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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Bitcoin Core with GUI and Wallet from GitHub.md

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-<p>In this article, we will show you how to download, install, and use bitcoin-qt on your computer. We will assume that you have some basic knowledge of Bitcoin and how it works. If not, you can check out our <a href="(^1^)">Bitcoin Basics</a> guide for beginners.</p>
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- <h1>Downloading bitcoin-qt</h1>
-<p>The first step to use bitcoin-qt is to download the latest version of the software for your operating system. You can get it from the official Bitcoin Core website at <a href="(^2^)">https://bitcoincore.org/en/download/</a>. You can choose between Windows, Mac OS X, Linux, or ARM Linux versions.</p>
-<p>The website will provide you with a link to download a .exe file for Windows, a .dmg file for Mac OS X, or a .tar.gz file for Linux. You can also verify the integrity of the file by checking its cryptographic signature or hash value against the ones provided on the website.</p>
-<p>Alternatively, you can get the source code from the Bitcoin Core GitHub page at <a href="(^3^)">https://github.com/bitcoin/bitcoin</a>. You can then compile it yourself using the instructions provided in the doc folder.</p>
- <h1>Installing bitcoin-qt</h1>
-<p>Once you have downloaded the file for your operating system, you can run the installer and follow the steps to set up bitcoin-qt on your computer. The installation process may vary slightly depending on your operating system, but generally it involves choosing a destination folder, agreeing to the terms of service, and clicking next until it finishes.</p>
-<p>After the installation is complete, you will see a shortcut icon for bitcoin-qt on your desktop or in your applications folder. You can double-click it to launch the program.</p>
- <h1>Using bitcoin-qt</h1>
-<p>When you launch bitcoin-qt for the first time, it will start downloading and validating the blockchain from scratch. This may take several hours or days depending on your connection speed and hardware. You will see a progress bar on the bottom right corner of the window that shows how many blocks have been downloaded and verified.</p>
-<p>You can still use some functions of bitcoin-qt while it is syncing with the network, such as creating a new wallet or importing an existing one. However, you will not be able to send or receive transactions until it is fully synced.</p>
-<p>To create a new wallet, <p>you can click on the File menu and select Create Wallet. You will be asked to choose a name for your wallet and a password to encrypt it. You can also choose to make it the default wallet or not. After you create your wallet, you will see it listed on the left side of the window under Wallets.</p>
-<p>To import an existing wallet, you can click on the File menu and select Open Wallet. You will be asked to locate the wallet file on your computer, which usually has a .dat extension. You can also choose to make it the default wallet or not. After you open your wallet, you will see it listed on the left side of the window under Wallets.</p>
-<p>You can switch between different wallets by clicking on them on the left side of the window. You can also create or open more wallets by clicking on the plus icon next to Wallets.</p>
- <h2>Sending and receiving transactions</h2>
-<p>To send and receive transactions, you need to create and manage addresses. An address is a string of letters and numbers that represents a destination for a Bitcoin payment. You can have multiple addresses for different purposes, such as receiving donations, paying bills, or saving for a goal.</p>
-<p>To create a new address, you can click on the Receive tab and then click on the Request Payment button. You will see a window that shows your new address, a QR code, and some options to customize your request. You can enter a label, an amount, and a message for your request. You can also choose to reuse an existing address or generate a new one every time.</p>
-<p>To manage your addresses, you can click on the File menu and select Receiving Addresses. You will see a list of all your addresses, their labels, and their balances. You can edit, copy, or delete any address by right-clicking on it.</p>
-<p>To send bitcoins to someone else, you need to know their address. You can either scan their QR code using your webcam or enter their address manually. To do so, you can click on the Send tab and then click on the Send button. You will see a window that allows you to enter the recipient's address, the amount, and a fee. You can also enter a label and a message for your payment. You can add more recipients by clicking on the Add Recipient button.</p>
-<p>To view your transaction history, you can click on the Transactions tab. You will see a list of all your transactions, their status, date, amount, and fee. You can filter them by type, date range, or amount range using the options on the top of the window. You can also view more details about any transaction by double-clicking on it.</p>
- <h2>Managing your wallet</h2>
-<p>To manage your wallet, you need to encrypt, backup, and restore it. Encrypting your wallet means protecting it with a password so that only you can access it. Backing up your wallet means saving a copy of it in a safe place so that you can recover it in case of loss or theft. Restoring your wallet means loading a backup copy of it into bitcoin-qt so that you can access your funds again.</p>
-<p>To encrypt your wallet, you can click on the Settings menu and select Encrypt Wallet. You will be asked to enter a password and confirm it. You will also see a warning message that tells you to remember your password and backup your wallet, as you will lose access to your funds if you forget it or lose your wallet file. After you enter and confirm your password, you will see a message that tells you to restart bitcoin-qt for the encryption to take effect.</p>
-<p>To backup your wallet, you can click on the File menu and select Backup Wallet. You will be asked to choose a location and a name for your backup file, which usually has a .dat extension. You should save your backup file in a secure place, such as an external hard drive, a USB flash drive, or a cloud storage service. You should also make multiple copies of your backup file and update them regularly.</p>
-<p>To restore your wallet, you need to have a backup file of your wallet. You can either replace the existing wallet file in the bitcoin-qt data directory with your backup file, or use the -wallet option to specify the location of your backup file when launching bitcoin-qt. You can find the bitcoin-qt data directory by clicking on the Help menu and selecting Debug Window. You will see the data directory under the Information tab.</p>
-<p>Some advanced features that you can use with your wallet are coin control and message signing. Coin control allows you to select which coins or outputs to use when creating a transaction, giving you more control over your privacy and fees. Message signing allows you to prove that you own a certain address by signing a message with its private key, which can be verified by anyone using bitcoin-qt or other tools.</p>
-<p>To use coin control, you need to enable it in the Options menu under the Wallet tab. You will then see a checkbox next to each output in the Send tab, which you can check or uncheck to include or exclude it from your transaction. You can also right-click on any output to see more details about it, such as its amount, address, confirmations, and age.</p>
-<p>To use message signing, you need to click on the File menu and select Sign Message. You will see a window that allows you to enter an address and a message. You can then click on the Sign Message button to generate a signature, which you can copy or save. You can also click on the Verify Message button to verify a signature from someone else, by entering their address, message, and signature.</p>
- <h1>Conclusion</h1>
-<p>Bitcoin-qt is a graphical user interface for the Bitcoin Core software, which allows you to interact with the Bitcoin network, send and receive transactions, and manage your wallet. It also supports some advanced features such as coin control and message signing. By using bitcoin-qt, you are also helping to secure and support the network by running a full node.</p>
-<p>However, bitcoin-qt also has some drawbacks, such as requiring a lot of disk space and bandwidth to store and verify the entire blockchain, which is over 300 GB as of June 2023. It also takes a long time to sync with the network for the first time, which can take several hours or days depending on your connection speed and hardware.</p>
-<p>If you are looking for an alternative Bitcoin client that is more lightweight and user-friendly, you may want to check out our <a href="">Bitcoin Wallets</a> guide for more options.</p>
- <h1>FAQs</h1>
-<h3>What is the difference between bitcoin-qt and bitcoind?</h3>
-<p>Bitcoin-qt and bitcoind are both part of the Bitcoin Core software package. Bitcoin-qt is the graphical user interface version, while bitcoind is the command-line version. They both perform the same functions, but bitcoin-qt is easier to use for beginners and non-technical users.</p>
-<h3>How can I update bitcoin-qt to the latest version?</h3>
-<p>You can update bitcoin-qt by downloading and installing the latest version from the official Bitcoin Core website at <a href="">https://bitcoincore.org/en/download/</a>. You should always backup your wallet before updating, as some updates may require a rescan or reindex of the blockchain.</p>
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-<p>You can speed up the sync process of bitcoin-qt by using a faster internet connection, a more powerful computer, or an SSD drive instead of an HDD drive. You can also use the -dbcache option to allocate more memory for caching blocks and transactions, which can reduce disk I/O operations.</p>
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-<p>You can reduce the disk space usage of bitcoin-qt by using the -prune option to delete old blocks that are no longer needed for validation. You can specify the amount of disk space in MB to keep for pruned blocks, such as -prune=1000 for 1 GB. However, pruning will disable some features, such as serving historical blocks to other nodes or creating raw transactions.</p>
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-<p>You can increase the security and privacy of bitcoin-qt by using a strong password to encrypt your wallet, backing up your wallet regularly, and using a VPN or Tor to hide your IP address. You can also use coin control to avoid linking your addresses, use message signing to prove your identity, and use raw transactions to create custom transactions.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Candy Crush The Original and Best Match 3 Game with Millions of Fans.md

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-<p>Boosters and power-ups are special items that can help you clear more candies or achieve your objectives faster. They can be earned by playing the game, watching ads, or buying them with real money. Some examples of boosters and power-ups are hammers, bombs, rockets, rainbows, lollipops, etc. However, you should not rely on them too much or waste them unnecessarily. You should save them for the harder levels or when you are stuck. You should also use them strategically, such as using a bomb to clear a large area of candies or using a rainbow to create a massive combo.</p>
- <h3>Plan your moves ahead and look for combos</h3>
-<p>One of the keys to playing candy match 3 games well is to plan your moves ahead and look for combos. You should not just swipe randomly or impulsively. You should think about how your move will affect the board and what matches it will create or enable. You should also look for opportunities to make combos of more than three candies, which will create special candies that have extra effects when matched. For example, matching four candies in a row or column will create a striped candy that will clear a whole row or column when matched. Matching five candies in a T or L shape will create a wrapped candy that will explode twice when matched. Matching five candies in a row will create a color bomb that will clear all the candies of the same color when matched.</p>
- <h3>Try different strategies and modes</h3>
-<p>Another way to improve your candy match 3 game skills is to try different strategies and modes. You should not stick to one way of playing or one type of game. You should experiment with different approaches and see what works best for you. For example, you can try to focus on clearing the bottom rows first, which will cause more candies to fall from the top and create more matches. You can also try to focus on clearing the obstacles first, such as jelly, chocolate, or ice, which will free up more space for matches. You can also try different modes of play, such as timed mode, where you have to reach a certain score within a time limit; moves mode, where you have a limited number of moves; or endless mode, where you can play as long as you want.</p>
- <h2>What are some of the best candy match 3 games to play?</h2>
-<p>There are many candy match 3 games available on the market, but some of them stand out from the rest for their quality and popularity. Here are some of the best candy match 3 games to play on your phone or browser:</p>
- <h3>Candy Crush Saga: The classic and iconic game from King</h3>
-<h3>Candy Crush Saga: The classic and iconic game from King</h3>
-<p>Candy Crush Saga is one of the most popular and successful candy match 3 games ever made. It has over one billion downloads on Google Play Store alone. It has thousands of levels with different objectives and challenges. It has many features that make it fun and addictive, such as boosters, power-ups, special candies, daily rewards, events, tournaments, and more. It also has a social aspect, where you can connect with your friends and compare your scores and progress. Candy Crush Saga is a game that will keep you hooked for hours and days.</p>
- <h3>Candy Match 3 - Sweet Crunch: A new and innovative game from Playflux</h3>
-<p>Candy Match 3 - Sweet Crunch is a new and innovative game from Playflux that takes candy match 3 games to the next level. It has a unique gameplay mechanic where you can build your own candy town by matching candies and collecting resources. You can also customize your town with different buildings, decorations, and characters. You can also challenge cookie monsters that will try to steal your candies and resources. You can also play with other players online and join a guild to cooperate and compete. Candy Match 3 - Sweet Crunch is a game that will let you unleash your creativity and imagination.</p>
- <h3>Candy Match: A simple and addictive game from CrazyGames</h3>
-<p>Candy Match is a simple and addictive game from CrazyGames that focuses on the core gameplay of matching candies without any distractions. It has a minimalist design and interface that makes it easy to play and enjoy. It has hundreds of levels with increasing difficulty and variety. It has no ads, no in-app purchases, no time limits, no lives, no boosters, no power-ups, no special candies, no objectives, no obstacles, no modes, no features, no nothing. Just pure candy matching fun. Candy Match is a game that will test your skills and concentration.</p>
- <h2>Conclusion: Candy match 3 games are here to stay</h2>
-<p>Candy match 3 games are one of the most popular and fun types of puzzle games that you can play on your phone or browser. They are simple to learn but hard to master. They are relaxing but challenging. They are creative but strategic. They are colorful but minimalist. They have something for everyone, whether you are looking for a casual distraction or a serious challenge. Candy match 3 games are here to stay, and they will continue to entertain and delight millions of players around the world.</p>
- <h4>FAQs</h4>
-<p>Here are some frequently asked questions about candy match 3 games:</p>
- <ul>
-<li><b>What is the best candy match 3 game?</b></li>
-<p>There is no definitive answer to this question, as different players may have different preferences and tastes. However, some of the most popular and well-reviewed candy match 3 games are Candy Crush Saga, Candy Match 3 - Sweet Crunch, Candy Match, Candy Blast Mania, Cookie Jam, etc.</p>
-<li><b>How do I get more boosters and power-ups in candy match 3 games?</b></li>
-<p>There are different ways to get more boosters and power-ups in candy match 3 games, depending on the game you are playing. Some common ways are playing the game regularly, completing levels or objectives, watching ads or videos, spinning wheels or lucky draws, joining events or tournaments, connecting with friends or social media, buying them with real money or in-game currency, etc.</p>
-<li><b>How do I clear jelly or chocolate in candy match 3 games?</b></li>
-<p>Jelly or chocolate are common obstacles in candy match 3 games that can block your matches or spread over the board. To clear them, you need to make matches adjacent to them or use special candies or boosters that can affect them. For example, a striped candy can clear a whole row or column of jelly or chocolate when matched.</p>
-<li><b>How do I create special candies in candy match 3 games?</b></li>
-<p>Special candies are candies that have extra effects when matched or activated. To create them, you need to make matches of more than three candies of the same color or shape. For example, matching four candies in a row or column will create a striped candy; matching five candies in a T or L shape will create a wrapped candy; matching five candies in a row will create a color bomb; etc.</p>
-<li><b>How do I play candy match 3 games online?</b></li>
-<li><b>How do I play candy match 3 games online?</b></li>
-<p>To play candy match 3 games online, you need to have a device that can access the internet and a browser that can run HTML5 games. You can then visit websites that offer free online candy match 3 games, such as CrazyGames, Poki, Kizi, etc. You can also download and install candy match 3 games from the app store or play store, such as Candy Crush Saga, Candy Match 3 - Sweet Crunch, Candy Match, etc.</p>
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spaces/1phancelerku/anime-remove-background/Clash Royale MOD APK v3.6.1 The Ultimate Guide to Unlimited Money Elixir and Gems.md

