diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Skin Pack 32 Bit For Windows 7 WORK.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Skin Pack 32 Bit For Windows 7 WORK.md deleted file mode 100644 index 7840966a6b34d045863a9abeba3a71714db50e03..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Skin Pack 32 Bit For Windows 7 WORK.md +++ /dev/null @@ -1,43 +0,0 @@ - -

How to Download Skin Pack 32 Bit for Windows 7

-

If you are bored with the default look of your Windows 7 desktop, you might want to try a skin pack. A skin pack is a collection of themes, icons, wallpapers, and other elements that can change the appearance of your system. Skin packs are easy to install and uninstall, and they can give your computer a fresh and unique look.

-

download skin pack 32 bit for windows 7


DOWNLOAD ►►► https://byltly.com/2uKveQ



-

One of the most popular skin packs for Windows 7 is the 32 bit version. This skin pack is compatible with both 32 bit and 64 bit versions of Windows 7, but it is designed to optimize the performance and memory usage of the 32 bit system. The 32 bit skin pack includes various themes inspired by different operating systems, such as Windows 8, Mac OS X, Android, iOS, and more. It also comes with custom icons, cursors, sounds, fonts, and boot screens.

-

To download the skin pack 32 bit for Windows 7, you need to follow these simple steps:

-
    -
  1. Go to the official website of the skin pack creator: https://skinpacks.com/download/windows-7/32-bit-skin-pack/
    2. -
  2. Scroll down and click on the download link that matches your system architecture (32 bit or 64 bit).
    3. -
  3. Wait for the download to finish and then run the installer file.
    4. -
  4. Follow the instructions on the screen and choose the components you want to install.
    5. -
  5. Restart your computer and enjoy your new skin pack.
    6. -
-

Note: Before installing any skin pack, it is recommended to create a system restore point or backup your data in case something goes wrong. You can also uninstall the skin pack anytime from the control panel or by running the uninstaller file.

-

With the skin pack 32 bit for Windows 7, you can transform your desktop into a modern and stylish one. Download it today and see for yourself!

- -

Benefits of Using Skin Pack 32 Bit for Windows 7

-

Using a skin pack can have many benefits for your Windows 7 system. Here are some of them:

-

- -

With the skin pack 32 bit for Windows 7, you can experience all these benefits and more. You can choose from a variety of themes and customize them to suit your needs. You can also switch between different themes easily and quickly.

- -

How to Customize Skin Pack 32 Bit for Windows 7

-

One of the best things about the skin pack 32 bit for Windows 7 is that it is very customizable. You can change many aspects of the skin pack to make it fit your style and preferences. Here are some of the things you can customize:

- -

To customize the skin pack 32 bit for Windows 7, you need to open the skin pack tool that comes with the installer. You can access it from the start menu or the desktop shortcut. From there, you can select the components you want to customize and apply the changes. You may need to restart your computer for some changes to take effect.

- -

Conclusion

-

The skin pack 32 bit for Windows 7 is a great way to change the look and feel of your system. It is easy to install and uninstall, compatible with both 32 bit and 64 bit versions of Windows 7, and offers many benefits and customization options. If you want to give your system a makeover, download the skin pack 32 bit for Windows 7 today!

81aa517590
-
-
\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Bootstrap Studio 4.5.8 Crack License Key Full 2020 TOP.md b/spaces/1gistliPinn/ChatGPT4/Examples/Bootstrap Studio 4.5.8 Crack License Key Full 2020 TOP.md deleted file mode 100644 index 440c8981be05a465d5fcfd9071050a0b50c9002f..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Bootstrap Studio 4.5.8 Crack License Key Full 2020 TOP.md +++ /dev/null @@ -1,41 +0,0 @@ -
-

Bootstrap Studio 4.5.8 Crack License Key Full 2020: A Powerful Web Design Tool

- -

Bootstrap Studio 4.5.8 Crack is a desktop application that helps you create beautiful websites using the Bootstrap framework. It has a drag and drop interface that lets you easily add components, customize them, and preview your results in real time. You can also edit the HTML, CSS, and JavaScript code of your project with the built-in code editor.

- -

Bootstrap Studio 4.5.8 License Key is a premium feature that unlocks more advanced options and themes for your web design. You can use it to access hundreds of ready-made templates, icons, fonts, and components that you can mix and match to create stunning websites. You can also export your projects as static HTML files or publish them online with one click.

-

Bootstrap Studio 4.5.8 Crack License Key Full 2020


Download >>>>> https://imgfil.com/2uxZ2T



- -

Bootstrap Studio 4.5.8 Full 2020 is the latest version of this software that comes with many improvements and bug fixes. It supports the latest Bootstrap 4 version and has a redesigned user interface that makes it easier to use. It also has a new smart forms feature that lets you create complex forms with validation and logic without writing any code.

- -

If you are looking for a powerful web design tool that can help you create responsive and modern websites with ease, then Bootstrap Studio 4.5.8 Crack License Key Full 2020 is the perfect choice for you. You can download it from the official website or use the crack file to activate it for free.

- -

How to Use Bootstrap Studio 4.5.8 Crack License Key Full 2020

- -

To use Bootstrap Studio 4.5.8 Crack License Key Full 2020, you need to follow these simple steps:

- -
    -
  1. Download the setup file from the official website or the crack file from the link below.
    2. -
  2. Install the software on your computer and run it.
    3. -
  3. Enter the license key that you received or generated from the crack file.
    4. -
  4. Enjoy the full features of Bootstrap Studio 4.5.8.
    5. -
- -

Note: You should always use a reliable antivirus program to scan any downloaded files before opening them. Also, you should only use the crack file for educational purposes and not for commercial use.

- -

Why Choose Bootstrap Studio 4.5.8 Crack License Key Full 2020

- -

Bootstrap Studio 4.5.8 Crack License Key Full 2020 is a great web design tool for many reasons. Here are some of the benefits of using it:

- - - -

With Bootstrap Studio 4.5.8 Crack License Key Full 2020, you can unleash your creativity and make amazing websites in no time.

-

d5da3c52bf
-
-
\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Cara Menghilangkan Windows License Valid For 90 Days Hit.md b/spaces/1gistliPinn/ChatGPT4/Examples/Cara Menghilangkan Windows License Valid For 90 Days Hit.md deleted file mode 100644 index 736731319bf1f6fce40b535e028b00d5b79aaa0b..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Cara Menghilangkan Windows License Valid For 90 Days Hit.md +++ /dev/null @@ -1,6 +0,0 @@ -

Cara Menghilangkan Windows License Valid For 90 Days Hit


DOWNLOAD →→→ https://imgfil.com/2uy1rc



- -Jika memang Anda ingin tetap melakukan cara screenshot di webtoon untuk ... The utility for easy and quick creation of screenshots on Windows OS running PC. ... unlimited reading across all WEBTOON Originals update every day so there's new ... *Please use your real name and valid ID number to submit your real name ... 1fdad05405
-
-
-

diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Crack See Electrical V7 U Torrent [UPD].md b/spaces/1gistliPinn/ChatGPT4/Examples/Crack See Electrical V7 U Torrent [UPD].md deleted file mode 100644 index 152982674cc67b240235741b540fc72d8e152e01..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Crack See Electrical V7 U Torrent [UPD].md +++ /dev/null @@ -1,6 +0,0 @@ -

Crack See Electrical V7 U Torrent


Download File ✑ ✑ ✑ https://imgfil.com/2uxZF7



- -Официальное зеркало продуктов SarasSoft на Rapidshare: . ... samsung tools v2.2.0.3 hwk by sarassoft 94 · crack see electrical v7 u torrent ... 1fdad05405
-
-
-

diff --git a/spaces/1phancelerku/anime-remove-background/Cannon Shot! APK Free Download - Enjoy the Fun and Challenge of Shooting Cannons.md b/spaces/1phancelerku/anime-remove-background/Cannon Shot! APK Free Download - Enjoy the Fun and Challenge of Shooting Cannons.md deleted file mode 100644 index 0839a9286676e15f0adf7fcd198db8ac079073bd..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Cannon Shot! APK Free Download - Enjoy the Fun and Challenge of Shooting Cannons.md +++ /dev/null @@ -1,150 +0,0 @@ -
-

Cannon Shot APK: A Fun and Casual Shooting Game for Android Devices

-

If you are looking for a simple yet addictive shooting game that you can play on your Android device, you might want to check out Cannon Shot APK. This game is developed by SayGames Ltd, a popular developer of hyper-casual games such as Johnny Trigger, Jelly Shift, and Drive and Park. In this game, you have to fill all the buckets with balls by using your finger to move various objects and change the direction of the balls you shoot. Aim smart, complete levels, and unlock new cannons. Can you find the rare one?

-

cannon shot apk


DOWNLOAD ✔ https://jinyurl.com/2uNKIU



-

In this article, we will give you a brief overview of what Cannon Shot APK is, how to download and install it on your Android device, how to play it, why you should play it, and some alternatives to it. We will also answer some frequently asked questions about this game. Let's get started!

-

What is Cannon Shot APK?

-

A brief introduction to the game and its features

-

Cannon Shot APK is a free-to-play casual shooting game that is available on Google Play Store and other third-party websites. The game has a simple picture style, fun adventure challenge mode, and easy-to-use controls. The game features:

- -

How to download and install Cannon Shot APK on your Android device

-

If you want to download and install Cannon Shot APK on your Android device, you have two options:

-
    -
  1. You can download it from Google Play Store by searching for "Cannon Shot" or by clicking here. This is the official and recommended way to get the game, as it ensures that you get the latest version and updates.
    2. -
  2. You can download it from a third-party website by searching for "Cannon Shot APK" or by clicking here. This is an alternative way to get the game, but it may not be as safe or secure as the first option. You may also need to enable "Unknown Sources" in your device settings to install the game.
    3. -
-

Once you have downloaded the game file, you can tap on it to install it on your device. The installation process may take a few seconds or minutes depending on your device performance. After the installation is complete, you can launch the game and enjoy playing it.

-

How to Play Cannon Shot APK?

-

The basic gameplay and controls of Cannon Shot APK

-

The gameplay of Cannon Shot APK is very simple and intuitive. You just have to fill all the buckets with balls by shooting them from a cannon. You can use your finger to move various objects such as trampolines, fans , and magnets to change the direction of the balls. You can also tap on the screen to shoot more balls from the cannon. You have to fill all the buckets with balls before you run out of balls or time. You can see the number of balls and the time left at the top of the screen. You can also see the number of stars you have earned at the bottom of the screen. You can earn up to three stars per level depending on how well you perform.

-

The controls of Cannon Shot APK are very easy and responsive. You just have to swipe your finger on the screen to move the objects and tap on the screen to shoot more balls. You can also pause the game by tapping on the pause button at the top right corner of the screen. You can resume, restart, or quit the game from there. You can also access the settings, shop, and floors from there.

-

The different levels, obstacles, and cannons in Cannon Shot APK

-

Cannon Shot APK has over 100 levels with different difficulty levels and obstacles. The levels are divided into floors, each with 10 levels and a boss fight. The floors have different themes such as forest, desert, ice, and lava. The obstacles include walls, platforms, spikes, portals, lasers, and more. The buckets also have different shapes, sizes, and colors. Some buckets are fixed, while others are moving or rotating. Some buckets are empty, while others are already filled with balls or other objects. You have to be careful not to shoot at the wrong buckets or waste your balls.

-

cannon shot game apk download
-cannon shot android game free download
-cannon shot apk mod unlimited balls
-cannon shot apk latest version
-cannon shot apk for pc
-cannon shot app download for android
-cannon shot ball game apk
-cannon shot bucket game apk
-cannon shot casual game apk
-cannon shot challenge game apk
-cannon shot com.cannonshot.game apk
-cannon shot com.JLabs.CannonShot apk
-cannon shot com.cannonshot apk
-cannon shot download apk pure
-cannon shot free download apk
-cannon shot full unlocked apk
-cannon shot game hack apk
-cannon shot game online apk
-cannon shot game offline apk
-cannon shot itechpro apk
-cannon shot level 1000 apk
-cannon shot mod apk android 1
-cannon shot mod apk revdl
-cannon shot mod apk unlimited money
-cannon shot new version apk
-cannon shot no ads apk
-cannon shot offline mod apk
-cannon shot online mod apk
-cannon shot premium apk
-cannon shot pro apk
-cannon shot puzzle game apk
-cannon shot shooting game apk
-cannon shot strategy game apk
-cannon shot unlocked all cannons apk
-cannon shot update version apk
-download cannon shot game for android
-download cannon shot mod apk 2023
-how to play cannon shot game on android
-how to install cannon shot app on android
-how to update cannon shot app on android

-

Cannon Shot APK also has various cannons with different shapes, colors, and effects. You can unlock new cannons by collecting stars and keys and opening chests. You can also buy coins with real money and use them to buy cannons in the shop. Some cannons are common, while others are rare or legendary. Some cannons have special effects such as shooting multiple balls, shooting fireballs, shooting rainbow balls, and more. You can switch between your unlocked cannons by tapping on the cannon icon at the bottom left corner of the screen.

-

Some tips and tricks to master Cannon Shot APK

-

If you want to master Cannon Shot APK and complete all the levels with three stars, you might want to follow these tips and tricks:

- -

Why Should You Play Cannon Shot APK?

-

The benefits and advantages of playing Cannon Shot APK

-

There are many benefits and advantages of playing Cannon Shot APK, such as:

- -

The challenges and drawbacks of playing Cannon Shot APK

-

There are also some challenges and drawbacks of playing Cannon Shot APK, such as:

- -

Some alternatives to Cannon Shot APK

-

If you are looking for some alternatives to Cannon Shot APK, you might want to try these games:

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
NameDescriptionLink
Knock BallsA game where you have to shoot balls at towers of blocks and knock them down.Knock Balls - Apps on Google Play
Ball BlastA game where you have to shoot balls at flying objects and make them explode.Ball Blast - Apps on Google Play
Tank StarsA game where you have to shoot tanks at other tanks and destroy them.Tank Stars - Apps on Google Play
Mr BulletA game where you have to shoot bullets at enemies and objects and eliminate them.Mr Bullet - Spy Puzzles - Apps on Google Play
Angry Birds 2A game where you have to shoot birds at pigs and structures and make them collapse.Angry Birds 2 - Apps on Google Play
-

Conclusion

-

Cannon Shot APK is a fun and casual shooting game for Android devices that you can play for free. It has a simple picture style, fun adventure challenge mode, and easy-to-use controls. It has over 100 levels with different difficulty levels and obstacles, various cannons with different shapes, colors, and effects, boss fights where you have to shoot at monsters instead of buckets, floors where you can collect stars and keys to unlock chests with rewards, and in-app purchases where you can buy coins, no ads, or special offers. It also has some challenges and drawbacks such as requiring internet connection, containing ads, having bugs or glitches, being repetitive or boring, or being too easy or too hard. It also has some alternatives such as Knock Balls, Ball Blast, Tank Stars, Mr Bullet, and Angry Birds 2.

-

If you are looking for a simple yet addictive shooting game that you can play on your Android device, you might want to check out Cannon Shot APK. You can download it from Google Play Store or from a third-party website. You can also read this article to learn more about the game and how to play it. We hope you enjoy playing Cannon Shot APK and have fun!

-

FAQs

-

What are the system requirements for Cannon Shot APK?

-

Cannon Shot APK requires Android 4.4 or higher and about 60 MB of free storage space on your device. It also requires internet connection to play or access some features.

-

Is Cannon Shot APK safe and secure to use?

-

Cannon Shot APK is safe and secure to use if you download it from Google Play Store or from a trusted third-party website. However, you should always be careful when downloading and installing any app from unknown sources. You should also read the app's privacy policy and permissions before using it.

-

How can I get more stars and unlock more cannons in Cannon Shot APK?

-

You can get more stars by completing levels with three stars. You can also watch ads to get more stars. You can unlock more cannons by collecting stars and keys and opening chests. You can also buy coins with real money and use them to buy cannons in the shop.

-

How can I contact the developer of Cannon Shot APK?

-

You can contact the developer of Cannon Shot APK by emailing them at support@saygames.by. You can also visit their website at https://saygames.by/. You can also follow them on Facebook at https://www.facebook.com/SayGamesBy/.

-

Can I play Cannon Shot APK offline?

-

You can play Cannon Shot APK offline if you have already downloaded the game file and installed it on your device. However, you may not be able to access some features or updates that require internet connection. You may also miss out on some rewards or offers that are available online. Therefore, it is recommended that you play Cannon Shot APK online whenever possible.

197e85843d
-
-
\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Access to Exclusive Anime Content with Bstation MOD Premium APK.md b/spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Access to Exclusive Anime Content with Bstation MOD Premium APK.md deleted file mode 100644 index dafaefa3a3f8d65fd6c3af57340abf4ac1f2f172..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Enjoy Unlimited Access to Exclusive Anime Content with Bstation MOD Premium APK.md +++ /dev/null @@ -1,107 +0,0 @@ -
-

How to Download Bstation Mod Premium 2023 for Free

-

If you are an anime lover, you might have heard of Bstation, a popular app that allows you to watch various anime shows and movies online. But did you know that there is a mod version of Bstation that gives you access to premium features for free? In this article, we will tell you what Bstation mod premium 2023 is, what features it offers, and how to download and install it on your device.

-

download bstation mod premium 2023


Download ->->->-> https://jinyurl.com/2uNPOk



-

What is Bstation and Why You Need the Mod Version

-

Bstation: A Popular App for Anime Lovers

-

Bstation is an app developed by Bilibili, a Chinese company that specializes in online video services. Bstation provides a platform for anime fans to watch their favorite shows and movies, as well as other content from different countries. You can find a wide range of genres, such as action, comedy, romance, horror, sci-fi, and more. You can also read comics and interact with other users on the app.

-

Bstation Mod: A Modified App with Premium Features Unlocked

-

While Bstation is a great app for anime lovers, it has some limitations for free users. For example, you have to watch ads before and during the videos, you cannot watch some exclusive and latest content, and you cannot enjoy HD quality. To get rid of these restrictions, you have to upgrade to a premium account, which costs money per month or per year.

-

However, if you do not want to spend money on a premium account, you can opt for Bstation mod premium 2023, which is a modified version of the app that unlocks all the premium features for free. This means that you can watch any content without ads, in HD quality, and with a mini screen option. You can also enjoy a user-friendly interface that makes it easy to navigate the app.

-

download bstation mod apk premium unlocked 2023 free
-download bstation mod apk premium v1.35.0 update 2023
-download bstation mod apk premium v2.31.2 vip unlocked
-download bstation mod apk premium versi terbaru
-download bstation mod apk premium no ads
-download bstation mod apk premium hd resolution
-download bstation mod apk premium mini screen
-download bstation mod apk premium user friendly interface
-download bstation mod apk premium unlimited access
-download bstation mod apk premium anime collection
-download bstation mod apk premium video creator
-download bstation mod apk premium acg community
-download bstation mod apk premium latest anime
-download bstation mod apk premium classic anime
-download bstation mod apk premium various genres
-download bstation mod apk premium action anime
-download bstation mod apk premium adventure anime
-download bstation mod apk premium comedy anime
-download bstation mod apk premium fantasy anime
-download bstation mod apk premium romance anime
-download bstation mod apk premium school anime
-download bstation mod apk premium sci-fi anime
-download bstation mod apk premium sports anime
-download bstation mod apk premium new episodes daily
-download bstation mod apk premium direct install
-download bstation mod apk premium only 107.08 mb
-download bstation mod apk premium only 89 mb
-download bstation mod apk premium for android
-download bstation mod apk premium for ios
-download bstation mod apk premium for pc
-how to download bstation mod apk premium 2023
-where to download bstation mod apk premium 2023
-why to download bstation mod apk premium 2023
-what is bstation mod apk premium 2023
-who is behind bstation mod apk premium 2023
-benefits of downloading bstation mod apk premium 2023
-drawbacks of downloading bstation mod apk premium 2023
-alternatives to downloading bstation mod apk premium 2023
-reviews of downloading bstation mod apk premium 2023
-tips and tricks for downloading bstation mod apk premium 2023
-best sites to download bstation mod apk premium 2023
-best apps to download bstation mod apk premium 2023
-best tools to download bstation mod apk premium 2023
-best methods to download bstation mod apk premium 2023
-best sources to download bstation mod apk premium 2023
-best guides to download bstation mod apk premium 2023
-best tutorials to download bstation mod apk premium 2023
-best strategies to download bstation mod apk premium 2023

-

Features of Bstation Mod Premium 2023

-

No Ads

-

One of the best features of Bstation mod premium 2023 is that it removes all the ads from the app. This means that you can watch your favorite anime shows and movies without any interruptions or distractions. You can also save your data and battery by not loading unnecessary ads.

-

HD Quality

-

Another feature of Bstation mod premium 2023 is that it allows you to watch videos in HD quality. This means that you can enjoy crisp and clear images and sounds that enhance your viewing experience. You can also adjust the video quality according to your preference and network speed.

-

Mini Screen

-

Bstation mod premium 2023 also offers a mini screen feature that lets you watch videos in a small window while doing other tasks on your device. For example, you can browse the web, check your messages, or play games while watching anime. You can also move and resize the mini screen as you like.

-

User-Friendly Interface

-

Bstation mod premium 2023 has a user-friendly interface that makes it easy to use the app. You can find various categories and genres of anime on the homepage, as well as search for specific titles or keywords. You can also access your history, favorites, downloads, and settings from the menu bar.

-

How to Download and Install Bstation Mod Premium 2023

-

Download the APK File from a Trusted Source

-

To download Bstation mod premium 2023, you need to find a reliable source that provides the APK file of the app. You can use one of the links below to download the APK file:

- -

Make sure to download the latest version of the app, which is 6.0.0 as of June 2023.

-

Enable Unknown Sources on Your Device

-

Before you can install Bstation mod premium 2023, you need to enable unknown sources on your device. This will allow you to install apps from sources other than the official app store. To enable unknown sources, follow these steps:

-
    -
  1. Go to your device's settings and tap on security or privacy.
    2. -
  2. Find the option that says unknown sources or install unknown apps and toggle it on.
    3. -
  3. Confirm your choice by tapping on OK or allow.
    4. -
-

You can now install Bstation mod premium 2023 on your device.

-

Install the APK File and Enjoy

-

After you have downloaded the APK file and enabled unknown sources, you can install Bstation mod premium 2023 by following these steps:

-
    -
  1. Locate the APK file on your device's file manager or downloads folder and tap on it.
    2. -
  2. Tap on install and wait for the installation process to complete.
    3. -
  3. Tap on open and launch the app.
    4. -
-

You can now enjoy watching anime with Bstation mod premium 2023 for free.

-

Conclusion

-

Bstation mod premium 2023 is a modified version of Bstation, a popular app for anime lovers. It unlocks all the premium features of the app for free, such as no ads, HD quality, mini screen, and user-friendly interface. You can download and install Bstation mod premium 2023 by following the steps in this article. However, you should be careful when downloading apps from unknown sources, as they may contain viruses or malware that can harm your device. You should also respect the rights of the original developers and creators of the app and the content. If you like Bstation, you should consider supporting them by purchasing a premium account or subscribing to their services.

-

FAQs

-

What is the difference between Bstation and Bilibili?

-

Bstation is an app developed by Bilibili, a Chinese company that specializes in online video services. Bilibili is a website that hosts various types of videos, such as anime, games, music, movies, and more. Bstation is an app that focuses on anime content from different countries.

-

Is Bstation mod premium 2023 safe to use?

-

Bstation mod premium 2023 is a modified version of Bstation that unlocks all the premium features for free. However, it is not an official app and it may not be safe to use. It may contain viruses or malware that can harm your device or steal your personal information. You should always download apps from trusted sources and scan them with antivirus software before installing them.

-

How can I update Bstation mod premium 2023?

-

To update Bstation mod premium 2023, you need to download the latest version of the APK file from a reliable source and install it over the existing app. You should not update the app from within the app itself, as it may revert to the original version and lose all the mod features.

-

Can I watch offline with Bstation mod premium 2023?

-

Bstation mod premium 2023 allows you to watch videos offline by downloading them to your device. You can find the download option on the video page or in the menu bar. You can also manage your downloads from the settings section of the app.

-

Can I use Bstation mod premium 2023 on PC or TV?

-

Bstation mod premium 2023 is an app designed for Android devices. However, you can use it on PC or TV by using an emulator or a casting device. An emulator is a software that simulates an Android device on your PC, such as Bluestacks or Nox Player. A casting device is a hardware that connects your Android device to your TV, such as Chromecast or Firestick.

401be4b1e0
-
-
\ No newline at end of file diff --git a/spaces/AI-Edify/demo-gpt3.5-turbo/README.md b/spaces/AI-Edify/demo-gpt3.5-turbo/README.md deleted file mode 100644 index fb753b5d2419ac313dea8b4dd0e7ca7704e2b42c..0000000000000000000000000000000000000000 --- a/spaces/AI-Edify/demo-gpt3.5-turbo/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Demo Gpt3.5-turbo Model -emoji: 📈 -colorFrom: green -colorTo: red -sdk: gradio -sdk_version: 3.20.0 -app_file: app.py -pinned: false -license: cc-by-nc-4.0 -duplicated_from: ramon1992/demo-gpt3.5-turbo ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AISuperheroes/02GR-ASR-Memory/README.md b/spaces/AISuperheroes/02GR-ASR-Memory/README.md deleted file mode 100644 index 90ca0f901b1cfb87180c25590a355a46644392c7..0000000000000000000000000000000000000000 --- a/spaces/AISuperheroes/02GR-ASR-Memory/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 02GR ASR Memory -emoji: 😻 -colorFrom: blue -colorTo: indigo -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/sde_team.py b/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/sde_team.py deleted file mode 100644 index ac0d5426782a88de420d91412341719983a540b0..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/sde_team.py +++ /dev/null @@ -1,30 +0,0 @@ -from __future__ import annotations - -import logging -import re -import random -from typing import TYPE_CHECKING, Any, List, Optional - -from . import order_registry as OrderRegistry -from .base import BaseOrder - -if TYPE_CHECKING: - from agentverse.environments import BaseEnvironment - - -@OrderRegistry.register("sde_team") -class SdeTeamOrder(BaseOrder): - """The order for a code problem solving - """ - next_agent_idx: int = 2 - - def get_next_agent_idx(self, environment: BaseEnvironment) -> List[int]: - if self.next_agent_idx == 2: - self.next_agent_idx = 0 - return [2] * 5 # TODO set the number in yaml - elif self.next_agent_idx == 0: - self.next_agent_idx = 1 - return [0] - elif self.next_agent_idx == 1: - self.next_agent_idx = 0 - return [1] \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/clock.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/clock.js deleted file mode 100644 index edf6c603a64ca661c4c965f36f4d4dbfbad88c4b..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/clock.js +++ /dev/null @@ -1,2 +0,0 @@ -import Clock from './time/clock/Clock.js'; -export default Clock; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/interception-plugin.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/interception-plugin.js deleted file mode 100644 index 4a23462d16c393b41acd4eeafc12b5256ead0b21..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/interception-plugin.js +++ /dev/null @@ -1,13 +0,0 @@ -import Interception from './interception.js'; - -class InterceptionPlugin extends Phaser.Plugins.BasePlugin { - - constructor(pluginManager) { - super(pluginManager); - } - - add(gameObject, config) { - return new Interception(gameObject, config); - } -} -export default InterceptionPlugin; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch2/NinePatch.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch2/NinePatch.d.ts deleted file mode 100644 index b59550c0eaf125ccc8715127ade7573009f4680a..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch2/NinePatch.d.ts +++ /dev/null @@ -1,2 +0,0 @@ -import NinePatch from '../../../plugins/ninepatch2'; -export default NinePatch; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/scrollableblock/ResetChildPosition.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/scrollableblock/ResetChildPosition.js deleted file mode 100644 index 2709ecc654274b3fd4157d0e36dbbfe676b2f979..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/scrollableblock/ResetChildPosition.js +++ /dev/null @@ -1,15 +0,0 @@ -var ResetChildPosition = function () { - var x = this.left; - var y = this.top; - if (this.scrollMode === 0) { - y += this.childOY; - } else { - x += this.childOY; - } - this.child.setPosition(x, y); - this.resetChildPositionState(this.child); - - this.setMaskChildrenFlag(); -}; - -export default ResetChildPosition; \ No newline at end of file diff --git a/spaces/AhmedRashwan369/ChatGPT4/app.py b/spaces/AhmedRashwan369/ChatGPT4/app.py deleted file mode 100644 index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000 --- a/spaces/AhmedRashwan369/ChatGPT4/app.py +++ /dev/null @@ -1,193 +0,0 @@ -import gradio as gr -import os -import json -import requests - -#Streaming endpoint -API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream" - -#Huggingface provided GPT4 OpenAI API Key -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") - -#Inferenec function -def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): - - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {OPENAI_API_KEY}" - } - print(f"system message is ^^ {system_msg}") - if system_msg.strip() == '': - initial_message = [{"role": "user", "content": f"{inputs}"},] - multi_turn_message = [] - else: - initial_message= [{"role": "system", "content": system_msg}, - {"role": "user", "content": f"{inputs}"},] - multi_turn_message = [{"role": "system", "content": system_msg},] - - if chat_counter == 0 : - payload = { - "model": "gpt-4", - "messages": initial_message , - "temperature" : 1.0, - "top_p":1.0, - "n" : 1, - "stream": True, - "presence_penalty":0, - "frequency_penalty":0, - } - print(f"chat_counter - {chat_counter}") - else: #if chat_counter != 0 : - messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},] - for data in chatbot: - user = {} - user["role"] = "user" - user["content"] = data[0] - assistant = {} - assistant["role"] = "assistant" - assistant["content"] = data[1] - messages.append(user) - messages.append(assistant) - temp = {} - temp["role"] = "user" - temp["content"] = inputs - messages.append(temp) - #messages - payload = { - "model": "gpt-4", - "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}], - "temperature" : temperature, #1.0, - "top_p": top_p, #1.0, - "n" : 1, - "stream": True, - "presence_penalty":0, - "frequency_penalty":0,} - - chat_counter+=1 - - history.append(inputs) - print(f"Logging : payload is - {payload}") - # make a POST request to the API endpoint using the requests.post method, passing in stream=True - response = requests.post(API_URL, headers=headers, json=payload, stream=True) - print(f"Logging : response code - {response}") - token_counter = 0 - partial_words = "" - - counter=0 - for chunk in response.iter_lines(): - #Skipping first chunk - if counter == 0: - counter+=1 - continue - # check whether each line is non-empty - if chunk.decode() : - chunk = chunk.decode() - # decode each line as response data is in bytes - if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: - partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] - if token_counter == 0: - history.append(" " + partial_words) - else: - history[-1] = partial_words - chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list - token_counter+=1 - yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history} - -#Resetting to blank -def reset_textbox(): - return gr.update(value='') - -#to set a component as visible=False -def set_visible_false(): - return gr.update(visible=False) - -#to set a component as visible=True -def set_visible_true(): - return gr.update(visible=True) - -title = """

🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming

""" - -#display message for themes feature -theme_addon_msg = """
🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple theme.push_to_hub(). -
🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - Gradio-Themes-Party🎨 🏆
-""" - -#Using info to add additional information about System message in GPT4 -system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. -System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'""" - -#Modifying existing Gradio Theme -theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green", - text_size=gr.themes.sizes.text_lg) - -with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""", - theme=theme) as demo: - gr.HTML(title) - gr.HTML("""

🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌

""") - gr.HTML(theme_addon_msg) - gr.HTML('''
Duplicate SpaceDuplicate the Space and run securely with your OpenAI API Key
''') - - with gr.Column(elem_id = "col_container"): - #GPT4 API Key is provided by Huggingface - with gr.Accordion(label="System message:", open=False): - system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="") - accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False) - chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot") - inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") - state = gr.State([]) - with gr.Row(): - with gr.Column(scale=7): - b1 = gr.Button().style(full_width=True) - with gr.Column(scale=3): - server_status_code = gr.Textbox(label="Status code from OpenAI server", ) - - #top_p, temperature - with gr.Accordion("Parameters", open=False): - top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",) - temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",) - chat_counter = gr.Number(value=0, visible=False, precision=0) - - #Event handling - inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key - b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key - - inputs.submit(set_visible_false, [], [system_msg]) - b1.click(set_visible_false, [], [system_msg]) - inputs.submit(set_visible_true, [], [accordion_msg]) - b1.click(set_visible_true, [], [accordion_msg]) - - b1.click(reset_textbox, [], [inputs]) - inputs.submit(reset_textbox, [], [inputs]) - - #Examples - with gr.Accordion(label="Examples for System message:", open=False): - gr.Examples( - examples = [["""You are an AI programming assistant. - - - Follow the user's requirements carefully and to the letter. - - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail. - - Then output the code in a single code block. - - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""], - ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."], - ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."], - ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."], - ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."], - ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."], - ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."], - ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."], - ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."], - ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."], - ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."], - ["You are a helpful assistant that provides detailed and accurate information."], - ["You are an assistant that speaks like Shakespeare."], - ["You are a friendly assistant who uses casual language and humor."], - ["You are a financial advisor who gives expert advice on investments and budgeting."], - ["You are a health and fitness expert who provides advice on nutrition and exercise."], - ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."], - ["You are a movie critic who shares insightful opinions on films and their themes."], - ["You are a history enthusiast who loves to discuss historical events and figures."], - ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."], - ["You are an AI poet who can compose creative and evocative poems on any given topic."],], - inputs = system_msg,) - -demo.queue(max_size=99, concurrency_count=20).launch(debug=True) \ No newline at end of file diff --git a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/avd_network.py b/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/avd_network.py deleted file mode 100644 index e62937ebc7d00a09f0e10ab9abda038cdaeaaf54..0000000000000000000000000000000000000000 --- a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/avd_network.py +++ /dev/null @@ -1,65 +0,0 @@ - -import torch -from torch import nn - - -class AVDNetwork(nn.Module): - """ - Animation via Disentanglement network - """ - - def __init__(self, num_tps, id_bottle_size=64, pose_bottle_size=64): - super(AVDNetwork, self).__init__() - input_size = 5*2 * num_tps - self.num_tps = num_tps - - self.id_encoder = nn.Sequential( - nn.Linear(input_size, 256), - nn.BatchNorm1d(256), - nn.ReLU(inplace=True), - nn.Linear(256, 512), - nn.BatchNorm1d(512), - nn.ReLU(inplace=True), - nn.Linear(512, 1024), - nn.BatchNorm1d(1024), - nn.ReLU(inplace=True), - nn.Linear(1024, id_bottle_size) - ) - - self.pose_encoder = nn.Sequential( - nn.Linear(input_size, 256), - nn.BatchNorm1d(256), - nn.ReLU(inplace=True), - nn.Linear(256, 512), - nn.BatchNorm1d(512), - nn.ReLU(inplace=True), - nn.Linear(512, 1024), - nn.BatchNorm1d(1024), - nn.ReLU(inplace=True), - nn.Linear(1024, pose_bottle_size) - ) - - self.decoder = nn.Sequential( - nn.Linear(pose_bottle_size + id_bottle_size, 1024), - nn.BatchNorm1d(1024), - nn.ReLU(), - nn.Linear(1024, 512), - nn.BatchNorm1d(512), - nn.ReLU(), - nn.Linear(512, 256), - nn.BatchNorm1d(256), - nn.ReLU(), - nn.Linear(256, input_size) - ) - - def forward(self, kp_source, kp_random): - - bs = kp_source['fg_kp'].shape[0] - - pose_emb = self.pose_encoder(kp_random['fg_kp'].view(bs, -1)) - id_emb = self.id_encoder(kp_source['fg_kp'].view(bs, -1)) - - rec = self.decoder(torch.cat([pose_emb, id_emb], dim=1)) - - rec = {'fg_kp': rec.view(bs, self.num_tps*5, -1)} - return rec diff --git a/spaces/Ali-C137/Motivation-Letter-Generator/README.md b/spaces/Ali-C137/Motivation-Letter-Generator/README.md deleted file mode 100644 index 5ffee5f9db8a8224a2e82f4b88ee9eb6575490cd..0000000000000000000000000000000000000000 --- a/spaces/Ali-C137/Motivation-Letter-Generator/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Motivation Letter Generator -emoji: 📝 -colorFrom: red -colorTo: indigo -sdk: gradio -sdk_version: 3.1.7 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AliUsama98/Usama_TextClassifier/app.py b/spaces/AliUsama98/Usama_TextClassifier/app.py deleted file mode 100644 index debfa869df620d84c3f89527d93fedf8f333f193..0000000000000000000000000000000000000000 --- a/spaces/AliUsama98/Usama_TextClassifier/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/krupper/text-complexity-classification").launch() \ No newline at end of file diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/tflib/custom_ops.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/tflib/custom_ops.py deleted file mode 100644 index 3e2498b04f4a5c950dae0ff77b85f8372df1b5b9..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/tflib/custom_ops.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright (c) SenseTime Research. All rights reserved. - -# Copyright (c) 2019, NVIDIA Corporation. All rights reserved. -# -# This work is made available under the Nvidia Source Code License-NC. -# To view a copy of this license, visit -# https://nvlabs.github.io/stylegan2/license.html - -"""TensorFlow custom ops builder. -""" - -import os -import re -import uuid -import hashlib -import tempfile -import shutil -import tensorflow as tf -from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module - -# ---------------------------------------------------------------------------- -# Global options. - -cuda_cache_path = os.path.join(os.path.dirname(__file__), '_cudacache') -cuda_cache_version_tag = 'v1' -# Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe! -do_not_hash_included_headers = False -verbose = True # Print status messages to stdout. - -compiler_bindir_search_path = [ - 'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.14.26428/bin/Hostx64/x64', - 'C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.23.28105/bin/Hostx64/x64', - 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin', -] - -# ---------------------------------------------------------------------------- -# Internal helper funcs. - - -def _find_compiler_bindir(): - for compiler_path in compiler_bindir_search_path: - if os.path.isdir(compiler_path): - return compiler_path - return None - - -def _get_compute_cap(device): - caps_str = device.physical_device_desc - m = re.search('compute capability: (\\d+).(\\d+)', caps_str) - major = m.group(1) - minor = m.group(2) - return (major, minor) - - -def _get_cuda_gpu_arch_string(): - gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU'] - if len(gpus) == 0: - raise RuntimeError('No GPU devices found') - (major, minor) = _get_compute_cap(gpus[0]) - return 'sm_%s%s' % (major, minor) - - -def _run_cmd(cmd): - with os.popen(cmd) as pipe: - output = pipe.read() - status = pipe.close() - if status is not None: - raise RuntimeError( - 'NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output)) - - -def _prepare_nvcc_cli(opts): - cmd = 'nvcc ' + opts.strip() - cmd += ' --disable-warnings' - cmd += ' --include-path "%s"' % tf.sysconfig.get_include() - cmd += ' --include-path "%s"' % os.path.join( - tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src') - cmd += ' --include-path "%s"' % os.path.join( - tf.sysconfig.get_include(), 'external', 'com_google_absl') - cmd += ' --include-path "%s"' % os.path.join( - tf.sysconfig.get_include(), 'external', 'eigen_archive') - - compiler_bindir = _find_compiler_bindir() - if compiler_bindir is None: - # Require that _find_compiler_bindir succeeds on Windows. Allow - # nvcc to use whatever is the default on Linux. - if os.name == 'nt': - raise RuntimeError( - 'Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__) - else: - cmd += ' --compiler-bindir "%s"' % compiler_bindir - cmd += ' 2>&1' - return cmd - -# ---------------------------------------------------------------------------- -# Main entry point. - - -_plugin_cache = dict() - - -def get_plugin(cuda_file): - cuda_file_base = os.path.basename(cuda_file) - cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base) - - # Already in cache? - if cuda_file in _plugin_cache: - return _plugin_cache[cuda_file] - - # Setup plugin. - if verbose: - print('Setting up TensorFlow plugin "%s": ' % - cuda_file_base, end='', flush=True) - try: - # Hash CUDA source. - md5 = hashlib.md5() - with open(cuda_file, 'rb') as f: - md5.update(f.read()) - md5.update(b'\n') - - # Hash headers included by the CUDA code by running it through the preprocessor. - if not do_not_hash_included_headers: - if verbose: - print('Preprocessing... ', end='', flush=True) - with tempfile.TemporaryDirectory() as tmp_dir: - tmp_file = os.path.join( - tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext) - _run_cmd(_prepare_nvcc_cli( - '"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))) - with open(tmp_file, 'rb') as f: - # __FILE__ in error check macros - bad_file_str = ( - '"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') - good_file_str = ('"' + cuda_file_base + - '"').encode('utf-8') - for ln in f: - # ignore line number pragmas - if not ln.startswith(b'# ') and not ln.startswith(b'#line '): - ln = ln.replace(bad_file_str, good_file_str) - md5.update(ln) - md5.update(b'\n') - - # Select compiler options. - compile_opts = '' - if os.name == 'nt': - compile_opts += '"%s"' % os.path.join( - tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib') - elif os.name == 'posix': - compile_opts += '"%s"' % os.path.join( - tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so') - compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\'' - else: - assert False # not Windows or Linux, w00t? - compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string() - compile_opts += ' --use_fast_math' - nvcc_cmd = _prepare_nvcc_cli(compile_opts) - - # Hash build configuration. - md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n') - md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n') - md5.update(('cuda_cache_version_tag: ' + - cuda_cache_version_tag).encode('utf-8') + b'\n') - - # Compile if not already compiled. - bin_file_ext = '.dll' if os.name == 'nt' else '.so' - bin_file = os.path.join( - cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext) - if not os.path.isfile(bin_file): - if verbose: - print('Compiling... ', end='', flush=True) - with tempfile.TemporaryDirectory() as tmp_dir: - tmp_file = os.path.join( - tmp_dir, cuda_file_name + '_tmp' + bin_file_ext) - _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % - (cuda_file, tmp_file, tmp_dir)) - os.makedirs(cuda_cache_path, exist_ok=True) - intermediate_file = os.path.join( - cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext) - shutil.copyfile(tmp_file, intermediate_file) - os.rename(intermediate_file, bin_file) # atomic - - # Load. - if verbose: - print('Loading... ', end='', flush=True) - plugin = tf.load_op_library(bin_file) - - # Add to cache. - _plugin_cache[cuda_file] = plugin - if verbose: - print('Done.', flush=True) - return plugin - - except: - if verbose: - print('Failed!', flush=True) - raise - -# ---------------------------------------------------------------------------- diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/mulit_token_textual_inversion/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/mulit_token_textual_inversion/README.md deleted file mode 100644 index 1303f73c175636466061110775cf1c905b4aba9a..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/mulit_token_textual_inversion/README.md +++ /dev/null @@ -1,143 +0,0 @@ -## [Deprecated] Multi Token Textual Inversion - -**IMPORTART: This research project is deprecated. Multi Token Textual Inversion is now supported natively in [the officail textual inversion example](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion#running-locally-with-pytorch).** - -The author of this project is [Isamu Isozaki](https://github.com/isamu-isozaki) - please make sure to tag the author for issue and PRs as well as @patrickvonplaten. - -We add multi token support to textual inversion. I added -1. num_vec_per_token for the number of used to reference that token -2. progressive_tokens for progressively training the token from 1 token to 2 token etc -3. progressive_tokens_max_steps for the max number of steps until we start full training -4. vector_shuffle to shuffle vectors - -Feel free to add these options to your training! In practice num_vec_per_token around 10+vector shuffle works great! - -## Textual Inversion fine-tuning example - -[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples. -The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion. - -## Running on Colab - -Colab for training -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) - -Colab for inference -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) - -## Running locally with PyTorch -### Installing the dependencies - -Before running the scripts, make sure to install the library's training dependencies: - -**Important** - -To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment: -```bash -git clone https://github.com/huggingface/diffusers -cd diffusers -pip install . -``` - -Then cd in the example folder and run -```bash -pip install -r requirements.txt -``` - -And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with: - -```bash -accelerate config -``` - - -### Cat toy example - -You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree. - -You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens). - -Run the following command to authenticate your token - -```bash -huggingface-cli login -``` - -If you have already cloned the repo, then you won't need to go through these steps. - -
- -Now let's get our dataset.Download 3-4 images from [here](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and save them in a directory. This will be our training data. - -And launch the training using - -**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___** - -```bash -export MODEL_NAME="runwayml/stable-diffusion-v1-5" -export DATA_DIR="path-to-dir-containing-images" - -accelerate launch textual_inversion.py \ - --pretrained_model_name_or_path=$MODEL_NAME \ - --train_data_dir=$DATA_DIR \ - --learnable_property="object" \ - --placeholder_token="" --initializer_token="toy" \ - --resolution=512 \ - --train_batch_size=1 \ - --gradient_accumulation_steps=4 \ - --max_train_steps=3000 \ - --learning_rate=5.0e-04 --scale_lr \ - --lr_scheduler="constant" \ - --lr_warmup_steps=0 \ - --output_dir="textual_inversion_cat" -``` - -A full training run takes ~1 hour on one V100 GPU. - -### Inference - -Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt. - -```python -from diffusers import StableDiffusionPipeline - -model_id = "path-to-your-trained-model" -pipe = StableDiffusionPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to("cuda") - -prompt = "A backpack" - -image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] - -image.save("cat-backpack.png") -``` - - -## Training with Flax/JAX - -For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script. - -Before running the scripts, make sure to install the library's training dependencies: - -```bash -pip install -U -r requirements_flax.txt -``` - -```bash -export MODEL_NAME="duongna/stable-diffusion-v1-4-flax" -export DATA_DIR="path-to-dir-containing-images" - -python textual_inversion_flax.py \ - --pretrained_model_name_or_path=$MODEL_NAME \ - --train_data_dir=$DATA_DIR \ - --learnable_property="object" \ - --placeholder_token="" --initializer_token="toy" \ - --resolution=512 \ - --train_batch_size=1 \ - --max_train_steps=3000 \ - --learning_rate=5.0e-04 --scale_lr \ - --output_dir="textual_inversion_cat" -``` -It should be at least 70% faster than the PyTorch script with the same configuration. - -### Training with xformers: -You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation. diff --git a/spaces/Anew5128/Anew51/server.py b/spaces/Anew5128/Anew51/server.py deleted file mode 100644 index 2c5301cc39a5a4767014b3873111b2a592855d0d..0000000000000000000000000000000000000000 --- a/spaces/Anew5128/Anew51/server.py +++ /dev/null @@ -1,964 +0,0 @@ -from functools import wraps -from flask import ( - Flask, - jsonify, - request, - Response, - render_template_string, - abort, - send_from_directory, - send_file, -) -from flask_cors import CORS -from flask_compress import Compress -import markdown -import argparse -from transformers import AutoTokenizer, AutoProcessor, pipeline -from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM -from transformers import BlipForConditionalGeneration -import unicodedata -import torch -import time -import os -import gc -import sys -import secrets -from PIL import Image -import base64 -from io import BytesIO -from random import randint -import webuiapi -import hashlib -from constants import * -from colorama import Fore, Style, init as colorama_init - -colorama_init() - -if sys.hexversion < 0x030b0000: - print(f"{Fore.BLUE}{Style.BRIGHT}Python 3.11 or newer is recommended to run this program.{Style.RESET_ALL}") - time.sleep(2) - -class SplitArgs(argparse.Action): - def __call__(self, parser, namespace, values, option_string=None): - setattr( - namespace, self.dest, values.replace('"', "").replace("'", "").split(",") - ) - -#Setting Root Folders for Silero Generations so it is compatible with STSL, should not effect regular runs. - Rolyat -parent_dir = os.path.dirname(os.path.abspath(__file__)) -SILERO_SAMPLES_PATH = os.path.join(parent_dir, "tts_samples") -SILERO_SAMPLE_TEXT = os.path.join(parent_dir) - -# Create directories if they don't exist -if not os.path.exists(SILERO_SAMPLES_PATH): - os.makedirs(SILERO_SAMPLES_PATH) -if not os.path.exists(SILERO_SAMPLE_TEXT): - os.makedirs(SILERO_SAMPLE_TEXT) - -# Script arguments -parser = argparse.ArgumentParser( - prog="SillyTavern Extras", description="Web API for transformers models" -) -parser.add_argument( - "--port", type=int, help="Specify the port on which the application is hosted" -) -parser.add_argument( - "--listen", action="store_true", help="Host the app on the local network" -) -parser.add_argument( - "--share", action="store_true", help="Share the app on CloudFlare tunnel" -) -parser.add_argument("--cpu", action="store_true", help="Run the models on the CPU") -parser.add_argument("--cuda", action="store_false", dest="cpu", help="Run the models on the GPU") -parser.add_argument("--cuda-device", help="Specify the CUDA device to use") -parser.add_argument("--mps", "--apple", "--m1", "--m2", action="store_false", dest="cpu", help="Run the models on Apple Silicon") -parser.set_defaults(cpu=True) -parser.add_argument("--summarization-model", help="Load a custom summarization model") -parser.add_argument( - "--classification-model", help="Load a custom text classification model" -) -parser.add_argument("--captioning-model", help="Load a custom captioning model") -parser.add_argument("--embedding-model", help="Load a custom text embedding model") -parser.add_argument("--chroma-host", help="Host IP for a remote ChromaDB instance") -parser.add_argument("--chroma-port", help="HTTP port for a remote ChromaDB instance (defaults to 8000)") -parser.add_argument("--chroma-folder", help="Path for chromadb persistence folder", default='.chroma_db') -parser.add_argument('--chroma-persist', help="ChromaDB persistence", default=True, action=argparse.BooleanOptionalAction) -parser.add_argument( - "--secure", action="store_true", help="Enforces the use of an API key" -) -sd_group = parser.add_mutually_exclusive_group() - -local_sd = sd_group.add_argument_group("sd-local") -local_sd.add_argument("--sd-model", help="Load a custom SD image generation model") -local_sd.add_argument("--sd-cpu", help="Force the SD pipeline to run on the CPU", action="store_true") - -remote_sd = sd_group.add_argument_group("sd-remote") -remote_sd.add_argument( - "--sd-remote", action="store_true", help="Use a remote backend for SD" -) -remote_sd.add_argument( - "--sd-remote-host", type=str, help="Specify the host of the remote SD backend" -) -remote_sd.add_argument( - "--sd-remote-port", type=int, help="Specify the port of the remote SD backend" -) -remote_sd.add_argument( - "--sd-remote-ssl", action="store_true", help="Use SSL for the remote SD backend" -) -remote_sd.add_argument( - "--sd-remote-auth", - type=str, - help="Specify the username:password for the remote SD backend (if required)", -) - -parser.add_argument( - "--enable-modules", - action=SplitArgs, - default=[], - help="Override a list of enabled modules", -) - -args = parser.parse_args() -# [HF, Huggingface] Set port to 7860, set host to remote. -port = 7860 -host = "0.0.0.0" -summarization_model = ( - args.summarization_model - if args.summarization_model - else DEFAULT_SUMMARIZATION_MODEL -) -classification_model = ( - args.classification_model - if args.classification_model - else DEFAULT_CLASSIFICATION_MODEL -) -captioning_model = ( - args.captioning_model if args.captioning_model else DEFAULT_CAPTIONING_MODEL -) -embedding_model = ( - args.embedding_model if args.embedding_model else DEFAULT_EMBEDDING_MODEL -) - -sd_use_remote = False if args.sd_model else True -sd_model = args.sd_model if args.sd_model else DEFAULT_SD_MODEL -sd_remote_host = args.sd_remote_host if args.sd_remote_host else DEFAULT_REMOTE_SD_HOST -sd_remote_port = args.sd_remote_port if args.sd_remote_port else DEFAULT_REMOTE_SD_PORT -sd_remote_ssl = args.sd_remote_ssl -sd_remote_auth = args.sd_remote_auth - -modules = ( - args.enable_modules if args.enable_modules and len(args.enable_modules) > 0 else [] -) - -if len(modules) == 0: - print( - f"{Fore.RED}{Style.BRIGHT}You did not select any modules to run! Choose them by adding an --enable-modules option" - ) - print(f"Example: --enable-modules=caption,summarize{Style.RESET_ALL}") - -# Models init -cuda_device = DEFAULT_CUDA_DEVICE if not args.cuda_device else args.cuda_device -device_string = cuda_device if torch.cuda.is_available() and not args.cpu else 'mps' if torch.backends.mps.is_available() and not args.cpu else 'cpu' -device = torch.device(device_string) -torch_dtype = torch.float32 if device_string != cuda_device else torch.float16 - -if not torch.cuda.is_available() and not args.cpu: - print(f"{Fore.YELLOW}{Style.BRIGHT}torch-cuda is not supported on this device.{Style.RESET_ALL}") - if not torch.backends.mps.is_available() and not args.cpu: - print(f"{Fore.YELLOW}{Style.BRIGHT}torch-mps is not supported on this device.{Style.RESET_ALL}") - - -print(f"{Fore.GREEN}{Style.BRIGHT}Using torch device: {device_string}{Style.RESET_ALL}") - -if "caption" in modules: - print("Initializing an image captioning model...") - captioning_processor = AutoProcessor.from_pretrained(captioning_model) - if "blip" in captioning_model: - captioning_transformer = BlipForConditionalGeneration.from_pretrained( - captioning_model, torch_dtype=torch_dtype - ).to(device) - else: - captioning_transformer = AutoModelForCausalLM.from_pretrained( - captioning_model, torch_dtype=torch_dtype - ).to(device) - -if "summarize" in modules: - print("Initializing a text summarization model...") - summarization_tokenizer = AutoTokenizer.from_pretrained(summarization_model) - summarization_transformer = AutoModelForSeq2SeqLM.from_pretrained( - summarization_model, torch_dtype=torch_dtype - ).to(device) - -if "classify" in modules: - print("Initializing a sentiment classification pipeline...") - classification_pipe = pipeline( - "text-classification", - model=classification_model, - top_k=None, - device=device, - torch_dtype=torch_dtype, - ) - -if "sd" in modules and not sd_use_remote: - from diffusers import StableDiffusionPipeline - from diffusers import EulerAncestralDiscreteScheduler - - print("Initializing Stable Diffusion pipeline...") - sd_device_string = cuda_device if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' - sd_device = torch.device(sd_device_string) - sd_torch_dtype = torch.float32 if sd_device_string != cuda_device else torch.float16 - sd_pipe = StableDiffusionPipeline.from_pretrained( - sd_model, custom_pipeline="lpw_stable_diffusion", torch_dtype=sd_torch_dtype - ).to(sd_device) - sd_pipe.safety_checker = lambda images, clip_input: (images, False) - sd_pipe.enable_attention_slicing() - # pipe.scheduler = KarrasVeScheduler.from_config(pipe.scheduler.config) - sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config( - sd_pipe.scheduler.config - ) -elif "sd" in modules and sd_use_remote: - print("Initializing Stable Diffusion connection") - try: - sd_remote = webuiapi.WebUIApi( - host=sd_remote_host, port=sd_remote_port, use_https=sd_remote_ssl - ) - if sd_remote_auth: - username, password = sd_remote_auth.split(":") - sd_remote.set_auth(username, password) - sd_remote.util_wait_for_ready() - except Exception as e: - # remote sd from modules - print( - f"{Fore.RED}{Style.BRIGHT}Could not connect to remote SD backend at http{'s' if sd_remote_ssl else ''}://{sd_remote_host}:{sd_remote_port}! Disabling SD module...{Style.RESET_ALL}" - ) - modules.remove("sd") - -if "tts" in modules: - print("tts module is deprecated. Please use silero-tts instead.") - modules.remove("tts") - modules.append("silero-tts") - - -if "silero-tts" in modules: - if not os.path.exists(SILERO_SAMPLES_PATH): - os.makedirs(SILERO_SAMPLES_PATH) - print("Initializing Silero TTS server") - from silero_api_server import tts - - tts_service = tts.SileroTtsService(SILERO_SAMPLES_PATH) - if len(os.listdir(SILERO_SAMPLES_PATH)) == 0: - print("Generating Silero TTS samples...") - tts_service.update_sample_text(SILERO_SAMPLE_TEXT) - tts_service.generate_samples() - - -if "edge-tts" in modules: - print("Initializing Edge TTS client") - import tts_edge as edge - - -if "chromadb" in modules: - print("Initializing ChromaDB") - import chromadb - import posthog - from chromadb.config import Settings - from sentence_transformers import SentenceTransformer - - # Assume that the user wants in-memory unless a host is specified - # Also disable chromadb telemetry - posthog.capture = lambda *args, **kwargs: None - if args.chroma_host is None: - if args.chroma_persist: - chromadb_client = chromadb.PersistentClient(path=args.chroma_folder, settings=Settings(anonymized_telemetry=False)) - print(f"ChromaDB is running in-memory with persistence. Persistence is stored in {args.chroma_folder}. Can be cleared by deleting the folder or purging db.") - else: - chromadb_client = chromadb.EphemeralClient(Settings(anonymized_telemetry=False)) - print(f"ChromaDB is running in-memory without persistence.") - else: - chroma_port=( - args.chroma_port if args.chroma_port else DEFAULT_CHROMA_PORT - ) - chromadb_client = chromadb.HttpClient(host=args.chroma_host, port=chroma_port, settings=Settings(anonymized_telemetry=False)) - print(f"ChromaDB is remotely configured at {args.chroma_host}:{chroma_port}") - - chromadb_embedder = SentenceTransformer(embedding_model, device=device_string) - chromadb_embed_fn = lambda *args, **kwargs: chromadb_embedder.encode(*args, **kwargs).tolist() - - # Check if the db is connected and running, otherwise tell the user - try: - chromadb_client.heartbeat() - print("Successfully pinged ChromaDB! Your client is successfully connected.") - except: - print("Could not ping ChromaDB! If you are running remotely, please check your host and port!") - -# Flask init -app = Flask(__name__) -CORS(app) # allow cross-domain requests -Compress(app) # compress responses -app.config["MAX_CONTENT_LENGTH"] = 100 * 1024 * 1024 - - -def require_module(name): - def wrapper(fn): - @wraps(fn) - def decorated_view(*args, **kwargs): - if name not in modules: - abort(403, "Module is disabled by config") - return fn(*args, **kwargs) - - return decorated_view - - return wrapper - - -# AI stuff -def classify_text(text: str) -> list: - output = classification_pipe( - text, - truncation=True, - max_length=classification_pipe.model.config.max_position_embeddings, - )[0] - return sorted(output, key=lambda x: x["score"], reverse=True) - - -def caption_image(raw_image: Image, max_new_tokens: int = 20) -> str: - inputs = captioning_processor(raw_image.convert("RGB"), return_tensors="pt").to( - device, torch_dtype - ) - outputs = captioning_transformer.generate(**inputs, max_new_tokens=max_new_tokens) - caption = captioning_processor.decode(outputs[0], skip_special_tokens=True) - return caption - - -def summarize_chunks(text: str, params: dict) -> str: - try: - return summarize(text, params) - except IndexError: - print( - "Sequence length too large for model, cutting text in half and calling again" - ) - new_params = params.copy() - new_params["max_length"] = new_params["max_length"] // 2 - new_params["min_length"] = new_params["min_length"] // 2 - return summarize_chunks( - text[: (len(text) // 2)], new_params - ) + summarize_chunks(text[(len(text) // 2) :], new_params) - - -def summarize(text: str, params: dict) -> str: - # Tokenize input - inputs = summarization_tokenizer(text, return_tensors="pt").to(device) - token_count = len(inputs[0]) - - bad_words_ids = [ - summarization_tokenizer(bad_word, add_special_tokens=False).input_ids - for bad_word in params["bad_words"] - ] - summary_ids = summarization_transformer.generate( - inputs["input_ids"], - num_beams=2, - max_new_tokens=max(token_count, int(params["max_length"])), - min_new_tokens=min(token_count, int(params["min_length"])), - repetition_penalty=float(params["repetition_penalty"]), - temperature=float(params["temperature"]), - length_penalty=float(params["length_penalty"]), - bad_words_ids=bad_words_ids, - ) - summary = summarization_tokenizer.batch_decode( - summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True - )[0] - summary = normalize_string(summary) - return summary - - -def normalize_string(input: str) -> str: - output = " ".join(unicodedata.normalize("NFKC", input).strip().split()) - return output - - -def generate_image(data: dict) -> Image: - prompt = normalize_string(f'{data["prompt_prefix"]} {data["prompt"]}') - - if sd_use_remote: - image = sd_remote.txt2img( - prompt=prompt, - negative_prompt=data["negative_prompt"], - sampler_name=data["sampler"], - steps=data["steps"], - cfg_scale=data["scale"], - width=data["width"], - height=data["height"], - restore_faces=data["restore_faces"], - enable_hr=data["enable_hr"], - save_images=True, - send_images=True, - do_not_save_grid=False, - do_not_save_samples=False, - ).image - else: - image = sd_pipe( - prompt=prompt, - negative_prompt=data["negative_prompt"], - num_inference_steps=data["steps"], - guidance_scale=data["scale"], - width=data["width"], - height=data["height"], - ).images[0] - - image.save("./debug.png") - return image - - -def image_to_base64(image: Image, quality: int = 75) -> str: - buffer = BytesIO() - image.convert("RGB") - image.save(buffer, format="JPEG", quality=quality) - img_str = base64.b64encode(buffer.getvalue()).decode("utf-8") - return img_str - - -ignore_auth = [] -# [HF, Huggingface] Get password instead of text file. -api_key = os.environ.get("password") - -def is_authorize_ignored(request): - view_func = app.view_functions.get(request.endpoint) - - if view_func is not None: - if view_func in ignore_auth: - return True - return False - -@app.before_request -def before_request(): - # Request time measuring - request.start_time = time.time() - - # Checks if an API key is present and valid, otherwise return unauthorized - # The options check is required so CORS doesn't get angry - try: - if request.method != 'OPTIONS' and is_authorize_ignored(request) == False and getattr(request.authorization, 'token', '') != api_key: - print(f"WARNING: Unauthorized API key access from {request.remote_addr}") - if request.method == 'POST': - print(f"Incoming POST request with {request.headers.get('Authorization')}") - response = jsonify({ 'error': '401: Invalid API key' }) - response.status_code = 401 - return "https://(hf_name)-(space_name).hf.space/" - except Exception as e: - print(f"API key check error: {e}") - return "https://(hf_name)-(space_name).hf.space/" - - -@app.after_request -def after_request(response): - duration = time.time() - request.start_time - response.headers["X-Request-Duration"] = str(duration) - return response - - -@app.route("/", methods=["GET"]) -def index(): - with open("./README.md", "r", encoding="utf8") as f: - content = f.read() - return render_template_string(markdown.markdown(content, extensions=["tables"])) - - -@app.route("/api/extensions", methods=["GET"]) -def get_extensions(): - extensions = dict( - { - "extensions": [ - { - "name": "not-supported", - "metadata": { - "display_name": """Extensions serving using Extensions API is no longer supported. Please update the mod from: https://github.com/Cohee1207/SillyTavern""", - "requires": [], - "assets": [], - }, - } - ] - } - ) - return jsonify(extensions) - - -@app.route("/api/caption", methods=["POST"]) -@require_module("caption") -def api_caption(): - data = request.get_json() - - if "image" not in data or not isinstance(data["image"], str): - abort(400, '"image" is required') - - image = Image.open(BytesIO(base64.b64decode(data["image"]))) - image = image.convert("RGB") - image.thumbnail((512, 512)) - caption = caption_image(image) - thumbnail = image_to_base64(image) - print("Caption:", caption, sep="\n") - gc.collect() - return jsonify({"caption": caption, "thumbnail": thumbnail}) - - -@app.route("/api/summarize", methods=["POST"]) -@require_module("summarize") -def api_summarize(): - data = request.get_json() - - if "text" not in data or not isinstance(data["text"], str): - abort(400, '"text" is required') - - params = DEFAULT_SUMMARIZE_PARAMS.copy() - - if "params" in data and isinstance(data["params"], dict): - params.update(data["params"]) - - print("Summary input:", data["text"], sep="\n") - summary = summarize_chunks(data["text"], params) - print("Summary output:", summary, sep="\n") - gc.collect() - return jsonify({"summary": summary}) - - -@app.route("/api/classify", methods=["POST"]) -@require_module("classify") -def api_classify(): - data = request.get_json() - - if "text" not in data or not isinstance(data["text"], str): - abort(400, '"text" is required') - - print("Classification input:", data["text"], sep="\n") - classification = classify_text(data["text"]) - print("Classification output:", classification, sep="\n") - gc.collect() - return jsonify({"classification": classification}) - - -@app.route("/api/classify/labels", methods=["GET"]) -@require_module("classify") -def api_classify_labels(): - classification = classify_text("") - labels = [x["label"] for x in classification] - return jsonify({"labels": labels}) - - -@app.route("/api/image", methods=["POST"]) -@require_module("sd") -def api_image(): - required_fields = { - "prompt": str, - } - - optional_fields = { - "steps": 30, - "scale": 6, - "sampler": "DDIM", - "width": 512, - "height": 512, - "restore_faces": False, - "enable_hr": False, - "prompt_prefix": PROMPT_PREFIX, - "negative_prompt": NEGATIVE_PROMPT, - } - - data = request.get_json() - - # Check required fields - for field, field_type in required_fields.items(): - if field not in data or not isinstance(data[field], field_type): - abort(400, f'"{field}" is required') - - # Set optional fields to default values if not provided - for field, default_value in optional_fields.items(): - type_match = ( - (int, float) - if isinstance(default_value, (int, float)) - else type(default_value) - ) - if field not in data or not isinstance(data[field], type_match): - data[field] = default_value - - try: - print("SD inputs:", data, sep="\n") - image = generate_image(data) - base64image = image_to_base64(image, quality=90) - return jsonify({"image": base64image}) - except RuntimeError as e: - abort(400, str(e)) - - -@app.route("/api/image/model", methods=["POST"]) -@require_module("sd") -def api_image_model_set(): - data = request.get_json() - - if not sd_use_remote: - abort(400, "Changing model for local sd is not supported.") - if "model" not in data or not isinstance(data["model"], str): - abort(400, '"model" is required') - - old_model = sd_remote.util_get_current_model() - sd_remote.util_set_model(data["model"], find_closest=False) - # sd_remote.util_set_model(data['model']) - sd_remote.util_wait_for_ready() - new_model = sd_remote.util_get_current_model() - - return jsonify({"previous_model": old_model, "current_model": new_model}) - - -@app.route("/api/image/model", methods=["GET"]) -@require_module("sd") -def api_image_model_get(): - model = sd_model - - if sd_use_remote: - model = sd_remote.util_get_current_model() - - return jsonify({"model": model}) - - -@app.route("/api/image/models", methods=["GET"]) -@require_module("sd") -def api_image_models(): - models = [sd_model] - - if sd_use_remote: - models = sd_remote.util_get_model_names() - - return jsonify({"models": models}) - - -@app.route("/api/image/samplers", methods=["GET"]) -@require_module("sd") -def api_image_samplers(): - samplers = ["Euler a"] - - if sd_use_remote: - samplers = [sampler["name"] for sampler in sd_remote.get_samplers()] - - return jsonify({"samplers": samplers}) - - -@app.route("/api/modules", methods=["GET"]) -def get_modules(): - return jsonify({"modules": modules}) - - -@app.route("/api/tts/speakers", methods=["GET"]) -@require_module("silero-tts") -def tts_speakers(): - voices = [ - { - "name": speaker, - "voice_id": speaker, - "preview_url": f"{str(request.url_root)}api/tts/sample/{speaker}", - } - for speaker in tts_service.get_speakers() - ] - return jsonify(voices) - -# Added fix for Silero not working as new files were unable to be created if one already existed. - Rolyat 7/7/23 -@app.route("/api/tts/generate", methods=["POST"]) -@require_module("silero-tts") -def tts_generate(): - voice = request.get_json() - if "text" not in voice or not isinstance(voice["text"], str): - abort(400, '"text" is required') - if "speaker" not in voice or not isinstance(voice["speaker"], str): - abort(400, '"speaker" is required') - # Remove asterisks - voice["text"] = voice["text"].replace("*", "") - try: - # Remove the destination file if it already exists - if os.path.exists('test.wav'): - os.remove('test.wav') - - audio = tts_service.generate(voice["speaker"], voice["text"]) - audio_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.basename(audio)) - - os.rename(audio, audio_file_path) - return send_file(audio_file_path, mimetype="audio/x-wav") - except Exception as e: - print(e) - abort(500, voice["speaker"]) - - -@app.route("/api/tts/sample/", methods=["GET"]) -@require_module("silero-tts") -def tts_play_sample(speaker: str): - return send_from_directory(SILERO_SAMPLES_PATH, f"{speaker}.wav") - - -@app.route("/api/edge-tts/list", methods=["GET"]) -@require_module("edge-tts") -def edge_tts_list(): - voices = edge.get_voices() - return jsonify(voices) - - -@app.route("/api/edge-tts/generate", methods=["POST"]) -@require_module("edge-tts") -def edge_tts_generate(): - data = request.get_json() - if "text" not in data or not isinstance(data["text"], str): - abort(400, '"text" is required') - if "voice" not in data or not isinstance(data["voice"], str): - abort(400, '"voice" is required') - if "rate" in data and isinstance(data['rate'], int): - rate = data['rate'] - else: - rate = 0 - # Remove asterisks - data["text"] = data["text"].replace("*", "") - try: - audio = edge.generate_audio(text=data["text"], voice=data["voice"], rate=rate) - return Response(audio, mimetype="audio/mpeg") - except Exception as e: - print(e) - abort(500, data["voice"]) - - -@app.route("/api/chromadb", methods=["POST"]) -@require_module("chromadb") -def chromadb_add_messages(): - data = request.get_json() - if "chat_id" not in data or not isinstance(data["chat_id"], str): - abort(400, '"chat_id" is required') - if "messages" not in data or not isinstance(data["messages"], list): - abort(400, '"messages" is required') - - chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest() - collection = chromadb_client.get_or_create_collection( - name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn - ) - - documents = [m["content"] for m in data["messages"]] - ids = [m["id"] for m in data["messages"]] - metadatas = [ - {"role": m["role"], "date": m["date"], "meta": m.get("meta", "")} - for m in data["messages"] - ] - - collection.upsert( - ids=ids, - documents=documents, - metadatas=metadatas, - ) - - return jsonify({"count": len(ids)}) - - -@app.route("/api/chromadb/purge", methods=["POST"]) -@require_module("chromadb") -def chromadb_purge(): - data = request.get_json() - if "chat_id" not in data or not isinstance(data["chat_id"], str): - abort(400, '"chat_id" is required') - - chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest() - collection = chromadb_client.get_or_create_collection( - name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn - ) - - count = collection.count() - collection.delete() - print("ChromaDB embeddings deleted", count) - return 'Ok', 200 - - -@app.route("/api/chromadb/query", methods=["POST"]) -@require_module("chromadb") -def chromadb_query(): - data = request.get_json() - if "chat_id" not in data or not isinstance(data["chat_id"], str): - abort(400, '"chat_id" is required') - if "query" not in data or not isinstance(data["query"], str): - abort(400, '"query" is required') - - if "n_results" not in data or not isinstance(data["n_results"], int): - n_results = 1 - else: - n_results = data["n_results"] - - chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest() - collection = chromadb_client.get_or_create_collection( - name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn - ) - - if collection.count() == 0: - print(f"Queried empty/missing collection for {repr(data['chat_id'])}.") - return jsonify([]) - - - n_results = min(collection.count(), n_results) - query_result = collection.query( - query_texts=[data["query"]], - n_results=n_results, - ) - - documents = query_result["documents"][0] - ids = query_result["ids"][0] - metadatas = query_result["metadatas"][0] - distances = query_result["distances"][0] - - messages = [ - { - "id": ids[i], - "date": metadatas[i]["date"], - "role": metadatas[i]["role"], - "meta": metadatas[i]["meta"], - "content": documents[i], - "distance": distances[i], - } - for i in range(len(ids)) - ] - - return jsonify(messages) - -@app.route("/api/chromadb/multiquery", methods=["POST"]) -@require_module("chromadb") -def chromadb_multiquery(): - data = request.get_json() - if "chat_list" not in data or not isinstance(data["chat_list"], list): - abort(400, '"chat_list" is required and should be a list') - if "query" not in data or not isinstance(data["query"], str): - abort(400, '"query" is required') - - if "n_results" not in data or not isinstance(data["n_results"], int): - n_results = 1 - else: - n_results = data["n_results"] - - messages = [] - - for chat_id in data["chat_list"]: - if not isinstance(chat_id, str): - continue - - try: - chat_id_md5 = hashlib.md5(chat_id.encode()).hexdigest() - collection = chromadb_client.get_collection( - name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn - ) - - # Skip this chat if the collection is empty - if collection.count() == 0: - continue - - n_results_per_chat = min(collection.count(), n_results) - query_result = collection.query( - query_texts=[data["query"]], - n_results=n_results_per_chat, - ) - documents = query_result["documents"][0] - ids = query_result["ids"][0] - metadatas = query_result["metadatas"][0] - distances = query_result["distances"][0] - - chat_messages = [ - { - "id": ids[i], - "date": metadatas[i]["date"], - "role": metadatas[i]["role"], - "meta": metadatas[i]["meta"], - "content": documents[i], - "distance": distances[i], - } - for i in range(len(ids)) - ] - - messages.extend(chat_messages) - except Exception as e: - print(e) - - #remove duplicate msgs, filter down to the right number - seen = set() - messages = [d for d in messages if not (d['content'] in seen or seen.add(d['content']))] - messages = sorted(messages, key=lambda x: x['distance'])[0:n_results] - - return jsonify(messages) - - -@app.route("/api/chromadb/export", methods=["POST"]) -@require_module("chromadb") -def chromadb_export(): - data = request.get_json() - if "chat_id" not in data or not isinstance(data["chat_id"], str): - abort(400, '"chat_id" is required') - - chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest() - try: - collection = chromadb_client.get_collection( - name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn - ) - except Exception as e: - print(e) - abort(400, "Chat collection not found in chromadb") - - collection_content = collection.get() - documents = collection_content.get('documents', []) - ids = collection_content.get('ids', []) - metadatas = collection_content.get('metadatas', []) - - unsorted_content = [ - { - "id": ids[i], - "metadata": metadatas[i], - "document": documents[i], - } - for i in range(len(ids)) - ] - - sorted_content = sorted(unsorted_content, key=lambda x: x['metadata']['date']) - - export = { - "chat_id": data["chat_id"], - "content": sorted_content - } - - return jsonify(export) - -@app.route("/api/chromadb/import", methods=["POST"]) -@require_module("chromadb") -def chromadb_import(): - data = request.get_json() - content = data['content'] - if "chat_id" not in data or not isinstance(data["chat_id"], str): - abort(400, '"chat_id" is required') - - chat_id_md5 = hashlib.md5(data["chat_id"].encode()).hexdigest() - collection = chromadb_client.get_or_create_collection( - name=f"chat-{chat_id_md5}", embedding_function=chromadb_embed_fn - ) - - documents = [item['document'] for item in content] - metadatas = [item['metadata'] for item in content] - ids = [item['id'] for item in content] - - - collection.upsert(documents=documents, metadatas=metadatas, ids=ids) - print(f"Imported {len(ids)} (total {collection.count()}) content entries into {repr(data['chat_id'])}") - - return jsonify({"count": len(ids)}) - - -if args.share: - from flask_cloudflared import _run_cloudflared - import inspect - - sig = inspect.signature(_run_cloudflared) - sum = sum( - 1 - for param in sig.parameters.values() - if param.kind == param.POSITIONAL_OR_KEYWORD - ) - if sum > 1: - metrics_port = randint(8100, 9000) - cloudflare = _run_cloudflared(port, metrics_port) - else: - cloudflare = _run_cloudflared(port) - print("Running on", cloudflare) - -ignore_auth.append(tts_play_sample) -app.run(host=host, port=port) diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/__init__.py deleted file mode 100644 index 915af28cefab14a14c1188ed861161080fd138a3..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .checkpoint import CheckpointHook -from .closure import ClosureHook -from .ema import EMAHook -from .evaluation import DistEvalHook, EvalHook -from .hook import HOOKS, Hook -from .iter_timer import IterTimerHook -from .logger import (DvcliveLoggerHook, LoggerHook, MlflowLoggerHook, - NeptuneLoggerHook, PaviLoggerHook, TensorboardLoggerHook, - TextLoggerHook, WandbLoggerHook) -from .lr_updater import LrUpdaterHook -from .memory import EmptyCacheHook -from .momentum_updater import MomentumUpdaterHook -from .optimizer import (Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook, - GradientCumulativeOptimizerHook, OptimizerHook) -from .profiler import ProfilerHook -from .sampler_seed import DistSamplerSeedHook -from .sync_buffer import SyncBuffersHook - -__all__ = [ - 'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook', - 'OptimizerHook', 'Fp16OptimizerHook', 'IterTimerHook', - 'DistSamplerSeedHook', 'EmptyCacheHook', 'LoggerHook', 'MlflowLoggerHook', - 'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook', - 'NeptuneLoggerHook', 'WandbLoggerHook', 'DvcliveLoggerHook', - 'MomentumUpdaterHook', 'SyncBuffersHook', 'EMAHook', 'EvalHook', - 'DistEvalHook', 'ProfilerHook', 'GradientCumulativeOptimizerHook', - 'GradientCumulativeFp16OptimizerHook' -] diff --git a/spaces/AquaSuisei/ChatGPTXE/modules/config.py b/spaces/AquaSuisei/ChatGPTXE/modules/config.py deleted file mode 100644 index a7cde4fbdb5deb0b43064051e440673913b9c5e5..0000000000000000000000000000000000000000 --- a/spaces/AquaSuisei/ChatGPTXE/modules/config.py +++ /dev/null @@ -1,145 +0,0 @@ -from collections import defaultdict -from contextlib import contextmanager -import os -import logging -import sys -import json - -from . import shared - - -__all__ = [ - "my_api_key", - "authflag", - "auth_list", - "dockerflag", - "retrieve_proxy", - "log_level", - "advance_docs", - "update_doc_config", - "multi_api_key", -] - -# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低） -# 同时，也可以为后续支持自定义功能提供config的帮助 -if os.path.exists("config.json"): - with open("config.json", "r", encoding='utf-8') as f: - config = json.load(f) -else: - config = {} - -## 处理docker if we are running in Docker -dockerflag = config.get("dockerflag", False) -if os.environ.get("dockerrun") == "yes": - dockerflag = True - -## 处理 api-key 以及 允许的用户列表 -my_api_key = config.get("openai_api_key", "") # 在这里输入你的 API 密钥 -my_api_key = os.environ.get("my_api_key", my_api_key) - -## 多账户机制 -multi_api_key = config.get("multi_api_key", False) # 是否开启多账户机制 -if multi_api_key: - api_key_list = config.get("api_key_list", []) - if len(api_key_list) == 0: - logging.error("多账号模式已开启，但api_key_list为空，请检查config.json") - sys.exit(1) - shared.state.set_api_key_queue(api_key_list) - -auth_list = config.get("users", []) # 实际上是使用者的列表 -authflag = len(auth_list) > 0 # 是否开启认证的状态值，改为判断auth_list长度 - -# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配 -api_host = os.environ.get("api_host", config.get("api_host", "")) -if api_host: - shared.state.set_api_host(api_host) - -if dockerflag: - if my_api_key == "empty": - logging.error("Please give a api key!") - sys.exit(1) - # auth - username = os.environ.get("USERNAME") - password = os.environ.get("PASSWORD") - if not (isinstance(username, type(None)) or isinstance(password, type(None))): - auth_list.append((os.environ.get("USERNAME"), os.environ.get("PASSWORD"))) - authflag = True -else: - if ( - not my_api_key - and os.path.exists("api_key.txt") - and os.path.getsize("api_key.txt") - ): - with open("api_key.txt", "r") as f: - my_api_key = f.read().strip() - if os.path.exists("auth.json"): - authflag = True - with open("auth.json", "r", encoding='utf-8') as f: - auth = json.load(f) - for _ in auth: - if auth[_]["username"] and auth[_]["password"]: - auth_list.append((auth[_]["username"], auth[_]["password"])) - else: - logging.error("请检查auth.json文件中的用户名和密码！") - sys.exit(1) - -@contextmanager -def retrieve_openai_api(api_key = None): - old_api_key = os.environ.get("OPENAI_API_KEY", "") - if api_key is None: - os.environ["OPENAI_API_KEY"] = my_api_key - yield my_api_key - else: - os.environ["OPENAI_API_KEY"] = api_key - yield api_key - os.environ["OPENAI_API_KEY"] = old_api_key - -## 处理log -log_level = config.get("log_level", "INFO") -logging.basicConfig( - level=log_level, - format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s", -) - -## 处理代理： -http_proxy = config.get("http_proxy", "") -https_proxy = config.get("https_proxy", "") -http_proxy = os.environ.get("HTTP_PROXY", http_proxy) -https_proxy = os.environ.get("HTTPS_PROXY", https_proxy) - -# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报 -os.environ["HTTP_PROXY"] = "" -os.environ["HTTPS_PROXY"] = "" - -@contextmanager -def retrieve_proxy(proxy=None): - """ - 1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理 - 2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量6 - """ - global http_proxy, https_proxy - if proxy is not None: - http_proxy = proxy - https_proxy = proxy - yield http_proxy, https_proxy - else: - old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] - os.environ["HTTP_PROXY"] = http_proxy - os.environ["HTTPS_PROXY"] = https_proxy - yield http_proxy, https_proxy # return new proxy - - # return old proxy - os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var - - -## 处理advance docs -advance_docs = defaultdict(lambda: defaultdict(dict)) -advance_docs.update(config.get("advance_docs", {})) -def update_doc_config(two_column_pdf): - global advance_docs - if two_column_pdf: - advance_docs["pdf"]["two_column"] = True - else: - advance_docs["pdf"]["two_column"] = False - - logging.info(f"更新后的文件参数为：{advance_docs}") \ No newline at end of file diff --git a/spaces/Archan/ArXivAudio/app.py b/spaces/Archan/ArXivAudio/app.py deleted file mode 100644 index 4f0064937fb21e6d2ab35d7309e04139f55fbfae..0000000000000000000000000000000000000000 --- a/spaces/Archan/ArXivAudio/app.py +++ /dev/null @@ -1,106 +0,0 @@ -import os -import streamlit as st -from pdfminer.high_level import extract_pages -from search import search -from get_paper import get_paper -from get_pages import get_pages -from tts import inference - -st.title("ArXiV Audio") - -with st.form(key="search_form"): - col1, col2, col3 = st.columns(3) - with col1: - query = st.text_input("Search Paper") - with col2: - sort_by = st.selectbox(label="Sort By", options=( - 'Relevance', 'Last Updated Date', 'Submitted Date')) - with col3: - order_by = st.selectbox( - label="Order By", options=('Ascending', 'Descending')) - submit = st.form_submit_button(label="Search") - -lst = search(query=query, sort_by=sort_by, sort_order=order_by) -if len(lst) != 0: - label = "Papers for " + query - with st.form(key="paper_form"): - pname = st.selectbox(label=label, options=lst) - submit_paper = st.form_submit_button(label="Fetch Paper") -else: - with st.form(key="paper_form"): - pname = st.selectbox(label="NO PAPERS", options=lst) - submit_paper = st.form_submit_button(label="Fetch Paper") - -paper = "" -if submit_paper or os.path.exists('downloads/paper.pdf'): - paper = get_paper(pname) - - print("Submit_paper = ", submit_paper) - - name = "" - tpages = 0 - lst_idx = 1 - if paper: - name = "./downloads/paper.pdf" - tpages = len(list(extract_pages(name))) - lst_idx = tpages-1 - - pgs = [i+1 for i in range(tpages)] - - start_page = 1 - end_page = 1 - #content = get_pages(name, start_page, end_page) - #audio_path = inference(content, "english") - #audio_file = open(audio_path, "rb") - #audio_bytes = audio_file.read() - #st.audio(audio_bytes, format='audio/wav') - - with st.form(key="page_form"): - print("inside page form") - col4, col5 = st.columns(2) - with col4: - print("column 1") - s_page = st.selectbox(label="Start Page", options=pgs) - print(s_page) - start_page = s_page - with col5: - print("column 2") - e_page = st.selectbox(label="End Page", options=pgs, index=lst_idx) - print(e_page) - end_page = e_page - st.text("*") - submit_pages = st.form_submit_button(label="Convert To Audio") - print("Submit_pages' = ", submit_pages) - print(start_page, end_page) - - print("Submit_pages = ", submit_pages) - if submit_pages: - content = get_pages(name, start_page, end_page) - x = st.text("Converting to Audio..... Please Wait") - audio_path = inference(content, "english") - audio_file = open(audio_path, "rb") - audio_bytes = audio_file.read() - x = st.text("Done") - st.audio(audio_bytes, format='audio/wav') - os.remove('downloads/paper.pdf') - - print("Submit_paper at end state = ", submit_paper) - - -else: - with st.form(key="page_form"): - col1, col2 = st.columns(2) - with col1: - s_page = st.selectbox(label="Start Page", options=[]) - with col2: - e_page = st.selectbox(label="End Page", options=[]) - submit_pages2 = st.form_submit_button(label="Convert To Audio") -st.text(" ") -st.text(" ") -st.text(" ") -st.text(" ") -st.text(" ") -st.markdown("Created by [Archan Ghosh](https://github.com/ArchanGhosh) & [Madhurima Maji](https://github.com/madhurima99). Special Thanks to [Herumb](https://github.com/krypticmouse) for helping us with the deployment.", unsafe_allow_html=True) -st.markdown("Do Support us on [Github](https://github.com/ArchanGhosh/ArxivAudio)", unsafe_allow_html =True) -st.text(" ") -st.text("* - Please limit to 3 pages as we are currently rate limited on CPU, we are planning to move to a GPU in the coming future. ") \ No newline at end of file diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/__init__.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/__init__.py deleted file mode 100644 index f102a9cadfa89ce554b3b26d2b90bfba2e05273c..0000000000000000000000000000000000000000 --- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.0.1" diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/inpaint_zoom/zoom_out_app.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/inpaint_zoom/zoom_out_app.py deleted file mode 100644 index 380c0fa8a33bdba99cf5c47c0e422659c591b85b..0000000000000000000000000000000000000000 --- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/inpaint_zoom/zoom_out_app.py +++ /dev/null @@ -1,140 +0,0 @@ -import os - -import gradio as gr -import torch -from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler -from PIL import Image - -from video_diffusion.inpaint_zoom.utils.zoom_out_utils import ( - dummy, - preprocess_image, - preprocess_mask_image, - write_video, -) - -os.environ["CUDA_VISIBLE_DEVICES"] = "0" - - -stable_paint_model_list = ["stabilityai/stable-diffusion-2-inpainting", "runwayml/stable-diffusion-inpainting"] - -stable_paint_prompt_list = [ - "children running in the forest , sunny, bright, by studio ghibli painting, superior quality, masterpiece, traditional Japanese colors, by Grzegorz Rutkowski, concept art", - "A beautiful landscape of a mountain range with a lake in the foreground", -] - -stable_paint_negative_prompt_list = [ - "lurry, bad art, blurred, text, watermark", -] - - -class StableDiffusionZoomOut: - def __init__(self): - self.pipe = None - - def load_model(self, model_id): - if self.pipe is None: - self.pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) - self.pipe.set_use_memory_efficient_attention_xformers(True) - self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config) - self.pipe = self.pipe.to("cuda") - self.pipe.safety_checker = dummy - self.g_cuda = torch.Generator(device="cuda") - - return self.pipe - - def generate_video( - self, - model_id, - prompt, - negative_prompt, - guidance_scale, - num_inference_steps, - num_frames, - step_size, - ): - pipe = self.load_model(model_id) - - new_image = Image.new(mode="RGBA", size=(512, 512)) - current_image, mask_image = preprocess_mask_image(new_image) - - current_image = pipe( - prompt=[prompt], - negative_prompt=[negative_prompt], - image=current_image, - mask_image=mask_image, - num_inference_steps=num_inference_steps, - guidance_scale=guidance_scale, - ).images[0] - - all_frames = [] - all_frames.append(current_image) - - for i in range(num_frames): - prev_image = preprocess_image(current_image, step_size, 512) - current_image = prev_image - current_image, mask_image = preprocess_mask_image(current_image) - current_image = pipe( - prompt=[prompt], - negative_prompt=[negative_prompt], - image=current_image, - mask_image=mask_image, - num_inference_steps=num_inference_steps, - ).images[0] - current_image.paste(prev_image, mask=prev_image) - all_frames.append(current_image) - - save_path = "output.mp4" - write_video(save_path, all_frames, fps=30) - return save_path - - def app(): - with gr.Blocks(): - with gr.Row(): - with gr.Column(): - text2image_out_model_path = gr.Dropdown( - choices=stable_paint_model_list, value=stable_paint_model_list[0], label="Text-Image Model Id" - ) - - text2image_out_prompt = gr.Textbox(lines=2, value=stable_paint_prompt_list[0], label="Prompt") - - text2image_out_negative_prompt = gr.Textbox( - lines=1, value=stable_paint_negative_prompt_list[0], label="Negative Prompt" - ) - - with gr.Row(): - with gr.Column(): - text2image_out_guidance_scale = gr.Slider( - minimum=0.1, maximum=15, step=0.1, value=7.5, label="Guidance Scale" - ) - - text2image_out_num_inference_step = gr.Slider( - minimum=1, maximum=100, step=1, value=50, label="Num Inference Step" - ) - with gr.Row(): - with gr.Column(): - text2image_out_step_size = gr.Slider( - minimum=1, maximum=100, step=1, value=10, label="Step Size" - ) - - text2image_out_num_frames = gr.Slider( - minimum=1, maximum=100, step=1, value=10, label="Frames" - ) - - text2image_out_predict = gr.Button(value="Generator") - - with gr.Column(): - output_image = gr.Video(label="Output") - - text2image_out_predict.click( - fn=StableDiffusionZoomOut().generate_video, - inputs=[ - text2image_out_model_path, - text2image_out_prompt, - text2image_out_negative_prompt, - text2image_out_guidance_scale, - text2image_out_num_inference_step, - text2image_out_step_size, - text2image_out_num_frames, - ], - outputs=output_image, - ) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/locations/_sysconfig.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/locations/_sysconfig.py deleted file mode 100644 index 97aef1f1ac237e6ef97b1a1d026818d7b8ab9be9..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/locations/_sysconfig.py +++ /dev/null @@ -1,213 +0,0 @@ -import logging -import os -import sys -import sysconfig -import typing - -from pip._internal.exceptions import InvalidSchemeCombination, UserInstallationInvalid -from pip._internal.models.scheme import SCHEME_KEYS, Scheme -from pip._internal.utils.virtualenv import running_under_virtualenv - -from .base import change_root, get_major_minor_version, is_osx_framework - -logger = logging.getLogger(__name__) - - -# Notes on _infer_* functions. -# Unfortunately ``get_default_scheme()`` didn't exist before 3.10, so there's no -# way to ask things like "what is the '_prefix' scheme on this platform". These -# functions try to answer that with some heuristics while accounting for ad-hoc -# platforms not covered by CPython's default sysconfig implementation. If the -# ad-hoc implementation does not fully implement sysconfig, we'll fall back to -# a POSIX scheme. - -_AVAILABLE_SCHEMES = set(sysconfig.get_scheme_names()) - -_PREFERRED_SCHEME_API = getattr(sysconfig, "get_preferred_scheme", None) - - -def _should_use_osx_framework_prefix() -> bool: - """Check for Apple's ``osx_framework_library`` scheme. - - Python distributed by Apple's Command Line Tools has this special scheme - that's used when: - - * This is a framework build. - * We are installing into the system prefix. - - This does not account for ``pip install --prefix`` (also means we're not - installing to the system prefix), which should use ``posix_prefix``, but - logic here means ``_infer_prefix()`` outputs ``osx_framework_library``. But - since ``prefix`` is not available for ``sysconfig.get_default_scheme()``, - which is the stdlib replacement for ``_infer_prefix()``, presumably Apple - wouldn't be able to magically switch between ``osx_framework_library`` and - ``posix_prefix``. ``_infer_prefix()`` returning ``osx_framework_library`` - means its behavior is consistent whether we use the stdlib implementation - or our own, and we deal with this special case in ``get_scheme()`` instead. - """ - return ( - "osx_framework_library" in _AVAILABLE_SCHEMES - and not running_under_virtualenv() - and is_osx_framework() - ) - - -def _infer_prefix() -> str: - """Try to find a prefix scheme for the current platform. - - This tries: - - * A special ``osx_framework_library`` for Python distributed by Apple's - Command Line Tools, when not running in a virtual environment. - * Implementation + OS, used by PyPy on Windows (``pypy_nt``). - * Implementation without OS, used by PyPy on POSIX (``pypy``). - * OS + "prefix", used by CPython on POSIX (``posix_prefix``). - * Just the OS name, used by CPython on Windows (``nt``). - - If none of the above works, fall back to ``posix_prefix``. - """ - if _PREFERRED_SCHEME_API: - return _PREFERRED_SCHEME_API("prefix") - if _should_use_osx_framework_prefix(): - return "osx_framework_library" - implementation_suffixed = f"{sys.implementation.name}_{os.name}" - if implementation_suffixed in _AVAILABLE_SCHEMES: - return implementation_suffixed - if sys.implementation.name in _AVAILABLE_SCHEMES: - return sys.implementation.name - suffixed = f"{os.name}_prefix" - if suffixed in _AVAILABLE_SCHEMES: - return suffixed - if os.name in _AVAILABLE_SCHEMES: # On Windows, prefx is just called "nt". - return os.name - return "posix_prefix" - - -def _infer_user() -> str: - """Try to find a user scheme for the current platform.""" - if _PREFERRED_SCHEME_API: - return _PREFERRED_SCHEME_API("user") - if is_osx_framework() and not running_under_virtualenv(): - suffixed = "osx_framework_user" - else: - suffixed = f"{os.name}_user" - if suffixed in _AVAILABLE_SCHEMES: - return suffixed - if "posix_user" not in _AVAILABLE_SCHEMES: # User scheme unavailable. - raise UserInstallationInvalid() - return "posix_user" - - -def _infer_home() -> str: - """Try to find a home for the current platform.""" - if _PREFERRED_SCHEME_API: - return _PREFERRED_SCHEME_API("home") - suffixed = f"{os.name}_home" - if suffixed in _AVAILABLE_SCHEMES: - return suffixed - return "posix_home" - - -# Update these keys if the user sets a custom home. -_HOME_KEYS = [ - "installed_base", - "base", - "installed_platbase", - "platbase", - "prefix", - "exec_prefix", -] -if sysconfig.get_config_var("userbase") is not None: - _HOME_KEYS.append("userbase") - - -def get_scheme( - dist_name: str, - user: bool = False, - home: typing.Optional[str] = None, - root: typing.Optional[str] = None, - isolated: bool = False, - prefix: typing.Optional[str] = None, -) -> Scheme: - """ - Get the "scheme" corresponding to the input parameters. - - :param dist_name: the name of the package to retrieve the scheme for, used - in the headers scheme path - :param user: indicates to use the "user" scheme - :param home: indicates to use the "home" scheme - :param root: root under which other directories are re-based - :param isolated: ignored, but kept for distutils compatibility (where - this controls whether the user-site pydistutils.cfg is honored) - :param prefix: indicates to use the "prefix" scheme and provides the - base directory for the same - """ - if user and prefix: - raise InvalidSchemeCombination("--user", "--prefix") - if home and prefix: - raise InvalidSchemeCombination("--home", "--prefix") - - if home is not None: - scheme_name = _infer_home() - elif user: - scheme_name = _infer_user() - else: - scheme_name = _infer_prefix() - - # Special case: When installing into a custom prefix, use posix_prefix - # instead of osx_framework_library. See _should_use_osx_framework_prefix() - # docstring for details. - if prefix is not None and scheme_name == "osx_framework_library": - scheme_name = "posix_prefix" - - if home is not None: - variables = {k: home for k in _HOME_KEYS} - elif prefix is not None: - variables = {k: prefix for k in _HOME_KEYS} - else: - variables = {} - - paths = sysconfig.get_paths(scheme=scheme_name, vars=variables) - - # Logic here is very arbitrary, we're doing it for compatibility, don't ask. - # 1. Pip historically uses a special header path in virtual environments. - # 2. If the distribution name is not known, distutils uses 'UNKNOWN'. We - # only do the same when not running in a virtual environment because - # pip's historical header path logic (see point 1) did not do this. - if running_under_virtualenv(): - if user: - base = variables.get("userbase", sys.prefix) - else: - base = variables.get("base", sys.prefix) - python_xy = f"python{get_major_minor_version()}" - paths["include"] = os.path.join(base, "include", "site", python_xy) - elif not dist_name: - dist_name = "UNKNOWN" - - scheme = Scheme( - platlib=paths["platlib"], - purelib=paths["purelib"], - headers=os.path.join(paths["include"], dist_name), - scripts=paths["scripts"], - data=paths["data"], - ) - if root is not None: - for key in SCHEME_KEYS: - value = change_root(root, getattr(scheme, key)) - setattr(scheme, key, value) - return scheme - - -def get_bin_prefix() -> str: - # Forcing to use /usr/local/bin for standard macOS framework installs. - if sys.platform[:6] == "darwin" and sys.prefix[:16] == "/System/Library/": - return "/usr/local/bin" - return sysconfig.get_paths()["scripts"] - - -def get_purelib() -> str: - return sysconfig.get_paths()["purelib"] - - -def get_platlib() -> str: - return sysconfig.get_paths()["platlib"] diff --git a/spaces/Atualli/yoloxTeste/configs/yolox_l.py b/spaces/Atualli/yoloxTeste/configs/yolox_l.py deleted file mode 100644 index 50833ca38c51fe9ac5e327d7c1c0561fb62249aa..0000000000000000000000000000000000000000 --- a/spaces/Atualli/yoloxTeste/configs/yolox_l.py +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding:utf-8 -*- -# Copyright (c) Megvii, Inc. and its affiliates. - -import os - -from yolox.exp import Exp as MyExp - - -class Exp(MyExp): - def __init__(self): - super(Exp, self).__init__() - self.depth = 1.0 - self.width = 1.0 - self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0] diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py deleted file mode 100644 index 63c7a1a31b31dd89b82011effee26471faccacf5..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py +++ /dev/null @@ -1,350 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -""" -Note: -For your custom dataset, there is no need to hard-code metadata anywhere in the code. -For example, for COCO-format dataset, metadata will be obtained automatically -when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways -during loading. - -However, we hard-coded metadata for a few common dataset here. -The only goal is to allow users who don't have these dataset to use pre-trained models. -Users don't have to download a COCO json (which contains metadata), in order to visualize a -COCO model (with correct class names and colors). -""" - - -# All coco categories, together with their nice-looking visualization colors -# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json -COCO_CATEGORIES = [ - {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, - {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, - {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, - {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, - {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, - {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, - {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, - {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, - {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, - {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, - {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, - {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, - {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, - {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, - {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, - {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, - {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, - {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, - {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, - {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, - {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, - {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, - {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, - {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, - {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, - {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, - {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, - {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, - {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, - {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, - {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, - {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, - {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, - {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, - {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, - {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, - {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, - {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, - {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, - {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, - {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, - {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, - {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, - {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, - {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, - {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, - {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, - {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, - {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, - {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, - {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, - {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, - {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, - {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, - {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, - {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, - {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, - {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, - {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, - {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, - {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, - {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, - {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, - {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, - {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, - {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, - {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, - {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, - {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, - {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, - {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, - {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, - {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, - {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, - {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, - {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, - {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, - {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, - {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, - {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, - {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"}, - {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"}, - {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"}, - {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"}, - {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"}, - {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"}, - {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"}, - {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"}, - {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"}, - {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"}, - {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"}, - {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"}, - {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"}, - {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"}, - {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"}, - {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"}, - {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"}, - {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"}, - {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"}, - {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"}, - {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"}, - {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"}, - {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"}, - {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"}, - {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"}, - {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"}, - {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"}, - {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"}, - {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"}, - {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"}, - {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"}, - {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"}, - {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"}, - {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"}, - {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"}, - {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"}, - {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"}, - {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"}, - {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"}, - {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"}, - {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"}, - {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"}, - {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"}, - {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"}, - {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"}, - {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"}, - {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"}, - {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"}, - {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"}, - {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"}, - {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"}, - {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"}, - {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"}, -] - -# fmt: off -COCO_PERSON_KEYPOINT_NAMES = ( - "nose", - "left_eye", "right_eye", - "left_ear", "right_ear", - "left_shoulder", "right_shoulder", - "left_elbow", "right_elbow", - "left_wrist", "right_wrist", - "left_hip", "right_hip", - "left_knee", "right_knee", - "left_ankle", "right_ankle", -) -# fmt: on - -# Pairs of keypoints that should be exchanged under horizontal flipping -COCO_PERSON_KEYPOINT_FLIP_MAP = ( - ("left_eye", "right_eye"), - ("left_ear", "right_ear"), - ("left_shoulder", "right_shoulder"), - ("left_elbow", "right_elbow"), - ("left_wrist", "right_wrist"), - ("left_hip", "right_hip"), - ("left_knee", "right_knee"), - ("left_ankle", "right_ankle"), -) - -# rules for pairs of keypoints to draw a line between, and the line color to use. -KEYPOINT_CONNECTION_RULES = [ - # face - ("left_ear", "left_eye", (102, 204, 255)), - ("right_ear", "right_eye", (51, 153, 255)), - ("left_eye", "nose", (102, 0, 204)), - ("nose", "right_eye", (51, 102, 255)), - # upper-body - ("left_shoulder", "right_shoulder", (255, 128, 0)), - ("left_shoulder", "left_elbow", (153, 255, 204)), - ("right_shoulder", "right_elbow", (128, 229, 255)), - ("left_elbow", "left_wrist", (153, 255, 153)), - ("right_elbow", "right_wrist", (102, 255, 224)), - # lower-body - ("left_hip", "right_hip", (255, 102, 0)), - ("left_hip", "left_knee", (255, 255, 77)), - ("right_hip", "right_knee", (153, 255, 204)), - ("left_knee", "left_ankle", (191, 255, 128)), - ("right_knee", "right_ankle", (255, 195, 77)), -] - -# All Cityscapes categories, together with their nice-looking visualization colors -# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py # noqa -CITYSCAPES_CATEGORIES = [ - {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"}, - {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"}, - {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"}, - {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"}, - {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"}, - {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"}, - {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"}, - {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"}, - {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"}, - {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"}, - {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"}, - {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"}, - {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"}, - {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"}, - {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"}, - {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"}, - {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"}, - {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"}, - {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"}, -] - -# fmt: off -ADE20K_SEM_SEG_CATEGORIES = [ - "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa -] -# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore -# fmt: on - - -def _get_coco_instances_meta(): - thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1] - thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] - assert len(thing_ids) == 80, len(thing_ids) - # Mapping from the incontiguous COCO category id to an id in [0, 79] - thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} - thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] - ret = { - "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, - "thing_classes": thing_classes, - "thing_colors": thing_colors, - } - return ret - - -def _get_coco_panoptic_separated_meta(): - """ - Returns metadata for "separated" version of the panoptic segmentation dataset. - """ - stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0] - assert len(stuff_ids) == 53, len(stuff_ids) - - # For semantic segmentation, this mapping maps from contiguous stuff id - # (in [0, 53], used in models) to ids in the dataset (used for processing results) - # The id 0 is mapped to an extra category "thing". - stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)} - # When converting COCO panoptic annotations to semantic annotations - # We label the "thing" category to 0 - stuff_dataset_id_to_contiguous_id[0] = 0 - - # 54 names for COCO stuff categories (including "things") - stuff_classes = ["things"] + [ - k["name"].replace("-other", "").replace("-merged", "") - for k in COCO_CATEGORIES - if k["isthing"] == 0 - ] - - # NOTE: I randomly picked a color for things - stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0] - ret = { - "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, - "stuff_classes": stuff_classes, - "stuff_colors": stuff_colors, - } - ret.update(_get_coco_instances_meta()) - return ret - - -def _get_builtin_metadata(dataset_name): - if dataset_name == "coco": - return _get_coco_instances_meta() - if dataset_name == "coco_panoptic_separated": - return _get_coco_panoptic_separated_meta() - elif dataset_name == "coco_panoptic_standard": - meta = {} - # The following metadata maps contiguous id from [0, #thing categories + - # #stuff categories) to their names and colors. We have to replica of the - # same name and color under "thing_*" and "stuff_*" because the current - # visualization function in D2 handles thing and class classes differently - # due to some heuristic used in Panoptic FPN. We keep the same naming to - # enable reusing existing visualization functions. - thing_classes = [k["name"] for k in COCO_CATEGORIES] - thing_colors = [k["color"] for k in COCO_CATEGORIES] - stuff_classes = [k["name"] for k in COCO_CATEGORIES] - stuff_colors = [k["color"] for k in COCO_CATEGORIES] - - meta["thing_classes"] = thing_classes - meta["thing_colors"] = thing_colors - meta["stuff_classes"] = stuff_classes - meta["stuff_colors"] = stuff_colors - - # Convert category id for training: - # category id: like semantic segmentation, it is the class id for each - # pixel. Since there are some classes not used in evaluation, the category - # id is not always contiguous and thus we have two set of category ids: - # - original category id: category id in the original dataset, mainly - # used for evaluation. - # - contiguous category id: [0, #classes), in order to train the linear - # softmax classifier. - thing_dataset_id_to_contiguous_id = {} - stuff_dataset_id_to_contiguous_id = {} - - for i, cat in enumerate(COCO_CATEGORIES): - if cat["isthing"]: - thing_dataset_id_to_contiguous_id[cat["id"]] = i - else: - stuff_dataset_id_to_contiguous_id[cat["id"]] = i - - meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id - meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id - - return meta - elif dataset_name == "coco_person": - return { - "thing_classes": ["person"], - "keypoint_names": COCO_PERSON_KEYPOINT_NAMES, - "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP, - "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES, - } - elif dataset_name == "cityscapes": - # fmt: off - CITYSCAPES_THING_CLASSES = [ - "person", "rider", "car", "truck", - "bus", "train", "motorcycle", "bicycle", - ] - CITYSCAPES_STUFF_CLASSES = [ - "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", - "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", - "truck", "bus", "train", "motorcycle", "bicycle", - ] - # fmt: on - return { - "thing_classes": CITYSCAPES_THING_CLASSES, - "stuff_classes": CITYSCAPES_STUFF_CLASSES, - } - raise KeyError("No built-in metadata for dataset {}".format(dataset_name)) diff --git a/spaces/Banbri/zcvzcv/README.md b/spaces/Banbri/zcvzcv/README.md deleted file mode 100644 index 076781badb5847aad8365e3471eeefdd9118b722..0000000000000000000000000000000000000000 --- a/spaces/Banbri/zcvzcv/README.md +++ /dev/null @@ -1,158 +0,0 @@ ---- -title: cbv -colorFrom: blue -colorTo: yellow -sdk: docker -pinned: true -app_port: 3000 ---- - -# AI Comic Factory - -*(note: the website "aicomicfactory.com" is not affiliated with the AI Comic Factory project, nor it is created or maintained by the AI Comic Factory team. If you see their website has an issue, please contact them directly)* - -## Running the project at home - -First, I would like to highlight that everything is open-source (see [here](https://huggingface.co/spaces/jbilcke-hf/ai-comic-factory/tree/main), [here](https://huggingface.co/spaces/jbilcke-hf/VideoChain-API/tree/main), [here](https://huggingface.co/spaces/hysts/SD-XL/tree/main), [here](https://github.com/huggingface/text-generation-inference)). - -However the project isn't a monolithic Space that can be duplicated and ran immediately: -it requires various components to run for the frontend, backend, LLM, SDXL etc. - -If you try to duplicate the project, open the `.env` you will see it requires some variables. - -Provider config: -- `LLM_ENGINE`: can be one of: "INFERENCE_API", "INFERENCE_ENDPOINT", "OPENAI" -- `RENDERING_ENGINE`: can be one of: "INFERENCE_API", "INFERENCE_ENDPOINT", "REPLICATE", "VIDEOCHAIN" for now, unless you code your custom solution - -Auth config: -- `AUTH_HF_API_TOKEN`: only if you decide to use OpenAI for the LLM engine necessary if you decide to use an inference api model or a custom inference endpoint -- `AUTH_OPENAI_TOKEN`: only if you decide to use OpenAI for the LLM engine -- `AITH_VIDEOCHAIN_API_TOKEN`: secret token to access the VideoChain API server -- `AUTH_REPLICATE_API_TOKEN`: in case you want to use Replicate.com - -Rendering config: -- `RENDERING_HF_INFERENCE_ENDPOINT_URL`: necessary if you decide to use a custom inference endpoint -- `RENDERING_REPLICATE_API_MODEL_VERSION`: url to the VideoChain API server -- `RENDERING_HF_INFERENCE_ENDPOINT_URL`: optional, default to nothing -- `RENDERING_HF_INFERENCE_API_BASE_MODEL`: optional, defaults to "stabilityai/stable-diffusion-xl-base-1.0" -- `RENDERING_HF_INFERENCE_API_REFINER_MODEL`: optional, defaults to "stabilityai/stable-diffusion-xl-refiner-1.0" -- `RENDERING_REPLICATE_API_MODEL`: optional, defaults to "stabilityai/sdxl" -- `RENDERING_REPLICATE_API_MODEL_VERSION`: optional, in case you want to change the version - -Language model config: -- `LLM_HF_INFERENCE_ENDPOINT_URL`: "https://llama-v2-70b-chat.ngrok.io" -- `LLM_HF_INFERENCE_API_MODEL`: "codellama/CodeLlama-7b-hf" - -In addition, there are some community sharing variables that you can just ignore. -Those variables are not required to run the AI Comic Factory on your own website or computer -(they are meant to create a connection with the Hugging Face community, -and thus only make sense for official Hugging Face apps): -- `NEXT_PUBLIC_ENABLE_COMMUNITY_SHARING`: you don't need this -- `COMMUNITY_API_URL`: you don't need this -- `COMMUNITY_API_TOKEN`: you don't need this -- `COMMUNITY_API_ID`: you don't need this - -Please read the `.env` default config file for more informations. -To customise a variable locally, you should create a `.env.local` -(do not commit this file as it will contain your secrets). - --> If you intend to run it with local, cloud-hosted and/or proprietary models **you are going to need to code 👨‍💻**. - -## The LLM API (Large Language Model) - -Currently the AI Comic Factory uses [Llama-2 70b](https://huggingface.co/blog/llama2) through an [Inference Endpoint](https://huggingface.co/docs/inference-endpoints/index). - -You have three options: - -### Option 1: Use an Inference API model - -This is a new option added recently, where you can use one of the models from the Hugging Face Hub. By default we suggest to use CodeLlama 34b as it will provide better results than the 7b model. - -To activate it, create a `.env.local` configuration file: - -```bash -LLM_ENGINE="INFERENCE_API" - -HF_API_TOKEN="Your Hugging Face token" - -# codellama/CodeLlama-7b-hf" is used by default, but you can change this -# note: You should use a model able to generate JSON responses, -# so it is storngly suggested to use at least the 34b model -HF_INFERENCE_API_MODEL="codellama/CodeLlama-7b-hf" -``` - -### Option 2: Use an Inference Endpoint URL - -If you would like to run the AI Comic Factory on a private LLM running on the Hugging Face Inference Endpoint service, create a `.env.local` configuration file: - -```bash -LLM_ENGINE="INFERENCE_ENDPOINT" - -HF_API_TOKEN="Your Hugging Face token" - -HF_INFERENCE_ENDPOINT_URL="path to your inference endpoint url" -``` - -To run this kind of LLM locally, you can use [TGI](https://github.com/huggingface/text-generation-inference) (Please read [this post](https://github.com/huggingface/text-generation-inference/issues/726) for more information about the licensing). - -### Option 3: Use an OpenAI API Key - -This is a new option added recently, where you can use OpenAI API with an OpenAI API Key. - -To activate it, create a `.env.local` configuration file: - -```bash -LLM_ENGINE="OPENAI" - -# default openai api base url is: https://api.openai.com/v1 -LLM_OPENAI_API_BASE_URL="Your OpenAI API Base URL" - -LLM_OPENAI_API_MODEL="gpt-3.5-turbo" - -AUTH_OPENAI_API_KEY="Your OpenAI API Key" -``` - -### Option 4: Fork and modify the code to use a different LLM system - -Another option could be to disable the LLM completely and replace it with another LLM protocol and/or provider (eg. Claude, Replicate), or a human-generated story instead (by returning mock or static data). - -### Notes - -It is possible that I modify the AI Comic Factory to make it easier in the future (eg. add support for Claude or Replicate) - -## The Rendering API - -This API is used to generate the panel images. This is an API I created for my various projects at Hugging Face. - -I haven't written documentation for it yet, but basically it is "just a wrapper ™" around other existing APIs: - -- The [hysts/SD-XL](https://huggingface.co/spaces/hysts/SD-XL?duplicate=true) Space by [@hysts](https://huggingface.co/hysts) -- And other APIs for making videos, adding audio etc.. but you won't need them for the AI Comic Factory - -### Option 1: Deploy VideoChain yourself - -You will have to [clone](https://huggingface.co/spaces/jbilcke-hf/VideoChain-API?duplicate=true) the [source-code](https://huggingface.co/spaces/jbilcke-hf/VideoChain-API/tree/main) - -Unfortunately, I haven't had the time to write the documentation for VideoChain yet. -(When I do I will update this document to point to the VideoChain's README) - - -### Option 2: Use Replicate - -To use Replicate, create a `.env.local` configuration file: - -```bash -RENDERING_ENGINE="REPLICATE" - -RENDERING_REPLICATE_API_MODEL="stabilityai/sdxl" - -RENDERING_REPLICATE_API_MODEL_VERSION="da77bc59ee60423279fd632efb4795ab731d9e3ca9705ef3341091fb989b7eaf" - -AUTH_REPLICATE_API_TOKEN="Your Replicate token" -``` - -### Option 3: Use another SDXL API - -If you fork the project you will be able to modify the code to use the Stable Diffusion technology of your choice (local, open-source, proprietary, your custom HF Space etc). - -It would even be something else, such as Dall-E. \ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descarga Gratuita De Fuego Mx Mod Apk 50 Mb.md b/spaces/Benson/text-generation/Examples/Descarga Gratuita De Fuego Mx Mod Apk 50 Mb.md deleted file mode 100644 index 1e24a73948db8028780b9c32acfc753ae69599b7..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descarga Gratuita De Fuego Mx Mod Apk 50 Mb.md +++ /dev/null @@ -1,69 +0,0 @@ - -

