Use alternative software. If you find Enscape too expensive or not suitable for your needs, you can also use alternative software that offers similar or better features and functions than Enscape. Some of the best alternative software ddb901b051
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-How to Use H Force Keygen Autocad 2010
-Autocad is one of the most popular and powerful software for designing and drafting in various fields such as architecture, engineering, construction, and more. However, it is also quite expensive and requires online activation to use it fully. If you want to use Autocad 2010 without paying or activating it online, you might be interested in using h force keygen autocad 2010.
-H force keygen autocad 2010 is a tool that can generate activation codes for any Autodesk product, including Autocad 2010. It can also bypass the online activation process and let you use Autocad 2010 offline. In this article, we will show you what h force keygen autocad 2010 is, why you might want to use it, how to download it, how to install and activate Autocad 2010 with it, and some tips and warnings for using it.
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- What is H Force Keygen Autocad 2010?
-A tool to generate activation codes for Autodesk products
-H force keygen autocad 2010 is a software that can create serial numbers and activation codes for any Autodesk product, such as Autocad, Revit, Maya, Inventor, etc. It works by using an algorithm that can crack the encryption of Autodesk's licensing system. By using h force keygen autocad 2010, you can get a valid activation code for your Autodesk product without having to pay or register online.
-A way to bypass the online activation process
-Another feature of h force keygen autocad 2010 is that it can bypass the online activation process of Autodesk products. Normally, when you install an Autodesk product, you have to enter a serial number and a product key, and then activate it online by connecting to Autodesk's servers. However, with h force keygen autocad 2010, you can skip this step and activate your Autodesk product offline. This way, you can use your Autodesk product without having to connect to the internet or worry about being detected by Autodesk.
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- Why Use H Force Keygen Autocad 2010?
-To save money and time
-One of the main reasons why you might want to use h force keygen autocad 2010 is to save money and time. As you may know, Autocad 2010 is not a cheap software. It costs hundreds or even thousands of dollars depending on the version and the license type. Moreover, it requires online activation every time you install it on a new computer or change your hardware configuration. This can be inconvenient and time-consuming if you have multiple computers or devices that you want to use Autocad on. By using h force keygen autocad 2010, you can avoid these costs and hassles and use Autocad 2010 on as many computers as you want.
-To access all the features and updates of Autocad 2010
-Another reason why you might want to use h force keygen autocad 2010 is to access all the features and updates of Autocad 2010. Autocad 2010 is a powerful software that has many features and functions that can help you create amazing designs and drawings. However, some of these features are only available for registered users or subscribers who have paid for a license. For example, some of the advanced tools, commands, options, settings, libraries, templates, etc. are only accessible for licensed users. Moreover, licensed users can also get regular updates and patches from Autodesk that can improve the performance and security of Autocad. By using h force keygen autocad 2010, you can unlock all these features and updates without having to pay or subscribe.
- How to Download H Force Keygen Autocad 2010?
-Find a reliable source online
-The first step to use h force keygen autocad 2010 is to find a reliable source online where you can download it safely. Since h force keygen autocad 2010 is an illegal software that violates Autodesk's terms of service and intellectual property rights, it is not available on official websites or platforms. Instead, you have to look for it on unofficial websites or platforms that host pirated software or cracks. However, not all of these websites or platforms are trustworthy or secure. Some of them may contain malware or viruses that can harm your computer or steal your personal information. Therefore, you have to be careful when choosing where to download h force keygen autocad 2010 from.
-One way to find a reliable source online is to use a search engine such as Google or Bing . You can type keywords such as "h force keygen autocad 2010", "h force keygen autocad", "xforce keygen autocad", etc. You will get many results that link to different websites or platforms that offer h force keygen autocad 2010 for download. However, before clicking on any of these links, you should check their credibility and reputation by looking at their domain name, reviews, ratings, comments, etc. You should avoid websites or platforms that have suspicious domain names (such as .ru .cn .tk .biz .info etc.), negative reviews or ratings (such as low stars or thumbs down), no comments or feedback (or only positive ones), etc.
-Another way to find a reliable source online is to use a peer-to-peer network such as BitTorrent or uTorrent . These are applications that allow users to share files with each other directly without using a central server. You can use these applications to download h force keygen autocad 2010 from other users who have already downloaded it before. However, before downloading any file from these applications, you should check its size (it should be around few MBs), name (it should match with h force keygen autocad), extension (it should be .exe .rar .zip etc.), seeders (the more the better), leechers (the less the better), etc.
-Extract the zip file and run the keygen
-The second step to use h force keygen autocad The response was too long so I had to truncate it.
The second step to use h force keygen autocad 2010 is to extract the zip file and run the keygen. After you have downloaded h force keygen autocad 2010 from a reliable source online, you will get a zip file that contains the keygen and some instructions. You need to extract this zip file using a software such as WinRAR or 7-Zip . You will get a folder that contains the keygen and some files. You need to run the keygen by double-clicking on it or right-clicking on it and choosing Run as administrator. You will see a window that looks like this:
-
-+---------------------------------------------------+ | X-FORCE KEYGEN | | Autodesk 2010 Products | | | | Serial: [Enter any serial number] | | Product Key: [Enter the product key for Autocad] | | Request Code: [Enter the request code from Autocad]| | | | [Patch] [Generate] [Copy] | +---------------------------------------------------+
- You need to enter some information in the keygen to generate an activation code for Autocad 2010. Here is what you need to do:
-
-- Enter any serial number in the Serial field. You can use any combination of numbers and letters, such as 123-4567890 or ABCD-EFGH-IJKL.
-- Enter the product key for Autocad 2010 in the Product Key field. You can find the product key for Autocad 2010 on the official website of Autodesk or on the installation CD or DVD. The product key for Autocad 2010 is 001B1.
-- Enter the request code from Autocad 2010 in the Request Code field. You can get the request code from Autocad 2010 by installing it and launching it for the first time. You will see an activation screen that asks you to enter a serial number and a product key. After you enter them, you will see another screen that shows you a request code. You need to copy this request code and paste it into the keygen.
-- Click on Patch in the keygen. You will see a message that says "Successfully patched". This means that the keygen has modified some files in your Autodesk product to bypass the online activation process.
-- Click on Generate in the keygen. You will see an activation code in the keygen. You need to copy this activation code and paste it into the activation screen of Autocad 2010.
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- How to Install and Activate Autocad 2010 with H Force Keygen?
-Install Autocad 2010 and restart your computer
-The third step to use h force keygen autocad 2010 is to install Autocad 2010 and restart your computer. Before you can use h force keygen autocad 2010, you need to have Autocad 2010 installed on your computer. You can install Autocad 2010 from a CD or DVD that you have bought or downloaded from an unofficial website or platform. To install Autocad 2010, you need to follow these steps:
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-- Insert the CD or DVD of Autocad 2010 into your computer's drive or mount the ISO file if you have downloaded it.
-- Run the setup.exe file or autorun.exe file from the CD or DVD or ISO file.
-- Follow the instructions on the screen to choose your language, accept the license agreement, enter your name and organization, etc.
-- Choose the type of installation you want: Typical (recommended), Custom (advanced), or Network (for multiple users).
-- Choose the components you want to install: Autocad 2010, Autodesk Design Review, Autodesk DWG TrueView, etc.
-- Choose the location where you want to install Autocad 2010 on your computer's hard drive.
-- Click on Install and wait for the installation process to complete.
-- Click on Finish and restart your computer.
-
- Disable your internet connection and antivirus
-The fourth step to use h force keygen autocad 2010 is to disable your internet connection and antivirus. Before you can activate Autocad 2010 with h force keygen autocad 2010, you need to disconnect your computer from the internet and disable your antivirus software. This is because h force keygen autocad 2010 is an illegal software that can be detected and blocked by Autodesk's servers or your antivirus software. To disable your internet connection and antivirus, you need to follow these steps:
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-- To disable your internet connection, you can unplug your ethernet cable, turn off your Wi-Fi adapter, or disable your network adapter from your computer's settings.
-- To disable your antivirus software, you can right-click on its icon in your system tray and choose Disable, Exit, Quit, or Turn off. Alternatively, you can open its main window and look for an option to disable it temporarily or permanently.
-
- Run the keygen and click on Patch
-The fifth step to use h force keygen autocad 2010 is to run the keygen and click on Patch. After you have disabled your internet connection and antivirus, you need to run h force keygen autocad 2010 and patch your Autodesk product. To do this, you need to follow these steps:
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-- Navigate to the folder where you have extracted h force keygen autocad The response was too long so I had to truncate it.
- Navigate to the folder where you have extracted h force keygen autocad 2010 and double-click on the keygen.exe file or right-click on it and choose Run as administrator.
-- Enter any serial number in the Serial field. You can use any combination of numbers and letters, such as 123-4567890 or ABCD-EFGH-IJKL.
-- Enter the product key for Autocad 2010 in the Product Key field. You can find the product key for Autocad 2010 on the official website of Autodesk or on the installation CD or DVD. The product key for Autocad 2010 is 001B1.
-- Click on Patch in the keygen. You will see a message that says "Successfully patched". This means that the keygen has modified some files in your Autodesk product to bypass the online activation process.
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- Copy the request code and paste it into the keygen
-The sixth step to use h force keygen autocad 2010 is to copy the request code and paste it into the keygen. After you have patched your Autodesk product with h force keygen autocad 2010, you need to copy the request code from Autocad 2010 and paste it into the keygen. To do this, you need to follow these steps:
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-- Launch Autocad 2010 by clicking on its icon on your desktop or start menu.
-- You will see an activation screen that asks you to enter a serial number and a product key. Enter the same serial number and product key that you have entered in the keygen.
-- You will see another screen that shows you a request code. This is a code that is generated by Autocad 2010 based on your computer's hardware configuration and software installation.
-- Copy this request code by selecting it with your mouse and pressing Ctrl+C or by clicking on Copy.
-- Go back to the keygen and paste the request code into the Request Code field by pressing Ctrl+V or by clicking on Paste.
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- Copy the activation code and paste it into the activation screen
-The seventh and final step to use h force keygen autocad 2010 is to copy the activation code and paste it into the activation screen. After you have entered the request code into the keygen, you need to copy the activation code from the keygen and paste it into the activation screen of Autocad 2010. To do this, you need to follow these steps:
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-- Click on Generate in the keygen. You will see an activation code in the keygen. This is a code that is generated by the keygen based on your request code.
-- Copy this activation code by selecting it with your mouse and pressing Ctrl+C or by clicking on Copy.
-- Go back to the activation screen of Autocad 2010 and paste the activation code into the Activation Code field by pressing Ctrl+V or by clicking on Paste.
-- Click on Next in the activation screen. You will see a message that says "Thank you for activating your Autodesk product". This means that you have successfully activated Autocad 2010 with h force keygen autocad 2010.
-- Click on Finish and enjoy using Autocad 2010 with all its features and updates.
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- Tips and Warnings for Using H Force Keygen Autocad 2010
-Use it at your own risk
-While using h force keygen autocad 2010 can save you money and time and give you access to all the features and updates of Autocad 2010, it also comes with some risks and drawbacks. Using h force keygen autocad 2010 is illegal and unethical, as it violates Autodesk's terms of service and intellectual property rights. By using h force keygen autocad 2010, you are not supporting Autodesk's development and innovation, and you are depriving them of their rightful revenue. Moreover, using h force keygen autocad 2010 can expose you to legal consequences, such as fines, lawsuits, or even criminal charges, if Autodesk finds out that you are using their software without a valid license. Therefore, use h force keygen autocad 2010 at your own risk and discretion.
-Be careful of malware and viruses
-Another risk of using h force keygen autocad 2010 is that it can contain malware or viruses that can harm your computer or steal your personal information. Since h force keygen autocad 2010 is an unofficial software that is not verified or approved by Autodesk or any other authority, it can be infected with malicious code that can damage your system files, corrupt your data, slow down your performance, spy on your activities, or even take control of your computer. Therefore, be careful of where you download h force keygen autocad 2010 from and what files you run on your computer. Always scan h force keygen autocad 2010 with a reputable antivirus software before using it, and never open any suspicious files or links that come with it.
-Do not update Autocad 2010 online
-A final tip for using h force keygen autocad 2010 is to avoid updating Autocad 2010 online. While h force keygen autocad 2010 can give you access to all the features and updates of Autocad 2010, it can also make your software vulnerable to detection and deactivation by Autodesk's servers. If you update Autocad 2010 online, you may lose your activation status and get a message that says "Your license is invalid" or "Your license has expired". Moreover, updating Autocad 2010 online may also overwrite some files that were patched by h force keygen autocad 2010, making it unusable or unstable. Therefore, do not update Autocad 2010 online if you have activated it with h force keygen autocad The response was too long so I had to truncate it. 2010. Instead, you can use offline updates or patches that are compatible with h force keygen autocad 2010.
Conclusion
-In conclusion, h force keygen autocad 2010 is a tool that can generate activation codes for Autocad 2010 and bypass the online activation process. It can help you save money and time and access all the features and updates of Autocad 2010. However, it also comes with some risks and drawbacks, such as being illegal and unethical, containing malware or viruses, and exposing you to legal consequences. Therefore, you should use h force keygen autocad 2010 at your own risk and discretion, and be careful of where you download it from and what files you run on your computer. Moreover, you should avoid updating Autocad 2010 online if you have activated it with h force keygen autocad 2010.
- FAQs
-What is Autocad 2010?
-Autocad 2010 is a software for designing and drafting in various fields such as architecture, engineering, construction, and more. It is developed by Autodesk, a leading company in software and technology. Autocad 2010 has many features and functions that can help you create amazing designs and drawings.
-What is h force keygen autocad 2010?
-H force keygen autocad 2010 is a tool that can generate activation codes for Autocad 2010 and bypass the online activation process. It can help you use Autocad 2010 without paying or activating it online.
-How to use h force keygen autocad 2010?
-To use h force keygen autocad 2010, you need to follow these steps:
-
-- Find a reliable source online where you can download h force keygen autocad 2010 safely.
-- Extract the zip file and run the keygen.
-- Install Autocad 2010 and restart your computer.
-- Disable your internet connection and antivirus.
-- Run the keygen and click on Patch.
-- Copy the request code from Autocad 2010 and paste it into the keygen.
-- Copy the activation code from the keygen and paste it into the activation screen of Autocad 2010.
-
- Is h force keygen autocad 2010 safe?
-H force keygen autocad 2010 is not safe, as it is an illegal software that violates Autodesk's terms of service and intellectual property rights. It can also contain malware or viruses that can harm your computer or steal your personal information. Moreover, it can expose you to legal consequences if Autodesk finds out that you are using their software without a valid license.
-Is h force keygen autocad 2010 legal?
-H force keygen autocad 2010 is not legal, as it violates Autodesk's terms of service and intellectual property rights. By using h force keygen autocad 2010, you are not supporting Autodesk's development and innovation, and you are depriving them of their rightful revenue. Moreover, you are breaking the law and risking fines, lawsuits, or even criminal charges if Autodesk finds out that you are using their software without a valid license.
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Artcut 2009 !!EXCLUSIVE!! Full Crack.md b/spaces/1gistliPinn/ChatGPT4/Examples/Artcut 2009 !!EXCLUSIVE!! Full Crack.md
deleted file mode 100644
index dcf4a45c0ac45b089beb9152c1b8670addbbb449..0000000000000000000000000000000000000000
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-- It is free to use for computer clients.
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-- It only supports EPS files for cutting out.
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-- Go to this link: https://isoriver.com/download-artcut-2009/
-- Click on the download button and wait for the file to be downloaded.
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-- Enjoy using Artcut 2009 for your cutting projects.
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-How to use Artcut 2009?
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-- Launch the software and select the cutting plotter you want to use.
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-- Preview the cutting path and make sure it is correct.
-- Send the cutting command to the plotter and wait for the cutting process to finish.
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-Artcut 2009 also has some advanced features that you can use to enhance your cutting projects. For example, you can use the color confirmation feature to correct the colors of your graphics. You can also use the phase compensation feature to adjust the cutting speed and pressure. You can also use the corner adjustment feature to smooth the corners of your graphics.
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-Artcut 2009 is not the only software that you can use for cutting vinyl and plastic. There are some alternatives that you can try if you want to explore other options. Some of the alternatives are:
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-- CorelDRAW: This is a popular vector graphics software that can also be used for cutting plotters. It has more features and tools than Artcut 2009 and supports more file formats. However, it is also more expensive and complex to use.
-- Inkscape: This is a free and open-source vector graphics software that can also be used for cutting plotters. It has a similar interface and functionality as CorelDRAW but with fewer features and tools. It supports SVG files for cutting.
-- SignCut Pro: This is a dedicated software for cutting plotters that can work with any plotter model. It has a simple interface and a powerful cutting engine. It supports EPS, PDF, AI, DXF, PLT, and other file formats for cutting.
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-Conclusion
-Artcut 2009 is a software that allows you to cut vinyl and plastic with ease. It is designed for cutting plotters and supports EPS files for cutting. It has many features and benefits that make it a popular software among users. However, it also has some drawbacks and limitations that you should be aware of. If you want to download and install Artcut 2009 for free on your Windows computer, you can follow the steps in this article. You can also try some alternatives to Artcut 2009 if you want to explore other options.
-Artcut 2009 is a software that allows you to cut vinyl and plastic with ease. It is designed for cutting plotters and supports EPS files for cutting. It has many features and benefits that make it a popular software among users. However, it also has some drawbacks and limitations that you should be aware of. If you want to download and install Artcut 2009 for free on your Windows computer, you can follow the steps in this article. You can also try some alternatives to Artcut 2009 if you want to explore other options.
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Cineasset Doremi Crack.md b/spaces/1gistliPinn/ChatGPT4/Examples/Cineasset Doremi Crack.md
deleted file mode 100644
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--- a/spaces/1gistliPinn/ChatGPT4/Examples/Cineasset Doremi Crack.md
+++ /dev/null
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-
\ No newline at end of file
diff --git a/spaces/1line/AutoGPT/autogpt/memory/pinecone.py b/spaces/1line/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/1line/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Black Commando Special Ops MOD APK The Best Offline FPS Shooting Game of 2023.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Black Commando Special Ops MOD APK The Best Offline FPS Shooting Game of 2023.md
deleted file mode 100644
index 973988f9c3e571c48cb843fc76c4d5e10a113ea4..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Black Commando Special Ops MOD APK The Best Offline FPS Shooting Game of 2023.md
+++ /dev/null
@@ -1,61 +0,0 @@
-
-Download Black Commando Special Ops Mod APK: A Thrilling Action Game for Android
- If you are looking for a challenging and exciting action game for your Android device, you should definitely check out Black Commando Special Ops. This is a first-person shooter game that puts you in the role of a black commando who has to complete various missions in different locations. You will face enemies, obstacles, and traps as you try to survive and accomplish your objectives.
- But what if you want to enjoy the game with more features and benefits? Well, there is a way to do that. You can download the mod apk version of Black Commando Special Ops, which gives you access to unlimited money, ammo, health, and more. With this mod apk, you can unlock all the weapons, items, and upgrades in the game and have more fun and satisfaction.
-download black commando special ops mod apk
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- In this article, we will tell you more about the features of Black Commando Special Ops mod apk, how to download and install it on your device, and some tips and tricks to improve your gameplay. So, let's get started!
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-
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- Now that you have installed Black Commando Special Ops mod apk on your device, you are ready to play the game. But before you do that, here are some tips and tricks that can help you improve your gameplay and performance:
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-- Choose your weapons wisely: There are many weapons available in the game, such as pistols, rifles, shotguns, snipers, rocket launchers, etc. Each weapon has its own advantages and disadvantages, such as range, accuracy, damage, fire rate, etc. You should choose your weapons according to your mission objectives and preferences. For example, if you need to snipe enemies from a distance, use with other players online?
-
A: Yes, you can play Black Commando Special Ops mod apk with other players online. You can join or create rooms and invite your friends or random players to join you. You can also chat with them and cooperate to complete the missions.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Bingo Crush - The Ultimate Bingo Experience on Android APK.md b/spaces/1phancelerku/anime-remove-background/Bingo Crush - The Ultimate Bingo Experience on Android APK.md
deleted file mode 100644
index 147bbe87b1985b9ddb10e9561ecd7d33dac08dcd..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Bingo Crush - The Ultimate Bingo Experience on Android APK.md
+++ /dev/null
@@ -1,95 +0,0 @@
-
-Bingo Crush APK: A Fun and Engaging Bingo Game for Android
-If you love playing bingo games, you might want to try Bingo Crush APK, a fun and engaging bingo game for Android devices. Bingo Crush APK is a game of chance where you can play bingo with players from around the world, enjoy different themes and rooms, and collect bonuses and rewards. In this article, we will tell you what Bingo Crush APK is, how to download and install it, and what are its pros and cons.
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-Enable unknown sources on your device
-Before you install Bingo Crush APK, you need to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-Install the APK file and launch the game
-After you download the APK file, locate it on your device and tap on it to install it. Follow the instructions on the screen to complete the installation. Once done, launch the game from your app drawer or home screen and enjoy playing bingo.
-Pros and cons of Bingo Crush APK
-Pros: fun, free, easy, social, diverse
-Bingo Crush APK has many advantages that make it a fun and engaging bingo game for Android users. Some of them are:
-
-- It - It is fun to play bingo with players from around the world and enjoy different themes and rooms. - It is free to play, but you can also purchase in-game items and coins to enhance your experience. - It is easy to play, as you just need to tap on the numbers that match the ones on your card. - It is social, as you can chat with other players, send them gifts, and make new friends. - It is diverse, as it has a variety of rooms, rules, prizes, and challenges.
-Cons: ads, bugs, compatibility issues, internet required
-Bingo Crush APK also has some disadvantages that might affect your enjoyment of the game. Some of them are:
-
-- It has ads that might interrupt your gameplay or consume your data. - It has bugs that might cause the game to crash or freeze. - It has compatibility issues that might prevent the game from running smoothly on some devices. - It requires an internet connection to play, which might limit your access or increase your costs.
-Conclusion
-Bingo Crush APK is a fun and engaging bingo game for Android devices that lets you play bingo with players from around the world, enjoy different themes and rooms, and collect bonuses and rewards. It is free to play, but you can also purchase in-game items and coins to enhance your experience. It is easy to play, social, and diverse. However, it also has some drawbacks, such as ads, bugs, compatibility issues, and internet requirement. If you want to try Bingo Crush APK, you can download it from a trusted source and install it on your device. You might find it a great way to pass the time and have fun.
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-You should try Bingo Crush APK if you:
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-- Love playing bingo games - Want to play with players from around the world - Enjoy different themes and rooms - Like collecting bonuses and rewards - Don't mind ads, bugs, compatibility issues, and internet requirement
-If you have any questions about Bingo Crush APK, you can check out the FAQs below or contact the developer for more information.
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-What is the difference between Bingo Crush APK and Bingo Crush from Google Play Store?
-Bingo Crush APK is an Android application package (APK) file that you can download and install from a third-party source. Bingo Crush from Google Play Store is the official version of the game that you can download and install from the Google Play Store. Both versions are similar in terms of gameplay and features, but they might have some differences in terms of updates, security, and performance.
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-Bingo Crush APK is generally safe to download and install if you get it from a trusted source. However, you should always be careful when downloading and installing APK files from unknown sources, as they might contain malware or viruses that could harm your device or steal your data. You should also enable unknown sources on your device before installing Bingo Crush APK, which might expose your device to potential risks.
-How can I get more coins and tickets in Bingo Crush APK?
-You can get more coins and tickets in Bingo Crush APK by:
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-- Logging in every day - Spinning the wheel - Completing tasks - Watching ads - Inviting friends - Purchasing in-game items and coins
-How can I join or create a club in Bingo Crush APK?
-You can join or create a club in Bingo Crush APK by:
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-- Tapping on the club icon on the main screen - Choosing to join an existing club or create your own - Following the instructions on the screen
-How can I contact the developer of Bingo Crush APK?
-You can contact the developer of Bingo Crush APK by:
-
-- Tapping on the settings icon on the main screen - Tapping on the feedback option - Filling out the form and submitting it
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Chess Universe Mod APK - The Ultimate Chess Experience for Android Users.md b/spaces/1phancelerku/anime-remove-background/Chess Universe Mod APK - The Ultimate Chess Experience for Android Users.md
deleted file mode 100644
index 7f586bb9d26c556586b531782afbbe36e1066ebb..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Chess Universe Mod APK - The Ultimate Chess Experience for Android Users.md
+++ /dev/null
@@ -1,108 +0,0 @@
-
-
Q2: Is Chess Universe Mod APK Apkpure safe?
Q3: How do I update Chess Universe Mod APK Apkpure?
Q4: Can I play online with Chess Universe Mod APK Apkpure?
Q5: How do I uninstall Chess Universe Mod APK Apkpure? | Here is the article based on the outline: Chess Universe Mod APK Apkpure: A Review
-If you are a fan of chess and looking for a fun and engaging chess app to play on your Android device, you might have heard of Chess Universe. It is a popular chess app that offers a lot of features and game modes to suit your preferences and skill level. But what if you want to enjoy all the premium features and resources of the app without spending any money? That's where Chess Universe Mod APK Apkpure comes in.
-In this article, we will review Chess Universe Mod APK Apkpure and tell you what it offers, how to download and install it on your device, some tips and tricks for playing the game, and some alternatives to try out. Let's get started!
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- Chess Universe: A Fun and Engaging Chess App
-Chess Universe is a chess app that is designed to motivate and guide chess players to improve their skills from beginner to master. It has a unique design and exciting gameplay that makes chess more fun and accessible for everyone.
-Some of the features of Chess Universe are:
-
-Chess Universe is a free app that you can download from the Google Play Store or the App Store. However, some of the features and resources of the app require in-app purchases or subscriptions. For example, you need to spend gems, coins, keys, or chests to unlock new chessboards, chess sets, emojis, special effects, academy towers, characters, or pets. You also need to watch ads to get hints or undo moves. If you want to avoid these limitations and enjoy the full potential of the app, you might want to try Chess Universe Mod APK Apkpure.
- Chess Universe Mod APK: What Does It Offer?
-A mod apk is a modified version of an original app that gives you access to all the premium features and resources of the app for free. Chess Universe Mod APK Apkpure is one such mod apk that you can download from the apkpure website, which is a reliable source for mod apks.
-Some of the benefits of Chess Universe Mod APK Apkpure are:
-
-- Unlimited gems, coins, keys, chests, hints, and undo moves: You can use these resources to unlock and use any chessboard, chess set, emoji, special effect, academy tower, character, or pet you want. You can also get hints or undo moves without watching ads.
-- All features unlocked: You can access all the features of the app without any restrictions or subscriptions. You can play online and offline chess games, choose any game mode, complete daily challenges, learn from the chess academy, and join or create chess clubs.
