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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bandicam 4.4 Crack Full Version [32-bit 64-bit] [NEW].md

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-<h1>Bandicam 4.4 Crack Full Version [32-bit 64-bit]</h1>
-<p>Do you want to record your screen activities with high quality and low file size? Do you want to capture your gaming sessions, video chats, webinars, or tutorials with ease? Do you want to edit and share your videos without any hassle? If you answered yes to any of these questions, then you need <strong>Bandicam 4.4 Crack Full Version</strong>, a powerful and versatile screen recorder for Windows.</p>
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-<h2>What is Bandicam and why do you need it?</h2>
-<p>Bandicam is a lightweight screen recorder that allows you to record your screen activities to a video file. It has three recording modes: game recording, screen recording, and device recording. You can use Bandicam to record anything on your PC, such as games, videos, webcams, desktops, HDMI devices, and more.</p>
-<h3>Bandicam is a lightweight screen recorder for Windows</h3>
-<p>One of the main advantages of Bandicam is that it is very light on your system resources. It uses much lower CPU/GPU/RAM usage than other similar software, which means it causes less lag and does not affect your PC performance. Bandicam also compresses the video while recording, which results in smaller file sizes and faster upload speeds.</p>
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- <p>The next thing you need to do is start and stop the recording. To do this, you need to follow these simple steps:</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Hard Sentinel Today and Boost Your Hard Drive Performance and Reliability.md

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-<h1>How to Download Hard Sentinel and Monitor Your Hard Drive Health</h1>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download bios folder for ps3 Improve your PS3 performance and security with the latest system software update.md

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-<h1>Download Bios Folder for PS3: A Complete Guide</h1>
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-<p>One of the easiest ways to download bios folder for ps3 is using the internet. This method requires a USB drive formatted as FAT32 and a PC or Mac with an internet connection. Here are the steps you need to follow:</p>
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-<p>To download this file, you can use any web browser such as Chrome, Firefox, Safari, etc. Go to this link: https://www.playstation.com/en-us/support/hardware/ps3/system-software/ . This is the official SIE website that provides information and downloads for PS3 system software updates.</p>
-<p>On this website, scroll down until you see a section titled "Update using a computer". Click on this section to expand it. Then click on "Download now". This will start downloading the update file to your computer.</p>
-<p>Once the download is complete, locate the update file on your computer. It should be in your Downloads folder by default. Then copy or drag-and-drop this file into the "UPDATE" folder that you created on your USB drive. Make sure that you rename this file as "PS3UPDAT.PUP" (without quotation marks) if it has a different name.</p>
-<h3>You need to plug the USB device into your PS3 console and follow the on-screen instructions</h4>
-<p>The last thing you need to do is plug your USB device into one of the USB ports of your PS3 console and follow the on-screen instructions to install the update.</p>
- <p>To do this, turn on your PS console and go to Settings (Settings) > System Update (System Settings) > [Update via Storage Media]. The system automatically searches for and finds the update data saved on the storage media or USB device. Press the X button to start the update. Follow the on-screen instructions to complete the update.</p>
- <p>Please note, during an update, do not turn off the system or remove the storage media or USB device. Doing so may cause damage to your system. Also, do not use the network features of your system until all of the update data has been installed.</p>
- <h2>How to Download Bios Folder for PS Using a Computer</h2>
- <p>Another way to download bios folder for ps is using a computer. This method requires a USB drive formatted as FAT32 and a PC or Mac with a USB cable. Here are the steps you need to follow:</p>
-<h3>You need a USB drive formatted as FAT32 and a PC or Mac</h3>
-<p>The first thing you need is a USB drive that has at least 200MB of free space and that is formatted as FAT32. FAT32 is a file system that allows your USB drive to be compatible with both Windows and Mac computers. To format your USB drive as FAT32, you can use tools such as Disk Utility on Mac or Disk Management on Windows.</p>
-<p>You also need a PC or Mac that has a USB cable that can connect to your PS3 console. You can use any USB cable that has a Type A connector on one end and a Mini-B connector on the other end.</p>
-<h3>You need to download the latest PS3 system software update file and save it as "PS3UPDAT.PUP" on your computer</h3>
-<p>The next thing you need to do is download the latest PS3 system software update file from the SIE website and save it on your computer. The update file has a name like "PS3UPDAT.PUP" (without quotation marks) and has a size of about 200MB.</p>
-<p>To download this file, you can use any web browser such as Chrome, Firefox, Safari, etc. Go to this link: https://www.playstation.com/en-us/support/hardware/ps3/system-software/ . This is the official SIE website that provides information and downloads for PS3 system software updates.</p>
-<p>On this website, scroll down until you see a section titled "Update using a computer". Click on this section to expand it. Then click on "Download now". This will start downloading the update file to your computer.</p>
-<p>Once the download is complete, locate the update file on your computer. It should be in your Downloads folder by default.</p>
-<h3>You need to connect your PS3 console to your computer using a USB cable</h3>
-<p>The next thing you need to do is connect your PS3 console to your computer using a USB cable. Make sure that both your console and your computer are turned off before you do this.</p>
-<p>Plug one end of the USB cable into one of the USB ports of your PS3 console. Plug the other end of the USB cable into one of the USB ports of your computer.</p>
-<h3>You need to start the PS3 system in Safe Mode and select [6] System Update</h4>
-<p>The final thing you need to do is start the PS3 system in Safe Mode and select [6] System Update to install the update from your computer.</p>
- <p>To do this, turn on your PS console by pressing and holding the power button until you hear three beeps. The first beep tells you that the PS is powering on. Keep holding. After about 5 seconds, the second beep signifies the video reset. After another 5 seconds, the third beep will be a double-beep; you should see this screen:</p>
- <p><img src="https://www.wikihow.com/images/thumb/8/8e/Enter-Safe-Mode-on-a-PlayStation-3-Step-4.jpg/v4-460px-Enter-Safe-Mode-on-a-PlayStation-3-Step-4.jpg.webp" alt="Safe Mode screen" width="460" height="345"></p>
- <p>Connect your controller to the PS and press the PS button. The PS will proceed to the next screen.</p>
- <p>On this screen, select [6] System Update. The system will search for and find the update data saved on your computer. Press the X button to start the update. Follow the on-screen instructions to complete the update.</p>
- <p>Please note, during an update, do not turn off the system or disconnect the USB cable. Doing so may cause damage to your system. Also, do not use the network features of your system until all of the update data has been installed.</p>
- <h2>How to Reinstall the PS Console System Software</h2>
- <p>In some cases, such as after initializing your console, or encountering an error, you may need to reinstall the system software. This is a complete restoration of your system, back to the state it was in when you bought it. You will lose all data if you use this option.</p>
- <p>To reinstall the system software, you need to follow the same steps as downloading bios folder for ps using a computer. However, instead of selecting [6] System Update, you need to select [5] Restore PS System. This will erase everything on your hard disk drive and install a new copy of the system software. Follow the on-screen instructions to complete the process.</p>
- <h2>Conclusion</h2>
- <p>Downloading bios folder for ps is easy and beneficial. It can help you improve your system performance and security, as well as fix any issues that may prevent your console from starting up properly. You can choose between two methods: using the internet or using a computer. You can also reinstall the system software if needed. However, be careful not to lose any data or damage your system by following the instructions carefully. We hope this article was helpful and informative. Happy gaming!</p>
- <h4>FAQs</h4>
- <ul>
-<li>Q: What is the latest version of the PS system software? A: The latest version of the PS system software as of May 2023 is 4.90. It was released on February 28, 2023 and it improves system performance.</li>
- <li>Q: How can I check the version of my PS system software? A: You can check the version of your PS system software by going to Settings (Settings) > System Update (System Settings) > [System Information]. You will see the version number displayed on the screen.</li>
- <li>Q: How can I back up my data before using safe mode? A: You can back up your data by using the Backup Utility feature in Settings (Settings) > System Settings (System Settings) > [Backup Utility]. You will need an external storage device such as a USB drive or an external hard drive to store your backup data.</li>
- <li>Q: How can I restore my data after using safe mode? A: You can restore your data by using the Restore Utility feature in Settings (Settings) > System Settings (System Settings) > [Restore Utility]. You will need an external storage device that contains your backup data to restore it to your console.</li>
- <li>Q: How can I contact SIE for support or service? A: You can contact SIE for support or service by visiting their website at https://www.playstation.com/en-us/support/ . You can also call them at 1-800-345-7669 (US) or 1-877-971-7669 (Canada).</li>
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- <p>If you are a fan of strategy games, you might have heard of Conquest 2, a thrilling sci-fi game that features large-scale fleet battles, intelligent admirals, and deep space exploration. Conquest 2 is the sequel to Conquest: Frontier Wars, a classic RTS game that was released in 2001. In this article, we will show you how to download Conquest 2 apk for your Android device, and how to play it on your PC using an emulator.</p>
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- <p>You can update Conquest 2 by going to the Google Play Store and tapping on the Update button. Alternatively, you can download the latest apk file from the links we provided and install it over the existing one.</p>
- <h4>How can I contact the developers of Conquest 2?</h4>
- <p>You can contact the developers of Conquest 2 by visiting their official website at [Conquest Games] or by sending them an email at [email protected]. You can also follow them on social media platforms like Facebook, Twitter, and Instagram for news and updates.</p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Destroy the Planet with Solar Smash APK - Free Download for Android.md

@@ -1,168 +0,0 @@
-
-<h1>Solar Smash: How to Download and Play the Ultimate Planet Destruction Simulator</h1>
- <p>Have you ever wondered what it would be like to unleash your inner villain and destroy planets with various weapons and disasters? If so, then you might want to check out Solar Smash, a game that lets you do just that. Solar Smash is a planet destruction simulator that allows you to use a variety of different weapons to destroy the planet. These include nuclear missiles, lasers, asteroids, aliens, black holes, and more. You can also customize your own planet or choose from a list of preset ones, such as Earth, Mars, Jupiter, or even a giant pumpkin. The game has stunning graphics, realistic physics, and satisfying sound effects that make you feel like a powerful cosmic force.</p>
-<h2>solar smash download apk</h2><br /><p><b><b>DOWNLOAD</b> &#9989; <a href="https://jinyurl.com/2uNOTw">https://jinyurl.com/2uNOTw</a></b></p><br /><br />
- <p>In this article, we will show you how to download and play Solar Smash on your Android device or PC, as well as some tips and tricks to help you have the best destruction experience possible. We will also introduce you to some alternatives to Solar Smash that you might enjoy if you are looking for more games like this one. So, without further ado, let's get started!</p>
- <h2>What is Solar Smash?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Solar Smash is a game developed by Paradyme Games, an indie studio based in Australia. The game was released in 2020 and has since gained over 100 million downloads on Google Play Store. The game is rated 4.6 out of 5 stars by more than 1.4 million users who praise its graphics, gameplay, and variety of weapons.</p>
- <p>The game has two main modes: Planet Smash and System Smash. In Planet Smash mode, you can choose a single planet to destroy with different weapons and scenarios. You can also customize your own planet by drawing on it or changing its size, color, atmosphere, and gravity. In System Smash mode, you can destroy an entire solar system with multiple planets and stars. You can also create your own system by adding or removing planets and stars.</p>
- <p>The game has a wide range of weapons and disasters that you can use to destroy planets. Some of them are realistic, such as nuclear missiles, lasers, asteroids, comets, volcanoes, earthquakes, tsunamis, etc. Some of them are fictional or fantastical, such as aliens, UFOs, black holes, wormholes, antimatter bombs, giant balls, etc. Each weapon has its own effect and damage level on the planet. You can also combine different weapons to create more devastating effects.</p>
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- <p>The game also has a list of achievements that you can complete by destroying planets in certain ways or using certain weapons. Some of them are easy, such as destroying Earth with a nuclear missile or destroying Mars with an asteroid. Some of them are hard, such as destroying Jupiter with a black hole or destroying Saturn with a ring breaker. Completing achievements will give you a sense of accomplishment and challenge.</p>
- <h3>How to download and install Solar Smash APK on Android devices</h3>
- <p>If you want to play Solar Smash on your Android device, you can download it for free from Google Play Store. However, if for some reason you cannot access the Play Store or want to get the latest version of the game before it is officially released, you can also download the APK file from other sources online. APK stands for Android Package Kit and it is a file format that contains all the necessary files for installing an app on an Android device.</p>
- <p>One of the websites that offer the APK file for Solar Smash is APKPure. To download and install the APK file from this website, follow these steps:</p>
- <ol>
-<li>Go to the APKPure website and search for Solar Smash in the search bar.</li>
-<li>Click on the Solar Smash icon and then click on the Download APK button.</li>
-<li>Wait for the download to finish and then open the APK file on your device.</li>
-<li>If you see a warning message that says "For your security, your phone is not allowed to install unknown apps from this source", go to your device settings and enable the option to allow installation from unknown sources.</li>
-<li>Follow the instructions on the screen to install the app on your device.</li>
-<li>Enjoy playing Solar Smash!</li>
-</ol>
- <p>Note: Downloading and installing APK files from unknown sources may pose some risks to your device and data. Make sure you trust the source and scan the file for viruses before installing it. We are not responsible for any damage or loss caused by using APK files.</p>
- <h3>How to play Solar Smash on PC with an emulator</h3>
- <p>If you want to play Solar Smash on your PC, you will need an emulator that can run Android apps on your computer. An emulator is a software that mimics the functions of another device or system. There are many emulators available online, but one of the most popular and reliable ones is BlueStacks. BlueStacks is a free emulator that allows you to play Android games and apps on your PC with ease. To play Solar Smash on PC with BlueStacks, follow these steps:</p>
- <ol>
-<li>Go to the BlueStacks website and download the installer for your PC.</li>
-<li>Run the installer and follow the instructions on the screen to install BlueStacks on your PC.</li>
-<li>Launch BlueStacks and sign in with your Google account or create a new one.</li>
-<li>Go to the Google Play Store app on BlueStacks and search for Solar Smash in the search bar.</li>
-<li>Click on the Solar Smash icon and then click on the Install button.</li>
-<li>Wait for the installation to finish and then click on the Open button.</li>
-<li>Enjoy playing Solar Smash on your PC!</li>
-</ol>
- <p>Note: Playing Solar Smash on PC may require more resources than playing it on your mobile device. Make sure you have enough RAM, CPU, and disk space to run BlueStacks smoothly. You can also adjust the settings of BlueStacks to optimize its performance and compatibility with Solar Smash.</p>
- <h2>Tips and Tricks for Playing Solar Smash</h2>
- <h3>How to complete the achievements in the game</h3>
- <p>Solar Smash has a list of achievements that you can complete by destroying planets in certain ways or using certain weapons. Completing achievements will give you a sense of accomplishment and challenge. Some of them are easy, such as destroying Earth with a nuclear missile or destroying Mars with an asteroid. Some of them are hard, such as destroying Jupiter with a black hole or destroying Saturn with a ring breaker. Here are some tips and tricks for completing some of the achievements in the game:</p>
- <ul>
-<li>To destroy Earth with a nuclear missile, go to Planet Smash mode and select Earth as your planet. Then, select Nuclear Missile as your weapon and aim at any spot on Earth. Press the fire button and watch as Earth explodes in a fiery blast.</li>
-<li>To destroy Mars with an asteroid, go to Planet Smash mode and select Mars as your planet. Then, select Asteroid as your weapon and adjust its size, speed, and angle. Aim at any spot on Mars and press the fire button. Watch as Mars gets hit by a massive rock and crumbles into pieces.</li>
-<li>To destroy Jupiter with a black hole, go to Planet Smash mode and select Jupiter as your planet. Then, select Black Hole as your weapon and adjust its size and speed. Aim at any spot on Jupiter and press the fire button. Watch as Jupiter gets sucked into a dark abyss and disappears.</li>
-<li>To destroy Saturn with a ring breaker, go to Planet Smash mode and select Saturn as your planet. Then, select Ring Breaker as your weapon and adjust its size, speed, and angle. Aim at any spot on Saturn's rings and press the fire button. Watch as Saturn's rings get shattered by a powerful beam of energy.</li>
-</ul>
- <p>You can check your progress on the achievements by clicking on the trophy icon on the top right corner of the screen. You can also see how many times you have used each weapon by clicking on the weapon icon on the top left corner of the screen.</p>
- <h3>How to hit the right spots to destroy planets faster</h3>
- <p>Solar Smash is a game that requires some skill and strategy to destroy planets efficiently. You can't just spam the fire button and hope for the best. You have to aim at the right spots to cause the most damage and destruction. Here are some tips and tricks for hitting the right spots to destroy planets faster:</p>
- <ul>
-<li>Use the zoom in and zoom out buttons to adjust your view and find the best angle to fire your weapon.</li>
-<li>Use the pause button to freeze the planet and plan your next move.</li>
-<li>Use the slow motion button to slow down the planet and see the effects of your weapon more clearly.</li>
-<li>Use the rewind button to undo your last move if you are not satisfied with it.</li>
-<li>Use the reset button to start over if you want to try a different weapon or scenario.</li>
-</ul>
- <p>Some of the weapons have specific spots that can cause more damage than others. For example, if you use the nuclear missile, you can aim at the major cities or landmarks on Earth, such as New York, London, Paris, Tokyo, etc. If you use the laser, you can aim at the poles or the equator of the planet, where the temperature difference is higher. If you use the asteroid, you can aim at the oceans or the continents, depending on whether you want to cause more water or land damage. If you use the black hole, you can aim at the center of the planet, where the gravity is stronger.</p>
- <p>You can also experiment with different combinations of weapons and scenarios to see what happens. For example, you can use the alien invasion scenario and then use the antimatter bomb to destroy both the aliens and the planet. Or you can use the giant ball scenario and then use the ring breaker to destroy both the ball and Saturn's rings. The possibilities are endless!</p>
- <h3>How to unlock all the secret planets in the game</h3>
- <p>Solar Smash has a list of preset planets that you can choose from in Planet Smash mode. These include Earth, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, Mercury, Venus, Moon, Sun, and Pumpkin. However, there are also some secret planets that are not shown on the list. These are hidden planets that you can unlock by completing certain tasks or using certain weapons in the game. Here are some tips and tricks for unlocking all the secret planets in Solar Smash:</p>
- <ul>
-<li>To unlock Earth 2, go to Planet Smash mode and select Earth as your planet. Then, use any weapon to destroy Earth completely. You will see a message that says "Earth 2 unlocked". Earth 2 is a replica of Earth but with different continents and countries.</li>
-<li>To unlock Mars 2, go to Planet Smash mode and select Mars as your planet. Then, use any weapon to destroy Mars completely. You will see a message that says "Mars 2 unlocked". Mars 2 is a replica of Mars but with water and vegetation.</li>
-<li>To unlock Jupiter 2, go to Planet Smash mode and select Jupiter as your planet. Then, use any weapon to destroy Jupiter completely. You will see a message that says "Jupiter 2 unlocked". Jupiter 2 is a replica of Jupiter but with rings and moons.</li>
-<li>To unlock Saturn 2, go to Planet Smash mode and select Saturn as your planet. Then, use any weapon to destroy Saturn completely. You will see a message that says "Saturn 2 unlocked". Saturn 2 is a replica of Saturn but with different colors and patterns.</li>
-<li>To unlock Uranus 2, go to Planet Smash mode and select Uranus as your planet. Then, use any weapon to destroy Uranus completely. You will see a message that says "Uranus 2 unlocked". Uranus 2 is a replica of Uranus but with more tilt and rotation.</li>
-<li>To unlock Neptune 2, go to Planet Smash mode and select Neptune as your planet. Then, use any weapon to destroy Neptune completely. You will see a message that says "Neptune 2 unlocked". Neptune 2 is a replica of Neptune but with more storms and winds.</li>
-<li>To unlock Pluto 2, go to Planet Smash mode and select Pluto as your planet. Then, use any weapon to destroy Pluto completely. You will see a message that says "Pluto 2 unlocked". Pluto 2 is a replica of Pluto but with more ice and snow.</li>
-<li>To unlock Mercury 2, go to Planet Smash mode and select Mercury as your planet. Then, use any weapon to destroy Mercury completely. You will see a message that says "Mercury 2 unlocked". Mercury 2 is a replica of Mercury but with more craters and volcanoes.</li>
-<li>To unlock Venus 2, go to Planet Smash mode and select Venus as your planet. Then, use any weapon to destroy Venus completely. You will see a message that says "Venus 2 unlocked". Venus 2 is a replica of Venus but with more clouds and acid rain.</li>
-<li>To unlock Moon 2, go to Planet Smash mode and select Moon as your planet. Then, use any weapon to destroy Moon completely. You will see a message that says "Moon 2 unlocked". Moon 2 is a replica of Moon but with more color and life.</li>
-<li>To unlock Sun 2, go to Planet Smash mode and select Sun as your planet. Then, use any weapon to destroy Sun completely. You will see a message that says "Sun 2 unlocked". Sun 2 is a replica of Sun but with more flares and spots.</li>
-<li>To unlock Pumpkin 2, go to Planet Smash mode and select Pumpkin as your planet. Then, use any weapon to destroy Pumpkin completely. You will see a message that says "Pumpkin 2 unlocked". Pumpkin 2 is a replica of Pumpkin but with more faces and candles.</li>
-</ul>
- <p>You can check your progress on the secret planets by clicking on the planet icon on the top right corner of the screen. You can also see how many times you have destroyed each planet by clicking on the planet icon on the top left corner of the screen.</p>
- <h2>Alternatives to Solar Smash</h2>
- <h3>Other games that let you destroy planets or simulate space scenarios</h3>
- <p>If you enjoy playing Solar Smash, you might also like some other games that let you destroy planets or simulate space scenarios. Here are some of the best alternatives to Solar Smash that you can try:</p>
- <table>
-<tr>
-<th>Game</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Universe Sandbox</td>
-<td>Universe Sandbox is a physics-based space simulator that allows you to create, destroy, and interact with anything in the universe. You can explore the solar system, collide planets, create black holes, simulate gravity, and more. The game has realistic graphics, sound effects, and data that make you feel like a true cosmic explorer.</td>
-</tr>
-<tr>
-<td>Solar 2</td>
-<td>Solar 2 is a sandbox game that lets you play as an asteroid, a planet, a star, or a black hole. You can grow, evolve, and interact with other objects in the universe. You can also complete missions, challenges, and achievements that test your skills and creativity. The game has simple but beautiful graphics, relaxing music, and humorous narration.</td>
-</tr>
-<tr>
-<td>Planet Bomber</td>
-<td>Planet Bomber is a casual game that lets you bomb planets with different weapons and upgrades. You can choose from various types of bombs, such as cluster bombs, nuclear bombs, plasma bombs, etc. You can also upgrade your bomber's speed, power, accuracy, and more. The game has colorful graphics, addictive gameplay, and satisfying explosions.</td>
-</tr>
-<tr>
-<td>Solar Smash 2</td>
-<td>Solar Smash 2 is the sequel to Solar Smash that adds more features and improvements to the original game. You can enjoy new weapons, scenarios, planets, systems, modes, and more. You can also play online with other players or offline with bots. The game has enhanced graphics, physics, and sound effects that make it more realistic and fun.</td>
-</tr>
-</table>
- <h3>Pros and cons of Solar Smash compared to other games</h3>
- <p>Solar Smash is a great game for anyone who likes to destroy planets or simulate space scenarios. However, it is not perfect and it has some pros and cons compared to other games in the same genre. Here are some of the pros and cons of Solar Smash:</p>
- <ul>
-<li><b>Pros:</b></li>
-<li>It is free to play and easy to download.</li>
-<li>It has stunning graphics, realistic physics, and satisfying sound effects.</li>
-<li>It has a wide range of weapons and disasters that you can use to destroy planets.</li>
-<li>It has two main modes: Planet Smash and System Smash that offer different gameplay options.</li>
-<li>It has a list of achievements and secret planets that you can complete and unlock for more challenge and fun.</li>
-<li>It has a simple and intuitive user interface that makes it easy to control and navigate.</li>
-</ul>
- <ul>
-<li><b>Cons:</b></li>
-<li>It may contain ads or in-app purchases that may interrupt or limit your gameplay.</li>
-<li>It may require a good internet connection and a compatible device to run the game smoothly.</li>
-<li>It may become repetitive or boring after a while if you run out of weapons or scenarios to try.</li>
-<li>It may not be very educational or realistic as some of the weapons or disasters are fictional or exaggerated.</li>
-<li>It may not be very suitable for children or sensitive people as some of the weapons or disasters are violent or disturbing.</li>
-</ul>
- <p>Of course, these pros and cons are subjective and may vary depending on your personal preferences and expectations. You can always try the game for yourself and see if you like it or not. After all, the best way to judge a game is to play it!</p>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and a call to action for the readers</h3>
- <p>Solar Smash is a planet destruction simulator that allows you to use a variety of different weapons to destroy the planet. You can also customize your own planet or choose from a list of preset ones, such as Earth, Mars, Jupiter, or even a giant pumpkin. The game has stunning graphics, realistic physics, and satisfying sound effects that make you feel like a powerful cosmic force.</p>
- <p>In this article, we have shown you how to download and play Solar Smash on your Android device or PC, as well as some tips and tricks to help you have the best destruction experience possible. We have also introduced you to some alternatives to Solar Smash that you might enjoy if you are looking for more games like this one.</p>
- <p>If you are interested in playing Solar Smash, you can download it for free from Google Play Store or from other sources online. You can also visit the official website or the Facebook page of Paradyme Games, the developer of Solar Smash, to learn more about the game and its updates.</p>
- <p>We hope you have enjoyed reading this article and found it useful and informative. If you have any questions, comments, or feedback, feel free to leave them below. We would love to hear from you!</p>
- <p>Now, go ahead and unleash your inner villain and destroy some planets with Solar Smash! Have fun!</p>
- <h2>FAQs</h2>
- <h3>Five unique questions and answers related to Solar Smash</h3>
- <ol>
-<li><b>Q: Is Solar Smash safe to play?</b></li>
-<li>A: Solar Smash is safe to play as long as you download it from a trusted source and scan it for viruses before installing it. However, the game may contain ads or in-app purchases that may require your permission or payment. The game may also contain violent or disturbing content that may not be suitable for children or sensitive people. You can always check the ratings and reviews of the game before playing it.</li>
-<li><b>Q: How can I play Solar Smash with my friends?</b></li>
-<li>A: Solar Smash has an online multiplayer mode that allows you to play with other players around the world. You can also play offline with bots if you prefer. To play online, you need to have a good internet connection and a compatible device. You can also create or join a room with your friends by using a code. To play offline, you need to select the offline mode in the settings menu.</li>
-<li><b>Q: How can I get more weapons or scenarios in Solar Smash?</b></li>
-<li>A: Solar Smash has a lot of weapons and scenarios that you can use to destroy planets. However, some of them are locked and require you to complete certain tasks or pay real money to unlock them. You can also get more weapons or scenarios by downloading updates or mods for the game. Updates are official releases by the developer that add new features or improvements to the game. Mods are unofficial modifications by other users that change or enhance the game in some way.</li>
-<li><b>Q: How can I contact the developer of Solar Smash?</b></li>
-<li>A: If you want to contact the developer of Solar Smash, Paradyme Games, you can visit their official website or their Facebook page. You can also send them an email at [email protected]. You can also follow them on Twitter or Instagram to get the latest news and updates about their games.</li>
-<li><b>Q: What are some other games by Paradyme Games?</b></li>
-<li>A: Paradyme Games is an indie studio based in Australia that develops games for Android devices. Some of their other games are Planet Miner, Planet Miner 2, Planet Miner Idle Tycoon, Planet Miner Clicker Game, and Planet Miner Space Simulator. You can find these games on Google Play Store or on their website.</li>
-</ol>
- <p>Thank you for reading this article and I hope you have learned something new and useful about Solar Smash. If you liked this article, please share it with your friends and family who might also enjoy playing Solar Smash. You can also leave a comment below and let me know what you think about the game or the article. I would love to hear your feedback and suggestions.</p>
- <p>Until next time, happy smashing!</p> 401be4b1e0<br />
-<br />
-<br />

spaces/801artistry/RVC801/demucs/wav.py

@@ -1,174 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import OrderedDict
-import hashlib
-import math
-import json
-from pathlib import Path
-
-import julius
-import torch as th
-from torch import distributed
-import torchaudio as ta
-from torch.nn import functional as F
-
-from .audio import convert_audio_channels
-from .compressed import get_musdb_tracks
-
-MIXTURE = "mixture"
-EXT = ".wav"
-
-
-def _track_metadata(track, sources):
-    track_length = None
-    track_samplerate = None
-    for source in sources + [MIXTURE]:
-        file = track / f"{source}{EXT}"
-        info = ta.info(str(file))
-        length = info.num_frames
-        if track_length is None:
-            track_length = length
-            track_samplerate = info.sample_rate
-        elif track_length != length:
-            raise ValueError(
-                f"Invalid length for file {file}: "
-                f"expecting {track_length} but got {length}.")
-        elif info.sample_rate != track_samplerate:
-            raise ValueError(
-                f"Invalid sample rate for file {file}: "
-                f"expecting {track_samplerate} but got {info.sample_rate}.")
-        if source == MIXTURE:
-            wav, _ = ta.load(str(file))
-            wav = wav.mean(0)
-            mean = wav.mean().item()
-            std = wav.std().item()
-
-    return {"length": length, "mean": mean, "std": std, "samplerate": track_samplerate}
-
-
-def _build_metadata(path, sources):
-    meta = {}
-    path = Path(path)
-    for file in path.iterdir():
-        meta[file.name] = _track_metadata(file, sources)
-    return meta
-
-
-class Wavset:
-    def __init__(
-            self,
-            root, metadata, sources,
-            length=None, stride=None, normalize=True,
-            samplerate=44100, channels=2):
-        """
-        Waveset (or mp3 set for that matter). Can be used to train
-        with arbitrary sources. Each track should be one folder inside of `path`.
-        The folder should contain files named `{source}.{ext}`.
-        Files will be grouped according to `sources` (each source is a list of
-        filenames).
-
-        Sample rate and channels will be converted on the fly.
-
-        `length` is the sample size to extract (in samples, not duration).
-        `stride` is how many samples to move by between each example.
-        """
-        self.root = Path(root)
-        self.metadata = OrderedDict(metadata)
-        self.length = length
-        self.stride = stride or length
-        self.normalize = normalize
-        self.sources = sources
-        self.channels = channels
-        self.samplerate = samplerate
-        self.num_examples = []
-        for name, meta in self.metadata.items():
-            track_length = int(self.samplerate * meta['length'] / meta['samplerate'])
-            if length is None or track_length < length:
-                examples = 1
-            else:
-                examples = int(math.ceil((track_length - self.length) / self.stride) + 1)
-            self.num_examples.append(examples)
-
-    def __len__(self):
-        return sum(self.num_examples)
-
-    def get_file(self, name, source):
-        return self.root / name / f"{source}{EXT}"
-
-    def __getitem__(self, index):
-        for name, examples in zip(self.metadata, self.num_examples):
-            if index >= examples:
-                index -= examples
-                continue
-            meta = self.metadata[name]
-            num_frames = -1
-            offset = 0
-            if self.length is not None:
-                offset = int(math.ceil(
-                    meta['samplerate'] * self.stride * index / self.samplerate))
-                num_frames = int(math.ceil(
-                    meta['samplerate'] * self.length / self.samplerate))
-            wavs = []
-            for source in self.sources:
-                file = self.get_file(name, source)
-                wav, _ = ta.load(str(file), frame_offset=offset, num_frames=num_frames)
-                wav = convert_audio_channels(wav, self.channels)
-                wavs.append(wav)
-
-            example = th.stack(wavs)
-            example = julius.resample_frac(example, meta['samplerate'], self.samplerate)
-            if self.normalize:
-                example = (example - meta['mean']) / meta['std']
-            if self.length:
-                example = example[..., :self.length]
-                example = F.pad(example, (0, self.length - example.shape[-1]))
-            return example
-
-
-def get_wav_datasets(args, samples, sources):
-    sig = hashlib.sha1(str(args.wav).encode()).hexdigest()[:8]
-    metadata_file = args.metadata / (sig + ".json")
-    train_path = args.wav / "train"
-    valid_path = args.wav / "valid"
-    if not metadata_file.is_file() and args.rank == 0:
-        train = _build_metadata(train_path, sources)
-        valid = _build_metadata(valid_path, sources)
-        json.dump([train, valid], open(metadata_file, "w"))
-    if args.world_size > 1:
-        distributed.barrier()
-    train, valid = json.load(open(metadata_file))
-    train_set = Wavset(train_path, train, sources,
-                       length=samples, stride=args.data_stride,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    valid_set = Wavset(valid_path, valid, [MIXTURE] + sources,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    return train_set, valid_set
-
-
-def get_musdb_wav_datasets(args, samples, sources):
-    metadata_file = args.metadata / "musdb_wav.json"
-    root = args.musdb / "train"
-    if not metadata_file.is_file() and args.rank == 0:
-        metadata = _build_metadata(root, sources)
-        json.dump(metadata, open(metadata_file, "w"))
-    if args.world_size > 1:
-        distributed.barrier()
-    metadata = json.load(open(metadata_file))
-
-    train_tracks = get_musdb_tracks(args.musdb, is_wav=True, subsets=["train"], split="train")
-    metadata_train = {name: meta for name, meta in metadata.items() if name in train_tracks}
-    metadata_valid = {name: meta for name, meta in metadata.items() if name not in train_tracks}
-    train_set = Wavset(root, metadata_train, sources,
-                       length=samples, stride=args.data_stride,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    valid_set = Wavset(root, metadata_valid, [MIXTURE] + sources,
-                       samplerate=args.samplerate, channels=args.audio_channels,
-                       normalize=args.norm_wav)
-    return train_set, valid_set

spaces/A1draw-12196y/DeepDanbooru_string/app.py

@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import html
-import pathlib
-import tarfile
-
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-import piexif
-import piexif.helper
-
-TITLE = 'DeepDanbooru String'
-
-TOKEN = os.environ['TOKEN']
-MODEL_REPO = 'CikeyQI/DeepDanbooru_string'
-MODEL_FILENAME = 'model-resnet_custom_v3.h5'
-LABEL_FILENAME = 'tags.txt'
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--score-slider-step', type=float, default=0.05)
-    parser.add_argument('--score-threshold', type=float, default=0.5)
-    parser.add_argument('--theme', type=str, default='dark-grass')
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
-
-
-def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
-    if not image_dir.exists():
-        dataset_repo = 'hysts/sample-images-TADNE'
-        path = huggingface_hub.hf_hub_download(dataset_repo,
-                                               'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=TOKEN)
-        with tarfile.open(path) as f:
-            f.extractall()
-    return sorted(image_dir.glob('*'))
-
-
-def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           MODEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    model = tf.keras.models.load_model(path)
-    return model
-
-
-def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           LABEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    with open(path) as f:
-        labels = [line.strip() for line in f.readlines()]
-    return labels
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def predict(image: PIL.Image.Image, score_threshold: float,
-            model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
-    rawimage = image
-    _, height, width, _ = model.input_shape
-    image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
-    image = image.numpy()
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
-    probs = model.predict(image[None, ...])[0]
-    probs = probs.astype(float)
-    res = dict()
-    for prob, label in zip(probs.tolist(), labels):
-        if prob < score_threshold:
-            continue
-        res[label] = prob
-    b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
-    a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
-    c = ', '.join(list(b.keys()))
-    
-    items = rawimage.info
-    geninfo = ''
-    
-    if "exif" in rawimage.info:
-        exif = piexif.load(rawimage.info["exif"])
-        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
-        try:
-            exif_comment = piexif.helper.UserComment.load(exif_comment)
-        except ValueError:
-            exif_comment = exif_comment.decode('utf8', errors="ignore")
-    
-        items['exif comment'] = exif_comment
-        geninfo = exif_comment
-    
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
-    
-    geninfo = items.get('parameters', geninfo)
-    
-    info = f"""
-<p><h4>PNG Info</h4></p>    
-"""
-    for key, text in items.items():
-        info += f"""
-<div>
-<p><b>{plaintext_to_html(str(key))}</b></p>
-<p>{plaintext_to_html(str(text))}</p>
-</div>
-""".strip()+"\n"
-    
-    if len(info) == 0:
-        message = "Nothing found in the image."
-        info = f"<div><p>{message}<p></div>"
-    
-    return (a,c,res,info)
-
-
-def main():
-    args = parse_args()
-    model = load_model()
-    labels = load_labels()
-
-    func = functools.partial(predict, model=model, labels=labels)
-    func = functools.update_wrapper(func, predict)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='pil', label='Input'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=args.score_slider_step,
-                             default=args.score_threshold,
-                             label='Score Threshold'),
-        ],
-        [
-            gr.outputs.Textbox(label='Output (string)'), 
-            gr.outputs.Textbox(label='Output (raw string)'), 
-            gr.outputs.Label(label='Output (label)'),
-            gr.outputs.HTML()
-        ],
-        examples=[
-        ['miku.jpg',0.5],
-        ['miku2.jpg',0.5]
-        ],
-        title=TITLE,
-        description='''
-Demo for [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) with "ready to copy" prompt and a prompt analyzer.
-
-Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-
-PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-        ''',
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()

spaces/A666sxr/Genshin_TTS/text/symbols.py

@@ -1,13 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-_pad        = '_'
-_punctuation = ';:,.!?¡¿—…"«»“” '
-_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
-# Special symbol ids
-SPACE_ID = symbols.index(" ")

