diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Advanced SystemCare Ultimate 12.1.0.120 Crack with License Code The Ultimate Solution for PC Problems.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Advanced SystemCare Ultimate 12.1.0.120 Crack with License Code The Ultimate Solution for PC Problems.md deleted file mode 100644 index 8028af8d189fb6d76a64357f1cf739bb185824b6..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Advanced SystemCare Ultimate 12.1.0.120 Crack with License Code The Ultimate Solution for PC Problems.md +++ /dev/null @@ -1,127 +0,0 @@ - -

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It has a user-friendly interface and easy-to-use featuresIt may cause some false positives or false negatives when scanning your PC for viruses or issues
It supports multiple languages and platformsIt may contain some bugs or errors that affect its performance or stability
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Have you ever encountered a situation where your Windows PC won't boot or crashes frequently? If so, you know how frustrating and stressful it can be. You may lose access to your important files, settings, and applications, and you may not know how to fix the problem or recover your data.

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Easy Recovery Essentials for Windows (EasyRE) is a bootable repair and recovery software from NeoSmart Technologies that can be used to repair non-booting/crashed laptops and PCs. It supports all Windows versions, including Windows XP, Vista, 7, 8, 10, and 11, as well as Windows Server editions.

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EasyRE is different from other repair solutions because it uses a scientific approach to understand how your PC works and where things go wrong. It tests and validates each component in your system, simulating the boot process and identifying the errors. It then automatically fixes the problems and restores your PC to a working state.

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EasyRE works by booting from a CD or USB that you create using another working PC. You don't need to install anything on your PC or access Windows. Once you boot from the CD or USB, EasyRE will scan your PC and display a list of recovery options. All you have to do is click 'begin'.

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In this section, we will show you how to use Easy Recovery Essentials to fix your Windows PC. You will need a blank CD or USB drive, a working PC with internet access, and the PC that needs fixing.

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Downloading EasyRE

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The first step is to download Easy Recovery Essentials from the official website: https://neosmart.net/EasyRE/. You can choose the version that matches your Windows version (XP/Vista/7/8/10/11) or the technicians' edition that supports all Windows versions.

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The download size is about 120 MB. After downloading the file, you will get an ISO image that contains the software.

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Burning EasyRE to a CD or USB

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The next step is to burn the ISO image to a CD or USB drive using another working PC. You can use any burning software that supports ISO images, such as ImgBurn, Rufus, PowerISO, etc.

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If you are using a CD, insert a blank CD into the CD/DVD drive of the working PC. Then open the burning software and select the ISO image as the source file and the CD/DVD drive as the destination. Start the burning process and wait until it finishes.

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If you are using a USB drive, insert a blank USB drive into a USB port of the working PC. Then open the burning software and select the ISO image as the source file and the USB drive as the destination. Start the burning process and wait until it finishes.

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You now have a bootable media with Easy Recovery Essentials on it.

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Booting from EasyRE

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The third step is to boot your PC that needs fixing from the CD or USB drive that contains Easy Recovery Essentials. To do this, you need to change the boot order in your BIOS or UEFI settings.

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To access the BIOS or UEFI settings, restart your PC and press a specific key (usually F2, F10, F12, Del, Esc) when you see the manufacturer's logo on the screen. This will take you to a menu where you can change various settings for your PC.

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Look for an option that says 'Boot' or 'Boot order' or something similar. Select it and change it so that the CD/DVD drive or USB drive is at the top of the list. This will make your PC boot from that device first instead of your hard drive.

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Save the changes and exit the BIOS or UEFI settings. Your PC will restart again and boot from the CD or USB drive.

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Running EasyRE

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The final step is to run Easy Recovery Essentials on your PC. When you boot from the CD or USB drive, you will see a welcome screen with some options. Choose 'Automated Repair' if you want EasyRE to automatically scan and fix your PC problems.

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You will then see a screen where you can select your Windows installation (if you have more than one). Choose the one that corresponds to your current Windows version (XP/Vista/7/8/10/11) or select 'All' if you are not sure.

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You will then see a screen where you can choose which tests and repairs you want EasyRE to perform on your PC. The default settings are recommended for most cases. Click 'Begin' when you are ready.

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Easy Recovery Essentials will then start scanning and repairing your PC using its scientific approach. You will see a progress bar and some messages on the screen indicating what it is doing. The process may take several minutes depending on your PC condition.

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When it finishes, you will see a screen with # Article with HTML formatting (continued)

a summary of the repair results and a message that says 'Repair complete'. You can click 'View details' to see more information about what EasyRE did.

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Now you can click 'Restart' to reboot your PC and check if it works normally. If not, you can try running EasyRE again with different options or contact the support team for help.

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Frequently asked questions about EasyRE

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In this section, we will answer some of the common questions that users may have about Easy Recovery Essentials.

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Is EasyRE free?

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No, EasyRE is not free. It is a paid software that costs $19.75 for a single license. However, you can download a free trial version that lets you scan your PC and see if EasyRE can fix it. The trial version does not perform any repairs, but it gives you an idea of what EasyRE can do for you.

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What are the system requirements for EasyRE?

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To use EasyRE, you need a PC or laptop that runs Windows XP, Vista, 7, 8, 10, or 11, or Windows Server editions. You also need a blank CD or USB drive with at least 256 MB of space, and another working PC with internet access to download and burn EasyRE.

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Does EasyRE support UEFI and GPT?

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Yes, EasyRE supports UEFI and GPT. It is the only repair solution that natively supports UEFI, the modern replacement for BIOS. It can also work with GPT disks, which are the new standard for disk partitioning. EasyRE can handle any PC configuration and technology.

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Does EasyRE erase any data from my PC?

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No, EasyRE does not erase any data from your PC. It only repairs the system files and settings that are causing your PC problems. It does not touch your personal files, documents, photos, music, videos, etc. However, if you want to backup or restore your data, you can use the 'Browse/Backup Files' option in EasyRE.

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How can I contact the support team for EasyRE?

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If you have any issues or questions about EasyRE, you can contact the support team via email at easyre@neosmart.net. They will respond to your queries as soon as possible and help you with troubleshooting your PC problems. Please note that there is no phone support for EasyRE at this time.

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Easy Recovery Essentials (EasyRE) Pro is a powerful tool that can help you repair and recover your Windows PC in a fast and easy way. It supports all Windows versions and PCs and laptops, and uses a scientific approach to fix your PC problems. It is easy to use, fast and effective, compatible and reliable, and safe for your data.

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If you want to try EasyRE Pro for yourself, you can download it from the official website: https://neosmart.net/EasyRE/. You can also watch a demo video of how it works here: https://www.youtube.com/watch?v=0QIg9k50T-w.

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We hope this article has given you a clear overview of what EasyRE Pro is and how to use it. If you have any feedback or questions, please feel free to leave a comment below or contact us via email.

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Bleach vs Naruto APK 3.8: A Review of the Latest Sports Version of the Anime Fighting Game

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If you are a fan of anime and fighting games, you might have heard of Bleach vs Naruto, a popular fan-made game that features characters from two famous anime series, Bleach and Naruto. The game has been updated regularly with new versions, adding more characters, stages, modes, and features.

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The sports version is made by the original author team of Bleach vs Naruto, with contributions from some modders. The sports version is different from the official version in that it has more balanced characters and assists, more game modes, more features, bug fixes, and optimizations. The sports version is also used in matches in China instead of the official version, because it is more competitive and fair.

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To play Bleach vs Naruto APK 3.8 on PC and Mac, you need to know the basic controls and commands of the game. Here are some of them:

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KeyFunction
OSelect/Confirm/Attack
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KJump
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IAssist/Tag
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JSkill 2/Special Attack
A/D/S/WMove Left/Right/Down/Up
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F11/F10/F9/F8/F7/F6/F5/F4/F3/F2/F1/Esc/Q/W/A/S/D/Z/X/C/V/B/N/M/,/.//Shift/Ctrl/Alt/Spacebar/Enter/Backspace/Delete/Home/End/Page Up/Page Down/Up Arrow/Down Arrow/Left Arrow/Right Arrow/Num Lock/*/-/+//0-9/Numpad 0-Numpad 9/Numpad /-Numpad +/Numpad Enter/Numpad . Customizable Keys (You can change them in the Options menu)
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Bleach vs Naruto APK 3.8 has several game modes that you can choose from, such as:

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Some of the disadvantages of Bleach vs Naruto APK 3.8 are:

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Conclusion

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Is Bleach vs Naruto APK 3.8 worth playing?

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In conclusion, Bleach vs Naruto APK 3.8 is a fun and exciting game that offers a lot of entertainment and challenge for anime and fighting game fans. It is a fan-made game that pays tribute to two of the most popular anime series in the world, Bleach and Naruto, as well as other anime and manga franchises. It has a large roster of characters and assists, a variety of game modes and options, a smooth and fast gameplay, a high-quality graphics and sound, a balanced and competitive gameplay, and a loyal and active fan base. It is free to download and play on PC and Mac.

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However, it is not an official game and it is not endorsed by the original creators of the anime and manga series. It may have some bugs and glitches that affect the gameplay or the performance. It may have some compatibility issues with some devices or operating systems. It may have some language barriers or cultural differences that make it hard to understand or enjoy for some players. It may have some legal or ethical issues that make it risky or controversial to play or distribute.

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Therefore, whether Bleach vs Naruto APK 3.8 is worth playing or not depends on your personal preference and situation. If you are a fan of anime and fighting games, and you don't mind the drawbacks of the game, you might find it enjoyable and satisfying. However, if you are looking for an official and authentic game, or you have concerns about the quality or the legality of the game, you might want to avoid it or look for alternatives.

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FAQs

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Q1: Is Bleach vs Naruto APK 3.8 available for Android?

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A1: Yes, Bleach vs Naruto APK 3.8 is available for Android devices. You can download the APK file from the official website or from a trusted source. However, you need to enable the installation of unknown sources in your device settings before installing the APK file.

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Q2: Who is the author of the sports version of Bleach vs Naruto APK 3.8?

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A2: The author of the sports version of Bleach vs Naruto APK 3.8 is 5Dplay, which is also the original author team of Bleach vs Naruto. They are a group of Chinese developers who create fan-made games based on anime and manga series.

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Q3: What is the difference between the sports version and the official version of Bleach vs Naruto?

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A3: The difference between the sports version and the official version of Bleach vs Naruto is that the sports version has more balanced characters and assists, more game modes, more features, bug fixes, and optimizations. The sports version is also used in matches in China instead of the official version, because it is more competitive and fair.

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Q4: How to fix the big buttons error in Bleach vs Naruto APK 3.8?

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A4: The big buttons error in Bleach vs Naruto APK 3.8 is a common problem that occurs when the game is not compatible with some devices or operating systems. To fix this error, you need to do the following steps: - Go to the Options menu in the game and select the Screen option. - Change the screen resolution to a lower value, such as 800x600 or 640x480. - Save the changes and restart the game. This should solve the big buttons error and make the game run normally on your device.

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Q5: How to learn all the moves and combos in Bleach vs Naruto APK 3.8?

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A5: To learn all the moves and combos in Bleach vs Naruto APK 3.8, you need to practice and experiment with different characters and assists. You can also use the Training mode in the game to practice your skills and learn new techniques. You can also watch gameplay videos and tutorials online to get some tips and tricks from other players.

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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car For Sale Simulator 2023 APK A Realistic and Fun Car Simulation Game.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car For Sale Simulator 2023 APK A Realistic and Fun Car Simulation Game.md deleted file mode 100644 index 8d16335a0657d2f7fd4ec6713cbc6c61d7eef02f..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car For Sale Simulator 2023 APK A Realistic and Fun Car Simulation Game.md +++ /dev/null @@ -1,115 +0,0 @@ - -

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If you are a fan of cars and simulation games, you might want to check out Car For Sale Simulator 2023, a game that lets you buy, repair, customize, and sell cars in a realistic and fun way. In this game, you can explore different locations, find various car models, negotiate with sellers, fix and upgrade your vehicles, and make a profit by selling them. You can also manage your own garage and workshop, expand your business, and become the best car dealer in town. In this article, we will tell you more about this game, its features, and how to download and install it on your Android device.

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Introduction

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What is Car For Sale Simulator 2023?

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Car For Sale Simulator 2023 is a car simulation game developed by Digital Melody Games. It is available for Android devices and can be downloaded from the Google Play Store or from other sources as an APK file. The game was released in August 2021 and has received positive reviews from players and critics. The game has over 500,000 downloads and a rating of 4.1 out of 5 stars on the Google Play Store.

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Car For Sale Simulator 2023 is a game that will appeal to anyone who loves cars and wants to experience the thrill of buying, repairing, and selling them. The game offers a realistic and immersive gameplay that will make you feel like a real car mechanic and dealer. You can choose from over 50 different car models and countless combinations of customization and modification options. You can also interact with various characters, such as sellers, buyers, experts, bankers, and tax officers. You can also enjoy the stunning graphics, sound effects, and music that enhance the atmosphere of the game.

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Features of Car For Sale Simulator 2023

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Buy, repair, and sell cars

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The main feature of Car For Sale Simulator 2023 is the ability to buy, repair, and sell cars. You can explore different locations, such as car markets, neighborhoods, junkyards, and auctions, where you can find various car models for sale. You can compare prices, negotiate with sellers, or even haggle for a better deal. You can also inspect the cars for any damages or defects before buying them.

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How to buy cars

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To buy a car in Car For Sale Simulator 2023, you need to have enough money in your account. You can earn money by selling cars or by taking loans from the bank. You can also get discounts by using coupons or by completing achievements. Once you have enough money, you can go to any location where cars are sold and select the car you want to buy. You can then check the details of the car, such as its model, year, mileage, condition, price, and seller's rating. You can also use the expert system to get a more accurate evaluation of the car's value and condition. If you are satisfied with the car, you can click on the buy button and confirm your purchase.

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How to repair cars

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After buying a car in Car For Sale Simulator 2023, you need to repair it if it has any damages or defects. You can do this by going to your garage or workshop and select the car you want to repair. You can then use various tools and parts to fix the car's body, engine, suspension, brakes, tires, and interior. You can also use the diagnostic system to detect any hidden problems or faults in the car. You can see the progress of your repair by checking the car's condition bar and the repair cost. You can also test drive the car to see how it performs on the road. Once you are done with the repair, you can click on the finish button and save your changes.

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How to sell cars

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When you have a car that is ready to be sold in Car For Sale Simulator 2023, you can go to the showroom and select the car you want to sell. You can then see the details of the car, such as its model, year, mileage, condition, price, and profit. You can also use the marketing system to increase the demand and value of your car by advertising it on various platforms, such as newspapers, magazines, radio, TV, or online. You can also use the pricing system to adjust the price of your car according to the market trends and your desired profit margin. Once you have set the price of your car, you can wait for potential buyers to contact you. You can then negotiate with them or accept their offers. You can also reject their offers if they are too low or if you change your mind. Once you agree on a deal, you can click on the sell button and confirm your sale.

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Customize and upgrade cars

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Another feature of Car For Sale Simulator 2023 is the ability to customize and upgrade cars. You can do this by going to your garage or workshop and selecting the car you want to modify. You can then use various options and accessories to change the appearance and performance of your car. You can customize your car's color, paint, decals, wheels, tires, windows, lights, spoilers, bumpers, hoods, grills, mirrors, exhausts, and license plates. You can also upgrade your car's engine, transmission, turbo, nitro, suspension, brakes, steering, battery, and fuel tank.

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How to customize cars

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To customize a car in Car For Sale Simulator 2023, you need to have enough money in your account. You can earn money by selling cars or by taking loans from the bank. You can also get discounts by using coupons or by completing achievements. Once you have enough money, you can go to your garage or workshop and select the car you want to customize. You can then click on the customize button and choose from various options and accessories that are available for your car model. You can see the preview of your customization by checking the car's appearance bar and the customization cost. You can also test drive the car to see how it looks on the road. Once you are done with the customization, you can click on the finish button and save your changes.

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How to upgrade cars

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To upgrade a car in Car For Sale Simulator 2023, you need to have enough money in your account. You can earn money by selling cars or by taking loans from the bank. You can also get discounts by using coupons or by completing achievements. Once you have enough money, you can go to your garage or workshop and select the car you want to upgrade. You can then click on the upgrade button and choose from various options that are available for your car model. You can see the preview of your upgrade by checking the car's performance bar and the upgrade cost. You can also test drive the car to see how it performs on the road. Once you are done with the upgrade, you can click on the finish button and save your changes.

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Manage your garage and workshop

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The last feature of Car For Sale Simulator 2023 is the ability to manage your own garage and workshop. You can do this by going to your garage or workshop and selecting the manage button. You can then use various options to expand your business and monitor your finances. You can expand your garage by buying more space, equipment, tools, and staff. You can also monitor your finances by checking your income, expenses, taxes, loans, and balance. You can also use the statistics system to track your progress and achievements in the game.

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How to expand your garage

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To expand your garage in Car For Sale Simulator 2023, you need to have enough money in your account. You can earn money by selling cars or by taking loans from the bank. You can also get discounts by using coupons or by completing achievements. Once you have enough money, you can go to your garage or workshop and select the manage button. You can then click on the expand button and choose from various options that are available for your garage. You can buy more space, equipment, tools, and staff that will help you repair and customize more cars faster and better. You can see the preview of your expansion by checking the garage's capacity bar and the expansion cost. Once you are done with the expansion, you can click on the finish button and save your changes.

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How to monitor your finances

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To monitor your finances in Car For Sale Simulator 2023, you need to go to your garage or workshop and select the manage button. You can then click on the finance button and see various information about your income, expenses, taxes, loans, and balance. You can also use the graph system to see the trends and fluctuations of your finances over time. You can also use the report system to see a detailed breakdown of your finances by category, such as car sales, car purchases, car repairs, car customizations, car upgrades, garage expansions, marketing costs, staff salaries, bank interests, tax payments, etc. You can also use the budget system to set a limit for your spending and saving goals.

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How to download and install Car For Sale Simulator 2023 APK

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Requirements for downloading and installing Car For Sale Simulator 2023 APK

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If you want to download and install Car For Sale Simulator 2023 APK on your Android device, you need to meet some requirements first. These are:

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Steps for downloading and installing Car For Sale Simulator 2023 APK

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Once you have met the requirements for downloading and installing Car For Sale Simulator 2023 APK on your Android device, you can follow these steps:

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  1. Go to a trusted website that provides Car For Sale Simulator 2023 APK file for download. For example, you can go to [this link] where you can find the latest version of Car For Sale Simulator 2023 APK file.
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  3. Locate the downloaded APK file on your Android device using a file manager app or by going to Downloads folder.
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  4. Tap on the APK file and follow the instructions on the screen to install Car For Sale Simulator 2023 APK on your Android device.
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  5. Launch Car For Sale Simulator 2023 APK from your app drawer or home screen and enjoy playing it.
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Conclusion

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Car For Sale Simulator 2023 is a game that will satisfy your passion for cars and simulation games. It is a game that will let you buy, repair, customize, and sell cars in a realistic and fun way. It is a game that will let you manage your own garage and workshop, expand your business, and become the best car dealer in town. It is a game that will challenge your skills and knowledge as a car mechanic and dealer. It is a game that will provide you with hours of entertainment and enjoyment. If you are interested in playing this game, you can download and install Car For Sale Simulator 2023 APK on your Android device by following the steps we have provided in this article. We hope you have found this article helpful and informative. Thank you for reading and happy gaming!

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FAQs

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Here are some frequently asked questions about Car For Sale Simulator 2023 APK:

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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cmo jugar a Dragon Z Quest Action RPG con mod apk gua para descargar y instalar.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cmo jugar a Dragon Z Quest Action RPG con mod apk gua para descargar y instalar.md deleted file mode 100644 index 0b03f016f7665297e6319df27103099735f05fc6..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cmo jugar a Dragon Z Quest Action RPG con mod apk gua para descargar y instalar.md +++ /dev/null @@ -1,112 +0,0 @@ - -

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If you are a fan of DB anime and you love platform action RPG games, then you should definitely try Dragon Z Quest Action RPG. This game is an epic adventure that lets you summon dozens of different ssj heroes and fight your way through various enemies and bosses. You can also explore a fantasy world and collect dragon star balls to rescue the world from dark forces. In this guide, we will show you how to download and install Dragon Z Quest Action RPG mod apk, which gives you unlimited resources, unlocks all heroes, removes ads, and works offline. We will also share some tips and tricks to help you master the game.

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What is Dragon Z Quest Action RPG?

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A Platform Action RPG Game Based on DB Anime

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Dragon Z Quest Action RPG is a game developed by DHZ SOFT, a studio that specializes in creating anime-inspired games. The game is inspired by the popular DB anime series, which features characters with superhuman abilities and transformations. The game is a combination of platform action RPG games and action games, where you can control your heroes with simple controls and perform various actions such as jumping, running, flying, attacking, dodging, and using special skills. The game has stunning 2D graphics that capture the essence of the anime style.

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Features of Dragon Z Quest Action RPG

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Some of the features of Dragon Z Quest Action RPG are:

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Why Download Dragon Z Quest Action RPG Mod APK?

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Unlimited Resources and Unlock All Heroes

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One of the reasons why you should download Dragon Z Quest Action RPG mod apk is that it gives you unlimited resources, such as coins, gems, energy, and tickets. You can use these resources to buy items, upgrade your teams, summon more heroes, and more. You can also unlock all heroes in the game without spending any money or waiting for hours. You can enjoy playing with any hero you want and experiment with different combinations.

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No Ads and No Internet Connection Required

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Another reason why you should download Dragon Z Quest Action RPG mod apk is that it removes all ads from the game. You can play the game without any interruptions or distractions from annoying ads. You can also play the game offline without any internet connection required. You can play the game anytime and anywhere you want without worrying about data usage or network issues.

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How to Download and Install Dragon Z Quest Action RPG Mod APK?

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Step 1: Download the Mod APK File from a Trusted Source

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The first step to download and install Dragon Z Quest Action RPG mod apk is to download the mod apk file from a trusted source. You can find many websites that offer mod apk files for various games, but you need to be careful and choose a reliable one. Some websites may contain malware or viruses that can harm your device or steal your personal information. You can use the following link to download the Dragon Z Quest Action RPG mod apk file safely and securely: [Download Dragon Z Quest Action RPG Mod APK].

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Step 2: Enable Unknown Sources on Your Device

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The second step to install Dragon Z Quest Action RPG mod apk is to enable unknown sources on your device. This is a security setting that prevents you from installing apps from sources other than the official Google Play Store. However, since you are installing a mod apk file, you need to enable this option temporarily. To do this, follow these steps:

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Once you have enabled unknown sources, you can proceed to the next step.

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Step 3: Install the Mod APK File and Enjoy the Game

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The third and final step to install Dragon Z Quest Action RPG mod apk is to install the mod apk file and enjoy the game. To do this, follow these steps:

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  1. Locate the downloaded mod apk file on your device's storage. You can use a file manager app to help you with this.
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Congratulations! You have successfully installed Dragon Z Quest Action RPG mod apk on your device. You can now enjoy playing the game with unlimited resources, all heroes unlocked, no ads, and offline mode.

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Tips and Tricks for Playing Dragon Z Quest Action RPG

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Choose the Right Heroes for Each Battle

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One of the tips for playing Dragon Z Quest Action RPG is to choose the right heroes for each battle. The game has dozens of different ssj heroes, each with their own strengths and weaknesses. You can summon up to four heroes at a time and switch between them during the battle. You should choose heroes that complement each other and match the enemy's type and level. For example, if you are facing a fire-type enemy, you should use water-type heroes or heroes with high defense. You should also consider the terrain and obstacles in each stage and choose heroes that can overcome them easily.

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Upgrade Your Teams and Items Regularly

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Another tip for playing Dragon Z Quest Action RPG is to upgrade your teams and items regularly. The game has a system of leveling up, evolving, and enhancing your heroes and items. You can use coins, gems, energy, tickets, and other resources to improve your heroes' stats, skills, outfits, and accessories. You can also use items such as potions, scrolls, bombs, and more to boost your performance in battle. You should upgrade your teams and items as much as possible to increase your chances of winning and progressing in the game.

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Use Special Skills and Combos Wisely

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A final tip for playing Dragon Z Quest Action RPG is to use special skills and combos wisely. The game has a system of special skills and combos that allow you to unleash powerful attacks on your enemies. Each hero has their own special skill that can be activated by filling up a gauge during the battle. You can also perform combos by tapping on different buttons in sequence or by switching between heroes at the right time. You should use special skills and combos strategically to deal maximum damage, stun enemies, heal allies, or escape danger.

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Conclusion

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Dragon Z Quest Action RPG is a game that will appeal to fans of DB anime and platform action RPG games alike. The game has stunning graphics, addictive gameplay, diverse characters, and an engaging story. By downloading Dragon Z Quest Action RPG mod apk, you can enjoy the game with unlimited resources, all heroes unlocked, no ads, and offline mode. You can also follow our tips and tricks to master the game and become a legend in the world of Dragon Z Quest Action RPG.

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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Abo Mvelo (feat. Mellow Amp Sleazy Amp M.j) Mixed __TOP__.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Abo Mvelo (feat. Mellow Amp Sleazy Amp M.j) Mixed __TOP__.md deleted file mode 100644 index 386476f27c86a026f7e9b77b1df6e977c3c5c7a9..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Abo Mvelo (feat. Mellow Amp Sleazy Amp M.j) Mixed __TOP__.md +++ /dev/null @@ -1,67 +0,0 @@ -
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Daliwonga is not the only artist who contributed to the song. He collaborated with three other talented musicians: Mellow, Sleazy, and M.J. Mellow and Sleazy are a duo of producers and DJs who are known for their amapiano hits like 'Sjepa', 'Pele Pele', and 'Tabela Hape'. They have also worked with other famous artists like Focalistic, Kabza De Small, Mr JazziQ, and Young Stunna. M.J is a rapper and singer who adds some spice to the song with his fast-paced verses and witty wordplay.

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Abo Mvelo has been well-received by both fans and critics since its release. The song has amassed over 800 thousand views on YouTube, over 600 thousand streams on Spotify, and over 500 thousand Shazams. The song has also been featured on various playlists and charts on Apple Music, Deezer, Audiomack, and other music platforms. The song has been praised for its catchy melody, uplifting message, and impressive collaboration.

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The benefits of downloading music

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Downloading music has many advantages over streaming music online. Some of them are:

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The legal and ethical issues of downloading music

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However, downloading music is not always a straightforward process. There are some legal and ethical issues that you need to be aware of before you download any song. Some of them are:

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The best sources and platforms to download music

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Therefore, it is important to download music from reliable and reputable sources and platforms that offer legal, safe, and high-quality downloads. Some of the best sources and platforms to download Abo Mvelo and other amapiano songs are:

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Source/PlatformDescriptionPrice
[Apple Music]A popular music streaming service that also allows you to download songs for offline listening. It has a large catalog of songs from various genres and regions, including amapiano. It also has curated playlists, radio stations, and podcasts.$9.99 per month for individual plan; $14.99 per month for family plan; $4.99 per month for student plan; free trial available
[Spotify]A leading music streaming service that also lets you download songs for offline listening. It has a huge library of songs from different genres and artists, including amapiano. It also has personalized recommendations, playlists, podcasts, and social features.$9.99 per month for premium plan; $14.99 per month for family plan; $4.99 per month for student plan; free version available with ads and limited features
[Deezer]A global music streaming service that also enables you to download songs for offline listening. It has a diverse collection of songs from various genres and countries, including amapiano. It also has smart playlists, radio channels, podcasts, and lyrics.$9.99 per month for premium plan; $14.99 per month for family plan; $4.99 per month for student plan; free version available with ads and limited features
[Audiomack]A free music streaming and downloading platform that focuses on emerging artists and genres. It has a lot of amapiano songs from South African artists like Daliwonga, Mellow, Sleazy, M.J., and others. It also has trending charts, playlists, and podcasts.Free; optional subscription for $4.99 per month to remove ads and unlock additional features
[Fakaza](https://fakaza.com/)A South African website that specializes in amapiano and other local genres like kwaito, gqom, afro house, and hip hop. It has a lot of amapiano songs from various artists like Daliwonga, Mellow, Sleazy, M.J., and others. It also has news, videos, albums, and mixtapes.Free; optional donation to support the website and the artists
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How to download Abo Mvelo?

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Now that you know the best sources and platforms to download Abo Mvelo, you might be wondering how to actually download the song. The process is not very complicated, but it may vary depending on the source or platform you choose. Here are the general steps that you can follow to download Abo Mvelo:

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Step 1: Choose a reliable and reputable website or app

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The first step is to choose a website or app that offers legal, safe, and high-quality downloads of Abo Mvelo. You can use any of the sources or platforms that we mentioned above, or you can do your own research and find other options. Make sure that the website or app is trustworthy, secure, and has good reviews from other users. Avoid websites or apps that look suspicious, have pop-up ads, or ask for your personal or financial information.

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Step 2: Search for the song by title, artist, or album

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The next step is to search for the song by typing its title, artist, or album in the search bar of the website or app. You can also browse through the categories, genres, playlists, or charts to find the song. Once you find the song, click on it to see more details, such as the duration, release date, genre, and lyrics.

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Step 3: Select the format and quality of the download

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The third step is to select the format and quality of the download that you want. Depending on the website or app, you may have different options for the format and quality of the audio file. Some of the common formats are MP3, WAV, FLAC, and AAC. Some of the common qualities are 128 kbps, 192 kbps, 320 kbps, and lossless. The format and quality of the download will affect the sound quality, file size, and compatibility of the song. Generally, higher formats and qualities offer better sound quality but larger file sizes and lower compatibility.

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Step 4: Confirm the purchase or download for free

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The fourth step is to confirm the purchase or download for free of the song. Depending on the website or app, you may have to pay a fee or subscribe to a plan to download the song. If so, you will have to enter your payment details and confirm your purchase. If not, you can simply click on the download button and start downloading the song for free. Some websites or apps may also ask you to create an account or sign in with your email or social media account before downloading.

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Step 5: Enjoy the music offline or transfer it to other devices

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The final step is to enjoy the music offline or transfer it to other devices. Once you have downloaded the song, you can listen to it offline on your device without an internet connection. You can also transfer it to other devices, such as your phone, tablet, laptop, or USB drive. To do so, you will need a USB cable or a wireless connection and follow the instructions of your device.

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Conclusion

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Abo Mvelo is a great song that deserves to be downloaded and enjoyed by all amapiano lovers. It is a song that celebrates life and encourages people to have fun with their friends and loved ones. It is also a song that showcases the talent and creativity of South African artists like Daliwonga, Mellow, Sleazy, and M.J. In this article, we have shown you what Abo Mvelo is, why you should download it, and how to download it easily and legally. We hope that this article has been helpful and informative for you. Now go ahead and download Abo Mvelo and enjoy its catchy beat, smooth vocals, and rap-inspired lyrics.

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FAQs

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197e85843d
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Facebook Messenger APK Old Version Features Benefits and Download Links.md b/spaces/1phancelerku/anime-remove-background/Facebook Messenger APK Old Version Features Benefits and Download Links.md deleted file mode 100644 index bd8c5d751d17c8d7e3b85861d893c41ca159fa2f..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Facebook Messenger APK Old Version Features Benefits and Download Links.md +++ /dev/null @@ -1,131 +0,0 @@ -
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How to Download Messenger Facebook APK Old Version

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If you are a Facebook user who likes to chat with your friends and family on your mobile device, you might be familiar with Messenger Facebook APK. This is the official messaging app for Facebook users that allows you to send and receive text messages, voice calls, video calls, stickers, emojis, GIFs, and more. But what if you are not happy with the latest version of the app and want to go back to an older version? In this article, we will show you how to download Messenger Facebook APK old version and why you might need it.