-- No ads: You can enjoy the app without any annoying or disruptive ads.
-
-Chess Universe Mod APK Apkpure is a great way to enhance your chess experience and have more fun with the app. However, you should be aware of some of the risks and drawbacks of using mod apks from third-party sources. For example, you might face compatibility issues with your device or the original app. You might also violate the terms and conditions of the app developer or expose your device to malware or viruses. Therefore, you should always scan the mod apk file before installing it on your device and use it at your own risk.
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-If you decide to download Chess Universe Mod APK Apkpure, you need to find a reliable source for it. Apkpure is one of the most popular websites that offer mod apks for various apps and games. It has a large collection of mod apks that are updated regularly and verified for safety and quality.
-Some of the advantages of using apkpure are:
-
-- Safe and secure downloads: Apkpure scans all the mod apks for viruses and malware before uploading them on their website. They also use SSL encryption to protect your data and privacy.
-- Fast and easy installation: Apkpure allows you to download mod apks directly from their website without any registration or verification. You just need to enable unknown sources in your device settings and install the apk file on your device.
-- Updated versions: Apkpure keeps track of the latest versions of the original apps and games and updates their mod apks accordingly. You can also check the version history and changelog of each mod apk on their website.
-- No registration required: Apkpure does not require you to create an account or sign in to use their services. You can download as many mod apks as you want without any hassle.
-
-Apkpure is a trusted and reputable website that has millions of users worldwide. However, you should still be careful when downloading mod apks from any third-party source and always backup your data before installing them on your device.
- How to Download and Install Chess Universe Mod APK from Apkpure
-If you want to download and install Chess Universe Mod APK from Apkpure, you need to follow these steps:
-
-- Visit the apkpure website: Go to https://apkpure.com on your browser and search for Chess Universe Mod APK. You can also use this direct link: https://apkpure.com/chess-universe-play-learn-online-with-friends/com.kingofchess.chessuniverse.
-- Click on download: On the mod apk page, you will see a green download button. Click on it and wait for the download to start. The file size is about 130 MB, so make sure you have enough space and a stable internet connection.
-- Enable unknown sources: Before you can install the mod apk on your device, you need to enable unknown sources in your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-- Install the apk file: Once the download is complete, locate the apk file in your device storage and tap on it. You will see a prompt asking you to confirm the installation. Tap on Install and wait for the process to finish.
-- Enjoy the game: After the installation is done, you can launch the game from your app drawer or home screen. You will see that you have unlimited resources and all features unlocked. You can now play chess with friends or online with other players without any limitations.
-
-Congratulations! You have successfully downloaded and installed Chess Universe Mod APK from Apkpure. Now you can enjoy the game and improve your chess skills.
- Tips and Tricks for Playing Chess Universe
-Chess Universe is a fun and engaging chess app that can help you learn and master chess. However, if you want to get better at the game and win more matches, you need to practice and apply some tips and tricks. Here are some suggestions that can help you improve your chess performance:
-
-- Learn the basic rules and moves of chess: If you are new to chess or need a refresher, you should learn the basic rules and moves of the game. You should know how each piece moves, how to capture, check, and checkmate, how to castle, en passant, and promote pawns, and how to avoid stalemate and draw situations. You can use the Chess Academy feature of the app to learn these basics.
-- Practice with puzzles and AI opponents: One of the best ways to improve your chess skills is to practice with puzzles and AI opponents. Puzzles can help you train your tactical vision, calculation, and problem-solving skills. AI opponents can help you practice your opening, middlegame, and endgame skills. You can use the Daily Challenges feature of the app to access puzzles and quizzes. You can also use the Offline Mode feature of the app to play against AI opponents with different difficulty levels.
-- Analyze your games and learn from your mistakes: Another important way to improve your chess skills is to analyze your games and learn from your mistakes. You should review your moves and identify where you went wrong or missed opportunities. You should also study the moves of your opponents and see what they did right or wrong. You can use the Analysis feature of the app to replay your games and get feedback on your moves.
-- Try different game modes and strategies: Chess Universe offers a variety of game modes and settings that can help you test your skills and have fun. You can try different time controls, board sizes, chess pieces, special effects, and game modes such as blitz chess, bullet chess, rapid chess or easy mode. You can also try different strategies such as attacking, defending, positional, or tactical play. You can use the Online Mode feature of the app to play with other players from all over the world.
-- Challenge yourself with daily quests and tournaments: Chess Universe also offers daily quests and tournaments that can help you challenge yourself and earn rewards. You can complete daily quests such as winning a certain number of games, solving puzzles, or using emojis. You can also join or create tournaments with different formats, rules, and prizes. You can use the Quests feature of the app to access daily quests. You can also use the Clubs feature of the app to join or create chess clubs and participate in club events and tournaments.
-
-Chess Universe is a great chess app that offers a lot of fun and learning opportunities for chess players of all levels. By following these tips and tricks, you can improve your chess skills and enjoy the game more.
-
-
-These are some of the FAQs about Chess Universe Mod APK Apkpure. If you have any other questions, please feel free to ask us in the comments section below.
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/repaint/pipeline_repaint.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/repaint/pipeline_repaint.py
deleted file mode 100644
index d73c1c7b5623fdb115260d0d0f12d03064e80ddf..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/repaint/pipeline_repaint.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import paddle
-import PIL
-
-from ...models import UNet2DModel
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import RePaintScheduler
-from ...utils import PIL_INTERPOLATION, logging
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
-def _preprocess_image(image: Union[List, PIL.Image.Image, paddle.Tensor]):
- if isinstance(image, paddle.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
-
- image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = paddle.to_tensor(image)
- elif isinstance(image[0], paddle.Tensor):
- image = paddle.concat(image, axis=0)
- return image
-
-
-def _preprocess_mask(mask: Union[List, PIL.Image.Image, paddle.Tensor]):
- if isinstance(mask, paddle.Tensor):
- return mask
- elif isinstance(mask, PIL.Image.Image):
- mask = [mask]
-
- if isinstance(mask[0], PIL.Image.Image):
- w, h = mask[0].size
- w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
- mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
- mask = np.concatenate(mask, axis=0)
- mask = mask.astype(np.float32) / 255.0
- mask[mask < 0.5] = 0
- mask[mask >= 0.5] = 1
- mask = paddle.to_tensor(mask)
- elif isinstance(mask[0], paddle.Tensor):
- mask = paddle.concat(mask, axis=0)
- return mask
-
-
-class RePaintPipeline(DiffusionPipeline):
- unet: UNet2DModel
- scheduler: RePaintScheduler
-
- def __init__(self, unet, scheduler):
- super().__init__()
- self.register_modules(unet=unet, scheduler=scheduler)
-
- @paddle.no_grad()
- def __call__(
- self,
- image: Union[paddle.Tensor, PIL.Image.Image],
- mask_image: Union[paddle.Tensor, PIL.Image.Image],
- num_inference_steps: int = 250,
- eta: float = 0.0,
- jump_length: int = 10,
- jump_n_sample: int = 10,
- generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- ) -> Union[ImagePipelineOutput, Tuple]:
- r"""
- Args:
- image (`paddle.Tensor` or `PIL.Image.Image`):
- The original image to inpaint on.
- mask_image (`paddle.Tensor` or `PIL.Image.Image`):
- The mask_image where 0.0 values define which part of the original image to inpaint (change).
- num_inference_steps (`int`, *optional*, defaults to 1000):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- eta (`float`):
- The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM
- and 1.0 is DDPM scheduler respectively.
- jump_length (`int`, *optional*, defaults to 10):
- The number of steps taken forward in time before going backward in time for a single jump ("j" in
- RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
- jump_n_sample (`int`, *optional*, defaults to 10):
- The number of times we will make forward time jump for a given chosen time sample. Take a look at
- Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
- generator (`paddle.Generator`, *optional*):
- One or a list of paddle generator(s) to make generation deterministic.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-
- Returns:
- [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
- `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
- generated images.
- """
- original_image = _preprocess_image(image)
- original_image = original_image.cast(self.unet.dtype)
- mask_image = _preprocess_mask(mask_image)
- mask_image = mask_image.cast(self.unet.dtype)
-
- batch_size = original_image.shape[0]
-
- # sample gaussian noise to begin the loop
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- image_shape = original_image.shape
- if isinstance(generator, list):
- shape = (1,) + image_shape[1:]
- image = [paddle.randn(shape, generator=generator[i], dtype=self.unet.dtype) for i in range(batch_size)]
- image = paddle.concat(image, axis=0)
- else:
- image = paddle.randn(image_shape, generator=generator, dtype=self.unet.dtype)
-
- # set step values
- self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample)
- self.scheduler.eta = eta
-
- t_last = self.scheduler.timesteps[0] + 1
- generator = generator[0] if isinstance(generator, list) else generator
- for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
- if t < t_last:
- # predict the noise residual
- model_output = self.unet(image, t).sample
- # compute previous image: x_t -> x_t-1
- image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample
-
- else:
- # compute the reverse: x_t-1 -> x_t
- image = self.scheduler.undo_step(image, t_last, generator)
- t_last = t
-
- image = (image / 2 + 0.5).clip(0, 1)
- image = image.transpose([0, 2, 3, 1]).numpy()
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
deleted file mode 100644
index 1a4ceea3c8d72ab29bcea1dd64af58e17cdc9dcf..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
+++ /dev/null
@@ -1,469 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import paddle
-import PIL
-
-from paddlenlp.transformers import CLIPTextModel, CLIPTokenizer
-
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import (
- DDIMScheduler,
- DDPMScheduler,
- LMSDiscreteScheduler,
- PNDMScheduler,
-)
-from ...utils import logging
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-def preprocess(image):
- if isinstance(image, paddle.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32
-
- image = [np.array(i.resize((w, h)))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = paddle.to_tensor(image)
- elif isinstance(image[0], paddle.Tensor):
- image = paddle.concat(image, axis=0)
- return image
-
-
-class StableDiffusionUpscalePipeline(DiffusionPipeline):
- r"""
- Pipeline for text-guided image super-resolution using Stable Diffusion 2.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving etc.)
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder ([`CLIPTextModel`]):
- Frozen text-encoder. Stable Diffusion uses the text portion of
- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
- the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- low_res_scheduler ([`SchedulerMixin`]):
- A scheduler used to add initial noise to the low res conditioning image. It must be an instance of
- [`DDPMScheduler`].
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- """
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- low_res_scheduler: DDPMScheduler,
- scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
- max_noise_level: int = 350,
- ):
- super().__init__()
-
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- low_res_scheduler=low_res_scheduler,
- scheduler=scheduler,
- )
- self.register_to_config(max_noise_level=max_noise_level)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
- def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `list(int)`):
- prompt to be encoded
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- """
- batch_size = len(prompt) if isinstance(prompt, list) else 1
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pd",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pd").input_ids
-
- if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not paddle.equal_all(
- text_input_ids, untruncated_ids
- ):
- removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- config = (
- self.text_encoder.config
- if isinstance(self.text_encoder.config, dict)
- else self.text_encoder.config.to_dict()
- )
- if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
- attention_mask = text_inputs.attention_mask
- else:
- attention_mask = None
-
- text_embeddings = self.text_encoder(
- text_input_ids,
- attention_mask=attention_mask,
- )
- text_embeddings = text_embeddings[0]
-
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- bs_embed, seq_len, _ = text_embeddings.shape
- text_embeddings = text_embeddings.tile([1, num_images_per_prompt, 1])
- text_embeddings = text_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1])
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
- elif isinstance(negative_prompt, str):
- uncond_tokens = [negative_prompt]
- elif batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
- else:
- uncond_tokens = negative_prompt
-
- max_length = text_input_ids.shape[-1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pd",
- )
-
- if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
- attention_mask = uncond_input.attention_mask
- else:
- attention_mask = None
-
- uncond_embeddings = self.text_encoder(
- uncond_input.input_ids,
- attention_mask=attention_mask,
- )
- uncond_embeddings = uncond_embeddings[0]
-
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = uncond_embeddings.shape[1]
- uncond_embeddings = uncond_embeddings.tile([1, num_images_per_prompt, 1])
- uncond_embeddings = uncond_embeddings.reshape([batch_size * num_images_per_prompt, seq_len, -1])
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- text_embeddings = paddle.concat([uncond_embeddings, text_embeddings])
-
- return text_embeddings
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents with 0.18215->0.08333
- def decode_latents(self, latents):
- latents = 1 / 0.08333 * latents
- image = self.vae.decode(latents).sample
- image = (image / 2 + 0.5).clip(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
- image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
- return image
-
- def check_inputs(self, prompt, image, noise_level, callback_steps):
- if not isinstance(prompt, str) and not isinstance(prompt, list):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if (
- not isinstance(image, paddle.Tensor)
- and not isinstance(image, PIL.Image.Image)
- and not isinstance(image, list)
- ):
- raise ValueError(
- f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or `list` but is {type(image)}"
- )
-
- # verify batch size of prompt and image are same if image is a list or tensor
- if isinstance(image, list) or isinstance(image, paddle.Tensor):
- if isinstance(prompt, str):
- batch_size = 1
- else:
- batch_size = len(prompt)
- if isinstance(image, list):
- image_batch_size = len(image)
- else:
- image_batch_size = image.shape[0]
- if batch_size != image_batch_size:
- raise ValueError(
- f"`prompt` has batch size {batch_size} and `image` has batch size {image_batch_size}."
- " Please make sure that passed `prompt` matches the batch size of `image`."
- )
-
- # check noise level
- if noise_level > self.config.max_noise_level:
- raise ValueError(f"`noise_level` has to be <= {self.config.max_noise_level} but is {noise_level}")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
- shape = [batch_size, num_channels_latents, height, width]
- if latents is None:
- latents = paddle.randn(shape, generator=generator, dtype=dtype)
- else:
- if latents.shape != shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
- return latents
-
- @paddle.no_grad()
- def __call__(
- self,
- prompt: Union[str, List[str]],
- image: Union[paddle.Tensor, PIL.Image.Image, List[PIL.Image.Image]],
- num_inference_steps: int = 75,
- guidance_scale: float = 9.0,
- noise_level: int = 20,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
- latents: Optional[paddle.Tensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
- callback_steps: Optional[int] = 1,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `paddle.Tensor`):
- `Image`, or tensor representing an image batch which will be upscaled. *
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`paddle.Generator`, *optional*):
- A [paddle generator] to make generation
- deterministic.
- latents (`paddle.Tensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
-
- # 1. Check inputs
- self.check_inputs(prompt, image, noise_level, callback_steps)
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 3. Encode input prompt
- text_embeddings = self._encode_prompt(
- prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
- )
-
- # 4. Preprocess image
- image = preprocess(image)
- image = image.cast(text_embeddings.dtype)
-
- # 5. set timesteps
- self.scheduler.set_timesteps(num_inference_steps)
- timesteps = self.scheduler.timesteps
-
- # 5. Add noise to image
- noise_level = paddle.to_tensor([noise_level], dtype="int64")
- noise = paddle.randn(image.shape, generator=generator, dtype=text_embeddings.dtype)
- image = self.low_res_scheduler.add_noise(image, noise, noise_level)
- batch_multiplier = 2 if do_classifier_free_guidance else 1
- image = paddle.concat([image] * batch_multiplier * num_images_per_prompt)
- noise_level = paddle.concat([noise_level] * image.shape[0])
-
- # 6. Prepare latent variables
- height, width = image.shape[2:]
- num_channels_latents = self.vae.config.latent_channels
- latents = self.prepare_latents(
- batch_size * num_images_per_prompt,
- num_channels_latents,
- height,
- width,
- text_embeddings.dtype,
- generator,
- latents,
- )
-
- # 7. Check that sizes of image and latents match
- num_channels_image = image.shape[1]
- if num_channels_latents + num_channels_image != self.unet.config.in_channels:
- raise ValueError(
- f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
- f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
- f" `num_channels_image`: {num_channels_image} "
- f" = {num_channels_latents+num_channels_image}. Please verify the config of"
- " `pipeline.unet` or your `image` input."
- )
-
- # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 9. Denoising loop
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-
- # concat latents, mask, masked_image_latents in the channel dimension
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
- latent_model_input = paddle.concat([latent_model_input, image], axis=1)
-
- # predict the noise residual
- noise_pred = self.unet(
- latent_model_input, t, encoder_hidden_states=text_embeddings, class_labels=noise_level
- ).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- # 10. Post-processing
- # make sure the VAE is in float32 mode, as it overflows in float16
- # self.vae.to(dtype=paddle.float32)
- image = self.decode_latents(latents.cast("float32"))
-
- # 11. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/AI-Naga/Roof_Element_Identification/app.py b/spaces/AI-Naga/Roof_Element_Identification/app.py
deleted file mode 100644
index 25213f3b58e25207b5e2f5e38bfe98185be8bd6e..0000000000000000000000000000000000000000
--- a/spaces/AI-Naga/Roof_Element_Identification/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import gradio as gr
-from gradio.outputs import Label
-import cv2
-import requests
-import os
-import numpy as np
-
-from ultralytics import YOLO
-import yolov5
-
-# Function for inference
-def yolov5_inference(
- image: gr.inputs.Image = None,
- model_path: gr.inputs.Dropdown = None,
- image_size: gr.inputs.Slider = 640,
- conf_threshold: gr.inputs.Slider = 0.25,
- iou_threshold: gr.inputs.Slider = 0.45 ):
-
- # Loading Yolo V5 model
- model = yolov5.load(model_path, device="cpu")
-
- # Setting model configuration
- model.conf = conf_threshold
- model.iou = iou_threshold
-
- # Inference
- results = model([image], size=image_size)
-
- # Cropping the predictions
- crops = results.crop(save=False)
- img_crops = []
- for i in range(len(crops)):
- img_crops.append(crops[i]["im"][..., ::-1])
- return results.render()[0], img_crops
-
-# gradio Input
-inputs = [
- gr.inputs.Image(type="pil", label="Input Image"),
- gr.inputs.Dropdown(["Roof_Elements_Y5.pt"], label="Model", default = 'Roof_Elements_Y5.pt'),
- gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
- gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
- gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-]
-
-# gradio Output
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-outputs_crops = gr.Gallery(label="Object crop")
-
-title = "Roof element identification"
-
-# gradio examples: "Image", "Model", "Image Size", "Confidence Threshold", "IOU Threshold"
-examples = [['image_0.jpg', 'Roof_Elements_Y5.pt', 640, 0.35, 0.45]
- ,['image_1.jpg', 'Roof_Elements_Y5.pt', 640, 0.35, 0.45]
- ,['image_2.jpg', 'Roof_Elements_Y5.pt', 640, 0.35, 0.45],
- ]
-
-# gradio app launch
-demo_app = gr.Interface(
- fn=yolov5_inference,
- inputs=inputs,
- outputs=[outputs,outputs_crops],
- title=title,
- examples=examples,
- cache_examples=True,
- live=True,
- theme='huggingface',
-)
-demo_app.launch(debug=True, enable_queue=True, width=50, height=50)
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/plot_for_paper.py b/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/plot_for_paper.py
deleted file mode 100644
index 25e799a7e7eea9ffc5bced214a8beb0a558842eb..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/utils/plot_for_paper.py
+++ /dev/null
@@ -1,565 +0,0 @@
-import os
-import sys
-import numpy as np
-import argparse
-import h5py
-import time
-import pickle
-import matplotlib.pyplot as plt
-import csv
-from sklearn import metrics
-
-from utilities import (create_folder, get_filename, d_prime)
-import config
-
-
-def load_statistics(statistics_path):
- statistics_dict = pickle.load(open(statistics_path, 'rb'))
-
- bal_map = np.array([statistics['average_precision'] for statistics in statistics_dict['bal']]) # (N, classes_num)
- bal_map = np.mean(bal_map, axis=-1)
- test_map = np.array([statistics['average_precision'] for statistics in statistics_dict['test']]) # (N, classes_num)
- test_map = np.mean(test_map, axis=-1)
-
- return bal_map, test_map
-
-
-def crop_label(label):
- max_len = 16
- if len(label) <= max_len:
- return label
- else:
- words = label.split(' ')
- cropped_label = ''
- for w in words:
- if len(cropped_label + ' ' + w) > max_len:
- break
- else:
- cropped_label += ' {}'.format(w)
- return cropped_label
-
-
-def add_comma(integer):
- """E.g., 1234567 -> 1,234,567
- """
- integer = int(integer)
- if integer >= 1000:
- return str(integer // 1000) + ',' + str(integer % 1000)
- else:
- return str(integer)
-
-
-def plot_classwise_iteration_map(args):
-
- # Paths
- save_out_path = 'results/classwise_iteration_map.pdf'
- create_folder(os.path.dirname(save_out_path))
-
- # Load statistics
- statistics_dict = pickle.load(open('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_WavegramLogmelCnn_balanced_mixup_bs32.pkl', 'rb'))
-
- mAP_mat = np.array([e['average_precision'] for e in statistics_dict['test']])
- mAP_mat = mAP_mat[0 : 300, :] # 300 * 2000 = 600k iterations
- sorted_indexes = np.argsort(config.full_samples_per_class)[::-1]
-
- fig, axs = plt.subplots(1, 3, figsize=(20, 5))
- ranges = [np.arange(0, 10), np.arange(250, 260), np.arange(517, 527)]
- axs[0].set_ylabel('AP')
-
- for col in range(0, 3):
- axs[col].set_ylim(0, 1.)
- axs[col].set_xlim(0, 301)
- axs[col].set_xlabel('Iterations')
- axs[col].set_ylabel('AP')
- axs[col].xaxis.set_ticks(np.arange(0, 301, 100))
- axs[col].xaxis.set_ticklabels(['0', '200k', '400k', '600k'])
- lines = []
- for _ix in ranges[col]:
- _label = crop_label(config.labels[sorted_indexes[_ix]]) + \
- ' ({})'.format(add_comma(config.full_samples_per_class[sorted_indexes[_ix]]))
- line, = axs[col].plot(mAP_mat[:, sorted_indexes[_ix]], label=_label)
- lines.append(line)
- box = axs[col].get_position()
- axs[col].set_position([box.x0, box.y0, box.width * 1., box.height])
- axs[col].legend(handles=lines, bbox_to_anchor=(1., 1.))
- axs[col].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3)
-
- plt.tight_layout(pad=4, w_pad=1, h_pad=1)
- plt.savefig(save_out_path)
- print(save_out_path)
-
-
-def plot_six_figures(args):
-
- # Arguments & parameters
- classes_num = config.classes_num
- labels = config.labels
- max_plot_iteration = 540000
- iterations = np.arange(0, max_plot_iteration, 2000)
-
- # Paths
- class_labels_indices_path = os.path.join('metadata', 'class_labels_indices.csv')
- save_out_path = 'results/six_figures.pdf'
- create_folder(os.path.dirname(save_out_path))
-
- # Plot
- fig, ax = plt.subplots(2, 3, figsize=(14, 7))
- bal_alpha = 0.3
- test_alpha = 1.0
- linewidth = 1.
