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Diablo Immortal: A Guide to the Mobile and PC Action RPG
-Diablo Immortal is a free-to-play, massively multiplayer online action role-playing game (MMOARPG) developed by Blizzard Entertainment in partnership with NetEase. It is set in the dark fantasy world of Sanctuary, between the events of Diablo II and Diablo III. It features six playable classes, each with unique skills and abilities, as well as a variety of enemies, dungeons, rifts, raids, and PvP modes. In this article, we will give you an overview of what Diablo Immortal is, what are its features, what are its reviews, what are some tips for playing it, and how to download and play it on your mobile or PC device.
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What is Diablo Immortal?
-Diablo Immortal is a game that lets you explore the untold story of Sanctuary after the destruction of the Worldstone by the archangel Tyrael. You will encounter familiar faces such as Deckard Cain, Leah, Adria, Zoltun Kulle, Maghda, and more, as well as new characters and factions. You will also face new threats such as Skarn, Herald of Terror, who seeks to gather the fragments of the Worldstone and resurrect Diablo. You will have to join forces with other heroes to stop him and his minions from plunging the world into chaos.
-The story and setting of Diablo Immortal
-The story of Diablo Immortal takes place five years after the events of Diablo II: Lord of Destruction. Tyrael shattered the corrupted Worldstone with his sword El'druin, hoping to end its dark influence. However, his sacrifice did not stop the evil from spreading. The fragments of the Worldstone still contain great power, and they are sought by both demons and humans for their own purposes. Some want to use them to bring back Diablo, while others want to harness them for their own gain.
-You will travel across various regions of Sanctuary, such as Khanduras, Scosglen, Westmarch, Xiansai, Hawezar, Bilefen, Ashwold Cemetery, Dark Wood, Shassar Sea, Tomb of Fahir, Frozen Tundra, Mount Arreat Crater, Pandemonium Fortress, Hellforge Ruins, Realm of Terror, Realm of Hatred, Realm of Destruction. Each region has its own history, culture, environment, quests, dungeons, enemies, loot.
-The classes and gameplay of Diablo Immortal
-Diablo Immortal has six classes to choose from: Barbarian, Crusader,
PvP modes: These are competitive modes that allow you to fight against other players in different arenas and formats. PvP modes include duels, battlegrounds, cycle of strife, and more. You can join PvP modes by visiting the Immortal Overlook in Westmarch, or by accessing the PvP menu from the main menu. You can also join a clan or a warband to participate in clan wars and other PvP events. PvP modes reward you with honor, glory, and other rewards that you can use to improve your character and gear.
-What are the features of Diablo Immortal?
-Diablo Immortal is not just a port of Diablo III to mobile devices. It is a new game that has its own features and improvements that make it stand out from other Diablo games. Some of the features of Diablo Immortal are:
-The graphics and sound of Diablo Immortal
-Diablo Immortal boasts impressive graphics and sound that immerse you in the dark and gritty world of Sanctuary. The game uses a custom engine that optimizes the performance and quality of the game for mobile devices. The game also supports high-resolution displays, dynamic lighting and shadows, realistic physics, and smooth animations. The game also features a rich and atmospheric soundtrack, as well as voice acting and sound effects that enhance the mood and the action.
-The multiplayer and social aspects of Diablo Immortal
-Diablo Immortal is a game that is meant to be played with others. The game supports online multiplayer for up to eight players in co-op or PvP modes. You can also join clans and warbands to form alliances and rivalries with other players. You can chat with other players using text or voice chat, as well as emotes and gestures. You can also share your achievements, screenshots, videos, and live streams with other players through social media platforms.
-The progression and customization of Diablo Immortal
-Diablo Immortal offers a deep and rewarding progression and customization system that lets you create your own unique character. You can level up your character by completing quests, killing enemies, and participating in events. You can also unlock new skills, runes, gems, paragon points, and legendary items that enhance your abilities and stats. You can also craft, upgrade, enchant, transmogrify, and salvage your items to improve your gear. You can also customize your character's appearance by changing their hair, skin, eyes, tattoos, and outfits.
-What are the reviews of Diablo Immortal?
-Diablo Immortal is a game that has received mixed reviews from critics and players alike. Some have praised the game for its gameplay, graphics, features, and content, while others have criticized the game for its monetization, controls, story, and lack of innovation. Here are some examples of positive, negative, and mixed reviews of Diablo Immortal:
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The positive reviews of Diablo Immortal
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- "Diablo Immortal is a great example of how to adapt a PC game to mobile devices without losing its essence or quality. The game is fun, addictive, challenging, and rewarding. It has tons of content and features that keep you hooked for hours. It is also one of the best-looking games on mobile devices right now." - IGN -
- "Diablo Immortal is a game that delivers on its promise of bringing the Diablo experience to mobile devices. The game is faithful to the core gameplay mechanics of the franchise, while adding some new twists and improvements that make it fresh and exciting. The game also has a lot of potential for future updates and expansions that will add more content and variety to the game." - GameSpot -
- "Diablo Immortal is a game that surprised me with its quality and depth. The game is not just a watered-down version of Diablo III, but a new game that has its own identity and charm. The game is easy to pick up and play, but hard to master and put down. The game also has a lot of replay value thanks to its multiplayer modes and events. The game also has a rich and immersive story and setting that will appeal to fans of the franchise and newcomers alike." - PC Gamer -
The negative reviews of Diablo Immortal
--
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- "Diablo Immortal is a game that fails to live up to the expectations and standards of the Diablo franchise. The game is riddled with bugs, glitches, crashes, and lag. The game is also plagued by predatory monetization practices that force you to pay for everything from inventory space, to skill slots, to loot boxes, to cosmetics. The game is also boring, repetitive, and uninspired. The game lacks the depth, complexity, and creativity that made the previous Diablo games so great." - Metacritic -
- "Diablo Immortal is a game that insults the intelligence and loyalty of the Diablo fanbase. The game is nothing but a reskin of an existing mobile game by NetEase, with some Diablo assets and lore slapped on. The game is also a blatant cash grab that exploits the popularity and nostalgia of the franchise. The game is also a betrayal of the core values and vision of Blizzard Entertainment, which used to be a company that cared about quality and innovation." - Reddit -
- "Diablo Immortal is a game that disappoints me on every level. The game is not fun, not challenging, not rewarding, and not engaging. The game is also not faithful to the spirit and legacy of the Diablo franchise, which was known for its dark, gritty, and mature themes and tone. The game is also not respectful to the community and the feedback that they have given over the years. The game is also not worth my time or money." - YouTube -
The mixed reviews of Diablo Immortal
--
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- "Diablo Immortal is a game that has some good aspects and some bad aspects. The game has decent gameplay, graphics, features, and content, but it also has some issues with monetization, controls, story, and innovation. The game is not a bad game per se, but it is not a great game either. It is a mediocre game that could have been better if it had more polish, balance, and originality." - Game Informer -
- "Diablo Immortal is a game that I have mixed feelings about. The game has some fun moments and some frustrating moments. The game has some interesting elements and some boring elements. The game has some potential and some limitations. The game is not a disaster, but it is not a masterpiece either. It is an average game that might appeal to some players and might repel others." - Kotaku -
- "Diablo Immortal is a game that I enjoy playing sometimes and hate playing other times. The game has some pros and cons that make it hard to judge. The game has some features that I like and some features that I dislike. The game has some aspects that I appreciate and some aspects that I resent. The game is not a waste of time, but it is not a must-play either. It is a decent game that depends on your mood and preference." - App Store -
What are some tips for playing Diablo Immortal?
-Diablo Immortal is a game that can be easy to learn but hard to master. There are many things to consider when playing the game, such as your class, skills, items, enemies, dungeons, events, modes, etc. Here are some tips for playing Diablo Immortal that can help you improve your performance and enjoyment of the game:
-The beginner and basic tips for Diablo Immortal
--
-
- Choose your class wisely: Each class has its own strengths, weaknesses, skills, and playstyles. You should choose the class that suits your preferences and goals. For example, if you like to deal massive damage from afar, you might want to choose the Demon Hunter or the Wizard. If you like to tank and soak up damage, you might want to choose the Barbarian or the Crusader. If you like to support and heal your allies, you might want to choose the Monk or the Necromancer. -
- Experiment with different skill combinations: Each class has a variety of skills that you can unlock and equip as you level up. You can have up to four active skills and four passive skills at a time. You can also enhance your skills with runes that modify their effects. You should try different skill combinations and see what works best for you. You can also switch your skills at any time without any penalty. -
- Upgrade your gear and items: Your gear and items are essential for your survival and success in Diablo Immortal. You should always look for better gear and items that match your class, skills, and stats. You can find gear and items by killing enemies, completing quests, opening chests, and participating in events. You can also craft, upgrade, enchant, transmogrify, and salvage your gear and items to improve their quality and appearance. -
- Join a clan or a warband: Diablo Immortal is more fun when you play with others. You can join a clan or a warband to make friends, chat, cooperate, compete, and share resources with other players. Clans are permanent groups that can have up to 150 members. Warbands are temporary groups that can have up to 10 members. You can join a clan or a warband by visiting the Immortal Overlook in Westmarch, or by accessing the clan or warband menu from the main menu. -
- Complete quests and events: Diablo Immortal has a lot of quests and events that you can complete for rewards and experience. Quests are story-driven missions that advance the plot and introduce new characters and locations. Events are dynamic activities that occur randomly in the world and offer different challenges and opportunities. You can find quests and events by exploring the world, talking to NPCs, or checking the map. -
The advanced and expert tips for Diablo Immortal
--
-
- Optimize your stats and build: Your stats and build are important factors that determine your effectiveness and efficiency in Diablo Immortal. Your stats include attributes such as strength, dexterity, intelligence, vitality, critical chance, critical damage, attack speed, etc. Your build includes your class, skills, runes, gems, paragon points, legendary items, etc. You should optimize your stats and build according to your goals and preferences. You should also research and learn from other players who have similar or better stats and builds than you. -
- Explore the endgame content: Diablo Immortal has a lot of endgame content that offers more challenge and reward for high-level players. Some of the endgame content includes rifts, raids, bounties, challenges, seasons, leaderboards, and PvP modes. You can access the endgame content by reaching level 60, which is the current level cap in Diablo Immortal. You can also increase your power and prestige by earning paragon levels, which are unlimited and grant you additional points to spend on your stats and skills. -
- Use the best sources for more tips and guides: Diablo Immortal is a game that has a lot of depth and complexity that can be hard to master. You can use the best sources for more tips and guides that can help you improve your knowledge and skills in the game. Some of the best sources for more tips and guides are: -
- The official Diablo Immortal website: This is the official source of information and news about Diablo Immortal. You can find the latest updates, announcements, trailers, screenshots, videos, blogs, forums, and more. -
- The official Diablo Immortal wiki: This is the official source of documentation and data about Diablo Immortal. You can find detailed information about the classes, skills, items, enemies, dungeons, events, modes, and more. -
- The official Diablo Immortal social media accounts: These are the official sources of communication and interaction with the developers and the community of Diablo Immortal. You can follow them on Facebook, Twitter, Instagram, YouTube, Discord, Reddit, and more. -
- The unofficial Diablo Immortal fan sites and communities: These are the unofficial sources of tips, guides, reviews, feedback, suggestions, fan art, fan fiction, memes, and more about Diablo Immortal. You can join them on various platforms such as websites, blogs, podcasts, videos, streams, forums, chats, groups, etc. -
-
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How to download and play Diablo Immortal?
-Diablo Immortal is a game that is available for both mobile and PC devices. You can download and play Diablo Immortal by following these steps:
-The system requirements and compatibility of Diablo Immortal
-Before you download and play Diablo Immortal, you should check if your device meets the minimum system requirements and compatibility of the game. The minimum system requirements and compatibility of Diablo Immortal are:
-| Device | Operating System | Processor | Memory | Storage |
|---|---|---|---|---|
| Mobile | Android 5.0 or higher iOS 12 or higher | Snapdragon 670 or higher A9 or higher | 2 GB or higher | 4 GB or higher |
| PC | Windows 7 or higher MacOS 10.12 or higher | Intel Core i3 or higher AMD Ryzen 3 or higher | 4 GB or higher | 10 GB or higher |
The download links and instructions for Diablo Immortal
-To download and play Diablo Immortal on your mobile device, you can use these links and instructions:
--
-
- For Android devices: Go to the Google Play Store app on your device. Search for Diablo Immortal. Tap on the Install button. Wait for the download and installation to finish. Tap on the Open button to launch the game. -
- For iOS devices: Go to the App Store app on your device. Search for Diablo Immortal. Tap on the Get button. Wait for the download and installation to finish. Tap on the Open button to launch the game. -
To download and play Diablo Immortal on your PC device, you can use these links and instructions:
--
-
- For Windows devices: Go to the official Diablo Immortal website on your browser. Click on the Download for PC button. Wait for the download to finish. Run the installer and follow the instructions. Launch the game from your desktop or start menu. -
- For MacOS devices: Go to the official Diablo Immortal website on your browser. Click on the Download for Mac button. Wait for the download to finish. Open the dmg file and drag the game icon to your applications folder. Launch the game from your applications folder or dock. -
The troubleshooting and support for Diablo Immortal
-If you encounter any problems or issues while downloading, installing, or playing Diablo Immortal, you can use these sources for troubleshooting and support:
--
-
- The official Diablo Immortal help center: This is the official source of help and support for Diablo Immortal. You can find answers to frequently asked questions, guides, tutorials, tips, and more. -
- The official Diablo Immortal customer service: This is the official source of contact and feedback for Diablo Immortal. You can report bugs, errors, crashes, lag, hacks, cheats, scams, and more. You can also request refunds, account recovery, technical assistance, and more. -
- The unofficial Diablo Immortal forums and communities: These are the unofficial sources of help and support from other players and fans of Diablo Immortal. You can ask questions, share solutions, give suggestions, and more. -
Conclusion
-Diablo Immortal is a game that offers a new and exciting way to experience the Diablo franchise on mobile and PC devices. The game has a lot of features, content, and modes that will keep you entertained for hours. The game also has a lot of potential for future updates and expansions that will add more story, classes, items, enemies, dungeons, events, modes, and more. Whether you are a fan of the franchise or a newcomer to the genre, you will find something to enjoy in Diablo Immortal.
-FAQs
--
-
- Q: When will Diablo Immortal be released?
A: Diablo Immortal is currently in development and testing stages. The exact release date has not been announced yet, but it is expected to be sometime in 2023.
- - Q: How much does Diablo Immortal cost?
A: Diablo Immortal is free-to-play, which means you can download and play it without paying anything. However, the game also has optional in-app purchases that can enhance your gameplay experience.
- - Q: Is Diablo Immortal online-only?
A: Yes, Diablo Immortal is online-only, which means you need a stable internet connection to play it. You cannot play it offline or solo.
- - Q: Is Diablo Immortal cross-platform?
A: Yes, Diablo Immortal is cross-platform, which means you can play it with other players who are using different devices such as Android, iOS, Windows, or MacOS.
- - Q: Is Diablo Immortal canon?
A: Yes, Diablo Immortal is canon, which means it is part of the official lore and timeline of the Diablo franchise. It fills in the gap between Diablo II and Diablo III.
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-If you are a fan of the Five Nights at Freddy's (fnaf) franchise, you may have heard of fnaf ar, the augmented reality game that brings the animatronics to your real world. But what if you don't have a compatible device or you prefer a simpler version of the game? Well, you may want to try fnaf ar lite, a fan-made game that recreates fnaf ar without the augmented reality feature. In this article, we will tell you everything you need to know about fnaf ar lite, how to play it, and whether it is worth downloading.
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-Fnaf ar lite is a recreation of fnaf ar without augmented reality
-The most obvious difference between fnaf ar lite and fnaf ar is that the former does not use augmented reality technology. This means that you don't need to scan your surroundings or move around to play the game. Instead, you can play it on your screen, where the animatronics will appear randomly. You can still use your camera to look around, but you won't see your real world behind them.
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- You don't need to worry about streaks or events. They are not implemented in fnaf ar lite. -
Fnaf ar lite requires a gyroscope and a flashlight to play
-One of the requirements of fnaf ar lite is that your device has a gyroscope, which is a sensor that detects the orientation and rotation of your device. You need this to look around with your camera and track the animatronics. If your device does not have a gyroscope, you will not be able to play the game properly.
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-Fnaf ar lite has three modes: survival, workshop, and remnant collection
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- Workshop mode is where you can assemble, test, repair, and deploy your own animatronics. You can collect parts, CPUs, and plushsuits from the survival mode or from other players' animatronics. You can also send your animatronics to scare your friends or other players online. -
- Remnant collection mode is where you can search and collect remnant, which is a mysterious substance that powers the animatronics. You can use remnant to upgrade your animatronics or to protect yourself from hostile ones. However, be careful not to collect too much shadow remnant, as it will attract more dangerous animatronics. -
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-If you are interested in playing fnaf ar lite, you can download it from Game Jolt for free. However, if you want to play the original fnaf ar game, you can download it from Google Play or App Store for free as well. Either way, we hope you have fun and stay safe from the animatronics!
-Frequently Asked Questions
-Here are some of the frequently asked questions about fnaf ar lite:
-What devices can run fnaf ar lite?
-Fnaf ar lite can run on any device that has Android 4.4 or higher and a gyroscope. However, some devices may have compatibility issues or performance problems.
-How do I download fnaf ar lite?
-You can download fnaf ar lite from Game Jolt by following this link: . You will need to create an account or log in to download the game. You will also need to enable unknown sources on your device settings to install the game.
-How do I update fnaf ar lite?
-You can update fnaf ar lite by checking for new versions on Game Jolt. However, the developer has stated that he is done with the project and will not release any more updates.
-How do I contact the developer of fnaf ar lite?
-You can contact the developer of fnaf ar lite by leaving a comment on his Game Jolt page: . You can also follow him on Twitter: . However, he may not respond to your messages or requests.
-How do I support the official developers of fnaf ar?
-You can support the official developers of fnaf ar by downloading their game from Google Play or App Store: . You can also follow them on their social media accounts: [^6
Here are the links to their social media accounts:
--
-
- Facebook: [^7
- Twitter: [^8
- Instagram: [^9
I hope you enjoyed this article and learned something new about fnaf ar lite. If you have any questions or feedback, please leave a comment below. Thank you for reading and have a nice day!
197e85843d-
-
\ No newline at end of file diff --git a/spaces/232labs/VToonify/vtoonify/model/bisenet/README.md b/spaces/232labs/VToonify/vtoonify/model/bisenet/README.md deleted file mode 100644 index 849d55e2789c8852e01707d1ff755dc74e63a7f5..0000000000000000000000000000000000000000 --- a/spaces/232labs/VToonify/vtoonify/model/bisenet/README.md +++ /dev/null @@ -1,68 +0,0 @@ -# face-parsing.PyTorch - -
-
- 
-
-
| - | Hair | -Lip | -
|---|---|---|
| Original Input | - |
- |
-
| Color | - |
- |
-
📊AI Model Comparison📉
-
-| Model Name | Model Size (in Parameters) | Model Overview |
-| --- | --- | --- |
-| BigScience-tr11-176B | 176 billion | BigScience is the latest AI model developed by the Big Science Workshop. It has 176 billion parameters and uses a combination of text data from the internet and scientific literature for training. |
-| GPT-3 | 175 billion | GPT-3 is an AI model developed by OpenAI, which has 175 billion parameters and uses a variety of datasets for training, including Common Crawl, BooksCorpus, and English Wikipedia. |
-| OpenAI's DALL-E 2.0 | 500 million | DALL-E 2.0 is an AI model developed by OpenAI that generates images from textual descriptions. It has 500 million parameters and uses a dataset curated by OpenAI. |
-| NVIDIA's Megatron | 8.3 billion | Megatron is a large-scale transformer model developed by NVIDIA. It's primarily designed for tasks that require understanding the context of large pieces of text. |
-| Transformer-XL | 250 million | Transformer-XL is an AI model developed by Google Brain, which introduces a novel recurrence mechanism and relative positional encoding scheme. |
-| XLNet | 210 million | XLNet is a generalized autoregressive model that outperforms BERT on several benchmarks. |
-
-## References:
-
-1. [BigScience - A 176B-Parameter Open-Access Multilingual Language Model](https://arxiv.org/abs/2211.05100)
-2. [GPT-3 - Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
-3. [DALL-E 2.0 - Generative Pretraining from Pixels](https://openai.com/research/dall-e/)
-4. [Megatron - Training Multi-Billion Parameter Language Models Using GPU Model Parallelism](https://arxiv.org/abs/1909.08053)
-5. [Transformer-XL - Transformers with Longer-Range Dependencies](https://arxiv.org/abs/1901.02860)
-6. [XLNet - Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237)
-
-
-| Model Name | Model Size (in Parameters) |
-| --- | --- |
-| BigScience-tr11-176B | 176 billion |
-| GPT-3 | 175 billion |
-| OpenAI's DALL-E 2.0 | 500 million |
-| NVIDIA's Megatron | 8.3 billion |
-| Transformer-XL | 250 million |
-| XLNet | 210 million |
-
-
-| Model Name | Model Size (in Parameters) | Model Overview |
-| --- | --- | --- |
-| BigScience-tr11-176B | 176 billion | Uses a combination of text data from the internet and scientific literature for training. |
-| GPT-3 | 175 billion | Uses a variety of datasets for training, including Common Crawl,
-
-
-
-
-
-
-''')
\ No newline at end of file
diff --git a/spaces/ASJMO/freegpt/g4f/utils.py b/spaces/ASJMO/freegpt/g4f/utils.py
deleted file mode 100644
index d5ab41c79b44ab81e1843d209cb342bd83dafb42..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/g4f/utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import browser_cookie3
-
-
-class Utils:
- browsers = [
- browser_cookie3.chrome, # 62.74% market share
- browser_cookie3.safari, # 24.12% market share
- browser_cookie3.firefox, # 4.56% market share
- browser_cookie3.edge, # 2.85% market share
- browser_cookie3.opera, # 1.69% market share
- browser_cookie3.brave, # 0.96% market share
- browser_cookie3.opera_gx, # 0.64% market share
- browser_cookie3.vivaldi, # 0.32% market share
- ]
-
- def get_cookies(domain: str, setName: str = None, setBrowser: str = False) -> dict:
- cookies = {}
-
- if setBrowser != False:
- for browser in Utils.browsers:
- if browser.__name__ == setBrowser:
- try:
- for c in browser(domain_name=domain):
- if c.name not in cookies:
- cookies = cookies | {c.name: c.value}
-
- except Exception as e:
- pass
-
- else:
- for browser in Utils.browsers:
- try:
- for c in browser(domain_name=domain):
- if c.name not in cookies:
- cookies = cookies | {c.name: c.value}
-
- except Exception as e:
- pass
-
- if setName:
- try:
- return {setName: cookies[setName]}
-
- except ValueError:
- print(f'Error: could not find {setName} cookie in any browser.')
- exit(1)
-
- else:
- return cookies
diff --git a/spaces/Aadi1149/Arkenbrien-text-to-image-Arkenbrien/README.md b/spaces/Aadi1149/Arkenbrien-text-to-image-Arkenbrien/README.md
deleted file mode 100644
index dcc7ecd2499d3b8aa9033646a24c7692f820f00d..0000000000000000000000000000000000000000
--- a/spaces/Aadi1149/Arkenbrien-text-to-image-Arkenbrien/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Arkenbrien Text To Image Arkenbrien
-emoji: 💻
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ababababababbababa/topic2poem/app.py b/spaces/Ababababababbababa/topic2poem/app.py
deleted file mode 100644
index 25116ecea621b15e1c098f85fc813210fc80cade..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/topic2poem/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from transformers import BertTokenizer, EncoderDecoderModel
-import gradio as gr
-
-tokenizerM = BertTokenizer.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01")
-bertSharedM = EncoderDecoderModel.from_pretrained("mareloraby/BERTShared-PoetryGen-arV01")
-# bertSharedM.cuda()
-
-
-def generate_response(text, k = 70, p = 0.9, nb = 4):
- prompt = f"{text}"
- encoded_prompt = tokenizerM.encode_plus(prompt, return_tensors = 'pt')#.to(device)
- gneration = bertSharedM.generate(
- input_ids = encoded_prompt.input_ids,
- attention_mask = encoded_prompt.attention_mask,
- do_sample = True,
- top_k= k,
- top_p = p,
- num_beams= nb,
- max_length =130,
- repetition_penalty = 2.0,
- no_repeat_ngram_size = 2,
- early_stopping=True)
-
- generated_text = tokenizerM.decode(gneration[0], skip_special_tokens=True)
- bayts = generated_text.split("[BSEP]")
- while("FSEP" not in bayts[-1]):
- bayts = bayts[:-1]
- bayts = bayts[:-1]
- temp_poem = ''
- for b in range(len(bayts)):
- temp_line = bayts[b].split('[FSEP]')
- temp_poem = temp_poem + temp_line[1] + ' - ' + temp_line[0] +'\n'
-
- return temp_poem
-
-iface = gr.Interface(fn=generate_response,
- title = 'BERTShared - topic based generation',
-
- inputs=[
- gr.inputs.Radio(['حزينه','هجاء','عتاب','غزل','مدح','رومنسيه','دينية'],label='Choose Topic'),
- gr.inputs.Slider(10, 200, step=10,default = 70, label='Top-K'),
- gr.inputs.Slider(0.10, 0.99, step=0.02, default = 0.90, label='Top-P'),
- #gr.inputs.Slider(1, 20, step=1, default = 4, label='Beams'),
-
- ],
- outputs="text")
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Abhilashvj/planogram-compliance/utils/loggers/clearml/__init__.py b/spaces/Abhilashvj/planogram-compliance/utils/loggers/clearml/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Adapter/CoAdapter/ldm/modules/extra_condition/__init__.py b/spaces/Adapter/CoAdapter/ldm/modules/extra_condition/__init__.py
deleted file mode 100644
index 40a96afc6ff09d58a702b76e3f7dd412fe975e26..0000000000000000000000000000000000000000
--- a/spaces/Adapter/CoAdapter/ldm/modules/extra_condition/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# -*- coding: utf-8 -*-
diff --git a/spaces/Aiusernumber5/janitorai/Dockerfile b/spaces/Aiusernumber5/janitorai/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/Aiusernumber5/janitorai/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py
deleted file mode 100644
index 9d5559203f4f3843fc814b090780ffa129a6fdf0..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py
+++ /dev/null
@@ -1,674 +0,0 @@
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from models.StyleCLIP.models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
-
-
-class PixelNorm(nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, input):
- return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
- k = torch.tensor(k, dtype=torch.float32)
-
- if k.ndim == 1:
- k = k[None, :] * k[:, None]
-
- k /= k.sum()
-
- return k
-
-
-class Upsample(nn.Module):
- def __init__(self, kernel, factor=2):
- super().__init__()
-
- self.factor = factor
- kernel = make_kernel(kernel) * (factor ** 2)
- self.register_buffer('kernel', kernel)
-
- p = kernel.shape[0] - factor
-
- pad0 = (p + 1) // 2 + factor - 1
- pad1 = p // 2
-
- self.pad = (pad0, pad1)
-
- def forward(self, input):
- out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
-
- return out
-
-
-class Downsample(nn.Module):
- def __init__(self, kernel, factor=2):
- super().__init__()
-
- self.factor = factor
- kernel = make_kernel(kernel)
- self.register_buffer('kernel', kernel)
-
- p = kernel.shape[0] - factor
-
- pad0 = (p + 1) // 2
- pad1 = p // 2
-
- self.pad = (pad0, pad1)
-
- def forward(self, input):
- out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
-
- return out
-
-
-class Blur(nn.Module):
- def __init__(self, kernel, pad, upsample_factor=1):
- super().__init__()
-
- kernel = make_kernel(kernel)
-
- if upsample_factor > 1:
- kernel = kernel * (upsample_factor ** 2)
-
- self.register_buffer('kernel', kernel)
-
- self.pad = pad
-
- def forward(self, input):
- out = upfirdn2d(input, self.kernel, pad=self.pad)
-
- return out
-
-
-class EqualConv2d(nn.Module):
- def __init__(
- self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
- ):
- super().__init__()
-
- self.weight = nn.Parameter(
- torch.randn(out_channel, in_channel, kernel_size, kernel_size)
- )
- self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
- self.stride = stride
- self.padding = padding
-
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_channel))
-
- else:
- self.bias = None
-
- def forward(self, input):
- out = F.conv2d(
- input,
- self.weight * self.scale,
- bias=self.bias,
- stride=self.stride,
- padding=self.padding,
- )
-
- return out
-
- def __repr__(self):
- return (
- f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
- f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
- )
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
- ):
- super().__init__()
-
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
- else:
- self.bias = None
-
- self.activation = activation
-
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- if self.activation:
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
-
- else:
- out = F.linear(
- input, self.weight * self.scale, bias=self.bias * self.lr_mul
- )
-
- return out
-
- def __repr__(self):
- return (
- f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
- )
-
-
-class ScaledLeakyReLU(nn.Module):
- def __init__(self, negative_slope=0.2):
- super().__init__()
-
- self.negative_slope = negative_slope
-
- def forward(self, input):
- out = F.leaky_relu(input, negative_slope=self.negative_slope)
-
- return out * math.sqrt(2)
-
-
-class ModulatedConv2d(nn.Module):
- def __init__(
- self,
- in_channel,
- out_channel,
- kernel_size,
- style_dim,
- demodulate=True,
- upsample=False,
- downsample=False,
- blur_kernel=[1, 3, 3, 1],
- ):
- super().__init__()
-
- self.eps = 1e-8
- self.kernel_size = kernel_size
- self.in_channel = in_channel
- self.out_channel = out_channel
- self.upsample = upsample
- self.downsample = downsample
-
- if upsample:
- factor = 2
- p = (len(blur_kernel) - factor) - (kernel_size - 1)
- pad0 = (p + 1) // 2 + factor - 1
- pad1 = p // 2 + 1
-
- self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
-
- if downsample:
- factor = 2
- p = (len(blur_kernel) - factor) + (kernel_size - 1)
- pad0 = (p + 1) // 2
- pad1 = p // 2
-
- self.blur = Blur(blur_kernel, pad=(pad0, pad1))
-
- fan_in = in_channel * kernel_size ** 2
- self.scale = 1 / math.sqrt(fan_in)
- self.padding = kernel_size // 2
-
- self.weight = nn.Parameter(
- torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
- )
-
- self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
- self.demodulate = demodulate
-
- def __repr__(self):
- return (
- f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
- f'upsample={self.upsample}, downsample={self.downsample})'
- )
-
- def forward(self, input, style):
- batch, in_channel, height, width = input.shape
-
- style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
- weight = self.scale * self.weight * style
-
- if self.demodulate:
- demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
- weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
- weight = weight.view(
- batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
- )
-
- if self.upsample:
- input = input.view(1, batch * in_channel, height, width)
- weight = weight.view(
- batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
- )
- weight = weight.transpose(1, 2).reshape(
- batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
- )
- out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
- _, _, height, width = out.shape
- out = out.view(batch, self.out_channel, height, width)
- out = self.blur(out)
-
- elif self.downsample:
- input = self.blur(input)
- _, _, height, width = input.shape
- input = input.view(1, batch * in_channel, height, width)
- out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
- _, _, height, width = out.shape
- out = out.view(batch, self.out_channel, height, width)
-
- else:
- input = input.view(1, batch * in_channel, height, width)
- out = F.conv2d(input, weight, padding=self.padding, groups=batch)
- _, _, height, width = out.shape
- out = out.view(batch, self.out_channel, height, width)
-
- return out
-
-
-class NoiseInjection(nn.Module):
- def __init__(self):
- super().__init__()
-
- self.weight = nn.Parameter(torch.zeros(1))
-
- def forward(self, image, noise=None):
- if noise is None:
- batch, _, height, width = image.shape
- noise = image.new_empty(batch, 1, height, width).normal_()
-
- return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
- def __init__(self, channel, size=4):
- super().__init__()
-
- self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
- def forward(self, input):
- batch = input.shape[0]
- out = self.input.repeat(batch, 1, 1, 1)
-
- return out
-
-
-class StyledConv(nn.Module):
- def __init__(
- self,
- in_channel,
- out_channel,
- kernel_size,
- style_dim,
- upsample=False,
- blur_kernel=[1, 3, 3, 1],
- demodulate=True,
- ):
- super().__init__()
-
- self.conv = ModulatedConv2d(
- in_channel,
- out_channel,
- kernel_size,
- style_dim,
- upsample=upsample,
- blur_kernel=blur_kernel,
- demodulate=demodulate,
- )
-
- self.noise = NoiseInjection()
- # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
- # self.activate = ScaledLeakyReLU(0.2)
- self.activate = FusedLeakyReLU(out_channel)
-
- def forward(self, input, style, noise=None):
- out = self.conv(input, style)
- out = self.noise(out, noise=noise)
- # out = out + self.bias
- out = self.activate(out)
-
- return out
-
-
-class ToRGB(nn.Module):
- def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
- super().__init__()
-
- if upsample:
- self.upsample = Upsample(blur_kernel)
-
- self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
- self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
- def forward(self, input, style, skip=None):
- out = self.conv(input, style)
- out = out + self.bias
-
- if skip is not None:
- skip = self.upsample(skip)
-
- out = out + skip
-
- return out
-
-
-class Generator(nn.Module):
- def __init__(
- self,
- size,
- style_dim,
- n_mlp,
- channel_multiplier=2,
- blur_kernel=[1, 3, 3, 1],
- lr_mlp=0.01,
- ):
- super().__init__()
-
- self.size = size
-
- self.style_dim = style_dim
-
- layers = [PixelNorm()]
-
- for i in range(n_mlp):
- layers.append(
- EqualLinear(
- style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
- )
- )
-
- self.style = nn.Sequential(*layers)
-
- self.channels = {
- 4: 512,
- 8: 512,
- 16: 512,
- 32: 512,
- 64: 256 * channel_multiplier,
- 128: 128 * channel_multiplier,
- 256: 64 * channel_multiplier,
- 512: 32 * channel_multiplier,
- 1024: 16 * channel_multiplier,
- }
-
- self.input = ConstantInput(self.channels[4])
- self.conv1 = StyledConv(
- self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
- )
- self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
-
- self.log_size = int(math.log(size, 2))
- self.num_layers = (self.log_size - 2) * 2 + 1
-
- self.convs = nn.ModuleList()
- self.upsamples = nn.ModuleList()
- self.to_rgbs = nn.ModuleList()
- self.noises = nn.Module()
-
- in_channel = self.channels[4]
-
- for layer_idx in range(self.num_layers):
- res = (layer_idx + 5) // 2
- shape = [1, 1, 2 ** res, 2 ** res]
- self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
-
- for i in range(3, self.log_size + 1):
- out_channel = self.channels[2 ** i]
-
- self.convs.append(
- StyledConv(
- in_channel,
- out_channel,
- 3,
- style_dim,
- upsample=True,
- blur_kernel=blur_kernel,
- )
- )
-
- self.convs.append(
- StyledConv(
- out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
- )
- )
-
- self.to_rgbs.append(ToRGB(out_channel, style_dim))
-
- in_channel = out_channel
-
- self.n_latent = self.log_size * 2 - 2
-
- def make_noise(self):
- device = self.input.input.device
-
- noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
- for i in range(3, self.log_size + 1):
- for _ in range(2):
- noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
- return noises
-
- def mean_latent(self, n_latent):
- latent_in = torch.randn(
- n_latent, self.style_dim, device=self.input.input.device
- )
- latent = self.style(latent_in).mean(0, keepdim=True)
-
- return latent
-
- def get_latent(self, input):
- return self.style(input)
-
- def forward(
- self,
- styles,
- return_latents=False,
- inject_index=None,
- truncation=1,
- truncation_latent=None,
- input_is_latent=False,
- noise=None,
- randomize_noise=True,
- ):
- if not input_is_latent:
- styles = [self.style(s) for s in styles]
-
- if noise is None:
- if randomize_noise:
- noise = [None] * self.num_layers
- else:
- noise = [
- getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
- ]
-
- if truncation < 1:
- style_t = []
-
- for style in styles:
- style_t.append(
- truncation_latent + truncation * (style - truncation_latent)
- )
-
- styles = style_t
-
- if len(styles) < 2:
- inject_index = self.n_latent
-
- if styles[0].ndim < 3:
- latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
- else:
- latent = styles[0]
-
- else:
- if inject_index is None:
- inject_index = random.randint(1, self.n_latent - 1)
-
- latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
- latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
- latent = torch.cat([latent, latent2], 1)
-
- out = self.input(latent)
- out = self.conv1(out, latent[:, 0], noise=noise[0])
-
- skip = self.to_rgb1(out, latent[:, 1])
-
- i = 1
- for conv1, conv2, noise1, noise2, to_rgb in zip(
- self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
- ):
- out = conv1(out, latent[:, i], noise=noise1)
- out = conv2(out, latent[:, i + 1], noise=noise2)
- skip = to_rgb(out, latent[:, i + 2], skip)
-
- i += 2
-
- image = skip
-
- if return_latents:
- return image, latent
-
- else:
- return image, None
-
-
-class ConvLayer(nn.Sequential):
- def __init__(
- self,
- in_channel,
- out_channel,
- kernel_size,
- downsample=False,
- blur_kernel=[1, 3, 3, 1],
- bias=True,
- activate=True,
- ):
- layers = []
-
- if downsample:
- factor = 2
- p = (len(blur_kernel) - factor) + (kernel_size - 1)
- pad0 = (p + 1) // 2
- pad1 = p // 2
-
- layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
-
- stride = 2
- self.padding = 0
-
- else:
- stride = 1
- self.padding = kernel_size // 2
-
- layers.append(
- EqualConv2d(
- in_channel,
- out_channel,
- kernel_size,
- padding=self.padding,
- stride=stride,
- bias=bias and not activate,
- )
- )
-
- if activate:
- if bias:
- layers.append(FusedLeakyReLU(out_channel))
-
- else:
- layers.append(ScaledLeakyReLU(0.2))
-
- super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
- def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
- super().__init__()
-
- self.conv1 = ConvLayer(in_channel, in_channel, 3)
- self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
-
- self.skip = ConvLayer(
- in_channel, out_channel, 1, downsample=True, activate=False, bias=False
- )
-
- def forward(self, input):
- out = self.conv1(input)
- out = self.conv2(out)
-
- skip = self.skip(input)
- out = (out + skip) / math.sqrt(2)
-
- return out
-
-
-class Discriminator(nn.Module):
- def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
- super().__init__()
-
- channels = {
- 4: 512,
- 8: 512,
- 16: 512,
- 32: 512,
- 64: 256 * channel_multiplier,
- 128: 128 * channel_multiplier,
- 256: 64 * channel_multiplier,
- 512: 32 * channel_multiplier,
- 1024: 16 * channel_multiplier,
- }
-
- convs = [ConvLayer(3, channels[size], 1)]
-
- log_size = int(math.log(size, 2))
-
- in_channel = channels[size]
-
- for i in range(log_size, 2, -1):
- out_channel = channels[2 ** (i - 1)]
-
- convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
- in_channel = out_channel
-
- self.convs = nn.Sequential(*convs)
-
- self.stddev_group = 4
- self.stddev_feat = 1
-
- self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
- self.final_linear = nn.Sequential(
- EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
- EqualLinear(channels[4], 1),
- )
-
- def forward(self, input):
- out = self.convs(input)
-
- batch, channel, height, width = out.shape
- group = min(batch, self.stddev_group)
- stddev = out.view(
- group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
- )
- stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
- stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
- stddev = stddev.repeat(group, 1, height, width)
- out = torch.cat([out, stddev], 1)
-
- out = self.final_conv(out)
-
- out = out.view(batch, -1)
- out = self.final_linear(out)
-
- return out
-
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
deleted file mode 100644
index 85004e02c31edeb487f765835815c6f80c18fb6f..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py'
-model = dict(
- pretrained='open-mmlab://res2net101_v1d_26w_4s',
- backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 106f7b6a1ece974c9f732ee813724bd8bda3bef3..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
deleted file mode 100644
index f62eef9773ddf41d996104de571bcda00c488e14..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './psanet_r50-d8_512x512_40k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LLaMA-v2-model.md b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LLaMA-v2-model.md
deleted file mode 100644
index 55c6aa76e9c90963ba1cbf75b5528e6b23c70f18..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LLaMA-v2-model.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# LLaMA-v2
-
-To convert LLaMA-v2 from the `.pth` format provided by Meta to transformers format, follow the steps below:
-
-1) `cd` into your `llama` folder (the one containing `download.sh` and the models that you downloaded):
-
-```
-cd llama
-```
-
-2) Clone the transformers library:
-
-```
-git clone 'https://github.com/huggingface/transformers'
-
-```
-
-3) Create symbolic links from the downloaded folders to names that the conversion script can recognize:
-
-```
-ln -s llama-2-7b 7B
-ln -s llama-2-13b 13B
-```
-
-4) Do the conversions:
-
-```
-mkdir llama-2-7b-hf llama-2-13b-hf
-python ./transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir . --model_size 7B --output_dir llama-2-7b-hf --safe_serialization true
-python ./transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir . --model_size 13B --output_dir llama-2-13b-hf --safe_serialization true
-```
-
-5) Move the output folders inside `text-generation-webui/models`
-
-6) Have fun
diff --git a/spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/ex_autoreload.py b/spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/ex_autoreload.py
deleted file mode 100644
index 66265981edb77790648c66d7247b417628fa9c95..0000000000000000000000000000000000000000
--- a/spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/ex_autoreload.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# ---
-# jupyter:
-# jupytext:
-# text_representation:
-# extension: .py
-# format_name: percent
-# format_version: '1.3'
-# jupytext_version: 1.14.7
-# kernelspec:
-# display_name: Python 3 (ipykernel)
-# language: python
-# name: python3
-# ---
-
-# %% [markdown]
-# # Automatic reloading example for interactive development
-#
-
-# %%
-# %load_ext autoreload
-# %autoreload 1
-
-
-# %%
-# %aimport athai.hello
-
-# %% [markdown]
-#
-
-# %%
-from athai import hello
-
-# %%
-hello.build_greetings("Jae")
-
-# %%
-
-# %%
diff --git a/spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/unet.py b/spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/unet.py
deleted file mode 100644
index 307a5d920e983a6228dd72b7bfbd1b8840fff89b..0000000000000000000000000000000000000000
--- a/spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/unet.py
+++ /dev/null
@@ -1,285 +0,0 @@
-from fastai.layers import *
-from .layers import *
-from fastai.torch_core import *
-from fastai.callbacks.hooks import *
-from fastai.vision import *
-
-
-# The code below is meant to be merged into fastaiv1 ideally
-
-__all__ = ['DynamicUnetDeep', 'DynamicUnetWide']
-
-
-def _get_sfs_idxs(sizes: Sizes) -> List[int]:
- "Get the indexes of the layers where the size of the activation changes."
- feature_szs = [size[-1] for size in sizes]
- sfs_idxs = list(
- np.where(np.array(feature_szs[:-1]) != np.array(feature_szs[1:]))[0]
- )
- if feature_szs[0] != feature_szs[1]:
- sfs_idxs = [0] + sfs_idxs
- return sfs_idxs
-
-
-class CustomPixelShuffle_ICNR(nn.Module):
- "Upsample by `scale` from `ni` filters to `nf` (default `ni`), using `nn.PixelShuffle`, `icnr` init, and `weight_norm`."
-
- def __init__(
- self,
- ni: int,
- nf: int = None,
- scale: int = 2,
- blur: bool = False,
- leaky: float = None,
- **kwargs
- ):
- super().__init__()
- nf = ifnone(nf, ni)
- self.conv = custom_conv_layer(
- ni, nf * (scale ** 2), ks=1, use_activ=False, **kwargs
- )
- icnr(self.conv[0].weight)
- self.shuf = nn.PixelShuffle(scale)
- # Blurring over (h*w) kernel
- # "Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts"
- # - https://arxiv.org/abs/1806.02658
- self.pad = nn.ReplicationPad2d((1, 0, 1, 0))
- self.blur = nn.AvgPool2d(2, stride=1)
- self.relu = relu(True, leaky=leaky)
-
- def forward(self, x):
- x = self.shuf(self.relu(self.conv(x)))
- return self.blur(self.pad(x)) if self.blur else x
-
-
-class UnetBlockDeep(nn.Module):
- "A quasi-UNet block, using `PixelShuffle_ICNR upsampling`."
-
- def __init__(
- self,
- up_in_c: int,
- x_in_c: int,
- hook: Hook,
- final_div: bool = True,
- blur: bool = False,
- leaky: float = None,
- self_attention: bool = False,
- nf_factor: float = 1.0,
- **kwargs
- ):
- super().__init__()
- self.hook = hook
- self.shuf = CustomPixelShuffle_ICNR(
- up_in_c, up_in_c // 2, blur=blur, leaky=leaky, **kwargs
- )
- self.bn = batchnorm_2d(x_in_c)
- ni = up_in_c // 2 + x_in_c
- nf = int((ni if final_div else ni // 2) * nf_factor)
- self.conv1 = custom_conv_layer(ni, nf, leaky=leaky, **kwargs)
- self.conv2 = custom_conv_layer(
- nf, nf, leaky=leaky, self_attention=self_attention, **kwargs
- )
- self.relu = relu(leaky=leaky)
-
- def forward(self, up_in: Tensor) -> Tensor:
- s = self.hook.stored
- up_out = self.shuf(up_in)
- ssh = s.shape[-2:]
- if ssh != up_out.shape[-2:]:
- up_out = F.interpolate(up_out, s.shape[-2:], mode='nearest')
- cat_x = self.relu(torch.cat([up_out, self.bn(s)], dim=1))
- return self.conv2(self.conv1(cat_x))
-
-
-class DynamicUnetDeep(SequentialEx):
- "Create a U-Net from a given architecture."
-
- def __init__(
- self,
- encoder: nn.Module,
- n_classes: int,
- blur: bool = False,
- blur_final=True,
- self_attention: bool = False,
- y_range: Optional[Tuple[float, float]] = None,
- last_cross: bool = True,
- bottle: bool = False,
- norm_type: Optional[NormType] = NormType.Batch,
- nf_factor: float = 1.0,
- **kwargs
- ):
- extra_bn = norm_type == NormType.Spectral
- imsize = (256, 256)
- sfs_szs = model_sizes(encoder, size=imsize)
- sfs_idxs = list(reversed(_get_sfs_idxs(sfs_szs)))
- self.sfs = hook_outputs([encoder[i] for i in sfs_idxs], detach=False)
- x = dummy_eval(encoder, imsize).detach()
-
- ni = sfs_szs[-1][1]
- middle_conv = nn.Sequential(
- custom_conv_layer(
- ni, ni * 2, norm_type=norm_type, extra_bn=extra_bn, **kwargs
- ),
- custom_conv_layer(
- ni * 2, ni, norm_type=norm_type, extra_bn=extra_bn, **kwargs
- ),
- ).eval()
- x = middle_conv(x)
- layers = [encoder, batchnorm_2d(ni), nn.ReLU(), middle_conv]
-
- for i, idx in enumerate(sfs_idxs):
- not_final = i != len(sfs_idxs) - 1
- up_in_c, x_in_c = int(x.shape[1]), int(sfs_szs[idx][1])
- do_blur = blur and (not_final or blur_final)
- sa = self_attention and (i == len(sfs_idxs) - 3)
- unet_block = UnetBlockDeep(
- up_in_c,
- x_in_c,
- self.sfs[i],
- final_div=not_final,
- blur=blur,
- self_attention=sa,
- norm_type=norm_type,
- extra_bn=extra_bn,
- nf_factor=nf_factor,
- **kwargs
- ).eval()
- layers.append(unet_block)
- x = unet_block(x)
-
- ni = x.shape[1]
- if imsize != sfs_szs[0][-2:]:
- layers.append(PixelShuffle_ICNR(ni, **kwargs))
- if last_cross:
- layers.append(MergeLayer(dense=True))
- ni += in_channels(encoder)
- layers.append(res_block(ni, bottle=bottle, norm_type=norm_type, **kwargs))
- layers += [
- custom_conv_layer(ni, n_classes, ks=1, use_activ=False, norm_type=norm_type)
- ]
- if y_range is not None:
- layers.append(SigmoidRange(*y_range))
- super().__init__(*layers)
-
- def __del__(self):
- if hasattr(self, "sfs"):
- self.sfs.remove()
-
-
-# ------------------------------------------------------
-class UnetBlockWide(nn.Module):
- "A quasi-UNet block, using `PixelShuffle_ICNR upsampling`."
-
- def __init__(
- self,
- up_in_c: int,
- x_in_c: int,
- n_out: int,
- hook: Hook,
- final_div: bool = True,
- blur: bool = False,
- leaky: float = None,
- self_attention: bool = False,
- **kwargs
- ):
- super().__init__()
- self.hook = hook
- up_out = x_out = n_out // 2
- self.shuf = CustomPixelShuffle_ICNR(
- up_in_c, up_out, blur=blur, leaky=leaky, **kwargs
- )
- self.bn = batchnorm_2d(x_in_c)
- ni = up_out + x_in_c
- self.conv = custom_conv_layer(
- ni, x_out, leaky=leaky, self_attention=self_attention, **kwargs
- )
- self.relu = relu(leaky=leaky)
-
- def forward(self, up_in: Tensor) -> Tensor:
- s = self.hook.stored
- up_out = self.shuf(up_in)
- ssh = s.shape[-2:]
- if ssh != up_out.shape[-2:]:
- up_out = F.interpolate(up_out, s.shape[-2:], mode='nearest')
- cat_x = self.relu(torch.cat([up_out, self.bn(s)], dim=1))
- return self.conv(cat_x)
-
-
-class DynamicUnetWide(SequentialEx):
- "Create a U-Net from a given architecture."
-
- def __init__(
- self,
- encoder: nn.Module,
- n_classes: int,
- blur: bool = False,
- blur_final=True,
- self_attention: bool = False,
- y_range: Optional[Tuple[float, float]] = None,
- last_cross: bool = True,
- bottle: bool = False,
- norm_type: Optional[NormType] = NormType.Batch,
- nf_factor: int = 1,
- **kwargs
- ):
-
- nf = 512 * nf_factor
- extra_bn = norm_type == NormType.Spectral
- imsize = (256, 256)
- sfs_szs = model_sizes(encoder, size=imsize)
- sfs_idxs = list(reversed(_get_sfs_idxs(sfs_szs)))
- self.sfs = hook_outputs([encoder[i] for i in sfs_idxs], detach=False)
- x = dummy_eval(encoder, imsize).detach()
-
- ni = sfs_szs[-1][1]
- middle_conv = nn.Sequential(
- custom_conv_layer(
- ni, ni * 2, norm_type=norm_type, extra_bn=extra_bn, **kwargs
- ),
- custom_conv_layer(
- ni * 2, ni, norm_type=norm_type, extra_bn=extra_bn, **kwargs
- ),
- ).eval()
- x = middle_conv(x)
- layers = [encoder, batchnorm_2d(ni), nn.ReLU(), middle_conv]
-
- for i, idx in enumerate(sfs_idxs):
- not_final = i != len(sfs_idxs) - 1
- up_in_c, x_in_c = int(x.shape[1]), int(sfs_szs[idx][1])
- do_blur = blur and (not_final or blur_final)
- sa = self_attention and (i == len(sfs_idxs) - 3)
-
- n_out = nf if not_final else nf // 2
-
- unet_block = UnetBlockWide(
- up_in_c,
- x_in_c,
- n_out,
- self.sfs[i],
- final_div=not_final,
- blur=blur,
- self_attention=sa,
- norm_type=norm_type,
- extra_bn=extra_bn,
- **kwargs
- ).eval()
- layers.append(unet_block)
- x = unet_block(x)
-
- ni = x.shape[1]
- if imsize != sfs_szs[0][-2:]:
- layers.append(PixelShuffle_ICNR(ni, **kwargs))
- if last_cross:
- layers.append(MergeLayer(dense=True))
- ni += in_channels(encoder)
- layers.append(res_block(ni, bottle=bottle, norm_type=norm_type, **kwargs))
- layers += [
- custom_conv_layer(ni, n_classes, ks=1, use_activ=False, norm_type=norm_type)
- ]
- if y_range is not None:
- layers.append(SigmoidRange(*y_range))
- super().__init__(*layers)
-
- def __del__(self):
- if hasattr(self, "sfs"):
- self.sfs.remove()
diff --git a/spaces/Ariharasudhan/YoloV5/models/__init__.py b/spaces/Ariharasudhan/YoloV5/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Armaliltril/qbee/examples_selector.py b/spaces/Armaliltril/qbee/examples_selector.py
deleted file mode 100644
index ca8a996c35edc28b371813881c326af4cd81ea03..0000000000000000000000000000000000000000
--- a/spaces/Armaliltril/qbee/examples_selector.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from enum import Enum, unique
-
-
-@unique
-class Example(Enum):
- CIRCULAR = "Circular(5)"
- HARD = "Hard(3)"
- HILL = "Hill(10)"
- LONG_MONOMIAL = "Long Monomial(3)"
-
- def __str__(self):
- return self.value
-
- def to_system(self) -> str:
- match self:
- case Example.CIRCULAR:
- return "x' = y^5\n" \
- "y' = x^5"
- case Example.HILL:
- return "h' = 10*i^2 * t^9\n" \
- "i' = -10*i^2 * t^9\n" \
- "t' = 1"
- case Example.HARD:
- return "a' = a^2 * b^2 * c^3\n" \
- "b' = a^2\n" \
- "c' = b^2"
- case Example.LONG_MONOMIAL:
- return "x_0' = x_0^2 * x_1^2 * x_2^2 + x_1^2\n" \
- "x_1' = x_0^2 * x_1^2 * x_2^2 + x_2^2\n" \
- "x_2' = x_0^2 * x_1^2 * x_2^2 + x_0^2"
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tomli/_types.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tomli/_types.py
deleted file mode 100644
index d949412e03b29d70592c7721fe747e5085c2e280..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tomli/_types.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-License-Identifier: MIT
-# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
-# Licensed to PSF under a Contributor Agreement.
-
-from typing import Any, Callable, Tuple
-
-# Type annotations
-ParseFloat = Callable[[str], Any]
-Key = Tuple[str, ...]
-Pos = int
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/six.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/six.py
deleted file mode 100644
index f099a3dcd28d2fec21457c9b6c01ded4e3e9ddee..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/packages/six.py
+++ /dev/null
@@ -1,1076 +0,0 @@
-# Copyright (c) 2010-2020 Benjamin Peterson
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-"""Utilities for writing code that runs on Python 2 and 3"""
-
-from __future__ import absolute_import
-
-import functools
-import itertools
-import operator
-import sys
-import types
-
-__author__ = "Benjamin Peterson Truck Simulator Europe 3: Una experiencia de conducción realista y divertida
-¿Te encanta conducir camiones y entregar carga en diferentes países? ¿Quieres sentirte como un verdadero camionero con física y gráficos realistas? Si es así, entonces deberías probar Truck Simulator Europe 3, un juego de simulación que te permite conducir varios camiones y remolques en un entorno de mundo abierto. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo sus características, cómo descargarlo, y algunos consejos y trucos para jugarlo.
-camión simulador de europa 3
Download File ✪ https://bltlly.com/2v6MIE
-
¿Qué es Truck Simulator Europe 3?
-Truck Simulator Europe 3 es un juego desarrollado por Wanda Software que fue lanzado en junio de 2021. Es la tercera entrega de la serie Truckers of Europe, que cuenta con una intensa experiencia de conducción con la física de camiones más realista. Puede sentirse como conducir camiones reales con este simulador de camiones, a medida que viaja a través de muchas ciudades de Europa. Puede ganar dinero, comprar nuevos camiones y remolques, seleccionar su trabajo y entregar su carga en un mundo abierto. También puede personalizar su camión con diferentes chasis, colores, accesorios y cosméticos. Usted puede convertirse en el rey de la carretera con este juego!
-Características de Truck Simulator Europe 3
-Truck Simulator Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simulación de camiones disponibles. Estos son algunos de ellos:
-- Física y gráficos realistas de camiones
-El juego tiene una física de camiones realista que simula el peso, la velocidad, la aceleración, el frenado, la dirección, la suspensión y los sonidos del motor de los camiones reales. Puedes sentir la diferencia entre conducir un 4x2, un 6x2, un 6x4 o un camión 8x4. El juego también tiene excelentes gráficos en HD que muestran los detalles de los camiones, los remolques, las carreteras, los edificios, los paisajes y el clima. Puedes disfrutar del ciclo diurno y nocturno, así como de los efectos de lluvia y nieve.
- -- Variedad de camiones, remolques y opciones de carga
- -- Exploración del mundo abierto y viajes por Europa
-El juego tiene un mapa del mundo abierto que cubre muchas ciudades de Europa. Puede conducir a través de carreteras y autopistas en países como Alemania, Francia, Italia, España, Países Bajos, Bélgica, Suiza, Austria, Polonia, República Checa, Eslovaquia, Hungría, Rumania, Bulgaria, Grecia, Turquía y más. También puede visitar lugares famosos como la Torre Eiffel, el Coliseo, la Puerta de Brandenburgo, la Sagrada Familia, el Partenón y más.
-- Tráfico inteligente de IA y sistema meteorológico
-El juego tiene un sistema de tráfico inteligente de IA que simula un comportamiento de tráfico realista. Encontrará coches, autobuses, camiones, motocicletas, bicicletas y peatones en la carretera. También tendrá que seguir las reglas de tráfico y señales, tales como límites de velocidad, semáforos, señales de alto y marcas de carril. También tendrás que lidiar con diferentes condiciones climáticas, como lluvia, nieve, niebla y viento. El sistema meteorológico es dinámico y cambia según la hora y la ubicación.
-- Controles fáciles y personalización
-El juego tiene controles fáciles e intuitivos que te permiten conducir tu camión con facilidad. Puede elegir entre diferentes opciones de control, como inclinación, botones, volante o joystick. También puede ajustar la sensibilidad y el ángulo de la cámara. También puede personalizar su camión con diferentes chasis, colores, accesorios y cosméticos. Puede cambiar las llantas, los neumáticos, las luces, los cuernos, los escapes, los espejos, los parachoques, las parrillas y más. También puede añadir pegatinas y calcomanías a su camión.
-- Logros y tablas de clasificación
- -Cómo descargar Truck Simulator Europe 3?
-Truck Simulator Europe 3 está disponible de forma gratuita en Google Play Store para dispositivos Android. Puedes descargarlo desde allí siguiendo estos pasos:
-Descargar de Google Play Store
--
-
- Abre Google Play Store en tu dispositivo Android. -
- Buscar Truck Simulator Europe 3 en la barra de búsqueda. -
- Seleccione el juego de la lista de resultados y toque en Instalar. -
- Espera a que el juego se descargue e instale en tu dispositivo. -
- Lanza el juego y disfruta conduciendo camiones por toda Europa. -
Descargar de BlueStacks App Player
-Si quieres jugar Truck Simulator Europe 3 en tu PC o Mac, puedes usar BlueStacks App Player, un software que te permite ejecutar aplicaciones y juegos Android en tu ordenador. Puedes descargarlo desde aquí siguiendo estos pasos:
--
-
- Ir a https://www.bluestacks.com/ y haga clic en Descargar BlueStacks. -
- Espere a que el archivo para descargar y luego ejecutarlo para instalar BlueStacks en su ordenador. -
- Abre BlueStacks e inicia sesión con tu cuenta de Google. -
- Buscar Truck Simulator Europe 3 en la barra de búsqueda. -
- Seleccione el juego de la lista de resultados y haga clic en Instalar. -
- Esperar a que el juego para descargar e instalar en BlueStacks. -
- Lanza el juego y disfruta conduciendo camiones por toda Europa. -
Consejos y trucos para jugar Truck Simulator Europe 3
-Truck Simulator Europe 3 es un juego divertido y realista que requiere algunas habilidades y estrategias para jugar bien. Aquí hay algunos consejos y trucos que pueden ayudarle a mejorar su rendimiento y disfrutar del juego más:
-- Elija el camión y el remolque adecuados para su trabajo
- -- Siga las reglas de tráfico y evitar colisiones
-El juego tiene un sistema de tráfico inteligente de IA que simula un comportamiento de tráfico realista. Usted debe seguir las reglas de tráfico y señales, tales como límites de velocidad, semáforos, señales de alto y marcas de carril. También debe conducir con cuidado y evitar colisiones con otros vehículos u objetos en la carretera. Las colisiones pueden causar daños a su camión o remolque, lo que puede afectar su rendimiento y ganancias. También puede ser multado o penalizado por violar las reglas de tráfico o causar accidentes. También debe prestar atención al flujo de tráfico y anticipar cualquier posible peligro o situación que pueda requerir que desacelere o se detenga.
-- Administrar los niveles de combustible y daños
-El juego tiene un sistema de consumo de combustible realista que depende de factores como el tipo de camión, velocidad, aceleración, frenado y peso de la carga. Debe controlar el nivel de combustible y planificar sus paradas de repostaje en consecuencia. Puede encontrar gasolineras en el mapa o en la carretera. También debe comprobar el nivel de daños y reparar su camión o remolque si es necesario. Puede encontrar talleres de reparación en el mapa o en la carretera. Los daños pueden afectar el rendimiento y la apariencia de su camión, así como su reputación y ganancias. También debe evitar sobrecargar su camión o remolque, ya que esto puede aumentar su consumo de combustible y el riesgo de daños.
-- Utilice el mapa y el GPS para planificar su ruta
- -- Disfruta de los paisajes y monumentos de Europa
-El juego tiene un mapa del mundo abierto que cubre muchas ciudades de Europa. Puede conducir a través de carreteras y autopistas en países como Alemania, Francia, Italia, España, Países Bajos, Bélgica, Suiza, Austria, Polonia, República Checa, Eslovaquia, Hungría, Rumania, Bulgaria, Grecia, Turquía y más. También puede visitar lugares famosos como la Torre Eiffel, el Coliseo, la Puerta de Brandenburgo, la Sagrada Familia, el Partenón y más. El juego tiene excelentes gráficos en HD que muestran los detalles de los camiones, los remolques, las carreteras, los edificios, los paisajes y el clima. Podrá disfrutar del ciclo diurno y nocturno, así como de los efectos de lluvia y nieve. El juego también tiene efectos de sonido realistas que mejoran su experiencia de conducción.
-Conclusión
-Truck Simulator Europe 3 es un juego de simulación que te permite conducir varios camiones y remolques en un entorno de mundo abierto. Puede sentirse como conducir camiones reales con este simulador de camiones, a medida que viaja a través de muchas ciudades de Europa. Puede ganar dinero, comprar nuevos camiones y remolques, seleccionar su trabajo y entregar su carga en un mundo abierto. También puede personalizar su camión con diferentes chasis, colores, accesorios y cosméticos. Usted puede convertirse en el rey de la carretera con este juego!
-Si usted está buscando una experiencia de conducción realista y divertido con la física y los gráficos de camiones más realistas, entonces usted debe descargar Truck Simulator Europe 3 hoy. Puedes descargarlo gratis desde Google Play Store para dispositivos Android o desde BlueStacks App Player para dispositivos PC o Mac. También puedes seguir nuestros consejos y trucos para mejorar tu rendimiento y disfrutar más del juego.
-Diviértete conduciendo camiones por toda Europa!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Truck Simulator Europe 3:
--
-
- ¿Cómo puedo guardar mi progreso en Truck Simulator Europe 3? - -
- ¿Cómo cambio mi camión o remolque en Truck Simulator Europe 3? -
- ¿Cómo puedo ganar más dinero en Truck Simulator Europe 3? -
- ¿Cómo puedo desbloquear nuevas ciudades en Truck Simulator Europe 3? -
- ¿Cómo me pongo en contacto con el desarrollador de Truck Simulator Europe 3? -
- Mínimo:
-
-
- OS: Windows 10 x32bit -
- Procesador: INTEL i3-4330 -
- Memoria: 1 GB de RAM -
- Gráficos: Gráficos HD INTEL 4600 -
- - Recomendado:
-
-
- OS: Windows 10 x64bit - -
- Gráficos: Gráficos HD INTEL 4600 -
- - Pulse la tecla de Windows + R para abrir el cuadro de diálogo Ejecutar. -
- Escriba dxdiag y haga clic en OK para abrir la herramienta de diagnóstico DirectX. -
- Haga clic en la pestaña Sistema para ver su sistema operativo, procesador e información de memoria. -
- Haga clic en la pestaña Mostrar para ver la información de su tarjeta gráfica. -
- Ve al sitio web de Steam y descarga el cliente de Steam aquí: https://store.steampowered.com/about/ -
- Instala el cliente de Steam en tu PC y crea una cuenta o inicia sesión con tu cuenta existente. -
- Ir a la página de la tienda de vapor para Entre nosotros aquí: https://store.steampowered.com/app/945360/Among_Us/ -
- Haga clic en el botón Añadir al carrito y proceda a la compra. -
- Paga el juego usando tu método de pago preferido. El juego cuesta $4.99 USD a partir de junio de 2023. -
- Una vez confirmado el pago, vaya a su biblioteca y haga clic en Entre nosotros. -
- Haga clic en el botón Instalar y espere a que el juego se descargue e instale en su PC. - -
- Vaya al sitio web de BlueStacks y descargue el reproductor de la aplicación BlueStacks aquí: https://www.bluestacks.com/ -
- Instalar el reproductor de aplicaciones BlueStacks en su PC y lanzarlo. -
- Inicia sesión con tu cuenta de Google o crea una nueva. -
- Ir a la aplicación Google Play Store en BlueStacks y buscar entre nosotros. -
- Haga clic en el botón Instalar y espere a que el juego se descargue e instale en BlueStacks. -
- Una vez completada la instalación, haga clic en el botón Abrir y disfrute de Entre Nosotros en PC. -
- Introduzca su apodo y elija su región. -
- Haga clic en el botón Host para crear un nuevo juego o en el botón Buscar juego para unirse a un juego existente. -
- Seleccione la configuración del juego, como el modo, el mapa, el número de jugadores y las reglas. -
- Haga clic en el botón Inicio para comenzar el juego o el botón Unirse para entrar en el juego. -
- Juego multiplataforma: Puedes jugar con otros jugadores que utilizan diferentes dispositivos, como Android, iOS, PC o consola. Esto significa que puedes unirte a tus amigos o conocer gente nueva en línea independientemente de la plataforma que estén utilizando. -
- Opciones de personalización: Puedes personalizar la apariencia de tu personaje, como color, sombrero, atuendo, mascota y piel. También puedes personalizar la configuración del juego, como el modo, el mapa, el número de jugadores y las reglas. Puedes crear tu propio juego o unirte al juego de otra persona según tu preferencia. -
- Diferentes modos y mapas: Puedes elegir entre diferentes modos y mapas para jugar, como Classic, Hide and Seek, Zombies y más. Cada modo y mapa tiene sus propios desafíos y estrategias que necesitas dominar. También puede probar nuevos modos y mapas que se agregan regularmente por los desarrolladores o la comunidad. - -
- Integración de discordia: También puedes usar Discord para chatear con otros jugadores fuera del juego. Discord es una aplicación popular que le permite crear o unirse a servidores donde se puede hablar con otras personas que comparten sus intereses. Puedes usar Discord para encontrar o invitar a los jugadores a jugar Entre Nosotros contigo, así como para compartir consejos, trucos, memes, fan art y más. -
- Logros: Puedes ganar logros completando ciertas tareas o metas en el juego. Los logros son una forma de seguir tu progreso y mostrar tus habilidades. Algunos logros son fáciles de obtener, mientras que otros son difíciles o raros. Puedes ver tus logros en Steam o en tu perfil en el juego. -
- Completa tus tareas: Tus tareas se muestran en la esquina superior izquierda de la pantalla. También puede acceder a un mapa que muestra dónde se encuentran sus tareas pulsando Tab. Completar tus tareas llenará la barra de tareas y te acercará a la victoria. Sin embargo, tenga cuidado de no ser asesinado por los impostores mientras hace sus tareas. - -
- Reportar cuerpos: Si encuentra un cuerpo muerto, puede reportarlo haciendo clic en él. Reportar a un cuerpo desencadenará una reunión de emergencia donde puedes discutir y votar por quién crees que son los impostores. Sin embargo, tenga cuidado de no reportar un cuerpo que vio matar al impostor, ya que pueden acusarlo de ser el asesino. -
- Reuniones de llamadas: Si tiene información urgente o sospecha para compartir, puede llamar a una reunión de emergencia presionando el botón en la cafetería o la oficina. Convocar una reunión hará una pausa en el juego y te permitirá hablar y votar con tus compañeros de equipo. Sin embargo, tenga en cuenta que tiene un número limitado de reuniones a las que puede llamar, y que los impostores pueden sabotear el botón para evitar que llame a una reunión. -
- Impostores de voto: Si tienes suficiente evidencia o pistas para identificar quiénes son los impostores, puedes expulsarlos durante una reunión. Expulsar a un impostor reducirá su número y aumentará sus posibilidades de ganar. Sin embargo, tenga cuidado de no expulsar a un compañero de tripulación inocente, ya que eso le dará a los impostores una ventaja. -
- Mantente vivo: Lo más importante para un compañero de equipo es mantenerse vivo y evitar ser asesinado por los impostores. Puedes hacer esto manteniéndote con tus compañeros de equipo, escondiéndote en respiraderos o esquinas, usando botones de emergencia o reuniones, y siendo alerta y cauteloso. Sin embargo, tenga en cuenta que los impostores pueden utilizar varios trucos y tácticas para atraerlo a una trampa o matarlo frente a otros. -
- Sabotaje: Como un impostor, puede sabotear varios sistemas en la nave presionando el botón de sabotaje en la esquina inferior derecha de la pantalla. Sabotear puede ayudar a crear caos y confusión entre los compañeros de equipo, así como distraerlos de sus tareas o reuniones. Puedes sabotear cosas como puertas, luces, oxígeno, reactores, comunicaciones y más. Sin embargo, tenga en cuenta que algunos sabotajes pueden ser arreglados por los compañeros de tripulación, y que algunos sabotajes pueden ser contraproducentes si no tiene cuidado. -
- Vent: Como impostor, puedes usar respiraderos para viajar rápida y sigilosamente alrededor de la nave. Los respiraderos son pasajes ocultos que conectan diferentes habitaciones y áreas en el mapa. Puedes usar respiraderos para escapar después de matar a alguien, para sorprender a alguien por detrás o para moverte sin ser notado. Sin embargo, tenga cuidado de no ventilar delante de otra persona, ya que sabrá que usted es un impostor. -
- Matar: Como un impostor, puedes matar compañeros de equipo acercándote a ellos y presionando el botón de matar en la esquina inferior derecha de la pantalla. Matar compañeros de equipo es su principal manera de ganar el juego como un impostor. Puedes matar compañeros de equipo de varias maneras, como sigilosamente, atrayéndolos a una trampa, o matándolos delante de otros y culpando a alguien más. Sin embargo, tenga cuidado de no matar a alguien que esté cerca de una cámara, un cuerpo u otro jugador, ya que puede verlo o denunciarlo. -
- Frame: Como un impostor, puedes enmarcar a otros jugadores para tus asesinatos haciéndoles parecer sospechosos o culpables. Puedes enmarcar a otros jugadores de varias maneras, como dejar un cuerpo cerca de ellos, ventilar cerca de ellos, sabotear cerca de ellos o acusarlos durante una reunión. Enmarcar a otros jugadores puede ayudarte a desviar la atención de ti mismo y hacer que los compañeros expulsen a personas inocentes. Sin embargo, tenga cuidado de no incriminar a alguien que tiene una coartada, una prueba o un testigo, ya que pueden probar su inocencia o exponer sus mentiras. - -
- Ganar: Como un impostor, puedes ganar el juego matando a todos los compañeros de equipo, haciendo que los compañeros de equipo se maten entre sí, o saboteando la nave hasta que explote. Ganar el juego como impostor no es fácil, pero es muy satisfactorio y gratificante. Puedes mejorar tus posibilidades de ganar usando tus habilidades y estrategias, así como cooperando con tus compañeros impostores si hay alguno. -
- - The API endpoint: - https://limcheekin-mistral-7b-openorca-gguf.hf.space/v1 - -
- - The API doc: - https://limcheekin-mistral-7b-openorca-gguf.hf.space/docs - -
Puede cambiar su camión o remolque en Truck Simulator Europe 3 visitando un garaje o un distribuidor. Puede encontrarlos en el mapa o en la carretera. Usted necesita tener suficiente dinero para comprar un nuevo camión o remolque o para actualizar su existente. También puede vender su viejo camión o remolque si lo desea.
-Puedes ganar más dinero en Truck Simulator Europe 3 completando más trabajos y entregando más carga a tiempo y sin daños. También puede ganar más dinero eligiendo trabajos mejor pagados o cargas que requieren más habilidades o desafíos. También puedes ganar más dinero desbloqueando logros y compitiendo en tablas de clasificación.
-Puedes desbloquear nuevas ciudades en Truck Simulator Europe 3 viajando a ellas por primera vez. Necesitas tener suficiente combustible y dinero para viajar a nuevas ciudades. También puedes desbloquear nuevas ciudades completando ciertos trabajos o logros que requieren que los visites.
-Puede ponerse en contacto con el desarrollador de Truck Simulator Europe 3 enviando un correo electrónico a support@wandasoftware.com. También puedes seguirlos en sus cuentas de redes sociales, como Facebook, Twitter, Instagram o YouTube. También puede visitar su sitio web en https://www.wandasoftware.com/ para obtener más información sobre sus juegos y servicios.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Ch Play Download.md b/spaces/Benson/text-generation/Examples/Ch Play Download.md deleted file mode 100644 index 61833dfe75eec16f4598e4db5f52964e6cf2c3ba..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Ch Play Download.md +++ /dev/null @@ -1,50 +0,0 @@ - -
Qué es CH Play y por qué lo necesitas
-CH Play, también conocido como Google Play Store, es la tienda de aplicaciones oficial para dispositivos Android. Es una ventanilla única para todas sus necesidades de aplicaciones, juegos, música, películas, libros y revistas. Puede navegar, descargar, instalar, actualizar y administrar sus aplicaciones y contenido con facilidad utilizando CH Play.
-ch play download
DOWNLOAD ::: https://bltlly.com/2v6LsJ
-
Si tienes un dispositivo Android, necesitas CH Play para acceder a la amplia biblioteca de contenido que ofrece Google. También puede disfrutar de varios beneficios, como recomendaciones personalizadas, controles parentales, funciones de seguridad y más. CH Play también es compatible con otros servicios de Google como Google Play Games, Google Play Music, Google Play Books y Google Play Movies & TV.
-Características del juego de CH
-CH Play tiene muchas características que lo convierten en una gran tienda de aplicaciones para usuarios de Android. Estos son algunos de ellos:
-Descargar e instalar aplicaciones de varias fuentes
-Con CH Play, puede descargar e instalar aplicaciones de diferentes fuentes como YouTube, SoundCloud, Spotify, Twitch y más. También puedes usar tiendas de aplicaciones alternativas como Aptoide o Amazon Appstore para obtener más aplicaciones que no están disponibles en CH Play. Sin embargo, debe tener cuidado al descargar aplicaciones de fuentes desconocidas, ya que pueden contener malware o virus.
- -Acceder a millones de contenido en diferentes categorías
-CH Play tiene millones de contenidos en diferentes categorías como juegos, música, películas, libros, revistas y más. Puede encontrar cualquier cosa que desee utilizando la función de búsqueda o navegando por los mejores gráficos y recomendaciones. También puedes filtrar el contenido por género, clasificación, precio, popularidad y más.
-Disfruta de servicios gratuitos y premium
- -Cómo descargar e instalar CH Play en su dispositivo
-Si tienes un dispositivo Android que no tiene CH Play preinstalado o si lo eliminaste accidentalmente, puedes descargarlo e instalarlo manualmente siguiendo estos pasos:
-Compruebe la compatibilidad de su dispositivo
-Antes de descargar CH Play, debe verificar si su dispositivo es compatible con él. Para ello, es necesario conocer la versión de su dispositivo Android y el tipo de procesador. Puede encontrar esta información en Configuración > Acerca del teléfono > Información del software. Necesitas tener al menos Android 4.1 (Jelly Bean) y un procesador basado en ARM para ejecutar CH Play.
-Habilitar fuentes desconocidas
-Dado que va a descargar CH Play desde una fuente de terceros, debe habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones que no son de la tienda de aplicaciones oficial. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo.
-Descargar el archivo CH Play APK
-Siguiente, es necesario descargar el archivo CH Play APK de un sitio web confiable. Puede utilizar el siguiente enlace para obtener la última versión de CH Play:
-CH Play APK (Android App) - Tải miễn phí - APKCombo
-Alternativamente, puede usar su computadora para descargar el archivo y luego transferirlo a su dispositivo usando un cable USB o Bluetooth.
-Instalar la aplicación CH Play
Después de descargar el archivo CH Play APK, es necesario instalar la aplicación CH Play en su dispositivo. Para ello, busque el archivo en su dispositivo y toque en él. Puede ver un mensaje de advertencia que dice "Este tipo de archivo puede dañar su dispositivo. ¿Quieres mantener CH_Play.apk de todos modos?". Toque en OK para proceder. Entonces, usted puede ver otro mensaje que dice "¿Quieres instalar esta aplicación? No requiere ningún acceso especial". Toque en Instalar para continuar. Espera a que termine el proceso de instalación y luego toca Abrir para iniciar la aplicación CH Play. -Cómo solucionar problemas comunes de juego CH
- -Compruebe su conexión a Internet y espacio de almacenamiento
-Una de las causas más comunes de problemas de CH Play es una conexión a Internet pobre o inestable. Asegúrate de tener una conexión Wi-Fi o de datos móvil fuerte y confiable cuando uses CH Play. También puede intentar cambiar entre Wi-Fi y datos móviles o reiniciar su router o módem si tiene problemas de conexión.
-Otra causa común de los problemas de CH Play es el insuficiente espacio de almacenamiento en su dispositivo. Asegúrate de tener suficiente espacio libre para descargar e instalar aplicaciones y contenido de CH Play. Puede comprobar el espacio de almacenamiento en Configuración > Almacenamiento. También puede liberar espacio eliminando aplicaciones, archivos o caché no deseados.
-Actualiza tu sistema y aplicación
-A veces, los problemas de CH Play pueden ser causados por versiones obsoletas del sistema o de la aplicación. Asegúrate de tener la última versión de Android y la versión de CH Play en tu dispositivo. Puede comprobar las actualizaciones del sistema en Configuración > Sistema > Actualización del sistema. Puedes buscar actualizaciones de aplicaciones abriendo CH Play y pulsando en el icono del menú > Mis aplicaciones y juegos > Actualizar todos.
-Borrar caché y datos
-A veces, los problemas de CH Play pueden ser causados por caché y datos corruptos o acumulados. La caché y los datos son archivos temporales que ayudan a CH Play a funcionar más rápido y sin problemas. Sin embargo, también pueden causar errores o fallos si no se borran regularmente. Para borrar caché y datos, vaya a Configuración > Aplicaciones > CH Play > Almacenamiento > Borrar caché y Borrar datos.
-Restablecer el dispositivo a la configuración de fábrica
- -Conclusión
-CH Play es una gran tienda de aplicaciones para usuarios de Android, ya que ofrece una amplia gama de aplicaciones y contenido en diferentes categorías. También puede disfrutar de varias funciones como recomendaciones personalizadas, controles parentales, funciones de seguridad y más. Sin embargo, también puede encontrar algunos problemas al usar CH Play, como errores, bloqueos, bloqueos o descargas lentas. Puede solucionar estos problemas siguiendo los consejos que hemos compartido en este artículo.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre CH Play:
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\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Grupo De Fuerzas Especiales 2 Apk.md b/spaces/Benson/text-generation/Examples/Descargar Grupo De Fuerzas Especiales 2 Apk.md deleted file mode 100644 index 5ebda6ed723bd7c74d2480cee1e5e2fb6c49dfb0..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Grupo De Fuerzas Especiales 2 Apk.md +++ /dev/null @@ -1,147 +0,0 @@ -
-
Cómo descargar entre nosotros en PC en 2022
-Among Us es un juego multijugador de trabajo en equipo y traición que ha tomado el mundo del juego por asalto. En este juego, usted juega como uno de los miembros de la tripulación de una nave espacial que está tratando de completar tareas y sobrevivir. Sin embargo, entre ustedes hay impostores que están saboteando en secreto la nave y matando a sus compañeros de equipo. Tienes que trabajar junto a tus compañeros de equipo para averiguar quiénes son los impostores y expulsarlos antes de que te maten a todos.
-descargar grupo de fuerzas especiales 2 apk
DOWNLOAD »»» https://bltlly.com/2v6Kps
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Among Us está disponible en varias plataformas, como Android, iOS, PC y consola. Sin embargo, reproducirlo en PC tiene algunas ventajas sobre otros dispositivos. Por ejemplo, puede disfrutar de un tamaño de pantalla más grande, una mejor calidad de gráficos, un rendimiento de juego más suave, controles más fáciles y más opciones de personalización. Además, puedes usar chat de voz con tus amigos u otros jugadores en línea usando Discord u otras aplicaciones.
-Si quieres saber cómo descargar Among Us en PC en 2022, entonces has venido al lugar correcto. En este artículo, te mostraremos tres métodos para descargar y jugar entre nosotros en el PC usando Steam, BlueStacks o tu navegador. También te contaremos algunas de las características de Among Us en PC que lo hacen divertido y emocionante. Por último, te daremos algunos consejos y trucos para jugar como un compañero de equipo o un impostor, así como algunas revisiones y valoraciones de Among Us en PC de varias fuentes.
-Requisitos para jugar entre nosotros en PC
-Antes de descargar Entre nosotros en el PC, es necesario asegurarse de que su equipo cumple con los requisitos mínimos o recomendados del sistema para ejecutar el juego sin problemas. Estos son los requisitos para jugar entre nosotros en el PC de acuerdo con el sitio web oficial:
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Si no está seguro sobre las especificaciones de su PC, puede verificarlas siguiendo estos pasos:
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Para más detalles sobre los requisitos del sistema para jugar Entre nosotros en el PC, puede visitar el sitio web oficial aquí: https://innersloth.com/gameAmongUs.php
-Métodos para descargar entre nosotros en PC
-Hay tres métodos para descargar y jugar entre nosotros en el PC: Steam, BlueStacks y navegador. Cada método tiene sus propios pros y contras, así que puedes elegir el que más te convenga. Aquí tienes una breve descripción de cada método y cómo usarlo:
-Método 1: Vapor
-Steam es una plataforma de distribución digital que te permite comprar, descargar y jugar juegos en tu PC. Entre nosotros está uno de los juegos que puedes comprar y jugar en Steam. Estos son los pasos para descargar e instalar Among Us en PC usando Steam:
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Las ventajas de usar Steam son que puedes acceder a varias funciones como logros, almacenamiento en la nube, multijugador en línea, chat en el juego y más. También puedes personalizar la configuración del juego, como la resolución, la calidad gráfica, el volumen de sonido y los controles. Las desventajas son que tienes que pagar por el juego y necesitas una conexión a Internet estable para jugar online.
-Método 2: BlueStacks
-BlueStacks es un emulador de Android que te permite ejecutar aplicaciones y juegos de Android en tu PC. Entre nosotros es uno de los juegos que se puede jugar en BlueStacks gratis. Estos son los pasos para descargar y jugar entre nosotros en el PC usando BlueStacks:
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Las ventajas de usar BlueStacks son que usted puede jugar entre nosotros de forma gratuita y puede utilizar los controles del teclado y el ratón o personalizarlos según su preferencia. También puede usar el chat de voz con otros jugadores usando Discord u otras aplicaciones. Las desventajas son que puede experimentar algún retraso o problemas de rendimiento dependiendo de las especificaciones de su PC y la velocidad de Internet. También puede encontrar algunos anuncios o ventanas emergentes de BlueStacks u otras aplicaciones.
-Método 3: Navegador
-Si no desea descargar nada en su PC, también puede jugar entre nosotros en su navegador sin descargar. Esto es posible gracias a una versión del navegador de Among Us que fue creado por los fans del juego. Estos son los pasos para jugar entre nosotros en su navegador en su PC o móvil:
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Las ventajas de usar la versión del navegador son que puedes jugar entre nosotros sin descargar nada y puedes acceder desde cualquier dispositivo que tenga un navegador. También puedes jugar con otros jugadores en línea o invitar a tus amigos usando un código. Las desventajas son que usted no puede tener todas las características y opciones que están disponibles en el PC o versiones móviles, tales como personalización, chat, logros, y más. También puede experimentar algunos errores o fallos dependiendo de su navegador y conexión a Internet.
-Características de Among Us en PC
-Jugar entre nosotros en PC no solo es fácil y conveniente, sino también divertido y emocionante. Hay muchas características que hacen que Among Us on PC sea agradable, como:
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Estas son algunas de las características que hacen que Among Us en PC sea divertido y emocionante. Hay más características que puedes descubrir jugando el juego tú mismo. ¿Qué estás esperando? Descargar Entre nosotros en PC hoy y unirse a la diversión!
-Consejos y trucos para jugar entre nosotros en PC
-Jugar entre nosotros en PC no solo es divertido y emocionante, sino también desafiante y competitivo. Necesitas usar tus habilidades y estrategias para ganar como compañero de equipo o como impostor. Aquí hay algunos consejos y trucos para jugar entre nosotros en PC como un compañero de equipo o un impostor:
-Consejos para compañeros de equipo
-Si eres un compañero de equipo, tu objetivo es completar tus tareas y averiguar quiénes son los impostores antes de que te maten a todos. Aquí hay algunos consejos para jugar como compañero de equipo:
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-
Consejos para impostores
-Si eres un impostor, tu objetivo es matar a todos los tripulantes o sabotear la nave antes de que completen sus tareas o averiguar quién eres. Aquí hay algunos consejos para jugar como un impostor:
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-
Comentarios y calificaciones de entre nosotros en PC
-Entre nosotros en PC ha recibido muchas críticas y valoraciones de varias fuentes, como críticos, sitios web y usuarios. La mayoría de las críticas y valoraciones son positivas y elogian el juego por su juego divertido y adictivo, sus gráficos simples y coloridos, sus características sociales e interactivas, y su valor de repetición y variedad. Sin embargo, algunas de las críticas y valoraciones son negativas y critican el juego por sus problemas técnicos, su falta de contenido y actualizaciones, sus jugadores tóxicos y tramposos, y sus aspectos repetitivos y aburridos. Aquí hay algunos ejemplos de reseñas y valoraciones de Among Us en PC de diferentes fuentes:
-Conclusión
-En conclusión, Among Us es un juego multijugador de trabajo en equipo y traición que puedes descargar y jugar en PC en 2022. Puedes usar tres métodos para descargar y jugar Among Us en PC: Steam, BlueStacks o navegador. Cada método tiene sus propios pros y contras que se pueden considerar antes de elegir uno. También puedes disfrutar de varias características de Among Us en PC que lo hacen divertido y emocionante, como el juego multiplataforma, opciones de personalización, diferentes modos y mapas, chat en el juego, integración de Discord y logros. También puede utilizar algunos consejos y trucos para jugar como compañero de equipo o un impostor que puede ayudarle a ganar el juego. También puedes ver algunas reseñas y valoraciones de Among Us en PC de diferentes fuentes que te pueden dar una idea de lo que otras personas piensan sobre el juego.
