
- 36, 60, 41, 76, 36, 5, 8, 17, 3, 39, 8, 48, 4, 73, 8,
- 2, 13, 27, 17, 36, 13, 61, 17, 70, 13, 15, 26, 24, 22, 33,
- 18, 58, 22, 67, 18, 12, 31, 21, 27, 46, 31, 55, 27, 79, 32,
- 9, 36, 29, 41, 43, 36, 59, 41, 77, 36, 6, 8, 16, 3, 40,
- 8, 49, 4, 74, 8, 3, 13, 28, 17, 37, 13, 62, 17, 71, 13,
- 0, 18, 25, 22, 34, 18, 59, 22, 68, 18, 13, 31, 22, 27, 47,
- 31, 56, 27, 78, 32, 10, 36, 28, 41, 44, 36, 58, 41, 78, 36,
- 7, 8, 16, 4, 41, 8, 50, 4, 75, 8, 4, 13, 29, 17, 38,
- 13, 63, 17, 72, 13, 1, 18, 26, 22, 35, 18, 60, 22, 69, 18,
- 14, 31, 23, 27, 47, 32, 57, 27, 77, 32, 11, 36, 27, 41, 45,
- 36, 57, 41, 79, 36, 8, 8, 17, 4, 42, 8, 51, 4, 76, 8,
- 5, 13, 30, 17, 39, 13, 48, 9, 73, 13, 2, 18, 27, 22, 36,
- 18, 61, 22, 70, 18, 15, 31, 24, 27, 46, 32, 58, 27, 76, 32,
- 12, 36, 26, 41, 46, 36, 56, 41, 79, 37, 9, 8, 18, 4, 43,
- 8, 52, 4, 77, 8, 6, 13, 31, 17, 40, 13, 49, 9, 74, 13,
- 3, 18, 28, 22, 37, 18, 62, 22, 71, 18, 15, 32, 25, 27, 45,
- 32, 59, 27, 75, 32, 13, 36, 25, 41, 47, 36, 55, 41, 78, 37,
- 10, 8, 19, 4, 44, 8, 53, 4, 78, 8, 7, 13, 16, 9, 41,
- 13, 50, 9, 75, 13, 4, 18, 29, 22, 38, 18, 63, 22, 72, 18,
- 14, 32, 26, 27, 44, 32, 60, 27, 74, 32, 14, 36, 24, 41, 47,
- 37, 54, 41, 77, 37, 11, 8, 20, 4, 45, 8, 54, 4, 79, 8,
- 8, 13, 17, 9, 42, 13, 51, 9, 76, 13, 5, 18, 30, 22, 39,
- 18, 63, 23, 73, 18, 13, 32, 27, 27, 43, 32, 61, 27, 73, 32,
- 15, 36, 23, 41, 46, 37, 53, 41, 76, 37, 12, 8, 21, 4, 46,
- 8, 55, 4, 64, 0, 9, 13, 18, 9, 43, 13, 52, 9, 77, 13,
- 6, 18, 31, 22, 40, 18, 62, 23, 74, 18, 12, 32, 28, 27, 42,
- 32, 62, 27, 72, 32, 15, 37, 22, 41, 45, 37, 52, 41, 75, 37,
- 13, 8, 22, 4, 47, 8, 56, 4, 65, 0, 10, 13, 19, 9, 44,
- 13, 53, 9, 78, 13, 7, 18, 31, 23, 41, 18, 61, 23, 75, 18,
- 11, 32, 29, 27, 41, 32, 63, 27, 71, 32, 14, 37, 21, 41, 44,
- 37, 51, 41, 74, 37, 14, 8, 23, 4, 32, 0, 57, 4, 66, 0,
- 11, 13, 20, 9, 45, 13, 54, 9, 79, 13, 8, 18, 30, 23, 42,
- 18, 60, 23, 76, 18, 10, 32, 30, 27, 40, 32, 63, 28, 70, 32,
- 13, 37, 20, 41, 43, 37, 50, 41, 73, 37, 15, 8, 24, 4, 33,
- 0, 58, 4, 67, 0, 12, 13, 21, 9, 46, 13, 55, 9, 79, 14,
- 9, 18, 29, 23, 43, 18, 59, 23, 77, 18, 9, 32, 31, 27, 39,
- 32, 62, 28, 69, 32, 12, 37, 19, 41, 42, 37, 49, 41, 72, 37,
-};
-
-static const uint8_t hq_tab_06[] = {
- 0, 0, 25, 8, 34, 0, 59, 8, 68, 0, 13, 25, 22, 17, 47,
- 25, 56, 17, 78, 26, 10, 34, 28, 43, 44, 34, 58, 43, 78, 34,
- 8, 60, 31, 52, 38, 60, 61, 52, 68, 60, 1, 0, 26, 8, 35,
- 0, 60, 8, 69, 0, 14, 25, 23, 17, 47, 26, 57, 17, 77, 26,
- 11, 34, 27, 43, 45, 34, 57, 43, 79, 34, 7, 60, 30, 52, 37,
- 60, 60, 52, 67, 60, 2, 0, 27, 8, 36, 0, 61, 8, 70, 0,
- 15, 25, 24, 17, 46, 26, 58, 17, 76, 26, 12, 34, 26, 43, 46,
- 34, 56, 43, 79, 35, 6, 60, 29, 52, 36, 60, 59, 52, 66, 60,
- 3, 0, 28, 8, 37, 0, 62, 8, 71, 0, 15, 26, 25, 17, 45,
- 26, 59, 17, 75, 26, 13, 34, 25, 43, 47, 34, 55, 43, 78, 35,
- 5, 60, 28, 52, 35, 60, 58, 52, 65, 60, 4, 0, 29, 8, 38,
- 0, 63, 8, 72, 0, 14, 26, 26, 17, 44, 26, 60, 17, 74, 26,
- 14, 34, 24, 43, 47, 35, 54, 43, 77, 35, 4, 60, 27, 52, 34,
- 60, 57, 52, 64, 60, 5, 0, 30, 8, 39, 0, 63, 9, 73, 0,
- 13, 26, 27, 17, 43, 26, 61, 17, 73, 26, 15, 34, 23, 43, 46,
- 35, 53, 43, 76, 35, 3, 60, 26, 52, 33, 60, 56, 52, 64, 61,
- 6, 0, 31, 8, 40, 0, 62, 9, 74, 0, 12, 26, 28, 17, 42,
- 26, 62, 17, 72, 26, 15, 35, 22, 43, 45, 35, 52, 43, 75, 35,
- 2, 60, 25, 52, 32, 60, 55, 52, 65, 61, 7, 0, 31, 9, 41,
- 0, 61, 9, 75, 0, 11, 26, 29, 17, 41, 26, 63, 17, 71, 26,
- 14, 35, 21, 43, 44, 35, 51, 43, 74, 35, 1, 60, 24, 52, 32,
- 61, 54, 52, 66, 61, 8, 0, 30, 9, 42, 0, 60, 9, 76, 0,
- 10, 26, 30, 17, 40, 26, 63, 18, 70, 26, 13, 35, 20, 43, 43,
- 35, 50, 43, 73, 35, 0, 60, 23, 52, 33, 61, 53, 52, 67, 61,
- 9, 0, 29, 9, 43, 0, 59, 9, 77, 0, 9, 26, 31, 17, 39,
- 26, 62, 18, 69, 26, 12, 35, 19, 43, 42, 35, 49, 43, 72, 35,
- 0, 61, 22, 52, 34, 61, 52, 52, 68, 61, 10, 0, 28, 9, 44,
- 0, 58, 9, 78, 0, 8, 26, 31, 18, 38, 26, 61, 18, 68, 26,
- 11, 35, 18, 43, 41, 35, 48, 43, 71, 35, 1, 61, 21, 52, 35,
- 61, 51, 52, 69, 61, 11, 0, 27, 9, 45, 0, 57, 9, 79, 0,
- 7, 26, 30, 18, 37, 26, 60, 18, 67, 26, 10, 35, 17, 43, 40,
- 35, 48, 44, 70, 35, 2, 61, 20, 52, 36, 61, 50, 52, 70, 61,
- 12, 0, 26, 9, 46, 0, 56, 9, 79, 1, 6, 26, 29, 18, 36,
- 26, 59, 18, 66, 26, 9, 35, 16, 43, 39, 35, 49, 44, 69, 35,
- 3, 61, 19, 52, 37, 61, 49, 52, 71, 61, 13, 0, 25, 9, 47,
- 0, 55, 9, 78, 1, 5, 26, 28, 18, 35, 26, 58, 18, 65, 26,
- 8, 35, 16, 44, 38, 35, 50, 44, 68, 35, 4, 61, 18, 52, 38,
- 61, 48, 52, 72, 61, 14, 0, 24, 9, 47, 1, 54, 9, 77, 1,
- 4, 26, 27, 18, 34, 26, 57, 18, 64, 26, 7, 35, 17, 44, 37,
- 35, 51, 44, 67, 35, 5, 61, 17, 52, 39, 61, 48, 53, 73, 61,
- 15, 0, 23, 9, 46, 1, 53, 9, 76, 1, 3, 26, 26, 18, 33,
- 26, 56, 18, 64, 27, 6, 35, 18, 44, 36, 35, 52, 44, 66, 35,
- 6, 61, 16, 52, 40, 61, 49, 53, 74, 61, 15, 1, 22, 9, 45,
- 1, 52, 9, 75, 1, 2, 26, 25, 18, 32, 26, 55, 18, 65, 27,
- 5, 35, 19, 44, 35, 35, 53, 44, 65, 35, 7, 61, 16, 53, 41,
- 61, 50, 53, 75, 61, 14, 1, 21, 9, 44, 1, 51, 9, 74, 1,
- 1, 26, 24, 18, 32, 27, 54, 18, 66, 27, 4, 35, 20, 44, 34,
- 35, 54, 44, 64, 35, 8, 61, 17, 53, 42, 61, 51, 53, 76, 61,
- 13, 1, 20, 9, 43, 1, 50, 9, 73, 1, 0, 26, 23, 18, 33,
- 27, 53, 18, 67, 27, 3, 35, 21, 44, 33, 35, 55, 44, 64, 36,
- 9, 61, 18, 53, 43, 61, 52, 53, 77, 61, 12, 1, 19, 9, 42,
- 1, 49, 9, 72, 1, 0, 27, 22, 18, 34, 27, 52, 18, 68, 27,
- 2, 35, 22, 44, 32, 35, 56, 44, 65, 36, 10, 61, 19, 53, 44,
- 61, 53, 53, 78, 61, 11, 1, 18, 9, 41, 1, 48, 9, 71, 1,
- 1, 27, 21, 18, 35, 27, 51, 18, 69, 27, 1, 35, 23, 44, 32,
- 36, 57, 44, 66, 36, 11, 61, 20, 53, 45, 61, 54, 53, 79, 61,
- 10, 1, 17, 9, 40, 1, 48, 10, 70, 1, 2, 27, 20, 18, 36,
- 27, 50, 18, 70, 27, 0, 35, 24, 44, 33, 36, 58, 44, 67, 36,
- 12, 61, 21, 53, 46, 61, 55, 53, 79, 62, 9, 1, 16, 9, 39,
- 1, 49, 10, 69, 1, 3, 27, 19, 18, 37, 27, 49, 18, 71, 27,
- 0, 36, 25, 44, 34, 36, 59, 44, 68, 36, 13, 61, 22, 53, 47,
- 61, 56, 53, 78, 62, 8, 1, 16, 10, 38, 1, 50, 10, 68, 1,
- 4, 27, 18, 18, 38, 27, 48, 18, 72, 27, 1, 36, 26, 44, 35,
- 36, 60, 44, 69, 36, 14, 61, 23, 53, 47, 62, 57, 53, 77, 62,
- 7, 1, 17, 10, 37, 1, 51, 10, 67, 1, 5, 27, 17, 18, 39,
- 27, 48, 19, 73, 27, 2, 36, 27, 44, 36, 36, 61, 44, 70, 36,
- 15, 61, 24, 53, 46, 62, 58, 53, 76, 62, 6, 1, 18, 10, 36,
- 1, 52, 10, 66, 1, 6, 27, 16, 18, 40, 27, 49, 19, 74, 27,
- 3, 36, 28, 44, 37, 36, 62, 44, 71, 36, 15, 62, 25, 53, 45,
- 62, 59, 53, 75, 62, 5, 1, 19, 10, 35, 1, 53, 10, 65, 1,
- 7, 27, 16, 19, 41, 27, 50, 19, 75, 27, 4, 36, 29, 44, 38,
- 36, 63, 44, 72, 36, 14, 62, 26, 53, 44, 62, 60, 53, 74, 62,
- 4, 1, 20, 10, 34, 1, 54, 10, 64, 1, 8, 27, 17, 19, 42,
- 27, 51, 19, 76, 27, 5, 36, 30, 44, 39, 36, 63, 45, 73, 36,
- 13, 62, 27, 53, 43, 62, 61, 53, 73, 62, 3, 1, 21, 10, 33,
- 1, 55, 10, 64, 2, 9, 27, 18, 19, 43, 27, 52, 19, 77, 27,
- 6, 36, 31, 44, 40, 36, 62, 45, 74, 36, 12, 62, 28, 53, 42,
- 62, 62, 53, 72, 62, 2, 1, 22, 10, 32, 1, 56, 10, 65, 2,
- 10, 27, 19, 19, 44, 27, 53, 19, 78, 27, 7, 36, 31, 45, 41,
- 36, 61, 45, 75, 36, 11, 62, 29, 53, 41, 62, 63, 53, 71, 62,
- 1, 1, 23, 10, 32, 2, 57, 10, 66, 2, 11, 27, 20, 19, 45,
- 27, 54, 19, 79, 27, 8, 36, 30, 45, 42, 36, 60, 45, 76, 36,
- 10, 62, 30, 53, 40, 62, 63, 54, 70, 62, 0, 1, 24, 10, 33,
- 2, 58, 10, 67, 2, 12, 27, 21, 19, 46, 27, 55, 19, 79, 28,
- 9, 36, 29, 45, 43, 36, 59, 45, 77, 36, 9, 62, 31, 53, 39,
- 62, 62, 54, 69, 62, 0, 2, 25, 10, 34, 2, 59, 10, 68, 2,
- 13, 27, 22, 19, 47, 27, 56, 19, 78, 28, 10, 36, 28, 45, 44,
- 36, 58, 45, 78, 36, 8, 62, 31, 54, 38, 62, 61, 54, 68, 62,
- 1, 2, 26, 10, 35, 2, 60, 10, 69, 2, 14, 27, 23, 19, 47,
- 28, 57, 19, 77, 28, 11, 36, 27, 45, 45, 36, 57, 45, 79, 36,
- 7, 62, 30, 54, 37, 62, 60, 54, 67, 62, 2, 2, 27, 10, 36,
- 2, 61, 10, 70, 2, 15, 27, 24, 19, 46, 28, 58, 19, 76, 28,
- 12, 36, 26, 45, 46, 36, 56, 45, 79, 37, 6, 62, 29, 54, 36,
- 62, 59, 54, 66, 62, 3, 2, 28, 10, 37, 2, 62, 10, 71, 2,
- 15, 28, 25, 19, 45, 28, 59, 19, 75, 28, 13, 36, 25, 45, 47,
- 36, 55, 45, 78, 37, 5, 62, 28, 54, 35, 62, 58, 54, 65, 62,
- 4, 2, 29, 10, 38, 2, 63, 10, 72, 2, 14, 28, 26, 19, 44,
- 28, 60, 19, 74, 28, 14, 36, 24, 45, 47, 37, 54, 45, 77, 37,
- 4, 62, 27, 54, 34, 62, 57, 54, 64, 62, 5, 2, 30, 10, 39,
- 2, 63, 11, 73, 2, 13, 28, 27, 19, 43, 28, 61, 19, 73, 28,
- 15, 36, 23, 45, 46, 37, 53, 45, 76, 37, 3, 62, 26, 54, 33,
- 62, 56, 54, 64, 63, 6, 2, 31, 10, 40, 2, 62, 11, 74, 2,
- 12, 28, 28, 19, 42, 28, 62, 19, 72, 28, 15, 37, 22, 45, 45,
- 37, 52, 45, 75, 37, 2, 62, 25, 54, 32, 62, 55, 54, 65, 63,
- 7, 2, 31, 11, 41, 2, 61, 11, 75, 2, 11, 28, 29, 19, 41,
- 28, 63, 19, 71, 28, 14, 37, 21, 45, 44, 37, 51, 45, 74, 37,
- 1, 62, 24, 54, 32, 63, 54, 54, 66, 63, 8, 2, 30, 11, 42,
- 2, 60, 11, 76, 2, 10, 28, 30, 19, 40, 28, 63, 20, 70, 28,
- 13, 37, 20, 45, 43, 37, 50, 45, 73, 37, 0, 62, 23, 54, 33,
- 63, 53, 54, 67, 63, 9, 2, 29, 11, 43, 2, 59, 11, 77, 2,
- 9, 28, 31, 19, 39, 28, 62, 20, 69, 28, 12, 37, 19, 45, 42,
- 37, 49, 45, 72, 37, 0, 63, 22, 54, 34, 63, 52, 54, 68, 63,
- 10, 2, 28, 11, 44, 2, 58, 11, 78, 2, 8, 28, 31, 20, 38,
- 28, 61, 20, 68, 28, 11, 37, 18, 45, 41, 37, 48, 45, 71, 37,
- 1, 63, 21, 54, 35, 63, 51, 54, 69, 63, 11, 2, 27, 11, 45,
- 2, 57, 11, 79, 2, 7, 28, 30, 20, 37, 28, 60, 20, 67, 28,
- 10, 37, 17, 45, 40, 37, 48, 46, 70, 37, 2, 63, 20, 54, 36,
- 63, 50, 54, 70, 63, 12, 2, 26, 11, 46, 2, 56, 11, 79, 3,
- 6, 28, 29, 20, 36, 28, 59, 20, 66, 28, 9, 37, 16, 45, 39,
- 37, 49, 46, 69, 37, 3, 63, 19, 54, 37, 63, 49, 54, 71, 63,
- 13, 2, 25, 11, 47, 2, 55, 11, 78, 3, 5, 28, 28, 20, 35,
- 28, 58, 20, 65, 28, 8, 37, 16, 46, 38, 37, 50, 46, 68, 37,
- 4, 63, 18, 54, 38, 63, 48, 54, 72, 63, 14, 2, 24, 11, 47,
- 3, 54, 11, 77, 3, 4, 28, 27, 20, 34, 28, 57, 20, 64, 28,
- 7, 37, 17, 46, 37, 37, 51, 46, 67, 37, 5, 63, 17, 54, 39,
- 63, 48, 55, 73, 63, 15, 2, 23, 11, 46, 3, 53, 11, 76, 3,
- 3, 28, 26, 20, 33, 28, 56, 20, 64, 29, 6, 37, 18, 46, 36,
- 37, 52, 46, 66, 37, 6, 63, 16, 54, 40, 63, 49, 55, 74, 63,
- 15, 3, 22, 11, 45, 3, 52, 11, 75, 3, 2, 28, 25, 20, 32,
- 28, 55, 20, 65, 29, 5, 37, 19, 46, 35, 37, 53, 46, 65, 37,
- 7, 63, 16, 55, 41, 63, 50, 55, 75, 63, 14, 3, 21, 11, 44,
- 3, 51, 11, 74, 3, 1, 28, 24, 20, 32, 29, 54, 20, 66, 29,
- 4, 37, 20, 46, 34, 37, 54, 46, 64, 37, 8, 63, 17, 55, 42,
- 63, 51, 55, 76, 63, 13, 3, 20, 11, 43, 3, 50, 11, 73, 3,
- 0, 28, 23, 20, 33, 29, 53, 20, 67, 29, 3, 37, 21, 46, 33,
- 37, 55, 46, 64, 38, 9, 63, 18, 55, 43, 63, 52, 55, 77, 63,
- 12, 3, 19, 11, 42, 3, 49, 11, 72, 3, 0, 29, 22, 20, 34,
- 29, 52, 20, 68, 29, 2, 37, 22, 46, 32, 37, 56, 46, 65, 38,
- 10, 63, 19, 55, 44, 63, 53, 55, 78, 63, 11, 3, 18, 11, 41,
- 3, 48, 11, 71, 3, 1, 29, 21, 20, 35, 29, 51, 20, 69, 29,
- 1, 37, 23, 46, 32, 38, 57, 46, 66, 38, 11, 63, 20, 55, 45,
- 63, 54, 55, 79, 63, 10, 3, 17, 11, 40, 3, 48, 12, 70, 3,
- 2, 29, 20, 20, 36, 29, 50, 20, 70, 29, 0, 37, 24, 46, 33,
- 38, 58, 46, 67, 38, 12, 63, 21, 55, 46, 63, 55, 55, 79, 64,
- 9, 3, 16, 11, 39, 3, 49, 12, 69, 3, 3, 29, 19, 20, 37,
- 29, 49, 20, 71, 29, 0, 38, 25, 46, 34, 38, 59, 46, 68, 38,
- 13, 63, 22, 55, 47, 63, 56, 55, 78, 64, 8, 3, 16, 12, 38,
- 3, 50, 12, 68, 3, 4, 29, 18, 20, 38, 29, 48, 20, 72, 29,
- 1, 38, 26, 46, 35, 38, 60, 46, 69, 38, 14, 63, 23, 55, 47,
- 64, 57, 55, 77, 64, 7, 3, 17, 12, 37, 3, 51, 12, 67, 3,
- 5, 29, 17, 20, 39, 29, 48, 21, 73, 29, 2, 38, 27, 46, 36,
- 38, 61, 46, 70, 38, 15, 63, 24, 55, 46, 64, 58, 55, 76, 64,
- 6, 3, 18, 12, 36, 3, 52, 12, 66, 3, 6, 29, 16, 20, 40,
- 29, 49, 21, 74, 29, 3, 38, 28, 46, 37, 38, 62, 46, 71, 38,
- 15, 64, 25, 55, 45, 64, 59, 55, 75, 64, 5, 3, 19, 12, 35,
- 3, 53, 12, 65, 3, 7, 29, 16, 21, 41, 29, 50, 21, 75, 29,
- 4, 38, 29, 46, 38, 38, 63, 46, 72, 38, 14, 64, 26, 55, 44,
- 64, 60, 55, 74, 64, 4, 3, 20, 12, 34, 3, 54, 12, 64, 3,
- 8, 29, 17, 21, 42, 29, 51, 21, 76, 29, 5, 38, 30, 46, 39,
- 38, 63, 47, 73, 38, 13, 64, 27, 55, 43, 64, 61, 55, 73, 64,
- 3, 3, 21, 12, 33, 3, 55, 12, 64, 4, 9, 29, 18, 21, 43,
- 29, 52, 21, 77, 29, 6, 38, 31, 46, 40, 38, 62, 47, 74, 38,
- 12, 64, 28, 55, 42, 64, 62, 55, 72, 64, 2, 3, 22, 12, 32,
- 3, 56, 12, 65, 4, 10, 29, 19, 21, 44, 29, 53, 21, 78, 29,
- 7, 38, 31, 47, 41, 38, 61, 47, 75, 38, 11, 64, 29, 55, 41,
- 64, 63, 55, 71, 64, 1, 3, 23, 12, 32, 4, 57, 12, 66, 4,
- 11, 29, 20, 21, 45, 29, 54, 21, 79, 29, 8, 38, 30, 47, 42,
- 38, 60, 47, 76, 38, 10, 64, 30, 55, 40, 64, 63, 56, 70, 64,
- 0, 3, 24, 12, 33, 4, 58, 12, 67, 4, 12, 29, 21, 21, 46,
- 29, 55, 21, 79, 30, 9, 38, 29, 47, 43, 38, 59, 47, 77, 38,
- 9, 64, 31, 55, 39, 64, 62, 56, 69, 64, 0, 4, 25, 12, 34,
- 4, 59, 12, 68, 4, 13, 29, 22, 21, 47, 29, 56, 21, 78, 30,
- 10, 38, 28, 47, 44, 38, 58, 47, 78, 38, 8, 64, 31, 56, 38,
- 64, 61, 56, 68, 64, 1, 4, 26, 12, 35, 4, 60, 12, 69, 4,
- 14, 29, 23, 21, 47, 30, 57, 21, 77, 30, 11, 38, 27, 47, 45,
- 38, 57, 47, 79, 38, 7, 64, 30, 56, 37, 64, 60, 56, 67, 64,
- 2, 4, 27, 12, 36, 4, 61, 12, 70, 4, 15, 29, 24, 21, 46,
- 30, 58, 21, 76, 30, 12, 38, 26, 47, 46, 38, 56, 47, 79, 39,
- 6, 64, 29, 56, 36, 64, 59, 56, 66, 64, 3, 4, 28, 12, 37,
- 4, 62, 12, 71, 4, 15, 30, 25, 21, 45, 30, 59, 21, 75, 30,
- 13, 38, 25, 47, 47, 38, 55, 47, 78, 39, 5, 64, 28, 56, 35,
- 64, 58, 56, 65, 64, 4, 4, 29, 12, 38, 4, 63, 12, 72, 4,
- 14, 30, 26, 21, 44, 30, 60, 21, 74, 30, 14, 38, 24, 47, 47,
- 39, 54, 47, 77, 39, 4, 64, 27, 56, 34, 64, 57, 56, 64, 64,
- 5, 4, 30, 12, 39, 4, 63, 13, 73, 4, 13, 30, 27, 21, 43,
- 30, 61, 21, 73, 30, 15, 38, 23, 47, 46, 39, 53, 47, 76, 39,
- 3, 64, 26, 56, 33, 64, 56, 56, 64, 65, 6, 4, 31, 12, 40,
- 4, 62, 13, 74, 4, 12, 30, 28, 21, 42, 30, 62, 21, 72, 30,
- 15, 39, 22, 47, 45, 39, 52, 47, 75, 39, 2, 64, 25, 56, 32,
- 64, 55, 56, 65, 65, 7, 4, 31, 13, 41, 4, 61, 13, 75, 4,
- 11, 30, 29, 21, 41, 30, 63, 21, 71, 30, 14, 39, 21, 47, 44,
- 39, 51, 47, 74, 39, 1, 64, 24, 56, 32, 65, 54, 56, 66, 65,
- 8, 4, 30, 13, 42, 4, 60, 13, 76, 4, 10, 30, 30, 21, 40,
- 30, 63, 22, 70, 30, 13, 39, 20, 47, 43, 39, 50, 47, 73, 39,
- 0, 64, 23, 56, 33, 65, 53, 56, 67, 65, 9, 4, 29, 13, 43,
- 4, 59, 13, 77, 4, 9, 30, 31, 21, 39, 30, 62, 22, 69, 30,
- 12, 39, 19, 47, 42, 39, 49, 47, 72, 39, 0, 65, 22, 56, 34,
- 65, 52, 56, 68, 65, 10, 4, 28, 13, 44, 4, 58, 13, 78, 4,
- 8, 30, 31, 22, 38, 30, 61, 22, 68, 30, 11, 39, 18, 47, 41,
- 39, 48, 47, 71, 39, 1, 65, 21, 56, 35, 65, 51, 56, 69, 65,
- 11, 4, 27, 13, 45, 4, 57, 13, 79, 4, 7, 30, 30, 22, 37,
- 30, 60, 22, 67, 30, 10, 39, 17, 47, 40, 39, 48, 48, 70, 39,
- 2, 65, 20, 56, 36, 65, 50, 56, 70, 65, 12, 4, 26, 13, 46,
- 4, 56, 13, 79, 5, 6, 30, 29, 22, 36, 30, 59, 22, 66, 30,
- 9, 39, 16, 47, 39, 39, 49, 48, 69, 39, 3, 65, 19, 56, 37,
- 65, 49, 56, 71, 65, 13, 4, 25, 13, 47, 4, 55, 13, 78, 5,
- 5, 30, 28, 22, 35, 30, 58, 22, 65, 30, 8, 39, 16, 48, 38,
- 39, 50, 48, 68, 39, 4, 65, 18, 56, 38, 65, 48, 56, 72, 65,
- 14, 4, 24, 13, 47, 5, 54, 13, 77, 5, 4, 30, 27, 22, 34,
- 30, 57, 22, 64, 30, 7, 39, 17, 48, 37, 39, 51, 48, 67, 39,
- 5, 65, 17, 56, 39, 65, 48, 57, 73, 65, 15, 4, 23, 13, 46,
- 5, 53, 13, 76, 5, 3, 30, 26, 22, 33, 30, 56, 22, 64, 31,
- 6, 39, 18, 48, 36, 39, 52, 48, 66, 39, 6, 65, 16, 56, 40,
- 65, 49, 57, 74, 65, 15, 5, 22, 13, 45, 5, 52, 13, 75, 5,
- 2, 30, 25, 22, 32, 30, 55, 22, 65, 31, 5, 39, 19, 48, 35,
- 39, 53, 48, 65, 39, 7, 65, 16, 57, 41, 65, 50, 57, 75, 65,
- 14, 5, 21, 13, 44, 5, 51, 13, 74, 5, 1, 30, 24, 22, 32,
- 31, 54, 22, 66, 31, 4, 39, 20, 48, 34, 39, 54, 48, 64, 39,
- 8, 65, 17, 57, 42, 65, 51, 57, 76, 65, 13, 5, 20, 13, 43,
- 5, 50, 13, 73, 5, 0, 30, 23, 22, 33, 31, 53, 22, 67, 31,
- 3, 39, 21, 48, 33, 39, 55, 48, 64, 40, 9, 65, 18, 57, 43,
- 65, 52, 57, 77, 65, 12, 5, 19, 13, 42, 5, 49, 13, 72, 5,
- 0, 31, 22, 22, 34, 31, 52, 22, 68, 31, 2, 39, 22, 48, 32,
- 39, 56, 48, 65, 40, 10, 65, 19, 57, 44, 65, 53, 57, 78, 65,
- 11, 5, 18, 13, 41, 5, 48, 13, 71, 5, 1, 31, 21, 22, 35,
- 31, 51, 22, 69, 31, 1, 39, 23, 48, 32, 40, 57, 48, 66, 40,
- 11, 65, 20, 57, 45, 65, 54, 57, 79, 65, 10, 5, 17, 13, 40,
- 5, 48, 14, 70, 5, 2, 31, 20, 22, 36, 31, 50, 22, 70, 31,
- 0, 39, 24, 48, 33, 40, 58, 48, 67, 40, 12, 65, 21, 57, 46,
- 65, 55, 57, 79, 66, 9, 5, 16, 13, 39, 5, 49, 14, 69, 5,
- 3, 31, 19, 22, 37, 31, 49, 22, 71, 31, 0, 40, 25, 48, 34,
- 40, 59, 48, 68, 40, 13, 65, 22, 57, 47, 65, 56, 57, 78, 66,
- 8, 5, 16, 14, 38, 5, 50, 14, 68, 5, 4, 31, 18, 22, 38,
- 31, 48, 22, 72, 31, 1, 40, 26, 48, 35, 40, 60, 48, 69, 40,
- 14, 65, 23, 57, 47, 66, 57, 57, 77, 66, 7, 5, 17, 14, 37,
- 5, 51, 14, 67, 5, 5, 31, 17, 22, 39, 31, 48, 23, 73, 31,
- 2, 40, 27, 48, 36, 40, 61, 48, 70, 40, 15, 65, 24, 57, 46,
- 66, 58, 57, 76, 66, 6, 5, 18, 14, 36, 5, 52, 14, 66, 5,
- 6, 31, 16, 22, 40, 31, 49, 23, 74, 31, 3, 40, 28, 48, 37,
- 40, 62, 48, 71, 40, 15, 66, 25, 57, 45, 66, 59, 57, 75, 66,
- 5, 5, 19, 14, 35, 5, 53, 14, 65, 5, 7, 31, 16, 23, 41,
- 31, 50, 23, 75, 31, 4, 40, 29, 48, 38, 40, 63, 48, 72, 40,
- 14, 66, 26, 57, 44, 66, 60, 57, 74, 66, 4, 5, 20, 14, 34,
- 5, 54, 14, 64, 5, 8, 31, 17, 23, 42, 31, 51, 23, 76, 31,
- 5, 40, 30, 48, 39, 40, 63, 49, 73, 40, 13, 66, 27, 57, 43,
- 66, 61, 57, 73, 66, 3, 5, 21, 14, 33, 5, 55, 14, 64, 6,
- 9, 31, 18, 23, 43, 31, 52, 23, 77, 31, 6, 40, 31, 48, 40,
- 40, 62, 49, 74, 40, 12, 66, 28, 57, 42, 66, 62, 57, 72, 66,
- 2, 5, 22, 14, 32, 5, 56, 14, 65, 6, 10, 31, 19, 23, 44,
- 31, 53, 23, 78, 31, 7, 40, 31, 49, 41, 40, 61, 49, 75, 40,
- 11, 66, 29, 57, 41, 66, 63, 57, 71, 66, 1, 5, 23, 14, 32,
- 6, 57, 14, 66, 6, 11, 31, 20, 23, 45, 31, 54, 23, 79, 31,
- 8, 40, 30, 49, 42, 40, 60, 49, 76, 40, 10, 66, 30, 57, 40,
- 66, 63, 58, 70, 66, 0, 5, 24, 14, 33, 6, 58, 14, 67, 6,
- 12, 31, 21, 23, 46, 31, 55, 23, 79, 32, 9, 40, 29, 49, 43,
- 40, 59, 49, 77, 40, 9, 66, 31, 57, 39, 66, 62, 58, 69, 66,
- 0, 6, 25, 14, 34, 6, 59, 14, 68, 6, 13, 31, 22, 23, 47,
- 31, 56, 23, 78, 32, 10, 40, 28, 49, 44, 40, 58, 49, 78, 40,
- 8, 66, 31, 58, 38, 66, 61, 58, 68, 66, 1, 6, 26, 14, 35,
- 6, 60, 14, 69, 6, 14, 31, 23, 23, 47, 32, 57, 23, 77, 32,
- 11, 40, 27, 49, 45, 40, 57, 49, 79, 40, 7, 66, 30, 58, 37,
- 66, 60, 58, 67, 66, 2, 6, 27, 14, 36, 6, 61, 14, 70, 6,
- 15, 31, 24, 23, 46, 32, 58, 23, 76, 32, 12, 40, 26, 49, 46,
- 40, 56, 49, 79, 41, 6, 66, 29, 58, 36, 66, 59, 58, 66, 66,
- 3, 6, 28, 14, 37, 6, 62, 14, 71, 6, 15, 32, 25, 23, 45,
- 32, 59, 23, 75, 32, 13, 40, 25, 49, 47, 40, 55, 49, 78, 41,
- 5, 66, 28, 58, 35, 66, 58, 58, 65, 66, 4, 6, 29, 14, 38,
- 6, 63, 14, 72, 6, 14, 32, 26, 23, 44, 32, 60, 23, 74, 32,
- 14, 40, 24, 49, 47, 41, 54, 49, 77, 41, 4, 66, 27, 58, 34,
- 66, 57, 58, 64, 66, 5, 6, 30, 14, 39, 6, 63, 15, 73, 6,
- 13, 32, 27, 23, 43, 32, 61, 23, 73, 32, 15, 40, 23, 49, 46,
- 41, 53, 49, 76, 41, 3, 66, 26, 58, 33, 66, 56, 58, 64, 67,
- 6, 6, 31, 14, 40, 6, 62, 15, 74, 6, 12, 32, 28, 23, 42,
- 32, 62, 23, 72, 32, 15, 41, 22, 49, 45, 41, 52, 49, 75, 41,
- 2, 66, 25, 58, 32, 66, 55, 58, 65, 67, 7, 6, 31, 15, 41,
- 6, 61, 15, 75, 6, 11, 32, 29, 23, 41, 32, 63, 23, 71, 32,
- 14, 41, 21, 49, 44, 41, 51, 49, 74, 41, 1, 66, 24, 58, 32,
- 67, 54, 58, 66, 67, 8, 6, 30, 15, 42, 6, 60, 15, 76, 6,
- 10, 32, 30, 23, 40, 32, 63, 24, 70, 32, 13, 41, 20, 49, 43,
- 41, 50, 49, 73, 41, 0, 66, 23, 58, 33, 67, 53, 58, 67, 67,
- 9, 6, 29, 15, 43, 6, 59, 15, 77, 6, 9, 32, 31, 23, 39,
- 32, 62, 24, 69, 32, 12, 41, 19, 49, 42, 41, 49, 49, 72, 41,
- 0, 67, 22, 58, 34, 67, 52, 58, 68, 67, 10, 6, 28, 15, 44,
- 6, 58, 15, 78, 6, 8, 32, 31, 24, 38, 32, 61, 24, 68, 32,
- 11, 41, 18, 49, 41, 41, 48, 49, 71, 41, 1, 67, 21, 58, 35,
- 67, 51, 58, 69, 67, 11, 6, 27, 15, 45, 6, 57, 15, 79, 6,
- 7, 32, 30, 24, 37, 32, 60, 24, 67, 32, 10, 41, 17, 49, 40,
- 41, 48, 50, 70, 41, 2, 67, 20, 58, 36, 67, 50, 58, 70, 67,
- 12, 6, 26, 15, 46, 6, 56, 15, 79, 7, 6, 32, 29, 24, 36,
- 32, 59, 24, 66, 32, 9, 41, 16, 49, 39, 41, 49, 50, 69, 41,
- 3, 67, 19, 58, 37, 67, 49, 58, 71, 67, 13, 6, 25, 15, 47,
- 6, 55, 15, 78, 7, 5, 32, 28, 24, 35, 32, 58, 24, 65, 32,
- 8, 41, 16, 50, 38, 41, 50, 50, 68, 41, 4, 67, 18, 58, 38,
- 67, 48, 58, 72, 67, 14, 6, 24, 15, 47, 7, 54, 15, 77, 7,
- 4, 32, 27, 24, 34, 32, 57, 24, 64, 32, 7, 41, 17, 50, 37,
- 41, 51, 50, 67, 41, 5, 67, 17, 58, 39, 67, 48, 59, 73, 67,
- 15, 6, 23, 15, 46, 7, 53, 15, 76, 7, 3, 32, 26, 24, 33,
- 32, 56, 24, 64, 33, 6, 41, 18, 50, 36, 41, 52, 50, 66, 41,
- 6, 67, 16, 58, 40, 67, 49, 59, 74, 67, 15, 7, 22, 15, 45,
- 7, 52, 15, 75, 7, 2, 32, 25, 24, 32, 32, 55, 24, 65, 33,
- 5, 41, 19, 50, 35, 41, 53, 50, 65, 41, 7, 67, 16, 59, 41,
- 67, 50, 59, 75, 67, 14, 7, 21, 15, 44, 7, 51, 15, 74, 7,
- 1, 32, 24, 24, 32, 33, 54, 24, 66, 33, 4, 41, 20, 50, 34,
- 41, 54, 50, 64, 41, 8, 67, 17, 59, 42, 67, 51, 59, 76, 67,
- 13, 7, 20, 15, 43, 7, 50, 15, 73, 7, 0, 32, 23, 24, 33,
- 33, 53, 24, 67, 33, 3, 41, 21, 50, 33, 41, 55, 50, 64, 42,
- 9, 67, 18, 59, 43, 67, 52, 59, 77, 67, 12, 7, 19, 15, 42,
- 7, 49, 15, 72, 7, 0, 33, 22, 24, 34, 33, 52, 24, 68, 33,
- 2, 41, 22, 50, 32, 41, 56, 50, 65, 42, 10, 67, 19, 59, 44,
- 67, 53, 59, 78, 67, 11, 7, 18, 15, 41, 7, 48, 15, 71, 7,
- 1, 33, 21, 24, 35, 33, 51, 24, 69, 33, 1, 41, 23, 50, 32,
- 42, 57, 50, 66, 42, 11, 67, 20, 59, 45, 67, 54, 59, 79, 67,
- 10, 7, 17, 15, 40, 7, 48, 16, 70, 7, 2, 33, 20, 24, 36,
- 33, 50, 24, 70, 33, 0, 41, 24, 50, 33, 42, 58, 50, 67, 42,
- 12, 67, 21, 59, 46, 67, 55, 59, 64, 51, 9, 7, 16, 15, 39,
- 7, 49, 16, 69, 7, 3, 33, 19, 24, 37, 33, 49, 24, 71, 33,
- 0, 42, 25, 50, 34, 42, 59, 50, 68, 42, 13, 67, 22, 59, 47,
- 67, 56, 59, 65, 51, 8, 7, 16, 16, 38, 7, 50, 16, 68, 7,
- 4, 33, 18, 24, 38, 33, 48, 24, 72, 33, 1, 42, 26, 50, 35,
- 42, 60, 50, 69, 42, 14, 67, 23, 59, 32, 51, 57, 59, 66, 51,
- 7, 7, 17, 16, 37, 7, 51, 16, 67, 7, 5, 33, 17, 24, 39,
- 33, 48, 25, 73, 33, 2, 42, 27, 50, 36, 42, 61, 50, 70, 42,
- 15, 67, 24, 59, 33, 51, 58, 59, 67, 51, 6, 7, 18, 16, 36,
- 7, 52, 16, 66, 7, 6, 33, 16, 24, 40, 33, 49, 25, 74, 33,
- 3, 42, 28, 50, 37, 42, 62, 50, 71, 42, 0, 51, 25, 59, 34,
- 51, 59, 59, 68, 51, 5, 7, 19, 16, 35, 7, 53, 16, 65, 7,
- 7, 33, 16, 25, 41, 33, 50, 25, 75, 33, 4, 42, 29, 50, 38,
- 42, 63, 50, 72, 42, 1, 51, 26, 59, 35, 51, 60, 59, 69, 51,
- 4, 7, 20, 16, 34, 7, 54, 16, 64, 7, 8, 33, 17, 25, 42,
- 33, 51, 25, 76, 33, 5, 42, 30, 50, 39, 42, 48, 34, 73, 42,
- 2, 51, 27, 59, 36, 51, 61, 59, 70, 51, 3, 7, 21, 16, 33,
- 7, 55, 16, 64, 8, 9, 33, 18, 25, 43, 33, 52, 25, 77, 33,
- 6, 42, 31, 50, 40, 42, 49, 34, 74, 42, 3, 51, 28, 59, 37,
- 51, 62, 59, 71, 51, 2, 7, 22, 16, 32, 7, 56, 16, 65, 8,
- 10, 33, 19, 25, 44, 33, 53, 25, 78, 33, 7, 42, 16, 34, 41,
- 42, 50, 34, 75, 42, 4, 51, 29, 59, 38, 51, 63, 59, 72, 51,
- 1, 7, 23, 16, 32, 8, 57, 16, 66, 8, 11, 33, 20, 25, 45,
- 33, 54, 25, 79, 33, 8, 42, 17, 34, 42, 42, 51, 34, 76, 42,
- 5, 51, 30, 59, 39, 51, 63, 60, 73, 51, 0, 7, 24, 16, 33,
- 8, 58, 16, 67, 8, 12, 33, 21, 25, 46, 33, 55, 25, 64, 17,
- 9, 42, 18, 34, 43, 42, 52, 34, 77, 42, 6, 51, 31, 59, 40,
- 51, 62, 60, 74, 51, 0, 8, 25, 16, 34, 8, 59, 16, 68, 8,
- 13, 33, 22, 25, 47, 33, 56, 25, 65, 17, 10, 42, 19, 34, 44,
- 42, 53, 34, 78, 42, 7, 51, 31, 60, 41, 51, 61, 60, 75, 51,
- 1, 8, 26, 16, 35, 8, 60, 16, 69, 8, 14, 33, 23, 25, 32,
- 17, 57, 25, 66, 17, 11, 42, 20, 34, 45, 42, 54, 34, 79, 42,
- 8, 51, 30, 60, 42, 51, 60, 60, 76, 51, 2, 8, 27, 16, 36,
- 8, 61, 16, 70, 8, 15, 33, 24, 25, 33, 17, 58, 25, 67, 17,
- 12, 42, 21, 34, 46, 42, 55, 34, 79, 43, 9, 51, 29, 60, 43,
- 51, 59, 60, 77, 51, 3, 8, 28, 16, 37, 8, 62, 16, 71, 8,
- 0, 17, 25, 25, 34, 17, 59, 25, 68, 17, 13, 42, 22, 34, 47,
- 42, 56, 34, 78, 43, 10, 51, 28, 60, 44, 51, 58, 60, 78, 51,
- 4, 8, 29, 16, 38, 8, 63, 16, 72, 8, 1, 17, 26, 25, 35,
- 17, 60, 25, 69, 17, 14, 42, 23, 34, 47, 43, 57, 34, 77, 43,
- 11, 51, 27, 60, 45, 51, 57, 60, 79, 51, 5, 8, 30, 16, 39,
- 8, 48, 0, 73, 8, 2, 17, 27, 25, 36, 17, 61, 25, 70, 17,
- 15, 42, 24, 34, 46, 43, 58, 34, 76, 43, 12, 51, 26, 60, 46,
- 51, 56, 60, 79, 52, 6, 8, 31, 16, 40, 8, 49, 0, 74, 8,
- 3, 17, 28, 25, 37, 17, 62, 25, 71, 17, 15, 43, 25, 34, 45,
- 43, 59, 34, 75, 43, 13, 51, 25, 60, 47, 51, 55, 60, 78, 52,
- 7, 8, 16, 0, 41, 8, 50, 0, 75, 8, 4, 17, 29, 25, 38,
- 17, 63, 25, 72, 17, 14, 43, 26, 34, 44, 43, 60, 34, 74, 43,
- 14, 51, 24, 60, 47, 52, 54, 60, 77, 52, 8, 8, 17, 0, 42,
- 8, 51, 0, 76, 8, 5, 17, 30, 25, 39, 17, 63, 26, 73, 17,
- 13, 43, 27, 34, 43, 43, 61, 34, 73, 43, 15, 51, 23, 60, 46,
- 52, 53, 60, 76, 52, 9, 8, 18, 0, 43, 8, 52, 0, 77, 8,
- 6, 17, 31, 25, 40, 17, 62, 26, 74, 17, 12, 43, 28, 34, 42,
- 43, 62, 34, 72, 43, 15, 52, 22, 60, 45, 52, 52, 60, 75, 52,
- 10, 8, 19, 0, 44, 8, 53, 0, 78, 8, 7, 17, 31, 26, 41,
- 17, 61, 26, 75, 17, 11, 43, 29, 34, 41, 43, 63, 34, 71, 43,
- 14, 52, 21, 60, 44, 52, 51, 60, 74, 52, 11, 8, 20, 0, 45,
- 8, 54, 0, 79, 8, 8, 17, 30, 26, 42, 17, 60, 26, 76, 17,
- 10, 43, 30, 34, 40, 43, 63, 35, 70, 43, 13, 52, 20, 60, 43,
- 52, 50, 60, 73, 52, 12, 8, 21, 0, 46, 8, 55, 0, 79, 9,
- 9, 17, 29, 26, 43, 17, 59, 26, 77, 17, 9, 43, 31, 34, 39,
- 43, 62, 35, 69, 43, 12, 52, 19, 60, 42, 52, 49, 60, 72, 52,
- 13, 8, 22, 0, 47, 8, 56, 0, 78, 9, 10, 17, 28, 26, 44,
- 17, 58, 26, 78, 17, 8, 43, 31, 35, 38, 43, 61, 35, 68, 43,
- 11, 52, 18, 60, 41, 52, 48, 60, 71, 52, 14, 8, 23, 0, 47,
- 9, 57, 0, 77, 9, 11, 17, 27, 26, 45, 17, 57, 26, 79, 17,
- 7, 43, 30, 35, 37, 43, 60, 35, 67, 43, 10, 52, 17, 60, 40,
- 52, 48, 61, 70, 52, 15, 8, 24, 0, 46, 9, 58, 0, 76, 9,
- 12, 17, 26, 26, 46, 17, 56, 26, 79, 18, 6, 43, 29, 35, 36,
- 43, 59, 35, 66, 43, 9, 52, 16, 60, 39, 52, 49, 61, 69, 52,
- 15, 9, 25, 0, 45, 9, 59, 0, 75, 9, 13, 17, 25, 26, 47,
- 17, 55, 26, 78, 18, 5, 43, 28, 35, 35, 43, 58, 35, 65, 43,
- 8, 52, 16, 61, 38, 52, 50, 61, 68, 52, 14, 9, 26, 0, 44,
- 9, 60, 0, 74, 9, 14, 17, 24, 26, 47, 18, 54, 26, 77, 18,
- 4, 43, 27, 35, 34, 43, 57, 35, 64, 43, 7, 52, 17, 61, 37,
- 52, 51, 61, 67, 52, 13, 9, 27, 0, 43, 9, 61, 0, 73, 9,
- 15, 17, 23, 26, 46, 18, 53, 26, 76, 18, 3, 43, 26, 35, 33,
- 43, 56, 35, 64, 44, 6, 52, 18, 61, 36, 52, 52, 61, 66, 52,
- 12, 9, 28, 0, 42, 9, 62, 0, 72, 9, 15, 18, 22, 26, 45,
- 18, 52, 26, 75, 18, 2, 43, 25, 35, 32, 43, 55, 35, 65, 44,
- 5, 52, 19, 61, 35, 52, 53, 61, 65, 52, 11, 9, 29, 0, 41,
- 9, 63, 0, 71, 9, 14, 18, 21, 26, 44, 18, 51, 26, 74, 18,
- 1, 43, 24, 35, 32, 44, 54, 35, 66, 44, 4, 52, 20, 61, 34,
- 52, 54, 61, 64, 52, 10, 9, 30, 0, 40, 9, 63, 1, 70, 9,
- 13, 18, 20, 26, 43, 18, 50, 26, 73, 18, 0, 43, 23, 35, 33,
- 44, 53, 35, 67, 44, 3, 52, 21, 61, 33, 52, 55, 61, 64, 53,
- 9, 9, 31, 0, 39, 9, 62, 1, 69, 9, 12, 18, 19, 26, 42,
- 18, 49, 26, 72, 18, 0, 44, 22, 35, 34, 44, 52, 35, 68, 44,
- 2, 52, 22, 61, 32, 52, 56, 61, 65, 53, 8, 9, 31, 1, 38,
- 9, 61, 1, 68, 9, 11, 18, 18, 26, 41, 18, 48, 26, 71, 18,
- 1, 44, 21, 35, 35, 44, 51, 35, 69, 44, 1, 52, 23, 61, 32,
- 53, 57, 61, 66, 53, 7, 9, 30, 1, 37, 9, 60, 1, 67, 9,
- 10, 18, 17, 26, 40, 18, 48, 27, 70, 18, 2, 44, 20, 35, 36,
- 44, 50, 35, 70, 44, 0, 52, 24, 61, 33, 53, 58, 61, 67, 53,
- 6, 9, 29, 1, 36, 9, 59, 1, 66, 9, 9, 18, 16, 26, 39,
- 18, 49, 27, 69, 18, 3, 44, 19, 35, 37, 44, 49, 35, 71, 44,
- 0, 53, 25, 61, 34, 53, 59, 61, 68, 53, 5, 9, 28, 1, 35,
- 9, 58, 1, 65, 9, 8, 18, 16, 27, 38, 18, 50, 27, 68, 18,
- 4, 44, 18, 35, 38, 44, 48, 35, 72, 44, 1, 53, 26, 61, 35,
- 53, 60, 61, 69, 53, 4, 9, 27, 1, 34, 9, 57, 1, 64, 9,
- 7, 18, 17, 27, 37, 18, 51, 27, 67, 18, 5, 44, 17, 35, 39,
- 44, 48, 36, 73, 44, 2, 53, 27, 61, 36, 53, 61, 61, 70, 53,
- 3, 9, 26, 1, 33, 9, 56, 1, 64, 10, 6, 18, 18, 27, 36,
- 18, 52, 27, 66, 18, 6, 44, 16, 35, 40, 44, 49, 36, 74, 44,
- 3, 53, 28, 61, 37, 53, 62, 61, 71, 53, 2, 9, 25, 1, 32,
- 9, 55, 1, 65, 10, 5, 18, 19, 27, 35, 18, 53, 27, 65, 18,
- 7, 44, 16, 36, 41, 44, 50, 36, 75, 44, 4, 53, 29, 61, 38,
- 53, 63, 61, 72, 53, 1, 9, 24, 1, 32, 10, 54, 1, 66, 10,
- 4, 18, 20, 27, 34, 18, 54, 27, 64, 18, 8, 44, 17, 36, 42,
- 44, 51, 36, 76, 44, 5, 53, 30, 61, 39, 53, 63, 62, 73, 53,
- 0, 9, 23, 1, 33, 10, 53, 1, 67, 10, 3, 18, 21, 27, 33,
- 18, 55, 27, 64, 19, 9, 44, 18, 36, 43, 44, 52, 36, 77, 44,
- 6, 53, 31, 61, 40, 53, 62, 62, 74, 53, 0, 10, 22, 1, 34,
- 10, 52, 1, 68, 10, 2, 18, 22, 27, 32, 18, 56, 27, 65, 19,
- 10, 44, 19, 36, 44, 44, 53, 36, 78, 44, 7, 53, 31, 62, 41,
- 53, 61, 62, 75, 53, 1, 10, 21, 1, 35, 10, 51, 1, 69, 10,
- 1, 18, 23, 27, 32, 19, 57, 27, 66, 19, 11, 44, 20, 36, 45,
- 44, 54, 36, 79, 44, 8, 53, 30, 62, 42, 53, 60, 62, 76, 53,
- 2, 10, 20, 1, 36, 10, 50, 1, 70, 10, 0, 18, 24, 27, 33,
- 19, 58, 27, 67, 19, 12, 44, 21, 36, 46, 44, 55, 36, 79, 45,
- 9, 53, 29, 62, 43, 53, 59, 62, 77, 53, 3, 10, 19, 1, 37,
- 10, 49, 1, 71, 10, 0, 19, 25, 27, 34, 19, 59, 27, 68, 19,
- 13, 44, 22, 36, 47, 44, 56, 36, 78, 45, 10, 53, 28, 62, 44,
- 53, 58, 62, 78, 53, 4, 10, 18, 1, 38, 10, 48, 1, 72, 10,
- 1, 19, 26, 27, 35, 19, 60, 27, 69, 19, 14, 44, 23, 36, 47,
- 45, 57, 36, 77, 45, 11, 53, 27, 62, 45, 53, 57, 62, 79, 53,
- 5, 10, 17, 1, 39, 10, 48, 2, 73, 10, 2, 19, 27, 27, 36,
- 19, 61, 27, 70, 19, 15, 44, 24, 36, 46, 45, 58, 36, 76, 45,
- 12, 53, 26, 62, 46, 53, 56, 62, 79, 54, 6, 10, 16, 1, 40,
- 10, 49, 2, 74, 10, 3, 19, 28, 27, 37, 19, 62, 27, 71, 19,
- 15, 45, 25, 36, 45, 45, 59, 36, 75, 45, 13, 53, 25, 62, 47,
- 53, 55, 62, 78, 54, 7, 10, 16, 2, 41, 10, 50, 2, 75, 10,
- 4, 19, 29, 27, 38, 19, 63, 27, 72, 19, 14, 45, 26, 36, 44,
- 45, 60, 36, 74, 45, 14, 53, 24, 62, 47, 54, 54, 62, 77, 54,
- 8, 10, 17, 2, 42, 10, 51, 2, 76, 10, 5, 19, 30, 27, 39,
- 19, 63, 28, 73, 19, 13, 45, 27, 36, 43, 45, 61, 36, 73, 45,
- 15, 53, 23, 62, 46, 54, 53, 62, 76, 54, 9, 10, 18, 2, 43,
- 10, 52, 2, 77, 10, 6, 19, 31, 27, 40, 19, 62, 28, 74, 19,
- 12, 45, 28, 36, 42, 45, 62, 36, 72, 45, 15, 54, 22, 62, 45,
- 54, 52, 62, 75, 54, 10, 10, 19, 2, 44, 10, 53, 2, 78, 10,
- 7, 19, 31, 28, 41, 19, 61, 28, 75, 19, 11, 45, 29, 36, 41,
- 45, 63, 36, 71, 45, 14, 54, 21, 62, 44, 54, 51, 62, 74, 54,
- 11, 10, 20, 2, 45, 10, 54, 2, 79, 10, 8, 19, 30, 28, 42,
- 19, 60, 28, 76, 19, 10, 45, 30, 36, 40, 45, 63, 37, 70, 45,
- 13, 54, 20, 62, 43, 54, 50, 62, 73, 54, 12, 10, 21, 2, 46,
- 10, 55, 2, 79, 11, 9, 19, 29, 28, 43, 19, 59, 28, 77, 19,
- 9, 45, 31, 36, 39, 45, 62, 37, 69, 45, 12, 54, 19, 62, 42,
- 54, 49, 62, 72, 54, 13, 10, 22, 2, 47, 10, 56, 2, 78, 11,
- 10, 19, 28, 28, 44, 19, 58, 28, 78, 19, 8, 45, 31, 37, 38,
- 45, 61, 37, 68, 45, 11, 54, 18, 62, 41, 54, 48, 62, 71, 54,
- 14, 10, 23, 2, 47, 11, 57, 2, 77, 11, 11, 19, 27, 28, 45,
- 19, 57, 28, 79, 19, 7, 45, 30, 37, 37, 45, 60, 37, 67, 45,
- 10, 54, 17, 62, 40, 54, 48, 63, 70, 54, 15, 10, 24, 2, 46,
- 11, 58, 2, 76, 11, 12, 19, 26, 28, 46, 19, 56, 28, 79, 20,
- 6, 45, 29, 37, 36, 45, 59, 37, 66, 45, 9, 54, 16, 62, 39,
- 54, 49, 63, 69, 54, 15, 11, 25, 2, 45, 11, 59, 2, 75, 11,
- 13, 19, 25, 28, 47, 19, 55, 28, 78, 20, 5, 45, 28, 37, 35,
- 45, 58, 37, 65, 45, 8, 54, 16, 63, 38, 54, 50, 63, 68, 54,
- 14, 11, 26, 2, 44, 11, 60, 2, 74, 11, 14, 19, 24, 28, 47,
- 20, 54, 28, 77, 20, 4, 45, 27, 37, 34, 45, 57, 37, 64, 45,
- 7, 54, 17, 63, 37, 54, 51, 63, 67, 54, 13, 11, 27, 2, 43,
- 11, 61, 2, 73, 11, 15, 19, 23, 28, 46, 20, 53, 28, 76, 20,
- 3, 45, 26, 37, 33, 45, 56, 37, 64, 46, 6, 54, 18, 63, 36,
- 54, 52, 63, 66, 54, 12, 11, 28, 2, 42, 11, 62, 2, 72, 11,
- 15, 20, 22, 28, 45, 20, 52, 28, 75, 20, 2, 45, 25, 37, 32,
- 45, 55, 37, 65, 46, 5, 54, 19, 63, 35, 54, 53, 63, 65, 54,
- 11, 11, 29, 2, 41, 11, 63, 2, 71, 11, 14, 20, 21, 28, 44,
- 20, 51, 28, 74, 20, 1, 45, 24, 37, 32, 46, 54, 37, 66, 46,
- 4, 54, 20, 63, 34, 54, 54, 63, 64, 54, 10, 11, 30, 2, 40,
- 11, 63, 3, 70, 11, 13, 20, 20, 28, 43, 20, 50, 28, 73, 20,
- 0, 45, 23, 37, 33, 46, 53, 37, 67, 46, 3, 54, 21, 63, 33,
- 54, 55, 63, 64, 55, 9, 11, 31, 2, 39, 11, 62, 3, 69, 11,
- 12, 20, 19, 28, 42, 20, 49, 28, 72, 20, 0, 46, 22, 37, 34,
- 46, 52, 37, 68, 46, 2, 54, 22, 63, 32, 54, 56, 63, 65, 55,
- 8, 11, 31, 3, 38, 11, 61, 3, 68, 11, 11, 20, 18, 28, 41,
- 20, 48, 28, 71, 20, 1, 46, 21, 37, 35, 46, 51, 37, 69, 46,
- 1, 54, 23, 63, 32, 55, 57, 63, 66, 55, 7, 11, 30, 3, 37,
- 11, 60, 3, 67, 11, 10, 20, 17, 28, 40, 20, 48, 29, 70, 20,
- 2, 46, 20, 37, 36, 46, 50, 37, 70, 46, 0, 54, 24, 63, 33,
- 55, 58, 63, 67, 55, 6, 11, 29, 3, 36, 11, 59, 3, 66, 11,
- 9, 20, 16, 28, 39, 20, 49, 29, 69, 20, 3, 46, 19, 37, 37,
- 46, 49, 37, 71, 46, 0, 55, 25, 63, 34, 55, 59, 63, 68, 55,
- 5, 11, 28, 3, 35, 11, 58, 3, 65, 11, 8, 20, 16, 29, 38,
- 20, 50, 29, 68, 20, 4, 46, 18, 37, 38, 46, 48, 37, 72, 46,
- 1, 55, 26, 63, 35, 55, 60, 63, 69, 55, 4, 11, 27, 3, 34,
- 11, 57, 3, 64, 11, 7, 20, 17, 29, 37, 20, 51, 29, 67, 20,
- 5, 46, 17, 37, 39, 46, 48, 38, 73, 46, 2, 55, 27, 63, 36,
- 55, 61, 63, 70, 55, 3, 11, 26, 3, 33, 11, 56, 3, 64, 12,
- 6, 20, 18, 29, 36, 20, 52, 29, 66, 20, 6, 46, 16, 37, 40,
- 46, 49, 38, 74, 46, 3, 55, 28, 63, 37, 55, 62, 63, 71, 55,
- 2, 11, 25, 3, 32, 11, 55, 3, 65, 12, 5, 20, 19, 29, 35,
- 20, 53, 29, 65, 20, 7, 46, 16, 38, 41, 46, 50, 38, 75, 46,
- 4, 55, 29, 63, 38, 55, 63, 63, 72, 55, 1, 11, 24, 3, 32,
- 12, 54, 3, 66, 12, 4, 20, 20, 29, 34, 20, 54, 29, 64, 20,
- 8, 46, 17, 38, 42, 46, 51, 38, 76, 46, 5, 55, 30, 63, 39,
- 55, 63, 64, 73, 55, 0, 11, 23, 3, 33, 12, 53, 3, 67, 12,
- 3, 20, 21, 29, 33, 20, 55, 29, 64, 21, 9, 46, 18, 38, 43,
- 46, 52, 38, 77, 46, 6, 55, 31, 63, 40, 55, 62, 64, 74, 55,
- 0, 12, 22, 3, 34, 12, 52, 3, 68, 12, 2, 20, 22, 29, 32,
- 20, 56, 29, 65, 21, 10, 46, 19, 38, 44, 46, 53, 38, 78, 46,
- 7, 55, 31, 64, 41, 55, 61, 64, 75, 55, 1, 12, 21, 3, 35,
- 12, 51, 3, 69, 12, 1, 20, 23, 29, 32, 21, 57, 29, 66, 21,
- 11, 46, 20, 38, 45, 46, 54, 38, 79, 46, 8, 55, 30, 64, 42,
- 55, 60, 64, 76, 55, 2, 12, 20, 3, 36, 12, 50, 3, 70, 12,
- 0, 20, 24, 29, 33, 21, 58, 29, 67, 21, 12, 46, 21, 38, 46,
- 46, 55, 38, 79, 47, 9, 55, 29, 64, 43, 55, 59, 64, 77, 55,
- 3, 12, 19, 3, 37, 12, 49, 3, 71, 12, 0, 21, 25, 29, 34,
- 21, 59, 29, 68, 21, 13, 46, 22, 38, 47, 46, 56, 38, 78, 47,
- 10, 55, 28, 64, 44, 55, 58, 64, 78, 55, 4, 12, 18, 3, 38,
- 12, 48, 3, 72, 12, 1, 21, 26, 29, 35, 21, 60, 29, 69, 21,
- 14, 46, 23, 38, 47, 47, 57, 38, 77, 47, 11, 55, 27, 64, 45,
- 55, 57, 64, 79, 55, 5, 12, 17, 3, 39, 12, 48, 4, 73, 12,
- 2, 21, 27, 29, 36, 21, 61, 29, 70, 21, 15, 46, 24, 38, 46,
- 47, 58, 38, 76, 47, 12, 55, 26, 64, 46, 55, 56, 64, 79, 56,
- 6, 12, 16, 3, 40, 12, 49, 4, 74, 12, 3, 21, 28, 29, 37,
- 21, 62, 29, 71, 21, 15, 47, 25, 38, 45, 47, 59, 38, 75, 47,
- 13, 55, 25, 64, 47, 55, 55, 64, 78, 56, 7, 12, 16, 4, 41,
- 12, 50, 4, 75, 12, 4, 21, 29, 29, 38, 21, 63, 29, 72, 21,
- 14, 47, 26, 38, 44, 47, 60, 38, 74, 47, 14, 55, 24, 64, 47,
- 56, 54, 64, 77, 56, 8, 12, 17, 4, 42, 12, 51, 4, 76, 12,
- 5, 21, 30, 29, 39, 21, 63, 30, 73, 21, 13, 47, 27, 38, 43,
- 47, 61, 38, 73, 47, 15, 55, 23, 64, 46, 56, 53, 64, 76, 56,
- 9, 12, 18, 4, 43, 12, 52, 4, 77, 12, 6, 21, 31, 29, 40,
- 21, 62, 30, 74, 21, 12, 47, 28, 38, 42, 47, 62, 38, 72, 47,
- 15, 56, 22, 64, 45, 56, 52, 64, 75, 56, 10, 12, 19, 4, 44,
- 12, 53, 4, 78, 12, 7, 21, 31, 30, 41, 21, 61, 30, 75, 21,
- 11, 47, 29, 38, 41, 47, 63, 38, 71, 47, 14, 56, 21, 64, 44,
- 56, 51, 64, 74, 56, 11, 12, 20, 4, 45, 12, 54, 4, 79, 12,
- 8, 21, 30, 30, 42, 21, 60, 30, 76, 21, 10, 47, 30, 38, 40,
- 47, 63, 39, 70, 47, 13, 56, 20, 64, 43, 56, 50, 64, 73, 56,
- 12, 12, 21, 4, 46, 12, 55, 4, 79, 13, 9, 21, 29, 30, 43,
- 21, 59, 30, 77, 21, 9, 47, 31, 38, 39, 47, 62, 39, 69, 47,
- 12, 56, 19, 64, 42, 56, 49, 64, 72, 56, 13, 12, 22, 4, 47,
- 12, 56, 4, 78, 13, 10, 21, 28, 30, 44, 21, 58, 30, 78, 21,
- 8, 47, 31, 39, 38, 47, 61, 39, 68, 47, 11, 56, 18, 64, 41,
- 56, 48, 64, 71, 56, 14, 12, 23, 4, 47, 13, 57, 4, 77, 13,
- 11, 21, 27, 30, 45, 21, 57, 30, 79, 21, 7, 47, 30, 39, 37,
- 47, 60, 39, 67, 47, 10, 56, 17, 64, 40, 56, 48, 65, 70, 56,
- 15, 12, 24, 4, 46, 13, 58, 4, 76, 13, 12, 21, 26, 30, 46,
- 21, 56, 30, 79, 22, 6, 47, 29, 39, 36, 47, 59, 39, 66, 47,
- 9, 56, 16, 64, 39, 56, 49, 65, 69, 56, 15, 13, 25, 4, 45,
- 13, 59, 4, 75, 13, 13, 21, 25, 30, 47, 21, 55, 30, 78, 22,
- 5, 47, 28, 39, 35, 47, 58, 39, 65, 47, 8, 56, 16, 65, 38,
- 56, 50, 65, 68, 56, 14, 13, 26, 4, 44, 13, 60, 4, 74, 13,
- 14, 21, 24, 30, 47, 22, 54, 30, 77, 22, 4, 47, 27, 39, 34,
- 47, 57, 39, 64, 47, 7, 56, 17, 65, 37, 56, 51, 65, 67, 56,
- 13, 13, 27, 4, 43, 13, 61, 4, 73, 13, 15, 21, 23, 30, 46,
- 22, 53, 30, 76, 22, 3, 47, 26, 39, 33, 47, 56, 39, 64, 48,
- 6, 56, 18, 65, 36, 56, 52, 65, 66, 56, 12, 13, 28, 4, 42,
- 13, 62, 4, 72, 13, 15, 22, 22, 30, 45, 22, 52, 30, 75, 22,
- 2, 47, 25, 39, 32, 47, 55, 39, 65, 48, 5, 56, 19, 65, 35,
- 56, 53, 65, 65, 56, 11, 13, 29, 4, 41, 13, 63, 4, 71, 13,
- 14, 22, 21, 30, 44, 22, 51, 30, 74, 22, 1, 47, 24, 39, 32,
- 48, 54, 39, 66, 48, 4, 56, 20, 65, 34, 56, 54, 65, 64, 56,
- 10, 13, 30, 4, 40, 13, 63, 5, 70, 13, 13, 22, 20, 30, 43,
- 22, 50, 30, 73, 22, 0, 47, 23, 39, 33, 48, 53, 39, 67, 48,
- 3, 56, 21, 65, 33, 56, 55, 65, 64, 57, 9, 13, 31, 4, 39,
- 13, 62, 5, 69, 13, 12, 22, 19, 30, 42, 22, 49, 30, 72, 22,
- 0, 48, 22, 39, 34, 48, 52, 39, 68, 48, 2, 56, 22, 65, 32,
- 56, 56, 65, 65, 57, 8, 13, 31, 5, 38, 13, 61, 5, 68, 13,
- 11, 22, 18, 30, 41, 22, 48, 30, 71, 22, 1, 48, 21, 39, 35,
- 48, 51, 39, 69, 48, 1, 56, 23, 65, 32, 57, 57, 65, 66, 57,
- 7, 13, 30, 5, 37, 13, 60, 5, 67, 13, 10, 22, 17, 30, 40,
- 22, 48, 31, 70, 22, 2, 48, 20, 39, 36, 48, 50, 39, 70, 48,
- 0, 56, 24, 65, 33, 57, 58, 65, 67, 57, 6, 13, 29, 5, 36,
- 13, 59, 5, 66, 13, 9, 22, 16, 30, 39, 22, 49, 31, 69, 22,
- 3, 48, 19, 39, 37, 48, 49, 39, 71, 48, 0, 57, 25, 65, 34,
- 57, 59, 65, 68, 57, 5, 13, 28, 5, 35, 13, 58, 5, 65, 13,
- 8, 22, 16, 31, 38, 22, 50, 31, 68, 22, 4, 48, 18, 39, 38,
- 48, 48, 39, 72, 48, 1, 57, 26, 65, 35, 57, 60, 65, 69, 57,
- 4, 13, 27, 5, 34, 13, 57, 5, 64, 13, 7, 22, 17, 31, 37,
- 22, 51, 31, 67, 22, 5, 48, 17, 39, 39, 48, 48, 40, 73, 48,
- 2, 57, 27, 65, 36, 57, 61, 65, 70, 57, 3, 13, 26, 5, 33,
- 13, 56, 5, 64, 14, 6, 22, 18, 31, 36, 22, 52, 31, 66, 22,
- 6, 48, 16, 39, 40, 48, 49, 40, 74, 48, 3, 57, 28, 65, 37,
- 57, 62, 65, 71, 57, 2, 13, 25, 5, 32, 13, 55, 5, 65, 14,
- 5, 22, 19, 31, 35, 22, 53, 31, 65, 22, 7, 48, 16, 40, 41,
- 48, 50, 40, 75, 48, 4, 57, 29, 65, 38, 57, 63, 65, 72, 57,
- 1, 13, 24, 5, 32, 14, 54, 5, 66, 14, 4, 22, 20, 31, 34,
- 22, 54, 31, 64, 22, 8, 48, 17, 40, 42, 48, 51, 40, 76, 48,
- 5, 57, 30, 65, 39, 57, 63, 66, 73, 57, 0, 13, 23, 5, 33,
- 14, 53, 5, 67, 14, 3, 22, 21, 31, 33, 22, 55, 31, 64, 23,
- 9, 48, 18, 40, 43, 48, 52, 40, 77, 48, 6, 57, 31, 65, 40,
- 57, 62, 66, 74, 57, 0, 14, 22, 5, 34, 14, 52, 5, 68, 14,
- 2, 22, 22, 31, 32, 22, 56, 31, 65, 23, 10, 48, 19, 40, 44,
- 48, 53, 40, 78, 48, 7, 57, 31, 66, 41, 57, 61, 66, 75, 57,
- 1, 14, 21, 5, 35, 14, 51, 5, 69, 14, 1, 22, 23, 31, 32,
- 23, 57, 31, 66, 23, 11, 48, 20, 40, 45, 48, 54, 40, 79, 48,
- 8, 57, 30, 66, 42, 57, 60, 66, 76, 57, 2, 14, 20, 5, 36,
- 14, 50, 5, 70, 14, 0, 22, 24, 31, 33, 23, 58, 31, 67, 23,
- 12, 48, 21, 40, 46, 48, 55, 40, 79, 49, 9, 57, 29, 66, 43,
- 57, 59, 66, 77, 57, 3, 14, 19, 5, 37, 14, 49, 5, 71, 14,
- 0, 23, 25, 31, 34, 23, 59, 31, 68, 23, 13, 48, 22, 40, 47,
- 48, 56, 40, 78, 49, 10, 57, 28, 66, 44, 57, 58, 66, 78, 57,
- 4, 14, 18, 5, 38, 14, 48, 5, 72, 14, 1, 23, 26, 31, 35,
- 23, 60, 31, 69, 23, 14, 48, 23, 40, 47, 49, 57, 40, 77, 49,
- 11, 57, 27, 66, 45, 57, 57, 66, 79, 57, 5, 14, 17, 5, 39,
- 14, 48, 6, 73, 14, 2, 23, 27, 31, 36, 23, 61, 31, 70, 23,
- 15, 48, 24, 40, 46, 49, 58, 40, 76, 49, 12, 57, 26, 66, 46,
- 57, 56, 66, 79, 58, 6, 14, 16, 5, 40, 14, 49, 6, 74, 14,
- 3, 23, 28, 31, 37, 23, 62, 31, 71, 23, 15, 49, 25, 40, 45,
- 49, 59, 40, 75, 49, 13, 57, 25, 66, 47, 57, 55, 66, 78, 58,
- 7, 14, 16, 6, 41, 14, 50, 6, 75, 14, 4, 23, 29, 31, 38,
- 23, 63, 31, 72, 23, 14, 49, 26, 40, 44, 49, 60, 40, 74, 49,
- 14, 57, 24, 66, 47, 58, 54, 66, 77, 58, 8, 14, 17, 6, 42,
- 14, 51, 6, 76, 14, 5, 23, 30, 31, 39, 23, 63, 32, 73, 23,
- 13, 49, 27, 40, 43, 49, 61, 40, 73, 49, 15, 57, 23, 66, 46,
- 58, 53, 66, 76, 58, 9, 14, 18, 6, 43, 14, 52, 6, 77, 14,
- 6, 23, 31, 31, 40, 23, 62, 32, 74, 23, 12, 49, 28, 40, 42,
- 49, 62, 40, 72, 49, 15, 58, 22, 66, 45, 58, 52, 66, 75, 58,
- 10, 14, 19, 6, 44, 14, 53, 6, 78, 14, 7, 23, 31, 32, 41,
- 23, 61, 32, 75, 23, 11, 49, 29, 40, 41, 49, 63, 40, 71, 49,
- 14, 58, 21, 66, 44, 58, 51, 66, 74, 58, 11, 14, 20, 6, 45,
- 14, 54, 6, 79, 14, 8, 23, 30, 32, 42, 23, 60, 32, 76, 23,
- 10, 49, 30, 40, 40, 49, 63, 41, 70, 49, 13, 58, 20, 66, 43,
- 58, 50, 66, 73, 58, 12, 14, 21, 6, 46, 14, 55, 6, 79, 15,
- 9, 23, 29, 32, 43, 23, 59, 32, 77, 23, 9, 49, 31, 40, 39,
- 49, 62, 41, 69, 49, 12, 58, 19, 66, 42, 58, 49, 66, 72, 58,
- 13, 14, 22, 6, 47, 14, 56, 6, 78, 15, 10, 23, 28, 32, 44,
- 23, 58, 32, 78, 23, 8, 49, 31, 41, 38, 49, 61, 41, 68, 49,
- 11, 58, 18, 66, 41, 58, 48, 66, 71, 58, 14, 14, 23, 6, 47,
- 15, 57, 6, 77, 15, 11, 23, 27, 32, 45, 23, 57, 32, 79, 23,
- 7, 49, 30, 41, 37, 49, 60, 41, 67, 49, 10, 58, 17, 66, 40,
- 58, 48, 67, 70, 58, 15, 14, 24, 6, 46, 15, 58, 6, 76, 15,
- 12, 23, 26, 32, 46, 23, 56, 32, 79, 24, 6, 49, 29, 41, 36,
- 49, 59, 41, 66, 49, 9, 58, 16, 66, 39, 58, 49, 67, 69, 58,
- 15, 15, 25, 6, 45, 15, 59, 6, 75, 15, 13, 23, 25, 32, 47,
- 23, 55, 32, 78, 24, 5, 49, 28, 41, 35, 49, 58, 41, 65, 49,
- 8, 58, 16, 67, 38, 58, 50, 67, 68, 58, 14, 15, 26, 6, 44,
- 15, 60, 6, 74, 15, 14, 23, 24, 32, 47, 24, 54, 32, 77, 24,
- 4, 49, 27, 41, 34, 49, 57, 41, 64, 49, 7, 58, 17, 67, 37,
- 58, 51, 67, 67, 58, 13, 15, 27, 6, 43, 15, 61, 6, 73, 15,
- 15, 23, 23, 32, 46, 24, 53, 32, 76, 24, 3, 49, 26, 41, 33,
- 49, 56, 41, 64, 50, 6, 58, 18, 67, 36, 58, 52, 67, 66, 58,
- 12, 15, 28, 6, 42, 15, 62, 6, 72, 15, 15, 24, 22, 32, 45,
- 24, 52, 32, 75, 24, 2, 49, 25, 41, 32, 49, 55, 41, 65, 50,
- 5, 58, 19, 67, 35, 58, 53, 67, 65, 58, 11, 15, 29, 6, 41,
- 15, 63, 6, 71, 15, 14, 24, 21, 32, 44, 24, 51, 32, 74, 24,
- 1, 49, 24, 41, 32, 50, 54, 41, 66, 50, 4, 58, 20, 67, 34,
- 58, 54, 67, 64, 58, 10, 15, 30, 6, 40, 15, 63, 7, 70, 15,
- 13, 24, 20, 32, 43, 24, 50, 32, 73, 24, 0, 49, 23, 41, 33,
- 50, 53, 41, 67, 50, 3, 58, 21, 67, 33, 58, 55, 67, 64, 59,
- 9, 15, 31, 6, 39, 15, 62, 7, 69, 15, 12, 24, 19, 32, 42,
- 24, 49, 32, 72, 24, 0, 50, 22, 41, 34, 50, 52, 41, 68, 50,
- 2, 58, 22, 67, 32, 58, 56, 67, 65, 59, 8, 15, 31, 7, 38,
- 15, 61, 7, 68, 15, 11, 24, 18, 32, 41, 24, 48, 32, 71, 24,
- 1, 50, 21, 41, 35, 50, 51, 41, 69, 50, 1, 58, 23, 67, 32,
- 59, 57, 67, 66, 59, 7, 15, 30, 7, 37, 15, 60, 7, 67, 15,
- 10, 24, 17, 32, 40, 24, 48, 33, 70, 24, 2, 50, 20, 41, 36,
- 50, 50, 41, 70, 50, 0, 58, 24, 67, 33, 59, 58, 67, 67, 59,
- 6, 15, 29, 7, 36, 15, 59, 7, 66, 15, 9, 24, 16, 32, 39,
- 24, 49, 33, 69, 24, 3, 50, 19, 41, 37, 50, 49, 41, 71, 50,
- 0, 59, 25, 67, 34, 59, 59, 67, 68, 59, 5, 15, 28, 7, 35,
- 15, 58, 7, 65, 15, 8, 24, 16, 33, 38, 24, 50, 33, 68, 24,
- 4, 50, 18, 41, 38, 50, 48, 41, 72, 50, 1, 59, 26, 67, 35,
- 59, 60, 67, 69, 59, 4, 15, 27, 7, 34, 15, 57, 7, 64, 15,
- 7, 24, 17, 33, 37, 24, 51, 33, 67, 24, 5, 50, 17, 41, 39,
- 50, 48, 42, 73, 50, 2, 59, 27, 67, 36, 59, 61, 67, 70, 59,
- 3, 15, 26, 7, 33, 15, 56, 7, 64, 16, 6, 24, 18, 33, 36,
- 24, 52, 33, 66, 24, 6, 50, 16, 41, 40, 50, 49, 42, 74, 50,
- 3, 59, 28, 67, 37, 59, 62, 67, 71, 59, 2, 15, 25, 7, 32,
- 15, 55, 7, 65, 16, 5, 24, 19, 33, 35, 24, 53, 33, 65, 24,
- 7, 50, 16, 42, 41, 50, 50, 42, 75, 50, 4, 59, 29, 67, 38,
- 59, 63, 67, 72, 59, 1, 15, 24, 7, 32, 16, 54, 7, 66, 16,
- 4, 24, 20, 33, 34, 24, 54, 33, 64, 24, 8, 50, 17, 42, 42,
- 50, 51, 42, 76, 50, 5, 59, 30, 67, 39, 59, 48, 51, 73, 59,
- 0, 15, 23, 7, 33, 16, 53, 7, 67, 16, 3, 24, 21, 33, 33,
- 24, 55, 33, 64, 25, 9, 50, 18, 42, 43, 50, 52, 42, 77, 50,
- 6, 59, 31, 67, 40, 59, 49, 51, 74, 59, 0, 16, 22, 7, 34,
- 16, 52, 7, 68, 16, 2, 24, 22, 33, 32, 24, 56, 33, 65, 25,
- 10, 50, 19, 42, 44, 50, 53, 42, 78, 50, 7, 59, 16, 51, 41,
- 59, 50, 51, 75, 59, 1, 16, 21, 7, 35, 16, 51, 7, 69, 16,
- 1, 24, 23, 33, 32, 25, 57, 33, 66, 25, 11, 50, 20, 42, 45,
- 50, 54, 42, 79, 50, 8, 59, 17, 51, 42, 59, 51, 51, 76, 59,
- 2, 16, 20, 7, 36, 16, 50, 7, 70, 16, 0, 24, 24, 33, 33,
- 25, 58, 33, 67, 25, 12, 50, 21, 42, 46, 50, 55, 42, 64, 34,
- 9, 59, 18, 51, 43, 59, 52, 51, 77, 59, 3, 16, 19, 7, 37,
- 16, 49, 7, 71, 16, 0, 25, 25, 33, 34, 25, 59, 33, 68, 25,
- 13, 50, 22, 42, 47, 50, 56, 42, 65, 34, 10, 59, 19, 51, 44,
- 59, 53, 51, 78, 59, 4, 16, 18, 7, 38, 16, 48, 7, 72, 16,
- 1, 25, 26, 33, 35, 25, 60, 33, 69, 25, 14, 50, 23, 42, 32,
- 34, 57, 42, 66, 34, 11, 59, 20, 51, 45, 59, 54, 51, 79, 59,
- 5, 16, 17, 7, 39, 16, 48, 8, 73, 16, 2, 25, 27, 33, 36,
- 25, 61, 33, 70, 25, 15, 50, 24, 42, 33, 34, 58, 42, 67, 34,
- 12, 59, 21, 51, 46, 59, 55, 51, 79, 60, 6, 16, 16, 7, 40,
- 16, 49, 8, 74, 16, 3, 25, 28, 33, 37, 25, 62, 33, 71, 25,
- 0, 34, 25, 42, 34, 34, 59, 42, 68, 34, 13, 59, 22, 51, 47,
- 59, 56, 51, 78, 60, 7, 16, 16, 8, 41, 16, 50, 8, 75, 16,
- 4, 25, 29, 33, 38, 25, 63, 33, 72, 25, 1, 34, 26, 42, 35,
- 34, 60, 42, 69, 34, 14, 59, 23, 51, 47, 60, 57, 51, 77, 60,
- 8, 16, 17, 8, 42, 16, 51, 8, 76, 16, 5, 25, 30, 33, 39,
- 25, 48, 17, 73, 25, 2, 34, 27, 42, 36, 34, 61, 42, 70, 34,
- 15, 59, 24, 51, 46, 60, 58, 51, 76, 60, 9, 16, 18, 8, 43,
- 16, 52, 8, 77, 16, 6, 25, 31, 33, 40, 25, 49, 17, 74, 25,
- 3, 34, 28, 42, 37, 34, 62, 42, 71, 34, 15, 60, 25, 51, 45,
- 60, 59, 51, 75, 60, 10, 16, 19, 8, 44, 16, 53, 8, 78, 16,
- 7, 25, 16, 17, 41, 25, 50, 17, 75, 25, 4, 34, 29, 42, 38,
- 34, 63, 42, 72, 34, 14, 60, 26, 51, 44, 60, 60, 51, 74, 60,
- 11, 16, 20, 8, 45, 16, 54, 8, 79, 16, 8, 25, 17, 17, 42,
- 25, 51, 17, 76, 25, 5, 34, 30, 42, 39, 34, 63, 43, 73, 34,
- 13, 60, 27, 51, 43, 60, 61, 51, 73, 60, 12, 16, 21, 8, 46,
- 16, 55, 8, 64, 0, 9, 25, 18, 17, 43, 25, 52, 17, 77, 25,
- 6, 34, 31, 42, 40, 34, 62, 43, 74, 34, 12, 60, 28, 51, 42,
- 60, 62, 51, 72, 60, 13, 16, 22, 8, 47, 16, 56, 8, 65, 0,
- 10, 25, 19, 17, 44, 25, 53, 17, 78, 25, 7, 34, 31, 43, 41,
- 34, 61, 43, 75, 34, 11, 60, 29, 51, 41, 60, 63, 51, 71, 60,
- 14, 16, 23, 8, 32, 0, 57, 8, 66, 0, 11, 25, 20, 17, 45,
- 25, 54, 17, 79, 25, 8, 34, 30, 43, 42, 34, 60, 43, 76, 34,
- 10, 60, 30, 51, 40, 60, 63, 52, 70, 60, 15, 16, 24, 8, 33,
- 0, 58, 8, 67, 0, 12, 25, 21, 17, 46, 25, 55, 17, 79, 26,
- 9, 34, 29, 43, 43, 34, 59, 43, 77, 34, 9, 60, 31, 51, 39,
- 60, 62, 52, 69, 60,
-};
-
-static const uint8_t hq_tab_07[] = {
- 0, 0, 23, 8, 32, 0, 55, 8, 64, 0, 87, 8, 13, 25, 22,
- 17, 44, 26, 54, 17, 72, 26, 86, 17, 12, 34, 24, 43, 44, 34,
- 52, 43, 73, 35, 80, 43, 4, 60, 25, 52, 32, 60, 53, 52, 60,
- 60, 81, 52, 1, 0, 24, 8, 33, 0, 56, 8, 65, 0, 88, 8,
- 14, 25, 23, 17, 43, 26, 55, 17, 71, 26, 87, 17, 13, 34, 23,
- 43, 44, 35, 51, 43, 72, 35, 79, 43, 3, 60, 24, 52, 31, 60,
- 52, 52, 60, 61, 80, 52, 2, 0, 25, 8, 34, 0, 57, 8, 66,
- 0, 89, 8, 14, 26, 24, 17, 42, 26, 56, 17, 70, 26, 88, 17,
- 14, 34, 22, 43, 43, 35, 50, 43, 71, 35, 78, 43, 2, 60, 23,
- 52, 30, 60, 51, 52, 61, 61, 79, 52, 3, 0, 26, 8, 35, 0,
- 58, 8, 67, 0, 89, 9, 13, 26, 25, 17, 41, 26, 57, 17, 69,
- 26, 89, 17, 14, 35, 21, 43, 42, 35, 49, 43, 70, 35, 77, 43,
- 1, 60, 22, 52, 30, 61, 50, 52, 62, 61, 78, 52, 4, 0, 27,
- 8, 36, 0, 59, 8, 68, 0, 88, 9, 12, 26, 26, 17, 40, 26,
- 58, 17, 68, 26, 89, 18, 13, 35, 20, 43, 41, 35, 48, 43, 69,
- 35, 76, 43, 0, 60, 21, 52, 31, 61, 49, 52, 63, 61, 77, 52,
- 5, 0, 28, 8, 37, 0, 59, 9, 69, 0, 87, 9, 11, 26, 27,
- 17, 39, 26, 59, 17, 67, 26, 88, 18, 12, 35, 19, 43, 40, 35,
- 47, 43, 68, 35, 75, 43, 0, 61, 20, 52, 32, 61, 48, 52, 64,
- 61, 76, 52, 6, 0, 29, 8, 38, 0, 58, 9, 70, 0, 86, 9,
- 10, 26, 28, 17, 38, 26, 59, 18, 66, 26, 87, 18, 11, 35, 18,
- 43, 39, 35, 46, 43, 67, 35, 75, 44, 1, 61, 19, 52, 33, 61,
- 47, 52, 65, 61, 75, 52, 7, 0, 29, 9, 39, 0, 57, 9, 71,
- 0, 85, 9, 9, 26, 29, 17, 37, 26, 58, 18, 65, 26, 86, 18,
- 10, 35, 17, 43, 38, 35, 45, 43, 66, 35, 76, 44, 2, 61, 18,
- 52, 34, 61, 46, 52, 66, 61, 75, 53, 8, 0, 28, 9, 40, 0,
- 56, 9, 72, 0, 84, 9, 8, 26, 29, 18, 36, 26, 57, 18, 64,
- 26, 85, 18, 9, 35, 16, 43, 37, 35, 45, 44, 65, 35, 77, 44,
- 3, 61, 17, 52, 35, 61, 45, 52, 67, 61, 76, 53, 9, 0, 27,
- 9, 41, 0, 55, 9, 73, 0, 83, 9, 7, 26, 28, 18, 35, 26,
- 56, 18, 63, 26, 84, 18, 8, 35, 15, 43, 36, 35, 46, 44, 64,
- 35, 78, 44, 4, 61, 16, 52, 36, 61, 45, 53, 68, 61, 77, 53,
- 10, 0, 26, 9, 42, 0, 54, 9, 74, 0, 82, 9, 6, 26, 27,
- 18, 34, 26, 55, 18, 62, 26, 83, 18, 7, 35, 15, 44, 35, 35,
- 47, 44, 63, 35, 79, 44, 5, 61, 15, 52, 37, 61, 46, 53, 69,
- 61, 78, 53, 11, 0, 25, 9, 43, 0, 53, 9, 74, 1, 81, 9,
- 5, 26, 26, 18, 33, 26, 54, 18, 61, 26, 82, 18, 6, 35, 16,
- 44, 34, 35, 48, 44, 62, 35, 80, 44, 6, 61, 15, 53, 38, 61,
- 47, 53, 70, 61, 79, 53, 12, 0, 24, 9, 44, 0, 52, 9, 73,
- 1, 80, 9, 4, 26, 25, 18, 32, 26, 53, 18, 60, 26, 81, 18,
- 5, 35, 17, 44, 33, 35, 49, 44, 61, 35, 81, 44, 7, 61, 16,
- 53, 39, 61, 48, 53, 71, 61, 80, 53, 13, 0, 23, 9, 44, 1,
- 51, 9, 72, 1, 79, 9, 3, 26, 24, 18, 31, 26, 52, 18, 60,
- 27, 80, 18, 4, 35, 18, 44, 32, 35, 50, 44, 60, 35, 82, 44,
- 8, 61, 17, 53, 40, 61, 49, 53, 72, 61, 81, 53, 14, 0, 22,
- 9, 43, 1, 50, 9, 71, 1, 78, 9, 2, 26, 23, 18, 30, 26,
- 51, 18, 61, 27, 79, 18, 3, 35, 19, 44, 31, 35, 51, 44, 60,
- 36, 83, 44, 9, 61, 18, 53, 41, 61, 50, 53, 73, 61, 82, 53,
- 14, 1, 21, 9, 42, 1, 49, 9, 70, 1, 77, 9, 1, 26, 22,
- 18, 30, 27, 50, 18, 62, 27, 78, 18, 2, 35, 20, 44, 30, 35,
- 52, 44, 61, 36, 84, 44, 10, 61, 19, 53, 42, 61, 51, 53, 74,
- 61, 83, 53, 13, 1, 20, 9, 41, 1, 48, 9, 69, 1, 76, 9,
- 0, 26, 21, 18, 31, 27, 49, 18, 63, 27, 77, 18, 1, 35, 21,
- 44, 30, 36, 53, 44, 62, 36, 85, 44, 11, 61, 20, 53, 43, 61,
- 52, 53, 74, 62, 84, 53, 12, 1, 19, 9, 40, 1, 47, 9, 68,
- 1, 75, 9, 0, 27, 20, 18, 32, 27, 48, 18, 64, 27, 76, 18,
- 0, 35, 22, 44, 31, 36, 54, 44, 63, 36, 86, 44, 12, 61, 21,
- 53, 44, 61, 53, 53, 73, 62, 85, 53, 11, 1, 18, 9, 39, 1,
- 46, 9, 67, 1, 75, 10, 1, 27, 19, 18, 33, 27, 47, 18, 65,
- 27, 75, 18, 0, 36, 23, 44, 32, 36, 55, 44, 64, 36, 87, 44,
- 13, 61, 22, 53, 44, 62, 54, 53, 72, 62, 86, 53, 10, 1, 17,
- 9, 38, 1, 45, 9, 66, 1, 76, 10, 2, 27, 18, 18, 34, 27,
- 46, 18, 66, 27, 75, 19, 1, 36, 24, 44, 33, 36, 56, 44, 65,
- 36, 88, 44, 14, 61, 23, 53, 43, 62, 55, 53, 71, 62, 87, 53,
- 9, 1, 16, 9, 37, 1, 45, 10, 65, 1, 77, 10, 3, 27, 17,
- 18, 35, 27, 45, 18, 67, 27, 76, 19, 2, 36, 25, 44, 34, 36,
- 57, 44, 66, 36, 89, 44, 14, 62, 24, 53, 42, 62, 56, 53, 70,
- 62, 88, 53, 8, 1, 15, 9, 36, 1, 46, 10, 64, 1, 78, 10,
- 4, 27, 16, 18, 36, 27, 45, 19, 68, 27, 77, 19, 3, 36, 26,
- 44, 35, 36, 58, 44, 67, 36, 89, 45, 13, 62, 25, 53, 41, 62,
- 57, 53, 69, 62, 89, 53, 7, 1, 15, 10, 35, 1, 47, 10, 63,
- 1, 79, 10, 5, 27, 15, 18, 37, 27, 46, 19, 69, 27, 78, 19,
- 4, 36, 27, 44, 36, 36, 59, 44, 68, 36, 88, 45, 12, 62, 26,
- 53, 40, 62, 58, 53, 68, 62, 89, 54, 6, 1, 16, 10, 34, 1,
- 48, 10, 62, 1, 80, 10, 6, 27, 15, 19, 38, 27, 47, 19, 70,
- 27, 79, 19, 5, 36, 28, 44, 37, 36, 59, 45, 69, 36, 87, 45,
- 11, 62, 27, 53, 39, 62, 59, 53, 67, 62, 88, 54, 5, 1, 17,
- 10, 33, 1, 49, 10, 61, 1, 81, 10, 7, 27, 16, 19, 39, 27,
- 48, 19, 71, 27, 80, 19, 6, 36, 29, 44, 38, 36, 58, 45, 70,
- 36, 86, 45, 10, 62, 28, 53, 38, 62, 59, 54, 66, 62, 87, 54,
- 4, 1, 18, 10, 32, 1, 50, 10, 60, 1, 82, 10, 8, 27, 17,
- 19, 40, 27, 49, 19, 72, 27, 81, 19, 7, 36, 29, 45, 39, 36,
- 57, 45, 71, 36, 85, 45, 9, 62, 29, 53, 37, 62, 58, 54, 65,
- 62, 86, 54, 3, 1, 19, 10, 31, 1, 51, 10, 60, 2, 83, 10,
- 9, 27, 18, 19, 41, 27, 50, 19, 73, 27, 82, 19, 8, 36, 28,
- 45, 40, 36, 56, 45, 72, 36, 84, 45, 8, 62, 29, 54, 36, 62,
- 57, 54, 64, 62, 85, 54, 2, 1, 20, 10, 30, 1, 52, 10, 61,
- 2, 84, 10, 10, 27, 19, 19, 42, 27, 51, 19, 74, 27, 83, 19,
- 9, 36, 27, 45, 41, 36, 55, 45, 73, 36, 83, 45, 7, 62, 28,
- 54, 35, 62, 56, 54, 63, 62, 84, 54, 1, 1, 21, 10, 30, 2,
- 53, 10, 62, 2, 85, 10, 11, 27, 20, 19, 43, 27, 52, 19, 74,
- 28, 84, 19, 10, 36, 26, 45, 42, 36, 54, 45, 74, 36, 82, 45,
- 6, 62, 27, 54, 34, 62, 55, 54, 62, 62, 83, 54, 0, 1, 22,
- 10, 31, 2, 54, 10, 63, 2, 86, 10, 12, 27, 21, 19, 44, 27,
- 53, 19, 73, 28, 85, 19, 11, 36, 25, 45, 43, 36, 53, 45, 74,
- 37, 81, 45, 5, 62, 26, 54, 33, 62, 54, 54, 61, 62, 82, 54,
- 0, 2, 23, 10, 32, 2, 55, 10, 64, 2, 87, 10, 13, 27, 22,
- 19, 44, 28, 54, 19, 72, 28, 86, 19, 12, 36, 24, 45, 44, 36,
- 52, 45, 73, 37, 80, 45, 4, 62, 25, 54, 32, 62, 53, 54, 60,
- 62, 81, 54, 1, 2, 24, 10, 33, 2, 56, 10, 65, 2, 88, 10,
- 14, 27, 23, 19, 43, 28, 55, 19, 71, 28, 87, 19, 13, 36, 23,
- 45, 44, 37, 51, 45, 72, 37, 79, 45, 3, 62, 24, 54, 31, 62,
- 52, 54, 60, 63, 80, 54, 2, 2, 25, 10, 34, 2, 57, 10, 66,
- 2, 89, 10, 14, 28, 24, 19, 42, 28, 56, 19, 70, 28, 88, 19,
- 14, 36, 22, 45, 43, 37, 50, 45, 71, 37, 78, 45, 2, 62, 23,
- 54, 30, 62, 51, 54, 61, 63, 79, 54, 3, 2, 26, 10, 35, 2,
- 58, 10, 67, 2, 89, 11, 13, 28, 25, 19, 41, 28, 57, 19, 69,
- 28, 89, 19, 14, 37, 21, 45, 42, 37, 49, 45, 70, 37, 77, 45,
- 1, 62, 22, 54, 30, 63, 50, 54, 62, 63, 78, 54, 4, 2, 27,
- 10, 36, 2, 59, 10, 68, 2, 88, 11, 12, 28, 26, 19, 40, 28,
- 58, 19, 68, 28, 89, 20, 13, 37, 20, 45, 41, 37, 48, 45, 69,
- 37, 76, 45, 0, 62, 21, 54, 31, 63, 49, 54, 63, 63, 77, 54,
- 5, 2, 28, 10, 37, 2, 59, 11, 69, 2, 87, 11, 11, 28, 27,
- 19, 39, 28, 59, 19, 67, 28, 88, 20, 12, 37, 19, 45, 40, 37,
- 47, 45, 68, 37, 75, 45, 0, 63, 20, 54, 32, 63, 48, 54, 64,
- 63, 76, 54, 6, 2, 29, 10, 38, 2, 58, 11, 70, 2, 86, 11,
- 10, 28, 28, 19, 38, 28, 59, 20, 66, 28, 87, 20, 11, 37, 18,
- 45, 39, 37, 46, 45, 67, 37, 75, 46, 1, 63, 19, 54, 33, 63,
- 47, 54, 65, 63, 75, 54, 7, 2, 29, 11, 39, 2, 57, 11, 71,
- 2, 85, 11, 9, 28, 29, 19, 37, 28, 58, 20, 65, 28, 86, 20,
- 10, 37, 17, 45, 38, 37, 45, 45, 66, 37, 76, 46, 2, 63, 18,
- 54, 34, 63, 46, 54, 66, 63, 75, 55, 8, 2, 28, 11, 40, 2,
- 56, 11, 72, 2, 84, 11, 8, 28, 29, 20, 36, 28, 57, 20, 64,
- 28, 85, 20, 9, 37, 16, 45, 37, 37, 45, 46, 65, 37, 77, 46,
- 3, 63, 17, 54, 35, 63, 45, 54, 67, 63, 76, 55, 9, 2, 27,
- 11, 41, 2, 55, 11, 73, 2, 83, 11, 7, 28, 28, 20, 35, 28,
- 56, 20, 63, 28, 84, 20, 8, 37, 15, 45, 36, 37, 46, 46, 64,
- 37, 78, 46, 4, 63, 16, 54, 36, 63, 45, 55, 68, 63, 77, 55,
- 10, 2, 26, 11, 42, 2, 54, 11, 74, 2, 82, 11, 6, 28, 27,
- 20, 34, 28, 55, 20, 62, 28, 83, 20, 7, 37, 15, 46, 35, 37,
- 47, 46, 63, 37, 79, 46, 5, 63, 15, 54, 37, 63, 46, 55, 69,
- 63, 78, 55, 11, 2, 25, 11, 43, 2, 53, 11, 74, 3, 81, 11,
- 5, 28, 26, 20, 33, 28, 54, 20, 61, 28, 82, 20, 6, 37, 16,
- 46, 34, 37, 48, 46, 62, 37, 80, 46, 6, 63, 15, 55, 38, 63,
- 47, 55, 70, 63, 79, 55, 12, 2, 24, 11, 44, 2, 52, 11, 73,
- 3, 80, 11, 4, 28, 25, 20, 32, 28, 53, 20, 60, 28, 81, 20,
- 5, 37, 17, 46, 33, 37, 49, 46, 61, 37, 81, 46, 7, 63, 16,
- 55, 39, 63, 48, 55, 71, 63, 80, 55, 13, 2, 23, 11, 44, 3,
- 51, 11, 72, 3, 79, 11, 3, 28, 24, 20, 31, 28, 52, 20, 60,
- 29, 80, 20, 4, 37, 18, 46, 32, 37, 50, 46, 60, 37, 82, 46,
- 8, 63, 17, 55, 40, 63, 49, 55, 72, 63, 81, 55, 14, 2, 22,
- 11, 43, 3, 50, 11, 71, 3, 78, 11, 2, 28, 23, 20, 30, 28,
- 51, 20, 61, 29, 79, 20, 3, 37, 19, 46, 31, 37, 51, 46, 60,
- 38, 83, 46, 9, 63, 18, 55, 41, 63, 50, 55, 73, 63, 82, 55,
- 14, 3, 21, 11, 42, 3, 49, 11, 70, 3, 77, 11, 1, 28, 22,
- 20, 30, 29, 50, 20, 62, 29, 78, 20, 2, 37, 20, 46, 30, 37,
- 52, 46, 61, 38, 84, 46, 10, 63, 19, 55, 42, 63, 51, 55, 74,
- 63, 83, 55, 13, 3, 20, 11, 41, 3, 48, 11, 69, 3, 76, 11,
- 0, 28, 21, 20, 31, 29, 49, 20, 63, 29, 77, 20, 1, 37, 21,
- 46, 30, 38, 53, 46, 62, 38, 85, 46, 11, 63, 20, 55, 43, 63,
- 52, 55, 74, 64, 84, 55, 12, 3, 19, 11, 40, 3, 47, 11, 68,
- 3, 75, 11, 0, 29, 20, 20, 32, 29, 48, 20, 64, 29, 76, 20,
- 0, 37, 22, 46, 31, 38, 54, 46, 63, 38, 86, 46, 12, 63, 21,
- 55, 44, 63, 53, 55, 73, 64, 85, 55, 11, 3, 18, 11, 39, 3,
- 46, 11, 67, 3, 75, 12, 1, 29, 19, 20, 33, 29, 47, 20, 65,
- 29, 75, 20, 0, 38, 23, 46, 32, 38, 55, 46, 64, 38, 87, 46,
- 13, 63, 22, 55, 44, 64, 54, 55, 72, 64, 86, 55, 10, 3, 17,
- 11, 38, 3, 45, 11, 66, 3, 76, 12, 2, 29, 18, 20, 34, 29,
- 46, 20, 66, 29, 75, 21, 1, 38, 24, 46, 33, 38, 56, 46, 65,
- 38, 88, 46, 14, 63, 23, 55, 43, 64, 55, 55, 71, 64, 87, 55,
- 9, 3, 16, 11, 37, 3, 45, 12, 65, 3, 77, 12, 3, 29, 17,
- 20, 35, 29, 45, 20, 67, 29, 76, 21, 2, 38, 25, 46, 34, 38,
- 57, 46, 66, 38, 89, 46, 14, 64, 24, 55, 42, 64, 56, 55, 70,
- 64, 88, 55, 8, 3, 15, 11, 36, 3, 46, 12, 64, 3, 78, 12,
- 4, 29, 16, 20, 36, 29, 45, 21, 68, 29, 77, 21, 3, 38, 26,
- 46, 35, 38, 58, 46, 67, 38, 89, 47, 13, 64, 25, 55, 41, 64,
- 57, 55, 69, 64, 89, 55, 7, 3, 15, 12, 35, 3, 47, 12, 63,
- 3, 79, 12, 5, 29, 15, 20, 37, 29, 46, 21, 69, 29, 78, 21,
- 4, 38, 27, 46, 36, 38, 59, 46, 68, 38, 88, 47, 12, 64, 26,
- 55, 40, 64, 58, 55, 68, 64, 89, 56, 6, 3, 16, 12, 34, 3,
- 48, 12, 62, 3, 80, 12, 6, 29, 15, 21, 38, 29, 47, 21, 70,
- 29, 79, 21, 5, 38, 28, 46, 37, 38, 59, 47, 69, 38, 87, 47,
- 11, 64, 27, 55, 39, 64, 59, 55, 67, 64, 88, 56, 5, 3, 17,
- 12, 33, 3, 49, 12, 61, 3, 81, 12, 7, 29, 16, 21, 39, 29,
- 48, 21, 71, 29, 80, 21, 6, 38, 29, 46, 38, 38, 58, 47, 70,
- 38, 86, 47, 10, 64, 28, 55, 38, 64, 59, 56, 66, 64, 87, 56,
- 4, 3, 18, 12, 32, 3, 50, 12, 60, 3, 82, 12, 8, 29, 17,
- 21, 40, 29, 49, 21, 72, 29, 81, 21, 7, 38, 29, 47, 39, 38,
- 57, 47, 71, 38, 85, 47, 9, 64, 29, 55, 37, 64, 58, 56, 65,
- 64, 86, 56, 3, 3, 19, 12, 31, 3, 51, 12, 60, 4, 83, 12,
- 9, 29, 18, 21, 41, 29, 50, 21, 73, 29, 82, 21, 8, 38, 28,
- 47, 40, 38, 56, 47, 72, 38, 84, 47, 8, 64, 29, 56, 36, 64,
- 57, 56, 64, 64, 85, 56, 2, 3, 20, 12, 30, 3, 52, 12, 61,
- 4, 84, 12, 10, 29, 19, 21, 42, 29, 51, 21, 74, 29, 83, 21,
- 9, 38, 27, 47, 41, 38, 55, 47, 73, 38, 83, 47, 7, 64, 28,
- 56, 35, 64, 56, 56, 63, 64, 84, 56, 1, 3, 21, 12, 30, 4,
- 53, 12, 62, 4, 85, 12, 11, 29, 20, 21, 43, 29, 52, 21, 74,
- 30, 84, 21, 10, 38, 26, 47, 42, 38, 54, 47, 74, 38, 82, 47,
- 6, 64, 27, 56, 34, 64, 55, 56, 62, 64, 83, 56, 0, 3, 22,
- 12, 31, 4, 54, 12, 63, 4, 86, 12, 12, 29, 21, 21, 44, 29,
- 53, 21, 73, 30, 85, 21, 11, 38, 25, 47, 43, 38, 53, 47, 74,
- 39, 81, 47, 5, 64, 26, 56, 33, 64, 54, 56, 61, 64, 82, 56,
- 0, 4, 23, 12, 32, 4, 55, 12, 64, 4, 87, 12, 13, 29, 22,
- 21, 44, 30, 54, 21, 72, 30, 86, 21, 12, 38, 24, 47, 44, 38,
- 52, 47, 73, 39, 80, 47, 4, 64, 25, 56, 32, 64, 53, 56, 60,
- 64, 81, 56, 1, 4, 24, 12, 33, 4, 56, 12, 65, 4, 88, 12,
- 14, 29, 23, 21, 43, 30, 55, 21, 71, 30, 87, 21, 13, 38, 23,
- 47, 44, 39, 51, 47, 72, 39, 79, 47, 3, 64, 24, 56, 31, 64,
- 52, 56, 60, 65, 80, 56, 2, 4, 25, 12, 34, 4, 57, 12, 66,
- 4, 89, 12, 14, 30, 24, 21, 42, 30, 56, 21, 70, 30, 88, 21,