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-<h1>Clash Royale Mod APK: Unlimited Money, Elixir and Gems 3.6 1</h1>
-<p>Are you a fan of Clash Royale, the popular real-time strategy game from Supercell? Do you want to enjoy the game without any limitations or restrictions? If yes, then you should try Clash Royale Mod APK, a modified version of the game that gives you unlimited money, elixir and gems, as well as access to all the cards and custom servers. In this article, we will tell you everything you need to know about Clash Royale Mod APK, including its features, how to download and install it, and some tips and tricks for playing it. Read on and discover how to take your Clash Royale experience to the next level.</p>
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- <h2>Features of Clash Royale Mod APK</h2>
-<p>Clash Royale Mod APK is not just a regular version of Clash Royale with some minor tweaks. It is a completely different game that offers you many amazing features that you won't find in the original game. Here are some of the most notable features of Clash Royale Mod APK:</p>
- <h3>Unlimited resources: How to get unlimited money, elixir and gems in the game</h3>
-<p>One of the main advantages of Clash Royale Mod APK is that it gives you unlimited resources in the game. This means that you can get as much money, elixir and gems as you want, without having to spend any real money or wait for hours. You can use these resources to upgrade your cards, buy new ones, open chests, enter tournaments, and more. You can also use them to speed up your progress and unlock new arenas, levels, and rewards. With unlimited resources, you can enjoy the game without any worries or limitations.</p>
- <h3>All cards unlocked: How to access all the cards in the game without spending real money</h3>
-<p>Another great feature of Clash Royale Mod APK is that it unlocks all the cards in the game for you. This means that you can access all the cards in the game, regardless of your arena or level. You don't have to spend real money or wait for a long time to get them. You can choose from over 90 cards, including common, rare, epic, and legendary ones. You can also experiment with different combinations and strategies with these cards. With all cards unlocked, you can have more fun and variety in the game.</p>
- <h3>Custom servers: How to join private servers with different game modes and rules</h3>
-<p>The last but not least feature of Clash Royale Mod APK is that it allows you to join custom servers with different game modes and rules. These are private servers that are hosted by other players or developers who have modified the game in various ways. You can join these servers by using a specific IP address <p>You can join these servers by using a specific IP address or a link that will redirect you to the server. You can find these links on various websites or forums that host these servers. Some of the most popular and reliable custom servers for Clash Royale are PlenixClash, Master Royale, and Null's Royale. These servers offer you different game modes and rules, such as unlimited elixir, custom cards, clan wars, tournaments, and more. You can also chat with other players and make new friends on these servers. Custom servers are a great way to enjoy Clash Royale in a new and exciting way.</p>
- <h2>How to download and install Clash Royale Mod APK</h2>
-<p>If you are interested in downloading and installing Clash Royale Mod APK on your Android device, you will need to follow some simple steps. Here is what you need to do:</p>
- <h3>Requirements: What you need to download and install the mod apk file</h3>
-<p>Before you download and install the mod apk file, you will need to make sure that your device meets some basic requirements. These are:</p>
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-<li>Your device should have Android 4.1 or higher version.</li>
-<li>Your device should have at least 100 MB of free storage space.</li>
-<li>Your device should have a stable internet connection.</li>
-<li>Your device should allow installation from unknown sources. You can enable this option by going to Settings > Security > Unknown Sources and turning it on.</li>
-</ul>
- <h3>Steps: How to download and install the mod apk file on your Android device</h3>
-<p>Once you have checked the requirements, you can proceed to download and install the mod apk file on your device. Here are the steps you need to follow:</p>
-<ol>
-<li>Go to a trusted website that provides the link to download the mod apk file. You can use Google or any other search engine to find such websites. Alternatively, you can use the links provided in this article for some of the best custom servers.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device. The file size may vary depending on the server you choose.</li>
-<li>After the file is downloaded, locate it in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to be completed.</li>
-<li>Once the installation is done, open the app and enjoy Clash Royale Mod APK with unlimited money, elixir, gems, and more.</li>
-</ol>
- <h3>Troubleshooting: How to fix common issues with the mod apk file</h3>
-<p>Sometimes, you may face some issues with the mod apk file, such as crashing, freezing, or not working properly. Here are some possible solutions to fix these issues:</p>
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-<li>Make sure that your device meets the requirements mentioned above.</li>
-<li>Make sure that you have downloaded the latest version of the mod apk file from a reliable source.</li>
-<li>Make sure that you have enabled installation from unknown sources on your device.</li>
-<li>Make sure that you have a stable internet connection and that you are connected to the right server.</li>
-<li>Clear the cache and data of the app by going to Settings > Apps > Clash Royale Mod APK > Storage > Clear Cache and Clear Data.</li>
-<li>Uninstall and reinstall the app if none of the above solutions work.</li>
-</ul>
- <h2>Tips and tricks for playing Clash Royale Mod APK</h2>
-<p>Now that you have downloaded and installed Clash Royale Mod APK on your device, you may be wondering how to play it effectively and win more battles. Here are some tips and tricks that will help you improve your skills and have more fun in the game:</p>
- <h3>Join a clan: How to find and join a clan that suits your play style and goals</h3>
-<p>One of the best features of Clash Royale Mod APK is that it allows you to join clans and chat with other players. Clans are groups of players who share a common interest and goal in the game. By joining a clan, you can benefit from many advantages, such as:</p>
-<ul>
-<li>You can request and donate cards to your clan members, which will help you upgrade your cards faster and earn more gold.</li>
-<li>You can participate in clan wars, which are special events where clans compete against each other for rewards and glory.</li>
-<li>You can chat with your clan members, share tips and strategies, ask for advice, or just have fun.</li>
-</ul>
- <p>To join a clan, you need to go to the Clan tab in the game menu and tap on Find a Clan. You can then browse through different clans based on their name, location, trophies, members, type, and type. You can also search for a specific clan by using the search bar. Once you find a clan that suits your play style and goals, you can tap on Join and wait for the clan leader or co-leader to accept your request. Alternatively, you can create your own clan by tapping on Create a Clan and inviting other players to join you.</p>
- <h3>Build your deck: How to create a balanced and effective deck with the cards you have</h3>
-<p>Another important aspect of playing Clash Royale Mod APK is building your deck. Your deck is the set of eight cards that you use in battles. You can have up to five different decks, which you can switch between before each battle. To build a good deck, you need to consider several factors, such as:</p>
-<ul>
-<li>The cost of your cards: Each card has a certain amount of elixir that it costs to play. You want to have a balanced mix of low-cost and high-cost cards, so that you can always have something to play and not run out of elixir.</li>
-<li>The synergy of your cards: Each card has a certain role and function in the game, such as offense, defense, support, spell, etc. You want to have cards that work well together and complement each other, so that you can create powerful combos and counter your opponent's moves.</li>
-<li>The versatility of your cards: Each card has a certain strength and weakness against different types of cards, such as air, ground, swarm, tank, etc. You want to have cards that can deal with different situations and threats, so that you can adapt to your opponent's strategy and not be vulnerable to any card.</li>
-</ul>
- <p>To build your deck, you need to go to the Cards tab in the game menu and tap on Deck. You can then drag and drop the cards you want to use from your collection to your deck slots. You can also use the Suggest a Deck feature, which will automatically generate a deck for you based on your available cards. You can then edit and customize the deck as you wish.</p>
- <h3>Manage your elixir: How to use your elixir wisely and efficiently in battles</h3>
-<p>One of the most crucial skills in playing Clash Royale Mod APK is managing your elixir. Elixir is the resource that you use to play cards in battles. You have a maximum of 10 elixir at any time, which regenerates over time. You need to use your elixir wisely and efficiently in battles, so that you can gain an advantage over your opponent and win more battles. Here are some tips on how to manage your elixir:</p>
-<ul>
-<li>Don't waste your elixir: Don't play cards that are not necessary or effective in the current situation. For example, don't play a high-cost card when you have low elixir or when your opponent has a counter for it. Save your elixir for better opportunities and more value plays.</li>
-<li>Don't overcommit your elixir: Don't play too many cards at once or on one lane, unless you are sure that you can overwhelm your opponent or take their tower. Otherwise, you will leave yourself with no elixir or defense for the other lane or their counterattack.</li>
-<li>Don't leak your elixir: Don't let your elixir bar fill up without playing any card, unless you are waiting for a specific card or timing. Otherwise, you will lose potential elixir that could have been used for playing more cards or gaining more value.</li>
-<li>Track your opponent's elixir: Try to estimate how much elixir your opponent has based on the cards they play and the time they take to play them. This will help you predict their next move and plan your own accordingly.</li>
-</ul>
- <h3>Learn from the best: How to watch replays and learn from other players' strategies and mistakes</h3>
-<p>The last tip we have for playing Clash Royale Mod APK is learning from the best. The game offers you many ways to watch replays and learn from other players' strategies and mistakes. These are:</p>
-<ul>
-<li>The TV Royale feature: This feature allows you to watch replays of some of the most exciting and high-level battles in the game. You can filter the replays by arena, card, or clan. You can also watch replays of global tournaments and special events.</li>
-<li>The Clan Chat feature: This feature allows you to watch replays of battles that were shared by your clan members or other clans that you are friendly with. You can also chat with them and ask for feedback or advice.</li>
-<li>The YouTube feature: This feature allows you to watch replays of battles that were uploaded by some of the most popular and skilled Clash Royale players and content creators on YouTube. You can also subscribe You can also subscribe to their channels and watch their live streams, videos, and tutorials.</li>
-</ul>
- <p>By watching replays, you can learn from the best players in the game and improve your own skills and strategies. You can also discover new tips and tricks, card interactions, deck ideas, and more. Watching replays is a great way to enhance your Clash Royale knowledge and enjoyment.</p>
- <h2>Conclusion</h2>
-<p>Clash Royale Mod APK is a fantastic version of Clash Royale that offers you unlimited money, elixir, gems, and more. It also unlocks all the cards in the game and lets you join custom servers with different game modes and rules. By downloading and installing Clash Royale Mod APK on your Android device, you can enjoy the game without any limitations or restrictions. You can also use some tips and tricks to play the game more effectively and win more battles. Clash Royale Mod APK is a must-try for any Clash Royale fan who wants to have more fun and excitement in the game.</p>
- <p>So what are you waiting for? Download Clash Royale Mod APK now and experience the ultimate Clash Royale adventure. You won't regret it!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most frequently asked questions about Clash Royale Mod APK:</p>
- <h4>Is Clash Royale Mod APK safe to download and use?</h4>
-<p>Yes, Clash Royale Mod APK is safe to download and use, as long as you download it from a trusted source and follow the instructions carefully. However, you should be aware that using mod apk files may violate the terms of service of the original game and may result in your account being banned or suspended. Therefore, you should use Clash Royale Mod APK at your own risk and discretion.</p>
- <h4>Can I play Clash Royale Mod APK with my friends who use the original game?</h4>
-<p>No, you cannot play Clash Royale Mod APK with your friends who use the original game. This is because Clash Royale Mod APK uses different servers than the original game, which means that they are not compatible with each other. If you want to play with your friends who use the original game, you will need to uninstall Clash Royale Mod APK and install the original game again.</p>
- <h4>Can I update Clash Royale Mod APK to the latest version of the original game?</h4>
-<p>No, you cannot update Clash Royale Mod APK to the latest version of the original game. This is because Clash Royale Mod APK is a modified version of the original game, which means that it is not compatible with the official updates. If you want to update Clash Royale Mod APK to the latest version of the original game, you will need to wait for the mod apk developers to release a new version of the mod apk file that matches the latest version of the original game.</p>
- <h4>Can I use Clash Royale Mod APK on iOS devices?</h4>
-<p>No, you cannot use Clash Royale Mod APK on iOS devices. This is because Clash Royale Mod APK is an apk file, which is a file format that is only compatible with Android devices. If you want to use Clash Royale Mod APK on iOS devices, you will need to use a different method, such as jailbreaking your device or using an emulator.</p>
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-<p>Yes, you can use multiple accounts on Clash Royale Mod APK. This is because Clash Royale Mod APK does not require you to sign in with your Google Play or Facebook account. You can create as many accounts as you want on Clash Royale Mod APK by using different email addresses or usernames. You can also switch between your accounts easily by going to Settings > Account > Switch Account.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Gunship Strike 3D Mod APK with Unlimited Coins and Gems.md

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-<h1>Gunship Strike 3D APK Mod Download: How to Enjoy Unlimited Coins and Gems in This Action Game</h1>
- <p>Do you love action games that involve flying helicopters and shooting enemies? If yes, then you might want to try Gunship Strike 3D, a thrilling game that lets you pilot a gunship and destroy terrorist bases, warships, tanks, and more. But what if you want to enjoy the game without any limitations or restrictions? Well, there is a way to do that. You can download and install Gunship Strike 3D mod apk, a modified version of the game that gives you unlimited coins and gems. In this article, we will tell you what Gunship Strike 3D is, how to download and install Gunship Strike 3D mod apk, and how to use it. Let's get started!</p>
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- <h2>What is Gunship Strike 3D?</h2>
- <p>Gunship Strike 3D is an action game developed by Candy Mobile, a popular game studio that has created many other games such as Zombie Roadkill 3D, West Gunfighter, and Racing Fever. In Gunship Strike 3D, you can choose from different types of helicopters, each with its own weapons and abilities. You can also upgrade your helicopter with more powerful guns, missiles, rockets, and bombs. Your mission is to fly your gunship over various locations and destroy the enemy targets. You can also complete different challenges and achievements to earn more coins and gems.</p>
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- <p>While Gunship Strike 3D is a fun and exciting game, it also has some drawbacks. For example, you might run out of coins and gems quickly, especially if you want to buy or upgrade your helicopters or weapons. You might also face ads or pop-ups that interrupt your gameplay. Moreover, you might encounter some bugs or glitches that affect your performance or experience. That's why some players prefer to use Gunship Strike 3D mod apk, a modified version of the game that gives you unlimited coins and gems. With this mod apk, you can enjoy the following benefits:</p>
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-<h1>Download Pinkfong: The Best App for Kids' Songs and Stories</h1> | <p>If you are looking for a fun and educational app for your kids, you should download Pinkfong. Pinkfong is an app that offers hundreds of songs and stories for children of all ages. You can enjoy Pinkfong's content on your smartphone, tablet, or computer. In this article, we will tell you what Pinkfong is, why you should download it, how to download it, and what are some features of it.</p>
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- <ol>
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spaces/1phancelerku/anime-remove-background/Enjoy GameCube and Wii Games on Your Android with Dolphin Emulator.md

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-<li>Go to the <a href="(^3^)">Dolphin Emulator download page</a> and click on the Windows x64 button to download the latest beta version of the emulator. You can also download the stable version, but it may not have the latest features and improvements.</li>
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-<p>If you are using Android, you can follow these steps to download and install Dolphin emulator on your Android device:</p>
-<ol>
-<li>Go to the <a href="(^13^)">Google Play Store</a> and search for Dolphin Emulator. You should see an app with a dolphin icon developed by Dolphin Emulator. Tap on it and then tap on Install.</li>
-<li>You can also go to the <a href="(^3^)">Dolphin Emulator download page</a> and click on the Android button to download an apk file with the latest beta version of the emulator. You can also download the stable version, but it may not have the latest features and improvements.</li>
-<li>If you downloaded an apk file, you will need to enable unknown sources in your device settings before installing it. You can find more details about this process <a href="(^12^)">here</a>.</li>
-<li>Once you have installed Dolphin Emulator on your Android device, you can open it from your app drawer or home screen.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />

spaces/AIConsultant/MusicGen/tests/modules/test_lstm.py

@@ -1,32 +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.
-
-import random
-import torch
-
-from audiocraft.modules.lstm import StreamableLSTM
-
-
-class TestStreamableLSTM:
-
-    def test_lstm(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=False)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        print(y.shape)
-        assert y.shape == torch.Size([B, C, T])
-
-    def test_lstm_skip(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=True)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        assert y.shape == torch.Size([B, C, T])