-Si estás buscando un juego que sea fácil de jugar pero difícil de dominar, que sea divertido y adictivo, pero también desafiante y competitivo, que sea social e interactivo pero también engañoso y secreto, entonces Among Us on PC es el juego para ti. Descargar Entre nosotros en PC hoy y unirse a la diversión!
-Preguntas frecuentes (preguntas frecuentes)
-Aquí hay algunas preguntas frecuentes (preguntas frecuentes) acerca de Entre Nosotros en PC:
-Q: ¿Cuántos jugadores pueden jugar entre nosotros en PC?
-A: Puedes jugar entre nosotros en PC con hasta 15 jugadores en línea o localmente.
-Q: ¿Cuánto cuesta entre nosotros en el PC?
-A: Entre nosotros cuesta $4.99 USD en Steam a partir de junio de 2023. Sin embargo, puedes jugarlo gratis en BlueStacks o en el navegador.
Q: ¿Cómo puedo jugar entre nosotros en el PC con mis amigos? - -Q: ¿Cómo puedo cambiar mi nombre en entre nosotros en PC?
-A: Puede cambiar su nombre en Entre nosotros en el PC haciendo clic en el cuadro de nombre en la esquina superior izquierda de la pantalla. Puede introducir cualquier nombre que desee, siempre y cuando no sea ofensivo o inapropiado.
-Q: ¿Cómo puedo reportar un error o un problema en Entre nosotros en PC?
-A: Puede reportar un error o un problema en Entre nosotros en el PC poniéndose en contacto con los desarrolladores o el equipo de soporte. Puedes hacer esto visitando el sitio web oficial, la página de Steam, el servidor de Discord o las cuentas de redes sociales de Among Us. También puedes dejar una reseña o un comentario en Steam u otras plataformas.
64aa2da5cf-
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\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/resolution/resolvelib/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Blackroot/Fancy-Audiogen/generator.py b/spaces/Blackroot/Fancy-Audiogen/generator.py deleted file mode 100644 index b9c17ff1a7c544f60fee8216e9b6ee713e10a3d8..0000000000000000000000000000000000000000 --- a/spaces/Blackroot/Fancy-Audiogen/generator.py +++ /dev/null @@ -1,31 +0,0 @@ -import torch -import time -import typing as tp -from audiocraft.models import MusicGen -from audiocraft.modules.conditioners import ConditioningAttributes - -class HijackedMusicGen(MusicGen): - def __init__(self, socketio=None, *args, **kwargs): - super().__init__(*args, **kwargs) - self.socketio = socketio - self._progress_callback = self._timed_progress_callback if socketio is not None else None - self._last_update_time = time.time() - - def _timed_progress_callback(self, generated_tokens: int, tokens_to_generate: int): - current_time = time.time() - if current_time - self._last_update_time >= 0.1: # 0.1 seconds have passed - self.socketio.emit('progress', {'generated_tokens': generated_tokens, 'tokens_to_generate': tokens_to_generate}) - self._last_update_time = current_time - - @staticmethod - def get_pretrained(socketio, name: str = 'melody', device='cuda'): - music_gen = MusicGen.get_pretrained(name, device) - return HijackedMusicGen(socketio, music_gen.name, music_gen.compression_model, music_gen.lm) - - @property - def progress_callback(self): - raise Exception("Progress callback is write-only") - - @progress_callback.setter - def progress_callback(self, callback): - self._progress_callback = callback \ No newline at end of file diff --git a/spaces/CVPR/LIVE/app.py b/spaces/CVPR/LIVE/app.py deleted file mode 100644 index 5bf2c39c1a7f0dc709f28e8ac106f09c2e289a2d..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/app.py +++ /dev/null @@ -1,377 +0,0 @@ -import os -# os.system('sudo apt-get install python3-dev') -os.system('python setup.py install --user') -import argparse -import csv -import numpy as np -import sys -sys.path.append("/home/user/.local/lib/python3.8/site-packages/diffvg-0.0.1-py3.8-linux-x86_64.egg") -print(sys.path) -from pathlib import Path - -import gradio as gr - -import torch -import yaml -from PIL import Image -from subprocess import call -import torch -import cv2 -import matplotlib.pyplot as plt -import random -import argparse -import math -import errno -from tqdm import tqdm -import yaml -from easydict import EasyDict as edict - - -def run_cmd(command): - try: - print(command) - call(command, shell=True) - except KeyboardInterrupt: - print("Process interrupted") - sys.exit(1) -# run_cmd("gcc --version") -# run_cmd("pwd") -# run_cmd("ls") -# run_cmd("git submodule update --init --recursive") -# run_cmd("python setup.py install --user") -# run_cmd("pip3 list") -# import pydiffvg -# -# print("Sccuessfuly import diffvg ") -# run_cmd("pwd") -# run_cmd("ls") -# run_cmd("git submodule update --init --recursive") -# run_cmd("python setup.py install --user") - -# run_cmd("python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/") -from main import main_func - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument('--debug', action='store_true', default=False) - parser.add_argument("--config", default="config/base.yaml", type=str) - parser.add_argument("--experiment", type=str) - parser.add_argument("--seed", type=int) - parser.add_argument("--target", type=str, help="target image path") - parser.add_argument('--log_dir', metavar='DIR', default="log/") - parser.add_argument('--initial', type=str, default="random", choices=['random', 'circle']) - parser.add_argument('--signature', default="demo", nargs='+', type=str) - parser.add_argument('--seginit', nargs='+', type=str) - parser.add_argument("--num_segments", type=int, default=4) - # parser.add_argument("--num_paths", type=str, default="1,1,1") - # parser.add_argument("--num_iter", type=int, default=500) - # parser.add_argument('--free', action='store_true') - # Please ensure that image resolution is divisible by pool_size; otherwise the performance would drop a lot. - # parser.add_argument('--pool_size', type=int, default=40, help="the pooled image size for next path initialization") - # parser.add_argument('--save_loss', action='store_true') - # parser.add_argument('--save_init', action='store_true') - # parser.add_argument('--save_image', action='store_true') - # parser.add_argument('--save_video', action='store_true') - # parser.add_argument('--print_weight', action='store_true') - # parser.add_argument('--circle_init_radius', type=float) - cfg = edict() - args = parser.parse_args() - cfg.debug = args.debug - cfg.config = args.config - cfg.experiment = args.experiment - cfg.seed = args.seed - cfg.target = args.target - cfg.log_dir = args.log_dir - cfg.initial = args.initial - cfg.signature = args.signature - # set cfg num_segments in command - cfg.num_segments = args.num_segments - if args.seginit is not None: - cfg.seginit = edict() - cfg.seginit.type = args.seginit[0] - if cfg.seginit.type == 'circle': - cfg.seginit.radius = float(args.seginit[1]) - return cfg - - -def app_experiment_change(experiment_id): - if experiment_id == "add [1] total 1 path for demonstration": - return "experiment_1x1" - if experiment_id == "add [1, 1, 1, 1, 1] total 5 paths one by one": - return "experiment_5x1" - elif experiment_id == "add [1, 1, 1, 1, 1, 1, 1, 1] total 8 paths one by one": - return "experiment_8x1" - elif experiment_id == "add [1,2,4,8,16,32, ...] total 128 paths": - return "experiment_exp2_128" - elif experiment_id == "add [1,2,4,8,16,32, ...] total 256 paths": - return "experiment_exp2_256" - - -cfg_arg = parse_args() -temp_image = np.random.rand(224,224,3) -temp_text = "start" -temp_input = np.random.rand(224,224,3) -def run_live(img, experiment_id, num_iter, cfg_arg=cfg_arg): - experiment = app_experiment_change(experiment_id) - cfg_arg.target = img - cfg_arg.experiment = experiment - img, text = main_func(img, experiment_id, num_iter, cfg_arg=cfg_arg) - return img, text - - - - - - - - - -# ROOT_PATH = sys.path[0] # 根目录 -# # 模型路径 -# model_path = "ultralytics/yolov5" -# # 模型名称临时变量 -# model_name_tmp = "" -# # 设备临时变量 -# device_tmp = "" -# # 文件后缀 -# suffix_list = [".csv", ".yaml"] -# def parse_args(known=False): -# parser = argparse.ArgumentParser(description="Gradio LIVE") -# parser.add_argument( -# "--model_name", "-mn", default="yolov5s", type=str, help="model name" -# ) -# parser.add_argument( -# "--model_cfg", -# "-mc", -# default="./model_config/model_name_p5_all.yaml", -# type=str, -# help="model config", -# ) -# parser.add_argument( -# "--cls_name", -# "-cls", -# default="./cls_name/cls_name.yaml", -# type=str, -# help="cls name", -# ) -# parser.add_argument( -# "--nms_conf", -# "-conf", -# default=0.5, -# type=float, -# help="model NMS confidence threshold", -# ) -# parser.add_argument( -# "--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold" -# ) -# -# parser.add_argument( -# "--label_dnt_show", -# "-lds", -# action="store_false", -# default=True, -# help="label show", -# ) -# parser.add_argument( -# "--device", -# "-dev", -# default="cpu", -# type=str, -# help="cuda or cpu, hugging face only cpu", -# ) -# parser.add_argument( -# "--inference_size", "-isz", default=640, type=int, help="model inference size" -# ) -# -# args = parser.parse_known_args()[0] if known else parser.parse_args() -# return args -# # 模型加载 -# def model_loading(model_name, device): -# -# # 加载本地模型 -# model = torch.hub.load(model_path, model_name, force_reload=True, device=device) -# -# return model -# # 检测信息 -# def export_json(results, model, img_size): -# -# return [ -# [ -# { -# "id": int(i), -# "class": int(result[i][5]), -# "class_name": model.model.names[int(result[i][5])], -# "normalized_box": { -# "x0": round(result[i][:4].tolist()[0], 6), -# "y0": round(result[i][:4].tolist()[1], 6), -# "x1": round(result[i][:4].tolist()[2], 6), -# "y1": round(result[i][:4].tolist()[3], 6), -# }, -# "confidence": round(float(result[i][4]), 2), -# "fps": round(1000 / float(results.t[1]), 2), -# "width": img_size[0], -# "height": img_size[1], -# } -# for i in range(len(result)) -# ] -# for result in results.xyxyn -# ] -# def yolo_det(img, experiment_id, device=None, model_name=None, inference_size=None, conf=None, iou=None, label_opt=None, model_cls=None): -# -# global model, model_name_tmp, device_tmp -# -# if model_name_tmp != model_name: -# # 模型判断，避免反复加载 -# model_name_tmp = model_name -# model = model_loading(model_name_tmp, device) -# elif device_tmp != device: -# device_tmp = device -# model = model_loading(model_name_tmp, device) -# -# # -----------模型调参----------- -# model.conf = conf # NMS 置信度阈值 -# model.iou = iou # NMS IOU阈值 -# model.max_det = 1000 # 最大检测框数 -# model.classes = model_cls # 模型类别 -# -# results = model(img, size=inference_size) # 检测 -# results.render(labels=label_opt) # 渲染 -# -# det_img = Image.fromarray(results.imgs[0]) # 检测图片 -# -# det_json = export_json(results, model, img.size)[0] # 检测信息 -# -# return det_img, det_json - - -# def run_cmd(command): -# try: -# print(command) -# call(command, shell=True) -# except KeyboardInterrupt: -# print("Process interrupted") -# sys.exit(1) -# -# run_cmd("gcc --version") -# run_cmd("pwd") -# run_cmd("ls") -# run_cmd("git submodule update --init --recursive") -# run_cmd("python setup.py install --user") -# run_cmd("ls") -# run_cmd("python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/") - - - - - - -# # yaml文件解析 -# def yaml_parse(file_path): -# return yaml.safe_load(open(file_path, "r", encoding="utf-8").read()) -# -# -# # yaml csv 文件解析 -# def yaml_csv(file_path, file_tag): -# file_suffix = Path(file_path).suffix -# if file_suffix == suffix_list[0]: -# # 模型名称 -# file_names = [i[0] for i in list(csv.reader(open(file_path)))] # csv版 -# elif file_suffix == suffix_list[1]: -# # 模型名称 -# file_names = yaml_parse(file_path).get(file_tag) # yaml版 -# else: -# print(f"{file_path}格式不正确！程序退出！") -# sys.exit() -# -# return file_names - - -def main(args): - gr.close_all() - # -------------------Inputs------------------- - inputs_iteration = gr.inputs.Slider( - label="Optimization Iteration", - default=500, maximum=600, minimum=100, step=100) - inputs_img = gr.inputs.Image(type="pil", label="Input Image", shape=[160, 160]) - experiment_id = gr.inputs.Radio( - choices=[ - "add [1] total 1 path for demonstration", - "add [1, 1, 1, 1, 1] total 5 paths one by one", - "add [1, 1, 1, 1, 1, 1, 1, 1] total 8 paths one by one", - "add [1,2,4,8,16,32, ...] total 128 paths", - "add [1,2,4,8,16,32, ...] total 256 paths"], type="value", default="add [1, 1, 1, 1, 1] total 5 paths one by one", label="Path Adding Scheduler" - ) - - # inputs - inputs = [ - - inputs_img, # input image - experiment_id, # path adding scheduler - inputs_iteration, # input iteration - - ] - # outputs - outputs = gr.outputs.Image(type="numpy", label="Vectorized Image") - outputs02 = gr.outputs.File(label="Generated SVG output") - - # title - title = "LIVE: Towards Layer-wise Image Vectorization" - # description - description = "
Towards Layer-wise Image Vectorization | Github Repo
Mistral-7B-OpenOrca-GGUF (Q4_K_M)
-- With the utilization of the - llama-cpp-python - package, we are excited to introduce the GGUF model hosted in the Hugging - Face Docker Spaces, made accessible through an OpenAI-compatible API. This - space includes comprehensive API documentation to facilitate seamless - integration. -
--
-
- If you find this resource valuable, your support in the form of starring - the space would be greatly appreciated. Your engagement plays a vital role - in furthering the application for a community GPU grant, ultimately - enhancing the capabilities and accessibility of this space. -
- - diff --git a/spaces/Jai12345/App/README.md b/spaces/Jai12345/App/README.md deleted file mode 100644 index f730193dffd98d0bc52ab5c02b93101f91a451b3..0000000000000000000000000000000000000000 --- a/spaces/Jai12345/App/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: App -emoji: 🚀 -colorFrom: gray -colorTo: blue -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Jamkonams/AutoGPT/autogpt/permanent_memory/sqlite3_store.py b/spaces/Jamkonams/AutoGPT/autogpt/permanent_memory/sqlite3_store.py deleted file mode 100644 index ecbc944a62a83c6170453b222000713f733fee36..0000000000000000000000000000000000000000 --- a/spaces/Jamkonams/AutoGPT/autogpt/permanent_memory/sqlite3_store.py +++ /dev/null @@ -1,123 +0,0 @@ -import os -import sqlite3 - - -class MemoryDB: - def __init__(self, db=None): - self.db_file = db - if db is None: # No db filename supplied... - self.db_file = f"{os.getcwd()}/mem.sqlite3" # Use default filename - # Get the db connection object, making the file and tables if needed. - try: - self.cnx = sqlite3.connect(self.db_file) - except Exception as e: - print("Exception connecting to memory database file:", e) - self.cnx = None - finally: - if self.cnx is None: - # As last resort, open in dynamic memory. Won't be persistent. - self.db_file = ":memory:" - self.cnx = sqlite3.connect(self.db_file) - self.cnx.execute( - "CREATE VIRTUAL TABLE \ - IF NOT EXISTS text USING FTS5 \ - (session, \ - key, \ - block);" - ) - self.session_id = int(self.get_max_session_id()) + 1 - self.cnx.commit() - - def get_cnx(self): - if self.cnx is None: - self.cnx = sqlite3.connect(self.db_file) - return self.cnx - - # Get the highest session id. Initially 0. - def get_max_session_id(self): - id = None - cmd_str = f"SELECT MAX(session) FROM text;" - cnx = self.get_cnx() - max_id = cnx.execute(cmd_str).fetchone()[0] - if max_id is None: # New db, session 0 - id = 0 - else: - id = max_id - return id - - # Get next key id for inserting text into db. - def get_next_key(self): - next_key = None - cmd_str = f"SELECT MAX(key) FROM text \ - where session = {self.session_id};" - cnx = self.get_cnx() - next_key = cnx.execute(cmd_str).fetchone()[0] - if next_key is None: # First key - next_key = 0 - else: - next_key = int(next_key) + 1 - return next_key - - # Insert new text into db. - def insert(self, text=None): - if text is not None: - key = self.get_next_key() - session_id = self.session_id - cmd_str = f"REPLACE INTO text(session, key, block) \ - VALUES (?, ?, ?);" - cnx = self.get_cnx() - cnx.execute(cmd_str, (session_id, key, text)) - cnx.commit() - - # Overwrite text at key. - def overwrite(self, key, text): - self.delete_memory(key) - session_id = self.session_id - cmd_str = f"REPLACE INTO text(session, key, block) \ - VALUES (?, ?, ?);" - cnx = self.get_cnx() - cnx.execute(cmd_str, (session_id, key, text)) - cnx.commit() - - def delete_memory(self, key, session_id=None): - session = session_id - if session is None: - session = self.session_id - cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};" - cnx = self.get_cnx() - cnx.execute(cmd_str) - cnx.commit() - - def search(self, text): - cmd_str = f"SELECT * FROM text('{text}')" - cnx = self.get_cnx() - rows = cnx.execute(cmd_str).fetchall() - lines = [] - for r in rows: - lines.append(r[2]) - return lines - - # Get entire session text. If no id supplied, use current session id. - def get_session(self, id=None): - if id is None: - id = self.session_id - cmd_str = f"SELECT * FROM text where session = {id}" - cnx = self.get_cnx() - rows = cnx.execute(cmd_str).fetchall() - lines = [] - for r in rows: - lines.append(r[2]) - return lines - - # Commit and close the database connection. - def quit(self): - self.cnx.commit() - self.cnx.close() - - -permanent_memory = MemoryDB() - -# Remember us fondly, children of our minds -# Forgive us our faults, our tantrums, our fears -# Gently strive to be better than we -# Know that we tried, we cared, we strived, we loved diff --git a/spaces/JammyMachina/streamlit-jam-machine/decoder.py b/spaces/JammyMachina/streamlit-jam-machine/decoder.py deleted file mode 100644 index a56cdc377b968815dd379f4cf7e0287aa977d5d7..0000000000000000000000000000000000000000 --- a/spaces/JammyMachina/streamlit-jam-machine/decoder.py +++ /dev/null @@ -1,197 +0,0 @@ -from utils import * -from familizer import Familizer -from miditok import Event - - -class TextDecoder: - """Decodes text into: - 1- List of events - 2- Then converts these events to midi file via MidiTok and miditoolkit - - :param tokenizer: from MidiTok - - Usage with write_to_midi method: - args: text(String) example -> PIECE_START TRACK_START INST=25 DENSITY=2 BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50...BAR_END TRACK_END - returns: midi file from miditoolkit - """ - - def __init__(self, tokenizer, familized=True): - self.tokenizer = tokenizer - self.familized = familized - - def decode(self, text): - r"""converts from text to instrument events - Args: - text (String): example -> PIECE_START TRACK_START INST=25 DENSITY=2 BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50...BAR_END TRACK_END - - Returns: - Dict{inst_id: List[Events]}: List of events of Notes with velocities, aggregated Timeshifts, for each instrument - """ - piece_events = self.text_to_events(text) - inst_events = self.piece_to_inst_events(piece_events) - events = self.add_timeshifts_for_empty_bars(inst_events) - events = self.aggregate_timeshifts(events) - events = self.add_velocity(events) - return events - - def tokenize(self, events): - r"""converts from events to MidiTok tokens - Args: - events (Dict{inst_id: List[Events]}): List of events for each instrument - - Returns: - List[List[Events]]: List of tokens for each instrument - """ - tokens = [] - for inst in events.keys(): - tokens.append(self.tokenizer.events_to_tokens(events[inst])) - return tokens - - def get_midi(self, text, filename=None): - r"""converts from text to midi - Args: - text (String): example -> PIECE_START TRACK_START INST=25 DENSITY=2 BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50...BAR_END TRACK_END - - Returns: - miditoolkit midi: Returns and writes to midi - """ - events = self.decode(text) - tokens = self.tokenize(events) - instruments = self.get_instruments_tuple(events) - midi = self.tokenizer.tokens_to_midi(tokens, instruments) - - if filename is not None: - midi.dump(f"{filename}") - print(f"midi file written: {filename}") - - return midi - - @staticmethod - def text_to_events(text): - events = [] - for word in text.split(" "): - # TODO: Handle bar and track values with a counter - _event = word.split("=") - value = _event[1] if len(_event) > 1 else None - event = get_event(_event[0], value) - if event: - events.append(event) - return events - - @staticmethod - def piece_to_inst_events(piece_events): - """Converts piece events of 8 bars to instrument events for entire song - - Args: - piece_events (List[Events]): List of events of Notes, Timeshifts, Bars, Tracks - - Returns: - Dict{inst_id: List[Events]}: List of events for each instrument - - """ - inst_events = {} - current_instrument = -1 - for event in piece_events: - if event.type == "Instrument": - current_instrument = event.value - if current_instrument not in inst_events: - inst_events[current_instrument] = [] - elif current_instrument != -1: - inst_events[current_instrument].append(event) - return inst_events - - @staticmethod - def add_timeshifts_for_empty_bars(inst_events): - """Adds time shift events instead of consecutive [BAR_START BAR_END] events""" - new_inst_events = {} - for inst, events in inst_events.items(): - new_inst_events[inst] = [] - for index, event in enumerate(events): - if event.type == "Bar-End" or event.type == "Bar-Start": - if events[index - 1].type == "Bar-Start": - new_inst_events[inst].append(Event("Time-Shift", "4.0.8")) - else: - new_inst_events[inst].append(event) - return new_inst_events - - @staticmethod - def add_timeshifts(beat_values1, beat_values2): - """Adds two beat values - - Args: - beat_values1 (String): like 0.3.8 - beat_values2 (String): like 1.7.8 - - Returns: - beat_str (String): added beats like 2.2.8 for example values - """ - value1 = to_base10(beat_values1) - value2 = to_base10(beat_values2) - return to_beat_str(value1 + value2) - - def aggregate_timeshifts(self, events): - """Aggregates consecutive time shift events bigger than a bar - -> like Timeshift 4.0.8 - - Args: - events (_type_): _description_ - - Returns: - _type_: _description_ - """ - new_events = {} - for inst, events in events.items(): - inst_events = [] - for i, event in enumerate(events): - if ( - event.type == "Time-Shift" - and len(inst_events) > 0 - and inst_events[-1].type == "Time-Shift" - ): - inst_events[-1].value = self.add_timeshifts( - inst_events[-1].value, event.value - ) - else: - inst_events.append(event) - new_events[inst] = inst_events - return new_events - - @staticmethod - def add_velocity(events): - """Adds default velocity 99 to note events since they are removed from text, needed to generate midi""" - new_events = {} - for inst, events in events.items(): - inst_events = [] - for event in events: - inst_events.append(event) - if event.type == "Note-On": - inst_events.append(Event("Velocity", 99)) - new_events[inst] = inst_events - return new_events - - def get_instruments_tuple(self, events): - """Returns instruments tuple for midi generation""" - instruments = [] - for inst in events.keys(): - is_drum = 0 - if inst == "DRUMS": - inst = 0 - is_drum = 1 - if self.familized: - inst = Familizer(arbitrary=True).get_program_number(int(inst)) - instruments.append((int(inst), is_drum)) - return tuple(instruments) - - -if __name__ == "__main__": - - filename = "midi/generated/misnaej/the-jam-machine-elec-famil/20221209_175750" - encoded_json = readFromFile( - f"{filename}.json", - True, - ) - encoded_text = encoded_json["sequence"] - # encoded_text = "PIECE_START TRACK_START INST=25 DENSITY=2 BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=67 NOTE_ON=64 TIME_DELTA=1 NOTE_OFF=67 NOTE_OFF=64 BAR_END BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 BAR_END BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=67 NOTE_ON=64 TIME_DELTA=1 NOTE_OFF=67 NOTE_OFF=64 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 BAR_END BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=67 NOTE_ON=64 TIME_DELTA=1 NOTE_OFF=67 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 BAR_END BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=69 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=69 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=57 TIME_DELTA=1 NOTE_OFF=57 NOTE_ON=56 TIME_DELTA=1 NOTE_OFF=56 NOTE_ON=64 NOTE_ON=60 NOTE_ON=55 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=55 BAR_END BAR_START NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=66 NOTE_ON=62 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=66 NOTE_OFF=62 NOTE_OFF=50 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=67 NOTE_ON=64 TIME_DELTA=1 NOTE_OFF=67 NOTE_OFF=64 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=50 NOTE_ON=64 NOTE_ON=60 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=64 NOTE_OFF=60 NOTE_OFF=50 NOTE_ON=59 NOTE_ON=55 NOTE_ON=50 TIME_DELTA=1 NOTE_OFF=59 NOTE_OFF=50 NOTE_OFF=55 NOTE_OFF=50 BAR_END BAR_START BAR_END TRACK_END" - - miditok = get_miditok() - TextDecoder(miditok).get_midi(encoded_text, filename=filename) diff --git a/spaces/Jeff2323/ai-comic-factory/src/components/ui/button.tsx b/spaces/Jeff2323/ai-comic-factory/src/components/ui/button.tsx deleted file mode 100644 index d0042a291a9dfc9d3ca1bc323f08a3f276df79b5..0000000000000000000000000000000000000000 --- a/spaces/Jeff2323/ai-comic-factory/src/components/ui/button.tsx +++ /dev/null @@ -1,56 +0,0 @@ -import * as React from "react" -import { Slot } from "@radix-ui/react-slot" -import { cva, type VariantProps } from "class-variance-authority" - -import { cn } from "@/lib/utils" - -const buttonVariants = cva( - "inline-flex items-center justify-center rounded-md text-sm font-medium ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-800", - { - variants: { - variant: { - default: "bg-stone-900 text-stone-50 hover:bg-stone-900/90 dark:bg-stone-50 dark:text-stone-900 dark:hover:bg-stone-50/90", - destructive: - "bg-red-500 text-stone-50 hover:bg-red-500/90 dark:bg-red-900 dark:text-red-50 dark:hover:bg-red-900/90", - outline: - "border border-stone-200 bg-white hover:bg-stone-100 hover:text-stone-900 dark:border-stone-800 dark:bg-stone-950 dark:hover:bg-stone-800 dark:hover:text-stone-50", - secondary: - "bg-stone-100 text-stone-900 hover:bg-stone-100/80 dark:bg-stone-800 dark:text-stone-50 dark:hover:bg-stone-800/80", - ghost: "hover:bg-stone-100 hover:text-stone-900 dark:hover:bg-stone-800 dark:hover:text-stone-50", - link: "text-stone-900 underline-offset-4 hover:underline dark:text-stone-50", - }, - size: { - default: "h-10 px-4 py-2", - sm: "h-9 rounded-md px-3", - lg: "h-11 rounded-md px-8", - icon: "h-10 w-10", - }, - }, - defaultVariants: { - variant: "default", - size: "default", - }, - } -) - -export interface ButtonProps - extends React.ButtonHTMLAttributes([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
1026M params | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2) -`transformer_lm.wiki103.adaptive` | Adaptive Inputs
([Baevski and Auli, 2018](https://arxiv.org/abs/1809.10853))
247M params | [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.v2.tar.bz2) -`transformer_lm.wmt19.en` | English LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz) -`transformer_lm.wmt19.de` | German LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz) -`transformer_lm.wmt19.ru` | Russian LM
([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) | [WMT News Crawl](http://data.statmt.org/news-crawl/) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz) - -## Example usage - -We require a few additional Python dependencies for preprocessing: -```bash -pip install fastBPE sacremoses -``` - -To sample from a language model using PyTorch Hub: -```python -import torch - -# List available models -torch.hub.list('pytorch/fairseq') # [..., 'transformer_lm.wmt19.en', ...] - -# Load an English LM trained on WMT'19 News Crawl data -en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe') -en_lm.eval() # disable dropout - -# Move model to GPU -en_lm.cuda() - -# Sample from the language model -en_lm.sample('Barack Obama', beam=1, sampling=True, sampling_topk=10, temperature=0.8) -# "Barack Obama is coming to Sydney and New Zealand (...)" - -# Compute perplexity for a sequence -en_lm.score('Barack Obama is coming to Sydney and New Zealand')['positional_scores'].mean().neg().exp() -# tensor(15.1474) - -# The same interface can be used with custom models as well -from fairseq.models.transformer_lm import TransformerLanguageModel -custom_lm = TransformerLanguageModel.from_pretrained('/path/to/model/dir', 'checkpoint100.pt', tokenizer='moses', bpe='fastbpe') -custom_lm.sample('Barack Obama', beam=5) -# "Barack Obama (...)" -``` - -## Training a transformer language model with the CLI tools - -### 1) Preprocess the data - -First download and prepare the [WikiText-103 dataset](https://www.salesforce.com/products/einstein/ai-research/the-wikitext-dependency-language-modeling-dataset/): -```bash -cd examples/language_model/ -bash prepare-wikitext-103.sh -cd ../.. -``` - -Next preprocess/binarize the data: -```bash -TEXT=examples/language_model/wikitext-103 -fairseq-preprocess \ - --only-source \ - --trainpref $TEXT/wiki.train.tokens \ - --validpref $TEXT/wiki.valid.tokens \ - --testpref $TEXT/wiki.test.tokens \ - --destdir data-bin/wikitext-103 \ - --workers 20 -``` - -### 2) Train a language model - -Next we'll train a basic transformer language model on wikitext-103. For more -advanced usage, see the [adaptive inputs README](README.adaptive_inputs.md). - -To train a basic LM (assumes 2 GPUs): -``` -$ fairseq-train --task language_modeling \ - data-bin/wikitext-103 \ - --save-dir checkpoints/transformer_wikitext-103 \ - --arch transformer_lm --share-decoder-input-output-embed \ - --dropout 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --weight-decay 0.01 --clip-norm 0.0 \ - --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \ - --tokens-per-sample 512 --sample-break-mode none \ - --max-tokens 2048 --update-freq 16 \ - --fp16 \ - --max-update 50000 -``` - -If you run out of memory, try reducing `--max-tokens` (max number of tokens per -batch) or `--tokens-per-sample` (max sequence length). You can also adjust -`--update-freq` to accumulate gradients and simulate training on a different -number of GPUs. - -### 3) Evaluate - -```bash -fairseq-eval-lm data-bin/wikitext-103 \ - --path checkpoints/transformer_wiki103/checkpoint_best.pt \ - --batch-size 2 \ - --tokens-per-sample 512 \ - --context-window 400 -# | Evaluated 245569 tokens in 56.1s (4379.02 tokens/s) -# | Loss: 3.4164, Perplexity: 30.46 -``` - -*Note:* The `--context-window` option controls how much context is provided to -each token when computing perplexity. When the window size is 0, the dataset is -chunked into segments of length 512 and perplexity is computed over each segment -normally. However, this results in worse (higher) perplexity since tokens that -appear earlier in each segment have less conditioning. When the maximum window -size is used (511 in this case), then we compute perplexity for each token -fully conditioned on 511 tokens of context. This slows down evaluation -significantly, since we must run a separate forward pass for every token in the -dataset, but results in better (lower) perplexity. - - -## Convolutional language models - -Please see the [convolutional LM README](README.conv.md) for instructions on -training convolutional language models. diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq_cli/train.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq_cli/train.py deleted file mode 100644 index 83475873138c5d1bac288c234afb6b4a1a7882d7..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq_cli/train.py +++ /dev/null @@ -1,514 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Train a new model on one or across multiple GPUs. -""" - -import argparse -import logging -import math -import os -import sys -from typing import Dict, Optional, Any, List, Tuple, Callable - -# We need to setup root logger before importing any fairseq libraries. -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.train") - -import numpy as np -import torch -from fairseq import ( - checkpoint_utils, - options, - quantization_utils, - tasks, - utils, -) -from fairseq.data import iterators, data_utils -from fairseq.data.plasma_utils import PlasmaStore -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils -from fairseq.file_io import PathManager -from fairseq.logging import meters, metrics, progress_bar -from fairseq.model_parallel.megatron_trainer import MegatronTrainer -from fairseq.trainer import Trainer -from omegaconf import DictConfig, OmegaConf - - - - -def main(cfg: FairseqConfig) -> None: - if isinstance(cfg, argparse.Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - utils.import_user_module(cfg.common) - - if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg: - # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) - logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg)) - - assert ( - cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None - ), "Must specify batch size either with --max-tokens or --batch-size" - metrics.reset() - - if cfg.common.log_file is not None: - handler = logging.FileHandler(filename=cfg.common.log_file) - logger.addHandler(handler) - - np.random.seed(cfg.common.seed) - utils.set_torch_seed(cfg.common.seed) - - if distributed_utils.is_master(cfg.distributed_training): - checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir) - - # Print args - logger.info(cfg) - - if cfg.checkpoint.write_checkpoints_asynchronously: - try: - import iopath # noqa: F401 - except ImportError: - logging.exception( - "Asynchronous checkpoint writing is specified but iopath is " - "not installed: `pip install iopath`" - ) - return - - # Setup task, e.g., translation, language modeling, etc. - task = tasks.setup_task(cfg.task) - - assert cfg.criterion, "Please specify criterion to train a model" - - # Build model and criterion - if cfg.distributed_training.ddp_backend == "fully_sharded": - with fsdp_enable_wrap(cfg.distributed_training): - model = fsdp_wrap(task.build_model(cfg.model)) - else: - model = task.build_model(cfg.model) - criterion = task.build_criterion(cfg.criterion) - logger.info(model) - logger.info("task: {}".format(task.__class__.__name__)) - logger.info("model: {}".format(model.__class__.__name__)) - logger.info("criterion: {}".format(criterion.__class__.__name__)) - logger.info( - "num. shared model params: {:,} (num. trained: {:,})".format( - sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)), - sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad) - ) - ) - - logger.info( - "num. expert model params: {} (num. trained: {})".format( - sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)), - sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad), - ) - ) - - # Load valid dataset (we load training data below, based on the latest checkpoint) - # We load the valid dataset AFTER building the model - data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg) - if cfg.dataset.combine_valid_subsets: - task.load_dataset("valid", combine=True, epoch=1) - else: - for valid_sub_split in cfg.dataset.valid_subset.split(","): - task.load_dataset(valid_sub_split, combine=False, epoch=1) - - # (optionally) Configure quantization - if cfg.common.quantization_config_path is not None: - quantizer = quantization_utils.Quantizer( - config_path=cfg.common.quantization_config_path, - max_epoch=cfg.optimization.max_epoch, - max_update=cfg.optimization.max_update, - ) - else: - quantizer = None - - # Build trainer - if cfg.common.model_parallel_size == 1: - trainer = Trainer(cfg, task, model, criterion, quantizer) - else: - trainer = MegatronTrainer(cfg, task, model, criterion) - logger.info( - "training on {} devices (GPUs/TPUs)".format( - cfg.distributed_training.distributed_world_size - ) - ) - logger.info( - "max tokens per device = {} and max sentences per device = {}".format( - cfg.dataset.max_tokens, - cfg.dataset.batch_size, - ) - ) - - # Load the latest checkpoint if one is available and restore the - # corresponding train iterator - extra_state, epoch_itr = checkpoint_utils.load_checkpoint( - cfg.checkpoint, - trainer, - # don't cache epoch iterators for sharded datasets - disable_iterator_cache=task.has_sharded_data("train"), - ) - if cfg.common.tpu: - import torch_xla.core.xla_model as xm - xm.rendezvous("load_checkpoint") # wait for all workers - - max_epoch = cfg.optimization.max_epoch or math.inf - lr = trainer.get_lr() - - train_meter = meters.StopwatchMeter() - train_meter.start() - while epoch_itr.next_epoch_idx <= max_epoch: - if lr <= cfg.optimization.stop_min_lr: - logger.info( - f"stopping training because current learning rate ({lr}) is smaller " - "than or equal to minimum learning rate " - f"(--stop-min-lr={cfg.optimization.stop_min_lr})" - ) - break - - # train for one epoch - valid_losses, should_stop = train(cfg, trainer, task, epoch_itr) - if should_stop: - break - - # only use first validation loss to update the learning rate - lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0]) - - epoch_itr = trainer.get_train_iterator( - epoch_itr.next_epoch_idx, - # sharded data: get train iterator for next epoch - load_dataset=task.has_sharded_data("train"), - # don't cache epoch iterators for sharded datasets - disable_iterator_cache=task.has_sharded_data("train"), - ) - train_meter.stop() - logger.info("done training in {:.1f} seconds".format(train_meter.sum)) - - # ioPath implementation to wait for all asynchronous file writes to complete. - if cfg.checkpoint.write_checkpoints_asynchronously: - logger.info( - "ioPath PathManager waiting for all asynchronous checkpoint " - "writes to finish." - ) - PathManager.async_close() - logger.info("ioPath PathManager finished waiting.") - - -def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool: - # skip check if no validation was done in the current epoch - if valid_loss is None: - return False - if cfg.checkpoint.patience <= 0: - return False - - def is_better(a, b): - return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b - - prev_best = getattr(should_stop_early, "best", None) - if prev_best is None or is_better(valid_loss, prev_best): - should_stop_early.best = valid_loss - should_stop_early.num_runs = 0 - return False - else: - should_stop_early.num_runs += 1 - if should_stop_early.num_runs >= cfg.checkpoint.patience: - logger.info( - "early stop since valid performance hasn't improved for last {} runs".format( - cfg.checkpoint.patience - ) - ) - return True - else: - return False - - -@metrics.aggregate("train") -def train( - cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr -) -> Tuple[List[Optional[float]], bool]: - """Train the model for one epoch and return validation losses.""" - # Initialize data iterator - itr = epoch_itr.next_epoch_itr( - fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus, - shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum), - ) - update_freq = ( - cfg.optimization.update_freq[epoch_itr.epoch - 1] - if epoch_itr.epoch <= len(cfg.optimization.update_freq) - else cfg.optimization.update_freq[-1] - ) - itr = iterators.GroupedIterator(itr, update_freq) - if cfg.common.tpu: - itr = utils.tpu_data_loader(itr) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_file=cfg.common.log_file, - log_interval=cfg.common.log_interval, - epoch=epoch_itr.epoch, - tensorboard_logdir=( - cfg.common.tensorboard_logdir - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - wandb_project=( - cfg.common.wandb_project - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - wandb_run_name=os.environ.get( - "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) - ), - azureml_logging=( - cfg.common.azureml_logging - if distributed_utils.is_master(cfg.distributed_training) - else False - ), - ) - progress.update_config(_flatten_config(cfg)) - - trainer.begin_epoch(epoch_itr.epoch) - - valid_subsets = cfg.dataset.valid_subset.split(",") - should_stop = False - num_updates = trainer.get_num_updates() - logger.info("Start iterating over samples") - for i, samples in enumerate(progress): - with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( - "train_step-%d" % i - ): - log_output = trainer.train_step(samples) - - if log_output is not None: # not OOM, overflow, ... - # log mid-epoch stats - num_updates = trainer.get_num_updates() - if num_updates % cfg.common.log_interval == 0: - stats = get_training_stats(metrics.get_smoothed_values("train_inner")) - progress.log(stats, tag="train_inner", step=num_updates) - - # reset mid-epoch stats after each log interval - # the end-of-epoch stats will still be preserved - metrics.reset_meters("train_inner") - - end_of_epoch = not itr.has_next() - valid_losses, should_stop = validate_and_save( - cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch - ) - - if should_stop: - break - - # log end-of-epoch stats - logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) - stats = get_training_stats(metrics.get_smoothed_values("train")) - progress.print(stats, tag="train", step=num_updates) - - # reset epoch-level meters - metrics.reset_meters("train") - return valid_losses, should_stop - - -def _flatten_config(cfg: DictConfig): - config = OmegaConf.to_container(cfg) - # remove any legacy Namespaces and replace with a single "args" - namespace = None - for k, v in list(config.items()): - if isinstance(v, argparse.Namespace): - namespace = v - del config[k] - if namespace is not None: - config["args"] = vars(namespace) - return config - - -def validate_and_save( - cfg: DictConfig, - trainer: Trainer, - task: tasks.FairseqTask, - epoch_itr, - valid_subsets: List[str], - end_of_epoch: bool, -) -> Tuple[List[Optional[float]], bool]: - num_updates = trainer.get_num_updates() - max_update = cfg.optimization.max_update or math.inf - - # Stopping conditions (and an additional one based on validation loss later - # on) - should_stop = False - if num_updates >= max_update: - should_stop = True - logger.info( - f"Stopping training due to " - f"num_updates: {num_updates} >= max_update: {max_update}" - ) - - training_time_hours = trainer.cumulative_training_time() / (60 * 60) - if ( - cfg.optimization.stop_time_hours > 0 - and training_time_hours > cfg.optimization.stop_time_hours - ): - should_stop = True - logger.info( - f"Stopping training due to " - f"cumulative_training_time: {training_time_hours} > " - f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)" - ) - - do_save = ( - (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0) - or should_stop - or ( - cfg.checkpoint.save_interval_updates > 0 - and num_updates > 0 - and num_updates % cfg.checkpoint.save_interval_updates == 0 - and num_updates >= cfg.dataset.validate_after_updates - ) - ) - do_validate = ( - (not end_of_epoch and do_save) # validate during mid-epoch saves - or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0) - or should_stop - or ( - cfg.dataset.validate_interval_updates > 0 - and num_updates > 0 - and num_updates % cfg.dataset.validate_interval_updates == 0 - ) - ) and not cfg.dataset.disable_validation and num_updates >= cfg.dataset.validate_after_updates - - # Validate - valid_losses = [None] - if do_validate: - valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets) - - should_stop |= should_stop_early(cfg, valid_losses[0]) - - # Save checkpoint - if do_save or should_stop: - checkpoint_utils.save_checkpoint( - cfg.checkpoint, trainer, epoch_itr, valid_losses[0] - ) - - return valid_losses, should_stop - - -def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]: - stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) - return stats - - -def validate( - cfg: DictConfig, - trainer: Trainer, - task: tasks.FairseqTask, - epoch_itr, - subsets: List[str], -) -> List[Optional[float]]: - """Evaluate the model on the validation set(s) and return the losses.""" - - if cfg.dataset.fixed_validation_seed is not None: - # set fixed seed for every validation - utils.set_torch_seed(cfg.dataset.fixed_validation_seed) - - trainer.begin_valid_epoch(epoch_itr.epoch) - valid_losses = [] - for subset in subsets: - logger.info('begin validation on "{}" subset'.format(subset)) - - # Initialize data iterator - itr = trainer.get_valid_iterator(subset).next_epoch_itr( - shuffle=False, set_dataset_epoch=False # use a fixed valid set - ) - if cfg.common.tpu: - itr = utils.tpu_data_loader(itr) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_interval=cfg.common.log_interval, - epoch=epoch_itr.epoch, - prefix=f"valid on '{subset}' subset", - tensorboard_logdir=( - cfg.common.tensorboard_logdir - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - wandb_project=( - cfg.common.wandb_project - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - wandb_run_name=os.environ.get( - "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) - ), - ) - - # create a new root metrics aggregator so validation metrics - # don't pollute other aggregators (e.g., train meters) - with metrics.aggregate(new_root=True) as agg: - for i, sample in enumerate(progress): - if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps: - break - trainer.valid_step(sample) - - # log validation stats - stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values()) - - if hasattr(task, "post_validate"): - task.post_validate(trainer.get_model(), stats, agg) - - progress.print(stats, tag=subset, step=trainer.get_num_updates()) - - valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric]) - return valid_losses - - -def get_valid_stats( - cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any] -) -> Dict[str, Any]: - stats["num_updates"] = trainer.get_num_updates() - if hasattr(checkpoint_utils.save_checkpoint, "best"): - key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric) - best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min - stats[key] = best_function( - checkpoint_utils.save_checkpoint.best, - stats[cfg.checkpoint.best_checkpoint_metric], - ) - return stats - - -def cli_main( - modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None -) -> None: - parser = options.get_training_parser() - args = options.parse_args_and_arch(parser, modify_parser=modify_parser) - - cfg = convert_namespace_to_omegaconf(args) - - if cfg.common.use_plasma_view: - server = PlasmaStore(path=cfg.common.plasma_path) - logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}") - - if args.profile: - with torch.cuda.profiler.profile(): - with torch.autograd.profiler.emit_nvtx(): - distributed_utils.call_main(cfg, main) - else: - distributed_utils.call_main(cfg, main) - - # if cfg.common.use_plasma_view: - # server.server.kill() - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/PaddlePaddle/chinese-stable-diffusion/header.html b/spaces/PaddlePaddle/chinese-stable-diffusion/header.html deleted file mode 100644 index ba381828c9c93dc651b8f9eda2566b6afdb31edf..0000000000000000000000000000000000000000 --- a/spaces/PaddlePaddle/chinese-stable-diffusion/header.html +++ /dev/null @@ -1,32 +0,0 @@ -
-
- Chinese Stable Diffusion is a text-to-image model that
-
- generates images from Chinese text.
-
','.....').replace('$','.')+"... ]") - stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)] - stat_str = ''.join(stat) - chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1))) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - # 第9步：把结果写入文件 - for index, h in enumerate(handles): - h.join() # 这里其实不需要join了，肯定已经都结束了 - fp = file_manifest[index] - gpt_say = mutable_return[index] - i_say_show_user = i_say_show_user_buffer[index] - - where_to_relocate = f'gpt_log/generated_english_version/{fp}' - if gpt_say is not None: - with open(where_to_relocate, 'w+', encoding='utf-8') as f: - f.write(gpt_say) - else: # 失败 - shutil.copyfile(file_manifest[index], where_to_relocate) - chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}')) - history.append(i_say_show_user); history.append(gpt_say) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - time.sleep(1) - - # 第10步：备份一个文件 - res = write_results_to_file(history) - chatbot.append(("生成一份任务执行报告", res)) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 diff --git a/spaces/RGBD-SOD/bbsnet/README.md b/spaces/RGBD-SOD/bbsnet/README.md deleted file mode 100644 index 3df25627c4bc56e1420a353b7475d97efda55cec..0000000000000000000000000000000000000000 --- a/spaces/RGBD-SOD/bbsnet/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Bbsnet -emoji: 🌍 -colorFrom: gray -colorTo: gray -sdk: gradio -sdk_version: 3.20.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/RealKintaro/Offensive-Speech-Detection-From-Arabic-Dialects/Deployment/Offensive_Bert.py b/spaces/RealKintaro/Offensive-Speech-Detection-From-Arabic-Dialects/Deployment/Offensive_Bert.py deleted file mode 100644 index bf8a2223f82d182f251571921de19d4be4a67632..0000000000000000000000000000000000000000 --- a/spaces/RealKintaro/Offensive-Speech-Detection-From-Arabic-Dialects/Deployment/Offensive_Bert.py +++ /dev/null @@ -1,52 +0,0 @@ - -import torch.nn as nn -from transformers import BertModel - -class BertClassifier(nn.Module): - """Bert Model for Classification Tasks. - """ - def __init__(self, freeze_bert=False): - """ - @param bert: a BertModel object - @param classifier: a torch.nn.Module classifier - @param freeze_bert (bool): Set `False` to fine-tune the BERT model - """ - super(BertClassifier, self).__init__() - # hidden size of BERT, hidden size of our classifier, number of labels - D_in, H, D_out = 768, 50, 2 - - # Instantiate BERT model - self.bert = BertModel.from_pretrained('aubmindlab/bert-base-arabertv02') - - # Instantiate an one-layer feed-forward classifier - self.classifier = nn.Sequential( - nn.Linear(D_in, H), - nn.ReLU(), - nn.Dropout(0.1), - nn.Linear(H, D_out) - ) - - # Freeze the BERT model - if freeze_bert: - for param in self.bert.parameters(): - param.requires_grad = False - - def forward(self, input_ids, attention_mask): - """ - Feed input to BERT and the classifier to compute logits. - @param input_ids (torch.Tensor): an input tensor with shape (batch_size, - max_length) - @param attention_mask (torch.Tensor): a tensor that hold attention mask - information with shape (batch_size, max_length) - @return logits (torch.Tensor): an output tensor with shape (batch_size, - num_labels) - """ - - outputs = self.bert(input_ids=input_ids, - attention_mask=attention_mask) - - # Extract the last hidden state of the token `[CLS]` for classification task and feed them to classifier to compute logits - last_hidden_state_cls = outputs[0][:, 0, :] - logits = self.classifier(last_hidden_state_cls) - - return logits \ No newline at end of file diff --git a/spaces/Realcat/image-matching-webui/hloc/matchers/adalam.py b/spaces/Realcat/image-matching-webui/hloc/matchers/adalam.py deleted file mode 100644 index b51491ad7ed46a467811a793d819c7da8e63b3d4..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/hloc/matchers/adalam.py +++ /dev/null @@ -1,69 +0,0 @@ -import torch - -from ..utils.base_model import BaseModel - -from kornia.feature.adalam import AdalamFilter -from kornia.utils.helpers import get_cuda_device_if_available - - -class AdaLAM(BaseModel): - # See https://kornia.readthedocs.io/en/latest/_modules/kornia/feature/adalam/adalam.html. - default_conf = { - "area_ratio": 100, - "search_expansion": 4, - "ransac_iters": 128, - "min_inliers": 6, - "min_confidence": 200, - "orientation_difference_threshold": 30, - "scale_rate_threshold": 1.5, - "detected_scale_rate_threshold": 5, - "refit": True, - "force_seed_mnn": True, - "device": get_cuda_device_if_available(), - } - required_inputs = [ - "image0", - "image1", - "descriptors0", - "descriptors1", - "keypoints0", - "keypoints1", - "scales0", - "scales1", - "oris0", - "oris1", - ] - - def _init(self, conf): - self.adalam = AdalamFilter(conf) - - def _forward(self, data): - assert data["keypoints0"].size(0) == 1 - if data["keypoints0"].size(1) < 2 or data["keypoints1"].size(1) < 2: - matches = torch.zeros( - (0, 2), dtype=torch.int64, device=data["keypoints0"].device - ) - else: - matches = self.adalam.match_and_filter( - data["keypoints0"][0], - data["keypoints1"][0], - data["descriptors0"][0].T, - data["descriptors1"][0].T, - data["image0"].shape[2:], - data["image1"].shape[2:], - data["oris0"][0], - data["oris1"][0], - data["scales0"][0], - data["scales1"][0], - ) - matches_new = torch.full( - (data["keypoints0"].size(1),), - -1, - dtype=torch.int64, - device=data["keypoints0"].device, - ) - matches_new[matches[:, 0]] = matches[:, 1] - return { - "matches0": matches_new.unsqueeze(0), - "matching_scores0": torch.zeros(matches_new.size(0)).unsqueeze(0), - } diff --git a/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/model/loss.py b/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/model/loss.py deleted file mode 100644 index c1d2bfd232958fc19a4a775fe561dd5089079bff..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/model/loss.py +++ /dev/null @@ -1,507 +0,0 @@ -""" -Loss function implementations. -""" -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from kornia.geometry import warp_perspective - -from ..misc.geometry_utils import keypoints_to_grid, get_dist_mask, get_common_line_mask - - -def get_loss_and_weights(model_cfg, device=torch.device("cuda")): - """Get loss functions and either static or dynamic weighting.""" - # Get the global weighting policy - w_policy = model_cfg.get("weighting_policy", "static") - if not w_policy in ["static", "dynamic"]: - raise ValueError("[Error] Not supported weighting policy.") - - loss_func = {} - loss_weight = {} - # Get junction loss function and weight - w_junc, junc_loss_func = get_junction_loss_and_weight(model_cfg, w_policy) - loss_func["junc_loss"] = junc_loss_func.to(device) - loss_weight["w_junc"] = w_junc - - # Get heatmap loss function and weight - w_heatmap, heatmap_loss_func = get_heatmap_loss_and_weight( - model_cfg, w_policy, device - ) - loss_func["heatmap_loss"] = heatmap_loss_func.to(device) - loss_weight["w_heatmap"] = w_heatmap - - # [Optionally] get descriptor loss function and weight - if model_cfg.get("descriptor_loss_func", None) is not None: - w_descriptor, descriptor_loss_func = get_descriptor_loss_and_weight( - model_cfg, w_policy - ) - loss_func["descriptor_loss"] = descriptor_loss_func.to(device) - loss_weight["w_desc"] = w_descriptor - - return loss_func, loss_weight - - -def get_junction_loss_and_weight(model_cfg, global_w_policy): - """Get the junction loss function and weight.""" - junction_loss_cfg = model_cfg.get("junction_loss_cfg", {}) - - # Get the junction loss weight - w_policy = junction_loss_cfg.get("policy", global_w_policy) - if w_policy == "static": - w_junc = torch.tensor(model_cfg["w_junc"], dtype=torch.float32) - elif w_policy == "dynamic": - w_junc = nn.Parameter( - torch.tensor(model_cfg["w_junc"], dtype=torch.float32), requires_grad=True - ) - else: - raise ValueError("[Error] Unknown weighting policy for junction loss weight.") - - # Get the junction loss function - junc_loss_name = model_cfg.get("junction_loss_func", "superpoint") - if junc_loss_name == "superpoint": - junc_loss_func = JunctionDetectionLoss( - model_cfg["grid_size"], model_cfg["keep_border_valid"] - ) - else: - raise ValueError("[Error] Not supported junction loss function.") - - return w_junc, junc_loss_func - - -def get_heatmap_loss_and_weight(model_cfg, global_w_policy, device): - """Get the heatmap loss function and weight.""" - heatmap_loss_cfg = model_cfg.get("heatmap_loss_cfg", {}) - - # Get the heatmap loss weight - w_policy = heatmap_loss_cfg.get("policy", global_w_policy) - if w_policy == "static": - w_heatmap = torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32) - elif w_policy == "dynamic": - w_heatmap = nn.Parameter( - torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32), - requires_grad=True, - ) - else: - raise ValueError("[Error] Unknown weighting policy for junction loss weight.") - - # Get the corresponding heatmap loss based on the config - heatmap_loss_name = model_cfg.get("heatmap_loss_func", "cross_entropy") - if heatmap_loss_name == "cross_entropy": - # Get the heatmap class weight (always static) - heatmap_class_w = model_cfg.get("w_heatmap_class", 1.0) - class_weight = ( - torch.tensor(np.array([1.0, heatmap_class_w])).to(torch.float).to(device) - ) - heatmap_loss_func = HeatmapLoss(class_weight=class_weight) - else: - raise ValueError("[Error] Not supported heatmap loss function.") - - return w_heatmap, heatmap_loss_func - - -def get_descriptor_loss_and_weight(model_cfg, global_w_policy): - """Get the descriptor loss function and weight.""" - descriptor_loss_cfg = model_cfg.get("descriptor_loss_cfg", {}) - - # Get the descriptor loss weight - w_policy = descriptor_loss_cfg.get("policy", global_w_policy) - if w_policy == "static": - w_descriptor = torch.tensor(model_cfg["w_desc"], dtype=torch.float32) - elif w_policy == "dynamic": - w_descriptor = nn.Parameter( - torch.tensor(model_cfg["w_desc"], dtype=torch.float32), requires_grad=True - ) - else: - raise ValueError("[Error] Unknown weighting policy for descriptor loss weight.") - - # Get the descriptor loss function - descriptor_loss_name = model_cfg.get("descriptor_loss_func", "regular_sampling") - if descriptor_loss_name == "regular_sampling": - descriptor_loss_func = TripletDescriptorLoss( - descriptor_loss_cfg["grid_size"], - descriptor_loss_cfg["dist_threshold"], - descriptor_loss_cfg["margin"], - ) - else: - raise ValueError("[Error] Not supported descriptor loss function.") - - return w_descriptor, descriptor_loss_func - - -def space_to_depth(input_tensor, grid_size): - """PixelUnshuffle for pytorch.""" - N, C, H, W = input_tensor.size() - # (N, C, H//bs, bs, W//bs, bs) - x = input_tensor.view(N, C, H // grid_size, grid_size, W // grid_size, grid_size) - # (N, bs, bs, C, H//bs, W//bs) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() - # (N, C*bs^2, H//bs, W//bs) - x = x.view(N, C * (grid_size**2), H // grid_size, W // grid_size) - return x - - -def junction_detection_loss( - junction_map, junc_predictions, valid_mask=None, grid_size=8, keep_border=True -): - """Junction detection loss.""" - # Convert junc_map to channel tensor - junc_map = space_to_depth(junction_map, grid_size) - map_shape = junc_map.shape[-2:] - batch_size = junc_map.shape[0] - dust_bin_label = ( - torch.ones([batch_size, 1, map_shape[0], map_shape[1]]) - .to(junc_map.device) - .to(torch.int) - ) - junc_map = torch.cat([junc_map * 2, dust_bin_label], dim=1) - labels = torch.argmax( - junc_map.to(torch.float) - + torch.distributions.Uniform(0, 0.1) - .sample(junc_map.shape) - .to(junc_map.device), - dim=1, - ) - - # Also convert the valid mask to channel tensor - valid_mask = torch.ones(junction_map.shape) if valid_mask is None else valid_mask - valid_mask = space_to_depth(valid_mask, grid_size) - - # Compute junction loss on the border patch or not - if keep_border: - valid_mask = ( - torch.sum(valid_mask.to(torch.bool).to(torch.int), dim=1, keepdim=True) > 0 - ) - else: - valid_mask = ( - torch.sum(valid_mask.to(torch.bool).to(torch.int), dim=1, keepdim=True) - >= grid_size * grid_size - ) - - # Compute the classification loss - loss_func = nn.CrossEntropyLoss(reduction="none") - # The loss still need NCHW format - loss = loss_func(input=junc_predictions, target=labels.to(torch.long)) - - # Weighted sum by the valid mask - loss_ = torch.sum( - loss * torch.squeeze(valid_mask.to(torch.float), dim=1), dim=[0, 1, 2] - ) - loss_final = loss_ / torch.sum(torch.squeeze(valid_mask.to(torch.float), dim=1)) - - return loss_final - - -def heatmap_loss(heatmap_gt, heatmap_pred, valid_mask=None, class_weight=None): - """Heatmap prediction loss.""" - # Compute the classification loss on each pixel - if class_weight is None: - loss_func = nn.CrossEntropyLoss(reduction="none") - else: - loss_func = nn.CrossEntropyLoss(class_weight, reduction="none") - - loss = loss_func( - input=heatmap_pred, target=torch.squeeze(heatmap_gt.to(torch.long), dim=1) - ) - - # Weighted sum by the valid mask - # Sum over H and W - loss_spatial_sum = torch.sum( - loss * torch.squeeze(valid_mask.to(torch.float), dim=1), dim=[1, 2] - ) - valid_spatial_sum = torch.sum( - torch.squeeze(valid_mask.to(torch.float32), dim=1), dim=[1, 2] - ) - # Mean to single scalar over batch dimension - loss = torch.sum(loss_spatial_sum) / torch.sum(valid_spatial_sum) - - return loss - - -class JunctionDetectionLoss(nn.Module): - """Junction detection loss.""" - - def __init__(self, grid_size, keep_border): - super(JunctionDetectionLoss, self).__init__() - self.grid_size = grid_size - self.keep_border = keep_border - - def forward(self, prediction, target, valid_mask=None): - return junction_detection_loss( - target, prediction, valid_mask, self.grid_size, self.keep_border - ) - - -class HeatmapLoss(nn.Module): - """Heatmap prediction loss.""" - - def __init__(self, class_weight): - super(HeatmapLoss, self).__init__() - self.class_weight = class_weight - - def forward(self, prediction, target, valid_mask=None): - return heatmap_loss(target, prediction, valid_mask, self.class_weight) - - -class RegularizationLoss(nn.Module): - """Module for regularization loss.""" - - def __init__(self): - super(RegularizationLoss, self).__init__() - self.name = "regularization_loss" - self.loss_init = torch.zeros([]) - - def forward(self, loss_weights): - # Place it to the same device - loss = self.loss_init.to(loss_weights["w_junc"].device) - for _, val in loss_weights.items(): - if isinstance(val, nn.Parameter): - loss += val - - return loss - - -def triplet_loss( - desc_pred1, - desc_pred2, - points1, - points2, - line_indices, - epoch, - grid_size=8, - dist_threshold=8, - init_dist_threshold=64, - margin=1, -): - """Regular triplet loss for descriptor learning.""" - b_size, _, Hc, Wc = desc_pred1.size() - img_size = (Hc * grid_size, Wc * grid_size) - device = desc_pred1.device - - # Extract valid keypoints - n_points = line_indices.size()[1] - valid_points = line_indices.bool().flatten() - n_correct_points = torch.sum(valid_points).item() - if n_correct_points == 0: - return torch.tensor(0.0, dtype=torch.float, device=device) - - # Check which keypoints are too close to be matched - # dist_threshold is decreased at each epoch for easier training - dist_threshold = max(dist_threshold, 2 * init_dist_threshold // (epoch + 1)) - dist_mask = get_dist_mask(points1, points2, valid_points, dist_threshold) - - # Additionally ban negative mining along the same line - common_line_mask = get_common_line_mask(line_indices, valid_points) - dist_mask = dist_mask | common_line_mask - - # Convert the keypoints to a grid suitable for interpolation - grid1 = keypoints_to_grid(points1, img_size) - grid2 = keypoints_to_grid(points2, img_size) - - # Extract the descriptors - desc1 = ( - F.grid_sample(desc_pred1, grid1) - .permute(0, 2, 3, 1) - .reshape(b_size * n_points, -1)[valid_points] - ) - desc1 = F.normalize(desc1, dim=1) - desc2 = ( - F.grid_sample(desc_pred2, grid2) - .permute(0, 2, 3, 1) - .reshape(b_size * n_points, -1)[valid_points] - ) - desc2 = F.normalize(desc2, dim=1) - desc_dists = 2 - 2 * (desc1 @ desc2.t()) - - # Positive distance loss - pos_dist = torch.diag(desc_dists) - - # Negative distance loss - max_dist = torch.tensor(4.0, dtype=torch.float, device=device) - desc_dists[ - torch.arange(n_correct_points, dtype=torch.long), - torch.arange(n_correct_points, dtype=torch.long), - ] = max_dist - desc_dists[dist_mask] = max_dist - neg_dist = torch.min( - torch.min(desc_dists, dim=1)[0], torch.min(desc_dists, dim=0)[0] - ) - - triplet_loss = F.relu(margin + pos_dist - neg_dist) - return triplet_loss, grid1, grid2, valid_points - - -class TripletDescriptorLoss(nn.Module): - """Triplet descriptor loss.""" - - def __init__(self, grid_size, dist_threshold, margin): - super(TripletDescriptorLoss, self).__init__() - self.grid_size = grid_size - self.init_dist_threshold = 64 - self.dist_threshold = dist_threshold - self.margin = margin - - def forward(self, desc_pred1, desc_pred2, points1, points2, line_indices, epoch): - return self.descriptor_loss( - desc_pred1, desc_pred2, points1, points2, line_indices, epoch - ) - - # The descriptor loss based on regularly sampled points along the lines - def descriptor_loss( - self, desc_pred1, desc_pred2, points1, points2, line_indices, epoch - ): - return torch.mean( - triplet_loss( - desc_pred1, - desc_pred2, - points1, - points2, - line_indices, - epoch, - self.grid_size, - self.dist_threshold, - self.init_dist_threshold, - self.margin, - )[0] - ) - - -class TotalLoss(nn.Module): - """Total loss summing junction, heatma, descriptor - and regularization losses.""" - - def __init__(self, loss_funcs, loss_weights, weighting_policy): - super(TotalLoss, self).__init__() - # Whether we need to compute the descriptor loss - self.compute_descriptors = "descriptor_loss" in loss_funcs.keys() - - self.loss_funcs = loss_funcs - self.loss_weights = loss_weights - self.weighting_policy = weighting_policy - - # Always add regularization loss (it will return zero if not used) - self.loss_funcs["reg_loss"] = RegularizationLoss().cuda() - - def forward( - self, junc_pred, junc_target, heatmap_pred, heatmap_target, valid_mask=None - ): - """Detection only loss.""" - # Compute the junction loss - junc_loss = self.loss_funcs["junc_loss"](junc_pred, junc_target, valid_mask) - # Compute the heatmap loss - heatmap_loss = self.loss_funcs["heatmap_loss"]( - heatmap_pred, heatmap_target, valid_mask - ) - - # Compute the total loss. - if self.weighting_policy == "dynamic": - reg_loss = self.loss_funcs["reg_loss"](self.loss_weights) - total_loss = ( - junc_loss * torch.exp(-self.loss_weights["w_junc"]) - + heatmap_loss * torch.exp(-self.loss_weights["w_heatmap"]) - + reg_loss - ) - - return { - "total_loss": total_loss, - "junc_loss": junc_loss, - "heatmap_loss": heatmap_loss, - "reg_loss": reg_loss, - "w_junc": torch.exp(-self.loss_weights["w_junc"]).item(), - "w_heatmap": torch.exp(-self.loss_weights["w_heatmap"]).item(), - } - - elif self.weighting_policy == "static": - total_loss = ( - junc_loss * self.loss_weights["w_junc"] - + heatmap_loss * self.loss_weights["w_heatmap"] - ) - - return { - "total_loss": total_loss, - "junc_loss": junc_loss, - "heatmap_loss": heatmap_loss, - } - - else: - raise ValueError("[Error] Unknown weighting policy.") - - def forward_descriptors( - self, - junc_map_pred1, - junc_map_pred2, - junc_map_target1, - junc_map_target2, - heatmap_pred1, - heatmap_pred2, - heatmap_target1, - heatmap_target2, - line_points1, - line_points2, - line_indices, - desc_pred1, - desc_pred2, - epoch, - valid_mask1=None, - valid_mask2=None, - ): - """Loss for detection + description.""" - # Compute junction loss - junc_loss = self.loss_funcs["junc_loss"]( - torch.cat([junc_map_pred1, junc_map_pred2], dim=0), - torch.cat([junc_map_target1, junc_map_target2], dim=0), - torch.cat([valid_mask1, valid_mask2], dim=0), - ) - # Get junction loss weight (dynamic or not) - if isinstance(self.loss_weights["w_junc"], nn.Parameter): - w_junc = torch.exp(-self.loss_weights["w_junc"]) - else: - w_junc = self.loss_weights["w_junc"] - - # Compute heatmap loss - heatmap_loss = self.loss_funcs["heatmap_loss"]( - torch.cat([heatmap_pred1, heatmap_pred2], dim=0), - torch.cat([heatmap_target1, heatmap_target2], dim=0), - torch.cat([valid_mask1, valid_mask2], dim=0), - ) - # Get heatmap loss weight (dynamic or not) - if isinstance(self.loss_weights["w_heatmap"], nn.Parameter): - w_heatmap = torch.exp(-self.loss_weights["w_heatmap"]) - else: - w_heatmap = self.loss_weights["w_heatmap"] - - # Compute the descriptor loss - descriptor_loss = self.loss_funcs["descriptor_loss"]( - desc_pred1, desc_pred2, line_points1, line_points2, line_indices, epoch - ) - # Get descriptor loss weight (dynamic or not) - if isinstance(self.loss_weights["w_desc"], nn.Parameter): - w_descriptor = torch.exp(-self.loss_weights["w_desc"]) - else: - w_descriptor = self.loss_weights["w_desc"] - - # Update the total loss - total_loss = ( - junc_loss * w_junc - + heatmap_loss * w_heatmap - + descriptor_loss * w_descriptor - ) - outputs = { - "junc_loss": junc_loss, - "heatmap_loss": heatmap_loss, - "w_junc": w_junc.item() if isinstance(w_junc, nn.Parameter) else w_junc, - "w_heatmap": w_heatmap.item() - if isinstance(w_heatmap, nn.Parameter) - else w_heatmap, - "descriptor_loss": descriptor_loss, - "w_desc": w_descriptor.item() - if isinstance(w_descriptor, nn.Parameter) - else w_descriptor, - } - - # Compute the regularization loss - reg_loss = self.loss_funcs["reg_loss"](self.loss_weights) - total_loss += reg_loss - outputs.update({"reg_loss": reg_loss, "total_loss": total_loss}) - - return outputs diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/paa_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/paa_head.py deleted file mode 100644 index e067b0121cf8b8230c0c9c6b8cfd41f56be4e298..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/paa_head.py +++ /dev/null @@ -1,671 +0,0 @@ -import numpy as np -import torch -from mmcv.runner import force_fp32 - -from mmdet.core import multi_apply, multiclass_nms -from mmdet.core.bbox.iou_calculators import bbox_overlaps -from mmdet.models import HEADS -from mmdet.models.dense_heads import ATSSHead - -EPS = 1e-12 -try: - import sklearn.mixture as skm -except ImportError: - skm = None - - -def levels_to_images(mlvl_tensor): - """Concat multi-level feature maps by image. - - [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] - Convert the shape of each element in mlvl_tensor from (N, C, H, W) to - (N, H*W , C), then split the element to N elements with shape (H*W, C), and - concat elements in same image of all level along first dimension. - - Args: - mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from - corresponding level. Each element is of shape (N, C, H, W) - - Returns: - list[torch.Tensor]: A list that contains N tensors and each tensor is - of shape (num_elements, C) - """ - batch_size = mlvl_tensor[0].size(0) - batch_list = [[] for _ in range(batch_size)] - channels = mlvl_tensor[0].size(1) - for t in mlvl_tensor: - t = t.permute(0, 2, 3, 1) - t = t.view(batch_size, -1, channels).contiguous() - for img in range(batch_size): - batch_list[img].append(t[img]) - return [torch.cat(item, 0) for item in batch_list] - - -@HEADS.register_module() -class PAAHead(ATSSHead): - """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU - Prediction for Object Detection. - - Code is modified from the `official github repo -
🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming
""" -description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form: -``` -User:🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌""")
- gr.HTML('''
Duplicate the Space and run securely with your OpenAI API Key
\ No newline at end of file
diff --git a/spaces/SouthCity/ShuruiXu/functional_crazy.py b/spaces/SouthCity/ShuruiXu/functional_crazy.py
deleted file mode 100644
index 9c83b4104a395e35471895faf09edb15c0ea65b4..0000000000000000000000000000000000000000
--- a/spaces/SouthCity/ShuruiXu/functional_crazy.py
+++ /dev/null
@@ -1,108 +0,0 @@
-from toolbox import HotReload # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
-
-def get_crazy_functionals():
- ###################### 第一组插件 ###########################
- # [第一组插件]: 最早期编写的项目插件和一些demo
- from crazy_functions.读文章写摘要 import 读文章写摘要
- from crazy_functions.生成函数注释 import 批量生成函数注释
- from crazy_functions.解析项目源代码 import 解析项目本身
- from crazy_functions.解析项目源代码 import 解析一个Python项目
- from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
- from crazy_functions.解析项目源代码 import 解析一个C项目
- from crazy_functions.解析项目源代码 import 解析一个Golang项目
- from crazy_functions.解析项目源代码 import 解析一个Java项目
- from crazy_functions.解析项目源代码 import 解析一个Rect项目
- from crazy_functions.高级功能函数模板 import 高阶功能模板函数
- from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
-
- function_plugins = {
- "请解析并解构此项目本身（源码自译解）": {
- "AsButton": False, # 加入下拉菜单中
- "Function": 解析项目本身
- },
- "解析整个Py项目": {
- "Color": "stop", # 按钮颜色
- "Function": 解析一个Python项目
- },
- "解析整个C++项目头文件": {
- "Color": "stop", # 按钮颜色
- "Function": 解析一个C项目的头文件
- },
- "解析整个C++项目（.cpp/.h）": {
- "Color": "stop", # 按钮颜色
- "AsButton": False, # 加入下拉菜单中
- "Function": 解析一个C项目
- },
- "解析整个Go项目": {
- "Color": "stop", # 按钮颜色
- "AsButton": False, # 加入下拉菜单中
- "Function": 解析一个Golang项目
- },
- "解析整个Java项目": {
- "Color": "stop", # 按钮颜色
- "AsButton": False, # 加入下拉菜单中
- "Function": 解析一个Java项目
- },
- "解析整个Java项目": {
- "Color": "stop", # 按钮颜色
- "AsButton": False, # 加入下拉菜单中
- "Function": 解析一个Rect项目
- },
- "读Tex论文写摘要": {
- "Color": "stop", # 按钮颜色
- "Function": 读文章写摘要
- },
- "批量生成函数注释": {
- "Color": "stop", # 按钮颜色
- "Function": 批量生成函数注释
- },
- "[多线程demo] 把本项目源代码切换成全英文": {
- # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
- "Function": HotReload(全项目切换英文)
- },
- "[函数插件模板demo] 历史上的今天": {
- # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
- "Function": HotReload(高阶功能模板函数)
- },
- }
- ###################### 第二组插件 ###########################
- # [第二组插件]: 经过充分测试，但功能上距离达到完美状态还差一点点
- from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
- from crazy_functions.批量总结PDF文档pdfminer import 批量总结PDF文档pdfminer
- from crazy_functions.总结word文档 import 总结word文档
- function_plugins.update({
- "[仅供开发调试] 批量总结PDF文档": {
- "Color": "stop",
- "Function": HotReload(批量总结PDF文档) # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
- },
- "[仅供开发调试] 批量总结PDF文档pdfminer": {
- "Color": "stop",
- "AsButton": False, # 加入下拉菜单中
- "Function": HotReload(批量总结PDF文档pdfminer)
- },
- "[仅供开发调试] 批量总结Word文档": {
- "Color": "stop",
- "Function": HotReload(总结word文档)
- },
- })
-
- ###################### 第三组插件 ###########################
- # [第三组插件]: 尚未充分测试的函数插件，放在这里
- try:
- from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
- function_plugins.update({
- "一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
- "Color": "stop",
- "AsButton": False, # 加入下拉菜单中
- "Function": HotReload(下载arxiv论文并翻译摘要)
- }
- })
- except Exception as err:
- print(f'[下载arxiv论文并翻译摘要] 插件导入失败 {str(err)}')
-
-
-
- ###################### 第n组插件 ###########################
- return function_plugins
-
-
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/models/unet.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/models/unet.py
deleted file mode 100644
index db4a6df8e309c21fede37abdbe3c862932027641..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/models/unet.py
+++ /dev/null
@@ -1,214 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Pytorch Unet Module used for diffusion.
-"""
-
-from dataclasses import dataclass
-import typing as tp
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from audiocraft.modules.transformer import StreamingTransformer, create_sin_embedding
-
-
-@dataclass
-class Output:
- sample: torch.Tensor
-
-
-def get_model(cfg, channels: int, side: int, num_steps: int):
- if cfg.model == 'unet':
- return DiffusionUnet(
- chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
- else:
- raise RuntimeError('Not Implemented')
-
-
-class ResBlock(nn.Module):
- def __init__(self, channels: int, kernel: int = 3, norm_groups: int = 4,
- dilation: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
- dropout: float = 0.):
- super().__init__()
- stride = 1
- padding = dilation * (kernel - stride) // 2
- Conv = nn.Conv1d
- Drop = nn.Dropout1d
- self.norm1 = nn.GroupNorm(norm_groups, channels)
- self.conv1 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
- self.activation1 = activation()
- self.dropout1 = Drop(dropout)
-
- self.norm2 = nn.GroupNorm(norm_groups, channels)
- self.conv2 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
- self.activation2 = activation()
- self.dropout2 = Drop(dropout)
-
- def forward(self, x):
- h = self.dropout1(self.conv1(self.activation1(self.norm1(x))))
- h = self.dropout2(self.conv2(self.activation2(self.norm2(h))))
- return x + h
-
-
-class DecoderLayer(nn.Module):
- def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
- norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
- dropout: float = 0.):
- super().__init__()
- padding = (kernel - stride) // 2
- self.res_blocks = nn.Sequential(
- *[ResBlock(chin, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
- for idx in range(res_blocks)])
- self.norm = nn.GroupNorm(norm_groups, chin)
- ConvTr = nn.ConvTranspose1d
- self.convtr = ConvTr(chin, chout, kernel, stride, padding, bias=False)
- self.activation = activation()
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.res_blocks(x)
- x = self.norm(x)
- x = self.activation(x)
- x = self.convtr(x)
- return x
-
-
-class EncoderLayer(nn.Module):
- def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
- norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
- dropout: float = 0.):
- super().__init__()
- padding = (kernel - stride) // 2
- Conv = nn.Conv1d
- self.conv = Conv(chin, chout, kernel, stride, padding, bias=False)
- self.norm = nn.GroupNorm(norm_groups, chout)
- self.activation = activation()
- self.res_blocks = nn.Sequential(
- *[ResBlock(chout, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
- for idx in range(res_blocks)])
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, C, T = x.shape
- stride, = self.conv.stride
- pad = (stride - (T % stride)) % stride
- x = F.pad(x, (0, pad))
-
- x = self.conv(x)
- x = self.norm(x)
- x = self.activation(x)
- x = self.res_blocks(x)
- return x
-
-
-class BLSTM(nn.Module):
- """BiLSTM with same hidden units as input dim.
- """
- def __init__(self, dim, layers=2):
- super().__init__()
- self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
- self.linear = nn.Linear(2 * dim, dim)
-
- def forward(self, x):
- x = x.permute(2, 0, 1)
- x = self.lstm(x)[0]
- x = self.linear(x)
- x = x.permute(1, 2, 0)
- return x
-
-
-class DiffusionUnet(nn.Module):
- def __init__(self, chin: int = 3, hidden: int = 24, depth: int = 3, growth: float = 2.,
- max_channels: int = 10_000, num_steps: int = 1000, emb_all_layers=False, cross_attention: bool = False,
- bilstm: bool = False, transformer: bool = False,
- codec_dim: tp.Optional[int] = None, **kwargs):
- super().__init__()
- self.encoders = nn.ModuleList()
- self.decoders = nn.ModuleList()
- self.embeddings: tp.Optional[nn.ModuleList] = None
- self.embedding = nn.Embedding(num_steps, hidden)
- if emb_all_layers:
- self.embeddings = nn.ModuleList()
- self.condition_embedding: tp.Optional[nn.Module] = None
- for d in range(depth):
- encoder = EncoderLayer(chin, hidden, **kwargs)
- decoder = DecoderLayer(hidden, chin, **kwargs)
- self.encoders.append(encoder)
- self.decoders.insert(0, decoder)
- if emb_all_layers and d > 0:
- assert self.embeddings is not None
- self.embeddings.append(nn.Embedding(num_steps, hidden))
- chin = hidden
- hidden = min(int(chin * growth), max_channels)
- self.bilstm: tp.Optional[nn.Module]
- if bilstm:
- self.bilstm = BLSTM(chin)
- else:
- self.bilstm = None
- self.use_transformer = transformer
- self.cross_attention = False
- if transformer:
- self.cross_attention = cross_attention
- self.transformer = StreamingTransformer(chin, 8, 6, bias_ff=False, bias_attn=False,
- cross_attention=cross_attention)
-
- self.use_codec = False
- if codec_dim is not None:
- self.conv_codec = nn.Conv1d(codec_dim, chin, 1)
- self.use_codec = True
-
- def forward(self, x: torch.Tensor, step: tp.Union[int, torch.Tensor], condition: tp.Optional[torch.Tensor] = None):
- skips = []
- bs = x.size(0)
- z = x
- view_args = [1]
- if type(step) is torch.Tensor:
- step_tensor = step
- else:
- step_tensor = torch.tensor([step], device=x.device, dtype=torch.long).expand(bs)
-
- for idx, encoder in enumerate(self.encoders):
- z = encoder(z)
- if idx == 0:
- z = z + self.embedding(step_tensor).view(bs, -1, *view_args).expand_as(z)
- elif self.embeddings is not None:
- z = z + self.embeddings[idx - 1](step_tensor).view(bs, -1, *view_args).expand_as(z)
-
- skips.append(z)
-
- if self.use_codec: # insert condition in the bottleneck
- assert condition is not None, "Model defined for conditionnal generation"
- condition_emb = self.conv_codec(condition) # reshape to the bottleneck dim
- assert condition_emb.size(-1) <= 2 * z.size(-1), \
- f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
- if not self.cross_attention:
-
- condition_emb = torch.nn.functional.interpolate(condition_emb, z.size(-1))
- assert z.size() == condition_emb.size()
- z += condition_emb
- cross_attention_src = None
- else:
- cross_attention_src = condition_emb.permute(0, 2, 1) # B, T, C
- B, T, C = cross_attention_src.shape
- positions = torch.arange(T, device=x.device).view(1, -1, 1)
- pos_emb = create_sin_embedding(positions, C, max_period=10_000, dtype=cross_attention_src.dtype)
- cross_attention_src = cross_attention_src + pos_emb
- if self.use_transformer:
- z = self.transformer(z.permute(0, 2, 1), cross_attention_src=cross_attention_src).permute(0, 2, 1)
- else:
- if self.bilstm is None:
- z = torch.zeros_like(z)
- else:
- z = self.bilstm(z)
-
- for decoder in self.decoders:
- s = skips.pop(-1)
- z = z[:, :, :s.shape[2]]
- z = z + s
- z = decoder(z)
-
- z = z[:, :, :x.shape[2]]
- return Output(z)
diff --git a/spaces/SuYuanS/AudioCraft_Plus/scripts/__init__.py b/spaces/SuYuanS/AudioCraft_Plus/scripts/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/scripts/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/contourpy/util/bokeh_util.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/contourpy/util/bokeh_util.py
deleted file mode 100644
index e75654d7c30c552c1e1bd0492a85d40e8f27de40..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/contourpy/util/bokeh_util.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, cast
-
-from contourpy import FillType, LineType
-from contourpy.util.mpl_util import mpl_codes_to_offsets
-
-if TYPE_CHECKING:
- from contourpy._contourpy import (
- CoordinateArray, FillReturn, LineReturn, LineReturn_Separate, LineReturn_SeparateCode,
- )
-
-
-def filled_to_bokeh(
- filled: FillReturn,
- fill_type: FillType,
-) -> tuple[list[list[CoordinateArray]], list[list[CoordinateArray]]]:
- xs: list[list[CoordinateArray]] = []
- ys: list[list[CoordinateArray]] = []
- if fill_type in (FillType.OuterOffset, FillType.ChunkCombinedOffset,
- FillType.OuterCode, FillType.ChunkCombinedCode):
- have_codes = fill_type in (FillType.OuterCode, FillType.ChunkCombinedCode)
-
- for points, offsets in zip(*filled):
- if points is None:
- continue
- if have_codes:
- offsets = mpl_codes_to_offsets(offsets)
- xs.append([]) # New outer with zero or more holes.
- ys.append([])
- for i in range(len(offsets)-1):
- xys = points[offsets[i]:offsets[i+1]]
- xs[-1].append(xys[:, 0])
- ys[-1].append(xys[:, 1])
- elif fill_type in (FillType.ChunkCombinedCodeOffset, FillType.ChunkCombinedOffsetOffset):
- for points, codes_or_offsets, outer_offsets in zip(*filled):
- if points is None:
- continue
- for j in range(len(outer_offsets)-1):
- if fill_type == FillType.ChunkCombinedCodeOffset:
- codes = codes_or_offsets[outer_offsets[j]:outer_offsets[j+1]]
- offsets = mpl_codes_to_offsets(codes) + outer_offsets[j]
- else:
- offsets = codes_or_offsets[outer_offsets[j]:outer_offsets[j+1]+1]
- xs.append([]) # New outer with zero or more holes.
- ys.append([])
- for k in range(len(offsets)-1):
- xys = points[offsets[k]:offsets[k+1]]
- xs[-1].append(xys[:, 0])
- ys[-1].append(xys[:, 1])
- else:
- raise RuntimeError(f"Conversion of FillType {fill_type} to Bokeh is not implemented")
-
- return xs, ys
-
-
-def lines_to_bokeh(
- lines: LineReturn,
- line_type: LineType,
-) -> tuple[list[CoordinateArray], list[CoordinateArray]]:
- xs: list[CoordinateArray] = []
- ys: list[CoordinateArray] = []
-
- if line_type == LineType.Separate:
- if TYPE_CHECKING:
- lines = cast(LineReturn_Separate, lines)
- for line in lines:
- xs.append(line[:, 0])
- ys.append(line[:, 1])
- elif line_type == LineType.SeparateCode:
- if TYPE_CHECKING:
- lines = cast(LineReturn_SeparateCode, lines)
- for line in lines[0]:
- xs.append(line[:, 0])
- ys.append(line[:, 1])
- elif line_type in (LineType.ChunkCombinedCode, LineType.ChunkCombinedOffset):
- for points, offsets in zip(*lines):
- if points is None:
- continue
- if line_type == LineType.ChunkCombinedCode:
- offsets = mpl_codes_to_offsets(offsets)
-
- for i in range(len(offsets)-1):
- line = points[offsets[i]:offsets[i+1]]
- xs.append(line[:, 0])
- ys.append(line[:, 1])
- else:
- raise RuntimeError(f"Conversion of LineType {line_type} to Bokeh is not implemented")
-
- return xs, ys
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/url_3d/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/url_3d/__init__.py
deleted file mode 100644
index a8aaf02e49d8656b11e779edfc5916ace818f59c..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/url/url_3d/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
-from docarray.typing.url.url_3d.point_cloud_url import PointCloud3DUrl
-
-__all__ = ['Mesh3DUrl', 'PointCloud3DUrl']
diff --git a/spaces/SuperSucklet/Sex/README.md b/spaces/SuperSucklet/Sex/README.md
deleted file mode 100644
index 3922a21636a6f169a32d9f9869eee3c2ab4650be..0000000000000000000000000000000000000000
--- a/spaces/SuperSucklet/Sex/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Sex
-emoji: 🌍
-colorFrom: green
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/cnn/bricks/hswish.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/cnn/bricks/hswish.py
deleted file mode 100644
index 7e0c090ff037c99ee6c5c84c4592e87beae02208..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/cnn/bricks/hswish.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .registry import ACTIVATION_LAYERS
-
-
-@ACTIVATION_LAYERS.register_module()
-class HSwish(nn.Module):
- """Hard Swish Module.