- 14, 38, 22, 47, 43, 39, 50, 47, 71, 39, 78, 47, 2, 64, 23,
- 56, 30, 64, 51, 56, 61, 65, 79, 56, 3, 4, 26, 12, 35, 4,
- 58, 12, 67, 4, 89, 13, 13, 30, 25, 21, 41, 30, 57, 21, 69,
- 30, 89, 21, 14, 39, 21, 47, 42, 39, 49, 47, 70, 39, 77, 47,
- 1, 64, 22, 56, 30, 65, 50, 56, 62, 65, 78, 56, 4, 4, 27,
- 12, 36, 4, 59, 12, 68, 4, 88, 13, 12, 30, 26, 21, 40, 30,
- 58, 21, 68, 30, 89, 22, 13, 39, 20, 47, 41, 39, 48, 47, 69,
- 39, 76, 47, 0, 64, 21, 56, 31, 65, 49, 56, 63, 65, 77, 56,
- 5, 4, 28, 12, 37, 4, 59, 13, 69, 4, 87, 13, 11, 30, 27,
- 21, 39, 30, 59, 21, 67, 30, 88, 22, 12, 39, 19, 47, 40, 39,
- 47, 47, 68, 39, 75, 47, 0, 65, 20, 56, 32, 65, 48, 56, 64,
- 65, 76, 56, 6, 4, 29, 12, 38, 4, 58, 13, 70, 4, 86, 13,
- 10, 30, 28, 21, 38, 30, 59, 22, 66, 30, 87, 22, 11, 39, 18,
- 47, 39, 39, 46, 47, 67, 39, 75, 48, 1, 65, 19, 56, 33, 65,
- 47, 56, 65, 65, 75, 56, 7, 4, 29, 13, 39, 4, 57, 13, 71,
- 4, 85, 13, 9, 30, 29, 21, 37, 30, 58, 22, 65, 30, 86, 22,
- 10, 39, 17, 47, 38, 39, 45, 47, 66, 39, 76, 48, 2, 65, 18,
- 56, 34, 65, 46, 56, 66, 65, 75, 57, 8, 4, 28, 13, 40, 4,
- 56, 13, 72, 4, 84, 13, 8, 30, 29, 22, 36, 30, 57, 22, 64,
- 30, 85, 22, 9, 39, 16, 47, 37, 39, 45, 48, 65, 39, 77, 48,
- 3, 65, 17, 56, 35, 65, 45, 56, 67, 65, 76, 57, 9, 4, 27,
- 13, 41, 4, 55, 13, 73, 4, 83, 13, 7, 30, 28, 22, 35, 30,
- 56, 22, 63, 30, 84, 22, 8, 39, 15, 47, 36, 39, 46, 48, 64,
- 39, 78, 48, 4, 65, 16, 56, 36, 65, 45, 57, 68, 65, 77, 57,
- 10, 4, 26, 13, 42, 4, 54, 13, 74, 4, 82, 13, 6, 30, 27,
- 22, 34, 30, 55, 22, 62, 30, 83, 22, 7, 39, 15, 48, 35, 39,
- 47, 48, 63, 39, 79, 48, 5, 65, 15, 56, 37, 65, 46, 57, 69,
- 65, 78, 57, 11, 4, 25, 13, 43, 4, 53, 13, 74, 5, 81, 13,
- 5, 30, 26, 22, 33, 30, 54, 22, 61, 30, 82, 22, 6, 39, 16,
- 48, 34, 39, 48, 48, 62, 39, 80, 48, 6, 65, 15, 57, 38, 65,
- 47, 57, 70, 65, 79, 57, 12, 4, 24, 13, 44, 4, 52, 13, 73,
- 5, 80, 13, 4, 30, 25, 22, 32, 30, 53, 22, 60, 30, 81, 22,
- 5, 39, 17, 48, 33, 39, 49, 48, 61, 39, 81, 48, 7, 65, 16,
- 57, 39, 65, 48, 57, 71, 65, 80, 57, 13, 4, 23, 13, 44, 5,
- 51, 13, 72, 5, 79, 13, 3, 30, 24, 22, 31, 30, 52, 22, 60,
- 31, 80, 22, 4, 39, 18, 48, 32, 39, 50, 48, 60, 39, 82, 48,
- 8, 65, 17, 57, 40, 65, 49, 57, 72, 65, 81, 57, 14, 4, 22,
- 13, 43, 5, 50, 13, 71, 5, 78, 13, 2, 30, 23, 22, 30, 30,
- 51, 22, 61, 31, 79, 22, 3, 39, 19, 48, 31, 39, 51, 48, 60,
- 40, 83, 48, 9, 65, 18, 57, 41, 65, 50, 57, 73, 65, 82, 57,
- 14, 5, 21, 13, 42, 5, 49, 13, 70, 5, 77, 13, 1, 30, 22,
- 22, 30, 31, 50, 22, 62, 31, 78, 22, 2, 39, 20, 48, 30, 39,
- 52, 48, 61, 40, 84, 48, 10, 65, 19, 57, 42, 65, 51, 57, 74,
- 65, 83, 57, 13, 5, 20, 13, 41, 5, 48, 13, 69, 5, 76, 13,
- 0, 30, 21, 22, 31, 31, 49, 22, 63, 31, 77, 22, 1, 39, 21,
- 48, 30, 40, 53, 48, 62, 40, 85, 48, 11, 65, 20, 57, 43, 65,
- 52, 57, 74, 66, 84, 57, 12, 5, 19, 13, 40, 5, 47, 13, 68,
- 5, 75, 13, 0, 31, 20, 22, 32, 31, 48, 22, 64, 31, 76, 22,
- 0, 39, 22, 48, 31, 40, 54, 48, 63, 40, 86, 48, 12, 65, 21,
- 57, 44, 65, 53, 57, 73, 66, 85, 57, 11, 5, 18, 13, 39, 5,
- 46, 13, 67, 5, 75, 14, 1, 31, 19, 22, 33, 31, 47, 22, 65,
- 31, 75, 22, 0, 40, 23, 48, 32, 40, 55, 48, 64, 40, 87, 48,
- 13, 65, 22, 57, 44, 66, 54, 57, 72, 66, 86, 57, 10, 5, 17,
- 13, 38, 5, 45, 13, 66, 5, 76, 14, 2, 31, 18, 22, 34, 31,
- 46, 22, 66, 31, 75, 23, 1, 40, 24, 48, 33, 40, 56, 48, 65,
- 40, 88, 48, 14, 65, 23, 57, 43, 66, 55, 57, 71, 66, 87, 57,
- 9, 5, 16, 13, 37, 5, 45, 14, 65, 5, 77, 14, 3, 31, 17,
- 22, 35, 31, 45, 22, 67, 31, 76, 23, 2, 40, 25, 48, 34, 40,
- 57, 48, 66, 40, 89, 48, 14, 66, 24, 57, 42, 66, 56, 57, 70,
- 66, 88, 57, 8, 5, 15, 13, 36, 5, 46, 14, 64, 5, 78, 14,
- 4, 31, 16, 22, 36, 31, 45, 23, 68, 31, 77, 23, 3, 40, 26,
- 48, 35, 40, 58, 48, 67, 40, 89, 49, 13, 66, 25, 57, 41, 66,
- 57, 57, 69, 66, 89, 57, 7, 5, 15, 14, 35, 5, 47, 14, 63,
- 5, 79, 14, 5, 31, 15, 22, 37, 31, 46, 23, 69, 31, 78, 23,
- 4, 40, 27, 48, 36, 40, 59, 48, 68, 40, 88, 49, 12, 66, 26,
- 57, 40, 66, 58, 57, 68, 66, 89, 58, 6, 5, 16, 14, 34, 5,
- 48, 14, 62, 5, 80, 14, 6, 31, 15, 23, 38, 31, 47, 23, 70,
- 31, 79, 23, 5, 40, 28, 48, 37, 40, 59, 49, 69, 40, 87, 49,
- 11, 66, 27, 57, 39, 66, 59, 57, 67, 66, 88, 58, 5, 5, 17,
- 14, 33, 5, 49, 14, 61, 5, 81, 14, 7, 31, 16, 23, 39, 31,
- 48, 23, 71, 31, 80, 23, 6, 40, 29, 48, 38, 40, 58, 49, 70,
- 40, 86, 49, 10, 66, 28, 57, 38, 66, 59, 58, 66, 66, 87, 58,
- 4, 5, 18, 14, 32, 5, 50, 14, 60, 5, 82, 14, 8, 31, 17,
- 23, 40, 31, 49, 23, 72, 31, 81, 23, 7, 40, 29, 49, 39, 40,
- 57, 49, 71, 40, 85, 49, 9, 66, 29, 57, 37, 66, 58, 58, 65,
- 66, 86, 58, 3, 5, 19, 14, 31, 5, 51, 14, 60, 6, 83, 14,
- 9, 31, 18, 23, 41, 31, 50, 23, 73, 31, 82, 23, 8, 40, 28,
- 49, 40, 40, 56, 49, 72, 40, 84, 49, 8, 66, 29, 58, 36, 66,
- 57, 58, 64, 66, 85, 58, 2, 5, 20, 14, 30, 5, 52, 14, 61,
- 6, 84, 14, 10, 31, 19, 23, 42, 31, 51, 23, 74, 31, 83, 23,
- 9, 40, 27, 49, 41, 40, 55, 49, 73, 40, 83, 49, 7, 66, 28,
- 58, 35, 66, 56, 58, 63, 66, 84, 58, 1, 5, 21, 14, 30, 6,
- 53, 14, 62, 6, 85, 14, 11, 31, 20, 23, 43, 31, 52, 23, 74,
- 32, 84, 23, 10, 40, 26, 49, 42, 40, 54, 49, 74, 40, 82, 49,
- 6, 66, 27, 58, 34, 66, 55, 58, 62, 66, 83, 58, 0, 5, 22,
- 14, 31, 6, 54, 14, 63, 6, 86, 14, 12, 31, 21, 23, 44, 31,
- 53, 23, 73, 32, 85, 23, 11, 40, 25, 49, 43, 40, 53, 49, 74,
- 41, 81, 49, 5, 66, 26, 58, 33, 66, 54, 58, 61, 66, 82, 58,
- 0, 6, 23, 14, 32, 6, 55, 14, 64, 6, 87, 14, 13, 31, 22,
- 23, 44, 32, 54, 23, 72, 32, 86, 23, 12, 40, 24, 49, 44, 40,
- 52, 49, 73, 41, 80, 49, 4, 66, 25, 58, 32, 66, 53, 58, 60,
- 66, 81, 58, 1, 6, 24, 14, 33, 6, 56, 14, 65, 6, 88, 14,
- 14, 31, 23, 23, 43, 32, 55, 23, 71, 32, 87, 23, 13, 40, 23,
- 49, 44, 41, 51, 49, 72, 41, 79, 49, 3, 66, 24, 58, 31, 66,
- 52, 58, 60, 67, 80, 58, 2, 6, 25, 14, 34, 6, 57, 14, 66,
- 6, 89, 14, 14, 32, 24, 23, 42, 32, 56, 23, 70, 32, 88, 23,
- 14, 40, 22, 49, 43, 41, 50, 49, 71, 41, 78, 49, 2, 66, 23,
- 58, 30, 66, 51, 58, 61, 67, 79, 58, 3, 6, 26, 14, 35, 6,
- 58, 14, 67, 6, 89, 15, 13, 32, 25, 23, 41, 32, 57, 23, 69,
- 32, 89, 23, 14, 41, 21, 49, 42, 41, 49, 49, 70, 41, 77, 49,
- 1, 66, 22, 58, 30, 67, 50, 58, 62, 67, 78, 58, 4, 6, 27,
- 14, 36, 6, 59, 14, 68, 6, 88, 15, 12, 32, 26, 23, 40, 32,
- 58, 23, 68, 32, 89, 24, 13, 41, 20, 49, 41, 41, 48, 49, 69,
- 41, 76, 49, 0, 66, 21, 58, 31, 67, 49, 58, 63, 67, 77, 58,
- 5, 6, 28, 14, 37, 6, 59, 15, 69, 6, 87, 15, 11, 32, 27,
- 23, 39, 32, 59, 23, 67, 32, 88, 24, 12, 41, 19, 49, 40, 41,
- 47, 49, 68, 41, 75, 49, 0, 67, 20, 58, 32, 67, 48, 58, 64,
- 67, 76, 58, 6, 6, 29, 14, 38, 6, 58, 15, 70, 6, 86, 15,
- 10, 32, 28, 23, 38, 32, 59, 24, 66, 32, 87, 24, 11, 41, 18,
- 49, 39, 41, 46, 49, 67, 41, 75, 50, 1, 67, 19, 58, 33, 67,
- 47, 58, 65, 67, 75, 58, 7, 6, 29, 15, 39, 6, 57, 15, 71,
- 6, 85, 15, 9, 32, 29, 23, 37, 32, 58, 24, 65, 32, 86, 24,
- 10, 41, 17, 49, 38, 41, 45, 49, 66, 41, 76, 50, 2, 67, 18,
- 58, 34, 67, 46, 58, 66, 67, 75, 59, 8, 6, 28, 15, 40, 6,
- 56, 15, 72, 6, 84, 15, 8, 32, 29, 24, 36, 32, 57, 24, 64,
- 32, 85, 24, 9, 41, 16, 49, 37, 41, 45, 50, 65, 41, 77, 50,
- 3, 67, 17, 58, 35, 67, 45, 58, 67, 67, 76, 59, 9, 6, 27,
- 15, 41, 6, 55, 15, 73, 6, 83, 15, 7, 32, 28, 24, 35, 32,
- 56, 24, 63, 32, 84, 24, 8, 41, 15, 49, 36, 41, 46, 50, 64,
- 41, 78, 50, 4, 67, 16, 58, 36, 67, 45, 59, 68, 67, 77, 59,
- 10, 6, 26, 15, 42, 6, 54, 15, 74, 6, 82, 15, 6, 32, 27,
- 24, 34, 32, 55, 24, 62, 32, 83, 24, 7, 41, 15, 50, 35, 41,
- 47, 50, 63, 41, 79, 50, 5, 67, 15, 58, 37, 67, 46, 59, 69,
- 67, 78, 59, 11, 6, 25, 15, 43, 6, 53, 15, 74, 7, 81, 15,
- 5, 32, 26, 24, 33, 32, 54, 24, 61, 32, 82, 24, 6, 41, 16,
- 50, 34, 41, 48, 50, 62, 41, 80, 50, 6, 67, 15, 59, 38, 67,
- 47, 59, 70, 67, 79, 59, 12, 6, 24, 15, 44, 6, 52, 15, 73,
- 7, 80, 15, 4, 32, 25, 24, 32, 32, 53, 24, 60, 32, 81, 24,
- 5, 41, 17, 50, 33, 41, 49, 50, 61, 41, 81, 50, 7, 67, 16,
- 59, 39, 67, 48, 59, 71, 67, 80, 59, 13, 6, 23, 15, 44, 7,
- 51, 15, 72, 7, 79, 15, 3, 32, 24, 24, 31, 32, 52, 24, 60,
- 33, 80, 24, 4, 41, 18, 50, 32, 41, 50, 50, 60, 41, 82, 50,
- 8, 67, 17, 59, 40, 67, 49, 59, 72, 67, 81, 59, 14, 6, 22,
- 15, 43, 7, 50, 15, 71, 7, 78, 15, 2, 32, 23, 24, 30, 32,
- 51, 24, 61, 33, 79, 24, 3, 41, 19, 50, 31, 41, 51, 50, 60,
- 42, 83, 50, 9, 67, 18, 59, 41, 67, 50, 59, 73, 67, 82, 59,
- 14, 7, 21, 15, 42, 7, 49, 15, 70, 7, 77, 15, 1, 32, 22,
- 24, 30, 33, 50, 24, 62, 33, 78, 24, 2, 41, 20, 50, 30, 41,
- 52, 50, 61, 42, 84, 50, 10, 67, 19, 59, 42, 67, 51, 59, 74,
- 67, 83, 59, 13, 7, 20, 15, 41, 7, 48, 15, 69, 7, 76, 15,
- 0, 32, 21, 24, 31, 33, 49, 24, 63, 33, 77, 24, 1, 41, 21,
- 50, 30, 42, 53, 50, 62, 42, 85, 50, 11, 67, 20, 59, 43, 67,
- 52, 59, 60, 51, 84, 59, 12, 7, 19, 15, 40, 7, 47, 15, 68,
- 7, 75, 15, 0, 33, 20, 24, 32, 33, 48, 24, 64, 33, 76, 24,
- 0, 41, 22, 50, 31, 42, 54, 50, 63, 42, 86, 50, 12, 67, 21,
- 59, 44, 67, 53, 59, 61, 51, 85, 59, 11, 7, 18, 15, 39, 7,
- 46, 15, 67, 7, 75, 16, 1, 33, 19, 24, 33, 33, 47, 24, 65,
- 33, 75, 24, 0, 42, 23, 50, 32, 42, 55, 50, 64, 42, 87, 50,
- 13, 67, 22, 59, 30, 51, 54, 59, 62, 51, 86, 59, 10, 7, 17,
- 15, 38, 7, 45, 15, 66, 7, 76, 16, 2, 33, 18, 24, 34, 33,
- 46, 24, 66, 33, 75, 25, 1, 42, 24, 50, 33, 42, 56, 50, 65,
- 42, 88, 50, 14, 67, 23, 59, 31, 51, 55, 59, 63, 51, 87, 59,
- 9, 7, 16, 15, 37, 7, 45, 16, 65, 7, 77, 16, 3, 33, 17,
- 24, 35, 33, 45, 24, 67, 33, 76, 25, 2, 42, 25, 50, 34, 42,
- 57, 50, 66, 42, 89, 50, 0, 51, 24, 59, 32, 51, 56, 59, 64,
- 51, 88, 59, 8, 7, 15, 15, 36, 7, 46, 16, 64, 7, 78, 16,
- 4, 33, 16, 24, 36, 33, 45, 25, 68, 33, 77, 25, 3, 42, 26,
- 50, 35, 42, 58, 50, 67, 42, 75, 34, 1, 51, 25, 59, 33, 51,
- 57, 59, 65, 51, 89, 59, 7, 7, 15, 16, 35, 7, 47, 16, 63,
- 7, 79, 16, 5, 33, 15, 24, 37, 33, 46, 25, 69, 33, 78, 25,
- 4, 42, 27, 50, 36, 42, 59, 50, 68, 42, 76, 34, 2, 51, 26,
- 59, 34, 51, 58, 59, 66, 51, 89, 60, 6, 7, 16, 16, 34, 7,
- 48, 16, 62, 7, 80, 16, 6, 33, 15, 25, 38, 33, 47, 25, 70,
- 33, 79, 25, 5, 42, 28, 50, 37, 42, 45, 34, 69, 42, 77, 34,
- 3, 51, 27, 59, 35, 51, 59, 59, 67, 51, 88, 60, 5, 7, 17,
- 16, 33, 7, 49, 16, 61, 7, 81, 16, 7, 33, 16, 25, 39, 33,
- 48, 25, 71, 33, 80, 25, 6, 42, 29, 50, 38, 42, 46, 34, 70,
- 42, 78, 34, 4, 51, 28, 59, 36, 51, 59, 60, 68, 51, 87, 60,
- 4, 7, 18, 16, 32, 7, 50, 16, 60, 7, 82, 16, 8, 33, 17,
- 25, 40, 33, 49, 25, 72, 33, 81, 25, 7, 42, 15, 34, 39, 42,
- 47, 34, 71, 42, 79, 34, 5, 51, 29, 59, 37, 51, 58, 60, 69,
- 51, 86, 60, 3, 7, 19, 16, 31, 7, 51, 16, 60, 8, 83, 16,
- 9, 33, 18, 25, 41, 33, 50, 25, 73, 33, 82, 25, 8, 42, 16,
- 34, 40, 42, 48, 34, 72, 42, 80, 34, 6, 51, 29, 60, 38, 51,
- 57, 60, 70, 51, 85, 60, 2, 7, 20, 16, 30, 7, 52, 16, 61,
- 8, 84, 16, 10, 33, 19, 25, 42, 33, 51, 25, 74, 33, 83, 25,
- 9, 42, 17, 34, 41, 42, 49, 34, 73, 42, 81, 34, 7, 51, 28,
- 60, 39, 51, 56, 60, 71, 51, 84, 60, 1, 7, 21, 16, 30, 8,
- 53, 16, 62, 8, 85, 16, 11, 33, 20, 25, 43, 33, 52, 25, 60,
- 17, 84, 25, 10, 42, 18, 34, 42, 42, 50, 34, 74, 42, 82, 34,
- 8, 51, 27, 60, 40, 51, 55, 60, 72, 51, 83, 60, 0, 7, 22,
- 16, 31, 8, 54, 16, 63, 8, 86, 16, 12, 33, 21, 25, 44, 33,
- 53, 25, 61, 17, 85, 25, 11, 42, 19, 34, 43, 42, 51, 34, 74,
- 43, 83, 34, 9, 51, 26, 60, 41, 51, 54, 60, 73, 51, 82, 60,
- 0, 8, 23, 16, 32, 8, 55, 16, 64, 8, 87, 16, 13, 33, 22,
- 25, 30, 17, 54, 25, 62, 17, 86, 25, 12, 42, 20, 34, 44, 42,
- 52, 34, 73, 43, 84, 34, 10, 51, 25, 60, 42, 51, 53, 60, 74,
- 51, 81, 60, 1, 8, 24, 16, 33, 8, 56, 16, 65, 8, 88, 16,
- 14, 33, 23, 25, 31, 17, 55, 25, 63, 17, 87, 25, 13, 42, 21,
- 34, 44, 43, 53, 34, 72, 43, 85, 34, 11, 51, 24, 60, 43, 51,
- 52, 60, 74, 52, 80, 60, 2, 8, 25, 16, 34, 8, 57, 16, 66,
- 8, 89, 16, 0, 17, 24, 25, 32, 17, 56, 25, 64, 17, 88, 25,
- 14, 42, 22, 34, 43, 43, 54, 34, 71, 43, 86, 34, 12, 51, 23,
- 60, 44, 51, 51, 60, 73, 52, 79, 60, 3, 8, 26, 16, 35, 8,
- 58, 16, 67, 8, 75, 0, 1, 17, 25, 25, 33, 17, 57, 25, 65,
- 17, 89, 25, 14, 43, 23, 34, 42, 43, 55, 34, 70, 43, 87, 34,
- 13, 51, 22, 60, 44, 52, 50, 60, 72, 52, 78, 60, 4, 8, 27,
- 16, 36, 8, 59, 16, 68, 8, 76, 0, 2, 17, 26, 25, 34, 17,
- 58, 25, 66, 17, 89, 26, 13, 43, 24, 34, 41, 43, 56, 34, 69,
- 43, 88, 34, 14, 51, 21, 60, 43, 52, 49, 60, 71, 52, 77, 60,
- 5, 8, 28, 16, 37, 8, 45, 0, 69, 8, 77, 0, 3, 17, 27,
- 25, 35, 17, 59, 25, 67, 17, 88, 26, 12, 43, 25, 34, 40, 43,
- 57, 34, 68, 43, 89, 34, 14, 52, 20, 60, 42, 52, 48, 60, 70,
- 52, 76, 60, 6, 8, 29, 16, 38, 8, 46, 0, 70, 8, 78, 0,
- 4, 17, 28, 25, 36, 17, 59, 26, 68, 17, 87, 26, 11, 43, 26,
- 34, 39, 43, 58, 34, 67, 43, 89, 35, 13, 52, 19, 60, 41, 52,
- 47, 60, 69, 52, 75, 60, 7, 8, 15, 0, 39, 8, 47, 0, 71,
- 8, 79, 0, 5, 17, 29, 25, 37, 17, 58, 26, 69, 17, 86, 26,
- 10, 43, 27, 34, 38, 43, 59, 34, 66, 43, 88, 35, 12, 52, 18,
- 60, 40, 52, 46, 60, 68, 52, 75, 61, 8, 8, 16, 0, 40, 8,
- 48, 0, 72, 8, 80, 0, 6, 17, 29, 26, 38, 17, 57, 26, 70,
- 17, 85, 26, 9, 43, 28, 34, 37, 43, 59, 35, 65, 43, 87, 35,
- 11, 52, 17, 60, 39, 52, 45, 60, 67, 52, 76, 61, 9, 8, 17,
- 0, 41, 8, 49, 0, 73, 8, 81, 0, 7, 17, 28, 26, 39, 17,
- 56, 26, 71, 17, 84, 26, 8, 43, 29, 34, 36, 43, 58, 35, 64,
- 43, 86, 35, 10, 52, 16, 60, 38, 52, 45, 61, 66, 52, 77, 61,
- 10, 8, 18, 0, 42, 8, 50, 0, 74, 8, 82, 0, 8, 17, 27,
- 26, 40, 17, 55, 26, 72, 17, 83, 26, 7, 43, 29, 35, 35, 43,
- 57, 35, 63, 43, 85, 35, 9, 52, 15, 60, 37, 52, 46, 61, 65,
- 52, 78, 61, 11, 8, 19, 0, 43, 8, 51, 0, 74, 9, 83, 0,
- 9, 17, 26, 26, 41, 17, 54, 26, 73, 17, 82, 26, 6, 43, 28,
- 35, 34, 43, 56, 35, 62, 43, 84, 35, 8, 52, 15, 61, 36, 52,
- 47, 61, 64, 52, 79, 61, 12, 8, 20, 0, 44, 8, 52, 0, 73,
- 9, 84, 0, 10, 17, 25, 26, 42, 17, 53, 26, 74, 17, 81, 26,
- 5, 43, 27, 35, 33, 43, 55, 35, 61, 43, 83, 35, 7, 52, 16,
- 61, 35, 52, 48, 61, 63, 52, 80, 61, 13, 8, 21, 0, 44, 9,
- 53, 0, 72, 9, 85, 0, 11, 17, 24, 26, 43, 17, 52, 26, 74,
- 18, 80, 26, 4, 43, 26, 35, 32, 43, 54, 35, 60, 43, 82, 35,
- 6, 52, 17, 61, 34, 52, 49, 61, 62, 52, 81, 61, 14, 8, 22,
- 0, 43, 9, 54, 0, 71, 9, 86, 0, 12, 17, 23, 26, 44, 17,
- 51, 26, 73, 18, 79, 26, 3, 43, 25, 35, 31, 43, 53, 35, 60,
- 44, 81, 35, 5, 52, 18, 61, 33, 52, 50, 61, 61, 52, 82, 61,
- 14, 9, 23, 0, 42, 9, 55, 0, 70, 9, 87, 0, 13, 17, 22,
- 26, 44, 18, 50, 26, 72, 18, 78, 26, 2, 43, 24, 35, 30, 43,
- 52, 35, 61, 44, 80, 35, 4, 52, 19, 61, 32, 52, 51, 61, 60,
- 52, 83, 61, 13, 9, 24, 0, 41, 9, 56, 0, 69, 9, 88, 0,
- 14, 17, 21, 26, 43, 18, 49, 26, 71, 18, 77, 26, 1, 43, 23,
- 35, 30, 44, 51, 35, 62, 44, 79, 35, 3, 52, 20, 61, 31, 52,
- 52, 61, 60, 53, 84, 61, 12, 9, 25, 0, 40, 9, 57, 0, 68,
- 9, 89, 0, 14, 18, 20, 26, 42, 18, 48, 26, 70, 18, 76, 26,
- 0, 43, 22, 35, 31, 44, 50, 35, 63, 44, 78, 35, 2, 52, 21,
- 61, 30, 52, 53, 61, 61, 53, 85, 61, 11, 9, 26, 0, 39, 9,
- 58, 0, 67, 9, 89, 1, 13, 18, 19, 26, 41, 18, 47, 26, 69,
- 18, 75, 26, 0, 44, 21, 35, 32, 44, 49, 35, 64, 44, 77, 35,
- 1, 52, 22, 61, 30, 53, 54, 61, 62, 53, 86, 61, 10, 9, 27,
- 0, 38, 9, 59, 0, 66, 9, 88, 1, 12, 18, 18, 26, 40, 18,
- 46, 26, 68, 18, 75, 27, 1, 44, 20, 35, 33, 44, 48, 35, 65,
- 44, 76, 35, 0, 52, 23, 61, 31, 53, 55, 61, 63, 53, 87, 61,
- 9, 9, 28, 0, 37, 9, 59, 1, 65, 9, 87, 1, 11, 18, 17,
- 26, 39, 18, 45, 26, 67, 18, 76, 27, 2, 44, 19, 35, 34, 44,
- 47, 35, 66, 44, 75, 35, 0, 53, 24, 61, 32, 53, 56, 61, 64,
- 53, 88, 61, 8, 9, 29, 0, 36, 9, 58, 1, 64, 9, 86, 1,
- 10, 18, 16, 26, 38, 18, 45, 27, 66, 18, 77, 27, 3, 44, 18,
- 35, 35, 44, 46, 35, 67, 44, 75, 36, 1, 53, 25, 61, 33, 53,
- 57, 61, 65, 53, 89, 61, 7, 9, 29, 1, 35, 9, 57, 1, 63,
- 9, 85, 1, 9, 18, 15, 26, 37, 18, 46, 27, 65, 18, 78, 27,
- 4, 44, 17, 35, 36, 44, 45, 35, 68, 44, 76, 36, 2, 53, 26,
- 61, 34, 53, 58, 61, 66, 53, 89, 62, 6, 9, 28, 1, 34, 9,
- 56, 1, 62, 9, 84, 1, 8, 18, 15, 27, 36, 18, 47, 27, 64,
- 18, 79, 27, 5, 44, 16, 35, 37, 44, 45, 36, 69, 44, 77, 36,
- 3, 53, 27, 61, 35, 53, 59, 61, 67, 53, 88, 62, 5, 9, 27,
- 1, 33, 9, 55, 1, 61, 9, 83, 1, 7, 18, 16, 27, 35, 18,
- 48, 27, 63, 18, 80, 27, 6, 44, 15, 35, 38, 44, 46, 36, 70,
- 44, 78, 36, 4, 53, 28, 61, 36, 53, 59, 62, 68, 53, 87, 62,
- 4, 9, 26, 1, 32, 9, 54, 1, 60, 9, 82, 1, 6, 18, 17,
- 27, 34, 18, 49, 27, 62, 18, 81, 27, 7, 44, 15, 36, 39, 44,
- 47, 36, 71, 44, 79, 36, 5, 53, 29, 61, 37, 53, 58, 62, 69,
- 53, 86, 62, 3, 9, 25, 1, 31, 9, 53, 1, 60, 10, 81, 1,
- 5, 18, 18, 27, 33, 18, 50, 27, 61, 18, 82, 27, 8, 44, 16,
- 36, 40, 44, 48, 36, 72, 44, 80, 36, 6, 53, 29, 62, 38, 53,
- 57, 62, 70, 53, 85, 62, 2, 9, 24, 1, 30, 9, 52, 1, 61,
- 10, 80, 1, 4, 18, 19, 27, 32, 18, 51, 27, 60, 18, 83, 27,
- 9, 44, 17, 36, 41, 44, 49, 36, 73, 44, 81, 36, 7, 53, 28,
- 62, 39, 53, 56, 62, 71, 53, 84, 62, 1, 9, 23, 1, 30, 10,
- 51, 1, 62, 10, 79, 1, 3, 18, 20, 27, 31, 18, 52, 27, 60,
- 19, 84, 27, 10, 44, 18, 36, 42, 44, 50, 36, 74, 44, 82, 36,
- 8, 53, 27, 62, 40, 53, 55, 62, 72, 53, 83, 62, 0, 9, 22,
- 1, 31, 10, 50, 1, 63, 10, 78, 1, 2, 18, 21, 27, 30, 18,
- 53, 27, 61, 19, 85, 27, 11, 44, 19, 36, 43, 44, 51, 36, 74,
- 45, 83, 36, 9, 53, 26, 62, 41, 53, 54, 62, 73, 53, 82, 62,
- 0, 10, 21, 1, 32, 10, 49, 1, 64, 10, 77, 1, 1, 18, 22,
- 27, 30, 19, 54, 27, 62, 19, 86, 27, 12, 44, 20, 36, 44, 44,
- 52, 36, 73, 45, 84, 36, 10, 53, 25, 62, 42, 53, 53, 62, 74,
- 53, 81, 62, 1, 10, 20, 1, 33, 10, 48, 1, 65, 10, 76, 1,
- 0, 18, 23, 27, 31, 19, 55, 27, 63, 19, 87, 27, 13, 44, 21,
- 36, 44, 45, 53, 36, 72, 45, 85, 36, 11, 53, 24, 62, 43, 53,
- 52, 62, 74, 54, 80, 62, 2, 10, 19, 1, 34, 10, 47, 1, 66,
- 10, 75, 1, 0, 19, 24, 27, 32, 19, 56, 27, 64, 19, 88, 27,
- 14, 44, 22, 36, 43, 45, 54, 36, 71, 45, 86, 36, 12, 53, 23,
- 62, 44, 53, 51, 62, 73, 54, 79, 62, 3, 10, 18, 1, 35, 10,
- 46, 1, 67, 10, 75, 2, 1, 19, 25, 27, 33, 19, 57, 27, 65,
- 19, 89, 27, 14, 45, 23, 36, 42, 45, 55, 36, 70, 45, 87, 36,
- 13, 53, 22, 62, 44, 54, 50, 62, 72, 54, 78, 62, 4, 10, 17,
- 1, 36, 10, 45, 1, 68, 10, 76, 2, 2, 19, 26, 27, 34, 19,
- 58, 27, 66, 19, 89, 28, 13, 45, 24, 36, 41, 45, 56, 36, 69,
- 45, 88, 36, 14, 53, 21, 62, 43, 54, 49, 62, 71, 54, 77, 62,
- 5, 10, 16, 1, 37, 10, 45, 2, 69, 10, 77, 2, 3, 19, 27,
- 27, 35, 19, 59, 27, 67, 19, 88, 28, 12, 45, 25, 36, 40, 45,
- 57, 36, 68, 45, 89, 36, 14, 54, 20, 62, 42, 54, 48, 62, 70,
- 54, 76, 62, 6, 10, 15, 1, 38, 10, 46, 2, 70, 10, 78, 2,
- 4, 19, 28, 27, 36, 19, 59, 28, 68, 19, 87, 28, 11, 45, 26,
- 36, 39, 45, 58, 36, 67, 45, 89, 37, 13, 54, 19, 62, 41, 54,
- 47, 62, 69, 54, 75, 62, 7, 10, 15, 2, 39, 10, 47, 2, 71,
- 10, 79, 2, 5, 19, 29, 27, 37, 19, 58, 28, 69, 19, 86, 28,
- 10, 45, 27, 36, 38, 45, 59, 36, 66, 45, 88, 37, 12, 54, 18,
- 62, 40, 54, 46, 62, 68, 54, 75, 63, 8, 10, 16, 2, 40, 10,
- 48, 2, 72, 10, 80, 2, 6, 19, 29, 28, 38, 19, 57, 28, 70,
- 19, 85, 28, 9, 45, 28, 36, 37, 45, 59, 37, 65, 45, 87, 37,
- 11, 54, 17, 62, 39, 54, 45, 62, 67, 54, 76, 63, 9, 10, 17,
- 2, 41, 10, 49, 2, 73, 10, 81, 2, 7, 19, 28, 28, 39, 19,
- 56, 28, 71, 19, 84, 28, 8, 45, 29, 36, 36, 45, 58, 37, 64,
- 45, 86, 37, 10, 54, 16, 62, 38, 54, 45, 63, 66, 54, 77, 63,
- 10, 10, 18, 2, 42, 10, 50, 2, 74, 10, 82, 2, 8, 19, 27,
- 28, 40, 19, 55, 28, 72, 19, 83, 28, 7, 45, 29, 37, 35, 45,
- 57, 37, 63, 45, 85, 37, 9, 54, 15, 62, 37, 54, 46, 63, 65,
- 54, 78, 63, 11, 10, 19, 2, 43, 10, 51, 2, 74, 11, 83, 2,
- 9, 19, 26, 28, 41, 19, 54, 28, 73, 19, 82, 28, 6, 45, 28,
- 37, 34, 45, 56, 37, 62, 45, 84, 37, 8, 54, 15, 63, 36, 54,
- 47, 63, 64, 54, 79, 63, 12, 10, 20, 2, 44, 10, 52, 2, 73,
- 11, 84, 2, 10, 19, 25, 28, 42, 19, 53, 28, 74, 19, 81, 28,
- 5, 45, 27, 37, 33, 45, 55, 37, 61, 45, 83, 37, 7, 54, 16,
- 63, 35, 54, 48, 63, 63, 54, 80, 63, 13, 10, 21, 2, 44, 11,
- 53, 2, 72, 11, 85, 2, 11, 19, 24, 28, 43, 19, 52, 28, 74,
- 20, 80, 28, 4, 45, 26, 37, 32, 45, 54, 37, 60, 45, 82, 37,
- 6, 54, 17, 63, 34, 54, 49, 63, 62, 54, 81, 63, 14, 10, 22,
- 2, 43, 11, 54, 2, 71, 11, 86, 2, 12, 19, 23, 28, 44, 19,
- 51, 28, 73, 20, 79, 28, 3, 45, 25, 37, 31, 45, 53, 37, 60,
- 46, 81, 37, 5, 54, 18, 63, 33, 54, 50, 63, 61, 54, 82, 63,
- 14, 11, 23, 2, 42, 11, 55, 2, 70, 11, 87, 2, 13, 19, 22,
- 28, 44, 20, 50, 28, 72, 20, 78, 28, 2, 45, 24, 37, 30, 45,
- 52, 37, 61, 46, 80, 37, 4, 54, 19, 63, 32, 54, 51, 63, 60,
- 54, 83, 63, 13, 11, 24, 2, 41, 11, 56, 2, 69, 11, 88, 2,
- 14, 19, 21, 28, 43, 20, 49, 28, 71, 20, 77, 28, 1, 45, 23,
- 37, 30, 46, 51, 37, 62, 46, 79, 37, 3, 54, 20, 63, 31, 54,
- 52, 63, 60, 55, 84, 63, 12, 11, 25, 2, 40, 11, 57, 2, 68,
- 11, 89, 2, 14, 20, 20, 28, 42, 20, 48, 28, 70, 20, 76, 28,
- 0, 45, 22, 37, 31, 46, 50, 37, 63, 46, 78, 37, 2, 54, 21,
- 63, 30, 54, 53, 63, 61, 55, 85, 63, 11, 11, 26, 2, 39, 11,
- 58, 2, 67, 11, 89, 3, 13, 20, 19, 28, 41, 20, 47, 28, 69,
- 20, 75, 28, 0, 46, 21, 37, 32, 46, 49, 37, 64, 46, 77, 37,
- 1, 54, 22, 63, 30, 55, 54, 63, 62, 55, 86, 63, 10, 11, 27,
- 2, 38, 11, 59, 2, 66, 11, 88, 3, 12, 20, 18, 28, 40, 20,
- 46, 28, 68, 20, 75, 29, 1, 46, 20, 37, 33, 46, 48, 37, 65,
- 46, 76, 37, 0, 54, 23, 63, 31, 55, 55, 63, 63, 55, 87, 63,
- 9, 11, 28, 2, 37, 11, 59, 3, 65, 11, 87, 3, 11, 20, 17,
- 28, 39, 20, 45, 28, 67, 20, 76, 29, 2, 46, 19, 37, 34, 46,
- 47, 37, 66, 46, 75, 37, 0, 55, 24, 63, 32, 55, 56, 63, 64,
- 55, 88, 63, 8, 11, 29, 2, 36, 11, 58, 3, 64, 11, 86, 3,
- 10, 20, 16, 28, 38, 20, 45, 29, 66, 20, 77, 29, 3, 46, 18,
- 37, 35, 46, 46, 37, 67, 46, 75, 38, 1, 55, 25, 63, 33, 55,
- 57, 63, 65, 55, 89, 63, 7, 11, 29, 3, 35, 11, 57, 3, 63,
- 11, 85, 3, 9, 20, 15, 28, 37, 20, 46, 29, 65, 20, 78, 29,
- 4, 46, 17, 37, 36, 46, 45, 37, 68, 46, 76, 38, 2, 55, 26,
- 63, 34, 55, 58, 63, 66, 55, 89, 64, 6, 11, 28, 3, 34, 11,
- 56, 3, 62, 11, 84, 3, 8, 20, 15, 29, 36, 20, 47, 29, 64,
- 20, 79, 29, 5, 46, 16, 37, 37, 46, 45, 38, 69, 46, 77, 38,
- 3, 55, 27, 63, 35, 55, 59, 63, 67, 55, 88, 64, 5, 11, 27,
- 3, 33, 11, 55, 3, 61, 11, 83, 3, 7, 20, 16, 29, 35, 20,
- 48, 29, 63, 20, 80, 29, 6, 46, 15, 37, 38, 46, 46, 38, 70,
- 46, 78, 38, 4, 55, 28, 63, 36, 55, 59, 64, 68, 55, 87, 64,
- 4, 11, 26, 3, 32, 11, 54, 3, 60, 11, 82, 3, 6, 20, 17,
- 29, 34, 20, 49, 29, 62, 20, 81, 29, 7, 46, 15, 38, 39, 46,
- 47, 38, 71, 46, 79, 38, 5, 55, 29, 63, 37, 55, 58, 64, 69,
- 55, 86, 64, 3, 11, 25, 3, 31, 11, 53, 3, 60, 12, 81, 3,
- 5, 20, 18, 29, 33, 20, 50, 29, 61, 20, 82, 29, 8, 46, 16,
- 38, 40, 46, 48, 38, 72, 46, 80, 38, 6, 55, 29, 64, 38, 55,
- 57, 64, 70, 55, 85, 64, 2, 11, 24, 3, 30, 11, 52, 3, 61,
- 12, 80, 3, 4, 20, 19, 29, 32, 20, 51, 29, 60, 20, 83, 29,
- 9, 46, 17, 38, 41, 46, 49, 38, 73, 46, 81, 38, 7, 55, 28,
- 64, 39, 55, 56, 64, 71, 55, 84, 64, 1, 11, 23, 3, 30, 12,
- 51, 3, 62, 12, 79, 3, 3, 20, 20, 29, 31, 20, 52, 29, 60,
- 21, 84, 29, 10, 46, 18, 38, 42, 46, 50, 38, 74, 46, 82, 38,
- 8, 55, 27, 64, 40, 55, 55, 64, 72, 55, 83, 64, 0, 11, 22,
- 3, 31, 12, 50, 3, 63, 12, 78, 3, 2, 20, 21, 29, 30, 20,
- 53, 29, 61, 21, 85, 29, 11, 46, 19, 38, 43, 46, 51, 38, 74,
- 47, 83, 38, 9, 55, 26, 64, 41, 55, 54, 64, 73, 55, 82, 64,
- 0, 12, 21, 3, 32, 12, 49, 3, 64, 12, 77, 3, 1, 20, 22,
- 29, 30, 21, 54, 29, 62, 21, 86, 29, 12, 46, 20, 38, 44, 46,
- 52, 38, 73, 47, 84, 38, 10, 55, 25, 64, 42, 55, 53, 64, 74,
- 55, 81, 64, 1, 12, 20, 3, 33, 12, 48, 3, 65, 12, 76, 3,
- 0, 20, 23, 29, 31, 21, 55, 29, 63, 21, 87, 29, 13, 46, 21,
- 38, 44, 47, 53, 38, 72, 47, 85, 38, 11, 55, 24, 64, 43, 55,
- 52, 64, 74, 56, 80, 64, 2, 12, 19, 3, 34, 12, 47, 3, 66,
- 12, 75, 3, 0, 21, 24, 29, 32, 21, 56, 29, 64, 21, 88, 29,
- 14, 46, 22, 38, 43, 47, 54, 38, 71, 47, 86, 38, 12, 55, 23,
- 64, 44, 55, 51, 64, 73, 56, 79, 64, 3, 12, 18, 3, 35, 12,
- 46, 3, 67, 12, 75, 4, 1, 21, 25, 29, 33, 21, 57, 29, 65,
- 21, 89, 29, 14, 47, 23, 38, 42, 47, 55, 38, 70, 47, 87, 38,
- 13, 55, 22, 64, 44, 56, 50, 64, 72, 56, 78, 64, 4, 12, 17,
- 3, 36, 12, 45, 3, 68, 12, 76, 4, 2, 21, 26, 29, 34, 21,
- 58, 29, 66, 21, 89, 30, 13, 47, 24, 38, 41, 47, 56, 38, 69,
- 47, 88, 38, 14, 55, 21, 64, 43, 56, 49, 64, 71, 56, 77, 64,
- 5, 12, 16, 3, 37, 12, 45, 4, 69, 12, 77, 4, 3, 21, 27,
- 29, 35, 21, 59, 29, 67, 21, 88, 30, 12, 47, 25, 38, 40, 47,
- 57, 38, 68, 47, 89, 38, 14, 56, 20, 64, 42, 56, 48, 64, 70,
- 56, 76, 64, 6, 12, 15, 3, 38, 12, 46, 4, 70, 12, 78, 4,
- 4, 21, 28, 29, 36, 21, 59, 30, 68, 21, 87, 30, 11, 47, 26,
- 38, 39, 47, 58, 38, 67, 47, 89, 39, 13, 56, 19, 64, 41, 56,
- 47, 64, 69, 56, 75, 64, 7, 12, 15, 4, 39, 12, 47, 4, 71,
- 12, 79, 4, 5, 21, 29, 29, 37, 21, 58, 30, 69, 21, 86, 30,
- 10, 47, 27, 38, 38, 47, 59, 38, 66, 47, 88, 39, 12, 56, 18,
- 64, 40, 56, 46, 64, 68, 56, 75, 65, 8, 12, 16, 4, 40, 12,
- 48, 4, 72, 12, 80, 4, 6, 21, 29, 30, 38, 21, 57, 30, 70,
- 21, 85, 30, 9, 47, 28, 38, 37, 47, 59, 39, 65, 47, 87, 39,
- 11, 56, 17, 64, 39, 56, 45, 64, 67, 56, 76, 65, 9, 12, 17,
- 4, 41, 12, 49, 4, 73, 12, 81, 4, 7, 21, 28, 30, 39, 21,
- 56, 30, 71, 21, 84, 30, 8, 47, 29, 38, 36, 47, 58, 39, 64,
- 47, 86, 39, 10, 56, 16, 64, 38, 56, 45, 65, 66, 56, 77, 65,
- 10, 12, 18, 4, 42, 12, 50, 4, 74, 12, 82, 4, 8, 21, 27,
- 30, 40, 21, 55, 30, 72, 21, 83, 30, 7, 47, 29, 39, 35, 47,
- 57, 39, 63, 47, 85, 39, 9, 56, 15, 64, 37, 56, 46, 65, 65,
- 56, 78, 65, 11, 12, 19, 4, 43, 12, 51, 4, 74, 13, 83, 4,
- 9, 21, 26, 30, 41, 21, 54, 30, 73, 21, 82, 30, 6, 47, 28,
- 39, 34, 47, 56, 39, 62, 47, 84, 39, 8, 56, 15, 65, 36, 56,
- 47, 65, 64, 56, 79, 65, 12, 12, 20, 4, 44, 12, 52, 4, 73,
- 13, 84, 4, 10, 21, 25, 30, 42, 21, 53, 30, 74, 21, 81, 30,
- 5, 47, 27, 39, 33, 47, 55, 39, 61, 47, 83, 39, 7, 56, 16,
- 65, 35, 56, 48, 65, 63, 56, 80, 65, 13, 12, 21, 4, 44, 13,
- 53, 4, 72, 13, 85, 4, 11, 21, 24, 30, 43, 21, 52, 30, 74,
- 22, 80, 30, 4, 47, 26, 39, 32, 47, 54, 39, 60, 47, 82, 39,
- 6, 56, 17, 65, 34, 56, 49, 65, 62, 56, 81, 65, 14, 12, 22,
- 4, 43, 13, 54, 4, 71, 13, 86, 4, 12, 21, 23, 30, 44, 21,
- 51, 30, 73, 22, 79, 30, 3, 47, 25, 39, 31, 47, 53, 39, 60,
- 48, 81, 39, 5, 56, 18, 65, 33, 56, 50, 65, 61, 56, 82, 65,
- 14, 13, 23, 4, 42, 13, 55, 4, 70, 13, 87, 4, 13, 21, 22,
- 30, 44, 22, 50, 30, 72, 22, 78, 30, 2, 47, 24, 39, 30, 47,
- 52, 39, 61, 48, 80, 39, 4, 56, 19, 65, 32, 56, 51, 65, 60,
- 56, 83, 65, 13, 13, 24, 4, 41, 13, 56, 4, 69, 13, 88, 4,
- 14, 21, 21, 30, 43, 22, 49, 30, 71, 22, 77, 30, 1, 47, 23,
- 39, 30, 48, 51, 39, 62, 48, 79, 39, 3, 56, 20, 65, 31, 56,
- 52, 65, 60, 57, 84, 65, 12, 13, 25, 4, 40, 13, 57, 4, 68,
- 13, 89, 4, 14, 22, 20, 30, 42, 22, 48, 30, 70, 22, 76, 30,
- 0, 47, 22, 39, 31, 48, 50, 39, 63, 48, 78, 39, 2, 56, 21,
- 65, 30, 56, 53, 65, 61, 57, 85, 65, 11, 13, 26, 4, 39, 13,
- 58, 4, 67, 13, 89, 5, 13, 22, 19, 30, 41, 22, 47, 30, 69,
- 22, 75, 30, 0, 48, 21, 39, 32, 48, 49, 39, 64, 48, 77, 39,
- 1, 56, 22, 65, 30, 57, 54, 65, 62, 57, 86, 65, 10, 13, 27,
- 4, 38, 13, 59, 4, 66, 13, 88, 5, 12, 22, 18, 30, 40, 22,
- 46, 30, 68, 22, 75, 31, 1, 48, 20, 39, 33, 48, 48, 39, 65,
- 48, 76, 39, 0, 56, 23, 65, 31, 57, 55, 65, 63, 57, 87, 65,
- 9, 13, 28, 4, 37, 13, 59, 5, 65, 13, 87, 5, 11, 22, 17,
- 30, 39, 22, 45, 30, 67, 22, 76, 31, 2, 48, 19, 39, 34, 48,
- 47, 39, 66, 48, 75, 39, 0, 57, 24, 65, 32, 57, 56, 65, 64,
- 57, 88, 65, 8, 13, 29, 4, 36, 13, 58, 5, 64, 13, 86, 5,
- 10, 22, 16, 30, 38, 22, 45, 31, 66, 22, 77, 31, 3, 48, 18,
- 39, 35, 48, 46, 39, 67, 48, 75, 40, 1, 57, 25, 65, 33, 57,
- 57, 65, 65, 57, 89, 65, 7, 13, 29, 5, 35, 13, 57, 5, 63,
- 13, 85, 5, 9, 22, 15, 30, 37, 22, 46, 31, 65, 22, 78, 31,
- 4, 48, 17, 39, 36, 48, 45, 39, 68, 48, 76, 40, 2, 57, 26,
- 65, 34, 57, 58, 65, 66, 57, 89, 66, 6, 13, 28, 5, 34, 13,
- 56, 5, 62, 13, 84, 5, 8, 22, 15, 31, 36, 22, 47, 31, 64,
- 22, 79, 31, 5, 48, 16, 39, 37, 48, 45, 40, 69, 48, 77, 40,
- 3, 57, 27, 65, 35, 57, 59, 65, 67, 57, 88, 66, 5, 13, 27,
- 5, 33, 13, 55, 5, 61, 13, 83, 5, 7, 22, 16, 31, 35, 22,
- 48, 31, 63, 22, 80, 31, 6, 48, 15, 39, 38, 48, 46, 40, 70,
- 48, 78, 40, 4, 57, 28, 65, 36, 57, 59, 66, 68, 57, 87, 66,
- 4, 13, 26, 5, 32, 13, 54, 5, 60, 13, 82, 5, 6, 22, 17,
- 31, 34, 22, 49, 31, 62, 22, 81, 31, 7, 48, 15, 40, 39, 48,
- 47, 40, 71, 48, 79, 40, 5, 57, 29, 65, 37, 57, 58, 66, 69,
- 57, 86, 66, 3, 13, 25, 5, 31, 13, 53, 5, 60, 14, 81, 5,
- 5, 22, 18, 31, 33, 22, 50, 31, 61, 22, 82, 31, 8, 48, 16,
- 40, 40, 48, 48, 40, 72, 48, 80, 40, 6, 57, 29, 66, 38, 57,
- 57, 66, 70, 57, 85, 66, 2, 13, 24, 5, 30, 13, 52, 5, 61,
- 14, 80, 5, 4, 22, 19, 31, 32, 22, 51, 31, 60, 22, 83, 31,
- 9, 48, 17, 40, 41, 48, 49, 40, 73, 48, 81, 40, 7, 57, 28,
- 66, 39, 57, 56, 66, 71, 57, 84, 66, 1, 13, 23, 5, 30, 14,
- 51, 5, 62, 14, 79, 5, 3, 22, 20, 31, 31, 22, 52, 31, 60,
- 23, 84, 31, 10, 48, 18, 40, 42, 48, 50, 40, 74, 48, 82, 40,
- 8, 57, 27, 66, 40, 57, 55, 66, 72, 57, 83, 66, 0, 13, 22,
- 5, 31, 14, 50, 5, 63, 14, 78, 5, 2, 22, 21, 31, 30, 22,
- 53, 31, 61, 23, 85, 31, 11, 48, 19, 40, 43, 48, 51, 40, 74,
- 49, 83, 40, 9, 57, 26, 66, 41, 57, 54, 66, 73, 57, 82, 66,
- 0, 14, 21, 5, 32, 14, 49, 5, 64, 14, 77, 5, 1, 22, 22,
- 31, 30, 23, 54, 31, 62, 23, 86, 31, 12, 48, 20, 40, 44, 48,
- 52, 40, 73, 49, 84, 40, 10, 57, 25, 66, 42, 57, 53, 66, 74,
- 57, 81, 66, 1, 14, 20, 5, 33, 14, 48, 5, 65, 14, 76, 5,
- 0, 22, 23, 31, 31, 23, 55, 31, 63, 23, 87, 31, 13, 48, 21,
- 40, 44, 49, 53, 40, 72, 49, 85, 40, 11, 57, 24, 66, 43, 57,
- 52, 66, 74, 58, 80, 66, 2, 14, 19, 5, 34, 14, 47, 5, 66,
- 14, 75, 5, 0, 23, 24, 31, 32, 23, 56, 31, 64, 23, 88, 31,
- 14, 48, 22, 40, 43, 49, 54, 40, 71, 49, 86, 40, 12, 57, 23,
- 66, 44, 57, 51, 66, 73, 58, 79, 66, 3, 14, 18, 5, 35, 14,
- 46, 5, 67, 14, 75, 6, 1, 23, 25, 31, 33, 23, 57, 31, 65,
- 23, 89, 31, 14, 49, 23, 40, 42, 49, 55, 40, 70, 49, 87, 40,
- 13, 57, 22, 66, 44, 58, 50, 66, 72, 58, 78, 66, 4, 14, 17,
- 5, 36, 14, 45, 5, 68, 14, 76, 6, 2, 23, 26, 31, 34, 23,
- 58, 31, 66, 23, 89, 32, 13, 49, 24, 40, 41, 49, 56, 40, 69,
- 49, 88, 40, 14, 57, 21, 66, 43, 58, 49, 66, 71, 58, 77, 66,
- 5, 14, 16, 5, 37, 14, 45, 6, 69, 14, 77, 6, 3, 23, 27,
- 31, 35, 23, 59, 31, 67, 23, 88, 32, 12, 49, 25, 40, 40, 49,
- 57, 40, 68, 49, 89, 40, 14, 58, 20, 66, 42, 58, 48, 66, 70,
- 58, 76, 66, 6, 14, 15, 5, 38, 14, 46, 6, 70, 14, 78, 6,
- 4, 23, 28, 31, 36, 23, 59, 32, 68, 23, 87, 32, 11, 49, 26,
- 40, 39, 49, 58, 40, 67, 49, 89, 41, 13, 58, 19, 66, 41, 58,
- 47, 66, 69, 58, 75, 66, 7, 14, 15, 6, 39, 14, 47, 6, 71,
- 14, 79, 6, 5, 23, 29, 31, 37, 23, 58, 32, 69, 23, 86, 32,
- 10, 49, 27, 40, 38, 49, 59, 40, 66, 49, 88, 41, 12, 58, 18,
- 66, 40, 58, 46, 66, 68, 58, 75, 67, 8, 14, 16, 6, 40, 14,
- 48, 6, 72, 14, 80, 6, 6, 23, 29, 32, 38, 23, 57, 32, 70,
- 23, 85, 32, 9, 49, 28, 40, 37, 49, 59, 41, 65, 49, 87, 41,
- 11, 58, 17, 66, 39, 58, 45, 66, 67, 58, 76, 67, 9, 14, 17,
- 6, 41, 14, 49, 6, 73, 14, 81, 6, 7, 23, 28, 32, 39, 23,
- 56, 32, 71, 23, 84, 32, 8, 49, 29, 40, 36, 49, 58, 41, 64,
- 49, 86, 41, 10, 58, 16, 66, 38, 58, 45, 67, 66, 58, 77, 67,
- 10, 14, 18, 6, 42, 14, 50, 6, 74, 14, 82, 6, 8, 23, 27,
- 32, 40, 23, 55, 32, 72, 23, 83, 32, 7, 49, 29, 41, 35, 49,
- 57, 41, 63, 49, 85, 41, 9, 58, 15, 66, 37, 58, 46, 67, 65,
- 58, 78, 67, 11, 14, 19, 6, 43, 14, 51, 6, 74, 15, 83, 6,
- 9, 23, 26, 32, 41, 23, 54, 32, 73, 23, 82, 32, 6, 49, 28,
- 41, 34, 49, 56, 41, 62, 49, 84, 41, 8, 58, 15, 67, 36, 58,
- 47, 67, 64, 58, 79, 67, 12, 14, 20, 6, 44, 14, 52, 6, 73,
- 15, 84, 6, 10, 23, 25, 32, 42, 23, 53, 32, 74, 23, 81, 32,
- 5, 49, 27, 41, 33, 49, 55, 41, 61, 49, 83, 41, 7, 58, 16,
- 67, 35, 58, 48, 67, 63, 58, 80, 67, 13, 14, 21, 6, 44, 15,
- 53, 6, 72, 15, 85, 6, 11, 23, 24, 32, 43, 23, 52, 32, 74,
- 24, 80, 32, 4, 49, 26, 41, 32, 49, 54, 41, 60, 49, 82, 41,
- 6, 58, 17, 67, 34, 58, 49, 67, 62, 58, 81, 67, 14, 14, 22,
- 6, 43, 15, 54, 6, 71, 15, 86, 6, 12, 23, 23, 32, 44, 23,
- 51, 32, 73, 24, 79, 32, 3, 49, 25, 41, 31, 49, 53, 41, 60,
- 50, 81, 41, 5, 58, 18, 67, 33, 58, 50, 67, 61, 58, 82, 67,
- 14, 15, 23, 6, 42, 15, 55, 6, 70, 15, 87, 6, 13, 23, 22,
- 32, 44, 24, 50, 32, 72, 24, 78, 32, 2, 49, 24, 41, 30, 49,
- 52, 41, 61, 50, 80, 41, 4, 58, 19, 67, 32, 58, 51, 67, 60,
- 58, 83, 67, 13, 15, 24, 6, 41, 15, 56, 6, 69, 15, 88, 6,
- 14, 23, 21, 32, 43, 24, 49, 32, 71, 24, 77, 32, 1, 49, 23,
- 41, 30, 50, 51, 41, 62, 50, 79, 41, 3, 58, 20, 67, 31, 58,
- 52, 67, 60, 59, 84, 67, 12, 15, 25, 6, 40, 15, 57, 6, 68,
- 15, 89, 6, 14, 24, 20, 32, 42, 24, 48, 32, 70, 24, 76, 32,
- 0, 49, 22, 41, 31, 50, 50, 41, 63, 50, 78, 41, 2, 58, 21,
- 67, 30, 58, 53, 67, 61, 59, 85, 67, 11, 15, 26, 6, 39, 15,
- 58, 6, 67, 15, 89, 7, 13, 24, 19, 32, 41, 24, 47, 32, 69,
- 24, 75, 32, 0, 50, 21, 41, 32, 50, 49, 41, 64, 50, 77, 41,
- 1, 58, 22, 67, 30, 59, 54, 67, 62, 59, 86, 67, 10, 15, 27,
- 6, 38, 15, 59, 6, 66, 15, 88, 7, 12, 24, 18, 32, 40, 24,
- 46, 32, 68, 24, 75, 33, 1, 50, 20, 41, 33, 50, 48, 41, 65,
- 50, 76, 41, 0, 58, 23, 67, 31, 59, 55, 67, 63, 59, 87, 67,
- 9, 15, 28, 6, 37, 15, 59, 7, 65, 15, 87, 7, 11, 24, 17,
- 32, 39, 24, 45, 32, 67, 24, 76, 33, 2, 50, 19, 41, 34, 50,
- 47, 41, 66, 50, 75, 41, 0, 59, 24, 67, 32, 59, 56, 67, 64,
- 59, 88, 67, 8, 15, 29, 6, 36, 15, 58, 7, 64, 15, 86, 7,
- 10, 24, 16, 32, 38, 24, 45, 33, 66, 24, 77, 33, 3, 50, 18,
- 41, 35, 50, 46, 41, 67, 50, 75, 42, 1, 59, 25, 67, 33, 59,
- 57, 67, 65, 59, 89, 67, 7, 15, 29, 7, 35, 15, 57, 7, 63,
- 15, 85, 7, 9, 24, 15, 32, 37, 24, 46, 33, 65, 24, 78, 33,
- 4, 50, 17, 41, 36, 50, 45, 41, 68, 50, 76, 42, 2, 59, 26,
- 67, 34, 59, 58, 67, 66, 59, 75, 51, 6, 15, 28, 7, 34, 15,
- 56, 7, 62, 15, 84, 7, 8, 24, 15, 33, 36, 24, 47, 33, 64,
- 24, 79, 33, 5, 50, 16, 41, 37, 50, 45, 42, 69, 50, 77, 42,
- 3, 59, 27, 67, 35, 59, 59, 67, 67, 59, 76, 51, 5, 15, 27,
- 7, 33, 15, 55, 7, 61, 15, 83, 7, 7, 24, 16, 33, 35, 24,
- 48, 33, 63, 24, 80, 33, 6, 50, 15, 41, 38, 50, 46, 42, 70,
- 50, 78, 42, 4, 59, 28, 67, 36, 59, 45, 51, 68, 59, 77, 51,
- 4, 15, 26, 7, 32, 15, 54, 7, 60, 15, 82, 7, 6, 24, 17,
- 33, 34, 24, 49, 33, 62, 24, 81, 33, 7, 50, 15, 42, 39, 50,
- 47, 42, 71, 50, 79, 42, 5, 59, 29, 67, 37, 59, 46, 51, 69,
- 59, 78, 51, 3, 15, 25, 7, 31, 15, 53, 7, 60, 16, 81, 7,
- 5, 24, 18, 33, 33, 24, 50, 33, 61, 24, 82, 33, 8, 50, 16,
- 42, 40, 50, 48, 42, 72, 50, 80, 42, 6, 59, 15, 51, 38, 59,
- 47, 51, 70, 59, 79, 51, 2, 15, 24, 7, 30, 15, 52, 7, 61,
- 16, 80, 7, 4, 24, 19, 33, 32, 24, 51, 33, 60, 24, 83, 33,
- 9, 50, 17, 42, 41, 50, 49, 42, 73, 50, 81, 42, 7, 59, 16,
- 51, 39, 59, 48, 51, 71, 59, 80, 51, 1, 15, 23, 7, 30, 16,
- 51, 7, 62, 16, 79, 7, 3, 24, 20, 33, 31, 24, 52, 33, 60,
- 25, 84, 33, 10, 50, 18, 42, 42, 50, 50, 42, 74, 50, 82, 42,
- 8, 59, 17, 51, 40, 59, 49, 51, 72, 59, 81, 51, 0, 15, 22,
- 7, 31, 16, 50, 7, 63, 16, 78, 7, 2, 24, 21, 33, 30, 24,
- 53, 33, 61, 25, 85, 33, 11, 50, 19, 42, 43, 50, 51, 42, 60,
- 34, 83, 42, 9, 59, 18, 51, 41, 59, 50, 51, 73, 59, 82, 51,
- 0, 16, 21, 7, 32, 16, 49, 7, 64, 16, 77, 7, 1, 24, 22,
- 33, 30, 25, 54, 33, 62, 25, 86, 33, 12, 50, 20, 42, 44, 50,
- 52, 42, 61, 34, 84, 42, 10, 59, 19, 51, 42, 59, 51, 51, 74,
- 59, 83, 51, 1, 16, 20, 7, 33, 16, 48, 7, 65, 16, 76, 7,
- 0, 24, 23, 33, 31, 25, 55, 33, 63, 25, 87, 33, 13, 50, 21,
- 42, 30, 34, 53, 42, 62, 34, 85, 42, 11, 59, 20, 51, 43, 59,
- 52, 51, 74, 60, 84, 51, 2, 16, 19, 7, 34, 16, 47, 7, 66,
- 16, 75, 7, 0, 25, 24, 33, 32, 25, 56, 33, 64, 25, 88, 33,
- 14, 50, 22, 42, 31, 34, 54, 42, 63, 34, 86, 42, 12, 59, 21,
- 51, 44, 59, 53, 51, 73, 60, 85, 51, 3, 16, 18, 7, 35, 16,
- 46, 7, 67, 16, 75, 8, 1, 25, 25, 33, 33, 25, 57, 33, 65,
- 25, 89, 33, 0, 34, 23, 42, 32, 34, 55, 42, 64, 34, 87, 42,
- 13, 59, 22, 51, 44, 60, 54, 51, 72, 60, 86, 51, 4, 16, 17,
- 7, 36, 16, 45, 7, 68, 16, 76, 8, 2, 25, 26, 33, 34, 25,
- 58, 33, 66, 25, 75, 17, 1, 34, 24, 42, 33, 34, 56, 42, 65,
- 34, 88, 42, 14, 59, 23, 51, 43, 60, 55, 51, 71, 60, 87, 51,
- 5, 16, 16, 7, 37, 16, 45, 8, 69, 16, 77, 8, 3, 25, 27,
- 33, 35, 25, 59, 33, 67, 25, 76, 17, 2, 34, 25, 42, 34, 34,
- 57, 42, 66, 34, 89, 42, 14, 60, 24, 51, 42, 60, 56, 51, 70,
- 60, 88, 51, 6, 16, 15, 7, 38, 16, 46, 8, 70, 16, 78, 8,
- 4, 25, 28, 33, 36, 25, 45, 17, 68, 25, 77, 17, 3, 34, 26,
- 42, 35, 34, 58, 42, 67, 34, 89, 43, 13, 60, 25, 51, 41, 60,
- 57, 51, 69, 60, 89, 51, 7, 16, 15, 8, 39, 16, 47, 8, 71,
- 16, 79, 8, 5, 25, 29, 33, 37, 25, 46, 17, 69, 25, 78, 17,
- 4, 34, 27, 42, 36, 34, 59, 42, 68, 34, 88, 43, 12, 60, 26,
- 51, 40, 60, 58, 51, 68, 60, 89, 52, 8, 16, 16, 8, 40, 16,
- 48, 8, 72, 16, 80, 8, 6, 25, 15, 17, 38, 25, 47, 17, 70,
- 25, 79, 17, 5, 34, 28, 42, 37, 34, 59, 43, 69, 34, 87, 43,
- 11, 60, 27, 51, 39, 60, 59, 51, 67, 60, 88, 52, 9, 16, 17,
- 8, 41, 16, 49, 8, 73, 16, 81, 8, 7, 25, 16, 17, 39, 25,
- 48, 17, 71, 25, 80, 17, 6, 34, 29, 42, 38, 34, 58, 43, 70,
- 34, 86, 43, 10, 60, 28, 51, 38, 60, 59, 52, 66, 60, 87, 52,
- 10, 16, 18, 8, 42, 16, 50, 8, 74, 16, 82, 8, 8, 25, 17,
- 17, 40, 25, 49, 17, 72, 25, 81, 17, 7, 34, 29, 43, 39, 34,
- 57, 43, 71, 34, 85, 43, 9, 60, 29, 51, 37, 60, 58, 52, 65,
- 60, 86, 52, 11, 16, 19, 8, 43, 16, 51, 8, 60, 0, 83, 8,
- 9, 25, 18, 17, 41, 25, 50, 17, 73, 25, 82, 17, 8, 34, 28,
- 43, 40, 34, 56, 43, 72, 34, 84, 43, 8, 60, 29, 52, 36, 60,
- 57, 52, 64, 60, 85, 52, 12, 16, 20, 8, 44, 16, 52, 8, 61,
- 0, 84, 8, 10, 25, 19, 17, 42, 25, 51, 17, 74, 25, 83, 17,
- 9, 34, 27, 43, 41, 34, 55, 43, 73, 34, 83, 43, 7, 60, 28,
- 52, 35, 60, 56, 52, 63, 60, 84, 52, 13, 16, 21, 8, 30, 0,
- 53, 8, 62, 0, 85, 8, 11, 25, 20, 17, 43, 25, 52, 17, 74,
- 26, 84, 17, 10, 34, 26, 43, 42, 34, 54, 43, 74, 34, 82, 43,
- 6, 60, 27, 52, 34, 60, 55, 52, 62, 60, 83, 52, 14, 16, 22,
- 8, 31, 0, 54, 8, 63, 0, 86, 8, 12, 25, 21, 17, 44, 25,
- 53, 17, 73, 26, 85, 17, 11, 34, 25, 43, 43, 34, 53, 43, 74,
- 35, 81, 43, 5, 60, 26, 52, 33, 60, 54, 52, 61, 60, 82, 52,
-};
-
-static const uint8_t hq_tab_08[] = {
- 0, 0, 31, 8, 42, 0, 73, 8, 84, 0, 115, 8, 16, 25, 27,
- 17, 58, 25, 69, 17, 99, 26, 111, 17, 12, 34, 36, 43, 54, 34,
- 74, 43, 96, 34, 112, 43, 11, 60, 39, 51, 49, 60, 78, 52, 87,
- 60, 116, 52, 1, 0, 32, 8, 43, 0, 74, 8, 85, 0, 116, 8,
- 17, 25, 28, 17, 59, 25, 70, 17, 98, 26, 112, 17, 13, 34, 35,
- 43, 55, 34, 73, 43, 97, 34, 111, 43, 10, 60, 39, 52, 48, 60,
- 77, 52, 86, 60, 115, 52, 2, 0, 33, 8, 44, 0, 75, 8, 86,
- 0, 117, 8, 18, 25, 29, 17, 59, 26, 71, 17, 97, 26, 113, 17,
- 14, 34, 34, 43, 56, 34, 72, 43, 98, 34, 110, 43, 9, 60, 38,
- 52, 47, 60, 76, 52, 85, 60, 114, 52, 3, 0, 34, 8, 45, 0,
- 76, 8, 87, 0, 118, 8, 19, 25, 30, 17, 58, 26, 72, 17, 96,
- 26, 114, 17, 15, 34, 33, 43, 57, 34, 71, 43, 99, 34, 109, 43,
- 8, 60, 37, 52, 46, 60, 75, 52, 84, 60, 113, 52, 4, 0, 35,
- 8, 46, 0, 77, 8, 88, 0, 119, 8, 19, 26, 31, 17, 57, 26,
- 73, 17, 95, 26, 115, 17, 16, 34, 32, 43, 58, 34, 70, 43, 99,
- 35, 108, 43, 7, 60, 36, 52, 45, 60, 74, 52, 83, 60, 112, 52,
- 5, 0, 36, 8, 47, 0, 78, 8, 89, 0, 119, 9, 18, 26, 32,
- 17, 56, 26, 74, 17, 94, 26, 116, 17, 17, 34, 31, 43, 59, 34,
- 69, 43, 98, 35, 107, 43, 6, 60, 35, 52, 44, 60, 73, 52, 82,
- 60, 111, 52, 6, 0, 37, 8, 48, 0, 79, 8, 90, 0, 118, 9,
- 17, 26, 33, 17, 55, 26, 75, 17, 93, 26, 117, 17, 18, 34, 30,
- 43, 59, 35, 68, 43, 97, 35, 106, 43, 5, 60, 34, 52, 43, 60,
- 72, 52, 81, 60, 110, 52, 7, 0, 38, 8, 49, 0, 79, 9, 91,
- 0, 117, 9, 16, 26, 34, 17, 54, 26, 76, 17, 92, 26, 118, 17,
- 19, 34, 29, 43, 58, 35, 67, 43, 96, 35, 105, 43, 4, 60, 33,
- 52, 42, 60, 71, 52, 80, 60, 109, 52, 8, 0, 39, 8, 50, 0,
- 78, 9, 92, 0, 116, 9, 15, 26, 35, 17, 53, 26, 77, 17, 91,
- 26, 119, 17, 19, 35, 28, 43, 57, 35, 66, 43, 95, 35, 104, 43,
- 3, 60, 32, 52, 41, 60, 70, 52, 80, 61, 108, 52, 9, 0, 39,
- 9, 51, 0, 77, 9, 93, 0, 115, 9, 14, 26, 36, 17, 52, 26,
- 78, 17, 90, 26, 119, 18, 18, 35, 27, 43, 56, 35, 65, 43, 94,
- 35, 103, 43, 2, 60, 31, 52, 40, 60, 69, 52, 81, 61, 107, 52,
- 10, 0, 38, 9, 52, 0, 76, 9, 94, 0, 114, 9, 13, 26, 37,
- 17, 51, 26, 79, 17, 89, 26, 118, 18, 17, 35, 26, 43, 55, 35,
- 64, 43, 93, 35, 102, 43, 1, 60, 30, 52, 40, 61, 68, 52, 82,
- 61, 106, 52, 11, 0, 37, 9, 53, 0, 75, 9, 95, 0, 113, 9,
- 12, 26, 38, 17, 50, 26, 79, 18, 88, 26, 117, 18, 16, 35, 25,
- 43, 54, 35, 63, 43, 92, 35, 101, 43, 0, 60, 29, 52, 41, 61,
- 67, 52, 83, 61, 105, 52, 12, 0, 36, 9, 54, 0, 74, 9, 96,
- 0, 112, 9, 11, 26, 39, 17, 49, 26, 78, 18, 87, 26, 116, 18,
- 15, 35, 24, 43, 53, 35, 62, 43, 91, 35, 100, 43, 0, 61, 28,
- 52, 42, 61, 66, 52, 84, 61, 104, 52, 13, 0, 35, 9, 55, 0,
- 73, 9, 97, 0, 111, 9, 10, 26, 39, 18, 48, 26, 77, 18, 86,
- 26, 115, 18, 14, 35, 23, 43, 52, 35, 61, 43, 90, 35, 100, 44,
- 1, 61, 27, 52, 43, 61, 65, 52, 85, 61, 103, 52, 14, 0, 34,
- 9, 56, 0, 72, 9, 98, 0, 110, 9, 9, 26, 38, 18, 47, 26,
- 76, 18, 85, 26, 114, 18, 13, 35, 22, 43, 51, 35, 60, 43, 89,
- 35, 101, 44, 2, 61, 26, 52, 44, 61, 64, 52, 86, 61, 102, 52,
- 15, 0, 33, 9, 57, 0, 71, 9, 99, 0, 109, 9, 8, 26, 37,
- 18, 46, 26, 75, 18, 84, 26, 113, 18, 12, 35, 21, 43, 50, 35,
- 60, 44, 88, 35, 102, 44, 3, 61, 25, 52, 45, 61, 63, 52, 87,
- 61, 101, 52, 16, 0, 32, 9, 58, 0, 70, 9, 99, 1, 108, 9,
- 7, 26, 36, 18, 45, 26, 74, 18, 83, 26, 112, 18, 11, 35, 20,
- 43, 49, 35, 61, 44, 87, 35, 103, 44, 4, 61, 24, 52, 46, 61,
- 62, 52, 88, 61, 100, 52, 17, 0, 31, 9, 59, 0, 69, 9, 98,
- 1, 107, 9, 6, 26, 35, 18, 44, 26, 73, 18, 82, 26, 111, 18,
- 10, 35, 20, 44, 48, 35, 62, 44, 86, 35, 104, 44, 5, 61, 23,
- 52, 47, 61, 61, 52, 89, 61, 100, 53, 18, 0, 30, 9, 59, 1,
- 68, 9, 97, 1, 106, 9, 5, 26, 34, 18, 43, 26, 72, 18, 81,
- 26, 110, 18, 9, 35, 21, 44, 47, 35, 63, 44, 85, 35, 105, 44,
- 6, 61, 22, 52, 48, 61, 60, 52, 90, 61, 101, 53, 19, 0, 29,
- 9, 58, 1, 67, 9, 96, 1, 105, 9, 4, 26, 33, 18, 42, 26,
- 71, 18, 80, 26, 109, 18, 8, 35, 22, 44, 46, 35, 64, 44, 84,
- 35, 106, 44, 7, 61, 21, 52, 49, 61, 60, 53, 91, 61, 102, 53,
- 19, 1, 28, 9, 57, 1, 66, 9, 95, 1, 104, 9, 3, 26, 32,
- 18, 41, 26, 70, 18, 80, 27, 108, 18, 7, 35, 23, 44, 45, 35,
- 65, 44, 83, 35, 107, 44, 8, 61, 20, 52, 50, 61, 61, 53, 92,
- 61, 103, 53, 18, 1, 27, 9, 56, 1, 65, 9, 94, 1, 103, 9,
- 2, 26, 31, 18, 40, 26, 69, 18, 81, 27, 107, 18, 6, 35, 24,
- 44, 44, 35, 66, 44, 82, 35, 108, 44, 9, 61, 20, 53, 51, 61,
- 62, 53, 93, 61, 104, 53, 17, 1, 26, 9, 55, 1, 64, 9, 93,
- 1, 102, 9, 1, 26, 30, 18, 40, 27, 68, 18, 82, 27, 106, 18,
- 5, 35, 25, 44, 43, 35, 67, 44, 81, 35, 109, 44, 10, 61, 21,
- 53, 52, 61, 63, 53, 94, 61, 105, 53, 16, 1, 25, 9, 54, 1,
- 63, 9, 92, 1, 101, 9, 0, 26, 29, 18, 41, 27, 67, 18, 83,
- 27, 105, 18, 4, 35, 26, 44, 42, 35, 68, 44, 80, 35, 110, 44,
- 11, 61, 22, 53, 53, 61, 64, 53, 95, 61, 106, 53, 15, 1, 24,
- 9, 53, 1, 62, 9, 91, 1, 100, 9, 0, 27, 28, 18, 42, 27,
- 66, 18, 84, 27, 104, 18, 3, 35, 27, 44, 41, 35, 69, 44, 80,
- 36, 111, 44, 12, 61, 23, 53, 54, 61, 65, 53, 96, 61, 107, 53,
- 14, 1, 23, 9, 52, 1, 61, 9, 90, 1, 100, 10, 1, 27, 27,
- 18, 43, 27, 65, 18, 85, 27, 103, 18, 2, 35, 28, 44, 40, 35,
- 70, 44, 81, 36, 112, 44, 13, 61, 24, 53, 55, 61, 66, 53, 97,
- 61, 108, 53, 13, 1, 22, 9, 51, 1, 60, 9, 89, 1, 101, 10,
- 2, 27, 26, 18, 44, 27, 64, 18, 86, 27, 102, 18, 1, 35, 29,
- 44, 40, 36, 71, 44, 82, 36, 113, 44, 14, 61, 25, 53, 56, 61,
- 67, 53, 98, 61, 109, 53, 12, 1, 21, 9, 50, 1, 60, 10, 88,
- 1, 102, 10, 3, 27, 25, 18, 45, 27, 63, 18, 87, 27, 101, 18,
- 0, 35, 30, 44, 41, 36, 72, 44, 83, 36, 114, 44, 15, 61, 26,
- 53, 57, 61, 68, 53, 99, 61, 110, 53, 11, 1, 20, 9, 49, 1,
- 61, 10, 87, 1, 103, 10, 4, 27, 24, 18, 46, 27, 62, 18, 88,
- 27, 100, 18, 0, 36, 31, 44, 42, 36, 73, 44, 84, 36, 115, 44,
- 16, 61, 27, 53, 58, 61, 69, 53, 99, 62, 111, 53, 10, 1, 20,
- 10, 48, 1, 62, 10, 86, 1, 104, 10, 5, 27, 23, 18, 47, 27,
- 61, 18, 89, 27, 100, 19, 1, 36, 32, 44, 43, 36, 74, 44, 85,
- 36, 116, 44, 17, 61, 28, 53, 59, 61, 70, 53, 98, 62, 112, 53,
- 9, 1, 21, 10, 47, 1, 63, 10, 85, 1, 105, 10, 6, 27, 22,
- 18, 48, 27, 60, 18, 90, 27, 101, 19, 2, 36, 33, 44, 44, 36,
- 75, 44, 86, 36, 117, 44, 18, 61, 29, 53, 59, 62, 71, 53, 97,
- 62, 113, 53, 8, 1, 22, 10, 46, 1, 64, 10, 84, 1, 106, 10,
- 7, 27, 21, 18, 49, 27, 60, 19, 91, 27, 102, 19, 3, 36, 34,
- 44, 45, 36, 76, 44, 87, 36, 118, 44, 19, 61, 30, 53, 58, 62,
- 72, 53, 96, 62, 114, 53, 7, 1, 23, 10, 45, 1, 65, 10, 83,
- 1, 107, 10, 8, 27, 20, 18, 50, 27, 61, 19, 92, 27, 103, 19,
- 4, 36, 35, 44, 46, 36, 77, 44, 88, 36, 119, 44, 19, 62, 31,
- 53, 57, 62, 73, 53, 95, 62, 115, 53, 6, 1, 24, 10, 44, 1,
- 66, 10, 82, 1, 108, 10, 9, 27, 20, 19, 51, 27, 62, 19, 93,
- 27, 104, 19, 5, 36, 36, 44, 47, 36, 78, 44, 89, 36, 119, 45,
- 18, 62, 32, 53, 56, 62, 74, 53, 94, 62, 116, 53, 5, 1, 25,
- 10, 43, 1, 67, 10, 81, 1, 109, 10, 10, 27, 21, 19, 52, 27,
- 63, 19, 94, 27, 105, 19, 6, 36, 37, 44, 48, 36, 79, 44, 90,
- 36, 118, 45, 17, 62, 33, 53, 55, 62, 75, 53, 93, 62, 117, 53,
- 4, 1, 26, 10, 42, 1, 68, 10, 80, 1, 110, 10, 11, 27, 22,
- 19, 53, 27, 64, 19, 95, 27, 106, 19, 7, 36, 38, 44, 49, 36,
- 79, 45, 91, 36, 117, 45, 16, 62, 34, 53, 54, 62, 76, 53, 92,
- 62, 118, 53, 3, 1, 27, 10, 41, 1, 69, 10, 80, 2, 111, 10,
- 12, 27, 23, 19, 54, 27, 65, 19, 96, 27, 107, 19, 8, 36, 39,
- 44, 50, 36, 78, 45, 92, 36, 116, 45, 15, 62, 35, 53, 53, 62,
- 77, 53, 91, 62, 119, 53, 2, 1, 28, 10, 40, 1, 70, 10, 81,
- 2, 112, 10, 13, 27, 24, 19, 55, 27, 66, 19, 97, 27, 108, 19,
- 9, 36, 39, 45, 51, 36, 77, 45, 93, 36, 115, 45, 14, 62, 36,
- 53, 52, 62, 78, 53, 90, 62, 119, 54, 1, 1, 29, 10, 40, 2,
- 71, 10, 82, 2, 113, 10, 14, 27, 25, 19, 56, 27, 67, 19, 98,
- 27, 109, 19, 10, 36, 38, 45, 52, 36, 76, 45, 94, 36, 114, 45,
- 13, 62, 37, 53, 51, 62, 79, 53, 89, 62, 118, 54, 0, 1, 30,
- 10, 41, 2, 72, 10, 83, 2, 114, 10, 15, 27, 26, 19, 57, 27,
- 68, 19, 99, 27, 110, 19, 11, 36, 37, 45, 53, 36, 75, 45, 95,
- 36, 113, 45, 12, 62, 38, 53, 50, 62, 79, 54, 88, 62, 117, 54,
- 0, 2, 31, 10, 42, 2, 73, 10, 84, 2, 115, 10, 16, 27, 27,
- 19, 58, 27, 69, 19, 99, 28, 111, 19, 12, 36, 36, 45, 54, 36,
- 74, 45, 96, 36, 112, 45, 11, 62, 39, 53, 49, 62, 78, 54, 87,
- 62, 116, 54, 1, 2, 32, 10, 43, 2, 74, 10, 85, 2, 116, 10,
- 17, 27, 28, 19, 59, 27, 70, 19, 98, 28, 112, 19, 13, 36, 35,
- 45, 55, 36, 73, 45, 97, 36, 111, 45, 10, 62, 39, 54, 48, 62,
- 77, 54, 86, 62, 115, 54, 2, 2, 33, 10, 44, 2, 75, 10, 86,
- 2, 117, 10, 18, 27, 29, 19, 59, 28, 71, 19, 97, 28, 113, 19,
- 14, 36, 34, 45, 56, 36, 72, 45, 98, 36, 110, 45, 9, 62, 38,
- 54, 47, 62, 76, 54, 85, 62, 114, 54, 3, 2, 34, 10, 45, 2,
- 76, 10, 87, 2, 118, 10, 19, 27, 30, 19, 58, 28, 72, 19, 96,
- 28, 114, 19, 15, 36, 33, 45, 57, 36, 71, 45, 99, 36, 109, 45,
- 8, 62, 37, 54, 46, 62, 75, 54, 84, 62, 113, 54, 4, 2, 35,
- 10, 46, 2, 77, 10, 88, 2, 119, 10, 19, 28, 31, 19, 57, 28,
- 73, 19, 95, 28, 115, 19, 16, 36, 32, 45, 58, 36, 70, 45, 99,
- 37, 108, 45, 7, 62, 36, 54, 45, 62, 74, 54, 83, 62, 112, 54,
- 5, 2, 36, 10, 47, 2, 78, 10, 89, 2, 119, 11, 18, 28, 32,
- 19, 56, 28, 74, 19, 94, 28, 116, 19, 17, 36, 31, 45, 59, 36,
- 69, 45, 98, 37, 107, 45, 6, 62, 35, 54, 44, 62, 73, 54, 82,
- 62, 111, 54, 6, 2, 37, 10, 48, 2, 79, 10, 90, 2, 118, 11,
- 17, 28, 33, 19, 55, 28, 75, 19, 93, 28, 117, 19, 18, 36, 30,
- 45, 59, 37, 68, 45, 97, 37, 106, 45, 5, 62, 34, 54, 43, 62,
- 72, 54, 81, 62, 110, 54, 7, 2, 38, 10, 49, 2, 79, 11, 91,
- 2, 117, 11, 16, 28, 34, 19, 54, 28, 76, 19, 92, 28, 118, 19,
- 19, 36, 29, 45, 58, 37, 67, 45, 96, 37, 105, 45, 4, 62, 33,
- 54, 42, 62, 71, 54, 80, 62, 109, 54, 8, 2, 39, 10, 50, 2,
- 78, 11, 92, 2, 116, 11, 15, 28, 35, 19, 53, 28, 77, 19, 91,
- 28, 119, 19, 19, 37, 28, 45, 57, 37, 66, 45, 95, 37, 104, 45,
- 3, 62, 32, 54, 41, 62, 70, 54, 80, 63, 108, 54, 9, 2, 39,
- 11, 51, 2, 77, 11, 93, 2, 115, 11, 14, 28, 36, 19, 52, 28,
- 78, 19, 90, 28, 119, 20, 18, 37, 27, 45, 56, 37, 65, 45, 94,
- 37, 103, 45, 2, 62, 31, 54, 40, 62, 69, 54, 81, 63, 107, 54,
- 10, 2, 38, 11, 52, 2, 76, 11, 94, 2, 114, 11, 13, 28, 37,
- 19, 51, 28, 79, 19, 89, 28, 118, 20, 17, 37, 26, 45, 55, 37,
- 64, 45, 93, 37, 102, 45, 1, 62, 30, 54, 40, 63, 68, 54, 82,
- 63, 106, 54, 11, 2, 37, 11, 53, 2, 75, 11, 95, 2, 113, 11,
- 12, 28, 38, 19, 50, 28, 79, 20, 88, 28, 117, 20, 16, 37, 25,
- 45, 54, 37, 63, 45, 92, 37, 101, 45, 0, 62, 29, 54, 41, 63,
- 67, 54, 83, 63, 105, 54, 12, 2, 36, 11, 54, 2, 74, 11, 96,
- 2, 112, 11, 11, 28, 39, 19, 49, 28, 78, 20, 87, 28, 116, 20,
- 15, 37, 24, 45, 53, 37, 62, 45, 91, 37, 100, 45, 0, 63, 28,
- 54, 42, 63, 66, 54, 84, 63, 104, 54, 13, 2, 35, 11, 55, 2,
- 73, 11, 97, 2, 111, 11, 10, 28, 39, 20, 48, 28, 77, 20, 86,
- 28, 115, 20, 14, 37, 23, 45, 52, 37, 61, 45, 90, 37, 100, 46,
- 1, 63, 27, 54, 43, 63, 65, 54, 85, 63, 103, 54, 14, 2, 34,
- 11, 56, 2, 72, 11, 98, 2, 110, 11, 9, 28, 38, 20, 47, 28,
- 76, 20, 85, 28, 114, 20, 13, 37, 22, 45, 51, 37, 60, 45, 89,
- 37, 101, 46, 2, 63, 26, 54, 44, 63, 64, 54, 86, 63, 102, 54,
- 15, 2, 33, 11, 57, 2, 71, 11, 99, 2, 109, 11, 8, 28, 37,
- 20, 46, 28, 75, 20, 84, 28, 113, 20, 12, 37, 21, 45, 50, 37,
- 60, 46, 88, 37, 102, 46, 3, 63, 25, 54, 45, 63, 63, 54, 87,
- 63, 101, 54, 16, 2, 32, 11, 58, 2, 70, 11, 99, 3, 108, 11,
- 7, 28, 36, 20, 45, 28, 74, 20, 83, 28, 112, 20, 11, 37, 20,
- 45, 49, 37, 61, 46, 87, 37, 103, 46, 4, 63, 24, 54, 46, 63,
- 62, 54, 88, 63, 100, 54, 17, 2, 31, 11, 59, 2, 69, 11, 98,
- 3, 107, 11, 6, 28, 35, 20, 44, 28, 73, 20, 82, 28, 111, 20,
- 10, 37, 20, 46, 48, 37, 62, 46, 86, 37, 104, 46, 5, 63, 23,
- 54, 47, 63, 61, 54, 89, 63, 100, 55, 18, 2, 30, 11, 59, 3,
- 68, 11, 97, 3, 106, 11, 5, 28, 34, 20, 43, 28, 72, 20, 81,
- 28, 110, 20, 9, 37, 21, 46, 47, 37, 63, 46, 85, 37, 105, 46,
- 6, 63, 22, 54, 48, 63, 60, 54, 90, 63, 101, 55, 19, 2, 29,
- 11, 58, 3, 67, 11, 96, 3, 105, 11, 4, 28, 33, 20, 42, 28,
- 71, 20, 80, 28, 109, 20, 8, 37, 22, 46, 46, 37, 64, 46, 84,
- 37, 106, 46, 7, 63, 21, 54, 49, 63, 60, 55, 91, 63, 102, 55,
- 19, 3, 28, 11, 57, 3, 66, 11, 95, 3, 104, 11, 3, 28, 32,
- 20, 41, 28, 70, 20, 80, 29, 108, 20, 7, 37, 23, 46, 45, 37,
- 65, 46, 83, 37, 107, 46, 8, 63, 20, 54, 50, 63, 61, 55, 92,
- 63, 103, 55, 18, 3, 27, 11, 56, 3, 65, 11, 94, 3, 103, 11,
- 2, 28, 31, 20, 40, 28, 69, 20, 81, 29, 107, 20, 6, 37, 24,
- 46, 44, 37, 66, 46, 82, 37, 108, 46, 9, 63, 20, 55, 51, 63,
- 62, 55, 93, 63, 104, 55, 17, 3, 26, 11, 55, 3, 64, 11, 93,
- 3, 102, 11, 1, 28, 30, 20, 40, 29, 68, 20, 82, 29, 106, 20,
- 5, 37, 25, 46, 43, 37, 67, 46, 81, 37, 109, 46, 10, 63, 21,
- 55, 52, 63, 63, 55, 94, 63, 105, 55, 16, 3, 25, 11, 54, 3,
- 63, 11, 92, 3, 101, 11, 0, 28, 29, 20, 41, 29, 67, 20, 83,
- 29, 105, 20, 4, 37, 26, 46, 42, 37, 68, 46, 80, 37, 110, 46,
- 11, 63, 22, 55, 53, 63, 64, 55, 95, 63, 106, 55, 15, 3, 24,
- 11, 53, 3, 62, 11, 91, 3, 100, 11, 0, 29, 28, 20, 42, 29,
- 66, 20, 84, 29, 104, 20, 3, 37, 27, 46, 41, 37, 69, 46, 80,
- 38, 111, 46, 12, 63, 23, 55, 54, 63, 65, 55, 96, 63, 107, 55,
- 14, 3, 23, 11, 52, 3, 61, 11, 90, 3, 100, 12, 1, 29, 27,
- 20, 43, 29, 65, 20, 85, 29, 103, 20, 2, 37, 28, 46, 40, 37,
- 70, 46, 81, 38, 112, 46, 13, 63, 24, 55, 55, 63, 66, 55, 97,
- 63, 108, 55, 13, 3, 22, 11, 51, 3, 60, 11, 89, 3, 101, 12,
- 2, 29, 26, 20, 44, 29, 64, 20, 86, 29, 102, 20, 1, 37, 29,
- 46, 40, 38, 71, 46, 82, 38, 113, 46, 14, 63, 25, 55, 56, 63,
- 67, 55, 98, 63, 109, 55, 12, 3, 21, 11, 50, 3, 60, 12, 88,
- 3, 102, 12, 3, 29, 25, 20, 45, 29, 63, 20, 87, 29, 101, 20,
- 0, 37, 30, 46, 41, 38, 72, 46, 83, 38, 114, 46, 15, 63, 26,
- 55, 57, 63, 68, 55, 99, 63, 110, 55, 11, 3, 20, 11, 49, 3,
- 61, 12, 87, 3, 103, 12, 4, 29, 24, 20, 46, 29, 62, 20, 88,
- 29, 100, 20, 0, 38, 31, 46, 42, 38, 73, 46, 84, 38, 115, 46,
- 16, 63, 27, 55, 58, 63, 69, 55, 99, 64, 111, 55, 10, 3, 20,
- 12, 48, 3, 62, 12, 86, 3, 104, 12, 5, 29, 23, 20, 47, 29,
- 61, 20, 89, 29, 100, 21, 1, 38, 32, 46, 43, 38, 74, 46, 85,
- 38, 116, 46, 17, 63, 28, 55, 59, 63, 70, 55, 98, 64, 112, 55,
- 9, 3, 21, 12, 47, 3, 63, 12, 85, 3, 105, 12, 6, 29, 22,
- 20, 48, 29, 60, 20, 90, 29, 101, 21, 2, 38, 33, 46, 44, 38,
- 75, 46, 86, 38, 117, 46, 18, 63, 29, 55, 59, 64, 71, 55, 97,
- 64, 113, 55, 8, 3, 22, 12, 46, 3, 64, 12, 84, 3, 106, 12,
- 7, 29, 21, 20, 49, 29, 60, 21, 91, 29, 102, 21, 3, 38, 34,
- 46, 45, 38, 76, 46, 87, 38, 118, 46, 19, 63, 30, 55, 58, 64,
- 72, 55, 96, 64, 114, 55, 7, 3, 23, 12, 45, 3, 65, 12, 83,
- 3, 107, 12, 8, 29, 20, 20, 50, 29, 61, 21, 92, 29, 103, 21,
- 4, 38, 35, 46, 46, 38, 77, 46, 88, 38, 119, 46, 19, 64, 31,
- 55, 57, 64, 73, 55, 95, 64, 115, 55, 6, 3, 24, 12, 44, 3,
- 66, 12, 82, 3, 108, 12, 9, 29, 20, 21, 51, 29, 62, 21, 93,
- 29, 104, 21, 5, 38, 36, 46, 47, 38, 78, 46, 89, 38, 119, 47,
- 18, 64, 32, 55, 56, 64, 74, 55, 94, 64, 116, 55, 5, 3, 25,
- 12, 43, 3, 67, 12, 81, 3, 109, 12, 10, 29, 21, 21, 52, 29,
- 63, 21, 94, 29, 105, 21, 6, 38, 37, 46, 48, 38, 79, 46, 90,
- 38, 118, 47, 17, 64, 33, 55, 55, 64, 75, 55, 93, 64, 117, 55,
- 4, 3, 26, 12, 42, 3, 68, 12, 80, 3, 110, 12, 11, 29, 22,
- 21, 53, 29, 64, 21, 95, 29, 106, 21, 7, 38, 38, 46, 49, 38,
- 79, 47, 91, 38, 117, 47, 16, 64, 34, 55, 54, 64, 76, 55, 92,
- 64, 118, 55, 3, 3, 27, 12, 41, 3, 69, 12, 80, 4, 111, 12,
- 12, 29, 23, 21, 54, 29, 65, 21, 96, 29, 107, 21, 8, 38, 39,
- 46, 50, 38, 78, 47, 92, 38, 116, 47, 15, 64, 35, 55, 53, 64,
- 77, 55, 91, 64, 119, 55, 2, 3, 28, 12, 40, 3, 70, 12, 81,
- 4, 112, 12, 13, 29, 24, 21, 55, 29, 66, 21, 97, 29, 108, 21,
- 9, 38, 39, 47, 51, 38, 77, 47, 93, 38, 115, 47, 14, 64, 36,
- 55, 52, 64, 78, 55, 90, 64, 119, 56, 1, 3, 29, 12, 40, 4,
- 71, 12, 82, 4, 113, 12, 14, 29, 25, 21, 56, 29, 67, 21, 98,
- 29, 109, 21, 10, 38, 38, 47, 52, 38, 76, 47, 94, 38, 114, 47,
- 13, 64, 37, 55, 51, 64, 79, 55, 89, 64, 118, 56, 0, 3, 30,
- 12, 41, 4, 72, 12, 83, 4, 114, 12, 15, 29, 26, 21, 57, 29,
- 68, 21, 99, 29, 110, 21, 11, 38, 37, 47, 53, 38, 75, 47, 95,
- 38, 113, 47, 12, 64, 38, 55, 50, 64, 79, 56, 88, 64, 117, 56,
- 0, 4, 31, 12, 42, 4, 73, 12, 84, 4, 115, 12, 16, 29, 27,
- 21, 58, 29, 69, 21, 99, 30, 111, 21, 12, 38, 36, 47, 54, 38,
- 74, 47, 96, 38, 112, 47, 11, 64, 39, 55, 49, 64, 78, 56, 87,
- 64, 116, 56, 1, 4, 32, 12, 43, 4, 74, 12, 85, 4, 116, 12,
- 17, 29, 28, 21, 59, 29, 70, 21, 98, 30, 112, 21, 13, 38, 35,
- 47, 55, 38, 73, 47, 97, 38, 111, 47, 10, 64, 39, 56, 48, 64,
- 77, 56, 86, 64, 115, 56, 2, 4, 33, 12, 44, 4, 75, 12, 86,
- 4, 117, 12, 18, 29, 29, 21, 59, 30, 71, 21, 97, 30, 113, 21,
- 14, 38, 34, 47, 56, 38, 72, 47, 98, 38, 110, 47, 9, 64, 38,
- 56, 47, 64, 76, 56, 85, 64, 114, 56, 3, 4, 34, 12, 45, 4,
- 76, 12, 87, 4, 118, 12, 19, 29, 30, 21, 58, 30, 72, 21, 96,
- 30, 114, 21, 15, 38, 33, 47, 57, 38, 71, 47, 99, 38, 109, 47,
- 8, 64, 37, 56, 46, 64, 75, 56, 84, 64, 113, 56, 4, 4, 35,
- 12, 46, 4, 77, 12, 88, 4, 119, 12, 19, 30, 31, 21, 57, 30,
- 73, 21, 95, 30, 115, 21, 16, 38, 32, 47, 58, 38, 70, 47, 99,
- 39, 108, 47, 7, 64, 36, 56, 45, 64, 74, 56, 83, 64, 112, 56,
- 5, 4, 36, 12, 47, 4, 78, 12, 89, 4, 119, 13, 18, 30, 32,
- 21, 56, 30, 74, 21, 94, 30, 116, 21, 17, 38, 31, 47, 59, 38,
- 69, 47, 98, 39, 107, 47, 6, 64, 35, 56, 44, 64, 73, 56, 82,
- 64, 111, 56, 6, 4, 37, 12, 48, 4, 79, 12, 90, 4, 118, 13,
- 17, 30, 33, 21, 55, 30, 75, 21, 93, 30, 117, 21, 18, 38, 30,
- 47, 59, 39, 68, 47, 97, 39, 106, 47, 5, 64, 34, 56, 43, 64,
- 72, 56, 81, 64, 110, 56, 7, 4, 38, 12, 49, 4, 79, 13, 91,
- 4, 117, 13, 16, 30, 34, 21, 54, 30, 76, 21, 92, 30, 118, 21,
- 19, 38, 29, 47, 58, 39, 67, 47, 96, 39, 105, 47, 4, 64, 33,
- 56, 42, 64, 71, 56, 80, 64, 109, 56, 8, 4, 39, 12, 50, 4,
- 78, 13, 92, 4, 116, 13, 15, 30, 35, 21, 53, 30, 77, 21, 91,
- 30, 119, 21, 19, 39, 28, 47, 57, 39, 66, 47, 95, 39, 104, 47,
- 3, 64, 32, 56, 41, 64, 70, 56, 80, 65, 108, 56, 9, 4, 39,
- 13, 51, 4, 77, 13, 93, 4, 115, 13, 14, 30, 36, 21, 52, 30,
- 78, 21, 90, 30, 119, 22, 18, 39, 27, 47, 56, 39, 65, 47, 94,
- 39, 103, 47, 2, 64, 31, 56, 40, 64, 69, 56, 81, 65, 107, 56,
- 10, 4, 38, 13, 52, 4, 76, 13, 94, 4, 114, 13, 13, 30, 37,
- 21, 51, 30, 79, 21, 89, 30, 118, 22, 17, 39, 26, 47, 55, 39,
- 64, 47, 93, 39, 102, 47, 1, 64, 30, 56, 40, 65, 68, 56, 82,
- 65, 106, 56, 11, 4, 37, 13, 53, 4, 75, 13, 95, 4, 113, 13,
- 12, 30, 38, 21, 50, 30, 79, 22, 88, 30, 117, 22, 16, 39, 25,
- 47, 54, 39, 63, 47, 92, 39, 101, 47, 0, 64, 29, 56, 41, 65,
- 67, 56, 83, 65, 105, 56, 12, 4, 36, 13, 54, 4, 74, 13, 96,
- 4, 112, 13, 11, 30, 39, 21, 49, 30, 78, 22, 87, 30, 116, 22,
- 15, 39, 24, 47, 53, 39, 62, 47, 91, 39, 100, 47, 0, 65, 28,
- 56, 42, 65, 66, 56, 84, 65, 104, 56, 13, 4, 35, 13, 55, 4,
- 73, 13, 97, 4, 111, 13, 10, 30, 39, 22, 48, 30, 77, 22, 86,
- 30, 115, 22, 14, 39, 23, 47, 52, 39, 61, 47, 90, 39, 100, 48,
- 1, 65, 27, 56, 43, 65, 65, 56, 85, 65, 103, 56, 14, 4, 34,
- 13, 56, 4, 72, 13, 98, 4, 110, 13, 9, 30, 38, 22, 47, 30,
- 76, 22, 85, 30, 114, 22, 13, 39, 22, 47, 51, 39, 60, 47, 89,
- 39, 101, 48, 2, 65, 26, 56, 44, 65, 64, 56, 86, 65, 102, 56,
- 15, 4, 33, 13, 57, 4, 71, 13, 99, 4, 109, 13, 8, 30, 37,
- 22, 46, 30, 75, 22, 84, 30, 113, 22, 12, 39, 21, 47, 50, 39,
- 60, 48, 88, 39, 102, 48, 3, 65, 25, 56, 45, 65, 63, 56, 87,
- 65, 101, 56, 16, 4, 32, 13, 58, 4, 70, 13, 99, 5, 108, 13,
- 7, 30, 36, 22, 45, 30, 74, 22, 83, 30, 112, 22, 11, 39, 20,
- 47, 49, 39, 61, 48, 87, 39, 103, 48, 4, 65, 24, 56, 46, 65,
- 62, 56, 88, 65, 100, 56, 17, 4, 31, 13, 59, 4, 69, 13, 98,
- 5, 107, 13, 6, 30, 35, 22, 44, 30, 73, 22, 82, 30, 111, 22,
- 10, 39, 20, 48, 48, 39, 62, 48, 86, 39, 104, 48, 5, 65, 23,
- 56, 47, 65, 61, 56, 89, 65, 100, 57, 18, 4, 30, 13, 59, 5,
- 68, 13, 97, 5, 106, 13, 5, 30, 34, 22, 43, 30, 72, 22, 81,
- 30, 110, 22, 9, 39, 21, 48, 47, 39, 63, 48, 85, 39, 105, 48,
- 6, 65, 22, 56, 48, 65, 60, 56, 90, 65, 101, 57, 19, 4, 29,
- 13, 58, 5, 67, 13, 96, 5, 105, 13, 4, 30, 33, 22, 42, 30,
- 71, 22, 80, 30, 109, 22, 8, 39, 22, 48, 46, 39, 64, 48, 84,
- 39, 106, 48, 7, 65, 21, 56, 49, 65, 60, 57, 91, 65, 102, 57,
- 19, 5, 28, 13, 57, 5, 66, 13, 95, 5, 104, 13, 3, 30, 32,
- 22, 41, 30, 70, 22, 80, 31, 108, 22, 7, 39, 23, 48, 45, 39,
- 65, 48, 83, 39, 107, 48, 8, 65, 20, 56, 50, 65, 61, 57, 92,
- 65, 103, 57, 18, 5, 27, 13, 56, 5, 65, 13, 94, 5, 103, 13,
- 2, 30, 31, 22, 40, 30, 69, 22, 81, 31, 107, 22, 6, 39, 24,
- 48, 44, 39, 66, 48, 82, 39, 108, 48, 9, 65, 20, 57, 51, 65,
- 62, 57, 93, 65, 104, 57, 17, 5, 26, 13, 55, 5, 64, 13, 93,
- 5, 102, 13, 1, 30, 30, 22, 40, 31, 68, 22, 82, 31, 106, 22,
- 5, 39, 25, 48, 43, 39, 67, 48, 81, 39, 109, 48, 10, 65, 21,
- 57, 52, 65, 63, 57, 94, 65, 105, 57, 16, 5, 25, 13, 54, 5,
- 63, 13, 92, 5, 101, 13, 0, 30, 29, 22, 41, 31, 67, 22, 83,
- 31, 105, 22, 4, 39, 26, 48, 42, 39, 68, 48, 80, 39, 110, 48,
- 11, 65, 22, 57, 53, 65, 64, 57, 95, 65, 106, 57, 15, 5, 24,
- 13, 53, 5, 62, 13, 91, 5, 100, 13, 0, 31, 28, 22, 42, 31,
- 66, 22, 84, 31, 104, 22, 3, 39, 27, 48, 41, 39, 69, 48, 80,
- 40, 111, 48, 12, 65, 23, 57, 54, 65, 65, 57, 96, 65, 107, 57,
- 14, 5, 23, 13, 52, 5, 61, 13, 90, 5, 100, 14, 1, 31, 27,