-
- # (a) Comparison of architectures
- if True:
- lines = []
-
- # Wavegram-Logmel-CNN
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_WavegramLogmelCnn_balanced_mixup_bs32.pkl')
- line, = ax[0, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 0].plot(test_map, label='Wavegram-Logmel-CNN', color='g', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Cnn14
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[0, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 0].plot(test_map, label='CNN14', color='r', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # MobileNetV1
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_MobileNetV1_balanced_mixup_bs32.pkl')
- line, = ax[0, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 0].plot(test_map, label='MobileNetV1', color='b', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- ax[0, 0].legend(handles=lines, loc=2)
- ax[0, 0].set_title('(a) Comparison of architectures')
-
- # (b) Comparison of training data and augmentation'
- if True:
- lines = []
-
- # Full data + balanced sampler + mixup
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[0, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (1.9m)', color='r', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Full data + balanced sampler + mixup in time domain
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_timedomain_bs32.pkl')
- line, = ax[0, 1].plot(bal_map, color='y', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup-wav (1.9m)', color='y', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Full data + balanced sampler + no mixup
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_nomixup_bs32.pkl')
- line, = ax[0, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (1.9m)', color='g', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Full data + uniform sampler + no mixup
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_nobalanced_nomixup_bs32.pkl')
- line, = ax[0, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 1].plot(test_map, label='CNN14,no-bal,no-mixup (1.9m)', color='b', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Balanced data + balanced sampler + mixup
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_balanced_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[0, 1].plot(bal_map, color='m', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 1].plot(test_map, label='CNN14,bal,mixup (20k)', color='m', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Balanced data + balanced sampler + no mixup
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_balanced_train_Cnn14_balanced_nomixup_bs32.pkl')
- line, = ax[0, 1].plot(bal_map, color='k', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 1].plot(test_map, label='CNN14,bal,no-mixup (20k)', color='k', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- ax[0, 1].legend(handles=lines, loc=2, fontsize=8)
- ax[0, 1].set_title('(b) Comparison of training data and augmentation')
-
- # (c) Comparison of embedding size
- if True:
- lines = []
-
- # Embedding size 2048
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[0, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 2].plot(test_map, label='CNN14,emb=2048', color='r', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Embedding size 128
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_emb128_balanced_mixup_bs32.pkl')
- line, = ax[0, 2].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 2].plot(test_map, label='CNN14,emb=128', color='g', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Embedding size 32
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_emb32_balanced_mixup_bs32.pkl')
- line, = ax[0, 2].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[0, 2].plot(test_map, label='CNN14,emb=32', color='b', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- ax[0, 2].legend(handles=lines, loc=2)
- ax[0, 2].set_title('(c) Comparison of embedding size')
-
- # (d) Comparison of amount of training data
- if True:
- lines = []
-
- # 100% of full training data
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 0].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 0].plot(test_map, label='CNN14 (100% full)', color='r', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # 80% of full training data
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_0.8full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 0].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 0].plot(test_map, label='CNN14 (80% full)', color='b', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # 50% of full training data
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_0.5full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 0].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 0].plot(test_map, label='cnn14 (50% full)', color='g', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- ax[1, 0].legend(handles=lines, loc=2)
- ax[1, 0].set_title('(d) Comparison of amount of training data')
-
- # (e) Comparison of sampling rate
- if True:
- lines = []
-
- # Cnn14 + 32 kHz
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 1].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 1].plot(test_map, label='CNN14,32kHz', color='r', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Cnn14 + 16 kHz
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_16k_balanced_mixup_bs32.pkl')
- line, = ax[1, 1].plot(bal_map, color='b', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 1].plot(test_map, label='CNN14,16kHz', color='b', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Cnn14 + 8 kHz
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_8k_balanced_mixup_bs32.pkl')
- line, = ax[1, 1].plot(bal_map, color='g', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 1].plot(test_map, label='CNN14,8kHz', color='g', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- ax[1, 1].legend(handles=lines, loc=2)
- ax[1, 1].set_title('(e) Comparison of sampling rate')
-
- # (f) Comparison of mel bins number
- if True:
- lines = []
-
- # Cnn14 + 128 mel bins
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel128_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 2].plot(bal_map, color='g', alpha=bal_alpha)
- line, = ax[1, 2].plot(test_map, label='CNN14,128-melbins', color='g', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Cnn14 + 64 mel bins
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel64_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 2].plot(bal_map, color='r', alpha=bal_alpha, linewidth=linewidth)
- line, = ax[1, 2].plot(test_map, label='CNN14,64-melbins', color='r', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- # Cnn14 + 32 mel bins
- (bal_map, test_map) = load_statistics('paper_statistics/statistics_sr32000_window1024_hop320_mel32_fmin50_fmax14000_full_train_Cnn14_balanced_mixup_bs32.pkl')
- line, = ax[1, 2].plot(bal_map, color='b', alpha=bal_alpha)
- line, = ax[1, 2].plot(test_map, label='CNN14,32-melbins', color='b', alpha=test_alpha, linewidth=linewidth)
- lines.append(line)
-
- ax[1, 2].legend(handles=lines, loc=2)
- ax[1, 2].set_title('(f) Comparison of mel bins number')
-
- for i in range(2):
- for j in range(3):
- ax[i, j].set_ylim(0, 0.8)
- ax[i, j].set_xlim(0, len(iterations))
- ax[i, j].set_xlabel('Iterations')
- ax[i, j].set_ylabel('mAP')
- ax[i, j].xaxis.set_ticks(np.arange(0, len(iterations), 50))
- ax[i, j].xaxis.set_ticklabels(['0', '100k', '200k', '300k', '400k', '500k'])
- ax[i, j].yaxis.set_ticks(np.arange(0, 0.81, 0.05))
- ax[i, j].yaxis.set_ticklabels(['0', '', '0.1', '', '0.2', '', '0.3',
- '', '0.4', '', '0.5', '', '0.6', '', '0.7', '', '0.8'])
- ax[i, j].yaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3)
- ax[i, j].xaxis.grid(color='k', linestyle='solid', alpha=0.3, linewidth=0.3)
-
- plt.tight_layout(0, 1, 0)
- plt.savefig(save_out_path)
- print('Save figure to {}'.format(save_out_path))
-
-
-def plot_complexity_map(args):
-
- # Paths
- save_out_path = 'results/complexity_mAP.pdf'
- create_folder(os.path.dirname(save_out_path))
-
- plt.figure(figsize=(5, 5))
- fig, ax = plt.subplots(1, 1)
-
- model_types = np.array(['Cnn6', 'Cnn10', 'Cnn14', 'ResNet22', 'ResNet38', 'ResNet54',
- 'MobileNetV1', 'MobileNetV2', 'DaiNet', 'LeeNet', 'LeeNet18',
- 'Res1dNet30', 'Res1dNet44', 'Wavegram-CNN', 'Wavegram-\nLogmel-CNN'])
- flops = np.array([21.986, 28.166, 42.220, 30.081, 48.962, 54.563, 3.614, 2.810,
- 30.395, 4.741, 26.369, 32.688, 61.833, 44.234, 53.510])
- mAPs = np.array([0.343, 0.380, 0.431, 0.430, 0.434, 0.429, 0.389, 0.383, 0.295,
- 0.266, 0.336, 0.365, 0.355, 0.389, 0.439])
-
- sorted_indexes = np.sort(flops)
- ax.scatter(flops, mAPs)
-
- shift = [[-5.5, -0.004], [1, -0.004], [-1, -0.014], [-2, 0.006], [-7, 0.006],
- [1, -0.01], [0.5, 0.004], [-1, -0.014], [1, -0.007], [0.8, -0.008],
- [1, -0.007], [1, 0.002], [-6, -0.015], [1, -0.008], [0.8, 0]]
-
- for i, model_type in enumerate(model_types):
- ax.annotate(model_type, (flops[i] + shift[i][0], mAPs[i] + shift[i][1]))
-
- ax.plot(flops[[0, 1, 2]], mAPs[[0, 1, 2]])
- ax.plot(flops[[3, 4, 5]], mAPs[[3, 4, 5]])
- ax.plot(flops[[6, 7]], mAPs[[6, 7]])
- ax.plot(flops[[9, 10]], mAPs[[9, 10]])
- ax.plot(flops[[11, 12]], mAPs[[11, 12]])
- ax.plot(flops[[13, 14]], mAPs[[13, 14]])
-
- ax.set_xlim(0, 70)
- ax.set_ylim(0.2, 0.5)
- ax.set_xlabel('Multi-load_statisticss (million)', fontsize=15)
- ax.set_ylabel('mAP', fontsize=15)
- ax.tick_params(axis='x', labelsize=12)
- ax.tick_params(axis='y', labelsize=12)
-
- plt.tight_layout(0, 0, 0)
-
- plt.savefig(save_out_path)
- print('Write out figure to {}'.format(save_out_path))
-
-
-def plot_long_fig(args):
-
- # Paths
- stats = pickle.load(open('paper_statistics/stats_for_long_fig.pkl', 'rb'))
-
- save_out_path = 'results/long_fig.pdf'
- create_folder(os.path.dirname(save_out_path))
-
- # Load meta
- N = len(config.labels)
- sorted_indexes = stats['sorted_indexes_for_plot']
- sorted_labels = np.array(config.labels)[sorted_indexes]
- audio_clips_per_class = stats['official_balanced_training_samples'] + stats['official_unbalanced_training_samples']
- audio_clips_per_class = audio_clips_per_class[sorted_indexes]
-
- # Prepare axes for plot
- (ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b) = prepare_plot_long_4_rows(sorted_labels)
-
- # plot the number of training samples
- ax1a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
- ax2a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
- ax3a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
- ax4a.bar(np.arange(N), audio_clips_per_class, alpha=0.3)
-
- # Load mAP of different systems
- """Average instance system of [1] with an mAP of 0.317.
- [1] Kong, Qiuqiang, Changsong Yu, Yong Xu, Turab Iqbal, Wenwu Wang, and
- Mark D. Plumbley. "Weakly labelled audioset tagging with attention neural
- networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing
- 27, no. 11 (2019): 1791-1802."""
- maps_avg_instances = stats['averaging_instance_system_avg_9_probs_from_10000_to_50000_iterations']['eval']['average_precision']
- maps_avg_instances = maps_avg_instances[sorted_indexes]
-
- # PANNs Cnn14
- maps_panns_cnn14 = stats['panns_cnn14']['eval']['average_precision']
- maps_panns_cnn14 = maps_panns_cnn14[sorted_indexes]
-
- # PANNs MobileNetV1
- maps_panns_mobilenetv1 = stats['panns_mobilenetv1']['eval']['average_precision']
- maps_panns_mobilenetv1 = maps_panns_mobilenetv1[sorted_indexes]
-
- # PANNs Wavegram-Logmel-Cnn14
- maps_panns_wavegram_logmel_cnn14 = stats['panns_wavegram_logmel_cnn14']['eval']['average_precision']
- maps_panns_wavegram_logmel_cnn14 = maps_panns_wavegram_logmel_cnn14[sorted_indexes]
-
- # Plot mAPs
- _scatter_4_rows(maps_panns_wavegram_logmel_cnn14, ax1b, ax2b, ax3b, ax4b, s=5, c='g')
- _scatter_4_rows(maps_panns_cnn14, ax1b, ax2b, ax3b, ax4b, s=5, c='r')
- _scatter_4_rows(maps_panns_mobilenetv1, ax1b, ax2b, ax3b, ax4b, s=5, c='b')
- _scatter_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, s=5, c='k')
-
- linewidth = 0.7
- line0te = _plot_4_rows(maps_panns_wavegram_logmel_cnn14, ax1b, ax2b, ax3b, ax4b,
- c='g', linewidth=linewidth, label='AP with Wavegram-Logmel-CNN')
- line1te = _plot_4_rows(maps_panns_cnn14, ax1b, ax2b, ax3b, ax4b, c='r',
- linewidth=linewidth, label='AP with CNN14')
- line2te = _plot_4_rows(maps_panns_mobilenetv1, ax1b, ax2b, ax3b, ax4b, c='b',
- linewidth=linewidth, label='AP with MobileNetV1')
- line3te = _plot_4_rows(maps_avg_instances, ax1b, ax2b, ax3b, ax4b, c='k',
- linewidth=linewidth, label='AP with averaging instances (baseline)')
-
- # Plot label quality
- label_quality = stats['label_quality']
- sorted_label_quality = np.array(label_quality)[sorted_indexes]
- for k in range(len(sorted_label_quality)):
- if sorted_label_quality[k] and sorted_label_quality[k] == 1:
- sorted_label_quality[k] = 0.99
-
- ax1b.scatter(np.arange(N)[sorted_label_quality != None],
- sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+')
- ax2b.scatter(np.arange(N)[sorted_label_quality != None],
- sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+')
- ax3b.scatter(np.arange(N)[sorted_label_quality != None],
- sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+')
- line_label_quality = ax4b.scatter(np.arange(N)[sorted_label_quality != None],
- sorted_label_quality[sorted_label_quality != None], s=12, c='r', linewidth=0.8, marker='+', label='Label quality')
- ax1b.scatter(np.arange(N)[sorted_label_quality == None],
- 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
- ax2b.scatter(np.arange(N)[sorted_label_quality == None],
- 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
- ax3b.scatter(np.arange(N)[sorted_label_quality == None],
- 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
- ax4b.scatter(np.arange(N)[sorted_label_quality == None],
- 0.5 * np.ones(len(np.arange(N)[sorted_label_quality == None])), s=12, c='r', linewidth=0.8, marker='_')
-
- plt.legend(handles=[line0te, line1te, line2te, line3te, line_label_quality], fontsize=6, loc=1)
- plt.tight_layout(0, 0, 0)
- plt.savefig(save_out_path)
- print('Save fig to {}'.format(save_out_path))
-
-
-def prepare_plot_long_4_rows(sorted_lbs):
- N = len(sorted_lbs)
-
- f,(ax1a, ax2a, ax3a, ax4a) = plt.subplots(4, 1, sharey=False, facecolor='w', figsize=(10, 10.5))
-
- fontsize = 5
-
- K = 132
- ax1a.set_xlim(0, K)
- ax2a.set_xlim(K, 2 * K)
- ax3a.set_xlim(2 * K, 3 * K)
- ax4a.set_xlim(3 * K, N)
-
- truncated_sorted_lbs = []
- for lb in sorted_lbs:
- lb = lb[0 : 25]
- words = lb.split(' ')
- if len(words[-1]) < 3:
- lb = ' '.join(words[0:-1])
- truncated_sorted_lbs.append(lb)
-
- ax1a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
- ax2a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
- ax3a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
- ax4a.grid(which='major', axis='x', linestyle='-', alpha=0.3)
-
- ax1a.set_yscale('log')
- ax2a.set_yscale('log')
- ax3a.set_yscale('log')
- ax4a.set_yscale('log')
-
- ax1b = ax1a.twinx()
- ax2b = ax2a.twinx()
- ax3b = ax3a.twinx()
- ax4b = ax4a.twinx()
- ax1b.set_ylim(0., 1.)
- ax2b.set_ylim(0., 1.)
- ax3b.set_ylim(0., 1.)
- ax4b.set_ylim(0., 1.)
- ax1b.set_ylabel('Average precision')
- ax2b.set_ylabel('Average precision')
- ax3b.set_ylabel('Average precision')
- ax4b.set_ylabel('Average precision')
-
- ax1b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
- ax2b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
- ax3b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
- ax4b.yaxis.grid(color='grey', linestyle='--', alpha=0.5)
-
- ax1a.xaxis.set_ticks(np.arange(K))
- ax1a.xaxis.set_ticklabels(truncated_sorted_lbs[0:K], rotation=90, fontsize=fontsize)
- ax1a.xaxis.tick_bottom()
- ax1a.set_ylabel("Number of audio clips")
-
- ax2a.xaxis.set_ticks(np.arange(K, 2*K))
- ax2a.xaxis.set_ticklabels(truncated_sorted_lbs[K:2*K], rotation=90, fontsize=fontsize)
- ax2a.xaxis.tick_bottom()
- ax2a.set_ylabel("Number of audio clips")
-
- ax3a.xaxis.set_ticks(np.arange(2*K, 3*K))
- ax3a.xaxis.set_ticklabels(truncated_sorted_lbs[2*K:3*K], rotation=90, fontsize=fontsize)
- ax3a.xaxis.tick_bottom()
- ax3a.set_ylabel("Number of audio clips")
-
- ax4a.xaxis.set_ticks(np.arange(3*K, N))
- ax4a.xaxis.set_ticklabels(truncated_sorted_lbs[3*K:], rotation=90, fontsize=fontsize)
- ax4a.xaxis.tick_bottom()
- ax4a.set_ylabel("Number of audio clips")
-
- ax1a.spines['right'].set_visible(False)
- ax1b.spines['right'].set_visible(False)
- ax2a.spines['left'].set_visible(False)
- ax2b.spines['left'].set_visible(False)
- ax2a.spines['right'].set_visible(False)
- ax2b.spines['right'].set_visible(False)
- ax3a.spines['left'].set_visible(False)
- ax3b.spines['left'].set_visible(False)
- ax3a.spines['right'].set_visible(False)
- ax3b.spines['right'].set_visible(False)
- ax4a.spines['left'].set_visible(False)
- ax4b.spines['left'].set_visible(False)
-
- plt.subplots_adjust(hspace = 0.8)
-
- return ax1a, ax2a, ax3a, ax4a, ax1b, ax2b, ax3b, ax4b
-
-
-def _scatter_4_rows(x, ax, ax2, ax3, ax4, s, c, marker='.', alpha=1.):
- N = len(x)
- ax.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
- ax2.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
- ax3.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
- ax4.scatter(np.arange(N), x, s=s, c=c, marker=marker, alpha=alpha)
-
-
-def _plot_4_rows(x, ax, ax2, ax3, ax4, c, linewidth=1.0, alpha=1.0, label=""):
- N = len(x)
- ax.plot(x, c=c, linewidth=linewidth, alpha=alpha)
- ax2.plot(x, c=c, linewidth=linewidth, alpha=alpha)
- ax3.plot(x, c=c, linewidth=linewidth, alpha=alpha)
- line, = ax4.plot(x, c=c, linewidth=linewidth, alpha=alpha, label=label)
- return line
-
-
-if __name__ == '__main__':
-
- parser = argparse.ArgumentParser(description='')
- subparsers = parser.add_subparsers(dest='mode')
-
- parser_classwise_iteration_map = subparsers.add_parser('plot_classwise_iteration_map')
- parser_six_figures = subparsers.add_parser('plot_six_figures')
- parser_complexity_map = subparsers.add_parser('plot_complexity_map')
- parser_long_fig = subparsers.add_parser('plot_long_fig')
-
- args = parser.parse_args()
-
- if args.mode == 'plot_classwise_iteration_map':
- plot_classwise_iteration_map(args)
-
- elif args.mode == 'plot_six_figures':
- plot_six_figures(args)
-
- elif args.mode == 'plot_complexity_map':
- plot_complexity_map(args)
-
- elif args.mode == 'plot_long_fig':
- plot_long_fig(args)
-
- else:
- raise Exception('Incorrect argument!')
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/sound_extraction/model/LASSNet.py b/spaces/AIGC-Audio/AudioGPT/sound_extraction/model/LASSNet.py
deleted file mode 100644
index a525e3c8b303753a52a8a001929414d27c08007f..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/sound_extraction/model/LASSNet.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from .text_encoder import Text_Encoder
-from .resunet_film import UNetRes_FiLM
-
-class LASSNet(nn.Module):
- def __init__(self, device='cuda'):
- super(LASSNet, self).__init__()
- self.text_embedder = Text_Encoder(device)
- self.UNet = UNetRes_FiLM(channels=1, cond_embedding_dim=256)
-
- def forward(self, x, caption):
- # x: (Batch, 1, T, 128))
- input_ids, attns_mask = self.text_embedder.tokenize(caption)
-
- cond_vec = self.text_embedder(input_ids, attns_mask)[0]
- dec_cond_vec = cond_vec
-
- mask = self.UNet(x, cond_vec, dec_cond_vec)
- mask = torch.sigmoid(mask)
- return mask
-
- def get_tokenizer(self):
- return self.text_embedder.tokenizer
diff --git a/spaces/AISuperheroes/02GR-ASR-Memory/app.py b/spaces/AISuperheroes/02GR-ASR-Memory/app.py
deleted file mode 100644
index 164fcb9287a9c572d6fe3d15b1cbb7e82f04244d..0000000000000000000000000000000000000000
--- a/spaces/AISuperheroes/02GR-ASR-Memory/app.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import gradio as gr
-import torch
-import time
-import librosa
-import soundfile
-import nemo.collections.asr as nemo_asr
-import tempfile
-import os
-import uuid
-
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-import torch
-
-# PersistDataset -----
-import os
-import csv
-import gradio as gr
-from gradio import inputs, outputs
-import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
-from datetime import datetime
-DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
-DATASET_REPO_ID = "awacke1/Carddata.csv"
-DATA_FILENAME = "Carddata.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-SCRIPT = """
-
-"""
-
-
-try:
- hf_hub_download(
- repo_id=DATASET_REPO_ID,
- filename=DATA_FILENAME,
- cache_dir=DATA_DIRNAME,
- force_filename=DATA_FILENAME
- )
-except:
- print("file not found")
-repo = Repository(
- local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
-)
-
-def generate_html() -> str:
- with open(DATA_FILE) as csvfile:
- reader = csv.DictReader(csvfile)
- rows = []
- for row in reader:
- rows.append(row)
- rows.reverse()
- if len(rows) == 0:
- return "no messages yet"
- else:
- html = ""
- for row in rows:
- html += "
"
- html += f"{row['inputs']}"
- html += f"{row['outputs']}"
- html += "
"
- html += "
'.join(note_history[0].split(' ')[2:])]
- history = history[1:]
- return inputs, note_history, history
-
-def add_note_to_history(note, note_history):
- """Add a note to the historical information"""
- note_history.append(note)
- note_history = ' '.join(note_history)
- return [note_history]
-
-
-def chat(message, history):
- history = history or []
- if history:
- history_useful = [' '.join([str(a[0])+' '+str(a[1]) for a in history])]
- else:
- history_useful = []
- history_useful = add_note_to_history(message, history_useful)
- inputs = tokenizer(history_useful, return_tensors="pt")
- inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
- reply_ids = model.generate(**inputs)
- response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
- history_useful = add_note_to_history(response, history_useful)
- list_history = history_useful[0].split(' ')
- history.append((list_history[-2], list_history[-1]))
- store_message(message, response) # Save to dataset
- return history, history
-
-SAMPLE_RATE = 16000
-model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
-model.change_decoding_strategy(None)
-model.eval()
-
-def process_audio_file(file):
- data, sr = librosa.load(file)
- if sr != SAMPLE_RATE:
- data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
- # monochannel
- data = librosa.to_mono(data)
- return data
-
-#def transcribe(audio, state = "", im4 = "", file = ""):
-#def transcribe(audio, state = "", im4 = None, file = None):
-def transcribe(audio, state = ""): # two parms - had been testing video and file inputs at same time.
- # Grant additional context
- # time.sleep(1)
- if state is None:
- state = ""
- audio_data = process_audio_file(audio)
- with tempfile.TemporaryDirectory() as tmpdir:
- # Filepath transcribe
- audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
- soundfile.write(audio_path, audio_data, SAMPLE_RATE)
- transcriptions = model.transcribe([audio_path])
- # Direct transcribe
- # transcriptions = model.transcribe([audio])
- # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
- if type(transcriptions) == tuple and len(transcriptions) == 2:
- transcriptions = transcriptions[0]
- transcriptions = transcriptions[0]
- store_message(transcriptions, state) # Save to dataset
- state = state + transcriptions + " "
- return state, state
-
-iface = gr.Interface(
- fn=transcribe,
- inputs=[
- gr.Audio(source="microphone", type='filepath', streaming=True),
- "state",
- #gr.Image(label="Webcam", source="webcam"),
- #gr.File(label="File"),
- ],
- outputs=[
- "textbox",
- "state",
- #gr.HighlightedText(label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}),
- #gr.HighlightedText(label="HighlightedText", show_legend=True),
- #gr.JSON(label="JSON"),
- #gr.HTML(label="HTML"),
- ],
- layout="horizontal",
- theme="huggingface",
- title="🗣️LiveSpeechRecognition🧠Memory💾",
- description=f"Live Automatic Speech Recognition (ASR) with Memory💾 Dataset.",
- allow_flagging='never',
- live=True,
- article=f"Result Output Saved to Memory💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
-)
-iface.launch()
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/extensions/vis_pred_save.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/extensions/vis_pred_save.py
deleted file mode 100644
index 000b7746bdfbb02cf29b09f5b4959a23b8ec3f7b..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/extensions/vis_pred_save.py
+++ /dev/null
@@ -1,209 +0,0 @@
-from .dataset_info import DatasetInfo
-import cv2
-import mmcv
-import numpy as np
-import os
-from os import path as osp
-import json
-
-
-def save_result(img,
- poses,
- img_name=None,
- radius=4,
- thickness=1,
- bbox_score_thr=None,
- kpt_score_thr=0.3,
- bbox_color='green',
- dataset_info=None,
- show=False,
- out_dir=None,
- vis_out_dir=None,
- pred_out_dir=None,):
- """Visualize the detection results on the image.
-
- Args:
- img (str | np.ndarray): Image filename or loaded image.
- poses (dict[dict]): a dict which contains pose_model and pose_results of different classes.
- And the pose_results contains bboxes, bbox_scores, keypoints and keypoint_scores.
- img_name (str): Image name.
- radius (int): Radius of circles.
- thickness (int): Thickness of lines.
- bbox_score_thr (float): The threshold to visualize the bounding boxes.
- kpt_score_thr (float): The threshold to visualize the keypoints.
- bbox_color (str | tuple[int]): Color of bounding boxes.
- dataset_info (DatasetInfo): Dataset info.
- show (bool): Whether to show the image. Default False.
- out_dir (str): The output directory to save the visualizations and predictions results.
- If vis_out_dir is None, visualizations will be saved in ${out_dir}/visualizations.
- If pred_out_dir is None, predictions will be saved in ${out_dir}/predictions.
- Default None.
- vis_out_dir (str): The output directory to save the visualization results. Default None.
- pred_out_dir (str): The output directory to save the predictions results. Default None.
- """
- # set flags
- vis_out_flag = False if vis_out_dir is None else vis_out_dir
- pred_out_flag = False if pred_out_dir is None else pred_out_dir
- if out_dir:
- if not vis_out_dir:
- vis_out_flag = osp.join(out_dir, 'visualizations')
- if not osp.exists(vis_out_flag):
- os.mkdir(vis_out_flag)
- if not pred_out_dir:
- pred_out_flag = osp.join(out_dir, 'predictions')
- if not osp.exists(pred_out_flag):
- os.mkdir(pred_out_flag)
-
- # read image
- img_path = None
- if isinstance(img, str):
- img_path = img
- img = mmcv.imread(img)
- elif isinstance(img, np.ndarray):
- img = img.copy()
- else:
- raise TypeError('img must be a filename or numpy array, '
- f'but got {type(img)}')
- bbox_list = []
- label_list = []
- class_name_list = []
- bbox_score_list = []
- idx = 0
- for label, v in poses.items():
- if len(v) == 0:
- continue
- pose_results = v['pose_results']
- bbox = pose_results[0].gt_instances.bboxes
- bbox_score = pose_results[0].gt_instances.bbox_scores
- for bbox_idx in range(len(bbox)):
- b = bbox[bbox_idx]
- s = bbox_score[bbox_idx]
- if bbox_score_thr is not None:
- b = np.append(b, values=s) # switch to x1, y1, x2, y2, score
- bbox_score_list.append(s.tolist())
- bbox_list.append(b)
- label_list.append(idx)
- class_name_list.append(label)
- idx += 1
- bbox_list = np.array(bbox_list)
- label_list = np.array(label_list)
-
- # draw bbox
- img = mmcv.imshow_det_bboxes(
- img,
- bbox_list,
- label_list,
- class_names=class_name_list,
- score_thr=bbox_score_thr if bbox_score_thr is not None else 0,
- bbox_color=bbox_color,
- text_color='white',
- show=False,
- # out_file=out_file
- )
-
- keypoints_list = []
- keypoint_scores_list = []
- # draw pose of different classes
- for label, v in poses.items():
- if len(v) == 0:
- continue
- pose_model = v['pose_model']
- pose_results = v['pose_results']
- keypoints = pose_results[0].pred_instances.keypoints
- for ks in keypoints:
- keypoints_list.append(ks.tolist())
- keypoint_scores = pose_results[0].pred_instances.keypoint_scores
- for kss in keypoint_scores:
- keypoint_scores_list.append(kss.tolist())
-
- # get dataset info
- if (dataset_info is None and hasattr(pose_model, 'cfg')
- and 'dataset_info' in pose_model.cfg):
- dataset_info = DatasetInfo(pose_model.cfg.dataset_info)
-
- if dataset_info is not None:
- skeleton = dataset_info.skeleton
-
- pose_kpt_color = dataset_info.pose_kpt_color
- pose_kpt_color_tmp = []
- for color in pose_kpt_color:
- pose_kpt_color_tmp.append(tuple([int(x) for x in color]))
- pose_kpt_color = pose_kpt_color_tmp
-
- pose_link_color = dataset_info.pose_link_color
- pose_link_color_tmp = []
- for color in pose_link_color:
- pose_link_color_tmp.append(tuple([int(x) for x in color]))
- pose_link_color = pose_link_color_tmp
- else:
- warnings.warn(
- 'dataset is deprecated.'
- 'Please set `dataset_info` in the config.'
- 'Check https://github.com/open-mmlab/mmpose/pull/663 for details.',
- DeprecationWarning)
- raise ValueError('dataset_info is not specified or set in the config file.')