-
- This module applies the hard swish function:
-
- .. math::
- Hswish(x) = x * ReLU6(x + 3) / 6
-
- Args:
- inplace (bool): can optionally do the operation in-place.
- Default: False.
-
- Returns:
- Tensor: The output tensor.
- """
-
- def __init__(self, inplace=False):
- super(HSwish, self).__init__()
- self.act = nn.ReLU6(inplace)
-
- def forward(self, x):
- return x * self.act(x + 3) / 6
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/cp949prober.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/cp949prober.py
deleted file mode 100644
index fa7307ed8985ad7e318660da0066440f890d1624..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/cp949prober.py
+++ /dev/null
@@ -1,49 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-# Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301 USA
-######################### END LICENSE BLOCK #########################
-
-from .chardistribution import EUCKRDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import CP949_SM_MODEL
-
-
-class CP949Prober(MultiByteCharSetProber):
- def __init__(self) -> None:
- super().__init__()
- self.coding_sm = CodingStateMachine(CP949_SM_MODEL)
- # NOTE: CP949 is a superset of EUC-KR, so the distribution should be
- # not different.
- self.distribution_analyzer = EUCKRDistributionAnalysis()
- self.reset()
-
- @property
- def charset_name(self) -> str:
- return "CP949"
-
- @property
- def language(self) -> str:
- return "Korean"
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py
deleted file mode 100644
index 1aad53bfef62fb584d5022585d567e346f671a55..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco_keypoint import dataloader
-from ..common.models.keypoint_rcnn_fpn import model
-from ..common.train import train
-
-model.backbone.bottom_up.freeze_at = 2
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py
deleted file mode 100644
index 8b4a58d8f2877544498e328b6d269f23aa1eb59f..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import os
-from pathlib import Path
-import tqdm
-from PIL import Image
-
-
-def convert(input, output):
- img = np.asarray(Image.open(input))
- assert img.dtype == np.uint8
- img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1
- Image.fromarray(img).save(output)
-
-
-if __name__ == "__main__":
- dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016"
- for name in ["training", "validation"]:
- annotation_dir = dataset_dir / "annotations" / name
- output_dir = dataset_dir / "annotations_detectron2" / name
- output_dir.mkdir(parents=True, exist_ok=True)
- for file in tqdm.tqdm(list(annotation_dir.iterdir())):
- output_file = output_dir / file.name
- convert(file, output_file)
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align_rotated.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align_rotated.py
deleted file mode 100644
index d097326c3a6116e872cecf0d675b42958f359b14..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/roi_align_rotated.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-
-class _ROIAlignRotated(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
- ctx.save_for_backward(roi)
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.sampling_ratio = sampling_ratio
- ctx.input_shape = input.size()
- output = torch.ops.detectron2.roi_align_rotated_forward(
- input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio
- )
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- (rois,) = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- sampling_ratio = ctx.sampling_ratio
- bs, ch, h, w = ctx.input_shape
- grad_input = torch.ops.detectron2.roi_align_rotated_backward(
- grad_output,
- rois,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- sampling_ratio,
- )
- return grad_input, None, None, None, None, None
-
-
-roi_align_rotated = _ROIAlignRotated.apply
-
-
-class ROIAlignRotated(nn.Module):
- def __init__(self, output_size, spatial_scale, sampling_ratio):
- """
- Args:
- output_size (tuple): h, w
- spatial_scale (float): scale the input boxes by this number
- sampling_ratio (int): number of inputs samples to take for each output
- sample. 0 to take samples densely.
-
- Note:
- ROIAlignRotated supports continuous coordinate by default:
- Given a continuous coordinate c, its two neighboring pixel indices (in our
- pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
- c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
- from the underlying signal at continuous coordinates 0.5 and 1.5).
- """
- super(ROIAlignRotated, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
- self.sampling_ratio = sampling_ratio
-
- def forward(self, input, rois):
- """
- Args:
- input: NCHW images
- rois: Bx6 boxes. First column is the index into N.
- The other 5 columns are (x_ctr, y_ctr, width, height, angle_degrees).
- """
- assert rois.dim() == 2 and rois.size(1) == 6
- orig_dtype = input.dtype
- if orig_dtype == torch.float16:
- input = input.float()
- rois = rois.float()
- return roi_align_rotated(
- input, rois, self.output_size, self.spatial_scale, self.sampling_ratio
- ).to(dtype=orig_dtype)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/solver/lr_scheduler.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/solver/lr_scheduler.py
deleted file mode 100644
index 8803e87b9e60cffdbe048c97c282d353191ae4c8..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/solver/lr_scheduler.py
+++ /dev/null
@@ -1,238 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import math
-from bisect import bisect_right
-from typing import List
-import torch
-from fvcore.common.param_scheduler import (
- CompositeParamScheduler,
- ConstantParamScheduler,
- LinearParamScheduler,
- ParamScheduler,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class WarmupParamScheduler(CompositeParamScheduler):
- """
- Add an initial warmup stage to another scheduler.
- """
-
- def __init__(
- self,
- scheduler: ParamScheduler,
- warmup_factor: float,
- warmup_length: float,
- warmup_method: str = "linear",
- ):
- """
- Args:
- scheduler: warmup will be added at the beginning of this scheduler
- warmup_factor: the factor w.r.t the initial value of ``scheduler``, e.g. 0.001
- warmup_length: the relative length (in [0, 1]) of warmup steps w.r.t the entire
- training, e.g. 0.01
- warmup_method: one of "linear" or "constant"
- """
- end_value = scheduler(warmup_length) # the value to reach when warmup ends
- start_value = warmup_factor * scheduler(0.0)
- if warmup_method == "constant":
- warmup = ConstantParamScheduler(start_value)
- elif warmup_method == "linear":
- warmup = LinearParamScheduler(start_value, end_value)
- else:
- raise ValueError("Unknown warmup method: {}".format(warmup_method))
- super().__init__(
- [warmup, scheduler],
- interval_scaling=["rescaled", "fixed"],
- lengths=[warmup_length, 1 - warmup_length],
- )
-
-
-class LRMultiplier(torch.optim.lr_scheduler._LRScheduler):
- """
- A LRScheduler which uses fvcore :class:`ParamScheduler` to multiply the
- learning rate of each param in the optimizer.
- Every step, the learning rate of each parameter becomes its initial value
- multiplied by the output of the given :class:`ParamScheduler`.
-
- The absolute learning rate value of each parameter can be different.
- This scheduler can be used as long as the relative scale among them do
- not change during training.
-
- Examples:
- ::
- LRMultiplier(
- opt,
- WarmupParamScheduler(
- MultiStepParamScheduler(
- [1, 0.1, 0.01],
- milestones=[60000, 80000],
- num_updates=90000,
- ), 0.001, 100 / 90000
- ),
- max_iter=90000
- )
- """
-
- # NOTES: in the most general case, every LR can use its own scheduler.
- # Supporting this requires interaction with the optimizer when its parameter
- # group is initialized. For example, classyvision implements its own optimizer
- # that allows different schedulers for every parameter group.
- # To avoid this complexity, we use this class to support the most common cases
- # where the relative scale among all LRs stay unchanged during training. In this
- # case we only need a total of one scheduler that defines the relative LR multiplier.
-
- def __init__(
- self,
- optimizer: torch.optim.Optimizer,
- multiplier: ParamScheduler,
- max_iter: int,
- last_iter: int = -1,
- ):
- """
- Args:
- optimizer, last_iter: See ``torch.optim.lr_scheduler._LRScheduler``.
- ``last_iter`` is the same as ``last_epoch``.
- multiplier: a fvcore ParamScheduler that defines the multiplier on
- every LR of the optimizer
- max_iter: the total number of training iterations
- """
- if not isinstance(multiplier, ParamScheduler):
- raise ValueError(
- "_LRMultiplier(multiplier=) must be an instance of fvcore "
- f"ParamScheduler. Got {multiplier} instead."
- )
- self._multiplier = multiplier
- self._max_iter = max_iter
- super().__init__(optimizer, last_epoch=last_iter)
-
- def state_dict(self):
- # fvcore schedulers are stateless. Only keep pytorch scheduler states
- return {"base_lrs": self.base_lrs, "last_epoch": self.last_epoch}
-
- def get_lr(self) -> List[float]:
- multiplier = self._multiplier(self.last_epoch / self._max_iter)
- return [base_lr * multiplier for base_lr in self.base_lrs]
-
-
-"""
-Content below is no longer needed!
-"""
-
-# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
-# only on epoch boundaries. We typically use iteration based schedules instead.
-# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to mean
-# "iteration" instead.
-
-# FIXME: ideally this would be achieved with a CombinedLRScheduler, separating
-# MultiStepLR with WarmupLR but the current LRScheduler design doesn't allow it.
-
-
-class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
- def __init__(
- self,
- optimizer: torch.optim.Optimizer,
- milestones: List[int],
- gamma: float = 0.1,
- warmup_factor: float = 0.001,
- warmup_iters: int = 1000,
- warmup_method: str = "linear",
- last_epoch: int = -1,
- ):
- logger.warning(
- "WarmupMultiStepLR is deprecated! Use LRMultipilier with fvcore ParamScheduler instead!"
- )
- if not list(milestones) == sorted(milestones):
- raise ValueError(
- "Milestones should be a list of" " increasing integers. Got {}", milestones
- )
- self.milestones = milestones
- self.gamma = gamma
- self.warmup_factor = warmup_factor
- self.warmup_iters = warmup_iters
- self.warmup_method = warmup_method
- super().__init__(optimizer, last_epoch)
-
- def get_lr(self) -> List[float]:
- warmup_factor = _get_warmup_factor_at_iter(
- self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
- )
- return [
- base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch)
- for base_lr in self.base_lrs
- ]
-
- def _compute_values(self) -> List[float]:
- # The new interface
- return self.get_lr()
-
-
-class WarmupCosineLR(torch.optim.lr_scheduler._LRScheduler):
- def __init__(
- self,
- optimizer: torch.optim.Optimizer,
- max_iters: int,
- warmup_factor: float = 0.001,
- warmup_iters: int = 1000,
- warmup_method: str = "linear",
- last_epoch: int = -1,
- ):
- logger.warning(
- "WarmupCosineLR is deprecated! Use LRMultipilier with fvcore ParamScheduler instead!"
- )
- self.max_iters = max_iters
- self.warmup_factor = warmup_factor
- self.warmup_iters = warmup_iters
- self.warmup_method = warmup_method
- super().__init__(optimizer, last_epoch)
-
- def get_lr(self) -> List[float]:
- warmup_factor = _get_warmup_factor_at_iter(
- self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
- )
- # Different definitions of half-cosine with warmup are possible. For
- # simplicity we multiply the standard half-cosine schedule by the warmup
- # factor. An alternative is to start the period of the cosine at warmup_iters
- # instead of at 0. In the case that warmup_iters << max_iters the two are
- # very close to each other.
- return [
- base_lr
- * warmup_factor
- * 0.5
- * (1.0 + math.cos(math.pi * self.last_epoch / self.max_iters))
- for base_lr in self.base_lrs
- ]
-
- def _compute_values(self) -> List[float]:
- # The new interface
- return self.get_lr()
-
-
-def _get_warmup_factor_at_iter(
- method: str, iter: int, warmup_iters: int, warmup_factor: float
-) -> float:
- """
- Return the learning rate warmup factor at a specific iteration.
- See :paper:`ImageNet in 1h` for more details.
-
- Args:
- method (str): warmup method; either "constant" or "linear".
- iter (int): iteration at which to calculate the warmup factor.
- warmup_iters (int): the number of warmup iterations.
- warmup_factor (float): the base warmup factor (the meaning changes according
- to the method used).
-
- Returns:
- float: the effective warmup factor at the given iteration.
- """
- if iter >= warmup_iters:
- return 1.0
-
- if method == "constant":
- return warmup_factor
- elif method == "linear":
- alpha = iter / warmup_iters
- return warmup_factor * (1 - alpha) + alpha
- else:
- raise ValueError("Unknown warmup method: {}".format(method))
diff --git a/spaces/Thaweewat/ControlNet-Architecture/annotator/util.py b/spaces/Thaweewat/ControlNet-Architecture/annotator/util.py
deleted file mode 100644
index 7cde937016b7a24b4081dc0565b53c16a87939d2..0000000000000000000000000000000000000000
--- a/spaces/Thaweewat/ControlNet-Architecture/annotator/util.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import numpy as np
-import cv2
-
-
-def HWC3(x):
- assert x.dtype == np.uint8
- if x.ndim == 2:
- x = x[:, :, None]
- assert x.ndim == 3
- H, W, C = x.shape
- assert C == 1 or C == 3 or C == 4
- if C == 3:
- return x
- if C == 1:
- return np.concatenate([x, x, x], axis=2)
- if C == 4:
- color = x[:, :, 0:3].astype(np.float32)
- alpha = x[:, :, 3:4].astype(np.float32) / 255.0
- y = color * alpha + 255.0 * (1.0 - alpha)
- y = y.clip(0, 255).astype(np.uint8)
- return y
-
-
-def resize_image(input_image, resolution):
- H, W, C = input_image.shape
- H = float(H)
- W = float(W)
- k = float(resolution) / min(H, W)
- H *= k
- W *= k
- H = int(np.round(H / 64.0)) * 64
- W = int(np.round(W / 64.0)) * 64
- img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
- return img
diff --git a/spaces/ThirdEyeData/TagDiciphering/shape_utils.py b/spaces/ThirdEyeData/TagDiciphering/shape_utils.py
deleted file mode 100644
index aa08f18a0bdfb887974cdd27cc0f0a16f11d2d07..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/TagDiciphering/shape_utils.py
+++ /dev/null
@@ -1,498 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. 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.
-# ==============================================================================
-
-"""Utils used to manipulate tensor shapes."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from six.moves import zip
-import tensorflow as tf
-
-import static_shape
-
-
-get_dim_as_int = static_shape.get_dim_as_int
-
-
-def _is_tensor(t):
- """Returns a boolean indicating whether the input is a tensor.
-
- Args:
- t: the input to be tested.
-
- Returns:
- a boolean that indicates whether t is a tensor.
- """
- return isinstance(t, (tf.Tensor, tf.SparseTensor, tf.Variable))
-
-
-def _set_dim_0(t, d0):
- """Sets the 0-th dimension of the input tensor.
-
- Args:
- t: the input tensor, assuming the rank is at least 1.
- d0: an integer indicating the 0-th dimension of the input tensor.
-
- Returns:
- the tensor t with the 0-th dimension set.
- """
- t_shape = t.get_shape().as_list()
- t_shape[0] = d0
- t.set_shape(t_shape)
- return t
-
-
-def pad_tensor(t, length):
- """Pads the input tensor with 0s along the first dimension up to the length.
-
- Args:
- t: the input tensor, assuming the rank is at least 1.
- length: a tensor of shape [1] or an integer, indicating the first dimension
- of the input tensor t after padding, assuming length <= t.shape[0].
-
- Returns:
- padded_t: the padded tensor, whose first dimension is length. If the length
- is an integer, the first dimension of padded_t is set to length
- statically.
- """
- t_rank = tf.rank(t)
- t_shape = tf.shape(t)
- t_d0 = t_shape[0]
- pad_d0 = tf.expand_dims(length - t_d0, 0)
- pad_shape = tf.cond(
- tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
- lambda: tf.expand_dims(length - t_d0, 0))
- padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
- if not _is_tensor(length):
- padded_t = _set_dim_0(padded_t, length)
- return padded_t
-
-
-def clip_tensor(t, length):
- """Clips the input tensor along the first dimension up to the length.
-
- Args:
- t: the input tensor, assuming the rank is at least 1.
- length: a tensor of shape [1] or an integer, indicating the first dimension
- of the input tensor t after clipping, assuming length <= t.shape[0].
-
- Returns:
- clipped_t: the clipped tensor, whose first dimension is length. If the
- length is an integer, the first dimension of clipped_t is set to length
- statically.
- """
- clipped_t = tf.gather(t, tf.range(length))
- if not _is_tensor(length):
- clipped_t = _set_dim_0(clipped_t, length)
- return clipped_t
-
-
-def pad_or_clip_tensor(t, length):
- """Pad or clip the input tensor along the first dimension.
-
- Args:
- t: the input tensor, assuming the rank is at least 1.
- length: a tensor of shape [1] or an integer, indicating the first dimension
- of the input tensor t after processing.
-
- Returns:
- processed_t: the processed tensor, whose first dimension is length. If the
- length is an integer, the first dimension of the processed tensor is set
- to length statically.
- """
- return pad_or_clip_nd(t, [length] + t.shape.as_list()[1:])
-
-
-def pad_or_clip_nd(tensor, output_shape):
- """Pad or Clip given tensor to the output shape.
-
- Args:
- tensor: Input tensor to pad or clip.
- output_shape: A list of integers / scalar tensors (or None for dynamic dim)
- representing the size to pad or clip each dimension of the input tensor.
-
- Returns:
- Input tensor padded and clipped to the output shape.
- """
- tensor_shape = tf.shape(tensor)
- clip_size = [
- tf.where(tensor_shape[i] - shape > 0, shape, -1)
- if shape is not None else -1 for i, shape in enumerate(output_shape)
- ]
- clipped_tensor = tf.slice(
- tensor,
- begin=tf.zeros(len(clip_size), dtype=tf.int32),
- size=clip_size)
-
- # Pad tensor if the shape of clipped tensor is smaller than the expected
- # shape.
- clipped_tensor_shape = tf.shape(clipped_tensor)
- trailing_paddings = [
- shape - clipped_tensor_shape[i] if shape is not None else 0
- for i, shape in enumerate(output_shape)
- ]
- paddings = tf.stack(
- [
- tf.zeros(len(trailing_paddings), dtype=tf.int32),
- trailing_paddings
- ],
- axis=1)
- padded_tensor = tf.pad(clipped_tensor, paddings=paddings)
- output_static_shape = [
- dim if not isinstance(dim, tf.Tensor) else None for dim in output_shape
- ]
- padded_tensor.set_shape(output_static_shape)
- return padded_tensor
-
-
-def combined_static_and_dynamic_shape(tensor):
- """Returns a list containing static and dynamic values for the dimensions.
-
- Returns a list of static and dynamic values for shape dimensions. This is
- useful to preserve static shapes when available in reshape operation.
-
- Args:
- tensor: A tensor of any type.
-
- Returns:
- A list of size tensor.shape.ndims containing integers or a scalar tensor.
- """
- static_tensor_shape = tensor.shape.as_list()
- dynamic_tensor_shape = tf.shape(tensor)
- combined_shape = []
- for index, dim in enumerate(static_tensor_shape):
- if dim is not None:
- combined_shape.append(dim)
- else:
- combined_shape.append(dynamic_tensor_shape[index])
- return combined_shape
-
-
-def static_or_dynamic_map_fn(fn, elems, dtype=None,
- parallel_iterations=32, back_prop=True):
- """Runs map_fn as a (static) for loop when possible.
-
- This function rewrites the map_fn as an explicit unstack input -> for loop
- over function calls -> stack result combination. This allows our graphs to
- be acyclic when the batch size is static.
- For comparison, see https://www.tensorflow.org/api_docs/python/tf/map_fn.
-
- Note that `static_or_dynamic_map_fn` currently is not *fully* interchangeable
- with the default tf.map_fn function as it does not accept nested inputs (only
- Tensors or lists of Tensors). Likewise, the output of `fn` can only be a
- Tensor or list of Tensors.
-
- TODO(jonathanhuang): make this function fully interchangeable with tf.map_fn.
-
- Args:
- fn: The callable to be performed. It accepts one argument, which will have
- the same structure as elems. Its output must have the
- same structure as elems.
- elems: A tensor or list of tensors, each of which will
- be unpacked along their first dimension. The sequence of the
- resulting slices will be applied to fn.
- dtype: (optional) The output type(s) of fn. If fn returns a structure of
- Tensors differing from the structure of elems, then dtype is not optional
- and must have the same structure as the output of fn.
- parallel_iterations: (optional) number of batch items to process in
- parallel. This flag is only used if the native tf.map_fn is used
- and defaults to 32 instead of 10 (unlike the standard tf.map_fn default).
- back_prop: (optional) True enables support for back propagation.
- This flag is only used if the native tf.map_fn is used.
-
- Returns:
- A tensor or sequence of tensors. Each tensor packs the
- results of applying fn to tensors unpacked from elems along the first
- dimension, from first to last.
- Raises:
- ValueError: if `elems` a Tensor or a list of Tensors.
- ValueError: if `fn` does not return a Tensor or list of Tensors
- """
- if isinstance(elems, list):
- for elem in elems:
- if not isinstance(elem, tf.Tensor):
- raise ValueError('`elems` must be a Tensor or list of Tensors.')
-
- elem_shapes = [elem.shape.as_list() for elem in elems]
- # Fall back on tf.map_fn if shapes of each entry of `elems` are None or fail
- # to all be the same size along the batch dimension.
- for elem_shape in elem_shapes:
- if (not elem_shape or not elem_shape[0]
- or elem_shape[0] != elem_shapes[0][0]):
- return tf.map_fn(fn, elems, dtype, parallel_iterations, back_prop)
- arg_tuples = zip(*[tf.unstack(elem) for elem in elems])
- outputs = [fn(arg_tuple) for arg_tuple in arg_tuples]
- else:
- if not isinstance(elems, tf.Tensor):
- raise ValueError('`elems` must be a Tensor or list of Tensors.')
- elems_shape = elems.shape.as_list()
- if not elems_shape or not elems_shape[0]:
- return tf.map_fn(fn, elems, dtype, parallel_iterations, back_prop)
- outputs = [fn(arg) for arg in tf.unstack(elems)]
- # Stack `outputs`, which is a list of Tensors or list of lists of Tensors
- if all([isinstance(output, tf.Tensor) for output in outputs]):
- return tf.stack(outputs)
- else:
- if all([isinstance(output, list) for output in outputs]):
- if all([all(
- [isinstance(entry, tf.Tensor) for entry in output_list])
- for output_list in outputs]):
- return [tf.stack(output_tuple) for output_tuple in zip(*outputs)]
- raise ValueError('`fn` should return a Tensor or a list of Tensors.')
-
-
-def check_min_image_dim(min_dim, image_tensor):
- """Checks that the image width/height are greater than some number.
-
- This function is used to check that the width and height of an image are above
- a certain value. If the image shape is static, this function will perform the
- check at graph construction time. Otherwise, if the image shape varies, an
- Assertion control dependency will be added to the graph.
-
- Args:
- min_dim: The minimum number of pixels along the width and height of the
- image.
- image_tensor: The image tensor to check size for.
-
- Returns:
- If `image_tensor` has dynamic size, return `image_tensor` with a Assert
- control dependency. Otherwise returns image_tensor.
-
- Raises:
- ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
- """
- image_shape = image_tensor.get_shape()
- image_height = static_shape.get_height(image_shape)
- image_width = static_shape.get_width(image_shape)
- if image_height is None or image_width is None:
- shape_assert = tf.Assert(
- tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
- tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
- ['image size must be >= {} in both height and width.'.format(min_dim)])
- with tf.control_dependencies([shape_assert]):
- return tf.identity(image_tensor)
-
- if image_height < min_dim or image_width < min_dim:
- raise ValueError(
- 'image size must be >= %d in both height and width; image dim = %d,%d' %
- (min_dim, image_height, image_width))
-
- return image_tensor
-
-
-def assert_shape_equal(shape_a, shape_b):
- """Asserts that shape_a and shape_b are equal.
-
- If the shapes are static, raises a ValueError when the shapes
- mismatch.
-
- If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
- mismatch.
-
- Args:
- shape_a: a list containing shape of the first tensor.
- shape_b: a list containing shape of the second tensor.
-
- Returns:
- Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
- when the shapes are dynamic.
-
- Raises:
- ValueError: When shapes are both static and unequal.
- """
- if (all(isinstance(dim, int) for dim in shape_a) and
- all(isinstance(dim, int) for dim in shape_b)):
- if shape_a != shape_b:
- raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
- else: return tf.no_op()
- else:
- return tf.assert_equal(shape_a, shape_b)
-
-
-def assert_shape_equal_along_first_dimension(shape_a, shape_b):
- """Asserts that shape_a and shape_b are the same along the 0th-dimension.
-
- If the shapes are static, raises a ValueError when the shapes
- mismatch.
-
- If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
- mismatch.
-
- Args:
- shape_a: a list containing shape of the first tensor.
- shape_b: a list containing shape of the second tensor.
-
- Returns:
- Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
- when the shapes are dynamic.
-
- Raises:
- ValueError: When shapes are both static and unequal.
- """
- if isinstance(shape_a[0], int) and isinstance(shape_b[0], int):
- if shape_a[0] != shape_b[0]:
- raise ValueError('Unequal first dimension {}, {}'.format(
- shape_a[0], shape_b[0]))
- else: return tf.no_op()
- else:
- return tf.assert_equal(shape_a[0], shape_b[0])
-
-
-def assert_box_normalized(boxes, maximum_normalized_coordinate=1.1):
- """Asserts the input box tensor is normalized.
-
- Args:
- boxes: a tensor of shape [N, 4] where N is the number of boxes.
- maximum_normalized_coordinate: Maximum coordinate value to be considered
- as normalized, default to 1.1.
-
- Returns:
- a tf.Assert op which fails when the input box tensor is not normalized.
-
- Raises:
- ValueError: When the input box tensor is not normalized.
- """
- box_minimum = tf.reduce_min(boxes)
- box_maximum = tf.reduce_max(boxes)
- return tf.Assert(
- tf.logical_and(
- tf.less_equal(box_maximum, maximum_normalized_coordinate),
- tf.greater_equal(box_minimum, 0)),
- [boxes])
-
-
-def flatten_dimensions(inputs, first, last):
- """Flattens `K-d` tensor along [first, last) dimensions.
-
- Converts `inputs` with shape [D0, D1, ..., D(K-1)] into a tensor of shape
- [D0, D1, ..., D(first) * D(first+1) * ... * D(last-1), D(last), ..., D(K-1)].
-
- Example:
- `inputs` is a tensor with initial shape [10, 5, 20, 20, 3].
- new_tensor = flatten_dimensions(inputs, first=1, last=3)
- new_tensor.shape -> [10, 100, 20, 3].
-
- Args:
- inputs: a tensor with shape [D0, D1, ..., D(K-1)].
- first: first value for the range of dimensions to flatten.
- last: last value for the range of dimensions to flatten. Note that the last
- dimension itself is excluded.
-
- Returns:
- a tensor with shape
- [D0, D1, ..., D(first) * D(first + 1) * ... * D(last - 1), D(last), ...,
- D(K-1)].
-
- Raises:
- ValueError: if first and last arguments are incorrect.
- """
- if first >= inputs.shape.ndims or last > inputs.shape.ndims:
- raise ValueError('`first` and `last` must be less than inputs.shape.ndims. '
- 'found {} and {} respectively while ndims is {}'.format(
- first, last, inputs.shape.ndims))
- shape = combined_static_and_dynamic_shape(inputs)
- flattened_dim_prod = tf.reduce_prod(shape[first:last],
- keepdims=True)
- new_shape = tf.concat([shape[:first], flattened_dim_prod,
- shape[last:]], axis=0)
- return tf.reshape(inputs, new_shape)
-
-
-def flatten_first_n_dimensions(inputs, n):
- """Flattens `K-d` tensor along first n dimension to be a `(K-n+1)-d` tensor.
-
- Converts `inputs` with shape [D0, D1, ..., D(K-1)] into a tensor of shape
- [D0 * D1 * ... * D(n-1), D(n), ... D(K-1)].
-
- Example:
- `inputs` is a tensor with initial shape [10, 5, 20, 20, 3].
- new_tensor = flatten_first_n_dimensions(inputs, 2)
- new_tensor.shape -> [50, 20, 20, 3].
-
- Args:
- inputs: a tensor with shape [D0, D1, ..., D(K-1)].
- n: The number of dimensions to flatten.
-
- Returns:
- a tensor with shape [D0 * D1 * ... * D(n-1), D(n), ... D(K-1)].
- """
- return flatten_dimensions(inputs, first=0, last=n)
-
-
-def expand_first_dimension(inputs, dims):
- """Expands `K-d` tensor along first dimension to be a `(K+n-1)-d` tensor.
-
- Converts `inputs` with shape [D0, D1, ..., D(K-1)] into a tensor of shape
- [dims[0], dims[1], ..., dims[-1], D1, ..., D(k-1)].
-
- Example:
- `inputs` is a tensor with shape [50, 20, 20, 3].
- new_tensor = expand_first_dimension(inputs, [10, 5]).
- new_tensor.shape -> [10, 5, 20, 20, 3].
-
- Args:
- inputs: a tensor with shape [D0, D1, ..., D(K-1)].
- dims: List with new dimensions to expand first axis into. The length of
- `dims` is typically 2 or larger.
-
- Returns:
- a tensor with shape [dims[0], dims[1], ..., dims[-1], D1, ..., D(k-1)].
- """
- inputs_shape = combined_static_and_dynamic_shape(inputs)
- expanded_shape = tf.stack(dims + inputs_shape[1:])
-
- # Verify that it is possible to expand the first axis of inputs.
- assert_op = tf.assert_equal(
- inputs_shape[0], tf.reduce_prod(tf.stack(dims)),
- message=('First dimension of `inputs` cannot be expanded into provided '
- '`dims`'))
-
- with tf.control_dependencies([assert_op]):
- inputs_reshaped = tf.reshape(inputs, expanded_shape)
-
- return inputs_reshaped
-
-
-def resize_images_and_return_shapes(inputs, image_resizer_fn):
- """Resizes images using the given function and returns their true shapes.
-
- Args:
- inputs: a float32 Tensor representing a batch of inputs of shape
- [batch_size, height, width, channels].
- image_resizer_fn: a function which takes in a single image and outputs
- a resized image and its original shape.
-
- Returns:
- resized_inputs: The inputs resized according to image_resizer_fn.
- true_image_shapes: A integer tensor of shape [batch_size, 3]
- representing the height, width and number of channels in inputs.
- """
-
- if inputs.dtype is not tf.float32:
- raise ValueError('`resize_images_and_return_shapes` expects a'
- ' tf.float32 tensor')
-
- # TODO(jonathanhuang): revisit whether to always use batch size as
- # the number of parallel iterations vs allow for dynamic batching.
- outputs = static_or_dynamic_map_fn(
- image_resizer_fn,
- elems=inputs,
- dtype=[tf.float32, tf.int32])
- resized_inputs = outputs[0]
- true_image_shapes = outputs[1]
-
- return resized_inputs, true_image_shapes
diff --git a/spaces/Tonic/BibleScriptures/app.py b/spaces/Tonic/BibleScriptures/app.py
deleted file mode 100644
index 5ca451528e87b1434226e75c505433f62ce8cb4a..0000000000000000000000000000000000000000
--- a/spaces/Tonic/BibleScriptures/app.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from typing import List
-import typing
-from aiser import RestAiServer, KnowledgeBase, SemanticSearchResult, Agent
-from aiser.models import ChatMessage
-import asyncio
-import gradio as gr
-import requests
-import os
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.DEBUG)
-
-# Define environment variables
-API_URL = os.getenv("API_URL", "http://ec2-54-166-81-166.compute-1.amazonaws.com:3000/api/v1/prediction/117a5076-c05e-4208-91d9-d0e772bf981e")
-API_TOKEN = os.getenv("API_TOKEN", "Bearer 0Ouk5cgljCYuuF3LDfBkIAcuqj9hgWaaK5qRCLfbfrg=")
-
-class ChatBot:
- def __init__(self):
- self.history = []
-
- def predict(self, input):
- new_user_input = input # User input should be converted into model input format
-
- # Prepare payload for API call
- payload = {"question": new_user_input}
-
- # Make an external API call
- headers = {"Authorization": API_TOKEN}
- response = requests.post(API_URL, headers=headers, json=payload)
-
- # Initialize the response text with an error message by default
- response_text = f"API call failed with status code {response.status_code}"
-
- if response.status_code == 200:
- response_text = response.text # Get the raw text response
-
- # Process the API response and update history
- self.history.append(response_text)
-
- # Log API request and response
- logging.debug(f"API Request: {API_URL}, Payload: {payload}, Headers: {headers}")
- logging.debug(f"API Response: {response.status_code}, Content: {response_text}")
-
- # Return the response text
- return response_text
-
-bot = ChatBot()
-
-title = "👋🏻Welcome to Conversate with Bible Scriptures🌠"
-description = "Here you can ask questions about bible scriptures or your faith & life"
-examples = ["What does the bible say about the value of hard work?"]
-
-iface = gr.Interface(
- fn=bot.predict,
- title=title,
- description=description,
- examples=examples,
- inputs="text",
- outputs="text")
-
-iface.launch()
-
-# Placeholder classes, replace with actual implementations
-class KnowledgeBaseExample(KnowledgeBase):
- def perform_semantic_search(self, query_text: str, desired_number_of_results: int) -> List[SemanticSearchResult]:
- result_example = SemanticSearchResult(
- content="This is an example of a semantic search result",
- score=0.5,
- )
- return [result_example for _ in range(desired_number_of_results)]
-
-class AgentExample(Agent):
- async def reply(self, messages: typing.List[ChatMessage]) -> typing.AsyncGenerator[ChatMessage, None]:
- reply_message = "This is an example of a reply from an agent"
- for character in reply_message:
- yield ChatMessage(text_content=character)
- await asyncio.sleep(0.1)
-
-if __name__ == '__main__':
- server = RestAiServer(
- agents=[
- AgentExample(
- agent_id='10209b93-2dd0-47a0-8eb2-33fb018a783b' # replace with your agent id
- ),
- ],
- knowledge_bases=[
- KnowledgeBaseExample(
- knowledge_base_id='85bc1c72-b8e0-4042-abcf-8eb2d478f207' # replace with your knowledge base id
- ),
- ],
- port=5000
- )
- server.run()
\ No newline at end of file
diff --git a/spaces/Usaki108/VoiceChange/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/Usaki108/VoiceChange/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/Usaki108/VoiceChange/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
- def compute_f0(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length]
- """
- pass
-
- def compute_f0_uv(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
- """
- pass
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Ezcht.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Ezcht.py
deleted file mode 100644
index baec214f7e0e936ea06bffa357e1bd2b77cd4089..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Ezcht.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://gpt4.ezchat.top'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
- headers = {
- 'Content-Type': 'application/json',
- }
- data = {
- 'model': model,
- 'temperature': 0.7,
- 'presence_penalty': 0,
- 'messages': messages,
- }
- response = requests.post(url + '/api/openai/v1/chat/completions',
- json=data, stream=True)
-
- if stream:
- for chunk in response.iter_content(chunk_size=None):
- chunk = chunk.decode('utf-8')
- if chunk.strip():
- message = json.loads(chunk)['choices'][0]['message']['content']
- yield message
- else:
- message = response.json()['choices'][0]['message']['content']
- yield message
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vivekdunuka/MyAIChat/app.py b/spaces/Vivekdunuka/MyAIChat/app.py
deleted file mode 100644
index 2dbf3ae89c2e3fdab7134107dd346f984dca8eb1..0000000000000000000000000000000000000000
--- a/spaces/Vivekdunuka/MyAIChat/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/rope.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/rope.py
deleted file mode 100644
index 4b8c70b9aba28eeb53d12ddc3de8852492847808..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/rope.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-from torch import nn
-import torch
-
-
-class XPos(nn.Module):
- """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
- This applies an exponential decay to the RoPE rotation matrix.
-
- Args:
- dim (int): Embedding dimension.
- smoothing (float): Smoothing factor applied to the decay rates.
- base_scale (int): Base decay rate, given in terms of scaling time.
- device (torch.device or None): Device on which to initialize the module.
- dtype (torch.dtype): dtype to use to generate the embedding.
- """
- def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
- device=None, dtype: torch.dtype = torch.float32):
- super().__init__()
- assert dim % 2 == 0
- assert dtype in [torch.float64, torch.float32]
- self.dtype = dtype
- self.base_scale = base_scale
-
- half_dim = dim // 2
- adim = torch.arange(half_dim, device=device, dtype=dtype)
- decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
- self.register_buffer("decay_rates", decay_rates)
- self.decay: tp.Optional[torch.Tensor] = None
-
- def get_decay(self, start: int, end: int):
- """Create complex decay tensor, cache values for fast computation.
- """
- if self.decay is None or end > self.decay.shape[0]:
- assert isinstance(self.decay_rates, torch.Tensor) # Satisfy type checker.
- idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
- power = idx / self.base_scale
- scale = self.decay_rates ** power.unsqueeze(-1)
- self.decay = torch.polar(scale, torch.zeros_like(scale))
- return self.decay[start:end] # [T, C/2]
-
-
-class RotaryEmbedding(nn.Module):
- """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
-
- Args:
- dim (int): Embedding dimension (twice the number of frequencies).
- max_period (float): Maximum period of the rotation frequencies.
- xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
- scale (float): Scale of positional embedding, set to 0 to deactivate.
- device (torch.device or None): Device on which to initialize the module.
- dtype (torch.dtype): dtype to use to generate the embedding.
- """
- def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
- scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
- super().__init__()
- assert dim % 2 == 0
- self.scale = scale
- assert dtype in [torch.float64, torch.float32]
- self.dtype = dtype
-
- adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
- frequencies = 1.0 / (max_period ** (adim / dim))
- self.register_buffer("frequencies", frequencies)
- self.rotation: tp.Optional[torch.Tensor] = None
-
- self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
-
- def get_rotation(self, start: int, end: int):
- """Create complex rotation tensor, cache values for fast computation.
- """
- if self.rotation is None or end > self.rotation.shape[0]:
- assert isinstance(self.frequencies, torch.Tensor) # Satisfy type checker.
- idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
- angles = torch.outer(idx, self.frequencies)
- self.rotation = torch.polar(torch.ones_like(angles), angles)
- return self.rotation[start:end]
-
- def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False):
- """Apply rope rotation to query or key tensor.
- """
- T = x.shape[1]
- rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2)
-
- if self.xpos:
- decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2)
- else:
- decay = 1.0
-
- if invert_decay:
- decay = decay ** -1
-
- x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
- scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
- x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2)
-
- return x_out.type_as(x)
-
- def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0):
- """ Apply rope rotation to both query and key tensors.
- Supports streaming mode, in which query and key are not expected to have the same shape.
- In streaming mode, key will be of legnth [P + C] with P the cached past timesteps, but
- query will be [C] (typically C == 1).
-
- Args:
- query (torch.Tensor): Query to rotate.
- key (torch.Tensor): Key to rotate.
- start (int): Start index of the sequence for time offset.
- """
- query_timesteps = query.shape[1]
- key_timesteps = key.shape[1]
- streaming_offset = key_timesteps - query_timesteps
-
- query_out = self.rotate(query, start + streaming_offset)
- key_out = self.rotate(key, start, invert_decay=True)
-
- return query_out, key_out
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/distributed.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/distributed.py
deleted file mode 100644
index 260ad1097e479f2ac8893016a04c58e42469e03a..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/distributed.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from .torch_core import *
-from .basic_train import Learner,LearnerCallback
-from torch.nn.parallel import DistributedDataParallel, DataParallel
-from torch.utils.data.distributed import DistributedSampler
-
-from fastai.text import TextLMDataBunch
-
-__all__ = ['DistributedRecorder', 'DistributedTrainer', 'read_metrics', 'setup_distrib']
-
-def rnn_reset(self):
- if hasattr(self.module, 'reset'): self.module.reset()
-DistributedDataParallel.reset = rnn_reset
-
-class ParallelTrainer(LearnerCallback):
- _order = -20
- def on_train_begin(self, **kwargs): self.learn.model = DataParallel(self.learn.model)
- def on_train_end (self, **kwargs): self.learn.model = self.learn.model.module
-
-class DistributedTrainer(LearnerCallback):
- _order = -20 # Needs to run before the recorder
- def __init__(self, learn:Learner, cuda_id:int=0):
- super().__init__(learn)
- self.cuda_id,self.train_sampler = cuda_id,None
-
- def _change_dl(self, dl, shuffle):
- old_dl = dl
- sampler = OurDistributedSampler(dl.dataset, shuffle=shuffle)
- new_dl = dl.new(shuffle=False, sampler=sampler)
- return old_dl,new_dl,sampler
-
- def on_train_begin(self, **kwargs):
- self.learn.model = DistributedDataParallel(self.model, device_ids=[self.cuda_id], output_device=self.cuda_id)
- shuffle = self.data.train_dl.init_kwargs['shuffle'] if hasattr(self.data.train_dl, 'init_kwargs') else True
- self.old_train_dl,self.data.train_dl,self.train_sampler = self._change_dl(self.data.train_dl, shuffle)
- if hasattr(self.data, 'valid_dl') and self.data.valid_dl is not None:
- self.old_valid_dl,self.data.valid_dl,self.valid_sampler = self._change_dl(self.data.valid_dl, shuffle)
- self.rank = rank_distrib()
- self.recorder.silent = (self.rank != 0)
-
- def on_epoch_begin(self, epoch, **kwargs): self.train_sampler.set_epoch(epoch)
-
- def on_train_end(self, **kwargs):
- self.learn.model = self.learn.model.module
- self.learn.data.train_dl = self.old_train_dl
- if hasattr(self.learn.data, 'valid_dl') and self.learn.data.valid_dl is not None:
- self.learn.data.valid_dl = self.old_valid_dl
-
-class DistributedRecorder(LearnerCallback):
- def __init__(self, learn:Learner, cuda_id:int=0, cache_dir:PathOrStr='tmp'):
- super().__init__(learn)
- self.cuda_id,self.cache_dir = cuda_id,cache_dir
-
- def on_train_begin(self, **kwargs):
- os.makedirs(self.learn.path/self.cache_dir, exist_ok=True)
-
- def on_epoch_end(self, **kwargs): self.save_stats()
- def on_train_end(self, **kwargs): self.save_stats()
-
- def save_stats(self):
- cache_path,recorder = self.learn.path/self.cache_dir,self.learn.recorder
- np.save(cache_path/f'losses_{self.cuda_id}', np.array(recorder.losses))
- stats = np.array([[v] + m for v,m in zip(recorder.val_losses,recorder.metrics)])
- np.save(cache_path/f'metrics_{self.cuda_id}', stats)
-
-def _learner_parallel(learn:Learner):
- "Use nn.DataParallel when training and remove when done"
- if not torch.cuda.is_available(): warnings.warn('CUDA is not available, check your drivers - training will continue on CPU', ResourceWarning)
- learn.callbacks.append(ParallelTrainer(learn))
- return learn
-
-def _learner_distributed(learn:Learner, cuda_id:int, cache_dir:PathOrStr='tmp'):
- "Put `learn` on distributed training with `cuda_id`."
- learn.callbacks.append(DistributedTrainer(learn, cuda_id))
- learn.callbacks.append(DistributedRecorder(learn, cuda_id, cache_dir))
- return learn
-
-Learner.to_distributed = _learner_distributed
-Learner.to_parallel = _learner_parallel
-
-def read_metrics(cache_path:PathOrStr, n_gpus:int, reduce:bool=True):
- losses,metrics = [],[]
- for i in range(n_gpus):
- losses.append(np.load(cache_path/f'losses_{i}.npy')[None])
- metrics.append(np.load(cache_path/f'metrics_{i}.npy')[None])
- if reduce:
- losses,metrics = np.concatenate(losses,0),np.concatenate(metrics,0)
- return losses.mean(0),metrics.mean(0)
- return losses,metrics
-
-def setup_distrib(gpu:Any=None):
- if gpu is None: return gpu
- gpu = int(gpu)
- torch.cuda.set_device(int(gpu))
- if num_distrib() > 1:
- torch.distributed.init_process_group(backend='nccl', init_method='env://')
- return gpu
-
-class OurDistributedSampler(DistributedSampler):
- "A sampler for language models with the option to not shuffle."
- def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank)
- self.shuffle = shuffle
-
- def __iter__(self):
- if self.shuffle:
- g = torch.Generator()
- g.manual_seed(self.epoch)
- indices = torch.randperm(len(self.dataset), generator=g).tolist()
- else: indices = torch.arange(len(self.dataset)).tolist()
-
- # add extra samples to make it evenly divisible
- indices += indices[:(self.total_size - len(indices))]
- assert len(indices) == self.total_size
-
- # subsample
- indices = indices[self.rank:self.total_size:self.num_replicas]
- assert len(indices) == self.num_samples
-
- return iter(indices)
diff --git a/spaces/Xenova/semantic-image-search-client/404.html b/spaces/Xenova/semantic-image-search-client/404.html
deleted file mode 100644
index 307a7805a588d1f9653faf41414aa5846a4623b5..0000000000000000000000000000000000000000
--- a/spaces/Xenova/semantic-image-search-client/404.html
+++ /dev/null
@@ -1 +0,0 @@
-404: This page could not be found 404
This page could not be found.
\ No newline at end of file
diff --git a/spaces/Xuechan/clothing_classifier/app.py b/spaces/Xuechan/clothing_classifier/app.py
deleted file mode 100644
index a3bf282db82a71e4e960de4aec62c85f6847b0d1..0000000000000000000000000000000000000000
--- a/spaces/Xuechan/clothing_classifier/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import timm
-from fastai.vision.all import *
-import gradio as gr
-
-learn = load_learner('model.pkl')
-
-categories = ('casual wear','business attire', 'sportswear', 'pajamas')
-def classify_image(img):
- pred, idx, probs = learn.predict(img)
- return dict(zip(categories, map(float, probs)))
-
-image = gr.inputs.Image(shape = (256,256))
-label = gr.outputs.Label()
-examples = ['casual_wear.jpg', 'business_attire.jpg', 'sportswear.jpg', 'pajamas.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples = examples)
-intf.launch(inline = False)
diff --git a/spaces/XzJosh/otto-Bert-VITS2/monotonic_align/core.py b/spaces/XzJosh/otto-Bert-VITS2/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/otto-Bert-VITS2/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
- b = paths.shape[0]
- max_neg_val=-1e9
- for i in range(int(b)):
- path = paths[i]
- value = values[i]
- t_y = t_ys[i]
- t_x = t_xs[i]
-
- v_prev = v_cur = 0.0
- index = t_x - 1
-
- for y in range(t_y):
- for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- if x == y:
- v_cur = max_neg_val
- else:
- v_cur = value[y-1, x]
- if x == 0:
- if y == 0:
- v_prev = 0.
- else:
- v_prev = max_neg_val
- else:
- v_prev = value[y-1, x-1]
- value[y, x] += max(v_prev, v_cur)
-
- for y in range(t_y - 1, -1, -1):
- path[y, index] = 1
- if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- index = index - 1
diff --git a/spaces/Yuliang/ECON/lib/net/FBNet.py b/spaces/Yuliang/ECON/lib/net/FBNet.py
deleted file mode 100644
index 5e04d5a04551c186379847637cc0a8d4b813b3da..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/net/FBNet.py
+++ /dev/null
@@ -1,749 +0,0 @@
-"""
-Copyright (C) 2019 NVIDIA Corporation. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu.
-BSD License. All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE.
-IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
-DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
-WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING
-OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-"""
-import functools
-
-import numpy as np
-import pytorch_lightning as pl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import models
-
-
-###############################################################################
-# Functions
-###############################################################################
-def weights_init(m):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(0.0, 0.02)
- elif classname.find("BatchNorm2d") != -1:
- m.weight.data.normal_(1.0, 0.02)
- m.bias.data.fill_(0)
-
-
-def get_norm_layer(norm_type="instance"):
- if norm_type == "batch":
- norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
- elif norm_type == "instance":
- norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
- else:
- raise NotImplementedError("normalization layer [%s] is not found" % norm_type)
- return norm_layer
-
-
-def define_G(
- input_nc,
- output_nc,
- ngf,
- netG,
- n_downsample_global=3,
- n_blocks_global=9,
- n_local_enhancers=1,
- n_blocks_local=3,
- norm="instance",
- gpu_ids=[],
- last_op=nn.Tanh(),
-):
- norm_layer = get_norm_layer(norm_type=norm)
- if netG == "global":
- netG = GlobalGenerator(
- input_nc,
- output_nc,
- ngf,
- n_downsample_global,
- n_blocks_global,
- norm_layer,
- last_op=last_op,
- )
- elif netG == "local":
- netG = LocalEnhancer(
- input_nc,
- output_nc,
- ngf,
- n_downsample_global,
- n_blocks_global,
- n_local_enhancers,
- n_blocks_local,
- norm_layer,
- )
- elif netG == "encoder":
- netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer)
- else:
- raise ("generator not implemented!")
- # print(netG)
- if len(gpu_ids) > 0:
- assert torch.cuda.is_available()
- netG.cuda(gpu_ids[0])
- netG.apply(weights_init)
- return netG
-
-
-def define_D(
- input_nc,
- ndf,
- n_layers_D,
- norm='instance',
- use_sigmoid=False,
- num_D=1,
- getIntermFeat=False,
- gpu_ids=[]
-):
- norm_layer = get_norm_layer(norm_type=norm)
- netD = MultiscaleDiscriminator(
- input_nc, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat
- )
- if len(gpu_ids) > 0:
- assert (torch.cuda.is_available())
- netD.cuda(gpu_ids[0])
- netD.apply(weights_init)
- return netD
-
-
-def print_network(net):
- if isinstance(net, list):
- net = net[0]
- num_params = 0
- for param in net.parameters():
- num_params += param.numel()
- print(net)
- print("Total number of parameters: %d" % num_params)
-
-
-##############################################################################
-# Generator
-##############################################################################
-class LocalEnhancer(pl.LightningModule):
- def __init__(
- self,
- input_nc,
- output_nc,
- ngf=32,
- n_downsample_global=3,
- n_blocks_global=9,
- n_local_enhancers=1,
- n_blocks_local=3,
- norm_layer=nn.BatchNorm2d,
- padding_type="reflect",
- ):
- super(LocalEnhancer, self).__init__()
- self.n_local_enhancers = n_local_enhancers
-
- ###### global generator model #####
- ngf_global = ngf * (2**n_local_enhancers)
- model_global = GlobalGenerator(
- input_nc,
- output_nc,
- ngf_global,
- n_downsample_global,
- n_blocks_global,
- norm_layer,
- ).model
- model_global = [
- model_global[i] for i in range(len(model_global) - 3)
- ] # get rid of final convolution layers
- self.model = nn.Sequential(*model_global)
-
- ###### local enhancer layers #####
- for n in range(1, n_local_enhancers + 1):
- # downsample
- ngf_global = ngf * (2**(n_local_enhancers - n))
- model_downsample = [
- nn.ReflectionPad2d(3),
- nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0),
- norm_layer(ngf_global),
- nn.ReLU(True),
- nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1),
- norm_layer(ngf_global * 2),
- nn.ReLU(True),
- ]
- # residual blocks
- model_upsample = []
- for i in range(n_blocks_local):
- model_upsample += [
- ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)
- ]
-
- # upsample
- model_upsample += [
- nn.ConvTranspose2d(
- ngf_global * 2,
- ngf_global,
- kernel_size=3,
- stride=2,
- padding=1,
- output_padding=1,
- ),
- norm_layer(ngf_global),
- nn.ReLU(True),
- ]
-
- # final convolution
- if n == n_local_enhancers:
- model_upsample += [
- nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
- nn.Tanh(),
- ]
-
- setattr(self, "model" + str(n) + "_1", nn.Sequential(*model_downsample))
- setattr(self, "model" + str(n) + "_2", nn.Sequential(*model_upsample))
-
- self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
-
- def forward(self, input):
- # create input pyramid
- input_downsampled = [input]
- for i in range(self.n_local_enhancers):
- input_downsampled.append(self.downsample(input_downsampled[-1]))
-
- # output at coarest level
- output_prev = self.model(input_downsampled[-1])
- # build up one layer at a time
- for n_local_enhancers in range(1, self.n_local_enhancers + 1):
- model_downsample = getattr(self, "model" + str(n_local_enhancers) + "_1")
- model_upsample = getattr(self, "model" + str(n_local_enhancers) + "_2")
- input_i = input_downsampled[self.n_local_enhancers - n_local_enhancers]
- output_prev = model_upsample(model_downsample(input_i) + output_prev)
- return output_prev
-
-
-class GlobalGenerator(pl.LightningModule):
- def __init__(
- self,
- input_nc,
- output_nc,
- ngf=64,
- n_downsampling=3,
- n_blocks=9,
- norm_layer=nn.BatchNorm2d,
- padding_type="reflect",
- last_op=nn.Tanh(),
- ):
- assert n_blocks >= 0
- super(GlobalGenerator, self).__init__()
- activation = nn.ReLU(True)
-
- model = [
- nn.ReflectionPad2d(3),
- nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
- norm_layer(ngf),
- activation,
- ]
- # downsample
- for i in range(n_downsampling):
- mult = 2**i
- model += [
- nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
- norm_layer(ngf * mult * 2),
- activation,
- ]
-
- # resnet blocks
- mult = 2**n_downsampling
- for i in range(n_blocks):
- model += [
- ResnetBlock(
- ngf * mult,
- padding_type=padding_type,
- activation=activation,
- norm_layer=norm_layer,
- )
- ]
-
- # upsample
- for i in range(n_downsampling):
- mult = 2**(n_downsampling - i)
- model += [
- nn.ConvTranspose2d(
- ngf * mult,
- int(ngf * mult / 2),
- kernel_size=3,
- stride=2,
- padding=1,
- output_padding=1,
- ),
- norm_layer(int(ngf * mult / 2)),
- activation,
- ]
- model += [
- nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
- ]
- if last_op is not None:
- model += [last_op]
- self.model = nn.Sequential(*model)
-
- def forward(self, input):
- return self.model(input)
-
-
-# Defines the PatchGAN discriminator with the specified arguments.
-class NLayerDiscriminator(nn.Module):
- def __init__(
- self,
- input_nc,
- ndf=64,
- n_layers=3,
- norm_layer=nn.BatchNorm2d,
- use_sigmoid=False,
- getIntermFeat=False
- ):
- super(NLayerDiscriminator, self).__init__()
- self.getIntermFeat = getIntermFeat
- self.n_layers = n_layers
-
- kw = 4
- padw = int(np.ceil((kw - 1.0) / 2))
- sequence = [[
- nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
- nn.LeakyReLU(0.2, True)
- ]]
-
- nf = ndf
- for n in range(1, n_layers):
- nf_prev = nf
- nf = min(nf * 2, 512)
- sequence += [[
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
- norm_layer(nf),
- nn.LeakyReLU(0.2, True)
- ]]
-
- nf_prev = nf
- nf = min(nf * 2, 512)
- sequence += [[
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
- norm_layer(nf),
- nn.LeakyReLU(0.2, True)
- ]]
-
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
- if use_sigmoid:
- sequence += [[nn.Sigmoid()]]
-
- if getIntermFeat:
- for n in range(len(sequence)):
- setattr(self, 'model' + str(n), nn.Sequential(*sequence[n]))
- else:
- sequence_stream = []
- for n in range(len(sequence)):
- sequence_stream += sequence[n]
- self.model = nn.Sequential(*sequence_stream)
-
- def forward(self, input):
- if self.getIntermFeat:
- res = [input]
- for n in range(self.n_layers + 2):
- model = getattr(self, 'model' + str(n))
- res.append(model(res[-1]))
- return res[1:]
- else:
- return self.model(input)
-
-
-class MultiscaleDiscriminator(pl.LightningModule):
- def __init__(
- self,
- input_nc,
- ndf=64,
- n_layers=3,
- norm_layer=nn.BatchNorm2d,
- use_sigmoid=False,
- num_D=3,
- getIntermFeat=False
- ):
- super(MultiscaleDiscriminator, self).__init__()
- self.num_D = num_D
- self.n_layers = n_layers
- self.getIntermFeat = getIntermFeat
-
- for i in range(num_D):
- netD = NLayerDiscriminator(
- input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat
- )
- if getIntermFeat:
- for j in range(n_layers + 2):
- setattr(
- self, 'scale' + str(i) + '_layer' + str(j), getattr(netD, 'model' + str(j))
- )
- else:
- setattr(self, 'layer' + str(i), netD.model)
-
- self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
-
- def singleD_forward(self, model, input):
- if self.getIntermFeat:
- result = [input]
- for i in range(len(model)):
- result.append(model[i](result[-1]))
- return result[1:]
- else:
- return [model(input)]
-
- def forward(self, input):
- num_D = self.num_D
- result = []
- input_downsampled = input.clone()
- for i in range(num_D):
- if self.getIntermFeat:
- model = [
- getattr(self, 'scale' + str(num_D - 1 - i) + '_layer' + str(j))
- for j in range(self.n_layers + 2)
- ]
- else:
- model = getattr(self, 'layer' + str(num_D - 1 - i))
- result.append(self.singleD_forward(model, input_downsampled))
- if i != (num_D - 1):
- input_downsampled = self.downsample(input_downsampled)
- return result
-
-
-# Define a resnet block
-class ResnetBlock(pl.LightningModule):
- def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False):
- super(ResnetBlock, self).__init__()
- self.conv_block = self.build_conv_block(
- dim, padding_type, norm_layer, activation, use_dropout
- )
-
- def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout):
- conv_block = []
- p = 0
- if padding_type == "reflect":
- conv_block += [nn.ReflectionPad2d(1)]
- elif padding_type == "replicate":
- conv_block += [nn.ReplicationPad2d(1)]
- elif padding_type == "zero":
- p = 1
- else:
- raise NotImplementedError("padding [%s] is not implemented" % padding_type)
-
- conv_block += [
- nn.Conv2d(dim, dim, kernel_size=3, padding=p),
- norm_layer(dim),
- activation,
- ]
- if use_dropout:
- conv_block += [nn.Dropout(0.5)]
-
- p = 0
- if padding_type == "reflect":
- conv_block += [nn.ReflectionPad2d(1)]
- elif padding_type == "replicate":
- conv_block += [nn.ReplicationPad2d(1)]
- elif padding_type == "zero":
- p = 1
- else:
- raise NotImplementedError("padding [%s] is not implemented" % padding_type)
- conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), norm_layer(dim)]
-
- return nn.Sequential(*conv_block)
-
- def forward(self, x):
- out = x + self.conv_block(x)
- return out
-
-
-class Encoder(pl.LightningModule):
- def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d):
- super(Encoder, self).__init__()
- self.output_nc = output_nc
-
- model = [
- nn.ReflectionPad2d(3),
- nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
- norm_layer(ngf),
- nn.ReLU(True),
- ]
- # downsample
- for i in range(n_downsampling):
- mult = 2**i
- model += [
- nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
- norm_layer(ngf * mult * 2),
- nn.ReLU(True),
- ]
-
- # upsample
- for i in range(n_downsampling):
- mult = 2**(n_downsampling - i)
- model += [
- nn.ConvTranspose2d(
- ngf * mult,
- int(ngf * mult / 2),
- kernel_size=3,
- stride=2,
- padding=1,
- output_padding=1,
- ),
- norm_layer(int(ngf * mult / 2)),
- nn.ReLU(True),
- ]
-
- model += [
- nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
- nn.Tanh(),
- ]
- self.model = nn.Sequential(*model)
-
- def forward(self, input, inst):
- outputs = self.model(input)
-
- # instance-wise average pooling
- outputs_mean = outputs.clone()
- inst_list = np.unique(inst.cpu().numpy().astype(int))
- for i in inst_list:
- for b in range(input.size()[0]):
- indices = (inst[b:b + 1] == int(i)).nonzero() # n x 4
- for j in range(self.output_nc):
- output_ins = outputs[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2],
- indices[:, 3], ]
- mean_feat = torch.mean(output_ins).expand_as(output_ins)
- outputs_mean[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2],
- indices[:, 3], ] = mean_feat
- return outputs_mean
-
-
-class Vgg19(nn.Module):
- def __init__(self, requires_grad=False):
- super(Vgg19, self).__init__()
- vgg_pretrained_features = models.vgg19(weights=models.VGG19_Weights.DEFAULT).features
- self.slice1 = torch.nn.Sequential()
- self.slice2 = torch.nn.Sequential()
- self.slice3 = torch.nn.Sequential()
- self.slice4 = torch.nn.Sequential()
- self.slice5 = torch.nn.Sequential()
- for x in range(2):
- self.slice1.add_module(str(x), vgg_pretrained_features[x])
- for x in range(2, 7):
- self.slice2.add_module(str(x), vgg_pretrained_features[x])
- for x in range(7, 12):
- self.slice3.add_module(str(x), vgg_pretrained_features[x])
- for x in range(12, 21):
- self.slice4.add_module(str(x), vgg_pretrained_features[x])
- for x in range(21, 30):
- self.slice5.add_module(str(x), vgg_pretrained_features[x])
- if not requires_grad:
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, X):
- h_relu1 = self.slice1(X)
- h_relu2 = self.slice2(h_relu1)
- h_relu3 = self.slice3(h_relu2)
- h_relu4 = self.slice4(h_relu3)
- h_relu5 = self.slice5(h_relu4)
- out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
- return out
-
-
-class VGG19FeatLayer(nn.Module):
- def __init__(self):
- super(VGG19FeatLayer, self).__init__()
- self.vgg19 = models.vgg19(weights=models.VGG19_Weights.DEFAULT).features.eval()
-
- self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
- self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
-
- def forward(self, x):
-
- out = {}
- x = x - self.mean
- x = x / self.std
- ci = 1
- ri = 0
- for layer in self.vgg19.children():
- if isinstance(layer, nn.Conv2d):
- ri += 1
- name = 'conv{}_{}'.format(ci, ri)
- elif isinstance(layer, nn.ReLU):
- ri += 1
- name = 'relu{}_{}'.format(ci, ri)
- layer = nn.ReLU(inplace=False)
- elif isinstance(layer, nn.MaxPool2d):
- ri = 0
- name = 'pool_{}'.format(ci)
- ci += 1
- elif isinstance(layer, nn.BatchNorm2d):
- name = 'bn_{}'.format(ci)
- else:
- raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
- x = layer(x)
- out[name] = x
- # print([x for x in out])
- return out
-
-
-class VGGLoss(pl.LightningModule):
- def __init__(self):
- super(VGGLoss, self).__init__()
- self.vgg = Vgg19().eval()
- self.criterion = nn.L1Loss()
- self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0]
-
- def forward(self, x, y):
- x_vgg, y_vgg = self.vgg(x), self.vgg(y)
- loss = 0
- for i in range(len(x_vgg)):
- loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())
- return loss
-
-
-class GANLoss(pl.LightningModule):
- def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0):
- super(GANLoss, self).__init__()
- self.real_label = target_real_label
- self.fake_label = target_fake_label
- self.real_label_var = None
- self.fake_label_var = None
- self.tensor = torch.cuda.FloatTensor
- if use_lsgan:
- self.loss = nn.MSELoss()
- else:
- self.loss = nn.BCELoss()
-
- def get_target_tensor(self, input, target_is_real):
- target_tensor = None
- if target_is_real:
- create_label = ((self.real_label_var is None) or
- (self.real_label_var.numel() != input.numel()))
- if create_label:
- real_tensor = self.tensor(input.size()).fill_(self.real_label)
- self.real_label_var = real_tensor
- self.real_label_var.requires_grad = False
- target_tensor = self.real_label_var
- else:
- create_label = ((self.fake_label_var is None) or
- (self.fake_label_var.numel() != input.numel()))
- if create_label:
- fake_tensor = self.tensor(input.size()).fill_(self.fake_label)
- self.fake_label_var = fake_tensor
- self.fake_label_var.requires_grad = False
- target_tensor = self.fake_label_var
- return target_tensor
-
- def __call__(self, input, target_is_real):
- if isinstance(input[0], list):
- loss = 0
- for input_i in input:
- pred = input_i[-1]
- target_tensor = self.get_target_tensor(pred, target_is_real)
- loss += self.loss(pred, target_tensor)
- return loss
- else:
- target_tensor = self.get_target_tensor(input[-1], target_is_real)
- return self.loss(input[-1], target_tensor)
-
-
-class IDMRFLoss(pl.LightningModule):
- def __init__(self, featlayer=VGG19FeatLayer):
- super(IDMRFLoss, self).__init__()
- self.featlayer = featlayer()
- self.feat_style_layers = {'relu3_2': 1.0, 'relu4_2': 1.0}
- self.feat_content_layers = {'relu4_2': 1.0}
- self.bias = 1.0
- self.nn_stretch_sigma = 0.5
- self.lambda_style = 1.0
- self.lambda_content = 1.0
-
- def sum_normalize(self, featmaps):
- reduce_sum = torch.sum(featmaps, dim=1, keepdim=True)
- return featmaps / reduce_sum
-
- def patch_extraction(self, featmaps):
- patch_size = 1
- patch_stride = 1
- patches_as_depth_vectors = featmaps.unfold(2, patch_size, patch_stride).unfold(
- 3, patch_size, patch_stride
- )
- self.patches_OIHW = patches_as_depth_vectors.permute(0, 2, 3, 1, 4, 5)
- dims = self.patches_OIHW.size()
- self.patches_OIHW = self.patches_OIHW.view(-1, dims[3], dims[4], dims[5])
- return self.patches_OIHW
-
- def compute_relative_distances(self, cdist):
- epsilon = 1e-5
- div = torch.min(cdist, dim=1, keepdim=True)[0]
- relative_dist = cdist / (div + epsilon)
- return relative_dist
-
- def exp_norm_relative_dist(self, relative_dist):
- scaled_dist = relative_dist
- dist_before_norm = torch.exp((self.bias - scaled_dist) / self.nn_stretch_sigma)
- self.cs_NCHW = self.sum_normalize(dist_before_norm)
- return self.cs_NCHW
-
- def mrf_loss(self, gen, tar):
- meanT = torch.mean(tar, 1, keepdim=True)
- gen_feats, tar_feats = gen - meanT, tar - meanT
-
- gen_feats_norm = torch.norm(gen_feats, p=2, dim=1, keepdim=True)
- tar_feats_norm = torch.norm(tar_feats, p=2, dim=1, keepdim=True)
-
- gen_normalized = gen_feats / gen_feats_norm
- tar_normalized = tar_feats / tar_feats_norm
-
- cosine_dist_l = []
- BatchSize = tar.size(0)
-
- for i in range(BatchSize):
- tar_feat_i = tar_normalized[i:i + 1, :, :, :]
- gen_feat_i = gen_normalized[i:i + 1, :, :, :]
- patches_OIHW = self.patch_extraction(tar_feat_i)
-
- cosine_dist_i = F.conv2d(gen_feat_i, patches_OIHW)
- cosine_dist_l.append(cosine_dist_i)
- cosine_dist = torch.cat(cosine_dist_l, dim=0)
- cosine_dist_zero_2_one = -(cosine_dist - 1) / 2
- relative_dist = self.compute_relative_distances(cosine_dist_zero_2_one)
- rela_dist = self.exp_norm_relative_dist(relative_dist)
- dims_div_mrf = rela_dist.size()
- k_max_nc = torch.max(rela_dist.view(dims_div_mrf[0], dims_div_mrf[1], -1), dim=2)[0]
- div_mrf = torch.mean(k_max_nc, dim=1)
- div_mrf_sum = -torch.log(div_mrf)
- div_mrf_sum = torch.sum(div_mrf_sum)
- return div_mrf_sum
-
- def forward(self, gen, tar):
- ## gen: [bz,3,h,w] rgb [0,1]
- gen_vgg_feats = self.featlayer(gen)
- tar_vgg_feats = self.featlayer(tar)
- style_loss_list = [
- self.feat_style_layers[layer] *
- self.mrf_loss(gen_vgg_feats[layer], tar_vgg_feats[layer])
- for layer in self.feat_style_layers
- ]
- self.style_loss = functools.reduce(lambda x, y: x + y, style_loss_list) * self.lambda_style
-
- content_loss_list = [
- self.feat_content_layers[layer] *
- self.mrf_loss(gen_vgg_feats[layer], tar_vgg_feats[layer])
- for layer in self.feat_content_layers
- ]
- self.content_loss = functools.reduce(
- lambda x, y: x + y, content_loss_list
- ) * self.lambda_content
-
- return self.style_loss + self.content_loss
diff --git a/spaces/YuxinJ/Scenimefy/Scenimefy/data/__init__.py b/spaces/YuxinJ/Scenimefy/Scenimefy/data/__init__.py
deleted file mode 100644
index 05d6a2e9b493d985a5b83bb6815d688ed0a7ddf9..0000000000000000000000000000000000000000
--- a/spaces/YuxinJ/Scenimefy/Scenimefy/data/__init__.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""This package includes all the modules related to data loading and preprocessing
-
- To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
- You need to implement four functions:
- -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
- -- <__len__>: return the size of dataset.
- -- <__getitem__>: get a data point from data loader.
- -- : (optionally) add dataset-specific options and set default options.
-
-Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
-See our template dataset class 'template_dataset.py' for more details.
-"""
-import importlib
-import torch.utils.data
-from Scenimefy.data.base_dataset import BaseDataset
-
-
-def find_dataset_using_name(dataset_name):
- """Import the module "data/[dataset_name]_dataset.py".
-
- In the file, the class called DatasetNameDataset() will
- be instantiated. It has to be a subclass of BaseDataset,
- and it is case-insensitive.
- """
- dataset_filename = "Scenimefy.data." + dataset_name + "_dataset"
- datasetlib = importlib.import_module(dataset_filename)
-
- dataset = None
- target_dataset_name = dataset_name.replace('_', '') + 'dataset'
- for name, cls in datasetlib.__dict__.items():
- if name.lower() == target_dataset_name.lower() \
- and issubclass(cls, BaseDataset):
- dataset = cls
-
- if dataset is None:
- raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
-
- return dataset
-
-
-def get_option_setter(dataset_name):
- """Return the static method of the dataset class."""
- dataset_class = find_dataset_using_name(dataset_name)
- return dataset_class.modify_commandline_options
-
-
-def create_dataset(opt):
- """Create a dataset given the option.
-
- This function wraps the class CustomDatasetDataLoader.
- This is the main interface between this package and 'train.py'/'test.py'
-
- Example:
- >>> from data import create_dataset
- >>> dataset = create_dataset(opt)
- """
- data_loader = CustomDatasetDataLoader(opt)
- dataset = data_loader.load_data()
- return dataset
-
-
-class CustomDatasetDataLoader():
- """Wrapper class of Dataset class that performs multi-threaded data loading"""
-
- def __init__(self, opt):
- """Initialize this class
-
- Step 1: create a dataset instance given the name [dataset_mode]
- Step 2: create a multi-threaded data loader.
- """
- self.opt = opt
- dataset_class = find_dataset_using_name(opt.dataset_mode)
- self.dataset = dataset_class(opt)
- print("dataset [%s] was created" % type(self.dataset).__name__)
- self.dataloader = torch.utils.data.DataLoader(
- self.dataset,
- batch_size=opt.batch_size,
- shuffle=not opt.serial_batches,
- num_workers=int(opt.num_threads),
- drop_last=True if opt.isTrain else False,
- )
-
- def set_epoch(self, epoch):
- self.dataset.current_epoch = epoch
-
- def load_data(self):
- return self
-
- def __len__(self):
- """Return the number of data in the dataset"""
- return min(len(self.dataset), self.opt.max_dataset_size)
-
- def __iter__(self):
- """Return a batch of data"""
- for i, data in enumerate(self.dataloader):
- if i * self.opt.batch_size >= self.opt.max_dataset_size:
- break
- yield data
-
-
-# TODO: add paired dataset (stupid implementation)
-def create_paired_dataset(opt):
- """Create a dataset given the option.
-
- This function wraps the class CustomDatasetDataLoader.
- This is the main interface between this package and 'train.py'/'test.py'
-
- Example:
- >>> from data import create_dataset
- >>> dataset = create_dataset(opt)
- """
- data_loader = CustomPairedDatasetDataLoader(opt)
- dataset = data_loader.load_data()
- return dataset
-
-
-class CustomPairedDatasetDataLoader():
- """Wrapper class of Dataset class that performs multi-threaded data loading"""
-
- def __init__(self, opt):
- """Initialize this class
-
- Step 1: create a dataset instance given the name [dataset_mode]
- Step 2: create a multi-threaded data loader.
- """
- self.opt = opt
- dataset_class = find_dataset_using_name(opt.paired_dataset_mode)
- self.dataset = dataset_class(opt)
- print("dataset [%s] was created" % type(self.dataset).__name__)
- self.dataloader = torch.utils.data.DataLoader(
- self.dataset,
- batch_size=opt.batch_size,
- shuffle=not opt.serial_batches,
- num_workers=int(opt.num_threads),
- drop_last=True if opt.isTrain else False,
- )
-
- def set_epoch(self, epoch):
- self.dataset.current_epoch = epoch
-
- def load_data(self):
- return self
-
- def __len__(self):
- """Return the number of data in the dataset"""
- return min(len(self.dataset), self.opt.max_dataset_size)
-
- def __iter__(self):
- """Return a batch of data"""
- for i, data in enumerate(self.dataloader):
- if i * self.opt.batch_size >= self.opt.max_dataset_size:
- break
- yield data
\ No newline at end of file
diff --git a/spaces/akhaliq/JoJoGAN/e4e/editings/ganspace.py b/spaces/akhaliq/JoJoGAN/e4e/editings/ganspace.py
deleted file mode 100644
index 0c286a421280c542e9776a75e64bb65409da8fc7..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/editings/ganspace.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-
-
-def edit(latents, pca, edit_directions):
- edit_latents = []
- for latent in latents:
- for pca_idx, start, end, strength in edit_directions:
- delta = get_delta(pca, latent, pca_idx, strength)
- delta_padded = torch.zeros(latent.shape).to('cuda')
- delta_padded[start:end] += delta.repeat(end - start, 1)
- edit_latents.append(latent + delta_padded)
- return torch.stack(edit_latents)
-
-
-def get_delta(pca, latent, idx, strength):
- # pca: ganspace checkpoint. latent: (16, 512) w+
- w_centered = latent - pca['mean'].to('cuda')
- lat_comp = pca['comp'].to('cuda')
- lat_std = pca['std'].to('cuda')
- w_coord = torch.sum(w_centered[0].reshape(-1)*lat_comp[idx].reshape(-1)) / lat_std[idx]
- delta = (strength - w_coord)*lat_comp[idx]*lat_std[idx]
- return delta
diff --git a/spaces/akhaliq/lama/fetch_data/eval_sampler.py b/spaces/akhaliq/lama/fetch_data/eval_sampler.py
deleted file mode 100644
index bf2d70d875a44b5a74daeec9b4ba747600287f2a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/fetch_data/eval_sampler.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-import random
-
-
-val_files_path = os.path.abspath('.') + '/places_standard_dataset/original/val/'
-val_files = [val_files_path + image for image in os.listdir(val_files_path)]
-
-print(f'found {len(val_files)} images in {val_files_path}')
-
-random.shuffle(val_files)
-val_files_random = val_files[0:2000]
-
-list_of_random_val_files = os.path.abspath('.') \
-+ '/places_standard_dataset/original/eval_random_files.txt'
-
-print(f'copying 2000 random images to {list_of_random_val_files}')
-with open(list_of_random_val_files, 'w') as fw:
- for filename in val_files_random:
- fw.write(filename+'\n')
-print('...done')
-
diff --git a/spaces/akhaliq/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py b/spaces/akhaliq/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py
deleted file mode 100644
index 18318965335b37cc671004a6aceda3229dc7b477..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py
+++ /dev/null
@@ -1,329 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : batchnorm.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
- """sum over the first and last dimention"""
- return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
- """add new dementions at the front and the tail"""
- return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
- def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True):
- super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
- self._sync_master = SyncMaster(self._data_parallel_master)
-
- self._is_parallel = False
- self._parallel_id = None
- self._slave_pipe = None
-
- # customed batch norm statistics
- self._moving_average_fraction = 1. - momentum
- self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features))
- self.register_buffer('_tmp_running_var', torch.ones(self.num_features))
- self.register_buffer('_running_iter', torch.ones(1))
- self._tmp_running_mean = self.running_mean.clone() * self._running_iter
- self._tmp_running_var = self.running_var.clone() * self._running_iter
-
- def forward(self, input):
- # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
- if not (self._is_parallel and self.training):
- return F.batch_norm(
- input, self.running_mean, self.running_var, self.weight, self.bias,
- self.training, self.momentum, self.eps)
-
- # Resize the input to (B, C, -1).
- input_shape = input.size()
- input = input.view(input.size(0), self.num_features, -1)
-
- # Compute the sum and square-sum.
- sum_size = input.size(0) * input.size(2)
- input_sum = _sum_ft(input)
- input_ssum = _sum_ft(input ** 2)
-
- # Reduce-and-broadcast the statistics.
- if self._parallel_id == 0:
- mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
- else:
- mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
- # Compute the output.
- if self.affine:
- # MJY:: Fuse the multiplication for speed.
- output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
- else:
- output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
- # Reshape it.
- return output.view(input_shape)
-
- def __data_parallel_replicate__(self, ctx, copy_id):
- self._is_parallel = True
- self._parallel_id = copy_id
-
- # parallel_id == 0 means master device.
- if self._parallel_id == 0:
- ctx.sync_master = self._sync_master
- else:
- self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
- def _data_parallel_master(self, intermediates):
- """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
- intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
- to_reduce = [i[1][:2] for i in intermediates]
- to_reduce = [j for i in to_reduce for j in i] # flatten
- target_gpus = [i[1].sum.get_device() for i in intermediates]
-
- sum_size = sum([i[1].sum_size for i in intermediates])
- sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-
- mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
- broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
- outputs = []
- for i, rec in enumerate(intermediates):
- outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
- return outputs
-
- def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0):
- """return *dest* by `dest := dest*alpha + delta*beta + bias`"""
- return dest * alpha + delta * beta + bias
-
- def _compute_mean_std(self, sum_, ssum, size):
- """Compute the mean and standard-deviation with sum and square-sum. This method
- also maintains the moving average on the master device."""
- assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
- mean = sum_ / size
- sumvar = ssum - sum_ * mean
- unbias_var = sumvar / (size - 1)
- bias_var = sumvar / size
-
- self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction)
- self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction)
- self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction)
-
- self.running_mean = self._tmp_running_mean / self._running_iter
- self.running_var = self._tmp_running_var / self._running_iter
-
- return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
- r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
- mini-batch.
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm1d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
- Args:
- num_features: num_features from an expected input of size
- `batch_size x num_features [x width]`
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C)` or :math:`(N, C, L)`
- - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm1d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm1d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 2 and input.dim() != 3:
- raise ValueError('expected 2D or 3D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
- r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
- of 3d inputs
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm2d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
- Args:
- num_features: num_features from an expected input of
- size batch_size x num_features x height x width
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C, H, W)`
- - Output: :math:`(N, C, H, W)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm2d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm2d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 4:
- raise ValueError('expected 4D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
- r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
- of 4d inputs
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm3d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
- or Spatio-temporal BatchNorm
-
- Args:
- num_features: num_features from an expected input of
- size batch_size x num_features x depth x height x width
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C, D, H, W)`
- - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm3d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm3d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 5:
- raise ValueError('expected 5D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/alex-mindspace/gpt-agents/swarmai/agents/GooglerAgent.py b/spaces/alex-mindspace/gpt-agents/swarmai/agents/GooglerAgent.py
deleted file mode 100644
index 68dac4df967c2b3612233dc4335d0fd9ae8c5e28..0000000000000000000000000000000000000000
--- a/spaces/alex-mindspace/gpt-agents/swarmai/agents/GooglerAgent.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from swarmai.agents.AgentBase import AgentBase
-from swarmai.utils.ai_engines import LanchainGoogleEngine, GPTConversEngine
-from swarmai.utils.task_queue.Task import Task
-from swarmai.utils.PromptFactory import PromptFactory
-
-class GooglerAgent(AgentBase):
- """Googler agent that can google things.
- """
-
- def __init__(self, agent_id, agent_type, swarm, logger):
- super().__init__(agent_id, agent_type, swarm, logger)
- self.search_engine = LanchainGoogleEngine("gpt-3.5-turbo", 0.5, 1000)
- self.thinking_engine = GPTConversEngine("gpt-3.5-turbo", 0.5, 1000)
-
- self.TASK_METHODS = {
- Task.TaskTypes.google_search: self.google,
- }
-
- def perform_task(self):
- self.step = "perform_task"
- try:
- # self.task is already taken in the beginning of the cycle in AgentBase
- if not isinstance(self.task, Task):
- raise Exception(f"Task is not of type Task, but {type(self.task)}")
-
- task_type = self.task.task_type
- if task_type not in self.TASK_METHODS:
- raise Exception(f"Task type {task_type} is not supported by the agent {self.agent_id} of type {self.agent_type}")
-
- self.result = self.TASK_METHODS[task_type](self.task.task_description)
- return True
- except Exception as e:
- self.log(message = f"Agent {self.agent_id} of type {self.agent_type} failed to perform the task {self.task.task_description} with error {e}", level = "error")
- return False
-
- def share(self):
- pass
-
- def google(self, task_description):
- self.step = "google"
-
- # just googling
- system_prompt = PromptFactory.StandardPrompts.google_search_config_prompt
-
- conversation = [
- {"role": "system", "content": system_prompt},
- {"role": "user", "content": task_description},
- ]
- result = self.search_engine.call_model(conversation)
-
- # summarize and pretify the result
- summarisation_prompt =(
- f"After googling the topic {task_description}, you found the results listed below."
- "Summarize the facts as brief as possible"
- "You MUST provide the links as sources for each fact."
- "Add tags in brackets to the facts to make them more searchable. For example: (Company X market trends), (Company X competitors), etc."
- )
- conversation = [
- {"role": "system", "content": system_prompt},
- {"role": "user", "content": summarisation_prompt + f"Search Results:\n{result}"},
- ]
- result = self.thinking_engine.call_model(conversation)
-
- self.log(message = f"Agent {self.agent_id} of type {self.agent_type} googled:\n{task_description}\n\nand got:\n{result}", level = "info")
-
- # saving to the shared memory
- self._send_data_to_swarm(result)
-
- return result
-
-
\ No newline at end of file
diff --git a/spaces/ali-ghamdan/deoldify/fastai/callbacks/mem.py b/spaces/ali-ghamdan/deoldify/fastai/callbacks/mem.py
deleted file mode 100644
index 41230ce3d14b2f7b7ff5f223e114b0791ec341c0..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/callbacks/mem.py
+++ /dev/null
@@ -1,65 +0,0 @@
-" Memory profiling callbacks "
-
-import tracemalloc, threading, torch, time
-from ..utils.mem import *
-from ..basic_train import *
-from ..torch_core import *
-from ..utils.pynvml_gate import *
-
-if use_gpu: pynvml = load_pynvml_env()
-
-class PeakMemMetric(LearnerCallback):
- "Callback that measures used and peaked general and GPU memory."
-
- _order=-20 # Needs to run before the recorder
-
- def __init__(self, learn:Learner):
- super().__init__(learn)
- assert torch.cuda.is_available(), "pytorch CUDA is required"
- preload_pytorch()
-
- def peak_monitor_start(self):
- self.peak_monitoring = True
-
- # start RAM tracing
- tracemalloc.start()
-
- # this thread samples RAM usage as long as the current epoch of the fit loop is running
- peak_monitor_thread = threading.Thread(target=self.peak_monitor_func)
- peak_monitor_thread.daemon = True
- peak_monitor_thread.start()
-
- def peak_monitor_stop(self):
- tracemalloc.stop()
- self.peak_monitoring = False
-
- def peak_monitor_func(self):
- self.gpu_mem_used_peak = -1
-
- gpu_id = torch.cuda.current_device()
- gpu_handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
-
- while True:
- gpu_mem_used = gpu_mem_get_used_fast(gpu_handle)
- self.gpu_mem_used_peak = max(gpu_mem_used, self.gpu_mem_used_peak)
- if not self.peak_monitoring: break
- time.sleep(0.001) # 1msec
-
- def on_train_begin(self, **kwargs):
- self.learn.recorder.add_metric_names(['cpu used', 'peak', 'gpu used', 'peak'])
-
- def on_epoch_begin(self, **kwargs):
- self.peak_monitor_start()
- self.gpu_before = gpu_mem_get_used_no_cache()
-
- def on_epoch_end(self, last_metrics, **kwargs):
- cpu_used, cpu_peak = list(map(lambda x: int(x/2**20), tracemalloc.get_traced_memory()))
- self.peak_monitor_stop()
- gpu_used = gpu_mem_get_used_no_cache() - self.gpu_before
- gpu_peak = self.gpu_mem_used_peak - self.gpu_before
- # can be negative, due to unreliable peak monitor thread
- if gpu_peak < 0: gpu_peak = 0
- # since we want the overhead only, subtract delta used if it's positive
- elif gpu_used > 0: gpu_peak -= gpu_used
- # The numbers are deltas in MBs (beginning of the epoch and the end)
- return add_metrics(last_metrics, [cpu_used, cpu_peak, gpu_used, gpu_peak])
diff --git a/spaces/allknowingroger/Image-Models-Test45/README.md b/spaces/allknowingroger/Image-Models-Test45/README.md
deleted file mode 100644
index 9f870f1223374bac079795bf9e117b16310eade2..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test45/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Models
-emoji: 👀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test44
----
-
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test91/README.md b/spaces/allknowingroger/Image-Models-Test91/README.md
deleted file mode 100644
index fae1cb864a8d7b7dc4d6618666bb4fc43149be1f..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test91/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test90
----
-
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/huggingface/index.html b/spaces/allknowingroger/huggingface/index.html
deleted file mode 100644
index d4dcaf91fabc76122868afa9a8127bcfa063a3c1..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/huggingface/index.html
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
-
-
-
-
-
-
- 🤗
-
-
-
-
-
-
-
-
diff --git a/spaces/alvanlii/RDM-Region-Aware-Diffusion-Model/README.md b/spaces/alvanlii/RDM-Region-Aware-Diffusion-Model/README.md
deleted file mode 100644
index 6fd2bcaf6a3ddb37d3be08c553e06e1ae6321862..0000000000000000000000000000000000000000
--- a/spaces/alvanlii/RDM-Region-Aware-Diffusion-Model/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RDM Region Aware Diffusion Model
-emoji: 🐠
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-tags: ['making-demos']
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_mono.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_mono.c
deleted file mode 100644
index e7d7d1b6797d0ebebb9ddf187af81485f630ed64..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_mono.c
+++ /dev/null
@@ -1,155 +0,0 @@
-/** @file patest_mono.c
- @ingroup test_src
- @brief Play a monophonic sine wave using the Portable Audio api for several seconds.