- 22, 43, 31, 65, 22, 85, 31, 103, 22, 2, 39, 28, 48, 40, 39,
- 70, 48, 81, 40, 112, 48, 13, 65, 24, 57, 55, 65, 66, 57, 97,
- 65, 108, 57, 13, 5, 22, 13, 51, 5, 60, 13, 89, 5, 101, 14,
- 2, 31, 26, 22, 44, 31, 64, 22, 86, 31, 102, 22, 1, 39, 29,
- 48, 40, 40, 71, 48, 82, 40, 113, 48, 14, 65, 25, 57, 56, 65,
- 67, 57, 98, 65, 109, 57, 12, 5, 21, 13, 50, 5, 60, 14, 88,
- 5, 102, 14, 3, 31, 25, 22, 45, 31, 63, 22, 87, 31, 101, 22,
- 0, 39, 30, 48, 41, 40, 72, 48, 83, 40, 114, 48, 15, 65, 26,
- 57, 57, 65, 68, 57, 99, 65, 110, 57, 11, 5, 20, 13, 49, 5,
- 61, 14, 87, 5, 103, 14, 4, 31, 24, 22, 46, 31, 62, 22, 88,
- 31, 100, 22, 0, 40, 31, 48, 42, 40, 73, 48, 84, 40, 115, 48,
- 16, 65, 27, 57, 58, 65, 69, 57, 99, 66, 111, 57, 10, 5, 20,
- 14, 48, 5, 62, 14, 86, 5, 104, 14, 5, 31, 23, 22, 47, 31,
- 61, 22, 89, 31, 100, 23, 1, 40, 32, 48, 43, 40, 74, 48, 85,
- 40, 116, 48, 17, 65, 28, 57, 59, 65, 70, 57, 98, 66, 112, 57,
- 9, 5, 21, 14, 47, 5, 63, 14, 85, 5, 105, 14, 6, 31, 22,
- 22, 48, 31, 60, 22, 90, 31, 101, 23, 2, 40, 33, 48, 44, 40,
- 75, 48, 86, 40, 117, 48, 18, 65, 29, 57, 59, 66, 71, 57, 97,
- 66, 113, 57, 8, 5, 22, 14, 46, 5, 64, 14, 84, 5, 106, 14,
- 7, 31, 21, 22, 49, 31, 60, 23, 91, 31, 102, 23, 3, 40, 34,
- 48, 45, 40, 76, 48, 87, 40, 118, 48, 19, 65, 30, 57, 58, 66,
- 72, 57, 96, 66, 114, 57, 7, 5, 23, 14, 45, 5, 65, 14, 83,
- 5, 107, 14, 8, 31, 20, 22, 50, 31, 61, 23, 92, 31, 103, 23,
- 4, 40, 35, 48, 46, 40, 77, 48, 88, 40, 119, 48, 19, 66, 31,
- 57, 57, 66, 73, 57, 95, 66, 115, 57, 6, 5, 24, 14, 44, 5,
- 66, 14, 82, 5, 108, 14, 9, 31, 20, 23, 51, 31, 62, 23, 93,
- 31, 104, 23, 5, 40, 36, 48, 47, 40, 78, 48, 89, 40, 119, 49,
- 18, 66, 32, 57, 56, 66, 74, 57, 94, 66, 116, 57, 5, 5, 25,
- 14, 43, 5, 67, 14, 81, 5, 109, 14, 10, 31, 21, 23, 52, 31,
- 63, 23, 94, 31, 105, 23, 6, 40, 37, 48, 48, 40, 79, 48, 90,
- 40, 118, 49, 17, 66, 33, 57, 55, 66, 75, 57, 93, 66, 117, 57,
- 4, 5, 26, 14, 42, 5, 68, 14, 80, 5, 110, 14, 11, 31, 22,
- 23, 53, 31, 64, 23, 95, 31, 106, 23, 7, 40, 38, 48, 49, 40,
- 79, 49, 91, 40, 117, 49, 16, 66, 34, 57, 54, 66, 76, 57, 92,
- 66, 118, 57, 3, 5, 27, 14, 41, 5, 69, 14, 80, 6, 111, 14,
- 12, 31, 23, 23, 54, 31, 65, 23, 96, 31, 107, 23, 8, 40, 39,
- 48, 50, 40, 78, 49, 92, 40, 116, 49, 15, 66, 35, 57, 53, 66,
- 77, 57, 91, 66, 119, 57, 2, 5, 28, 14, 40, 5, 70, 14, 81,
- 6, 112, 14, 13, 31, 24, 23, 55, 31, 66, 23, 97, 31, 108, 23,
- 9, 40, 39, 49, 51, 40, 77, 49, 93, 40, 115, 49, 14, 66, 36,
- 57, 52, 66, 78, 57, 90, 66, 119, 58, 1, 5, 29, 14, 40, 6,
- 71, 14, 82, 6, 113, 14, 14, 31, 25, 23, 56, 31, 67, 23, 98,
- 31, 109, 23, 10, 40, 38, 49, 52, 40, 76, 49, 94, 40, 114, 49,
- 13, 66, 37, 57, 51, 66, 79, 57, 89, 66, 118, 58, 0, 5, 30,
- 14, 41, 6, 72, 14, 83, 6, 114, 14, 15, 31, 26, 23, 57, 31,
- 68, 23, 99, 31, 110, 23, 11, 40, 37, 49, 53, 40, 75, 49, 95,
- 40, 113, 49, 12, 66, 38, 57, 50, 66, 79, 58, 88, 66, 117, 58,
- 0, 6, 31, 14, 42, 6, 73, 14, 84, 6, 115, 14, 16, 31, 27,
- 23, 58, 31, 69, 23, 99, 32, 111, 23, 12, 40, 36, 49, 54, 40,
- 74, 49, 96, 40, 112, 49, 11, 66, 39, 57, 49, 66, 78, 58, 87,
- 66, 116, 58, 1, 6, 32, 14, 43, 6, 74, 14, 85, 6, 116, 14,
- 17, 31, 28, 23, 59, 31, 70, 23, 98, 32, 112, 23, 13, 40, 35,
- 49, 55, 40, 73, 49, 97, 40, 111, 49, 10, 66, 39, 58, 48, 66,
- 77, 58, 86, 66, 115, 58, 2, 6, 33, 14, 44, 6, 75, 14, 86,
- 6, 117, 14, 18, 31, 29, 23, 59, 32, 71, 23, 97, 32, 113, 23,
- 14, 40, 34, 49, 56, 40, 72, 49, 98, 40, 110, 49, 9, 66, 38,
- 58, 47, 66, 76, 58, 85, 66, 114, 58, 3, 6, 34, 14, 45, 6,
- 76, 14, 87, 6, 118, 14, 19, 31, 30, 23, 58, 32, 72, 23, 96,
- 32, 114, 23, 15, 40, 33, 49, 57, 40, 71, 49, 99, 40, 109, 49,
- 8, 66, 37, 58, 46, 66, 75, 58, 84, 66, 113, 58, 4, 6, 35,
- 14, 46, 6, 77, 14, 88, 6, 119, 14, 19, 32, 31, 23, 57, 32,
- 73, 23, 95, 32, 115, 23, 16, 40, 32, 49, 58, 40, 70, 49, 99,
- 41, 108, 49, 7, 66, 36, 58, 45, 66, 74, 58, 83, 66, 112, 58,
- 5, 6, 36, 14, 47, 6, 78, 14, 89, 6, 119, 15, 18, 32, 32,
- 23, 56, 32, 74, 23, 94, 32, 116, 23, 17, 40, 31, 49, 59, 40,
- 69, 49, 98, 41, 107, 49, 6, 66, 35, 58, 44, 66, 73, 58, 82,
- 66, 111, 58, 6, 6, 37, 14, 48, 6, 79, 14, 90, 6, 118, 15,
- 17, 32, 33, 23, 55, 32, 75, 23, 93, 32, 117, 23, 18, 40, 30,
- 49, 59, 41, 68, 49, 97, 41, 106, 49, 5, 66, 34, 58, 43, 66,
- 72, 58, 81, 66, 110, 58, 7, 6, 38, 14, 49, 6, 79, 15, 91,
- 6, 117, 15, 16, 32, 34, 23, 54, 32, 76, 23, 92, 32, 118, 23,
- 19, 40, 29, 49, 58, 41, 67, 49, 96, 41, 105, 49, 4, 66, 33,
- 58, 42, 66, 71, 58, 80, 66, 109, 58, 8, 6, 39, 14, 50, 6,
- 78, 15, 92, 6, 116, 15, 15, 32, 35, 23, 53, 32, 77, 23, 91,
- 32, 119, 23, 19, 41, 28, 49, 57, 41, 66, 49, 95, 41, 104, 49,
- 3, 66, 32, 58, 41, 66, 70, 58, 80, 67, 108, 58, 9, 6, 39,
- 15, 51, 6, 77, 15, 93, 6, 115, 15, 14, 32, 36, 23, 52, 32,
- 78, 23, 90, 32, 119, 24, 18, 41, 27, 49, 56, 41, 65, 49, 94,
- 41, 103, 49, 2, 66, 31, 58, 40, 66, 69, 58, 81, 67, 107, 58,
- 10, 6, 38, 15, 52, 6, 76, 15, 94, 6, 114, 15, 13, 32, 37,
- 23, 51, 32, 79, 23, 89, 32, 118, 24, 17, 41, 26, 49, 55, 41,
- 64, 49, 93, 41, 102, 49, 1, 66, 30, 58, 40, 67, 68, 58, 82,
- 67, 106, 58, 11, 6, 37, 15, 53, 6, 75, 15, 95, 6, 113, 15,
- 12, 32, 38, 23, 50, 32, 79, 24, 88, 32, 117, 24, 16, 41, 25,
- 49, 54, 41, 63, 49, 92, 41, 101, 49, 0, 66, 29, 58, 41, 67,
- 67, 58, 83, 67, 105, 58, 12, 6, 36, 15, 54, 6, 74, 15, 96,
- 6, 112, 15, 11, 32, 39, 23, 49, 32, 78, 24, 87, 32, 116, 24,
- 15, 41, 24, 49, 53, 41, 62, 49, 91, 41, 100, 49, 0, 67, 28,
- 58, 42, 67, 66, 58, 84, 67, 104, 58, 13, 6, 35, 15, 55, 6,
- 73, 15, 97, 6, 111, 15, 10, 32, 39, 24, 48, 32, 77, 24, 86,
- 32, 115, 24, 14, 41, 23, 49, 52, 41, 61, 49, 90, 41, 100, 50,
- 1, 67, 27, 58, 43, 67, 65, 58, 85, 67, 103, 58, 14, 6, 34,
- 15, 56, 6, 72, 15, 98, 6, 110, 15, 9, 32, 38, 24, 47, 32,
- 76, 24, 85, 32, 114, 24, 13, 41, 22, 49, 51, 41, 60, 49, 89,
- 41, 101, 50, 2, 67, 26, 58, 44, 67, 64, 58, 86, 67, 102, 58,
- 15, 6, 33, 15, 57, 6, 71, 15, 99, 6, 109, 15, 8, 32, 37,
- 24, 46, 32, 75, 24, 84, 32, 113, 24, 12, 41, 21, 49, 50, 41,
- 60, 50, 88, 41, 102, 50, 3, 67, 25, 58, 45, 67, 63, 58, 87,
- 67, 101, 58, 16, 6, 32, 15, 58, 6, 70, 15, 99, 7, 108, 15,
- 7, 32, 36, 24, 45, 32, 74, 24, 83, 32, 112, 24, 11, 41, 20,
- 49, 49, 41, 61, 50, 87, 41, 103, 50, 4, 67, 24, 58, 46, 67,
- 62, 58, 88, 67, 100, 58, 17, 6, 31, 15, 59, 6, 69, 15, 98,
- 7, 107, 15, 6, 32, 35, 24, 44, 32, 73, 24, 82, 32, 111, 24,
- 10, 41, 20, 50, 48, 41, 62, 50, 86, 41, 104, 50, 5, 67, 23,
- 58, 47, 67, 61, 58, 89, 67, 100, 59, 18, 6, 30, 15, 59, 7,
- 68, 15, 97, 7, 106, 15, 5, 32, 34, 24, 43, 32, 72, 24, 81,
- 32, 110, 24, 9, 41, 21, 50, 47, 41, 63, 50, 85, 41, 105, 50,
- 6, 67, 22, 58, 48, 67, 60, 58, 90, 67, 101, 59, 19, 6, 29,
- 15, 58, 7, 67, 15, 96, 7, 105, 15, 4, 32, 33, 24, 42, 32,
- 71, 24, 80, 32, 109, 24, 8, 41, 22, 50, 46, 41, 64, 50, 84,
- 41, 106, 50, 7, 67, 21, 58, 49, 67, 60, 59, 91, 67, 102, 59,
- 19, 7, 28, 15, 57, 7, 66, 15, 95, 7, 104, 15, 3, 32, 32,
- 24, 41, 32, 70, 24, 80, 33, 108, 24, 7, 41, 23, 50, 45, 41,
- 65, 50, 83, 41, 107, 50, 8, 67, 20, 58, 50, 67, 61, 59, 92,
- 67, 103, 59, 18, 7, 27, 15, 56, 7, 65, 15, 94, 7, 103, 15,
- 2, 32, 31, 24, 40, 32, 69, 24, 81, 33, 107, 24, 6, 41, 24,
- 50, 44, 41, 66, 50, 82, 41, 108, 50, 9, 67, 20, 59, 51, 67,
- 62, 59, 93, 67, 104, 59, 17, 7, 26, 15, 55, 7, 64, 15, 93,
- 7, 102, 15, 1, 32, 30, 24, 40, 33, 68, 24, 82, 33, 106, 24,
- 5, 41, 25, 50, 43, 41, 67, 50, 81, 41, 109, 50, 10, 67, 21,
- 59, 52, 67, 63, 59, 94, 67, 105, 59, 16, 7, 25, 15, 54, 7,
- 63, 15, 92, 7, 101, 15, 0, 32, 29, 24, 41, 33, 67, 24, 83,
- 33, 105, 24, 4, 41, 26, 50, 42, 41, 68, 50, 80, 41, 110, 50,
- 11, 67, 22, 59, 53, 67, 64, 59, 95, 67, 106, 59, 15, 7, 24,
- 15, 53, 7, 62, 15, 91, 7, 100, 15, 0, 33, 28, 24, 42, 33,
- 66, 24, 84, 33, 104, 24, 3, 41, 27, 50, 41, 41, 69, 50, 80,
- 42, 111, 50, 12, 67, 23, 59, 54, 67, 65, 59, 96, 67, 107, 59,
- 14, 7, 23, 15, 52, 7, 61, 15, 90, 7, 100, 16, 1, 33, 27,
- 24, 43, 33, 65, 24, 85, 33, 103, 24, 2, 41, 28, 50, 40, 41,
- 70, 50, 81, 42, 112, 50, 13, 67, 24, 59, 55, 67, 66, 59, 97,
- 67, 108, 59, 13, 7, 22, 15, 51, 7, 60, 15, 89, 7, 101, 16,
- 2, 33, 26, 24, 44, 33, 64, 24, 86, 33, 102, 24, 1, 41, 29,
- 50, 40, 42, 71, 50, 82, 42, 113, 50, 14, 67, 25, 59, 56, 67,
- 67, 59, 98, 67, 109, 59, 12, 7, 21, 15, 50, 7, 60, 16, 88,
- 7, 102, 16, 3, 33, 25, 24, 45, 33, 63, 24, 87, 33, 101, 24,
- 0, 41, 30, 50, 41, 42, 72, 50, 83, 42, 114, 50, 15, 67, 26,
- 59, 57, 67, 68, 59, 99, 67, 110, 59, 11, 7, 20, 15, 49, 7,
- 61, 16, 87, 7, 103, 16, 4, 33, 24, 24, 46, 33, 62, 24, 88,
- 33, 100, 24, 0, 42, 31, 50, 42, 42, 73, 50, 84, 42, 115, 50,
- 16, 67, 27, 59, 58, 67, 69, 59, 80, 51, 111, 59, 10, 7, 20,
- 16, 48, 7, 62, 16, 86, 7, 104, 16, 5, 33, 23, 24, 47, 33,
- 61, 24, 89, 33, 100, 25, 1, 42, 32, 50, 43, 42, 74, 50, 85,
- 42, 116, 50, 17, 67, 28, 59, 59, 67, 70, 59, 81, 51, 112, 59,
- 9, 7, 21, 16, 47, 7, 63, 16, 85, 7, 105, 16, 6, 33, 22,
- 24, 48, 33, 60, 24, 90, 33, 101, 25, 2, 42, 33, 50, 44, 42,
- 75, 50, 86, 42, 117, 50, 18, 67, 29, 59, 40, 51, 71, 59, 82,
- 51, 113, 59, 8, 7, 22, 16, 46, 7, 64, 16, 84, 7, 106, 16,
- 7, 33, 21, 24, 49, 33, 60, 25, 91, 33, 102, 25, 3, 42, 34,
- 50, 45, 42, 76, 50, 87, 42, 118, 50, 19, 67, 30, 59, 41, 51,
- 72, 59, 83, 51, 114, 59, 7, 7, 23, 16, 45, 7, 65, 16, 83,
- 7, 107, 16, 8, 33, 20, 24, 50, 33, 61, 25, 92, 33, 103, 25,
- 4, 42, 35, 50, 46, 42, 77, 50, 88, 42, 119, 50, 0, 51, 31,
- 59, 42, 51, 73, 59, 84, 51, 115, 59, 6, 7, 24, 16, 44, 7,
- 66, 16, 82, 7, 108, 16, 9, 33, 20, 25, 51, 33, 62, 25, 93,
- 33, 104, 25, 5, 42, 36, 50, 47, 42, 78, 50, 89, 42, 100, 34,
- 1, 51, 32, 59, 43, 51, 74, 59, 85, 51, 116, 59, 5, 7, 25,
- 16, 43, 7, 67, 16, 81, 7, 109, 16, 10, 33, 21, 25, 52, 33,
- 63, 25, 94, 33, 105, 25, 6, 42, 37, 50, 48, 42, 79, 50, 90,
- 42, 101, 34, 2, 51, 33, 59, 44, 51, 75, 59, 86, 51, 117, 59,
- 4, 7, 26, 16, 42, 7, 68, 16, 80, 7, 110, 16, 11, 33, 22,
- 25, 53, 33, 64, 25, 95, 33, 106, 25, 7, 42, 38, 50, 49, 42,
- 60, 34, 91, 42, 102, 34, 3, 51, 34, 59, 45, 51, 76, 59, 87,
- 51, 118, 59, 3, 7, 27, 16, 41, 7, 69, 16, 80, 8, 111, 16,
- 12, 33, 23, 25, 54, 33, 65, 25, 96, 33, 107, 25, 8, 42, 39,
- 50, 50, 42, 61, 34, 92, 42, 103, 34, 4, 51, 35, 59, 46, 51,
- 77, 59, 88, 51, 119, 59, 2, 7, 28, 16, 40, 7, 70, 16, 81,
- 8, 112, 16, 13, 33, 24, 25, 55, 33, 66, 25, 97, 33, 108, 25,
- 9, 42, 20, 34, 51, 42, 62, 34, 93, 42, 104, 34, 5, 51, 36,
- 59, 47, 51, 78, 59, 89, 51, 119, 60, 1, 7, 29, 16, 40, 8,
- 71, 16, 82, 8, 113, 16, 14, 33, 25, 25, 56, 33, 67, 25, 98,
- 33, 109, 25, 10, 42, 21, 34, 52, 42, 63, 34, 94, 42, 105, 34,
- 6, 51, 37, 59, 48, 51, 79, 59, 90, 51, 118, 60, 0, 7, 30,
- 16, 41, 8, 72, 16, 83, 8, 114, 16, 15, 33, 26, 25, 57, 33,
- 68, 25, 99, 33, 110, 25, 11, 42, 22, 34, 53, 42, 64, 34, 95,
- 42, 106, 34, 7, 51, 38, 59, 49, 51, 79, 60, 91, 51, 117, 60,
- 0, 8, 31, 16, 42, 8, 73, 16, 84, 8, 115, 16, 16, 33, 27,
- 25, 58, 33, 69, 25, 80, 17, 111, 25, 12, 42, 23, 34, 54, 42,
- 65, 34, 96, 42, 107, 34, 8, 51, 39, 59, 50, 51, 78, 60, 92,
- 51, 116, 60, 1, 8, 32, 16, 43, 8, 74, 16, 85, 8, 116, 16,
- 17, 33, 28, 25, 59, 33, 70, 25, 81, 17, 112, 25, 13, 42, 24,
- 34, 55, 42, 66, 34, 97, 42, 108, 34, 9, 51, 39, 60, 51, 51,
- 77, 60, 93, 51, 115, 60, 2, 8, 33, 16, 44, 8, 75, 16, 86,
- 8, 117, 16, 18, 33, 29, 25, 40, 17, 71, 25, 82, 17, 113, 25,
- 14, 42, 25, 34, 56, 42, 67, 34, 98, 42, 109, 34, 10, 51, 38,
- 60, 52, 51, 76, 60, 94, 51, 114, 60, 3, 8, 34, 16, 45, 8,
- 76, 16, 87, 8, 118, 16, 19, 33, 30, 25, 41, 17, 72, 25, 83,
- 17, 114, 25, 15, 42, 26, 34, 57, 42, 68, 34, 99, 42, 110, 34,
- 11, 51, 37, 60, 53, 51, 75, 60, 95, 51, 113, 60, 4, 8, 35,
- 16, 46, 8, 77, 16, 88, 8, 119, 16, 0, 17, 31, 25, 42, 17,
- 73, 25, 84, 17, 115, 25, 16, 42, 27, 34, 58, 42, 69, 34, 99,
- 43, 111, 34, 12, 51, 36, 60, 54, 51, 74, 60, 96, 51, 112, 60,
- 5, 8, 36, 16, 47, 8, 78, 16, 89, 8, 100, 0, 1, 17, 32,
- 25, 43, 17, 74, 25, 85, 17, 116, 25, 17, 42, 28, 34, 59, 42,
- 70, 34, 98, 43, 112, 34, 13, 51, 35, 60, 55, 51, 73, 60, 97,
- 51, 111, 60, 6, 8, 37, 16, 48, 8, 79, 16, 90, 8, 101, 0,
- 2, 17, 33, 25, 44, 17, 75, 25, 86, 17, 117, 25, 18, 42, 29,
- 34, 59, 43, 71, 34, 97, 43, 113, 34, 14, 51, 34, 60, 56, 51,
- 72, 60, 98, 51, 110, 60, 7, 8, 38, 16, 49, 8, 60, 0, 91,
- 8, 102, 0, 3, 17, 34, 25, 45, 17, 76, 25, 87, 17, 118, 25,
- 19, 42, 30, 34, 58, 43, 72, 34, 96, 43, 114, 34, 15, 51, 33,
- 60, 57, 51, 71, 60, 99, 51, 109, 60, 8, 8, 39, 16, 50, 8,
- 61, 0, 92, 8, 103, 0, 4, 17, 35, 25, 46, 17, 77, 25, 88,
- 17, 119, 25, 19, 43, 31, 34, 57, 43, 73, 34, 95, 43, 115, 34,
- 16, 51, 32, 60, 58, 51, 70, 60, 99, 52, 108, 60, 9, 8, 20,
- 0, 51, 8, 62, 0, 93, 8, 104, 0, 5, 17, 36, 25, 47, 17,
- 78, 25, 89, 17, 119, 26, 18, 43, 32, 34, 56, 43, 74, 34, 94,
- 43, 116, 34, 17, 51, 31, 60, 59, 51, 69, 60, 98, 52, 107, 60,
- 10, 8, 21, 0, 52, 8, 63, 0, 94, 8, 105, 0, 6, 17, 37,
- 25, 48, 17, 79, 25, 90, 17, 118, 26, 17, 43, 33, 34, 55, 43,
- 75, 34, 93, 43, 117, 34, 18, 51, 30, 60, 59, 52, 68, 60, 97,
- 52, 106, 60, 11, 8, 22, 0, 53, 8, 64, 0, 95, 8, 106, 0,
- 7, 17, 38, 25, 49, 17, 79, 26, 91, 17, 117, 26, 16, 43, 34,
- 34, 54, 43, 76, 34, 92, 43, 118, 34, 19, 51, 29, 60, 58, 52,
- 67, 60, 96, 52, 105, 60, 12, 8, 23, 0, 54, 8, 65, 0, 96,
- 8, 107, 0, 8, 17, 39, 25, 50, 17, 78, 26, 92, 17, 116, 26,
- 15, 43, 35, 34, 53, 43, 77, 34, 91, 43, 119, 34, 19, 52, 28,
- 60, 57, 52, 66, 60, 95, 52, 104, 60, 13, 8, 24, 0, 55, 8,
- 66, 0, 97, 8, 108, 0, 9, 17, 39, 26, 51, 17, 77, 26, 93,
- 17, 115, 26, 14, 43, 36, 34, 52, 43, 78, 34, 90, 43, 119, 35,
- 18, 52, 27, 60, 56, 52, 65, 60, 94, 52, 103, 60, 14, 8, 25,
- 0, 56, 8, 67, 0, 98, 8, 109, 0, 10, 17, 38, 26, 52, 17,
- 76, 26, 94, 17, 114, 26, 13, 43, 37, 34, 51, 43, 79, 34, 89,
- 43, 118, 35, 17, 52, 26, 60, 55, 52, 64, 60, 93, 52, 102, 60,
- 15, 8, 26, 0, 57, 8, 68, 0, 99, 8, 110, 0, 11, 17, 37,
- 26, 53, 17, 75, 26, 95, 17, 113, 26, 12, 43, 38, 34, 50, 43,
- 79, 35, 88, 43, 117, 35, 16, 52, 25, 60, 54, 52, 63, 60, 92,
- 52, 101, 60, 16, 8, 27, 0, 58, 8, 69, 0, 99, 9, 111, 0,
- 12, 17, 36, 26, 54, 17, 74, 26, 96, 17, 112, 26, 11, 43, 39,
- 34, 49, 43, 78, 35, 87, 43, 116, 35, 15, 52, 24, 60, 53, 52,
- 62, 60, 91, 52, 100, 60, 17, 8, 28, 0, 59, 8, 70, 0, 98,
- 9, 112, 0, 13, 17, 35, 26, 55, 17, 73, 26, 97, 17, 111, 26,
- 10, 43, 39, 35, 48, 43, 77, 35, 86, 43, 115, 35, 14, 52, 23,
- 60, 52, 52, 61, 60, 90, 52, 100, 61, 18, 8, 29, 0, 59, 9,
- 71, 0, 97, 9, 113, 0, 14, 17, 34, 26, 56, 17, 72, 26, 98,
- 17, 110, 26, 9, 43, 38, 35, 47, 43, 76, 35, 85, 43, 114, 35,
- 13, 52, 22, 60, 51, 52, 60, 60, 89, 52, 101, 61, 19, 8, 30,
- 0, 58, 9, 72, 0, 96, 9, 114, 0, 15, 17, 33, 26, 57, 17,
- 71, 26, 99, 17, 109, 26, 8, 43, 37, 35, 46, 43, 75, 35, 84,
- 43, 113, 35, 12, 52, 21, 60, 50, 52, 60, 61, 88, 52, 102, 61,
- 19, 9, 31, 0, 57, 9, 73, 0, 95, 9, 115, 0, 16, 17, 32,
- 26, 58, 17, 70, 26, 99, 18, 108, 26, 7, 43, 36, 35, 45, 43,
- 74, 35, 83, 43, 112, 35, 11, 52, 20, 60, 49, 52, 61, 61, 87,
- 52, 103, 61, 18, 9, 32, 0, 56, 9, 74, 0, 94, 9, 116, 0,
- 17, 17, 31, 26, 59, 17, 69, 26, 98, 18, 107, 26, 6, 43, 35,
- 35, 44, 43, 73, 35, 82, 43, 111, 35, 10, 52, 20, 61, 48, 52,
- 62, 61, 86, 52, 104, 61, 17, 9, 33, 0, 55, 9, 75, 0, 93,
- 9, 117, 0, 18, 17, 30, 26, 59, 18, 68, 26, 97, 18, 106, 26,
- 5, 43, 34, 35, 43, 43, 72, 35, 81, 43, 110, 35, 9, 52, 21,
- 61, 47, 52, 63, 61, 85, 52, 105, 61, 16, 9, 34, 0, 54, 9,
- 76, 0, 92, 9, 118, 0, 19, 17, 29, 26, 58, 18, 67, 26, 96,
- 18, 105, 26, 4, 43, 33, 35, 42, 43, 71, 35, 80, 43, 109, 35,
- 8, 52, 22, 61, 46, 52, 64, 61, 84, 52, 106, 61, 15, 9, 35,
- 0, 53, 9, 77, 0, 91, 9, 119, 0, 19, 18, 28, 26, 57, 18,
- 66, 26, 95, 18, 104, 26, 3, 43, 32, 35, 41, 43, 70, 35, 80,
- 44, 108, 35, 7, 52, 23, 61, 45, 52, 65, 61, 83, 52, 107, 61,
- 14, 9, 36, 0, 52, 9, 78, 0, 90, 9, 119, 1, 18, 18, 27,
- 26, 56, 18, 65, 26, 94, 18, 103, 26, 2, 43, 31, 35, 40, 43,
- 69, 35, 81, 44, 107, 35, 6, 52, 24, 61, 44, 52, 66, 61, 82,
- 52, 108, 61, 13, 9, 37, 0, 51, 9, 79, 0, 89, 9, 118, 1,
- 17, 18, 26, 26, 55, 18, 64, 26, 93, 18, 102, 26, 1, 43, 30,
- 35, 40, 44, 68, 35, 82, 44, 106, 35, 5, 52, 25, 61, 43, 52,
- 67, 61, 81, 52, 109, 61, 12, 9, 38, 0, 50, 9, 79, 1, 88,
- 9, 117, 1, 16, 18, 25, 26, 54, 18, 63, 26, 92, 18, 101, 26,
- 0, 43, 29, 35, 41, 44, 67, 35, 83, 44, 105, 35, 4, 52, 26,
- 61, 42, 52, 68, 61, 80, 52, 110, 61, 11, 9, 39, 0, 49, 9,
- 78, 1, 87, 9, 116, 1, 15, 18, 24, 26, 53, 18, 62, 26, 91,
- 18, 100, 26, 0, 44, 28, 35, 42, 44, 66, 35, 84, 44, 104, 35,
- 3, 52, 27, 61, 41, 52, 69, 61, 80, 53, 111, 61, 10, 9, 39,
- 1, 48, 9, 77, 1, 86, 9, 115, 1, 14, 18, 23, 26, 52, 18,
- 61, 26, 90, 18, 100, 27, 1, 44, 27, 35, 43, 44, 65, 35, 85,
- 44, 103, 35, 2, 52, 28, 61, 40, 52, 70, 61, 81, 53, 112, 61,
- 9, 9, 38, 1, 47, 9, 76, 1, 85, 9, 114, 1, 13, 18, 22,
- 26, 51, 18, 60, 26, 89, 18, 101, 27, 2, 44, 26, 35, 44, 44,
- 64, 35, 86, 44, 102, 35, 1, 52, 29, 61, 40, 53, 71, 61, 82,
- 53, 113, 61, 8, 9, 37, 1, 46, 9, 75, 1, 84, 9, 113, 1,
- 12, 18, 21, 26, 50, 18, 60, 27, 88, 18, 102, 27, 3, 44, 25,
- 35, 45, 44, 63, 35, 87, 44, 101, 35, 0, 52, 30, 61, 41, 53,
- 72, 61, 83, 53, 114, 61, 7, 9, 36, 1, 45, 9, 74, 1, 83,
- 9, 112, 1, 11, 18, 20, 26, 49, 18, 61, 27, 87, 18, 103, 27,
- 4, 44, 24, 35, 46, 44, 62, 35, 88, 44, 100, 35, 0, 53, 31,
- 61, 42, 53, 73, 61, 84, 53, 115, 61, 6, 9, 35, 1, 44, 9,
- 73, 1, 82, 9, 111, 1, 10, 18, 20, 27, 48, 18, 62, 27, 86,
- 18, 104, 27, 5, 44, 23, 35, 47, 44, 61, 35, 89, 44, 100, 36,
- 1, 53, 32, 61, 43, 53, 74, 61, 85, 53, 116, 61, 5, 9, 34,
- 1, 43, 9, 72, 1, 81, 9, 110, 1, 9, 18, 21, 27, 47, 18,
- 63, 27, 85, 18, 105, 27, 6, 44, 22, 35, 48, 44, 60, 35, 90,
- 44, 101, 36, 2, 53, 33, 61, 44, 53, 75, 61, 86, 53, 117, 61,
- 4, 9, 33, 1, 42, 9, 71, 1, 80, 9, 109, 1, 8, 18, 22,
- 27, 46, 18, 64, 27, 84, 18, 106, 27, 7, 44, 21, 35, 49, 44,
- 60, 36, 91, 44, 102, 36, 3, 53, 34, 61, 45, 53, 76, 61, 87,
- 53, 118, 61, 3, 9, 32, 1, 41, 9, 70, 1, 80, 10, 108, 1,
- 7, 18, 23, 27, 45, 18, 65, 27, 83, 18, 107, 27, 8, 44, 20,
- 35, 50, 44, 61, 36, 92, 44, 103, 36, 4, 53, 35, 61, 46, 53,
- 77, 61, 88, 53, 119, 61, 2, 9, 31, 1, 40, 9, 69, 1, 81,
- 10, 107, 1, 6, 18, 24, 27, 44, 18, 66, 27, 82, 18, 108, 27,
- 9, 44, 20, 36, 51, 44, 62, 36, 93, 44, 104, 36, 5, 53, 36,
- 61, 47, 53, 78, 61, 89, 53, 119, 62, 1, 9, 30, 1, 40, 10,
- 68, 1, 82, 10, 106, 1, 5, 18, 25, 27, 43, 18, 67, 27, 81,
- 18, 109, 27, 10, 44, 21, 36, 52, 44, 63, 36, 94, 44, 105, 36,
- 6, 53, 37, 61, 48, 53, 79, 61, 90, 53, 118, 62, 0, 9, 29,
- 1, 41, 10, 67, 1, 83, 10, 105, 1, 4, 18, 26, 27, 42, 18,
- 68, 27, 80, 18, 110, 27, 11, 44, 22, 36, 53, 44, 64, 36, 95,
- 44, 106, 36, 7, 53, 38, 61, 49, 53, 79, 62, 91, 53, 117, 62,
- 0, 10, 28, 1, 42, 10, 66, 1, 84, 10, 104, 1, 3, 18, 27,
- 27, 41, 18, 69, 27, 80, 19, 111, 27, 12, 44, 23, 36, 54, 44,
- 65, 36, 96, 44, 107, 36, 8, 53, 39, 61, 50, 53, 78, 62, 92,
- 53, 116, 62, 1, 10, 27, 1, 43, 10, 65, 1, 85, 10, 103, 1,
- 2, 18, 28, 27, 40, 18, 70, 27, 81, 19, 112, 27, 13, 44, 24,
- 36, 55, 44, 66, 36, 97, 44, 108, 36, 9, 53, 39, 62, 51, 53,
- 77, 62, 93, 53, 115, 62, 2, 10, 26, 1, 44, 10, 64, 1, 86,
- 10, 102, 1, 1, 18, 29, 27, 40, 19, 71, 27, 82, 19, 113, 27,
- 14, 44, 25, 36, 56, 44, 67, 36, 98, 44, 109, 36, 10, 53, 38,
- 62, 52, 53, 76, 62, 94, 53, 114, 62, 3, 10, 25, 1, 45, 10,
- 63, 1, 87, 10, 101, 1, 0, 18, 30, 27, 41, 19, 72, 27, 83,
- 19, 114, 27, 15, 44, 26, 36, 57, 44, 68, 36, 99, 44, 110, 36,
- 11, 53, 37, 62, 53, 53, 75, 62, 95, 53, 113, 62, 4, 10, 24,
- 1, 46, 10, 62, 1, 88, 10, 100, 1, 0, 19, 31, 27, 42, 19,
- 73, 27, 84, 19, 115, 27, 16, 44, 27, 36, 58, 44, 69, 36, 99,
- 45, 111, 36, 12, 53, 36, 62, 54, 53, 74, 62, 96, 53, 112, 62,
- 5, 10, 23, 1, 47, 10, 61, 1, 89, 10, 100, 2, 1, 19, 32,
- 27, 43, 19, 74, 27, 85, 19, 116, 27, 17, 44, 28, 36, 59, 44,
- 70, 36, 98, 45, 112, 36, 13, 53, 35, 62, 55, 53, 73, 62, 97,
- 53, 111, 62, 6, 10, 22, 1, 48, 10, 60, 1, 90, 10, 101, 2,
- 2, 19, 33, 27, 44, 19, 75, 27, 86, 19, 117, 27, 18, 44, 29,
- 36, 59, 45, 71, 36, 97, 45, 113, 36, 14, 53, 34, 62, 56, 53,
- 72, 62, 98, 53, 110, 62, 7, 10, 21, 1, 49, 10, 60, 2, 91,
- 10, 102, 2, 3, 19, 34, 27, 45, 19, 76, 27, 87, 19, 118, 27,
- 19, 44, 30, 36, 58, 45, 72, 36, 96, 45, 114, 36, 15, 53, 33,
- 62, 57, 53, 71, 62, 99, 53, 109, 62, 8, 10, 20, 1, 50, 10,
- 61, 2, 92, 10, 103, 2, 4, 19, 35, 27, 46, 19, 77, 27, 88,
- 19, 119, 27, 19, 45, 31, 36, 57, 45, 73, 36, 95, 45, 115, 36,
- 16, 53, 32, 62, 58, 53, 70, 62, 99, 54, 108, 62, 9, 10, 20,
- 2, 51, 10, 62, 2, 93, 10, 104, 2, 5, 19, 36, 27, 47, 19,
- 78, 27, 89, 19, 119, 28, 18, 45, 32, 36, 56, 45, 74, 36, 94,
- 45, 116, 36, 17, 53, 31, 62, 59, 53, 69, 62, 98, 54, 107, 62,
- 10, 10, 21, 2, 52, 10, 63, 2, 94, 10, 105, 2, 6, 19, 37,
- 27, 48, 19, 79, 27, 90, 19, 118, 28, 17, 45, 33, 36, 55, 45,
- 75, 36, 93, 45, 117, 36, 18, 53, 30, 62, 59, 54, 68, 62, 97,
- 54, 106, 62, 11, 10, 22, 2, 53, 10, 64, 2, 95, 10, 106, 2,
- 7, 19, 38, 27, 49, 19, 79, 28, 91, 19, 117, 28, 16, 45, 34,
- 36, 54, 45, 76, 36, 92, 45, 118, 36, 19, 53, 29, 62, 58, 54,
- 67, 62, 96, 54, 105, 62, 12, 10, 23, 2, 54, 10, 65, 2, 96,
- 10, 107, 2, 8, 19, 39, 27, 50, 19, 78, 28, 92, 19, 116, 28,
- 15, 45, 35, 36, 53, 45, 77, 36, 91, 45, 119, 36, 19, 54, 28,
- 62, 57, 54, 66, 62, 95, 54, 104, 62, 13, 10, 24, 2, 55, 10,
- 66, 2, 97, 10, 108, 2, 9, 19, 39, 28, 51, 19, 77, 28, 93,
- 19, 115, 28, 14, 45, 36, 36, 52, 45, 78, 36, 90, 45, 119, 37,
- 18, 54, 27, 62, 56, 54, 65, 62, 94, 54, 103, 62, 14, 10, 25,
- 2, 56, 10, 67, 2, 98, 10, 109, 2, 10, 19, 38, 28, 52, 19,
- 76, 28, 94, 19, 114, 28, 13, 45, 37, 36, 51, 45, 79, 36, 89,
- 45, 118, 37, 17, 54, 26, 62, 55, 54, 64, 62, 93, 54, 102, 62,
- 15, 10, 26, 2, 57, 10, 68, 2, 99, 10, 110, 2, 11, 19, 37,
- 28, 53, 19, 75, 28, 95, 19, 113, 28, 12, 45, 38, 36, 50, 45,
- 79, 37, 88, 45, 117, 37, 16, 54, 25, 62, 54, 54, 63, 62, 92,
- 54, 101, 62, 16, 10, 27, 2, 58, 10, 69, 2, 99, 11, 111, 2,
- 12, 19, 36, 28, 54, 19, 74, 28, 96, 19, 112, 28, 11, 45, 39,
- 36, 49, 45, 78, 37, 87, 45, 116, 37, 15, 54, 24, 62, 53, 54,
- 62, 62, 91, 54, 100, 62, 17, 10, 28, 2, 59, 10, 70, 2, 98,
- 11, 112, 2, 13, 19, 35, 28, 55, 19, 73, 28, 97, 19, 111, 28,
- 10, 45, 39, 37, 48, 45, 77, 37, 86, 45, 115, 37, 14, 54, 23,
- 62, 52, 54, 61, 62, 90, 54, 100, 63, 18, 10, 29, 2, 59, 11,
- 71, 2, 97, 11, 113, 2, 14, 19, 34, 28, 56, 19, 72, 28, 98,
- 19, 110, 28, 9, 45, 38, 37, 47, 45, 76, 37, 85, 45, 114, 37,
- 13, 54, 22, 62, 51, 54, 60, 62, 89, 54, 101, 63, 19, 10, 30,
- 2, 58, 11, 72, 2, 96, 11, 114, 2, 15, 19, 33, 28, 57, 19,
- 71, 28, 99, 19, 109, 28, 8, 45, 37, 37, 46, 45, 75, 37, 84,
- 45, 113, 37, 12, 54, 21, 62, 50, 54, 60, 63, 88, 54, 102, 63,
- 19, 11, 31, 2, 57, 11, 73, 2, 95, 11, 115, 2, 16, 19, 32,
- 28, 58, 19, 70, 28, 99, 20, 108, 28, 7, 45, 36, 37, 45, 45,
- 74, 37, 83, 45, 112, 37, 11, 54, 20, 62, 49, 54, 61, 63, 87,
- 54, 103, 63, 18, 11, 32, 2, 56, 11, 74, 2, 94, 11, 116, 2,
- 17, 19, 31, 28, 59, 19, 69, 28, 98, 20, 107, 28, 6, 45, 35,
- 37, 44, 45, 73, 37, 82, 45, 111, 37, 10, 54, 20, 63, 48, 54,
- 62, 63, 86, 54, 104, 63, 17, 11, 33, 2, 55, 11, 75, 2, 93,
- 11, 117, 2, 18, 19, 30, 28, 59, 20, 68, 28, 97, 20, 106, 28,
- 5, 45, 34, 37, 43, 45, 72, 37, 81, 45, 110, 37, 9, 54, 21,
- 63, 47, 54, 63, 63, 85, 54, 105, 63, 16, 11, 34, 2, 54, 11,
- 76, 2, 92, 11, 118, 2, 19, 19, 29, 28, 58, 20, 67, 28, 96,
- 20, 105, 28, 4, 45, 33, 37, 42, 45, 71, 37, 80, 45, 109, 37,
- 8, 54, 22, 63, 46, 54, 64, 63, 84, 54, 106, 63, 15, 11, 35,
- 2, 53, 11, 77, 2, 91, 11, 119, 2, 19, 20, 28, 28, 57, 20,
- 66, 28, 95, 20, 104, 28, 3, 45, 32, 37, 41, 45, 70, 37, 80,
- 46, 108, 37, 7, 54, 23, 63, 45, 54, 65, 63, 83, 54, 107, 63,
- 14, 11, 36, 2, 52, 11, 78, 2, 90, 11, 119, 3, 18, 20, 27,
- 28, 56, 20, 65, 28, 94, 20, 103, 28, 2, 45, 31, 37, 40, 45,
- 69, 37, 81, 46, 107, 37, 6, 54, 24, 63, 44, 54, 66, 63, 82,
- 54, 108, 63, 13, 11, 37, 2, 51, 11, 79, 2, 89, 11, 118, 3,
- 17, 20, 26, 28, 55, 20, 64, 28, 93, 20, 102, 28, 1, 45, 30,
- 37, 40, 46, 68, 37, 82, 46, 106, 37, 5, 54, 25, 63, 43, 54,
- 67, 63, 81, 54, 109, 63, 12, 11, 38, 2, 50, 11, 79, 3, 88,
- 11, 117, 3, 16, 20, 25, 28, 54, 20, 63, 28, 92, 20, 101, 28,
- 0, 45, 29, 37, 41, 46, 67, 37, 83, 46, 105, 37, 4, 54, 26,
- 63, 42, 54, 68, 63, 80, 54, 110, 63, 11, 11, 39, 2, 49, 11,
- 78, 3, 87, 11, 116, 3, 15, 20, 24, 28, 53, 20, 62, 28, 91,
- 20, 100, 28, 0, 46, 28, 37, 42, 46, 66, 37, 84, 46, 104, 37,
- 3, 54, 27, 63, 41, 54, 69, 63, 80, 55, 111, 63, 10, 11, 39,
- 3, 48, 11, 77, 3, 86, 11, 115, 3, 14, 20, 23, 28, 52, 20,
- 61, 28, 90, 20, 100, 29, 1, 46, 27, 37, 43, 46, 65, 37, 85,
- 46, 103, 37, 2, 54, 28, 63, 40, 54, 70, 63, 81, 55, 112, 63,
- 9, 11, 38, 3, 47, 11, 76, 3, 85, 11, 114, 3, 13, 20, 22,
- 28, 51, 20, 60, 28, 89, 20, 101, 29, 2, 46, 26, 37, 44, 46,
- 64, 37, 86, 46, 102, 37, 1, 54, 29, 63, 40, 55, 71, 63, 82,
- 55, 113, 63, 8, 11, 37, 3, 46, 11, 75, 3, 84, 11, 113, 3,
- 12, 20, 21, 28, 50, 20, 60, 29, 88, 20, 102, 29, 3, 46, 25,
- 37, 45, 46, 63, 37, 87, 46, 101, 37, 0, 54, 30, 63, 41, 55,
- 72, 63, 83, 55, 114, 63, 7, 11, 36, 3, 45, 11, 74, 3, 83,
- 11, 112, 3, 11, 20, 20, 28, 49, 20, 61, 29, 87, 20, 103, 29,
- 4, 46, 24, 37, 46, 46, 62, 37, 88, 46, 100, 37, 0, 55, 31,
- 63, 42, 55, 73, 63, 84, 55, 115, 63, 6, 11, 35, 3, 44, 11,
- 73, 3, 82, 11, 111, 3, 10, 20, 20, 29, 48, 20, 62, 29, 86,
- 20, 104, 29, 5, 46, 23, 37, 47, 46, 61, 37, 89, 46, 100, 38,
- 1, 55, 32, 63, 43, 55, 74, 63, 85, 55, 116, 63, 5, 11, 34,
- 3, 43, 11, 72, 3, 81, 11, 110, 3, 9, 20, 21, 29, 47, 20,
- 63, 29, 85, 20, 105, 29, 6, 46, 22, 37, 48, 46, 60, 37, 90,
- 46, 101, 38, 2, 55, 33, 63, 44, 55, 75, 63, 86, 55, 117, 63,
- 4, 11, 33, 3, 42, 11, 71, 3, 80, 11, 109, 3, 8, 20, 22,
- 29, 46, 20, 64, 29, 84, 20, 106, 29, 7, 46, 21, 37, 49, 46,
- 60, 38, 91, 46, 102, 38, 3, 55, 34, 63, 45, 55, 76, 63, 87,
- 55, 118, 63, 3, 11, 32, 3, 41, 11, 70, 3, 80, 12, 108, 3,
- 7, 20, 23, 29, 45, 20, 65, 29, 83, 20, 107, 29, 8, 46, 20,
- 37, 50, 46, 61, 38, 92, 46, 103, 38, 4, 55, 35, 63, 46, 55,
- 77, 63, 88, 55, 119, 63, 2, 11, 31, 3, 40, 11, 69, 3, 81,
- 12, 107, 3, 6, 20, 24, 29, 44, 20, 66, 29, 82, 20, 108, 29,
- 9, 46, 20, 38, 51, 46, 62, 38, 93, 46, 104, 38, 5, 55, 36,
- 63, 47, 55, 78, 63, 89, 55, 119, 64, 1, 11, 30, 3, 40, 12,
- 68, 3, 82, 12, 106, 3, 5, 20, 25, 29, 43, 20, 67, 29, 81,
- 20, 109, 29, 10, 46, 21, 38, 52, 46, 63, 38, 94, 46, 105, 38,
- 6, 55, 37, 63, 48, 55, 79, 63, 90, 55, 118, 64, 0, 11, 29,
- 3, 41, 12, 67, 3, 83, 12, 105, 3, 4, 20, 26, 29, 42, 20,
- 68, 29, 80, 20, 110, 29, 11, 46, 22, 38, 53, 46, 64, 38, 95,
- 46, 106, 38, 7, 55, 38, 63, 49, 55, 79, 64, 91, 55, 117, 64,
- 0, 12, 28, 3, 42, 12, 66, 3, 84, 12, 104, 3, 3, 20, 27,
- 29, 41, 20, 69, 29, 80, 21, 111, 29, 12, 46, 23, 38, 54, 46,
- 65, 38, 96, 46, 107, 38, 8, 55, 39, 63, 50, 55, 78, 64, 92,
- 55, 116, 64, 1, 12, 27, 3, 43, 12, 65, 3, 85, 12, 103, 3,
- 2, 20, 28, 29, 40, 20, 70, 29, 81, 21, 112, 29, 13, 46, 24,
- 38, 55, 46, 66, 38, 97, 46, 108, 38, 9, 55, 39, 64, 51, 55,
- 77, 64, 93, 55, 115, 64, 2, 12, 26, 3, 44, 12, 64, 3, 86,
- 12, 102, 3, 1, 20, 29, 29, 40, 21, 71, 29, 82, 21, 113, 29,
- 14, 46, 25, 38, 56, 46, 67, 38, 98, 46, 109, 38, 10, 55, 38,
- 64, 52, 55, 76, 64, 94, 55, 114, 64, 3, 12, 25, 3, 45, 12,
- 63, 3, 87, 12, 101, 3, 0, 20, 30, 29, 41, 21, 72, 29, 83,
- 21, 114, 29, 15, 46, 26, 38, 57, 46, 68, 38, 99, 46, 110, 38,
- 11, 55, 37, 64, 53, 55, 75, 64, 95, 55, 113, 64, 4, 12, 24,
- 3, 46, 12, 62, 3, 88, 12, 100, 3, 0, 21, 31, 29, 42, 21,
- 73, 29, 84, 21, 115, 29, 16, 46, 27, 38, 58, 46, 69, 38, 99,
- 47, 111, 38, 12, 55, 36, 64, 54, 55, 74, 64, 96, 55, 112, 64,
- 5, 12, 23, 3, 47, 12, 61, 3, 89, 12, 100, 4, 1, 21, 32,
- 29, 43, 21, 74, 29, 85, 21, 116, 29, 17, 46, 28, 38, 59, 46,
- 70, 38, 98, 47, 112, 38, 13, 55, 35, 64, 55, 55, 73, 64, 97,
- 55, 111, 64, 6, 12, 22, 3, 48, 12, 60, 3, 90, 12, 101, 4,
- 2, 21, 33, 29, 44, 21, 75, 29, 86, 21, 117, 29, 18, 46, 29,
- 38, 59, 47, 71, 38, 97, 47, 113, 38, 14, 55, 34, 64, 56, 55,
- 72, 64, 98, 55, 110, 64, 7, 12, 21, 3, 49, 12, 60, 4, 91,
- 12, 102, 4, 3, 21, 34, 29, 45, 21, 76, 29, 87, 21, 118, 29,
- 19, 46, 30, 38, 58, 47, 72, 38, 96, 47, 114, 38, 15, 55, 33,
- 64, 57, 55, 71, 64, 99, 55, 109, 64, 8, 12, 20, 3, 50, 12,
- 61, 4, 92, 12, 103, 4, 4, 21, 35, 29, 46, 21, 77, 29, 88,
- 21, 119, 29, 19, 47, 31, 38, 57, 47, 73, 38, 95, 47, 115, 38,
- 16, 55, 32, 64, 58, 55, 70, 64, 99, 56, 108, 64, 9, 12, 20,
- 4, 51, 12, 62, 4, 93, 12, 104, 4, 5, 21, 36, 29, 47, 21,
- 78, 29, 89, 21, 119, 30, 18, 47, 32, 38, 56, 47, 74, 38, 94,
- 47, 116, 38, 17, 55, 31, 64, 59, 55, 69, 64, 98, 56, 107, 64,
- 10, 12, 21, 4, 52, 12, 63, 4, 94, 12, 105, 4, 6, 21, 37,
- 29, 48, 21, 79, 29, 90, 21, 118, 30, 17, 47, 33, 38, 55, 47,
- 75, 38, 93, 47, 117, 38, 18, 55, 30, 64, 59, 56, 68, 64, 97,
- 56, 106, 64, 11, 12, 22, 4, 53, 12, 64, 4, 95, 12, 106, 4,
- 7, 21, 38, 29, 49, 21, 79, 30, 91, 21, 117, 30, 16, 47, 34,
- 38, 54, 47, 76, 38, 92, 47, 118, 38, 19, 55, 29, 64, 58, 56,
- 67, 64, 96, 56, 105, 64, 12, 12, 23, 4, 54, 12, 65, 4, 96,
- 12, 107, 4, 8, 21, 39, 29, 50, 21, 78, 30, 92, 21, 116, 30,
- 15, 47, 35, 38, 53, 47, 77, 38, 91, 47, 119, 38, 19, 56, 28,
- 64, 57, 56, 66, 64, 95, 56, 104, 64, 13, 12, 24, 4, 55, 12,
- 66, 4, 97, 12, 108, 4, 9, 21, 39, 30, 51, 21, 77, 30, 93,
- 21, 115, 30, 14, 47, 36, 38, 52, 47, 78, 38, 90, 47, 119, 39,
- 18, 56, 27, 64, 56, 56, 65, 64, 94, 56, 103, 64, 14, 12, 25,
- 4, 56, 12, 67, 4, 98, 12, 109, 4, 10, 21, 38, 30, 52, 21,
- 76, 30, 94, 21, 114, 30, 13, 47, 37, 38, 51, 47, 79, 38, 89,
- 47, 118, 39, 17, 56, 26, 64, 55, 56, 64, 64, 93, 56, 102, 64,
- 15, 12, 26, 4, 57, 12, 68, 4, 99, 12, 110, 4, 11, 21, 37,
- 30, 53, 21, 75, 30, 95, 21, 113, 30, 12, 47, 38, 38, 50, 47,
- 79, 39, 88, 47, 117, 39, 16, 56, 25, 64, 54, 56, 63, 64, 92,
- 56, 101, 64, 16, 12, 27, 4, 58, 12, 69, 4, 99, 13, 111, 4,
- 12, 21, 36, 30, 54, 21, 74, 30, 96, 21, 112, 30, 11, 47, 39,
- 38, 49, 47, 78, 39, 87, 47, 116, 39, 15, 56, 24, 64, 53, 56,
- 62, 64, 91, 56, 100, 64, 17, 12, 28, 4, 59, 12, 70, 4, 98,
- 13, 112, 4, 13, 21, 35, 30, 55, 21, 73, 30, 97, 21, 111, 30,
- 10, 47, 39, 39, 48, 47, 77, 39, 86, 47, 115, 39, 14, 56, 23,
- 64, 52, 56, 61, 64, 90, 56, 100, 65, 18, 12, 29, 4, 59, 13,
- 71, 4, 97, 13, 113, 4, 14, 21, 34, 30, 56, 21, 72, 30, 98,
- 21, 110, 30, 9, 47, 38, 39, 47, 47, 76, 39, 85, 47, 114, 39,
- 13, 56, 22, 64, 51, 56, 60, 64, 89, 56, 101, 65, 19, 12, 30,
- 4, 58, 13, 72, 4, 96, 13, 114, 4, 15, 21, 33, 30, 57, 21,
- 71, 30, 99, 21, 109, 30, 8, 47, 37, 39, 46, 47, 75, 39, 84,
- 47, 113, 39, 12, 56, 21, 64, 50, 56, 60, 65, 88, 56, 102, 65,
- 19, 13, 31, 4, 57, 13, 73, 4, 95, 13, 115, 4, 16, 21, 32,
- 30, 58, 21, 70, 30, 99, 22, 108, 30, 7, 47, 36, 39, 45, 47,
- 74, 39, 83, 47, 112, 39, 11, 56, 20, 64, 49, 56, 61, 65, 87,
- 56, 103, 65, 18, 13, 32, 4, 56, 13, 74, 4, 94, 13, 116, 4,
- 17, 21, 31, 30, 59, 21, 69, 30, 98, 22, 107, 30, 6, 47, 35,
- 39, 44, 47, 73, 39, 82, 47, 111, 39, 10, 56, 20, 65, 48, 56,
- 62, 65, 86, 56, 104, 65, 17, 13, 33, 4, 55, 13, 75, 4, 93,
- 13, 117, 4, 18, 21, 30, 30, 59, 22, 68, 30, 97, 22, 106, 30,
- 5, 47, 34, 39, 43, 47, 72, 39, 81, 47, 110, 39, 9, 56, 21,
- 65, 47, 56, 63, 65, 85, 56, 105, 65, 16, 13, 34, 4, 54, 13,
- 76, 4, 92, 13, 118, 4, 19, 21, 29, 30, 58, 22, 67, 30, 96,
- 22, 105, 30, 4, 47, 33, 39, 42, 47, 71, 39, 80, 47, 109, 39,
- 8, 56, 22, 65, 46, 56, 64, 65, 84, 56, 106, 65, 15, 13, 35,
- 4, 53, 13, 77, 4, 91, 13, 119, 4, 19, 22, 28, 30, 57, 22,
- 66, 30, 95, 22, 104, 30, 3, 47, 32, 39, 41, 47, 70, 39, 80,
- 48, 108, 39, 7, 56, 23, 65, 45, 56, 65, 65, 83, 56, 107, 65,
- 14, 13, 36, 4, 52, 13, 78, 4, 90, 13, 119, 5, 18, 22, 27,
- 30, 56, 22, 65, 30, 94, 22, 103, 30, 2, 47, 31, 39, 40, 47,
- 69, 39, 81, 48, 107, 39, 6, 56, 24, 65, 44, 56, 66, 65, 82,
- 56, 108, 65, 13, 13, 37, 4, 51, 13, 79, 4, 89, 13, 118, 5,
- 17, 22, 26, 30, 55, 22, 64, 30, 93, 22, 102, 30, 1, 47, 30,
- 39, 40, 48, 68, 39, 82, 48, 106, 39, 5, 56, 25, 65, 43, 56,
- 67, 65, 81, 56, 109, 65, 12, 13, 38, 4, 50, 13, 79, 5, 88,
- 13, 117, 5, 16, 22, 25, 30, 54, 22, 63, 30, 92, 22, 101, 30,
- 0, 47, 29, 39, 41, 48, 67, 39, 83, 48, 105, 39, 4, 56, 26,
- 65, 42, 56, 68, 65, 80, 56, 110, 65, 11, 13, 39, 4, 49, 13,
- 78, 5, 87, 13, 116, 5, 15, 22, 24, 30, 53, 22, 62, 30, 91,
- 22, 100, 30, 0, 48, 28, 39, 42, 48, 66, 39, 84, 48, 104, 39,
- 3, 56, 27, 65, 41, 56, 69, 65, 80, 57, 111, 65, 10, 13, 39,
- 5, 48, 13, 77, 5, 86, 13, 115, 5, 14, 22, 23, 30, 52, 22,
- 61, 30, 90, 22, 100, 31, 1, 48, 27, 39, 43, 48, 65, 39, 85,
- 48, 103, 39, 2, 56, 28, 65, 40, 56, 70, 65, 81, 57, 112, 65,
- 9, 13, 38, 5, 47, 13, 76, 5, 85, 13, 114, 5, 13, 22, 22,
- 30, 51, 22, 60, 30, 89, 22, 101, 31, 2, 48, 26, 39, 44, 48,
- 64, 39, 86, 48, 102, 39, 1, 56, 29, 65, 40, 57, 71, 65, 82,
- 57, 113, 65, 8, 13, 37, 5, 46, 13, 75, 5, 84, 13, 113, 5,
- 12, 22, 21, 30, 50, 22, 60, 31, 88, 22, 102, 31, 3, 48, 25,
- 39, 45, 48, 63, 39, 87, 48, 101, 39, 0, 56, 30, 65, 41, 57,
- 72, 65, 83, 57, 114, 65, 7, 13, 36, 5, 45, 13, 74, 5, 83,
- 13, 112, 5, 11, 22, 20, 30, 49, 22, 61, 31, 87, 22, 103, 31,
- 4, 48, 24, 39, 46, 48, 62, 39, 88, 48, 100, 39, 0, 57, 31,
- 65, 42, 57, 73, 65, 84, 57, 115, 65, 6, 13, 35, 5, 44, 13,
- 73, 5, 82, 13, 111, 5, 10, 22, 20, 31, 48, 22, 62, 31, 86,
- 22, 104, 31, 5, 48, 23, 39, 47, 48, 61, 39, 89, 48, 100, 40,
- 1, 57, 32, 65, 43, 57, 74, 65, 85, 57, 116, 65, 5, 13, 34,
- 5, 43, 13, 72, 5, 81, 13, 110, 5, 9, 22, 21, 31, 47, 22,
- 63, 31, 85, 22, 105, 31, 6, 48, 22, 39, 48, 48, 60, 39, 90,
- 48, 101, 40, 2, 57, 33, 65, 44, 57, 75, 65, 86, 57, 117, 65,
- 4, 13, 33, 5, 42, 13, 71, 5, 80, 13, 109, 5, 8, 22, 22,
- 31, 46, 22, 64, 31, 84, 22, 106, 31, 7, 48, 21, 39, 49, 48,
- 60, 40, 91, 48, 102, 40, 3, 57, 34, 65, 45, 57, 76, 65, 87,
- 57, 118, 65, 3, 13, 32, 5, 41, 13, 70, 5, 80, 14, 108, 5,
- 7, 22, 23, 31, 45, 22, 65, 31, 83, 22, 107, 31, 8, 48, 20,
- 39, 50, 48, 61, 40, 92, 48, 103, 40, 4, 57, 35, 65, 46, 57,
- 77, 65, 88, 57, 119, 65, 2, 13, 31, 5, 40, 13, 69, 5, 81,
- 14, 107, 5, 6, 22, 24, 31, 44, 22, 66, 31, 82, 22, 108, 31,
- 9, 48, 20, 40, 51, 48, 62, 40, 93, 48, 104, 40, 5, 57, 36,
- 65, 47, 57, 78, 65, 89, 57, 119, 66, 1, 13, 30, 5, 40, 14,
- 68, 5, 82, 14, 106, 5, 5, 22, 25, 31, 43, 22, 67, 31, 81,
- 22, 109, 31, 10, 48, 21, 40, 52, 48, 63, 40, 94, 48, 105, 40,
- 6, 57, 37, 65, 48, 57, 79, 65, 90, 57, 118, 66, 0, 13, 29,
- 5, 41, 14, 67, 5, 83, 14, 105, 5, 4, 22, 26, 31, 42, 22,
- 68, 31, 80, 22, 110, 31, 11, 48, 22, 40, 53, 48, 64, 40, 95,
- 48, 106, 40, 7, 57, 38, 65, 49, 57, 79, 66, 91, 57, 117, 66,
- 0, 14, 28, 5, 42, 14, 66, 5, 84, 14, 104, 5, 3, 22, 27,
- 31, 41, 22, 69, 31, 80, 23, 111, 31, 12, 48, 23, 40, 54, 48,
- 65, 40, 96, 48, 107, 40, 8, 57, 39, 65, 50, 57, 78, 66, 92,
- 57, 116, 66, 1, 14, 27, 5, 43, 14, 65, 5, 85, 14, 103, 5,
- 2, 22, 28, 31, 40, 22, 70, 31, 81, 23, 112, 31, 13, 48, 24,
- 40, 55, 48, 66, 40, 97, 48, 108, 40, 9, 57, 39, 66, 51, 57,
- 77, 66, 93, 57, 115, 66, 2, 14, 26, 5, 44, 14, 64, 5, 86,
- 14, 102, 5, 1, 22, 29, 31, 40, 23, 71, 31, 82, 23, 113, 31,
- 14, 48, 25, 40, 56, 48, 67, 40, 98, 48, 109, 40, 10, 57, 38,
- 66, 52, 57, 76, 66, 94, 57, 114, 66, 3, 14, 25, 5, 45, 14,
- 63, 5, 87, 14, 101, 5, 0, 22, 30, 31, 41, 23, 72, 31, 83,
- 23, 114, 31, 15, 48, 26, 40, 57, 48, 68, 40, 99, 48, 110, 40,
- 11, 57, 37, 66, 53, 57, 75, 66, 95, 57, 113, 66, 4, 14, 24,
- 5, 46, 14, 62, 5, 88, 14, 100, 5, 0, 23, 31, 31, 42, 23,
- 73, 31, 84, 23, 115, 31, 16, 48, 27, 40, 58, 48, 69, 40, 99,
- 49, 111, 40, 12, 57, 36, 66, 54, 57, 74, 66, 96, 57, 112, 66,
- 5, 14, 23, 5, 47, 14, 61, 5, 89, 14, 100, 6, 1, 23, 32,
- 31, 43, 23, 74, 31, 85, 23, 116, 31, 17, 48, 28, 40, 59, 48,
- 70, 40, 98, 49, 112, 40, 13, 57, 35, 66, 55, 57, 73, 66, 97,
- 57, 111, 66, 6, 14, 22, 5, 48, 14, 60, 5, 90, 14, 101, 6,
- 2, 23, 33, 31, 44, 23, 75, 31, 86, 23, 117, 31, 18, 48, 29,
- 40, 59, 49, 71, 40, 97, 49, 113, 40, 14, 57, 34, 66, 56, 57,
- 72, 66, 98, 57, 110, 66, 7, 14, 21, 5, 49, 14, 60, 6, 91,
- 14, 102, 6, 3, 23, 34, 31, 45, 23, 76, 31, 87, 23, 118, 31,
- 19, 48, 30, 40, 58, 49, 72, 40, 96, 49, 114, 40, 15, 57, 33,
- 66, 57, 57, 71, 66, 99, 57, 109, 66, 8, 14, 20, 5, 50, 14,
- 61, 6, 92, 14, 103, 6, 4, 23, 35, 31, 46, 23, 77, 31, 88,
- 23, 119, 31, 19, 49, 31, 40, 57, 49, 73, 40, 95, 49, 115, 40,
- 16, 57, 32, 66, 58, 57, 70, 66, 99, 58, 108, 66, 9, 14, 20,
- 6, 51, 14, 62, 6, 93, 14, 104, 6, 5, 23, 36, 31, 47, 23,
- 78, 31, 89, 23, 119, 32, 18, 49, 32, 40, 56, 49, 74, 40, 94,
- 49, 116, 40, 17, 57, 31, 66, 59, 57, 69, 66, 98, 58, 107, 66,
- 10, 14, 21, 6, 52, 14, 63, 6, 94, 14, 105, 6, 6, 23, 37,
- 31, 48, 23, 79, 31, 90, 23, 118, 32, 17, 49, 33, 40, 55, 49,
- 75, 40, 93, 49, 117, 40, 18, 57, 30, 66, 59, 58, 68, 66, 97,
- 58, 106, 66, 11, 14, 22, 6, 53, 14, 64, 6, 95, 14, 106, 6,
- 7, 23, 38, 31, 49, 23, 79, 32, 91, 23, 117, 32, 16, 49, 34,
- 40, 54, 49, 76, 40, 92, 49, 118, 40, 19, 57, 29, 66, 58, 58,
- 67, 66, 96, 58, 105, 66, 12, 14, 23, 6, 54, 14, 65, 6, 96,
- 14, 107, 6, 8, 23, 39, 31, 50, 23, 78, 32, 92, 23, 116, 32,
- 15, 49, 35, 40, 53, 49, 77, 40, 91, 49, 119, 40, 19, 58, 28,
- 66, 57, 58, 66, 66, 95, 58, 104, 66, 13, 14, 24, 6, 55, 14,
- 66, 6, 97, 14, 108, 6, 9, 23, 39, 32, 51, 23, 77, 32, 93,
- 23, 115, 32, 14, 49, 36, 40, 52, 49, 78, 40, 90, 49, 119, 41,
- 18, 58, 27, 66, 56, 58, 65, 66, 94, 58, 103, 66, 14, 14, 25,
- 6, 56, 14, 67, 6, 98, 14, 109, 6, 10, 23, 38, 32, 52, 23,
- 76, 32, 94, 23, 114, 32, 13, 49, 37, 40, 51, 49, 79, 40, 89,
- 49, 118, 41, 17, 58, 26, 66, 55, 58, 64, 66, 93, 58, 102, 66,
- 15, 14, 26, 6, 57, 14, 68, 6, 99, 14, 110, 6, 11, 23, 37,
- 32, 53, 23, 75, 32, 95, 23, 113, 32, 12, 49, 38, 40, 50, 49,
- 79, 41, 88, 49, 117, 41, 16, 58, 25, 66, 54, 58, 63, 66, 92,
- 58, 101, 66, 16, 14, 27, 6, 58, 14, 69, 6, 99, 15, 111, 6,
- 12, 23, 36, 32, 54, 23, 74, 32, 96, 23, 112, 32, 11, 49, 39,
- 40, 49, 49, 78, 41, 87, 49, 116, 41, 15, 58, 24, 66, 53, 58,
- 62, 66, 91, 58, 100, 66, 17, 14, 28, 6, 59, 14, 70, 6, 98,
- 15, 112, 6, 13, 23, 35, 32, 55, 23, 73, 32, 97, 23, 111, 32,
- 10, 49, 39, 41, 48, 49, 77, 41, 86, 49, 115, 41, 14, 58, 23,
- 66, 52, 58, 61, 66, 90, 58, 100, 67, 18, 14, 29, 6, 59, 15,
- 71, 6, 97, 15, 113, 6, 14, 23, 34, 32, 56, 23, 72, 32, 98,
- 23, 110, 32, 9, 49, 38, 41, 47, 49, 76, 41, 85, 49, 114, 41,
- 13, 58, 22, 66, 51, 58, 60, 66, 89, 58, 101, 67, 19, 14, 30,
- 6, 58, 15, 72, 6, 96, 15, 114, 6, 15, 23, 33, 32, 57, 23,
- 71, 32, 99, 23, 109, 32, 8, 49, 37, 41, 46, 49, 75, 41, 84,
- 49, 113, 41, 12, 58, 21, 66, 50, 58, 60, 67, 88, 58, 102, 67,
- 19, 15, 31, 6, 57, 15, 73, 6, 95, 15, 115, 6, 16, 23, 32,
- 32, 58, 23, 70, 32, 99, 24, 108, 32, 7, 49, 36, 41, 45, 49,
- 74, 41, 83, 49, 112, 41, 11, 58, 20, 66, 49, 58, 61, 67, 87,
- 58, 103, 67, 18, 15, 32, 6, 56, 15, 74, 6, 94, 15, 116, 6,
- 17, 23, 31, 32, 59, 23, 69, 32, 98, 24, 107, 32, 6, 49, 35,
- 41, 44, 49, 73, 41, 82, 49, 111, 41, 10, 58, 20, 67, 48, 58,
- 62, 67, 86, 58, 104, 67, 17, 15, 33, 6, 55, 15, 75, 6, 93,
- 15, 117, 6, 18, 23, 30, 32, 59, 24, 68, 32, 97, 24, 106, 32,
- 5, 49, 34, 41, 43, 49, 72, 41, 81, 49, 110, 41, 9, 58, 21,
- 67, 47, 58, 63, 67, 85, 58, 105, 67, 16, 15, 34, 6, 54, 15,
- 76, 6, 92, 15, 118, 6, 19, 23, 29, 32, 58, 24, 67, 32, 96,
- 24, 105, 32, 4, 49, 33, 41, 42, 49, 71, 41, 80, 49, 109, 41,
- 8, 58, 22, 67, 46, 58, 64, 67, 84, 58, 106, 67, 15, 15, 35,
- 6, 53, 15, 77, 6, 91, 15, 119, 6, 19, 24, 28, 32, 57, 24,
- 66, 32, 95, 24, 104, 32, 3, 49, 32, 41, 41, 49, 70, 41, 80,
- 50, 108, 41, 7, 58, 23, 67, 45, 58, 65, 67, 83, 58, 107, 67,
- 14, 15, 36, 6, 52, 15, 78, 6, 90, 15, 119, 7, 18, 24, 27,
- 32, 56, 24, 65, 32, 94, 24, 103, 32, 2, 49, 31, 41, 40, 49,
- 69, 41, 81, 50, 107, 41, 6, 58, 24, 67, 44, 58, 66, 67, 82,
- 58, 108, 67, 13, 15, 37, 6, 51, 15, 79, 6, 89, 15, 118, 7,
- 17, 24, 26, 32, 55, 24, 64, 32, 93, 24, 102, 32, 1, 49, 30,
- 41, 40, 50, 68, 41, 82, 50, 106, 41, 5, 58, 25, 67, 43, 58,
- 67, 67, 81, 58, 109, 67, 12, 15, 38, 6, 50, 15, 79, 7, 88,
- 15, 117, 7, 16, 24, 25, 32, 54, 24, 63, 32, 92, 24, 101, 32,
- 0, 49, 29, 41, 41, 50, 67, 41, 83, 50, 105, 41, 4, 58, 26,
- 67, 42, 58, 68, 67, 80, 58, 110, 67, 11, 15, 39, 6, 49, 15,
- 78, 7, 87, 15, 116, 7, 15, 24, 24, 32, 53, 24, 62, 32, 91,
- 24, 100, 32, 0, 50, 28, 41, 42, 50, 66, 41, 84, 50, 104, 41,
- 3, 58, 27, 67, 41, 58, 69, 67, 80, 59, 111, 67, 10, 15, 39,
- 7, 48, 15, 77, 7, 86, 15, 115, 7, 14, 24, 23, 32, 52, 24,
- 61, 32, 90, 24, 100, 33, 1, 50, 27, 41, 43, 50, 65, 41, 85,
- 50, 103, 41, 2, 58, 28, 67, 40, 58, 70, 67, 81, 59, 112, 67,
- 9, 15, 38, 7, 47, 15, 76, 7, 85, 15, 114, 7, 13, 24, 22,
- 32, 51, 24, 60, 32, 89, 24, 101, 33, 2, 50, 26, 41, 44, 50,
- 64, 41, 86, 50, 102, 41, 1, 58, 29, 67, 40, 59, 71, 67, 82,
- 59, 113, 67, 8, 15, 37, 7, 46, 15, 75, 7, 84, 15, 113, 7,
- 12, 24, 21, 32, 50, 24, 60, 33, 88, 24, 102, 33, 3, 50, 25,
- 41, 45, 50, 63, 41, 87, 50, 101, 41, 0, 58, 30, 67, 41, 59,
- 72, 67, 83, 59, 114, 67, 7, 15, 36, 7, 45, 15, 74, 7, 83,
- 15, 112, 7, 11, 24, 20, 32, 49, 24, 61, 33, 87, 24, 103, 33,
- 4, 50, 24, 41, 46, 50, 62, 41, 88, 50, 100, 41, 0, 59, 31,
- 67, 42, 59, 73, 67, 84, 59, 115, 67, 6, 15, 35, 7, 44, 15,
- 73, 7, 82, 15, 111, 7, 10, 24, 20, 33, 48, 24, 62, 33, 86,
- 24, 104, 33, 5, 50, 23, 41, 47, 50, 61, 41, 89, 50, 100, 42,
- 1, 59, 32, 67, 43, 59, 74, 67, 85, 59, 116, 67, 5, 15, 34,
- 7, 43, 15, 72, 7, 81, 15, 110, 7, 9, 24, 21, 33, 47, 24,
- 63, 33, 85, 24, 105, 33, 6, 50, 22, 41, 48, 50, 60, 41, 90,
- 50, 101, 42, 2, 59, 33, 67, 44, 59, 75, 67, 86, 59, 117, 67,
- 4, 15, 33, 7, 42, 15, 71, 7, 80, 15, 109, 7, 8, 24, 22,
- 33, 46, 24, 64, 33, 84, 24, 106, 33, 7, 50, 21, 41, 49, 50,
- 60, 42, 91, 50, 102, 42, 3, 59, 34, 67, 45, 59, 76, 67, 87,
- 59, 118, 67, 3, 15, 32, 7, 41, 15, 70, 7, 80, 16, 108, 7,
- 7, 24, 23, 33, 45, 24, 65, 33, 83, 24, 107, 33, 8, 50, 20,
- 41, 50, 50, 61, 42, 92, 50, 103, 42, 4, 59, 35, 67, 46, 59,
- 77, 67, 88, 59, 119, 67, 2, 15, 31, 7, 40, 15, 69, 7, 81,
- 16, 107, 7, 6, 24, 24, 33, 44, 24, 66, 33, 82, 24, 108, 33,
- 9, 50, 20, 42, 51, 50, 62, 42, 93, 50, 104, 42, 5, 59, 36,
- 67, 47, 59, 78, 67, 89, 59, 100, 51, 1, 15, 30, 7, 40, 16,
- 68, 7, 82, 16, 106, 7, 5, 24, 25, 33, 43, 24, 67, 33, 81,
- 24, 109, 33, 10, 50, 21, 42, 52, 50, 63, 42, 94, 50, 105, 42,
- 6, 59, 37, 67, 48, 59, 79, 67, 90, 59, 101, 51, 0, 15, 29,
- 7, 41, 16, 67, 7, 83, 16, 105, 7, 4, 24, 26, 33, 42, 24,
- 68, 33, 80, 24, 110, 33, 11, 50, 22, 42, 53, 50, 64, 42, 95,
- 50, 106, 42, 7, 59, 38, 67, 49, 59, 60, 51, 91, 59, 102, 51,
- 0, 16, 28, 7, 42, 16, 66, 7, 84, 16, 104, 7, 3, 24, 27,
- 33, 41, 24, 69, 33, 80, 25, 111, 33, 12, 50, 23, 42, 54, 50,
- 65, 42, 96, 50, 107, 42, 8, 59, 39, 67, 50, 59, 61, 51, 92,
- 59, 103, 51, 1, 16, 27, 7, 43, 16, 65, 7, 85, 16, 103, 7,
- 2, 24, 28, 33, 40, 24, 70, 33, 81, 25, 112, 33, 13, 50, 24,
- 42, 55, 50, 66, 42, 97, 50, 108, 42, 9, 59, 20, 51, 51, 59,
- 62, 51, 93, 59, 104, 51, 2, 16, 26, 7, 44, 16, 64, 7, 86,
- 16, 102, 7, 1, 24, 29, 33, 40, 25, 71, 33, 82, 25, 113, 33,
- 14, 50, 25, 42, 56, 50, 67, 42, 98, 50, 109, 42, 10, 59, 21,
- 51, 52, 59, 63, 51, 94, 59, 105, 51, 3, 16, 25, 7, 45, 16,
- 63, 7, 87, 16, 101, 7, 0, 24, 30, 33, 41, 25, 72, 33, 83,
- 25, 114, 33, 15, 50, 26, 42, 57, 50, 68, 42, 99, 50, 110, 42,
- 11, 59, 22, 51, 53, 59, 64, 51, 95, 59, 106, 51, 4, 16, 24,
- 7, 46, 16, 62, 7, 88, 16, 100, 7, 0, 25, 31, 33, 42, 25,
- 73, 33, 84, 25, 115, 33, 16, 50, 27, 42, 58, 50, 69, 42, 80,
- 34, 111, 42, 12, 59, 23, 51, 54, 59, 65, 51, 96, 59, 107, 51,
- 5, 16, 23, 7, 47, 16, 61, 7, 89, 16, 100, 8, 1, 25, 32,
- 33, 43, 25, 74, 33, 85, 25, 116, 33, 17, 50, 28, 42, 59, 50,
- 70, 42, 81, 34, 112, 42, 13, 59, 24, 51, 55, 59, 66, 51, 97,
- 59, 108, 51, 6, 16, 22, 7, 48, 16, 60, 7, 90, 16, 101, 8,
- 2, 25, 33, 33, 44, 25, 75, 33, 86, 25, 117, 33, 18, 50, 29,
- 42, 40, 34, 71, 42, 82, 34, 113, 42, 14, 59, 25, 51, 56, 59,
- 67, 51, 98, 59, 109, 51, 7, 16, 21, 7, 49, 16, 60, 8, 91,
- 16, 102, 8, 3, 25, 34, 33, 45, 25, 76, 33, 87, 25, 118, 33,
- 19, 50, 30, 42, 41, 34, 72, 42, 83, 34, 114, 42, 15, 59, 26,
- 51, 57, 59, 68, 51, 99, 59, 110, 51, 8, 16, 20, 7, 50, 16,
- 61, 8, 92, 16, 103, 8, 4, 25, 35, 33, 46, 25, 77, 33, 88,
- 25, 119, 33, 0, 34, 31, 42, 42, 34, 73, 42, 84, 34, 115, 42,
- 16, 59, 27, 51, 58, 59, 69, 51, 99, 60, 111, 51, 9, 16, 20,
- 8, 51, 16, 62, 8, 93, 16, 104, 8, 5, 25, 36, 33, 47, 25,
- 78, 33, 89, 25, 100, 17, 1, 34, 32, 42, 43, 34, 74, 42, 85,
- 34, 116, 42, 17, 59, 28, 51, 59, 59, 70, 51, 98, 60, 112, 51,
- 10, 16, 21, 8, 52, 16, 63, 8, 94, 16, 105, 8, 6, 25, 37,
- 33, 48, 25, 79, 33, 90, 25, 101, 17, 2, 34, 33, 42, 44, 34,
- 75, 42, 86, 34, 117, 42, 18, 59, 29, 51, 59, 60, 71, 51, 97,
- 60, 113, 51, 11, 16, 22, 8, 53, 16, 64, 8, 95, 16, 106, 8,
- 7, 25, 38, 33, 49, 25, 60, 17, 91, 25, 102, 17, 3, 34, 34,
- 42, 45, 34, 76, 42, 87, 34, 118, 42, 19, 59, 30, 51, 58, 60,
- 72, 51, 96, 60, 114, 51, 12, 16, 23, 8, 54, 16, 65, 8, 96,
- 16, 107, 8, 8, 25, 39, 33, 50, 25, 61, 17, 92, 25, 103, 17,
- 4, 34, 35, 42, 46, 34, 77, 42, 88, 34, 119, 42, 19, 60, 31,
- 51, 57, 60, 73, 51, 95, 60, 115, 51, 13, 16, 24, 8, 55, 16,
- 66, 8, 97, 16, 108, 8, 9, 25, 20, 17, 51, 25, 62, 17, 93,
- 25, 104, 17, 5, 34, 36, 42, 47, 34, 78, 42, 89, 34, 119, 43,
- 18, 60, 32, 51, 56, 60, 74, 51, 94, 60, 116, 51, 14, 16, 25,
- 8, 56, 16, 67, 8, 98, 16, 109, 8, 10, 25, 21, 17, 52, 25,
- 63, 17, 94, 25, 105, 17, 6, 34, 37, 42, 48, 34, 79, 42, 90,
- 34, 118, 43, 17, 60, 33, 51, 55, 60, 75, 51, 93, 60, 117, 51,
- 15, 16, 26, 8, 57, 16, 68, 8, 99, 16, 110, 8, 11, 25, 22,
- 17, 53, 25, 64, 17, 95, 25, 106, 17, 7, 34, 38, 42, 49, 34,
- 79, 43, 91, 34, 117, 43, 16, 60, 34, 51, 54, 60, 76, 51, 92,
- 60, 118, 51, 16, 16, 27, 8, 58, 16, 69, 8, 80, 0, 111, 8,
- 12, 25, 23, 17, 54, 25, 65, 17, 96, 25, 107, 17, 8, 34, 39,
- 42, 50, 34, 78, 43, 92, 34, 116, 43, 15, 60, 35, 51, 53, 60,
- 77, 51, 91, 60, 119, 51, 17, 16, 28, 8, 59, 16, 70, 8, 81,
- 0, 112, 8, 13, 25, 24, 17, 55, 25, 66, 17, 97, 25, 108, 17,
- 9, 34, 39, 43, 51, 34, 77, 43, 93, 34, 115, 43, 14, 60, 36,
- 51, 52, 60, 78, 51, 90, 60, 119, 52, 18, 16, 29, 8, 40, 0,
- 71, 8, 82, 0, 113, 8, 14, 25, 25, 17, 56, 25, 67, 17, 98,
- 25, 109, 17, 10, 34, 38, 43, 52, 34, 76, 43, 94, 34, 114, 43,
- 13, 60, 37, 51, 51, 60, 79, 51, 89, 60, 118, 52, 19, 16, 30,
- 8, 41, 0, 72, 8, 83, 0, 114, 8, 15, 25, 26, 17, 57, 25,
- 68, 17, 99, 25, 110, 17, 11, 34, 37, 43, 53, 34, 75, 43, 95,
- 34, 113, 43, 12, 60, 38, 51, 50, 60, 79, 52, 88, 60, 117, 52,
- 0, 0, 0, 0, 0, 0, 0, 0,
-};
-
-static const uint8_t hq_tab_09[] = {
- 0, 0, 10, 3, 18, 0, 24, 3, 36, 0, 1, 10, 14, 6, 19,
- 10, 31, 7, 37, 10, 5, 13, 15, 16, 19, 13, 30, 17, 33, 13,
- 4, 23, 8, 20, 18, 23, 26, 20, 32, 23, 4, 26, 9, 24, 22,
- 26, 27, 24, 39, 27, 1, 0, 9, 3, 19, 0, 24, 4, 37, 0,
- 2, 10, 15, 6, 20, 10, 30, 7, 38, 10, 4, 13, 15, 17, 18,
- 13, 29, 17, 32, 13, 3, 23, 9, 20, 17, 23, 27, 20, 32, 18,
- 5, 26, 10, 24, 23, 26, 28, 24, 38, 27, 2, 0, 8, 3, 20,
- 0, 25, 4, 38, 0, 3, 10, 15, 7, 21, 10, 29, 7, 39, 10,
- 3, 13, 14, 17, 17, 13, 28, 17, 32, 14, 2, 23, 10, 20, 16,
- 23, 28, 20, 33, 18, 6, 26, 11, 24, 23, 27, 29, 24, 37, 27,
- 3, 0, 8, 4, 21, 0, 26, 4, 39, 0, 4, 10, 14, 7, 22,
- 10, 28, 7, 39, 11, 2, 13, 13, 17, 16, 13, 27, 17, 33, 14,
- 1, 23, 11, 20, 16, 18, 29, 20, 34, 18, 7, 26, 12, 24, 22,
- 27, 30, 24, 36, 27, 4, 0, 9, 4, 22, 0, 27, 4, 39, 1,
- 5, 10, 13, 7, 23, 10, 27, 7, 38, 11, 1, 13, 12, 17, 16,
- 14, 26, 17, 34, 14, 0, 23, 12, 20, 17, 18, 30, 20, 35, 18,
- 7, 27, 13, 24, 21, 27, 31, 24, 35, 27, 5, 0, 10, 4, 23,
- 0, 28, 4, 38, 1, 6, 10, 12, 7, 23, 11, 26, 7, 37, 11,
- 0, 13, 11, 17, 17, 14, 25, 17, 35, 14, 0, 18, 13, 20, 18,
- 18, 31, 20, 36, 18, 6, 27, 14, 24, 20, 27, 31, 25, 34, 27,
- 6, 0, 11, 4, 23, 1, 29, 4, 37, 1, 7, 10, 11, 7, 22,
- 11, 25, 7, 36, 11, 0, 14, 10, 17, 18, 14, 24, 17, 36, 14,
- 1, 18, 14, 20, 19, 18, 31, 21, 37, 18, 5, 27, 15, 24, 19,
- 27, 30, 25, 33, 27, 7, 0, 12, 4, 22, 1, 30, 4, 36, 1,
- 7, 11, 10, 7, 21, 11, 24, 7, 35, 11, 1, 14, 9, 17, 19,
- 14, 24, 12, 37, 14, 2, 18, 15, 20, 20, 18, 30, 21, 38, 18,
- 4, 27, 15, 25, 18, 27, 29, 25, 32, 27, 7, 1, 13, 4, 21,
- 1, 31, 4, 35, 1, 6, 11, 9, 7, 20, 11, 24, 8, 34, 11,
- 2, 14, 8, 17, 20, 14, 25, 12, 38, 14, 3, 18, 15, 21, 21,
- 18, 29, 21, 39, 18, 3, 27, 14, 25, 17, 27, 28, 25, 32, 28,
- 6, 1, 14, 4, 20, 1, 31, 5, 34, 1, 5, 11, 8, 7, 19,
- 11, 25, 8, 33, 11, 3, 14, 8, 12, 21, 14, 26, 12, 39, 14,
- 4, 18, 14, 21, 22, 18, 28, 21, 39, 19, 2, 27, 13, 25, 16,
- 27, 27, 25, 33, 28, 5, 1, 15, 4, 19, 1, 30, 5, 33, 1,
- 4, 11, 8, 8, 18, 11, 26, 8, 32, 11, 4, 14, 9, 12, 22,
- 14, 27, 12, 39, 15, 5, 18, 13, 21, 23, 18, 27, 21, 38, 19,
- 1, 27, 12, 25, 16, 28, 26, 25, 34, 28, 4, 1, 15, 5, 18,
- 1, 29, 5, 32, 1, 3, 11, 9, 8, 17, 11, 27, 8, 32, 6,
- 5, 14, 10, 12, 23, 14, 28, 12, 38, 15, 6, 18, 12, 21, 23,
- 19, 26, 21, 37, 19, 0, 27, 11, 25, 17, 28, 25, 25, 35, 28,
- 3, 1, 14, 5, 17, 1, 28, 5, 32, 2, 2, 11, 10, 8, 16,
- 11, 28, 8, 33, 6, 6, 14, 11, 12, 23, 15, 29, 12, 37, 15,
- 7, 18, 11, 21, 22, 19, 25, 21, 36, 19, 0, 28, 10, 25, 18,
- 28, 24, 25, 36, 28, 2, 1, 13, 5, 16, 1, 27, 5, 33, 2,
- 1, 11, 11, 8, 16, 6, 29, 8, 34, 6, 7, 14, 12, 12, 22,
- 15, 30, 12, 36, 15, 7, 19, 10, 21, 21, 19, 24, 21, 35, 19,
- 1, 28, 9, 25, 19, 28, 24, 26, 37, 28, 1, 1, 12, 5, 16,
- 2, 26, 5, 34, 2, 0, 11, 12, 8, 17, 6, 30, 8, 35, 6,
- 7, 15, 13, 12, 21, 15, 31, 12, 35, 15, 6, 19, 9, 21, 20,
- 19, 24, 22, 34, 19, 2, 28, 8, 25, 20, 28, 25, 26, 38, 28,
- 0, 1, 11, 5, 17, 2, 25, 5, 35, 2, 0, 6, 13, 8, 18,
- 6, 31, 8, 36, 6, 6, 15, 14, 12, 20, 15, 31, 13, 34, 15,
- 5, 19, 8, 21, 19, 19, 25, 22, 33, 19, 3, 28, 8, 26, 21,
- 28, 26, 26, 39, 28, 0, 2, 10, 5, 18, 2, 24, 5, 36, 2,
- 1, 6, 14, 8, 19, 6, 31, 9, 37, 6, 5, 15, 15, 12, 19,
- 15, 30, 13, 33, 15, 4, 19, 8, 22, 18, 19, 26, 22, 32, 19,
- 4, 28, 9, 26, 22, 28, 27, 26, 39, 29, 1, 2, 9, 5, 19,
- 2, 24, 0, 37, 2, 2, 6, 15, 8, 20, 6, 30, 9, 38, 6,
- 4, 15, 15, 13, 18, 15, 29, 13, 32, 15, 3, 19, 9, 22, 17,
- 19, 27, 22, 32, 20, 5, 28, 10, 26, 23, 28, 28, 26, 38, 29,
- 2, 2, 8, 5, 20, 2, 25, 0, 38, 2, 3, 6, 15, 9, 21,
- 6, 29, 9, 39, 6, 3, 15, 14, 13, 17, 15, 28, 13, 32, 16,
- 2, 19, 10, 22, 16, 19, 28, 22, 33, 20, 6, 28, 11, 26, 23,
- 29, 29, 26, 37, 29, 3, 2, 8, 0, 21, 2, 26, 0, 39, 2,
- 4, 6, 14, 9, 22, 6, 28, 9, 39, 7, 2, 15, 13, 13, 16,
- 15, 27, 13, 33, 16, 1, 19, 11, 22, 16, 20, 29, 22, 34, 20,
- 7, 28, 12, 26, 22, 29, 30, 26, 36, 29, 4, 2, 9, 0, 22,
- 2, 27, 0, 39, 3, 5, 6, 13, 9, 23, 6, 27, 9, 38, 7,
- 1, 15, 12, 13, 16, 16, 26, 13, 34, 16, 0, 19, 12, 22, 17,
- 20, 30, 22, 35, 20, 7, 29, 13, 26, 21, 29, 31, 26, 35, 29,
- 5, 2, 10, 0, 23, 2, 28, 0, 38, 3, 6, 6, 12, 9, 23,
- 7, 26, 9, 37, 7, 0, 15, 11, 13, 17, 16, 25, 13, 35, 16,
- 0, 20, 13, 22, 18, 20, 31, 22, 36, 20, 6, 29, 14, 26, 20,
- 29, 31, 27, 34, 29, 6, 2, 11, 0, 23, 3, 29, 0, 37, 3,
- 7, 6, 11, 9, 22, 7, 25, 9, 36, 7, 0, 16, 10, 13, 18,
- 16, 24, 13, 36, 16, 1, 20, 14, 22, 19, 20, 31, 23, 37, 20,
- 5, 29, 15, 26, 19, 29, 30, 27, 33, 29, 7, 2, 12, 0, 22,
- 3, 30, 0, 36, 3, 7, 7, 10, 9, 21, 7, 24, 9, 35, 7,
- 1, 16, 9, 13, 19, 16, 24, 14, 37, 16, 2, 20, 15, 22, 20,
- 20, 30, 23, 38, 20, 4, 29, 15, 27, 18, 29, 29, 27, 32, 29,
- 7, 3, 13, 0, 21, 3, 31, 0, 35, 3, 6, 7, 9, 9, 20,
- 7, 24, 10, 34, 7, 2, 16, 8, 13, 20, 16, 25, 14, 38, 16,
- 3, 20, 15, 23, 21, 20, 29, 23, 39, 20, 3, 29, 14, 27, 17,
- 29, 28, 27, 32, 24, 6, 3, 14, 0, 20, 3, 31, 1, 34, 3,
- 5, 7, 8, 9, 19, 7, 25, 10, 33, 7, 3, 16, 8, 14, 21,
- 16, 26, 14, 39, 16, 4, 20, 14, 23, 22, 20, 28, 23, 39, 21,
- 2, 29, 13, 27, 16, 29, 27, 27, 33, 24, 5, 3, 15, 0, 19,
- 3, 30, 1, 33, 3, 4, 7, 8, 10, 18, 7, 26, 10, 32, 7,
- 4, 16, 9, 14, 22, 16, 27, 14, 39, 17, 5, 20, 13, 23, 23,
- 20, 27, 23, 38, 21, 1, 29, 12, 27, 16, 24, 26, 27, 34, 24,
- 4, 3, 15, 1, 18, 3, 29, 1, 32, 3, 3, 7, 9, 10, 17,
- 7, 27, 10, 32, 8, 5, 16, 10, 14, 23, 16, 28, 14, 38, 17,
- 6, 20, 12, 23, 23, 21, 26, 23, 37, 21, 0, 29, 11, 27, 17,
- 24, 25, 27, 35, 24, 3, 3, 14, 1, 17, 3, 28, 1, 32, 4,
- 2, 7, 10, 10, 16, 7, 28, 10, 33, 8, 6, 16, 11, 14, 23,
- 17, 29, 14, 37, 17, 7, 20, 11, 23, 22, 21, 25, 23, 36, 21,
- 0, 24, 10, 27, 18, 24, 24, 27, 36, 24, 2, 3, 13, 1, 16,
- 3, 27, 1, 33, 4, 1, 7, 11, 10, 16, 8, 29, 10, 34, 8,
- 7, 16, 12, 14, 22, 17, 30, 14, 36, 17, 7, 21, 10, 23, 21,
- 21, 24, 23, 35, 21, 1, 24, 9, 27, 19, 24, 24, 28, 37, 24,
- 1, 3, 12, 1, 16, 4, 26, 1, 34, 4, 0, 7, 12, 10, 17,
- 8, 30, 10, 35, 8, 7, 17, 13, 14, 21, 17, 31, 14, 35, 17,
- 6, 21, 9, 23, 20, 21, 24, 18, 34, 21, 2, 24, 8, 27, 20,
- 24, 25, 28, 38, 24, 0, 3, 11, 1, 17, 4, 25, 1, 35, 4,
- 0, 8, 13, 10, 18, 8, 31, 10, 36, 8, 6, 17, 14, 14, 20,
- 17, 31, 15, 34, 17, 5, 21, 8, 23, 19, 21, 25, 18, 33, 21,
- 3, 24, 8, 28, 21, 24, 26, 28, 39, 24, 0, 4, 10, 1, 18,
- 4, 24, 1, 36, 4, 1, 8, 14, 10, 19, 8, 31, 11, 37, 8,
- 5, 17, 15, 14, 19, 17, 30, 15, 33, 17, 4, 21, 8, 18, 18,
- 21, 26, 18, 32, 21, 4, 24, 9, 28, 22, 24, 27, 28, 39, 25,
- 1, 4, 9, 1, 19, 4, 24, 2, 37, 4, 2, 8, 15, 10, 20,
- 8, 30, 11, 38, 8, 4, 17, 15, 15, 18, 17, 29, 15, 32, 17,
- 3, 21, 9, 18, 17, 21, 27, 18, 32, 22, 5, 24, 10, 28, 23,
- 24, 28, 28, 38, 25, 2, 4, 8, 1, 20, 4, 25, 2, 38, 4,
- 3, 8, 15, 11, 21, 8, 29, 11, 39, 8, 3, 17, 14, 15, 17,
- 17, 28, 15, 32, 12, 2, 21, 10, 18, 16, 21, 28, 18, 33, 22,
- 6, 24, 11, 28, 23, 25, 29, 28, 37, 25, 3, 4, 8, 2, 21,
- 4, 26, 2, 39, 4, 4, 8, 14, 11, 22, 8, 28, 11, 39, 9,
- 2, 17, 13, 15, 16, 17, 27, 15, 33, 12, 1, 21, 11, 18, 16,
- 22, 29, 18, 34, 22, 7, 24, 12, 28, 22, 25, 30, 28, 36, 25,
- 4, 4, 9, 2, 22, 4, 27, 2, 39, 5, 5, 8, 13, 11, 23,
- 8, 27, 11, 38, 9, 1, 17, 12, 15, 16, 12, 26, 15, 34, 12,
- 0, 21, 12, 18, 17, 22, 30, 18, 35, 22, 7, 25, 13, 28, 21,
- 25, 31, 28, 35, 25, 5, 4, 10, 2, 23, 4, 28, 2, 38, 5,
- 6, 8, 12, 11, 23, 9, 26, 11, 37, 9, 0, 17, 11, 15, 17,
- 12, 25, 15, 35, 12, 0, 22, 13, 18, 18, 22, 31, 18, 36, 22,
- 6, 25, 14, 28, 20, 25, 31, 29, 34, 25, 6, 4, 11, 2, 23,
- 5, 29, 2, 37, 5, 7, 8, 11, 11, 22, 9, 25, 11, 36, 9,
- 0, 12, 10, 15, 18, 12, 24, 15, 36, 12, 1, 22, 14, 18, 19,
- 22, 31, 19, 37, 22, 5, 25, 15, 28, 19, 25, 30, 29, 33, 25,
- 7, 4, 12, 2, 22, 5, 30, 2, 36, 5, 7, 9, 10, 11, 21,
- 9, 24, 11, 35, 9, 1, 12, 9, 15, 19, 12, 24, 16, 37, 12,
- 2, 22, 15, 18, 20, 22, 30, 19, 38, 22, 4, 25, 15, 29, 18,
- 25, 29, 29, 32, 25, 7, 5, 13, 2, 21, 5, 31, 2, 35, 5,
- 6, 9, 9, 11, 20, 9, 24, 6, 34, 9, 2, 12, 8, 15, 20,
- 12, 25, 16, 38, 12, 3, 22, 15, 19, 21, 22, 29, 19, 39, 22,
- 3, 25, 14, 29, 17, 25, 28, 29, 32, 26, 6, 5, 14, 2, 20,
- 5, 31, 3, 34, 5, 5, 9, 8, 11, 19, 9, 25, 6, 33, 9,
- 3, 12, 8, 16, 21, 12, 26, 16, 39, 12, 4, 22, 14, 19, 22,
- 22, 28, 19, 39, 23, 2, 25, 13, 29, 16, 25, 27, 29, 33, 26,
- 5, 5, 15, 2, 19, 5, 30, 3, 33, 5, 4, 9, 8, 6, 18,
- 9, 26, 6, 32, 9, 4, 12, 9, 16, 22, 12, 27, 16, 39, 13,
- 5, 22, 13, 19, 23, 22, 27, 19, 38, 23, 1, 25, 12, 29, 16,
- 26, 26, 29, 34, 26, 4, 5, 15, 3, 18, 5, 29, 3, 32, 5,
- 3, 9, 9, 6, 17, 9, 27, 6, 32, 10, 5, 12, 10, 16, 23,
- 12, 28, 16, 38, 13, 6, 22, 12, 19, 23, 23, 26, 19, 37, 23,
- 0, 25, 11, 29, 17, 26, 25, 29, 35, 26, 3, 5, 14, 3, 17,
- 5, 28, 3, 32, 0, 2, 9, 10, 6, 16, 9, 28, 6, 33, 10,
- 6, 12, 11, 16, 23, 13, 29, 16, 37, 13, 7, 22, 11, 19, 22,
- 23, 25, 19, 36, 23, 0, 26, 10, 29, 18, 26, 24, 29, 36, 26,
- 2, 5, 13, 3, 16, 5, 27, 3, 33, 0, 1, 9, 11, 6, 16,
- 10, 29, 6, 34, 10, 7, 12, 12, 16, 22, 13, 30, 16, 36, 13,
- 7, 23, 10, 19, 21, 23, 24, 19, 35, 23, 1, 26, 9, 29, 19,
- 26, 24, 24, 37, 26, 1, 5, 12, 3, 16, 0, 26, 3, 34, 0,
- 0, 9, 12, 6, 17, 10, 30, 6, 35, 10, 7, 13, 13, 16, 21,
- 13, 31, 16, 35, 13, 6, 23, 9, 19, 20, 23, 24, 20, 34, 23,
- 2, 26, 8, 29, 20, 26, 25, 24, 38, 26, 0, 5, 11, 3, 17,
- 0, 25, 3, 35, 0, 0, 10, 13, 6, 18, 10, 31, 6, 36, 10,
- 6, 13, 14, 16, 20, 13, 31, 17, 34, 13, 5, 23, 8, 19, 19,
- 23, 25, 20, 33, 23, 3, 26, 8, 24, 21, 26, 26, 24, 39, 26,
-};
-
-static const uint8_t hq_tab_10[] = {
- 0, 0, 4, 1, 10, 0, 13, 2, 19, 1, 0, 5, 6, 4, 10,
- 3, 15, 5, 16, 3, 2, 8, 6, 7, 8, 6, 12, 7, 18, 6,
- 2, 11, 4, 10, 8, 11, 14, 10, 18, 9, 0, 14, 7, 12, 10,
- 14, 14, 13, 16, 12, 1, 0, 4, 2, 11, 0, 14, 2, 18, 1,
- 3, 3, 7, 4, 9, 3, 14, 5, 16, 4, 3, 8, 5, 7, 9,
- 6, 12, 8, 19, 6, 1, 11, 5, 10, 11, 9, 15, 10, 17, 9,
- 1, 14, 7, 13, 11, 14, 13, 13, 17, 12, 2, 0, 5, 2, 11,
- 1, 15, 2, 17, 1, 2, 3, 7, 5, 8, 3, 13, 5, 17, 4,
- 0, 6, 4, 7, 10, 6, 13, 8, 19, 7, 0, 11, 6, 10, 10,
- 9, 15, 11, 16, 9, 2, 14, 6, 13, 8, 12, 12, 13, 18, 12,
- 3, 0, 6, 2, 10, 1, 12, 0, 16, 1, 1, 3, 6, 5, 8,
- 4, 12, 5, 18, 4, 1, 6, 4, 8, 11, 6, 14, 8, 18, 7,
- 3, 9, 7, 10, 9, 9, 14, 11, 16, 10, 3, 14, 5, 13, 9,
- 12, 12, 14, 19, 12, 3, 1, 7, 2, 9, 1, 13, 0, 16, 2,
- 0, 3, 5, 5, 9, 4, 15, 3, 19, 4, 2, 6, 5, 8, 11,
- 7, 15, 8, 17, 7, 2, 9, 7, 11, 8, 9, 13, 11, 17, 10,
- 0, 12, 4, 13, 10, 12, 13, 14, 19, 13, 2, 1, 4, 0, 8,
- 1, 14, 0, 17, 2, 0, 4, 4, 5, 10, 4, 14, 3, 19, 5,
- 3, 6, 6, 8, 10, 7, 12, 6, 16, 7, 1, 9, 6, 11, 8,
- 10, 12, 11, 18, 10, 1, 12, 4, 14, 11, 12, 14, 14, 18, 13,
- 1, 1, 5, 0, 8, 2, 15, 0, 18, 2, 1, 4, 7, 3, 11,
- 4, 13, 3, 18, 5, 3, 7, 7, 8, 9, 7, 13, 6, 16, 8,
- 0, 9, 5, 11, 9, 10, 15, 9, 19, 10, 2, 12, 5, 14, 11,
- 13, 15, 14, 17, 13, 0, 1, 6, 0, 9, 2, 15, 1, 19, 2,
- 2, 4, 6, 3, 11, 5, 12, 3, 17, 5, 2, 7, 4, 6, 8,
- 7, 14, 6, 17, 8, 0, 10, 4, 11, 10, 10, 14, 9, 19, 11,
- 3, 12, 6, 14, 10, 13, 12, 12, 16, 13, 0, 2, 7, 0, 10,
- 2, 14, 1, 16, 0, 3, 4, 5, 3, 10, 5, 12, 4, 16, 5,
- 1, 7, 5, 6, 8, 8, 15, 6, 18, 8, 1, 10, 7, 9, 11,
- 10, 13, 9, 18, 11, 3, 13, 7, 14, 9, 13, 13, 12, 16, 14,
- 1, 2, 7, 1, 11, 2, 13, 1, 17, 0, 3, 5, 4, 3, 9,
- 5, 13, 4, 19, 3, 0, 7, 6, 6, 9, 8, 15, 7, 19, 8,
- 2, 10, 6, 9, 11, 11, 12, 9, 17, 11, 2, 13, 4, 12, 8,
- 13, 14, 12, 17, 14, 2, 2, 6, 1, 8, 0, 12, 1, 18, 0,
- 2, 5, 4, 4, 8, 5, 14, 4, 18, 3, 0, 8, 7, 6, 10,
- 8, 14, 7, 16, 6, 3, 10, 5, 9, 10, 11, 12, 10, 16, 11,
- 1, 13, 5, 12, 8, 14, 15, 12, 18, 14, 3, 2, 5, 1, 9,
- 0, 12, 2, 19, 0, 1, 5, 5, 4, 11, 3, 15, 4, 17, 3,
- 1, 8, 7, 7, 11, 8, 13, 7, 17, 6, 3, 11, 4, 9, 9,
- 11, 13, 10, 19, 9, 0, 13, 6, 12, 9, 14, 15, 13, 19, 14,
-};
-
-static const uint8_t hq_tab_11[] = {
