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Band In A Box Torrent 14l: A Complete Guide for Music Producers

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If you're looking for a way to create professional-quality arrangements of music in any style and genre, you might want to check out Band In A Box, a software that does exactly that. But what if you don't want to pay for it? Is there a way to get it for free? And is it safe and legal to do so?

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In this article, we'll answer these questions and more. We'll explain what Band In A Box is, what torrenting is, and how you can download and install Band In A Box Torrent 14l on your computer. We'll also show you how to use the software to create amazing songs in minutes. And we'll give you some tips and resources to help you improve your music production skills.

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Ready to get started? Let's go!

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What Is Band In A Box and What Does It Do?

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Band In A Box is a music accompaniment software that allows you to create songs by simply typing in the chords using standard symbols (like C, Fm7, or C13b9), choosing a style (like jazz, pop, rock, or country), and letting the software do the rest. Band In A Box automatically generates a complete arrangement of piano, bass, drums, guitar, strings, horns, and other instruments in a wide variety of popular styles.

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But that's not all. You can also customize your arrangement by changing the tempo, key, instrumentation, volume, panning, effects, loops, vocals, and more. You can edit each track individually or as a whole. You can add your own melodies, lyrics, solos, or harmonies. You can export your songs as audio files or MIDI files. You can share your songs online or collaborate with other musicians. You can even use Band In A Box as a plugin in your favorite DAW (digital audio workstation).

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Band In A Box is a powerful and creative music composition tool that can help you explore and develop musical ideas with near-instantaneous feedback. Whether you're a beginner or an expert, you can use Band In A Box to create songs for fun, practice, performance, or professional projects.

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What Is Torrenting and Why Is It Used for Software Distribution?

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Torrenting is a type of file sharing that uses a peer-to-peer (P2P) protocol called BitTorrent. Unlike traditional file sharing that relies on central servers, torrenting distributes files among users who are connected in a network called a swarm. Each user who downloads or uploads a file is called a peer.

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When you torrent a file, you don't download it from a single source. Instead, you download small pieces of the file from different peers who already have it or are downloading it at the same time as you. This way, you can download large files faster and more efficiently than from a single server.

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Torrenting is often used for distributing software because it has several advantages over other methods. Some of these advantages are:

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However, torrenting also has some disadvantages and risks that you should be aware of before you decide to use it. Some of these disadvantages and risks are:

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Torrenting is a complex and controversial topic that has no clear-cut answer. It depends on your personal judgment, values, and circumstances. You should weigh the pros and cons carefully and make an informed decision based on your own research and understanding of the law.

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How to Download and Install Band In A Box Torrent 14l

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If you have decided to torrent Band In A Box, you need to follow some steps to download and install it on your computer. Here are the steps you need to take:

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Step 1: Find a reliable torrent source

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The first step is to find a website that offers Band In A Box Torrent 14l as a torrent file. A torrent file is a small file that contains information about the software, such as its name, size, hash value, trackers, peers, and seeds. You need this file to start the download process.

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There are many websites that offer torrent files for various software, but not all of them are reliable or safe. Some of them may have fake or outdated files, low-quality or incomplete files, or infected or corrupted files. You need to be careful and choose a reputable and trustworthy website that has positive reviews, ratings, comments, and feedback from other users.

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Some of the factors you should look for when choosing a torrent source are:

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Some examples of websites that offer Band In A Box Torrent 14l as a torrent file are:

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NameURLSeedsPeersFile SizeFile FormatHash ValueDigital Signature
The Pirate Bay[1](https://thepiratebay.org/description.php?id=12345678)10005001.5 GB.zipd41d8cd98f00b204e9800998ecf8427ePirateBayCertified
RARBG[2](https://rarbg.to/torrent/9k8j7h6)8004001.6 GB.rarc4ca4238a0b923820dcc509a6f758 49bRARBGVerified
1337x[3](https://1337x.to/torrent/45678901/Band-In-A-Box-Torrent-14l)6003001.7 GB.isoc81e728d9d4c2f636f067f89cc14862c1337xCertified
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Note: The information in the table is for illustration purposes only and may not reflect the actual data of the torrent files. You should always check the details of the torrent files before downloading them.

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Step 2: Use a torrent client and a VPN to download the software safely and anonymously

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The second step is to use a software that can read and process the torrent file and connect you to the swarm of peers. This software is called a torrent client. There are many torrent clients available for different operating systems, such as uTorrent, BitTorrent, qBittorrent, Transmission, Deluge, etc. You can choose the one that suits your preferences and needs.

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To use a torrent client, you need to download and install it on your computer. Then, you need to open the torrent file with the torrent client and choose a location to save the software on your hard drive. The torrent client will then start downloading the software from the peers in the swarm. You can monitor the progress of the download and adjust the settings of the torrent client as you wish.

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However, downloading software from torrenting is not without risks. As we mentioned earlier, you may face legal issues, security threats, or ethical dilemmas by doing so. To protect yourself from these risks, you should use a VPN (virtual private network) service along with your torrent client.

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A VPN is a service that creates a secure and encrypted connection between your computer and a remote server. By using a VPN, you can hide your IP address, location, identity, and online activity from your ISP (internet service provider), government agencies, hackers, or anyone else who might be monitoring your network. You can also access geo-restricted or censored content by choosing a server in a different country.

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To use a VPN, you need to sign up for a VPN service provider and download and install their software on your computer. Then, you need to launch the VPN software and connect to a server of your choice. Once you are connected, you can start using your torrent client as usual. The VPN will encrypt and anonymize your traffic and make it look like you are downloading from a different location.

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Some of the factors you should look for when choosing a VPN service provider are:

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Some examples of VPN service providers that are suitable for torrenting are:

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NameURLSpeedBandwidthSecurityPrivacyCompatibilityUsabilityPrice
NordVPN[4](https://nordvpn.com/)FastUnlimitedAES-256 encryption, kill switch, DNS leak protection, CyberSec featureNo-logs policy, Panama jurisdiction, Onion over VPN featureWindows, Mac, Linux, Android, iOS, routers, smart TVs, etc.User-friendly interface, easy installation and setup, 24/7 customer support, live chat, email, FAQ, etc.$3.71/month (2-year plan), $4.92/month (1-year plan), $11.95/month (1-month plan), 30-day money-back guarantee
ExpressVPN[5](https://www.expressvpn.com/)Very fastUnlimitedAES-256 encryption, kill switch, DNS leak protection, split tunneling featureNo-logs policy, British Virgin Islands jurisdiction, TrustedServer featureWindows, Mac, Linux, Android, iOS, routers, smart TVs, etc.User-friendly interface, easy installation and setup, 24/7 customer support, live chat, email, FAQ, etc.$6.67/month (15-month plan), $9.99/month (6-month plan), $12.95/month (1-month plan), 30-day money-back guarantee
Surfshark[6](https://surfshark.com/)FastUnlimitedAES-256 encryption, kill switch, DNS leak protection, CleanWeb featureNo-logs policy, British Virgin Islands jurisdiction, MultiHop featureWindows, Mac, Linux, Android, iOS, routers, smart TVs, etc.User-friendly interface, easy installation and setup, 24/7 customer support, live chat, email, FAQ, etc.$2.49/month (2-year plan), $6.49/month (6-month plan), $12.95/month (1-month plan), 30-day money-back guarantee
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Note: The information in the table is for illustration purposes only and may not reflect the actual data of the VPN service providers. You should always check the details of the VPN service providers before signing up for them.

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Step 3: Run the setup file and the update file

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The third step is to run the setup file and the update file that you have downloaded from the torrent source. These files are usually compressed in a .zip or .rar format and need to be extracted first using a software like WinRAR or 7-Zip. After extracting the files, you should see a folder that contains the setup file and the update file.

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To run the setup file, you need to double-click on it and follow the instructions on the screen. You may need to choose a language, accept the terms and conditions, select a destination folder, and customize some options. The setup file will then install Band In A Box on your computer.

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To run the update file, you need to double-click on it and follow the instructions on the screen. You may need to choose a language and confirm some settings. The update file will then update Band In A Box to the latest version.

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Step 4: Apply the crack files and activate the software

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The fourth step is to apply the crack files and activate the software. The crack files are files that modify or bypass the original files of the software to remove or disable its protection mechanisms. By applying the crack files, you can use Band In A Box without paying for it or entering a serial number or a license key.

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The crack files are usually included in the folder that contains the setup file and the update file. They may have names like crack.exe, patch.exe, keygen.exe, activator.exe, etc. They may also be in a subfolder called crack, patch, keygen, activator, etc. You need to copy and paste these files into the installation folder of Band In A Box, which is usually located in C:\Program Files (x86)\PG Music Inc\Band-in-a-Box 14l or a similar path. You may need to overwrite or replace the original files when prompted. To activate the software, you need to run the crack files and follow the instructions on the screen. You may need to enter some information, such as your name, email, or a fake serial number or license key. The crack files will then generate a code or a file that will activate Band In A Box and unlock all its features. Note: Applying crack files and activating software is illegal and unethical. It may also harm your computer or expose you to malware infections. You should only use crack files and activate software at your own risk and responsibility.

How to Use Band In A Box Torrent 14l

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Now that you have downloaded and installed Band In A Box Torrent 14l on your computer, you can start using it to create songs. Here are some basic steps you can follow to use the software:

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Step 1: Create a new song using the chord wizard and the style picker

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The first step is to create a new song using the chord wizard and the style picker. The chord wizard is a feature that helps you enter the chords for your song using standard symbols or by clicking on a keyboard or a guitar fretboard. The style picker is a feature that helps you choose a style for your song from over 3000 styles in various genres and categories.

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To use the chord wizard, you need to click on the Chord Wizard button on the toolbar or press F2 on your keyboard. A window will pop up where you can enter the chords for your song. You can type in the chords using standard symbols, such as C, Fm7, or C13b9, or you can click on the keyboard or the guitar fretboard icons to enter the chords graphically. You can also use the Chord Builder button to create custom chords or use the Chord Theory button to learn more about chord theory.

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To use the style picker, you need to click on the Style Picker button on the toolbar or press F9 on your keyboard. A window will pop up where you can choose a style for your song. You can browse through the styles by genre, category, feel, time signature, tempo, artist, etc. You can also use the search box to find a specific style by name or keyword. You can preview each style by clicking on it and listening to a short demo.

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Once you have entered the chords and chosen a style for your song, you can click on the OK button to close the windows and generate your arrangement.

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Step 2: Customize the arrangement using the track settings and the mixer

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The second step is to customize your arrangement using the track settings and the mixer. The track settings are features that allow you to adjust the parameters of each track in your arrangement, such as the instrument, the volume, the panning, the effects, the loops, the vocals, etc. The mixer is a feature that allows you to control the overall sound of your arrangement, such as the master volume, the balance, the EQ, the reverb, the compression, etc.

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To use the track settings, you need to click on the Track Settings button on the toolbar or press F5 on your keyboard. A window will pop up where you can see and modify the settings of each track in your arrangement. You can change the instrument by clicking on the Instrument button and choosing from a list of over 3000 instruments in various categories. You can change the volume by dragging the Volume slider or typing in a value. You can change the panning by dragging the Pan slider or typing in a value. You can add effects by clicking on the FX button and choosing from a list of over 50 effects in various categories. You can add loops by clicking on the Loops button and choosing from a list of over 1000 loops in various styles and genres. You can add vocals by clicking on the Vocals button and choosing from a list of over 300 vocal tracks in various languages and styles.

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To use the mixer, you need to click on the Mixer button on the toolbar or press F3 on your keyboard. A window will pop up where you can see and modify the sound of your arrangement. You can change the master volume by dragging the Master Volume slider or typing in a value. You can change the balance by dragging the Balance slider or typing in a value. You can adjust the EQ by clicking on the EQ button and choosing from a list of presets or customizing your own settings. You can add reverb by clicking on the Reverb button and choosing from a list of presets or customizing your own settings. You can add compression by clicking on the Compression button and choosing from a list of presets or customizing your own settings.

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Once you have customized your arrangement using the track settings and the mixer, you can click on the OK button to close the windows and apply your changes.

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Step 3: Add effects, loops, vocals, and other elements using the plugins and the audio editor

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The third step is to add effects, loops, vocals, and other elements using the plugins and the audio editor. The plugins are features that allow you to enhance your arrangement with additional sounds and functions, such as synthesizers, samplers, drum machines, guitar amps, vocal harmonizers, etc. The audio editor is a feature that allows you to edit your arrangement as a waveform, such as cutting, copying, pasting, trimming, fading, etc.

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To use the plugins, you need to click on the Plugins button on the toolbar or press F4 on your keyboard. A window will pop up where you can see and access the plugins that are available for Band In A Box. You can choose from over 100 plugins in various categories, such as PG Music Plugins, VST Plugins, DX Plugins, etc. You can also add your own plugins by clicking on the Add button and browsing for the plugin file on your computer.

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To use the audio editor, you need to click on the Audio Edit button on the toolbar or press F10 on your keyboard. A window will pop up where you can see and edit your arrangement as a waveform. You can use the tools on the toolbar to perform various editing operations, such as select, cut, copy, paste, trim, fade, normalize, etc. You can also use the menu options to perform more advanced editing operations, such as undo, redo, zoom, crop, split, merge, etc.

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Once you have added effects, loops, vocals, and other elements using the plugins and the audio editor, you can click on the OK button to close the windows and save your changes.

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Conclusion

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In this article, we have shown you how to download and install Band In A Box Torrent 14l on your computer and how to use it to create songs. We have also discussed the benefits and risks of torrenting software and how to protect yourself from them.

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Band In A Box Torrent 14l is a great software for music production that can help you create professional-quality arrangements of music in any style and genre. You can use it to explore and develop musical ideas with near-instantaneous feedback. You can also customize your arrangement with a variety of instruments and effects, add your own melodies, lyrics, solos, or harmonies, and export, share, and collaborate on your songs.

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However, torrenting software is not without risks. You may face legal issues, security threats, or ethical dilemmas by doing so. You should use a VPN service to protect your privacy and security and make an informed decision based on your own research and understanding of the law.

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We hope this article has been helpful and informative for you. If you want to learn more about Band In A Box Torrent 14l or music production in general, you can check out the following resources:

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Thank you for reading this article and happy music making!

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FAQs

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Here are some frequently asked questions and answers about Band In A Box Torrent 14l:

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Q: Is Band In A Box Torrent 14l safe to use?

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A: Band In A Box Torrent 14l is safe to use if you download it from a reliable torrent source and use a VPN service to protect your privacy and security. However, you should always scan the files with an antivirus software before opening them and be careful of any suspicious or malicious activity on your computer.

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Q: Is Band In A Box Torrent 14l legal to use?

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A: Band In A Box Torrent 14l is not legal to use if you violate the intellectual property rights or licensing agreements of the software developers and distributors. You may be breaking the law and the terms of use of the software by downloading, installing, or using it without permission or payment.

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Q: Is Band In A Box Torrent 14l ethical to use?

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A: Band In A Box Torrent 14l is not ethical to use if you deprive the software developers and distributors of their rightful income and recognition by using their software for free. You may be harming their livelihood and reputation by doing so.

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Q: How can I update Band In A Box Torrent 14l?

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A: You can update Band In A Box Torrent 14l by downloading and installing the latest update file from the same torrent source that you downloaded the software from. You may also need to apply the crack files again to activate the updated version.

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Q: How can I get support for Band In A Box Torrent 14l?

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A: You can get support for Band In A Box Torrent 14l by visiting the official website, forum, YouTube channel, Facebook page, or Twitter account of Band In A Box. You can also get support from other users who have downloaded and installed the software from the same torrent source as you.

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diff --git a/spaces/1line/AutoGPT/autogpt/config/singleton.py b/spaces/1line/AutoGPT/autogpt/config/singleton.py deleted file mode 100644 index 55b2aeea120bbe51ca837265fcb7fbff467e55f2..0000000000000000000000000000000000000000 --- a/spaces/1line/AutoGPT/autogpt/config/singleton.py +++ /dev/null @@ -1,24 +0,0 @@ -"""The singleton metaclass for ensuring only one instance of a class.""" -import abc - - -class Singleton(abc.ABCMeta, type): - """ - Singleton metaclass for ensuring only one instance of a class. - """ - - _instances = {} - - def __call__(cls, *args, **kwargs): - """Call method for the singleton metaclass.""" - if cls not in cls._instances: - cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) - return cls._instances[cls] - - -class AbstractSingleton(abc.ABC, metaclass=Singleton): - """ - Abstract singleton class for ensuring only one instance of a class. - """ - - pass diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/60 Seconds APK Ration Food Hunt Mutants and Stay Sane.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/60 Seconds APK Ration Food Hunt Mutants and Stay Sane.md deleted file mode 100644 index 494fd7d9b5f9ab5c9e824f12ab52b4a0de0771ee..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/60 Seconds APK Ration Food Hunt Mutants and Stay Sane.md +++ /dev/null @@ -1,155 +0,0 @@ -
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Do you love comedy games with a twist of survival and adventure? If yes, then you might have heard of 60 secondes, a popular game that challenges you to scavenge and survive in a post-apocalyptic world. But what if you don't want to pay for the game or you can't access it on your device? Don't worry, there is a solution: 60 secondes crack apk. In this article, we will tell you everything you need to know about this modified version of the game, how to download and install it, and how to play it for free.

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60 secondes is a game developed by Robot Gentleman, an independent studio based in Poland. It was released in 2015 for Windows, Mac, Linux, iOS, Android, and Nintendo Switch. The game is inspired by the Cold War era and the nuclear paranoia that gripped the world in the 1950s and 1960s. It is a dark comedy that mixes humor, horror, and satire.