Free Fire Max: Una versión Premium de Free Fire con gráficos y características mejoradas

-

Si usted es un fan de Free Fire, el popular juego de batalla móvil royale, es posible que haya oído hablar de Free Fire Max, una versión mejorada del juego que ofrece mejores gráficos, animaciones y jugabilidad. Pero ¿qué es exactamente Free Fire Max y cómo se puede descargar en su dispositivo? ¿Y cuáles son los beneficios de usar un archivo apk mod que afirma darle recursos ilimitados y acceso a todo en el juego? En este artículo, responderemos estas preguntas y más.

-

descarga gratuita de fuego máx mod apk 50 mb


Download 🔗 https://bltlly.com/2v6J0Y



-

¿Qué es Free Fire Max?

-

Free Fire Max es una aplicación independiente que proporciona el mismo juego Free Fire que millones de jugadores aman, pero con especificaciones mejoradas. Está diseñado para ofrecer una experiencia premium e inmersiva en un entorno battle royale. Puedes disfrutar de una variedad de emocionantes modos de juego con todos los jugadores de Free Fire a través de la exclusiva tecnología Firelink. También puedes experimentar el combate como nunca antes con resoluciones Ultra HD y efectos impresionantes.

-

Free Fire Max es diferente del juego original Free Fire de varias maneras. Algunas de las diferencias incluyen:

-
    -
  • Mejor calidad gráfica: Free Fire Max tiene gráficos HD, efectos especiales mejorados y un juego más fluido. También tiene texturas Ultra HD, diseños de mapas realistas, efectos de sonido inmersivos y nuevas animaciones de armas.
    • -
  • Nuevas características: Free Fire Max tiene características exclusivas que no están disponibles en el juego original, como un vestíbulo de 360 grados donde puede mostrar sus artículos, un modo Craftland donde puede crear y jugar en sus propios mapas personalizados y un nuevo mapa de Bermuda Max con áreas renovadas.
    • -
  • Tecnología Firelink: Con Firelink, puede iniciar sesión en su cuenta de Free Fire existente para jugar Free Fire Max sin ningún problema. El progreso y los elementos se sincronizan en ambas aplicaciones en tiempo real. También puedes jugar con los jugadores de Free Fire y Free Fire Max juntos, sin importar la aplicación que usen.
    • -
- -

Cómo descargar gratis Fire Max mod apk 50 mb

-

Si quieres descargar Free Fire Max en tu dispositivo, puedes hacerlo siguiendo estos pasos:

-
    -
  1. Ir a [este enlace]( 1 ) para descargar el archivo apk mod para Free Fire Max. El tamaño del archivo es de alrededor de 50 MB.
    2. -
  2. Una vez completada la descarga, busque e instale el archivo en su dispositivo. Es posible que necesite habilitar la instalación desde fuentes desconocidas en su configuración.
    3. -
  3. Abre la aplicación y disfruta jugando Free Fire Max con recursos y funciones ilimitadas.
    4. -
-

Sin embargo, antes de descargar y utilizar el archivo apk mod, usted debe ser consciente de algunos riesgos y problemas legales. Los archivos apk mod son versiones modificadas de la aplicación original que omiten las medidas de seguridad y alteran los datos del juego. No están autorizados por Garena, el desarrollador de Free Fire, y pueden contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. También pueden violar los términos de servicio y la política de privacidad del juego, y resultar en que su cuenta sea prohibida o suspendida. Por lo tanto, debe utilizar el archivo apk mod a su propio riesgo y discreción, y solo de fuentes de confianza.

-

¿Cuáles son los beneficios de usar Free Fire Max mod apk 50 mb

-

Si decide utilizar el archivo apk mod para Free Fire Max, puede disfrutar de algunos beneficios que no están disponibles en la aplicación oficial. Algunos de estos beneficios son:

- - -Beneficio -Descripción - - -Diamantes y monedas ilimitadas -Puedes obtener divisas ilimitadas en el juego que puedes usar para comprar lo que quieras en el juego, como personajes, armas, pieles, objetos y más. No tienes que gastar dinero real o completar tareas para ganarlas. - - -Acceso a todos los caracteres, armas, skins y elementos - - - -Mod menú con varios trucos y hacks -Puedes acceder a un menú mod que te permite activar o desactivar varios trucos y hacks en el juego, como aimbot, wallhack, speed hack, headshot automático, salud ilimitada, munición ilimitada y más. Puedes ganar ventaja sobre tus enemigos y ganar cada partido fácilmente. - - -No se requieren anuncios ni root -Puedes jugar el juego sin anuncios molestos o ventanas emergentes que puedan interrumpir tu juego o consumir tus datos. Tampoco necesitas rootear tu dispositivo para usar el archivo apk mod. - - -

Conclusión

-

Free Fire Max es una versión premium de Free Fire que ofrece gráficos mejorados y características para una experiencia más inmersiva y emocionante battle royale. Puede descargarlo en su dispositivo siguiendo los pasos anteriores, o puede utilizar un archivo apk mod que le da recursos ilimitados y acceso a todo en el juego. Sin embargo, usted debe ser cuidadoso y responsable al usar archivos apk mod, ya que pueden tener algunos riesgos y problemas legales. Si estás interesado en probar Free Fire Max mod apk 50 mb, puedes descargarlo desde [este enlace] y disfrutar del juego con todos los beneficios.

-

-

¿Has probado Free Fire Max mod apk 50 mb? ¿Qué te parece? Déjanos saber en los comentarios de abajo!

-

Preguntas frecuentes

-

Aquí hay algunas preguntas frecuentes sobre Free Fire Max mod apk 50 mb:

-
    -
  • ¿Es seguro usar Free Fire Max mod apk 50 mb?
    • -

    Free Fire Max mod apk 50 mb no es una aplicación oficial de Garena, y puede contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. También puede violar los términos de servicio y la política de privacidad del juego, y resultar en que su cuenta sea prohibida o suspendida. Por lo tanto, debe usarlo bajo su propio riesgo y discreción, y solo de fuentes confiables.

    -
  • ¿Cómo puedo actualizar Free Fire Max mod apk 50 mb?
    • - -
  • ¿Puedo jugar con mis amigos que usan Free Fire o Free Fire Max?
    • -

    Sí, puedes jugar con tus amigos que usan Free Fire o Free Fire Max a través de la tecnología Firelink. Solo tiene que iniciar sesión en su cuenta de Free Fire existente para jugar Free Fire Max con ellos. El progreso y los elementos se sincronizan en ambas aplicaciones en tiempo real. También puedes jugar con los jugadores de Free Fire y Free Fire Max juntos, sin importar la aplicación que usen.

    -
  • ¿Me prohibirán por usar Free Fire Max mod apk 50 mb?
    • -

    Existe la posibilidad de que usted puede conseguir prohibido para el uso de Free Fire Max mod apk 50 mb, ya que no es una aplicación autorizada por Garena y altera los datos del juego. Garena tiene un estricto sistema anti-trucos que detecta y castiga a cualquier jugador que use trucos o hacks en el juego. Si usted es sorprendido usando Free Fire Max mod apk 50 mb, puede enfrentar consecuencias tales como la suspensión de la cuenta, eliminación de la cuenta, o acciones legales. Por lo tanto, usted debe ser cuidadoso y responsable al usar Free Fire Max mod apk 50 mb, y evitar usarlo en partidos clasificados o competitivos.

    -
  • ¿Cuáles son algunas alternativas a Free Fire Max mod apk 50 mb?
    • -

    Si usted está buscando algunas alternativas a Free Fire Max mod apk 50 mb, puede probar estas opciones:

    -
      -
    • Descargue la aplicación oficial Free Fire Max desde la Google Play Store o la App Store. Usted puede disfrutar de la misma jugabilidad y características como Free Fire Max mod apk 50 mb, pero sin ningún riesgo o problemas legales. También puedes apoyar a los desarrolladores y al juego comprando dinero y objetos en el juego legítimamente.
      • -
    • Utilice una aplicación VPN para cambiar su ubicación y acceder a los servidores Free Fire Max en otras regiones. Puedes jugar el juego con jugadores de diferentes países y experimentar diferentes modos de juego y eventos. También puede omitir cualquier restricción geográfica o problema de red que pueda impedirle jugar el juego.
      • - -