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/syntactic_graph_buider.py

@@ -1,294 +0,0 @@
-from copy import deepcopy
-import torch
-import dgl
-import stanza 
-import networkx as nx
-
-class Sentence2GraphParser:
-    def __init__(self, language='zh', use_gpu=False, download=False):
-        self.language = language
-        if download:
-            self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu)
-        else:
-            self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu, download_method=None)
-
-    def parse(self, clean_sentence=None, words=None, ph_words=None):
-        if self.language == 'zh':
-            assert words is not None and ph_words is not None
-            ret = self._parse_zh(words, ph_words)
-        elif self.language == 'en':
-            assert clean_sentence is not None
-            ret = self._parse_en(clean_sentence)
-        else:
-            raise NotImplementedError
-        return ret
-
-    def _parse_zh(self, words, ph_words, enable_backward_edge=True, enable_recur_edge=True,
-                  enable_inter_sentence_edge=True, sequential_edge=False):
-        """
-        words: <List of str>, each character in chinese is one item
-        ph_words: <List of str>, each character in chinese is one item, represented by the phoneme
-        Example:
-                text1 = '宝马配挂跛骡鞍,貂蝉怨枕董翁榻.'
-                words = ['<BOS>', '宝', '马', '配', '挂', '跛', '骡', '鞍', ','
-                        , '貂', '蝉', '怨', '枕', '董', '翁', '榻', '<EOS>']
-                ph_words = ['<BOS>', 'b_ao3_|', 'm_a3_#', 'p_ei4_|', 'g_ua4_#',
-                            'b_o3_#', 'l_uo2_|', 'an1', ',', 'd_iao1_|',
-                            'ch_an2_#', 'van4_#', 'zh_en3_#', 'd_ong3_|', 'ueng1_#', 't_a4', '<EOS>']
-        """
-        words, ph_words = words[1:-1], ph_words[1:-1]  # delete <BOS> and <EOS>
-        for i, p_w in enumerate(ph_words):
-            if p_w == ',':
-                # change english ',' into chinese
-                # we found it necessary in stanza's dependency parsing
-                words[i], ph_words[i] = '，', '，'
-        tmp_words = deepcopy(words)
-        num_added_space = 0
-        for i, p_w in enumerate(ph_words):
-            if p_w.endswith("#"):
-                # add a blank after the p_w with '#', to separate words
-                tmp_words.insert(num_added_space + i + 1, " ")
-                num_added_space += 1
-            if p_w in ['，', ',']:
-                # add one blank before and after ', ', respectively
-                tmp_words.insert(num_added_space + i + 1, " ")  # insert behind ',' first
-                tmp_words.insert(num_added_space + i, " ")  # insert before
-                num_added_space += 2
-        clean_text = ''.join(tmp_words).strip()
-        parser_out = self.stanza_parser(clean_text)
-
-        idx_to_word = {i + 1: w for i, w in enumerate(words)}
-
-        vocab_nodes = {}
-        vocab_idx_offset = 0
-        for sentence in parser_out.sentences:
-            num_nodes_in_current_sentence = 0
-            for vocab_node in sentence.words:
-                num_nodes_in_current_sentence += 1
-                vocab_idx = vocab_node.id + vocab_idx_offset
-                vocab_text = vocab_node.text.replace(" ", "")  # delete blank in vocab
-                vocab_nodes[vocab_idx] = vocab_text
-            vocab_idx_offset += num_nodes_in_current_sentence
-
-        # start vocab-to-word alignment
-        vocab_to_word = {}
-        current_word_idx = 1
-        for vocab_i in vocab_nodes.keys():
-            vocab_to_word[vocab_i] = []
-            for w_in_vocab_i in vocab_nodes[vocab_i]:
-                if w_in_vocab_i != idx_to_word[current_word_idx]:
-                    raise ValueError("Word Mismatch!")
-                vocab_to_word[vocab_i].append(current_word_idx)  # add a path (vocab_node_idx, word_global_idx)
-                current_word_idx += 1
-
-        # then we compute the vocab-level edges
-        if len(parser_out.sentences) > 5:
-            print("Detect more than 5 input sentence! pls check whether the sentence is too long!")
-        vocab_level_source_id, vocab_level_dest_id = [], []
-        vocab_level_edge_types = []
-        sentences_heads = []
-        vocab_id_offset = 0
-        # get forward edges
-        for s in parser_out.sentences:
-            for w in s.words:
-                w_idx = w.id + vocab_id_offset  # it starts from 1, just same as binarizer
-                w_dest_idx = w.head + vocab_id_offset
-                if w.head == 0:
-                    sentences_heads.append(w_idx)
-                    continue
-                vocab_level_source_id.append(w_idx)
-                vocab_level_dest_id.append(w_dest_idx)
-            vocab_id_offset += len(s.words)
-        vocab_level_edge_types += [0] * len(vocab_level_source_id)
-        num_vocab = vocab_id_offset
-
-        # optional: get backward edges
-        if enable_backward_edge:
-            back_source, back_dest = deepcopy(vocab_level_dest_id), deepcopy(vocab_level_source_id)
-            vocab_level_source_id += back_source
-            vocab_level_dest_id += back_dest
-            vocab_level_edge_types += [1] * len(back_source)
-
-        # optional: get inter-sentence edges if num_sentences > 1
-        inter_sentence_source, inter_sentence_dest = [], []
-        if enable_inter_sentence_edge and len(sentences_heads) > 1:
-            def get_full_graph_edges(nodes):
-                tmp_edges = []
-                for i, node_i in enumerate(nodes):
-                    for j, node_j in enumerate(nodes):
-                        if i == j:
-                            continue
-                        tmp_edges.append((node_i, node_j))
-                return tmp_edges
-
-            tmp_edges = get_full_graph_edges(sentences_heads)
-            for (source, dest) in tmp_edges:
-                inter_sentence_source.append(source)
-                inter_sentence_dest.append(dest)
-            vocab_level_source_id += inter_sentence_source
-            vocab_level_dest_id += inter_sentence_dest
-            vocab_level_edge_types += [3] * len(inter_sentence_source)
-
-        if sequential_edge:
-            seq_source, seq_dest = list(range(1, num_vocab)) + list(range(num_vocab, 0, -1)), \
-                                   list(range(2, num_vocab + 1)) + list(range(num_vocab - 1, -1, -1))
-            vocab_level_source_id += seq_source
-            vocab_level_dest_id += seq_dest
-            vocab_level_edge_types += [4] * (num_vocab - 1) + [5] * (num_vocab - 1)
-
-        # Then, we use the vocab-level edges and the vocab-to-word path, to construct the word-level graph
-        num_word = len(words)
-        source_id, dest_id, edge_types = [], [], []
-        for (vocab_start, vocab_end, vocab_edge_type) in zip(vocab_level_source_id, vocab_level_dest_id,
-                                                             vocab_level_edge_types):
-            # connect the first word in the vocab
-            word_start = min(vocab_to_word[vocab_start])
-            word_end = min(vocab_to_word[vocab_end])
-            source_id.append(word_start)
-            dest_id.append(word_end)
-            edge_types.append(vocab_edge_type)
-
-        # sequential connection in words
-        for word_indices_in_v in vocab_to_word.values():
-            for i, word_idx in enumerate(word_indices_in_v):
-                if i + 1 < len(word_indices_in_v):
-                    source_id.append(word_idx)
-                    dest_id.append(word_idx + 1)
-                    edge_types.append(4)
-                if i - 1 >= 0:
-                    source_id.append(word_idx)
-                    dest_id.append(word_idx - 1)
-                    edge_types.append(5)
-
-        # optional: get recurrent edges
-        if enable_recur_edge:
-            recur_source, recur_dest = list(range(1, num_word + 1)), list(range(1, num_word + 1))
-            source_id += recur_source
-            dest_id += recur_dest
-            edge_types += [2] * len(recur_source)
-
-        # add <BOS> and <EOS>
-        source_id += [0, num_word + 1, 1, num_word]
-        dest_id += [1, num_word, 0, num_word + 1]
-        edge_types += [4, 4, 5, 5]  # 4 represents sequentially forward, 5 is sequential backward
-
-        edges = (torch.LongTensor(source_id), torch.LongTensor(dest_id))
-        dgl_graph = dgl.graph(edges)
-        assert dgl_graph.num_edges() == len(edge_types)
-        return dgl_graph, torch.LongTensor(edge_types)
-
-    def _parse_en(self, clean_sentence, enable_backward_edge=True, enable_recur_edge=True,
-                  enable_inter_sentence_edge=True, sequential_edge=False, consider_bos_for_index=True):
-        """
-        clean_sentence: <str>, each word or punctuation should be separated by one blank.
-        """
-        edge_types = []  # required for gated graph neural network
-        clean_sentence = clean_sentence.strip()
-        if clean_sentence.endswith((" .", " ,", " ;", " :", " ?", " !")):
-            clean_sentence = clean_sentence[:-2]
-        if clean_sentence.startswith(". "):
-            clean_sentence = clean_sentence[2:]
-        parser_out = self.stanza_parser(clean_sentence)
-        if len(parser_out.sentences) > 5:
-            print("Detect more than 5 input sentence! pls check whether the sentence is too long!")
-            print(clean_sentence)
-        source_id, dest_id = [], []
-        sentences_heads = []
-        word_id_offset = 0
-        # get forward edges
-        for s in parser_out.sentences:
-            for w in s.words:
-                w_idx = w.id + word_id_offset  # it starts from 1, just same as binarizer
-                w_dest_idx = w.head + word_id_offset
-                if w.head == 0:
-                    sentences_heads.append(w_idx)
-                    continue
-                source_id.append(w_idx)
-                dest_id.append(w_dest_idx)
-            word_id_offset += len(s.words)
-        num_word = word_id_offset
-        edge_types += [0] * len(source_id)
-
-        # optional: get backward edges
-        if enable_backward_edge:
-            back_source, back_dest = deepcopy(dest_id), deepcopy(source_id)
-            source_id += back_source
-            dest_id += back_dest
-            edge_types += [1] * len(back_source)
-
-        # optional: get recurrent edges
-        if enable_recur_edge:
-            recur_source, recur_dest = list(range(1, num_word + 1)), list(range(1, num_word + 1))
-            source_id += recur_source
-            dest_id += recur_dest
-            edge_types += [2] * len(recur_source)
-
-        # optional: get inter-sentence edges if num_sentences > 1
-        inter_sentence_source, inter_sentence_dest = [], []
-        if enable_inter_sentence_edge and len(sentences_heads) > 1:
-            def get_full_graph_edges(nodes):
-                tmp_edges = []
-                for i, node_i in enumerate(nodes):
-                    for j, node_j in enumerate(nodes):
-                        if i == j:
-                            continue
-                        tmp_edges.append((node_i, node_j))
-                return tmp_edges
-
-            tmp_edges = get_full_graph_edges(sentences_heads)
-            for (source, dest) in tmp_edges:
-                inter_sentence_source.append(source)
-                inter_sentence_dest.append(dest)
-            source_id += inter_sentence_source
-            dest_id += inter_sentence_dest
-            edge_types += [3] * len(inter_sentence_source)
-
-        # add <BOS> and <EOS>
-        source_id += [0, num_word + 1, 1, num_word]
-        dest_id += [1, num_word, 0, num_word + 1]
-        edge_types += [4, 4, 5, 5]  # 4 represents sequentially forward, 5 is sequential backward
-
-        # optional: sequential edge
-        if sequential_edge:
-            seq_source, seq_dest = list(range(1, num_word)) + list(range(num_word, 0, -1)), \
-                                   list(range(2, num_word + 1)) + list(range(num_word - 1, -1, -1))
-            source_id += seq_source
-            dest_id += seq_dest
-            edge_types += [4] * (num_word - 1) + [5] * (num_word - 1)
-        if consider_bos_for_index:
-            edges = (torch.LongTensor(source_id), torch.LongTensor(dest_id))
-        else:
-            edges = (torch.LongTensor(source_id) - 1, torch.LongTensor(dest_id) - 1)
-        dgl_graph = dgl.graph(edges)
-        assert dgl_graph.num_edges() == len(edge_types)
-        return dgl_graph, torch.LongTensor(edge_types)
-
-
-def plot_dgl_sentence_graph(dgl_graph, labels):
-    """
-    labels = {idx: word for idx,word in enumerate(sentence.split(" ")) }
-    """
-    import matplotlib.pyplot as plt
-    nx_graph = dgl_graph.to_networkx()
-    pos = nx.random_layout(nx_graph)
-    nx.draw(nx_graph, pos, with_labels=False)
-    nx.draw_networkx_labels(nx_graph, pos, labels)
-    plt.show()
-
-if __name__ == '__main__':
-
-    # Unit Test for Chinese Graph Builder
-    parser = Sentence2GraphParser("zh")
-    text1 = '宝马配挂跛骡鞍,貂蝉怨枕董翁榻.'
-    words = ['<BOS>', '宝', '马', '配', '挂', '跛', '骡', '鞍', ',', '貂', '蝉', '怨', '枕', '董', '翁', '榻', '<EOS>']
-    ph_words = ['<BOS>', 'b_ao3_|', 'm_a3_#', 'p_ei4_|', 'g_ua4_#', 'b_o3_#', 'l_uo2_|', 'an1', ',', 'd_iao1_|',
-                'ch_an2_#', 'van4_#', 'zh_en3_#', 'd_ong3_|', 'ueng1_#', 't_a4', '<EOS>']
-    graph1, etypes1 = parser.parse(text1, words, ph_words)
-    plot_dgl_sentence_graph(graph1, {i: w for i, w in enumerate(ph_words)})
-
-    # Unit Test for English Graph Builder
-    parser = Sentence2GraphParser("en")
-    text2 = "I love you . You love me . Mixue ice-scream and tea ."
-    graph2, etypes2 = parser.parse(text2)
-    plot_dgl_sentence_graph(graph2, {i: w for i, w in enumerate(("<BOS> " + text2 + " <EOS>").split(" "))})
-    

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/tts/portaspeech/portaspeech.py

@@ -1,233 +0,0 @@
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import Linear
-
-from text_to_speech.modules.commons.conv import ConvBlocks, ConditionalConvBlocks
-from text_to_speech.modules.commons.layers import Embedding
-from text_to_speech.modules.commons.rel_transformer import RelTransformerEncoder
-from text_to_speech.modules.commons.transformer import MultiheadAttention, FFTBlocks
-from text_to_speech.modules.tts.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word
-from text_to_speech.modules.tts.fs import FS_DECODERS, FastSpeech
-from text_to_speech.modules.tts.portaspeech.fvae import FVAE
-from text_to_speech.utils.commons.meters import Timer
-from text_to_speech.utils.nn.seq_utils import group_hidden_by_segs
-
-
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        """
-
-        :param x: [B, T]
-        :return: [B, T, H]
-        """
-        device = x.device
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
-        emb = x[:, :, None] * emb[None, :]
-        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
-        return emb
-
-
-class PortaSpeech(FastSpeech):
-    def __init__(self, ph_dict_size, word_dict_size, hparams, out_dims=None):
-        self.hparams = hparams
-        super().__init__(ph_dict_size, hparams, out_dims)
-        # build linguistic encoder
-        if hparams['use_word_encoder']: 
-            # default False, use independent word embedding instead of phoneme encoding to represent word
-            self.word_encoder = RelTransformerEncoder(
-                word_dict_size, self.hidden_size, self.hidden_size, self.hidden_size, 2,
-                hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
-        if hparams['dur_level'] == 'word':
-            if hparams['word_encoder_type'] == 'rel_fft':
-                self.ph2word_encoder = RelTransformerEncoder(
-                    0, self.hidden_size, self.hidden_size, self.hidden_size, 2,
-                    hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
-            if hparams['word_encoder_type'] == 'fft':
-                self.ph2word_encoder = FFTBlocks(
-                    self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads'])
-            self.sin_pos = SinusoidalPosEmb(self.hidden_size)
-            self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False)
-            self.attn.enable_torch_version = False
-            if hparams['text_encoder_postnet']:
-                self.text_encoder_postnet = ConvBlocks(
-                    self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2)
-        else:
-            self.sin_pos = SinusoidalPosEmb(self.hidden_size)
-        # build VAE decoder
-        if hparams['use_fvae']:
-            del self.decoder
-            del self.mel_out
-            self.fvae = FVAE(
-                c_in_out=self.out_dims,
-                hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'],
-                kernel_size=hparams['fvae_kernel_size'],
-                enc_n_layers=hparams['fvae_enc_n_layers'],
-                dec_n_layers=hparams['fvae_dec_n_layers'],
-                c_cond=self.hidden_size,
-                use_prior_flow=hparams['use_prior_flow'],
-                flow_hidden=hparams['prior_flow_hidden'],
-                flow_kernel_size=hparams['prior_flow_kernel_size'],
-                flow_n_steps=hparams['prior_flow_n_blocks'],
-                strides=[hparams['fvae_strides']],
-                encoder_type=hparams['fvae_encoder_type'],
-                decoder_type=hparams['fvae_decoder_type'],
-            )
-        else:
-            self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
-            self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
-        if hparams['use_pitch_embed']:
-            self.pitch_embed = Embedding(300, self.hidden_size, 0)
-        if self.hparams['add_word_pos']:
-            self.word_pos_proj = Linear(self.hidden_size, self.hidden_size)
-
-    def build_embedding(self, dictionary, embed_dim):
-        num_embeddings = len(dictionary)
-        emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
-        return emb
-
-    def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None,
-                spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None,
-                global_step=None, *args, **kwargs):
-        ret = {}
-        style_embed = self.forward_style_embed(spk_embed, spk_id)
-        x, tgt_nonpadding = self.run_text_encoder(
-            txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, **kwargs)
-        x = x * tgt_nonpadding
-        ret['nonpadding'] = tgt_nonpadding
-        if self.hparams['use_pitch_embed']:
-            x = x + self.pitch_embed(pitch)
-        ret['decoder_inp'] = x
-        ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step)
-        return ret
-
-    def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, **kwargs):
-        word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1  # [B, T_mel, T_word]
-        src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-        use_bert = self.hparams.get("use_bert") is True
-        if use_bert:
-            ph_encoder_out = self.ph_encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=ph2word,
-                graph_lst=kwargs['graph_lst'], etypes_lst=kwargs['etypes_lst'],
-                cl_feats=kwargs['cl_feats'], ret=ret) * src_nonpadding + style_embed
-        else:
-            ph_encoder_out = self.ph_encoder(txt_tokens) * src_nonpadding + style_embed
-        if self.hparams['use_word_encoder']:
-            word_encoder_out = self.word_encoder(word_tokens) + style_embed
-            ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word)
-        if self.hparams['dur_level'] == 'word':
-            word_encoder_out = 0
-            h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0]
-            word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word)
-            if self.hparams['use_word_encoder']:
-                word_encoder_out = word_encoder_out + self.word_encoder(word_tokens)
-            mel2word = self.forward_dur(ph_encoder_out, mel2word, ret, ph2word=ph2word, word_len=word_len)
-            mel2word = clip_mel2token_to_multiple(mel2word, self.hparams['frames_multiple'])
-            tgt_nonpadding = (mel2word > 0).float()[:, :, None]
-            enc_pos = self.get_pos_embed(word2word, ph2word)  # [B, T_ph, H]
-            dec_pos = self.get_pos_embed(word2word, mel2word)  # [B, T_mel, H]
-            dec_word_mask = build_word_mask(mel2word, ph2word)  # [B, T_mel, T_ph]
-            x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask)
-            if self.hparams['add_word_pos']:
-                x = x + self.word_pos_proj(dec_pos)
-            ret['attn'] = weight
-        else:
-            mel2ph = self.forward_dur(ph_encoder_out, mel2ph, ret)
-            mel2ph = clip_mel2token_to_multiple(mel2ph, self.hparams['frames_multiple'])
-            mel2word = mel2ph_to_mel2word(mel2ph, ph2word)
-            x = expand_states(ph_encoder_out, mel2ph)
-            if self.hparams['add_word_pos']:
-                dec_pos = self.get_pos_embed(word2word, mel2word)  # [B, T_mel, H]
-                x = x + self.word_pos_proj(dec_pos)
-            tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-        if self.hparams['use_word_encoder']:
-            x = x + expand_states(word_encoder_out, mel2word)
-        return x, tgt_nonpadding
-
-    def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask):
-        ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1))
-        word_enc_out_expend = expand_states(word_encoder_out, mel2word)
-        word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1)
-        if self.hparams['text_encoder_postnet']:
-            word_enc_out_expend = self.dec_res_proj(word_enc_out_expend)
-            word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend)
-            dec_q = x_res = word_enc_out_expend
-        else:
-            dec_q = self.dec_query_proj(word_enc_out_expend)
-            x_res = self.dec_res_proj(word_enc_out_expend)
-        ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1)
-        x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9)
-        x = x.transpose(0, 1)
-        x = x + x_res
-        return x, weight
-
-    def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0):
-        if not self.hparams['use_fvae']:
-            x = self.decoder(x)
-            x = self.mel_out(x)
-            ret['kl'] = 0
-            return x * tgt_nonpadding
-        else:
-            decoder_inp = x
-            x = x.transpose(1, 2)  # [B, H, T]
-            tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2)  # [B, H, T]
-            if infer:
-                z = self.fvae(cond=x, infer=True)
-            else:
-                tgt_mels = tgt_mels.transpose(1, 2)  # [B, 80, T]
-                z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae(
-                    tgt_mels, tgt_nonpadding_BHT, cond=x)
-                if global_step < self.hparams['posterior_start_steps']:
-                    z = torch.randn_like(z)
-            x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2)
-            ret['pre_mel_out'] = x_recon
-            return x_recon
-
-    def forward_dur(self, dur_input, mel2word, ret, **kwargs):
-        """
-
-        :param dur_input: [B, T_txt, H]
-        :param mel2ph: [B, T_mel]
-        :param txt_tokens: [B, T_txt]
-        :param ret:
-        :return:
-        """
-        src_padding = dur_input.data.abs().sum(-1) == 0
-        dur_input = dur_input.detach() + self.hparams['predictor_grad'] * (dur_input - dur_input.detach())
-        dur = self.dur_predictor(dur_input, src_padding)
-        if self.hparams['dur_level'] == 'word':
-            word_len = kwargs['word_len']
-            ph2word = kwargs['ph2word']
-            B, T_ph = ph2word.shape
-            dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur)
-            dur = dur[:, 1:]
-        ret['dur'] = dur
-        if mel2word is None:
-            mel2word = self.length_regulator(dur).detach()
-        return mel2word
-
-    def get_pos_embed(self, word2word, x2word):
-        x_pos = build_word_mask(word2word, x2word).float()  # [B, T_word, T_ph]
-        x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1)
-        x_pos = self.sin_pos(x_pos.float())  # [B, T_ph, H]
-        return x_pos
-
-    def store_inverse_all(self):
-        def remove_weight_norm(m):
-            try:
-                if hasattr(m, 'store_inverse'):
-                    m.store_inverse()
-                nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(remove_weight_norm)

spaces/AfrodreamsAI/afrodreams/Home.py

@@ -1,164 +0,0 @@
-import neural_style
-
-import streamlit as st
-import os 
-import random
-import numpy as np
-from  PIL import Image, ImageEnhance
-from io import BytesIO
-import matplotlib.pyplot as plt
-import streamlit_ext as ste #for download button not to rerun
-from huggingface_hub import upload_file
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-st.set_page_config(layout="wide")
-
-st.markdown('<p class="font">Afrodreams.AI</p>', unsafe_allow_html=True)
-st.subheader("This app takes in your image and styles it with a unique african art.")
-
-#Create two columns with different width
-col1, col2 = st.columns( [0.8, 0.2])
-import time
-
-
-
-with col1:               # To display the header text using css style
-    st.markdown(""" <style> .font {
-    font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;} 
-    </style> """, unsafe_allow_html=True)
-    st.markdown('<p class="font">Upload your photo here...</p>', unsafe_allow_html=True)
-    
-    
-
-
-
-#Add file uploader to allow users to upload photos
-uploaded_file = st.file_uploader("", type=['jpg','png','jpeg'])
-
-# add slider to side bar
-style_weight = st.slider("Select Style Weight", min_value=10, max_value=100, value=12)
-img_size_slider= st.select_slider(label= 'Seleet Output Quality Level',
-                                         options = ['Very Low', 'Low', 'Normal', 'High', 'Very High'],
-                                         value='Normal')
-img_size_mapping  = {'Very Low':128, 'Low':300, 'Normal':400, 'High':500, 'Very High':600}
-
-
-def get_random_subset(list_, num_imgs):
-    return random.sample(list_, num_imgs)
-
-
-def display_random_images(five_rand_imgs, display_type, size= (15, 6)):
-    fig = plt.figure(figsize=size)
-    fig.subplots_adjust(wspace=0.2) 
-    for i in range(1, len(five_rand_imgs)+1):
-        ith_image = Image.open(five_rand_imgs[i-1])
-    
-        ax = fig.add_subplot(1, 5, i)
-        ax.imshow(ith_image)
-        ax.set_title(f'{display_type} {i}')
-        plt.axis('off')
-    
-    st.pyplot(fig)
-
-
-
-path = 'stylesv2'
-    
-    
-#expander for style selection
-with st.expander("Expand to select style type"):
-    img_names = [os.path.join(path, img) for img in os.listdir(path)]
-    five_rand_imgs0 = get_random_subset(img_names, 5)
-    if 'selected_image' not in st.session_state:
-        st.session_state.selected_image = five_rand_imgs0
-    five_rand_imgs = st.session_state.selected_image   
-    display_random_images(five_rand_imgs, 'Style')
-    chosen_style = st.selectbox(
-    'Select the style you want to use',
-    options = five_rand_imgs, format_func = lambda x: "Style " + str(five_rand_imgs.index(x) + 1),
-    key= 'expander1'
-    )
-    
- 
-
-#put notificaation
-#with st.empty():
-    #for seconds in range(5):
-        #st.info('Please note that by using this app, you agree that your image  be will be showcased on this app.')
-        #time.sleep(1)
-    #st.empty()
-
-#Add 'before' and 'after' columns
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    
-    col1, col2 = st.columns( [0.5, 0.5])
-    with col1:
-        st.markdown('<p style="text-align: center;">Before</p>',unsafe_allow_html=True)
-        st.image(image,width=300)  
-
-    with col2:
-        st.markdown('<p style="text-align: center;">After</p>',unsafe_allow_html=True)
-        
-    # add a button
-    run = st.button('Generate Art')
-    my_bar = st.progress(0)
-    params = neural_style.TransferParams()
-    params.gpu = "c" #0
-    params.backend = "mkl"
-
-    
-    params.image_size = img_size_mapping[img_size_slider]
-    
-    params.content_image = uploaded_file
-    params.style_weight = style_weight
-
-
-
-    keep_style = False
-    if run==True:
-        # run image selection if keep style is false
-        if keep_style==False:
-            
-            styles = os.listdir(path)
-            #params.style_image = path + '/' + random.choice(styles)
-            params.style_image = chosen_style
-            
-        st.session_state.submitted = True   
-        with st.spinner('Wait for it...'): 
-            neural_style.transfer(params)
-
-        #display image when done.
-        with col2:
-            if 'submitted' in st.session_state:
-                result = Image.open('out.png')
-                st.image(result, width=300)
-                buf = BytesIO()
-                result.save(buf, format="png")
-
-                img_file_name = f"generated_samples/{str(len(os.listdir('generated_samples')))}.png"
-            
-                _ = upload_file(path_or_fileobj = 'out.png',
-                        path_in_repo = img_file_name,
-                        repo_id='AfrodreamsAI/afrodreams',
-                        repo_type='space',
-                        token=HF_TOKEN
-                    ) 
-                    
-                byte_im = buf.getvalue()
-                run = ste.download_button("Download Image", data=byte_im, file_name="afrodreams.png")
-                
-
-                #if run==True: 
-# selectiuing random iamges to be displayed
-img_names = [os.path.join('generated_samples', img) for img in os.listdir('generated_samples')]
-five_rand_imgs1 = get_random_subset(img_names, 5)
-st.subheader('\n\n\n\n\n\n\n\n\n Examples of some Generate Images')
-display_random_images(five_rand_imgs1, 'Generate image', size=(20, 15))
-                    
-        
-
-
-
-

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/dialog-quest/QuestMethods.js

@@ -1,18 +0,0 @@
-export default {
-    start(key) {
-        this.questionManager
-            .restartQuest()
-            .getNextQuestion(key);
-        return this;
-    },
-
-    next(key) {
-        this.questionManager
-            .getNextQuestion(key);
-        return this;
-    },
-
-    isLast() {
-        return this.questionManager.isLastQuestion();
-    },
-};

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/checkbox/Factory.d.ts

@@ -1,19 +0,0 @@
-import Checkbox from './Checkbox';
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    color?: number,
-    config?: Checkbox.IConfig
-): Checkbox;
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    config?: Checkbox.IConfig
-): Checkbox;
-
-
-export default function (
-    config?: Checkbox.IConfig
-): Checkbox;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/skew/Skew.js

@@ -1,2 +0,0 @@
-import { ContainerSkew } from '../../../plugins/quadimage.js';
-export default ContainerSkew;

spaces/Akmyradov/TurkmenSpeechRecogntion/README.md

@@ -1,12 +0,0 @@
----
-title: TurkmenSpeechRecognition
-emoji: ⚡
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlekseyKorshuk/thin-plate-spline-motion-model/run.py

@@ -1,89 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-
-import os, sys
-import yaml
-from argparse import ArgumentParser
-from time import gmtime, strftime
-from shutil import copy
-from frames_dataset import FramesDataset
-
-from modules.inpainting_network import InpaintingNetwork
-from modules.keypoint_detector import KPDetector
-from modules.bg_motion_predictor import BGMotionPredictor
-from modules.dense_motion import DenseMotionNetwork
-from modules.avd_network import AVDNetwork
-import torch
-from train import train
-from train_avd import train_avd
-from reconstruction import reconstruction
-import os 
-
-
-if __name__ == "__main__":
-    
-    if sys.version_info[0] < 3:
-        raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
-
-    parser = ArgumentParser()
-    parser.add_argument("--config", default="config/vox-256.yaml", help="path to config")
-    parser.add_argument("--mode", default="train", choices=["train", "reconstruction", "train_avd"])
-    parser.add_argument("--log_dir", default='log', help="path to log into")
-    parser.add_argument("--checkpoint", default=None, help="path to checkpoint to restore")
-    parser.add_argument("--device_ids", default="0,1", type=lambda x: list(map(int, x.split(','))),
-                        help="Names of the devices comma separated.")
-
-    opt = parser.parse_args()
-    with open(opt.config) as f:
-        config = yaml.load(f)
-
-    if opt.checkpoint is not None:
-        log_dir = os.path.join(*os.path.split(opt.checkpoint)[:-1])
-    else:
-        log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0])
-        log_dir += ' ' + strftime("%d_%m_%y_%H.%M.%S", gmtime())
-
-    inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
-                                        **config['model_params']['common_params'])
-
-    if torch.cuda.is_available():
-        cuda_device = torch.device('cuda:'+str(opt.device_ids[0]))
-        inpainting.to(cuda_device)
-
-    kp_detector = KPDetector(**config['model_params']['common_params'])
-    dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
-                                              **config['model_params']['dense_motion_params'])
-                                                           
-    if torch.cuda.is_available():
-        kp_detector.to(opt.device_ids[0])
-        dense_motion_network.to(opt.device_ids[0])
-
-    bg_predictor = None
-    if (config['model_params']['common_params']['bg']):
-        bg_predictor = BGMotionPredictor()
-        if torch.cuda.is_available():
-            bg_predictor.to(opt.device_ids[0])
-
-    avd_network = None
-    if opt.mode == "train_avd":
-        avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
-                             **config['model_params']['avd_network_params'])
-        if torch.cuda.is_available():
-            avd_network.to(opt.device_ids[0])
-
-    dataset = FramesDataset(is_train=(opt.mode.startswith('train')), **config['dataset_params'])
-
-    if not os.path.exists(log_dir):
-        os.makedirs(log_dir)
-    if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))):
-        copy(opt.config, log_dir)
-
-    if opt.mode == 'train':
-        print("Training...")
-        train(config, inpainting, kp_detector, bg_predictor, dense_motion_network, opt.checkpoint, log_dir, dataset)
-    elif opt.mode == 'train_avd':
-        print("Training Animation via Disentaglement...")
-        train_avd(config, inpainting, kp_detector, bg_predictor, dense_motion_network, avd_network, opt.checkpoint, log_dir, dataset)
-    elif opt.mode == 'reconstruction':
-        print("Reconstruction...")
-        reconstruction(config, inpainting, kp_detector, bg_predictor, dense_motion_network, opt.checkpoint, log_dir, dataset)