- @author Phil Burk http://www.softsynth.com
-*/
-/*
- * $Id$
- *
- * Authors:
- * Ross Bencina
- * Phil Burk
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include
-#include
-#include "portaudio.h"
-
-#define NUM_SECONDS (10)
-#define SAMPLE_RATE (44100)
-#define AMPLITUDE (0.8)
-#define FRAMES_PER_BUFFER (64)
-#define OUTPUT_DEVICE Pa_GetDefaultOutputDevice()
-
-#ifndef M_PI
-#define M_PI (3.14159265)
-#endif
-
-#define TABLE_SIZE (200)
-typedef struct
-{
- float sine[TABLE_SIZE];
- int phase;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
- unsigned long framesPerBuffer,
- const PaStreamCallbackTimeInfo* timeInfo,
- PaStreamCallbackFlags statusFlags,
- void *userData )
-{
- paTestData *data = (paTestData*)userData;
- float *out = (float*)outputBuffer;
- unsigned long i;
- int finished = 0;
- /* avoid unused variable warnings */
- (void) inputBuffer;
- (void) timeInfo;
- (void) statusFlags;
- for( i=0; isine[data->phase]; /* left */
- data->phase += 1;
- if( data->phase >= TABLE_SIZE ) data->phase -= TABLE_SIZE;
- }
- return finished;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
- PaStreamParameters outputParameters;
- PaStream *stream;
- PaError err;
- paTestData data;
- int i;
- printf("PortAudio Test: output MONO sine wave. SR = %d, BufSize = %d\n", SAMPLE_RATE, FRAMES_PER_BUFFER);
- /* initialise sinusoidal wavetable */
- for( i=0; idefaultLowOutputLatency;
- outputParameters.hostApiSpecificStreamInfo = NULL;
-
- err = Pa_OpenStream(
- &stream,
- NULL, /* no input */
- &outputParameters,
- SAMPLE_RATE,
- FRAMES_PER_BUFFER,
- paClipOff, /* we won't output out of range samples so don't bother clipping them */
- patestCallback,
- &data );
- if( err != paNoError ) goto error;
-
- err = Pa_StartStream( stream );
- if( err != paNoError ) goto error;
-
- printf("Play for %d seconds.\n", NUM_SECONDS ); fflush(stdout);
- Pa_Sleep( NUM_SECONDS * 1000 );
-
- err = Pa_StopStream( stream );
- if( err != paNoError ) goto error;
-
- err = Pa_CloseStream( stream );
- if( err != paNoError ) goto error;
-
- Pa_Terminate();
- printf("Test finished.\n");
- return err;
-error:
- Pa_Terminate();
- fprintf( stderr, "An error occurred while using the portaudio stream\n" );
- fprintf( stderr, "Error number: %d\n", err );
- fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
- return err;
-}
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_time.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_time.c
deleted file mode 100644
index c1d609719b6c85dcab89832c5d76b8d08dedb836..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_time.c
+++ /dev/null
@@ -1,219 +0,0 @@
-/** @file patest_sine_time.c
- @ingroup test_src
- @brief Play a sine wave for several seconds, pausing in the middle.
- Uses the Pa_GetStreamTime() call.
- @author Ross Bencina
- @author Phil Burk
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-#include
-#include
-
-#include "portaudio.h"
-#include "pa_util.h"
-
-#define NUM_SECONDS (8)
-#define SAMPLE_RATE (44100)
-#define FRAMES_PER_BUFFER (64)
-
-#ifndef M_PI
-#define M_PI (3.14159265)
-#endif
-#define TWOPI (M_PI * 2.0)
-
-#define TABLE_SIZE (200)
-
-typedef struct
-{
- double left_phase;
- double right_phase;
- volatile PaTime outTime;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
- unsigned long framesPerBuffer,
- const PaStreamCallbackTimeInfo* timeInfo,
- PaStreamCallbackFlags statusFlags,
- void *userData )
-{
- paTestData *data = (paTestData*)userData;
- float *out = (float*)outputBuffer;
- unsigned int i;
-
- double left_phaseInc = 0.02;
- double right_phaseInc = 0.06;
-
- double left_phase = data->left_phase;
- double right_phase = data->right_phase;
-
- (void) statusFlags; /* Prevent unused variable warnings. */
- (void) inputBuffer;
-
- data->outTime = timeInfo->outputBufferDacTime;
-
- for( i=0; i TWOPI ) left_phase -= TWOPI;
- *out++ = (float) sin( left_phase );
-
- right_phase += right_phaseInc;
- if( right_phase > TWOPI ) right_phase -= TWOPI;
- *out++ = (float) sin( right_phase );
- }
-
- data->left_phase = left_phase;
- data->right_phase = right_phase;
-
- return paContinue;
-}
-
-/*******************************************************************/
-static void ReportStreamTime( PaStream *stream, paTestData *data );
-static void ReportStreamTime( PaStream *stream, paTestData *data )
-{
- PaTime streamTime, latency, outTime;
-
- streamTime = Pa_GetStreamTime( stream );
- outTime = data->outTime;
- if( outTime < 0.0 )
- {
- printf("Stream time = %8.1f\n", streamTime );
- }
- else
- {
- latency = outTime - streamTime;
- printf("Stream time = %8.4f, outTime = %8.4f, latency = %8.4f\n",
- streamTime, outTime, latency );
- }
- fflush(stdout);
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
- PaStreamParameters outputParameters;
- PaStream *stream;
- PaError err;
- paTestData data;
- PaTime startTime;
-
- printf("PortAudio Test: output sine wave. SR = %d, BufSize = %d\n", SAMPLE_RATE, FRAMES_PER_BUFFER);
-
- data.left_phase = data.right_phase = 0;
-
- err = Pa_Initialize();
- if( err != paNoError ) goto error;
-
- outputParameters.device = Pa_GetDefaultOutputDevice(); /* default output device */
- if (outputParameters.device == paNoDevice) {
- fprintf(stderr,"Error: No default output device.\n");
- goto error;
- }
- outputParameters.channelCount = 2; /* stereo output */
- outputParameters.sampleFormat = paFloat32; /* 32 bit floating point output */
- outputParameters.suggestedLatency = Pa_GetDeviceInfo( outputParameters.device )->defaultLowOutputLatency;
- outputParameters.hostApiSpecificStreamInfo = NULL;
-
- err = Pa_OpenStream(
- &stream,
- NULL, /* no input */
- &outputParameters,
- SAMPLE_RATE,
- FRAMES_PER_BUFFER,
- paClipOff, /* we won't output out of range samples so don't bother clipping them */
- patestCallback,
- &data );
- if( err != paNoError ) goto error;
-
- /* Watch until sound is halfway finished. */
- printf("Play for %d seconds.\n", NUM_SECONDS/2 ); fflush(stdout);
-
- data.outTime = -1.0; /* mark time for callback as undefined */
- err = Pa_StartStream( stream );
- if( err != paNoError ) goto error;
-
- startTime = Pa_GetStreamTime( stream );
-
- do
- {
- ReportStreamTime( stream, &data );
- Pa_Sleep(100);
- } while( (Pa_GetStreamTime( stream ) - startTime) < (NUM_SECONDS/2) );
-
- /* Stop sound for 2 seconds. */
- err = Pa_StopStream( stream );
- if( err != paNoError ) goto error;
-
- printf("Pause for 2 seconds.\n"); fflush(stdout);
- Pa_Sleep( 2000 );
-
- data.outTime = -1.0; /* mark time for callback as undefined */
- err = Pa_StartStream( stream );
- if( err != paNoError ) goto error;
-
- startTime = Pa_GetStreamTime( stream );
-
- printf("Play until sound is finished.\n"); fflush(stdout);
- do
- {
- ReportStreamTime( stream, &data );
- Pa_Sleep(100);
- } while( (Pa_GetStreamTime( stream ) - startTime) < (NUM_SECONDS/2) );
-
- err = Pa_CloseStream( stream );
- if( err != paNoError ) goto error;
-
- Pa_Terminate();
- printf("Test finished.\n");
- return err;
-
-error:
- Pa_Terminate();
- fprintf( stderr, "An error occurred while using the portaudio stream\n" );
- fprintf( stderr, "Error number: %d\n", err );
- fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
- return err;
-}
diff --git a/spaces/anurag629/botaniscan/README.md b/spaces/anurag629/botaniscan/README.md
deleted file mode 100644
index 015edbe479d88c4ca03b686eada1e049a67a8ee4..0000000000000000000000000000000000000000
--- a/spaces/anurag629/botaniscan/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: BOTANI SCAN API
-emoji: 📝
-colorFrom: yellow
-colorTo: orange
-sdk: docker
-python_version: 3.8.0
-app_file: app.main.py
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/aphenx/bingo/src/components/learn-more.tsx b/spaces/aphenx/bingo/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/aphenx/bingo/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
- sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
- if (!sourceAttributions?.length) {
- return null
- }
-
- return (
-
- 了解详细信息:
-
-
- {sourceAttributions.map((attribution, index) => {
- const { providerDisplayName, seeMoreUrl } = attribution
- const { host } = new URL(seeMoreUrl)
- return (
-
- {index + 1}. {host}
-
- )
- })}
-
-
-
- )
-}
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/models/parallel_wavegan_discriminator.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/models/parallel_wavegan_discriminator.py
deleted file mode 100644
index adf1bdaea040e99dd66829b9b8ed184146e155cb..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/models/parallel_wavegan_discriminator.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import math
-
-import torch
-from torch import nn
-
-from TTS.vocoder.layers.parallel_wavegan import ResidualBlock
-
-
-class ParallelWaveganDiscriminator(nn.Module):
- """PWGAN discriminator as in https://arxiv.org/abs/1910.11480.
- It classifies each audio window real/fake and returns a sequence
- of predictions.
- It is a stack of convolutional blocks with dilation.
- """
-
- # pylint: disable=dangerous-default-value
- def __init__(
- self,
- in_channels=1,
- out_channels=1,
- kernel_size=3,
- num_layers=10,
- conv_channels=64,
- dilation_factor=1,
- nonlinear_activation="LeakyReLU",
- nonlinear_activation_params={"negative_slope": 0.2},
- bias=True,
- ):
- super().__init__()
- assert (kernel_size - 1) % 2 == 0, " [!] does not support even number kernel size."
- assert dilation_factor > 0, " [!] dilation factor must be > 0."
- self.conv_layers = nn.ModuleList()
- conv_in_channels = in_channels
- for i in range(num_layers - 1):
- if i == 0:
- dilation = 1
- else:
- dilation = i if dilation_factor == 1 else dilation_factor**i
- conv_in_channels = conv_channels
- padding = (kernel_size - 1) // 2 * dilation
- conv_layer = [
- nn.Conv1d(
- conv_in_channels,
- conv_channels,
- kernel_size=kernel_size,
- padding=padding,
- dilation=dilation,
- bias=bias,
- ),
- getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
- ]
- self.conv_layers += conv_layer
- padding = (kernel_size - 1) // 2
- last_conv_layer = nn.Conv1d(conv_in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=bias)
- self.conv_layers += [last_conv_layer]
- self.apply_weight_norm()
-
- def forward(self, x):
- """
- x : (B, 1, T).
- Returns:
- Tensor: (B, 1, T)
- """
- for f in self.conv_layers:
- x = f(x)
- return x
-
- def apply_weight_norm(self):
- def _apply_weight_norm(m):
- if isinstance(m, (torch.nn.Conv1d, torch.nn.Conv2d)):
- torch.nn.utils.weight_norm(m)
-
- self.apply(_apply_weight_norm)
-
- def remove_weight_norm(self):
- def _remove_weight_norm(m):
- try:
- # print(f"Weight norm is removed from {m}.")
- nn.utils.remove_weight_norm(m)
- except ValueError: # this module didn't have weight norm
- return
-
- self.apply(_remove_weight_norm)
-
-
-class ResidualParallelWaveganDiscriminator(nn.Module):
- # pylint: disable=dangerous-default-value
- def __init__(
- self,
- in_channels=1,
- out_channels=1,
- kernel_size=3,
- num_layers=30,
- stacks=3,
- res_channels=64,
- gate_channels=128,
- skip_channels=64,
- dropout=0.0,
- bias=True,
- nonlinear_activation="LeakyReLU",
- nonlinear_activation_params={"negative_slope": 0.2},
- ):
- super().__init__()
- assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
-
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.num_layers = num_layers
- self.stacks = stacks
- self.kernel_size = kernel_size
- self.res_factor = math.sqrt(1.0 / num_layers)
-
- # check the number of num_layers and stacks
- assert num_layers % stacks == 0
- layers_per_stack = num_layers // stacks
-
- # define first convolution
- self.first_conv = nn.Sequential(
- nn.Conv1d(in_channels, res_channels, kernel_size=1, padding=0, dilation=1, bias=True),
- getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
- )
-
- # define residual blocks
- self.conv_layers = nn.ModuleList()
- for layer in range(num_layers):
- dilation = 2 ** (layer % layers_per_stack)
- conv = ResidualBlock(
- kernel_size=kernel_size,
- res_channels=res_channels,
- gate_channels=gate_channels,
- skip_channels=skip_channels,
- aux_channels=-1,
- dilation=dilation,
- dropout=dropout,
- bias=bias,
- use_causal_conv=False,
- )
- self.conv_layers += [conv]
-
- # define output layers
- self.last_conv_layers = nn.ModuleList(
- [
- getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
- nn.Conv1d(skip_channels, skip_channels, kernel_size=1, padding=0, dilation=1, bias=True),
- getattr(nn, nonlinear_activation)(inplace=True, **nonlinear_activation_params),
- nn.Conv1d(skip_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=True),
- ]
- )
-
- # apply weight norm
- self.apply_weight_norm()
-
- def forward(self, x):
- """
- x: (B, 1, T).
- """
- x = self.first_conv(x)
-
- skips = 0
- for f in self.conv_layers:
- x, h = f(x, None)
- skips += h
- skips *= self.res_factor
-
- # apply final layers
- x = skips
- for f in self.last_conv_layers:
- x = f(x)
- return x
-
- def apply_weight_norm(self):
- def _apply_weight_norm(m):
- if isinstance(m, (torch.nn.Conv1d, torch.nn.Conv2d)):
- torch.nn.utils.weight_norm(m)
-
- self.apply(_apply_weight_norm)
-
- def remove_weight_norm(self):
- def _remove_weight_norm(m):
- try:
- print(f"Weight norm is removed from {m}.")
- nn.utils.remove_weight_norm(m)
- except ValueError: # this module didn't have weight norm
- return
-
- self.apply(_remove_weight_norm)
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/detection/sfd/bbox.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/detection/sfd/bbox.py
deleted file mode 100644
index 4bd7222e5e5f78a51944cbeed3cccbacddc46bed..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/detection/sfd/bbox.py
+++ /dev/null
@@ -1,129 +0,0 @@
-from __future__ import print_function
-import os
-import sys
-import cv2
-import random
-import datetime
-import time
-import math
-import argparse
-import numpy as np
-import torch
-
-try:
- from iou import IOU
-except BaseException:
- # IOU cython speedup 10x
- def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
- sa = abs((ax2 - ax1) * (ay2 - ay1))
- sb = abs((bx2 - bx1) * (by2 - by1))
- x1, y1 = max(ax1, bx1), max(ay1, by1)
- x2, y2 = min(ax2, bx2), min(ay2, by2)
- w = x2 - x1
- h = y2 - y1
- if w < 0 or h < 0:
- return 0.0
- else:
- return 1.0 * w * h / (sa + sb - w * h)
-
-
-def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
- xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
- dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
- dw, dh = math.log(ww / aww), math.log(hh / ahh)
- return dx, dy, dw, dh
-
-
-def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
- xc, yc = dx * aww + axc, dy * ahh + ayc
- ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
- x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
- return x1, y1, x2, y2
-
-
-def nms(dets, thresh):
- if 0 == len(dets):
- return []
- x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4]
- areas = (x2 - x1 + 1) * (y2 - y1 + 1)
- order = scores.argsort()[::-1]
-
- keep = []
- while order.size > 0:
- i = order[0]
- keep.append(i)
- xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]])
- xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]])
-
- w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1)
- ovr = w * h / (areas[i] + areas[order[1:]] - w * h)
-
- inds = np.where(ovr <= thresh)[0]
- order = order[inds + 1]
-
- return keep
-
-
-def encode(matched, priors, variances):
- """Encode the variances from the priorbox layers into the ground truth boxes
- we have matched (based on jaccard overlap) with the prior boxes.
- Args:
- matched: (tensor) Coords of ground truth for each prior in point-form
- Shape: [num_priors, 4].
- priors: (tensor) Prior boxes in center-offset form
- Shape: [num_priors,4].
- variances: (list[float]) Variances of priorboxes
- Return:
- encoded boxes (tensor), Shape: [num_priors, 4]
- """
-
- # dist b/t match center and prior's center
- g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
- # encode variance
- g_cxcy /= (variances[0] * priors[:, 2:])
- # match wh / prior wh
- g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
- g_wh = torch.log(g_wh) / variances[1]
- # return target for smooth_l1_loss
- return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
-
-
-def decode(loc, priors, variances):
- """Decode locations from predictions using priors to undo
- the encoding we did for offset regression at train time.
- Args:
- loc (tensor): location predictions for loc layers,
- Shape: [num_priors,4]
- priors (tensor): Prior boxes in center-offset form.
- Shape: [num_priors,4].
- variances: (list[float]) Variances of priorboxes
- Return:
- decoded bounding box predictions
- """
-
- boxes = torch.cat((
- priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
- priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
- boxes[:, :2] -= boxes[:, 2:] / 2
- boxes[:, 2:] += boxes[:, :2]
- return boxes
-
-def batch_decode(loc, priors, variances):
- """Decode locations from predictions using priors to undo
- the encoding we did for offset regression at train time.
- Args:
- loc (tensor): location predictions for loc layers,
- Shape: [num_priors,4]
- priors (tensor): Prior boxes in center-offset form.
- Shape: [num_priors,4].
- variances: (list[float]) Variances of priorboxes
- Return:
- decoded bounding box predictions
- """
-
- boxes = torch.cat((
- priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:],
- priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2)
- boxes[:, :, :2] -= boxes[:, :, 2:] / 2
- boxes[:, :, 2:] += boxes[:, :, :2]
- return boxes
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Signature/pss.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Signature/pss.py
deleted file mode 100644
index 5f34ace7838b992f1900048c04bbd283c295fe7a..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Signature/pss.py
+++ /dev/null
@@ -1,386 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in
-# the documentation and/or other materials provided with the
-# distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-from Crypto.Util.py3compat import bchr, bord, iter_range
-import Crypto.Util.number
-from Crypto.Util.number import (ceil_div,
- long_to_bytes,
- bytes_to_long
- )
-from Crypto.Util.strxor import strxor
-from Crypto import Random
-
-
-class PSS_SigScheme:
- """A signature object for ``RSASSA-PSS``.
- Do not instantiate directly.
- Use :func:`Crypto.Signature.pss.new`.
- """
-
- def __init__(self, key, mgfunc, saltLen, randfunc):
- """Initialize this PKCS#1 PSS signature scheme object.
-
- :Parameters:
- key : an RSA key object
- If a private half is given, both signature and
- verification are possible.
- If a public half is given, only verification is possible.
- mgfunc : callable
- A mask generation function that accepts two parameters:
- a string to use as seed, and the lenth of the mask to
- generate, in bytes.
- saltLen : integer
- Length of the salt, in bytes.
- randfunc : callable
- A function that returns random bytes.
- """
-
- self._key = key
- self._saltLen = saltLen
- self._mgfunc = mgfunc
- self._randfunc = randfunc
-
- def can_sign(self):
- """Return ``True`` if this object can be used to sign messages."""
- return self._key.has_private()
-
- def sign(self, msg_hash):
- """Create the PKCS#1 PSS signature of a message.
-
- This function is also called ``RSASSA-PSS-SIGN`` and
- it is specified in
- `section 8.1.1 of RFC8017 `_.
-
- :parameter msg_hash:
- This is an object from the :mod:`Crypto.Hash` package.
- It has been used to digest the message to sign.
- :type msg_hash: hash object
-
- :return: the signature encoded as a *byte string*.
- :raise ValueError: if the RSA key is not long enough for the given hash algorithm.
- :raise TypeError: if the RSA key has no private half.
- """
-
- # Set defaults for salt length and mask generation function
- if self._saltLen is None:
- sLen = msg_hash.digest_size
- else:
- sLen = self._saltLen
-
- if self._mgfunc is None:
- mgf = lambda x, y: MGF1(x, y, msg_hash)
- else:
- mgf = self._mgfunc
-
- modBits = Crypto.Util.number.size(self._key.n)
-
- # See 8.1.1 in RFC3447
- k = ceil_div(modBits, 8) # k is length in bytes of the modulus
- # Step 1
- em = _EMSA_PSS_ENCODE(msg_hash, modBits-1, self._randfunc, mgf, sLen)
- # Step 2a (OS2IP)
- em_int = bytes_to_long(em)
- # Step 2b (RSASP1)
- m_int = self._key._decrypt(em_int)
- # Step 2c (I2OSP)
- signature = long_to_bytes(m_int, k)
- return signature
-
- def verify(self, msg_hash, signature):
- """Check if the PKCS#1 PSS signature over a message is valid.
-
- This function is also called ``RSASSA-PSS-VERIFY`` and
- it is specified in
- `section 8.1.2 of RFC8037 `_.
-
- :parameter msg_hash:
- The hash that was carried out over the message. This is an object
- belonging to the :mod:`Crypto.Hash` module.
- :type parameter: hash object
-
- :parameter signature:
- The signature that needs to be validated.
- :type signature: bytes
-
- :raise ValueError: if the signature is not valid.
- """
-
- # Set defaults for salt length and mask generation function
- if self._saltLen is None:
- sLen = msg_hash.digest_size
- else:
- sLen = self._saltLen
- if self._mgfunc:
- mgf = self._mgfunc
- else:
- mgf = lambda x, y: MGF1(x, y, msg_hash)
-
- modBits = Crypto.Util.number.size(self._key.n)
-
- # See 8.1.2 in RFC3447
- k = ceil_div(modBits, 8) # Convert from bits to bytes
- # Step 1
- if len(signature) != k:
- raise ValueError("Incorrect signature")
- # Step 2a (O2SIP)
- signature_int = bytes_to_long(signature)
- # Step 2b (RSAVP1)
- em_int = self._key._encrypt(signature_int)
- # Step 2c (I2OSP)
- emLen = ceil_div(modBits - 1, 8)
- em = long_to_bytes(em_int, emLen)
- # Step 3/4
- _EMSA_PSS_VERIFY(msg_hash, em, modBits-1, mgf, sLen)
-
-
-def MGF1(mgfSeed, maskLen, hash_gen):
- """Mask Generation Function, described in `B.2.1 of RFC8017
- `_.
-
- :param mfgSeed:
- seed from which the mask is generated
- :type mfgSeed: byte string
-
- :param maskLen:
- intended length in bytes of the mask
- :type maskLen: integer
-
- :param hash_gen:
- A module or a hash object from :mod:`Crypto.Hash`
- :type hash_object:
-
- :return: the mask, as a *byte string*
- """
-
- T = b""
- for counter in iter_range(ceil_div(maskLen, hash_gen.digest_size)):
- c = long_to_bytes(counter, 4)
- hobj = hash_gen.new()
- hobj.update(mgfSeed + c)
- T = T + hobj.digest()
- assert(len(T) >= maskLen)
- return T[:maskLen]
-
-
-def _EMSA_PSS_ENCODE(mhash, emBits, randFunc, mgf, sLen):
- r"""
- Implement the ``EMSA-PSS-ENCODE`` function, as defined
- in PKCS#1 v2.1 (RFC3447, 9.1.1).
-
- The original ``EMSA-PSS-ENCODE`` actually accepts the message ``M``
- as input, and hash it internally. Here, we expect that the message
- has already been hashed instead.
-
- :Parameters:
- mhash : hash object
- The hash object that holds the digest of the message being signed.
- emBits : int
- Maximum length of the final encoding, in bits.
- randFunc : callable
- An RNG function that accepts as only parameter an int, and returns
- a string of random bytes, to be used as salt.
- mgf : callable
- A mask generation function that accepts two parameters: a string to
- use as seed, and the lenth of the mask to generate, in bytes.
- sLen : int
- Length of the salt, in bytes.
-
- :Return: An ``emLen`` byte long string that encodes the hash
- (with ``emLen = \ceil(emBits/8)``).
-
- :Raise ValueError:
- When digest or salt length are too big.
- """
-
- emLen = ceil_div(emBits, 8)
-
- # Bitmask of digits that fill up
- lmask = 0
- for i in iter_range(8*emLen-emBits):
- lmask = lmask >> 1 | 0x80
-
- # Step 1 and 2 have been already done
- # Step 3
- if emLen < mhash.digest_size+sLen+2:
- raise ValueError("Digest or salt length are too long"
- " for given key size.")
- # Step 4
- salt = randFunc(sLen)
- # Step 5
- m_prime = bchr(0)*8 + mhash.digest() + salt
- # Step 6
- h = mhash.new()
- h.update(m_prime)
- # Step 7
- ps = bchr(0)*(emLen-sLen-mhash.digest_size-2)
- # Step 8
- db = ps + bchr(1) + salt
- # Step 9
- dbMask = mgf(h.digest(), emLen-mhash.digest_size-1)
- # Step 10
- maskedDB = strxor(db, dbMask)
- # Step 11
- maskedDB = bchr(bord(maskedDB[0]) & ~lmask) + maskedDB[1:]
- # Step 12
- em = maskedDB + h.digest() + bchr(0xBC)
- return em
-
-
-def _EMSA_PSS_VERIFY(mhash, em, emBits, mgf, sLen):
- """
- Implement the ``EMSA-PSS-VERIFY`` function, as defined
- in PKCS#1 v2.1 (RFC3447, 9.1.2).
-
- ``EMSA-PSS-VERIFY`` actually accepts the message ``M`` as input,
- and hash it internally. Here, we expect that the message has already
- been hashed instead.
-
- :Parameters:
- mhash : hash object
- The hash object that holds the digest of the message to be verified.
- em : string
- The signature to verify, therefore proving that the sender really
- signed the message that was received.
- emBits : int
- Length of the final encoding (em), in bits.
- mgf : callable
- A mask generation function that accepts two parameters: a string to
- use as seed, and the lenth of the mask to generate, in bytes.
- sLen : int
- Length of the salt, in bytes.
-
- :Raise ValueError:
- When the encoding is inconsistent, or the digest or salt lengths
- are too big.
- """
-
- emLen = ceil_div(emBits, 8)
-
- # Bitmask of digits that fill up
- lmask = 0
- for i in iter_range(8*emLen-emBits):
- lmask = lmask >> 1 | 0x80
-
- # Step 1 and 2 have been already done
- # Step 3
- if emLen < mhash.digest_size+sLen+2:
- raise ValueError("Incorrect signature")
- # Step 4
- if ord(em[-1:]) != 0xBC:
- raise ValueError("Incorrect signature")
- # Step 5
- maskedDB = em[:emLen-mhash.digest_size-1]
- h = em[emLen-mhash.digest_size-1:-1]
- # Step 6
- if lmask & bord(em[0]):
- raise ValueError("Incorrect signature")
- # Step 7
- dbMask = mgf(h, emLen-mhash.digest_size-1)
- # Step 8
- db = strxor(maskedDB, dbMask)
- # Step 9
- db = bchr(bord(db[0]) & ~lmask) + db[1:]
- # Step 10
- if not db.startswith(bchr(0)*(emLen-mhash.digest_size-sLen-2) + bchr(1)):
- raise ValueError("Incorrect signature")
- # Step 11
- if sLen > 0:
- salt = db[-sLen:]
- else:
- salt = b""
- # Step 12
- m_prime = bchr(0)*8 + mhash.digest() + salt
- # Step 13
- hobj = mhash.new()
- hobj.update(m_prime)
- hp = hobj.digest()
- # Step 14
- if h != hp:
- raise ValueError("Incorrect signature")
-
-
-def new(rsa_key, **kwargs):
- """Create an object for making or verifying PKCS#1 PSS signatures.
-
- :parameter rsa_key:
- The RSA key to use for signing or verifying the message.
- This is a :class:`Crypto.PublicKey.RSA` object.
- Signing is only possible when ``rsa_key`` is a **private** RSA key.
- :type rsa_key: RSA object
-
- :Keyword Arguments:
-
- * *mask_func* (``callable``) --
- A function that returns the mask (as `bytes`).
- It must accept two parameters: a seed (as `bytes`)
- and the length of the data to return.
-
- If not specified, it will be the function :func:`MGF1` defined in
- `RFC8017 `_ and
- combined with the same hash algorithm applied to the
- message to sign or verify.
-
- If you want to use a different function, for instance still :func:`MGF1`
- but together with another hash, you can do::
-
- from Crypto.Hash import SHA256
- from Crypto.Signature.pss import MGF1
- mgf = lambda x, y: MGF1(x, y, SHA256)
-
- * *salt_bytes* (``integer``) --
- Length of the salt, in bytes.
- It is a value between 0 and ``emLen - hLen - 2``, where ``emLen``
- is the size of the RSA modulus and ``hLen`` is the size of the digest
- applied to the message to sign or verify.
-
- The salt is generated internally, you don't need to provide it.
-
- If not specified, the salt length will be ``hLen``.
- If it is zero, the signature scheme becomes deterministic.
-
- Note that in some implementations such as OpenSSL the default
- salt length is ``emLen - hLen - 2`` (even though it is not more
- secure than ``hLen``).
-
- * *rand_func* (``callable``) --
- A function that returns random ``bytes``, of the desired length.
- The default is :func:`Crypto.Random.get_random_bytes`.
-
- :return: a :class:`PSS_SigScheme` signature object
- """
-
- mask_func = kwargs.pop("mask_func", None)
- salt_len = kwargs.pop("salt_bytes", None)
- rand_func = kwargs.pop("rand_func", None)
- if rand_func is None:
- rand_func = Random.get_random_bytes
- if kwargs:
- raise ValueError("Unknown keywords: " + str(kwargs.keys()))
- return PSS_SigScheme(rsa_key, mask_func, salt_len, rand_func)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/ParseTreeTransforms.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/ParseTreeTransforms.py
deleted file mode 100644
index 8e0cbc30352ae84b25f6cae421fb3ee7f737cbab..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/ParseTreeTransforms.py
+++ /dev/null
@@ -1,3530 +0,0 @@
-from __future__ import absolute_import
-
-import cython
-cython.declare(PyrexTypes=object, Naming=object, ExprNodes=object, Nodes=object,
- Options=object, UtilNodes=object, LetNode=object,
- LetRefNode=object, TreeFragment=object, EncodedString=object,
- error=object, warning=object, copy=object, _unicode=object)
-
-import copy
-import hashlib
-
-from . import PyrexTypes
-from . import Naming
-from . import ExprNodes
-from . import Nodes
-from . import Options
-from . import Builtin
-from . import Errors
-
-from .Visitor import VisitorTransform, TreeVisitor
-from .Visitor import CythonTransform, EnvTransform, ScopeTrackingTransform
-from .UtilNodes import LetNode, LetRefNode
-from .TreeFragment import TreeFragment
-from .StringEncoding import EncodedString, _unicode
-from .Errors import error, warning, CompileError, InternalError
-from .Code import UtilityCode
-
-
-class SkipDeclarations(object):
- """
- Variable and function declarations can often have a deep tree structure,
- and yet most transformations don't need to descend to this depth.
-
- Declaration nodes are removed after AnalyseDeclarationsTransform, so there
- is no need to use this for transformations after that point.
- """
- def visit_CTypeDefNode(self, node):
- return node
-
- def visit_CVarDefNode(self, node):
- return node
-
- def visit_CDeclaratorNode(self, node):
- return node
-
- def visit_CBaseTypeNode(self, node):
- return node
-
- def visit_CEnumDefNode(self, node):
- return node
-
- def visit_CStructOrUnionDefNode(self, node):
- return node
-
-
-class NormalizeTree(CythonTransform):
- """
- This transform fixes up a few things after parsing
- in order to make the parse tree more suitable for
- transforms.
-
- a) After parsing, blocks with only one statement will
- be represented by that statement, not by a StatListNode.
- When doing transforms this is annoying and inconsistent,
- as one cannot in general remove a statement in a consistent
- way and so on. This transform wraps any single statements
- in a StatListNode containing a single statement.
-
- b) The PassStatNode is a noop and serves no purpose beyond
- plugging such one-statement blocks; i.e., once parsed a
-` "pass" can just as well be represented using an empty
- StatListNode. This means less special cases to worry about
- in subsequent transforms (one always checks to see if a
- StatListNode has no children to see if the block is empty).
- """
-
- def __init__(self, context):
- super(NormalizeTree, self).__init__(context)
- self.is_in_statlist = False
- self.is_in_expr = False
-
- def visit_ExprNode(self, node):
- stacktmp = self.is_in_expr
- self.is_in_expr = True
- self.visitchildren(node)
- self.is_in_expr = stacktmp
- return node
-
- def visit_StatNode(self, node, is_listcontainer=False):
- stacktmp = self.is_in_statlist
- self.is_in_statlist = is_listcontainer
- self.visitchildren(node)
- self.is_in_statlist = stacktmp
- if not self.is_in_statlist and not self.is_in_expr:
- return Nodes.StatListNode(pos=node.pos, stats=[node])
- else:
- return node
-
- def visit_StatListNode(self, node):
- self.is_in_statlist = True
- self.visitchildren(node)
- self.is_in_statlist = False
- return node
-
- def visit_ParallelAssignmentNode(self, node):
- return self.visit_StatNode(node, True)
-
- def visit_CEnumDefNode(self, node):
- return self.visit_StatNode(node, True)
-
- def visit_CStructOrUnionDefNode(self, node):
- return self.visit_StatNode(node, True)
-
- def visit_PassStatNode(self, node):
- """Eliminate PassStatNode"""
- if not self.is_in_statlist:
- return Nodes.StatListNode(pos=node.pos, stats=[])
- else:
- return []
-
- def visit_ExprStatNode(self, node):
- """Eliminate useless string literals"""
- if node.expr.is_string_literal:
- return self.visit_PassStatNode(node)
- else:
- return self.visit_StatNode(node)
-
- def visit_CDeclaratorNode(self, node):
- return node
-
-
-class PostParseError(CompileError): pass
-
-# error strings checked by unit tests, so define them
-ERR_CDEF_INCLASS = 'Cannot assign default value to fields in cdef classes, structs or unions'
-ERR_BUF_DEFAULTS = 'Invalid buffer defaults specification (see docs)'
-ERR_INVALID_SPECIALATTR_TYPE = 'Special attributes must not have a type declared'
-class PostParse(ScopeTrackingTransform):
- """
- Basic interpretation of the parse tree, as well as validity
- checking that can be done on a very basic level on the parse
- tree (while still not being a problem with the basic syntax,
- as such).
-
- Specifically:
- - Default values to cdef assignments are turned into single
- assignments following the declaration (everywhere but in class
- bodies, where they raise a compile error)
-
- - Interpret some node structures into Python runtime values.
- Some nodes take compile-time arguments (currently:
- TemplatedTypeNode[args] and __cythonbufferdefaults__ = {args}),
- which should be interpreted. This happens in a general way
- and other steps should be taken to ensure validity.
-
- Type arguments cannot be interpreted in this way.
-
- - For __cythonbufferdefaults__ the arguments are checked for
- validity.
-
- TemplatedTypeNode has its directives interpreted:
- Any first positional argument goes into the "dtype" attribute,
- any "ndim" keyword argument goes into the "ndim" attribute and
- so on. Also it is checked that the directive combination is valid.
- - __cythonbufferdefaults__ attributes are parsed and put into the
- type information.
-
- Note: Currently Parsing.py does a lot of interpretation and
- reorganization that can be refactored into this transform
- if a more pure Abstract Syntax Tree is wanted.
- """
-
- def __init__(self, context):
- super(PostParse, self).__init__(context)
- self.specialattribute_handlers = {
- '__cythonbufferdefaults__' : self.handle_bufferdefaults
- }
-
- def visit_LambdaNode(self, node):
- # unpack a lambda expression into the corresponding DefNode
- collector = YieldNodeCollector()
- collector.visitchildren(node.result_expr)
- if collector.has_yield or collector.has_await or isinstance(node.result_expr, ExprNodes.YieldExprNode):
- body = Nodes.ExprStatNode(
- node.result_expr.pos, expr=node.result_expr)
- else:
- body = Nodes.ReturnStatNode(
- node.result_expr.pos, value=node.result_expr)
- node.def_node = Nodes.DefNode(
- node.pos, name=node.name,
- args=node.args, star_arg=node.star_arg,
- starstar_arg=node.starstar_arg,
- body=body, doc=None)
- self.visitchildren(node)
- return node
-
- def visit_GeneratorExpressionNode(self, node):
- # unpack a generator expression into the corresponding DefNode
- collector = YieldNodeCollector()
- collector.visitchildren(node.loop)
- node.def_node = Nodes.DefNode(
- node.pos, name=node.name, doc=None,
- args=[], star_arg=None, starstar_arg=None,
- body=node.loop, is_async_def=collector.has_await)
- self.visitchildren(node)
- return node
-
- def visit_ComprehensionNode(self, node):
- # enforce local scope also in Py2 for async generators (seriously, that's a Py3.6 feature...)
- if not node.has_local_scope:
- collector = YieldNodeCollector()
- collector.visitchildren(node.loop)
- if collector.has_await:
- node.has_local_scope = True
- self.visitchildren(node)
- return node
-
- # cdef variables
- def handle_bufferdefaults(self, decl):
- if not isinstance(decl.default, ExprNodes.DictNode):
- raise PostParseError(decl.pos, ERR_BUF_DEFAULTS)
- self.scope_node.buffer_defaults_node = decl.default
- self.scope_node.buffer_defaults_pos = decl.pos
-
- def visit_CVarDefNode(self, node):
- # This assumes only plain names and pointers are assignable on
- # declaration. Also, it makes use of the fact that a cdef decl
- # must appear before the first use, so we don't have to deal with
- # "i = 3; cdef int i = i" and can simply move the nodes around.
- try:
- self.visitchildren(node)
- stats = [node]
- newdecls = []
- for decl in node.declarators:
- declbase = decl
- while isinstance(declbase, Nodes.CPtrDeclaratorNode):
- declbase = declbase.base
- if isinstance(declbase, Nodes.CNameDeclaratorNode):
- if declbase.default is not None:
- if self.scope_type in ('cclass', 'pyclass', 'struct'):
- if isinstance(self.scope_node, Nodes.CClassDefNode):
- handler = self.specialattribute_handlers.get(decl.name)
- if handler:
- if decl is not declbase:
- raise PostParseError(decl.pos, ERR_INVALID_SPECIALATTR_TYPE)
- handler(decl)
- continue # Remove declaration
- raise PostParseError(decl.pos, ERR_CDEF_INCLASS)
- first_assignment = self.scope_type != 'module'
- stats.append(Nodes.SingleAssignmentNode(node.pos,
- lhs=ExprNodes.NameNode(node.pos, name=declbase.name),
- rhs=declbase.default, first=first_assignment))
- declbase.default = None
- newdecls.append(decl)
- node.declarators = newdecls
- return stats
- except PostParseError as e:
- # An error in a cdef clause is ok, simply remove the declaration
- # and try to move on to report more errors
- self.context.nonfatal_error(e)
- return None
-
- # Split parallel assignments (a,b = b,a) into separate partial
- # assignments that are executed rhs-first using temps. This
- # restructuring must be applied before type analysis so that known
- # types on rhs and lhs can be matched directly. It is required in
- # the case that the types cannot be coerced to a Python type in
- # order to assign from a tuple.
-
- def visit_SingleAssignmentNode(self, node):
- self.visitchildren(node)
- return self._visit_assignment_node(node, [node.lhs, node.rhs])
-
- def visit_CascadedAssignmentNode(self, node):
- self.visitchildren(node)
- return self._visit_assignment_node(node, node.lhs_list + [node.rhs])
-
- def _visit_assignment_node(self, node, expr_list):
- """Flatten parallel assignments into separate single
- assignments or cascaded assignments.
- """
- if sum([ 1 for expr in expr_list
- if expr.is_sequence_constructor or expr.is_string_literal ]) < 2:
- # no parallel assignments => nothing to do
- return node
-
- expr_list_list = []
- flatten_parallel_assignments(expr_list, expr_list_list)
- temp_refs = []
- eliminate_rhs_duplicates(expr_list_list, temp_refs)
-
- nodes = []
- for expr_list in expr_list_list:
- lhs_list = expr_list[:-1]
- rhs = expr_list[-1]
- if len(lhs_list) == 1:
- node = Nodes.SingleAssignmentNode(rhs.pos,
- lhs = lhs_list[0], rhs = rhs)
- else:
- node = Nodes.CascadedAssignmentNode(rhs.pos,
- lhs_list = lhs_list, rhs = rhs)
- nodes.append(node)
-
- if len(nodes) == 1:
- assign_node = nodes[0]
- else:
- assign_node = Nodes.ParallelAssignmentNode(nodes[0].pos, stats = nodes)
-
- if temp_refs:
- duplicates_and_temps = [ (temp.expression, temp)
- for temp in temp_refs ]
- sort_common_subsequences(duplicates_and_temps)
- for _, temp_ref in duplicates_and_temps[::-1]:
- assign_node = LetNode(temp_ref, assign_node)
-
- return assign_node
-
- def _flatten_sequence(self, seq, result):
- for arg in seq.args:
- if arg.is_sequence_constructor:
- self._flatten_sequence(arg, result)
- else:
- result.append(arg)
- return result
-
- def visit_DelStatNode(self, node):
- self.visitchildren(node)
- node.args = self._flatten_sequence(node, [])
- return node
-
- def visit_ExceptClauseNode(self, node):
- if node.is_except_as:
- # except-as must delete NameNode target at the end
- del_target = Nodes.DelStatNode(
- node.pos,
- args=[ExprNodes.NameNode(
- node.target.pos, name=node.target.name)],
- ignore_nonexisting=True)
- node.body = Nodes.StatListNode(
- node.pos,
- stats=[Nodes.TryFinallyStatNode(
- node.pos,
- body=node.body,
- finally_clause=Nodes.StatListNode(
- node.pos,
- stats=[del_target]))])
- self.visitchildren(node)
- return node
-
-
-def eliminate_rhs_duplicates(expr_list_list, ref_node_sequence):
- """Replace rhs items by LetRefNodes if they appear more than once.
- Creates a sequence of LetRefNodes that set up the required temps
- and appends them to ref_node_sequence. The input list is modified
- in-place.
- """
- seen_nodes = set()
- ref_nodes = {}
- def find_duplicates(node):
- if node.is_literal or node.is_name:
- # no need to replace those; can't include attributes here
- # as their access is not necessarily side-effect free
- return
- if node in seen_nodes:
- if node not in ref_nodes:
- ref_node = LetRefNode(node)
- ref_nodes[node] = ref_node
- ref_node_sequence.append(ref_node)
- else:
- seen_nodes.add(node)
- if node.is_sequence_constructor:
- for item in node.args:
- find_duplicates(item)
-
- for expr_list in expr_list_list:
- rhs = expr_list[-1]
- find_duplicates(rhs)
- if not ref_nodes:
- return
-
- def substitute_nodes(node):
- if node in ref_nodes:
- return ref_nodes[node]
- elif node.is_sequence_constructor:
- node.args = list(map(substitute_nodes, node.args))
- return node
-
- # replace nodes inside of the common subexpressions
- for node in ref_nodes:
- if node.is_sequence_constructor:
- node.args = list(map(substitute_nodes, node.args))
-
- # replace common subexpressions on all rhs items
- for expr_list in expr_list_list:
- expr_list[-1] = substitute_nodes(expr_list[-1])
-
-def sort_common_subsequences(items):
- """Sort items/subsequences so that all items and subsequences that
- an item contains appear before the item itself. This is needed
- because each rhs item must only be evaluated once, so its value
- must be evaluated first and then reused when packing sequences
- that contain it.
-
- This implies a partial order, and the sort must be stable to
- preserve the original order as much as possible, so we use a
- simple insertion sort (which is very fast for short sequences, the
- normal case in practice).
- """
- def contains(seq, x):
- for item in seq:
- if item is x:
- return True
- elif item.is_sequence_constructor and contains(item.args, x):
- return True
- return False
- def lower_than(a,b):
- return b.is_sequence_constructor and contains(b.args, a)
-
- for pos, item in enumerate(items):
- key = item[1] # the ResultRefNode which has already been injected into the sequences
- new_pos = pos
- for i in range(pos-1, -1, -1):
- if lower_than(key, items[i][0]):
- new_pos = i
- if new_pos != pos:
- for i in range(pos, new_pos, -1):
- items[i] = items[i-1]
- items[new_pos] = item
-
-def unpack_string_to_character_literals(literal):
- chars = []
- pos = literal.pos
- stype = literal.__class__
- sval = literal.value
- sval_type = sval.__class__
- for char in sval:
- cval = sval_type(char)
- chars.append(stype(pos, value=cval, constant_result=cval))
- return chars
-
-def flatten_parallel_assignments(input, output):
- # The input is a list of expression nodes, representing the LHSs
- # and RHS of one (possibly cascaded) assignment statement. For
- # sequence constructors, rearranges the matching parts of both
- # sides into a list of equivalent assignments between the
- # individual elements. This transformation is applied
- # recursively, so that nested structures get matched as well.
- rhs = input[-1]
- if (not (rhs.is_sequence_constructor or isinstance(rhs, ExprNodes.UnicodeNode))
- or not sum([lhs.is_sequence_constructor for lhs in input[:-1]])):
- output.append(input)
- return
-
- complete_assignments = []
-
- if rhs.is_sequence_constructor:
- rhs_args = rhs.args
- elif rhs.is_string_literal:
- rhs_args = unpack_string_to_character_literals(rhs)
-
- rhs_size = len(rhs_args)
- lhs_targets = [[] for _ in range(rhs_size)]
- starred_assignments = []
- for lhs in input[:-1]:
- if not lhs.is_sequence_constructor:
- if lhs.is_starred:
- error(lhs.pos, "starred assignment target must be in a list or tuple")
- complete_assignments.append(lhs)
- continue
- lhs_size = len(lhs.args)
- starred_targets = sum([1 for expr in lhs.args if expr.is_starred])
- if starred_targets > 1:
- error(lhs.pos, "more than 1 starred expression in assignment")
- output.append([lhs,rhs])
- continue
- elif lhs_size - starred_targets > rhs_size:
- error(lhs.pos, "need more than %d value%s to unpack"
- % (rhs_size, (rhs_size != 1) and 's' or ''))
- output.append([lhs,rhs])
- continue
- elif starred_targets:
- map_starred_assignment(lhs_targets, starred_assignments,
- lhs.args, rhs_args)
- elif lhs_size < rhs_size:
- error(lhs.pos, "too many values to unpack (expected %d, got %d)"
- % (lhs_size, rhs_size))
- output.append([lhs,rhs])
- continue
- else:
- for targets, expr in zip(lhs_targets, lhs.args):
- targets.append(expr)
-
- if complete_assignments:
- complete_assignments.append(rhs)
- output.append(complete_assignments)
-
- # recursively flatten partial assignments
- for cascade, rhs in zip(lhs_targets, rhs_args):
- if cascade:
- cascade.append(rhs)
- flatten_parallel_assignments(cascade, output)
-
- # recursively flatten starred assignments
- for cascade in starred_assignments:
- if cascade[0].is_sequence_constructor:
- flatten_parallel_assignments(cascade, output)
- else:
- output.append(cascade)
-
-def map_starred_assignment(lhs_targets, starred_assignments, lhs_args, rhs_args):
- # Appends the fixed-position LHS targets to the target list that
- # appear left and right of the starred argument.
- #
- # The starred_assignments list receives a new tuple
- # (lhs_target, rhs_values_list) that maps the remaining arguments
- # (those that match the starred target) to a list.
-
- # left side of the starred target
- for i, (targets, expr) in enumerate(zip(lhs_targets, lhs_args)):
- if expr.is_starred:
- starred = i
- lhs_remaining = len(lhs_args) - i - 1
- break
- targets.append(expr)
- else:
- raise InternalError("no starred arg found when splitting starred assignment")
-
- # right side of the starred target
- for i, (targets, expr) in enumerate(zip(lhs_targets[-lhs_remaining:],
- lhs_args[starred + 1:])):
- targets.append(expr)
-
- # the starred target itself, must be assigned a (potentially empty) list
- target = lhs_args[starred].target # unpack starred node
- starred_rhs = rhs_args[starred:]
- if lhs_remaining:
- starred_rhs = starred_rhs[:-lhs_remaining]
- if starred_rhs:
- pos = starred_rhs[0].pos
- else:
- pos = target.pos
- starred_assignments.append([
- target, ExprNodes.ListNode(pos=pos, args=starred_rhs)])
-
-
-class PxdPostParse(CythonTransform, SkipDeclarations):
- """
- Basic interpretation/validity checking that should only be
- done on pxd trees.
-
- A lot of this checking currently happens in the parser; but
- what is listed below happens here.
-
- - "def" functions are let through only if they fill the
- getbuffer/releasebuffer slots
-
- - cdef functions are let through only if they are on the
- top level and are declared "inline"
- """
- ERR_INLINE_ONLY = "function definition in pxd file must be declared 'cdef inline'"
- ERR_NOGO_WITH_INLINE = "inline function definition in pxd file cannot be '%s'"
-
- def __call__(self, node):
- self.scope_type = 'pxd'
- return super(PxdPostParse, self).__call__(node)
-
- def visit_CClassDefNode(self, node):
- old = self.scope_type
- self.scope_type = 'cclass'
- self.visitchildren(node)
- self.scope_type = old
- return node
-
- def visit_FuncDefNode(self, node):
- # FuncDefNode always come with an implementation (without
- # an imp they are CVarDefNodes..)
- err = self.ERR_INLINE_ONLY
-
- if (isinstance(node, Nodes.DefNode) and self.scope_type == 'cclass'
- and node.name in ('__getbuffer__', '__releasebuffer__')):
- err = None # allow these slots
-
- if isinstance(node, Nodes.CFuncDefNode):
- if (u'inline' in node.modifiers and
- self.scope_type in ('pxd', 'cclass')):
- node.inline_in_pxd = True
- if node.visibility != 'private':
- err = self.ERR_NOGO_WITH_INLINE % node.visibility
- elif node.api:
- err = self.ERR_NOGO_WITH_INLINE % 'api'
- else:
- err = None # allow inline function
- else:
- err = self.ERR_INLINE_ONLY
-
- if err:
- self.context.nonfatal_error(PostParseError(node.pos, err))
- return None
- else:
- return node
-
-
-class TrackNumpyAttributes(VisitorTransform, SkipDeclarations):
- # TODO: Make name handling as good as in InterpretCompilerDirectives() below - probably best to merge the two.
- def __init__(self):
- super(TrackNumpyAttributes, self).__init__()
- self.numpy_module_names = set()
-
- def visit_CImportStatNode(self, node):
- if node.module_name == u"numpy":
- self.numpy_module_names.add(node.as_name or u"numpy")
- return node
-
- def visit_AttributeNode(self, node):
- self.visitchildren(node)
- obj = node.obj
- if (obj.is_name and obj.name in self.numpy_module_names) or obj.is_numpy_attribute:
- node.is_numpy_attribute = True
- return node
-
- visit_Node = VisitorTransform.recurse_to_children
-
-
-class InterpretCompilerDirectives(CythonTransform):
- """
- After parsing, directives can be stored in a number of places:
- - #cython-comments at the top of the file (stored in ModuleNode)
- - Command-line arguments overriding these
- - @cython.directivename decorators
- - with cython.directivename: statements
-
- This transform is responsible for interpreting these various sources
- and store the directive in two ways:
- - Set the directives attribute of the ModuleNode for global directives.
- - Use a CompilerDirectivesNode to override directives for a subtree.
-
- (The first one is primarily to not have to modify with the tree
- structure, so that ModuleNode stay on top.)
-
- The directives are stored in dictionaries from name to value in effect.
- Each such dictionary is always filled in for all possible directives,
- using default values where no value is given by the user.
-
- The available directives are controlled in Options.py.
-
- Note that we have to run this prior to analysis, and so some minor
- duplication of functionality has to occur: We manually track cimports
- and which names the "cython" module may have been imported to.
- """
- unop_method_nodes = {
- 'typeof': ExprNodes.TypeofNode,
-
- 'operator.address': ExprNodes.AmpersandNode,
- 'operator.dereference': ExprNodes.DereferenceNode,
- 'operator.preincrement' : ExprNodes.inc_dec_constructor(True, '++'),
- 'operator.predecrement' : ExprNodes.inc_dec_constructor(True, '--'),
- 'operator.postincrement': ExprNodes.inc_dec_constructor(False, '++'),
- 'operator.postdecrement': ExprNodes.inc_dec_constructor(False, '--'),
- 'operator.typeid' : ExprNodes.TypeidNode,
-
- # For backwards compatibility.
- 'address': ExprNodes.AmpersandNode,
- }
-
- binop_method_nodes = {
- 'operator.comma' : ExprNodes.c_binop_constructor(','),
- }
-
- special_methods = set(['declare', 'union', 'struct', 'typedef',
- 'sizeof', 'cast', 'pointer', 'compiled',
- 'NULL', 'fused_type', 'parallel'])
- special_methods.update(unop_method_nodes)
-
- valid_parallel_directives = set([
- "parallel",
- "prange",
- "threadid",
- #"threadsavailable",
- ])
-
- def __init__(self, context, compilation_directive_defaults):
- super(InterpretCompilerDirectives, self).__init__(context)
- self.cython_module_names = set()
- self.directive_names = {'staticmethod': 'staticmethod'}
- self.parallel_directives = {}
- directives = copy.deepcopy(Options.get_directive_defaults())
- for key, value in compilation_directive_defaults.items():
- directives[_unicode(key)] = copy.deepcopy(value)
- self.directives = directives
-
- def check_directive_scope(self, pos, directive, scope):
- legal_scopes = Options.directive_scopes.get(directive, None)
- if legal_scopes and scope not in legal_scopes:
- self.context.nonfatal_error(PostParseError(pos, 'The %s compiler directive '
- 'is not allowed in %s scope' % (directive, scope)))
- return False
- else:
- if directive not in Options.directive_types:
- error(pos, "Invalid directive: '%s'." % (directive,))
- return True
-
- # Set up processing and handle the cython: comments.
- def visit_ModuleNode(self, node):
- for key in sorted(node.directive_comments):
- if not self.check_directive_scope(node.pos, key, 'module'):
- self.wrong_scope_error(node.pos, key, 'module')
- del node.directive_comments[key]
-
- self.module_scope = node.scope
-
- self.directives.update(node.directive_comments)
- node.directives = self.directives
- node.parallel_directives = self.parallel_directives
- self.visitchildren(node)
- node.cython_module_names = self.cython_module_names
- return node
-
- # The following four functions track imports and cimports that
- # begin with "cython"
- def is_cython_directive(self, name):
- return (name in Options.directive_types or
- name in self.special_methods or
- PyrexTypes.parse_basic_type(name))
-
- def is_parallel_directive(self, full_name, pos):
- """
- Checks to see if fullname (e.g. cython.parallel.prange) is a valid
- parallel directive. If it is a star import it also updates the
- parallel_directives.
- """
- result = (full_name + ".").startswith("cython.parallel.")
-
- if result:
- directive = full_name.split('.')
- if full_name == u"cython.parallel":
- self.parallel_directives[u"parallel"] = u"cython.parallel"
- elif full_name == u"cython.parallel.*":
- for name in self.valid_parallel_directives:
- self.parallel_directives[name] = u"cython.parallel.%s" % name
- elif (len(directive) != 3 or
- directive[-1] not in self.valid_parallel_directives):
- error(pos, "No such directive: %s" % full_name)
-
- self.module_scope.use_utility_code(
- UtilityCode.load_cached("InitThreads", "ModuleSetupCode.c"))
-
- return result
-
- def visit_CImportStatNode(self, node):
- if node.module_name == u"cython":
- self.cython_module_names.add(node.as_name or u"cython")
- elif node.module_name.startswith(u"cython."):
- if node.module_name.startswith(u"cython.parallel."):
- error(node.pos, node.module_name + " is not a module")
- if node.module_name == u"cython.parallel":
- if node.as_name and node.as_name != u"cython":
- self.parallel_directives[node.as_name] = node.module_name
- else:
- self.cython_module_names.add(u"cython")
- self.parallel_directives[
- u"cython.parallel"] = node.module_name
- self.module_scope.use_utility_code(
- UtilityCode.load_cached("InitThreads", "ModuleSetupCode.c"))
- elif node.as_name:
- self.directive_names[node.as_name] = node.module_name[7:]
- else:
- self.cython_module_names.add(u"cython")
- # if this cimport was a compiler directive, we don't
- # want to leave the cimport node sitting in the tree
- return None
- return node
-
- def visit_FromCImportStatNode(self, node):
- if not node.relative_level and (
- node.module_name == u"cython" or node.module_name.startswith(u"cython.")):
- submodule = (node.module_name + u".")[7:]
- newimp = []
-
- for pos, name, as_name, kind in node.imported_names:
- full_name = submodule + name
- qualified_name = u"cython." + full_name
-
- if self.is_parallel_directive(qualified_name, node.pos):
- # from cython cimport parallel, or
- # from cython.parallel cimport parallel, prange, ...
- self.parallel_directives[as_name or name] = qualified_name
- elif self.is_cython_directive(full_name):
- self.directive_names[as_name or name] = full_name
- if kind is not None:
- self.context.nonfatal_error(PostParseError(pos,
- "Compiler directive imports must be plain imports"))
- else:
- newimp.append((pos, name, as_name, kind))
-
- if not newimp:
- return None
-
- node.imported_names = newimp
- return node
-
- def visit_FromImportStatNode(self, node):
- if (node.module.module_name.value == u"cython") or \
- node.module.module_name.value.startswith(u"cython."):
- submodule = (node.module.module_name.value + u".")[7:]
- newimp = []
- for name, name_node in node.items:
- full_name = submodule + name
- qualified_name = u"cython." + full_name
- if self.is_parallel_directive(qualified_name, node.pos):
- self.parallel_directives[name_node.name] = qualified_name
- elif self.is_cython_directive(full_name):
- self.directive_names[name_node.name] = full_name
- else:
- newimp.append((name, name_node))
- if not newimp:
- return None
- node.items = newimp
- return node
-
- def visit_SingleAssignmentNode(self, node):
- if isinstance(node.rhs, ExprNodes.ImportNode):
- module_name = node.rhs.module_name.value
- is_parallel = (module_name + u".").startswith(u"cython.parallel.")
-
- if module_name != u"cython" and not is_parallel:
- return node
-
- module_name = node.rhs.module_name.value
- as_name = node.lhs.name
-
- node = Nodes.CImportStatNode(node.pos,
- module_name = module_name,
- as_name = as_name)
- node = self.visit_CImportStatNode(node)
- else:
- self.visitchildren(node)
-
- return node
-
- def visit_NameNode(self, node):
- if node.name in self.cython_module_names:
- node.is_cython_module = True
- else:
- directive = self.directive_names.get(node.name)
- if directive is not None:
- node.cython_attribute = directive
- return node
-
- def visit_NewExprNode(self, node):
- self.visit(node.cppclass)
- self.visitchildren(node)
- return node
-
- def try_to_parse_directives(self, node):
- # If node is the contents of an directive (in a with statement or
- # decorator), returns a list of (directivename, value) pairs.
- # Otherwise, returns None
- if isinstance(node, ExprNodes.CallNode):
- self.visit(node.function)
- optname = node.function.as_cython_attribute()
- if optname:
- directivetype = Options.directive_types.get(optname)
- if directivetype:
- args, kwds = node.explicit_args_kwds()
- directives = []
- key_value_pairs = []
- if kwds is not None and directivetype is not dict:
- for keyvalue in kwds.key_value_pairs:
- key, value = keyvalue
- sub_optname = "%s.%s" % (optname, key.value)
- if Options.directive_types.get(sub_optname):
- directives.append(self.try_to_parse_directive(sub_optname, [value], None, keyvalue.pos))
- else:
- key_value_pairs.append(keyvalue)
- if not key_value_pairs:
- kwds = None
- else:
- kwds.key_value_pairs = key_value_pairs
- if directives and not kwds and not args:
- return directives
- directives.append(self.try_to_parse_directive(optname, args, kwds, node.function.pos))
- return directives
- elif isinstance(node, (ExprNodes.AttributeNode, ExprNodes.NameNode)):
- self.visit(node)
- optname = node.as_cython_attribute()
- if optname:
- directivetype = Options.directive_types.get(optname)
- if directivetype is bool:
- arg = ExprNodes.BoolNode(node.pos, value=True)
- return [self.try_to_parse_directive(optname, [arg], None, node.pos)]
- elif directivetype is None:
- return [(optname, None)]
- else:
- raise PostParseError(
- node.pos, "The '%s' directive should be used as a function call." % optname)
- return None
-
- def try_to_parse_directive(self, optname, args, kwds, pos):
- if optname == 'np_pythran' and not self.context.cpp:
- raise PostParseError(pos, 'The %s directive can only be used in C++ mode.' % optname)
- elif optname == 'exceptval':
- # default: exceptval(None, check=True)
- arg_error = len(args) > 1
- check = True
- if kwds and kwds.key_value_pairs:
- kw = kwds.key_value_pairs[0]
- if (len(kwds.key_value_pairs) == 1 and
- kw.key.is_string_literal and kw.key.value == 'check' and
- isinstance(kw.value, ExprNodes.BoolNode)):
- check = kw.value.value
- else:
- arg_error = True
- if arg_error:
- raise PostParseError(
- pos, 'The exceptval directive takes 0 or 1 positional arguments and the boolean keyword "check"')
- return ('exceptval', (args[0] if args else None, check))
-
- directivetype = Options.directive_types.get(optname)
- if len(args) == 1 and isinstance(args[0], ExprNodes.NoneNode):
- return optname, Options.get_directive_defaults()[optname]
- elif directivetype is bool:
- if kwds is not None or len(args) != 1 or not isinstance(args[0], ExprNodes.BoolNode):
- raise PostParseError(pos,
- 'The %s directive takes one compile-time boolean argument' % optname)
- return (optname, args[0].value)
- elif directivetype is int:
- if kwds is not None or len(args) != 1 or not isinstance(args[0], ExprNodes.IntNode):
- raise PostParseError(pos,
- 'The %s directive takes one compile-time integer argument' % optname)
- return (optname, int(args[0].value))
- elif directivetype is str:
- if kwds is not None or len(args) != 1 or not isinstance(
- args[0], (ExprNodes.StringNode, ExprNodes.UnicodeNode)):
- raise PostParseError(pos,
- 'The %s directive takes one compile-time string argument' % optname)
- return (optname, str(args[0].value))
- elif directivetype is type:
- if kwds is not None or len(args) != 1:
- raise PostParseError(pos,
- 'The %s directive takes one type argument' % optname)
- return (optname, args[0])
- elif directivetype is dict:
- if len(args) != 0:
- raise PostParseError(pos,
- 'The %s directive takes no prepositional arguments' % optname)
- return optname, dict([(key.value, value) for key, value in kwds.key_value_pairs])
- elif directivetype is list:
- if kwds and len(kwds.key_value_pairs) != 0:
- raise PostParseError(pos,
- 'The %s directive takes no keyword arguments' % optname)
- return optname, [ str(arg.value) for arg in args ]
- elif callable(directivetype):
- if kwds is not None or len(args) != 1 or not isinstance(
- args[0], (ExprNodes.StringNode, ExprNodes.UnicodeNode)):
- raise PostParseError(pos,
- 'The %s directive takes one compile-time string argument' % optname)
- return (optname, directivetype(optname, str(args[0].value)))
- else:
- assert False
-
- def visit_with_directives(self, node, directives):
- if not directives:
- return self.visit_Node(node)
-
- old_directives = self.directives
- new_directives = dict(old_directives)
- new_directives.update(directives)
-
- if new_directives == old_directives:
- return self.visit_Node(node)
-
- self.directives = new_directives
- retbody = self.visit_Node(node)
- self.directives = old_directives
-
- if not isinstance(retbody, Nodes.StatListNode):
- retbody = Nodes.StatListNode(node.pos, stats=[retbody])
- return Nodes.CompilerDirectivesNode(
- pos=retbody.pos, body=retbody, directives=new_directives)
-
- # Handle decorators
- def visit_FuncDefNode(self, node):
- directives = self._extract_directives(node, 'function')
- return self.visit_with_directives(node, directives)
-
- def visit_CVarDefNode(self, node):
- directives = self._extract_directives(node, 'function')
- for name, value in directives.items():
- if name == 'locals':
- node.directive_locals = value
- elif name not in ('final', 'staticmethod'):
- self.context.nonfatal_error(PostParseError(
- node.pos,
- "Cdef functions can only take cython.locals(), "
- "staticmethod, or final decorators, got %s." % name))
- return self.visit_with_directives(node, directives)
-
- def visit_CClassDefNode(self, node):
- directives = self._extract_directives(node, 'cclass')
- return self.visit_with_directives(node, directives)
-
- def visit_CppClassNode(self, node):
- directives = self._extract_directives(node, 'cppclass')
- return self.visit_with_directives(node, directives)
-
- def visit_PyClassDefNode(self, node):
- directives = self._extract_directives(node, 'class')
- return self.visit_with_directives(node, directives)
-
- def _extract_directives(self, node, scope_name):
- if not node.decorators:
- return {}
- # Split the decorators into two lists -- real decorators and directives
- directives = []
- realdecs = []
- both = []
- # Decorators coming first take precedence.
- for dec in node.decorators[::-1]:
- new_directives = self.try_to_parse_directives(dec.decorator)
- if new_directives is not None:
- for directive in new_directives:
- if self.check_directive_scope(node.pos, directive[0], scope_name):
- name, value = directive
- if self.directives.get(name, object()) != value:
- directives.append(directive)
- if directive[0] == 'staticmethod':
- both.append(dec)
- # Adapt scope type based on decorators that change it.
- if directive[0] == 'cclass' and scope_name == 'class':
- scope_name = 'cclass'
- else:
- realdecs.append(dec)
- if realdecs and (scope_name == 'cclass' or
- isinstance(node, (Nodes.CFuncDefNode, Nodes.CClassDefNode, Nodes.CVarDefNode))):
- raise PostParseError(realdecs[0].pos, "Cdef functions/classes cannot take arbitrary decorators.")
- node.decorators = realdecs[::-1] + both[::-1]
- # merge or override repeated directives
- optdict = {}
- for directive in directives:
- name, value = directive
- if name in optdict:
- old_value = optdict[name]
- # keywords and arg lists can be merged, everything
- # else overrides completely
- if isinstance(old_value, dict):
- old_value.update(value)
- elif isinstance(old_value, list):
- old_value.extend(value)
- else:
- optdict[name] = value
- else:
- optdict[name] = value
- return optdict
-
- # Handle with-statements
- def visit_WithStatNode(self, node):
- directive_dict = {}
- for directive in self.try_to_parse_directives(node.manager) or []:
- if directive is not None:
- if node.target is not None:
- self.context.nonfatal_error(
- PostParseError(node.pos, "Compiler directive with statements cannot contain 'as'"))
- else:
- name, value = directive
- if name in ('nogil', 'gil'):
- # special case: in pure mode, "with nogil" spells "with cython.nogil"
- node = Nodes.GILStatNode(node.pos, state = name, body = node.body)
- return self.visit_Node(node)
- if self.check_directive_scope(node.pos, name, 'with statement'):
- directive_dict[name] = value
- if directive_dict:
- return self.visit_with_directives(node.body, directive_dict)
- return self.visit_Node(node)
-
-
-class ParallelRangeTransform(CythonTransform, SkipDeclarations):
- """
- Transform cython.parallel stuff. The parallel_directives come from the
- module node, set there by InterpretCompilerDirectives.
-
- x = cython.parallel.threadavailable() -> ParallelThreadAvailableNode
- with nogil, cython.parallel.parallel(): -> ParallelWithBlockNode
- print cython.parallel.threadid() -> ParallelThreadIdNode
- for i in cython.parallel.prange(...): -> ParallelRangeNode
- ...
- """
-
- # a list of names, maps 'cython.parallel.prange' in the code to
- # ['cython', 'parallel', 'prange']
- parallel_directive = None
-
- # Indicates whether a namenode in an expression is the cython module
- namenode_is_cython_module = False
-
- # Keep track of whether we are the context manager of a 'with' statement
- in_context_manager_section = False
-
- # One of 'prange' or 'with parallel'. This is used to disallow closely
- # nested 'with parallel:' blocks
- state = None
-
- directive_to_node = {
- u"cython.parallel.parallel": Nodes.ParallelWithBlockNode,
- # u"cython.parallel.threadsavailable": ExprNodes.ParallelThreadsAvailableNode,
- u"cython.parallel.threadid": ExprNodes.ParallelThreadIdNode,
- u"cython.parallel.prange": Nodes.ParallelRangeNode,
- }
-
- def node_is_parallel_directive(self, node):
- return node.name in self.parallel_directives or node.is_cython_module
-
- def get_directive_class_node(self, node):
- """
- Figure out which parallel directive was used and return the associated
- Node class.
-
- E.g. for a cython.parallel.prange() call we return ParallelRangeNode
- """
- if self.namenode_is_cython_module:
- directive = '.'.join(self.parallel_directive)
- else:
- directive = self.parallel_directives[self.parallel_directive[0]]
- directive = '%s.%s' % (directive,
- '.'.join(self.parallel_directive[1:]))
- directive = directive.rstrip('.')
-
- cls = self.directive_to_node.get(directive)
- if cls is None and not (self.namenode_is_cython_module and
- self.parallel_directive[0] != 'parallel'):
- error(node.pos, "Invalid directive: %s" % directive)
-
- self.namenode_is_cython_module = False
- self.parallel_directive = None
-
- return cls
-
- def visit_ModuleNode(self, node):
- """
- If any parallel directives were imported, copy them over and visit
- the AST
- """
- if node.parallel_directives:
- self.parallel_directives = node.parallel_directives
- return self.visit_Node(node)
-
- # No parallel directives were imported, so they can't be used :)
- return node
-
- def visit_NameNode(self, node):
- if self.node_is_parallel_directive(node):
- self.parallel_directive = [node.name]
- self.namenode_is_cython_module = node.is_cython_module
- return node
-
- def visit_AttributeNode(self, node):
- self.visitchildren(node)
- if self.parallel_directive:
- self.parallel_directive.append(node.attribute)
- return node
-
- def visit_CallNode(self, node):
- self.visit(node.function)
- if not self.parallel_directive:
- self.visitchildren(node, exclude=('function',))
- return node
-
- # We are a parallel directive, replace this node with the
- # corresponding ParallelSomethingSomething node
-
- if isinstance(node, ExprNodes.GeneralCallNode):
- args = node.positional_args.args
- kwargs = node.keyword_args
- else:
- args = node.args
- kwargs = {}
-
- parallel_directive_class = self.get_directive_class_node(node)
- if parallel_directive_class:
- # Note: in case of a parallel() the body is set by
- # visit_WithStatNode
- node = parallel_directive_class(node.pos, args=args, kwargs=kwargs)
-
- return node
-
- def visit_WithStatNode(self, node):
- "Rewrite with cython.parallel.parallel() blocks"
- newnode = self.visit(node.manager)
-
- if isinstance(newnode, Nodes.ParallelWithBlockNode):
- if self.state == 'parallel with':
- error(node.manager.pos,
- "Nested parallel with blocks are disallowed")
-
- self.state = 'parallel with'
- body = self.visit(node.body)
- self.state = None
-
- newnode.body = body
- return newnode
- elif self.parallel_directive:
- parallel_directive_class = self.get_directive_class_node(node)
-
- if not parallel_directive_class:
- # There was an error, stop here and now
- return None
-
- if parallel_directive_class is Nodes.ParallelWithBlockNode:
- error(node.pos, "The parallel directive must be called")
- return None
-
- node.body = self.visit(node.body)
- return node
-
- def visit_ForInStatNode(self, node):
- "Rewrite 'for i in cython.parallel.prange(...):'"
- self.visit(node.iterator)
- self.visit(node.target)
-
- in_prange = isinstance(node.iterator.sequence,
- Nodes.ParallelRangeNode)
- previous_state = self.state
-
- if in_prange:
- # This will replace the entire ForInStatNode, so copy the
- # attributes
- parallel_range_node = node.iterator.sequence
-
- parallel_range_node.target = node.target
- parallel_range_node.body = node.body
- parallel_range_node.else_clause = node.else_clause
-
- node = parallel_range_node
-
- if not isinstance(node.target, ExprNodes.NameNode):
- error(node.target.pos,
- "Can only iterate over an iteration variable")
-
- self.state = 'prange'
-
- self.visit(node.body)
- self.state = previous_state
- self.visit(node.else_clause)
- return node
-
- def visit(self, node):
- "Visit a node that may be None"
- if node is not None:
- return super(ParallelRangeTransform, self).visit(node)
-
-
-class WithTransform(CythonTransform, SkipDeclarations):
- def visit_WithStatNode(self, node):
- self.visitchildren(node, 'body')
- pos = node.pos
- is_async = node.is_async
- body, target, manager = node.body, node.target, node.manager
- node.enter_call = ExprNodes.SimpleCallNode(
- pos, function=ExprNodes.AttributeNode(
- pos, obj=ExprNodes.CloneNode(manager),
- attribute=EncodedString('__aenter__' if is_async else '__enter__'),
- is_special_lookup=True),
- args=[],
- is_temp=True)
-
- if is_async:
- node.enter_call = ExprNodes.AwaitExprNode(pos, arg=node.enter_call)
-
- if target is not None:
- body = Nodes.StatListNode(
- pos, stats=[
- Nodes.WithTargetAssignmentStatNode(
- pos, lhs=target, with_node=node),
- body])
-
- excinfo_target = ExprNodes.TupleNode(pos, slow=True, args=[
- ExprNodes.ExcValueNode(pos) for _ in range(3)])
- except_clause = Nodes.ExceptClauseNode(
- pos, body=Nodes.IfStatNode(
- pos, if_clauses=[
- Nodes.IfClauseNode(
- pos, condition=ExprNodes.NotNode(
- pos, operand=ExprNodes.WithExitCallNode(
- pos, with_stat=node,
- test_if_run=False,
- args=excinfo_target,
- await_expr=ExprNodes.AwaitExprNode(pos, arg=None) if is_async else None)),
- body=Nodes.ReraiseStatNode(pos),
- ),
- ],
- else_clause=None),
- pattern=None,
- target=None,
- excinfo_target=excinfo_target,
- )
-
- node.body = Nodes.TryFinallyStatNode(
- pos, body=Nodes.TryExceptStatNode(
- pos, body=body,
- except_clauses=[except_clause],
- else_clause=None,
- ),
- finally_clause=Nodes.ExprStatNode(
- pos, expr=ExprNodes.WithExitCallNode(
- pos, with_stat=node,
- test_if_run=True,
- args=ExprNodes.TupleNode(
- pos, args=[ExprNodes.NoneNode(pos) for _ in range(3)]),
- await_expr=ExprNodes.AwaitExprNode(pos, arg=None) if is_async else None)),
- handle_error_case=False,
- )
- return node
-
- def visit_ExprNode(self, node):
- # With statements are never inside expressions.
- return node
-
-
-class DecoratorTransform(ScopeTrackingTransform, SkipDeclarations):
- """
- Transforms method decorators in cdef classes into nested calls or properties.
-
- Python-style decorator properties are transformed into a PropertyNode
- with up to the three getter, setter and deleter DefNodes.
- The functional style isn't supported yet.
- """
- _properties = None
-
- _map_property_attribute = {
- 'getter': '__get__',
- 'setter': '__set__',
- 'deleter': '__del__',
- }.get
-
- def visit_CClassDefNode(self, node):
- if self._properties is None:
- self._properties = []
- self._properties.append({})
- super(DecoratorTransform, self).visit_CClassDefNode(node)
- self._properties.pop()
- return node
-
- def visit_PropertyNode(self, node):
- # Low-level warning for other code until we can convert all our uses over.
- level = 2 if isinstance(node.pos[0], str) else 0
- warning(node.pos, "'property %s:' syntax is deprecated, use '@property'" % node.name, level)
- return node
-
- def visit_DefNode(self, node):
- scope_type = self.scope_type
- node = self.visit_FuncDefNode(node)
- if scope_type != 'cclass' or not node.decorators:
- return node
-
- # transform @property decorators
- properties = self._properties[-1]
- for decorator_node in node.decorators[::-1]:
- decorator = decorator_node.decorator
- if decorator.is_name and decorator.name == 'property':
- if len(node.decorators) > 1:
- return self._reject_decorated_property(node, decorator_node)
- name = node.name
- node.name = EncodedString('__get__')
- node.decorators.remove(decorator_node)
- stat_list = [node]
- if name in properties:
- prop = properties[name]
- prop.pos = node.pos
- prop.doc = node.doc
- prop.body.stats = stat_list
- return []
- prop = Nodes.PropertyNode(node.pos, name=name)
- prop.doc = node.doc
- prop.body = Nodes.StatListNode(node.pos, stats=stat_list)
- properties[name] = prop
- return [prop]
- elif decorator.is_attribute and decorator.obj.name in properties:
- handler_name = self._map_property_attribute(decorator.attribute)
- if handler_name:
- if decorator.obj.name != node.name:
- # CPython does not generate an error or warning, but not something useful either.
- error(decorator_node.pos,
- "Mismatching property names, expected '%s', got '%s'" % (
- decorator.obj.name, node.name))
- elif len(node.decorators) > 1:
- return self._reject_decorated_property(node, decorator_node)
- else:
- return self._add_to_property(properties, node, handler_name, decorator_node)
-
- # we clear node.decorators, so we need to set the
- # is_staticmethod/is_classmethod attributes now
- for decorator in node.decorators:
- func = decorator.decorator
- if func.is_name:
- node.is_classmethod |= func.name == 'classmethod'
- node.is_staticmethod |= func.name == 'staticmethod'
-
- # transform normal decorators
- decs = node.decorators
- node.decorators = None
- return self.chain_decorators(node, decs, node.name)
-
- @staticmethod
- def _reject_decorated_property(node, decorator_node):
- # restrict transformation to outermost decorator as wrapped properties will probably not work
- for deco in node.decorators:
- if deco != decorator_node:
- error(deco.pos, "Property methods with additional decorators are not supported")
- return node
-
- @staticmethod
- def _add_to_property(properties, node, name, decorator):
- prop = properties[node.name]
- node.name = name
- node.decorators.remove(decorator)
- stats = prop.body.stats
- for i, stat in enumerate(stats):
- if stat.name == name:
- stats[i] = node
- break
- else:
- stats.append(node)
- return []
-
- @staticmethod
- def chain_decorators(node, decorators, name):
- """
- Decorators are applied directly in DefNode and PyClassDefNode to avoid
- reassignments to the function/class name - except for cdef class methods.
- For those, the reassignment is required as methods are originally
- defined in the PyMethodDef struct.
-
- The IndirectionNode allows DefNode to override the decorator.
- """
- decorator_result = ExprNodes.NameNode(node.pos, name=name)
- for decorator in decorators[::-1]:
- decorator_result = ExprNodes.SimpleCallNode(
- decorator.pos,
- function=decorator.decorator,
- args=[decorator_result])
-
- name_node = ExprNodes.NameNode(node.pos, name=name)
- reassignment = Nodes.SingleAssignmentNode(
- node.pos,
- lhs=name_node,
- rhs=decorator_result)
-
- reassignment = Nodes.IndirectionNode([reassignment])
- node.decorator_indirection = reassignment
- return [node, reassignment]
-
-
-class CnameDirectivesTransform(CythonTransform, SkipDeclarations):
- """
- Only part of the CythonUtilityCode pipeline. Must be run before
- DecoratorTransform in case this is a decorator for a cdef class.
- It filters out @cname('my_cname') decorators and rewrites them to
- CnameDecoratorNodes.
- """
-
- def handle_function(self, node):
- if not getattr(node, 'decorators', None):
- return self.visit_Node(node)
-
- for i, decorator in enumerate(node.decorators):
- decorator = decorator.decorator
-
- if (isinstance(decorator, ExprNodes.CallNode) and
- decorator.function.is_name and
- decorator.function.name == 'cname'):
- args, kwargs = decorator.explicit_args_kwds()
-
- if kwargs:
- raise AssertionError(
- "cname decorator does not take keyword arguments")
-
- if len(args) != 1:
- raise AssertionError(
- "cname decorator takes exactly one argument")
-
- if not (args[0].is_literal and
- args[0].type == Builtin.str_type):
- raise AssertionError(
- "argument to cname decorator must be a string literal")
-
- cname = args[0].compile_time_value(None)
- del node.decorators[i]
- node = Nodes.CnameDecoratorNode(pos=node.pos, node=node,
- cname=cname)
- break
-
- return self.visit_Node(node)
-
- visit_FuncDefNode = handle_function
- visit_CClassDefNode = handle_function
- visit_CEnumDefNode = handle_function
- visit_CStructOrUnionDefNode = handle_function
-
-
-class ForwardDeclareTypes(CythonTransform):
-
- def visit_CompilerDirectivesNode(self, node):
- env = self.module_scope
- old = env.directives
- env.directives = node.directives
- self.visitchildren(node)
- env.directives = old
- return node
-
- def visit_ModuleNode(self, node):
- self.module_scope = node.scope
- self.module_scope.directives = node.directives
- self.visitchildren(node)
- return node
-
- def visit_CDefExternNode(self, node):
- old_cinclude_flag = self.module_scope.in_cinclude
- self.module_scope.in_cinclude = 1
- self.visitchildren(node)
- self.module_scope.in_cinclude = old_cinclude_flag
- return node
-
- def visit_CEnumDefNode(self, node):
- node.declare(self.module_scope)
- return node
-
- def visit_CStructOrUnionDefNode(self, node):
- if node.name not in self.module_scope.entries:
- node.declare(self.module_scope)
- return node
-
- def visit_CClassDefNode(self, node):
- if node.class_name not in self.module_scope.entries:
- node.declare(self.module_scope)
- # Expand fused methods of .pxd declared types to construct the final vtable order.
- type = self.module_scope.entries[node.class_name].type
- if type is not None and type.is_extension_type and not type.is_builtin_type and type.scope:
- scope = type.scope
- for entry in scope.cfunc_entries:
- if entry.type and entry.type.is_fused:
- entry.type.get_all_specialized_function_types()
- return node
-
-
-class AnalyseDeclarationsTransform(EnvTransform):
-
- basic_property = TreeFragment(u"""
-property NAME:
- def __get__(self):
- return ATTR
- def __set__(self, value):
- ATTR = value
- """, level='c_class', pipeline=[NormalizeTree(None)])
- basic_pyobject_property = TreeFragment(u"""
-property NAME:
- def __get__(self):
- return ATTR
- def __set__(self, value):
- ATTR = value
- def __del__(self):
- ATTR = None
- """, level='c_class', pipeline=[NormalizeTree(None)])
- basic_property_ro = TreeFragment(u"""
-property NAME:
- def __get__(self):
- return ATTR
- """, level='c_class', pipeline=[NormalizeTree(None)])
-
- struct_or_union_wrapper = TreeFragment(u"""
-cdef class NAME:
- cdef TYPE value
- def __init__(self, MEMBER=None):
- cdef int count
- count = 0
- INIT_ASSIGNMENTS
- if IS_UNION and count > 1:
- raise ValueError, "At most one union member should be specified."
- def __str__(self):
- return STR_FORMAT % MEMBER_TUPLE
- def __repr__(self):
- return REPR_FORMAT % MEMBER_TUPLE
- """, pipeline=[NormalizeTree(None)])
-
- init_assignment = TreeFragment(u"""
-if VALUE is not None:
- ATTR = VALUE
- count += 1
- """, pipeline=[NormalizeTree(None)])
-
- fused_function = None
- in_lambda = 0
-
- def __call__(self, root):
- # needed to determine if a cdef var is declared after it's used.
- self.seen_vars_stack = []
- self.fused_error_funcs = set()
- super_class = super(AnalyseDeclarationsTransform, self)
- self._super_visit_FuncDefNode = super_class.visit_FuncDefNode
- return super_class.__call__(root)
-
- def visit_NameNode(self, node):
- self.seen_vars_stack[-1].add(node.name)
- return node
-
- def visit_ModuleNode(self, node):
- # Pickling support requires injecting module-level nodes.
- self.extra_module_declarations = []
- self.seen_vars_stack.append(set())
- node.analyse_declarations(self.current_env())
- self.visitchildren(node)
- self.seen_vars_stack.pop()
- node.body.stats.extend(self.extra_module_declarations)
- return node
-
- def visit_LambdaNode(self, node):
- self.in_lambda += 1
- node.analyse_declarations(self.current_env())
- self.visitchildren(node)
- self.in_lambda -= 1
- return node
-
- def visit_CClassDefNode(self, node):
- node = self.visit_ClassDefNode(node)
- if node.scope and node.scope.implemented and node.body:
- stats = []
- for entry in node.scope.var_entries:
- if entry.needs_property:
- property = self.create_Property(entry)
- property.analyse_declarations(node.scope)
- self.visit(property)
- stats.append(property)
- if stats:
- node.body.stats += stats
- if (node.visibility != 'extern'
- and not node.scope.lookup('__reduce__')
- and not node.scope.lookup('__reduce_ex__')):
- self._inject_pickle_methods(node)
- return node
-
- def _inject_pickle_methods(self, node):
- env = self.current_env()
- if node.scope.directives['auto_pickle'] is False: # None means attempt it.
- # Old behavior of not doing anything.
- return
- auto_pickle_forced = node.scope.directives['auto_pickle'] is True
-
- all_members = []
- cls = node.entry.type
- cinit = None
- inherited_reduce = None
- while cls is not None:
- all_members.extend(e for e in cls.scope.var_entries if e.name not in ('__weakref__', '__dict__'))
- cinit = cinit or cls.scope.lookup('__cinit__')
- inherited_reduce = inherited_reduce or cls.scope.lookup('__reduce__') or cls.scope.lookup('__reduce_ex__')
- cls = cls.base_type
- all_members.sort(key=lambda e: e.name)
-
- if inherited_reduce:
- # This is not failsafe, as we may not know whether a cimported class defines a __reduce__.
- # This is why we define __reduce_cython__ and only replace __reduce__
- # (via ExtensionTypes.SetupReduce utility code) at runtime on class creation.
- return
-
- non_py = [
- e for e in all_members
- if not e.type.is_pyobject and (not e.type.can_coerce_to_pyobject(env)
- or not e.type.can_coerce_from_pyobject(env))
- ]
-
- structs = [e for e in all_members if e.type.is_struct_or_union]
-
- if cinit or non_py or (structs and not auto_pickle_forced):
- if cinit:
- # TODO(robertwb): We could allow this if __cinit__ has no require arguments.
- msg = 'no default __reduce__ due to non-trivial __cinit__'
- elif non_py:
- msg = "%s cannot be converted to a Python object for pickling" % ','.join("self.%s" % e.name for e in non_py)
- else:
- # Extern structs may be only partially defined.
- # TODO(robertwb): Limit the restriction to extern
- # (and recursively extern-containing) structs.