- 0, 0, 3, 3, 5, 1, 6, 0, 8, 2, 1, 5, 3, 7, 4,
- 6, 6, 4, 9, 7, 1, 0, 2, 3, 4, 1, 7, 0, 9, 2,
- 0, 5, 2, 7, 5, 6, 7, 4, 8, 7, 1, 1, 2, 0, 4,
- 2, 7, 1, 9, 3, 0, 6, 2, 4, 5, 7, 7, 5, 8, 4,
- 0, 1, 3, 0, 5, 2, 6, 1, 8, 3, 1, 6, 3, 4, 4,
- 7, 6, 5, 9, 4, 0, 2, 3, 1, 5, 3, 6, 2, 8, 0,
- 1, 7, 3, 5, 4, 4, 6, 6, 9, 5, 1, 2, 2, 1, 4,
- 3, 7, 2, 9, 0, 0, 7, 2, 5, 5, 4, 7, 6, 8, 5,
- 1, 3, 2, 2, 4, 0, 7, 3, 9, 1, 0, 4, 2, 6, 5,
- 5, 7, 7, 8, 6, 0, 3, 3, 2, 5, 0, 6, 3, 8, 1,
- 1, 4, 3, 6, 4, 5, 6, 7, 9, 6,
-};
-
-static const uint8_t hq_tab_12[] = {
- 0, 0, 13, 3, 22, 0, 31, 3, 44, 0, 3, 11, 12, 8, 21,
- 11, 34, 8, 40, 12, 2, 16, 11, 19, 24, 16, 30, 20, 46, 16,
- 1, 27, 14, 24, 20, 28, 36, 24, 42, 28, 4, 32, 10, 36, 26,
- 32, 32, 36, 48, 32, 1, 0, 12, 3, 23, 0, 30, 3, 45, 0,
- 2, 11, 13, 8, 20, 11, 35, 8, 41, 12, 3, 16, 10, 19, 25,
- 16, 31, 20, 47, 16, 0, 27, 15, 24, 21, 28, 37, 24, 43, 28,
- 5, 32, 11, 36, 27, 32, 33, 36, 49, 32, 2, 0, 11, 3, 24,
- 0, 30, 4, 46, 0, 1, 11, 14, 8, 20, 12, 36, 8, 42, 12,
- 4, 16, 10, 20, 26, 16, 32, 20, 48, 16, 0, 28, 16, 24, 22,
- 28, 38, 24, 44, 28, 6, 32, 12, 36, 28, 32, 34, 36, 49, 33,
- 3, 0, 10, 3, 25, 0, 31, 4, 47, 0, 0, 11, 15, 8, 21,
- 12, 37, 8, 43, 12, 5, 16, 11, 20, 27, 16, 33, 20, 49, 16,
- 1, 28, 17, 24, 23, 28, 39, 24, 45, 28, 7, 32, 13, 36, 29,
- 32, 35, 36, 48, 33, 4, 0, 10, 4, 26, 0, 32, 4, 48, 0,
- 0, 12, 16, 8, 22, 12, 38, 8, 44, 12, 6, 16, 12, 20, 28,
- 16, 34, 20, 49, 17, 2, 28, 18, 24, 24, 28, 39, 25, 46, 28,
- 8, 32, 14, 36, 29, 33, 36, 36, 47, 33, 5, 0, 11, 4, 27,
- 0, 33, 4, 49, 0, 1, 12, 17, 8, 23, 12, 39, 8, 45, 12,
- 7, 16, 13, 20, 29, 16, 35, 20, 48, 17, 3, 28, 19, 24, 25,
- 28, 38, 25, 47, 28, 9, 32, 15, 36, 28, 33, 37, 36, 46, 33,
- 6, 0, 12, 4, 28, 0, 34, 4, 49, 1, 2, 12, 18, 8, 24,
- 12, 39, 9, 46, 12, 8, 16, 14, 20, 29, 17, 36, 20, 47, 17,
- 4, 28, 19, 25, 26, 28, 37, 25, 48, 28, 9, 33, 16, 36, 27,
- 33, 38, 36, 45, 33, 7, 0, 13, 4, 29, 0, 35, 4, 48, 1,
- 3, 12, 19, 8, 25, 12, 38, 9, 47, 12, 9, 16, 15, 20, 28,
- 17, 37, 20, 46, 17, 5, 28, 18, 25, 27, 28, 36, 25, 49, 28,
- 8, 33, 17, 36, 26, 33, 39, 36, 44, 33, 8, 0, 14, 4, 29,
- 1, 36, 4, 47, 1, 4, 12, 19, 9, 26, 12, 37, 9, 48, 12,
- 9, 17, 16, 20, 27, 17, 38, 20, 45, 17, 6, 28, 17, 25, 28,
- 28, 35, 25, 49, 29, 7, 33, 18, 36, 25, 33, 39, 37, 43, 33,
- 9, 0, 15, 4, 28, 1, 37, 4, 46, 1, 5, 12, 18, 9, 27,
- 12, 36, 9, 49, 12, 8, 17, 17, 20, 26, 17, 39, 20, 44, 17,
- 7, 28, 16, 25, 29, 28, 34, 25, 48, 29, 6, 33, 19, 36, 24,
- 33, 38, 37, 42, 33, 9, 1, 16, 4, 27, 1, 38, 4, 45, 1,
- 6, 12, 17, 9, 28, 12, 35, 9, 49, 13, 7, 17, 18, 20, 25,
- 17, 39, 21, 43, 17, 8, 28, 15, 25, 29, 29, 33, 25, 47, 29,
- 5, 33, 19, 37, 23, 33, 37, 37, 41, 33, 8, 1, 17, 4, 26,
- 1, 39, 4, 44, 1, 7, 12, 16, 9, 29, 12, 34, 9, 48, 13,
- 6, 17, 19, 20, 24, 17, 38, 21, 42, 17, 9, 28, 14, 25, 28,
- 29, 32, 25, 46, 29, 4, 33, 18, 37, 22, 33, 36, 37, 40, 33,
- 7, 1, 18, 4, 25, 1, 39, 5, 43, 1, 8, 12, 15, 9, 29,
- 13, 33, 9, 47, 13, 5, 17, 19, 21, 23, 17, 37, 21, 41, 17,
- 9, 29, 13, 25, 27, 29, 31, 25, 45, 29, 3, 33, 17, 37, 21,
- 33, 35, 37, 40, 34, 6, 1, 19, 4, 24, 1, 38, 5, 42, 1,
- 9, 12, 14, 9, 28, 13, 32, 9, 46, 13, 4, 17, 18, 21, 22,
- 17, 36, 21, 40, 17, 8, 29, 12, 25, 26, 29, 30, 25, 44, 29,
- 2, 33, 16, 37, 20, 33, 34, 37, 41, 34, 5, 1, 19, 5, 23,
- 1, 37, 5, 41, 1, 9, 13, 13, 9, 27, 13, 31, 9, 45, 13,
- 3, 17, 17, 21, 21, 17, 35, 21, 40, 18, 7, 29, 11, 25, 25,
- 29, 30, 26, 43, 29, 1, 33, 15, 37, 20, 34, 33, 37, 42, 34,
- 4, 1, 18, 5, 22, 1, 36, 5, 40, 1, 8, 13, 12, 9, 26,
- 13, 30, 9, 44, 13, 2, 17, 16, 21, 20, 17, 34, 21, 41, 18,
- 6, 29, 10, 25, 24, 29, 31, 26, 42, 29, 0, 33, 14, 37, 21,
- 34, 32, 37, 43, 34, 3, 1, 17, 5, 21, 1, 35, 5, 40, 2,
- 7, 13, 11, 9, 25, 13, 30, 10, 43, 13, 1, 17, 15, 21, 20,
- 18, 33, 21, 42, 18, 5, 29, 10, 26, 23, 29, 32, 26, 41, 29,
- 0, 34, 13, 37, 22, 34, 31, 37, 44, 34, 2, 1, 16, 5, 20,
- 1, 34, 5, 41, 2, 6, 13, 10, 9, 24, 13, 31, 10, 42, 13,
- 0, 17, 14, 21, 21, 18, 32, 21, 43, 18, 4, 29, 11, 26, 22,
- 29, 33, 26, 40, 29, 1, 34, 12, 37, 23, 34, 30, 37, 45, 34,
- 1, 1, 15, 5, 20, 2, 33, 5, 42, 2, 5, 13, 10, 10, 23,
- 13, 32, 10, 41, 13, 0, 18, 13, 21, 22, 18, 31, 21, 44, 18,
- 3, 29, 12, 26, 21, 29, 34, 26, 40, 30, 2, 34, 11, 37, 24,
- 34, 34, 30, 46, 34, 0, 1, 14, 5, 21, 2, 32, 5, 43, 2,
- 4, 13, 11, 10, 22, 13, 33, 10, 40, 13, 1, 18, 12, 21, 23,
- 18, 30, 21, 45, 18, 2, 29, 13, 26, 20, 29, 35, 26, 41, 30,
- 3, 34, 10, 37, 25, 34, 35, 30, 47, 34, 0, 2, 13, 5, 22,
- 2, 31, 5, 44, 2, 3, 13, 12, 10, 21, 13, 34, 10, 40, 14,
- 2, 18, 11, 21, 24, 18, 30, 22, 46, 18, 1, 29, 14, 26, 20,
- 30, 36, 26, 42, 30, 4, 34, 14, 30, 26, 34, 36, 30, 48, 34,
- 1, 2, 12, 5, 23, 2, 30, 5, 45, 2, 2, 13, 13, 10, 20,
- 13, 35, 10, 41, 14, 3, 18, 10, 21, 25, 18, 31, 22, 47, 18,
- 0, 29, 15, 26, 21, 30, 37, 26, 43, 30, 5, 34, 15, 30, 27,
- 34, 37, 30, 49, 34, 2, 2, 11, 5, 24, 2, 30, 6, 46, 2,
- 1, 13, 14, 10, 20, 14, 36, 10, 42, 14, 4, 18, 10, 22, 26,
- 18, 32, 22, 48, 18, 0, 30, 16, 26, 22, 30, 38, 26, 48, 22,
- 6, 34, 16, 30, 28, 34, 38, 30, 49, 35, 3, 2, 10, 5, 25,
- 2, 31, 6, 47, 2, 0, 13, 15, 10, 21, 14, 37, 10, 43, 14,
- 5, 18, 11, 22, 27, 18, 33, 22, 49, 18, 1, 30, 17, 26, 23,
- 30, 39, 26, 49, 22, 7, 34, 17, 30, 29, 34, 39, 30, 48, 35,
- 4, 2, 10, 6, 26, 2, 32, 6, 48, 2, 0, 14, 16, 10, 22,
- 14, 38, 10, 44, 14, 6, 18, 12, 22, 28, 18, 34, 22, 49, 19,
- 2, 30, 18, 26, 28, 22, 39, 27, 49, 23, 8, 34, 18, 30, 29,
- 35, 39, 31, 47, 35, 5, 2, 11, 6, 27, 2, 33, 6, 49, 2,
- 1, 14, 17, 10, 23, 14, 39, 10, 45, 14, 7, 18, 13, 22, 29,
- 18, 35, 22, 48, 19, 3, 30, 19, 26, 29, 22, 38, 27, 48, 23,
- 9, 34, 19, 30, 28, 35, 38, 31, 46, 35, 6, 2, 12, 6, 28,
- 2, 34, 6, 49, 3, 2, 14, 18, 10, 24, 14, 39, 11, 46, 14,
- 8, 18, 14, 22, 29, 19, 36, 22, 47, 19, 8, 22, 19, 27, 29,
- 23, 37, 27, 47, 23, 9, 35, 19, 31, 27, 35, 37, 31, 45, 35,
- 7, 2, 13, 6, 29, 2, 35, 6, 48, 3, 3, 14, 19, 10, 25,
- 14, 38, 11, 47, 14, 9, 18, 15, 22, 28, 19, 37, 22, 46, 19,
- 9, 22, 18, 27, 28, 23, 36, 27, 46, 23, 8, 35, 18, 31, 26,
- 35, 36, 31, 44, 35, 8, 2, 14, 6, 29, 3, 36, 6, 47, 3,
- 4, 14, 19, 11, 26, 14, 37, 11, 48, 14, 9, 19, 16, 22, 27,
- 19, 37, 15, 45, 19, 9, 23, 17, 27, 27, 23, 35, 27, 45, 23,
- 7, 35, 17, 31, 25, 35, 35, 31, 43, 35, 9, 2, 15, 6, 28,
- 3, 37, 6, 46, 3, 5, 14, 18, 11, 27, 14, 36, 11, 49, 14,
- 8, 19, 17, 22, 26, 19, 36, 15, 44, 19, 8, 23, 16, 27, 26,
- 23, 34, 27, 44, 23, 6, 35, 16, 31, 24, 35, 34, 31, 42, 35,
- 9, 3, 16, 6, 27, 3, 38, 6, 45, 3, 6, 14, 17, 11, 28,
- 14, 35, 11, 49, 15, 7, 19, 17, 15, 25, 19, 35, 15, 43, 19,
- 7, 23, 15, 27, 25, 23, 33, 27, 43, 23, 5, 35, 15, 31, 23,
- 35, 33, 31, 41, 35, 8, 3, 17, 6, 26, 3, 39, 6, 44, 3,
- 7, 14, 16, 11, 29, 14, 34, 11, 48, 15, 6, 19, 16, 15, 24,
- 19, 34, 15, 42, 19, 6, 23, 14, 27, 24, 23, 32, 27, 42, 23,
- 4, 35, 14, 31, 22, 35, 32, 31, 40, 35, 7, 3, 18, 6, 25,
- 3, 39, 7, 43, 3, 8, 14, 15, 11, 29, 15, 33, 11, 43, 7,
- 5, 19, 15, 15, 23, 19, 33, 15, 41, 19, 5, 23, 13, 27, 23,
- 23, 31, 27, 41, 23, 3, 35, 13, 31, 21, 35, 31, 31, 40, 36,
- 6, 3, 19, 6, 24, 3, 38, 7, 42, 3, 9, 14, 14, 11, 28,
- 15, 32, 11, 42, 7, 4, 19, 14, 15, 22, 19, 32, 15, 40, 19,
- 4, 23, 12, 27, 22, 23, 30, 27, 40, 23, 2, 35, 12, 31, 20,
- 35, 30, 31, 41, 36, 5, 3, 19, 7, 23, 3, 37, 7, 41, 3,
- 9, 15, 13, 11, 23, 7, 31, 11, 41, 7, 3, 19, 13, 15, 21,
- 19, 31, 15, 40, 20, 3, 23, 11, 27, 21, 23, 30, 28, 40, 24,
- 1, 35, 11, 31, 20, 36, 30, 32, 42, 36, 4, 3, 18, 7, 22,
- 3, 36, 7, 40, 3, 8, 15, 12, 11, 22, 7, 30, 11, 40, 7,
- 2, 19, 12, 15, 20, 19, 30, 15, 41, 20, 2, 23, 10, 27, 20,
- 23, 31, 28, 41, 24, 0, 35, 10, 31, 21, 36, 31, 32, 43, 36,
- 3, 3, 17, 7, 21, 3, 35, 7, 40, 4, 3, 7, 11, 11, 21,
- 7, 30, 12, 40, 8, 1, 19, 11, 15, 20, 20, 30, 16, 42, 20,
- 1, 23, 10, 28, 20, 24, 32, 28, 42, 24, 0, 36, 10, 32, 22,
- 36, 32, 32, 44, 36, 2, 3, 16, 7, 20, 3, 34, 7, 41, 4,
- 2, 7, 10, 11, 20, 7, 31, 12, 41, 8, 0, 19, 10, 15, 21,
- 20, 31, 16, 43, 20, 0, 23, 11, 28, 21, 24, 33, 28, 43, 24,
- 1, 36, 11, 32, 23, 36, 33, 32, 45, 36, 1, 3, 15, 7, 20,
- 4, 30, 0, 42, 4, 1, 7, 10, 12, 20, 8, 32, 12, 42, 8,
- 0, 20, 10, 16, 22, 20, 32, 16, 44, 20, 0, 24, 12, 28, 22,
- 24, 34, 28, 44, 24, 2, 36, 12, 32, 24, 36, 34, 32, 46, 36,
- 0, 3, 14, 7, 21, 4, 31, 0, 43, 4, 0, 7, 11, 12, 21,
- 8, 33, 12, 43, 8, 1, 20, 11, 16, 23, 20, 33, 16, 45, 20,
- 1, 24, 13, 28, 23, 24, 35, 28, 45, 24, 3, 36, 13, 32, 25,
- 36, 35, 32, 47, 36, 0, 4, 10, 0, 22, 4, 32, 0, 44, 4,
- 0, 8, 12, 12, 22, 8, 34, 12, 44, 8, 2, 20, 12, 16, 24,
- 20, 34, 16, 46, 20, 2, 24, 14, 28, 24, 24, 36, 28, 46, 24,
- 4, 36, 14, 32, 26, 36, 36, 32, 48, 36, 1, 4, 11, 0, 23,
- 4, 33, 0, 45, 4, 1, 8, 13, 12, 23, 8, 35, 12, 45, 8,
- 3, 20, 13, 16, 25, 20, 35, 16, 47, 20, 3, 24, 15, 28, 25,
- 24, 37, 28, 47, 24, 5, 36, 15, 32, 27, 36, 37, 32, 49, 36,
- 2, 4, 12, 0, 24, 4, 34, 0, 46, 4, 2, 8, 14, 12, 24,
- 8, 36, 12, 46, 8, 4, 20, 14, 16, 26, 20, 36, 16, 48, 20,
- 4, 24, 16, 28, 26, 24, 38, 28, 48, 24, 6, 36, 16, 32, 28,
- 36, 38, 32, 49, 37, 3, 4, 13, 0, 25, 4, 35, 0, 47, 4,
- 3, 8, 15, 12, 25, 8, 37, 12, 47, 8, 5, 20, 15, 16, 27,
- 20, 37, 16, 49, 20, 5, 24, 17, 28, 27, 24, 39, 28, 49, 24,
- 7, 36, 17, 32, 29, 36, 39, 32, 48, 37, 4, 4, 14, 0, 26,
- 4, 36, 0, 48, 4, 4, 8, 16, 12, 26, 8, 38, 12, 48, 8,
- 6, 20, 16, 16, 28, 20, 38, 16, 49, 21, 6, 24, 18, 28, 28,
- 24, 39, 29, 49, 25, 8, 36, 18, 32, 29, 37, 39, 33, 47, 37,
- 5, 4, 15, 0, 27, 4, 37, 0, 49, 4, 5, 8, 17, 12, 27,
- 8, 39, 12, 49, 8, 7, 20, 17, 16, 29, 20, 39, 16, 48, 21,
- 7, 24, 19, 28, 29, 24, 38, 29, 48, 25, 9, 36, 19, 32, 28,
- 37, 38, 33, 46, 37, 6, 4, 16, 0, 28, 4, 38, 0, 49, 5,
- 6, 8, 18, 12, 28, 8, 39, 13, 49, 9, 8, 20, 18, 16, 29,
- 21, 39, 17, 47, 21, 8, 24, 19, 29, 29, 25, 37, 29, 47, 25,
- 9, 37, 19, 33, 27, 37, 37, 33, 45, 37, 7, 4, 17, 0, 29,
- 4, 39, 0, 48, 5, 7, 8, 19, 12, 29, 8, 38, 13, 48, 9,
- 9, 20, 19, 16, 28, 21, 38, 17, 46, 21, 9, 24, 18, 29, 28,
- 25, 36, 29, 46, 25, 8, 37, 18, 33, 26, 37, 36, 33, 44, 37,
- 8, 4, 18, 0, 29, 5, 39, 1, 47, 5, 8, 8, 19, 13, 29,
- 9, 37, 13, 47, 9, 9, 21, 19, 17, 27, 21, 37, 17, 45, 21,
- 9, 25, 17, 29, 27, 25, 35, 29, 45, 25, 7, 37, 17, 33, 25,
- 37, 35, 33, 43, 37, 9, 4, 19, 0, 28, 5, 38, 1, 46, 5,
- 9, 8, 18, 13, 28, 9, 36, 13, 46, 9, 8, 21, 18, 17, 26,
- 21, 36, 17, 44, 21, 8, 25, 16, 29, 26, 25, 34, 29, 44, 25,
- 6, 37, 16, 33, 24, 37, 34, 33, 42, 37, 9, 5, 19, 1, 27,
- 5, 37, 1, 45, 5, 9, 9, 17, 13, 27, 9, 35, 13, 45, 9,
- 7, 21, 17, 17, 25, 21, 35, 17, 43, 21, 7, 25, 15, 29, 25,
- 25, 33, 29, 43, 25, 5, 37, 15, 33, 23, 37, 33, 33, 41, 37,
- 8, 5, 18, 1, 26, 5, 36, 1, 44, 5, 8, 9, 16, 13, 26,
- 9, 34, 13, 44, 9, 6, 21, 16, 17, 24, 21, 34, 17, 42, 21,
- 6, 25, 14, 29, 24, 25, 32, 29, 42, 25, 4, 37, 14, 33, 22,
- 37, 32, 33, 40, 37, 7, 5, 17, 1, 25, 5, 35, 1, 43, 5,
- 7, 9, 15, 13, 25, 9, 33, 13, 43, 9, 5, 21, 15, 17, 23,
- 21, 33, 17, 41, 21, 5, 25, 13, 29, 23, 25, 31, 29, 41, 25,
- 3, 37, 13, 33, 21, 37, 31, 33, 44, 30, 6, 5, 16, 1, 24,
- 5, 34, 1, 42, 5, 6, 9, 14, 13, 24, 9, 32, 13, 42, 9,
- 4, 21, 14, 17, 22, 21, 32, 17, 40, 21, 4, 25, 12, 29, 22,
- 25, 30, 29, 40, 25, 2, 37, 12, 33, 20, 37, 30, 33, 45, 30,
- 5, 5, 15, 1, 23, 5, 33, 1, 41, 5, 5, 9, 13, 13, 23,
- 9, 31, 13, 41, 9, 3, 21, 13, 17, 21, 21, 31, 17, 40, 22,
- 3, 25, 11, 29, 21, 25, 30, 30, 40, 26, 1, 37, 11, 33, 24,
- 30, 30, 34, 46, 30, 4, 5, 14, 1, 22, 5, 32, 1, 40, 5,
- 4, 9, 12, 13, 22, 9, 30, 13, 40, 9, 2, 21, 12, 17, 20,
- 21, 30, 17, 41, 22, 2, 25, 10, 29, 20, 25, 31, 30, 41, 26,
- 0, 37, 10, 33, 25, 30, 31, 34, 47, 30, 3, 5, 13, 1, 21,
- 5, 31, 1, 40, 6, 3, 9, 11, 13, 21, 9, 30, 14, 40, 10,
- 1, 21, 11, 17, 20, 22, 30, 18, 42, 22, 1, 25, 10, 30, 20,
- 26, 32, 30, 42, 26, 4, 30, 10, 34, 26, 30, 32, 34, 48, 30,
- 2, 5, 12, 1, 20, 5, 30, 1, 41, 6, 2, 9, 10, 13, 20,
- 9, 31, 14, 41, 10, 0, 21, 10, 17, 21, 22, 31, 18, 43, 22,
- 0, 25, 11, 30, 21, 26, 33, 30, 43, 26, 5, 30, 11, 34, 27,
- 30, 33, 34, 49, 30, 1, 5, 11, 1, 20, 6, 30, 2, 42, 6,
- 1, 9, 10, 14, 20, 10, 32, 14, 42, 10, 0, 22, 10, 18, 22,
- 22, 32, 18, 44, 22, 0, 26, 12, 30, 22, 26, 38, 22, 44, 26,
- 6, 30, 12, 34, 28, 30, 34, 34, 49, 31, 0, 5, 10, 1, 21,
- 6, 31, 2, 43, 6, 0, 9, 11, 14, 21, 10, 33, 14, 43, 10,
- 1, 22, 11, 18, 23, 22, 33, 18, 45, 22, 1, 26, 13, 30, 23,
- 26, 39, 22, 45, 26, 7, 30, 13, 34, 29, 30, 35, 34, 48, 31,
- 0, 6, 10, 2, 22, 6, 32, 2, 44, 6, 0, 10, 12, 14, 22,
- 10, 34, 14, 44, 10, 2, 22, 12, 18, 24, 22, 34, 18, 46, 22,
- 2, 26, 18, 22, 24, 26, 39, 23, 46, 26, 8, 30, 14, 34, 29,
- 31, 36, 34, 47, 31, 1, 6, 11, 2, 23, 6, 33, 2, 45, 6,
- 1, 10, 13, 14, 23, 10, 35, 14, 45, 10, 3, 22, 13, 18, 25,
- 22, 35, 18, 47, 22, 3, 26, 19, 22, 25, 26, 38, 23, 47, 26,
- 9, 30, 15, 34, 28, 31, 37, 34, 46, 31, 2, 6, 12, 2, 24,
- 6, 34, 2, 46, 6, 2, 10, 14, 14, 24, 10, 36, 14, 46, 10,
- 4, 22, 14, 18, 26, 22, 36, 18, 47, 15, 4, 26, 19, 23, 26,
- 26, 37, 23, 48, 26, 9, 31, 16, 34, 27, 31, 38, 34, 45, 31,
- 3, 6, 13, 2, 25, 6, 35, 2, 47, 6, 3, 10, 15, 14, 25,
- 10, 37, 14, 47, 10, 5, 22, 15, 18, 27, 22, 37, 18, 46, 15,
- 5, 26, 18, 23, 27, 26, 36, 23, 49, 26, 8, 31, 17, 34, 26,
- 31, 39, 34, 44, 31, 4, 6, 14, 2, 26, 6, 36, 2, 48, 6,
- 4, 10, 16, 14, 26, 10, 38, 14, 48, 10, 6, 22, 16, 18, 27,
- 15, 38, 18, 45, 15, 6, 26, 17, 23, 28, 26, 35, 23, 49, 27,
- 7, 31, 18, 34, 25, 31, 39, 35, 43, 31, 5, 6, 15, 2, 27,
- 6, 37, 2, 49, 6, 5, 10, 17, 14, 27, 10, 39, 14, 49, 10,
- 7, 22, 17, 18, 26, 15, 39, 18, 44, 15, 7, 26, 16, 23, 29,
- 26, 34, 23, 48, 27, 6, 31, 19, 34, 24, 31, 38, 35, 42, 31,
- 6, 6, 16, 2, 28, 6, 38, 2, 49, 7, 6, 10, 18, 14, 28,
- 10, 39, 15, 49, 11, 7, 15, 18, 18, 25, 15, 39, 19, 43, 15,
- 8, 26, 15, 23, 29, 27, 33, 23, 47, 27, 5, 31, 19, 35, 23,
- 31, 37, 35, 41, 31, 7, 6, 17, 2, 29, 6, 39, 2, 48, 7,
- 7, 10, 19, 14, 29, 10, 38, 15, 48, 11, 6, 15, 19, 18, 24,
- 15, 38, 19, 42, 15, 9, 26, 14, 23, 28, 27, 32, 23, 46, 27,
- 4, 31, 18, 35, 22, 31, 36, 35, 40, 31, 8, 6, 18, 2, 29,
- 7, 39, 3, 47, 7, 8, 10, 19, 15, 29, 11, 33, 7, 47, 11,
- 5, 15, 19, 19, 23, 15, 37, 19, 41, 15, 9, 27, 13, 23, 27,
- 27, 31, 23, 45, 27, 3, 31, 17, 35, 21, 31, 35, 35, 40, 32,
- 9, 6, 19, 2, 28, 7, 38, 3, 46, 7, 9, 10, 18, 15, 28,
- 11, 32, 7, 46, 11, 4, 15, 18, 19, 22, 15, 36, 19, 40, 15,
- 8, 27, 12, 23, 26, 27, 30, 23, 44, 27, 2, 31, 16, 35, 20,
- 31, 34, 35, 41, 32, 9, 7, 19, 3, 27, 7, 37, 3, 45, 7,
- 9, 11, 13, 7, 27, 11, 31, 7, 45, 11, 3, 15, 17, 19, 21,
- 15, 35, 19, 40, 16, 7, 27, 11, 23, 25, 27, 30, 24, 43, 27,
- 1, 31, 15, 35, 20, 32, 33, 35, 42, 32, 8, 7, 18, 3, 26,
- 7, 36, 3, 44, 7, 8, 11, 12, 7, 26, 11, 30, 7, 44, 11,
- 2, 15, 16, 19, 20, 15, 34, 19, 41, 16, 6, 27, 10, 23, 24,
- 27, 31, 24, 42, 27, 0, 31, 14, 35, 21, 32, 32, 35, 43, 32,
- 7, 7, 17, 3, 25, 7, 35, 3, 40, 0, 7, 11, 11, 7, 25,
- 11, 30, 8, 43, 11, 1, 15, 15, 19, 20, 16, 33, 19, 42, 16,
- 5, 27, 10, 24, 23, 27, 32, 24, 41, 27, 0, 32, 13, 35, 22,
- 32, 31, 35, 44, 32, 6, 7, 16, 3, 24, 7, 34, 3, 41, 0,
- 6, 11, 10, 7, 24, 11, 31, 8, 42, 11, 0, 15, 14, 19, 21,
- 16, 32, 19, 43, 16, 4, 27, 11, 24, 22, 27, 33, 24, 40, 27,
- 1, 32, 12, 35, 23, 32, 30, 35, 45, 32, 5, 7, 15, 3, 20,
- 0, 33, 3, 42, 0, 5, 11, 10, 8, 23, 11, 32, 8, 41, 11,
- 0, 16, 13, 19, 22, 16, 31, 19, 44, 16, 3, 27, 12, 24, 21,
- 27, 34, 24, 40, 28, 2, 32, 11, 35, 24, 32, 30, 36, 46, 32,
- 4, 7, 14, 3, 21, 0, 32, 3, 43, 0, 4, 11, 11, 8, 22,
- 11, 33, 8, 40, 11, 1, 16, 12, 19, 23, 16, 30, 19, 45, 16,
- 2, 27, 13, 24, 20, 27, 35, 24, 41, 28, 3, 32, 10, 35, 25,
- 32, 31, 36, 47, 32,
-};
-
-static const uint8_t hq_tab_13[] = {
- 0, 0, 15, 1, 7, 4, 18, 3, 4, 6, 11, 7, 10, 11, 14,
- 9, 8, 12, 14, 14, 6, 17, 11, 16, 9, 19, 18, 20, 2, 23,
- 15, 22, 5, 25, 11, 24, 9, 27, 19, 28, 1, 0, 14, 1, 8,
- 4, 17, 3, 5, 6, 11, 8, 9, 11, 13, 9, 9, 12, 15, 14,
- 5, 17, 12, 16, 8, 19, 19, 20, 1, 23, 16, 22, 4, 25, 12,
- 24, 8, 27, 20, 28, 2, 0, 13, 1, 9, 4, 16, 3, 6, 6,
- 12, 8, 8, 11, 12, 9, 10, 12, 16, 14, 4, 17, 13, 16, 7,
- 19, 20, 20, 0, 23, 17, 22, 3, 25, 13, 24, 7, 27, 21, 28,
- 3, 0, 12, 1, 10, 4, 15, 3, 7, 6, 13, 8, 7, 11, 11,
- 9, 10, 13, 17, 14, 3, 17, 14, 16, 6, 19, 21, 20, 10, 21,
- 18, 22, 2, 25, 14, 24, 6, 27, 21, 29, 4, 0, 11, 1, 10,
- 5, 14, 3, 8, 6, 14, 8, 6, 11, 11, 10, 9, 13, 18, 14,
- 2, 17, 15, 16, 5, 19, 11, 18, 9, 21, 19, 22, 1, 25, 15,
- 24, 5, 27, 20, 29, 5, 0, 11, 2, 9, 5, 13, 3, 9, 6,
- 15, 8, 5, 11, 12, 10, 8, 13, 19, 14, 1, 17, 16, 16, 4,
- 19, 12, 18, 8, 21, 20, 22, 0, 25, 16, 24, 4, 27, 19, 29,
- 6, 0, 12, 2, 8, 5, 12, 3, 10, 6, 16, 8, 4, 11, 13,
- 10, 7, 13, 20, 14, 0, 17, 17, 16, 3, 19, 13, 18, 7, 21,
- 21, 22, 0, 26, 17, 24, 3, 27, 18, 29, 7, 0, 13, 2, 7,
- 5, 11, 3, 10, 7, 17, 8, 3, 11, 14, 10, 6, 13, 21, 14,
- 10, 15, 18, 16, 2, 19, 14, 18, 6, 21, 21, 23, 1, 26, 18,
- 24, 2, 27, 17, 29, 8, 0, 14, 2, 6, 5, 11, 4, 9, 7,
- 18, 8, 2, 11, 15, 10, 5, 13, 11, 12, 9, 15, 19, 16, 1,
- 19, 15, 18, 5, 21, 20, 23, 2, 26, 19, 24, 1, 27, 16, 29,
- 9, 0, 15, 2, 5, 5, 12, 4, 8, 7, 19, 8, 1, 11, 16,
- 10, 4, 13, 12, 12, 8, 15, 20, 16, 0, 19, 16, 18, 4, 21,
- 19, 23, 3, 26, 20, 24, 0, 27, 15, 29, 10, 0, 16, 2, 4,
- 5, 13, 4, 7, 7, 20, 8, 0, 11, 17, 10, 3, 13, 13, 12,
- 7, 15, 21, 16, 0, 20, 17, 18, 3, 21, 18, 23, 4, 26, 21,
- 24, 0, 28, 14, 29, 10, 1, 17, 2, 3, 5, 14, 4, 6, 7,
- 21, 8, 10, 9, 18, 10, 2, 13, 14, 12, 6, 15, 21, 17, 1,
- 20, 18, 18, 2, 21, 17, 23, 5, 26, 21, 25, 1, 28, 13, 29,
- 9, 1, 18, 2, 2, 5, 15, 4, 5, 7, 11, 6, 9, 9, 19,
- 10, 1, 13, 15, 12, 5, 15, 20, 17, 2, 20, 19, 18, 1, 21,
- 16, 23, 6, 26, 20, 25, 2, 28, 12, 29, 8, 1, 19, 2, 1,
- 5, 16, 4, 4, 7, 12, 6, 8, 9, 20, 10, 0, 13, 16, 12,
- 4, 15, 19, 17, 3, 20, 20, 18, 0, 21, 15, 23, 7, 26, 19,
- 25, 3, 28, 11, 29, 7, 1, 20, 2, 0, 5, 17, 4, 3, 7,
- 13, 6, 7, 9, 21, 10, 0, 14, 17, 12, 3, 15, 18, 17, 4,
- 20, 21, 18, 0, 22, 14, 23, 8, 26, 18, 25, 4, 28, 21, 27,
- 6, 1, 21, 2, 10, 3, 18, 4, 2, 7, 14, 6, 6, 9, 21,
- 11, 1, 14, 18, 12, 2, 15, 17, 17, 5, 20, 21, 19, 1, 22,
- 13, 23, 9, 26, 17, 25, 5, 28, 20, 27, 5, 1, 11, 0, 9,
- 3, 19, 4, 1, 7, 15, 6, 5, 9, 20, 11, 2, 14, 19, 12,
- 1, 15, 16, 17, 6, 20, 20, 19, 2, 22, 12, 23, 10, 26, 16,
- 25, 6, 28, 19, 27, 4, 1, 12, 0, 8, 3, 20, 4, 0, 7,
- 16, 6, 4, 9, 19, 11, 3, 14, 20, 12, 0, 15, 15, 17, 7,
- 20, 19, 19, 3, 22, 11, 23, 0, 24, 15, 25, 7, 28, 18, 27,
- 3, 1, 13, 0, 7, 3, 21, 4, 0, 8, 17, 6, 3, 9, 18,
- 11, 4, 14, 21, 12, 0, 16, 14, 17, 8, 20, 18, 19, 4, 22,
- 21, 21, 1, 24, 14, 25, 8, 28, 17, 27, 2, 1, 14, 0, 6,
- 3, 21, 5, 1, 8, 18, 6, 2, 9, 17, 11, 5, 14, 21, 13,
- 1, 16, 13, 17, 9, 20, 17, 19, 5, 22, 20, 21, 2, 24, 13,
- 25, 9, 28, 16, 27, 1, 1, 15, 0, 5, 3, 20, 5, 2, 8,
- 19, 6, 1, 9, 16, 11, 6, 14, 20, 13, 2, 16, 12, 17, 10,
- 20, 16, 19, 6, 22, 19, 21, 3, 24, 12, 25, 10, 28, 15, 27,
- 0, 1, 16, 0, 4, 3, 19, 5, 3, 8, 20, 6, 0, 9, 15,
- 11, 7, 14, 19, 13, 3, 16, 11, 17, 0, 18, 15, 19, 7, 22,
- 18, 21, 4, 24, 11, 25, 10, 29, 14, 27, 0, 2, 17, 0, 3,
- 3, 18, 5, 4, 8, 21, 6, 0, 10, 14, 11, 8, 14, 18, 13,
- 4, 16, 21, 15, 1, 18, 14, 19, 8, 22, 17, 21, 5, 24, 11,
- 26, 9, 29, 13, 27, 1, 2, 18, 0, 2, 3, 17, 5, 5, 8,
- 21, 7, 1, 10, 13, 11, 9, 14, 17, 13, 5, 16, 20, 15, 2,
- 18, 13, 19, 9, 22, 16, 21, 6, 24, 12, 26, 8, 29, 12, 27,
- 2, 2, 19, 0, 1, 3, 16, 5, 6, 8, 20, 7, 2, 10, 12,
- 11, 10, 14, 16, 13, 6, 16, 19, 15, 3, 18, 12, 19, 10, 22,
- 15, 21, 7, 24, 13, 26, 7, 29, 11, 27, 3, 2, 20, 0, 0,
- 3, 15, 5, 7, 8, 19, 7, 3, 10, 11, 11, 0, 12, 15, 13,
- 7, 16, 18, 15, 4, 18, 11, 19, 10, 23, 14, 21, 8, 24, 14,
- 26, 6, 29, 11, 28, 4, 2, 21, 0, 0, 4, 14, 5, 8, 8,
- 18, 7, 4, 10, 21, 9, 1, 12, 14, 13, 8, 16, 17, 15, 5,
- 18, 11, 20, 9, 23, 13, 21, 9, 24, 15, 26, 5, 29, 12, 28,
- 5, 2, 21, 1, 1, 4, 13, 5, 9, 8, 17, 7, 5, 10, 20,
- 9, 2, 12, 13, 13, 9, 16, 16, 15, 6, 18, 12, 20, 8, 23,
- 12, 21, 10, 24, 16, 26, 4, 29, 13, 28, 6, 2, 20, 1, 2,
- 4, 12, 5, 10, 8, 16, 7, 6, 10, 19, 9, 3, 12, 12, 13,
- 10, 16, 15, 15, 7, 18, 13, 20, 7, 23, 11, 21, 10, 25, 17,
- 26, 3, 29, 14, 28, 7, 2, 19, 1, 3, 4, 11, 5, 0, 6,
- 15, 7, 7, 10, 18, 9, 4, 12, 11, 13, 10, 17, 14, 15, 8,
- 18, 14, 20, 6, 23, 11, 22, 9, 25, 18, 26, 2, 29, 15, 28,
- 8, 2, 18, 1, 4, 4, 21, 3, 1, 6, 14, 7, 8, 10, 17,
- 9, 5, 12, 11, 14, 9, 17, 13, 15, 9, 18, 15, 20, 5, 23,
- 12, 22, 8, 25, 19, 26, 1, 29, 16, 28, 9, 2, 17, 1, 5,
- 4, 20, 3, 2, 6, 13, 7, 9, 10, 16, 9, 6, 12, 12, 14,
- 8, 17, 12, 15, 10, 18, 16, 20, 4, 23, 13, 22, 7, 25, 20,
- 26, 0, 29, 17, 28, 10, 2, 16, 1, 6, 4, 19, 3, 3, 6,
- 12, 7, 10, 10, 15, 9, 7, 12, 13, 14, 7, 17, 11, 15, 10,
- 19, 17, 20, 3, 23, 14, 22, 6, 25, 21, 26, 10, 27, 18, 28,
-};
-
-static const uint8_t hq_tab_14[] = {
- 0, 0, 17, 0, 0, 2, 14, 1, 9, 3, 14, 3, 9, 5, 19,
- 4, 2, 6, 19, 6, 9, 6, 12, 7, 7, 9, 16, 8, 0, 9,
- 15, 9, 10, 11, 15, 11, 10, 13, 18, 12, 1, 14, 18, 14, 1,
- 0, 18, 0, 1, 2, 13, 1, 8, 3, 13, 3, 8, 5, 20, 4,
- 3, 6, 16, 5, 10, 6, 11, 7, 6, 9, 17, 8, 0, 10, 14,
- 9, 9, 11, 14, 11, 9, 13, 19, 12, 2, 14, 19, 14, 2, 0,
- 19, 0, 2, 2, 12, 1, 7, 3, 12, 3, 7, 5, 21, 4, 4,
- 6, 15, 5, 10, 7, 11, 8, 5, 9, 18, 8, 1, 10, 13, 9,
- 8, 11, 13, 11, 8, 13, 20, 12, 3, 14, 20, 14, 3, 0, 20,
- 0, 3, 2, 11, 1, 6, 3, 11, 3, 6, 5, 21, 5, 5, 6,
- 14, 5, 9, 7, 12, 8, 2, 8, 19, 8, 2, 10, 12, 9, 7,
- 11, 12, 11, 7, 13, 21, 12, 4, 14, 21, 14, 4, 0, 21, 0,
- 4, 2, 11, 2, 5, 3, 11, 4, 1, 4, 20, 5, 6, 6, 13,
- 5, 8, 7, 20, 6, 3, 8, 20, 8, 3, 10, 11, 9, 6, 11,
- 11, 11, 6, 13, 21, 13, 5, 14, 14, 13, 5, 0, 21, 1, 5,
- 2, 12, 2, 4, 3, 19, 2, 2, 4, 19, 5, 7, 6, 12, 5,
- 7, 7, 21, 6, 4, 8, 21, 8, 4, 10, 11, 10, 5, 11, 11,
- 12, 5, 13, 20, 13, 6, 14, 13, 13, 6, 0, 20, 1, 6, 2,
- 13, 2, 3, 3, 20, 2, 3, 4, 18, 5, 8, 6, 11, 5, 6,
- 7, 21, 7, 5, 8, 21, 9, 5, 10, 12, 10, 4, 11, 12, 12,
- 4, 13, 19, 13, 7, 14, 12, 13, 7, 0, 19, 1, 7, 2, 14,
- 2, 2, 3, 21, 2, 4, 4, 17, 5, 5, 5, 11, 6, 5, 7,
- 20, 7, 6, 8, 20, 9, 6, 10, 13, 10, 3, 11, 13, 12, 3,
- 12, 18, 13, 8, 14, 11, 13, 8, 0, 18, 1, 6, 1, 15, 2,
- 1, 3, 21, 3, 5, 4, 12, 4, 4, 5, 12, 6, 4, 7, 19,
- 7, 7, 8, 19, 9, 7, 10, 14, 10, 2, 11, 21, 10, 4, 12,
- 17, 13, 9, 14, 11, 14, 9, 0, 11, 0, 5, 1, 16, 2, 0,
- 3, 20, 3, 6, 4, 13, 4, 3, 5, 13, 6, 3, 7, 18, 7,
- 8, 8, 18, 9, 8, 10, 15, 10, 1, 11, 21, 11, 5, 12, 16,
- 13, 10, 14, 12, 14, 10, 0, 12, 0, 4, 1, 17, 2, 0, 4,
- 19, 3, 7, 4, 14, 4, 2, 5, 14, 6, 2, 7, 17, 7, 9,
- 8, 17, 9, 9, 10, 16, 10, 0, 11, 20, 11, 6, 12, 15, 13,
- 3, 13, 13, 14, 10, 1, 13, 0, 3, 1, 18, 2, 8, 2, 18,
- 3, 8, 4, 15, 4, 1, 5, 15, 6, 1, 7, 16, 7, 10, 8,
- 16, 9, 4, 9, 17, 10, 0, 12, 19, 11, 7, 12, 14, 12, 2,
- 13, 14, 14, 9, 1, 14, 0, 2, 1, 17, 1, 9, 2, 17, 3,
- 9, 4, 16, 4, 0, 5, 16, 6, 0, 7, 15, 7, 10, 9, 13,
- 8, 3, 9, 18, 10, 1, 12, 18, 11, 8, 12, 15, 12, 1, 13,
- 15, 14, 8, 1, 15, 0, 1, 1, 16, 1, 10, 2, 16, 3, 10,
- 4, 17, 4, 0, 6, 17, 6, 0, 8, 14, 7, 9, 9, 14, 8,
- 2, 9, 19, 10, 2, 12, 17, 11, 9, 12, 16, 12, 0, 13, 16,
- 14, 7, 1, 16, 0, 0, 1, 15, 1, 10, 3, 15, 3, 10, 5,
- 18, 4, 1, 6, 18, 6, 1, 8, 13, 7, 8, 9, 15, 8, 1,
- 9, 20, 10, 10, 10, 16, 11, 10, 12, 17, 12, 0, 14, 17, 14,
-};
-
-static const uint8_t hq_tab_15[] = {
- 0, 0, 15, 1, 3, 3, 14, 2, 4, 4, 11, 5, 0, 6, 18,
- 6, 8, 8, 14, 8, 4, 10, 21, 11, 9, 13, 18, 12, 8, 14,
- 17, 15, 5, 17, 21, 17, 1, 0, 14, 1, 2, 3, 15, 2, 5,
- 4, 11, 4, 1, 6, 19, 6, 9, 8, 15, 8, 5, 10, 20, 11,
- 8, 13, 19, 12, 9, 14, 16, 15, 4, 17, 20, 17, 2, 0, 13,
- 1, 1, 3, 16, 2, 6, 4, 12, 4, 2, 6, 20, 6, 10, 8,
- 16, 8, 6, 10, 19, 11, 7, 13, 20, 12, 10, 14, 15, 15, 3,
- 17, 19, 17, 3, 0, 12, 1, 0, 3, 17, 2, 7, 4, 13, 4,
- 3, 6, 21, 6, 10, 9, 17, 8, 7, 10, 18, 11, 6, 13, 21,
- 12, 10, 15, 14, 15, 2, 17, 18, 17, 4, 0, 11, 1, 0, 2,
- 18, 2, 8, 4, 14, 4, 4, 6, 21, 7, 9, 9, 18, 8, 8,
- 10, 17, 11, 5, 13, 21, 13, 9, 15, 13, 15, 1, 17, 17, 17,
- 5, 0, 11, 0, 1, 2, 19, 2, 9, 4, 15, 4, 5, 6, 20,
- 7, 8, 9, 19, 8, 9, 10, 16, 11, 4, 13, 20, 13, 8, 15,
- 12, 15, 0, 17, 16, 17, 6, 0, 12, 0, 2, 2, 20, 2, 10,
- 4, 16, 4, 6, 6, 19, 7, 7, 9, 20, 8, 10, 10, 15, 11,
- 3, 13, 19, 13, 7, 15, 11, 15, 0, 16, 15, 17, 7, 0, 13,
- 0, 3, 2, 21, 2, 10, 5, 17, 4, 7, 6, 18, 7, 6, 9,
- 21, 8, 10, 11, 14, 11, 2, 13, 18, 13, 6, 15, 11, 14, 1,
- 16, 14, 17, 8, 0, 14, 0, 4, 2, 21, 3, 9, 5, 18, 4,
- 8, 6, 17, 7, 5, 9, 21, 9, 9, 11, 13, 11, 1, 13, 17,
- 13, 5, 15, 12, 14, 2, 16, 13, 17, 9, 0, 15, 0, 5, 2,
- 20, 3, 8, 5, 19, 4, 9, 6, 16, 7, 4, 9, 20, 9, 8,
- 11, 12, 11, 0, 13, 16, 13, 4, 15, 13, 14, 3, 16, 12, 17,
- 10, 0, 16, 0, 6, 2, 19, 3, 7, 5, 20, 4, 10, 6, 15,
- 7, 3, 9, 19, 9, 7, 11, 11, 11, 0, 12, 15, 13, 3, 15,
- 14, 14, 4, 16, 11, 17, 10, 1, 17, 0, 7, 2, 18, 3, 6,
- 5, 21, 4, 10, 7, 14, 7, 2, 9, 18, 9, 6, 11, 11, 10,
- 1, 12, 14, 13, 2, 15, 15, 14, 5, 16, 11, 16, 9, 1, 18,
- 0, 8, 2, 17, 3, 5, 5, 21, 5, 9, 7, 13, 7, 1, 9,
- 17, 9, 5, 11, 12, 10, 2, 12, 13, 13, 1, 15, 16, 14, 6,
- 16, 12, 16, 8, 1, 19, 0, 9, 2, 16, 3, 4, 5, 20, 5,
- 8, 7, 12, 7, 0, 9, 16, 9, 4, 11, 13, 10, 3, 12, 12,
- 13, 0, 15, 17, 14, 7, 16, 13, 16, 7, 1, 20, 0, 10, 2,
- 15, 3, 3, 5, 19, 5, 7, 7, 11, 7, 0, 8, 15, 9, 3,
- 11, 14, 10, 4, 12, 11, 13, 0, 14, 18, 14, 8, 16, 14, 16,
- 6, 1, 21, 0, 10, 3, 14, 3, 2, 5, 18, 5, 6, 7, 11,
- 6, 1, 8, 14, 9, 2, 11, 15, 10, 5, 12, 11, 12, 1, 14,
- 19, 14, 9, 16, 15, 16, 5, 1, 21, 1, 9, 3, 13, 3, 1,
- 5, 17, 5, 5, 7, 12, 6, 2, 8, 13, 9, 1, 11, 16, 10,
- 6, 12, 12, 12, 2, 14, 20, 14, 10, 16, 16, 16, 4, 1, 20,
- 1, 8, 3, 12, 3, 0, 5, 16, 5, 4, 7, 13, 6, 3, 8,
- 12, 9, 0, 11, 17, 10, 7, 12, 13, 12, 3, 14, 21, 14, 10,
- 17, 17, 16, 3, 1, 19, 1, 7, 3, 11, 3, 0, 4, 15, 5,
- 3, 7, 14, 6, 4, 8, 11, 9, 0, 10, 18, 10, 8, 12, 14,
- 12, 4, 14, 21, 15, 9, 17, 18, 16, 2, 1, 18, 1, 6, 3,
- 11, 2, 1, 4, 14, 5, 2, 7, 15, 6, 5, 8, 11, 8, 1,
- 10, 19, 10, 9, 12, 15, 12, 5, 14, 20, 15, 8, 17, 19, 16,
- 1, 1, 17, 1, 5, 3, 12, 2, 2, 4, 13, 5, 1, 7, 16,
- 6, 6, 8, 12, 8, 2, 10, 20, 10, 10, 12, 16, 12, 6, 14,
- 19, 15, 7, 17, 20, 16, 0, 1, 16, 1, 4, 3, 13, 2, 3,
- 4, 12, 5, 0, 7, 17, 6, 7, 8, 13, 8, 3, 10, 21, 10,
- 10, 13, 17, 12, 7, 14, 18, 15, 6, 17, 21, 16,
-};
-
-static const uint8_t hq_tab_16[] = {
- 0, 0, 4, 1, 2, 2, 2, 3, 4, 4, 0, 5, 6, 6, 9,
- 7, 8, 8, 1, 0, 3, 1, 3, 2, 1, 3, 5, 4, 10, 5,
- 7, 6, 8, 7, 9, 8, 2, 0, 2, 1, 4, 2, 0, 3, 6,
- 4, 9, 5, 8, 6, 7, 7, 10, 8, 3, 0, 1, 1, 5, 2,
- 10, 3, 7, 4, 8, 5, 9, 6, 6, 7, 0, 8, 4, 0, 0,
- 1, 6, 2, 9, 3, 8, 4, 7, 5, 10, 6, 5, 7, 1, 8,
- 5, 0, 10, 1, 7, 2, 8, 3, 9, 4, 6, 5, 0, 6, 4,
- 7, 2, 8, 6, 0, 9, 1, 8, 2, 7, 3, 10, 4, 5, 5,
- 1, 6, 3, 7, 3, 8, 7, 0, 8, 1, 9, 2, 6, 3, 0,
- 4, 4, 5, 2, 6, 2, 7, 4, 8, 8, 0, 7, 1, 10, 2,
- 5, 3, 1, 4, 3, 5, 3, 6, 1, 7, 5, 8, 9, 0, 6,
- 1, 0, 2, 4, 3, 2, 4, 2, 5, 4, 6, 0, 7, 6, 8,
- 10, 0, 5, 1, 1, 2, 3, 3, 3, 4, 1, 5, 5, 6, 10,
- 7, 7, 8, 0, 0,
-};
-
-static const uint8_t hq_tab_17[] = {
- 0, 0, 25, 8, 34, 0, 59, 8, 68, 0, 13, 24, 22, 16, 47,
- 24, 56, 16, 78, 25, 10, 32, 28, 41, 44, 32, 58, 41, 78, 32,
- 8, 57, 31, 49, 38, 57, 61, 49, 68, 57, 1, 0, 26, 8, 35,
- 0, 60, 8, 69, 0, 14, 24, 23, 16, 47, 25, 57, 16, 77, 25,
- 11, 32, 27, 41, 45, 32, 57, 41, 79, 32, 7, 57, 30, 49, 37,
- 57, 60, 49, 67, 57, 2, 0, 27, 8, 36, 0, 61, 8, 70, 0,
- 15, 24, 24, 16, 46, 25, 58, 16, 76, 25, 12, 32, 26, 41, 46,
- 32, 56, 41, 79, 33, 6, 57, 29, 49, 36, 57, 59, 49, 66, 57,
- 3, 0, 28, 8, 37, 0, 62, 8, 71, 0, 15, 25, 25, 16, 45,
- 25, 59, 16, 75, 25, 13, 32, 25, 41, 47, 32, 55, 41, 78, 33,
- 5, 57, 28, 49, 35, 57, 58, 49, 65, 57, 4, 0, 29, 8, 38,
- 0, 63, 8, 72, 0, 14, 25, 26, 16, 44, 25, 60, 16, 74, 25,
- 14, 32, 24, 41, 47, 33, 54, 41, 77, 33, 4, 57, 27, 49, 34,
- 57, 57, 49, 64, 57, 5, 0, 30, 8, 39, 0, 63, 9, 73, 0,
- 13, 25, 27, 16, 43, 25, 61, 16, 73, 25, 15, 32, 23, 41, 46,
- 33, 53, 41, 76, 33, 3, 57, 26, 49, 33, 57, 56, 49, 64, 58,
- 6, 0, 31, 8, 40, 0, 62, 9, 74, 0, 12, 25, 28, 16, 42,
- 25, 62, 16, 72, 25, 15, 33, 22, 41, 45, 33, 52, 41, 75, 33,
- 2, 57, 25, 49, 32, 57, 55, 49, 65, 58, 7, 0, 31, 9, 41,
- 0, 61, 9, 75, 0, 11, 25, 29, 16, 41, 25, 63, 16, 71, 25,
- 14, 33, 21, 41, 44, 33, 51, 41, 74, 33, 1, 57, 24, 49, 32,
- 58, 54, 49, 66, 58, 8, 0, 30, 9, 42, 0, 60, 9, 76, 0,
- 10, 25, 30, 16, 40, 25, 63, 17, 70, 25, 13, 33, 20, 41, 43,
- 33, 50, 41, 73, 33, 0, 57, 23, 49, 33, 58, 53, 49, 67, 58,
- 9, 0, 29, 9, 43, 0, 59, 9, 77, 0, 9, 25, 31, 16, 39,
- 25, 62, 17, 69, 25, 12, 33, 19, 41, 42, 33, 49, 41, 72, 33,
- 0, 58, 22, 49, 34, 58, 52, 49, 68, 58, 10, 0, 28, 9, 44,
- 0, 58, 9, 78, 0, 8, 25, 31, 17, 38, 25, 61, 17, 68, 25,
- 11, 33, 18, 41, 41, 33, 48, 41, 71, 33, 1, 58, 21, 49, 35,
- 58, 51, 49, 69, 58, 11, 0, 27, 9, 45, 0, 57, 9, 79, 0,
- 7, 25, 30, 17, 37, 25, 60, 17, 67, 25, 10, 33, 17, 41, 40,
- 33, 48, 42, 70, 33, 2, 58, 20, 49, 36, 58, 50, 49, 70, 58,
- 12, 0, 26, 9, 46, 0, 56, 9, 79, 1, 6, 25, 29, 17, 36,
- 25, 59, 17, 66, 25, 9, 33, 16, 41, 39, 33, 49, 42, 69, 33,
- 3, 58, 19, 49, 37, 58, 49, 49, 71, 58, 13, 0, 25, 9, 47,
- 0, 55, 9, 78, 1, 5, 25, 28, 17, 35, 25, 58, 17, 65, 25,
- 8, 33, 16, 42, 38, 33, 50, 42, 68, 33, 4, 58, 18, 49, 38,
- 58, 48, 49, 72, 58, 14, 0, 24, 9, 47, 1, 54, 9, 77, 1,
- 4, 25, 27, 17, 34, 25, 57, 17, 64, 25, 7, 33, 17, 42, 37,
- 33, 51, 42, 67, 33, 5, 58, 17, 49, 39, 58, 48, 50, 73, 58,
- 15, 0, 23, 9, 46, 1, 53, 9, 76, 1, 3, 25, 26, 17, 33,
- 25, 56, 17, 64, 26, 6, 33, 18, 42, 36, 33, 52, 42, 66, 33,
- 6, 58, 16, 49, 40, 58, 49, 50, 74, 58, 15, 1, 22, 9, 45,
- 1, 52, 9, 75, 1, 2, 25, 25, 17, 32, 25, 55, 17, 65, 26,
- 5, 33, 19, 42, 35, 33, 53, 42, 65, 33, 7, 58, 16, 50, 41,
- 58, 50, 50, 75, 58, 14, 1, 21, 9, 44, 1, 51, 9, 74, 1,
- 1, 25, 24, 17, 32, 26, 54, 17, 66, 26, 4, 33, 20, 42, 34,
- 33, 54, 42, 64, 33, 8, 58, 17, 50, 42, 58, 51, 50, 76, 58,
- 13, 1, 20, 9, 43, 1, 50, 9, 73, 1, 0, 25, 23, 17, 33,
- 26, 53, 17, 67, 26, 3, 33, 21, 42, 33, 33, 55, 42, 64, 34,
- 9, 58, 18, 50, 43, 58, 52, 50, 77, 58, 12, 1, 19, 9, 42,
- 1, 49, 9, 72, 1, 0, 26, 22, 17, 34, 26, 52, 17, 68, 26,
- 2, 33, 22, 42, 32, 33, 56, 42, 65, 34, 10, 58, 19, 50, 44,
- 58, 53, 50, 78, 58, 11, 1, 18, 9, 41, 1, 48, 9, 71, 1,
- 1, 26, 21, 17, 35, 26, 51, 17, 69, 26, 1, 33, 23, 42, 32,
- 34, 57, 42, 66, 34, 11, 58, 20, 50, 45, 58, 54, 50, 79, 58,
- 10, 1, 17, 9, 40, 1, 48, 10, 70, 1, 2, 26, 20, 17, 36,
- 26, 50, 17, 70, 26, 0, 33, 24, 42, 33, 34, 58, 42, 67, 34,
- 12, 58, 21, 50, 46, 58, 55, 50, 79, 59, 9, 1, 16, 9, 39,
- 1, 49, 10, 69, 1, 3, 26, 19, 17, 37, 26, 49, 17, 71, 26,
- 0, 34, 25, 42, 34, 34, 59, 42, 68, 34, 13, 58, 22, 50, 47,
- 58, 56, 50, 78, 59, 8, 1, 16, 10, 38, 1, 50, 10, 68, 1,
- 4, 26, 18, 17, 38, 26, 48, 17, 72, 26, 1, 34, 26, 42, 35,
- 34, 60, 42, 69, 34, 14, 58, 23, 50, 47, 59, 57, 50, 77, 59,
- 7, 1, 17, 10, 37, 1, 51, 10, 67, 1, 5, 26, 17, 17, 39,
- 26, 48, 18, 73, 26, 2, 34, 27, 42, 36, 34, 61, 42, 70, 34,
- 15, 58, 24, 50, 46, 59, 58, 50, 76, 59, 6, 1, 18, 10, 36,
- 1, 52, 10, 66, 1, 6, 26, 16, 17, 40, 26, 49, 18, 74, 26,
- 3, 34, 28, 42, 37, 34, 62, 42, 71, 34, 15, 59, 25, 50, 45,
- 59, 59, 50, 75, 59, 5, 1, 19, 10, 35, 1, 53, 10, 65, 1,
- 7, 26, 16, 18, 41, 26, 50, 18, 75, 26, 4, 34, 29, 42, 38,
- 34, 63, 42, 72, 34, 14, 59, 26, 50, 44, 59, 60, 50, 74, 59,
- 4, 1, 20, 10, 34, 1, 54, 10, 64, 1, 8, 26, 17, 18, 42,
- 26, 51, 18, 76, 26, 5, 34, 30, 42, 39, 34, 63, 43, 73, 34,
- 13, 59, 27, 50, 43, 59, 61, 50, 73, 59, 3, 1, 21, 10, 33,
- 1, 55, 10, 64, 2, 9, 26, 18, 18, 43, 26, 52, 18, 77, 26,
- 6, 34, 31, 42, 40, 34, 62, 43, 74, 34, 12, 59, 28, 50, 42,
- 59, 62, 50, 72, 59, 2, 1, 22, 10, 32, 1, 56, 10, 65, 2,
- 10, 26, 19, 18, 44, 26, 53, 18, 78, 26, 7, 34, 31, 43, 41,
- 34, 61, 43, 75, 34, 11, 59, 29, 50, 41, 59, 63, 50, 71, 59,
- 1, 1, 23, 10, 32, 2, 57, 10, 66, 2, 11, 26, 20, 18, 45,
- 26, 54, 18, 79, 26, 8, 34, 30, 43, 42, 34, 60, 43, 76, 34,
- 10, 59, 30, 50, 40, 59, 63, 51, 70, 59, 0, 1, 24, 10, 33,
- 2, 58, 10, 67, 2, 12, 26, 21, 18, 46, 26, 55, 18, 79, 27,
- 9, 34, 29, 43, 43, 34, 59, 43, 77, 34, 9, 59, 31, 50, 39,
- 59, 62, 51, 69, 59, 0, 2, 25, 10, 34, 2, 59, 10, 68, 2,
- 13, 26, 22, 18, 47, 26, 56, 18, 78, 27, 10, 34, 28, 43, 44,
- 34, 58, 43, 78, 34, 8, 59, 31, 51, 38, 59, 61, 51, 68, 59,
- 1, 2, 26, 10, 35, 2, 60, 10, 69, 2, 14, 26, 23, 18, 47,
- 27, 57, 18, 77, 27, 11, 34, 27, 43, 45, 34, 57, 43, 79, 34,
- 7, 59, 30, 51, 37, 59, 60, 51, 67, 59, 2, 2, 27, 10, 36,
- 2, 61, 10, 70, 2, 15, 26, 24, 18, 46, 27, 58, 18, 76, 27,
- 12, 34, 26, 43, 46, 34, 56, 43, 79, 35, 6, 59, 29, 51, 36,
- 59, 59, 51, 66, 59, 3, 2, 28, 10, 37, 2, 62, 10, 71, 2,
- 15, 27, 25, 18, 45, 27, 59, 18, 75, 27, 13, 34, 25, 43, 47,
- 34, 55, 43, 78, 35, 5, 59, 28, 51, 35, 59, 58, 51, 65, 59,
- 4, 2, 29, 10, 38, 2, 63, 10, 72, 2, 14, 27, 26, 18, 44,
- 27, 60, 18, 74, 27, 14, 34, 24, 43, 47, 35, 54, 43, 77, 35,
- 4, 59, 27, 51, 34, 59, 57, 51, 64, 59, 5, 2, 30, 10, 39,
- 2, 63, 11, 73, 2, 13, 27, 27, 18, 43, 27, 61, 18, 73, 27,
- 15, 34, 23, 43, 46, 35, 53, 43, 76, 35, 3, 59, 26, 51, 33,
- 59, 56, 51, 64, 60, 6, 2, 31, 10, 40, 2, 62, 11, 74, 2,
- 12, 27, 28, 18, 42, 27, 62, 18, 72, 27, 15, 35, 22, 43, 45,
- 35, 52, 43, 75, 35, 2, 59, 25, 51, 32, 59, 55, 51, 65, 60,
- 7, 2, 31, 11, 41, 2, 61, 11, 75, 2, 11, 27, 29, 18, 41,
- 27, 63, 18, 71, 27, 14, 35, 21, 43, 44, 35, 51, 43, 74, 35,
- 1, 59, 24, 51, 32, 60, 54, 51, 66, 60, 8, 2, 30, 11, 42,
- 2, 60, 11, 76, 2, 10, 27, 30, 18, 40, 27, 63, 19, 70, 27,
- 13, 35, 20, 43, 43, 35, 50, 43, 73, 35, 0, 59, 23, 51, 33,
- 60, 53, 51, 67, 60, 9, 2, 29, 11, 43, 2, 59, 11, 77, 2,
- 9, 27, 31, 18, 39, 27, 62, 19, 69, 27, 12, 35, 19, 43, 42,
- 35, 49, 43, 72, 35, 0, 60, 22, 51, 34, 60, 52, 51, 68, 60,
- 10, 2, 28, 11, 44, 2, 58, 11, 78, 2, 8, 27, 31, 19, 38,
- 27, 61, 19, 68, 27, 11, 35, 18, 43, 41, 35, 48, 43, 71, 35,
- 1, 60, 21, 51, 35, 60, 51, 51, 69, 60, 11, 2, 27, 11, 45,
- 2, 57, 11, 79, 2, 7, 27, 30, 19, 37, 27, 60, 19, 67, 27,
- 10, 35, 17, 43, 40, 35, 48, 44, 70, 35, 2, 60, 20, 51, 36,
- 60, 50, 51, 70, 60, 12, 2, 26, 11, 46, 2, 56, 11, 79, 3,
- 6, 27, 29, 19, 36, 27, 59, 19, 66, 27, 9, 35, 16, 43, 39,
- 35, 49, 44, 69, 35, 3, 60, 19, 51, 37, 60, 49, 51, 71, 60,
- 13, 2, 25, 11, 47, 2, 55, 11, 78, 3, 5, 27, 28, 19, 35,
- 27, 58, 19, 65, 27, 8, 35, 16, 44, 38, 35, 50, 44, 68, 35,
- 4, 60, 18, 51, 38, 60, 48, 51, 72, 60, 14, 2, 24, 11, 47,
- 3, 54, 11, 77, 3, 4, 27, 27, 19, 34, 27, 57, 19, 64, 27,
- 7, 35, 17, 44, 37, 35, 51, 44, 67, 35, 5, 60, 17, 51, 39,
- 60, 48, 52, 73, 60, 15, 2, 23, 11, 46, 3, 53, 11, 76, 3,
- 3, 27, 26, 19, 33, 27, 56, 19, 64, 28, 6, 35, 18, 44, 36,
- 35, 52, 44, 66, 35, 6, 60, 16, 51, 40, 60, 49, 52, 74, 60,
- 15, 3, 22, 11, 45, 3, 52, 11, 75, 3, 2, 27, 25, 19, 32,
- 27, 55, 19, 65, 28, 5, 35, 19, 44, 35, 35, 53, 44, 65, 35,
- 7, 60, 16, 52, 41, 60, 50, 52, 75, 60, 14, 3, 21, 11, 44,
- 3, 51, 11, 74, 3, 1, 27, 24, 19, 32, 28, 54, 19, 66, 28,
- 4, 35, 20, 44, 34, 35, 54, 44, 64, 35, 8, 60, 17, 52, 42,
- 60, 51, 52, 76, 60, 13, 3, 20, 11, 43, 3, 50, 11, 73, 3,
- 0, 27, 23, 19, 33, 28, 53, 19, 67, 28, 3, 35, 21, 44, 33,
- 35, 55, 44, 64, 36, 9, 60, 18, 52, 43, 60, 52, 52, 77, 60,
- 12, 3, 19, 11, 42, 3, 49, 11, 72, 3, 0, 28, 22, 19, 34,
- 28, 52, 19, 68, 28, 2, 35, 22, 44, 32, 35, 56, 44, 65, 36,
- 10, 60, 19, 52, 44, 60, 53, 52, 78, 60, 11, 3, 18, 11, 41,
- 3, 48, 11, 71, 3, 1, 28, 21, 19, 35, 28, 51, 19, 69, 28,
- 1, 35, 23, 44, 32, 36, 57, 44, 66, 36, 11, 60, 20, 52, 45,
- 60, 54, 52, 79, 60, 10, 3, 17, 11, 40, 3, 48, 12, 70, 3,
- 2, 28, 20, 19, 36, 28, 50, 19, 70, 28, 0, 35, 24, 44, 33,
- 36, 58, 44, 67, 36, 12, 60, 21, 52, 46, 60, 55, 52, 79, 61,
- 9, 3, 16, 11, 39, 3, 49, 12, 69, 3, 3, 28, 19, 19, 37,
- 28, 49, 19, 71, 28, 0, 36, 25, 44, 34, 36, 59, 44, 68, 36,
- 13, 60, 22, 52, 47, 60, 56, 52, 78, 61, 8, 3, 16, 12, 38,
- 3, 50, 12, 68, 3, 4, 28, 18, 19, 38, 28, 48, 19, 72, 28,
- 1, 36, 26, 44, 35, 36, 60, 44, 69, 36, 14, 60, 23, 52, 47,
- 61, 57, 52, 77, 61, 7, 3, 17, 12, 37, 3, 51, 12, 67, 3,
- 5, 28, 17, 19, 39, 28, 48, 20, 73, 28, 2, 36, 27, 44, 36,
- 36, 61, 44, 70, 36, 15, 60, 24, 52, 46, 61, 58, 52, 76, 61,
- 6, 3, 18, 12, 36, 3, 52, 12, 66, 3, 6, 28, 16, 19, 40,
- 28, 49, 20, 74, 28, 3, 36, 28, 44, 37, 36, 62, 44, 71, 36,
- 15, 61, 25, 52, 45, 61, 59, 52, 75, 61, 5, 3, 19, 12, 35,
- 3, 53, 12, 65, 3, 7, 28, 16, 20, 41, 28, 50, 20, 75, 28,
- 4, 36, 29, 44, 38, 36, 63, 44, 72, 36, 14, 61, 26, 52, 44,
- 61, 60, 52, 74, 61, 4, 3, 20, 12, 34, 3, 54, 12, 64, 3,
- 8, 28, 17, 20, 42, 28, 51, 20, 76, 28, 5, 36, 30, 44, 39,
- 36, 63, 45, 73, 36, 13, 61, 27, 52, 43, 61, 61, 52, 73, 61,
- 3, 3, 21, 12, 33, 3, 55, 12, 64, 4, 9, 28, 18, 20, 43,
- 28, 52, 20, 77, 28, 6, 36, 31, 44, 40, 36, 62, 45, 74, 36,
- 12, 61, 28, 52, 42, 61, 62, 52, 72, 61, 2, 3, 22, 12, 32,
- 3, 56, 12, 65, 4, 10, 28, 19, 20, 44, 28, 53, 20, 78, 28,
- 7, 36, 31, 45, 41, 36, 61, 45, 75, 36, 11, 61, 29, 52, 41,
- 61, 63, 52, 71, 61, 1, 3, 23, 12, 32, 4, 57, 12, 66, 4,
- 11, 28, 20, 20, 45, 28, 54, 20, 79, 28, 8, 36, 30, 45, 42,
- 36, 60, 45, 76, 36, 10, 61, 30, 52, 40, 61, 63, 53, 70, 61,
- 0, 3, 24, 12, 33, 4, 58, 12, 67, 4, 12, 28, 21, 20, 46,
- 28, 55, 20, 79, 29, 9, 36, 29, 45, 43, 36, 59, 45, 77, 36,
- 9, 61, 31, 52, 39, 61, 62, 53, 69, 61, 0, 4, 25, 12, 34,
- 4, 59, 12, 68, 4, 13, 28, 22, 20, 47, 28, 56, 20, 78, 29,
- 10, 36, 28, 45, 44, 36, 58, 45, 78, 36, 8, 61, 31, 53, 38,
- 61, 61, 53, 68, 61, 1, 4, 26, 12, 35, 4, 60, 12, 69, 4,
- 14, 28, 23, 20, 47, 29, 57, 20, 77, 29, 11, 36, 27, 45, 45,
- 36, 57, 45, 79, 36, 7, 61, 30, 53, 37, 61, 60, 53, 67, 61,
- 2, 4, 27, 12, 36, 4, 61, 12, 70, 4, 15, 28, 24, 20, 46,
- 29, 58, 20, 76, 29, 12, 36, 26, 45, 46, 36, 56, 45, 79, 37,
- 6, 61, 29, 53, 36, 61, 59, 53, 66, 61, 3, 4, 28, 12, 37,
- 4, 62, 12, 71, 4, 15, 29, 25, 20, 45, 29, 59, 20, 75, 29,
- 13, 36, 25, 45, 47, 36, 55, 45, 78, 37, 5, 61, 28, 53, 35,
- 61, 58, 53, 65, 61, 4, 4, 29, 12, 38, 4, 63, 12, 72, 4,
- 14, 29, 26, 20, 44, 29, 60, 20, 74, 29, 14, 36, 24, 45, 47,
- 37, 54, 45, 77, 37, 4, 61, 27, 53, 34, 61, 57, 53, 64, 61,
- 5, 4, 30, 12, 39, 4, 63, 13, 73, 4, 13, 29, 27, 20, 43,
- 29, 61, 20, 73, 29, 15, 36, 23, 45, 46, 37, 53, 45, 76, 37,
- 3, 61, 26, 53, 33, 61, 56, 53, 64, 62, 6, 4, 31, 12, 40,
- 4, 62, 13, 74, 4, 12, 29, 28, 20, 42, 29, 62, 20, 72, 29,
- 15, 37, 22, 45, 45, 37, 52, 45, 75, 37, 2, 61, 25, 53, 32,
- 61, 55, 53, 65, 62, 7, 4, 31, 13, 41, 4, 61, 13, 75, 4,
- 11, 29, 29, 20, 41, 29, 63, 20, 71, 29, 14, 37, 21, 45, 44,
- 37, 51, 45, 74, 37, 1, 61, 24, 53, 32, 62, 54, 53, 66, 62,
- 8, 4, 30, 13, 42, 4, 60, 13, 76, 4, 10, 29, 30, 20, 40,
- 29, 63, 21, 70, 29, 13, 37, 20, 45, 43, 37, 50, 45, 73, 37,
- 0, 61, 23, 53, 33, 62, 53, 53, 67, 62, 9, 4, 29, 13, 43,
- 4, 59, 13, 77, 4, 9, 29, 31, 20, 39, 29, 62, 21, 69, 29,
- 12, 37, 19, 45, 42, 37, 49, 45, 72, 37, 0, 62, 22, 53, 34,
- 62, 52, 53, 68, 62, 10, 4, 28, 13, 44, 4, 58, 13, 78, 4,
- 8, 29, 31, 21, 38, 29, 61, 21, 68, 29, 11, 37, 18, 45, 41,
- 37, 48, 45, 71, 37, 1, 62, 21, 53, 35, 62, 51, 53, 69, 62,
- 11, 4, 27, 13, 45, 4, 57, 13, 79, 4, 7, 29, 30, 21, 37,
- 29, 60, 21, 67, 29, 10, 37, 17, 45, 40, 37, 48, 46, 70, 37,
- 2, 62, 20, 53, 36, 62, 50, 53, 70, 62, 12, 4, 26, 13, 46,
- 4, 56, 13, 79, 5, 6, 29, 29, 21, 36, 29, 59, 21, 66, 29,
- 9, 37, 16, 45, 39, 37, 49, 46, 69, 37, 3, 62, 19, 53, 37,
- 62, 49, 53, 71, 62, 13, 4, 25, 13, 47, 4, 55, 13, 78, 5,
- 5, 29, 28, 21, 35, 29, 58, 21, 65, 29, 8, 37, 16, 46, 38,
- 37, 50, 46, 68, 37, 4, 62, 18, 53, 38, 62, 48, 53, 72, 62,
- 14, 4, 24, 13, 47, 5, 54, 13, 77, 5, 4, 29, 27, 21, 34,
- 29, 57, 21, 64, 29, 7, 37, 17, 46, 37, 37, 51, 46, 67, 37,
- 5, 62, 17, 53, 39, 62, 48, 54, 73, 62, 15, 4, 23, 13, 46,
- 5, 53, 13, 76, 5, 3, 29, 26, 21, 33, 29, 56, 21, 64, 30,
- 6, 37, 18, 46, 36, 37, 52, 46, 66, 37, 6, 62, 16, 53, 40,
- 62, 49, 54, 74, 62, 15, 5, 22, 13, 45, 5, 52, 13, 75, 5,
- 2, 29, 25, 21, 32, 29, 55, 21, 65, 30, 5, 37, 19, 46, 35,
- 37, 53, 46, 65, 37, 7, 62, 16, 54, 41, 62, 50, 54, 75, 62,
- 14, 5, 21, 13, 44, 5, 51, 13, 74, 5, 1, 29, 24, 21, 32,
- 30, 54, 21, 66, 30, 4, 37, 20, 46, 34, 37, 54, 46, 64, 37,
- 8, 62, 17, 54, 42, 62, 51, 54, 76, 62, 13, 5, 20, 13, 43,
- 5, 50, 13, 73, 5, 0, 29, 23, 21, 33, 30, 53, 21, 67, 30,
- 3, 37, 21, 46, 33, 37, 55, 46, 64, 38, 9, 62, 18, 54, 43,
- 62, 52, 54, 77, 62, 12, 5, 19, 13, 42, 5, 49, 13, 72, 5,
- 0, 30, 22, 21, 34, 30, 52, 21, 68, 30, 2, 37, 22, 46, 32,
- 37, 56, 46, 65, 38, 10, 62, 19, 54, 44, 62, 53, 54, 78, 62,
- 11, 5, 18, 13, 41, 5, 48, 13, 71, 5, 1, 30, 21, 21, 35,
- 30, 51, 21, 69, 30, 1, 37, 23, 46, 32, 38, 57, 46, 66, 38,
- 11, 62, 20, 54, 45, 62, 54, 54, 79, 62, 10, 5, 17, 13, 40,
- 5, 48, 14, 70, 5, 2, 30, 20, 21, 36, 30, 50, 21, 70, 30,
- 0, 37, 24, 46, 33, 38, 58, 46, 67, 38, 12, 62, 21, 54, 46,
- 62, 55, 54, 79, 63, 9, 5, 16, 13, 39, 5, 49, 14, 69, 5,
- 3, 30, 19, 21, 37, 30, 49, 21, 71, 30, 0, 38, 25, 46, 34,
- 38, 59, 46, 68, 38, 13, 62, 22, 54, 47, 62, 56, 54, 78, 63,
- 8, 5, 16, 14, 38, 5, 50, 14, 68, 5, 4, 30, 18, 21, 38,
- 30, 48, 21, 72, 30, 1, 38, 26, 46, 35, 38, 60, 46, 69, 38,
- 14, 62, 23, 54, 47, 63, 57, 54, 77, 63, 7, 5, 17, 14, 37,
- 5, 51, 14, 67, 5, 5, 30, 17, 21, 39, 30, 48, 22, 73, 30,
- 2, 38, 27, 46, 36, 38, 61, 46, 70, 38, 15, 62, 24, 54, 46,
- 63, 58, 54, 76, 63, 6, 5, 18, 14, 36, 5, 52, 14, 66, 5,
- 6, 30, 16, 21, 40, 30, 49, 22, 74, 30, 3, 38, 28, 46, 37,
- 38, 62, 46, 71, 38, 15, 63, 25, 54, 45, 63, 59, 54, 75, 63,
- 5, 5, 19, 14, 35, 5, 53, 14, 65, 5, 7, 30, 16, 22, 41,
- 30, 50, 22, 75, 30, 4, 38, 29, 46, 38, 38, 63, 46, 72, 38,
- 14, 63, 26, 54, 44, 63, 60, 54, 74, 63, 4, 5, 20, 14, 34,
- 5, 54, 14, 64, 5, 8, 30, 17, 22, 42, 30, 51, 22, 76, 30,
- 5, 38, 30, 46, 39, 38, 63, 47, 73, 38, 13, 63, 27, 54, 43,
- 63, 61, 54, 73, 63, 3, 5, 21, 14, 33, 5, 55, 14, 64, 6,
- 9, 30, 18, 22, 43, 30, 52, 22, 77, 30, 6, 38, 31, 46, 40,
- 38, 62, 47, 74, 38, 12, 63, 28, 54, 42, 63, 62, 54, 72, 63,
- 2, 5, 22, 14, 32, 5, 56, 14, 65, 6, 10, 30, 19, 22, 44,
- 30, 53, 22, 78, 30, 7, 38, 31, 47, 41, 38, 61, 47, 75, 38,
- 11, 63, 29, 54, 41, 63, 63, 54, 71, 63, 1, 5, 23, 14, 32,
- 6, 57, 14, 66, 6, 11, 30, 20, 22, 45, 30, 54, 22, 79, 30,
- 8, 38, 30, 47, 42, 38, 60, 47, 76, 38, 10, 63, 30, 54, 40,
- 63, 63, 55, 70, 63, 0, 5, 24, 14, 33, 6, 58, 14, 67, 6,
- 12, 30, 21, 22, 46, 30, 55, 22, 79, 31, 9, 38, 29, 47, 43,
- 38, 59, 47, 77, 38, 9, 63, 31, 54, 39, 63, 62, 55, 69, 63,
- 0, 6, 25, 14, 34, 6, 59, 14, 68, 6, 13, 30, 22, 22, 47,
- 30, 56, 22, 78, 31, 10, 38, 28, 47, 44, 38, 58, 47, 78, 38,
- 8, 63, 31, 55, 38, 63, 61, 55, 68, 63, 1, 6, 26, 14, 35,
- 6, 60, 14, 69, 6, 14, 30, 23, 22, 47, 31, 57, 22, 77, 31,
- 11, 38, 27, 47, 45, 38, 57, 47, 79, 38, 7, 63, 30, 55, 37,
- 63, 60, 55, 67, 63, 2, 6, 27, 14, 36, 6, 61, 14, 70, 6,
- 15, 30, 24, 22, 46, 31, 58, 22, 76, 31, 12, 38, 26, 47, 46,
- 38, 56, 47, 79, 39, 6, 63, 29, 55, 36, 63, 59, 55, 66, 63,
- 3, 6, 28, 14, 37, 6, 62, 14, 71, 6, 15, 31, 25, 22, 45,
- 31, 59, 22, 75, 31, 13, 38, 25, 47, 47, 38, 55, 47, 78, 39,
- 5, 63, 28, 55, 35, 63, 58, 55, 65, 63, 4, 6, 29, 14, 38,
- 6, 63, 14, 72, 6, 14, 31, 26, 22, 44, 31, 60, 22, 74, 31,
- 14, 38, 24, 47, 47, 39, 54, 47, 77, 39, 4, 63, 27, 55, 34,
- 63, 57, 55, 64, 63, 5, 6, 30, 14, 39, 6, 63, 15, 73, 6,
- 13, 31, 27, 22, 43, 31, 61, 22, 73, 31, 15, 38, 23, 47, 46,
- 39, 53, 47, 76, 39, 3, 63, 26, 55, 33, 63, 56, 55, 64, 48,
- 6, 6, 31, 14, 40, 6, 62, 15, 74, 6, 12, 31, 28, 22, 42,
- 31, 62, 22, 72, 31, 15, 39, 22, 47, 45, 39, 52, 47, 75, 39,
- 2, 63, 25, 55, 32, 63, 55, 55, 65, 48, 7, 6, 31, 15, 41,
- 6, 61, 15, 75, 6, 11, 31, 29, 22, 41, 31, 63, 22, 71, 31,
- 14, 39, 21, 47, 44, 39, 51, 47, 74, 39, 1, 63, 24, 55, 32,
- 48, 54, 55, 66, 48, 8, 6, 30, 15, 42, 6, 60, 15, 76, 6,
- 10, 31, 30, 22, 40, 31, 63, 23, 70, 31, 13, 39, 20, 47, 43,
- 39, 50, 47, 73, 39, 0, 63, 23, 55, 33, 48, 53, 55, 67, 48,
- 9, 6, 29, 15, 43, 6, 59, 15, 77, 6, 9, 31, 31, 22, 39,
- 31, 62, 23, 69, 31, 12, 39, 19, 47, 42, 39, 49, 47, 72, 39,
- 0, 48, 22, 55, 34, 48, 52, 55, 68, 48, 10, 6, 28, 15, 44,
- 6, 58, 15, 78, 6, 8, 31, 31, 23, 38, 31, 61, 23, 68, 31,
- 11, 39, 18, 47, 41, 39, 48, 47, 71, 39, 1, 48, 21, 55, 35,
- 48, 51, 55, 69, 48, 11, 6, 27, 15, 45, 6, 57, 15, 79, 6,
- 7, 31, 30, 23, 37, 31, 60, 23, 67, 31, 10, 39, 17, 47, 40,
- 39, 48, 32, 70, 39, 2, 48, 20, 55, 36, 48, 50, 55, 70, 48,
- 12, 6, 26, 15, 46, 6, 56, 15, 79, 7, 6, 31, 29, 23, 36,
- 31, 59, 23, 66, 31, 9, 39, 16, 47, 39, 39, 49, 32, 69, 39,
- 3, 48, 19, 55, 37, 48, 49, 55, 71, 48, 13, 6, 25, 15, 47,
- 6, 55, 15, 78, 7, 5, 31, 28, 23, 35, 31, 58, 23, 65, 31,
- 8, 39, 16, 32, 38, 39, 50, 32, 68, 39, 4, 48, 18, 55, 38,
- 48, 48, 55, 72, 48, 14, 6, 24, 15, 47, 7, 54, 15, 77, 7,
- 4, 31, 27, 23, 34, 31, 57, 23, 64, 31, 7, 39, 17, 32, 37,
- 39, 51, 32, 67, 39, 5, 48, 17, 55, 39, 48, 48, 56, 73, 48,
- 15, 6, 23, 15, 46, 7, 53, 15, 76, 7, 3, 31, 26, 23, 33,
- 31, 56, 23, 64, 16, 6, 39, 18, 32, 36, 39, 52, 32, 66, 39,
- 6, 48, 16, 55, 40, 48, 49, 56, 74, 48, 15, 7, 22, 15, 45,
- 7, 52, 15, 75, 7, 2, 31, 25, 23, 32, 31, 55, 23, 65, 16,
- 5, 39, 19, 32, 35, 39, 53, 32, 65, 39, 7, 48, 16, 56, 41,
- 48, 50, 56, 75, 48, 14, 7, 21, 15, 44, 7, 51, 15, 74, 7,
- 1, 31, 24, 23, 32, 16, 54, 23, 66, 16, 4, 39, 20, 32, 34,
- 39, 54, 32, 64, 39, 8, 48, 17, 56, 42, 48, 51, 56, 76, 48,
- 13, 7, 20, 15, 43, 7, 50, 15, 73, 7, 0, 31, 23, 23, 33,
- 16, 53, 23, 67, 16, 3, 39, 21, 32, 33, 39, 55, 32, 64, 40,
- 9, 48, 18, 56, 43, 48, 52, 56, 77, 48, 12, 7, 19, 15, 42,
- 7, 49, 15, 72, 7, 0, 16, 22, 23, 34, 16, 52, 23, 68, 16,
- 2, 39, 22, 32, 32, 39, 56, 32, 65, 40, 10, 48, 19, 56, 44,
- 48, 53, 56, 78, 48, 11, 7, 18, 15, 41, 7, 48, 15, 71, 7,
- 1, 16, 21, 23, 35, 16, 51, 23, 69, 16, 1, 39, 23, 32, 32,
- 40, 57, 32, 66, 40, 11, 48, 20, 56, 45, 48, 54, 56, 79, 48,
- 10, 7, 17, 15, 40, 7, 48, 0, 70, 7, 2, 16, 20, 23, 36,
- 16, 50, 23, 70, 16, 0, 39, 24, 32, 33, 40, 58, 32, 67, 40,
- 12, 48, 21, 56, 46, 48, 55, 56, 79, 49, 9, 7, 16, 15, 39,
- 7, 49, 0, 69, 7, 3, 16, 19, 23, 37, 16, 49, 23, 71, 16,
- 0, 40, 25, 32, 34, 40, 59, 32, 68, 40, 13, 48, 22, 56, 47,
- 48, 56, 56, 78, 49, 8, 7, 16, 0, 38, 7, 50, 0, 68, 7,
- 4, 16, 18, 23, 38, 16, 48, 23, 72, 16, 1, 40, 26, 32, 35,
- 40, 60, 32, 69, 40, 14, 48, 23, 56, 47, 49, 57, 56, 77, 49,
- 7, 7, 17, 0, 37, 7, 51, 0, 67, 7, 5, 16, 17, 23, 39,
- 16, 48, 24, 73, 16, 2, 40, 27, 32, 36, 40, 61, 32, 70, 40,
- 15, 48, 24, 56, 46, 49, 58, 56, 76, 49, 6, 7, 18, 0, 36,
- 7, 52, 0, 66, 7, 6, 16, 16, 23, 40, 16, 49, 24, 74, 16,
- 3, 40, 28, 32, 37, 40, 62, 32, 71, 40, 15, 49, 25, 56, 45,
- 49, 59, 56, 75, 49, 5, 7, 19, 0, 35, 7, 53, 0, 65, 7,
- 7, 16, 16, 24, 41, 16, 50, 24, 75, 16, 4, 40, 29, 32, 38,
- 40, 63, 32, 72, 40, 14, 49, 26, 56, 44, 49, 60, 56, 74, 49,
- 4, 7, 20, 0, 34, 7, 54, 0, 64, 7, 8, 16, 17, 24, 42,
- 16, 51, 24, 76, 16, 5, 40, 30, 32, 39, 40, 63, 33, 73, 40,
- 13, 49, 27, 56, 43, 49, 61, 56, 73, 49, 3, 7, 21, 0, 33,
- 7, 55, 0, 64, 8, 9, 16, 18, 24, 43, 16, 52, 24, 77, 16,
- 6, 40, 31, 32, 40, 40, 62, 33, 74, 40, 12, 49, 28, 56, 42,
- 49, 62, 56, 72, 49, 2, 7, 22, 0, 32, 7, 56, 0, 65, 8,
- 10, 16, 19, 24, 44, 16, 53, 24, 78, 16, 7, 40, 31, 33, 41,
- 40, 61, 33, 75, 40, 11, 49, 29, 56, 41, 49, 63, 56, 71, 49,
- 1, 7, 23, 0, 32, 8, 57, 0, 66, 8, 11, 16, 20, 24, 45,
- 16, 54, 24, 79, 16, 8, 40, 30, 33, 42, 40, 60, 33, 76, 40,
- 10, 49, 30, 56, 40, 49, 63, 57, 70, 49, 0, 7, 24, 0, 33,
- 8, 58, 0, 67, 8, 12, 16, 21, 24, 46, 16, 55, 24, 79, 17,
- 9, 40, 29, 33, 43, 40, 59, 33, 77, 40, 9, 49, 31, 56, 39,
- 49, 62, 57, 69, 49, 0, 8, 25, 0, 34, 8, 59, 0, 68, 8,
- 13, 16, 22, 24, 47, 16, 56, 24, 78, 17, 10, 40, 28, 33, 44,
- 40, 58, 33, 78, 40, 8, 49, 31, 57, 38, 49, 61, 57, 68, 49,
- 1, 8, 26, 0, 35, 8, 60, 0, 69, 8, 14, 16, 23, 24, 47,
- 17, 57, 24, 77, 17, 11, 40, 27, 33, 45, 40, 57, 33, 79, 40,
- 7, 49, 30, 57, 37, 49, 60, 57, 67, 49, 2, 8, 27, 0, 36,
- 8, 61, 0, 70, 8, 15, 16, 24, 24, 46, 17, 58, 24, 76, 17,
- 12, 40, 26, 33, 46, 40, 56, 33, 79, 41, 6, 49, 29, 57, 36,
- 49, 59, 57, 66, 49, 3, 8, 28, 0, 37, 8, 62, 0, 71, 8,
- 15, 17, 25, 24, 45, 17, 59, 24, 75, 17, 13, 40, 25, 33, 47,
- 40, 55, 33, 78, 41, 5, 49, 28, 57, 35, 49, 58, 57, 65, 49,
- 4, 8, 29, 0, 38, 8, 63, 0, 72, 8, 14, 17, 26, 24, 44,
- 17, 60, 24, 74, 17, 14, 40, 24, 33, 47, 41, 54, 33, 77, 41,
- 4, 49, 27, 57, 34, 49, 57, 57, 64, 49, 5, 8, 30, 0, 39,
- 8, 63, 1, 73, 8, 13, 17, 27, 24, 43, 17, 61, 24, 73, 17,
- 15, 40, 23, 33, 46, 41, 53, 33, 76, 41, 3, 49, 26, 57, 33,
- 49, 56, 57, 64, 50, 6, 8, 31, 0, 40, 8, 62, 1, 74, 8,
- 12, 17, 28, 24, 42, 17, 62, 24, 72, 17, 15, 41, 22, 33, 45,
- 41, 52, 33, 75, 41, 2, 49, 25, 57, 32, 49, 55, 57, 65, 50,
- 7, 8, 31, 1, 41, 8, 61, 1, 75, 8, 11, 17, 29, 24, 41,
- 17, 63, 24, 71, 17, 14, 41, 21, 33, 44, 41, 51, 33, 74, 41,
- 1, 49, 24, 57, 32, 50, 54, 57, 66, 50, 8, 8, 30, 1, 42,
- 8, 60, 1, 76, 8, 10, 17, 30, 24, 40, 17, 63, 25, 70, 17,
- 13, 41, 20, 33, 43, 41, 50, 33, 73, 41, 0, 49, 23, 57, 33,
- 50, 53, 57, 67, 50, 9, 8, 29, 1, 43, 8, 59, 1, 77, 8,
- 9, 17, 31, 24, 39, 17, 62, 25, 69, 17, 12, 41, 19, 33, 42,
- 41, 49, 33, 72, 41, 0, 50, 22, 57, 34, 50, 52, 57, 68, 50,
- 10, 8, 28, 1, 44, 8, 58, 1, 78, 8, 8, 17, 31, 25, 38,
- 17, 61, 25, 68, 17, 11, 41, 18, 33, 41, 41, 48, 33, 71, 41,
- 1, 50, 21, 57, 35, 50, 51, 57, 69, 50, 11, 8, 27, 1, 45,
- 8, 57, 1, 79, 8, 7, 17, 30, 25, 37, 17, 60, 25, 67, 17,
- 10, 41, 17, 33, 40, 41, 48, 34, 70, 41, 2, 50, 20, 57, 36,
- 50, 50, 57, 70, 50, 12, 8, 26, 1, 46, 8, 56, 1, 79, 9,
- 6, 17, 29, 25, 36, 17, 59, 25, 66, 17, 9, 41, 16, 33, 39,
- 41, 49, 34, 69, 41, 3, 50, 19, 57, 37, 50, 49, 57, 71, 50,
- 13, 8, 25, 1, 47, 8, 55, 1, 78, 9, 5, 17, 28, 25, 35,
- 17, 58, 25, 65, 17, 8, 41, 16, 34, 38, 41, 50, 34, 68, 41,
- 4, 50, 18, 57, 38, 50, 48, 57, 72, 50, 14, 8, 24, 1, 47,
- 9, 54, 1, 77, 9, 4, 17, 27, 25, 34, 17, 57, 25, 64, 17,
- 7, 41, 17, 34, 37, 41, 51, 34, 67, 41, 5, 50, 17, 57, 39,
- 50, 48, 58, 73, 50, 15, 8, 23, 1, 46, 9, 53, 1, 76, 9,
- 3, 17, 26, 25, 33, 17, 56, 25, 64, 18, 6, 41, 18, 34, 36,
- 41, 52, 34, 66, 41, 6, 50, 16, 57, 40, 50, 49, 58, 74, 50,
- 15, 9, 22, 1, 45, 9, 52, 1, 75, 9, 2, 17, 25, 25, 32,
- 17, 55, 25, 65, 18, 5, 41, 19, 34, 35, 41, 53, 34, 65, 41,
- 7, 50, 16, 58, 41, 50, 50, 58, 75, 50, 14, 9, 21, 1, 44,
- 9, 51, 1, 74, 9, 1, 17, 24, 25, 32, 18, 54, 25, 66, 18,
- 4, 41, 20, 34, 34, 41, 54, 34, 64, 41, 8, 50, 17, 58, 42,
- 50, 51, 58, 76, 50, 13, 9, 20, 1, 43, 9, 50, 1, 73, 9,
- 0, 17, 23, 25, 33, 18, 53, 25, 67, 18, 3, 41, 21, 34, 33,
- 41, 55, 34, 64, 42, 9, 50, 18, 58, 43, 50, 52, 58, 77, 50,
- 12, 9, 19, 1, 42, 9, 49, 1, 72, 9, 0, 18, 22, 25, 34,
- 18, 52, 25, 68, 18, 2, 41, 22, 34, 32, 41, 56, 34, 65, 42,
- 10, 50, 19, 58, 44, 50, 53, 58, 78, 50, 11, 9, 18, 1, 41,
- 9, 48, 1, 71, 9, 1, 18, 21, 25, 35, 18, 51, 25, 69, 18,
- 1, 41, 23, 34, 32, 42, 57, 34, 66, 42, 11, 50, 20, 58, 45,
- 50, 54, 58, 79, 50, 10, 9, 17, 1, 40, 9, 48, 2, 70, 9,
- 2, 18, 20, 25, 36, 18, 50, 25, 70, 18, 0, 41, 24, 34, 33,
- 42, 58, 34, 67, 42, 12, 50, 21, 58, 46, 50, 55, 58, 79, 51,
- 9, 9, 16, 1, 39, 9, 49, 2, 69, 9, 3, 18, 19, 25, 37,
- 18, 49, 25, 71, 18, 0, 42, 25, 34, 34, 42, 59, 34, 68, 42,
- 13, 50, 22, 58, 47, 50, 56, 58, 78, 51, 8, 9, 16, 2, 38,
- 9, 50, 2, 68, 9, 4, 18, 18, 25, 38, 18, 48, 25, 72, 18,
- 1, 42, 26, 34, 35, 42, 60, 34, 69, 42, 14, 50, 23, 58, 47,
- 51, 57, 58, 77, 51, 7, 9, 17, 2, 37, 9, 51, 2, 67, 9,
- 5, 18, 17, 25, 39, 18, 48, 26, 73, 18, 2, 42, 27, 34, 36,
- 42, 61, 34, 70, 42, 15, 50, 24, 58, 46, 51, 58, 58, 76, 51,
- 6, 9, 18, 2, 36, 9, 52, 2, 66, 9, 6, 18, 16, 25, 40,
- 18, 49, 26, 74, 18, 3, 42, 28, 34, 37, 42, 62, 34, 71, 42,
- 15, 51, 25, 58, 45, 51, 59, 58, 75, 51, 5, 9, 19, 2, 35,
- 9, 53, 2, 65, 9, 7, 18, 16, 26, 41, 18, 50, 26, 75, 18,
- 4, 42, 29, 34, 38, 42, 63, 34, 72, 42, 14, 51, 26, 58, 44,
- 51, 60, 58, 74, 51, 4, 9, 20, 2, 34, 9, 54, 2, 64, 9,
- 8, 18, 17, 26, 42, 18, 51, 26, 76, 18, 5, 42, 30, 34, 39,
- 42, 63, 35, 73, 42, 13, 51, 27, 58, 43, 51, 61, 58, 73, 51,
- 3, 9, 21, 2, 33, 9, 55, 2, 64, 10, 9, 18, 18, 26, 43,
- 18, 52, 26, 77, 18, 6, 42, 31, 34, 40, 42, 62, 35, 74, 42,
- 12, 51, 28, 58, 42, 51, 62, 58, 72, 51, 2, 9, 22, 2, 32,
- 9, 56, 2, 65, 10, 10, 18, 19, 26, 44, 18, 53, 26, 78, 18,
- 7, 42, 31, 35, 41, 42, 61, 35, 75, 42, 11, 51, 29, 58, 41,
- 51, 63, 58, 71, 51, 1, 9, 23, 2, 32, 10, 57, 2, 66, 10,
- 11, 18, 20, 26, 45, 18, 54, 26, 79, 18, 8, 42, 30, 35, 42,
- 42, 60, 35, 76, 42, 10, 51, 30, 58, 40, 51, 63, 59, 70, 51,
- 0, 9, 24, 2, 33, 10, 58, 2, 67, 10, 12, 18, 21, 26, 46,
- 18, 55, 26, 79, 19, 9, 42, 29, 35, 43, 42, 59, 35, 77, 42,
- 9, 51, 31, 58, 39, 51, 62, 59, 69, 51, 0, 10, 25, 2, 34,
- 10, 59, 2, 68, 10, 13, 18, 22, 26, 47, 18, 56, 26, 78, 19,
- 10, 42, 28, 35, 44, 42, 58, 35, 78, 42, 8, 51, 31, 59, 38,
- 51, 61, 59, 68, 51, 1, 10, 26, 2, 35, 10, 60, 2, 69, 10,
- 14, 18, 23, 26, 47, 19, 57, 26, 77, 19, 11, 42, 27, 35, 45,
- 42, 57, 35, 79, 42, 7, 51, 30, 59, 37, 51, 60, 59, 67, 51,
- 2, 10, 27, 2, 36, 10, 61, 2, 70, 10, 15, 18, 24, 26, 46,
- 19, 58, 26, 76, 19, 12, 42, 26, 35, 46, 42, 56, 35, 79, 43,
- 6, 51, 29, 59, 36, 51, 59, 59, 66, 51, 3, 10, 28, 2, 37,
- 10, 62, 2, 71, 10, 15, 19, 25, 26, 45, 19, 59, 26, 75, 19,
- 13, 42, 25, 35, 47, 42, 55, 35, 78, 43, 5, 51, 28, 59, 35,
- 51, 58, 59, 65, 51, 4, 10, 29, 2, 38, 10, 63, 2, 72, 10,
- 14, 19, 26, 26, 44, 19, 60, 26, 74, 19, 14, 42, 24, 35, 47,
- 43, 54, 35, 77, 43, 4, 51, 27, 59, 34, 51, 57, 59, 64, 51,
- 5, 10, 30, 2, 39, 10, 63, 3, 73, 10, 13, 19, 27, 26, 43,
- 19, 61, 26, 73, 19, 15, 42, 23, 35, 46, 43, 53, 35, 76, 43,
- 3, 51, 26, 59, 33, 51, 56, 59, 64, 52, 6, 10, 31, 2, 40,
- 10, 62, 3, 74, 10, 12, 19, 28, 26, 42, 19, 62, 26, 72, 19,
- 15, 43, 22, 35, 45, 43, 52, 35, 75, 43, 2, 51, 25, 59, 32,
- 51, 55, 59, 65, 52, 7, 10, 31, 3, 41, 10, 61, 3, 75, 10,
- 11, 19, 29, 26, 41, 19, 63, 26, 71, 19, 14, 43, 21, 35, 44,
- 43, 51, 35, 74, 43, 1, 51, 24, 59, 32, 52, 54, 59, 66, 52,
- 8, 10, 30, 3, 42, 10, 60, 3, 76, 10, 10, 19, 30, 26, 40,
- 19, 63, 27, 70, 19, 13, 43, 20, 35, 43, 43, 50, 35, 73, 43,
- 0, 51, 23, 59, 33, 52, 53, 59, 67, 52, 9, 10, 29, 3, 43,
- 10, 59, 3, 77, 10, 9, 19, 31, 26, 39, 19, 62, 27, 69, 19,
- 12, 43, 19, 35, 42, 43, 49, 35, 72, 43, 0, 52, 22, 59, 34,
- 52, 52, 59, 68, 52, 10, 10, 28, 3, 44, 10, 58, 3, 78, 10,
- 8, 19, 31, 27, 38, 19, 61, 27, 68, 19, 11, 43, 18, 35, 41,
- 43, 48, 35, 71, 43, 1, 52, 21, 59, 35, 52, 51, 59, 69, 52,
- 11, 10, 27, 3, 45, 10, 57, 3, 79, 10, 7, 19, 30, 27, 37,
- 19, 60, 27, 67, 19, 10, 43, 17, 35, 40, 43, 48, 36, 70, 43,
- 2, 52, 20, 59, 36, 52, 50, 59, 70, 52, 12, 10, 26, 3, 46,
- 10, 56, 3, 79, 11, 6, 19, 29, 27, 36, 19, 59, 27, 66, 19,
- 9, 43, 16, 35, 39, 43, 49, 36, 69, 43, 3, 52, 19, 59, 37,
- 52, 49, 59, 71, 52, 13, 10, 25, 3, 47, 10, 55, 3, 78, 11,
- 5, 19, 28, 27, 35, 19, 58, 27, 65, 19, 8, 43, 16, 36, 38,
- 43, 50, 36, 68, 43, 4, 52, 18, 59, 38, 52, 48, 59, 72, 52,
- 14, 10, 24, 3, 47, 11, 54, 3, 77, 11, 4, 19, 27, 27, 34,
- 19, 57, 27, 64, 19, 7, 43, 17, 36, 37, 43, 51, 36, 67, 43,
- 5, 52, 17, 59, 39, 52, 48, 60, 73, 52, 15, 10, 23, 3, 46,
- 11, 53, 3, 76, 11, 3, 19, 26, 27, 33, 19, 56, 27, 64, 20,
- 6, 43, 18, 36, 36, 43, 52, 36, 66, 43, 6, 52, 16, 59, 40,
- 52, 49, 60, 74, 52, 15, 11, 22, 3, 45, 11, 52, 3, 75, 11,
- 2, 19, 25, 27, 32, 19, 55, 27, 65, 20, 5, 43, 19, 36, 35,
- 43, 53, 36, 65, 43, 7, 52, 16, 60, 41, 52, 50, 60, 75, 52,
- 14, 11, 21, 3, 44, 11, 51, 3, 74, 11, 1, 19, 24, 27, 32,
- 20, 54, 27, 66, 20, 4, 43, 20, 36, 34, 43, 54, 36, 64, 43,
- 8, 52, 17, 60, 42, 52, 51, 60, 76, 52, 13, 11, 20, 3, 43,
- 11, 50, 3, 73, 11, 0, 19, 23, 27, 33, 20, 53, 27, 67, 20,
- 3, 43, 21, 36, 33, 43, 55, 36, 64, 44, 9, 52, 18, 60, 43,
- 52, 52, 60, 77, 52, 12, 11, 19, 3, 42, 11, 49, 3, 72, 11,
- 0, 20, 22, 27, 34, 20, 52, 27, 68, 20, 2, 43, 22, 36, 32,
- 43, 56, 36, 65, 44, 10, 52, 19, 60, 44, 52, 53, 60, 78, 52,
- 11, 11, 18, 3, 41, 11, 48, 3, 71, 11, 1, 20, 21, 27, 35,
- 20, 51, 27, 69, 20, 1, 43, 23, 36, 32, 44, 57, 36, 66, 44,
- 11, 52, 20, 60, 45, 52, 54, 60, 79, 52, 10, 11, 17, 3, 40,
- 11, 48, 4, 70, 11, 2, 20, 20, 27, 36, 20, 50, 27, 70, 20,
- 0, 43, 24, 36, 33, 44, 58, 36, 67, 44, 12, 52, 21, 60, 46,
- 52, 55, 60, 79, 53, 9, 11, 16, 3, 39, 11, 49, 4, 69, 11,
- 3, 20, 19, 27, 37, 20, 49, 27, 71, 20, 0, 44, 25, 36, 34,
- 44, 59, 36, 68, 44, 13, 52, 22, 60, 47, 52, 56, 60, 78, 53,
- 8, 11, 16, 4, 38, 11, 50, 4, 68, 11, 4, 20, 18, 27, 38,
- 20, 48, 27, 72, 20, 1, 44, 26, 36, 35, 44, 60, 36, 69, 44,
- 14, 52, 23, 60, 47, 53, 57, 60, 77, 53, 7, 11, 17, 4, 37,
- 11, 51, 4, 67, 11, 5, 20, 17, 27, 39, 20, 48, 28, 73, 20,
- 2, 44, 27, 36, 36, 44, 61, 36, 70, 44, 15, 52, 24, 60, 46,
- 53, 58, 60, 76, 53, 6, 11, 18, 4, 36, 11, 52, 4, 66, 11,
- 6, 20, 16, 27, 40, 20, 49, 28, 74, 20, 3, 44, 28, 36, 37,
- 44, 62, 36, 71, 44, 15, 53, 25, 60, 45, 53, 59, 60, 75, 53,
- 5, 11, 19, 4, 35, 11, 53, 4, 65, 11, 7, 20, 16, 28, 41,
- 20, 50, 28, 75, 20, 4, 44, 29, 36, 38, 44, 63, 36, 72, 44,
- 14, 53, 26, 60, 44, 53, 60, 60, 74, 53, 4, 11, 20, 4, 34,
- 11, 54, 4, 64, 11, 8, 20, 17, 28, 42, 20, 51, 28, 76, 20,
- 5, 44, 30, 36, 39, 44, 63, 37, 73, 44, 13, 53, 27, 60, 43,
- 53, 61, 60, 73, 53, 3, 11, 21, 4, 33, 11, 55, 4, 64, 12,
- 9, 20, 18, 28, 43, 20, 52, 28, 77, 20, 6, 44, 31, 36, 40,
- 44, 62, 37, 74, 44, 12, 53, 28, 60, 42, 53, 62, 60, 72, 53,
- 2, 11, 22, 4, 32, 11, 56, 4, 65, 12, 10, 20, 19, 28, 44,
- 20, 53, 28, 78, 20, 7, 44, 31, 37, 41, 44, 61, 37, 75, 44,
- 11, 53, 29, 60, 41, 53, 63, 60, 71, 53, 1, 11, 23, 4, 32,
- 12, 57, 4, 66, 12, 11, 20, 20, 28, 45, 20, 54, 28, 79, 20,
- 8, 44, 30, 37, 42, 44, 60, 37, 76, 44, 10, 53, 30, 60, 40,
- 53, 63, 61, 70, 53, 0, 11, 24, 4, 33, 12, 58, 4, 67, 12,
- 12, 20, 21, 28, 46, 20, 55, 28, 79, 21, 9, 44, 29, 37, 43,
- 44, 59, 37, 77, 44, 9, 53, 31, 60, 39, 53, 62, 61, 69, 53,
- 0, 12, 25, 4, 34, 12, 59, 4, 68, 12, 13, 20, 22, 28, 47,
- 20, 56, 28, 78, 21, 10, 44, 28, 37, 44, 44, 58, 37, 78, 44,
- 8, 53, 31, 61, 38, 53, 61, 61, 68, 53, 1, 12, 26, 4, 35,
- 12, 60, 4, 69, 12, 14, 20, 23, 28, 47, 21, 57, 28, 77, 21,
- 11, 44, 27, 37, 45, 44, 57, 37, 79, 44, 7, 53, 30, 61, 37,
- 53, 60, 61, 67, 53, 2, 12, 27, 4, 36, 12, 61, 4, 70, 12,
- 15, 20, 24, 28, 46, 21, 58, 28, 76, 21, 12, 44, 26, 37, 46,
- 44, 56, 37, 79, 45, 6, 53, 29, 61, 36, 53, 59, 61, 66, 53,
- 3, 12, 28, 4, 37, 12, 62, 4, 71, 12, 15, 21, 25, 28, 45,
- 21, 59, 28, 75, 21, 13, 44, 25, 37, 47, 44, 55, 37, 78, 45,
- 5, 53, 28, 61, 35, 53, 58, 61, 65, 53, 4, 12, 29, 4, 38,
- 12, 63, 4, 72, 12, 14, 21, 26, 28, 44, 21, 60, 28, 74, 21,
- 14, 44, 24, 37, 47, 45, 54, 37, 77, 45, 4, 53, 27, 61, 34,