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

@@ -1,358 +0,0 @@
-# modified from https://github.com/jaywalnut310/vits
-import os
-import random
-
-import torch
-import torch.utils.data
-
-import commons
-from analysis import Pitch
-from mel_processing import spectrogram_torch
-from text import cleaned_text_to_sequence
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-""" Modified from Multi speaker version of VITS"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-  """
-      1) loads audio, speaker_id, text pairs
-      2) normalizes text and converts them to sequences of integers
-      3) computes spectrograms from audio files.
-  """
-
-  def __init__(self, audiopaths_sid_text, hparams, pt_run=False):
-    self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-    self.sampling_rate = hparams.sampling_rate
-    self.filter_length = hparams.filter_length
-    self.hop_length = hparams.hop_length
-    self.win_length = hparams.win_length
-
-    self.add_blank = hparams.add_blank
-    self.min_text_len = 1
-    self.max_text_len = 190
-
-    self.speaker_dict = {
-      speaker: idx
-      for idx, speaker in enumerate(hparams.speakers)
-    }
-    self.data_path = hparams.data_path
-
-    self.pitch = Pitch(sr=hparams.sampling_rate,
-                       W=hparams.tau_max,
-                       tau_max=hparams.tau_max,
-                       midi_start=hparams.midi_start,
-                       midi_end=hparams.midi_end,
-                       octave_range=hparams.octave_range)
-
-    random.seed(1234)
-    random.shuffle(self.audiopaths_sid_text)
-    self._filter()
-    if pt_run:
-      for _audiopaths_sid_text in self.audiopaths_sid_text:
-        _ = self.get_audio_text_speaker_pair(_audiopaths_sid_text,
-                                             True)
-
-  def _filter(self):
-    """
-    Filter text & store spec lengths
-    """
-    # Store spectrogram lengths for Bucketing
-    # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-    # spec_length = wav_length // hop_length
-
-    audiopaths_sid_text_new = []
-    lengths = []
-    for audiopath, spk, text, lang in self.audiopaths_sid_text:
-      if self.min_text_len <= len(text) and len(
-          text) <= self.max_text_len:
-        audiopath = os.path.join(self.data_path, audiopath)
-        if not os.path.exists(audiopath):
-          print(audiopath, "not exist!")
-          continue
-        try:
-          audio, sampling_rate = load_wav_to_torch(audiopath)
-        except:
-          print(audiopath, "load error!")
-          continue
-        audiopaths_sid_text_new.append([audiopath, spk, text, lang])
-        lengths.append(
-          os.path.getsize(audiopath) // (2 * self.hop_length))
-    self.audiopaths_sid_text = audiopaths_sid_text_new
-    self.lengths = lengths
-
-  def get_audio_text_speaker_pair(self, audiopath_sid_text, pt_run=False):
-    # separate filename, speaker_id and text
-    audiopath, spk, text, lang = audiopath_sid_text
-    text, lang = self.get_text(text, lang)
-    spec, ying, wav = self.get_audio(audiopath, pt_run)
-    sid = self.get_sid(self.speaker_dict[spk])
-    return (text, spec, ying, wav, sid, lang)
-
-  def get_audio(self, filename, pt_run=False):
-    audio, sampling_rate = load_wav_to_torch(filename)
-    if sampling_rate != self.sampling_rate:
-      raise ValueError("{} {} SR doesn't match target {} SR".format(
-        sampling_rate, self.sampling_rate))
-    audio_norm = audio.unsqueeze(0)
-    spec_filename = filename.replace(".wav", ".spec.pt")
-    ying_filename = filename.replace(".wav", ".ying.pt")
-    if os.path.exists(spec_filename) and not pt_run:
-      spec = torch.load(spec_filename, map_location='cpu')
-    else:
-      spec = spectrogram_torch(audio_norm,
-                               self.filter_length,
-                               self.sampling_rate,
-                               self.hop_length,
-                               self.win_length,
-                               center=False)
-      spec = torch.squeeze(spec, 0)
-      torch.save(spec, spec_filename)
-    if os.path.exists(ying_filename) and not pt_run:
-      ying = torch.load(ying_filename, map_location='cpu')
-    else:
-      wav = torch.nn.functional.pad(
-        audio_norm.unsqueeze(0),
-        (self.filter_length - self.hop_length,
-         self.filter_length - self.hop_length +
-         (-audio_norm.shape[1]) % self.hop_length + self.hop_length * (audio_norm.shape[1] % self.hop_length == 0)),
-        mode='constant').squeeze(0)
-      ying = self.pitch.yingram(wav)[0]
-      torch.save(ying, ying_filename)
-    return spec, ying, audio_norm
-
-  def get_text(self, text, lang):
-    text_norm = cleaned_text_to_sequence(text)
-    lang = [int(i) for i in lang.split(" ")]
-    if self.add_blank:
-      text_norm, lang = commons.intersperse_with_language_id(text_norm, lang, 0)
-    text_norm = torch.LongTensor(text_norm)
-    lang = torch.LongTensor(lang)
-    return text_norm, lang
-
-  def get_sid(self, sid):
-    sid = torch.LongTensor([int(sid)])
-    return sid
-
-  def __getitem__(self, index):
-    return self.get_audio_text_speaker_pair(
-      self.audiopaths_sid_text[index])
-
-  def __len__(self):
-    return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-  """ Zero-pads model inputs and targets"""
-
-  def __init__(self, return_ids=False):
-    self.return_ids = return_ids
-
-  def __call__(self, batch):
-    """Collate's training batch from normalized text, audio and speaker identities
-    PARAMS
-    ------
-    batch: [text_normalized, spec_normalized, wav_normalized, sid]
-    """
-    # Right zero-pad all one-hot text sequences to max input length
-    _, ids_sorted_decreasing = torch.sort(torch.LongTensor(
-      [x[1].size(1) for x in batch]),
-      dim=0,
-      descending=True)
-
-    max_text_len = max([len(x[0]) for x in batch])
-    max_spec_len = max([x[1].size(1) for x in batch])
-    max_ying_len = max([x[2].size(1) for x in batch])
-    max_wav_len = max([x[3].size(1) for x in batch])
-
-    text_lengths = torch.LongTensor(len(batch))
-    spec_lengths = torch.LongTensor(len(batch))
-    ying_lengths = torch.LongTensor(len(batch))
-    wav_lengths = torch.LongTensor(len(batch))
-    sid = torch.LongTensor(len(batch))
-
-    text_padded = torch.LongTensor(len(batch), max_text_len)
-    tone_padded = torch.LongTensor(len(batch), max_text_len)
-    spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0),
-                                    max_spec_len)
-    ying_padded = torch.FloatTensor(len(batch), batch[0][2].size(0),
-                                    max_ying_len)
-    wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-    text_padded.zero_()
-    tone_padded.zero_()
-    spec_padded.zero_()
-    ying_padded.zero_()
-    wav_padded.zero_()
-    for i in range(len(ids_sorted_decreasing)):
-      row = batch[ids_sorted_decreasing[i]]
-
-      text = row[0]
-      text_padded[i, :text.size(0)] = text
-      text_lengths[i] = text.size(0)
-
-      spec = row[1]
-      spec_padded[i, :, :spec.size(1)] = spec
-      spec_lengths[i] = spec.size(1)
-
-      ying = row[2]
-      ying_padded[i, :, :ying.size(1)] = ying
-      ying_lengths[i] = ying.size(1)
-
-      wav = row[3]
-      wav_padded[i, :, :wav.size(1)] = wav
-      wav_lengths[i] = wav.size(1)
-
-      tone = row[5]
-      tone_padded[i, :text.size(0)] = tone
-
-      sid[i] = row[4]
-
-    if self.return_ids:
-      return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
-    return text_padded, text_lengths, spec_padded, spec_lengths, ying_padded, ying_lengths, wav_padded, wav_lengths, sid, tone_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler
-                               ):
-  """
-  Maintain similar input lengths in a batch.
-  Length groups are specified by boundaries.
-  Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-  It removes samples which are not included in the boundaries.
-  Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-  """
-
-  def __init__(self,
-               dataset,
-               batch_size,
-               boundaries,
-               num_replicas=None,
-               rank=None,
-               shuffle=True):
-    super().__init__(dataset,
-                     num_replicas=num_replicas,
-                     rank=rank,
-                     shuffle=shuffle)
-    self.lengths = dataset.lengths
-    self.batch_size = batch_size
-    self.boundaries = boundaries
-
-    self.buckets, self.num_samples_per_bucket = self._create_buckets()
-    self.total_size = sum(self.num_samples_per_bucket)
-    self.num_samples = self.total_size // self.num_replicas
-
-  def _create_buckets(self):
-    buckets = [[] for _ in range(len(self.boundaries) - 1)]
-    for i in range(len(self.lengths)):
-      length = self.lengths[i]
-      idx_bucket = self._bisect(length)
-      if idx_bucket != -1:
-        buckets[idx_bucket].append(i)
-
-    for i in range(len(buckets) - 1, -1, -1):
-      if len(buckets[i]) == 0:
-        buckets.pop(i)
-        self.boundaries.pop(i + 1)
-
-    num_samples_per_bucket = []
-    for i in range(len(buckets)):
-      len_bucket = len(buckets[i])
-      total_batch_size = self.num_replicas * self.batch_size
-      rem = (total_batch_size -
-             (len_bucket % total_batch_size)) % total_batch_size
-      num_samples_per_bucket.append(len_bucket + rem)
-    return buckets, num_samples_per_bucket
-
-  def __iter__(self):
-    # deterministically shuffle based on epoch
-    g = torch.Generator()
-    g.manual_seed(self.epoch)
-
-    indices = []
-    if self.shuffle:
-      for bucket in self.buckets:
-        indices.append(
-          torch.randperm(len(bucket), generator=g).tolist())
-    else:
-      for bucket in self.buckets:
-        indices.append(list(range(len(bucket))))
-
-    batches = []
-    for i in range(len(self.buckets)):
-      bucket = self.buckets[i]
-      len_bucket = len(bucket)
-      ids_bucket = indices[i]
-      num_samples_bucket = self.num_samples_per_bucket[i]
-
-      # add extra samples to make it evenly divisible
-      rem = num_samples_bucket - len_bucket
-      ids_bucket = ids_bucket + ids_bucket * \
-                   (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
-      # subsample
-      ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
-      # batching
-      for j in range(len(ids_bucket) // self.batch_size):
-        batch = [
-          bucket[idx]
-          for idx in ids_bucket[j * self.batch_size:(j + 1) *
-                                                    self.batch_size]
-        ]
-        batches.append(batch)
-
-    if self.shuffle:
-      batch_ids = torch.randperm(len(batches), generator=g).tolist()
-      batches = [batches[i] for i in batch_ids]
-    self.batches = batches
-
-    assert len(self.batches) * self.batch_size == self.num_samples
-    return iter(self.batches)
-
-  def _bisect(self, x, lo=0, hi=None):
-    if hi is None:
-      hi = len(self.boundaries) - 1
-
-    if hi > lo:
-      mid = (hi + lo) // 2
-      if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-        return mid
-      elif x <= self.boundaries[mid]:
-        return self._bisect(x, lo, mid)
-      else:
-        return self._bisect(x, mid + 1, hi)
-    else:
-      return -1
-
-  def __len__(self):
-    return self.num_samples // self.batch_size
-
-
-def create_spec(audiopaths_sid_text, hparams):
-  audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-  for audiopath, _, _, _ in audiopaths_sid_text:
-    audiopath = os.path.join(hparams.data_path, audiopath)
-    if not os.path.exists(audiopath):
-      print(audiopath, "not exist!")
-      continue
-    try:
-      audio, sampling_rate = load_wav_to_torch(audiopath)
-    except:
-      print(audiopath, "load error!")
-      continue
-    if sampling_rate != hparams.sampling_rate:
-      raise ValueError("{} {} SR doesn't match target {} SR".format(
-        sampling_rate, hparams.sampling_rate))
-    audio_norm = audio.unsqueeze(0)
-    specpath = audiopath.replace(".wav", ".spec.pt")
-
-    if not os.path.exists(specpath):
-      spec = spectrogram_torch(audio_norm,
-                               hparams.filter_length,
-                               hparams.sampling_rate,
-                               hparams.hop_length,
-                               hparams.win_length,
-                               center=False)
-      spec = torch.squeeze(spec, 0)
-      torch.save(spec, specpath)

spaces/Amrrs/hubble-jwst-compare/README.md

@@ -1,13 +0,0 @@
----
-title: Hubble Jwst Compare
-emoji: 😻
-colorFrom: pink
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/conceptual/philosophy.md

@@ -1,110 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Philosophy
-
-🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
-Its purpose is to serve as a **modular toolbox** for both inference and training.
-
-We aim at building a library that stands the test of time and therefore take API design very seriously.
-
-In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
-
-## Usability over Performance
-
-- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.
-- Diffusers aim at being a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.
-- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.
-
-## Simple over easy
-
-As PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library: 
-- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.
-- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.
-- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.
-- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. Dreambooth or textual inversion training 
-is very simple thanks to diffusers' ability to separate single components of the diffusion pipeline.
-
-## Tweakable, contributor-friendly over abstraction
-
-For large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself). 
-In short, just like Transformers does for modeling files, diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.
-Functions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable. 
-**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:
-- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.
-- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions.
-- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel.
-
-At Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look
-at [this blog post](https://huggingface.co/blog/transformers-design-philosophy).
-
-In diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such 
-as [DDPM](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [UnCLIP (Dalle-2)](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/unclip#overview) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models#diffusers.UNet2DConditionModel).
-
-Great, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗. 
-We try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️  to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
-
-## Design Philosophy in Details
-
-Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consist of three major classes, [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
-Let's walk through more in-detail design decisions for each class.
-
-### Pipelines
-
-Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.
-
-The following design principles are followed:
-- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
-- Pipelines all inherit from [`DiffusionPipeline`].
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
-- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
-- Pipelines should be used **only** for inference.
-- Pipelines should be very readable, self-explanatory, and easy to tweak.
-- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
-- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
-- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
-- Pipelines should be named after the task they are intended to solve.
-- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
-
-### Models
-
-Models are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**.
-
-The following design principles are followed:
-- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
-- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
-- Models intend to expose complexity, just like PyTorch's module does, and give clear error messages.
-- Models all inherit from `ModelMixin` and `ConfigMixin`.
-- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain.
-- Models should by default have the highest precision and lowest performance setting.
-- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
-- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
-- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and 
-readable longterm, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-### Schedulers
-
-Schedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**.
-
-The following design principles are followed:
-- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers). 
-- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained. 
-- One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper). 
-- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
-- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.md).
-- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
-- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
-- The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
-- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
-- In almost all cases, novel schedulers shall be implemented in a new scheduling file.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_pndm.py

@@ -1,242 +0,0 @@
-import tempfile
-
-import torch
-
-from diffusers import PNDMScheduler
-
-from .test_schedulers import SchedulerCommonTest
-
-
-class PNDMSchedulerTest(SchedulerCommonTest):
-    scheduler_classes = (PNDMScheduler,)
-    forward_default_kwargs = (("num_inference_steps", 50),)
-
-    def get_scheduler_config(self, **kwargs):
-        config = {
-            "num_train_timesteps": 1000,
-            "beta_start": 0.0001,
-            "beta_end": 0.02,
-            "beta_schedule": "linear",
-        }
-
-        config.update(**kwargs)
-        return config
-
-    def check_over_configs(self, time_step=0, **config):
-        kwargs = dict(self.forward_default_kwargs)
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-        sample = self.dummy_sample
-        residual = 0.1 * sample
-        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
-
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config(**config)
-            scheduler = scheduler_class(**scheduler_config)
-            scheduler.set_timesteps(num_inference_steps)
-            # copy over dummy past residuals
-            scheduler.ets = dummy_past_residuals[:]
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-                new_scheduler.set_timesteps(num_inference_steps)
-                # copy over dummy past residuals
-                new_scheduler.ets = dummy_past_residuals[:]
-
-            output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-            output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def test_from_save_pretrained(self):
-        pass
-
-    def check_over_forward(self, time_step=0, **forward_kwargs):
-        kwargs = dict(self.forward_default_kwargs)
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-        sample = self.dummy_sample
-        residual = 0.1 * sample
-        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
-
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-            scheduler.set_timesteps(num_inference_steps)
-
-            # copy over dummy past residuals (must be after setting timesteps)
-            scheduler.ets = dummy_past_residuals[:]
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                scheduler.save_config(tmpdirname)
-                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
-                # copy over dummy past residuals
-                new_scheduler.set_timesteps(num_inference_steps)
-
-                # copy over dummy past residual (must be after setting timesteps)
-                new_scheduler.ets = dummy_past_residuals[:]
-
-            output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-            output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
-            new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
-
-            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
-
-    def full_loop(self, **config):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(**config)
-        scheduler = scheduler_class(**scheduler_config)
-
-        num_inference_steps = 10
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter
-        scheduler.set_timesteps(num_inference_steps)
-
-        for i, t in enumerate(scheduler.prk_timesteps):
-            residual = model(sample, t)
-            sample = scheduler.step_prk(residual, t, sample).prev_sample
-
-        for i, t in enumerate(scheduler.plms_timesteps):
-            residual = model(sample, t)
-            sample = scheduler.step_plms(residual, t, sample).prev_sample
-
-        return sample
-
-    def test_step_shape(self):
-        kwargs = dict(self.forward_default_kwargs)
-
-        num_inference_steps = kwargs.pop("num_inference_steps", None)
-
-        for scheduler_class in self.scheduler_classes:
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            sample = self.dummy_sample
-            residual = 0.1 * sample
-
-            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
-                scheduler.set_timesteps(num_inference_steps)
-            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
-                kwargs["num_inference_steps"] = num_inference_steps
-
-            # copy over dummy past residuals (must be done after set_timesteps)
-            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
-            scheduler.ets = dummy_past_residuals[:]
-
-            output_0 = scheduler.step_prk(residual, 0, sample, **kwargs).prev_sample
-            output_1 = scheduler.step_prk(residual, 1, sample, **kwargs).prev_sample
-
-            self.assertEqual(output_0.shape, sample.shape)
-            self.assertEqual(output_0.shape, output_1.shape)
-
-            output_0 = scheduler.step_plms(residual, 0, sample, **kwargs).prev_sample
-            output_1 = scheduler.step_plms(residual, 1, sample, **kwargs).prev_sample
-
-            self.assertEqual(output_0.shape, sample.shape)
-            self.assertEqual(output_0.shape, output_1.shape)
-
-    def test_timesteps(self):
-        for timesteps in [100, 1000]:
-            self.check_over_configs(num_train_timesteps=timesteps)
-
-    def test_steps_offset(self):
-        for steps_offset in [0, 1]:
-            self.check_over_configs(steps_offset=steps_offset)
-
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(steps_offset=1)
-        scheduler = scheduler_class(**scheduler_config)
-        scheduler.set_timesteps(10)
-        assert torch.equal(
-            scheduler.timesteps,
-            torch.LongTensor(
-                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]
-            ),
-        )
-
-    def test_betas(self):
-        for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):
-            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
-
-    def test_schedules(self):
-        for schedule in ["linear", "squaredcos_cap_v2"]:
-            self.check_over_configs(beta_schedule=schedule)
-
-    def test_prediction_type(self):
-        for prediction_type in ["epsilon", "v_prediction"]:
-            self.check_over_configs(prediction_type=prediction_type)
-
-    def test_time_indices(self):
-        for t in [1, 5, 10]:
-            self.check_over_forward(time_step=t)
-
-    def test_inference_steps(self):
-        for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):
-            self.check_over_forward(num_inference_steps=num_inference_steps)
-
-    def test_pow_of_3_inference_steps(self):
-        # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
-        num_inference_steps = 27
-
-        for scheduler_class in self.scheduler_classes:
-            sample = self.dummy_sample
-            residual = 0.1 * sample
-
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            scheduler.set_timesteps(num_inference_steps)
-
-            # before power of 3 fix, would error on first step, so we only need to do two
-            for i, t in enumerate(scheduler.prk_timesteps[:2]):
-                sample = scheduler.step_prk(residual, t, sample).prev_sample
-
-    def test_inference_plms_no_past_residuals(self):
-        with self.assertRaises(ValueError):
-            scheduler_class = self.scheduler_classes[0]
-            scheduler_config = self.get_scheduler_config()
-            scheduler = scheduler_class(**scheduler_config)
-
-            scheduler.step_plms(self.dummy_sample, 1, self.dummy_sample).prev_sample
-
-    def test_full_loop_no_noise(self):
-        sample = self.full_loop()
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 198.1318) < 1e-2
-        assert abs(result_mean.item() - 0.2580) < 1e-3
-
-    def test_full_loop_with_v_prediction(self):
-        sample = self.full_loop(prediction_type="v_prediction")
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 67.3986) < 1e-2
-        assert abs(result_mean.item() - 0.0878) < 1e-3
-
-    def test_full_loop_with_set_alpha_to_one(self):
-        # We specify different beta, so that the first alpha is 0.99
-        sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 230.0399) < 1e-2
-        assert abs(result_mean.item() - 0.2995) < 1e-3
-
-    def test_full_loop_with_no_set_alpha_to_one(self):
-        # We specify different beta, so that the first alpha is 0.99
-        sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 186.9482) < 1e-2
-        assert abs(result_mean.item() - 0.2434) < 1e-3

spaces/Andy1621/uniformer_image_detection/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py

@@ -1,13 +0,0 @@
-_base_ = './retinanet_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/mmdet/core/evaluation/mean_ap.py

@@ -1,469 +0,0 @@
-from multiprocessing import Pool
-
-import mmcv
-import numpy as np
-from mmcv.utils import print_log
-from terminaltables import AsciiTable
-
-from .bbox_overlaps import bbox_overlaps
-from .class_names import get_classes
-
-
-def average_precision(recalls, precisions, mode='area'):
-    """Calculate average precision (for single or multiple scales).
-
-    Args:
-        recalls (ndarray): shape (num_scales, num_dets) or (num_dets, )
-        precisions (ndarray): shape (num_scales, num_dets) or (num_dets, )
-        mode (str): 'area' or '11points', 'area' means calculating the area
-            under precision-recall curve, '11points' means calculating
-            the average precision of recalls at [0, 0.1, ..., 1]
-
-    Returns:
-        float or ndarray: calculated average precision
-    """
-    no_scale = False
-    if recalls.ndim == 1:
-        no_scale = True
-        recalls = recalls[np.newaxis, :]
-        precisions = precisions[np.newaxis, :]
-    assert recalls.shape == precisions.shape and recalls.ndim == 2
-    num_scales = recalls.shape[0]
-    ap = np.zeros(num_scales, dtype=np.float32)
-    if mode == 'area':
-        zeros = np.zeros((num_scales, 1), dtype=recalls.dtype)
-        ones = np.ones((num_scales, 1), dtype=recalls.dtype)
-        mrec = np.hstack((zeros, recalls, ones))
-        mpre = np.hstack((zeros, precisions, zeros))
-        for i in range(mpre.shape[1] - 1, 0, -1):
-            mpre[:, i - 1] = np.maximum(mpre[:, i - 1], mpre[:, i])
-        for i in range(num_scales):
-            ind = np.where(mrec[i, 1:] != mrec[i, :-1])[0]
-            ap[i] = np.sum(
-                (mrec[i, ind + 1] - mrec[i, ind]) * mpre[i, ind + 1])
-    elif mode == '11points':
-        for i in range(num_scales):
-            for thr in np.arange(0, 1 + 1e-3, 0.1):
-                precs = precisions[i, recalls[i, :] >= thr]
-                prec = precs.max() if precs.size > 0 else 0
-                ap[i] += prec
-        ap /= 11
-    else:
-        raise ValueError(
-            'Unrecognized mode, only "area" and "11points" are supported')
-    if no_scale:
-        ap = ap[0]
-    return ap
-
-
-def tpfp_imagenet(det_bboxes,
-                  gt_bboxes,
-                  gt_bboxes_ignore=None,
-                  default_iou_thr=0.5,
-                  area_ranges=None):
-    """Check if detected bboxes are true positive or false positive.
-
-    Args:
-        det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
-        gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
-        gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
-            of shape (k, 4). Default: None
-        default_iou_thr (float): IoU threshold to be considered as matched for
-            medium and large bboxes (small ones have special rules).
-            Default: 0.5.
-        area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,
-            in the format [(min1, max1), (min2, max2), ...]. Default: None.
-
-    Returns:
-        tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
-            each array is (num_scales, m).
-    """
-    # an indicator of ignored gts
-    gt_ignore_inds = np.concatenate(
-        (np.zeros(gt_bboxes.shape[0], dtype=np.bool),
-         np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool)))
-    # stack gt_bboxes and gt_bboxes_ignore for convenience
-    gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
-
-    num_dets = det_bboxes.shape[0]
-    num_gts = gt_bboxes.shape[0]
-    if area_ranges is None:
-        area_ranges = [(None, None)]
-    num_scales = len(area_ranges)
-    # tp and fp are of shape (num_scales, num_gts), each row is tp or fp
-    # of a certain scale.
-    tp = np.zeros((num_scales, num_dets), dtype=np.float32)
-    fp = np.zeros((num_scales, num_dets), dtype=np.float32)
-    if gt_bboxes.shape[0] == 0:
-        if area_ranges == [(None, None)]:
-            fp[...] = 1
-        else:
-            det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * (
-                det_bboxes[:, 3] - det_bboxes[:, 1])
-            for i, (min_area, max_area) in enumerate(area_ranges):
-                fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
-        return tp, fp
-    ious = bbox_overlaps(det_bboxes, gt_bboxes - 1)
-    gt_w = gt_bboxes[:, 2] - gt_bboxes[:, 0]
-    gt_h = gt_bboxes[:, 3] - gt_bboxes[:, 1]
-    iou_thrs = np.minimum((gt_w * gt_h) / ((gt_w + 10.0) * (gt_h + 10.0)),
-                          default_iou_thr)
-    # sort all detections by scores in descending order
-    sort_inds = np.argsort(-det_bboxes[:, -1])
-    for k, (min_area, max_area) in enumerate(area_ranges):
-        gt_covered = np.zeros(num_gts, dtype=bool)
-        # if no area range is specified, gt_area_ignore is all False
-        if min_area is None:
-            gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
-        else:
-            gt_areas = gt_w * gt_h
-            gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
-        for i in sort_inds:
-            max_iou = -1
-            matched_gt = -1
-            # find best overlapped available gt
-            for j in range(num_gts):
-                # different from PASCAL VOC: allow finding other gts if the
-                # best overlapped ones are already matched by other det bboxes
-                if gt_covered[j]:
-                    continue
-                elif ious[i, j] >= iou_thrs[j] and ious[i, j] > max_iou:
-                    max_iou = ious[i, j]
-                    matched_gt = j
-            # there are 4 cases for a det bbox:
-            # 1. it matches a gt, tp = 1, fp = 0
-            # 2. it matches an ignored gt, tp = 0, fp = 0
-            # 3. it matches no gt and within area range, tp = 0, fp = 1
-            # 4. it matches no gt but is beyond area range, tp = 0, fp = 0
-            if matched_gt >= 0:
-                gt_covered[matched_gt] = 1
-                if not (gt_ignore_inds[matched_gt]
-                        or gt_area_ignore[matched_gt]):
-                    tp[k, i] = 1
-            elif min_area is None:
-                fp[k, i] = 1
-            else:
-                bbox = det_bboxes[i, :4]
-                area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
-                if area >= min_area and area < max_area:
-                    fp[k, i] = 1
-    return tp, fp
-
-
-def tpfp_default(det_bboxes,
-                 gt_bboxes,
-                 gt_bboxes_ignore=None,
-                 iou_thr=0.5,
-                 area_ranges=None):
-    """Check if detected bboxes are true positive or false positive.
-
-    Args:
-        det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
-        gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
-        gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
-            of shape (k, 4). Default: None
-        iou_thr (float): IoU threshold to be considered as matched.
-            Default: 0.5.
-        area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,
-            in the format [(min1, max1), (min2, max2), ...]. Default: None.
-
-    Returns:
-        tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
-            each array is (num_scales, m).
-    """
-    # an indicator of ignored gts
-    gt_ignore_inds = np.concatenate(
-        (np.zeros(gt_bboxes.shape[0], dtype=np.bool),
-         np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool)))
-    # stack gt_bboxes and gt_bboxes_ignore for convenience
-    gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
-
-    num_dets = det_bboxes.shape[0]
-    num_gts = gt_bboxes.shape[0]
-    if area_ranges is None:
-        area_ranges = [(None, None)]
-    num_scales = len(area_ranges)
-    # tp and fp are of shape (num_scales, num_gts), each row is tp or fp of
-    # a certain scale
-    tp = np.zeros((num_scales, num_dets), dtype=np.float32)
-    fp = np.zeros((num_scales, num_dets), dtype=np.float32)
-
-    # if there is no gt bboxes in this image, then all det bboxes
-    # within area range are false positives
-    if gt_bboxes.shape[0] == 0:
-        if area_ranges == [(None, None)]:
-            fp[...] = 1
-        else:
-            det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * (
-                det_bboxes[:, 3] - det_bboxes[:, 1])
-            for i, (min_area, max_area) in enumerate(area_ranges):
-                fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
-        return tp, fp
-
-    ious = bbox_overlaps(det_bboxes, gt_bboxes)
-    # for each det, the max iou with all gts
-    ious_max = ious.max(axis=1)
-    # for each det, which gt overlaps most with it
-    ious_argmax = ious.argmax(axis=1)
-    # sort all dets in descending order by scores
-    sort_inds = np.argsort(-det_bboxes[:, -1])
-    for k, (min_area, max_area) in enumerate(area_ranges):
-        gt_covered = np.zeros(num_gts, dtype=bool)
-        # if no area range is specified, gt_area_ignore is all False
-        if min_area is None:
-            gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
-        else:
-            gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
-                gt_bboxes[:, 3] - gt_bboxes[:, 1])
-            gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
-        for i in sort_inds:
-            if ious_max[i] >= iou_thr:
-                matched_gt = ious_argmax[i]
-                if not (gt_ignore_inds[matched_gt]
-                        or gt_area_ignore[matched_gt]):
-                    if not gt_covered[matched_gt]:
-                        gt_covered[matched_gt] = True
-                        tp[k, i] = 1
-                    else:
-                        fp[k, i] = 1
-                # otherwise ignore this detected bbox, tp = 0, fp = 0
-            elif min_area is None:
-                fp[k, i] = 1
-            else:
-                bbox = det_bboxes[i, :4]
-                area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
-                if area >= min_area and area < max_area:
-                    fp[k, i] = 1
-    return tp, fp
-
-
-def get_cls_results(det_results, annotations, class_id):
-    """Get det results and gt information of a certain class.
-
-    Args:
-        det_results (list[list]): Same as `eval_map()`.
-        annotations (list[dict]): Same as `eval_map()`.
-        class_id (int): ID of a specific class.
-
-    Returns:
-        tuple[list[np.ndarray]]: detected bboxes, gt bboxes, ignored gt bboxes
-    """
-    cls_dets = [img_res[class_id] for img_res in det_results]
-    cls_gts = []
-    cls_gts_ignore = []
-    for ann in annotations:
-        gt_inds = ann['labels'] == class_id
-        cls_gts.append(ann['bboxes'][gt_inds, :])
-
-        if ann.get('labels_ignore', None) is not None:
-            ignore_inds = ann['labels_ignore'] == class_id
-            cls_gts_ignore.append(ann['bboxes_ignore'][ignore_inds, :])
-        else:
-            cls_gts_ignore.append(np.empty((0, 4), dtype=np.float32))
-
-    return cls_dets, cls_gts, cls_gts_ignore
-
-
-def eval_map(det_results,
-             annotations,
-             scale_ranges=None,
-             iou_thr=0.5,
-             dataset=None,
-             logger=None,
-             tpfp_fn=None,
-             nproc=4):
-    """Evaluate mAP of a dataset.
-
-    Args:
-        det_results (list[list]): [[cls1_det, cls2_det, ...], ...].
-            The outer list indicates images, and the inner list indicates
-            per-class detected bboxes.
-        annotations (list[dict]): Ground truth annotations where each item of
-            the list indicates an image. Keys of annotations are:
-
-            - `bboxes`: numpy array of shape (n, 4)
-            - `labels`: numpy array of shape (n, )
-            - `bboxes_ignore` (optional): numpy array of shape (k, 4)
-            - `labels_ignore` (optional): numpy array of shape (k, )
-        scale_ranges (list[tuple] | None): Range of scales to be evaluated,
-            in the format [(min1, max1), (min2, max2), ...]. A range of
-            (32, 64) means the area range between (32**2, 64**2).
-            Default: None.
-        iou_thr (float): IoU threshold to be considered as matched.
-            Default: 0.5.
-        dataset (list[str] | str | None): Dataset name or dataset classes,
-            there are minor differences in metrics for different datsets, e.g.
-            "voc07", "imagenet_det", etc. Default: None.
-        logger (logging.Logger | str | None): The way to print the mAP
-            summary. See `mmcv.utils.print_log()` for details. Default: None.
-        tpfp_fn (callable | None): The function used to determine true/
-            false positives. If None, :func:`tpfp_default` is used as default
-            unless dataset is 'det' or 'vid' (:func:`tpfp_imagenet` in this
-            case). If it is given as a function, then this function is used
-            to evaluate tp & fp. Default None.
-        nproc (int): Processes used for computing TP and FP.
-            Default: 4.
-
-    Returns:
-        tuple: (mAP, [dict, dict, ...])
-    """
-    assert len(det_results) == len(annotations)
-
-    num_imgs = len(det_results)
-    num_scales = len(scale_ranges) if scale_ranges is not None else 1
-    num_classes = len(det_results[0])  # positive class num
-    area_ranges = ([(rg[0]**2, rg[1]**2) for rg in scale_ranges]
-                   if scale_ranges is not None else None)
-
-    pool = Pool(nproc)
-    eval_results = []
-    for i in range(num_classes):
-        # get gt and det bboxes of this class
-        cls_dets, cls_gts, cls_gts_ignore = get_cls_results(
-            det_results, annotations, i)
-        # choose proper function according to datasets to compute tp and fp
-        if tpfp_fn is None:
-            if dataset in ['det', 'vid']:
-                tpfp_fn = tpfp_imagenet
-            else:
-                tpfp_fn = tpfp_default
-        if not callable(tpfp_fn):
-            raise ValueError(
-                f'tpfp_fn has to be a function or None, but got {tpfp_fn}')
-
-        # compute tp and fp for each image with multiple processes
-        tpfp = pool.starmap(
-            tpfp_fn,
-            zip(cls_dets, cls_gts, cls_gts_ignore,
-                [iou_thr for _ in range(num_imgs)],
-                [area_ranges for _ in range(num_imgs)]))
-        tp, fp = tuple(zip(*tpfp))
-        # calculate gt number of each scale
-        # ignored gts or gts beyond the specific scale are not counted
-        num_gts = np.zeros(num_scales, dtype=int)
-        for j, bbox in enumerate(cls_gts):
-            if area_ranges is None:
-                num_gts[0] += bbox.shape[0]
-            else:
-                gt_areas = (bbox[:, 2] - bbox[:, 0]) * (
-                    bbox[:, 3] - bbox[:, 1])
-                for k, (min_area, max_area) in enumerate(area_ranges):
-                    num_gts[k] += np.sum((gt_areas >= min_area)
-                                         & (gt_areas < max_area))
-        # sort all det bboxes by score, also sort tp and fp
-        cls_dets = np.vstack(cls_dets)
-        num_dets = cls_dets.shape[0]
-        sort_inds = np.argsort(-cls_dets[:, -1])
-        tp = np.hstack(tp)[:, sort_inds]
-        fp = np.hstack(fp)[:, sort_inds]
-        # calculate recall and precision with tp and fp
-        tp = np.cumsum(tp, axis=1)
-        fp = np.cumsum(fp, axis=1)
-        eps = np.finfo(np.float32).eps
-        recalls = tp / np.maximum(num_gts[:, np.newaxis], eps)
-        precisions = tp / np.maximum((tp + fp), eps)
-        # calculate AP
-        if scale_ranges is None:
-            recalls = recalls[0, :]
-            precisions = precisions[0, :]
-            num_gts = num_gts.item()
-        mode = 'area' if dataset != 'voc07' else '11points'
-        ap = average_precision(recalls, precisions, mode)
-        eval_results.append({
-            'num_gts': num_gts,
-            'num_dets': num_dets,
-            'recall': recalls,
-            'precision': precisions,
-            'ap': ap
-        })
-    pool.close()
-    if scale_ranges is not None:
-        # shape (num_classes, num_scales)
-        all_ap = np.vstack([cls_result['ap'] for cls_result in eval_results])
-        all_num_gts = np.vstack(
-            [cls_result['num_gts'] for cls_result in eval_results])
-        mean_ap = []
-        for i in range(num_scales):
-            if np.any(all_num_gts[:, i] > 0):
-                mean_ap.append(all_ap[all_num_gts[:, i] > 0, i].mean())
-            else:
-                mean_ap.append(0.0)
-    else:
-        aps = []
-        for cls_result in eval_results:
-            if cls_result['num_gts'] > 0:
-                aps.append(cls_result['ap'])
-        mean_ap = np.array(aps).mean().item() if aps else 0.0
-
-    print_map_summary(
-        mean_ap, eval_results, dataset, area_ranges, logger=logger)
-
-    return mean_ap, eval_results
-
-
-def print_map_summary(mean_ap,
-                      results,
-                      dataset=None,
-                      scale_ranges=None,
-                      logger=None):
-    """Print mAP and results of each class.
-
-    A table will be printed to show the gts/dets/recall/AP of each class and
-    the mAP.
-
-    Args:
-        mean_ap (float): Calculated from `eval_map()`.
-        results (list[dict]): Calculated from `eval_map()`.
-        dataset (list[str] | str | None): Dataset name or dataset classes.
-        scale_ranges (list[tuple] | None): Range of scales to be evaluated.
-        logger (logging.Logger | str | None): The way to print the mAP
-            summary. See `mmcv.utils.print_log()` for details. Default: None.
-    """
-
-    if logger == 'silent':
-        return
-
-    if isinstance(results[0]['ap'], np.ndarray):
-        num_scales = len(results[0]['ap'])
-    else:
-        num_scales = 1
-
-    if scale_ranges is not None:
-        assert len(scale_ranges) == num_scales
-
-    num_classes = len(results)
-
-    recalls = np.zeros((num_scales, num_classes), dtype=np.float32)
-    aps = np.zeros((num_scales, num_classes), dtype=np.float32)
-    num_gts = np.zeros((num_scales, num_classes), dtype=int)
-    for i, cls_result in enumerate(results):
-        if cls_result['recall'].size > 0:
-            recalls[:, i] = np.array(cls_result['recall'], ndmin=2)[:, -1]
-        aps[:, i] = cls_result['ap']
-        num_gts[:, i] = cls_result['num_gts']
-
-    if dataset is None:
-        label_names = [str(i) for i in range(num_classes)]
-    elif mmcv.is_str(dataset):
-        label_names = get_classes(dataset)
-    else:
-        label_names = dataset
-
-    if not isinstance(mean_ap, list):
-        mean_ap = [mean_ap]
-
-    header = ['class', 'gts', 'dets', 'recall', 'ap']
-    for i in range(num_scales):
-        if scale_ranges is not None:
-            print_log(f'Scale range {scale_ranges[i]}', logger=logger)
-        table_data = [header]
-        for j in range(num_classes):
-            row_data = [
-                label_names[j], num_gts[i, j], results[j]['num_dets'],
-                f'{recalls[i, j]:.3f}', f'{aps[i, j]:.3f}'
-            ]
-            table_data.append(row_data)
-        table_data.append(['mAP', '', '', '', f'{mean_ap[i]:.3f}'])
-        table = AsciiTable(table_data)
-        table.inner_footing_row_border = True
-        print_log('\n' + table.table, logger=logger)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/_distutils_hack/__init__.py