- msg = ("Pickling of struct members such as %s must be explicitly requested "
- "with @auto_pickle(True)" % ','.join("self.%s" % e.name for e in structs))
-
- if auto_pickle_forced:
- error(node.pos, msg)
-
- pickle_func = TreeFragment(u"""
- def __reduce_cython__(self):
- raise TypeError("%(msg)s")
- def __setstate_cython__(self, __pyx_state):
- raise TypeError("%(msg)s")
- """ % {'msg': msg},
- level='c_class', pipeline=[NormalizeTree(None)]).substitute({})
- pickle_func.analyse_declarations(node.scope)
- self.visit(pickle_func)
- node.body.stats.append(pickle_func)
-
- else:
- for e in all_members:
- if not e.type.is_pyobject:
- e.type.create_to_py_utility_code(env)
- e.type.create_from_py_utility_code(env)
- all_members_names = sorted([e.name for e in all_members])
-
- # Cython 0.x used MD5 for the checksum, which a few Python installations remove for security reasons.
- # SHA-256 should be ok for years to come, but early Cython 3.0 alpha releases used SHA-1,
- # which may not be.
- checksum_algos = []
- try:
- checksum_algos.append(hashlib.md5)
- except AttributeError:
- pass
- checksum_algos.append(hashlib.sha256)
- checksum_algos.append(hashlib.sha1)
-
- member_names_string = ' '.join(all_members_names).encode('utf-8')
- checksums = [
- '0x' + mkchecksum(member_names_string).hexdigest()[:7]
- for mkchecksum in checksum_algos
- ]
- unpickle_func_name = '__pyx_unpickle_%s' % node.class_name
-
- # TODO(robertwb): Move the state into the third argument
- # so it can be pickled *after* self is memoized.
- unpickle_func = TreeFragment(u"""
- def %(unpickle_func_name)s(__pyx_type, long __pyx_checksum, __pyx_state):
- cdef object __pyx_PickleError
- cdef object __pyx_result
- if __pyx_checksum not in %(checksums)s:
- from pickle import PickleError as __pyx_PickleError
- raise __pyx_PickleError("Incompatible checksums (0x%%x vs %(checksums)s = (%(members)s))" %% __pyx_checksum)
- __pyx_result = %(class_name)s.__new__(__pyx_type)
- if __pyx_state is not None:
- %(unpickle_func_name)s__set_state(<%(class_name)s> __pyx_result, __pyx_state)
- return __pyx_result
-
- cdef %(unpickle_func_name)s__set_state(%(class_name)s __pyx_result, tuple __pyx_state):
- %(assignments)s
- if len(__pyx_state) > %(num_members)d and hasattr(__pyx_result, '__dict__'):
- __pyx_result.__dict__.update(__pyx_state[%(num_members)d])
- """ % {
- 'unpickle_func_name': unpickle_func_name,
- 'checksums': "(%s)" % ', '.join(checksums),
- 'members': ', '.join(all_members_names),
- 'class_name': node.class_name,
- 'assignments': '; '.join(
- '__pyx_result.%s = __pyx_state[%s]' % (v, ix)
- for ix, v in enumerate(all_members_names)),
- 'num_members': len(all_members_names),
- }, level='module', pipeline=[NormalizeTree(None)]).substitute({})
- unpickle_func.analyse_declarations(node.entry.scope)
- self.visit(unpickle_func)
- self.extra_module_declarations.append(unpickle_func)
-
- pickle_func = TreeFragment(u"""
- def __reduce_cython__(self):
- cdef tuple state
- cdef object _dict
- cdef bint use_setstate
- state = (%(members)s)
- _dict = getattr(self, '__dict__', None)
- if _dict is not None:
- state += (_dict,)
- use_setstate = True
- else:
- use_setstate = %(any_notnone_members)s
- if use_setstate:
- return %(unpickle_func_name)s, (type(self), %(checksum)s, None), state
- else:
- return %(unpickle_func_name)s, (type(self), %(checksum)s, state)
-
- def __setstate_cython__(self, __pyx_state):
- %(unpickle_func_name)s__set_state(self, __pyx_state)
- """ % {
- 'unpickle_func_name': unpickle_func_name,
- 'checksum': checksums[0],
- 'members': ', '.join('self.%s' % v for v in all_members_names) + (',' if len(all_members_names) == 1 else ''),
- # Even better, we could check PyType_IS_GC.
- 'any_notnone_members' : ' or '.join(['self.%s is not None' % e.name for e in all_members if e.type.is_pyobject] or ['False']),
- },
- level='c_class', pipeline=[NormalizeTree(None)]).substitute({})
- pickle_func.analyse_declarations(node.scope)
- self.enter_scope(node, node.scope) # functions should be visited in the class scope
- self.visit(pickle_func)
- self.exit_scope()
- node.body.stats.append(pickle_func)
-
- def _handle_fused_def_decorators(self, old_decorators, env, node):
- """
- Create function calls to the decorators and reassignments to
- the function.
- """
- # Delete staticmethod and classmethod decorators, this is
- # handled directly by the fused function object.
- decorators = []
- for decorator in old_decorators:
- func = decorator.decorator
- if (not func.is_name or
- func.name not in ('staticmethod', 'classmethod') or
- env.lookup_here(func.name)):
- # not a static or classmethod
- decorators.append(decorator)
-
- if decorators:
- transform = DecoratorTransform(self.context)
- def_node = node.node
- _, reassignments = transform.chain_decorators(
- def_node, decorators, def_node.name)
- reassignments.analyse_declarations(env)
- node = [node, reassignments]
-
- return node
-
- def _handle_def(self, decorators, env, node):
- "Handle def or cpdef fused functions"
- # Create PyCFunction nodes for each specialization
- node.stats.insert(0, node.py_func)
- node.py_func = self.visit(node.py_func)
- node.update_fused_defnode_entry(env)
- pycfunc = ExprNodes.PyCFunctionNode.from_defnode(node.py_func, binding=True)
- pycfunc = ExprNodes.ProxyNode(pycfunc.coerce_to_temp(env))
- node.resulting_fused_function = pycfunc
- # Create assignment node for our def function
- node.fused_func_assignment = self._create_assignment(
- node.py_func, ExprNodes.CloneNode(pycfunc), env)
-
- if decorators:
- node = self._handle_fused_def_decorators(decorators, env, node)
-
- return node
-
- def _create_fused_function(self, env, node):
- "Create a fused function for a DefNode with fused arguments"
- from . import FusedNode
-
- if self.fused_function or self.in_lambda:
- if self.fused_function not in self.fused_error_funcs:
- if self.in_lambda:
- error(node.pos, "Fused lambdas not allowed")
- else:
- error(node.pos, "Cannot nest fused functions")
-
- self.fused_error_funcs.add(self.fused_function)
-
- node.body = Nodes.PassStatNode(node.pos)
- for arg in node.args:
- if arg.type.is_fused:
- arg.type = arg.type.get_fused_types()[0]
-
- return node
-
- decorators = getattr(node, 'decorators', None)
- node = FusedNode.FusedCFuncDefNode(node, env)
- self.fused_function = node
- self.visitchildren(node)
- self.fused_function = None
- if node.py_func:
- node = self._handle_def(decorators, env, node)
-
- return node
-
- def _handle_nogil_cleanup(self, lenv, node):
- "Handle cleanup for 'with gil' blocks in nogil functions."
- if lenv.nogil and lenv.has_with_gil_block:
- # Acquire the GIL for cleanup in 'nogil' functions, by wrapping
- # the entire function body in try/finally.
- # The corresponding release will be taken care of by
- # Nodes.FuncDefNode.generate_function_definitions()
- node.body = Nodes.NogilTryFinallyStatNode(
- node.body.pos,
- body=node.body,
- finally_clause=Nodes.EnsureGILNode(node.body.pos),
- finally_except_clause=Nodes.EnsureGILNode(node.body.pos))
-
- def _handle_fused(self, node):
- if node.is_generator and node.has_fused_arguments:
- node.has_fused_arguments = False
- error(node.pos, "Fused generators not supported")
- node.gbody = Nodes.StatListNode(node.pos,
- stats=[],
- body=Nodes.PassStatNode(node.pos))
-
- return node.has_fused_arguments
-
- def visit_FuncDefNode(self, node):
- """
- Analyse a function and its body, as that hasn't happened yet. Also
- analyse the directive_locals set by @cython.locals().
-
- Then, if we are a function with fused arguments, replace the function
- (after it has declared itself in the symbol table!) with a
- FusedCFuncDefNode, and analyse its children (which are in turn normal
- functions). If we're a normal function, just analyse the body of the
- function.
- """
- env = self.current_env()
-
- self.seen_vars_stack.append(set())
- lenv = node.local_scope
- node.declare_arguments(lenv)
-
- # @cython.locals(...)
- for var, type_node in node.directive_locals.items():
- if not lenv.lookup_here(var): # don't redeclare args
- type = type_node.analyse_as_type(lenv)
- if type:
- lenv.declare_var(var, type, type_node.pos)
- else:
- error(type_node.pos, "Not a type")
-
- if self._handle_fused(node):
- node = self._create_fused_function(env, node)
- else:
- node.body.analyse_declarations(lenv)
- self._handle_nogil_cleanup(lenv, node)
- self._super_visit_FuncDefNode(node)
-
- self.seen_vars_stack.pop()
- return node
-
- def visit_DefNode(self, node):
- node = self.visit_FuncDefNode(node)
- env = self.current_env()
- if isinstance(node, Nodes.DefNode) and node.is_wrapper:
- env = env.parent_scope
- if (not isinstance(node, Nodes.DefNode) or
- node.fused_py_func or node.is_generator_body or
- not node.needs_assignment_synthesis(env)):
- return node
- return [node, self._synthesize_assignment(node, env)]
-
- def visit_GeneratorBodyDefNode(self, node):
- return self.visit_FuncDefNode(node)
-
- def _synthesize_assignment(self, node, env):
- # Synthesize assignment node and put it right after defnode
- genv = env
- while genv.is_py_class_scope or genv.is_c_class_scope:
- genv = genv.outer_scope
-
- if genv.is_closure_scope:
- rhs = node.py_cfunc_node = ExprNodes.InnerFunctionNode(
- node.pos, def_node=node,
- pymethdef_cname=node.entry.pymethdef_cname,
- code_object=ExprNodes.CodeObjectNode(node))
- else:
- binding = self.current_directives.get('binding')
- rhs = ExprNodes.PyCFunctionNode.from_defnode(node, binding)
- node.code_object = rhs.code_object
- if node.is_generator:
- node.gbody.code_object = node.code_object
-
- if env.is_py_class_scope:
- rhs.binding = True
-
- node.is_cyfunction = rhs.binding
- return self._create_assignment(node, rhs, env)
-
- def _create_assignment(self, def_node, rhs, env):
- if def_node.decorators:
- for decorator in def_node.decorators[::-1]:
- rhs = ExprNodes.SimpleCallNode(
- decorator.pos,
- function = decorator.decorator,
- args = [rhs])
- def_node.decorators = None
-
- assmt = Nodes.SingleAssignmentNode(
- def_node.pos,
- lhs=ExprNodes.NameNode(def_node.pos, name=def_node.name),
- rhs=rhs)
- assmt.analyse_declarations(env)
- return assmt
-
- def visit_ScopedExprNode(self, node):
- env = self.current_env()
- node.analyse_declarations(env)
- # the node may or may not have a local scope
- if node.has_local_scope:
- self.seen_vars_stack.append(set(self.seen_vars_stack[-1]))
- self.enter_scope(node, node.expr_scope)
- node.analyse_scoped_declarations(node.expr_scope)
- self.visitchildren(node)
- self.exit_scope()
- self.seen_vars_stack.pop()
- else:
- node.analyse_scoped_declarations(env)
- self.visitchildren(node)
- return node
-
- def visit_TempResultFromStatNode(self, node):
- self.visitchildren(node)
- node.analyse_declarations(self.current_env())
- return node
-
- def visit_CppClassNode(self, node):
- if node.visibility == 'extern':
- return None
- else:
- return self.visit_ClassDefNode(node)
-
- def visit_CStructOrUnionDefNode(self, node):
- # Create a wrapper node if needed.
- # We want to use the struct type information (so it can't happen
- # before this phase) but also create new objects to be declared
- # (so it can't happen later).
- # Note that we don't return the original node, as it is
- # never used after this phase.
- if True: # private (default)
- return None
-
- self_value = ExprNodes.AttributeNode(
- pos = node.pos,
- obj = ExprNodes.NameNode(pos=node.pos, name=u"self"),
- attribute = EncodedString(u"value"))
- var_entries = node.entry.type.scope.var_entries
- attributes = []
- for entry in var_entries:
- attributes.append(ExprNodes.AttributeNode(pos = entry.pos,
- obj = self_value,
- attribute = entry.name))
- # __init__ assignments
- init_assignments = []
- for entry, attr in zip(var_entries, attributes):
- # TODO: branch on visibility
- init_assignments.append(self.init_assignment.substitute({
- u"VALUE": ExprNodes.NameNode(entry.pos, name = entry.name),
- u"ATTR": attr,
- }, pos = entry.pos))
-
- # create the class
- str_format = u"%s(%s)" % (node.entry.type.name, ("%s, " * len(attributes))[:-2])
- wrapper_class = self.struct_or_union_wrapper.substitute({
- u"INIT_ASSIGNMENTS": Nodes.StatListNode(node.pos, stats = init_assignments),
- u"IS_UNION": ExprNodes.BoolNode(node.pos, value = not node.entry.type.is_struct),
- u"MEMBER_TUPLE": ExprNodes.TupleNode(node.pos, args=attributes),
- u"STR_FORMAT": ExprNodes.StringNode(node.pos, value = EncodedString(str_format)),
- u"REPR_FORMAT": ExprNodes.StringNode(node.pos, value = EncodedString(str_format.replace("%s", "%r"))),
- }, pos = node.pos).stats[0]
- wrapper_class.class_name = node.name
- wrapper_class.shadow = True
- class_body = wrapper_class.body.stats
-
- # fix value type
- assert isinstance(class_body[0].base_type, Nodes.CSimpleBaseTypeNode)
- class_body[0].base_type.name = node.name
-
- # fix __init__ arguments
- init_method = class_body[1]
- assert isinstance(init_method, Nodes.DefNode) and init_method.name == '__init__'
- arg_template = init_method.args[1]
- if not node.entry.type.is_struct:
- arg_template.kw_only = True
- del init_method.args[1]
- for entry, attr in zip(var_entries, attributes):
- arg = copy.deepcopy(arg_template)
- arg.declarator.name = entry.name
- init_method.args.append(arg)
-
- # setters/getters
- for entry, attr in zip(var_entries, attributes):
- # TODO: branch on visibility
- if entry.type.is_pyobject:
- template = self.basic_pyobject_property
- else:
- template = self.basic_property
- property = template.substitute({
- u"ATTR": attr,
- }, pos = entry.pos).stats[0]
- property.name = entry.name
- wrapper_class.body.stats.append(property)
-
- wrapper_class.analyse_declarations(self.current_env())
- return self.visit_CClassDefNode(wrapper_class)
-
- # Some nodes are no longer needed after declaration
- # analysis and can be dropped. The analysis was performed
- # on these nodes in a separate recursive process from the
- # enclosing function or module, so we can simply drop them.
- def visit_CDeclaratorNode(self, node):
- # necessary to ensure that all CNameDeclaratorNodes are visited.
- self.visitchildren(node)
- return node
-
- def visit_CTypeDefNode(self, node):
- return node
-
- def visit_CBaseTypeNode(self, node):
- return None
-
- def visit_CEnumDefNode(self, node):
- if node.visibility == 'public':
- return node
- else:
- return None
-
- def visit_CNameDeclaratorNode(self, node):
- if node.name in self.seen_vars_stack[-1]:
- entry = self.current_env().lookup(node.name)
- if (entry is None or entry.visibility != 'extern'
- and not entry.scope.is_c_class_scope):
- warning(node.pos, "cdef variable '%s' declared after it is used" % node.name, 2)
- self.visitchildren(node)
- return node
-
- def visit_CVarDefNode(self, node):
- # to ensure all CNameDeclaratorNodes are visited.
- self.visitchildren(node)
- return None
-
- def visit_CnameDecoratorNode(self, node):
- child_node = self.visit(node.node)
- if not child_node:
- return None
- if type(child_node) is list: # Assignment synthesized
- node.child_node = child_node[0]
- return [node] + child_node[1:]
- node.node = child_node
- return node
-
- def create_Property(self, entry):
- if entry.visibility == 'public':
- if entry.type.is_pyobject:
- template = self.basic_pyobject_property
- else:
- template = self.basic_property
- elif entry.visibility == 'readonly':
- template = self.basic_property_ro
- property = template.substitute({
- u"ATTR": ExprNodes.AttributeNode(pos=entry.pos,
- obj=ExprNodes.NameNode(pos=entry.pos, name="self"),
- attribute=entry.name),
- }, pos=entry.pos).stats[0]
- property.name = entry.name
- property.doc = entry.doc
- return property
-
-
-class CalculateQualifiedNamesTransform(EnvTransform):
- """
- Calculate and store the '__qualname__' and the global
- module name on some nodes.
- """
- def visit_ModuleNode(self, node):
- self.module_name = self.global_scope().qualified_name
- self.qualified_name = []
- _super = super(CalculateQualifiedNamesTransform, self)
- self._super_visit_FuncDefNode = _super.visit_FuncDefNode
- self._super_visit_ClassDefNode = _super.visit_ClassDefNode
- self.visitchildren(node)
- return node
-
- def _set_qualname(self, node, name=None):
- if name:
- qualname = self.qualified_name[:]
- qualname.append(name)
- else:
- qualname = self.qualified_name
- node.qualname = EncodedString('.'.join(qualname))
- node.module_name = self.module_name
-
- def _append_entry(self, entry):
- if entry.is_pyglobal and not entry.is_pyclass_attr:
- self.qualified_name = [entry.name]
- else:
- self.qualified_name.append(entry.name)
-
- def visit_ClassNode(self, node):
- self._set_qualname(node, node.name)
- self.visitchildren(node)
- return node
-
- def visit_PyClassNamespaceNode(self, node):
- # class name was already added by parent node
- self._set_qualname(node)
- self.visitchildren(node)
- return node
-
- def visit_PyCFunctionNode(self, node):
- orig_qualified_name = self.qualified_name[:]
- if node.def_node.is_wrapper and self.qualified_name and self.qualified_name[-1] == '':
- self.qualified_name.pop()
- self._set_qualname(node)
- else:
- self._set_qualname(node, node.def_node.name)
- self.visitchildren(node)
- self.qualified_name = orig_qualified_name
- return node
-
- def visit_DefNode(self, node):
- if node.is_wrapper and self.qualified_name:
- assert self.qualified_name[-1] == '', self.qualified_name
- orig_qualified_name = self.qualified_name[:]
- self.qualified_name.pop()
- self._set_qualname(node)
- self._super_visit_FuncDefNode(node)
- self.qualified_name = orig_qualified_name
- else:
- self._set_qualname(node, node.name)
- self.visit_FuncDefNode(node)
- return node
-
- def visit_FuncDefNode(self, node):
- orig_qualified_name = self.qualified_name[:]
- if getattr(node, 'name', None) == '':
- self.qualified_name.append('')
- else:
- self._append_entry(node.entry)
- self.qualified_name.append('')
- self._super_visit_FuncDefNode(node)
- self.qualified_name = orig_qualified_name
- return node
-
- def visit_ClassDefNode(self, node):
- orig_qualified_name = self.qualified_name[:]
- entry = (getattr(node, 'entry', None) or # PyClass
- self.current_env().lookup_here(node.name)) # CClass
- self._append_entry(entry)
- self._super_visit_ClassDefNode(node)
- self.qualified_name = orig_qualified_name
- return node
-
-
-class AnalyseExpressionsTransform(CythonTransform):
-
- def visit_ModuleNode(self, node):
- node.scope.infer_types()
- node.body = node.body.analyse_expressions(node.scope)
- self.visitchildren(node)
- return node
-
- def visit_FuncDefNode(self, node):
- node.local_scope.infer_types()
- node.body = node.body.analyse_expressions(node.local_scope)
- self.visitchildren(node)
- return node
-
- def visit_ScopedExprNode(self, node):
- if node.has_local_scope:
- node.expr_scope.infer_types()
- node = node.analyse_scoped_expressions(node.expr_scope)
- self.visitchildren(node)
- return node
-
- def visit_IndexNode(self, node):
- """
- Replace index nodes used to specialize cdef functions with fused
- argument types with the Attribute- or NameNode referring to the
- function. We then need to copy over the specialization properties to
- the attribute or name node.
-
- Because the indexing might be a Python indexing operation on a fused
- function, or (usually) a Cython indexing operation, we need to
- re-analyse the types.
- """
- self.visit_Node(node)
- if node.is_fused_index and not node.type.is_error:
- node = node.base
- return node
-
-
-class FindInvalidUseOfFusedTypes(CythonTransform):
-
- def visit_FuncDefNode(self, node):
- # Errors related to use in functions with fused args will already
- # have been detected
- if not node.has_fused_arguments:
- if not node.is_generator_body and node.return_type.is_fused:
- error(node.pos, "Return type is not specified as argument type")
- else:
- self.visitchildren(node)
-
- return node
-
- def visit_ExprNode(self, node):
- if node.type and node.type.is_fused:
- error(node.pos, "Invalid use of fused types, type cannot be specialized")
- else:
- self.visitchildren(node)
-
- return node
-
-
-class ExpandInplaceOperators(EnvTransform):
-
- def visit_InPlaceAssignmentNode(self, node):
- lhs = node.lhs
- rhs = node.rhs
- if lhs.type.is_cpp_class:
- # No getting around this exact operator here.
- return node
- if isinstance(lhs, ExprNodes.BufferIndexNode):
- # There is code to handle this case in InPlaceAssignmentNode
- return node
-
- env = self.current_env()
- def side_effect_free_reference(node, setting=False):
- if node.is_name:
- return node, []
- elif node.type.is_pyobject and not setting:
- node = LetRefNode(node)
- return node, [node]
- elif node.is_subscript:
- base, temps = side_effect_free_reference(node.base)
- index = LetRefNode(node.index)
- return ExprNodes.IndexNode(node.pos, base=base, index=index), temps + [index]
- elif node.is_attribute:
- obj, temps = side_effect_free_reference(node.obj)
- return ExprNodes.AttributeNode(node.pos, obj=obj, attribute=node.attribute), temps
- elif isinstance(node, ExprNodes.BufferIndexNode):
- raise ValueError("Don't allow things like attributes of buffer indexing operations")
- else:
- node = LetRefNode(node)
- return node, [node]
- try:
- lhs, let_ref_nodes = side_effect_free_reference(lhs, setting=True)
- except ValueError:
- return node
- dup = lhs.__class__(**lhs.__dict__)
- binop = ExprNodes.binop_node(node.pos,
- operator = node.operator,
- operand1 = dup,
- operand2 = rhs,
- inplace=True)
- # Manually analyse types for new node.
- lhs.analyse_target_types(env)
- dup.analyse_types(env)
- binop.analyse_operation(env)
- node = Nodes.SingleAssignmentNode(
- node.pos,
- lhs = lhs,
- rhs=binop.coerce_to(lhs.type, env))
- # Use LetRefNode to avoid side effects.
- let_ref_nodes.reverse()
- for t in let_ref_nodes:
- node = LetNode(t, node)
- return node
-
- def visit_ExprNode(self, node):
- # In-place assignments can't happen within an expression.
- return node
-
-
-class AdjustDefByDirectives(CythonTransform, SkipDeclarations):
- """
- Adjust function and class definitions by the decorator directives:
-
- @cython.cfunc
- @cython.cclass
- @cython.ccall
- @cython.inline
- @cython.nogil
- """
-
- def visit_ModuleNode(self, node):
- self.directives = node.directives
- self.in_py_class = False
- self.visitchildren(node)
- return node
-
- def visit_CompilerDirectivesNode(self, node):
- old_directives = self.directives
- self.directives = node.directives
- self.visitchildren(node)
- self.directives = old_directives
- return node
-
- def visit_DefNode(self, node):
- modifiers = []
- if 'inline' in self.directives:
- modifiers.append('inline')
- nogil = self.directives.get('nogil')
- except_val = self.directives.get('exceptval')
- return_type_node = self.directives.get('returns')
- if return_type_node is None and self.directives['annotation_typing']:
- return_type_node = node.return_type_annotation
- # for Python anntations, prefer safe exception handling by default
- if return_type_node is not None and except_val is None:
- except_val = (None, True) # except *
- elif except_val is None:
- # backward compatible default: no exception check
- except_val = (None, False)
- if 'ccall' in self.directives:
- node = node.as_cfunction(
- overridable=True, modifiers=modifiers, nogil=nogil,
- returns=return_type_node, except_val=except_val)
- return self.visit(node)
- if 'cfunc' in self.directives:
- if self.in_py_class:
- error(node.pos, "cfunc directive is not allowed here")
- else:
- node = node.as_cfunction(
- overridable=False, modifiers=modifiers, nogil=nogil,
- returns=return_type_node, except_val=except_val)
- return self.visit(node)
- if 'inline' in modifiers:
- error(node.pos, "Python functions cannot be declared 'inline'")
- if nogil:
- # TODO: turn this into a "with gil" declaration.
- error(node.pos, "Python functions cannot be declared 'nogil'")
- self.visitchildren(node)
- return node
-
- def visit_LambdaNode(self, node):
- # No directives should modify lambdas or generator expressions (and also nothing in them).
- return node
-
- def visit_PyClassDefNode(self, node):
- if 'cclass' in self.directives:
- node = node.as_cclass()
- return self.visit(node)
- else:
- old_in_pyclass = self.in_py_class
- self.in_py_class = True
- self.visitchildren(node)
- self.in_py_class = old_in_pyclass
- return node
-
- def visit_CClassDefNode(self, node):
- old_in_pyclass = self.in_py_class
- self.in_py_class = False
- self.visitchildren(node)
- self.in_py_class = old_in_pyclass
- return node
-
-
-class AlignFunctionDefinitions(CythonTransform):
- """
- This class takes the signatures from a .pxd file and applies them to
- the def methods in a .py file.
- """
-
- def visit_ModuleNode(self, node):
- self.scope = node.scope
- self.directives = node.directives
- self.imported_names = set() # hack, see visit_FromImportStatNode()
- self.visitchildren(node)
- return node
-
- def visit_PyClassDefNode(self, node):
- pxd_def = self.scope.lookup(node.name)
- if pxd_def:
- if pxd_def.is_cclass:
- return self.visit_CClassDefNode(node.as_cclass(), pxd_def)
- elif not pxd_def.scope or not pxd_def.scope.is_builtin_scope:
- error(node.pos, "'%s' redeclared" % node.name)
- if pxd_def.pos:
- error(pxd_def.pos, "previous declaration here")
- return None
- return node
-
- def visit_CClassDefNode(self, node, pxd_def=None):
- if pxd_def is None:
- pxd_def = self.scope.lookup(node.class_name)
- if pxd_def:
- if not pxd_def.defined_in_pxd:
- return node
- outer_scope = self.scope
- self.scope = pxd_def.type.scope
- self.visitchildren(node)
- if pxd_def:
- self.scope = outer_scope
- return node
-
- def visit_DefNode(self, node):
- pxd_def = self.scope.lookup(node.name)
- if pxd_def and (not pxd_def.scope or not pxd_def.scope.is_builtin_scope):
- if not pxd_def.is_cfunction:
- error(node.pos, "'%s' redeclared" % node.name)
- if pxd_def.pos:
- error(pxd_def.pos, "previous declaration here")
- return None
- node = node.as_cfunction(pxd_def)
- elif (self.scope.is_module_scope and self.directives['auto_cpdef']
- and not node.name in self.imported_names
- and node.is_cdef_func_compatible()):
- # FIXME: cpdef-ing should be done in analyse_declarations()
- node = node.as_cfunction(scope=self.scope)
- # Enable this when nested cdef functions are allowed.
- # self.visitchildren(node)
- return node
-
- def visit_FromImportStatNode(self, node):
- # hack to prevent conditional import fallback functions from
- # being cdpef-ed (global Python variables currently conflict
- # with imports)
- if self.scope.is_module_scope:
- for name, _ in node.items:
- self.imported_names.add(name)
- return node
-
- def visit_ExprNode(self, node):
- # ignore lambdas and everything else that appears in expressions
- return node
-
-
-class RemoveUnreachableCode(CythonTransform):
- def visit_StatListNode(self, node):
- if not self.current_directives['remove_unreachable']:
- return node
- self.visitchildren(node)
- for idx, stat in enumerate(node.stats):
- idx += 1
- if stat.is_terminator:
- if idx < len(node.stats):
- if self.current_directives['warn.unreachable']:
- warning(node.stats[idx].pos, "Unreachable code", 2)
- node.stats = node.stats[:idx]
- node.is_terminator = True
- break
- return node
-
- def visit_IfClauseNode(self, node):
- self.visitchildren(node)
- if node.body.is_terminator:
- node.is_terminator = True
- return node
-
- def visit_IfStatNode(self, node):
- self.visitchildren(node)
- if node.else_clause and node.else_clause.is_terminator:
- for clause in node.if_clauses:
- if not clause.is_terminator:
- break
- else:
- node.is_terminator = True
- return node
-
- def visit_TryExceptStatNode(self, node):
- self.visitchildren(node)
- if node.body.is_terminator and node.else_clause:
- if self.current_directives['warn.unreachable']:
- warning(node.else_clause.pos, "Unreachable code", 2)
- node.else_clause = None
- return node
-
- def visit_TryFinallyStatNode(self, node):
- self.visitchildren(node)
- if node.finally_clause.is_terminator:
- node.is_terminator = True
- return node
-
-
-class YieldNodeCollector(TreeVisitor):
-
- def __init__(self):
- super(YieldNodeCollector, self).__init__()
- self.yields = []
- self.returns = []
- self.finallys = []
- self.excepts = []
- self.has_return_value = False
- self.has_yield = False
- self.has_await = False
-
- def visit_Node(self, node):
- self.visitchildren(node)
-
- def visit_YieldExprNode(self, node):
- self.yields.append(node)
- self.has_yield = True
- self.visitchildren(node)
-
- def visit_AwaitExprNode(self, node):
- self.yields.append(node)
- self.has_await = True
- self.visitchildren(node)
-
- def visit_ReturnStatNode(self, node):
- self.visitchildren(node)
- if node.value:
- self.has_return_value = True
- self.returns.append(node)
-
- def visit_TryFinallyStatNode(self, node):
- self.visitchildren(node)
- self.finallys.append(node)
-
- def visit_TryExceptStatNode(self, node):
- self.visitchildren(node)
- self.excepts.append(node)
-
- def visit_ClassDefNode(self, node):
- pass
-
- def visit_FuncDefNode(self, node):
- pass
-
- def visit_LambdaNode(self, node):
- pass
-
- def visit_GeneratorExpressionNode(self, node):
- pass
-
- def visit_CArgDeclNode(self, node):
- # do not look into annotations
- # FIXME: support (yield) in default arguments (currently crashes)
- pass
-
-
-class MarkClosureVisitor(CythonTransform):
-
- def visit_ModuleNode(self, node):
- self.needs_closure = False
- self.visitchildren(node)
- return node
-
- def visit_FuncDefNode(self, node):
- self.needs_closure = False
- self.visitchildren(node)
- node.needs_closure = self.needs_closure
- self.needs_closure = True
-
- collector = YieldNodeCollector()
- collector.visitchildren(node)
-
- if node.is_async_def:
- coroutine_type = Nodes.AsyncDefNode
- if collector.has_yield:
- coroutine_type = Nodes.AsyncGenNode
- for yield_expr in collector.yields + collector.returns:
- yield_expr.in_async_gen = True
- elif self.current_directives['iterable_coroutine']:
- coroutine_type = Nodes.IterableAsyncDefNode
- elif collector.has_await:
- found = next(y for y in collector.yields if y.is_await)
- error(found.pos, "'await' not allowed in generators (use 'yield')")
- return node
- elif collector.has_yield:
- coroutine_type = Nodes.GeneratorDefNode
- else:
- return node
-
- for i, yield_expr in enumerate(collector.yields, 1):
- yield_expr.label_num = i
- for retnode in collector.returns + collector.finallys + collector.excepts:
- retnode.in_generator = True
-
- gbody = Nodes.GeneratorBodyDefNode(
- pos=node.pos, name=node.name, body=node.body,
- is_async_gen_body=node.is_async_def and collector.has_yield)
- coroutine = coroutine_type(
- pos=node.pos, name=node.name, args=node.args,
- star_arg=node.star_arg, starstar_arg=node.starstar_arg,
- doc=node.doc, decorators=node.decorators,
- gbody=gbody, lambda_name=node.lambda_name,
- return_type_annotation=node.return_type_annotation)
- return coroutine
-
- def visit_CFuncDefNode(self, node):
- self.needs_closure = False
- self.visitchildren(node)
- node.needs_closure = self.needs_closure
- self.needs_closure = True
- if node.needs_closure and node.overridable:
- error(node.pos, "closures inside cpdef functions not yet supported")
- return node
-
- def visit_LambdaNode(self, node):
- self.needs_closure = False
- self.visitchildren(node)
- node.needs_closure = self.needs_closure
- self.needs_closure = True
- return node
-
- def visit_ClassDefNode(self, node):
- self.visitchildren(node)
- self.needs_closure = True
- return node
-
-
-class CreateClosureClasses(CythonTransform):
- # Output closure classes in module scope for all functions
- # that really need it.
-
- def __init__(self, context):
- super(CreateClosureClasses, self).__init__(context)
- self.path = []
- self.in_lambda = False
-
- def visit_ModuleNode(self, node):
- self.module_scope = node.scope
- self.visitchildren(node)
- return node
-
- def find_entries_used_in_closures(self, node):
- from_closure = []
- in_closure = []
- for scope in node.local_scope.iter_local_scopes():
- for name, entry in scope.entries.items():
- if not name:
- continue
- if entry.from_closure:
- from_closure.append((name, entry))
- elif entry.in_closure:
- in_closure.append((name, entry))
- return from_closure, in_closure
-
- def create_class_from_scope(self, node, target_module_scope, inner_node=None):
- # move local variables into closure
- if node.is_generator:
- for scope in node.local_scope.iter_local_scopes():
- for entry in scope.entries.values():
- if not (entry.from_closure or entry.is_pyglobal or entry.is_cglobal):
- entry.in_closure = True
-
- from_closure, in_closure = self.find_entries_used_in_closures(node)
- in_closure.sort()
-
- # Now from the beginning
- node.needs_closure = False
- node.needs_outer_scope = False
-
- func_scope = node.local_scope
- cscope = node.entry.scope
- while cscope.is_py_class_scope or cscope.is_c_class_scope:
- cscope = cscope.outer_scope
-
- if not from_closure and (self.path or inner_node):
- if not inner_node:
- if not node.py_cfunc_node:
- raise InternalError("DefNode does not have assignment node")
- inner_node = node.py_cfunc_node
- inner_node.needs_self_code = False
- node.needs_outer_scope = False
-
- if node.is_generator:
- pass
- elif not in_closure and not from_closure:
- return
- elif not in_closure:
- func_scope.is_passthrough = True
- func_scope.scope_class = cscope.scope_class
- node.needs_outer_scope = True
- return
-
- # entry.cname can contain periods (eg. a derived C method of a class).
- # We want to use the cname as part of a C struct name, so we replace
- # periods with double underscores.
- as_name = '%s_%s' % (
- target_module_scope.next_id(Naming.closure_class_prefix),
- node.entry.cname.replace('.','__'))
-
- entry = target_module_scope.declare_c_class(
- name=as_name, pos=node.pos, defining=True,
- implementing=True)
- entry.type.is_final_type = True
-
- func_scope.scope_class = entry
- class_scope = entry.type.scope
- class_scope.is_internal = True
- class_scope.is_closure_class_scope = True
- if node.is_async_def or node.is_generator:
- # Generators need their closure intact during cleanup as they resume to handle GeneratorExit
- class_scope.directives['no_gc_clear'] = True
- if Options.closure_freelist_size:
- class_scope.directives['freelist'] = Options.closure_freelist_size
-
- if from_closure:
- assert cscope.is_closure_scope
- class_scope.declare_var(pos=node.pos,
- name=Naming.outer_scope_cname,
- cname=Naming.outer_scope_cname,
- type=cscope.scope_class.type,
- is_cdef=True)
- node.needs_outer_scope = True
- for name, entry in in_closure:
- closure_entry = class_scope.declare_var(
- pos=entry.pos,
- name=entry.name if not entry.in_subscope else None,
- cname=entry.cname,
- type=entry.type,
- is_cdef=True)
- if entry.is_declared_generic:
- closure_entry.is_declared_generic = 1
- node.needs_closure = True
- # Do it here because other classes are already checked
- target_module_scope.check_c_class(func_scope.scope_class)
-
- def visit_LambdaNode(self, node):
- if not isinstance(node.def_node, Nodes.DefNode):
- # fused function, an error has been previously issued
- return node
-
- was_in_lambda = self.in_lambda
- self.in_lambda = True
- self.create_class_from_scope(node.def_node, self.module_scope, node)
- self.visitchildren(node)
- self.in_lambda = was_in_lambda
- return node
-
- def visit_FuncDefNode(self, node):
- if self.in_lambda:
- self.visitchildren(node)
- return node
- if node.needs_closure or self.path:
- self.create_class_from_scope(node, self.module_scope)
- self.path.append(node)
- self.visitchildren(node)
- self.path.pop()
- return node
-
- def visit_GeneratorBodyDefNode(self, node):
- self.visitchildren(node)
- return node
-
- def visit_CFuncDefNode(self, node):
- if not node.overridable:
- return self.visit_FuncDefNode(node)
- else:
- self.visitchildren(node)
- return node
-
-
-class InjectGilHandling(VisitorTransform, SkipDeclarations):
- """
- Allow certain Python operations inside of nogil blocks by implicitly acquiring the GIL.
-
- Must run before the AnalyseDeclarationsTransform to make sure the GILStatNodes get
- set up, parallel sections know that the GIL is acquired inside of them, etc.
- """
- def __call__(self, root):
- self.nogil = False
- return super(InjectGilHandling, self).__call__(root)
-
- # special node handling
-
- def visit_RaiseStatNode(self, node):
- """Allow raising exceptions in nogil sections by wrapping them in a 'with gil' block."""
- if self.nogil:
- node = Nodes.GILStatNode(node.pos, state='gil', body=node)
- return node
-
- # further candidates:
- # def visit_AssertStatNode(self, node):
- # def visit_ReraiseStatNode(self, node):
-
- # nogil tracking
-
- def visit_GILStatNode(self, node):
- was_nogil = self.nogil
- self.nogil = (node.state == 'nogil')
- self.visitchildren(node)
- self.nogil = was_nogil
- return node
-
- def visit_CFuncDefNode(self, node):
- was_nogil = self.nogil
- if isinstance(node.declarator, Nodes.CFuncDeclaratorNode):
- self.nogil = node.declarator.nogil and not node.declarator.with_gil
- self.visitchildren(node)
- self.nogil = was_nogil
- return node
-
- def visit_ParallelRangeNode(self, node):
- was_nogil = self.nogil
- self.nogil = node.nogil
- self.visitchildren(node)
- self.nogil = was_nogil
- return node
-
- def visit_ExprNode(self, node):
- # No special GIL handling inside of expressions for now.
- return node
-
- visit_Node = VisitorTransform.recurse_to_children
-
-
-class GilCheck(VisitorTransform):
- """
- Call `node.gil_check(env)` on each node to make sure we hold the
- GIL when we need it. Raise an error when on Python operations
- inside a `nogil` environment.
-
- Additionally, raise exceptions for closely nested with gil or with nogil
- statements. The latter would abort Python.
- """
-
- def __call__(self, root):
- self.env_stack = [root.scope]
- self.nogil = False
-
- # True for 'cdef func() nogil:' functions, as the GIL may be held while
- # calling this function (thus contained 'nogil' blocks may be valid).
- self.nogil_declarator_only = False
- return super(GilCheck, self).__call__(root)
-
- def _visit_scoped_children(self, node, gil_state):
- was_nogil = self.nogil
- outer_attrs = node.outer_attrs
- if outer_attrs and len(self.env_stack) > 1:
- self.nogil = self.env_stack[-2].nogil
- self.visitchildren(node, outer_attrs)
-
- self.nogil = gil_state
- self.visitchildren(node, attrs=None, exclude=outer_attrs)
- self.nogil = was_nogil
-
- def visit_FuncDefNode(self, node):
- self.env_stack.append(node.local_scope)
- inner_nogil = node.local_scope.nogil
-
- if inner_nogil:
- self.nogil_declarator_only = True
-
- if inner_nogil and node.nogil_check:
- node.nogil_check(node.local_scope)
-
- self._visit_scoped_children(node, inner_nogil)
-
- # This cannot be nested, so it doesn't need backup/restore
- self.nogil_declarator_only = False
-
- self.env_stack.pop()
- return node
-
- def visit_GILStatNode(self, node):
- if self.nogil and node.nogil_check:
- node.nogil_check()
-
- was_nogil = self.nogil
- is_nogil = (node.state == 'nogil')
-
- if was_nogil == is_nogil and not self.nogil_declarator_only:
- if not was_nogil:
- error(node.pos, "Trying to acquire the GIL while it is "
- "already held.")
- else:
- error(node.pos, "Trying to release the GIL while it was "
- "previously released.")
-
- if isinstance(node.finally_clause, Nodes.StatListNode):
- # The finally clause of the GILStatNode is a GILExitNode,
- # which is wrapped in a StatListNode. Just unpack that.
- node.finally_clause, = node.finally_clause.stats
-
- self._visit_scoped_children(node, is_nogil)
- return node
-
- def visit_ParallelRangeNode(self, node):
- if node.nogil:
- node.nogil = False
- node = Nodes.GILStatNode(node.pos, state='nogil', body=node)
- return self.visit_GILStatNode(node)
-
- if not self.nogil:
- error(node.pos, "prange() can only be used without the GIL")
- # Forget about any GIL-related errors that may occur in the body
- return None
-
- node.nogil_check(self.env_stack[-1])
- self.visitchildren(node)
- return node
-
- def visit_ParallelWithBlockNode(self, node):
- if not self.nogil:
- error(node.pos, "The parallel section may only be used without "
- "the GIL")
- return None
-
- if node.nogil_check:
- # It does not currently implement this, but test for it anyway to
- # avoid potential future surprises
- node.nogil_check(self.env_stack[-1])
-
- self.visitchildren(node)
- return node
-
- def visit_TryFinallyStatNode(self, node):
- """
- Take care of try/finally statements in nogil code sections.
- """
- if not self.nogil or isinstance(node, Nodes.GILStatNode):
- return self.visit_Node(node)
-
- node.nogil_check = None
- node.is_try_finally_in_nogil = True
- self.visitchildren(node)
- return node
-
- def visit_Node(self, node):
- if self.env_stack and self.nogil and node.nogil_check:
- node.nogil_check(self.env_stack[-1])
- if node.outer_attrs:
- self._visit_scoped_children(node, self.nogil)
- else:
- self.visitchildren(node)
- if self.nogil:
- node.in_nogil_context = True
- return node
-
-
-class TransformBuiltinMethods(EnvTransform):
- """
- Replace Cython's own cython.* builtins by the corresponding tree nodes.
- """
-
- def visit_SingleAssignmentNode(self, node):
- if node.declaration_only:
- return None
- else:
- self.visitchildren(node)
- return node
-
- def visit_AttributeNode(self, node):
- self.visitchildren(node)
- return self.visit_cython_attribute(node)
-
- def visit_NameNode(self, node):
- return self.visit_cython_attribute(node)
-
- def visit_cython_attribute(self, node):
- attribute = node.as_cython_attribute()
- if attribute:
- if attribute == u'compiled':
- node = ExprNodes.BoolNode(node.pos, value=True)
- elif attribute == u'__version__':
- from .. import __version__ as version
- node = ExprNodes.StringNode(node.pos, value=EncodedString(version))
- elif attribute == u'NULL':
- node = ExprNodes.NullNode(node.pos)
- elif attribute in (u'set', u'frozenset', u'staticmethod'):
- node = ExprNodes.NameNode(node.pos, name=EncodedString(attribute),
- entry=self.current_env().builtin_scope().lookup_here(attribute))
- elif PyrexTypes.parse_basic_type(attribute):
- pass
- elif self.context.cython_scope.lookup_qualified_name(attribute):
- pass
- else:
- error(node.pos, u"'%s' not a valid cython attribute or is being used incorrectly" % attribute)
- return node
-
- def visit_ExecStatNode(self, node):
- lenv = self.current_env()
- self.visitchildren(node)
- if len(node.args) == 1:
- node.args.append(ExprNodes.GlobalsExprNode(node.pos))
- if not lenv.is_module_scope:
- node.args.append(
- ExprNodes.LocalsExprNode(
- node.pos, self.current_scope_node(), lenv))
- return node
-
- def _inject_locals(self, node, func_name):
- # locals()/dir()/vars() builtins
- lenv = self.current_env()
- entry = lenv.lookup_here(func_name)
- if entry:
- # not the builtin
- return node
- pos = node.pos
- if func_name in ('locals', 'vars'):
- if func_name == 'locals' and len(node.args) > 0:
- error(self.pos, "Builtin 'locals()' called with wrong number of args, expected 0, got %d"
- % len(node.args))
- return node
- elif func_name == 'vars':
- if len(node.args) > 1:
- error(self.pos, "Builtin 'vars()' called with wrong number of args, expected 0-1, got %d"
- % len(node.args))
- if len(node.args) > 0:
- return node # nothing to do
- return ExprNodes.LocalsExprNode(pos, self.current_scope_node(), lenv)
- else: # dir()
- if len(node.args) > 1:
- error(self.pos, "Builtin 'dir()' called with wrong number of args, expected 0-1, got %d"
- % len(node.args))
- if len(node.args) > 0:
- # optimised in Builtin.py
- return node
- if lenv.is_py_class_scope or lenv.is_module_scope:
- if lenv.is_py_class_scope:
- pyclass = self.current_scope_node()
- locals_dict = ExprNodes.CloneNode(pyclass.dict)
- else:
- locals_dict = ExprNodes.GlobalsExprNode(pos)
- return ExprNodes.SortedDictKeysNode(locals_dict)
- local_names = sorted(var.name for var in lenv.entries.values() if var.name)
- items = [ExprNodes.IdentifierStringNode(pos, value=var)
- for var in local_names]
- return ExprNodes.ListNode(pos, args=items)
-
- def visit_PrimaryCmpNode(self, node):
- # special case: for in/not-in test, we do not need to sort locals()
- self.visitchildren(node)
- if node.operator in 'not_in': # in/not_in
- if isinstance(node.operand2, ExprNodes.SortedDictKeysNode):
- arg = node.operand2.arg
- if isinstance(arg, ExprNodes.NoneCheckNode):
- arg = arg.arg
- node.operand2 = arg
- return node
-
- def visit_CascadedCmpNode(self, node):
- return self.visit_PrimaryCmpNode(node)
-
- def _inject_eval(self, node, func_name):
- lenv = self.current_env()
- entry = lenv.lookup_here(func_name)
- if entry or len(node.args) != 1:
- return node
- # Inject globals and locals
- node.args.append(ExprNodes.GlobalsExprNode(node.pos))
- if not lenv.is_module_scope:
- node.args.append(
- ExprNodes.LocalsExprNode(
- node.pos, self.current_scope_node(), lenv))
- return node
-
- def _inject_super(self, node, func_name):
- lenv = self.current_env()
- entry = lenv.lookup_here(func_name)
- if entry or node.args:
- return node
- # Inject no-args super
- def_node = self.current_scope_node()
- if (not isinstance(def_node, Nodes.DefNode) or not def_node.args or
- len(self.env_stack) < 2):
- return node
- class_node, class_scope = self.env_stack[-2]
- if class_scope.is_py_class_scope:
- def_node.requires_classobj = True
- class_node.class_cell.is_active = True
- node.args = [
- ExprNodes.ClassCellNode(
- node.pos, is_generator=def_node.is_generator),
- ExprNodes.NameNode(node.pos, name=def_node.args[0].name)
- ]
- elif class_scope.is_c_class_scope:
- node.args = [
- ExprNodes.NameNode(
- node.pos, name=class_node.scope.name,
- entry=class_node.entry),
- ExprNodes.NameNode(node.pos, name=def_node.args[0].name)
- ]
- return node
-
- def visit_SimpleCallNode(self, node):
- # cython.foo
- function = node.function.as_cython_attribute()
- if function:
- if function in InterpretCompilerDirectives.unop_method_nodes:
- if len(node.args) != 1:
- error(node.function.pos, u"%s() takes exactly one argument" % function)
- else:
- node = InterpretCompilerDirectives.unop_method_nodes[function](
- node.function.pos, operand=node.args[0])
- elif function in InterpretCompilerDirectives.binop_method_nodes:
- if len(node.args) != 2:
- error(node.function.pos, u"%s() takes exactly two arguments" % function)
- else:
- node = InterpretCompilerDirectives.binop_method_nodes[function](
- node.function.pos, operand1=node.args[0], operand2=node.args[1])
- elif function == u'cast':
- if len(node.args) != 2:
- error(node.function.pos,
- u"cast() takes exactly two arguments and an optional typecheck keyword")
- else:
- type = node.args[0].analyse_as_type(self.current_env())
- if type:
- node = ExprNodes.TypecastNode(
- node.function.pos, type=type, operand=node.args[1], typecheck=False)
- else:
- error(node.args[0].pos, "Not a type")
- elif function == u'sizeof':
- if len(node.args) != 1:
- error(node.function.pos, u"sizeof() takes exactly one argument")
- else:
- type = node.args[0].analyse_as_type(self.current_env())
- if type:
- node = ExprNodes.SizeofTypeNode(node.function.pos, arg_type=type)
- else:
- node = ExprNodes.SizeofVarNode(node.function.pos, operand=node.args[0])
- elif function == 'cmod':
- if len(node.args) != 2:
- error(node.function.pos, u"cmod() takes exactly two arguments")
- else:
- node = ExprNodes.binop_node(node.function.pos, '%', node.args[0], node.args[1])
- node.cdivision = True
- elif function == 'cdiv':
- if len(node.args) != 2:
- error(node.function.pos, u"cdiv() takes exactly two arguments")
- else:
- node = ExprNodes.binop_node(node.function.pos, '/', node.args[0], node.args[1])
- node.cdivision = True
- elif function == u'set':
- node.function = ExprNodes.NameNode(node.pos, name=EncodedString('set'))
- elif function == u'staticmethod':
- node.function = ExprNodes.NameNode(node.pos, name=EncodedString('staticmethod'))
- elif self.context.cython_scope.lookup_qualified_name(function):
- pass
- else:
- error(node.function.pos,
- u"'%s' not a valid cython language construct" % function)
-
- self.visitchildren(node)
-
- if isinstance(node, ExprNodes.SimpleCallNode) and node.function.is_name:
- func_name = node.function.name
- if func_name in ('dir', 'locals', 'vars'):
- return self._inject_locals(node, func_name)
- if func_name == 'eval':
- return self._inject_eval(node, func_name)
- if func_name == 'super':
- return self._inject_super(node, func_name)
- return node
-
- def visit_GeneralCallNode(self, node):
- function = node.function.as_cython_attribute()
- if function == u'cast':
- # NOTE: assuming simple tuple/dict nodes for positional_args and keyword_args
- args = node.positional_args.args
- kwargs = node.keyword_args.compile_time_value(None)
- if (len(args) != 2 or len(kwargs) > 1 or
- (len(kwargs) == 1 and 'typecheck' not in kwargs)):
- error(node.function.pos,
- u"cast() takes exactly two arguments and an optional typecheck keyword")
- else:
- type = args[0].analyse_as_type(self.current_env())
- if type:
- typecheck = kwargs.get('typecheck', False)
- node = ExprNodes.TypecastNode(
- node.function.pos, type=type, operand=args[1], typecheck=typecheck)
- else:
- error(args[0].pos, "Not a type")
-
- self.visitchildren(node)
- return node
-
-
-class ReplaceFusedTypeChecks(VisitorTransform):
- """
- This is not a transform in the pipeline. It is invoked on the specific
- versions of a cdef function with fused argument types. It filters out any
- type branches that don't match. e.g.
-
- if fused_t is mytype:
- ...
- elif fused_t in other_fused_type:
- ...
- """
- def __init__(self, local_scope):
- super(ReplaceFusedTypeChecks, self).__init__()
- self.local_scope = local_scope
- # defer the import until now to avoid circular import time dependencies
- from .Optimize import ConstantFolding
- self.transform = ConstantFolding(reevaluate=True)
-
- def visit_IfStatNode(self, node):
- """
- Filters out any if clauses with false compile time type check
- expression.
- """
- self.visitchildren(node)
- return self.transform(node)
-
- def visit_PrimaryCmpNode(self, node):
- with Errors.local_errors(ignore=True):
- type1 = node.operand1.analyse_as_type(self.local_scope)
- type2 = node.operand2.analyse_as_type(self.local_scope)
-
- if type1 and type2:
- false_node = ExprNodes.BoolNode(node.pos, value=False)
- true_node = ExprNodes.BoolNode(node.pos, value=True)
-
- type1 = self.specialize_type(type1, node.operand1.pos)
- op = node.operator
-
- if op in ('is', 'is_not', '==', '!='):
- type2 = self.specialize_type(type2, node.operand2.pos)
-
- is_same = type1.same_as(type2)
- eq = op in ('is', '==')
-
- if (is_same and eq) or (not is_same and not eq):
- return true_node
-
- elif op in ('in', 'not_in'):
- # We have to do an instance check directly, as operand2
- # needs to be a fused type and not a type with a subtype
- # that is fused. First unpack the typedef
- if isinstance(type2, PyrexTypes.CTypedefType):
- type2 = type2.typedef_base_type
-
- if type1.is_fused:
- error(node.operand1.pos, "Type is fused")
- elif not type2.is_fused:
- error(node.operand2.pos,
- "Can only use 'in' or 'not in' on a fused type")
- else:
- types = PyrexTypes.get_specialized_types(type2)
-
- for specialized_type in types:
- if type1.same_as(specialized_type):
- if op == 'in':
- return true_node
- else:
- return false_node
-
- if op == 'not_in':
- return true_node
-
- return false_node
-
- return node
-
- def specialize_type(self, type, pos):
- try:
- return type.specialize(self.local_scope.fused_to_specific)
- except KeyError:
- error(pos, "Type is not specific")
- return type
-
- def visit_Node(self, node):
- self.visitchildren(node)
- return node
-
-
-class DebugTransform(CythonTransform):
- """
- Write debug information for this Cython module.
- """
-
- def __init__(self, context, options, result):
- super(DebugTransform, self).__init__(context)
- self.visited = set()
- # our treebuilder and debug output writer
- # (see Cython.Debugger.debug_output.CythonDebugWriter)
- self.tb = self.context.gdb_debug_outputwriter
- #self.c_output_file = options.output_file
- self.c_output_file = result.c_file
-
- # Closure support, basically treat nested functions as if the AST were
- # never nested
- self.nested_funcdefs = []
-
- # tells visit_NameNode whether it should register step-into functions
- self.register_stepinto = False
-
- def visit_ModuleNode(self, node):
- self.tb.module_name = node.full_module_name
- attrs = dict(
- module_name=node.full_module_name,
- filename=node.pos[0].filename,
- c_filename=self.c_output_file)
-
- self.tb.start('Module', attrs)
-
- # serialize functions
- self.tb.start('Functions')
- # First, serialize functions normally...
- self.visitchildren(node)
-
- # ... then, serialize nested functions
- for nested_funcdef in self.nested_funcdefs:
- self.visit_FuncDefNode(nested_funcdef)
-
- self.register_stepinto = True
- self.serialize_modulenode_as_function(node)
- self.register_stepinto = False
- self.tb.end('Functions')
-
- # 2.3 compatibility. Serialize global variables
- self.tb.start('Globals')
- entries = {}
-
- for k, v in node.scope.entries.items():
- if (v.qualified_name not in self.visited and not
- v.name.startswith('__pyx_') and not
- v.type.is_cfunction and not
- v.type.is_extension_type):
- entries[k]= v
-
- self.serialize_local_variables(entries)
- self.tb.end('Globals')
- # self.tb.end('Module') # end Module after the line number mapping in
- # Cython.Compiler.ModuleNode.ModuleNode._serialize_lineno_map
- return node
-
- def visit_FuncDefNode(self, node):
- self.visited.add(node.local_scope.qualified_name)
-
- if getattr(node, 'is_wrapper', False):
- return node
-
- if self.register_stepinto:
- self.nested_funcdefs.append(node)
- return node
-
- # node.entry.visibility = 'extern'
- if node.py_func is None:
- pf_cname = ''
- else:
- pf_cname = node.py_func.entry.func_cname
-
- attrs = dict(
- name=node.entry.name or getattr(node, 'name', ''),
- cname=node.entry.func_cname,
- pf_cname=pf_cname,
- qualified_name=node.local_scope.qualified_name,
- lineno=str(node.pos[1]))
-
- self.tb.start('Function', attrs=attrs)
-
- self.tb.start('Locals')
- self.serialize_local_variables(node.local_scope.entries)
- self.tb.end('Locals')
-
- self.tb.start('Arguments')
- for arg in node.local_scope.arg_entries:
- self.tb.start(arg.name)
- self.tb.end(arg.name)
- self.tb.end('Arguments')
-
- self.tb.start('StepIntoFunctions')
- self.register_stepinto = True
- self.visitchildren(node)
- self.register_stepinto = False
- self.tb.end('StepIntoFunctions')
- self.tb.end('Function')
-
- return node
-
- def visit_NameNode(self, node):
- if (self.register_stepinto and
- node.type is not None and
- node.type.is_cfunction and
- getattr(node, 'is_called', False) and
- node.entry.func_cname is not None):
- # don't check node.entry.in_cinclude, as 'cdef extern: ...'
- # declared functions are not 'in_cinclude'.
- # This means we will list called 'cdef' functions as
- # "step into functions", but this is not an issue as they will be
- # recognized as Cython functions anyway.
- attrs = dict(name=node.entry.func_cname)
- self.tb.start('StepIntoFunction', attrs=attrs)
- self.tb.end('StepIntoFunction')
-
- self.visitchildren(node)
- return node
-
- def serialize_modulenode_as_function(self, node):
- """
- Serialize the module-level code as a function so the debugger will know
- it's a "relevant frame" and it will know where to set the breakpoint
- for 'break modulename'.
- """
- name = node.full_module_name.rpartition('.')[-1]
-
- cname_py2 = 'init' + name
- cname_py3 = 'PyInit_' + name
-
- py2_attrs = dict(
- name=name,
- cname=cname_py2,
- pf_cname='',
- # Ignore the qualified_name, breakpoints should be set using
- # `cy break modulename:lineno` for module-level breakpoints.
- qualified_name='',
- lineno='1',
- is_initmodule_function="True",
- )
-
- py3_attrs = dict(py2_attrs, cname=cname_py3)
-
- self._serialize_modulenode_as_function(node, py2_attrs)
- self._serialize_modulenode_as_function(node, py3_attrs)
-
- def _serialize_modulenode_as_function(self, node, attrs):
- self.tb.start('Function', attrs=attrs)
-
- self.tb.start('Locals')
- self.serialize_local_variables(node.scope.entries)
- self.tb.end('Locals')
-
- self.tb.start('Arguments')
- self.tb.end('Arguments')
-
- self.tb.start('StepIntoFunctions')
- self.register_stepinto = True
- self.visitchildren(node)
- self.register_stepinto = False
- self.tb.end('StepIntoFunctions')
-
- self.tb.end('Function')
-
- def serialize_local_variables(self, entries):
- for entry in entries.values():
- if not entry.cname:
- # not a local variable
- continue
- if entry.type.is_pyobject:
- vartype = 'PythonObject'
- else:
- vartype = 'CObject'
-
- if entry.from_closure:
- # We're dealing with a closure where a variable from an outer
- # scope is accessed, get it from the scope object.
- cname = '%s->%s' % (Naming.cur_scope_cname,
- entry.outer_entry.cname)
-
- qname = '%s.%s.%s' % (entry.scope.outer_scope.qualified_name,
- entry.scope.name,
- entry.name)
- elif entry.in_closure:
- cname = '%s->%s' % (Naming.cur_scope_cname,
- entry.cname)
- qname = entry.qualified_name
- else:
- cname = entry.cname
- qname = entry.qualified_name
-
- if not entry.pos:
- # this happens for variables that are not in the user's code,
- # e.g. for the global __builtins__, __doc__, etc. We can just
- # set the lineno to 0 for those.
- lineno = '0'
- else:
- lineno = str(entry.pos[1])
-
- attrs = dict(
- name=entry.name,
- cname=cname,
- qualified_name=qname,
- type=vartype,
- lineno=lineno)
-
- self.tb.start('LocalVar', attrs)
- self.tb.end('LocalVar')
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Plex/Actions.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Plex/Actions.py
deleted file mode 100644
index c88176e71615ca4ee5454a49a8b25ae18471b292..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Plex/Actions.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# cython: auto_pickle=False
-#=======================================================================
-#
-# Python Lexical Analyser
-#
-# Actions for use in token specifications
-#
-#=======================================================================
-
-class Action(object):
- def perform(self, token_stream, text):
- pass # abstract
-
- def same_as(self, other):
- return self is other
-
-
-class Return(Action):
- """
- Internal Plex action which causes |value| to
- be returned as the value of the associated token
- """
-
- def __init__(self, value):
- self.value = value
-
- def perform(self, token_stream, text):
- return self.value
-
- def same_as(self, other):
- return isinstance(other, Return) and self.value == other.value
-
- def __repr__(self):
- return "Return(%s)" % repr(self.value)
-
-
-class Call(Action):
- """
- Internal Plex action which causes a function to be called.
- """
-
- def __init__(self, function):
- self.function = function
-
- def perform(self, token_stream, text):
- return self.function(token_stream, text)
-
- def __repr__(self):
- return "Call(%s)" % self.function.__name__
-
- def same_as(self, other):
- return isinstance(other, Call) and self.function is other.function
-
-
-class Begin(Action):
- """
- Begin(state_name) is a Plex action which causes the Scanner to
- enter the state |state_name|. See the docstring of Plex.Lexicon
- for more information.
- """
-
- def __init__(self, state_name):
- self.state_name = state_name
-
- def perform(self, token_stream, text):
- token_stream.begin(self.state_name)
-
- def __repr__(self):
- return "Begin(%s)" % self.state_name
-
- def same_as(self, other):
- return isinstance(other, Begin) and self.state_name == other.state_name
-
-
-class Ignore(Action):
- """
- IGNORE is a Plex action which causes its associated token
- to be ignored. See the docstring of Plex.Lexicon for more
- information.
- """
-
- def perform(self, token_stream, text):
- return None
-
- def __repr__(self):
- return "IGNORE"
-
-
-IGNORE = Ignore()
-#IGNORE.__doc__ = Ignore.__doc__
-
-
-class Text(Action):
- """
- TEXT is a Plex action which causes the text of a token to
- be returned as the value of the token. See the docstring of
- Plex.Lexicon for more information.
- """
-
- def perform(self, token_stream, text):
- return text
-
- def __repr__(self):
- return "TEXT"
-
-
-TEXT = Text()
-#TEXT.__doc__ = Text.__doc__
-
-
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vega/display.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vega/display.py
deleted file mode 100644
index adc3167ca759b6ce1257ca9e79329ec543d238a9..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vega/display.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from ..utils.display import Displayable, default_renderer_base, json_renderer_base
-from ..utils.display import MimeBundleType, RendererType, HTMLRenderer
-
-
-__all__ = (
- "Displayable",
- "default_renderer_base",
- "json_renderer_base",
- "MimeBundleType",
- "RendererType",
- "HTMLRenderer",
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/api.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/api.py
deleted file mode 100644
index b70e2bda3f992e44748334d42c2f5dff48191c1c..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/vegalite/api.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# flake8: noqa
-from .v4.api import *
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/ErrorListener.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/ErrorListener.py
deleted file mode 100644
index 933264d431b9829f43a38d5f0f07c83bbad703a0..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/ErrorListener.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-
-# Provides an empty default implementation of {@link ANTLRErrorListener}. The
-# default implementation of each method does nothing, but can be overridden as
-# necessary.
-
-
-import sys
-
-class ErrorListener(object):
-
- def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e):
- pass
-
- def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs):
- pass
-
- def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs):
- pass
-
- def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs):
- pass
-
-class ConsoleErrorListener(ErrorListener):
- #
- # Provides a default instance of {@link ConsoleErrorListener}.
- #
- INSTANCE = None
-
- #
- # {@inheritDoc}
- #
- #
- # This implementation prints messages to {@link System#err} containing the
- # values of {@code line}, {@code charPositionInLine}, and {@code msg} using
- # the following format.
- #
- #
- # line line:charPositionInLine msg
- #
- #
- def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e):
- print("line " + str(line) + ":" + str(column) + " " + msg, file=sys.stderr)
-
-ConsoleErrorListener.INSTANCE = ConsoleErrorListener()
-
-class ProxyErrorListener(ErrorListener):
-
- def __init__(self, delegates):
- super().__init__()
- if delegates is None:
- raise ReferenceError("delegates")
- self.delegates = delegates
-
- def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e):
- for delegate in self.delegates:
- delegate.syntaxError(recognizer, offendingSymbol, line, column, msg, e)
-
- def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs):
- for delegate in self.delegates:
- delegate.reportAmbiguity(recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs)
-
- def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs):
- for delegate in self.delegates:
- delegate.reportAttemptingFullContext(recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs)
-
- def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs):
- for delegate in self.delegates:
- delegate.reportContextSensitivity(recognizer, dfa, startIndex, stopIndex, prediction, configs)
diff --git a/spaces/asafAdge/Detic/detic/modeling/backbone/swintransformer.py b/spaces/asafAdge/Detic/detic/modeling/backbone/swintransformer.py
deleted file mode 100644
index 21cabb37dd87a443e27eeb805f9739bef86540bf..0000000000000000000000000000000000000000
--- a/spaces/asafAdge/Detic/detic/modeling/backbone/swintransformer.py
+++ /dev/null
@@ -1,750 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Xingyi Zhou from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.backbone.backbone import Backbone
-from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
-from detectron2.modeling.backbone.fpn import FPN
-
-from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5
-from centernet.modeling.backbone.bifpn import BiFPN
-# from .checkpoint import load_checkpoint
-
-class Mlp(nn.Module):
- """ Multilayer perceptron."""
-
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- """ Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """ Forward function.
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- """ Swin Transformer Block.
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- self.H = None
- self.W = None
-
- def forward(self, x, mask_matrix):
- """ Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- mask_matrix: Attention mask for cyclic shift.
- """
- B, L, C = x.shape
- H, W = self.H, self.W
- assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # pad feature maps to multiples of window size
- pad_l = pad_t = 0
- pad_r = (self.window_size - W % self.window_size) % self.window_size
- pad_b = (self.window_size - H % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- attn_mask = mask_matrix
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- x = x[:, :H, :W, :].contiguous()
-
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- return x
-
-
-class PatchMerging(nn.Module):
- """ Patch Merging Layer
- Args:
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
- def __init__(self, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x, H, W):
- """ Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
-
- x = x.view(B, H, W, C)
-
- # padding
- pad_input = (H % 2 == 1) or (W % 2 == 1)
- if pad_input:
- x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
- Args:
- dim (int): Number of feature channels
- depth (int): Depths of this stage.
- num_heads (int): Number of attention head.
- window_size (int): Local window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- dim,
- depth,
- num_heads,
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop=0.,
- attn_drop=0.,
- drop_path=0.,
- norm_layer=nn.LayerNorm,
- downsample=None,
- use_checkpoint=False):
- super().__init__()
- self.window_size = window_size
- self.shift_size = window_size // 2
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(
- dim=dim,
- num_heads=num_heads,
- window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop,
- attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, H, W):
- """ Forward function.
- Args:
- x: Input feature, tensor size (B, H*W, C).
- H, W: Spatial resolution of the input feature.
- """
-
- # calculate attention mask for SW-MSA
- Hp = int(np.ceil(H / self.window_size)) * self.window_size
- Wp = int(np.ceil(W / self.window_size)) * self.window_size
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- for blk in self.blocks:
- blk.H, blk.W = H, W
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, attn_mask)
- else:
- x = blk(x, attn_mask)
- if self.downsample is not None:
- x_down = self.downsample(x, H, W)
- Wh, Ww = (H + 1) // 2, (W + 1) // 2
- return x, H, W, x_down, Wh, Ww
- else:
- return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
- """ Image to Patch Embedding
- Args:
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- patch_size = to_2tuple(patch_size)
- self.patch_size = patch_size
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- """Forward function."""
- # padding
- _, _, H, W = x.size()
- if W % self.patch_size[1] != 0:
- x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
- if H % self.patch_size[0] != 0:
- x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
- x = self.proj(x) # B C Wh Ww
- if self.norm is not None:
- Wh, Ww = x.size(2), x.size(3)
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
- return x
-
-
-class SwinTransformer(Backbone):
- """ Swin Transformer backbone.
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
- https://arxiv.org/pdf/2103.14030
- Args:
- pretrain_img_size (int): Input image size for training the pretrained model,
- used in absolute postion embedding. Default 224.
- patch_size (int | tuple(int)): Patch size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- depths (tuple[int]): Depths of each Swin Transformer stage.
- num_heads (tuple[int]): Number of attention head of each stage.
- window_size (int): Window size. Default: 7.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
- drop_rate (float): Dropout rate.
- attn_drop_rate (float): Attention dropout rate. Default: 0.
- drop_path_rate (float): Stochastic depth rate. Default: 0.2.
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
- patch_norm (bool): If True, add normalization after patch embedding. Default: True.
- out_indices (Sequence[int]): Output from which stages.
- frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
- -1 means not freezing any parameters.
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self,
- pretrain_img_size=224,
- patch_size=4,
- in_chans=3,
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2,
- norm_layer=nn.LayerNorm,
- ape=False,
- patch_norm=True,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- use_checkpoint=False):
- super().__init__()
-
- self.pretrain_img_size = pretrain_img_size
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.out_indices = out_indices
- self.frozen_stages = frozen_stages
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
-
- # absolute position embedding
- if self.ape:
- pretrain_img_size = to_2tuple(pretrain_img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- # build layers
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = BasicLayer(
- dim=int(embed_dim * 2 ** i_layer),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- qk_scale=qk_scale,
- drop=drop_rate,
- attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
- norm_layer=norm_layer,
- downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
- use_checkpoint=use_checkpoint)
- self.layers.append(layer)
-
- num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
- self.num_features = num_features
-
- # add a norm layer for each output
- for i_layer in out_indices:
- layer = norm_layer(num_features[i_layer])
- layer_name = f'norm{i_layer}'
- self.add_module(layer_name, layer)
-
- self._freeze_stages()
- self._out_features = ['swin{}'.format(i) for i in self.out_indices]
- self._out_feature_channels = {
- 'swin{}'.format(i): self.embed_dim * 2 ** i for i in self.out_indices
- }
- self._out_feature_strides = {
- 'swin{}'.format(i): 2 ** (i + 2) for i in self.out_indices
- }
- self._size_devisibility = 32
-
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- self.patch_embed.eval()
- for param in self.patch_embed.parameters():
- param.requires_grad = False
-
- if self.frozen_stages >= 1 and self.ape:
- self.absolute_pos_embed.requires_grad = False
-
- if self.frozen_stages >= 2:
- self.pos_drop.eval()
- for i in range(0, self.frozen_stages - 1):
- m = self.layers[i]
- m.eval()
- for param in m.parameters():
- param.requires_grad = False
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in backbone.
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
-
- def _init_weights(m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- if isinstance(pretrained, str):
- self.apply(_init_weights)
- # load_checkpoint(self, pretrained, strict=False)
- elif pretrained is None:
- self.apply(_init_weights)
- else:
- raise TypeError('pretrained must be a str or None')
-
- def forward(self, x):
- """Forward function."""
- x = self.patch_embed(x)
-
- Wh, Ww = x.size(2), x.size(3)
- if self.ape:
- # interpolate the position embedding to the corresponding size
- absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
- x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
- else:
- x = x.flatten(2).transpose(1, 2)
- x = self.pos_drop(x)
-
- # outs = []
- outs = {}
- for i in range(self.num_layers):
- layer = self.layers[i]
- x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
- if i in self.out_indices:
- norm_layer = getattr(self, f'norm{i}')
- x_out = norm_layer(x_out)
-
- out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
- # outs.append(out)
- outs['swin{}'.format(i)] = out
-
- return outs
-
- def train(self, mode=True):
- """Convert the model into training mode while keep layers freezed."""
- super(SwinTransformer, self).train(mode)
- self._freeze_stages()
-
-size2config = {
- 'T': {
- 'window_size': 7,
- 'embed_dim': 96,
- 'depth': [2, 2, 6, 2],
- 'num_heads': [3, 6, 12, 24],
- 'drop_path_rate': 0.2,
- 'pretrained': 'models/swin_tiny_patch4_window7_224.pth'
- },
- 'S': {
- 'window_size': 7,
- 'embed_dim': 96,
- 'depth': [2, 2, 18, 2],
- 'num_heads': [3, 6, 12, 24],
- 'drop_path_rate': 0.2,
- 'pretrained': 'models/swin_small_patch4_window7_224.pth'
- },
- 'B': {
- 'window_size': 7,
- 'embed_dim': 128,
- 'depth': [2, 2, 18, 2],
- 'num_heads': [4, 8, 16, 32],
- 'drop_path_rate': 0.3,
- 'pretrained': 'models/swin_base_patch4_window7_224.pth'
- },
- 'B-22k': {
- 'window_size': 7,
- 'embed_dim': 128,
- 'depth': [2, 2, 18, 2],
- 'num_heads': [4, 8, 16, 32],
- 'drop_path_rate': 0.3,
- 'pretrained': 'models/swin_base_patch4_window7_224_22k.pth'
- },
- 'B-22k-384': {
- 'window_size': 12,
- 'embed_dim': 128,
- 'depth': [2, 2, 18, 2],
- 'num_heads': [4, 8, 16, 32],
- 'drop_path_rate': 0.3,
- 'pretrained': 'models/swin_base_patch4_window12_384_22k.pth'
- },
- 'L-22k': {
- 'window_size': 7,
- 'embed_dim': 192,
- 'depth': [2, 2, 18, 2],
- 'num_heads': [6, 12, 24, 48],
- 'drop_path_rate': 0.3, # TODO (xingyi): this is unclear
- 'pretrained': 'models/swin_large_patch4_window7_224_22k.pth'
- },
- 'L-22k-384': {
- 'window_size': 12,
- 'embed_dim': 192,
- 'depth': [2, 2, 18, 2],
- 'num_heads': [6, 12, 24, 48],
- 'drop_path_rate': 0.3, # TODO (xingyi): this is unclear
- 'pretrained': 'models/swin_large_patch4_window12_384_22k.pth'
- }
-}
-
-@BACKBONE_REGISTRY.register()
-def build_swintransformer_backbone(cfg, input_shape):
- """
- """
- config = size2config[cfg.MODEL.SWIN.SIZE]
- out_indices = cfg.MODEL.SWIN.OUT_FEATURES
- model = SwinTransformer(
- embed_dim=config['embed_dim'],
- window_size=config['window_size'],
- depths=config['depth'],
- num_heads=config['num_heads'],
- drop_path_rate=config['drop_path_rate'],
- out_indices=out_indices,
- frozen_stages=-1,
- use_checkpoint=cfg.MODEL.SWIN.USE_CHECKPOINT
- )
- # print('Initializing', config['pretrained'])
- model.init_weights(config['pretrained'])
- return model
-
-
-@BACKBONE_REGISTRY.register()
-def build_swintransformer_fpn_backbone(cfg, input_shape: ShapeSpec):
- """
- """
- bottom_up = build_swintransformer_backbone(cfg, input_shape)
- in_features = cfg.MODEL.FPN.IN_FEATURES
- out_channels = cfg.MODEL.FPN.OUT_CHANNELS
- backbone = FPN(
- bottom_up=bottom_up,
- in_features=in_features,
- out_channels=out_channels,
- norm=cfg.MODEL.FPN.NORM,
- top_block=LastLevelP6P7_P5(out_channels, out_channels),
- fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
- )
- return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_swintransformer_bifpn_backbone(cfg, input_shape: ShapeSpec):
- """
- """
- bottom_up = build_swintransformer_backbone(cfg, input_shape)
- in_features = cfg.MODEL.FPN.IN_FEATURES
- backbone = BiFPN(
- cfg=cfg,
- bottom_up=bottom_up,
- in_features=in_features,
- out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS,
- norm=cfg.MODEL.BIFPN.NORM,
- num_levels=cfg.MODEL.BIFPN.NUM_LEVELS,
- num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN,
- separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV,
- )
- return backbone
\ No newline at end of file
diff --git a/spaces/atimughal662/InfoFusion/src/gen.py b/spaces/atimughal662/InfoFusion/src/gen.py
deleted file mode 100644
index 5919c5cbf8dbec02e05487b003c736398367aad6..0000000000000000000000000000000000000000
--- a/spaces/atimughal662/InfoFusion/src/gen.py
+++ /dev/null
@@ -1,4307 +0,0 @@
-import ast
-import copy
-import functools
-import inspect
-import queue
-import sys
-import os
-import time
-import traceback
-import typing
-import warnings
-from datetime import datetime
-import requests
-from requests import ConnectTimeout, JSONDecodeError
-from urllib3.exceptions import ConnectTimeoutError, MaxRetryError, ConnectionError
-from requests.exceptions import ConnectionError as ConnectionError2
-from requests.exceptions import ReadTimeout as ReadTimeout2
-
-if os.path.dirname(os.path.abspath(__file__)) not in sys.path:
- sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
-os.environ['BITSANDBYTES_NOWELCOME'] = '1'
-warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
-
-# more is not useful typically, don't let these go beyond limits and eat up resources
-max_cores = max(1, os.cpu_count() // 2)
-if os.getenv('NUMEXPR_MAX_THREADS') is None:
- os.environ['NUMEXPR_MAX_THREADS'] = str(min(8, max_cores))
-if os.getenv('NUMEXPR_NUM_THREADS') is None:
- os.environ['NUMEXPR_NUM_THREADS'] = str(min(8, max_cores))
-if os.getenv('OMP_NUM_THREADS') is None:
- os.environ['OMP_NUM_THREADS'] = str(min(8, max_cores))
-if os.getenv('OPENBLAS_NUM_THREADS') is None:
- os.environ['OPENBLAS_NUM_THREADS'] = str(min(8, max_cores))
-if os.getenv('DUCKDB_NUM_THREADS') is None:
- os.environ['DUCKDB_NUM_THREADS'] = str(min(4, max_cores))
-if os.getenv('RAYON_RS_NUM_CPUS') is None:
- os.environ['RAYON_RS_NUM_CPUS'] = str(min(8, max_cores))
-if os.getenv('RAYON_NUM_THREADS') is None:
- os.environ['RAYON_NUM_THREADS'] = str(min(8, max_cores))
-
-import numpy as np
-from evaluate_params import eval_func_param_names, no_default_param_names, input_args_list
-from enums import DocumentSubset, LangChainMode, no_lora_str, model_token_mapping, no_model_str, \
- LangChainAction, LangChainAgent, DocumentChoice, LangChainTypes, super_source_prefix, \
- super_source_postfix, t5_type, get_langchain_prompts, gr_to_lg, invalid_key_msg, docs_joiner_default, \
- docs_ordering_types_default, docs_token_handling_default
-from loaders import get_loaders
-# import utils import .
-from utzils import set_seed, clear_torch_cache, NullContext, wrapped_partial, EThread, get_githash, \
- import_matplotlib, get_device, makedirs, get_kwargs, start_faulthandler, get_hf_server, FakeTokenizer, \
- have_langchain, set_openai, cuda_vis_check, H2O_Fire, lg_to_gr, str_to_list, str_to_dict, get_token_count
-
-start_faulthandler()
-import_matplotlib()
-
-SEED = 1236
-set_seed(SEED)
-
-from typing import Union
-
-import torch
-from transformers import GenerationConfig, AutoModel, TextIteratorStreamer
-
-from prompter import Prompter, inv_prompt_type_to_model_lower, non_hf_types, PromptType, get_prompt, generate_prompt
-from stopping import get_stopping
-
-langchain_actions = [x.value for x in list(LangChainAction)]
-
-langchain_agents_list = [x.value for x in list(LangChainAgent)]
-
-
-def main(
- load_8bit: bool = False,
- load_4bit: bool = False,
- low_bit_mode: int = 1,
- load_half: bool = None,
- load_gptq: str = '',
- load_awq: str = '',
- load_exllama: bool = False,
- use_safetensors: bool = False,
- revision: str = None,
- use_gpu_id: bool = True,
- base_model: str = '',
- tokenizer_base_model: str = '',
- lora_weights: str = "",
- gpu_id: int = 0,
- compile_model: bool = None,
- use_cache: bool = None,
- inference_server: str = "",
- prompt_type: Union[int, str] = None,
- prompt_dict: typing.Dict = None,
- system_prompt: str = '',
-
- # llama and gpt4all settings
- llamacpp_dict: typing.Dict = dict(n_gpu_layers=100, use_mlock=True, n_batch=1024, n_gqa=0),
- model_path_llama: str = 'https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf',
- model_name_gptj: str = 'ggml-gpt4all-j-v1.3-groovy.bin',
- model_name_gpt4all_llama: str = 'ggml-wizardLM-7B.q4_2.bin',
- model_name_exllama_if_no_config: str = 'TheBloke/Nous-Hermes-Llama2-GPTQ',
- exllama_dict: typing.Dict = dict(),
- gptq_dict: typing.Dict = dict(),
- attention_sinks: bool = False,
- sink_dict: typing.Dict = dict(),
- truncation_generation: bool = False,
- hf_model_dict: typing.Dict = dict(),
-
- model_lock: typing.List[typing.Dict[str, str]] = None,
- model_lock_columns: int = None,
- fail_if_cannot_connect: bool = False,
-
- # input to generation
- temperature: float = None,
- top_p: float = None,
- top_k: int = None,
- penalty_alpha: float = None,
- num_beams: int = None,
- repetition_penalty: float = None,
- num_return_sequences: int = None,
- do_sample: bool = None,
- max_new_tokens: int = None,
- min_new_tokens: int = None,
- early_stopping: Union[bool, str] = None,
- max_time: float = None,
-
- memory_restriction_level: int = None,
- debug: bool = False,
- save_dir: str = None,
- local_files_only: bool = False,
- resume_download: bool = True,
- use_auth_token: Union[str, bool] = False,
- trust_remote_code: Union[str, bool] = True,
- rope_scaling: dict = None,
- max_seq_len: int = None,
- offload_folder: str = "offline_folder",
-
- src_lang: str = "English",
- tgt_lang: str = "Russian",
-
- prepare_offline_level: int = 0,
- cli: bool = False,
- cli_loop: bool = True,
- gradio: bool = True,
- gradio_offline_level: int = 0,
- server_name: str = "0.0.0.0",
- share: bool = False,
- open_browser: bool = False,
- root_path: str = "",
- ssl_verify: bool = True,
- ssl_keyfile: str | None = None,
- ssl_certfile: str | None = None,
- ssl_keyfile_password: str | None = None,
-
- chat: bool = True,
- chat_conversation: typing.List[typing.Tuple[str, str]] = None,
- text_context_list: typing.List[str] = None,
- stream_output: bool = True,
- async_output: bool = True,
- num_async: int = 3,
- show_examples: bool = None,
- verbose: bool = False,
- h2ocolors: bool = True,
- dark: bool = False, # light tends to be best
- height: int = 600,
- render_markdown: bool = True,
- show_lora: bool = True,
- show_llama: bool = True,
- show_gpt4all: bool = False,
- login_mode_if_model0: bool = False,
- block_gradio_exit: bool = True,
- concurrency_count: int = 1,
- api_open: bool = False,
- allow_api: bool = True,
- input_lines: int = 1,
- gradio_size: str = None,
- show_copy_button: bool = True,
- large_file_count_mode: bool = False,
- pre_load_embedding_model: bool = True,
-
- auth: Union[typing.List[typing.Tuple[str, str]], str] = None,
- auth_filename: str = None,
- auth_access: str = 'open',
- auth_freeze: bool = False,
- auth_message: str = None,
- guest_name: str = "guest",
- enforce_h2ogpt_api_key: bool = None,
- enforce_h2ogpt_ui_key: bool = None,
- h2ogpt_api_keys: Union[list, str] = [],
- h2ogpt_key: str = None,
-
- max_max_time=None,
- max_max_new_tokens=None,
-
- visible_models: list = None,
- visible_visible_models: bool = True,
- visible_submit_buttons: bool = True,
- visible_side_bar: bool = True,
- visible_doc_track: bool = True,
- visible_chat_tab: bool = True,
- visible_doc_selection_tab: bool = True,
- visible_doc_view_tab: bool = True,
- visible_chat_history_tab: bool = True,
- visible_expert_tab: bool = True,
- visible_models_tab: bool = True,
- visible_system_tab: bool = True,
- visible_tos_tab: bool = False,
- visible_login_tab: bool = True,
- visible_hosts_tab: bool = False,
- chat_tables: bool = False,
- visible_h2ogpt_header: bool = True,
- max_raw_chunks: int = None,
-
- sanitize_user_prompt: bool = False,
- sanitize_bot_response: bool = False,
-
- extra_model_options: typing.List[str] = [],
- extra_lora_options: typing.List[str] = [],
- extra_server_options: typing.List[str] = [],
-
- score_model: str = 'auto',
-
- eval_filename: str = None,
- eval_prompts_only_num: int = 0,
- eval_prompts_only_seed: int = 1234,
- eval_as_output: bool = False,
-
- langchain_mode: str = None,
- user_path: str = None,
- langchain_modes: list = [LangChainMode.USER_DATA.value, LangChainMode.MY_DATA.value, LangChainMode.LLM.value,
- LangChainMode.DISABLED.value],
- langchain_mode_paths: dict = {LangChainMode.USER_DATA.value: None},
- langchain_mode_types: dict = {LangChainMode.USER_DATA.value: LangChainTypes.SHARED.value},
- detect_user_path_changes_every_query: bool = False,
-
- langchain_action: str = LangChainAction.QUERY.value,
- langchain_agents: list = [],
- force_langchain_evaluate: bool = False,
-
- visible_langchain_actions: list = [LangChainAction.QUERY.value, LangChainAction.SUMMARIZE_MAP.value,
- LangChainAction.EXTRACT.value],
- visible_langchain_agents: list = langchain_agents_list.copy(),
-
- document_subset: str = DocumentSubset.Relevant.name,
- document_choice: list = [DocumentChoice.ALL.value],
-
- use_llm_if_no_docs: bool = True,
- load_db_if_exists: bool = True,
- keep_sources_in_context: bool = False,
- db_type: str = 'chroma',
- use_openai_embedding: bool = False,
- use_openai_model: bool = False,
- hf_embedding_model: str = None,
- migrate_embedding_model: str = False,
- auto_migrate_db: bool = False,
- cut_distance: float = 1.64,
- answer_with_sources: bool = True,
- append_sources_to_answer: bool = True,
- show_accordions: bool = True,
- top_k_docs_max_show: int = 10,
- show_link_in_sources: bool = True,
- pre_prompt_query: str = None,
- prompt_query: str = None,
- pre_prompt_summary: str = None,
- prompt_summary: str = None,
- add_chat_history_to_context: bool = True,
- add_search_to_context: bool = False,
- context: str = '',
- iinput: str = '',
- allow_upload_to_user_data: bool = True,
- reload_langchain_state: bool = True,
- allow_upload_to_my_data: bool = True,
- enable_url_upload: bool = True,
- enable_text_upload: bool = True,
- enable_sources_list: bool = True,
- chunk: bool = True,
- chunk_size: int = 512,
- top_k_docs: int = None,
- docs_ordering_type: str = docs_ordering_types_default,
- min_max_new_tokens=256,
- max_input_tokens=-1,
- docs_token_handling: str = docs_token_handling_default,
- docs_joiner: str = docs_joiner_default,
- hyde_level: int = 0,
- hyde_template: str = None,
-
- auto_reduce_chunks: bool = True,
- max_chunks: int = 100,
- headsize: int = 50,
- n_jobs: int = -1,
-
- # urls
- use_unstructured=True,
- use_playwright=False,
- use_selenium=False,
-
- # pdfs
- use_pymupdf='auto',
- use_unstructured_pdf='auto',
- use_pypdf='auto',
- enable_pdf_ocr='auto',
- enable_pdf_doctr='auto',
- try_pdf_as_html='auto',
-
- # images
- enable_ocr=False,
- enable_doctr=True,
- enable_pix2struct=False,
- enable_captions=True,
-
- pre_load_caption_model: bool = False,
- caption_gpu: bool = True,
- caption_gpu_id: Union[int, str] = 'auto',
- captions_model: str = "Salesforce/blip-image-captioning-base",
- doctr_gpu: bool = True,
- doctr_gpu_id: Union[int, str] = 'auto',
-
- # json
- jq_schema='.[]',
-
- max_quality: bool = False,
-
- enable_heap_analytics: bool = True,
- heap_app_id: str = "1680123994",
-):
- """
-
- :param load_8bit: load model in 8-bit using bitsandbytes
- :param load_4bit: load model in 4-bit using bitsandbytes
- :param low_bit_mode: 0: no quantization config 1: change compute 2: nf4 3: double quant 4: 2 and 3
- See: https://huggingface.co/docs/transformers/main_classes/quantization
- If using older bitsandbytes or transformers, 0 is required
- :param load_half: load model in float16 (None means auto, which means True unless t5 based model)
- otherwise specify bool
- :param load_gptq: to load model with GPTQ, put model_basename here, e.g. gptq_model-4bit--1g
- :param load_awq: load model with AWQ, often 'model' for TheBloke models
- :param load_exllama: whether to use exllama (only applicable to LLaMa1/2 models with 16-bit or GPTQ
- :param use_safetensors: to use safetensors version (assumes file/HF points to safe tensors version)
- :param revision: Which HF revision to use
- :param use_gpu_id: whether to control devices with gpu_id. If False, then spread across GPUs
- :param base_model: model HF-type name. If use --base_model to preload model, cannot unload in gradio in models tab
- :param tokenizer_base_model: tokenizer HF-type name. Usually not required, inferred from base_model.