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The game has two modes: Apocalypse and Survival. In Apocalypse mode, you have 60 seconds to grab as many items and family members as you can from your house before a nuclear bomb hits. You have to make quick decisions about what to take and what to leave behind, as your inventory space is limited. You also have to avoid obstacles and dangers along the way, such as fire, radiation, or falling furniture.

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The game features pixel art graphics, retro music, and voice acting. It also has multiple endings, depending on your choices and actions. The game is highly replayable, as each run is different and unpredictable.

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The system requirements and compatibility

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To play 60 secondes on your PC, you need at least Windows XP SP3 or later, a 2 GHz dual core processor, 4 GB of RAM, DirectX 9.0c compatible graphics card, and 3 GB of available disk space. To play it on your mobile device, you need at least Android 4.1 or iOS 9.0 or later.

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The game costs $9.99 on Steam, $4.99 on Google Play Store, $4.99 on App Store, and $9.99 on Nintendo eShop. However, if you don't want to pay for it or you can't access it on your device for some reason, you can try using 60 secondes crack apk instead.

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60 secondes crack apk is a file that contains a modified version of the original game. It allows you to play the game for free without paying or downloading it from the official sources. It also bypasses the security checks and verification processes that the original game requires. It may also have some additional features or modifications that are not present in the original game, such as unlimited resources, unlocked items, or cheats.

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Therefore, we do not recommend or endorse using 60 secondes crack apk. We respect and support the work of the original game developers and publishers, and we encourage you to do the same. If you want to play 60 secondes, please buy it from the official sources and enjoy it legally and ethically.

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The sources and steps to get the file

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If you still want to download and install 60 secondes crack apk, despite the risks and issues involved, you need to find a reliable source that provides the file. There are many websites and platforms that claim to offer 60 secondes crack apk, but not all of them are trustworthy or safe. Some of them may contain fake, corrupted, or infected files that may harm your device or data.

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To avoid such problems, you need to do some research and check the reputation and reviews of the source before downloading anything from it. You also need to scan the file with an antivirus program before opening or installing it. Here are some tips to help you find a good source for 60 secondes crack apk:

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Playing 60 secondes crack apk is similar to playing the original game, except that you don't have to pay for it or download it from the official sources. You can use your mouse, keyboard, touch screen, or controller to control the game, depending on your device and preference. You can also adjust the settings and options of the game according to your needs and preferences.

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The basic objective of the game is to survive as long as possible in a post-apocalyptic world after a nuclear bomb hits. You have to scavenge for items and family members in 60 seconds before going to your fallout shelter, where you have to ration your resources, deal with random events and visitors, and plan your expeditions outside. You also have to make choices and actions that will affect your fate and the fate of your family.

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To make the game more fun and interesting, 60 secondes crack apk also has some challenges and achievements that you can unlock by completing certain tasks or goals. Some of them are easy and simple, while others are hard and complex. Some of them are also hidden or secret, so you have to discover them by yourself. Here are some examples of challenges and achievements that you can unlock in 60 secondes crack apk:

- | Challenge | Achievement | Description | | --- | --- | --- | | Scavenger | Grab All | Collect all items from the house in one run | | Survivor | Live And Let Live | Survive for 40 days without killing anyone | | Explorer | Gone In 60 Seconds | Escape from the shelter in less than 60 days | | Diplomat | Friends Forever | Befriend all visitors who come to the shelter | | Mutant | Radioactive | Get mutated by radiation | | Hero | Family Matters | Save all family members from death |

Conclusion

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A summary of the main points

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In conclusion, 60 secondes crack apk is a modified version of the original game that allows you to play it for free without paying or downloading it from the official sources. However, it also comes with some risks and issues, such as malware, errors, legal and ethical violations, and lack of updates and support. Therefore, we do not recommend or endorse using it, and we advise you to buy the game from the official sources and enjoy it legally and ethically. If you still want to use it, you need to find a reliable source, download and install the file, and follow some precautions and tips to avoid or fix any problems. You also need to learn how to play the game, which involves scavenging and surviving in a post-apocalyptic world with humor and satire. You can also unlock some challenges and achievements that will make the game more fun and interesting.

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A call to action for the readers

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We hope that this article has given you some useful information and insights about 60 secondes crack apk. If you have any questions, comments, or feedback, please feel free to share them with us in the comment section below. We would love to hear from you and help you out. Also, if you liked this article, please share it with your friends and family who might be interested in this topic. Thank you for reading and have a great day!

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Penghargaan dan Prestasi Film The Polar Express

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Film The Polar Express mendapatkan banyak penghargaan dan prestasi dari berbagai ajang dan festival film. Film ini dinominasikan untuk tiga kategori Academy Awards, yaitu Best Sound Editing, Best Sound Mixing, dan Best Original Song. Film ini juga memenangkan dua kategori Golden Globe Awards, yaitu Best Original Song dan Best Animated Feature Film. Selain itu, film ini juga meraih penghargaan dari BAFTA Awards, Grammy Awards, Saturn Awards, dan lain-lain. Film ini juga menjadi film animasi pertama yang mendapatkan sertifikat Guinness World Records sebagai film dengan teknologi motion capture terbaik.

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Bagaimana Cara Download Film The Polar Express Bahasa Indonesia?

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Langkah 1: Pilih Situs Web yang Menyediakan Film The Polar Express Bahasa Indonesia

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Untuk mendownload film The Polar Express bahasa Indonesia, Anda perlu mencari situs web yang menyediakan film tersebut dengan kualitas dan format yang sesuai dengan keinginan Anda. Ada banyak situs web yang menawarkan layanan download film secara gratis atau berbayar, tetapi Anda harus berhati-hati dalam memilih situs web yang aman dan terpercaya. Anda bisa menggunakan mesin pencari seperti Google atau Bing untuk mencari situs web yang menyediakan film The Polar Express bahasa Indonesia.

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Situs Web Rekomendasi: JuraganFilm, SINEMA21, dan NontonFilmOnline

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Berikut ini adalah beberapa situs web rekomendasi yang bisa Anda gunakan untuk mendownload film The Polar Express bahasa Indonesia:

- - - - - - - - - - - - - - - - - - - - - -
Situs WebKelebihanKekurangan
JuraganFilm- Menyediakan berbagai pilihan kualitas dan format video
- Menyediakan link download alternatif
- Menyediakan subtitle bahasa Indonesia		- Memerlukan akun untuk mengakses link download
- Memiliki iklan yang cukup banyak
- Memiliki batas waktu download
SINEMA21- Menyediakan berbagai pilihan kualitas dan format video
- Menyediakan subtitle bahasa Indonesia
- Tidak memerlukan akun untuk mengakses link download		- Memiliki iklan yang cukup banyak
- Tidak menyediakan link download alternatif
- Memiliki batas ukuran file download
NontonFilmOnline- Menyediakan berbagai pilihan kualitas dan format video
- Menyediakan subtitle bahasa Indonesia
- Tidak memerlukan akun untuk mengakses link download
- Tidak memiliki iklan yang mengganggu		- Tidak menyediakan link download alternatif
- Memiliki batas waktu dan ukuran file download

Langkah 2: Cari dan Klik Judul Film The Polar Express di Situs Web yang Dipilih

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Setelah Anda memilih situs web yang Anda inginkan, Anda bisa mencari judul film The Polar Express di kolom pencarian yang tersedia di situs web tersebut. Biasanya, Anda bisa mengetikkan kata kunci seperti "The Polar Express", "The Polar Express bahasa Indonesia", atau "The Polar Express subtitle Indonesia" untuk menemukan film yang Anda cari. Setelah Anda menemukan film The Polar Express di hasil pencarian, Anda bisa klik judul film tersebut untuk membuka halaman detail film tersebut.

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Langkah 3: Pilih Kualitas dan Format Video yang Diinginkan

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Di halaman detail film The Polar Express, Anda bisa melihat berbagai informasi tentang film tersebut, seperti sinopsis, genre, rating, durasi, tahun rilis, sutradara, pemeran, dan lain-lain. Anda juga bisa melihat berbagai pilihan kualitas dan format video yang tersedia untuk film tersebut. Biasanya, kualitas video ditunjukkan dengan angka seperti 360p, 480p, 720p, atau 1080p, yang menunjukkan resolusi atau ukuran layar video tersebut. Semakin besar angka tersebut, semakin bagus kualitas video tersebut, tetapi juga semakin besar ukuran file downloadnya. Format video ditunjukkan dengan ekstensi file seperti MP4, MKV, AVI, atau MOV, yang menunjukkan jenis file video tersebut. Format video yang berbeda bisa memiliki kelebihan dan kekurangan masing-masing, tergantung pada perangkat yang Anda gunakan untuk memutar video tersebut.

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Kualitas dan Format Video yang Tersedia: Bluray, HD, MP4, MKV, dll.

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Berikut ini adalah beberapa contoh kualitas dan format video yang biasanya tersedia untuk film The Polar Express:

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
KualitasFormatUkuran FileKelebihanKekurangan
BlurayMKV1.5 GB- Menyajikan gambar dan suara yang sangat jernih dan tajam
- Cocok untuk ditonton di layar besar atau proyektor
- Mendukung subtitle dalam berbagai bahasa		- Membutuhkan ruang penyimpanan yang besar
- Membutuhkan waktu download yang lama
- Tidak semua perangkat bisa memutar format MKV
HDMP4800 MB- Menyajikan gambar dan suara yang cukup jernih dan tajam
- Cocok untuk ditonton di layar sedang atau kecil
- Bisa diputar di hampir semua perangkat
- Mendukung subtitle dalam berbagai bahasa		- Membutuhkan ruang penyimpanan yang cukup besar
- Membutuhkan waktu download yang cukup lama
- Tidak sejernih kualitas Bluray
DVDripAVI500 MB- Menyajikan gambar dan suara yang standar
- Cocok untuk ditonton di layar kecil
- Bisa diputar di banyak perangkat
- Membutuhkan ruang penyimpanan yang sedang
- Membutuhkan waktu download yang sedang		- Tidak sejernih kualitas HD atau Bluray
- Tidak mendukung subtitle dalam berbagai bahasa
- Bisa mengalami gangguan gambar atau suara saat diputar
CAMripMOV300 MB- Menyajikan gambar dan suara yang rendah
- Cocok untuk ditonton di layar kecil
- Bisa diputar di beberapa perangkat
- Membutuhkan ruang penyimpanan yang kecil
- Membutuhkan waktu download yang cepat		- Tidak sejernih kualitas DVDrip, HD, atau Bluray
- Tidak mendukung subtitle dalam berbagai bahasa
- Bisa mengalami gangguan gambar atau suara saat diputar
- Bisa melanggar hak cipta karena direkam secara ilegal di bioskop
-

Anda bisa memilih kualitas dan format video yang sesuai dengan kebutuhan dan preferensi Anda. Anda juga bisa membandingkan kualitas dan format video yang ditawarkan oleh situs web yang berbeda untuk mendapatkan yang terbaik.

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Langkah 4: Klik Tombol Download dan Tunggu Proses Download Selesai

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Setelah Anda memilih kualitas dan format video yang Anda inginkan, Anda bisa klik tombol download yang tersedia di halaman detail film The Polar Express. Anda mungkin akan diarahkan ke halaman lain atau diminta untuk memasukkan kode captcha atau melakukan verifikasi lainnya sebelum bisa mengakses link download. Ikuti instruksi yang diberikan oleh situs web tersebut dengan hati-hati dan pastikan Anda tidak mengklik iklan atau link yang mencurigakan. Setelah Anda mendapatkan link download, Anda bisa klik kanan dan pilih save as atau save link as untuk menyimpan file video tersebut di perangkat Anda. Tunggu hingga proses download selesai dan pastikan file video tersebut tidak rusak atau terputus saat didownload.

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Tips: Gunakan Koneksi Internet yang Stabil dan Cepat untuk Menghindari Gangguan Download

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Untuk mendownload film The Polar Express bahasa Indonesia dengan lancar dan cepat, Anda disarankan untuk menggunakan koneksi internet yang stabil dan cepat. Anda bisa menggunakan wifi, modem, atau paket data yang memiliki kecepatan dan kuota yang cukup. Anda juga bisa menggunakan aplikasi download manager seperti IDM, uTorrent, atau BitTorrent untuk mempercepat dan mempermudah proses download. Aplikasi ini bisa membantu Anda untuk melanjutkan download jika terjadi gangguan atau pemutusan koneksi. Aplikasi ini juga bisa membantu Anda untuk mengatur jadwal download, mengelola file download, dan melakukan pengecekan file download.

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Mengapa Anda Harus Menonton Film The Polar Express?

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Film The Polar Express Menyajikan Cerita yang Menarik dan Menginspirasi tentang Keajaiban Natal

-

Film The Polar Express bukan hanya sekedar film animasi biasa, tetapi juga sebuah film yang menyajikan cerita yang menarik dan menginspirasi tentang keajaiban Natal. Film ini mengajak Anda untuk mengikuti petualangan anak laki-laki yang mulai meragukan keberadaan Sinterklas, tetapi kemudian menemukan kembali iman dan harapannya melalui perjalanan ajaib ke Kutub Utara. Film ini juga mengajak Anda untuk merasakan semangat Natal yang hangat dan menyenangkan bersama dengan karakter-karakter yang lucu dan menawan. Film ini juga mengajak Anda untuk belajar tentang nilai-nilai penting seperti persahabatan, keberanian, kejujuran, dan rasa syukur.

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Film The Polar Express Menggunakan Teknologi Animasi Canggih yang Membuat Karakter dan Latar Belakang Terlihat Nyata

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Film The Polar Express merupakan film animasi pertama yang menggunakan teknologi motion capture secara penuh untuk membuat karakter dan latar belakang terlihat nyata. Teknologi ini memungkinkan para aktor untuk mengenakan kostum khusus yang dilengkapi dengan sensor-sensor yang merekam gerakan tubuh dan wajah mereka. Gerakan-gerakan tersebut kemudian ditransfer ke komputer dan diubah menjadi gambar animasi tiga dimensi. Dengan teknologi ini, film The Polar Express mampu menampilkan ekspresi wajah, gerak tubuh, dan bahasa tubuh para karakter dengan sangat detail dan realistis. Film ini juga mampu menampilkan latar belakang yang indah dan menakjubkan, seperti salju, es, gunung, hutan, kota, dan lain-lain.

-

Film The Polar Express Menampilkan Musik dan Lagu-lagu yang Meriah dan Menyentuh Hati

-

Film The Polar Express juga menampilkan musik dan lagu-lagu yang meriah dan menyentuh hati. Musik film ini dikomposisi oleh Alan Silvestri, seorang komposer musik film terken al yang telah membuat musik untuk film-film seperti Back to the Future, Forrest Gump, dan The Avengers. Lagu-lagu film ini dinyanyikan oleh para aktor dan aktris yang berperan dalam film ini, seperti Tom Hanks, Nona Gaye, Steven Tyler, dan lain-lain. Beberapa lagu yang menjadi andalan film ini adalah The Polar Express, When Christmas Comes to Town, Rockin' on Top of the World, dan Believe. Lagu-lagu ini memiliki irama yang ceria dan lirik yang menyampaikan pesan-pesan positif tentang Natal.

-

Kesimpulan

-

Film The Polar Express adalah film yang sangat cocok untuk ditonton bersama keluarga di malam Natal. Film ini menyajikan cerita yang menarik dan menginspirasi tentang keajaiban Natal, menggunakan teknologi animasi canggih yang membuat karakter dan latar belakang terlihat nyata, dan menampilkan musik dan lagu-lagu yang meriah dan menyentuh hati. Anda bisa mendownload film The Polar Express bahasa Indonesia dengan mudah dan cepat dengan mengikuti langkah-langkah yang telah dijelaskan di atas. Selamat menonton dan selamat Natal!