@@ -1,222 +0,0 @@
-# don't import any costly modules
-import sys
-import os
-
-
-is_pypy = '__pypy__' in sys.builtin_module_names
-
-
-def warn_distutils_present():
-    if 'distutils' not in sys.modules:
-        return
-    if is_pypy and sys.version_info < (3, 7):
-        # PyPy for 3.6 unconditionally imports distutils, so bypass the warning
-        # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
-        return
-    import warnings
-
-    warnings.warn(
-        "Distutils was imported before Setuptools, but importing Setuptools "
-        "also replaces the `distutils` module in `sys.modules`. This may lead "
-        "to undesirable behaviors or errors. To avoid these issues, avoid "
-        "using distutils directly, ensure that setuptools is installed in the "
-        "traditional way (e.g. not an editable install), and/or make sure "
-        "that setuptools is always imported before distutils."
-    )
-
-
-def clear_distutils():
-    if 'distutils' not in sys.modules:
-        return
-    import warnings
-
-    warnings.warn("Setuptools is replacing distutils.")
-    mods = [
-        name
-        for name in sys.modules
-        if name == "distutils" or name.startswith("distutils.")
-    ]
-    for name in mods:
-        del sys.modules[name]
-
-
-def enabled():
-    """
-    Allow selection of distutils by environment variable.
-    """
-    which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
-    return which == 'local'
-
-
-def ensure_local_distutils():
-    import importlib
-
-    clear_distutils()
-
-    # With the DistutilsMetaFinder in place,
-    # perform an import to cause distutils to be
-    # loaded from setuptools._distutils. Ref #2906.
-    with shim():
-        importlib.import_module('distutils')
-
-    # check that submodules load as expected
-    core = importlib.import_module('distutils.core')
-    assert '_distutils' in core.__file__, core.__file__
-    assert 'setuptools._distutils.log' not in sys.modules
-
-
-def do_override():
-    """
-    Ensure that the local copy of distutils is preferred over stdlib.
-
-    See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
-    for more motivation.
-    """
-    if enabled():
-        warn_distutils_present()
-        ensure_local_distutils()
-
-
-class _TrivialRe:
-    def __init__(self, *patterns):
-        self._patterns = patterns
-
-    def match(self, string):
-        return all(pat in string for pat in self._patterns)
-
-
-class DistutilsMetaFinder:
-    def find_spec(self, fullname, path, target=None):
-        # optimization: only consider top level modules and those
-        # found in the CPython test suite.
-        if path is not None and not fullname.startswith('test.'):
-            return
-
-        method_name = 'spec_for_{fullname}'.format(**locals())
-        method = getattr(self, method_name, lambda: None)
-        return method()
-
-    def spec_for_distutils(self):
-        if self.is_cpython():
-            return
-
-        import importlib
-        import importlib.abc
-        import importlib.util
-
-        try:
-            mod = importlib.import_module('setuptools._distutils')
-        except Exception:
-            # There are a couple of cases where setuptools._distutils
-            # may not be present:
-            # - An older Setuptools without a local distutils is
-            #   taking precedence. Ref #2957.
-            # - Path manipulation during sitecustomize removes
-            #   setuptools from the path but only after the hook
-            #   has been loaded. Ref #2980.
-            # In either case, fall back to stdlib behavior.
-            return
-
-        class DistutilsLoader(importlib.abc.Loader):
-            def create_module(self, spec):
-                mod.__name__ = 'distutils'
-                return mod
-
-            def exec_module(self, module):
-                pass
-
-        return importlib.util.spec_from_loader(
-            'distutils', DistutilsLoader(), origin=mod.__file__
-        )
-
-    @staticmethod
-    def is_cpython():
-        """
-        Suppress supplying distutils for CPython (build and tests).
-        Ref #2965 and #3007.
-        """
-        return os.path.isfile('pybuilddir.txt')
-
-    def spec_for_pip(self):
-        """
-        Ensure stdlib distutils when running under pip.
-        See pypa/pip#8761 for rationale.
-        """
-        if self.pip_imported_during_build():
-            return
-        clear_distutils()
-        self.spec_for_distutils = lambda: None
-
-    @classmethod
-    def pip_imported_during_build(cls):
-        """
-        Detect if pip is being imported in a build script. Ref #2355.
-        """
-        import traceback
-
-        return any(
-            cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
-        )
-
-    @staticmethod
-    def frame_file_is_setup(frame):
-        """
-        Return True if the indicated frame suggests a setup.py file.
-        """
-        # some frames may not have __file__ (#2940)
-        return frame.f_globals.get('__file__', '').endswith('setup.py')
-
-    def spec_for_sensitive_tests(self):
-        """
-        Ensure stdlib distutils when running select tests under CPython.
-
-        python/cpython#91169
-        """
-        clear_distutils()
-        self.spec_for_distutils = lambda: None
-
-    sensitive_tests = (
-        [
-            'test.test_distutils',
-            'test.test_peg_generator',
-            'test.test_importlib',
-        ]
-        if sys.version_info < (3, 10)
-        else [
-            'test.test_distutils',
-        ]
-    )
-
-
-for name in DistutilsMetaFinder.sensitive_tests:
-    setattr(
-        DistutilsMetaFinder,
-        f'spec_for_{name}',
-        DistutilsMetaFinder.spec_for_sensitive_tests,
-    )
-
-
-DISTUTILS_FINDER = DistutilsMetaFinder()
-
-
-def add_shim():
-    DISTUTILS_FINDER in sys.meta_path or insert_shim()
-
-
-class shim:
-    def __enter__(self):
-        insert_shim()
-
-    def __exit__(self, exc, value, tb):
-        remove_shim()
-
-
-def insert_shim():
-    sys.meta_path.insert(0, DISTUTILS_FINDER)
-
-
-def remove_shim():
-    try:
-        sys.meta_path.remove(DISTUTILS_FINDER)
-    except ValueError:
-        pass

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/socks.py

@@ -1,216 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module contains provisional support for SOCKS proxies from within
-urllib3. This module supports SOCKS4, SOCKS4A (an extension of SOCKS4), and
-SOCKS5. To enable its functionality, either install PySocks or install this
-module with the ``socks`` extra.
-
-The SOCKS implementation supports the full range of urllib3 features. It also
-supports the following SOCKS features:
-
-- SOCKS4A (``proxy_url='socks4a://...``)
-- SOCKS4 (``proxy_url='socks4://...``)
-- SOCKS5 with remote DNS (``proxy_url='socks5h://...``)
-- SOCKS5 with local DNS (``proxy_url='socks5://...``)
-- Usernames and passwords for the SOCKS proxy
-
-.. note::
-   It is recommended to use ``socks5h://`` or ``socks4a://`` schemes in
-   your ``proxy_url`` to ensure that DNS resolution is done from the remote
-   server instead of client-side when connecting to a domain name.
-
-SOCKS4 supports IPv4 and domain names with the SOCKS4A extension. SOCKS5
-supports IPv4, IPv6, and domain names.
-
-When connecting to a SOCKS4 proxy the ``username`` portion of the ``proxy_url``
-will be sent as the ``userid`` section of the SOCKS request:
-
-.. code-block:: python
-
-    proxy_url="socks4a://<userid>@proxy-host"
-
-When connecting to a SOCKS5 proxy the ``username`` and ``password`` portion
-of the ``proxy_url`` will be sent as the username/password to authenticate
-with the proxy:
-
-.. code-block:: python
-
-    proxy_url="socks5h://<username>:<password>@proxy-host"
-
-"""
-from __future__ import absolute_import
-
-try:
-    import socks
-except ImportError:
-    import warnings
-
-    from ..exceptions import DependencyWarning
-
-    warnings.warn(
-        (
-            "SOCKS support in urllib3 requires the installation of optional "
-            "dependencies: specifically, PySocks.  For more information, see "
-            "https://urllib3.readthedocs.io/en/1.26.x/contrib.html#socks-proxies"
-        ),
-        DependencyWarning,
-    )
-    raise
-
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-from ..connection import HTTPConnection, HTTPSConnection
-from ..connectionpool import HTTPConnectionPool, HTTPSConnectionPool
-from ..exceptions import ConnectTimeoutError, NewConnectionError
-from ..poolmanager import PoolManager
-from ..util.url import parse_url
-
-try:
-    import ssl
-except ImportError:
-    ssl = None
-
-
-class SOCKSConnection(HTTPConnection):
-    """
-    A plain-text HTTP connection that connects via a SOCKS proxy.
-    """
-
-    def __init__(self, *args, **kwargs):
-        self._socks_options = kwargs.pop("_socks_options")
-        super(SOCKSConnection, self).__init__(*args, **kwargs)
-
-    def _new_conn(self):
-        """
-        Establish a new connection via the SOCKS proxy.
-        """
-        extra_kw = {}
-        if self.source_address:
-            extra_kw["source_address"] = self.source_address
-
-        if self.socket_options:
-            extra_kw["socket_options"] = self.socket_options
-
-        try:
-            conn = socks.create_connection(
-                (self.host, self.port),
-                proxy_type=self._socks_options["socks_version"],
-                proxy_addr=self._socks_options["proxy_host"],
-                proxy_port=self._socks_options["proxy_port"],
-                proxy_username=self._socks_options["username"],
-                proxy_password=self._socks_options["password"],
-                proxy_rdns=self._socks_options["rdns"],
-                timeout=self.timeout,
-                **extra_kw
-            )
-
-        except SocketTimeout:
-            raise ConnectTimeoutError(
-                self,
-                "Connection to %s timed out. (connect timeout=%s)"
-                % (self.host, self.timeout),
-            )
-
-        except socks.ProxyError as e:
-            # This is fragile as hell, but it seems to be the only way to raise
-            # useful errors here.
-            if e.socket_err:
-                error = e.socket_err
-                if isinstance(error, SocketTimeout):
-                    raise ConnectTimeoutError(
-                        self,
-                        "Connection to %s timed out. (connect timeout=%s)"
-                        % (self.host, self.timeout),
-                    )
-                else:
-                    raise NewConnectionError(
-                        self, "Failed to establish a new connection: %s" % error
-                    )
-            else:
-                raise NewConnectionError(
-                    self, "Failed to establish a new connection: %s" % e
-                )
-
-        except SocketError as e:  # Defensive: PySocks should catch all these.
-            raise NewConnectionError(
-                self, "Failed to establish a new connection: %s" % e
-            )
-
-        return conn
-
-
-# We don't need to duplicate the Verified/Unverified distinction from
-# urllib3/connection.py here because the HTTPSConnection will already have been
-# correctly set to either the Verified or Unverified form by that module. This
-# means the SOCKSHTTPSConnection will automatically be the correct type.
-class SOCKSHTTPSConnection(SOCKSConnection, HTTPSConnection):
-    pass
-
-
-class SOCKSHTTPConnectionPool(HTTPConnectionPool):
-    ConnectionCls = SOCKSConnection
-
-
-class SOCKSHTTPSConnectionPool(HTTPSConnectionPool):
-    ConnectionCls = SOCKSHTTPSConnection
-
-
-class SOCKSProxyManager(PoolManager):
-    """
-    A version of the urllib3 ProxyManager that routes connections via the
-    defined SOCKS proxy.
-    """
-
-    pool_classes_by_scheme = {
-        "http": SOCKSHTTPConnectionPool,
-        "https": SOCKSHTTPSConnectionPool,
-    }
-
-    def __init__(
-        self,
-        proxy_url,
-        username=None,
-        password=None,
-        num_pools=10,
-        headers=None,
-        **connection_pool_kw
-    ):
-        parsed = parse_url(proxy_url)
-
-        if username is None and password is None and parsed.auth is not None:
-            split = parsed.auth.split(":")
-            if len(split) == 2:
-                username, password = split
-        if parsed.scheme == "socks5":
-            socks_version = socks.PROXY_TYPE_SOCKS5
-            rdns = False
-        elif parsed.scheme == "socks5h":
-            socks_version = socks.PROXY_TYPE_SOCKS5
-            rdns = True
-        elif parsed.scheme == "socks4":
-            socks_version = socks.PROXY_TYPE_SOCKS4
-            rdns = False
-        elif parsed.scheme == "socks4a":
-            socks_version = socks.PROXY_TYPE_SOCKS4
-            rdns = True
-        else:
-            raise ValueError("Unable to determine SOCKS version from %s" % proxy_url)
-
-        self.proxy_url = proxy_url
-
-        socks_options = {
-            "socks_version": socks_version,
-            "proxy_host": parsed.host,
-            "proxy_port": parsed.port,
-            "username": username,
-            "password": password,
-            "rdns": rdns,
-        }
-        connection_pool_kw["_socks_options"] = socks_options
-
-        super(SOCKSProxyManager, self).__init__(
-            num_pools, headers, **connection_pool_kw
-        )
-
-        self.pool_classes_by_scheme = SOCKSProxyManager.pool_classes_by_scheme

spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/factory.py

@@ -1,277 +0,0 @@
-import json
-import logging
-import os
-import pathlib
-import re
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-
-from .model import CLAP, convert_weights_to_fp16
-from .openai import load_openai_model
-from .pretrained import get_pretrained_url, download_pretrained
-from .transform import image_transform
-
-_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
-_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
-
-
-def _natural_key(string_):
-    return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())]
-
-
-def _rescan_model_configs():
-    global _MODEL_CONFIGS
-
-    config_ext = (".json",)
-    config_files = []
-    for config_path in _MODEL_CONFIG_PATHS:
-        if config_path.is_file() and config_path.suffix in config_ext:
-            config_files.append(config_path)
-        elif config_path.is_dir():
-            for ext in config_ext:
-                config_files.extend(config_path.glob(f"*{ext}"))
-
-    for cf in config_files:
-        if os.path.basename(cf)[0] == ".":
-            continue  # Ignore hidden files
-
-        with open(cf, "r") as f:
-            model_cfg = json.load(f)
-            if all(a in model_cfg for a in ("embed_dim", "audio_cfg", "text_cfg")):
-                _MODEL_CONFIGS[cf.stem] = model_cfg
-
-    _MODEL_CONFIGS = {
-        k: v
-        for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))
-    }
-
-
-_rescan_model_configs()  # initial populate of model config registry
-
-
-def load_state_dict(checkpoint_path: str, map_location="cpu", skip_params=True):
-    checkpoint = torch.load(checkpoint_path, map_location=map_location)
-    if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
-        state_dict = checkpoint["state_dict"]
-    else:
-        state_dict = checkpoint
-    if skip_params:
-        if next(iter(state_dict.items()))[0].startswith("module"):
-            state_dict = {k[7:]: v for k, v in state_dict.items()}
-    # for k in state_dict:
-    #     if k.startswith('transformer'):
-    #         v = state_dict.pop(k)
-    #         state_dict['text_branch.' + k[12:]] = v
-    return state_dict
-
-
-def create_model(
-    amodel_name: str,
-    tmodel_name: str,
-    pretrained: str = "",
-    precision: str = "fp32",
-    device: torch.device = torch.device("cpu"),
-    jit: bool = False,
-    force_quick_gelu: bool = False,
-    openai_model_cache_dir: str = os.path.expanduser("~/.cache/clip"),
-    skip_params=True,
-    pretrained_audio: str = "",
-    pretrained_text: str = "",
-    enable_fusion: bool = False,
-    fusion_type: str = "None"
-    # pretrained_image: bool = False,
-):
-    amodel_name = amodel_name.replace(
-        "/", "-"
-    )  # for callers using old naming with / in ViT names
-    pretrained_orig = pretrained
-    pretrained = pretrained.lower()
-    if pretrained == "openai":
-        if amodel_name in _MODEL_CONFIGS:
-            logging.info(f"Loading {amodel_name} model config.")
-            model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
-        else:
-            logging.error(
-                f"Model config for {amodel_name} not found; available models {list_models()}."
-            )
-            raise RuntimeError(f"Model config for {amodel_name} not found.")
-
-        logging.info(f"Loading pretrained ViT-B-16 text encoder from OpenAI.")
-        # Hard Code in model name
-        model_cfg["text_cfg"]["model_type"] = tmodel_name
-        model = load_openai_model(
-            "ViT-B-16",
-            model_cfg,
-            device=device,
-            jit=jit,
-            cache_dir=openai_model_cache_dir,
-            enable_fusion=enable_fusion,
-            fusion_type=fusion_type,
-        )
-        # See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372
-        if precision == "amp" or precision == "fp32":
-            model = model.float()
-    else:
-        if amodel_name in _MODEL_CONFIGS:
-            logging.info(f"Loading {amodel_name} model config.")
-            model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
-        else:
-            logging.error(
-                f"Model config for {amodel_name} not found; available models {list_models()}."
-            )
-            raise RuntimeError(f"Model config for {amodel_name} not found.")
-
-        if force_quick_gelu:
-            # override for use of QuickGELU on non-OpenAI transformer models
-            model_cfg["quick_gelu"] = True
-
-        # if pretrained_image:
-        #     if 'timm_amodel_name' in model_cfg.get('vision_cfg', {}):
-        #         # pretrained weight loading for timm models set via vision_cfg
-        #         model_cfg['vision_cfg']['timm_model_pretrained'] = True
-        #     else:
-        #         assert False, 'pretrained image towers currently only supported for timm models'
-        model_cfg["text_cfg"]["model_type"] = tmodel_name
-        model_cfg["enable_fusion"] = enable_fusion
-        model_cfg["fusion_type"] = fusion_type
-        model = CLAP(**model_cfg)
-
-        if pretrained:
-            checkpoint_path = ""
-            url = get_pretrained_url(amodel_name, pretrained)
-            if url:
-                checkpoint_path = download_pretrained(url, root=openai_model_cache_dir)
-            elif os.path.exists(pretrained_orig):
-                checkpoint_path = pretrained_orig
-            if checkpoint_path:
-                logging.info(
-                    f"Loading pretrained {amodel_name}-{tmodel_name} weights ({pretrained})."
-                )
-                ckpt = load_state_dict(checkpoint_path, skip_params=True)
-                model.load_state_dict(ckpt)
-                param_names = [n for n, p in model.named_parameters()]
-                # for n in param_names:
-                #     print(n, "\t", "Loaded" if n in ckpt else "Unloaded")
-            else:
-                logging.warning(
-                    f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
-                )
-                raise RuntimeError(
-                    f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
-                )
-
-        if pretrained_audio:
-            if amodel_name.startswith("PANN"):
-                if "Cnn14_mAP" in pretrained_audio:  # official checkpoint
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["model"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if (
-                            "spectrogram_extractor" not in key
-                            and "logmel_extractor" not in key
-                        ):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "PANN"
-                ):  # checkpoint trained via HTSAT codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["state_dict"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith("sed_model"):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "finetuned"
-                ):  # checkpoint trained via linear probe codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                else:
-                    raise ValueError("Unknown audio checkpoint")
-            elif amodel_name.startswith("HTSAT"):
-                if "HTSAT_AudioSet_Saved" in pretrained_audio:  # official checkpoint
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["state_dict"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith("sed_model") and (
-                            "spectrogram_extractor" not in key
-                            and "logmel_extractor" not in key
-                        ):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "HTSAT"
-                ):  # checkpoint trained via HTSAT codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["state_dict"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith("sed_model"):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "finetuned"
-                ):  # checkpoint trained via linear probe codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                else:
-                    raise ValueError("Unknown audio checkpoint")
-            else:
-                raise f"this audio encoder pretrained checkpoint is not support"
-
-            model.load_state_dict(audio_ckpt, strict=False)
-            logging.info(
-                f"Loading pretrained {amodel_name} weights ({pretrained_audio})."
-            )
-            param_names = [n for n, p in model.named_parameters()]
-            for n in param_names:
-                print(n, "\t", "Loaded" if n in audio_ckpt else "Unloaded")
-
-        model.to(device=device)
-        if precision == "fp16":
-            assert device.type != "cpu"
-            convert_weights_to_fp16(model)
-
-        if jit:
-            model = torch.jit.script(model)
-
-    return model, model_cfg
-
-
-def create_model_and_transforms(
-    model_name: str,
-    pretrained: str = "",
-    precision: str = "fp32",
-    device: torch.device = torch.device("cpu"),
-    jit: bool = False,
-    force_quick_gelu: bool = False,
-    # pretrained_image: bool = False,
-):
-    model = create_model(
-        model_name,
-        pretrained,
-        precision,
-        device,
-        jit,
-        force_quick_gelu=force_quick_gelu,
-        # pretrained_image=pretrained_image
-    )
-    preprocess_train = image_transform(model.visual.image_size, is_train=True)
-    preprocess_val = image_transform(model.visual.image_size, is_train=False)
-    return model, preprocess_train, preprocess_val
-
-
-def list_models():
-    """enumerate available model architectures based on config files"""
-    return list(_MODEL_CONFIGS.keys())
-
-
-def add_model_config(path):
-    """add model config path or file and update registry"""
-    if not isinstance(path, Path):
-        path = Path(path)
-    _MODEL_CONFIG_PATHS.append(path)
-    _rescan_model_configs()

spaces/BOXNYC/shirley/README.md

@@ -1,17 +0,0 @@
----
-title: Shirley
-emoji: 🚀
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Developed using this tutorial (Thanks!) https://medium.com/@sohaibshaheen/train-chatgpt-with-custom-data-and-create-your-own-chat-bot-using-macos-fb78c2f9646d
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-Notes:
- - Changed model from 'text-davinci-003' to 'gpt-4'. Another model is 'gpt-3.5-turbo'.

spaces/Banbri/zcvzcv/src/app/interface/bottom-bar/index.tsx

@@ -1,187 +0,0 @@
-import { useStore } from "@/app/store"
-import { HuggingClap } from "@/components/icons/hugging-clap"
-import { Button } from "@/components/ui/button"
-import { base64ToFile } from "@/lib/base64ToFile"
-import { uploadToHuggingFace } from "@/lib/uploadToHuggingFace"
-import { cn } from "@/lib/utils"
-import { startTransition, useState } from "react"
-import { upscaleImage } from "@/app/engine/render"
-import { sleep } from "@/lib/sleep"
-
-export function BottomBar() {
-  const download = useStore(state => state.download)
-  const isGeneratingStory = useStore(state => state.isGeneratingStory)
-  const prompt = useStore(state => state.prompt)
-  const panelGenerationStatus = useStore(state => state.panelGenerationStatus)
-  const page = useStore(state => state.page)
-  const preset = useStore(state => state.preset)
-  const pageToImage = useStore(state => state.pageToImage)
-
-  const allStatus = Object.values(panelGenerationStatus)
-  const remainingImages = allStatus.reduce((acc, s) => (acc + (s ? 1 : 0)), 0)
-
-  const upscaleQueue = useStore(state => state.upscaleQueue)
-  const renderedScenes = useStore(state => state.renderedScenes)
-  const removeFromUpscaleQueue = useStore(state => state.removeFromUpscaleQueue)
-  const setRendered = useStore(state => state.setRendered)
-  const [isUpscaling, setUpscaling] = useState(false)
-
-  const handleUpscale = () => {
-    setUpscaling(true)
-    startTransition(() => {
-      const fn = async () => {
-        for (let [panelId, renderedScene] of Object.entries(upscaleQueue)) {
-          try {
-            console.log(`upscaling panel ${panelId} (${renderedScene.renderId})`)
-            const result = await upscaleImage(renderedScene.assetUrl)
-            await sleep(1000)
-            if (result.assetUrl) {
-              console.log(`upscale successful, removing ${panelId} (${renderedScene.renderId}) from upscale queue`)
-              setRendered(panelId, {
-                ...renderedScene,
-                assetUrl: result.assetUrl
-              })
-              removeFromUpscaleQueue(panelId)
-            }
-
-          } catch (err) {
-            console.error(`failed to upscale: ${err}`)
-          }
-        }
-        
-        setUpscaling(false)
-      }
-
-      fn()
-    })
-  }
-const handleBuyMeACoffee = () => {
-    window.open("https://www.buymeacoffee.com/aicomicfactory", '_blank');
-  }
-  const handleShare = async () => {
-    const dataUrl = await pageToImage()
-    // console.log("dataUrl:", dataUrl)
-    const fileToUpload = base64ToFile(dataUrl, "comic.png")
-    let uploadUrl = ""
-    try {
-      uploadUrl = await uploadToHuggingFace(fileToUpload)
-      console.log("uploadUrl:", uploadUrl)
-    } catch (err) {
-      console.error("Failed to upload the image to Hugging Face")
-    }
-
-
-    const descriptionMd = `
-#### Prompt:
-\`\`\`${prompt}\`\`\`
-
-#### Preset:
-\`\`\`${preset.label}\`\`\`
-
-#### Comic:
-${uploadUrl
-  ? (`![${prompt}](${uploadUrl})`)
-  : (`(please drag & drop your JPG image here)`)}
-`;
-
-    console.log("descriptionMd:", descriptionMd)
-
-    const params = new URLSearchParams({
-      title: `[Comic] ${prompt}`,
-      description: descriptionMd,
-      });
-    const paramsStr = params.toString();
-    window.open(`https://huggingface.co/spaces/jbilcke-hf/comic-factory/discussions/new?${paramsStr}`, '_blank');
-  }
-
-  const handlePrint = () => {
-    window.print()
-  }
-  return (
-    <div className={cn(
-      `print:hidden`,
-      `fixed bottom-2 md:bottom-4 left-2 right-0 md:left-3 md:right-1`,
-      `flex flex-row`,
-      `justify-between`,
-      `pointer-events-none`
-    )}>
-      <div className={cn(
-        `flex flex-row`,
-        `items-end`,
-        `pointer-events-auto`,
-        `animation-all duration-300 ease-in-out`,
-        isGeneratingStory ? `scale-0 opacity-0` : ``,
-        `space-x-3`,
-        `scale-[0.9]`
-      )}>
-<Button variant="outline" onClick={handleBuyMeACoffee}>
-          <span className="hidden md:inline">Buy me a coffee</span>
-          <span className="inline md:hidden">Support</span>
-        </Button>
-      </div>
-      <div className={cn(
-      `flex flex-row`,
-      `pointer-events-auto`,
-      `animation-all duration-300 ease-in-out`,
-      isGeneratingStory ? `scale-0 opacity-0` : ``,
-      `space-x-3`,
-      `scale-[0.9]`
-    )}>
-      <div>
-        {
-       // there is an issue, this env check doesn't work..
-        // process.env.NEXT_PUBLIC_CAN_UPSCALE === "true" ?
-        <Button
-          onClick={handleUpscale}
-          disabled={!prompt?.length || remainingImages > 0 || isUpscaling || !Object.values(upscaleQueue).length}
-        >
-          {isUpscaling
-            ? `${allStatus.length - Object.values(upscaleQueue).length}/${allStatus.length} ⌛`
-            : "Upscale"}
-        </Button>
-        // : null
-      }
-      </div>
-        <div>
-          <Button
-            onClick={handlePrint}
-            disabled={!prompt?.length}
-          >
-            Print
-          </Button>
-        </div>
-        <div>
-          <Button
-            onClick={download}
-            disabled={!prompt?.length}
-          >
-            <span className="hidden md:inline">{
-            remainingImages ? `${allStatus.length - remainingImages}/${allStatus.length} panels ⌛` : `Save`
-            }</span>
-            <span className="inline md:hidden">{
-              remainingImages ? `${allStatus.length - remainingImages}/${allStatus.length} ⌛` : `Save`
-            }</span>
-           </Button>
-        </div>
-        <div>
-          {
-          // there is an issue, this env check doesn't work..
-          // process.env.NEXT_PUBLIC_ENABLE_COMMUNITY_SHARING === "true" ? 
-          <Button
-            onClick={handleShare}
-            disabled={!prompt?.length}
-            className="space-x-2"
-          >
-            <div className="scale-105"><HuggingClap /></div>
-            <div>
-              <span className="hidden md:inline">Share to community</span>
-              <span className="inline md:hidden">Share</span>
-            </div>
-          </Button> 
-          //: null
-        }
-        </div>
-      </div>
-    </div>
-  )
-}

spaces/Bart92/RVC_HF/extract_locale.py

@@ -1,34 +0,0 @@
-import json
-import re
-
-# Define regular expression patterns
-pattern = r"""i18n\([\s\n\t]*(["'][^"']+["'])[\s\n\t]*\)"""
-
-# Initialize the dictionary to store key-value pairs
-data = {}
-
-
-def process(fn: str):
-    global data
-    with open(fn, "r", encoding="utf-8") as f:
-        contents = f.read()
-        matches = re.findall(pattern, contents)
-        for key in matches:
-            key = eval(key)
-            print("extract:", key)
-            data[key] = key
-
-
-print("processing infer-web.py")
-process("infer-web.py")
-
-print("processing gui_v0.py")
-process("gui_v0.py")
-
-print("processing gui_v1.py")
-process("gui_v1.py")
-
-# Save as a JSON file
-with open("./i18n/en_US.json", "w", encoding="utf-8") as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)
-    f.write("\n")

spaces/Benson/text-generation/Examples/Camin Simulador ltimo Coche Descarga Apk.md