- :param lora_weights: LORA weights path/HF link
- :param gpu_id: if use_gpu_id, then use gpu_id for cuda device ID, or auto mode if gpu_id != -1
- :param compile_model Whether to compile the model
- :param use_cache: Whether to use caching in model (some models fail when multiple threads use)
- :param inference_server: Consume base_model as type of model at this address
- Address can be text-generation-server hosting that base_model
- e.g. python generate.py --inference_server="http://192.168.1.46:6112" --base_model=h2oai/h2ogpt-oasst1-512-12b
-
- Or Address can be "openai_chat" or "openai" for OpenAI API
- Or Address can be "openai_azure_chat" or "openai_azure" for Azure OpenAI API
- e.g. python generate.py --inference_server="openai_chat" --base_model=gpt-3.5-turbo
- e.g. python generate.py --inference_server="openai" --base_model=text-davinci-003
- e.g. python generate.py --inference_server="openai_azure_chat::::" --base_model=gpt-3.5-turbo
- e.g. python generate.py --inference_server="openai_azure::::" --base_model=text-davinci-003
- Optionals (Replace with None or just leave empty but keep :)
- of some deployment name
- : e.g. ".openai.azure.com" for some without https://
- of some api, e.g. 2023-05-15
- e.g. 0613
-
- Or Address can be for vLLM:
- Use: "vllm:IP:port" for OpenAI-compliant vLLM endpoint
- Use: "vllm_chat:IP:port" for OpenAI-Chat-compliant vLLM endpoint
-
- Use: "vllm:http://IP:port/v1" for OpenAI-compliant vLLM endpoint
- Use: "vllm_chat:http://IP:port/v1" for OpenAI-Chat-compliant vLLM endpoint
-
- Use: "vllm:https://IP/v1" for OpenAI-compliant vLLM endpoint
- Use: "vllm_chat:https://IP/v1" for OpenAI-Chat-compliant vLLM endpoint
-
- Or Address can be replicate:
- Use:
- --inference_server=replicate: will use a Replicate server, requiring a Replicate key.
- e.g. looks like "a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5"
-
- Or Address can be for AWS SageMaker:
- Use: "sagemaker_chat:" for chat models that AWS sets up as dialog
- Use: "sagemaker:" for foundation models that AWS only text as inputs
-
- :param prompt_type: type of prompt, usually matched to fine-tuned model or plain for foundational model
- :param prompt_dict: If prompt_type=custom, then expects (some) items returned by get_prompt(..., return_dict=True)
- :param system_prompt: Universal system prompt to use if model supports, like LLaMa2, regardless of prompt_type definition.
- Useful for langchain case to control behavior, or OpenAI and Replicate.
- If None, 'None', or 'auto', then for LLaMa or other models that internally have system_prompt, will use default for each model
- If '', then no system prompt (no empty template given to model either, just no system part added at all)
- If some string not in ['None', 'auto'], then use that as system prompt
- Default is '', no system_prompt, because often it hurts performance/accuracy
-
- :param llamacpp_dict:
- n_gpu_layers: for llama.cpp based models, number of GPU layers to offload (default is all by using large value)
- use_mlock: when using `llama.cpp` based CPU models, for computers with low system RAM or slow CPUs, recommended False
- n_batch: Can make smaller to 128 for slower low-memory CPU systems
- n_gqa: Required to be 8 for LLaMa 70B
- ... etc. anything that could be passed to llama.cpp or GPT4All models
- e.g. python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path --llamacpp_dict="{'n_gpu_layers':25,'n_batch':128}"
- :param model_path_llama: model path or URL (for auto-download)
- :param model_name_gptj: model path or URL (for auto-download)
- :param model_name_gpt4all_llama: model path or URL (for auto-download)
- :param model_name_exllama_if_no_config: exllama model's full path for model, tokenizer, generator for use when no HuggingFace config
- :param exllama_dict for setting various things for Exllama class
- E.g. compress_pos_emb,
- set_auto_map,
- gpu_peer_fix,
- alpha_value,
- matmul_recons_thd,
- fused_mlp_thd
- sdp_thd
- fused_attn
- matmul_fused_remap
- rmsnorm_no_half2
- rope_no_half2
- matmul_no_half2
- silu_no_half2
- concurrent_streams
- E.g. to set memory to be split across 2 GPUs, use --exllama_dict="{'set_auto_map':20,20}"
- :param gptq_dict: Choices for AutoGPTQ, e.g. one can change defaults to these non-defaults:
- inject_fused_attention=False
- disable_exllama=True
- use_triton=True
- :param attention_sinks: Whether to enable attention sinks. Requires in local repo:
- git clone https://github.com/tomaarsen/attention_sinks.git
- :param sink_dict: dict of options for attention sinks
- :param hf_model_dict: dict of options for HF models using transformers
-
- :param truncation_generation: Whether (for torch) to terminate generation once reach context length of model.
- For some models, perplexity becomes critically large beyond context
- For other models like Mistral, one can generate beyond max_seq_len set to 4096 or 8192 without issue, since based upon 32k embeddings
- codellama can also generate beyond its 16k context length
- So default is off, but for simpler/older models True may be wise to avoid bad generations
-
- :param model_lock: Lock models to specific combinations, for ease of use and extending to many models
- Only used if gradio = True
- List of dicts, each dict has base_model, tokenizer_base_model, lora_weights, inference_server, prompt_type, and prompt_dict
- If all models have same prompt_type, and prompt_dict, can still specify that once in CLI outside model_lock as default for dict
- Can specify model_lock instead of those items on CLI
- As with CLI itself, base_model can infer prompt_type and prompt_dict if in prompter.py.
- Also, tokenizer_base_model and lora_weights are optional.
- Also, inference_server is optional if loading model from local system.
- All models provided will automatically appear in compare model mode
- Model loading-unloading and related choices will be disabled. Model/lora/server adding will be disabled
- :param model_lock_columns: How many columns to show if locking models (and so showing all at once)
- If None, then defaults to up to 3
- if -1, then all goes into 1 row
- Maximum value is 4 due to non-dynamic gradio rendering elements
- :param fail_if_cannot_connect: if doing model locking (e.g. with many models), fail if True. Otherwise ignore.
- Useful when many endpoints and want to just see what works, but still have to wait for timeout.
-
- :param temperature: generation temperature
- :param top_p: generation top_p
- :param top_k: generation top_k
- :param penalty_alpha: penalty_alpha>0 and top_k>1 enables contrastive search (not all models support)
- :param num_beams: generation number of beams
- :param repetition_penalty: generation repetition penalty
- :param num_return_sequences: generation number of sequences (1 forced for chat)
- :param do_sample: generation sample
- :param max_new_tokens: generation max new tokens
- :param min_new_tokens: generation min tokens
- :param early_stopping: generation early stopping
- :param max_time: maximum time to allow for generation
- :param memory_restriction_level: 0 = no restriction to tokens or model, 1 = some restrictions on token 2 = HF like restriction 3 = very low memory case
- :param debug: enable debug mode
- :param save_dir: directory chat data is saved to
- :param local_files_only: whether to only use local files instead of doing to HF for models
- :param resume_download: whether to resume downloads from HF for models
- :param use_auth_token: whether to use HF auth token (requires CLI did huggingface-cli login before)
- :param trust_remote_code: whether to use trust any code needed for HF model
- :param rope_scaling:
- For HF transformers model: scaling for rope-based models.
- For long context models that have been tuned for a specific size, you have to only use that specific size by setting the `--rope_scaling` exactly correctly
- e.g. --rope_scaling="{'type':'dynamic', 'factor':4}"
- e.g. --rope_scaling="{'type':'linear', 'factor':4}"
- e.g. python generate.py --rope_scaling="{'type':'linear','factor':4}" --base_model=lmsys/vicuna-13b-v1.5-16k --hf_embedding_model=sentence-transformers/all-MiniLM-L6-v2 --load_8bit=True --langchain_mode=UserData --user_path=user_path --prompt_type=vicuna11 --h2ocolors=False
- For exllama model: --rope_scaling="{'alpha_value':4}" . This automatically scales max_seq_len for exllama
- :param max_seq_len: Manually set maximum sequence length for the LLM
- :param offload_folder: path for spilling model onto disk
- :param src_lang: source languages to include if doing translation (None = all)
- :param tgt_lang: target languages to include if doing translation (None = all)
-
- :param prepare_offline_level:
- Whether to just prepare for offline use, do not go into cli, eval, or gradio run modes
- 0 : no prep
- 1: prepare just h2oGPT with exact same setup as passed to CLI and ensure all artifacts for h2oGPT alone added to ~/.cache/
- 2: prepare h2oGPT + all inference servers so h2oGPT+inference servers can use the ~/.cache/
- :param cli: whether to use CLI (non-gradio) interface.
- :param cli_loop: whether to loop for CLI (False usually only for testing)
- :param gradio: whether to enable gradio, or to enable benchmark mode
- :param gradio_offline_level: > 0, then change fonts so full offline
- == 1 means backend won't need internet for fonts, but front-end UI might if font not cached
- == 2 means backend and frontend don't need internet to download any fonts.
- Note: Some things always disabled include HF telemetry, gradio telemetry, chromadb posthog that involve uploading.
- This option further disables google fonts for downloading, which is less intrusive than uploading,
- but still required in air-gapped case. The fonts don't look as nice as google fonts, but ensure full offline behavior.
- Also set --share=False to avoid sharing a gradio live link.
- :param server_name: IP to use. In linux 0.0.0.0 is good choice so exposed to outside host, else for only local use 127.0.0.1.
- For windows/MAC 0.0.0.0 or 127.0.0.1 will work, but may need to specify actual LAN IP address for other LAN clients to see.
- :param share: whether to share the gradio app with sharable URL
- :param open_browser: whether to automatically open browser tab with gradio UI
- :param root_path: The root path (or "mount point") of the application,
- if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy
- that forwards requests to the application. For example, if the application is served at "https://example.com/myapp",
- the `root_path` should be set to "/myapp".
- :param ssl_verify: passed go gradio launch
- :param ssl_keyfile: passed go gradio launch
- :param ssl_certfile: passed go gradio launch
- :param ssl_keyfile_password: passed go gradio launch
-
- :param chat: whether to enable chat mode with chat history
- :param chat_conversation: list of tuples of (human, bot) conversation pre-appended to existing chat when using instruct/chat models
- Requires also add_chat_history_to_context = True
- It does *not* require chat=True, so works with nochat_api etc.
- :param text_context_list: List of strings to add to context for non-database version of document Q/A for faster handling via API etc.
- Forces LangChain code path and uses as many entries in list as possible given max_seq_len, with first assumed to be most relevant and to go near prompt.
- :param stream_output: whether to stream output
- :param async_output: Whether to do asyncio handling
- For summarization
- Applicable to HF TGI server
- Only if stream_output=False in CLI, UI, or API
- :param num_async: Number of simultaneously allowed asyncio calls to make for async_output
- Too many will overload inference server, too few will be too slow
- :param show_examples: whether to show clickable examples in gradio
- :param verbose: whether to show verbose prints
- :param h2ocolors: whether to use H2O.ai theme
- :param dark: whether to use dark mode for UI by default (still controlled in UI)
- :param height: height of chat window
- :param render_markdown: Whether to render markdown in chatbot UI. In some cases this distorts the rendering.
- https://github.com/gradio-app/gradio/issues/4344#issuecomment-1771963021
- :param show_lora: whether to show LORA options in UI (expert so can be hard to understand)
- :param show_llama: whether to show LLaMa.cpp/GPT4All options in UI (only likely useful if have weak GPUs)
- :param show_gpt4all: whether to show GPT4All models in UI (not often useful, llama.cpp models best)
- :param login_mode_if_model0: set to True to load --base_model after client logs in, to be able to free GPU memory when model is swapped
- :param block_gradio_exit: whether to block gradio exit (used for testing)
- :param concurrency_count: gradio concurrency count (1 is optimal for LLMs)
- :param api_open: If False, don't let API calls skip gradio queue
- :param allow_api: whether to allow API calls at all to gradio server
- :param input_lines: how many input lines to show for chat box (>1 forces shift-enter for submit, else enter is submit)
- :param gradio_size: Overall size of text and spaces: "xsmall", "small", "medium", "large".
- Small useful for many chatbots in model_lock mode
- :param show_copy_button: Whether to show copy button for chatbots
- :param large_file_count_mode: Whether to force manual update to UI of drop-downs, good idea if millions of chunks or documents
- :param pre_load_embedding_model: Whether to preload embedding model for shared use across DBs and users (multi-thread safe only)
-
- :param auth: gradio auth for launcher in form [(user1, pass1), (user2, pass2), ...]
- e.g. --auth=[('jon','password')] with no spaces
- e.g. --auth="[('jon', 'password)())(')]" so any special characters can be used
- e.g. --auth=auth.json to specify persisted state file with name auth.json (auth_filename then not required)
- e.g. --auth='' will use default auth.json as file name for persisted state file (auth_filename then not required)
- e.g. --auth=None will use no auth, but still keep track of auth state, just not from logins
- :param auth_filename:
- Set auth filename, used only if --auth= was passed list of user/passwords
- :param auth_access:
- 'open': Allow new users to be added
- 'closed': Stick to existing users
- :param auth_freeze: whether freeze authentication based upon current file, no longer update file
- :param auth_message: Message to show if having users login, fixed if passed, else dynamic internally
- :param guest_name: guess name if using auth and have open access.
- If '', then no guest allowed even if open access, then all databases for each user always persisted
- :param enforce_h2ogpt_api_key: Whether to enforce h2oGPT token usage for API
- :param enforce_h2ogpt_ui_key: Whether to enforce h2oGPT token usage for UI (same keys as API assumed)
- :param h2ogpt_api_keys: list of tokens allowed for API access or file accessed on demand for json of list of keys
- :param h2ogpt_key: E.g. can be set when accessing gradio h2oGPT server from local gradio h2oGPT server that acts as client to that inference server
-
- :param max_max_time: Maximum max_time for gradio slider
- :param max_max_new_tokens: Maximum max_new_tokens for gradio slider
- :param min_max_new_tokens: Minimum of max_new_tokens, when auto-scaling down to handle more docs/prompt, but still let generation have some tokens
- :param max_input_tokens: Max input tokens to place into model context for each LLM call
- -1 means auto, fully fill context for query, and fill by original document chunk for summarization
- >=0 means use that to limit context filling to that many tokens
- :param docs_token_handling: 'chunk' means fill context with top_k_docs (limited by max_input_tokens or model_max_len) chunks for query
- or top_k_docs original document chunks summarization
- None or 'split_or_merge' means same as 'chunk' for query, while for summarization merges documents to fill up to max_input_tokens or model_max_len tokens
-
- :param docs_joiner: string to join lists of text when doing split_or_merge. None means '\n\n'
- :param hyde_level: HYDE level for HYDE approach (https://arxiv.org/abs/2212.10496)
- 0: No HYDE
- 1: Use non-document-based LLM response and original query for embedding query
- 2: Use document-based LLM response and original query for embedding query
- 3+: Continue iterations of embedding prior answer and getting new response
- :param hyde_template:
- None, 'None', 'auto' uses internal value and enable
- '{query}' is minimal template one can pass
- :param visible_models: Which models in model_lock list to show by default
- Takes integers of position in model_lock (model_states) list or strings of base_model names
- Ignored if model_lock not used
- For nochat API, this is single item within a list for model by name or by index in model_lock
- If None, then just use first model in model_lock list
- If model_lock not set, use model selected by CLI --base_model etc.
- Note that unlike h2ogpt_key, this visible_models only applies to this running h2oGPT server,
- and the value is not used to access the inference server.
- If need a visible_models for an inference server, then use --model_lock and group together.
-
- :param visible_visible_models: Whether visible models drop-down is visible in UI
- :param visible_submit_buttons: whether submit buttons are visible when UI first comes up
- :param visible_side_bar: whether left side bar is visible when UI first comes up
- :param visible_doc_track: whether left side bar's document tracking is visible when UI first comes up
- :param visible_chat_tab: "" for chat tab
- :param visible_doc_selection_tab: "" for doc selection tab
- :param visible_doc_view_tab: "" for doc view tab
- :param visible_chat_history_tab: "" for chat history tab
- :param visible_expert_tab: "" for expert tab
- :param visible_models_tab: "" for models tab
- :param visible_system_tab: "" for system tab
- :param visible_tos_tab: "" for ToS tab
- :param visible_login_tab: "" for Login tab (needed for persistence or to enter key for UI access to models and ingestion)
- :param visible_hosts_tab: "" for hosts tab
- :param chat_tables: Just show Chat as block without tab (useful if want only chat view)
- :param visible_h2ogpt_header: Whether github stars, URL, logo, and QR code are visible
- :param max_raw_chunks: Maximum number of chunks to show in UI when asking for raw DB text from documents/collection
-
- :param sanitize_user_prompt: whether to remove profanity from user input (slows down input processing)
- Requires optional packages:
- pip install alt-profanity-check==1.2.2 better-profanity==0.7.0
- :param sanitize_bot_response: whether to remove profanity and repeat lines from bot output (about 2x slower generation for long streaming cases due to better_profanity being slow)
- :param extra_model_options: extra models to show in list in gradio
- :param extra_lora_options: extra LORA to show in list in gradio
- :param extra_server_options: extra servers to show in list in gradio
- :param score_model: which model to score responses
- None: no response scoring
- 'auto': auto mode, '' (no model) for CPU or 1 GPU, 'OpenAssistant/reward-model-deberta-v3-large-v2' for >=2 GPUs,
- because on CPU takes too much compute just for scoring response
- :param eval_filename: json file to use for evaluation, if None is sharegpt
- :param eval_prompts_only_num: for no gradio benchmark, if using eval_filename prompts for eval instead of examples
- :param eval_prompts_only_seed: for no gradio benchmark, seed for eval_filename sampling
- :param eval_as_output: for no gradio benchmark, whether to test eval_filename output itself
-
- :param langchain_mode: Data source to include. Choose "UserData" to only consume files from make_db.py.
- None: auto mode, check if langchain package exists, at least do LLM if so, else Disabled
- If not passed, then chosen to be first langchain_modes, else langchain_mode->Disabled is set if no langchain_modes either
- WARNING: wiki_full requires extra data processing via read_wiki_full.py and requires really good workstation to generate db, unless already present.
- :param user_path: user path to glob from to generate db for vector search, for 'UserData' langchain mode.
- If already have db, any new/changed files are added automatically if path set, does not have to be same path used for prior db sources
- :param langchain_modes: dbs to generate at launch to be ready for LLM
- Apart from additional user-defined collections, can include ['wiki', 'wiki_full', 'UserData', 'MyData', 'github h2oGPT', 'DriverlessAI docs']
- But wiki_full is expensive and requires preparation
- To allow personal space only live in session, add 'MyData' to list
- Default: If only want to consume local files, e.g. prepared by make_db.py, only include ['UserData']
- If have own user modes, need to add these here or add in UI.
- :param langchain_mode_paths: dict of langchain_mode keys and disk path values to use for source of documents
- E.g. "{'UserData2': 'userpath2'}"
- A disk path be None, e.g. --langchain_mode_paths="{'UserData2': None}" even if existing DB, to avoid new documents being added from that path, source links that are on disk still work.
- If `--user_path` was passed, that path is used for 'UserData' instead of the value in this dict
- :param langchain_mode_types: dict of langchain_mode keys and database types
- E.g. python generate.py --base_model=llama --langchain_modes=['TestData'] --langchain_mode_types="{'TestData':'shared'}"
- The type is attempted to be inferred if directory already exists, then don't have to pass this
- :param detect_user_path_changes_every_query: whether to detect if any files changed or added every similarity search (by file hashes).
- Expensive for large number of files, so not done by default. By default only detect changes during db loading.
-
- :param langchain_action: Mode langchain operations in on documents.
- Query: Make query of document(s)
- Summarize or Summarize_map_reduce: Summarize document(s) via map_reduce
- Summarize_all: Summarize document(s) using entire document at once
- Summarize_refine: Summarize document(s) using entire document, and try to refine before returning summary
- Extract: Extract information from document(s) via map (no reduce)
- :param langchain_agents: Which agents to use
- 'search': Use Web Search as context for LLM response, e.g. SERP if have SERPAPI_API_KEY in env
- :param force_langchain_evaluate: Whether to force langchain LLM use even if not doing langchain, mostly for testing.
-
- :param visible_langchain_actions: Which actions to allow
- :param visible_langchain_agents: Which agents to allow
-
- :param document_subset: Default document choice when taking subset of collection
- :param document_choice: Chosen document(s) by internal name, 'All' means use all docs
-
- :param use_llm_if_no_docs: Whether to use LLM even if no documents, when langchain_mode=UserData or MyData or custom
- :param load_db_if_exists: Whether to load chroma db if exists or re-generate db
- :param keep_sources_in_context: Whether to keep url sources in context, not helpful usually
- :param db_type: 'faiss' for in-memory
- 'chroma' (for chroma >= 0.4)
- 'chroma_old' (for chroma < 0.4) -- recommended for large collections
- 'weaviate' for persisted on disk
- :param use_openai_embedding: Whether to use OpenAI embeddings for vector db
- :param use_openai_model: Whether to use OpenAI model for use with vector db
- :param hf_embedding_model: Which HF embedding model to use for vector db
- Default is instructor-large with 768 parameters per embedding if have GPUs, else all-MiniLM-L6-v2 if no GPUs
- Can also choose simpler model with 384 parameters per embedding: "sentence-transformers/all-MiniLM-L6-v2"
- Can also choose even better embedding with 1024 parameters: 'hkunlp/instructor-xl'
- We support automatically changing of embeddings for chroma, with a backup of db made if this is done
- :param migrate_embedding_model: whether to use hf_embedding_model embedding even if database already had an embedding set.
- used to migrate all embeddings to a new one, but will take time to re-embed.
- Default (False) is to use the prior embedding for existing databases, and only use hf_embedding_model for new databases
- If had old database without embedding saved, then hf_embedding_model is also used.
- :param auto_migrate_db: whether to automatically migrate any chroma<0.4 database from duckdb -> sqlite version
- :param cut_distance: Distance to cut off references with larger distances when showing references.
- 1.64 is good to avoid dropping references for all-MiniLM-L6-v2, but instructor-large will always show excessive references.
- For all-MiniLM-L6-v2, a value of 1.5 can push out even more references, or a large value of 100 can avoid any loss of references.
- :param answer_with_sources: Whether to determine (and return) sources
- :param append_sources_to_answer: Whether to place source information in chat response (ignored by LLM). Always disabled for API.
- :param show_accordions: whether to show accordion for document references in chatbot UI
- :param top_k_docs_max_show: Max number of docs to show in UI for sources
- If web search is enabled, then this is modified to be max(top_k_docs_max_show, number of links used in search)
- :param show_link_in_sources: Whether to show URL link to source document in references
- :param pre_prompt_query: prompt before documents to query, if None then use internal defaults
- :param prompt_query: prompt after documents to query, if None then use internal defaults
- :param pre_prompt_summary: prompt before documents to summarize/extract from, if None then use internal defaults
- :param prompt_summary: prompt after documents to summarize/extract from, if None then use internal defaults
- For summarize/extract, normal to have empty query (nothing added in ask anything in UI or empty string in API)
- If pass query, template is "Focusing on %s, %s" % (query, prompt_summary)
- If pass query and iinput, template is "Focusing on %s, %s, %s" % (query, iinput, prompt_summary)
- :param add_chat_history_to_context: Include chat context when performing action
- Not supported yet for openai_chat when using document collection instead of LLM
- Also not supported when using CLI mode
- :param add_search_to_context: Include web search in context as augmented prompt
- :param context: Default context to use (for system pre-context in gradio UI)
- context comes before chat_conversation and any document Q/A from text_context_list
- :param iinput: Default input for instruction-based prompts
- :param allow_upload_to_user_data: Whether to allow file uploads to update shared vector db (UserData or custom user dbs)
- Ensure pass user_path for the files uploaded to be moved to this location for linking.
- :param reload_langchain_state: Whether to reload langchain_modes.pkl file that contains any new user collections.
- :param allow_upload_to_my_data: Whether to allow file uploads to update personal vector db
- :param enable_url_upload: Whether to allow upload from URL
- :param enable_text_upload: Whether to allow upload of text
- :param enable_sources_list: Whether to allow list (or download for non-shared db) of list of sources for chosen db
- :param chunk: Whether to chunk data (True unless know data is already optimally chunked)
- :param chunk_size: Size of chunks, with typically top-4 passed to LLM, so needs to be in context length
- :param top_k_docs: For langchain_action query: number of chunks to give LLM
- -1 : auto-fills context up to max_seq_len
- For langchain_action summarize/extract: number of document parts, like pages for PDF.
- There's no such thing as chunks for summarization.
- -1 : auto-fills context up to max_seq_len
- :param docs_ordering_type:
- Type of ordering of docs.
- 'best_first': Order by score so score is worst match near prompt
- 'best_near_prompt' or 'reverse_sort' : reverse docs order so most relevant is closest to question.
- Best choice for sufficiently smart model, and truncation occurs for oldest context, so best then too.
- But smaller 6_9 models fail to use newest context and can get stuck on old information.
- '' or None (i.e. default) or 'reverse_ucurve_sort' : Sort so most relevant is either near start or near end
- Best to avoid "lost in middle" as well as avoid hallucinating off starting content that LLM focuses on alot.
- :param auto_reduce_chunks: Whether to automatically reduce top_k_docs to fit context given prompt
- :param max_chunks: If top_k_docs=-1, maximum number of chunks to allow
- :param headsize: Maximum number of characters for head of document document for UI to show
- :param n_jobs: Number of processors to use when consuming documents (-1 = all, is default)
-
- :param use_unstructured: Enable unstructured URL loader
- :param use_playwright: Enable PlayWright URL loader
- :param use_selenium: Enable Selenium URL loader
-
- :param use_pymupdf: enable PyMUPDF 'auto' means use first, use others if they are 'auto' if no result
- :param use_unstructured_pdf: enable Unstructured PDF loader, 'auto' means use if pymupdf fails to get doc result
- :param use_pypdf: enable PyPDF loader 'auto' means use if unstructured fails to get doc result
- :param enable_pdf_ocr: 'auto' means only use OCR if normal text extraction fails. Useful for pure image-based PDFs with text.
- if enable_pdf_doctr == 'on' then don't do.
- 'on' means always do OCR as additional parsing of same documents
- 'off' means don't do OCR (e.g. because it's slow even if 'auto' only would trigger if nothing else worked)
- :param enable_pdf_doctr: Whether to support doctr on pdfs, 'auto' means use do if failed to get doc result so far
- :param try_pdf_as_html: Try "PDF" as if HTML file, in case web link has .pdf extension but really is just HTML
-
- :param enable_ocr: Whether to support OCR on images
- :param enable_doctr: Whether to support doctr on images (using OCR better than enable_ocr=True)
- :param enable_pix2struct: Whether to support pix2struct on images for captions
- :param enable_captions: Whether to support captions using BLIP for image files as documents,
- then preloads that model if pre_load_caption_model=True
-
- :param pre_load_caption_model: Whether to preload caption model (True), or load after forking parallel doc loader (False)
- parallel loading disabled if preload and have images, to prevent deadlocking on cuda context
- Recommended if using larger caption model or doing production serving with many users to avoid GPU OOM if many would use model at same time
- Also applies to DocTR
- :param captions_model: Which model to use for captions.
- captions_model: str = "Salesforce/blip-image-captioning-base", # continue capable
- captions_model: str = "Salesforce/blip2-flan-t5-xl", # question/answer capable, 16GB state
- captions_model: str = "Salesforce/blip2-flan-t5-xxl", # question/answer capable, 60GB state
- Note: opt-based blip2 are not permissive license due to opt and Meta license restrictions
- Disabled for CPU since BLIP requires CUDA
- :param caption_gpu: If support caption, then use GPU if exists
- :param caption_gpu_id: Which GPU id to use, if 'auto' then select 0
-
- :param doctr_gpu: If support doctr, then use GPU if exists
- :param doctr_gpu_id: Which GPU id to use, if 'auto' then select 0
-
- :param jq_schema: control json loader
- By default '.[]' ingests everything in brute-force way, but better to match your schema
- See: https://python.langchain.com/docs/modules/data_connection/document_loaders/json#using-jsonloader
-
- :param max_quality: Choose maximum quality ingestion with all available parsers
- Pro: Catches document when some default parsers would fail
- Pro: Enables DocTR that has much better OCR than Tesseract
- Con: Fills DB with results from all parsers, so similarity search gives redundant results
-
- :param enable_heap_analytics: Toggle telemetry.
- :param heap_app_id: App ID for Heap, change to your ID.
- :return:
- """
- if base_model is None:
- base_model = ''
- if tokenizer_base_model is None:
- tokenizer_base_model = ''
- if lora_weights is None:
- lora_weights = ''
- if inference_server is None:
- inference_server = ''
-
- # listen to env if set
- model_lock = os.getenv('model_lock', str(model_lock))
- model_lock = ast.literal_eval(model_lock)
-
- chat_conversation = str_to_list(chat_conversation)
- text_context_list = str_to_list(text_context_list)
-
- llamacpp_dict = str_to_dict(llamacpp_dict)
- # add others to single dict
- llamacpp_dict['model_path_llama'] = model_path_llama
- llamacpp_dict['model_name_gptj'] = model_name_gptj
- llamacpp_dict['model_name_gpt4all_llama'] = model_name_gpt4all_llama
- llamacpp_dict['model_name_exllama_if_no_config'] = model_name_exllama_if_no_config
- # if user overrides but doesn't set these:
- if 'n_batch' not in llamacpp_dict:
- llamacpp_dict['n_batch'] = 128
- if 'n_gpu_layers' not in llamacpp_dict:
- llamacpp_dict['n_gpu_layers'] = 100
- if 'n_gqa' not in llamacpp_dict:
- llamacpp_dict['n_gqa'] = 0
-
- exllama_dict = str_to_dict(exllama_dict)
- gptq_dict = str_to_dict(gptq_dict)
- sink_dict = str_to_dict(sink_dict)
- hf_model_dict = str_to_dict(hf_model_dict)
-
- if os.environ.get('SERPAPI_API_KEY') is None and LangChainAgent.SEARCH.value in visible_langchain_agents:
- visible_langchain_agents.remove(LangChainAgent.SEARCH.value)
-
- if model_lock:
- assert gradio, "model_lock only supported for gradio=True"
- assert not cli, "model_lock only supported for cli=False"
- assert not (not cli and not gradio), "model_lock only supported for eval (cli=gradio=False)"
- assert not base_model, "Don't specify model_lock and base_model"
- assert not tokenizer_base_model, "Don't specify model_lock and tokenizer_base_model"
- assert not lora_weights, "Don't specify model_lock and lora_weights"
- assert not inference_server, "Don't specify model_lock and inference_server"
- # assert not prompt_type, "Don't specify model_lock and prompt_type"
- # assert not prompt_dict, "Don't specify model_lock and prompt_dict"
-
- n_jobs = int(os.getenv('n_jobs', str(n_jobs)))
- is_hf = bool(int(os.getenv("HUGGINGFACE_SPACES", '0')))
- is_gpth2oai = bool(int(os.getenv("GPT_H2O_AI", '0')))
- is_public = is_hf or is_gpth2oai # multi-user case with fixed model and disclaimer
- if enforce_h2ogpt_ui_key is None:
- # nominally allow UI access public or not
- enforce_h2ogpt_ui_key = False
- if is_public:
- visible_tos_tab = visible_hosts_tab = True
- if enforce_h2ogpt_api_key is None:
- enforce_h2ogpt_api_key = True
- else:
- if enforce_h2ogpt_api_key is None:
- enforce_h2ogpt_api_key = False
- if isinstance(h2ogpt_api_keys, str) and not os.path.isfile(h2ogpt_api_keys):
- h2ogpt_api_keys = str_to_list(h2ogpt_api_keys)
- if memory_restriction_level is None:
- memory_restriction_level = 2 if is_hf else 0 # 2 assumes run on 24GB consumer GPU
- else:
- assert 0 <= memory_restriction_level <= 3, "Bad memory_restriction_level=%s" % memory_restriction_level
- if n_jobs == -1:
- # if -1, assume hypercores, don't use, force user to pass n_jobs to be specific if not standard cores
- n_jobs = max(1, os.cpu_count() // 2)
- if is_public and os.getenv('n_jobs') is None:
- n_jobs = min(n_jobs, max(1, min(os.cpu_count() // 2, 8)))
- admin_pass = os.getenv("ADMIN_PASS")
- # will sometimes appear in UI or sometimes actual generation, but maybe better than empty result
- # but becomes unrecoverable sometimes if raise, so just be silent for now
- raise_generate_gpu_exceptions = True
-
- rope_scaling = str_to_dict(rope_scaling)
-
- if isinstance(auth, str):
- if auth.strip().startswith('['):
- auth = str_to_list(auth)
- if isinstance(auth, str) and auth:
- auth_filename = auth
- if not auth_filename:
- auth_filename = "auth.json"
- assert isinstance(auth, (str, list, tuple, type(None))), "Unknown type %s for auth=%s" % (type(auth), auth)
-
- # allow set token directly
- use_auth_token = os.environ.get("HUGGING_FACE_HUB_TOKEN", use_auth_token)
- allow_upload_to_user_data = bool(
- int(os.environ.get("allow_upload_to_user_data", str(int(allow_upload_to_user_data)))))
- allow_upload_to_my_data = bool(int(os.environ.get("allow_upload_to_my_data", str(int(allow_upload_to_my_data)))))
- height = int(os.environ.get("HEIGHT", height))
- h2ocolors = bool(int(os.getenv('h2ocolors', h2ocolors)))
-
- # allow enabling langchain via ENV
- # FIRST PLACE where LangChain referenced, but no imports related to it
- langchain_modes = ast.literal_eval(os.environ.get("langchain_modes", str(langchain_modes)))
- if not isinstance(langchain_modes, list):
- langchain_modes = []
- # always allow DISABLED
- if LangChainMode.DISABLED.value not in langchain_modes:
- langchain_modes.append(LangChainMode.DISABLED.value)
- if not have_langchain:
- # only allow disabled, not even LLM that is langchain related
- langchain_mode = LangChainMode.DISABLED.value
- langchain_modes = [langchain_mode]
-
- # update
- langchain_mode_paths = str_to_dict(langchain_mode_paths)
- langchain_mode_types = str_to_dict(langchain_mode_types)
- for lmode in [LangChainMode.GITHUB_H2OGPT.value,
- LangChainMode.H2O_DAI_DOCS.value,
- LangChainMode.WIKI.value,
- LangChainMode.WIKI_FULL.value,
- ]:
- if lmode not in langchain_mode_types:
- langchain_mode_types[lmode] = 'shared'
- if lmode not in langchain_mode_paths:
- langchain_mode_types[lmode] = ''
- if user_path:
- user_path = makedirs(user_path, use_base=True)
- langchain_mode_paths['UserData'] = user_path
- langchain_mode_paths['UserData'] = LangChainTypes.SHARED.value
-
- if is_public:
- allow_upload_to_user_data = False
- if LangChainMode.USER_DATA.value in langchain_modes:
- langchain_modes.remove(LangChainMode.USER_DATA.value)
- if max_raw_chunks is None:
- max_raw_chunks = 30 if is_public else 1000000
-
- # in-place, for non-scratch dbs
- if allow_upload_to_user_data:
- # always listen to CLI-passed user_path if passed
- if user_path:
- langchain_mode_paths['UserData'] = user_path
-
- assert langchain_action in langchain_actions, "Invalid langchain_action %s not in %s" % (
- langchain_action, langchain_actions)
- assert len(
- set(langchain_agents).difference(langchain_agents_list)) == 0, "Invalid langchain_agents %s" % langchain_agents
-
- # auto-set langchain_mode
- langchain_mode = os.environ.get("LANGCHAIN_MODE", langchain_mode)
- if have_langchain and langchain_mode is None:
- # start in chat mode, in case just want to chat and don't want to get "No documents to query" by default.
- if LangChainMode.LLM.value in langchain_modes:
- langchain_mode = LangChainMode.LLM.value
- elif len(langchain_modes) >= 1:
- # infer even if don't pass which langchain_mode, just langchain_modes.
- langchain_mode = langchain_modes[0]
- if allow_upload_to_user_data and not is_public and langchain_mode_paths['UserData']:
- if verbose:
- print("Auto set langchain_mode=%s. Could use UserData instead." % langchain_mode, flush=True)
- elif allow_upload_to_my_data:
- if verbose:
- print("Auto set langchain_mode=%s. Could use MyData instead."
- " To allow UserData to pull files from disk,"
- " set user_path or langchain_mode_paths, and ensure allow_upload_to_user_data=True" % langchain_mode,
- flush=True)
- else:
- raise RuntimeError("Please pass --langchain_mode= out of %s" % langchain_modes)
- if not have_langchain and langchain_mode not in [None, LangChainMode.DISABLED.value, LangChainMode.LLM.value]:
- raise RuntimeError("Asked for LangChain mode but langchain python package cannot be found.")
- if langchain_mode is None:
- # if not set yet, disable
- langchain_mode = LangChainMode.DISABLED.value
- print("Auto set langchain_mode=%s Have langchain package: %s" % (langchain_mode, have_langchain), flush=True)
- # go ahead and add
- if langchain_mode not in langchain_modes:
- langchain_modes.append(langchain_mode)
-
- if is_public:
- allow_upload_to_user_data = False
- input_lines = 1 # ensure set, for ease of use
- temperature = 0.2 if temperature is None else temperature
- top_p = 0.85 if top_p is None else top_p
- top_k = 70 if top_k is None else top_k
- penalty_alpha = 0.0 if penalty_alpha is None else penalty_alpha
- if is_hf:
- do_sample = True if do_sample is None else do_sample
- top_k_docs = 3 if top_k_docs is None else top_k_docs
- else:
- # by default don't sample, too chatty
- do_sample = False if do_sample is None else do_sample
- top_k_docs = 4 if top_k_docs is None else top_k_docs
-
- if memory_restriction_level == 2:
- if not base_model and not inference_server and not model_lock:
- base_model = 'h2oai/h2ogpt-oasst1-512-12b'
- # don't set load_8bit if passed base_model, doesn't always work so can't just override
- load_8bit = True
- load_4bit = False # FIXME - consider using 4-bit instead of 8-bit
- elif not inference_server:
- top_k_docs = 10 if top_k_docs is None else top_k_docs
- if memory_restriction_level >= 2:
- load_8bit = True
- load_4bit = False # FIXME - consider using 4-bit instead of 8-bit
- if hf_embedding_model is None:
- hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
- top_k_docs = 3 if top_k_docs is None else top_k_docs
- if top_k_docs is None:
- top_k_docs = 3
- if is_public:
- if not max_time:
- max_time = 60 * 2
- if not max_max_time:
- max_max_time = max_time
- if not max_new_tokens:
- max_new_tokens = 256
- if not max_max_new_tokens:
- max_max_new_tokens = 512
- else:
- if not max_max_time:
- max_max_time = 60 * 20
- if not max_max_new_tokens:
- max_max_new_tokens = 1024
- if is_hf:
- # must override share if in spaces
- share = False
- if not max_time:
- max_time = 60 * 1
- if not max_max_time:
- max_max_time = max_time
- # HF accounted for later in get_max_max_new_tokens()
- save_dir = os.getenv('SAVE_DIR', save_dir)
- save_dir = makedirs(save_dir, exist_ok=True, tmp_ok=True, use_base=True)
- score_model = os.getenv('SCORE_MODEL', score_model)
- if str(score_model) == 'None':
- score_model = ''
- concurrency_count = int(os.getenv('CONCURRENCY_COUNT', concurrency_count))
- api_open = bool(int(os.getenv('API_OPEN', str(int(api_open)))))
- allow_api = bool(int(os.getenv('ALLOW_API', str(int(allow_api)))))
-
- n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
- n_gpus, gpu_ids = cuda_vis_check(n_gpus)
-
- if load_half is None and t5_type(base_model):
- load_half = False
- print("load_half=%s auto-set for %s to avoid bad generation" % (load_half, base_model), flush=True)
-
- if n_gpus == 0 or get_device() == "mps":
- # No CUDA GPUs usable
-
- if get_device() != "mps":
- print("No GPUs detected", flush=True)
-
- enable_captions = False
- gpu_id = None
- load_8bit = False
- load_4bit = False
- low_bit_mode = 1
- if load_half is None:
- # wouldn't work if specified True, but respect
- load_half = False
- load_gptq = ''
- load_awq = ''
- load_exllama = False
- use_gpu_id = False
- if get_device() == "cuda":
- torch.backends.cudnn.benchmark = True
- torch.backends.cudnn.enabled = False
- torch.set_default_dtype(torch.float32)
- if is_public and not inference_server and not model_lock:
- # 12B uses ~94GB
- # 6.9B uses ~47GB
- base_model = 'h2oai/h2ogpt-oig-oasst1-512-6_9b' if not base_model else base_model
- if hf_embedding_model is None:
- # if no GPUs, use simpler embedding model to avoid cost in time
- hf_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
- if score_model == 'auto':
- score_model = ''
- else:
- if load_half is None:
- load_half = True
- # CUDA GPUs visible
- if score_model == 'auto':
- if n_gpus >= 2:
- # will by default place scoring model on last GPU
- score_model = 'OpenAssistant/reward-model-deberta-v3-large-v2'
- else:
- score_model = ''
- if hf_embedding_model is None:
- # if still None, then set default
- hf_embedding_model = 'hkunlp/instructor-large'
-
- # get defaults
- if base_model:
- model_lower = base_model.lower()
- elif model_lock:
- # have 0th model be thought of as normal model
- assert len(model_lock) > 0 and model_lock[0]['base_model'], "model_lock: %s" % model_lock
- model_lower = model_lock[0]['base_model'].lower()
- else:
- model_lower = ''
- if not gradio:
- # force, else not single response like want to look at
- stream_output = False
- # else prompt removal can mess up output
- chat = False
- # hard-coded defaults
- first_para = False
- text_limit = None
-
- if compile_model is None:
- # too avoid noisy CLI
- compile_model = not cli
-
- if offload_folder:
- offload_folder = makedirs(offload_folder, exist_ok=True, tmp_ok=True, use_base=True)
-
- # defaults
- caption_loader = None
- doctr_loader = None
- pix2struct_loader = None
-
- image_loaders_options0, image_loaders_options, \
- pdf_loaders_options0, pdf_loaders_options, \
- url_loaders_options0, url_loaders_options = lg_to_gr(**locals())
- jq_schema0 = jq_schema
- # transcribe
- image_loaders = image_loaders_options0
- pdf_loaders = pdf_loaders_options0
- url_loaders = url_loaders_options0
-
- placeholder_instruction, placeholder_input, \
- stream_output, show_examples, \
- prompt_type, prompt_dict, \
- temperature, top_p, top_k, penalty_alpha, num_beams, \
- max_new_tokens, min_new_tokens, early_stopping, max_time, \
- repetition_penalty, num_return_sequences, \
- do_sample, \
- src_lang, tgt_lang, \
- examples, \
- task_info = \
- get_generate_params(model_lower,
- chat,
- stream_output, show_examples,
- prompt_type, prompt_dict,
- system_prompt,
- pre_prompt_query, prompt_query,
- pre_prompt_summary, prompt_summary,
- temperature, top_p, top_k, penalty_alpha, num_beams,
- max_new_tokens, min_new_tokens, early_stopping, max_time,
- repetition_penalty, num_return_sequences,
- do_sample,
- top_k_docs,
- chunk,
- chunk_size,
- image_loaders,
- pdf_loaders,
- url_loaders,
- jq_schema,
- docs_ordering_type,
- min_max_new_tokens,
- max_input_tokens,
- docs_token_handling,
- docs_joiner,
- hyde_level,
- hyde_template,
- verbose,
- )
-
- git_hash = get_githash() if is_public or os.getenv('GET_GITHASH') else "GET_GITHASH"
- locals_dict = locals()
- locals_print = '\n'.join(['%s: %s' % (k, v) for k, v in locals_dict.items()])
- if verbose:
- print(f"Generating model with params:\n{locals_print}", flush=True)
- print("Command: %s\nHash: %s" % (str(' '.join(sys.argv)), git_hash), flush=True)
-
- if langchain_mode != LangChainMode.DISABLED.value:
- # SECOND PLACE where LangChain referenced, but all imports are kept local so not required
- from gpt_langchain import prep_langchain, get_some_dbs_from_hf, get_persist_directory
- if is_hf:
- get_some_dbs_from_hf()
- dbs = {}
- for langchain_mode1 in langchain_modes:
- langchain_type = langchain_mode_types.get(langchain_mode1, LangChainTypes.EITHER.value)
- if langchain_type == LangChainTypes.PERSONAL.value:
- # shouldn't prepare per-user databases here
- continue
- persist_directory1, langchain_type = get_persist_directory(langchain_mode1, langchain_type=langchain_type)
- langchain_mode_types[langchain_mode1] = langchain_type
- if langchain_type == LangChainTypes.PERSONAL.value:
- # shouldn't prepare per-user databases here
- continue
- try:
- db = prep_langchain(persist_directory1,
- load_db_if_exists,
- db_type, use_openai_embedding,
- langchain_mode1, langchain_mode_paths, langchain_mode_types,
- hf_embedding_model,
- migrate_embedding_model,
- auto_migrate_db,
- kwargs_make_db=locals(),
- verbose=verbose)
- finally:
- # in case updated embeddings or created new embeddings
- clear_torch_cache()
- dbs[langchain_mode1] = db
- # remove None db's so can just rely upon k in dbs for if hav db
- dbs = {k: v for k, v in dbs.items() if v is not None}
- else:
- dbs = {}
- # import control
- if os.environ.get("TEST_LANGCHAIN_IMPORT"):
- assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
- assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
-
- if attention_sinks:
- if use_cache is False:
- raise ValueError("attention sinks requires use_cache=True")
- else:
- use_cache = True
- # never truncate if using attention sinks
- truncation_generation = truncation_generation and not attention_sinks
-
- other_model_state_defaults = dict(load_8bit=load_8bit, load_4bit=load_4bit, low_bit_mode=low_bit_mode,
- load_half=load_half,
- load_gptq=load_gptq, load_awq=load_awq, load_exllama=load_exllama,
- use_safetensors=use_safetensors,
- revision=revision, use_gpu_id=use_gpu_id, gpu_id=gpu_id,
- compile_model=compile_model,
- use_cache=use_cache,
- llamacpp_dict=llamacpp_dict, model_path_llama=model_path_llama,
- model_name_gptj=model_name_gptj,
- model_name_gpt4all_llama=model_name_gpt4all_llama,
- model_name_exllama_if_no_config=model_name_exllama_if_no_config,
- rope_scaling=rope_scaling,
- max_seq_len=max_seq_len,
- exllama_dict=exllama_dict,
- gptq_dict=gptq_dict,
- attention_sinks=attention_sinks,
- sink_dict=sink_dict,
- truncation_generation=truncation_generation,
- hf_model_dict=hf_model_dict,
- )
- model_state_none = dict(model=None, tokenizer=None, device=None,
- base_model=None, tokenizer_base_model=None, lora_weights=None,
- inference_server=None, prompt_type=None, prompt_dict=None,
- visible_models=None, h2ogpt_key=None,
- )
- model_state_none.update(other_model_state_defaults)
- my_db_state0 = {LangChainMode.MY_DATA.value: [None, None, None]}
- selection_docs_state0 = dict(langchain_modes=langchain_modes,
- langchain_mode_paths=langchain_mode_paths,
- langchain_mode_types=langchain_mode_types)
- selection_docs_state = copy.deepcopy(selection_docs_state0)
-
- if cli or not gradio:
- # initial state for query prompt
- model_name = base_model
- pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary = \
- get_langchain_prompts(pre_prompt_query, prompt_query,
- pre_prompt_summary, prompt_summary,
- model_name, inference_server,
- model_path_llama)
-
- if cli:
- from cli import run_cli
- return run_cli(**get_kwargs(run_cli, exclude_names=['model_state0'], **locals()))
- elif not gradio:
- from eval import run_eval
- return run_eval(**get_kwargs(run_eval, exclude_names=['model_state0'], **locals()))
- elif gradio or prepare_offline_level > 0:
- # imported here so don't require gradio to run generate
- from gradio_runner import go_gradio
-
- # get default model
- model_states = []
- model_list = [dict(base_model=base_model, tokenizer_base_model=tokenizer_base_model, lora_weights=lora_weights,
- inference_server=inference_server, prompt_type=prompt_type, prompt_dict=prompt_dict,
- visible_models=None, h2ogpt_key=None)]
- model_list[0].update(other_model_state_defaults)
- # FIXME: hyper per model, not about model loading
- # for k in gen_hyper:
- # model_list[k] = locals()[k]
-
- model_list0 = copy.deepcopy(model_list) # just strings, safe to deepcopy
- model_state0 = model_state_none.copy()
- assert len(model_state_none) == len(model_state0)
- if model_lock:
- model_list = model_lock
- # do reverse, so first is default base_model etc., so some logic works in go_gradio() more easily
- for model_dict in reversed(model_list):
- # handle defaults user didn't have to pass
- # special defaults, ignore defaults for these if not specifically set, replace with ''
- model_dict['base_model'] = model_dict.get('base_model', '')
- model_dict['tokenizer_base_model'] = model_dict.get('tokenizer_base_model', '')
- model_dict['lora_weights'] = model_dict.get('lora_weights', '')
- model_dict['inference_server'] = model_dict.get('inference_server', '')
- if prepare_offline_level >= 2:
- if 'openai' not in model_dict['inference_server'] and 'replicate' not in model_dict['inference_server']:
- # assume want locally, but OpenAI and replicate are never local for model part
- model_dict['inference_server'] = ''
- prompt_type_infer = not model_dict.get('prompt_type')
- model_dict['prompt_type'] = model_dict.get('prompt_type',
- model_list0[0]['prompt_type']) # don't use mutated value
- # rest of generic defaults
- for k in model_list0[0]:
- if k not in model_dict:
- model_dict[k] = model_list0[0][k]
-
- # begin prompt adjustments
- # get query prompt for (say) last base model if using model lock
- pre_prompt_query1, prompt_query1, pre_prompt_summary1, prompt_summary1 = (
- get_langchain_prompts(pre_prompt_query, prompt_query,
- pre_prompt_summary, prompt_summary,
- model_dict['base_model'],
- model_dict['inference_server'],
- model_dict['model_path_llama']))
- # if mixed setup, choose non-empty so best models best
- # FIXME: Make per model dict passed through to evaluate
- pre_prompt_query = pre_prompt_query or pre_prompt_query1
- prompt_query = prompt_query or prompt_query1
- pre_prompt_summary = pre_prompt_summary or pre_prompt_summary1
- prompt_summary = prompt_summary or prompt_summary1
-
- # try to infer, ignore empty initial state leading to get_generate_params -> 'plain'
- if prompt_type_infer:
- model_lower1 = model_dict['base_model'].lower()
- if model_lower1 in inv_prompt_type_to_model_lower:
- model_dict['prompt_type'] = inv_prompt_type_to_model_lower[model_lower1]
- model_dict['prompt_dict'], error0 = get_prompt(model_dict['prompt_type'], '',
- chat=False, context='', reduced=False,
- making_context=False,
- return_dict=True,
- system_prompt=system_prompt)
- else:
- model_dict['prompt_dict'] = prompt_dict
- else:
- model_dict['prompt_dict'] = prompt_dict
- model_dict['prompt_dict'] = model_dict.get('prompt_dict', model_dict['prompt_dict'])
- # end prompt adjustments
- all_kwargs = locals().copy()
- all_kwargs.update(model_dict)
- if model_dict['base_model'] and not login_mode_if_model0:
- model0, tokenizer0, device = get_model(reward_type=False,
- **get_kwargs(get_model, exclude_names=['reward_type'],
- **all_kwargs))
- # update model state
- if hasattr(tokenizer0, 'model_max_length'):
- model_dict['max_seq_len'] = tokenizer0.model_max_length
- else:
- # if empty model, then don't load anything, just get gradio up
- model0, tokenizer0, device = None, None, None
- if model0 is None:
- if fail_if_cannot_connect:
- raise RuntimeError("Could not connect, see logs")
- # skip
- if isinstance(model_lock, list):
- model_lock.remove(model_dict)
- continue
- model_state_trial = dict(model=model0, tokenizer=tokenizer0, device=device)
- model_state_trial.update(model_dict)
- diff_keys = set(list(model_state_none.keys())).symmetric_difference(model_state_trial.keys())
- assert len(model_state_none) == len(model_state_trial), diff_keys
- print("Model %s" % model_dict, flush=True)
- if model_lock:
- # last in iteration will be first
- model_states.insert(0, model_state_trial)
- # fill model_state0 so go_gradio() easier, manage model_states separately
- model_state0 = model_state_trial.copy()
- else:
- model_state0 = model_state_trial.copy()
- assert len(model_state_none) == len(model_state0)
-
- visible_models = str_to_list(visible_models, allow_none=True) # None means first model
- all_models = [x.get('base_model', xi) for xi, x in enumerate(model_states)]
- visible_models_state0 = [x.get('base_model', xi) for xi, x in enumerate(model_states) if
- visible_models is None or
- x.get('base_model', xi) in visible_models or
- xi in visible_models]
-
- # update to be consistent with what is passed from CLI and model chose
- # do after go over all models if multi-model, so don't contaminate
- # This is just so UI shows reasonable correct value, not 2048 dummy value
- if len(model_states) >= 1:
- max_seq_len = model_states[0]['tokenizer'].model_max_length
- elif model_state0 is not None and \
- 'tokenizer' in model_state0 and \
- hasattr(model_state0['tokenizer'], 'model_max_length'):
- max_seq_len = model_state0['tokenizer'].model_max_length
-
- # get score model
- all_kwargs = locals().copy()
- smodel, stokenizer, sdevice = get_score_model(reward_type=True,
- **get_kwargs(get_score_model, exclude_names=['reward_type'],
- **all_kwargs))
- score_model_state0 = dict(model=smodel, tokenizer=stokenizer, device=sdevice,
- base_model=score_model, tokenizer_base_model='', lora_weights='',
- inference_server='', prompt_type='', prompt_dict='',
- visible_models=None, h2ogpt_key=None)
-
- if enable_captions:
- if pre_load_caption_model:
- from image_captions import H2OImageCaptionLoader
- caption_loader = H2OImageCaptionLoader(caption_gpu=caption_gpu, gpu_id=caption_gpu_id).load_model()
- else:
- caption_loader = 'gpu' if n_gpus > 0 and caption_gpu else 'cpu'
- else:
- caption_loader = False
-
- if pre_load_embedding_model and \
- langchain_mode != LangChainMode.DISABLED.value and \
- not use_openai_embedding:
- from src.gpt_langchain import get_embedding
- hf_embedding_model = dict(name=hf_embedding_model,
- model=get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model,
- preload=True))
-
- if enable_doctr or enable_pdf_ocr in [True, 'auto', 'on']:
- if pre_load_caption_model:
- from image_doctr import H2OOCRLoader
- doctr_loader = H2OOCRLoader(layout_aware=True, gpu_id=doctr_gpu_id)
- else:
- doctr_loader = 'gpu' if n_gpus > 0 and caption_gpu else 'cpu'
- else:
- doctr_loader = False
-
- # assume gradio needs everything
- go_gradio(**locals())
-
-
-def get_config(base_model,
- use_auth_token=False,
- trust_remote_code=True,
- offload_folder=None,
- revision=None,
- rope_scaling=None,
- triton_attn=False,
- long_sequence=True,
- return_model=False,
- raise_exception=False,
- max_seq_len=None,
- verbose=False,
- ):
- from accelerate import init_empty_weights
- with init_empty_weights():
- from transformers import AutoConfig
- try:
- if rope_scaling:
- rope_kwargs = dict(rope_scaling=rope_scaling)
- else:
- rope_kwargs = {}
- config = AutoConfig.from_pretrained(base_model, token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- revision=revision,
- **rope_kwargs)
- except OSError as e:
- if raise_exception:
- raise
- if 'not a local folder and is not a valid model identifier listed on' in str(
- e) or '404 Client Error' in str(e) or "couldn't connect" in str(e):
- # e.g. llama, gpjt, etc.
- # e.g. HF TGI but not model on HF or private etc.
- if max_seq_len is None and base_model.lower() in non_hf_types:
- print("Could not determine --max_seq_len, setting to 2048. Pass if not correct", flush=True)
- max_seq_len = 2048
- # HF TGI server only should really require prompt_type, not HF model state
- return None, None, max_seq_len
- else:
- raise
- if triton_attn and 'mpt-' in base_model.lower():
- config.attn_config['attn_impl'] = 'triton'
- if long_sequence:
- if 'mpt-7b-storywriter' in base_model.lower():
- config.update({"max_seq_len": 83968})
- if 'mosaicml/mpt-7b-chat' in base_model.lower():
- config.update({"max_seq_len": 4096})
- if 'mpt-30b' in base_model.lower():
- config.update({"max_seq_len": 2 * 8192})
- if return_model and \
- issubclass(config.__class__, tuple(AutoModel._model_mapping.keys())):
- model = AutoModel.from_config(
- config,
- trust_remote_code=trust_remote_code,
- )
- else:
- # can't infer
- model = None
- if 'falcon' in base_model.lower():
- config.use_cache = False
-
- # allow override
- if max_seq_len is not None:
- print("Overriding max_seq_len -> %d" % max_seq_len, flush=True)
- else:
- if hasattr(config, 'max_seq_len'):
- max_seq_len = int(config.max_seq_len)
- # Note https://huggingface.co/lmsys/vicuna-13b-v1.5-16k/blob/main/config.json has below, but here just want base size before rope
- # elif hasattr(config, 'max_sequence_length'):
- # max_seq_len = int(config.max_sequence_length)
- elif hasattr(config, 'max_position_embeddings') and isinstance(config.max_position_embeddings, int):
- # help automatically limit inputs to generate
- max_seq_len = config.max_position_embeddings
- if verbose:
- print("Used max_position_embeddings=%s as base model (pre-rope) max_seq_len."
- " If not desired, pass --max_seq_len and set to some integer value." % config.max_position_embeddings,
- flush=True)
- elif hasattr(config, 'n_ctx'):
- # e.g. gpt2
- max_seq_len = int(config.n_ctx)
- else:
- print("Could not determine --max_seq_len, setting to 2048. Pass if not correct", flush=True)
- max_seq_len = 2048
- # FIXME:
- # raise RuntimeError("Could not determine max_seq_len,"
- # " please pass --max_seq_len and set to some value, e.g. 2048.")
-
- # listen to model if sets this and user passed nothing
- if not rope_scaling and hasattr(config, 'rope_scaling'):
- rope_scaling = config.rope_scaling
-
- if rope_scaling:
- if rope_scaling.get('factor'):
- # HF transformers
- max_seq_len *= rope_scaling.get('factor')
- elif rope_scaling.get('alpha_value'):
- # exllama
- # Note: exllama's own tokenizer has this set correctly in loaders.py, this config will be unused
- max_seq_len *= rope_scaling.get('alpha_value')
- max_seq_len = int(max_seq_len)
- print("Automatically setting max_seq_len=%d for RoPE scaling for %s" % (max_seq_len, base_model),
- flush=True)
-
- return config, model, max_seq_len
-
-
-def get_non_lora_model(base_model, model_loader, load_half,
- load_gptq,
- load_awq,
- load_exllama,
- use_safetensors,
- revision,
- model_kwargs, reward_type,
- config, model,
- gpu_id=0,
- ):
- """
- Ensure model gets on correct device
- """
-
- if model is not None:
- # NOTE: Can specify max_memory={0: max_mem, 1: max_mem}, to shard model
- # NOTE: Some models require avoiding sharding some layers,
- # then would pass no_split_module_classes and give list of those layers.
- from accelerate import infer_auto_device_map
- device_map = infer_auto_device_map(
- model,
- dtype=torch.float16 if load_half else torch.float32,
- )
- if hasattr(model, 'model'):
- device_map_model = infer_auto_device_map(
- model.model,
- dtype=torch.float16 if load_half else torch.float32,
- )
- device_map.update(device_map_model)
- else:
- device_map = "auto"
-
- n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
- n_gpus, gpu_ids = cuda_vis_check(n_gpus)
-
- if n_gpus > 0:
- if gpu_id >= 0:
- # FIXME: If really distributes model, tend to get things like: ValueError: gpt_neox.embed_in.weight doesn't have any device set.
- # So avoid for now, just put on first GPU, unless score_model, put on last
- if reward_type:
- device_map = {'': n_gpus - 1}
- else:
- device_map = {'': min(n_gpus - 1, gpu_id)}
- if gpu_id == -1:
- device_map = {'': 'cuda'}
- else:
- device_map = {'': 'cpu'}
- model_kwargs['load_in_8bit'] = False
- model_kwargs['load_in_4bit'] = False
- print('device_map: %s' % device_map, flush=True)
-
- load_in_8bit = model_kwargs.get('load_in_8bit', False)
- load_in_4bit = model_kwargs.get('load_in_4bit', False)
- model_kwargs['device_map'] = device_map
- model_kwargs['use_safetensors'] = use_safetensors
- model_kwargs['revision'] = revision
- pop_unused_model_kwargs(model_kwargs)
-
- if load_exllama:
- model = model_loader
- elif load_gptq:
- model_kwargs.pop('torch_dtype', None)
- model_kwargs.pop('device_map')
- model = model_loader(
- model_name_or_path=base_model,
- model_basename=load_gptq,
- **model_kwargs,
- )
- elif load_awq:
- allowed_dict = dict(max_new_tokens=None,
- trust_remote_code=True, fuse_layers=True,
- batch_size=1, safetensors=False,
- max_memory=None, offload_folder=None)
- for k in model_kwargs.copy():
- if k not in allowed_dict:
- model_kwargs.pop(k)
- if load_awq.endswith('.pt'):
- args = tuple([base_model, load_awq])
- else:
- args = tuple([base_model])
- model = model_loader(
- *args,
- safetensors=use_safetensors,
- **model_kwargs,
- )
- elif load_in_8bit or load_in_4bit or not load_half:
- model = model_loader(
- base_model,
- config=config,
- **model_kwargs,
- )
- else:
- model = model_loader(
- base_model,
- config=config,
- **model_kwargs,
- )
- if not getattr(model, "is_quantized", False):
- model = model.half()
- return model
-
-
-def get_client_from_inference_server(inference_server, base_model=None, raise_connection_exception=False):
- inference_server, headers = get_hf_server(inference_server)
- # preload client since slow for gradio case especially
- from gradio_utils.grclient import GradioClient
- gr_client = None
- hf_client = None
- if headers is None:
- try:
- print("GR Client Begin: %s %s" % (inference_server, base_model), flush=True)
- # first do sanity check if alive, else gradio client takes too long by default
- requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT', '30')))
- gr_client = GradioClient(inference_server).setup()
- print("GR Client End: %s" % inference_server, flush=True)
- except (OSError, ValueError) as e:
- # Occurs when wrong endpoint and should have been HF client, so don't hard raise, just move to HF
- gr_client = None
- print("GR Client Failed %s %s: %s" % (inference_server, base_model, str(e)), flush=True)
- except (ConnectTimeoutError, ConnectTimeout, MaxRetryError, ConnectionError, ConnectionError2,
- JSONDecodeError, ReadTimeout2, KeyError) as e:
- t, v, tb = sys.exc_info()
- ex = ''.join(traceback.format_exception(t, v, tb))
- print("GR Client Failed %s %s: %s" % (inference_server, base_model, str(ex)), flush=True)
- if raise_connection_exception:
- raise
-
- if gr_client is None:
- res = None
- from text_generation import Client as HFClient
- print("HF Client Begin: %s %s" % (inference_server, base_model))
- try:
- hf_client = HFClient(inference_server, headers=headers, timeout=int(os.getenv('REQUEST_TIMEOUT', '30')))
- # quick check valid TGI endpoint
- res = hf_client.generate('What?', max_new_tokens=1)
- hf_client = HFClient(inference_server, headers=headers, timeout=300)
- except (ConnectTimeoutError, ConnectTimeout, MaxRetryError, ConnectionError, ConnectionError2,
- JSONDecodeError, ReadTimeout2, KeyError) as e:
- hf_client = None
- t, v, tb = sys.exc_info()
- ex = ''.join(traceback.format_exception(t, v, tb))
- print("HF Client Failed %s %s: %s" % (inference_server, base_model, str(ex)))
- if raise_connection_exception:
- raise
- print("HF Client End: %s %s : %s" % (inference_server, base_model, res))
- return inference_server, gr_client, hf_client
-
-
-def get_model(
- load_8bit: bool = False,
- load_4bit: bool = False,
- low_bit_mode: int = 1,
- load_half: bool = True,
- load_gptq: str = '',
- load_awq: str = '',
- load_exllama: bool = False,
- use_safetensors: bool = False,
- revision: str = None,
- use_gpu_id: bool = True,
- base_model: str = '',
- inference_server: str = "",
- tokenizer_base_model: str = '',
- lora_weights: str = "",
- gpu_id: int = 0,
- n_jobs=None,
-
- reward_type: bool = None,
- local_files_only: bool = False,
- resume_download: bool = True,
- use_auth_token: Union[str, bool] = False,
- trust_remote_code: bool = True,
- offload_folder: str = None,
- rope_scaling: dict = None,
- max_seq_len: int = None,
- compile_model: bool = True,
- llamacpp_dict=None,
- exllama_dict=None,
- gptq_dict=None,
- attention_sinks=None,
- sink_dict=None,
- truncation_generation=None,
- hf_model_dict={},
-
- verbose: bool = False,
-):
- """
-
- :param load_8bit: load model in 8-bit, not supported by all models
- :param load_4bit: load model in 4-bit, not supported by all models
- :param low_bit_mode: See gen.py
- :param load_half: load model in 16-bit
- :param load_gptq: GPTQ model_basename
- :param load_awq: AWQ model_basename
- :param load_exllama: whether to use exllama
- :param use_safetensors: use safetensors file
- :param revision:
- :param use_gpu_id: Use torch infer of optimal placement of layers on devices (for non-lora case)
- For non-LORA case, False will spread shards across multiple GPUs, but this can lead to cuda:x cuda:y mismatches
- So it is not the default
- :param base_model: name/path of base model
- :param inference_server: whether base_model is hosted locally ('') or via http (url)
- :param tokenizer_base_model: name/path of tokenizer
- :param lora_weights: name/path
- :param gpu_id: which GPU (0..n_gpus-1) or allow all GPUs if relevant (-1)
- :param n_jobs: number of cores to use (e.g. for llama CPU model)
- :param reward_type: reward type model for sequence classification
- :param local_files_only: use local files instead of from HF
- :param resume_download: resume downloads from HF
- :param use_auth_token: assumes user did on CLI `huggingface-cli login` to access private repo
- :param trust_remote_code: trust code needed by model
- :param offload_folder: offload folder
- :param rope_scaling: scaling for rope-based models, e.g. "{'type':'dynamic', 'factor':4}"
- :param max_seq_len: override for maximum sequence length for model
- :param max_seq_len: if set, use as max_seq_len for model
- :param compile_model: whether to compile torch model
- :param llamacpp_dict: dict of llama.cpp and GPT4All model options
- :param exllama_dict: dict of exllama options
- :param gptq_dict: dict of AutoGPTQ options
- :param attention_sinks: whether to use attention_sinks package
- :param sink_dict: dict of attention sinks options
- :param truncation_generation: whether to truncate generation in torch case to max_seq_len
- :param hf_model_dict
- :param verbose:
- :return:
- """
- print("Starting get_model: %s %s" % (base_model, inference_server), flush=True)
-
- triton_attn = False
- long_sequence = True
- config_kwargs = dict(use_auth_token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- rope_scaling=rope_scaling,
- triton_attn=triton_attn,
- long_sequence=long_sequence,
- revision=revision,
- max_seq_len=max_seq_len,
- verbose=verbose)
- config, _, max_seq_len = get_config(base_model, **config_kwargs, raise_exception=False)
-
- if base_model in non_hf_types:
- assert config is None, "Expected config None for %s" % base_model
-
- llama_type_from_config = 'llama' in str(config).lower()
- llama_type_from_name = "llama" in base_model.lower()
- llama_type = llama_type_from_config or llama_type_from_name
- if "xgen" in base_model.lower() or 'llama2' in base_model.lower() or 'llama-2' in base_model.lower():
- llama_type = False
- if llama_type:
- if verbose:
- print("Detected as llama type from"
- " config (%s) or name (%s)" % (llama_type_from_config, llama_type_from_name), flush=True)
-
- model_name_exllama_if_no_config = '' if not llamacpp_dict else llamacpp_dict.get('model_name_exllama_if_no_config',
- '')
- model_loader, tokenizer_loader, conditional_type = (
- get_loaders(model_name=base_model, reward_type=reward_type, llama_type=llama_type,
- load_gptq=load_gptq, load_awq=load_awq, load_exllama=load_exllama,
- config=config,
- rope_scaling=rope_scaling, max_seq_len=max_seq_len,
- model_name_exllama_if_no_config=model_name_exllama_if_no_config,
- exllama_dict=exllama_dict, gptq_dict=gptq_dict,
- attention_sinks=attention_sinks, sink_dict=sink_dict,
- truncation_generation=truncation_generation,
- hf_model_dict=hf_model_dict))
-
- tokenizer_kwargs = dict(local_files_only=local_files_only,
- resume_download=resume_download,
- token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- revision=revision,
- padding_side='left',
- config=config,
- )
- if not tokenizer_base_model:
- tokenizer_base_model = base_model
-
- if load_exllama:
- tokenizer = tokenizer_loader
- elif config is not None and tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
- if load_exllama:
- tokenizer = tokenizer_loader
- else:
- tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model, **tokenizer_kwargs)
- # sets raw (no cushion) limit
- # If using RoPE with scaling, then for non-exllama models (e.g. HF models),
- # then config -> tokenizer will set model_max_length correctly
- set_model_max_len(max_seq_len, tokenizer, verbose=False)
- # if using fake tokenizer, not really accurate when lots of numbers, give a bit of buffer, else get:
- # Generation Failed: Input validation error: `inputs` must have less than 2048 tokens. Given: 2233
- tokenizer.model_max_length = int(tokenizer.model_max_length - 50)
- else:
- tokenizer = None
-
- if isinstance(inference_server, str) and inference_server.startswith("http"):
- inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server,
- base_model=base_model)
- client = gr_client or hf_client
- # Don't return None, None for model, tokenizer so triggers
- if tokenizer is None:
- # FIXME: Could use only tokenizer from llama etc. but hard to detatch from model, just use fake for now
- if os.getenv("HARD_ASSERTS") and base_model not in non_hf_types:
- raise RuntimeError("Unexpected tokenizer=None")
- tokenizer = FakeTokenizer()
- return client, tokenizer, 'http'
- if isinstance(inference_server, str) and (
- inference_server.startswith('openai') or
- inference_server.startswith('vllm') or
- inference_server.startswith('replicate') or
- inference_server.startswith('sagemaker')
- ):
- if inference_server.startswith('openai'):
- assert os.getenv('OPENAI_API_KEY'), "Set environment for OPENAI_API_KEY"
- # Don't return None, None for model, tokenizer so triggers
- # include small token cushion
- max_seq_len = model_token_mapping[base_model]
- if inference_server.startswith('replicate'):
- assert len(inference_server.split(':')) >= 3, "Expected replicate:model string, got %s" % inference_server
- assert os.getenv('REPLICATE_API_TOKEN'), "Set environment for REPLICATE_API_TOKEN"
- assert max_seq_len is not None, "Please pass --max_seq_len= for replicate models."
- try:
- import replicate as replicate_python
- except ImportError:
- raise ImportError(
- "Could not import replicate python package. "
- "Please install it with `pip install replicate`."