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/pann_model.py

@@ -1,703 +0,0 @@
-# PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
-# Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn
-# Some layers are re-designed for CLAP
-import os
-
-os.environ["NUMBA_CACHE_DIR"] = "/tmp/"
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-from torchlibrosa.augmentation import SpecAugmentation
-
-from .utils import do_mixup, interpolate, pad_framewise_output
-from .feature_fusion import iAFF, AFF, DAF
-
-
-def init_layer(layer):
-    """Initialize a Linear or Convolutional layer."""
-    nn.init.xavier_uniform_(layer.weight)
-
-    if hasattr(layer, "bias"):
-        if layer.bias is not None:
-            layer.bias.data.fill_(0.0)
-
-def init_bn(bn):
-    """Initialize a Batchnorm layer."""
-    bn.bias.data.fill_(0.0)
-    bn.weight.data.fill_(1.0)
-
-
-class ConvBlock(nn.Module):
-    def __init__(self, in_channels, out_channels):
-
-        super(ConvBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=(3, 3),
-            stride=(1, 1),
-            padding=(1, 1),
-            bias=False,
-        )
-
-        self.conv2 = nn.Conv2d(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=(3, 3),
-            stride=(1, 1),
-            padding=(1, 1),
-            bias=False,
-        )
-
-        self.bn1 = nn.BatchNorm2d(out_channels)
-        self.bn2 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_layer(self.conv2)
-        init_bn(self.bn1)
-        init_bn(self.bn2)
-
-    def forward(self, input, pool_size=(2, 2), pool_type="avg"):
-
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        x = F.relu_(self.bn2(self.conv2(x)))
-        if pool_type == "max":
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg":
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg+max":
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception("Incorrect argument!")
-
-        return x
-
-
-class ConvBlock5x5(nn.Module):
-    def __init__(self, in_channels, out_channels):
-
-        super(ConvBlock5x5, self).__init__()
-
-        self.conv1 = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=(5, 5),
-            stride=(1, 1),
-            padding=(2, 2),
-            bias=False,
-        )
-
-        self.bn1 = nn.BatchNorm2d(out_channels)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_layer(self.conv1)
-        init_bn(self.bn1)
-
-    def forward(self, input, pool_size=(2, 2), pool_type="avg"):
-
-        x = input
-        x = F.relu_(self.bn1(self.conv1(x)))
-        if pool_type == "max":
-            x = F.max_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg":
-            x = F.avg_pool2d(x, kernel_size=pool_size)
-        elif pool_type == "avg+max":
-            x1 = F.avg_pool2d(x, kernel_size=pool_size)
-            x2 = F.max_pool2d(x, kernel_size=pool_size)
-            x = x1 + x2
-        else:
-            raise Exception("Incorrect argument!")
-
-        return x
-
-
-class AttBlock(nn.Module):
-    def __init__(self, n_in, n_out, activation="linear", temperature=1.0):
-        super(AttBlock, self).__init__()
-
-        self.activation = activation
-        self.temperature = temperature
-        self.att = nn.Conv1d(
-            in_channels=n_in,
-            out_channels=n_out,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=True,
-        )
-        self.cla = nn.Conv1d(
-            in_channels=n_in,
-            out_channels=n_out,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=True,
-        )
-
-        self.bn_att = nn.BatchNorm1d(n_out)
-        self.init_weights()
-
-    def init_weights(self):
-        init_layer(self.att)
-        init_layer(self.cla)
-        init_bn(self.bn_att)
-
-    def forward(self, x):
-        # x: (n_samples, n_in, n_time)
-        norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
-        cla = self.nonlinear_transform(self.cla(x))
-        x = torch.sum(norm_att * cla, dim=2)
-        return x, norm_att, cla
-
-    def nonlinear_transform(self, x):
-        if self.activation == "linear":
-            return x
-        elif self.activation == "sigmoid":
-            return torch.sigmoid(x)
-
-
-class Cnn14(nn.Module):
-    def __init__(
-        self,
-        sample_rate,
-        window_size,
-        hop_size,
-        mel_bins,
-        fmin,
-        fmax,
-        classes_num,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-
-        super(Cnn14, self).__init__()
-
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=window_size,
-            hop_length=hop_size,
-            win_length=window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=sample_rate,
-            n_fft=window_size,
-            n_mels=mel_bins,
-            fmin=fmin,
-            fmax=fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        if (self.enable_fusion) and (self.fusion_type == "channel_map"):
-            self.conv_block1 = ConvBlock(in_channels=4, out_channels=64)
-        else:
-            self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-        self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
-
-        self.fc1 = nn.Linear(2048, 2048, bias=True)
-        self.fc_audioset = nn.Linear(2048, classes_num, bias=True)
-
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]
-        ):
-            self.mel_conv1d = nn.Sequential(
-                nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
-                nn.BatchNorm1d(64),  # No Relu
-            )
-            if self.fusion_type == "daf_1d":
-                self.fusion_model = DAF()
-            elif self.fusion_type == "aff_1d":
-                self.fusion_model = AFF(channels=64, type="1D")
-            elif self.fusion_type == "iaff_1d":
-                self.fusion_model = iAFF(channels=64, type="1D")
-
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
-        ):
-            self.mel_conv2d = nn.Sequential(
-                nn.Conv2d(1, 64, kernel_size=(5, 5), stride=(6, 2), padding=(2, 2)),
-                nn.BatchNorm2d(64),
-                nn.ReLU(inplace=True),
-            )
-
-            if self.fusion_type == "daf_2d":
-                self.fusion_model = DAF()
-            elif self.fusion_type == "aff_2d":
-                self.fusion_model = AFF(channels=64, type="2D")
-            elif self.fusion_type == "iaff_2d":
-                self.fusion_model = iAFF(channels=64, type="2D")
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        if self.enable_fusion and input["longer"].sum() == 0:
-            # if no audio is longer than 10s, then randomly select one audio to be longer
-            input["longer"][torch.randint(0, input["longer"].shape[0], (1,))] = True
-
-        if not self.enable_fusion:
-            x = self.spectrogram_extractor(
-                input["waveform"].to(device=device, non_blocking=True)
-            )  # (batch_size, 1, time_steps, freq_bins)
-            x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-        else:
-            longer_list = input["longer"].to(device=device, non_blocking=True)
-            x = input["mel_fusion"].to(device=device, non_blocking=True)
-            longer_list_idx = torch.where(longer_list)[0]
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            if self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]:
-                new_x = x[:, 0:1, :, :].clone().contiguous()
-                # local processing
-                if len(longer_list_idx) > 0:
-                    fusion_x_local = x[longer_list_idx, 1:, :, :].clone().contiguous()
-                    FB, FC, FT, FF = fusion_x_local.size()
-                    fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
-                    fusion_x_local = torch.permute(
-                        fusion_x_local, (0, 2, 1)
-                    ).contiguous()
-                    fusion_x_local = self.mel_conv1d(fusion_x_local)
-                    fusion_x_local = fusion_x_local.view(
-                        FB, FC, FF, fusion_x_local.size(-1)
-                    )
-                    fusion_x_local = (
-                        torch.permute(fusion_x_local, (0, 2, 1, 3))
-                        .contiguous()
-                        .flatten(2)
-                    )
-                    if fusion_x_local.size(-1) < FT:
-                        fusion_x_local = torch.cat(
-                            [
-                                fusion_x_local,
-                                torch.zeros(
-                                    (FB, FF, FT - fusion_x_local.size(-1)),
-                                    device=device,
-                                ),
-                            ],
-                            dim=-1,
-                        )
-                    else:
-                        fusion_x_local = fusion_x_local[:, :, :FT]
-                    # 1D fusion
-                    new_x = new_x.squeeze(1).permute((0, 2, 1)).contiguous()
-                    new_x[longer_list_idx] = self.fusion_model(
-                        new_x[longer_list_idx], fusion_x_local
-                    )
-                    x = new_x.permute((0, 2, 1)).contiguous()[:, None, :, :]
-                else:
-                    x = new_x
-            elif self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d", "channel_map"]:
-                x = x  # no change
-
-        if self.training:
-            x = self.spec_augmenter(x)
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
-        ):
-            global_x = x[:, 0:1, :, :]
-
-            # global processing
-            B, C, H, W = global_x.shape
-            global_x = self.conv_block1(global_x, pool_size=(2, 2), pool_type="avg")
-            if len(longer_list_idx) > 0:
-                local_x = x[longer_list_idx, 1:, :, :].contiguous()
-                TH = global_x.size(-2)
-                # local processing
-                B, C, H, W = local_x.shape
-                local_x = local_x.view(B * C, 1, H, W)
-                local_x = self.mel_conv2d(local_x)
-                local_x = local_x.view(
-                    B, C, local_x.size(1), local_x.size(2), local_x.size(3)
-                )
-                local_x = local_x.permute((0, 2, 1, 3, 4)).contiguous().flatten(2, 3)
-                TB, TC, _, TW = local_x.size()
-                if local_x.size(-2) < TH:
-                    local_x = torch.cat(
-                        [
-                            local_x,
-                            torch.zeros(
-                                (TB, TC, TH - local_x.size(-2), TW),
-                                device=global_x.device,
-                            ),
-                        ],
-                        dim=-2,
-                    )
-                else:
-                    local_x = local_x[:, :, :TH, :]
-
-                global_x[longer_list_idx] = self.fusion_model(
-                    global_x[longer_list_idx], local_x
-                )
-            x = global_x
-        else:
-            x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
-
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block6(x, pool_size=(1, 1), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 32)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {
-            "clipwise_output": clipwise_output,
-            "embedding": embedding,
-            "fine_grained_embedding": latent_output,
-        }
-        return output_dict
-
-
-class Cnn6(nn.Module):
-    def __init__(
-        self,
-        sample_rate,
-        window_size,
-        hop_size,
-        mel_bins,
-        fmin,
-        fmax,
-        classes_num,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-
-        super(Cnn6, self).__init__()
-
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=window_size,
-            hop_length=hop_size,
-            win_length=window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=sample_rate,
-            n_fft=window_size,
-            n_mels=mel_bins,
-            fmin=fmin,
-            fmax=fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512)
-
-        self.fc1 = nn.Linear(512, 512, bias=True)
-        self.fc_audioset = nn.Linear(512, classes_num, bias=True)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.spectrogram_extractor(input)  # (batch_size, 1, time_steps, freq_bins)
-        x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-
-        x = x.transpose(1, 3)
-        x = self.bn0(x)
-        x = x.transpose(1, 3)
-
-        if self.training:
-            x = self.spec_augmenter(x)
-
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 16)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {
-            "clipwise_output": clipwise_output,
-            "embedding": embedding,
-            "fine_grained_embedding": latent_output,
-        }
-
-        return output_dict
-
-
-class Cnn10(nn.Module):
-    def __init__(
-        self,
-        sample_rate,
-        window_size,
-        hop_size,
-        mel_bins,
-        fmin,
-        fmax,
-        classes_num,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-
-        super(Cnn10, self).__init__()
-
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=window_size,
-            hop_length=hop_size,
-            win_length=window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=sample_rate,
-            n_fft=window_size,
-            n_mels=mel_bins,
-            fmin=fmin,
-            fmax=fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )
-
-        self.bn0 = nn.BatchNorm2d(64)
-
-        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
-        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
-        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
-        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
-        self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
-
-        self.fc1 = nn.Linear(1024, 1024, bias=True)
-        self.fc_audioset = nn.Linear(1024, classes_num, bias=True)
-
-        self.init_weight()
-
-    def init_weight(self):
-        init_bn(self.bn0)
-        init_layer(self.fc1)
-        init_layer(self.fc_audioset)
-
-    def forward(self, input, mixup_lambda=None, device=None):
-        """
-        Input: (batch_size, data_length)"""
-
-        x = self.spectrogram_extractor(input)  # (batch_size, 1, time_steps, freq_bins)
-        x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-
-        x = x.transpose(1, 3)
-        x = self.bn0(x)
-        x = x.transpose(1, 3)
-
-        if self.training:
-            x = self.spec_augmenter(x)
-
-        # Mixup on spectrogram
-        if self.training and mixup_lambda is not None:
-            x = do_mixup(x, mixup_lambda)
-
-        x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
-        x = F.dropout(x, p=0.2, training=self.training)
-        x = torch.mean(x, dim=3)
-
-        latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)
-        latent_x = latent_x1 + latent_x2
-        latent_x = latent_x.transpose(1, 2)
-        latent_x = F.relu_(self.fc1(latent_x))
-        latent_output = interpolate(latent_x, 32)
-
-        (x1, _) = torch.max(x, dim=2)
-        x2 = torch.mean(x, dim=2)
-        x = x1 + x2
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = F.relu_(self.fc1(x))
-        embedding = F.dropout(x, p=0.5, training=self.training)
-        clipwise_output = torch.sigmoid(self.fc_audioset(x))
-
-        output_dict = {
-            "clipwise_output": clipwise_output,
-            "embedding": embedding,
-            "fine_grained_embedding": latent_output,
-        }
-
-        return output_dict
-
-
-def create_pann_model(audio_cfg, enable_fusion=False, fusion_type="None"):
-    try:
-        ModelProto = eval(audio_cfg.model_name)
-        model = ModelProto(
-            sample_rate=audio_cfg.sample_rate,
-            window_size=audio_cfg.window_size,
-            hop_size=audio_cfg.hop_size,
-            mel_bins=audio_cfg.mel_bins,
-            fmin=audio_cfg.fmin,
-            fmax=audio_cfg.fmax,
-            classes_num=audio_cfg.class_num,
-            enable_fusion=enable_fusion,
-            fusion_type=fusion_type,
-        )
-        return model
-    except:
-        raise RuntimeError(
-            f"Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough."
-        )

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/image_degradation/utils_image.py

@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)

spaces/AIWaves/Software_Company/gradio_backend.py

@@ -1,127 +0,0 @@
-import os
-import argparse
-import sys
-sys.path.append("src/agents")
-from utils import extract
-from SOP import SOP
-from Agent import Agent
-from Environment import Environment
-from Memory import Memory
-from gradio_base import Client, convert2list4agentname
-
-def process(action):
-    response = action.response
-    send_name = action.name
-    send_role = action.role
-    if not action.is_user:
-        print(f"{send_name}({send_role}):{response}")
-    memory = Memory(send_role, send_name, response)
-    return memory
-
-def gradio_process(action,current_state):
-    response = action.response
-    all = ""
-    for i,res in enumerate(response):
-        all+=res
-        state = 10
-        if action.is_user:
-            state = 30
-        elif action.state_begin:
-            state = 12
-            action.state_begin = False
-        elif i>0:
-            state = 11
-        send_name = f"{action.name}({action.role})"
-        Client.send_server(str([state, send_name, res, current_state.name]))
-        if state == 30:
-            # print("client: waiting for user input")
-            data: list = next(Client.receive_server)
-            content = ""
-            for item in data:
-                if item.startswith("<USER>"):
-                    content = item.split("<USER>")[1]
-                    break
-            # print(f"client: received `{content}` from server.")
-            action.response = content
-            break
-        else:
-            action.response = all
-
-def init(config): 
-    if not os.path.exists("logs"):
-        os.mkdir("logs")
-    sop = SOP.from_config(config)
-    agents,roles_to_names,names_to_roles = Agent.from_config(config)
-    environment = Environment.from_config(config)
-    environment.agents = agents
-    environment.roles_to_names,environment.names_to_roles = roles_to_names,names_to_roles
-    sop.roles_to_names,sop.names_to_roles = roles_to_names,names_to_roles
-    for name,agent in agents.items():
-        agent.environment = environment
-    return agents,sop,environment
-
-def block_when_next(current_agent, current_state):
-    if Client.LAST_USER:
-        assert not current_agent.is_user
-        Client.LAST_USER = False
-        return
-    if current_agent.is_user:
-        # if next turn is user, we don't handle it here
-        Client.LAST_USER = True
-        return
-    if Client.FIRST_RUN:
-        Client.FIRST_RUN = False
-    else:
-        # block current process
-        if Client.mode == Client.SINGLE_MODE:
-            Client.send_server(str([98, f"{current_agent.name}({current_agent.state_roles[current_state.name]})", " ", current_state.name]))
-            data: list = next(Client.receive_server)
-
-def run(agents,sop,environment):
-    while True:      
-        current_state,current_agent= sop.next(environment,agents)
-        if sop.finished:
-            print("finished!")
-            Client.send_server(str([99, ' ', ' ', 'done']))
-            os.environ.clear()
-            break
-        block_when_next(current_agent, current_state)
-        action = current_agent.step(current_state)   #component_dict = current_state[self.role[current_node.name]]   current_agent.compile(component_dict) 
-        gradio_process(action,current_state)
-        memory = process(action)
-        environment.update_memory(memory,current_state)
-
-def prepare(agents, sop, environment):
-    client = Client()
-    Client.send_server = client.send_message
-
-    requirement_game_name = extract(sop.states['design_state'].environment_prompt,"target")
-    client.send_message(
-        {
-            "requirement": requirement_game_name,
-            "agents_name": convert2list4agentname(sop)[0],
-            # "only_name":  DebateUI.convert2list4agentname(sop)[1],
-            "only_name":  convert2list4agentname(sop)[0],
-            "default_cos_play_id": -1,
-            "api_key": os.environ["API_KEY"]
-        }
-    )
-    # print(f"client: send {requirement_game_name}")
-    client.listening_for_start_()
-    client.mode = Client.mode = client.cache["mode"]
-    new_requirement = Client.cache['requirement']
-    os.environ["API_KEY"] = client.cache["api_key"]
-    for state in sop.states.values():
-        state.environment_prompt = state.environment_prompt.replace("<target>a snake game with python</target>", f"<target>{new_requirement}</target>")
-    # print(f"client: received {Client.cache['requirement']} from server.")
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='A demo of chatbot')
-    parser.add_argument('--agent', type=str, help='path to SOP json', default="config.json")
-    args = parser.parse_args()
-    
-    agents,sop,environment = init(args.agent)
-    # add================================
-    prepare(agents, sop, environment)
-    # ===================================
-    run(agents,sop,environment)

spaces/AIZeroToHero/3-NLP-MLM-MaskedLanguageModel/README.md

@@ -1,13 +0,0 @@
----
-title: 3 NLP MLM MaskedLanguageModel
-emoji: 🌖
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Adapter/T2I-Adapter/ldm/modules/diffusionmodules/util.py

@@ -1,270 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/Init.js

@@ -1,9 +0,0 @@
-/* 
-1. Fill background tiles
-*/
-var Init = function () {
-    // TODO: assign symobls of board via callback
-    return this;
-}
-
-export default Init;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorcomponents/Factory.js

@@ -1,13 +0,0 @@
-import ColorComponents from './ColorComponents.js';
-import ObjectFactory from '../../ObjectFactory.js';
-import SetValue from '../../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('colorComponents', function (config) {
-    var gameObject = new ColorComponents(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.ColorComponents', ColorComponents);
-
-export default ColorComponents;

spaces/AhmedTambal/malaria/app.py

@@ -1,74 +0,0 @@
-from __future__ import division, print_function
-import numpy as np # linear algebra
-import pandas as pd
-import os
-import tensorflow as tf
-
-import numpy as np
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-
-
-
-
-#!pip install gradio
-
-#!git clone https://huggingface.co/spaces/AhmedTambal/malaria
-
-
-
-
-import gradio as gr
-import skimage
-import keras.preprocessing.image
-from tensorflow.keras.utils import load_img
-from tensorflow.keras.utils import img_to_array
-
-# coding=utf-8
-import sys
-import os
-import glob
-import re
-import numpy as np
-
-# Keras
-from tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-
-MODEL_PATH ='/content/drive/MyDrive/Fraud Detection/malaria5.h5'
-
-saved_model = tf.keras.models.load_model(MODEL_PATH)
-
-image = gr.inputs.Image(shape=(224,224))
-
-label = gr.outputs.Label(num_top_classes=2)
-
-
-def predict_input_image(img):
-  img_4d=img.reshape(-1,224,224,3)
-  prediction=saved_model.predict(img_4d)[0]
-
-  p_pred1 = prediction.flatten()
-  print(p_pred1.round(2))
-   # [1. 0.01 0.91 0.87 0.06 0.95 0.24 0.58 0.78 ...
-
-   # extract the predicted class labels
-  y_pred1 = np.where(p_pred1 > 0.5, 1, 0)
-  
-
-  if y_pred1==1:
-        y_pred1="The cells is not Infected With the parasitized"
-  else:
-        y_pred1="The cells is Infected With the parasitized"
-
-  return y_pred1
-
-import gradio as gr
-gr.Interface(fn=predict_input_image,
-             inputs=image,
-             outputs=label).launch(share=True,debug=False)
-
-#$ git add app.py
-#$ git commit -m "Add application file"
-#$ git push