@@ -1,48 +0,0 @@
-<br />
-<h1>Hay día Hack monedas ilimitadas y diamantes APK Descargar</h1>
-<p>Hay Day es uno de los juegos de simulación de agricultura más populares en dispositivos Android e iOS. En este juego, puedes crear tu propia granja, cultivar, criar animales, comerciar con otros jugadores y más. Sin embargo, para disfrutar de todas las características y beneficios del juego, necesitas monedas y diamantes, que son las principales monedas en Hay Day. Las monedas se utilizan para comprar objetos, mejorar edificios, ampliar tu terreno y más. Los diamantes se utilizan para acelerar los procesos, desbloquear objetos especiales y mucho más. Sin embargo, ganar monedas y diamantes en el juego puede ser lento y desafiante, especialmente si quieres progresar más rápido y divertirte más. Es por eso que muchos jugadores están buscando una manera de obtener monedas y diamantes ilimitados en Hay Day sin gastar dinero real. </p>
-<p>Si eres uno de ellos, entonces estás de suerte. En este artículo, le mostraremos cómo descargar e instalar Hay Day Hack APK, que es una versión modificada del juego original que le da monedas ilimitadas y diamantes gratis. También le mostraremos cómo usarlo, qué características y beneficios ofrece, y algunos consejos y trucos para jugar Hay Day con Hay Day Hack APK. Así que, sin más preámbulos, empecemos. </p>
-<h2>camión simulador último coche descarga apk</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://bltlly.com/2v6JfM">https://bltlly.com/2v6JfM</a></b></p><br /><br />
-<h2>Cómo descargar e instalar Hay Day Hack APK</h2>
-<p>Descargar e instalar Hay Day Hack APK es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Descargar el archivo APK de una fuente de confianza. Puede utilizar el siguiente enlace para descargarlo directamente desde nuestro sitio web. El tamaño del archivo es de unos 150 MB, así que asegúrate de tener suficiente espacio en tu dispositivo. </li>
-<li>Habilitar fuentes desconocidas en su dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store.</li>
-
-</ol>
-<h2>Cómo utilizar Hay Día Hack APK</h2>
-<p>El uso de Hay Día Hack APK es muy fácil y simple, así. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Inicia sesión con tu cuenta de Facebook o crea una nueva. Cuando inicies el juego por primera vez, se te pedirá que inicies sesión con tu cuenta de Facebook o que crees una nueva. Recomendamos usar una cuenta de Facebook falsa o secundaria para este propósito, ya que el uso de su cuenta de Facebook real o principal puede resultar en una prohibición del juego o de Facebook en sí. </li>
-<li>Elija la cantidad de monedas y diamantes que desea generar. Una vez que hayas iniciado sesión, verás un menú en la esquina superior derecha de la pantalla con dos botones: Monedas y Diamantes. Toque en cualquiera de ellos e introduzca la cantidad de monedas o diamantes que desea generar. Puede elegir la cantidad que desee, del 1 al 9999999. </li>
-<li>Espere a que el hack para completar y disfrutar de sus recursos. Después de introducir la cantidad de monedas o diamantes que desea, toque en el botón Generar y espere unos segundos. El hack procesará su solicitud y agregará los recursos a su cuenta. Verá un mensaje de confirmación en la pantalla cuando el hack esté terminado. A continuación, puede cerrar el menú y disfrutar de sus monedas y diamantes ilimitados. </li>
-</ol>
-<h2>Características y beneficios de Hay Day Hack APK</h2>
-<p>Hay Día Hack APK no es solo un simple mod que le da monedas ilimitadas y diamantes. También ofrece muchas otras características y beneficios que lo convierten en uno de los mejores hacks para Hay Day. Estos son algunos de ellos:</p>
-<ul>
-<li><b>Monedas y diamantes ilimitados</b>: Esta es la característica principal de Hay Day Hack APK. Puede generar tantas monedas y diamantes como desee, en cualquier momento que desee, sin gastar dinero real. Puedes usarlos para comprar artículos, mejorar edificios, expandir tu tierra, acelerar procesos, desbloquear artículos especiales y más. También puede utilizarlos para comprar y revender artículos de otros jugadores, obteniendo más ganancias. </li>
-
-<li><b>Seguro y seguro</b>: Hay Día Hack APK es seguro y seguro de usar. No contiene ningún virus, malware, spyware u otros elementos dañinos que podrían dañar su dispositivo o su cuenta. Tampoco solicita información personal ni acceso a los datos de su dispositivo. Solo utiliza tu cuenta de Facebook para iniciar sesión en el juego y generar los recursos. También tiene un sistema anti-van que protege su cuenta de ser detectado o prohibido por los servidores del juego. </li>
-<li><b>Compatible con todos los dispositivos y versiones</b>: Hay Day Hack APK es compatible con todos los dispositivos y versiones del juego. Puede usarlo en cualquier dispositivo Android o iOS, ya sea un teléfono inteligente, tableta o emulador. También puedes usarlo en cualquier versión del juego, ya sea viejo o nuevo, oficial o modificado. Funciona perfectamente con todos ellos. </li>
-</ul>
-<h2>Consejos y trucos para jugar Hay Día con Hay Día Hack APK</h2>
-<p>Ahora que tienes monedas y diamantes ilimitados en Hay Day, puedes preguntarte cómo sacarles el máximo partido. Aquí hay algunos consejos y trucos para jugar Hay Día con Hay Día Hack APK:</p>
-<ul>
-<li><b>Usa tus monedas y diamantes sabiamente</b>: A pesar de que tienes monedas y diamantes ilimitados en Hay Day, todavía debes usarlos sabiamente. No los malgastes en cosas innecesarias ni los gastes todos a la vez. Guarda algunos para más tarde o para emergencias. Nunca sabes cuándo los necesitarás. </li>
-<li><b>Plantar cultivos de crecimiento lento por la noche o durante las horas de trabajo</b>: Una forma de maximizar su productividad y ganancias en Hay Day es plantar cultivos de crecimiento lento por la noche o durante las horas de trabajo. Estos cultivos tardan más en crecer, pero producen más productos y dinero. Por ejemplo, el trigo tarda 2 minutos en crecer, pero solo da 1 producto, mientras que el añil tarda 2 horas en crecer, pero da 2 productos. Al plantar cultivos de crecimiento lento cuando no estás jugando, puedes cosecharlos cuando vuelvas y obtener más ganancias. </li>
-
-<li><b>Comprar y revender artículos de otros jugadores</b>: Una forma inteligente de obtener más ganancias en Hay Day es comprar y revender artículos de otros jugadores. Puedes usar tus monedas y diamantes ilimitados para comprar artículos de los stands de otros jugadores a precios bajos y luego revenderlos a precios más altos en tu propio stand. También puedes usar esta estrategia para completar pedidos más rápido y ganar más recompensas. </li>
-</ <h2>Conclusión y preguntas frecuentes</h2>
-<p>En conclusión, Hay Día Hack APK es una gran manera de disfrutar de Hay Día con monedas ilimitadas y diamantes. Puede descargarlo e instalarlo de forma fácil y segura, y usarlo para comprar artículos, actualizar edificios, expandir su tierra, acelerar los procesos, desbloquear artículos especiales y más. También puedes usarlo para ganar más dinero vendiendo tus productos y comprando y revendiendo artículos de otros jugadores. Hay Día Hack APK es compatible con todos los dispositivos y versiones del juego, y no requiere ninguna raíz o jailbreak. También tiene un sistema anti-van que protege tu cuenta de ser detectada o prohibida por los servidores del juego. </p>
-<p>Si usted es un fan de Hay Día y quiere tener más diversión y libertad en el juego, entonces usted debe probar definitivamente Hay Día Hack APK. Hará que su experiencia agrícola sea más agradable y gratificante. Sin embargo, también debes ser cuidadoso y responsable al usarlo, ya que puede afectar el equilibrio y la equidad del juego. También debes respetar a otros jugadores y no abusar ni acosarlos con tus recursos ilimitados. Recuerde, Hay Day es un juego para el entretenimiento y la relajación, no para hacer trampa o intimidación. </p>
-<p></p>
-<p>Si usted tiene alguna pregunta o duda acerca de Hay Day Hack APK, puede consultar estas preguntas frecuentes a continuación:</p>
-<ol>
-
-<li><b> ¿Es seguro Hay Día Hack APK? </b>: Hay Día Hack APK es seguro de usar, ya que no contiene ningún virus, malware, spyware, u otros elementos dañinos que podrían dañar su dispositivo o su cuenta. Tampoco solicita información personal ni acceso a los datos de su dispositivo. Solo utiliza tu cuenta de Facebook para iniciar sesión en el juego y generar los recursos. También tiene un sistema anti-van que protege su cuenta de ser detectado o prohibido por los servidores del juego. </li>
-<li><b> ¿Con qué frecuencia puedo utilizar Hay Day Hack APK? </b>: Puede utilizar Hay Día Hack APK tan a menudo como quieras, ya que no hay límite a la cantidad de monedas y diamantes que puede generar. Sin embargo, debes usarlo de forma moderada y razonable, ya que generar demasiados recursos a la vez puede levantar sospechas o causar errores en el juego. También debe evitar usarlo durante eventos o competiciones, ya que puede arruinar la diversión y el desafío para usted y otros. </li>
-<li><b> ¿Voy a ser prohibido para el uso de Hay Día Hack APK? </b>: Hay una baja probabilidad de ser prohibido por el uso de Hay Day Hack APK, ya que tiene un sistema anti-van que protege su cuenta de ser detectado o prohibido por los servidores del juego. Sin embargo, todavía existe el riesgo de ser prohibido si lo usas de manera excesiva o irresponsable, como generar demasiados recursos a la vez, utilizarlos para acosar o intimidar a otros jugadores, o participar en eventos o competiciones con ellos. También debes evitar usar tu cuenta de Facebook real o principal para este propósito, ya que usar una cuenta de Facebook falsa o secundaria puede reducir el riesgo de ser prohibida. </li>
-
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/colorama/ansitowin32.py

@@ -1,277 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import re
-import sys
-import os
-
-from .ansi import AnsiFore, AnsiBack, AnsiStyle, Style, BEL
-from .winterm import enable_vt_processing, WinTerm, WinColor, WinStyle
-from .win32 import windll, winapi_test
-
-
-winterm = None
-if windll is not None:
-    winterm = WinTerm()
-
-
-class StreamWrapper(object):
-    '''
-    Wraps a stream (such as stdout), acting as a transparent proxy for all
-    attribute access apart from method 'write()', which is delegated to our
-    Converter instance.
-    '''
-    def __init__(self, wrapped, converter):
-        # double-underscore everything to prevent clashes with names of
-        # attributes on the wrapped stream object.
-        self.__wrapped = wrapped
-        self.__convertor = converter
-
-    def __getattr__(self, name):
-        return getattr(self.__wrapped, name)
-
-    def __enter__(self, *args, **kwargs):
-        # special method lookup bypasses __getattr__/__getattribute__, see
-        # https://stackoverflow.com/questions/12632894/why-doesnt-getattr-work-with-exit
-        # thus, contextlib magic methods are not proxied via __getattr__
-        return self.__wrapped.__enter__(*args, **kwargs)
-
-    def __exit__(self, *args, **kwargs):
-        return self.__wrapped.__exit__(*args, **kwargs)
-
-    def __setstate__(self, state):
-        self.__dict__ = state
-
-    def __getstate__(self):
-        return self.__dict__
-
-    def write(self, text):
-        self.__convertor.write(text)
-
-    def isatty(self):
-        stream = self.__wrapped
-        if 'PYCHARM_HOSTED' in os.environ:
-            if stream is not None and (stream is sys.__stdout__ or stream is sys.__stderr__):
-                return True
-        try:
-            stream_isatty = stream.isatty
-        except AttributeError:
-            return False
-        else:
-            return stream_isatty()
-
-    @property
-    def closed(self):
-        stream = self.__wrapped
-        try:
-            return stream.closed
-        # AttributeError in the case that the stream doesn't support being closed
-        # ValueError for the case that the stream has already been detached when atexit runs
-        except (AttributeError, ValueError):
-            return True
-
-
-class AnsiToWin32(object):
-    '''
-    Implements a 'write()' method which, on Windows, will strip ANSI character
-    sequences from the text, and if outputting to a tty, will convert them into
-    win32 function calls.
-    '''
-    ANSI_CSI_RE = re.compile('\001?\033\\[((?:\\d|;)*)([a-zA-Z])\002?')   # Control Sequence Introducer
-    ANSI_OSC_RE = re.compile('\001?\033\\]([^\a]*)(\a)\002?')             # Operating System Command
-
-    def __init__(self, wrapped, convert=None, strip=None, autoreset=False):
-        # The wrapped stream (normally sys.stdout or sys.stderr)
-        self.wrapped = wrapped
-
-        # should we reset colors to defaults after every .write()
-        self.autoreset = autoreset
-
-        # create the proxy wrapping our output stream
-        self.stream = StreamWrapper(wrapped, self)
-
-        on_windows = os.name == 'nt'
-        # We test if the WinAPI works, because even if we are on Windows
-        # we may be using a terminal that doesn't support the WinAPI
-        # (e.g. Cygwin Terminal). In this case it's up to the terminal
-        # to support the ANSI codes.
-        conversion_supported = on_windows and winapi_test()
-        try:
-            fd = wrapped.fileno()
-        except Exception:
-            fd = -1
-        system_has_native_ansi = not on_windows or enable_vt_processing(fd)
-        have_tty = not self.stream.closed and self.stream.isatty()
-        need_conversion = conversion_supported and not system_has_native_ansi
-
-        # should we strip ANSI sequences from our output?
-        if strip is None:
-            strip = need_conversion or not have_tty
-        self.strip = strip
-
-        # should we should convert ANSI sequences into win32 calls?
-        if convert is None:
-            convert = need_conversion and have_tty
-        self.convert = convert
-
-        # dict of ansi codes to win32 functions and parameters
-        self.win32_calls = self.get_win32_calls()
-
-        # are we wrapping stderr?
-        self.on_stderr = self.wrapped is sys.stderr
-
-    def should_wrap(self):
-        '''
-        True if this class is actually needed. If false, then the output
-        stream will not be affected, nor will win32 calls be issued, so
-        wrapping stdout is not actually required. This will generally be
-        False on non-Windows platforms, unless optional functionality like
-        autoreset has been requested using kwargs to init()
-        '''
-        return self.convert or self.strip or self.autoreset
-
-    def get_win32_calls(self):
-        if self.convert and winterm:
-            return {
-                AnsiStyle.RESET_ALL: (winterm.reset_all, ),
-                AnsiStyle.BRIGHT: (winterm.style, WinStyle.BRIGHT),
-                AnsiStyle.DIM: (winterm.style, WinStyle.NORMAL),
-                AnsiStyle.NORMAL: (winterm.style, WinStyle.NORMAL),
-                AnsiFore.BLACK: (winterm.fore, WinColor.BLACK),
-                AnsiFore.RED: (winterm.fore, WinColor.RED),
-                AnsiFore.GREEN: (winterm.fore, WinColor.GREEN),
-                AnsiFore.YELLOW: (winterm.fore, WinColor.YELLOW),
-                AnsiFore.BLUE: (winterm.fore, WinColor.BLUE),
-                AnsiFore.MAGENTA: (winterm.fore, WinColor.MAGENTA),
-                AnsiFore.CYAN: (winterm.fore, WinColor.CYAN),
-                AnsiFore.WHITE: (winterm.fore, WinColor.GREY),
-                AnsiFore.RESET: (winterm.fore, ),
-                AnsiFore.LIGHTBLACK_EX: (winterm.fore, WinColor.BLACK, True),
-                AnsiFore.LIGHTRED_EX: (winterm.fore, WinColor.RED, True),
-                AnsiFore.LIGHTGREEN_EX: (winterm.fore, WinColor.GREEN, True),
-                AnsiFore.LIGHTYELLOW_EX: (winterm.fore, WinColor.YELLOW, True),
-                AnsiFore.LIGHTBLUE_EX: (winterm.fore, WinColor.BLUE, True),
-                AnsiFore.LIGHTMAGENTA_EX: (winterm.fore, WinColor.MAGENTA, True),
-                AnsiFore.LIGHTCYAN_EX: (winterm.fore, WinColor.CYAN, True),
-                AnsiFore.LIGHTWHITE_EX: (winterm.fore, WinColor.GREY, True),
-                AnsiBack.BLACK: (winterm.back, WinColor.BLACK),
-                AnsiBack.RED: (winterm.back, WinColor.RED),
-                AnsiBack.GREEN: (winterm.back, WinColor.GREEN),
-                AnsiBack.YELLOW: (winterm.back, WinColor.YELLOW),
-                AnsiBack.BLUE: (winterm.back, WinColor.BLUE),
-                AnsiBack.MAGENTA: (winterm.back, WinColor.MAGENTA),
-                AnsiBack.CYAN: (winterm.back, WinColor.CYAN),
-                AnsiBack.WHITE: (winterm.back, WinColor.GREY),
-                AnsiBack.RESET: (winterm.back, ),
-                AnsiBack.LIGHTBLACK_EX: (winterm.back, WinColor.BLACK, True),
-                AnsiBack.LIGHTRED_EX: (winterm.back, WinColor.RED, True),
-                AnsiBack.LIGHTGREEN_EX: (winterm.back, WinColor.GREEN, True),
-                AnsiBack.LIGHTYELLOW_EX: (winterm.back, WinColor.YELLOW, True),
-                AnsiBack.LIGHTBLUE_EX: (winterm.back, WinColor.BLUE, True),
-                AnsiBack.LIGHTMAGENTA_EX: (winterm.back, WinColor.MAGENTA, True),
-                AnsiBack.LIGHTCYAN_EX: (winterm.back, WinColor.CYAN, True),
-                AnsiBack.LIGHTWHITE_EX: (winterm.back, WinColor.GREY, True),
-            }
-        return dict()
-
-    def write(self, text):
-        if self.strip or self.convert:
-            self.write_and_convert(text)
-        else:
-            self.wrapped.write(text)
-            self.wrapped.flush()
-        if self.autoreset:
-            self.reset_all()
-
-
-    def reset_all(self):
-        if self.convert:
-            self.call_win32('m', (0,))
-        elif not self.strip and not self.stream.closed:
-            self.wrapped.write(Style.RESET_ALL)
-
-
-    def write_and_convert(self, text):
-        '''
-        Write the given text to our wrapped stream, stripping any ANSI
-        sequences from the text, and optionally converting them into win32
-        calls.
-        '''
-        cursor = 0
-        text = self.convert_osc(text)
-        for match in self.ANSI_CSI_RE.finditer(text):
-            start, end = match.span()
-            self.write_plain_text(text, cursor, start)
-            self.convert_ansi(*match.groups())
-            cursor = end
-        self.write_plain_text(text, cursor, len(text))
-
-
-    def write_plain_text(self, text, start, end):
-        if start < end:
-            self.wrapped.write(text[start:end])
-            self.wrapped.flush()
-
-
-    def convert_ansi(self, paramstring, command):
-        if self.convert:
-            params = self.extract_params(command, paramstring)
-            self.call_win32(command, params)
-
-
-    def extract_params(self, command, paramstring):
-        if command in 'Hf':
-            params = tuple(int(p) if len(p) != 0 else 1 for p in paramstring.split(';'))
-            while len(params) < 2:
-                # defaults:
-                params = params + (1,)
-        else:
-            params = tuple(int(p) for p in paramstring.split(';') if len(p) != 0)
-            if len(params) == 0:
-                # defaults:
-                if command in 'JKm':
-                    params = (0,)
-                elif command in 'ABCD':
-                    params = (1,)
-
-        return params
-
-
-    def call_win32(self, command, params):
-        if command == 'm':
-            for param in params:
-                if param in self.win32_calls:
-                    func_args = self.win32_calls[param]
-                    func = func_args[0]
-                    args = func_args[1:]
-                    kwargs = dict(on_stderr=self.on_stderr)
-                    func(*args, **kwargs)
-        elif command in 'J':
-            winterm.erase_screen(params[0], on_stderr=self.on_stderr)
-        elif command in 'K':
-            winterm.erase_line(params[0], on_stderr=self.on_stderr)
-        elif command in 'Hf':     # cursor position - absolute
-            winterm.set_cursor_position(params, on_stderr=self.on_stderr)
-        elif command in 'ABCD':   # cursor position - relative
-            n = params[0]
-            # A - up, B - down, C - forward, D - back
-            x, y = {'A': (0, -n), 'B': (0, n), 'C': (n, 0), 'D': (-n, 0)}[command]
-            winterm.cursor_adjust(x, y, on_stderr=self.on_stderr)
-
-
-    def convert_osc(self, text):
-        for match in self.ANSI_OSC_RE.finditer(text):
-            start, end = match.span()
-            text = text[:start] + text[end:]
-            paramstring, command = match.groups()
-            if command == BEL:
-                if paramstring.count(";") == 1:
-                    params = paramstring.split(";")
-                    # 0 - change title and icon (we will only change title)
-                    # 1 - change icon (we don't support this)
-                    # 2 - change title
-                    if params[0] in '02':
-                        winterm.set_title(params[1])
-        return text
-
-
-    def flush(self):
-        self.wrapped.flush()

spaces/Boadiwaa/Recipes/openai/api_resources/edit.py

@@ -1,32 +0,0 @@
-import time
-
-from openai import util
-from openai.api_resources.abstract.engine_api_resource import EngineAPIResource
-from openai.error import InvalidRequestError, TryAgain
-
-
-class Edit(EngineAPIResource):
-    engine_required = False
-    OBJECT_NAME = "edits"
-
-    @classmethod
-    def create(cls, *args, **kwargs):
-        """
-        Creates a new edit for the provided input, instruction, and parameters.
-        """
-        start = time.time()
-        timeout = kwargs.pop("timeout", None)
-        if kwargs.get("model", None) is None and kwargs.get("engine", None) is None:
-            raise InvalidRequestError(
-                "Must provide an 'engine' or 'model' parameter to create an Edit.",
-                param="engine",
-            )
-
-        while True:
-            try:
-                return super().create(*args, **kwargs)
-            except TryAgain as e:
-                if timeout is not None and time.time() > start + timeout:
-                    raise
-
-                util.log_info("Waiting for model to warm up", error=e)

spaces/BwayKC/prompthero-openjourney-v2/README.md

@@ -1,14 +0,0 @@
----
-title: Prompthero Openjourney V2
-emoji: 🐢
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: lizhome/prompthero-openjourney-v2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/pybind11/include/pybind11/detail/common.h