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What is Messenger Facebook APK?

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The official messaging app for Facebook users

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Messenger Facebook APK is an Android application package (APK) file that contains the installation files for Messenger, the official messaging app for Facebook users. You can use Messenger to chat with your Facebook friends and contacts, as well as anyone who has your phone number. You can also create group chats, send money, play games, share your location, and access other apps and services within Messenger.

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messenger facebook apk old version


Download File ✑ ✑ ✑ https://jinyurl.com/2uNOJm



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The features and benefits of Messenger Facebook APK

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Messenger Facebook APK has many features and benefits that make it a popular and convenient app for communication. Some of them are:

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Why would you need Messenger Facebook APK old version?

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The compatibility issues with newer versions of the app

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One of the reasons why you might need Messenger Facebook APK old version is that the newer versions of the app might not be compatible with your device or operating system. For example, if you have an older Android device or a device with low memory or storage space, you might not be able to run the latest version of Messenger smoothly or at all. In that case, you might want to download an older version of the app that works better with your device specifications.

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The preference for older features and design of the app

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Another reason why you might need Messenger Facebook APK old version is that you might prefer the older features and design of the app over the newer ones. For example, if you like the simplicity and functionality of the old interface of the app, you might not like the changes and updates that Facebook has made to the app over time. For example, you might not like the new logo, the new chat bubbles, the new swipe gestures, or the new features that you don't use or need. In that case, you might want to download an older version of the app that suits your taste and preferences better.

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How to find and download Messenger Facebook APK old version?

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The sources and websites that offer old versions of the app

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If you want to download Messenger Facebook APK old version, you need to find a reliable and trustworthy source that offers old versions of the app. You cannot download old versions of the app from the official Google Play Store or the Facebook website, as they only provide the latest version of the app. However, there are some websites and platforms that archive and host old versions of Android apps, including Messenger Facebook APK. Some of them are:

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The steps and precautions to download and install the app

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Once you have found a source and a version of Messenger Facebook APK that you want to download, you need to follow some steps and precautions to download and install the app on your device. Here are some of them:

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    -
  1. Before you download any APK file from a third-party source, you need to make sure that your device allows installation from unknown sources. To do this, go to your device settings, then security, then enable unknown sources.
    2. -
  2. After you have enabled unknown sources, you can download the APK file from the source website or platform. You can either use your device browser or a file manager app to download the file.
    3. -
  3. Once you have downloaded the APK file, you need to locate it on your device storage and tap on it to start the installation process. You might see some warnings or prompts asking for your permission to install the app. You need to grant those permissions and follow the instructions on the screen.
    4. -
  4. After you have installed the app, you can launch it from your app drawer or home screen and sign in with your Facebook account or phone number. You can then enjoy using Messenger Facebook APK old version on your device.
    5. -
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However, you also need to be aware of some risks and drawbacks of downloading and installing Messenger Facebook APK old version. Some of them are:

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Conclusion

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Messenger Facebook APK is a great app for communication with your Facebook friends and contacts, as well as anyone who has your phone number. However, if you are not satisfied with the latest version of the app and want to use an older version instead, you need to know how to find and download Messenger Facebook APK old version from a reliable source. You also need to follow some steps and precautions to install the app on your device safely and securely. However, you also need to be aware of some risks and drawbacks of using an outdated version of the app.

-

If you found this article helpful, please share it with your friends and family who might be interested in downloading Messenger Facebook APK old version. Also, feel free to leave a comment below if you have any questions or feedback about this topic. Thank you for reading!

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FAQs

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Q1: Is Messenger Facebook APK safe to download?

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A1: It depends on where you download it from. If you download it from a reputable and trustworthy source that verifies and scans the APK files for malware and viruses, such as APKMirror, Uptodown, or APKPure, then it is safe to download. However, if you download it from an unknown or unverified source that might have tampered with or modified the APK files, then it is not safe to download. You should always check the source website, the file size, the file signature, and the user reviews before downloading any APK file from a third-party source.

-

Q2: How can I update Messenger Facebook APK if I want to?

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A2: If you want to update Messenger Facebook APK to the latest version, you have two options. One option is to uninstall the old version of the app from your device and then download and install the latest version of the app from the official Google Play Store or the Facebook website. Another option is to download and install the latest version of the app from a third-party source that offers updated versions of Messenger Facebook APK, such as APKMirror, Uptodown, or APKPure. However, you should be careful and cautious when downloading and installing any APK file from a third-party source, as there might be some risks and drawbacks involved.

-

Q3: What are the advantages and disadvantages of using Messenger Facebook APK old version?

-

A3: There are some advantages and disadvantages of using Messenger Facebook APK old version. Some of the advantages are:

- -

Some of the disadvantages are:

- -

Q4: How can I backup my messages and data on Messenger Facebook APK?

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A4: If you want to backup your messages and data on Messenger Facebook APK, you have two options. One option is to use the built-in backup feature of Messenger that allows you to sync your messages and data with your Facebook account. To do this, go to your Messenger settings, then account settings, then sync contacts. Another option is to use a third-party app or tool that allows you to backup your messages and data on Messenger Facebook APK, such as Backup Text for FB Messenger, SMS Backup & Restore, or Titanium Backup. However, you should be careful and cautious when using any third-party app or tool, as there might be some risks and drawbacks involved.

-

Q5: How can I contact Facebook support if I have any issues with Messenger Facebook APK?

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A5: If you have any issues with Messenger Facebook APK, such as installation problems, login errors, performance issues, or feature requests, you can contact Facebook support through various channels. Some of them are:

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401be4b1e0
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\ No newline at end of file diff --git a/spaces/232labs/VToonify/vtoonify/model/raft/train.py b/spaces/232labs/VToonify/vtoonify/model/raft/train.py deleted file mode 100644 index 307573097f13ee30c67bbe11658f457fdf1ead3c..0000000000000000000000000000000000000000 --- a/spaces/232labs/VToonify/vtoonify/model/raft/train.py +++ /dev/null @@ -1,247 +0,0 @@ -from __future__ import print_function, division -import sys -sys.path.append('core') - -import argparse -import os -import cv2 -import time -import numpy as np -import matplotlib.pyplot as plt - -import torch -import torch.nn as nn -import torch.optim as optim -import torch.nn.functional as F - -from torch.utils.data import DataLoader -from raft import RAFT -import evaluate -import datasets - -from torch.utils.tensorboard import SummaryWriter - -try: - from torch.cuda.amp import GradScaler -except: - # dummy GradScaler for PyTorch < 1.6 - class GradScaler: - def __init__(self): - pass - def scale(self, loss): - return loss - def unscale_(self, optimizer): - pass - def step(self, optimizer): - optimizer.step() - def update(self): - pass - - -# exclude extremly large displacements -MAX_FLOW = 400 -SUM_FREQ = 100 -VAL_FREQ = 5000 - - -def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8, max_flow=MAX_FLOW): - """ Loss function defined over sequence of flow predictions """ - - n_predictions = len(flow_preds) - flow_loss = 0.0 - - # exlude invalid pixels and extremely large diplacements - mag = torch.sum(flow_gt**2, dim=1).sqrt() - valid = (valid >= 0.5) & (mag < max_flow) - - for i in range(n_predictions): - i_weight = gamma**(n_predictions - i - 1) - i_loss = (flow_preds[i] - flow_gt).abs() - flow_loss += i_weight * (valid[:, None] * i_loss).mean() - - epe = torch.sum((flow_preds[-1] - flow_gt)**2, dim=1).sqrt() - epe = epe.view(-1)[valid.view(-1)] - - metrics = { - 'epe': epe.mean().item(), - '1px': (epe < 1).float().mean().item(), - '3px': (epe < 3).float().mean().item(), - '5px': (epe < 5).float().mean().item(), - } - - return flow_loss, metrics - - -def count_parameters(model): - return sum(p.numel() for p in model.parameters() if p.requires_grad) - - -def fetch_optimizer(args, model): - """ Create the optimizer and learning rate scheduler """ - optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon) - - scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100, - pct_start=0.05, cycle_momentum=False, anneal_strategy='linear') - - return optimizer, scheduler - - -class Logger: - def __init__(self, model, scheduler): - self.model = model - self.scheduler = scheduler - self.total_steps = 0 - self.running_loss = {} - self.writer = None - - def _print_training_status(self): - metrics_data = [self.running_loss[k]/SUM_FREQ for k in sorted(self.running_loss.keys())] - training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, self.scheduler.get_last_lr()[0]) - metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data) - - # print the training status - print(training_str + metrics_str) - - if self.writer is None: - self.writer = SummaryWriter() - - for k in self.running_loss: - self.writer.add_scalar(k, self.running_loss[k]/SUM_FREQ, self.total_steps) - self.running_loss[k] = 0.0 - - def push(self, metrics): - self.total_steps += 1 - - for key in metrics: - if key not in self.running_loss: - self.running_loss[key] = 0.0 - - self.running_loss[key] += metrics[key] - - if self.total_steps % SUM_FREQ == SUM_FREQ-1: - self._print_training_status() - self.running_loss = {} - - def write_dict(self, results): - if self.writer is None: - self.writer = SummaryWriter() - - for key in results: - self.writer.add_scalar(key, results[key], self.total_steps) - - def close(self): - self.writer.close() - - -def train(args): - - model = nn.DataParallel(RAFT(args), device_ids=args.gpus) - print("Parameter Count: %d" % count_parameters(model)) - - if args.restore_ckpt is not None: - model.load_state_dict(torch.load(args.restore_ckpt), strict=False) - - model.cuda() - model.train() - - if args.stage != 'chairs': - model.module.freeze_bn() - - train_loader = datasets.fetch_dataloader(args) - optimizer, scheduler = fetch_optimizer(args, model) - - total_steps = 0 - scaler = GradScaler(enabled=args.mixed_precision) - logger = Logger(model, scheduler) - - VAL_FREQ = 5000 - add_noise = True - - should_keep_training = True - while should_keep_training: - - for i_batch, data_blob in enumerate(train_loader): - optimizer.zero_grad() - image1, image2, flow, valid = [x.cuda() for x in data_blob] - - if args.add_noise: - stdv = np.random.uniform(0.0, 5.0) - image1 = (image1 + stdv * torch.randn(*image1.shape).cuda()).clamp(0.0, 255.0) - image2 = (image2 + stdv * torch.randn(*image2.shape).cuda()).clamp(0.0, 255.0) - - flow_predictions = model(image1, image2, iters=args.iters) - - loss, metrics = sequence_loss(flow_predictions, flow, valid, args.gamma) - scaler.scale(loss).backward() - scaler.unscale_(optimizer) - torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip) - - scaler.step(optimizer) - scheduler.step() - scaler.update() - - logger.push(metrics) - - if total_steps % VAL_FREQ == VAL_FREQ - 1: - PATH = 'checkpoints/%d_%s.pth' % (total_steps+1, args.name) - torch.save(model.state_dict(), PATH) - - results = {} - for val_dataset in args.validation: - if val_dataset == 'chairs': - results.update(evaluate.validate_chairs(model.module)) - elif val_dataset == 'sintel': - results.update(evaluate.validate_sintel(model.module)) - elif val_dataset == 'kitti': - results.update(evaluate.validate_kitti(model.module)) - - logger.write_dict(results) - - model.train() - if args.stage != 'chairs': - model.module.freeze_bn() - - total_steps += 1 - - if total_steps > args.num_steps: - should_keep_training = False - break - - logger.close() - PATH = 'checkpoints/%s.pth' % args.name - torch.save(model.state_dict(), PATH) - - return PATH - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--name', default='raft', help="name your experiment") - parser.add_argument('--stage', help="determines which dataset to use for training") - parser.add_argument('--restore_ckpt', help="restore checkpoint") - parser.add_argument('--small', action='store_true', help='use small model') - parser.add_argument('--validation', type=str, nargs='+') - - parser.add_argument('--lr', type=float, default=0.00002) - parser.add_argument('--num_steps', type=int, default=100000) - parser.add_argument('--batch_size', type=int, default=6) - parser.add_argument('--image_size', type=int, nargs='+', default=[384, 512]) - parser.add_argument('--gpus', type=int, nargs='+', default=[0,1]) - parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision') - - parser.add_argument('--iters', type=int, default=12) - parser.add_argument('--wdecay', type=float, default=.00005) - parser.add_argument('--epsilon', type=float, default=1e-8) - parser.add_argument('--clip', type=float, default=1.0) - parser.add_argument('--dropout', type=float, default=0.0) - parser.add_argument('--gamma', type=float, default=0.8, help='exponential weighting') - parser.add_argument('--add_noise', action='store_true') - args = parser.parse_args() - - torch.manual_seed(1234) - np.random.seed(1234) - - if not os.path.isdir('checkpoints'): - os.mkdir('checkpoints') - - train(args) \ No newline at end of file diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/pann_model.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/pann_model.py deleted file mode 100644 index 109db5f418a0bad32cae2452742589ff52a19b85..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/pann_model.py +++ /dev/null @@ -1,543 +0,0 @@ -# PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition -# Reference from https://github.com/qiuqiangkong/audioset_tagging_cnn -# Some layers are re-designed for CLAP -import os -os.environ['NUMBA_CACHE_DIR'] = '/tmp/' - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torchlibrosa.stft import Spectrogram, LogmelFilterBank -from torchlibrosa.augmentation import SpecAugmentation - -from .utils import do_mixup, interpolate, pad_framewise_output -from .feature_fusion import iAFF, AFF, DAF - - -def init_layer(layer): - """Initialize a Linear or Convolutional layer. """ - nn.init.xavier_uniform_(layer.weight) - - if hasattr(layer, 'bias'): - if layer.bias is not None: - layer.bias.data.fill_(0.) - - -def init_bn(bn): - """Initialize a Batchnorm layer. """ - bn.bias.data.fill_(0.) - bn.weight.data.fill_(1.) - - -class ConvBlock(nn.Module): - def __init__(self, in_channels, out_channels): - - super(ConvBlock, self).__init__() - - self.conv1 = nn.Conv2d(in_channels=in_channels, - out_channels=out_channels, - kernel_size=(3, 3), stride=(1, 1), - padding=(1, 1), bias=False) - - self.conv2 = nn.Conv2d(in_channels=out_channels, - out_channels=out_channels, - kernel_size=(3, 3), stride=(1, 1), - padding=(1, 1), bias=False) - - self.bn1 = nn.BatchNorm2d(out_channels) - self.bn2 = nn.BatchNorm2d(out_channels) - - self.init_weight() - - def init_weight(self): - init_layer(self.conv1) - init_layer(self.conv2) - init_bn(self.bn1) - init_bn(self.bn2) - - - def forward(self, input, pool_size=(2, 2), pool_type='avg'): - - x = input - x = F.relu_(self.bn1(self.conv1(x))) - x = F.relu_(self.bn2(self.conv2(x))) - if pool_type == 'max': - x = F.max_pool2d(x, kernel_size=pool_size) - elif pool_type == 'avg': - x = F.avg_pool2d(x, kernel_size=pool_size) - elif pool_type == 'avg+max': - x1 = F.avg_pool2d(x, kernel_size=pool_size) - x2 = F.max_pool2d(x, kernel_size=pool_size) - x = x1 + x2 - else: - raise Exception('Incorrect argument!') - - return x - - -class ConvBlock5x5(nn.Module): - def __init__(self, in_channels, out_channels): - - super(ConvBlock5x5, self).__init__() - - self.conv1 = nn.Conv2d(in_channels=in_channels, - out_channels=out_channels, - kernel_size=(5, 5), stride=(1, 1), - padding=(2, 2), bias=False) - - self.bn1 = nn.BatchNorm2d(out_channels) - - self.init_weight() - - def init_weight(self): - init_layer(self.conv1) - init_bn(self.bn1) - - - def forward(self, input, pool_size=(2, 2), pool_type='avg'): - - x = input - x = F.relu_(self.bn1(self.conv1(x))) - if pool_type == 'max': - x = F.max_pool2d(x, kernel_size=pool_size) - elif pool_type == 'avg': - x = F.avg_pool2d(x, kernel_size=pool_size) - elif pool_type == 'avg+max': - x1 = F.avg_pool2d(x, kernel_size=pool_size) - x2 = F.max_pool2d(x, kernel_size=pool_size) - x = x1 + x2 - else: - raise Exception('Incorrect argument!') - - return x - - -class AttBlock(nn.Module): - def __init__(self, n_in, n_out, activation='linear', temperature=1.): - super(AttBlock, self).__init__() - - self.activation = activation - self.temperature = temperature - self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) - self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) - - self.bn_att = nn.BatchNorm1d(n_out) - self.init_weights() - - def init_weights(self): - init_layer(self.att) - init_layer(self.cla) - init_bn(self.bn_att) - - def forward(self, x): - # x: (n_samples, n_in, n_time) - norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) - cla = self.nonlinear_transform(self.cla(x)) - x = torch.sum(norm_att * cla, dim=2) - return x, norm_att, cla - - def nonlinear_transform(self, x): - if self.activation == 'linear': - return x - elif self.activation == 'sigmoid': - return torch.sigmoid(x) - - -class Cnn14(nn.Module): - def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, classes_num, enable_fusion=False, fusion_type='None'): - - super(Cnn14, self).__init__() - - window = 'hann' - center = True - pad_mode = 'reflect' - ref = 1.0 - amin = 1e-10 - top_db = None - - self.enable_fusion = enable_fusion - self.fusion_type = fusion_type - - # Spectrogram extractor - self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, - win_length=window_size, window=window, center=center, pad_mode=pad_mode, - freeze_parameters=True) - - # Logmel feature extractor - self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, - n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, - freeze_parameters=True) - - # Spec augmenter - self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, - freq_drop_width=8, freq_stripes_num=2) - - self.bn0 = nn.BatchNorm2d(64) - - if (self.enable_fusion) and (self.fusion_type == 'channel_map'): - self.conv_block1 = ConvBlock(in_channels=4, out_channels=64) - else: - self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) - self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) - self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) - self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) - self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) - self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) - - self.fc1 = nn.Linear(2048, 2048, bias=True) - self.fc_audioset = nn.Linear(2048, classes_num, bias=True) - - if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']): - self.mel_conv1d = nn.Sequential( - nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2), - nn.BatchNorm1d(64) # No Relu - ) - if self.fusion_type == 'daf_1d': - self.fusion_model = DAF() - elif self.fusion_type == 'aff_1d': - self.fusion_model = AFF(channels=64, type='1D') - elif self.fusion_type == 'iaff_1d': - self.fusion_model = iAFF(channels=64, type='1D') - - if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): - self.mel_conv2d = nn.Sequential( - nn.Conv2d(1, 64, kernel_size=(5,5), stride=(6, 2), padding=(2,2)), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True) - ) - - if self.fusion_type == 'daf_2d': - self.fusion_model = DAF() - elif self.fusion_type == 'aff_2d': - self.fusion_model = AFF(channels=64, type='2D') - elif self.fusion_type == 'iaff_2d': - self.fusion_model = iAFF(channels=64, type='2D') - self.init_weight() - - def init_weight(self): - init_bn(self.bn0) - init_layer(self.fc1) - init_layer(self.fc_audioset) - - def forward(self, input, mixup_lambda=None, device=None): - """ - Input: (batch_size, data_length)""" - - if self.enable_fusion and input["longer"].sum() == 0: - # if no audio is longer than 10s, then randomly select one audio to be longer - input["longer"][torch.randint(0, input["longer"].shape[0], (1,))] = True - - if not self.enable_fusion: - x = self.spectrogram_extractor(input['waveform'].to(device=device, non_blocking=True)) # (batch_size, 1, time_steps, freq_bins) - x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) - - x = x.transpose(1, 3) - x = self.bn0(x) - x = x.transpose(1, 3) - else: - longer_list = input["longer"].to(device=device, non_blocking=True) - x = input["mel_fusion"].to(device=device, non_blocking=True) - longer_list_idx = torch.where(longer_list)[0] - x = x.transpose(1, 3) - x = self.bn0(x) - x = x.transpose(1, 3) - if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']: - new_x = x[:,0:1,:,:].clone().contiguous() - # local processing - if len(longer_list_idx) > 0: - fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous() - FB,FC,FT,FF = fusion_x_local.size() - fusion_x_local = fusion_x_local.view(FB * FC, FT, FF) - fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous() - fusion_x_local = self.mel_conv1d(fusion_x_local) - fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1)) - fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2) - if fusion_x_local.size(-1) < FT: - fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1) - else: - fusion_x_local = fusion_x_local[:,:,:FT] - # 1D fusion - new_x = new_x.squeeze(1).permute((0,2,1)).contiguous() - new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local) - x = new_x.permute((0,2,1)).contiguous()[:,None,:,:] - else: - x = new_x - elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']: - x = x # no change - - if self.training: - x = self.spec_augmenter(x) - # Mixup on spectrogram - if self.training and mixup_lambda is not None: - x = do_mixup(x, mixup_lambda) - if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']): - global_x = x[:,0:1,:,:] - - # global processing - B, C, H, W = global_x.shape - global_x = self.conv_block1(global_x, pool_size=(2, 2), pool_type='avg') - if len(longer_list_idx) > 0: - local_x = x[longer_list_idx,1:,:,:].contiguous() - TH = global_x.size(-2) - # local processing - B, C, H, W = local_x.shape - local_x = local_x.view(B*C,1,H,W) - local_x = self.mel_conv2d(local_x) - local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3)) - local_x = local_x.permute((0,2,1,3,4)).contiguous().flatten(2,3) - TB,TC,_,TW = local_x.size() - if local_x.size(-2) < TH: - local_x = torch.cat([local_x, torch.zeros((TB,TC,TH-local_x.size(-2),TW), device=global_x.device)], dim=-2) - else: - local_x = local_x[:,:,:TH,:] - - global_x[longer_list_idx] = self.fusion_model(global_x[longer_list_idx],local_x) - x = global_x - else: - x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') - - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = torch.mean(x, dim=3) - - latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) - latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) - latent_x = latent_x1 + latent_x2 - latent_x = latent_x.transpose(1, 2) - latent_x = F.relu_(self.fc1(latent_x)) - latent_output = interpolate(latent_x, 32) - - - (x1, _) = torch.max(x, dim=2) - x2 = torch.mean(x, dim=2) - x = x1 + x2 - x = F.dropout(x, p=0.5, training=self.training) - x = F.relu_(self.fc1(x)) - embedding = F.dropout(x, p=0.5, training=self.training) - clipwise_output = torch.sigmoid(self.fc_audioset(x)) - - output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} - return output_dict - - -class Cnn6(nn.Module): - def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, classes_num, enable_fusion=False, fusion_type='None'): - - super(Cnn6, self).__init__() - - window = 'hann' - center = True - pad_mode = 'reflect' - ref = 1.0 - amin = 1e-10 - top_db = None - - self.enable_fusion = enable_fusion - self.fusion_type = fusion_type - - # Spectrogram extractor - self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, - win_length=window_size, window=window, center=center, pad_mode=pad_mode, - freeze_parameters=True) - - # Logmel feature extractor - self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, - n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, - freeze_parameters=True) - - # Spec augmenter - self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, - freq_drop_width=8, freq_stripes_num=2) - - self.bn0 = nn.BatchNorm2d(64) - - self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64) - self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128) - self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256) - self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512) - - self.fc1 = nn.Linear(512, 512, bias=True) - self.fc_audioset = nn.Linear(512, classes_num, bias=True) - - self.init_weight() - - def init_weight(self): - init_bn(self.bn0) - init_layer(self.fc1) - init_layer(self.fc_audioset) - - def forward(self, input, mixup_lambda=None, device=None): - """ - Input: (batch_size, data_length)""" - - x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) - x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) - - x = x.transpose(1, 3) - x = self.bn0(x) - x = x.transpose(1, 3) - - if self.training: - x = self.spec_augmenter(x) - - # Mixup on spectrogram - if self.training and mixup_lambda is not None: - x = do_mixup(x, mixup_lambda) - - x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = torch.mean(x, dim=3) - - latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) - latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) - latent_x = latent_x1 + latent_x2 - latent_x = latent_x.transpose(1, 2) - latent_x = F.relu_(self.fc1(latent_x)) - latent_output = interpolate(latent_x, 16) - - (x1, _) = torch.max(x, dim=2) - x2 = torch.mean(x, dim=2) - x = x1 + x2 - x = F.dropout(x, p=0.5, training=self.training) - x = F.relu_(self.fc1(x)) - embedding = F.dropout(x, p=0.5, training=self.training) - clipwise_output = torch.sigmoid(self.fc_audioset(x)) - - output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} - - return output_dict - - -class Cnn10(nn.Module): - def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, - fmax, classes_num, enable_fusion=False, fusion_type='None'): - - super(Cnn10, self).__init__() - - window = 'hann' - center = True - pad_mode = 'reflect' - ref = 1.0 - amin = 1e-10 - top_db = None - - self.enable_fusion = enable_fusion - self.fusion_type = fusion_type - - # Spectrogram extractor - self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, - win_length=window_size, window=window, center=center, pad_mode=pad_mode, - freeze_parameters=True) - - # Logmel feature extractor - self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, - n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, - freeze_parameters=True) - - # Spec augmenter - self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, - freq_drop_width=8, freq_stripes_num=2) - - self.bn0 = nn.BatchNorm2d(64) - - self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) - self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) - self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) - self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) - self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) - - self.fc1 = nn.Linear(1024, 1024, bias=True) - self.fc_audioset = nn.Linear(1024, classes_num, bias=True) - - self.init_weight() - - def init_weight(self): - init_bn(self.bn0) - init_layer(self.fc1) - init_layer(self.fc_audioset) - - def forward(self, input, mixup_lambda=None, device=None): - """ - Input: (batch_size, data_length)""" - - x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) - x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) - - x = x.transpose(1, 3) - x = self.bn0(x) - x = x.transpose(1, 3) - - if self.training: - x = self.spec_augmenter(x) - - # Mixup on spectrogram - if self.training and mixup_lambda is not None: - x = do_mixup(x, mixup_lambda) - - x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') - x = F.dropout(x, p=0.2, training=self.training) - x = torch.mean(x, dim=3) - - latent_x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1) - latent_x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1) - latent_x = latent_x1 + latent_x2 - latent_x = latent_x.transpose(1, 2) - latent_x = F.relu_(self.fc1(latent_x)) - latent_output = interpolate(latent_x, 32) - - (x1, _) = torch.max(x, dim=2) - x2 = torch.mean(x, dim=2) - x = x1 + x2 - x = F.dropout(x, p=0.5, training=self.training) - x = F.relu_(self.fc1(x)) - embedding = F.dropout(x, p=0.5, training=self.training) - clipwise_output = torch.sigmoid(self.fc_audioset(x)) - - output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding, 'fine_grained_embedding': latent_output} - - return output_dict - - -def create_pann_model(audio_cfg, enable_fusion=False, fusion_type='None'): - try: - ModelProto = eval(audio_cfg.model_name) - model = ModelProto( - sample_rate = audio_cfg.sample_rate, - window_size = audio_cfg.window_size, - hop_size =audio_cfg.hop_size, - mel_bins = audio_cfg.mel_bins, - fmin = audio_cfg.fmin, - fmax = audio_cfg.fmax, - classes_num = audio_cfg.class_num, - enable_fusion = enable_fusion, - fusion_type = fusion_type - ) - return model - except: - raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.') - diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Aibn.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Aibn.py deleted file mode 100644 index 3399d613fd4c40ab594154a8e9c5f0ec04054a4e..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Aibn.py +++ /dev/null @@ -1,52 +0,0 @@ -from __future__ import annotations - -import time -import hashlib - -from ..typing import AsyncGenerator -from ..requests import StreamSession -from .base_provider import AsyncGeneratorProvider - - -class Aibn(AsyncGeneratorProvider): - url = "https://aibn.cc" - supports_gpt_35_turbo = True - working = True - - @classmethod - async def create_async_generator( - cls, - model: str, - messages: list[dict[str, str]], - timeout: int = 30, - **kwargs - ) -> AsyncGenerator: - async with StreamSession(impersonate="chrome107", timeout=timeout) as session: - timestamp = int(time.time()) - data = { - "messages": messages, - "pass": None, - "sign": generate_signature(timestamp, messages[-1]["content"]), - "time": timestamp - } - async with session.post(f"{cls.url}/api/generate", json=data) as response: - response.raise_for_status() - async for chunk in response.iter_content(): - yield chunk.decode() - - @classmethod - @property - def params(cls): - params = [ - ("model", "str"), - ("messages", "list[dict[str, str]]"), - ("stream", "bool"), - ("temperature", "float"), - ] - param = ", ".join([": ".join(p) for p in params]) - return f"g4f.provider.{cls.__name__} supports: ({param})" - - -def generate_signature(timestamp: int, message: str, secret: str = "undefined"): - data = f"{timestamp}:{message}:{secret}" - return hashlib.sha256(data.encode()).hexdigest() \ No newline at end of file diff --git a/spaces/Adapter/CoAdapter/ldm/modules/extra_condition/midas/api.py b/spaces/Adapter/CoAdapter/ldm/modules/extra_condition/midas/api.py deleted file mode 100644 index 9a6e194545c40ec263e65a140678b53a5a2abd54..0000000000000000000000000000000000000000 --- a/spaces/Adapter/CoAdapter/ldm/modules/extra_condition/midas/api.py +++ /dev/null @@ -1,175 +0,0 @@ -# based on https://github.com/isl-org/MiDaS -import os - -import cv2 -import torch -import torch.nn as nn -from torchvision.transforms import Compose - -from ldm.modules.extra_condition.midas.midas.dpt_depth import DPTDepthModel -from ldm.modules.extra_condition.midas.midas.midas_net import MidasNet -from ldm.modules.extra_condition.midas.midas.midas_net_custom import MidasNet_small -from ldm.modules.extra_condition.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet - - -ISL_PATHS = { - "dpt_large": "models/dpt_large-midas-2f21e586.pt", - "dpt_hybrid": "models/dpt_hybrid-midas-501f0c75.pt", - "midas_v21": "", - "midas_v21_small": "", -} - -remote_model_path = "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt" - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -def load_midas_transform(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load transform only - if model_type == "dpt_large": # DPT-Large - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - elif model_type == "midas_v21_small": - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - else: - assert False, f"model_type '{model_type}' not implemented, use: --model_type large" - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return transform - - -def load_model(model_type): - # https://github.com/isl-org/MiDaS/blob/master/run.py - # load network - model_path = ISL_PATHS[model_type] - if model_type == "dpt_large": # DPT-Large - model = DPTDepthModel( - path=model_path, - backbone="vitl16_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "dpt_hybrid": # DPT-Hybrid - if not os.path.exists(model_path): - from basicsr.utils.download_util import load_file_from_url - load_file_from_url(remote_model_path, model_dir='models') - - model = DPTDepthModel( - path=model_path, - backbone="vitb_rn50_384", - non_negative=True, - ) - net_w, net_h = 384, 384 - resize_mode = "minimal" - normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - - elif model_type == "midas_v21": - model = MidasNet(model_path, non_negative=True) - net_w, net_h = 384, 384 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - elif model_type == "midas_v21_small": - model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, - non_negative=True, blocks={'expand': True}) - net_w, net_h = 256, 256 - resize_mode = "upper_bound" - normalization = NormalizeImage( - mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] - ) - - else: - print(f"model_type '{model_type}' not implemented, use: --model_type large") - assert False - - transform = Compose( - [ - Resize( - net_w, - net_h, - resize_target=None, - keep_aspect_ratio=True, - ensure_multiple_of=32, - resize_method=resize_mode, - image_interpolation_method=cv2.INTER_CUBIC, - ), - normalization, - PrepareForNet(), - ] - ) - - return model.eval(), transform - - -class MiDaSInference(nn.Module): - MODEL_TYPES_TORCH_HUB = [ - "DPT_Large", - "DPT_Hybrid", - "MiDaS_small" - ] - MODEL_TYPES_ISL = [ - "dpt_large", - "dpt_hybrid", - "midas_v21", - "midas_v21_small", - ] - - def __init__(self, model_type): - super().__init__() - assert (model_type in self.MODEL_TYPES_ISL) - model, _ = load_model(model_type) - self.model = model - self.model.train = disabled_train - - def forward(self, x): - # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array - # NOTE: we expect that the correct transform has been called during dataloading. - with torch.no_grad(): - prediction = self.model(x) - prediction = torch.nn.functional.interpolate( - prediction.unsqueeze(1), - size=x.shape[2:], - mode="bicubic", - align_corners=False, - ) - assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3]) - return prediction diff --git a/spaces/Aditya9790/yolo7-object-tracking/detect_or_track.py b/spaces/Aditya9790/yolo7-object-tracking/detect_or_track.py deleted file mode 100644 index d16bf2b6a8d946324458adc8b95093c4d9d7bc21..0000000000000000000000000000000000000000 --- a/spaces/Aditya9790/yolo7-object-tracking/detect_or_track.py +++ /dev/null @@ -1,285 +0,0 @@ -import argparse -import time -from pathlib import Path -import cv2 -import torch -import torch.backends.cudnn as cudnn -from numpy import random - -from models.experimental import attempt_load -from utils.datasets import LoadStreams, LoadImages -from utils.general import check_img_size, check_requirements, \ - check_imshow, non_max_suppression, apply_classifier, \ - scale_coords, xyxy2xywh, strip_optimizer, set_logging, \ - increment_path -from utils.plots import plot_one_box -from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel - -from sort import * - - -"""Function to Draw Bounding boxes""" -def draw_boxes(img, bbox, identities=None, categories=None, confidences = None, names=None, colors = None): - for i, box in enumerate(bbox): - x1, y1, x2, y2 = [int(i) for i in box] - tl = opt.thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness - - cat = int(categories[i]) if categories is not None else 0 - id = int(identities[i]) if identities is not None else 0 - # conf = confidences[i] if confidences is not None else 0 - - color = colors[cat] - - if not opt.nobbox: - cv2.rectangle(img, (x1, y1), (x2, y2), color, tl) - - if not opt.nolabel: - label = str(id) + ":"+ names[cat] if identities is not None else f'{names[cat]} {confidences[i]:.2f}' - tf = max(tl - 1, 1) # font thickness - t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] - c2 = x1 + t_size[0], y1 - t_size[1] - 3 - cv2.rectangle(img, (x1, y1), c2, color, -1, cv2.LINE_AA) # filled - cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) - - - return img - - -def detect(save_img=False): - source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace - save_img = not opt.nosave and not source.endswith('.txt') # save inference images - webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( - ('rtsp://', 'rtmp://', 'http://', 'https://')) - save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run - if not opt.nosave: - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Initialize - set_logging() - device = select_device(opt.device) - half = device.type != 'cpu' # half precision only supported on CUDA - - # Load model - model = attempt_load(weights, map_location=device) # load FP32 model - stride = int(model.stride.max()) # model stride - imgsz = check_img_size(imgsz, s=stride) # check img_size - - if trace: - model = TracedModel(model, device, opt.img_size) - - if half: - model.half() # to FP16 - - # Second-stage classifier - classify = False - if classify: - modelc = load_classifier(name='resnet101', n=2) # initialize - modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() - - # Set Dataloader - vid_path, vid_writer = None, None - if webcam: - view_img = check_imshow() - cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz, stride=stride) - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride) - - # Get names and colors - names = model.module.names if hasattr(model, 'module') else model.names - colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] - - # Run inference - if device.type != 'cpu': - model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once - old_img_w = old_img_h = imgsz - old_img_b = 1 - - t0 = time.time() - ################################### - startTime = 0 - ################################### - for path, img, im0s, vid_cap in dataset: - img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 - img /= 255.0 # 0 - 255 to 0.0 - 1.0 - if img.ndimension() == 3: - img = img.unsqueeze(0) - - # Warmup - if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): - old_img_b = img.shape[0] - old_img_h = img.shape[2] - old_img_w = img.shape[3] - for i in range(3): - model(img, augment=opt.augment)[0] - - # Inference - t1 = time_synchronized() - pred = model(img, augment=opt.augment)[0] - t2 = time_synchronized() - - # Apply NMS - pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) - t3 = time_synchronized() - - # Apply Classifier - if classify: - pred = apply_classifier(pred, modelc, img, im0s) - - # Process detections - for i, det in enumerate(pred): # detections per image - if webcam: # batch_size >= 1 - p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count - else: - p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # img.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh - if len(det): - # Rescale boxes from img_size to im0 size - det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() - - # Print results - for c in det[:, -1].unique(): - n = (det[:, -1] == c).sum() # detections per class - s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string - - dets_to_sort = np.empty((0,6)) - # NOTE: We send in detected object class too - for x1,y1,x2,y2,conf,detclass in det.cpu().detach().numpy(): - dets_to_sort = np.vstack((dets_to_sort, - np.array([x1, y1, x2, y2, conf, detclass]))) - - - if opt.track: - - tracked_dets = sort_tracker.update(dets_to_sort, opt.unique_track_color) - tracks =sort_tracker.getTrackers() - - # draw boxes for visualization - if len(tracked_dets)>0: - bbox_xyxy = tracked_dets[:,:4] - identities = tracked_dets[:, 8] - categories = tracked_dets[:, 4] - confidences = None - - if opt.show_track: - #loop over tracks - for t, track in enumerate(tracks): - - track_color = colors[int(track.detclass)] if not opt.unique_track_color else sort_tracker.color_list[t] - - [cv2.line(im0, (int(track.centroidarr[i][0]), - int(track.centroidarr[i][1])), - (int(track.centroidarr[i+1][0]), - int(track.centroidarr[i+1][1])), - track_color, thickness=opt.thickness) - for i,_ in enumerate(track.centroidarr) - if i < len(track.centroidarr)-1 ] - else: - bbox_xyxy = dets_to_sort[:,:4] - identities = None - categories = dets_to_sort[:, 5] - confidences = dets_to_sort[:, 4] - - im0 = draw_boxes(im0, bbox_xyxy, identities, categories, confidences, names, colors) - - - - - - # Print time (inference + NMS) - print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') - - # Stream results - ###################################################### - if dataset.mode != 'image' and opt.show_fps: - currentTime = time.time() - - fps = 1/(currentTime - startTime) - startTime = currentTime - cv2.putText(im0, "FPS: " + str(int(fps)), (20, 70), cv2.FONT_HERSHEY_PLAIN, 2, (0,255,0),2) - - ####################################################### - if view_img: - cv2.imshow(str(p), im0) - cv2.waitKey(1) # 1 millisecond - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - print(f" The image with the result is saved in: {save_path}") - else: # 'video' or 'stream' - if vid_path != save_path: # new video - vid_path = save_path - if isinstance(vid_writer, cv2.VideoWriter): - vid_writer.release() # release previous video writer - if vid_cap: # video - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - else: # stream - fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path += '.mp4' - vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer.write(im0) - - if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - #print(f"Results saved to {save_dir}{s}") - - print(f'Done. ({time.time() - t0:.3f}s)') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)') - parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam - parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='display results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default='runs/detect', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--no-trace', action='store_true', help='don`t trace model') - - parser.add_argument('--track', action='store_true', help='run tracking') - parser.add_argument('--show-track', action='store_true', help='show tracked path') - parser.add_argument('--show-fps', action='store_true', help='show fps') - parser.add_argument('--thickness', type=int, default=2, help='bounding box and font size thickness') - parser.add_argument('--seed', type=int, default=1, help='random seed to control bbox colors') - parser.add_argument('--nobbox', action='store_true', help='don`t show bounding box') - parser.add_argument('--nolabel', action='store_true', help='don`t show label') - parser.add_argument('--unique-track-color', action='store_true', help='show each track in unique color') - - - opt = parser.parse_args() - print(opt) - np.random.seed(opt.seed) - - sort_tracker = Sort(max_age=5, - min_hits=2, - iou_threshold=0.2) - - #check_requirements(exclude=('pycocotools', 'thop')) - - with torch.no_grad(): - if opt.update: # update all models (to fix SourceChangeWarning) - for opt.weights in ['yolov7.pt']: - detect() - strip_optimizer(opt.weights) - else: - detect() diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetTotalColumnProportions.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetTotalColumnProportions.js deleted file mode 100644 index 8f440fdcdf070e24d5a7f49210bdfa2c41da5625..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetTotalColumnProportions.js +++ /dev/null @@ -1,13 +0,0 @@ -var GetTotalColumnProportions = function () { - var result = 0, - proportion; - for (var i = 0; i < this.columnCount; i++) { - proportion = this.columnProportions[i]; - if (proportion > 0) { - result += proportion; - } - } - return result; -} - -export default GetTotalColumnProportions; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetExpandedChildWidth.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetExpandedChildWidth.js deleted file mode 100644 index 484fa3132721a102f02b622b2bf8384cc4065871..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetExpandedChildWidth.js +++ /dev/null @@ -1,16 +0,0 @@ -var GetExpandedChildWidth = function (child, parentWidth) { - if (parentWidth === undefined) { - parentWidth = this.width; - } - - var childWidth; - var childConfig = child.rexSizer; - if (childConfig.expandWidth) { - var innerWidth = parentWidth - this.space.left - this.space.right; - var padding = childConfig.padding; - childWidth = innerWidth - padding.left - padding.right; - } - return childWidth; -} - -export default GetExpandedChildWidth; \ No newline at end of file diff --git a/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/css/app.4dc5e420.css b/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/css/app.4dc5e420.css deleted file mode 100644 index 8f40ec95983d11fb4424b4576d1043a74ef222e1..0000000000000000000000000000000000000000 --- a/spaces/Alcedo/yunmedia/resources/chatgpt-plugin/css/app.4dc5e420.css +++ /dev/null @@ -1,22 +0,0 @@ -/*! - -========================================================= -* Vue Notus - v1.1.0 based on Tailwind Starter Kit by Creative Tim -========================================================= - -* Product Page: https://www.creative-tim.com/product/vue-notus -* Copyright 2021 Creative Tim (https://www.creative-tim.com) -* Licensed under MIT (https://github.com/creativetimofficial/vue-notus/blob/main/LICENSE.md) - -* Tailwind Starter Kit Page: https://www.creative-tim.com/learning-lab/tailwind-starter-kit/presentation - -* Coded by Creative Tim - -========================================================= - -* The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - -*/ -/* -! tailwindcss v3.3.1 | MIT 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figure:last-child,.v-md-plugin-tip.details p:last-child{margin-bottom:0;padding-bottom:0}.v-md-plugin-tip.details summary{outline:none;cursor:pointer} \ No newline at end of file diff --git a/spaces/Alexxggs/ggvpnewen/constants.py b/spaces/Alexxggs/ggvpnewen/constants.py deleted file mode 100644 index f14d6585dc2d150810a4bf276240f67b170147ac..0000000000000000000000000000000000000000 --- a/spaces/Alexxggs/ggvpnewen/constants.py +++ /dev/null @@ -1,7 +0,0 @@ -import numpy as np - -MUBERT_TAGS_STRING = 'рэп,russian,tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic' -MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(',')) -MUBERT_LICENSE = "ttmmubertlicense#f0acYBenRcfeFpNT4wpYGaTQIyDI4mJGv5MfIhBFz97NXDwDNFHmMRsBSzmGsJwbTpP1A6i07AXcIeAHo5" -MUBERT_MODE = "loop" -MUBERT_TOKEN = "4951f6428e83172a4f39de05d5b3ab10d58560b8" \ No newline at end of file diff --git a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/transformer.py b/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/transformer.py deleted file mode 100644 index e69cca829d774d0b8b36c0de9b7924373da81b43..0000000000000000000000000000000000000000 --- a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/transformer.py +++ /dev/null @@ -1,747 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Transformer model, with streaming support, xformer attention support -and easy causal attention with a potentially finite receptive field. - -See `StreamingTransformer` for more information. - -Unlike regular PyTorch Transformer, we make the hard choice that batches are first. -""" - -import typing as tp - -from einops import rearrange -import torch -import torch.nn as nn -from torch.nn import functional as F -from torch.utils.checkpoint import checkpoint as torch_checkpoint -from xformers import ops - -from .rope import RotaryEmbedding -from .streaming import StreamingModule - -_efficient_attention_backend: str = 'torch' - - -def set_efficient_attention_backend(backend: str = 'torch'): - # Using torch by default, it seems a bit faster on older P100 GPUs (~20% faster). - global _efficient_attention_backend - assert _efficient_attention_backend in ['xformers', 'torch'] - _efficient_attention_backend = backend - - -def _get_attention_time_dimension() -> int: - if _efficient_attention_backend == 'torch': - return 2 - else: - return 1 - - -def _is_profiled() -> bool: - # Return true if we are currently running with a xformers profiler activated. - try: - from xformers.profiler import profiler - except ImportError: - return False - return profiler._Profiler._CURRENT_PROFILER is not None - - -def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module: - """Create normalization module for transformer encoder layer. - - Args: - norm_type (str): Normalization method. - dim (int): Dimension of the normalized layer. - **kwargs (dict): Additional parameters for normalization layer. - Returns: - nn.Module: Normalization module. - """ - if norm_type == 'layer_norm': - return nn.LayerNorm(dim, eps=1e-5, **kwargs) - else: - raise ValueError(f"Unknown norm type: {norm_type}") - - -def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000, - dtype: torch.dtype = torch.float32) -> torch.Tensor: - """Create sinusoidal positional embedding, with shape `[B, T, C]`. - - Args: - positions (torch.Tensor): LongTensor of positions. - dim (int): Dimension of the embedding. - max_period (float): Maximum period of the cosine/sine functions. - dtype (torch.dtype or str): dtype to use to generate the embedding. - Returns: - torch.Tensor: Sinusoidal positional embedding. - """ - # We aim for BTC format - assert dim % 2 == 0 - half_dim = dim // 2 - positions = positions.to(dtype) - adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1) - max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype) # avoid sync point - phase = positions / (max_period_tensor ** (adim / (half_dim - 1))) - return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1) - - -def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor: - """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers""" - if n_rep == 1: - return x - if _efficient_attention_backend == 'torch': - bs, n_kv_heads, slen, head_dim = x.shape - return ( - x[:, :, None, :, :] - .expand(bs, n_kv_heads, n_rep, slen, head_dim) - .reshape(bs, n_kv_heads * n_rep, slen, head_dim) - ) - else: - bs, slen, n_kv_heads, head_dim = x.shape - return ( - x[:, :, :, None, :] - .expand(bs, slen, n_kv_heads, n_rep, head_dim) - .reshape(bs, slen, n_kv_heads * n_rep, head_dim) - ) - - -class LayerScale(nn.Module): - """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf). - This rescales diagonaly the residual outputs close to 0, with a learnt scale. - - Args: - channels (int): Number of channels. - init (float): Initial scale. - channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`. - device (torch.device or None): Device on which to initialize the module. - dtype (torch.dtype or None): dtype to use to initialize the module. - """ - def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True, - device=None, dtype=None): - super().__init__() - self.channel_last = channel_last - self.scale = nn.Parameter( - torch.full((channels,), init, - requires_grad=True, device=device, dtype=dtype)) - - def forward(self, x: torch.Tensor): - if self.channel_last: - return self.scale * x - else: - return self.scale[:, None] * x - - -class StreamingMultiheadAttention(StreamingModule): - """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation. - - Args: - embed_dim (int): Dimension to project to. - num_heads (int): Number of heads. - dropout (float): Dropout level. - bias (bool): Use bias in projections. - causal (bool): Causal mask applied automatically. - past_context (int or None): Receptive field for the causal mask, infinite if None. - custom (bool): Use custom MHA implementation, for testing / benchmarking. - memory_efficient (bool): Use xformers based memory efficient attention. - attention_as_float32 (bool): Perform the attention as float32 - (especially important with memory_efficient as autocast won't do this automatically). - rope (`RotaryEmbedding` or None): Rope embedding to use. - cross_attention: Should be true when used as a cross attention. - All keys and values must be available at once, streaming is only for the queries. - Cannot be used with `causal` or `rope` (as it wouldn't make sens to - intepret the time steps in the keys relative to those in the queries). - safe_streaming (bool): Bug fix, will go away with xformers update. - qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product. - kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads). - This will lead to faster decoding time on A100 or other GPUs with tensorcore. - device (torch.device or None): Sevice on which to initialize. - dtype (torch.dtype or None): dtype to use. - """ - def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True, - causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False, - memory_efficient: bool = False, attention_as_float32: bool = False, - rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False, - safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1, - device=None, dtype=None): - super().