- 53, 57, 61, 64, 53, 5, 12, 30, 4, 39, 12, 63, 5, 73, 12,
- 13, 21, 27, 28, 43, 21, 61, 28, 73, 21, 15, 44, 23, 37, 46,
- 45, 53, 37, 76, 45, 3, 53, 26, 61, 33, 53, 56, 61, 64, 54,
- 6, 12, 31, 4, 40, 12, 62, 5, 74, 12, 12, 21, 28, 28, 42,
- 21, 62, 28, 72, 21, 15, 45, 22, 37, 45, 45, 52, 37, 75, 45,
- 2, 53, 25, 61, 32, 53, 55, 61, 65, 54, 7, 12, 31, 5, 41,
- 12, 61, 5, 75, 12, 11, 21, 29, 28, 41, 21, 63, 28, 71, 21,
- 14, 45, 21, 37, 44, 45, 51, 37, 74, 45, 1, 53, 24, 61, 32,
- 54, 54, 61, 66, 54, 8, 12, 30, 5, 42, 12, 60, 5, 76, 12,
- 10, 21, 30, 28, 40, 21, 63, 29, 70, 21, 13, 45, 20, 37, 43,
- 45, 50, 37, 73, 45, 0, 53, 23, 61, 33, 54, 53, 61, 67, 54,
- 9, 12, 29, 5, 43, 12, 59, 5, 77, 12, 9, 21, 31, 28, 39,
- 21, 62, 29, 69, 21, 12, 45, 19, 37, 42, 45, 49, 37, 72, 45,
- 0, 54, 22, 61, 34, 54, 52, 61, 68, 54, 10, 12, 28, 5, 44,
- 12, 58, 5, 78, 12, 8, 21, 31, 29, 38, 21, 61, 29, 68, 21,
- 11, 45, 18, 37, 41, 45, 48, 37, 71, 45, 1, 54, 21, 61, 35,
- 54, 51, 61, 69, 54, 11, 12, 27, 5, 45, 12, 57, 5, 79, 12,
- 7, 21, 30, 29, 37, 21, 60, 29, 67, 21, 10, 45, 17, 37, 40,
- 45, 48, 38, 70, 45, 2, 54, 20, 61, 36, 54, 50, 61, 70, 54,
- 12, 12, 26, 5, 46, 12, 56, 5, 79, 13, 6, 21, 29, 29, 36,
- 21, 59, 29, 66, 21, 9, 45, 16, 37, 39, 45, 49, 38, 69, 45,
- 3, 54, 19, 61, 37, 54, 49, 61, 71, 54, 13, 12, 25, 5, 47,
- 12, 55, 5, 78, 13, 5, 21, 28, 29, 35, 21, 58, 29, 65, 21,
- 8, 45, 16, 38, 38, 45, 50, 38, 68, 45, 4, 54, 18, 61, 38,
- 54, 48, 61, 72, 54, 14, 12, 24, 5, 47, 13, 54, 5, 77, 13,
- 4, 21, 27, 29, 34, 21, 57, 29, 64, 21, 7, 45, 17, 38, 37,
- 45, 51, 38, 67, 45, 5, 54, 17, 61, 39, 54, 48, 62, 73, 54,
- 15, 12, 23, 5, 46, 13, 53, 5, 76, 13, 3, 21, 26, 29, 33,
- 21, 56, 29, 64, 22, 6, 45, 18, 38, 36, 45, 52, 38, 66, 45,
- 6, 54, 16, 61, 40, 54, 49, 62, 74, 54, 15, 13, 22, 5, 45,
- 13, 52, 5, 75, 13, 2, 21, 25, 29, 32, 21, 55, 29, 65, 22,
- 5, 45, 19, 38, 35, 45, 53, 38, 65, 45, 7, 54, 16, 62, 41,
- 54, 50, 62, 75, 54, 14, 13, 21, 5, 44, 13, 51, 5, 74, 13,
- 1, 21, 24, 29, 32, 22, 54, 29, 66, 22, 4, 45, 20, 38, 34,
- 45, 54, 38, 64, 45, 8, 54, 17, 62, 42, 54, 51, 62, 76, 54,
- 13, 13, 20, 5, 43, 13, 50, 5, 73, 13, 0, 21, 23, 29, 33,
- 22, 53, 29, 67, 22, 3, 45, 21, 38, 33, 45, 55, 38, 64, 46,
- 9, 54, 18, 62, 43, 54, 52, 62, 77, 54, 12, 13, 19, 5, 42,
- 13, 49, 5, 72, 13, 0, 22, 22, 29, 34, 22, 52, 29, 68, 22,
- 2, 45, 22, 38, 32, 45, 56, 38, 65, 46, 10, 54, 19, 62, 44,
- 54, 53, 62, 78, 54, 11, 13, 18, 5, 41, 13, 48, 5, 71, 13,
- 1, 22, 21, 29, 35, 22, 51, 29, 69, 22, 1, 45, 23, 38, 32,
- 46, 57, 38, 66, 46, 11, 54, 20, 62, 45, 54, 54, 62, 79, 54,
- 10, 13, 17, 5, 40, 13, 48, 6, 70, 13, 2, 22, 20, 29, 36,
- 22, 50, 29, 70, 22, 0, 45, 24, 38, 33, 46, 58, 38, 67, 46,
- 12, 54, 21, 62, 46, 54, 55, 62, 79, 55, 9, 13, 16, 5, 39,
- 13, 49, 6, 69, 13, 3, 22, 19, 29, 37, 22, 49, 29, 71, 22,
- 0, 46, 25, 38, 34, 46, 59, 38, 68, 46, 13, 54, 22, 62, 47,
- 54, 56, 62, 78, 55, 8, 13, 16, 6, 38, 13, 50, 6, 68, 13,
- 4, 22, 18, 29, 38, 22, 48, 29, 72, 22, 1, 46, 26, 38, 35,
- 46, 60, 38, 69, 46, 14, 54, 23, 62, 47, 55, 57, 62, 77, 55,
- 7, 13, 17, 6, 37, 13, 51, 6, 67, 13, 5, 22, 17, 29, 39,
- 22, 48, 30, 73, 22, 2, 46, 27, 38, 36, 46, 61, 38, 70, 46,
- 15, 54, 24, 62, 46, 55, 58, 62, 76, 55, 6, 13, 18, 6, 36,
- 13, 52, 6, 66, 13, 6, 22, 16, 29, 40, 22, 49, 30, 74, 22,
- 3, 46, 28, 38, 37, 46, 62, 38, 71, 46, 15, 55, 25, 62, 45,
- 55, 59, 62, 75, 55, 5, 13, 19, 6, 35, 13, 53, 6, 65, 13,
- 7, 22, 16, 30, 41, 22, 50, 30, 75, 22, 4, 46, 29, 38, 38,
- 46, 63, 38, 72, 46, 14, 55, 26, 62, 44, 55, 60, 62, 74, 55,
- 4, 13, 20, 6, 34, 13, 54, 6, 64, 13, 8, 22, 17, 30, 42,
- 22, 51, 30, 76, 22, 5, 46, 30, 38, 39, 46, 63, 39, 73, 46,
- 13, 55, 27, 62, 43, 55, 61, 62, 73, 55, 3, 13, 21, 6, 33,
- 13, 55, 6, 64, 14, 9, 22, 18, 30, 43, 22, 52, 30, 77, 22,
- 6, 46, 31, 38, 40, 46, 62, 39, 74, 46, 12, 55, 28, 62, 42,
- 55, 62, 62, 72, 55, 2, 13, 22, 6, 32, 13, 56, 6, 65, 14,
- 10, 22, 19, 30, 44, 22, 53, 30, 78, 22, 7, 46, 31, 39, 41,
- 46, 61, 39, 75, 46, 11, 55, 29, 62, 41, 55, 63, 62, 71, 55,
- 1, 13, 23, 6, 32, 14, 57, 6, 66, 14, 11, 22, 20, 30, 45,
- 22, 54, 30, 79, 22, 8, 46, 30, 39, 42, 46, 60, 39, 76, 46,
- 10, 55, 30, 62, 40, 55, 63, 63, 70, 55, 0, 13, 24, 6, 33,
- 14, 58, 6, 67, 14, 12, 22, 21, 30, 46, 22, 55, 30, 79, 23,
- 9, 46, 29, 39, 43, 46, 59, 39, 77, 46, 9, 55, 31, 62, 39,
- 55, 62, 63, 69, 55, 0, 14, 25, 6, 34, 14, 59, 6, 68, 14,
- 13, 22, 22, 30, 47, 22, 56, 30, 78, 23, 10, 46, 28, 39, 44,
- 46, 58, 39, 78, 46, 8, 55, 31, 63, 38, 55, 61, 63, 68, 55,
- 1, 14, 26, 6, 35, 14, 60, 6, 69, 14, 14, 22, 23, 30, 47,
- 23, 57, 30, 77, 23, 11, 46, 27, 39, 45, 46, 57, 39, 79, 46,
- 7, 55, 30, 63, 37, 55, 60, 63, 67, 55, 2, 14, 27, 6, 36,
- 14, 61, 6, 70, 14, 15, 22, 24, 30, 46, 23, 58, 30, 76, 23,
- 12, 46, 26, 39, 46, 46, 56, 39, 79, 47, 6, 55, 29, 63, 36,
- 55, 59, 63, 66, 55, 3, 14, 28, 6, 37, 14, 62, 6, 71, 14,
- 15, 23, 25, 30, 45, 23, 59, 30, 75, 23, 13, 46, 25, 39, 47,
- 46, 55, 39, 78, 47, 5, 55, 28, 63, 35, 55, 58, 63, 65, 55,
- 4, 14, 29, 6, 38, 14, 63, 6, 72, 14, 14, 23, 26, 30, 44,
- 23, 60, 30, 74, 23, 14, 46, 24, 39, 47, 47, 54, 39, 77, 47,
- 4, 55, 27, 63, 34, 55, 57, 63, 64, 55, 5, 14, 30, 6, 39,
- 14, 63, 7, 73, 14, 13, 23, 27, 30, 43, 23, 61, 30, 73, 23,
- 15, 46, 23, 39, 46, 47, 53, 39, 76, 47, 3, 55, 26, 63, 33,
- 55, 56, 63, 64, 56, 6, 14, 31, 6, 40, 14, 62, 7, 74, 14,
- 12, 23, 28, 30, 42, 23, 62, 30, 72, 23, 15, 47, 22, 39, 45,
- 47, 52, 39, 75, 47, 2, 55, 25, 63, 32, 55, 55, 63, 65, 56,
- 7, 14, 31, 7, 41, 14, 61, 7, 75, 14, 11, 23, 29, 30, 41,
- 23, 63, 30, 71, 23, 14, 47, 21, 39, 44, 47, 51, 39, 74, 47,
- 1, 55, 24, 63, 32, 56, 54, 63, 66, 56, 8, 14, 30, 7, 42,
- 14, 60, 7, 76, 14, 10, 23, 30, 30, 40, 23, 63, 31, 70, 23,
- 13, 47, 20, 39, 43, 47, 50, 39, 73, 47, 0, 55, 23, 63, 33,
- 56, 53, 63, 67, 56, 9, 14, 29, 7, 43, 14, 59, 7, 77, 14,
- 9, 23, 31, 30, 39, 23, 62, 31, 69, 23, 12, 47, 19, 39, 42,
- 47, 49, 39, 72, 47, 0, 56, 22, 63, 34, 56, 52, 63, 68, 56,
- 10, 14, 28, 7, 44, 14, 58, 7, 78, 14, 8, 23, 31, 31, 38,
- 23, 61, 31, 68, 23, 11, 47, 18, 39, 41, 47, 48, 39, 71, 47,
- 1, 56, 21, 63, 35, 56, 51, 63, 69, 56, 11, 14, 27, 7, 45,
- 14, 57, 7, 79, 14, 7, 23, 30, 31, 37, 23, 60, 31, 67, 23,
- 10, 47, 17, 39, 40, 47, 48, 40, 70, 47, 2, 56, 20, 63, 36,
- 56, 50, 63, 70, 56, 12, 14, 26, 7, 46, 14, 56, 7, 79, 15,
- 6, 23, 29, 31, 36, 23, 59, 31, 66, 23, 9, 47, 16, 39, 39,
- 47, 49, 40, 69, 47, 3, 56, 19, 63, 37, 56, 49, 63, 71, 56,
- 13, 14, 25, 7, 47, 14, 55, 7, 78, 15, 5, 23, 28, 31, 35,
- 23, 58, 31, 65, 23, 8, 47, 16, 40, 38, 47, 50, 40, 68, 47,
- 4, 56, 18, 63, 38, 56, 48, 63, 72, 56, 14, 14, 24, 7, 47,
- 15, 54, 7, 77, 15, 4, 23, 27, 31, 34, 23, 57, 31, 64, 23,
- 7, 47, 17, 40, 37, 47, 51, 40, 67, 47, 5, 56, 17, 63, 39,
- 56, 48, 48, 73, 56, 15, 14, 23, 7, 46, 15, 53, 7, 76, 15,
- 3, 23, 26, 31, 33, 23, 56, 31, 64, 24, 6, 47, 18, 40, 36,
- 47, 52, 40, 66, 47, 6, 56, 16, 63, 40, 56, 49, 48, 74, 56,
- 15, 15, 22, 7, 45, 15, 52, 7, 75, 15, 2, 23, 25, 31, 32,
- 23, 55, 31, 65, 24, 5, 47, 19, 40, 35, 47, 53, 40, 65, 47,
- 7, 56, 16, 48, 41, 56, 50, 48, 75, 56, 14, 15, 21, 7, 44,
- 15, 51, 7, 74, 15, 1, 23, 24, 31, 32, 24, 54, 31, 66, 24,
- 4, 47, 20, 40, 34, 47, 54, 40, 64, 47, 8, 56, 17, 48, 42,
- 56, 51, 48, 76, 56, 13, 15, 20, 7, 43, 15, 50, 7, 73, 15,
- 0, 23, 23, 31, 33, 24, 53, 31, 67, 24, 3, 47, 21, 40, 33,
- 47, 55, 40, 64, 32, 9, 56, 18, 48, 43, 56, 52, 48, 77, 56,
- 12, 15, 19, 7, 42, 15, 49, 7, 72, 15, 0, 24, 22, 31, 34,
- 24, 52, 31, 68, 24, 2, 47, 22, 40, 32, 47, 56, 40, 65, 32,
- 10, 56, 19, 48, 44, 56, 53, 48, 78, 56, 11, 15, 18, 7, 41,
- 15, 48, 7, 71, 15, 1, 24, 21, 31, 35, 24, 51, 31, 69, 24,
- 1, 47, 23, 40, 32, 32, 57, 40, 66, 32, 11, 56, 20, 48, 45,
- 56, 54, 48, 79, 56, 10, 15, 17, 7, 40, 15, 48, 8, 70, 15,
- 2, 24, 20, 31, 36, 24, 50, 31, 70, 24, 0, 47, 24, 40, 33,
- 32, 58, 40, 67, 32, 12, 56, 21, 48, 46, 56, 55, 48, 79, 57,
- 9, 15, 16, 7, 39, 15, 49, 8, 69, 15, 3, 24, 19, 31, 37,
- 24, 49, 31, 71, 24, 0, 32, 25, 40, 34, 32, 59, 40, 68, 32,
- 13, 56, 22, 48, 47, 56, 56, 48, 78, 57, 8, 15, 16, 8, 38,
- 15, 50, 8, 68, 15, 4, 24, 18, 31, 38, 24, 48, 31, 72, 24,
- 1, 32, 26, 40, 35, 32, 60, 40, 69, 32, 14, 56, 23, 48, 47,
- 57, 57, 48, 77, 57, 7, 15, 17, 8, 37, 15, 51, 8, 67, 15,
- 5, 24, 17, 31, 39, 24, 48, 16, 73, 24, 2, 32, 27, 40, 36,
- 32, 61, 40, 70, 32, 15, 56, 24, 48, 46, 57, 58, 48, 76, 57,
- 6, 15, 18, 8, 36, 15, 52, 8, 66, 15, 6, 24, 16, 31, 40,
- 24, 49, 16, 74, 24, 3, 32, 28, 40, 37, 32, 62, 40, 71, 32,
- 15, 57, 25, 48, 45, 57, 59, 48, 75, 57, 5, 15, 19, 8, 35,
- 15, 53, 8, 65, 15, 7, 24, 16, 16, 41, 24, 50, 16, 75, 24,
- 4, 32, 29, 40, 38, 32, 63, 40, 72, 32, 14, 57, 26, 48, 44,
- 57, 60, 48, 74, 57, 4, 15, 20, 8, 34, 15, 54, 8, 64, 15,
- 8, 24, 17, 16, 42, 24, 51, 16, 76, 24, 5, 32, 30, 40, 39,
- 32, 63, 41, 73, 32, 13, 57, 27, 48, 43, 57, 61, 48, 73, 57,
- 3, 15, 21, 8, 33, 15, 55, 8, 64, 0, 9, 24, 18, 16, 43,
- 24, 52, 16, 77, 24, 6, 32, 31, 40, 40, 32, 62, 41, 74, 32,
- 12, 57, 28, 48, 42, 57, 62, 48, 72, 57, 2, 15, 22, 8, 32,
- 15, 56, 8, 65, 0, 10, 24, 19, 16, 44, 24, 53, 16, 78, 24,
- 7, 32, 31, 41, 41, 32, 61, 41, 75, 32, 11, 57, 29, 48, 41,
- 57, 63, 48, 71, 57, 1, 15, 23, 8, 32, 0, 57, 8, 66, 0,
- 11, 24, 20, 16, 45, 24, 54, 16, 79, 24, 8, 32, 30, 41, 42,
- 32, 60, 41, 76, 32, 10, 57, 30, 48, 40, 57, 63, 49, 70, 57,
- 0, 15, 24, 8, 33, 0, 58, 8, 67, 0, 12, 24, 21, 16, 46,
- 24, 55, 16, 79, 25, 9, 32, 29, 41, 43, 32, 59, 41, 77, 32,
- 9, 57, 31, 48, 39, 57, 62, 49, 69, 57,
-};
-
-static const uint8_t hq_tab_18[] = {
- 0, 0, 25, 6, 34, 0, 59, 6, 68, 0, 13, 18, 22, 12, 47,
- 18, 56, 12, 78, 19, 10, 24, 28, 31, 44, 24, 58, 31, 78, 24,
- 8, 43, 31, 37, 38, 43, 61, 37, 68, 43, 11, 49, 18, 55, 41,
- 49, 48, 55, 71, 49, 1, 0, 26, 6, 35, 0, 60, 6, 69, 0,
- 14, 18, 23, 12, 47, 19, 57, 12, 77, 19, 11, 24, 27, 31, 45,
- 24, 57, 31, 79, 24, 7, 43, 30, 37, 37, 43, 60, 37, 67, 43,
- 10, 49, 17, 55, 40, 49, 48, 56, 70, 49, 2, 0, 27, 6, 36,
- 0, 61, 6, 70, 0, 15, 18, 24, 12, 46, 19, 58, 12, 76, 19,
- 12, 24, 26, 31, 46, 24, 56, 31, 79, 25, 6, 43, 29, 37, 36,
- 43, 59, 37, 66, 43, 9, 49, 16, 55, 39, 49, 49, 56, 69, 49,
- 3, 0, 28, 6, 37, 0, 62, 6, 71, 0, 15, 19, 25, 12, 45,
- 19, 59, 12, 75, 19, 13, 24, 25, 31, 47, 24, 55, 31, 78, 25,
- 5, 43, 28, 37, 35, 43, 58, 37, 65, 43, 8, 49, 16, 56, 38,
- 49, 50, 56, 68, 49, 4, 0, 29, 6, 38, 0, 63, 6, 72, 0,
- 14, 19, 26, 12, 44, 19, 60, 12, 74, 19, 14, 24, 24, 31, 47,
- 25, 54, 31, 77, 25, 4, 43, 27, 37, 34, 43, 57, 37, 64, 43,
- 7, 49, 17, 56, 37, 49, 51, 56, 67, 49, 5, 0, 30, 6, 39,
- 0, 63, 7, 73, 0, 13, 19, 27, 12, 43, 19, 61, 12, 73, 19,
- 15, 24, 23, 31, 46, 25, 53, 31, 76, 25, 3, 43, 26, 37, 33,
- 43, 56, 37, 64, 44, 6, 49, 18, 56, 36, 49, 52, 56, 66, 49,
- 6, 0, 31, 6, 40, 0, 62, 7, 74, 0, 12, 19, 28, 12, 42,
- 19, 62, 12, 72, 19, 15, 25, 22, 31, 45, 25, 52, 31, 75, 25,
- 2, 43, 25, 37, 32, 43, 55, 37, 65, 44, 5, 49, 19, 56, 35,
- 49, 53, 56, 65, 49, 7, 0, 31, 7, 41, 0, 61, 7, 75, 0,
- 11, 19, 29, 12, 41, 19, 63, 12, 71, 19, 14, 25, 21, 31, 44,
- 25, 51, 31, 74, 25, 1, 43, 24, 37, 32, 44, 54, 37, 66, 44,
- 4, 49, 20, 56, 34, 49, 54, 56, 64, 49, 8, 0, 30, 7, 42,
- 0, 60, 7, 76, 0, 10, 19, 30, 12, 40, 19, 63, 13, 70, 19,
- 13, 25, 20, 31, 43, 25, 50, 31, 73, 25, 0, 43, 23, 37, 33,
- 44, 53, 37, 67, 44, 3, 49, 21, 56, 33, 49, 55, 56, 64, 50,
- 9, 0, 29, 7, 43, 0, 59, 7, 77, 0, 9, 19, 31, 12, 39,
- 19, 62, 13, 69, 19, 12, 25, 19, 31, 42, 25, 49, 31, 72, 25,
- 0, 44, 22, 37, 34, 44, 52, 37, 68, 44, 2, 49, 22, 56, 32,
- 49, 56, 56, 65, 50, 10, 0, 28, 7, 44, 0, 58, 7, 78, 0,
- 8, 19, 31, 13, 38, 19, 61, 13, 68, 19, 11, 25, 18, 31, 41,
- 25, 48, 31, 71, 25, 1, 44, 21, 37, 35, 44, 51, 37, 69, 44,
- 1, 49, 23, 56, 32, 50, 57, 56, 66, 50, 11, 0, 27, 7, 45,
- 0, 57, 7, 79, 0, 7, 19, 30, 13, 37, 19, 60, 13, 67, 19,
- 10, 25, 17, 31, 40, 25, 48, 32, 70, 25, 2, 44, 20, 37, 36,
- 44, 50, 37, 70, 44, 0, 49, 24, 56, 33, 50, 58, 56, 67, 50,
- 12, 0, 26, 7, 46, 0, 56, 7, 79, 1, 6, 19, 29, 13, 36,
- 19, 59, 13, 66, 19, 9, 25, 16, 31, 39, 25, 49, 32, 69, 25,
- 3, 44, 19, 37, 37, 44, 49, 37, 71, 44, 0, 50, 25, 56, 34,
- 50, 59, 56, 68, 50, 13, 0, 25, 7, 47, 0, 55, 7, 78, 1,
- 5, 19, 28, 13, 35, 19, 58, 13, 65, 19, 8, 25, 16, 32, 38,
- 25, 50, 32, 68, 25, 4, 44, 18, 37, 38, 44, 48, 37, 72, 44,
- 1, 50, 26, 56, 35, 50, 60, 56, 69, 50, 14, 0, 24, 7, 47,
- 1, 54, 7, 77, 1, 4, 19, 27, 13, 34, 19, 57, 13, 64, 19,
- 7, 25, 17, 32, 37, 25, 51, 32, 67, 25, 5, 44, 17, 37, 39,
- 44, 48, 38, 73, 44, 2, 50, 27, 56, 36, 50, 61, 56, 70, 50,
- 15, 0, 23, 7, 46, 1, 53, 7, 76, 1, 3, 19, 26, 13, 33,
- 19, 56, 13, 64, 20, 6, 25, 18, 32, 36, 25, 52, 32, 66, 25,
- 6, 44, 16, 37, 40, 44, 49, 38, 74, 44, 3, 50, 28, 56, 37,
- 50, 62, 56, 71, 50, 15, 1, 22, 7, 45, 1, 52, 7, 75, 1,
- 2, 19, 25, 13, 32, 19, 55, 13, 65, 20, 5, 25, 19, 32, 35,
- 25, 53, 32, 65, 25, 7, 44, 16, 38, 41, 44, 50, 38, 75, 44,
- 4, 50, 29, 56, 38, 50, 63, 56, 72, 50, 14, 1, 21, 7, 44,
- 1, 51, 7, 74, 1, 1, 19, 24, 13, 32, 20, 54, 13, 66, 20,
- 4, 25, 20, 32, 34, 25, 54, 32, 64, 25, 8, 44, 17, 38, 42,
- 44, 51, 38, 76, 44, 5, 50, 30, 56, 39, 50, 63, 57, 73, 50,
- 13, 1, 20, 7, 43, 1, 50, 7, 73, 1, 0, 19, 23, 13, 33,
- 20, 53, 13, 67, 20, 3, 25, 21, 32, 33, 25, 55, 32, 64, 26,
- 9, 44, 18, 38, 43, 44, 52, 38, 77, 44, 6, 50, 31, 56, 40,
- 50, 62, 57, 74, 50, 12, 1, 19, 7, 42, 1, 49, 7, 72, 1,
- 0, 20, 22, 13, 34, 20, 52, 13, 68, 20, 2, 25, 22, 32, 32,
- 25, 56, 32, 65, 26, 10, 44, 19, 38, 44, 44, 53, 38, 78, 44,
- 7, 50, 31, 57, 41, 50, 61, 57, 75, 50, 11, 1, 18, 7, 41,
- 1, 48, 7, 71, 1, 1, 20, 21, 13, 35, 20, 51, 13, 69, 20,
- 1, 25, 23, 32, 32, 26, 57, 32, 66, 26, 11, 44, 20, 38, 45,
- 44, 54, 38, 79, 44, 8, 50, 30, 57, 42, 50, 60, 57, 76, 50,
- 10, 1, 17, 7, 40, 1, 48, 8, 70, 1, 2, 20, 20, 13, 36,
- 20, 50, 13, 70, 20, 0, 25, 24, 32, 33, 26, 58, 32, 67, 26,
- 12, 44, 21, 38, 46, 44, 55, 38, 79, 45, 9, 50, 29, 57, 43,
- 50, 59, 57, 77, 50, 9, 1, 16, 7, 39, 1, 49, 8, 69, 1,
- 3, 20, 19, 13, 37, 20, 49, 13, 71, 20, 0, 26, 25, 32, 34,
- 26, 59, 32, 68, 26, 13, 44, 22, 38, 47, 44, 56, 38, 78, 45,
- 10, 50, 28, 57, 44, 50, 58, 57, 78, 50, 8, 1, 16, 8, 38,
- 1, 50, 8, 68, 1, 4, 20, 18, 13, 38, 20, 48, 13, 72, 20,
- 1, 26, 26, 32, 35, 26, 60, 32, 69, 26, 14, 44, 23, 38, 47,
- 45, 57, 38, 77, 45, 11, 50, 27, 57, 45, 50, 57, 57, 79, 50,
- 7, 1, 17, 8, 37, 1, 51, 8, 67, 1, 5, 20, 17, 13, 39,
- 20, 48, 14, 73, 20, 2, 26, 27, 32, 36, 26, 61, 32, 70, 26,
- 15, 44, 24, 38, 46, 45, 58, 38, 76, 45, 12, 50, 26, 57, 46,
- 50, 56, 57, 79, 51, 6, 1, 18, 8, 36, 1, 52, 8, 66, 1,
- 6, 20, 16, 13, 40, 20, 49, 14, 74, 20, 3, 26, 28, 32, 37,
- 26, 62, 32, 71, 26, 15, 45, 25, 38, 45, 45, 59, 38, 75, 45,
- 13, 50, 25, 57, 47, 50, 55, 57, 78, 51, 5, 1, 19, 8, 35,
- 1, 53, 8, 65, 1, 7, 20, 16, 14, 41, 20, 50, 14, 75, 20,
- 4, 26, 29, 32, 38, 26, 63, 32, 72, 26, 14, 45, 26, 38, 44,
- 45, 60, 38, 74, 45, 14, 50, 24, 57, 47, 51, 54, 57, 77, 51,
- 4, 1, 20, 8, 34, 1, 54, 8, 64, 1, 8, 20, 17, 14, 42,
- 20, 51, 14, 76, 20, 5, 26, 30, 32, 39, 26, 63, 33, 73, 26,
- 13, 45, 27, 38, 43, 45, 61, 38, 73, 45, 15, 50, 23, 57, 46,
- 51, 53, 57, 76, 51, 3, 1, 21, 8, 33, 1, 55, 8, 64, 2,
- 9, 20, 18, 14, 43, 20, 52, 14, 77, 20, 6, 26, 31, 32, 40,
- 26, 62, 33, 74, 26, 12, 45, 28, 38, 42, 45, 62, 38, 72, 45,
- 15, 51, 22, 57, 45, 51, 52, 57, 75, 51, 2, 1, 22, 8, 32,
- 1, 56, 8, 65, 2, 10, 20, 19, 14, 44, 20, 53, 14, 78, 20,
- 7, 26, 31, 33, 41, 26, 61, 33, 75, 26, 11, 45, 29, 38, 41,
- 45, 63, 38, 71, 45, 14, 51, 21, 57, 44, 51, 51, 57, 74, 51,
- 1, 1, 23, 8, 32, 2, 57, 8, 66, 2, 11, 20, 20, 14, 45,
- 20, 54, 14, 79, 20, 8, 26, 30, 33, 42, 26, 60, 33, 76, 26,
- 10, 45, 30, 38, 40, 45, 63, 39, 70, 45, 13, 51, 20, 57, 43,
- 51, 50, 57, 73, 51, 0, 1, 24, 8, 33, 2, 58, 8, 67, 2,
- 12, 20, 21, 14, 46, 20, 55, 14, 79, 21, 9, 26, 29, 33, 43,
- 26, 59, 33, 77, 26, 9, 45, 31, 38, 39, 45, 62, 39, 69, 45,
- 12, 51, 19, 57, 42, 51, 49, 57, 72, 51, 0, 2, 25, 8, 34,
- 2, 59, 8, 68, 2, 13, 20, 22, 14, 47, 20, 56, 14, 78, 21,
- 10, 26, 28, 33, 44, 26, 58, 33, 78, 26, 8, 45, 31, 39, 38,
- 45, 61, 39, 68, 45, 11, 51, 18, 57, 41, 51, 48, 57, 71, 51,
- 1, 2, 26, 8, 35, 2, 60, 8, 69, 2, 14, 20, 23, 14, 47,
- 21, 57, 14, 77, 21, 11, 26, 27, 33, 45, 26, 57, 33, 79, 26,
- 7, 45, 30, 39, 37, 45, 60, 39, 67, 45, 10, 51, 17, 57, 40,
- 51, 48, 58, 70, 51, 2, 2, 27, 8, 36, 2, 61, 8, 70, 2,
- 15, 20, 24, 14, 46, 21, 58, 14, 76, 21, 12, 26, 26, 33, 46,
- 26, 56, 33, 79, 27, 6, 45, 29, 39, 36, 45, 59, 39, 66, 45,
- 9, 51, 16, 57, 39, 51, 49, 58, 69, 51, 3, 2, 28, 8, 37,
- 2, 62, 8, 71, 2, 15, 21, 25, 14, 45, 21, 59, 14, 75, 21,
- 13, 26, 25, 33, 47, 26, 55, 33, 78, 27, 5, 45, 28, 39, 35,
- 45, 58, 39, 65, 45, 8, 51, 16, 58, 38, 51, 50, 58, 68, 51,
- 4, 2, 29, 8, 38, 2, 63, 8, 72, 2, 14, 21, 26, 14, 44,
- 21, 60, 14, 74, 21, 14, 26, 24, 33, 47, 27, 54, 33, 77, 27,
- 4, 45, 27, 39, 34, 45, 57, 39, 64, 45, 7, 51, 17, 58, 37,
- 51, 51, 58, 67, 51, 5, 2, 30, 8, 39, 2, 63, 9, 73, 2,
- 13, 21, 27, 14, 43, 21, 61, 14, 73, 21, 15, 26, 23, 33, 46,
- 27, 53, 33, 76, 27, 3, 45, 26, 39, 33, 45, 56, 39, 64, 46,
- 6, 51, 18, 58, 36, 51, 52, 58, 66, 51, 6, 2, 31, 8, 40,
- 2, 62, 9, 74, 2, 12, 21, 28, 14, 42, 21, 62, 14, 72, 21,
- 15, 27, 22, 33, 45, 27, 52, 33, 75, 27, 2, 45, 25, 39, 32,
- 45, 55, 39, 65, 46, 5, 51, 19, 58, 35, 51, 53, 58, 65, 51,
- 7, 2, 31, 9, 41, 2, 61, 9, 75, 2, 11, 21, 29, 14, 41,
- 21, 63, 14, 71, 21, 14, 27, 21, 33, 44, 27, 51, 33, 74, 27,
- 1, 45, 24, 39, 32, 46, 54, 39, 66, 46, 4, 51, 20, 58, 34,
- 51, 54, 58, 64, 51, 8, 2, 30, 9, 42, 2, 60, 9, 76, 2,
- 10, 21, 30, 14, 40, 21, 63, 15, 70, 21, 13, 27, 20, 33, 43,
- 27, 50, 33, 73, 27, 0, 45, 23, 39, 33, 46, 53, 39, 67, 46,
- 3, 51, 21, 58, 33, 51, 55, 58, 64, 52, 9, 2, 29, 9, 43,
- 2, 59, 9, 77, 2, 9, 21, 31, 14, 39, 21, 62, 15, 69, 21,
- 12, 27, 19, 33, 42, 27, 49, 33, 72, 27, 0, 46, 22, 39, 34,
- 46, 52, 39, 68, 46, 2, 51, 22, 58, 32, 51, 56, 58, 65, 52,
- 10, 2, 28, 9, 44, 2, 58, 9, 78, 2, 8, 21, 31, 15, 38,
- 21, 61, 15, 68, 21, 11, 27, 18, 33, 41, 27, 48, 33, 71, 27,
- 1, 46, 21, 39, 35, 46, 51, 39, 69, 46, 1, 51, 23, 58, 32,
- 52, 57, 58, 66, 52, 11, 2, 27, 9, 45, 2, 57, 9, 79, 2,
- 7, 21, 30, 15, 37, 21, 60, 15, 67, 21, 10, 27, 17, 33, 40,
- 27, 48, 34, 70, 27, 2, 46, 20, 39, 36, 46, 50, 39, 70, 46,
- 0, 51, 24, 58, 33, 52, 58, 58, 67, 52, 12, 2, 26, 9, 46,
- 2, 56, 9, 79, 3, 6, 21, 29, 15, 36, 21, 59, 15, 66, 21,
- 9, 27, 16, 33, 39, 27, 49, 34, 69, 27, 3, 46, 19, 39, 37,
- 46, 49, 39, 71, 46, 0, 52, 25, 58, 34, 52, 59, 58, 68, 52,
- 13, 2, 25, 9, 47, 2, 55, 9, 78, 3, 5, 21, 28, 15, 35,
- 21, 58, 15, 65, 21, 8, 27, 16, 34, 38, 27, 50, 34, 68, 27,
- 4, 46, 18, 39, 38, 46, 48, 39, 72, 46, 1, 52, 26, 58, 35,
- 52, 60, 58, 69, 52, 14, 2, 24, 9, 47, 3, 54, 9, 77, 3,
- 4, 21, 27, 15, 34, 21, 57, 15, 64, 21, 7, 27, 17, 34, 37,
- 27, 51, 34, 67, 27, 5, 46, 17, 39, 39, 46, 48, 40, 73, 46,
- 2, 52, 27, 58, 36, 52, 61, 58, 70, 52, 15, 2, 23, 9, 46,
- 3, 53, 9, 76, 3, 3, 21, 26, 15, 33, 21, 56, 15, 64, 22,
- 6, 27, 18, 34, 36, 27, 52, 34, 66, 27, 6, 46, 16, 39, 40,
- 46, 49, 40, 74, 46, 3, 52, 28, 58, 37, 52, 62, 58, 71, 52,
- 15, 3, 22, 9, 45, 3, 52, 9, 75, 3, 2, 21, 25, 15, 32,
- 21, 55, 15, 65, 22, 5, 27, 19, 34, 35, 27, 53, 34, 65, 27,
- 7, 46, 16, 40, 41, 46, 50, 40, 75, 46, 4, 52, 29, 58, 38,
- 52, 63, 58, 72, 52, 14, 3, 21, 9, 44, 3, 51, 9, 74, 3,
- 1, 21, 24, 15, 32, 22, 54, 15, 66, 22, 4, 27, 20, 34, 34,
- 27, 54, 34, 64, 27, 8, 46, 17, 40, 42, 46, 51, 40, 76, 46,
- 5, 52, 30, 58, 39, 52, 63, 59, 73, 52, 13, 3, 20, 9, 43,
- 3, 50, 9, 73, 3, 0, 21, 23, 15, 33, 22, 53, 15, 67, 22,
- 3, 27, 21, 34, 33, 27, 55, 34, 64, 28, 9, 46, 18, 40, 43,
- 46, 52, 40, 77, 46, 6, 52, 31, 58, 40, 52, 62, 59, 74, 52,
- 12, 3, 19, 9, 42, 3, 49, 9, 72, 3, 0, 22, 22, 15, 34,
- 22, 52, 15, 68, 22, 2, 27, 22, 34, 32, 27, 56, 34, 65, 28,
- 10, 46, 19, 40, 44, 46, 53, 40, 78, 46, 7, 52, 31, 59, 41,
- 52, 61, 59, 75, 52, 11, 3, 18, 9, 41, 3, 48, 9, 71, 3,
- 1, 22, 21, 15, 35, 22, 51, 15, 69, 22, 1, 27, 23, 34, 32,
- 28, 57, 34, 66, 28, 11, 46, 20, 40, 45, 46, 54, 40, 79, 46,
- 8, 52, 30, 59, 42, 52, 60, 59, 76, 52, 10, 3, 17, 9, 40,
- 3, 48, 10, 70, 3, 2, 22, 20, 15, 36, 22, 50, 15, 70, 22,
- 0, 27, 24, 34, 33, 28, 58, 34, 67, 28, 12, 46, 21, 40, 46,
- 46, 55, 40, 79, 47, 9, 52, 29, 59, 43, 52, 59, 59, 77, 52,
- 9, 3, 16, 9, 39, 3, 49, 10, 69, 3, 3, 22, 19, 15, 37,
- 22, 49, 15, 71, 22, 0, 28, 25, 34, 34, 28, 59, 34, 68, 28,
- 13, 46, 22, 40, 47, 46, 56, 40, 78, 47, 10, 52, 28, 59, 44,
- 52, 58, 59, 78, 52, 8, 3, 16, 10, 38, 3, 50, 10, 68, 3,
- 4, 22, 18, 15, 38, 22, 48, 15, 72, 22, 1, 28, 26, 34, 35,
- 28, 60, 34, 69, 28, 14, 46, 23, 40, 47, 47, 57, 40, 77, 47,
- 11, 52, 27, 59, 45, 52, 57, 59, 79, 52, 7, 3, 17, 10, 37,
- 3, 51, 10, 67, 3, 5, 22, 17, 15, 39, 22, 48, 16, 73, 22,
- 2, 28, 27, 34, 36, 28, 61, 34, 70, 28, 15, 46, 24, 40, 46,
- 47, 58, 40, 76, 47, 12, 52, 26, 59, 46, 52, 56, 59, 79, 53,
- 6, 3, 18, 10, 36, 3, 52, 10, 66, 3, 6, 22, 16, 15, 40,
- 22, 49, 16, 74, 22, 3, 28, 28, 34, 37, 28, 62, 34, 71, 28,
- 15, 47, 25, 40, 45, 47, 59, 40, 75, 47, 13, 52, 25, 59, 47,
- 52, 55, 59, 78, 53, 5, 3, 19, 10, 35, 3, 53, 10, 65, 3,
- 7, 22, 16, 16, 41, 22, 50, 16, 75, 22, 4, 28, 29, 34, 38,
- 28, 63, 34, 72, 28, 14, 47, 26, 40, 44, 47, 60, 40, 74, 47,
- 14, 52, 24, 59, 47, 53, 54, 59, 77, 53, 4, 3, 20, 10, 34,
- 3, 54, 10, 64, 3, 8, 22, 17, 16, 42, 22, 51, 16, 76, 22,
- 5, 28, 30, 34, 39, 28, 63, 35, 73, 28, 13, 47, 27, 40, 43,
- 47, 61, 40, 73, 47, 15, 52, 23, 59, 46, 53, 53, 59, 76, 53,
- 3, 3, 21, 10, 33, 3, 55, 10, 64, 4, 9, 22, 18, 16, 43,
- 22, 52, 16, 77, 22, 6, 28, 31, 34, 40, 28, 62, 35, 74, 28,
- 12, 47, 28, 40, 42, 47, 62, 40, 72, 47, 15, 53, 22, 59, 45,
- 53, 52, 59, 75, 53, 2, 3, 22, 10, 32, 3, 56, 10, 65, 4,
- 10, 22, 19, 16, 44, 22, 53, 16, 78, 22, 7, 28, 31, 35, 41,
- 28, 61, 35, 75, 28, 11, 47, 29, 40, 41, 47, 63, 40, 71, 47,
- 14, 53, 21, 59, 44, 53, 51, 59, 74, 53, 1, 3, 23, 10, 32,
- 4, 57, 10, 66, 4, 11, 22, 20, 16, 45, 22, 54, 16, 79, 22,
- 8, 28, 30, 35, 42, 28, 60, 35, 76, 28, 10, 47, 30, 40, 40,
- 47, 63, 41, 70, 47, 13, 53, 20, 59, 43, 53, 50, 59, 73, 53,
- 0, 3, 24, 10, 33, 4, 58, 10, 67, 4, 12, 22, 21, 16, 46,
- 22, 55, 16, 79, 23, 9, 28, 29, 35, 43, 28, 59, 35, 77, 28,
- 9, 47, 31, 40, 39, 47, 62, 41, 69, 47, 12, 53, 19, 59, 42,
- 53, 49, 59, 72, 53, 0, 4, 25, 10, 34, 4, 59, 10, 68, 4,
- 13, 22, 22, 16, 47, 22, 56, 16, 78, 23, 10, 28, 28, 35, 44,
- 28, 58, 35, 78, 28, 8, 47, 31, 41, 38, 47, 61, 41, 68, 47,
- 11, 53, 18, 59, 41, 53, 48, 59, 71, 53, 1, 4, 26, 10, 35,
- 4, 60, 10, 69, 4, 14, 22, 23, 16, 47, 23, 57, 16, 77, 23,
- 11, 28, 27, 35, 45, 28, 57, 35, 79, 28, 7, 47, 30, 41, 37,
- 47, 60, 41, 67, 47, 10, 53, 17, 59, 40, 53, 48, 48, 70, 53,
- 2, 4, 27, 10, 36, 4, 61, 10, 70, 4, 15, 22, 24, 16, 46,
- 23, 58, 16, 76, 23, 12, 28, 26, 35, 46, 28, 56, 35, 79, 29,
- 6, 47, 29, 41, 36, 47, 59, 41, 66, 47, 9, 53, 16, 59, 39,
- 53, 49, 48, 69, 53, 3, 4, 28, 10, 37, 4, 62, 10, 71, 4,
- 15, 23, 25, 16, 45, 23, 59, 16, 75, 23, 13, 28, 25, 35, 47,
- 28, 55, 35, 78, 29, 5, 47, 28, 41, 35, 47, 58, 41, 65, 47,
- 8, 53, 16, 48, 38, 53, 50, 48, 68, 53, 4, 4, 29, 10, 38,
- 4, 63, 10, 72, 4, 14, 23, 26, 16, 44, 23, 60, 16, 74, 23,
- 14, 28, 24, 35, 47, 29, 54, 35, 77, 29, 4, 47, 27, 41, 34,
- 47, 57, 41, 64, 47, 7, 53, 17, 48, 37, 53, 51, 48, 67, 53,
- 5, 4, 30, 10, 39, 4, 63, 11, 73, 4, 13, 23, 27, 16, 43,
- 23, 61, 16, 73, 23, 15, 28, 23, 35, 46, 29, 53, 35, 76, 29,
- 3, 47, 26, 41, 33, 47, 56, 41, 64, 36, 6, 53, 18, 48, 36,
- 53, 52, 48, 66, 53, 6, 4, 31, 10, 40, 4, 62, 11, 74, 4,
- 12, 23, 28, 16, 42, 23, 62, 16, 72, 23, 15, 29, 22, 35, 45,
- 29, 52, 35, 75, 29, 2, 47, 25, 41, 32, 47, 55, 41, 65, 36,
- 5, 53, 19, 48, 35, 53, 53, 48, 65, 53, 7, 4, 31, 11, 41,
- 4, 61, 11, 75, 4, 11, 23, 29, 16, 41, 23, 63, 16, 71, 23,
- 14, 29, 21, 35, 44, 29, 51, 35, 74, 29, 1, 47, 24, 41, 32,
- 36, 54, 41, 66, 36, 4, 53, 20, 48, 34, 53, 54, 48, 64, 53,
- 8, 4, 30, 11, 42, 4, 60, 11, 76, 4, 10, 23, 30, 16, 40,
- 23, 63, 17, 70, 23, 13, 29, 20, 35, 43, 29, 50, 35, 73, 29,
- 0, 47, 23, 41, 33, 36, 53, 41, 67, 36, 3, 53, 21, 48, 33,
- 53, 55, 48, 64, 54, 9, 4, 29, 11, 43, 4, 59, 11, 77, 4,
- 9, 23, 31, 16, 39, 23, 62, 17, 69, 23, 12, 29, 19, 35, 42,
- 29, 49, 35, 72, 29, 0, 36, 22, 41, 34, 36, 52, 41, 68, 36,
- 2, 53, 22, 48, 32, 53, 56, 48, 65, 54, 10, 4, 28, 11, 44,
- 4, 58, 11, 78, 4, 8, 23, 31, 17, 38, 23, 61, 17, 68, 23,
- 11, 29, 18, 35, 41, 29, 48, 35, 71, 29, 1, 36, 21, 41, 35,
- 36, 51, 41, 69, 36, 1, 53, 23, 48, 32, 54, 57, 48, 66, 54,
- 11, 4, 27, 11, 45, 4, 57, 11, 79, 4, 7, 23, 30, 17, 37,
- 23, 60, 17, 67, 23, 10, 29, 17, 35, 40, 29, 48, 24, 70, 29,
- 2, 36, 20, 41, 36, 36, 50, 41, 70, 36, 0, 53, 24, 48, 33,
- 54, 58, 48, 67, 54, 12, 4, 26, 11, 46, 4, 56, 11, 79, 5,
- 6, 23, 29, 17, 36, 23, 59, 17, 66, 23, 9, 29, 16, 35, 39,
- 29, 49, 24, 69, 29, 3, 36, 19, 41, 37, 36, 49, 41, 71, 36,
- 0, 54, 25, 48, 34, 54, 59, 48, 68, 54, 13, 4, 25, 11, 47,
- 4, 55, 11, 78, 5, 5, 23, 28, 17, 35, 23, 58, 17, 65, 23,
- 8, 29, 16, 24, 38, 29, 50, 24, 68, 29, 4, 36, 18, 41, 38,
- 36, 48, 41, 72, 36, 1, 54, 26, 48, 35, 54, 60, 48, 69, 54,
- 14, 4, 24, 11, 47, 5, 54, 11, 77, 5, 4, 23, 27, 17, 34,
- 23, 57, 17, 64, 23, 7, 29, 17, 24, 37, 29, 51, 24, 67, 29,
- 5, 36, 17, 41, 39, 36, 48, 42, 73, 36, 2, 54, 27, 48, 36,
- 54, 61, 48, 70, 54, 15, 4, 23, 11, 46, 5, 53, 11, 76, 5,
- 3, 23, 26, 17, 33, 23, 56, 17, 64, 12, 6, 29, 18, 24, 36,
- 29, 52, 24, 66, 29, 6, 36, 16, 41, 40, 36, 49, 42, 74, 36,
- 3, 54, 28, 48, 37, 54, 62, 48, 71, 54, 15, 5, 22, 11, 45,
- 5, 52, 11, 75, 5, 2, 23, 25, 17, 32, 23, 55, 17, 65, 12,
- 5, 29, 19, 24, 35, 29, 53, 24, 65, 29, 7, 36, 16, 42, 41,
- 36, 50, 42, 75, 36, 4, 54, 29, 48, 38, 54, 63, 48, 72, 54,
- 14, 5, 21, 11, 44, 5, 51, 11, 74, 5, 1, 23, 24, 17, 32,
- 12, 54, 17, 66, 12, 4, 29, 20, 24, 34, 29, 54, 24, 64, 29,
- 8, 36, 17, 42, 42, 36, 51, 42, 76, 36, 5, 54, 30, 48, 39,
- 54, 63, 49, 73, 54, 13, 5, 20, 11, 43, 5, 50, 11, 73, 5,
- 0, 23, 23, 17, 33, 12, 53, 17, 67, 12, 3, 29, 21, 24, 33,
- 29, 55, 24, 64, 30, 9, 36, 18, 42, 43, 36, 52, 42, 77, 36,
- 6, 54, 31, 48, 40, 54, 62, 49, 74, 54, 12, 5, 19, 11, 42,
- 5, 49, 11, 72, 5, 0, 12, 22, 17, 34, 12, 52, 17, 68, 12,
- 2, 29, 22, 24, 32, 29, 56, 24, 65, 30, 10, 36, 19, 42, 44,
- 36, 53, 42, 78, 36, 7, 54, 31, 49, 41, 54, 61, 49, 75, 54,
- 11, 5, 18, 11, 41, 5, 48, 11, 71, 5, 1, 12, 21, 17, 35,
- 12, 51, 17, 69, 12, 1, 29, 23, 24, 32, 30, 57, 24, 66, 30,
- 11, 36, 20, 42, 45, 36, 54, 42, 79, 36, 8, 54, 30, 49, 42,
- 54, 60, 49, 76, 54, 10, 5, 17, 11, 40, 5, 48, 0, 70, 5,
- 2, 12, 20, 17, 36, 12, 50, 17, 70, 12, 0, 29, 24, 24, 33,
- 30, 58, 24, 67, 30, 12, 36, 21, 42, 46, 36, 55, 42, 79, 37,
- 9, 54, 29, 49, 43, 54, 59, 49, 77, 54, 9, 5, 16, 11, 39,
- 5, 49, 0, 69, 5, 3, 12, 19, 17, 37, 12, 49, 17, 71, 12,
- 0, 30, 25, 24, 34, 30, 59, 24, 68, 30, 13, 36, 22, 42, 47,
- 36, 56, 42, 78, 37, 10, 54, 28, 49, 44, 54, 58, 49, 78, 54,
- 8, 5, 16, 0, 38, 5, 50, 0, 68, 5, 4, 12, 18, 17, 38,
- 12, 48, 17, 72, 12, 1, 30, 26, 24, 35, 30, 60, 24, 69, 30,
- 14, 36, 23, 42, 47, 37, 57, 42, 77, 37, 11, 54, 27, 49, 45,
- 54, 57, 49, 79, 54, 7, 5, 17, 0, 37, 5, 51, 0, 67, 5,
- 5, 12, 17, 17, 39, 12, 48, 18, 73, 12, 2, 30, 27, 24, 36,
- 30, 61, 24, 70, 30, 15, 36, 24, 42, 46, 37, 58, 42, 76, 37,
- 12, 54, 26, 49, 46, 54, 56, 49, 79, 55, 6, 5, 18, 0, 36,
- 5, 52, 0, 66, 5, 6, 12, 16, 17, 40, 12, 49, 18, 74, 12,
- 3, 30, 28, 24, 37, 30, 62, 24, 71, 30, 15, 37, 25, 42, 45,
- 37, 59, 42, 75, 37, 13, 54, 25, 49, 47, 54, 55, 49, 78, 55,
- 5, 5, 19, 0, 35, 5, 53, 0, 65, 5, 7, 12, 16, 18, 41,
- 12, 50, 18, 75, 12, 4, 30, 29, 24, 38, 30, 63, 24, 72, 30,
- 14, 37, 26, 42, 44, 37, 60, 42, 74, 37, 14, 54, 24, 49, 47,
- 55, 54, 49, 77, 55, 4, 5, 20, 0, 34, 5, 54, 0, 64, 5,
- 8, 12, 17, 18, 42, 12, 51, 18, 76, 12, 5, 30, 30, 24, 39,
- 30, 63, 25, 73, 30, 13, 37, 27, 42, 43, 37, 61, 42, 73, 37,
- 15, 54, 23, 49, 46, 55, 53, 49, 76, 55, 3, 5, 21, 0, 33,
- 5, 55, 0, 64, 6, 9, 12, 18, 18, 43, 12, 52, 18, 77, 12,
- 6, 30, 31, 24, 40, 30, 62, 25, 74, 30, 12, 37, 28, 42, 42,
- 37, 62, 42, 72, 37, 15, 55, 22, 49, 45, 55, 52, 49, 75, 55,
- 2, 5, 22, 0, 32, 5, 56, 0, 65, 6, 10, 12, 19, 18, 44,
- 12, 53, 18, 78, 12, 7, 30, 31, 25, 41, 30, 61, 25, 75, 30,
- 11, 37, 29, 42, 41, 37, 63, 42, 71, 37, 14, 55, 21, 49, 44,
- 55, 51, 49, 74, 55, 1, 5, 23, 0, 32, 6, 57, 0, 66, 6,
- 11, 12, 20, 18, 45, 12, 54, 18, 79, 12, 8, 30, 30, 25, 42,
- 30, 60, 25, 76, 30, 10, 37, 30, 42, 40, 37, 63, 43, 70, 37,
- 13, 55, 20, 49, 43, 55, 50, 49, 73, 55, 0, 5, 24, 0, 33,
- 6, 58, 0, 67, 6, 12, 12, 21, 18, 46, 12, 55, 18, 79, 13,
- 9, 30, 29, 25, 43, 30, 59, 25, 77, 30, 9, 37, 31, 42, 39,
- 37, 62, 43, 69, 37, 12, 55, 19, 49, 42, 55, 49, 49, 72, 55,
- 0, 6, 25, 0, 34, 6, 59, 0, 68, 6, 13, 12, 22, 18, 47,
- 12, 56, 18, 78, 13, 10, 30, 28, 25, 44, 30, 58, 25, 78, 30,
- 8, 37, 31, 43, 38, 37, 61, 43, 68, 37, 11, 55, 18, 49, 41,
- 55, 48, 49, 71, 55, 1, 6, 26, 0, 35, 6, 60, 0, 69, 6,
- 14, 12, 23, 18, 47, 13, 57, 18, 77, 13, 11, 30, 27, 25, 45,
- 30, 57, 25, 79, 30, 7, 37, 30, 43, 37, 37, 60, 43, 67, 37,
- 10, 55, 17, 49, 40, 55, 48, 50, 70, 55, 2, 6, 27, 0, 36,
- 6, 61, 0, 70, 6, 15, 12, 24, 18, 46, 13, 58, 18, 76, 13,
- 12, 30, 26, 25, 46, 30, 56, 25, 79, 31, 6, 37, 29, 43, 36,
- 37, 59, 43, 66, 37, 9, 55, 16, 49, 39, 55, 49, 50, 69, 55,
- 3, 6, 28, 0, 37, 6, 62, 0, 71, 6, 15, 13, 25, 18, 45,
- 13, 59, 18, 75, 13, 13, 30, 25, 25, 47, 30, 55, 25, 78, 31,
- 5, 37, 28, 43, 35, 37, 58, 43, 65, 37, 8, 55, 16, 50, 38,
- 55, 50, 50, 68, 55, 4, 6, 29, 0, 38, 6, 63, 0, 72, 6,
- 14, 13, 26, 18, 44, 13, 60, 18, 74, 13, 14, 30, 24, 25, 47,
- 31, 54, 25, 77, 31, 4, 37, 27, 43, 34, 37, 57, 43, 64, 37,
- 7, 55, 17, 50, 37, 55, 51, 50, 67, 55, 5, 6, 30, 0, 39,
- 6, 63, 1, 73, 6, 13, 13, 27, 18, 43, 13, 61, 18, 73, 13,
- 15, 30, 23, 25, 46, 31, 53, 25, 76, 31, 3, 37, 26, 43, 33,
- 37, 56, 43, 64, 38, 6, 55, 18, 50, 36, 55, 52, 50, 66, 55,
- 6, 6, 31, 0, 40, 6, 62, 1, 74, 6, 12, 13, 28, 18, 42,
- 13, 62, 18, 72, 13, 15, 31, 22, 25, 45, 31, 52, 25, 75, 31,
- 2, 37, 25, 43, 32, 37, 55, 43, 65, 38, 5, 55, 19, 50, 35,
- 55, 53, 50, 65, 55, 7, 6, 31, 1, 41, 6, 61, 1, 75, 6,
- 11, 13, 29, 18, 41, 13, 63, 18, 71, 13, 14, 31, 21, 25, 44,
- 31, 51, 25, 74, 31, 1, 37, 24, 43, 32, 38, 54, 43, 66, 38,
- 4, 55, 20, 50, 34, 55, 54, 50, 64, 55, 8, 6, 30, 1, 42,
- 6, 60, 1, 76, 6, 10, 13, 30, 18, 40, 13, 63, 19, 70, 13,
- 13, 31, 20, 25, 43, 31, 50, 25, 73, 31, 0, 37, 23, 43, 33,
- 38, 53, 43, 67, 38, 3, 55, 21, 50, 33, 55, 55, 50, 64, 56,
- 9, 6, 29, 1, 43, 6, 59, 1, 77, 6, 9, 13, 31, 18, 39,
- 13, 62, 19, 69, 13, 12, 31, 19, 25, 42, 31, 49, 25, 72, 31,
- 0, 38, 22, 43, 34, 38, 52, 43, 68, 38, 2, 55, 22, 50, 32,
- 55, 56, 50, 65, 56, 10, 6, 28, 1, 44, 6, 58, 1, 78, 6,
- 8, 13, 31, 19, 38, 13, 61, 19, 68, 13, 11, 31, 18, 25, 41,
- 31, 48, 25, 71, 31, 1, 38, 21, 43, 35, 38, 51, 43, 69, 38,
- 1, 55, 23, 50, 32, 56, 57, 50, 66, 56, 11, 6, 27, 1, 45,
- 6, 57, 1, 79, 6, 7, 13, 30, 19, 37, 13, 60, 19, 67, 13,
- 10, 31, 17, 25, 40, 31, 48, 26, 70, 31, 2, 38, 20, 43, 36,
- 38, 50, 43, 70, 38, 0, 55, 24, 50, 33, 56, 58, 50, 67, 56,
- 12, 6, 26, 1, 46, 6, 56, 1, 79, 7, 6, 13, 29, 19, 36,
- 13, 59, 19, 66, 13, 9, 31, 16, 25, 39, 31, 49, 26, 69, 31,
- 3, 38, 19, 43, 37, 38, 49, 43, 71, 38, 0, 56, 25, 50, 34,
- 56, 59, 50, 68, 56, 13, 6, 25, 1, 47, 6, 55, 1, 78, 7,
- 5, 13, 28, 19, 35, 13, 58, 19, 65, 13, 8, 31, 16, 26, 38,
- 31, 50, 26, 68, 31, 4, 38, 18, 43, 38, 38, 48, 43, 72, 38,
- 1, 56, 26, 50, 35, 56, 60, 50, 69, 56, 14, 6, 24, 1, 47,
- 7, 54, 1, 77, 7, 4, 13, 27, 19, 34, 13, 57, 19, 64, 13,
- 7, 31, 17, 26, 37, 31, 51, 26, 67, 31, 5, 38, 17, 43, 39,
- 38, 48, 44, 73, 38, 2, 56, 27, 50, 36, 56, 61, 50, 70, 56,
- 15, 6, 23, 1, 46, 7, 53, 1, 76, 7, 3, 13, 26, 19, 33,
- 13, 56, 19, 64, 14, 6, 31, 18, 26, 36, 31, 52, 26, 66, 31,
- 6, 38, 16, 43, 40, 38, 49, 44, 74, 38, 3, 56, 28, 50, 37,
- 56, 62, 50, 71, 56, 15, 7, 22, 1, 45, 7, 52, 1, 75, 7,
- 2, 13, 25, 19, 32, 13, 55, 19, 65, 14, 5, 31, 19, 26, 35,
- 31, 53, 26, 65, 31, 7, 38, 16, 44, 41, 38, 50, 44, 75, 38,
- 4, 56, 29, 50, 38, 56, 63, 50, 72, 56, 14, 7, 21, 1, 44,
- 7, 51, 1, 74, 7, 1, 13, 24, 19, 32, 14, 54, 19, 66, 14,
- 4, 31, 20, 26, 34, 31, 54, 26, 64, 31, 8, 38, 17, 44, 42,
- 38, 51, 44, 76, 38, 5, 56, 30, 50, 39, 56, 63, 51, 73, 56,
- 13, 7, 20, 1, 43, 7, 50, 1, 73, 7, 0, 13, 23, 19, 33,
- 14, 53, 19, 67, 14, 3, 31, 21, 26, 33, 31, 55, 26, 64, 32,
- 9, 38, 18, 44, 43, 38, 52, 44, 77, 38, 6, 56, 31, 50, 40,
- 56, 62, 51, 74, 56, 12, 7, 19, 1, 42, 7, 49, 1, 72, 7,
- 0, 14, 22, 19, 34, 14, 52, 19, 68, 14, 2, 31, 22, 26, 32,
- 31, 56, 26, 65, 32, 10, 38, 19, 44, 44, 38, 53, 44, 78, 38,
- 7, 56, 31, 51, 41, 56, 61, 51, 75, 56, 11, 7, 18, 1, 41,
- 7, 48, 1, 71, 7, 1, 14, 21, 19, 35, 14, 51, 19, 69, 14,
- 1, 31, 23, 26, 32, 32, 57, 26, 66, 32, 11, 38, 20, 44, 45,
- 38, 54, 44, 79, 38, 8, 56, 30, 51, 42, 56, 60, 51, 76, 56,
- 10, 7, 17, 1, 40, 7, 48, 2, 70, 7, 2, 14, 20, 19, 36,
- 14, 50, 19, 70, 14, 0, 31, 24, 26, 33, 32, 58, 26, 67, 32,
- 12, 38, 21, 44, 46, 38, 55, 44, 79, 39, 9, 56, 29, 51, 43,
- 56, 59, 51, 77, 56, 9, 7, 16, 1, 39, 7, 49, 2, 69, 7,
- 3, 14, 19, 19, 37, 14, 49, 19, 71, 14, 0, 32, 25, 26, 34,
- 32, 59, 26, 68, 32, 13, 38, 22, 44, 47, 38, 56, 44, 78, 39,
- 10, 56, 28, 51, 44, 56, 58, 51, 78, 56, 8, 7, 16, 2, 38,
- 7, 50, 2, 68, 7, 4, 14, 18, 19, 38, 14, 48, 19, 72, 14,
- 1, 32, 26, 26, 35, 32, 60, 26, 69, 32, 14, 38, 23, 44, 47,
- 39, 57, 44, 77, 39, 11, 56, 27, 51, 45, 56, 57, 51, 79, 56,
- 7, 7, 17, 2, 37, 7, 51, 2, 67, 7, 5, 14, 17, 19, 39,
- 14, 48, 20, 73, 14, 2, 32, 27, 26, 36, 32, 61, 26, 70, 32,
- 15, 38, 24, 44, 46, 39, 58, 44, 76, 39, 12, 56, 26, 51, 46,
- 56, 56, 51, 79, 57, 6, 7, 18, 2, 36, 7, 52, 2, 66, 7,
- 6, 14, 16, 19, 40, 14, 49, 20, 74, 14, 3, 32, 28, 26, 37,
- 32, 62, 26, 71, 32, 15, 39, 25, 44, 45, 39, 59, 44, 75, 39,
- 13, 56, 25, 51, 47, 56, 55, 51, 78, 57, 5, 7, 19, 2, 35,
- 7, 53, 2, 65, 7, 7, 14, 16, 20, 41, 14, 50, 20, 75, 14,
- 4, 32, 29, 26, 38, 32, 63, 26, 72, 32, 14, 39, 26, 44, 44,
- 39, 60, 44, 74, 39, 14, 56, 24, 51, 47, 57, 54, 51, 77, 57,
- 4, 7, 20, 2, 34, 7, 54, 2, 64, 7, 8, 14, 17, 20, 42,
- 14, 51, 20, 76, 14, 5, 32, 30, 26, 39, 32, 63, 27, 73, 32,
- 13, 39, 27, 44, 43, 39, 61, 44, 73, 39, 15, 56, 23, 51, 46,
- 57, 53, 51, 76, 57, 3, 7, 21, 2, 33, 7, 55, 2, 64, 8,
- 9, 14, 18, 20, 43, 14, 52, 20, 77, 14, 6, 32, 31, 26, 40,
- 32, 62, 27, 74, 32, 12, 39, 28, 44, 42, 39, 62, 44, 72, 39,
- 15, 57, 22, 51, 45, 57, 52, 51, 75, 57, 2, 7, 22, 2, 32,
- 7, 56, 2, 65, 8, 10, 14, 19, 20, 44, 14, 53, 20, 78, 14,
- 7, 32, 31, 27, 41, 32, 61, 27, 75, 32, 11, 39, 29, 44, 41,
- 39, 63, 44, 71, 39, 14, 57, 21, 51, 44, 57, 51, 51, 74, 57,
- 1, 7, 23, 2, 32, 8, 57, 2, 66, 8, 11, 14, 20, 20, 45,
- 14, 54, 20, 79, 14, 8, 32, 30, 27, 42, 32, 60, 27, 76, 32,
- 10, 39, 30, 44, 40, 39, 63, 45, 70, 39, 13, 57, 20, 51, 43,
- 57, 50, 51, 73, 57, 0, 7, 24, 2, 33, 8, 58, 2, 67, 8,
- 12, 14, 21, 20, 46, 14, 55, 20, 79, 15, 9, 32, 29, 27, 43,
- 32, 59, 27, 77, 32, 9, 39, 31, 44, 39, 39, 62, 45, 69, 39,
- 12, 57, 19, 51, 42, 57, 49, 51, 72, 57, 0, 8, 25, 2, 34,
- 8, 59, 2, 68, 8, 13, 14, 22, 20, 47, 14, 56, 20, 78, 15,
- 10, 32, 28, 27, 44, 32, 58, 27, 78, 32, 8, 39, 31, 45, 38,
- 39, 61, 45, 68, 39, 11, 57, 18, 51, 41, 57, 48, 51, 71, 57,
- 1, 8, 26, 2, 35, 8, 60, 2, 69, 8, 14, 14, 23, 20, 47,
- 15, 57, 20, 77, 15, 11, 32, 27, 27, 45, 32, 57, 27, 79, 32,
- 7, 39, 30, 45, 37, 39, 60, 45, 67, 39, 10, 57, 17, 51, 40,
- 57, 48, 52, 70, 57, 2, 8, 27, 2, 36, 8, 61, 2, 70, 8,
- 15, 14, 24, 20, 46, 15, 58, 20, 76, 15, 12, 32, 26, 27, 46,
- 32, 56, 27, 79, 33, 6, 39, 29, 45, 36, 39, 59, 45, 66, 39,
- 9, 57, 16, 51, 39, 57, 49, 52, 69, 57, 3, 8, 28, 2, 37,
- 8, 62, 2, 71, 8, 15, 15, 25, 20, 45, 15, 59, 20, 75, 15,
- 13, 32, 25, 27, 47, 32, 55, 27, 78, 33, 5, 39, 28, 45, 35,
- 39, 58, 45, 65, 39, 8, 57, 16, 52, 38, 57, 50, 52, 68, 57,
- 4, 8, 29, 2, 38, 8, 63, 2, 72, 8, 14, 15, 26, 20, 44,
- 15, 60, 20, 74, 15, 14, 32, 24, 27, 47, 33, 54, 27, 77, 33,
- 4, 39, 27, 45, 34, 39, 57, 45, 64, 39, 7, 57, 17, 52, 37,
- 57, 51, 52, 67, 57, 5, 8, 30, 2, 39, 8, 63, 3, 73, 8,
- 13, 15, 27, 20, 43, 15, 61, 20, 73, 15, 15, 32, 23, 27, 46,
- 33, 53, 27, 76, 33, 3, 39, 26, 45, 33, 39, 56, 45, 64, 40,
- 6, 57, 18, 52, 36, 57, 52, 52, 66, 57, 6, 8, 31, 2, 40,
- 8, 62, 3, 74, 8, 12, 15, 28, 20, 42, 15, 62, 20, 72, 15,
- 15, 33, 22, 27, 45, 33, 52, 27, 75, 33, 2, 39, 25, 45, 32,
- 39, 55, 45, 65, 40, 5, 57, 19, 52, 35, 57, 53, 52, 65, 57,
- 7, 8, 31, 3, 41, 8, 61, 3, 75, 8, 11, 15, 29, 20, 41,
- 15, 63, 20, 71, 15, 14, 33, 21, 27, 44, 33, 51, 27, 74, 33,
- 1, 39, 24, 45, 32, 40, 54, 45, 66, 40, 4, 57, 20, 52, 34,
- 57, 54, 52, 64, 57, 8, 8, 30, 3, 42, 8, 60, 3, 76, 8,
- 10, 15, 30, 20, 40, 15, 63, 21, 70, 15, 13, 33, 20, 27, 43,
- 33, 50, 27, 73, 33, 0, 39, 23, 45, 33, 40, 53, 45, 67, 40,
- 3, 57, 21, 52, 33, 57, 55, 52, 64, 58, 9, 8, 29, 3, 43,
- 8, 59, 3, 77, 8, 9, 15, 31, 20, 39, 15, 62, 21, 69, 15,
- 12, 33, 19, 27, 42, 33, 49, 27, 72, 33, 0, 40, 22, 45, 34,
- 40, 52, 45, 68, 40, 2, 57, 22, 52, 32, 57, 56, 52, 65, 58,
- 10, 8, 28, 3, 44, 8, 58, 3, 78, 8, 8, 15, 31, 21, 38,
- 15, 61, 21, 68, 15, 11, 33, 18, 27, 41, 33, 48, 27, 71, 33,
- 1, 40, 21, 45, 35, 40, 51, 45, 69, 40, 1, 57, 23, 52, 32,
- 58, 57, 52, 66, 58, 11, 8, 27, 3, 45, 8, 57, 3, 79, 8,
- 7, 15, 30, 21, 37, 15, 60, 21, 67, 15, 10, 33, 17, 27, 40,
- 33, 48, 28, 70, 33, 2, 40, 20, 45, 36, 40, 50, 45, 70, 40,
- 0, 57, 24, 52, 33, 58, 58, 52, 67, 58, 12, 8, 26, 3, 46,
- 8, 56, 3, 79, 9, 6, 15, 29, 21, 36, 15, 59, 21, 66, 15,
- 9, 33, 16, 27, 39, 33, 49, 28, 69, 33, 3, 40, 19, 45, 37,
- 40, 49, 45, 71, 40, 0, 58, 25, 52, 34, 58, 59, 52, 68, 58,
- 13, 8, 25, 3, 47, 8, 55, 3, 78, 9, 5, 15, 28, 21, 35,
- 15, 58, 21, 65, 15, 8, 33, 16, 28, 38, 33, 50, 28, 68, 33,
- 4, 40, 18, 45, 38, 40, 48, 45, 72, 40, 1, 58, 26, 52, 35,
- 58, 60, 52, 69, 58, 14, 8, 24, 3, 47, 9, 54, 3, 77, 9,
- 4, 15, 27, 21, 34, 15, 57, 21, 64, 15, 7, 33, 17, 28, 37,
- 33, 51, 28, 67, 33, 5, 40, 17, 45, 39, 40, 48, 46, 73, 40,
- 2, 58, 27, 52, 36, 58, 61, 52, 70, 58, 15, 8, 23, 3, 46,
- 9, 53, 3, 76, 9, 3, 15, 26, 21, 33, 15, 56, 21, 64, 16,
- 6, 33, 18, 28, 36, 33, 52, 28, 66, 33, 6, 40, 16, 45, 40,
- 40, 49, 46, 74, 40, 3, 58, 28, 52, 37, 58, 62, 52, 71, 58,
- 15, 9, 22, 3, 45, 9, 52, 3, 75, 9, 2, 15, 25, 21, 32,
- 15, 55, 21, 65, 16, 5, 33, 19, 28, 35, 33, 53, 28, 65, 33,
- 7, 40, 16, 46, 41, 40, 50, 46, 75, 40, 4, 58, 29, 52, 38,
- 58, 63, 52, 72, 58, 14, 9, 21, 3, 44, 9, 51, 3, 74, 9,
- 1, 15, 24, 21, 32, 16, 54, 21, 66, 16, 4, 33, 20, 28, 34,
- 33, 54, 28, 64, 33, 8, 40, 17, 46, 42, 40, 51, 46, 76, 40,
- 5, 58, 30, 52, 39, 58, 63, 53, 73, 58, 13, 9, 20, 3, 43,
- 9, 50, 3, 73, 9, 0, 15, 23, 21, 33, 16, 53, 21, 67, 16,
- 3, 33, 21, 28, 33, 33, 55, 28, 64, 34, 9, 40, 18, 46, 43,
- 40, 52, 46, 77, 40, 6, 58, 31, 52, 40, 58, 62, 53, 74, 58,
- 12, 9, 19, 3, 42, 9, 49, 3, 72, 9, 0, 16, 22, 21, 34,
- 16, 52, 21, 68, 16, 2, 33, 22, 28, 32, 33, 56, 28, 65, 34,
- 10, 40, 19, 46, 44, 40, 53, 46, 78, 40, 7, 58, 31, 53, 41,
- 58, 61, 53, 75, 58, 11, 9, 18, 3, 41, 9, 48, 3, 71, 9,
- 1, 16, 21, 21, 35, 16, 51, 21, 69, 16, 1, 33, 23, 28, 32,
- 34, 57, 28, 66, 34, 11, 40, 20, 46, 45, 40, 54, 46, 79, 40,
- 8, 58, 30, 53, 42, 58, 60, 53, 76, 58, 10, 9, 17, 3, 40,
- 9, 48, 4, 70, 9, 2, 16, 20, 21, 36, 16, 50, 21, 70, 16,
- 0, 33, 24, 28, 33, 34, 58, 28, 67, 34, 12, 40, 21, 46, 46,
- 40, 55, 46, 79, 41, 9, 58, 29, 53, 43, 58, 59, 53, 77, 58,
- 9, 9, 16, 3, 39, 9, 49, 4, 69, 9, 3, 16, 19, 21, 37,
- 16, 49, 21, 71, 16, 0, 34, 25, 28, 34, 34, 59, 28, 68, 34,
- 13, 40, 22, 46, 47, 40, 56, 46, 78, 41, 10, 58, 28, 53, 44,
- 58, 58, 53, 78, 58, 8, 9, 16, 4, 38, 9, 50, 4, 68, 9,
- 4, 16, 18, 21, 38, 16, 48, 21, 72, 16, 1, 34, 26, 28, 35,
- 34, 60, 28, 69, 34, 14, 40, 23, 46, 47, 41, 57, 46, 77, 41,
- 11, 58, 27, 53, 45, 58, 57, 53, 79, 58, 7, 9, 17, 4, 37,
- 9, 51, 4, 67, 9, 5, 16, 17, 21, 39, 16, 48, 22, 73, 16,
- 2, 34, 27, 28, 36, 34, 61, 28, 70, 34, 15, 40, 24, 46, 46,
- 41, 58, 46, 76, 41, 12, 58, 26, 53, 46, 58, 56, 53, 79, 59,
- 6, 9, 18, 4, 36, 9, 52, 4, 66, 9, 6, 16, 16, 21, 40,
- 16, 49, 22, 74, 16, 3, 34, 28, 28, 37, 34, 62, 28, 71, 34,
- 15, 41, 25, 46, 45, 41, 59, 46, 75, 41, 13, 58, 25, 53, 47,
- 58, 55, 53, 78, 59, 5, 9, 19, 4, 35, 9, 53, 4, 65, 9,
- 7, 16, 16, 22, 41, 16, 50, 22, 75, 16, 4, 34, 29, 28, 38,
- 34, 63, 28, 72, 34, 14, 41, 26, 46, 44, 41, 60, 46, 74, 41,
- 14, 58, 24, 53, 47, 59, 54, 53, 77, 59, 4, 9, 20, 4, 34,
- 9, 54, 4, 64, 9, 8, 16, 17, 22, 42, 16, 51, 22, 76, 16,
- 5, 34, 30, 28, 39, 34, 63, 29, 73, 34, 13, 41, 27, 46, 43,
- 41, 61, 46, 73, 41, 15, 58, 23, 53, 46, 59, 53, 53, 76, 59,
- 3, 9, 21, 4, 33, 9, 55, 4, 64, 10, 9, 16, 18, 22, 43,
- 16, 52, 22, 77, 16, 6, 34, 31, 28, 40, 34, 62, 29, 74, 34,
- 12, 41, 28, 46, 42, 41, 62, 46, 72, 41, 15, 59, 22, 53, 45,
- 59, 52, 53, 75, 59, 2, 9, 22, 4, 32, 9, 56, 4, 65, 10,
- 10, 16, 19, 22, 44, 16, 53, 22, 78, 16, 7, 34, 31, 29, 41,
- 34, 61, 29, 75, 34, 11, 41, 29, 46, 41, 41, 63, 46, 71, 41,
- 14, 59, 21, 53, 44, 59, 51, 53, 74, 59, 1, 9, 23, 4, 32,
- 10, 57, 4, 66, 10, 11, 16, 20, 22, 45, 16, 54, 22, 79, 16,
- 8, 34, 30, 29, 42, 34, 60, 29, 76, 34, 10, 41, 30, 46, 40,
- 41, 63, 47, 70, 41, 13, 59, 20, 53, 43, 59, 50, 53, 73, 59,
- 0, 9, 24, 4, 33, 10, 58, 4, 67, 10, 12, 16, 21, 22, 46,
- 16, 55, 22, 79, 17, 9, 34, 29, 29, 43, 34, 59, 29, 77, 34,
- 9, 41, 31, 46, 39, 41, 62, 47, 69, 41, 12, 59, 19, 53, 42,
- 59, 49, 53, 72, 59, 0, 10, 25, 4, 34, 10, 59, 4, 68, 10,
- 13, 16, 22, 22, 47, 16, 56, 22, 78, 17, 10, 34, 28, 29, 44,
- 34, 58, 29, 78, 34, 8, 41, 31, 47, 38, 41, 61, 47, 68, 41,
- 11, 59, 18, 53, 41, 59, 48, 53, 71, 59, 1, 10, 26, 4, 35,
- 10, 60, 4, 69, 10, 14, 16, 23, 22, 47, 17, 57, 22, 77, 17,
- 11, 34, 27, 29, 45, 34, 57, 29, 79, 34, 7, 41, 30, 47, 37,
- 41, 60, 47, 67, 41, 10, 59, 17, 53, 40, 59, 48, 54, 70, 59,
- 2, 10, 27, 4, 36, 10, 61, 4, 70, 10, 15, 16, 24, 22, 46,
- 17, 58, 22, 76, 17, 12, 34, 26, 29, 46, 34, 56, 29, 79, 35,
- 6, 41, 29, 47, 36, 41, 59, 47, 66, 41, 9, 59, 16, 53, 39,
- 59, 49, 54, 69, 59, 3, 10, 28, 4, 37, 10, 62, 4, 71, 10,
- 15, 17, 25, 22, 45, 17, 59, 22, 75, 17, 13, 34, 25, 29, 47,
- 34, 55, 29, 78, 35, 5, 41, 28, 47, 35, 41, 58, 47, 65, 41,
- 8, 59, 16, 54, 38, 59, 50, 54, 68, 59, 4, 10, 29, 4, 38,
- 10, 63, 4, 72, 10, 14, 17, 26, 22, 44, 17, 60, 22, 74, 17,
- 14, 34, 24, 29, 47, 35, 54, 29, 77, 35, 4, 41, 27, 47, 34,
- 41, 57, 47, 64, 41, 7, 59, 17, 54, 37, 59, 51, 54, 67, 59,
- 5, 10, 30, 4, 39, 10, 63, 5, 73, 10, 13, 17, 27, 22, 43,
- 17, 61, 22, 73, 17, 15, 34, 23, 29, 46, 35, 53, 29, 76, 35,
- 3, 41, 26, 47, 33, 41, 56, 47, 64, 42, 6, 59, 18, 54, 36,
- 59, 52, 54, 66, 59, 6, 10, 31, 4, 40, 10, 62, 5, 74, 10,
- 12, 17, 28, 22, 42, 17, 62, 22, 72, 17, 15, 35, 22, 29, 45,
- 35, 52, 29, 75, 35, 2, 41, 25, 47, 32, 41, 55, 47, 65, 42,
- 5, 59, 19, 54, 35, 59, 53, 54, 65, 59, 7, 10, 31, 5, 41,
- 10, 61, 5, 75, 10, 11, 17, 29, 22, 41, 17, 63, 22, 71, 17,
- 14, 35, 21, 29, 44, 35, 51, 29, 74, 35, 1, 41, 24, 47, 32,
- 42, 54, 47, 66, 42, 4, 59, 20, 54, 34, 59, 54, 54, 64, 59,
- 8, 10, 30, 5, 42, 10, 60, 5, 76, 10, 10, 17, 30, 22, 40,
- 17, 63, 23, 70, 17, 13, 35, 20, 29, 43, 35, 50, 29, 73, 35,
- 0, 41, 23, 47, 33, 42, 53, 47, 67, 42, 3, 59, 21, 54, 33,
- 59, 55, 54, 64, 48, 9, 10, 29, 5, 43, 10, 59, 5, 77, 10,
- 9, 17, 31, 22, 39, 17, 62, 23, 69, 17, 12, 35, 19, 29, 42,
- 35, 49, 29, 72, 35, 0, 42, 22, 47, 34, 42, 52, 47, 68, 42,
- 2, 59, 22, 54, 32, 59, 56, 54, 65, 48, 10, 10, 28, 5, 44,
- 10, 58, 5, 78, 10, 8, 17, 31, 23, 38, 17, 61, 23, 68, 17,
- 11, 35, 18, 29, 41, 35, 48, 29, 71, 35, 1, 42, 21, 47, 35,
- 42, 51, 47, 69, 42, 1, 59, 23, 54, 32, 48, 57, 54, 66, 48,
- 11, 10, 27, 5, 45, 10, 57, 5, 79, 10, 7, 17, 30, 23, 37,
- 17, 60, 23, 67, 17, 10, 35, 17, 29, 40, 35, 48, 30, 70, 35,
- 2, 42, 20, 47, 36, 42, 50, 47, 70, 42, 0, 59, 24, 54, 33,
- 48, 58, 54, 67, 48, 12, 10, 26, 5, 46, 10, 56, 5, 79, 11,
- 6, 17, 29, 23, 36, 17, 59, 23, 66, 17, 9, 35, 16, 29, 39,
- 35, 49, 30, 69, 35, 3, 42, 19, 47, 37, 42, 49, 47, 71, 42,
- 0, 48, 25, 54, 34, 48, 59, 54, 68, 48, 13, 10, 25, 5, 47,
- 10, 55, 5, 78, 11, 5, 17, 28, 23, 35, 17, 58, 23, 65, 17,
- 8, 35, 16, 30, 38, 35, 50, 30, 68, 35, 4, 42, 18, 47, 38,
- 42, 48, 47, 72, 42, 1, 48, 26, 54, 35, 48, 60, 54, 69, 48,
- 14, 10, 24, 5, 47, 11, 54, 5, 77, 11, 4, 17, 27, 23, 34,
- 17, 57, 23, 64, 17, 7, 35, 17, 30, 37, 35, 51, 30, 67, 35,
- 5, 42, 17, 47, 39, 42, 48, 36, 73, 42, 2, 48, 27, 54, 36,
- 48, 61, 54, 70, 48, 15, 10, 23, 5, 46, 11, 53, 5, 76, 11,
- 3, 17, 26, 23, 33, 17, 56, 23, 64, 18, 6, 35, 18, 30, 36,
- 35, 52, 30, 66, 35, 6, 42, 16, 47, 40, 42, 49, 36, 74, 42,
- 3, 48, 28, 54, 37, 48, 62, 54, 71, 48, 15, 11, 22, 5, 45,
- 11, 52, 5, 75, 11, 2, 17, 25, 23, 32, 17, 55, 23, 65, 18,
- 5, 35, 19, 30, 35, 35, 53, 30, 65, 35, 7, 42, 16, 36, 41,
- 42, 50, 36, 75, 42, 4, 48, 29, 54, 38, 48, 63, 54, 72, 48,
- 14, 11, 21, 5, 44, 11, 51, 5, 74, 11, 1, 17, 24, 23, 32,
- 18, 54, 23, 66, 18, 4, 35, 20, 30, 34, 35, 54, 30, 64, 35,
- 8, 42, 17, 36, 42, 42, 51, 36, 76, 42, 5, 48, 30, 54, 39,
- 48, 63, 55, 73, 48, 13, 11, 20, 5, 43, 11, 50, 5, 73, 11,
- 0, 17, 23, 23, 33, 18, 53, 23, 67, 18, 3, 35, 21, 30, 33,
- 35, 55, 30, 64, 24, 9, 42, 18, 36, 43, 42, 52, 36, 77, 42,
- 6, 48, 31, 54, 40, 48, 62, 55, 74, 48, 12, 11, 19, 5, 42,
- 11, 49, 5, 72, 11, 0, 18, 22, 23, 34, 18, 52, 23, 68, 18,
- 2, 35, 22, 30, 32, 35, 56, 30, 65, 24, 10, 42, 19, 36, 44,
- 42, 53, 36, 78, 42, 7, 48, 31, 55, 41, 48, 61, 55, 75, 48,
- 11, 11, 18, 5, 41, 11, 48, 5, 71, 11, 1, 18, 21, 23, 35,
- 18, 51, 23, 69, 18, 1, 35, 23, 30, 32, 24, 57, 30, 66, 24,
- 11, 42, 20, 36, 45, 42, 54, 36, 79, 42, 8, 48, 30, 55, 42,
- 48, 60, 55, 76, 48, 10, 11, 17, 5, 40, 11, 48, 6, 70, 11,
- 2, 18, 20, 23, 36, 18, 50, 23, 70, 18, 0, 35, 24, 30, 33,
- 24, 58, 30, 67, 24, 12, 42, 21, 36, 46, 42, 55, 36, 79, 43,
- 9, 48, 29, 55, 43, 48, 59, 55, 77, 48, 9, 11, 16, 5, 39,
- 11, 49, 6, 69, 11, 3, 18, 19, 23, 37, 18, 49, 23, 71, 18,
- 0, 24, 25, 30, 34, 24, 59, 30, 68, 24, 13, 42, 22, 36, 47,
- 42, 56, 36, 78, 43, 10, 48, 28, 55, 44, 48, 58, 55, 78, 48,
- 8, 11, 16, 6, 38, 11, 50, 6, 68, 11, 4, 18, 18, 23, 38,
- 18, 48, 23, 72, 18, 1, 24, 26, 30, 35, 24, 60, 30, 69, 24,
- 14, 42, 23, 36, 47, 43, 57, 36, 77, 43, 11, 48, 27, 55, 45,
- 48, 57, 55, 79, 48, 7, 11, 17, 6, 37, 11, 51, 6, 67, 11,
- 5, 18, 17, 23, 39, 18, 48, 12, 73, 18, 2, 24, 27, 30, 36,
- 24, 61, 30, 70, 24, 15, 42, 24, 36, 46, 43, 58, 36, 76, 43,
- 12, 48, 26, 55, 46, 48, 56, 55, 79, 49, 6, 11, 18, 6, 36,
- 11, 52, 6, 66, 11, 6, 18, 16, 23, 40, 18, 49, 12, 74, 18,
- 3, 24, 28, 30, 37, 24, 62, 30, 71, 24, 15, 43, 25, 36, 45,
- 43, 59, 36, 75, 43, 13, 48, 25, 55, 47, 48, 55, 55, 78, 49,
- 5, 11, 19, 6, 35, 11, 53, 6, 65, 11, 7, 18, 16, 12, 41,
- 18, 50, 12, 75, 18, 4, 24, 29, 30, 38, 24, 63, 30, 72, 24,
- 14, 43, 26, 36, 44, 43, 60, 36, 74, 43, 14, 48, 24, 55, 47,
- 49, 54, 55, 77, 49, 4, 11, 20, 6, 34, 11, 54, 6, 64, 11,
- 8, 18, 17, 12, 42, 18, 51, 12, 76, 18, 5, 24, 30, 30, 39,
- 24, 63, 31, 73, 24, 13, 43, 27, 36, 43, 43, 61, 36, 73, 43,
- 15, 48, 23, 55, 46, 49, 53, 55, 76, 49, 3, 11, 21, 6, 33,
- 11, 55, 6, 64, 0, 9, 18, 18, 12, 43, 18, 52, 12, 77, 18,
- 6, 24, 31, 30, 40, 24, 62, 31, 74, 24, 12, 43, 28, 36, 42,
- 43, 62, 36, 72, 43, 15, 49, 22, 55, 45, 49, 52, 55, 75, 49,
- 2, 11, 22, 6, 32, 11, 56, 6, 65, 0, 10, 18, 19, 12, 44,
- 18, 53, 12, 78, 18, 7, 24, 31, 31, 41, 24, 61, 31, 75, 24,
- 11, 43, 29, 36, 41, 43, 63, 36, 71, 43, 14, 49, 21, 55, 44,
- 49, 51, 55, 74, 49, 1, 11, 23, 6, 32, 0, 57, 6, 66, 0,
- 11, 18, 20, 12, 45, 18, 54, 12, 79, 18, 8, 24, 30, 31, 42,
- 24, 60, 31, 76, 24, 10, 43, 30, 36, 40, 43, 63, 37, 70, 43,
- 13, 49, 20, 55, 43, 49, 50, 55, 73, 49, 0, 11, 24, 6, 33,
- 0, 58, 6, 67, 0, 12, 18, 21, 12, 46, 18, 55, 12, 79, 19,
- 9, 24, 29, 31, 43, 24, 59, 31, 77, 24, 9, 43, 31, 36, 39,
- 43, 62, 37, 69, 43, 12, 49, 19, 55, 42, 49, 49, 55, 72, 49,
-};
-
-static const uint8_t hq_tab_19[] = {
- 0, 0, 25, 4, 34, 0, 59, 4, 12, 12, 21, 8, 46, 12, 55,
- 8, 8, 16, 30, 21, 42, 16, 60, 21, 11, 29, 29, 24, 41, 29,
- 63, 24, 15, 33, 22, 37, 45, 33, 52, 37, 3, 45, 26, 41, 33,
- 45, 56, 41, 1, 0, 26, 4, 35, 0, 60, 4, 13, 12, 22, 8,
- 47, 12, 56, 8, 9, 16, 29, 21, 43, 16, 59, 21, 10, 29, 30,
- 24, 40, 29, 63, 25, 14, 33, 21, 37, 44, 33, 51, 37, 2, 45,
- 25, 41, 32, 45, 55, 41, 2, 0, 27, 4, 36, 0, 61, 4, 14,
- 12, 23, 8, 47, 13, 57, 8, 10, 16, 28, 21, 44, 16, 58, 21,
- 9, 29, 31, 24, 39, 29, 62, 25, 13, 33, 20, 37, 43, 33, 50,
- 37, 1, 45, 24, 41, 32, 46, 54, 41, 3, 0, 28, 4, 37, 0,
- 62, 4, 15, 12, 24, 8, 46, 13, 58, 8, 11, 16, 27, 21, 45,
- 16, 57, 21, 8, 29, 31, 25, 38, 29, 61, 25, 12, 33, 19, 37,
- 42, 33, 49, 37, 0, 45, 23, 41, 33, 46, 53, 41, 4, 0, 29,
- 4, 38, 0, 63, 4, 15, 13, 25, 8, 45, 13, 59, 8, 12, 16,
- 26, 21, 46, 16, 56, 21, 7, 29, 30, 25, 37, 29, 60, 25, 11,
- 33, 18, 37, 41, 33, 48, 37, 0, 46, 22, 41, 34, 46, 52, 41,
- 5, 0, 30, 4, 39, 0, 63, 5, 14, 13, 26, 8, 44, 13, 60,
- 8, 13, 16, 25, 21, 47, 16, 55, 21, 6, 29, 29, 25, 36, 29,
- 59, 25, 10, 33, 17, 37, 40, 33, 48, 38, 1, 46, 21, 41, 35,
- 46, 51, 41, 6, 0, 31, 4, 40, 0, 62, 5, 13, 13, 27, 8,
- 43, 13, 61, 8, 14, 16, 24, 21, 47, 17, 54, 21, 5, 29, 28,
- 25, 35, 29, 58, 25, 9, 33, 16, 37, 39, 33, 49, 38, 2, 46,
- 20, 41, 36, 46, 50, 41, 7, 0, 31, 5, 41, 0, 61, 5, 12,
- 13, 28, 8, 42, 13, 62, 8, 15, 16, 23, 21, 46, 17, 53, 21,
- 4, 29, 27, 25, 34, 29, 57, 25, 8, 33, 16, 38, 38, 33, 50,
- 38, 3, 46, 19, 41, 37, 46, 49, 41, 8, 0, 30, 5, 42, 0,
- 60, 5, 11, 13, 29, 8, 41, 13, 63, 8, 15, 17, 22, 21, 45,
- 17, 52, 21, 3, 29, 26, 25, 33, 29, 56, 25, 7, 33, 17, 38,
- 37, 33, 51, 38, 4, 46, 18, 41, 38, 46, 48, 41, 9, 0, 29,
- 5, 43, 0, 59, 5, 10, 13, 30, 8, 40, 13, 63, 9, 14, 17,
- 21, 21, 44, 17, 51, 21, 2, 29, 25, 25, 32, 29, 55, 25, 6,
- 33, 18, 38, 36, 33, 52, 38, 5, 46, 17, 41, 39, 46, 48, 42,
- 10, 0, 28, 5, 44, 0, 58, 5, 9, 13, 31, 8, 39, 13, 62,
- 9, 13, 17, 20, 21, 43, 17, 50, 21, 1, 29, 24, 25, 32, 30,
- 54, 25, 5, 33, 19, 38, 35, 33, 53, 38, 6, 46, 16, 41, 40,
- 46, 49, 42, 11, 0, 27, 5, 45, 0, 57, 5, 8, 13, 31, 9,
- 38, 13, 61, 9, 12, 17, 19, 21, 42, 17, 49, 21, 0, 29, 23,
- 25, 33, 30, 53, 25, 4, 33, 20, 38, 34, 33, 54, 38, 7, 46,
- 16, 42, 41, 46, 50, 42, 12, 0, 26, 5, 46, 0, 56, 5, 7,
- 13, 30, 9, 37, 13, 60, 9, 11, 17, 18, 21, 41, 17, 48, 21,
- 0, 30, 22, 25, 34, 30, 52, 25, 3, 33, 21, 38, 33, 33, 55,
- 38, 8, 46, 17, 42, 42, 46, 51, 42, 13, 0, 25, 5, 47, 0,
- 55, 5, 6, 13, 29, 9, 36, 13, 59, 9, 10, 17, 17, 21, 40,
- 17, 48, 22, 1, 30, 21, 25, 35, 30, 51, 25, 2, 33, 22, 38,
- 32, 33, 56, 38, 9, 46, 18, 42, 43, 46, 52, 42, 14, 0, 24,
- 5, 47, 1, 54, 5, 5, 13, 28, 9, 35, 13, 58, 9, 9, 17,
- 16, 21, 39, 17, 49, 22, 2, 30, 20, 25, 36, 30, 50, 25, 1,
- 33, 23, 38, 32, 34, 57, 38, 10, 46, 19, 42, 44, 46, 53, 42,
- 15, 0, 23, 5, 46, 1, 53, 5, 4, 13, 27, 9, 34, 13, 57,
- 9, 8, 17, 16, 22, 38, 17, 50, 22, 3, 30, 19, 25, 37, 30,
- 49, 25, 0, 33, 24, 38, 33, 34, 58, 38, 11, 46, 20, 42, 45,
- 46, 54, 42, 15, 1, 22, 5, 45, 1, 52, 5, 3, 13, 26, 9,
- 33, 13, 56, 9, 7, 17, 17, 22, 37, 17, 51, 22, 4, 30, 18,
- 25, 38, 30, 48, 25, 0, 34, 25, 38, 34, 34, 59, 38, 12, 46,
- 21, 42, 46, 46, 55, 42, 14, 1, 21, 5, 44, 1, 51, 5, 2,
- 13, 25, 9, 32, 13, 55, 9, 6, 17, 18, 22, 36, 17, 52, 22,
- 5, 30, 17, 25, 39, 30, 48, 26, 1, 34, 26, 38, 35, 34, 60,
- 38, 13, 46, 22, 42, 47, 46, 56, 42, 13, 1, 20, 5, 43, 1,
- 50, 5, 1, 13, 24, 9, 32, 14, 54, 9, 5, 17, 19, 22, 35,
- 17, 53, 22, 6, 30, 16, 25, 40, 30, 49, 26, 2, 34, 27, 38,
- 36, 34, 61, 38, 14, 46, 23, 42, 47, 47, 57, 42, 12, 1, 19,
- 5, 42, 1, 49, 5, 0, 13, 23, 9, 33, 14, 53, 9, 4, 17,
- 20, 22, 34, 17, 54, 22, 7, 30, 16, 26, 41, 30, 50, 26, 3,
- 34, 28, 38, 37, 34, 62, 38, 15, 46, 24, 42, 46, 47, 58, 42,
- 11, 1, 18, 5, 41, 1, 48, 5, 0, 14, 22, 9, 34, 14, 52,
- 9, 3, 17, 21, 22, 33, 17, 55, 22, 8, 30, 17, 26, 42, 30,
- 51, 26, 4, 34, 29, 38, 38, 34, 63, 38, 15, 47, 25, 42, 45,
- 47, 59, 42, 10, 1, 17, 5, 40, 1, 48, 6, 1, 14, 21, 9,
- 35, 14, 51, 9, 2, 17, 22, 22, 32, 17, 56, 22, 9, 30, 18,
- 26, 43, 30, 52, 26, 5, 34, 30, 38, 39, 34, 63, 39, 14, 47,
- 26, 42, 44, 47, 60, 42, 9, 1, 16, 5, 39, 1, 49, 6, 2,
- 14, 20, 9, 36, 14, 50, 9, 1, 17, 23, 22, 32, 18, 57, 22,
- 10, 30, 19, 26, 44, 30, 53, 26, 6, 34, 31, 38, 40, 34, 62,
- 39, 13, 47, 27, 42, 43, 47, 61, 42, 8, 1, 16, 6, 38, 1,
- 50, 6, 3, 14, 19, 9, 37, 14, 49, 9, 0, 17, 24, 22, 33,
- 18, 58, 22, 11, 30, 20, 26, 45, 30, 54, 26, 7, 34, 31, 39,
- 41, 34, 61, 39, 12, 47, 28, 42, 42, 47, 62, 42, 7, 1, 17,
- 6, 37, 1, 51, 6, 4, 14, 18, 9, 38, 14, 48, 9, 0, 18,
- 25, 22, 34, 18, 59, 22, 12, 30, 21, 26, 46, 30, 55, 26, 8,
- 34, 30, 39, 42, 34, 60, 39, 11, 47, 29, 42, 41, 47, 63, 42,
- 6, 1, 18, 6, 36, 1, 52, 6, 5, 14, 17, 9, 39, 14, 48,
- 10, 1, 18, 26, 22, 35, 18, 60, 22, 13, 30, 22, 26, 47, 30,
- 56, 26, 9, 34, 29, 39, 43, 34, 59, 39, 10, 47, 30, 42, 40,
- 47, 63, 43, 5, 1, 19, 6, 35, 1, 53, 6, 6, 14, 16, 9,
- 40, 14, 49, 10, 2, 18, 27, 22, 36, 18, 61, 22, 14, 30, 23,
- 26, 47, 31, 57, 26, 10, 34, 28, 39, 44, 34, 58, 39, 9, 47,
- 31, 42, 39, 47, 62, 43, 4, 1, 20, 6, 34, 1, 54, 6, 7,
- 14, 16, 10, 41, 14, 50, 10, 3, 18, 28, 22, 37, 18, 62, 22,
- 15, 30, 24, 26, 46, 31, 58, 26, 11, 34, 27, 39, 45, 34, 57,
- 39, 8, 47, 31, 43, 38, 47, 61, 43, 3, 1, 21, 6, 33, 1,
- 55, 6, 8, 14, 17, 10, 42, 14, 51, 10, 4, 18, 29, 22, 38,
- 18, 63, 22, 15, 31, 25, 26, 45, 31, 59, 26, 12, 34, 26, 39,
- 46, 34, 56, 39, 7, 47, 30, 43, 37, 47, 60, 43, 2, 1, 22,
- 6, 32, 1, 56, 6, 9, 14, 18, 10, 43, 14, 52, 10, 5, 18,
- 30, 22, 39, 18, 63, 23, 14, 31, 26, 26, 44, 31, 60, 26, 13,
- 34, 25, 39, 47, 34, 55, 39, 6, 47, 29, 43, 36, 47, 59, 43,
- 1, 1, 23, 6, 32, 2, 57, 6, 10, 14, 19, 10, 44, 14, 53,
- 10, 6, 18, 31, 22, 40, 18, 62, 23, 13, 31, 27, 26, 43, 31,
- 61, 26, 14, 34, 24, 39, 47, 35, 54, 39, 5, 47, 28, 43, 35,
- 47, 58, 43, 0, 1, 24, 6, 33, 2, 58, 6, 11, 14, 20, 10,
- 45, 14, 54, 10, 7, 18, 31, 23, 41, 18, 61, 23, 12, 31, 28,
- 26, 42, 31, 62, 26, 15, 34, 23, 39, 46, 35, 53, 39, 4, 47,
- 27, 43, 34, 47, 57, 43, 0, 2, 25, 6, 34, 2, 59, 6, 12,
- 14, 21, 10, 46, 14, 55, 10, 8, 18, 30, 23, 42, 18, 60, 23,
- 11, 31, 29, 26, 41, 31, 63, 26, 15, 35, 22, 39, 45, 35, 52,
- 39, 3, 47, 26, 43, 33, 47, 56, 43, 1, 2, 26, 6, 35, 2,
- 60, 6, 13, 14, 22, 10, 47, 14, 56, 10, 9, 18, 29, 23, 43,
- 18, 59, 23, 10, 31, 30, 26, 40, 31, 63, 27, 14, 35, 21, 39,
- 44, 35, 51, 39, 2, 47, 25, 43, 32, 47, 55, 43, 2, 2, 27,
- 6, 36, 2, 61, 6, 14, 14, 23, 10, 47, 15, 57, 10, 10, 18,
- 28, 23, 44, 18, 58, 23, 9, 31, 31, 26, 39, 31, 62, 27, 13,
- 35, 20, 39, 43, 35, 50, 39, 1, 47, 24, 43, 32, 40, 54, 43,
- 3, 2, 28, 6, 37, 2, 62, 6, 15, 14, 24, 10, 46, 15, 58,
- 10, 11, 18, 27, 23, 45, 18, 57, 23, 8, 31, 31, 27, 38, 31,
- 61, 27, 12, 35, 19, 39, 42, 35, 49, 39, 0, 47, 23, 43, 33,
- 40, 53, 43, 4, 2, 29, 6, 38, 2, 63, 6, 15, 15, 25, 10,
- 45, 15, 59, 10, 12, 18, 26, 23, 46, 18, 56, 23, 7, 31, 30,
- 27, 37, 31, 60, 27, 11, 35, 18, 39, 41, 35, 48, 39, 0, 40,
- 22, 43, 34, 40, 52, 43, 5, 2, 30, 6, 39, 2, 63, 7, 14,
- 15, 26, 10, 44, 15, 60, 10, 13, 18, 25, 23, 47, 18, 55, 23,
- 6, 31, 29, 27, 36, 31, 59, 27, 10, 35, 17, 39, 40, 35, 48,
- 32, 1, 40, 21, 43, 35, 40, 51, 43, 6, 2, 31, 6, 40, 2,
- 62, 7, 13, 15, 27, 10, 43, 15, 61, 10, 14, 18, 24, 23, 47,
- 19, 54, 23, 5, 31, 28, 27, 35, 31, 58, 27, 9, 35, 16, 39,
- 39, 35, 49, 32, 2, 40, 20, 43, 36, 40, 50, 43, 7, 2, 31,
- 7, 41, 2, 61, 7, 12, 15, 28, 10, 42, 15, 62, 10, 15, 18,
- 23, 23, 46, 19, 53, 23, 4, 31, 27, 27, 34, 31, 57, 27, 8,
- 35, 16, 32, 38, 35, 50, 32, 3, 40, 19, 43, 37, 40, 49, 43,
- 8, 2, 30, 7, 42, 2, 60, 7, 11, 15, 29, 10, 41, 15, 63,
- 10, 15, 19, 22, 23, 45, 19, 52, 23, 3, 31, 26, 27, 33, 31,
- 56, 27, 7, 35, 17, 32, 37, 35, 51, 32, 4, 40, 18, 43, 38,
- 40, 48, 43, 9, 2, 29, 7, 43, 2, 59, 7, 10, 15, 30, 10,
- 40, 15, 63, 11, 14, 19, 21, 23, 44, 19, 51, 23, 2, 31, 25,
- 27, 32, 31, 55, 27, 6, 35, 18, 32, 36, 35, 52, 32, 5, 40,
- 17, 43, 39, 40, 48, 44, 10, 2, 28, 7, 44, 2, 58, 7, 9,
- 15, 31, 10, 39, 15, 62, 11, 13, 19, 20, 23, 43, 19, 50, 23,
- 1, 31, 24, 27, 32, 24, 54, 27, 5, 35, 19, 32, 35, 35, 53,
- 32, 6, 40, 16, 43, 40, 40, 49, 44, 11, 2, 27, 7, 45, 2,
- 57, 7, 8, 15, 31, 11, 38, 15, 61, 11, 12, 19, 19, 23, 42,
- 19, 49, 23, 0, 31, 23, 27, 33, 24, 53, 27, 4, 35, 20, 32,
- 34, 35, 54, 32, 7, 40, 16, 44, 41, 40, 50, 44, 12, 2, 26,
- 7, 46, 2, 56, 7, 7, 15, 30, 11, 37, 15, 60, 11, 11, 19,
- 18, 23, 41, 19, 48, 23, 0, 24, 22, 27, 34, 24, 52, 27, 3,
- 35, 21, 32, 33, 35, 55, 32, 8, 40, 17, 44, 42, 40, 51, 44,
- 13, 2, 25, 7, 47, 2, 55, 7, 6, 15, 29, 11, 36, 15, 59,
- 11, 10, 19, 17, 23, 40, 19, 48, 16, 1, 24, 21, 27, 35, 24,
- 51, 27, 2, 35, 22, 32, 32, 35, 56, 32, 9, 40, 18, 44, 43,
- 40, 52, 44, 14, 2, 24, 7, 47, 3, 54, 7, 5, 15, 28, 11,
- 35, 15, 58, 11, 9, 19, 16, 23, 39, 19, 49, 16, 2, 24, 20,
- 27, 36, 24, 50, 27, 1, 35, 23, 32, 32, 36, 57, 32, 10, 40,
- 19, 44, 44, 40, 53, 44, 15, 2, 23, 7, 46, 3, 53, 7, 4,
- 15, 27, 11, 34, 15, 57, 11, 8, 19, 16, 16, 38, 19, 50, 16,
- 3, 24, 19, 27, 37, 24, 49, 27, 0, 35, 24, 32, 33, 36, 58,
- 32, 11, 40, 20, 44, 45, 40, 54, 44, 15, 3, 22, 7, 45, 3,
- 52, 7, 3, 15, 26, 11, 33, 15, 56, 11, 7, 19, 17, 16, 37,
- 19, 51, 16, 4, 24, 18, 27, 38, 24, 48, 27, 0, 36, 25, 32,
- 34, 36, 59, 32, 12, 40, 21, 44, 46, 40, 55, 44, 14, 3, 21,
- 7, 44, 3, 51, 7, 2, 15, 25, 11, 32, 15, 55, 11, 6, 19,
- 18, 16, 36, 19, 52, 16, 5, 24, 17, 27, 39, 24, 48, 28, 1,
- 36, 26, 32, 35, 36, 60, 32, 13, 40, 22, 44, 47, 40, 56, 44,
- 13, 3, 20, 7, 43, 3, 50, 7, 1, 15, 24, 11, 32, 8, 54,
- 11, 5, 19, 19, 16, 35, 19, 53, 16, 6, 24, 16, 27, 40, 24,
- 49, 28, 2, 36, 27, 32, 36, 36, 61, 32, 14, 40, 23, 44, 47,
- 41, 57, 44, 12, 3, 19, 7, 42, 3, 49, 7, 0, 15, 23, 11,
- 33, 8, 53, 11, 4, 19, 20, 16, 34, 19, 54, 16, 7, 24, 16,
- 28, 41, 24, 50, 28, 3, 36, 28, 32, 37, 36, 62, 32, 15, 40,
- 24, 44, 46, 41, 58, 44, 11, 3, 18, 7, 41, 3, 48, 7, 0,