@@ -1,837 +0,0 @@
-/*
-    pybind11/detail/common.h -- Basic macros
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#define PYBIND11_VERSION_MAJOR 2
-#define PYBIND11_VERSION_MINOR 6
-#define PYBIND11_VERSION_PATCH dev0
-
-#define PYBIND11_NAMESPACE_BEGIN(name) namespace name {
-#define PYBIND11_NAMESPACE_END(name) }
-
-// Robust support for some features and loading modules compiled against different pybind versions
-// requires forcing hidden visibility on pybind code, so we enforce this by setting the attribute on
-// the main `pybind11` namespace.
-#if !defined(PYBIND11_NAMESPACE)
-#  ifdef __GNUG__
-#    define PYBIND11_NAMESPACE pybind11 __attribute__((visibility("hidden")))
-#  else
-#    define PYBIND11_NAMESPACE pybind11
-#  endif
-#endif
-
-#if !(defined(_MSC_VER) && __cplusplus == 199711L) && !defined(__INTEL_COMPILER)
-#  if __cplusplus >= 201402L
-#    define PYBIND11_CPP14
-#    if __cplusplus >= 201703L
-#      define PYBIND11_CPP17
-#    endif
-#  endif
-#elif defined(_MSC_VER) && __cplusplus == 199711L
-// MSVC sets _MSVC_LANG rather than __cplusplus (supposedly until the standard is fully implemented)
-// Unless you use the /Zc:__cplusplus flag on Visual Studio 2017 15.7 Preview 3 or newer
-#  if _MSVC_LANG >= 201402L
-#    define PYBIND11_CPP14
-#    if _MSVC_LANG > 201402L && _MSC_VER >= 1910
-#      define PYBIND11_CPP17
-#    endif
-#  endif
-#endif
-
-// Compiler version assertions
-#if defined(__INTEL_COMPILER)
-#  if __INTEL_COMPILER < 1700
-#    error pybind11 requires Intel C++ compiler v17 or newer
-#  endif
-#elif defined(__clang__) && !defined(__apple_build_version__)
-#  if __clang_major__ < 3 || (__clang_major__ == 3 && __clang_minor__ < 3)
-#    error pybind11 requires clang 3.3 or newer
-#  endif
-#elif defined(__clang__)
-// Apple changes clang version macros to its Xcode version; the first Xcode release based on
-// (upstream) clang 3.3 was Xcode 5:
-#  if __clang_major__ < 5
-#    error pybind11 requires Xcode/clang 5.0 or newer
-#  endif
-#elif defined(__GNUG__)
-#  if __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 8)
-#    error pybind11 requires gcc 4.8 or newer
-#  endif
-#elif defined(_MSC_VER)
-// Pybind hits various compiler bugs in 2015u2 and earlier, and also makes use of some stl features
-// (e.g. std::negation) added in 2015u3:
-#  if _MSC_FULL_VER < 190024210
-#    error pybind11 requires MSVC 2015 update 3 or newer
-#  endif
-#endif
-
-#if !defined(PYBIND11_EXPORT)
-#  if defined(WIN32) || defined(_WIN32)
-#    define PYBIND11_EXPORT __declspec(dllexport)
-#  else
-#    define PYBIND11_EXPORT __attribute__ ((visibility("default")))
-#  endif
-#endif
-
-#if defined(_MSC_VER)
-#  define PYBIND11_NOINLINE __declspec(noinline)
-#else
-#  define PYBIND11_NOINLINE __attribute__ ((noinline))
-#endif
-
-#if defined(PYBIND11_CPP14)
-#  define PYBIND11_DEPRECATED(reason) [[deprecated(reason)]]
-#else
-#  define PYBIND11_DEPRECATED(reason) __attribute__((deprecated(reason)))
-#endif
-
-#if defined(PYBIND11_CPP17)
-#  define PYBIND11_MAYBE_UNUSED [[maybe_unused]]
-#elif defined(_MSC_VER) && !defined(__clang__)
-#  define PYBIND11_MAYBE_UNUSED
-#else
-#  define PYBIND11_MAYBE_UNUSED __attribute__ ((__unused__))
-#endif
-
-/* Don't let Python.h #define (v)snprintf as macro because they are implemented
-   properly in Visual Studio since 2015. */
-#if defined(_MSC_VER) && _MSC_VER >= 1900
-#  define HAVE_SNPRINTF 1
-#endif
-
-/// Include Python header, disable linking to pythonX_d.lib on Windows in debug mode
-#if defined(_MSC_VER)
-#  if (PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION < 4)
-#    define HAVE_ROUND 1
-#  endif
-#  pragma warning(push)
-#  pragma warning(disable: 4510 4610 4512 4005)
-#  if defined(_DEBUG) && !defined(Py_DEBUG)
-#    define PYBIND11_DEBUG_MARKER
-#    undef _DEBUG
-#  endif
-#endif
-
-#include <Python.h>
-#include <frameobject.h>
-#include <pythread.h>
-
-/* Python #defines overrides on all sorts of core functions, which
-   tends to weak havok in C++ codebases that expect these to work
-   like regular functions (potentially with several overloads) */
-#if defined(isalnum)
-#  undef isalnum
-#  undef isalpha
-#  undef islower
-#  undef isspace
-#  undef isupper
-#  undef tolower
-#  undef toupper
-#endif
-
-#if defined(copysign)
-#  undef copysign
-#endif
-
-#if defined(_MSC_VER)
-#  if defined(PYBIND11_DEBUG_MARKER)
-#    define _DEBUG
-#    undef PYBIND11_DEBUG_MARKER
-#  endif
-#  pragma warning(pop)
-#endif
-
-#include <cstddef>
-#include <cstring>
-#include <forward_list>
-#include <vector>
-#include <string>
-#include <stdexcept>
-#include <unordered_set>
-#include <unordered_map>
-#include <memory>
-#include <typeindex>
-#include <type_traits>
-
-#if PY_MAJOR_VERSION >= 3 /// Compatibility macros for various Python versions
-#define PYBIND11_INSTANCE_METHOD_NEW(ptr, class_) PyInstanceMethod_New(ptr)
-#define PYBIND11_INSTANCE_METHOD_CHECK PyInstanceMethod_Check
-#define PYBIND11_INSTANCE_METHOD_GET_FUNCTION PyInstanceMethod_GET_FUNCTION
-#define PYBIND11_BYTES_CHECK PyBytes_Check
-#define PYBIND11_BYTES_FROM_STRING PyBytes_FromString
-#define PYBIND11_BYTES_FROM_STRING_AND_SIZE PyBytes_FromStringAndSize
-#define PYBIND11_BYTES_AS_STRING_AND_SIZE PyBytes_AsStringAndSize
-#define PYBIND11_BYTES_AS_STRING PyBytes_AsString
-#define PYBIND11_BYTES_SIZE PyBytes_Size
-#define PYBIND11_LONG_CHECK(o) PyLong_Check(o)
-#define PYBIND11_LONG_AS_LONGLONG(o) PyLong_AsLongLong(o)
-#define PYBIND11_LONG_FROM_SIGNED(o) PyLong_FromSsize_t((ssize_t) o)
-#define PYBIND11_LONG_FROM_UNSIGNED(o) PyLong_FromSize_t((size_t) o)
-#define PYBIND11_BYTES_NAME "bytes"
-#define PYBIND11_STRING_NAME "str"
-#define PYBIND11_SLICE_OBJECT PyObject
-#define PYBIND11_FROM_STRING PyUnicode_FromString
-#define PYBIND11_STR_TYPE ::pybind11::str
-#define PYBIND11_BOOL_ATTR "__bool__"
-#define PYBIND11_NB_BOOL(ptr) ((ptr)->nb_bool)
-// Providing a separate declaration to make Clang's -Wmissing-prototypes happy.
-// See comment for PYBIND11_MODULE below for why this is marked "maybe unused".
-#define PYBIND11_PLUGIN_IMPL(name) \
-    extern "C" PYBIND11_MAYBE_UNUSED PYBIND11_EXPORT PyObject *PyInit_##name(); \
-    extern "C" PYBIND11_EXPORT PyObject *PyInit_##name()
-
-#else
-#define PYBIND11_INSTANCE_METHOD_NEW(ptr, class_) PyMethod_New(ptr, nullptr, class_)
-#define PYBIND11_INSTANCE_METHOD_CHECK PyMethod_Check
-#define PYBIND11_INSTANCE_METHOD_GET_FUNCTION PyMethod_GET_FUNCTION
-#define PYBIND11_BYTES_CHECK PyString_Check
-#define PYBIND11_BYTES_FROM_STRING PyString_FromString
-#define PYBIND11_BYTES_FROM_STRING_AND_SIZE PyString_FromStringAndSize
-#define PYBIND11_BYTES_AS_STRING_AND_SIZE PyString_AsStringAndSize
-#define PYBIND11_BYTES_AS_STRING PyString_AsString
-#define PYBIND11_BYTES_SIZE PyString_Size
-#define PYBIND11_LONG_CHECK(o) (PyInt_Check(o) || PyLong_Check(o))
-#define PYBIND11_LONG_AS_LONGLONG(o) (PyInt_Check(o) ? (long long) PyLong_AsLong(o) : PyLong_AsLongLong(o))
-#define PYBIND11_LONG_FROM_SIGNED(o) PyInt_FromSsize_t((ssize_t) o) // Returns long if needed.
-#define PYBIND11_LONG_FROM_UNSIGNED(o) PyInt_FromSize_t((size_t) o) // Returns long if needed.
-#define PYBIND11_BYTES_NAME "str"
-#define PYBIND11_STRING_NAME "unicode"
-#define PYBIND11_SLICE_OBJECT PySliceObject
-#define PYBIND11_FROM_STRING PyString_FromString
-#define PYBIND11_STR_TYPE ::pybind11::bytes
-#define PYBIND11_BOOL_ATTR "__nonzero__"
-#define PYBIND11_NB_BOOL(ptr) ((ptr)->nb_nonzero)
-// Providing a separate PyInit decl to make Clang's -Wmissing-prototypes happy.
-// See comment for PYBIND11_MODULE below for why this is marked "maybe unused".
-#define PYBIND11_PLUGIN_IMPL(name) \
-    static PyObject *pybind11_init_wrapper();                           \
-    extern "C" PYBIND11_MAYBE_UNUSED PYBIND11_EXPORT void init##name(); \
-    extern "C" PYBIND11_EXPORT void init##name() {                      \
-        (void)pybind11_init_wrapper();                                  \
-    }                                                                   \
-    PyObject *pybind11_init_wrapper()
-#endif
-
-#if PY_VERSION_HEX >= 0x03050000 && PY_VERSION_HEX < 0x03050200
-extern "C" {
-    struct _Py_atomic_address { void *value; };
-    PyAPI_DATA(_Py_atomic_address) _PyThreadState_Current;
-}
-#endif
-
-#define PYBIND11_TRY_NEXT_OVERLOAD ((PyObject *) 1) // special failure return code
-#define PYBIND11_STRINGIFY(x) #x
-#define PYBIND11_TOSTRING(x) PYBIND11_STRINGIFY(x)
-#define PYBIND11_CONCAT(first, second) first##second
-#define PYBIND11_ENSURE_INTERNALS_READY \
-    pybind11::detail::get_internals();
-
-#define PYBIND11_CHECK_PYTHON_VERSION \
-    {                                                                          \
-        const char *compiled_ver = PYBIND11_TOSTRING(PY_MAJOR_VERSION)         \
-            "." PYBIND11_TOSTRING(PY_MINOR_VERSION);                           \
-        const char *runtime_ver = Py_GetVersion();                             \
-        size_t len = std::strlen(compiled_ver);                                \
-        if (std::strncmp(runtime_ver, compiled_ver, len) != 0                  \
-                || (runtime_ver[len] >= '0' && runtime_ver[len] <= '9')) {     \
-            PyErr_Format(PyExc_ImportError,                                    \
-                "Python version mismatch: module was compiled for Python %s, " \
-                "but the interpreter version is incompatible: %s.",            \
-                compiled_ver, runtime_ver);                                    \
-            return nullptr;                                                    \
-        }                                                                      \
-    }
-
-#define PYBIND11_CATCH_INIT_EXCEPTIONS \
-        catch (pybind11::error_already_set &e) {                               \
-            PyErr_SetString(PyExc_ImportError, e.what());                      \
-            return nullptr;                                                    \
-        } catch (const std::exception &e) {                                    \
-            PyErr_SetString(PyExc_ImportError, e.what());                      \
-            return nullptr;                                                    \
-        }                                                                      \
-
-/** \rst
-    ***Deprecated in favor of PYBIND11_MODULE***
-
-    This macro creates the entry point that will be invoked when the Python interpreter
-    imports a plugin library. Please create a `module` in the function body and return
-    the pointer to its underlying Python object at the end.
-
-    .. code-block:: cpp
-
-        PYBIND11_PLUGIN(example) {
-            pybind11::module m("example", "pybind11 example plugin");
-            /// Set up bindings here
-            return m.ptr();
-        }
-\endrst */
-#define PYBIND11_PLUGIN(name)                                                  \
-    PYBIND11_DEPRECATED("PYBIND11_PLUGIN is deprecated, use PYBIND11_MODULE")  \
-    static PyObject *pybind11_init();                                          \
-    PYBIND11_PLUGIN_IMPL(name) {                                               \
-        PYBIND11_CHECK_PYTHON_VERSION                                          \
-        PYBIND11_ENSURE_INTERNALS_READY                                        \
-        try {                                                                  \
-            return pybind11_init();                                            \
-        } PYBIND11_CATCH_INIT_EXCEPTIONS                                       \
-    }                                                                          \
-    PyObject *pybind11_init()
-
-/** \rst
-    This macro creates the entry point that will be invoked when the Python interpreter
-    imports an extension module. The module name is given as the fist argument and it
-    should not be in quotes. The second macro argument defines a variable of type
-    `py::module` which can be used to initialize the module.
-
-    The entry point is marked as "maybe unused" to aid dead-code detection analysis:
-    since the entry point is typically only looked up at runtime and not referenced
-    during translation, it would otherwise appear as unused ("dead") code.
-
-    .. code-block:: cpp
-
-        PYBIND11_MODULE(example, m) {
-            m.doc() = "pybind11 example module";
-
-            // Add bindings here
-            m.def("foo", []() {
-                return "Hello, World!";
-            });
-        }
-\endrst */
-#define PYBIND11_MODULE(name, variable)                                        \
-    PYBIND11_MAYBE_UNUSED                                                      \
-    static void PYBIND11_CONCAT(pybind11_init_, name)(pybind11::module &);     \
-    PYBIND11_PLUGIN_IMPL(name) {                                               \
-        PYBIND11_CHECK_PYTHON_VERSION                                          \
-        PYBIND11_ENSURE_INTERNALS_READY                                        \
-        auto m = pybind11::module(PYBIND11_TOSTRING(name));                    \
-        try {                                                                  \
-            PYBIND11_CONCAT(pybind11_init_, name)(m);                          \
-            return m.ptr();                                                    \
-        } PYBIND11_CATCH_INIT_EXCEPTIONS                                       \
-    }                                                                          \
-    void PYBIND11_CONCAT(pybind11_init_, name)(pybind11::module &variable)
-
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-
-using ssize_t = Py_ssize_t;
-using size_t  = std::size_t;
-
-/// Approach used to cast a previously unknown C++ instance into a Python object
-enum class return_value_policy : uint8_t {
-    /** This is the default return value policy, which falls back to the policy
-        return_value_policy::take_ownership when the return value is a pointer.
-        Otherwise, it uses return_value::move or return_value::copy for rvalue
-        and lvalue references, respectively. See below for a description of what
-        all of these different policies do. */
-    automatic = 0,
-
-    /** As above, but use policy return_value_policy::reference when the return
-        value is a pointer. This is the default conversion policy for function
-        arguments when calling Python functions manually from C++ code (i.e. via
-        handle::operator()). You probably won't need to use this. */
-    automatic_reference,
-
-    /** Reference an existing object (i.e. do not create a new copy) and take
-        ownership. Python will call the destructor and delete operator when the
-        object’s reference count reaches zero. Undefined behavior ensues when
-        the C++ side does the same.. */
-    take_ownership,
-
-    /** Create a new copy of the returned object, which will be owned by
-        Python. This policy is comparably safe because the lifetimes of the two
-        instances are decoupled. */
-    copy,
-
-    /** Use std::move to move the return value contents into a new instance
-        that will be owned by Python. This policy is comparably safe because the
-        lifetimes of the two instances (move source and destination) are
-        decoupled. */
-    move,
-
-    /** Reference an existing object, but do not take ownership. The C++ side
-        is responsible for managing the object’s lifetime and deallocating it
-        when it is no longer used. Warning: undefined behavior will ensue when
-        the C++ side deletes an object that is still referenced and used by
-        Python. */
-    reference,
-
-    /** This policy only applies to methods and properties. It references the
-        object without taking ownership similar to the above
-        return_value_policy::reference policy. In contrast to that policy, the
-        function or property’s implicit this argument (called the parent) is
-        considered to be the the owner of the return value (the child).
-        pybind11 then couples the lifetime of the parent to the child via a
-        reference relationship that ensures that the parent cannot be garbage
-        collected while Python is still using the child. More advanced
-        variations of this scheme are also possible using combinations of
-        return_value_policy::reference and the keep_alive call policy */
-    reference_internal
-};
-
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-inline static constexpr int log2(size_t n, int k = 0) { return (n <= 1) ? k : log2(n >> 1, k + 1); }
-
-// Returns the size as a multiple of sizeof(void *), rounded up.
-inline static constexpr size_t size_in_ptrs(size_t s) { return 1 + ((s - 1) >> log2(sizeof(void *))); }
-
-/**
- * The space to allocate for simple layout instance holders (see below) in multiple of the size of
- * a pointer (e.g.  2 means 16 bytes on 64-bit architectures).  The default is the minimum required
- * to holder either a std::unique_ptr or std::shared_ptr (which is almost always
- * sizeof(std::shared_ptr<T>)).
- */
-constexpr size_t instance_simple_holder_in_ptrs() {
-    static_assert(sizeof(std::shared_ptr<int>) >= sizeof(std::unique_ptr<int>),
-            "pybind assumes std::shared_ptrs are at least as big as std::unique_ptrs");
-    return size_in_ptrs(sizeof(std::shared_ptr<int>));
-}
-
-// Forward declarations
-struct type_info;
-struct value_and_holder;
-
-struct nonsimple_values_and_holders {
-    void **values_and_holders;
-    uint8_t *status;
-};
-
-/// The 'instance' type which needs to be standard layout (need to be able to use 'offsetof')
-struct instance {
-    PyObject_HEAD
-    /// Storage for pointers and holder; see simple_layout, below, for a description
-    union {
-        void *simple_value_holder[1 + instance_simple_holder_in_ptrs()];
-        nonsimple_values_and_holders nonsimple;
-    };
-    /// Weak references
-    PyObject *weakrefs;
-    /// If true, the pointer is owned which means we're free to manage it with a holder.
-    bool owned : 1;
-    /**
-     * An instance has two possible value/holder layouts.
-     *
-     * Simple layout (when this flag is true), means the `simple_value_holder` is set with a pointer
-     * and the holder object governing that pointer, i.e. [val1*][holder].  This layout is applied
-     * whenever there is no python-side multiple inheritance of bound C++ types *and* the type's
-     * holder will fit in the default space (which is large enough to hold either a std::unique_ptr
-     * or std::shared_ptr).
-     *
-     * Non-simple layout applies when using custom holders that require more space than `shared_ptr`
-     * (which is typically the size of two pointers), or when multiple inheritance is used on the
-     * python side.  Non-simple layout allocates the required amount of memory to have multiple
-     * bound C++ classes as parents.  Under this layout, `nonsimple.values_and_holders` is set to a
-     * pointer to allocated space of the required space to hold a sequence of value pointers and
-     * holders followed `status`, a set of bit flags (1 byte each), i.e.
-     * [val1*][holder1][val2*][holder2]...[bb...]  where each [block] is rounded up to a multiple of
-     * `sizeof(void *)`.  `nonsimple.status` is, for convenience, a pointer to the
-     * beginning of the [bb...] block (but not independently allocated).
-     *
-     * Status bits indicate whether the associated holder is constructed (&
-     * status_holder_constructed) and whether the value pointer is registered (&
-     * status_instance_registered) in `registered_instances`.
-     */
-    bool simple_layout : 1;
-    /// For simple layout, tracks whether the holder has been constructed
-    bool simple_holder_constructed : 1;
-    /// For simple layout, tracks whether the instance is registered in `registered_instances`
-    bool simple_instance_registered : 1;
-    /// If true, get_internals().patients has an entry for this object
-    bool has_patients : 1;
-
-    /// Initializes all of the above type/values/holders data (but not the instance values themselves)
-    void allocate_layout();
-
-    /// Destroys/deallocates all of the above
-    void deallocate_layout();
-
-    /// Returns the value_and_holder wrapper for the given type (or the first, if `find_type`
-    /// omitted).  Returns a default-constructed (with `.inst = nullptr`) object on failure if
-    /// `throw_if_missing` is false.
-    value_and_holder get_value_and_holder(const type_info *find_type = nullptr, bool throw_if_missing = true);
-
-    /// Bit values for the non-simple status flags
-    static constexpr uint8_t status_holder_constructed  = 1;
-    static constexpr uint8_t status_instance_registered = 2;
-};
-
-static_assert(std::is_standard_layout<instance>::value, "Internal error: `pybind11::detail::instance` is not standard layout!");
-
-/// from __cpp_future__ import (convenient aliases from C++14/17)
-#if defined(PYBIND11_CPP14) && (!defined(_MSC_VER) || _MSC_VER >= 1910)
-using std::enable_if_t;
-using std::conditional_t;
-using std::remove_cv_t;
-using std::remove_reference_t;
-#else
-template <bool B, typename T = void> using enable_if_t = typename std::enable_if<B, T>::type;
-template <bool B, typename T, typename F> using conditional_t = typename std::conditional<B, T, F>::type;
-template <typename T> using remove_cv_t = typename std::remove_cv<T>::type;
-template <typename T> using remove_reference_t = typename std::remove_reference<T>::type;
-#endif
-
-/// Index sequences
-#if defined(PYBIND11_CPP14)
-using std::index_sequence;
-using std::make_index_sequence;
-#else
-template<size_t ...> struct index_sequence  { };
-template<size_t N, size_t ...S> struct make_index_sequence_impl : make_index_sequence_impl <N - 1, N - 1, S...> { };
-template<size_t ...S> struct make_index_sequence_impl <0, S...> { typedef index_sequence<S...> type; };
-template<size_t N> using make_index_sequence = typename make_index_sequence_impl<N>::type;
-#endif
-
-/// Make an index sequence of the indices of true arguments
-template <typename ISeq, size_t, bool...> struct select_indices_impl { using type = ISeq; };
-template <size_t... IPrev, size_t I, bool B, bool... Bs> struct select_indices_impl<index_sequence<IPrev...>, I, B, Bs...>
-    : select_indices_impl<conditional_t<B, index_sequence<IPrev..., I>, index_sequence<IPrev...>>, I + 1, Bs...> {};
-template <bool... Bs> using select_indices = typename select_indices_impl<index_sequence<>, 0, Bs...>::type;
-
-/// Backports of std::bool_constant and std::negation to accommodate older compilers
-template <bool B> using bool_constant = std::integral_constant<bool, B>;
-template <typename T> struct negation : bool_constant<!T::value> { };
-
-template <typename...> struct void_t_impl { using type = void; };
-template <typename... Ts> using void_t = typename void_t_impl<Ts...>::type;
-
-/// Compile-time all/any/none of that check the boolean value of all template types
-#if defined(__cpp_fold_expressions) && !(defined(_MSC_VER) && (_MSC_VER < 1916))
-template <class... Ts> using all_of = bool_constant<(Ts::value && ...)>;
-template <class... Ts> using any_of = bool_constant<(Ts::value || ...)>;
-#elif !defined(_MSC_VER)
-template <bool...> struct bools {};
-template <class... Ts> using all_of = std::is_same<
-    bools<Ts::value..., true>,
-    bools<true, Ts::value...>>;
-template <class... Ts> using any_of = negation<all_of<negation<Ts>...>>;
-#else
-// MSVC has trouble with the above, but supports std::conjunction, which we can use instead (albeit
-// at a slight loss of compilation efficiency).
-template <class... Ts> using all_of = std::conjunction<Ts...>;
-template <class... Ts> using any_of = std::disjunction<Ts...>;
-#endif
-template <class... Ts> using none_of = negation<any_of<Ts...>>;
-
-template <class T, template<class> class... Predicates> using satisfies_all_of = all_of<Predicates<T>...>;
-template <class T, template<class> class... Predicates> using satisfies_any_of = any_of<Predicates<T>...>;
-template <class T, template<class> class... Predicates> using satisfies_none_of = none_of<Predicates<T>...>;
-
-/// Strip the class from a method type
-template <typename T> struct remove_class { };
-template <typename C, typename R, typename... A> struct remove_class<R (C::*)(A...)> { typedef R type(A...); };
-template <typename C, typename R, typename... A> struct remove_class<R (C::*)(A...) const> { typedef R type(A...); };
-
-/// Helper template to strip away type modifiers
-template <typename T> struct intrinsic_type                       { typedef T type; };
-template <typename T> struct intrinsic_type<const T>              { typedef typename intrinsic_type<T>::type type; };
-template <typename T> struct intrinsic_type<T*>                   { typedef typename intrinsic_type<T>::type type; };
-template <typename T> struct intrinsic_type<T&>                   { typedef typename intrinsic_type<T>::type type; };
-template <typename T> struct intrinsic_type<T&&>                  { typedef typename intrinsic_type<T>::type type; };
-template <typename T, size_t N> struct intrinsic_type<const T[N]> { typedef typename intrinsic_type<T>::type type; };
-template <typename T, size_t N> struct intrinsic_type<T[N]>       { typedef typename intrinsic_type<T>::type type; };
-template <typename T> using intrinsic_t = typename intrinsic_type<T>::type;
-
-/// Helper type to replace 'void' in some expressions
-struct void_type { };
-
-/// Helper template which holds a list of types
-template <typename...> struct type_list { };
-
-/// Compile-time integer sum
-#ifdef __cpp_fold_expressions
-template <typename... Ts> constexpr size_t constexpr_sum(Ts... ns) { return (0 + ... + size_t{ns}); }
-#else
-constexpr size_t constexpr_sum() { return 0; }
-template <typename T, typename... Ts>
-constexpr size_t constexpr_sum(T n, Ts... ns) { return size_t{n} + constexpr_sum(ns...); }
-#endif
-
-PYBIND11_NAMESPACE_BEGIN(constexpr_impl)
-/// Implementation details for constexpr functions
-constexpr int first(int i) { return i; }
-template <typename T, typename... Ts>
-constexpr int first(int i, T v, Ts... vs) { return v ? i : first(i + 1, vs...); }
-
-constexpr int last(int /*i*/, int result) { return result; }
-template <typename T, typename... Ts>
-constexpr int last(int i, int result, T v, Ts... vs) { return last(i + 1, v ? i : result, vs...); }
-PYBIND11_NAMESPACE_END(constexpr_impl)
-
-/// Return the index of the first type in Ts which satisfies Predicate<T>.  Returns sizeof...(Ts) if
-/// none match.
-template <template<typename> class Predicate, typename... Ts>
-constexpr int constexpr_first() { return constexpr_impl::first(0, Predicate<Ts>::value...); }
-
-/// Return the index of the last type in Ts which satisfies Predicate<T>, or -1 if none match.
-template <template<typename> class Predicate, typename... Ts>
-constexpr int constexpr_last() { return constexpr_impl::last(0, -1, Predicate<Ts>::value...); }
-
-/// Return the Nth element from the parameter pack
-template <size_t N, typename T, typename... Ts>
-struct pack_element { using type = typename pack_element<N - 1, Ts...>::type; };
-template <typename T, typename... Ts>
-struct pack_element<0, T, Ts...> { using type = T; };
-
-/// Return the one and only type which matches the predicate, or Default if none match.
-/// If more than one type matches the predicate, fail at compile-time.
-template <template<typename> class Predicate, typename Default, typename... Ts>
-struct exactly_one {
-    static constexpr auto found = constexpr_sum(Predicate<Ts>::value...);
-    static_assert(found <= 1, "Found more than one type matching the predicate");
-
-    static constexpr auto index = found ? constexpr_first<Predicate, Ts...>() : 0;
-    using type = conditional_t<found, typename pack_element<index, Ts...>::type, Default>;
-};
-template <template<typename> class P, typename Default>
-struct exactly_one<P, Default> { using type = Default; };
-
-template <template<typename> class Predicate, typename Default, typename... Ts>
-using exactly_one_t = typename exactly_one<Predicate, Default, Ts...>::type;
-
-/// Defer the evaluation of type T until types Us are instantiated
-template <typename T, typename... /*Us*/> struct deferred_type { using type = T; };
-template <typename T, typename... Us> using deferred_t = typename deferred_type<T, Us...>::type;
-
-/// Like is_base_of, but requires a strict base (i.e. `is_strict_base_of<T, T>::value == false`,
-/// unlike `std::is_base_of`)
-template <typename Base, typename Derived> using is_strict_base_of = bool_constant<
-    std::is_base_of<Base, Derived>::value && !std::is_same<Base, Derived>::value>;
-
-/// Like is_base_of, but also requires that the base type is accessible (i.e. that a Derived pointer
-/// can be converted to a Base pointer)
-template <typename Base, typename Derived> using is_accessible_base_of = bool_constant<
-    std::is_base_of<Base, Derived>::value && std::is_convertible<Derived *, Base *>::value>;
-
-template <template<typename...> class Base>
-struct is_template_base_of_impl {
-    template <typename... Us> static std::true_type check(Base<Us...> *);
-    static std::false_type check(...);
-};
-
-/// Check if a template is the base of a type. For example:
-/// `is_template_base_of<Base, T>` is true if `struct T : Base<U> {}` where U can be anything
-template <template<typename...> class Base, typename T>
-#if !defined(_MSC_VER)
-using is_template_base_of = decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T>*)nullptr));
-#else // MSVC2015 has trouble with decltype in template aliases
-struct is_template_base_of : decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T>*)nullptr)) { };
-#endif
-
-/// Check if T is an instantiation of the template `Class`. For example:
-/// `is_instantiation<shared_ptr, T>` is true if `T == shared_ptr<U>` where U can be anything.
-template <template<typename...> class Class, typename T>
-struct is_instantiation : std::false_type { };
-template <template<typename...> class Class, typename... Us>
-struct is_instantiation<Class, Class<Us...>> : std::true_type { };
-
-/// Check if T is std::shared_ptr<U> where U can be anything
-template <typename T> using is_shared_ptr = is_instantiation<std::shared_ptr, T>;
-
-/// Check if T looks like an input iterator
-template <typename T, typename = void> struct is_input_iterator : std::false_type {};
-template <typename T>
-struct is_input_iterator<T, void_t<decltype(*std::declval<T &>()), decltype(++std::declval<T &>())>>
-    : std::true_type {};
-
-template <typename T> using is_function_pointer = bool_constant<
-    std::is_pointer<T>::value && std::is_function<typename std::remove_pointer<T>::type>::value>;
-
-template <typename F> struct strip_function_object {
-    using type = typename remove_class<decltype(&F::operator())>::type;
-};
-
-// Extracts the function signature from a function, function pointer or lambda.
-template <typename Function, typename F = remove_reference_t<Function>>
-using function_signature_t = conditional_t<
-    std::is_function<F>::value,
-    F,
-    typename conditional_t<
-        std::is_pointer<F>::value || std::is_member_pointer<F>::value,
-        std::remove_pointer<F>,
-        strip_function_object<F>
-    >::type
->;
-
-/// Returns true if the type looks like a lambda: that is, isn't a function, pointer or member
-/// pointer.  Note that this can catch all sorts of other things, too; this is intended to be used
-/// in a place where passing a lambda makes sense.
-template <typename T> using is_lambda = satisfies_none_of<remove_reference_t<T>,
-        std::is_function, std::is_pointer, std::is_member_pointer>;
-
-/// Ignore that a variable is unused in compiler warnings
-inline void ignore_unused(const int *) { }
-
-/// Apply a function over each element of a parameter pack
-#ifdef __cpp_fold_expressions
-#define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN) (((PATTERN), void()), ...)
-#else
-using expand_side_effects = bool[];
-#define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN) (void)pybind11::detail::expand_side_effects{ ((PATTERN), void(), false)..., false }
-#endif
-
-PYBIND11_NAMESPACE_END(detail)
-
-/// C++ bindings of builtin Python exceptions
-class builtin_exception : public std::runtime_error {
-public:
-    using std::runtime_error::runtime_error;
-    /// Set the error using the Python C API
-    virtual void set_error() const = 0;
-};
-
-#define PYBIND11_RUNTIME_EXCEPTION(name, type) \
-    class name : public builtin_exception { public: \
-        using builtin_exception::builtin_exception; \
-        name() : name("") { } \
-        void set_error() const override { PyErr_SetString(type, what()); } \
-    };
-
-PYBIND11_RUNTIME_EXCEPTION(stop_iteration, PyExc_StopIteration)
-PYBIND11_RUNTIME_EXCEPTION(index_error, PyExc_IndexError)
-PYBIND11_RUNTIME_EXCEPTION(key_error, PyExc_KeyError)
-PYBIND11_RUNTIME_EXCEPTION(value_error, PyExc_ValueError)
-PYBIND11_RUNTIME_EXCEPTION(type_error, PyExc_TypeError)
-PYBIND11_RUNTIME_EXCEPTION(buffer_error, PyExc_BufferError)
-PYBIND11_RUNTIME_EXCEPTION(import_error, PyExc_ImportError)
-PYBIND11_RUNTIME_EXCEPTION(cast_error, PyExc_RuntimeError) /// Thrown when pybind11::cast or handle::call fail due to a type casting error
-PYBIND11_RUNTIME_EXCEPTION(reference_cast_error, PyExc_RuntimeError) /// Used internally
-
-[[noreturn]] PYBIND11_NOINLINE inline void pybind11_fail(const char *reason) { throw std::runtime_error(reason); }
-[[noreturn]] PYBIND11_NOINLINE inline void pybind11_fail(const std::string &reason) { throw std::runtime_error(reason); }
-
-template <typename T, typename SFINAE = void> struct format_descriptor { };
-
-PYBIND11_NAMESPACE_BEGIN(detail)
-// Returns the index of the given type in the type char array below, and in the list in numpy.h
-// The order here is: bool; 8 ints ((signed,unsigned)x(8,16,32,64)bits); float,double,long double;
-// complex float,double,long double.  Note that the long double types only participate when long
-// double is actually longer than double (it isn't under MSVC).
-// NB: not only the string below but also complex.h and numpy.h rely on this order.
-template <typename T, typename SFINAE = void> struct is_fmt_numeric { static constexpr bool value = false; };
-template <typename T> struct is_fmt_numeric<T, enable_if_t<std::is_arithmetic<T>::value>> {
-    static constexpr bool value = true;
-    static constexpr int index = std::is_same<T, bool>::value ? 0 : 1 + (
-        std::is_integral<T>::value ? detail::log2(sizeof(T))*2 + std::is_unsigned<T>::value : 8 + (
-        std::is_same<T, double>::value ? 1 : std::is_same<T, long double>::value ? 2 : 0));
-};
-PYBIND11_NAMESPACE_END(detail)
-
-template <typename T> struct format_descriptor<T, detail::enable_if_t<std::is_arithmetic<T>::value>> {
-    static constexpr const char c = "?bBhHiIqQfdg"[detail::is_fmt_numeric<T>::index];
-    static constexpr const char value[2] = { c, '\0' };
-    static std::string format() { return std::string(1, c); }
-};
-
-#if !defined(PYBIND11_CPP17)
-
-template <typename T> constexpr const char format_descriptor<
-    T, detail::enable_if_t<std::is_arithmetic<T>::value>>::value[2];
-
-#endif
-
-/// RAII wrapper that temporarily clears any Python error state
-struct error_scope {
-    PyObject *type, *value, *trace;
-    error_scope() { PyErr_Fetch(&type, &value, &trace); }
-    ~error_scope() { PyErr_Restore(type, value, trace); }
-};
-
-/// Dummy destructor wrapper that can be used to expose classes with a private destructor
-struct nodelete { template <typename T> void operator()(T*) { } };
-
-PYBIND11_NAMESPACE_BEGIN(detail)
-template <typename... Args>
-struct overload_cast_impl {
-    constexpr overload_cast_impl() {} // MSVC 2015 needs this
-
-    template <typename Return>
-    constexpr auto operator()(Return (*pf)(Args...)) const noexcept
-                              -> decltype(pf) { return pf; }
-
-    template <typename Return, typename Class>
-    constexpr auto operator()(Return (Class::*pmf)(Args...), std::false_type = {}) const noexcept
-                              -> decltype(pmf) { return pmf; }
-
-    template <typename Return, typename Class>
-    constexpr auto operator()(Return (Class::*pmf)(Args...) const, std::true_type) const noexcept
-                              -> decltype(pmf) { return pmf; }
-};
-PYBIND11_NAMESPACE_END(detail)
-
-// overload_cast requires variable templates: C++14
-#if defined(PYBIND11_CPP14)
-#define PYBIND11_OVERLOAD_CAST 1
-/// Syntax sugar for resolving overloaded function pointers:
-///  - regular: static_cast<Return (Class::*)(Arg0, Arg1, Arg2)>(&Class::func)
-///  - sweet:   overload_cast<Arg0, Arg1, Arg2>(&Class::func)
-template <typename... Args>
-static constexpr detail::overload_cast_impl<Args...> overload_cast = {};
-// MSVC 2015 only accepts this particular initialization syntax for this variable template.
-#endif
-
-/// Const member function selector for overload_cast
-///  - regular: static_cast<Return (Class::*)(Arg) const>(&Class::func)
-///  - sweet:   overload_cast<Arg>(&Class::func, const_)
-static constexpr auto const_ = std::true_type{};
-
-#if !defined(PYBIND11_CPP14) // no overload_cast: providing something that static_assert-fails:
-template <typename... Args> struct overload_cast {
-    static_assert(detail::deferred_t<std::false_type, Args...>::value,
-                  "pybind11::overload_cast<...> requires compiling in C++14 mode");
-};
-#endif // overload_cast
-
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-// Adaptor for converting arbitrary container arguments into a vector; implicitly convertible from
-// any standard container (or C-style array) supporting std::begin/std::end, any singleton
-// arithmetic type (if T is arithmetic), or explicitly constructible from an iterator pair.
-template <typename T>
-class any_container {
-    std::vector<T> v;
-public:
-    any_container() = default;
-
-    // Can construct from a pair of iterators
-    template <typename It, typename = enable_if_t<is_input_iterator<It>::value>>
-    any_container(It first, It last) : v(first, last) { }
-
-    // Implicit conversion constructor from any arbitrary container type with values convertible to T
-    template <typename Container, typename = enable_if_t<std::is_convertible<decltype(*std::begin(std::declval<const Container &>())), T>::value>>
-    any_container(const Container &c) : any_container(std::begin(c), std::end(c)) { }
-
-    // initializer_list's aren't deducible, so don't get matched by the above template; we need this
-    // to explicitly allow implicit conversion from one:
-    template <typename TIn, typename = enable_if_t<std::is_convertible<TIn, T>::value>>
-    any_container(const std::initializer_list<TIn> &c) : any_container(c.begin(), c.end()) { }
-
-    // Avoid copying if given an rvalue vector of the correct type.
-    any_container(std::vector<T> &&v) : v(std::move(v)) { }
-
-    // Moves the vector out of an rvalue any_container
-    operator std::vector<T> &&() && { return std::move(v); }
-
-    // Dereferencing obtains a reference to the underlying vector
-    std::vector<T> &operator*() { return v; }
-    const std::vector<T> &operator*() const { return v; }
-
-    // -> lets you call methods on the underlying vector
-    std::vector<T> *operator->() { return &v; }
-    const std::vector<T> *operator->() const { return &v; }
-};
-
-PYBIND11_NAMESPACE_END(detail)
-
-
-
-PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)

spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/uninitialized_copy.h

@@ -1,23 +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>
-
-// this system inherits uninitialized_copy
-#include <thrust/system/cpp/detail/uninitialized_copy.h>
-

spaces/CVPR/transfiner/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py

@@ -1,14 +0,0 @@
-from .mask_rcnn_R_50_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-
-train.max_iter //= 2  # 100ep -> 50ep
-
-lr_multiplier.scheduler.milestones = [
-    milestone // 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter

spaces/Catmeow/Face2Painting_From_Photo/face_detection.py

@@ -1,140 +0,0 @@
-# Copyright (c) 2021 Justin Pinkney
-
-import dlib
-import numpy as np
-import os
-from PIL import Image
-from PIL import ImageOps
-from scipy.ndimage import gaussian_filter
-import cv2
-
-
-MODEL_PATH = "shape_predictor_5_face_landmarks.dat"
-detector = dlib.get_frontal_face_detector()
-
-
-def align(image_in, face_index=0, output_size=256):
-    try:
-        image_in = ImageOps.exif_transpose(image_in)
-    except:
-        print("exif problem, not rotating")
-
-    landmarks = list(get_landmarks(image_in))
-    n_faces = len(landmarks)
-    face_index = min(n_faces-1, face_index)
-    if n_faces == 0:
-        aligned_image = image_in
-        quad = None
-    else:
-        aligned_image, quad = image_align(image_in, landmarks[face_index], output_size=output_size)
-
-    return aligned_image, n_faces, quad
-
-
-def composite_images(quad, img, output):
-    """Composite an image into and output canvas according to transformed co-ords"""
-    output = output.convert("RGBA")
-    img = img.convert("RGBA")
-    input_size = img.size
-    src = np.array(((0, 0), (0, input_size[1]), input_size, (input_size[0], 0)), dtype=np.float32)
-    dst = np.float32(quad)
-    mtx = cv2.getPerspectiveTransform(dst, src)
-    img = img.transform(output.size, Image.PERSPECTIVE, mtx.flatten(), Image.BILINEAR)
-    output.alpha_composite(img)
-
-    return output.convert("RGB")
-    
-
-def get_landmarks(image):
-    """Get landmarks from PIL image"""
-    shape_predictor = dlib.shape_predictor(MODEL_PATH)
-
-    max_size = max(image.size)
-    reduction_scale = int(max_size/512)
-    if reduction_scale == 0:
-        reduction_scale = 1
-    downscaled = image.reduce(reduction_scale)
-    img = np.array(downscaled)
-    detections = detector(img, 0)
-    
-    for detection in detections:
-        try:
-            face_landmarks = [(reduction_scale*item.x, reduction_scale*item.y) for item in shape_predictor(img, detection).parts()]
-            yield face_landmarks
-        except Exception as e:
-            print(e)
-
-
-def image_align(src_img, face_landmarks, output_size=512, transform_size=2048, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False):
-        # Align function modified from ffhq-dataset
-        # See https://github.com/NVlabs/ffhq-dataset for license
-
-        lm = np.array(face_landmarks)
-        lm_eye_left      = lm[2:3]  # left-clockwise
-        lm_eye_right     = lm[0:1]  # left-clockwise
-
-        # Calculate auxiliary vectors.
-        eye_left     = np.mean(lm_eye_left, axis=0)
-        eye_right    = np.mean(lm_eye_right, axis=0)
-        eye_avg      = (eye_left + eye_right) * 0.5
-        eye_to_eye   = 0.71*(eye_right - eye_left)
-        mouth_avg    = lm[4]
-        eye_to_mouth = 1.35*(mouth_avg - eye_avg)
-
-        # Choose oriented crop rectangle.
-        x = eye_to_eye.copy()
-        x /= np.hypot(*x)
-        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-        x *= x_scale
-        y = np.flipud(x) * [-y_scale, y_scale]
-        c = eye_avg + eye_to_mouth * em_scale
-        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-        quad_orig = quad.copy()
-        qsize = np.hypot(*x) * 2        
-        
-        img = src_img.convert('RGBA').convert('RGB')
-
-        # Shrink.
-        shrink = int(np.floor(qsize / output_size * 0.5))
-        if shrink > 1:
-            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-            img = img.resize(rsize, Image.ANTIALIAS)
-            quad /= shrink
-            qsize /= shrink
-
-        # Crop.
-        border = max(int(np.rint(qsize * 0.1)), 3)
-        crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
-        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
-        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-            img = img.crop(crop)
-            quad -= crop[0:2]
-
-        # Pad.
-        pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
-        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
-        if enable_padding and max(pad) > border - 4:
-            pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-            img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-            h, w, _ = img.shape
-            y, x, _ = np.ogrid[:h, :w, :1]
-            mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))
-            blur = qsize * 0.02
-            img += (gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-            img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
-            img = np.uint8(np.clip(np.rint(img), 0, 255))
-            if alpha:
-                mask = 1-np.clip(3.0 * mask, 0.0, 1.0)
-                mask = np.uint8(np.clip(np.rint(mask*255), 0, 255))
-                img = np.concatenate((img, mask), axis=2)
-                img = Image.fromarray(img, 'RGBA')
-            else:
-                img = Image.fromarray(img, 'RGB')
-            quad += pad[:2]
-
-        # Transform.
-        img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
-        if output_size < transform_size:
-            img = img.resize((output_size, output_size), Image.ANTIALIAS)
-
-        return img, quad_orig