spaces/AlexZou/Deploy_Restoration/model/global_net.py

@@ -1,132 +0,0 @@
-import imp
-import torch
-import torch.nn as nn
-from timm.models.layers import trunc_normal_, DropPath, to_2tuple
-import os
-from model.blocks import Mlp
-
-
-class query_Attention(nn.Module):
-    def __init__(self, dim, num_heads=2, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.q = nn.Parameter(torch.ones((1, 10, dim)), requires_grad=True)
-        self.k = nn.Linear(dim, dim, bias=qkv_bias)
-        self.v = nn.Linear(dim, dim, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-        q = self.q.expand(B, -1, -1).view(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
-        # k = self.k(x).reshape(B, N, self.num_heads, torch.div(C,self.num_heads, rounding_mode='floor')).permute(0, 2, 1, 3)
-        # v = self.v(x).reshape(B, N, self.num_heads, torch.div(C,self.num_heads, rounding_mode='floor')).permute(0, 2, 1, 3)
-        # q = self.q.expand(B, -1, -1).view(B, -1, self.num_heads, torch.div(C,self.num_heads, rounding_mode='floor')).permute(0, 2, 1, 3)
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, 10, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class query_SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = query_Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x.flatten(2).transpose(1, 2)
-        x = self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class conv_embedding(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        super(conv_embedding, self).__init__()
-        self.proj = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
-            nn.BatchNorm2d(out_channels // 2),
-            nn.GELU(),
-            # nn.Conv2d(out_channels // 2, out_channels // 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
-            # nn.BatchNorm2d(out_channels // 2),
-            # nn.GELU(),
-            nn.Conv2d(out_channels // 2, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
-            nn.BatchNorm2d(out_channels),
-        )
-
-    def forward(self, x):
-        x = self.proj(x)
-        return x
-
-
-class Global_pred(nn.Module):
-    def __init__(self, in_channels=3, out_channels=64, num_heads=4, type='exp'):
-        super(Global_pred, self).__init__()
-        if type == 'exp':
-            self.gamma_base = nn.Parameter(torch.ones((1)), requires_grad=False) # False in exposure correction
-        else:
-            self.gamma_base = nn.Parameter(torch.ones((1)), requires_grad=True)  
-        self.color_base = nn.Parameter(torch.eye((3)), requires_grad=True)  # basic color matrix
-        # main blocks
-        self.conv_large = conv_embedding(in_channels, out_channels)
-        self.generator = query_SABlock(dim=out_channels, num_heads=num_heads)
-        self.gamma_linear = nn.Linear(out_channels, 1)
-        self.color_linear = nn.Linear(out_channels, 1)
-
-        self.apply(self._init_weights)
-
-        for name, p in self.named_parameters():
-            if name == 'generator.attn.v.weight':
-                nn.init.constant_(p, 0)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-
-    def forward(self, x):
-        #print(self.gamma_base)
-        x = self.conv_large(x)
-        x = self.generator(x)
-        gamma, color = x[:, 0].unsqueeze(1), x[:, 1:]
-        gamma = self.gamma_linear(gamma).squeeze(-1) + self.gamma_base
-        #print(self.gamma_base, self.gamma_linear(gamma))
-        color = self.color_linear(color).squeeze(-1).view(-1, 3, 3) + self.color_base
-        return gamma, color
-
-if __name__ == "__main__":
-    os.environ['CUDA_VISIBLE_DEVICES']='3'
-    #net = Local_pred_new().cuda()
-    img = torch.Tensor(8, 3, 400, 600)
-    global_net = Global_pred()
-    gamma, color = global_net(img)
-    print(gamma.shape, color.shape)

spaces/Aloento/9Nine-PITS/utils.py

@@ -1,302 +0,0 @@
-# from https://github.com/jaywalnut310/vits
-import logging
-import os
-import subprocess
-import sys
-import glob
-
-import numpy as np
-import torch
-from omegaconf import OmegaConf
-from scipy.io.wavfile import read
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(
-  stream=sys.stdout,
-  level=logging.INFO,
-  format='[%(levelname)s|%(filename)s:%(lineno)s][%(asctime)s] >>> %(message)s'
-)
-logger = logging
-
-
-def latest_checkpoint_path(dir_path, regex="*.pth"):
-  f_list = glob.glob(os.path.join(dir_path, regex))
-  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-  x = f_list[-1]
-  print(x)
-  return x
-
-
-def load_checkpoint(checkpoint_path, rank=0, model_g=None, model_d=None, optim_g=None, optim_d=None):
-  is_train = os.path.isdir(checkpoint_path)
-
-  if is_train:
-    train = latest_checkpoint_path(checkpoint_path, "*_Train_*.pth")
-    val = latest_checkpoint_path(checkpoint_path, "*_Eval_*.pth")
-
-    assert train is not None
-    assert val is not None
-
-    train_dict = torch.load(train, map_location='cpu')
-    iteration = train_dict['iteration']
-  else:
-    assert os.path.isfile(checkpoint_path)
-    val = checkpoint_path
-    iteration = "Eval"
-
-  val_dict = torch.load(val, map_location='cpu')
-
-  assert model_g is not None
-  model_g = load_model(
-    model_g,
-    val_dict['model_g']
-  )
-
-  if is_train:
-    if optim_g is not None:
-      optim_g.load_state_dict(train_dict['optimizer_g'])
-
-    if model_d is not None:
-      model_d = load_model(
-        model_d,
-        train_dict['model_d']
-      )
-
-    if optim_d is not None:
-      optim_d.load_state_dict(train_dict['optimizer_d'])
-
-  if rank == 0:
-    logger.info(
-      "Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path,
-        iteration
-      )
-    )
-
-  return model_g, model_d, optim_g, optim_d, iteration
-
-
-def load_model(model, model_state_dict):
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-
-  for k, v in model_state_dict.items():
-    if k in state_dict and state_dict[k].size() == v.size():
-      state_dict[k] = v
-
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(state_dict)
-  else:
-    model.load_state_dict(state_dict)
-
-  return model
-
-
-def save_checkpoint(net_g, optim_g, net_d, optim_d, hps, epoch, global_step):
-  def get_state_dict(model):
-    if hasattr(model, 'module'):
-      state_dict = model.module.state_dict()
-    else:
-      state_dict = model.state_dict()
-    return state_dict
-
-  torch.save(
-    {
-      'model_d': get_state_dict(net_d),
-      'optimizer_g': optim_g.state_dict(),
-      'optimizer_d': optim_d.state_dict(),
-      'iteration': epoch,
-    }, os.path.join(
-      hps.model_dir, "{}_Train_{}.pth".format(hps.model_name, global_step)
-    )
-  )
-
-  torch.save(
-    {
-      'model_g': get_state_dict(net_g),
-    }, os.path.join(
-      hps.model_dir, "{}_Eval_{}.pth".format(hps.model_name, global_step)
-    )
-  )
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-  for k, v in scalars.items():
-    writer.add_scalar(k, v, global_step)
-  for k, v in histograms.items():
-    writer.add_histogram(k, v, global_step)
-  for k, v in images.items():
-    writer.add_image(k, v, global_step, dataformats='HWC')
-  for k, v in audios.items():
-    writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10, 2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                 interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                 interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-    xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_wav_to_torch(full_path):
-  sampling_rate, wav = read(full_path)
-
-  if len(wav.shape) == 2:
-    wav = wav[:, 0]
-
-  if wav.dtype == np.int16:
-    wav = wav / 32768.0
-  elif wav.dtype == np.int32:
-    wav = wav / 2147483648.0
-  elif wav.dtype == np.uint8:
-    wav = (wav - 128) / 128.0
-  wav = wav.astype(np.float32)
-  return torch.FloatTensor(wav), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(args, init=True):
-  config = OmegaConf.load(args.config)
-  hparams = HParams(**config)
-  model_dir = os.path.join(hparams.train.log_path, args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  hparams.model_name = args.model
-  hparams.model_dir = model_dir
-  config_save_path = os.path.join(model_dir, "config.yaml")
-
-  if init:
-    OmegaConf.save(config, config_save_path)
-
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  config = OmegaConf.load(config_path)
-  hparams = HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter(
-    "%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()

spaces/Alpaca233/SadTalker/src/face3d/util/my_awing_arch.py

@@ -1,378 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def calculate_points(heatmaps):
-    # change heatmaps to landmarks
-    B, N, H, W = heatmaps.shape
-    HW = H * W
-    BN_range = np.arange(B * N)
-
-    heatline = heatmaps.reshape(B, N, HW)
-    indexes = np.argmax(heatline, axis=2)
-
-    preds = np.stack((indexes % W, indexes // W), axis=2)
-    preds = preds.astype(np.float, copy=False)
-
-    inr = indexes.ravel()
-
-    heatline = heatline.reshape(B * N, HW)
-    x_up = heatline[BN_range, inr + 1]
-    x_down = heatline[BN_range, inr - 1]
-    # y_up = heatline[BN_range, inr + W]
-
-    if any((inr + W) >= 4096):
-        y_up = heatline[BN_range, 4095]
-    else:
-        y_up = heatline[BN_range, inr + W]
-    if any((inr - W) <= 0):
-        y_down = heatline[BN_range, 0]
-    else:
-        y_down = heatline[BN_range, inr - W]
-
-    think_diff = np.sign(np.stack((x_up - x_down, y_up - y_down), axis=1))
-    think_diff *= .25
-
-    preds += think_diff.reshape(B, N, 2)
-    preds += .5
-    return preds
-
-
-class AddCoordsTh(nn.Module):
-
-    def __init__(self, x_dim=64, y_dim=64, with_r=False, with_boundary=False):
-        super(AddCoordsTh, self).__init__()
-        self.x_dim = x_dim
-        self.y_dim = y_dim
-        self.with_r = with_r
-        self.with_boundary = with_boundary
-
-    def forward(self, input_tensor, heatmap=None):
-        """
-        input_tensor: (batch, c, x_dim, y_dim)
-        """
-        batch_size_tensor = input_tensor.shape[0]
-
-        xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32, device=input_tensor.device)
-        xx_ones = xx_ones.unsqueeze(-1)
-
-        xx_range = torch.arange(self.x_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0)
-        xx_range = xx_range.unsqueeze(1)
-
-        xx_channel = torch.matmul(xx_ones.float(), xx_range.float())
-        xx_channel = xx_channel.unsqueeze(-1)
-
-        yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32, device=input_tensor.device)
-        yy_ones = yy_ones.unsqueeze(1)
-
-        yy_range = torch.arange(self.y_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0)
-        yy_range = yy_range.unsqueeze(-1)
-
-        yy_channel = torch.matmul(yy_range.float(), yy_ones.float())
-        yy_channel = yy_channel.unsqueeze(-1)
-
-        xx_channel = xx_channel.permute(0, 3, 2, 1)
-        yy_channel = yy_channel.permute(0, 3, 2, 1)
-
-        xx_channel = xx_channel / (self.x_dim - 1)
-        yy_channel = yy_channel / (self.y_dim - 1)
-
-        xx_channel = xx_channel * 2 - 1
-        yy_channel = yy_channel * 2 - 1
-
-        xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1)
-        yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1)
-
-        if self.with_boundary and heatmap is not None:
-            boundary_channel = torch.clamp(heatmap[:, -1:, :, :], 0.0, 1.0)
-
-            zero_tensor = torch.zeros_like(xx_channel)
-            xx_boundary_channel = torch.where(boundary_channel > 0.05, xx_channel, zero_tensor)
-            yy_boundary_channel = torch.where(boundary_channel > 0.05, yy_channel, zero_tensor)
-        if self.with_boundary and heatmap is not None:
-            xx_boundary_channel = xx_boundary_channel.to(input_tensor.device)
-            yy_boundary_channel = yy_boundary_channel.to(input_tensor.device)
-        ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1)
-
-        if self.with_r:
-            rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2))
-            rr = rr / torch.max(rr)
-            ret = torch.cat([ret, rr], dim=1)
-
-        if self.with_boundary and heatmap is not None:
-            ret = torch.cat([ret, xx_boundary_channel, yy_boundary_channel], dim=1)
-        return ret
-
-
-class CoordConvTh(nn.Module):
-    """CoordConv layer as in the paper."""
-
-    def __init__(self, x_dim, y_dim, with_r, with_boundary, in_channels, first_one=False, *args, **kwargs):
-        super(CoordConvTh, self).__init__()
-        self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, with_boundary=with_boundary)
-        in_channels += 2
-        if with_r:
-            in_channels += 1
-        if with_boundary and not first_one:
-            in_channels += 2
-        self.conv = nn.Conv2d(in_channels=in_channels, *args, **kwargs)
-
-    def forward(self, input_tensor, heatmap=None):
-        ret = self.addcoords(input_tensor, heatmap)
-        last_channel = ret[:, -2:, :, :]
-        ret = self.conv(ret)
-        return ret, last_channel
-
-
-def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False, dilation=1):
-    '3x3 convolution with padding'
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias, dilation=dilation)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        # self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        # self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ConvBlock(nn.Module):
-
-    def __init__(self, in_planes, out_planes):
-        super(ConvBlock, self).__init__()
-        self.bn1 = nn.BatchNorm2d(in_planes)
-        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
-        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
-        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4), padding=1, dilation=1)
-        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
-        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4), padding=1, dilation=1)
-
-        if in_planes != out_planes:
-            self.downsample = nn.Sequential(
-                nn.BatchNorm2d(in_planes),
-                nn.ReLU(True),
-                nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False),
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        residual = x
-
-        out1 = self.bn1(x)
-        out1 = F.relu(out1, True)
-        out1 = self.conv1(out1)
-
-        out2 = self.bn2(out1)
-        out2 = F.relu(out2, True)
-        out2 = self.conv2(out2)
-
-        out3 = self.bn3(out2)
-        out3 = F.relu(out3, True)
-        out3 = self.conv3(out3)
-
-        out3 = torch.cat((out1, out2, out3), 1)
-
-        if self.downsample is not None:
-            residual = self.downsample(residual)
-
-        out3 += residual
-
-        return out3
-
-
-class HourGlass(nn.Module):
-
-    def __init__(self, num_modules, depth, num_features, first_one=False):
-        super(HourGlass, self).__init__()
-        self.num_modules = num_modules
-        self.depth = depth
-        self.features = num_features
-        self.coordconv = CoordConvTh(
-            x_dim=64,
-            y_dim=64,
-            with_r=True,
-            with_boundary=True,
-            in_channels=256,
-            first_one=first_one,
-            out_channels=256,
-            kernel_size=1,
-            stride=1,
-            padding=0)
-        self._generate_network(self.depth)
-
-    def _generate_network(self, level):
-        self.add_module('b1_' + str(level), ConvBlock(256, 256))
-
-        self.add_module('b2_' + str(level), ConvBlock(256, 256))
-
-        if level > 1:
-            self._generate_network(level - 1)
-        else:
-            self.add_module('b2_plus_' + str(level), ConvBlock(256, 256))
-
-        self.add_module('b3_' + str(level), ConvBlock(256, 256))
-
-    def _forward(self, level, inp):
-        # Upper branch
-        up1 = inp
-        up1 = self._modules['b1_' + str(level)](up1)
-
-        # Lower branch
-        low1 = F.avg_pool2d(inp, 2, stride=2)
-        low1 = self._modules['b2_' + str(level)](low1)
-
-        if level > 1:
-            low2 = self._forward(level - 1, low1)
-        else:
-            low2 = low1
-            low2 = self._modules['b2_plus_' + str(level)](low2)
-
-        low3 = low2
-        low3 = self._modules['b3_' + str(level)](low3)
-
-        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
-
-        return up1 + up2
-
-    def forward(self, x, heatmap):
-        x, last_channel = self.coordconv(x, heatmap)
-        return self._forward(self.depth, x), last_channel
-
-
-class FAN(nn.Module):
-
-    def __init__(self, num_modules=1, end_relu=False, gray_scale=False, num_landmarks=68, device='cuda'):
-        super(FAN, self).__init__()
-        self.device = device
-        self.num_modules = num_modules
-        self.gray_scale = gray_scale
-        self.end_relu = end_relu
-        self.num_landmarks = num_landmarks
-
-        # Base part
-        if self.gray_scale:
-            self.conv1 = CoordConvTh(
-                x_dim=256,
-                y_dim=256,
-                with_r=True,
-                with_boundary=False,
-                in_channels=3,
-                out_channels=64,
-                kernel_size=7,
-                stride=2,
-                padding=3)
-        else:
-            self.conv1 = CoordConvTh(
-                x_dim=256,
-                y_dim=256,
-                with_r=True,
-                with_boundary=False,
-                in_channels=3,
-                out_channels=64,
-                kernel_size=7,
-                stride=2,
-                padding=3)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.conv2 = ConvBlock(64, 128)
-        self.conv3 = ConvBlock(128, 128)
-        self.conv4 = ConvBlock(128, 256)
-
-        # Stacking part
-        for hg_module in range(self.num_modules):
-            if hg_module == 0:
-                first_one = True
-            else:
-                first_one = False
-            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256, first_one))
-            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
-            self.add_module('conv_last' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
-            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
-            self.add_module('l' + str(hg_module), nn.Conv2d(256, num_landmarks + 1, kernel_size=1, stride=1, padding=0))
-
-            if hg_module < self.num_modules - 1:
-                self.add_module('bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
-                self.add_module('al' + str(hg_module),
-                                nn.Conv2d(num_landmarks + 1, 256, kernel_size=1, stride=1, padding=0))
-
-    def forward(self, x):
-        x, _ = self.conv1(x)
-        x = F.relu(self.bn1(x), True)
-        # x = F.relu(self.bn1(self.conv1(x)), True)
-        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
-        x = self.conv3(x)
-        x = self.conv4(x)
-
-        previous = x
-
-        outputs = []
-        boundary_channels = []
-        tmp_out = None
-        for i in range(self.num_modules):
-            hg, boundary_channel = self._modules['m' + str(i)](previous, tmp_out)
-
-            ll = hg
-            ll = self._modules['top_m_' + str(i)](ll)
-
-            ll = F.relu(self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll)), True)
-
-            # Predict heatmaps
-            tmp_out = self._modules['l' + str(i)](ll)
-            if self.end_relu:
-                tmp_out = F.relu(tmp_out)  # HACK: Added relu
-            outputs.append(tmp_out)
-            boundary_channels.append(boundary_channel)
-
-            if i < self.num_modules - 1:
-                ll = self._modules['bl' + str(i)](ll)
-                tmp_out_ = self._modules['al' + str(i)](tmp_out)
-                previous = previous + ll + tmp_out_
-
-        return outputs, boundary_channels
-
-    def get_landmarks(self, img):
-        H, W, _ = img.shape
-        offset = W / 64, H / 64, 0, 0
-
-        img = cv2.resize(img, (256, 256))
-        inp = img[..., ::-1]
-        inp = torch.from_numpy(np.ascontiguousarray(inp.transpose((2, 0, 1)))).float()
-        inp = inp.to(self.device)
-        inp.div_(255.0).unsqueeze_(0)
-
-        outputs, _ = self.forward(inp)
-        out = outputs[-1][:, :-1, :, :]
-        heatmaps = out.detach().cpu().numpy()
-
-        pred = calculate_points(heatmaps).reshape(-1, 2)
-
-        pred *= offset[:2]
-        pred += offset[-2:]
-
-        return pred