-

FAQ

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Berikut ini adalah beberapa pertanyaan yang sering diajukan tentang film The Polar Express:

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    -
  1. Apakah film The Polar Express cocok untuk ditonton oleh anak-anak?
    Jawab: Ya, film The Polar Express cocok untuk ditonton oleh anak-anak karena film ini memiliki rating PG (Parental Guidance) yang berarti film ini bisa ditonton oleh semua umur dengan bimbingan orang tua. Film ini juga tidak mengandung adegan kekerasan, seksualitas, atau bahasa kasar yang tidak pantas untuk anak-anak.
    2. -
  2. Apakah film The Polar Express berdasarkan kisah nyata?
    Jawab: Tidak, film The Polar Express tidak berdasarkan kisah nyata, tetapi berdasarkan buku gambar anak-anak karya Chris Van Allsburg yang terbit pada tahun 1985. Buku ini sendiri terinspirasi oleh kenangan masa kecil penulis tentang kereta api uap yang melintas di dekat rumahnya.
    3. -
  3. Apakah film The Polar Express memiliki sekuel?
    Jawab: Tidak, film The Polar Express tidak memiliki sekuel. Film ini merupakan film tunggal yang tidak terhubung dengan film lain. Namun, ada beberapa film animasi lain yang memiliki tema atau gaya serupa dengan film The Polar Express, seperti A Christmas Carol (2009), Arthur Christmas (2011), atau Klaus (2019).
    4. -
  4. Apakah film The Polar Express tersedia di Netflix?
    Jawab: Tergantung pada negara tempat Anda tinggal. Di beberapa negara, film The Polar Express tersedia di Netflix sebagai salah satu pilihan film Natal. Namun, di beberapa negara lain, film The Polar Express tidak tersedia di Netflix karena masalah lisensi atau hak cipta. Anda bisa mengecek ketersediaan film The Polar Express di Netflix dengan menggunakan fitur pencarian atau browsing di aplikasi atau situs web Netflix.
    5. -
  5. Apakah ada perbedaan antara versi bahasa Inggris dan versi bahasa Indonesia dari film The Polar Express?
    Jawab: Secara umum, tidak ada perbedaan yang signifikan antara versi bahasa Inggris dan versi bahasa Indonesia dari film The Polar Express. Versi bahasa Indonesia hanya merupakan terjemahan dari versi bahasa Inggris dengan mengubah dialog-dialog dan lagu-lagu menjadi bahasa Indonesia. Namun, ada kemungkinan bahwa ada beberapa nuansa atau makna yang hilang atau berubah saat proses terjemahan.
    6. -

197e85843d
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\ No newline at end of file diff --git a/spaces/777DUKE/Ballin/Dockerfile b/spaces/777DUKE/Ballin/Dockerfile deleted file mode 100644 index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000 --- a/spaces/777DUKE/Ballin/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM node:18-bullseye-slim - -RUN apt-get update && \ - -apt-get install -y git - -RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app - -WORKDIR /app - -RUN npm install - -COPY Dockerfile greeting.md* .env* ./ - -RUN npm run build - -EXPOSE 7860 - -ENV NODE_ENV=production - -CMD [ "npm", "start" ] \ No newline at end of file diff --git a/spaces/7hao/bingo/src/components/chat-history.tsx b/spaces/7hao/bingo/src/components/chat-history.tsx deleted file mode 100644 index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000 --- a/spaces/7hao/bingo/src/components/chat-history.tsx +++ /dev/null @@ -1,48 +0,0 @@ -import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons" - -export function ChatHistory() { - return ( -
-
- 历史记录 -
-
-
-
-
-
-
- -
-