-
- # create circles_list
- circles_list = []
- for bbox_idx, circles in enumerate(keypoints):
- c_dict = {}
- for c_idx, c in enumerate(circles):
- if keypoint_scores[bbox_idx][c_idx] >= kpt_score_thr:
- c_dict[c_idx] = c
- # else:
- # c_dict[c_idx] = None
- circles_list.append(c_dict)
-
- # create lines_list
- lines_list = []
- for bbox_idx, _ in enumerate(keypoints):
- s_dict = {}
- for s_idx, s in enumerate(skeleton):
- if s[0] in circles_list[bbox_idx].keys() and s[1] in circles_list[bbox_idx].keys():
- s_dict[s_idx] = True
- else:
- s_dict[s_idx] = False
- lines_list.append(s_dict)
-
- # draw circle
- for _, circles in enumerate(circles_list):
- for c_idx, c in circles.items():
- if c is not None:
- cv2.circle(img, (int(c[0]), int(c[1])), radius, pose_kpt_color[c_idx], -1)
-
- # draw line
- for bbox_idx, lines in enumerate(lines_list):
- for l_idx, l in lines.items():
- if l:
- s = skeleton[l_idx][0] # idx of start point
- e = skeleton[l_idx][1] # idx of end point
- cv2.line(img,
- (int(circles_list[bbox_idx][s][0]), int(circles_list[bbox_idx][s][1])),
- (int(circles_list[bbox_idx][e][0]), int(circles_list[bbox_idx][e][1])),
- pose_link_color[l_idx], thickness)
-
- if show:
- mmcv.imshow(img, wait_time=0)
- if img_path is None:
- if img_name is not None:
- img_path = img_name
- else:
- img_path = 'demo.jpg'
- if vis_out_flag:
- out_file = osp.join(vis_out_flag, osp.basename(img_path))
- mmcv.imwrite(img, out_file)
- if pred_out_flag:
- pred_list = []
- for bbox_idx in range(len(bbox_list)):
- bbl = bbox_list[bbox_idx].tolist()
- pred_list.append(dict(
- keypoints=keypoints_list[bbox_idx],
- keypoint_scores=keypoint_scores_list[bbox_idx],
- bbox=[bbl],
- bbox_score=bbox_score_list[bbox_idx],
- ))
- # replace .jpg or .png with .json
- out_file = osp.join(pred_out_flag, osp.basename(img_path).rsplit('.', 1)[0] + '.json')
- json.dump(pred_list, open(out_file, 'w'))
-
- return img
diff --git a/spaces/AchyuthGamer/text-to-speech-client/assets/worker-7f2d1abe.js b/spaces/AchyuthGamer/text-to-speech-client/assets/worker-7f2d1abe.js
deleted file mode 100644
index e6b33e408a300394a8ae35d6e48413e8d8076aaa..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/text-to-speech-client/assets/worker-7f2d1abe.js
+++ /dev/null
@@ -1,1790 +0,0 @@
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r=Object.keys(e),i=0;i>>3){case 1:d.tensorName=e.string();break;case 2:d.quantParameterTensorNames&&d.quantParameterTensorNames.length||(d.quantParameterTensorNames=[]),d.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!l.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r>>3){case 1:d.node&&d.node.length||(d.node=[]),d.node.push(o.onnx.NodeProto.decode(e,e.uint32()));break;case 2:d.name=e.string();break;case 5:d.initializer&&d.initializer.length||(d.initializer=[]),d.initializer.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 10:d.docString=e.string();break;case 11:d.input&&d.input.length||(d.input=[]),d.input.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:d.output&&d.output.length||(d.output=[]),d.output.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:d.valueInfo&&d.valueInfo.length||(d.valueInfo=[]),d.valueInfo.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:d.quantizationAnnotation&&d.quantizationAnnotation.length||(d.quantizationAnnotation=[]),d.quantizationAnnotation.push(o.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var r=0;r>>3){case 1:if(d.dims&&d.dims.length||(d.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=o.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i>>0,e.int64Data[i].high>>>0).toNumber())}if(e.name!=null&&(r.name=String(e.name)),e.docString!=null&&(r.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?l.base64.decode(e.rawData,r.rawData=l.newBuffer(l.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i>>0,e.uint64Data[i].high>>>0).toNumber(!0))}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.dims=[],i.floatData=[],i.int32Data=[],i.stringData=[],i.int64Data=[],i.doubleData=[],i.uint64Data=[],i.externalData=[]),r.defaults&&(i.dataType=0,i.segment=null,i.name="",r.bytes===String?i.rawData="":(i.rawData=[],r.bytes!==Array&&(i.rawData=l.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var 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h||(r=new h(r)),this.decode(r,r.uint32())},e.verify=function(r){return typeof r!="object"||r===null?"object expected":r.begin!=null&&r.hasOwnProperty("begin")&&!(l.isInteger(r.begin)||r.begin&&l.isInteger(r.begin.low)&&l.isInteger(r.begin.high))?"begin: integer|Long expected":r.end!=null&&r.hasOwnProperty("end")&&!(l.isInteger(r.end)||r.end&&l.isInteger(r.end.low)&&l.isInteger(r.end.high))?"end: integer|Long expected":null},e.fromObject=function(r){if(r instanceof o.onnx.TensorProto.Segment)return r;var i=new o.onnx.TensorProto.Segment;return r.begin!=null&&(l.Long?(i.begin=l.Long.fromValue(r.begin)).unsigned=!1:typeof r.begin=="string"?i.begin=parseInt(r.begin,10):typeof r.begin=="number"?i.begin=r.begin:typeof r.begin=="object"&&(i.begin=new l.LongBits(r.begin.low>>>0,r.begin.high>>>0).toNumber())),r.end!=null&&(l.Long?(i.end=l.Long.fromValue(r.end)).unsigned=!1:typeof r.end=="string"?i.end=parseInt(r.end,10):typeof r.end=="number"?i.end=r.end:typeof r.end=="object"&&(i.end=new 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- vec2 offsetToCoords(int offset, int width, int height) {
- int t = offset / width;
- int s = offset - t*width;
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- }
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- float s = coords.s * float(width);
- float t = coords.t * float(height);
- int offset = int(t) * width + int(s);
- return offset;
- }
- `)}}getOutputSamplingSnippet(){const o=this.context.outputTextureLayout;return o.isPacked?this.getPackedOutputSamplingSnippet(o):this.getUnpackedOutputSamplingSnippet(o)}getPackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const d=`
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- ${(0,p.getGlsl)(this.context.glContext.version).output} = val;
- }
- `;return r.floatTextureSetRGBA=new c.GlslLibRoutine(d),r}getUnpackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,e);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,e);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,e);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}const d=`
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- }
- `;return r.floatTextureSetR=new c.GlslLibRoutine(d),r}getOutputScalarCoords(){return new c.GlslLibRoutine(`
- int getOutputCoords() {
- return 0;
- }
- `)}getOutputPacked1DCoords(o,t){const e=t;let r="";return e[0]===1?(r=`
- int getOutputCoords() {
- return 2 * int(TexCoords.y * ${e[1]}.0);
- }
- `,new c.GlslLibRoutine(r)):e[1]===1?(r=`
- int getOutputCoords() {
- return 2 * int(TexCoords.x * ${e[0]}.0);
- }
- `,new c.GlslLibRoutine(r)):(r=`
- int getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${e[0]}, ${e[1]}));
- return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
- }
- `,new c.GlslLibRoutine(r))}getOutputPacked2DCoords(o,t){let e="";if(u.ArrayUtil.arraysEqual(o,t))return e=`
- ivec2 getOutputCoords() {
- return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
- }
- `,new c.GlslLibRoutine(e);const r=t,i=Math.ceil(o[1]/2);return e=`
- ivec2 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${r[0]}, ${r[1]}));
-
- int index = resTexRC.y * ${r[0]} + resTexRC.x;
-
- // reverse r and c order for packed texture
- int r = imod(index, ${i}) * 2;
- int c = 2 * (index / ${i});
-
- return ivec2(r, c);
- }
- `,new c.GlslLibRoutine(e)}getOutputPacked3DCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[2]/2),i=r*Math.ceil(o[1]/2),d=`
- ivec3 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${e[0]}, ${e[1]}));
- int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
- int b = index / ${i};
- index -= b * ${i};
-
- // reverse r and c order for packed texture
- int r = imod(index, ${r}) * 2;
- int c = 2 * (index / ${r});
-
- return ivec3(b, r, c);
- }
- `;return new c.GlslLibRoutine(d)}getOutputPackedNDCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[o.length-1]/2),i=r*Math.ceil(o[o.length-2]/2);let d=i,g="",m="b, r, c";for(let y=2;y=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
- ivec3 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec3(r, c, d);
- }
- `,new c.GlslLibRoutine(e)}getOutputUnpacked4DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
- ivec4 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec4(r, c, d, d2);
- }
- `,new c.GlslLibRoutine(e)}getOutputUnpacked5DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2","d3"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
- ivec5 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec5(r, c, d, d2, d3);
- }
- `,new c.GlslLibRoutine(e)}getOutputUnpacked6DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2","d3","d4"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
- ivec6 getOutputCoords() {
- ivec2 resTexRC = ivec2(TexCoords.xy *
- vec2(${t[0]}, ${t[1]}));
- int index = resTexRC.y * ${t[0]} + resTexRC.x;
- ${g}
- return ivec6(r, c, d, d2, d3, d4);
- }
- `,new c.GlslLibRoutine(e)}getCommonUtilFuncs(){const o={};let t="uvFromFlat";o[t]=new c.GlslLibRoutine(`
- vec2 uvFromFlat(int texNumR, int texNumC, int index) {
- int texC = index / texNumR;
- int texR = index - texC * texNumR;
- // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
- // v.
- return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
- }
- `),t="packedUVfrom1D",o[t]=new c.GlslLibRoutine(`
- vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
- int texelIndex = index / 2;
- int texR = texelIndex / texNumC;
- int texC = texelIndex - texR * texNumC;
- return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
- }
- `),t="packedUVfrom2D",o[t]=new c.GlslLibRoutine(`
- vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
- int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
- int texR = texelIndex / texNumC;
- int texC = texelIndex - texR * texNumC;
- return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
- }
- `),t="packedUVfrom3D",o[t]=new c.GlslLibRoutine(`
- vec2 packedUVfrom3D(int texNumR, int texNumC,
- int texelsInBatch, int texelsInLogicalRow, int b,
- int row, int col) {
- int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
- int texR = index / texNumC;
- int texC = index - texR * texNumC;
- return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
- }
- `),t="sampleTexture";const e=(0,p.getGlsl)(this.context.glContext.version);return o[t]=new c.GlslLibRoutine(`
- float sampleTexture(sampler2D textureSampler, vec2 uv) {
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-`);let T="";T=y<2&&_>0?"coords":i.map((L,H)=>`coords.${A[H+S]}`).join(", ");let M="return outputValue;";const N=u.ShapeUtil.size(i)===1,B=u.ShapeUtil.size(d)===1;if(_!==1||N||B){if(N&&!B)M=y===1?`
- return vec4(outputValue.x, outputValue.x, 0., 0.);
- `:`
- return vec4(outputValue.x);
- `;else if(w.length){const L=_-2,H=_-1;w.indexOf(L)>-1&&w.indexOf(H)>-1?M="return vec4(outputValue.x);":w.indexOf(L)>-1?M="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":w.indexOf(H)>-1&&(M="return vec4(outputValue.xx, outputValue.zz);")}}else M=`
- return vec4(outputValue.xy, outputValue.xy);
- `;const $=`
- vec4 ${o}() {
- ${v} coords = getOutputCoords();
-
- int lastDim = coords.${A[y-1]};
- coords.${A[y-1]} = coords.${A[y-2]};
- coords.${A[y-2]} = lastDim;
-
- ${O}
- vec4 outputValue = ${m}(${T});
- ${M}
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- int input_channel = group_id * ${w[1]} + wInChannel;
- for (int wHeight = 0; wHeight < ${w[2]}; wHeight++) {
- int xHeight = xRCCorner.x + wHeight * ${m.dilations[0]};
-
- if (xHeight < 0 || xHeight >= ${y[2]}) {
- continue;
- }
-
- for (int wWidth = 0; wWidth < ${w[3]}; wWidth++) {
- int xWidth = xRCCorner.y + wWidth * ${m.dilations[1]};
- if (xWidth < 0 || xWidth >= ${y[3]}) {
- continue;
- }
-
- float xVal = getX(batch, input_channel, xWidth, xHeight);
- float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
- value += xVal*wVal;
- }
- }
- }
- ${_}
- ${T}
- ${O.output} = vec4(value, .0, .0, .0);
- }
-`;return Object.assign(Object.assign({},g),{output:{dims:S,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:M,hasMain:!0})})(f,l,t,o)})}},1386:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.conv2DPacked=n.conv2DPackedPointwise=void 0;const u=a(8138),c=a(8555),p=a(708);n.conv2DPackedPointwise=(s,h,f)=>{const l=h[0].dims,o=h[1].dims,t=(0,u.calculateOutputShape)(l,o,f.dilations,f.pads,f.strides),e=s.reshapePacked(h[0],[l[1],l[2]*l[3]]),r=s.reshapePacked(h[1],[o[0],o[1]]),i=h.length>2?[r,e,h[2]]:[r,e],d=s.run((0,p.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(d,t)},n.conv2DPacked=(s,h,f)=>{const l=h[0].dims,o=h[1].dims,t=(0,u.calculateOutputShape)(l,o,f.dilations,f.pads,f.strides),e=s.run((0,c.createPackedIm2ColProgramInfoLoader)(s,h[0],h[1],t,f),[h[0]]),r=s.reshapePacked(h[1],[o[0],o[1]*o[2]*o[3]]),i=h.length===3?[r,e,h[2]]:[r,e],d=s.run((0,p.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(d,t)}},9663:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvTransposeAttributes=n.convTranspose=void 0;const u=a(246),c=a(5060),p=a(2039),s=a(2823),h=(r,i,d,g,m,_)=>(r-1)*i+d+(g-1)*m+1-_,f=(r,i,d,g,m)=>{const _=Math.floor(r/2);i==="SAME_UPPER"?(d[g]=_,d[m]=r-_):i==="SAME_LOWER"&&(d[g]=r-_,d[m]=_)};n.convTranspose=(r,i,d)=>(e(i,d),l(r,i,d));const l=(r,i,d)=>{const g=t(d,i);return[o(r,i,g)]},o=(r,i,d)=>r.run(((g,m,_)=>{const y=(w=m.length>2,v=_.cacheKey,{name:"ConvTranspose",inputNames:w?["X","W","B"]:["X","W"],inputTypes:w?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],cacheHint:v});var w,v;return Object.assign(Object.assign({},y),{get:()=>((S,O,A,T)=>{const M=O.length>2?"getB(output_channel)":"0.0",N=O[0].dims,B=O[1].dims,$=B[1],L=B[0]/T.group,H=[O[0].dims[0],O[1].dims[1]*T.group,...T.outputShape],C=(0,c.getGlsl)(S.session.backend.glContext.version),{activationFunction:z,applyActivation:J}=(0,s.getActivationSnippet)(T),X=`
- const ivec2 strides = ivec2(${T.strides[0]}, ${T.strides[1]});
- const ivec2 pads = ivec2(${T.pads[0]}, ${T.pads[1]});
- ${z}
- void main() {
- ivec4 coords = getOutputCoords();
- int batch = coords.x;
- int output_channel = coords.y;
-
- ivec2 loc = coords.zw + pads;
-
- int group_id = output_channel / ${$};
- int wOutChannel = output_channel - group_id * ${$};
-
- float value = ${M};
- for (int inChannelOffset = 0; inChannelOffset < ${L}; inChannelOffset++) {
- int input_channel = group_id * ${L} + inChannelOffset;
- for (int wWOff = 0; wWOff < ${B[2]}; wWOff++) {
- for (int wHOff = 0; wHOff < ${B[3]}; wHOff++) {
- ivec2 wOff = ivec2(wWOff * ${T.dilations[0]}, wHOff * ${T.dilations[1]});
- ivec2 wLoc = loc - wOff;
- ivec2 wLocIn = wLoc / strides;
- if (
- wLocIn * strides == wLoc &&
- wLocIn.x >= 0 && wLocIn.x < ${N[2]} &&
- wLocIn.y >= 0 && wLocIn.y < ${N[3]}
- ) {
- float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
- float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
- value += xVal * wVal;
- }
- }
- }
- }
- ${J}
- ${C.output} = vec4(value, .0, .0, .0);
- }
-`;return Object.assign(Object.assign({},A),{output:{dims:H,type:O[0].type,textureType:p.TextureType.unpacked},shaderSource:X,hasMain:!0})})(g,m,y,_)})})(r,i,d),i),t=(r,i)=>{const d=r.kernelShape.slice();if(r.kernelShape.length===0)for(let y=2;y{const N=y.length-2,B=M.length===0;for(let $=0;${const i=r.attributes,d=(0,s.parseInternalActivationAttributes)(i),g=i.getString("auto_pad","NOTSET"),m=i.getInts("dilations",[1,1]),_=i.getInt("group",1),y=i.getInts("kernel_shape",[]),w=i.getInts("output_padding",[0,0]),v=i.getInts("output_shape",[]),S=i.getInts("pads",[0,0,0,0]),O=i.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:g,dilations:m,group:_,kernelShape:y,outputPadding:w,outputShape:v,pads:S,strides:O},d))};const e=(r,i)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(r[0].dims[1]!==r[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");const d=r[1].dims[1]*i.group;if(r.length===3&&(r[2].dims.length!==1||r[2].dims[0]!==d))throw new Error("invalid bias");const g=r[0].dims.length-2;if(i.dilations.length!==g)throw new Error(`dilations should be ${g}D`);if(i.strides.length!==g)throw new Error(`strides should be ${g}D`);if(i.pads.length!==2*g)throw new Error(`pads should be ${2*g}D`);if(i.outputPadding.length!==g)throw new Error(`output_padding should be ${g}D`);if(i.kernelShape.length!==0&&i.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(i.outputShape.length!==0&&i.outputShape.length!==r[0].dims.length-2)throw new Error("invalid output shape");if(r[0].type!=="float32"||r[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(r.length===3&&r[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}},8138:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvAttributes=n.conv=n.calculateOutputShape=void 0;const u=a(246),c=a(2517),p=a(4770),s=a(1386),h=a(9828),f=a(2823),l=a(3248),o=a(5623);n.calculateOutputShape=(g,m,_,y,w)=>{const v=g[0],S=g.slice(2),O=S.length,A=m[0],T=m.slice(2).map((N,B)=>N+(N-1)*(_[B]-1)),M=S.map((N,B)=>N+y[B]+y[B+O]).map((N,B)=>Math.floor((N-T[B]+w[B])/w[B]));return[v,A].concat(...M)},n.conv=(g,m,_)=>(d(m,_),t(g,m,_));const t=(g,m,_)=>{const y=i(_,m),w=g.session.pack,v=y.kernelShape[0]===1&&y.kernelShape[1]===1;return y.group>1?[g.run((0,p.createUnpackedGroupedConvProgramInfoLoader)(g,m,y),m)]:v&&w?[e(g,m,y)]:w&&m[0].dims.length===4&&m[0].dims[0]===1&&!v?[(0,s.conv2DPacked)(g,m,y)]:[r(g,m,y)]},e=(g,m,_)=>{const y=m[0].dims,w=m[1].dims,v=(0,n.calculateOutputShape)(y,w,_.dilations,_.pads,_.strides),S=g.reshapeUnpacked(m[0],[y[1],y[2]*y[3]]),O=g.reshapeUnpacked(m[1],[w[0],w[1]]),A=m.length>2?[O,S,m[2]]:[O,S],T=g.run((0,o.createMatmulProgramInfoLoader)(A,_),A);return g.reshapeUnpacked(T,v)},r=(g,m,_)=>{const y=m[0].dims,w=m[1].dims,v=(0,n.calculateOutputShape)(y,w,_.dilations,_.pads,_.strides),S=g.run((0,l.createIm2ColProgramInfoLoader)(g,m[0],m[1],v,_),[m[0]]),O=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,h.createDotProductProgramInfoLoader)(g,m,v,_),O)},i=(g,m)=>{const _=g.kernelShape.slice();if(g.kernelShape.length===0)for(let v=2;v{const m=g.attributes,_=(0,f.parseInternalActivationAttributes)(m),y=m.getString("auto_pad","NOTSET"),w=m.getInts("dilations",[1,1]),v=m.getInt("group",1),S=m.getInts("kernel_shape",[]),O=m.getInts("pads",[0,0,0,0]),A=m.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:y,dilations:w,group:v,kernelShape:S,pads:O,strides:A},_))};const d=(g,m)=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(g[0].dims.length!==4||g[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(g[0].dims[1]!==g[1].dims[1]*m.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(g.length===3&&(g[2].dims.length!==1||g[1].dims[0]!==g[2].dims[0]))throw new Error("invalid bias");const _=g[0].dims.length-2;if(m.dilations.length!==_)throw new Error(`dilations should be ${_}D`);if(m.strides.length!==_)throw new Error(`strides should be ${_}D`);if(m.pads.length!==2*_)throw new Error(`pads should be ${2*_}D`);if(m.kernelShape.length!==0&&m.kernelShape.length!==g[1].dims.length-2)throw new Error("invalid kernel shape");if(g[0].type!=="float32"||g[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(g.length===3&&g[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseDepthToSpaceAttributes=n.depthToSpace=void 0;const u=a(3738);n.depthToSpace=(p,s,h)=>{c(s);const f=h.blocksize,l=f*f,o=h.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=h.mode==="DCR"?[s[0].dims[0],f,f,s[0].dims[1]/l,s[0].dims[2],s[0].dims[3]]:[s[0].dims[0],s[0].dims[1]/l,f,f,s[0].dims[2],s[0].dims[3]],e=p.reshapeUnpacked(s[0],t),r={perm:o,cacheKey:`${o}`},[i]=(0,u.transpose)(p,[e],r),d=[s[0].dims[0],s[0].dims[1]/l,s[0].dims[2]*f,s[0].dims[3]*f];return[p.reshapeUnpacked(i,d)]},n.parseDepthToSpaceAttributes=p=>{const s=p.attributes.getInt("blocksize");if(s<1)throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);const h=p.attributes.getString("mode","DCR");if(h!=="DCR"&&h!=="CRD")throw new Error(`unrecognized mode: ${h} for DepthToSpace`);return{mode:h,blocksize:s}};const c=p=>{if(p.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${p.length}`);if(p[0].type==="string"||p[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const u=a(2517),c=a(5060),p=a(2039),s=a(2823),h=a(3248);n.createDotProductProgramInfoLoader=(f,l,o,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[p.TextureType.unpacked,p.TextureType.packedLastDimension,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(l.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,d,g,m)=>{const _=d[0].dims,y=d[1].dims,w=[y[0],Math.ceil(_[1]*y[2]*y[3]/4)],v=(0,h.calculateIm2ColDims)(_,y,g),[S,O]=r.calculateTextureWidthAndHeight(w,p.TextureType.packedLastDimension),A=u.ShapeUtil.computeStrides(v),[T,M]=r.calculateTextureWidthAndHeight(v,p.TextureType.packedLastDimension),N=g.length,B=d.length<3?"0.0":"_B(b)",$=Math.ceil(_[1]*y[2]*y[3]/4),{activationFunction:L,applyActivation:H}=(0,s.getActivationSnippet)(m),C=(0,c.getGlsl)(r.session.backend.glContext.version),z=`
-${L}
-float process(int indices[${N}]) {
- int b[1];
- b[0] = indices[1];
- int im2col[4];
- im2col[0] = indices[0];
- im2col[1] = indices[2];
- im2col[2] = indices[3];
- int im2colOffset = im2col[0] * ${A[0]} + im2col[1] * ${A[1]} + im2col[2] * ${A[2]};
- int kernelOffset = indices[1] * ${w[1]};
- float value = ${B};
- for (int i = 0; i < ${$}; ++i) {
- vec2 im2colCoords = offsetToCoords(im2colOffset, ${T}, ${M});
- vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${O});
- value += dot(${C.texture2D}(Im2Col, im2colCoords), ${C.texture2D}(K, kernelCoords));
- ++im2colOffset;
- ++kernelOffset;
- }
- ${H}
- return value;
-}`;return Object.assign(Object.assign({},i),{output:{dims:g,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:z})})(f,e,l,o,t)})}},7992:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const u=a(2517);n.flatten=(p,s,h)=>{c(s,h);const f=u.ShapeUtil.flattenShape(s[0].dims,h);return[p.reshapeUnpacked(s[0],f)]},n.parseFlattenAttributes=p=>p.attributes.getInt("axis",1);const c=(p,s)=>{if(!p||p.length!==1)throw new Error("Flatten requires 1 input.");const h=p[0].dims.length;if(h===0)throw new Error("scalar tensor is not supported.");if(s<-h||s>h)throw new Error("Invalid axis");if(p[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const u=a(2517),c=a(4909);n.getActivationSnippet=function(p){let s;switch(p.activation){case"Relu":s=(0,c.glslRelu)();break;case"Sigmoid":s=(0,c.glslSigmoid)();break;case"Clip":s=(0,c.glslClip)(p.clipMin,p.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const h=s.name;return{activationFunction:s.body,applyActivation:`value = ${h}_(value);`}},n.parseInternalActivationAttributes=p=>{const s=p.getString("activation","");if(s==="Clip"){const[h,f]=p.getFloats("activation_params",[u.MIN_CLIP,u.MAX_CLIP]);return{activation:s,clipMax:f,clipMin:h,activationCacheKey:`${s}:${h},${f}`}}return{activation:s,activationCacheKey:s}}},1253:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const u=a(246),c=a(782),p=a(2517),s=a(2039);n.gather=(o,t,e)=>(l(t,e.axis),[o.run(f(o,t,e),t)]),n.parseGatherAttributes=o=>(0,u.createAttributeWithCacheKey)({axis:o.attributes.getInt("axis",0)});const h={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(o,t,e)=>{const r=Object.assign(Object.assign({},h),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,d,g,m)=>{const _=g[0].dims.slice(),y=g[1].dims.slice(),w=new Array(_.length+y.length-1);m=p.ShapeUtil.normalizeAxis(m,_.length);const v=[];for(let O=0;O{if(!o||o.length!==2)throw new Error("Gather requires 2 inputs.");const e=o[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(c.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invaid input type.");if(o[1].type!=="int32"&&o[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const u=a(246),c=a(2517),p=a(2039);n.gemm=(o,t,e)=>(l(t,e),[o.run(h(t,e),t)]);const s=(o,t)=>{const e=o.attributes.getInt("transA",0)!==0,r=o.attributes.getInt("transB",0)!==0,i=o.attributes.getFloat("alpha",1),d=o.attributes.getFloat("beta",1);return(0,u.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:d,isOptionalC:t})};n.parseGemmAttributesV7=o=>s(o,!1),n.parseGemmAttributesV11=o=>s(o,!0);const h=(o,t)=>{const e={name:"Gemm",inputNames:o.length===3?["A","B","C"]:["A","B"],inputTypes:o.length===3?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,o,t)})},f=(o,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[d,g]=c.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[d,g];if(!m)throw new Error("Can't use gemm on the given tensors");let _=r[r.length-1],y="";e.transA&&(_=r[0]),e.transA&&e.transB?y="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?y="value += _A_T(a) * _B(b);":!e.transA&&e.transB?y="value += _A(a) * _B_T(b);":e.transA||e.transB||(y="value += _A(a) * _B(b);");const w=m.length,v=`
- float process(int indices[${w}]) {
- int a[${w}];
- int b[${w}];
- ${t.length===3?`int c[${t[2].dims.length}];`:""}
-
- copyVec(indices, a);
- copyVec(indices, b);
- ${t.length===3?"bcastIndices_C(indices, c);":""}
-
- float value = 0.0;