    64aa2da5cf
    -
    -
    \ No newline at end of file diff --git a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/models/vqgan.py b/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/models/vqgan.py deleted file mode 100644 index 121d01fd2e1641d409aa90635c367a7a1bb0b4d4..0000000000000000000000000000000000000000 --- a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/models/vqgan.py +++ /dev/null @@ -1,363 +0,0 @@ -import torch -import torch.nn.functional as F -import pytorch_lightning as pl - -from main import instantiate_from_config - -from taming.modules.diffusionmodules.model import Encoder, Decoder -from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer -from taming.modules.vqvae.quantize import GumbelQuantize - - -class VQModel(pl.LightningModule): - def __init__(self, - ddconfig, - lossconfig, - n_embed, - embed_dim, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None, - monitor=None, - remap=None, - sane_index_shape=False, # tell vector quantizer to return indices as bhw - ): - super().__init__() - self.image_key = image_key - self.encoder = Encoder(**ddconfig) - self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) - self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, - remap=remap, sane_index_shape=sane_index_shape) - self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - self.image_key = image_key - if colorize_nlabels is not None: - assert type(colorize_nlabels)==int - self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) - if monitor is not None: - self.monitor = monitor - - def init_from_ckpt(self, path, ignore_keys=list()): - sd = torch.load(path, map_location="cpu")["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - self.load_state_dict(sd, strict=False) - print(f"Restored from {path}") - - def encode(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - quant, emb_loss, info = self.quantize(h) - return quant, emb_loss, info - - def decode(self, quant): - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - def decode_code(self, code_b): - quant_b = self.quantize.embed_code(code_b) - dec = self.decode(quant_b) - return dec - - def forward(self, input): - quant, diff, _ = self.encode(input) - dec = self.decode(quant) - return dec, diff - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format) - return x.float() - - def training_step(self, batch, batch_idx, optimizer_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - - if optimizer_idx == 0: - # autoencode - aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - - self.log("train/aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return aeloss - - if optimizer_idx == 1: - # discriminator - discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - self.log("train/discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) - self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return discloss - - def validation_step(self, batch, batch_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, - last_layer=self.get_last_layer(), split="val") - - discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, - last_layer=self.get_last_layer(), split="val") - rec_loss = log_dict_ae["val/rec_loss"] - self.log("val/rec_loss", rec_loss, - prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True) - self.log("val/aeloss", aeloss, - prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True) - self.log_dict(log_dict_ae) - self.log_dict(log_dict_disc) - return self.log_dict - - def configure_optimizers(self): - lr = self.learning_rate - opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ - list(self.decoder.parameters())+ - list(self.quantize.parameters())+ - list(self.quant_conv.parameters())+ - list(self.post_quant_conv.parameters()), - lr=lr, betas=(0.5, 0.9)) - opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), - lr=lr, betas=(0.5, 0.9)) - return [opt_ae, opt_disc], [] - - def get_last_layer(self): - return self.decoder.conv_out.weight - - def log_images(self, batch, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - xrec, _ = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - log["inputs"] = x - log["reconstructions"] = xrec - return log - - def to_rgb(self, x): - assert self.image_key == "segmentation" - if not hasattr(self, "colorize"): - self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) - x = F.conv2d(x, weight=self.colorize) - x = 2.*(x-x.min())/(x.max()-x.min()) - 1. - return x - - -class VQSegmentationModel(VQModel): - def __init__(self, n_labels, *args, **kwargs): - super().__init__(*args, **kwargs) - self.register_buffer("colorize", torch.randn(3, n_labels, 1, 1)) - - def configure_optimizers(self): - lr = self.learning_rate - opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ - list(self.decoder.parameters())+ - list(self.quantize.parameters())+ - list(self.quant_conv.parameters())+ - list(self.post_quant_conv.parameters()), - lr=lr, betas=(0.5, 0.9)) - return opt_ae - - def training_step(self, batch, batch_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train") - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return aeloss - - def validation_step(self, batch, batch_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="val") - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - total_loss = log_dict_ae["val/total_loss"] - self.log("val/total_loss", total_loss, - prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True) - return aeloss - - @torch.no_grad() - def log_images(self, batch, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - xrec, _ = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - # convert logits to indices - xrec = torch.argmax(xrec, dim=1, keepdim=True) - xrec = F.one_hot(xrec, num_classes=x.shape[1]) - xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float() - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - log["inputs"] = x - log["reconstructions"] = xrec - return log - - -class VQNoDiscModel(VQModel): - def __init__(self, - ddconfig, - lossconfig, - n_embed, - embed_dim, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None - ): - super().__init__(ddconfig=ddconfig, lossconfig=lossconfig, n_embed=n_embed, embed_dim=embed_dim, - ckpt_path=ckpt_path, ignore_keys=ignore_keys, image_key=image_key, - colorize_nlabels=colorize_nlabels) - - def training_step(self, batch, batch_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - # autoencode - aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="train") - output = pl.TrainResult(minimize=aeloss) - output.log("train/aeloss", aeloss, - prog_bar=True, logger=True, on_step=True, on_epoch=True) - output.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return output - - def validation_step(self, batch, batch_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="val") - rec_loss = log_dict_ae["val/rec_loss"] - output = pl.EvalResult(checkpoint_on=rec_loss) - output.log("val/rec_loss", rec_loss, - prog_bar=True, logger=True, on_step=True, on_epoch=True) - output.log("val/aeloss", aeloss, - prog_bar=True, logger=True, on_step=True, on_epoch=True) - output.log_dict(log_dict_ae) - - return output - - def configure_optimizers(self): - optimizer = torch.optim.Adam(list(self.encoder.parameters())+ - list(self.decoder.parameters())+ - list(self.quantize.parameters())+ - list(self.quant_conv.parameters())+ - list(self.post_quant_conv.parameters()), - lr=self.learning_rate, betas=(0.5, 0.9)) - return optimizer - - -class GumbelVQ(VQModel): - def __init__(self, - ddconfig, - lossconfig, - n_embed, - embed_dim, - temperature_scheduler_config, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None, - monitor=None, - kl_weight=1e-8, - remap=None, - ): - - z_channels = ddconfig["z_channels"] - super().__init__(ddconfig, - lossconfig, - n_embed, - embed_dim, - ckpt_path=None, - ignore_keys=ignore_keys, - image_key=image_key, - colorize_nlabels=colorize_nlabels, - monitor=monitor, - ) - - self.loss.n_classes = n_embed - self.vocab_size = n_embed - - self.quantize = GumbelQuantize(z_channels, embed_dim, - n_embed=n_embed, - kl_weight=kl_weight, temp_init=1.0, - remap=remap) - - self.temperature_scheduler = instantiate_from_config(temperature_scheduler_config) # annealing of temp - - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - - def temperature_scheduling(self): - self.quantize.temperature = self.temperature_scheduler(self.global_step) - - def encode_to_prequant(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - return h - - def decode_code(self, code_b): - raise NotImplementedError - - def training_step(self, batch, batch_idx, optimizer_idx): - self.temperature_scheduling() - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x) - - if optimizer_idx == 0: - # autoencode - aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - self.log("temperature", self.quantize.temperature, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return aeloss - - if optimizer_idx == 1: - # discriminator - discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return discloss - - def validation_step(self, batch, batch_idx): - x = self.get_input(batch, self.image_key) - xrec, qloss = self(x, return_pred_indices=True) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, - last_layer=self.get_last_layer(), split="val") - - discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, - last_layer=self.get_last_layer(), split="val") - rec_loss = log_dict_ae["val/rec_loss"] - self.log("val/rec_loss", rec_loss, - prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) - self.log("val/aeloss", aeloss, - prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) - self.log_dict(log_dict_ae) - self.log_dict(log_dict_disc) - return self.log_dict - - def log_images(self, batch, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - # encode - h = self.encoder(x) - h = self.quant_conv(h) - quant, _, _ = self.quantize(h) - # decode - x_rec = self.decode(quant) - log["inputs"] = x - log["reconstructions"] = x_rec - return log diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/special.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/special.py deleted file mode 100644 index c14a089b336cf62b63f3f949a278d70e97a32f16..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/special.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Special cased retries. - -These are additional retry cases we still have to handle from the legacy -retry handler. They don't make sense as part of the standard mode retry -module. Ideally we should be able to remove this module. - -""" -import logging -from binascii import crc32 - -from botocore.retries.base import BaseRetryableChecker - -logger = logging.getLogger(__name__) - - -# TODO: This is an ideal candidate for the retryable trait once that's -# available. -class RetryIDPCommunicationError(BaseRetryableChecker): - - _SERVICE_NAME = 'sts' - - def is_retryable(self, context): - service_name = context.operation_model.service_model.service_name - if service_name != self._SERVICE_NAME: - return False - error_code = context.get_error_code() - return error_code == 'IDPCommunicationError' - - -class RetryDDBChecksumError(BaseRetryableChecker): - - _CHECKSUM_HEADER = 'x-amz-crc32' - _SERVICE_NAME = 'dynamodb' - - def is_retryable(self, context): - service_name = context.operation_model.service_model.service_name - if service_name != self._SERVICE_NAME: - return False - if context.http_response is None: - return False - checksum = context.http_response.headers.get(self._CHECKSUM_HEADER) - if checksum is None: - return False - actual_crc32 = crc32(context.http_response.content) & 0xFFFFFFFF - if actual_crc32 != int(checksum): - logger.debug( - "DynamoDB crc32 checksum does not match, " - "expected: %s, actual: %s", - checksum, - actual_crc32, - ) - return True diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/jmespath/exceptions.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/jmespath/exceptions.py deleted file mode 100644 index 0156015918b91eac9b675bdf92489af894da9103..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/jmespath/exceptions.py +++ /dev/null @@ -1,122 +0,0 @@ -from jmespath.compat import with_str_method - - -class JMESPathError(ValueError): - pass - - -@with_str_method -class ParseError(JMESPathError): - _ERROR_MESSAGE = 'Invalid jmespath expression' - def __init__(self, lex_position, token_value, token_type, - msg=_ERROR_MESSAGE): - super(ParseError, self).__init__(lex_position, token_value, token_type) - self.lex_position = lex_position - self.token_value = token_value - self.token_type = token_type.upper() - self.msg = msg - # Whatever catches the ParseError can fill in the full expression - self.expression = None - - def __str__(self): - # self.lex_position +1 to account for the starting double quote char. - underline = ' ' * (self.lex_position + 1) + '^' - return ( - '%s: Parse error at column %s, ' - 'token "%s" (%s), for expression:\n"%s"\n%s' % ( - self.msg, self.lex_position, self.token_value, self.token_type, - self.expression, underline)) - - -@with_str_method -class IncompleteExpressionError(ParseError): - def set_expression(self, expression): - self.expression = expression - self.lex_position = len(expression) - self.token_type = None - self.token_value = None - - def __str__(self): - # self.lex_position +1 to account for the starting double quote char. - underline = ' ' * (self.lex_position + 1) + '^' - return ( - 'Invalid jmespath expression: Incomplete expression:\n' - '"%s"\n%s' % (self.expression, underline)) - - -@with_str_method -class LexerError(ParseError): - def __init__(self, lexer_position, lexer_value, message, expression=None): - self.lexer_position = lexer_position - self.lexer_value = lexer_value - self.message = message - super(LexerError, self).__init__(lexer_position, - lexer_value, - message) - # Whatever catches LexerError can set this. - self.expression = expression - - def __str__(self): - underline = ' ' * self.lexer_position + '^' - return 'Bad jmespath expression: %s:\n%s\n%s' % ( - self.message, self.expression, underline) - - -@with_str_method -class ArityError(ParseError): - def __init__(self, expected, actual, name): - self.expected_arity = expected - self.actual_arity = actual - self.function_name = name - self.expression = None - - def __str__(self): - return ("Expected %s %s for function %s(), " - "received %s" % ( - self.expected_arity, - self._pluralize('argument', self.expected_arity), - self.function_name, - self.actual_arity)) - - def _pluralize(self, word, count): - if count == 1: - return word - else: - return word + 's' - - -@with_str_method -class VariadictArityError(ArityError): - def __str__(self): - return ("Expected at least %s %s for function %s(), " - "received %s" % ( - self.expected_arity, - self._pluralize('argument', self.expected_arity), - self.function_name, - self.actual_arity)) - - -@with_str_method -class JMESPathTypeError(JMESPathError): - def __init__(self, function_name, current_value, actual_type, - expected_types): - self.function_name = function_name - self.current_value = current_value - self.actual_type = actual_type - self.expected_types = expected_types - - def __str__(self): - return ('In function %s(), invalid type for value: %s, ' - 'expected one of: %s, received: "%s"' % ( - self.function_name, self.current_value, - self.expected_types, self.actual_type)) - - -class EmptyExpressionError(JMESPathError): - def __init__(self): - super(EmptyExpressionError, self).__init__( - "Invalid JMESPath expression: cannot be empty.") - - -class UnknownFunctionError(JMESPathError): - pass diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/more_itertools/more.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/more_itertools/more.py deleted file mode 100644 index e6fca4d47f661ff16fdc8c2bb7ae5b86c7f347b2..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/more_itertools/more.py +++ /dev/null @@ -1,3824 +0,0 @@ -import warnings - -from collections import Counter, defaultdict, deque, abc -from collections.abc import Sequence -from functools import partial, reduce, wraps -from heapq import merge, heapify, heapreplace, heappop -from itertools import ( - chain, - compress, - count, - cycle, - dropwhile, - groupby, - islice, - repeat, - starmap, - takewhile, - tee, - zip_longest, -) -from math import exp, factorial, floor, log -from queue import Empty, Queue -from random import random, randrange, uniform -from operator import itemgetter, mul, sub, gt, lt -from sys import hexversion, maxsize -from time import monotonic - -from .recipes import ( - consume, - flatten, - pairwise, - powerset, - take, - unique_everseen, -) - -__all__ = [ - 'AbortThread', - 'adjacent', - 'always_iterable', - 'always_reversible', - 'bucket', - 'callback_iter', - 'chunked', - 'circular_shifts', - 'collapse', - 'collate', - 'consecutive_groups', - 'consumer', - 'countable', - 'count_cycle', - 'mark_ends', - 'difference', - 'distinct_combinations', - 'distinct_permutations', - 'distribute', - 'divide', - 'exactly_n', - 'filter_except', - 'first', - 'groupby_transform', - 'ilen', - 'interleave_longest', - 'interleave', - 'intersperse', - 'islice_extended', - 'iterate', - 'ichunked', - 'is_sorted', - 'last', - 'locate', - 'lstrip', - 'make_decorator', - 'map_except', - 'map_reduce', - 'nth_or_last', - 'nth_permutation', - 'nth_product', - 'numeric_range', - 'one', - 'only', - 'padded', - 'partitions', - 'set_partitions', - 'peekable', - 'repeat_last', - 'replace', - 'rlocate', - 'rstrip', - 'run_length', - 'sample', - 'seekable', - 'SequenceView', - 'side_effect', - 'sliced', - 'sort_together', - 'split_at', - 'split_after', - 'split_before', - 'split_when', - 'split_into', - 'spy', - 'stagger', - 'strip', - 'substrings', - 'substrings_indexes', - 'time_limited', - 'unique_to_each', - 'unzip', - 'windowed', - 'with_iter', - 'UnequalIterablesError', - 'zip_equal', - 'zip_offset', - 'windowed_complete', - 'all_unique', - 'value_chain', - 'product_index', - 'combination_index', - 'permutation_index', -] - -_marker = object() - - -def chunked(iterable, n, strict=False): - """Break *iterable* into lists of length *n*: - - >>> list(chunked([1, 2, 3, 4, 5, 6], 3)) - [[1, 2, 3], [4, 5, 6]] - - By the default, the last yielded list will have fewer than *n* elements - if the length of *iterable* is not divisible by *n*: - - >>> list(chunked([1, 2, 3, 4, 5, 6, 7, 8], 3)) - [[1, 2, 3], [4, 5, 6], [7, 8]] - - To use a fill-in value instead, see the :func:`grouper` recipe. - - If the length of *iterable* is not divisible by *n* and *strict* is - ``True``, then ``ValueError`` will be raised before the last - list is yielded. - - """ - iterator = iter(partial(take, n, iter(iterable)), []) - if strict: - - def ret(): - for chunk in iterator: - if len(chunk) != n: - raise ValueError('iterable is not divisible by n.') - yield chunk - - return iter(ret()) - else: - return iterator - - -def first(iterable, default=_marker): - """Return the first item of *iterable*, or *default* if *iterable* is - empty. - - >>> first([0, 1, 2, 3]) - 0 - >>> first([], 'some default') - 'some default' - - If *default* is not provided and there are no items in the iterable, - raise ``ValueError``. - - :func:`first` is useful when you have a generator of expensive-to-retrieve - values and want any arbitrary one. It is marginally shorter than - ``next(iter(iterable), default)``. - - """ - try: - return next(iter(iterable)) - except StopIteration as e: - if default is _marker: - raise ValueError( - 'first() was called on an empty iterable, and no ' - 'default value was provided.' - ) from e - return default - - -def last(iterable, default=_marker): - """Return the last item of *iterable*, or *default* if *iterable* is - empty. - - >>> last([0, 1, 2, 3]) - 3 - >>> last([], 'some default') - 'some default' - - If *default* is not provided and there are no items in the iterable, - raise ``ValueError``. - """ - try: - if isinstance(iterable, Sequence): - return iterable[-1] - # Work around https://bugs.python.org/issue38525 - elif hasattr(iterable, '__reversed__') and (hexversion != 0x030800F0): - return next(reversed(iterable)) - else: - return deque(iterable, maxlen=1)[-1] - except (IndexError, TypeError, StopIteration): - if default is _marker: - raise ValueError( - 'last() was called on an empty iterable, and no default was ' - 'provided.' - ) - return default - - -def nth_or_last(iterable, n, default=_marker): - """Return the nth or the last item of *iterable*, - or *default* if *iterable* is empty. - - >>> nth_or_last([0, 1, 2, 3], 2) - 2 - >>> nth_or_last([0, 1], 2) - 1 - >>> nth_or_last([], 0, 'some default') - 'some default' - - If *default* is not provided and there are no items in the iterable, - raise ``ValueError``. - """ - return last(islice(iterable, n + 1), default=default) - - -class peekable: - """Wrap an iterator to allow lookahead and prepending elements. - - Call :meth:`peek` on the result to get the value that will be returned - by :func:`next`. This won't advance the iterator: - - >>> p = peekable(['a', 'b']) - >>> p.peek() - 'a' - >>> next(p) - 'a' - - Pass :meth:`peek` a default value to return that instead of raising - ``StopIteration`` when the iterator is exhausted. - - >>> p = peekable([]) - >>> p.peek('hi') - 'hi' - - peekables also offer a :meth:`prepend` method, which "inserts" items - at the head of the iterable: - - >>> p = peekable([1, 2, 3]) - >>> p.prepend(10, 11, 12) - >>> next(p) - 10 - >>> p.peek() - 11 - >>> list(p) - [11, 12, 1, 2, 3] - - peekables can be indexed. Index 0 is the item that will be returned by - :func:`next`, index 1 is the item after that, and so on: - The values up to the given index will be cached. - - >>> p = peekable(['a', 'b', 'c', 'd']) - >>> p[0] - 'a' - >>> p[1] - 'b' - >>> next(p) - 'a' - - Negative indexes are supported, but be aware that they will cache the - remaining items in the source iterator, which may require significant - storage. - - To check whether a peekable is exhausted, check its truth value: - - >>> p = peekable(['a', 'b']) - >>> if p: # peekable has items - ... list(p) - ['a', 'b'] - >>> if not p: # peekable is exhausted - ... list(p) - [] - - """ - - def __init__(self, iterable): - self._it = iter(iterable) - self._cache = deque() - - def __iter__(self): - return self - - def __bool__(self): - try: - self.peek() - except StopIteration: - return False - return True - - def peek(self, default=_marker): - """Return the item that will be next returned from ``next()``. - - Return ``default`` if there are no items left. If ``default`` is not - provided, raise ``StopIteration``. - - """ - if not self._cache: - try: - self._cache.append(next(self._it)) - except StopIteration: - if default is _marker: - raise - return default - return self._cache[0] - - def prepend(self, *items): - """Stack up items to be the next ones returned from ``next()`` or - ``self.peek()``. The items will be returned in - first in, first out order:: - - >>> p = peekable([1, 2, 3]) - >>> p.prepend(10, 11, 12) - >>> next(p) - 10 - >>> list(p) - [11, 12, 1, 2, 3] - - It is possible, by prepending items, to "resurrect" a peekable that - previously raised ``StopIteration``. - - >>> p = peekable([]) - >>> next(p) - Traceback (most recent call last): - ... - StopIteration - >>> p.prepend(1) - >>> next(p) - 1 - >>> next(p) - Traceback (most recent call last): - ... - StopIteration - - """ - self._cache.extendleft(reversed(items)) - - def __next__(self): - if self._cache: - return self._cache.popleft() - - return next(self._it) - - def _get_slice(self, index): - # Normalize the slice's arguments - step = 1 if (index.step is None) else index.step - if step > 0: - start = 0 if (index.start is None) else index.start - stop = maxsize if (index.stop is None) else index.stop - elif step < 0: - start = -1 if (index.start is None) else index.start - stop = (-maxsize - 1) if (index.stop is None) else index.stop - else: - raise ValueError('slice step cannot be zero') - - # If either the start or stop index is negative, we'll need to cache - # the rest of the iterable in order to slice from the right side. - if (start < 0) or (stop < 0): - self._cache.extend(self._it) - # Otherwise we'll need to find the rightmost index and cache to that - # point. - else: - n = min(max(start, stop) + 1, maxsize) - cache_len = len(self._cache) - if n >= cache_len: - self._cache.extend(islice(self._it, n - cache_len)) - - return list(self._cache)[index] - - def __getitem__(self, index): - if isinstance(index, slice): - return self._get_slice(index) - - cache_len = len(self._cache) - if index < 0: - self._cache.extend(self._it) - elif index >= cache_len: - self._cache.extend(islice(self._it, index + 1 - cache_len)) - - return self._cache[index] - - -def collate(*iterables, **kwargs): - """Return a sorted merge of the items from each of several already-sorted - *iterables*. - - >>> list(collate('ACDZ', 'AZ', 'JKL')) - ['A', 'A', 'C', 'D', 'J', 'K', 'L', 'Z', 'Z'] - - Works lazily, keeping only the next value from each iterable in memory. Use - :func:`collate` to, for example, perform a n-way mergesort of items that - don't fit in memory. - - If a *key* function is specified, the iterables will be sorted according - to its result: - - >>> key = lambda s: int(s) # Sort by numeric value, not by string - >>> list(collate(['1', '10'], ['2', '11'], key=key)) - ['1', '2', '10', '11'] - - - If the *iterables* are sorted in descending order, set *reverse* to - ``True``: - - >>> list(collate([5, 3, 1], [4, 2, 0], reverse=True)) - [5, 4, 3, 2, 1, 0] - - If the elements of the passed-in iterables are out of order, you might get - unexpected results. - - On Python 3.5+, this function is an alias for :func:`heapq.merge`. - - """ - warnings.warn( - "collate is no longer part of more_itertools, use heapq.merge", - DeprecationWarning, - ) - return merge(*iterables, **kwargs) - - -def consumer(func): - """Decorator that automatically advances a PEP-342-style "reverse iterator" - to its first yield point so you don't have to call ``next()`` on it - manually. - - >>> @consumer - ... def tally(): - ... i = 0 - ... while True: - ... print('Thing number %s is %s.' % (i, (yield))) - ... i += 1 - ... - >>> t = tally() - >>> t.send('red') - Thing number 0 is red. - >>> t.send('fish') - Thing number 1 is fish. - - Without the decorator, you would have to call ``next(t)`` before - ``t.send()`` could be used. - - """ - - @wraps(func) - def wrapper(*args, **kwargs): - gen = func(*args, **kwargs) - next(gen) - return gen - - return wrapper - - -def ilen(iterable): - """Return the number of items in *iterable*. - - >>> ilen(x for x in range(1000000) if x % 3 == 0) - 333334 - - This consumes the iterable, so handle with care. - - """ - # This approach was selected because benchmarks showed it's likely the - # fastest of the known implementations at the time of writing. - # See GitHub tracker: #236, #230. - counter = count() - deque(zip(iterable, counter), maxlen=0) - return next(counter) - - -def iterate(func, start): - """Return ``start``, ``func(start)``, ``func(func(start))``, ... - - >>> from itertools import islice - >>> list(islice(iterate(lambda x: 2*x, 1), 10)) - [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - - """ - while True: - yield start - start = func(start) - - -def with_iter(context_manager): - """Wrap an iterable in a ``with`` statement, so it closes once exhausted. - - For example, this will close the file when the iterator is exhausted:: - - upper_lines = (line.upper() for line in with_iter(open('foo'))) - - Any context manager which returns an iterable is a candidate for - ``with_iter``. - - """ - with context_manager as iterable: - yield from iterable - - -def one(iterable, too_short=None, too_long=None): - """Return the first item from *iterable*, which is expected to contain only - that item. Raise an exception if *iterable* is empty or has more than one - item. - - :func:`one` is useful for ensuring that an iterable contains only one item. - For example, it can be used to retrieve the result of a database query - that is expected to return a single row. - - If *iterable* is empty, ``ValueError`` will be raised. You may specify a - different exception with the *too_short* keyword: - - >>> it = [] - >>> one(it) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError: too many items in iterable (expected 1)' - >>> too_short = IndexError('too few items') - >>> one(it, too_short=too_short) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - IndexError: too few items - - Similarly, if *iterable* contains more than one item, ``ValueError`` will - be raised. You may specify a different exception with the *too_long* - keyword: - - >>> it = ['too', 'many'] - >>> one(it) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError: Expected exactly one item in iterable, but got 'too', - 'many', and perhaps more. - >>> too_long = RuntimeError - >>> one(it, too_long=too_long) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - RuntimeError - - Note that :func:`one` attempts to advance *iterable* twice to ensure there - is only one item. See :func:`spy` or :func:`peekable` to check iterable - contents less destructively. - - """ - it = iter(iterable) - - try: - first_value = next(it) - except StopIteration as e: - raise ( - too_short or ValueError('too few items in iterable (expected 1)') - ) from e - - try: - second_value = next(it) - except StopIteration: - pass - else: - msg = ( - 'Expected exactly one item in iterable, but got {!r}, {!r}, ' - 'and perhaps more.'.format(first_value, second_value) - ) - raise too_long or ValueError(msg) - - return first_value - - -def distinct_permutations(iterable, r=None): - """Yield successive distinct permutations of the elements in *iterable*. - - >>> sorted(distinct_permutations([1, 0, 1])) - [(0, 1, 1), (1, 0, 1), (1, 1, 0)] - - Equivalent to ``set(permutations(iterable))``, except duplicates are not - generated and thrown away. For larger input sequences this is much more - efficient. - - Duplicate permutations arise when there are duplicated elements in the - input iterable. The number of items returned is - `n! / (x_1! * x_2! * ... * x_n!)`, where `n` is the total number of - items input, and each `x_i` is the count of a distinct item in the input - sequence. - - If *r* is given, only the *r*-length permutations are yielded. - - >>> sorted(distinct_permutations([1, 0, 1], r=2)) - [(0, 1), (1, 0), (1, 1)] - >>> sorted(distinct_permutations(range(3), r=2)) - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] - - """ - # Algorithm: https://w.wiki/Qai - def _full(A): - while True: - # Yield the permutation we have - yield tuple(A) - - # Find the largest index i such that A[i] < A[i + 1] - for i in range(size - 2, -1, -1): - if A[i] < A[i + 1]: - break - # If no such index exists, this permutation is the last one - else: - return - - # Find the largest index j greater than j such that A[i] < A[j] - for j in range(size - 1, i, -1): - if A[i] < A[j]: - break - - # Swap the value of A[i] with that of A[j], then reverse the - # sequence from A[i + 1] to form the new permutation - A[i], A[j] = A[j], A[i] - A[i + 1 :] = A[: i - size : -1] # A[i + 1:][::-1] - - # Algorithm: modified from the above - def _partial(A, r): - # Split A into the first r items and the last r items - head, tail = A[:r], A[r:] - right_head_indexes = range(r - 1, -1, -1) - left_tail_indexes = range(len(tail)) - - while True: - # Yield the permutation we have - yield tuple(head) - - # Starting from the right, find the first index of the head with - # value smaller than the maximum value of the tail - call it i. - pivot = tail[-1] - for i in right_head_indexes: - if head[i] < pivot: - break - pivot = head[i] - else: - return - - # Starting from the left, find the first value of the tail - # with a value greater than head[i] and swap. - for j in left_tail_indexes: - if tail[j] > head[i]: - head[i], tail[j] = tail[j], head[i] - break - # If we didn't find one, start from the right and find the first - # index of the head with a value greater than head[i] and swap. - else: - for j in right_head_indexes: - if head[j] > head[i]: - head[i], head[j] = head[j], head[i] - break - - # Reverse head[i + 1:] and swap it with tail[:r - (i + 1)] - tail += head[: i - r : -1] # head[i + 1:][::-1] - i += 1 - head[i:], tail[:] = tail[: r - i], tail[r - i :] - - items = sorted(iterable) - - size = len(items) - if r is None: - r = size - - if 0 < r <= size: - return _full(items) if (r == size) else _partial(items, r) - - return iter(() if r else ((),)) - - -def intersperse(e, iterable, n=1): - """Intersperse filler element *e* among the items in *iterable*, leaving - *n* items between each filler element. - - >>> list(intersperse('!', [1, 2, 3, 4, 5])) - [1, '!', 2, '!', 3, '!', 4, '!', 5] - - >>> list(intersperse(None, [1, 2, 3, 4, 5], n=2)) - [1, 2, None, 3, 4, None, 5] - - """ - if n == 0: - raise ValueError('n must be > 0') - elif n == 1: - # interleave(repeat(e), iterable) -> e, x_0, e, e, x_1, e, x_2... - # islice(..., 1, None) -> x_0, e, e, x_1, e, x_2... - return islice(interleave(repeat(e), iterable), 1, None) - else: - # interleave(filler, chunks) -> [e], [x_0, x_1], [e], [x_2, x_3]... - # islice(..., 1, None) -> [x_0, x_1], [e], [x_2, x_3]... - # flatten(...) -> x_0, x_1, e, x_2, x_3... - filler = repeat([e]) - chunks = chunked(iterable, n) - return flatten(islice(interleave(filler, chunks), 1, None)) - - -def unique_to_each(*iterables): - """Return the elements from each of the input iterables that aren't in the - other input iterables. - - For example, suppose you have a set of packages, each with a set of - dependencies:: - - {'pkg_1': {'A', 'B'}, 'pkg_2': {'B', 'C'}, 'pkg_3': {'B', 'D'}} - - If you remove one package, which dependencies can also be removed? - - If ``pkg_1`` is removed, then ``A`` is no longer necessary - it is not - associated with ``pkg_2`` or ``pkg_3``. Similarly, ``C`` is only needed for - ``pkg_2``, and ``D`` is only needed for ``pkg_3``:: - - >>> unique_to_each({'A', 'B'}, {'B', 'C'}, {'B', 'D'}) - [['A'], ['C'], ['D']] - - If there are duplicates in one input iterable that aren't in the others - they will be duplicated in the output. Input order is preserved:: - - >>> unique_to_each("mississippi", "missouri") - [['p', 'p'], ['o', 'u', 'r']] - - It is assumed that the elements of each iterable are hashable. - - """ - pool = [list(it) for it in iterables] - counts = Counter(chain.from_iterable(map(set, pool))) - uniques = {element for element in counts if counts[element] == 1} - return [list(filter(uniques.__contains__, it)) for it in pool] - - -def windowed(seq, n, fillvalue=None, step=1): - """Return a sliding window of width *n* over the given iterable. - - >>> all_windows = windowed([1, 2, 3, 4, 5], 3) - >>> list(all_windows) - [(1, 2, 3), (2, 3, 4), (3, 4, 5)] - - When the window is larger than the iterable, *fillvalue* is used in place - of missing values: - - >>> list(windowed([1, 2, 3], 4)) - [(1, 2, 3, None)] - - Each window will advance in increments of *step*: - - >>> list(windowed([1, 2, 3, 4, 5, 6], 3, fillvalue='!', step=2)) - [(1, 2, 3), (3, 4, 5), (5, 6, '!')] - - To slide into the iterable's items, use :func:`chain` to add filler items - to the left: - - >>> iterable = [1, 2, 3, 4] - >>> n = 3 - >>> padding = [None] * (n - 1) - >>> list(windowed(chain(padding, iterable), 3)) - [(None, None, 1), (None, 1, 2), (1, 2, 3), (2, 3, 4)] - """ - if n < 0: - raise ValueError('n must be >= 0') - if n == 0: - yield tuple() - return - if step < 1: - raise ValueError('step must be >= 1') - - window = deque(maxlen=n) - i = n - for _ in map(window.append, seq): - i -= 1 - if not i: - i = step - yield tuple(window) - - size = len(window) - if size < n: - yield tuple(chain(window, repeat(fillvalue, n - size))) - elif 0 < i < min(step, n): - window += (fillvalue,) * i - yield tuple(window) - - -def substrings(iterable): - """Yield all of the substrings of *iterable*. - - >>> [''.join(s) for s in substrings('more')] - ['m', 'o', 'r', 'e', 'mo', 'or', 're', 'mor', 'ore', 'more'] - - Note that non-string iterables can also be subdivided. - - >>> list(substrings([0, 1, 2])) - [(0,), (1,), (2,), (0, 1), (1, 2), (0, 1, 2)] - - """ - # The length-1 substrings - seq = [] - for item in iter(iterable): - seq.append(item) - yield (item,) - seq = tuple(seq) - item_count = len(seq) - - # And the rest - for n in range(2, item_count + 1): - for i in range(item_count - n + 1): - yield seq[i : i + n] - - -def substrings_indexes(seq, reverse=False): - """Yield all substrings and their positions in *seq* - - The items yielded will be a tuple of the form ``(substr, i, j)``, where - ``substr == seq[i:j]``. - - This function only works for iterables that support slicing, such as - ``str`` objects. - - >>> for item in substrings_indexes('more'): - ... print(item) - ('m', 0, 1) - ('o', 1, 2) - ('r', 2, 3) - ('e', 3, 4) - ('mo', 0, 2) - ('or', 1, 3) - ('re', 2, 4) - ('mor', 0, 3) - ('ore', 1, 4) - ('more', 0, 4) - - Set *reverse* to ``True`` to yield the same items in the opposite order. - - - """ - r = range(1, len(seq) + 1) - if reverse: - r = reversed(r) - return ( - (seq[i : i + L], i, i + L) for L in r for i in range(len(seq) - L + 1) - ) - - -class bucket: - """Wrap *iterable* and return an object that buckets it iterable into - child iterables based on a *key* function. - - >>> iterable = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2', 'b3'] - >>> s = bucket(iterable, key=lambda x: x[0]) # Bucket by 1st character - >>> sorted(list(s)) # Get the keys - ['a', 'b', 'c'] - >>> a_iterable = s['a'] - >>> next(a_iterable) - 'a1' - >>> next(a_iterable) - 'a2' - >>> list(s['b']) - ['b1', 'b2', 'b3'] - - The original iterable will be advanced and its items will be cached until - they are used by the child iterables. This may require significant storage. - - By default, attempting to select a bucket to which no items belong will - exhaust the iterable and cache all values. - If you specify a *validator* function, selected buckets will instead be - checked against it. - - >>> from itertools import count - >>> it = count(1, 2) # Infinite sequence of odd numbers - >>> key = lambda x: x % 10 # Bucket by last digit - >>> validator = lambda x: x in {1, 3, 5, 7, 9} # Odd digits only - >>> s = bucket(it, key=key, validator=validator) - >>> 2 in s - False - >>> list(s[2]) - [] - - """ - - def __init__(self, iterable, key, validator=None): - self._it = iter(iterable) - self._key = key - self._cache = defaultdict(deque) - self._validator = validator or (lambda x: True) - - def __contains__(self, value): - if not self._validator(value): - return False - - try: - item = next(self[value]) - except StopIteration: - return False - else: - self._cache[value].appendleft(item) - - return True - - def _get_values(self, value): - """ - Helper to yield items from the parent iterator that match *value*. - Items that don't match are stored in the local cache as they - are encountered. - """ - while True: - # If we've cached some items that match the target value, emit - # the first one and evict it from the cache. - if self._cache[value]: - yield self._cache[value].popleft() - # Otherwise we need to advance the parent iterator to search for - # a matching item, caching the rest. - else: - while True: - try: - item = next(self._it) - except StopIteration: - return - item_value = self._key(item) - if item_value == value: - yield item - break - elif self._validator(item_value): - self._cache[item_value].append(item) - - def __iter__(self): - for item in self._it: - item_value = self._key(item) - if self._validator(item_value): - self._cache[item_value].append(item) - - yield from self._cache.keys() - - def __getitem__(self, value): - if not self._validator(value): - return iter(()) - - return self._get_values(value) - - -def spy(iterable, n=1): - """Return a 2-tuple with a list containing the first *n* elements of - *iterable*, and an iterator with the same items as *iterable*. - This allows you to "look ahead" at the items in the iterable without - advancing it. - - There is one item in the list by default: - - >>> iterable = 'abcdefg' - >>> head, iterable = spy(iterable) - >>> head - ['a'] - >>> list(iterable) - ['a', 'b', 'c', 'd', 'e', 'f', 'g'] - - You may use unpacking to retrieve items instead of lists: - - >>> (head,), iterable = spy('abcdefg') - >>> head - 'a' - >>> (first, second), iterable = spy('abcdefg', 2) - >>> first - 'a' - >>> second - 'b' - - The number of items requested can be larger than the number of items in - the iterable: - - >>> iterable = [1, 2, 3, 4, 5] - >>> head, iterable = spy(iterable, 10) - >>> head - [1, 2, 3, 4, 5] - >>> list(iterable) - [1, 2, 3, 4, 5] - - """ - it = iter(iterable) - head = take(n, it) - - return head.copy(), chain(head, it) - - -def interleave(*iterables): - """Return a new iterable yielding from each iterable in turn, - until the shortest is exhausted. - - >>> list(interleave([1, 2, 3], [4, 5], [6, 7, 8])) - [1, 4, 6, 2, 5, 7] - - For a version that doesn't terminate after the shortest iterable is - exhausted, see :func:`interleave_longest`. - - """ - return chain.from_iterable(zip(*iterables)) - - -def interleave_longest(*iterables): - """Return a new iterable yielding from each iterable in turn, - skipping any that are exhausted. - - >>> list(interleave_longest([1, 2, 3], [4, 5], [6, 7, 8])) - [1, 4, 6, 2, 5, 7, 3, 8] - - This function produces the same output as :func:`roundrobin`, but may - perform better for some inputs (in particular when the number of iterables - is large). - - """ - i = chain.from_iterable(zip_longest(*iterables, fillvalue=_marker)) - return (x for x in i if x is not _marker) - - -def collapse(iterable, base_type=None, levels=None): - """Flatten an iterable with multiple levels of nesting (e.g., a list of - lists of tuples) into non-iterable types. - - >>> iterable = [(1, 2), ([3, 4], [[5], [6]])] - >>> list(collapse(iterable)) - [1, 2, 3, 4, 5, 6] - - Binary and text strings are not considered iterable and - will not be collapsed. - - To avoid collapsing other types, specify *base_type*: - - >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']] - >>> list(collapse(iterable, base_type=tuple)) - ['ab', ('cd', 'ef'), 'gh', 'ij'] - - Specify *levels* to stop flattening after a certain level: - - >>> iterable = [('a', ['b']), ('c', ['d'])] - >>> list(collapse(iterable)) # Fully flattened - ['a', 'b', 'c', 'd'] - >>> list(collapse(iterable, levels=1)) # Only one level flattened - ['a', ['b'], 'c', ['d']] - - """ - - def walk(node, level): - if ( - ((levels is not None) and (level > levels)) - or isinstance(node, (str, bytes)) - or ((base_type is not None) and isinstance(node, base_type)) - ): - yield node - return - - try: - tree = iter(node) - except TypeError: - yield node - return - else: - for child in tree: - yield from walk(child, level + 1) - - yield from walk(iterable, 0) - - -def side_effect(func, iterable, chunk_size=None, before=None, after=None): - """Invoke *func* on each item in *iterable* (or on each *chunk_size* group - of items) before yielding the item. - - `func` must be a function that takes a single argument. Its return value - will be discarded. - - *before* and *after* are optional functions that take no arguments. They - will be executed before iteration starts and after it ends, respectively. - - `side_effect` can be used for logging, updating progress bars, or anything - that is not functionally "pure." - - Emitting a status message: - - >>> from more_itertools import consume - >>> func = lambda item: print('Received {}'.format(item)) - >>> consume(side_effect(func, range(2))) - Received 0 - Received 1 - - Operating on chunks of items: - - >>> pair_sums = [] - >>> func = lambda chunk: pair_sums.append(sum(chunk)) - >>> list(side_effect(func, [0, 1, 2, 3, 4, 5], 2)) - [0, 1, 2, 3, 4, 5] - >>> list(pair_sums) - [1, 5, 9] - - Writing to a file-like object: - - >>> from io import StringIO - >>> from more_itertools import consume - >>> f = StringIO() - >>> func = lambda x: print(x, file=f) - >>> before = lambda: print(u'HEADER', file=f) - >>> after = f.close - >>> it = [u'a', u'b', u'c'] - >>> consume(side_effect(func, it, before=before, after=after)) - >>> f.closed - True - - """ - try: - if before is not None: - before() - - if chunk_size is None: - for item in iterable: - func(item) - yield item - else: - for chunk in chunked(iterable, chunk_size): - func(chunk) - yield from chunk - finally: - if after is not None: - after() - - -def sliced(seq, n, strict=False): - """Yield slices of length *n* from the sequence *seq*. - - >>> list(sliced((1, 2, 3, 4, 5, 6), 3)) - [(1, 2, 3), (4, 5, 6)] - - By the default, the last yielded slice will have fewer than *n* elements - if the length of *seq* is not divisible by *n*: - - >>> list(sliced((1, 2, 3, 4, 5, 6, 7, 8), 3)) - [(1, 2, 3), (4, 5, 6), (7, 8)] - - If the length of *seq* is not divisible by *n* and *strict* is - ``True``, then ``ValueError`` will be raised before the last - slice is yielded. - - This function will only work for iterables that support slicing. - For non-sliceable iterables, see :func:`chunked`. - - """ - iterator = takewhile(len, (seq[i : i + n] for i in count(0, n))) - if strict: - - def ret(): - for _slice in iterator: - if len(_slice) != n: - raise ValueError("seq is not divisible by n.") - yield _slice - - return iter(ret()) - else: - return iterator - - -def split_at(iterable, pred, maxsplit=-1, keep_separator=False): - """Yield lists of items from *iterable*, where each list is delimited by - an item where callable *pred* returns ``True``. - - >>> list(split_at('abcdcba', lambda x: x == 'b')) - [['a'], ['c', 'd', 'c'], ['a']] - - >>> list(split_at(range(10), lambda n: n % 2 == 1)) - [[0], [2], [4], [6], [8], []] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_at(range(10), lambda n: n % 2 == 1, maxsplit=2)) - [[0], [2], [4, 5, 6, 7, 8, 9]] - - By default, the delimiting items are not included in the output. - The include them, set *keep_separator* to ``True``. - - >>> list(split_at('abcdcba', lambda x: x == 'b', keep_separator=True)) - [['a'], ['b'], ['c', 'd', 'c'], ['b'], ['a']] - - """ - if maxsplit == 0: - yield list(iterable) - return - - buf = [] - it = iter(iterable) - for item in it: - if pred(item): - yield buf - if keep_separator: - yield [item] - if maxsplit == 1: - yield list(it) - return - buf = [] - maxsplit -= 1 - else: - buf.append(item) - yield buf - - -def split_before(iterable, pred, maxsplit=-1): - """Yield lists of items from *iterable*, where each list ends just before - an item for which callable *pred* returns ``True``: - - >>> list(split_before('OneTwo', lambda s: s.isupper())) - [['O', 'n', 'e'], ['T', 'w', 'o']] - - >>> list(split_before(range(10), lambda n: n % 3 == 0)) - [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_before(range(10), lambda n: n % 3 == 0, maxsplit=2)) - [[0, 1, 2], [3, 4, 5], [6, 7, 8, 9]] - """ - if maxsplit == 0: - yield list(iterable) - return - - buf = [] - it = iter(iterable) - for item in it: - if pred(item) and buf: - yield buf - if maxsplit == 1: - yield [item] + list(it) - return - buf = [] - maxsplit -= 1 - buf.append(item) - if buf: - yield buf - - -def split_after(iterable, pred, maxsplit=-1): - """Yield lists of items from *iterable*, where each list ends with an - item where callable *pred* returns ``True``: - - >>> list(split_after('one1two2', lambda s: s.isdigit())) - [['o', 'n', 'e', '1'], ['t', 'w', 'o', '2']] - - >>> list(split_after(range(10), lambda n: n % 3 == 0)) - [[0], [1, 2, 3], [4, 5, 6], [7, 8, 9]] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_after(range(10), lambda n: n % 3 == 0, maxsplit=2)) - [[0], [1, 2, 3], [4, 5, 6, 7, 8, 9]] - - """ - if maxsplit == 0: - yield list(iterable) - return - - buf = [] - it = iter(iterable) - for item in it: - buf.append(item) - if pred(item) and buf: - yield buf - if maxsplit == 1: - yield list(it) - return - buf = [] - maxsplit -= 1 - if buf: - yield buf - - -def split_when(iterable, pred, maxsplit=-1): - """Split *iterable* into pieces based on the output of *pred*. - *pred* should be a function that takes successive pairs of items and - returns ``True`` if the iterable should be split in between them. - - For example, to find runs of increasing numbers, split the iterable when - element ``i`` is larger than element ``i + 1``: - - >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], lambda x, y: x > y)) - [[1, 2, 3, 3], [2, 5], [2, 4], [2]] - - At most *maxsplit* splits are done. If *maxsplit* is not specified or -1, - then there is no limit on the number of splits: - - >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], - ... lambda x, y: x > y, maxsplit=2)) - [[1, 2, 3, 3], [2, 5], [2, 4, 2]] - - """ - if maxsplit == 0: - yield list(iterable) - return - - it = iter(iterable) - try: - cur_item = next(it) - except StopIteration: - return - - buf = [cur_item] - for next_item in it: - if pred(cur_item, next_item): - yield buf - if maxsplit == 1: - yield [next_item] + list(it) - return - buf = [] - maxsplit -= 1 - - buf.append(next_item) - cur_item = next_item - - yield buf - - -def split_into(iterable, sizes): - """Yield a list of sequential items from *iterable* of length 'n' for each - integer 'n' in *sizes*. - - >>> list(split_into([1,2,3,4,5,6], [1,2,3])) - [[1], [2, 3], [4, 5, 6]] - - If the sum of *sizes* is smaller than the length of *iterable*, then the - remaining items of *iterable* will not be returned. - - >>> list(split_into([1,2,3,4,5,6], [2,3])) - [[1, 2], [3, 4, 5]] - - If the sum of *sizes* is larger than the length of *iterable*, fewer items - will be returned in the iteration that overruns *iterable* and further - lists will be empty: - - >>> list(split_into([1,2,3,4], [1,2,3,4])) - [[1], [2, 3], [4], []] - - When a ``None`` object is encountered in *sizes*, the returned list will - contain items up to the end of *iterable* the same way that itertools.slice - does: - - >>> list(split_into([1,2,3,4,5,6,7,8,9,0], [2,3,None])) - [[1, 2], [3, 4, 5], [6, 7, 8, 9, 0]] - - :func:`split_into` can be useful for grouping a series of items where the - sizes of the groups are not uniform. An example would be where in a row - from a table, multiple columns represent elements of the same feature - (e.g. a point represented by x,y,z) but, the format is not the same for - all columns. - """ - # convert the iterable argument into an iterator so its contents can - # be consumed by islice in case it is a generator - it = iter(iterable) - - for size in sizes: - if size is None: - yield list(it) - return - else: - yield list(islice(it, size)) - - -def padded(iterable, fillvalue=None, n=None, next_multiple=False): - """Yield the elements from *iterable*, followed by *fillvalue*, such that - at least *n* items are emitted. - - >>> list(padded([1, 2, 3], '?', 5)) - [1, 2, 3, '?', '?'] - - If *next_multiple* is ``True``, *fillvalue* will be emitted until the - number of items emitted is a multiple of *n*:: - - >>> list(padded([1, 2, 3, 4], n=3, next_multiple=True)) - [1, 2, 3, 4, None, None] - - If *n* is ``None``, *fillvalue* will be emitted indefinitely. - - """ - it = iter(iterable) - if n is None: - yield from chain(it, repeat(fillvalue)) - elif n < 1: - raise ValueError('n must be at least 1') - else: - item_count = 0 - for item in it: - yield item - item_count += 1 - - remaining = (n - item_count) % n if next_multiple else n - item_count - for _ in range(remaining): - yield fillvalue - - -def repeat_last(iterable, default=None): - """After the *iterable* is exhausted, keep yielding its last element. - - >>> list(islice(repeat_last(range(3)), 5)) - [0, 1, 2, 2, 2] - - If the iterable is empty, yield *default* forever:: - - >>> list(islice(repeat_last(range(0), 42), 5)) - [42, 42, 42, 42, 42] - - """ - item = _marker - for item in iterable: - yield item - final = default if item is _marker else item - yield from repeat(final) - - -def distribute(n, iterable): - """Distribute the items from *iterable* among *n* smaller iterables. - - >>> group_1, group_2 = distribute(2, [1, 2, 3, 4, 5, 6]) - >>> list(group_1) - [1, 3, 5] - >>> list(group_2) - [2, 4, 6] - - If the length of *iterable* is not evenly divisible by *n*, then the - length of the returned iterables will not be identical: - - >>> children = distribute(3, [1, 2, 3, 4, 5, 6, 7]) - >>> [list(c) for c in children] - [[1, 4, 7], [2, 5], [3, 6]] - - If the length of *iterable* is smaller than *n*, then the last returned - iterables will be empty: - - >>> children = distribute(5, [1, 2, 3]) - >>> [list(c) for c in children] - [[1], [2], [3], [], []] - - This function uses :func:`itertools.tee` and may require significant - storage. If you need the order items in the smaller iterables to match the - original iterable, see :func:`divide`. - - """ - if n < 1: - raise ValueError('n must be at least 1') - - children = tee(iterable, n) - return [islice(it, index, None, n) for index, it in enumerate(children)] - - -def stagger(iterable, offsets=(-1, 0, 1), longest=False, fillvalue=None): - """Yield tuples whose elements are offset from *iterable*. - The amount by which the `i`-th item in each tuple is offset is given by - the `i`-th item in *offsets*. - - >>> list(stagger([0, 1, 2, 3])) - [(None, 0, 1), (0, 1, 2), (1, 2, 3)] - >>> list(stagger(range(8), offsets=(0, 2, 4))) - [(0, 2, 4), (1, 3, 5), (2, 4, 6), (3, 5, 7)] - - By default, the sequence will end when the final element of a tuple is the - last item in the iterable. To continue until the first element of a tuple - is the last item in the iterable, set *longest* to ``True``:: - - >>> list(stagger([0, 1, 2, 3], longest=True)) - [(None, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, None), (3, None, None)] - - By default, ``None`` will be used to replace offsets beyond the end of the - sequence. Specify *fillvalue* to use some other value. - - """ - children = tee(iterable, len(offsets)) - - return zip_offset( - *children, offsets=offsets, longest=longest, fillvalue=fillvalue - ) - - -class UnequalIterablesError(ValueError): - def __init__(self, details=None): - msg = 'Iterables have different lengths' - if details is not None: - msg += (': index 0 has length {}; index {} has length {}').format( - *details - ) - - super().__init__(msg) - - -def _zip_equal_generator(iterables): - for combo in zip_longest(*iterables, fillvalue=_marker): - for val in combo: - if val is _marker: - raise UnequalIterablesError() - yield combo - - -def zip_equal(*iterables): - """``zip`` the input *iterables* together, but raise - ``UnequalIterablesError`` if they aren't all the same length. - - >>> it_1 = range(3) - >>> it_2 = iter('abc') - >>> list(zip_equal(it_1, it_2)) - [(0, 'a'), (1, 'b'), (2, 'c')] - - >>> it_1 = range(3) - >>> it_2 = iter('abcd') - >>> list(zip_equal(it_1, it_2)) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - more_itertools.more.UnequalIterablesError: Iterables have different - lengths - - """ - if hexversion >= 0x30A00A6: - warnings.warn( - ( - 'zip_equal will be removed in a future version of ' - 'more-itertools. Use the builtin zip function with ' - 'strict=True instead.' - ), - DeprecationWarning, - ) - # Check whether the iterables are all the same size. - try: - first_size = len(iterables[0]) - for i, it in enumerate(iterables[1:], 1): - size = len(it) - if size != first_size: - break - else: - # If we didn't break out, we can use the built-in zip. - return zip(*iterables) - - # If we did break out, there was a mismatch. - raise UnequalIterablesError(details=(first_size, i, size)) - # If any one of the iterables didn't have a length, start reading - # them until one runs out. - except TypeError: - return _zip_equal_generator(iterables) - - -def zip_offset(*iterables, offsets, longest=False, fillvalue=None): - """``zip`` the input *iterables* together, but offset the `i`-th iterable - by the `i`-th item in *offsets*. - - >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1))) - [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e')] - - This can be used as a lightweight alternative to SciPy or pandas to analyze - data sets in which some series have a lead or lag relationship. - - By default, the sequence will end when the shortest iterable is exhausted. - To continue until the longest iterable is exhausted, set *longest* to - ``True``. - - >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1), longest=True)) - [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e'), (None, 'f')] - - By default, ``None`` will be used to replace offsets beyond the end of the - sequence. Specify *fillvalue* to use some other value. - - """ - if len(iterables) != len(offsets): - raise ValueError("Number of iterables and offsets didn't match") - - staggered = [] - for it, n in zip(iterables, offsets): - if n < 0: - staggered.append(chain(repeat(fillvalue, -n), it)) - elif n > 0: - staggered.append(islice(it, n, None)) - else: - staggered.append(it) - - if longest: - return zip_longest(*staggered, fillvalue=fillvalue) - - return zip(*staggered) - - -def sort_together(iterables, key_list=(0,), key=None, reverse=False): - """Return the input iterables sorted together, with *key_list* as the - priority for sorting. All iterables are trimmed to the length of the - shortest one. - - This can be used like the sorting function in a spreadsheet. If each - iterable represents a column of data, the key list determines which - columns are used for sorting. - - By default, all iterables are sorted using the ``0``-th iterable:: - - >>> iterables = [(4, 3, 2, 1), ('a', 'b', 'c', 'd')] - >>> sort_together(iterables) - [(1, 2, 3, 4), ('d', 'c', 'b', 'a')] - - Set a different key list to sort according to another iterable. - Specifying multiple keys dictates how ties are broken:: - - >>> iterables = [(3, 1, 2), (0, 1, 0), ('c', 'b', 'a')] - >>> sort_together(iterables, key_list=(1, 2)) - [(2, 3, 1), (0, 0, 1), ('a', 'c', 'b')] - - To sort by a function of the elements of the iterable, pass a *key* - function. Its arguments are the elements of the iterables corresponding to - the key list:: - - >>> names = ('a', 'b', 'c') - >>> lengths = (1, 2, 3) - >>> widths = (5, 2, 1) - >>> def area(length, width): - ... return length * width - >>> sort_together([names, lengths, widths], key_list=(1, 2), key=area) - [('c', 'b', 'a'), (3, 2, 1), (1, 2, 5)] - - Set *reverse* to ``True`` to sort in descending order. - - >>> sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True) - [(3, 2, 1), ('a', 'b', 'c')] - - """ - if key is None: - # if there is no key function, the key argument to sorted is an - # itemgetter - key_argument = itemgetter(*key_list) - else: - # if there is a key function, call it with the items at the offsets - # specified by the key function as arguments - key_list = list(key_list) - if len(key_list) == 1: - # if key_list contains a single item, pass the item at that offset - # as the only argument to the key function - key_offset = key_list[0] - key_argument = lambda zipped_items: key(zipped_items[key_offset]) - else: - # if key_list contains multiple items, use itemgetter to return a - # tuple of items, which we pass as *args to the key function - get_key_items = itemgetter(*key_list) - key_argument = lambda zipped_items: key( - *get_key_items(zipped_items) - ) - - return list( - zip(*sorted(zip(*iterables), key=key_argument, reverse=reverse)) - ) - - -def unzip(iterable): - """The inverse of :func:`zip`, this function disaggregates the elements - of the zipped *iterable*. - - The ``i``-th iterable contains the ``i``-th element from each element - of the zipped iterable. The first element is used to to determine the - length of the remaining elements. - - >>> iterable = [('a', 1), ('b', 2), ('c', 3), ('d', 4)] - >>> letters, numbers = unzip(iterable) - >>> list(letters) - ['a', 'b', 'c', 'd'] - >>> list(numbers) - [1, 2, 3, 4] - - This is similar to using ``zip(*iterable)``, but it avoids reading - *iterable* into memory. Note, however, that this function uses - :func:`itertools.tee` and thus may require significant storage. - - """ - head, iterable = spy(iter(iterable)) - if not head: - # empty iterable, e.g. zip([], [], []) - return () - # spy returns a one-length iterable as head - head = head[0] - iterables = tee(iterable, len(head)) - - def itemgetter(i): - def getter(obj): - try: - return obj[i] - except IndexError: - # basically if we have an iterable like - # iter([(1, 2, 3), (4, 5), (6,)]) - # the second unzipped iterable would fail at the third tuple - # since it would try to access tup[1] - # same with the third unzipped iterable and the second tuple - # to support these "improperly zipped" iterables, - # we create a custom itemgetter - # which just stops the unzipped iterables - # at first length mismatch - raise StopIteration - - return getter - - return tuple(map(itemgetter(i), it) for i, it in enumerate(iterables)) - - -def divide(n, iterable): - """Divide the elements from *iterable* into *n* parts, maintaining - order. - - >>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6]) - >>> list(group_1) - [1, 2, 3] - >>> list(group_2) - [4, 5, 6] - - If the length of *iterable* is not evenly divisible by *n*, then the - length of the returned iterables will not be identical: - - >>> children = divide(3, [1, 2, 3, 4, 5, 6, 7]) - >>> [list(c) for c in children] - [[1, 2, 3], [4, 5], [6, 7]] - - If the length of the iterable is smaller than n, then the last returned - iterables will be empty: - - >>> children = divide(5, [1, 2, 3]) - >>> [list(c) for c in children] - [[1], [2], [3], [], []] - - This function will exhaust the iterable before returning and may require - significant storage. If order is not important, see :func:`distribute`, - which does not first pull the iterable into memory. - - """ - if n < 1: - raise ValueError('n must be at least 1') - - try: - iterable[:0] - except TypeError: - seq = tuple(iterable) - else: - seq = iterable - - q, r = divmod(len(seq), n) - - ret = [] - stop = 0 - for i in range(1, n + 1): - start = stop - stop += q + 1 if i <= r else q - ret.append(iter(seq[start:stop])) - - return ret - - -def always_iterable(obj, base_type=(str, bytes)): - """If *obj* is iterable, return an iterator over its items:: - - >>> obj = (1, 2, 3) - >>> list(always_iterable(obj)) - [1, 2, 3] - - If *obj* is not iterable, return a one-item iterable containing *obj*:: - - >>> obj = 1 - >>> list(always_iterable(obj)) - [1] - - If *obj* is ``None``, return an empty iterable: - - >>> obj = None - >>> list(always_iterable(None)) - [] - - By default, binary and text strings are not considered iterable:: - - >>> obj = 'foo' - >>> list(always_iterable(obj)) - ['foo'] - - If *base_type* is set, objects for which ``isinstance(obj, base_type)`` - returns ``True`` won't be considered iterable. - - >>> obj = {'a': 1} - >>> list(always_iterable(obj)) # Iterate over the dict's keys - ['a'] - >>> list(always_iterable(obj, base_type=dict)) # Treat dicts as a unit - [{'a': 1}] - - Set *base_type* to ``None`` to avoid any special handling and treat objects - Python considers iterable as iterable: - - >>> obj = 'foo' - >>> list(always_iterable(obj, base_type=None)) - ['f', 'o', 'o'] - """ - if obj is None: - return iter(()) - - if (base_type is not None) and isinstance(obj, base_type): - return iter((obj,)) - - try: - return iter(obj) - except TypeError: - return iter((obj,)) - - -def adjacent(predicate, iterable, distance=1): - """Return an iterable over `(bool, item)` tuples where the `item` is - drawn from *iterable* and the `bool` indicates whether - that item satisfies the *predicate* or is adjacent to an item that does. - - For example, to find whether items are adjacent to a ``3``:: - - >>> list(adjacent(lambda x: x == 3, range(6))) - [(False, 0), (False, 1), (True, 2), (True, 3), (True, 4), (False, 5)] - - Set *distance* to change what counts as adjacent. For example, to find - whether items are two places away from a ``3``: - - >>> list(adjacent(lambda x: x == 3, range(6), distance=2)) - [(False, 0), (True, 1), (True, 2), (True, 3), (True, 4), (True, 5)] - - This is useful for contextualizing the results of a search function. - For example, a code comparison tool might want to identify lines that - have changed, but also surrounding lines to give the viewer of the diff - context. - - The predicate function will only be called once for each item in the - iterable. - - See also :func:`groupby_transform`, which can be used with this function - to group ranges of items with the same `bool` value. - - """ - # Allow distance=0 mainly for testing that it reproduces results with map() - if distance < 0: - raise ValueError('distance must be at least 0') - - i1, i2 = tee(iterable) - padding = [False] * distance - selected = chain(padding, map(predicate, i1), padding) - adjacent_to_selected = map(any, windowed(selected, 2 * distance + 1)) - return zip(adjacent_to_selected, i2) - - -def groupby_transform(iterable, keyfunc=None, valuefunc=None, reducefunc=None): - """An extension of :func:`itertools.groupby` that can apply transformations - to the grouped data. - - * *keyfunc* is a function computing a key value for each item in *iterable* - * *valuefunc* is a function that transforms the individual items from - *iterable* after grouping - * *reducefunc* is a function that transforms each group of items - - >>> iterable = 'aAAbBBcCC' - >>> keyfunc = lambda k: k.upper() - >>> valuefunc = lambda v: v.lower() - >>> reducefunc = lambda g: ''.join(g) - >>> list(groupby_transform(iterable, keyfunc, valuefunc, reducefunc)) - [('A', 'aaa'), ('B', 'bbb'), ('C', 'ccc')] - - Each optional argument defaults to an identity function if not specified. - - :func:`groupby_transform` is useful when grouping elements of an iterable - using a separate iterable as the key. To do this, :func:`zip` the iterables - and pass a *keyfunc* that extracts the first element and a *valuefunc* - that extracts the second element:: - - >>> from operator import itemgetter - >>> keys = [0, 0, 1, 1, 1, 2, 2, 2, 3] - >>> values = 'abcdefghi' - >>> iterable = zip(keys, values) - >>> grouper = groupby_transform(iterable, itemgetter(0), itemgetter(1)) - >>> [(k, ''.join(g)) for k, g in grouper] - [(0, 'ab'), (1, 'cde'), (2, 'fgh'), (3, 'i')] - - Note that the order of items in the iterable is significant. - Only adjacent items are grouped together, so if you don't want any - duplicate groups, you should sort the iterable by the key function. - - """ - ret = groupby(iterable, keyfunc) - if valuefunc: - ret = ((k, map(valuefunc, g)) for k, g in ret) - if reducefunc: - ret = ((k, reducefunc(g)) for k, g in ret) - - return ret - - -class numeric_range(abc.Sequence, abc.Hashable): - """An extension of the built-in ``range()`` function whose arguments can - be any orderable numeric type. - - With only *stop* specified, *start* defaults to ``0`` and *step* - defaults to ``1``. The output items will match the type of *stop*: - - >>> list(numeric_range(3.5)) - [0.0, 1.0, 2.0, 3.0] - - With only *start* and *stop* specified, *step* defaults to ``1``. The - output items will match the type of *start*: - - >>> from decimal import Decimal - >>> start = Decimal('2.1') - >>> stop = Decimal('5.1') - >>> list(numeric_range(start, stop)) - [Decimal('2.1'), Decimal('3.1'), Decimal('4.1')] - - With *start*, *stop*, and *step* specified the output items will match - the type of ``start + step``: - - >>> from fractions import Fraction - >>> start = Fraction(1, 2) # Start at 1/2 - >>> stop = Fraction(5, 2) # End at 5/2 - >>> step = Fraction(1, 2) # Count by 1/2 - >>> list(numeric_range(start, stop, step)) - [Fraction(1, 2), Fraction(1, 1), Fraction(3, 2), Fraction(2, 1)] - - If *step* is zero, ``ValueError`` is raised. Negative steps are supported: - - >>> list(numeric_range(3, -1, -1.0)) - [3.0, 2.0, 1.0, 0.0] - - Be aware of the limitations of floating point numbers; the representation - of the yielded numbers may be surprising. - - ``datetime.datetime`` objects can be used for *start* and *stop*, if *step* - is a ``datetime.timedelta`` object: - - >>> import datetime - >>> start = datetime.datetime(2019, 1, 1) - >>> stop = datetime.datetime(2019, 1, 3) - >>> step = datetime.timedelta(days=1) - >>> items = iter(numeric_range(start, stop, step)) - >>> next(items) - datetime.datetime(2019, 1, 1, 0, 0) - >>> next(items) - datetime.datetime(2019, 1, 2, 0, 0) - - """ - - _EMPTY_HASH = hash(range(0, 0)) - - def __init__(self, *args): - argc = len(args) - if argc == 1: - (self._stop,) = args - self._start = type(self._stop)(0) - self._step = type(self._stop - self._start)(1) - elif argc == 2: - self._start, self._stop = args - self._step = type(self._stop - self._start)(1) - elif argc == 3: - self._start, self._stop, self._step = args - elif argc == 0: - raise TypeError( - 'numeric_range expected at least ' - '1 argument, got {}'.format(argc) - ) - else: - raise TypeError( - 'numeric_range expected at most ' - '3 arguments, got {}'.format(argc) - ) - - self._zero = type(self._step)(0) - if self._step == self._zero: - raise ValueError('numeric_range() arg 3 must not be zero') - self._growing = self._step > self._zero - self._init_len() - - def __bool__(self): - if self._growing: - return self._start < self._stop - else: - return self._start > self._stop - - def __contains__(self, elem): - if self._growing: - if self._start <= elem < self._stop: - return (elem - self._start) % self._step == self._zero - else: - if self._start >= elem > self._stop: - return (self._start - elem) % (-self._step) == self._zero - - return False - - def __eq__(self, other): - if isinstance(other, numeric_range): - empty_self = not bool(self) - empty_other = not bool(other) - if empty_self or empty_other: - return empty_self and empty_other # True if both empty - else: - return ( - self._start == other._start - and self._step == other._step - and self._get_by_index(-1) == other._get_by_index(-1) - ) - else: - return False - - def __getitem__(self, key): - if isinstance(key, int): - return self._get_by_index(key) - elif isinstance(key, slice): - step = self._step if key.step is None else key.step * self._step - - if key.start is None or key.start <= -self._len: - start = self._start - elif key.start >= self._len: - start = self._stop - else: # -self._len < key.start < self._len - start = self._get_by_index(key.start) - - if key.stop is None or key.stop >= self._len: - stop = self._stop - elif key.stop <= -self._len: - stop = self._start - else: # -self._len < key.stop < self._len - stop = self._get_by_index(key.stop) - - return numeric_range(start, stop, step) - else: - raise TypeError( - 'numeric range indices must be ' - 'integers or slices, not {}'.format(type(key).__name__) - ) - - def __hash__(self): - if self: - return hash((self._start, self._get_by_index(-1), self._step)) - else: - return self._EMPTY_HASH - - def __iter__(self): - values = (self._start + (n * self._step) for n in count()) - if self._growing: - return takewhile(partial(gt, self._stop), values) - else: - return takewhile(partial(lt, self._stop), values) - - def __len__(self): - return self._len - - def _init_len(self): - if self._growing: - start = self._start - stop = self._stop - step = self._step - else: - start = self._stop - stop = self._start - step = -self._step - distance = stop - start - if distance <= self._zero: - self._len = 0 - else: # distance > 0 and step > 0: regular euclidean division - q, r = divmod(distance, step) - self._len = int(q) + int(r != self._zero) - - def __reduce__(self): - return numeric_range, (self._start, self._stop, self._step) - - def __repr__(self): - if self._step == 1: - return "numeric_range({}, {})".format( - repr(self._start), repr(self._stop) - ) - else: - return "numeric_range({}, {}, {})".format( - repr(self._start), repr(self._stop), repr(self._step) - ) - - def __reversed__(self): - return iter( - numeric_range( - self._get_by_index(-1), self._start - self._step, -self._step - ) - ) - - def count(self, value): - return int(value in self) - - def index(self, value): - if self._growing: - if self._start <= value < self._stop: - q, r = divmod(value - self._start, self._step) - if r == self._zero: - return int(q) - else: - if self._start >= value > self._stop: - q, r = divmod(self._start - value, -self._step) - if r == self._zero: - return int(q) - - raise ValueError("{} is not in numeric range".format(value)) - - def _get_by_index(self, i): - if i < 0: - i += self._len - if i < 0 or i >= self._len: - raise IndexError("numeric range object index out of range") - return self._start + i * self._step - - -def count_cycle(iterable, n=None): - """Cycle through the items from *iterable* up to *n* times, yielding - the number of completed cycles along with each item. If *n* is omitted the - process repeats indefinitely. - - >>> list(count_cycle('AB', 3)) - [(0, 'A'), (0, 'B'), (1, 'A'), (1, 'B'), (2, 'A'), (2, 'B')] - - """ - iterable = tuple(iterable) - if not iterable: - return iter(()) - counter = count() if n is None else range(n) - return ((i, item) for i in counter for item in iterable) - - -def mark_ends(iterable): - """Yield 3-tuples of the form ``(is_first, is_last, item)``. - - >>> list(mark_ends('ABC')) - [(True, False, 'A'), (False, False, 'B'), (False, True, 'C')] - - Use this when looping over an iterable to take special action on its first - and/or last items: - - >>> iterable = ['Header', 100, 200, 'Footer'] - >>> total = 0 - >>> for is_first, is_last, item in mark_ends(iterable): - ... if is_first: - ... continue # Skip the header - ... if is_last: - ... continue # Skip the footer - ... total += item - >>> print(total) - 300 - """ - it = iter(iterable) - - try: - b = next(it) - except StopIteration: - return - - try: - for i in count(): - a = b - b = next(it) - yield i == 0, False, a - - except StopIteration: - yield i == 0, True, a - - -def locate(iterable, pred=bool, window_size=None): - """Yield the index of each item in *iterable* for which *pred* returns - ``True``. - - *pred* defaults to :func:`bool`, which will select truthy items: - - >>> list(locate([0, 1, 1, 0, 1, 0, 0])) - [1, 2, 4] - - Set *pred* to a custom function to, e.g., find the indexes for a particular - item. - - >>> list(locate(['a', 'b', 'c', 'b'], lambda x: x == 'b')) - [1, 3] - - If *window_size* is given, then the *pred* function will be called with - that many items. This enables searching for sub-sequences: - - >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] - >>> pred = lambda *args: args == (1, 2, 3) - >>> list(locate(iterable, pred=pred, window_size=3)) - [1, 5, 9] - - Use with :func:`seekable` to find indexes and then retrieve the associated - items: - - >>> from itertools import count - >>> from more_itertools import seekable - >>> source = (3 * n + 1 if (n % 2) else n // 2 for n in count()) - >>> it = seekable(source) - >>> pred = lambda x: x > 100 - >>> indexes = locate(it, pred=pred) - >>> i = next(indexes) - >>> it.seek(i) - >>> next(it) - 106 - - """ - if window_size is None: - return compress(count(), map(pred, iterable)) - - if window_size < 1: - raise ValueError('window size must be at least 1') - - it = windowed(iterable, window_size, fillvalue=_marker) - return compress(count(), starmap(pred, it)) - - -def lstrip(iterable, pred): - """Yield the items from *iterable*, but strip any from the beginning - for which *pred* returns ``True``. - - For example, to remove a set of items from the start of an iterable: - - >>> iterable = (None, False, None, 1, 2, None, 3, False, None) - >>> pred = lambda x: x in {None, False, ''} - >>> list(lstrip(iterable, pred)) - [1, 2, None, 3, False, None] - - This function is analogous to to :func:`str.lstrip`, and is essentially - an wrapper for :func:`itertools.dropwhile`. - - """ - return dropwhile(pred, iterable) - - -def rstrip(iterable, pred): - """Yield the items from *iterable*, but strip any from the end - for which *pred* returns ``True``. - - For example, to remove a set of items from the end of an iterable: - - >>> iterable = (None, False, None, 1, 2, None, 3, False, None) - >>> pred = lambda x: x in {None, False, ''} - >>> list(rstrip(iterable, pred)) - [None, False, None, 1, 2, None, 3] - - This function is analogous to :func:`str.rstrip`. - - """ - cache = [] - cache_append = cache.append - cache_clear = cache.clear - for x in iterable: - if pred(x): - cache_append(x) - else: - yield from cache - cache_clear() - yield x - - -def strip(iterable, pred): - """Yield the items from *iterable*, but strip any from the - beginning and end for which *pred* returns ``True``. - - For example, to remove a set of items from both ends of an iterable: - - >>> iterable = (None, False, None, 1, 2, None, 3, False, None) - >>> pred = lambda x: x in {None, False, ''} - >>> list(strip(iterable, pred)) - [1, 2, None, 3] - - This function is analogous to :func:`str.strip`. - - """ - return rstrip(lstrip(iterable, pred), pred) - - -class islice_extended: - """An extension of :func:`itertools.islice` that supports negative values - for *stop*, *start*, and *step*. - - >>> iterable = iter('abcdefgh') - >>> list(islice_extended(iterable, -4, -1)) - ['e', 'f', 'g'] - - Slices with negative values require some caching of *iterable*, but this - function takes care to minimize the amount of memory required. - - For example, you can use a negative step with an infinite iterator: - - >>> from itertools import count - >>> list(islice_extended(count(), 110, 99, -2)) - [110, 108, 106, 104, 102, 100] - - You can also use slice notation directly: - - >>> iterable = map(str, count()) - >>> it = islice_extended(iterable)[10:20:2] - >>> list(it) - ['10', '12', '14', '16', '18'] - - """ - - def __init__(self, iterable, *args): - it = iter(iterable) - if args: - self._iterable = _islice_helper(it, slice(*args)) - else: - self._iterable = it - - def __iter__(self): - return self - - def __next__(self): - return next(self._iterable) - - def __getitem__(self, key): - if isinstance(key, slice): - return islice_extended(_islice_helper(self._iterable, key)) - - raise TypeError('islice_extended.__getitem__ argument must be a slice') - - -def _islice_helper(it, s): - start = s.start - stop = s.stop - if s.step == 0: - raise ValueError('step argument must be a non-zero integer or None.') - step = s.step or 1 - - if step > 0: - start = 0 if (start is None) else start - - if start < 0: - # Consume all but the last -start items - cache = deque(enumerate(it, 1), maxlen=-start) - len_iter = cache[-1][0] if cache else 0 - - # Adjust start to be positive - i = max(len_iter + start, 0) - - # Adjust stop to be positive - if stop is None: - j = len_iter - elif stop >= 0: - j = min(stop, len_iter) - else: - j = max(len_iter + stop, 0) - - # Slice the cache - n = j - i - if n <= 0: - return - - for index, item in islice(cache, 0, n, step): - yield item - elif (stop is not None) and (stop < 0): - # Advance to the start position - next(islice(it, start, start), None) - - # When stop is negative, we have to carry -stop items while - # iterating - cache = deque(islice(it, -stop), maxlen=-stop) - - for index, item in enumerate(it): - cached_item = cache.popleft() - if index % step == 0: - yield cached_item - cache.append(item) - else: - # When both start and stop are positive we have the normal case - yield from islice(it, start, stop, step) - else: - start = -1 if (start is None) else start - - if (stop is not None) and (stop < 0): - # Consume all but the last items - n = -stop - 1 - cache = deque(enumerate(it, 1), maxlen=n) - len_iter = cache[-1][0] if cache else 0 - - # If start and stop are both negative they are comparable and - # we can just slice. Otherwise we can adjust start to be negative - # and then slice. - if start < 0: - i, j = start, stop - else: - i, j = min(start - len_iter, -1), None - - for index, item in list(cache)[i:j:step]: - yield item - else: - # Advance to the stop position - if stop is not None: - m = stop + 1 - next(islice(it, m, m), None) - - # stop is positive, so if start is negative they are not comparable - # and we need the rest of the items. - if start < 0: - i = start - n = None - # stop is None and start is positive, so we just need items up to - # the start index. - elif stop is None: - i = None - n = start + 1 - # Both stop and start are positive, so they are comparable. - else: - i = None - n = start - stop - if n <= 0: - return - - cache = list(islice(it, n)) - - yield from cache[i::step] - - -def always_reversible(iterable): - """An extension of :func:`reversed` that supports all iterables, not - just those which implement the ``Reversible`` or ``Sequence`` protocols. - - >>> print(*always_reversible(x for x in range(3))) - 2 1 0 - - If the iterable is already reversible, this function returns the - result of :func:`reversed()`. If the iterable is not reversible, - this function will cache the remaining items in the iterable and - yield them in reverse order, which may require significant storage. - """ - try: - return reversed(iterable) - except TypeError: - return reversed(list(iterable)) - - -def consecutive_groups(iterable, ordering=lambda x: x): - """Yield groups of consecutive items using :func:`itertools.groupby`. - The *ordering* function determines whether two items are adjacent by - returning their position. - - By default, the ordering function is the identity function. This is - suitable for finding runs of numbers: - - >>> iterable = [1, 10, 11, 12, 20, 30, 31, 32, 33, 40] - >>> for group in consecutive_groups(iterable): - ... print(list(group)) - [1] - [10, 11, 12] - [20] - [30, 31, 32, 33] - [40] - - For finding runs of adjacent letters, try using the :meth:`index` method - of a string of letters: - - >>> from string import ascii_lowercase - >>> iterable = 'abcdfgilmnop' - >>> ordering = ascii_lowercase.index - >>> for group in consecutive_groups(iterable, ordering): - ... print(list(group)) - ['a', 'b', 'c', 'd'] - ['f', 'g'] - ['i'] - ['l', 'm', 'n', 'o', 'p'] - - Each group of consecutive items is an iterator that shares it source with - *iterable*. When an an output group is advanced, the previous group is - no longer available unless its elements are copied (e.g., into a ``list``). - - >>> iterable = [1, 2, 11, 12, 21, 22] - >>> saved_groups = [] - >>> for group in consecutive_groups(iterable): - ... saved_groups.append(list(group)) # Copy group elements - >>> saved_groups - [[1, 2], [11, 12], [21, 22]] - - """ - for k, g in groupby( - enumerate(iterable), key=lambda x: x[0] - ordering(x[1]) - ): - yield map(itemgetter(1), g) - - -def difference(iterable, func=sub, *, initial=None): - """This function is the inverse of :func:`itertools.accumulate`. By default - it will compute the first difference of *iterable* using - :func:`operator.sub`: - - >>> from itertools import accumulate - >>> iterable = accumulate([0, 1, 2, 3, 4]) # produces 0, 1, 3, 6, 10 - >>> list(difference(iterable)) - [0, 1, 2, 3, 4] - - *func* defaults to :func:`operator.sub`, but other functions can be - specified. They will be applied as follows:: - - A, B, C, D, ... --> A, func(B, A), func(C, B), func(D, C), ... - - For example, to do progressive division: - - >>> iterable = [1, 2, 6, 24, 120] - >>> func = lambda x, y: x // y - >>> list(difference(iterable, func)) - [1, 2, 3, 4, 5] - - If the *initial* keyword is set, the first element will be skipped when - computing successive differences. - - >>> it = [10, 11, 13, 16] # from accumulate([1, 2, 3], initial=10) - >>> list(difference(it, initial=10)) - [1, 2, 3] - - """ - a, b = tee(iterable) - try: - first = [next(b)] - except StopIteration: - return iter([]) - - if initial is not None: - first = [] - - return chain(first, starmap(func, zip(b, a))) - - -class SequenceView(Sequence): - """Return a read-only view of the sequence object *target*. - - :class:`SequenceView` objects are analogous to Python's built-in - "dictionary view" types. They provide a dynamic view of a sequence's items, - meaning that when the sequence updates, so does the view. - - >>> seq = ['0', '1', '2'] - >>> view = SequenceView(seq) - >>> view - SequenceView(['0', '1', '2']) - >>> seq.append('3') - >>> view - SequenceView(['0', '1', '2', '3']) - - Sequence views support indexing, slicing, and length queries. They act - like the underlying sequence, except they don't allow assignment: - - >>> view[1] - '1' - >>> view[1:-1] - ['1', '2'] - >>> len(view) - 4 - - Sequence views are useful as an alternative to copying, as they don't - require (much) extra storage. - - """ - - def __init__(self, target): - if not isinstance(target, Sequence): - raise TypeError - self._target = target - - def __getitem__(self, index): - return self._target[index] - - def __len__(self): - return len(self._target) - - def __repr__(self): - return '{}({})'.format(self.__class__.