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/reinforcement_learning/README.md

@@ -1,22 +0,0 @@
-# Overview
-
-These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers. 
-There are two ways to use the script, `run_diffuser_locomotion.py`.
-
-The key option is a change of the variable `n_guide_steps`. 
-When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment.
-By default, `n_guide_steps=2` to match the original implementation.
- 
-
-You will need some RL specific requirements to run the examples:
-
-```
-pip install -f https://download.pytorch.org/whl/torch_stable.html \
-                free-mujoco-py \
-                einops \
-                gym==0.24.1 \
-                protobuf==3.20.1 \
-                git+https://github.com/rail-berkeley/d4rl.git \
-                mediapy \
-                Pillow==9.0.0
-```

spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/ssd300_coco_v1.py

@@ -1,79 +0,0 @@
-_base_ = [
-    '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
-]
-# model settings
-input_size = 300
-model = dict(
-    bbox_head=dict(
-        type='SSDHead',
-        anchor_generator=dict(
-            type='LegacySSDAnchorGenerator',
-            scale_major=False,
-            input_size=input_size,
-            basesize_ratio_range=(0.15, 0.9),
-            strides=[8, 16, 32, 64, 100, 300],
-            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
-        bbox_coder=dict(
-            type='LegacyDeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.1, 0.1, 0.2, 0.2])))
-# dataset settings
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='Expand',
-        mean=img_norm_cfg['mean'],
-        to_rgb=img_norm_cfg['to_rgb'],
-        ratio_range=(1, 4)),
-    dict(
-        type='MinIoURandomCrop',
-        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
-        min_crop_size=0.3),
-    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(300, 300),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=False),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=3,
-    train=dict(
-        _delete_=True,
-        type='RepeatDataset',
-        times=5,
-        dataset=dict(
-            type=dataset_type,
-            ann_file=data_root + 'annotations/instances_train2017.json',
-            img_prefix=data_root + 'train2017/',
-            pipeline=train_pipeline)),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
-optimizer_config = dict(_delete_=True)
-dist_params = dict(backend='nccl', port=29555)

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Apex-X/GODROOP/README.md

@@ -1,13 +0,0 @@
----
-title: GODROOP
-emoji: 🐢
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/network/xmlrpc.py

@@ -1,60 +0,0 @@
-"""xmlrpclib.Transport implementation
-"""
-
-import logging
-import urllib.parse
-import xmlrpc.client
-from typing import TYPE_CHECKING, Tuple
-
-from pip._internal.exceptions import NetworkConnectionError
-from pip._internal.network.session import PipSession
-from pip._internal.network.utils import raise_for_status
-
-if TYPE_CHECKING:
-    from xmlrpc.client import _HostType, _Marshallable
-
-logger = logging.getLogger(__name__)
-
-
-class PipXmlrpcTransport(xmlrpc.client.Transport):
-    """Provide a `xmlrpclib.Transport` implementation via a `PipSession`
-    object.
-    """
-
-    def __init__(
-        self, index_url: str, session: PipSession, use_datetime: bool = False
-    ) -> None:
-        super().__init__(use_datetime)
-        index_parts = urllib.parse.urlparse(index_url)
-        self._scheme = index_parts.scheme
-        self._session = session
-
-    def request(
-        self,
-        host: "_HostType",
-        handler: str,
-        request_body: bytes,
-        verbose: bool = False,
-    ) -> Tuple["_Marshallable", ...]:
-        assert isinstance(host, str)
-        parts = (self._scheme, host, handler, None, None, None)
-        url = urllib.parse.urlunparse(parts)
-        try:
-            headers = {"Content-Type": "text/xml"}
-            response = self._session.post(
-                url,
-                data=request_body,
-                headers=headers,
-                stream=True,
-            )
-            raise_for_status(response)
-            self.verbose = verbose
-            return self.parse_response(response.raw)
-        except NetworkConnectionError as exc:
-            assert exc.response
-            logger.critical(
-                "HTTP error %s while getting %s",
-                exc.response.status_code,
-                url,
-            )
-            raise

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/live_render.py

@@ -1,113 +0,0 @@
-import sys
-from typing import Optional, Tuple
-
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from pip._vendor.typing_extensions import Literal  # pragma: no cover
-
-
-from ._loop import loop_last
-from .console import Console, ConsoleOptions, RenderableType, RenderResult
-from .control import Control
-from .segment import ControlType, Segment
-from .style import StyleType
-from .text import Text
-
-VerticalOverflowMethod = Literal["crop", "ellipsis", "visible"]
-
-
-class LiveRender:
-    """Creates a renderable that may be updated.
-
-    Args:
-        renderable (RenderableType): Any renderable object.
-        style (StyleType, optional): An optional style to apply to the renderable. Defaults to "".
-    """
-
-    def __init__(
-        self,
-        renderable: RenderableType,
-        style: StyleType = "",
-        vertical_overflow: VerticalOverflowMethod = "ellipsis",
-    ) -> None:
-        self.renderable = renderable
-        self.style = style
-        self.vertical_overflow = vertical_overflow
-        self._shape: Optional[Tuple[int, int]] = None
-
-    def set_renderable(self, renderable: RenderableType) -> None:
-        """Set a new renderable.
-
-        Args:
-            renderable (RenderableType): Any renderable object, including str.
-        """
-        self.renderable = renderable
-
-    def position_cursor(self) -> Control:
-        """Get control codes to move cursor to beginning of live render.
-
-        Returns:
-            Control: A control instance that may be printed.
-        """
-        if self._shape is not None:
-            _, height = self._shape
-            return Control(
-                ControlType.CARRIAGE_RETURN,
-                (ControlType.ERASE_IN_LINE, 2),
-                *(
-                    (
-                        (ControlType.CURSOR_UP, 1),
-                        (ControlType.ERASE_IN_LINE, 2),
-                    )
-                    * (height - 1)
-                )
-            )
-        return Control()
-
-    def restore_cursor(self) -> Control:
-        """Get control codes to clear the render and restore the cursor to its previous position.
-
-        Returns:
-            Control: A Control instance that may be printed.
-        """
-        if self._shape is not None:
-            _, height = self._shape
-            return Control(
-                ControlType.CARRIAGE_RETURN,
-                *((ControlType.CURSOR_UP, 1), (ControlType.ERASE_IN_LINE, 2)) * height
-            )
-        return Control()
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-
-        renderable = self.renderable
-        style = console.get_style(self.style)
-        lines = console.render_lines(renderable, options, style=style, pad=False)
-        shape = Segment.get_shape(lines)
-
-        _, height = shape
-        if height > options.size.height:
-            if self.vertical_overflow == "crop":
-                lines = lines[: options.size.height]
-                shape = Segment.get_shape(lines)
-            elif self.vertical_overflow == "ellipsis":
-                lines = lines[: (options.size.height - 1)]
-                overflow_text = Text(
-                    "...",
-                    overflow="crop",
-                    justify="center",
-                    end="",
-                    style="live.ellipsis",
-                )
-                lines.append(list(console.render(overflow_text)))
-                shape = Segment.get_shape(lines)
-        self._shape = shape
-
-        new_line = Segment.line()
-        for last, line in loop_last(lines):
-            yield from line
-            if not last:
-                yield new_line

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/_manylinux.py

@@ -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" if self.e_ident_data == self.ELFDATA2LSB else ">H"
-        format_i = "<I" if self.e_ident_data == self.ELFDATA2LSB else ">I"
-        format_q = "<Q" if self.e_ident_data == self.ELFDATA2LSB else ">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<major>[0-9]+)\.(?P<minor>[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)

spaces/Banbri/zcvzcv/src/app/main.tsx

@@ -1,155 +0,0 @@
-"use client"
-
-import { useEffect, useState, useTransition } from "react"
-
-import { cn } from "@/lib/utils"
-import { TopMenu } from "./interface/top-menu"
-import { fonts } from "@/lib/fonts"
-import { useStore } from "./store"
-import { Zoom } from "./interface/zoom"
-import { getStory } from "./queries/getStory"
-import { BottomBar } from "./interface/bottom-bar"
-import { Page } from "./interface/page"
-import { LLMResponse } from "@/types"
-
-export default function Main() {
-  const [_isPending, startTransition] = useTransition()
-
-  const isGeneratingStory = useStore(state => state.isGeneratingStory)
-  const setGeneratingStory = useStore(state => state.setGeneratingStory)
-
-  const font = useStore(state => state.font)
-  const preset = useStore(state => state.preset)
-  const prompt = useStore(state => state.prompt)
-
-  const setLayouts = useStore(state => state.setLayouts)
-
-  const setPanels = useStore(state => state.setPanels)
-  const setCaptions = useStore(state => state.setCaptions)
-
-  const zoomLevel = useStore(state => state.zoomLevel)
-
-  const [waitABitMore, setWaitABitMore] = useState(false)
-
-  // react to prompt changes
-  useEffect(() => {
-    if (!prompt) { return }
-
-    startTransition(async () => {
-      setWaitABitMore(false)
-      setGeneratingStory(true)
-
-      // I don't think we are going to need a rate limiter on the LLM part anymore
-      const enableRateLimiter = false // `${process.env.NEXT_PUBLIC_ENABLE_RATE_LIMITER}`  === "true"
-
-      const nbPanels = 4
-
-      let llmResponse: LLMResponse = []
-
-      try {
-        llmResponse = await getStory({ preset, prompt })
-        console.log("LLM responded:", llmResponse)
-
-      } catch (err) {
-        console.log("LLM step failed due to:", err)
-        console.log("we are now switching to a degraded mode, using 4 similar panels")
-        
-        llmResponse = []
-        for (let p = 0; p < nbPanels; p++) {
-          llmResponse.push({
-            panel: p,
-            instructions: `${prompt} ${".".repeat(p)}`,
-            caption: "(Sorry, LLM generation failed: using degraded mode)"
-          })
-        }
-        console.error(err)
-      }
-
-      // we have to limit the size of the prompt, otherwise the rest of the style won't be followed
-
-      let limitedPrompt = prompt.slice(0, 77)
-      if (limitedPrompt.length !== prompt.length) {
-        console.log("Sorry folks, the prompt was cut to:", limitedPrompt)
-      }
-
-      const panelPromptPrefix = preset.imagePrompt(limitedPrompt).join(", ")
-
-      const newPanels: string[] = []
-      const newCaptions: string[] = []
-      setWaitABitMore(true)
-      console.log("Panel prompts for SDXL:")
-      for (let p = 0; p < nbPanels; p++) {
-        newCaptions.push(llmResponse[p]?.caption || "...")
-        const newPanel = [panelPromptPrefix, llmResponse[p]?.instructions || ""].map(chunk => chunk).join(", ")
-        newPanels.push(newPanel)
-        console.log(newPanel)
-      }
-   
-      setCaptions(newCaptions)
-      setPanels(newPanels)
-
-      setTimeout(() => {
-        setGeneratingStory(false)
-        setWaitABitMore(false)
-      }, enableRateLimiter ? 12000 : 0)
- 
-    })
-  }, [prompt, preset?.label]) // important: we need to react to preset changes too
-
-  return (
-    <div>
-      <TopMenu />
-      <div className={cn(
-        `flex items-start w-screen h-screen pt-24 md:pt-[72px] overflow-y-scroll`,
-        `transition-all duration-200 ease-in-out`,
-        zoomLevel > 105 ? `px-0` : `pl-1 pr-8 md:pl-16 md:pr-16`,
-        `print:pt-0 print:px-0 print:pl-0 print:pr-0`,
-        fonts.actionman.className
-      )}>
-        <div
-          className={cn(
-            `flex flex-col w-full`,
-            zoomLevel > 105 ? `items-start` : `items-center`
-          )}>
-          <div
-            className={cn(
-              `comic-page`,
-              `flex flex-col md:flex-row md:space-x-16 md:items-center md:justify-start`,
-            )}
-            style={{
-              width: `${zoomLevel}%`
-            }}>
-            <Page page={0} />
-
-            {/*
-            // we could support multiple pages here,
-            // but let's disable it for now
-            <Page page={1} />
-            */}
-          </div>
-        </div>
-      </div>
-      <Zoom />
-      <BottomBar />
-      <div className={cn(
-        `print:hidden`,
-        `z-20 fixed inset-0`,
-        `flex flex-row items-center justify-center`,
-        `transition-all duration-300 ease-in-out`,
-        isGeneratingStory
-          ? `bg-zinc-100/10 backdrop-blur-md`
-          : `bg-zinc-100/0 backdrop-blur-none pointer-events-none`,
-        fonts.actionman.className
-      )}>
-        <div className={cn(
-          `text-center text-xl text-stone-700 w-[70%]`,
-          isGeneratingStory ? ``: `scale-0 opacity-0`,
-          `transition-all duration-300 ease-in-out`,
-        )}>
-          {waitABitMore ? `Story is ready, but server is a bit busy!`: 'Generating a new story..'}<br/>
-          {waitABitMore ? `Please hold tight..` : ''}
-        </div>
-      </div>
-    </div>
-  )
-}

spaces/Bart92/RVC_HF/infer/lib/infer_pack/modules.py

@@ -1,521 +0,0 @@
-import copy
-import math
-
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, weight_norm
-
-from infer.lib.infer_pack import commons
-from infer.lib.infer_pack.commons import get_padding, init_weights
-from infer.lib.infer_pack.transforms import piecewise_rational_quadratic_transform
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x