- for (int k=0; k<${_}; ++k) {
- a[${w-1}] = k;
- b[${w-2}] = k;
- ${y}
- }
-
- value = value * alpha;
- ${t.length===3?"value += beta * _C(c);":""}
- return value;
- }`;return Object.assign(Object.assign({},o),{output:{dims:m,type:t[0].type,textureType:p.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:v})},l=(o,t)=>{if(!o)throw new Error("Input is missing");if(t.isOptionalC&&(o.length<2||o.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&o.length!==3)throw new Error("Gemm requires 3 inputs");if(o.length===3&&o[2].dims.length!==1&&o[2].dims.length!==2)throw new Error("Invalid input shape of C");if(o[0].type!=="float32"&&o[0].type!=="float64"||o[1].type!=="float32"&&o[1].type!=="float64"||o.length===3&&o[2].type!=="float32"&&o[2].type!=="float64")throw new Error("Invalid input type.");if(o[0].type!==o[1].type||o.length===3&&o[0].type!==o[2].type)throw new Error("Input types are mismatched")}},8555:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const u=a(5060),c=a(2039),p=a(2827);n.createPackedIm2ColProgramInfoLoader=(s,h,f,l,o)=>{const t=(e=o.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[c.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,d,g,m,_)=>{const y=d.dims,w=g.dims,v=m.length,S=[w[1]*w[2]*w[3],m[2]*m[3]],O=w[2]*w[3],A=(0,p.unpackFromChannel)(),T=(0,u.getGlsl)(r.session.backend.glContext.version);let M="";for(let B=0;B<=1;B++)for(let $=0;$<=1;$++)M+=`
- blockIndex = rc.x + ${$};
- pos = rc.y + ${B};
-
- if(blockIndex < ${S[1]} && pos < ${S[0]}) {
- offsetY = int(blockIndex / (${m[v-1]})) * ${_.strides[0]} -
- ${_.pads[0]};
- d0 = offsetY + ${_.dilations[0]} * (imod(pos, ${O}) / ${w[2]});
-
- if(d0 < ${y[2]} && d0 >= 0) {
- offsetX = imod(blockIndex, ${m[v-1]}) * ${_.strides[1]} -
- ${_.pads[1]};
- d1 = offsetX + ${_.dilations[1]} * imod(imod(pos, ${O}), ${w[2]});
-
- if(d1 < ${y[3]} && d1 >= 0) {
-
- ch = int(float(pos)/ ${O}.);
- innerDims = vec2(d0, d1);
- result[${2*B+$}] = getChannel(
- getA(0, ch, int(innerDims.x),
- int(innerDims.y)), innerDims);
- }
- }
- }
-
- `;const N=`
- ${A}
-
- void main() {
- ivec2 rc = getOutputCoords();
- vec4 result = vec4(0.0);
- int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
- vec2 innerDims;
- ${M}
- ${T.output} = result;
- }
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- const int XH = ${m[2]};
- const int XW = ${m[3]};
- const int KH = ${g.kernelShape[0]};
- const int KW = ${g.kernelShape[1]};
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- const int dilationW = ${g.dilations[1]};
- const int strideH = ${g.strides[0]};
- const int strideW = ${g.strides[1]};
- const int padH = ${g.pads[0]};
- const int padW = ${g.pads[1]};
- const int KHKW = KH*KW;
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- const int outputChannels = 4;
- vec4 process(int indices[${y}]) {
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- int p = indices[3] * outputChannels; //patch
- vec4 value = vec4(0.0);
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- int x[${m.length}];
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- x[2] = xh2;
- x[3] = xw2;
- if(xh2 >= 0 &&
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- xw2 < XW) {
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- }
- ++p;
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- return value;
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- for(int a3=0; a3<${r[3]}; a3++) {
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- }
- float mean = temp / float(${d});
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- a[2] = a2;
- for(int a3=0; a3<${r[3]}; a3++) {
- a[3] = a3;
- float x = _X(a);
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- }
- }
- v.r = mean;
- v.g = temp / float(${d});
-
- return v;
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- int offset = indicesToOffset_MeanAndVariance(mv);
- vec2 coords = offsetToCoords(offset, ${O}, ${A});
- return ${w.texture2D}(MeanAndVariance, coords);
- }
-
- float process(int[4] indices) {
- int mv[2];
- mv[0] = indices[0];
- mv[1] = indices[1];
- vec4 mean_and_variance = get_MeanAndVariance(mv);
- float mean = mean_and_variance.r;
- float variance = mean_and_variance.g;
-
- int sb[1];
- sb[0] = indices[1];
- float scale = _Scale(sb);
- float b = _B(sb);
-
- return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;
- }`;return Object.assign(Object.assign({},g),{output:{dims:m.dims,type:m.type,textureType:c.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:_}],shaderSource:T})})(o,i,t,e,r)})},l=o=>{if(!o||o.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");const t=o[0],e=o[1],r=o[2];if(t.dims.length<3||e.dims.length!==1||r.dims.length!==1)throw new Error("Invalid input shape.");if(e.dims[0]!==t.dims[1]||r.dims[0]!==t.dims[1])throw new Error("Input shapes are mismatched.");if(t.type!=="float32"&&t.type!=="float64"||e.type!=="float32"&&e.type!=="float64"||r.type!=="float32"&&r.type!=="float64")throw new Error("Invalid input type.");if(o[0].dims.length!==4)throw new Error("Only support 4-D input shape.")}},708:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedMatmulProgramInfoLoader=void 0;const u=a(2517),c=a(5060),p=a(2039),s=a(9390),h=a(2823),f=a(5623);n.createPackedMatmulProgramInfoLoader=(l,o,t)=>{const e=(r=o.length>2,i=t.activationCacheKey,{name:"MatMul (packed)",inputNames:r?["A","B","Bias"]:["A","B"],inputTypes:r?[p.TextureType.packed,p.TextureType.packed,p.TextureType.packed]:[p.TextureType.packed,p.TextureType.packed],cacheHint:i});var r,i;return Object.assign(Object.assign({},e),{get:()=>((d,g,m,_)=>{const y=m.length>2,w=y?"value += getBiasForMatmul();":"",v=m[0].dims,S=m[1].dims,O=u.BroadcastUtil.calcShape(v,S,!0),A=!u.ShapeUtil.areEqual(m[0].dims,m[1].dims);if(!O)throw new Error("Can't use matmul on the given tensors");const T=v[v.length-1],M=Math.ceil(T/2),N=v.length,B=S.length,$=(0,c.getGlsl)(d.session.backend.glContext.version),L=(0,s.getCoordsDataType)(O.length),H=O.length,C=(0,s.getGlChannels)(),{activationFunction:z,applyActivation:J}=(0,h.getActivationSnippet)(_),X=y?`${(0,f.getBiasForMatmul)(L,C,m[2].dims,O,!0)}`:"",te=A?`${function(Oe,ce,Te,ye){let Fe=[],He=[];const Ae=Te[0].dims,Ne=Te[1].dims,De=Ae.length,Pe=Ne.length,ve=ye.length,Be=ve-De,Ue=ve-Pe;Fe=Ae.map((Se,$e)=>`coords.${ce[$e+Be]}`),Fe[De-1]="i*2",Fe.join(", "),He=Ne.map((Se,$e)=>`coords.${ce[$e+Ue]}`),He[Pe-2]="i*2",He.join(", ");const Ve=u.BroadcastUtil.getBroadcastDims(Ae,ye),Xe=u.BroadcastUtil.getBroadcastDims(Ne,ye),Qe=Ve.map(Se=>`coords.${ce[Se+Be]} = 0;`).join(`
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-vec4 getAAtOutCoordsMatmul(int i) {
- ${Oe} coords = getOutputCoords();
- ${ze}
- ${Qe}
- vec4 outputValue = getA(${Fe});
- return outputValue;
-}
-
-vec4 getBAtOutCoordsMatmul(int i) {
- ${Oe} coords = getOutputCoords();
- ${ze}
- ${Ge}
- vec4 outputValue = getB(${He});
- return outputValue;
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- ${A}
- ${B}
- float process(int indices[${$}]) {
- int a[${L}];
- int b[${H}];
- bcastMatmulIndices_A(indices, a);
- bcastMatmulIndices_B(indices, b);
-
- float value;
- for (int k=0; k<${y[y.length-1]}; ++k) {
- a[${L-1}] = k;
- b[${H-2}] = k;
- value += _A(a) * _B(b);
- }
- ${N}
- ${T}
- return value;
- }`;return Object.assign(Object.assign({},g),{output:{dims:v,type:m[0].type,textureType:c.TextureType.unpacked},shaderSource:C})}(r,t,e)})}n.matMul=(t,e,r)=>(l(e),t.session.pack?[t.run((0,h.createPackedMatmulProgramInfoLoader)(t,e,r),e)]:[t.run(f(e,r),e)]),n.parseMatMulAttributes=t=>(0,s.parseInternalActivationAttributes)(t.attributes),n.createMatmulProgramInfoLoader=f;const l=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.");if(t[0].type!=="float32"&&t[0].type!=="float64"||t[1].type!=="float32"&&t[1].type!=="float64")throw new Error("inputs should be float type");if(t[0].type!==t[1].type)throw new Error("inputs types should match")};function o(t,e,r,i,d){let g="";const m=r.length,_=i.length,y=_-m;g=_<2&&m>0?"coords":r.map((S,O)=>`coords.${e[O+y]}`).join(", ");const w=u.BroadcastUtil.getBroadcastDims(r,i).map(S=>`coords.${e[S+y]} = 0;`).join(`
-`);let v="vec4(outputValue.xx, outputValue.yy)";return u.ShapeUtil.size(r)===1&&(v="vec4(outputValue.x)"),d?`
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- ${w}
- vec4 outputValue = getBias(${g});
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-float getBiasForMatmul() {
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- ${w}
- return getBias(coords.x);
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- int r = ${w[y-2]};
- int c = ${w[y-1]};
- int rp1 = ${w[y-2]} + 1;
- int cp1 = ${w[y-1]} + 1;
- bool rEdge = rp1 >= ${S};
- bool cEdge = cp1 >= ${v};
- `);var y,w,v,S;let O;O=i===0?[1,1]:i===1?[r[0],1]:[r[d-1],r[d-2]];const A=function(N,B,$){if(N===0)return"false";if(N===1)return`rc > ${B[0]}`;let L="";for(let H=N-2;H= ${B[H-N+2]}`,H= ${N[0]} ? 0. : getA(rc + 1),
- 0, 0`;let L="";if($>2)for(let H=0;H<$-2;++H)L+=`${B[H]},`;return`getA(${L}r, c),
- rEdge ? 0. : getA(${L}rp1, c),
- cEdge ? 0. : getA(${L}r, cp1),
- rEdge || cEdge ? 0. : getA(${L}rp1, cp1)`}(r,m),M=`
- void main() {
- ${g} rc = getOutputCoords();
-
- if(${A}) {
- ${e.output} = vec4(0);
- } else {
- ${_}
-
- ${e.output} = vec4(${T});
- }
- }
- `;return Object.assign(Object.assign({},h),{hasMain:!0,output:{dims:t.dims,type:t.type,textureType:c.TextureType.packed},shaderSource:M})})(f,l)})},2827:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.unpackFromChannel=n.getChannels=n.getVecChannels=void 0;const u=a(9390);function c(p,s){return(0,u.getGlChannels)(s).map(h=>`${p}.${h}`)}n.getVecChannels=c,n.getChannels=function(p,s){return s===1?[p]:c(p,s)},n.unpackFromChannel=function(){return`
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- int modCoord = imod(dim, 2);
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- }
-
- float getChannel(vec4 frag, vec2 innerDims) {
- vec2 modCoord = mod(innerDims, 2.);
- return modCoord.x == 0. ?
- (modCoord.y == 0. ? frag.r : frag.g) :
- (modCoord.y == 0. ? frag.b : frag.a);
- }
- `}},2870:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const u=a(246),c=a(2517),p=a(5060),s=a(2039),h={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,_)=>(o(m),[g.run(Object.assign(Object.assign({},h),{cacheHint:_.cacheKey,get:()=>l(g,m[0],_)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),_=g.attributes.getFloat("value",0),y=g.attributes.getInts("pads");return(0,u.createAttributeWithCacheKey)({mode:m,value:_,pads:y})},n.padV11=(g,m,_)=>{t(m);const y=f(g,m,_);return(0,n.padV2)(g,[m[0]],y)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const f=(g,m,_)=>{if(!g.session.isInitializer(m[1].dataId)||m.length>=3&&!g.session.isInitializer(m[2].dataId))throw new Error("dynamic pad attributes are not allowed");const y=Array.from(m[1].integerData),w=m.length>=3?m[2].floatData[0]:0;return(0,u.createAttributeWithCacheKey)({mode:_,pads:y,value:w})},l=(g,m,_)=>{const y=c.ShapeUtil.padShape(m.dims.slice(),_.pads),w=y.length,v=`
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- `;return`
- float padA(int m[${O}]) {
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- int offset = 0;
- int k = 0;
- ${A}
- vec2 coords = offsetToCoords(offset, ${y}, ${w});
- float value = getColorAsFloat(${g.texture2D}(A, coords));
- return value;
- }
- `},i=(g,m,_,y,w,v)=>{const S=m.length;let O="";for(let A=S-1;A>=0;--A)O+=`
- k = m[${A}] - ${v[A]};
- if (k < 0) { k = -k; }
- {
- const int _2n_1 = ${2*(m[A]-1)};
- k = int( mod( float(k), float(_2n_1) ) ) ;
- if(k >= ${m[A]}) { k = _2n_1 - k; }
- }
- offset += k * ${_[A]};
- `;return`
- float padA(int m[${S}]) {
- int offset = 0;
- int k = 0;
- ${O}
- vec2 coords = offsetToCoords(offset, ${y}, ${w});
- float value = getColorAsFloat(${g.texture2D}(A, coords));
- return value;
- }
- `},d=(g,m,_,y,w,v)=>{const S=m.length;let O="";for(let A=S-1;A>=0;--A)O+=`
- k = m[${A}] - ${v[A]};
- if (k < 0) k = 0;
- if (k >= ${m[A]}) k = ${m[A]-1};
- offset += k * ${_[A]};
- `;return`
- float padA(int m[${S}]) {
- int offset = 0;
- int k = 0;
- ${O}
- vec2 coords = offsetToCoords(offset, ${y}, ${w});
- float value = getColorAsFloat(${g.texture2D}(A, coords));
- return value;
- }
- `}},2143:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.globalMaxPool=n.parseMaxPoolAttributes=n.maxPool=n.parseGlobalAveragePoolAttributes=n.globalAveragePool=n.parseAveragePoolAttributes=n.averagePool=void 0;const u=a(246),c=a(2517),p=a(2039);n.averagePool=(d,g,m)=>{t(g);const _={name:"AveragePool",inputNames:["X"],inputTypes:[p.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},_),{get:()=>s(g,_,!1,m)}),g)]},n.parseAveragePoolAttributes=d=>{const g=d.attributes.getString("auto_pad","NOTSET"),m=d.attributes.getInt("ceil_mode",0),_=d.attributes.getInt("count_include_pad",0)!==0,y=d.attributes.getInts("kernel_shape"),w=d.attributes.getInts("strides",[]),v=d.attributes.getInts("pads",[]);if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,u.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:_,kernelShape:y,strides:w,pads:v})};const s=(d,g,m,_)=>{const[y,w]=f(d,_,m),v=c.ShapeUtil.size(y.kernelShape);let S="";y.countIncludePad?S+=`value /= float(${v});`:S+=`value /= float(${v} - pad);`;const O=`
- ${e(d[0].dims,y,"value += _X(x);",S,"0.0")}
- `;return Object.assign(Object.assign({},g),{output:{dims:w,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:O})};n.globalAveragePool=(d,g,m)=>{t(g);const _={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[p.TextureType.unpacked],cacheHint:`${m.countIncludePad}`};return[d.run(Object.assign(Object.assign({},_),{get:()=>s(g,_,!0,m)}),g)]},n.parseGlobalAveragePoolAttributes=d=>{const g=d.attributes.getInt("count_include_pad",0)!==0;return(0,u.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:g,kernelShape:[],strides:[],pads:[]})},n.maxPool=(d,g,m)=>{t(g);const _={name:"MaxPool",inputNames:["X"],inputTypes:[p.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},_),{get:()=>h(g,_,!1,m)}),g)]},n.parseMaxPoolAttributes=d=>{const g=d.attributes.getString("auto_pad","NOTSET"),m=d.attributes.getInt("ceil_mode",0),_=d.attributes.getInts("kernel_shape"),y=d.attributes.getInts("strides",[]),w=d.attributes.getInts("pads",[]),v=d.attributes.getInt("storage_order",0),S=d.attributes.getInts("dilations",[]);if(v!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return(0,u.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:!1,kernelShape:_,strides:y,pads:w,storageOrder:v,dilations:S})};const h=(d,g,m,_)=>{const[y,w]=f(d,_,m),v=`
- ${e(d[0].dims,y,`
- value = max(_X(x), value);
- `,"","-1e5")}
- `;return Object.assign(Object.assign({},g),{output:{dims:w,type:d[0].type,textureType:p.TextureType.unpacked},shaderSource:v})},f=(d,g,m)=>{const _=d[0].dims.slice(),y=Object.hasOwnProperty.call(g,"dilations"),w=g.kernelShape.slice(),v=g.strides.slice(),S=y?g.dilations.slice():[],O=g.pads.slice();c.PoolConvUtil.adjustPoolAttributes(m,_,w,v,S,O);const A=c.PoolConvUtil.computePoolOutputShape(m,_,v,S,w,O,g.autoPad),T=Object.assign({},g);return y?Object.assign(T,{kernelShape:w,strides:v,pads:O,dilations:S,cacheKey:g.cacheKey}):Object.assign(T,{kernelShape:w,strides:v,pads:O,cacheKey:g.cacheKey}),[T,A]},l={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},o={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[p.TextureType.unpacked]};n.globalMaxPool=(d,g)=>(t(g),[d.run(Object.assign(Object.assign({},o),{get:()=>h(g,o,!0,l)}),g)]);const t=d=>{if(!d||d.length!==1)throw new Error("Pool ops requires 1 input.");if(d[0].type!=="float32"&&d[0].type!=="float64")throw new Error("Invalid input type.")},e=(d,g,m,_,y)=>{const w=d.length;if(g.kernelShape.length<=2){const v=g.kernelShape[g.kernelShape.length-1],S=g.strides[g.strides.length-1],O=g.pads[g.pads.length/2-1],A=g.pads[g.pads.length-1],T=d[w-1];let M="",N="",B="";if(M=O+A!==0?`
- for (int i = 0; i < ${v}; i++) {
- x[${w} - 1] = indices[${w} - 1] * ${S} - ${O} + i;
- if (x[${w} - 1] < 0 || x[${w} - 1] >= ${T}) {
- pad++;
- continue;
- }
- ${m}
- }`:`
- for (int i = 0; i < ${v}; i++) {
- x[${w} - 1] = indices[${w} - 1] * ${S} - ${O} + i;
- ${m}
- }`,g.kernelShape.length===2){const $=g.kernelShape[g.kernelShape.length-2],L=g.strides[g.strides.length-2],H=g.pads[g.pads.length/2-2],C=g.pads[g.pads.length-2],z=d[w-2];N=H+C!==0?`
- for (int j = 0; j < ${$}; j++) {
- x[${w} - 2] = indices[${w} - 2] * ${L} - ${H} + j;
- if (x[${w} - 2] < 0 || x[${w} - 2] >= ${z}) {
- pad+= ${v};
- continue;
- }
- `:`
- for (int j = 0; j < ${$}; j++) {
- x[${w} - 2] = indices[${w} - 2] * ${L} - ${H} + j;
- `,B=`
- }
- `}return`
- float process(int indices[${w}]) {
- int x[${w}];
- copyVec(indices, x);
-
- float value = ${y};
- int pad = 0;
- ${N}
- ${M}
- ${B}
- ${_}
- return value;
- }
- `}{const v=c.ShapeUtil.size(g.kernelShape),S=c.ShapeUtil.computeStrides(g.kernelShape),O=S.length,A=g.pads.length,T=i(O),M=r(d,"inputDims"),N=r(g.pads,"pads"),B=r(S,"kernelStrides"),$=r(g.strides,"strides");let L="";return L=g.pads.reduce((H,C)=>H+C)?`
- if (x[j] >= inputDims[j] || x[j] < 0) {
- pad++;
- isPad = true;
- break;
- }
- }
- if (!isPad) {
- ${m}
- }`:`
- }
- ${m}
- `,`
- ${T}
- float process(int indices[${w}]) {
- int x[${w}];
- copyVec(indices, x);
- int offset[${O}];
- int pads[${A}];
- int inputDims[${w}];
- int kernelStrides[${O}];
- int strides[${O}];
- ${N}
- ${M}
- ${$}
- ${B}
-
- float value = ${y};
- int pad = 0;
- bool isPad = false;
- for (int i = 0; i < ${v}; i++) {
- offsetToIndices(i, kernelStrides, offset);
- isPad = false;
- for (int j = ${w} - ${O}; j < ${w}; j++) {
- x[j] = indices[j] * strides[j - ${w} + ${O}]
- + offset[j - ${w} + ${O}] - pads[j - 2];
- ${L}
- }
- ${_}
-
- return value;
- }
- `}},r=(d,g)=>{let m="";for(let _=0;_`
- void offsetToIndices(int offset, int[${d}] strides, out int[${d}] indices) {
- if (${d} == 0) {
- return;
- }
- for (int i = 0; i < ${d} - 1; ++i) {
- indices[i] = offset / strides[i];
- offset -= indices[i] * strides[i];
- }
- indices[${d} - 1] = offset;
- }`},4939:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reduceLogSumSquare=n.reduceLogSum=n.reduceProd=n.reduceMin=n.reduceMax=n.reduceMean=n.reduceSum=n.parseReduceAttributes=void 0;const u=a(246),c=a(782),p=a(2517),s=a(2039),h=(o,t,e,r,i)=>{l(t);const d={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[o.run(Object.assign(Object.assign({},d),{cacheHint:e.cacheKey,get:()=>f(o,t,e,r,i,d)}),t)]};n.parseReduceAttributes=o=>{const t=o.attributes.getInts("axes",[]),e=o.attributes.getInt("keepdims",1)===1;return(0,u.createAttributeWithCacheKey)({axes:t,keepDims:e})};const f=(o,t,e,r,i,d)=>{const g=[],m=t[0].dims.length||1,_=[],y=p.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),w=i(t,y);let v=w[1];for(let O=0;O=0||y.length===0?(e.keepDims&&g.push(1),v=`
- for(int j${O} = 0; j${O} < ${t[0].dims[O]}; j${O}++) {
- inputIdx[${O}] = j${O};
- ${v}
- }`):(_.push(`inputIdx[${O}] = outputIdx[${g.length}];`),g.push(t[0].dims[O]));const S=`
- float process(int outputIdx[${g.length||1}]) {
- float value; // final result
- int inputIdx[${m}]; // addressing input data
- ${_.join(`
-`)}
- ${w[0]} // init ops for reduce max/min
- ${v}
- ${w[2]} // final computation for reduce mean
- return value;
- }`;return Object.assign(Object.assign({},d),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},l=o=>{if(!o||o.length!==1)throw new Error("Reduce op requires 1 input.");if(c.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(o,t,e)=>h(o,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(o,t,e)=>h(o,t,e,"ReduceMean",(r,i)=>{let d=1;for(let g=0;g=0||i.length===0)&&(d*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${d}.;`]}),n.reduceMax=(o,t,e)=>h(o,t,e,"ReduceMax",(r,i)=>{const d=[];for(let g=0;g=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(`
-`)}
-value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(o,t,e)=>h(o,t,e,"ReduceMin",(r,i)=>{const d=[];for(let g=0;g=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(`
-`)}
-value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(o,t,e)=>h(o,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(o,t,e)=>h(o,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(o,t,e)=>h(o,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const u=a(2517),c=a(5060),p=a(2039),s=a(2827);n.createPackedReshape3DProgramInfoLoader=(h,f,l)=>{const o=(t=>({name:"Reshape (packed)",inputTypes:[p.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(l);return Object.assign(Object.assign({},o),{get:()=>((t,e,r,i)=>{const d=e.dims,g=i;let m="";for(let w=0;w<4;w++){let v="";switch(w){case 0:v="outputCoords = rc;";break;case 1:v="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:v="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:v="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=`
- ${v}
- ${w>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
- int flattenedIndex = getFlattenedIndex(outputCoords);
-
- ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
- vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
-
- result[${w}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
-
- ${w>0?"}":""}
- `}const _=(0,c.getGlsl)(t.session.backend.glContext.version),y=`
- ${function(w){const v=u.ShapeUtil.computeStrides(w),S=["b","r","c"],O="index";return`
- ivec3 inputCoordsFromReshapedOutCoords(int index) {
- ${v.map((A,T)=>`int ${S[T]} = ${O} / ${A}; ${T===v.length-1?`int ${S[T+1]} = ${O} - ${S[T]} * ${A}`:`index -= ${S[T]} * ${A}`};`).join("")}
- return ivec3(b, r, c);
- }
- `}(d)}
- ${function(w){const v=u.ShapeUtil.computeStrides(w);return`
- int getFlattenedIndex(ivec3 coords) {
- // reverse y, z order
- return coords.x * ${v[0]} + coords.z * ${v[1]} + coords.y;
- }
-`}(g)}
- ${(0,s.unpackFromChannel)()}
-
- void main() {
- ivec3 rc = getOutputCoords();
-
- vec4 result = vec4(0.0);
-
- ivec3 outputCoords;
- int rows = ${g[2]};
- int cols = ${g[1]};
-
- ${m}
- ${_.output} = result;
- }
- `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:p.TextureType.packed},shaderSource:y,hasMain:!0})})(h,f,o,l)})},n.processDims3D=function(h){if(h.length===0)return[1,1,1];let f=1;for(let l=0;l1?h[h.length-2]:1,h[h.length-1]]},n.isReshapeCheap=function(h,f){let l=!1;return l=h.length===0||f.length===0||(h.length<2||f.length<2?h[h.length-1]===f[f.length-1]:h[h.length-1]===f[f.length-1]&&h[h.length-2]===f[f.length-2]),l}},718:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const u=a(2517);n.reshape=(c,p)=>{const s=u.ShapeUtil.calculateReshapedDims(p[0].dims,p[1].integerData);return c.session.pack?[c.reshapePacked(p[0],s)]:[c.reshapeUnpacked(p[0],s)]}},2268:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const u=a(5060),c=a(2039),p=a(9390),s=a(2827),h=a(9793),f={name:"Resize",inputNames:["A"],inputTypes:[c.TextureType.packed]};n.resize=(r,i,d)=>((0,h.validateInputs)(i,d),[r.run(Object.assign(Object.assign({},f),{cacheHint:d.cacheKey,get:()=>l(r,i,d)}),i)]),n.parseResizeAttributesV10=r=>(0,h.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,h.parseUpsampleAttributes)(r,11);const l=(r,i,d)=>{const g=(0,u.getGlsl)(r.session.backend.glContext.version),[m,_]=o(i,d);if(m.every(L=>L===1)&&d.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},f),{output:{dims:_,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:`void main() {
- vec4 v = ${g.texture2D}(X, TexCoords);
- ${g.output} = v;
- }`});const y=_.length;if(y<2)throw new Error(`output dimension should be at least 2, but got ${y}`);const w=_[y-2],v=_[y-1],S=i[0].dims;if(y!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${y}`);const O=S[y-2],A=S[y-1],T=m[y-2],M=m[y-1];let N="";if(d.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${d.mode}'`);switch(d.coordinateTransformMode){case"asymmetric":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- return vec4(coords) / scaleWHWH;
- }
- `;break;case"half_pixel":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
- }
- `;break;case"pytorch_half_pixel":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- vec4 fcoords = vec4(coords);
- return vec4(
- ${v}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
- ${w}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
- ${v}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
- ${w}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
- );
- }
- `;break;case"align_corners":N=`
- vec4 getSourceFracIndex(ivec4 coords) {
- vec4 resized = vec4(${v}.0 - 1.0, ${w}.0 - 1.0, ${v}.0 - 1.0,
- ${w}.0 - 1.0);
- vec4 original = vec4(${A}.0 - 1.0, ${O}.0 - 1.0, ${A}.0 - 1.0,
- ${O}.0 - 1.0);
- vec4 new_scale = original / resized;
- return vec4(coords) * new_scale;
- }
- `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode: '${d.coordinateTransformMode}'`)}const B=(0,p.getCoordsDataType)(y),$=`
- const vec2 inputWH = vec2(${O}.0, ${A}.0);
- const vec4 scaleWHWH = vec4(float(${T}), float(${M}), float(${T}), float(${M}));
- ${(0,s.unpackFromChannel)()}
- ${N}
- float getAValue(int x10, int r, int c, int d) {
- return getChannel(getA(x10, r, c, d), vec2(c, d));
- }
- void main() {
- ${B} rc = getOutputCoords();
-
- int batch = rc[0];
- int depth = rc[1];
-
- // retrieve the 4 coordinates that is used in the 4 packed output values.
- ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);
-
- // calculate the source index in fraction
- vec4 sourceFrac = getSourceFracIndex(coords);
-
- // get the lower and upper bound of the 4 values that will be packed into one texel.
- ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));
- ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));
- ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));
- ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));
-
- bool hasNextRow = rc.w < ${w-1};
- bool hasNextCol = rc.z < ${v-1};
-
- // pack x00, x01, x10, x11's top-left corner into one vec4 structure
- vec4 topLeft = vec4(
- getAValue(batch, depth, x00.x, x00.y),
- hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);
-
- // pack x00, x01, x10, x11's top-right corner into one vec4 structure
- vec4 topRight = vec4(
- getAValue(batch, depth, x00.x, x00.w),
- hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);
-
- // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure
- vec4 bottomLeft = vec4(
- getAValue(batch, depth, x00.z, x00.y),
- hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);
-
- // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure
- vec4 bottomRight = vec4(
- getAValue(batch, depth, x00.z, x00.w),
- hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,
- hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,
- (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);
-
- // calculate the interpolation fraction on u and v direction
- vec4 frac = vec4(sourceFrac) - floor(sourceFrac);
- vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));
-
- vec4 top = mix(topLeft, topRight, clampFrac.ywyw);
- vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);
- vec4 newValue = mix(top, bottom, clampFrac.xxzz);
-
- ${g.output} = vec4(newValue);
- }
- `;return Object.assign(Object.assign({},f),{output:{dims:_,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:$})},o=(r,i)=>{const d=r[0].dims;let g,m=i.scales;if(m.length===0){const y=r[i.scalesInputIdx];if(y&&y.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(y,i.mode,i.isResize)}else{const w=r[i.sizesInputIdx];if(!w||w.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(w.integerData),m=e(g,d,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const _=g||d.map((y,w)=>Math.floor(y*m[w]));return[m,_]},t=(r,i,d)=>{const g=Array.from(r.floatData);return(0,h.scalesValidation)(g,i,d),g},e=(r,i,d,g)=>{const m=i.length,_=new Array(m);for(let y=0,w=m;y{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const u=a(9162);n.shape=(p,s)=>(c(s),[new u.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const c=p=>{if(!p||p.length!==1)throw new Error("Shape requires 1 input.")}},2278:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const u=a(246),c=a(782),p=a(2517),s=a(2039),h={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(l(r),[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),d=e.attributes.getInts("axes",[]);return(0,u.createAttributeWithCacheKey)({starts:r,ends:i,axes:d})};const f=(e,r,i)=>{const d=i.axes.length===0?r.dims.slice(0).map((S,O)=>O):i.axes,g=p.ShapeUtil.normalizeAxes(d,r.dims.length),m=i.starts.map((S,O)=>S>r.dims[g[O]]-1?r.dims[g[O]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[O]])),_=i.ends.map((S,O)=>S>r.dims[g[O]]-1?r.dims[g[O]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[O]])),y=r.dims.slice(),w=[];for(let S=0;S0&&w.push(`outputIdx[${g[S]}] += ${m[S]};`);const v=`
- float process(int outputIdx[${y.length}]) {
- ${w.join(`
- `)}
- return _A(outputIdx);
- }`;return Object.assign(Object.assign({},h),{output:{dims:y,type:r.type,textureType:s.TextureType.unpacked},shaderSource:v})},l=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(c.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=o(e,r);return[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),[r[0]])]};const o=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),d=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:d,axes:g,cacheKey:`${g};${i};${d}`}},t=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if(e[1].type!=="int32"||e[1].dims.length!==1)throw new Error("Invalid input type.");if(e[2].type!=="int32"||e[2].dims.length!==1)throw new Error("Invalid input type.");if(e.length>=4&&(e[3].type!=="int32"||e[3].dims.length!==1))throw new Error("Invalid input type.");if(e.length>=5&&(e[4].type!=="int32"||e[4].dims.length!==1))throw new Error("Invalid input type.")}},5524:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.softmaxV13=n.parseSoftmaxAttributesV13=n.parseSoftmaxAttributes=n.softmax=void 0;const u=a(246),c=a(2517),p=a(5060),s=a(2039),h=a(3738),f={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[s.TextureType.unpacked]},l={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},o={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]};n.softmax=(g,m,_)=>{d(m);const y=m[0].dims.slice(),w=c.ShapeUtil.normalizeAxis(_.axis,y.length),v=c.ShapeUtil.sizeToDimension(y,w),S=c.ShapeUtil.sizeFromDimension(y,w);return t(g,m,_,v,S)},n.parseSoftmaxAttributes=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",1)}),n.parseSoftmaxAttributesV13=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",-1)}),n.softmaxV13=(g,m,_)=>{d(m);const y=m[0].dims.slice(),w=c.ShapeUtil.normalizeAxis(_.axis,y.length),v=y.length,S=w!==v-1,O=[];let A,T=[],M=[];S&&(T=Array.from({length:v}).map((L,H)=>H),T[w]=v-1,T[v-1]=w,T.map(L=>O.push(y[L])),A=(0,u.createAttributeWithCacheKey)({perm:T}),M=(0,h.transpose)(g,m,A));const N=S?c.ShapeUtil.sizeToDimension(O,v-1):c.ShapeUtil.sizeToDimension(y,v-1),B=S?c.ShapeUtil.sizeFromDimension(O,v-1):c.ShapeUtil.sizeFromDimension(y,v-1),$=t(g,S?M:m,_,N,B);return S?(0,h.transpose)(g,$,A):$};const t=(g,m,_,y,w)=>{const v=e(g,m[0],y,w,[y]),S=g.run(Object.assign(Object.assign({},f),{cacheHint:_.cacheKey,get:()=>v}),m),O=r(g,m[0],y,w,v.output.dims,[y]),A=g.run(Object.assign(Object.assign({},l),{cacheHint:_.cacheKey,get:()=>O}),[m[0],S]),T=i(g,m[0],y,w,v.output.dims,O.output.dims);return[g.run(Object.assign(Object.assign({},o),{cacheHint:_.cacheKey,get:()=>T}),[m[0],S,A])]},e=(g,m,_,y,w)=>{const[v,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=w.length;if(_<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1)throw new Error("Dimensionality of the output should be 1");if(w[0]!==_)throw new Error("Shape of the output should be equal to logical row count");const A=(0,p.getGlsl)(g.session.backend.glContext.version),T=`
- float process(int[${O}] indices) {
- int logical_row_start_offset = indices[0] * ${y};
-
- float max = getColorAsFloat(${A.texture2D}(A, offsetToCoords(logical_row_start_offset, ${v},
- ${S} )));
- for(int i=1; i<${y}; ++i)
- {
- float current = getColorAsFloat(${A.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
- ${v}, ${S})));
- if(current > max)
- max = current;
- }
-
- return max;
- }`;return Object.assign(Object.assign({},f),{output:{dims:w,type:m.type,textureType:s.TextureType.unpacked},shaderSource:T})},r=(g,m,_,y,w,v)=>{const[S,O]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),A=v.length;if(_<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(v.length!==1)throw new Error("Dimensionality of the output should be 1");if(v[0]!==_)throw new Error("Shape of the output should be equal to logical row count");if(w.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==_)throw new Error("Shape of the intermediate results should be equal to logical row count");const T=`
- float process(int[${A}] indices) {
- int logical_row_start_offset = indices[0] * ${y};
-
- float norm_factor = 0.0;
- float max = _Max(indices);
- for(int i=0; i<${y}; ++i)
- {
- norm_factor += exp(getColorAsFloat(${(0,p.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
- ${S}, ${O}))) - max);
- }
-
- return norm_factor;
- }`;return Object.assign(Object.assign({},l),{output:{dims:v,type:m.type,textureType:s.TextureType.unpacked},shaderSource:T})},i=(g,m,_,y,w,v)=>{const[S,O]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),A=m.dims.length;if(_<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1||v.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==_||v[0]!==_)throw new Error("Shape of the intermediate results should be equal to logical row count");const T=`
- float process(int[${A}] indices) {
-
- // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
- int offset = coordsToOffset(TexCoords, ${S}, ${O});
-
- //determine the logical row for this index
- int logical_row_index[1];
- logical_row_index[0] = offset / ${y};
-
- float norm_factor = _Norm(logical_row_index);
-
- // avoid possible division by 0
- // if norm_facor is 0, all elements are zero
- // if so, return 0
- if(norm_factor == 0.0)
- return 0.0;
-
- return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
- }`;return Object.assign(Object.assign({},o),{output:{dims:m.dims,type:m.type,textureType:s.TextureType.unpacked},shaderSource:T})},d=g=>{if(!g||g.length!==1)throw new Error("Softmax requires 1 input.");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type")}},5975:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSplitAttributes=n.split=void 0;const u=a(246),c=a(2517),p=a(2039),s={name:"Split",inputNames:["A"],inputTypes:[p.TextureType.unpacked]};n.split=(o,t,e)=>{l(t);const r=c.ShapeUtil.normalizeAxis(e.axis,t[0].dims.length),i=h(o,t,r,e),d=[];for(let g=0;gf(o,t[0],e,r,g)}),t));return d},n.parseSplitAttributes=o=>{const t=o.attributes.getInt("axis",0),e=o.attributes.getInts("split",[]),r=o.outputs.length;return(0,u.createAttributeWithCacheKey)({axis:t,split:e,numOutputs:r})};const h=(o,t,e,r)=>{const[,i]=c.SplitUtil.splitShape(t[0].dims,e,r.split,r.numOutputs);return i.length},f=(o,t,e,r,i)=>{const[d,g]=c.SplitUtil.splitShape(t.dims,r,e.split,e.numOutputs),m=g[i],_=d[i],y=`
- float process(int indices[${_.length}]) {
- indices[${r}] += ${m};
- return _A(indices);
- }
- `;return Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey}:${i}`,output:{dims:_,type:t.type,textureType:p.TextureType.unpacked},shaderSource:y})},l=o=>{if(!o||o.length!==1)throw new Error("Split requires one input.");if(o[0].type!=="int8"&&o[0].type!=="uint8"&&o[0].type!=="int16"&&o[0].type!=="uint16"&&o[0].type!=="int32"&&o[0].type!=="uint32"&&o[0].type!=="float32"&&o[0].type!=="float64"&&o[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSqueezeAttributes=n.squeezeV13=n.squeeze=void 0;const u=a(2517);n.squeeze=(s,h,f)=>{c(h);const l=u.ShapeUtil.squeezeShape(h[0].dims,f);return[s.reshapeUnpacked(h[0],l)]},n.squeezeV13=(s,h)=>(p(h),(0,n.squeeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseSqueezeAttributes=s=>s.attributes.getInts("axes");const c=s=>{if(!s||s.length!==1)throw new Error("Squeeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},p=s=>{if(!s||s.length!==2)throw new Error("Squeeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sum=void 0;const u=a(5060),c=a(2039);n.sum=(h,f)=>{s(f);const l={name:"Sum",inputNames:f.map((o,t)=>`X${t}`),inputTypes:new Array(f.length).fill(c.TextureType.unpacked)};return[h.run(Object.assign(Object.assign({},l),{get:()=>p(h,f,l)}),f)]};const p=(h,f,l)=>{const o=(0,u.getGlsl)(h.session.backend.glContext.version),t=f[0].dims.slice(),e=`
- void main() {
- vec4 result = ${f.map((r,i)=>`${o.texture2D}(X${i},TexCoords)`).join(" + ")};
- ${o.output} = result;
- }
- `;return Object.assign(Object.assign({},l),{output:{dims:t,type:f[0].type,textureType:c.TextureType.unpacked},hasMain:!0,shaderSource:e})},s=h=>{if(!h||h.length===0)throw new Error("Sum requires inputs.");const f=h[0].dims.length;for(let l=1;l{Object.defineProperty(n,"__esModule",{value:!0}),n.tile=void 0;const u=a(782),c=a(2039);n.tile=(h,f)=>{s(f);const l={name:"Tile",inputNames:["A"],inputTypes:[c.TextureType.unpacked]};return[h.run(Object.assign(Object.assign({},l),{get:()=>p(h,f,l)}),f)]};const p=(h,f,l)=>{const o=f[0].dims.slice(),t=new Array(o.length),e=[];for(let d=0;d{if(!h||h.length!==2)throw new Error("Tile requires 2 input.");if(h[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(h[1].dims[0]!==h[0].dims.length)throw new Error("Invalid input shape.");if(u.NUMBER_TYPES.indexOf(h[0].type)===-1)throw new Error("Invalid input type.");if(h[1].type!=="int32"&&h[1].type!=="int16")throw new Error("Invalid repeat type.")}},3738:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseTransposeAttributes=n.transpose=void 0;const u=a(246),c=a(2517),p=a(2039),s={name:"Transpose",inputNames:["A"],inputTypes:[p.TextureType.unpacked]};n.transpose=(e,r,i)=>(t(r),[e.run(Object.assign(Object.assign({},s),{cacheHint:i.cacheKey,get:()=>h(e,r[0],i.perm)}),r)]),n.parseTransposeAttributes=e=>(0,u.createAttributeWithCacheKey)({perm:e.attributes.getInts("perm",[])});const h=(e,r,i)=>{const d=r.dims;i=f(d,i);const g=l(d,i),m=d.length,_=`
- ${o("perm",i,m)}
- float process(int indices[${m}]) {
- int a[${m}];
- perm(a, indices);
- return _A(a);
- }`;return Object.assign(Object.assign({},s),{output:{dims:g,type:r.type,textureType:p.TextureType.unpacked},shaderSource:_})},f=(e,r)=>(r&&r.length!==e.length&&(r=[...e.keys()].reverse()),r),l=(e,r)=>(r=f(e,r),c.ShapeUtil.sortBasedOnPerm(e,r)),o=(e,r,i)=>{const d=[];d.push(`void ${e}(out int a[${i}], int src[${i}]) {`);for(let g=0;g{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(e[0].type!=="float32"&&e[0].type!=="float64")throw new Error("input should be float tensor")}},8710:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const u=a(5060),c=a(2039);n.encodeAsUint8=(p,s)=>{const h=s.shape,f=(0,u.getGlsl)(p.session.backend.glContext.version),l=`
- const float FLOAT_MAX = 1.70141184e38;
- const float FLOAT_MIN = 1.17549435e-38;
-
- bool isNaN(float val) {
- return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;
- }
-
- highp vec4 encodeAsUint8(highp float v) {
- if (isNaN(v)) {
- return vec4(255, 255, 255, 255);
- }
-
- highp float av = abs(v);
-
- if(av < FLOAT_MIN) {
- return vec4(0.0, 0.0, 0.0, 0.0);
- } else if(v > FLOAT_MAX) {
- return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
- } else if(v < -FLOAT_MAX) {
- return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
- }
-
- highp vec4 c = vec4(0,0,0,0);
-
- highp float e = floor(log2(av));
- highp float m = exp2(fract(log2(av))) - 1.0;
-
- c[2] = floor(128.0 * m);
- m -= c[2] / 128.0;
- c[1] = floor(32768.0 * m);
- m -= c[1] / 32768.0;
- c[0] = floor(8388608.0 * m);
-
- highp float ebias = e + 127.0;
- c[3] = floor(ebias / 2.0);
- ebias -= c[3] * 2.0;
- c[2] += floor(ebias) * 128.0;
-
- c[3] += 128.0 * step(0.0, -v);
-
- return c / 255.0;
- }
-
- void main() {
- float value = ${f.texture2D}(X,TexCoords).r;
- ${f.output} = encodeAsUint8(value);
- }`,o={name:"Uint8Encode",inputTypes:[c.TextureType.unpacked],inputNames:["X"],output:{dims:h,type:s.tensor.type,textureType:c.TextureType.downloadUint8AsFloat},shaderSource:l,hasMain:!0};return p.executeProgram(o,[s.tensor])}},4909:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tanh=n.tan=n.sqrt=n.sin=n.sigmoid=n.relu=n.not=n.neg=n.log=n.parseLeakyReluAttributes=n.leakyRelu=n.identity=n.floor=n.exp=n.parseEluAttributes=n.elu=n.cos=n.ceil=n.clipV11=n.parseClipAttributes=n.clip=n.atan=n.asin=n.acos=n.abs=n.glslTanh=n.glslTan=n.glslSqrt=n.glslSigmoid=n.glslRelu=n.glslSin=n.glslNot=n.glslNeg=n.glslLog=n.glslLeakyRelu=n.glslIdentity=n.glslClip=n.glslFloor=n.glslExp=n.glslElu=n.glslCos=n.glslCeil=n.glslAtan=n.glslAsin=n.glslAcos=n.glslAbs=void 0;const u=a(246),c=a(2517),p=a(8520),s=a(5060),h=a(2039);function f(){return $("abs")}function l(){return $("acos")}function o(){return $("asin")}function t(){return $("atan")}function e(){return $("ceil")}function r(){return $("cos")}function i(C){const z="elu";return{body:`
- const float alpha = float(${C});
-
- float ${z}_(float a) {
- return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
- }
- vec4 ${z}_(vec4 v) {
- return vec4(${z}_(v.x), ${z}_(v.y), ${z}_(v.z), ${z}_(v.w));
- }
- `,name:z,type:p.FunctionType.ValueBased}}function d(){return $("exp")}function g(){return $("floor")}function m(C,z){const J="clip";return{body:`
- const float min = float(${C});
- const float max = float(${z});
-
- float ${J}_(float a) {
- return clamp(a, min, max);
- }
- vec4 ${J}_(vec4 v) {
- return clamp(v, min, max);
- }
- `,name:J,type:p.FunctionType.ValueBased}}function _(){const C="indentity";return{body:`
- float ${C}_(float a) {
- return a;
- }
- vec4 ${C}_(vec4 v) {
- return v;
- }
- `,name:C,type:p.FunctionType.ValueBased}}function y(C){const z="leakyRelu";return{body:`
- const float alpha = float(${C});
-
- float ${z}_(float a) {
- return a < 0.0 ? a * alpha : a;
- }
- vec4 ${z}_(vec4 v) {
- return vec4(${z}_(v.x), ${z}_(v.y), ${z}_(v.z), ${z}_(v.w));
- }
- `,name:z,type:p.FunctionType.ValueBased}}function w(){return $("log")}function v(){const C="neg";return{body:`
- float ${C}_(float a) {
- return -a;
- }
- vec4 ${C}_(vec4 v) {
- return -v;
- }
- `,name:C,type:p.FunctionType.ValueBased}}function S(){const C="not";return{body:`
- float ${C}_(float a) {
- return float( ! bool(a) );
- }
- bool ${C}_(bool a) {
- return !a;
- }
- vec4 ${C}_(vec4 v) {
- return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
- }
- bvec4 ${C}_(bvec4 v) {
- return bvec4(!v.x, !v.y, !v.z, !v.w);
- }
- `,name:C,type:p.FunctionType.ValueBased}}function O(){return $("sin")}function A(){const C="relu";return{body:`
- float ${C}_(float a) {
- return max( a, 0.0 );
- }
- vec4 ${C}_(vec4 v) {
- return max( v, 0.0 );
- }
- `,name:C,type:p.FunctionType.ValueBased}}function T(){const C="sigmoid";return{body:`
- float ${C}_(float a) {
- return 1.0 / (1.0 + exp(-a));
- }
- vec4 ${C}_(vec4 v) {
- return 1.0 / (1.0 + exp(-v));
- }
- `,name:C,type:p.FunctionType.ValueBased}}function M(){return $("sqrt")}function N(){return $("tan")}function B(){const C="tanh";return{body:`
- float ${C}_(float a) {
- a = clamp(a, -10., 10.);
- a = exp(2.*a);
- return (a - 1.) / (a + 1.);
- }
- vec4 ${C}_(vec4 v) {
- v = clamp(v, -10., 10.);
- v = exp(2.*v);
- return (v - 1.) / (v + 1.);
- }
- `,name:C,type:p.FunctionType.ValueBased}}function $(C){return{body:`
- float ${C}_(float a) {
- return ${C}(a);
- }
- vec4 ${C}_(vec4 v) {
- return ${C}(v);
- }
- `,name:C,type:p.FunctionType.ValueBased}}n.glslAbs=f,n.glslAcos=l,n.glslAsin=o,n.glslAtan=t,n.glslCeil=e,n.glslCos=r,n.glslElu=i,n.glslExp=d,n.glslFloor=g,n.glslClip=m,n.glslIdentity=_,n.glslLeakyRelu=y,n.glslLog=w,n.glslNeg=v,n.glslNot=S,n.glslSin=O,n.glslRelu=A,n.glslSigmoid=T,n.glslSqrt=M,n.glslTan=N,n.glslTanh=B;const L=(C,z,J,X)=>{const te=C.session.pack?h.TextureType.packed:h.TextureType.unpacked,ne={name:J.name,inputTypes:[te],inputNames:["A"],cacheHint:X};return Object.assign(Object.assign({},ne),{get:()=>((me,Me,Oe,ce)=>{const Te=me.session.pack?h.TextureType.packed:h.TextureType.unpacked,ye=(0,s.getGlsl)(me.session.backend.glContext.version);return Object.assign(Object.assign({},Me),{output:{dims:Oe.dims,type:Oe.type,textureType:Te},shaderSource:`
- ${ce.body}
- void main() {
- vec4 v = ${ye.texture2D}(A, TexCoords);
- v = ${ce.name}_(v);
- ${ye.output} = v;
- }
- `,hasMain:!0})})(C,ne,z,J)})};n.abs=(C,z)=>[C.run(L(C,z[0],f()),z)],n.acos=(C,z)=>[C.run(L(C,z[0],l()),z)],n.asin=(C,z)=>[C.run(L(C,z[0],o()),z)],n.atan=(C,z)=>[C.run(L(C,z[0],t()),z)],n.clip=(C,z,J)=>[C.run(L(C,z[0],m(J.min,J.max),J.cacheKey),z)],n.parseClipAttributes=C=>(0,u.createAttributeWithCacheKey)({min:C.attributes.getFloat("min",c.MIN_CLIP),max:C.attributes.getFloat("max",c.MAX_CLIP)}),n.clipV11=(C,z)=>{const J=H(C,z);return(0,n.clip)(C,[z[0]],J)};const H=(C,z)=>{if(z.length>=3&&(!C.session.isInitializer(z[1].dataId)||!C.session.isInitializer(z[2].dataId)))throw new Error("dynamic clip attributes are not allowed");const J=z.length>=3?z[1].numberData[0]:c.MIN_CLIP,X=z.length>=3?z[2].numberData[0]:c.MAX_CLIP;return(0,u.createAttributeWithCacheKey)({min:J,max:X})};n.ceil=(C,z)=>[C.run(L(C,z[0],e()),z)],n.cos=(C,z)=>[C.run(L(C,z[0],r()),z)],n.elu=(C,z,J)=>[C.run(L(C,z[0],i(J.alpha),J.cacheKey),z)],n.parseEluAttributes=C=>(0,u.createAttributeWithCacheKey)({alpha:C.attributes.getFloat("alpha",1)}),n.exp=(C,z)=>[C.run(L(C,z[0],d()),z)],n.floor=(C,z)=>[C.run(L(C,z[0],g()),z)],n.identity=(C,z)=>[C.run(L(C,z[0],_()),z)],n.leakyRelu=(C,z,J)=>[C.run(L(C,z[0],y(J.alpha),J.cacheKey),z)],n.parseLeakyReluAttributes=C=>(0,u.createAttributeWithCacheKey)({alpha:C.attributes.getFloat("alpha",.01)}),n.log=(C,z)=>[C.run(L(C,z[0],w()),z)],n.neg=(C,z)=>[C.run(L(C,z[0],v()),z)],n.not=(C,z)=>[C.run(L(C,z[0],S()),z)],n.relu=(C,z)=>[C.run(L(C,z[0],A()),z)],n.sigmoid=(C,z)=>[C.run(L(C,z[0],T()),z)],n.sin=(C,z)=>[C.run(L(C,z[0],O()),z)],n.sqrt=(C,z)=>[C.run(L(C,z[0],M()),z)],n.tan=(C,z)=>[C.run(L(C,z[0],N()),z)],n.tanh=(C,z)=>[C.run(L(C,z[0],B()),z)]},5611:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createUnpackProgramInfoLoader=n.createUnpackProgramInfo=void 0;const u=a(5060),c=a(2039),p=a(9390),s=a(2827),h={name:"unpack",inputNames:["A"],inputTypes:[c.TextureType.packed]};n.createUnpackProgramInfo=(f,l)=>{const o=l.dims.length,t=(0,s.getChannels)("rc",o),e=t.slice(-2),r=(0,p.getCoordsDataType)(o),i=(0,s.unpackFromChannel)(),d=l.dims.length===0?"":function(_,y){if(_===1)return"rc";let w="";for(let v=0;v<_;v++)w+=y[v],v<_-1&&(w+=",");return w}(o,t),g=o<=1?"rc":`vec2(${e.join(",")})`,m=`
- ${i}
- void main() {
- ${r} rc = getOutputCoords();
-
- // Sample the texture with the coords to get the rgba channel value.