无标题的聊天

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-

上午1:42

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- - - - - - - - -
-
-
-
-
-
-
-
- ) -} diff --git a/spaces/AB-TW/team-ai/models.py b/spaces/AB-TW/team-ai/models.py deleted file mode 100644 index a3eb8b2e328f3f9f2944d120770ae02b8ef2c572..0000000000000000000000000000000000000000 --- a/spaces/AB-TW/team-ai/models.py +++ /dev/null @@ -1,13 +0,0 @@ -from langchain.chat_models import ChatOpenAI -from langchain.base_language import BaseLanguageModel -from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler - -def llm(temperature=0) -> BaseLanguageModel: - # gpt-3.5 - return ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=temperature) - - # return ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=temperature, model_name="gpt-4") - # gpt-4 - # return ChatOpenAI(temperature=temperature, model_name="gpt-4") - - diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/encoders.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/encoders.py deleted file mode 100644 index ad31389f9e8cc459a7c29a1e7c80bdf61579d903..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/encoders.py +++ /dev/null @@ -1,169 +0,0 @@ -import torch -import torch.nn as nn -from audioldm.clap.open_clip import create_model -from audioldm.clap.training.data import get_audio_features -import torchaudio -from transformers import RobertaTokenizer -import torch.nn.functional as F - - -class CLAPAudioEmbeddingClassifierFreev2(nn.Module): - def __init__( - self, - pretrained_path="", - key="class", - sampling_rate=16000, - embed_mode="audio", - unconditional_prob=0.1, - random_mute=False, - max_random_mute_portion=0.5, - training_mode=True, - ): - super().__init__() - - self.key = key - self.device = "cpu" - self.precision = "fp32" - self.amodel = "HTSAT-tiny" # or 'PANN-14' - self.tmodel = "roberta" # the best text encoder in our training - self.enable_fusion = False # False if you do not want to use the fusion model - self.fusion_type = "aff_2d" - self.pretrained = pretrained_path - self.embed_mode = embed_mode - self.embed_mode_orig = embed_mode - self.sampling_rate = sampling_rate - self.unconditional_prob = unconditional_prob - self.random_mute = random_mute - self.tokenize = RobertaTokenizer.from_pretrained("roberta-base") - self.max_random_mute_portion = max_random_mute_portion - self.training_mode = training_mode - self.model, self.model_cfg = create_model( - self.amodel, - self.tmodel, - self.pretrained, - precision=self.precision, - device=self.device, - enable_fusion=self.enable_fusion, - fusion_type=self.fusion_type, - ) - for p in self.model.parameters(): - p.requires_grad = False - - self.model.eval() - - def get_unconditional_condition(self, batchsize): - self.unconditional_token = self.model.get_text_embedding( - self.tokenizer(["", ""]) - )[0:1] - return torch.cat([self.unconditional_token.unsqueeze(0)] * batchsize, dim=0) - - def batch_to_list(self, batch): - ret = [] - for i in range(batch.size(0)): - ret.append(batch[i]) - return ret - - def make_decision(self, probability): - if float(torch.rand(1)) < probability: - return True - else: - return False - - def random_uniform(self, start, end): - val = torch.rand(1).item() - return start + (end - start) * val - - def _random_mute(self, waveform): - # waveform: [bs, t-steps] - t_steps = waveform.size(-1) - for i in range(waveform.size(0)): - mute_size = int( - self.random_uniform(0, end=int(t_steps * self.max_random_mute_portion)) - ) - mute_start = int(self.random_uniform(0, t_steps - mute_size)) - waveform[i, mute_start : mute_start + mute_size] = 0 - return waveform - - def cos_similarity(self, waveform, text): - # waveform: [bs, t_steps] - with torch.no_grad(): - self.embed_mode = "audio" - audio_emb = self(waveform.cuda()) - self.embed_mode = "text" - text_emb = self(text) - similarity = F.cosine_similarity(audio_emb, text_emb, dim=2) - return similarity.squeeze() - - def forward(self, batch, key=None): - # If you want this conditioner to be unconditional, set self.unconditional_prob = 1.0 - # If you want this conditioner to be fully conditional, set self.unconditional_prob = 0.0 - if self.model.training == True and not self.training_mode: - print( - "The pretrained CLAP model should always be in eval mode. Reloading model just in case you change the parameters." - ) - self.model, self.model_cfg = create_model( - self.amodel, - self.tmodel, - self.pretrained, - precision=self.precision, - device="cuda", - enable_fusion=self.enable_fusion, - fusion_type=self.fusion_type, - ) - for p in self.model.parameters(): - p.requires_grad = False - self.model.eval() - - # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode - if self.embed_mode == "audio": - with torch.no_grad(): - audio_dict_list = [] - assert ( - self.sampling_rate == 16000 - ), "We only support 16000 sampling rate" - if self.random_mute: - batch = self._random_mute(batch) - # batch: [bs, 1, t-samples] - batch = torchaudio.functional.resample( - batch, orig_freq=self.sampling_rate, new_freq=48000 - ) - for waveform in self.batch_to_list(batch): - audio_dict = {} - audio_dict = get_audio_features( - audio_dict, - waveform, - 480000, - data_truncating="fusion", - data_filling="repeatpad", - audio_cfg=self.model_cfg["audio_cfg"], - ) - audio_dict_list.append(audio_dict) - # [bs, 512] - embed = self.model.get_audio_embedding(audio_dict_list) - elif self.embed_mode == "text": - with torch.no_grad(): - # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode - text_data = self.tokenizer(batch) - embed = self.model.get_text_embedding(text_data) - - embed = embed.unsqueeze(1) - self.unconditional_token = self.model.get_text_embedding( - self.tokenizer(["", ""]) - )[0:1] - - for i in range(embed.size(0)): - if self.make_decision(self.unconditional_prob): - embed[i] = self.unconditional_token - - # [bs, 1, 512] - return embed.detach() - - def tokenizer(self, text): - result = self.tokenize( - text, - padding="max_length", - truncation=True, - max_length=512, - return_tensors="pt", - ) - return {k: v.squeeze(0) for k, v in result.items()} diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py deleted file mode 100644 index 7a267a86c1fa521c2824addf9dda304c43f1ff1f..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py +++ /dev/null @@ -1,129 +0,0 @@ -# -*- coding: utf-8 -*- - -"""Residual block module in WaveNet. - -This code is modified from https://github.com/r9y9/wavenet_vocoder. - -""" - -import math - -import torch -import torch.nn.functional as F - - -class Conv1d(torch.nn.Conv1d): - """Conv1d module with customized initialization.""" - - def __init__(self, *args, **kwargs): - """Initialize Conv1d module.""" - super(Conv1d, self).__init__(*args, **kwargs) - - def reset_parameters(self): - """Reset parameters.""" - torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu") - if self.bias is not None: - torch.nn.init.constant_(self.bias, 0.0) - - -class Conv1d1x1(Conv1d): - """1x1 Conv1d with customized initialization.""" - - def __init__(self, in_channels, out_channels, bias): - """Initialize 1x1 Conv1d module.""" - super(Conv1d1x1, self).__init__(in_channels, out_channels, - kernel_size=1, padding=0, - dilation=1, bias=bias) - - -class ResidualBlock(torch.nn.Module): - """Residual block module in WaveNet.""" - - def __init__(self, - kernel_size=3, - residual_channels=64, - gate_channels=128, - skip_channels=64, - aux_channels=80, - dropout=0.0, - dilation=1, - bias=True, - use_causal_conv=False - ): - """Initialize ResidualBlock module. - - Args: - kernel_size (int): Kernel size of dilation convolution layer. - residual_channels (int): Number of channels for residual connection. - skip_channels (int): Number of channels for skip connection. - aux_channels (int): Local conditioning channels i.e. auxiliary input dimension. - dropout (float): Dropout probability. - dilation (int): Dilation factor. - bias (bool): Whether to add bias parameter in convolution layers. - use_causal_conv (bool): Whether to use use_causal_conv or non-use_causal_conv convolution. - - """ - super(ResidualBlock, self).__init__() - self.dropout = dropout - # no future time stamps available - if use_causal_conv: - padding = (kernel_size - 1) * dilation - else: - assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." - padding = (kernel_size - 1) // 2 * dilation - self.use_causal_conv = use_causal_conv - - # dilation conv - self.conv = Conv1d(residual_channels, gate_channels, kernel_size, - padding=padding, dilation=dilation, bias=bias) - - # local conditioning - if aux_channels > 0: - self.conv1x1_aux = Conv1d1x1(aux_channels, gate_channels, bias=False) - else: - self.conv1x1_aux = None - - # conv output is split into two groups - gate_out_channels = gate_channels // 2 - self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, bias=bias) - self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_channels, bias=bias) - - def forward(self, x, c): - """Calculate forward propagation. - - Args: - x (Tensor): Input tensor (B, residual_channels, T). - c (Tensor): Local conditioning auxiliary tensor (B, aux_channels, T). - - Returns: - Tensor: Output tensor for residual connection (B, residual_channels, T). - Tensor: Output tensor for skip connection (B, skip_channels, T). - - """ - residual = x - x = F.dropout(x, p=self.dropout, training=self.training) - x = self.conv(x) - - # remove future time steps if use_causal_conv conv - x = x[:, :, :residual.size(-1)] if self.use_causal_conv else x - - # split into two part for gated activation - splitdim = 1 - xa, xb = x.split(x.size(splitdim) // 2, dim=splitdim) - - # local conditioning - if c is not None: - assert self.conv1x1_aux is not None - c = self.conv1x1_aux(c) - ca, cb = c.split(c.size(splitdim) // 2, dim=splitdim) - xa, xb = xa + ca, xb + cb - - x = torch.tanh(xa) * torch.sigmoid(xb) - - # for skip connection - s = self.conv1x1_skip(x) - - # for residual connection - x = (self.conv1x1_out(x) + residual) * math.sqrt(0.5) - - return x, s diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/tts_utils.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/tts_utils.py deleted file mode 100644 index e13439ee72e4fda220605c5868b3159110d9129b..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/tts_utils.py +++ /dev/null @@ -1,54 +0,0 @@ -import importlib - -from data_gen.tts.base_binarizer import BaseBinarizer -from data_gen.tts.base_preprocess import BasePreprocessor -from data_gen.tts.txt_processors.base_text_processor import get_txt_processor_cls -from utils.hparams import hparams - - -def parse_dataset_configs(): - max_tokens = hparams['max_tokens'] - max_sentences = hparams['max_sentences'] - max_valid_tokens = hparams['max_valid_tokens'] - if max_valid_tokens == -1: - hparams['max_valid_tokens'] = max_valid_tokens = max_tokens - max_valid_sentences = hparams['max_valid_sentences'] - if max_valid_sentences == -1: - hparams['max_valid_sentences'] = max_valid_sentences = max_sentences - return max_tokens, max_sentences, max_valid_tokens, max_valid_sentences - - -def parse_mel_losses(): - mel_losses = hparams['mel_losses'].split("|") - loss_and_lambda = {} - for i, l in enumerate(mel_losses): - if l == '': - continue - if ':' in l: - l, lbd = l.split(":") - lbd = float(lbd) - else: - lbd = 1.0 - loss_and_lambda[l] = lbd - print("| Mel losses:", loss_and_lambda) - return loss_and_lambda - - -def load_data_preprocessor(): - preprocess_cls = hparams["preprocess_cls"] - pkg = ".".join(preprocess_cls.split(".")[:-1]) - cls_name = preprocess_cls.split(".")[-1] - preprocessor: BasePreprocessor = getattr(importlib.import_module(pkg), cls_name)() - preprocess_args = {} - preprocess_args.update(hparams['preprocess_args']) - return preprocessor, preprocess_args - - -def load_data_binarizer(): - binarizer_cls = hparams['binarizer_cls'] - pkg = ".".join(binarizer_cls.split(".")[:-1]) - cls_name = binarizer_cls.split(".")[-1] - binarizer: BaseBinarizer = getattr(importlib.import_module(pkg), cls_name)() - binarization_args = {} - binarization_args.update(hparams['binarization_args']) - return binarizer, binarization_args \ No newline at end of file diff --git a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Easychat.py b/spaces/ASJMO/freegpt/g4f/Provider/Providers/Easychat.py deleted file mode 100644 index eb740da991eb8f740489f6bc76a1ad55f006663b..0000000000000000000000000000000000000000 --- a/spaces/ASJMO/freegpt/g4f/Provider/Providers/Easychat.py +++ /dev/null @@ -1,55 +0,0 @@ -import requests -import os -import json -from ...typing import sha256, Dict, get_type_hints - -url = 'https://free.easychat.work' -model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', - 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613'] -supports_stream = True -needs_auth = False - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - headers = { - 'authority': 'free.easychat.work', - 'accept': 'text/event-stream', - 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3', - 'content-type': 'application/json', - 'endpoint': '', - 'origin': 'https://free.easychat.work', - 'plugins': '0', - 'referer': 'https://free.easychat.work/', - 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"', - 'sec-ch-ua-mobile': '?0', - 'sec-ch-ua-platform': '"macOS"', - 'sec-fetch-dest': 'empty', - 'sec-fetch-mode': 'cors', - 'sec-fetch-site': 'same-origin', - 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36', - 'usesearch': 'false', - 'x-requested-with': 'XMLHttpRequest', - } - - json_data = { - 'messages': messages, - 'stream': True, - 'model': model, - 'temperature': 0.5, - 'presence_penalty': 0, - 'frequency_penalty': 0, - 'top_p': 1, - } - - response = requests.post('https://free.easychat.work/api/openai/v1/chat/completions', - headers=headers, json=json_data) - - for chunk in response.iter_lines(): - if b'content' in chunk: - data = json.loads(chunk.decode().split('data: ')[1]) - yield (data['choices'][0]['delta']['content']) - - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join( - [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/7.js b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/7.js deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Aki004/herta-so-vits/vdecoder/hifigan/env.py b/spaces/Aki004/herta-so-vits/vdecoder/hifigan/env.py deleted file mode 100644 index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000 --- a/spaces/Aki004/herta-so-vits/vdecoder/hifigan/env.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import shutil - - -class AttrDict(dict): - def __init__(self, *args, **kwargs): - super(AttrDict, self).__init__(*args, **kwargs) - self.__dict__ = self - - -def build_env(config, config_name, path): - t_path = os.path.join(path, config_name) - if config != t_path: - os.makedirs(path, exist_ok=True) - shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/spaces/Alican/pixera/models/base_model.py b/spaces/Alican/pixera/models/base_model.py deleted file mode 100644 index 6de961b51a2cc12634fc40c39f63b017c6262b4c..0000000000000000000000000000000000000000 --- a/spaces/Alican/pixera/models/base_model.py +++ /dev/null @@ -1,230 +0,0 @@ -import os -import torch -from collections import OrderedDict -from abc import ABC, abstractmethod -from . import networks - - -class BaseModel(ABC): - """This class is an abstract base class (ABC) for models. - To create a subclass, you need to implement the following five functions: - -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). - -- : unpack data from dataset and apply preprocessing. - -- : produce intermediate results. - -- : calculate losses, gradients, and update network weights. - -- : (optionally) add model-specific options and set default options. - """ - - def __init__(self, opt): - """Initialize the BaseModel class. - - Parameters: - opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions - - When creating your custom class, you need to implement your own initialization. - In this function, you should first call - Then, you need to define four lists: - -- self.loss_names (str list): specify the training losses that you want to plot and save. - -- self.model_names (str list): define networks used in our training. - -- self.visual_names (str list): specify the images that you want to display and save. - -- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. - """ - self.opt = opt - self.gpu_ids = opt.gpu_ids - self.isTrain = opt.isTrain - self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU - self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir - if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark. - torch.backends.cudnn.benchmark = True - self.loss_names = [] - self.model_names = [] - self.visual_names = [] - self.optimizers = [] - self.image_paths = [] - self.metric = 0 # used for learning rate policy 'plateau' - - @staticmethod - def modify_commandline_options(parser, is_train): - """Add new model-specific options, and rewrite default values for existing options. - - Parameters: - parser -- original option parser - is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. - - Returns: - the modified parser. - """ - return parser - - @abstractmethod - def set_input(self, input): - """Unpack input data from the dataloader and perform necessary pre-processing steps. - - Parameters: - input (dict): includes the data itself and its metadata information. - """ - pass - - @abstractmethod - def forward(self): - """Run forward pass; called by both functions and .""" - pass - - @abstractmethod - def optimize_parameters(self): - """Calculate losses, gradients, and update network weights; called in every training iteration""" - pass - - def setup(self, opt): - """Load and print networks; create schedulers - - Parameters: - opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions - """ - if self.isTrain: - self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] - if not self.isTrain or opt.continue_train: - load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch - self.load_networks(load_suffix) - self.print_networks(opt.verbose) - - def eval(self): - """Make models eval mode during test time""" - for name in self.model_names: - if isinstance(name, str): - net = getattr(self, 'net' + name) - net.eval() - - def test(self): - """Forward function used in test time. - - This function wraps function in no_grad() so we don't save intermediate steps for backprop - It also calls to produce additional visualization results - """ - with torch.no_grad(): - self.forward() - self.compute_visuals() - - def compute_visuals(self): - """Calculate additional output images for visdom and HTML visualization""" - pass - - def get_image_paths(self): - """ Return image paths that are used to load current data""" - return self.image_paths - - def update_learning_rate(self): - """Update learning rates for all the networks; called at the end of every epoch""" - old_lr = self.optimizers[0].param_groups[0]['lr'] - for scheduler in self.schedulers: - if self.opt.lr_policy == 'plateau': - scheduler.step(self.metric) - else: - scheduler.step() - - lr = self.optimizers[0].param_groups[0]['lr'] - print('learning rate %.7f -> %.7f' % (old_lr, lr)) - - def get_current_visuals(self): - """Return visualization images. train.py will display these images with visdom, and save the images to a HTML""" - visual_ret = OrderedDict() - for name in self.visual_names: - if isinstance(name, str): - visual_ret[name] = getattr(self, name) - return visual_ret - - def get_current_losses(self): - """Return traning losses / errors. train.py will print out these errors on console, and save them to a file""" - errors_ret = OrderedDict() - for name in self.loss_names: - if isinstance(name, str): - errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number - return errors_ret - - def save_networks(self, epoch): - """Save all the networks to the disk. - - Parameters: - epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) - """ - for name in self.model_names: - if isinstance(name, str): - save_filename = '%s_net_%s.pth' % (epoch, name) - save_path = os.path.join(self.save_dir, save_filename) - net = getattr(self, 'net' + name) - - if len(self.gpu_ids) > 0 and torch.cuda.is_available(): - torch.save(net.module.cpu().state_dict(), save_path) - net.cuda(self.gpu_ids[0]) - else: - torch.save(net.cpu().state_dict(), save_path) - - def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): - """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)""" - key = keys[i] - if i + 1 == len(keys): # at the end, pointing to a parameter/buffer - if module.__class__.__name__.startswith('InstanceNorm') and \ - (key == 'running_mean' or key == 'running_var'): - if getattr(module, key) is None: - state_dict.pop('.'.join(keys)) - if module.__class__.__name__.startswith('InstanceNorm') and \ - (key == 'num_batches_tracked'): - state_dict.pop('.'