__init__() - factory_kwargs = {'device': device, 'dtype': dtype} - if past_context is not None: - assert causal - - self.embed_dim = embed_dim - self.causal = causal - self.past_context = past_context - self.memory_efficient = memory_efficient - self.attention_as_float32 = attention_as_float32 - self.rope = rope - self.cross_attention = cross_attention - self.safe_streaming = safe_streaming - self.num_heads = num_heads - self.dropout = dropout - self.kv_repeat = kv_repeat - if cross_attention: - assert not causal, "Causal cannot work with cross attention." - assert rope is None, "Rope cannot work with cross attention." - - if memory_efficient: - _verify_xformers_memory_efficient_compat() - - self.custom = _is_custom(custom, memory_efficient) - if self.custom: - out_dim = embed_dim - assert num_heads % kv_repeat == 0 - assert not cross_attention or kv_repeat == 1 - num_kv = num_heads // kv_repeat - kv_dim = (embed_dim // num_heads) * num_kv - out_dim += 2 * kv_dim - in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs) - # We try to follow the default PyTorch MHA convention, to easily compare results. - self.in_proj_weight = in_proj.weight - self.in_proj_bias = in_proj.bias - if bias: - self.in_proj_bias.data.zero_() # Following Pytorch convention - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs) - if bias: - self.out_proj.bias.data.zero_() - else: - assert not qk_layer_norm - assert kv_repeat == 1 - self.mha = nn.MultiheadAttention( - embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True, - **factory_kwargs) - self.qk_layer_norm = qk_layer_norm - if qk_layer_norm: - assert self.custom - assert kv_repeat == 1 - ln_dim = embed_dim - self.q_layer_norm = nn.LayerNorm(ln_dim) - self.k_layer_norm = nn.LayerNorm(ln_dim) - - def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): - if not self.custom: - # Support compat with regular MHA - keys = [n for n, _ in self.mha.named_parameters()] - for key in keys: - if prefix + key in state_dict: - state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key) - super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) - - def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype): - # Return a causal mask, accounting for potentially stored past keys/values - # We actually return a bias for the attention score, as this has the same - # convention both in the builtin MHA in Pytorch, and Xformers functions. - time_dim = _get_attention_time_dimension() - if self.memory_efficient: - from xformers.ops import LowerTriangularMask - if current_steps == 1: - # If we only have one step, then we do not need a mask. - return None - elif 'past_keys' in self._streaming_state: - raise RuntimeError('Not supported at the moment') - else: - # Then we can safely use a lower triangular mask - return LowerTriangularMask() - if self._streaming_state: - past_keys = self._streaming_state['past_keys'] - past_steps = past_keys.shape[time_dim] - else: - past_steps = 0 - - queries_pos = torch.arange( - past_steps, current_steps + past_steps, device=device).view(-1, 1) - keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1) - delta = queries_pos - keys_pos - valid = delta >= 0 - if self.past_context is not None: - valid &= (delta <= self.past_context) - return torch.where( - valid, - torch.zeros([], device=device, dtype=dtype), - torch.full([], float('-inf'), device=device, dtype=dtype)) - - def _complete_kv(self, k, v): - time_dim = _get_attention_time_dimension() - if self.cross_attention: - # With cross attention we assume all keys and values - # are already available, and streaming is with respect - # to the queries only. - return k, v - # Complete the key/value pair using the streaming state. - if self._streaming_state: - pk = self._streaming_state['past_keys'] - nk = torch.cat([pk, k], dim=time_dim) - if v is k: - nv = nk - else: - pv = self._streaming_state['past_values'] - nv = torch.cat([pv, v], dim=time_dim) - else: - nk = k - nv = v - - assert nk.shape[time_dim] == nv.shape[time_dim] - offset = 0 - if self.past_context is not None: - offset = max(0, nk.shape[time_dim] - self.past_context) - if self._is_streaming: - self._streaming_state['past_keys'] = nk[:, offset:] - if v is not k: - self._streaming_state['past_values'] = nv[:, offset:] - if 'offset' in self._streaming_state: - self._streaming_state['offset'] += offset - else: - self._streaming_state['offset'] = torch.tensor(0) - return nk, nv - - def _apply_rope(self, query: torch.Tensor, key: torch.Tensor): - # TODO: fix and verify layout. - assert _efficient_attention_backend == 'xformers', 'Rope not supported with torch attn.' - # Apply rope embeddings to query and key tensors. - assert self.rope is not None - if 'past_keys' in self._streaming_state: - past_keys_offset = self._streaming_state['past_keys'].shape[1] - else: - past_keys_offset = 0 - if 'offset' in self._streaming_state: - past_context_offset = int(self._streaming_state['offset'].item()) - else: - past_context_offset = 0 - streaming_offset = past_context_offset + past_keys_offset - return self.rope.rotate_qk(query, key, start=streaming_offset) - - def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, - key_padding_mask=None, need_weights=False, attn_mask=None, - average_attn_weights=True, is_causal=False): - assert attn_mask is None - assert not is_causal, ("new param added in torch 2.0.1 not supported, " - "use the causal args in the constructor.") - - time_dim = _get_attention_time_dimension() - if time_dim == 2: - layout = "b h t d" - else: - layout = "b t h d" - dtype = query.dtype - if self._is_streaming: - assert self.causal or self.cross_attention, \ - "Streaming only available for causal or cross attention" - - if self.causal: - # At the moment we specialize only for the self-attention case. - assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value" - assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value" - attn_mask = self._get_mask(query.shape[1], query.device, query.dtype) - - if self.custom: - # custom implementation - assert need_weights is False - assert key_padding_mask is None - if self.cross_attention: - # Different queries, keys, values, we have to spit manually the weights - # before applying the linear. - dim = self.in_proj_weight.shape[0] // 3 - if self.in_proj_bias is None: - bias_q, bias_k, bias_v = None, None, None - else: - bias_q = self.in_proj_bias[:dim] - bias_k = self.in_proj_bias[dim: 2 * dim] - bias_v = self.in_proj_bias[2 * dim:] - q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q) - # todo: when streaming, we could actually save k, v and check the shape actually match. - k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k) - v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v) - if self.qk_layer_norm is True: - q = self.q_layer_norm(q) - k = self.k_layer_norm(k) - q, k, v = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k, v]] - else: - if not _is_profiled(): - # profiling breaks that propertysomehow. - assert query is key, "specialized implementation" - assert value is key, "specialized implementation" - projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias) - if self.kv_repeat == 1: - if time_dim == 2: - bound_layout = "b h p t d" - else: - bound_layout = "b t p h d" - packed = rearrange(projected, f"b t (p h d) -> {bound_layout}", p=3, h=self.num_heads) - q, k, v = ops.unbind(packed, dim=2) - else: - embed_dim = self.embed_dim - per_head_dim = (embed_dim // self.num_heads) - kv_heads = self.num_heads // self.kv_repeat - q = projected[:, :, :embed_dim] - start = embed_dim - end = start + per_head_dim * kv_heads - k = projected[:, :, start: end] - v = projected[:, :, end:] - q = rearrange(q, f"b t (h d) -> {layout}", h=self.num_heads) - k = rearrange(k, f"b t (h d) -> {layout}", h=kv_heads) - v = rearrange(v, f"b t (h d) -> {layout}", h=kv_heads) - - if self.qk_layer_norm is True: - assert self.kv_repeat == 1 - q, k = [rearrange(x, f"{layout} -> b t (h d)") for x in [q, k]] - q = self.q_layer_norm(q) - k = self.k_layer_norm(k) - q, k = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k]] - if self.rope: - q, k = self._apply_rope(q, k) - k, v = self._complete_kv(k, v) - if self.kv_repeat > 1: - k = expand_repeated_kv(k, self.kv_repeat) - v = expand_repeated_kv(v, self.kv_repeat) - if self.attention_as_float32: - q, k, v = [x.float() for x in [q, k, v]] - if self.memory_efficient: - p = self.dropout if self.training else 0 - if _efficient_attention_backend == 'torch': - x = torch.nn.functional.scaled_dot_product_attention( - q, k, v, is_causal=attn_mask is not None, dropout_p=p) - else: - x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p) - else: - # We include the dot product as float32, for consistency - # with the other implementations that include that step - # as part of the attention. Note that when using `autocast`, - # the einsums would be done as bfloat16, but the softmax - # would be done as bfloat16, so `attention_as_float32` will - # extend a bit the range of operations done in float32, - # although this should make no difference. - q = q / q.shape[-1] ** 0.5 - key_layout = layout.replace('t', 'k') - query_layout = layout - if self._is_streaming and self.safe_streaming and q.device.type == 'cuda': - with torch.autocast(device_type=q.device.type, dtype=torch.float32): - pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k) - else: - pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k) - if attn_mask is not None: - pre_w = pre_w + attn_mask - w = torch.softmax(pre_w, dim=-1) - w = F.dropout(w, self.dropout, training=self.training).to(v) - # Key and value have the same format. - x = torch.einsum(f"b h t k, {key_layout} -> {layout}", w, v) - x = x.to(dtype) - x = rearrange(x, f"{layout} -> b t (h d)", h=self.num_heads) - x = self.out_proj(x) - else: - key, value = self._complete_kv(key, value) - if self.attention_as_float32: - query, key, value = [x.float() for x in [query, key, value]] - x, _ = self.mha( - query, key, value, key_padding_mask, - need_weights, attn_mask, average_attn_weights) - x = x.to(dtype) - - return x, None - - -class StreamingTransformerLayer(nn.TransformerEncoderLayer): - """TransformerLayer with Streaming / Causal support. - This also integrates cross_attention, when passing `cross_attention=True`, - rather than having two separate classes like in PyTorch. - - Args: - d_model (int): Dimension of the data. - num_heads (int): Number of heads. - dim_feedforward (int): Intermediate dimension of FF module. - dropout (float): Dropout both for MHA and FF. - bias_ff (bool): Use bias for FF. - bias_attn (bool): Use bias for MHA. - causal (bool): Causal mask applied automatically. - past_context (int or None): Receptive field for the causal mask, infinite if None. - custom (bool): Use custom MHA implementation, for testing / benchmarking. - memory_efficient (bool): Use xformers based memory efficient attention. - attention_as_float32 (bool): Perform the attention as float32 - (especially important with memory_efficient as autocast won't do this automatically). - qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention. - qk_layer_norm_cross (bool): Same for the cross attention. - cross_attention (bool): If True, expect to get secondary input for cross-attention. - Cross attention will use the default MHA, as it typically won't require - special treatment. - layer_scale (float or None): If not None, LayerScale will be used with - the given value as initial scale. - rope (`RotaryEmbedding` or None): Rope embedding to use. - attention_dropout (float or None): If not None, separate the value of the dimension dropout - in FFN and of the attention dropout. - kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads). - This will lead to faster decoding time on A100 or other GPUs with tensorcore. - device (torch.device or None): Device on which to initialize. - dtype (torch.dtype or None): dtype to use. - **kwargs: See `nn.TransformerEncoderLayer`. - """ - def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1, - bias_ff: bool = True, bias_attn: bool = True, causal: bool = False, - past_context: tp.Optional[int] = None, custom: bool = False, - memory_efficient: bool = False, attention_as_float32: bool = False, - qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False, - cross_attention: bool = False, layer_scale: tp.Optional[float] = None, - rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None, - kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs): - super().__init__(d_model, num_heads, dim_feedforward, dropout, - device=device, dtype=dtype, batch_first=True, **kwargs) - factory_kwargs = {'device': device, 'dtype': dtype} - # Redefine self_attn to our streaming multi-head attention - attn_kwargs: tp.Dict[str, tp.Any] = { - 'embed_dim': d_model, - 'num_heads': num_heads, - 'dropout': dropout if attention_dropout is None else attention_dropout, - 'bias': bias_attn, - 'custom': custom, - 'memory_efficient': memory_efficient, - 'attention_as_float32': attention_as_float32, - } - self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention( - causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm, - kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs) # type: ignore - # Redefine feedforward layers to expose bias parameter - self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs) - self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs) - - self.layer_scale_1: nn.Module - self.layer_scale_2: nn.Module - if layer_scale is None: - self.layer_scale_1 = nn.Identity() - self.layer_scale_2 = nn.Identity() - else: - self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs) - self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs) - - self.cross_attention: tp.Optional[nn.Module] = None - if cross_attention: - self.cross_attention = StreamingMultiheadAttention( - cross_attention=True, qk_layer_norm=qk_layer_norm_cross, - **attn_kwargs, **factory_kwargs) - # Norm and dropout - self.dropout_cross = nn.Dropout(dropout) - # eps value matching that used in PyTorch reference implementation. - self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs) - self.layer_scale_cross: nn.Module - if layer_scale is None: - self.layer_scale_cross = nn.Identity() - else: - self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs) - self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs) # type: ignore - self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs) # type: ignore - - def _cross_attention_block(self, src: torch.Tensor, - cross_attention_src: torch.Tensor) -> torch.Tensor: - assert self.cross_attention is not None - # queries are from src, keys and values from cross_attention_src. - x = self.cross_attention( - src, cross_attention_src, cross_attention_src, need_weights=False)[0] - return self.dropout_cross(x) # type: ignore - - def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None, # type: ignore - src_key_padding_mask: tp.Optional[torch.Tensor] = None, - cross_attention_src: tp.Optional[torch.Tensor] = None): - if self.cross_attention is None: - assert cross_attention_src is None - else: - assert cross_attention_src is not None - x = src - if self.norm_first: - x = x + self.layer_scale_1( - self._sa_block(self.norm1(x), src_mask, src_key_padding_mask)) - if cross_attention_src is not None: - x = x + self.layer_scale_cross( - self._cross_attention_block( - self.norm_cross(x), cross_attention_src)) - x = x + self.layer_scale_2(self._ff_block(self.norm2(x))) - else: - x = self.norm1(x + self.layer_scale_1( - self._sa_block(x, src_mask, src_key_padding_mask))) - if cross_attention_src is not None: - x = self.norm_cross( - x + self.layer_scale_cross( - self._cross_attention_block(src, cross_attention_src))) - x = self.norm2(x + self.layer_scale_2(self._ff_block(x))) - return x - - -class StreamingTransformer(StreamingModule): - """Transformer with Streaming / Causal support. - - Args: - d_model (int): Dimension of the data. - num_heads (int): Number of heads. - dim_feedforward (int): Intermediate dimension of FF module. - dropout (float): Dropout both for MHA and FF. - bias_ff (bool): Use bias for FF. - bias_attn (bool): Use bias for MHA. - causal (bool): Causal mask applied automatically. - past_context (int or None): Receptive field for the causal mask, infinite if None. - custom (bool): Use custom MHA implementation, for testing / benchmarking. - memory_efficient (bool): Use xformers based memory efficient attention. - attention_as_float32 (bool): Perform the attention as float32 - (especially important with memory_efficient as autocast won't do this automatically). - cross_attention (bool): If True, expect to get secondary input for cross-attention. - layer_scale (float or None): If not None, LayerScale will be used - with the given value as initial scale. - positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope). - max_period (float): Maximum period of the time embedding. - positional_scale (float): Scale of positional embedding, set to 0 to deactivate. - xpos (bool): Apply xpos exponential decay to positional embedding (rope only). - lr (float or None): learning rate override through the `make_optim_group` API. - weight_decay (float or None): Weight_decay override through the `make_optim_group` API. - layer_class: (subclass of `StreamingTransformerLayer): class to use - to initialize the layers, allowing further customization outside of Audiocraft. - checkpointing (str): Checkpointing strategy to reduce memory usage. - No checkpointing if set to 'none'. Per layer checkpointing using PyTorch - if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice, - minimal memory usage, but maximal runtime). Finally, `xformers_default` provide - a policy for opting-out some operations of the checkpointing like - linear layers and attention, providing a middle ground between speed and memory. - device (torch.device or None): Device on which to initialize. - dtype (torch.dtype or None): dtype to use. - **kwargs: See `nn.TransformerEncoderLayer`. - """ - def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048, - dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True, - causal: bool = False, past_context: tp.Optional[int] = None, - custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False, - cross_attention: bool = False, layer_scale: tp.Optional[float] = None, - positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1., - xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None, - layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer, - checkpointing: str = 'none', device=None, dtype=None, **kwargs): - super().__init__() - assert d_model % num_heads == 0 - - self.positional_embedding = positional_embedding - self.max_period = max_period - self.positional_scale = positional_scale - self.weight_decay = weight_decay - self.lr = lr - - assert positional_embedding in ['sin', 'rope', 'sin_rope'] - self.rope: tp.Optional[RotaryEmbedding] = None - if self.positional_embedding in ['rope', 'sin_rope']: - assert _is_custom(custom, memory_efficient) - self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period, - xpos=xpos, scale=positional_scale, device=device) - - self.checkpointing = checkpointing - - assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm'] - if self.checkpointing.startswith('xformers'): - _verify_xformers_internal_compat() - - self.layers = nn.ModuleList() - for idx in range(num_layers): - self.layers.append( - layer_class( - d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward, - dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn, - causal=causal, past_context=past_context, custom=custom, - memory_efficient=memory_efficient, attention_as_float32=attention_as_float32, - cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope, - device=device, dtype=dtype, **kwargs)) - - if self.checkpointing != 'none': - for layer in self.layers: - # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the - # backward hook inside of FSDP... - layer._magma_checkpointed = True # type: ignore - assert layer.layer_drop == 0., "Need further checking" # type: ignore - - def _apply_layer(self, layer, *args, **kwargs): - method = self.checkpointing - if method == 'none': - return layer(*args, **kwargs) - elif method == 'torch': - return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs) - elif method.startswith('xformers'): - from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy - if method == 'xformers_default': - # those operations will be saved, and not recomputed. - # According to Francisco we can get smarter policies but this is a good start. - allow_list = [ - "xformers.efficient_attention_forward_cutlass.default", - "xformers_flash.flash_fwd.default", - "aten.addmm.default", - "aten.mm.default", - ] - elif method == 'xformers_mm': - # those operations will be saved, and not recomputed. - # According to Francisco we can get smarter policies but this is a good start. - allow_list = [ - "aten.addmm.default", - "aten.mm.default", - ] - else: - raise ValueError(f"xformers checkpointing xformers policy {method} is not known.") - policy_fn = _get_default_policy(allow_list) - return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs) - else: - raise ValueError(f"Checkpointing method {method} is unknown.") - - def forward(self, x: torch.Tensor, *args, **kwargs): - B, T, C = x.shape - - if 'offsets' in self._streaming_state: - offsets = self._streaming_state['offsets'] - else: - offsets = torch.zeros(B, dtype=torch.long, device=x.device) - - if self.positional_embedding in ['sin', 'sin_rope']: - positions = torch.arange(T, device=x.device).view(1, -1, 1) - positions = positions + offsets.view(-1, 1, 1) - pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype) - x = x + self.positional_scale * pos_emb - - for layer in self.layers: - x = self._apply_layer(layer, x, *args, **kwargs) - - if self._is_streaming: - self._streaming_state['offsets'] = offsets + T - - return x - - def make_optim_group(self): - group = {"params": list(self.parameters())} - if self.lr is not None: - group["lr"] = self.lr - if self.weight_decay is not None: - group["weight_decay"] = self.weight_decay - return group - - -# special attention attention related function - -def _verify_xformers_memory_efficient_compat(): - try: - from xformers.ops import memory_efficient_attention, LowerTriangularMask # noqa - except ImportError: - raise ImportError( - "xformers is not installed. Please install it and try again.\n" - "To install on AWS and Azure, run \n" - "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n" - "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n" - "To install on FAIR Cluster, run \n" - "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n" - "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n") - - -def _verify_xformers_internal_compat(): - try: - from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy # noqa - except ImportError: - raise ImportError( - "Francisco's fairinternal xformers is not installed. Please install it and try again.\n" - "To install on AWS and Azure, run \n" - "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n" - "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n" - "To install on FAIR Cluster, run \n" - "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n" - "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n") - - -def _is_custom(custom: bool, memory_efficient: bool): - return custom or memory_efficient diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/initialise.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/initialise.py deleted file mode 100644 index d5fd4b71fed1bb4871717f978f0c470280f099c1..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/initialise.py +++ /dev/null @@ -1,121 +0,0 @@ -# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file. -import atexit -import contextlib -import sys - -from .ansitowin32 import AnsiToWin32 - - -def _wipe_internal_state_for_tests(): - global orig_stdout, orig_stderr - orig_stdout = None - orig_stderr = None - - global wrapped_stdout, wrapped_stderr - wrapped_stdout = None - wrapped_stderr = None - - global atexit_done - atexit_done = False - - global fixed_windows_console - fixed_windows_console = False - - try: - # no-op if it wasn't registered - atexit.unregister(reset_all) - except AttributeError: - # python 2: no atexit.unregister. Oh well, we did our best. - pass - - -def reset_all(): - if AnsiToWin32 is not None: # Issue #74: objects might become None at exit - AnsiToWin32(orig_stdout).reset_all() - - -def init(autoreset=False, convert=None, strip=None, wrap=True): - - if not wrap and any([autoreset, convert, strip]): - raise ValueError('wrap=False conflicts with any other arg=True') - - global wrapped_stdout, wrapped_stderr - global orig_stdout, orig_stderr - - orig_stdout = sys.stdout - orig_stderr = sys.stderr - - if sys.stdout is None: - wrapped_stdout = None - else: - sys.stdout = wrapped_stdout = \ - wrap_stream(orig_stdout, convert, strip, autoreset, wrap) - if sys.stderr is None: - wrapped_stderr = None - else: - sys.stderr = wrapped_stderr = \ - wrap_stream(orig_stderr, convert, strip, autoreset, wrap) - - global atexit_done - if not atexit_done: - atexit.register(reset_all) - atexit_done = True - - -def deinit(): - if orig_stdout is not None: - sys.stdout = orig_stdout - if orig_stderr is not None: - sys.stderr = orig_stderr - - -def just_fix_windows_console(): - global fixed_windows_console - - if sys.platform != "win32": - return - if fixed_windows_console: - return - if wrapped_stdout is not None or wrapped_stderr is not None: - # Someone already ran init() and it did stuff, so we won't second-guess them - return - - # On newer versions of Windows, AnsiToWin32.__init__ will implicitly enable the - # native ANSI support in the console as a side-effect. We only need to actually - # replace sys.stdout/stderr if we're in the old-style conversion mode. - new_stdout = AnsiToWin32(sys.stdout, convert=None, strip=None, autoreset=False) - if new_stdout.convert: - sys.stdout = new_stdout - new_stderr = AnsiToWin32(sys.stderr, convert=None, strip=None, autoreset=False) - if new_stderr.convert: - sys.stderr = new_stderr - - fixed_windows_console = True - -@contextlib.contextmanager -def colorama_text(*args, **kwargs): - init(*args, **kwargs) - try: - yield - finally: - deinit() - - -def reinit(): - if wrapped_stdout is not None: - sys.stdout = wrapped_stdout - if wrapped_stderr is not None: - sys.stderr = wrapped_stderr - - -def wrap_stream(stream, convert, strip, autoreset, wrap): - if wrap: - wrapper = AnsiToWin32(stream, - convert=convert, strip=strip, autoreset=autoreset) - if wrapper.should_wrap(): - stream = wrapper.stream - return stream - - -# Use this for initial setup as well, to reduce code duplication -_wipe_internal_state_for_tests() diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/config.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/config.py deleted file mode 100644 index 49a55b1bc87509e2bb24b902ae12c21d5aaeda81..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/config.py +++ /dev/null @@ -1,265 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -import functools -import inspect -import logging -from fvcore.common.config import CfgNode as _CfgNode - -from detectron2.utils.file_io import PathManager - - -class CfgNode(_CfgNode): - """ - The same as `fvcore.common.config.CfgNode`, but different in: - - 1. Use unsafe yaml loading by default. - Note that this may lead to arbitrary code execution: you must not - load a config file from untrusted sources before manually inspecting - the content of the file. - 2. Support config versioning. - When attempting to merge an old config, it will convert the old config automatically. - - .. automethod:: clone - .. automethod:: freeze - .. automethod:: defrost - .. automethod:: is_frozen - .. automethod:: load_yaml_with_base - .. automethod:: merge_from_list - .. automethod:: merge_from_other_cfg - """ - - @classmethod - def _open_cfg(cls, filename): - return PathManager.open(filename, "r") - - # Note that the default value of allow_unsafe is changed to True - def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None: - """ - Load content from the given config file and merge it into self. - - Args: - cfg_filename: config filename - allow_unsafe: allow unsafe yaml syntax - """ - assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!" - loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe) - loaded_cfg = type(self)(loaded_cfg) - - # defaults.py needs to import CfgNode - from .defaults import _C - - latest_ver = _C.VERSION - assert ( - latest_ver == self.VERSION - ), "CfgNode.merge_from_file is only allowed on a config object of latest version!" - - logger = logging.getLogger(__name__) - - loaded_ver = loaded_cfg.get("VERSION", None) - if loaded_ver is None: - from .compat import guess_version - - loaded_ver = guess_version(loaded_cfg, cfg_filename) - assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format( - loaded_ver, self.VERSION - ) - - if loaded_ver == self.VERSION: - self.merge_from_other_cfg(loaded_cfg) - else: - # compat.py needs to import CfgNode - from .compat import upgrade_config, downgrade_config - - logger.warning( - "Loading an old v{} config file '{}' by automatically upgrading to v{}. " - "See docs/CHANGELOG.md for instructions to update your files.".format( - loaded_ver, cfg_filename, self.VERSION - ) - ) - # To convert, first obtain a full config at an old version - old_self = downgrade_config(self, to_version=loaded_ver) - old_self.merge_from_other_cfg(loaded_cfg) - new_config = upgrade_config(old_self) - self.clear() - self.update(new_config) - - def dump(self, *args, **kwargs): - """ - Returns: - str: a yaml string representation of the config - """ - # to make it show up in docs - return super().dump(*args, **kwargs) - - -global_cfg = CfgNode() - - -def get_cfg() -> CfgNode: - """ - Get a copy of the default config. - - Returns: - a detectron2 CfgNode instance. - """ - from .defaults import _C - - return _C.clone() - - -def set_global_cfg(cfg: CfgNode) -> None: - """ - Let the global config point to the given cfg. - - Assume that the given "cfg" has the key "KEY", after calling - `set_global_cfg(cfg)`, the key can be accessed by: - :: - from detectron2.config import global_cfg - print(global_cfg.KEY) - - By using a hacky global config, you can access these configs anywhere, - without having to pass the config object or the values deep into the code. - This is a hacky feature introduced for quick prototyping / research exploration. - """ - global global_cfg - global_cfg.clear() - global_cfg.update(cfg) - - -def configurable(init_func=None, *, from_config=None): - """ - Decorate a function or a class's __init__ method so that it can be called - with a :class:`CfgNode` object using a :func:`from_config` function that translates - :class:`CfgNode` to arguments. - - Examples: - :: - # Usage 1: Decorator on __init__: - class A: - @configurable - def __init__(self, a, b=2, c=3): - pass - - @classmethod - def from_config(cls, cfg): # 'cfg' must be the first argument - # Returns kwargs to be passed to __init__ - return {"a": cfg.A, "b": cfg.B} - - a1 = A(a=1, b=2) # regular construction - a2 = A(cfg) # construct with a cfg - a3 = A(cfg, b=3, c=4) # construct with extra overwrite - - # Usage 2: Decorator on any function. Needs an extra from_config argument: - @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B}) - def a_func(a, b=2, c=3): - pass - - a1 = a_func(a=1, b=2) # regular call - a2 = a_func(cfg) # call with a cfg - a3 = a_func(cfg, b=3, c=4) # call with extra overwrite - - Args: - init_func (callable): a class's ``__init__`` method in usage 1. The - class must have a ``from_config`` classmethod which takes `cfg` as - the first argument. - from_config (callable): the from_config function in usage 2. It must take `cfg` - as its first argument. - """ - - if init_func is not None: - assert ( - inspect.isfunction(init_func) - and from_config is None - and init_func.__name__ == "__init__" - ), "Incorrect use of @configurable. Check API documentation for examples." - - @functools.wraps(init_func) - def wrapped(self, *args, **kwargs): - try: - from_config_func = type(self).from_config - except AttributeError as e: - raise AttributeError( - "Class with @configurable must have a 'from_config' classmethod." - ) from e - if not inspect.ismethod(from_config_func): - raise TypeError("Class with @configurable must have a 'from_config' classmethod.") - - if _called_with_cfg(*args, **kwargs): - explicit_args = _get_args_from_config(from_config_func, *args, **kwargs) - init_func(self, **explicit_args) - else: - init_func(self, *args, **kwargs) - - return wrapped - - else: - if from_config is None: - return configurable # @configurable() is made equivalent to @configurable - assert inspect.isfunction( - from_config - ), "from_config argument of configurable must be a function!" - - def wrapper(orig_func): - @functools.wraps(orig_func) - def wrapped(*args, **kwargs): - if _called_with_cfg(*args, **kwargs): - explicit_args = _get_args_from_config(from_config, *args, **kwargs) - return orig_func(**explicit_args) - else: - return orig_func(*args, **kwargs) - - wrapped.from_config = from_config - return wrapped - - return wrapper - - -def _get_args_from_config(from_config_func, *args, **kwargs): - """ - Use `from_config` to obtain explicit arguments. - - Returns: - dict: arguments to be used for cls.__init__ - """ - signature = inspect.signature(from_config_func) - if list(signature.parameters.keys())[0] != "cfg": - if inspect.isfunction(from_config_func): - name = from_config_func.__name__ - else: - name = f"{from_config_func.__self__}.from_config" - raise TypeError(f"{name} must take 'cfg' as the first argument!") - support_var_arg = any( - param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD] - for param in signature.parameters.values() - ) - if support_var_arg: # forward all arguments to from_config, if from_config accepts them - ret = from_config_func(*args, **kwargs) - else: - # forward supported arguments to from_config - supported_arg_names = set(signature.parameters.keys()) - extra_kwargs = {} - for name in list(kwargs.keys()): - if name not in supported_arg_names: - extra_kwargs[name] = kwargs.pop(name) - ret = from_config_func(*args, **kwargs) - # forward the other arguments to __init__ - ret.update(extra_kwargs) - return ret - - -def _called_with_cfg(*args, **kwargs): - """ - Returns: - bool: whether the arguments contain CfgNode and should be considered - forwarded to from_config. - """ - from omegaconf import DictConfig - - if len(args) and isinstance(args[0], (_CfgNode, DictConfig)): - return True - if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)): - return True - # `from_config`'s first argument is forced to be "cfg". - # So the above check covers all cases. - return False diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/matcher.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/matcher.py deleted file mode 100644 index c7597cab5a89a7e828b8eee53d1a3712be6dbc62..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/matcher.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from typing import List -import torch - -from detectron2.layers import nonzero_tuple - - -# TODO: the name is too general -class Matcher(object): - """ - This class assigns to each predicted "element" (e.g., a box) a ground-truth - element. Each predicted element will have exactly zero or one matches; each - ground-truth element may be matched to zero or more predicted elements. - - The matching is determined by the MxN match_quality_matrix, that characterizes - how well each (ground-truth, prediction)-pair match each other. For example, - if the elements are boxes, this matrix may contain box intersection-over-union - overlap values. - - The matcher returns (a) a vector of length N containing the index of the - ground-truth element m in [0, M) that matches to prediction n in [0, N). - (b) a vector of length N containing the labels for each prediction. - """ - - def __init__( - self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False - ): - """ - Args: - thresholds (list): a list of thresholds used to stratify predictions - into levels. - labels (list): a list of values to label predictions belonging at - each level. A label can be one of {-1, 0, 1} signifying - {ignore, negative class, positive class}, respectively. - allow_low_quality_matches (bool): if True, produce additional matches - for predictions with maximum match quality lower than high_threshold. - See set_low_quality_matches_ for more details. - - For example, - thresholds = [0.3, 0.5] - labels = [0, -1, 1] - All predictions with iou < 0.3 will be marked with 0 and - thus will be considered as false positives while training. - All predictions with 0.3 <= iou < 0.5 will be marked with -1 and - thus will be ignored. - All predictions with 0.5 <= iou will be marked with 1 and - thus will be considered as true positives. - """ - # Add -inf and +inf to first and last position in thresholds - thresholds = thresholds[:] - assert thresholds[0] > 0 - thresholds.insert(0, -float("inf")) - thresholds.append(float("inf")) - # Currently torchscript does not support all + generator - assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])]) - assert all([l in [-1, 0, 1] for l in labels]) - assert len(labels) == len(thresholds) - 1 - self.thresholds = thresholds - self.labels = labels - self.allow_low_quality_matches = allow_low_quality_matches - - def __call__(self, match_quality_matrix): - """ - Args: - match_quality_matrix (Tensor[float]): an MxN tensor, containing the - pairwise quality between M ground-truth elements and N predicted - elements. All elements must be >= 0 (due to the us of `torch.nonzero` - for selecting indices in :meth:`set_low_quality_matches_`). - - Returns: - matches (Tensor[int64]): a vector of length N, where matches[i] is a matched - ground-truth index in [0, M) - match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates - whether a prediction is a true or false positive or ignored - """ - assert match_quality_matrix.dim() == 2 - if match_quality_matrix.numel() == 0: - default_matches = match_quality_matrix.new_full( - (match_quality_matrix.size(1),), 0, dtype=torch.int64 - ) - # When no gt boxes exist, we define IOU = 0 and therefore set labels - # to `self.labels[0]`, which usually defaults to background class 0 - # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds - default_match_labels = match_quality_matrix.new_full( - (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 - ) - return default_matches, default_match_labels - - assert torch.all(match_quality_matrix >= 0) - - # match_quality_matrix is M (gt) x N (predicted) - # Max over gt elements (dim 0) to find best gt candidate for each prediction - matched_vals, matches = match_quality_matrix.max(dim=0) - - match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) - - for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): - low_high = (matched_vals >= low) & (matched_vals < high) - match_labels[low_high] = l - - if self.allow_low_quality_matches: - self.set_low_quality_matches_(match_labels, match_quality_matrix) - - return matches, match_labels - - def set_low_quality_matches_(self, match_labels, match_quality_matrix): - """ - Produce additional matches for predictions that have only low-quality matches. - Specifically, for each ground-truth G find the set of predictions that have - maximum overlap with it (including ties); for each prediction in that set, if - it is unmatched, then match it to the ground-truth G. - - This function implements the RPN assignment case (i) in Sec. 3.1.2 of - :paper:`Faster R-CNN`. - """ - # For each gt, find the prediction with which it has highest quality - highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) - # Find the highest quality match available, even if it is low, including ties. - # Note that the matches qualities must be positive due to the use of - # `torch.nonzero`. - _, pred_inds_with_highest_quality = nonzero_tuple( - match_quality_matrix == highest_quality_foreach_gt[:, None] - ) - # If an anchor was labeled positive only due to a low-quality match - # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B. - # This follows the implementation in Detectron, and is found to have no significant impact. - match_labels[pred_inds_with_highest_quality] = 1 diff --git a/spaces/B10915003/B10915003-autotrain-jimmy-test-face-identification-53251125423/app.py b/spaces/B10915003/B10915003-autotrain-jimmy-test-face-identification-53251125423/app.py deleted file mode 100644 index 1694623513c3e22a4bb69673e3f4b33a11b8c595..0000000000000000000000000000000000000000 --- a/spaces/B10915003/B10915003-autotrain-jimmy-test-face-identification-53251125423/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/B10915003/autotrain-jimmy-test-face-identification-53251125423").launch() \ No newline at end of file diff --git a/spaces/Bart92/RVC_HF/infer/modules/vc/pipeline.py b/spaces/Bart92/RVC_HF/infer/modules/vc/pipeline.py deleted file mode 100644 index 76e712c649b95e21f9bbe6416ae8b7050317b479..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/infer/modules/vc/pipeline.py +++ /dev/null @@ -1,655 +0,0 @@ -import os -import sys -import traceback -import logging - -logger = logging.getLogger(__name__) - -from functools import lru_cache -from time import time as ttime -from torch import Tensor -import faiss -import librosa -import numpy as np -import parselmouth -import pyworld -import torch -import torch.nn.functional as F -import torchcrepe -from scipy import signal -from tqdm import tqdm - -import random -now_dir = os.getcwd() -sys.path.append(now_dir) -import re -from functools import partial -bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) - -input_audio_path2wav = {} -from LazyImport import lazyload -torchcrepe = lazyload("torchcrepe") # Fork Feature. Crepe algo for training and preprocess -torch = lazyload("torch") -from infer.lib.rmvpe import RMVPE - -@lru_cache -def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period): - audio = input_audio_path2wav[input_audio_path] - f0, t = pyworld.harvest( - audio, - fs=fs, - f0_ceil=f0max, - f0_floor=f0min, - frame_period=frame_period, - ) - f0 = pyworld.stonemask(audio, f0, t, fs) - return f0 - - -def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比 - # print(data1.max(),data2.max()) - rms1 = librosa.feature.rms( - y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2 - ) # 每半秒一个点 - rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2) - rms1 = torch.from_numpy(rms1) - rms1 = F.interpolate( - rms1.unsqueeze(0), size=data2.shape[0], mode="linear" - ).squeeze() - rms2 = torch.from_numpy(rms2) - rms2 = F.interpolate( - rms2.unsqueeze(0), size=data2.shape[0], mode="linear" - ).squeeze() - rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6) - data2 *= ( - torch.pow(rms1, torch.tensor(1 - rate)) - * torch.pow(rms2, torch.tensor(rate - 1)) - ).numpy() - return data2 - - -class Pipeline(object): - def __init__(self, tgt_sr, config): - self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = ( - config.x_pad, - config.x_query, - config.x_center, - config.x_max, - config.is_half, - ) - self.sr = 16000 # hubert输入采样率 - self.window = 160 # 每帧点数 - self.t_pad = self.sr * self.x_pad # 每条前后pad时间 - self.t_pad_tgt = tgt_sr * self.x_pad - self.t_pad2 = self.t_pad * 2 - self.t_query = self.sr * self.x_query # 查询切点前后查询时间 - self.t_center = self.sr * self.x_center # 查询切点位置 - self.t_max = self.sr * self.x_max # 免查询时长阈值 - self.device = config.device - self.model_rmvpe = RMVPE("%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device) - self.f0_method_dict = { - "pm": self.get_pm, - "harvest": self.get_harvest, - "dio": self.get_dio, - "rmvpe": self.get_rmvpe, - "rmvpe+": self.get_pitch_dependant_rmvpe, - "crepe": self.get_f0_official_crepe_computation, - "crepe-tiny": partial(self.get_f0_official_crepe_computation, model='model'), - "mangio-crepe": self.get_f0_crepe_computation, - "mangio-crepe-tiny": partial(self.get_f0_crepe_computation, model='model'), - - } - self.note_dict = [ - 65.41, 69.30, 73.42, 77.78, 82.41, 87.31, - 92.50, 98.00, 103.83, 110.00, 116.54, 123.47, - 130.81, 138.59, 146.83, 155.56, 164.81, 174.61, - 185.00, 196.00, 207.65, 220.00, 233.08, 246.94, - 261.63, 277.18, 293.66, 311.13, 329.63, 349.23, - 369.99, 392.00, 415.30, 440.00, 466.16, 493.88, - 523.25, 554.37, 587.33, 622.25, 659.25, 698.46, - 739.99, 783.99, 830.61, 880.00, 932.33, 987.77, - 1046.50, 1108.73, 1174.66, 1244.51, 1318.51, 1396.91, - 1479.98, 1567.98, 1661.22, 1760.00, 1864.66, 1975.53, - 2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83, - 2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07 - ] - - # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device) - def get_optimal_torch_device(self, index: int = 0) -> torch.device: - if torch.cuda.is_available(): - return torch.device( - f"cuda:{index % torch.cuda.device_count()}" - ) # Very fast - elif torch.backends.mps.is_available(): - return torch.device("mps") - return torch.device("cpu") - - # Fork Feature: Compute f0 with the crepe method - def get_f0_crepe_computation( - self, - x, - f0_min, - f0_max, - p_len, - *args, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time. - **kwargs, # Either use crepe-tiny "tiny" or crepe "full". Default is full - ): - x = x.astype( - np.float32 - ) # fixes the F.conv2D exception. We needed to convert double to float. - x /= np.quantile(np.abs(x), 0.999) - torch_device = self.get_optimal_torch_device() - audio = torch.from_numpy(x).to(torch_device, copy=True) - audio = torch.unsqueeze(audio, dim=0) - if audio.ndim == 2 and audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True).detach() - audio = audio.detach() - hop_length = kwargs.get('crepe_hop_length', 160) - model = kwargs.get('model', 'full') - print("Initiating prediction with a crepe_hop_length of: " + str(hop_length)) - pitch: Tensor = torchcrepe.predict( - audio, - self.sr, - hop_length, - f0_min, - f0_max, - model, - batch_size=hop_length * 2, - device=torch_device, - pad=True, - ) - p_len = p_len or x.shape[0] // hop_length - # Resize the pitch for final f0 - source = np.array(pitch.squeeze(0).cpu().float().numpy()) - source[source < 0.001] = np.nan - target = np.interp( - np.arange(0, len(source) * p_len, len(source)) / p_len, - np.arange(0, len(source)), - source, - ) - f0 = np.nan_to_num(target) - return f0 # Resized f0 - - def get_f0_official_crepe_computation( - self, - x, - f0_min, - f0_max, - *args, - **kwargs - ): - # Pick a batch size that doesn't cause memory errors on your gpu - batch_size = 512 - # Compute pitch using first gpu - audio = torch.tensor(np.copy(x))[None].float() - model = kwargs.get('model', 'full') - f0, pd = torchcrepe.predict( - audio, - self.sr, - self.window, - f0_min, - f0_max, - model, - batch_size=batch_size, - device=self.device, - return_periodicity=True, - ) - pd = torchcrepe.filter.median(pd, 3) - f0 = torchcrepe.filter.mean(f0, 3) - f0[pd < 0.1] = 0 - f0 = f0[0].cpu().numpy() - return f0 - - # Fork Feature: Compute pYIN f0 method - def get_f0_pyin_computation(self, x, f0_min, f0_max): - y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True) - f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max) - f0 = f0[1:] # Get rid of extra first frame - return f0 - - def get_pm(self, x, p_len, *args, **kwargs): - f0 = parselmouth.Sound(x, self.sr).to_pitch_ac( - time_step=160 / 16000, - voicing_threshold=0.6, - pitch_floor=kwargs.get('f0_min'), - pitch_ceiling=kwargs.get('f0_max'), - ).selected_array["frequency"] - - return np.pad( - f0, - [[max(0, (p_len - len(f0) + 1) // 2), max(0, p_len - len(f0) - (p_len - len(f0) + 1) // 2)]], - mode="constant" - ) - - def get_harvest(self, x, *args, **kwargs): - f0_spectral = pyworld.harvest( - x.astype(np.double), - fs=self.sr, - f0_ceil=kwargs.get('f0_max'), - f0_floor=kwargs.get('f0_min'), - frame_period=1000 * kwargs.get('hop_length', 160) / self.sr, - ) - return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr) - - def get_dio(self, x, *args, **kwargs): - f0_spectral = pyworld.dio( - x.astype(np.double), - fs=self.sr, - f0_ceil=kwargs.get('f0_max'), - f0_floor=kwargs.get('f0_min'), - frame_period=1000 * kwargs.get('hop_length', 160) / self.sr, - ) - return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr) - - - def get_rmvpe(self, x, *args, **kwargs): - if not hasattr(self, "model_rmvpe"): - from infer.lib.rmvpe import RMVPE - - logger.info( - "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"] - ) - self.model_rmvpe = RMVPE( - "%s/rmvpe.pt" % os.environ["rmvpe_root"], - is_half=self.is_half, - device=self.device, - ) - f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) - - return f0 - - - def get_pitch_dependant_rmvpe(self, x, f0_min=1, f0_max=40000, *args, **kwargs): - return self.model_rmvpe.infer_from_audio_with_pitch(x, thred=0.03, f0_min=f0_min, f0_max=f0_max) - - def autotune_f0(self, f0): - autotuned_f0 = [] - for freq in f0: - closest_notes = [x for x in self.note_dict if abs(x - freq) == min(abs(n - freq) for n in self.note_dict)] - autotuned_f0.append(random.choice(closest_notes)) - return np.array(autotuned_f0, np.float64) - - # Fork Feature: Acquire median hybrid f0 estimation calculation - def get_f0_hybrid_computation( - self, - methods_str, - input_audio_path, - x, - f0_min, - f0_max, - p_len, - filter_radius, - crepe_hop_length, - time_step - ): - # Get various f0 methods from input to use in the computation stack - params = {'x': x, 'p_len': p_len, 'f0_min': f0_min, - 'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius, - 'crepe_hop_length': crepe_hop_length, 'model': "full" - } - methods_str = re.search('hybrid\[(.+)\]', methods_str) - if methods_str: # Ensure a match was found - methods = [method.strip() for method in methods_str.group(1).split('+')] - f0_computation_stack = [] - - print(f"Calculating f0 pitch estimations for methods: {str(methods)}") - x = x.astype(np.float32) - x /= np.quantile(np.abs(x), 0.999) - # Get f0 calculations for all methods specified - - for method in methods: - if method not in self.f0_method_dict: - print(f"Method {method} not found.") - continue - f0 = self.f0_method_dict[method](**params) - if method == 'harvest' and filter_radius > 2: - f0 = signal.medfilt(f0, 3) - f0 = f0[1:] # Get rid of first frame. - f0_computation_stack.append(f0) - - for fc in f0_computation_stack: - print(len(fc)) - - print(f"Calculating hybrid median f0 from the stack of: {str(methods)}") - f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0) - return f0_median_hybrid - - def get_f0( - self, - input_audio_path, - x, - p_len, - f0_up_key, - f0_method, - filter_radius, - crepe_hop_length, - f0_autotune, - inp_f0=None, - f0_min=50, - f0_max=1100, - ): - global input_audio_path2wav - time_step = self.window / self.sr * 1000 - f0_min = 50 - f0_max = 1100 - f0_mel_min = 1127 * np.log(1 + f0_min / 700) - f0_mel_max = 1127 * np.log(1 + f0_max / 700) - params = {'x': x, 'p_len': p_len, 'f0_up_key': f0_up_key, 'f0_min': f0_min, - 'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius, - 'crepe_hop_length': crepe_hop_length, 'model': "full" - } - - if "hybrid" in f0_method: - # Perform hybrid median pitch estimation - input_audio_path2wav[input_audio_path] = x.astype(np.double) - f0 = self.get_f0_hybrid_computation( - f0_method,+ - input_audio_path, - x, - f0_min, - f0_max, - p_len, - filter_radius, - crepe_hop_length, - time_step, - ) - else: - f0 = self.f0_method_dict[f0_method](**params) - - if "privateuseone" in str(self.device): # clean ortruntime memory - del self.model_rmvpe.model - del self.model_rmvpe - logger.info("Cleaning ortruntime memory") - - if f0_autotune: - f0 = self.autotune_f0(f0) - - f0 *= pow(2, f0_up_key / 12) - # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - tf0 = self.sr // self.window # 每秒f0点数 - if inp_f0 is not None: - delta_t = np.round( - (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 - ).astype("int16") - replace_f0 = np.interp( - list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] - ) - shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0] - f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[ - :shape - ] - # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) - f0bak = f0.copy() - f0_mel = 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( - f0_mel_max - f0_mel_min - ) + 1 - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > 255] = 255 - f0_coarse = np.rint(f0_mel).astype(np.int32) - return f0_coarse, f0bak # 1-0 - - def vc( - self, - model, - net_g, - sid, - audio0, - pitch, - pitchf, - times, - index, - big_npy, - index_rate, - version, - protect, - ): # ,file_index,file_big_npy - feats = torch.from_numpy(audio0) - if self.is_half: - feats = feats.half() - else: - feats = feats.float() - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) - - inputs = { - "source": feats.to(self.device), - "padding_mask": padding_mask, - "output_layer": 9 if version == "v1" else 12, - } - t0 = ttime() - with torch.no_grad(): - logits = model.extract_features(**inputs) - feats = model.final_proj(logits[0]) if version == "v1" else logits[0] - if protect < 0.5 and pitch is not None and pitchf is not None: - feats0 = feats.clone() - if ( - not isinstance(index, type(None)) - and not isinstance(big_npy, type(None)) - and index_rate != 0 - ): - npy = feats[0].cpu().numpy() - if self.is_half: - npy = npy.astype("float32") - - # _, I = index.search(npy, 1) - # npy = big_npy[I.squeeze()] - - score, ix = index.search(npy, k=8) - weight = np.square(1 / score) - weight /= weight.sum(axis=1, keepdims=True) - npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) - - if self.is_half: - npy = npy.astype("float16") - feats = ( - torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate - + (1 - index_rate) * feats - ) - - feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) - if protect < 0.5 and pitch is not None and pitchf is not None: - feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute( - 0, 2, 1 - ) - t1 = ttime() - p_len = audio0.shape[0] // self.window - if feats.shape[1] < p_len: - p_len = feats.shape[1] - if pitch is not None and pitchf is not None: - pitch = pitch[:, :p_len] - pitchf = pitchf[:, :p_len] - - if protect < 0.5 and pitch is not None and pitchf is not None: - pitchff = pitchf.clone() - pitchff[pitchf > 0] = 1 - pitchff[pitchf < 1] = protect - pitchff = pitchff.unsqueeze(-1) - feats = feats * pitchff + feats0 * (1 - pitchff) - feats = feats.to(feats0.dtype) - p_len = torch.tensor([p_len], device=self.device).long() - with torch.no_grad(): - hasp = pitch is not None and pitchf is not None - arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid) - audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy() - del hasp, arg - del feats, p_len, padding_mask - if torch.cuda.is_available(): - torch.cuda.empty_cache() - t2 = ttime() - times[0] += t1 - t0 - times[2] += t2 - t1 - return audio1 - def process_t(self, t, s, window, audio_pad, pitch, pitchf, times, index, big_npy, index_rate, version, protect, t_pad_tgt, if_f0, sid, model, net_g): - t = t // window * window - if if_f0 == 1: - return self.vc( - model, - net_g, - sid, - audio_pad[s : t + t_pad_tgt + window], - pitch[:, s // window : (t + t_pad_tgt) // window], - pitchf[:, s // window : (t + t_pad_tgt) // window], - times, - index, - big_npy, - index_rate, - version, - protect, - )[t_pad_tgt : -t_pad_tgt] - else: - return self.vc( - model, - net_g, - sid, - audio_pad[s : t + t_pad_tgt + window], - None, - None, - times, - index, - big_npy, - index_rate, - version, - protect, - )[t_pad_tgt : -t_pad_tgt] - - - def pipeline( - self, - model, - net_g, - sid, - audio, - input_audio_path, - times, - f0_up_key, - f0_method, - file_index, - index_rate, - if_f0, - filter_radius, - tgt_sr, - resample_sr, - rms_mix_rate, - version, - protect, - crepe_hop_length, - f0_autotune, - f0_file=None, - f0_min=50, - f0_max=1100 - ): - if ( - file_index != "" - # and file_big_npy != "" - # and os.path.exists(file_big_npy) == True - and os.path.exists(file_index) - and index_rate != 0 - ): - try: - index = faiss.read_index(file_index) - # big_npy = np.load(file_big_npy) - big_npy = index.reconstruct_n(0, index.ntotal) - except: - traceback.print_exc() - index = big_npy = None - else: - index = big_npy = None - audio = signal.filtfilt(bh, ah, audio) - audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") - opt_ts = [] - if audio_pad.shape[0] > self.t_max: - audio_sum = np.zeros_like(audio) - for i in range(self.window): - audio_sum += audio_pad[i : i - self.window] - for t in range(self.t_center, audio.shape[0], self.t_center): - opt_ts.append( - t - - self.t_query - + np.where( - np.abs(audio_sum[t - self.t_query : t + self.t_query]) - == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min() - )[0][0] - ) - s = 0 - audio_opt = [] - t = None - t1 = ttime() - audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") - p_len = audio_pad.shape[0] // self.window - inp_f0 = None - if hasattr(f0_file, "name"): - try: - with open(f0_file.name, "r") as f: - lines = f.read().strip("\n").split("\n") - inp_f0 = [] - for line in lines: - inp_f0.append([float(i) for i in line.split(",")]) - inp_f0 = np.array(inp_f0, dtype="float32") - except: - traceback.print_exc() - sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() - pitch, pitchf = None, None - if if_f0: - pitch, pitchf = self.get_f0( - input_audio_path, - audio_pad, - p_len, - f0_up_key, - f0_method, - filter_radius, - crepe_hop_length, - f0_autotune, - inp_f0, - f0_min, - f0_max - ) - pitch = pitch[:p_len] - pitchf = pitchf[:p_len] - if self.device == "mps" or "xpu" in self.device: - pitchf = pitchf.astype(np.float32) - pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() - pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() - t2 = ttime() - times[1] += t2 - t1 - - with tqdm(total=len(opt_ts), desc="Processing", unit="window") as pbar: - for i, t in enumerate(opt_ts): - t = t // self.window * self.window - start = s - end = t + self.t_pad2 + self.window - audio_slice = audio_pad[start:end] - pitch_slice = pitch[:, start // self.window:end // self.window] if if_f0 else None - pitchf_slice = pitchf[:, start // self.window:end // self.window] if if_f0 else None - audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt]) - s = t - pbar.update(1) - pbar.refresh() - - audio_slice = audio_pad[t:] - pitch_slice = pitch[:, t // self.window:] if if_f0 and t is not None else pitch - pitchf_slice = pitchf[:, t // self.window:] if if_f0 and t is not None else pitchf - audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt]) - - audio_opt = np.concatenate(audio_opt) - if rms_mix_rate != 1: - audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate) - if tgt_sr != resample_sr >= 16000: - audio_opt = librosa.resample( - audio_opt, orig_sr=tgt_sr, target_sr=resample_sr - ) - audio_max = np.abs(audio_opt).max() / 0.99 - max_int16 = 32768 - if audio_max > 1: - max_int16 /= audio_max - audio_opt = (audio_opt * max_int16).astype(np.int16) - del pitch, pitchf, sid - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - print("Returning completed audio...") - print("-------------------") - return audio_opt diff --git a/spaces/Benson/text-generation/Examples/Descargar Entre Nosotros 3.29.md b/spaces/Benson/text-generation/Examples/Descargar Entre Nosotros 3.29.md deleted file mode 100644 index 71c9d93f8ddca5ca854c2afb28e7480e2b7c6745..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Entre Nosotros 3.29.md +++ /dev/null @@ -1,122 +0,0 @@ -
-