- 8, 22, 11, 34, 8, 52, 11, 3, 19, 21, 16, 33, 19, 55, 16,
- 8, 24, 17, 28, 42, 24, 51, 28, 4, 36, 29, 32, 38, 36, 63,
- 32, 15, 41, 25, 44, 45, 41, 59, 44, 10, 3, 17, 7, 40, 3,
- 48, 0, 1, 8, 21, 11, 35, 8, 51, 11, 2, 19, 22, 16, 32,
- 19, 56, 16, 9, 24, 18, 28, 43, 24, 52, 28, 5, 36, 30, 32,
- 39, 36, 63, 33, 14, 41, 26, 44, 44, 41, 60, 44, 9, 3, 16,
- 7, 39, 3, 49, 0, 2, 8, 20, 11, 36, 8, 50, 11, 1, 19,
- 23, 16, 32, 20, 57, 16, 10, 24, 19, 28, 44, 24, 53, 28, 6,
- 36, 31, 32, 40, 36, 62, 33, 13, 41, 27, 44, 43, 41, 61, 44,
- 8, 3, 16, 0, 38, 3, 50, 0, 3, 8, 19, 11, 37, 8, 49,
- 11, 0, 19, 24, 16, 33, 20, 58, 16, 11, 24, 20, 28, 45, 24,
- 54, 28, 7, 36, 31, 33, 41, 36, 61, 33, 12, 41, 28, 44, 42,
- 41, 62, 44, 7, 3, 17, 0, 37, 3, 51, 0, 4, 8, 18, 11,
- 38, 8, 48, 11, 0, 20, 25, 16, 34, 20, 59, 16, 12, 24, 21,
- 28, 46, 24, 55, 28, 8, 36, 30, 33, 42, 36, 60, 33, 11, 41,
- 29, 44, 41, 41, 63, 44, 6, 3, 18, 0, 36, 3, 52, 0, 5,
- 8, 17, 11, 39, 8, 48, 12, 1, 20, 26, 16, 35, 20, 60, 16,
- 13, 24, 22, 28, 47, 24, 56, 28, 9, 36, 29, 33, 43, 36, 59,
- 33, 10, 41, 30, 44, 40, 41, 63, 45, 5, 3, 19, 0, 35, 3,
- 53, 0, 6, 8, 16, 11, 40, 8, 49, 12, 2, 20, 27, 16, 36,
- 20, 61, 16, 14, 24, 23, 28, 47, 25, 57, 28, 10, 36, 28, 33,
- 44, 36, 58, 33, 9, 41, 31, 44, 39, 41, 62, 45, 4, 3, 20,
- 0, 34, 3, 54, 0, 7, 8, 16, 12, 41, 8, 50, 12, 3, 20,
- 28, 16, 37, 20, 62, 16, 15, 24, 24, 28, 46, 25, 58, 28, 11,
- 36, 27, 33, 45, 36, 57, 33, 8, 41, 31, 45, 38, 41, 61, 45,
- 3, 3, 21, 0, 33, 3, 55, 0, 8, 8, 17, 12, 42, 8, 51,
- 12, 4, 20, 29, 16, 38, 20, 63, 16, 15, 25, 25, 28, 45, 25,
- 59, 28, 12, 36, 26, 33, 46, 36, 56, 33, 7, 41, 30, 45, 37,
- 41, 60, 45, 2, 3, 22, 0, 32, 3, 56, 0, 9, 8, 18, 12,
- 43, 8, 52, 12, 5, 20, 30, 16, 39, 20, 63, 17, 14, 25, 26,
- 28, 44, 25, 60, 28, 13, 36, 25, 33, 47, 36, 55, 33, 6, 41,
- 29, 45, 36, 41, 59, 45, 1, 3, 23, 0, 32, 4, 57, 0, 10,
- 8, 19, 12, 44, 8, 53, 12, 6, 20, 31, 16, 40, 20, 62, 17,
- 13, 25, 27, 28, 43, 25, 61, 28, 14, 36, 24, 33, 47, 37, 54,
- 33, 5, 41, 28, 45, 35, 41, 58, 45, 0, 3, 24, 0, 33, 4,
- 58, 0, 11, 8, 20, 12, 45, 8, 54, 12, 7, 20, 31, 17, 41,
- 20, 61, 17, 12, 25, 28, 28, 42, 25, 62, 28, 15, 36, 23, 33,
- 46, 37, 53, 33, 4, 41, 27, 45, 34, 41, 57, 45, 0, 4, 25,
- 0, 34, 4, 59, 0, 12, 8, 21, 12, 46, 8, 55, 12, 8, 20,
- 30, 17, 42, 20, 60, 17, 11, 25, 29, 28, 41, 25, 63, 28, 15,
- 37, 22, 33, 45, 37, 52, 33, 3, 41, 26, 45, 33, 41, 56, 45,
- 1, 4, 26, 0, 35, 4, 60, 0, 13, 8, 22, 12, 47, 8, 56,
- 12, 9, 20, 29, 17, 43, 20, 59, 17, 10, 25, 30, 28, 40, 25,
- 63, 29, 14, 37, 21, 33, 44, 37, 51, 33, 2, 41, 25, 45, 32,
- 41, 55, 45, 2, 4, 27, 0, 36, 4, 61, 0, 14, 8, 23, 12,
- 47, 9, 57, 12, 10, 20, 28, 17, 44, 20, 58, 17, 9, 25, 31,
- 28, 39, 25, 62, 29, 13, 37, 20, 33, 43, 37, 50, 33, 1, 41,
- 24, 45, 32, 42, 54, 45, 3, 4, 28, 0, 37, 4, 62, 0, 15,
- 8, 24, 12, 46, 9, 58, 12, 11, 20, 27, 17, 45, 20, 57, 17,
- 8, 25, 31, 29, 38, 25, 61, 29, 12, 37, 19, 33, 42, 37, 49,
- 33, 0, 41, 23, 45, 33, 42, 53, 45, 4, 4, 29, 0, 38, 4,
- 63, 0, 15, 9, 25, 12, 45, 9, 59, 12, 12, 20, 26, 17, 46,
- 20, 56, 17, 7, 25, 30, 29, 37, 25, 60, 29, 11, 37, 18, 33,
- 41, 37, 48, 33, 0, 42, 22, 45, 34, 42, 52, 45, 5, 4, 30,
- 0, 39, 4, 63, 1, 14, 9, 26, 12, 44, 9, 60, 12, 13, 20,
- 25, 17, 47, 20, 55, 17, 6, 25, 29, 29, 36, 25, 59, 29, 10,
- 37, 17, 33, 40, 37, 48, 34, 1, 42, 21, 45, 35, 42, 51, 45,
- 6, 4, 31, 0, 40, 4, 62, 1, 13, 9, 27, 12, 43, 9, 61,
- 12, 14, 20, 24, 17, 47, 21, 54, 17, 5, 25, 28, 29, 35, 25,
- 58, 29, 9, 37, 16, 33, 39, 37, 49, 34, 2, 42, 20, 45, 36,
- 42, 50, 45, 7, 4, 31, 1, 41, 4, 61, 1, 12, 9, 28, 12,
- 42, 9, 62, 12, 15, 20, 23, 17, 46, 21, 53, 17, 4, 25, 27,
- 29, 34, 25, 57, 29, 8, 37, 16, 34, 38, 37, 50, 34, 3, 42,
- 19, 45, 37, 42, 49, 45, 8, 4, 30, 1, 42, 4, 60, 1, 11,
- 9, 29, 12, 41, 9, 63, 12, 15, 21, 22, 17, 45, 21, 52, 17,
- 3, 25, 26, 29, 33, 25, 56, 29, 7, 37, 17, 34, 37, 37, 51,
- 34, 4, 42, 18, 45, 38, 42, 48, 45, 9, 4, 29, 1, 43, 4,
- 59, 1, 10, 9, 30, 12, 40, 9, 63, 13, 14, 21, 21, 17, 44,
- 21, 51, 17, 2, 25, 25, 29, 32, 25, 55, 29, 6, 37, 18, 34,
- 36, 37, 52, 34, 5, 42, 17, 45, 39, 42, 48, 46, 10, 4, 28,
- 1, 44, 4, 58, 1, 9, 9, 31, 12, 39, 9, 62, 13, 13, 21,
- 20, 17, 43, 21, 50, 17, 1, 25, 24, 29, 32, 26, 54, 29, 5,
- 37, 19, 34, 35, 37, 53, 34, 6, 42, 16, 45, 40, 42, 49, 46,
- 11, 4, 27, 1, 45, 4, 57, 1, 8, 9, 31, 13, 38, 9, 61,
- 13, 12, 21, 19, 17, 42, 21, 49, 17, 0, 25, 23, 29, 33, 26,
- 53, 29, 4, 37, 20, 34, 34, 37, 54, 34, 7, 42, 16, 46, 41,
- 42, 50, 46, 12, 4, 26, 1, 46, 4, 56, 1, 7, 9, 30, 13,
- 37, 9, 60, 13, 11, 21, 18, 17, 41, 21, 48, 17, 0, 26, 22,
- 29, 34, 26, 52, 29, 3, 37, 21, 34, 33, 37, 55, 34, 8, 42,
- 17, 46, 42, 42, 51, 46, 13, 4, 25, 1, 47, 4, 55, 1, 6,
- 9, 29, 13, 36, 9, 59, 13, 10, 21, 17, 17, 40, 21, 48, 18,
- 1, 26, 21, 29, 35, 26, 51, 29, 2, 37, 22, 34, 32, 37, 56,
- 34, 9, 42, 18, 46, 43, 42, 52, 46, 14, 4, 24, 1, 47, 5,
- 54, 1, 5, 9, 28, 13, 35, 9, 58, 13, 9, 21, 16, 17, 39,
- 21, 49, 18, 2, 26, 20, 29, 36, 26, 50, 29, 1, 37, 23, 34,
- 32, 38, 57, 34, 10, 42, 19, 46, 44, 42, 53, 46, 15, 4, 23,
- 1, 46, 5, 53, 1, 4, 9, 27, 13, 34, 9, 57, 13, 8, 21,
- 16, 18, 38, 21, 50, 18, 3, 26, 19, 29, 37, 26, 49, 29, 0,
- 37, 24, 34, 33, 38, 58, 34, 11, 42, 20, 46, 45, 42, 54, 46,
- 15, 5, 22, 1, 45, 5, 52, 1, 3, 9, 26, 13, 33, 9, 56,
- 13, 7, 21, 17, 18, 37, 21, 51, 18, 4, 26, 18, 29, 38, 26,
- 48, 29, 0, 38, 25, 34, 34, 38, 59, 34, 12, 42, 21, 46, 46,
- 42, 55, 46, 14, 5, 21, 1, 44, 5, 51, 1, 2, 9, 25, 13,
- 32, 9, 55, 13, 6, 21, 18, 18, 36, 21, 52, 18, 5, 26, 17,
- 29, 39, 26, 48, 30, 1, 38, 26, 34, 35, 38, 60, 34, 13, 42,
- 22, 46, 47, 42, 56, 46, 13, 5, 20, 1, 43, 5, 50, 1, 1,
- 9, 24, 13, 32, 10, 54, 13, 5, 21, 19, 18, 35, 21, 53, 18,
- 6, 26, 16, 29, 40, 26, 49, 30, 2, 38, 27, 34, 36, 38, 61,
- 34, 14, 42, 23, 46, 47, 43, 57, 46, 12, 5, 19, 1, 42, 5,
- 49, 1, 0, 9, 23, 13, 33, 10, 53, 13, 4, 21, 20, 18, 34,
- 21, 54, 18, 7, 26, 16, 30, 41, 26, 50, 30, 3, 38, 28, 34,
- 37, 38, 62, 34, 15, 42, 24, 46, 46, 43, 58, 46, 11, 5, 18,
- 1, 41, 5, 48, 1, 0, 10, 22, 13, 34, 10, 52, 13, 3, 21,
- 21, 18, 33, 21, 55, 18, 8, 26, 17, 30, 42, 26, 51, 30, 4,
- 38, 29, 34, 38, 38, 63, 34, 15, 43, 25, 46, 45, 43, 59, 46,
- 10, 5, 17, 1, 40, 5, 48, 2, 1, 10, 21, 13, 35, 10, 51,
- 13, 2, 21, 22, 18, 32, 21, 56, 18, 9, 26, 18, 30, 43, 26,
- 52, 30, 5, 38, 30, 34, 39, 38, 63, 35, 14, 43, 26, 46, 44,
- 43, 60, 46, 9, 5, 16, 1, 39, 5, 49, 2, 2, 10, 20, 13,
- 36, 10, 50, 13, 1, 21, 23, 18, 32, 22, 57, 18, 10, 26, 19,
- 30, 44, 26, 53, 30, 6, 38, 31, 34, 40, 38, 62, 35, 13, 43,
- 27, 46, 43, 43, 61, 46, 8, 5, 16, 2, 38, 5, 50, 2, 3,
- 10, 19, 13, 37, 10, 49, 13, 0, 21, 24, 18, 33, 22, 58, 18,
- 11, 26, 20, 30, 45, 26, 54, 30, 7, 38, 31, 35, 41, 38, 61,
- 35, 12, 43, 28, 46, 42, 43, 62, 46, 7, 5, 17, 2, 37, 5,
- 51, 2, 4, 10, 18, 13, 38, 10, 48, 13, 0, 22, 25, 18, 34,
- 22, 59, 18, 12, 26, 21, 30, 46, 26, 55, 30, 8, 38, 30, 35,
- 42, 38, 60, 35, 11, 43, 29, 46, 41, 43, 63, 46, 6, 5, 18,
- 2, 36, 5, 52, 2, 5, 10, 17, 13, 39, 10, 48, 14, 1, 22,
- 26, 18, 35, 22, 60, 18, 13, 26, 22, 30, 47, 26, 56, 30, 9,
- 38, 29, 35, 43, 38, 59, 35, 10, 43, 30, 46, 40, 43, 63, 47,
- 5, 5, 19, 2, 35, 5, 53, 2, 6, 10, 16, 13, 40, 10, 49,
- 14, 2, 22, 27, 18, 36, 22, 61, 18, 14, 26, 23, 30, 47, 27,
- 57, 30, 10, 38, 28, 35, 44, 38, 58, 35, 9, 43, 31, 46, 39,
- 43, 62, 47, 4, 5, 20, 2, 34, 5, 54, 2, 7, 10, 16, 14,
- 41, 10, 50, 14, 3, 22, 28, 18, 37, 22, 62, 18, 15, 26, 24,
- 30, 46, 27, 58, 30, 11, 38, 27, 35, 45, 38, 57, 35, 8, 43,
- 31, 47, 38, 43, 61, 47, 3, 5, 21, 2, 33, 5, 55, 2, 8,
- 10, 17, 14, 42, 10, 51, 14, 4, 22, 29, 18, 38, 22, 63, 18,
- 15, 27, 25, 30, 45, 27, 59, 30, 12, 38, 26, 35, 46, 38, 56,
- 35, 7, 43, 30, 47, 37, 43, 60, 47, 2, 5, 22, 2, 32, 5,
- 56, 2, 9, 10, 18, 14, 43, 10, 52, 14, 5, 22, 30, 18, 39,
- 22, 63, 19, 14, 27, 26, 30, 44, 27, 60, 30, 13, 38, 25, 35,
- 47, 38, 55, 35, 6, 43, 29, 47, 36, 43, 59, 47, 1, 5, 23,
- 2, 32, 6, 57, 2, 10, 10, 19, 14, 44, 10, 53, 14, 6, 22,
- 31, 18, 40, 22, 62, 19, 13, 27, 27, 30, 43, 27, 61, 30, 14,
- 38, 24, 35, 47, 39, 54, 35, 5, 43, 28, 47, 35, 43, 58, 47,
- 0, 5, 24, 2, 33, 6, 58, 2, 11, 10, 20, 14, 45, 10, 54,
- 14, 7, 22, 31, 19, 41, 22, 61, 19, 12, 27, 28, 30, 42, 27,
- 62, 30, 15, 38, 23, 35, 46, 39, 53, 35, 4, 43, 27, 47, 34,
- 43, 57, 47, 0, 6, 25, 2, 34, 6, 59, 2, 12, 10, 21, 14,
- 46, 10, 55, 14, 8, 22, 30, 19, 42, 22, 60, 19, 11, 27, 29,
- 30, 41, 27, 63, 30, 15, 39, 22, 35, 45, 39, 52, 35, 3, 43,
- 26, 47, 33, 43, 56, 47, 1, 6, 26, 2, 35, 6, 60, 2, 13,
- 10, 22, 14, 47, 10, 56, 14, 9, 22, 29, 19, 43, 22, 59, 19,
- 10, 27, 30, 30, 40, 27, 63, 31, 14, 39, 21, 35, 44, 39, 51,
- 35, 2, 43, 25, 47, 32, 43, 55, 47, 2, 6, 27, 2, 36, 6,
- 61, 2, 14, 10, 23, 14, 47, 11, 57, 14, 10, 22, 28, 19, 44,
- 22, 58, 19, 9, 27, 31, 30, 39, 27, 62, 31, 13, 39, 20, 35,
- 43, 39, 50, 35, 1, 43, 24, 47, 32, 44, 54, 47, 3, 6, 28,
- 2, 37, 6, 62, 2, 15, 10, 24, 14, 46, 11, 58, 14, 11, 22,
- 27, 19, 45, 22, 57, 19, 8, 27, 31, 31, 38, 27, 61, 31, 12,
- 39, 19, 35, 42, 39, 49, 35, 0, 43, 23, 47, 33, 44, 53, 47,
- 4, 6, 29, 2, 38, 6, 63, 2, 15, 11, 25, 14, 45, 11, 59,
- 14, 12, 22, 26, 19, 46, 22, 56, 19, 7, 27, 30, 31, 37, 27,
- 60, 31, 11, 39, 18, 35, 41, 39, 48, 35, 0, 44, 22, 47, 34,
- 44, 52, 47, 5, 6, 30, 2, 39, 6, 63, 3, 14, 11, 26, 14,
- 44, 11, 60, 14, 13, 22, 25, 19, 47, 22, 55, 19, 6, 27, 29,
- 31, 36, 27, 59, 31, 10, 39, 17, 35, 40, 39, 48, 36, 1, 44,
- 21, 47, 35, 44, 51, 47, 6, 6, 31, 2, 40, 6, 62, 3, 13,
- 11, 27, 14, 43, 11, 61, 14, 14, 22, 24, 19, 47, 23, 54, 19,
- 5, 27, 28, 31, 35, 27, 58, 31, 9, 39, 16, 35, 39, 39, 49,
- 36, 2, 44, 20, 47, 36, 44, 50, 47, 7, 6, 31, 3, 41, 6,
- 61, 3, 12, 11, 28, 14, 42, 11, 62, 14, 15, 22, 23, 19, 46,
- 23, 53, 19, 4, 27, 27, 31, 34, 27, 57, 31, 8, 39, 16, 36,
- 38, 39, 50, 36, 3, 44, 19, 47, 37, 44, 49, 47, 8, 6, 30,
- 3, 42, 6, 60, 3, 11, 11, 29, 14, 41, 11, 63, 14, 15, 23,
- 22, 19, 45, 23, 52, 19, 3, 27, 26, 31, 33, 27, 56, 31, 7,
- 39, 17, 36, 37, 39, 51, 36, 4, 44, 18, 47, 38, 44, 48, 47,
- 9, 6, 29, 3, 43, 6, 59, 3, 10, 11, 30, 14, 40, 11, 63,
- 15, 14, 23, 21, 19, 44, 23, 51, 19, 2, 27, 25, 31, 32, 27,
- 55, 31, 6, 39, 18, 36, 36, 39, 52, 36, 5, 44, 17, 47, 39,
- 44, 48, 40, 10, 6, 28, 3, 44, 6, 58, 3, 9, 11, 31, 14,
- 39, 11, 62, 15, 13, 23, 20, 19, 43, 23, 50, 19, 1, 27, 24,
- 31, 32, 28, 54, 31, 5, 39, 19, 36, 35, 39, 53, 36, 6, 44,
- 16, 47, 40, 44, 49, 40, 11, 6, 27, 3, 45, 6, 57, 3, 8,
- 11, 31, 15, 38, 11, 61, 15, 12, 23, 19, 19, 42, 23, 49, 19,
- 0, 27, 23, 31, 33, 28, 53, 31, 4, 39, 20, 36, 34, 39, 54,
- 36, 7, 44, 16, 40, 41, 44, 50, 40, 12, 6, 26, 3, 46, 6,
- 56, 3, 7, 11, 30, 15, 37, 11, 60, 15, 11, 23, 18, 19, 41,
- 23, 48, 19, 0, 28, 22, 31, 34, 28, 52, 31, 3, 39, 21, 36,
- 33, 39, 55, 36, 8, 44, 17, 40, 42, 44, 51, 40, 13, 6, 25,
- 3, 47, 6, 55, 3, 6, 11, 29, 15, 36, 11, 59, 15, 10, 23,
- 17, 19, 40, 23, 48, 20, 1, 28, 21, 31, 35, 28, 51, 31, 2,
- 39, 22, 36, 32, 39, 56, 36, 9, 44, 18, 40, 43, 44, 52, 40,
- 14, 6, 24, 3, 47, 7, 54, 3, 5, 11, 28, 15, 35, 11, 58,
- 15, 9, 23, 16, 19, 39, 23, 49, 20, 2, 28, 20, 31, 36, 28,
- 50, 31, 1, 39, 23, 36, 32, 32, 57, 36, 10, 44, 19, 40, 44,
- 44, 53, 40, 15, 6, 23, 3, 46, 7, 53, 3, 4, 11, 27, 15,
- 34, 11, 57, 15, 8, 23, 16, 20, 38, 23, 50, 20, 3, 28, 19,
- 31, 37, 28, 49, 31, 0, 39, 24, 36, 33, 32, 58, 36, 11, 44,
- 20, 40, 45, 44, 54, 40, 15, 7, 22, 3, 45, 7, 52, 3, 3,
- 11, 26, 15, 33, 11, 56, 15, 7, 23, 17, 20, 37, 23, 51, 20,
- 4, 28, 18, 31, 38, 28, 48, 31, 0, 32, 25, 36, 34, 32, 59,
- 36, 12, 44, 21, 40, 46, 44, 55, 40, 14, 7, 21, 3, 44, 7,
- 51, 3, 2, 11, 25, 15, 32, 11, 55, 15, 6, 23, 18, 20, 36,
- 23, 52, 20, 5, 28, 17, 31, 39, 28, 48, 24, 1, 32, 26, 36,
- 35, 32, 60, 36, 13, 44, 22, 40, 47, 44, 56, 40, 13, 7, 20,
- 3, 43, 7, 50, 3, 1, 11, 24, 15, 32, 12, 54, 15, 5, 23,
- 19, 20, 35, 23, 53, 20, 6, 28, 16, 31, 40, 28, 49, 24, 2,
- 32, 27, 36, 36, 32, 61, 36, 14, 44, 23, 40, 47, 45, 57, 40,
- 12, 7, 19, 3, 42, 7, 49, 3, 0, 11, 23, 15, 33, 12, 53,
- 15, 4, 23, 20, 20, 34, 23, 54, 20, 7, 28, 16, 24, 41, 28,
- 50, 24, 3, 32, 28, 36, 37, 32, 62, 36, 15, 44, 24, 40, 46,
- 45, 58, 40, 11, 7, 18, 3, 41, 7, 48, 3, 0, 12, 22, 15,
- 34, 12, 52, 15, 3, 23, 21, 20, 33, 23, 55, 20, 8, 28, 17,
- 24, 42, 28, 51, 24, 4, 32, 29, 36, 38, 32, 63, 36, 15, 45,
- 25, 40, 45, 45, 59, 40, 10, 7, 17, 3, 40, 7, 48, 4, 1,
- 12, 21, 15, 35, 12, 51, 15, 2, 23, 22, 20, 32, 23, 56, 20,
- 9, 28, 18, 24, 43, 28, 52, 24, 5, 32, 30, 36, 39, 32, 63,
- 37, 14, 45, 26, 40, 44, 45, 60, 40, 9, 7, 16, 3, 39, 7,
- 49, 4, 2, 12, 20, 15, 36, 12, 50, 15, 1, 23, 23, 20, 32,
- 16, 57, 20, 10, 28, 19, 24, 44, 28, 53, 24, 6, 32, 31, 36,
- 40, 32, 62, 37, 13, 45, 27, 40, 43, 45, 61, 40, 8, 7, 16,
- 4, 38, 7, 50, 4, 3, 12, 19, 15, 37, 12, 49, 15, 0, 23,
- 24, 20, 33, 16, 58, 20, 11, 28, 20, 24, 45, 28, 54, 24, 7,
- 32, 31, 37, 41, 32, 61, 37, 12, 45, 28, 40, 42, 45, 62, 40,
- 7, 7, 17, 4, 37, 7, 51, 4, 4, 12, 18, 15, 38, 12, 48,
- 15, 0, 16, 25, 20, 34, 16, 59, 20, 12, 28, 21, 24, 46, 28,
- 55, 24, 8, 32, 30, 37, 42, 32, 60, 37, 11, 45, 29, 40, 41,
- 45, 63, 40, 6, 7, 18, 4, 36, 7, 52, 4, 5, 12, 17, 15,
- 39, 12, 48, 8, 1, 16, 26, 20, 35, 16, 60, 20, 13, 28, 22,
- 24, 47, 28, 56, 24, 9, 32, 29, 37, 43, 32, 59, 37, 10, 45,
- 30, 40, 40, 45, 63, 41, 5, 7, 19, 4, 35, 7, 53, 4, 6,
- 12, 16, 15, 40, 12, 49, 8, 2, 16, 27, 20, 36, 16, 61, 20,
- 14, 28, 23, 24, 47, 29, 57, 24, 10, 32, 28, 37, 44, 32, 58,
- 37, 9, 45, 31, 40, 39, 45, 62, 41, 4, 7, 20, 4, 34, 7,
- 54, 4, 7, 12, 16, 8, 41, 12, 50, 8, 3, 16, 28, 20, 37,
- 16, 62, 20, 15, 28, 24, 24, 46, 29, 58, 24, 11, 32, 27, 37,
- 45, 32, 57, 37, 8, 45, 31, 41, 38, 45, 61, 41, 3, 7, 21,
- 4, 33, 7, 55, 4, 8, 12, 17, 8, 42, 12, 51, 8, 4, 16,
- 29, 20, 38, 16, 63, 20, 15, 29, 25, 24, 45, 29, 59, 24, 12,
- 32, 26, 37, 46, 32, 56, 37, 7, 45, 30, 41, 37, 45, 60, 41,
- 2, 7, 22, 4, 32, 7, 56, 4, 9, 12, 18, 8, 43, 12, 52,
- 8, 5, 16, 30, 20, 39, 16, 63, 21, 14, 29, 26, 24, 44, 29,
- 60, 24, 13, 32, 25, 37, 47, 32, 55, 37, 6, 45, 29, 41, 36,
- 45, 59, 41, 1, 7, 23, 4, 32, 0, 57, 4, 10, 12, 19, 8,
- 44, 12, 53, 8, 6, 16, 31, 20, 40, 16, 62, 21, 13, 29, 27,
- 24, 43, 29, 61, 24, 14, 32, 24, 37, 47, 33, 54, 37, 5, 45,
- 28, 41, 35, 45, 58, 41, 0, 7, 24, 4, 33, 0, 58, 4, 11,
- 12, 20, 8, 45, 12, 54, 8, 7, 16, 31, 21, 41, 16, 61, 21,
- 12, 29, 28, 24, 42, 29, 62, 24, 15, 32, 23, 37, 46, 33, 53,
- 37, 4, 45, 27, 41, 34, 45, 57, 41,
-};
-
-static const uint8_t hq_tab_20[] = {
- 0, 0, 15, 3, 24, 0, 35, 3, 1, 9, 16, 6, 22, 10, 40,
- 6, 8, 12, 14, 16, 32, 12, 38, 16, 6, 22, 19, 19, 30, 22,
- 39, 19, 5, 25, 21, 29, 25, 25, 41, 29, 1, 0, 14, 3, 25,
- 0, 34, 3, 0, 9, 17, 6, 23, 10, 41, 6, 9, 12, 15, 16,
- 32, 13, 39, 16, 7, 22, 18, 19, 31, 22, 38, 19, 4, 25, 20,
- 29, 24, 25, 40, 29, 2, 0, 13, 3, 26, 0, 33, 3, 0, 10,
- 18, 6, 24, 10, 42, 6, 10, 12, 16, 16, 31, 13, 40, 16, 8,
- 22, 17, 19, 32, 22, 37, 19, 3, 25, 19, 29, 23, 25, 39, 29,
- 3, 0, 12, 3, 27, 0, 33, 4, 1, 10, 19, 6, 25, 10, 43,
- 6, 10, 13, 17, 16, 30, 13, 41, 16, 9, 22, 16, 19, 32, 23,
- 36, 19, 2, 25, 18, 29, 22, 25, 38, 29, 4, 0, 11, 3, 28,
- 0, 34, 4, 2, 10, 20, 6, 26, 10, 43, 7, 9, 13, 18, 16,
- 29, 13, 42, 16, 10, 22, 15, 19, 31, 23, 35, 19, 1, 25, 17,
- 29, 22, 26, 37, 29, 5, 0, 11, 4, 29, 0, 35, 4, 3, 10,
- 21, 6, 27, 10, 42, 7, 8, 13, 19, 16, 28, 13, 43, 16, 10,
- 23, 14, 19, 30, 23, 34, 19, 0, 25, 16, 29, 23, 26, 36, 29,
- 6, 0, 12, 4, 30, 0, 36, 4, 4, 10, 21, 7, 28, 10, 41,
- 7, 7, 13, 20, 16, 27, 13, 43, 17, 9, 23, 13, 19, 29, 23,
- 33, 19, 0, 26, 15, 29, 24, 26, 35, 29, 7, 0, 13, 4, 31,
- 0, 37, 4, 5, 10, 20, 7, 29, 10, 40, 7, 6, 13, 21, 16,
- 26, 13, 42, 17, 8, 23, 12, 19, 28, 23, 33, 20, 1, 26, 14,
- 29, 25, 26, 34, 29, 8, 0, 14, 4, 32, 0, 38, 4, 6, 10,
- 19, 7, 30, 10, 39, 7, 5, 13, 21, 17, 25, 13, 41, 17, 7,
- 23, 11, 19, 27, 23, 34, 20, 2, 26, 13, 29, 26, 26, 33, 29,
- 9, 0, 15, 4, 32, 1, 39, 4, 7, 10, 18, 7, 31, 10, 38,
- 7, 4, 13, 20, 17, 24, 13, 40, 17, 6, 23, 11, 20, 26, 23,
- 35, 20, 3, 26, 12, 29, 27, 26, 33, 24, 10, 0, 16, 4, 31,
- 1, 40, 4, 8, 10, 17, 7, 32, 10, 37, 7, 3, 13, 19, 17,
- 23, 13, 39, 17, 5, 23, 12, 20, 25, 23, 36, 20, 4, 26, 11,
- 29, 28, 26, 34, 24, 10, 1, 17, 4, 30, 1, 41, 4, 9, 10,
- 16, 7, 32, 11, 36, 7, 2, 13, 18, 17, 22, 13, 38, 17, 4,
- 23, 13, 20, 24, 23, 37, 20, 5, 26, 11, 24, 29, 26, 35, 24,
- 9, 1, 18, 4, 29, 1, 42, 4, 10, 10, 15, 7, 31, 11, 35,
- 7, 1, 13, 17, 17, 22, 14, 37, 17, 3, 23, 14, 20, 23, 23,
- 38, 20, 6, 26, 12, 24, 30, 26, 36, 24, 8, 1, 19, 4, 28,
- 1, 43, 4, 10, 11, 14, 7, 30, 11, 34, 7, 0, 13, 16, 17,
- 23, 14, 36, 17, 2, 23, 15, 20, 22, 23, 39, 20, 7, 26, 13,
- 24, 31, 26, 37, 24, 7, 1, 20, 4, 27, 1, 43, 5, 9, 11,
- 13, 7, 29, 11, 33, 7, 0, 14, 15, 17, 24, 14, 35, 17, 1,
- 23, 16, 20, 22, 18, 40, 20, 8, 26, 14, 24, 32, 26, 38, 24,
- 6, 1, 21, 4, 26, 1, 42, 5, 8, 11, 12, 7, 28, 11, 33,
- 8, 1, 14, 14, 17, 25, 14, 34, 17, 0, 23, 17, 20, 23, 18,
- 41, 20, 9, 26, 15, 24, 32, 27, 39, 24, 5, 1, 21, 5, 25,
- 1, 41, 5, 7, 11, 11, 7, 27, 11, 34, 8, 2, 14, 13, 17,
- 26, 14, 33, 17, 0, 18, 18, 20, 24, 18, 42, 20, 10, 26, 16,
- 24, 31, 27, 40, 24, 4, 1, 20, 5, 24, 1, 40, 5, 6, 11,
- 11, 8, 26, 11, 35, 8, 3, 14, 12, 17, 27, 14, 33, 12, 1,
- 18, 19, 20, 25, 18, 43, 20, 10, 27, 17, 24, 30, 27, 41, 24,
- 3, 1, 19, 5, 23, 1, 39, 5, 5, 11, 12, 8, 25, 11, 36,
- 8, 4, 14, 11, 17, 28, 14, 34, 12, 2, 18, 20, 20, 26, 18,
- 43, 21, 9, 27, 18, 24, 29, 27, 42, 24, 2, 1, 18, 5, 22,
- 1, 38, 5, 4, 11, 13, 8, 24, 11, 37, 8, 5, 14, 11, 12,
- 29, 14, 35, 12, 3, 18, 21, 20, 27, 18, 42, 21, 8, 27, 19,
- 24, 28, 27, 43, 24, 1, 1, 17, 5, 22, 2, 37, 5, 3, 11,
- 14, 8, 23, 11, 38, 8, 6, 14, 12, 12, 30, 14, 36, 12, 4,
- 18, 21, 21, 28, 18, 41, 21, 7, 27, 20, 24, 27, 27, 43, 25,
- 0, 1, 16, 5, 23, 2, 36, 5, 2, 11, 15, 8, 22, 11, 39,
- 8, 7, 14, 13, 12, 31, 14, 37, 12, 5, 18, 20, 21, 29, 18,
- 40, 21, 6, 27, 21, 24, 26, 27, 42, 25, 0, 2, 15, 5, 24,
- 2, 35, 5, 1, 11, 16, 8, 22, 6, 40, 8, 8, 14, 14, 12,
- 32, 14, 38, 12, 6, 18, 19, 21, 30, 18, 39, 21, 5, 27, 21,
- 25, 25, 27, 41, 25, 1, 2, 14, 5, 25, 2, 34, 5, 0, 11,
- 17, 8, 23, 6, 41, 8, 9, 14, 15, 12, 32, 15, 39, 12, 7,
- 18, 18, 21, 31, 18, 38, 21, 4, 27, 20, 25, 24, 27, 40, 25,
- 2, 2, 13, 5, 26, 2, 33, 5, 0, 6, 18, 8, 24, 6, 42,
- 8, 10, 14, 16, 12, 31, 15, 40, 12, 8, 18, 17, 21, 32, 18,
- 37, 21, 3, 27, 19, 25, 23, 27, 39, 25, 3, 2, 12, 5, 27,
- 2, 33, 0, 1, 6, 19, 8, 25, 6, 43, 8, 10, 15, 17, 12,
- 30, 15, 41, 12, 9, 18, 16, 21, 32, 19, 36, 21, 2, 27, 18,
- 25, 22, 27, 38, 25, 4, 2, 11, 5, 28, 2, 34, 0, 2, 6,
- 20, 8, 26, 6, 43, 9, 9, 15, 18, 12, 29, 15, 42, 12, 10,
- 18, 15, 21, 31, 19, 35, 21, 1, 27, 17, 25, 22, 28, 37, 25,
- 5, 2, 11, 0, 29, 2, 35, 0, 3, 6, 21, 8, 27, 6, 42,
- 9, 8, 15, 19, 12, 28, 15, 43, 12, 10, 19, 14, 21, 30, 19,
- 34, 21, 0, 27, 16, 25, 23, 28, 36, 25, 6, 2, 12, 0, 30,
- 2, 36, 0, 4, 6, 21, 9, 28, 6, 41, 9, 7, 15, 20, 12,
- 27, 15, 43, 13, 9, 19, 13, 21, 29, 19, 33, 21, 0, 28, 15,
- 25, 24, 28, 35, 25, 7, 2, 13, 0, 31, 2, 37, 0, 5, 6,
- 20, 9, 29, 6, 40, 9, 6, 15, 21, 12, 26, 15, 42, 13, 8,
- 19, 12, 21, 28, 19, 33, 22, 1, 28, 14, 25, 25, 28, 34, 25,
- 8, 2, 14, 0, 32, 2, 38, 0, 6, 6, 19, 9, 30, 6, 39,
- 9, 5, 15, 21, 13, 25, 15, 41, 13, 7, 19, 11, 21, 27, 19,
- 34, 22, 2, 28, 13, 25, 26, 28, 33, 25, 9, 2, 15, 0, 32,
- 3, 39, 0, 7, 6, 18, 9, 31, 6, 38, 9, 4, 15, 20, 13,
- 24, 15, 40, 13, 6, 19, 11, 22, 26, 19, 35, 22, 3, 28, 12,
- 25, 27, 28, 33, 26, 10, 2, 16, 0, 31, 3, 40, 0, 8, 6,
- 17, 9, 32, 6, 37, 9, 3, 15, 19, 13, 23, 15, 39, 13, 5,
- 19, 12, 22, 25, 19, 36, 22, 4, 28, 11, 25, 28, 28, 34, 26,
- 10, 3, 17, 0, 30, 3, 41, 0, 9, 6, 16, 9, 32, 7, 36,
- 9, 2, 15, 18, 13, 22, 15, 38, 13, 4, 19, 13, 22, 24, 19,
- 37, 22, 5, 28, 11, 26, 29, 28, 35, 26, 9, 3, 18, 0, 29,
- 3, 42, 0, 10, 6, 15, 9, 31, 7, 35, 9, 1, 15, 17, 13,
- 22, 16, 37, 13, 3, 19, 14, 22, 23, 19, 38, 22, 6, 28, 12,
- 26, 30, 28, 36, 26, 8, 3, 19, 0, 28, 3, 43, 0, 10, 7,
- 14, 9, 30, 7, 34, 9, 0, 15, 16, 13, 23, 16, 36, 13, 2,
- 19, 15, 22, 22, 19, 39, 22, 7, 28, 13, 26, 31, 28, 37, 26,
- 7, 3, 20, 0, 27, 3, 43, 1, 9, 7, 13, 9, 29, 7, 33,
- 9, 0, 16, 15, 13, 24, 16, 35, 13, 1, 19, 16, 22, 22, 20,
- 40, 22, 8, 28, 14, 26, 32, 28, 38, 26, 6, 3, 21, 0, 26,
- 3, 42, 1, 8, 7, 12, 9, 28, 7, 33, 10, 1, 16, 14, 13,
- 25, 16, 34, 13, 0, 19, 17, 22, 23, 20, 41, 22, 9, 28, 15,
- 26, 32, 29, 39, 26, 5, 3, 21, 1, 25, 3, 41, 1, 7, 7,
- 11, 9, 27, 7, 34, 10, 2, 16, 13, 13, 26, 16, 33, 13, 0,
- 20, 18, 22, 24, 20, 42, 22, 10, 28, 16, 26, 31, 29, 40, 26,
- 4, 3, 20, 1, 24, 3, 40, 1, 6, 7, 11, 10, 26, 7, 35,
- 10, 3, 16, 12, 13, 27, 16, 33, 14, 1, 20, 19, 22, 25, 20,
- 43, 22, 10, 29, 17, 26, 30, 29, 41, 26, 3, 3, 19, 1, 23,
- 3, 39, 1, 5, 7, 12, 10, 25, 7, 36, 10, 4, 16, 11, 13,
- 28, 16, 34, 14, 2, 20, 20, 22, 26, 20, 43, 23, 9, 29, 18,
- 26, 29, 29, 42, 26, 2, 3, 18, 1, 22, 3, 38, 1, 4, 7,
- 13, 10, 24, 7, 37, 10, 5, 16, 11, 14, 29, 16, 35, 14, 3,
- 20, 21, 22, 27, 20, 42, 23, 8, 29, 19, 26, 28, 29, 43, 26,
- 1, 3, 17, 1, 22, 4, 37, 1, 3, 7, 14, 10, 23, 7, 38,
- 10, 6, 16, 12, 14, 30, 16, 36, 14, 4, 20, 21, 23, 28, 20,
- 41, 23, 7, 29, 20, 26, 27, 29, 43, 27, 0, 3, 16, 1, 23,
- 4, 36, 1, 2, 7, 15, 10, 22, 7, 39, 10, 7, 16, 13, 14,
- 31, 16, 37, 14, 5, 20, 20, 23, 29, 20, 40, 23, 6, 29, 21,
- 26, 26, 29, 42, 27, 0, 4, 15, 1, 24, 4, 35, 1, 1, 7,
- 16, 10, 22, 8, 40, 10, 8, 16, 14, 14, 32, 16, 38, 14, 6,
- 20, 19, 23, 30, 20, 39, 23, 5, 29, 21, 27, 25, 29, 41, 27,
- 1, 4, 14, 1, 25, 4, 34, 1, 0, 7, 17, 10, 23, 8, 41,
- 10, 9, 16, 15, 14, 32, 17, 39, 14, 7, 20, 18, 23, 31, 20,
- 38, 23, 4, 29, 20, 27, 24, 29, 40, 27, 2, 4, 13, 1, 26,
- 4, 33, 1, 0, 8, 18, 10, 24, 8, 42, 10, 10, 16, 16, 14,
- 31, 17, 40, 14, 8, 20, 17, 23, 32, 20, 37, 23, 3, 29, 19,
- 27, 23, 29, 39, 27, 3, 4, 12, 1, 27, 4, 33, 2, 1, 8,
- 19, 10, 25, 8, 43, 10, 10, 17, 17, 14, 30, 17, 41, 14, 9,
- 20, 16, 23, 32, 21, 36, 23, 2, 29, 18, 27, 22, 29, 38, 27,
- 4, 4, 11, 1, 28, 4, 34, 2, 2, 8, 20, 10, 26, 8, 43,
- 11, 9, 17, 18, 14, 29, 17, 42, 14, 10, 20, 15, 23, 31, 21,
- 35, 23, 1, 29, 17, 27, 22, 24, 37, 27, 5, 4, 11, 2, 29,
- 4, 35, 2, 3, 8, 21, 10, 27, 8, 42, 11, 8, 17, 19, 14,
- 28, 17, 43, 14, 10, 21, 14, 23, 30, 21, 34, 23, 0, 29, 16,
- 27, 23, 24, 36, 27, 6, 4, 12, 2, 30, 4, 36, 2, 4, 8,
- 21, 11, 28, 8, 41, 11, 7, 17, 20, 14, 27, 17, 43, 15, 9,
- 21, 13, 23, 29, 21, 33, 23, 0, 24, 15, 27, 24, 24, 35, 27,
- 7, 4, 13, 2, 31, 4, 37, 2, 5, 8, 20, 11, 29, 8, 40,
- 11, 6, 17, 21, 14, 26, 17, 42, 15, 8, 21, 12, 23, 28, 21,
- 33, 18, 1, 24, 14, 27, 25, 24, 34, 27, 8, 4, 14, 2, 32,
- 4, 38, 2, 6, 8, 19, 11, 30, 8, 39, 11, 5, 17, 21, 15,
- 25, 17, 41, 15, 7, 21, 11, 23, 27, 21, 34, 18, 2, 24, 13,
- 27, 26, 24, 33, 27, 9, 4, 15, 2, 32, 5, 39, 2, 7, 8,
- 18, 11, 31, 8, 38, 11, 4, 17, 20, 15, 24, 17, 40, 15, 6,
- 21, 11, 18, 26, 21, 35, 18, 3, 24, 12, 27, 27, 24, 33, 28,
- 10, 4, 16, 2, 31, 5, 40, 2, 8, 8, 17, 11, 32, 8, 37,
- 11, 3, 17, 19, 15, 23, 17, 39, 15, 5, 21, 12, 18, 25, 21,
- 36, 18, 4, 24, 11, 27, 28, 24, 34, 28, 10, 5, 17, 2, 30,
- 5, 41, 2, 9, 8, 16, 11, 32, 9, 36, 11, 2, 17, 18, 15,
- 22, 17, 38, 15, 4, 21, 13, 18, 24, 21, 37, 18, 5, 24, 11,
- 28, 29, 24, 35, 28, 9, 5, 18, 2, 29, 5, 42, 2, 10, 8,
- 15, 11, 31, 9, 35, 11, 1, 17, 17, 15, 22, 12, 37, 15, 3,
- 21, 14, 18, 23, 21, 38, 18, 6, 24, 12, 28, 30, 24, 36, 28,
- 8, 5, 19, 2, 28, 5, 43, 2, 10, 9, 14, 11, 30, 9, 34,
- 11, 0, 17, 16, 15, 23, 12, 36, 15, 2, 21, 15, 18, 22, 21,
- 39, 18, 7, 24, 13, 28, 31, 24, 37, 28, 7, 5, 20, 2, 27,
- 5, 43, 3, 9, 9, 13, 11, 29, 9, 33, 11, 0, 12, 15, 15,
- 24, 12, 35, 15, 1, 21, 16, 18, 22, 22, 40, 18, 8, 24, 14,
- 28, 32, 24, 38, 28, 6, 5, 21, 2, 26, 5, 42, 3, 8, 9,
- 12, 11, 28, 9, 33, 6, 1, 12, 14, 15, 25, 12, 34, 15, 0,
- 21, 17, 18, 23, 22, 41, 18, 9, 24, 15, 28, 32, 25, 39, 28,
- 5, 5, 21, 3, 25, 5, 41, 3, 7, 9, 11, 11, 27, 9, 34,
- 6, 2, 12, 13, 15, 26, 12, 33, 15, 0, 22, 18, 18, 24, 22,
- 42, 18, 10, 24, 16, 28, 31, 25, 40, 28, 4, 5, 20, 3, 24,
- 5, 40, 3, 6, 9, 11, 6, 26, 9, 35, 6, 3, 12, 12, 15,
- 27, 12, 33, 16, 1, 22, 19, 18, 25, 22, 43, 18, 10, 25, 17,
- 28, 30, 25, 41, 28, 3, 5, 19, 3, 23, 5, 39, 3, 5, 9,
- 12, 6, 25, 9, 36, 6, 4, 12, 11, 15, 28, 12, 34, 16, 2,
- 22, 20, 18, 26, 22, 43, 19, 9, 25, 18, 28, 29, 25, 42, 28,
- 2, 5, 18, 3, 22, 5, 38, 3, 4, 9, 13, 6, 24, 9, 37,
- 6, 5, 12, 11, 16, 29, 12, 35, 16, 3, 22, 21, 18, 27, 22,
- 42, 19, 8, 25, 19, 28, 28, 25, 43, 28, 1, 5, 17, 3, 22,
- 0, 37, 3, 3, 9, 14, 6, 23, 9, 38, 6, 6, 12, 12, 16,
- 30, 12, 36, 16, 4, 22, 21, 19, 28, 22, 41, 19, 7, 25, 20,
- 28, 27, 25, 43, 29, 0, 5, 16, 3, 23, 0, 36, 3, 2, 9,
- 15, 6, 22, 9, 39, 6, 7, 12, 13, 16, 31, 12, 37, 16, 5,
- 22, 20, 19, 29, 22, 40, 19, 6, 25, 21, 28, 26, 25, 42, 29,
-};
-
-static const uint8_t hq_tab_21[] = {
- 0, 0, 15, 3, 24, 0, 35, 3, 1, 9, 16, 6, 22, 10, 40,
- 6, 8, 12, 14, 16, 32, 12, 38, 16, 6, 22, 19, 19, 30, 22,
- 39, 19, 5, 25, 21, 29, 25, 25, 41, 29, 7, 35, 11, 31, 27,
- 35, 34, 32, 1, 0, 14, 3, 25, 0, 34, 3, 0, 9, 17, 6,
- 23, 10, 41, 6, 9, 12, 15, 16, 32, 13, 39, 16, 7, 22, 18,
- 19, 31, 22, 38, 19, 4, 25, 20, 29, 24, 25, 40, 29, 6, 35,
- 11, 32, 26, 35, 35, 32, 2, 0, 13, 3, 26, 0, 33, 3, 0,
- 10, 18, 6, 24, 10, 42, 6, 10, 12, 16, 16, 31, 13, 40, 16,
- 8, 22, 17, 19, 32, 22, 37, 19, 3, 25, 19, 29, 23, 25, 39,
- 29, 5, 35, 12, 32, 25, 35, 36, 32, 3, 0, 12, 3, 27, 0,
- 33, 4, 1, 10, 19, 6, 25, 10, 43, 6, 10, 13, 17, 16, 30,
- 13, 41, 16, 9, 22, 16, 19, 32, 23, 36, 19, 2, 25, 18, 29,
- 22, 25, 38, 29, 4, 35, 13, 32, 24, 35, 37, 32, 4, 0, 11,
- 3, 28, 0, 34, 4, 2, 10, 20, 6, 26, 10, 43, 7, 9, 13,
- 18, 16, 29, 13, 42, 16, 10, 22, 15, 19, 31, 23, 35, 19, 1,
- 25, 17, 29, 22, 26, 37, 29, 3, 35, 14, 32, 23, 35, 38, 32,
- 5, 0, 11, 4, 29, 0, 35, 4, 3, 10, 21, 6, 27, 10, 42,
- 7, 8, 13, 19, 16, 28, 13, 43, 16, 10, 23, 14, 19, 30, 23,
- 34, 19, 0, 25, 16, 29, 23, 26, 36, 29, 2, 35, 15, 32, 22,
- 35, 39, 32, 6, 0, 12, 4, 30, 0, 36, 4, 4, 10, 21, 7,
- 28, 10, 41, 7, 7, 13, 20, 16, 27, 13, 43, 17, 9, 23, 13,
- 19, 29, 23, 33, 19, 0, 26, 15, 29, 24, 26, 35, 29, 1, 35,
- 16, 32, 22, 30, 40, 32, 7, 0, 13, 4, 31, 0, 37, 4, 5,
- 10, 20, 7, 29, 10, 40, 7, 6, 13, 21, 16, 26, 13, 42, 17,
- 8, 23, 12, 19, 28, 23, 33, 20, 1, 26, 14, 29, 25, 26, 34,
- 29, 0, 35, 17, 32, 23, 30, 41, 32, 8, 0, 14, 4, 32, 0,
- 38, 4, 6, 10, 19, 7, 30, 10, 39, 7, 5, 13, 21, 17, 25,
- 13, 41, 17, 7, 23, 11, 19, 27, 23, 34, 20, 2, 26, 13, 29,
- 26, 26, 33, 29, 0, 30, 18, 32, 24, 30, 42, 32, 9, 0, 15,
- 4, 32, 1, 39, 4, 7, 10, 18, 7, 31, 10, 38, 7, 4, 13,
- 20, 17, 24, 13, 40, 17, 6, 23, 11, 20, 26, 23, 35, 20, 3,
- 26, 12, 29, 27, 26, 33, 24, 1, 30, 19, 32, 25, 30, 43, 32,
- 10, 0, 16, 4, 31, 1, 40, 4, 8, 10, 17, 7, 32, 10, 37,
- 7, 3, 13, 19, 17, 23, 13, 39, 17, 5, 23, 12, 20, 25, 23,
- 36, 20, 4, 26, 11, 29, 28, 26, 34, 24, 2, 30, 20, 32, 26,
- 30, 43, 33, 10, 1, 17, 4, 30, 1, 41, 4, 9, 10, 16, 7,
- 32, 11, 36, 7, 2, 13, 18, 17, 22, 13, 38, 17, 4, 23, 13,
- 20, 24, 23, 37, 20, 5, 26, 11, 24, 29, 26, 35, 24, 3, 30,
- 21, 32, 27, 30, 42, 33, 9, 1, 18, 4, 29, 1, 42, 4, 10,
- 10, 15, 7, 31, 11, 35, 7, 1, 13, 17, 17, 22, 14, 37, 17,
- 3, 23, 14, 20, 23, 23, 38, 20, 6, 26, 12, 24, 30, 26, 36,
- 24, 4, 30, 21, 33, 28, 30, 41, 33, 8, 1, 19, 4, 28, 1,
- 43, 4, 10, 11, 14, 7, 30, 11, 34, 7, 0, 13, 16, 17, 23,
- 14, 36, 17, 2, 23, 15, 20, 22, 23, 39, 20, 7, 26, 13, 24,
- 31, 26, 37, 24, 5, 30, 20, 33, 29, 30, 40, 33, 7, 1, 20,
- 4, 27, 1, 43, 5, 9, 11, 13, 7, 29, 11, 33, 7, 0, 14,
- 15, 17, 24, 14, 35, 17, 1, 23, 16, 20, 22, 18, 40, 20, 8,
- 26, 14, 24, 32, 26, 38, 24, 6, 30, 19, 33, 30, 30, 39, 33,
- 6, 1, 21, 4, 26, 1, 42, 5, 8, 11, 12, 7, 28, 11, 33,
- 8, 1, 14, 14, 17, 25, 14, 34, 17, 0, 23, 17, 20, 23, 18,
- 41, 20, 9, 26, 15, 24, 32, 27, 39, 24, 7, 30, 18, 33, 31,
- 30, 38, 33, 5, 1, 21, 5, 25, 1, 41, 5, 7, 11, 11, 7,
- 27, 11, 34, 8, 2, 14, 13, 17, 26, 14, 33, 17, 0, 18, 18,
- 20, 24, 18, 42, 20, 10, 26, 16, 24, 31, 27, 40, 24, 8, 30,
- 17, 33, 32, 30, 37, 33, 4, 1, 20, 5, 24, 1, 40, 5, 6,
- 11, 11, 8, 26, 11, 35, 8, 3, 14, 12, 17, 27, 14, 33, 12,
- 1, 18, 19, 20, 25, 18, 43, 20, 10, 27, 17, 24, 30, 27, 41,
- 24, 9, 30, 16, 33, 32, 31, 36, 33, 3, 1, 19, 5, 23, 1,
- 39, 5, 5, 11, 12, 8, 25, 11, 36, 8, 4, 14, 11, 17, 28,
- 14, 34, 12, 2, 18, 20, 20, 26, 18, 43, 21, 9, 27, 18, 24,
- 29, 27, 42, 24, 10, 30, 15, 33, 31, 31, 35, 33, 2, 1, 18,
- 5, 22, 1, 38, 5, 4, 11, 13, 8, 24, 11, 37, 8, 5, 14,
- 11, 12, 29, 14, 35, 12, 3, 18, 21, 20, 27, 18, 42, 21, 8,
- 27, 19, 24, 28, 27, 43, 24, 10, 31, 14, 33, 30, 31, 34, 33,
- 1, 1, 17, 5, 22, 2, 37, 5, 3, 11, 14, 8, 23, 11, 38,
- 8, 6, 14, 12, 12, 30, 14, 36, 12, 4, 18, 21, 21, 28, 18,
- 41, 21, 7, 27, 20, 24, 27, 27, 43, 25, 9, 31, 13, 33, 29,
- 31, 33, 33, 0, 1, 16, 5, 23, 2, 36, 5, 2, 11, 15, 8,
- 22, 11, 39, 8, 7, 14, 13, 12, 31, 14, 37, 12, 5, 18, 20,
- 21, 29, 18, 40, 21, 6, 27, 21, 24, 26, 27, 42, 25, 8, 31,
- 12, 33, 28, 31, 33, 34, 0, 2, 15, 5, 24, 2, 35, 5, 1,
- 11, 16, 8, 22, 6, 40, 8, 8, 14, 14, 12, 32, 14, 38, 12,
- 6, 18, 19, 21, 30, 18, 39, 21, 5, 27, 21, 25, 25, 27, 41,
- 25, 7, 31, 11, 33, 27, 31, 34, 34, 1, 2, 14, 5, 25, 2,
- 34, 5, 0, 11, 17, 8, 23, 6, 41, 8, 9, 14, 15, 12, 32,
- 15, 39, 12, 7, 18, 18, 21, 31, 18, 38, 21, 4, 27, 20, 25,
- 24, 27, 40, 25, 6, 31, 11, 34, 26, 31, 35, 34, 2, 2, 13,
- 5, 26, 2, 33, 5, 0, 6, 18, 8, 24, 6, 42, 8, 10, 14,
- 16, 12, 31, 15, 40, 12, 8, 18, 17, 21, 32, 18, 37, 21, 3,
- 27, 19, 25, 23, 27, 39, 25, 5, 31, 12, 34, 25, 31, 36, 34,
- 3, 2, 12, 5, 27, 2, 33, 0, 1, 6, 19, 8, 25, 6, 43,
- 8, 10, 15, 17, 12, 30, 15, 41, 12, 9, 18, 16, 21, 32, 19,
- 36, 21, 2, 27, 18, 25, 22, 27, 38, 25, 4, 31, 13, 34, 24,
- 31, 37, 34, 4, 2, 11, 5, 28, 2, 34, 0, 2, 6, 20, 8,
- 26, 6, 43, 9, 9, 15, 18, 12, 29, 15, 42, 12, 10, 18, 15,
- 21, 31, 19, 35, 21, 1, 27, 17, 25, 22, 28, 37, 25, 3, 31,
- 14, 34, 23, 31, 38, 34, 5, 2, 11, 0, 29, 2, 35, 0, 3,
- 6, 21, 8, 27, 6, 42, 9, 8, 15, 19, 12, 28, 15, 43, 12,
- 10, 19, 14, 21, 30, 19, 34, 21, 0, 27, 16, 25, 23, 28, 36,
- 25, 2, 31, 15, 34, 22, 31, 39, 34, 6, 2, 12, 0, 30, 2,
- 36, 0, 4, 6, 21, 9, 28, 6, 41, 9, 7, 15, 20, 12, 27,
- 15, 43, 13, 9, 19, 13, 21, 29, 19, 33, 21, 0, 28, 15, 25,
- 24, 28, 35, 25, 1, 31, 16, 34, 22, 32, 40, 34, 7, 2, 13,
- 0, 31, 2, 37, 0, 5, 6, 20, 9, 29, 6, 40, 9, 6, 15,
- 21, 12, 26, 15, 42, 13, 8, 19, 12, 21, 28, 19, 33, 22, 1,
- 28, 14, 25, 25, 28, 34, 25, 0, 31, 17, 34, 23, 32, 41, 34,
- 8, 2, 14, 0, 32, 2, 38, 0, 6, 6, 19, 9, 30, 6, 39,
- 9, 5, 15, 21, 13, 25, 15, 41, 13, 7, 19, 11, 21, 27, 19,
- 34, 22, 2, 28, 13, 25, 26, 28, 33, 25, 0, 32, 18, 34, 24,
- 32, 42, 34, 9, 2, 15, 0, 32, 3, 39, 0, 7, 6, 18, 9,
- 31, 6, 38, 9, 4, 15, 20, 13, 24, 15, 40, 13, 6, 19, 11,
- 22, 26, 19, 35, 22, 3, 28, 12, 25, 27, 28, 33, 26, 1, 32,
- 19, 34, 25, 32, 43, 34, 10, 2, 16, 0, 31, 3, 40, 0, 8,
- 6, 17, 9, 32, 6, 37, 9, 3, 15, 19, 13, 23, 15, 39, 13,
- 5, 19, 12, 22, 25, 19, 36, 22, 4, 28, 11, 25, 28, 28, 34,
- 26, 2, 32, 20, 34, 26, 32, 43, 35, 10, 3, 17, 0, 30, 3,
- 41, 0, 9, 6, 16, 9, 32, 7, 36, 9, 2, 15, 18, 13, 22,
- 15, 38, 13, 4, 19, 13, 22, 24, 19, 37, 22, 5, 28, 11, 26,
- 29, 28, 35, 26, 3, 32, 21, 34, 27, 32, 42, 35, 9, 3, 18,
- 0, 29, 3, 42, 0, 10, 6, 15, 9, 31, 7, 35, 9, 1, 15,
- 17, 13, 22, 16, 37, 13, 3, 19, 14, 22, 23, 19, 38, 22, 6,
- 28, 12, 26, 30, 28, 36, 26, 4, 32, 21, 35, 28, 32, 41, 35,
- 8, 3, 19, 0, 28, 3, 43, 0, 10, 7, 14, 9, 30, 7, 34,
- 9, 0, 15, 16, 13, 23, 16, 36, 13, 2, 19, 15, 22, 22, 19,
- 39, 22, 7, 28, 13, 26, 31, 28, 37, 26, 5, 32, 20, 35, 29,
- 32, 40, 35, 7, 3, 20, 0, 27, 3, 43, 1, 9, 7, 13, 9,
- 29, 7, 33, 9, 0, 16, 15, 13, 24, 16, 35, 13, 1, 19, 16,
- 22, 22, 20, 40, 22, 8, 28, 14, 26, 32, 28, 38, 26, 6, 32,
- 19, 35, 30, 32, 39, 35, 6, 3, 21, 0, 26, 3, 42, 1, 8,
- 7, 12, 9, 28, 7, 33, 10, 1, 16, 14, 13, 25, 16, 34, 13,
- 0, 19, 17, 22, 23, 20, 41, 22, 9, 28, 15, 26, 32, 29, 39,
- 26, 7, 32, 18, 35, 31, 32, 38, 35, 5, 3, 21, 1, 25, 3,
- 41, 1, 7, 7, 11, 9, 27, 7, 34, 10, 2, 16, 13, 13, 26,
- 16, 33, 13, 0, 20, 18, 22, 24, 20, 42, 22, 10, 28, 16, 26,
- 31, 29, 40, 26, 8, 32, 17, 35, 32, 32, 37, 35, 4, 3, 20,
- 1, 24, 3, 40, 1, 6, 7, 11, 10, 26, 7, 35, 10, 3, 16,
- 12, 13, 27, 16, 33, 14, 1, 20, 19, 22, 25, 20, 43, 22, 10,
- 29, 17, 26, 30, 29, 41, 26, 9, 32, 16, 35, 32, 33, 36, 35,
- 3, 3, 19, 1, 23, 3, 39, 1, 5, 7, 12, 10, 25, 7, 36,
- 10, 4, 16, 11, 13, 28, 16, 34, 14, 2, 20, 20, 22, 26, 20,
- 43, 23, 9, 29, 18, 26, 29, 29, 42, 26, 10, 32, 15, 35, 31,
- 33, 35, 35, 2, 3, 18, 1, 22, 3, 38, 1, 4, 7, 13, 10,
- 24, 7, 37, 10, 5, 16, 11, 14, 29, 16, 35, 14, 3, 20, 21,
- 22, 27, 20, 42, 23, 8, 29, 19, 26, 28, 29, 43, 26, 10, 33,
- 14, 35, 30, 33, 34, 35, 1, 3, 17, 1, 22, 4, 37, 1, 3,
- 7, 14, 10, 23, 7, 38, 10, 6, 16, 12, 14, 30, 16, 36, 14,
- 4, 20, 21, 23, 28, 20, 41, 23, 7, 29, 20, 26, 27, 29, 43,
- 27, 9, 33, 13, 35, 29, 33, 33, 35, 0, 3, 16, 1, 23, 4,
- 36, 1, 2, 7, 15, 10, 22, 7, 39, 10, 7, 16, 13, 14, 31,
- 16, 37, 14, 5, 20, 20, 23, 29, 20, 40, 23, 6, 29, 21, 26,
- 26, 29, 42, 27, 8, 33, 12, 35, 28, 33, 33, 30, 0, 4, 15,
- 1, 24, 4, 35, 1, 1, 7, 16, 10, 22, 8, 40, 10, 8, 16,
- 14, 14, 32, 16, 38, 14, 6, 20, 19, 23, 30, 20, 39, 23, 5,
- 29, 21, 27, 25, 29, 41, 27, 7, 33, 11, 35, 27, 33, 34, 30,
- 1, 4, 14, 1, 25, 4, 34, 1, 0, 7, 17, 10, 23, 8, 41,
- 10, 9, 16, 15, 14, 32, 17, 39, 14, 7, 20, 18, 23, 31, 20,
- 38, 23, 4, 29, 20, 27, 24, 29, 40, 27, 6, 33, 11, 30, 26,
- 33, 35, 30, 2, 4, 13, 1, 26, 4, 33, 1, 0, 8, 18, 10,
- 24, 8, 42, 10, 10, 16, 16, 14, 31, 17, 40, 14, 8, 20, 17,
- 23, 32, 20, 37, 23, 3, 29, 19, 27, 23, 29, 39, 27, 5, 33,
- 12, 30, 25, 33, 36, 30, 3, 4, 12, 1, 27, 4, 33, 2, 1,
- 8, 19, 10, 25, 8, 43, 10, 10, 17, 17, 14, 30, 17, 41, 14,
- 9, 20, 16, 23, 32, 21, 36, 23, 2, 29, 18, 27, 22, 29, 38,
- 27, 4, 33, 13, 30, 24, 33, 37, 30, 4, 4, 11, 1, 28, 4,
- 34, 2, 2, 8, 20, 10, 26, 8, 43, 11, 9, 17, 18, 14, 29,
- 17, 42, 14, 10, 20, 15, 23, 31, 21, 35, 23, 1, 29, 17, 27,
- 22, 24, 37, 27, 3, 33, 14, 30, 23, 33, 38, 30, 5, 4, 11,
- 2, 29, 4, 35, 2, 3, 8, 21, 10, 27, 8, 42, 11, 8, 17,
- 19, 14, 28, 17, 43, 14, 10, 21, 14, 23, 30, 21, 34, 23, 0,
- 29, 16, 27, 23, 24, 36, 27, 2, 33, 15, 30, 22, 33, 39, 30,
- 6, 4, 12, 2, 30, 4, 36, 2, 4, 8, 21, 11, 28, 8, 41,
- 11, 7, 17, 20, 14, 27, 17, 43, 15, 9, 21, 13, 23, 29, 21,
- 33, 23, 0, 24, 15, 27, 24, 24, 35, 27, 1, 33, 16, 30, 22,
- 34, 40, 30, 7, 4, 13, 2, 31, 4, 37, 2, 5, 8, 20, 11,
- 29, 8, 40, 11, 6, 17, 21, 14, 26, 17, 42, 15, 8, 21, 12,
- 23, 28, 21, 33, 18, 1, 24, 14, 27, 25, 24, 34, 27, 0, 33,
- 17, 30, 23, 34, 41, 30, 8, 4, 14, 2, 32, 4, 38, 2, 6,
- 8, 19, 11, 30, 8, 39, 11, 5, 17, 21, 15, 25, 17, 41, 15,
- 7, 21, 11, 23, 27, 21, 34, 18, 2, 24, 13, 27, 26, 24, 33,
- 27, 0, 34, 18, 30, 24, 34, 42, 30, 9, 4, 15, 2, 32, 5,
- 39, 2, 7, 8, 18, 11, 31, 8, 38, 11, 4, 17, 20, 15, 24,
- 17, 40, 15, 6, 21, 11, 18, 26, 21, 35, 18, 3, 24, 12, 27,
- 27, 24, 33, 28, 1, 34, 19, 30, 25, 34, 43, 30, 10, 4, 16,
- 2, 31, 5, 40, 2, 8, 8, 17, 11, 32, 8, 37, 11, 3, 17,
- 19, 15, 23, 17, 39, 15, 5, 21, 12, 18, 25, 21, 36, 18, 4,
- 24, 11, 27, 28, 24, 34, 28, 2, 34, 20, 30, 26, 34, 43, 31,
- 10, 5, 17, 2, 30, 5, 41, 2, 9, 8, 16, 11, 32, 9, 36,
- 11, 2, 17, 18, 15, 22, 17, 38, 15, 4, 21, 13, 18, 24, 21,
- 37, 18, 5, 24, 11, 28, 29, 24, 35, 28, 3, 34, 21, 30, 27,
- 34, 42, 31, 9, 5, 18, 2, 29, 5, 42, 2, 10, 8, 15, 11,
- 31, 9, 35, 11, 1, 17, 17, 15, 22, 12, 37, 15, 3, 21, 14,
- 18, 23, 21, 38, 18, 6, 24, 12, 28, 30, 24, 36, 28, 4, 34,
- 21, 31, 28, 34, 41, 31, 8, 5, 19, 2, 28, 5, 43, 2, 10,
- 9, 14, 11, 30, 9, 34, 11, 0, 17, 16, 15, 23, 12, 36, 15,
- 2, 21, 15, 18, 22, 21, 39, 18, 7, 24, 13, 28, 31, 24, 37,
- 28, 5, 34, 20, 31, 29, 34, 40, 31, 7, 5, 20, 2, 27, 5,
- 43, 3, 9, 9, 13, 11, 29, 9, 33, 11, 0, 12, 15, 15, 24,
- 12, 35, 15, 1, 21, 16, 18, 22, 22, 40, 18, 8, 24, 14, 28,
- 32, 24, 38, 28, 6, 34, 19, 31, 30, 34, 39, 31, 6, 5, 21,
- 2, 26, 5, 42, 3, 8, 9, 12, 11, 28, 9, 33, 6, 1, 12,
- 14, 15, 25, 12, 34, 15, 0, 21, 17, 18, 23, 22, 41, 18, 9,
- 24, 15, 28, 32, 25, 39, 28, 7, 34, 18, 31, 31, 34, 38, 31,
- 5, 5, 21, 3, 25, 5, 41, 3, 7, 9, 11, 11, 27, 9, 34,
- 6, 2, 12, 13, 15, 26, 12, 33, 15, 0, 22, 18, 18, 24, 22,
- 42, 18, 10, 24, 16, 28, 31, 25, 40, 28, 8, 34, 17, 31, 32,
- 34, 37, 31, 4, 5, 20, 3, 24, 5, 40, 3, 6, 9, 11, 6,
- 26, 9, 35, 6, 3, 12, 12, 15, 27, 12, 33, 16, 1, 22, 19,
- 18, 25, 22, 43, 18, 10, 25, 17, 28, 30, 25, 41, 28, 9, 34,
- 16, 31, 32, 35, 36, 31, 3, 5, 19, 3, 23, 5, 39, 3, 5,
- 9, 12, 6, 25, 9, 36, 6, 4, 12, 11, 15, 28, 12, 34, 16,
- 2, 22, 20, 18, 26, 22, 43, 19, 9, 25, 18, 28, 29, 25, 42,
- 28, 10, 34, 15, 31, 31, 35, 35, 31, 2, 5, 18, 3, 22, 5,
- 38, 3, 4, 9, 13, 6, 24, 9, 37, 6, 5, 12, 11, 16, 29,
- 12, 35, 16, 3, 22, 21, 18, 27, 22, 42, 19, 8, 25, 19, 28,
- 28, 25, 43, 28, 10, 35, 14, 31, 30, 35, 34, 31, 1, 5, 17,
- 3, 22, 0, 37, 3, 3, 9, 14, 6, 23, 9, 38, 6, 6, 12,
- 12, 16, 30, 12, 36, 16, 4, 22, 21, 19, 28, 22, 41, 19, 7,
- 25, 20, 28, 27, 25, 43, 29, 9, 35, 13, 31, 29, 35, 33, 31,
- 0, 5, 16, 3, 23, 0, 36, 3, 2, 9, 15, 6, 22, 9, 39,
- 6, 7, 12, 13, 16, 31, 12, 37, 16, 5, 22, 20, 19, 29, 22,
- 40, 19, 6, 25, 21, 28, 26, 25, 42, 29, 8, 35, 12, 31, 28,
- 35, 33, 32,
-};
-
-/* List of profiles, order is important */
-const HQProfile ff_hq_profile[NUM_HQ_PROFILES] = {
- { hq_tab_11, 160, 120, 8, 10, 8 }, // case 0 (default) = case 11
- { hq_tab_01, 720, 480, 8, 25, 54 },
- { hq_tab_02, 720, 486, 8, 15, 93 },
- { hq_tab_03, 720, 576, 8, 20, 81 },
- { hq_tab_04, 960, 720, 12, 25, 108 },
- { hq_tab_05, 1280, 720, 16, 25, 144 },
- { hq_tab_06, 1280, 1080, 16, 20, 272 },
- { hq_tab_07, 1440, 1080, 16, 24, 255 },
- { hq_tab_08, 1920, 1080, 20, 24, 340 },
- { hq_tab_09, 640, 480, 8, 25, 48 },
- { hq_tab_10, 320, 240, 8, 25, 12 },
- { hq_tab_11, 160, 120, 8, 10, 8 },
- { hq_tab_12, 800, 600, 8, 25, 76 },
- { hq_tab_13, 352, 480, 8, 20, 33 },
- { hq_tab_14, 352, 240, 8, 22, 15 },
- { hq_tab_15, 352, 288, 8, 18, 22 },
- { hq_tab_16, 176, 144, 8, 9, 11 },
- { hq_tab_17, 1280, 1024, 16, 20, 256 },
- { hq_tab_18, 1280, 960, 16, 25, 192 },
- { hq_tab_19, 1024, 768, 16, 24, 128 },
- { hq_tab_20, 704, 480, 8, 20, 66 },
- { hq_tab_21, 704, 576, 8, 24, 66 },
-};
-
-av_cold int ff_hq_init_vlcs(HQContext *c)
-{
- int ret = init_vlc(&c->hqa_cbp_vlc, 5, FF_ARRAY_ELEMS(cbp_vlc_lens),
- cbp_vlc_lens, 1, 1, cbp_vlc_bits, 1, 1, 0);
- if (ret < 0)
- return ret;
-
- return init_vlc(&c->hq_ac_vlc, 9, NUM_HQ_AC_ENTRIES,
- hq_ac_bits, 1, 1, hq_ac_codes, 2, 2, 0);
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Ultraman Trigger Episode Z The Movie That Follows New Generation Tiga.md b/spaces/congsaPfin/Manga-OCR/logs/Download Ultraman Trigger Episode Z The Movie That Follows New Generation Tiga.md
deleted file mode 100644
index 5e689d94534d4bad28d80901e382a8f344ca0c5b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Ultraman Trigger Episode Z The Movie That Follows New Generation Tiga.md
+++ /dev/null
@@ -1,101 +0,0 @@
-Download Ultraman Trigger Episode Z: The Ultimate Conclusion to the New Generation Tiga Series
-If you are a fan of Ultraman Trigger, you don't want to miss this movie. Ultraman Trigger Episode Z is a Japanese superhero kaiju film that serves as the conclusion to the 2021-22 Ultra Series television series Ultraman Trigger: New Generation Tiga. It is a spectacular and satisfying finale that will make you cheer, cry, and smile.
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In this article, we will tell you everything you need to know about Ultraman Trigger Episode Z, including what it is, how to watch it, why you should watch it, and some tips for enjoying it. Let's get started!
- What is Ultraman Trigger Episode Z?
-Ultraman Trigger Episode Z is a movie that follows Ultraman Trigger: New Generation Tiga, set two years after the series finale. It features the return of Kengo Manaka, who merged into the Eternity Core as Ultraman Trigger to stabilize it, and his friends from GUTS-Select, who face a new threat from a mysterious cult that seeks to revive the Ultra Ancient Civilization.
-The movie also features guest appearances from other Ultras, such as Ultraman Z and Haruki Natsukawa, who join forces with Kengo and his allies to stop Celebro, an alien parasite that has possessed Haruki and turned Z into Z Red Damage. The movie also reveals the true identity of Yuzare, a mysterious woman who guided Kengo throughout his journey as an Ultra.
-The movie is directed by Masayoshi Takesue, written by Toshizo Nemoto, and produced by Tsuburaya Productions. It stars Raiga Terasaka as Kengo Manaka/Ultraman Trigger, Runa Toyoda as Yuna Shizuma/Yuzare, Shunya Kaneko as Akito Hijiri/Ultrawoman Grigio, Meiku Harukawa as Himari Nanase/Ultrawoman Lila, Tadashi Mizuno as Tesshin Sakuma/Ultrawoman Rosso, Katsuya Takagi as Daigo Ishijima/Ultrawoman Blu, Kei Hosogai as Ignis/Ultrawoman Groob, Kohshu Hirano as Seiya Tatsumi/Ultrawoman Ruebe, Akinori Ando as Ryuichi Tokioka/Zabil, Yūichi Nakamura as Haruki Natsukawa/Ultraman Z, and Tatsuya Kishida as Celebro. It was released in Japan on January 8, 2023, and has received positive reviews from critics and fans alike.
- How to watch Ultraman Trigger Episode Z?
-There are two ways to watch Ultraman Trigger Episode Z: in theaters or online streaming. Here are the details of each option:
-How to download ultraman trigger episode z movie online
- In theaters
-If you live in Japan or plan to visit there soon, you can watch Ultraman Trigger Episode Z on the big screen. The movie is currently showing in more than 300 theaters across the country, and you can find the nearest one to you by using this website . You can also book your tickets online or at the theater's box office. The movie is rated G, which means it is suitable for all ages.
-Watching Ultraman Trigger Episode Z in theaters will give you the best experience of the movie's stunning visuals, sound effects, and music. You will also get to enjoy some exclusive bonuses, such as a special booklet, a poster, and a card featuring the movie's characters. Plus, you will get to share your excitement and emotions with other fans in the theater.
- Online streaming
-If you can't go to the theaters or prefer to watch Ultraman Trigger Episode Z at home, you can stream it online via Tsuburaya Imagination, a pay per view website that offers various Ultra Series content. You can access the website here , and register for an account. You will need to pay 1,500 yen (about $13) to watch the movie, and you can use credit cards or other payment methods.
-Streaming Ultraman Trigger Episode Z online will give you the convenience and flexibility of watching it anytime and anywhere. You can also pause, rewind, or fast forward the movie as you like. However, you will need a stable internet connection and a compatible device to stream the movie smoothly. You will also miss out on some of the bonuses and perks that come with watching it in theaters.
Why should you watch Ultraman Trigger Episode Z?
-Ultraman Trigger Episode Z is not just another movie. It is a masterpiece that will leave you breathless, amazed, and moved. Here are some of the reasons why you should watch it:
- It is a thrilling and emotional finale
-Ultraman Trigger Episode Z is the culmination of the New Generation Tiga series, and it delivers on every level. The movie is packed with action, suspense, and surprises, as Kengo and his friends face their greatest challenge yet. You will witness epic battles, stunning transformations, and shocking revelations that will keep you on the edge of your seat.
-The movie is also a heartfelt and touching story, as Kengo and his friends grow and bond as a team and as individuals. You will feel their joy, pain, and love, as they overcome their fears and doubts, and discover their true selves. You will also see how they honor the legacy of Ultraman Trigger and Tiga, and how they inspire hope and courage for the future.
- It features guest appearances from other Ultras
-Ultraman Trigger Episode Z is not only a celebration of Ultraman Trigger, but also of the entire Ultra Series. The movie features guest appearances from other Ultras, such as Ultraman Z and Haruki Natsukawa, who join forces with Kengo and his allies to stop Celebro. You will also see the return of Ultraman Tiga, the original hero who inspired Kengo to become an Ultra.
-The movie showcases the diversity and unity of the Ultra Series, as different Ultras from different worlds and timelines come together to fight for a common cause. You will also enjoy the interactions and chemistry between the Ultras and their human partners, as they share their experiences, skills, and personalities. You will also witness some of the most powerful and iconic moves and forms of the Ultras, such as Z's Final Shining Zero Twin Breaker or Tiga's Glitter Tiga.
- It celebrates the legacy of Ultraman Trigger and Tiga
-Ultraman Trigger Episode Z is a tribute to Ultraman Trigger and Tiga, two of the most beloved and influential Ultras in history. The movie explores the themes and messages of both series, such as the importance of friendship, courage, justice, peace, and love. The movie also reveals the connection between Trigger and Tiga, and how they are both part of a larger destiny that spans across time and space.
-The movie honors the legacy of Ultraman Trigger and Tiga, by showing how they have inspired generations of fans and heroes alike. The movie also pays homage to some of the classic scenes and elements of both series, such as the Spark Lens, the Dark Giants, or the Ultra Ancient Civilization. The movie also features some of the original cast members of Tiga, such as Hiroshi Nagano as Daigo Madoka/Ultraman Tiga or Takami Yoshimoto as Rena Yanase.
What are some tips for enjoying Ultraman Trigger Episode Z?
-Ultraman Trigger Episode Z is a movie that you will want to watch more than once. To make the most of your viewing experience, here are some tips that we recommend:
- Watch the TV series first
-Ultraman Trigger Episode Z is a sequel to Ultraman Trigger: New Generation Tiga, which is a 25-episode TV series that aired from July 2021 to January 2022. The TV series introduces the characters, the setting, and the story of Ultraman Trigger, and sets up the events of the movie. If you want to fully understand and appreciate the movie, we suggest that you watch the TV series first.
-You can watch Ultraman Trigger: New Generation Tiga on Tsuburaya Imagination, the same website that streams the movie. You can also watch it on YouTube, where Tsuburaya Productions uploads the episodes with English subtitles every week. You can find the playlist here .
- Stay until the end credits
-Ultraman Trigger Episode Z is not over until it's over. After the movie ends, don't leave your seat or close your browser just yet. There is a post-credits scene that you don't want to miss. The scene teases a possible sequel or spin-off to Ultraman Trigger, featuring a new character and a new threat. We won't spoil it for you, but we will say that it will make you excited for what's next.
- Check out the merchandise and other media
-If you love Ultraman Trigger Episode Z, you will love the merchandise and other media that are related to it. Tsuburaya Productions has released various products and content that will enhance your enjoyment of the movie and the series. For example, you can check out:
-
-The official website, where you can find news, videos, images, and information about Ultraman Trigger.
-The official social media accounts, where you can follow the latest updates, interact with other fans, and participate in contests and events.
-The official online store, where you can buy DVDs, Blu-rays, toys, books, clothing, accessories, and more.
-The official soundtrack album, where you can listen to the music of Ultraman Trigger, composed by Kenji Kawai.
-The official manga adaptation, where you can read a different version of Ultraman Trigger's story, illustrated by Eiichi Shimizu and Tomohiro Shimoguchi.
-
- Conclusion
-Ultraman Trigger Episode Z is a movie that every Ultra fan should watch. It is a thrilling and emotional finale to the New Generation Tiga series, featuring guest appearances from other Ultras, and celebrating the legacy of Ultraman Trigger and Tiga. It is also a movie that anyone can enjoy, as it offers a fun and inspiring story of friendship, courage, justice, peace, and love.
-If you want to watch Ultraman Trigger Episode Z, you can either go to the theaters or stream it online. Either way, you will have a blast watching it. And if you want to enjoy it even more, you can follow our tips for watching the TV series first, staying until the end credits, and checking out the merchandise and other media.
-So what are you waiting for? Download Ultraman Trigger Episode Z today and join Kengo and his friends on their final adventure as Ultras!
- Frequently Asked Questions
-
-Q: When was Ultraman Trigger Episode Z released? A: Ultraman Trigger Episode Z was released in Japan on January 8, 2023.
-Q: Who are the main characters of Ultraman Trigger Episode Z? A: The main characters of Ultraman Trigger Episode Z are Kengo Manaka/Ultraman Trigger, Yuna Shizuma/Yuzare, Akito Hijiri/Ultrawoman Grigio, Himari Nanase/Ultrawoman Lila, Tesshin Sakuma/Ultrawoman Rosso, Daigo Ishijima/Ultrawoman Blu, Ignis/Ultrawoman Groob, Seiya Tatsumi/Ultrawoman Ruebe, Ryuichi Tokioka/Zabil, Haruki Natsukawa/Ultraman Z, and Celebro.
-Q: How long is Ultraman Trigger Episode Z? A: Ultraman Trigger Episode Z is about 90 minutes long.
-Q: How much does it cost to watch Ultraman Trigger Episode Z? A: It depends on how you watch it. If you watch it in theaters, you will need to pay for the ticket, which varies depending on the theater and the time. If you watch it online, you will need to pay 1,500 yen (about $13) to stream it on Tsuburaya Imagination.
-Q: Where can I find more information about Ultraman Trigger Episode Z? A: You can find more information about Ultraman Trigger Episode Z on the official website, social media accounts, online store, soundtrack album, and manga adaptation of Ultraman Trigger.
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- Melna kleita ir viens no visuniversālākajiem un elegantiem apģērba gabaliem sieviešu garderobē. Bet vai jūs zināt, ka melnai kleitai var piešķirt papildus šarmu un oriģinalitāti, ja tai ir balts apkaklis? Šis kontrastējošais elements padara jebkuru melno kleitu par interesantu un pievilcīgu tērpu, kas piemērots dažādām situācijām. Bet kā izvēlēties pareizo melno kleitu ar baltu apkakli atbilstoši jūsu figūrai, stilam un sezonai? Un kā saskaņot to ar citiem apģērba gabaliem, lai izveidotu harmonisku un modernu izskatu? Šajā rakstā mēs jums pastāstīsim visu, ko jums jāzina par melnajām kleitām ar baltiem apkakļiem, to vēsturi, veidiem, izvēles kritērijiem un kombinācijas iespējām.
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Kas ir melna kleita ar baltu apkakli?
- Melna kleita ar baltu apkakli ir apģērbs, kas sastāv no vienkāršas vai dekoratīvas melnas auduma virskleitas un balta auduma kakla daļas, kas var būt dažāda forma, izmēra un st
ila. Melna kleita ar baltu apkakli ir klasiska un laikmeta apģērba kombinācija, kas ir iecienīta daudzu sieviešu vidū, jo tā rada kontrastu starp melno un baltu krāsu, kas ir vienkāršas, bet efektīvas. Melna kleita ar baltu apkakli var būt gan garša, gan īsa, gan ar piedurknēm, gan bez tām, gan ar ciešu, gan ar brīvu siluetu. Galvenais ir tas, ka baltais apkaklis izceļas uz melnā fona un pievērš uzmanību sejai un kaklam.
- Melnas kleitas ar baltu apkakli vēsture
- Melna kleita ar baltu apkakli nav jauns modes izgudrojums. Šis apģērbs ir bijis populārs jau kopš 19. gadsimta beigām, kad to valkāja dāmas ar augstu sabiedrisko statusu un labu gaumi. Melna kleita ar baltu apkakli bija simbols elegancijai, rafinētībai un aristokrātijai. Šo apģērbu iecienīja tādas slavenas sievietes kā Koko Šanelle, Odrīja Hepberna, Džekijs Kenedijs un citas. Melna kleita ar baltu apkakli bija arī viens no mīļākajiem tērpiem filmu zvaigznēm Holivudas zelta laikmetā.
- Melna kleita ar baltu apkakli ir saglabājusi savu popularitāti līdz mūsdienām, jo tā ir viegli pielāgojama dažādiem stiliem un tendencēm. Šodien melnu kleitu ar baltu apkakli var atrast gan klasiskajos, gan modernajos dizainos, gan sportiskajos, gan romantiskajos variantos. Melna kleita ar baltu apkakli ir piemērota gan ikdienai, gan svētkiem, gan darbam, gan atpūtai.
-melna kleita ar baltu apkakli un aprocēm
- Melnas kleitas ar baltu apkakli veidi
- Melnu kleitu ar baltu apkakli var iedalīt dažādos veidos atkarībā no to formām, materiāliem un detaļām. Šeit ir daži no populārākajiem melno kleitu ar baltu apkakli veidiem:
- Kleita ar aprocēm
- Kleita ar aprocēm ir viens no klasiskajiem melno kleitu ar baltu apkakli veidiem, kas radies 20. gadsimta sākumā. Šis apģērbs sastāv no melnas virskleitas ar aprocēm pie krūtīm un pleciem un balta kakla daļas, kas var būt apaļa vai kvadrātveida. Kleita ar aprocēm ir piemērota gan vasarai, gan ziemai, jo to var valkāt gan ar plānu, gan biezu audumu. Kleita ar aprocēm ir ideāla sievietēm ar mazu vai vidēju krūšu izmēru, jo aprocēs vizuāli palielina krūtis.
- Kleita ar stingru apkakli
- Kleita ar stingru apkakli ir vēl viens klasiskais melno kleitu ar baltu apkakli veids, kas radies 20. gadsimta vidū. Šis apģērbs sastāv no melnas virskleitas un balta kakla daļas, kas ir cieši pieguļoša kaklam un var būt līdzena vai rievota. Kleita ar sting
ra apkakli ir piemērota gan īsām, gan garām kleitām, gan ar piedurknēm, gan bez tām. Kleita ar stingru apkakli ir ideāla sievietēm ar garu vai šauru kaklu, jo stingra apkakle vizuāli saīsina un sabalansē kaklu.
- Kleita ar atloku apkakli
- Kleita ar atloku apkakli ir moderns un dinamisks melno kleitu ar baltu apkakli veids, kas radies 21. gadsimta sākumā. Šis apģērbs sastāv no melnas virskleitas un balta kakla daļas, kas ir atlocīta uz leju un var būt dažāda platuma un garuma. Kleita ar atloku apkakli ir piemērota gan sportiskam, gan biznesa stilam, gan ar svārkiem, gan ar biksēm. Kleita ar atloku apkakli ir ideāla sievietēm ar apaļu vai īsu kaklu, jo atloka apkakle vizuāli pagarina un slaidina kaklu.
- Kleita ar mežģīņu apkakli
- Kleita ar mežģīņu apkakli ir romantisks un maigs melno kleitu ar baltu apkakli veids, kas radies 21. gadsimta vidū. Šis apģērbs sastāv no melnas virskleitas un balta kakla daļas, kas ir izgatavota no mežģīņu auduma un var būt dažāda forma un detaļas. Kleita ar mežģīņu apkakli ir piemērota gan vasarai, gan ziemai, gan ar vieglu, gan ar smagu audumu. Kleita ar mežģīņu apkakli ir ideāla sievietēm ar jebkuru kakla formu, jo mežģīņu apkakle piešķir mīlestību un nevainību.
- Kā izvēlēties melnu kleitu ar baltu apkakli atbilstoši figūrai
- Melna kleita ar baltu apkakli ir apģērbs, kas var izcelt jūsu figūras priekšrocības un noslēpt trūkumus, ja jūs izvēlaties pareizo modeli un izmēru. Lai to izdarītu, jums jāzina jūsu figūras tips un jāņem vērā daži padomi:
- Melna kleita ar baltu apkakli plakanai figūrai
- Ja jums ir plakana figūra bez izteiktas krūšu un gurnu līnijas, jums jāizvēlas melna kleita ar baltu apkakli, kas piešķir apjomu un formas jūsu ķermenim. Jums derēs kleitas ar aprocēm, ruf liem, volāniem, drapējumiem vai citām dekoratīvām detaļām, kas palielina krūtis un gurnus. Jums derēs arī kleitas ar stingru apkakli, kas akcentē kaklu un seju. Jums jāizvairās no kleitām ar atloku apkakli, kas var izskatīties pārāk lielas un neveiklas uz jūsu plakanas figūras.
- Melna kleita ar baltu apkakli ābolveida figūrai
- Ja jums ir ābolveida figūra ar lielām krūtīm un vēderu, bet šauriem gurniem un kājām, jums jāizvēlas melna kleita ar baltu apkakli, kas slēpj jūsu problēmzonas un izceļ jūsu priekšrocības. Jums derēs kleitas ar atloku apkakli, kas vizuāli samazina krūtis un piešķir eleganci. Jums derēs arī kleitas ar mežģīņu apkakli, kas pievieno maigumu un romantiku. Jums jāizvairās no kleitām ar aprocēm vai citām detaļām pie krūtīm, kas var izskatīties pārblīvētas un neestētiskas.
- Melna kleita ar baltu apkakli smilšu pulksteņa figūrai
- Ja jums ir smilšu pulksteņa figūra ar proporcionālām krūtīm un gurniem, bet izteiktu vidukli, jums jāizvēlas melna kleita ar baltu apkakli, kas uzsver jūsu ideālo siluetu un padara to vēl skaistāku. Jums derēs kleitas ar ciešu vai elastīgu audumu, kas pieguļ jūsu ķermenim un akcentē jūsu formas. Jums derēs arī kleitas ar aprocēm vai mežģīņu apkakli, kas piešķir šarmu un sievišķību. Jums jāizvairās no kleitām ar atloku apkakli vai citiem elementiem, kas var traucēt jūsu proporcijas.
- Melna kleita ar baltu apkakli taisnstūra figūrai
- Ja jums ir taisnstūra figūra bez izteiktas krūšu, vidukļa un gurnu līnijas, jums jāizvēlas melna kleita ar baltu apkakli, kas piešķir formas un apjomu jūsu ķermenim. Jums derēs kleitas ar drapējumiem, volāniem, rufliem vai citiem elementiem, kas palielina krūtis un gurnus. Jums derēs arī kleitas ar atloku apkakli vai mežģīņu apkakli, kas pievieno dinamiku un maigumu. Jums jāizvairās no kleitām ar stingru apkakli vai ciešu audumu, kas var izskatīties pārāk stingras un garlaicīgas.
- Kā saskaņot melnu kleitu ar baltu apkakli ar citiem apģērba gabaliem
- Melna kleita ar baltu apkakli ir viegli saskaņojama ar citiem apģērba gabaliem, jo tā ir univers āla un eleganta krāsu kombinācija. Tomēr, lai izveidotu harmonisku un modernu izskatu, jums jāņem vērā daži padomi par to, kādiem apaviem, jakām, mēteļiem, somām un rotaslietām jābūt melnai kleitai ar baltu apkakli:
- Kādiem apaviem jābūt melnai kleitai ar baltu apkakli
- Melna kleita ar baltu apkakli ir saskaņojama ar dažādiem apavu veidiem, atkarībā no kleitas garuma, stila un sezonas. Šeit ir daži no populārākajiem apavu variantiem melnai kleitai ar baltu apkakli:
-
-Ja jūs valkājat īsu melnu kleitu ar baltu apkakli, jums derēs augstpapēžu kurpes vai sandales melnā, baltā vai kontrastējošā krāsā, piemēram, sarkanā, zilā vai rozā. Šie apavi piešķirs jūsu tērpam seksualitāti un eleganci.