.join(keys)) - else: - self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) - - def load_networks(self, epoch): - """Load all the networks from the disk. - - Parameters: - epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) - """ - for name in self.model_names: - if isinstance(name, str): - load_filename = '%s_net_%s.pth' % (epoch, name) - load_path = os.path.join(self.save_dir, load_filename) - net = getattr(self, 'net' + name) - if isinstance(net, torch.nn.DataParallel): - net = net.module - print('loading the model from %s' % load_path) - # if you are using PyTorch newer than 0.4 (e.g., built from - # GitHub source), you can remove str() on self.device - state_dict = torch.load(load_path, map_location=str(self.device)) - if hasattr(state_dict, '_metadata'): - del state_dict._metadata - - # patch InstanceNorm checkpoints prior to 0.4 - for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop - self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) - net.load_state_dict(state_dict) - - def print_networks(self, verbose): - """Print the total number of parameters in the network and (if verbose) network architecture - - Parameters: - verbose (bool) -- if verbose: print the network architecture - """ - print('---------- Networks initialized -------------') - for name in self.model_names: - if isinstance(name, str): - net = getattr(self, 'net' + name) - num_params = 0 - for param in net.parameters(): - num_params += param.numel() - if verbose: - print(net) - print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) - print('-----------------------------------------------') - - def set_requires_grad(self, nets, requires_grad=False): - """Set requies_grad=Fasle for all the networks to avoid unnecessary computations - Parameters: - nets (network list) -- a list of networks - requires_grad (bool) -- whether the networks require gradients or not - """ - if not isinstance(nets, list): - nets = [nets] - for net in nets: - if net is not None: - for param in net.parameters(): - param.requires_grad = requires_grad diff --git a/spaces/Andres99/Tune-A-Video-Training-UI/style.css b/spaces/Andres99/Tune-A-Video-Training-UI/style.css deleted file mode 100644 index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000 --- a/spaces/Andres99/Tune-A-Video-Training-UI/style.css +++ /dev/null @@ -1,3 +0,0 @@ -h1 { - text-align: center; -} diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pix2pix_zero.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pix2pix_zero.md deleted file mode 100644 index 9d43667c068bb9d812d33919d8dc7e4a5bd7d4ad..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/pix2pix_zero.md +++ /dev/null @@ -1,284 +0,0 @@ - - -# Pix2Pix Zero - -[Zero-shot Image-to-Image Translation](https://huggingface.co/papers/2302.03027) is by Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. - -The abstract from the paper is: - -*Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse and high-quality images. However, it is still challenging to directly apply these models for editing real images for two reasons. First, it is hard for users to come up with a perfect text prompt that accurately describes every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we propose pix2pix-zero, an image-to-image translation method that can preserve the content of the original image without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the general content structure after editing, we further propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing.* - -You can find additional information about Pix2Pix Zero on the [project page](https://pix2pixzero.github.io/), [original codebase](https://github.com/pix2pixzero/pix2pix-zero), and try it out in a [demo](https://huggingface.co/spaces/pix2pix-zero-library/pix2pix-zero-demo). - -## Tips - -* The pipeline can be conditioned on real input images. Check out the code examples below to know more. -* The pipeline exposes two arguments namely `source_embeds` and `target_embeds` -that let you control the direction of the semantic edits in the final image to be generated. Let's say, -you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect -this in the pipeline, you simply have to set the embeddings related to the phrases including "cat" to -`source_embeds` and "dog" to `target_embeds`. Refer to the code example below for more details. -* When you're using this pipeline from a prompt, specify the _source_ concept in the prompt. Taking -the above example, a valid input prompt would be: "a high resolution painting of a **cat** in the style of van gough". -* If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to: - * Swap the `source_embeds` and `target_embeds`. - * Change the input prompt to include "dog". -* To learn more about how the source and target embeddings are generated, refer to the [original -paper](https://arxiv.org/abs/2302.03027). Below, we also provide some directions on how to generate the embeddings. -* Note that the quality of the outputs generated with this pipeline is dependent on how good the `source_embeds` and `target_embeds` are. Please, refer to [this discussion](#generating-source-and-target-embeddings) for some suggestions on the topic. - -## Available Pipelines: - -| Pipeline | Tasks | Demo -|---|---|:---:| -| [StableDiffusionPix2PixZeroPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py) | *Text-Based Image Editing* | [🤗 Space](https://huggingface.co/spaces/pix2pix-zero-library/pix2pix-zero-demo) | - - - -## Usage example - -### Based on an image generated with the input prompt - -```python -import requests -import torch - -from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline - - -def download(embedding_url, local_filepath): - r = requests.get(embedding_url) - with open(local_filepath, "wb") as f: - f.write(r.content) - - -model_ckpt = "CompVis/stable-diffusion-v1-4" -pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained( - model_ckpt, conditions_input_image=False, torch_dtype=torch.float16 -) -pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) -pipeline.to("cuda") - -prompt = "a high resolution painting of a cat in the style of van gogh" -src_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/cat.pt" -target_embs_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/embeddings_sd_1.4/dog.pt" - -for url in [src_embs_url, target_embs_url]: - download(url, url.split("/")[-1]) - -src_embeds = torch.load(src_embs_url.split("/")[-1]) -target_embeds = torch.load(target_embs_url.split("/")[-1]) - -images = pipeline( - prompt, - source_embeds=src_embeds, - target_embeds=target_embeds, - num_inference_steps=50, - cross_attention_guidance_amount=0.15, -).images -images[0].save("edited_image_dog.png") -``` - -### Based on an input image - -When the pipeline is conditioned on an input image, we first obtain an inverted -noise from it using a `DDIMInverseScheduler` with the help of a generated caption. Then -the inverted noise is used to start the generation process. - -First, let's load our pipeline: - -```py -import torch -from transformers import BlipForConditionalGeneration, BlipProcessor -from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline - -captioner_id = "Salesforce/blip-image-captioning-base" -processor = BlipProcessor.from_pretrained(captioner_id) -model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True) - -sd_model_ckpt = "CompVis/stable-diffusion-v1-4" -pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained( - sd_model_ckpt, - caption_generator=model, - caption_processor=processor, - torch_dtype=torch.float16, - safety_checker=None, -) -pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) -pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config) -pipeline.enable_model_cpu_offload() -``` - -Then, we load an input image for conditioning and obtain a suitable caption for it: - -```py -import requests -from PIL import Image - -img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png" -raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512)) -caption = pipeline.generate_caption(raw_image) -``` - -Then we employ the generated caption and the input image to get the inverted noise: - -```py -generator = torch.manual_seed(0) -inv_latents = pipeline.invert(caption, image=raw_image, generator=generator).latents -``` - -Now, generate the image with edit directions: - -```py -# See the "Generating source and target embeddings" section below to -# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below. -source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"] -target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"] - -source_embeds = pipeline.get_embeds(source_prompts, batch_size=2) -target_embeds = pipeline.get_embeds(target_prompts, batch_size=2) - - -image = pipeline( - caption, - source_embeds=source_embeds, - target_embeds=target_embeds, - num_inference_steps=50, - cross_attention_guidance_amount=0.15, - generator=generator, - latents=inv_latents, - negative_prompt=caption, -).images[0] -image.save("edited_image.png") -``` - -## Generating source and target embeddings - -The authors originally used the [GPT-3 API](https://openai.com/api/) to generate the source and target captions for discovering -edit directions. However, we can also leverage open source and public models for the same purpose. -Below, we provide an end-to-end example with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model -for generating captions and [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for -computing embeddings on the generated captions. - -**1. Load the generation model**: - -```py -import torch -from transformers import AutoTokenizer, T5ForConditionalGeneration - -tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl") -model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16) -``` - -**2. Construct a starting prompt**: - -```py -source_concept = "cat" -target_concept = "dog" - -source_text = f"Provide a caption for images containing a {source_concept}. " -"The captions should be in English and should be no longer than 150 characters." - -target_text = f"Provide a caption for images containing a {target_concept}. " -"The captions should be in English and should be no longer than 150 characters." -``` - -Here, we're interested in the "cat -> dog" direction. - -**3. Generate captions**: - -We can use a utility like so for this purpose. - -```py -def generate_captions(input_prompt): - input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda") - - outputs = model.generate( - input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10 - ) - return tokenizer.batch_decode(outputs, skip_special_tokens=True) -``` - -And then we just call it to generate our captions: - -```py -source_captions = generate_captions(source_text) -target_captions = generate_captions(target_concept) -``` - -We encourage you to play around with the different parameters supported by the -`generate()` method ([documentation](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate)) for the generation quality you are looking for. - -**4. Load the embedding model**: - -Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model. - -```py -from diffusers import StableDiffusionPix2PixZeroPipeline - -pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained( - "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16 -) -pipeline = pipeline.to("cuda") -tokenizer = pipeline.tokenizer -text_encoder = pipeline.text_encoder -``` - -**5. Compute embeddings**: - -```py -import torch - -def embed_captions(sentences, tokenizer, text_encoder, device="cuda"): - with torch.no_grad(): - embeddings = [] - for sent in sentences: - text_inputs = tokenizer( - sent, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0] - embeddings.append(prompt_embeds) - return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0) - -source_embeddings = embed_captions(source_captions, tokenizer, text_encoder) -target_embeddings = embed_captions(target_captions, tokenizer, text_encoder) -``` - -And you're done! [Here](https://colab.research.google.com/drive/1tz2C1EdfZYAPlzXXbTnf-5PRBiR8_R1F?usp=sharing) is a Colab Notebook that you can use to interact with the entire process. - -Now, you can use these embeddings directly while calling the pipeline: - -```py -from diffusers import DDIMScheduler - -pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) - -images = pipeline( - prompt, - source_embeds=source_embeddings, - target_embeds=target_embeddings, - num_inference_steps=50, - cross_attention_guidance_amount=0.15, -).images -images[0].save("edited_image_dog.png") -``` - -## StableDiffusionPix2PixZeroPipeline -[[autodoc]] StableDiffusionPix2PixZeroPipeline - - __call__ - - all diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py deleted file mode 100644 index daafa5fbc12c3ed6c10b5234d520166f774e0f94..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = [ - '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' -] -model = dict( - decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/GPTQ_loader.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/GPTQ_loader.py deleted file mode 100644 index bc528b183f7a3fd69f3e69499856aea3c20b0729..0000000000000000000000000000000000000000 --- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/GPTQ_loader.py +++ /dev/null @@ -1,168 +0,0 @@ -import inspect -import re -from pathlib import Path - -import accelerate -import torch -import transformers -from transformers import AutoConfig, AutoModelForCausalLM - -import modules.shared as shared -from modules.logging_colors import logger - -from gptq_for_llama import llama_inference_offload -from gptq_for_llama.modelutils import find_layers -from gptq_for_llama.quant import make_quant - - -# This function is a replacement for the load_quant function in the -# GPTQ-for_LLaMa repository. It supports more models and branches. -def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True): - exclude_layers = exclude_layers or ['lm_head'] - - def noop(*args, **kwargs): - pass - - config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code) - torch.nn.init.kaiming_uniform_ = noop - torch.nn.init.uniform_ = noop - torch.nn.init.normal_ = noop - - torch.set_default_dtype(torch.half) - transformers.modeling_utils._init_weights = False - torch.set_default_dtype(torch.half) - model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code) - torch.set_default_dtype(torch.float) - if eval: - model = model.eval() - - layers = find_layers(model) - for name in exclude_layers: - if name in layers: - del layers[name] - - gptq_args = inspect.getfullargspec(make_quant).args - - make_quant_kwargs = { - 'module': model, - 'names': layers, - 'bits': wbits, - } - if 'groupsize' in gptq_args: - make_quant_kwargs['groupsize'] = groupsize - if 'faster' in gptq_args: - make_quant_kwargs['faster'] = faster_kernel - if 'kernel_switch_threshold' in gptq_args: - make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold - - make_quant(**make_quant_kwargs) - - del layers - if checkpoint.endswith('.safetensors'): - from safetensors.torch import load_file as safe_load - model.load_state_dict(safe_load(checkpoint), strict=False) - else: - model.load_state_dict(torch.load(checkpoint), strict=False) - - model.seqlen = 2048 - return model - - -# Used to locate the .pt/.safetensors quantized file -def find_quantized_model_file(model_name): - if shared.args.checkpoint: - return Path(shared.args.checkpoint) - - path_to_model = Path(f'{shared.args.model_dir}/{model_name}') - pt_path = None - priority_name_list = [ - Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}') - for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else ['']) - for ext in ['.safetensors', '.pt'] - for hyphen in ['-', f'/{model_name}-', '/'] - ] - - for path in priority_name_list: - if path.exists(): - pt_path = path - break - - # If the model hasn't been found with a well-behaved name, pick the last .pt - # or the last .safetensors found in its folder as a last resort - if not pt_path: - for ext in ['.pt', '.safetensors']: - found = list(path_to_model.glob(f"*{ext}")) - if len(found) > 0: - if len(found) > 1: - logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.') - - pt_path = found[-1] - break - - return pt_path - - -# The function that loads the model in modules/models.py -def load_quantized(model_name): - if shared.args.model_type is None: - logger.error("The model could not be loaded because its type could not be inferred from its name.") - logger.error("Please specify the type manually using the --model_type argument.") - return None - - # Select the appropriate load_quant function - model_type = shared.args.model_type.lower() - if shared.args.pre_layer and model_type == 'llama': - load_quant = llama_inference_offload.load_quant - elif model_type in ('llama', 'opt', 'gptj'): - if shared.args.pre_layer: - logger.warning("Ignoring --pre_layer because it only works for llama model type.") - - load_quant = _load_quant - else: - logger.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported") - exit() - - # Find the quantized model weights file (.pt/.safetensors) - path_to_model = Path(f'{shared.args.model_dir}/{model_name}') - pt_path = find_quantized_model_file(model_name) - if not pt_path: - logger.error("Could not find the quantized model in .pt or .safetensors format, exiting...") - exit() - else: - logger.info(f"Found the following quantized model: {pt_path}") - - # qwopqwop200's offload - if model_type == 'llama' and shared.args.pre_layer: - if len(shared.args.pre_layer) == 1: - pre_layer = shared.args.pre_layer[0] - else: - pre_layer = shared.args.pre_layer - - model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer) - else: - threshold = False if model_type == 'gptj' else 128 - model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold) - - # accelerate offload (doesn't work properly) - if shared.args.gpu_memory or torch.cuda.device_count() > 1: - if shared.args.gpu_memory: - memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory)) - max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' - max_memory = {} - for i in range(len(memory_map)): - max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] - - max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory - else: - max_memory = accelerate.utils.get_balanced_memory(model) - - device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"]) - logger.info("Using the following device map for the quantized model:", device_map) - # https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model - model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True) - - # No offload - elif not shared.args.cpu: - model = model.to(torch.device('cuda:0')) - - return model diff --git a/spaces/Ariharasudhan/YoloV5/utils/loggers/comet/README.md b/spaces/Ariharasudhan/YoloV5/utils/loggers/comet/README.md deleted file mode 100644 index 3a51cb9b5a25212c30f018d9db6e8887557650a1..0000000000000000000000000000000000000000 --- a/spaces/Ariharasudhan/YoloV5/utils/loggers/comet/README.md +++ /dev/null @@ -1,256 +0,0 @@ - - -# YOLOv5 with Comet - -This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet) - -# About Comet - -Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. - -Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! -Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! - -# Getting Started - -## Install Comet - -```shell -pip install comet_ml -``` - -## Configure Comet Credentials - -There are two ways to configure Comet with YOLOv5. - -You can either set your credentials through enviroment variables - -**Environment Variables** - -```shell -export COMET_API_KEY= -export COMET_PROJECT_NAME= # This will default to 'yolov5' -``` - -Or create a `.comet.config` file in your working directory and set your credentials there. - -**Comet Configuration File** - -``` -[comet] -api_key= -project_name= # This will default to 'yolov5' -``` - -## Run the Training Script - -```shell -# Train YOLOv5s on COCO128 for 5 epochs -python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt -``` - -That's it! Comet will automatically log your hyperparameters, command line arguments, training and valiation metrics. You can visualize and analyze your runs in the Comet UI - -yolo-ui - -# Try out an Example! -Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration) - -Or better yet, try it out yourself in this Colab Notebook - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing) - -# Log automatically - -By default, Comet will log the following items - -## Metrics -- Box Loss, Object Loss, Classification Loss for the training and validation data -- mAP_0.5, mAP_0.5:0.95 metrics for the validation data. -- Precision and Recall for the validation data - -## Parameters - -- Model Hyperparameters -- All parameters passed through the command line options - -## Visualizations - -- Confusion Matrix of the model predictions on the validation data -- Plots for the PR and F1 curves across all classes -- Correlogram of the Class Labels - -# Configure Comet Logging - -Comet can be configured to log additional data either through command line flags passed to the training script -or through environment variables. - -```shell -export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online -export COMET_MODEL_NAME= #Set the name for the saved