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

@@ -1,347 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# PPM support for PIL
-#
-# History:
-#       96-03-24 fl     Created
-#       98-03-06 fl     Write RGBA images (as RGB, that is)
-#
-# Copyright (c) Secret Labs AB 1997-98.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-from . import Image, ImageFile
-from ._binary import i16be as i16
-from ._binary import o8
-from ._binary import o32le as o32
-
-#
-# --------------------------------------------------------------------
-
-b_whitespace = b"\x20\x09\x0a\x0b\x0c\x0d"
-
-MODES = {
-    # standard
-    b"P1": "1",
-    b"P2": "L",
-    b"P3": "RGB",
-    b"P4": "1",
-    b"P5": "L",
-    b"P6": "RGB",
-    # extensions
-    b"P0CMYK": "CMYK",
-    # PIL extensions (for test purposes only)
-    b"PyP": "P",
-    b"PyRGBA": "RGBA",
-    b"PyCMYK": "CMYK",
-}
-
-
-def _accept(prefix):
-    return prefix[0:1] == b"P" and prefix[1] in b"0123456y"
-
-
-##
-# Image plugin for PBM, PGM, and PPM images.
-
-
-class PpmImageFile(ImageFile.ImageFile):
-    format = "PPM"
-    format_description = "Pbmplus image"
-
-    def _read_magic(self):
-        magic = b""
-        # read until whitespace or longest available magic number
-        for _ in range(6):
-            c = self.fp.read(1)
-            if not c or c in b_whitespace:
-                break
-            magic += c
-        return magic
-
-    def _read_token(self):
-        token = b""
-        while len(token) <= 10:  # read until next whitespace or limit of 10 characters
-            c = self.fp.read(1)
-            if not c:
-                break
-            elif c in b_whitespace:  # token ended
-                if not token:
-                    # skip whitespace at start
-                    continue
-                break
-            elif c == b"#":
-                # ignores rest of the line; stops at CR, LF or EOF
-                while self.fp.read(1) not in b"\r\n":
-                    pass
-                continue
-            token += c
-        if not token:
-            # Token was not even 1 byte
-            msg = "Reached EOF while reading header"
-            raise ValueError(msg)
-        elif len(token) > 10:
-            msg = f"Token too long in file header: {token.decode()}"
-            raise ValueError(msg)
-        return token
-
-    def _open(self):
-        magic_number = self._read_magic()
-        try:
-            mode = MODES[magic_number]
-        except KeyError:
-            msg = "not a PPM file"
-            raise SyntaxError(msg)
-
-        if magic_number in (b"P1", b"P4"):
-            self.custom_mimetype = "image/x-portable-bitmap"
-        elif magic_number in (b"P2", b"P5"):
-            self.custom_mimetype = "image/x-portable-graymap"
-        elif magic_number in (b"P3", b"P6"):
-            self.custom_mimetype = "image/x-portable-pixmap"
-
-        maxval = None
-        decoder_name = "raw"
-        if magic_number in (b"P1", b"P2", b"P3"):
-            decoder_name = "ppm_plain"
-        for ix in range(3):
-            token = int(self._read_token())
-            if ix == 0:  # token is the x size
-                xsize = token
-            elif ix == 1:  # token is the y size
-                ysize = token
-                if mode == "1":
-                    self.mode = "1"
-                    rawmode = "1;I"
-                    break
-                else:
-                    self.mode = rawmode = mode
-            elif ix == 2:  # token is maxval
-                maxval = token
-                if not 0 < maxval < 65536:
-                    msg = "maxval must be greater than 0 and less than 65536"
-                    raise ValueError(msg)
-                if maxval > 255 and mode == "L":
-                    self.mode = "I"
-
-                if decoder_name != "ppm_plain":
-                    # If maxval matches a bit depth, use the raw decoder directly
-                    if maxval == 65535 and mode == "L":
-                        rawmode = "I;16B"
-                    elif maxval != 255:
-                        decoder_name = "ppm"
-
-        args = (rawmode, 0, 1) if decoder_name == "raw" else (rawmode, maxval)
-        self._size = xsize, ysize
-        self.tile = [(decoder_name, (0, 0, xsize, ysize), self.fp.tell(), args)]
-
-
-#
-# --------------------------------------------------------------------
-
-
-class PpmPlainDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def _read_block(self):
-        return self.fd.read(ImageFile.SAFEBLOCK)
-
-    def _find_comment_end(self, block, start=0):
-        a = block.find(b"\n", start)
-        b = block.find(b"\r", start)
-        return min(a, b) if a * b > 0 else max(a, b)  # lowest nonnegative index (or -1)
-
-    def _ignore_comments(self, block):
-        if self._comment_spans:
-            # Finish current comment
-            while block:
-                comment_end = self._find_comment_end(block)
-                if comment_end != -1:
-                    # Comment ends in this block
-                    # Delete tail of comment
-                    block = block[comment_end + 1 :]
-                    break
-                else:
-                    # Comment spans whole block
-                    # So read the next block, looking for the end
-                    block = self._read_block()
-
-        # Search for any further comments
-        self._comment_spans = False
-        while True:
-            comment_start = block.find(b"#")
-            if comment_start == -1:
-                # No comment found
-                break
-            comment_end = self._find_comment_end(block, comment_start)
-            if comment_end != -1:
-                # Comment ends in this block
-                # Delete comment
-                block = block[:comment_start] + block[comment_end + 1 :]
-            else:
-                # Comment continues to next block(s)
-                block = block[:comment_start]
-                self._comment_spans = True
-                break
-        return block
-
-    def _decode_bitonal(self):
-        """
-        This is a separate method because in the plain PBM format, all data tokens are
-        exactly one byte, so the inter-token whitespace is optional.
-        """
-        data = bytearray()
-        total_bytes = self.state.xsize * self.state.ysize
-
-        while len(data) != total_bytes:
-            block = self._read_block()  # read next block
-            if not block:
-                # eof
-                break
-
-            block = self._ignore_comments(block)
-
-            tokens = b"".join(block.split())
-            for token in tokens:
-                if token not in (48, 49):
-                    msg = b"Invalid token for this mode: %s" % bytes([token])
-                    raise ValueError(msg)
-            data = (data + tokens)[:total_bytes]
-        invert = bytes.maketrans(b"01", b"\xFF\x00")
-        return data.translate(invert)
-
-    def _decode_blocks(self, maxval):
-        data = bytearray()
-        max_len = 10
-        out_byte_count = 4 if self.mode == "I" else 1
-        out_max = 65535 if self.mode == "I" else 255
-        bands = Image.getmodebands(self.mode)
-        total_bytes = self.state.xsize * self.state.ysize * bands * out_byte_count
-
-        half_token = False
-        while len(data) != total_bytes:
-            block = self._read_block()  # read next block
-            if not block:
-                if half_token:
-                    block = bytearray(b" ")  # flush half_token
-                else:
-                    # eof
-                    break
-
-            block = self._ignore_comments(block)
-
-            if half_token:
-                block = half_token + block  # stitch half_token to new block
-                half_token = False
-
-            tokens = block.split()
-
-            if block and not block[-1:].isspace():  # block might split token
-                half_token = tokens.pop()  # save half token for later
-                if len(half_token) > max_len:  # prevent buildup of half_token
-                    msg = (
-                        b"Token too long found in data: %s" % half_token[: max_len + 1]
-                    )
-                    raise ValueError(msg)
-
-            for token in tokens:
-                if len(token) > max_len:
-                    msg = b"Token too long found in data: %s" % token[: max_len + 1]
-                    raise ValueError(msg)
-                value = int(token)
-                if value > maxval:
-                    msg = f"Channel value too large for this mode: {value}"
-                    raise ValueError(msg)
-                value = round(value / maxval * out_max)
-                data += o32(value) if self.mode == "I" else o8(value)
-                if len(data) == total_bytes:  # finished!
-                    break
-        return data
-
-    def decode(self, buffer):
-        self._comment_spans = False
-        if self.mode == "1":
-            data = self._decode_bitonal()
-            rawmode = "1;8"
-        else:
-            maxval = self.args[-1]
-            data = self._decode_blocks(maxval)
-            rawmode = "I;32" if self.mode == "I" else self.mode
-        self.set_as_raw(bytes(data), rawmode)
-        return -1, 0
-
-
-class PpmDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def decode(self, buffer):
-        data = bytearray()
-        maxval = self.args[-1]
-        in_byte_count = 1 if maxval < 256 else 2
-        out_byte_count = 4 if self.mode == "I" else 1
-        out_max = 65535 if self.mode == "I" else 255
-        bands = Image.getmodebands(self.mode)
-        while len(data) < self.state.xsize * self.state.ysize * bands * out_byte_count:
-            pixels = self.fd.read(in_byte_count * bands)
-            if len(pixels) < in_byte_count * bands:
-                # eof
-                break
-            for b in range(bands):
-                value = (
-                    pixels[b] if in_byte_count == 1 else i16(pixels, b * in_byte_count)
-                )
-                value = min(out_max, round(value / maxval * out_max))
-                data += o32(value) if self.mode == "I" else o8(value)
-        rawmode = "I;32" if self.mode == "I" else self.mode
-        self.set_as_raw(bytes(data), rawmode)
-        return -1, 0
-
-
-#
-# --------------------------------------------------------------------
-
-
-def _save(im, fp, filename):
-    if im.mode == "1":
-        rawmode, head = "1;I", b"P4"
-    elif im.mode == "L":
-        rawmode, head = "L", b"P5"
-    elif im.mode == "I":
-        rawmode, head = "I;16B", b"P5"
-    elif im.mode in ("RGB", "RGBA"):
-        rawmode, head = "RGB", b"P6"
-    else:
-        msg = f"cannot write mode {im.mode} as PPM"
-        raise OSError(msg)
-    fp.write(head + b"\n%d %d\n" % im.size)
-    if head == b"P6":
-        fp.write(b"255\n")
-    elif head == b"P5":
-        if rawmode == "L":
-            fp.write(b"255\n")
-        else:
-            fp.write(b"65535\n")
-    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, 1))])
-
-    # ALTERNATIVE: save via builtin debug function
-    # im._dump(filename)
-
-
-#
-# --------------------------------------------------------------------
-
-
-Image.register_open(PpmImageFile.format, PpmImageFile, _accept)
-Image.register_save(PpmImageFile.format, _save)
-
-Image.register_decoder("ppm", PpmDecoder)
-Image.register_decoder("ppm_plain", PpmPlainDecoder)
-
-Image.register_extensions(PpmImageFile.format, [".pbm", ".pgm", ".ppm", ".pnm"])
-
-Image.register_mime(PpmImageFile.format, "image/x-portable-anymap")

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

@@ -1,560 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# TIFF tags
-#
-# This module provides clear-text names for various well-known
-# TIFF tags.  the TIFF codec works just fine without it.
-#
-# Copyright (c) Secret Labs AB 1999.
-#
-# See the README file for information on usage and redistribution.
-#
-
-##
-# This module provides constants and clear-text names for various
-# well-known TIFF tags.
-##
-
-from collections import namedtuple
-
-
-class TagInfo(namedtuple("_TagInfo", "value name type length enum")):
-    __slots__ = []
-
-    def __new__(cls, value=None, name="unknown", type=None, length=None, enum=None):
-        return super().__new__(cls, value, name, type, length, enum or {})
-
-    def cvt_enum(self, value):
-        # Using get will call hash(value), which can be expensive
-        # for some types (e.g. Fraction). Since self.enum is rarely
-        # used, it's usually better to test it first.
-        return self.enum.get(value, value) if self.enum else value
-
-
-def lookup(tag, group=None):
-    """
-    :param tag: Integer tag number
-    :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in
-
-    .. versionadded:: 8.3.0
-
-    :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible,
-        otherwise just populating the value and name from ``TAGS``.
-        If the tag is not recognized, "unknown" is returned for the name
-
-    """
-
-    if group is not None:
-        info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None
-    else:
-        info = TAGS_V2.get(tag)
-    return info or TagInfo(tag, TAGS.get(tag, "unknown"))
-
-
-##
-# Map tag numbers to tag info.
-#
-#  id: (Name, Type, Length, enum_values)
-#
-# The length here differs from the length in the tiff spec.  For
-# numbers, the tiff spec is for the number of fields returned. We
-# agree here.  For string-like types, the tiff spec uses the length of
-# field in bytes.  In Pillow, we are using the number of expected
-# fields, in general 1 for string-like types.
-
-
-BYTE = 1
-ASCII = 2
-SHORT = 3
-LONG = 4
-RATIONAL = 5
-SIGNED_BYTE = 6
-UNDEFINED = 7
-SIGNED_SHORT = 8
-SIGNED_LONG = 9
-SIGNED_RATIONAL = 10
-FLOAT = 11
-DOUBLE = 12
-IFD = 13
-LONG8 = 16
-
-TAGS_V2 = {
-    254: ("NewSubfileType", LONG, 1),
-    255: ("SubfileType", SHORT, 1),
-    256: ("ImageWidth", LONG, 1),
-    257: ("ImageLength", LONG, 1),
-    258: ("BitsPerSample", SHORT, 0),
-    259: (
-        "Compression",
-        SHORT,
-        1,
-        {
-            "Uncompressed": 1,
-            "CCITT 1d": 2,
-            "Group 3 Fax": 3,
-            "Group 4 Fax": 4,
-            "LZW": 5,
-            "JPEG": 6,
-            "PackBits": 32773,
-        },
-    ),
-    262: (
-        "PhotometricInterpretation",
-        SHORT,
-        1,
-        {
-            "WhiteIsZero": 0,
-            "BlackIsZero": 1,
-            "RGB": 2,
-            "RGB Palette": 3,
-            "Transparency Mask": 4,
-            "CMYK": 5,
-            "YCbCr": 6,
-            "CieLAB": 8,
-            "CFA": 32803,  # TIFF/EP, Adobe DNG
-            "LinearRaw": 32892,  # Adobe DNG
-        },
-    ),
-    263: ("Threshholding", SHORT, 1),
-    264: ("CellWidth", SHORT, 1),
-    265: ("CellLength", SHORT, 1),
-    266: ("FillOrder", SHORT, 1),
-    269: ("DocumentName", ASCII, 1),
-    270: ("ImageDescription", ASCII, 1),
-    271: ("Make", ASCII, 1),
-    272: ("Model", ASCII, 1),
-    273: ("StripOffsets", LONG, 0),
-    274: ("Orientation", SHORT, 1),
-    277: ("SamplesPerPixel", SHORT, 1),
-    278: ("RowsPerStrip", LONG, 1),
-    279: ("StripByteCounts", LONG, 0),
-    280: ("MinSampleValue", SHORT, 0),
-    281: ("MaxSampleValue", SHORT, 0),
-    282: ("XResolution", RATIONAL, 1),
-    283: ("YResolution", RATIONAL, 1),
-    284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}),
-    285: ("PageName", ASCII, 1),
-    286: ("XPosition", RATIONAL, 1),
-    287: ("YPosition", RATIONAL, 1),
-    288: ("FreeOffsets", LONG, 1),
-    289: ("FreeByteCounts", LONG, 1),
-    290: ("GrayResponseUnit", SHORT, 1),
-    291: ("GrayResponseCurve", SHORT, 0),
-    292: ("T4Options", LONG, 1),
-    293: ("T6Options", LONG, 1),
-    296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}),
-    297: ("PageNumber", SHORT, 2),
-    301: ("TransferFunction", SHORT, 0),
-    305: ("Software", ASCII, 1),
-    306: ("DateTime", ASCII, 1),
-    315: ("Artist", ASCII, 1),
-    316: ("HostComputer", ASCII, 1),
-    317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}),
-    318: ("WhitePoint", RATIONAL, 2),
-    319: ("PrimaryChromaticities", RATIONAL, 6),
-    320: ("ColorMap", SHORT, 0),
-    321: ("HalftoneHints", SHORT, 2),
-    322: ("TileWidth", LONG, 1),
-    323: ("TileLength", LONG, 1),
-    324: ("TileOffsets", LONG, 0),
-    325: ("TileByteCounts", LONG, 0),
-    330: ("SubIFDs", LONG, 0),
-    332: ("InkSet", SHORT, 1),
-    333: ("InkNames", ASCII, 1),
-    334: ("NumberOfInks", SHORT, 1),
-    336: ("DotRange", SHORT, 0),
-    337: ("TargetPrinter", ASCII, 1),
-    338: ("ExtraSamples", SHORT, 0),
-    339: ("SampleFormat", SHORT, 0),
-    340: ("SMinSampleValue", DOUBLE, 0),
-    341: ("SMaxSampleValue", DOUBLE, 0),
-    342: ("TransferRange", SHORT, 6),
-    347: ("JPEGTables", UNDEFINED, 1),
-    # obsolete JPEG tags
-    512: ("JPEGProc", SHORT, 1),
-    513: ("JPEGInterchangeFormat", LONG, 1),
-    514: ("JPEGInterchangeFormatLength", LONG, 1),
-    515: ("JPEGRestartInterval", SHORT, 1),
-    517: ("JPEGLosslessPredictors", SHORT, 0),
-    518: ("JPEGPointTransforms", SHORT, 0),
-    519: ("JPEGQTables", LONG, 0),
-    520: ("JPEGDCTables", LONG, 0),
-    521: ("JPEGACTables", LONG, 0),
-    529: ("YCbCrCoefficients", RATIONAL, 3),
-    530: ("YCbCrSubSampling", SHORT, 2),
-    531: ("YCbCrPositioning", SHORT, 1),
-    532: ("ReferenceBlackWhite", RATIONAL, 6),
-    700: ("XMP", BYTE, 0),
-    33432: ("Copyright", ASCII, 1),
-    33723: ("IptcNaaInfo", UNDEFINED, 1),
-    34377: ("PhotoshopInfo", BYTE, 0),
-    # FIXME add more tags here
-    34665: ("ExifIFD", LONG, 1),
-    34675: ("ICCProfile", UNDEFINED, 1),
-    34853: ("GPSInfoIFD", LONG, 1),
-    36864: ("ExifVersion", UNDEFINED, 1),
-    37724: ("ImageSourceData", UNDEFINED, 1),
-    40965: ("InteroperabilityIFD", LONG, 1),
-    41730: ("CFAPattern", UNDEFINED, 1),
-    # MPInfo
-    45056: ("MPFVersion", UNDEFINED, 1),
-    45057: ("NumberOfImages", LONG, 1),
-    45058: ("MPEntry", UNDEFINED, 1),
-    45059: ("ImageUIDList", UNDEFINED, 0),  # UNDONE, check
-    45060: ("TotalFrames", LONG, 1),
-    45313: ("MPIndividualNum", LONG, 1),
-    45569: ("PanOrientation", LONG, 1),
-    45570: ("PanOverlap_H", RATIONAL, 1),
-    45571: ("PanOverlap_V", RATIONAL, 1),
-    45572: ("BaseViewpointNum", LONG, 1),
-    45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1),
-    45574: ("BaselineLength", RATIONAL, 1),
-    45575: ("VerticalDivergence", SIGNED_RATIONAL, 1),
-    45576: ("AxisDistance_X", SIGNED_RATIONAL, 1),
-    45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1),
-    45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1),
-    45579: ("YawAngle", SIGNED_RATIONAL, 1),
-    45580: ("PitchAngle", SIGNED_RATIONAL, 1),
-    45581: ("RollAngle", SIGNED_RATIONAL, 1),
-    40960: ("FlashPixVersion", UNDEFINED, 1),
-    50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}),
-    50780: ("BestQualityScale", RATIONAL, 1),
-    50838: ("ImageJMetaDataByteCounts", LONG, 0),  # Can be more than one
-    50839: ("ImageJMetaData", UNDEFINED, 1),  # see Issue #2006
-}
-TAGS_V2_GROUPS = {
-    # ExifIFD
-    34665: {
-        36864: ("ExifVersion", UNDEFINED, 1),
-        40960: ("FlashPixVersion", UNDEFINED, 1),
-        40965: ("InteroperabilityIFD", LONG, 1),
-        41730: ("CFAPattern", UNDEFINED, 1),
-    },
-    # GPSInfoIFD
-    34853: {
-        0: ("GPSVersionID", BYTE, 4),
-        1: ("GPSLatitudeRef", ASCII, 2),
-        2: ("GPSLatitude", RATIONAL, 3),
-        3: ("GPSLongitudeRef", ASCII, 2),
-        4: ("GPSLongitude", RATIONAL, 3),
-        5: ("GPSAltitudeRef", BYTE, 1),
-        6: ("GPSAltitude", RATIONAL, 1),
-        7: ("GPSTimeStamp", RATIONAL, 3),
-        8: ("GPSSatellites", ASCII, 0),
-        9: ("GPSStatus", ASCII, 2),
-        10: ("GPSMeasureMode", ASCII, 2),
-        11: ("GPSDOP", RATIONAL, 1),
-        12: ("GPSSpeedRef", ASCII, 2),
-        13: ("GPSSpeed", RATIONAL, 1),
-        14: ("GPSTrackRef", ASCII, 2),
-        15: ("GPSTrack", RATIONAL, 1),
-        16: ("GPSImgDirectionRef", ASCII, 2),
-        17: ("GPSImgDirection", RATIONAL, 1),
-        18: ("GPSMapDatum", ASCII, 0),
-        19: ("GPSDestLatitudeRef", ASCII, 2),
-        20: ("GPSDestLatitude", RATIONAL, 3),
-        21: ("GPSDestLongitudeRef", ASCII, 2),
-        22: ("GPSDestLongitude", RATIONAL, 3),
-        23: ("GPSDestBearingRef", ASCII, 2),
-        24: ("GPSDestBearing", RATIONAL, 1),
-        25: ("GPSDestDistanceRef", ASCII, 2),
-        26: ("GPSDestDistance", RATIONAL, 1),
-        27: ("GPSProcessingMethod", UNDEFINED, 0),
-        28: ("GPSAreaInformation", UNDEFINED, 0),
-        29: ("GPSDateStamp", ASCII, 11),
-        30: ("GPSDifferential", SHORT, 1),
-    },
-    # InteroperabilityIFD
-    40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)},
-}
-
-# Legacy Tags structure
-# these tags aren't included above, but were in the previous versions
-TAGS = {
-    347: "JPEGTables",
-    700: "XMP",
-    # Additional Exif Info
-    32932: "Wang Annotation",
-    33434: "ExposureTime",
-    33437: "FNumber",
-    33445: "MD FileTag",
-    33446: "MD ScalePixel",
-    33447: "MD ColorTable",
-    33448: "MD LabName",
-    33449: "MD SampleInfo",
-    33450: "MD PrepDate",
-    33451: "MD PrepTime",
-    33452: "MD FileUnits",
-    33550: "ModelPixelScaleTag",
-    33723: "IptcNaaInfo",
-    33918: "INGR Packet Data Tag",
-    33919: "INGR Flag Registers",
-    33920: "IrasB Transformation Matrix",
-    33922: "ModelTiepointTag",
-    34264: "ModelTransformationTag",
-    34377: "PhotoshopInfo",
-    34735: "GeoKeyDirectoryTag",
-    34736: "GeoDoubleParamsTag",
-    34737: "GeoAsciiParamsTag",
-    34850: "ExposureProgram",
-    34852: "SpectralSensitivity",
-    34855: "ISOSpeedRatings",
-    34856: "OECF",
-    34864: "SensitivityType",
-    34865: "StandardOutputSensitivity",
-    34866: "RecommendedExposureIndex",
-    34867: "ISOSpeed",
-    34868: "ISOSpeedLatitudeyyy",
-    34869: "ISOSpeedLatitudezzz",
-    34908: "HylaFAX FaxRecvParams",
-    34909: "HylaFAX FaxSubAddress",
-    34910: "HylaFAX FaxRecvTime",
-    36864: "ExifVersion",
-    36867: "DateTimeOriginal",
-    36868: "DateTimeDigitized",
-    37121: "ComponentsConfiguration",
-    37122: "CompressedBitsPerPixel",
-    37724: "ImageSourceData",
-    37377: "ShutterSpeedValue",
-    37378: "ApertureValue",
-    37379: "BrightnessValue",
-    37380: "ExposureBiasValue",
-    37381: "MaxApertureValue",
-    37382: "SubjectDistance",
-    37383: "MeteringMode",
-    37384: "LightSource",
-    37385: "Flash",
-    37386: "FocalLength",
-    37396: "SubjectArea",
-    37500: "MakerNote",
-    37510: "UserComment",
-    37520: "SubSec",
-    37521: "SubSecTimeOriginal",
-    37522: "SubsecTimeDigitized",
-    40960: "FlashPixVersion",
-    40961: "ColorSpace",
-    40962: "PixelXDimension",
-    40963: "PixelYDimension",
-    40964: "RelatedSoundFile",
-    40965: "InteroperabilityIFD",
-    41483: "FlashEnergy",
-    41484: "SpatialFrequencyResponse",
-    41486: "FocalPlaneXResolution",
-    41487: "FocalPlaneYResolution",
-    41488: "FocalPlaneResolutionUnit",
-    41492: "SubjectLocation",
-    41493: "ExposureIndex",
-    41495: "SensingMethod",
-    41728: "FileSource",
-    41729: "SceneType",
-    41730: "CFAPattern",
-    41985: "CustomRendered",
-    41986: "ExposureMode",
-    41987: "WhiteBalance",
-    41988: "DigitalZoomRatio",
-    41989: "FocalLengthIn35mmFilm",
-    41990: "SceneCaptureType",
-    41991: "GainControl",
-    41992: "Contrast",
-    41993: "Saturation",
-    41994: "Sharpness",
-    41995: "DeviceSettingDescription",
-    41996: "SubjectDistanceRange",
-    42016: "ImageUniqueID",
-    42032: "CameraOwnerName",
-    42033: "BodySerialNumber",
-    42034: "LensSpecification",
-    42035: "LensMake",
-    42036: "LensModel",
-    42037: "LensSerialNumber",
-    42112: "GDAL_METADATA",
-    42113: "GDAL_NODATA",
-    42240: "Gamma",
-    50215: "Oce Scanjob Description",
-    50216: "Oce Application Selector",
-    50217: "Oce Identification Number",
-    50218: "Oce ImageLogic Characteristics",
-    # Adobe DNG
-    50706: "DNGVersion",
-    50707: "DNGBackwardVersion",
-    50708: "UniqueCameraModel",
-    50709: "LocalizedCameraModel",
-    50710: "CFAPlaneColor",
-    50711: "CFALayout",
-    50712: "LinearizationTable",
-    50713: "BlackLevelRepeatDim",
-    50714: "BlackLevel",
-    50715: "BlackLevelDeltaH",
-    50716: "BlackLevelDeltaV",
-    50717: "WhiteLevel",
-    50718: "DefaultScale",
-    50719: "DefaultCropOrigin",
-    50720: "DefaultCropSize",
-    50721: "ColorMatrix1",
-    50722: "ColorMatrix2",
-    50723: "CameraCalibration1",
-    50724: "CameraCalibration2",
-    50725: "ReductionMatrix1",
-    50726: "ReductionMatrix2",
-    50727: "AnalogBalance",
-    50728: "AsShotNeutral",
-    50729: "AsShotWhiteXY",
-    50730: "BaselineExposure",
-    50731: "BaselineNoise",
-    50732: "BaselineSharpness",
-    50733: "BayerGreenSplit",
-    50734: "LinearResponseLimit",
-    50735: "CameraSerialNumber",
-    50736: "LensInfo",
-    50737: "ChromaBlurRadius",
-    50738: "AntiAliasStrength",
-    50740: "DNGPrivateData",
-    50778: "CalibrationIlluminant1",
-    50779: "CalibrationIlluminant2",
-    50784: "Alias Layer Metadata",
-}
-
-
-def _populate():
-    for k, v in TAGS_V2.items():
-        # Populate legacy structure.
-        TAGS[k] = v[0]
-        if len(v) == 4:
-            for sk, sv in v[3].items():
-                TAGS[(k, sv)] = sk
-
-        TAGS_V2[k] = TagInfo(k, *v)
-
-    for group, tags in TAGS_V2_GROUPS.items():
-        for k, v in tags.items():
-            tags[k] = TagInfo(k, *v)
-
-
-_populate()
-##
-# Map type numbers to type names -- defined in ImageFileDirectory.
-
-TYPES = {}
-
-# was:
-# TYPES = {
-#     1: "byte",
-#     2: "ascii",
-#     3: "short",
-#     4: "long",
-#     5: "rational",
-#     6: "signed byte",
-#     7: "undefined",
-#     8: "signed short",
-#     9: "signed long",
-#     10: "signed rational",
-#     11: "float",
-#     12: "double",
-# }
-
-#
-# These tags are handled by default in libtiff, without
-# adding to the custom dictionary. From tif_dir.c, searching for
-# case TIFFTAG in the _TIFFVSetField function:
-# Line: item.
-# 148: case TIFFTAG_SUBFILETYPE:
-# 151: case TIFFTAG_IMAGEWIDTH:
-# 154: case TIFFTAG_IMAGELENGTH:
-# 157: case TIFFTAG_BITSPERSAMPLE:
-# 181: case TIFFTAG_COMPRESSION:
-# 202: case TIFFTAG_PHOTOMETRIC:
-# 205: case TIFFTAG_THRESHHOLDING:
-# 208: case TIFFTAG_FILLORDER:
-# 214: case TIFFTAG_ORIENTATION:
-# 221: case TIFFTAG_SAMPLESPERPIXEL:
-# 228: case TIFFTAG_ROWSPERSTRIP:
-# 238: case TIFFTAG_MINSAMPLEVALUE:
-# 241: case TIFFTAG_MAXSAMPLEVALUE:
-# 244: case TIFFTAG_SMINSAMPLEVALUE:
-# 247: case TIFFTAG_SMAXSAMPLEVALUE:
-# 250: case TIFFTAG_XRESOLUTION:
-# 256: case TIFFTAG_YRESOLUTION:
-# 262: case TIFFTAG_PLANARCONFIG:
-# 268: case TIFFTAG_XPOSITION:
-# 271: case TIFFTAG_YPOSITION:
-# 274: case TIFFTAG_RESOLUTIONUNIT:
-# 280: case TIFFTAG_PAGENUMBER:
-# 284: case TIFFTAG_HALFTONEHINTS:
-# 288: case TIFFTAG_COLORMAP:
-# 294: case TIFFTAG_EXTRASAMPLES:
-# 298: case TIFFTAG_MATTEING:
-# 305: case TIFFTAG_TILEWIDTH:
-# 316: case TIFFTAG_TILELENGTH:
-# 327: case TIFFTAG_TILEDEPTH:
-# 333: case TIFFTAG_DATATYPE:
-# 344: case TIFFTAG_SAMPLEFORMAT:
-# 361: case TIFFTAG_IMAGEDEPTH:
-# 364: case TIFFTAG_SUBIFD:
-# 376: case TIFFTAG_YCBCRPOSITIONING:
-# 379: case TIFFTAG_YCBCRSUBSAMPLING:
-# 383: case TIFFTAG_TRANSFERFUNCTION:
-# 389: case TIFFTAG_REFERENCEBLACKWHITE:
-# 393: case TIFFTAG_INKNAMES:
-
-# Following pseudo-tags are also handled by default in libtiff:
-# TIFFTAG_JPEGQUALITY 65537
-
-# some of these are not in our TAGS_V2 dict and were included from tiff.h
-
-# This list also exists in encode.c
-LIBTIFF_CORE = {
-    255,
-    256,
-    257,
-    258,
-    259,
-    262,
-    263,
-    266,
-    274,
-    277,
-    278,
-    280,
-    281,
-    340,
-    341,
-    282,
-    283,
-    284,
-    286,
-    287,
-    296,
-    297,
-    321,
-    320,
-    338,
-    32995,
-    322,
-    323,
-    32998,
-    32996,
-    339,
-    32997,
-    330,
-    531,
-    530,
-    301,
-    532,
-    333,
-    # as above
-    269,  # this has been in our tests forever, and works
-    65537,
-}
-
-LIBTIFF_CORE.remove(255)  # We don't have support for subfiletypes
-LIBTIFF_CORE.remove(322)  # We don't have support for writing tiled images with libtiff
-LIBTIFF_CORE.remove(323)  # Tiled images
-LIBTIFF_CORE.remove(333)  # Ink Names either
-
-# Note to advanced users: There may be combinations of these
-# parameters and values that when added properly, will work and
-# produce valid tiff images that may work in your application.
-# It is safe to add and remove tags from this set from Pillow's point
-# of view so long as you test against libtiff.