-Ja jūs valkājat garu melnu kleitu ar baltu apkakli, jums derēs zemu papēžu kurpes vai balerīnas melnā, baltā vai neitrālā krāsā, piemēram, pelēkā, brūnā vai krēmkrāsas. Šie apavi piešķirs jūsu tērpam komfortu un vieglumu.
-Ja jūs valkājat sportisku melnu kleitu ar baltu apkakli, jums derēs kedas vai botas melnā, baltā vai spilgtā krāsā, piemēram, zaļā, dzeltenā vai oranžā. Šie apavi piešķirs jūsu tērpam dinamiku un enerģiju.
-
- Kādas jakas un mēteļus var valkāt ar melnu kleitu ar baltu apkakli
- Melna kleita ar baltu apkakli ir saskaņojama ar dažādiem jaku un mēteļu veidiem, atkarīb ā. Šī soma piešķirs jūsu tērpam praktiskumu un akcentu. Jums derēs arī vienkāršas vai elegantes rotaslietas, piemēram, auskari, kaklarota, gredzens vai aproce melnā, baltā vai sudraba krāsā. Šīs rotaslietas piešķirs jūsu tērpam glītumu un šarmu.
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Ja jūs valkājat garu melnu kleitu ar baltu apkakli, jums derēs liela vai vidēja izmēra soma ar rokturi vai plecu siksnas melnā, baltā vai neitrālā krāsā, piemēram, pelēkā, brūnā vai krēmkrāsas. Šī soma piešķirs jūsu tērpam eleganci un klasi. Jums derēs arī klasiskas vai maigas rotaslietas, piemēram, auskari, kaklarota, gredzens vai aproce melnā, baltā vai zelta krāsā. Šīs rotaslietas piešķirs jūsu tērpam mīlestību un nevainību.
-Ja jūs valkājat sportisku melnu kleitu ar baltu apkakli, jums derēs sportiska vai funkcionāla soma ar rokturi vai plecu siksnas melnā, baltā vai spilgtā krāsā, piemēram, zaļā, dzeltenā vai oranžā. Šī soma piešķirs jūsu tērpam dinamiku un enerģiju. Jums derēs arī sportiskas vai modernas rotaslietas, piemēram, auskari, kaklarota, gredzens vai aproce melnā, baltā vai metāliskā krāsā. Šīs rotaslietas piešķirs jūsu tērpam stilu un oriģinalitāti.
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- Kādas ir melnas kleitas ar baltu apkakli priekšrocības un trūkumi
- Melna kleita ar baltu apkakli ir apģērbs, kas ir iecienīts daudzu sieviešu vidū, jo tam ir daudzas priekšrocības un tikai daži trūkumi. Šeit ir daži no tiem:
- Priekšrocības
-
-Melna kleita ar baltu apkakli ir universāla un eleganta krāsu kombinācija, kas piestāv jebkurai sievietei un jebkuram gadalaikam.
-Melna kleita ar baltu apkakli ir viegli saskaņojama ar citiem apģērba gabaliem, jo t ā ir klasiskas un neitrālas krāsas, kas der ar jebkuru citu krāsu.
-Melna kleita ar baltu apkakli ir pieejama dažādos veidos, formās un materiālos, kas atbilst jebkurai figūrai, stilam un gaumei.
-Melna kleita ar baltu apkakli ir apģērbs, kas rada kontrastu starp melno un baltu krāsu, kas ir vienkāršas, bet efektīvas. Melna kleita ar baltu apkakli izceļ seju un kaklu, kā arī pievērš uzmanību citiem apģērba elementiem.
-
- Trūkumi
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- Secinājums
- Melna kleita ar baltu apkakli ir apģērbs, kas ir klasisks un elegants, bet arī interesants un oriģināls. Šis apģērbs ir piemērots dažādām situācijām un sezonām, ja to izvēlas un saskaņo pareizi. Melna kleita ar baltu apkakli var izcelt jūsu figūru, seju un kaklu, kā arī piešķirt jūsu tērpam šarmu un stilu. Melna kleita ar baltu apkakli ir apģērbs, kas ir vērts iekļaut jūsu garderobē, ja jūs vēlaties būt eleganta un moderna sieviete.
- FAQ
- Šeit ir daži no biežāk uzdotajiem jautājumiem par melno kleitu ar baltu apkakli:
-
-Kur var nopirkt melnu kleitu ar baltu apkakli?
-Melnu kleitu ar baltu apkakli var nopirkt gan veikalos, gan interneta platformās, kas piedāvā dažādus apģērba zīmolus un dizainerus. Piemēram, jūs varat meklēt melnas kleitas ar baltiem apkakļiem šādās vietnēs: [Zara], [H&M], [Asos], [Mango] un citās.
-Kad var valkāt melnu kleitu ar baltu apkakli?
-Melnu kleitu ar baltu apkakli var valkāt jebkurā gadalaikā un situācijā, ja to saskaņo ar citiem apģērba gabaliem atbilstoši laikapstākļiem un notikuma raksturam. Piemēram, jūs varat valkāt melnu kleitu ar baltu apkakli vasarā ar sandalēm un somiņu, ziemā ar zābakiem un mēteli, darbā ar kurpēm un žaketi, ballītē ar augstpapēžu kurpēm un rotaslietām.
-Kas der melnai kleitai ar baltu apkakli?
-Melna kleita ar baltu apkakli der ar daudziem citiem apģērba gabaliem, jo t
ā ir klasiskas un neitrālas krāsas, kas der ar jebkuru citu krāsu. Tomēr, lai izveidotu harmonisku un modernu izskatu, jums jāņem vērā daži padomi par to, kādiem apaviem, jakām, mēteļiem, somām un rotaslietām jābūt melnai kleitai ar baltu apkakli. Šeit ir daži no tiem:
-
-Apaviem jābūt melnai, baltai vai kontrastējošai krāsai, kas saskan ar kleitas stilu un garumu. Jūs varat izvēlēties augstpapēžu kurpes, sandales, zemu papēžu kurpes, balerīnas, kedas vai botas atkarībā no tā, vai jūs valkājat īsu, garu vai sportisku kleitu.
-Jakām un mēteļiem jābūt melnai, baltai vai neitrālai krāsai, kas saskan ar kleitas stilu un sezonu. Jūs varat izvēlēties īsu, vidēju vai garu jaku vai mēteli atkarībā no tā, vai jūs valkājat īsu, garu vai sportisku kleitu.
-Somām un rotaslietām jābūt melnai, baltai vai spilgtai krāsai, kas saskan ar kleitas stilu un situāciju. Jūs varat izvēlēties nelielu, vidēju vai lielu somu ar rokturi vai plecu siksnas atkarībā no tā, vai jūs valkājat īsu, garu vai sportisku kleitu. Jūs varat izvēlēties vienkāršas, elegantes, klasiskas, maigas, sportiskas vai modernas rotaslietas atkarībā no tā, vai jūs valkājat īsu, garu vai sportisku kleitu.
- 401be4b1e0How to Download and Play Rampage: Total Destruction on Android
- Do you love smashing buildings, eating people, and causing mayhem as a giant monster? If so, you might want to check out Rampage: Total Destruction, a sequel to the classic arcade game that lets you do all that and more. In this article, we will show you how to download and play Rampage: Total Destruction on your Android device, as well as some tips and tricks to enjoy the game.
- What is Rampage: Total Destruction?
- A brief introduction to the game and its features
- Rampage: Total Destruction is a video game developed by Pipeworks Software and published by Midway Games in 2006. It is available for GameCube, PlayStation 2, and Wii consoles, but you can also play it on your Android device using an emulator. The game is a sequel to the original Rampage arcade game from 1986, which was also ported to various platforms over the years.
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The game features over 30 different monsters that you can unlock and play as, each with their own special abilities and moves. You can also customize your monster's appearance, name, and stats. The game has four modes of play: campaign, king of the city, king of the world, and time trial. In each mode, you have to destroy as many buildings, vehicles, and people as possible in various cities around the world, while avoiding the military forces that try to stop you. You can also collect tokens, power-ups, health items, and bonus items along the way.
- The plot and gameplay of the game
- The plot of the game revolves around a soda company called Scum Labs that accidentally creates a batch of soda that turns people into giant monsters. The company tries to cover up their mistake by freezing the monsters in cryo-tubes and hiding them around the world. However, some of the monsters escape and start rampaging through the cities, while others are freed by other monsters or by curious humans.
- The gameplay of the game is simple and fun. You control your monster using the emulator's buttons or touch screen. You can move left or right, jump, climb buildings, punch, kick, grab, throw, eat, roar, rampage, and more. You can also interact with other monsters in cooperative or competitive ways. You can team up with them to destroy more buildings faster, or you can fight them for dominance or food. You can also unlock new moves and abilities by completing challenges or eating certain items.
- How to Download and Install Rampage: Total Destruction on Android
- The requirements and steps for downloading and installing the game
- To play Rampage: Total Destruction on your Android device, you will need two things: a ROM file of the game and an emulator that can run it. A ROM file is a digital copy of a video game that you can download from various websites. An emulator is a software that mimics the functions of a console and allows you to play its games on your device.
- Before you download anything, make sure that your device has enough storage space and meets the minimum requirements for running the game smoothly. You will also need a stable internet connection and a file manager app to access the downloaded files. You will also need to enable the installation of apps from unknown sources in your device's settings. This will allow you to install the emulator and the game without any problems.
- Here are the steps for downloading and installing Rampage: Total Destruction on your Android device:
-
-Go to a reputable website that offers ROM files of GameCube, PlayStation 2, or Wii games. For example, you can use [ROMsMania] or [EmuParadise]. Search for Rampage: Total Destruction and choose the version that you want to download. Make sure that the file is compatible with your device and has good ratings and reviews. Download the file and save it to your device's storage.
-Go to another website that offers emulators for Android devices. For example, you can use [Dolphin Emulator] or [DamonPS2]. These are two of the best emulators for playing GameCube, PlayStation 2, and Wii games on Android. Download the emulator that matches the version of the game that you downloaded. Install the emulator on your device and launch it.
-Open the emulator and locate the ROM file of Rampage: Total Destruction that you downloaded. Tap on the file and wait for it to load. You might need to adjust some settings in the emulator to optimize the performance and graphics of the game. You can also customize the controls and layout of the buttons or touch screen.
-Enjoy playing Rampage: Total Destruction on your Android device!
-
- The best emulator and settings for playing the game
- While there are many emulators that can run Rampage: Total Destruction on Android, we recommend using Dolphin Emulator for GameCube and Wii versions, and DamonPS2 for PlayStation 2 version. These are two of the most popular and reliable emulators that have many features and options to enhance your gaming experience.
-rampage total destruction android apk download
- Dolphin Emulator is an open-source emulator that can run GameCube and Wii games on Android devices. It has a high compatibility rate, supports HD graphics, saves states, cheats, multiplayer mode, and more. You can download it from its official website or from Google Play Store. To play Rampage: Total Destruction on Dolphin Emulator, you will need a device with at least 1 GB of RAM, a quad-core processor, and Android 5.0 or higher.
- DamonPS2 is a paid emulator that can run PlayStation 2 games on Android devices. It has a fast speed, supports high-resolution graphics, saves states, cheats, gamepad support, and more. You can download it from Google Play Store or from its official website. To play Rampage: Total Destruction on DamonPS2, you will need a device with at least 2 GB of RAM, an octa-core processor, and Android 5.1 or higher.
- Depending on your device's specifications and preferences, you might need to tweak some settings in the emulator to get the best performance and quality of the game. Here are some general tips for optimizing the settings:
-
-Choose the lowest resolution possible that still looks good on your screen. This will reduce the lag and improve the frame rate of the game.
-Enable or disable anti-aliasing, anisotropic filtering, texture filtering, and other graphical enhancements according to your taste. These will improve the appearance of the game but might also affect its speed.
-Adjust the audio settings to match your device's speakers or headphones. You can also mute or lower the volume of the game if you find it too loud or annoying.
-Configure the controls to suit your style and comfort. You can use the default buttons or touch screen layout, or you can customize them according to your preference. You can also use a gamepad or a keyboard if your device supports them.
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- How to Enjoy Rampage: Total Destruction on Android
- Some tips and tricks for playing the game
- Rampage: Total Destruction is a fun and addictive game that will keep you entertained for hours. However, it can also be challenging and frustrating at times, especially if you are new to it or if you want to complete all the objectives and unlock all the monsters. Here are some tips and tricks that will help you enjoy the game more:
-
-Experiment with different monsters and find out their strengths and weaknesses. Each monster has its own special ability that can give you an edge in certain situations. For example, George can climb faster, Lizzie can swim better, Ralph can jump higher, etc.
-Use combos and rampages to increase your score and destroy more buildings faster . Combos are sequences of attacks that you can perform by pressing different buttons. Rampages are special modes that you can activate by filling up your rampage meter. When you are in a rampage, you become invincible, faster, and stronger for a limited time.
-Collect and use power-ups and bonus items to boost your performance and score. Power-ups are items that give you temporary benefits, such as extra health, speed, strength, etc. Bonus items are items that give you extra points, such as money, jewels, gold bars, etc.
-Explore the cities and find hidden secrets and easter eggs. Some buildings and objects contain hidden tokens, health items, power-ups, bonus items, or even new monsters. You can also find references to other games, movies, or pop culture in some places.
-Have fun and don't take the game too seriously. Rampage: Total Destruction is a game that is meant to be enjoyed and not to be stressed over. Don't worry too much about completing all the objectives or unlocking all the monsters. Just have fun destroying everything in sight and causing chaos as a giant monster.
-
- Some benefits and drawbacks of playing the game on Android
- Playing Rampage: Total Destruction on your Android device has some advantages and disadvantages compared to playing it on a console. Here are some of them:
-
-
-Benefits
-Drawbacks
-
-
-You can play the game anytime and anywhere you want, as long as you have your device with you.
-You might experience some lag, glitches, or crashes depending on your device's specifications and the emulator's settings.
-
-
-You can save your progress and resume the game whenever you want, without losing any data.
-You might have some difficulties with the controls, especially if you are using the touch screen or a small screen.
-
-
-You can customize the game's graphics, sound, and controls according to your preference and comfort.
-You might miss some features or options that are available on the console versions, such as multiplayer mode or online connectivity.
-
-
-You can access other apps and functions on your device while playing the game, such as browsing the internet or listening to music.
-You might drain your device's battery faster or overheat it if you play the game for too long or at high settings.
-
-
- Conclusion
- A summary of the main points and a call to action
- Rampage: Total Destruction is a fun and addictive game that lets you unleash your inner monster and destroy everything in sight. You can play it on your Android device using an emulator, such as Dolphin Emulator or DamonPS2. You can also enjoy the game more by following some tips and tricks that we shared in this article.
- If you are looking for a game that is simple, entertaining, and satisfying, then Rampage: Total Destruction is the game for you. Download it now and start rampaging through the cities as a giant monster!
- FAQs
- Q1: Is Rampage: Total Destruction free to download and play?
- A1: No, Rampage: Total Destruction is not free to download and play. You will need to buy the original game disc or download a legal copy of it from an authorized source. You will also need to pay for the emulator that you will use to play the game on your Android device. However, there are some websites that offer ROM files and emulators for free, but they might be illegal or unsafe to use.
- Q2: How many monsters and cities are there in Rampage: Total Destruction?
- A2: There are 30 playable monsters and 7 hidden monsters in Rampage: Total Destruction. Each monster has its own special ability and moves. There are also 40 cities in Rampage: Total Destruction, each with its own landmarks and challenges.
- Q3: Can I play Rampage: Total Destruction with other players online?
- A3: No, you cannot play Rampage: Total Destruction with other players online. The game does not have an online multiplayer mode or feature. However, you can play with up to three other players locally using a split-screen mode on the console versions of the game.
- Q4: What are some other games like Rampage: Total Destruction?
- A4: Some other games like Rampage: Total Destruction are:
-
-Rampage World Tour (1997): The first sequel to the original Rampage arcade game that introduced new monsters and locations.
-R ampage 2: Universal Tour (1999): The second sequel to the original Rampage arcade game that added more monsters and locations, as well as a space theme.
-War of the Monsters (2003): A 3D fighting game that features giant monsters inspired by various movies and genres.
-Destroy All Humans! (2005): A humorous action-adventure game that lets you play as an alien who invades Earth and wreaks havoc with various weapons and abilities.
-Godzilla: Unleashed (2007): A fighting game that features Godzilla and other kaiju from the Toho franchise.
-
- Q5: Where can I find more information about Rampage: Total Destruction?
- A5: You can find more information about Rampage: Total Destruction on the following sources:
-
-The official website of the game: [Rampage: Total Destruction]
-The Wikipedia page of the game: [Rampage: Total Destruction - Wikipedia]
-The IGN review of the game: [Rampage: Total Destruction Review - IGN]
-The YouTube gameplay video of the game: [Rampage: Total Destruction Gameplay - YouTube]
- 401be4b1e0Wheelie Life 2: The Best Online Wheelie Game of the Moment
-Do you love motorcycles and wheelies? Do you want to experience the thrill of riding a bike on one wheel and doing amazing stunts? If yes, then you should try Wheelie Life 2, the best online wheelie game of the moment. Wheelie Life 2 is a physics-based racing game that lets you join rooms and play with your friends and other players from around the world. You can also practice your wheelie skills in offline mode and customize your bike and rider. In this article, we will show you how to download Wheelie Life 2 apk for Android devices, as well as how to play it on PC and Mac using an emulator. We will also give you some tips and tricks to help you master the game and have more fun.
- How to Play Wheelie Life 2
-Wheelie Life 2 is easy to learn but hard to master. The game has two modes: online and offline. In online mode, you can create or join rooms to play with your friends and other players. You can chat with them, watch their replays, and compete in leaderboards and tournaments. In offline mode, you can practice your wheelie skills on different maps and do amazing tricks. You can also unlock new bikes and colors by completing challenges.
-wheelie life 2 download apk Download ✪ https://urlca.com/2uOdPW
Online Mode
-To play online, you need to have an internet connection and a Google account. You can sign in with your Google account or play as a guest. Once you are in the game, you can choose from three options:
-
-Create Room: You can create your own room and invite your friends or other players to join. You can set the room name, password, map, bike type, number of players, time limit, etc.
-Join Room: You can join an existing room created by another player. You can see the room name, map, bike type, number of players, etc. You can also filter the rooms by region, language, bike type, etc.
-Quick Join: You can join a random room with other players who are looking for a match.
-
-Once you are in a room, you can see the other players' names, bikes, and colors. You can also chat with them, send emojis, and mute them if you want. To start the game, you need to press the ready button. The game will start when all the players are ready or when the host starts it manually.
- The goal of the game is to do as many wheelies as possible and score points. You can also do tricks like flips, spins, hops, etc. to earn extra points. The game will end when the time limit is reached or when all the players fall off their bikes. The player with the highest score will win the game and get a trophy. You can also see your rank, score, and best wheelie time on the screen. You can also watch the replays of other players and learn from their moves.
- Offline Mode
-If you don't have an internet connection or you just want to practice your wheelie skills, you can play offline mode. In offline mode, you can choose from different maps and bike types. You can also adjust the settings like gravity, wind, camera angle, etc. to make the game more challenging or realistic.
- In offline mode, you can do unlimited wheelies and tricks without worrying about falling off or running out of time. You can also pause and resume the game anytime you want. You can also see your score and best wheelie time on the screen. You can also record your gameplay and share it with your friends or on social media.
- Controls
-The controls of Wheelie Life 2 are simple and intuitive. You can use your keyboard or mouse to play the game on PC or Mac, or use your touchscreen to play the game on Android devices. Here are the basic controls of the game:
-wheelie life 2 game free download for android
-
-Balance: Use the left and right arrow keys or tilt your device to balance your bike and keep it on one wheel.
-Steer: Use the up and down arrow keys or swipe up and down on your screen to steer your bike and change direction.
-Accelerate: Use the spacebar or tap on the right side of your screen to accelerate your bike and gain speed.
-Brake: Use the shift key or tap on the left side of your screen to brake your bike and slow down.
-Customize: Use the C key or tap on the gear icon on your screen to customize your bike and rider. You can change the color, suspension, tire pressure, gear ratio, helmet, suit, etc.
-
- How to Download Wheelie Life 2 APK for Android
-If you want to play Wheelie Life 2 on your Android device, you need to download and install the APK file of the game. The APK file is a package that contains all the files and data needed to run the game on your device. Here are the steps to download Wheelie Life 2 APK for Android:
- Step 1: Go to the official website of Wheelie Life 2 or Google Play Store
-You can download Wheelie Life 2 APK from two sources: the official website of Wheelie Life 2 or Google Play Store. The official website of Wheelie Life 2 is https://wheelielife2.com/ . The Google Play Store link is https://play.google.com/store/apps/details?id=com.wheelielife.wheelielife2 . Both sources are safe and reliable.
- Step 2: Click on the download button and wait for the file to be downloaded
-Once you are on the website or Google Play Store page of Wheelie Life 2, you will see a download button that says "Download APK" or "Install". Click on it and wait for the file to be downloaded on your device. The file size is about 100 MB, so make sure you have enough space and a stable internet connection.
- Step 3: Open the file and install the game on your device
-After downloading the file, you need to open it and install it on your device. To do this, you may need to enable unknown sources in your device settings. This will allow you to install apps from sources other than Google Play Store. To enable unknown sources, go to Settings > Security > Unknown Sources and toggle it on.
- Then, go to your file manager and locate the downloaded file. It should be in your Downloads folder or wherever you saved it. Tap on it and follow the instructions on the screen to install the game on your device. It may take a few minutes to complete the installation process.
- Step 4: Enjoy the game and have fun
-Once the installation is done, you can launch the game and start playing. You will see the Wheelie Life 2 icon on your home screen or app drawer. Tap on it and sign in with your Google account or play as a guest. You can then choose your mode, bike, map, and settings. Enjoy the game and have fun!
- How to Download Wheelie Life 2 for PC and Mac
-If you want to play Wheelie Life 2 on your PC or Mac, you need to download and install an emulator. An emulator is a software that mimics the Android operating system on your computer. This way, you can run Android apps and games on your PC or Mac. There are many emulators available online, but we recommend using BlueStacks or NoxPlayer. They are both free, fast, and easy to use. Here are the steps to download Wheelie Life 2 for PC and Mac using an emulator:
- Step 1: Download and install an emulator like BlueStacks or NoxPlayer
-To download an emulator, you need to go to its official website and click on the download button. The official website of BlueStacks is https://www.bluestacks.com/ . The official website of NoxPlayer is https://www.bignox.com/ . Both websites are safe and reliable.
- Once you download the emulator file, you need to open it and install it on your computer. To do this, you may need to accept the terms and conditions and follow the instructions on the screen. It may take a few minutes to complete the installation process.
- Step 2: Launch the emulator and sign in with your Google account
-After installing the emulator, you need to launch it and sign in with your Google account. This will allow you to access the Google Play Store and other Google services on the emulator. To sign in with your Google account, you need to enter your email and password and agree to the terms and conditions.
- If you don't have a Google account, you can create one for free by clicking on the create account button. You will need to provide some basic information like your name, birthday, gender, etc. You will also need to verify your email and phone number.
- Step 3: Search for Wheelie Life 2 in the emulator's app store or Google Play Store
-Once you are signed in with your Google account, you can search for Wheelie Life 2 in the emulator's app store or Google Play Store. The app store or Google Play Store icon should be on the home screen or app drawer of the emulator. Click on it and type "Wheelie Life 2" in the search bar. You will see the game icon and name on the results page.
- Step 4: Install the game and start playing on your PC or Mac
-To install the game, you need to click on the game icon and then click on the install button. Wait for the game to be downloaded and installed on your computer. It may take a few minutes depending on your internet speed and computer performance.
- Once the installation is done, you can launch the game and start playing. You will see the Wheelie Life 2 icon on your home screen or app drawer of the emulator. Click on it and sign in with your Google account or play as a guest. You can then choose your mode, bike, map, and settings. Enjoy the game and have fun!
- Tips and Tricks for Wheelie Life 2
-Wheelie Life 2 is a fun and realistic wheelie game that requires skill and practice. To help you master the game and have more fun, we have compiled some tips and tricks for you:
- Tip 1: Adjust your bike's suspension, tire pressure, and gear ratio to suit your style and terrain
-One of the cool features of Wheelie Life 2 is that you can customize your bike's suspension, tire pressure, and gear ratio. These settings affect how your bike behaves on different terrains and how easy or hard it is to do wheelies.
- To adjust these settings, you need to go to customize mode by pressing C or tapping on the gear icon on your screen. You will see three sliders that you can move to change the suspension, tire pressure, and gear ratio of your bike. You will also see a preview of how your bike looks and sounds.
- The suspension affects how bouncy or stiff your bike is. A higher suspension makes your bike more bouncy and responsive, but also more unstable and prone to falling. A lower suspension makes your bike more stiff and stable, but also less agile and fun.
- The tire pressure affects how grippy or slippery your tires are. A higher tire pressure makes your tires more grippy and responsive, but also more rigid and prone to popping. A lower tire pressure makes your tires more slippery and smooth, but also less agile and fast.
- The gear ratio affects how fast or slow your bike accelerates and decelerates. A higher gear ratio makes your bike accelerate faster and reach higher speeds, but also consume more fuel and make more noise. A lower gear ratio makes your bike accelerate slower and reach lower speeds, but also consume less fuel and make less noise.
- You can experiment with different settings to find the ones that suit your style and terrain. For example, if you are playing on a flat and smooth map, you may want to use a higher suspension, tire pressure, and gear ratio to make your wheelies easier and faster. If you are playing on a bumpy and rough map, you may want to use a lower suspension, tire pressure, and gear ratio to make your wheelies more stable and controlled.
- Tip 2: Use the brake to control your wheelie height and avoid falling backwards
-One of the most important skills in Wheelie Life 2 is to control your wheelie height and avoid falling backwards. Falling backwards will end your wheelie and reduce your score. To prevent this, you need to use the brake wisely.
- The brake is not only used to slow down or stop your bike, but also to adjust your wheelie height. When you press the brake, your bike will lean forward and lower its front wheel. When you release the brake, your bike will lean backward and raise its front wheel. By tapping the brake repeatedly, you can balance your bike on one wheel and keep it at a comfortable height.
- You can also use the brake to do tricks like hops, spins, or flips. To do a hop, you need to press the brake when your front wheel is high enough to lift your back wheel off the ground. To do a spin, you need to press the brake when your front wheel is low enough to make your bike rotate around it. To do a flip, you need to press the brake when your front wheel is high enough to make your bike flip over it.
- You can also combine these tricks with steering or accelerating to create more complex and impressive stunts. For example, you can do a hop-spin by hopping and then steering left or right. You can do a flip-spin by flipping and then steering left or right. You can do a hop-flip by hopping and then accelerating forward or backward.
- Tip 3: Try different bikes and colors to find your favorite one
-Wheelie Life 2 has many bikes and colors for you to choose from. Each bike has its own characteristics, such as weight, power, speed, sound, etc. Each color has its own style, such as bright, dark, metallic, neon, etc. You can try different bikes and colors to find your favorite one.
- To change your bike or color, you need to go to customize mode by pressing C or tapping on the gear icon on your screen. You will see two tabs: Bike and Color. You can switch between them by clicking on them or swiping left or right on your screen.
- In the Bike tab, you can see all the bikes that are available for you to use. Some of them are unlocked by default, while others are locked until you complete certain challenges or buy them with coins. You can see the name, price, and stats of each bike by hovering over it or tapping on it.
- In the Color tab, you can see all the colors that are available for you to use. Some of them are unlocked by default, while others are locked until you complete certain challenges or buy them with coins. You can see the name and price of each color by hovering over it or tapping on it.
- To select a bike or color, you need to click on it or tap on it. You will see a check mark on the bike or color that you have selected. You will also see a preview of how your bike and rider look and sound. You can change your bike or color anytime you want before or after a game.
- Tip 4: Learn from other players and watch their replays to improve your skills
-One of the best ways to improve your skills in Wheelie Life 2 is to learn from other players and watch their replays. You can see how they do wheelies and tricks, how they balance and steer their bikes, how they customize their settings, etc. You can also get inspired by their styles and techniques.
- To watch other players' replays, you need to go to online mode and join a room. You will see a list of players who are in the room. You can click on their names or tap on their icons to see their profiles. You will see their names, bikes, colors, ranks, scores, best wheelie times, etc. You will also see a button that says "Watch Replay". Click on it or tap on it to watch their replays.
- You can also watch your own replays by going to offline mode and clicking on the replay button or tapping on the camera icon on your screen. You will see a list of your recent games. You can click on them or tap on them to watch your replays.
- You can control the replay by using the buttons or gestures on your screen. You can pause, play, rewind, fast forward, slow motion, zoom in, zoom out, etc. You can also change the camera angle by dragging your mouse or finger on your screen. You can also record your replay and share it with your friends or on social media.
- Tip 5: Challenge yourself and compete with others in online leaderboards and tournaments
-Another way to have more fun and excitement in Wheelie Life 2 is to challenge yourself and compete with others in online leaderboards and tournaments. You can test your skills and see how you rank among other players from around the world. You can also win prizes and rewards by participating in tournaments.
- To access the online leaderboards, you need to go to online mode and click on the leaderboard button or tap on the trophy icon on your screen. You will see a list of players who have the highest scores and best wheelie times in different categories, such as global, regional, friends, bike type, etc. You can also see your own rank and stats in each category.
- To access the online tournaments, you need to go to online mode and click on the tournament button or tap on the cup icon on your screen. You will see a list of tournaments that are available for you to join. Each tournament has its own name, description, rules, prizes, duration, etc. You can also see the number of players who have joined or are waiting to join each tournament.
- To join a tournament, you need to click on it or tap on it. You will see a confirmation message that asks you if you want to join the tournament. Click on yes or tap on yes to join the tournament. You will then be taken to a room where you will play with other players who have joined the tournament. The tournament will start when all the players are ready or when the host starts it manually.
- The goal of the tournament is to score as many points as possible by doing wheelies and tricks. The tournament will end when the time limit is reached or when all the players fall off their bikes. The player with the highest score will win the tournament and get the prize. The prize may vary depending on the tournament, but it usually includes coins, bikes, colors, etc.
- Conclusion
-Wheelie Life 2 is a fun and realistic wheelie game that you can play online or offline on your Android or PC/Mac device. You can join rooms and play with your friends and other players from around the world. You can also practice your wheelie skills on different maps and do amazing tricks. You can also customize your bike and rider to suit your style and preference.
- Wheelie Life 2 is easy to download and install on your device. You just need to follow the steps we have provided in this article. You can also use an emulator to play Wheelie Life 2 on your PC or Mac device.
- Wheelie Life 2 is also easy to learn but hard to master. You need to use the controls and settings wisely to balance, steer, accelerate, and brake your bike. You also need to use the brake to control your wheelie height and avoid falling backwards. You can also try different bikes and colors to find your favorite one. You can also learn from other players and watch their replays to improve your skills. You can also challenge yourself and compete with others in online leaderboards and tournaments.
- Wheelie Life 2 is a game that will keep you entertained and engaged for hours. It is a game that will test your skills and creativity. It is a game that will make you feel the thrill of riding a bike on one wheel and doing amazing stunts. It is a game that you should download and play today.
- So what are you waiting for? Download Wheelie Life 2 apk for Android or play it on PC or Mac using an emulator. Join the wheelie community and have fun!
- FAQs
-Here are some frequently asked questions about Wheelie Life 2:
- Q: Is Wheelie Life 2 free to play?
-A: Yes, Wheelie Life 2 is free to play. You can download and install the game on your device without paying anything. However, the game may contain ads and in-app purchases that can enhance your gaming experience.
- Q: Is Wheelie Life 2 safe to play?
-A: Yes, Wheelie Life 2 is safe to play. The game does not contain any harmful or inappropriate content that may harm your device or offend your sensibilities. The game also does not collect or share any personal or sensitive information from you or your device.
- Q: Is Wheelie Life 2 compatible with my device?
-A: Wheelie Life 2 is compatible with most Android devices that have Android 4.4 or higher. The game may not work properly on some older or low-end devices due to performance issues. The game is also compatible with most PC and Mac devices that have Windows 7 or higher or Mac OS X 10.9 or higher. You just need to use an emulator to run the game on your computer.
- Q: How can I contact the developers of Wheelie Life 2?
-A: If you have any questions, feedback, suggestions, or issues about Wheelie Life 2, you can contact the developers of the game by emailing them at support@wheelielife2.com . You can also follow them on social media platforms like Facebook, Twitter, Instagram, YouTube, etc. to get the latest news and updates about the game.
- Q: How can I support the developers of Wheelie Life 2?
-A: If you like Wheelie Life 2 and want to support the developers of the game, you can do so by rating and reviewing the game on Google Play Store or App Store. You can also share the game with your friends and family and invite them to play with you. You can also buy coins or premium items in the game to unlock more features and content.
401be4b1e0My static Space
- ' SHOULD WRITE BEFORE the code and triple quote.
-
-## Implementation approach: Provide as Plain text. Analyze the difficult points of the requirements, select the appropriate open-source framework.
-
-## Python package name: Provide as Python str with python triple quoto, concise and clear, characters only use a combination of all lowercase and underscores
-
-## File list: Provided as Python list[str], the list of ONLY REQUIRED files needed to write the program(LESS IS MORE!). Only need relative paths, comply with PEP8 standards. ALWAYS write a main.py or app.py here
-
-## Data structures and interface definitions: Use mermaid classDiagram code syntax, including classes (INCLUDING __init__ method) and functions (with type annotations), CLEARLY MARK the RELATIONSHIPS between classes, and comply with PEP8 standards. The data structures SHOULD BE VERY DETAILED and the API should be comprehensive with a complete design.
-
-## Program call flow: Use sequenceDiagram code syntax, COMPLETE and VERY DETAILED, using CLASSES AND API DEFINED ABOVE accurately, covering the CRUD AND INIT of each object, SYNTAX MUST BE CORRECT.
-
-## Anything UNCLEAR: Provide as Plain text. Make clear here.
-
-"""
-FORMAT_EXAMPLE = """
----
-## Implementation approach
-We will ...
-
-## Python package name
-```python
-"snake_game"
-```
-
-## File list
-```python
-[
- "main.py",
-]
-```
-
-## Data structures and interface definitions
-```mermaid
-classDiagram
- class Game{
- +int score
- }
- ...
- Game "1" -- "1" Food: has
-```
-
-## Program call flow
-```mermaid
-sequenceDiagram
- participant M as Main
- ...
- G->>M: end game
-```
-
-## Anything UNCLEAR
-The requirement is clear to me.
----
-"""
-OUTPUT_MAPPING = {
- "Implementation approach": (str, ...),
- "Python package name": (str, ...),
- "File list": (List[str], ...),
- "Data structures and interface definitions": (str, ...),
- "Program call flow": (str, ...),
- "Anything UNCLEAR": (str, ...),
-}
-
-
-class WriteDesign(Action):
- def __init__(self, name, context=None, llm=None):
- super().__init__(name, context, llm)
- self.desc = (
- "Based on the PRD, think about the system design, and design the corresponding APIs, "
- "data structures, library tables, processes, and paths. Please provide your design, feedback "
- "clearly and in detail."
- )
-
- async def _save_system_design(self, docs_path, resources_path, content):
- data_api_design = CodeParser.parse_code(block="Data structures and interface definitions", text=content)
- seq_flow = CodeParser.parse_code(block="Program call flow", text=content)
- await mermaid_to_file(data_api_design, resources_path / "data_api_design")
- await mermaid_to_file(seq_flow, resources_path / "seq_flow")
- system_design_file = docs_path / "system_design.md"
- logger.info(f"Saving System Designs to {system_design_file}")
- async with aiofiles.open(system_design_file, "w") as f:
- await f.write(content)
-
- async def _save(self, system_design: str):
- workspace = CONFIG.workspace
- docs_path = workspace / "docs"
- resources_path = workspace / "resources"
- docs_path.mkdir(parents=True, exist_ok=True)
- resources_path.mkdir(parents=True, exist_ok=True)
- await self._save_system_design(docs_path, resources_path, system_design)
-
- async def run(self, context, **kwargs):
- prompt = PROMPT_TEMPLATE.format(context=context, format_example=FORMAT_EXAMPLE)
- system_design = await self._aask_v1(prompt, "system_design", OUTPUT_MAPPING)
- await self._save(system_design.content)
- return system_design
diff --git a/spaces/dhruvshettty/dutch-whisperer/app.py b/spaces/dhruvshettty/dutch-whisperer/app.py
deleted file mode 100644
index df682993af207c54943e0b59e3ee1a00b5414a25..0000000000000000000000000000000000000000
--- a/spaces/dhruvshettty/dutch-whisperer/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from transformers import pipeline
-import gradio as gr
-
-pipe = pipeline(model="renesteeman/whisper-base-dutch-25") # change to "your-username/the-name-you-picked"
-
-def transcribe(audio):
- text = pipe(audio)["text"]
- return text
-
-iface = gr.Interface(
- fn=transcribe,
- inputs=gr.Audio(source="microphone", type="filepath"),
- outputs="text",
- title="Whisper Small Dutch",
- description="Realtime demo for Dutch speech recognition using a fine-tuned Whisper small model.",
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Malizia 1973 Movie Free Download In Hindi Mp4 Movies __HOT__.md b/spaces/diacanFperku/AutoGPT/Malizia 1973 Movie Free Download In Hindi Mp4 Movies __HOT__.md
deleted file mode 100644
index e6a5a03cba2a5be9d8e0f37ba7830af302dc7c00..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Malizia 1973 Movie Free Download In Hindi Mp4 Movies __HOT__.md
+++ /dev/null
@@ -1,6 +0,0 @@
-malizia 1973 movie free download in hindi mp4 movies Download File ↔ https://gohhs.com/2uFVjF
diff --git a/spaces/diaoren/OpenSetObstacleDetection/opendet2/config/defaults.py b/spaces/diaoren/OpenSetObstacleDetection/opendet2/config/defaults.py
deleted file mode 100644
index d0ae5fb64067050fea0068053b9f1e1fb37cf72b..0000000000000000000000000000000000000000
--- a/spaces/diaoren/OpenSetObstacleDetection/opendet2/config/defaults.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from detectron2.config import CfgNode as CN
-
-
-def add_opendet_config(cfg):
- _C = cfg
-
- # unknown probability loss
- _C.UPLOSS = CN()
- _C.UPLOSS.START_ITER = 100 # usually the same as warmup iter
- _C.UPLOSS.SAMPLING_METRIC = "min_score"
- _C.UPLOSS.TOPK = 3
- _C.UPLOSS.ALPHA = 1.0
- _C.UPLOSS.WEIGHT = 0.5
-
- # instance contrastive loss
- _C.ICLOSS = CN()
- _C.ICLOSS.OUT_DIM = 128
- _C.ICLOSS.QUEUE_SIZE = 256
- _C.ICLOSS.IN_QUEUE_SIZE = 16
- _C.ICLOSS.BATCH_IOU_THRESH = 0.5
- _C.ICLOSS.QUEUE_IOU_THRESH = 0.7
- _C.ICLOSS.TEMPERATURE = 0.1
- _C.ICLOSS.WEIGHT = 0.1
-
- _C.EDLLOSS = CN()
- _C.EDLLOSS.HAS_EDL = False
-
- # register RoI output layer
- _C.MODEL.ROI_BOX_HEAD.OUTPUT_LAYERS = "FastRCNNOutputLayers"
- # known classes
- _C.MODEL.ROI_HEADS.NUM_KNOWN_CLASSES = 20
- _C.MODEL.RETINANET.NUM_KNOWN_CLASSES = 20
- # thresh for visualization results.
- _C.MODEL.ROI_HEADS.VIS_IOU_THRESH = 1
- # scale for cosine classifier
- _C.MODEL.ROI_HEADS.COSINE_SCALE = 20
-
- # swin transformer
- _C.MODEL.SWINT = CN()
- _C.MODEL.SWINT.EMBED_DIM = 96
- _C.MODEL.SWINT.OUT_FEATURES = ["stage2", "stage3", "stage4", "stage5"]
- _C.MODEL.SWINT.DEPTHS = [2, 2, 6, 2]
- _C.MODEL.SWINT.NUM_HEADS = [3, 6, 12, 24]
- _C.MODEL.SWINT.WINDOW_SIZE = 7
- _C.MODEL.SWINT.MLP_RATIO = 4
- _C.MODEL.SWINT.DROP_PATH_RATE = 0.2
- _C.MODEL.SWINT.APE = False
- _C.MODEL.BACKBONE.FREEZE_AT = -1
- _C.MODEL.FPN.TOP_LEVELS = 2
-
- # solver, e.g., adamw for swin
- _C.SOLVER.OPTIMIZER = 'SGD'
- _C.SOLVER.BETAS = (0.9, 0.999)
diff --git a/spaces/digitalxingtong/Azuma-Bert-VITS2/preprocess_text.py b/spaces/digitalxingtong/Azuma-Bert-VITS2/preprocess_text.py
deleted file mode 100644
index 44c35fecd9b7f21016e80e9597d6055254cba3f7..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Azuma-Bert-VITS2/preprocess_text.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import json
-from random import shuffle
-
-import tqdm
-from text.cleaner import clean_text
-from collections import defaultdict
-import shutil
-stage = [1,2,3]
-
-transcription_path = 'filelists/short_character_anno.list'
-train_path = 'filelists/train.list'
-val_path = 'filelists/val.list'
-config_path = "configs/config.json"
-val_per_spk = 4
-max_val_total = 8
-
-if 1 in stage:
- with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f:
- for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()):
- try:
- utt, spk, language, text = line.strip().split('|')
- #language = "ZH"
- norm_text, phones, tones, word2ph = clean_text(text, language)
- f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones),
- " ".join([str(i) for i in tones]),
- " ".join([str(i) for i in word2ph])))
- except:
- print("err!", utt)
-
-if 2 in stage:
- spk_utt_map = defaultdict(list)
- spk_id_map = {}
- current_sid = 0
-
- with open( transcription_path+'.cleaned', encoding='utf-8') as f:
- for line in f.readlines():
- utt, spk, language, text, phones, tones, word2ph = line.strip().split('|')
- spk_utt_map[spk].append(line)
- if spk not in spk_id_map.keys():
- spk_id_map[spk] = current_sid
- current_sid += 1
- train_list = []
- val_list = []
- for spk, utts in spk_utt_map.items():
- shuffle(utts)
- val_list+=utts[:val_per_spk]
- train_list+=utts[val_per_spk:]
- if len(val_list) > max_val_total:
- train_list+=val_list[max_val_total:]
- val_list = val_list[:max_val_total]
-
- with open( train_path,"w", encoding='utf-8') as f:
- for line in train_list:
- f.write(line)
-
- file_path = transcription_path+'.cleaned'
- shutil.copy(file_path,'./filelists/train.list')
-
- with open(val_path, "w", encoding='utf-8') as f:
- for line in val_list:
- f.write(line)
-
-if 3 in stage:
- assert 2 in stage
- config = json.load(open(config_path))
- config['data']["n_speakers"] = current_sid #
- config["data"]['spk2id'] = spk_id_map
- with open(config_path, 'w', encoding='utf-8') as f:
- json.dump(config, f, indent=2, ensure_ascii=False)
diff --git a/spaces/digitalxingtong/Luzao-Bert-Vits2/README_zh.md b/spaces/digitalxingtong/Luzao-Bert-Vits2/README_zh.md
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Luzao-Bert-Vits2/README_zh.md
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/chinese_bert.py b/spaces/digitalxingtong/Miiu-Bert-Vits2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
- with torch.no_grad():
- inputs = tokenizer(text, return_tensors='pt')
- for i in inputs:
- inputs[i] = inputs[i].to(device)
- res = model(**inputs, output_hidden_states=True)
- res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
- assert len(word2ph) == len(text)+2
- word2phone = word2ph
- phone_level_feature = []
- for i in range(len(word2phone)):
- repeat_feature = res[i].repeat(word2phone[i], 1)
- phone_level_feature.append(repeat_feature)
-
- phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
- return phone_level_feature.T
-
-if __name__ == '__main__':
- # feature = get_bert_feature('你好,我是说的道理。')
- import torch
-
- word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
- word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
- # 计算总帧数
- total_frames = sum(word2phone)
- print(word_level_feature.shape)
- print(word2phone)
- phone_level_feature = []
- for i in range(len(word2phone)):
- print(word_level_feature[i].shape)
-
- # 对每个词重复word2phone[i]次
- repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
- phone_level_feature.append(repeat_feature)
-
- phone_level_feature = torch.cat(phone_level_feature, dim=0)
- print(phone_level_feature.shape) # torch.Size([36, 1024])
-
diff --git a/spaces/dorkai/SINGPT-Temporary/extensions/google_translate/script.py b/spaces/dorkai/SINGPT-Temporary/extensions/google_translate/script.py
deleted file mode 100644
index 68bc54b293086bed1a070a310d276060ee939d44..0000000000000000000000000000000000000000
--- a/spaces/dorkai/SINGPT-Temporary/extensions/google_translate/script.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import gradio as gr
-from deep_translator import GoogleTranslator
-
-params = {
- "language string": "ja",
-}
-
-language_codes = {'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chinese (Simplified)': 'zh-CN', 'Chinese (Traditional)': 'zh-TW', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Nyanja (Chichewa)': 'ny', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese (Portugal, Brazil)': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala (Sinhalese)': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog (Filipino)': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu'}
-
-def input_modifier(string):
- """
- This function is applied to your text inputs before
- they are fed into the model.
- """
-
- return GoogleTranslator(source=params['language string'], target='en').translate(string)
-
-def output_modifier(string):
- """
- This function is applied to the model outputs.
- """
-
- return GoogleTranslator(source='en', target=params['language string']).translate(string)
-
-def bot_prefix_modifier(string):
- """
- This function is only applied in chat mode. It modifies
- the prefix text for the Bot and can be used to bias its
- behavior.
- """
-
- return string
-
-def ui():
- # Finding the language name from the language code to use as the default value
- language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])]
-
- # Gradio elements
- language = gr.Dropdown(value=language_name, choices=[k for k in language_codes], label='Language')
-
- # Event functions to update the parameters in the backend
- language.change(lambda x: params.update({"language string": language_codes[x]}), language, None)
diff --git a/spaces/dorkai/SINGPT-Temporary/run.py b/spaces/dorkai/SINGPT-Temporary/run.py
deleted file mode 100644
index 2c966a2f5691c6444c3329365c39e78b74fdbf95..0000000000000000000000000000000000000000
--- a/spaces/dorkai/SINGPT-Temporary/run.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import os
-os.system('python download-model.py PygmalionAI/pygmalion-350m --branch main')
-# os.system('python download-model.py waifu-workshop/pygmalion-6b --branch original-sharded')
-os.system('python server.py --cpu --chat --model pygmalion-350m --no-stream --auto-devices')
\ No newline at end of file
diff --git a/spaces/dpc/mmstts/mm_num2word.py b/spaces/dpc/mmstts/mm_num2word.py
deleted file mode 100644
index 654b13e49ebbd664de675dfed864433874a33de7..0000000000000000000000000000000000000000
--- a/spaces/dpc/mmstts/mm_num2word.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""
-This file is adapted from https://github.com/hpbyte/Myanmar_Number_to_Words
-"""
-import re
-
-mm_digit = {
- "၀": "သုည",
- "၁": "တစ်",
- "၂": "နှစ်",
- "၃": "သုံ:",
- "၄": "လေ:",
- "၅": "ငါ:",
- "၆": "ခြောက်",
- "၇": "ခုနှစ်",
- "၈": "ရှစ်",
- "၉": "ကို:",
-}
-
-# regular expressions
-rgxPh = "^(၀၁|၀၉)"
-rgxDate = "[၀-၉]{1,2}-[၀-၉]{1,2}-[၀-၉]{4}|[၀-၉]{1,2}\/[၀-၉]{1,2}\/[၀-၉]{4}"
-rgxTime = "[၀-၉]{1,2}:[၀-၉]{1,2}"
-rgxDec = "[၀-၉]*\.[၀-၉]*"
-rgxAmt = "[,၀-၉]+"
-
-
-def convert_digit(num):
- """
- @type num str
- @param num Myanmar number
- @rtype str
- @return converted Myanmar spoken words
- """
-
- converted = ""
- nb_digits = len(num)
-
- def check_if_zero(pos):
- return not num[-pos] == "၀"
-
- def hundred_thousandth_val():
- n = num[:-5]
- return (
- ("သိန်: " + mm_num2word(n))
- if (n[-2:] == "၀၀")
- else (mm_num2word(n) + "သိန်: ")
- )
-
- def thousandth_val():
- return mm_digit[num[-4]] + ("ထောင် " if (num[-3:] == "၀၀၀") else "ထောင့် ")
-
- def hundredth_val():
- return mm_digit[num[-3]] + (
- "ရာ့ "
- if (
- (num[-2] == "၀" and re.match(r"[၁-၉]", num[-1]))
- or (re.match(r"[၁-၉]", num[-2]) and num[-1] == "၀")
- )
- else "ရာ "
- )
-
- def tenth_val():
- return ("" if (num[-2] == "၁") else mm_digit[num[-2]]) + (
- "ဆယ် " if (num[-1] == "၀") else "ဆယ့် "
- )
-
- if nb_digits > 5:
- converted += hundred_thousandth_val()
- if (nb_digits > 4) and check_if_zero(5):
- converted += mm_digit[num[-5]] + "သောင်: "
- if (nb_digits > 3) and check_if_zero(4):
- converted += thousandth_val()
- if (nb_digits > 2) and check_if_zero(3):
- converted += hundredth_val()
- if (nb_digits > 1) and check_if_zero(2):
- converted += tenth_val()
- if (nb_digits > 0) and check_if_zero(1):
- converted += mm_digit[num[-1]]
-
- return converted
-
-
-def mm_num2word(num):
- """
- Detect type of number and convert accordingly
-
- @type num str
- @param num Myanmar number
- @rtype str
- @return converted Myanmar spoken words
- """
-
- word = ""
-
- # phone number
- if re.match(r"" + rgxPh, num[:2]):
- word = " ".join([(mm_digit[d] if not d == "၇" else "ခွန်") for d in num])
- # date
- elif re.match(r"" + rgxDate, num):
- n = re.split(r"-|/", num)
- word = (
- convert_digit(n[-1])
- + " ခုနှစ် "
- + convert_digit(n[1])
- + " လပိုင်: "
- + convert_digit(n[0])
- + " ရက်"
- )
- # time
- elif re.match(r"" + rgxTime, num):
- n = re.split(r":", num)
- word = (convert_digit(n[0]) + " နာရီ ") + (
- "ခွဲ" if (n[1] == "၃၀") else (convert_digit(n[1]) + " မိနစ်")
- )
- # decimal
- elif re.match(r"" + rgxDec, num):
- n = re.split(r"\.", num)
- word = convert_digit(n[0]) + " ဒဿမ " + " ".join([mm_digit[d] for d in n[1]])
- # amount
- elif re.match(r"" + rgxAmt, num):
- word = convert_digit(num.replace(",", ""))
- # default
- else:
- raise Exception("Cannot convert the provided number format!")
-
- return word
-
-
-def extract_num(S):
- """
- Extract numbers from the input string
-
- @type S str
- @param S Myanmar sentence
- @rtype list
- @return a list of Myanmar numbers
- """
- matchedNums = re.compile(
- "%s|%s|%s|%s" % (rgxDate, rgxTime, rgxDec, rgxAmt)
- ).findall(S)
-
- return matchedNums
diff --git a/spaces/drdevinhopkins/llSourcell-medllama2_7b/README.md b/spaces/drdevinhopkins/llSourcell-medllama2_7b/README.md
deleted file mode 100644
index 4ff2f532475ba6cf3176db34d47c3af527335ce0..0000000000000000000000000000000000000000
--- a/spaces/drdevinhopkins/llSourcell-medllama2_7b/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: LlSourcell-medllama2 7b
-emoji: 🌖
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/duycse1603/math2tex/HybridViT/recognizers/build_pred.py b/spaces/duycse1603/math2tex/HybridViT/recognizers/build_pred.py
deleted file mode 100644
index 8caa1be4de6b79477b871a6d00b13ed515c4d3fd..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/HybridViT/recognizers/build_pred.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-import torch.nn as nn
-from ..module.component.prediction_head import (Attention,
- AttentionV2,
- TransformerPrediction
- )
-
-
-class PredictBuilder(nn.Module):
- def __init__(self, flow, config, SequenceModeling_output):
- super().__init__()
- self.flow =flow
- self.config=config
- if flow['Pred'] == 'CTC':
- self.Prediction = nn.Linear(SequenceModeling_output, config['num_class'])
-
- elif flow['Pred'] == 'Attn':
- config['Prediction']['params']['num_classes'] = config['num_class']
- config['Prediction']['params']['device'] = config['device']
- self.Prediction = Attention(
- **config['Prediction']['params']
- )
- elif flow['Pred'] == 'Attnv2':
- config['Prediction']['params']['num_classes'] = config['num_class']
- config['Prediction']['params']['device'] = config['device']
- self.Prediction = AttentionV2(
- **config['Prediction']['params']
- )
- elif flow['Pred'] == 'Multistage_Attn':
- config['Prediction']['params']['num_classes'] = config['num_class']
- config['Prediction']['params']['device'] = config['device']
- self.Prediction = AttentionV2(
- **config['Prediction']['params']
- )
- elif flow['Pred'] == 'TFM':
- config['Prediction']['params']['num_classes'] = config['num_class']
- config['Prediction']['params']['device'] = config['device']
- self.Prediction = TransformerPrediction(
- **config['Prediction']['params']
- )
- else:
- raise ValueError('Prediction name is not suppported')
-
- def forward(self, contextual_feature, text, is_train=True, is_test=False, rtl_text=None):
- beam_size = self.config.get('beam_size', 1)
-
- addition_outputs = {}
- decoder_attn = None
-
- if self.flow['Pred'] == 'CTC':
- prediction = self.Prediction(contextual_feature.contiguous())
-
- elif self.flow['Pred'] in ['Attn', 'Attnv2']:
- prediction, logits, decoder_attn = self.Prediction(beam_size, contextual_feature.contiguous(), text, is_train=is_train,
- is_test=is_test, batch_max_length=self.config['batch_max_length'])
-
- elif self.flow['Pred'] == 'TFM':
- prediction, logits = self.Prediction(beam_size, contextual_feature.contiguous(), text, is_test)
- self.Prediction.reset_beam()
-
- return prediction, logits, decoder_attn, addition_outputs
diff --git a/spaces/dylanebert/list-of-splats/index.html b/spaces/dylanebert/list-of-splats/index.html
deleted file mode 100644
index d5f3cbca6544defc96774b2211622b52ee1ba367..0000000000000000000000000000000000000000
--- a/spaces/dylanebert/list-of-splats/index.html
+++ /dev/null
@@ -1,81 +0,0 @@
-
-
-
- My static Space
- List of Splats
-
- List of viewers, tools, and implementations of
- 3D Gaussian Splatting
-
-
- a larger compilation of resources can be found
- here
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/edjdhug3/chat-with-pdfs/README.md b/spaces/edjdhug3/chat-with-pdfs/README.md
deleted file mode 100644
index 51b0416c6ff176a11d7467fb621bd37a51a90c00..0000000000000000000000000000000000000000
--- a/spaces/edjdhug3/chat-with-pdfs/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chat With Pdfs
-emoji: 🏢
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/enzostvs/hub-api-playground/Dockerfile b/spaces/enzostvs/hub-api-playground/Dockerfile
deleted file mode 100644
index 99581361f9cd6829032e647c0087ca22709096fe..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/hub-api-playground/Dockerfile
+++ /dev/null
@@ -1,25 +0,0 @@
-# Dockerfile
-
-# Use an official Node.js runtime as the base image
-FROM node:18
-
-# Set the working directory in the container
-WORKDIR /usr/src/app
-
-# Copy package.json and package-lock.json to the container
-COPY package.json package-lock.json ./
-
-# Install dependencies
-RUN npm install
-
-# Copy the rest of the application files to the container
-COPY . .
-
-# Build the Next.js application for production
-RUN npm run build
-
-# Expose the application port (assuming your app runs on port 3000)
-EXPOSE 3002
-
-# Start the application
-CMD ["npm", "start"]
\ No newline at end of file
diff --git a/spaces/epexVfeibi/Imagedeblurr/Acoustica Mixcraft Pro 9.0 Build 439 Keygen VERIFIED.md b/spaces/epexVfeibi/Imagedeblurr/Acoustica Mixcraft Pro 9.0 Build 439 Keygen VERIFIED.md
deleted file mode 100644
index cb4356aa68ab2e92e564b3191388ec9fc72c8265..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/Acoustica Mixcraft Pro 9.0 Build 439 Keygen VERIFIED.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
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diff --git "a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py" "b/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py"
deleted file mode 100644
index c1e5dadd142de683323463d3df260cbe6eefa6d8..0000000000000000000000000000000000000000
--- "a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py"
+++ /dev/null
@@ -1,60 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-@CatchException
-def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
- llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
- plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行
- chatbot 聊天显示框的句柄，用于显示给用户
- history 聊天历史，前情提要
- system_prompt 给gpt的静默提醒
- web_port 当前软件运行的端口号
- """
- history = [] # 清空历史，以免输入溢出
- chatbot.append((txt, "正在同时咨询gpt-3.5和gpt-4……"))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
- # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
- llm_kwargs['llm_model'] = 'gpt-3.5-turbo&gpt-4' # 支持任意数量的llm接口，用&符号分隔
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=txt, inputs_show_user=txt,
- llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
- sys_prompt=system_prompt,
- retry_times_at_unknown_error=0
- )
-
- history.append(txt)
- history.append(gpt_say)
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-
-@CatchException
-def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
- llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
- plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行
- chatbot 聊天显示框的句柄，用于显示给用户
- history 聊天历史，前情提要
- system_prompt 给gpt的静默提醒
- web_port 当前软件运行的端口号
- """
- history = [] # 清空历史，以免输入溢出
- chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
- if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
- # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
- llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=txt, inputs_show_user=txt,
- llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
- sys_prompt=system_prompt,
- retry_times_at_unknown_error=0
- )
-
- history.append(txt)
- history.append(gpt_say)
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/examples/clue_sim/loss.py b/spaces/fclong/summary/fengshen/examples/clue_sim/loss.py
deleted file mode 100644
index 537e2347f65aa952b0eb852c23a39901b0fef52e..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/clue_sim/loss.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. 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.
-import torch
-from torch.nn import functional as F
-
-
-class FocalLoss(torch.nn.Module):
- """Multi-class Focal loss implementation"""
-
- def __init__(self, gamma=2, weight=None, ignore_index=-100):
- super(FocalLoss, self).__init__()
- self.gamma = gamma
- self.weight = weight
- self.ignore_index = ignore_index
-
- def forward(self, input, target):
- """
- input: [N, C]
- target: [N, ]
- """
- logpt = F.log_softmax(input, dim=1)
- pt = torch.exp(logpt)
- logpt = (1-pt)**self.gamma * logpt
- loss = F.nll_loss(logpt, target, self.weight, ignore_index=self.ignore_index)
- return loss
-
-# 交叉熵平滑滤波 防止过拟合
-
-
-class LabelSmoothingCorrectionCrossEntropy(torch.nn.Module):
- def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
- super(LabelSmoothingCorrectionCrossEntropy, self).__init__()
- self.eps = eps
- self.reduction = reduction
- self.ignore_index = ignore_index
-
- def forward(self, output, target):
- c = output.size()[-1]
- log_preds = F.log_softmax(output, dim=-1)
- if self.reduction == 'sum':
- loss = -log_preds.sum()
- else:
- loss = -log_preds.sum(dim=-1)
- if self.reduction == 'mean':
- loss = loss.mean()
-
- # task specific
- labels_hat = torch.argmax(output, dim=1)
- lt_sum = labels_hat + target
- abs_lt_sub = abs(labels_hat - target)
- correction_loss = 0
- for i in range(c):
- if lt_sum[i] == 0:
- pass
- elif lt_sum[i] == 1:
- if abs_lt_sub[i] == 1:
- pass
- else:
- correction_loss -= self.eps*(0.5945275813408382)
- else:
- correction_loss += self.eps*(1/0.32447699714575207)
- correction_loss /= c
- # print(correction_loss)
- return loss*self.eps/c + (1-self.eps) * \
- F.nll_loss(log_preds, target, reduction=self.reduction, ignore_index=self.ignore_index) + correction_loss
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/501 Essential Backgammon Problems PDF A Comprehensive Collection of Problems for Every Phase of the Game.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/501 Essential Backgammon Problems PDF A Comprehensive Collection of Problems for Every Phase of the Game.md
deleted file mode 100644
index 71e1a5e283b9f2eda745148fd9276c12ca5df33a..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/501 Essential Backgammon Problems PDF A Comprehensive Collection of Problems for Every Phase of the Game.md
+++ /dev/null
@@ -1,157 +0,0 @@
-
-501 Essential Backgammon Problems: A Must-Have Book for Backgammon Enthusiasts
-If you love playing backgammon and want to take your game to the next level, you need to get your hands on a copy of 501 Essential Backgammon Problems. This book is widely regarded as one of the best backgammon books ever written, and it will help you master the most important aspects of this fascinating game. In this article, we will tell you what this book is about, how you can download it in PDF format, and how you can use it to improve your backgammon skills.
- What is 501 Essential Backgammon Problems?
-501 Essential Backgammon Problems is a book that contains 501 carefully selected backgammon positions that cover various stages and scenarios of the game. The book is designed to test your knowledge, sharpen your skills, and challenge your mind.
-501 essential backgammon problems pdf download DOWNLOAD ⚙ https://gohhs.com/2uPuXn
The author and the publisher
-The author of this book is Bill Robertie, a two-time world champion backgammon player and a renowned backgammon writer and teacher. He has written several other books on backgammon, such as Modern Backgammon, Advanced Backgammon, and Backgammon for Winners. The publisher of this book is Cardoza Publishing, a leading publisher of gaming and gambling books since 1981.
- The content and the format
-The book is divided into six chapters, each focusing on a different aspect of the game. The chapters are:
-
-Chapter 1: Opening Rolls
-Chapter 2: Middle Game
-Chapter 3: Bearing In
-Chapter 4: Bearing Off
-Chapter 5: Doubling
-Chapter 6: Endgame
-
-Each chapter contains a number of problems that range from easy to hard. Each problem is presented with a diagram of the board position, a brief description of the situation, and a question that asks you to make the best move or decision. The answers are given at the end of each chapter, along with detailed explanations and analysis. The book also includes an introduction that explains the basics of backgammon notation, terminology, and concepts.
- The benefits and the challenges
-The benefits of reading this book are many. You will learn how to:
-
-Evaluate different positions and options
-Apply fundamental principles and strategies
-Avoid common mistakes and pitfalls
-Recognize key patterns and themes
-Use the doubling cube effectively
-Calculate odds and probabilities
-Plan ahead and anticipate your opponent's moves
-Develop your intuition and judgment
-
-The challenges of reading this book are also many. You will need to:
-501 essential backgammon problems ebook free download
-
-Spend time and effort to study each problem carefully
-Compare your answers with the author's answers and understand the differences
-Review your mistakes and learn from them
-Practice what you learn by playing against strong opponents or computer programs
-Keep an open mind and be willing to change your views if necessary
-Have fun and enjoy the process of learning
-
- How to Download 501 Essential Backgammon Problems PDF?
-If you want to download this book in PDF format, you have two options:
- The official website and the price
-You can buy this book directly from the official website of Cardoza Publishing. The price of the book is $24.95, and you can pay with a credit card or PayPal. You will receive a link to download the PDF file after completing the purchase. You can also order a paperback copy of the book if you prefer. The website of Cardoza Publishing is .
- The alternative sources and the precautions
-You can also find some alternative sources that offer this book in PDF format for free or for a lower price. However, you should be careful when using these sources, as they may not be authorized, legal, or safe. Some of the risks involved are:
-
-You may download a corrupted, incomplete, or outdated version of the book
-You may download a file that contains viruses, malware, or spyware that can harm your device or compromise your privacy
-You may violate the intellectual property rights of the author and the publisher and face legal consequences
-You may miss out on the updates, corrections, and support that the official website provides
-
-Some of the alternative sources that claim to offer this book in PDF format are and . We do not endorse or recommend these sources, and we advise you to use them at your own risk.
- How to Improve Your Backgammon Skills with 501 Essential Backgammon Problems?
-Downloading this book in PDF format is only the first step. To really improve your backgammon skills, you need to study and practice with this book regularly and systematically. Here are some tips on how to do that:
- The basic backgammon rules and strategies
-Before you start solving the problems in this book, you should make sure that you are familiar with the basic rules and strategies of backgammon. If you are a beginner or need a refresher, you can find many online resources that explain the basics of backgammon, such as and . You should also learn how to use backgammon notation, which is a system of symbols and abbreviations that represent the moves and positions on the board. This book uses a standard backgammon notation that is explained in the introduction.
- The tips and tricks for solving the problems
-When you are ready to tackle the problems in this book, you should follow these steps:
-
-Pick a chapter that matches your level of skill and interest
-Read the description of the situation and look at the diagram of the board position
-Try to find the best move or decision for yourself without looking at the answer
-Check your answer with the author's answer and read the explanation and analysis
-Compare your reasoning with the author's reasoning and understand why your answer was right or wrong
-Review your mistakes and learn from them
-Repeat the process with another problem until you finish the chapter
-
-Some tricks that can help you solve the problems faster and better are:
-
-Use a real backgammon board or a computer program to set up and play out the positions
-Use a pencil and paper to write down your thoughts and calculations
-Use a timer to limit your thinking time and simulate real game conditions
-Use a friend or a partner to discuss and compare your answers and opinions
-Use a rating system to track your performance and progress
-
- The feedback and the progress
-The ultimate goal of reading this book is to improve your backgammon skills and enjoy playing this game more. To achieve this goal, you need to get feedback on your results and measure your progress. Some ways to do that are:
-
-Play against stronger opponents or computer programs and see how you fare against them
-Join online or offline backgammon clubs, forums, or communities and interact with other players
-Participate in online or offline backgammon tournaments and test your skills under pressure
-Read other backgammon books, articles, or blogs and expand your knowledge and perspective
-Watch online or offline backgammon videos, podcasts, or streams and learn from experts and professionals
-
- Conclusion
-501 Essential Backgammon Problems is a must-have book for backgammon enthusiasts who want to improve their game. It contains 501 challenging and instructive problems that cover all aspects of backgammon. It is written by a world-class backgammon player and teacher who provides clear and detailed answers and explanations for each problem. It is available in PDF format from the official website of the publisher or from some alternative sources. It is a valuable resource for learning and practicing backgammon, and it can help you improve your skills and enjoy the game more. We hope you found this article helpful and informative, and we encourage you to download this book and start solving the problems today.
- FAQs
-Here are some frequently asked questions about 501 Essential Backgammon Problems:
-
-Q: How long does it take to read this book?
-A: It depends on your reading speed, your level of skill, and your interest in the topic. However, you should not rush through this book, as it is meant to be studied and practiced carefully. You should spend enough time on each problem to understand the situation, find the best move or decision, and compare your answer with the author's answer. You should also review your mistakes and learn from them. A reasonable pace would be to solve one or two problems per day, which would take you about a year to finish the book.
-Q: Do I need any prior knowledge or experience to read this book?
-A: You do not need any prior knowledge or experience to read this book, as it explains the basics of backgammon notation, terminology, and concepts in the introduction. However, you should have some familiarity with the rules and strategies of backgammon, as this book is not a beginner's guide. If you are a complete novice, you should first learn the basics of backgammon from other sources before reading this book.
-Q: Is this book suitable for all levels of players?
-A: This book is suitable for all levels of players, from beginners to experts. The problems in this book are arranged in order of difficulty, so you can start with the easier ones and progress to the harder ones as you improve your skills. The problems cover all aspects of backgammon, so you can learn something new from each one. The answers and explanations are clear and detailed, so you can understand the logic and reasoning behind each move or decision.
-Q: Can I use this book as a reference or a guide?
-A: You can use this book as a reference or a guide, but not as a substitute for your own thinking and judgment. This book provides you with examples and principles that can help you improve your backgammon skills, but it does not give you definitive answers or solutions for every situation. You should always analyze each position and option for yourself, and use your own intuition and experience to make the best move or decision. You should also be aware that backgammon is a dynamic and complex game, and that there may be exceptions or variations to the rules and strategies presented in this book.
-Q: Where can I find more information or resources on backgammon?
-A: There are many information and resources on backgammon available online or offline. Some of them are:
-
-The official website of the World Backgammon Association (WBA), which is the governing body of international backgammon tournaments and events
-The official website of the United States Backgammon Federation (USBGF), which is the national organization for backgammon players in the US
-The official website of GammonLife, which is an online magazine that covers news, articles, interviews, videos, podcasts, and streams on backgammon
-The official website of Backgammon Galaxy, which is an online platform that allows you to play backgammon against other players or computer programs
-The official website of XG Mobile Backgammon Analyzer, which is an app that analyzes your backgammon games and gives you feedback and advice
- 197e85843dZeroscope Text-to-Video
-
- A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output.
-
- """
- )
-
- prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="prompt-in")
- #neg_prompt = gr.Textbox(label="Negative prompt", value="text, watermark, copyright, blurry, nsfw", elem_id="neg-prompt-in")
- #inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
- submit_btn = gr.Button("Submit")
- video_result = gr.Video(label="Video Output", elem_id="video-output")
-
- with gr.Row():
- with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
- community_icon = gr.HTML(community_icon_html)
- loading_icon = gr.HTML(loading_icon_html)
- share_button = gr.Button("Share with Community", elem_id="share-btn")
-
- gr.Markdown("""
- [](https://huggingface.co/spaces/fffiloni/zeroscope-cloning?duplicate=true)
- """)
-
- gr.HTML("""
-
-
- """)
-
- submit_btn.click(fn=infer,
- inputs=[prompt_in],
- outputs=[video_result, share_group],
- api_name="zrscp")
-
- share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=12).launch(show_api=True)
-
\ No newline at end of file
diff --git a/spaces/flax-community/chef-transformer/utils/draw.py b/spaces/flax-community/chef-transformer/utils/draw.py
deleted file mode 100644
index de7ef90167a979b7ddac2671e7c5ab178754424a..0000000000000000000000000000000000000000
--- a/spaces/flax-community/chef-transformer/utils/draw.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from PIL import (
- Image,
- ImageDraw
-)
-import textwrap
-from utils.utils import load_image_from_url
-from utils.ext import (
- ingredients as ext_ingredients,
- directions as ext_directions
-)
-
-
-# from .utils import load_image_from_url
-# from .ext import (
-# ingredients as ext_ingredients,
-# directions as ext_directions
-# )
-
-
-def generate_food_with_logo_image(bg_path, logo_path, food_url, no_food="asset/frame/no_food.png"):
- bg = Image.open(bg_path)
- width, height = bg.size
-
- logo = Image.open(logo_path)
- logo_width, logo_height, logo_ratio, logo_rb, logo_mb = logo.size + (3, -20, 45)
- logo_width, logo_height = (logo_width // logo_ratio, logo_height // logo_ratio)
- logo = logo.resize((logo_width, logo_height))
-
- food = load_image_from_url(food_url, rgba_mode=True, default_image=no_food)
-
- food_width, food_height = (300, 300)
- food = food.resize((food_width, food_height))
-
- bg.paste(food, (0, 0), food)
- bg.paste(logo, (width - logo_width - logo_rb, height - logo_height - logo_mb), logo)
-
- return bg
-
-
-def generate_recipe_image(
- recipe_data,
- bg_path,
- food_logo_ia,
- fonts,
- bg_color="#ffffff"
-):
- bg = Image.open(bg_path)
- bg.paste(food_logo_ia, (50, 50), food_logo_ia)
- bg_color = Image.new("RGBA", bg.size, bg_color)
- bg_color.paste(bg, mask=bg)
-
- im_editable = ImageDraw.Draw(bg_color)
- im_editable.text(
- (418, 30),
- textwrap.fill(recipe_data["title"], 15).replace(" \n", "\n"),
- (61, 61, 70),
- font=fonts["title"],
- )
-
- im_editable.text(
- (100, 450),
- "Ingredients",
- (61, 61, 70),
- font=fonts["body_bold"],
- )
- ingredients = recipe_data["ingredients"]
- ingredients = ext_ingredients(ingredients, [], without_mapping=True)
- ingredients = [textwrap.fill(item, 30).replace("\n", "\n ") for item in ingredients]
-
- im_editable.text(
- (50, 520),
- "\n".join([f"- {item}" for item in ingredients]),
- (61, 61, 70),
- font=fonts["body"],
- )
-
- im_editable.text(
- (700, 450),
- "Directions",
- (61, 61, 70),
- font=fonts["body_bold"],
- )
-
- directions = recipe_data["directions"]
- directions = ext_directions(directions)
- directions = [textwrap.fill(item, 70).replace("\n", "\n ").capitalize() for item in directions]
-
- im_editable.text(
- (430, 520),
- "\n".join([f"{i + 1}. {item}" for i, item in enumerate(directions)]).strip(),
- (61, 61, 70),
- font=fonts["body"],
- )
- return bg_color
diff --git a/spaces/florim/MedGPT/autogpt/processing/html.py b/spaces/florim/MedGPT/autogpt/processing/html.py
deleted file mode 100644
index 81387b12adab5023150c55f2075ddd40b554f386..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/processing/html.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""HTML processing functions"""
-from __future__ import annotations
-
-from bs4 import BeautifulSoup
-from requests.compat import urljoin
-
-
-def extract_hyperlinks(soup: BeautifulSoup, base_url: str) -> list[tuple[str, str]]:
- """Extract hyperlinks from a BeautifulSoup object
-
- Args:
- soup (BeautifulSoup): The BeautifulSoup object
- base_url (str): The base URL
-
- Returns:
- List[Tuple[str, str]]: The extracted hyperlinks
- """
- return [
- (link.text, urljoin(base_url, link["href"]))
- for link in soup.find_all("a", href=True)
- ]
-
-
-def format_hyperlinks(hyperlinks: list[tuple[str, str]]) -> list[str]:
- """Format hyperlinks to be displayed to the user
-
- Args:
- hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
-
- Returns:
- List[str]: The formatted hyperlinks
- """
- return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
diff --git a/spaces/flowers-team/SocialAISchool/scripts/LLM_test_old.py b/spaces/flowers-team/SocialAISchool/scripts/LLM_test_old.py
deleted file mode 100644
index a16900195b616ffa992295c07638c29312812af6..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/scripts/LLM_test_old.py
+++ /dev/null
@@ -1,586 +0,0 @@
-# python -m scripts.LLM_test --gif test_GPT_boxes --episodes 1 --max-steps 8 --model text-davinci-003 --env-args size 6 --env-name SocialAI-ColorBoxesLLMCSParamEnv-v1 --in-context-path llm_data/in_context_color_boxes.txt
-# python -m scripts.LLM_test --gif test_GPT_asoc --episodes 1 --max-steps 8 --model text-ada-001 --env-args size 6 --env-name SocialAI-AsocialBoxInformationSeekingParamEnv-v1 --in-context-path llm_data/in_context_asocial_box.txt --feed-full-ep
-
-# python -m scripts.LLM_test --gif test_GPT_boxes --episodes 1 --max-steps 8 --model bloom_560m --env-args size 6 --env-name SocialAI-ColorBoxesLLMCSParamEnv-v1 --in-context-path llm_data/in_context_color_boxes.txt
-# python -m scripts.LLM_test --gif test_GPT_asoc --episodes 1 --max-steps 8 --model bloom_560m --env-args size 6 --env-name SocialAI-AsocialBoxInformationSeekingParamEnv-v1 --in-context-path llm_data/in_context_asocial_box.txt --feed-full-ep
-
-## bloom 560m
-# boxes
-# python -m scripts.LLM_test --log llm_log/bloom_560m_boxes_no_hist --gif evaluation --episodes 20 --max-steps 10 --model bloom_560m --env-args size 6 --env-name SocialAI-ColorBoxesLLMCSParamEnv-v1 --in-context-path llm_data/in_context_color_boxes.txt
-
-# asocial
-# python -m scripts.LLM_test --log llm_log/bloom_560m_asocial_no_hist --gif evaluation --episodes 20 --max-steps 10 --model bloom_560m --env-args size 6 --env-name SocialAI-AsocialBoxInformationSeekingParamEnv-v1 --in-context-path llm_data/in_context_asocial_box.txt
-
-# random
-# python -m scripts.LLM_test --log llm_log/random_boxes --gif evaluation --episodes 20 --max-steps 10 --model random --env-args size 6 --env-name SocialAI-ColorBoxesLLMCSParamEnv-v1 --in-context-path llm_data/in_context_color_boxes.txt
-
-import argparse
-import json
-import requests
-import time
-import warnings
-from n_tokens import estimate_price
-
-import numpy as np
-import torch
-from pathlib import Path
-
-from utils.babyai_utils.baby_agent import load_agent
-from utils import *
-from models import *
-import subprocess
-import os
-
-from matplotlib import pyplot as plt
-
-from gym_minigrid.wrappers import *
-from gym_minigrid.window import Window
-from datetime import datetime
-
-from imageio import mimsave
-
-def prompt_preprocessor(llm_prompt):
- # remove peer observations
- lines = llm_prompt.split("\n")
- new_lines = []
- for line in lines:
- if line.startswith("#"):
- continue
-
- elif line.startswith("Conversation"):
- continue
-
- elif "peer" in line:
- caretaker = True
- if caretaker:
- # show only the location of the caretaker
-
- # this is very ugly, todo: refactor this
- assert "there is a" in line
- start_index = line.index('there is a') + 11
- new_line = line[:start_index] + 'caretaker'
-
- new_lines.append(new_line)
-
- else:
- # no caretaker at all
- if line.startswith("Obs :") and "peer" in line:
- # remove only the peer descriptions
- line = "Obs :"
- new_lines.append(line)
- else:
- assert "peer" in line
-
- elif "Caretaker:" in line:
- # line = line.replace("Caretaker:", "Caretaker says: '") + "'"
- new_lines.append(line)
-
- else:
- new_lines.append(line)
-
- return "\n".join(new_lines)
-
-
-# Parse arguments
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--model", required=False,
- help="text-ada-001")
-parser.add_argument("--seed", type=int, default=0,
- help="Seed of the first episode. The seed for the following episodes will be used in order: seed, seed + 1, ... seed + (n_episodes-1) (default: 0)")
-parser.add_argument("--max-steps", type=int, default=5,
- help="max num of steps")
-parser.add_argument("--shift", type=int, default=0,
- help="number of times the environment is reset at the beginning (default: 0)")
-parser.add_argument("--argmax", action="store_true", default=False,
- help="select the action with highest probability (default: False)")
-parser.add_argument("--pause", type=float, default=0.5,
- help="pause duration between two consequent actions of the agent (default: 0.5)")
-parser.add_argument("--env-name", type=str,
- # default="SocialAI-ELangColorBoxesTestInformationSeekingParamEnv-v1",
- # default="SocialAI-AsocialBoxInformationSeekingParamEnv-v1",
- default="SocialAI-ColorBoxesLLMCSParamEnv-v1",
- required=False,
- help="env name")
-parser.add_argument("--in-context-path", type=str,
- # default='llm_data/short_in_context_boxes.txt'
- # default='llm_data/in_context_asocial_box.txt'
- default='llm_data/in_context_color_boxes.txt',
- required=False,
- help="path to in context examples")
-parser.add_argument("--gif", type=str, default="visualization",
- help="store output as gif with the given filename", required=False)
-parser.add_argument("--episodes", type=int, default=1,
- help="number of episodes to visualize")
-parser.add_argument("--env-args", nargs='*', default=None)
-parser.add_argument("--agent_view", default=False, help="draw the agent sees (partially observable view)", action='store_true' )
-parser.add_argument("--tile_size", type=int, help="size at which to render tiles", default=32 )
-parser.add_argument("--mask-unobserved", default=False, help="mask cells that are not observed by the agent", action='store_true' )
-parser.add_argument("--log", type=str, default="llm_log/episodes_log", help="log from the run", required=False)
-parser.add_argument("--feed-full-ep", default=False, help="weather to append the whole episode to the prompt", action='store_true')
-parser.add_argument("--skip-check", default=False, help="Don't estimate the price.", action="store_true")
-
-args = parser.parse_args()
-
-# Set seed for all randomness sources
-
-seed(args.seed)
-
-model = args.model
-
-
-in_context_examples_path = args.in_context_path
-
-print("env name:", args.env_name)
-print("examples:", in_context_examples_path)
-print("model:", args.model)
-
-# datetime
-now = datetime.now()
-datetime_string = now.strftime("%d_%m_%Y_%H:%M:%S")
-print(datetime_string)
-
-# log filenames
-
-log_folder = args.log+"_"+datetime_string+"/"
-os.mkdir(log_folder)
-evaluation_log_filename = log_folder+"evaluation_log.json"
-prompt_log_filename = log_folder + "prompt_log.txt"
-ep_h_log_filename = log_folder+"episode_history_query.txt"
-gif_savename = log_folder + args.gif + ".gif"
-
-assert "viz" not in gif_savename # don't use viz anymore
-
-
-env_args = env_args_str_to_dict(args.env_args)
-env = make_env(args.env_name, args.seed, env_args)
-
-# env = gym.make(args.env_name, **env_args)
-print(f"Environment {args.env_name} and args: {env_args_str_to_dict(args.env_args)}\n")
-
-# Define agent
-print("Agent loaded\n")
-
-# prepare models
-
-if args.model in ["text-davinci-003", "text-ada-001", "gpt-3.5-turbo-0301"]:
- import openai
- openai.api_key = os.getenv("OPENAI_API_KEY")
-
-elif args.model in ["gpt2_large", "api_bloom"]:
- HF_TOKEN = os.getenv("HF_TOKEN")
-
-elif args.model in ["bloom_560m"]:
- from transformers import BloomForCausalLM
- from transformers import BloomTokenizerFast
-
- hf_tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
- hf_model = BloomForCausalLM.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
-
-elif args.model in ["bloom"]:
- from transformers import BloomForCausalLM
- from transformers import BloomTokenizerFast
-
- hf_tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom", cache_dir=".cache/huggingface/")
- hf_model = BloomForCausalLM.from_pretrained("bigscience/bloom", cache_dir=".cache/huggingface/")
-
-
-def plt_2_rgb(env):
- # data = np.frombuffer(env.window.fig.canvas.tostring_rgb(), dtype=np.uint8)
- # data = data.reshape(env.window.fig.canvas.get_width_height()[::-1] + (3,))
-
- width, height = env.window.fig.get_size_inches() * env.window.fig.get_dpi()
- data = np.fromstring(env.window.fig.canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
- return data
-
-def generate(text_input, model):
- # return "(a) move forward"
- if model == "dummy":
- print("dummy action forward")
- return "move forward"
-
- elif model == "random":
- print("random agent")
- return random.choice([
- "move forward",
- "turn left",
- "turn right",
- "toggle",
- ])
-
- elif model in ["gpt-3.5-turbo-0301"]:
- while True:
- try:
- c = openai.ChatCompletion.create(
- model=model,
- messages=[
- # {"role": "system", "content": ""},
- # {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
- # {"role": "user", "content": "Continue the following text in the most logical way.\n"+text_input}
- {"role": "user", "content": text_input}
- ],
- max_tokens=3,
- n=1,
- temperature=0,
- request_timeout=30,
- )
- break
- except Exception as e:
- print(e)
- print("Pausing")
- time.sleep(10)
- continue
- print("generation: ", c['choices'][0]['message']['content'])
- return c['choices'][0]['message']['content']
-
- elif model in ["text-davinci-003", "text-ada-001"]:
- while True:
- try:
- response = openai.Completion.create(
- model=model,
- prompt=text_input,
- # temperature=0.7,
- temperature=0.0,
- max_tokens=3,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0,
- timeout=30
- )
- break
-
- except Exception as e:
- print(e)
- print("Pausing")
- time.sleep(10)
- continue
-
- choices = response["choices"]
- assert len(choices) == 1
- return choices[0]["text"].strip().lower() # remove newline from the end
-
- elif model in ["gpt2_large", "api_bloom"]:
- # HF_TOKEN = os.getenv("HF_TOKEN")
- if model == "gpt2_large":
- API_URL = "https://api-inference.huggingface.co/models/gpt2-large"
-
- elif model == "api_bloom":
- API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-
- else:
- raise ValueError(f"Undefined model {model}.")
-
- headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
- def query(text_prompt, n_tokens=3):
-
- input = text_prompt
-
- # make n_tokens request and append the output each time - one request generates one token
-
- for _ in range(n_tokens):
- # prepare request
- payload = {
- "inputs": input,
- "parameters": {
- "do_sample": False,
- 'temperature': 0,
- 'wait_for_model': True,
- # "max_length": 500, # for gpt2
- # "max_new_tokens": 250 # fot gpt2-xl
- },
- }
- data = json.dumps(payload)
-
- # request
- response = requests.request("POST", API_URL, headers=headers, data=data)
- response_json = json.loads(response.content.decode("utf-8"))
-
- if type(response_json) is list and len(response_json) == 1:
- # generated_text contains the input + the response
- response_full_text = response_json[0]['generated_text']
-
- # we use this as the next input
- input = response_full_text
-
- else:
- print("Invalid request to huggingface api")
- from IPython import embed; embed()
-
- # remove the prompt from the beginning
- assert response_full_text.startswith(text_prompt)
- response_text = response_full_text[len(text_prompt):]
-
- return response_text
-
- response = query(text_input).strip().lower()
- return response
-
- elif model in ["bloom_560m"]:
- # from transformers import BloomForCausalLM
- # from transformers import BloomTokenizerFast
- #
- # tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
- # model = BloomForCausalLM.from_pretrained("bigscience/bloom-560m", cache_dir=".cache/huggingface/")
-
- inputs = hf_tokenizer(text_input, return_tensors="pt")
- # 3 words
- result_length = inputs['input_ids'].shape[-1]+3
- full_output = hf_tokenizer.decode(hf_model.generate(inputs["input_ids"], max_length=result_length)[0])
-
- assert full_output.startswith(text_input)
- response = full_output[len(text_input):]
-
- response = response.strip().lower()
-
- return response
-
- else:
- raise ValueError("Unknown model.")
-
-def get_parsed_action(text_action):
- if "move forward" in text_action:
- return "move forward"
-
- elif "turn left" in text_action:
- return "turn left"
-
- elif "turn right" in text_action:
- return "turn right"
-
- elif "toggle" in text_action:
- return "toggle"
-
- elif "no_op" in text_action:
- return "no_op"
- else:
- warnings.warn(f"Undefined action {text_action}")
- return "no_op"
-
-
-def step(text_action):
- text_action = get_parsed_action(text_action)
-
- if "move forward" == text_action:
- action = [int(env.actions.forward), np.nan, np.nan]
-
- elif "turn left" == text_action:
- action = [int(env.actions.left), np.nan, np.nan]
-
- elif "turn right" == text_action:
- action = [int(env.actions.right), np.nan, np.nan]
-
- elif "toggle" == text_action:
- action = [int(env.actions.toggle), np.nan, np.nan]
-
- elif "no_op" == text_action:
- action = [np.nan, np.nan, np.nan]
-
- # if text_action.startswith("a"):
- # action = [int(env.actions.forward), np.nan, np.nan]
- #
- # elif text_action.startswith("b"):
- # action = [int(env.actions.left), np.nan, np.nan]
- #
- # elif text_action.startswith("c"):
- # action = [int(env.actions.right), np.nan, np.nan]
- #
- # elif text_action.startswith("d"):
- # action = [int(env.actions.toggle), np.nan, np.nan]
- #
- # elif text_action.startswith("e"):
- # action = [np.nan, np.nan, np.nan]
- #
- # else:
- # print("Unknown action.")
-
- obs, reward, done, info = env.step(action)
-
- return obs, reward, done, info
-
-
-
-def reset(env):
- env.reset()
- # a dirty trick just to get obs and info
- return step("no_op")
-
-
-def generate_text_obs(obs, info):
- llm_prompt = "Obs : "
- llm_prompt += "".join(info["descriptions"])
- if obs["utterance_history"] != "Conversation: \n":
- utt_hist = obs['utterance_history']
- utt_hist = utt_hist.replace("Conversation: \n","")
- llm_prompt += utt_hist
-
- return llm_prompt
-
-
-def action_query():
- # llm_prompt = ""
- # llm_prompt += "Your possible actions are:\n"
- # llm_prompt += "(a) move forward\n"
- # llm_prompt += "(b) turn left\n"
- # llm_prompt += "(c) turn right\n"
- # llm_prompt += "(d) toggle\n"
- # llm_prompt += "(e) no_op\n"
- # llm_prompt += "Your next action is: ("
- llm_prompt = "Act :"
- return llm_prompt
-
-# lod context examples
-with open(in_context_examples_path, "r") as f:
- in_context_examples = f.read()
-
-with open(prompt_log_filename, "a+") as f:
- f.write(datetime_string)
-
-with open(ep_h_log_filename, "a+") as f:
- f.write(datetime_string)
-
-feed_episode_history = args.feed_full_ep
-
-# asoc
-in_context_n_tokens = 800
-ep_obs_len = 50 * 3
-
-# color
-in_context_n_tokens = 1434
-# ep_obs_len = 70
-
-# feed only current obs
-if feed_episode_history:
- ep_obs_len = 50
-
-else:
- # last_n = 1
- # last_n = 2
- last_n = 3
- ep_obs_len = 50 * last_n
-
-_, price = estimate_price(
- num_of_episodes=args.episodes,
- in_context_len=in_context_n_tokens,
- ep_obs_len=ep_obs_len,
- n_steps=args.max_steps,
- model=args.model,
- feed_episode_history=feed_episode_history
-)
-if not args.skip_check:
- input(f"You will spend: {price} dollars. (in context: {in_context_n_tokens} obs: {ep_obs_len}), ok?")
-
-# prepare frames list to save to gif
-frames = []
-
-assert args.max_steps <= 20
-
-success_rates = []
-# episodes start
-for episode in range(args.episodes):
- print("Episode:", episode)
- new_episode_text = "New episode.\n"
- episode_history_text = new_episode_text
-
- success = False
- episode_seed = args.seed + episode
- env = make_env(args.env_name, episode_seed, env_args)
-
- with open(prompt_log_filename, "a+") as f:
- f.write("\n\n")
-
- observations = []
- actions = []
- for i in range(int(args.max_steps)):
- if i == 0:
- obs, reward, done, info = reset(env)
- action_text = ""
-
- else:
- with open(prompt_log_filename, "a+") as f:
- f.write("\nnew prompt: -----------------------------------\n")
- f.write(llm_prompt)
-
- text_action = generate(llm_prompt, args.model)
- obs, reward, done, info = step(text_action)
- action_text = f"Act : {get_parsed_action(text_action)}\n"
- actions.append(action_text)
-
- print(action_text)
-
- text_obs = generate_text_obs(obs, info)
- observations.append(text_obs)
- print(prompt_preprocessor(text_obs))
-
- # feed the full episode history
- episode_history_text += prompt_preprocessor(action_text + text_obs) # append to history of this episode
-
- if feed_episode_history:
- # feed full episode history
- llm_prompt = in_context_examples + episode_history_text + action_query()
-
- else:
- n = min(last_n, len(observations))
- obs = observations[-n:]
- act = (actions + [action_query()])[-n:]
-
- episode_text = "".join([o+a for o,a in zip(obs, act)])
-
- llm_prompt = in_context_examples + new_episode_text + episode_text
-
- llm_prompt = prompt_preprocessor(llm_prompt)
-
-
- # save the image
- env.render(mode="human")
- rgb_img = plt_2_rgb(env)
- frames.append(rgb_img)
-
- if env.current_env.box.blocked and not env.current_env.box.is_open:
- # target box is blocked -> apple can't be obtained
- # break to save compute
- break
-
- if done:
- # quadruple last frame to pause between episodes
- for i in range(3):
- same_img = np.copy(rgb_img)
- # toggle a pixel between frames to avoid cropping when going from gif to mp4
- same_img[0, 0, 2] = 0 if (i % 2) == 0 else 255
- frames.append(same_img)
-
- if reward > 0:
- print("Success!")
- episode_history_text += "Success!\n"
- success = True
- else:
- episode_history_text += "Failure!\n"
-
- with open(ep_h_log_filename, "a+") as f:
- f.write("\nnew prompt: -----------------------------------\n")
- f.write(episode_history_text)
-
- break
-
- else:
- with open(ep_h_log_filename, "a+") as f:
- f.write("\nnew prompt: -----------------------------------\n")
- f.write(episode_history_text)
-
- print(f"{'Success' if success else 'Failure'}")
- success_rates.append(success)
-
-mean_success_rate = np.mean(success_rates)
-print("Success rate:", mean_success_rate)
-print(f"Saving gif to {gif_savename}.")
-mimsave(gif_savename, frames, duration=args.pause)
-
-print("Done.")
-
-log_data_dict = vars(args)
-log_data_dict["success_rates"] = success_rates
-log_data_dict["mean_success_rate"] = mean_success_rate
-
-print("Evaluation log: ", evaluation_log_filename)
-with open(evaluation_log_filename, "w") as f:
- f.write(json.dumps(log_data_dict))
diff --git a/spaces/freddyaboulton/structured-data-classification/app.py b/spaces/freddyaboulton/structured-data-classification/app.py
deleted file mode 100644
index 6a8778266f8d94a8cb77897a42745777e397e5fc..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/structured-data-classification/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import numpy as np
-import tensorflow as tf
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-
-# download the already pushed model
-model = from_pretrained_keras("keras-io/structured-data-classification")
-
-def convert_and_predict(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
-
- # some conversions from the gradio interface are needed
- sample_converted = {
- "age": age,
- "sex": sex,
- "cp": cp+1,
- "trestbps": trestbps,
- "chol": chol,
- "fbs": 0 if fbs<=120 else 1,
- "restecg": restecg,
- "thalach": thalach,
- "exang": exang,
- "oldpeak": oldpeak,
- "slope": slope+1,
- "ca": ca,
- "thal": thal,
-}
-
- input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample_converted.items()}
- predictions = model.predict(input_dict)
-
- return f'{predictions[0][0]:.2%}'
-
-
-# the app uses slider and number fields for numerical inputs
-# while radio buttons for the categoricals
-inputs = [
- gr.Slider(minimum=1, maximum=120, step=1, label='age', value=60),
- gr.Radio(choices=['female','male'], label='sex', type='index',value='male'),
- gr.Radio(choices=['typical angina',
- 'atypical angina',
- 'non-anginal pain',
- 'asymptomatic'],
- type='index', label=f'chest pain type', value='typical angina'),
- gr.Number(label='blood pressure in mmHg', value=145),
- gr.Number(label='serum cholestoral in mg/dl', value=233),
- gr.Number(label='fasting blood sugar in mg/dl', value=150),
- gr.Radio(choices=['normal','T-T wave abnormality','probable or definite left ventricular hypertrophy'],
- label='resting ecg', type='index',value='probable or definite left ventricular hypertrophy'),
- gr.Number(label='maximum heart rate achieved', value=150),
- gr.Radio(choices=['no','yes',], type='index', label='exercise induced angina',value='no'),
- gr.Number(label='ST depression induced by exercise relative to rest', value=2.3),
- gr.Radio(choices=['psloping','flat','downsloping'], label='slope of the peak exercise ST segment', type='index', value='downsloping'),
- gr.Number(label ='number of major vessels (0-3) colored by flourosopy',value=0),
- gr.Radio(['normal','fixed','reversable'],label ='thal', value='fixed')
- ]
-
-
-# the app outputs text
-output = gr.Textbox(label='Probability of having a heart disease, as evaluated by our model:')
-# it's good practice to pass examples, description and a title to guide users
-title = "Structured Data Classification 🧮"
-description = "Binary classification of structured data including numerical and categorical features for Heart Disease prediction."
-
-article = "Author: Marco Buiani . Based on this keras example by François Chollet. HuggingFace Model here "
-
-examples = [[41, 'female', 'atypical angina', 130, 204, 100, 'normal', 150, 'yes', 1.4, 'psloping', 2, 'reversible'],
- [63, 'male', 'typical angina', 145, 233, 150, 'T-T wave abnormality', 150, 'no', 2.3, 'flat', 0, 'fixed']]
-
-gr.Interface(convert_and_predict, inputs, output, examples= examples, allow_flagging='never',
- title=title, description=description, article=article, live=True).launch()
\ No newline at end of file
diff --git a/spaces/futuristicdude/The_First_Principle_thinker/README.md b/spaces/futuristicdude/The_First_Principle_thinker/README.md
deleted file mode 100644
index c3c454f133d0511c51fc7908f8d5bfc1320be05d..0000000000000000000000000000000000000000
--- a/spaces/futuristicdude/The_First_Principle_thinker/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: The First Principle Thinker
-emoji: 👁
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/geninhu/whisper-vietnamese/app.py b/spaces/geninhu/whisper-vietnamese/app.py
deleted file mode 100644
index 4fcf293736a8d25ef8ea7b8d7fa7130138cf04ed..0000000000000000000000000000000000000000
--- a/spaces/geninhu/whisper-vietnamese/app.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import torch
-
-import gradio as gr
-import pytube as pt
-from transformers import pipeline
-from huggingface_hub import model_info
-
-MODEL_NAME = "geninhu/whisper-large-v2-multiset-vi" #this always needs to stay in line 8 :D sorry for the hackiness
-lang = "vi"
-
-device = 0 if torch.cuda.is_available() else "cpu"
-pipe = pipeline(
- task="automatic-speech-recognition",
- model=MODEL_NAME,
- chunk_length_s=30,
- device=device,
-)
-
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
-
-def transcribe(microphone, file_upload):
- warn_output = ""
- if (microphone is not None) and (file_upload is not None):
- warn_output = (
- "WARNING: You've uploaded an audio file and used the microphone. "
- "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
- )
-
- elif (microphone is None) and (file_upload is None):
- return "ERROR: You have to either use the microphone or upload an audio file"
-
- file = microphone if microphone is not None else file_upload
-
- text = pipe(file)["text"]
-
- return warn_output + text
-
-
-def _return_yt_html_embed(yt_url):
- video_id = yt_url.split("?v=")[-1]
- HTML_str = (
- f' VIDEO '
- " "
- )
- return HTML_str
-
-
-def yt_transcribe(yt_url):
- yt = pt.YouTube(yt_url)
- html_embed_str = _return_yt_html_embed(yt_url)
- stream = yt.streams.filter(only_audio=True)[0]
- stream.download(filename="audio.mp3")
-
- text = pipe("audio.mp3")["text"]
-
- return html_embed_str, text
-
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
- fn=transcribe,
- inputs=[
- gr.inputs.Audio(source="microphone", type="filepath", optional=True),
- gr.inputs.Audio(source="upload", type="filepath", optional=True),
- ],
- outputs="text",
- layout="horizontal",
- theme="huggingface",
- title="Whisper Demo: Transcribe Audio",
- description=(
- "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
- f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
- " of arbitrary length."
- ),
- allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
- fn=yt_transcribe,
- inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
- outputs=["html", "text"],
- layout="horizontal",
- theme="huggingface",
- title="Whisper Demo: Transcribe YouTube",
- description=(
- "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
- f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
- " arbitrary length."
- ),
- allow_flagging="never",
-)
-
-with demo:
- gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
-
-demo.launch(enable_queue=True)
diff --git a/spaces/gotiQspiryo/whisper-ui/Mercedes-Benz-Navigation-Cd-Ntg2-Audio-50-Aps-Europa-Version-14-WORK.md b/spaces/gotiQspiryo/whisper-ui/Mercedes-Benz-Navigation-Cd-Ntg2-Audio-50-Aps-Europa-Version-14-WORK.md
deleted file mode 100644
index 0e90e100c6d72e65b75bf6a7238d08cd9630046f..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/Mercedes-Benz-Navigation-Cd-Ntg2-Audio-50-Aps-Europa-Version-14-WORK.md
+++ /dev/null
@@ -1,142 +0,0 @@
-## Mercedes Benz Navigation Cd Ntg2 Audio 50 Aps Europa Version 14
-
-
-
-
-
-
-
-
-
-**Click Here >>>>> [https://miimms.com/2txSSY](https://miimms.com/2txSSY)**
-
-
-
-
-
-
-
-
-
-
-
-
-
-# How to Update Your Mercedes-Benz Audio 50 APS Navigation System
-
-
-
-If you own a Mercedes-Benz vehicle with an Audio 50 APS navigation system, you might be wondering how to keep it updated with the latest maps and points of interest. In this article, we will show you how to get the latest version of the navigation software and how to install it on your device.
-
-
-
-## What is the Audio 50 APS Navigation System?
-
-
-
-The Audio 50 APS is a navigation system that was available for some Mercedes-Benz models from 2004 to 2013. It features a 6.5-inch color display, a DVD player, a radio, a CD changer, and an integrated hard drive. The system uses DVDs to store the map data for different regions of Europe.
-
-
-
-## What is the Latest Version of the Navigation Software?
-
-
-
-The latest version of the navigation software for the Audio 50 APS is the \*\*Europe Version 2019\*\*[^2^] [^3^]. This is the final version of the software, as Mercedes-Benz has discontinued the support for this system. The Europe Version 2019 covers 39 European countries with detailed road networks, towns, villages, and more than 4.4 million points of interest. It also includes dynamic route guidance based on the latest traffic data (TMC/RDS) and the locations of 2966 Mercedes-Benz service outlets.
-
-
-
-## How to Get the Latest Version of the Navigation Software?
-
-
-
-To get the latest version of the navigation software, you need to purchase four DVDs from the Mercedes-Benz Genuine Accessories website[^1^] [^2^] [^3^]. The DVDs cost 1,651.87 RON (Romanian Leu) or about $390 USD. You can order them online or contact your local Mercedes-Benz dealer for assistance.
-
-
-
-## How to Install the Latest Version of the Navigation Software?
-
-
-
-To install the latest version of the navigation software, you need to follow these steps:
-
-
-
-1. Turn on your Audio 50 APS device and insert DVD 1 into the DVD slot.
-
-2. Wait for the system to recognize the DVD and display a message asking if you want to update the software.
-
-3. Select "Yes" and confirm your choice.
-
-4. Wait for the system to copy the data from the DVD to the hard drive. This may take up to 30 minutes.
-
-5. Eject DVD 1 and insert DVD 2 when prompted.
-
-6. Repeat steps 4 and 5 for DVDs 3 and 4.
-
-7. When the installation is complete, eject DVD 4 and restart your device.
-
-8. Enjoy your updated navigation system!
-
-
-
-Note: Do not turn off your device or remove the DVDs during the installation process. This may damage your device or corrupt your data.
-
-
-
-## Conclusion
-
-
-
-The Audio 50 APS navigation system is a useful feature for Mercedes-Benz drivers who want to navigate Europe with ease. However, it requires regular updates to keep up with the changes in road networks and points of interest. By following this guide, you can get and install the latest version of the navigation software and enjoy a smooth and safe driving experience.
-
-
-
-## What are the Benefits of Updating Your Navigation System?
-
-
-
-Updating your navigation system is not only a matter of convenience, but also a matter of safety and efficiency. Here are some of the benefits of having the latest version of the navigation software on your Audio 50 APS device:
-
-
-
-- You can drive the most fuel-efficient routes, saving money on gas with up to date directions[^1^]. The system will also alert you of any traffic jams, road closures, or accidents that might affect your journey.
-
-- You can access millions of points of interest, including gas stations, shopping, medical services, parking, restaurants, and accommodation. You can also find the nearest Mercedes-Benz service outlet in case you need any assistance with your vehicle.
-
-- You can get to your destination on time with improved routing and time planning. The system will calculate the best route based on your preferences and current conditions. You can also set waypoints, avoid toll roads, and choose between different modes of transportation.
-
-- You can reduce your carbon footprint as you drive less miles with up to date directions[^2^]. By avoiding unnecessary detours and traffic congestion, you can also reduce your emissions and contribute to a cleaner environment.
-
-
-
-As you can see, updating your navigation system is a smart investment that will enhance your driving experience and keep you safe and informed on the road.
-
-
-
-## How to Troubleshoot Your Navigation System?
-
-
-
-Even with the latest version of the navigation software, you might encounter some issues with your Audio 50 APS device. Here are some common problems and how to fix them:
-
-
-
-- The system does not recognize the DVD: Make sure you are using the correct DVD for your region and device. Clean the DVD with a soft cloth and check for any scratches or damage. If the problem persists, contact your Mercedes-Benz dealer for a replacement DVD.
-
-- The system does not display the map: Make sure you have selected the map mode on the screen. Check the settings and adjust the brightness and contrast if needed. If the map is still not visible, try restarting your device or ejecting and reinserting the DVD.
-
-- The system does not provide voice guidance: Make sure you have turned on the voice guidance option in the settings. Check the volume level and mute button on your device and your car's audio system. If you still cannot hear any voice guidance, try changing the language or voice type in the settings.
-
-- The system provides incorrect or outdated directions: Make sure you have updated your navigation software to the latest version. Check the GPS signal strength and make sure your device has a clear view of the sky. If you encounter any errors or inaccuracies in the map data, report them to Navteq through their website[^4^].
-
-
-
-If none of these solutions work, consult your Mercedes-Benz dealer or refer to your owner's manual for more troubleshooting tips.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Girls Gitting Fucked For First Time [Extra Quality].md b/spaces/gotiQspiryo/whisper-ui/examples/Girls Gitting Fucked For First Time [Extra Quality].md
deleted file mode 100644
index ad282179a5a2043ef316eba8a90c5a2ea0a162f5..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Girls Gitting Fucked For First Time [Extra Quality].md
+++ /dev/null
@@ -1,19 +0,0 @@
-
-After watching a movie a busty lesbian girlfriend dares her straight friend to kiss her.After their first kiss she sucks on her perky tits before giving her her first lesbian pussy licking.All horny her friend gets to taste pussy for the first time
-girls gitting fucked for first time Download Zip ✯ https://urlgoal.com/2uyMuO
my gf suprised me with sucking my cock soo good and b4 i knew wht happened she already had her vibrator buried n my ass n started sliding it n out my ass with it on the highest vibrating speed made it feel so good getting my ass fucked bby my gf and every since tht time she bought her a strap on (black mamba) and n instead of her only getting fucked now we take turns n i get fucked like her bitch n its sweet
-I love anal sex! I was surprised the first time but I loved everything. I loved him rubbing my anus and sliding fingers inside me. We used a lot of lube and it took a few tries to get his penis all the way in but after he got it in and started thrusting it felt incredible. After he came and pulled out, I wanted it again.