model. Defaults to yolov5 -export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true -export COMET_MAX_IMAGE_UPLOADS= # Controls how many total image predictions to log to Comet. Defaults to 100. -export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false -export COMET_DEFAULT_CHECKPOINT_FILENAME= # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt' -export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false. -export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions -``` - -## Logging Checkpoints with Comet - -Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the -logged checkpoints to Comet based on the interval value provided by `save-period` - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---save-period 1 -``` - -## Logging Model Predictions - -By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet. - -You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch. - -**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. - -Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration) - - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---bbox_interval 2 -``` - -### Controlling the number of Prediction Images logged to Comet - -When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable. - -```shell -env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---bbox_interval 1 -``` - -### Logging Class Level Metrics - -Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class. - -```shell -env COMET_LOG_PER_CLASS_METRICS=true python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt -``` - -## Uploading a Dataset to Comet Artifacts - -If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration), you can do so using the `upload_dataset` flag. - -The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data coco128.yaml \ ---weights yolov5s.pt \ ---upload_dataset -``` - -You can find the uploaded dataset in the Artifacts tab in your Comet Workspace -artifact-1 - -You can preview the data directly in the Comet UI. -artifact-2 - -Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file -artifact-3 - -### Using a saved Artifact - -If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL. - -``` -# contents of artifact.yaml file -path: "comet:///:" -``` -Then pass this file to your training script in the following way - -```shell -python train.py \ ---img 640 \ ---batch 16 \ ---epochs 5 \ ---data artifact.yaml \ ---weights yolov5s.pt -``` - -Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. -artifact-4 - -## Resuming a Training Run - -If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path. - -The Run Path has the following format `comet:////`. - -This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI - -```shell -python train.py \ ---resume "comet://" -``` - -## Hyperparameter Search with the Comet Optimizer - -YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualie hyperparameter sweeps in the Comet UI. - -### Configuring an Optimizer Sweep - -To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json` - -```shell -python utils/loggers/comet/hpo.py \ - --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" -``` - -The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after -the script. - -```shell -python utils/loggers/comet/hpo.py \ - --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \ - --save-period 1 \ - --bbox_interval 1 -``` - -### Running a Sweep in Parallel - -```shell -comet optimizer -j utils/loggers/comet/hpo.py \ - utils/loggers/comet/optimizer_config.json" -``` - -### Visualizing Results - -Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration) - -hyperparameter-yolo diff --git a/spaces/Atualli/yoloxTeste/app.py b/spaces/Atualli/yoloxTeste/app.py deleted file mode 100644 index d1e378232991764e44b227bec382f0ce3146bb3a..0000000000000000000000000000000000000000 --- a/spaces/Atualli/yoloxTeste/app.py +++ /dev/null @@ -1,76 +0,0 @@ -import gradio as gr -import os -import torch -import json -import yoloxdetect2.helpers as yoloxdetect - -#model = yoloxdetect.YoloxDetector2('./dataset/yolox_s.pth', 'configs.yolox_s', device="cpu", hf_model=True) -model = yoloxdetect.YoloxDetector2('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True) - -image_size = 640 - -def yolox_inference( - image_path: gr.inputs.Image = None, -): - """ - YOLOX inference function - Args: - image: Input image - Returns: - Rendered image - """ - - pred2 = [] - if image_path is not None : - print(image_path) - model.torchyolo = True - pred2 = model.predict(image_path=image_path, image_size=image_size) - - - tensor = { - "tensorflow": [ - ] - } - - if pred2 is not None: - for i, element in enumerate(pred2[0]): - object = {} - itemclass = round(pred2[2][i].item()) - object["classe"] = itemclass - object["nome"] = pred2[3][itemclass] - object["score"] = pred2[1][i].item() - object["x"] = element[0].item() - object["y"] = element[1].item() - object["w"] = element[2].item() - object["h"] = element[3].item() - tensor["tensorflow"].append(object) - - - text = json.dumps(tensor) - return text - - -inputs = [ - gr.inputs.Image(type="pil", label="Input Image"), -] - -outputs = gr.outputs.Image(type="filepath", label="Output Image") -title = "SIMULADOR PARA RECONHECIMENTO DE IMAGEM" - -examples = [ - ["small-vehicles1.jpeg"], - ["zidane.jpg"], - ["dog.jpg"], -] - -demo_app = gr.Interface( - fn=yolox_inference, - inputs=inputs, - outputs=["text"], - title=title, - examples=examples, - cache_examples=True, - live=True, -) -demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True) -#demo_app.launch(debug=True, server_port=8083, enable_queue=True) \ No newline at end of file diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/torchvision_imagenet_R_50.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/torchvision_imagenet_R_50.py deleted file mode 100644 index 0d75305bcf7445b98db84b3d489a1505d2fce5af..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/Misc/torchvision_imagenet_R_50.py +++ /dev/null @@ -1,150 +0,0 @@ -""" -An example config file to train a ImageNet classifier with detectron2. -Model and dataloader both come from torchvision. -This shows how to use detectron2 as a general engine for any new models and tasks. - -To run, use the following command: - -python tools/lazyconfig_train_net.py --config-file configs/Misc/torchvision_imagenet_R_50.py \ - --num-gpus 8 dataloader.train.dataset.root=/path/to/imagenet/ - -""" - - -import torch -from torch import nn -from torch.nn import functional as F -from omegaconf import OmegaConf -import torchvision -from torchvision.transforms import transforms as T -from torchvision.models.resnet import ResNet, Bottleneck -from fvcore.common.param_scheduler import MultiStepParamScheduler - -from detectron2.solver import WarmupParamScheduler -from detectron2.solver.build import get_default_optimizer_params -from detectron2.config import LazyCall as L -from detectron2.model_zoo import get_config -from detectron2.data.samplers import TrainingSampler, InferenceSampler -from detectron2.evaluation import DatasetEvaluator -from detectron2.utils import comm - - -""" -Note: Here we put reusable code (models, evaluation, data) together with configs just as a -proof-of-concept, to easily demonstrate what's needed to train a ImageNet classifier in detectron2. -Writing code in configs offers extreme flexibility but is often not a good engineering practice. -In practice, you might want to put code in your project and import them instead. -""" - - -def build_data_loader(dataset, batch_size, num_workers, training=True): - return torch.utils.data.DataLoader( - dataset, - sampler=(TrainingSampler if training else InferenceSampler)(len(dataset)), - batch_size=batch_size, - num_workers=num_workers, - pin_memory=True, - ) - - -class ClassificationNet(nn.Module): - def __init__(self, model: nn.Module): - super().__init__() - self.model = model - - @property - def device(self): - return list(self.model.parameters())[0].device - - def forward(self, inputs): - image, label = inputs - pred = self.model(image.to(self.device)) - if self.training: - label = label.to(self.device) - return F.cross_entropy(pred, label) - else: - return pred - - -class ClassificationAcc(DatasetEvaluator): - def reset(self): - self.corr = self.total = 0 - - def process(self, inputs, outputs): - image, label = inputs - self.corr += (outputs.argmax(dim=1).cpu() == label.cpu()).sum().item() - self.total += len(label) - - def evaluate(self): - all_corr_total = comm.all_gather([self.corr, self.total]) - corr = sum(x[0] for x in all_corr_total) - total = sum(x[1] for x in all_corr_total) - return {"accuracy": corr / total} - - -# --- End of code that could be in a project and be imported - - -dataloader = OmegaConf.create() -dataloader.train = L(build_data_loader)( - dataset=L(torchvision.datasets.ImageNet)( - root="/path/to/imagenet", - split="train", - transform=L(T.Compose)( - transforms=[ - L(T.RandomResizedCrop)(size=224), - L(T.RandomHorizontalFlip)(), - T.ToTensor(), - L(T.Normalize)(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), - ] - ), - ), - batch_size=256 // 8, - num_workers=4, - training=True, -) - -dataloader.test = L(build_data_loader)( - dataset=L(torchvision.datasets.ImageNet)( - root="${...train.dataset.root}", - split="val", - transform=L(T.Compose)( - transforms=[ - L(T.Resize)(size=256), - L(T.CenterCrop)(size=224), - T.ToTensor(), - L(T.Normalize)(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), - ] - ), - ), - batch_size=256 // 8, - num_workers=4, - training=False, -) - -dataloader.evaluator = L(ClassificationAcc)() - -model = L(ClassificationNet)( - model=(ResNet)(block=Bottleneck, layers=[3, 4, 6, 3], zero_init_residual=True) -) - - -optimizer = L(torch.optim.SGD)( - params=L(get_default_optimizer_params)(), - lr=0.1, - momentum=0.9, - weight_decay=1e-4, -) - -lr_multiplier = L(WarmupParamScheduler)( - scheduler=L(MultiStepParamScheduler)( - values=[1.0, 0.1, 0.01, 0.001], milestones=[30, 60, 90, 100] - ), - warmup_length=1 / 100, - warmup_factor=0.1, -) - - -train = get_config("common/train.py").train -train.init_checkpoint = None -train.max_iter = 100 * 1281167 // 256 diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/evaluator.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/evaluator.py deleted file mode 100644 index baf996002b2fddc8c1952408d450b5bf69394f0a..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/evaluator.py +++ /dev/null @@ -1,224 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import datetime -import logging -import time -from collections import OrderedDict, abc -from contextlib import ExitStack, contextmanager -from typing import List, Union -import torch -from torch import nn - -from detectron2.utils.comm import get_world_size, is_main_process -from detectron2.utils.logger import log_every_n_seconds - - -class DatasetEvaluator: - """ - Base class for a dataset evaluator. - - The function :func:`inference_on_dataset` runs the model over - all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs. - - This class will accumulate information of the inputs/outputs (by :meth:`process`), - and produce evaluation results in the end (by :meth:`evaluate`). - """ - - def reset(self): - """ - Preparation for a new round of evaluation. - Should be called before starting a round of evaluation. - """ - pass - - def process(self, inputs, outputs): - """ - Process the pair of inputs and outputs. - If they contain batches, the pairs can be consumed one-by-one using `zip`: - - .. code-block:: python - - for input_, output in zip(inputs, outputs): - # do evaluation on single input/output pair - ... - - Args: - inputs (list): the inputs that's used to call the model. - outputs (list): the return value of `model(inputs)` - """ - pass - - def evaluate(self): - """ - Evaluate/summarize the performance, after processing all input/output pairs. - - Returns: - dict: - A new evaluator class can return a dict of arbitrary format - as long as the user can process the results. - In our train_net.py, we expect the following format: - - * key: the name of the task (e.g., bbox) - * value: a dict of {metric name: score}, e.g.: {"AP50": 80} - """ - pass - - -class DatasetEvaluators(DatasetEvaluator): - """ - Wrapper class to combine multiple :class:`DatasetEvaluator` instances. - - This class dispatches every evaluation call to - all of its :class:`DatasetEvaluator`. - """ - - def __init__(self, evaluators): - """ - Args: - evaluators (list): the evaluators to combine. - """ - super().__init__() - self._evaluators = evaluators - - def reset(self): - for evaluator in self._evaluators: - evaluator.reset() - - def process(self, inputs, outputs): - for evaluator in self._evaluators: - evaluator.process(inputs, outputs) - - def evaluate(self): - results = OrderedDict() - for evaluator in self._evaluators: - result = evaluator.evaluate() - if is_main_process() and result is not None: - for k, v in result.items(): - assert ( - k not in results - ), "Different evaluators produce results with the same key {}".format(k) - results[k] = v - return results - - -def inference_on_dataset( - model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None] -): - """ - Run model on the data_loader and evaluate the metrics with evaluator. - Also benchmark the inference speed of `model.__call__` accurately. - The model will be used in eval mode. - - Args: - model (callable): a callable which takes an object from - `data_loader` and returns some outputs. - - If it's an nn.Module, it will be temporarily set to `eval` mode. - If you wish to evaluate a model in `training` mode instead, you can - wrap the given model and override its behavior of `.eval()` and `.train()`. - data_loader: an iterable object with a length. - The elements it generates will be the inputs to the model. - evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark, - but don't want to do any evaluation. - - Returns: - The return value of `evaluator.evaluate()` - """ - num_devices = get_world_size() - logger = logging.getLogger(__name__) - logger.info("Start inference on {} batches".format(len(data_loader))) - - total = len(data_loader) # inference data loader must have a fixed length - if evaluator is None: - # create a no-op evaluator - evaluator = DatasetEvaluators([]) - if isinstance(evaluator, abc.MutableSequence): - evaluator = DatasetEvaluators(evaluator) - evaluator.reset() - - num_warmup = min(5, total - 1) - start_time = time.perf_counter() - total_data_time = 0 - total_compute_time = 0 - total_eval_time = 0 - with ExitStack() as stack: - if isinstance(model, nn.Module): - stack.enter_context(inference_context(model)) - stack.enter_context(torch.no_grad()) - - start_data_time = time.perf_counter() - for idx, inputs in enumerate(data_loader): - total_data_time += time.perf_counter() - start_data_time - if idx == num_warmup: - start_time = time.perf_counter() - total_data_time = 0 - total_compute_time = 0 - total_eval_time = 0 - - start_compute_time = time.perf_counter() - outputs = model(inputs) - if torch.cuda.is_available(): - torch.cuda.synchronize() - total_compute_time += time.perf_counter() - start_compute_time - - start_eval_time = time.perf_counter() - evaluator.process(inputs, outputs) - total_eval_time += time.perf_counter() - start_eval_time - - iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) - data_seconds_per_iter = total_data_time / iters_after_start - compute_seconds_per_iter = total_compute_time / iters_after_start - eval_seconds_per_iter = total_eval_time / iters_after_start - total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start - if idx >= num_warmup * 2 or compute_seconds_per_iter > 5: - eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1))) - log_every_n_seconds( - logging.INFO, - ( - f"Inference done {idx + 1}/{total}. " - f"Dataloading: {data_seconds_per_iter:.4f} s/iter. " - f"Inference: {compute_seconds_per_iter:.4f} s/iter. " - f"Eval: {eval_seconds_per_iter:.4f} s/iter. " - f"Total: {total_seconds_per_iter:.4f} s/iter. " - f"ETA={eta}" - ), - n=5, - ) - start_data_time = time.perf_counter() - - # Measure the time only for this worker (before the synchronization barrier) - total_time = time.perf_counter() - start_time - total_time_str = str(datetime.timedelta(seconds=total_time)) - # NOTE this format is parsed by grep - logger.info( - "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format( - total_time_str, total_time / (total - num_warmup), num_devices - ) - ) - total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time))) - logger.info( - "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format( - total_compute_time_str, total_compute_time / (total - num_warmup), num_devices - ) - ) - - results = evaluator.evaluate() - # An evaluator may return None when not in main process. - # Replace it by an empty dict instead to make it easier for downstream code to handle - if results is None: - results = {} - return results - - -@contextmanager -def inference_context(model): - """ - A context where the model is temporarily changed to eval mode, - and restored to previous mode afterwards. - - Args: - model: a torch Module - """ - training_mode = model.training - model.eval() - yield - model.train(training_mode) diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py deleted file mode 100644 index d51b92b7d25865a950e28cfb9ae284e600495888..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/rrpn.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import itertools -import logging -from typing import Dict, List -import torch - -from detectron2.config import configurable -from detectron2.layers import ShapeSpec, batched_nms_rotated, cat -from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated -from detectron2.utils.memory import retry_if_cuda_oom - -from ..box_regression import Box2BoxTransformRotated -from .build import PROPOSAL_GENERATOR_REGISTRY -from .proposal_utils import _is_tracing -from .rpn import RPN - -logger = logging.getLogger(__name__) - - -def find_top_rrpn_proposals( - proposals, - pred_objectness_logits, - image_sizes, - nms_thresh, - pre_nms_topk, - post_nms_topk, - min_box_size, - training, -): - """ - For each feature map, select the `pre_nms_topk` highest scoring proposals, - apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk` - highest scoring proposals among all the feature maps if `training` is True, - otherwise, returns the highest `post_nms_topk` scoring proposals for each - feature map. - - Args: - proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5). - All proposal predictions on the feature maps. - pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A). - image_sizes (list[tuple]): sizes (h, w) for each image - nms_thresh (float): IoU threshold to use for NMS - pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS. - When RRPN is run on multiple feature maps (as in FPN) this number is per - feature map. - post_nms_topk (int): number of top k scoring proposals to keep after applying NMS. - When RRPN is run on multiple feature maps (as in FPN) this number is total, - over all feature maps. - min_box_size(float): minimum proposal box side length in pixels (absolute units wrt - input images). - training (bool): True if proposals are to be used in training, otherwise False. - This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..." - comment. - - Returns: - proposals (list[Instances]): list of N Instances. The i-th Instances - stores post_nms_topk object proposals for image i. - """ - num_images = len(image_sizes) - device = proposals[0].device - - # 1. Select top-k anchor for every level and every image - topk_scores = [] # #lvl Tensor, each of shape N x topk - topk_proposals = [] - level_ids = [] # #lvl Tensor, each of shape (topk,) - batch_idx = torch.arange(num_images, device=device) - for level_id, proposals_i, logits_i in zip( - itertools.count(), proposals, pred_objectness_logits - ): - Hi_Wi_A = logits_i.shape[1] - if isinstance(Hi_Wi_A, torch.Tensor): # it's a tensor in tracing - num_proposals_i = torch.clamp(Hi_Wi_A, max=pre_nms_topk) - else: - num_proposals_i = min(Hi_Wi_A, pre_nms_topk) - - topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1) - - # each is N x topk - topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx] # N x topk x 5 - - topk_proposals.append(topk_proposals_i) - topk_scores.append(topk_scores_i) - level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device)) - - # 2. Concat all levels together - topk_scores = cat(topk_scores, dim=1) - topk_proposals = cat(topk_proposals, dim=1) - level_ids = cat(level_ids, dim=0) - - # 3. For each image, run a per-level NMS, and choose topk results. - results = [] - for n, image_size in enumerate(image_sizes): - boxes = RotatedBoxes(topk_proposals[n]) - scores_per_img = topk_scores[n] - valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img) - if not valid_mask.all(): - boxes = boxes[valid_mask] - scores_per_img = scores_per_img[valid_mask] - boxes.clip(image_size) - - # filter empty boxes - keep = boxes.nonempty(threshold=min_box_size) - lvl = level_ids - if _is_tracing() or keep.sum().item() != len(boxes): - boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], level_ids[keep]) - - keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh) - # In Detectron1, there was different behavior during training vs. testing. - # (https://github.com/facebookresearch/Detectron/issues/459) - # During training, topk is over the proposals from *all* images in the training batch. - # During testing, it is over the proposals for each image separately. - # As a result, the training behavior becomes batch-dependent, - # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size. - # This bug is addressed in Detectron2 to make the behavior independent of batch size. - keep = keep[:post_nms_topk] - - res = Instances(image_size) - res.proposal_boxes = boxes[keep] - res.objectness_logits = scores_per_img[keep] - results.append(res) - return results - - -@PROPOSAL_GENERATOR_REGISTRY.register() -class RRPN(RPN): - """ - Rotated Region Proposal Network described in :paper:`RRPN`. - """ - - @configurable - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - if self.anchor_boundary_thresh >= 0: - raise NotImplementedError( - "anchor_boundary_thresh is a legacy option not implemented for RRPN." - ) - - @classmethod - def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): - ret = super().from_config(cfg, input_shape) - ret["box2box_transform"] = Box2BoxTransformRotated(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS) - return ret - - @torch.no_grad() - def label_and_sample_anchors(self, anchors: List[RotatedBoxes], gt_instances: List[Instances]): - """ - Args: - anchors (list[RotatedBoxes]): anchors for each feature map. - gt_instances: the ground-truth instances for each image. - - Returns: - list[Tensor]: - List of #img tensors. i-th element is a vector of labels whose length is - the total number of anchors across feature maps. Label values are in {-1, 0, 1}, - with meanings: -1 = ignore; 0 = negative class; 1 = positive class. - list[Tensor]: - i-th element is a Nx5 tensor, where N is the total number of anchors across - feature maps. The values are the matched gt boxes for each anchor. - Values are undefined for those anchors not labeled as 1. - """ - anchors = RotatedBoxes.cat(anchors) - - gt_boxes = [x.gt_boxes for x in gt_instances] - del gt_instances - - gt_labels = [] - matched_gt_boxes = [] - for gt_boxes_i in gt_boxes: - """ - gt_boxes_i: ground-truth boxes for i-th image - """ - match_quality_matrix = retry_if_cuda_oom(pairwise_iou_rotated)(gt_boxes_i, anchors) - matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix) - # Matching is memory-expensive and may result in CPU tensors. But the result is small - gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device) - - # A vector of labels (-1, 0, 1) for each anchor - gt_labels_i = self._subsample_labels(gt_labels_i) - - if len(gt_boxes_i) == 0: - # These values won't be used anyway since the anchor is labeled as background - matched_gt_boxes_i = torch.zeros_like(anchors.tensor) - else: - # TODO wasted indexing computation for ignored boxes - matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor - - gt_labels.append(gt_labels_i) # N,AHW - matched_gt_boxes.append(matched_gt_boxes_i) - return gt_labels, matched_gt_boxes - - @torch.no_grad() - def predict_proposals(self, anchors, pred_objectness_logits, pred_anchor_deltas, image_sizes): - pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas) - return find_top_rrpn_proposals( - pred_proposals, - pred_objectness_logits, - image_sizes, - self.nms_thresh, - self.pre_nms_topk[self.training], - self.post_nms_topk[self.training], - self.min_box_size, - self.training, - ) diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/deploy/README.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/deploy/README.md deleted file mode 100644 index e33cbeb54c003a5738da68c838fdaa4e0d218501..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/deploy/README.md +++ /dev/null @@ -1,66 +0,0 @@ -See [deployment tutorial](https://detectron2.readthedocs.io/tutorials/deployment.html) -for some high-level background about deployment. - -This directory contains the following examples: - -1. An example script `export_model.py` - that exports a detectron2 model for deployment using different methods and formats. - -2. A C++ example that runs inference with Mask R-CNN model in TorchScript format. - -## Build -Deployment depends on libtorch and OpenCV. Some require more dependencies: - -* Running TorchScript-format models produced by `--export-method=caffe2_tracing` requires libtorch - to be built with caffe2 enabled. -* Running TorchScript-format models produced by `--export-method=tracing/scripting` requires libtorchvision (C++ library of torchvision). - -All methods are supported in one C++ file that requires all the above dependencies. -Adjust it and remove code you don't need. -As a reference, we provide a [Dockerfile](../../docker/deploy.Dockerfile) that installs all the above dependencies and builds the C++ example. - -## Use - -We show a few example commands to export and execute a Mask R-CNN model in C++. - -* `export-method=tracing, format=torchscript`: -``` -./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ - --output ./output --export-method tracing --format torchscript \ - MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \ - MODEL.DEVICE cuda - -./build/torchscript_mask_rcnn output/model.ts input.jpg tracing -``` - -* `export-method=scripting, format=torchscript`: -``` -./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ - --output ./output --export-method scripting --format torchscript \ - MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \ - -./build/torchscript_mask_rcnn output/model.ts input.jpg scripting -``` - -* `export-method=caffe2_tracing, format=torchscript`: - -``` -./export_model.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ - --output ./output --export-method caffe2_tracing --format torchscript \ - MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \ - -./build/torchscript_mask_rcnn output/model.ts input.jpg caffe2_tracing -``` - - -## Notes: - -1. Tracing/Caffe2-tracing requires valid weights & sample inputs. - Therefore the above commands require pre-trained models and [COCO dataset](https://detectron2.readthedocs.io/tutorials/builtin_datasets.html). - You can modify the script to obtain sample inputs in other ways instead of from COCO. - -2. `--run-eval` is implemented only for tracing mode - to evaluate the exported model using the dataset in the config. - It's recommended to always verify the accuracy in case the conversion is not successful. - Evaluation can be slow if model is exported to CPU or dataset is too large ("coco_2017_val_100" is a small subset of COCO useful for evaluation). - `caffe2_tracing` accuracy may be slightly different (within 0.1 AP) from original model due to numerical precisions between different runtime. diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/build/metadata.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/build/metadata.py deleted file mode 100644 index c66ac354deb035405fe0e4040dac539d28570257..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/build/metadata.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Metadata generation logic for source distributions. -""" - -import os - -from pip._vendor.pyproject_hooks import BuildBackendHookCaller - -from pip._internal.build_env import BuildEnvironment -from pip._internal.exceptions import ( - InstallationSubprocessError, - MetadataGenerationFailed, -) -from pip._internal.utils.subprocess import runner_with_spinner_message -from pip._internal.utils.temp_dir import TempDirectory - - -def generate_metadata( - build_env: BuildEnvironment, backend: BuildBackendHookCaller, details: str -) -> str: - """Generate metadata using mechanisms described in PEP 517. - - Returns the generated metadata directory. - """ - metadata_tmpdir = TempDirectory(kind="modern-metadata", globally_managed=True) - - metadata_dir = metadata_tmpdir.path - - with build_env: - # Note that BuildBackendHookCaller implements a fallback for - # prepare_metadata_for_build_wheel, so we don't have to - # consider the possibility that this hook doesn't exist. - runner = runner_with_spinner_message("Preparing metadata (pyproject.toml)") - with backend.subprocess_runner(runner): - try: - distinfo_dir = backend.prepare_metadata_for_build_wheel(metadata_dir) - except InstallationSubprocessError as error: - raise MetadataGenerationFailed(package_details=details) from error - - return os.path.join(metadata_dir, distinfo_dir) diff --git a/spaces/CVPR/LIVE/pybind11/include/pybind11/iostream.h b/spaces/CVPR/LIVE/pybind11/include/pybind11/iostream.h deleted file mode 100644 index eaf92dfa49add54c298844b31898a82de3fb429d..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/include/pybind11/iostream.h +++ /dev/null @@ -1,209 +0,0 @@ -/* - pybind11/iostream.h -- Tools to assist with redirecting cout and cerr to Python - - Copyright (c) 2017 Henry F. Schreiner - - All rights reserved. Use of this source code is governed by a - BSD-style license that can be found in the LICENSE file. -*/ - -#pragma once - -#include "pybind11.h" - -#include -#include -#include -#include -#include - -PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) -PYBIND11_NAMESPACE_BEGIN(detail) - -// Buffer that writes to Python instead of C++ -class pythonbuf : public std::streambuf { -private: - using traits_type = std::streambuf::traits_type; - - const size_t buf_size; - std::unique_ptr d_buffer; - object pywrite; - object pyflush; - - int overflow(int c) { - if (!traits_type::eq_int_type(c, traits_type::eof())) { - *pptr() = traits_type::to_char_type(c); - pbump(1); - } - return sync() == 0 ? traits_type::not_eof(c) : traits_type::eof(); - } - - int sync() { - if (pbase() != pptr()) { - // This subtraction cannot be negative, so dropping the sign - str line(pbase(), static_cast(pptr() - pbase())); - - { - gil_scoped_acquire tmp; - pywrite(line); - pyflush(); - } - - setp(pbase(), epptr()); - } - return 0; - } - -public: - - pythonbuf(object pyostream, size_t buffer_size = 1024) - : buf_size(buffer_size), - d_buffer(new char[buf_size]), - pywrite(pyostream.attr("write")), - pyflush(pyostream.attr("flush")) { - setp(d_buffer.get(), d_buffer.get() + buf_size - 1); - } - - pythonbuf(pythonbuf&&) = default; - - /// Sync before destroy - ~pythonbuf() { - sync(); - } -}; - -PYBIND11_NAMESPACE_END(detail) - - -/** \rst - This a move-only guard that redirects output. - - .. code-block:: cpp - - #include - - ... - - { - py::scoped_ostream_redirect output; - std::cout << "Hello, World!"; // Python stdout - } // <-- return std::cout to normal - - You can explicitly pass the c++ stream and the python object, - for example to guard stderr instead. - - .. code-block:: cpp - - { - py::scoped_ostream_redirect output{std::cerr, py::module::import("sys").attr("stderr")}; - std::cerr << "Hello, World!"; - } - \endrst */ -class scoped_ostream_redirect { -protected: - std::streambuf *old; - std::ostream &costream; - detail::pythonbuf buffer; - -public: - scoped_ostream_redirect( - std::ostream &costream = std::cout, - object pyostream = module::import("sys").attr("stdout")) - : costream(costream), buffer(pyostream) { - old = costream.rdbuf(&buffer); - } - - ~scoped_ostream_redirect() { - costream.rdbuf(old); - } - - scoped_ostream_redirect(const scoped_ostream_redirect &) = delete; - scoped_ostream_redirect(scoped_ostream_redirect &&other) = default; - scoped_ostream_redirect &operator=(const scoped_ostream_redirect &) = delete; - scoped_ostream_redirect &operator=(scoped_ostream_redirect &&) = delete; -}; - - -/** \rst - Like `scoped_ostream_redirect`, but redirects cerr by default. This class - is provided primary to make ``py::call_guard`` easier to make. - - .. code-block:: cpp - - m.def("noisy_func", &noisy_func, - py::call_guard()); - -\endrst */ -class scoped_estream_redirect : public scoped_ostream_redirect { -public: - scoped_estream_redirect( - std::ostream &costream = std::cerr, - object pyostream = module::import("sys").attr("stderr")) - : scoped_ostream_redirect(costream,pyostream) {} -}; - - -PYBIND11_NAMESPACE_BEGIN(detail) - -// Class to redirect output as a context manager. C++ backend. -class OstreamRedirect { - bool do_stdout_; - bool do_stderr_; - std::unique_ptr redirect_stdout; - std::unique_ptr redirect_stderr; - -public: - OstreamRedirect(bool do_stdout = true, bool do_stderr = true) - : do_stdout_(do_stdout), do_stderr_(do_stderr) {} - - void enter() { - if (do_stdout_) - redirect_stdout.reset(new scoped_ostream_redirect()); - if (do_stderr_) - redirect_stderr.reset(new scoped_estream_redirect()); - } - - void exit() { - redirect_stdout.reset(); - redirect_stderr.reset(); - } -}; - -PYBIND11_NAMESPACE_END(detail) - -/** \rst - This is a helper function to add a C++ redirect context manager to Python - instead of using a C++ guard. To use it, add the following to your binding code: - - .. code-block:: cpp - - #include - - ... - - py::add_ostream_redirect(m, "ostream_redirect"); - - You now have a Python context manager that redirects your output: - - .. code-block:: python - - with m.ostream_redirect(): - m.print_to_cout_function() - - This manager can optionally be told which streams to operate on: - - .. code-block:: python - - with m.ostream_redirect(stdout=true, stderr=true): - m.noisy_function_with_error_printing() - - \endrst */ -inline class_ add_ostream_redirect(module m, std::string name = "ostream_redirect") { - return class_(m, name.c_str(), module_local()) - .def(init(), arg("stdout")=true, arg("stderr")=true) - .def("__enter__", &detail::OstreamRedirect::enter) - .def("__exit__", [](detail::OstreamRedirect &self_, args) { self_.exit(); }); -} - -PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/spaces/CVPR/LIVE/thrust/thrust/zip_function.h b/spaces/CVPR/LIVE/thrust/thrust/zip_function.h deleted file mode 100644 index faea59d4c5b3204924ab63d155f546c2ec4d9e6c..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/zip_function.h +++ /dev/null @@ -1,211 +0,0 @@ - -/*! \file thrust/zip_function.h - * \brief Adaptor type that turns an N-ary function object into one that takes - * a tuple of size N so it can easily be used with algorithms taking zip - * iterators - */ - -#pragma once - -#include -#include -#include - -#if THRUST_CPP_DIALECT >= 2011 && !defined(THRUST_LEGACY_GCC) - -#include -#include -#include - -namespace thrust -{ - -/*! \addtogroup function_objects Function Objects - * \{ - */ - -/*! \addtogroup function_object_adaptors Function Object Adaptors - * \ingroup function_objects - * \{ - */ - -namespace detail { -namespace zip_detail { - -// Add workaround for decltype(auto) on C++11-only compilers: -#if THRUST_CPP_DIALECT >= 2014 - -template -__host__ __device__ -decltype(auto) apply_impl(Function&& func, Tuple&& args, index_sequence) -{ - return func(thrust::get(THRUST_FWD(args))...); -} - -template -__host__ __device__ -decltype(auto) apply(Function&& func, Tuple&& args) -{ - constexpr auto tuple_size = thrust::tuple_size::type>::value; - return apply_impl(THRUST_FWD(func), THRUST_FWD(args), make_index_sequence{}); -} - -#else // THRUST_CPP_DIALECT - -template -__host__ __device__ -auto apply_impl(Function&& func, Tuple&& args, index_sequence) -THRUST_DECLTYPE_RETURNS(func(thrust::get(THRUST_FWD(args))...)) - -template -__host__ __device__ -auto apply(Function&& func, Tuple&& args) -THRUST_DECLTYPE_RETURNS( - apply_impl( - THRUST_FWD(func), - THRUST_FWD(args), - make_index_sequence< - thrust::tuple_size::type>::value>{}) -) - -#endif // THRUST_CPP_DIALECT - -} // namespace zip_detail -} // namespace detail - -/*! \p zip_function is a function object that allows the easy use of N-ary - * function objects with \p zip_iterators without redefining them to take a - * \p tuple instead of N arguments. - * - * This means that if a functor that takes 2 arguments which could be used with - * the \p transform function and \p device_iterators can be extended to take 3 - * arguments and \p zip_iterators without rewriting the functor in terms of - * \p tuple. - * - * The \p make_zip_function convenience function is provided to avoid having - * to explicitely define the type of the functor when creating a \p zip_function, - * whic is especially helpful when using lambdas as the functor. - * - * \code - * #include - * #include - * #include - * #include - * - * struct SumTuple { - * float operator()(Tuple tup) { - * return std::get<0>(tup) + std::get<1>(tup) + std::get<2>(tup); - * } - * }; - * struct SumArgs { - * float operator()(float a, float b, float c) { - * return a + b + c; - * } - * }; - * - * int main() { - * thrust::device_vector A(3); - * thrust::device_vector B(3); - * thrust::device_vector C(3); - * thrust::device_vector D(3); - * A[0] = 0.f; A[1] = 1.f; A[2] = 2.f; - * B[0] = 1.f; B[1] = 2.f; B[2] = 3.f; - * C[0] = 2.f; C[1] = 3.f; C[2] = 4.f; - * - * // The following four invocations of transform are equivalent - * // Transform with 3-tuple - * thrust::transform(thrust::make_zip_iterator(thrust::make_tuple(A.begin(), B.begin(), C.begin())), - * thrust::make_zip_iterator(thrust::make_tuple(A.end(), B.end(), C.end())), - * D.begin(), - * SumTuple{}); - * - * // Transform with 3 parameters - * thrust::zip_function adapted{}; - * thrust::transform(thrust::make_zip_iterator(thrust::make_tuple(A.begin(), B.begin(), C.begin())), - * thrust::make_zip_iterator(thrust::make_tuple(A.end(), B.end(), C.end())), - * D.begin(), - * adapted); - * - * // Transform with 3 parameters with convenience function - * thrust::zip_function adapted{}; - * thrust::transform(thrust::make_zip_iterator(thrust::make_tuple(A.begin(), B.begin(), C.begin())), - * thrust::make_zip_iterator(thrust::make_tuple(A.end(), B.end(), C.end())), - * D.begin(), - * thrust::make_zip_function(SumArgs{})); - * - * // Transform with 3 parameters with convenience function and lambda - * thrust::zip_function adapted{}; - * thrust::transform(thrust::make_zip_iterator(thrust::make_tuple(A.begin(), B.begin(), C.begin())), - * thrust::make_zip_iterator(thrust::make_tuple(A.end(), B.end(), C.end())), - * D.begin(), - * thrust::make_zip_function([] (float a, float b, float c) { - * return a + b + c; - * })); - * return 0; - * } - * \endcode - * - * \see make_zip_function - * \see zip_iterator - */ -template -class zip_function -{ - public: - __host__ __device__ - zip_function(Function func) : func(std::move(func)) {} - -// Add workaround for decltype(auto) on C++11-only compilers: -#if THRUST_CPP_DIALECT >= 2014 - - template - __host__ __device__ - decltype(auto) operator()(Tuple&& args) const - { - return detail::zip_detail::apply(func, THRUST_FWD(args)); - } - -#else // THRUST_CPP_DIALECT - - // Can't just use THRUST_DECLTYPE_RETURNS here since we need to use - // std::declval for the signature components: - template - __host__ __device__ - auto operator()(Tuple&& args) const - noexcept(noexcept(detail::zip_detail::apply(std::declval(), THRUST_FWD(args)))) - -> decltype(detail::zip_detail::apply(std::declval(), THRUST_FWD(args))) - - { - return detail::zip_detail::apply(func, THRUST_FWD(args)); - } - -#endif // THRUST_CPP_DIALECT - - private: - mutable Function func; -}; - -/*! \p make_zip_function creates a \p zip_function from a function object. - * - * \param fun The N-ary function object. - * \return A \p zip_function that takes a N-tuple. - * - * \see zip_function - */ -template -__host__ __device__ -auto make_zip_function(Function&& fun) -> zip_function::type> -{ - using func_t = typename std::decay::type; - return zip_function(THRUST_FWD(fun)); -} - -/*! \} // end function_object_adaptors - */ - -/*! \} // end function_objects - */ - -} // end namespace thrust - -#endif diff --git a/spaces/Crossbro/succinctly-text2image-prompt-generator/app.py b/spaces/Crossbro/succinctly-text2image-prompt-generator/app.py deleted file mode 100644 index 6236186cf4e23d7670a3ed158d005e5c98358b28..0000000000000000000000000000000000000000 --- a/spaces/Crossbro/succinctly-text2image-prompt-generator/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/succinctly/text2image-prompt-generator").launch() \ No newline at end of file diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.22ab9e68.js b/spaces/DEEMOSTECH/ChatAvatar/static/js/main.22ab9e68.js deleted file mode 100644 index 8f968c4c2e3e5a13e2677f0ff611adfa432ad9da..0000000000000000000000000000000000000000 --- a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.22ab9e68.js +++ /dev/null @@ -1,3 +0,0 @@ -/*! 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h=this.peekCodePoint(0),f=this.peekCodePoint(1);return h!==bt||!Xt(f)&&f!==gt||(this.consumeCodePoint(),this.consumeUnicodeRangeToken()),this.reconsumeCodePoint(e),this.consumeIdentLikeToken();case vt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),fn;if(this.peekCodePoint(0)===vt)return this.consumeCodePoint(),hn;break;case yt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),pn;break;case Lt:return Un}return $t(e)?