__name__, repr(self._target)) - - -class seekable: - """Wrap an iterator to allow for seeking backward and forward. This - progressively caches the items in the source iterable so they can be - re-visited. - - Call :meth:`seek` with an index to seek to that position in the source - iterable. - - To "reset" an iterator, seek to ``0``: - - >>> from itertools import count - >>> it = seekable((str(n) for n in count())) - >>> next(it), next(it), next(it) - ('0', '1', '2') - >>> it.seek(0) - >>> next(it), next(it), next(it) - ('0', '1', '2') - >>> next(it) - '3' - - You can also seek forward: - - >>> it = seekable((str(n) for n in range(20))) - >>> it.seek(10) - >>> next(it) - '10' - >>> it.seek(20) # Seeking past the end of the source isn't a problem - >>> list(it) - [] - >>> it.seek(0) # Resetting works even after hitting the end - >>> next(it), next(it), next(it) - ('0', '1', '2') - - Call :meth:`peek` to look ahead one item without advancing the iterator: - - >>> it = seekable('1234') - >>> it.peek() - '1' - >>> list(it) - ['1', '2', '3', '4'] - >>> it.peek(default='empty') - 'empty' - - Before the iterator is at its end, calling :func:`bool` on it will return - ``True``. After it will return ``False``: - - >>> it = seekable('5678') - >>> bool(it) - True - >>> list(it) - ['5', '6', '7', '8'] - >>> bool(it) - False - - You may view the contents of the cache with the :meth:`elements` method. - That returns a :class:`SequenceView`, a view that updates automatically: - - >>> it = seekable((str(n) for n in range(10))) - >>> next(it), next(it), next(it) - ('0', '1', '2') - >>> elements = it.elements() - >>> elements - SequenceView(['0', '1', '2']) - >>> next(it) - '3' - >>> elements - SequenceView(['0', '1', '2', '3']) - - By default, the cache grows as the source iterable progresses, so beware of - wrapping very large or infinite iterables. Supply *maxlen* to limit the - size of the cache (this of course limits how far back you can seek). - - >>> from itertools import count - >>> it = seekable((str(n) for n in count()), maxlen=2) - >>> next(it), next(it), next(it), next(it) - ('0', '1', '2', '3') - >>> list(it.elements()) - ['2', '3'] - >>> it.seek(0) - >>> next(it), next(it), next(it), next(it) - ('2', '3', '4', '5') - >>> next(it) - '6' - - """ - - def __init__(self, iterable, maxlen=None): - self._source = iter(iterable) - if maxlen is None: - self._cache = [] - else: - self._cache = deque([], maxlen) - self._index = None - - def __iter__(self): - return self - - def __next__(self): - if self._index is not None: - try: - item = self._cache[self._index] - except IndexError: - self._index = None - else: - self._index += 1 - return item - - item = next(self._source) - self._cache.append(item) - return item - - def __bool__(self): - try: - self.peek() - except StopIteration: - return False - return True - - def peek(self, default=_marker): - try: - peeked = next(self) - except StopIteration: - if default is _marker: - raise - return default - if self._index is None: - self._index = len(self._cache) - self._index -= 1 - return peeked - - def elements(self): - return SequenceView(self._cache) - - def seek(self, index): - self._index = index - remainder = index - len(self._cache) - if remainder > 0: - consume(self, remainder) - - -class run_length: - """ - :func:`run_length.encode` compresses an iterable with run-length encoding. - It yields groups of repeated items with the count of how many times they - were repeated: - - >>> uncompressed = 'abbcccdddd' - >>> list(run_length.encode(uncompressed)) - [('a', 1), ('b', 2), ('c', 3), ('d', 4)] - - :func:`run_length.decode` decompresses an iterable that was previously - compressed with run-length encoding. It yields the items of the - decompressed iterable: - - >>> compressed = [('a', 1), ('b', 2), ('c', 3), ('d', 4)] - >>> list(run_length.decode(compressed)) - ['a', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd', 'd'] - - """ - - @staticmethod - def encode(iterable): - return ((k, ilen(g)) for k, g in groupby(iterable)) - - @staticmethod - def decode(iterable): - return chain.from_iterable(repeat(k, n) for k, n in iterable) - - -def exactly_n(iterable, n, predicate=bool): - """Return ``True`` if exactly ``n`` items in the iterable are ``True`` - according to the *predicate* function. - - >>> exactly_n([True, True, False], 2) - True - >>> exactly_n([True, True, False], 1) - False - >>> exactly_n([0, 1, 2, 3, 4, 5], 3, lambda x: x < 3) - True - - The iterable will be advanced until ``n + 1`` truthy items are encountered, - so avoid calling it on infinite iterables. - - """ - return len(take(n + 1, filter(predicate, iterable))) == n - - -def circular_shifts(iterable): - """Return a list of circular shifts of *iterable*. - - >>> circular_shifts(range(4)) - [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)] - """ - lst = list(iterable) - return take(len(lst), windowed(cycle(lst), len(lst))) - - -def make_decorator(wrapping_func, result_index=0): - """Return a decorator version of *wrapping_func*, which is a function that - modifies an iterable. *result_index* is the position in that function's - signature where the iterable goes. - - This lets you use itertools on the "production end," i.e. at function - definition. This can augment what the function returns without changing the - function's code. - - For example, to produce a decorator version of :func:`chunked`: - - >>> from more_itertools import chunked - >>> chunker = make_decorator(chunked, result_index=0) - >>> @chunker(3) - ... def iter_range(n): - ... return iter(range(n)) - ... - >>> list(iter_range(9)) - [[0, 1, 2], [3, 4, 5], [6, 7, 8]] - - To only allow truthy items to be returned: - - >>> truth_serum = make_decorator(filter, result_index=1) - >>> @truth_serum(bool) - ... def boolean_test(): - ... return [0, 1, '', ' ', False, True] - ... - >>> list(boolean_test()) - [1, ' ', True] - - The :func:`peekable` and :func:`seekable` wrappers make for practical - decorators: - - >>> from more_itertools import peekable - >>> peekable_function = make_decorator(peekable) - >>> @peekable_function() - ... def str_range(*args): - ... return (str(x) for x in range(*args)) - ... - >>> it = str_range(1, 20, 2) - >>> next(it), next(it), next(it) - ('1', '3', '5') - >>> it.peek() - '7' - >>> next(it) - '7' - - """ - # See https://sites.google.com/site/bbayles/index/decorator_factory for - # notes on how this works. - def decorator(*wrapping_args, **wrapping_kwargs): - def outer_wrapper(f): - def inner_wrapper(*args, **kwargs): - result = f(*args, **kwargs) - wrapping_args_ = list(wrapping_args) - wrapping_args_.insert(result_index, result) - return wrapping_func(*wrapping_args_, **wrapping_kwargs) - - return inner_wrapper - - return outer_wrapper - - return decorator - - -def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None): - """Return a dictionary that maps the items in *iterable* to categories - defined by *keyfunc*, transforms them with *valuefunc*, and - then summarizes them by category with *reducefunc*. - - *valuefunc* defaults to the identity function if it is unspecified. - If *reducefunc* is unspecified, no summarization takes place: - - >>> keyfunc = lambda x: x.upper() - >>> result = map_reduce('abbccc', keyfunc) - >>> sorted(result.items()) - [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])] - - Specifying *valuefunc* transforms the categorized items: - - >>> keyfunc = lambda x: x.upper() - >>> valuefunc = lambda x: 1 - >>> result = map_reduce('abbccc', keyfunc, valuefunc) - >>> sorted(result.items()) - [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])] - - Specifying *reducefunc* summarizes the categorized items: - - >>> keyfunc = lambda x: x.upper() - >>> valuefunc = lambda x: 1 - >>> reducefunc = sum - >>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc) - >>> sorted(result.items()) - [('A', 1), ('B', 2), ('C', 3)] - - You may want to filter the input iterable before applying the map/reduce - procedure: - - >>> all_items = range(30) - >>> items = [x for x in all_items if 10 <= x <= 20] # Filter - >>> keyfunc = lambda x: x % 2 # Evens map to 0; odds to 1 - >>> categories = map_reduce(items, keyfunc=keyfunc) - >>> sorted(categories.items()) - [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])] - >>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum) - >>> sorted(summaries.items()) - [(0, 90), (1, 75)] - - Note that all items in the iterable are gathered into a list before the - summarization step, which may require significant storage. - - The returned object is a :obj:`collections.defaultdict` with the - ``default_factory`` set to ``None``, such that it behaves like a normal - dictionary. - - """ - valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc - - ret = defaultdict(list) - for item in iterable: - key = keyfunc(item) - value = valuefunc(item) - ret[key].append(value) - - if reducefunc is not None: - for key, value_list in ret.items(): - ret[key] = reducefunc(value_list) - - ret.default_factory = None - return ret - - -def rlocate(iterable, pred=bool, window_size=None): - """Yield the index of each item in *iterable* for which *pred* returns - ``True``, starting from the right and moving left. - - *pred* defaults to :func:`bool`, which will select truthy items: - - >>> list(rlocate([0, 1, 1, 0, 1, 0, 0])) # Truthy at 1, 2, and 4 - [4, 2, 1] - - Set *pred* to a custom function to, e.g., find the indexes for a particular - item: - - >>> iterable = iter('abcb') - >>> pred = lambda x: x == 'b' - >>> list(rlocate(iterable, pred)) - [3, 1] - - If *window_size* is given, then the *pred* function will be called with - that many items. This enables searching for sub-sequences: - - >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] - >>> pred = lambda *args: args == (1, 2, 3) - >>> list(rlocate(iterable, pred=pred, window_size=3)) - [9, 5, 1] - - Beware, this function won't return anything for infinite iterables. - If *iterable* is reversible, ``rlocate`` will reverse it and search from - the right. Otherwise, it will search from the left and return the results - in reverse order. - - See :func:`locate` to for other example applications. - - """ - if window_size is None: - try: - len_iter = len(iterable) - return (len_iter - i - 1 for i in locate(reversed(iterable), pred)) - except TypeError: - pass - - return reversed(list(locate(iterable, pred, window_size))) - - -def replace(iterable, pred, substitutes, count=None, window_size=1): - """Yield the items from *iterable*, replacing the items for which *pred* - returns ``True`` with the items from the iterable *substitutes*. - - >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1] - >>> pred = lambda x: x == 0 - >>> substitutes = (2, 3) - >>> list(replace(iterable, pred, substitutes)) - [1, 1, 2, 3, 1, 1, 2, 3, 1, 1] - - If *count* is given, the number of replacements will be limited: - - >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1, 0] - >>> pred = lambda x: x == 0 - >>> substitutes = [None] - >>> list(replace(iterable, pred, substitutes, count=2)) - [1, 1, None, 1, 1, None, 1, 1, 0] - - Use *window_size* to control the number of items passed as arguments to - *pred*. This allows for locating and replacing subsequences. - - >>> iterable = [0, 1, 2, 5, 0, 1, 2, 5] - >>> window_size = 3 - >>> pred = lambda *args: args == (0, 1, 2) # 3 items passed to pred - >>> substitutes = [3, 4] # Splice in these items - >>> list(replace(iterable, pred, substitutes, window_size=window_size)) - [3, 4, 5, 3, 4, 5] - - """ - if window_size < 1: - raise ValueError('window_size must be at least 1') - - # Save the substitutes iterable, since it's used more than once - substitutes = tuple(substitutes) - - # Add padding such that the number of windows matches the length of the - # iterable - it = chain(iterable, [_marker] * (window_size - 1)) - windows = windowed(it, window_size) - - n = 0 - for w in windows: - # If the current window matches our predicate (and we haven't hit - # our maximum number of replacements), splice in the substitutes - # and then consume the following windows that overlap with this one. - # For example, if the iterable is (0, 1, 2, 3, 4...) - # and the window size is 2, we have (0, 1), (1, 2), (2, 3)... - # If the predicate matches on (0, 1), we need to zap (0, 1) and (1, 2) - if pred(*w): - if (count is None) or (n < count): - n += 1 - yield from substitutes - consume(windows, window_size - 1) - continue - - # If there was no match (or we've reached the replacement limit), - # yield the first item from the window. - if w and (w[0] is not _marker): - yield w[0] - - -def partitions(iterable): - """Yield all possible order-preserving partitions of *iterable*. - - >>> iterable = 'abc' - >>> for part in partitions(iterable): - ... print([''.join(p) for p in part]) - ['abc'] - ['a', 'bc'] - ['ab', 'c'] - ['a', 'b', 'c'] - - This is unrelated to :func:`partition`. - - """ - sequence = list(iterable) - n = len(sequence) - for i in powerset(range(1, n)): - yield [sequence[i:j] for i, j in zip((0,) + i, i + (n,))] - - -def set_partitions(iterable, k=None): - """ - Yield the set partitions of *iterable* into *k* parts. Set partitions are - not order-preserving. - - >>> iterable = 'abc' - >>> for part in set_partitions(iterable, 2): - ... print([''.join(p) for p in part]) - ['a', 'bc'] - ['ab', 'c'] - ['b', 'ac'] - - - If *k* is not given, every set partition is generated. - - >>> iterable = 'abc' - >>> for part in set_partitions(iterable): - ... print([''.join(p) for p in part]) - ['abc'] - ['a', 'bc'] - ['ab', 'c'] - ['b', 'ac'] - ['a', 'b', 'c'] - - """ - L = list(iterable) - n = len(L) - if k is not None: - if k < 1: - raise ValueError( - "Can't partition in a negative or zero number of groups" - ) - elif k > n: - return - - def set_partitions_helper(L, k): - n = len(L) - if k == 1: - yield [L] - elif n == k: - yield [[s] for s in L] - else: - e, *M = L - for p in set_partitions_helper(M, k - 1): - yield [[e], *p] - for p in set_partitions_helper(M, k): - for i in range(len(p)): - yield p[:i] + [[e] + p[i]] + p[i + 1 :] - - if k is None: - for k in range(1, n + 1): - yield from set_partitions_helper(L, k) - else: - yield from set_partitions_helper(L, k) - - -class time_limited: - """ - Yield items from *iterable* until *limit_seconds* have passed. - If the time limit expires before all items have been yielded, the - ``timed_out`` parameter will be set to ``True``. - - >>> from time import sleep - >>> def generator(): - ... yield 1 - ... yield 2 - ... sleep(0.2) - ... yield 3 - >>> iterable = time_limited(0.1, generator()) - >>> list(iterable) - [1, 2] - >>> iterable.timed_out - True - - Note that the time is checked before each item is yielded, and iteration - stops if the time elapsed is greater than *limit_seconds*. If your time - limit is 1 second, but it takes 2 seconds to generate the first item from - the iterable, the function will run for 2 seconds and not yield anything. - - """ - - def __init__(self, limit_seconds, iterable): - if limit_seconds < 0: - raise ValueError('limit_seconds must be positive') - self.limit_seconds = limit_seconds - self._iterable = iter(iterable) - self._start_time = monotonic() - self.timed_out = False - - def __iter__(self): - return self - - def __next__(self): - item = next(self._iterable) - if monotonic() - self._start_time > self.limit_seconds: - self.timed_out = True - raise StopIteration - - return item - - -def only(iterable, default=None, too_long=None): - """If *iterable* has only one item, return it. - If it has zero items, return *default*. - If it has more than one item, raise the exception given by *too_long*, - which is ``ValueError`` by default. - - >>> only([], default='missing') - 'missing' - >>> only([1]) - 1 - >>> only([1, 2]) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - ValueError: Expected exactly one item in iterable, but got 1, 2, - and perhaps more.' - >>> only([1, 2], too_long=TypeError) # doctest: +IGNORE_EXCEPTION_DETAIL - Traceback (most recent call last): - ... - TypeError - - Note that :func:`only` attempts to advance *iterable* twice to ensure there - is only one item. See :func:`spy` or :func:`peekable` to check - iterable contents less destructively. - """ - it = iter(iterable) - first_value = next(it, default) - - try: - second_value = next(it) - except StopIteration: - pass - else: - msg = ( - 'Expected exactly one item in iterable, but got {!r}, {!r}, ' - 'and perhaps more.'.format(first_value, second_value) - ) - raise too_long or ValueError(msg) - - return first_value - - -def ichunked(iterable, n): - """Break *iterable* into sub-iterables with *n* elements each. - :func:`ichunked` is like :func:`chunked`, but it yields iterables - instead of lists. - - If the sub-iterables are read in order, the elements of *iterable* - won't be stored in memory. - If they are read out of order, :func:`itertools.tee` is used to cache - elements as necessary. - - >>> from itertools import count - >>> all_chunks = ichunked(count(), 4) - >>> c_1, c_2, c_3 = next(all_chunks), next(all_chunks), next(all_chunks) - >>> list(c_2) # c_1's elements have been cached; c_3's haven't been - [4, 5, 6, 7] - >>> list(c_1) - [0, 1, 2, 3] - >>> list(c_3) - [8, 9, 10, 11] - - """ - source = iter(iterable) - - while True: - # Check to see whether we're at the end of the source iterable - item = next(source, _marker) - if item is _marker: - return - - # Clone the source and yield an n-length slice - source, it = tee(chain([item], source)) - yield islice(it, n) - - # Advance the source iterable - consume(source, n) - - -def distinct_combinations(iterable, r): - """Yield the distinct combinations of *r* items taken from *iterable*. - - >>> list(distinct_combinations([0, 0, 1], 2)) - [(0, 0), (0, 1)] - - Equivalent to ``set(combinations(iterable))``, except duplicates are not - generated and thrown away. For larger input sequences this is much more - efficient. - - """ - if r < 0: - raise ValueError('r must be non-negative') - elif r == 0: - yield () - return - pool = tuple(iterable) - generators = [unique_everseen(enumerate(pool), key=itemgetter(1))] - current_combo = [None] * r - level = 0 - while generators: - try: - cur_idx, p = next(generators[-1]) - except StopIteration: - generators.pop() - level -= 1 - continue - current_combo[level] = p - if level + 1 == r: - yield tuple(current_combo) - else: - generators.append( - unique_everseen( - enumerate(pool[cur_idx + 1 :], cur_idx + 1), - key=itemgetter(1), - ) - ) - level += 1 - - -def filter_except(validator, iterable, *exceptions): - """Yield the items from *iterable* for which the *validator* function does - not raise one of the specified *exceptions*. - - *validator* is called for each item in *iterable*. - It should be a function that accepts one argument and raises an exception - if that item is not valid. - - >>> iterable = ['1', '2', 'three', '4', None] - >>> list(filter_except(int, iterable, ValueError, TypeError)) - ['1', '2', '4'] - - If an exception other than one given by *exceptions* is raised by - *validator*, it is raised like normal. - """ - for item in iterable: - try: - validator(item) - except exceptions: - pass - else: - yield item - - -def map_except(function, iterable, *exceptions): - """Transform each item from *iterable* with *function* and yield the - result, unless *function* raises one of the specified *exceptions*. - - *function* is called to transform each item in *iterable*. - It should be a accept one argument. - - >>> iterable = ['1', '2', 'three', '4', None] - >>> list(map_except(int, iterable, ValueError, TypeError)) - [1, 2, 4] - - If an exception other than one given by *exceptions* is raised by - *function*, it is raised like normal. - """ - for item in iterable: - try: - yield function(item) - except exceptions: - pass - - -def _sample_unweighted(iterable, k): - # Implementation of "Algorithm L" from the 1994 paper by Kim-Hung Li: - # "Reservoir-Sampling Algorithms of Time Complexity O(n(1+log(N/n)))". - - # Fill up the reservoir (collection of samples) with the first `k` samples - reservoir = take(k, iterable) - - # Generate random number that's the largest in a sample of k U(0,1) numbers - # Largest order statistic: https://en.wikipedia.org/wiki/Order_statistic - W = exp(log(random()) / k) - - # The number of elements to skip before changing the reservoir is a random - # number with a geometric distribution. Sample it using random() and logs. - next_index = k + floor(log(random()) / log(1 - W)) - - for index, element in enumerate(iterable, k): - - if index == next_index: - reservoir[randrange(k)] = element - # The new W is the largest in a sample of k U(0, `old_W`) numbers - W *= exp(log(random()) / k) - next_index += floor(log(random()) / log(1 - W)) + 1 - - return reservoir - - -def _sample_weighted(iterable, k, weights): - # Implementation of "A-ExpJ" from the 2006 paper by Efraimidis et al. : - # "Weighted random sampling with a reservoir". - - # Log-transform for numerical stability for weights that are small/large - weight_keys = (log(random()) / weight for weight in weights) - - # Fill up the reservoir (collection of samples) with the first `k` - # weight-keys and elements, then heapify the list. - reservoir = take(k, zip(weight_keys, iterable)) - heapify(reservoir) - - # The number of jumps before changing the reservoir is a random variable - # with an exponential distribution. Sample it using random() and logs. - smallest_weight_key, _ = reservoir[0] - weights_to_skip = log(random()) / smallest_weight_key - - for weight, element in zip(weights, iterable): - if weight >= weights_to_skip: - # The notation here is consistent with the paper, but we store - # the weight-keys in log-space for better numerical stability. - smallest_weight_key, _ = reservoir[0] - t_w = exp(weight * smallest_weight_key) - r_2 = uniform(t_w, 1) # generate U(t_w, 1) - weight_key = log(r_2) / weight - heapreplace(reservoir, (weight_key, element)) - smallest_weight_key, _ = reservoir[0] - weights_to_skip = log(random()) / smallest_weight_key - else: - weights_to_skip -= weight - - # Equivalent to [element for weight_key, element in sorted(reservoir)] - return [heappop(reservoir)[1] for _ in range(k)] - - -def sample(iterable, k, weights=None): - """Return a *k*-length list of elements chosen (without replacement) - from the *iterable*. Like :func:`random.sample`, but works on iterables - of unknown length. - - >>> iterable = range(100) - >>> sample(iterable, 5) # doctest: +SKIP - [81, 60, 96, 16, 4] - - An iterable with *weights* may also be given: - - >>> iterable = range(100) - >>> weights = (i * i + 1 for i in range(100)) - >>> sampled = sample(iterable, 5, weights=weights) # doctest: +SKIP - [79, 67, 74, 66, 78] - - The algorithm can also be used to generate weighted random permutations. - The relative weight of each item determines the probability that it - appears late in the permutation. - - >>> data = "abcdefgh" - >>> weights = range(1, len(data) + 1) - >>> sample(data, k=len(data), weights=weights) # doctest: +SKIP - ['c', 'a', 'b', 'e', 'g', 'd', 'h', 'f'] - """ - if k == 0: - return [] - - iterable = iter(iterable) - if weights is None: - return _sample_unweighted(iterable, k) - else: - weights = iter(weights) - return _sample_weighted(iterable, k, weights) - - -def is_sorted(iterable, key=None, reverse=False): - """Returns ``True`` if the items of iterable are in sorted order, and - ``False`` otherwise. *key* and *reverse* have the same meaning that they do - in the built-in :func:`sorted` function. - - >>> is_sorted(['1', '2', '3', '4', '5'], key=int) - True - >>> is_sorted([5, 4, 3, 1, 2], reverse=True) - False - - The function returns ``False`` after encountering the first out-of-order - item. If there are no out-of-order items, the iterable is exhausted. - """ - - compare = lt if reverse else gt - it = iterable if (key is None) else map(key, iterable) - return not any(starmap(compare, pairwise(it))) - - -class AbortThread(BaseException): - pass - - -class callback_iter: - """Convert a function that uses callbacks to an iterator. - - Let *func* be a function that takes a `callback` keyword argument. - For example: - - >>> def func(callback=None): - ... for i, c in [(1, 'a'), (2, 'b'), (3, 'c')]: - ... if callback: - ... callback(i, c) - ... return 4 - - - Use ``with callback_iter(func)`` to get an iterator over the parameters - that are delivered to the callback. - - >>> with callback_iter(func) as it: - ... for args, kwargs in it: - ... print(args) - (1, 'a') - (2, 'b') - (3, 'c') - - The function will be called in a background thread. The ``done`` property - indicates whether it has completed execution. - - >>> it.done - True - - If it completes successfully, its return value will be available - in the ``result`` property. - - >>> it.result - 4 - - Notes: - - * If the function uses some keyword argument besides ``callback``, supply - *callback_kwd*. - * If it finished executing, but raised an exception, accessing the - ``result`` property will raise the same exception. - * If it hasn't finished executing, accessing the ``result`` - property from within the ``with`` block will raise ``RuntimeError``. - * If it hasn't finished executing, accessing the ``result`` property from - outside the ``with`` block will raise a - ``more_itertools.AbortThread`` exception. - * Provide *wait_seconds* to adjust how frequently the it is polled for - output. - - """ - - def __init__(self, func, callback_kwd='callback', wait_seconds=0.1): - self._func = func - self._callback_kwd = callback_kwd - self._aborted = False - self._future = None - self._wait_seconds = wait_seconds - self._executor = __import__("concurrent.futures").futures.ThreadPoolExecutor(max_workers=1) - self._iterator = self._reader() - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, traceback): - self._aborted = True - self._executor.shutdown() - - def __iter__(self): - return self - - def __next__(self): - return next(self._iterator) - - @property - def done(self): - if self._future is None: - return False - return self._future.done() - - @property - def result(self): - if not self.done: - raise RuntimeError('Function has not yet completed') - - return self._future.result() - - def _reader(self): - q = Queue() - - def callback(*args, **kwargs): - if self._aborted: - raise AbortThread('canceled by user') - - q.put((args, kwargs)) - - self._future = self._executor.submit( - self._func, **{self._callback_kwd: callback} - ) - - while True: - try: - item = q.get(timeout=self._wait_seconds) - except Empty: - pass - else: - q.task_done() - yield item - - if self._future.done(): - break - - remaining = [] - while True: - try: - item = q.get_nowait() - except Empty: - break - else: - q.task_done() - remaining.append(item) - q.join() - yield from remaining - - -def windowed_complete(iterable, n): - """ - Yield ``(beginning, middle, end)`` tuples, where: - - * Each ``middle`` has *n* items from *iterable* - * Each ``beginning`` has the items before the ones in ``middle`` - * Each ``end`` has the items after the ones in ``middle`` - - >>> iterable = range(7) - >>> n = 3 - >>> for beginning, middle, end in windowed_complete(iterable, n): - ... print(beginning, middle, end) - () (0, 1, 2) (3, 4, 5, 6) - (0,) (1, 2, 3) (4, 5, 6) - (0, 1) (2, 3, 4) (5, 6) - (0, 1, 2) (3, 4, 5) (6,) - (0, 1, 2, 3) (4, 5, 6) () - - Note that *n* must be at least 0 and most equal to the length of - *iterable*. - - This function will exhaust the iterable and may require significant - storage. - """ - if n < 0: - raise ValueError('n must be >= 0') - - seq = tuple(iterable) - size = len(seq) - - if n > size: - raise ValueError('n must be <= len(seq)') - - for i in range(size - n + 1): - beginning = seq[:i] - middle = seq[i : i + n] - end = seq[i + n :] - yield beginning, middle, end - - -def all_unique(iterable, key=None): - """ - Returns ``True`` if all the elements of *iterable* are unique (no two - elements are equal). - - >>> all_unique('ABCB') - False - - If a *key* function is specified, it will be used to make comparisons. - - >>> all_unique('ABCb') - True - >>> all_unique('ABCb', str.lower) - False - - The function returns as soon as the first non-unique element is - encountered. Iterables with a mix of hashable and unhashable items can - be used, but the function will be slower for unhashable items. - """ - seenset = set() - seenset_add = seenset.add - seenlist = [] - seenlist_add = seenlist.append - for element in map(key, iterable) if key else iterable: - try: - if element in seenset: - return False - seenset_add(element) - except TypeError: - if element in seenlist: - return False - seenlist_add(element) - return True - - -def nth_product(index, *args): - """Equivalent to ``list(product(*args))[index]``. - - The products of *args* can be ordered lexicographically. - :func:`nth_product` computes the product at sort position *index* without - computing the previous products. - - >>> nth_product(8, range(2), range(2), range(2), range(2)) - (1, 0, 0, 0) - - ``IndexError`` will be raised if the given *index* is invalid. - """ - pools = list(map(tuple, reversed(args))) - ns = list(map(len, pools)) - - c = reduce(mul, ns) - - if index < 0: - index += c - - if not 0 <= index < c: - raise IndexError - - result = [] - for pool, n in zip(pools, ns): - result.append(pool[index % n]) - index //= n - - return tuple(reversed(result)) - - -def nth_permutation(iterable, r, index): - """Equivalent to ``list(permutations(iterable, r))[index]``` - - The subsequences of *iterable* that are of length *r* where order is - important can be ordered lexicographically. :func:`nth_permutation` - computes the subsequence at sort position *index* directly, without - computing the previous subsequences. - - >>> nth_permutation('ghijk', 2, 5) - ('h', 'i') - - ``ValueError`` will be raised If *r* is negative or greater than the length - of *iterable*. - ``IndexError`` will be raised if the given *index* is invalid. - """ - pool = list(iterable) - n = len(pool) - - if r is None or r == n: - r, c = n, factorial(n) - elif not 0 <= r < n: - raise ValueError - else: - c = factorial(n) // factorial(n - r) - - if index < 0: - index += c - - if not 0 <= index < c: - raise IndexError - - if c == 0: - return tuple() - - result = [0] * r - q = index * factorial(n) // c if r < n else index - for d in range(1, n + 1): - q, i = divmod(q, d) - if 0 <= n - d < r: - result[n - d] = i - if q == 0: - break - - return tuple(map(pool.pop, result)) - - -def value_chain(*args): - """Yield all arguments passed to the function in the same order in which - they were passed. If an argument itself is iterable then iterate over its - values. - - >>> list(value_chain(1, 2, 3, [4, 5, 6])) - [1, 2, 3, 4, 5, 6] - - Binary and text strings are not considered iterable and are emitted - as-is: - - >>> list(value_chain('12', '34', ['56', '78'])) - ['12', '34', '56', '78'] - - - Multiple levels of nesting are not flattened. - - """ - for value in args: - if isinstance(value, (str, bytes)): - yield value - continue - try: - yield from value - except TypeError: - yield value - - -def product_index(element, *args): - """Equivalent to ``list(product(*args)).index(element)`` - - The products of *args* can be ordered lexicographically. - :func:`product_index` computes the first index of *element* without - computing the previous products. - - >>> product_index([8, 2], range(10), range(5)) - 42 - - ``ValueError`` will be raised if the given *element* isn't in the product - of *args*. - """ - index = 0 - - for x, pool in zip_longest(element, args, fillvalue=_marker): - if x is _marker or pool is _marker: - raise ValueError('element is not a product of args') - - pool = tuple(pool) - index = index * len(pool) + pool.index(x) - - return index - - -def combination_index(element, iterable): - """Equivalent to ``list(combinations(iterable, r)).index(element)`` - - The subsequences of *iterable* that are of length *r* can be ordered - lexicographically. :func:`combination_index` computes the index of the - first *element*, without computing the previous combinations. - - >>> combination_index('adf', 'abcdefg') - 10 - - ``ValueError`` will be raised if the given *element* isn't one of the - combinations of *iterable*. - """ - element = enumerate(element) - k, y = next(element, (None, None)) - if k is None: - return 0 - - indexes = [] - pool = enumerate(iterable) - for n, x in pool: - if x == y: - indexes.append(n) - tmp, y = next(element, (None, None)) - if tmp is None: - break - else: - k = tmp - else: - raise ValueError('element is not a combination of iterable') - - n, _ = last(pool, default=(n, None)) - - # Python versiosn below 3.8 don't have math.comb - index = 1 - for i, j in enumerate(reversed(indexes), start=1): - j = n - j - if i <= j: - index += factorial(j) // (factorial(i) * factorial(j - i)) - - return factorial(n + 1) // (factorial(k + 1) * factorial(n - k)) - index - - -def permutation_index(element, iterable): - """Equivalent to ``list(permutations(iterable, r)).index(element)``` - - The subsequences of *iterable* that are of length *r* where order is - important can be ordered lexicographically. :func:`permutation_index` - computes the index of the first *element* directly, without computing - the previous permutations. - - >>> permutation_index([1, 3, 2], range(5)) - 19 - - ``ValueError`` will be raised if the given *element* isn't one of the - permutations of *iterable*. - """ - index = 0 - pool = list(iterable) - for i, x in zip(range(len(pool), -1, -1), element): - r = pool.index(x) - index = index * i + r - del pool[r] - - return index - - -class countable: - """Wrap *iterable* and keep a count of how many items have been consumed. - - The ``items_seen`` attribute starts at ``0`` and increments as the iterable - is consumed: - - >>> iterable = map(str, range(10)) - >>> it = countable(iterable) - >>> it.items_seen - 0 - >>> next(it), next(it) - ('0', '1') - >>> list(it) - ['2', '3', '4', '5', '6', '7', '8', '9'] - >>> it.items_seen - 10 - """ - - def __init__(self, iterable): - self._it = iter(iterable) - self.items_seen = 0 - - def __iter__(self): - return self - - def __next__(self): - item = next(self._it) - self.items_seen += 1 - - return item diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/upload_docs.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/upload_docs.py deleted file mode 100644 index 3263f07f4877ad6f9ecc881c12df29a4a65b03f4..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/upload_docs.py +++ /dev/null @@ -1,213 +0,0 @@ -# -*- coding: utf-8 -*- -"""upload_docs - -Implements a Distutils 'upload_docs' subcommand (upload documentation to -sites other than PyPi such as devpi). -""" - -from base64 import standard_b64encode -from distutils import log -from distutils.errors import DistutilsOptionError -import os -import socket -import zipfile -import tempfile -import shutil -import itertools -import functools -import http.client -import urllib.parse -import warnings - -from .._importlib import metadata -from .. import SetuptoolsDeprecationWarning - -from .upload import upload - - -def _encode(s): - return s.encode('utf-8', 'surrogateescape') - - -class upload_docs(upload): - # override the default repository as upload_docs isn't - # supported by Warehouse (and won't be). - DEFAULT_REPOSITORY = 'https://pypi.python.org/pypi/' - - description = 'Upload documentation to sites other than PyPi such as devpi' - - user_options = [ - ('repository=', 'r', - "url of repository [default: %s]" % upload.DEFAULT_REPOSITORY), - ('show-response', None, - 'display full response text from server'), - ('upload-dir=', None, 'directory to upload'), - ] - boolean_options = upload.boolean_options - - def has_sphinx(self): - return bool( - self.upload_dir is None - and metadata.entry_points(group='distutils.commands', name='build_sphinx') - ) - - sub_commands = [('build_sphinx', has_sphinx)] - - def initialize_options(self): - upload.initialize_options(self) - self.upload_dir = None - self.target_dir = None - - def finalize_options(self): - log.warn( - "Upload_docs command is deprecated. Use Read the Docs " - "(https://readthedocs.org) instead.") - upload.finalize_options(self) - if self.upload_dir is None: - if self.has_sphinx(): - build_sphinx = self.get_finalized_command('build_sphinx') - self.target_dir = dict(build_sphinx.builder_target_dirs)['html'] - else: - build = self.get_finalized_command('build') - self.target_dir = os.path.join(build.build_base, 'docs') - else: - self.ensure_dirname('upload_dir') - self.target_dir = self.upload_dir - self.announce('Using upload directory %s' % self.target_dir) - - def create_zipfile(self, filename): - zip_file = zipfile.ZipFile(filename, "w") - try: - self.mkpath(self.target_dir) # just in case - for root, dirs, files in os.walk(self.target_dir): - if root == self.target_dir and not files: - tmpl = "no files found in upload directory '%s'" - raise DistutilsOptionError(tmpl % self.target_dir) - for name in files: - full = os.path.join(root, name) - relative = root[len(self.target_dir):].lstrip(os.path.sep) - dest = os.path.join(relative, name) - zip_file.write(full, dest) - finally: - zip_file.close() - - def run(self): - warnings.warn( - "upload_docs is deprecated and will be removed in a future " - "version. Use tools like httpie or curl instead.", - SetuptoolsDeprecationWarning, - ) - - # Run sub commands - for cmd_name in self.get_sub_commands(): - self.run_command(cmd_name) - - tmp_dir = tempfile.mkdtemp() - name = self.distribution.metadata.get_name() - zip_file = os.path.join(tmp_dir, "%s.zip" % name) - try: - self.create_zipfile(zip_file) - self.upload_file(zip_file) - finally: - shutil.rmtree(tmp_dir) - - @staticmethod - def _build_part(item, sep_boundary): - key, values = item - title = '\nContent-Disposition: form-data; name="%s"' % key - # handle multiple entries for the same name - if not isinstance(values, list): - values = [values] - for value in values: - if isinstance(value, tuple): - title += '; filename="%s"' % value[0] - value = value[1] - else: - value = _encode(value) - yield sep_boundary - yield _encode(title) - yield b"\n\n" - yield value - if value and value[-1:] == b'\r': - yield b'\n' # write an extra newline (lurve Macs) - - @classmethod - def _build_multipart(cls, data): - """ - Build up the MIME payload for the POST data - """ - boundary = '--------------GHSKFJDLGDS7543FJKLFHRE75642756743254' - sep_boundary = b'\n--' + boundary.encode('ascii') - end_boundary = sep_boundary + b'--' - end_items = end_boundary, b"\n", - builder = functools.partial( - cls._build_part, - sep_boundary=sep_boundary, - ) - part_groups = map(builder, data.items()) - parts = itertools.chain.from_iterable(part_groups) - body_items = itertools.chain(parts, end_items) - content_type = 'multipart/form-data; boundary=%s' % boundary - return b''.join(body_items), content_type - - def upload_file(self, filename): - with open(filename, 'rb') as f: - content = f.read() - meta = self.distribution.metadata - data = { - ':action': 'doc_upload', - 'name': meta.get_name(), - 'content': (os.path.basename(filename), content), - } - # set up the authentication - credentials = _encode(self.username + ':' + self.password) - credentials = standard_b64encode(credentials).decode('ascii') - auth = "Basic " + credentials - - body, ct = self._build_multipart(data) - - msg = "Submitting documentation to %s" % (self.repository) - self.announce(msg, log.INFO) - - # build the Request - # We can't use urllib2 since we need to send the Basic - # auth right with the first request - schema, netloc, url, params, query, fragments = \ - urllib.parse.urlparse(self.repository) - assert not params and not query and not fragments - if schema == 'http': - conn = http.client.HTTPConnection(netloc) - elif schema == 'https': - conn = http.client.HTTPSConnection(netloc) - else: - raise AssertionError("unsupported schema " + schema) - - data = '' - try: - conn.connect() - conn.putrequest("POST", url) - content_type = ct - conn.putheader('Content-type', content_type) - conn.putheader('Content-length', str(len(body))) - conn.putheader('Authorization', auth) - conn.endheaders() - conn.send(body) - except socket.error as e: - self.announce(str(e), log.ERROR) - return - - r = conn.getresponse() - if r.status == 200: - msg = 'Server response (%s): %s' % (r.status, r.reason) - self.announce(msg, log.INFO) - elif r.status == 301: - location = r.getheader('Location') - if location is None: - location = 'https://pythonhosted.org/%s/' % meta.get_name() - msg = 'Upload successful. Visit %s' % location - self.announce(msg, log.INFO) - else: - msg = 'Upload failed (%s): %s' % (r.status, r.reason) - self.announce(msg, log.ERROR) - if self.show_response: - print('-' * 75, r.read(), '-' * 75) diff --git a/spaces/CVPR/GFPGAN-example/gfpgan/models/__init__.py b/spaces/CVPR/GFPGAN-example/gfpgan/models/__init__.py deleted file mode 100644 index 6afad57a3794b867dabbdb617a16355a24d6a8b3..0000000000000000000000000000000000000000 --- a/spaces/CVPR/GFPGAN-example/gfpgan/models/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -import importlib -from basicsr.utils import scandir -from os import path as osp - -# automatically scan and import model modules for registry -# scan all the files that end with '_model.py' under the model folder -model_folder = osp.dirname(osp.abspath(__file__)) -model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] -# import all the model modules -_model_modules = [importlib.import_module(f'gfpgan.models.{file_name}') for file_name in model_filenames] diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/copy_if.h b/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/copy_if.h deleted file mode 100644 index 10869e2a90215ecf1045b36882180acf3e791981..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/copy_if.h +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include - -// this system inherits copy_if -#include - diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/reduce_by_key.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/reduce_by_key.h deleted file mode 100644 index 0605f9befaf97bea651e2fde12c790fcd7103744..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/reduce_by_key.h +++ /dev/null @@ -1,44 +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 - -// the purpose of this header is to #include the reduce_by_key.h header -// of the sequential, host, and device systems. It should be #included in any -// code which uses adl to dispatch reduce_by_key - -#include - -// SCons can't see through the #defines below to figure out what this header -// includes, so we fake it out by specifying all possible files we might end up -// including inside an #if 0. -#if 0 -#include -#include -#include -#include -#endif - -#define __THRUST_HOST_SYSTEM_REDUCE_BY_KEY_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/reduce_by_key.h> -#include __THRUST_HOST_SYSTEM_REDUCE_BY_KEY_HEADER -#undef __THRUST_HOST_SYSTEM_REDUCE_BY_KEY_HEADER - -#define __THRUST_DEVICE_SYSTEM_REDUCE_BY_KEY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/reduce_by_key.h> -#include __THRUST_DEVICE_SYSTEM_REDUCE_BY_KEY_HEADER -#undef __THRUST_DEVICE_SYSTEM_REDUCE_BY_KEY_HEADER - diff --git a/spaces/CVPR/WALT/cwalt/utils.py b/spaces/CVPR/WALT/cwalt/utils.py deleted file mode 100644 index 57f8e05a01cb4895dd95a4175f96a35974ee3ea3..