spaces/Benson/text-generation/Examples/Descargar Clicker Hroes Mod Apk.md

@@ -1,70 +0,0 @@
-<br />
-<h1>Descargar Clicker Heroes Mod APK: Una guía para principiantes</h1>
-<p>Si usted está buscando un divertido y adictivo juego de ocio que le mantendrá entretenido durante horas, es posible que desee probar Clicker Heroes. Este juego es uno de los juegos de clickers más populares del mercado, con millones de descargas y críticas positivas. Pero lo que si quieres disfrutar del juego sin limitaciones o restricciones? Ahí es donde Clicker Heroes mod apk entra en juego. En este artículo, le diremos todo lo que necesita saber sobre Clicker Heroes, por qué debe descargar su apk mod, y cómo hacerlo de forma segura y fácil. </p>
-<h2>descargar clicker héroes mod apk</h2><br /><p><b><b>Download File</b> >>> <a href="https://bltlly.com/2v6JzL">https://bltlly.com/2v6JzL</a></b></p><br /><br />
- <h2>¿Qué es Clicker Heroes? </h2>
-<p>Clicker Heroes es un juego desarrollado por Playsaurus, un estudio especializado en juegos casuales y ociosos. El juego fue lanzado en 2014 para navegadores, y posteriormente portado a dispositivos móviles y consolas. El juego ha recibido varias actualizaciones y expansiones a lo largo de los años, añadiendo nuevas características y contenido. </p>
- <h3>El juego de Clicker Heroes</h3>
-<p>La jugabilidad de Clicker Heroes es simple y directa. Empiezas tocando la pantalla para matar monstruos y ganar oro. Puedes usar el oro para contratar y actualizar héroes, que te ayudarán a luchar contra más monstruos y jefes. También puedes desbloquear habilidades y habilidades que aumentarán tu daño y velocidad. El juego no tiene fin, ya que siempre puedes avanzar a niveles y mundos más altos, donde te enfrentarás a enemigos y desafíos más fuertes. </p>
- <h3>Las características de Clicker Heroes</h3>
-<p>Algunas de las características que hacen que Clicker Heroes se destaque de otros juegos de clicker son:</p>
-<p></p>
-<ul>
-<li>Más de 1000 héroes para recoger y actualizar, cada uno con sus propias habilidades y habilidades únicas. </li>
-<li>Más de 10.000 niveles para explorar, cada uno con diferentes temas y entornos. </li>
-<li>Una variedad de modos de juego, como incursiones, clanes, torneos, eventos, misiones, logros y más. </li>
-<li>Un estilo gráfico vibrante y colorido, con animaciones y efectos suaves. </li>
-
-<li>Una comunidad amigable y activa, con foros, wikis, guías, consejos, trucos y más. </li>
-</ul>
- <h2>¿Por qué descargar Clicker Heroes mod apk? </h2>
-<p>Aunque Clicker Heroes es un juego gratuito, también tiene algunas compras en la aplicación que pueden mejorar tu experiencia de juego. Por ejemplo, puedes comprar rubíes, que son la moneda premium del juego. Puedes usar rubíes para comprar pieles, cofres, dorados, auto-clickers, timelapses y más. Sin embargo, estas compras pueden ser bastante caras, especialmente si desea obtener los mejores artículos y mejoras. Es por eso que algunos jugadores prefieren descargar Clicker Heroes mod apk en su lugar. </p>
- <h3>Los beneficios de Clicker Heroes mod apk</h3>
-<p>Clicker Heroes mod apk es una versión modificada del juego original que le da acceso a recursos y características ilimitadas. Algunos de los beneficios de descargar Clicker Heroes mod apk son:</p>
-<ul>
-<li>Puedes obtener rubíes ilimitados gratis, sin gastar dinero real. </li>
-<li> Puedes desbloquear todos los héroes y sus mejoras al instante, sin moler o esperar. </li>
-<li>Puedes saltarte cualquier nivel o jefe que encuentres demasiado duro o aburrido. </li>
-<li>Puedes personalizar la configuración del juego según tus preferencias. </li>
-<li>Puedes disfrutar del juego sin anuncios ni interrupciones. </li>
-</ul>
- <h3>Los inconvenientes de Clicker Heroes mod apk</h3>
-<p>Sin embargo, descargar Clicker Heroes mod apk también tiene algunos inconvenientes que usted debe tener en cuenta. Algunos de los inconvenientes de descargar Clicker Heroes mod apk son:</p>
-<ul>
-<li> Es posible que se enfrentan a algunos problemas de compatibilidad o rendimiento, como el apk mod no puede ser actualizado o optimizado para su dispositivo. </li>
-<li> Usted puede encontrar algunos errores o fallos, como el apk mod no puede ser probado o verificado por la calidad. </li>
-<li>Usted puede arriesgarse a perder su progreso o datos, como el apk mod no puede ser sincronizado o respaldado con su cuenta. </li>
-<li>Usted puede violar los términos y condiciones del juego, como el apk mod puede ser considerado como trampa o piratería. </li>
-
-</ul>
- <h2>Cómo descargar e instalar Clicker Heroes mod apk? </h2>
-<p>Si usted ha decidido descargar Clicker Heroes mod apk, usted debe seguir estos pasos cuidadosamente para asegurar una instalación segura y exitosa. Antes de proceder, asegúrese de que tiene suficiente espacio de almacenamiento en su dispositivo y que tiene una conexión a Internet estable. </p>
- <h3>Paso 1: Encuentra una fuente confiable</h3>
-<p>El primer paso es encontrar una fuente confiable que ofrece Clicker Heroes mod apk para descargar. Usted puede buscar en línea para varios sitios web o plataformas que proporcionan este servicio, pero tenga cuidado de los sitios falsos o estafa que pueden engañar a descargar algo más. También puedes consultar las reseñas y valoraciones de otros usuarios para ver si han tenido una buena experiencia con la fuente. Alternativamente, puede utilizar el siguiente enlace para descargar Clicker Heroes mod apk de una fuente de confianza:</p>
-<p><a href=">Haga clic aquí para descargar Clicker Heroes mod apk</a></p>
- <h3>Paso 2: Descargar el archivo apk</h3>
-<p>El siguiente paso es descargar el archivo apk de Clicker Heroes mod apk de la fuente. Puede hacer esto haciendo clic en el botón de descarga o enlace, y luego esperar a que el archivo se descargue en su dispositivo. El tamaño del archivo puede variar dependiendo de la versión y características de la apk mod, pero no debe tardar mucho en completarse. </p>
- <h3>Paso 3: Habilitar fuentes desconocidas</h3>
-<p>El tercer paso es habilitar fuentes desconocidas en su dispositivo. Esta es una configuración de seguridad que le impide instalar aplicaciones desde fuentes distintas de la tienda de aplicaciones oficial. Sin embargo, ya que está instalando un apk mod, es necesario habilitar esta opción temporalmente. Puede hacer esto yendo a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y luego activarlo. También puede ver un mensaje emergente pidiendo su permiso para instalar aplicaciones de fuentes desconocidas. Simplemente toque en permitir o aceptar para proceder. </p>
- <h3>Paso 4: Instalar el archivo apk</h3>
-
- <h3>Paso 5: Disfruta del juego</h3>
-<p>El paso final es disfrutar del juego. Puede hacerlo abriendo el icono de la aplicación en la pantalla de inicio o en el cajón de la aplicación, y luego lanzando el juego. Deberías ver una versión modificada de Clicker Heroes, con recursos y funciones ilimitadas. Ahora puedes jugar el juego tanto como quieras, sin limitaciones ni restricciones. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, Clicker Heroes es un divertido y adictivo juego de ocio que te mantendrá entretenido durante horas. Sin embargo, si desea disfrutar del juego sin limitaciones o restricciones, puede descargar Clicker Heroes mod apk lugar. Esto le dará acceso a recursos y características ilimitadas, pero también algunos inconvenientes que debe tener en cuenta. Para descargar e instalar Clicker Heroes mod apk de forma segura y fácil, puede seguir los pasos que hemos descrito en este artículo. Esperamos que esta guía haya sido útil e informativa para usted. ¡Feliz clic! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Clicker Heroes mod apk:</p>
-<ul>
-<li><b>Q: ¿Es seguro Clicker Heroes mod apk? </b></li>
-<li>A: Clicker Heroes mod apk es generalmente seguro, siempre y cuando se descarga desde una fuente confiable y siga los pasos de instalación correctamente. Sin embargo, siempre hay un riesgo de malware o virus al descargar cualquier apk mod, por lo que siempre debe escanear su dispositivo con una aplicación antivirus antes y después de instalarlo. </li>
-<li><b>Q: ¿Es Clicker Heroes mod apk legal? </b></li>
-<li>A: Clicker Heroes mod apk no es legal, ya que viola los términos y condiciones del juego. Al usarlo, estás haciendo trampa o hackeando el juego, lo que puede resultar en una prohibición o suspensión de tu cuenta. Por lo tanto, no recomendamos ni apoyamos el uso de Clicker Heroes mod apk, y le recomendamos que juegue el juego de forma justa y responsable. </li>
-<li><b>Q: Es Clicker Heroes mod apk compatible con mi dispositivo? </b></li>
-
-<li><b>Q: Es Clicker Heroes mod apk actualizado? </b></li>
-<li>A: Clicker Heroes mod apk se actualiza regularmente, como los desarrolladores tratan de mantenerse al día con la última versión y características del juego original. Sin embargo, puede haber algunos retrasos o discrepancias entre las actualizaciones, como el mod apk requiere más tiempo y esfuerzo para modificar y probar. Por lo tanto, siempre debe comprobar la fuente y la versión del apk mod antes de descargarlo, y asegúrese de que coincide con la versión actual del juego. </li>
-<li><b>Q: ¿Puedo jugar Clicker Heroes mod apk online? </b></li>
-<li>A: Clicker Heroes mod apk se puede jugar en línea, ya que se conecta a los mismos servidores y bases de datos que el juego original. Sin embargo, esto también significa que puede ser detectado por el sistema de seguridad del juego, que puede marcar su cuenta como sospechosa o fraudulenta. Esto puede resultar en una prohibición o suspensión de su cuenta, o una pérdida de su progreso o datos. Por lo tanto, le sugerimos que juegue Clicker Heroes mod apk offline, o use una VPN para ocultar su dirección IP y ubicación. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/discovery.py

@@ -1,282 +0,0 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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 logging
-import time
-import weakref
-
-from botocore import xform_name
-from botocore.exceptions import BotoCoreError, ConnectionError, HTTPClientError
-from botocore.model import OperationNotFoundError
-from botocore.utils import CachedProperty
-
-logger = logging.getLogger(__name__)
-
-
-class EndpointDiscoveryException(BotoCoreError):
-    pass
-
-
-class EndpointDiscoveryRequired(EndpointDiscoveryException):
-    """Endpoint Discovery is disabled but is required for this operation."""
-
-    fmt = 'Endpoint Discovery is not enabled but this operation requires it.'
-
-
-class EndpointDiscoveryRefreshFailed(EndpointDiscoveryException):
-    """Endpoint Discovery failed to the refresh the known endpoints."""
-
-    fmt = 'Endpoint Discovery failed to refresh the required endpoints.'
-
-
-def block_endpoint_discovery_required_operations(model, **kwargs):
-    endpoint_discovery = model.endpoint_discovery
-    if endpoint_discovery and endpoint_discovery.get('required'):
-        raise EndpointDiscoveryRequired()
-
-
-class EndpointDiscoveryModel:
-    def __init__(self, service_model):
-        self._service_model = service_model
-
-    @CachedProperty
-    def discovery_operation_name(self):
-        discovery_operation = self._service_model.endpoint_discovery_operation
-        return xform_name(discovery_operation.name)
-
-    @CachedProperty
-    def discovery_operation_keys(self):
-        discovery_operation = self._service_model.endpoint_discovery_operation
-        keys = []
-        if discovery_operation.input_shape:
-            keys = list(discovery_operation.input_shape.members.keys())
-        return keys
-
-    def discovery_required_for(self, operation_name):
-        try:
-            operation_model = self._service_model.operation_model(
-                operation_name
-            )
-            return operation_model.endpoint_discovery.get('required', False)
-        except OperationNotFoundError:
-            return False
-
-    def discovery_operation_kwargs(self, **kwargs):
-        input_keys = self.discovery_operation_keys
-        # Operation and Identifiers are only sent if there are Identifiers
-        if not kwargs.get('Identifiers'):
-            kwargs.pop('Operation', None)
-            kwargs.pop('Identifiers', None)
-        return {k: v for k, v in kwargs.items() if k in input_keys}
-
-    def gather_identifiers(self, operation, params):
-        return self._gather_ids(operation.input_shape, params)
-
-    def _gather_ids(self, shape, params, ids=None):
-        # Traverse the input shape and corresponding parameters, gathering
-        # any input fields labeled as an endpoint discovery id
-        if ids is None:
-            ids = {}
-        for member_name, member_shape in shape.members.items():
-            if member_shape.metadata.get('endpointdiscoveryid'):
-                ids[member_name] = params[member_name]
-            elif (
-                member_shape.type_name == 'structure' and member_name in params
-            ):
-                self._gather_ids(member_shape, params[member_name], ids)
-        return ids
-
-
-class EndpointDiscoveryManager:
-    def __init__(
-        self, client, cache=None, current_time=None, always_discover=True
-    ):
-        if cache is None:
-            cache = {}
-        self._cache = cache
-        self._failed_attempts = {}
-        if current_time is None:
-            current_time = time.time
-        self._time = current_time
-        self._always_discover = always_discover
-
-        # This needs to be a weak ref in order to prevent memory leaks on
-        # python 2.6
-        self._client = weakref.proxy(client)
-        self._model = EndpointDiscoveryModel(client.meta.service_model)
-
-    def _parse_endpoints(self, response):
-        endpoints = response['Endpoints']
-        current_time = self._time()
-        for endpoint in endpoints:
-            cache_time = endpoint.get('CachePeriodInMinutes')
-            endpoint['Expiration'] = current_time + cache_time * 60
-        return endpoints
-
-    def _cache_item(self, value):
-        if isinstance(value, dict):
-            return tuple(sorted(value.items()))
-        else:
-            return value
-
-    def _create_cache_key(self, **kwargs):
-        kwargs = self._model.discovery_operation_kwargs(**kwargs)
-        return tuple(self._cache_item(v) for k, v in sorted(kwargs.items()))
-
-    def gather_identifiers(self, operation, params):
-        return self._model.gather_identifiers(operation, params)
-
-    def delete_endpoints(self, **kwargs):
-        cache_key = self._create_cache_key(**kwargs)
-        if cache_key in self._cache:
-            del self._cache[cache_key]
-
-    def _describe_endpoints(self, **kwargs):
-        # This is effectively a proxy to whatever name/kwargs the service
-        # supports for endpoint discovery.
-        kwargs = self._model.discovery_operation_kwargs(**kwargs)
-        operation_name = self._model.discovery_operation_name
-        discovery_operation = getattr(self._client, operation_name)
-        logger.debug('Discovering endpoints with kwargs: %s', kwargs)
-        return discovery_operation(**kwargs)
-
-    def _get_current_endpoints(self, key):
-        if key not in self._cache:
-            return None
-        now = self._time()
-        return [e for e in self._cache[key] if now < e['Expiration']]
-
-    def _refresh_current_endpoints(self, **kwargs):
-        cache_key = self._create_cache_key(**kwargs)
-        try:
-            response = self._describe_endpoints(**kwargs)
-            endpoints = self._parse_endpoints(response)
-            self._cache[cache_key] = endpoints
-            self._failed_attempts.pop(cache_key, None)
-            return endpoints
-        except (ConnectionError, HTTPClientError):
-            self._failed_attempts[cache_key] = self._time() + 60
-            return None
-
-    def _recently_failed(self, cache_key):
-        if cache_key in self._failed_attempts:
-            now = self._time()
-            if now < self._failed_attempts[cache_key]:
-                return True
-            del self._failed_attempts[cache_key]
-        return False
-
-    def _select_endpoint(self, endpoints):
-        return endpoints[0]['Address']
-
-    def describe_endpoint(self, **kwargs):
-        operation = kwargs['Operation']
-        discovery_required = self._model.discovery_required_for(operation)
-
-        if not self._always_discover and not discovery_required:
-            # Discovery set to only run on required operations
-            logger.debug(
-                'Optional discovery disabled. Skipping discovery for Operation: %s'
-                % operation
-            )
-            return None
-
-        # Get the endpoint for the provided operation and identifiers
-        cache_key = self._create_cache_key(**kwargs)
-        endpoints = self._get_current_endpoints(cache_key)
-        if endpoints:
-            return self._select_endpoint(endpoints)
-        # All known endpoints are stale
-        recently_failed = self._recently_failed(cache_key)
-        if not recently_failed:
-            # We haven't failed to discover recently, go ahead and refresh
-            endpoints = self._refresh_current_endpoints(**kwargs)
-            if endpoints:
-                return self._select_endpoint(endpoints)
-        # Discovery has failed recently, do our best to get an endpoint
-        logger.debug('Endpoint Discovery has failed for: %s', kwargs)
-        stale_entries = self._cache.get(cache_key, None)
-        if stale_entries:
-            # We have stale entries, use those while discovery is failing
-            return self._select_endpoint(stale_entries)
-        if discovery_required:
-            # It looks strange to be checking recently_failed again but,
-            # this informs us as to whether or not we tried to refresh earlier
-            if recently_failed:
-                # Discovery is required and we haven't already refreshed
-                endpoints = self._refresh_current_endpoints(**kwargs)
-                if endpoints:
-                    return self._select_endpoint(endpoints)
-            # No endpoints even refresh, raise hard error
-            raise EndpointDiscoveryRefreshFailed()
-        # Discovery is optional, just use the default endpoint for now
-        return None
-
-
-class EndpointDiscoveryHandler:
-    def __init__(self, manager):
-        self._manager = manager
-
-    def register(self, events, service_id):
-        events.register(
-            'before-parameter-build.%s' % service_id, self.gather_identifiers
-        )
-        events.register_first(
-            'request-created.%s' % service_id, self.discover_endpoint
-        )
-        events.register('needs-retry.%s' % service_id, self.handle_retries)
-
-    def gather_identifiers(self, params, model, context, **kwargs):
-        endpoint_discovery = model.endpoint_discovery
-        # Only continue if the operation supports endpoint discovery
-        if endpoint_discovery is None:
-            return
-        ids = self._manager.gather_identifiers(model, params)
-        context['discovery'] = {'identifiers': ids}
-
-    def discover_endpoint(self, request, operation_name, **kwargs):
-        ids = request.context.get('discovery', {}).get('identifiers')
-        if ids is None:
-            return
-        endpoint = self._manager.describe_endpoint(
-            Operation=operation_name, Identifiers=ids
-        )
-        if endpoint is None:
-            logger.debug('Failed to discover and inject endpoint')
-            return
-        if not endpoint.startswith('http'):
-            endpoint = 'https://' + endpoint
-        logger.debug('Injecting discovered endpoint: %s', endpoint)
-        request.url = endpoint
-
-    def handle_retries(self, request_dict, response, operation, **kwargs):
-        if response is None:
-            return None
-
-        _, response = response
-        status = response.get('ResponseMetadata', {}).get('HTTPStatusCode')
-        error_code = response.get('Error', {}).get('Code')
-        if status != 421 and error_code != 'InvalidEndpointException':
-            return None
-
-        context = request_dict.get('context', {})
-        ids = context.get('discovery', {}).get('identifiers')
-        if ids is None:
-            return None
-
-        # Delete the cached endpoints, forcing a refresh on retry
-        # TODO: Improve eviction behavior to only evict the bad endpoint if
-        # there are multiple. This will almost certainly require a lock.
-        self._manager.delete_endpoints(
-            Operation=operation.name, Identifiers=ids
-        )
-        return 0