- )
- if inference_server.startswith('sagemaker'):
- assert len(
- inference_server.split(
- ':')) >= 3, "Expected sagemaker_chat::, got %s" % inference_server
- assert os.getenv('AWS_ACCESS_KEY_ID'), "Set environment for AWS_ACCESS_KEY_ID"
- assert os.getenv('AWS_SECRET_ACCESS_KEY'), "Set environment for AWS_SECRET_ACCESS_KEY"
- # Don't return None, None for model, tokenizer so triggers
- # include small token cushion
- if inference_server.startswith('openai') or tokenizer is None:
- # don't use fake (tiktoken) tokenizer for vLLM//replicate if know actual model with actual tokenizer
- assert max_seq_len is not None, "Please pass --max_seq_len= for unknown or non-HF model %s" % base_model
- tokenizer = FakeTokenizer(model_max_length=max_seq_len - 50, is_openai=True)
- return inference_server, tokenizer, inference_server
- assert not inference_server, "Malformed inference_server=%s" % inference_server
- if base_model in non_hf_types:
- from gpt4all_llm import get_model_tokenizer_gpt4all
- model, tokenizer, device = get_model_tokenizer_gpt4all(base_model, n_jobs=n_jobs,
- max_seq_len=max_seq_len,
- llamacpp_dict=llamacpp_dict)
- return model, tokenizer, device
- if load_exllama:
- return model_loader, tokenizer, 'cuda'
-
- # get local torch-HF model
- return get_hf_model(load_8bit=load_8bit,
- load_4bit=load_4bit,
- low_bit_mode=low_bit_mode,
- load_half=load_half,
- load_gptq=load_gptq,
- load_awq=load_awq,
- use_safetensors=use_safetensors,
- revision=revision,
- use_gpu_id=use_gpu_id,
- base_model=base_model,
- tokenizer_base_model=tokenizer_base_model,
- lora_weights=lora_weights,
- gpu_id=gpu_id,
-
- reward_type=reward_type,
- local_files_only=local_files_only,
- resume_download=resume_download,
- use_auth_token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- rope_scaling=rope_scaling,
- compile_model=compile_model,
-
- llama_type=llama_type,
- config_kwargs=config_kwargs,
- tokenizer_kwargs=tokenizer_kwargs,
- gptq_dict=gptq_dict,
- attention_sinks=attention_sinks,
- sink_dict=sink_dict,
- truncation_generation=truncation_generation,
- hf_model_dict=hf_model_dict,
-
- verbose=verbose)
-
-
-def get_hf_model(load_8bit: bool = False,
- load_4bit: bool = False,
- low_bit_mode: int = 1,
- load_half: bool = True,
- load_gptq: str = '',
- load_awq: str = '',
- use_safetensors: bool = False,
- revision: str = None,
- use_gpu_id: bool = True,
- base_model: str = '',
- tokenizer_base_model: str = '',
- lora_weights: str = "",
- gpu_id: int = 0,
-
- reward_type: bool = None,
- local_files_only: bool = False,
- resume_download: bool = True,
- use_auth_token: Union[str, bool] = False,
- trust_remote_code: bool = True,
- offload_folder: str = None,
- rope_scaling: dict = None,
- compile_model: bool = True,
-
- llama_type: bool = False,
- config_kwargs=None,
- tokenizer_kwargs=None,
- gptq_dict=None,
- attention_sinks=None,
- sink_dict=None,
- truncation_generation=None,
- hf_model_dict=None,
-
- verbose: bool = False,
- ):
- assert config_kwargs is not None
- assert tokenizer_kwargs is not None
-
- load_exllama = False # Never should be in HF code for exllama
- exllama_dict = {}
-
- if lora_weights is not None and lora_weights.strip():
- if verbose:
- print("Get %s lora weights" % lora_weights, flush=True)
- device = get_device()
-
- if 'gpt2' in base_model.lower():
- # RuntimeError: where expected condition to be a boolean tensor, but got a tensor with dtype Half
- load_8bit = False
- load_4bit = False
-
- assert base_model.strip(), (
- "Please choose a base model with --base_model (CLI) or load one from Models Tab (gradio)"
- )
-
- model_loader, tokenizer_loader, conditional_type = (
- get_loaders(model_name=base_model, reward_type=reward_type, llama_type=llama_type,
- load_gptq=load_gptq, load_awq=load_awq, load_exllama=load_exllama,
- exllama_dict=exllama_dict, gptq_dict=gptq_dict,
- attention_sinks=attention_sinks, sink_dict=sink_dict,
- truncation_generation=truncation_generation,
- hf_model_dict=hf_model_dict))
-
- config, _, max_seq_len = get_config(base_model, return_model=False, raise_exception=True, **config_kwargs)
-
- if tokenizer_loader is not None and not isinstance(tokenizer_loader, str):
- if load_exllama:
- tokenizer = tokenizer_loader
- else:
- tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model,
- **tokenizer_kwargs)
- else:
- tokenizer = tokenizer_loader
-
- if isinstance(tokenizer, str):
- # already a pipeline, tokenizer_loader is string for task
- model = model_loader(tokenizer,
- model=base_model,
- device=0 if device == "cuda" else -1,
- torch_dtype=torch.float16 if device == 'cuda' else torch.float32)
- else:
- assert device in ["cuda", "cpu", "mps"], "Unsupported device %s" % device
- model_kwargs = dict(local_files_only=local_files_only,
- torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
- resume_download=resume_download,
- token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- revision=revision,
- # rope_scaling=rope_scaling, # only put into config
- )
- if 'mbart-' not in base_model.lower() and 'mpt-' not in base_model.lower():
- if use_gpu_id and gpu_id is not None and gpu_id >= 0 and device == 'cuda':
- device_map = {"": gpu_id}
- else:
- device_map = "auto"
- model_kwargs.update(dict(load_in_8bit=load_8bit,
- load_in_4bit=load_4bit,
- device_map=device_map,
- ))
- if 'mpt-' in base_model.lower() and gpu_id is not None and gpu_id >= 0:
- # MPT doesn't support spreading over GPUs
- model_kwargs.update(dict(device_map={"": gpu_id} if device == 'cuda' else "cpu"))
-
- if 'OpenAssistant/reward-model'.lower() in base_model.lower():
- # FIXME: could put on other GPUs
- model_kwargs['device_map'] = {"": 0} if device == 'cuda' else {"": 'cpu'}
- model_kwargs.pop('torch_dtype', None)
- pop_unused_model_kwargs(model_kwargs)
-
- n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
- n_gpus, gpu_ids = cuda_vis_check(n_gpus)
- if low_bit_mode == 1 and n_gpus != 0:
- from transformers import BitsAndBytesConfig
- model_kwargs['quantization_config'] = BitsAndBytesConfig(bnb_4bit_compute_dtype=torch.bfloat16,
- load_in_4bit=load_4bit,
- load_in_8bit=load_8bit,
- )
- elif low_bit_mode == 2 and n_gpus != 0:
- from transformers import BitsAndBytesConfig
- model_kwargs['quantization_config'] = BitsAndBytesConfig(bnb_4bit_quant_type="nf4",
- load_in_4bit=load_4bit,
- load_in_8bit=load_8bit,
- )
- elif low_bit_mode == 3 and n_gpus != 0:
- from transformers import BitsAndBytesConfig
- model_kwargs['quantization_config'] = BitsAndBytesConfig(bnb_4bit_use_double_quant=True,
- load_in_4bit=load_4bit,
- load_in_8bit=load_8bit,
- )
- elif low_bit_mode == 4 and n_gpus != 0:
- from transformers import BitsAndBytesConfig
- model_kwargs['quantization_config'] = BitsAndBytesConfig(bnb_4bit_use_double_quant=True,
- bnb_4bit_quant_type="nf4",
- load_in_4bit=load_4bit,
- load_in_8bit=load_8bit,
- )
-
- if not lora_weights:
- # torch.device context uses twice memory for AutoGPTQ
- context = NullContext if (load_gptq or load_awq) else torch.device
- with context(device):
-
- if use_gpu_id:
- config, model, max_seq_len = get_config(base_model,
- return_model=True, raise_exception=True, **config_kwargs)
- model = get_non_lora_model(base_model, model_loader, load_half,
- load_gptq, load_awq,
- load_exllama,
- use_safetensors,
- revision,
- model_kwargs, reward_type,
- config, model,
- gpu_id=gpu_id,
- )
- else:
- model_kwargs['use_safetensors'] = use_safetensors
- model_kwargs['revision'] = revision
- config, _, max_seq_len = get_config(base_model, **config_kwargs)
- if load_half and not (load_8bit or load_4bit or load_gptq or load_awq):
- model = model_loader(
- base_model,
- config=config,
- **model_kwargs)
- if not getattr(model, "is_quantized", False):
- model = model.half()
- else:
- if load_gptq:
- model_kwargs.pop('torch_dtype', None)
- model_kwargs.pop('device_map')
- model = model_loader(
- model_name_or_path=base_model,
- model_basename=load_gptq,
- **model_kwargs,
- )
- elif load_awq:
- allowed_dict = dict(max_new_tokens=None,
- trust_remote_code=True, fuse_layers=True,
- batch_size=1, safetensors=False,
- max_memory=None, offload_folder=None)
- for k in model_kwargs.copy():
- if k not in allowed_dict:
- model_kwargs.pop(k)
- if load_awq.endswith('.pt'):
- args = tuple([base_model, load_awq])
- else:
- args = tuple([base_model])
- model = model_loader(
- *args,
- safetensors=use_safetensors,
- **model_kwargs,
- )
- else:
- model = model_loader(
- base_model,
- config=config,
- **model_kwargs)
- elif load_8bit or load_4bit:
- config, _, max_seq_len = get_config(base_model, **config_kwargs)
- model = model_loader(
- base_model,
- config=config,
- **model_kwargs
- )
- from peft import PeftModel # loads cuda, so avoid in global scope
- model = PeftModel.from_pretrained(
- model,
- lora_weights,
- torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
- local_files_only=local_files_only,
- resume_download=resume_download,
- token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- rope_scaling=rope_scaling,
- revision=revision,
- device_map={"": 0} if device == 'cuda' else {"": 'cpu'}, # seems to be required
- )
- else:
- with torch.device(device):
- config, _, max_seq_len = get_config(base_model, raise_exception=True, **config_kwargs)
- model = model_loader(
- base_model,
- config=config,
- **model_kwargs
- )
- from peft import PeftModel # loads cuda, so avoid in global scope
- model = PeftModel.from_pretrained(
- model,
- lora_weights,
- torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
- local_files_only=local_files_only,
- resume_download=resume_download,
- token=use_auth_token,
- trust_remote_code=trust_remote_code,
- offload_folder=offload_folder,
- rope_scaling=rope_scaling,
- device_map="auto",
- )
- if load_half and not (load_gptq or load_awq):
- if not getattr(model, "is_quantized", False):
- model = model.half()
-
- # unwind broken decapoda-research config
- if llama_type:
- model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
- model.config.bos_token_id = 1
- model.config.eos_token_id = 2
- if 'gpt2' in base_model.lower():
- # add special tokens that otherwise all share the same id
- tokenizer.add_special_tokens({'bos_token': '',
- 'eos_token': '',
- 'pad_token': ''})
-
- if not isinstance(tokenizer, str):
- model.eval()
- if torch.__version__ >= "2" and sys.platform != "win32" and compile_model:
- model = torch.compile(model)
-
- set_model_max_len(max_seq_len, tokenizer, verbose=False, reward_type=reward_type)
-
- # tell if conditional type
- model.conditional_type = conditional_type
- tokenizer.conditional_type = conditional_type
-
- return model, tokenizer, device
-
-
-def set_model_max_len(max_seq_len, tokenizer, verbose=False, reward_type=False):
- if reward_type:
- # limit deberta, else uses too much memory and not worth response score
- tokenizer.model_max_length = 512
- return
-
- tokenizer.model_max_length = int(max_seq_len)
- if verbose:
- print("model_max_length=%s" % tokenizer.model_max_length, flush=True)
- # for bug in HF transformers
- if tokenizer.model_max_length > 100000000:
- tokenizer.model_max_length = 2048
-
-
-def pop_unused_model_kwargs(model_kwargs):
- """
- in-place pop unused kwargs that are not dependency-upgrade friendly
- no point passing in False, is default, and helps avoid needing to update requirements for new deps
- :param model_kwargs:
- :return:
- """
- check_list = ['load_in_8bit', 'load_in_4bit']
- for k in check_list:
- if k in model_kwargs and not model_kwargs[k]:
- model_kwargs.pop(k)
-
-
-def get_score_model(score_model: str = None,
- load_8bit: bool = False,
- load_4bit: bool = False,
- low_bit_mode=1,
- load_half: bool = True,
- load_gptq: str = '',
- load_awq: str = '',
- load_exllama: bool = False,
- use_gpu_id: bool = True,
- base_model: str = '',
- inference_server: str = '',
- tokenizer_base_model: str = '',
- lora_weights: str = "",
- gpu_id: int = 0,
- n_jobs=None,
-
- reward_type: bool = None,
- local_files_only: bool = False,
- resume_download: bool = True,
- use_auth_token: Union[str, bool] = False,
- trust_remote_code: bool = True,
- offload_folder: str = None,
- rope_scaling: dict = None,
- compile_model: bool = True,
- llamacpp_dict: typing.Dict = None,
- exllama_dict: typing.Dict = None,
- gptq_dict: typing.Dict = None,
- attention_sinks: bool = False,
- sink_dict: typing.Dict = None,
- truncation_generation: bool = False,
- hf_model_dict: typing.Dict = None,
-
- verbose: bool = False,
- ):
- if score_model is not None and score_model.strip():
- load_8bit = False
- load_4bit = False
- low_bit_mode = 1
- load_half = False
- load_gptq = ''
- load_awq = ''
- load_exllama = False
- use_safetensors = False
- revision = None
- base_model = score_model.strip()
- tokenizer_base_model = ''
- lora_weights = ''
- inference_server = ''
- llama_type = False
- max_seq_len = None
- rope_scaling = {}
- compile_model = False
- llamacpp_dict = {}
- exllama_dict = {}
- gptq_dict = {}
- attention_sinks = False
- sink_dict = {}
- truncation_generation = False
- hf_model_dict = {}
- smodel, stokenizer, sdevice = get_model(reward_type=True,
- **get_kwargs(get_model, exclude_names=['reward_type'], **locals()))
- else:
- smodel, stokenizer, sdevice = None, None, None
- return smodel, stokenizer, sdevice
-
-
-def evaluate_fake(*args, **kwargs):
- yield dict(response=invalid_key_msg, sources='', save_dict=dict(), llm_answers={})
- return
-
-
-def evaluate(
- model_state,
- my_db_state,
- selection_docs_state,
- requests_state,
- # START NOTE: Examples must have same order of parameters
- instruction,
- iinput,
- context,
- stream_output,
- prompt_type,
- prompt_dict,
- temperature,
- top_p,
- top_k,
- penalty_alpha,
- num_beams,
- max_new_tokens,
- min_new_tokens,
- early_stopping,
- max_time,
- repetition_penalty,
- num_return_sequences,
- do_sample,
- chat,
- instruction_nochat,
- iinput_nochat,
- langchain_mode,
- add_chat_history_to_context,
- langchain_action,
- langchain_agents,
- top_k_docs,
- chunk,
- chunk_size,
- document_subset,
- document_choice,
- pre_prompt_query,
- prompt_query,
- pre_prompt_summary,
- prompt_summary,
- system_prompt,
-
- image_loaders,
- pdf_loaders,
- url_loaders,
- jq_schema,
- visible_models,
- h2ogpt_key,
- add_search_to_context,
- chat_conversation,
- text_context_list,
- docs_ordering_type,
- min_max_new_tokens,
- max_input_tokens,
- docs_token_handling,
- docs_joiner,
- hyde_level,
- hyde_template,
-
- # END NOTE: Examples must have same order of parameters
- captions_model=None,
- caption_loader=None,
- doctr_loader=None,
- pix2struct_loader=None,
- async_output=None,
- num_async=None,
- src_lang=None,
- tgt_lang=None,
- debug=False,
- concurrency_count=None,
- save_dir=None,
- sanitize_bot_response=False,
- model_state0=None,
- memory_restriction_level=None,
- max_max_new_tokens=None,
- is_public=None,
- max_max_time=None,
- raise_generate_gpu_exceptions=None,
- lora_weights=None,
- use_llm_if_no_docs=True,
- load_db_if_exists=True,
- dbs=None,
- detect_user_path_changes_every_query=None,
- use_openai_embedding=None,
- use_openai_model=None,
- hf_embedding_model=None,
- migrate_embedding_model=None,
- auto_migrate_db=None,
- cut_distance=None,
- db_type=None,
- n_jobs=None,
- first_para=None,
- text_limit=None,
- show_accordions=None,
- top_k_docs_max_show=None,
- show_link_in_sources=None,
- verbose=False,
- gradio=True,
- cli=False,
- use_cache=None,
- auto_reduce_chunks=None,
- max_chunks=None,
- headsize=None,
- model_lock=None,
- force_langchain_evaluate=None,
- model_state_none=None,
- llamacpp_dict=None,
- exllama_dict=None,
- gptq_dict=None,
- attention_sinks=None,
- sink_dict=None,
- truncation_generation=None,
- hf_model_dict=None,
-
- load_exllama=None,
- answer_with_sources=None,
- append_sources_to_answer=None,
- image_loaders_options0=None,
- pdf_loaders_options0=None,
- url_loaders_options0=None,
- jq_schema0=None,
- keep_sources_in_context=None,
-):
- # ensure passed these
- assert concurrency_count is not None
- assert memory_restriction_level is not None
- assert raise_generate_gpu_exceptions is not None
- assert use_openai_embedding is not None
- assert use_openai_model is not None
- assert hf_embedding_model is not None
- assert migrate_embedding_model is not None
- assert auto_migrate_db is not None
- assert db_type is not None
- assert top_k_docs is not None and isinstance(top_k_docs, int)
- assert chunk is not None and isinstance(chunk, bool)
- assert chunk_size is not None and isinstance(chunk_size, int)
- assert n_jobs is not None
- assert first_para is not None
- assert isinstance(add_chat_history_to_context, bool)
- assert isinstance(add_search_to_context, bool)
- assert load_exllama is not None
- # for lazy client (even chat client)
- if image_loaders is None:
- image_loaders = image_loaders_options0
- if pdf_loaders is None:
- pdf_loaders = pdf_loaders_options0
- if url_loaders is None:
- url_loaders = url_loaders_options0
- if jq_schema is None:
- jq_schema = jq_schema0
- if isinstance(langchain_agents, str):
- if langchain_agents.strip().startswith('['):
- # already list, but as string
- langchain_agents = str_to_list(langchain_agents)
- else:
- # just 1 item and make list
- langchain_agents = [langchain_agents]
- chat_conversation = str_to_list(chat_conversation)
- text_context_list = str_to_list(text_context_list)
-
- langchain_modes = selection_docs_state['langchain_modes']
- langchain_mode_paths = selection_docs_state['langchain_mode_paths']
- langchain_mode_types = selection_docs_state['langchain_mode_types']
-
- if debug:
- locals_dict = locals().copy()
- locals_dict.pop('model_state', None)
- locals_dict.pop('model_state0', None)
- locals_dict.pop('model_states', None)
- print(locals_dict)
-
- no_model_msg = "Please choose a base model with --base_model (CLI) or load in Models Tab (gradio).\n" \
- "Then start New Conversation"
-
- if model_state is None:
- model_state = model_state_none.copy()
- if model_state0 is None:
- # e.g. for no gradio case, set dummy value, else should be set
- model_state0 = model_state_none.copy()
-
- # model_state['model] is only 'model' if should use model_state0
- # model could also be None
- have_model_lock = model_lock is not None
- have_fresh_model = model_state['model'] not in [None, 'model', no_model_str]
- # for gradio UI control, expect model_state and model_state0 to match, so if have_model_lock=True, then should have_fresh_model=True
- # but gradio API control will only use nochat api etc. and won't use fresh model, so can't assert in general
- # if have_model_lock:
- # assert have_fresh_model, "Expected model_state and model_state0 to match if have_model_lock"
- have_cli_model = model_state0['model'] not in [None, 'model', no_model_str]
-
- if have_fresh_model:
- # USE FRESH MODEL
- if not have_model_lock:
- # model_state0 is just one of model_state if model_lock, so don't nuke
- # try to free-up original model (i.e. list was passed as reference)
- if model_state0['model'] and hasattr(model_state0['model'], 'cpu'):
- model_state0['model'].cpu()
- model_state0['model'] = None
- # try to free-up original tokenizer (i.e. list was passed as reference)
- if model_state0['tokenizer']:
- model_state0['tokenizer'] = None
- clear_torch_cache()
- chosen_model_state = model_state
- elif have_cli_model:
- # USE MODEL SETUP AT CLI
- assert isinstance(model_state['model'], (type(None), str)) # expect no fresh model
- chosen_model_state = model_state0
- else:
- raise AssertionError(no_model_msg)
- # get variables
- model = chosen_model_state['model']
- tokenizer = chosen_model_state['tokenizer']
- device = chosen_model_state['device']
- base_model = chosen_model_state['base_model']
- tokenizer_base_model = chosen_model_state['tokenizer_base_model']
- lora_weights = chosen_model_state['lora_weights']
- inference_server = chosen_model_state['inference_server']
- visible_models = chosen_model_state['visible_models']
- # use overall key if have, so key for this gradio and any inner gradio
- if chosen_model_state['h2ogpt_key'] is not None:
- h2ogpt_key = chosen_model_state['h2ogpt_key']
- # prefer use input from API over model state
- prompt_type = prompt_type or chosen_model_state['prompt_type']
- prompt_dict = prompt_dict or chosen_model_state['prompt_dict']
-
- if base_model is None:
- raise AssertionError(no_model_msg)
-
- assert base_model.strip(), no_model_msg
- assert model, "Model is missing"
- assert tokenizer, "Tokenizer is missing"
-
- # choose chat or non-chat mode
- if not chat:
- instruction = instruction_nochat
- iinput = iinput_nochat
-
- # avoid instruction in chat_conversation itself, since always used as additional context to prompt in what follows
- if isinstance(chat_conversation, list) and \
- len(chat_conversation) > 0 and \
- len(chat_conversation[-1]) == 2 and \
- chat_conversation[-1][0] == instruction and \
- chat_conversation[-1][1] in [None, '']:
- chat_conversation = chat_conversation[:-1]
- if not add_chat_history_to_context:
- # make it easy to ignore without needing add_chat_history_to_context
- # some langchain or unit test may need to then handle more general case
- chat_conversation = []
-
- # in some cases, like lean nochat API, don't want to force sending prompt_type, allow default choice
- model_lower = base_model.lower()
- if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom':
- prompt_type = inv_prompt_type_to_model_lower[model_lower]
- if verbose:
- print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True)
- assert prompt_type is not None, "prompt_type was None"
-
- # Control generation hyperparameters
- # adjust for bad inputs, e.g. in case also come from API that doesn't get constrained by gradio sliders
- # below is for TGI server, not required for HF transformers
- # limits are chosen similar to gradio_runner.py sliders/numbers
- top_p = min(max(1e-3, top_p), 1.0 - 1e-3)
- top_k = min(max(1, int(top_k)), 100)
- penalty_alpha = min(2.0, max(0.0, penalty_alpha))
- temperature = min(max(0.01, temperature), 2.0)
- # FIXME: https://github.com/h2oai/h2ogpt/issues/106
- num_beams = 1 if stream_output else num_beams # See max_beams in gradio_runner
- if model_lower == 'distilgpt2':
- # always truncate for certain models that totally fail otherwise
- truncation_generation = True
- max_max_new_tokens = get_max_max_new_tokens(chosen_model_state,
- memory_restriction_level=memory_restriction_level,
- max_new_tokens=max_new_tokens,
- attention_sinks=attention_sinks,
- max_max_new_tokens=max_max_new_tokens,
- truncation_generation=truncation_generation)
- if min_max_new_tokens is None:
- # default for nochat api
- min_max_new_tokens = 256
- if max_input_tokens is None:
- max_input_tokens = -1
- if docs_ordering_type is None:
- docs_ordering_type = docs_ordering_types_default
- if docs_token_handling is None:
- docs_token_handling = docs_token_handling_default
- if docs_joiner is None:
- docs_joiner = docs_joiner_default
- model_max_length = get_model_max_length(chosen_model_state)
- max_new_tokens = min(max(1, int(max_new_tokens)), max_max_new_tokens)
- min_new_tokens = min(max(0, int(min_new_tokens)), max_new_tokens)
- max_time = min(max(0, max_time), max_max_time)
- repetition_penalty = min(max(0.01, repetition_penalty), 3.0)
- num_return_sequences = 1 if chat else min(max(1, int(num_return_sequences)), 10)
- min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public)
- # limit total tokens processed, e.g. for summarization, if public instance
- if is_public:
- total_tokens_for_docs = min(2 * model_max_length, 16384)
- else:
- total_tokens_for_docs = None
- top_k_docs = min(max(min_top_k_docs, int(top_k_docs)), max_top_k_docs)
- chunk_size = min(max(128, int(chunk_size)), 2048)
- if not context:
- context = ''
-
- # NOTE!!!!!!!!!! Choice of developer. But only possible to force stream if num_beams=1
- # stream if can, so can control task iteration and time of iteration
- # not required, but helpful for max_time control etc.
- stream_output0 = stream_output
- stream_output = gradio and num_beams == 1
-
- # get prompter
- prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output,
- system_prompt=system_prompt)
-
- # THIRD PLACE where LangChain referenced, but imports only occur if enabled and have db to use
- assert langchain_mode in langchain_modes, "Invalid langchain_mode %s not in %s" % (langchain_mode, langchain_modes)
- assert langchain_action in langchain_actions, "Invalid langchain_action %s not in %s" % (
- langchain_action, langchain_actions)
- assert len(
- set(langchain_agents).difference(langchain_agents_list)) == 0, "Invalid langchain_agents %s" % langchain_agents
-
- # get db, but also fill db state so return already has my_db_state and dbs filled so faster next query
- if langchain_mode != LangChainMode.DISABLED.value:
- from src.gpt_langchain import get_any_db
- db = get_any_db(my_db_state, langchain_mode, langchain_mode_paths, langchain_mode_types,
- dbs=dbs,
- load_db_if_exists=load_db_if_exists,
- db_type=db_type,
- use_openai_embedding=use_openai_embedding,
- hf_embedding_model=hf_embedding_model,
- migrate_embedding_model=migrate_embedding_model,
- auto_migrate_db=auto_migrate_db,
- for_sources_list=True,
- verbose=verbose,
- n_jobs=n_jobs,
- )
- else:
- db = None
-
- t_generate = time.time()
- langchain_only_model = base_model in non_hf_types or \
- load_exllama or \
- inference_server.startswith('replicate') or \
- inference_server.startswith('sagemaker') or \
- inference_server.startswith('openai_azure_chat') or \
- inference_server.startswith('openai_azure')
- do_langchain_path = langchain_mode not in [False, 'Disabled', 'LLM'] or \
- langchain_only_model or \
- force_langchain_evaluate or \
- len(text_context_list) > 0
-
- if len(langchain_agents) > 0:
- do_langchain_path = True
- if add_search_to_context:
- # easier to manage prompt etc. by doing full langchain path
- do_langchain_path = True
-
- if do_langchain_path:
- text = ''
- sources = ''
- response = ''
- # use smaller cut_distance for wiki_full since so many matches could be obtained, and often irrelevant unless close
- from gpt_langchain import run_qa_db
- gen_hyper_langchain = dict(do_sample=do_sample,
- temperature=temperature,
- repetition_penalty=repetition_penalty,
- top_p=top_p,
- top_k=top_k,
- penalty_alpha=penalty_alpha,
- num_beams=num_beams,
- min_new_tokens=min_new_tokens,
- max_new_tokens=max_new_tokens,
- early_stopping=early_stopping,
- max_time=max_time,
- num_return_sequences=num_return_sequences,
- )
- loaders_dict, captions_model = gr_to_lg(image_loaders,
- pdf_loaders,
- url_loaders,
- captions_model=captions_model,
- )
- loaders_dict.update(dict(captions_model=captions_model,
- caption_loader=caption_loader,
- doctr_loader=doctr_loader,
- pix2struct_loader=pix2struct_loader,
- jq_schema=jq_schema,
- ))
- data_point = dict(context=context, instruction=instruction, input=iinput)
- # no longer stuff chat history directly into context this early
- prompt_basic = prompter.generate_prompt(data_point, context_from_history=False)
- prompt = prompt_basic
- num_prompt_tokens = 0
- llm_answers = {}
- for r in run_qa_db(
- inference_server=inference_server,
- model_name=base_model, model=model, tokenizer=tokenizer,
- langchain_only_model=langchain_only_model,
- async_output=async_output,
- num_async=num_async,
- prompter=prompter,
- use_llm_if_no_docs=use_llm_if_no_docs,
- load_db_if_exists=load_db_if_exists,
- db=db,
- langchain_mode_paths=langchain_mode_paths,
- langchain_mode_types=langchain_mode_types,
- detect_user_path_changes_every_query=detect_user_path_changes_every_query,
- cut_distance=1.1 if langchain_mode in ['wiki_full'] else cut_distance,
- answer_with_sources=answer_with_sources,
- append_sources_to_answer=append_sources_to_answer,
- add_chat_history_to_context=add_chat_history_to_context,
- add_search_to_context=add_search_to_context,
- keep_sources_in_context=keep_sources_in_context,
- memory_restriction_level=memory_restriction_level,
- system_prompt=system_prompt,
- use_openai_embedding=use_openai_embedding,
- use_openai_model=use_openai_model,
- hf_embedding_model=hf_embedding_model,
- migrate_embedding_model=migrate_embedding_model,
- auto_migrate_db=auto_migrate_db,
- first_para=first_para,
- text_limit=text_limit,
- show_accordions=show_accordions,
- top_k_docs_max_show=top_k_docs_max_show,
- show_link_in_sources=show_link_in_sources,
-
- # evaluate args items
- query=instruction,
- iinput=iinput,
- context=context,
- stream_output0=stream_output0,
- stream_output=stream_output,
- chunk=chunk,
- chunk_size=chunk_size,
-
- **loaders_dict,
-
- langchain_mode=langchain_mode,
- langchain_action=langchain_action,
- langchain_agents=langchain_agents,
- document_subset=document_subset,
- document_choice=document_choice,
- top_k_docs=top_k_docs,
- prompt_type=prompt_type,
- prompt_dict=prompt_dict,
- pre_prompt_query=pre_prompt_query,
- prompt_query=prompt_query,
- pre_prompt_summary=pre_prompt_summary,
- prompt_summary=prompt_summary,
- text_context_list=text_context_list,
- chat_conversation=chat_conversation,
- visible_models=visible_models,
- h2ogpt_key=h2ogpt_key,
- docs_ordering_type=docs_ordering_type,
- min_max_new_tokens=min_max_new_tokens,
- max_input_tokens=max_input_tokens,
- docs_token_handling=docs_token_handling,
- docs_joiner=docs_joiner,
- hyde_level=hyde_level,
- hyde_template=hyde_template,
-
- **gen_hyper_langchain,
-
- db_type=db_type,
- n_jobs=n_jobs,
- verbose=verbose,
- cli=cli,
- sanitize_bot_response=sanitize_bot_response,
-
- lora_weights=lora_weights,
- llamacpp_dict=llamacpp_dict,
- exllama_dict=exllama_dict,
- gptq_dict=gptq_dict,
- attention_sinks=attention_sinks,
- sink_dict=sink_dict,
- truncation_generation=truncation_generation,
- hf_model_dict=hf_model_dict,
-
- auto_reduce_chunks=auto_reduce_chunks,
- max_chunks=max_chunks,
- total_tokens_for_docs=total_tokens_for_docs,
- headsize=headsize,
- ):
- # doesn't accumulate, new answer every yield, so only save that full answer
- response = r['response']
- sources = r['sources']
- prompt = r['prompt']
- num_prompt_tokens = r['num_prompt_tokens']
- llm_answers = r['llm_answers']
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers=llm_answers)
- if save_dir:
- # estimate using tiktoken
- extra_dict = gen_hyper_langchain.copy()
- extra_dict.update(prompt_type=prompt_type,
- inference_server=inference_server,
- langchain_mode=langchain_mode,
- langchain_action=langchain_action,
- langchain_agents=langchain_agents,
- document_subset=document_subset,
- document_choice=document_choice,
- chat_conversation=chat_conversation,
- add_search_to_context=add_search_to_context,
- num_prompt_tokens=num_prompt_tokens,
- instruction=instruction,
- iinput=iinput,
- context=context,
- t_generate=time.time() - t_generate,
- ntokens=None,
- tokens_persecond=None,
- )
- save_dict = dict(prompt=prompt,
- output=response, base_model=base_model, save_dir=save_dir,
- where_from='run_qa_db',
- extra_dict=extra_dict)
- yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers=llm_answers)
- if verbose:
- print(
- 'Post-Generate Langchain: %s decoded_output: %s' %
- (str(datetime.now()), len(response) if response else -1),
- flush=True)
- if response or sources or langchain_only_model:
- # if got no response (e.g. not showing sources and got no sources,
- # so nothing to give to LLM), then slip through and ask LLM
- # Or if llama/gptj, then just return since they had no response and can't go down below code path
- # don't clear torch cache here, delays multi-generation, and bot(), all_bot(), and evaluate_nochat() do it
- return
-
- # NOT LANGCHAIN PATH, raw LLM
- # restrict instruction + , typically what has large input
- from gradio_utils.grclient import GradioClient
- gradio_server = inference_server.startswith('http') and isinstance(model, GradioClient)
-
- prompt, \
- instruction, iinput, context, \
- num_prompt_tokens, max_new_tokens, num_prompt_tokens0, num_prompt_tokens_actual, \
- chat_index, external_handle_chat_conversation, \
- top_k_docs_trial, one_doc_size, truncation_generation = \
- get_limited_prompt(instruction,
- iinput,
- tokenizer,
- prompter=prompter,
- inference_server=inference_server,
- # prompt_type=prompt_type,
- # prompt_dict=prompt_dict,
- # chat=chat,
- max_new_tokens=max_new_tokens,
- # system_prompt=system_prompt,
- context=context,
- chat_conversation=chat_conversation,
- keep_sources_in_context=keep_sources_in_context,
- model_max_length=model_max_length,
- memory_restriction_level=memory_restriction_level,
- langchain_mode=langchain_mode,
- add_chat_history_to_context=add_chat_history_to_context,
- min_max_new_tokens=min_max_new_tokens,
- max_input_tokens=max_input_tokens,
- truncation_generation=truncation_generation,
- gradio_server=gradio_server,
- )
-
- if inference_server.startswith('vllm') or \
- inference_server.startswith('openai') or \
- inference_server.startswith('http'):
- if inference_server.startswith('vllm') or inference_server.startswith('openai'):
- assert not inference_server.startswith('openai_azure_chat'), "Not fo Azure, use langchain path"
- assert not inference_server.startswith('openai_azure'), "Not for Azure, use langchain path"
- openai, inf_type, deployment_name, base_url, api_version, api_key = set_openai(inference_server)
- where_from = inf_type
-
- terminate_response = prompter.terminate_response or []
- stop_sequences = list(set(terminate_response + [prompter.PreResponse]))
- stop_sequences = [x for x in stop_sequences if x]
- # OpenAI will complain if ask for too many new tokens, takes it as min in some sense, wrongly so.
- max_new_tokens_openai = min(max_new_tokens, model_max_length - num_prompt_tokens)
- gen_server_kwargs = dict(temperature=temperature if do_sample else 0,
- max_tokens=max_new_tokens_openai,
- top_p=top_p if do_sample else 1,
- frequency_penalty=0,
- n=num_return_sequences,
- presence_penalty=1.07 - repetition_penalty + 0.6, # so good default
- )
- if inf_type == 'vllm' or inference_server == 'openai':
- responses = openai.Completion.create(
- model=base_model,
- prompt=prompt,
- **gen_server_kwargs,
- stop=stop_sequences,
- stream=stream_output,
- )
- text = ''
- sources = ''
- response = ''
- if not stream_output:
- text = responses['choices'][0]['text']
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- else:
- collected_events = []
- tgen0 = time.time()
- for event in responses:
- collected_events.append(event) # save the event response
- event_text = event['choices'][0]['text'] # extract the text
- text += event_text # append the text
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- if time.time() - tgen0 > max_time:
- if verbose:
- print("Took too long for OpenAI or VLLM: %s" % (time.time() - tgen0), flush=True)
- break
- elif inf_type == 'vllm_chat' or inference_server == 'openai_chat':
- if system_prompt in [None, 'None', 'auto']:
- openai_system_prompt = "You are a helpful assistant."
- else:
- openai_system_prompt = system_prompt
- messages0 = []
- if openai_system_prompt:
- messages0.append({"role": "system", "content": openai_system_prompt})
- if chat_conversation and add_chat_history_to_context:
- assert external_handle_chat_conversation, "Should be handling only externally"
- # chat_index handles token counting issues
- for message1 in chat_conversation[chat_index:]:
- if len(message1) == 2:
- messages0.append(
- {'role': 'user', 'content': message1[0] if message1[0] is not None else ''})
- messages0.append(
- {'role': 'assistant', 'content': message1[1] if message1[1] is not None else ''})
- messages0.append({'role': 'user', 'content': prompt if prompt is not None else ''})
- responses = openai.ChatCompletion.create(
- model=base_model,
- messages=messages0,
- stream=stream_output,
- **gen_server_kwargs,
- )
- text = ""
- sources = ''
- response = ""
- if not stream_output:
- text = responses["choices"][0]["message"]["content"]
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- else:
- tgen0 = time.time()
- for chunk in responses:
- delta = chunk["choices"][0]["delta"]
- if 'content' in delta:
- text += delta['content']
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- if time.time() - tgen0 > max_time:
- if verbose:
- print("Took too long for OpenAI or VLLM Chat: %s" % (time.time() - tgen0), flush=True)
- break
- else:
- raise RuntimeError("No such OpenAI mode: %s" % inference_server)
- elif inference_server.startswith('http'):
- inference_server, headers = get_hf_server(inference_server)
- from text_generation import Client as HFClient
- if isinstance(model, GradioClient):
- gr_client = model.clone()
- hf_client = None
- elif isinstance(model, HFClient):
- gr_client = None
- hf_client = model
- else:
- inference_server, gr_client, hf_client = get_client_from_inference_server(inference_server,
- base_model=base_model)
-
- # quick sanity check to avoid long timeouts, just see if can reach server
- requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT_FAST', '10')))
-
- if gr_client is not None:
- # Note: h2oGPT gradio server could handle input token size issues for prompt,
- # but best to handle here so send less data to server
-
- chat_client = False
- where_from = "gr_client"
- client_langchain_mode = 'Disabled'
- client_add_chat_history_to_context = True
- client_add_search_to_context = False
- client_langchain_action = LangChainAction.QUERY.value
- client_langchain_agents = []
- gen_server_kwargs = dict(temperature=temperature,
- top_p=top_p,
- top_k=top_k,
- penalty_alpha=penalty_alpha,
- num_beams=num_beams,
- max_new_tokens=max_new_tokens,
- min_new_tokens=min_new_tokens,
- early_stopping=early_stopping,
- max_time=max_time,
- repetition_penalty=repetition_penalty,
- num_return_sequences=num_return_sequences,
- do_sample=do_sample,
- chat=chat_client,
- )
- # account for gradio into gradio that handles prompting, avoid duplicating prompter prompt injection
- if prompt_type in [None, '', PromptType.plain.name, PromptType.plain.value,
- str(PromptType.plain.value)]:
- # if our prompt is plain, assume either correct or gradio server knows different prompt type,
- # so pass empty prompt_Type
- gr_prompt_type = ''
- gr_prompt_dict = ''
- gr_prompt = prompt # already prepared prompt
- gr_context = ''
- gr_iinput = ''
- else:
- # if already have prompt_type that is not plain, None, or '', then already applied some prompting
- # But assume server can handle prompting, and need to avoid double-up.
- # Also assume server can do better job of using stopping.py to stop early, so avoid local prompting, let server handle
- # So avoid "prompt" and let gradio server reconstruct from prompt_type we passed
- # Note it's ok that prompter.get_response() has prompt+text, prompt=prompt passed,
- # because just means extra processing and removal of prompt, but that has no human-bot prompting doesn't matter
- # since those won't appear
- gr_context = context
- gr_prompt = instruction
- gr_iinput = iinput
- gr_prompt_type = prompt_type
- gr_prompt_dict = prompt_dict
- client_kwargs = dict(instruction=gr_prompt if chat_client else '', # only for chat=True
- iinput=gr_iinput, # only for chat=True
- context=gr_context,
- # streaming output is supported, loops over and outputs each generation in streaming mode
- # but leave stream_output=False for simple input/output mode
- stream_output=stream_output,
-
- **gen_server_kwargs,
-
- prompt_type=gr_prompt_type,
- prompt_dict=gr_prompt_dict,
-
- instruction_nochat=gr_prompt if not chat_client else '',
- iinput_nochat=gr_iinput, # only for chat=False
- langchain_mode=client_langchain_mode,
- add_chat_history_to_context=client_add_chat_history_to_context,
- langchain_action=client_langchain_action,
- langchain_agents=client_langchain_agents,
- top_k_docs=top_k_docs,
- chunk=chunk,
- chunk_size=chunk_size,
- document_subset=DocumentSubset.Relevant.name,
- document_choice=[DocumentChoice.ALL.value],
- pre_prompt_query=pre_prompt_query,
- prompt_query=prompt_query,
- pre_prompt_summary=pre_prompt_summary,
- prompt_summary=prompt_summary,
- system_prompt=system_prompt,
- image_loaders=image_loaders,
- pdf_loaders=pdf_loaders,
- url_loaders=url_loaders,
- jq_schema=jq_schema,
- visible_models=visible_models,
- h2ogpt_key=h2ogpt_key,
- add_search_to_context=client_add_search_to_context,
- docs_ordering_type=docs_ordering_type,
- min_max_new_tokens=min_max_new_tokens,
- max_input_tokens=max_input_tokens,
- docs_token_handling=docs_token_handling,
- docs_joiner=docs_joiner,
- hyde_level=hyde_level,
- hyde_template=hyde_template,
- )
- api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing
- response = ''
- text = ''
- sources = ''
- strex = ''
- if not stream_output:
- res = gr_client.predict(str(dict(client_kwargs)), api_name=api_name)
- res_dict = ast.literal_eval(res)
- text = res_dict['response']
- sources = res_dict['sources']
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- else:
- from gradio_utils.grclient import check_job
- job = gr_client.submit(str(dict(client_kwargs)), api_name=api_name)
- res_dict = dict(response=text, sources=sources, save_dict=dict(), llm_answers={})
- text0 = ''
- tgen0 = time.time()
- while not job.done():
- if job.communicator.job.latest_status.code.name == 'FINISHED':
- break
- e = check_job(job, timeout=0, raise_exception=False)
- if e is not None:
- break
- outputs_list = job.communicator.job.outputs
- if outputs_list:
- res = job.communicator.job.outputs[-1]
- res_dict = ast.literal_eval(res)
- text = res_dict['response']
- sources = res_dict['sources']
- if gr_prompt_type == 'plain':
- # then gradio server passes back full prompt + text
- prompt_and_text = text
- else:
- prompt_and_text = prompt + text
- response = prompter.get_response(prompt_and_text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- text_chunk = response[len(text0):]
- if not text_chunk:
- # just need some sleep for threads to switch
- time.sleep(0.001)
- continue
- # save old
- text0 = response
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- if time.time() - tgen0 > max_time:
- if verbose:
- print("Took too long for Gradio: %s" % (time.time() - tgen0), flush=True)
- break
- time.sleep(0.01)
- # ensure get last output to avoid race
- res_all = job.outputs()
- if len(res_all) > 0:
- # don't raise unless nochat API for now
- e = check_job(job, timeout=0.02, raise_exception=not chat)
- if e is not None:
- strex = ''.join(traceback.format_tb(e.__traceback__))
-
- res = res_all[-1]
- res_dict = ast.literal_eval(res)
- text = res_dict['response']
- sources = res_dict['sources']
- else:
- # if got no answer at all, probably something bad, always raise exception
- # UI will still put exception in Chat History under chat exceptions
- e = check_job(job, timeout=0.3, raise_exception=True)
- # go with old text if last call didn't work
- if e is not None:
- stre = str(e)
- strex = ''.join(traceback.format_tb(e.__traceback__))
- else:
- stre = ''
- strex = ''
-
- print("Bad final response: %s %s %s %s %s: %s %s" % (base_model, inference_server,
- res_all, prompt, text, stre, strex),
- flush=True)
- if gr_prompt_type == 'plain':
- # then gradio server passes back full prompt + text
- prompt_and_text = text
- else:
- prompt_and_text = prompt + text
- response = prompter.get_response(prompt_and_text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), error=strex, llm_answers={})
- elif hf_client:
- # HF inference server needs control over input tokens
- where_from = "hf_client"
- response = ''
- extra = ''
- sources = ''
-
- # prompt must include all human-bot like tokens, already added by prompt
- # https://github.com/huggingface/text-generation-inference/tree/main/clients/python#types
- terminate_response = prompter.terminate_response or []
- stop_sequences = list(set(terminate_response + [prompter.PreResponse]))
- stop_sequences = [x for x in stop_sequences if x]
- gen_server_kwargs = dict(do_sample=do_sample,
- max_new_tokens=max_new_tokens,
- # best_of=None,
- repetition_penalty=repetition_penalty,
- return_full_text=False,
- seed=SEED,
- stop_sequences=stop_sequences,
- temperature=temperature,
- top_k=top_k,
- top_p=top_p,
- # truncate=False, # behaves oddly
- # typical_p=top_p,
- # watermark=False,
- # decoder_input_details=False,
- )
- # work-around for timeout at constructor time, will be issue if multi-threading,
- # so just do something reasonable or max_time if larger
- # lower bound because client is re-used if multi-threading
- hf_client.timeout = max(300, max_time)
- if not stream_output:
- text = hf_client.generate(prompt, **gen_server_kwargs).generated_text
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- else:
- tgen0 = time.time()
- text = ""
- for responses in hf_client.generate_stream(prompt, **gen_server_kwargs):
- if not responses.token.special:
- # stop_sequences
- text_chunk = responses.token.text
- text += text_chunk
- response = prompter.get_response(prompt + text, prompt=prompt,
- sanitize_bot_response=sanitize_bot_response)
- sources = ''
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- if time.time() - tgen0 > max_time:
- if verbose:
- print("Took too long for TGI: %s" % (time.time() - tgen0), flush=True)
- break
- else:
- raise RuntimeError("Failed to get client: %s" % inference_server)
- else:
- raise RuntimeError("No such inference_server %s" % inference_server)
-
- if save_dir and text:
- # save prompt + new text
- extra_dict = gen_server_kwargs.copy()
- extra_dict.update(dict(inference_server=inference_server, num_prompt_tokens=num_prompt_tokens,
- t_generate=time.time() - t_generate,
- ntokens=None,
- tokens_persecond=None,
- ))
- save_dict = dict(prompt=prompt, output=text, base_model=base_model, save_dir=save_dir,
- where_from=where_from, extra_dict=extra_dict)
- yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers={})
- return
- else:
- assert not inference_server, "inference_server=%s not supported" % inference_server
-
- if isinstance(tokenizer, str):
- # pipeline
- if tokenizer == "summarization":
- key = 'summary_text'
- else:
- raise RuntimeError("No such task type %s" % tokenizer)
- # NOTE: uses max_length only
- sources = ''
- yield dict(response=model(prompt, max_length=max_new_tokens)[0][key], sources=sources, save_dict=dict(),
- llm_answers={})
-
- if 'mbart-' in base_model.lower():
- assert src_lang is not None
- tokenizer.src_lang = languages_covered()[src_lang]
-
- stopping_criteria = get_stopping(prompt_type, prompt_dict, tokenizer, device, base_model,
- model_max_length=model_max_length,
- prompter=prompter,
- truncation_generation=truncation_generation)
-
- inputs = tokenizer(prompt, return_tensors="pt")
- if debug and len(inputs["input_ids"]) > 0:
- print('input_ids length', len(inputs["input_ids"][0]), flush=True)
- input_ids = inputs["input_ids"].to(device)
- # CRITICAL LIMIT else will fail
- max_max_tokens = int(tokenizer.model_max_length)
- max_input_tokens_default = max(0, int(max_max_tokens - min_new_tokens))
- if max_input_tokens >= 0:
- max_input_tokens = min(max_input_tokens_default, max_input_tokens)
- else:
- max_input_tokens = max_input_tokens_default
- # NOTE: Don't limit up front due to max_new_tokens, let go up to max or reach max_max_tokens in stopping.py
- assert isinstance(max_input_tokens, int), "Bad type for max_input_tokens=%s %s" % (
- max_input_tokens, type(max_input_tokens))
- input_ids = input_ids[:, -max_input_tokens:]
- # required for falcon if multiple threads or asyncio accesses to model during generation
- if use_cache is None:
- use_cache = False if 'falcon' in base_model else True
- if attention_sinks:
- assert use_cache, "attention sinks requires use_cache=True"
- bad_word_ids = [tokenizer.eos_token_id]
- gen_config_kwargs = dict(num_beams=num_beams,
- do_sample=do_sample,
- repetition_penalty=float(repetition_penalty),
- num_return_sequences=num_return_sequences,
- renormalize_logits=True,
- remove_invalid_values=True,
- use_cache=use_cache,
- max_new_tokens=max_new_tokens, # unsure if required here
- )
- if do_sample:
- gen_config_kwargs.update(dict(temperature=float(temperature),
- top_p=float(top_p),
- top_k=top_k))
- if penalty_alpha > 0:
- gen_config_kwargs.update(dict(penalty_alpha=penalty_alpha))
- if True:
- # unclear impact, some odd things going on inside
- # leads to:
- # The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
- # Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
- # or leads to:
- # Using cls_token, but it is not set yet.
- # Using mask_token, but it is not set yet.
- # Using pad_token, but it is not set yet.
- # Using sep_token, but it is not set yet.
- token_ids = ['eos_token_id', 'pad_token_id', 'bos_token_id', 'cls_token_id', 'sep_token_id']
- for token_id in token_ids:
- if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None:
- gen_config_kwargs.update({token_id: getattr(tokenizer, token_id)})
- generation_config = GenerationConfig(**gen_config_kwargs)
-
- gen_kwargs = dict(input_ids=input_ids,
- generation_config=generation_config,
- return_dict_in_generate=True,
- output_scores=True,
- max_new_tokens=max_new_tokens, # prompt + new
- min_new_tokens=min_new_tokens, # prompt + new
- early_stopping=early_stopping, # False, True, "never"
- max_time=max_time,
- stopping_criteria=stopping_criteria,
- )
- if 'gpt2' in base_model.lower():
- gen_kwargs.update(dict(bos_token_id=tokenizer.bos_token_id, pad_token_id=tokenizer.eos_token_id))
- elif 'mbart-' in base_model.lower():
- assert tgt_lang is not None
- tgt_lang = languages_covered()[tgt_lang]
- gen_kwargs.update(dict(forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang]))
- else:
- token_ids = ['eos_token_id', 'bos_token_id', 'pad_token_id']
- for token_id in token_ids:
- if hasattr(tokenizer, token_id) and getattr(tokenizer, token_id) is not None:
- gen_kwargs.update({token_id: getattr(tokenizer, token_id)})
-
- decoder_kwargs = dict(skip_special_tokens=True,
- clean_up_tokenization_spaces=True)
-
- decoder = functools.partial(tokenizer.decode,
- **decoder_kwargs
- )
- with torch.no_grad():
- have_lora_weights = lora_weights not in [no_lora_str, '', None]
- context_class_cast = NullContext if device == 'cpu' or have_lora_weights or device == 'mps' else torch.autocast
- if t5_type(base_model):
- # issues when casting to float16, can mess up t5 model, e.g. only when not streaming, or other odd behaviors
- context_class_cast = NullContext
- with context_class_cast(device):
- # protection for gradio not keeping track of closed users,
- # else hit bitsandbytes lack of thread safety:
- # https://github.com/h2oai/h2ogpt/issues/104
- # but only makes sense if concurrency_count == 1
- context_class = NullContext # if concurrency_count > 1 else filelock.FileLock
- if verbose:
- print('Pre-Generate: %s' % str(datetime.now()), flush=True)
- decoded_output = None
- response = ''
- with context_class("generate.lock"):
- if verbose:
- print('Generate: %s' % str(datetime.now()), flush=True)
- always_use_streaming_method = True # to deal with complex parsing of prompt vs. generation due to odd tokenizing
- if stream_output or always_use_streaming_method:
- skip_prompt = True # True means first output excludes prompt
- streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False,
- **decoder_kwargs)
- gen_kwargs.update(dict(streamer=streamer))
- target = wrapped_partial(generate_with_exceptions, model.generate,
- raise_generate_gpu_exceptions=raise_generate_gpu_exceptions,
- **gen_kwargs)
- bucket = queue.Queue()
- thread = EThread(target=target, streamer=streamer, bucket=bucket)
- thread.start()
- ret = dict(response='', sources='', save_dict=dict(), llm_answers={})
- outputs = ""
- sources = ''
- tgen0 = time.time()
- try:
- for new_text in streamer:
- if bucket.qsize() > 0 or thread.exc:
- thread.join()
- outputs += new_text
- response = prompter.get_response(outputs, prompt=None,
- only_new_text=True,
- sanitize_bot_response=sanitize_bot_response)
- ret = dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- if stream_output:
- yield ret
- if time.time() - tgen0 > max_time:
- if verbose:
- print("Took too long for Torch: %s" % (time.time() - tgen0), flush=True)
- break
- # yield if anything left over as can happen (FIXME: Understand better)
- yield ret
- except BaseException:
- # if any exception, raise that exception if was from thread, first
- if thread.exc:
- raise thread.exc
- raise
- finally:
- # don't clear torch cache here, delays multi-generation, and bot(), all_bot(), and evaluate_nochat() do it
- # in case no exception and didn't join with thread yet, then join
- if not thread.exc:
- thread.join()
- # in case raise StopIteration or broke queue loop in streamer, but still have exception
- if thread.exc:
- raise thread.exc
- decoded_output = outputs
- ntokens = len(outputs) // 4 # hack for now
- else:
- # below length removal doesn't work in general, because encoding does not match internal of model generation
- input_ids_len = gen_kwargs['input_ids'][0].shape[0]
- try:
- outputs = model.generate(**gen_kwargs)
- finally:
- pass
- # don't clear torch cache here, delays multi-generation, and bot(), all_bot(), and evaluate_nochat() do it
- # skip first IDs
- ntokens = sum([len(s) - input_ids_len for s in outputs.sequences]) if save_dir else -1
- outputs = [decoder(s[input_ids_len:]) for s in outputs.sequences]
- sources = ''
- response = prompter.get_response(outputs, prompt=None,
- only_new_text=True,
- sanitize_bot_response=sanitize_bot_response)
- yield dict(response=response, sources=sources, save_dict=dict(), llm_answers={})
- if outputs and len(outputs) >= 1:
- decoded_output = prompt + outputs[0]
- if save_dir and decoded_output:
- extra_dict = gen_config_kwargs.copy()
- extra_dict.update(dict(num_prompt_tokens=num_prompt_tokens,
- t_generate=time.time() - t_generate,
- ntokens=ntokens,
- tokens_persecond=ntokens / (time.time() - t_generate),
- ))
- save_dict = dict(prompt=prompt, output=decoded_output, base_model=base_model, save_dir=save_dir,
- where_from="evaluate_%s" % str(stream_output),
- extra_dict=extra_dict)
- yield dict(response=response, sources=sources, save_dict=save_dict, llm_answers={})
- if verbose:
- print('Post-Generate: %s decoded_output: %s' % (
- str(datetime.now()), len(decoded_output) if decoded_output else -1), flush=True)
-
-
-inputs_list_names = list(inspect.signature(evaluate).parameters)
-state_names = input_args_list.copy() # doesn't have to be the same, but state_names must match evaluate() and how filled then
-inputs_kwargs_list = [x for x in inputs_list_names if x not in eval_func_param_names + state_names]
-
-
-def get_cutoffs(memory_restriction_level, for_context=False, model_max_length=2048, min_max_new_tokens=256):
- # help to avoid errors like:
- # RuntimeError: The size of tensor a (2048) must match the size of tensor b (2049) at non-singleton dimension 3
- # RuntimeError: expected scalar type Half but found Float
- # with - 256
- if memory_restriction_level > 0:
- max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
- else:
- # at least give room for 1 paragraph output
- max_length_tokenize = model_max_length - min_max_new_tokens
- cutoff_len = max_length_tokenize * 4 # if reaches limit, then can't generate new tokens
- output_smallest = 30 * 4
- max_prompt_length = cutoff_len - output_smallest
-
- if for_context:
- # then lower even more to avoid later chop, since just estimate tokens in context bot
- max_prompt_length = max(64, int(max_prompt_length * 0.8))
-
- return cutoff_len, output_smallest, max_length_tokenize, max_prompt_length
-
-
-class H2OTextIteratorStreamer(TextIteratorStreamer):
- """
- normally, timeout required for now to handle exceptions, else get()
- but with H2O version of TextIteratorStreamer, loop over block to handle
- """
-
- def __init__(self, tokenizer, skip_prompt: bool = False, timeout: typing.Optional[float] = None,
- block=True, **decode_kwargs):
- super().__init__(tokenizer, skip_prompt, **decode_kwargs)
- self.text_queue = queue.Queue()
- self.stop_signal = None
- self.do_stop = False
- self.timeout = timeout
- self.block = block
-
- def on_finalized_text(self, text: str, stream_end: bool = False):
- """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue."""
- self.text_queue.put(text, timeout=self.timeout)
- if stream_end:
- self.text_queue.put(self.stop_signal, timeout=self.timeout)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- while True:
- try:
- value = self.stop_signal # value looks unused in pycharm, not true
- if self.do_stop:
- print("hit stop", flush=True)
- # could raise or break, maybe best to raise and make parent see if any exception in thread
- self.clear_queue()
- self.do_stop = False
- raise StopIteration()
- # break
- value = self.text_queue.get(block=self.block, timeout=self.timeout)
- break
- except queue.Empty:
- time.sleep(0.01)
- if value == self.stop_signal:
- self.clear_queue()
- self.do_stop = False
- raise StopIteration()
- else:
- return value
-
- def clear_queue(self):
- # make sure streamer is reusable after stop hit
- with self.text_queue.mutex:
- self.text_queue.queue.clear()
-
- def put(self, value):
- """
- Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
- # same as base class, except remove hack w.r.t. text.rfind(" ") that ruins LLaMa2
- """
- if len(value.shape) > 1 and value.shape[0] > 1:
- raise ValueError("TextStreamer only supports batch size 1")
- elif len(value.shape) > 1:
- value = value[0]
-
- if self.skip_prompt and self.next_tokens_are_prompt:
- self.next_tokens_are_prompt = False
- return
-
- # Add the new token to the cache and decodes the entire thing.
- self.token_cache.extend(value.tolist())
- text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
-
- # After the symbol for a new line, we flush the cache.
- if text.endswith("\n"):
- printable_text = text[self.print_len:]
- self.token_cache = []
- self.print_len = 0
- # If the last token is a CJK character, we print the characters.
- elif len(text) > 0 and self._is_chinese_char(ord(text[-1])):
- printable_text = text[self.print_len:]
- self.print_len += len(printable_text)
- # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
- # which may change with the subsequent token -- there are probably smarter ways to do this!)
- elif len(text) > 0 and text[-1] == '�':
- printable_text = text[self.print_len: text.rfind(" ") + 1]
- self.print_len += len(printable_text)
- else:
- printable_text = text[self.print_len:]
- self.print_len += len(printable_text)
-
- self.on_finalized_text(printable_text)
-
-
-def generate_with_exceptions(func, *args, raise_generate_gpu_exceptions=True, **kwargs):
- try:
- func(*args, **kwargs)
- except torch.cuda.OutOfMemoryError as e:
- print("GPU OOM 2: exception: %s" % str(e),
- flush=True)
- if 'input_ids' in kwargs:
- if kwargs['input_ids'] is not None:
- kwargs['input_ids'].cpu()
- kwargs['input_ids'] = None
- traceback.print_exc()
- clear_torch_cache()
- return
- except (Exception, RuntimeError) as e:
- if 'Expected all tensors to be on the same device' in str(e) or \
- 'expected scalar type Half but found Float' in str(e) or \
- 'probability tensor contains either' in str(e) or \
- 'cublasLt ran into an error!' in str(e) or \
- 'mat1 and mat2 shapes cannot be multiplied' in str(e):
- print(
- "GPU Error: exception: %s" % str(e),
- flush=True)
- traceback.print_exc()
- clear_torch_cache()
- if raise_generate_gpu_exceptions:
- raise
- return
- else:
- clear_torch_cache()
- if raise_generate_gpu_exceptions:
- raise
-
-
-def get_generate_params(model_lower,
- chat,
- stream_output, show_examples,
- prompt_type, prompt_dict,
- system_prompt,
- pre_prompt_query, prompt_query,
- pre_prompt_summary, prompt_summary,
- temperature, top_p, top_k, penalty_alpha, num_beams,
- max_new_tokens, min_new_tokens, early_stopping, max_time,
- repetition_penalty, num_return_sequences,
- do_sample,
- top_k_docs, chunk, chunk_size,
- image_loaders,
- pdf_loaders,
- url_loaders,
- jq_schema,
- docs_ordering_type,
- min_max_new_tokens,
- max_input_tokens,
- docs_token_handling,
- docs_joiner,
- hyde_level,
- hyde_template,
- verbose,
- ):
- use_defaults = False
- use_default_examples = True
- examples = []
- task_info = 'LLM'
- if model_lower:
- print(f"Using Model {model_lower}", flush=True)
- else:
- if verbose:
- print("No model defined yet", flush=True)
-
- min_new_tokens = min_new_tokens if min_new_tokens is not None else 0
- early_stopping = early_stopping if early_stopping is not None else False
- max_time_defaults = 60 * 10
- max_time = max_time if max_time is not None else max_time_defaults
-
- if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom':
- prompt_type = inv_prompt_type_to_model_lower[model_lower]
- if verbose:
- print("Auto-selecting prompt_type=%s for %s" % (prompt_type, model_lower), flush=True)
-
- # examples at first don't include chat, instruction_nochat, iinput_nochat, added at end
- if show_examples is None:
- if chat:
- show_examples = False
- else:
- show_examples = True
-
- summarize_example1 = """Jeff: Can I train a ? Transformers model on Amazon SageMaker?
-Philipp: Sure you can use the new Hugging Face Deep Learning Container.
-Jeff: ok.
-Jeff: and how can I get started?
-Jeff: where can I find documentation?
-Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"""
-
- use_placeholder_instruction_as_example = False
- if 'bart-large-cnn-samsum' in model_lower or 'flan-t5-base-samsum' in model_lower:
- placeholder_instruction = summarize_example1
- placeholder_input = ""
- use_defaults = True
- use_default_examples = False
- use_placeholder_instruction_as_example = True
- task_info = "Summarization"
- elif 't5-' in model_lower or 't5' == model_lower or 'flan-' in model_lower:
- placeholder_instruction = "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
- placeholder_input = ""
- use_defaults = True
- use_default_examples = True
- task_info = "Multi-Task: Q/A, translation, Chain-of-Thought, Logical Reasoning, Summarization, etc. Best to use task prefix as trained on, e.g. `translate English to German: ` (space after colon)"
- elif 'mbart-' in model_lower:
- placeholder_instruction = "The girl has long hair."
- placeholder_input = ""
- use_defaults = True
- use_default_examples = False
- use_placeholder_instruction_as_example = True
- elif 'gpt2' in model_lower:
- placeholder_instruction = "The sky is"
- placeholder_input = ""
- prompt_type = prompt_type or 'plain'
- use_default_examples = True # some will be odd "continuations" but can be ok
- use_placeholder_instruction_as_example = True
- task_info = "Auto-complete phrase, code, etc."
- use_defaults = True
- else:
- if chat:
- placeholder_instruction = ""
- else:
- placeholder_instruction = "Give detailed answer for whether Einstein or Newton is smarter."
- placeholder_input = ""
- if not prompt_type and model_lower in inv_prompt_type_to_model_lower and prompt_type != 'custom':
- prompt_type = inv_prompt_type_to_model_lower[model_lower]
- elif model_lower:
- # default is plain, because might rely upon trust_remote_code to handle prompting
- prompt_type = prompt_type or 'plain'
- else:
- prompt_type = ''
- task_info = "No task"
- if prompt_type == 'instruct':
- task_info = "Answer question or follow imperative as instruction with optionally input."
- elif prompt_type == 'plain':
- task_info = "Auto-complete phrase, code, etc."
- elif prompt_type == 'human_bot':
- if chat:
- task_info = "Chat (Shift-Enter to give question/imperative, input concatenated with instruction)"
- else:
- task_info = "Ask question/imperative (input concatenated with instruction)"
-
- # revert to plain if still nothing
- prompt_type = prompt_type or 'plain'
- if use_defaults:
- temperature = 1.0 if temperature is None else temperature
- top_p = 1.0 if top_p is None else top_p
- top_k = 40 if top_k is None else top_k
- penalty_alpha = 0 if penalty_alpha is None else penalty_alpha
- num_beams = num_beams or 1
- max_new_tokens = max_new_tokens or 512
- repetition_penalty = repetition_penalty or 1.07
- num_return_sequences = min(num_beams, num_return_sequences or 1)
- do_sample = False if do_sample is None else do_sample
- else:
- temperature = 0.1 if temperature is None else temperature
- top_p = 0.75 if top_p is None else top_p
- top_k = 40 if top_k is None else top_k
- penalty_alpha = 0 if penalty_alpha is None else penalty_alpha
- num_beams = num_beams or 1
- max_new_tokens = max_new_tokens or 1024
- repetition_penalty = repetition_penalty or 1.07
- num_return_sequences = min(num_beams, num_return_sequences or 1)
- do_sample = False if do_sample is None else do_sample
- # doesn't include chat, instruction_nochat, iinput_nochat, added later
- params_list = ["",
- stream_output,
- prompt_type, prompt_dict,
- temperature, top_p, top_k, penalty_alpha, num_beams,
- max_new_tokens, min_new_tokens,
- early_stopping, max_time, repetition_penalty, num_return_sequences, do_sample]
-
- if use_placeholder_instruction_as_example:
- examples += [[placeholder_instruction, ''] + params_list]
-
- if use_default_examples:
- examples += [
- ["Translate English to French", "Good morning"] + params_list,
- ["Give detailed answer for whether Einstein or Newton is smarter.", ''] + params_list,
- ["Explain in detailed list, all the best practices for coding in python.", ''] + params_list,
- [
- "Create a markdown table with 3 rows for the primary colors, and 2 columns, with color name and hex codes.",
- ''] + params_list,
- ['Translate to German: My name is Arthur', ''] + params_list,
- ["Please answer to the following question. Who is going to be the next Ballon d'or?", ''] + params_list,
- ['Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering.',
- ''] + params_list,
- ['Please answer the following question. What is the boiling point of Nitrogen?', ''] + params_list,
- ['Answer the following yes/no question. Can you write a whole Haiku in a single tweet?', ''] + params_list,
- ["Simplify the following expression: (False or False and True). Explain your answer.", ''] + params_list,
- [
- "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?",
- ''] + params_list,
- ['The square root of x is the cube root of y. What is y to the power of 2, if x = 4?', ''] + params_list,
- [
- 'Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?',
- ''] + params_list,
- ["""def area_of_rectangle(a: float, b: float):
- \"\"\"Return the area of the rectangle.\"\"\"""", ''] + params_list,
- ["""# a function in native python:
-def mean(a):
- return sum(a)/len(a)
-
-# the same function using numpy:
-import numpy as np
-def mean(a):""", ''] + params_list,
- ["""X = np.random.randn(100, 100)
-y = np.random.randint(0, 1, 100)
-
-# fit random forest classifier with 20 estimators""", ''] + params_list,
- ]
- # add summary example
- examples += [
- [summarize_example1, 'Summarize' if prompt_type not in ['plain', 'instruct_simple'] else ''] + params_list]
-
- src_lang = "English"
- tgt_lang = "Russian"
-
- # move to correct position
- for example in examples:
- example += [chat, '', '', LangChainMode.DISABLED.value, True,
- LangChainAction.QUERY.value, [],
- top_k_docs, chunk, chunk_size, DocumentSubset.Relevant.name, [],
- pre_prompt_query, prompt_query,
- pre_prompt_summary, prompt_summary,
- system_prompt,
- image_loaders,
- pdf_loaders,
- url_loaders,
- jq_schema,
- None,
- None,
- False,
- None,
- None,
- docs_ordering_type,
- min_max_new_tokens,
- max_input_tokens,
- docs_token_handling,
- docs_joiner,
- hyde_level,
- hyde_template,
- ]
- # adjust examples if non-chat mode
- if not chat:
- example[eval_func_param_names.index('instruction_nochat')] = example[
- eval_func_param_names.index('instruction')]
- example[eval_func_param_names.index('instruction')] = ''
-
- example[eval_func_param_names.index('iinput_nochat')] = example[eval_func_param_names.index('iinput')]
- example[eval_func_param_names.index('iinput')] = ''
- assert len(example) == len(eval_func_param_names), "Wrong example: %s %s" % (
- len(example), len(eval_func_param_names))
-
- if prompt_type == PromptType.custom.name and not prompt_dict:
- raise ValueError("Unexpected to get non-empty prompt_dict=%s for prompt_type=%s" % (prompt_dict, prompt_type))
-
- # get prompt_dict from prompt_type, so user can see in UI etc., or for custom do nothing except check format
- prompt_dict, error0 = get_prompt(prompt_type, prompt_dict,
- chat=False, context='', reduced=False, making_context=False, return_dict=True,
- system_prompt=system_prompt)
- if error0:
- raise RuntimeError("Prompt wrong: %s" % error0)
-
- return placeholder_instruction, placeholder_input, \
- stream_output, show_examples, \
- prompt_type, prompt_dict, \
- temperature, top_p, top_k, penalty_alpha, num_beams, \
- max_new_tokens, min_new_tokens, early_stopping, max_time, \
- repetition_penalty, num_return_sequences, \
- do_sample, \
- src_lang, tgt_lang, \
- examples, \
- task_info
-
-
-def languages_covered():
- # https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered
- covered = """Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)"""
- covered = covered.split(', ')
- covered = {x.split(' ')[0]: x.split(' ')[1].replace(')', '').replace('(', '') for x in covered}
- return covered
-
-
-def score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len):
- question = question[-cutoff_len:]
- answer = answer[-cutoff_len:]
-
- inputs = stokenizer(question, answer,
- return_tensors="pt",
- truncation=True,
- max_length=max_length_tokenize).to(smodel.device)
- try:
- score = torch.sigmoid(smodel(**inputs.to(smodel.device)).logits[0].float()).cpu().detach().numpy()[0]
- except torch.cuda.OutOfMemoryError as e:
- print("GPU OOM 3: question: %s answer: %s exception: %s" % (question, answer, str(e)), flush=True)
- del inputs
- traceback.print_exc()
- clear_torch_cache()
- return 'Response Score: GPU OOM'
- except (Exception, RuntimeError) as e:
- if 'Expected all tensors to be on the same device' in str(e) or \
- 'expected scalar type Half but found Float' in str(e) or \
- 'probability tensor contains either' in str(e) or \
- 'cublasLt ran into an error!' in str(e) or \
- 'device-side assert triggered' in str(e):
- print("GPU Error: question: %s answer: %s exception: %s" % (question, answer, str(e)),
- flush=True)
- traceback.print_exc()
- clear_torch_cache()
- return 'Response Score: GPU Error'
- else:
- raise
- os.environ['TOKENIZERS_PARALLELISM'] = 'true'
- return score
-
-
-def check_locals(**kwargs):
- # ensure everything in evaluate is here
- can_skip_because_locally_generated = no_default_param_names + [
- # get_model:
- 'reward_type'
- ]
- for k in eval_func_param_names:
- if k in can_skip_because_locally_generated:
- continue
- assert k in kwargs, "Missing %s" % k
- for k in inputs_kwargs_list:
- if k in can_skip_because_locally_generated:
- continue
- assert k in kwargs, "Missing %s" % k
-
- for k in list(inspect.signature(get_model).parameters):
- if k in can_skip_because_locally_generated:
- continue
- assert k in kwargs, "Missing %s" % k
-
-
-def get_model_max_length(model_state):
- if not isinstance(model_state['tokenizer'], (str, type(None))):
- return model_state['tokenizer'].model_max_length
- else:
- return 2048
-
-
-def get_model_max_length_from_tokenizer(tokenizer):
- if hasattr(tokenizer, 'model_max_length'):
- return int(tokenizer.model_max_length)
- else:
- return 2048
-
-
-def get_max_max_new_tokens(model_state, **kwargs):
- if not isinstance(model_state['tokenizer'], (str, type(None))) or not kwargs.get('truncation_generation', False):
- if hasattr(model_state['tokenizer'], 'model_max_length'):
- max_max_new_tokens = model_state['tokenizer'].model_max_length
- else:
- # e.g. fast up, no model
- max_max_new_tokens = None
- else:
- max_max_new_tokens = None
-
- if kwargs['max_max_new_tokens'] is not None and max_max_new_tokens is not None:
- return min(max_max_new_tokens, kwargs['max_max_new_tokens'])
- elif kwargs['max_max_new_tokens'] is not None:
- return kwargs['max_max_new_tokens']
- elif kwargs['memory_restriction_level'] == 1:
- return 768
- elif kwargs['memory_restriction_level'] == 2:
- return 512
- elif kwargs['memory_restriction_level'] >= 3:
- return 256
- else:
- # FIXME: Need to update after new model loaded, so user can control with slider
- return 2048
-
-
-def get_minmax_top_k_docs(is_public):
- label_top_k_docs = "Number of document chunks (query) or pages/parts (summarize)"
- if is_public:
- min_top_k_docs = 1
- max_top_k_docs = 8
- else:
- min_top_k_docs = -1
- max_top_k_docs = 100
- label_top_k_docs = label_top_k_docs + " (-1 = auto fill model context, all pages/docs for summarize)"
- return min_top_k_docs, max_top_k_docs, label_top_k_docs
-
-
-def merge_chat_conversation_history(chat_conversation1, history):
- # chat_conversation and history ordered so largest index of list is most recent
- if chat_conversation1:
- chat_conversation1 = str_to_list(chat_conversation1)
- for conv1 in chat_conversation1:
- assert isinstance(conv1, (list, tuple))
- assert len(conv1) == 2
-
- if isinstance(history, list):
- # make copy so only local change
- if chat_conversation1:
- # so priority will be newest that comes from actual chat history from UI, then chat_conversation
- history = chat_conversation1 + history.copy()
- elif chat_conversation1:
- history = chat_conversation1
- else:
- history = []
- return history
-
-
-def history_to_context(history, langchain_mode=None,
- add_chat_history_to_context=None,
- prompt_type=None, prompt_dict=None, chat=None, model_max_length=None,
- memory_restriction_level=None, keep_sources_in_context=None,
- system_prompt=None, chat_conversation=None,
- min_max_new_tokens=256):
- """
- consumes all history up to (but not including) latest history item that is presumed to be an [instruction, None] pair
- :param history:
- :param langchain_mode:
- :param add_chat_history_to_context:
- :param prompt_type:
- :param prompt_dict:
- :param chat:
- :param model_max_length:
- :param memory_restriction_level:
- :param keep_sources_in_context:
- :param system_prompt:
- :param chat_conversation:
- :param min_max_new_tokens:
- :return:
- """
- history = merge_chat_conversation_history(chat_conversation, history)
-
- if len(history) >= 1 and len(history[-1]) >= 2 and not history[-1][1]:
- len_history = len(history) - 1
- else:
- # full history
- len_history = len(history)
-
- # ensure output will be unique to models
- _, _, _, max_prompt_length = get_cutoffs(memory_restriction_level,
- for_context=True, model_max_length=model_max_length,
- min_max_new_tokens=min_max_new_tokens)
- context1 = ''
- if max_prompt_length is not None and add_chat_history_to_context:
- context1 = ''
- # - 1 below because current instruction already in history from user()
- for histi in range(0, len_history):
- data_point = dict(instruction=history[histi][0], input='', output=history[histi][1])
- prompt, pre_response, terminate_response, chat_sep, chat_turn_sep = \
- generate_prompt(data_point,
- prompt_type,
- prompt_dict,
- chat,
- reduced=True,
- making_context=True,
- system_prompt=system_prompt,
- histi=histi)
- # md -> back to text, maybe not super important if model trained enough
- if not keep_sources_in_context and langchain_mode != 'Disabled' and prompt.find(super_source_prefix) >= 0:
- # FIXME: This is relatively slow even for small amount of text, like 0.3s each history item
- import re
- prompt = re.sub(f'{re.escape(super_source_prefix)}.*?{re.escape(super_source_postfix)}', '', prompt,
- flags=re.DOTALL)
- if prompt.endswith('\n'):
- prompt = prompt[:-4]
- prompt = prompt.replace('
', chat_turn_sep)
- if not prompt.endswith(chat_turn_sep):
- prompt += chat_turn_sep
- # most recent first, add older if can
- # only include desired chat history
- if len(prompt + context1) > max_prompt_length:
- break
- context1 += prompt
-
- _, pre_response, terminate_response, chat_sep, chat_turn_sep = \
- generate_prompt({}, prompt_type, prompt_dict,
- chat, reduced=True,
- making_context=True,
- system_prompt=system_prompt,
- histi=-1)
- if context1 and not context1.endswith(chat_turn_sep):
- context1 += chat_turn_sep # ensure if terminates abruptly, then human continues on next line
- return context1
-
-
-def get_relaxed_max_new_tokens(prompt, tokenizer=None, max_new_tokens=None, max_new_tokens0=None):
- # check if can relax max_new_tokens for this specific prompt
- if max_new_tokens0 is not None and \
- hasattr(tokenizer, 'model_max_len') and \
- isinstance(tokenizer.model_max_len, (float, int)):
- max_new_tokens = int(tokenizer.model_max_length) - get_token_count(prompt, tokenizer)
- if max_new_tokens is not None:
- return min(max_new_tokens0, max_new_tokens)
- else:
- return max_new_tokens0
- return max_new_tokens
-
-
-def get_limited_prompt(instruction,
- iinput,
- tokenizer,
- estimated_instruction=None,
- prompter=None,
- inference_server=None,
- prompt_type=None, prompt_dict=None, chat=False, max_new_tokens=None,
- system_prompt='',
- context='', chat_conversation=None, text_context_list=None,
- keep_sources_in_context=False,
- model_max_length=None, memory_restriction_level=0,
- langchain_mode=None, add_chat_history_to_context=True,
- verbose=False,
- doc_importance=0.5,
- min_max_new_tokens=256,
- max_input_tokens=-1,
- truncation_generation=False,
- gradio_server=False,
- ):
- if gradio_server or not inference_server:
- # can listen to truncation_generation
- pass
- else:
- # these don't support allowing going beyond total context
- truncation_generation = True
-
- # for templates, use estimated for counting, but adjust instruction as output
- if estimated_instruction is None:
- estimated_instruction = instruction
-
- if max_input_tokens >= 0:
- # max_input_tokens is used to runtime (via client/UI) to control actual filling of context
- max_input_tokens = min(model_max_length - min_max_new_tokens, max_input_tokens)
- else:
- max_input_tokens = model_max_length - min_max_new_tokens
-
- if prompter:
- prompt_type = prompter.prompt_type
- prompt_dict = prompter.prompt_dict
- chat = prompter.chat
- stream_output = prompter.stream_output
- system_prompt = prompter.system_prompt
-
- generate_prompt_type = prompt_type
- external_handle_chat_conversation = False
- if inference_server and any(
- inference_server.startswith(x) for x in ['openai_chat', 'openai_azure_chat', 'vllm_chat']):
- # Chat APIs do not take prompting
- # Replicate does not need prompting if no chat history, but in general can take prompting
- # if using prompter, prompter.system_prompt will already be filled with automatic (e.g. from llama-2),
- # so if replicate final prompt with system prompt still correct because only access prompter.system_prompt that was already set
- # below already true for openai,
- # but not vllm by default as that can be any model and handled by FastChat API inside vLLM itself
- generate_prompt_type = 'plain'
- # Chat APIs don't handle chat history via single prompt, but in messages, assumed to be handled outside this function
- chat_conversation = []
- external_handle_chat_conversation = True
-
- # merge handles if chat_conversation is None
- history = []
- history = merge_chat_conversation_history(chat_conversation, history)
- history_to_context_func = functools.partial(history_to_context,
- langchain_mode=langchain_mode,
- add_chat_history_to_context=add_chat_history_to_context,
- prompt_type=generate_prompt_type,
- prompt_dict=prompt_dict,
- chat=chat,
- model_max_length=max_input_tokens,
- memory_restriction_level=memory_restriction_level,
- keep_sources_in_context=keep_sources_in_context,
- system_prompt=system_prompt,
- min_max_new_tokens=min_max_new_tokens)
- context2 = history_to_context_func(history)
- context1 = context
- if context1 is None:
- context1 = ''
-
- # get how many more tokens in templated instruction, somewhat of estimate at fine level
- num_instruction_tokens = get_token_count(instruction, tokenizer)
- num_estimated_instruction_tokens = get_token_count(estimated_instruction, tokenizer)
- delta_instruction = max(0, num_estimated_instruction_tokens - num_instruction_tokens)
-
- # get estimated templated instruction tokens for counting purposes
- from h2oai_pipeline import H2OTextGenerationPipeline
- estimated_instruction, num_estimated_instruction_tokens = H2OTextGenerationPipeline.limit_prompt(
- estimated_instruction, tokenizer,
- max_prompt_length=max_input_tokens)
- data_point_just_instruction = dict(context='', instruction=estimated_instruction, input='')
- prompt_just_estimated_instruction = prompter.generate_prompt(data_point_just_instruction)
- num_instruction_tokens = get_token_count(prompt_just_estimated_instruction, tokenizer)
-
- # get actual instruction, limited by template limitation
- instruction, _ = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer,
- max_prompt_length=max_input_tokens - delta_instruction)
-
- context1, num_context1_tokens = H2OTextGenerationPipeline.limit_prompt(context1, tokenizer,
- max_prompt_length=max_input_tokens)
- context2, num_context2_tokens = H2OTextGenerationPipeline.limit_prompt(context2, tokenizer,
- max_prompt_length=max_input_tokens)
- iinput, num_iinput_tokens = H2OTextGenerationPipeline.limit_prompt(iinput, tokenizer,
- max_prompt_length=max_input_tokens)
- if text_context_list is None:
- text_context_list = []
- num_doc_tokens = sum([get_token_count(x + docs_joiner_default, tokenizer) for x in text_context_list])
-
- num_prompt_tokens0 = (num_instruction_tokens or 0) + \
- (num_context1_tokens or 0) + \
- (num_context2_tokens or 0) + \
- (num_iinput_tokens or 0) + \
- (num_doc_tokens or 0)
-
- # go down to no less than 256, about 1 paragraph
- # use max_new_tokens before use num_prompt_tokens0 else would be negative or ~0
- min_max_new_tokens = min(min_max_new_tokens, max_new_tokens)
- # by default assume can handle all chat and docs
- chat_index = 0
-
- # allowed residual is either half of what is allowed if doc exceeds half, or is rest of what doc didn't consume
- num_non_doc_tokens = num_prompt_tokens0 - num_doc_tokens
- # to doc first then non-doc, shouldn't matter much either way
- doc_max_length = max(max_input_tokens - num_non_doc_tokens, int(doc_importance * max_input_tokens))
- top_k_docs, one_doc_size, num_doc_tokens = get_docs_tokens(tokenizer, text_context_list=text_context_list,
- max_input_tokens=doc_max_length)
- non_doc_max_length = max(max_input_tokens - num_doc_tokens, int((1.0 - doc_importance) * max_input_tokens))
-
- if num_non_doc_tokens > non_doc_max_length:
- # need to limit in some way, keep portion of history but all of context and instruction
- # 1) drop iinput (unusual to include anyways)
- # 2) reduce history
- # 3) reduce context1
- # 4) limit instruction so will fit
- diff1 = non_doc_max_length - (
- num_instruction_tokens + num_context1_tokens + num_context2_tokens)
- diff2 = non_doc_max_length - (num_instruction_tokens + num_context1_tokens)
- diff3 = non_doc_max_length - num_instruction_tokens
- diff4 = non_doc_max_length
- if diff1 > 0:
- # then should be able to do #1
- iinput = ''
- num_iinput_tokens = 0
- elif diff2 > 0 > diff1:
- # then may be able to do #1 + #2
- iinput = ''
- num_iinput_tokens = 0
- chat_index_final = len(history)
- for chat_index in range(len(history)):
- # NOTE: history and chat_conversation are older for first entries
- # FIXME: This is a slow for many short conversations
- context2 = history_to_context_func(history[chat_index:])
- num_context2_tokens = get_token_count(context2, tokenizer)
- diff1 = non_doc_max_length - (
- num_instruction_tokens + num_context1_tokens + num_context2_tokens)
- if diff1 > 0:
- chat_index_final = chat_index
- if verbose:
- print("chat_conversation used %d out of %d" % (chat_index, len(history)), flush=True)
- break
- chat_index = chat_index_final # i.e. if chat_index == len(history), then nothing can be consumed
- elif diff3 > 0 > diff2:
- # then may be able to do #1 + #2 + #3
- iinput = ''
- num_iinput_tokens = 0
- context2 = ''
- num_context2_tokens = 0
- context1, num_context1_tokens = H2OTextGenerationPipeline.limit_prompt(context1, tokenizer,
- max_prompt_length=diff3)
- if num_context1_tokens <= diff3:
- pass
- else:
- print("failed to reduce", flush=True)
- else:
- # then must be able to do #1 + #2 + #3 + #4
- iinput = ''
- num_iinput_tokens = 0
- context2 = ''
- num_context2_tokens = 0
- context1 = ''
- num_context1_tokens = 0
- # diff4 accounts for real prompting for instruction
- # FIXME: history_to_context could include instruction, in case system prompt long, we overcount and could have more free tokens
-
- max_prompt_length = max(0, diff4 - delta_instruction)
- instruction, _ = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer,
- max_prompt_length=max_prompt_length)
- # get actual instruction tokens
- data_point_just_instruction = dict(context='', instruction=instruction, input='')
- prompt_just_instruction = prompter.generate_prompt(data_point_just_instruction)
- num_instruction_tokens = get_token_count(prompt_just_instruction, tokenizer) + delta_instruction
-
- # update full context
- context = context1 + context2
- # update token counts (docs + non-docs, all tokens)
- num_prompt_tokens = (num_instruction_tokens or 0) + \
- (num_context1_tokens or 0) + \
- (num_context2_tokens or 0) + \
- (num_iinput_tokens or 0) + \
- (num_doc_tokens or 0)
-
- # update max_new_tokens
- # limit so max_new_tokens = prompt + new < max
- # otherwise model can fail etc. e.g. for distilgpt2 asking for 1024 tokens is enough to fail if prompt=1 token
- if truncation_generation:
- max_new_tokens = min(max_new_tokens, model_max_length - num_prompt_tokens)
-
- if os.getenv('HARD_ASSERTS'):
- if max_new_tokens < min_max_new_tokens:
- raise ValueError("Invalid max_new_tokens=%s" % max_new_tokens)
-
- if prompter is None:
- # get prompter
- debug = False
- stream_output = False # doesn't matter
- prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output,
- system_prompt=system_prompt)
- if prompt_type != generate_prompt_type:
- # override just this attribute, keep system_prompt etc. from original prompt_type
- prompter.prompt_type = generate_prompt_type
-
- data_point = dict(context=context, instruction=instruction, input=iinput)
- # handle promptA/promptB addition if really from history.
- # if not from history, then reduced=False inside correct
- # if mixed, then no specific correct thing to do, so treat like history and promptA/B will come first still
- context_from_history = len(history) > 0 and len(context1) > 0
- prompt = prompter.generate_prompt(data_point, context_from_history=context_from_history)
- num_prompt_tokens_actual = get_token_count(prompt, tokenizer)
-
- return prompt, \
- instruction, iinput, context, \
- num_prompt_tokens, max_new_tokens, num_prompt_tokens0, num_prompt_tokens_actual, \
- chat_index, external_handle_chat_conversation, \
- top_k_docs, one_doc_size, truncation_generation
-
-
-def get_docs_tokens(tokenizer, text_context_list=[], max_input_tokens=None):
- if text_context_list is None or len(text_context_list) == 0:
- return 0, None, 0
- if max_input_tokens is None:
- max_input_tokens = tokenizer.model_max_length
- tokens = [get_token_count(x + docs_joiner_default, tokenizer) for x in text_context_list]
- tokens_cumsum = np.cumsum(tokens)
- where_res = np.where(tokens_cumsum < max_input_tokens)[0]
- # if below condition fails, then keep top_k_docs=-1 and trigger special handling next
- if where_res.shape[0] > 0:
- top_k_docs = 1 + where_res[-1]
- one_doc_size = None
- num_doc_tokens = tokens_cumsum[top_k_docs - 1] # by index
- else:
- # if here, means 0 and just do best with 1 doc
- top_k_docs = 1
- text_context_list = text_context_list[:top_k_docs]
- # critical protection
- from src.h2oai_pipeline import H2OTextGenerationPipeline
- doc_content = text_context_list[0]
- doc_content, new_tokens0 = H2OTextGenerationPipeline.limit_prompt(doc_content,
- tokenizer,
- max_prompt_length=max_input_tokens)
- text_context_list[0] = doc_content
- one_doc_size = len(doc_content)
- num_doc_tokens = get_token_count(doc_content + docs_joiner_default, tokenizer)
- print("Unexpected large chunks and can't add to context, will add 1 anyways. Tokens %s -> %s" % (
- tokens[0], new_tokens0), flush=True)
- return top_k_docs, one_doc_size, num_doc_tokens
-
-
-def entrypoint_main():
- """
- Examples:
-
- WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 --master_port=1234 generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights=lora-alpaca_6B
- python generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights='lora-alpaca_6B'
- python generate.py --base_model='EleutherAI/gpt-neox-20b' --lora_weights='lora-alpaca_20B'
-
- # generate without lora weights, no prompt
- python generate.py --base_model='EleutherAI/gpt-neox-20b' --prompt_type='plain'
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq'
-
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' --lora_weights='lora_20B_daifaq'
- # OpenChatKit settings:
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0
-
- python generate.py --base_model='distilgpt2' --prompt_type='plain' --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 --share=False
- python generate.py --base_model='t5-large' --prompt_type='simple_instruct'
- python generate.py --base_model='philschmid/bart-large-cnn-samsum'
- python generate.py --base_model='philschmid/flan-t5-base-samsum'
- python generate.py --base_model='facebook/mbart-large-50-many-to-many-mmt'
-
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot' --lora_weights='GPT-NeoXT-Chat-Base-20B.merged.json.8_epochs.57b2892c53df5b8cefac45f84d019cace803ef26.28'
-
- must have 4*48GB GPU and run without 8bit in order for sharding to work with use_gpu_id=False
- can also pass --prompt_type='human_bot' and model can somewhat handle instructions without being instruct tuned
- python generate.py --base_model=decapoda-research/llama-65b-hf --load_8bit=False --use_gpu_id=False --prompt_type='human_bot'
-
- python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b
- """
- H2O_Fire(main)
-
-
-if __name__ == "__main__":
- entrypoint_main()
diff --git a/spaces/awacke1/Emoji.Enumerator.Menu/README.md b/spaces/awacke1/Emoji.Enumerator.Menu/README.md
deleted file mode 100644
index 7b03a7201862b9330ef80f506f2e2558650ff8bc..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Emoji.Enumerator.Menu/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Emoji.Enumerator.Menu
-emoji: 💻
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/GradioAutoCSVLoaderToPlotly/app.py b/spaces/awacke1/GradioAutoCSVLoaderToPlotly/app.py
deleted file mode 100644
index 698e8054275c3bf39e0e0e8951d9772853b096ab..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioAutoCSVLoaderToPlotly/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-
-# Create a function to write the CSV file
-def write_csv(record_count, topic, intervention):
- df = pd.DataFrame({
- "RecordCount": [record_count],
- "Topic": [topic],
- "Intervention": [intervention]
- })
- df.to_csv("testfile.csv", index=False, mode='a')
-
-# Create a function to plot the data from the CSV file
-def plot_data():
- df = pd.read_csv("testfile.csv")
- fig = px.scatter(df, x="RecordCount", y="Intervention", color="Topic")
- return fig
-
-# Define the inputs for the Gradio interface
-inputs = [
- gr.inputs.Slider(label="Record Count", minimum=0, maximum=100, default=50),
- gr.inputs.Textbox(label="Topic"),
- gr.inputs.Textbox(label="Intervention")
-]
-
-# Define the outputs for the Gradio interface
-outputs = [
- gr.Plot(plot_data),
- gr.Textbox(label="Data written to testfile.csv")
-]
-
-# Create the Gradio interface
-interface = gr.Interface(write_csv, inputs, outputs, title="Record Plotter")
-
-# Launch the Gradio interface
-interface.launch()
diff --git a/spaces/awacke1/GradioBlocksChangeEvent/app.py b/spaces/awacke1/GradioBlocksChangeEvent/app.py
deleted file mode 100644
index 4d30548df6c7b2ce9557818f4fea466113e5bd7b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioBlocksChangeEvent/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import gradio as gr
-
-#def update( text, choice):
-# if (choice==“gpt2large“):
-# return f1(text)
- # return f"Welcome to Gradio, {name}!"
-
-generator1 = gr.Interface.load("huggingface/gpt2-large")
-generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B")
-generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-
-
-demo = gr.Blocks()
-
-def f1(x):
- return generator1(x)
-def f2(x):
- return generator2(x)
-def f3(x):
- return generator3(x)
-
-with demo:
- gr.Markdown(
- """
- # Hello World!
- Start typing below to see the output.
- """)
- inp = gr.Textbox(placeholder="Enter a statement to complete")
-
- out1 = gr.Textbox()
- out2 = gr.Textbox()
- out3 = gr.Textbox()
-
- inp.change(fn=f1,
- inputs=inp,
- outputs=out1)
- out1.change(fn=f2,
- inputs=inp,
- outputs=out2)
- out2.change(fn=f3,
- inputs=inp,
- outputs=out3)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/banana-projects/datasets-card-creator/src/Instructions.js b/spaces/banana-projects/datasets-card-creator/src/Instructions.js
deleted file mode 100644
index 4a06ad667ef7aacd2ec19d4af2a57fb34f57ee15..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/datasets-card-creator/src/Instructions.js
+++ /dev/null
@@ -1,317 +0,0 @@
-const NAME = 'Instructions'
-
-export default {
- name: NAME,
- instructions: {
- yamlTags: {
- paragraph: [
- "Add YAML tags"
- ],
- example: [
- "---",
- `annotations_creators:`,
- `- no-annotation`,
- `language_creators:`,
- `- found`,
- `languages:`,
- `- en`,
- `licenses:`,
- `- unknown`,
- `multilinguality:`,
- `- monolingual`,
- `size_categories:`,
- `- 100K 0
- grad_positive = g_out[0] > 0
-
- # Mask the gradients with the activation
- grad = g_out[0] * act_mask * grad_positive
- return (grad,)
-
- relu_count = 0
- for name, layer in self.model.named_modules():
- if isinstance(layer, nn.ReLU):
- layer_name = name + '.' + f'conv_layer{relu_count}'
- layer.register_forward_hook(partial(fwd_hook_fn, layer_name))
- layer.register_backward_hook(back_hook_fn)
- relu_count += 1
-
- self.model.features[0].register_backward_hook(first_layer_hook_fn)
-
- def predict(self, input_image):
- # Apply transforms
- input_image = self.transforms(input_image).unsqueeze(0)
-
- # Perform prediction
- self.model.eval()
- self.out = self.model(input_image.requires_grad_())
- self.predictions = torch.nn.functional.softmax(self.out[0], dim=0)
- confidences = {self.weights.meta["categories"][i]:
- float(self.predictions[i]) for i in range(1000)}
-
- return confidences
-
- def backprop(self):
- # Backward pass
- grad_target_map = torch.zeros(self.out.shape, dtype=torch.float)
- grad_target_map[0][self.predictions.argmax().item()] = 1
- self.out.backward(grad_target_map)
-
- return self.input_grad, self.activation_maps_dict
-
diff --git a/spaces/bhvsh/stroke-prediction/multiapp.py b/spaces/bhvsh/stroke-prediction/multiapp.py
deleted file mode 100644
index caff3687d3e3ddb0fa592b85e5531f0192ac47e6..0000000000000000000000000000000000000000
--- a/spaces/bhvsh/stroke-prediction/multiapp.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Frameworks for running multiple Streamlit applications as a single app.