- vec4 packedInput = getA(${d});
-
- ${(0,u.getGlsl)(f.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${g}), 0, 0, 0);
- }
- `;return Object.assign(Object.assign({},h),{hasMain:!0,output:{dims:l.dims,type:l.type,textureType:c.TextureType.unpacked},shaderSource:m})},n.createUnpackProgramInfoLoader=(f,l)=>Object.assign(Object.assign({},h),{get:()=>(0,n.createUnpackProgramInfo)(f,l)})},8428:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseUnsqueezeAttributes=n.unsqueezeV13=n.unsqueeze=void 0;const u=a(2517);n.unsqueeze=(s,h,f)=>{c(h);const l=u.ShapeUtil.unsqueezeShape(h[0].dims,f);return[s.reshapeUnpacked(h[0],l)]},n.unsqueezeV13=(s,h)=>(p(h),(0,n.unsqueeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseUnsqueezeAttributes=s=>s.attributes.getInts("axes");const c=s=>{if(!s||s.length!==1)throw new Error("Unsqueeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},p=s=>{if(!s||s.length!==2)throw new Error("Unsqueeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},9793:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.scalesValidation=n.validateInputs=n.parseUpsampleAttributes=n.parseUpsampleAttributesV9=n.parseUpsampleAttributesV7=n.upsample=void 0;const u=a(246),c=a(5060),p=a(2039),s={name:"Upsample",inputNames:["X"],inputTypes:[p.TextureType.unpacked]};n.upsample=(f,l,o)=>((0,n.validateInputs)(l,o),[f.run(Object.assign(Object.assign({},s),{cacheHint:o.cacheKey,get:()=>h(f,l,o)}),l)]),n.parseUpsampleAttributesV7=f=>(0,n.parseUpsampleAttributes)(f,7),n.parseUpsampleAttributesV9=f=>(0,n.parseUpsampleAttributes)(f,9),n.parseUpsampleAttributes=(f,l)=>{const o=l>=10,t=f.attributes.getString("mode","nearest");if(t!=="nearest"&&t!=="linear"&&(l<11||t!=="cubic"))throw new Error(`unrecognized mode: ${t}`);let e=[];l<9&&(e=f.attributes.getFloats("scales"),(0,n.scalesValidation)(e,t,o));const r=f.attributes.getFloat("extrapolation_value",0),i=l>10?f.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(i)===-1)throw new Error(`coordinate_transform_mode '${i}' is not supported`);const d=i==="tf_crop_and_resize",g=d,m=t==="nearest"&&l>=11?f.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(m)===-1)throw new Error(`nearest_mode '${m}' is not supported`);const _=f.attributes.getFloat("cubic_coeff_a",-.75),y=f.attributes.getInt("exclude_outside",0)!==0;if(y&&t!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");const w=l<11||t==="nearest"&&i==="asymmetric"&&m==="floor";let v=0,S=0,O=0;return l>10?f.inputs.length>2?(v=1,S=2,O=3):(S=1,O=2):l===9&&(S=1),(0,u.createAttributeWithCacheKey)({opset:l,isResize:o,mode:t,scales:e,extrapolationValue:r,coordinateTransformMode:i,useExtrapolation:g,needRoiInput:d,nearestMode:m,cubicCoefficientA:_,excludeOutside:y,useNearest2xOptimization:w,roiInputIdx:v,scalesInputIdx:S,sizesInputIdx:O})};const h=(f,l,o)=>{const t=(0,c.getGlsl)(f.session.backend.glContext.version),[e,r]=f.calculateTextureWidthAndHeight(l[0].dims,p.TextureType.unpacked),i=l[0].dims.map((O,A)=>Math.floor(O*o.scales[A])),[d,g]=f.calculateTextureWidthAndHeight(i,p.TextureType.unpacked),m=i.length,_=new Array(m),y=new Array(m);let w=`
- int output_pitches[${m}];
- int input_pitches[${m}];
- `;for(let O=m-1;O>=0;O--)_[O]=O===m-1?1:_[O+1]*i[O+1],y[O]=O===m-1?1:y[O+1]*l[0].dims[O+1],w+=`
- output_pitches[${O}] = ${_[O]};
- input_pitches[${O}] = ${y[O]};
- `;const v=`
- float getInputFloat(int index) {
- vec2 coords = offsetToCoords(index, ${e}, ${r});
- float value = getColorAsFloat(${t.texture2D}(X, coords));
- return value;
- }
- `,S=o.mode==="nearest"?`
- ${v}
- float process(int indices[${m}]) {
- int input_index = 0;
- int output_index = coordsToOffset(TexCoords, ${d}, ${g});
-
- ${w}
-
- int d, m;
- for (int dim = 0; dim < ${m}; ++dim) {
- d = output_index / output_pitches[dim];
- m = output_index - d * output_pitches[dim];
- output_index = m;
-
- if (scales[dim] != 1 && d > 0) {
- int d2 = d / scales[dim];
- m = d - d2 * scales[dim];
- d = d2;
- }
- input_index += input_pitches[dim] * d;
- }
-
- return getInputFloat(input_index);
- }`:m===4?`
- ${v}
- float process(int indices[4]) {
- int input_index = 0;
- int output_index = coordsToOffset(TexCoords, ${d}, ${g});
-
- ${w}
-
- int m;
- int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
- index_of_dim0 = output_index / output_pitches[0];
- m = output_index - index_of_dim0 * output_pitches[0];
- index_of_dim1 = m / output_pitches[1];
- m = m - index_of_dim1 * output_pitches[1];
- index_of_dim2 = m / output_pitches[2];
- m = m - index_of_dim2 * output_pitches[2];
- index_of_dim3 = m;
-
- int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
- index_of_input_dim2 = index_of_dim2 / scales[2];
- y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
- index_of_input_dim3 = index_of_dim3 / scales[3];
- x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
-
- input_index = index_of_dim0 * input_pitches[0] +
- index_of_dim1 * input_pitches[1] +
- index_of_input_dim2 * input_pitches[2] +
- index_of_input_dim3;
-
- float x00 = getInputFloat(input_index);
- float x10, x01, x11;
-
- bool end_of_dim2 = false;
- if (index_of_input_dim2 == (${l[0].dims[2]} - 1)) {
- // It's the end in dimension 2
- x01 = x00;
- end_of_dim2 = true;
- } else {
- x01 = getInputFloat(input_index + input_pitches[2]);
- }
-
- if (index_of_input_dim3 == (input_pitches[2] - 1)) {
- // It's the end in dimension 3
- x10 = x00;
- x11 = x01;
- }
- else {
- x10 = getInputFloat(input_index + 1);
- x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
- }
-
- float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
- float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
- return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
- }`:`
- ${v}
- float process(int indices[2]) {
- int input_index = 0;
- int output_index = coordsToOffset(TexCoords, ${d}, ${g});
-
- ${w}
-
- int m;
- int index_of_dim0, index_of_dim1;
- index_of_dim0 = output_index / output_pitches[0];
- m = output_index - index_of_dim0 * output_pitches[0];
- index_of_dim1 = m;
-
- int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
- index_of_input_dim0 = index_of_dim0 / scales[0];
- y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
- index_of_input_dim1 = index_of_dim1 / scales[1];
- x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
-
- input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
-
- float x00 = getInputFloat(input_index);
- float x10, x01, x11;
-
- bool end_of_dim0 = false;
- if (index_of_input_dim0 == (${l[0].dims[0]} - 1)) {
- // It's the end in dimension 0
- x01 = x00;
- end_of_dim0 = true;
- } else {
- x01 = getInputFloat(input_index + input_pitches[0]);
- }
-
- if (index_of_input_dim1 == (input_pitches[0] - 1)) {
- // It's the end in dimension 1
- x10 = x00;
- x11 = x01;
- }
- else {
- x10 = getInputFloat(input_index + 1);
- x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
- }
-
- float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
- float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
- return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
- }`;return Object.assign(Object.assign({},s),{output:{dims:i,type:l[0].type,textureType:p.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(O=>Math.ceil(O))}]})};n.validateInputs=(f,l)=>{if(!f||l.opset<9&&f.length!==1||l.opset>=9&&l.opset<11&&f.length!==2||l.opset>=11&&f.length<2)throw new Error("invalid inputs.");if(l.scales.length>0&&f[0].dims.length!==l.scales.length)throw new Error("Invalid input shape.");if(f[0].type==="string")throw new Error("Invalid input tensor types.")},n.scalesValidation=(f,l,o)=>{if(o){for(const t of f)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(const t of f)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if(!(l!=="linear"&&l!=="cubic"||f.length===2||f.length===4&&f[0]===1&&f[1]===1))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the ${o?"Resize":"Upsample"} opeartor.`)}},1958:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ProgramManager=void 0;const u=a(1670),c=a(6231),p=a(8879),s=a(5060);n.ProgramManager=class{constructor(h,f,l){this.profiler=h,this.glContext=f,this.textureLayoutStrategy=l,this.repo=new Map,this.attributesBound=!1}getArtifact(h){return this.repo.get(h)}setArtifact(h,f){this.repo.set(h,f)}run(h,f,l){var o;this.profiler.event("op",`ProgramManager.run ${(o=h.programInfo.name)!==null&&o!==void 0?o:"unknown kernel"}`,()=>{var t;const e=this.glContext.gl,r=h.program;e.useProgram(r);try{this.bindOutput(l),this.attributesBound||this.bindAttributes(h.attribLocations),this.bindUniforms(h.uniformLocations,(t=h.programInfo.variables)!==null&&t!==void 0?t:[],f)}catch(i){throw c.Logger.error("ProgramManager",h.programInfo.shaderSource),i}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(h=>this.glContext.deleteProgram(h.program))}build(h,f,l){return this.profiler.event("backend","ProgramManager.build",()=>{const o=new p.GlslPreprocessor(this.glContext,h,f,l),t=o.preprocess(),e=this.compile(t);return{programInfo:h,program:e,uniformLocations:this.getUniformLocations(e,o.context.programInfo.inputNames,o.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(h){if(!this.vertexShader){c.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const o=(0,s.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(o,this.glContext.gl.VERTEX_SHADER)}u.env.debug&&c.Logger.verbose("ProrgramManager",`FragShader:
-${h}
-`);const f=this.glContext.compileShader(h,this.glContext.gl.FRAGMENT_SHADER),l=this.glContext.createProgram(this.vertexShader,f);return this.glContext.deleteShader(f),l}bindOutput(h){const f=h.width,l=h.height;c.Logger.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${f}/${l}, shape=${h.shape}, type=${h.tensor.type}`),this.glContext.attachFramebuffer(h.texture,f,l)}bindAttributes(h){const f=h.position,l=h.textureCoord;this.glContext.setVertexAttributes(f,l),this.attributesBound=!0}bindUniforms(h,f,l){var o;const t=this.glContext.gl;let e=0;for(const{name:r,type:i,location:d,arrayLength:g}of h){const m=(o=f.find(_=>_.name===r))===null||o===void 0?void 0:o.data;if(i!=="sampler2D"&&!m)throw new Error(`variable '${r}' does not have data defined in program info`);switch(i){case"sampler2D":this.bindTexture(l[e],d,e),e++;break;case"float":g?t.uniform1fv(d,m):t.uniform1f(d,m);break;case"int":g?t.uniform1iv(d,m):t.uniform1i(d,m);break;default:throw new Error(`Uniform not implemented: ${i}`)}}}bindTexture(h,f,l){this.glContext.bindTextureToUniform(h.texture,l,f)}getAttribLocations(h){return{position:this.getAttribLocation(h,"position"),textureCoord:this.getAttribLocation(h,"textureCoord")}}getUniformLocations(h,f,l){const o=[];if(f)for(const t of f)o.push({name:t,type:"sampler2D",location:this.getUniformLocation(h,t)});if(l)for(const t of l)o.push(Object.assign(Object.assign({},t),{location:this.getUniformLocation(h,t.name)}));return o}getUniformLocation(h,f){const l=this.glContext.gl.getUniformLocation(h,f);if(l===null)throw new Error(`Uniform ${f} not found.`);return l}getAttribLocation(h,f){return this.glContext.gl.getAttribLocation(h,f)}}},6416:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.WebGLSessionHandler=void 0;const u=a(6231),c=a(1047),p=a(8316),s=a(1640),h=a(1958),f=a(7859),l=a(5702);n.WebGLSessionHandler=class{constructor(o,t){this.backend=o,this.context=t,this.layoutStrategy=new f.PreferLogicalStrategy(o.glContext.maxTextureSize),this.programManager=new h.ProgramManager(this.context.profiler,o.glContext,this.layoutStrategy),this.textureManager=new l.TextureManager(o.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:o.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=o.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new p.WebGLInferenceHandler(this)}onGraphInitialized(o){const t=o.getValues().filter(e=>e.from===-1&&e.tensor).map(e=>e.tensor.dataId);this.initializers=new Set(t)}isInitializer(o){return!!this.initializers&&this.initializers.has(o)}addInitializer(o){this.initializers.add(o)}getTextureData(o,t){return t?this.packedTextureDataCache.get(o):this.unpackedTextureDataCache.get(o)}setTextureData(o,t,e=!1){u.Logger.verbose("WebGLSessionHandler","Storing Texture data in cache"),e?this.packedTextureDataCache.set(o,t):this.unpackedTextureDataCache.set(o,t)}dispose(){this.programManager.dispose(),this.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(o=>this.textureManager.releaseTexture(o,!0)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(o=>this.textureManager.releaseTexture(o,!0)),this.unpackedTextureDataCache=new Map}resolve(o,t,e){const r=(0,c.resolveOperator)(o,t,s.WEBGL_OP_RESOLVE_RULES);return{impl:r.opImpl,context:r.opInit?r.opInit(o,e):o}}}},7769:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Uint8DataEncoder=n.RGBAFloatDataEncoder=n.RedFloat32DataEncoder=void 0;const u=a(6231);n.RedFloat32DataEncoder=class{constructor(c,p=1){if(p===1)this.internalFormat=c.R32F,this.format=c.RED,this.textureType=c.FLOAT,this.channelSize=p;else{if(p!==4)throw new Error(`Invalid number of channels: ${p}`);this.internalFormat=c.RGBA32F,this.format=c.RGBA,this.textureType=c.FLOAT,this.channelSize=p}}encode(c,p){let s,h;return c.constructor!==Float32Array&&(u.Logger.warning("Encoder","data was not of type Float32; creating new Float32Array"),h=new Float32Array(c)),p*this.channelSize>c.length?(u.Logger.warning("Encoder","Source data too small. 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Error(`Internal error in dynamic time warping. 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u=this._heap[n];this._heap[n]=this._heap[a],this._heap[a]=u}_siftUp(){let n=this.size-1;for(;n>0&&this._greater(n,this._parent(n));)this._swap(n,this._parent(n)),n=this._parent(n)}_siftDown(){let n=0;for(;this._left(n)[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const c=new TokenLatticeNode(this.bosTokenId,0,0,0,0),p=new TokenLatticeNode(this.eosTokenId,1,this.len,0,0);this.nodes.push(c.clone()),this.nodes.push(p.clone()),this.beginNodes[this.len].push(p),this.endNodes[0].push(c)}insert(n,a,u,c){const p=this.nodes.length,s=new TokenLatticeNode(c,p,n,a,u);this.beginNodes[n].push(s),this.endNodes[n+a].push(s),this.nodes.push(s)}viterbi(){const n=this.len;let a=0;for(;a<=n;){if(this.beginNodes[a].length==0)return[];for(let h of this.beginNodes[a]){h.prev=null;let f=0,l=null;for(let o of this.endNodes[a]){const t=o.backtraceScore+h.score;(l===null||t>f)&&(l=o.clone(),f=t)}if(l!==null)h.prev=l,h.backtraceScore=f;else return[]}++a}const u=[],p=this.beginNodes[n][0].prev;if(p===null)return[];let s=p.clone();for(;s.prev!==null;)u.push(s.clone()),s=s.clone().prev.clone();return u.reverse(),u}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(a=>this.piece(a))}tokenIds(){return this.viterbi().map(a=>a.tokenId)}}class TokenLatticeNode{constructor(n,a,u,c,p){this.tokenId=n,this.nodeId=a,this.pos=u,this.length=c,this.score=p,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}async function loadTokenizer(b,n){return await Promise.all([getModelJSON(b,"tokenizer.json",!0,n),getModelJSON(b,"tokenizer_config.json",!0,n)])}function createPattern(b,n=!0){return b.Regex!==void 0?new RegExp(n?b.Regex:`(${b.Regex})`,"gu"):b.String!==void 0?b.String:(console.warn("Unknown pattern type:",b),null)}function objectToMap(b){return new Map(Object.entries(b))}function clean_up_tokenization(b){return b.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function remove_accents(b){return b.replace(/[\u0300-\u036f]/g,"")}function lowercase_and_remove_accent(b){return remove_accents(b.toLowerCase())}function fuse(b,n){let a=[],u=0;for(;uthis.tokens_to_ids.get(u)??this.unk_token_id);return this.fuse_unk&&(a=fuse(a,this.unk_token_id)),a}convert_ids_to_tokens(n){return n.map(a=>this.vocab[a]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=objectToMap(n.vocab),this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[a,u]of this.tokens_to_ids)this.vocab[u]=a}encode(n){let a=[];for(let u of n){let c=[...u],p=!1,s=0,h=[];for(;s0&&(o=this.config.continuing_subword_prefix+o),this.tokens_to_ids.has(o)){l=o;break}--f}if(l===null){p=!0;break}h.push(l),s=f}p?a.push(this.unk_token):a.push(...h)}return a}}class Unigram extends TokenizerModel{constructor(n,a){super(n);const u=n.vocab.length;this.vocab=new Array(u),this.scores=new Array(u);for(let c=0;c[c,p])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=a.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=min(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(n){const a=n.sentence,u=a.length;let c=0;for(;c{const b=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(c,p)=>p+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(c,p)=>p+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(c,p)=>p+"®".charCodeAt(0))];let n=b.slice(),a=0;for(let c=0;c<256;++c)b.includes(c)||(b.push(c),n.push(256+a),a+=1);let u=n.map(c=>String.fromCharCode(c));return Object.fromEntries(b.map((c,p)=>[c,u[p]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=objectToMap(n.vocab),this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[a,u]of this.tokens_to_ids)this.vocab[u]=a;this.bpe_ranks=new Map(n.merges.map((a,u)=>[a,u])),this.merges=n.merges.map(a=>a.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=n.end_of_word_suffix,this.continuing_subword_suffix=n.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(n){if(n.length===0)return[];const a=this.cache.get(n);if(a!==void 0)return a;const u=Array.from(n);this.end_of_word_suffix&&(u[u.length-1]+=this.end_of_word_suffix);let c=[];if(u.length>1){const p=new PriorityQueue((f,l)=>f.score`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):a.push(this.unk_token)}return a}}class LegacyTokenizerModel extends TokenizerModel{constructor(n,a){super(n),this.tokens_to_ids=objectToMap(a.target_lang?n.vocab[a.target_lang]:n.vocab),this.bos_token=a.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=a.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=a.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=a.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[u,c]of this.tokens_to_ids)this.vocab[c]=u}encode(n){return n}}class Normalizer extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"BertNormalizer":return new BertNormalizer(n);case"Precompiled":return new Precompiled(n);case"Sequence":return new NormalizerSequence(n);case"Replace":return new Replace(n);case"NFC":return new NFC(n);case"NFKD":return new NFKD(n);case"Strip":return new StripNormalizer(n);case"StripAccents":return new StripAccents(n);case"Lowercase":return new Lowercase(n);case"Prepend":return new Prepend(n);default:throw new Error(`Unknown Normalizer type: ${n.type}`)}}normalize(n){throw Error("normalize should be implemented in subclass.")}_call(n){return this.normalize(n)}}class Replace extends Normalizer{normalize(n){let a=createPattern(this.config.pattern);return a===null||(n=n.replaceAll(a,this.config.content)),n}}class NFC extends Normalizer{normalize(n){return n=n.normalize("NFC"),n}}class NFKD extends Normalizer{normalize(n){return n=n.normalize("NFKD"),n}}class StripNormalizer extends Normalizer{normalize(n){return this.config.strip_left&&this.config.strip_right?n=n.trim():(this.config.strip_left&&(n=n.trimStart()),this.config.strip_right&&(n=n.trimEnd())),n}}class StripAccents extends Normalizer{normalize(n){return n=remove_accents(n),n}}class Lowercase extends Normalizer{normalize(n){return n=n.toLowerCase(),n}}class Prepend extends Normalizer{normalize(n){return n=this.config.prepend+n,n}}class NormalizerSequence extends Normalizer{constructor(n){super(n),this.normalizers=n.normalizers.map(a=>Normalizer.fromConfig(a))}normalize(n){return this.normalizers.reduce((a,u)=>u.normalize(a),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let a=[];for(let u=0;u=19968&&n<=40959||n>=13312&&n<=19903||n>=131072&&n<=173791||n>=173824&&n<=177983||n>=177984&&n<=178207||n>=178208&&n<=183983||n>=63744&&n<=64255||n>=194560&&n<=195103}stripAccents(n){return n.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(n){return this.config.handle_chinese_chars&&(n=this._tokenize_chinese_chars(n)),this.config.lowercase?(n=n.toLowerCase(),this.config.strip_accents!==!1&&(n=this.stripAccents(n))):this.config.strip_accents&&(n=this.stripAccents(n)),n}}class PreTokenizer extends Callable{static fromConfig(n){if(n===null)return null;switch(n.type){case"BertPreTokenizer":return new BertPreTokenizer(n);case"Sequence":return new PreTokenizerSequence(n);case"WhitespaceSplit":return new WhitespaceSplit(n);case"Metaspace":return new MetaspacePreTokenizer(n);case"ByteLevel":return new ByteLevelPreTokenizer(n);case"Split":return new SplitPreTokenizer(n);case"Punctuation":return new PunctuationPreTokenizer(n);case"Digits":return new DigitsPreTokenizer(n);case"Replace":return new ReplacePreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let a=[];return Array.isArray(n)?a=n.map(u=>this.pre_tokenize_text(u)):a=this.pre_tokenize_text(n),a.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return this.add_prefix_space&&!n.startsWith(" ")&&(n=" "+n),(this.use_regex?n.match(this.pattern)||[]:[n]).map(u=>Array.from(this.text_encoder.encode(u),c=>this.byte_encoder[c]).join(""))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(a=>a)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const a=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(a,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"TemplateProcessing":return new TemplateProcessing(n);case"ByteLevel":return new ByteLevelPostProcessor(n);case"RobertaProcessing":return new RobertaProcessing(n);case"BertProcessing":return new BertProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...a){throw Error("post_process should be implemented in subclass.")}_call(n,...a){return this.post_process(n,...a)}}class BertProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,a=null){return n=mergeArrays([this.cls],n,[this.sep]),a!==null&&(n=mergeArrays(n,[this.sep],a,[this.sep])),n}}class RobertaProcessing extends BertProcessing{}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,a=null){let u=a===null?this.single:this.pair,c=[];for(let p of u)"SpecialToken"in p?c.push(p.SpecialToken.id):"Sequence"in p&&(p.Sequence.id==="A"?c=mergeArrays(c,n):p.Sequence.id==="B"&&(c=mergeArrays(c,a)));return c}}class ByteLevelPostProcessor extends PostProcessor{post_process(n){return n}}class Decoder extends Callable{constructor(n){super(),this.config=n,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=n.trim_offsets}static fromConfig(n){switch(n.type){case"WordPiece":return new WordPieceDecoder(n);case"Metaspace":return new MetaspaceDecoder(n);case"ByteLevel":return new ByteLevelDecoder(n);case"Replace":return new ReplaceDecoder(n);case"ByteFallback":return new ByteFallback(n);case"Fuse":return new FuseDecoder(n);case"Strip":return new StripDecoder(n);case"Sequence":return new DecoderSequence(n);case"CTC":return new CTCDecoder(n);case"BPEDecoder":return new BPEDecoder(n);default:throw new Error(`Unknown Decoder type: ${n.type}`)}}_call(n){return this.decode(n)}decode(n){return this.decode_chain(n).join("")}decode_chain(n){throw Error("`decode_chain` should be implemented in subclass.")}}class ReplaceDecoder extends Decoder{decode_chain(n){let a=createPattern(this.config.pattern);return a===null?n:n.map(u=>u.replaceAll(a,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let a=[],u=[];for(let c of n){let p=null;if(c.length===6&&c.startsWith("<0x")&&c.endsWith(">")){let s=parseInt(c.slice(3,5),16);isNaN(s)||(p=s)}if(p!==null)u.push(p);else{if(u.length>0){let s=this.text_decoder.decode(Uint8Array.from(u));a.push(s),u=[]}a.push(c)}}if(u.length>0){let c=this.text_decoder.decode(Uint8Array.from(u));a.push(c),u=[]}return a}}class FuseDecoder extends Decoder{decode_chain(n){return[n.join("")]}}class StripDecoder extends Decoder{constructor(n){super(n),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(n){return n.map(a=>{let u=0;for(let p=0;p(u!==0&&(a.startsWith(this.config.prefix)?a=a.replace(this.config.prefix,""):a=" "+a),this.cleanup&&(a=clean_up_tokenization(a)),a))}}class ByteLevelDecoder extends Decoder{constructor(n){super(n),this.byte_decoder=UNICODE_TO_BYTES,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(n){let a=n.join(""),u=new Uint8Array([...a].map(p=>this.byte_decoder[p]));return this.text_decoder.decode(u)}decode_chain(n){let a=[],u=[];for(let c of n)this.added_tokens.includes(c)?(u.length>0&&(a.push(this.convert_tokens_to_string(u)),u=[]),a.push(c)):u.push(c);return u.length>0&&a.push(this.convert_tokens_to_string(u)),a}}class CTCDecoder extends Decoder{constructor(n){super(n),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(n){if(n.length===0)return"";let a=[n[0]];for(let p=1;pp!