Cómo descargar entre nosotros 3.29 en su dispositivo

-

Among Us es uno de los juegos multijugador más populares y adictivos de los últimos años. Es un juego de trabajo en equipo y traición, donde tienes que trabajar junto a otros jugadores para preparar tu nave espacial para la salida, evitando ser asesinado por uno o más impostores que se esconden entre vosotros. Si usted está buscando una manera divertida y emocionante para pasar su tiempo con sus amigos o extraños en línea, entonces usted debe descargar definitivamente entre nosotros 3.29, la última versión del juego, en su dispositivo.

-

En este artículo, le diremos todo lo que necesita saber sobre Among Us 3.29, incluyendo qué es, por qué debe descargarlo, cómo descargarlo en diferentes plataformas y cómo reproducirlo después de descargarlo. Al final de este artículo, estarás listo para unirte a los millones de jugadores que están disfrutando de este juego todos los días.

-

descargar entre nosotros 3.29


Download File ★ https://bltlly.com/2v6IHW



-

¿Qué hay entre nosotros 3.29?

-

Una breve introducción al juego y sus características

-

Among Us es un juego que fue lanzado en 2018 por Innersloth, un pequeño estudio de juegos indie. Es un juego que se puede jugar en línea o a través de WiFi local con 4-15 jugadores. El juego tiene cuatro mapas diferentes para elegir: The Skeld, MIRA HQ, Polus y Airship. Cada mapa tiene su propio diseño, tareas, respiraderos y sabotajes.

-

El juego tiene dos papeles principales: compañeros de equipo e impostores. Los compañeros de equipo son los jugadores inocentes que tienen que completar tareas alrededor del mapa para llenar una barra de progreso y ganar el juego. Los impostores son los enemigos ocultos que tienen que matar compañeros de tripulación, sabotear el mapa y evitar ser atrapados. El juego puede tener hasta tres impostores por partido, dependiendo de la configuración del juego.

- -

La última actualización y lo que incluye

-

Among Us 3.29 es la última actualización para el juego que fue lanzado el 31 de marzo de 2021. Es una actualización gratuita que añade algunas nuevas características y mejoras al juego. Estos son algunos de los aspectos más destacados de esta actualización:

- -

Por qué debería descargar entre nosotros 3.29Por qué debería descargar entre nosotros 3.29

-

Si todavía te estás preguntando por qué deberías descargar Among Us 3.29, estas son algunas de las razones por las que no deberías perderte esta oportunidad:

-

Los beneficios de jugar con amigos y extraños en línea

-

Una de las mejores cosas de Among Us es que puedes jugar con cualquiera, en cualquier lugar y en cualquier momento. Puedes jugar con tus amigos y familiares, o puedes unirte a un lobby público y conocer gente nueva de todo el mundo. También puede utilizar el chat de voz o de texto para comunicarse con otros jugadores y tener conversaciones divertidas. Jugar con otros en línea puede ayudarte a mejorar tus habilidades sociales, hacer nuevos amigos y pasar un buen rato.

-

La diversión y el desafío de ser un compañero de equipo o un impostor

- -

Las opciones de personalización y juego disponibles

-

Una tercera razón por la que deberías descargar Among Us 3.29 es que te da muchas opciones de personalización y juegos para elegir. Puedes personalizar la apariencia, nombre, color, sombrero, visera, piel y mascota de tu personaje. También puedes cambiar la configuración del juego para adaptarla a tus preferencias, como el número de impostores, tareas, roles, velocidad, visión, tiempo de reutilización, tiempo de votación y más. También puedes probar diferentes modos, como Classic o Hide n Seek. Puedes hacer el juego tan fácil o tan difícil como quieras, y tener una experiencia diferente cada vez.

-

Cómo descargar entre nosotros 3.29 en diferentes plataformas

-

Ahora que sabes lo que es Among Us 3.29 y por qué deberías descargarlo, veamos cómo puedes descargarlo en diferentes plataformas. El juego está disponible en dispositivos Android, iOS, PC y Nintendo Switch. Estos son los pasos para descargarlo en cada plataforma:

-

Cómo descargar en dispositivos Android

-

Si tienes un dispositivo Android, puedes descargar Among Us 3.29 desde Google Play Store. Estos son los pasos para hacerlo:

-
    -
  1. Abra la aplicación Google Play Store en su dispositivo.
    2. -
  2. Buscar entre nosotros en la barra de búsqueda.
    3. -
  3. Toca el icono del juego y luego toca Instalar.
    4. -
  4. Espera a que el juego se descargue e instale en tu dispositivo.
    5. -
  5. Abre el juego y disfruta jugando.
    6. -
-

Nota: Necesitas tener Android 6.0 o superior y al menos 70 MB de espacio libre en tu dispositivo para descargar y jugar entre nosotros 3.29.

-

Cómo descargar en dispositivos iOS

-

Si tienes un dispositivo iOS, puedes descargar Among Us 3.29 desde la App Store. Estos son los pasos para hacerlo:

-
    -
  1. Abra la aplicación App Store en su dispositivo.
    2. -
  2. Buscar entre nosotros en la barra de búsqueda.
    3. -
  3. Toque en el icono del juego y luego toque en Get.
    4. -
  4. Espera a que el juego se descargue e instale en tu dispositivo.
    5. -
  5. Abre el juego y disfruta jugando.
    6. -
- -

Cómo descargar en PC

-

Si tienes un PC, puedes descargar Among Us 3.29 de Steam o Epic Games Store. Estos son los pasos para hacerlo:

-
    -
  1. Abra Steam o Epic Games Store en su PC.
    2. -
  2. Buscar entre nosotros en la barra de búsqueda.
    3. -
  3. Haga clic en el icono del juego y luego haga clic en Añadir al carrito o Comprar ahora.
    4. -
  4. Complete el proceso de pago y espere a que el juego se descargue e instale en su PC.
    5. -
  5. Abre el juego y disfruta jugando.
    6. -
-

Nota: Necesita tener Windows 7 SP1 o superior, 1 GB de RAM y 250 MB de espacio libre en su PC para descargar y jugar Entre nosotros 3.29. El juego cuesta $4.99 USD en Steam y Epic Games Store.

-

Cómo descargar en Nintendo Switch

-

Si tienes un Nintendo Switch, puedes descargar Among Us 3.29 desde la Nintendo eShop. Estos son los pasos para hacerlo:

-
    -
  1. Abre la Nintendo eShop en tu Switch.
    2. -
  2. Buscar entre nosotros en la barra de búsqueda.
    3. -
  3. Seleccione el icono del juego y luego seleccione Proceder a la compra o descargar Demo.
    4. -
  4. Complete el proceso de pago o confirme la descarga de la versión demo.
    5. -
  5. Espera a que el juego se descargue e instale en tu Switch.
    6. -
  6. Abre el juego y disfruta jugando.
    7. -
-

Nota: Necesitas tener una suscripción a Nintendo Switch Online, una cuenta de Nintendo y al menos 421 MB de espacio libre en tu Switch para descargar y jugar Among Us 3.29. El juego cuesta $5 USD en Nintendo eShop, pero también puedes probar la versión demo gratis.

-

Cómo jugar entre nosotros 3.29 Después de descargar

-

Después de haber descargado Entre nosotros 3.29 en su dispositivo, puede comenzar a jugar de inmediato. Aquí hay algunos consejos sobre cómo jugar después de descargar:

-

Cómo crear o unirse a un lobby

- -
    -
  1. Seleccione Online desde el menú principal del juego.
    2. -
  2. Seleccione Host si desea crear un lobby, o Buscar juego si desea unirse a un lobby.
    3. -
  3. Si selecciona Host, elija un mapa, el número de impostores, el idioma de chat y el número máximo de jugadores. Luego toca Confirmar y espera a que otros jugadores se unan a tu lobby. También puedes invitar a tus amigos usando tu código de amigo o código de lobby.
    4. -
  4. Si selecciona Buscar juego, elija un mapa, el número de impostores, el idioma de chat y el número máximo de jugadores. A continuación, toque en Confirmar y navegue por los lobbies disponibles. Toque en uno que se adapte a sus preferencias y únase a él.
    5. -
-

Para crear o unirse a un lobby a través de WiFi local, siga estos pasos:

-
    -
  1. Seleccione Local en el menú principal del juego.
    2. -
  2. Seleccione Host si desea crear un lobby, o Unirse al juego si desea unirse a un lobby.
    3. -
  3. Si selecciona Host, elija un mapa, el número de impostores, el idioma de chat y el número máximo de jugadores. Luego toca Confirmar y espera a que otros jugadores se unan a tu lobby. Tú y los otros jugadores deben estar conectados a la misma red WiFi.
    4. -
  4. Si selecciona Unirse al juego, navegue por los lobbies disponibles que están en la misma red WiFi que usted. Toca en uno que se adapte a tus preferencias y únete a él.
    5. -
-

Cómo completar tareas o sabotear como compañero de equipo o como impostor

-

Una vez que estés en un lobby, el juego te asignará al azar un rol: compañero de equipo o impostor. Dependiendo de tu rol, tienes diferentes objetivos y habilidades. Como compañero de equipo, su objetivo es completar tareas alrededor del mapa o encontrar y expulsar a los impostores. Como impostor, tu objetivo es matar compañeros de equipo, sabotear el mapa y evitar ser expulsado. Aquí hay algunos consejos sobre cómo completar tareas o sabotear como compañero de equipo o como impostor:

-

Como un compañero de equipo:

- -

Como un impostor:

- -

Cómo comunicarse y votar durante las reuniones

-

Durante el juego, las reuniones pueden ser convocadas ya sea reportando un cadáver o presionando el botón de emergencia. Las reuniones son donde los jugadores pueden comunicarse y votar por quien creen que es el impostor. Aquí hay algunos consejos sobre cómo comunicarse y votar durante las reuniones:

- -

Conclusión y preguntas frecuentes

-

En conclusión, Among Us 3.29 es un divertido y emocionante juego multijugador que debes descargar en tu dispositivo. Es un juego de trabajo en equipo y traición, donde tienes que trabajar junto a otros jugadores para preparar tu nave espacial para la salida, evitando ser asesinado por uno o más impostores que se esconden entre vosotros. Se puede jugar en línea o a través de WiFi local con 4-15 jugadores. También puedes personalizar tu personaje y cambiar la configuración del juego para que sea más desafiante o divertido.

-

Si tiene alguna pregunta sobre Among Us 3.29, aquí hay algunas preguntas frecuentes que podrían ayudarlo:

- -PreguntaRespuesta -¿Cómo actualizo Among Us a 3.29? Si ya tienes Among Us en tu dispositivo, puedes actualizarlo yendo a Google Play Store, App Store, Steam, Epic Games Store o Nintendo eShop, dependiendo de tu plataforma. Luego, encuentra el juego y toca Actualizar o Instalar. Si no tienes Among Us en tu dispositivo, puedes seguir los pasos de este artículo para descargarlo. -Cómo puedo agregar amigos en Among Us 3.29? Para agregar amigos en Among Us 3.29, necesita usar la nueva función de lista de amigos. Para acceder a ella, toque en el icono de amigos en la esquina superior derecha del menú principal. Luego, toca Agregar amigo e ingresa el código de tu amigo o comparte tu propio código. También puedes enviar y recibir solicitudes de amistad, invitar a amigos a tu lobby, ver con quién has jugado recientemente, bloquear jugadores, mostrar u ocultar tu código de amigo, y activar o desactivar las notificaciones de invitación al lobby. - -¿Cómo se juega al modo Ocultar n Buscar en Among Us 3.29? Para jugar al modo Ocultar n Buscar en Entre Nosotros 3.29, necesitas crear o unirte a un lobby con ajustes de juego personalizados. Los ajustes recomendados son: un impostor, visión de compañero de equipo bajo, visión de impostor alto, tiempo de reutilización bajo, sin expulsar confirmaciones, sin tareas visuales, sin votos anónimos y sin reuniones de emergencia. El impostor tiene que anunciarse al comienzo del juego y luego tratar de encontrar y matar a todos los compañeros de equipo que se esconden y hacer tareas. Los compañeros de equipo tienen que evitar al impostor y completar todas sus tareas para ganar. -¿Cómo informo de un error o problema con Among Us 3.29? Para reportar un error o problema con Among Us 3.29, puede ponerse en contacto con Innersloth, los desarrolladores del juego, a través de su sitio web, correo electrónico, Twitter o Discord. También puede consultar su página de preguntas frecuentes para encontrar problemas y soluciones comunes. - -

Espero que este artículo fue útil e informativo para usted. Si te gustó, por favor compártelo con tus amigos y familiares que podrían estar interesados en jugar Among Us 3.29. Gracias por leer y divertirse jugando!