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/web_server.py

@@ -1,62 +0,0 @@
-"""Low level HTTP server."""
-import asyncio
-from typing import Any, Awaitable, Callable, Dict, List, Optional  # noqa
-
-from .abc import AbstractStreamWriter
-from .helpers import get_running_loop
-from .http_parser import RawRequestMessage
-from .streams import StreamReader
-from .web_protocol import RequestHandler, _RequestFactory, _RequestHandler
-from .web_request import BaseRequest
-
-__all__ = ("Server",)
-
-
-class Server:
-    def __init__(
-        self,
-        handler: _RequestHandler,
-        *,
-        request_factory: Optional[_RequestFactory] = None,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-        **kwargs: Any
-    ) -> None:
-        self._loop = get_running_loop(loop)
-        self._connections: Dict[RequestHandler, asyncio.Transport] = {}
-        self._kwargs = kwargs
-        self.requests_count = 0
-        self.request_handler = handler
-        self.request_factory = request_factory or self._make_request
-
-    @property
-    def connections(self) -> List[RequestHandler]:
-        return list(self._connections.keys())
-
-    def connection_made(
-        self, handler: RequestHandler, transport: asyncio.Transport
-    ) -> None:
-        self._connections[handler] = transport
-
-    def connection_lost(
-        self, handler: RequestHandler, exc: Optional[BaseException] = None
-    ) -> None:
-        if handler in self._connections:
-            del self._connections[handler]
-
-    def _make_request(
-        self,
-        message: RawRequestMessage,
-        payload: StreamReader,
-        protocol: RequestHandler,
-        writer: AbstractStreamWriter,
-        task: "asyncio.Task[None]",
-    ) -> BaseRequest:
-        return BaseRequest(message, payload, protocol, writer, task, self._loop)
-
-    async def shutdown(self, timeout: Optional[float] = None) -> None:
-        coros = [conn.shutdown(timeout) for conn in self._connections]
-        await asyncio.gather(*coros)
-        self._connections.clear()
-
-    def __call__(self) -> RequestHandler:
-        return RequestHandler(self, loop=self._loop, **self._kwargs)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/roundTools.py

@@ -1,109 +0,0 @@
-"""
-Various round-to-integer helpers.
-"""
-
-import math
-import functools
-import logging
-
-log = logging.getLogger(__name__)
-
-__all__ = [
-    "noRound",
-    "otRound",
-    "maybeRound",
-    "roundFunc",
-]
-
-
-def noRound(value):
-    return value
-
-
-def otRound(value):
-    """Round float value to nearest integer towards ``+Infinity``.
-
-    The OpenType spec (in the section on `"normalization" of OpenType Font Variations <https://docs.microsoft.com/en-us/typography/opentype/spec/otvaroverview#coordinate-scales-and-normalization>`_)
-    defines the required method for converting floating point values to
-    fixed-point. In particular it specifies the following rounding strategy:
-
-            for fractional values of 0.5 and higher, take the next higher integer;
-            for other fractional values, truncate.
-
-    This function rounds the floating-point value according to this strategy
-    in preparation for conversion to fixed-point.
-
-    Args:
-            value (float): The input floating-point value.
-
-    Returns
-            float: The rounded value.
-    """
-    # See this thread for how we ended up with this implementation:
-    # https://github.com/fonttools/fonttools/issues/1248#issuecomment-383198166
-    return int(math.floor(value + 0.5))
-
-
-def maybeRound(v, tolerance, round=otRound):
-    rounded = round(v)
-    return rounded if abs(rounded - v) <= tolerance else v
-
-
-def roundFunc(tolerance, round=otRound):
-    if tolerance < 0:
-        raise ValueError("Rounding tolerance must be positive")
-
-    if tolerance == 0:
-        return noRound
-
-    if tolerance >= 0.5:
-        return round
-
-    return functools.partial(maybeRound, tolerance=tolerance, round=round)
-
-
-def nearestMultipleShortestRepr(value: float, factor: float) -> str:
-    """Round to nearest multiple of factor and return shortest decimal representation.
-
-    This chooses the float that is closer to a multiple of the given factor while
-    having the shortest decimal representation (the least number of fractional decimal
-    digits).
-
-    For example, given the following:
-
-    >>> nearestMultipleShortestRepr(-0.61883544921875, 1.0/(1<<14))
-    '-0.61884'
-
-    Useful when you need to serialize or print a fixed-point number (or multiples
-    thereof, such as F2Dot14 fractions of 180 degrees in COLRv1 PaintRotate) in
-    a human-readable form.
-
-    Args:
-        value (value): The value to be rounded and serialized.
-        factor (float): The value which the result is a close multiple of.
-
-    Returns:
-        str: A compact string representation of the value.
-    """
-    if not value:
-        return "0.0"
-
-    value = otRound(value / factor) * factor
-    eps = 0.5 * factor
-    lo = value - eps
-    hi = value + eps
-    # If the range of valid choices spans an integer, return the integer.
-    if int(lo) != int(hi):
-        return str(float(round(value)))
-
-    fmt = "%.8f"
-    lo = fmt % lo
-    hi = fmt % hi
-    assert len(lo) == len(hi) and lo != hi
-    for i in range(len(lo)):
-        if lo[i] != hi[i]:
-            break
-    period = lo.find(".")
-    assert period < i
-    fmt = "%%.%df" % (i - period)
-    return fmt % value

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/inference/_text_generation.py

@@ -1,479 +0,0 @@
-# coding=utf-8
-# Copyright 2023-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# Original implementation taken from the `text-generation` Python client (see https://pypi.org/project/text-generation/
-# and https://github.com/huggingface/text-generation-inference/tree/main/clients/python)
-#
-# Changes compared to original implementation:
-# - use pydantic.dataclasses instead of BaseModel
-# - default to Python's dataclasses if Pydantic is not installed (same implementation but no validation)
-# - added default values for all parameters (not needed in BaseModel but dataclasses yes)
-# - integrated in `huggingface_hub.InferenceClient``
-# - added `stream: bool` and `details: bool` in the `text_generation` method instead of having different methods for each use case
-# - NO asyncio support yet => TODO soon
-
-from dataclasses import field
-from enum import Enum
-from typing import List, NoReturn, Optional
-
-from requests import HTTPError
-
-from ..utils import is_pydantic_available
-
-
-if is_pydantic_available():
-    from pydantic import validator
-    from pydantic.dataclasses import dataclass
-else:
-    # No validation if Pydantic is not installed
-    from dataclasses import dataclass  # type: ignore
-
-    def validator(x):  # type: ignore
-        return lambda y: y
-
-
-@dataclass
-class TextGenerationParameters:
-    """
-    Parameters for text generation.
-
-    Args:
-        do_sample (`bool`, *optional*):
-            Activate logits sampling. Defaults to False.
-        max_new_tokens (`int`, *optional*):
-            Maximum number of generated tokens. Defaults to 20.
-        repetition_penalty (`Optional[float]`, *optional*):
-            The parameter for repetition penalty. A value of 1.0 means no penalty. See [this paper](https://arxiv.org/pdf/1909.05858.pdf)
-            for more details. Defaults to None.
-        return_full_text (`bool`, *optional*):
-            Whether to prepend the prompt to the generated text. Defaults to False.
-        stop (`List[str]`, *optional*):
-            Stop generating tokens if a member of `stop_sequences` is generated. Defaults to an empty list.
-        seed (`Optional[int]`, *optional*):
-            Random sampling seed. Defaults to None.
-        temperature (`Optional[float]`, *optional*):
-            The value used to modulate the logits distribution. Defaults to None.
-        top_k (`Optional[int]`, *optional*):
-            The number of highest probability vocabulary tokens to keep for top-k-filtering. Defaults to None.
-        top_p (`Optional[float]`, *optional*):
-            If set to a value less than 1, only the smallest set of most probable tokens with probabilities that add up
-            to `top_p` or higher are kept for generation. Defaults to None.
-        truncate (`Optional[int]`, *optional*):
-            Truncate input tokens to the given size. Defaults to None.
-        typical_p (`Optional[float]`, *optional*):
-            Typical Decoding mass. See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
-            for more information. Defaults to None.
-        best_of (`Optional[int]`, *optional*):
-            Generate `best_of` sequences and return the one with the highest token logprobs. Defaults to None.
-        watermark (`bool`, *optional*):
-            Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226). Defaults to False.
-        details (`bool`, *optional*):
-            Get generation details. Defaults to False.
-        decoder_input_details (`bool`, *optional*):
-            Get decoder input token logprobs and ids. Defaults to False.
-    """
-
-    # Activate logits sampling
-    do_sample: bool = False
-    # Maximum number of generated tokens
-    max_new_tokens: int = 20
-    # The parameter for repetition penalty. 1.0 means no penalty.
-    # See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
-    repetition_penalty: Optional[float] = None
-    # Whether to prepend the prompt to the generated text
-    return_full_text: bool = False
-    # Stop generating tokens if a member of `stop_sequences` is generated
-    stop: List[str] = field(default_factory=lambda: [])
-    # Random sampling seed
-    seed: Optional[int] = None
-    # The value used to module the logits distribution.
-    temperature: Optional[float] = None
-    # The number of highest probability vocabulary tokens to keep for top-k-filtering.
-    top_k: Optional[int] = None
-    # If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
-    # higher are kept for generation.
-    top_p: Optional[float] = None
-    # truncate inputs tokens to the given size
-    truncate: Optional[int] = None
-    # Typical Decoding mass
-    # See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
-    typical_p: Optional[float] = None
-    # Generate best_of sequences and return the one if the highest token logprobs
-    best_of: Optional[int] = None
-    # Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
-    watermark: bool = False
-    # Get generation details
-    details: bool = False
-    # Get decoder input token logprobs and ids
-    decoder_input_details: bool = False
-
-    @validator("best_of")
-    def valid_best_of(cls, field_value, values):
-        if field_value is not None:
-            if field_value <= 0:
-                raise ValueError("`best_of` must be strictly positive")
-            if field_value > 1 and values["seed"] is not None:
-                raise ValueError("`seed` must not be set when `best_of` is > 1")
-            sampling = (
-                values["do_sample"]
-                | (values["temperature"] is not None)
-                | (values["top_k"] is not None)
-                | (values["top_p"] is not None)
-                | (values["typical_p"] is not None)
-            )
-            if field_value > 1 and not sampling:
-                raise ValueError("you must use sampling when `best_of` is > 1")
-
-        return field_value
-
-    @validator("repetition_penalty")
-    def valid_repetition_penalty(cls, v):
-        if v is not None and v <= 0:
-            raise ValueError("`repetition_penalty` must be strictly positive")
-        return v
-
-    @validator("seed")
-    def valid_seed(cls, v):
-        if v is not None and v < 0:
-            raise ValueError("`seed` must be positive")
-        return v
-
-    @validator("temperature")
-    def valid_temp(cls, v):
-        if v is not None and v <= 0:
-            raise ValueError("`temperature` must be strictly positive")
-        return v
-
-    @validator("top_k")
-    def valid_top_k(cls, v):
-        if v is not None and v <= 0:
-            raise ValueError("`top_k` must be strictly positive")
-        return v
-
-    @validator("top_p")
-    def valid_top_p(cls, v):
-        if v is not None and (v <= 0 or v >= 1.0):
-            raise ValueError("`top_p` must be > 0.0 and < 1.0")
-        return v
-
-    @validator("truncate")
-    def valid_truncate(cls, v):
-        if v is not None and v <= 0:
-            raise ValueError("`truncate` must be strictly positive")
-        return v
-
-    @validator("typical_p")
-    def valid_typical_p(cls, v):
-        if v is not None and (v <= 0 or v >= 1.0):
-            raise ValueError("`typical_p` must be > 0.0 and < 1.0")
-        return v
-
-
-@dataclass
-class TextGenerationRequest:
-    """
-    Request object for text generation (only for internal use).
-
-    Args:
-        inputs (`str`):
-            The prompt for text generation.
-        parameters (`Optional[TextGenerationParameters]`, *optional*):
-            Generation parameters.
-        stream (`bool`, *optional*):
-            Whether to stream output tokens. Defaults to False.
-    """
-
-    # Prompt
-    inputs: str
-    # Generation parameters
-    parameters: Optional[TextGenerationParameters] = None
-    # Whether to stream output tokens
-    stream: bool = False
-
-    @validator("inputs")
-    def valid_input(cls, v):
-        if not v:
-            raise ValueError("`inputs` cannot be empty")
-        return v
-
-    @validator("stream")
-    def valid_best_of_stream(cls, field_value, values):
-        parameters = values["parameters"]
-        if parameters is not None and parameters.best_of is not None and parameters.best_of > 1 and field_value:
-            raise ValueError("`best_of` != 1 is not supported when `stream` == True")
-        return field_value
-
-
-# Decoder input tokens
-@dataclass
-class InputToken:
-    """
-    Represents an input token.
-
-    Args:
-        id (`int`):
-            Token ID from the model tokenizer.
-        text (`str`):
-            Token text.
-        logprob (`float` or `None`):
-            Log probability of the token. Optional since the logprob of the first token cannot be computed.
-    """
-
-    # Token ID from the model tokenizer
-    id: int
-    # Token text
-    text: str
-    # Logprob
-    # Optional since the logprob of the first token cannot be computed
-    logprob: Optional[float] = None
-
-
-# Generated tokens
-@dataclass
-class Token:
-    """
-    Represents a token.
-
-    Args:
-        id (`int`):
-            Token ID from the model tokenizer.
-        text (`str`):
-            Token text.
-        logprob (`float`):
-            Log probability of the token.
-        special (`bool`):
-            Indicates whether the token is a special token. It can be used to ignore
-            tokens when concatenating.
-    """
-
-    # Token ID from the model tokenizer
-    id: int
-    # Token text
-    text: str
-    # Logprob
-    logprob: float
-    # Is the token a special token
-    # Can be used to ignore tokens when concatenating
-    special: bool
-
-
-# Generation finish reason
-class FinishReason(str, Enum):
-    # number of generated tokens == `max_new_tokens`
-    Length = "length"
-    # the model generated its end of sequence token
-    EndOfSequenceToken = "eos_token"
-    # the model generated a text included in `stop_sequences`
-    StopSequence = "stop_sequence"
-
-
-# Additional sequences when using the `best_of` parameter
-@dataclass
-class BestOfSequence:
-    """
-    Represents a best-of sequence generated during text generation.
-
-    Args:
-        generated_text (`str`):
-            The generated text.
-        finish_reason (`FinishReason`):
-            The reason for the generation to finish, represented by a `FinishReason` value.
-        generated_tokens (`int`):
-            The number of generated tokens in the sequence.
-        seed (`Optional[int]`):
-            The sampling seed if sampling was activated.
-        prefill (`List[InputToken]`):
-            The decoder input tokens. Empty if `decoder_input_details` is False. Defaults to an empty list.
-        tokens (`List[Token]`):
-            The generated tokens. Defaults to an empty list.
-    """
-
-    # Generated text
-    generated_text: str
-    # Generation finish reason
-    finish_reason: FinishReason
-    # Number of generated tokens
-    generated_tokens: int
-    # Sampling seed if sampling was activated
-    seed: Optional[int] = None
-    # Decoder input tokens, empty if decoder_input_details is False
-    prefill: List[InputToken] = field(default_factory=lambda: [])
-    # Generated tokens
-    tokens: List[Token] = field(default_factory=lambda: [])
-
-
-# `generate` details
-@dataclass
-class Details:
-    """
-    Represents details of a text generation.
-
-    Args:
-        finish_reason (`FinishReason`):
-            The reason for the generation to finish, represented by a `FinishReason` value.
-        generated_tokens (`int`):
-            The number of generated tokens.
-        seed (`Optional[int]`):
-            The sampling seed if sampling was activated.
-        prefill (`List[InputToken]`, *optional*):
-            The decoder input tokens. Empty if `decoder_input_details` is False. Defaults to an empty list.
-        tokens (`List[Token]`):
-            The generated tokens. Defaults to an empty list.
-        best_of_sequences (`Optional[List[BestOfSequence]]`):
-            Additional sequences when using the `best_of` parameter.
-    """
-
-    # Generation finish reason
-    finish_reason: FinishReason
-    # Number of generated tokens
-    generated_tokens: int
-    # Sampling seed if sampling was activated
-    seed: Optional[int] = None
-    # Decoder input tokens, empty if decoder_input_details is False
-    prefill: List[InputToken] = field(default_factory=lambda: [])
-    # Generated tokens
-    tokens: List[Token] = field(default_factory=lambda: [])
-    # Additional sequences when using the `best_of` parameter
-    best_of_sequences: Optional[List[BestOfSequence]] = None
-
-
-# `generate` return value
-@dataclass
-class TextGenerationResponse:
-    """
-    Represents a response for text generation.
-
-    In practice, if `details=False` is passed (default), only the generated text is returned.
-
-    Args:
-        generated_text (`str`):
-            The generated text.
-        details (`Optional[Details]`):
-            Generation details. Returned only if `details=True` is sent to the server.
-    """
-
-    # Generated text
-    generated_text: str
-    # Generation details
-    details: Optional[Details] = None
-
-
-# `generate_stream` details
-@dataclass
-class StreamDetails:
-    """
-    Represents details of a text generation stream.
-
-    Args:
-        finish_reason (`FinishReason`):
-            The reason for the generation to finish, represented by a `FinishReason` value.
-        generated_tokens (`int`):
-            The number of generated tokens.
-        seed (`Optional[int]`):
-            The sampling seed if sampling was activated.
-    """
-
-    # Generation finish reason
-    finish_reason: FinishReason
-    # Number of generated tokens
-    generated_tokens: int
-    # Sampling seed if sampling was activated
-    seed: Optional[int] = None
-
-
-# `generate_stream` return value
-@dataclass
-class TextGenerationStreamResponse:
-    """
-    Represents a response for text generation when `stream=True` is passed
-
-    Args:
-        token (`Token`):
-            The generated token.
-        generated_text (`Optional[str]`, *optional*):
-            The complete generated text. Only available when the generation is finished.
-        details (`Optional[StreamDetails]`, *optional*):
-            Generation details. Only available when the generation is finished.
-    """
-
-    # Generated token
-    token: Token
-    # Complete generated text
-    # Only available when the generation is finished
-    generated_text: Optional[str] = None
-    # Generation details
-    # Only available when the generation is finished
-    details: Optional[StreamDetails] = None
-
-
-# TEXT GENERATION ERRORS
-# ----------------------
-# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
-# inference project (https://github.com/huggingface/text-generation-inference).
-# ----------------------
-
-
-class TextGenerationError(HTTPError):
-    """Generic error raised if text-generation went wrong."""
-
-
-# Text Generation Inference Errors
-class ValidationError(TextGenerationError):
-    """Server-side validation error."""
-
-
-class GenerationError(TextGenerationError):
-    pass
-
-
-class OverloadedError(TextGenerationError):
-    pass
-
-
-class IncompleteGenerationError(TextGenerationError):
-    pass
-
-
-def raise_text_generation_error(http_error: HTTPError) -> NoReturn:
-    """
-    Try to parse text-generation-inference error message and raise HTTPError in any case.
-
-    Args:
-        error (`HTTPError`):
-            The HTTPError that have been raised.
-    """
-    # Try to parse a Text Generation Inference error
-
-    try:
-        # Hacky way to retrieve payload in case of aiohttp error
-        payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
-        message = payload.get("error")
-        error_type = payload.get("error_type")
-    except Exception:  # no payload
-        raise http_error
-
-    # If error_type => more information than `hf_raise_for_status`
-    if error_type is not None:
-        if error_type == "generation":
-            raise GenerationError(message) from http_error
-        if error_type == "incomplete_generation":
-            raise IncompleteGenerationError(message) from http_error
-        if error_type == "overloaded":
-            raise OverloadedError(message) from http_error
-        if error_type == "validation":
-            raise ValidationError(message) from http_error
-
-    # Otherwise, fallback to default error
-    raise http_error

spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/distributed.py

@@ -1,126 +0,0 @@
-import math
-import pickle
-
-import torch
-from torch import distributed as dist
-from torch.utils.data.sampler import Sampler
-
-
-def get_rank():
-    if not dist.is_available():
-        return 0
-
-    if not dist.is_initialized():
-        return 0
-
-    return dist.get_rank()
-
-
-def synchronize():
-    if not dist.is_available():
-        return
-
-    if not dist.is_initialized():
-        return
-
-    world_size = dist.get_world_size()
-
-    if world_size == 1:
-        return
-
-    dist.barrier()
-
-
-def get_world_size():
-    if not dist.is_available():
-        return 1
-
-    if not dist.is_initialized():
-        return 1
-
-    return dist.get_world_size()
-
-
-def reduce_sum(tensor):
-    if not dist.is_available():
-        return tensor
-
-    if not dist.is_initialized():
-        return tensor
-
-    tensor = tensor.clone()
-    dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
-
-    return tensor
-
-
-def gather_grad(params):
-    world_size = get_world_size()
-    
-    if world_size == 1:
-        return
-
-    for param in params:
-        if param.grad is not None:
-            dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
-            param.grad.data.div_(world_size)
-
-
-def all_gather(data):
-    world_size = get_world_size()
-
-    if world_size == 1:
-        return [data]
-
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to('cuda')
-
-    local_size = torch.IntTensor([tensor.numel()]).to('cuda')
-    size_list = [torch.IntTensor([0]).to('cuda') for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    tensor_list = []
-    for _ in size_list:
-        tensor_list.append(torch.ByteTensor(size=(max_size,)).to('cuda'))
-
-    if local_size != max_size:
-        padding = torch.ByteTensor(size=(max_size - local_size,)).to('cuda')
-        tensor = torch.cat((tensor, padding), 0)
-
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    return data_list
-
-
-def reduce_loss_dict(loss_dict):
-    world_size = get_world_size()
-
-    if world_size < 2:
-        return loss_dict
-
-    with torch.no_grad():
-        keys = []
-        losses = []
-
-        for k in sorted(loss_dict.keys()):
-            keys.append(k)
-            losses.append(loss_dict[k])
-
-        losses = torch.stack(losses, 0)
-        dist.reduce(losses, dst=0)
-
-        if dist.get_rank() == 0:
-            losses /= world_size
-
-        reduced_losses = {k: v for k, v in zip(keys, losses)}
-
-    return reduced_losses

spaces/ECCV2022/bytetrack/yolox/data/dataloading.py

@@ -1,178 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import torch
-from torch.utils.data.dataloader import DataLoader as torchDataLoader
-from torch.utils.data.dataloader import default_collate
-
-import os
-import random
-
-from .samplers import YoloBatchSampler
-
-
-def get_yolox_datadir():
-    """
-    get dataset dir of YOLOX. If environment variable named `YOLOX_DATADIR` is set,
-    this function will return value of the environment variable. Otherwise, use data
-    """
-    yolox_datadir = os.getenv("YOLOX_DATADIR", None)
-    if yolox_datadir is None:
-        import yolox
-
-        yolox_path = os.path.dirname(os.path.dirname(yolox.__file__))
-        yolox_datadir = os.path.join(yolox_path, "datasets")
-    return yolox_datadir
-
-
-class DataLoader(torchDataLoader):
-    """
-    Lightnet dataloader that enables on the fly resizing of the images.
-    See :class:`torch.utils.data.DataLoader` for more information on the arguments.
-    Check more on the following website:
-    https://gitlab.com/EAVISE/lightnet/-/blob/master/lightnet/data/_dataloading.py
-
-    Note:
-        This dataloader only works with :class:`lightnet.data.Dataset` based datasets.
-
-    Example:
-        >>> class CustomSet(ln.data.Dataset):
-        ...     def __len__(self):
-        ...         return 4
-        ...     @ln.data.Dataset.resize_getitem
-        ...     def __getitem__(self, index):
-        ...         # Should return (image, anno) but here we return (input_dim,)
-        ...         return (self.input_dim,)
-        >>> dl = ln.data.DataLoader(
-        ...     CustomSet((200,200)),
-        ...     batch_size = 2,
-        ...     collate_fn = ln.data.list_collate   # We want the data to be grouped as a list
-        ... )
-        >>> dl.dataset.input_dim    # Default input_dim
-        (200, 200)
-        >>> for d in dl:
-        ...     d
-        [[(200, 200), (200, 200)]]
-        [[(200, 200), (200, 200)]]
-        >>> dl.change_input_dim(320, random_range=None)
-        (320, 320)
-        >>> for d in dl:
-        ...     d
-        [[(320, 320), (320, 320)]]
-        [[(320, 320), (320, 320)]]
-        >>> dl.change_input_dim((480, 320), random_range=None)
-        (480, 320)
-        >>> for d in dl:
-        ...     d
-        [[(480, 320), (480, 320)]]
-        [[(480, 320), (480, 320)]]
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.__initialized = False
-        shuffle = False
-        batch_sampler = None
-        if len(args) > 5:
-            shuffle = args[2]
-            sampler = args[3]
-            batch_sampler = args[4]
-        elif len(args) > 4:
-            shuffle = args[2]
-            sampler = args[3]
-            if "batch_sampler" in kwargs:
-                batch_sampler = kwargs["batch_sampler"]
-        elif len(args) > 3:
-            shuffle = args[2]
-            if "sampler" in kwargs:
-                sampler = kwargs["sampler"]
-            if "batch_sampler" in kwargs:
-                batch_sampler = kwargs["batch_sampler"]
-        else:
-            if "shuffle" in kwargs:
-                shuffle = kwargs["shuffle"]
-            if "sampler" in kwargs:
-                sampler = kwargs["sampler"]
-            if "batch_sampler" in kwargs:
-                batch_sampler = kwargs["batch_sampler"]
-
-        # Use custom BatchSampler
-        if batch_sampler is None:
-            if sampler is None:
-                if shuffle:
-                    sampler = torch.utils.data.sampler.RandomSampler(self.dataset)
-                    # sampler = torch.utils.data.DistributedSampler(self.dataset)
-                else:
-                    sampler = torch.utils.data.sampler.SequentialSampler(self.dataset)
-            batch_sampler = YoloBatchSampler(
-                sampler,
-                self.batch_size,
-                self.drop_last,
-                input_dimension=self.dataset.input_dim,
-            )
-            # batch_sampler = IterationBasedBatchSampler(batch_sampler, num_iterations =
-
-        self.batch_sampler = batch_sampler
-
-        self.__initialized = True
-
-    def close_mosaic(self):
-        self.batch_sampler.mosaic = False
-
-    def change_input_dim(self, multiple=32, random_range=(10, 19)):
-        """This function will compute a new size and update it on the next mini_batch.
-
-        Args:
-            multiple (int or tuple, optional): values to multiply the randomly generated range by.
-                Default **32**
-            random_range (tuple, optional): This (min, max) tuple sets the range
-                for the randomisation; Default **(10, 19)**
-
-        Return:
-            tuple: width, height tuple with new dimension
-
-        Note:
-            The new size is generated as follows: |br|
-            First we compute a random integer inside ``[random_range]``.
-            We then multiply that number with the ``multiple`` argument,
-            which gives our final new input size. |br|
-            If ``multiple`` is an integer we generate a square size. If you give a tuple
-            of **(width, height)**, the size is computed
-            as :math:`rng * multiple[0], rng * multiple[1]`.
-
-        Note:
-            You can set the ``random_range`` argument to **None** to set
-            an exact size of multiply. |br|
-            See the example above for how this works.
-        """
-        if random_range is None:
-            size = 1
-        else:
-            size = random.randint(*random_range)
-
-        if isinstance(multiple, int):
-            size = (size * multiple, size * multiple)
-        else:
-            size = (size * multiple[0], size * multiple[1])
-
-        self.batch_sampler.new_input_dim = size
-
-        return size
-
-
-def list_collate(batch):
-    """
-    Function that collates lists or tuples together into one list (of lists/tuples).
-    Use this as the collate function in a Dataloader, if you want to have a list of
-    items as an output, as opposed to tensors (eg. Brambox.boxes).
-    """
-    items = list(zip(*batch))
-
-    for i in range(len(items)):
-        if isinstance(items[i][0], (list, tuple)):
-            items[i] = list(items[i])
-        else:
-            items[i] = default_collate(items[i])
-
-    return items

spaces/Eddycrack864/Applio-Inference/venv.sh

@@ -1 +0,0 @@
-python3.8 -m venv .venv

spaces/EronSamez/RVC_HFmeu/Applio-RVC-Fork/utils/clonerepo_experimental.py

@@ -1,253 +0,0 @@
-import os
-import subprocess
-import shutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from tqdm.notebook import tqdm
-from pathlib import Path
-import requests
-
-def run_script():
-    def run_cmd(cmd):
-        process = subprocess.run(cmd, shell=True, check=True, text=True)
-        return process.stdout
-
-    # Change the current directory to /content/
-    os.chdir('/content/')
-    print("Changing dir to /content/")
-
-    # Your function to edit the file
-    def edit_file(file_path):
-        temp_file_path = "/tmp/temp_file.py"
-        changes_made = False
-        with open(file_path, "r") as file, open(temp_file_path, "w") as temp_file:
-            previous_line = ""
-            second_previous_line = ""
-            for line in file:
-                new_line = line.replace("value=160", "value=128")
-                if new_line != line:
-                    print("Replaced 'value=160' with 'value=128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("crepe hop length: 160", "crepe hop length: 128")
-                if new_line != line:
-                    print("Replaced 'crepe hop length: 160' with 'crepe hop length: 128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("value=0.88", "value=0.75")
-                if new_line != line:
-                    print("Replaced 'value=0.88' with 'value=0.75'")
-                    changes_made = True
-                line = new_line
-
-                if "label=i18n(\"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络\")" in previous_line and "value=1," in line:
-                    new_line = line.replace("value=1,", "value=0.25,")
-                    if new_line != line:
-                        print("Replaced 'value=1,' with 'value=0.25,' based on the condition")
-                        changes_made = True
-                    line = new_line
-
-                if "label=i18n(\"总训练轮数total_epoch\")" in previous_line and "value=20," in line:
-                    new_line = line.replace("value=20,", "value=500,")
-                    if new_line != line:
-                        print("Replaced 'value=20,' with 'value=500,' based on the condition for DEFAULT EPOCH")
-                        changes_made = True
-                    line = new_line
-
-                if 'choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny' in previous_line:
-                    if 'value="pm",' in line:
-                        new_line = line.replace('value="pm",', 'value="mangio-crepe",')
-                        if new_line != line:
-                            print("Replaced 'value=\"pm\",' with 'value=\"mangio-crepe\",' based on the condition")
-                            changes_made = True
-                        line = new_line
-
-                new_line = line.replace('label=i18n("输入训练文件夹路径"), value="E:\\\\语音音频+标注\\\\米津玄师\\\\src"', 'label=i18n("输入训练文件夹路径"), value="/content/dataset/"')
-                if new_line != line:
-                    print("Replaced 'label=i18n(\"输入训练文件夹路径\"), value=\"E:\\\\语音音频+标注\\\\米津玄师\\\\src\"' with 'label=i18n(\"输入训练文件夹路径\"), value=\"/content/dataset/\"'")
-                    changes_made = True
-                line = new_line
-
-                if 'label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE ONLY LATEST")
-                            changes_made = True
-                        line = new_line
-
-                if 'label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE SMALL WEIGHTS")
-                            changes_made = True
-                        line = new_line
-
-                temp_file.write(line)
-                second_previous_line = previous_line
-                previous_line = line
-
-        # After finished, we replace the original file with the temp one
-        import shutil
-        shutil.move(temp_file_path, file_path)
-
-        if changes_made:
-            print("Changes made and file saved successfully.")
-        else:
-            print("No changes were needed.")
-
-    # Define the repo path
-    repo_path = '/content/Applio-RVC-Fork'
-
-    def copy_all_files_in_directory(src_dir, dest_dir):
-        # Iterate over all files in source directory
-        for item in Path(src_dir).glob('*'):
-            if item.is_file():
-                # Copy each file to destination directory
-                shutil.copy(item, dest_dir)
-            else:
-                # If it's a directory, make a new directory in the destination and copy the files recursively
-                new_dest = Path(dest_dir) / item.name
-                new_dest.mkdir(exist_ok=True)
-                copy_all_files_in_directory(str(item), str(new_dest))
-
-    def clone_and_copy_repo(repo_path):
-        # New repository link
-        new_repo_link = "https://github.com/IAHispano/Applio-RVC-Fork/"
-        # Temporary path to clone the repository
-        temp_repo_path = "/content/temp_Applio-RVC-Fork"
-        # New folder name
-        new_folder_name = "Applio-RVC-Fork"
-
-        # Clone the latest code from the new repository to a temporary location
-        run_cmd(f"git clone {new_repo_link} {temp_repo_path}")
-        os.chdir(temp_repo_path)
-
-        run_cmd(f"git checkout 3fa4dad3d8961e5ca2522e9e12c0b4ddb71ad402")
-        run_cmd(f"git checkout f9e606c279cb49420597519b0a83b92be81e42e4")
-        run_cmd(f"git checkout 9e305588844c5442d58add1061b29beeca89d679")
-        run_cmd(f"git checkout bf92dc1eb54b4f28d6396a4d1820a25896cc9af8")
-        run_cmd(f"git checkout c3810e197d3cb98039973b2f723edf967ecd9e61")
-        run_cmd(f"git checkout a33159efd134c2413b0afe26a76b7dc87926d2de")
-        run_cmd(f"git checkout 24e251fb62c662e39ac5cf9253cc65deb9be94ec")
-        run_cmd(f"git checkout ad5667d3017e93232dba85969cddac1322ba2902")
-        run_cmd(f"git checkout ce9715392cf52dd5a0e18e00d1b5e408f08dbf27")
-        run_cmd(f"git checkout 7c7da3f2ac68f3bd8f3ad5ca5c700f18ab9f90eb")
-        run_cmd(f"git checkout 4ac395eab101955e8960b50d772c26f592161764")
-        run_cmd(f"git checkout b15b358702294c7375761584e5276c811ffab5e8")
-        run_cmd(f"git checkout 1501793dc490982db9aca84a50647764caa66e51")
-        run_cmd(f"git checkout 21f7faf57219c75e6ba837062350391a803e9ae2")
-        run_cmd(f"git checkout b5eb689fbc409b49f065a431817f822f554cebe7")
-        run_cmd(f"git checkout 7e02fae1ebf24cb151bf6cbe787d06734aa65862")
-        run_cmd(f"git checkout 6aea5ea18ed0b9a1e03fa5d268d6bc3c616672a9")
-        run_cmd(f"git checkout f0f9b25717e59116473fb42bd7f9252cfc32b398")
-        run_cmd(f"git checkout b394de424088a81fc081224bc27338a8651ad3b2")
-        run_cmd(f"git checkout f1999406a88b80c965d2082340f5ea2bfa9ab67a")
-        run_cmd(f"git checkout d98a0fa8dc715308dfc73eac5c553b69c6ee072b")
-        run_cmd(f"git checkout d73267a415fb0eba98477afa43ef71ffd82a7157")
-        run_cmd(f"git checkout 1a03d01356ae79179e1fb8d8915dc9cc79925742")
-        run_cmd(f"git checkout 81497bb3115e92c754300c9b3992df428886a3e9")
-        run_cmd(f"git checkout c5af1f8edcf79cb70f065c0110e279e78e48caf9")
-        run_cmd(f"git checkout cdb3c90109387fa4dfa92f53c3864c71170ffc77")
-
-        # Edit the file here, before copying
-        #edit_file(f"{temp_repo_path}/infer-web.py")
-
-        # Copy all files from the cloned repository to the existing path
-        copy_all_files_in_directory(temp_repo_path, repo_path)
-        print(f"Copying all {new_folder_name} files from GitHub.")
-
-        # Change working directory back to /content/
-        os.chdir('/content/')
-        print("Changed path back to /content/")
-        
-        # Remove the temporary cloned repository
-        shutil.rmtree(temp_repo_path)
-
-    # Call the function
-    clone_and_copy_repo(repo_path)
-
-    # Download the credentials file for RVC archive sheet
-    os.makedirs('/content/Applio-RVC-Fork/stats/', exist_ok=True)
-    run_cmd("wget -q https://cdn.discordapp.com/attachments/945486970883285045/1114717554481569802/peppy-generator-388800-07722f17a188.json -O /content/Applio-RVC-Fork/stats/peppy-generator-388800-07722f17a188.json")
-
-    # Forcefully delete any existing torchcrepe dependencies downloaded from an earlier run just in case
-    shutil.rmtree('/content/Applio-RVC-Fork/torchcrepe', ignore_errors=True)
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)
-
-    # Download the torchcrepe folder from the maxrmorrison/torchcrepe repository
-    run_cmd("git clone https://github.com/maxrmorrison/torchcrepe.git")
-    shutil.move('/content/torchcrepe/torchcrepe', '/content/Applio-RVC-Fork/')
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)  # Delete the torchcrepe repository folder
-
-    # Change the current directory to /content/Applio-RVC-Fork
-    os.chdir('/content/Applio-RVC-Fork')
-    os.makedirs('pretrained', exist_ok=True)
-    os.makedirs('uvr5_weights', exist_ok=True)
-
-def download_file(url, filepath):
-    response = requests.get(url, stream=True)
-    response.raise_for_status()
-
-    with open(filepath, "wb") as file:
-        for chunk in response.iter_content(chunk_size=8192):
-            if chunk:
-                file.write(chunk)
-
-def download_pretrained_models():
-    pretrained_models = {
-        "pretrained": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth"
-        ],
-        "pretrained_v2": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth",
-            "f0G48k.pth",
-            "f0D48k.pth"
-        ],
-        "uvr5_weights": [
-            "HP2-人声vocals+非人声instrumentals.pth",
-            "HP5-主旋律人声vocals+其他instrumentals.pth",
-            "VR-DeEchoNormal.pth",
-            "VR-DeEchoDeReverb.pth",
-            "VR-DeEchoAggressive.pth",
-            "HP5_only_main_vocal.pth",
-            "HP3_all_vocals.pth",
-            "HP2_all_vocals.pth"
-        ]
-    }
-    part2 = "I"
-    base_url = "https://huggingface.co/lj1995/VoiceConversionWebU" + part2 + "/resolve/main/"
-    base_path = "/content/Applio-RVC-Fork/"
-    base_pathm = base_path
-
-    # Calculate total number of files to download
-    total_files = sum(len(files) for files in pretrained_models.values()) + 1  # +1 for hubert_base.pt
-
-    with tqdm(total=total_files, desc="Downloading files") as pbar:
-        for folder, models in pretrained_models.items():
-            folder_path = os.path.join(base_path, folder)
-            os.makedirs(folder_path, exist_ok=True)
-            for model in models:
-                url = base_url + folder + "/" + model
-                filepath = os.path.join(folder_path, model)
-                download_file(url, filepath)
-                pbar.update()
-
-        # Download hubert_base.pt to the base path
-        hubert_url = base_url + "hubert_base.pt"
-        hubert_filepath = os.path.join(base_pathm, "hubert_base.pt")
-        download_file(hubert_url, hubert_filepath)
-        pbar.update()
-def clone_repository(run_download):
-    with ThreadPoolExecutor(max_workers=2) as executor:
-        executor.submit(run_script)
-        if run_download:
-            executor.submit(download_pretrained_models)