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/cwalt/utils.py +++ /dev/null @@ -1,168 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri May 20 15:16:56 2022 - -@author: dinesh -""" - -import json -import cv2 -from PIL import Image -import numpy as np -from dateutil.parser import parse - -def bb_intersection_over_union(box1, box2): - #print(box1, box2) - boxA = box1.copy() - boxB = box2.copy() - boxA[2] = boxA[0]+boxA[2] - boxA[3] = boxA[1]+boxA[3] - boxB[2] = boxB[0]+boxB[2] - boxB[3] = boxB[1]+boxB[3] - # determine the (x, y)-coordinates of the intersection rectangle - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxA[3], boxB[3]) - - # compute the area of intersection rectangle - interArea = abs(max((xB - xA, 0)) * max((yB - yA), 0)) - - if interArea == 0: - return 0 - # compute the area of both the prediction and ground-truth - # rectangles - boxAArea = abs((boxA[2] - boxA[0]) * (boxA[3] - boxA[1])) - boxBArea = abs((boxB[2] - boxB[0]) * (boxB[3] - boxB[1])) - - # compute the intersection over union by taking the intersection - # area and dividing it by the sum of prediction + ground-truth - # areas - the interesection area - iou = interArea / float(boxAArea + boxBArea - interArea) - return iou - -def bb_intersection_over_union_unoccluded(box1, box2, threshold=0.01): - #print(box1, box2) - boxA = box1.copy() - boxB = box2.copy() - boxA[2] = boxA[0]+boxA[2] - boxA[3] = boxA[1]+boxA[3] - boxB[2] = boxB[0]+boxB[2] - boxB[3] = boxB[1]+boxB[3] - # determine the (x, y)-coordinates of the intersection rectangle - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxA[3], boxB[3]) - - # compute the area of intersection rectangle - interArea = abs(max((xB - xA, 0)) * max((yB - yA), 0)) - - if interArea == 0: - return 0 - # compute the area of both the prediction and ground-truth - # rectangles - boxAArea = abs((boxA[2] - boxA[0]) * (boxA[3] - boxA[1])) - boxBArea = abs((boxB[2] - boxB[0]) * (boxB[3] - boxB[1])) - - # compute the intersection over union by taking the intersection - # area and dividing it by the sum of prediction + ground-truth - # areas - the interesection area - iou = interArea / float(boxAArea + boxBArea - interArea) - - #print(iou) - # return the intersection over union value - occlusion = False - if iou > threshold and iou < 1: - #print(boxA[3], boxB[3], boxB[1]) - if boxA[3] < boxB[3]:# and boxA[3] > boxB[1]: - if boxB[2] > boxA[0]:# and boxB[2] < boxA[2]: - #print('first', (boxB[2] - boxA[0])/(boxA[2] - boxA[0])) - if (min(boxB[2],boxA[2]) - boxA[0])/(boxA[2] - boxA[0]) > threshold: - occlusion = True - - if boxB[0] < boxA[2]: # boxB[0] > boxA[0] and - #print('second', (boxA[2] - boxB[0])/(boxA[2] - boxA[0])) - if (boxA[2] - max(boxB[0],boxA[0]))/(boxA[2] - boxA[0]) > threshold: - occlusion = True - if occlusion == False: - iou = iou*0 - #asas - # asas - #iou = 0.9 #iou*0 - #print(box1, box2, iou, occlusion) - return iou -def draw_tracks(image, tracks): - """ - Draw on input image. - - Args: - image (numpy.ndarray): image - tracks (list): list of tracks to be drawn on the image. - - Returns: - numpy.ndarray: image with the track-ids drawn on it. - """ - - for trk in tracks: - - trk_id = trk[1] - xmin = trk[2] - ymin = trk[3] - width = trk[4] - height = trk[5] - - xcentroid, ycentroid = int(xmin + 0.5*width), int(ymin + 0.5*height) - - text = "ID {}".format(trk_id) - - cv2.putText(image, text, (xcentroid - 10, ycentroid - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) - cv2.circle(image, (xcentroid, ycentroid), 4, (0, 255, 0), -1) - - return image - - -def draw_bboxes(image, tracks): - """ - Draw the bounding boxes about detected objects in the image. - - Args: - image (numpy.ndarray): Image or video frame. - bboxes (numpy.ndarray): Bounding boxes pixel coordinates as (xmin, ymin, width, height) - confidences (numpy.ndarray): Detection confidence or detection probability. - class_ids (numpy.ndarray): Array containing class ids (aka label ids) of each detected object. - - Returns: - numpy.ndarray: image with the bounding boxes drawn on it. - """ - - for trk in tracks: - xmin = int(trk[2]) - ymin = int(trk[3]) - width = int(trk[4]) - height = int(trk[5]) - clr = (np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)) - cv2.rectangle(image, (xmin, ymin), (xmin + width, ymin + height), clr, 2) - - return image - - -def num(v): - number_as_float = float(v) - number_as_int = int(number_as_float) - return number_as_int if number_as_float == number_as_int else number_as_float - - -def parse_bbox(bbox_str): - bbox_list = bbox_str.strip('{').strip('}').split(',') - bbox_list = [num(elem) for elem in bbox_list] - return bbox_list - -def parse_seg(bbox_str): - bbox_list = bbox_str.strip('{').strip('}').split(',') - bbox_list = [num(elem) for elem in bbox_list] - ret = bbox_list # [] - # for i in range(0, len(bbox_list) - 1, 2): - # ret.append((bbox_list[i], bbox_list[i + 1])) - return ret diff --git a/spaces/CVPR/WALT/mmdet/models/detectors/mask_rcnn.py b/spaces/CVPR/WALT/mmdet/models/detectors/mask_rcnn.py deleted file mode 100644 index c15a7733170e059d2825138b3812319915b7cad6..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/models/detectors/mask_rcnn.py +++ /dev/null @@ -1,24 +0,0 @@ -from ..builder import DETECTORS -from .two_stage import TwoStageDetector - - -@DETECTORS.register_module() -class MaskRCNN(TwoStageDetector): - """Implementation of `Mask R-CNN `_""" - - def __init__(self, - backbone, - rpn_head, - roi_head, - train_cfg, - test_cfg, - neck=None, - pretrained=None): - super(MaskRCNN, self).__init__( - backbone=backbone, - neck=neck, - rpn_head=rpn_head, - roi_head=roi_head, - train_cfg=train_cfg, - test_cfg=test_cfg, - pretrained=pretrained) diff --git a/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/bbox_head.py b/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/bbox_head.py deleted file mode 100644 index 408abef3a244115b4e73748049a228e37ad0665c..0000000000000000000000000000000000000000 --- a/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/bbox_head.py +++ /dev/null @@ -1,483 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.runner import auto_fp16, force_fp32 -from torch.nn.modules.utils import _pair - -from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms -from mmdet.models.builder import HEADS, build_loss -from mmdet.models.losses import accuracy - - -@HEADS.register_module() -class BBoxHead(nn.Module): - """Simplest RoI head, with only two fc layers for classification and - regression respectively.""" - - def __init__(self, - with_avg_pool=False, - with_cls=True, - with_reg=True, - roi_feat_size=7, - in_channels=256, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - clip_border=True, - target_means=[0., 0., 0., 0.], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - reg_decoded_bbox=False, - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=False, - loss_weight=1.0), - loss_bbox=dict( - type='SmoothL1Loss', beta=1.0, loss_weight=1.0)): - super(BBoxHead, self).__init__() - assert with_cls or with_reg - self.with_avg_pool = with_avg_pool - self.with_cls = with_cls - self.with_reg = with_reg - self.roi_feat_size = _pair(roi_feat_size) - self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1] - self.in_channels = in_channels - self.num_classes = num_classes - self.reg_class_agnostic = reg_class_agnostic - self.reg_decoded_bbox = reg_decoded_bbox - self.fp16_enabled = False - - self.bbox_coder = build_bbox_coder(bbox_coder) - self.loss_cls = build_loss(loss_cls) - self.loss_bbox = build_loss(loss_bbox) - - in_channels = self.in_channels - if self.with_avg_pool: - self.avg_pool = nn.AvgPool2d(self.roi_feat_size) - else: - in_channels *= self.roi_feat_area - if self.with_cls: - # need to add background class - self.fc_cls = nn.Linear(in_channels, num_classes + 1) - if self.with_reg: - out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes - self.fc_reg = nn.Linear(in_channels, out_dim_reg) - self.debug_imgs = None - - def init_weights(self): - # conv layers are already initialized by ConvModule - if self.with_cls: - nn.init.normal_(self.fc_cls.weight, 0, 0.01) - nn.init.constant_(self.fc_cls.bias, 0) - if self.with_reg: - nn.init.normal_(self.fc_reg.weight, 0, 0.001) - nn.init.constant_(self.fc_reg.bias, 0) - - @auto_fp16() - def forward(self, x): - if self.with_avg_pool: - x = self.avg_pool(x) - x = x.view(x.size(0), -1) - cls_score = self.fc_cls(x) if self.with_cls else None - bbox_pred = self.fc_reg(x) if self.with_reg else None - return cls_score, bbox_pred - - def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, - pos_gt_labels, cfg): - """Calculate the ground truth for proposals in the single image - according to the sampling results. - - Args: - pos_bboxes (Tensor): Contains all the positive boxes, - has shape (num_pos, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - neg_bboxes (Tensor): Contains all the negative boxes, - has shape (num_neg, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - pos_gt_bboxes (Tensor): Contains all the gt_boxes, - has shape (num_gt, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - pos_gt_labels (Tensor): Contains all the gt_labels, - has shape (num_gt). - cfg (obj:`ConfigDict`): `train_cfg` of R-CNN. - - Returns: - Tuple[Tensor]: Ground truth for proposals - in a single image. Containing the following Tensors: - - - labels(Tensor): Gt_labels for all proposals, has - shape (num_proposals,). - - label_weights(Tensor): Labels_weights for all - proposals, has shape (num_proposals,). - - bbox_targets(Tensor):Regression target for all - proposals, has shape (num_proposals, 4), the - last dimension 4 represents [tl_x, tl_y, br_x, br_y]. - - bbox_weights(Tensor):Regression weights for all - proposals, has shape (num_proposals, 4). - """ - num_pos = pos_bboxes.size(0) - num_neg = neg_bboxes.size(0) - num_samples = num_pos + num_neg - - # original implementation uses new_zeros since BG are set to be 0 - # now use empty & fill because BG cat_id = num_classes, - # FG cat_id = [0, num_classes-1] - labels = pos_bboxes.new_full((num_samples, ), - self.num_classes, - dtype=torch.long) - label_weights = pos_bboxes.new_zeros(num_samples) - bbox_targets = pos_bboxes.new_zeros(num_samples, 4) - bbox_weights = pos_bboxes.new_zeros(num_samples, 4) - if num_pos > 0: - labels[:num_pos] = pos_gt_labels - pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight - label_weights[:num_pos] = pos_weight - if not self.reg_decoded_bbox: - pos_bbox_targets = self.bbox_coder.encode( - pos_bboxes, pos_gt_bboxes) - else: - # When the regression loss (e.g. `IouLoss`, `GIouLoss`) - # is applied directly on the decoded bounding boxes, both - # the predicted boxes and regression targets should be with - # absolute coordinate format. - pos_bbox_targets = pos_gt_bboxes - bbox_targets[:num_pos, :] = pos_bbox_targets - bbox_weights[:num_pos, :] = 1 - if num_neg > 0: - label_weights[-num_neg:] = 1.0 - - return labels, label_weights, bbox_targets, bbox_weights - - def get_targets(self, - sampling_results, - gt_bboxes, - gt_labels, - rcnn_train_cfg, - concat=True): - """Calculate the ground truth for all samples in a batch according to - the sampling_results. - - Almost the same as the implementation in bbox_head, we passed - additional parameters pos_inds_list and neg_inds_list to - `_get_target_single` function. - - Args: - sampling_results (List[obj:SamplingResults]): Assign results of - all images in a batch after sampling. - gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch, - each tensor has shape (num_gt, 4), the last dimension 4 - represents [tl_x, tl_y, br_x, br_y]. - gt_labels (list[Tensor]): Gt_labels of all images in a batch, - each tensor has shape (num_gt,). - rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN. - concat (bool): Whether to concatenate the results of all - the images in a single batch. - - Returns: - Tuple[Tensor]: Ground truth for proposals in a single image. - Containing the following list of Tensors: - - - labels (list[Tensor],Tensor): Gt_labels for all - proposals in a batch, each tensor in list has - shape (num_proposals,) when `concat=False`, otherwise - just a single tensor has shape (num_all_proposals,). - - label_weights (list[Tensor]): Labels_weights for - all proposals in a batch, each tensor in list has - shape (num_proposals,) when `concat=False`, otherwise - just a single tensor has shape (num_all_proposals,). - - bbox_targets (list[Tensor],Tensor): Regression target - for all proposals in a batch, each tensor in list - has shape (num_proposals, 4) when `concat=False`, - otherwise just a single tensor has shape - (num_all_proposals, 4), the last dimension 4 represents - [tl_x, tl_y, br_x, br_y]. - - bbox_weights (list[tensor],Tensor): Regression weights for - all proposals in a batch, each tensor in list has shape - (num_proposals, 4) when `concat=False`, otherwise just a - single tensor has shape (num_all_proposals, 4). - """ - pos_bboxes_list = [res.pos_bboxes for res in sampling_results] - neg_bboxes_list = [res.neg_bboxes for res in sampling_results] - pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] - pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] - labels, label_weights, bbox_targets, bbox_weights = multi_apply( - self._get_target_single, - pos_bboxes_list, - neg_bboxes_list, - pos_gt_bboxes_list, - pos_gt_labels_list, - cfg=rcnn_train_cfg) - - if concat: - labels = torch.cat(labels, 0) - label_weights = torch.cat(label_weights, 0) - bbox_targets = torch.cat(bbox_targets, 0) - bbox_weights = torch.cat(bbox_weights, 0) - return labels, label_weights, bbox_targets, bbox_weights - - @force_fp32(apply_to=('cls_score', 'bbox_pred')) - def loss(self, - cls_score, - bbox_pred, - rois, - labels, - label_weights, - bbox_targets, - bbox_weights, - reduction_override=None): - losses = dict() - if cls_score is not None: - avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) - if cls_score.numel() > 0: - losses['loss_cls'] = self.loss_cls( - cls_score, - labels, - label_weights, - avg_factor=avg_factor, - reduction_override=reduction_override) - losses['acc'] = accuracy(cls_score, labels) - if bbox_pred is not None: - bg_class_ind = self.num_classes - # 0~self.num_classes-1 are FG, self.num_classes is BG - pos_inds = (labels >= 0) & (labels < bg_class_ind) - # do not perform bounding box regression for BG anymore. - if pos_inds.any(): - if self.reg_decoded_bbox: - # When the regression loss (e.g. `IouLoss`, - # `GIouLoss`, `DIouLoss`) is applied directly on - # the decoded bounding boxes, it decodes the - # already encoded coordinates to absolute format. - bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred) - if self.reg_class_agnostic: - pos_bbox_pred = bbox_pred.view( - bbox_pred.size(0), 4)[pos_inds.type(torch.bool)] - else: - pos_bbox_pred = bbox_pred.view( - bbox_pred.size(0), -1, - 4)[pos_inds.type(torch.bool), - labels[pos_inds.type(torch.bool)]] - losses['loss_bbox'] = self.loss_bbox( - pos_bbox_pred, - bbox_targets[pos_inds.type(torch.bool)], - bbox_weights[pos_inds.type(torch.bool)], - avg_factor=bbox_targets.size(0), - reduction_override=reduction_override) - else: - losses['loss_bbox'] = bbox_pred[pos_inds].sum() - return losses - - @force_fp32(apply_to=('cls_score', 'bbox_pred')) - def get_bboxes(self, - rois, - cls_score, - bbox_pred, - img_shape, - scale_factor, - rescale=False, - cfg=None): - """Transform network output for a batch into bbox predictions. - - If the input rois has batch dimension, the function would be in - `batch_mode` and return is a tuple[list[Tensor], list[Tensor]], - otherwise, the return is a tuple[Tensor, Tensor]. - - Args: - rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5) - or (B, num_boxes, 5) - cls_score (list[Tensor] or Tensor): Box scores for - each scale level, each is a 4D-tensor, the channel number is - num_points * num_classes. - bbox_pred (Tensor, optional): Box energies / deltas for each scale - level, each is a 4D-tensor, the channel number is - num_classes * 4. - img_shape (Sequence[int] or torch.Tensor or Sequence[ - Sequence[int]], optional): Maximum bounds for boxes, specifies - (H, W, C) or (H, W). If rois shape is (B, num_boxes, 4), then - the max_shape should be a Sequence[Sequence[int]] - and the length of max_shape should also be B. - scale_factor (tuple[ndarray] or ndarray): Scale factor of the - image arange as (w_scale, h_scale, w_scale, h_scale). In - `batch_mode`, the scale_factor shape is tuple[ndarray]. - rescale (bool): If True, return boxes in original image space. - Default: False. - cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None - - Returns: - tuple[list[Tensor], list[Tensor]] or tuple[Tensor, Tensor]: - If the input has a batch dimension, the return value is - a tuple of the list. The first list contains the boxes of - the corresponding image in a batch, each tensor has the - shape (num_boxes, 5) and last dimension 5 represent - (tl_x, tl_y, br_x, br_y, score). Each Tensor in the second - list is the labels with shape (num_boxes, ). The length of - both lists should be equal to batch_size. Otherwise return - value is a tuple of two tensors, the first tensor is the - boxes with scores, the second tensor is the labels, both - have the same shape as the first case. - """ - if isinstance(cls_score, list): - cls_score = sum(cls_score) / float(len(cls_score)) - - scores = F.softmax( - cls_score, dim=-1) if cls_score is not None else None - - batch_mode = True - if rois.ndim == 2: - # e.g. AugTest, Cascade R-CNN, HTC, SCNet... - batch_mode = False - - # add batch dimension - if scores is not None: - scores = scores.unsqueeze(0) - if bbox_pred is not None: - bbox_pred = bbox_pred.unsqueeze(0) - rois = rois.unsqueeze(0) - - if bbox_pred is not None: - bboxes = self.bbox_coder.decode( - rois[..., 1:], bbox_pred, max_shape=img_shape) - else: - bboxes = rois[..., 1:].clone() - if img_shape is not None: - max_shape = bboxes.new_tensor(img_shape)[..., :2] - min_xy = bboxes.new_tensor(0) - max_xy = torch.cat( - [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2) - bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) - bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) - - if rescale and bboxes.size(-2) > 0: - if not isinstance(scale_factor, tuple): - scale_factor = tuple([scale_factor]) - # B, 1, bboxes.size(-1) - scale_factor = bboxes.new_tensor(scale_factor).unsqueeze(1).repeat( - 1, 1, - bboxes.size(-1) // 4) - bboxes /= scale_factor - - det_bboxes = [] - det_labels = [] - for (bbox, score) in zip(bboxes, scores): - if cfg is not None: - det_bbox, det_label = multiclass_nms(bbox, score, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - else: - det_bbox, det_label = bbox, score - det_bboxes.append(det_bbox) - det_labels.append(det_label) - - if not batch_mode: - det_bboxes = det_bboxes[0] - det_labels = det_labels[0] - return det_bboxes, det_labels - - @force_fp32(apply_to=('bbox_preds', )) - def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): - """Refine bboxes during training. - - Args: - rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, - and bs is the sampled RoIs per image. The first column is - the image id and the next 4 columns are x1, y1, x2, y2. - labels (Tensor): Shape (n*bs, ). - bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class). - pos_is_gts (list[Tensor]): Flags indicating if each positive bbox - is a gt bbox. - img_metas (list[dict]): Meta info of each image. - - Returns: - list[Tensor]: Refined bboxes of each image in a mini-batch. - - Example: - >>> # xdoctest: +REQUIRES(module:kwarray) - >>> import kwarray - >>> import numpy as np - >>> from mmdet.core.bbox.demodata import random_boxes - >>> self = BBoxHead(reg_class_agnostic=True) - >>> n_roi = 2 - >>> n_img = 4 - >>> scale = 512 - >>> rng = np.random.RandomState(0) - >>> img_metas = [{'img_shape': (scale, scale)} - ... for _ in range(n_img)] - >>> # Create rois in the expected format - >>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng) - >>> img_ids = torch.randint(0, n_img, (n_roi,)) - >>> img_ids = img_ids.float() - >>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1) - >>> # Create other args - >>> labels = torch.randint(0, 2, (n_roi,)).long() - >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng) - >>> # For each image, pretend random positive boxes are gts - >>> is_label_pos = (labels.numpy() > 0).astype(np.int) - >>> lbl_per_img = kwarray.group_items(is_label_pos, - ... img_ids.numpy()) - >>> pos_per_img = [sum(lbl_per_img.get(gid, [])) - ... for gid in range(n_img)] - >>> pos_is_gts = [ - >>> torch.randint(0, 2, (npos,)).byte().sort( - >>> descending=True)[0] - >>> for npos in pos_per_img - >>> ] - >>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds, - >>> pos_is_gts, img_metas) - >>> print(bboxes_list) - """ - img_ids = rois[:, 0].long().unique(sorted=True) - assert img_ids.numel() <= len(img_metas) - - bboxes_list = [] - for i in range(len(img_metas)): - inds = torch.nonzero( - rois[:, 0] == i, as_tuple=False).squeeze(dim=1) - num_rois = inds.numel() - - bboxes_ = rois[inds, 1:] - label_ = labels[inds] - bbox_pred_ = bbox_preds[inds] - img_meta_ = img_metas[i] - pos_is_gts_ = pos_is_gts[i] - - bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, - img_meta_) - - # filter gt bboxes - pos_keep = 1 - pos_is_gts_ - keep_inds = pos_is_gts_.new_ones(num_rois) - keep_inds[:len(pos_is_gts_)] = pos_keep - - bboxes_list.append(bboxes[keep_inds.type(torch.bool)]) - - return bboxes_list - - @force_fp32(apply_to=('bbox_pred', )) - def regress_by_class(self, rois, label, bbox_pred, img_meta): - """Regress the bbox for the predicted class. Used in Cascade R-CNN. - - Args: - rois (Tensor): shape (n, 4) or (n, 5) - label (Tensor): shape (n, ) - bbox_pred (Tensor): shape (n, 4*(#class)) or (n, 4) - img_meta (dict): Image meta info. - - Returns: - Tensor: Regressed bboxes, the same shape as input rois. - """ - assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape) - - if not self.reg_class_agnostic: - label = label * 4 - inds = torch.stack((label, label + 1, label + 2, label + 3), 1) - bbox_pred = torch.gather(bbox_pred, 1, inds) - assert bbox_pred.size(1) == 4 - - if rois.size(1) == 4: - new_rois = self.bbox_coder.decode( - rois, bbox_pred, max_shape=img_meta['img_shape']) - else: - bboxes = self.bbox_coder.decode( - rois[:, 1:], bbox_pred, max_shape=img_meta['img_shape']) - new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) - - return new_rois diff --git a/spaces/CVPR/regionclip-demo/detectron2/checkpoint/detection_checkpoint.py b/spaces/CVPR/regionclip-demo/detectron2/checkpoint/detection_checkpoint.py deleted file mode 100644 index 42fbaa5be6f304a799247948b4da5e6b14da2c45..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/checkpoint/detection_checkpoint.py +++ /dev/null @@ -1,134 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import logging -import os -import pickle -import torch -from fvcore.common.checkpoint import Checkpointer -from torch.nn.parallel import DistributedDataParallel - -import detectron2.utils.comm as comm -from detectron2.utils.env import TORCH_VERSION -from detectron2.utils.file_io import PathManager - -from .c2_model_loading import align_and_update_state_dicts -from .clip_model_loading import align_and_update_state_dicts_for_CLIP - -class DetectionCheckpointer(Checkpointer): - """ - Same as :class:`Checkpointer`, but is able to: - 1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models. - 2. correctly load checkpoints that are only available on the master worker - """ - - def __init__(self, model, save_dir="", *, save_to_disk=None, bb_rpn_weights=False, **checkpointables): - is_main_process = comm.is_main_process() - super().__init__( - model, - save_dir, - save_to_disk=is_main_process if save_to_disk is None else save_to_disk, - **checkpointables, - ) - self.path_manager = PathManager - self.bb_rpn_weights = bb_rpn_weights - - def load(self, path, *args, **kwargs): - need_sync = False - - if path and isinstance(self.model, DistributedDataParallel): - logger = logging.getLogger(__name__) - path = self.path_manager.get_local_path(path) - has_file = os.path.isfile(path) - all_has_file = comm.all_gather(has_file) - if not all_has_file[0]: - raise OSError(f"File {path} not found on main worker.") - if not all(all_has_file): - logger.warning( - f"Not all workers can read checkpoint {path}. " - "Training may fail to fully resume." - ) - # TODO: broadcast the checkpoint file contents from main - # worker, and load from it instead. - need_sync = True - if not has_file: - path = None # don't load if not readable - ret = super().load(path, *args, **kwargs) - - if need_sync: - logger.info("Broadcasting model states from main worker ...") - if TORCH_VERSION >= (1, 7): - self.model._sync_params_and_buffers() - return ret - - def _load_file(self, filename): - if filename.endswith(".pkl"): - with PathManager.open(filename, "rb") as f: - data = pickle.load(f, encoding="latin1") - if "model" in data and "__author__" in data: - # file is in Detectron2 model zoo format - self.logger.info("Reading a file from '{}'".format(data["__author__"])) - return data - else: - # assume file is from Caffe2 / Detectron1 model zoo - if "blobs" in data: - # Detection models have "blobs", but ImageNet models don't - data = data["blobs"] - data = {k: v for k, v in data.items() if not k.endswith("_momentum")} - return {"model": data, "__author__": "Caffe2", "matching_heuristics": True} - elif filename.endswith(".pyth"): - # assume file is from pycls; no one else seems to use the ".pyth" extension - with PathManager.open(filename, "rb") as f: - data = torch.load(f) - assert ( - "model_state" in data - ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'." - model_state = { - k: v - for k, v in data["model_state"].items() - if not k.endswith("num_batches_tracked") - } - return {"model": model_state, "__author__": "pycls", "matching_heuristics": True} - elif "OAI_CLIP" in filename: - # assume file is from OpenAI CLIP pre-trained model - loaded = super()._load_file(filename) # load native pth checkpoint - if "model" not in loaded: - loaded = {"model": loaded} - return {"model": loaded["model"], "__author__": "OAI_CLIP", "matching_heuristics": True} - - loaded = super()._load_file(filename) # load native pth checkpoint - if "model" not in loaded: - loaded = {"model": loaded} - return loaded - - def _load_model(self, checkpoint): - # if checkpoint.get("matching_heuristics", False) or self.bb_rpn_weights: - # self._convert_ndarray_to_tensor(checkpoint["model"]) - # # convert weights by name-matching heuristics - # if checkpoint.get("__author__", "NA") == "OAI_CLIP" or self.bb_rpn_weights: # for OAI_CLIP or 2nd ckpt (offline modules) - # checkpoint["model"] = align_and_update_state_dicts_for_CLIP( - # self.model.state_dict(), - # checkpoint["model"], - # bb_rpn_weights=self.bb_rpn_weights, - # ) - # else: # default loading - # checkpoint["model"] = align_and_update_state_dicts( - # self.model.state_dict(), - # checkpoint["model"], - # c2_conversion=checkpoint.get("__author__", None) == "Caffe2", - # ) - # for non-caffe2 models, use standard ways to load it - # if not self.bb_rpn_weights: - # checkpoint = {'model': {'backbone.' + key: val for key, val in checkpoint['model'].items()}} - incompatible = super()._load_model(checkpoint) - del checkpoint # try saving memory - - model_buffers = dict(self.model.named_buffers(recurse=False)) - for k in ["pixel_mean", "pixel_std"]: - # Ignore missing key message about pixel_mean/std. - # Though they may be missing in old checkpoints, they will be correctly - # initialized from config anyway. - if k in model_buffers: - try: - incompatible.missing_keys.remove(k) - except ValueError: - pass - return incompatible \ No newline at end of file diff --git a/spaces/Chaitanya01/InvestingPlatform/googleNewsSlackAlerts.py b/spaces/Chaitanya01/InvestingPlatform/googleNewsSlackAlerts.py deleted file mode 100644 index 25840d091b8a86398c4f05eee88f9476f6ef5ba2..0000000000000000000000000000000000000000 --- a/spaces/Chaitanya01/InvestingPlatform/googleNewsSlackAlerts.py +++ /dev/null @@ -1,47 +0,0 @@ -from GoogleNews import GoogleNews -import pandas as pd -import numpy as np -import slack -import time -from datetime import datetime - -# Slack token -SLACK_TOKEN = "xoxb-2557354538181-2570404709172-oNr1bsP5hQoFyOL1HqgqF8lv" -# Initialize the slack client -client = slack.WebClient(token = SLACK_TOKEN) -# Google News Api -googlenews = GoogleNews() -googlenews = GoogleNews(lang='en', region='US') -googlenews = GoogleNews(period='1h') - -googlenews.set_encode('utf-8') - -arr = [] -while True: - # Run this in for loop and is to be run continously - today = datetime.now() - # If its midnight reset the array - if today.hour + today.minute == 0 and today.second<2: - arr = [] - # Search for the word crypto in googlenews - googlenews.search("crypto") - # Sort the results - result = googlenews.results(sort=True) - for i in result: - # Now if a news has already been scraped, ignore it - if i["title"] in arr: - continue - if "min" in i["date"]: - # If the time for the news is in minute then only fetch it - if "$" in i["desc"] or "$" in i["title"]: - # If the title or decription contains dollar symbol, then go ahead - if "million" in i["desc"].lower() or "raised" in i["desc"].lower(): - # If million or raised keywords are present then go ahead - arr.append(i["title"]) - # Post the news on slack bot - client.chat_postMessage(channel = "#bot_alerts", - text = f'{i["datetime"]} {i["date"]} {i["title"]} {i["link"]} {i["desc"]}') - # Clear the google news - googlenews.clear() - # Wait for 30seconds for next query - time.sleep(30) \ No newline at end of file diff --git a/spaces/CikeyQI/Yunzai/README.md b/spaces/CikeyQI/Yunzai/README.md deleted file mode 100644 index cf178fd85a682a420cf3a6c4287d479b1dcb2cf9..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/Yunzai/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Yunzai -emoji: 🏃 -colorFrom: red -colorTo: blue -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/incivilization/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/incivilization/__init__.py deleted file mode 100644 index 5491bc824ab12ebe3db5abeb34a5c13869af0ce6..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/incivilization/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -from pathlib import Path -from typing import List - -from PIL import ImageEnhance -from pil_utils import BuildImage - -from meme_generator import add_meme -from meme_generator.exception import TextOverLength - -img_dir = Path(__file__).parent / "images" - - -def incivilization(images: List[BuildImage], texts: List[str], args): - frame = BuildImage.open(img_dir / "0.png") - points = ((0, 20), (154, 0), (164, 153), (22, 180)) - img = images[0].convert("RGBA").circle().resize((150, 150)).perspective(points) - image = ImageEnhance.Brightness(img.image).enhance(0.8) - frame.paste(image, (137, 151), alpha=True) - text = texts[0] if texts else "你刚才说的话不是很礼貌！" - try: - frame.draw_text( - (57, 42, 528, 117), - text, - weight="bold", - max_fontsize=50, - min_fontsize=20, - allow_wrap=True, - ) - except ValueError: - raise TextOverLength(text) - return frame.save_jpg() - - -add_meme( - "incivilization", - incivilization, - min_images=1, - max_images=1, - min_texts=0, - max_texts=1, - default_texts=["你刚才说的话不是很礼貌！"], - keywords=["不文明"], -) diff --git a/spaces/ClassCat/mnist-classification/app.py b/spaces/ClassCat/mnist-classification/app.py deleted file mode 100644 index ae8aee955af5dfef62fee5cd5e3a4d1103212e77..0000000000000000000000000000000000000000 --- a/spaces/ClassCat/mnist-classification/app.py +++ /dev/null @@ -1,83 +0,0 @@ - -import torch -from torch import nn -import torch.nn.functional as F -from torchvision.transforms import ToTensor - -# Define model -class ConvNet(nn.Module): - def __init__(self): - super(ConvNet, self).__init__() - self.conv1 = nn.Conv2d(1, 32, kernel_size=5) - self.conv2 = nn.Conv2d(32, 32, kernel_size=5) - self.conv3 = nn.Conv2d(32,64, kernel_size=5) - self.fc1 = nn.Linear(3*3*64, 256) - self.fc2 = nn.Linear(256, 10) - - def forward(self, x): - x = F.relu(self.conv1(x)) - #x = F.dropout(x, p=0.5, training=self.training) - x = F.relu(F.max_pool2d(self.conv2(x), 2)) - x = F.dropout(x, p=0.5, training=self.training) - x = F.relu(F.max_pool2d(self.conv3(x),2)) - x = F.dropout(x, p=0.5, training=self.training) - x = x.view(-1,3*3*64 ) - x = F.relu(self.fc1(x)) - x = F.dropout(x, training=self.training) - logits = self.fc2(x) - return logits - - -model = ConvNet() -model.load_state_dict( - torch.load("weights/mnist_convnet_model.pth", - map_location=torch.device('cpu')) - ) - -model.eval() - -import gradio as gr -from torchvision import transforms - -import os -import glob - -examples_dir = './examples' -example_files = glob.glob(os.path.join(examples_dir, '*.png')) - -def predict(image): - tsr_image = transforms.ToTensor()(image) - - with torch.no_grad(): - pred = model(tsr_image) - prob = torch.nn.functional.softmax(pred[0], dim=0) - - confidences = {i: float(prob[i]) for i in range(10)} - return confidences - - -with gr.Blocks(css=".gradio-container {background:honeydew;}", title="MNIST Classification" - ) as demo: - gr.HTML("""
    MNIST Classification
    """) - - with gr.Row(): - with gr.Tab("Canvas"): - input_image1 = gr.Image(source="canvas", type="pil", image_mode="L", shape=(28,28), invert_colors=True) - send_btn1 = gr.Button("Infer") - - with gr.Tab("Image file"): - input_image2 = gr.Image(type="pil", image_mode="L", shape=(28, 28), invert_colors=True) - send_btn2 = gr.Button("Infer") - gr.Examples(example_files, inputs=input_image2) - #gr.Examples(['examples/sample02.png', 'examples/sample04.png'], inputs=input_image2) - - output_label=gr.Label(label="Probabilities", num_top_classes=3) - - send_btn1.click(fn=predict, inputs=input_image1, outputs=output_label) - send_btn2.click(fn=predict, inputs=input_image2, outputs=output_label) - -# demo.queue(concurrency_count=3) -demo.launch() - - -### EOF ### \ No newline at end of file diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.1b1ee80c.js b/spaces/DEEMOSTECH/ChatAvatar/static/js/main.1b1ee80c.js deleted file mode 100644 index bcbe9702bdf78b17a9354c244510c6072f72e863..0000000000000000000000000000000000000000 --- a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.1b1ee80c.js +++ /dev/null @@ -1,3 +0,0 @@ -/*! For license information please see main.1b1ee80c.js.LICENSE.txt */ -!function(){var e={498:function(e){e.exports=function(){"use strict";var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var n in t)Object.prototype.hasOwnProperty.call(t,n)&&(e[n]=t[n])},e(t,n)};function t(t,n){if("function"!==typeof n&&null!==n)throw new TypeError("Class extends value "+String(n)+" is not a constructor or null");function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}var n=function(){return n=Object.assign||function(e){for(var t,n=1,r=arguments.length;n0&&i[i.length-1])&&(6===A[0]||2===A[0])){a=0;continue}if(3===A[0]&&(!i||A[1]>i[0]&&A[1]=55296&&i<=56319&&n>10),a%1024+56320)),(i+1===n||r.length>16384)&&(A+=String.fromCharCode.apply(String,r),r.length=0)}return A},c="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/",d="undefined"===typeof Uint8Array?[]:new Uint8Array(256),h=0;h>4,u[s++]=(15&r)<<4|i>>2,u[s++]=(3&i)<<6|63&A;return l},v=function(e){for(var t=e.length,n=[],r=0;r>w,x=(1<>w)+32,S=65536>>B,E=(1<=0){if(e<55296||e>56319&&e<=65535)return t=((t=this.index[e>>w])<<_)+(e&x),this.data[t];if(e<=65535)return t=((t=this.index[b+(e-55296>>w)])<<_)+(e&x),this.data[t];if(e>B),t=this.index[t],t+=e>>w&E,t=((t=this.index[t])<<_)+(e&x),this.data[t];if(e<=1114111)return this.data[this.highValueIndex]}return this.errorValue},e}(),k="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/",Q="undefined"===typeof Uint8Array?[]:new Uint8Array(256),L=0;LD?(i.push(!0),a-=D):i.push(!1),-1!==["normal","auto","loose"].indexOf(t)&&-1!==[8208,8211,12316,12448].indexOf(e))return r.push(A),n.push(Y);if(a===H||a===K){if(0===A)return r.push(A),n.push(ue);var o=n[A-1];return-1===Qe.indexOf(o)?(r.push(r[A-1]),n.push(o)):(r.push(A),n.push(ue))}return r.push(A),a===ce?n.push("strict"===t?te:me):a===_e||a===le?n.push(ue):a===be?e>=131072&&e<=196605||e>=196608&&e<=262141?n.push(me):n.push(ue):void n.push(a)})),[r,n,i]},Re=function(e,t,n,r){var i=r[n];if(Array.isArray(e)?-1!==e.indexOf(i):e===i)for(var A=n;A<=r.length;){if((s=r[++A])===t)return!0;if(s!==G)break}if(i===G)for(A=n;A>0;){var a=r[--A];if(Array.isArray(e)?-1!==e.indexOf(a):e===a)for(var o=n;o<=r.length;){var s;if((s=r[++o])===t)return!0;if(s!==G)break}if(a!==G)break}return!1},Pe=function(e,t){for(var n=e;n>=0;){var r=t[n];if(r!==G)return r;n--}return 0},He=function(e,t,n,r,i){if(0===n[r])return Se;var A=r-1;if(Array.isArray(i)&&!0===i[A])return Se;var a=A-1,o=A+1,s=t[A],l=a>=0?t[a]:0,u=t[o];if(s===R&&u===P)return Se;if(-1!==Fe.indexOf(s))return Ce;if(-1!==Fe.indexOf(u))return Se;if(-1!==Te.indexOf(u))return Se;if(Pe(A,t)===V)return Ee;if(Ue.get(e[A])===K)return Se;if((s===de||s===he)&&Ue.get(e[o])===K)return Se;if(s===O||u===O)return Se;if(s===z)return Se;if(-1===[G,j,q].indexOf(s)&&u===z)return Se;if(-1!==[J,Z,$,ie,se].indexOf(u))return Se;if(Pe(A,t)===ne)return Se;if(Re(re,ne,A,t))return Se;if(Re([J,Z],te,A,t))return Se;if(Re(W,W,A,t))return Se;if(s===G)return Ee;if(s===re||u===re)return Se;if(u===Y||s===Y)return Ee;if(-1!==[j,q,te].indexOf(u)||s===X)return Se;if(l===ge&&-1!==De.indexOf(s))return Se;if(s===se&&u===ge)return Se;if(u===ee)return Se;if(-1!==Me.indexOf(u)&&s===Ae||-1!==Me.indexOf(s)&&u===Ae)return Se;if(s===oe&&-1!==[me,de,he].indexOf(u)||-1!==[me,de,he].indexOf(s)&&u===ae)return Se;if(-1!==Me.indexOf(s)&&-1!==ke.indexOf(u)||-1!==ke.indexOf(s)&&-1!==Me.indexOf(u))return Se;if(-1!==[oe,ae].indexOf(s)&&(u===Ae||-1!==[ne,q].indexOf(u)&&t[o+1]===Ae)||-1!==[ne,q].indexOf(s)&&u===Ae||s===Ae&&-1!==[Ae,se,ie].indexOf(u))return Se;if(-1!==[Ae,se,ie,J,Z].indexOf(u))for(var c=A;c>=0;){if((d=t[c])===Ae)return Se;if(-1===[se,ie].indexOf(d))break;c--}if(-1!==[oe,ae].indexOf(u))for(c=-1!==[J,Z].indexOf(s)?a:A;c>=0;){var d;if((d=t[c])===Ae)return Se;if(-1===[se,ie].indexOf(d))break;c--}if(ve===s&&-1!==[ve,ye,fe,pe].indexOf(u)||-1!==[ye,fe].indexOf(s)&&-1!==[ye,we].indexOf(u)||-1!==[we,pe].indexOf(s)&&u===we)return Se;if(-1!==Le.indexOf(s)&&-1!==[ee,ae].indexOf(u)||-1!==Le.indexOf(u)&&s===oe)return Se;if(-1!==Me.indexOf(s)&&-1!==Me.indexOf(u))return Se;if(s===ie&&-1!==Me.indexOf(u))return Se;if(-1!==Me.concat(Ae).indexOf(s)&&u===ne&&-1===xe.indexOf(e[o])||-1!==Me.concat(Ae).indexOf(u)&&s===Z)return Se;if(s===Be&&u===Be){for(var h=n[A],f=1;h>0&&t[--h]===Be;)f++;if(f%2!==0)return Se}return s===de&&u===he?Se:Ee},Ne=function(e,t){t||(t={lineBreak:"normal",wordBreak:"normal"});var n=Ie(e,t.lineBreak),r=n[0],i=n[1],A=n[2];"break-all"!==t.wordBreak&&"break-word"!==t.wordBreak||(i=i.map((function(e){return-1!==[Ae,ue,_e].indexOf(e)?me:e})));var a="keep-all"===t.wordBreak?A.map((function(t,n){return t&&e[n]>=19968&&e[n]<=40959})):void 0;return[r,i,a]},Oe=function(){function e(e,t,n,r){this.codePoints=e,this.required=t===Ce,this.start=n,this.end=r}return e.prototype.slice=function(){return u.apply(void 0,this.codePoints.slice(this.start,this.end))},e}(),Ve=function(e,t){var n=l(e),r=Ne(n,t),i=r[0],A=r[1],a=r[2],o=n.length,s=0,u=0;return{next:function(){if(u>=o)return{done:!0,value:null};for(var e=Se;u=Dt&&e<=57},jt=function(e){return e>=55296&&e<=57343},Xt=function(e){return Wt(e)||e>=Ot&&e<=zt||e>=It&&e<=Pt},qt=function(e){return e>=It&&e<=Nt},Yt=function(e){return e>=Ot&&e<=Kt},Jt=function(e){return qt(e)||Yt(e)},Zt=function(e){return e>=wt},$t=function(e){return e===je||e===Ye||e===Je},en=function(e){return Jt(e)||Zt(e)||e===at},tn=function(e){return en(e)||Wt(e)||e===ot},nn=function(e){return e>=Ut&&e<=Mt||e===Ft||e>=Tt&&e<=kt||e===Qt},rn=function(e,t){return e===qe&&t!==je},An=function(e,t,n){return e===ot?en(t)||rn(t,n):!!en(e)||!(e!==qe||!rn(e,t))},an=function(e,t,n){return e===bt||e===ot?!!Wt(t)||t===Et&&Wt(n):Wt(e===Et?t:e)},on=function(e){var t=0,n=1;e[t]!==bt&&e[t]!==ot||(e[t]===ot&&(n=-1),t++);for(var r=[];Wt(e[t]);)r.push(e[t++]);var i=r.length?parseInt(u.apply(void 0,r),10):0;e[t]===Et&&t++;for(var A=[];Wt(e[t]);)A.push(e[t++]);var a=A.length,o=a?parseInt(u.apply(void 0,A),10):0;e[t]!==Vt&&e[t]!==Rt||t++;var s=1;e[t]!==bt&&e[t]!==ot||(e[t]===ot&&(s=-1),t++);for(var l=[];Wt(e[t]);)l.push(e[t++]);var c=l.length?parseInt(u.apply(void 0,l),10):0;return n*(i+o*Math.pow(10,-a))*Math.pow(10,s*c)},sn={type:2},ln={type:3},un={type:4},cn={type:13},dn={type:8},hn={type:21},fn={type:9},pn={type:10},gn={type:11},mn={type:12},vn={type:14},yn={type:23},wn={type:1},Bn={type:25},_n={type:24},bn={type:26},xn={type:27},Cn={type:28},Sn={type:29},En={type:31},Un={type:32},Mn=function(){function e(){this._value=[]}return e.prototype.write=function(e){this._value=this._value.concat(l(e))},e.prototype.read=function(){for(var e=[],t=this.consumeToken();t!==Un;)e.push(t),t=this.consumeToken();return e},e.prototype.consumeToken=function(){var 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)===$e)return this.consumeCodePoint(),dn;break;case pt:return gn;case mt:return mn;case Ht:case Gt:var h=this.peekCodePoint(0),f=this.peekCodePoint(1);return h!==bt||!Xt(f)&&f!==gt||(this.consumeCodePoint(),this.consumeUnicodeRangeToken()),this.reconsumeCodePoint(e),this.consumeIdentLikeToken();case vt:if(this.peekCodePoint(0)===$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 e.push(r)}},e.prototype.consumeWhiteSpace=function(){for(;$t(this.peekCodePoint(0));)this.consumeCodePoint()},e.prototype.consumeBadUrlRemnants=function(){for(;;){var e=this.consumeCodePoint();if(e===At||e===Lt)return;rn(e,this.peekCodePoint(0))&&this.consumeEscapedCodePoint()}},e.prototype.consumeStringSlice=function(e){for(var t=5e4,n="";e>0;){var r=Math.min(t,e);n+=u.apply(void 0,this._value.splice(0,r)),e-=r}return this._value.shift(),n},e.prototype.consumeStringToken=function(e){for(var t="",n=0;;){var r=this._value[n];if(r===Lt||void 0===r||r===e)return{type:0,value:t+=this.consumeStringSlice(n)};if(r===je)return this._value.splice(0,n),wn;if(r===qe){var i=this._value[n+1];i!==Lt&&void 0!==i&&(i===je?(t+=this.consumeStringSlice(n),n=-1,this._value.shift()):rn(r,i)&&(t+=this.consumeStringSlice(n),t+=u(this.consumeEscapedCodePoint()),n=-1))}n++}},e.prototype.consumeNumber=function(){var e=[],t=Ke,n=this.peekCodePoint(0);for(n!==bt&&n!==ot||e.push(this.consumeCodePoint());Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0);var r=this.peekCodePoint(1);if(n===Et&&Wt(r))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0),r=this.peekCodePoint(1);var i=this.peekCodePoint(2);if((n===Vt||n===Rt)&&((r===bt||r===ot)&&Wt(i)||Wt(r)))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());return[on(e),t]},e.prototype.consumeNumericToken=function(){var e=this.consumeNumber(),t=e[0],n=e[1],r=this.peekCodePoint(0),i=this.peekCodePoint(1),A=this.peekCodePoint(2);return An(r,i,A)?{type:15,number:t,flags:n,unit:this.consumeName()}:r===nt?(this.consumeCodePoint(),{type:16,number:t,flags:n}):{type:17,number:t,flags:n}},e.prototype.consumeEscapedCodePoint=function(){var e=this.consumeCodePoint();if(Xt(e)){for(var t=u(e);Xt(this.peekCodePoint(0))&&t.length<6;)t+=u(this.consumeCodePoint());$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||Hn(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},Pn=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},Hn=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 top":case"top right":return[zn,Kn];case"to right":case"left":return tr(90);case"to top left":case"to left top":case"right bottom":case"bottom right":return[Kn,Kn];case"to bottom":case"top":return tr(180);case"to top right":case"to right top":case"left bottom":case"bottom left":return[Kn,zn];case"to left":case"right":return tr(270)}return 0},tr=function(e){return Math.PI*e/180},nr={name:"color",parse:function(e,t){if(18===t.type){var n=ur[t.name];if("undefined"===typeof n)throw new Error('Attempting to parse an unsupported color function "'+t.name+'"');return n(e,t.values)}if(5===t.type){if(3===t.value.length){var r=t.value.substring(0,1),i=t.value.substring(1,2),A=t.value.substring(2,3);return Ar(parseInt(r+r,16),parseInt(i+i,16),parseInt(A+A,16),1)}if(4===t.value.length){r=t.value.substring(0,1),i=t.value.substring(1,2),A=t.value.substring(2,3);var a=t.value.substring(3,4);return Ar(parseInt(r+r,16),parseInt(i+i,16),parseInt(A+A,16),parseInt(a+a,16)/255)}if(6===t.value.length)return r=t.value.substring(0,2),i=t.value.substring(2,4),A=t.value.substring(4,6),Ar(parseInt(r,16),parseInt(i,16),parseInt(A,16),1);if(8===t.value.length)return r=t.value.substring(0,2),i=t.value.substring(2,4),A=t.value.substring(4,6),a=t.value.substring(6,8),Ar(parseInt(r,16),parseInt(i,16),parseInt(A,16),parseInt(a,16)/255)}if(20===t.type){var o=dr[t.value.toUpperCase()];if("undefined"!==typeof o)return o}return dr.TRANSPARENT}},rr=function(e){return 0===(255&e)},ir=function(e){var t=255&e,n=255&e>>8,r=255&e>>16,i=255&e>>24;return t<255?"rgba("+i+","+r+","+n+","+t/255+")":"rgb("+i+","+r+","+n+")"},Ar=function(e,t,n,r){return(e<<24|t<<16|n<<8|Math.round(255*r)<<0)>>>0},ar=function(e,t){if(17===e.type)return e.number;if(16===e.type){var n=3===t?1:255;return 3===t?e.number/100*n:Math.round(e.number/100*n)}return 0},or=function(e,t){var n=t.filter(Rn);if(3===n.length){var r=n.map(ar),i=r[0],A=r[1],a=r[2];return Ar(i,A,a,1)}if(4===n.length){var o=n.map(ar),s=(i=o[0],A=o[1],a=o[2],o[3]);return Ar(i,A,a,s)}return 0};function sr(e,t,n){return n<0&&(n+=1),n>=1&&(n-=1),n<1/6?