64aa2da5cf
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-
\ No newline at end of file diff --git a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/losses/segmentation.py b/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/losses/segmentation.py deleted file mode 100644 index 4ba77deb5159a6307ed2acba9945e4764a4ff0a5..0000000000000000000000000000000000000000 --- a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/losses/segmentation.py +++ /dev/null @@ -1,22 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F - - -class BCELoss(nn.Module): - def forward(self, prediction, target): - loss = F.binary_cross_entropy_with_logits(prediction,target) - return loss, {} - - -class BCELossWithQuant(nn.Module): - def __init__(self, codebook_weight=1.): - super().__init__() - self.codebook_weight = codebook_weight - - def forward(self, qloss, target, prediction, split): - bce_loss = F.binary_cross_entropy_with_logits(prediction,target) - loss = bce_loss + self.codebook_weight*qloss - return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(), - "{}/bce_loss".format(split): bce_loss.detach().mean(), - "{}/quant_loss".format(split): qloss.detach().mean() - } diff --git a/spaces/BetterAPI/BetterChat/src/routes/conversation/[id]/share/+server.ts b/spaces/BetterAPI/BetterChat/src/routes/conversation/[id]/share/+server.ts deleted file mode 100644 index 5f97daa091152c8074797f1d9f48ebc93fdde718..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat/src/routes/conversation/[id]/share/+server.ts +++ /dev/null @@ -1,54 +0,0 @@ -import { base } from "$app/paths"; -import { PUBLIC_ORIGIN } from "$env/static/public"; -import { collections } from "$lib/server/database.js"; -import type { SharedConversation } from "$lib/types/SharedConversation.js"; -import { sha256 } from "$lib/utils/sha256.js"; -import { error } from "@sveltejs/kit"; -import { ObjectId } from "mongodb"; -import { nanoid } from "nanoid"; - -export async function POST({ params, url, locals }) { - const conversation = await collections.conversations.findOne({ - _id: new ObjectId(params.id), - sessionId: locals.sessionId, - }); - - if (!conversation) { - throw error(404, "Conversation not found"); - } - - const hash = await sha256(JSON.stringify(conversation.messages)); - - const existingShare = await collections.sharedConversations.findOne({ hash }); - - if (existingShare) { - return new Response( - JSON.stringify({ - url: getShareUrl(url, existingShare._id), - }), - { headers: { "Content-Type": "application/json" } } - ); - } - - const shared: SharedConversation = { - _id: nanoid(7), - createdAt: new Date(), - messages: conversation.messages, - hash, - updatedAt: new Date(), - title: conversation.title, - }; - - await collections.sharedConversations.insertOne(shared); - - return new Response( - JSON.stringify({ - url: getShareUrl(url, shared._id), - }), - { headers: { "Content-Type": "application/json" } } - ); -} - -function getShareUrl(url: URL, shareId: string): string { - return `${PUBLIC_ORIGIN || url.origin}${base}/r/${shareId}`; -} diff --git a/spaces/BetterAPI/BetterChat_new/src/routes/r/[id]/+page.server.ts b/spaces/BetterAPI/BetterChat_new/src/routes/r/[id]/+page.server.ts deleted file mode 100644 index 1630b38f1a9bb264a5c54eb09d7533a19337b16e..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat_new/src/routes/r/[id]/+page.server.ts +++ /dev/null @@ -1,18 +0,0 @@ -import type { PageServerLoad } from "./$types"; -import { collections } from "$lib/server/database"; -import { error } from "@sveltejs/kit"; - -export const load: PageServerLoad = async ({ params }) => { - const conversation = await collections.sharedConversations.findOne({ - _id: params.id, - }); - - if (!conversation) { - throw error(404, "Conversation not found"); - } - - return { - messages: conversation.messages, - title: conversation.title, - }; -}; diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/sjisprober.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/sjisprober.py deleted file mode 100644 index 91df077961b6310b8e1c708b74003d5343bff6a8..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/sjisprober.py +++ /dev/null @@ -1,105 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# The Original Code is mozilla.org code. -# -# The Initial Developer of the Original Code is -# Netscape Communications Corporation. -# Portions created by the Initial Developer are Copyright (C) 1998 -# the Initial Developer. All Rights Reserved. -# -# Contributor(s): -# Mark Pilgrim - port to Python -# -# This library is free software; you can redistribute it and/or -# modify it under the terms of the GNU Lesser General Public -# License as published by the Free Software Foundation; either -# version 2.1 of the License, or (at your option) any later version. -# -# This library is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU -# Lesser General Public License for more details. -# -# You should have received a copy of the GNU Lesser General Public -# License along with this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### - -from typing import Union - -from .chardistribution import SJISDistributionAnalysis -from .codingstatemachine import CodingStateMachine -from .enums import MachineState, ProbingState -from .jpcntx import SJISContextAnalysis -from .mbcharsetprober import MultiByteCharSetProber -from .mbcssm import SJIS_SM_MODEL - - -class SJISProber(MultiByteCharSetProber): - def __init__(self) -> None: - super().__init__() - self.coding_sm = CodingStateMachine(SJIS_SM_MODEL) - self.distribution_analyzer = SJISDistributionAnalysis() - self.context_analyzer = SJISContextAnalysis() - self.reset() - - def reset(self) -> None: - super().reset() - self.context_analyzer.reset() - - @property - def charset_name(self) -> str: - return self.context_analyzer.charset_name - - @property - def language(self) -> str: - return "Japanese" - - def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: - assert self.coding_sm is not None - assert self.distribution_analyzer is not None - - for i, byte in enumerate(byte_str): - coding_state = self.coding_sm.next_state(byte) - if coding_state == MachineState.ERROR: - self.logger.debug( - "%s %s prober hit error at byte %s", - self.charset_name, - self.language, - i, - ) - self._state = ProbingState.NOT_ME - break - if coding_state == MachineState.ITS_ME: - self._state = ProbingState.FOUND_IT - break - if coding_state == MachineState.START: - char_len = self.coding_sm.get_current_charlen() - if i == 0: - self._last_char[1] = byte - self.context_analyzer.feed( - self._last_char[2 - char_len :], char_len - ) - self.distribution_analyzer.feed(self._last_char, char_len) - else: - self.context_analyzer.feed( - byte_str[i + 1 - char_len : i + 3 - char_len], char_len - ) - self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len) - - self._last_char[0] = byte_str[-1] - - if self.state == ProbingState.DETECTING: - if self.context_analyzer.got_enough_data() and ( - self.get_confidence() > self.SHORTCUT_THRESHOLD - ): - self._state = ProbingState.FOUND_IT - - return self.state - - def get_confidence(self) -> float: - assert self.distribution_analyzer is not None - - context_conf = self.context_analyzer.get_confidence() - distrib_conf = self.distribution_analyzer.get_confidence() - return max(context_conf, distrib_conf) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/common.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/common.py deleted file mode 100644 index 1859fb79cc4e78850b69742fca56698041ce59f8..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/common.py +++ /dev/null @@ -1,424 +0,0 @@ -# common.py -from .core import * -from .helpers import delimited_list, any_open_tag, any_close_tag -from datetime import datetime - - -# some other useful expressions - using lower-case class name since we are really using this as a namespace -class pyparsing_common: - """Here are some common low-level expressions that may be useful in - jump-starting parser development: - - - numeric forms (:class:`integers`, :class:`reals`, - :class:`scientific notation`) - - common :class:`programming identifiers` - - network addresses (:class:`MAC`, - :class:`IPv4`, :class:`IPv6`) - - ISO8601 :class:`dates` and - :class:`datetime` - - :class:`UUID` - - :class:`comma-separated list` - - :class:`url` - - Parse actions: - - - :class:`convertToInteger` - - :class:`convertToFloat` - - :class:`convertToDate` - - :class:`convertToDatetime` - - :class:`stripHTMLTags` - - :class:`upcaseTokens` - - :class:`downcaseTokens` - - Example:: - - pyparsing_common.number.runTests(''' - # any int or real number, returned as the appropriate type - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - ''') - - pyparsing_common.fnumber.runTests(''' - # any int or real number, returned as float - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - ''') - - pyparsing_common.hex_integer.runTests(''' - # hex numbers - 100 - FF - ''') - - pyparsing_common.fraction.runTests(''' - # fractions - 1/2 - -3/4 - ''') - - pyparsing_common.mixed_integer.runTests(''' - # mixed fractions - 1 - 1/2 - -3/4 - 1-3/4 - ''') - - import uuid - pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID)) - pyparsing_common.uuid.runTests(''' - # uuid - 12345678-1234-5678-1234-567812345678 - ''') - - prints:: - - # any int or real number, returned as the appropriate type - 100 - [100] - - -100 - [-100] - - +100 - [100] - - 3.14159 - [3.14159] - - 6.02e23 - [6.02e+23] - - 1e-12 - [1e-12] - - # any int or real number, returned as float - 100 - [100.0] - - -100 - [-100.0] - - +100 - [100.0] - - 3.14159 - [3.14159] - - 6.02e23 - [6.02e+23] - - 1e-12 - [1e-12] - - # hex numbers - 100 - [256] - - FF - [255] - - # fractions - 1/2 - [0.5] - - -3/4 - [-0.75] - - # mixed fractions - 1 - [1] - - 1/2 - [0.5] - - -3/4 - [-0.75] - - 1-3/4 - [1.75] - - # uuid - 12345678-1234-5678-1234-567812345678 - [UUID('12345678-1234-5678-1234-567812345678')] - """ - - convert_to_integer = token_map(int) - """ - Parse action for converting parsed integers to Python int - """ - - convert_to_float = token_map(float) - """ - Parse action for converting parsed numbers to Python float - """ - - integer = Word(nums).set_name("integer").set_parse_action(convert_to_integer) - """expression that parses an unsigned integer, returns an int""" - - hex_integer = ( - Word(hexnums).set_name("hex integer").set_parse_action(token_map(int, 16)) - ) - """expression that parses a hexadecimal integer, returns an int""" - - signed_integer = ( - Regex(r"[+-]?\d+") - .set_name("signed integer") - .set_parse_action(convert_to_integer) - ) - """expression that parses an integer with optional leading sign, returns an int""" - - fraction = ( - signed_integer().set_parse_action(convert_to_float) - + "/" - + signed_integer().set_parse_action(convert_to_float) - ).set_name("fraction") - """fractional expression of an integer divided by an integer, returns a float""" - fraction.add_parse_action(lambda tt: tt[0] / tt[-1]) - - mixed_integer = ( - fraction | signed_integer + Opt(Opt("-").suppress() + fraction) - ).set_name("fraction or mixed integer-fraction") - """mixed integer of the form 'integer - fraction', with optional leading integer, returns float""" - mixed_integer.add_parse_action(sum) - - real = ( - Regex(r"[+-]?(?:\d+\.\d*|\.\d+)") - .set_name("real number") - .set_parse_action(convert_to_float) - ) - """expression that parses a floating point number and returns a float""" - - sci_real = ( - Regex(r"[+-]?(?:\d+(?:[eE][+-]?\d+)|(?:\d+\.\d*|\.\d+)(?:[eE][+-]?\d+)?)") - .set_name("real number with scientific notation") - .set_parse_action(convert_to_float) - ) - """expression that parses a floating point number with optional - scientific notation and returns a float""" - - # streamlining this expression makes the docs nicer-looking - number = (sci_real | real | signed_integer).setName("number").streamline() - """any numeric expression, returns the corresponding Python type""" - - fnumber = ( - Regex(r"[+-]?\d+\.?\d*([eE][+-]?\d+)?") - .set_name("fnumber") - .set_parse_action(convert_to_float) - ) - """any int or real number, returned as float""" - - identifier = Word(identchars, identbodychars).set_name("identifier") - """typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')""" - - ipv4_address = Regex( - r"(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}" - ).set_name("IPv4 address") - "IPv4 address (``0.0.0.0 - 255.255.255.255``)" - - _ipv6_part = Regex(r"[0-9a-fA-F]{1,4}").set_name("hex_integer") - _full_ipv6_address = (_ipv6_part + (":" + _ipv6_part) * 7).set_name( - "full IPv6 address" - ) - _short_ipv6_address = ( - Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6)) - + "::" - + Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6)) - ).set_name("short IPv6 address") - _short_ipv6_address.add_condition( - lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8 - ) - _mixed_ipv6_address = ("::ffff:" + ipv4_address).set_name("mixed IPv6 address") - ipv6_address = Combine( - (_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).set_name( - "IPv6 address" - ) - ).set_name("IPv6 address") - "IPv6 address (long, short, or mixed form)" - - mac_address = Regex( - r"[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}" - ).set_name("MAC address") - "MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)" - - @staticmethod - def convert_to_date(fmt: str = "%Y-%m-%d"): - """ - Helper to create a parse action for converting parsed date string to Python datetime.date - - Params - - - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``) - - Example:: - - date_expr = pyparsing_common.iso8601_date.copy() - date_expr.setParseAction(pyparsing_common.convertToDate()) - print(date_expr.parseString("1999-12-31")) - - prints:: - - [datetime.date(1999, 12, 31)] - """ - - def cvt_fn(ss, ll, tt): - try: - return datetime.strptime(tt[0], fmt).date() - except ValueError as ve: - raise ParseException(ss, ll, str(ve)) - - return cvt_fn - - @staticmethod - def convert_to_datetime(fmt: str = "%Y-%m-%dT%H:%M:%S.%f"): - """Helper to create a parse action for converting parsed - datetime string to Python datetime.datetime - - Params - - - fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``) - - Example:: - - dt_expr = pyparsing_common.iso8601_datetime.copy() - dt_expr.setParseAction(pyparsing_common.convertToDatetime()) - print(dt_expr.parseString("1999-12-31T23:59:59.999")) - - prints:: - - [datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)] - """ - - def cvt_fn(s, l, t): - try: - return datetime.strptime(t[0], fmt) - except ValueError as ve: - raise ParseException(s, l, str(ve)) - - return cvt_fn - - iso8601_date = Regex( - r"(?P\d{4})(?:-(?P\d\d)(?:-(?P\d\d))?)?" - ).set_name("ISO8601 date") - "ISO8601 date (``yyyy-mm-dd``)" - - iso8601_datetime = Regex( - r"(?P\d{4})-(?P\d\d)-(?P\d\d)[T ](?P\d\d):(?P\d\d)(:(?P\d\d(\.\d*)?)?)?(?PZ|[+-]\d\d:?\d\d)?" - ).set_name("ISO8601 datetime") - "ISO8601 datetime (``yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)``) - trailing seconds, milliseconds, and timezone optional; accepts separating ``'T'`` or ``' '``" - - uuid = Regex(r"[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}").set_name("UUID") - "UUID (``xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx``)" - - _html_stripper = any_open_tag.suppress() | any_close_tag.suppress() - - @staticmethod - def strip_html_tags(s: str, l: int, tokens: ParseResults): - """Parse action to remove HTML tags from web page HTML source - - Example:: - - # strip HTML links from normal text - text = 'More info at the pyparsing wiki page' - td, td_end = makeHTMLTags("TD") - table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end - print(table_text.parseString(text).body) - - Prints:: - - More info at the pyparsing wiki page - """ - return pyparsing_common._html_stripper.transform_string(tokens[0]) - - _commasepitem = ( - Combine( - OneOrMore( - ~Literal(",") - + ~LineEnd() - + Word(printables, exclude_chars=",") - + Opt(White(" \t") + ~FollowedBy(LineEnd() | ",")) - ) - ) - .streamline() - .set_name("commaItem") - ) - comma_separated_list = delimited_list( - Opt(quoted_string.copy() | _commasepitem, default="") - ).set_name("comma separated list") - """Predefined expression of 1 or more printable words or quoted strings, separated by commas.""" - - upcase_tokens = staticmethod(token_map(lambda t: t.upper())) - """Parse action to convert tokens to upper case.""" - - downcase_tokens = staticmethod(token_map(lambda t: t.lower())) - """Parse action to convert tokens to lower case.""" - - # fmt: off - url = Regex( - # https://mathiasbynens.be/demo/url-regex - # https://gist.github.com/dperini/729294 - r"^" + - # protocol identifier (optional) - # short syntax // still required - r"(?:(?:(?Phttps?|ftp):)?\/\/)" + - # user:pass BasicAuth (optional) - r"(?:(?P\S+(?::\S*)?)@)?" + - r"(?P" + - # IP address exclusion - # private & local networks - r"(?!(?:10|127)(?:\.\d{1,3}){3})" + - r"(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})" + - r"(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})" + - # IP address dotted notation octets - # excludes loopback network 0.0.0.0 - # excludes reserved space >= 224.0.0.0 - # excludes network & broadcast addresses - # (first & last IP address of each class) - r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])" + - r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}" + - r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))" + - r"|" + - # host & domain names, may end with dot - # can be replaced by a shortest alternative - # (?![-_])(?:[-\w\u00a1-\uffff]{0,63}[^-_]\.)+ - r"(?:" + - r"(?:" + - r"[a-z0-9\u00a1-\uffff]" + - r"[a-z0-9\u00a1-\uffff_-]{0,62}" + - r")?" + - r"[a-z0-9\u00a1-\uffff]\." + - r")+" + - # TLD identifier name, may end with dot - r"(?:[a-z\u00a1-\uffff]{2,}\.?)" + - r")" + - # port number (optional) - r"(:(?P\d{2,5}))?" + - # resource path (optional) - r"(?P\/[^?# ]*)?" + - # query string (optional) - r"(\?(?P[^#]*))?" + - # fragment (optional) - r"(#(?P\S*))?" + - r"$" - ).set_name("url") - # fmt: on - - # pre-PEP8 compatibility names - convertToInteger = convert_to_integer - convertToFloat = convert_to_float - convertToDate = convert_to_date - convertToDatetime = convert_to_datetime - stripHTMLTags = strip_html_tags - upcaseTokens = upcase_tokens - downcaseTokens = downcase_tokens - - -_builtin_exprs = [ - v for v in vars(pyparsing_common).values() if isinstance(v, ParserElement) -] diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/_securetransport/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/contrib/_securetransport/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Boadiwaa/Recipes/openai/validators.py b/spaces/Boadiwaa/Recipes/openai/validators.py deleted file mode 100644 index 0d4d85d4f2f44ef66b88d29cad25e3bc52554175..0000000000000000000000000000000000000000 --- a/spaces/Boadiwaa/Recipes/openai/validators.py +++ /dev/null @@ -1,860 +0,0 @@ -import os -import sys -from typing import Any, Callable, NamedTuple, Optional - -import pandas as pd - - -class Remediation(NamedTuple): - name: str - immediate_msg: Optional[str] = None - necessary_msg: Optional[str] = None - necessary_fn: Optional[Callable[[Any], Any]] = None - optional_msg: Optional[str] = None - optional_fn: Optional[Callable[[Any], Any]] = None - error_msg: Optional[str] = None - - -def num_examples_validator(df): - """ - This validator will only print out the number of examples and recommend to the user to increase the number of examples if less than 100. - """ - MIN_EXAMPLES = 100 - optional_suggestion = ( - "" - if len(df) >= MIN_EXAMPLES - else ". In general, we recommend having at least a few hundred examples. We've found that performance tends to linearly increase for every doubling of the number of examples" - ) - immediate_msg = ( - f"\n- Your file contains {len(df)} prompt-completion pairs{optional_suggestion}" - ) - return Remediation(name="num_examples", immediate_msg=immediate_msg) - - -def necessary_column_validator(df, necessary_column): - """ - This validator will ensure that the necessary column is present in the dataframe. - """ - - def lower_case_column(df, column): - cols = [c for c in df.columns if c.lower() == column] - df.rename(columns={cols[0]: column.lower()}, inplace=True) - return df - - immediate_msg = None - necessary_fn = None - necessary_msg = None - error_msg = None - - if necessary_column not in df.columns: - if necessary_column in [c.lower() for c in df.columns]: - - def lower_case_column_creator(df): - return lower_case_column(df, necessary_column) - - necessary_fn = lower_case_column_creator - immediate_msg = ( - f"\n- The `{necessary_column}` column/key should be lowercase" - ) - necessary_msg = f"Lower case column name to `{necessary_column}`" - else: - error_msg = f"`{necessary_column}` column/key is missing. Please make sure you name your columns/keys appropriately, then retry" - - return Remediation( - name="necessary_column", - immediate_msg=immediate_msg, - necessary_msg=necessary_msg, - necessary_fn=necessary_fn, - error_msg=error_msg, - ) - - -def additional_column_validator(df, fields=["prompt", "completion"]): - """ - This validator will remove additional columns from the dataframe. - """ - additional_columns = [] - necessary_msg = None - immediate_msg = None - necessary_fn = None - if len(df.columns) > 2: - additional_columns = [c for c in df.columns if c not in fields] - warn_message = "" - for ac in additional_columns: - dups = [c for c in additional_columns if ac in c] - if len(dups) > 0: - warn_message += f"\n WARNING: Some of the additional columns/keys contain `{ac}` in their name. These will be ignored, and the column/key `{ac}` will be used instead. This could also result from a duplicate column/key in the provided file." - immediate_msg = f"\n- The input file should contain exactly two columns/keys per row. Additional columns/keys present are: {additional_columns}{warn_message}" - necessary_msg = f"Remove additional columns/keys: {additional_columns}" - - def necessary_fn(x): - return x[fields] - - return Remediation( - name="additional_column", - immediate_msg=immediate_msg, - necessary_msg=necessary_msg, - necessary_fn=necessary_fn, - ) - - -def non_empty_field_validator(df, field="completion"): - """ - This validator will ensure that no completion is empty. - """ - necessary_msg = None - necessary_fn = None - immediate_msg = None - - if df[field].apply(lambda x: x == "").any() or df[field].isnull().any(): - empty_rows = (df[field] == "") | (df[field].isnull()) - empty_indexes = df.reset_index().index[empty_rows].tolist() - immediate_msg = f"\n- `{field}` column/key should not contain empty strings. These are rows: {empty_indexes}" - - def necessary_fn(x): - return x[x[field] != ""].dropna(subset=[field]) - - necessary_msg = f"Remove {len(empty_indexes)} rows with empty {field}s" - return Remediation( - name=f"empty_{field}", - immediate_msg=immediate_msg, - necessary_msg=necessary_msg, - necessary_fn=necessary_fn, - ) - - -def duplicated_rows_validator(df, fields=["prompt", "completion"]): - """ - This validator will suggest to the user to remove duplicate rows if they exist. - """ - duplicated_rows = df.duplicated(subset=fields) - duplicated_indexes = df.reset_index().index[duplicated_rows].tolist() - immediate_msg = None - optional_msg = None - optional_fn = None - - if len(duplicated_indexes) > 0: - immediate_msg = f"\n- There are {len(duplicated_indexes)} duplicated {'-'.join(fields)} sets. These are rows: {duplicated_indexes}" - optional_msg = f"Remove {len(duplicated_indexes)} duplicate rows" - - def optional_fn(x): - return x.drop_duplicates(subset=fields) - - return Remediation( - name="duplicated_rows", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - ) - - -def long_examples_validator(df): - """ - This validator will suggest to the user to remove examples that are too long. - """ - immediate_msg = None - optional_msg = None - optional_fn = None - - ft_type = infer_task_type(df) - if ft_type != "open-ended generation": - long_examples = df.apply( - lambda x: len(x.prompt) + len(x.completion) > 10000, axis=1 - ) - long_indexes = df.reset_index().index[long_examples].tolist() - - if len(long_indexes) > 0: - immediate_msg = f"\n- There are {len(long_indexes)} examples that are very long. These are rows: {long_indexes}\nFor conditional generation, and for classification the examples shouldn't be longer than 2048 tokens." - optional_msg = f"Remove {len(long_indexes)} long examples" - - def optional_fn(x): - return x.drop(long_indexes) - - return Remediation( - name="long_examples", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - ) - - -def common_prompt_suffix_validator(df): - """ - This validator will suggest to add a common suffix to the prompt if one doesn't already exist in case of classification or conditional generation. - """ - error_msg = None - immediate_msg = None - optional_msg = None - optional_fn = None - - # Find a suffix which is not contained within the prompt otherwise - suggested_suffix = "\n\n### =>\n\n" - suffix_options = [ - " ->", - "\n\n###\n\n", - "\n\n===\n\n", - "\n\n---\n\n", - "\n\n===>\n\n", - "\n\n--->\n\n", - ] - for suffix_option in suffix_options: - if suffix_option == " ->": - if df.prompt.str.contains("\n").any(): - continue - if df.prompt.str.contains(suffix_option, regex=False).any(): - continue - suggested_suffix = suffix_option - break - display_suggested_suffix = suggested_suffix.replace("\n", "\\n") - - ft_type = infer_task_type(df) - if ft_type == "open-ended generation": - return Remediation(name="common_suffix") - - def add_suffix(x, suffix): - x["prompt"] += suffix - return x - - common_suffix = get_common_xfix(df.prompt, xfix="suffix") - if (df.prompt == common_suffix).all(): - error_msg = f"All prompts are identical: `{common_suffix}`\nConsider leaving the prompts blank if you want to do open-ended generation, otherwise ensure prompts are different" - return Remediation(name="common_suffix", error_msg=error_msg) - - if common_suffix != "": - common_suffix_new_line_handled = common_suffix.replace("\n", "\\n") - immediate_msg = ( - f"\n- All prompts end with suffix `{common_suffix_new_line_handled}`" - ) - if len(common_suffix) > 10: - immediate_msg += f". This suffix seems very long. Consider replacing with a shorter suffix, such as `{display_suggested_suffix}`" - if ( - df.prompt.str[: -len(common_suffix)] - .str.contains(common_suffix, regex=False) - .any() - ): - immediate_msg += f"\n WARNING: Some of your prompts contain the suffix `{common_suffix}` more than once. We strongly suggest that you review your prompts and add a unique suffix" - - else: - immediate_msg = "\n- Your data does not contain a common separator at the end of your prompts. Having a separator string appended to the end of the prompt makes it clearer to the fine-tuned model where the completion should begin. See https://beta.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more detail and examples. If you intend to do open-ended generation, then you should leave the prompts empty" - - if common_suffix == "": - optional_msg = ( - f"Add a suffix separator `{display_suggested_suffix}` to all prompts" - ) - - def optional_fn(x): - return add_suffix(x, suggested_suffix) - - return Remediation( - name="common_completion_suffix", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - error_msg=error_msg, - ) - - -def common_prompt_prefix_validator(df): - """ - This validator will suggest to remove a common prefix from the prompt if a long one exist. - """ - MAX_PREFIX_LEN = 12 - - immediate_msg = None - optional_msg = None - optional_fn = None - - common_prefix = get_common_xfix(df.prompt, xfix="prefix") - if common_prefix == "": - return Remediation(name="common_prefix") - - def remove_common_prefix(x, prefix): - x["prompt"] = x["prompt"].str[len(prefix) :] - return x - - if (df.prompt == common_prefix).all(): - # already handled by common_suffix_validator - return Remediation(name="common_prefix") - - if common_prefix != "": - immediate_msg = f"\n- All prompts start with prefix `{common_prefix}`" - if MAX_PREFIX_LEN < len(common_prefix): - immediate_msg += ". Fine-tuning doesn't require the instruction specifying the task, or a few-shot example scenario. Most of the time you should only add the input data into the prompt, and the desired output into the completion" - optional_msg = f"Remove prefix `{common_prefix}` from all prompts" - - def optional_fn(x): - return remove_common_prefix(x, common_prefix) - - return Remediation( - name="common_prompt_prefix", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - ) - - -def common_completion_prefix_validator(df): - """ - This validator will suggest to remove a common prefix from the completion if a long one exist. - """ - MAX_PREFIX_LEN = 5 - - common_prefix = get_common_xfix(df.completion, xfix="prefix") - ws_prefix = len(common_prefix) > 0 and common_prefix[0] == " " - if len(common_prefix) < MAX_PREFIX_LEN: - return Remediation(name="common_prefix") - - def remove_common_prefix(x, prefix, ws_prefix): - x["completion"] = x["completion"].str[len(prefix) :] - if ws_prefix: - # keep the single whitespace as prefix - x["completion"] = " " + x["completion"] - return x - - if (df.completion == common_prefix).all(): - # already handled by common_suffix_validator - return Remediation(name="common_prefix") - - immediate_msg = f"\n- All completions start with prefix `{common_prefix}`. Most of the time you should only add the output data into the completion, without any prefix" - optional_msg = f"Remove prefix `{common_prefix}` from all completions" - - def optional_fn(x): - return remove_common_prefix(x, common_prefix, ws_prefix) - - return Remediation( - name="common_completion_prefix", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - ) - - -def common_completion_suffix_validator(df): - """ - This validator will suggest to add a common suffix to the completion if one doesn't already exist in case of classification or conditional generation. - """ - error_msg = None - immediate_msg = None - optional_msg = None - optional_fn = None - - ft_type = infer_task_type(df) - if ft_type == "open-ended generation" or ft_type == "classification": - return Remediation(name="common_suffix") - - common_suffix = get_common_xfix(df.completion, xfix="suffix") - if (df.completion == common_suffix).all(): - error_msg = f"All completions are identical: `{common_suffix}`\nEnsure completions are different, otherwise the model will just repeat `{common_suffix}`" - return Remediation(name="common_suffix", error_msg=error_msg) - - # Find a suffix which is not contained within the completion otherwise - suggested_suffix = " [END]" - suffix_options = [ - "\n", - ".", - " END", - "***", - "+++", - "&&&", - "$$$", - "@@@", - "%%%", - ] - for suffix_option in suffix_options: - if df.completion.str.contains(suffix_option, regex=False).any(): - continue - suggested_suffix = suffix_option - break - display_suggested_suffix = suggested_suffix.replace("\n", "\\n") - - def add_suffix(x, suffix): - x["completion"] += suffix - return x - - if common_suffix != "": - common_suffix_new_line_handled = common_suffix.replace("\n", "\\n") - immediate_msg = ( - f"\n- All completions end with suffix `{common_suffix_new_line_handled}`" - ) - if len(common_suffix) > 10: - immediate_msg += f". This suffix seems very long. Consider replacing with a shorter suffix, such as `{display_suggested_suffix}`" - if ( - df.completion.str[: -len(common_suffix)] - .str.contains(common_suffix, regex=False) - .any() - ): - immediate_msg += f"\n WARNING: Some of your completions contain the suffix `{common_suffix}` more than once. We suggest that you review your completions and add a unique ending" - - else: - immediate_msg = "\n- Your data does not contain a common ending at the end of your completions. Having a common ending string appended to the end of the completion makes it clearer to the fine-tuned model where the completion should end. See https://beta.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more detail and examples." - - if common_suffix == "": - optional_msg = ( - f"Add a suffix ending `{display_suggested_suffix}` to all completions" - ) - - def optional_fn(x): - return add_suffix(x, suggested_suffix) - - return Remediation( - name="common_completion_suffix", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - error_msg=error_msg, - ) - - -def completions_space_start_validator(df): - """ - This validator will suggest to add a space at the start of the completion if it doesn't already exist. This helps with tokenization. - """ - - def add_space_start(x): - x["completion"] = x["completion"].apply( - lambda x: ("" if x[0] == " " else " ") + x - ) - return x - - optional_msg = None - optional_fn = None - immediate_msg = None - - if df.completion.str[:1].nunique() != 1 or df.completion.values[0][0] != " ": - immediate_msg = "\n- The completion should start with a whitespace character (` `). This tends to produce better results due to the tokenization we use. See https://beta.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more details" - optional_msg = "Add a whitespace character to the beginning of the completion" - optional_fn = add_space_start - return Remediation( - name="completion_space_start", - immediate_msg=immediate_msg, - optional_msg=optional_msg, - optional_fn=optional_fn, - ) - - -def lower_case_validator(df, column): - """ - This validator will suggest to lowercase the column values, if more than a third of letters are uppercase. - """ - - def lower_case(x): - x[column] = x[column].str.lower() - return x - - count_upper = ( - df[column] - .apply(lambda x: sum(1 for c in x if c.isalpha() and c.isupper())) - .sum() - ) - count_lower = ( - df[column] - .apply(lambda x: sum(1 for c in x if c.isalpha() and c.islower())) - .sum() - ) - - if count_upper * 2 > count_lower: - return Remediation( - name="lower_case", - immediate_msg=f"\n- More than a third of your `{column}` column/key is uppercase. Uppercase {column}s tends to perform worse than a mixture of case encountered in normal language. We recommend to lower case the data if that makes sense in your domain. See https://beta.