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

@@ -1,43 +0,0 @@
-"""
-    pygments.modeline
-    ~~~~~~~~~~~~~~~~~
-
-    A simple modeline parser (based on pymodeline).
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-__all__ = ['get_filetype_from_buffer']
-
-
-modeline_re = re.compile(r'''
-    (?: vi | vim | ex ) (?: [<=>]? \d* )? :
-    .* (?: ft | filetype | syn | syntax ) = ( [^:\s]+ )
-''', re.VERBOSE)
-
-
-def get_filetype_from_line(l):
-    m = modeline_re.search(l)
-    if m:
-        return m.group(1)
-
-
-def get_filetype_from_buffer(buf, max_lines=5):
-    """
-    Scan the buffer for modelines and return filetype if one is found.
-    """
-    lines = buf.splitlines()
-    for l in lines[-1:-max_lines-1:-1]:
-        ret = get_filetype_from_line(l)
-        if ret:
-            return ret
-    for i in range(max_lines, -1, -1):
-        if i < len(lines):
-            ret = get_filetype_from_line(lines[i])
-            if ret:
-                return ret
-
-    return None

spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/utils.py

@@ -1,802 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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 functools
-import logging
-import math
-import os
-import random
-import socket
-import stat
-import string
-import threading
-from collections import defaultdict
-
-from botocore.exceptions import IncompleteReadError, ReadTimeoutError
-
-from s3transfer.compat import SOCKET_ERROR, fallocate, rename_file
-
-MAX_PARTS = 10000
-# The maximum file size you can upload via S3 per request.
-# See: http://docs.aws.amazon.com/AmazonS3/latest/dev/UploadingObjects.html
-# and: http://docs.aws.amazon.com/AmazonS3/latest/dev/qfacts.html
-MAX_SINGLE_UPLOAD_SIZE = 5 * (1024**3)
-MIN_UPLOAD_CHUNKSIZE = 5 * (1024**2)
-logger = logging.getLogger(__name__)
-
-
-S3_RETRYABLE_DOWNLOAD_ERRORS = (
-    socket.timeout,
-    SOCKET_ERROR,
-    ReadTimeoutError,
-    IncompleteReadError,
-)
-
-
-def random_file_extension(num_digits=8):
-    return ''.join(random.choice(string.hexdigits) for _ in range(num_digits))
-
-
-def signal_not_transferring(request, operation_name, **kwargs):
-    if operation_name in ['PutObject', 'UploadPart'] and hasattr(
-        request.body, 'signal_not_transferring'
-    ):
-        request.body.signal_not_transferring()
-
-
-def signal_transferring(request, operation_name, **kwargs):
-    if operation_name in ['PutObject', 'UploadPart'] and hasattr(
-        request.body, 'signal_transferring'
-    ):
-        request.body.signal_transferring()
-
-
-def calculate_num_parts(size, part_size):
-    return int(math.ceil(size / float(part_size)))
-
-
-def calculate_range_parameter(
-    part_size, part_index, num_parts, total_size=None
-):
-    """Calculate the range parameter for multipart downloads/copies
-
-    :type part_size: int
-    :param part_size: The size of the part
-
-    :type part_index: int
-    :param part_index: The index for which this parts starts. This index starts
-        at zero
-
-    :type num_parts: int
-    :param num_parts: The total number of parts in the transfer
-
-    :returns: The value to use for Range parameter on downloads or
-        the CopySourceRange parameter for copies
-    """
-    # Used to calculate the Range parameter
-    start_range = part_index * part_size
-    if part_index == num_parts - 1:
-        end_range = ''
-        if total_size is not None:
-            end_range = str(total_size - 1)
-    else:
-        end_range = start_range + part_size - 1
-    range_param = f'bytes={start_range}-{end_range}'
-    return range_param
-
-
-def get_callbacks(transfer_future, callback_type):
-    """Retrieves callbacks from a subscriber
-
-    :type transfer_future: s3transfer.futures.TransferFuture
-    :param transfer_future: The transfer future the subscriber is associated
-        to.
-
-    :type callback_type: str
-    :param callback_type: The type of callback to retrieve from the subscriber.
-        Valid types include:
-            * 'queued'
-            * 'progress'
-            * 'done'
-
-    :returns: A list of callbacks for the type specified. All callbacks are
-        preinjected with the transfer future.
-    """
-    callbacks = []
-    for subscriber in transfer_future.meta.call_args.subscribers:
-        callback_name = 'on_' + callback_type
-        if hasattr(subscriber, callback_name):
-            callbacks.append(
-                functools.partial(
-                    getattr(subscriber, callback_name), future=transfer_future
-                )
-            )
-    return callbacks
-
-
-def invoke_progress_callbacks(callbacks, bytes_transferred):
-    """Calls all progress callbacks
-
-    :param callbacks: A list of progress callbacks to invoke
-    :param bytes_transferred: The number of bytes transferred. This is passed
-        to the callbacks. If no bytes were transferred the callbacks will not
-        be invoked because no progress was achieved. It is also possible
-        to receive a negative amount which comes from retrying a transfer
-        request.
-    """
-    # Only invoke the callbacks if bytes were actually transferred.
-    if bytes_transferred:
-        for callback in callbacks:
-            callback(bytes_transferred=bytes_transferred)
-
-
-def get_filtered_dict(original_dict, whitelisted_keys):
-    """Gets a dictionary filtered by whitelisted keys
-
-    :param original_dict: The original dictionary of arguments to source keys
-        and values.
-    :param whitelisted_key: A list of keys to include in the filtered
-        dictionary.
-
-    :returns: A dictionary containing key/values from the original dictionary
-        whose key was included in the whitelist
-    """
-    filtered_dict = {}
-    for key, value in original_dict.items():
-        if key in whitelisted_keys:
-            filtered_dict[key] = value
-    return filtered_dict
-
-
-class CallArgs:
-    def __init__(self, **kwargs):
-        """A class that records call arguments
-
-        The call arguments must be passed as keyword arguments. It will set
-        each keyword argument as an attribute of the object along with its
-        associated value.
-        """
-        for arg, value in kwargs.items():
-            setattr(self, arg, value)
-
-
-class FunctionContainer:
-    """An object that contains a function and any args or kwargs to call it
-
-    When called the provided function will be called with provided args
-    and kwargs.
-    """
-
-    def __init__(self, func, *args, **kwargs):
-        self._func = func
-        self._args = args
-        self._kwargs = kwargs
-
-    def __repr__(self):
-        return 'Function: {} with args {} and kwargs {}'.format(
-            self._func, self._args, self._kwargs
-        )
-
-    def __call__(self):
-        return self._func(*self._args, **self._kwargs)
-
-
-class CountCallbackInvoker:
-    """An abstraction to invoke a callback when a shared count reaches zero
-
-    :param callback: Callback invoke when finalized count reaches zero
-    """
-
-    def __init__(self, callback):
-        self._lock = threading.Lock()
-        self._callback = callback
-        self._count = 0
-        self._is_finalized = False
-
-    @property
-    def current_count(self):
-        with self._lock:
-            return self._count
-
-    def increment(self):
-        """Increment the count by one"""
-        with self._lock:
-            if self._is_finalized:
-                raise RuntimeError(
-                    'Counter has been finalized it can no longer be '
-                    'incremented.'
-                )
-            self._count += 1
-
-    def decrement(self):
-        """Decrement the count by one"""
-        with self._lock:
-            if self._count == 0:
-                raise RuntimeError(
-                    'Counter is at zero. It cannot dip below zero'
-                )
-            self._count -= 1
-            if self._is_finalized and self._count == 0:
-                self._callback()
-
-    def finalize(self):
-        """Finalize the counter
-
-        Once finalized, the counter never be incremented and the callback
-        can be invoked once the count reaches zero
-        """
-        with self._lock:
-            self._is_finalized = True
-            if self._count == 0:
-                self._callback()
-
-
-class OSUtils:
-    _MAX_FILENAME_LEN = 255
-
-    def get_file_size(self, filename):
-        return os.path.getsize(filename)
-
-    def open_file_chunk_reader(self, filename, start_byte, size, callbacks):
-        return ReadFileChunk.from_filename(
-            filename, start_byte, size, callbacks, enable_callbacks=False
-        )
-
-    def open_file_chunk_reader_from_fileobj(
-        self,
-        fileobj,
-        chunk_size,
-        full_file_size,
-        callbacks,
-        close_callbacks=None,
-    ):
-        return ReadFileChunk(
-            fileobj,
-            chunk_size,
-            full_file_size,
-            callbacks=callbacks,
-            enable_callbacks=False,
-            close_callbacks=close_callbacks,
-        )
-
-    def open(self, filename, mode):
-        return open(filename, mode)
-
-    def remove_file(self, filename):
-        """Remove a file, noop if file does not exist."""
-        # Unlike os.remove, if the file does not exist,
-        # then this method does nothing.
-        try:
-            os.remove(filename)
-        except OSError:
-            pass
-
-    def rename_file(self, current_filename, new_filename):
-        rename_file(current_filename, new_filename)
-
-    def is_special_file(cls, filename):
-        """Checks to see if a file is a special UNIX file.
-
-        It checks if the file is a character special device, block special
-        device, FIFO, or socket.
-
-        :param filename: Name of the file
-
-        :returns: True if the file is a special file. False, if is not.
-        """
-        # If it does not exist, it must be a new file so it cannot be
-        # a special file.
-        if not os.path.exists(filename):
-            return False
-        mode = os.stat(filename).st_mode
-        # Character special device.
-        if stat.S_ISCHR(mode):
-            return True
-        # Block special device
-        if stat.S_ISBLK(mode):
-            return True
-        # Named pipe / FIFO
-        if stat.S_ISFIFO(mode):
-            return True
-        # Socket.
-        if stat.S_ISSOCK(mode):
-            return True
-        return False
-
-    def get_temp_filename(self, filename):
-        suffix = os.extsep + random_file_extension()
-        path = os.path.dirname(filename)
-        name = os.path.basename(filename)
-        temp_filename = name[: self._MAX_FILENAME_LEN - len(suffix)] + suffix
-        return os.path.join(path, temp_filename)
-
-    def allocate(self, filename, size):
-        try:
-            with self.open(filename, 'wb') as f:
-                fallocate(f, size)
-        except OSError:
-            self.remove_file(filename)
-            raise
-
-
-class DeferredOpenFile:
-    def __init__(self, filename, start_byte=0, mode='rb', open_function=open):
-        """A class that defers the opening of a file till needed
-
-        This is useful for deferring opening of a file till it is needed
-        in a separate thread, as there is a limit of how many open files
-        there can be in a single thread for most operating systems. The
-        file gets opened in the following methods: ``read()``, ``seek()``,
-        and ``__enter__()``
-
-        :type filename: str
-        :param filename: The name of the file to open
-
-        :type start_byte: int
-        :param start_byte: The byte to seek to when the file is opened.
-
-        :type mode: str
-        :param mode: The mode to use to open the file
-
-        :type open_function: function
-        :param open_function: The function to use to open the file
-        """
-        self._filename = filename
-        self._fileobj = None
-        self._start_byte = start_byte
-        self._mode = mode
-        self._open_function = open_function
-
-    def _open_if_needed(self):
-        if self._fileobj is None:
-            self._fileobj = self._open_function(self._filename, self._mode)
-            if self._start_byte != 0:
-                self._fileobj.seek(self._start_byte)
-
-    @property
-    def name(self):
-        return self._filename
-
-    def read(self, amount=None):
-        self._open_if_needed()
-        return self._fileobj.read(amount)
-
-    def write(self, data):
-        self._open_if_needed()
-        self._fileobj.write(data)
-
-    def seek(self, where, whence=0):
-        self._open_if_needed()
-        self._fileobj.seek(where, whence)
-
-    def tell(self):
-        if self._fileobj is None:
-            return self._start_byte
-        return self._fileobj.tell()
-
-    def close(self):
-        if self._fileobj:
-            self._fileobj.close()
-
-    def __enter__(self):
-        self._open_if_needed()
-        return self
-
-    def __exit__(self, *args, **kwargs):
-        self.close()
-
-
-class ReadFileChunk:
-    def __init__(
-        self,
-        fileobj,
-        chunk_size,
-        full_file_size,
-        callbacks=None,
-        enable_callbacks=True,
-        close_callbacks=None,
-    ):
-        """
-
-        Given a file object shown below::
-
-            |___________________________________________________|
-            0          |                 |                 full_file_size
-                       |----chunk_size---|
-                    f.tell()
-
-        :type fileobj: file
-        :param fileobj: File like object
-
-        :type chunk_size: int
-        :param chunk_size: The max chunk size to read.  Trying to read
-            pass the end of the chunk size will behave like you've
-            reached the end of the file.
-
-        :type full_file_size: int
-        :param full_file_size: The entire content length associated
-            with ``fileobj``.
-
-        :type callbacks: A list of function(amount_read)
-        :param callbacks: Called whenever data is read from this object in the
-            order provided.
-
-        :type enable_callbacks: boolean
-        :param enable_callbacks: True if to run callbacks. Otherwise, do not
-            run callbacks
-
-        :type close_callbacks: A list of function()
-        :param close_callbacks: Called when close is called. The function
-            should take no arguments.
-        """
-        self._fileobj = fileobj
-        self._start_byte = self._fileobj.tell()
-        self._size = self._calculate_file_size(
-            self._fileobj,
-            requested_size=chunk_size,
-            start_byte=self._start_byte,
-            actual_file_size=full_file_size,
-        )
-        # _amount_read represents the position in the chunk and may exceed
-        # the chunk size, but won't allow reads out of bounds.
-        self._amount_read = 0
-        self._callbacks = callbacks
-        if callbacks is None:
-            self._callbacks = []
-        self._callbacks_enabled = enable_callbacks
-        self._close_callbacks = close_callbacks
-        if close_callbacks is None:
-            self._close_callbacks = close_callbacks
-
-    @classmethod
-    def from_filename(
-        cls,
-        filename,
-        start_byte,
-        chunk_size,
-        callbacks=None,
-        enable_callbacks=True,
-    ):
-        """Convenience factory function to create from a filename.
-
-        :type start_byte: int
-        :param start_byte: The first byte from which to start reading.
-
-        :type chunk_size: int
-        :param chunk_size: The max chunk size to read.  Trying to read
-            pass the end of the chunk size will behave like you've
-            reached the end of the file.
-
-        :type full_file_size: int
-        :param full_file_size: The entire content length associated
-            with ``fileobj``.
-
-        :type callbacks: function(amount_read)
-        :param callbacks: Called whenever data is read from this object.
-
-        :type enable_callbacks: bool
-        :param enable_callbacks: Indicate whether to invoke callback
-            during read() calls.
-
-        :rtype: ``ReadFileChunk``
-        :return: A new instance of ``ReadFileChunk``
-
-        """
-        f = open(filename, 'rb')
-        f.seek(start_byte)
-        file_size = os.fstat(f.fileno()).st_size
-        return cls(f, chunk_size, file_size, callbacks, enable_callbacks)
-
-    def _calculate_file_size(
-        self, fileobj, requested_size, start_byte, actual_file_size
-    ):
-        max_chunk_size = actual_file_size - start_byte
-        return min(max_chunk_size, requested_size)
-
-    def read(self, amount=None):
-        amount_left = max(self._size - self._amount_read, 0)
-        if amount is None:
-            amount_to_read = amount_left
-        else:
-            amount_to_read = min(amount_left, amount)
-        data = self._fileobj.read(amount_to_read)
-        self._amount_read += len(data)
-        if self._callbacks is not None and self._callbacks_enabled:
-            invoke_progress_callbacks(self._callbacks, len(data))
-        return data
-
-    def signal_transferring(self):
-        self.enable_callback()
-        if hasattr(self._fileobj, 'signal_transferring'):
-            self._fileobj.signal_transferring()
-
-    def signal_not_transferring(self):
-        self.disable_callback()
-        if hasattr(self._fileobj, 'signal_not_transferring'):
-            self._fileobj.signal_not_transferring()
-
-    def enable_callback(self):
-        self._callbacks_enabled = True
-
-    def disable_callback(self):
-        self._callbacks_enabled = False
-
-    def seek(self, where, whence=0):
-        if whence not in (0, 1, 2):
-            # Mimic io's error for invalid whence values
-            raise ValueError(f"invalid whence ({whence}, should be 0, 1 or 2)")
-
-        # Recalculate where based on chunk attributes so seek from file
-        # start (whence=0) is always used
-        where += self._start_byte
-        if whence == 1:
-            where += self._amount_read
-        elif whence == 2:
-            where += self._size
-
-        self._fileobj.seek(max(where, self._start_byte))
-        if self._callbacks is not None and self._callbacks_enabled:
-            # To also rewind the callback() for an accurate progress report
-            bounded_where = max(min(where - self._start_byte, self._size), 0)
-            bounded_amount_read = min(self._amount_read, self._size)
-            amount = bounded_where - bounded_amount_read
-            invoke_progress_callbacks(
-                self._callbacks, bytes_transferred=amount
-            )
-        self._amount_read = max(where - self._start_byte, 0)
-
-    def close(self):
-        if self._close_callbacks is not None and self._callbacks_enabled:
-            for callback in self._close_callbacks:
-                callback()
-        self._fileobj.close()
-
-    def tell(self):
-        return self._amount_read
-
-    def __len__(self):
-        # __len__ is defined because requests will try to determine the length
-        # of the stream to set a content length.  In the normal case
-        # of the file it will just stat the file, but we need to change that
-        # behavior.  By providing a __len__, requests will use that instead
-        # of stat'ing the file.
-        return self._size
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args, **kwargs):
-        self.close()
-
-    def __iter__(self):
-        # This is a workaround for http://bugs.python.org/issue17575
-        # Basically httplib will try to iterate over the contents, even
-        # if its a file like object.  This wasn't noticed because we've
-        # already exhausted the stream so iterating over the file immediately
-        # stops, which is what we're simulating here.
-        return iter([])
-
-
-class StreamReaderProgress:
-    """Wrapper for a read only stream that adds progress callbacks."""
-
-    def __init__(self, stream, callbacks=None):