(this.consumeWhiteSpace(),En):Wt(e)?(this.reconsumeCodePoint(e),this.consumeNumericToken()):en(e)?(this.reconsumeCodePoint(e),this.consumeIdentLikeToken()):{type:6,value:u(e)}},e.prototype.consumeCodePoint=function(){var e=this._value.shift();return"undefined"===typeof e?-1:e},e.prototype.reconsumeCodePoint=function(e){this._value.unshift(e)},e.prototype.peekCodePoint=function(e){return e>=this._value.length?-1:this._value[e]},e.prototype.consumeUnicodeRangeToken=function(){for(var e=[],t=this.consumeCodePoint();Xt(t)&&e.length<6;)e.push(t),t=this.consumeCodePoint();for(var n=!1;t===gt&&e.length<6;)e.push(t),t=this.consumeCodePoint(),n=!0;if(n)return{type:30,start:parseInt(u.apply(void 0,e.map((function(e){return e===gt?Dt:e}))),16),end:parseInt(u.apply(void 0,e.map((function(e){return e===gt?zt:e}))),16)};var r=parseInt(u.apply(void 0,e),16);if(this.peekCodePoint(0)===ot&&Xt(this.peekCodePoint(1))){this.consumeCodePoint(),t=this.consumeCodePoint();for(var i=[];Xt(t)&&i.length<6;)i.push(t),t=this.consumeCodePoint();return{type:30,start:r,end:parseInt(u.apply(void 0,i),16)}}return{type:30,start:r,end:r}},e.prototype.consumeIdentLikeToken=function(){var e=this.consumeName();return"url"===e.toLowerCase()&&this.peekCodePoint(0)===it?(this.consumeCodePoint(),this.consumeUrlToken()):this.peekCodePoint(0)===it?(this.consumeCodePoint(),{type:19,value:e}):{type:20,value:e}},e.prototype.consumeUrlToken=function(){var e=[];if(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt)return{type:22,value:""};var t=this.peekCodePoint(0);if(t===rt||t===Ze){var n=this.consumeStringToken(this.consumeCodePoint());return 0===n.type&&(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At)?(this.consumeCodePoint(),{type:22,value:n.value}):(this.consumeBadUrlRemnants(),yn)}for(;;){var r=this.consumeCodePoint();if(r===Lt||r===At)return{type:22,value:u.apply(void 0,e)};if($t(r))return this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At?(this.consumeCodePoint(),{type:22,value:u.apply(void 0,e)}):(this.consumeBadUrlRemnants(),yn);if(r===Ze||r===rt||r===it||nn(r))return this.consumeBadUrlRemnants(),yn;if(r===qe){if(!rn(r,this.peekCodePoint(0)))return this.consumeBadUrlRemnants(),yn;e.push(this.consumeEscapedCodePoint())}else 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e=[],t=Ke,n=this.peekCodePoint(0);for(n!==bt&&n!==ot||e.push(this.consumeCodePoint());Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0);var r=this.peekCodePoint(1);if(n===Et&&Wt(r))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0),r=this.peekCodePoint(1);var i=this.peekCodePoint(2);if((n===Vt||n===Rt)&&((r===bt||r===ot)&&Wt(i)||Wt(r)))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());return[on(e),t]},e.prototype.consumeNumericToken=function(){var e=this.consumeNumber(),t=e[0],n=e[1],r=this.peekCodePoint(0),i=this.peekCodePoint(1),A=this.peekCodePoint(2);return An(r,i,A)?{type:15,number:t,flags:n,unit:this.consumeName()}:r===nt?(this.consumeCodePoint(),{type:16,number:t,flags:n}):{type:17,number:t,flags:n}},e.prototype.consumeEscapedCodePoint=function(){var e=this.consumeCodePoint();if(Xt(e)){for(var t=u(e);Xt(this.peekCodePoint(0))&&t.length<6;)t+=u(this.consumeCodePoint());$t(this.peekCodePoint(0))&&this.consumeCodePoint();var n=parseInt(t,16);return 0===n||jt(n)||n>1114111?Bt:n}return e===Lt?Bt:e},e.prototype.consumeName=function(){for(var e="";;){var t=this.consumeCodePoint();if(tn(t))e+=u(t);else{if(!rn(t,this.peekCodePoint(0)))return this.reconsumeCodePoint(t),e;e+=u(this.consumeEscapedCodePoint())}}},e}(),Fn=function(){function e(e){this._tokens=e}return e.create=function(t){var n=new Mn;return n.write(t),new e(n.read())},e.parseValue=function(t){return e.create(t).parseComponentValue()},e.parseValues=function(t){return e.create(t).parseComponentValues()},e.prototype.parseComponentValue=function(){for(var e=this.consumeToken();31===e.type;)e=this.consumeToken();if(32===e.type)throw new SyntaxError("Error parsing CSS component value, unexpected EOF");this.reconsumeToken(e);var t=this.consumeComponentValue();do{e=this.consumeToken()}while(31===e.type);if(32===e.type)return t;throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one")},e.prototype.parseComponentValues=function(){for(var e=[];;){var t=this.consumeComponentValue();if(32===t.type)return e;e.push(t),e.push()}},e.prototype.consumeComponentValue=function(){var e=this.consumeToken();switch(e.type){case 11:case 28:case 2:return this.consumeSimpleBlock(e.type);case 19:return this.consumeFunction(e)}return e},e.prototype.consumeSimpleBlock=function(e){for(var t={type:e,values:[]},n=this.consumeToken();;){if(32===n.type||Pn(n,e))return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue()),n=this.consumeToken()}},e.prototype.consumeFunction=function(e){for(var t={name:e.value,values:[],type:18};;){var n=this.consumeToken();if(32===n.type||3===n.type)return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue())}},e.prototype.consumeToken=function(){var e=this._tokens.shift();return"undefined"===typeof e?Un:e},e.prototype.reconsumeToken=function(e){this._tokens.unshift(e)},e}(),Tn=function(e){return 15===e.type},kn=function(e){return 17===e.type},Qn=function(e){return 20===e.type},Ln=function(e){return 0===e.type},Dn=function(e,t){return Qn(e)&&e.value===t},In=function(e){return 31!==e.type},Rn=function(e){return 31!==e.type&&4!==e.type},Hn=function(e){var t=[],n=[];return e.forEach((function(e){if(4===e.type){if(0===n.length)throw new Error("Error parsing function args, zero tokens for arg");return t.push(n),void(n=[])}31!==e.type&&n.push(e)})),n.length&&t.push(n),t},Pn=function(e,t){return 11===t&&12===e.type||28===t&&29===e.type||2===t&&3===e.type},Nn=function(e){return 17===e.type||15===e.type},On=function(e){return 16===e.type||Nn(e)},Vn=function(e){return 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Io(e,"\u30a2\u30a4\u30a6\u30a8\u30aa\u30ab\u30ad\u30af\u30b1\u30b3\u30b5\u30b7\u30b9\u30bb\u30bd\u30bf\u30c1\u30c4\u30c6\u30c8\u30ca\u30cb\u30cc\u30cd\u30ce\u30cf\u30d2\u30d5\u30d8\u30db\u30de\u30df\u30e0\u30e1\u30e2\u30e4\u30e6\u30e8\u30e9\u30ea\u30eb\u30ec\u30ed\u30ef\u30f0\u30f1\u30f2\u30f3",i);case 29:return Io(e,"\u30a4\u30ed\u30cf\u30cb\u30db\u30d8\u30c8\u30c1\u30ea\u30cc\u30eb\u30f2\u30ef\u30ab\u30e8\u30bf\u30ec\u30bd\u30c4\u30cd\u30ca\u30e9\u30e0\u30a6\u30f0\u30ce\u30aa\u30af\u30e4\u30de\u30b1\u30d5\u30b3\u30a8\u30c6\u30a2\u30b5\u30ad\u30e6\u30e1\u30df\u30b7\u30f1\u30d2\u30e2\u30bb\u30b9",i);case 34:return Do(e,3792,3801,!0,r);case 37:return Do(e,6160,6169,!0,r);case 38:return Do(e,4160,4169,!0,r);case 39:return Do(e,2918,2927,!0,r);case 40:return Do(e,1776,1785,!0,r);case 43:return Do(e,3046,3055,!0,r);case 44:return Do(e,3174,3183,!0,r);case 45:return Do(e,3664,3673,!0,r);case 46:return Do(e,3872,3881,!0,r);default:return 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wo(t)&&(wo(e)&&e.currentSrc&&e.currentSrc!==e.src&&(t.src=e.currentSrc,t.srcset=""),"lazy"===t.loading&&(t.loading="eager")),Eo(t)?this.createCustomElementClone(t):t},e.prototype.createCustomElementClone=function(e){var t=document.createElement("html2canvascustomelement");return ts(e.style,t),t},e.prototype.createStyleClone=function(e){try{var t=e.sheet;if(t&&t.cssRules){var n=[].slice.call(t.cssRules,0).reduce((function(e,t){return t&&"string"===typeof t.cssText?e+t.cssText:e}),""),r=e.cloneNode(!1);return r.textContent=n,r}}catch(Rt){if(this.context.logger.error("Unable to access cssRules property",Rt),"SecurityError"!==Rt.name)throw Rt}return e.cloneNode(!1)},e.prototype.createCanvasClone=function(e){var t;if(this.options.inlineImages&&e.ownerDocument){var n=e.ownerDocument.createElement("img");try{return n.src=e.toDataURL(),n}catch(Rt){this.context.logger.info("Unable to inline canvas contents, canvas is tainted",e)}}var r=e.cloneNode(!1);try{r.width=e.width,r.height=e.height;var i=e.getContext("2d"),A=r.getContext("2d");if(A)if(!this.options.allowTaint&&i)A.putImageData(i.getImageData(0,0,e.width,e.height),0,0);else{var a=null!==(t=e.getContext("webgl2"))&&void 0!==t?t:e.getContext("webgl");if(a){var o=a.getContextAttributes();!1===(null===o||void 0===o?void 0:o.preserveDrawingBuffer)&&this.context.logger.warn("Unable to clone WebGL context as it has preserveDrawingBuffer=false",e)}A.drawImage(e,0,0)}return r}catch(Rt){this.context.logger.info("Unable to clone canvas as it is tainted",e)}return r},e.prototype.createVideoClone=function(e){var t=e.ownerDocument.createElement("canvas");t.width=e.offsetWidth,t.height=e.offsetHeight;var n=t.getContext("2d");try{return n&&(n.drawImage(e,0,0,t.width,t.height),this.options.allowTaint||n.getImageData(0,0,t.width,t.height)),t}catch(Rt){this.context.logger.info("Unable to clone video as it is tainted",e)}var r=e.ownerDocument.createElement("canvas");return r.width=e.offsetWidth,r.height=e.offsetHeight,r},e.prototype.appendChildNode=function(e,t,n){so(t)&&(bo(t)||t.hasAttribute(jo)||"function"===typeof this.options.ignoreElements&&this.options.ignoreElements(t))||this.options.copyStyles&&so(t)&&_o(t)||e.appendChild(this.cloneNode(t,n))},e.prototype.cloneChildNodes=function(e,t,n){for(var r=this,i=e.shadowRoot?e.shadowRoot.firstChild:e.firstChild;i;i=i.nextSibling)if(so(i)&&So(i)&&"function"===typeof i.assignedNodes){var A=i.assignedNodes();A.length&&A.forEach((function(e){return r.appendChildNode(t,e,n)}))}else this.appendChildNode(t,i,n)},e.prototype.cloneNode=function(e,t){if(oo(e))return document.createTextNode(e.data);if(!e.ownerDocument)return e.cloneNode(!1);var n=e.ownerDocument.defaultView;if(n&&so(e)&&(lo(e)||uo(e))){var r=this.createElementClone(e);r.style.transitionProperty="none";var i=n.getComputedStyle(e),A=n.getComputedStyle(e,":before"),a=n.getComputedStyle(e,":after");this.referenceElement===e&&lo(r)&&(this.clonedReferenceElement=r),mo(r)&&us(r);var o=this.counters.parse(new mA(this.context,i)),s=this.resolvePseudoContent(e,r,A,KA.BEFORE);Eo(e)&&(t=!0),yo(e)||this.cloneChildNodes(e,r,t),s&&r.insertBefore(s,r.firstChild);var l=this.resolvePseudoContent(e,r,a,KA.AFTER);return l&&r.appendChild(l),this.counters.pop(o),(i&&(this.options.copyStyles||uo(e))&&!Bo(e)||t)&&ts(i,r),0===e.scrollTop&&0===e.scrollLeft||this.scrolledElements.push([r,e.scrollLeft,e.scrollTop]),(xo(e)||Co(e))&&(xo(r)||Co(r))&&(r.value=e.value),r}return e.cloneNode(!1)},e.prototype.resolvePseudoContent=function(e,t,n,r){var i=this;if(n){var A=n.content,a=t.ownerDocument;if(a&&A&&"none"!==A&&"-moz-alt-content"!==A&&"none"!==n.display){this.counters.parse(new mA(this.context,n));var o=new 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t=e.styles,n=e.bounds,r=Wn(t.borderTopLeftRadius,n.width,n.height),i=r[0],A=r[1],a=Wn(t.borderTopRightRadius,n.width,n.height),o=a[0],s=a[1],l=Wn(t.borderBottomRightRadius,n.width,n.height),u=l[0],c=l[1],d=Wn(t.borderBottomLeftRadius,n.width,n.height),h=d[0],f=d[1],p=[];p.push((i+o)/n.width),p.push((h+u)/n.width),p.push((A+f)/n.height),p.push((s+c)/n.height);var g=Math.max.apply(Math,p);g>1&&(i/=g,A/=g,o/=g,s/=g,u/=g,c/=g,h/=g,f/=g);var m=n.width-o,v=n.height-c,y=n.width-u,w=n.height-f,B=t.borderTopWidth,_=t.borderRightWidth,b=t.borderBottomWidth,x=t.borderLeftWidth,C=jn(t.paddingTop,e.bounds.width),S=jn(t.paddingRight,e.bounds.width),E=jn(t.paddingBottom,e.bounds.width),U=jn(t.paddingLeft,e.bounds.width);this.topLeftBorderDoubleOuterBox=i>0||A>0?Es(n.left+x/3,n.top+B/3,i-x/3,A-B/3,qo.TOP_LEFT):new _s(n.left+x/3,n.top+B/3),this.topRightBorderDoubleOuterBox=i>0||A>0?Es(n.left+m,n.top+B/3,o-_/3,s-B/3,qo.TOP_RIGHT):new _s(n.left+n.width-_/3,n.top+B/3),this.bottomRightBorderDoubleOuterBox=u>0||c>0?Es(n.left+y,n.top+v,u-_/3,c-b/3,qo.BOTTOM_RIGHT):new _s(n.left+n.width-_/3,n.top+n.height-b/3),this.bottomLeftBorderDoubleOuterBox=h>0||f>0?Es(n.left+x/3,n.top+w,h-x/3,f-b/3,qo.BOTTOM_LEFT):new _s(n.left+x/3,n.top+n.height-b/3),this.topLeftBorderDoubleInnerBox=i>0||A>0?Es(n.left+2*x/3,n.top+2*B/3,i-2*x/3,A-2*B/3,qo.TOP_LEFT):new _s(n.left+2*x/3,n.top+2*B/3),this.topRightBorderDoubleInnerBox=i>0||A>0?Es(n.left+m,n.top+2*B/3,o-2*_/3,s-2*B/3,qo.TOP_RIGHT):new _s(n.left+n.width-2*_/3,n.top+2*B/3),this.bottomRightBorderDoubleInnerBox=u>0||c>0?Es(n.left+y,n.top+v,u-2*_/3,c-2*b/3,qo.BOTTOM_RIGHT):new _s(n.left+n.width-2*_/3,n.top+n.height-2*b/3),this.bottomLeftBorderDoubleInnerBox=h>0||f>0?Es(n.left+2*x/3,n.top+w,h-2*x/3,f-2*b/3,qo.BOTTOM_LEFT):new _s(n.left+2*x/3,n.top+n.height-2*b/3),this.topLeftBorderStroke=i>0||A>0?Es(n.left+x/2,n.top+B/2,i-x/2,A-B/2,qo.TOP_LEFT):new _s(n.left+x/2,n.top+B/2),this.topRightBorderStroke=i>0||A>0?Es(n.left+m,n.top+B/2,o-_/2,s-B/2,qo.TOP_RIGHT):new _s(n.left+n.width-_/2,n.top+B/2),this.bottomRightBorderStroke=u>0||c>0?Es(n.left+y,n.top+v,u-_/2,c-b/2,qo.BOTTOM_RIGHT):new _s(n.left+n.width-_/2,n.top+n.height-b/2),this.bottomLeftBorderStroke=h>0||f>0?Es(n.left+x/2,n.top+w,h-x/2,f-b/2,qo.BOTTOM_LEFT):new _s(n.left+x/2,n.top+n.height-b/2),this.topLeftBorderBox=i>0||A>0?Es(n.left,n.top,i,A,qo.TOP_LEFT):new _s(n.left,n.top),this.topRightBorderBox=o>0||s>0?Es(n.left+m,n.top,o,s,qo.TOP_RIGHT):new _s(n.left+n.width,n.top),this.bottomRightBorderBox=u>0||c>0?Es(n.left+y,n.top+v,u,c,qo.BOTTOM_RIGHT):new _s(n.left+n.width,n.top+n.height),this.bottomLeftBorderBox=h>0||f>0?Es(n.left,n.top+w,h,f,qo.BOTTOM_LEFT):new _s(n.left,n.top+n.height),this.topLeftPaddingBox=i>0||A>0?Es(n.left+x,n.top+B,Math.max(0,i-x),Math.max(0,A-B),qo.TOP_LEFT):new _s(n.left+x,n.top+B),this.topRightPaddingBox=o>0||s>0?Es(n.left+Math.min(m,n.width-_),n.top+B,m>n.width+_?0:Math.max(0,o-_),Math.max(0,s-B),qo.TOP_RIGHT):new _s(n.left+n.width-_,n.top+B),this.bottomRightPaddingBox=u>0||c>0?Es(n.left+Math.min(y,n.width-x),n.top+Math.min(v,n.height-b),Math.max(0,u-_),Math.max(0,c-b),qo.BOTTOM_RIGHT):new _s(n.left+n.width-_,n.top+n.height-b),this.bottomLeftPaddingBox=h>0||f>0?Es(n.left+x,n.top+Math.min(w,n.height-b),Math.max(0,h-x),Math.max(0,f-b),qo.BOTTOM_LEFT):new _s(n.left+x,n.top+n.height-b),this.topLeftContentBox=i>0||A>0?Es(n.left+x+U,n.top+B+C,Math.max(0,i-(x+U)),Math.max(0,A-(B+C)),qo.TOP_LEFT):new _s(n.left+x+U,n.top+B+C),this.topRightContentBox=o>0||s>0?Es(n.left+Math.min(m,n.width+x+U),n.top+B+C,m>n.width+x+U?0:o-x+U,s-(B+C),qo.TOP_RIGHT):new _s(n.left+n.width-(_+S),n.top+B+C),this.bottomRightContentBox=u>0||c>0?Es(n.left+Math.min(y,n.width-(x+U)),n.top+Math.min(v,n.height+B+C),Math.max(0,u-(_+S)),c-(b+E),qo.BOTTOM_RIGHT):new _s(n.left+n.width-(_+S),n.top+n.height-(b+E)),this.bottomLeftContentBox=h>0||f>0?Es(n.left+x+U,n.top+w,Math.max(0,h-(x+U)),f-(b+E),qo.BOTTOM_LEFT):new _s(n.left+x+U,n.top+n.height-(b+E))}return e}();!function(e){e[e.TOP_LEFT=0]="TOP_LEFT",e[e.TOP_RIGHT=1]="TOP_RIGHT",e[e.BOTTOM_RIGHT=2]="BOTTOM_RIGHT",e[e.BOTTOM_LEFT=3]="BOTTOM_LEFT"}(qo||(qo={}));var Es=function(e,t,n,r,i){var A=(Math.sqrt(2)-1)/3*4,a=n*A,o=r*A,s=e+n,l=t+r;switch(i){case qo.TOP_LEFT:return new xs(new _s(e,l),new _s(e,l-o),new _s(s-a,t),new _s(s,t));case qo.TOP_RIGHT:return new xs(new _s(e,t),new _s(e+a,t),new _s(s,l-o),new _s(s,l));case qo.BOTTOM_RIGHT:return new xs(new _s(s,t),new _s(s,t+o),new _s(e+a,l),new _s(e,l));case qo.BOTTOM_LEFT:default:return new xs(new _s(s,l),new _s(s-a,l),new _s(e,t+o),new _s(e,t))}},Us=function(e){return[e.topLeftBorderBox,e.topRightBorderBox,e.bottomRightBorderBox,e.bottomLeftBorderBox]},Ms=function(e){return[e.topLeftContentBox,e.topRightContentBox,e.bottomRightContentBox,e.bottomLeftContentBox]},Fs=function(e){return[e.topLeftPaddingBox,e.topRightPaddingBox,e.bottomRightPaddingBox,e.bottomLeftPaddingBox]},Ts=function(){function e(e,t,n){this.offsetX=e,this.offsetY=t,this.matrix=n,this.type=0,this.target=6}return e}(),ks=function(){function e(e,t){this.path=e,this.target=t,this.type=1}return e}(),Qs=function(){function e(e){this.opacity=e,this.type=2,this.target=6}return e}(),Ls=function(e){return 0===e.type},Ds=function(e){return 1===e.type},Is=function(e){return 2===e.type},Rs=function(e,t){return e.length===t.length&&e.some((function(e,n){return e===t[n]}))},Hs=function(e,t,n,r,i){return e.map((function(e,A){switch(A){case 0:return e.add(t,n);case 1:return e.add(t+r,n);case 2:return e.add(t+r,n+i);case 3:return e.add(t,n+i)}return e}))},Ps=function(){function e(e){this.element=e,this.inlineLevel=[],this.nonInlineLevel=[],this.negativeZIndex=[],this.zeroOrAutoZIndexOrTransformedOrOpacity=[],this.positiveZIndex=[],this.nonPositionedFloats=[],this.nonPositionedInlineLevel=[]}return e}(),Ns=function(){function e(e,t){if(this.container=e,this.parent=t,this.effects=[],this.curves=new Ss(this.container),this.container.styles.opacity<1&&this.effects.push(new Qs(this.container.styles.opacity)),null!==this.container.styles.transform){var n=this.container.bounds.left+this.container.styles.transformOrigin[0].number,r=this.container.bounds.top+this.container.styles.transformOrigin[1].number,i=this.container.styles.transform;this.effects.push(new Ts(n,r,i))}if(0!==this.container.styles.overflowX){var A=Us(this.curves),a=Fs(this.curves);Rs(A,a)?this.effects.push(new ks(A,6)):(this.effects.push(new ks(A,2)),this.effects.push(new ks(a,4)))}}return e.prototype.getEffects=function(e){for(var t=-1===[2,3].indexOf(this.container.styles.position),n=this.parent,r=this.effects.slice(0);n;){var i=n.effects.filter((function(e){return!Ds(e)}));if(t||0!==n.container.styles.position||!n.parent){if(r.unshift.apply(r,i),t=-1===[2,3].indexOf(n.container.styles.position),0!==n.container.styles.overflowX){var A=Us(n.curves),a=Fs(n.curves);Rs(A,a)||r.unshift(new ks(a,6))}}else r.unshift.apply(r,i);n=n.parent}return r.filter((function(t){return iA(t.target,e)}))},e}(),Os=function e(t,n,r,i){t.container.elements.forEach((function(A){var a=iA(A.flags,4),o=iA(A.flags,2),s=new Ns(A,t);iA(A.styles.display,2048)&&i.push(s);var l=iA(A.flags,8)?[]:i;if(a||o){var u=a||A.styles.isPositioned()?r:n,c=new Ps(s);if(A.styles.isPositioned()||A.styles.opacity<1||A.styles.isTransformed()){var d=A.styles.zIndex.order;if(d<0){var h=0;u.negativeZIndex.some((function(e,t){return d>e.element.container.styles.zIndex.order?(h=t,!1):h>0})),u.negativeZIndex.splice(h,0,c)}else if(d>0){var f=0;u.positiveZIndex.some((function(e,t){return d>=e.element.container.styles.zIndex.order?(f=t+1,!1):f>0})),u.positiveZIndex.splice(f,0,c)}else u.zeroOrAutoZIndexOrTransformedOrOpacity.push(c)}else A.styles.isFloating()?u.nonPositionedFloats.push(c):u.nonPositionedInlineLevel.push(c);e(s,c,a?c:r,l)}else A.styles.isInlineLevel()?n.inlineLevel.push(s):n.nonInlineLevel.push(s),e(s,n,r,l);iA(A.flags,8)&&Vs(A,l)}))},Vs=function(e,t){for(var n=e instanceof Va?e.start:1,r=e instanceof Va&&e.reversed,i=0;i0&&e.intrinsicHeight>0){var r=Js(e),i=Fs(t);this.path(i),this.ctx.save(),this.ctx.clip(),this.ctx.drawImage(n,0,0,e.intrinsicWidth,e.intrinsicHeight,r.left,r.top,r.width,r.height),this.ctx.restore()}},n.prototype.renderNodeContent=function(e){return r(this,void 0,void 0,(function(){var t,r,A,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B;return i(this,(function(i){switch(i.label){case 0:this.applyEffects(e.getEffects(4)),t=e.container,r=e.curves,A=t.styles,o=0,s=t.textNodes,i.label=1;case 1:return o0&&x>0&&(v=r.ctx.createPattern(p,"repeat"),r.renderRepeat(w,v,S,E))):Qr(n)&&(y=el(e,t,[null,null,null]),w=y[0],B=y[1],_=y[2],b=y[3],x=y[4],C=0===n.position.length?[Gn]:n.position,S=jn(C[0],b),E=jn(C[C.length-1],x),U=Br(n,S,E,b,x),M=U[0],F=U[1],M>0&&F>0&&(T=r.ctx.createRadialGradient(B+S,_+E,0,B+S,_+E,M),gr(n.stops,2*M).forEach((function(e){return T.addColorStop(e.stop,ir(e.color))})),r.path(w),r.ctx.fillStyle=T,M!==F?(k=e.bounds.left+.5*e.bounds.width,Q=e.bounds.top+.5*e.bounds.height,D=1/(L=F/M),r.ctx.save(),r.ctx.translate(k,Q),r.ctx.transform(1,0,0,L,0,0),r.ctx.translate(-k,-Q),r.ctx.fillRect(B,D*(_-Q)+Q,b,x*D),r.ctx.restore()):r.ctx.fill())),i.label=6;case 6:return t--,[2]}}))},r=this,A=0,a=e.styles.backgroundImage.slice(0).reverse(),s.label=1;case 1:return A0?2!==l.style?[3,5]:[4,this.renderDashedDottedBorder(l.color,l.width,a,e.curves,2)]:[3,11]:[3,13];case 4:return i.sent(),[3,11];case 5:return 3!==l.style?[3,7]:[4,this.renderDashedDottedBorder(l.color,l.width,a,e.curves,3)];case 6:return i.sent(),[3,11];case 7:return 4!==l.style?[3,9]:[4,this.renderDoubleBorder(l.color,l.width,a,e.curves)];case 8:return i.sent(),[3,11];case 9:return[4,this.renderSolidBorder(l.color,a,e.curves)];case 10:i.sent(),i.label=11;case 11:a++,i.label=12;case 12:return o++,[3,3];case 13:return[2]}}))}))},n.prototype.renderDashedDottedBorder=function(e,t,n,A,a){return r(this,void 0,void 0,(function(){var r,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B;return i(this,(function(i){return this.ctx.save(),r=js(A,n),o=Gs(A,n),2===a&&(this.path(o),this.ctx.clip()),Cs(o[0])?(s=o[0].start.x,l=o[0].start.y):(s=o[0].x,l=o[0].y),Cs(o[1])?(u=o[1].end.x,c=o[1].end.y):(u=o[1].x,c=o[1].y),d=0===n||2===n?Math.abs(s-u):Math.abs(l-c),this.ctx.beginPath(),3===a?this.formatPath(r):this.formatPath(o.slice(0,2)),h=t<3?3*t:2*t,f=t<3?2*t:t,3===a&&(h=t,f=t),p=!0,d<=2*h?p=!1:d<=2*h+f?(h*=g=d/(2*h+f),f*=g):(m=Math.floor((d+f)/(h+f)),v=(d-m*h)/(m-1),f=(y=(d-(m+1)*h)/m)<=0||Math.abs(f-v)