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more details", - optional_msg=f"Lowercase all your data in column/key `{column}`", - optional_fn=lower_case, - ) - - -def read_any_format(fname, fields=["prompt", "completion"]): - """ - This function will read a file saved in .csv, .json, .txt, .xlsx or .tsv format using pandas. - - for .xlsx it will read the first sheet - - for .txt it will assume completions and split on newline - """ - remediation = None - necessary_msg = None - immediate_msg = None - error_msg = None - df = None - - if os.path.isfile(fname): - for ending, separator in [(".csv", ","), (".tsv", "\t")]: - if fname.lower().endswith(ending): - immediate_msg = f"\n- Based on your file extension, your file is formatted as a {ending[1:].upper()} file" - necessary_msg = ( - f"Your format `{ending[1:].upper()}` will be converted to `JSONL`" - ) - df = pd.read_csv(fname, sep=separator, dtype=str) - if fname.lower().endswith(".xlsx"): - immediate_msg = "\n- Based on your file extension, your file is formatted as an Excel file" - necessary_msg = "Your format `XLSX` will be converted to `JSONL`" - xls = pd.ExcelFile(fname) - sheets = xls.sheet_names - if len(sheets) > 1: - immediate_msg += "\n- Your Excel file contains more than one sheet. Please either save as csv or ensure all data is present in the first sheet. WARNING: Reading only the first sheet..." - df = pd.read_excel(fname, dtype=str) - if fname.lower().endswith(".txt"): - immediate_msg = "\n- Based on your file extension, you provided a text file" - necessary_msg = "Your format `TXT` will be converted to `JSONL`" - with open(fname, "r") as f: - content = f.read() - df = pd.DataFrame( - [["", line] for line in content.split("\n")], - columns=fields, - dtype=str, - ) - if fname.lower().endswith("jsonl") or fname.lower().endswith("json"): - try: - df = pd.read_json(fname, lines=True, dtype=str) - except (ValueError, TypeError): - df = pd.read_json(fname, dtype=str) - immediate_msg = "\n- Your file appears to be in a .JSON format. Your file will be converted to JSONL format" - necessary_msg = "Your format `JSON` will be converted to `JSONL`" - - if df is None: - error_msg = ( - "Your file is not saved as a .CSV, .TSV, .XLSX, .TXT or .JSONL file." - ) - if "." in fname: - error_msg += ( - f" Your file `{fname}` appears to end with `.{fname.split('.')[1]}`" - ) - else: - error_msg += f" Your file `{fname}` does not appear to have a file ending. Please ensure your filename ends with one of the supported file endings." - else: - df.fillna("", inplace=True) - else: - error_msg = f"File {fname} does not exist." - - remediation = Remediation( - name="read_any_format", - necessary_msg=necessary_msg, - immediate_msg=immediate_msg, - error_msg=error_msg, - ) - return df, remediation - - -def format_inferrer_validator(df): - """ - This validator will infer the likely fine-tuning format of the data, and display it to the user if it is classification. - It will also suggest to use ada and explain train/validation split benefits. - """ - ft_type = infer_task_type(df) - immediate_msg = None - if ft_type == "classification": - immediate_msg = f"\n- Based on your data it seems like you're trying to fine-tune a model for {ft_type}\n- For classification, we recommend you try one of the faster and cheaper models, such as `ada`\n- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used for training" - return Remediation(name="num_examples", immediate_msg=immediate_msg) - - -def apply_necessary_remediation(df, remediation): - """ - This function will apply a necessary remediation to a dataframe, or print an error message if one exists. - """ - if remediation.error_msg is not None: - sys.stderr.write( - f"\n\nERROR in {remediation.name} validator: {remediation.error_msg}\n\nAborting..." - ) - sys.exit(1) - if remediation.immediate_msg is not None: - sys.stdout.write(remediation.immediate_msg) - if remediation.necessary_fn is not None: - df = remediation.necessary_fn(df) - return df - - -def accept_suggestion(input_text, auto_accept): - sys.stdout.write(input_text) - if auto_accept: - sys.stdout.write("Y\n") - return True - return input().lower() != "n" - - -def apply_optional_remediation(df, remediation, auto_accept): - """ - This function will apply an optional remediation to a dataframe, based on the user input. - """ - optional_applied = False - input_text = f"- [Recommended] {remediation.optional_msg} [Y/n]: " - if remediation.optional_msg is not None: - if accept_suggestion(input_text, auto_accept): - df = remediation.optional_fn(df) - optional_applied = True - if remediation.necessary_msg is not None: - sys.stdout.write(f"- [Necessary] {remediation.necessary_msg}\n") - return df, optional_applied - - -def estimate_fine_tuning_time(df): - """ - Estimate the time it'll take to fine-tune the dataset - """ - ft_format = infer_task_type(df) - expected_time = 1.0 - if ft_format == "classification": - num_examples = len(df) - expected_time = num_examples * 1.44 - else: - size = df.memory_usage(index=True).sum() - expected_time = size * 0.0515 - - def format_time(time): - if time < 60: - return f"{round(time, 2)} seconds" - elif time < 3600: - return f"{round(time / 60, 2)} minutes" - elif time < 86400: - return f"{round(time / 3600, 2)} hours" - else: - return f"{round(time / 86400, 2)} days" - - time_string = format_time(expected_time + 140) - sys.stdout.write( - f"Once your model starts training, it'll approximately take {time_string} to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\n" - ) - - -def get_outfnames(fname, split): - suffixes = ["_train", "_valid"] if split else [""] - i = 0 - while True: - index_suffix = f" ({i})" if i > 0 else "" - candidate_fnames = [ - os.path.splitext(fname)[0] + "_prepared" + suffix + index_suffix + ".jsonl" - for suffix in suffixes - ] - if not any(os.path.isfile(f) for f in candidate_fnames): - return candidate_fnames - i += 1 - - -def get_classification_hyperparams(df): - n_classes = df.completion.nunique() - pos_class = None - if n_classes == 2: - pos_class = df.completion.value_counts().index[0] - return n_classes, pos_class - - -def write_out_file(df, fname, any_remediations, auto_accept): - """ - This function will write out a dataframe to a file, if the user would like to proceed, and also offer a fine-tuning command with the newly created file. - For classification it will optionally ask the user if they would like to split the data into train/valid files, and modify the suggested command to include the valid set. - """ - ft_format = infer_task_type(df) - common_prompt_suffix = get_common_xfix(df.prompt, xfix="suffix") - common_completion_suffix = get_common_xfix(df.completion, xfix="suffix") - - split = False - input_text = "- [Recommended] Would you like to split into training and validation set? [Y/n]: " - if ft_format == "classification": - if accept_suggestion(input_text, auto_accept): - split = True - - additional_params = "" - common_prompt_suffix_new_line_handled = common_prompt_suffix.replace("\n", "\\n") - common_completion_suffix_new_line_handled = common_completion_suffix.replace( - "\n", "\\n" - ) - optional_ending_string = ( - f' Make sure to include `stop=["{common_completion_suffix_new_line_handled}"]` so that the generated texts ends at the expected place.' - if len(common_completion_suffix_new_line_handled) > 0 - else "" - ) - - input_text = "\n\nYour data will be written to a new JSONL file. Proceed [Y/n]: " - - if not any_remediations and not split: - sys.stdout.write( - f'\nYou can use your file for fine-tuning:\n> openai api fine_tunes.create -t "{fname}"{additional_params}\n\nAfter you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `{common_prompt_suffix_new_line_handled}` for the model to start generating completions, rather than continuing with the prompt.{optional_ending_string}\n' - ) - estimate_fine_tuning_time(df) - - elif accept_suggestion(input_text, auto_accept): - fnames = get_outfnames(fname, split) - if split: - assert len(fnames) == 2 and "train" in fnames[0] and "valid" in fnames[1] - MAX_VALID_EXAMPLES = 1000 - n_train = max(len(df) - MAX_VALID_EXAMPLES, int(len(df) * 0.8)) - df_train = df.sample(n=n_train, random_state=42) - df_valid = df.drop(df_train.index) - df_train[["prompt", "completion"]].to_json( - fnames[0], lines=True, orient="records", force_ascii=False - ) - df_valid[["prompt", "completion"]].to_json( - fnames[1], lines=True, orient="records", force_ascii=False - ) - - n_classes, pos_class = get_classification_hyperparams(df) - additional_params += " --compute_classification_metrics" - if n_classes == 2: - additional_params += f' --classification_positive_class "{pos_class}"' - else: - additional_params += f" --classification_n_classes {n_classes}" - else: - assert len(fnames) == 1 - df[["prompt", "completion"]].to_json( - fnames[0], lines=True, orient="records", force_ascii=False - ) - - # Add -v VALID_FILE if we split the file into train / valid - files_string = ("s" if split else "") + " to `" + ("` and `".join(fnames)) - valid_string = f' -v "{fnames[1]}"' if split else "" - separator_reminder = ( - "" - if len(common_prompt_suffix_new_line_handled) == 0 - else f"After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `{common_prompt_suffix_new_line_handled}` for the model to start generating completions, rather than continuing with the prompt." - ) - sys.stdout.write( - f'\nWrote modified file{files_string}`\nFeel free to take a look!\n\nNow use that file when fine-tuning:\n> openai api fine_tunes.create -t "{fnames[0]}"{valid_string}{additional_params}\n\n{separator_reminder}{optional_ending_string}\n' - ) - estimate_fine_tuning_time(df) - else: - sys.stdout.write("Aborting... did not write the file\n") - - -def write_out_search_file(df, fname, any_remediations, auto_accept, fields, purpose): - """ - This function will write out a dataframe to a file, if the user would like to proceed. - """ - input_text = "\n\nYour data will be written to a new JSONL file. Proceed [Y/n]: " - - if not any_remediations: - sys.stdout.write( - f'\nYou can upload your file:\n> openai api files.create -f "{fname}" -p {purpose}' - ) - - elif accept_suggestion(input_text, auto_accept): - fnames = get_outfnames(fname, split=False) - - assert len(fnames) == 1 - df[fields].to_json(fnames[0], lines=True, orient="records", force_ascii=False) - - sys.stdout.write( - f'\nWrote modified file to {fnames[0]}`\nFeel free to take a look!\n\nNow upload that file:\n> openai api files.create -f "{fnames[0]}" -p {purpose}' - ) - else: - sys.stdout.write("Aborting... did not write the file\n") - - -def infer_task_type(df): - """ - Infer the likely fine-tuning task type from the data - """ - CLASSIFICATION_THRESHOLD = 3 # min_average instances of each class - if sum(df.prompt.str.len()) == 0: - return "open-ended generation" - - if len(df.completion.unique()) < len(df) / CLASSIFICATION_THRESHOLD: - return "classification" - - return "conditional generation" - - -def get_common_xfix(series, xfix="suffix"): - """ - Finds the longest common suffix or prefix of all the values in a series - """ - common_xfix = "" - while True: - common_xfixes = ( - series.str[-(len(common_xfix) + 1) :] - if xfix == "suffix" - else series.str[: len(common_xfix) + 1] - ) # first few or last few characters - if ( - common_xfixes.nunique() != 1 - ): # we found the character at which we don't have a unique xfix anymore - break - elif ( - common_xfix == common_xfixes.values[0] - ): # the entire first row is a prefix of every other row - break - else: # the first or last few characters are still common across all rows - let's try to add one more - common_xfix = common_xfixes.values[0] - return common_xfix - - -def get_validators(): - return [ - num_examples_validator, - lambda x: necessary_column_validator(x, "prompt"), - lambda x: necessary_column_validator(x, "completion"), - additional_column_validator, - non_empty_field_validator, - format_inferrer_validator, - duplicated_rows_validator, - long_examples_validator, - lambda x: lower_case_validator(x, "prompt"), - lambda x: lower_case_validator(x, "completion"), - common_prompt_suffix_validator, - common_prompt_prefix_validator, - common_completion_prefix_validator, - common_completion_suffix_validator, - completions_space_start_validator, - ] - - -def get_search_validators(required_fields, optional_fields): - validators = [ - lambda x: necessary_column_validator(x, field) for field in required_fields - ] - validators += [ - lambda x: non_empty_field_validator(x, field) for field in required_fields - ] - validators += [lambda x: duplicated_rows_validator(x, required_fields)] - validators += [ - lambda x: additional_column_validator( - x, fields=required_fields + optional_fields - ), - ] - - return validators - - -def apply_validators( - df, - fname, - remediation, - validators, - auto_accept, - write_out_file_func, -): - optional_remediations = [] - if remediation is not None: - optional_remediations.append(remediation) - for validator in validators: - remediation = validator(df) - if remediation is not None: - optional_remediations.append(remediation) - df = apply_necessary_remediation(df, remediation) - - any_optional_or_necessary_remediations = any( - [ - remediation - for remediation in optional_remediations - if remediation.optional_msg is not None - or remediation.necessary_msg is not None - ] - ) - any_necessary_applied = any( - [ - remediation - for remediation in optional_remediations - if remediation.necessary_msg is not None - ] - ) - any_optional_applied = False - - if any_optional_or_necessary_remediations: - sys.stdout.write( - "\n\nBased on the analysis we will perform the following actions:\n" - ) - for remediation in optional_remediations: - df, optional_applied = apply_optional_remediation( - df, remediation, auto_accept - ) - any_optional_applied = any_optional_applied or optional_applied - else: - sys.stdout.write("\n\nNo remediations found.\n") - - any_optional_or_necessary_applied = any_optional_applied or any_necessary_applied - - write_out_file_func(df, fname, any_optional_or_necessary_applied, auto_accept) diff --git a/spaces/BramVanroy/spacey_conll/app.py b/spaces/BramVanroy/spacey_conll/app.py deleted file mode 100644 index 27ba3c5133b445f03e9bab6adfa7b90a1c8bd6d9..0000000000000000000000000000000000000000 --- a/spaces/BramVanroy/spacey_conll/app.py +++ /dev/null @@ -1,129 +0,0 @@ -from io import StringIO -import logging - -import streamlit as st - -from utils import MODEL_MAP, create_download_link, load_nlp - - -def _init(): - st.set_page_config(page_title="Parse text into CoNLL-U format", page_icon="📝") - st.markdown("""""", unsafe_allow_html=True) - - if "selected_model" not in st.session_state: - st.session_state["selected_model"] = "en_core_web_sm" - - if "nlp" not in st.session_state: - st.session_state["nlp"] = None - - if "text" not in st.session_state: - st.session_state["text"] = None - - st.title("📝 Parse text into CoNLL-U format") - - -def _model_selection(): - st.markdown("## Model ✨") - lang_col, model_text_col, custom_col = st.columns((4, 1, 4)) - - selected_lang = lang_col.selectbox("Language", tuple(MODEL_MAP.keys()), - index=list(MODEL_MAP.keys()).index("English")) - st.session_state["selected_model"] = MODEL_MAP[selected_lang] - - model_text_col.markdown("**-- or --**", unsafe_allow_html=True) - - st.session_state["selected_model"] = custom_col.text_input(label="Model to load", - help="You can find spaCy models here: https://spacy.io/models/." - " Only official spaCy models are supported.", - value=st.session_state["selected_model"]).split("/")[-1] - - # Set extra options for the parser: disable tokenization/sentence segmentation - tok_col, sbd_col, status_col = st.columns(3) - is_tokenized = tok_col.checkbox("Is pre-tokenized?", help="When this option is enabled, tokens will be split by" - " white-space and sentence splitting will also be disabled.") - disable_sbd = sbd_col.checkbox("Disable sentence splitting?") - - ################################### - # (Down)oading model with options # - ################################### - load_info = status_col.info(f"Loading {st.session_state['selected_model']}...") - - # Check if the model exists, if not download it. Return None when there was an error downloading the model - try: - st.session_state["nlp"] = load_nlp(st.session_state["selected_model"], - is_tokenized=is_tokenized, - disable_sbd=disable_sbd) - except Exception: - logging.exception("Could not load model.") - load_info.error(f"Error when trying to load {st.session_state['selected_model']}!") - else: - load_info.success(f"{st.session_state['selected_model']} loaded!") - - -def _data_input(): - inp_data_heading, input_col = st.columns((3, 1)) - inp_data_heading.markdown("## Input data 📄") - fupload_check = input_col.checkbox("File upload?") - - if fupload_check: - uploaded_file = st.file_uploader("Choose a plain text file to parse as CoNLL", - label_visibility="hidden") - if uploaded_file is not None: - stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) - st.session_state["text"] = stringio.read() - else: - st.session_state["text"] = None - else: - st.session_state["text"] = st.text_area(label="Text to parse as CoNLL", - value="Grandma is baking cookies! I love her cookies.", - label_visibility="hidden") - - -def _parse(): - if ("text" in st.session_state and st.session_state["text"] - and "nlp" in st.session_state and st.session_state["nlp"]): - parse_process = st.info("Parsing...") - download_ct = st.empty() - - doc = st.session_state["nlp"](st.session_state["text"]) - - df = doc._.conll_pd - st.dataframe(df) - - excel_link = create_download_link(df, - "conll.xlsx", - "Excel file") - txt_link = create_download_link(df.to_csv(index=False, encoding="utf-8", sep="\t"), - "conll.txt", - "tab-separated file") - - parse_process.success("Done parsing!") - download_ct.markdown(f"You can download the table as an {excel_link}, or as a {txt_link}.", - unsafe_allow_html=True) - - -def _footer(): - st.markdown("## Github repository and contact ✒️") - st.markdown("This demo shows limited options of what the `spacy-conll` package can do. For instance, it also works" - " with stanza and UDPipe and you can parse CoNLL files directly into spaCy objects." - " You can check it out on [Github](https://github.com/BramVanroy/spacy_conll).") - st.markdown("Would you like additional functionality in the library? Or just want to get in touch?" - " Give me a shout on [Twitter](https://twitter.com/BramVanroy)!") - - -def main(): - _init() - _model_selection() - _data_input() - _parse() - _footer() - - -if __name__ == '__main__': - main() diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/distance.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/distance.h deleted file mode 100644 index 03b0fb5564a8efd630e78dac14ce52794281d603..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/distance.h +++ /dev/null @@ -1,43 +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 -#include - -namespace thrust -{ -namespace system -{ -namespace detail -{ -namespace generic -{ - -template -inline __host__ __device__ - typename thrust::iterator_traits::difference_type - distance(InputIterator first, InputIterator last); - -} // end namespace generic -} // end namespace detail -} // end namespace system -} // end namespace thrust - -#include - diff --git a/spaces/CVPR/Text2Human/Text2Human/utils/options.py b/spaces/CVPR/Text2Human/Text2Human/utils/options.py deleted file mode 100644 index 1045dd07381bd680b623d0187be5353e2e3dee80..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Text2Human/Text2Human/utils/options.py +++ /dev/null @@ -1,129 +0,0 @@ -import os -import os.path as osp -from collections import OrderedDict - -import yaml - - -def ordered_yaml(): - """Support OrderedDict for yaml. - - Returns: - yaml Loader and Dumper. - """ - try: - from yaml import CDumper as Dumper - from yaml import CLoader as Loader - except ImportError: - from yaml import Dumper, Loader - - _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG - - def dict_representer(dumper, data): - return dumper.represent_dict(data.items()) - - def dict_constructor(loader, node): - return OrderedDict(loader.construct_pairs(node)) - - Dumper.add_representer(OrderedDict, dict_representer) - Loader.add_constructor(_mapping_tag, dict_constructor) - return Loader, Dumper - - -def parse(opt_path, is_train=True): - """Parse option file. - - Args: - opt_path (str): Option file path. - is_train (str): Indicate whether in training or not. Default: True. - - Returns: - (dict): Options. - """ - with open(opt_path, mode='r') as f: - Loader, _ = ordered_yaml() - opt = yaml.load(f, Loader=Loader) - - gpu_list = ','.join(str(x) for x in opt['gpu_ids']) - if opt.get('set_CUDA_VISIBLE_DEVICES', None): - os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list - print('export CUDA_VISIBLE_DEVICES=' + gpu_list, flush=True) - else: - print('gpu_list: ', gpu_list, flush=True) - - opt['is_train'] = is_train - - # paths - opt['path'] = {} - opt['path']['root'] = osp.abspath( - osp.join(__file__, osp.pardir, osp.pardir)) - if is_train: - experiments_root = osp.join(opt['path']['root'], 'experiments', - opt['name']) - opt['path']['experiments_root'] = experiments_root - opt['path']['models'] = osp.join(experiments_root, 'models') - opt['path']['log'] = experiments_root - opt['path']['visualization'] = osp.join(experiments_root, - 'visualization') - - # change some options for debug mode - if 'debug' in opt['name']: - opt['debug'] = True - opt['val_freq'] = 1 - opt['print_freq'] = 1 - opt['save_checkpoint_freq'] = 1 - else: # test - results_root = osp.join(opt['path']['root'], 'results', opt['name']) - opt['path']['results_root'] = results_root - opt['path']['log'] = results_root - opt['path']['visualization'] = osp.join(results_root, 'visualization') - - return opt - - -def dict2str(opt, indent_level=1): - """dict to string for printing options. - - Args: - opt (dict): Option dict. - indent_level (int): Indent level. Default: 1. - - Return: - (str): Option string for printing. - """ - msg = '' - for k, v in opt.items(): - if isinstance(v, dict): - msg += ' ' * (indent_level * 2) + k + ':[\n' - msg += dict2str(v, indent_level + 1) - msg += ' ' * (indent_level * 2) + ']\n' - else: - msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n' - return msg - - -class NoneDict(dict): - """None dict. It will return none if key is not in the dict.""" - - def __missing__(self, key): - return None - - -def dict_to_nonedict(opt): - """Convert to NoneDict, which returns None for missing keys. - - Args: - opt (dict): Option dict. - - Returns: - (dict): NoneDict for options. - """ - if isinstance(opt, dict): - new_opt = dict() - for key, sub_opt in opt.items(): - new_opt[key] = dict_to_nonedict(sub_opt) - return NoneDict(**new_opt) - elif isinstance(opt, list): - return [dict_to_nonedict(sub_opt) for sub_opt in opt] - else: - return opt diff --git a/spaces/CVPR/regionclip-demo/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp b/spaces/CVPR/regionclip-demo/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp deleted file mode 100644 index c843487b5fa4e8077dd27402ec99009266ddda8d..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// Copyright (c) Facebook, Inc. and its affiliates. -#include "box_iou_rotated.h" -#include "box_iou_rotated_utils.h" - -namespace detectron2 { - -template -void box_iou_rotated_cpu_kernel( - const at::Tensor& boxes1, - const at::Tensor& boxes2, - at::Tensor& ious) { - auto num_boxes1 = boxes1.size(0); - auto num_boxes2 = boxes2.size(0); - - for (int i = 0; i < num_boxes1; i++) { - for (int j = 0; j < num_boxes2; j++) { - ious[i * num_boxes2 + j] = single_box_iou_rotated( - boxes1[i].data_ptr(), boxes2[j].data_ptr()); - } - } -} - -at::Tensor box_iou_rotated_cpu( - // input must be contiguous: - const at::Tensor& boxes1, - const at::Tensor& boxes2) { - auto num_boxes1 = boxes1.size(0); - auto num_boxes2 = boxes2.size(0); - at::Tensor ious = - at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat)); - - box_iou_rotated_cpu_kernel(boxes1, boxes2, ious); - - // reshape from 1d array to 2d array - auto shape = std::vector{num_boxes1, num_boxes2}; - return ious.reshape(shape); -} - -} // namespace detectron2 diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/visualizer.py b/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/visualizer.py deleted file mode 100644 index 7a1b7b101e9b73f75f9136bc67f2063c7c1cf1c1..0000000000000000000000000000000000000000 --- a/spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/util/visualizer.py +++ /dev/null @@ -1,318 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@File : visualizer.py -@Time : 2022/04/05 11:39:33 -@Author : Shilong Liu -@Contact : slongliu86@gmail.com -""" - -import datetime -import os - -import cv2 -import matplotlib.pyplot as plt -import numpy as np -import torch -from matplotlib import transforms -from matplotlib.collections import PatchCollection -from matplotlib.patches import Polygon -from pycocotools import mask as maskUtils - - -def renorm( - img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] -) -> torch.FloatTensor: - # img: tensor(3,H,W) or tensor(B,3,H,W) - # return: same as img - assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim() - if img.dim() == 3: - assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % ( - img.size(0), - str(img.size()), - ) - img_perm = img.permute(1, 2, 0) - mean = torch.Tensor(mean) - std = torch.Tensor(std) - img_res = img_perm * std + mean - return img_res.permute(2, 0, 1) - else: # img.dim() == 4 - assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % ( - img.size(1), - str(img.size()), - ) - img_perm = img.permute(0, 2, 3, 1) - mean = torch.Tensor(mean) - std = torch.Tensor(std) - img_res = img_perm * std + mean - return img_res.permute(0, 3, 1, 2) - - -class ColorMap: - def __init__(self, basergb=[255, 255, 0]): - self.basergb = np.array(basergb) - - def __call__(self, attnmap): - # attnmap: h, w. np.uint8. - # return: h, w, 4. np.uint8. - assert attnmap.dtype == np.uint8 - h, w = attnmap.shape - res = self.basergb.copy() - res = res[None][None].repeat(h, 0).repeat(w, 1) # h, w, 3 - attn1 = attnmap.copy()[..., None] # h, w, 1 - res = np.concatenate((res, attn1), axis=-1).astype(np.uint8) - return res - - -def rainbow_text(x, y, ls, lc, **kw): - """ - Take a list of strings ``ls`` and colors ``lc`` and place them next to each - other, with text ls[i] being shown in color lc[i]. - - This example shows how to do both vertical and horizontal text, and will - pass all keyword arguments to plt.text, so you can set the font size, - family, etc. - """ - t = plt.gca().transData - fig = plt.gcf() - plt.show() - - # horizontal version - for s, c in zip(ls, lc): - text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw) - text.draw(fig.canvas.get_renderer()) - ex = text.get_window_extent() - t = transforms.offset_copy(text._transform, x=ex.width, units="dots") - - # #vertical version - # for s,c in zip(ls,lc): - # text = plt.text(x,y," "+s+" ",color=c, transform=t, - # rotation=90,va='bottom',ha='center',**kw) - # text.draw(fig.canvas.get_renderer()) - # ex = text.get_window_extent() - # t = transforms.offset_copy(text._transform, y=ex.height, units='dots') - - -class COCOVisualizer: - def __init__(self, coco=None, tokenlizer=None) -> None: - self.coco = coco - - def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"): - """ - img: tensor(3, H, W) - tgt: make sure they are all on cpu. - must have items: 'image_id', 'boxes', 'size' - """ - plt.figure(dpi=dpi) - plt.rcParams["font.size"] = "5" - ax = plt.gca() - img = renorm(img).permute(1, 2, 0) - # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': - # import ipdb; ipdb.set_trace() - ax.imshow(img) - - self.addtgt(tgt) - - if tgt is None: - image_id = 0 - elif "image_id" not in tgt: - image_id = 0 - else: - image_id = tgt["image_id"] - - if caption is None: - savename = "{}/{}-{}.png".format( - savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-") - ) - else: - savename = "{}/{}-{}-{}.png".format( - savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-") - ) - print("savename: {}".format(savename)) - os.makedirs(os.path.dirname(savename), exist_ok=True) - plt.savefig(savename) - plt.close() - - def addtgt(self, tgt): - """ """ - if tgt is None or not "boxes" in tgt: - ax = plt.gca() - - if "caption" in tgt: - ax.set_title(tgt["caption"], wrap=True) - - ax.set_axis_off() - return - - ax = plt.gca() - H, W = tgt["size"] - numbox = tgt["boxes"].shape[0] - - color = [] - polygons = [] - boxes = [] - for box in tgt["boxes"].cpu(): - unnormbbox = box * torch.Tensor([W, H, W, H]) - unnormbbox[:2] -= unnormbbox[2:] / 2 - [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist() - boxes.append([bbox_x, bbox_y, bbox_w, bbox_h]) - poly = [ - [bbox_x, bbox_y], - [bbox_x, bbox_y + bbox_h], - [bbox_x + bbox_w, bbox_y + bbox_h], - [bbox_x + bbox_w, bbox_y], - ] - np_poly = np.array(poly).reshape((4, 2)) - polygons.append(Polygon(np_poly)) - c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] - color.append(c) - - p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1) - ax.add_collection(p) - p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2) - ax.add_collection(p) - - if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0: - assert ( - len(tgt["strings_positive"]) == numbox - ), f"{len(tgt['strings_positive'])} = {numbox}, " - for idx, strlist in enumerate(tgt["strings_positive"]): - cate_id = int(tgt["labels"][idx]) - _string = str(cate_id) + ":" + " ".join(strlist) - bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx] - # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1}) - ax.text( - bbox_x, - bbox_y, - _string, - color="black", - bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1}, - ) - - if "box_label" in tgt: - assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, " - for idx, bl in enumerate(tgt["box_label"]): - _string = str(bl) - bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx] - # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1}) - ax.text( - bbox_x, - bbox_y, - _string, - color="black", - bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1}, - ) - - if "caption" in tgt: - ax.set_title(tgt["caption"], wrap=True) - # plt.figure() - # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(), - # ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black']) - - if "attn" in tgt: - # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO': - # import ipdb; ipdb.set_trace() - if isinstance(tgt["attn"], tuple): - tgt["attn"] = [tgt["attn"]] - for item in tgt["attn"]: - attn_map, basergb = item - attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3) - attn_map = (attn_map * 255).astype(np.uint8) - cm = ColorMap(basergb) - heatmap = cm(attn_map) - ax.imshow(heatmap) - ax.set_axis_off() - - def showAnns(self, anns, draw_bbox=False): - """ - Display the specified annotations. - :param anns (array of object): annotations to display - :return: None - """ - if len(anns) == 0: - return 0 - if "segmentation" in anns[0] or "keypoints" in anns[0]: - datasetType = "instances" - elif "caption" in anns[0]: - datasetType = "captions" - else: - raise Exception("datasetType not supported") - if datasetType == "instances": - ax = plt.gca() - ax.set_autoscale_on(False) - polygons = [] - color = [] - for ann in anns: - c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] - if "segmentation" in ann: - if type(ann["segmentation"]) == list: - # polygon - for seg in ann["segmentation"]: - poly = np.array(seg).reshape((int(len(seg) / 2), 2)) - polygons.append(Polygon(poly)) - color.append(c) - else: - # mask - t = self.imgs[ann["image_id"]] - if type(ann["segmentation"]["counts"]) == list: - rle = maskUtils.frPyObjects( - [ann["segmentation"]], t["height"], t["width"] - ) - else: - rle = [ann["segmentation"]] - m = maskUtils.decode(rle) - img = np.ones((m.shape[0], m.shape[1], 3)) - if ann["iscrowd"] == 1: - color_mask = np.array([2.0, 166.0, 101.0]) / 255 - if ann["iscrowd"] == 0: - color_mask = np.random.random((1, 3)).tolist()[0] - for i in range(3): - img[:, :, i] = color_mask[i] - ax.imshow(np.dstack((img, m * 0.5))) - if "keypoints" in ann and type(ann["keypoints"]) == list: - # turn skeleton into zero-based index - sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1 - kp = np.array(ann["keypoints"]) - x = kp[0::3] - y = kp[1::3] - v = kp[2::3] - for sk in sks: - if np.all(v[sk] > 0): - plt.plot(x[sk], y[sk], linewidth=3, color=c) - plt.plot( - x[v > 0], - y[v > 0], - "o", - markersize=8, - markerfacecolor=c, - markeredgecolor="k", - markeredgewidth=2, - ) - plt.plot( - x[v > 1], - y[v > 1], - "o", - markersize=8, - markerfacecolor=c, - markeredgecolor=c, - markeredgewidth=2, - ) - - if draw_bbox: - [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"] - poly = [ - [bbox_x, bbox_y], - [bbox_x, bbox_y + bbox_h], - [bbox_x + bbox_w, bbox_y + bbox_h], - [bbox_x + bbox_w, bbox_y], - ] - np_poly = np.array(poly).reshape((4, 2)) - polygons.append(Polygon(np_poly)) - color.append(c) - - # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) - # ax.add_collection(p) - p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2) - ax.add_collection(p) - elif datasetType == "captions": - for ann in anns: - print(ann["caption"]) diff --git a/spaces/CofAI/CalculatorUI/style.css b/spaces/CofAI/CalculatorUI/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/CofAI/CalculatorUI/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/CofAI/chat.b4/g4f/Provider/Providers/DeepAi.py b/spaces/CofAI/chat.b4/g4f/Provider/Providers/DeepAi.py deleted file mode 100644 index 02b08120ec8ef50c91c9237047a4f36c822a7bfc..0000000000000000000000000000000000000000 --- a/spaces/CofAI/chat.b4/g4f/Provider/Providers/DeepAi.py +++ /dev/null @@ -1,46 +0,0 @@ -import os -import json -import random -import hashlib -import requests - -from ...typing import sha256, Dict, get_type_hints - -url = 'https://deepai.org' -model = ['gpt-3.5-turbo'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - def md5(text: str) -> str: - return hashlib.md5(text.encode()).hexdigest()[::-1] - - - def get_api_key(user_agent: str) -> str: - part1 = str(random.randint(0, 10**11)) - part2 = md5(user_agent + md5(user_agent + md5(user_agent + part1 + "x"))) - - return f"tryit-{part1}-{part2}" - - user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36' - - headers = { - "api-key": get_api_key(user_agent), - "user-agent": user_agent - } - - files = { - "chat_style": (None, "chat"), - "chatHistory": (None, json.dumps(messages)) - } - - r = requests.post("https://api.deepai.org/chat_response", headers=headers, files=files, stream=True) - - for chunk in r.iter_content(chunk_size=None): - r.raise_for_status() - yield chunk.decode() - - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join( - [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) diff --git a/spaces/Cpp4App/Cpp4App/CDM/input_examples/README.md b/spaces/Cpp4App/Cpp4App/CDM/input_examples/README.md deleted file mode 100644 index 55ccbc5a96d37a7c24f5d253e52c7c10fbc779cb..0000000000000000000000000000000000000000 --- a/spaces/Cpp4App/Cpp4App/CDM/input_examples/README.md +++ /dev/null @@ -1,80 +0,0 @@ -# UIED - UI element detection, detecting UI elements from UI screenshots or drawnings - -This project is still ongoing and this repo may be updated irregularly, I developed a web app for the UIED in http://uied.online - -## Related Publications: -[1. UIED: a hybrid tool for GUI element detection](https://dl.acm.org/doi/10.1145/3368089.3417940) - -[2. Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?](https://arxiv.org/abs/2008.05132) - ->The repo has been **upgraded with Google OCR** for GUI text detection, to use the original version in our paper (using [EAST](https://github.com/argman/EAST) as text detector), check the relase [v2.3](https://github.com/MulongXie/UIED/releases/tag/v2.3) and download the pre-trained model in [this link](https://drive.google.com/drive/folders/1MK0Om7Lx0wRXGDfNcyj21B0FL1T461v5?usp=sharing). - -## What is it? - -UI Element Detection (UIED) is an old-fashioned computer vision (CV) based element detection approach for graphic user interface. - -The input of UIED could be various UI image, such as mobile app or web page screenshot, UI design drawn by Photoshop or Sketch, and even some hand-drawn UI design. Then the approach detects and classifies text and graphic UI elements, and exports the detection result as JSON file for future application. - -UIED comprises two parts to detect UI text and graphic elements, such as button, image and input bar. -* For text, it leverages [Google OCR](https://cloud.google.com/vision/docs/ocr) to perfrom detection. - -* For graphical elements, it uses old-fashioned CV approaches to locate the elements and a CNN classifier to achieve classification. - -> UIED is highly customizable, you can replace both parts by your choice (e.g. other text detection approaches). Unlike black-box end-to-end deep learning approach, you can revise the algorithms in the non-text detection and merging (partially or entirely) easily to fit your task. - -![UIED Approach](https://github.com/MulongXie/UIED/blob/master/data/demo/approach.png) - -## How to use? - -### Dependency -* **Python 3.5** -* **Opencv 3.4.2** -* **Pandas** - - -### Installation - - - - -The new version of UIED equipped with Google OCR is easy to deploy and no pre-trained model is needed. Simply donwload the repo along with the dependencies. - -> Please replace the Google OCR key at `detect_text/ocr.py line 28` with your own (apply in [Google website](https://cloud.google.com/vision)). - -### Usage -To test your own image(s): -* To test single image, change *input_path_img* in ``run_single.py`` to your input image and the results will be output to *output_root*. -* To test mutiple images, change *input_img_root* in ``run_batch.py`` to your input directory and the results will be output to *output_root*. -* To adjust the parameters lively, using ``run_testing.py`` - -> Note: The best set of parameters vary for different types of GUI image (Mobile App, Web, PC). I highly recommend to first play with the ``run_testing.py`` to pick a good set of parameters for your data. - -## Folder structure -``cnn/`` -* Used to train classifier for graphic UI elements -* Set path of the CNN classification model - -``config/`` -* Set data paths -* Set parameters for graphic elements detection - -``data/`` -* Input UI images and output detection results - -``detect_compo/`` -* Non-text GUI component detection - -``detect_text/`` -* GUI text detection using Google OCR - -``detect_merge/`` -* Merge the detection results of non-text and text GUI elements - -The major detection algorithms are in ``detect_compo/``, ``detect_text/`` and ``detect_merge/`` - -## Demo -GUI element detection result for web screenshot - -![UI Components detection result](https://github.com/MulongXie/UIED/blob/master/data/demo/demo.png) diff --git a/spaces/Cvandi/remake/tests/test_utils.py b/spaces/Cvandi/remake/tests/test_utils.py deleted file mode 100644 index 7919b74905495b4b6f4aa957a1f0b5d7a174c782..0000000000000000000000000000000000000000 --- a/spaces/Cvandi/remake/tests/test_utils.py +++ /dev/null @@ -1,87 +0,0 @@ -import numpy as np -from basicsr.archs.rrdbnet_arch import RRDBNet - -from realesrgan.utils import RealESRGANer - - -def test_realesrganer(): - # initialize with default model - restorer = RealESRGANer( - scale=4, - model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth', - model=None, - tile=10, - tile_pad=10, - pre_pad=2, - half=False) - assert isinstance(restorer.model, RRDBNet) - assert restorer.half is False - # initialize with user-defined model - model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) - restorer = RealESRGANer( - scale=4, - model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth', - model=model, - tile=10, - tile_pad=10, - pre_pad=2, - half=True) - # test attribute - assert isinstance(restorer.model, RRDBNet) - assert restorer.half is True - - # ------------------ test pre_process ---------------- # - img = np.random.random((12, 12, 3)).astype(np.float32) - restorer.pre_process(img) - assert restorer.img.shape == (1, 3, 14, 14) - # with modcrop - restorer.scale = 1 - restorer.pre_process(img) - assert restorer.img.shape == (1, 3, 16, 16) - - # ------------------ test process ---------------- # - restorer.process() - assert restorer.output.shape == (1, 3, 64, 64) - - # ------------------ test post_process ---------------- # - restorer.mod_scale = 4 - output = restorer.post_process() - assert output.shape == (1, 3, 60, 60) - - # ------------------ test tile_process ---------------- # - restorer.scale = 4 - img = np.random.random((12, 12, 3)).astype(np.float32) - restorer.pre_process(img) - restorer.tile_process() - assert restorer.output.shape == (1, 3, 64, 64) - - # ------------------ test enhance ---------------- # - img = np.random.random((12, 12, 3)).astype(np.float32) - result = restorer.enhance(img, outscale=2) - assert result[0].shape == (24, 24, 3) - assert result[1] == 'RGB' - - # ------------------ test enhance with 16-bit image---------------- # - img = np.random.random((4, 4, 3)).astype(np.uint16) + 512 - result = restorer.enhance(img, outscale=2) - assert result[0].shape == (8, 8, 3) - assert result[1] == 'RGB' - - # ------------------ test enhance with gray image---------------- # - img = np.random.random((4, 4)).astype(np.float32) - result = restorer.enhance(img, outscale=2) - assert result[0].shape == (8, 8) - assert result[1] == 'L' - - # ------------------ test enhance with RGBA---------------- # - img = np.random.random((4, 4, 4)).astype(np.float32) - result = restorer.enhance(img, outscale=2) - assert result[0].shape == (8, 8, 4) - assert result[1] == 'RGBA' - - # ------------------ test enhance with RGBA, alpha_upsampler---------------- # - restorer.tile_size = 0 - img = np.random.random((4, 4, 4)).astype(np.float32) - result = restorer.enhance(img, outscale=2, alpha_upsampler=None) - assert result[0].shape == (8, 8, 4) - assert result[1] == 'RGBA' diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.d852ae94.js b/spaces/DEEMOSTECH/ChatAvatar/static/js/main.d852ae94.js deleted file mode 100644 index 6e5f95f4bfdb7ef3c27e44ed597ea75103add552..0000000000000000000000000000000000000000 --- a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.d852ae94.js +++ /dev/null @@ -1,3 +0,0 @@ -/*! 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e=this.consumeCodePoint();switch(e){case Ze:return this.consumeStringToken(Ze);case et:var t=this.peekCodePoint(0),n=this.peekCodePoint(1),r=this.peekCodePoint(2);if(tn(t)||rn(n,r)){var i=An(t,n,r)?Ge:ze;return{type:5,value:this.consumeName(),flags:i}}break;case tt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),cn;break;case rt:return this.consumeStringToken(rt);case it:return sn;case At:return ln;case _t:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),vn;break;case bt:if(an(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case xt:return un;case ot:var A=e,a=this.peekCodePoint(0),o=this.peekCodePoint(1);if(an(A,a,o))return this.reconsumeCodePoint(e),this.consumeNumericToken();if(An(A,a,o))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();if(a===ot&&o===ut)return this.consumeCodePoint(),this.consumeCodePoint(),_n;break;case Et:if(an(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case Xe:if(this.peekCodePoint(0)===_t)for(this.consumeCodePoint();;){var s=this.consumeCodePoint();if(s===_t&&(s=this.consumeCodePoint())===Xe)return this.consumeToken();if(s===Lt)return this.consumeToken()}break;case Ct:return bn;case St:return xn;case lt:if(this.peekCodePoint(0)===st&&this.peekCodePoint(1)===ot&&this.peekCodePoint(2)===ot)return this.consumeCodePoint(),this.consumeCodePoint(),Bn;break;case ct:var l=this.peekCodePoint(0),c=this.peekCodePoint(1),d=this.peekCodePoint(2);if(An(l,c,d))return{type:7,value:this.consumeName()};break;case dt:return Cn;case qe:if(rn(e,this.peekCodePoint(0)))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();break;case ht:return Sn;case ft:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),dn;break;case pt:return gn;case mt:return mn;case Pt:case Gt:var h=this.peekCodePoint(0),f=this.peekCodePoint(1);return h!==bt||!Xt(f)&&f!==gt||(this.consumeCodePoint(),this.consumeUnicodeRangeToken()),this.reconsumeCodePoint(e),this.consumeIdentLikeToken();case vt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),fn;if(this.peekCodePoint(0)===vt)return this.consumeCodePoint(),hn;break;case yt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),pn;break;case Lt:return Un}return $t(e)?(this.consumeWhiteSpace(),En):Wt(e)?(this.reconsumeCodePoint(e),this.consumeNumericToken()):en(e)?(this.reconsumeCodePoint(e),this.consumeIdentLikeToken()):{type:6,value:u(e)}},e.prototype.consumeCodePoint=function(){var e=this._value.shift();return"undefined"===typeof e?-1:e},e.prototype.reconsumeCodePoint=function(e){this._value.unshift(e)},e.prototype.peekCodePoint=function(e){return e>=this._value.length?-1:this._value[e]},e.prototype.consumeUnicodeRangeToken=function(){for(var e=[],t=this.consumeCodePoint();Xt(t)&&e.length<6;)e.push(t),t=this.consumeCodePoint();for(var n=!1;t===gt&&e.length<6;)e.push(t),t=this.consumeCodePoint(),n=!0;if(n)return{type:30,start:parseInt(u.apply(void 0,e.map((function(e){return e===gt?Dt:e}))),16),end:parseInt(u.apply(void 0,e.map((function(e){return e===gt?zt:e}))),16)};var r=parseInt(u.apply(void 0,e),16);if(this.peekCodePoint(0)===ot&&Xt(this.peekCodePoint(1))){this.consumeCodePoint(),t=this.consumeCodePoint();for(var i=[];Xt(t)&&i.length<6;)i.push(t),t=this.consumeCodePoint();return{type:30,start:r,end:parseInt(u.apply(void 0,i),16)}}return{type:30,start:r,end:r}},e.prototype.consumeIdentLikeToken=function(){var e=this.consumeName();return"url"===e.toLowerCase()&&this.peekCodePoint(0)===it?(this.consumeCodePoint(),this.consumeUrlToken()):this.peekCodePoint(0)===it?(this.consumeCodePoint(),{type:19,value:e}):{type:20,value:e}},e.prototype.consumeUrlToken=function(){var e=[];if(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt)return{type:22,value:""};var t=this.peekCodePoint(0);if(t===rt||t===Ze){var n=this.consumeStringToken(this.consumeCodePoint());return 0===n.type&&(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At)?(this.consumeCodePoint(),{type:22,value:n.value}):(this.consumeBadUrlRemnants(),yn)}for(;;){var r=this.consumeCodePoint();if(r===Lt||r===At)return{type:22,value:u.apply(void 0,e)};if($t(r))return this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At?(this.consumeCodePoint(),{type:22,value:u.apply(void 0,e)}):(this.consumeBadUrlRemnants(),yn);if(r===Ze||r===rt||r===it||nn(r))return this.consumeBadUrlRemnants(),yn;if(r===qe){if(!rn(r,this.peekCodePoint(0)))return this.consumeBadUrlRemnants(),yn;e.push(this.consumeEscapedCodePoint())}else 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e=[],t=Ke,n=this.peekCodePoint(0);for(n!==bt&&n!==ot||e.push(this.consumeCodePoint());Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0);var r=this.peekCodePoint(1);if(n===Et&&Wt(r))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0),r=this.peekCodePoint(1);var i=this.peekCodePoint(2);if((n===Vt||n===Rt)&&((r===bt||r===ot)&&Wt(i)||Wt(r)))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());return[on(e),t]},e.prototype.consumeNumericToken=function(){var e=this.consumeNumber(),t=e[0],n=e[1],r=this.peekCodePoint(0),i=this.peekCodePoint(1),A=this.peekCodePoint(2);return An(r,i,A)?{type:15,number:t,flags:n,unit:this.consumeName()}:r===nt?(this.consumeCodePoint(),{type:16,number:t,flags:n}):{type:17,number:t,flags:n}},e.prototype.consumeEscapedCodePoint=function(){var e=this.consumeCodePoint();if(Xt(e)){for(var t=u(e);Xt(this.peekCodePoint(0))&&t.length<6;)t+=u(this.consumeCodePoint());$t(this.peekCodePoint(0))&&this.consumeCodePoint();var n=parseInt(t,16);return 0===n||jt(n)||n>1114111?Bt:n}return e===Lt?Bt:e},e.prototype.consumeName=function(){for(var e="";;){var t=this.consumeCodePoint();if(tn(t))e+=u(t);else{if(!rn(t,this.peekCodePoint(0)))return this.reconsumeCodePoint(t),e;e+=u(this.consumeEscapedCodePoint())}}},e}(),Fn=function(){function e(e){this._tokens=e}return e.create=function(t){var n=new Mn;return n.write(t),new e(n.read())},e.parseValue=function(t){return e.create(t).parseComponentValue()},e.parseValues=function(t){return e.create(t).parseComponentValues()},e.prototype.parseComponentValue=function(){for(var e=this.consumeToken();31===e.type;)e=this.consumeToken();if(32===e.type)throw new SyntaxError("Error parsing CSS component value, unexpected EOF");this.reconsumeToken(e);var t=this.consumeComponentValue();do{e=this.consumeToken()}while(31===e.type);if(32===e.type)return t;throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one")},e.prototype.parseComponentValues=function(){for(var e=[];;){var t=this.consumeComponentValue();if(32===t.type)return e;e.push(t),e.push()}},e.prototype.consumeComponentValue=function(){var e=this.consumeToken();switch(e.type){case 11:case 28:case 2:return this.consumeSimpleBlock(e.type);case 19:return this.consumeFunction(e)}return e},e.prototype.consumeSimpleBlock=function(e){for(var t={type:e,values:[]},n=this.consumeToken();;){if(32===n.type||Pn(n,e))return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue()),n=this.consumeToken()}},e.prototype.consumeFunction=function(e){for(var t={name:e.value,values:[],type:18};;){var n=this.consumeToken();if(32===n.type||3===n.type)return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue())}},e.prototype.consumeToken=function(){var e=this._tokens.shift();return"undefined"===typeof e?Un:e},e.prototype.reconsumeToken=function(e){this._tokens.unshift(e)},e}(),Tn=function(e){return 15===e.type},kn=function(e){return 17===e.type},Qn=function(e){return 20===e.type},Ln=function(e){return 0===e.type},Dn=function(e,t){return Qn(e)&&e.value===t},In=function(e){return 31!==e.type},Rn=function(e){return 31!==e.type&&4!==e.type},Hn=function(e){var t=[],n=[];return e.forEach((function(e){if(4===e.type){if(0===n.length)throw new Error("Error parsing function args, zero tokens for arg");return t.push(n),void(n=[])}31!==e.type&&n.push(e)})),n.length&&t.push(n),t},Pn=function(e,t){return 11===t&&12===e.type||28===t&&29===e.type||2===t&&3===e.type},Nn=function(e){return 17===e.type||15===e.type},On=function(e){return 16===e.type||Nn(e)},Vn=function(e){return e.length>1?[e[0],e[1]]:[e[0]]},zn={type:17,number:0,flags:Ke},Gn={type:16,number:50,flags:Ke},Kn={type:16,number:100,flags:Ke},Wn=function(e,t,n){var r=e[0],i=e[1];return[jn(r,t),jn("undefined"!==typeof i?i:r,n)]},jn=function(e,t){if(16===e.type)return e.number/100*t;if(Tn(e))switch(e.unit){case"rem":case"em":return 16*e.number;default:return e.number}return e.number},Xn="deg",qn="grad",Yn="rad",Jn="turn",Zn={name:"angle",parse:function(e,t){if(15===t.type)switch(t.unit){case Xn:return Math.PI*t.number/180;case qn:return Math.PI/200*t.number;case Yn:return t.number;case Jn:return 2*Math.PI*t.number}throw new Error("Unsupported angle type")}},$n=function(e){return 15===e.type&&(e.unit===Xn||e.unit===qn||e.unit===Yn||e.unit===Jn)},er=function(e){switch(e.filter(Qn).map((function(e){return e.value})).join(" ")){case"to bottom right":case"to right bottom":case"left top":case"top left":return[zn,zn];case"to top":case"bottom":return tr(0);case"to bottom left":case"to left bottom":case"right 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Io(e,"\u30a2\u30a4\u30a6\u30a8\u30aa\u30ab\u30ad\u30af\u30b1\u30b3\u30b5\u30b7\u30b9\u30bb\u30bd\u30bf\u30c1\u30c4\u30c6\u30c8\u30ca\u30cb\u30cc\u30cd\u30ce\u30cf\u30d2\u30d5\u30d8\u30db\u30de\u30df\u30e0\u30e1\u30e2\u30e4\u30e6\u30e8\u30e9\u30ea\u30eb\u30ec\u30ed\u30ef\u30f0\u30f1\u30f2\u30f3",i);case 29:return Io(e,"\u30a4\u30ed\u30cf\u30cb\u30db\u30d8\u30c8\u30c1\u30ea\u30cc\u30eb\u30f2\u30ef\u30ab\u30e8\u30bf\u30ec\u30bd\u30c4\u30cd\u30ca\u30e9\u30e0\u30a6\u30f0\u30ce\u30aa\u30af\u30e4\u30de\u30b1\u30d5\u30b3\u30a8\u30c6\u30a2\u30b5\u30ad\u30e6\u30e1\u30df\u30b7\u30f1\u30d2\u30e2\u30bb\u30b9",i);case 34:return Do(e,3792,3801,!0,r);case 37:return Do(e,6160,6169,!0,r);case 38:return Do(e,4160,4169,!0,r);case 39:return Do(e,2918,2927,!0,r);case 40:return Do(e,1776,1785,!0,r);case 43:return Do(e,3046,3055,!0,r);case 44:return Do(e,3174,3183,!0,r);case 45:return Do(e,3664,3673,!0,r);case 46:return Do(e,3872,3881,!0,r);default:return 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wo(t)&&(wo(e)&&e.currentSrc&&e.currentSrc!==e.src&&(t.src=e.currentSrc,t.srcset=""),"lazy"===t.loading&&(t.loading="eager")),Eo(t)?this.createCustomElementClone(t):t},e.prototype.createCustomElementClone=function(e){var t=document.createElement("html2canvascustomelement");return ts(e.style,t),t},e.prototype.createStyleClone=function(e){try{var t=e.sheet;if(t&&t.cssRules){var n=[].slice.call(t.cssRules,0).reduce((function(e,t){return t&&"string"===typeof t.cssText?e+t.cssText:e}),""),r=e.cloneNode(!1);return r.textContent=n,r}}catch(Rt){if(this.context.logger.error("Unable to access cssRules property",Rt),"SecurityError"!==Rt.name)throw Rt}return e.cloneNode(!1)},e.prototype.createCanvasClone=function(e){var t;if(this.options.inlineImages&&e.ownerDocument){var n=e.ownerDocument.createElement("img");try{return n.src=e.toDataURL(),n}catch(Rt){this.context.logger.info("Unable to inline canvas contents, canvas is tainted",e)}}var r=e.cloneNode(!1);try{r.width=e.width,r.height=e.height;var i=e.getContext("2d"),A=r.getContext("2d");if(A)if(!this.options.allowTaint&&i)A.putImageData(i.getImageData(0,0,e.width,e.height),0,0);else{var a=null!==(t=e.getContext("webgl2"))&&void 0!==t?t:e.getContext("webgl");if(a){var o=a.getContextAttributes();!1===(null===o||void 0===o?void 0:o.preserveDrawingBuffer)&&this.context.logger.warn("Unable to clone WebGL context as it has preserveDrawingBuffer=false",e)}A.drawImage(e,0,0)}return r}catch(Rt){this.context.logger.info("Unable to clone canvas as it is tainted",e)}return r},e.prototype.createVideoClone=function(e){var t=e.ownerDocument.createElement("canvas");t.width=e.offsetWidth,t.height=e.offsetHeight;var n=t.getContext("2d");try{return n&&(n.drawImage(e,0,0,t.width,t.height),this.options.allowTaint||n.getImageData(0,0,t.width,t.height)),t}catch(Rt){this.context.logger.info("Unable to clone video as it is tainted",e)}var r=e.ownerDocument.createElement("canvas");return r.width=e.offsetWidth,r.height=e.offsetHeight,r},e.prototype.appendChildNode=function(e,t,n){so(t)&&(bo(t)||t.hasAttribute(jo)||"function"===typeof this.options.ignoreElements&&this.options.ignoreElements(t))||this.options.copyStyles&&so(t)&&_o(t)||e.appendChild(this.cloneNode(t,n))},e.prototype.cloneChildNodes=function(e,t,n){for(var r=this,i=e.shadowRoot?e.shadowRoot.firstChild:e.firstChild;i;i=i.nextSibling)if(so(i)&&So(i)&&"function"===typeof i.assignedNodes){var A=i.assignedNodes();A.length&&A.forEach((function(e){return r.appendChildNode(t,e,n)}))}else this.appendChildNode(t,i,n)},e.prototype.cloneNode=function(e,t){if(oo(e))return document.createTextNode(e.data);if(!e.ownerDocument)return e.cloneNode(!1);var n=e.ownerDocument.defaultView;if(n&&so(e)&&(lo(e)||uo(e))){var r=this.createElementClone(e);r.style.transitionProperty="none";var i=n.getComputedStyle(e),A=n.getComputedStyle(e,":before"),a=n.getComputedStyle(e,":after");this.referenceElement===e&&lo(r)&&(this.clonedReferenceElement=r),mo(r)&&us(r);var o=this.counters.parse(new mA(this.context,i)),s=this.resolvePseudoContent(e,r,A,KA.BEFORE);Eo(e)&&(t=!0),yo(e)||this.cloneChildNodes(e,r,t),s&&r.insertBefore(s,r.firstChild);var l=this.resolvePseudoContent(e,r,a,KA.AFTER);return l&&r.appendChild(l),this.counters.pop(o),(i&&(this.options.copyStyles||uo(e))&&!Bo(e)||t)&&ts(i,r),0===e.scrollTop&&0===e.scrollLeft||this.scrolledElements.push([r,e.scrollLeft,e.scrollTop]),(xo(e)||Co(e))&&(xo(r)||Co(r))&&(r.value=e.value),r}return e.cloneNode(!1)},e.prototype.resolvePseudoContent=function(e,t,n,r){var i=this;if(n){var A=n.content,a=t.ownerDocument;if(a&&A&&"none"!==A&&"-moz-alt-content"!==A&&"none"!==n.display){this.counters.parse(new mA(this.context,n));var o=new 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t=e.styles,n=e.bounds,r=Wn(t.borderTopLeftRadius,n.width,n.height),i=r[0],A=r[1],a=Wn(t.borderTopRightRadius,n.width,n.height),o=a[0],s=a[1],l=Wn(t.borderBottomRightRadius,n.width,n.height),u=l[0],c=l[1],d=Wn(t.borderBottomLeftRadius,n.width,n.height),h=d[0],f=d[1],p=[];p.push((i+o)/n.width),p.push((h+u)/n.width),p.push((A+f)/n.height),p.push((s+c)/n.height);var g=Math.max.apply(Math,p);g>1&&(i/=g,A/=g,o/=g,s/=g,u/=g,c/=g,h/=g,f/=g);var m=n.width-o,v=n.height-c,y=n.width-u,w=n.height-f,B=t.borderTopWidth,_=t.borderRightWidth,b=t.borderBottomWidth,x=t.borderLeftWidth,C=jn(t.paddingTop,e.bounds.width),S=jn(t.paddingRight,e.bounds.width),E=jn(t.paddingBottom,e.bounds.width),U=jn(t.paddingLeft,e.bounds.width);this.topLeftBorderDoubleOuterBox=i>0||A>0?Es(n.left+x/3,n.top+B/3,i-x/3,A-B/3,qo.TOP_LEFT):new _s(n.left+x/3,n.top+B/3),this.topRightBorderDoubleOuterBox=i>0||A>0?Es(n.left+m,n.top+B/3,o-_/3,s-B/3,qo.TOP_RIGHT):new _s(n.left+n.width-_/3,n.top+B/3),this.bottomRightBorderDoubleOuterBox=u>0||c>0?Es(n.left+y,n.top+v,u-_/3,c-b/3,qo.BOTTOM_RIGHT):new _s(n.left+n.width-_/3,n.top+n.height-b/3),this.bottomLeftBorderDoubleOuterBox=h>0||f>0?Es(n.left+x/3,n.top+w,h-x/3,f-b/3,qo.BOTTOM_LEFT):new _s(n.left+x/3,n.top+n.height-b/3),this.topLeftBorderDoubleInnerBox=i>0||A>0?Es(n.left+2*x/3,n.top+2*B/3,i-2*x/3,A-2*B/3,qo.TOP_LEFT):new _s(n.left+2*x/3,n.top+2*B/3),this.topRightBorderDoubleInnerBox=i>0||A>0?Es(n.left+m,n.top+2*B/3,o-2*_/3,s-2*B/3,qo.TOP_RIGHT):new _s(n.left+n.width-2*_/3,n.top+2*B/3),this.bottomRightBorderDoubleInnerBox=u>0||c>0?Es(n.left+y,n.top+v,u-2*_/3,c-2*b/3,qo.BOTTOM_RIGHT):new _s(n.left+n.width-2*_/3,n.top+n.height-2*b/3),this.bottomLeftBorderDoubleInnerBox=h>0||f>0?Es(n.left+2*x/3,n.top+w,h-2*x/3,f-2*b/3,qo.BOTTOM_LEFT):new _s(n.left+2*x/3,n.top+n.height-2*b/3),this.topLeftBorderStroke=i>0||A>0?Es(n.left+x/2,n.top+B/2,i-x/2,A-B/2,qo.TOP_LEFT):new _s(n.left+x/2,n.top+B/2),this.topRightBorderStroke=i>0||A>0?Es(n.left+m,n.top+B/2,o-_/2,s-B/2,qo.TOP_RIGHT):new _s(n.left+n.width-_/2,n.top+B/2),this.bottomRightBorderStroke=u>0||c>0?Es(n.left+y,n.top+v,u-_/2,c-b/2,qo.BOTTOM_RIGHT):new _s(n.left+n.width-_/2,n.top+n.height-b/2),this.bottomLeftBorderStroke=h>0||f>0?Es(n.left+x/2,n.top+w,h-x/2,f-b/2,qo.BOTTOM_LEFT):new _s(n.left+x/2,n.top+n.height-b/2),this.topLeftBorderBox=i>0||A>0?Es(n.left,n.top,i,A,qo.TOP_LEFT):new _s(n.left,n.top),this.topRightBorderBox=o>0||s>0?Es(n.left+m,n.top,o,s,qo.TOP_RIGHT):new _s(n.left+n.width,n.top),this.bottomRightBorderBox=u>0||c>0?Es(n.left+y,n.top+v,u,c,qo.BOTTOM_RIGHT):new _s(n.left+n.width,n.top+n.height),this.bottomLeftBorderBox=h>0||f>0?Es(n.left,n.top+w,h,f,qo.BOTTOM_LEFT):new _s(n.left,n.top+n.height),this.topLeftPaddingBox=i>0||A>0?Es(n.left+x,n.top+B,Math.max(0,i-x),Math.max(0,A-B),qo.TOP_LEFT):new _s(n.left+x,n.top+B),this.topRightPaddingBox=o>0||s>0?Es(n.left+Math.min(m,n.width-_),n.top+B,m>n.width+_?0:Math.max(0,o-_),Math.max(0,s-B),qo.TOP_RIGHT):new _s(n.left+n.width-_,n.top+B),this.bottomRightPaddingBox=u>0||c>0?Es(n.left+Math.min(y,n.width-x),n.top+Math.min(v,n.height-b),Math.max(0,u-_),Math.max(0,c-b),qo.BOTTOM_RIGHT):new _s(n.left+n.width-_,n.top+n.height-b),this.bottomLeftPaddingBox=h>0||f>0?Es(n.left+x,n.top+Math.min(w,n.height-b),Math.max(0,h-x),Math.max(0,f-b),qo.BOTTOM_LEFT):new _s(n.left+x,n.top+n.height-b),this.topLeftContentBox=i>0||A>0?Es(n.left+x+U,n.top+B+C,Math.max(0,i-(x+U)),Math.max(0,A-(B+C)),qo.TOP_LEFT):new _s(n.left+x+U,n.top+B+C),this.topRightContentBox=o>0||s>0?Es(n.left+Math.min(m,n.width+x+U),n.top+B+C,m>n.width+x+U?0:o-x+U,s-(B+C),qo.TOP_RIGHT):new _s(n.left+n.width-(_+S),n.top+B+C),this.bottomRightContentBox=u>0||c>0?Es(n.left+Math.min(y,n.width-(x+U)),n.top+Math.min(v,n.height+B+C),Math.max(0,u-(_+S)),c-(b+E),qo.BOTTOM_RIGHT):new _s(n.left+n.width-(_+S),n.top+n.height-(b+E)),this.bottomLeftContentBox=h>0||f>0?Es(n.left+x+U,n.top+w,Math.max(0,h-(x+U)),f-(b+E),qo.BOTTOM_LEFT):new _s(n.left+x+U,n.top+n.height-(b+E))}return e}();!function(e){e[e.TOP_LEFT=0]="TOP_LEFT",e[e.TOP_RIGHT=1]="TOP_RIGHT",e[e.BOTTOM_RIGHT=2]="BOTTOM_RIGHT",e[e.BOTTOM_LEFT=3]="BOTTOM_LEFT"}(qo||(qo={}));var Es=function(e,t,n,r,i){var A=(Math.sqrt(2)-1)/3*4,a=n*A,o=r*A,s=e+n,l=t+r;switch(i){case qo.TOP_LEFT:return new xs(new _s(e,l),new _s(e,l-o),new _s(s-a,t),new _s(s,t));case qo.TOP_RIGHT:return new xs(new _s(e,t),new _s(e+a,t),new _s(s,l-o),new _s(s,l));case qo.BOTTOM_RIGHT:return new xs(new _s(s,t),new _s(s,t+o),new _s(e+a,l),new _s(e,l));case qo.BOTTOM_LEFT:default:return new xs(new _s(s,l),new _s(s-a,l),new _s(e,t+o),new _s(e,t))}},Us=function(e){return[e.topLeftBorderBox,e.topRightBorderBox,e.bottomRightBorderBox,e.bottomLeftBorderBox]},Ms=function(e){return[e.topLeftContentBox,e.topRightContentBox,e.bottomRightContentBox,e.bottomLeftContentBox]},Fs=function(e){return[e.topLeftPaddingBox,e.topRightPaddingBox,e.bottomRightPaddingBox,e.bottomLeftPaddingBox]},Ts=function(){function e(e,t,n){this.offsetX=e,this.offsetY=t,this.matrix=n,this.type=0,this.target=6}return e}(),ks=function(){function e(e,t){this.path=e,this.target=t,this.type=1}return e}(),Qs=function(){function e(e){this.opacity=e,this.type=2,this.target=6}return e}(),Ls=function(e){return 0===e.type},Ds=function(e){return 1===e.type},Is=function(e){return 2===e.type},Rs=function(e,t){return e.length===t.length&&e.some((function(e,n){return e===t[n]}))},Hs=function(e,t,n,r,i){return e.map((function(e,A){switch(A){case 0:return e.add(t,n);case 1:return e.add(t+r,n);case 2:return e.add(t+r,n+i);case 3:return e.add(t,n+i)}return e}))},Ps=function(){function e(e){this.element=e,this.inlineLevel=[],this.nonInlineLevel=[],this.negativeZIndex=[],this.zeroOrAutoZIndexOrTransformedOrOpacity=[],this.positiveZIndex=[],this.nonPositionedFloats=[],this.nonPositionedInlineLevel=[]}return e}(),Ns=function(){function e(e,t){if(this.container=e,this.parent=t,this.effects=[],this.curves=new Ss(this.container),this.container.styles.opacity<1&&this.effects.push(new Qs(this.container.styles.opacity)),null!==this.container.styles.transform){var n=this.container.bounds.left+this.container.styles.transformOrigin[0].number,r=this.container.bounds.top+this.container.styles.transformOrigin[1].number,i=this.container.styles.transform;this.effects.push(new Ts(n,r,i))}if(0!==this.container.styles.overflowX){var A=Us(this.curves),a=Fs(this.curves);Rs(A,a)?this.effects.push(new ks(A,6)):(this.effects.push(new ks(A,2)),this.effects.push(new ks(a,4)))}}return e.prototype.getEffects=function(e){for(var t=-1===[2,3].indexOf(this.container.styles.position),n=this.parent,r=this.effects.slice(0);n;){var i=n.effects.filter((function(e){return!Ds(e)}));if(t||0!==n.container.styles.position||!n.parent){if(r.unshift.apply(r,i),t=-1===[2,3].indexOf(n.container.styles.position),0!==n.container.styles.overflowX){var A=Us(n.curves),a=Fs(n.curves);Rs(A,a)||r.unshift(new ks(a,6))}}else r.unshift.apply(r,i);n=n.parent}return r.filter((function(t){return iA(t.target,e)}))},e}(),Os=function e(t,n,r,i){t.container.elements.forEach((function(A){var a=iA(A.flags,4),o=iA(A.flags,2),s=new Ns(A,t);iA(A.styles.display,2048)&&i.push(s);var l=iA(A.flags,8)?[]:i;if(a||o){var u=a||A.styles.isPositioned()?r:n,c=new Ps(s);if(A.styles.isPositioned()||A.styles.opacity<1||A.styles.isTransformed()){var d=A.styles.zIndex.order;if(d<0){var h=0;u.negativeZIndex.some((function(e,t){return d>e.element.container.styles.zIndex.order?(h=t,!1):h>0})),u.negativeZIndex.splice(h,0,c)}else if(d>0){var f=0;u.positiveZIndex.some((function(e,t){return d>=e.element.container.styles.zIndex.order?(f=t+1,!1):f>0})),u.positiveZIndex.splice(f,0,c)}else u.zeroOrAutoZIndexOrTransformedOrOpacity.push(c)}else A.styles.isFloating()?u.nonPositionedFloats.push(c):u.nonPositionedInlineLevel.push(c);e(s,c,a?c:r,l)}else A.styles.isInlineLevel()?n.inlineLevel.push(s):n.nonInlineLevel.push(s),e(s,n,r,l);iA(A.flags,8)&&Vs(A,l)}))},Vs=function(e,t){for(var n=e instanceof Va?e.start:1,r=e instanceof Va&&e.reversed,i=0;i0&&e.intrinsicHeight>0){var r=Js(e),i=Fs(t);this.path(i),this.ctx.save(),this.ctx.clip(),this.ctx.drawImage(n,0,0,e.intrinsicWidth,e.intrinsicHeight,r.left,r.top,r.width,r.height),this.ctx.restore()}},n.prototype.renderNodeContent=function(e){return r(this,void 0,void 0,(function(){var t,r,A,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B;return i(this,(function(i){switch(i.label){case 0:this.applyEffects(e.getEffects(4)),t=e.container,r=e.curves,A=t.styles,o=0,s=t.textNodes,i.label=1;case 1:return o0&&x>0&&(v=r.ctx.createPattern(p,"repeat"),r.renderRepeat(w,v,S,E))):Qr(n)&&(y=el(e,t,[null,null,null]),w=y[0],B=y[1],_=y[2],b=y[3],x=y[4],C=0===n.position.length?[Gn]:n.position,S=jn(C[0],b),E=jn(C[C.length-1],x),U=Br(n,S,E,b,x),M=U[0],F=U[1],M>0&&F>0&&(T=r.ctx.createRadialGradient(B+S,_+E,0,B+S,_+E,M),gr(n.stops,2*M).forEach((function(e){return T.addColorStop(e.stop,ir(e.color))})),r.path(w),r.ctx.fillStyle=T,M!==F?(k=e.bounds.left+.5*e.bounds.width,Q=e.bounds.top+.5*e.bounds.height,D=1/(L=F/M),r.ctx.save(),r.ctx.translate(k,Q),r.ctx.transform(1,0,0,L,0,0),r.ctx.translate(-k,-Q),r.ctx.fillRect(B,D*(_-Q)+Q,b,x*D),r.ctx.restore()):r.ctx.fill())),i.label=6;case 6:return t--,[2]}}))},r=this,A=0,a=e.styles.backgroundImage.slice(0).reverse(),s.label=1;case 1:return A0?2!==l.style?[3,5]:[4,this.renderDashedDottedBorder(l.color,l.width,a,e.curves,2)]:[3,11]:[3,13];case 4:return i.sent(),[3,11];case 5:return 3!==l.style?[3,7]:[4,this.renderDashedDottedBorder(l.color,l.width,a,e.curves,3)];case 6:return i.sent(),[3,11];case 7:return 4!==l.style?[3,9]:[4,this.renderDoubleBorder(l.color,l.width,a,e.curves)];case 8:return i.sent(),[3,11];case 9:return[4,this.renderSolidBorder(l.color,a,e.curves)];case 10:i.sent(),i.label=11;case 11:a++,i.label=12;case 12:return o++,[3,3];case 13:return[2]}}))}))},n.prototype.renderDashedDottedBorder=function(e,t,n,A,a){return r(this,void 0,void 0,(function(){var r,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B;return i(this,(function(i){return this.ctx.save(),r=js(A,n),o=Gs(A,n),2===a&&(this.path(o),this.ctx.clip()),Cs(o[0])?(s=o[0].start.x,l=o[0].start.y):(s=o[0].x,l=o[0].y),Cs(o[1])?(u=o[1].end.x,c=o[1].end.y):(u=o[1].x,c=o[1].y),d=0===n||2===n?Math.abs(s-u):Math.abs(l-c),this.ctx.beginPath(),3===a?this.formatPath(r):this.formatPath(o.slice(0,2)),h=t<3?3*t:2*t,f=t<3?2*t:t,3===a&&(h=t,f=t),p=!0,d<=2*h?p=!1:d<=2*h+f?(h*=g=d/(2*h+f),f*=g):(m=Math.floor((d+f)/(h+f)),v=(d-m*h)/(m-1),f=(y=(d-(m+1)*h)/m)<=0||Math.abs(f-v)