-Me and my husband like having sex like daily using techniques i learned from sean but anal sex sometimes we wait a few days or weeks in between sometines not however when its been awhile it does hurt for like 10 seconds when he does penetrate sometimes he does use his fingers when I give him a blow job other tines he just gently penetrates but we never use lube or condoms i hate the feeling of condoms but What can I do to make it less painful that first 10 seconds and to make me actually orgams like a OMG orgams because from anal I never received a mind blowing orgasms
-Beautiful young Russian girls enjoy their teen anal sex with guys who have big cocks ready to fuck their virgin buttholes. First-time anal videos don't get any hotter than this, as these gorgeous and inexperienced Eastern European girls have their tight teenage assholes penetrated for the very first time. These barely legal anal sweethearts love it all, blowjobs, hardcore sex, ass licking and taking sex toys up their narrow rectums. First Anal Quest is the best teen anal porn on the internet. Young Slavic girls seem innocent, but they are eager to have their teenage asses fucked hard and long
-
-Whether it's your first real relationship or you have a string in your past there are a few apps that dating experts recommend consistently. These are apps where you can meet great girls that want something long-term as well as those just looking to keep it casual:
-The very title of Julia Alvarez's How the Garcia Girls Lost Their Accents reveals the central role that language plays in a novel that chronicles the difficult paths that four young sisters from the Dominican Republic take while growing up in the United States. The novel, made up of three parts of five chapters each, traces the Garcia girls' story backwards in time, beginning with American adulthood in 1989 and ending with their Dominican childhood in 1956. As Joan M. Hoffman has observed, despite this unique chronological structure, language is one of the unifying symbols for the four sisters' transition from one culture to another. Hoffman writes:
-This aspect of Hoffman's article is particularly important, especially since she correctly notes that the Garcia girls have to acquire the English language skills necessary to tell their story in several of the novel's first-person narratives. Nevertheless, Hoffman also concludes that language is a powerful symbol of the four sisters' successful bicultural assimilation into the American way of life:
-In Ayanna Dozier\u2019s little book about The Velvet Rope, from the 33 1/3 series, she gives context that I knew nothing about at the time I first listened to \u201CI Get Lonely.\u201D Janet wanted to write a personal album, one about her battle with depression, among other things, but the media, mostly dominated by white men, didn\u2019t get it (or in some cases actively went after it). No one believed a star at the height of her game could also be depressed. Those who believed it found it laughable. People dismissed the album as a failure, and wanted to talk about her weight fluctuations instead. But the music lives to tell the tale. It\u2019s fucking good.
-Thinking about this album and how worthy it is of celebrating made me think about the year it was released, 1997, and all that was going on in our ear waves. That year, I transition from eighth grade to high school. I became a full-fledged theater kid. I smoked weed for the first time, in my sister\u2019s bedroom, and wrote some really, really solid poetry. I touched my first penis.
-I don\u2019t remember the first time I laid eyes on my husband, sadly, but I do remember the exact moment when I first Erykah Badu\u2019s voice, on the single track from Baduzim, \"Next Lifetime.\u201D I was in the 90s-aesthetic clothing store Allston Beat in Harvard Square, paying a conjugal visit to a pair of pink patent leather platform Converse All-Stars, which I\u2019d been longing for but didn\u2019t have the money to buy. I stumbled to the register and asked something akin to \u201Cwhat in the gorgeous, heart-breaking fuck is this???\u201D and the cool clerk pointed towards the CD case, displayed on the counter like they used to do in those days before iPods and smartphones. There I saw her, though she hides her face from the camera, hands grasping the back of her head-wrapped-head in either resignation or certainty or anguish or all of the above and I knew something different was coming. I must have bought the album soon after that, maybe even that very day, because by the time my eighth grade class took an all night bus ride to Washington DC that spring, I had almost worn it out. Newly rejected by my group of friends in that horrible, mundane way middle-school girls are wont to do, I listened to Baduizm on repeat on my Discman, through the night and the interstate highways, feeling a profound loneliness, but relieved to have Erykah feeling it with me.
-That was not the first time that I had made my family wait for me sothat "work" could get done. There were the weekends I was out of town,the hours and hours spent in front of the computer, the unexpectedcrises that had to be solved immediately. I've lost track of the numberof times I told my family, "hold on, it will be just a minute," only tofinally emerge from behind the computer three hours later. The thingwas, it wasn't that I loved my job. It wasn't that most of the work Iwas doing was even paid work. It was that the work I was doing was"revolutionary." It was "liberatory" and "world changing," and"necessary."
-A genuine smile crosses my face for the first time since my Grandma Lane died. The thought of seeing my mom and dad and the rest of my family makes my heart sing. From what I've gathered, family members that we\u2019ve lost touch with or haven\u2019t seen in a while will be at Grandma Lane's funeral. It will be like one big family reunion, except the reason for meeting is bittersweet.
-I want to keep talking, but I hear the engines revving up. Oh, fuck, we're about to take off. You would think that as many times as I've flown, I would be used to it all. But I feel like a newborn calf trying to stand up for the first time. I tighten my grip on the armrest. There\u2019s a light brush of fingers over the top of my hand, then a calloused yet soft hand envelopes mine. If I think too hard about the feel of those hands on me and in me, I'm not going to make it through this flight. I swear, if we were on a private jet, I would be tossing my\u2014you know what\u2014in the air like a female cat in heat. Taking, throw that ass in a circle to new heights.
aaccfb2cb3DPT Demo . It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
-examples = [["examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg"]]
-
-iface = gr.Interface(fn=process_image,
- inputs=[gr.Image(
- type="filepath", label="Input Image")],
- outputs=[gr.Image(label="predicted depth", type="pil"),
- gr.Model3D(label="3d mesh reconstruction", clear_color=[
- 1.0, 1.0, 1.0, 1.0]),
- gr.File(label="3d gLTF")],
- title=title,
- description=description,
- examples=examples,
- allow_flagging="never",
- cache_examples=False)
-
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/greyskyAI/ChatRAS/README.md b/spaces/greyskyAI/ChatRAS/README.md
deleted file mode 100644
index bc25a9d119d4c19a7df07c436aa597cf3de9a103..0000000000000000000000000000000000000000
--- a/spaces/greyskyAI/ChatRAS/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatRAS
-emoji: 🦀
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gulabpatel/Real-ESRGAN/experiments/pretrained_models/README.md b/spaces/gulabpatel/Real-ESRGAN/experiments/pretrained_models/README.md
deleted file mode 100644
index d0cc4afcbdd2c733f6b946bb86bd00baa90e8295..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/Real-ESRGAN/experiments/pretrained_models/README.md
+++ /dev/null
@@ -1 +0,0 @@
-# Put downloaded pre-trained models here
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/__init__.py b/spaces/gwang-kim/DATID-3D/pose_estimation/nvdiffrast/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/discriminator.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/discriminator.py
deleted file mode 100644
index 16bf3722c7f2e35cdc9bd177a33ed0975e67200d..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/discriminator.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from torch import nn
-
-
-class LatentCodesDiscriminator(nn.Module):
- def __init__(self, style_dim, n_mlp):
- super().__init__()
-
- self.style_dim = style_dim
-
- layers = []
- for i in range(n_mlp-1):
- layers.append(
- nn.Linear(style_dim, style_dim)
- )
- layers.append(nn.LeakyReLU(0.2))
- layers.append(nn.Linear(512, 1))
- self.mlp = nn.Sequential(*layers)
-
- def forward(self, w):
- return self.mlp(w)
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/training_scripts/sg2/training/networks.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/training_scripts/sg2/training/networks.py
deleted file mode 100644
index 291d1f6d157aeab10896bc106c15fe4d03fcb145..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/training_scripts/sg2/training/networks.py
+++ /dev/null
@@ -1,966 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import numpy as np
-import torch
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_resample
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import bias_act
-from torch_utils.ops import fma
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def normalize_2nd_moment(x, dim=1, eps=1e-8):
- return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def modulated_conv2d(
- # Input tensor of shape [batch_size, in_channels, in_height, in_width].
- x,
- # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
- weight,
- # Modulation coefficients of shape [batch_size, in_channels].
- styles,
- noise=None, # Optional noise tensor to add to the output activations.
- up=1, # Integer upsampling factor.
- down=1, # Integer downsampling factor.
- padding=0, # Padding with respect to the upsampled image.
- # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
- resample_filter=None,
- demodulate=True, # Apply weight demodulation?
- # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
- flip_weight=True,
- # Perform modulation, convolution, and demodulation as a single fused operation?
- fused_modconv=True,
-):
- batch_size = x.shape[0]
- out_channels, in_channels, kh, kw = weight.shape
- misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
- misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
- misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
-
- # Pre-normalize inputs to avoid FP16 overflow.
- if x.dtype == torch.float16 and demodulate:
- weight = weight * (1 / np.sqrt(in_channels * kh * kw) /
- weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk
- styles = styles / \
- styles.norm(float('inf'), dim=1, keepdim=True) # max_I
-
- # Calculate per-sample weights and demodulation coefficients.
- w = None
- dcoefs = None
- if demodulate or fused_modconv:
- w = weight.unsqueeze(0) # [NOIkk]
- w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
- if demodulate:
- dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO]
- if demodulate and fused_modconv:
- w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
-
- # Execute by scaling the activations before and after the convolution.
- if not fused_modconv:
- x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
- x = conv2d_resample.conv2d_resample(x=x, w=weight.to(
- x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
- if demodulate and noise is not None:
- x = fma.fma(x, dcoefs.to(x.dtype).reshape(
- batch_size, -1, 1, 1), noise.to(x.dtype))
- elif demodulate:
- x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
- elif noise is not None:
- x = x.add_(noise.to(x.dtype))
- return x
-
- # Execute as one fused op using grouped convolution.
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- batch_size = int(batch_size)
- misc.assert_shape(x, [batch_size, in_channels, None, None])
- x = x.reshape(1, -1, *x.shape[2:])
- w = w.reshape(-1, in_channels, kh, kw)
- x = conv2d_resample.conv2d_resample(x=x, w=w.to(
- x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
- x = x.reshape(batch_size, -1, *x.shape[2:])
- if noise is not None:
- x = x.add_(noise)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
- def __init__(self,
- in_features, # Number of input features.
- out_features, # Number of output features.
- bias=True, # Apply additive bias before the activation function?
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- lr_multiplier=1, # Learning rate multiplier.
- bias_init=0, # Initial value for the additive bias.
- ):
- super().__init__()
- self.activation = activation
- self.weight = torch.nn.Parameter(torch.randn(
- [out_features, in_features]) / lr_multiplier)
- self.bias = torch.nn.Parameter(torch.full(
- [out_features], np.float32(bias_init))) if bias else None
- self.weight_gain = lr_multiplier / np.sqrt(in_features)
- self.bias_gain = lr_multiplier
-
- def forward(self, x):
- w = self.weight.to(x.dtype) * self.weight_gain
- b = self.bias
- if b is not None:
- b = b.to(x.dtype)
- if self.bias_gain != 1:
- b = b * self.bias_gain
-
- if self.activation == 'linear' and b is not None:
- x = torch.addmm(b.unsqueeze(0), x, w.t())
- else:
- x = x.matmul(w.t())
- x = bias_act.bias_act(x, b, act=self.activation)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Conv2dLayer(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Width and height of the convolution kernel.
- kernel_size,
- bias=True, # Apply additive bias before the activation function?
- # Activation function: 'relu', 'lrelu', etc.
- activation='linear',
- up=1, # Integer upsampling factor.
- down=1, # Integer downsampling factor.
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output to +-X, None = disable clamping.
- conv_clamp=None,
- channels_last=False, # Expect the input to have memory_format=channels_last?
- trainable=True, # Update the weights of this layer during training?
- ):
- super().__init__()
- self.activation = activation
- self.up = up
- self.down = down
- self.conv_clamp = conv_clamp
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.padding = kernel_size // 2
- self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
- self.act_gain = bias_act.activation_funcs[activation].def_gain
-
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
- memory_format=memory_format)
- bias = torch.zeros([out_channels]) if bias else None
- if trainable:
- self.weight = torch.nn.Parameter(weight)
- self.bias = torch.nn.Parameter(bias) if bias is not None else None
- else:
- self.register_buffer('weight', weight)
- if bias is not None:
- self.register_buffer('bias', bias)
- else:
- self.bias = None
-
- def forward(self, x, gain=1):
- w = self.weight * self.weight_gain
- b = self.bias.to(x.dtype) if self.bias is not None else None
- flip_weight = (self.up == 1) # slightly faster
- x = conv2d_resample.conv2d_resample(x=x, w=w.to(
- x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
-
- act_gain = self.act_gain * gain
- act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
- x = bias_act.bias_act(x, b, act=self.activation,
- gain=act_gain, clamp=act_clamp)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
- def __init__(self,
- # Input latent (Z) dimensionality, 0 = no latent.
- z_dim,
- # Conditioning label (C) dimensionality, 0 = no label.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Number of intermediate latents to output, None = do not broadcast.
- num_ws,
- num_layers=8, # Number of mapping layers.
- # Label embedding dimensionality, None = same as w_dim.
- embed_features=None,
- # Number of intermediate features in the mapping layers, None = same as w_dim.
- layer_features=None,
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Learning rate multiplier for the mapping layers.
- lr_multiplier=0.01,
- # Decay for tracking the moving average of W during training, None = do not track.
- w_avg_beta=0.995,
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.num_ws = num_ws
- self.num_layers = num_layers
- self.w_avg_beta = w_avg_beta
-
- if embed_features is None:
- embed_features = w_dim
- if c_dim == 0:
- embed_features = 0
- if layer_features is None:
- layer_features = w_dim
- features_list = [z_dim + embed_features] + \
- [layer_features] * (num_layers - 1) + [w_dim]
-
- if c_dim > 0:
- self.embed = FullyConnectedLayer(c_dim, embed_features)
- for idx in range(num_layers):
- in_features = features_list[idx]
- out_features = features_list[idx + 1]
- layer = FullyConnectedLayer(
- in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
- setattr(self, f'fc{idx}', layer)
-
- if num_ws is not None and w_avg_beta is not None:
- self.register_buffer('w_avg', torch.zeros([w_dim]))
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False):
- # Embed, normalize, and concat inputs.
- x = None
- with torch.autograd.profiler.record_function('input'):
- if self.z_dim > 0:
- misc.assert_shape(z, [None, self.z_dim])
- x = normalize_2nd_moment(z.to(torch.float32))
- if self.c_dim > 0:
- misc.assert_shape(c, [None, self.c_dim])
- y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
- x = torch.cat([x, y], dim=1) if x is not None else y
-
- # Main layers.
- for idx in range(self.num_layers):
- layer = getattr(self, f'fc{idx}')
- x = layer(x)
-
- # Update moving average of W.
- if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
- with torch.autograd.profiler.record_function('update_w_avg'):
- self.w_avg.copy_(x.detach().mean(
- dim=0).lerp(self.w_avg, self.w_avg_beta))
-
- # Broadcast.
- if self.num_ws is not None:
- with torch.autograd.profiler.record_function('broadcast'):
- x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-
- # Apply truncation.
- if truncation_psi != 1:
- with torch.autograd.profiler.record_function('truncate'):
- assert self.w_avg_beta is not None
- if self.num_ws is None or truncation_cutoff is None:
- x = self.w_avg.lerp(x, truncation_psi)
- else:
- x[:, :truncation_cutoff] = self.w_avg.lerp(
- x[:, :truncation_cutoff], truncation_psi)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- out_channels, # Number of output channels.
- # Intermediate latent (W) dimensionality.
- w_dim,
- resolution, # Resolution of this layer.
- kernel_size=3, # Convolution kernel size.
- up=1, # Integer upsampling factor.
- use_noise=True, # Enable noise input?
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- channels_last=False, # Use channels_last format for the weights?
- square=False, # default if for rectangle images
- ):
- super().__init__()
- self.resolution = resolution
- self.up = up
- self.use_noise = use_noise
- self.activation = activation
- self.conv_clamp = conv_clamp
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.padding = kernel_size // 2
- self.act_gain = bias_act.activation_funcs[activation].def_gain
- self.square = square
-
- self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- self.weight = torch.nn.Parameter(torch.randn(
- [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
- if use_noise:
- if self.square:
- self.register_buffer(
- 'noise_const', torch.randn([resolution, resolution]))
- else:
- self.register_buffer('noise_const', torch.randn(
- [resolution, resolution // 2]))
- self.noise_strength = torch.nn.Parameter(torch.zeros([]))
- self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-
- def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
- assert noise_mode in ['random', 'const', 'none']
- in_resolution = self.resolution // self.up
- if self.square:
- misc.assert_shape(
- x, [None, self.weight.shape[1], in_resolution, in_resolution])
- else:
- misc.assert_shape(
- x, [None, self.weight.shape[1], in_resolution, in_resolution // 2])
- styles = self.affine(w)
-
- noise = None
- if self.use_noise and noise_mode == 'random':
- if self.square:
- noise = torch.randn(
- [x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
- else:
- noise = torch.randn(
- [x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength
- if self.use_noise and noise_mode == 'const':
- noise = self.noise_const * self.noise_strength
-
- flip_weight = (self.up == 1) # slightly faster
- x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
- padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
-
- act_gain = self.act_gain * gain
- act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
- x = bias_act.bias_act(x, self.bias.to(
- x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class ToRGBLayer(torch.nn.Module):
- def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
- super().__init__()
- self.conv_clamp = conv_clamp
- self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
- memory_format = torch.channels_last if channels_last else torch.contiguous_format
- self.weight = torch.nn.Parameter(torch.randn(
- [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
- self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
- self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-
- def forward(self, x, w, fused_modconv=True):
- styles = self.affine(w) * self.weight_gain
- x = modulated_conv2d(x=x, weight=self.weight, styles=styles,
- demodulate=False, fused_modconv=fused_modconv)
- x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisBlock(torch.nn.Module):
- def __init__(self,
- # Number of input channels, 0 = first block.
- in_channels,
- # Number of output channels.
- out_channels,
- # Intermediate latent (W) dimensionality.
- w_dim,
- # Resolution of this block.
- resolution,
- # Number of output color channels.
- img_channels,
- is_last, # Is this the last block?
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='skip',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- use_fp16=False, # Use FP16 for this block?
- fp16_channels_last=False, # Use channels-last memory format with FP16?
- square=False, # default is for rectangle images
- # Arguments for SynthesisLayer.
- **layer_kwargs,
- ):
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.w_dim = w_dim
- self.resolution = resolution
- self.img_channels = img_channels
- self.is_last = is_last
- self.architecture = architecture
- self.use_fp16 = use_fp16
- self.channels_last = (use_fp16 and fp16_channels_last)
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.num_conv = 0
- self.num_torgb = 0
- self.square = square
-
- if in_channels == 0:
- if self.square:
- self.const = torch.nn.Parameter(torch.randn(
- [out_channels, resolution, resolution]))
- else: # rectangle
- self.const = torch.nn.Parameter(torch.randn(
- [out_channels, resolution, resolution // 2]))
-
- if in_channels != 0:
- self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
- resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs)
- self.num_conv += 1
-
- self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
- conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs)
- self.num_conv += 1
-
- if is_last or architecture == 'skip':
- self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
- conv_clamp=conv_clamp, channels_last=self.channels_last)
- self.num_torgb += 1
-
- if in_channels != 0 and architecture == 'resnet':
- self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
- resample_filter=resample_filter, channels_last=self.channels_last)
-
- def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **layer_kwargs):
- misc.assert_shape(
- ws, [None, self.num_conv + self.num_torgb, self.w_dim])
- w_iter = iter(ws.unbind(dim=1))
- dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
- memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
- if fused_modconv is None:
- with misc.suppress_tracer_warnings(): # this value will be treated as a constant
- fused_modconv = (not self.training) and (
- dtype == torch.float32 or int(x.shape[0]) == 1)
-
- # Input.
- if self.in_channels == 0:
- x = self.const.to(dtype=dtype, memory_format=memory_format)
- x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
- else:
- if self.square:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
- else: # rectangle
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution // 2, self.resolution // 4])
- x = x.to(dtype=dtype, memory_format=memory_format)
-
- # Main layers.
- if self.in_channels == 0:
- x = self.conv1(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- elif self.architecture == 'resnet':
- y = self.skip(x, gain=np.sqrt(0.5))
- x = self.conv0(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv,
- gain=np.sqrt(0.5), **layer_kwargs)
- x = y.add_(x)
- else:
- x = self.conv0(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
- x = self.conv1(x, next(w_iter),
- fused_modconv=fused_modconv, **layer_kwargs)
-
- # ToRGB.
- if img is not None:
- if self.square:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
- else:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution // 2, self.resolution // 4])
- img = upfirdn2d.upsample2d(img, self.resample_filter)
- if self.is_last or self.architecture == 'skip':
- y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
- y = y.to(dtype=torch.float32,
- memory_format=torch.contiguous_format)
- img = img.add_(y) if img is not None else y
-
- assert x.dtype == dtype
- assert img is None or img.dtype == torch.float32
- return x, img
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
- def __init__(self,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output image resolution.
- img_channels, # Number of color channels.
- square,
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Use FP16 for the N highest resolutions.
- num_fp16_res=0,
- **block_kwargs, # Arguments for SynthesisBlock.
- ):
- assert img_resolution >= 4 and img_resolution & (
- img_resolution - 1) == 0
- super().__init__()
- self.w_dim = w_dim
- self.img_resolution = img_resolution
- self.img_resolution_log2 = int(np.log2(img_resolution))
- self.img_channels = img_channels
- self.square = square
- self.block_resolutions = [
- 2 ** i for i in range(2, self.img_resolution_log2 + 1)]
- channels_dict = {res: min(channel_base // res, channel_max)
- for res in self.block_resolutions}
- fp16_resolution = max(
- 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
- self.num_ws = 0
- for res in self.block_resolutions:
- in_channels = channels_dict[res // 2] if res > 4 else 0
- out_channels = channels_dict[res]
- use_fp16 = (res >= fp16_resolution)
- is_last = (res == self.img_resolution)
- block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
- img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, square=square, **block_kwargs)
- self.num_ws += block.num_conv
- if is_last:
- self.num_ws += block.num_torgb
- setattr(self, f'b{res}', block)
-
- def forward(self, ws, return_feature=False, **block_kwargs):
- block_ws = []
- features = []
- with torch.autograd.profiler.record_function('split_ws'):
- misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
- ws = ws.to(torch.float32)
- w_idx = 0
- for res in self.block_resolutions:
- block = getattr(self, f'b{res}')
- block_ws.append(
- ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
- w_idx += block.num_conv
-
- x = img = None
- for res, cur_ws in zip(self.block_resolutions, block_ws):
- block = getattr(self, f'b{res}')
- x, img = block(x, img, cur_ws, **block_kwargs)
- features.append(x)
- if return_feature:
- return img, features
- else:
- return img
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
- def __init__(self,
- z_dim, # Input latent (Z) dimensionality.
- # Conditioning label (C) dimensionality.
- c_dim,
- # Intermediate latent (W) dimensionality.
- w_dim,
- img_resolution, # Output resolution.
- square,
- img_channels, # Number of output color channels.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- synthesis_kwargs={}, # Arguments for SynthesisNetwork.
- padding=False
- ):
- super().__init__()
- self.z_dim = z_dim
- self.c_dim = c_dim
- self.w_dim = w_dim
- self.square = square
- self.img_resolution = img_resolution
- self.img_channels = img_channels
- self.padding = padding
- self.synthesis = SynthesisNetwork(
- w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=square, **synthesis_kwargs)
- self.num_ws = self.synthesis.num_ws
- self.mapping = MappingNetwork(
- z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-
- def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, input_is_w=False, return_feature=False, **synthesis_kwargs):
- if input_is_w:
- ws = z
- if ws.dim() == 2:
- ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1])
- else:
- ws = self.mapping(z, c, truncation_psi=truncation_psi,
- truncation_cutoff=truncation_cutoff)
- img = self.synthesis(
- ws, return_feature=return_feature, **synthesis_kwargs)
- if return_feature:
- img, feature = img
- if self.padding:
- pad = (img.size(2) - img.size(3)) // 2
- img = torch.nn.functional.pad(img, (pad, pad), "constant", 1)
- if return_feature:
- for i, feat in enumerate(feature):
- pad = (feat.size(2) - feat.size(3)) // 2
- feature[i] = torch.nn.functional.pad(
- feat, (pad, pad), "constant", 0)
- if return_feature:
- return img, feature
- else:
- return img
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorBlock(torch.nn.Module):
- def __init__(self,
- # Number of input channels, 0 = first block.
- in_channels,
- # Number of intermediate channels.
- tmp_channels,
- # Number of output channels.
- out_channels,
- # Resolution of this block.
- resolution,
- # Number of input color channels.
- img_channels,
- # Index of the first layer.
- first_layer_idx,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Low-pass filter to apply when resampling activations.
- resample_filter=[1, 3, 3, 1],
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- use_fp16=False, # Use FP16 for this block?
- fp16_channels_last=False, # Use channels-last memory format with FP16?
- # Freeze-D: Number of layers to freeze.
- freeze_layers=0,
- square=False,
- ):
- assert in_channels in [0, tmp_channels]
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.resolution = resolution
- self.img_channels = img_channels
- self.first_layer_idx = first_layer_idx
- self.architecture = architecture
- self.use_fp16 = use_fp16
- self.channels_last = (use_fp16 and fp16_channels_last)
- self.register_buffer(
- 'resample_filter', upfirdn2d.setup_filter(resample_filter))
- self.square = square
-
- self.num_layers = 0
-
- def trainable_gen():
- while True:
- layer_idx = self.first_layer_idx + self.num_layers
- trainable = (layer_idx >= freeze_layers)
- self.num_layers += 1
- yield trainable
- trainable_iter = trainable_gen()
-
- if in_channels == 0 or architecture == 'skip':
- self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
- trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
- trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
- trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
-
- if architecture == 'resnet':
- self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
- trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
-
- def forward(self, x, img, force_fp32=False):
- dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
- memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-
- # Input.
- if x is not None:
- if self.square:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution, self.resolution])
- else:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution, self.resolution // 2])
- x = x.to(dtype=dtype, memory_format=memory_format)
-
- # FromRGB.
- if self.in_channels == 0 or self.architecture == 'skip':
- if self.square:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution])
- else:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution // 2])
- img = img.to(dtype=dtype, memory_format=memory_format)
- y = self.fromrgb(img)
- x = x + y if x is not None else y
- img = upfirdn2d.downsample2d(
- img, self.resample_filter) if self.architecture == 'skip' else None
-
- # Main layers.
- if self.architecture == 'resnet':
- y = self.skip(x, gain=np.sqrt(0.5))
- x = self.conv0(x)
- x = self.conv1(x, gain=np.sqrt(0.5))
- x = y.add_(x)
- else:
- x = self.conv0(x)
- x = self.conv1(x)
-
- assert x.dtype == dtype
- return x, img
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class MinibatchStdLayer(torch.nn.Module):
- def __init__(self, group_size, num_channels=1):
- super().__init__()
- self.group_size = group_size
- self.num_channels = num_channels
-
- def forward(self, x):
- N, C, H, W = x.shape
- with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
- G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(
- N)) if self.group_size is not None else N
- F = self.num_channels
- c = C // F
-
- # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
- y = x.reshape(G, -1, F, c, H, W)
- # [GnFcHW] Subtract mean over group.
- y = y - y.mean(dim=0)
- # [nFcHW] Calc variance over group.
- y = y.square().mean(dim=0)
- y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
- # [nF] Take average over channels and pixels.
- y = y.mean(dim=[2, 3, 4])
- y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
- # [NFHW] Replicate over group and pixels.
- y = y.repeat(G, 1, H, W)
- # [NCHW] Append to input as new channels.
- x = torch.cat([x, y], dim=1)
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class DiscriminatorEpilogue(torch.nn.Module):
- def __init__(self,
- in_channels, # Number of input channels.
- # Dimensionality of mapped conditioning label, 0 = no label.
- cmap_dim,
- resolution, # Resolution of this block.
- # Number of input color channels.
- img_channels,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Group size for the minibatch standard deviation layer, None = entire minibatch.
- mbstd_group_size=4,
- # Number of features for the minibatch standard deviation layer, 0 = disable.
- mbstd_num_channels=1,
- # Activation function: 'relu', 'lrelu', etc.
- activation='lrelu',
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- square=False,
- ):
- assert architecture in ['orig', 'skip', 'resnet']
- super().__init__()
- self.in_channels = in_channels
- self.cmap_dim = cmap_dim
- self.resolution = resolution
- self.img_channels = img_channels
- self.architecture = architecture
- self.square = square
-
- if architecture == 'skip':
- self.fromrgb = Conv2dLayer(
- img_channels, in_channels, kernel_size=1, activation=activation)
- self.mbstd = MinibatchStdLayer(
- group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
- self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels,
- kernel_size=3, activation=activation, conv_clamp=conv_clamp)
-
- if self.square:
- self.fc = FullyConnectedLayer(
- in_channels * (resolution ** 2), in_channels, activation=activation)
- else:
- self.fc = FullyConnectedLayer(
- in_channels * (resolution ** 2 // 2), in_channels, activation=activation)
-
- self.out = FullyConnectedLayer(
- in_channels, 1 if cmap_dim == 0 else cmap_dim)
-
- def forward(self, x, img, cmap, force_fp32=False):
- if self.square:
- misc.assert_shape(x, [None, self.in_channels,
- self.resolution, self.resolution])
- else:
- misc.assert_shape(
- x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [NCHW]
- _ = force_fp32 # unused
- dtype = torch.float32
- memory_format = torch.contiguous_format
-
- # FromRGB.
- x = x.to(dtype=dtype, memory_format=memory_format)
- if self.architecture == 'skip':
- if self.square:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution])
- else:
- misc.assert_shape(
- img, [None, self.img_channels, self.resolution, self.resolution // 2])
- img = img.to(dtype=dtype, memory_format=memory_format)
- x = x + self.fromrgb(img)
-
- # Main layers.
- if self.mbstd is not None:
- x = self.mbstd(x)
- x = self.conv(x)
- x = self.fc(x.flatten(1))
- x = self.out(x)
-
- # Conditioning.
- if self.cmap_dim > 0:
- misc.assert_shape(cmap, [None, self.cmap_dim])
- x = (x * cmap).sum(dim=1, keepdim=True) * \
- (1 / np.sqrt(self.cmap_dim))
-
- assert x.dtype == dtype
- return x
-
-# ----------------------------------------------------------------------------
-
-
-@persistence.persistent_class
-class Discriminator(torch.nn.Module):
- def __init__(self,
- # Conditioning label (C) dimensionality.
- c_dim,
- img_resolution, # Input resolution.
- # Number of input color channels.
- img_channels,
- # Architecture: 'orig', 'skip', 'resnet'.
- architecture='resnet',
- # Overall multiplier for the number of channels.
- channel_base=32768,
- # Maximum number of channels in any layer.
- channel_max=512,
- # Use FP16 for the N highest resolutions.
- num_fp16_res=0,
- # Clamp the output of convolution layers to +-X, None = disable clamping.
- conv_clamp=None,
- # Dimensionality of mapped conditioning label, None = default.
- cmap_dim=None,
- square=False, # default for rectangle images
- block_kwargs={}, # Arguments for DiscriminatorBlock.
- mapping_kwargs={}, # Arguments for MappingNetwork.
- # Arguments for DiscriminatorEpilogue.
- epilogue_kwargs={},
- ):
- super().__init__()
- self.c_dim = c_dim
- self.img_resolution = img_resolution
- self.img_resolution_log2 = int(np.log2(img_resolution))
- self.img_channels = img_channels
- self.square = square
- self.block_resolutions = [
- 2 ** i for i in range(self.img_resolution_log2, 2, -1)]
- channels_dict = {res: min(channel_base // res, channel_max)
- for res in self.block_resolutions + [4]}
- fp16_resolution = max(
- 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
- if cmap_dim is None:
- cmap_dim = channels_dict[4]
- if c_dim == 0:
- cmap_dim = 0
-
- common_kwargs = dict(img_channels=img_channels,
- architecture=architecture, conv_clamp=conv_clamp)
- cur_layer_idx = 0
- for res in self.block_resolutions:
- in_channels = channels_dict[res] if res < img_resolution else 0
- tmp_channels = channels_dict[res]
- out_channels = channels_dict[res // 2]
- use_fp16 = (res >= fp16_resolution)
- block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
- first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs)
- setattr(self, f'b{res}', block)
- cur_layer_idx += block.num_layers
- if c_dim > 0:
- self.mapping = MappingNetwork(
- z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
- self.b4 = DiscriminatorEpilogue(
- channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs)
-
- def forward(self, img, c, **block_kwargs):
- x = None
- for res in self.block_resolutions:
- block = getattr(self, f'b{res}')
- x, img = block(x, img, **block_kwargs)
-
- cmap = None
- if self.c_dim > 0:
- cmap = self.mapping(None, c)
- x = self.b4(x, img, cmap)
- return x
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/h2oai/h2ogpt-chatbot2/src/gradio_utils/grclient.py b/spaces/h2oai/h2ogpt-chatbot2/src/gradio_utils/grclient.py
deleted file mode 100644
index 8346a61cad99d492f8a10de17851454488364b83..0000000000000000000000000000000000000000
--- a/spaces/h2oai/h2ogpt-chatbot2/src/gradio_utils/grclient.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import traceback
-from typing import Callable
-import os
-
-from gradio_client.client import Job
-
-os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
-
-from gradio_client import Client
-
-
-class GradioClient(Client):
- """
- Parent class of gradio client
- To handle automatically refreshing client if detect gradio server changed
- """
-
- def __init__(self, *args, **kwargs):
- self.args = args
- self.kwargs = kwargs
- super().__init__(*args, **kwargs)
- self.server_hash = self.get_server_hash()
-
- def get_server_hash(self):
- """
- Get server hash using super without any refresh action triggered
- Returns: git hash of gradio server
- """
- return super().submit(api_name='/system_hash').result()
-
- def refresh_client_if_should(self):
- # get current hash in order to update api_name -> fn_index map in case gradio server changed
- # FIXME: Could add cli api as hash
- server_hash = self.get_server_hash()
- if self.server_hash != server_hash:
- self.refresh_client()
- self.server_hash = server_hash
- else:
- self.reset_session()
-
- def refresh_client(self):
- """
- Ensure every client call is independent
- Also ensure map between api_name and fn_index is updated in case server changed (e.g. restarted with new code)
- Returns:
- """
- # need session hash to be new every time, to avoid "generator already executing"
- self.reset_session()
-
- client = Client(*self.args, **self.kwargs)
- for k, v in client.__dict__.items():
- setattr(self, k, v)
-
- def submit(
- self,
- *args,
- api_name: str | None = None,
- fn_index: int | None = None,
- result_callbacks: Callable | list[Callable] | None = None,
- ) -> Job:
- # Note predict calls submit
- try:
- self.refresh_client_if_should()
- job = super().submit(*args, api_name=api_name, fn_index=fn_index)
- except Exception as e:
- print("Hit e=%s" % str(e), flush=True)
- # force reconfig in case only that
- self.refresh_client()
- job = super().submit(*args, api_name=api_name, fn_index=fn_index)
-
- # see if immediately failed
- e = job.future._exception
- if e is not None:
- print("GR job failed: %s %s" % (str(e), ''.join(traceback.format_tb(e.__traceback__))), flush=True)
- # force reconfig in case only that
- self.refresh_client()
- job = super().submit(*args, api_name=api_name, fn_index=fn_index)
- e2 = job.future._exception
- if e2 is not None:
- print("GR job failed again: %s\n%s" % (str(e2), ''.join(traceback.format_tb(e2.__traceback__))), flush=True)
-
- return job
diff --git a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/pretrained.py b/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/pretrained.py
deleted file mode 100644
index 73643f95dced25c0a3c82d439bbea47f495aafd1..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/open_clip/pretrained.py
+++ /dev/null
@@ -1,345 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from functools import partial
-from typing import Dict, Union
-
-from tqdm import tqdm
-
-from .version import __version__
-
-try:
- from huggingface_hub import hf_hub_download
- hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__)
- _has_hf_hub = True
-except ImportError:
- hf_hub_download = None
- _has_hf_hub = False
-
-
-def _pcfg(url='', hf_hub='', mean=None, std=None):
- return dict(
- url=url,
- hf_hub=hf_hub,
- mean=mean,
- std=std,
- )
-
-
-_RN50 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
- yfcc15m=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
- cc12m=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
-)
-
-_RN50_quickgelu = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
- yfcc15m=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
- cc12m=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
-)
-
-_RN101 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
- yfcc15m=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
-)
-
-_RN101_quickgelu = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
- yfcc15m=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
-)
-
-_RN50x4 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"),
-)
-
-_RN50x16 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"),
-)
-
-_RN50x64 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"),
-)
-
-_VITB32 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
- laion400m_e31=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
- laion400m_e32=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
- laion2b_e16=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"),
- laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/')
-)
-
-_VITB32_quickgelu = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
- laion400m_e31=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
- laion400m_e32=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
-)
-
-_VITB16 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"),
- laion400m_e31=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"),
- laion400m_e32=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"),
- # laion400m_32k=_pcfg(
- # url="",
- # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
- # laion400m_64k=_pcfg(
- # url="",
- # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
- laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'),
-)
-
-_VITB16_PLUS_240 = dict(
- laion400m_e31=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"),
- laion400m_e32=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"),
-)
-
-_VITL14 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"),
- laion400m_e31=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"),
- laion400m_e32=_pcfg(
- "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"),
- laion2b_s32b_b82k=_pcfg(
- hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/',
- mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-)
-
-_VITL14_336 = dict(
- openai=_pcfg(
- "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"),
-)
-
-_VITH14 = dict(
- laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'),
-)
-
-_VITg14 = dict(
- laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'),
-)
-
-_VITbigG14 = dict(
- laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'),
-)
-
-_robertaViTB32 = dict(
- laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'),
-)
-
-_xlmRobertaBaseViTB32 = dict(
- laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'),
-)
-
-_xlmRobertaLargeFrozenViTH14 = dict(
- frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'),
-)
-
-_convnext_base = dict(
- laion400m_s13b_b51k=_pcfg(hf_hub='convnext_base-laion400M-s13B-b51K'),
-)
-
-_convnext_base_w = dict(
- laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'),
- laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'),
- laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'),
-)
-
-_convnext_base_w_320 = dict(
- laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'),
- laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'),
-)
-
-
-_PRETRAINED = {
- "RN50": _RN50,
- "RN50-quickgelu": _RN50_quickgelu,
- "RN101": _RN101,
- "RN101-quickgelu": _RN101_quickgelu,
- "RN50x4": _RN50x4,
- "RN50x16": _RN50x16,
- "RN50x64": _RN50x64,
- "ViT-B-32": _VITB32,
- "ViT-B-32-quickgelu": _VITB32_quickgelu,
- "ViT-B-16": _VITB16,
- "ViT-B-16-plus-240": _VITB16_PLUS_240,
- "ViT-L-14": _VITL14,
- "ViT-L-14-336": _VITL14_336,
- "ViT-H-14": _VITH14,
- "ViT-g-14": _VITg14,
- "ViT-bigG-14": _VITbigG14,
- "roberta-ViT-B-32": _robertaViTB32,
- "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32,
- "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14,
- "convnext_base": _convnext_base,
- "convnext_base_w": _convnext_base_w,
- "convnext_base_w_320": _convnext_base_w_320,
-}
-
-
-def _clean_tag(tag: str):
- # normalize pretrained tags
- return tag.lower().replace('-', '_')
-
-
-def list_pretrained(as_str: bool = False):
- """ returns list of pretrained models
- Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True
- """
- return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()]
-
-
-def list_pretrained_models_by_tag(tag: str):
- """ return all models having the specified pretrain tag """
- models = []
- tag = _clean_tag(tag)
- for k in _PRETRAINED.keys():
- if tag in _PRETRAINED[k]:
- models.append(k)
- return models
-
-
-def list_pretrained_tags_by_model(model: str):
- """ return all pretrain tags for the specified model architecture """
- tags = []
- if model in _PRETRAINED:
- tags.extend(_PRETRAINED[model].keys())
- return tags
-
-
-def is_pretrained_cfg(model: str, tag: str):
- if model not in _PRETRAINED:
- return False
- return _clean_tag(tag) in _PRETRAINED[model]
-
-
-def get_pretrained_cfg(model: str, tag: str):
- if model not in _PRETRAINED:
- return {}
- model_pretrained = _PRETRAINED[model]
- return model_pretrained.get(_clean_tag(tag), {})
-
-
-def get_pretrained_url(model: str, tag: str):
- cfg = get_pretrained_cfg(model, _clean_tag(tag))
- return cfg.get('url', '')
-
-
-def download_pretrained_from_url(
- url: str,
- cache_dir: Union[str, None] = None,
-):
- if not cache_dir:
- cache_dir = os.path.expanduser("~/.cache/clip")
- os.makedirs(cache_dir, exist_ok=True)
- filename = os.path.basename(url)
-
- if 'openaipublic' in url:
- expected_sha256 = url.split("/")[-2]
- elif 'mlfoundations' in url:
- expected_sha256 = os.path.splitext(filename)[0].split("-")[-1]
- else:
- expected_sha256 = ''
-
- download_target = os.path.join(cache_dir, filename)
-
- if os.path.exists(download_target) and not os.path.isfile(download_target):
- raise RuntimeError(f"{download_target} exists and is not a regular file")
-
- if os.path.isfile(download_target):
- if expected_sha256:
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
- return download_target
- else:
- warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
- else:
- return download_target
-
- with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
- with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
- while True:
- buffer = source.read(8192)
- if not buffer:
- break
-
- output.write(buffer)
- loop.update(len(buffer))
-
- if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
- raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
-
- return download_target
-
-
-def has_hf_hub(necessary=False):
- if not _has_hf_hub and necessary:
- # if no HF Hub module installed, and it is necessary to continue, raise error
- raise RuntimeError(
- 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
- return _has_hf_hub
-
-
-def download_pretrained_from_hf(
- model_id: str,
- filename: str = 'open_clip_pytorch_model.bin',
- revision=None,
- cache_dir: Union[str, None] = None,
-):
- has_hf_hub(True)
- cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir)
- return cached_file
-
-
-def download_pretrained(
- cfg: Dict,
- force_hf_hub: bool = False,
- cache_dir: Union[str, None] = None,
-):
- target = ''
- if not cfg:
- return target
-
- download_url = cfg.get('url', '')
- download_hf_hub = cfg.get('hf_hub', '')
- if download_hf_hub and force_hf_hub:
- # use HF hub even if url exists
- download_url = ''
-
- if download_url:
- target = download_pretrained_from_url(download_url, cache_dir=cache_dir)
- elif download_hf_hub:
- has_hf_hub(True)
- # we assume the hf_hub entries in pretrained config combine model_id + filename in
- # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and
- # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'.
- model_id, filename = os.path.split(download_hf_hub)
- if filename:
- target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir)
- else:
- target = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
-
- return target
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py
deleted file mode 100644
index 91aaf065e30c748671a9e76a153a3dca7161b0aa..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/evaluation/flickr/flickr_eval.py
+++ /dev/null
@@ -1,440 +0,0 @@
-from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-import json
-import numpy as np
-import os.path as osp
-import os
-from prettytable import PrettyTable
-
-import xml.etree.ElementTree as ET
-from collections import defaultdict
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
-
-import maskrcnn_benchmark.utils.mdetr_dist as dist
-#### The following loading utilities are imported from
-#### https://github.com/BryanPlummer/flickr30k_entities/blob/68b3d6f12d1d710f96233f6bd2b6de799d6f4e5b/flickr30k_entities_utils.py
-# Changelog:
-# - Added typing information
-# - Completed docstrings
-
-def get_sentence_data(filename) -> List[Dict[str, Any]]:
- """
- Parses a sentence file from the Flickr30K Entities dataset
-
- input:
- filename - full file path to the sentence file to parse
-
- output:
- a list of dictionaries for each sentence with the following fields:
- sentence - the original sentence
- phrases - a list of dictionaries for each phrase with the
- following fields:
- phrase - the text of the annotated phrase
- first_word_index - the position of the first word of
- the phrase in the sentence
- phrase_id - an identifier for this phrase
- phrase_type - a list of the coarse categories this
- phrase belongs to
-
- """
- with open(filename, "r") as f:
- sentences = f.read().split("\n")
-
- annotations = []
- for sentence in sentences:
- if not sentence:
- continue
-
- first_word = []
- phrases = []
- phrase_id = []
- phrase_type = []
- words = []
- current_phrase = []
- add_to_phrase = False
- for token in sentence.split():
- if add_to_phrase:
- if token[-1] == "]":
- add_to_phrase = False
- token = token[:-1]
- current_phrase.append(token)
- phrases.append(" ".join(current_phrase))
- current_phrase = []
- else:
- current_phrase.append(token)
-
- words.append(token)
- else:
- if token[0] == "[":
- add_to_phrase = True
- first_word.append(len(words))
- parts = token.split("/")
- phrase_id.append(parts[1][3:])
- phrase_type.append(parts[2:])
- else:
- words.append(token)
-
- sentence_data = {"sentence": " ".join(words), "phrases": []}
- for index, phrase, p_id, p_type in zip(first_word, phrases, phrase_id, phrase_type):
- sentence_data["phrases"].append(
- {"first_word_index": index, "phrase": phrase, "phrase_id": p_id, "phrase_type": p_type}
- )
-
- annotations.append(sentence_data)
-
- return annotations
-
-
-def get_annotations(filename) -> Dict[str, Union[int, List[str], Dict[str, List[List[int]]]]]:
- """
- Parses the xml files in the Flickr30K Entities dataset
-
- input:
- filename - full file path to the annotations file to parse
-
- output:
- dictionary with the following fields:
- scene - list of identifiers which were annotated as
- pertaining to the whole scene
- nobox - list of identifiers which were annotated as
- not being visible in the image
- boxes - a dictionary where the fields are identifiers
- and the values are its list of boxes in the
- [xmin ymin xmax ymax] format
- height - int representing the height of the image
- width - int representing the width of the image
- depth - int representing the depth of the image
- """
- tree = ET.parse(filename)
- root = tree.getroot()
- size_container = root.findall("size")[0]
- anno_info: Dict[str, Union[int, List[str], Dict[str, List[List[int]]]]] = {}
- all_boxes: Dict[str, List[List[int]]] = {}
- all_noboxes: List[str] = []
- all_scenes: List[str] = []
- for size_element in size_container:
- assert size_element.text
- anno_info[size_element.tag] = int(size_element.text)
-
- for object_container in root.findall("object"):
- for names in object_container.findall("name"):
- box_id = names.text
- assert box_id
- box_container = object_container.findall("bndbox")
- if len(box_container) > 0:
- if box_id not in all_boxes:
- all_boxes[box_id] = []
- xmin = int(box_container[0].findall("xmin")[0].text)
- ymin = int(box_container[0].findall("ymin")[0].text)
- xmax = int(box_container[0].findall("xmax")[0].text)
- ymax = int(box_container[0].findall("ymax")[0].text)
- all_boxes[box_id].append([xmin, ymin, xmax, ymax])
- else:
- nobndbox = int(object_container.findall("nobndbox")[0].text)
- if nobndbox > 0:
- all_noboxes.append(box_id)
-
- scene = int(object_container.findall("scene")[0].text)
- if scene > 0:
- all_scenes.append(box_id)
- anno_info["boxes"] = all_boxes
- anno_info["nobox"] = all_noboxes
- anno_info["scene"] = all_scenes
-
- return anno_info
-
-
-#### END of import from flickr30k_entities
-
-
-#### Bounding box utilities imported from torchvision and converted to numpy
-def box_area(boxes: np.array) -> np.array:
- """
- Computes the area of a set of bounding boxes, which are specified by its
- (x1, y1, x2, y2) coordinates.
-
- Args:
- boxes (Tensor[N, 4]): boxes for which the area will be computed. They
- are expected to be in (x1, y1, x2, y2) format with
- ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-
- Returns:
- area (Tensor[N]): area for each box
- """
- assert boxes.ndim == 2 and boxes.shape[-1] == 4
- return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
-
-# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
-# with slight modifications
-def _box_inter_union(boxes1: np.array, boxes2: np.array) -> Tuple[np.array, np.array]:
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- lt = np.maximum(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
- rb = np.minimum(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
-
- wh = (rb - lt).clip(min=0) # [N,M,2]
- inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
-
- union = area1[:, None] + area2 - inter
-
- return inter, union
-
-
-def box_iou(boxes1: np.array, boxes2: np.array) -> np.array:
- """
- Return intersection-over-union (Jaccard index) of boxes.
-
- Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
- ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-
- Args:
- boxes1 (Tensor[N, 4])
- boxes2 (Tensor[M, 4])
-
- Returns:
- iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
- """
- inter, union = _box_inter_union(boxes1, boxes2)
- iou = inter / union
- return iou
-
-
-#### End of import of box utilities
-
-def _merge_boxes(boxes: List[List[int]]) -> List[List[int]]:
- """
- Return the boxes corresponding to the smallest enclosing box containing all the provided boxes
- The boxes are expected in [x1, y1, x2, y2] format
- """
- if len(boxes) == 1:
- return boxes
-
- np_boxes = np.asarray(boxes)
-
- return [[np_boxes[:, 0].min(), np_boxes[:, 1].min(), np_boxes[:, 2].max(), np_boxes[:, 3].max()]]
-
-
-class RecallTracker:
- """ Utility class to track recall@k for various k, split by categories"""
-
- def __init__(self, topk: Sequence[int]):
- """
- Parameters:
- - topk : tuple of ints corresponding to the recalls being tracked (eg, recall@1, recall@10, ...)
- """
-
- self.total_byk_bycat: Dict[int, Dict[str, int]] = {k: defaultdict(int) for k in topk}
- self.positives_byk_bycat: Dict[int, Dict[str, int]] = {k: defaultdict(int) for k in topk}
-
- def add_positive(self, k: int, category: str):
- """Log a positive hit @k for given category"""
- if k not in self.total_byk_bycat:
- raise RuntimeError(f"{k} is not a valid recall threshold")
- self.total_byk_bycat[k][category] += 1
- self.positives_byk_bycat[k][category] += 1
-
- def add_negative(self, k: int, category: str):
- """Log a negative hit @k for given category"""
- if k not in self.total_byk_bycat:
- raise RuntimeError(f"{k} is not a valid recall threshold")
- self.total_byk_bycat[k][category] += 1
-
- def report(self) -> Dict[int, Dict[str, float]]:
- """Return a condensed report of the results as a dict of dict.
- report[k][cat] is the recall@k for the given category
- """
- report: Dict[int, Dict[str, float]] = {}
- for k in self.total_byk_bycat:
- assert k in self.positives_byk_bycat
- report[k] = {
- cat: self.positives_byk_bycat[k][cat] / self.total_byk_bycat[k][cat] for cat in self.total_byk_bycat[k]
- }
- return report
-
-
-class Flickr30kEntitiesRecallEvaluator:
- def __init__(
- self,
- flickr_path: str,
- subset: str = "test",
- topk: Sequence[int] = (1, 5, 10, -1),
- iou_thresh: float = 0.5,
- merge_boxes: bool = False,
- verbose: bool = True,
- ):
- assert subset in ["train", "test", "val"], f"Wrong flickr subset {subset}"
-
- self.topk = topk
- self.iou_thresh = iou_thresh
-
- flickr_path = Path(flickr_path)
-
- # We load the image ids corresponding to the current subset
- with open(flickr_path / f"{subset}.txt") as file_d:
- self.img_ids = [line.strip() for line in file_d]
-
- if verbose:
- print(f"Flickr subset contains {len(self.img_ids)} images")
-
- # Read the box annotations for all the images
- self.imgid2boxes: Dict[str, Dict[str, List[List[int]]]] = {}
-
- if verbose:
- print("Loading annotations...")
-
- for img_id in self.img_ids:
- anno_info = get_annotations(flickr_path / "Annotations" / f"{img_id}.xml")["boxes"]
- if merge_boxes:
- merged = {}
- for phrase_id, boxes in anno_info.items():
- merged[phrase_id] = _merge_boxes(boxes)
- anno_info = merged
- self.imgid2boxes[img_id] = anno_info
-
- # Read the sentences annotations
- self.imgid2sentences: Dict[str, List[List[Optional[Dict]]]] = {}
-
- if verbose:
- print("Loading annotations...")
-
- self.all_ids: List[str] = []
- tot_phrases = 0
- for img_id in self.img_ids:
- sentence_info = get_sentence_data(flickr_path / "Sentences" / f"{img_id}.txt")
- self.imgid2sentences[img_id] = [None for _ in range(len(sentence_info))]
-
- # Some phrases don't have boxes, we filter them.
- for sent_id, sentence in enumerate(sentence_info):
- phrases = [phrase for phrase in sentence["phrases"] if phrase["phrase_id"] in self.imgid2boxes[img_id]]
- if len(phrases) > 0:
- self.imgid2sentences[img_id][sent_id] = phrases
- tot_phrases += len(phrases)
-
- self.all_ids += [
- f"{img_id}_{k}" for k in range(len(sentence_info)) if self.imgid2sentences[img_id][k] is not None
- ]
-
- if verbose:
- print(f"There are {tot_phrases} phrases in {len(self.all_ids)} sentences to evaluate")
-
- def evaluate(self, predictions: List[Dict]):
- evaluated_ids = set()
-
- recall_tracker = RecallTracker(self.topk)
-
- for pred in predictions:
- cur_id = f"{pred['image_id']}_{pred['sentence_id']}"
- if cur_id in evaluated_ids:
- print(
- "Warning, multiple predictions found for sentence"
- f"{pred['sentence_id']} in image {pred['image_id']}"
- )
- continue
-
- # Skip the sentences with no valid phrase
- if cur_id not in self.all_ids:
- if len(pred["boxes"]) != 0:
- print(
- f"Warning, in image {pred['image_id']} we were not expecting predictions "
- f"for sentence {pred['sentence_id']}. Ignoring them."
- )
- continue
-
- evaluated_ids.add(cur_id)
-
- pred_boxes = pred["boxes"]
- if str(pred["image_id"]) not in self.imgid2sentences:
- raise RuntimeError(f"Unknown image id {pred['image_id']}")
- if not 0 <= int(pred["sentence_id"]) < len(self.imgid2sentences[str(pred["image_id"])]):
- raise RuntimeError(f"Unknown sentence id {pred['sentence_id']}" f" in image {pred['image_id']}")
- target_sentence = self.imgid2sentences[str(pred["image_id"])][int(pred["sentence_id"])]
-
- phrases = self.imgid2sentences[str(pred["image_id"])][int(pred["sentence_id"])]
- if len(pred_boxes) != len(phrases):
- raise RuntimeError(
- f"Error, got {len(pred_boxes)} predictions, expected {len(phrases)} "
- f"for sentence {pred['sentence_id']} in image {pred['image_id']}"
- )
-
- for cur_boxes, phrase in zip(pred_boxes, phrases):
- target_boxes = self.imgid2boxes[str(pred["image_id"])][phrase["phrase_id"]]
-
- ious = box_iou(np.asarray(cur_boxes), np.asarray(target_boxes))
- for k in self.topk:
- maxi = 0
- if k == -1:
- maxi = ious.max()
- else:
- assert k > 0
- maxi = ious[:k].max()
- if maxi >= self.iou_thresh:
- recall_tracker.add_positive(k, "all")
- for phrase_type in phrase["phrase_type"]:
- recall_tracker.add_positive(k, phrase_type)
- else:
- recall_tracker.add_negative(k, "all")
- for phrase_type in phrase["phrase_type"]:
- recall_tracker.add_negative(k, phrase_type)
-
- if len(evaluated_ids) != len(self.all_ids):
- print("ERROR, the number of evaluated sentence doesn't match. Missing predictions:")
- un_processed = set(self.all_ids) - evaluated_ids
- for missing in un_processed:
- img_id, sent_id = missing.split("_")
- print(f"\t sentence {sent_id} in image {img_id}")
- raise RuntimeError("Missing predictions")
-
- return recall_tracker.report()
-
-
-class FlickrEvaluator(object):
- def __init__(
- self,
- flickr_path,
- subset,
- top_k=(1, 5, 10, -1),
- iou_thresh=0.5,
- merge_boxes=False,
- ):
- assert isinstance(top_k, (list, tuple))
-
- self.evaluator = Flickr30kEntitiesRecallEvaluator(
- flickr_path, subset=subset, topk=top_k, iou_thresh=iou_thresh, merge_boxes=merge_boxes, verbose=False
- )
- self.predictions = []
- self.results = None
-
- def accumulate(self):
- pass
-
- def update(self, predictions):
- self.predictions += predictions
-
- def synchronize_between_processes(self):
- all_predictions = dist.all_gather(self.predictions)
- self.predictions = sum(all_predictions, [])
-
- def summarize(self):
- if dist.is_main_process():
- self.results = self.evaluator.evaluate(self.predictions)
- table = PrettyTable()
- all_cat = sorted(list(self.results.values())[0].keys())
- table.field_names = ["Recall@k"] + all_cat
-
- score = {}
- for k, v in self.results.items():
- cur_results = [v[cat] for cat in all_cat]
- header = "Upper_bound" if k == -1 else f"Recall@{k}"
-
- for cat in all_cat:
- score[f"{header}_{cat}"] = v[cat]
- table.add_row([header] + cur_results)
-
- print(table)
-
- return score
-
- return None, None
diff --git a/spaces/hezhaoqia/vits-simple-api/logger.py b/spaces/hezhaoqia/vits-simple-api/logger.py
deleted file mode 100644
index d8e83f51f8aa5079b8ceaba0530668c5a7c0bba5..0000000000000000000000000000000000000000
--- a/spaces/hezhaoqia/vits-simple-api/logger.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import sys
-import logging
-import logzero
-import config
-from logging.handlers import TimedRotatingFileHandler
-
-logzero.loglevel(logging.WARNING)
-logger = logging.getLogger("vits-simple-api")
-level = getattr(config, "LOGGING_LEVEL", "DEBUG")
-level_dict = {'DEBUG': logging.DEBUG, 'INFO': logging.INFO, 'WARNING': logging.WARNING, 'ERROR': logging.ERROR,
- 'CRITICAL': logging.CRITICAL}
-logging.basicConfig(level=level_dict[level])
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger("langid.langid").setLevel(logging.INFO)
-logging.getLogger("apscheduler.scheduler").setLevel(logging.INFO)
-
-os.makedirs(config.LOGS_PATH, exist_ok=True)
-log_file = os.path.join(config.LOGS_PATH, 'latest.log')
-backup_count = getattr(config, "LOGS_BACKUPCOUNT", 30)
-handler = TimedRotatingFileHandler(log_file, when="midnight", interval=1, backupCount=backup_count, encoding='utf-8')
-handler.suffix = "%Y-%m-%d.log"
-formatter = logging.Formatter('%(levelname)s:%(name)s %(message)s')
-handler.setFormatter(formatter)
-logger.addHandler(handler)
-
-logging.getLogger("werkzeug").addHandler(handler)
-logging.getLogger("apscheduler.scheduler").addHandler(handler)
-
-
-# Custom function to handle uncaught exceptions
-def handle_exception(exc_type, exc_value, exc_traceback):
- # If it's a keyboard interrupt, don't handle it, just return
- if issubclass(exc_type, KeyboardInterrupt):
- sys.__excepthook__(exc_type, exc_value, exc_traceback)
- return
-
- logger.error("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))
-
-
-# Set the global exception handler in Python
-sys.excepthook = handle_exception
diff --git a/spaces/hjzhp/cgpt-online/src/types.ts b/spaces/hjzhp/cgpt-online/src/types.ts
deleted file mode 100644
index 0d3d63e729b98215278c71f7e35ec1a7cccfc560..0000000000000000000000000000000000000000
--- a/spaces/hjzhp/cgpt-online/src/types.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-export interface ChatMessage {
- role: 'system' | 'user' | 'assistant'
- content: string
-}
-
-export interface ErrorMessage {
- code: string
- message: string
-}
diff --git a/spaces/hkunlp/Binder/README.md b/spaces/hkunlp/Binder/README.md
deleted file mode 100644
index 4b351ecfb355b9afb40b0efe10af99897d036bf2..0000000000000000000000000000000000000000
--- a/spaces/hkunlp/Binder/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Binder
-emoji: 🔗
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: true
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/utilities/to_torch.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/utilities/to_torch.py
deleted file mode 100644
index ab68035eb19774540b7ca46a177a4d26d6fb3a4f..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/utilities/to_torch.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# 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 torch
-
-
-def maybe_to_torch(d):
- if isinstance(d, list):
- d = [maybe_to_torch(i) if not isinstance(i, torch.Tensor) else i for i in d]
- elif not isinstance(d, torch.Tensor):
- d = torch.from_numpy(d).float()
- return d
-
-
-def to_cuda(data, non_blocking=True, gpu_id=0):
- if isinstance(data, list):
- data = [i.cuda(gpu_id, non_blocking=non_blocking) for i in data]
- else:
- data = data.cuda(gpu_id, non_blocking=non_blocking)
- return data
diff --git a/spaces/hpi-dhc/FairEval/FairEval.py b/spaces/hpi-dhc/FairEval/FairEval.py
deleted file mode 100644
index d76c6096aa5341061d59d541dece20326e1552d8..0000000000000000000000000000000000000000
--- a/spaces/hpi-dhc/FairEval/FairEval.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-
-# huggingface packages
-import evaluate
-import datasets
-
-# faireval functions
-from .FairEvalUtils import *
-
-# packages to manage input formats
-import importlib
-from typing import List, Optional, Union
-from seqeval.metrics.v1 import check_consistent_length
-from seqeval.scheme import Entities, Token, auto_detect
-
-_CITATION = """\
-@inproceedings{ortmann2022,
- title = {Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans},
- author = {Katrin Ortmann},
- url = {https://aclanthology.org/2022.lrec-1.150},
- year = {2022},
- date = {2022-06-21},
- booktitle = {Proceedings of the Language Resources and Evaluation Conference (LREC)},
- pages = {1400-1407},
- publisher = {European Language Resources Association},
- address = {Marseille, France},
- pubstate = {published},
- type = {inproceedings}
-}
-"""
-
-_DESCRIPTION = """\
-New evaluation method that more accurately reflects true annotation quality by ensuring that every error is counted
-only once - avoiding the penalty to close-to-target annotations happening in traditional evaluation.
-In addition to the traditional categories of true positives (TP), false positives (FP), and false negatives
-(FN), the new method takes into account more fine-grained error types: labeling errors (LE), boundary errors (BE),
-and labeling-boundary errors (LBE).
-"""
-
-_KWARGS_DESCRIPTION = """
-Outputs the error count (TP, FP, etc.) and resulting scores (Precision, Recall and F1) from a reference list of
-spans compared against a predicted one. The user can choose to see traditional or fair error counts and scores by
-switching the argument 'mode'.
-For the computation of the fair metrics from the error count please refer to: https://aclanthology.org/2022.lrec-1.150.pdf
-Args:
- predictions: a list of lists of predicted labels, i.e. estimated targets as returned by a tagger.
- references: list of ground truth reference labels. Predicted sentences must have the same number of tokens as the references.
- mode: 'fair', 'traditional' ot 'weighted. Controls the desired output. The default value is 'fair'.
- - 'traditional': equivalent to seqeval's metrics / classic span-based evaluation.
- - 'fair': default fair score calculation. It will also show traditional scores for comparison.
- - 'weighted': custom score calculation with the weights passed. It will also show traditional scores for comparison.
- weights: dictionary with the weight of each error for the custom score calculation.
- If none is passed and the mode is set to 'weighted', the following is used:
- {"TP": {"TP": 1},
- "FP": {"FP": 1},
- "FN": {"FN": 1},
- "LE": {"TP": 0, "FP": 0.5, "FN": 0.5},
- "BE": {"TP": 0.5, "FP": 0.25, "FN": 0.25},
- "LBE": {"TP": 0, "FP": 0.5, "FN": 0.5}}
- error_format: 'count', 'error_ratio' or 'entity_ratio'. Controls the desired output for TP, FP, BE, LE, etc:. Default value is 'count'.
- - 'count': absolute count of each parameter.
- - 'error_ratio': precentage with respect to the total errors that each parameter represents.
- - 'entity_ratio': precentage with respect to the total number of ground truth entites that each parameter represents.
- zero_division: which value to substitute as a metric value when encountering zero division. Should be one of [0,1,"warn"]. "warn" acts as 0, but the warning is raised.
- suffix: True if the IOB tag is a suffix (after type) instead of a prefix (before type), False otherwise. The default value is False, i.e. the IOB tag is a prefix (before type).
- scheme: the target tagging scheme, which can be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU]. The default value is None.
-Returns:
- A dictionary with:
- - Overall error parameter count (or ratio) and resulting scores.
- - A nested dictionary per label with its respective error parameter count (or ratio) and resulting scores
-
- If mode is 'traditional', the error parameters shown are the classical TP, FP and FN. If mode is 'fair' or
- 'weighted', TP remains the same, FP and FN are shown as per the fair definition and additional errors BE, LE and LBE are shown.
-
-Examples:
- >>> faireval = evaluate.load("hpi-dhc/FairEval")
- >>> pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O', 'B-PER', 'I-PER', 'O']]
- >>> ref = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'B-PER', 'I-PER', 'O']]
- >>> results = faireval.compute(predictions=pred, references=ref, mode='fair', error_format='count')
- >>> print(results)
- {
- "MISC": {
- "precision": 0.0,
- "recall": 0.0,
- "f1": 0.0,
- "trad_prec": 0.0,
- "trad_rec": 0.0,
- "trad_f1": 0.0,
- "TP": 0,
- "FP": 0.0,
- "FN": 0.0,
- "LE": 0.0,
- "BE": 1.0,
- "LBE": 0.0
- },
- "PER": {
- "precision": 1.0,
- "recall": 1.0,
- "f1": 1.0,
- "trad_prec": 1.0,
- "trad_rec": 1.0,
- "trad_f1": 1.0,
- "TP": 1,
- "FP": 0.0,
- "FN": 0.0,
- "LE": 0.0,
- "BE": 0.0,
- "LBE": 0.0
- },
- "overall_precision": 0.6666666666666666,
- "overall_recall": 0.6666666666666666,
- "overall_f1": 0.6666666666666666,
- "overall_trad_prec": 0.5,
- "overall_trad_rec": 0.5,
- "overall_trad_f1": 0.5,
- "TP": 1,
- "FP": 0.0,
- "FN": 0.0,
- "LE": 0.0,
- "BE": 1.0,
- "LBE": 0.0
- }
- """
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class FairEval(evaluate.Metric):
-
- def _info(self):
- return evaluate.MetricInfo(
- # This is the description that will appear on the modules page.
- module_type="metric",
- description=_DESCRIPTION,
- citation=_CITATION,
- inputs_description=_KWARGS_DESCRIPTION,
- # This defines the format of each prediction and reference
- features=datasets.Features({
- "predictions": datasets.Sequence(datasets.Value("string", id="label"), id="sequence"),
- "references": datasets.Sequence(datasets.Value("string", id="label"), id="sequence"),
- }),
- # Homepage of the module for documentation
- homepage="https://huggingface.co/spaces/hpi-dhc/FairEval",
- # Additional links to the codebase or references
- codebase_urls=["https://github.com/rubcompling/FairEval#acknowledgement"],
- reference_urls=["https://aclanthology.org/2022.lrec-1.150.pdf"]
- )
-
- def _compute(
- self,
- predictions,
- references,
- suffix: bool = False,
- scheme: Optional[str] = None,
- mode: Optional[str] = 'fair',
- weights: dict = None,
- error_format: Optional[str] = 'count',
- zero_division: Union[str, int] = "warn",
- ):
- """Returns the error parameter counts and scores"""
- # (1) SEQEVAL INPUT MANAGEMENT
- if scheme is not None:
- try:
- scheme_module = importlib.import_module("seqeval.scheme")
- scheme = getattr(scheme_module, scheme)
- except AttributeError:
- raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}")
-
- y_true = references
- y_pred = predictions
-
- check_consistent_length(y_true, y_pred)
-
- if scheme is None or not issubclass(scheme, Token):
- scheme = auto_detect(y_true, suffix)
-
- true_spans = Entities(y_true, scheme, suffix).entities
- pred_spans = Entities(y_pred, scheme, suffix).entities
-
- # (2) TRANSFORM FROM SEQEVAL TO FAIREVAL SPAN FORMAT
- true_spans = seq_to_fair(true_spans)
- pred_spans = seq_to_fair(pred_spans)
-
- # (3) COUNT ERRORS AND CALCULATE SCORES (counting total ground truth entities too)
- total_errors = compare_spans([], [])
- total_ref_entities = 0
- for i in range(len(true_spans)):
- total_ref_entities += len(true_spans[i])
- sentence_errors = compare_spans(true_spans[i], pred_spans[i])
- total_errors = add_dict(total_errors, sentence_errors)
-
- if weights is None and mode == 'weighted':
- weights = {"TP": {"TP": 1},
- "FP": {"FP": 1},
- "FN": {"FN": 1},
- "LE": {"TP": 0, "FP": 0.5, "FN": 0.5},
- "BE": {"TP": 0.5, "FP": 0.25, "FN": 0.25},
- "LBE": {"TP": 0, "FP": 0.5, "FN": 0.5}}
- print("The chosen mode is \'weighted\', but no weights are given. Setting weights to:")
- for k in weights:
- print(k, ":", weights[k])
-
- config = {"labels": "all", "eval_method": ['traditional', 'fair', 'weighted'], "weights": weights,}
- results = calculate_results(total_errors, config)
- del results['conf']
-
- # (4) SELECT OUTPUT MODE AND REFORMAT AS SEQEVAL-HUGGINGFACE OUTPUT
- # initialize empty dictionary and count errors
- output = {}
- # control the denominator for the error_format (count, proportion over total errors or over total entities)
- if error_format == 'count':
- trad_divider = 1
- fair_divider = 1
- elif error_format == 'entity_ratio':
- trad_divider = total_ref_entities
- fair_divider = total_ref_entities
- elif error_format == 'error_ratio':
- trad_divider = results['overall']['traditional']['FP'] + results['overall']['traditional']['FN']
- fair_divider = results['overall']['fair']['FP'] + results['overall']['fair']['FN'] + \
- results['overall']['fair']['LE'] + results['overall']['fair']['BE'] + \
- results['overall']['fair']['LBE']
-
- # assert valid options
- assert mode in ['traditional', 'fair', 'weighted'], 'mode must be \'traditional\', \'fair\' or \'weighted\''
- assert error_format in ['count', 'error_ratio', 'entity_ratio'], 'error_format must be \'count\', \'error_ratio\' or \'entity_ratio\''
-
- # append entity-level errors and scores
- if mode == 'traditional':
- for k, v in results['per_label'][mode].items():
- output[k] = {# traditional scores
- 'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'],
-
- # traditional errors
- 'TP': v['TP'] / trad_divider if error_format == 'entity_ratio' else v['TP'],
- 'FP': v['FP'] / trad_divider, 'FN': v['FN'] / trad_divider}
- elif mode == 'fair' or mode == 'weighted':
- for k, v in results['per_label'][mode].items():
- output[k] = {# fair/weighted scores
- 'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'],
-
- # traditional scores
- 'trad_prec': results['per_label']['traditional'][k]['Prec'],
- 'trad_rec': results['per_label']['traditional'][k]['Rec'],
- 'trad_f1': results['per_label']['traditional'][k]['F1'],
-
- # fair/weighted errors
- 'TP': v['TP'] / fair_divider if error_format == 'entity_ratio' else v['TP'],
- 'FP': v['FP'] / fair_divider, 'FN': v['FN'] / fair_divider,
- 'LE': v['LE'] / fair_divider, 'BE': v['BE'] / fair_divider, 'LBE': v['LBE'] / fair_divider}
-
- # append overall scores
- output['overall_precision'] = results['overall'][mode]['Prec']
- output['overall_recall'] = results['overall'][mode]['Rec']
- output['overall_f1'] = results['overall'][mode]['F1']
-
- # append overall error counts (and trad scores if mode is fair)
- if mode == 'traditional':
- output['TP'] = results['overall'][mode]['TP'] / trad_divider if error_format == 'entity_ratio' else \
- results['overall'][mode]['TP']
- output['FP'] = results['overall'][mode]['FP'] / trad_divider
- output['FN'] = results['overall'][mode]['FN'] / trad_divider
- elif mode == 'fair' or 'weighted':
- output['overall_trad_prec'] = results['overall']['traditional']['Prec']
- output['overall_trad_rec'] = results['overall']['traditional']['Rec']
- output['overall_trad_f1'] = results['overall']['traditional']['F1']
- output['TP'] = results['overall'][mode]['TP'] / fair_divider if error_format == 'entity_ratio' else \
- results['overall'][mode]['TP']
- output['FP'] = results['overall'][mode]['FP'] / fair_divider
- output['FN'] = results['overall'][mode]['FN'] / fair_divider
- output['LE'] = results['overall'][mode]['LE'] / fair_divider
- output['BE'] = results['overall'][mode]['BE'] / fair_divider
- output['LBE'] = results['overall'][mode]['LBE'] / fair_divider
-
- return output
-
-
-def seq_to_fair(seq_sentences):
- "Transforms input annotated sentences from seqeval span format to FairEval span format"
- out = []
- for seq_sentence in seq_sentences:
- sentence = []
- for entity in seq_sentence:
- span = str(entity).replace('(', '').replace(')', '').replace(' ', '').split(',')
- span = span[1:]
- span[-1] = int(span[-1]) - 1
- span[1] = int(span[1])
- span.append({i for i in range(span[1], span[2] + 1)})
- sentence.append(span)
- out.append(sentence)
- return out
diff --git a/spaces/hrishikeshagi/ImagePromptGenerator/README.md b/spaces/hrishikeshagi/ImagePromptGenerator/README.md
deleted file mode 100644
index 7852acc0fb7ccb44346029d26bac58b85020c465..0000000000000000000000000000000000000000
--- a/spaces/hrishikeshagi/ImagePromptGenerator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ImagePromptGenerator
-emoji: 💻
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hysts/Yet-Another-Anime-Segmenter/style.css b/spaces/hysts/Yet-Another-Anime-Segmenter/style.css
deleted file mode 100644
index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000
--- a/spaces/hysts/Yet-Another-Anime-Segmenter/style.css
+++ /dev/null
@@ -1,3 +0,0 @@
-h1 {
- text-align: center;
-}
diff --git a/spaces/hyxue/HiFiFace-inference-demo/data_process/utils.py b/spaces/hyxue/HiFiFace-inference-demo/data_process/utils.py
deleted file mode 100644
index db42917be61e0baca077a9631eaa92c7962a0f37..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/data_process/utils.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from typing import Tuple
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-class SoftErosion(torch.nn.Module):
- def __init__(self, kernel_size: int = 15, threshold: float = 0.6, iterations: int = 1):
- super(SoftErosion, self).__init__()
- r = kernel_size // 2
- self.padding = r
- self.iterations = iterations
- self.threshold = threshold
-
- # Create kernel
- y_indices, x_indices = torch.meshgrid(torch.arange(0.0, kernel_size), torch.arange(0.0, kernel_size))
- dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
- kernel = dist.max() - dist
- kernel /= kernel.sum()
- kernel = kernel.view(1, 1, *kernel.shape)
- self.register_buffer("weight", kernel)
-
- def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- for i in range(self.iterations - 1):
- x = torch.min(
- x,
- F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding),
- )
- x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
-
- mask = x >= self.threshold
-
- x[mask] = 1.0
- # add small epsilon to avoid Nans
- x[~mask] /= x[~mask].max() + 1e-7
-
- return x, mask
-
-
-def encode_segmentation_rgb(segmentation: np.ndarray, no_neck: bool = True) -> np.ndarray:
- parse = segmentation
- # https://github.com/zllrunning/face-parsing.PyTorch/blob/master/prepropess_data.py
- face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
- mouth_id = 11
- # hair_id = 17
- face_map = np.zeros([parse.shape[0], parse.shape[1]])
- mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
- # hair_map = np.zeros([parse.shape[0], parse.shape[1]])
-
- for valid_id in face_part_ids:
- valid_index = np.where(parse == valid_id)
- face_map[valid_index] = 255
- valid_index = np.where(parse == mouth_id)
- mouth_map[valid_index] = 255
- # valid_index = np.where(parse==hair_id)
- # hair_map[valid_index] = 255
- # return np.stack([face_map, mouth_map,hair_map], axis=2)
- return np.stack([face_map, mouth_map], axis=2)
-
-
-def encode_segmentation_rgb_batch(segmentation: torch.Tensor, no_neck: bool = True) -> torch.Tensor:
- # https://github.com/zllrunning/face-parsing.PyTorch/blob/master/prepropess_data.py
- face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
- mouth_id = 11
- # hair_id = 17
- segmentation = segmentation.int()
- face_map = torch.zeros_like(segmentation)
- mouth_map = torch.zeros_like(segmentation)
- # hair_map = np.zeros([parse.shape[0], parse.shape[1]])
-
- white_tensor = face_map + 255
- for valid_id in face_part_ids:
- face_map = torch.where(segmentation == valid_id, white_tensor, face_map)
- mouth_map = torch.where(segmentation == mouth_id, white_tensor, mouth_map)
-
- return torch.cat([face_map, mouth_map], dim=1)
-
-
-def postprocess(
- swapped_face: np.ndarray,
- target: np.ndarray,
- target_mask: np.ndarray,
- smooth_mask: torch.nn.Module,
-) -> np.ndarray:
- # target_mask = cv2.resize(target_mask, (self.size, self.size))
-
- mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1 / 255.0).cuda()
- face_mask_tensor = mask_tensor[0] + mask_tensor[1]
-
- soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
- soft_face_mask_tensor.squeeze_()
-
- soft_face_mask = soft_face_mask_tensor.cpu().numpy()
- soft_face_mask = soft_face_mask[:, :, np.newaxis]
-
- result = swapped_face * soft_face_mask + target * (1 - soft_face_mask)
- result = result[:, :, ::-1] # .astype(np.uint8)
- return result
diff --git a/spaces/inamXcontru/PoeticTTS/Asphalt 9 1.7.3a Modificado Nitro Infinito How to Unlock All the Cars and Tracks.md b/spaces/inamXcontru/PoeticTTS/Asphalt 9 1.7.3a Modificado Nitro Infinito How to Unlock All the Cars and Tracks.md
deleted file mode 100644
index 803496632182bdf5ecaf7095cdff9b7f92ac6129..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Asphalt 9 1.7.3a Modificado Nitro Infinito How to Unlock All the Cars and Tracks.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-Asphalt 9: Legends é uma nova palavra em games de esportes para dispositivos móveis, que chama a atenção por seus gráficos de qualidade. A asphalt 9 apk mod dinheiro infinito download prometeu-nos o nível da consola e não nos enganaram. Os gráficos do jogo são realmente incríveis com seu desempenho. E se levarmos em conta que se passaram 5 anos desde o lançamento da última série Asphalt, então podemos dizer que a expectativa valeu a pena. No momento, cerca de 50 carros já estão disponíveis no jogo, cada um dos quais pode ser ajustado ao seu gosto graças a um excelente editor visual. Além disso, o jogo possui uma coleção de 70 faixas reais e um novo sistema de gerenciamento chamado Touch Drive.
-Gracias a este mod podrás conseguir dinero infinito en asphalt 9 2022 para comprar todo lo que te haga falta dentro del juego. Podrás conseguir Monedas infinitas, puedes compartir este mod con tus amigos!
-Asphalt 9 1.7.3a modificado nitro infinito Download --->>> https://gohhs.com/2uz401
Digital Music Mentor Full Version Crack Download Zip --->>> https://urlin.us/2uEvjV
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Foxit Pdf Editor Licence Key.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Foxit Pdf Editor Licence Key.md
deleted file mode 100644
index 987b7f114c8d4b75710d5510fb87ea7bb5461458..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Foxit Pdf Editor Licence Key.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Foxit Pdf Editor Licence Key DOWNLOAD ⚙ https://urlin.us/2uEwU0
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Genesys Rf And Microwave Design Software Crack Download PATCHED.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Genesys Rf And Microwave Design Software Crack Download PATCHED.md
deleted file mode 100644
index 9de948f420bc1b0f9bb58f80fea41f81bd6c3cc0..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Genesys Rf And Microwave Design Software Crack Download PATCHED.md
+++ /dev/null
@@ -1,26 +0,0 @@
-genesys rf and microwave design software crack download Download Zip >>> https://urlin.us/2uEvWq
diff --git a/spaces/isabel/testing-streamlit/README.md b/spaces/isabel/testing-streamlit/README.md
deleted file mode 100644
index e67b943362f39b8f311bbea028bb1f6c434ce37e..0000000000000000000000000000000000000000
--- a/spaces/isabel/testing-streamlit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Testing Streamlit
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.9.0
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/ivntl/MMS/uroman/lib/JSON.pm b/spaces/ivntl/MMS/uroman/lib/JSON.pm
deleted file mode 100644
index 8bac7eb5b90b530b828b25d41cec812d2dc2cf8f..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/uroman/lib/JSON.pm
+++ /dev/null
@@ -1,2317 +0,0 @@
-package JSON;
-
-
-use strict;
-use Carp ();
-use base qw(Exporter);
-@JSON::EXPORT = qw(from_json to_json jsonToObj objToJson encode_json decode_json);
-
-BEGIN {
- $JSON::VERSION = '2.90';
- $JSON::DEBUG = 0 unless (defined $JSON::DEBUG);
- $JSON::DEBUG = $ENV{ PERL_JSON_DEBUG } if exists $ENV{ PERL_JSON_DEBUG };
-}
-
-my $Module_XS = 'JSON::XS';
-my $Module_PP = 'JSON::PP';
-my $Module_bp = 'JSON::backportPP'; # included in JSON distribution
-my $PP_Version = '2.27203';
-my $XS_Version = '2.34';
-
-
-# XS and PP common methods
-
-my @PublicMethods = qw/
- ascii latin1 utf8 pretty indent space_before space_after relaxed canonical allow_nonref
- allow_blessed convert_blessed filter_json_object filter_json_single_key_object
- shrink max_depth max_size encode decode decode_prefix allow_unknown
-/;
-
-my @Properties = qw/
- ascii latin1 utf8 indent space_before space_after relaxed canonical allow_nonref
- allow_blessed convert_blessed shrink max_depth max_size allow_unknown
-/;
-
-my @XSOnlyMethods = qw/allow_tags/; # Currently nothing
-
-my @PPOnlyMethods = qw/
- indent_length sort_by
- allow_singlequote allow_bignum loose allow_barekey escape_slash as_nonblessed
-/; # JSON::PP specific
-
-
-# used in _load_xs and _load_pp ($INSTALL_ONLY is not used currently)
-my $_INSTALL_DONT_DIE = 1; # When _load_xs fails to load XS, don't die.
-my $_INSTALL_ONLY = 2; # Don't call _set_methods()
-my $_ALLOW_UNSUPPORTED = 0;
-my $_UNIV_CONV_BLESSED = 0;
-my $_USSING_bpPP = 0;
-
-
-# Check the environment variable to decide worker module.
-
-unless ($JSON::Backend) {
- $JSON::DEBUG and Carp::carp("Check used worker module...");
-
- my $backend = exists $ENV{PERL_JSON_BACKEND} ? $ENV{PERL_JSON_BACKEND} : 1;
-
- if ($backend eq '1' or $backend =~ /JSON::XS\s*,\s*JSON::PP/) {
- _load_xs($_INSTALL_DONT_DIE) or _load_pp();
- }
- elsif ($backend eq '0' or $backend eq 'JSON::PP') {
- _load_pp();
- }
- elsif ($backend eq '2' or $backend eq 'JSON::XS') {
- _load_xs();
- }
- elsif ($backend eq 'JSON::backportPP') {
- $_USSING_bpPP = 1;
- _load_pp();
- }
- else {
- Carp::croak "The value of environmental variable 'PERL_JSON_BACKEND' is invalid.";
- }
-}
-
-
-sub import {
- my $pkg = shift;
- my @what_to_export;
- my $no_export;
-
- for my $tag (@_) {
- if ($tag eq '-support_by_pp') {
- if (!$_ALLOW_UNSUPPORTED++) {
- JSON::Backend::XS
- ->support_by_pp(@PPOnlyMethods) if ($JSON::Backend eq $Module_XS);
- }
- next;
- }
- elsif ($tag eq '-no_export') {
- $no_export++, next;
- }
- elsif ( $tag eq '-convert_blessed_universally' ) {
- eval q|
- require B;
- *UNIVERSAL::TO_JSON = sub {
- my $b_obj = B::svref_2object( $_[0] );
- return $b_obj->isa('B::HV') ? { %{ $_[0] } }
- : $b_obj->isa('B::AV') ? [ @{ $_[0] } ]
- : undef
- ;
- }
- | if ( !$_UNIV_CONV_BLESSED++ );
- next;
- }
- push @what_to_export, $tag;
- }
-
- return if ($no_export);
-
- __PACKAGE__->export_to_level(1, $pkg, @what_to_export);
-}
-
-
-# OBSOLETED
-
-sub jsonToObj {
- my $alternative = 'from_json';
- if (defined $_[0] and UNIVERSAL::isa($_[0], 'JSON')) {
- shift @_; $alternative = 'decode';
- }
- Carp::carp "'jsonToObj' will be obsoleted. Please use '$alternative' instead.";
- return JSON::from_json(@_);
-};
-
-sub objToJson {
- my $alternative = 'to_json';
- if (defined $_[0] and UNIVERSAL::isa($_[0], 'JSON')) {
- shift @_; $alternative = 'encode';
- }
- Carp::carp "'objToJson' will be obsoleted. Please use '$alternative' instead.";
- JSON::to_json(@_);
-};
-
-
-# INTERFACES
-
-sub to_json ($@) {
- if (
- ref($_[0]) eq 'JSON'
- or (@_ > 2 and $_[0] eq 'JSON')
- ) {
- Carp::croak "to_json should not be called as a method.";
- }
- my $json = JSON->new;
-
- if (@_ == 2 and ref $_[1] eq 'HASH') {
- my $opt = $_[1];
- for my $method (keys %$opt) {
- $json->$method( $opt->{$method} );
- }
- }
-
- $json->encode($_[0]);
-}
-
-
-sub from_json ($@) {
- if ( ref($_[0]) eq 'JSON' or $_[0] eq 'JSON' ) {
- Carp::croak "from_json should not be called as a method.";
- }
- my $json = JSON->new;
-
- if (@_ == 2 and ref $_[1] eq 'HASH') {
- my $opt = $_[1];
- for my $method (keys %$opt) {
- $json->$method( $opt->{$method} );
- }
- }
-
- return $json->decode( $_[0] );
-}
-
-
-
-sub true { $JSON::true }
-
-sub false { $JSON::false }
-
-sub null { undef; }
-
-
-sub require_xs_version { $XS_Version; }
-
-sub backend {
- my $proto = shift;
- $JSON::Backend;
-}
-
-#*module = *backend;
-
-
-sub is_xs {
- return $_[0]->backend eq $Module_XS;
-}
-
-
-sub is_pp {
- return not $_[0]->is_xs;
-}
-
-
-sub pureperl_only_methods { @PPOnlyMethods; }
-
-
-sub property {
- my ($self, $name, $value) = @_;
-
- if (@_ == 1) {
- my %props;
- for $name (@Properties) {
- my $method = 'get_' . $name;
- if ($name eq 'max_size') {
- my $value = $self->$method();
- $props{$name} = $value == 1 ? 0 : $value;
- next;
- }
- $props{$name} = $self->$method();
- }
- return \%props;
- }
- elsif (@_ > 3) {
- Carp::croak('property() can take only the option within 2 arguments.');
- }
- elsif (@_ == 2) {
- if ( my $method = $self->can('get_' . $name) ) {
- if ($name eq 'max_size') {
- my $value = $self->$method();
- return $value == 1 ? 0 : $value;
- }
- $self->$method();
- }
- }
- else {
- $self->$name($value);
- }
-
-}
-
-
-
-# INTERNAL
-
-sub _load_xs {
- my $opt = shift;
-
- $JSON::DEBUG and Carp::carp "Load $Module_XS.";
-
- # if called after install module, overload is disable.... why?
- JSON::Boolean::_overrride_overload($Module_XS);
- JSON::Boolean::_overrride_overload($Module_PP);
-
- eval qq|
- use $Module_XS $XS_Version ();
- |;
-
- if ($@) {
- if (defined $opt and $opt & $_INSTALL_DONT_DIE) {
- $JSON::DEBUG and Carp::carp "Can't load $Module_XS...($@)";
- return 0;
- }
- Carp::croak $@;
- }
-
- unless (defined $opt and $opt & $_INSTALL_ONLY) {
- _set_module( $JSON::Backend = $Module_XS );
- my $data = join("", ); # this code is from Jcode 2.xx.
- close(DATA);
- eval $data;
- JSON::Backend::XS->init;
- }
-
- return 1;
-};
-
-
-sub _load_pp {
- my $opt = shift;
- my $backend = $_USSING_bpPP ? $Module_bp : $Module_PP;
-
- $JSON::DEBUG and Carp::carp "Load $backend.";
-
- # if called after install module, overload is disable.... why?
- JSON::Boolean::_overrride_overload($Module_XS);
- JSON::Boolean::_overrride_overload($backend);
-
- if ( $_USSING_bpPP ) {
- eval qq| require $backend |;
- }
- else {
- eval qq| use $backend $PP_Version () |;
- }
-
- if ($@) {
- if ( $backend eq $Module_PP ) {
- $JSON::DEBUG and Carp::carp "Can't load $Module_PP ($@), so try to load $Module_bp";
- $_USSING_bpPP++;
- $backend = $Module_bp;
- JSON::Boolean::_overrride_overload($backend);
- local $^W; # if PP installed but invalid version, backportPP redefines methods.
- eval qq| require $Module_bp |;
- }
- Carp::croak $@ if $@;
- }
-
- unless (defined $opt and $opt & $_INSTALL_ONLY) {
- _set_module( $JSON::Backend = $Module_PP ); # even if backportPP, set $Backend with 'JSON::PP'
- JSON::Backend::PP->init;
- }
-};
-
-
-sub _set_module {
- return if defined $JSON::true;
-
- my $module = shift;
-
- local $^W;
- no strict qw(refs);
-
- $JSON::true = ${"$module\::true"};
- $JSON::false = ${"$module\::false"};
-
- push @JSON::ISA, $module;
- if ( JSON->is_xs and JSON->backend->VERSION < 3 ) {
- eval 'package JSON::PP::Boolean';
- push @{"$module\::Boolean::ISA"}, qw(JSON::PP::Boolean);
- }
-
- *{"JSON::is_bool"} = \&{"$module\::is_bool"};
-
- for my $method ($module eq $Module_XS ? @PPOnlyMethods : @XSOnlyMethods) {
- *{"JSON::$method"} = sub {
- Carp::carp("$method is not supported in $module.");
- $_[0];
- };
- }
-
- return 1;
-}
-
-
-
-#
-# JSON Boolean
-#
-
-package JSON::Boolean;
-
-my %Installed;
-
-sub _overrride_overload {
- return; # this function is currently disable.
- return if ($Installed{ $_[0] }++);
-
- my $boolean = $_[0] . '::Boolean';
-
- eval sprintf(q|
- package %s;
- use overload (
- '""' => sub { ${$_[0]} == 1 ? 'true' : 'false' },
- 'eq' => sub {
- my ($obj, $op) = ref ($_[0]) ? ($_[0], $_[1]) : ($_[1], $_[0]);
- if ($op eq 'true' or $op eq 'false') {
- return "$obj" eq 'true' ? 'true' eq $op : 'false' eq $op;
- }
- else {
- return $obj ? 1 == $op : 0 == $op;
- }
- },
- );
- |, $boolean);
-
- if ($@) { Carp::croak $@; }
-
- if ( exists $INC{'JSON/XS.pm'} and $boolean eq 'JSON::XS::Boolean' ) {
- local $^W;
- my $true = do { bless \(my $dummy = 1), $boolean };
- my $false = do { bless \(my $dummy = 0), $boolean };
- *JSON::XS::true = sub () { $true };
- *JSON::XS::false = sub () { $false };
- }
- elsif ( exists $INC{'JSON/PP.pm'} and $boolean eq 'JSON::PP::Boolean' ) {
- local $^W;
- my $true = do { bless \(my $dummy = 1), $boolean };
- my $false = do { bless \(my $dummy = 0), $boolean };
- *JSON::PP::true = sub { $true };
- *JSON::PP::false = sub { $false };
- }
-
- return 1;
-}
-
-
-#
-# Helper classes for Backend Module (PP)
-#
-
-package JSON::Backend::PP;
-
-sub init {
- local $^W;
- no strict qw(refs); # this routine may be called after JSON::Backend::XS init was called.
- *{"JSON::decode_json"} = \&{"JSON::PP::decode_json"};
- *{"JSON::encode_json"} = \&{"JSON::PP::encode_json"};
- *{"JSON::PP::is_xs"} = sub { 0 };
- *{"JSON::PP::is_pp"} = sub { 1 };
- return 1;
-}
-
-#
-# To save memory, the below lines are read only when XS backend is used.
-#
-
-package JSON;
-
-1;
-__DATA__
-
-
-#
-# Helper classes for Backend Module (XS)
-#
-
-package JSON::Backend::XS;
-
-use constant INDENT_LENGTH_FLAG => 15 << 12;
-
-use constant UNSUPPORTED_ENCODE_FLAG => {
- ESCAPE_SLASH => 0x00000010,
- ALLOW_BIGNUM => 0x00000020,
- AS_NONBLESSED => 0x00000040,
- EXPANDED => 0x10000000, # for developer's
-};
-
-use constant UNSUPPORTED_DECODE_FLAG => {
- LOOSE => 0x00000001,
- ALLOW_BIGNUM => 0x00000002,
- ALLOW_BAREKEY => 0x00000004,
- ALLOW_SINGLEQUOTE => 0x00000008,
- EXPANDED => 0x20000000, # for developer's
-};
-
-
-sub init {
- local $^W;
- no strict qw(refs);
- *{"JSON::decode_json"} = \&{"JSON::XS::decode_json"};
- *{"JSON::encode_json"} = \&{"JSON::XS::encode_json"};
- *{"JSON::XS::is_xs"} = sub { 1 };
- *{"JSON::XS::is_pp"} = sub { 0 };
- return 1;
-}
-
-
-sub support_by_pp {
- my ($class, @methods) = @_;
-
- local $^W;
- no strict qw(refs);
-
- my $JSON_XS_encode_orignal = \&JSON::XS::encode;
- my $JSON_XS_decode_orignal = \&JSON::XS::decode;
- my $JSON_XS_incr_parse_orignal = \&JSON::XS::incr_parse;
-
- *JSON::XS::decode = \&JSON::Backend::XS::Supportable::_decode;
- *JSON::XS::encode = \&JSON::Backend::XS::Supportable::_encode;
- *JSON::XS::incr_parse = \&JSON::Backend::XS::Supportable::_incr_parse;
-
- *{JSON::XS::_original_decode} = $JSON_XS_decode_orignal;
- *{JSON::XS::_original_encode} = $JSON_XS_encode_orignal;
- *{JSON::XS::_original_incr_parse} = $JSON_XS_incr_parse_orignal;
-
- push @JSON::Backend::XS::Supportable::ISA, 'JSON';
-
- my $pkg = 'JSON::Backend::XS::Supportable';
-
- *{JSON::new} = sub {
- my $proto = JSON::XS->new; $$proto = 0;
- bless $proto, $pkg;
- };
-
-
- for my $method (@methods) {
- my $flag = uc($method);
- my $type |= (UNSUPPORTED_ENCODE_FLAG->{$flag} || 0);
- $type |= (UNSUPPORTED_DECODE_FLAG->{$flag} || 0);
-
- next unless($type);
-
- $pkg->_make_unsupported_method($method => $type);
- }
-
-# push @{"JSON::XS::Boolean::ISA"}, qw(JSON::PP::Boolean);
-# push @{"JSON::PP::Boolean::ISA"}, qw(JSON::Boolean);
-
- $JSON::DEBUG and Carp::carp("set -support_by_pp mode.");
-
- return 1;
-}
-
-
-
-
-#
-# Helper classes for XS
-#
-
-package JSON::Backend::XS::Supportable;
-
-$Carp::Internal{'JSON::Backend::XS::Supportable'} = 1;
-
-sub _make_unsupported_method {
- my ($pkg, $method, $type) = @_;
-
- local $^W;
- no strict qw(refs);
-
- *{"$pkg\::$method"} = sub {
- local $^W;
- if (defined $_[1] ? $_[1] : 1) {
- ${$_[0]} |= $type;
- }
- else {
- ${$_[0]} &= ~$type;
- }
- $_[0];
- };
-
- *{"$pkg\::get_$method"} = sub {
- ${$_[0]} & $type ? 1 : '';
- };
-
-}
-
-
-sub _set_for_pp {
- JSON::_load_pp( $_INSTALL_ONLY );
-
- my $type = shift;
- my $pp = JSON::PP->new;
- my $prop = $_[0]->property;
-
- for my $name (keys %$prop) {
- $pp->$name( $prop->{$name} ? $prop->{$name} : 0 );
- }
-
- my $unsupported = $type eq 'encode' ? JSON::Backend::XS::UNSUPPORTED_ENCODE_FLAG
- : JSON::Backend::XS::UNSUPPORTED_DECODE_FLAG;
- my $flags = ${$_[0]} || 0;
-
- for my $name (keys %$unsupported) {
- next if ($name eq 'EXPANDED'); # for developer's
- my $enable = ($flags & $unsupported->{$name}) ? 1 : 0;
- my $method = lc $name;
- $pp->$method($enable);
- }
-
- $pp->indent_length( $_[0]->get_indent_length );
-
- return $pp;
-}
-
-sub _encode { # using with PP encode
- if (${$_[0]}) {
- _set_for_pp('encode' => @_)->encode($_[1]);
- }
- else {
- $_[0]->_original_encode( $_[1] );
- }
-}
-
-
-sub _decode { # if unsupported-flag is set, use PP
- if (${$_[0]}) {
- _set_for_pp('decode' => @_)->decode($_[1]);
- }
- else {
- $_[0]->_original_decode( $_[1] );
- }
-}
-
-
-sub decode_prefix { # if unsupported-flag is set, use PP
- _set_for_pp('decode' => @_)->decode_prefix($_[1]);
-}
-
-
-sub _incr_parse {
- if (${$_[0]}) {
- _set_for_pp('decode' => @_)->incr_parse($_[1]);
- }
- else {
- $_[0]->_original_incr_parse( $_[1] );
- }
-}
-
-
-sub get_indent_length {
- ${$_[0]} << 4 >> 16;
-}
-
-
-sub indent_length {
- my $length = $_[1];
-
- if (!defined $length or $length > 15 or $length < 0) {
- Carp::carp "The acceptable range of indent_length() is 0 to 15.";
- }
- else {
- local $^W;
- $length <<= 12;
- ${$_[0]} &= ~ JSON::Backend::XS::INDENT_LENGTH_FLAG;
- ${$_[0]} |= $length;
- *JSON::XS::encode = \&JSON::Backend::XS::Supportable::_encode;
- }
-
- $_[0];
-}
-
-
-1;
-__END__
-
-=head1 NAME
-
-JSON - JSON (JavaScript Object Notation) encoder/decoder
-
-=head1 SYNOPSIS
-
- use JSON; # imports encode_json, decode_json, to_json and from_json.
-
- # simple and fast interfaces (expect/generate UTF-8)
-
- $utf8_encoded_json_text = encode_json $perl_hash_or_arrayref;
- $perl_hash_or_arrayref = decode_json $utf8_encoded_json_text;
-
- # OO-interface
-
- $json = JSON->new->allow_nonref;
-
- $json_text = $json->encode( $perl_scalar );
- $perl_scalar = $json->decode( $json_text );
-
- $pretty_printed = $json->pretty->encode( $perl_scalar ); # pretty-printing
-
- # If you want to use PP only support features, call with '-support_by_pp'
- # When XS unsupported feature is enable, using PP (de|en)code instead of XS ones.
-
- use JSON -support_by_pp;
-
- # option-acceptable interfaces (expect/generate UNICODE by default)
-
- $json_text = to_json( $perl_scalar, { ascii => 1, pretty => 1 } );
- $perl_scalar = from_json( $json_text, { utf8 => 1 } );
-
- # Between (en|de)code_json and (to|from)_json, if you want to write
- # a code which communicates to an outer world (encoded in UTF-8),
- # recommend to use (en|de)code_json.
-
-=head1 VERSION
-
- 2.90
-
-This version is compatible with JSON::XS B<2.34> and later.
-(Not yet compatble to JSON::XS B<3.0x>.)
-
-
-=head1 NOTE
-
-JSON::PP was earlier included in the C distribution, but
-has since Perl 5.14 been a core module. For this reason,
-L was removed from the JSON distribution and can now
-be found also in the Perl5 repository at
-
-=over
-
-=item * L
-
-=back
-
-(The newest JSON::PP version still exists in CPAN.)
-
-Instead, the C distribution will include JSON::backportPP
-for backwards computability. JSON.pm should thus work as it did
-before.
-
-=head1 DESCRIPTION
-
- *************************** CAUTION **************************************
- * *
- * INCOMPATIBLE CHANGE (JSON::XS version 2.90) *
- * *
- * JSON.pm had patched JSON::XS::Boolean and JSON::PP::Boolean internally *
- * on loading time for making these modules inherit JSON::Boolean. *
- * But since JSON::XS v3.0 it use Types::Serialiser as boolean class. *
- * Then now JSON.pm breaks boolean classe overload features and *
- * -support_by_pp if JSON::XS v3.0 or later is installed. *
- * *
- * JSON::true and JSON::false returned JSON::Boolean objects. *
- * For workaround, they return JSON::PP::Boolean objects in this version. *
- * *
- * isa_ok(JSON::true, 'JSON::PP::Boolean'); *
- * *
- * And it discards a feature: *
- * *
- * ok(JSON::true eq 'true'); *
- * *
- * In other word, JSON::PP::Boolean overload numeric only. *
- * *
- * ok( JSON::true == 1 ); *
- * *
- **************************************************************************
-
- ************************** CAUTION ********************************
- * This is 'JSON module version 2' and there are many differences *
- * to version 1.xx *
- * Please check your applications using old version. *
- * See to 'INCOMPATIBLE CHANGES TO OLD VERSION' *
- *******************************************************************
-
-JSON (JavaScript Object Notation) is a simple data format.
-See to L(L).
-
-This module converts Perl data structures to JSON and vice versa using either
-L or L.
-
-JSON::XS is the fastest and most proper JSON module on CPAN which must be
-compiled and installed in your environment.
-JSON::PP is a pure-Perl module which is bundled in this distribution and
-has a strong compatibility to JSON::XS.
-
-This module try to use JSON::XS by default and fail to it, use JSON::PP instead.
-So its features completely depend on JSON::XS or JSON::PP.
-
-See to L.
-
-To distinguish the module name 'JSON' and the format type JSON,
-the former is quoted by CEE (its results vary with your using media),
-and the latter is left just as it is.
-
-Module name : C
-
-Format type : JSON
-
-=head2 FEATURES
-
-=over
-
-=item * correct unicode handling
-
-This module (i.e. backend modules) knows how to handle Unicode, documents
-how and when it does so, and even documents what "correct" means.
-
-Even though there are limitations, this feature is available since Perl version 5.6.
-
-JSON::XS requires Perl 5.8.2 (but works correctly in 5.8.8 or later), so in older versions
-C should call JSON::PP as the backend which can be used since Perl 5.005.
-
-With Perl 5.8.x JSON::PP works, but from 5.8.0 to 5.8.2, because of a Perl side problem,
-JSON::PP works slower in the versions. And in 5.005, the Unicode handling is not available.
-See to L for more information.
-
-See also to L
-and L.
-
-
-=item * round-trip integrity
-
-When you serialise a perl data structure using only data types supported
-by JSON and Perl, the deserialised data structure is identical on the Perl
-level. (e.g. the string "2.0" doesn't suddenly become "2" just because
-it looks like a number). There I minor exceptions to this, read the
-L section below to learn about those.
-
-
-=item * strict checking of JSON correctness
-
-There is no guessing, no generating of illegal JSON texts by default,
-and only JSON is accepted as input by default (the latter is a security
-feature).
-
-See to L and L.
-
-=item * fast
-
-This module returns a JSON::XS object itself if available.
-Compared to other JSON modules and other serialisers such as Storable,
-JSON::XS usually compares favorably in terms of speed, too.
-
-If not available, C returns a JSON::PP object instead of JSON::XS and
-it is very slow as pure-Perl.
-
-=item * simple to use
-
-This module has both a simple functional interface as well as an
-object oriented interface interface.
-
-=item * reasonably versatile output formats
-
-You can choose between the most compact guaranteed-single-line format possible
-(nice for simple line-based protocols), a pure-ASCII format (for when your transport
-is not 8-bit clean, still supports the whole Unicode range), or a pretty-printed
-format (for when you want to read that stuff). Or you can combine those features
-in whatever way you like.
-
-=back
-
-=head1 FUNCTIONAL INTERFACE
-
-Some documents are copied and modified from L.
-C and C are additional functions.
-
-=head2 encode_json
-
- $json_text = encode_json $perl_scalar
-
-Converts the given Perl data structure to a UTF-8 encoded, binary string.
-
-This function call is functionally identical to:
-
- $json_text = JSON->new->utf8->encode($perl_scalar)
-
-=head2 decode_json
-
- $perl_scalar = decode_json $json_text
-
-The opposite of C: expects an UTF-8 (binary) string and tries
-to parse that as an UTF-8 encoded JSON text, returning the resulting
-reference.
-
-This function call is functionally identical to:
-
- $perl_scalar = JSON->new->utf8->decode($json_text)
-
-
-=head2 to_json
-
- $json_text = to_json($perl_scalar)
-
-Converts the given Perl data structure to a json string.
-
-This function call is functionally identical to:
-
- $json_text = JSON->new->encode($perl_scalar)
-
-Takes a hash reference as the second.
-
- $json_text = to_json($perl_scalar, $flag_hashref)
-
-So,
-
- $json_text = to_json($perl_scalar, {utf8 => 1, pretty => 1})
-
-equivalent to:
-
- $json_text = JSON->new->utf8(1)->pretty(1)->encode($perl_scalar)
-
-If you want to write a modern perl code which communicates to outer world,
-you should use C (supposed that JSON data are encoded in UTF-8).
-
-=head2 from_json
-
- $perl_scalar = from_json($json_text)
-
-The opposite of C: expects a json string and tries
-to parse it, returning the resulting reference.
-
-This function call is functionally identical to:
-
- $perl_scalar = JSON->decode($json_text)
-
-Takes a hash reference as the second.
-
- $perl_scalar = from_json($json_text, $flag_hashref)
-
-So,
-
- $perl_scalar = from_json($json_text, {utf8 => 1})
-
-equivalent to:
-
- $perl_scalar = JSON->new->utf8(1)->decode($json_text)
-
-If you want to write a modern perl code which communicates to outer world,
-you should use C (supposed that JSON data are encoded in UTF-8).
-
-=head2 JSON::is_bool
-
- $is_boolean = JSON::is_bool($scalar)
-
-Returns true if the passed scalar represents either JSON::true or
-JSON::false, two constants that act like C<1> and C<0> respectively
-and are also used to represent JSON C and C 
- }
- } catch (e) {
- }
- return