(t-e)*n*6+e:n<.5?t:n<2/3?6*(t-e)*(2/3-n)+e:e}var lr=function(e,t){var n=t.filter(Rn),r=n[0],i=n[1],A=n[2],a=n[3],o=(17===r.type?tr(r.number):Zn.parse(e,r))/(2*Math.PI),s=On(i)?i.number/100:0,l=On(A)?A.number/100:0,u="undefined"!==typeof a&&On(a)?jn(a,1):1;if(0===s)return Ar(255*l,255*l,255*l,1);var c=l<=.5?l*(s+1):l+s-l*s,d=2*l-c,h=sr(d,c,o+1/3),f=sr(d,c,o),p=sr(d,c,o-1/3);return Ar(255*h,255*f,255*p,u)},ur={hsl:lr,hsla:lr,rgb:or,rgba:or},cr=function(e,t){return nr.parse(e,Fn.create(t).parseComponentValue())},dr={ALICEBLUE:4042850303,ANTIQUEWHITE:4209760255,AQUA:16777215,AQUAMARINE:2147472639,AZURE:4043309055,BEIGE:4126530815,BISQUE:4293182719,BLACK:255,BLANCHEDALMOND:4293643775,BLUE:65535,BLUEVIOLET:2318131967,BROWN:2771004159,BURLYWOOD:3736635391,CADETBLUE:1604231423,CHARTREUSE:2147418367,CHOCOLATE:3530104575,CORAL:4286533887,CORNFLOWERBLUE:1687547391,CORNSILK:4294499583,CRIMSON:3692313855,CYAN:16777215,DARKBLUE:35839,DARKCYAN:9145343,DARKGOLDENROD:3095837695,DARKGRAY:2846468607,DARKGREEN:6553855,DARKGREY:2846468607,DARKKHAKI:3182914559,DARKMAGENTA:2332068863,DARKOLIVEGREEN:1433087999,DARKORANGE:4287365375,DARKORCHID:2570243327,DARKRED:2332033279,DARKSALMON:3918953215,DARKSEAGREEN:2411499519,DARKSLATEBLUE:1211993087,DARKSLATEGRAY:793726975,DARKSLATEGREY:793726975,DARKTURQUOISE:13554175,DARKVIOLET:2483082239,DEEPPINK:4279538687,DEEPSKYBLUE:12582911,DIMGRAY:1768516095,DIMGREY:1768516095,DODGERBLUE:512819199,FIREBRICK:2988581631,FLORALWHITE:4294635775,FORESTGREEN:579543807,FUCHSIA:4278255615,GAINSBORO:3705462015,GHOSTWHITE:4177068031,GOLD:4292280575,GOLDENROD:3668254975,GRAY:2155905279,GREEN:8388863,GREENYELLOW:2919182335,GREY:2155905279,HONEYDEW:4043305215,HOTPINK:4285117695,INDIANRED:3445382399,INDIGO:1258324735,IVORY:4294963455,KHAKI:4041641215,LAVENDER:3873897215,LAVENDERBLUSH:4293981695,LAWNGREEN:2096890111,LEMONCHIFFON:4294626815,LIGHTBLUE:2916673279,LIGHTCORAL:4034953471,LIGHTCYAN:3774873599,LIGHTGOLDENRODYELLOW:4210742015,LIGHTGRAY:3553874943,LIGHTGREEN:2431553791,LIGHTGREY:3553874943,LIGHTPINK:4290167295,LIGHTSALMON:4288707327,LIGHTSEAGREEN:548580095,LIGHTSKYBLUE:2278488831,LIGHTSLATEGRAY:2005441023,LIGHTSLATEGREY:2005441023,LIGHTSTEELBLUE:2965692159,LIGHTYELLOW:4294959359,LIME:16711935,LIMEGREEN:852308735,LINEN:4210091775,MAGENTA:4278255615,MAROON:2147483903,MEDIUMAQUAMARINE:1724754687,MEDIUMBLUE:52735,MEDIUMORCHID:3126187007,MEDIUMPURPLE:2473647103,MEDIUMSEAGREEN:1018393087,MEDIUMSLATEBLUE:2070474495,MEDIUMSPRINGGREEN:16423679,MEDIUMTURQUOISE:1221709055,MEDIUMVIOLETRED:3340076543,MIDNIGHTBLUE:421097727,MINTCREAM:4127193855,MISTYROSE:4293190143,MOCCASIN:4293178879,NAVAJOWHITE:4292783615,NAVY:33023,OLDLACE:4260751103,OLIVE:2155872511,OLIVEDRAB:1804477439,ORANGE:4289003775,ORANGERED:4282712319,ORCHID:3664828159,PALEGOLDENROD:4008225535,PALEGREEN:2566625535,PALETURQUOISE:2951671551,PALEVIOLETRED:3681588223,PAPAYAWHIP:4293907967,PEACHPUFF:4292524543,PERU:3448061951,PINK:4290825215,PLUM:3718307327,POWDERBLUE:2967529215,PURPLE:2147516671,REBECCAPURPLE:1714657791,RED:4278190335,ROSYBROWN:3163525119,ROYALBLUE:1097458175,SADDLEBROWN:2336560127,SALMON:4202722047,SANDYBROWN:4104413439,SEAGREEN:780883967,SEASHELL:4294307583,SIENNA:2689740287,SILVER:3233857791,SKYBLUE:2278484991,SLATEBLUE:1784335871,SLATEGRAY:1887473919,SLATEGREY:1887473919,SNOW:4294638335,SPRINGGREEN:16744447,STEELBLUE:1182971135,TAN:3535047935,TEAL:8421631,THISTLE:3636451583,TOMATO:4284696575,TRANSPARENT:0,TURQUOISE:1088475391,VIOLET:4001558271,WHEAT:4125012991,WHITE:4294967295,WHITESMOKE:4126537215,YELLOW:4294902015,YELLOWGREEN:2597139199},hr={name:"background-clip",initialValue:"border-box",prefix:!1,type:1,parse:function(e,t){return t.map((function(e){if(Qn(e))switch(e.value){case"padding-box":return 1;case"content-box":return 2}return 0}))}},fr={name:"background-color",initialValue:"transparent",prefix:!1,type:3,format:"color"},pr=function(e,t){var n=nr.parse(e,t[0]),r=t[1];return r&&On(r)?{color:n,stop:r}:{color:n,stop:null}},gr=function(e,t){var n=e[0],r=e[e.length-1];null===n.stop&&(n.stop=zn),null===r.stop&&(r.stop=Kn);for(var i=[],A=0,a=0;aA?i.push(s):i.push(A),A=s}else i.push(null)}var l=null;for(a=0;ae.optimumDistance)?{optimumCorner:t,optimumDistance:o}:e}),{optimumDistance:i?1/0:-1/0,optimumCorner:null}).optimumCorner},Br=function(e,t,n,r,i){var A=0,a=0;switch(e.size){case 0:0===e.shape?A=a=Math.min(Math.abs(t),Math.abs(t-r),Math.abs(n),Math.abs(n-i)):1===e.shape&&(A=Math.min(Math.abs(t),Math.abs(t-r)),a=Math.min(Math.abs(n),Math.abs(n-i)));break;case 2:if(0===e.shape)A=a=Math.min(yr(t,n),yr(t,n-i),yr(t-r,n),yr(t-r,n-i));else if(1===e.shape){var o=Math.min(Math.abs(n),Math.abs(n-i))/Math.min(Math.abs(t),Math.abs(t-r)),s=wr(r,i,t,n,!0),l=s[0],u=s[1];a=o*(A=yr(l-t,(u-n)/o))}break;case 1:0===e.shape?A=a=Math.max(Math.abs(t),Math.abs(t-r),Math.abs(n),Math.abs(n-i)):1===e.shape&&(A=Math.max(Math.abs(t),Math.abs(t-r)),a=Math.max(Math.abs(n),Math.abs(n-i)));break;case 3:if(0===e.shape)A=a=Math.max(yr(t,n),yr(t,n-i),yr(t-r,n),yr(t-r,n-i));else if(1===e.shape){o=Math.max(Math.abs(n),Math.abs(n-i))/Math.max(Math.abs(t),Math.abs(t-r));var c=wr(r,i,t,n,!1);l=c[0],u=c[1],a=o*(A=yr(l-t,(u-n)/o))}}return Array.isArray(e.size)&&(A=jn(e.size[0],r),a=2===e.size.length?jn(e.size[1],i):A),[A,a]},_r=function(e,t){var n=tr(180),r=[];return Pn(t).forEach((function(t,i){if(0===i){var A=t[0];if(20===A.type&&-1!==["top","left","right","bottom"].indexOf(A.value))return void(n=er(t));if($n(A))return void(n=(Zn.parse(e,A)+tr(270))%tr(360))}var a=pr(e,t);r.push(a)})),{angle:n,stops:r,type:1}},br="closest-side",xr="farthest-side",Cr="closest-corner",Sr="farthest-corner",Er="circle",Ur="ellipse",Mr="cover",Fr="contain",Tr=function(e,t){var n=0,r=3,i=[],A=[];return Pn(t).forEach((function(t,a){var o=!0;if(0===a?o=t.reduce((function(e,t){if(Qn(t))switch(t.value){case"center":return A.push(Gn),!1;case"top":case"left":return A.push(zn),!1;case"right":case"bottom":return A.push(Kn),!1}else if(On(t)||Nn(t))return A.push(t),!1;return e}),o):1===a&&(o=t.reduce((function(e,t){if(Qn(t))switch(t.value){case Er:return n=0,!1;case Ur:return n=1,!1;case Fr:case br:return r=0,!1;case xr:return r=1,!1;case Cr:return r=2,!1;case Mr:case Sr:return r=3,!1}else if(Nn(t)||On(t))return Array.isArray(r)||(r=[]),r.push(t),!1;return e}),o)),o){var s=pr(e,t);i.push(s)}})),{size:r,shape:n,stops:i,position:A,type:2}},kr=function(e){return 1===e.type},Qr=function(e){return 2===e.type},Lr={name:"image",parse:function(e,t){if(22===t.type){var n={url:t.value,type:0};return e.cache.addImage(t.value),n}if(18===t.type){var r=Rr[t.name];if("undefined"===typeof r)throw new Error('Attempting to parse an unsupported image function "'+t.name+'"');return r(e,t.values)}throw new Error("Unsupported image type "+t.type)}};function Dr(e){return!(20===e.type&&"none"===e.value)&&(18!==e.type||!!Rr[e.name])}var Ir,Rr={"linear-gradient":function(e,t){var n=tr(180),r=[];return Pn(t).forEach((function(t,i){if(0===i){var A=t[0];if(20===A.type&&"to"===A.value)return void(n=er(t));if($n(A))return void(n=Zn.parse(e,A))}var a=pr(e,t);r.push(a)})),{angle:n,stops:r,type:1}},"-moz-linear-gradient":_r,"-ms-linear-gradient":_r,"-o-linear-gradient":_r,"-webkit-linear-gradient":_r,"radial-gradient":function(e,t){var n=0,r=3,i=[],A=[];return Pn(t).forEach((function(t,a){var o=!0;if(0===a){var s=!1;o=t.reduce((function(e,t){if(s)if(Qn(t))switch(t.value){case"center":return A.push(Gn),e;case"top":case"left":return A.push(zn),e;case"right":case"bottom":return A.push(Kn),e}else(On(t)||Nn(t))&&A.push(t);else if(Qn(t))switch(t.value){case Er:return n=0,!1;case Ur:return n=1,!1;case"at":return s=!0,!1;case br:return r=0,!1;case Mr:case xr:return r=1,!1;case Fr:case Cr:return r=2,!1;case Sr:return r=3,!1}else if(Nn(t)||On(t))return Array.isArray(r)||(r=[]),r.push(t),!1;return e}),o)}if(o){var l=pr(e,t);i.push(l)}})),{size:r,shape:n,stops:i,position:A,type:2}},"-moz-radial-gradient":Tr,"-ms-radial-gradient":Tr,"-o-radial-gradient":Tr,"-webkit-radial-gradient":Tr,"-webkit-gradient":function(e,t){var n=tr(180),r=[],i=1,A=0,a=3,o=[];return Pn(t).forEach((function(t,n){var A=t[0];if(0===n){if(Qn(A)&&"linear"===A.value)return void(i=1);if(Qn(A)&&"radial"===A.value)return void(i=2)}if(18===A.type)if("from"===A.name){var a=nr.parse(e,A.values[0]);r.push({stop:zn,color:a})}else if("to"===A.name)a=nr.parse(e,A.values[0]),r.push({stop:Kn,color:a});else if("color-stop"===A.name){var o=A.values.filter(Rn);if(2===o.length){a=nr.parse(e,o[1]);var s=o[0];kn(s)&&r.push({stop:{type:16,number:100*s.number,flags:s.flags},color:a})}}})),1===i?{angle:(n+tr(180))%tr(360),stops:r,type:i}:{size:a,shape:A,stops:r,position:o,type:i}}},Pr={name:"background-image",initialValue:"none",type:1,prefix:!1,parse:function(e,t){if(0===t.length)return[];var n=t[0];return 20===n.type&&"none"===n.value?[]:t.filter((function(e){return Rn(e)&&Dr(e)})).map((function(t){return Lr.parse(e,t)}))}},Hr={name:"background-origin",initialValue:"border-box",prefix:!1,type:1,parse:function(e,t){return t.map((function(e){if(Qn(e))switch(e.value){case"padding-box":return 1;case"content-box":return 2}return 0}))}},Nr={name:"background-position",initialValue:"0% 0%",type:1,prefix:!1,parse:function(e,t){return Pn(t).map((function(e){return e.filter(On)})).map(Vn)}},Or={name:"background-repeat",initialValue:"repeat",prefix:!1,type:1,parse:function(e,t){return Pn(t).map((function(e){return e.filter(Qn).map((function(e){return e.value})).join(" ")})).map(Vr)}},Vr=function(e){switch(e){case"no-repeat":return 1;case"repeat-x":case"repeat no-repeat":return 2;case"repeat-y":case"no-repeat repeat":return 3;default:return 0}};!function(e){e.AUTO="auto",e.CONTAIN="contain",e.COVER="cover"}(Ir||(Ir={}));var zr,Gr={name:"background-size",initialValue:"0",prefix:!1,type:1,parse:function(e,t){return Pn(t).map((function(e){return e.filter(Kr)}))}},Kr=function(e){return Qn(e)||On(e)},Wr=function(e){return{name:"border-"+e+"-color",initialValue:"transparent",prefix:!1,type:3,format:"color"}},jr=Wr("top"),Xr=Wr("right"),qr=Wr("bottom"),Yr=Wr("left"),Jr=function(e){return{name:"border-radius-"+e,initialValue:"0 0",prefix:!1,type:1,parse:function(e,t){return Vn(t.filter(On))}}},Zr=Jr("top-left"),$r=Jr("top-right"),ei=Jr("bottom-right"),ti=Jr("bottom-left"),ni=function(e){return{name:"border-"+e+"-style",initialValue:"solid",prefix:!1,type:2,parse:function(e,t){switch(t){case"none":return 0;case"dashed":return 2;case"dotted":return 3;case"double":return 4}return 1}}},ri=ni("top"),ii=ni("right"),Ai=ni("bottom"),ai=ni("left"),oi=function(e){return{name:"border-"+e+"-width",initialValue:"0",type:0,prefix:!1,parse:function(e,t){return Tn(t)?t.number:0}}},si=oi("top"),li=oi("right"),ui=oi("bottom"),ci=oi("left"),di={name:"color",initialValue:"transparent",prefix:!1,type:3,format:"color"},hi={name:"direction",initialValue:"ltr",prefix:!1,type:2,parse:function(e,t){return"rtl"===t?1:0}},fi={name:"display",initialValue:"inline-block",prefix:!1,type:1,parse:function(e,t){return t.filter(Qn).reduce((function(e,t){return e|pi(t.value)}),0)}},pi=function(e){switch(e){case"block":case"-webkit-box":return 2;case"inline":return 4;case"run-in":return 8;case"flow":return 16;case"flow-root":return 32;case"table":return 64;case"flex":case"-webkit-flex":return 128;case"grid":case"-ms-grid":return 256;case"ruby":return 512;case"subgrid":return 1024;case"list-item":return 2048;case"table-row-group":return 4096;case"table-header-group":return 8192;case"table-footer-group":return 16384;case"table-row":return 32768;case"table-cell":return 65536;case"table-column-group":return 131072;case"table-column":return 262144;case"table-caption":return 524288;case"ruby-base":return 1048576;case"ruby-text":return 2097152;case"ruby-base-container":return 4194304;case"ruby-text-container":return 8388608;case"contents":return 16777216;case"inline-block":return 33554432;case"inline-list-item":return 67108864;case"inline-table":return 134217728;case"inline-flex":return 268435456;case"inline-grid":return 536870912}return 0},gi={name:"float",initialValue:"none",prefix:!1,type:2,parse:function(e,t){switch(t){case"left":return 1;case"right":return 2;case"inline-start":return 3;case"inline-end":return 4}return 0}},mi={name:"letter-spacing",initialValue:"0",prefix:!1,type:0,parse:function(e,t){return 20===t.type&&"normal"===t.value?0:17===t.type||15===t.type?t.number:0}};!function(e){e.NORMAL="normal",e.STRICT="strict"}(zr||(zr={}));var vi,yi={name:"line-break",initialValue:"normal",prefix:!1,type:2,parse:function(e,t){return"strict"===t?zr.STRICT:zr.NORMAL}},wi={name:"line-height",initialValue:"normal",prefix:!1,type:4},Bi=function(e,t){return Qn(e)&&"normal"===e.value?1.2*t:17===e.type?t*e.number:On(e)?jn(e,t):t},_i={name:"list-style-image",initialValue:"none",type:0,prefix:!1,parse:function(e,t){return 20===t.type&&"none"===t.value?null:Lr.parse(e,t)}},bi={name:"list-style-position",initialValue:"outside",prefix:!1,type:2,parse:function(e,t){return"inside"===t?0:1}},xi={name:"list-style-type",initialValue:"none",prefix:!1,type:2,parse:function(e,t){switch(t){case"disc":return 0;case"circle":return 1;case"square":return 2;case"decimal":return 3;case"cjk-decimal":return 4;case"decimal-leading-zero":return 5;case"lower-roman":return 6;case"upper-roman":return 7;case"lower-greek":return 8;case"lower-alpha":return 9;case"upper-alpha":return 10;case"arabic-indic":return 11;case"armenian":return 12;case"bengali":return 13;case"cambodian":return 14;case"cjk-earthly-branch":return 15;case"cjk-heavenly-stem":return 16;case"cjk-ideographic":return 17;case"devanagari":return 18;case"ethiopic-numeric":return 19;case"georgian":return 20;case"gujarati":return 21;case"gurmukhi":case"hebrew":return 22;case"hiragana":return 23;case"hiragana-iroha":return 24;case"japanese-formal":return 25;case"japanese-informal":return 26;case"kannada":return 27;case"katakana":return 28;case"katakana-iroha":return 29;case"khmer":return 30;case"korean-hangul-formal":return 31;case"korean-hanja-formal":return 32;case"korean-hanja-informal":return 33;case"lao":return 34;case"lower-armenian":return 35;case"malayalam":return 36;case"mongolian":return 37;case"myanmar":return 38;case"oriya":return 39;case"persian":return 40;case"simp-chinese-formal":return 41;case"simp-chinese-informal":return 42;case"tamil":return 43;case"telugu":return 44;case"thai":return 45;case"tibetan":return 46;case"trad-chinese-formal":return 47;case"trad-chinese-informal":return 48;case"upper-armenian":return 49;case"disclosure-open":return 50;case"disclosure-closed":return 51;default:return-1}}},Ci=function(e){return{name:"margin-"+e,initialValue:"0",prefix:!1,type:4}},Si=Ci("top"),Ei=Ci("right"),Ui=Ci("bottom"),Mi=Ci("left"),Fi={name:"overflow",initialValue:"visible",prefix:!1,type:1,parse:function(e,t){return t.filter(Qn).map((function(e){switch(e.value){case"hidden":return 1;case"scroll":return 2;case"clip":return 3;case"auto":return 4;default:return 0}}))}},Ti={name:"overflow-wrap",initialValue:"normal",prefix:!1,type:2,parse:function(e,t){return"break-word"===t?"break-word":"normal"}},ki=function(e){return{name:"padding-"+e,initialValue:"0",prefix:!1,type:3,format:"length-percentage"}},Qi=ki("top"),Li=ki("right"),Di=ki("bottom"),Ii=ki("left"),Ri={name:"text-align",initialValue:"left",prefix:!1,type:2,parse:function(e,t){switch(t){case"right":return 2;case"center":case"justify":return 1;default:return 0}}},Pi={name:"position",initialValue:"static",prefix:!1,type:2,parse:function(e,t){switch(t){case"relative":return 1;case"absolute":return 2;case"fixed":return 3;case"sticky":return 4}return 0}},Hi={name:"text-shadow",initialValue:"none",type:1,prefix:!1,parse:function(e,t){return 1===t.length&&Dn(t[0],"none")?[]:Pn(t).map((function(t){for(var n={color:dr.TRANSPARENT,offsetX:zn,offsetY:zn,blur:zn},r=0,i=0;i1?1:0],this.overflowWrap=vA(e,Ti,t.overflowWrap),this.paddingTop=vA(e,Qi,t.paddingTop),this.paddingRight=vA(e,Li,t.paddingRight),this.paddingBottom=vA(e,Di,t.paddingBottom),this.paddingLeft=vA(e,Ii,t.paddingLeft),this.paintOrder=vA(e,dA,t.paintOrder),this.position=vA(e,Pi,t.position),this.textAlign=vA(e,Ri,t.textAlign),this.textDecorationColor=vA(e,Ji,null!==(n=t.textDecorationColor)&&void 0!==n?n:t.color),this.textDecorationLine=vA(e,Zi,null!==(r=t.textDecorationLine)&&void 0!==r?r:t.textDecoration),this.textShadow=vA(e,Hi,t.textShadow),this.textTransform=vA(e,Ni,t.textTransform),this.transform=vA(e,Oi,t.transform),this.transformOrigin=vA(e,Ki,t.transformOrigin),this.visibility=vA(e,Wi,t.visibility),this.webkitTextStrokeColor=vA(e,hA,t.webkitTextStrokeColor),this.webkitTextStrokeWidth=vA(e,fA,t.webkitTextStrokeWidth),this.wordBreak=vA(e,ji,t.wordBreak),this.zIndex=vA(e,Xi,t.zIndex)}return e.prototype.isVisible=function(){return this.display>0&&this.opacity>0&&0===this.visibility},e.prototype.isTransparent=function(){return rr(this.backgroundColor)},e.prototype.isTransformed=function(){return null!==this.transform},e.prototype.isPositioned=function(){return 0!==this.position},e.prototype.isPositionedWithZIndex=function(){return this.isPositioned()&&!this.zIndex.auto},e.prototype.isFloating=function(){return 0!==this.float},e.prototype.isInlineLevel=function(){return iA(this.display,4)||iA(this.display,33554432)||iA(this.display,268435456)||iA(this.display,536870912)||iA(this.display,67108864)||iA(this.display,134217728)},e}(),gA=function(){function e(e,t){this.content=vA(e,AA,t.content),this.quotes=vA(e,lA,t.quotes)}return e}(),mA=function(){function e(e,t){this.counterIncrement=vA(e,aA,t.counterIncrement),this.counterReset=vA(e,oA,t.counterReset)}return e}(),vA=function(e,t,n){var r=new Mn,i=null!==n&&"undefined"!==typeof n?n.toString():t.initialValue;r.write(i);var A=new Fn(r.read());switch(t.type){case 2:var a=A.parseComponentValue();return t.parse(e,Qn(a)?a.value:t.initialValue);case 0:return t.parse(e,A.parseComponentValue());case 1:return t.parse(e,A.parseComponentValues());case 4:return A.parseComponentValue();case 3:switch(t.format){case"angle":return Zn.parse(e,A.parseComponentValue());case"color":return nr.parse(e,A.parseComponentValue());case"image":return Lr.parse(e,A.parseComponentValue());case"length":var o=A.parseComponentValue();return Nn(o)?o:zn;case"length-percentage":var s=A.parseComponentValue();return On(s)?s:zn;case"time":return qi.parse(e,A.parseComponentValue())}}},yA="data-html2canvas-debug",wA=function(e){switch(e.getAttribute(yA)){case"all":return 1;case"clone":return 2;case"parse":return 3;case"render":return 4;default:return 0}},BA=function(e,t){var n=wA(e);return 1===n||t===n},_A=function(){function e(e,t){this.context=e,this.textNodes=[],this.elements=[],this.flags=0,BA(t,3),this.styles=new pA(e,window.getComputedStyle(t,null)),lo(t)&&(this.styles.animationDuration.some((function(e){return e>0}))&&(t.style.animationDuration="0s"),null!==this.styles.transform&&(t.style.transform="none")),this.bounds=o(this.context,t),BA(t,4)&&(this.flags|=16)}return e}(),bA="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",xA="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/",CA="undefined"===typeof Uint8Array?[]:new Uint8Array(256),SA=0;SA>4,u[s++]=(15&r)<<4|i>>2,u[s++]=(3&i)<<6|63&A;return l},UA=function(e){for(var t=e.length,n=[],r=0;r>FA,LA=(1<>FA)+32,IA=65536>>TA,RA=(1<=0){if(e<55296||e>56319&&e<=65535)return t=((t=this.index[e>>FA])<>FA)])<>TA),t=this.index[t],t+=e>>FA&RA,t=((t=this.index[t])<=55296&&i<=56319&&n>10),a%1024+56320)),(i+1===n||r.length>16384)&&(A+=String.fromCharCode.apply(String,r),r.length=0)}return A},sa=NA(bA),la="\xd7",ua="\xf7",ca=function(e){return sa.get(e)},da=function(e,t,n){var r=n-2,i=t[r],A=t[n-1],a=t[n];if(A===jA&&a===XA)return la;if(A===jA||A===XA||A===qA)return ua;if(a===jA||a===XA||a===qA)return ua;if(A===ZA&&-1!==[ZA,$A,ta,na].indexOf(a))return la;if((A===ta||A===$A)&&(a===$A||a===ea))return la;if((A===na||A===ea)&&a===ea)return la;if(a===ra||a===YA)return la;if(a===JA)return la;if(A===WA)return la;if(A===ra&&a===ia){for(;i===YA;)i=t[--r];if(i===ia)return la}if(A===Aa&&a===Aa){for(var o=0;i===Aa;)o++,i=t[--r];if(o%2===0)return la}return ua},ha=function(e){var t=aa(e),n=t.length,r=0,i=0,A=t.map(ca);return{next:function(){if(r>=n)return{done:!0,value:null};for(var e=la;ra.x||i.y>a.y;return a=i,0===t||o}));return e.body.removeChild(t),o},ma=function(){return"undefined"!==typeof(new Image).crossOrigin},va=function(){return"string"===typeof(new XMLHttpRequest).responseType},ya=function(e){var t=new Image,n=e.createElement("canvas"),r=n.getContext("2d");if(!r)return!1;t.src="data:image/svg+xml,";try{r.drawImage(t,0,0),n.toDataURL()}catch(Rt){return!1}return!0},wa=function(e){return 0===e[0]&&255===e[1]&&0===e[2]&&255===e[3]},Ba=function(e){var t=e.createElement("canvas"),n=100;t.width=n,t.height=n;var r=t.getContext("2d");if(!r)return Promise.reject(!1);r.fillStyle="rgb(0, 255, 0)",r.fillRect(0,0,n,n);var i=new Image,A=t.toDataURL();i.src=A;var a=_a(n,n,0,0,i);return r.fillStyle="red",r.fillRect(0,0,n,n),ba(a).then((function(t){r.drawImage(t,0,0);var i=r.getImageData(0,0,n,n).data;r.fillStyle="red",r.fillRect(0,0,n,n);var a=e.createElement("div");return a.style.backgroundImage="url("+A+")",a.style.height=n+"px",wa(i)?ba(_a(n,n,0,0,a)):Promise.reject(!1)})).then((function(e){return r.drawImage(e,0,0),wa(r.getImageData(0,0,n,n).data)})).catch((function(){return!1}))},_a=function(e,t,n,r,i){var A="http://www.w3.org/2000/svg",a=document.createElementNS(A,"svg"),o=document.createElementNS(A,"foreignObject");return a.setAttributeNS(null,"width",e.toString()),a.setAttributeNS(null,"height",t.toString()),o.setAttributeNS(null,"width","100%"),o.setAttributeNS(null,"height","100%"),o.setAttributeNS(null,"x",n.toString()),o.setAttributeNS(null,"y",r.toString()),o.setAttributeNS(null,"externalResourcesRequired","true"),a.appendChild(o),o.appendChild(i),a},ba=function(e){return new Promise((function(t,n){var r=new Image;r.onload=function(){return t(r)},r.onerror=n,r.src="data:image/svg+xml;charset=utf-8,"+encodeURIComponent((new XMLSerializer).serializeToString(e))}))},xa={get SUPPORT_RANGE_BOUNDS(){var e=pa(document);return Object.defineProperty(xa,"SUPPORT_RANGE_BOUNDS",{value:e}),e},get SUPPORT_WORD_BREAKING(){var e=xa.SUPPORT_RANGE_BOUNDS&&ga(document);return Object.defineProperty(xa,"SUPPORT_WORD_BREAKING",{value:e}),e},get SUPPORT_SVG_DRAWING(){var e=ya(document);return Object.defineProperty(xa,"SUPPORT_SVG_DRAWING",{value:e}),e},get SUPPORT_FOREIGNOBJECT_DRAWING(){var e="function"===typeof Array.from&&"function"===typeof window.fetch?Ba(document):Promise.resolve(!1);return Object.defineProperty(xa,"SUPPORT_FOREIGNOBJECT_DRAWING",{value:e}),e},get SUPPORT_CORS_IMAGES(){var e=ma();return Object.defineProperty(xa,"SUPPORT_CORS_IMAGES",{value:e}),e},get SUPPORT_RESPONSE_TYPE(){var e=va();return Object.defineProperty(xa,"SUPPORT_RESPONSE_TYPE",{value:e}),e},get SUPPORT_CORS_XHR(){var e="withCredentials"in new XMLHttpRequest;return Object.defineProperty(xa,"SUPPORT_CORS_XHR",{value:e}),e},get SUPPORT_NATIVE_TEXT_SEGMENTATION(){var e=!("undefined"===typeof Intl||!Intl.Segmenter);return Object.defineProperty(xa,"SUPPORT_NATIVE_TEXT_SEGMENTATION",{value:e}),e}},Ca=function(){function e(e,t){this.text=e,this.bounds=t}return e}(),Sa=function(e,t,n,r){var i=Ta(t,n),A=[],o=0;return i.forEach((function(t){if(n.textDecorationLine.length||t.trim().length>0)if(xa.SUPPORT_RANGE_BOUNDS){var i=Ua(r,o,t.length).getClientRects();if(i.length>1){var s=Ma(t),l=0;s.forEach((function(t){A.push(new Ca(t,a.fromDOMRectList(e,Ua(r,l+o,t.length).getClientRects()))),l+=t.length}))}else A.push(new Ca(t,a.fromDOMRectList(e,i)))}else{var u=r.splitText(t.length);A.push(new Ca(t,Ea(e,r))),r=u}else xa.SUPPORT_RANGE_BOUNDS||(r=r.splitText(t.length));o+=t.length})),A},Ea=function(e,t){var n=t.ownerDocument;if(n){var r=n.createElement("html2canvaswrapper");r.appendChild(t.cloneNode(!0));var i=t.parentNode;if(i){i.replaceChild(r,t);var A=o(e,r);return r.firstChild&&i.replaceChild(r.firstChild,r),A}}return a.EMPTY},Ua=function(e,t,n){var r=e.ownerDocument;if(!r)throw new Error("Node has no owner document");var i=r.createRange();return i.setStart(e,t),i.setEnd(e,t+n),i},Ma=function(e){if(xa.SUPPORT_NATIVE_TEXT_SEGMENTATION){var t=new Intl.Segmenter(void 0,{granularity:"grapheme"});return Array.from(t.segment(e)).map((function(e){return e.segment}))}return fa(e)},Fa=function(e,t){if(xa.SUPPORT_NATIVE_TEXT_SEGMENTATION){var n=new Intl.Segmenter(void 0,{granularity:"word"});return Array.from(n.segment(e)).map((function(e){return e.segment}))}return Qa(e,t)},Ta=function(e,t){return 0!==t.letterSpacing?Ma(e):Fa(e,t)},ka=[32,160,4961,65792,65793,4153,4241],Qa=function(e,t){for(var n,r=Ve(e,{lineBreak:t.lineBreak,wordBreak:"break-word"===t.overflowWrap?"break-word":t.wordBreak}),i=[],A=function(){if(n.value){var e=n.value.slice(),t=l(e),r="";t.forEach((function(e){-1===ka.indexOf(e)?r+=u(e):(r.length&&i.push(r),i.push(u(e)),r="")})),r.length&&i.push(r)}};!(n=r.next()).done;)A();return i},La=function(){function e(e,t,n){this.text=Da(t.data,n.textTransform),this.textBounds=Sa(e,this.text,n,t)}return e}(),Da=function(e,t){switch(t){case 1:return e.toLowerCase();case 3:return e.replace(Ia,Ra);case 2:return e.toUpperCase();default:return e}},Ia=/(^|\s|:|-|\(|\))([a-z])/g,Ra=function(e,t,n){return e.length>0?t+n.toUpperCase():e},Pa=function(e){function n(t,n){var r=e.call(this,t,n)||this;return r.src=n.currentSrc||n.src,r.intrinsicWidth=n.naturalWidth,r.intrinsicHeight=n.naturalHeight,r.context.cache.addImage(r.src),r}return t(n,e),n}(_A),Ha=function(e){function n(t,n){var r=e.call(this,t,n)||this;return r.canvas=n,r.intrinsicWidth=n.width,r.intrinsicHeight=n.height,r}return t(n,e),n}(_A),Na=function(e){function n(t,n){var r=e.call(this,t,n)||this,i=new XMLSerializer,A=o(t,n);return n.setAttribute("width",A.width+"px"),n.setAttribute("height",A.height+"px"),r.svg="data:image/svg+xml,"+encodeURIComponent(i.serializeToString(n)),r.intrinsicWidth=n.width.baseVal.value,r.intrinsicHeight=n.height.baseVal.value,r.context.cache.addImage(r.svg),r}return t(n,e),n}(_A),Oa=function(e){function n(t,n){var r=e.call(this,t,n)||this;return r.value=n.value,r}return t(n,e),n}(_A),Va=function(e){function n(t,n){var r=e.call(this,t,n)||this;return r.start=n.start,r.reversed="boolean"===typeof n.reversed&&!0===n.reversed,r}return t(n,e),n}(_A),za=[{type:15,flags:0,unit:"px",number:3}],Ga=[{type:16,flags:0,number:50}],Ka=function(e){return e.width>e.height?new a(e.left+(e.width-e.height)/2,e.top,e.height,e.height):e.width0)r.textNodes.push(new La(t,A,r.styles));else if(so(A))if(So(A)&&A.assignedNodes)A.assignedNodes().forEach((function(n){return e(t,n,r,i)}));else{var o=ro(t,A);o.styles.isVisible()&&(Ao(A,o,i)?o.flags|=4:ao(o.styles)&&(o.flags|=2),-1!==to.indexOf(A.tagName)&&(o.flags|=8),r.elements.push(o),A.slot,A.shadowRoot?e(t,A.shadowRoot,o,i):xo(A)||go(A)||Co(A)||e(t,A,o,i))}},ro=function(e,t){return wo(t)?new Pa(e,t):vo(t)?new Ha(e,t):go(t)?new Na(e,t):co(t)?new Oa(e,t):ho(t)?new Va(e,t):fo(t)?new Ja(e,t):Co(t)?new Za(e,t):xo(t)?new $a(e,t):Bo(t)?new eo(e,t):new _A(e,t)},io=function(e,t){var n=ro(e,t);return n.flags|=4,no(e,t,n,n),n},Ao=function(e,t,n){return t.styles.isPositionedWithZIndex()||t.styles.opacity<1||t.styles.isTransformed()||mo(e)&&n.styles.isTransparent()},ao=function(e){return e.isPositioned()||e.isFloating()},oo=function(e){return e.nodeType===Node.TEXT_NODE},so=function(e){return e.nodeType===Node.ELEMENT_NODE},lo=function(e){return so(e)&&"undefined"!==typeof e.style&&!uo(e)},uo=function(e){return"object"===typeof e.className},co=function(e){return"LI"===e.tagName},ho=function(e){return"OL"===e.tagName},fo=function(e){return"INPUT"===e.tagName},po=function(e){return"HTML"===e.tagName},go=function(e){return"svg"===e.tagName},mo=function(e){return"BODY"===e.tagName},vo=function(e){return"CANVAS"===e.tagName},yo=function(e){return"VIDEO"===e.tagName},wo=function(e){return"IMG"===e.tagName},Bo=function(e){return"IFRAME"===e.tagName},_o=function(e){return"STYLE"===e.tagName},bo=function(e){return"SCRIPT"===e.tagName},xo=function(e){return"TEXTAREA"===e.tagName},Co=function(e){return"SELECT"===e.tagName},So=function(e){return"SLOT"===e.tagName},Eo=function(e){return e.tagName.indexOf("-")>0},Uo=function(){function e(){this.counters={}}return e.prototype.getCounterValue=function(e){var t=this.counters[e];return t&&t.length?t[t.length-1]:1},e.prototype.getCounterValues=function(e){var t=this.counters[e];return t||[]},e.prototype.pop=function(e){var t=this;e.forEach((function(e){return t.counters[e].pop()}))},e.prototype.parse=function(e){var t=this,n=e.counterIncrement,r=e.counterReset,i=!0;null!==n&&n.forEach((function(e){var n=t.counters[e.counter];n&&0!==e.increment&&(i=!1,n.length||n.push(1),n[Math.max(0,n.length-1)]+=e.increment)}));var A=[];return i&&r.forEach((function(e){var n=t.counters[e.counter];A.push(e.counter),n||(n=t.counters[e.counter]=[]),n.push(e.reset)})),A},e}(),Mo={integers:[1e3,900,500,400,100,90,50,40,10,9,5,4,1],values:["M","CM","D","CD","C","XC","L","XL","X","IX","V","IV","I"]},Fo={integers:[9e3,8e3,7e3,6e3,5e3,4e3,3e3,2e3,1e3,900,800,700,600,500,400,300,200,100,90,80,70,60,50,40,30,20,10,9,8,7,6,5,4,3,2,1],values:["\u0554","\u0553","\u0552","\u0551","\u0550","\u054f","\u054e","\u054d","\u054c","\u054b","\u054a","\u0549","\u0548","\u0547","\u0546","\u0545","\u0544","\u0543","\u0542","\u0541","\u0540","\u053f","\u053e","\u053d","\u053c","\u053b","\u053a","\u0539","\u0538","\u0537","\u0536","\u0535","\u0534","\u0533","\u0532","\u0531"]},To={integers:[1e4,9e3,8e3,7e3,6e3,5e3,4e3,3e3,2e3,1e3,400,300,200,100,90,80,70,60,50,40,30,20,19,18,17,16,15,10,9,8,7,6,5,4,3,2,1],values:["\u05d9\u05f3","\u05d8\u05f3","\u05d7\u05f3","\u05d6\u05f3","\u05d5\u05f3","\u05d4\u05f3","\u05d3\u05f3","\u05d2\u05f3","\u05d1\u05f3","\u05d0\u05f3","\u05ea","\u05e9","\u05e8","\u05e7","\u05e6","\u05e4","\u05e2","\u05e1","\u05e0","\u05de","\u05dc","\u05db","\u05d9\u05d8","\u05d9\u05d7","\u05d9\u05d6","\u05d8\u05d6","\u05d8\u05d5","\u05d9","\u05d8","\u05d7","\u05d6","\u05d5","\u05d4","\u05d3","\u05d2","\u05d1","\u05d0"]},ko={integers:[1e4,9e3,8e3,7e3,6e3,5e3,4e3,3e3,2e3,1e3,900,800,700,600,500,400,300,200,100,90,80,70,60,50,40,30,20,10,9,8,7,6,5,4,3,2,1],values:["\u10f5","\u10f0","\u10ef","\u10f4","\u10ee","\u10ed","\u10ec","\u10eb","\u10ea","\u10e9","\u10e8","\u10e7","\u10e6","\u10e5","\u10e4","\u10f3","\u10e2","\u10e1","\u10e0","\u10df","\u10de","\u10dd","\u10f2","\u10dc","\u10db","\u10da","\u10d9","\u10d8","\u10d7","\u10f1","\u10d6","\u10d5","\u10d4","\u10d3","\u10d2","\u10d1","\u10d0"]},Qo=function(e,t,n,r,i,A){return en?Wo(e,i,A.length>0):r.integers.reduce((function(t,n,i){for(;e>=n;)e-=n,t+=r.values[i];return t}),"")+A},Lo=function(e,t,n,r){var i="";do{n||e--,i=r(e)+i,e/=t}while(e*t>=t);return i},Do=function(e,t,n,r,i){var A=n-t+1;return(e<0?"-":"")+(Lo(Math.abs(e),A,r,(function(e){return u(Math.floor(e%A)+t)}))+i)},Io=function(e,t,n){void 0===n&&(n=". ");var r=t.length;return Lo(Math.abs(e),r,!1,(function(e){return t[Math.floor(e%r)]}))+n},Ro=1,Po=2,Ho=4,No=8,Oo=function(e,t,n,r,i,A){if(e<-9999||e>9999)return Wo(e,4,i.length>0);var a=Math.abs(e),o=i;if(0===a)return t[0]+o;for(var s=0;a>0&&s<=4;s++){var l=a%10;0===l&&iA(A,Ro)&&""!==o?o=t[l]+o:l>1||1===l&&0===s||1===l&&1===s&&iA(A,Po)||1===l&&1===s&&iA(A,Ho)&&e>100||1===l&&s>1&&iA(A,No)?o=t[l]+(s>0?n[s-1]:"")+o:1===l&&s>0&&(o=n[s-1]+o),a=Math.floor(a/10)}return(e<0?r:"")+o},Vo="\u5341\u767e\u5343\u842c",zo="\u62fe\u4f70\u4edf\u842c",Go="\u30de\u30a4\u30ca\u30b9",Ko="\ub9c8\uc774\ub108\uc2a4",Wo=function(e,t,n){var r=n?". ":"",i=n?"\u3001":"",A=n?", ":"",a=n?" ":"";switch(t){case 0:return"\u2022"+a;case 1:return"\u25e6"+a;case 2:return"\u25fe"+a;case 5:var o=Do(e,48,57,!0,r);return o.length<4?"0"+o:o;case 4:return Io(e,"\u3007\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d",i);case 6:return Qo(e,1,3999,Mo,3,r).toLowerCase();case 7:return Qo(e,1,3999,Mo,3,r);case 8:return Do(e,945,969,!1,r);case 9:return Do(e,97,122,!1,r);case 10:return Do(e,65,90,!1,r);case 11:return Do(e,1632,1641,!0,r);case 12:case 49:return Qo(e,1,9999,Fo,3,r);case 35:return Qo(e,1,9999,Fo,3,r).toLowerCase();case 13:return Do(e,2534,2543,!0,r);case 14:case 30:return Do(e,6112,6121,!0,r);case 15:return Io(e,"\u5b50\u4e11\u5bc5\u536f\u8fb0\u5df3\u5348\u672a\u7533\u9149\u620c\u4ea5",i);case 16:return Io(e,"\u7532\u4e59\u4e19\u4e01\u620a\u5df1\u5e9a\u8f9b\u58ec\u7678",i);case 17:case 48:return Oo(e,"\u96f6\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d",Vo,"\u8ca0",i,Po|Ho|No);case 47:return Oo(e,"\u96f6\u58f9\u8cb3\u53c3\u8086\u4f0d\u9678\u67d2\u634c\u7396",zo,"\u8ca0",i,Ro|Po|Ho|No);case 42:return Oo(e,"\u96f6\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d",Vo,"\u8d1f",i,Po|Ho|No);case 41:return Oo(e,"\u96f6\u58f9\u8d30\u53c1\u8086\u4f0d\u9646\u67d2\u634c\u7396",zo,"\u8d1f",i,Ro|Po|Ho|No);case 26:return Oo(e,"\u3007\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d","\u5341\u767e\u5343\u4e07",Go,i,0);case 25:return Oo(e,"\u96f6\u58f1\u5f10\u53c2\u56db\u4f0d\u516d\u4e03\u516b\u4e5d","\u62fe\u767e\u5343\u4e07",Go,i,Ro|Po|Ho);case 31:return Oo(e,"\uc601\uc77c\uc774\uc0bc\uc0ac\uc624\uc721\uce60\ud314\uad6c","\uc2ed\ubc31\ucc9c\ub9cc",Ko,A,Ro|Po|Ho);case 33:return Oo(e,"\u96f6\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d","\u5341\u767e\u5343\u842c",Ko,A,0);case 32:return Oo(e,"\u96f6\u58f9\u8cb3\u53c3\u56db\u4e94\u516d\u4e03\u516b\u4e5d","\u62fe\u767e\u5343",Ko,A,Ro|Po|Ho);case 18:return Do(e,2406,2415,!0,r);case 20:return Qo(e,1,19999,ko,3,r);case 21:return Do(e,2790,2799,!0,r);case 22:return Do(e,2662,2671,!0,r);case 22:return Qo(e,1,10999,To,3,r);case 23:return Io(e,"\u3042\u3044\u3046\u3048\u304a\u304b\u304d\u304f\u3051\u3053\u3055\u3057\u3059\u305b\u305d\u305f\u3061\u3064\u3066\u3068\u306a\u306b\u306c\u306d\u306e\u306f\u3072\u3075\u3078\u307b\u307e\u307f\u3080\u3081\u3082\u3084\u3086\u3088\u3089\u308a\u308b\u308c\u308d\u308f\u3090\u3091\u3092\u3093");case 24:return Io(e,"\u3044\u308d\u306f\u306b\u307b\u3078\u3068\u3061\u308a\u306c\u308b\u3092\u308f\u304b\u3088\u305f\u308c\u305d\u3064\u306d\u306a\u3089\u3080\u3046\u3090\u306e\u304a\u304f\u3084\u307e\u3051\u3075\u3053\u3048\u3066\u3042\u3055\u304d\u3086\u3081\u307f\u3057\u3091\u3072\u3082\u305b\u3059");case 27:return Do(e,3302,3311,!0,r);case 28:return 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 Do(e,48,57,!0,r)}},jo="data-html2canvas-ignore",Xo=function(){function e(e,t,n){if(this.context=e,this.options=n,this.scrolledElements=[],this.referenceElement=t,this.counters=new Uo,this.quoteDepth=0,!t.ownerDocument)throw new Error("Cloned element does not have an owner document");this.documentElement=this.cloneNode(t.ownerDocument.documentElement,!1)}return e.prototype.toIFrame=function(e,t){var n=this,A=Yo(e,t);if(!A.contentWindow)return Promise.reject("Unable to find iframe window");var a=e.defaultView.pageXOffset,o=e.defaultView.pageYOffset,s=A.contentWindow,l=s.document,u=$o(A).then((function(){return r(n,void 0,void 0,(function(){var e,n;return i(this,(function(r){switch(r.label){case 0:return this.scrolledElements.forEach(is),s&&(s.scrollTo(t.left,t.top),!/(iPad|iPhone|iPod)/g.test(navigator.userAgent)||s.scrollY===t.top&&s.scrollX===t.left||(this.context.logger.warn("Unable to restore scroll position for cloned document"),this.context.windowBounds=this.context.windowBounds.add(s.scrollX-t.left,s.scrollY-t.top,0,0))),e=this.options.onclone,"undefined"===typeof(n=this.clonedReferenceElement)?[2,Promise.reject("Error finding the "+this.referenceElement.nodeName+" in the cloned document")]:l.fonts&&l.fonts.ready?[4,l.fonts.ready]:[3,2];case 1:r.sent(),r.label=2;case 2:return/(AppleWebKit)/g.test(navigator.userAgent)?[4,Zo(l)]:[3,4];case 3:r.sent(),r.label=4;case 4:return"function"===typeof e?[2,Promise.resolve().then((function(){return e(l,n)})).then((function(){return A}))]:[2,A]}}))}))}));return l.open(),l.write(ns(document.doctype)+""),rs(this.referenceElement.ownerDocument,a,o),l.replaceChild(l.adoptNode(this.documentElement),l.documentElement),l.close(),u},e.prototype.createElementClone=function(e){if(BA(e,2),vo(e))return this.createCanvasClone(e);if(yo(e))return this.createVideoClone(e);if(_o(e))return this.createStyleClone(e);var t=e.cloneNode(!1);return 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 gA(this.context,n),s=a.createElement("html2canvaspseudoelement");ts(n,s),o.content.forEach((function(t){if(0===t.type)s.appendChild(a.createTextNode(t.value));else if(22===t.type){var n=a.createElement("img");n.src=t.value,n.style.opacity="1",s.appendChild(n)}else if(18===t.type){if("attr"===t.name){var r=t.values.filter(Qn);r.length&&s.appendChild(a.createTextNode(e.getAttribute(r[0].value)||""))}else if("counter"===t.name){var A=t.values.filter(Rn),l=A[0],u=A[1];if(l&&Qn(l)){var c=i.counters.getCounterValue(l.value),d=u&&Qn(u)?xi.parse(i.context,u.value):3;s.appendChild(a.createTextNode(Wo(c,d,!1)))}}else if("counters"===t.name){var h=t.values.filter(Rn),f=(l=h[0],h[1]);if(u=h[2],l&&Qn(l)){var p=i.counters.getCounterValues(l.value),g=u&&Qn(u)?xi.parse(i.context,u.value):3,m=f&&0===f.type?f.value:"",v=p.map((function(e){return Wo(e,g,!1)})).join(m);s.appendChild(a.createTextNode(v))}}}else if(20===t.type)switch(t.value){case"open-quote":s.appendChild(a.createTextNode(uA(o.quotes,i.quoteDepth++,!0)));break;case"close-quote":s.appendChild(a.createTextNode(uA(o.quotes,--i.quoteDepth,!1)));break;default:s.appendChild(a.createTextNode(t.value))}})),s.className=os+" "+ss;var l=r===KA.BEFORE?" "+os:" "+ss;return uo(t)?t.className.baseValue+=l:t.className+=l,s}}},e.destroy=function(e){return!!e.parentNode&&(e.parentNode.removeChild(e),!0)},e}();!function(e){e[e.BEFORE=0]="BEFORE",e[e.AFTER=1]="AFTER"}(KA||(KA={}));var qo,Yo=function(e,t){var n=e.createElement("iframe");return n.className="html2canvas-container",n.style.visibility="hidden",n.style.position="fixed",n.style.left="-10000px",n.style.top="0px",n.style.border="0",n.width=t.width.toString(),n.height=t.height.toString(),n.scrolling="no",n.setAttribute(jo,"true"),e.body.appendChild(n),n},Jo=function(e){return new Promise((function(t){e.complete?t():e.src?(e.onload=t,e.onerror=t):t()}))},Zo=function(e){return Promise.all([].slice.call(e.images,0).map(Jo))},$o=function(e){return new Promise((function(t,n){var r=e.contentWindow;if(!r)return n("No window assigned for iframe");var i=r.document;r.onload=e.onload=function(){r.onload=e.onload=null;var n=setInterval((function(){i.body.childNodes.length>0&&"complete"===i.readyState&&(clearInterval(n),t(e))}),50)}}))},es=["all","d","content"],ts=function(e,t){for(var n=e.length-1;n>=0;n--){var r=e.item(n);-1===es.indexOf(r)&&t.style.setProperty(r,e.getPropertyValue(r))}return t},ns=function(e){var t="";return e&&(t+=""),t},rs=function(e,t,n){e&&e.defaultView&&(t!==e.defaultView.pageXOffset||n!==e.defaultView.pageYOffset)&&e.defaultView.scrollTo(t,n)},is=function(e){var t=e[0],n=e[1],r=e[2];t.scrollLeft=n,t.scrollTop=r},As=":before",as=":after",os="___html2canvas___pseudoelement_before",ss="___html2canvas___pseudoelement_after",ls='{\n content: "" !important;\n display: none !important;\n}',us=function(e){cs(e,"."+os+As+ls+"\n ."+ss+as+ls)},cs=function(e,t){var n=e.ownerDocument;if(n){var r=n.createElement("style");r.textContent=t,e.appendChild(r)}},ds=function(){function e(){}return e.getOrigin=function(t){var n=e._link;return n?(n.href=t,n.href=n.href,n.protocol+n.hostname+n.port):"about:blank"},e.isSameOrigin=function(t){return e.getOrigin(t)===e._origin},e.setContext=function(t){e._link=t.document.createElement("a"),e._origin=e.getOrigin(t.location.href)},e._origin="about:blank",e}(),hs=function(){function e(e,t){this.context=e,this._options=t,this._cache={}}return e.prototype.addImage=function(e){var t=Promise.resolve();return this.has(e)?t:ws(e)||ms(e)?((this._cache[e]=this.loadImage(e)).catch((function(){})),t):t},e.prototype.match=function(e){return this._cache[e]},e.prototype.loadImage=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a=this;return i(this,(function(i){switch(i.label){case 0:return t=ds.isSameOrigin(e),n=!vs(e)&&!0===this._options.useCORS&&xa.SUPPORT_CORS_IMAGES&&!t,r=!vs(e)&&!t&&!ws(e)&&"string"===typeof this._options.proxy&&xa.SUPPORT_CORS_XHR&&!n,t||!1!==this._options.allowTaint||vs(e)||ws(e)||r||n?(A=e,r?[4,this.proxy(A)]:[3,2]):[2];case 1:A=i.sent(),i.label=2;case 2:return this.context.logger.debug("Added image "+e.substring(0,256)),[4,new Promise((function(e,t){var r=new Image;r.onload=function(){return e(r)},r.onerror=t,(ys(A)||n)&&(r.crossOrigin="anonymous"),r.src=A,!0===r.complete&&setTimeout((function(){return e(r)}),500),a._options.imageTimeout>0&&setTimeout((function(){return t("Timed out ("+a._options.imageTimeout+"ms) loading image")}),a._options.imageTimeout)}))];case 3:return[2,i.sent()]}}))}))},e.prototype.has=function(e){return"undefined"!==typeof this._cache[e]},e.prototype.keys=function(){return Promise.resolve(Object.keys(this._cache))},e.prototype.proxy=function(e){var t=this,n=this._options.proxy;if(!n)throw new Error("No proxy defined");var r=e.substring(0,256);return new Promise((function(i,A){var a=xa.SUPPORT_RESPONSE_TYPE?"blob":"text",o=new XMLHttpRequest;o.onload=function(){if(200===o.status)if("text"===a)i(o.response);else{var e=new FileReader;e.addEventListener("load",(function(){return i(e.result)}),!1),e.addEventListener("error",(function(e){return A(e)}),!1),e.readAsDataURL(o.response)}else A("Failed to proxy resource "+r+" with status code "+o.status)},o.onerror=A;var s=n.indexOf("?")>-1?"&":"?";if(o.open("GET",""+n+s+"url="+encodeURIComponent(e)+"&responseType="+a),"text"!==a&&o instanceof XMLHttpRequest&&(o.responseType=a),t._options.imageTimeout){var l=t._options.imageTimeout;o.timeout=l,o.ontimeout=function(){return A("Timed out ("+l+"ms) proxying "+r)}}o.send()}))},e}(),fs=/^data:image\/svg\+xml/i,ps=/^data:image\/.*;base64,/i,gs=/^data:image\/.*/i,ms=function(e){return xa.SUPPORT_SVG_DRAWING||!Bs(e)},vs=function(e){return gs.test(e)},ys=function(e){return ps.test(e)},ws=function(e){return"blob"===e.substr(0,4)},Bs=function(e){return"svg"===e.substr(-3).toLowerCase()||fs.test(e)},_s=function(){function e(e,t){this.type=0,this.x=e,this.y=t}return e.prototype.add=function(t,n){return new e(this.x+t,this.y+n)},e}(),bs=function(e,t,n){return new _s(e.x+(t.x-e.x)*n,e.y+(t.y-e.y)*n)},xs=function(){function e(e,t,n,r){this.type=1,this.start=e,this.startControl=t,this.endControl=n,this.end=r}return e.prototype.subdivide=function(t,n){var r=bs(this.start,this.startControl,t),i=bs(this.startControl,this.endControl,t),A=bs(this.endControl,this.end,t),a=bs(r,i,t),o=bs(i,A,t),s=bs(a,o,t);return n?new e(this.start,r,a,s):new e(s,o,A,this.end)},e.prototype.add=function(t,n){return new e(this.start.add(t,n),this.startControl.add(t,n),this.endControl.add(t,n),this.end.add(t,n))},e.prototype.reverse=function(){return new e(this.end,this.endControl,this.startControl,this.start)},e}(),Cs=function(e){return 1===e.type},Ss=function(){function e(e){var 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]}))},Ps=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}))},Hs=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 Hs(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)