-        self._stream = stream
-        self._callbacks = callbacks
-        if callbacks is None:
-            self._callbacks = []
-
-    def read(self, *args, **kwargs):
-        value = self._stream.read(*args, **kwargs)
-        invoke_progress_callbacks(self._callbacks, len(value))
-        return value
-
-
-class NoResourcesAvailable(Exception):
-    pass
-
-
-class TaskSemaphore:
-    def __init__(self, count):
-        """A semaphore for the purpose of limiting the number of tasks
-
-        :param count: The size of semaphore
-        """
-        self._semaphore = threading.Semaphore(count)
-
-    def acquire(self, tag, blocking=True):
-        """Acquire the semaphore
-
-        :param tag: A tag identifying what is acquiring the semaphore. Note
-            that this is not really needed to directly use this class but is
-            needed for API compatibility with the SlidingWindowSemaphore
-            implementation.
-        :param block: If True, block until it can be acquired. If False,
-            do not block and raise an exception if cannot be acquired.
-
-        :returns: A token (can be None) to use when releasing the semaphore
-        """
-        logger.debug("Acquiring %s", tag)
-        if not self._semaphore.acquire(blocking):
-            raise NoResourcesAvailable("Cannot acquire tag '%s'" % tag)
-
-    def release(self, tag, acquire_token):
-        """Release the semaphore
-
-        :param tag: A tag identifying what is releasing the semaphore
-        :param acquire_token:  The token returned from when the semaphore was
-            acquired. Note that this is not really needed to directly use this
-            class but is needed for API compatibility with the
-            SlidingWindowSemaphore implementation.
-        """
-        logger.debug(f"Releasing acquire {tag}/{acquire_token}")
-        self._semaphore.release()
-
-
-class SlidingWindowSemaphore(TaskSemaphore):
-    """A semaphore used to coordinate sequential resource access.
-
-    This class is similar to the stdlib BoundedSemaphore:
-
-    * It's initialized with a count.
-    * Each call to ``acquire()`` decrements the counter.
-    * If the count is at zero, then ``acquire()`` will either block until the
-      count increases, or if ``blocking=False``, then it will raise
-      a NoResourcesAvailable exception indicating that it failed to acquire the
-      semaphore.
-
-    The main difference is that this semaphore is used to limit
-    access to a resource that requires sequential access.  For example,
-    if I want to access resource R that has 20 subresources R_0 - R_19,
-    this semaphore can also enforce that you only have a max range of
-    10 at any given point in time.  You must also specify a tag name
-    when you acquire the semaphore.  The sliding window semantics apply
-    on a per tag basis.  The internal count will only be incremented
-    when the minimum sequence number for a tag is released.
-
-    """
-
-    def __init__(self, count):
-        self._count = count
-        # Dict[tag, next_sequence_number].
-        self._tag_sequences = defaultdict(int)
-        self._lowest_sequence = {}
-        self._lock = threading.Lock()
-        self._condition = threading.Condition(self._lock)
-        # Dict[tag, List[sequence_number]]
-        self._pending_release = {}
-
-    def current_count(self):
-        with self._lock:
-            return self._count
-
-    def acquire(self, tag, blocking=True):
-        logger.debug("Acquiring %s", tag)
-        self._condition.acquire()
-        try:
-            if self._count == 0:
-                if not blocking:
-                    raise NoResourcesAvailable("Cannot acquire tag '%s'" % tag)
-                else:
-                    while self._count == 0:
-                        self._condition.wait()
-            # self._count is no longer zero.
-            # First, check if this is the first time we're seeing this tag.
-            sequence_number = self._tag_sequences[tag]
-            if sequence_number == 0:
-                # First time seeing the tag, so record we're at 0.
-                self._lowest_sequence[tag] = sequence_number
-            self._tag_sequences[tag] += 1
-            self._count -= 1
-            return sequence_number
-        finally:
-            self._condition.release()
-
-    def release(self, tag, acquire_token):
-        sequence_number = acquire_token
-        logger.debug("Releasing acquire %s/%s", tag, sequence_number)
-        self._condition.acquire()
-        try:
-            if tag not in self._tag_sequences:
-                raise ValueError("Attempted to release unknown tag: %s" % tag)
-            max_sequence = self._tag_sequences[tag]
-            if self._lowest_sequence[tag] == sequence_number:
-                # We can immediately process this request and free up
-                # resources.
-                self._lowest_sequence[tag] += 1
-                self._count += 1
-                self._condition.notify()
-                queued = self._pending_release.get(tag, [])
-                while queued:
-                    if self._lowest_sequence[tag] == queued[-1]:
-                        queued.pop()
-                        self._lowest_sequence[tag] += 1
-                        self._count += 1
-                    else:
-                        break
-            elif self._lowest_sequence[tag] < sequence_number < max_sequence:
-                # We can't do anything right now because we're still waiting
-                # for the min sequence for the tag to be released.  We have
-                # to queue this for pending release.
-                self._pending_release.setdefault(tag, []).append(
-                    sequence_number
-                )
-                self._pending_release[tag].sort(reverse=True)
-            else:
-                raise ValueError(
-                    "Attempted to release unknown sequence number "
-                    "%s for tag: %s" % (sequence_number, tag)
-                )
-        finally:
-            self._condition.release()
-
-
-class ChunksizeAdjuster:
-    def __init__(
-        self,
-        max_size=MAX_SINGLE_UPLOAD_SIZE,
-        min_size=MIN_UPLOAD_CHUNKSIZE,
-        max_parts=MAX_PARTS,
-    ):
-        self.max_size = max_size
-        self.min_size = min_size
-        self.max_parts = max_parts
-
-    def adjust_chunksize(self, current_chunksize, file_size=None):
-        """Get a chunksize close to current that fits within all S3 limits.
-
-        :type current_chunksize: int
-        :param current_chunksize: The currently configured chunksize.
-
-        :type file_size: int or None
-        :param file_size: The size of the file to upload. This might be None
-            if the object being transferred has an unknown size.
-
-        :returns: A valid chunksize that fits within configured limits.
-        """
-        chunksize = current_chunksize
-        if file_size is not None:
-            chunksize = self._adjust_for_max_parts(chunksize, file_size)
-        return self._adjust_for_chunksize_limits(chunksize)
-
-    def _adjust_for_chunksize_limits(self, current_chunksize):
-        if current_chunksize > self.max_size:
-            logger.debug(
-                "Chunksize greater than maximum chunksize. "
-                "Setting to %s from %s." % (self.max_size, current_chunksize)
-            )
-            return self.max_size
-        elif current_chunksize < self.min_size:
-            logger.debug(
-                "Chunksize less than minimum chunksize. "
-                "Setting to %s from %s." % (self.min_size, current_chunksize)
-            )
-            return self.min_size
-        else:
-            return current_chunksize
-
-    def _adjust_for_max_parts(self, current_chunksize, file_size):
-        chunksize = current_chunksize
-        num_parts = int(math.ceil(file_size / float(chunksize)))
-
-        while num_parts > self.max_parts:
-            chunksize *= 2
-            num_parts = int(math.ceil(file_size / float(chunksize)))
-
-        if chunksize != current_chunksize:
-            logger.debug(
-                "Chunksize would result in the number of parts exceeding the "
-                "maximum. Setting to %s from %s."
-                % (chunksize, current_chunksize)
-            )
-
-        return chunksize

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/grouped_batch_sampler.py

@@ -1,47 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-from torch.utils.data.sampler import BatchSampler, Sampler
-
-
-class GroupedBatchSampler(BatchSampler):
-    """
-    Wraps another sampler to yield a mini-batch of indices.
-    It enforces that the batch only contain elements from the same group.
-    It also tries to provide mini-batches which follows an ordering which is
-    as close as possible to the ordering from the original sampler.
-    """
-
-    def __init__(self, sampler, group_ids, batch_size):
-        """
-        Args:
-            sampler (Sampler): Base sampler.
-            group_ids (list[int]): If the sampler produces indices in range [0, N),
-                `group_ids` must be a list of `N` ints which contains the group id of each sample.
-                The group ids must be a set of integers in the range [0, num_groups).
-            batch_size (int): Size of mini-batch.
-        """
-        if not isinstance(sampler, Sampler):
-            raise ValueError(
-                "sampler should be an instance of "
-                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
-            )
-        self.sampler = sampler
-        self.group_ids = np.asarray(group_ids)
-        assert self.group_ids.ndim == 1
-        self.batch_size = batch_size
-        groups = np.unique(self.group_ids).tolist()
-
-        # buffer the indices of each group until batch size is reached
-        self.buffer_per_group = {k: [] for k in groups}
-
-    def __iter__(self):
-        for idx in self.sampler:
-            group_id = self.group_ids[idx]
-            group_buffer = self.buffer_per_group[group_id]
-            group_buffer.append(idx)
-            if len(group_buffer) == self.batch_size:
-                yield group_buffer[:]  # yield a copy of the list
-                del group_buffer[:]
-
-    def __len__(self):
-        raise NotImplementedError("len() of GroupedBatchSampler is not well-defined.")

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/proposal_utils.py

@@ -1,57 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import math
-import torch
-
-from detectron2.structures import Instances
-
-
-def add_ground_truth_to_proposals(gt_boxes, proposals):
-    """
-    Call `add_ground_truth_to_proposals_single_image` for all images.
-
-    Args:
-        gt_boxes(list[Boxes]): list of N elements. Element i is a Boxes
-            representing the gound-truth for image i.
-        proposals (list[Instances]): list of N elements. Element i is a Instances
-            representing the proposals for image i.
-
-    Returns:
-        list[Instances]: list of N Instances. Each is the proposals for the image,
-            with field "proposal_boxes" and "objectness_logits".
-    """
-    assert gt_boxes is not None
-
-    assert len(proposals) == len(gt_boxes)
-    if len(proposals) == 0:
-        return proposals
-
-    return [
-        add_ground_truth_to_proposals_single_image(gt_boxes_i, proposals_i)
-        for gt_boxes_i, proposals_i in zip(gt_boxes, proposals)
-    ]
-
-
-def add_ground_truth_to_proposals_single_image(gt_boxes, proposals):
-    """
-    Augment `proposals` with ground-truth boxes from `gt_boxes`.
-
-    Args:
-        Same as `add_ground_truth_to_proposals`, but with gt_boxes and proposals
-        per image.
-
-    Returns:
-        Same as `add_ground_truth_to_proposals`, but for only one image.
-    """
-    device = proposals.objectness_logits.device
-    # Concatenating gt_boxes with proposals requires them to have the same fields
-    # Assign all ground-truth boxes an objectness logit corresponding to P(object) \approx 1.
-    gt_logit_value = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10)))
-
-    gt_logits = gt_logit_value * torch.ones(len(gt_boxes), device=device)
-    gt_proposal = Instances(proposals.image_size)
-
-    gt_proposal.proposal_boxes = gt_boxes
-    gt_proposal.objectness_logits = gt_logits
-    new_proposals = Instances.cat([proposals, gt_proposal])
-
-    return new_proposals

spaces/CVPR/GFPGAN-example/gfpgan/train.py

@@ -1,11 +0,0 @@
-# flake8: noqa
-import os.path as osp
-from basicsr.train import train_pipeline
-
-import gfpgan.archs
-import gfpgan.data
-import gfpgan.models
-
-if __name__ == '__main__':
-    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
-    train_pipeline(root_path)

spaces/CVPR/LIVE/pybind11/tests/test_union.py

@@ -1,9 +0,0 @@
-# -*- coding: utf-8 -*-
-from pybind11_tests import union_ as m
-
-
-def test_union():
-    instance = m.TestUnion()
-
-    instance.as_uint = 10
-    assert instance.as_int == 10

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/swap_ranges.h

@@ -1,47 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  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.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-template<typename DerivedPolicy,
-         typename ForwardIterator1,
-         typename ForwardIterator2>
-__host__ __device__
-  ForwardIterator2 swap_ranges(thrust::execution_policy<DerivedPolicy> &exec,
-                               ForwardIterator1 first1,
-                               ForwardIterator1 last1,
-                               ForwardIterator2 first2);
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/swap_ranges.inl>
-

spaces/CofAI/chat.b4/client/css/label.css

@@ -1,16 +0,0 @@
-label {
-	cursor: pointer;
-	text-indent: -9999px;
-	width: 50px;
-	height: 30px;
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-	display: block;
-	border-radius: 100px;
-	position: relative;
-	overflow: hidden;
-	transition: 0.33s;
-}

spaces/CofAI/chat.b4/server/bp.py

@@ -1,6 +0,0 @@
-from flask import Blueprint
-
-bp = Blueprint('bp', __name__,
-               template_folder='./../client/html',
-               static_folder='./../client',
-               static_url_path='assets')

spaces/Curranj/Regex_Generator/app.py

@@ -1,35 +0,0 @@
-import openai
-import gradio as gr
-import os
-
-
-#OpenAi call
-
-
-def gpt3(texts):
-    openai.api_key = os.environ["Secret"]
-    response = openai.Completion.create(
-      engine="code-davinci-002",
-      prompt= texts,
-          temperature=0,
-          max_tokens=750,
-          top_p=1,
-          frequency_penalty=0.0,
-          presence_penalty=0.0,
-          stop = ("'","#", "</code>")
-    )
-    x = response.choices[0].text 
-    
-    return x
-
-# Function to elicit regex response from model
-def greet(prompt):
-    txt= (f"""#---Regex Generator--- \n #Prompt: {prompt}\n#Regex String :\n#'""")
-    regex = gpt3(txt)
-    return regex
-
-
-#Code to set up Gradio UI
-iface = gr.Interface(greet, inputs = ["text"], outputs = "text",title="Natural Language to Regex ", description="Enter any prompt and get a regex statement back!")
-iface.launch()
-

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/PaletteFile.py

@@ -1,51 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read simple, teragon-style palette files
-#
-# History:
-#       97-08-23 fl     Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from ._binary import o8
-
-
-class PaletteFile:
-    """File handler for Teragon-style palette files."""
-
-    rawmode = "RGB"
-
-    def __init__(self, fp):
-        self.palette = [(i, i, i) for i in range(256)]
-
-        while True:
-            s = fp.readline()
-
-            if not s:
-                break
-            if s[:1] == b"#":
-                continue
-            if len(s) > 100:
-                msg = "bad palette file"
-                raise SyntaxError(msg)
-
-            v = [int(x) for x in s.split()]
-            try:
-                [i, r, g, b] = v
-            except ValueError:
-                [i, r] = v
-                g = b = r
-
-            if 0 <= i <= 255:
-                self.palette[i] = o8(r) + o8(g) + o8(b)
-
-        self.palette = b"".join(self.palette)
-
-    def getpalette(self):
-        return self.palette, self.rawmode

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/qu2cuPen.py

@@ -1,105 +0,0 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
-# Copyright 2023 Behdad Esfahbod. 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 fontTools.qu2cu import quadratic_to_curves
-from fontTools.pens.filterPen import ContourFilterPen
-from fontTools.pens.reverseContourPen import ReverseContourPen
-import math
-
-
-class Qu2CuPen(ContourFilterPen):
-    """A filter pen to convert quadratic bezier splines to cubic curves
-    using the FontTools SegmentPen protocol.
-
-    Args:
-
-        other_pen: another SegmentPen used to draw the transformed outline.
-        max_err: maximum approximation error in font units. For optimal results,
-            if you know the UPEM of the font, we recommend setting this to a
-            value equal, or close to UPEM / 1000.
-        reverse_direction: flip the contours' direction but keep starting point.
-        stats: a dictionary counting the point numbers of cubic segments.
-    """
-
-    def __init__(
-        self,
-        other_pen,
-        max_err,
-        all_cubic=False,
-        reverse_direction=False,
-        stats=None,
-    ):
-        if reverse_direction:
-            other_pen = ReverseContourPen(other_pen)
-        super().__init__(other_pen)
-        self.all_cubic = all_cubic
-        self.max_err = max_err
-        self.stats = stats
-
-    def _quadratics_to_curve(self, q):
-        curves = quadratic_to_curves(q, self.max_err, all_cubic=self.all_cubic)
-        if self.stats is not None:
-            for curve in curves:
-                n = str(len(curve) - 2)
-                self.stats[n] = self.stats.get(n, 0) + 1
-        for curve in curves:
-            if len(curve) == 4:
-                yield ("curveTo", curve[1:])
-            else:
-                yield ("qCurveTo", curve[1:])
-
-    def filterContour(self, contour):
-        quadratics = []
-        currentPt = None
-        newContour = []
-        for op, args in contour:
-            if op == "qCurveTo" and (
-                self.all_cubic or (len(args) > 2 and args[-1] is not None)
-            ):
-                if args[-1] is None:
-                    raise NotImplementedError(
-                        "oncurve-less contours with all_cubic not implemented"
-                    )
-                quadratics.append((currentPt,) + args)
-            else:
-                if quadratics:
-                    newContour.extend(self._quadratics_to_curve(quadratics))
-                    quadratics = []
-                newContour.append((op, args))
-            currentPt = args[-1] if args else None
-        if quadratics:
-            newContour.extend(self._quadratics_to_curve(quadratics))
-
-        if not self.all_cubic:
-            # Add back implicit oncurve points
-            contour = newContour
-            newContour = []
-            for op, args in contour:
-                if op == "qCurveTo" and newContour and newContour[-1][0] == "qCurveTo":
-                    pt0 = newContour[-1][1][-2]
-                    pt1 = newContour[-1][1][-1]
-                    pt2 = args[0]
-                    if (
-                        pt1 is not None
-                        and math.isclose(pt2[0] - pt1[0], pt1[0] - pt0[0])
-                        and math.isclose(pt2[1] - pt1[1], pt1[1] - pt0[1])
-                    ):
-                        newArgs = newContour[-1][1][:-1] + args
-                        newContour[-1] = (op, newArgs)
-                        continue
-
-                newContour.append((op, args))
-
-        return newContour

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-9a8f514c.js

@@ -1,2 +0,0 @@
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