==this.pad_token).join("");return this.cleanup&&(c=clean_up_tokenization(c).replaceAll(this.word_delimiter_token," ").trim()),c}decode_chain(n){return[this.convert_tokens_to_string(n)]}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(a=>Decoder.fromConfig(a))}decode_chain(n){return this.decoders.reduce((a,u)=>u.decode_chain(a),n)}}class BPEDecoder extends Decoder{constructor(n){super(n),this.suffix=this.config.suffix}decode_chain(n){return n.map((a,u)=>a.replaceAll(this.suffix,u===n.length-1?"":" "))}}class MetaspacePreTokenizer extends PreTokenizer{constructor(n){super(),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement,this.strRep=n.str_rep||this.replacement}pre_tokenize(n){typeof n=="string"&&(n=n.trimStart().split(/\s+/));const a=[];for(let u of n){let c=u.replaceAll(" ",this.strRep);this.addPrefixSpace&&!c.startsWith(this.replacement)&&(c=this.strRep+c),a.push(c)}return a}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let a=[];for(let u=0;uu.normalize("NFKC")).join("～"):n=n.normalize("NFKC"),n}}class PreTokenizerSequence extends PreTokenizer{constructor(n){super(),this.tokenizers=n.pretokenizers.map(a=>PreTokenizer.fromConfig(a))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((a,u)=>u.pre_tokenize(a),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class ReplacePreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern),this.content=this.config.content}pre_tokenize_text(n){return this.pattern===null?[n]:[n.replaceAll(this.pattern,this.config.content)]}}class PreTrainedTokenizer extends Callable{constructor(n,a){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),this.model=TokenizerModel.fromConfig(n.model,a),this.post_processor=PostProcessor.fromConfig(n.post_processor),this.decoder=Decoder.fromConfig(n.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(let u of n.added_tokens){let c=u.id,p=u.content;this.added_tokens.push(p),this.model.tokens_to_ids.set(p,c),this.model.vocab[c]=p,u.special&&(this.special_tokens.push(p),this.all_special_ids.push(c))}this.special_tokens.push(...a.additional_special_tokens??[]),this.special_tokens=[...new Set(this.special_tokens)],this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=this.added_tokens.length>0?new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"):null,this.mask_token=this.getToken(a,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(a,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(a,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=a.model_max_length,this.remove_space=a.remove_space,this.clean_up_tokenization_spaces=a.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=a.do_lowercase_and_remove_accent??!1,this.padding_side="right"}getToken(n,...a){for(let u of a){let c=n[u];if(c)if(typeof c=="object"){if(c.__type==="AddedToken")return c.content;throw Error(`Unknown token: ${c}`)}else return c}return null}static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:c=null,local_files_only:p=!1,revision:s="main"}={}){let h=await loadTokenizer(n,{progress_callback:a,config:u,cache_dir:c,local_files_only:p,revision:s});return new this(...h)}prepare_model_inputs(n){return n}_call(n,{text_pair:a=null,add_special_tokens:u=!0,padding:c=!1,truncation:p=null,max_length:s=null,return_tensor:h=!0}={}){let f;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(a!==null){if(Array.isArray(a)){if(n.length!==a.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");f=n.map((e,r)=>this.encode(e,a[r],{add_special_tokens:u}))}else f=n.map(e=>this.encode(e,null,{add_special_tokens:u}))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(a))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");f=[this.encode(n,a,{add_special_tokens:u})]}let l=max(f.map(e=>e.length))[0];s===null&&(s=l),s=Math.min(s,this.model_max_length);let o=[];if(c||p)for(let e=0;es)p&&(f[e]=f[e].slice(0,s)),o.push(new Array(f[e].length).fill(1));else if(c){let r=s-f[e].length;this.padding_side==="right"?(o.push(new Array(f[e].length).fill(1).concat(new Array(r).fill(0))),f[e].push(...new Array(r).fill(this.pad_token_id))):(o.push(new Array(r).fill(0).concat(new Array(f[e].length).fill(1))),f[e].unshift(...new Array(r).fill(this.pad_token_id)))}else o.push(new Array(f[e].length).fill(1));else o=f.map(e=>new Array(e.length).fill(1));if(h){if(!(c&&p)&&f.some(r=>r.length!==f[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let e=[f.length,f[0].length];f=new Tensor("int64",BigInt64Array.from(f.flat().map(BigInt)),e),o=new Tensor("int64",BigInt64Array.from(o.flat().map(BigInt)),e)}else Array.isArray(n)||(f=f[0],o=o[0]);let t={input_ids:f,attention_mask:o};return t=this.prepare_model_inputs(t),t}_encode_text(n){return n===null?null:(this.added_tokens_regex?n.split(this.added_tokens_regex).filter(c=>c):[n]).map(c=>{if(this.added_tokens.includes(c))return c;{this.remove_space===!0&&(c=c.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(c=lowercase_and_remove_accent(c)),this.normalizer!==null&&(c=this.normalizer(c));let p=this.pre_tokenizer!==null?this.pre_tokenizer(c):[c];return this.model(p)}}).flat()}encode(n,a=null,{add_special_tokens:u=!0}={}){let c=this._encode_text(n),p=this._encode_text(a),s=this.post_processor!==null&&u?this.post_processor(c,p):mergeArrays(c??[],p??[]);return this.model.convert_tokens_to_ids(s)}batch_decode(n,a={}){return n.map(u=>this.decode(u,a))}decode(n,a={}){if(!Array.isArray(n)||n.length===0||!isIntegralNumber(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,a)}decode_single(n,{skip_special_tokens:a=!1,clean_up_tokenization_spaces:u=null}){let c=this.model.convert_ids_to_tokens(n);a&&(c=c.filter(s=>!this.special_tokens.includes(s)));let p=this.decoder(c);return this.decoder.end_of_word_suffix&&(p=p.replaceAll(this.decoder.end_of_word_suffix," "),a&&(p=p.trim())),(u??this.clean_up_tokenization_spaces)&&(p=clean_up_tokenization(p)),p}}function add_token_types(b){if(b.input_ids instanceof Tensor)b.token_type_ids=new Tensor("int64",new BigInt64Array(b.input_ids.data.length),b.input_ids.dims);else if(Array.isArray(b.input_ids))Array.isArray(b.input_ids[0])?b.token_type_ids=b.input_ids.map(n=>new Array(n.length).fill(0)):b.token_type_ids=new Array(b.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return b}class BertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class AlbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class MobileBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class SqueezeBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DebertaTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DebertaV2Tokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class HerbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class CamembertTokenizer extends PreTrainedTokenizer{}class XLMTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}prepare_model_inputs(n){return add_token_types(n)}}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class MBartTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)),this.lang_to_token=u=>u}_build_translation_inputs(n,a,u){return _build_translation_inputs(this,n,a,u)}}class MBart50Tokenizer extends MBartTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{constructor(n,a){var p,s;const u=".,!?…。，、।۔،",c=(s=(p=n.pre_tokenizer)==null?void 0:p.pretokenizers[0])==null?void 0:s.pattern;c&&c.Regex===` ?[^(\\s|[${u}])]+`&&(c.Regex=` ?[^\\s${u}]+`),super(n,a)}}class LlamaTokenizer extends PreTrainedTokenizer{}class CodeLlamaTokenizer extends PreTrainedTokenizer{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{}class GPTNeoXTokenizer extends PreTrainedTokenizer{}function _build_translation_inputs(b,n,a,u){if(!("language_codes"in b)||!Array.isArray(b.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in b)||!(b.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in b)||typeof b.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const c=u.src_lang,p=u.tgt_lang;if(!b.language_codes.includes(p))throw new Error(`Target language code "${p}" is not valid. Must be one of: {${b.language_codes.join(", ")}}`);if(c!==void 0){if(!b.language_codes.includes(c))throw new Error(`Source language code "${c}" is not valid. Must be one of: {${b.language_codes.join(", ")}}`);for(let s of b.post_processor.config.single)if("SpecialToken"in s&&b.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=b.lang_to_token(c);break}}return u.forced_bos_token_id=b.model.convert_tokens_to_ids([b.lang_to_token(p)])[0],b._call(n,a)}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)),this.lang_to_token=u=>u}_build_translation_inputs(n,a,u){return _build_translation_inputs(this,n,a,u)}}class M2M100Tokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)).map(u=>u.slice(2,-2)),this.lang_to_token=u=>`__${u}__`}_build_translation_inputs(n,a,u){return _build_translation_inputs(this,n,a,u)}}const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],WHISPER_LANGUAGE_MAPPING=new Map(WHISPER_LANGUAGES),WHISPER_TO_LANGUAGE_CODE_MAPPING=new Map([...WHISPER_LANGUAGES.map(([b,n])=>[n,b]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class WhisperTokenizer extends PreTrainedTokenizer{_decode_asr(n,{return_timestamps:a=!1,return_language:u=!1,time_precision:c=null,force_full_sequences:p=!0}={}){if(c===null)throw Error("Must specify time_precision");let s=null;const h=a==="word";function f(){return{language:s,timestamp:[null,null],text:""}}const l=[];let o=f(),t=0;const e=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let r=[],i=[],d=!1,g=null;const m=new Set(this.all_special_ids);for(let w of n){const v=w.tokens,S=h?w.token_timestamps:null;let O=null,A=e;if("stride"in w){const[N,B,$]=w.stride;if(t-=B,g=N-$,B&&(A=B/c+e),$)for(let L=v.length-1;L>=0;--L){const H=v[L];if(H>=e){if(O!==null&&(H-e)*c=e){const $=(B-e)*c+t,L=round($,2);if(O!==null&&B>=O)d=!0;else if(d||r.length>0&&B0?(r.push(T),h&&i.push(M)):r.every(N=>N.length===0)&&(o=f(),r=[],T=[],i=[],M=[])}if(r.length>0){if(p&&a)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[w,v]=this.findLongestCommonSequence(r,i),S=this.decode(w);o.text=S,h&&(o.words=this.collateWordTimestamps(w,v,s)),l.push(o)}let _=Object.create(null);const y=l.map(w=>w.text).join("");if(a||u){for(let w=0;w0;let h=s?[]:null,f=s?a[0]:null;for(let l=1;lL===N[H]).length,$=B/w+v;B>1&&$>t&&(t=$,e=[S,O,T,M])}const[i,d,g,m]=e,_=Math.floor((d+i)/2),y=Math.floor((m+g)/2);p.push(...u.slice(0,_)),u=o.slice(y),c=u.length,s&&(h.push(...f.slice(0,_)),f=a[l].slice(y))}return p.push(...u),s?(h.push(...f),[p,h]):[p,[]]}collateWordTimestamps(n,a,u){let[c,p,s]=this.combineTokensIntoWords(n,u),h=[];for(let f=0;f=c){let h=(s-c)*u;h=round(h,2),p.push(`<|${h}|>`),p.push([])}else p[p.length-1].push(s);return p=p.map(s=>typeof s=="string"?s:super.decode(s,a)),p.join("")}splitTokensOnUnicode(n){const a=this.decode(n,{decode_with_timestamps:!0}),u="�";let c=[],p=[],s=[],h=[],f=[],l=0;for(let o=0;o=this.model.tokens_to_ids.get("<|endoftext|>"),i=o.startsWith(" "),d=o.trim(),g=f.test(d);if(r||i||g||p.length===0)p.push(o),s.push(t),h.push(e);else{const m=p.length-1;p[m]+=o,s[m].push(...t),h[m].push(...e)}}return[p,s,h]}mergePunctuations(n,a,u,c,p){let s=structuredClone(n),h=structuredClone(a),f=structuredClone(u),l=s.length-2,o=s.length-1;for(;l>=0;)s[l].startsWith(" ")&&c.includes(s[l].trim())?(s[o]=s[l]+s[o],h[o]=mergeArrays(h[l],h[o]),f[o]=mergeArrays(f[l],f[o]),s[l]="",h[l]=[],f[l]=[]):o=l,--l;for(l=0,o=1;ot),h.filter(t=>t.length>0),f.filter(t=>t.length>0)]}get_decoder_prompt_ids({language:n=null,task:a=null,no_timestamps:u=!0}={}){let c=[];if(n){n=n.toLowerCase();let p=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(p===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))p=n;else{const f=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${p}|>`);if(s===void 0)throw new Error(`Unable to find language "${p}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);c.push(s)}else c.push(null);if(a){if(a=a.toLowerCase(),a!=="transcribe"&&a!=="translate")throw new Error(`Task "${a}" is not supported. Must be one of: ["transcribe", "translate"]`);let p=this.model.tokens_to_ids.get(`<|${a}|>`);if(p===void 0)throw new Error(`Unable to find task "${a}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);c.push(p)}else c.push(null);if(u){let p=this.model.tokens_to_ids.get("<|notimestamps|>");if(p===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');c.push(p)}return c.map((p,s)=>[s+1,p]).filter(p=>p[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,a){super(n,a),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(u=>this.languageRegex.test(u)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(n){if(n===null)return null;let[a,...u]=n.trim().split(this.languageRegex);if(u.length===0)return super._encode_text(a);if(u.length===2){let[c,p]=u;return this.supported_language_codes.includes(c)||console.warn(`Unsupported language code "${c}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([c],super._encode_text(p))}}}class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer{}class BlenderbotTokenizer extends PreTrainedTokenizer{}class BlenderbotSmallTokenizer extends PreTrainedTokenizer{}class SpeechT5Tokenizer extends PreTrainedTokenizer{}class AutoTokenizer{static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let[f,l]=await loadTokenizer(n,{quantized:a,progress_callback:u,config:c,cache_dir:p,local_files_only:s,revision:h}),o=l.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[o];return t||(console.warn(`Unknown tokenizer class "${o}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,l)}}je(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,CamembertTokenizer,DebertaTokenizer,DebertaV2Tokenizer,BertTokenizer,HerbertTokenizer,XLMTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,MBartTokenizer,MBart50Tokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,M2M100Tokenizer,LlamaTokenizer,CodeLlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,Wav2Vec2CTCTokenizer,BlenderbotTokenizer,BlenderbotSmallTokenizer,SpeechT5Tokenizer,PreTrainedTokenizer});async function loadConfig(b,n){return await getModelJSON(b,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:c=null,local_files_only:p=!1,revision:s="main"}={}){let h=u??await loadConfig(n,{progress_callback:a,config:u,cache_dir:c,local_files_only:p,revision:s});return new this(h)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,a){for(let u of a)this.processors.forEach(c=>c(n,u))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,a){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.force_token_map=Object.fromEntries(n??[])}_call(n,a){let u=this.force_token_map[n.length];return exists(u)&&(a.data.fill(-1/0),a.data[u]=0),a}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,a){return n.length===1&&(a.data.fill(-1/0),a.data[this.bos_token_id]=0),a}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.max_length=n,this.forced_eos_token_id=a}_call(n,a){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.begin_suppress_tokens=n,this.begin_index=a}_call(n,a){if(n.length===this.begin_index)for(let u of this.begin_suppress_tokens)a.data[u]=-1/0;return a}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.eos_token_id=n.eos_token_id,this.no_timestamps_token_id=n.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(n.forced_decoder_ids||[]).length+2,n.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=n.max_initial_timestamp_index}_call(n,a){if(a.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return a.data.fill(-1/0),a.data[this.timestamp_begin]=0,a;const u=n.slice(this.begin_index),c=u.length>=1&&u[u.length-1]>=this.timestamp_begin,p=u.length<2||u[u.length-2]>=this.timestamp_begin;if(c&&(p?a.data.subarray(this.timestamp_begin).fill(-1/0):a.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const l=this.timestamp_begin+this.max_initial_timestamp_index;a.data.subarray(l+1).fill(-1/0)}const s=log_softmax(a.data),h=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((l,o)=>l+o)),f=max(s.subarray(0,this.timestamp_begin))[0];return h>f&&a.data.subarray(0,this.timestamp_begin).fill(-1/0),a}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const a=n.length,u=[];for(let p=0;p0&&(c=c.map(p=>p/this.generation_config.temperature)),c}randomSelect(n){let a=n.reduce((c,p)=>c+p,0),u=Math.random()*a;for(let c=0;c1)return new BeamSearchSampler(n);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,a=-1){let u=this.getLogits(n,a);return[[max(u)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const c=this.getLogits(n,a),p=getTopItems(c,u),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},()=>{const h=this.randomSelect(s);return[p[h][0],Math.log(s[h])]})}}class BeamSearchSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const c=this.getLogits(n,a),p=getTopItems(c,u),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},(h,f)=>[p[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX,MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(b,n,a){let u=`onnx/${n}${a.quantized?"_quantized":""}.onnx`,c=await getModelFile(b,u,!0,a);try{return await InferenceSession.create(c,{executionProviders})}catch(p){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw p;return console.warn(p),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(c,{executionProviders:["wasm"]})}}async function validateInputs(b,n){const a={},u=[];for(let s of b.inputNames)n[s]===void 0?u.push(s):a[s]=n[s];if(u.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);const c=Object.keys(n).length,p=b.inputNames.length;if(c>p){let s=Object.keys(n).filter(h=>!b.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${c} > ${p}). The following inputs will be ignored: "${s.join(", ")}".`)}return a}async function sessionRun(b,n){const a=await validateInputs(b,n);try{let u=await b.run(a);return u=replaceTensors(u),u}catch(u){throw console.error(`An error occurred during model execution: "${u}".`),console.error("Inputs given to model:",a),u}}function replaceTensors(b){for(let n in b)b[n]instanceof ONNXTensor?b[n]=new Tensor(b[n]):typeof b[n]=="object"&&replaceTensors(b[n]);return b}function toI64Tensor(b){if(b instanceof Tensor)return b;if(b.length===0)throw Error("items must be non-empty");if(Array.isArray(b[0])){if(b.some(n=>n.length!==b[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(b.flat().map(n=>BigInt(n))),[b.length,b[0].length])}else return new Tensor("int64",BigInt64Array.from(b.map(n=>BigInt(n))),[1,b.length])}function prepareAttentionMask(b,n){let a=b.config.pad_token_id??null,u=b.config.eos_token_id??null;isIntegralNumber(u)&&(u=[u]);let c=n.indexOf(a)!==-1,p=u===null||!u.includes(a);if(c&&p){let s=BigInt64Array.from(n.data.map(h=>h!=a));return new Tensor("int64",s,n.dims)}else return ones_like(n)}function boolTensor(b){return new Tensor("bool",[b],[1])}async function seq2seqForward(b,n){let{encoder_outputs:a,past_key_values:u}=n;a||(a=(await encoderForward(b,n)).last_hidden_state);let c={input_ids:n.decoder_input_ids,encoder_hidden_states:a,use_cache_branch:boolTensor(!!u)};b.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(c.encoder_attention_mask=n.attention_mask),b.addPastKeyValues(c,u);const p=await sessionRun(b.decoder_merged_session,c);let s=p.logits;u=b.getPastKeyValues(p,u);const h=b.getAttentions(p);return new Seq2SeqLMOutput({logits:s,past_key_values:u,encoder_outputs:a,...h})}function seq2seqStartBeams(b,n,a,u){let c=[],p=0;const s=b.requires_attention_mask??!0;let h=a.decoder_input_ids??a.decoder_start_token_id??a.bos_token_id??a.eos_token_id;h instanceof Tensor?h=h.tolist().flat():Array.isArray(h)||(h=[h]);for(let f of n){f.dims=[1,...f.dims];let l={inputs:f,encoder_outputs:null,prev_model_outputs:null,output_token_ids:h,done:!1,score:0,id:p++};s&&(l.attention_mask=prepareAttentionMask(b,f)),c.push(l)}return c}async function seq2seqRunBeam(b,n){var s;const a=b.main_input_name;let u=n.output_token_ids;n.prev_model_outputs&&(u=u.slice(-1));let c={[a]:n.inputs,decoder_input_ids:toI64Tensor(u),encoder_outputs:n.encoder_outputs,past_key_values:(s=n.prev_model_outputs)==null?void 0:s.past_key_values};n.attention_mask&&(c.attention_mask=n.attention_mask);let p=await b.forward(c);return n.prev_model_outputs=p,n.encoder_outputs=p.encoder_outputs,p}function seq2seqUpdatebeam(b,n){b.output_token_ids=[...b.output_token_ids,n]}async function encoderForward(b,n){let a={};for(let u of b.session.inputNames)a[u]=n[u];return await sessionRun(b.session,a)}async function decoderForward(b,n){let{input_ids:a,past_key_values:u,attention_mask:c}=n,p={input_ids:a,attention_mask:c??prepareAttentionMask(b,a),use_cache_branch:boolTensor(!!u)};b.addPastKeyValues(p,u);let s=await sessionRun(b.session,p),h=s.logits;return u=b.getPastKeyValues(s,u),{logits:h,past_key_values:u}}function decoderStartBeams(b,n,a,u,c){let p=[],s=0;for(let h of n){let f=h.tolist().map(Number);h.dims=[1,...h.dims];let l;c?(l=c[s],l.dims=[1,...l.dims]):l=prepareAttentionMask(b,h);let o={input:h,model_input_ids:h,attention_mask:l,prev_model_outputs:null,output_token_ids:f,num_output_tokens:u,done:!1,score:0,id:s++};p.push(o)}return p}async function decoderRunBeam(b,n){var p;let a=new BigInt64Array(n.output_token_ids.length).fill(1n),u={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",a,[1,a.length]),past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values},c=await b.forward(u);return n.prev_model_outputs=c,c}function decoderUpdatebeam(b,n){b.output_token_ids=[...b.output_token_ids,n],b.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(a,u){super();je(this,"main_input_name","input_ids");this.config=a,this.session=u;const c=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),p=MODEL_TYPE_MAPPING.get(c);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,p===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):p===MODEL_TYPES.Seq2Seq||p===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):p===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){let a=[];for(let u of Object.keys(this)){let c=this[u];c instanceof InferenceSession&&a.push(c.handler.dispose())}return await Promise.all(a)}static async from_pretrained(a,{quantized:u=!0,progress_callback:c=null,config:p=null,cache_dir:s=null,local_files_only:h=!1,revision:f="main",model_file_name:l=null}={}){let o={quantized:u,progress_callback:c,config:p,cache_dir:s,local_files_only:h,revision:f,model_file_name:l};const t=MODEL_CLASS_TO_NAME_MAPPING.get(this),e=MODEL_TYPE_MAPPING.get(t);let r;return e===MODEL_TYPES.DecoderOnly?r=await Promise.all([AutoConfig.from_pretrained(a,o),constructSession(a,o.model_file_name??"decoder_model_merged",o),getModelJSON(a,"generation_config.json",!1,o)]):e===MODEL_TYPES.Seq2Seq||e===MODEL_TYPES.Vision2Seq?r=await Promise.all([AutoConfig.from_pretrained(a,o),constructSession(a,"encoder_model",o),constructSession(a,"decoder_model_merged",o),getModelJSON(a,"generation_config.json",!1,o)]):e===MODEL_TYPES.EncoderDecoder?r=await Promise.all([AutoConfig.from_pretrained(a,o),constructSession(a,"encoder_model",o),constructSession(a,"decoder_model_merged",o)]):(e!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${t}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),r=await Promise.all([AutoConfig.from_pretrained(a,o),constructSession(a,o.model_file_name??"model",o)])),new this(...r)}async _call(a){return await this.forward(a)}async forward(a){return await this._forward(this,a)}_get_logits_processor(a,u,c=null){const p=new LogitsProcessorList;if(a.repetition_penalty!==null&&a.repetition_penalty!==1&&p.push(new RepetitionPenaltyLogitsProcessor(a.repetition_penalty)),a.no_repeat_ngram_size!==null&&a.no_repeat_ngram_size>0&&p.push(new NoRepeatNGramLogitsProcessor(a.no_repeat_ngram_size)),a.min_length!==null&&a.eos_token_id!==null&&a.min_length>0&&p.push(new MinLengthLogitsProcessor(a.min_length,a.eos_token_id)),a.min_new_tokens!==null&&a.eos_token_id!==null&&a.min_new_tokens>0&&p.push(new MinNewTokensLengthLogitsProcessor(u,a.min_new_tokens,a.eos_token_id)),a.forced_bos_token_id!==null&&p.push(new ForcedBOSTokenLogitsProcessor(a.forced_bos_token_id)),a.forced_eos_token_id!==null&&p.push(new ForcedEOSTokenLogitsProcessor(a.max_length,a.forced_eos_token_id)),a.begin_suppress_tokens!==null){let s=u>1||a.forced_bos_token_id===null?u:u+1;a.forced_decoder_ids!==null&&(s+=a.forced_decoder_ids[a.forced_decoder_ids.length-1][0]),p.push(new SuppressTokensAtBeginLogitsProcessor(a.begin_suppress_tokens,s))}return a.forced_decoder_ids!==null&&p.push(new ForceTokensLogitsProcessor(a.forced_decoder_ids)),c!==null&&p.extend(c),p}_get_generation_config(a){let u=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(u,this.generation_config),a!==null&&Object.assign(u,a),u}async generate(a,u=null,c=null,{inputs_attention_mask:p=null}={}){if(!this.can_generate){let m=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const _=this.config.model_type,y=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(_)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(_)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(_)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(_);throw y&&(m+=` Please use the following class instead: '${y[0]}'`),Error(m)}if(!(a instanceof Tensor)&&!isTypedArray(a)&&!Array.isArray(a))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${a.constructor.name}".`);let s;if(this.config.is_encoder_decoder)s=0;else if(s=a instanceof Tensor?a.dims.at(-1):a.length,s===0)throw Error("Must supply a non-empty array of input token ids.");u=this._get_generation_config(u),c=c??new LogitsProcessorList,c=this._get_logits_processor(u,s,c);let h=u.eos_token_id;h!==null&&!Array.isArray(h)&&(h=[h]);let f=1;const l=f+(u.max_new_tokens??1/0),o=Number.isInteger(u.max_length)&&(u.max_new_tokens??null)===null;let t=Sampler.getSampler(u),e=this.getStartBeams(a,u,f,p);for(;e.some(g=>!g.done)&&f