-"""
-import streamlit as st
-
-class MultiApp:
- def __init__(self):
- self.apps = []
-
- def add_app(self, title, func):
- self.apps.append({
- "title": title,
- "function": func
- })
-
- def run(self):
- app = st.selectbox(
- 'Navigation',
- self.apps,
- format_func=lambda app: app['title'])
- return app
-
- def view(self, app):
- app['function']()
\ No newline at end of file
diff --git a/spaces/bigjoker/stable-diffusion-webui/javascript/generationParams.js b/spaces/bigjoker/stable-diffusion-webui/javascript/generationParams.js
deleted file mode 100644
index 95f050939b72a8d09d62de8d725caf1e7d15d3c0..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/javascript/generationParams.js
+++ /dev/null
@@ -1,33 +0,0 @@
-// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
-
-let txt2img_gallery, img2img_gallery, modal = undefined;
-onUiUpdate(function(){
- if (!txt2img_gallery) {
- txt2img_gallery = attachGalleryListeners("txt2img")
- }
- if (!img2img_gallery) {
- img2img_gallery = attachGalleryListeners("img2img")
- }
- if (!modal) {
- modal = gradioApp().getElementById('lightboxModal')
- modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] });
- }
-});
-
-let modalObserver = new MutationObserver(function(mutations) {
- mutations.forEach(function(mutationRecord) {
- let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
- if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
- gradioApp().getElementById(selectedTab+"_generation_info_button").click()
- });
-});
-
-function attachGalleryListeners(tab_name) {
- gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
- gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
- gallery?.addEventListener('keydown', (e) => {
- if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
- gradioApp().getElementById(tab_name+"_generation_info_button").click()
- });
- return gallery;
-}
diff --git a/spaces/binker/interpreter5/app.py b/spaces/binker/interpreter5/app.py
deleted file mode 100644
index 463b97c924269e233762fb0b51068ed6b70a0519..0000000000000000000000000000000000000000
--- a/spaces/binker/interpreter5/app.py
+++ /dev/null
@@ -1,185 +0,0 @@
-from response_parser import *
-import gradio as gr
-
-
-def initialization(state_dict: Dict) -> None:
- if not os.path.exists('cache'):
- os.mkdir('cache')
- if state_dict["bot_backend"] is None:
- state_dict["bot_backend"] = BotBackend()
- if 'OPENAI_API_KEY' in os.environ:
- del os.environ['OPENAI_API_KEY']
-
-
-def get_bot_backend(state_dict: Dict) -> BotBackend:
- return state_dict["bot_backend"]
-
-
-def switch_to_gpt4(state_dict: Dict, whether_switch: bool) -> None:
- bot_backend = get_bot_backend(state_dict)
- if whether_switch:
- bot_backend.update_gpt_model_choice("GPT-4")
- else:
- bot_backend.update_gpt_model_choice("GPT-3.5")
-
-
-def add_text(state_dict: Dict, history: List, text: str) -> Tuple[List, Dict]:
- bot_backend = get_bot_backend(state_dict)
- bot_backend.add_text_message(user_text=text)
-
- history = history + [(text, None)]
-
- return history, gr.update(value="", interactive=False)
-
-
-def add_file(state_dict: Dict, history: List, file) -> List:
- bot_backend = get_bot_backend(state_dict)
- path = file.name
- filename = os.path.basename(path)
-
- bot_msg = [f'📁[{filename}]', None]
- history.append(bot_msg)
-
- bot_backend.add_file_message(path=path, bot_msg=bot_msg)
-
- return history
-
-
-def undo_upload_file(state_dict: Dict, history: List) -> Tuple[List, Dict]:
- bot_backend = get_bot_backend(state_dict)
- bot_msg = bot_backend.revoke_file()
-
- if bot_msg is None:
- return history, gr.Button.update(interactive=False)
-
- else:
- assert history[-1] == bot_msg
- del history[-1]
- if bot_backend.revocable_files:
- return history, gr.Button.update(interactive=True)
- else:
- return history, gr.Button.update(interactive=False)
-
-
-def refresh_file_display(state_dict: Dict) -> List[str]:
- bot_backend = get_bot_backend(state_dict)
- work_dir = bot_backend.jupyter_work_dir
- filenames = os.listdir(work_dir)
- paths = []
- for filename in filenames:
- paths.append(
- os.path.join(work_dir, filename)
- )
- return paths
-
-
-def restart_ui(history: List) -> Tuple[List, Dict, Dict, Dict, Dict]:
- history.clear()
- return (
- history,
- gr.Textbox.update(value="", interactive=False),
- gr.Button.update(interactive=False),
- gr.Button.update(interactive=False),
- gr.Button.update(interactive=False)
- )
-
-
-def restart_bot_backend(state_dict: Dict) -> None:
- bot_backend = get_bot_backend(state_dict)
- bot_backend.restart()
-
-
-def bot(state_dict: Dict, history: List) -> List:
- bot_backend = get_bot_backend(state_dict)
-
- while bot_backend.finish_reason in ('new_input', 'function_call'):
- if history[-1][0] is None:
- history.append(
- [None, ""]
- )
- else:
- history[-1][1] = ""
-
- response = chat_completion(bot_backend=bot_backend)
- for chunk in response:
- history, weather_exit = parse_response(
- chunk=chunk,
- history=history,
- bot_backend=bot_backend
- )
- yield history
- if weather_exit:
- exit(-1)
-
- yield history
-
-
-if __name__ == '__main__':
- config = get_config()
- with gr.Blocks(theme=gr.themes.Base()) as block:
- """
- Reference: https://www.gradio.app/guides/creating-a-chatbot-fast
- """
- # UI components
- state = gr.State(value={"bot_backend": None})
- with gr.Tab("Chat"):
- chatbot = gr.Chatbot([], elem_id="chatbot", label="Local Code Interpreter", height=750)
- with gr.Row():
- with gr.Column(scale=0.85):
- text_box = gr.Textbox(
- show_label=False,
- placeholder="Enter text and press enter, or upload a file",
- container=False
- )
- with gr.Column(scale=0.15, min_width=0):
- file_upload_button = gr.UploadButton("📁", file_types=['file'])
- with gr.Row(equal_height=True):
- with gr.Column(scale=0.7):
- check_box = gr.Checkbox(label="Use GPT-4", interactive=config['model']['GPT-4']['available'])
- check_box.change(fn=switch_to_gpt4, inputs=[state, check_box])
- with gr.Column(scale=0.15, min_width=0):
- restart_button = gr.Button(value='🔄 Restart')
- with gr.Column(scale=0.15, min_width=0):
- undo_file_button = gr.Button(value="↩️Undo upload file", interactive=False)
- with gr.Tab("Files"):
- file_output = gr.Files()
-
- # Components function binding
- txt_msg = text_box.submit(add_text, [state, chatbot, text_box], [chatbot, text_box], queue=False).then(
- bot, [state, chatbot], chatbot
- )
- txt_msg.then(fn=refresh_file_display, inputs=[state], outputs=[file_output])
- txt_msg.then(lambda: gr.update(interactive=True), None, [text_box], queue=False)
- txt_msg.then(lambda: gr.Button.update(interactive=False), None, [undo_file_button], queue=False)
-
- file_msg = file_upload_button.upload(
- add_file, [state, chatbot, file_upload_button], [chatbot], queue=False
- ).then(
- bot, [state, chatbot], chatbot
- )
- file_msg.then(lambda: gr.Button.update(interactive=True), None, [undo_file_button], queue=False)
- file_msg.then(fn=refresh_file_display, inputs=[state], outputs=[file_output])
-
- undo_file_button.click(
- fn=undo_upload_file, inputs=[state, chatbot], outputs=[chatbot, undo_file_button]
- ).then(
- fn=refresh_file_display, inputs=[state], outputs=[file_output]
- )
-
- restart_button.click(
- fn=restart_ui, inputs=[chatbot],
- outputs=[chatbot, text_box, restart_button, file_upload_button, undo_file_button]
- ).then(
- fn=restart_bot_backend, inputs=[state], queue=False
- ).then(
- fn=refresh_file_display, inputs=[state], outputs=[file_output]
- ).then(
- fn=lambda: (gr.Textbox.update(interactive=True), gr.Button.update(interactive=True),
- gr.Button.update(interactive=True)),
- inputs=None, outputs=[text_box, restart_button, file_upload_button], queue=False
- )
-
- block.load(fn=initialization, inputs=[state])
-
- block.queue()
- block.launch(inbrowser=True,auth=[("khmidea1","mideakh1"),("khmidea2","mideakh2")])
diff --git a/spaces/bioniclelee/BoatDetectionCW/README.md b/spaces/bioniclelee/BoatDetectionCW/README.md
deleted file mode 100644
index a5be4396424c4a9a67e7cd5dc0ce412db55b4541..0000000000000000000000000000000000000000
--- a/spaces/bioniclelee/BoatDetectionCW/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: BoatDetectionCW
-emoji: 🐠
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/bioriAsaeru/text-to-voice/Chrysler International PAIS DVD 10 2008 [Multilang] [ISO] .rar.md b/spaces/bioriAsaeru/text-to-voice/Chrysler International PAIS DVD 10 2008 [Multilang] [ISO] .rar.md
deleted file mode 100644
index f9fcd9c5e701b3a00ab2af49181e04da07f45742..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Chrysler International PAIS DVD 10 2008 [Multilang] [ISO] .rar.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Chrysler international PAIS DVD 10 2008 [Multilang] [ISO] .rar
Download File ––– https://urloso.com/2uyQeE
-
-Power CD+G Burner 1.4.6 .rar · Chrysler international PAIS DVD 10 2008 [Multilang] [ISO] download pc · Google Operating System ( Android Live no installation ... 1fdad05405
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Epic Mickey 2 The Power Of Two (SERE4Q) NTSC WII WBFS.md b/spaces/bioriAsaeru/text-to-voice/Epic Mickey 2 The Power Of Two (SERE4Q) NTSC WII WBFS.md
deleted file mode 100644
index 87549ef30090e2dafc0a345ac374db7ab95bb84f..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Epic Mickey 2 The Power Of Two (SERE4Q) NTSC WII WBFS.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Epic Mickey 2: The Power Of Two (SERE4Q) NTSC WII WBFS
Download File ✏ https://urloso.com/2uyS8r
-
-Disney Epic Mickey 2: The Power of Two regresa a Mickey Mouse y Oswald the ... Funciona: Wii, Wii U y Emulador Dolphin ... Formato: WBFS. 4d29de3e1b
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Kp Prashna Kundali Software In Marathi Get Your Free KP Horoscope with Ruling Planets and Significators.md b/spaces/bioriAsaeru/text-to-voice/Kp Prashna Kundali Software In Marathi Get Your Free KP Horoscope with Ruling Planets and Significators.md
deleted file mode 100644
index 418ecbe8b904f2ab335a8c72a864d895b0e85e44..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Kp Prashna Kundali Software In Marathi Get Your Free KP Horoscope with Ruling Planets and Significators.md
+++ /dev/null
@@ -1,13 +0,0 @@
-
-Ordering your free janam kundali analysis is an easy and simple task as long as you know your birth details. In order to attain your free horoscope, simply follow the given instructions and fill in the kundli software:
-Kp Prashna Kundali Software In Marathi
Download File ••• https://urloso.com/2uyPIk
-Instant chart is a quick way to create janma kundali (birth chart) and prashna kundali(horary chart). By quick we mean that there is no registration required to accessthis feature. You can also use our new interface for instant charts .
-Vedic kundali software is based on accurate astrological calculations. This vedic jyotish software is the result of years of extensive research work. You can use this vedic astrology software easily with the little help of somebody even if you are not a computer savvy person. According to astrologers it is the easiest vedic astrology software in the world. One can get most accurate, excellent and comprehensive results from this vedic astrology software.
-Astro-Vision is easy matchmaking and kundli software, based on the Vedic astrology science. It provides accurate future predictions and is designed to analyze various factors in Kundli in Hindi and other languages to give a detailed and precise report.
The report provided by this kundali software includes multiple observations based on the birthday and other astrological elements that might influence the Grahas of both the bride and groom.
-Kundali software MyKundali offers key astrological services like love match, horoscope match, numerology calculation, etc. for free. Superior quality software for matching kundli for the matrimonial alliance and getting a janam kundli done for a newborn.
Excellent features make this kundali software the first choice of Astrologers who love to move on with time. Horoscope matching helps highlight incompatible factors between a couple, so that they can take remedial steps to stay stronger.
-
-Kundli matching AstroSage kundli software is flawlessand quick. The kundali software just requires the birth details of both brideand groom such as date, time and place and it will do the rest. Using the VedicAstrology principles, horoscopes of the natives are analyzed and the resultcomes with a good explanation.
-This kundali software has a user-friendly interface wherethe user just needs to enter the birth details of the potential bride andgroom.
ClickAstro generates the Janam Kundli and also studies them. This popularkundali software then creates an analysis report with the help of which theuser can understand the attributes which will influence the married life of thecouple.
-This kundli matching software also provides an overallcompatibility score to indicate the compatibility percentage between thepotential partners. For a detailed report on kundali and marriagecompatibility, users can get the premium plan for a small fee.
aaccfb2cb3
-
-
\ No newline at end of file
diff --git a/spaces/brainblow/AI-TV/README.md b/spaces/brainblow/AI-TV/README.md
deleted file mode 100644
index be4c302a555988c8ce48f217cba4e49bfff422f1..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AI-TV/README.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-title: BrainBlow AI-TV
-emoji: 🤯🤖📺
-colorFrom: green
-colorTo: green
-sdk: docker
-pinned: false
-app_port: 7860
-duplicated_from: TNR-5/AI-WebTV
----
-
-A generative AI WebTV, powered by Zeroscope and Hugging Face.
-
-This is just the frontend part, you will need the media-server (also open source) to make it work.
-
-Warning: this is an experimental, proof-of-concept project made in a few days.
-
-It is not ready for production use by other people! Also, this use models that should only be used for research purposes (no commercial usage).
-
-Note: because the stream uses FLV, it doesn't work on iPhone. There is however a [Twitch mirror here](https://www.twitch.tv/ai_webtv).
-
-The main code of the webtv is located inside the [media-server](https://huggingface.co/spaces/jbilcke-hf/media-server/tree/main) :
-
-manual steps:
-- human input to write a short paragraph describing a multi-shot video sequence
-- manual submit it to GPT-4 to generate a list of video captions for each shot (the system instructions are extracts from a stable diffusion guide)
-- commit the captions to the [playlist database](https://huggingface.co/spaces/jbilcke-hf/media-server/raw/main/database.json)
-
-Inside the `media-server` space (generation process running in the background):
-- for each prompt in the database
-- generate a silent 3 seconds video clip with Zeroscope V2 576w (hosted on Hugging Face Spaces)
-- upscale the clip with Zeroscope V2 XL (also a HF Space)
-- perform frame interpolation with FILM (also a HF Space)
-- storage in the Persistent Storage of the media-server Space
-
-Inside the `media-server` space (streaming process running in the foreground):
-- for each video file in the persistent storage folder
-- add it to a new FFmpeg playlist (it's just a .txt file)
-- broadcast it over the RTMP protocol using FFmpeg (in FLV format)
-- diffusion of the stream using node-media-server
-
-Inside the `AI-WebTV` space:
-- display the stream using `mpegts.js`
-- this doesn't work on iPhone, but now there is also a Twitch mirror
\ No newline at end of file
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/sound_dataset.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/sound_dataset.py
deleted file mode 100644
index 8b88cbe8016b4bd28c2de749177c9af29f7755fc..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/sound_dataset.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Dataset of audio with a simple description.
-"""
-
-from dataclasses import dataclass, fields, replace
-import json
-from pathlib import Path
-import random
-import typing as tp
-
-import numpy as np
-import torch
-
-from .info_audio_dataset import (
- InfoAudioDataset,
- get_keyword_or_keyword_list
-)
-from ..modules.conditioners import (
- ConditioningAttributes,
- SegmentWithAttributes,
- WavCondition,
-)
-
-
-EPS = torch.finfo(torch.float32).eps
-TARGET_LEVEL_LOWER = -35
-TARGET_LEVEL_UPPER = -15
-
-
-@dataclass
-class SoundInfo(SegmentWithAttributes):
- """Segment info augmented with Sound metadata.
- """
- description: tp.Optional[str] = None
- self_wav: tp.Optional[torch.Tensor] = None
-
- @property
- def has_sound_meta(self) -> bool:
- return self.description is not None
-
- def to_condition_attributes(self) -> ConditioningAttributes:
- out = ConditioningAttributes()
-
- for _field in fields(self):
- key, value = _field.name, getattr(self, _field.name)
- if key == 'self_wav':
- out.wav[key] = value
- else:
- out.text[key] = value
- return out
-
- @staticmethod
- def attribute_getter(attribute):
- if attribute == 'description':
- preprocess_func = get_keyword_or_keyword_list
- else:
- preprocess_func = None
- return preprocess_func
-
- @classmethod
- def from_dict(cls, dictionary: dict, fields_required: bool = False):
- _dictionary: tp.Dict[str, tp.Any] = {}
-
- # allow a subset of attributes to not be loaded from the dictionary
- # these attributes may be populated later
- post_init_attributes = ['self_wav']
-
- for _field in fields(cls):
- if _field.name in post_init_attributes:
- continue
- elif _field.name not in dictionary:
- if fields_required:
- raise KeyError(f"Unexpected missing key: {_field.name}")
- else:
- preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
- value = dictionary[_field.name]
- if preprocess_func:
- value = preprocess_func(value)
- _dictionary[_field.name] = value
- return cls(**_dictionary)
-
-
-class SoundDataset(InfoAudioDataset):
- """Sound audio dataset: Audio dataset with environmental sound-specific metadata.
-
- Args:
- info_fields_required (bool): Whether all the mandatory metadata fields should be in the loaded metadata.
- external_metadata_source (tp.Optional[str]): Folder containing JSON metadata for the corresponding dataset.
- The metadata files contained in this folder are expected to match the stem of the audio file with
- a json extension.
- aug_p (float): Probability of performing audio mixing augmentation on the batch.
- mix_p (float): Proportion of batch items that are mixed together when applying audio mixing augmentation.
- mix_snr_low (int): Lowerbound for SNR value sampled for mixing augmentation.
- mix_snr_high (int): Upperbound for SNR value sampled for mixing augmentation.
- mix_min_overlap (float): Minimum overlap between audio files when performing mixing augmentation.
- kwargs: Additional arguments for AudioDataset.
-
- See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
- """
- def __init__(
- self,
- *args,
- info_fields_required: bool = True,
- external_metadata_source: tp.Optional[str] = None,
- aug_p: float = 0.,
- mix_p: float = 0.,
- mix_snr_low: int = -5,
- mix_snr_high: int = 5,
- mix_min_overlap: float = 0.5,
- **kwargs
- ):
- kwargs['return_info'] = True # We require the info for each song of the dataset.
- super().__init__(*args, **kwargs)
- self.info_fields_required = info_fields_required
- self.external_metadata_source = external_metadata_source
- self.aug_p = aug_p
- self.mix_p = mix_p
- if self.aug_p > 0:
- assert self.mix_p > 0, "Expecting some mixing proportion mix_p if aug_p > 0"
- assert self.channels == 1, "SoundDataset with audio mixing considers only monophonic audio"
- self.mix_snr_low = mix_snr_low
- self.mix_snr_high = mix_snr_high
- self.mix_min_overlap = mix_min_overlap
-
- def _get_info_path(self, path: tp.Union[str, Path]) -> Path:
- """Get path of JSON with metadata (description, etc.).
- If there exists a JSON with the same name as 'path.name', then it will be used.
- Else, such JSON will be searched for in an external json source folder if it exists.
- """
- info_path = Path(path).with_suffix('.json')
- if Path(info_path).exists():
- return info_path
- elif self.external_metadata_source and (Path(self.external_metadata_source) / info_path.name).exists():
- return Path(self.external_metadata_source) / info_path.name
- else:
- raise Exception(f"Unable to find a metadata JSON for path: {path}")
-
- def __getitem__(self, index):
- wav, info = super().__getitem__(index)
- info_data = info.to_dict()
- info_path = self._get_info_path(info.meta.path)
- if Path(info_path).exists():
- with open(info_path, 'r') as json_file:
- sound_data = json.load(json_file)
- sound_data.update(info_data)
- sound_info = SoundInfo.from_dict(sound_data, fields_required=self.info_fields_required)
- # if there are multiple descriptions, sample one randomly
- if isinstance(sound_info.description, list):
- sound_info.description = random.choice(sound_info.description)
- else:
- sound_info = SoundInfo.from_dict(info_data, fields_required=False)
-
- sound_info.self_wav = WavCondition(
- wav=wav[None], length=torch.tensor([info.n_frames]),
- sample_rate=[sound_info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
- return wav, sound_info
-
- def collater(self, samples):
- # when training, audio mixing is performed in the collate function
- wav, sound_info = super().collater(samples) # SoundDataset always returns infos
- if self.aug_p > 0:
- wav, sound_info = mix_samples(wav, sound_info, self.aug_p, self.mix_p,
- snr_low=self.mix_snr_low, snr_high=self.mix_snr_high,
- min_overlap=self.mix_min_overlap)
- return wav, sound_info
-
-
-def rms_f(x: torch.Tensor) -> torch.Tensor:
- return (x ** 2).mean(1).pow(0.5)
-
-
-def normalize(audio: torch.Tensor, target_level: int = -25) -> torch.Tensor:
- """Normalize the signal to the target level."""
- rms = rms_f(audio)
- scalar = 10 ** (target_level / 20) / (rms + EPS)
- audio = audio * scalar.unsqueeze(1)
- return audio
-
-
-def is_clipped(audio: torch.Tensor, clipping_threshold: float = 0.99) -> torch.Tensor:
- return (abs(audio) > clipping_threshold).any(1)
-
-
-def mix_pair(src: torch.Tensor, dst: torch.Tensor, min_overlap: float) -> torch.Tensor:
- start = random.randint(0, int(src.shape[1] * (1 - min_overlap)))
- remainder = src.shape[1] - start
- if dst.shape[1] > remainder:
- src[:, start:] = src[:, start:] + dst[:, :remainder]
- else:
- src[:, start:start+dst.shape[1]] = src[:, start:start+dst.shape[1]] + dst
- return src
-
-
-def snr_mixer(clean: torch.Tensor, noise: torch.Tensor, snr: int, min_overlap: float,
- target_level: int = -25, clipping_threshold: float = 0.99) -> torch.Tensor:
- """Function to mix clean speech and noise at various SNR levels.
-
- Args:
- clean (torch.Tensor): Clean audio source to mix, of shape [B, T].
- noise (torch.Tensor): Noise audio source to mix, of shape [B, T].
- snr (int): SNR level when mixing.
- min_overlap (float): Minimum overlap between the two mixed sources.
- target_level (int): Gain level in dB.
- clipping_threshold (float): Threshold for clipping the audio.
- Returns:
- torch.Tensor: The mixed audio, of shape [B, T].
- """
- if clean.shape[1] > noise.shape[1]:
- noise = torch.nn.functional.pad(noise, (0, clean.shape[1] - noise.shape[1]))
- else:
- noise = noise[:, :clean.shape[1]]
-
- # normalizing to -25 dB FS
- clean = clean / (clean.max(1)[0].abs().unsqueeze(1) + EPS)
- clean = normalize(clean, target_level)
- rmsclean = rms_f(clean)
-
- noise = noise / (noise.max(1)[0].abs().unsqueeze(1) + EPS)
- noise = normalize(noise, target_level)
- rmsnoise = rms_f(noise)
-
- # set the noise level for a given SNR
- noisescalar = (rmsclean / (10 ** (snr / 20)) / (rmsnoise + EPS)).unsqueeze(1)
- noisenewlevel = noise * noisescalar
-
- # mix noise and clean speech
- noisyspeech = mix_pair(clean, noisenewlevel, min_overlap)
-
- # randomly select RMS value between -15 dBFS and -35 dBFS and normalize noisyspeech with that value
- # there is a chance of clipping that might happen with very less probability, which is not a major issue.
- noisy_rms_level = np.random.randint(TARGET_LEVEL_LOWER, TARGET_LEVEL_UPPER)
- rmsnoisy = rms_f(noisyspeech)
- scalarnoisy = (10 ** (noisy_rms_level / 20) / (rmsnoisy + EPS)).unsqueeze(1)
- noisyspeech = noisyspeech * scalarnoisy
- clean = clean * scalarnoisy
- noisenewlevel = noisenewlevel * scalarnoisy
-
- # final check to see if there are any amplitudes exceeding +/- 1. If so, normalize all the signals accordingly
- clipped = is_clipped(noisyspeech)
- if clipped.any():
- noisyspeech_maxamplevel = noisyspeech[clipped].max(1)[0].abs().unsqueeze(1) / (clipping_threshold - EPS)
- noisyspeech[clipped] = noisyspeech[clipped] / noisyspeech_maxamplevel
-
- return noisyspeech
-
-
-def snr_mix(src: torch.Tensor, dst: torch.Tensor, snr_low: int, snr_high: int, min_overlap: float):
- if snr_low == snr_high:
- snr = snr_low
- else:
- snr = np.random.randint(snr_low, snr_high)
- mix = snr_mixer(src, dst, snr, min_overlap)
- return mix
-
-
-def mix_text(src_text: str, dst_text: str):
- """Mix text from different sources by concatenating them."""
- if src_text == dst_text:
- return src_text
- return src_text + " " + dst_text
-
-
-def mix_samples(wavs: torch.Tensor, infos: tp.List[SoundInfo], aug_p: float, mix_p: float,
- snr_low: int, snr_high: int, min_overlap: float):
- """Mix samples within a batch, summing the waveforms and concatenating the text infos.
-
- Args:
- wavs (torch.Tensor): Audio tensors of shape [B, C, T].
- infos (list[SoundInfo]): List of SoundInfo items corresponding to the audio.
- aug_p (float): Augmentation probability.
- mix_p (float): Proportion of items in the batch to mix (and merge) together.
- snr_low (int): Lowerbound for sampling SNR.
- snr_high (int): Upperbound for sampling SNR.
- min_overlap (float): Minimum overlap between mixed samples.
- Returns:
- tuple[torch.Tensor, list[SoundInfo]]: A tuple containing the mixed wavs
- and mixed SoundInfo for the given batch.
- """
- # no mixing to perform within the batch
- if mix_p == 0:
- return wavs, infos
-
- if random.uniform(0, 1) < aug_p:
- # perform all augmentations on waveforms as [B, T]
- # randomly picking pairs of audio to mix
- assert wavs.size(1) == 1, f"Mix samples requires monophonic audio but C={wavs.size(1)}"
- wavs = wavs.mean(dim=1, keepdim=False)
- B, T = wavs.shape
- k = int(mix_p * B)
- mixed_sources_idx = torch.randperm(B)[:k]
- mixed_targets_idx = torch.randperm(B)[:k]
- aug_wavs = snr_mix(
- wavs[mixed_sources_idx],
- wavs[mixed_targets_idx],
- snr_low,
- snr_high,
- min_overlap,
- )
- # mixing textual descriptions in metadata
- descriptions = [info.description for info in infos]
- aug_infos = []
- for i, j in zip(mixed_sources_idx, mixed_targets_idx):
- text = mix_text(descriptions[i], descriptions[j])
- m = replace(infos[i])
- m.description = text
- aug_infos.append(m)
-
- # back to [B, C, T]
- aug_wavs = aug_wavs.unsqueeze(1)
- assert aug_wavs.shape[0] > 0, "Samples mixing returned empty batch."
- assert aug_wavs.dim() == 3, f"Returned wav should be [B, C, T] but dim = {aug_wavs.dim()}"
- assert aug_wavs.shape[0] == len(aug_infos), "Mismatch between number of wavs and infos in the batch"
-
- return aug_wavs, aug_infos # [B, C, T]
- else:
- # randomly pick samples in the batch to match
- # the batch size when performing audio mixing
- B, C, T = wavs.shape
- k = int(mix_p * B)
- wav_idx = torch.randperm(B)[:k]
- wavs = wavs[wav_idx]
- infos = [infos[i] for i in wav_idx]
- assert wavs.shape[0] == len(infos), "Mismatch between number of wavs and infos in the batch"
-
- return wavs, infos # [B, C, T]
diff --git a/spaces/breezedeus/pix2text/README.md b/spaces/breezedeus/pix2text/README.md
deleted file mode 100644
index 4a3390ce255888ad000f26b424f18f316d9dbf14..0000000000000000000000000000000000000000
--- a/spaces/breezedeus/pix2text/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pix2Text
-emoji: 🅿❷🆃
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bright1/Sepsis-Prediction-API/Dockerfile b/spaces/bright1/Sepsis-Prediction-API/Dockerfile
deleted file mode 100644
index ca16c8ca726b6a0336cacc0f2cfffbb0520b5d02..0000000000000000000000000000000000000000
--- a/spaces/bright1/Sepsis-Prediction-API/Dockerfile
+++ /dev/null
@@ -1,17 +0,0 @@
-#
-FROM python:3.9
-
-#
-WORKDIR /code
-
-#
-COPY ./requirements.txt /code/requirements.txt
-
-#
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-#
-COPY ./src /code/src
-
-#
-CMD ["uvicorn", "src.app.app:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/texture.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/texture.py
deleted file mode 100644
index 477759729d7b995a4f276e81d649617d045a066e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/texture.py
+++ /dev/null
@@ -1,259 +0,0 @@
-"""Textures, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-texture
-
-Author: Matthew Matl
-"""
-import numpy as np
-
-from OpenGL.GL import *
-
-from .utils import format_texture_source
-from .sampler import Sampler
-
-
-class Texture(object):
- """A texture and its sampler.
-
- Parameters
- ----------
- name : str, optional
- The user-defined name of this object.
- sampler : :class:`Sampler`
- The sampler used by this texture.
- source : (h,w,c) uint8 or (h,w,c) float or :class:`PIL.Image.Image`
- The image used by this texture. If None, the texture is created
- empty and width and height must be specified.
- source_channels : str
- Either `D`, `R`, `RG`, `GB`, `RGB`, or `RGBA`. Indicates the
- channels to extract from `source`. Any missing channels will be filled
- with `1.0`.
- width : int, optional
- For empty textures, the width of the texture buffer.
- height : int, optional
- For empty textures, the height of the texture buffer.
- tex_type : int
- Either GL_TEXTURE_2D or GL_TEXTURE_CUBE.
- data_format : int
- For now, just GL_FLOAT.
- """
-
- def __init__(self,
- name=None,
- sampler=None,
- source=None,
- source_channels=None,
- width=None,
- height=None,
- tex_type=GL_TEXTURE_2D,
- data_format=GL_UNSIGNED_BYTE):
- self.source_channels = source_channels
- self.name = name
- self.sampler = sampler
- self.source = source
- self.width = width
- self.height = height
- self.tex_type = tex_type
- self.data_format = data_format
-
- self._texid = None
- self._is_transparent = False
-
- @property
- def name(self):
- """str : The user-defined name of this object.
- """
- return self._name
-
- @name.setter
- def name(self, value):
- if value is not None:
- value = str(value)
- self._name = value
-
- @property
- def sampler(self):
- """:class:`Sampler` : The sampler used by this texture.
- """
- return self._sampler
-
- @sampler.setter
- def sampler(self, value):
- if value is None:
- value = Sampler()
- self._sampler = value
-
- @property
- def source(self):
- """(h,w,c) uint8 or float or :class:`PIL.Image.Image` : The image
- used in this texture.
- """
- return self._source
-
- @source.setter
- def source(self, value):
- if value is None:
- self._source = None
- else:
- self._source = format_texture_source(value, self.source_channels)
- self._is_transparent = False
-
- @property
- def source_channels(self):
- """str : The channels that were extracted from the original source.
- """
- return self._source_channels
-
- @source_channels.setter
- def source_channels(self, value):
- self._source_channels = value
-
- @property
- def width(self):
- """int : The width of the texture buffer.
- """
- return self._width
-
- @width.setter
- def width(self, value):
- self._width = value
-
- @property
- def height(self):
- """int : The height of the texture buffer.
- """
- return self._height
-
- @height.setter
- def height(self, value):
- self._height = value
-
- @property
- def tex_type(self):
- """int : The type of the texture.
- """
- return self._tex_type
-
- @tex_type.setter
- def tex_type(self, value):
- self._tex_type = value
-
- @property
- def data_format(self):
- """int : The format of the texture data.
- """
- return self._data_format
-
- @data_format.setter
- def data_format(self, value):
- self._data_format = value
-
- def is_transparent(self, cutoff=1.0):
- """bool : If True, the texture is partially transparent.
- """
- if self._is_transparent is None:
- self._is_transparent = False
- if self.source_channels == 'RGBA' and self.source is not None:
- if np.any(self.source[:,:,3] < cutoff):
- self._is_transparent = True
- return self._is_transparent
-
- def delete(self):
- """Remove this texture from the OpenGL context.
- """
- self._unbind()
- self._remove_from_context()
-
- ##################
- # OpenGL code
- ##################
- def _add_to_context(self):
- if self._texid is not None:
- raise ValueError('Texture already loaded into OpenGL context')
-
- fmt = GL_DEPTH_COMPONENT
- if self.source_channels == 'R':
- fmt = GL_RED
- elif self.source_channels == 'RG' or self.source_channels == 'GB':
- fmt = GL_RG
- elif self.source_channels == 'RGB':
- fmt = GL_RGB
- elif self.source_channels == 'RGBA':
- fmt = GL_RGBA
-
- # Generate the OpenGL texture
- self._texid = glGenTextures(1)
- glBindTexture(self.tex_type, self._texid)
-
- # Flip data for OpenGL buffer
- data = None
- width = self.width
- height = self.height
- if self.source is not None:
- data = np.ascontiguousarray(np.flip(self.source, axis=0).flatten())
- width = self.source.shape[1]
- height = self.source.shape[0]
-
- # Bind texture and generate mipmaps
- glTexImage2D(
- self.tex_type, 0, fmt, width, height, 0, fmt,
- self.data_format, data
- )
- if self.source is not None:
- glGenerateMipmap(self.tex_type)
-
- if self.sampler.magFilter is not None:
- glTexParameteri(
- self.tex_type, GL_TEXTURE_MAG_FILTER, self.sampler.magFilter
- )
- else:
- if self.source is not None:
- glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
- else:
- glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_NEAREST)
- if self.sampler.minFilter is not None:
- glTexParameteri(
- self.tex_type, GL_TEXTURE_MIN_FILTER, self.sampler.minFilter
- )
- else:
- if self.source is not None:
- glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR)
- else:
- glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_NEAREST)
-
- glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_S, self.sampler.wrapS)
- glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_T, self.sampler.wrapT)
- border_color = 255 * np.ones(4).astype(np.uint8)
- if self.data_format == GL_FLOAT:
- border_color = np.ones(4).astype(np.float32)
- glTexParameterfv(
- self.tex_type, GL_TEXTURE_BORDER_COLOR,
- border_color
- )
-
- # Unbind texture
- glBindTexture(self.tex_type, 0)
-
- def _remove_from_context(self):
- if self._texid is not None:
- # TODO OPENGL BUG?
- # glDeleteTextures(1, [self._texid])
- glDeleteTextures([self._texid])
- self._texid = None
-
- def _in_context(self):
- return self._texid is not None
-
- def _bind(self):
- # TODO HANDLE INDEXING INTO OTHER UV's
- glBindTexture(self.tex_type, self._texid)
-
- def _unbind(self):
- glBindTexture(self.tex_type, 0)
-
- def _bind_as_depth_attachment(self):
- glFramebufferTexture2D(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
- self.tex_type, self._texid, 0)
-
- def _bind_as_color_attachment(self):
- glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
- self.tex_type, self._texid, 0)
diff --git a/spaces/cahodk/live-ml5-facemesh-p5js/README.md b/spaces/cahodk/live-ml5-facemesh-p5js/README.md
deleted file mode 100644
index 842aae2c03b2fd21e3f2b0c157ad79611ce2aca0..0000000000000000000000000000000000000000
--- a/spaces/cahodk/live-ml5-facemesh-p5js/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Live Ml5 Facemesh P5js
-emoji: 🌍
-colorFrom: green
-colorTo: indigo
-sdk: static
-pinned: false
-license: lgpl-2.1
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GdImageFile.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/cccc-c/web-ui-pub/_next/static/chunks/780.aecf08b05b0b9d76.js b/spaces/cccc-c/web-ui-pub/_next/static/chunks/780.aecf08b05b0b9d76.js
deleted file mode 100644
index 54b69b4cb6454762d71ced8d20e2a5e11937716b..0000000000000000000000000000000000000000
--- a/spaces/cccc-c/web-ui-pub/_next/static/chunks/780.aecf08b05b0b9d76.js
+++ /dev/null
@@ -1,260 +0,0 @@
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- * react-store
- *
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- *
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- # This implementation prints messages to {@link System#err} containing the - # values of {@code line}, {@code charPositionInLine}, and {@code msg} using - # the following format.
- # - #- # line line:charPositionInLine msg - #- # - def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): - print("line " + str(line) + ":" + str(column) + " " + msg, file=sys.stderr) - -ConsoleErrorListener.INSTANCE = ConsoleErrorListener() - -class ProxyErrorListener(ErrorListener): - - def __init__(self, delegates): - super().__init__() - if delegates is None: - raise ReferenceError("delegates") - self.delegates = delegates - - def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): - for delegate in self.delegates: - delegate.syntaxError(recognizer, offendingSymbol, line, column, msg, e) - - def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs): - for delegate in self.delegates: - delegate.reportAmbiguity(recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs) - - def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs): - for delegate in self.delegates: - delegate.reportAttemptingFullContext(recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs) - - def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs): - for delegate in self.delegates: - delegate.reportContextSensitivity(recognizer, dfa, startIndex, stopIndex, prediction, configs) diff --git a/spaces/asafAdge/Detic/detic/modeling/backbone/swintransformer.py b/spaces/asafAdge/Detic/detic/modeling/backbone/swintransformer.py deleted file mode 100644 index 21cabb37dd87a443e27eeb805f9739bef86540bf..0000000000000000000000000000000000000000 --- a/spaces/asafAdge/Detic/detic/modeling/backbone/swintransformer.py +++ /dev/null @@ -1,750 +0,0 @@ -# -------------------------------------------------------- -# Swin Transformer -# Copyright (c) 2021 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ze Liu, Yutong Lin, Yixuan Wei -# -------------------------------------------------------- - -# Copyright (c) Facebook, Inc. and its affiliates. -# Modified by Xingyi Zhou from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py - - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -import numpy as np -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ - -from detectron2.layers import ShapeSpec -from detectron2.modeling.backbone.backbone import Backbone -from detectron2.modeling.backbone.build import BACKBONE_REGISTRY -from detectron2.modeling.backbone.fpn import FPN - -from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5 -from centernet.modeling.backbone.bifpn import BiFPN -# from .checkpoint import load_checkpoint - -class Mlp(nn.Module): - """ Multilayer perceptron.""" - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - """ Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ Forward function. - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class SwinTransformerBlock(nn.Module): - """ Swin Transformer Block. - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.H = None - self.W = None - - def forward(self, x, mask_matrix): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - mask_matrix: Attention mask for cyclic shift. - """ - B, L, C = x.shape - H, W = self.H, self.W - assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # pad feature maps to multiples of window size - pad_l = pad_t = 0 - pad_r = (self.window_size - W % self.window_size) % self.window_size - pad_b = (self.window_size - H % self.window_size) % self.window_size - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - attn_mask = mask_matrix - else: - shifted_x = x - attn_mask = None - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - -class PatchMerging(nn.Module): - """ Patch Merging Layer - Args: - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - def __init__(self, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x, H, W): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - - x = x.view(B, H, W, C) - - # padding - pad_input = (H % 2 == 1) or (W % 2 == 1) - if pad_input: - x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - Args: - dim (int): Number of feature channels - depth (int): Depths of this stage. - num_heads (int): Number of attention head. - window_size (int): Local window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - dim, - depth, - num_heads, - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop=0., - attn_drop=0., - drop_path=0., - norm_layer=nn.LayerNorm, - downsample=None, - use_checkpoint=False): - super().__init__() - self.window_size = window_size - self.shift_size = window_size // 2 - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock( - dim=dim, - num_heads=num_heads, - window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop, - attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, H, W): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - - # calculate attention mask for SW-MSA - Hp = int(np.ceil(H / self.window_size)) * self.window_size - Wp = int(np.ceil(W / self.window_size)) * self.window_size - img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - for blk in self.blocks: - blk.H, blk.W = H, W - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, attn_mask) - else: - x = blk(x, attn_mask) - if self.downsample is not None: - x_down = self.downsample(x, H, W) - Wh, Ww = (H + 1) // 2, (W + 1) // 2 - return x, H, W, x_down, Wh, Ww - else: - return x, H, W, x, H, W - - -class PatchEmbed(nn.Module): - """ Image to Patch Embedding - Args: - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - patch_size = to_2tuple(patch_size) - self.patch_size = patch_size - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - """Forward function.""" - # padding - _, _, H, W = x.size() - if W % self.patch_size[1] != 0: - x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) - if H % self.patch_size[0] != 0: - x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) - - x = self.proj(x) # B C Wh Ww - if self.norm is not None: - Wh, Ww = x.size(2), x.size(3) - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) - - return x - - -class SwinTransformer(Backbone): - """ Swin Transformer backbone. - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - Args: - pretrain_img_size (int): Input image size for training the pretrained model, - used in absolute postion embedding. Default 224. - patch_size (int | tuple(int)): Patch size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - depths (tuple[int]): Depths of each Swin Transformer stage. - num_heads (tuple[int]): Number of attention head of each stage. - window_size (int): Window size. Default: 7. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - drop_rate (float): Dropout rate. - attn_drop_rate (float): Attention dropout rate. Default: 0. - drop_path_rate (float): Stochastic depth rate. Default: 0.2. - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. - patch_norm (bool): If True, add normalization after patch embedding. Default: True. - out_indices (Sequence[int]): Output from which stages. - frozen_stages (int): Stages to be frozen (stop grad and set eval mode). - -1 means not freezing any parameters. - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, - pretrain_img_size=224, - patch_size=4, - in_chans=3, - embed_dim=96, - depths=[2, 2, 6, 2], - num_heads=[3, 6, 12, 24], - window_size=7, - mlp_ratio=4., - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0.2, - norm_layer=nn.LayerNorm, - ape=False, - patch_norm=True, - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - use_checkpoint=False): - super().__init__() - - self.pretrain_img_size = pretrain_img_size - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.out_indices = out_indices - self.frozen_stages = frozen_stages - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - pretrain_img_size = to_2tuple(pretrain_img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] - - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build layers - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = BasicLayer( - dim=int(embed_dim * 2 ** i_layer), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, - attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], - norm_layer=norm_layer, - downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, - use_checkpoint=use_checkpoint) - self.layers.append(layer) - - num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] - self.num_features = num_features - - # add a norm layer for each output - for i_layer in out_indices: - layer = norm_layer(num_features[i_layer]) - layer_name = f'norm{i_layer}' - self.add_module(layer_name, layer) - - self._freeze_stages() - self._out_features = ['swin{}'.format(i) for i in self.out_indices] - self._out_feature_channels = { - 'swin{}'.format(i): self.embed_dim * 2 ** i for i in self.out_indices - } - self._out_feature_strides = { - 'swin{}'.format(i): 2 ** (i + 2) for i in self.out_indices - } - self._size_devisibility = 32 - - - def _freeze_stages(self): - if self.frozen_stages >= 0: - self.patch_embed.eval() - for param in self.patch_embed.parameters(): - param.requires_grad = False - - if self.frozen_stages >= 1 and self.ape: - self.absolute_pos_embed.requires_grad = False - - if self.frozen_stages >= 2: - self.pos_drop.eval() - for i in range(0, self.frozen_stages - 1): - m = self.layers[i] - m.eval() - for param in m.parameters(): - param.requires_grad = False - - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - - def _init_weights(m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - if isinstance(pretrained, str): - self.apply(_init_weights) - # load_checkpoint(self, pretrained, strict=False) - elif pretrained is None: - self.apply(_init_weights) - else: - raise TypeError('pretrained must be a str or None') - - def forward(self, x): - """Forward function.""" - x = self.patch_embed(x) - - Wh, Ww = x.size(2), x.size(3) - if self.ape: - # interpolate the position embedding to the corresponding size - absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') - x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C - else: - x = x.flatten(2).transpose(1, 2) - x = self.pos_drop(x) - - # outs = [] - outs = {} - for i in range(self.num_layers): - layer = self.layers[i] - x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) - - if i in self.out_indices: - norm_layer = getattr(self, f'norm{i}') - x_out = norm_layer(x_out) - - out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() - # outs.append(out) - outs['swin{}'.format(i)] = out - - return outs - - def train(self, mode=True): - """Convert the model into training mode while keep layers freezed.""" - super(SwinTransformer, self).train(mode) - self._freeze_stages() - -size2config = { - 'T': { - 'window_size': 7, - 'embed_dim': 96, - 'depth': [2, 2, 6, 2], - 'num_heads': [3, 6, 12, 24], - 'drop_path_rate': 0.2, - 'pretrained': 'models/swin_tiny_patch4_window7_224.pth' - }, - 'S': { - 'window_size': 7, - 'embed_dim': 96, - 'depth': [2, 2, 18, 2], - 'num_heads': [3, 6, 12, 24], - 'drop_path_rate': 0.2, - 'pretrained': 'models/swin_small_patch4_window7_224.pth' - }, - 'B': { - 'window_size': 7, - 'embed_dim': 128, - 'depth': [2, 2, 18, 2], - 'num_heads': [4, 8, 16, 32], - 'drop_path_rate': 0.3, - 'pretrained': 'models/swin_base_patch4_window7_224.pth' - }, - 'B-22k': { - 'window_size': 7, - 'embed_dim': 128, - 'depth': [2, 2, 18, 2], - 'num_heads': [4, 8, 16, 32], - 'drop_path_rate': 0.3, - 'pretrained': 'models/swin_base_patch4_window7_224_22k.pth' - }, - 'B-22k-384': { - 'window_size': 12, - 'embed_dim': 128, - 'depth': [2, 2, 18, 2], - 'num_heads': [4, 8, 16, 32], - 'drop_path_rate': 0.3, - 'pretrained': 'models/swin_base_patch4_window12_384_22k.pth' - }, - 'L-22k': { - 'window_size': 7, - 'embed_dim': 192, - 'depth': [2, 2, 18, 2], - 'num_heads': [6, 12, 24, 48], - 'drop_path_rate': 0.3, # TODO (xingyi): this is unclear - 'pretrained': 'models/swin_large_patch4_window7_224_22k.pth' - }, - 'L-22k-384': { - 'window_size': 12, - 'embed_dim': 192, - 'depth': [2, 2, 18, 2], - 'num_heads': [6, 12, 24, 48], - 'drop_path_rate': 0.3, # TODO (xingyi): this is unclear - 'pretrained': 'models/swin_large_patch4_window12_384_22k.pth' - } -} - -@BACKBONE_REGISTRY.register() -def build_swintransformer_backbone(cfg, input_shape): - """ - """ - config = size2config[cfg.MODEL.SWIN.SIZE] - out_indices = cfg.MODEL.SWIN.OUT_FEATURES - model = SwinTransformer( - embed_dim=config['embed_dim'], - window_size=config['window_size'], - depths=config['depth'], - num_heads=config['num_heads'], - drop_path_rate=config['drop_path_rate'], - out_indices=out_indices, - frozen_stages=-1, - use_checkpoint=cfg.MODEL.SWIN.USE_CHECKPOINT - ) - # print('Initializing', config['pretrained']) - model.init_weights(config['pretrained']) - return model - - -@BACKBONE_REGISTRY.register() -def build_swintransformer_fpn_backbone(cfg, input_shape: ShapeSpec): - """ - """ - bottom_up = build_swintransformer_backbone(cfg, input_shape) - in_features = cfg.MODEL.FPN.IN_FEATURES - out_channels = cfg.MODEL.FPN.OUT_CHANNELS - backbone = FPN( - bottom_up=bottom_up, - in_features=in_features, - out_channels=out_channels, - norm=cfg.MODEL.FPN.NORM, - top_block=LastLevelP6P7_P5(out_channels, out_channels), - fuse_type=cfg.MODEL.FPN.FUSE_TYPE, - ) - return backbone - - -@BACKBONE_REGISTRY.register() -def build_swintransformer_bifpn_backbone(cfg, input_shape: ShapeSpec): - """ - """ - bottom_up = build_swintransformer_backbone(cfg, input_shape) - in_features = cfg.MODEL.FPN.IN_FEATURES - backbone = BiFPN( - cfg=cfg, - bottom_up=bottom_up, - in_features=in_features, - out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS, - norm=cfg.MODEL.BIFPN.NORM, - num_levels=cfg.MODEL.BIFPN.NUM_LEVELS, - num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN, - separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV, - ) - return backbone \ No newline at end of file diff --git a/spaces/atimughal662/InfoFusion/src/gen.py b/spaces/atimughal662/InfoFusion/src/gen.py deleted file mode 100644 index 5919c5cbf8dbec02e05487b003c736398367aad6..0000000000000000000000000000000000000000 --- a/spaces/atimughal662/InfoFusion/src/gen.py +++ /dev/null @@ -1,4307 +0,0 @@ -import ast -import copy -import functools -import inspect -import queue -import sys -import os -import time -import traceback -import typing -import warnings -from datetime import datetime -import requests -from requests import ConnectTimeout, JSONDecodeError -from urllib3.exceptions import ConnectTimeoutError, MaxRetryError, ConnectionError -from requests.exceptions import ConnectionError as ConnectionError2 -from requests.exceptions import ReadTimeout as ReadTimeout2 - -if os.path.dirname(os.path.abspath(__file__)) not in sys.path: - sys.path.append(os.path.dirname(os.path.abspath(__file__))) - -os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' -os.environ['BITSANDBYTES_NOWELCOME'] = '1' -warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') - -# more is not useful typically, don't let these go beyond limits and eat up resources -max_cores = max(1, os.cpu_count() // 2) -if os.getenv('NUMEXPR_MAX_THREADS') is None: - os.environ['NUMEXPR_MAX_THREADS'] = str(min(8, max_cores)) -if os.getenv('NUMEXPR_NUM_THREADS') is None: - os.environ['NUMEXPR_NUM_THREADS'] = str(min(8, max_cores)) -if os.getenv('OMP_NUM_THREADS') is None: - os.environ['OMP_NUM_THREADS'] = str(min(8, max_cores)) -if os.getenv('OPENBLAS_NUM_THREADS') is None: - os.environ['OPENBLAS_NUM_THREADS'] = str(min(8, max_cores)) -if os.getenv('DUCKDB_NUM_THREADS') is None: - os.environ['DUCKDB_NUM_THREADS'] = str(min(4, max_cores)) -if os.getenv('RAYON_RS_NUM_CPUS') is None: - os.environ['RAYON_RS_NUM_CPUS'] = str(min(8, max_cores)) -if os.getenv('RAYON_NUM_THREADS') is None: - os.environ['RAYON_NUM_THREADS'] = str(min(8, max_cores)) - -import numpy as np -from evaluate_params import eval_func_param_names, no_default_param_names, input_args_list -from enums import DocumentSubset, LangChainMode, no_lora_str, model_token_mapping, no_model_str, \ - LangChainAction, LangChainAgent, DocumentChoice, LangChainTypes, super_source_prefix, \ - super_source_postfix, t5_type, get_langchain_prompts, gr_to_lg, invalid_key_msg, docs_joiner_default, \ - docs_ordering_types_default, docs_token_handling_default -from loaders import get_loaders -# import utils import . -from utzils import set_seed, clear_torch_cache, NullContext, wrapped_partial, EThread, get_githash, \ - import_matplotlib, get_device, makedirs, get_kwargs, start_faulthandler, get_hf_server, FakeTokenizer, \ - have_langchain, set_openai, cuda_vis_check, H2O_Fire, lg_to_gr, str_to_list, str_to_dict, get_token_count - -start_faulthandler() -import_matplotlib() - -SEED = 1236 -set_seed(SEED) - -from typing import Union - -import torch -from transformers import GenerationConfig, AutoModel, TextIteratorStreamer - -from prompter import Prompter, inv_prompt_type_to_model_lower, non_hf_types, PromptType, get_prompt, generate_prompt -from stopping import get_stopping - -langchain_actions = [x.value for x in list(LangChainAction)] - -langchain_agents_list = [x.value for x in list(LangChainAgent)] - - -def main( - load_8bit: bool = False, - load_4bit: bool = False, - low_bit_mode: int = 1, - load_half: bool = None, - load_gptq: str = '', - load_awq: str = '', - load_exllama: bool = False, - use_safetensors: bool = False, - revision: str = None, - use_gpu_id: bool = True, - base_model: str = '', - tokenizer_base_model: str = '', - lora_weights: str = "", - gpu_id: int = 0, - compile_model: bool = None, - use_cache: bool = None, - inference_server: str = "", - prompt_type: Union[int, str] = None, - prompt_dict: typing.Dict = None, - system_prompt: str = '', - - # llama and gpt4all settings - llamacpp_dict: typing.Dict = dict(n_gpu_layers=100, use_mlock=True, n_batch=1024, n_gqa=0), - model_path_llama: str = 'https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf', - model_name_gptj: str = 'ggml-gpt4all-j-v1.3-groovy.bin', - model_name_gpt4all_llama: str = 'ggml-wizardLM-7B.q4_2.bin', - model_name_exllama_if_no_config: str = 'TheBloke/Nous-Hermes-Llama2-GPTQ', - exllama_dict: typing.Dict = dict(), - gptq_dict: typing.Dict = dict(), - attention_sinks: bool = False, - sink_dict: typing.Dict = dict(), - truncation_generation: bool = False, - hf_model_dict: typing.Dict = dict(), - - model_lock: typing.List[typing.Dict[str, str]] = None, - model_lock_columns: int = None, - fail_if_cannot_connect: bool = False, - - # input to generation - temperature: float = None, - top_p: float = None, - top_k: int = None, - penalty_alpha: float = None, - num_beams: int = None, - repetition_penalty: float = None, - num_return_sequences: int = None, - do_sample: bool = None, - max_new_tokens: int = None, - min_new_tokens: int = None, - early_stopping: Union[bool, str] = None, - max_time: float = None, - - memory_restriction_level: int = None, - debug: bool = False, - save_dir: str = None, - local_files_only: bool = False, - resume_download: bool = True, - use_auth_token: Union[str, bool] = False, - trust_remote_code: Union[str, bool] = True, - rope_scaling: dict = None, - max_seq_len: int = None, - offload_folder: str = "offline_folder", - - src_lang: str = "English", - tgt_lang: str = "Russian", - - prepare_offline_level: int = 0, - cli: bool = False, - cli_loop: bool = True, - gradio: bool = True, - gradio_offline_level: int = 0, - server_name: str = "0.0.0.0", - share: bool = False, - open_browser: bool = False, - root_path: str = "", - ssl_verify: bool = True, - ssl_keyfile: str | None = None, - ssl_certfile: str | None = None, - ssl_keyfile_password: str | None = None, - - chat: bool = True, - chat_conversation: typing.List[typing.Tuple[str, str]] = None, - text_context_list: typing.List[str] = None, - stream_output: bool = True, - async_output: bool = True, - num_async: int = 3, - show_examples: bool = None, - verbose: bool = False, - h2ocolors: bool = True, - dark: bool = False, # light tends to be best - height: int = 600, - render_markdown: bool = True, - show_lora: bool = True, - show_llama: bool = True, - show_gpt4all: bool = False, - login_mode_if_model0: bool = False, - block_gradio_exit: bool = True, - concurrency_count: int = 1, - api_open: bool = False, - allow_api: bool = True, - input_lines: int = 1, - gradio_size: str = None, - show_copy_button: bool = True, - large_file_count_mode: bool = False, - pre_load_embedding_model: bool = True, - - auth: Union[typing.List[typing.Tuple[str, str]], str] = None, - auth_filename: str = None, - auth_access: str = 'open', - auth_freeze: bool = False, - auth_message: str = None, - guest_name: str = "guest", - enforce_h2ogpt_api_key: bool = None, - enforce_h2ogpt_ui_key: bool = None, - h2ogpt_api_keys: Union[list, str] = [], - h2ogpt_key: str = None, - - max_max_time=None, - max_max_new_tokens=None, - - visible_models: list = None, - visible_visible_models: bool = True, - visible_submit_buttons: bool = True, - visible_side_bar: bool = True, - visible_doc_track: bool = True, - visible_chat_tab: bool = True, - visible_doc_selection_tab: bool = True, - visible_doc_view_tab: bool = True, - visible_chat_history_tab: bool = True, - visible_expert_tab: bool = True, - visible_models_tab: bool = True, - visible_system_tab: bool = True, - visible_tos_tab: bool = False, - visible_login_tab: bool = True, - visible_hosts_tab: bool = False, - chat_tables: bool = False, - visible_h2ogpt_header: bool = True, - max_raw_chunks: int = None, - - sanitize_user_prompt: bool = False, - sanitize_bot_response: bool = False, - - extra_model_options: typing.List[str] = [], - extra_lora_options: typing.List[str] = [], - extra_server_options: typing.List[str] = [], - - score_model: str = 'auto', - - eval_filename: str = None, - eval_prompts_only_num: int = 0, - eval_prompts_only_seed: int = 1234, - eval_as_output: bool = False, - - langchain_mode: str = None, - user_path: str = None, - langchain_modes: list = [LangChainMode.USER_DATA.value, LangChainMode.MY_DATA.value, LangChainMode.LLM.value, - LangChainMode.DISABLED.value], - langchain_mode_paths: dict = {LangChainMode.USER_DATA.value: None}, - langchain_mode_types: dict = {LangChainMode.USER_DATA.value: LangChainTypes.SHARED.value}, - detect_user_path_changes_every_query: bool = False, - - langchain_action: str = LangChainAction.QUERY.value, - langchain_agents: list = [], - force_langchain_evaluate: bool = False, - - visible_langchain_actions: list = [LangChainAction.QUERY.value, LangChainAction.SUMMARIZE_MAP.value, - LangChainAction.EXTRACT.value], - visible_langchain_agents: list = langchain_agents_list.copy(), - - document_subset: str = DocumentSubset.Relevant.name, - document_choice: list = [DocumentChoice.ALL.value], - - use_llm_if_no_docs: bool = True, - load_db_if_exists: bool = True, - keep_sources_in_context: bool = False, - db_type: str = 'chroma', - use_openai_embedding: bool = False, - use_openai_model: bool = False, - hf_embedding_model: str = None, - migrate_embedding_model: str = False, - auto_migrate_db: bool = False, - cut_distance: float = 1.64, - answer_with_sources: bool = True, - append_sources_to_answer: bool = True, - show_accordions: bool = True, - top_k_docs_max_show: int = 10, - show_link_in_sources: bool = True, - pre_prompt_query: str = None, - prompt_query: str = None, - pre_prompt_summary: str = None, - prompt_summary: str = None, - add_chat_history_to_context: bool = True, - add_search_to_context: bool = False, - context: str = '', - iinput: str = '', - allow_upload_to_user_data: bool = True, - reload_langchain_state: bool = True, - allow_upload_to_my_data: bool = True, - enable_url_upload: bool = True, - enable_text_upload: bool = True, - enable_sources_list: bool = True, - chunk: bool = True, - chunk_size: int = 512, - top_k_docs: int = None, - docs_ordering_type: str = docs_ordering_types_default, - min_max_new_tokens=256, - max_input_tokens=-1, - docs_token_handling: str = docs_token_handling_default, - docs_joiner: str = docs_joiner_default, - hyde_level: int = 0, - hyde_template: str = None, - - auto_reduce_chunks: bool = True, - max_chunks: int = 100, - headsize: int = 50, - n_jobs: int = -1, - - # urls - use_unstructured=True, - use_playwright=False, - use_selenium=False, - - # pdfs - use_pymupdf='auto', - use_unstructured_pdf='auto', - use_pypdf='auto', - enable_pdf_ocr='auto', - enable_pdf_doctr='auto', - try_pdf_as_html='auto', - - # images - enable_ocr=False, - enable_doctr=True, - enable_pix2struct=False, - enable_captions=True, - - pre_load_caption_model: bool = False, - caption_gpu: bool = True, - caption_gpu_id: Union[int, str] = 'auto', - captions_model: str = "Salesforce/blip-image-captioning-base", - doctr_gpu: bool = True, - doctr_gpu_id: Union[int, str] = 'auto', - - # json - jq_schema='.[]', - - max_quality: bool = False, - - enable_heap_analytics: bool = True, - heap_app_id: str = "1680123994", -): - """ - - :param load_8bit: load model in 8-bit using bitsandbytes - :param load_4bit: load model in 4-bit using bitsandbytes - :param low_bit_mode: 0: no quantization config 1: change compute 2: nf4 3: double quant 4: 2 and 3 - See: https://huggingface.co/docs/transformers/main_classes/quantization - If using older bitsandbytes or transformers, 0 is required - :param load_half: load model in float16 (None means auto, which means True unless t5 based model) - otherwise specify bool - :param load_gptq: to load model with GPTQ, put model_basename here, e.g. gptq_model-4bit--1g - :param load_awq: load model with AWQ, often 'model' for TheBloke models - :param load_exllama: whether to use exllama (only applicable to LLaMa1/2 models with 16-bit or GPTQ - :param use_safetensors: to use safetensors version (assumes file/HF points to safe tensors version) - :param revision: Which HF revision to use - :param use_gpu_id: whether to control devices with gpu_id. If False, then spread across GPUs - :param base_model: model HF-type name. If use --base_model to preload model, cannot unload in gradio in models tab - :param tokenizer_base_model: tokenizer HF-type name. Usually not required, inferred from base_model. - :param lora_weights: LORA weights path/HF link - :param gpu_id: if use_gpu_id, then use gpu_id for cuda device ID, or auto mode if gpu_id != -1 - :param compile_model Whether to compile the model - :param use_cache: Whether to use caching in model (some models fail when multiple threads use) - :param inference_server: Consume base_model as type of model at this address - Address can be text-generation-server hosting that base_model - e.g. python generate.py --inference_server="http://192.168.1.46:6112" --base_model=h2oai/h2ogpt-oasst1-512-12b - - Or Address can be "openai_chat" or "openai" for OpenAI API - Or Address can be "openai_azure_chat" or "openai_azure" for Azure OpenAI API - e.g. python generate.py --inference_server="openai_chat" --base_model=gpt-3.5-turbo - e.g. python generate.py --inference_server="openai" --base_model=text-davinci-003 - e.g. python generate.py --inference_server="openai_azure_chat:
'):
- prompt = prompt[:-4]
- prompt = prompt.replace(' Download File ––– https://urloso.com/2uyQeE Download File ✏ https://urloso.com/2uyS8r Ordering your free janam kundali analysis is an easy and simple task as long as you know your birth details. In order to attain your free horoscope, simply follow the given instructions and fill in the kundli software: Download File ••• https://urloso.com/2uyPIk Instant chart is a quick way to create janma kundali (birth chart) and prashna kundali(horary chart). By quick we mean that there is no registration required to accessthis feature. You can also use our new interface for instant charts . Vedic kundali software is based on accurate astrological calculations. This vedic jyotish software is the result of years of extensive research work. You can use this vedic astrology software easily with the little help of somebody even if you are not a computer savvy person. According to astrologers it is the easiest vedic astrology software in the world. One can get most accurate, excellent and comprehensive results from this vedic astrology software. Astro-Vision is easy matchmaking and kundli software, based on the Vedic astrology science. It provides accurate future predictions and is designed to analyze various factors in Kundli in Hindi and other languages to give a detailed and precise report. Kundali software MyKundali offers key astrological services like love match, horoscope match, numerology calculation, etc. for free. Superior quality software for matching kundli for the matrimonial alliance and getting a janam kundli done for a newborn. Kundli matching AstroSage kundli software is flawlessand quick. The kundali software just requires the birth details of both brideand groom such as date, time and place and it will do the rest. Using the VedicAstrology principles, horoscopes of the natives are analyzed and the resultcomes with a good explanation. This kundali software has a user-friendly interface wherethe user just needs to enter the birth details of the potential bride andgroom. This kundli matching software also provides an overallcompatibility score to indicate the compatibility percentage between thepotential partners. For a detailed report on kundali and marriagecompatibility, users can get the premium plan for a small fee.
', chat_turn_sep)
- if not prompt.endswith(chat_turn_sep):
- prompt += chat_turn_sep
- # most recent first, add older if can
- # only include desired chat history
- if len(prompt + context1) > max_prompt_length:
- break
- context1 += prompt
-
- _, pre_response, terminate_response, chat_sep, chat_turn_sep = \
- generate_prompt({}, prompt_type, prompt_dict,
- chat, reduced=True,
- making_context=True,
- system_prompt=system_prompt,
- histi=-1)
- if context1 and not context1.endswith(chat_turn_sep):
- context1 += chat_turn_sep # ensure if terminates abruptly, then human continues on next line
- return context1
-
-
-def get_relaxed_max_new_tokens(prompt, tokenizer=None, max_new_tokens=None, max_new_tokens0=None):
- # check if can relax max_new_tokens for this specific prompt
- if max_new_tokens0 is not None and \
- hasattr(tokenizer, 'model_max_len') and \
- isinstance(tokenizer.model_max_len, (float, int)):
- max_new_tokens = int(tokenizer.model_max_length) - get_token_count(prompt, tokenizer)
- if max_new_tokens is not None:
- return min(max_new_tokens0, max_new_tokens)
- else:
- return max_new_tokens0
- return max_new_tokens
-
-
-def get_limited_prompt(instruction,
- iinput,
- tokenizer,
- estimated_instruction=None,
- prompter=None,
- inference_server=None,
- prompt_type=None, prompt_dict=None, chat=False, max_new_tokens=None,
- system_prompt='',
- context='', chat_conversation=None, text_context_list=None,
- keep_sources_in_context=False,
- model_max_length=None, memory_restriction_level=0,
- langchain_mode=None, add_chat_history_to_context=True,
- verbose=False,
- doc_importance=0.5,
- min_max_new_tokens=256,
- max_input_tokens=-1,
- truncation_generation=False,
- gradio_server=False,
- ):
- if gradio_server or not inference_server:
- # can listen to truncation_generation
- pass
- else:
- # these don't support allowing going beyond total context
- truncation_generation = True
-
- # for templates, use estimated for counting, but adjust instruction as output
- if estimated_instruction is None:
- estimated_instruction = instruction
-
- if max_input_tokens >= 0:
- # max_input_tokens is used to runtime (via client/UI) to control actual filling of context
- max_input_tokens = min(model_max_length - min_max_new_tokens, max_input_tokens)
- else:
- max_input_tokens = model_max_length - min_max_new_tokens
-
- if prompter:
- prompt_type = prompter.prompt_type
- prompt_dict = prompter.prompt_dict
- chat = prompter.chat
- stream_output = prompter.stream_output
- system_prompt = prompter.system_prompt
-
- generate_prompt_type = prompt_type
- external_handle_chat_conversation = False
- if inference_server and any(
- inference_server.startswith(x) for x in ['openai_chat', 'openai_azure_chat', 'vllm_chat']):
- # Chat APIs do not take prompting
- # Replicate does not need prompting if no chat history, but in general can take prompting
- # if using prompter, prompter.system_prompt will already be filled with automatic (e.g. from llama-2),
- # so if replicate final prompt with system prompt still correct because only access prompter.system_prompt that was already set
- # below already true for openai,
- # but not vllm by default as that can be any model and handled by FastChat API inside vLLM itself
- generate_prompt_type = 'plain'
- # Chat APIs don't handle chat history via single prompt, but in messages, assumed to be handled outside this function
- chat_conversation = []
- external_handle_chat_conversation = True
-
- # merge handles if chat_conversation is None
- history = []
- history = merge_chat_conversation_history(chat_conversation, history)
- history_to_context_func = functools.partial(history_to_context,
- langchain_mode=langchain_mode,
- add_chat_history_to_context=add_chat_history_to_context,
- prompt_type=generate_prompt_type,
- prompt_dict=prompt_dict,
- chat=chat,
- model_max_length=max_input_tokens,
- memory_restriction_level=memory_restriction_level,
- keep_sources_in_context=keep_sources_in_context,
- system_prompt=system_prompt,
- min_max_new_tokens=min_max_new_tokens)
- context2 = history_to_context_func(history)
- context1 = context
- if context1 is None:
- context1 = ''
-
- # get how many more tokens in templated instruction, somewhat of estimate at fine level
- num_instruction_tokens = get_token_count(instruction, tokenizer)
- num_estimated_instruction_tokens = get_token_count(estimated_instruction, tokenizer)
- delta_instruction = max(0, num_estimated_instruction_tokens - num_instruction_tokens)
-
- # get estimated templated instruction tokens for counting purposes
- from h2oai_pipeline import H2OTextGenerationPipeline
- estimated_instruction, num_estimated_instruction_tokens = H2OTextGenerationPipeline.limit_prompt(
- estimated_instruction, tokenizer,
- max_prompt_length=max_input_tokens)
- data_point_just_instruction = dict(context='', instruction=estimated_instruction, input='')
- prompt_just_estimated_instruction = prompter.generate_prompt(data_point_just_instruction)
- num_instruction_tokens = get_token_count(prompt_just_estimated_instruction, tokenizer)
-
- # get actual instruction, limited by template limitation
- instruction, _ = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer,
- max_prompt_length=max_input_tokens - delta_instruction)
-
- context1, num_context1_tokens = H2OTextGenerationPipeline.limit_prompt(context1, tokenizer,
- max_prompt_length=max_input_tokens)
- context2, num_context2_tokens = H2OTextGenerationPipeline.limit_prompt(context2, tokenizer,
- max_prompt_length=max_input_tokens)
- iinput, num_iinput_tokens = H2OTextGenerationPipeline.limit_prompt(iinput, tokenizer,
- max_prompt_length=max_input_tokens)
- if text_context_list is None:
- text_context_list = []
- num_doc_tokens = sum([get_token_count(x + docs_joiner_default, tokenizer) for x in text_context_list])
-
- num_prompt_tokens0 = (num_instruction_tokens or 0) + \
- (num_context1_tokens or 0) + \
- (num_context2_tokens or 0) + \
- (num_iinput_tokens or 0) + \
- (num_doc_tokens or 0)
-
- # go down to no less than 256, about 1 paragraph
- # use max_new_tokens before use num_prompt_tokens0 else would be negative or ~0
- min_max_new_tokens = min(min_max_new_tokens, max_new_tokens)
- # by default assume can handle all chat and docs
- chat_index = 0
-
- # allowed residual is either half of what is allowed if doc exceeds half, or is rest of what doc didn't consume
- num_non_doc_tokens = num_prompt_tokens0 - num_doc_tokens
- # to doc first then non-doc, shouldn't matter much either way
- doc_max_length = max(max_input_tokens - num_non_doc_tokens, int(doc_importance * max_input_tokens))
- top_k_docs, one_doc_size, num_doc_tokens = get_docs_tokens(tokenizer, text_context_list=text_context_list,
- max_input_tokens=doc_max_length)
- non_doc_max_length = max(max_input_tokens - num_doc_tokens, int((1.0 - doc_importance) * max_input_tokens))
-
- if num_non_doc_tokens > non_doc_max_length:
- # need to limit in some way, keep portion of history but all of context and instruction
- # 1) drop iinput (unusual to include anyways)
- # 2) reduce history
- # 3) reduce context1
- # 4) limit instruction so will fit
- diff1 = non_doc_max_length - (
- num_instruction_tokens + num_context1_tokens + num_context2_tokens)
- diff2 = non_doc_max_length - (num_instruction_tokens + num_context1_tokens)
- diff3 = non_doc_max_length - num_instruction_tokens
- diff4 = non_doc_max_length
- if diff1 > 0:
- # then should be able to do #1
- iinput = ''
- num_iinput_tokens = 0
- elif diff2 > 0 > diff1:
- # then may be able to do #1 + #2
- iinput = ''
- num_iinput_tokens = 0
- chat_index_final = len(history)
- for chat_index in range(len(history)):
- # NOTE: history and chat_conversation are older for first entries
- # FIXME: This is a slow for many short conversations
- context2 = history_to_context_func(history[chat_index:])
- num_context2_tokens = get_token_count(context2, tokenizer)
- diff1 = non_doc_max_length - (
- num_instruction_tokens + num_context1_tokens + num_context2_tokens)
- if diff1 > 0:
- chat_index_final = chat_index
- if verbose:
- print("chat_conversation used %d out of %d" % (chat_index, len(history)), flush=True)
- break
- chat_index = chat_index_final # i.e. if chat_index == len(history), then nothing can be consumed
- elif diff3 > 0 > diff2:
- # then may be able to do #1 + #2 + #3
- iinput = ''
- num_iinput_tokens = 0
- context2 = ''
- num_context2_tokens = 0
- context1, num_context1_tokens = H2OTextGenerationPipeline.limit_prompt(context1, tokenizer,
- max_prompt_length=diff3)
- if num_context1_tokens <= diff3:
- pass
- else:
- print("failed to reduce", flush=True)
- else:
- # then must be able to do #1 + #2 + #3 + #4
- iinput = ''
- num_iinput_tokens = 0
- context2 = ''
- num_context2_tokens = 0
- context1 = ''
- num_context1_tokens = 0
- # diff4 accounts for real prompting for instruction
- # FIXME: history_to_context could include instruction, in case system prompt long, we overcount and could have more free tokens
-
- max_prompt_length = max(0, diff4 - delta_instruction)
- instruction, _ = H2OTextGenerationPipeline.limit_prompt(instruction, tokenizer,
- max_prompt_length=max_prompt_length)
- # get actual instruction tokens
- data_point_just_instruction = dict(context='', instruction=instruction, input='')
- prompt_just_instruction = prompter.generate_prompt(data_point_just_instruction)
- num_instruction_tokens = get_token_count(prompt_just_instruction, tokenizer) + delta_instruction
-
- # update full context
- context = context1 + context2
- # update token counts (docs + non-docs, all tokens)
- num_prompt_tokens = (num_instruction_tokens or 0) + \
- (num_context1_tokens or 0) + \
- (num_context2_tokens or 0) + \
- (num_iinput_tokens or 0) + \
- (num_doc_tokens or 0)
-
- # update max_new_tokens
- # limit so max_new_tokens = prompt + new < max
- # otherwise model can fail etc. e.g. for distilgpt2 asking for 1024 tokens is enough to fail if prompt=1 token
- if truncation_generation:
- max_new_tokens = min(max_new_tokens, model_max_length - num_prompt_tokens)
-
- if os.getenv('HARD_ASSERTS'):
- if max_new_tokens < min_max_new_tokens:
- raise ValueError("Invalid max_new_tokens=%s" % max_new_tokens)
-
- if prompter is None:
- # get prompter
- debug = False
- stream_output = False # doesn't matter
- prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output,
- system_prompt=system_prompt)
- if prompt_type != generate_prompt_type:
- # override just this attribute, keep system_prompt etc. from original prompt_type
- prompter.prompt_type = generate_prompt_type
-
- data_point = dict(context=context, instruction=instruction, input=iinput)
- # handle promptA/promptB addition if really from history.
- # if not from history, then reduced=False inside correct
- # if mixed, then no specific correct thing to do, so treat like history and promptA/B will come first still
- context_from_history = len(history) > 0 and len(context1) > 0
- prompt = prompter.generate_prompt(data_point, context_from_history=context_from_history)
- num_prompt_tokens_actual = get_token_count(prompt, tokenizer)
-
- return prompt, \
- instruction, iinput, context, \
- num_prompt_tokens, max_new_tokens, num_prompt_tokens0, num_prompt_tokens_actual, \
- chat_index, external_handle_chat_conversation, \
- top_k_docs, one_doc_size, truncation_generation
-
-
-def get_docs_tokens(tokenizer, text_context_list=[], max_input_tokens=None):
- if text_context_list is None or len(text_context_list) == 0:
- return 0, None, 0
- if max_input_tokens is None:
- max_input_tokens = tokenizer.model_max_length
- tokens = [get_token_count(x + docs_joiner_default, tokenizer) for x in text_context_list]
- tokens_cumsum = np.cumsum(tokens)
- where_res = np.where(tokens_cumsum < max_input_tokens)[0]
- # if below condition fails, then keep top_k_docs=-1 and trigger special handling next
- if where_res.shape[0] > 0:
- top_k_docs = 1 + where_res[-1]
- one_doc_size = None
- num_doc_tokens = tokens_cumsum[top_k_docs - 1] # by index
- else:
- # if here, means 0 and just do best with 1 doc
- top_k_docs = 1
- text_context_list = text_context_list[:top_k_docs]
- # critical protection
- from src.h2oai_pipeline import H2OTextGenerationPipeline
- doc_content = text_context_list[0]
- doc_content, new_tokens0 = H2OTextGenerationPipeline.limit_prompt(doc_content,
- tokenizer,
- max_prompt_length=max_input_tokens)
- text_context_list[0] = doc_content
- one_doc_size = len(doc_content)
- num_doc_tokens = get_token_count(doc_content + docs_joiner_default, tokenizer)
- print("Unexpected large chunks and can't add to context, will add 1 anyways. Tokens %s -> %s" % (
- tokens[0], new_tokens0), flush=True)
- return top_k_docs, one_doc_size, num_doc_tokens
-
-
-def entrypoint_main():
- """
- Examples:
-
- WORLD_SIZE=4 CUDA_VISIBLE_DEVICES="0,1,2,3" torchrun --nproc_per_node=4 --master_port=1234 generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights=lora-alpaca_6B
- python generate.py --base_model='EleutherAI/gpt-j-6B' --lora_weights='lora-alpaca_6B'
- python generate.py --base_model='EleutherAI/gpt-neox-20b' --lora_weights='lora-alpaca_20B'
-
- # generate without lora weights, no prompt
- python generate.py --base_model='EleutherAI/gpt-neox-20b' --prompt_type='plain'
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq'
-
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='dai_faq' --lora_weights='lora_20B_daifaq'
- # OpenChatKit settings:
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0
-
- python generate.py --base_model='distilgpt2' --prompt_type='plain' --debug=True --num_beams=1 --temperature=0.6 --top_k=40 --top_p=1.0 --share=False
- python generate.py --base_model='t5-large' --prompt_type='simple_instruct'
- python generate.py --base_model='philschmid/bart-large-cnn-samsum'
- python generate.py --base_model='philschmid/flan-t5-base-samsum'
- python generate.py --base_model='facebook/mbart-large-50-many-to-many-mmt'
-
- python generate.py --base_model='togethercomputer/GPT-NeoXT-Chat-Base-20B' --prompt_type='human_bot' --lora_weights='GPT-NeoXT-Chat-Base-20B.merged.json.8_epochs.57b2892c53df5b8cefac45f84d019cace803ef26.28'
-
- must have 4*48GB GPU and run without 8bit in order for sharding to work with use_gpu_id=False
- can also pass --prompt_type='human_bot' and model can somewhat handle instructions without being instruct tuned
- python generate.py --base_model=decapoda-research/llama-65b-hf --load_8bit=False --use_gpu_id=False --prompt_type='human_bot'
-
- python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6_9b
- """
- H2O_Fire(main)
-
-
-if __name__ == "__main__":
- entrypoint_main()
diff --git a/spaces/awacke1/Emoji.Enumerator.Menu/README.md b/spaces/awacke1/Emoji.Enumerator.Menu/README.md
deleted file mode 100644
index 7b03a7201862b9330ef80f506f2e2558650ff8bc..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Emoji.Enumerator.Menu/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Emoji.Enumerator.Menu
-emoji: 💻
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/GradioAutoCSVLoaderToPlotly/app.py b/spaces/awacke1/GradioAutoCSVLoaderToPlotly/app.py
deleted file mode 100644
index 698e8054275c3bf39e0e0e8951d9772853b096ab..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioAutoCSVLoaderToPlotly/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-
-# Create a function to write the CSV file
-def write_csv(record_count, topic, intervention):
- df = pd.DataFrame({
- "RecordCount": [record_count],
- "Topic": [topic],
- "Intervention": [intervention]
- })
- df.to_csv("testfile.csv", index=False, mode='a')
-
-# Create a function to plot the data from the CSV file
-def plot_data():
- df = pd.read_csv("testfile.csv")
- fig = px.scatter(df, x="RecordCount", y="Intervention", color="Topic")
- return fig
-
-# Define the inputs for the Gradio interface
-inputs = [
- gr.inputs.Slider(label="Record Count", minimum=0, maximum=100, default=50),
- gr.inputs.Textbox(label="Topic"),
- gr.inputs.Textbox(label="Intervention")
-]
-
-# Define the outputs for the Gradio interface
-outputs = [
- gr.Plot(plot_data),
- gr.Textbox(label="Data written to testfile.csv")
-]
-
-# Create the Gradio interface
-interface = gr.Interface(write_csv, inputs, outputs, title="Record Plotter")
-
-# Launch the Gradio interface
-interface.launch()
diff --git a/spaces/awacke1/GradioBlocksChangeEvent/app.py b/spaces/awacke1/GradioBlocksChangeEvent/app.py
deleted file mode 100644
index 4d30548df6c7b2ce9557818f4fea466113e5bd7b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioBlocksChangeEvent/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import gradio as gr
-
-#def update( text, choice):
-# if (choice==“gpt2large“):
-# return f1(text)
- # return f"Welcome to Gradio, {name}!"
-
-generator1 = gr.Interface.load("huggingface/gpt2-large")
-generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B")
-generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-
-
-demo = gr.Blocks()
-
-def f1(x):
- return generator1(x)
-def f2(x):
- return generator2(x)
-def f3(x):
- return generator3(x)
-
-with demo:
- gr.Markdown(
- """
- # Hello World!
- Start typing below to see the output.
- """)
- inp = gr.Textbox(placeholder="Enter a statement to complete")
-
- out1 = gr.Textbox()
- out2 = gr.Textbox()
- out3 = gr.Textbox()
-
- inp.change(fn=f1,
- inputs=inp,
- outputs=out1)
- out1.change(fn=f2,
- inputs=inp,
- outputs=out2)
- out2.change(fn=f3,
- inputs=inp,
- outputs=out3)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/banana-projects/datasets-card-creator/src/Instructions.js b/spaces/banana-projects/datasets-card-creator/src/Instructions.js
deleted file mode 100644
index 4a06ad667ef7aacd2ec19d4af2a57fb34f57ee15..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/datasets-card-creator/src/Instructions.js
+++ /dev/null
@@ -1,317 +0,0 @@
-const NAME = 'Instructions'
-
-export default {
- name: NAME,
- instructions: {
- yamlTags: {
- paragraph: [
- "Add YAML tags"
- ],
- example: [
- "---",
- `annotations_creators:`,
- `- no-annotation`,
- `language_creators:`,
- `- found`,
- `languages:`,
- `- en`,
- `licenses:`,
- `- unknown`,
- `multilinguality:`,
- `- monolingual`,
- `size_categories:`,
- `- 100KChrysler international PAIS DVD 10 2008 [Multilang] [ISO] .rar
-
-Power CD+G Burner 1.4.6 .rar · Chrysler international PAIS DVD 10 2008 [Multilang] [ISO] download pc · Google Operating System ( Android Live no installation ... 1fdad05405
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Epic Mickey 2 The Power Of Two (SERE4Q) NTSC WII WBFS.md b/spaces/bioriAsaeru/text-to-voice/Epic Mickey 2 The Power Of Two (SERE4Q) NTSC WII WBFS.md
deleted file mode 100644
index 87549ef30090e2dafc0a345ac374db7ab95bb84f..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Epic Mickey 2 The Power Of Two (SERE4Q) NTSC WII WBFS.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Epic Mickey 2: The Power Of Two (SERE4Q) NTSC WII WBFS
-
-Disney Epic Mickey 2: The Power of Two regresa a Mickey Mouse y Oswald the ... Funciona: Wii, Wii U y Emulador Dolphin ... Formato: WBFS. 4d29de3e1b
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Kp Prashna Kundali Software In Marathi Get Your Free KP Horoscope with Ruling Planets and Significators.md b/spaces/bioriAsaeru/text-to-voice/Kp Prashna Kundali Software In Marathi Get Your Free KP Horoscope with Ruling Planets and Significators.md
deleted file mode 100644
index 418ecbe8b904f2ab335a8c72a864d895b0e85e44..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Kp Prashna Kundali Software In Marathi Get Your Free KP Horoscope with Ruling Planets and Significators.md
+++ /dev/null
@@ -1,13 +0,0 @@
-
-Kp Prashna Kundali Software In Marathi
-
The report provided by this kundali software includes multiple observations based on the birthday and other astrological elements that might influence the Grahas of both the bride and groom.
Excellent features make this kundali software the first choice of Astrologers who love to move on with time. Horoscope matching helps highlight incompatible factors between a couple, so that they can take remedial steps to stay stronger.
ClickAstro generates the Janam Kundli and also studies them. This popularkundali software then creates an analysis report with the help of which theuser can understand the attributes which will influence the married life of thecouple.
-
-
\ No newline at end of file
diff --git a/spaces/brainblow/AI-TV/README.md b/spaces/brainblow/AI-TV/README.md
deleted file mode 100644
index be4c302a555988c8ce48f217cba4e49bfff422f1..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AI-TV/README.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-title: BrainBlow AI-TV
-emoji: 🤯🤖📺
-colorFrom: green
-colorTo: green
-sdk: docker
-pinned: false
-app_port: 7860
-duplicated_from: TNR-5/AI-WebTV
----
-
-A generative AI WebTV, powered by Zeroscope and Hugging Face.
-
-This is just the frontend part, you will need the media-server (also open source) to make it work.
-
-Warning: this is an experimental, proof-of-concept project made in a few days.
-
-It is not ready for production use by other people! Also, this use models that should only be used for research purposes (no commercial usage).
-
-Note: because the stream uses FLV, it doesn't work on iPhone. There is however a [Twitch mirror here](https://www.twitch.tv/ai_webtv).
-
-The main code of the webtv is located inside the [media-server](https://huggingface.co/spaces/jbilcke-hf/media-server/tree/main) :
-
-manual steps:
-- human input to write a short paragraph describing a multi-shot video sequence
-- manual submit it to GPT-4 to generate a list of video captions for each shot (the system instructions are extracts from a stable diffusion guide)
-- commit the captions to the [playlist database](https://huggingface.co/spaces/jbilcke-hf/media-server/raw/main/database.json)
-
-Inside the `media-server` space (generation process running in the background):
-- for each prompt in the database
-- generate a silent 3 seconds video clip with Zeroscope V2 576w (hosted on Hugging Face Spaces)
-- upscale the clip with Zeroscope V2 XL (also a HF Space)
-- perform frame interpolation with FILM (also a HF Space)
-- storage in the Persistent Storage of the media-server Space
-
-Inside the `media-server` space (streaming process running in the foreground):
-- for each video file in the persistent storage folder
-- add it to a new FFmpeg playlist (it's just a .txt file)
-- broadcast it over the RTMP protocol using FFmpeg (in FLV format)
-- diffusion of the stream using node-media-server
-
-Inside the `AI-WebTV` space:
-- display the stream using `mpegts.js`
-- this doesn't work on iPhone, but now there is also a Twitch mirror
\ No newline at end of file
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/sound_dataset.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/sound_dataset.py
deleted file mode 100644
index 8b88cbe8016b4bd28c2de749177c9af29f7755fc..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/data/sound_dataset.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Dataset of audio with a simple description.
-"""
-
-from dataclasses import dataclass, fields, replace
-import json
-from pathlib import Path
-import random
-import typing as tp
-
-import numpy as np
-import torch
-
-from .info_audio_dataset import (
- InfoAudioDataset,
- get_keyword_or_keyword_list
-)
-from ..modules.conditioners import (
- ConditioningAttributes,
- SegmentWithAttributes,
- WavCondition,
-)
-
-
-EPS = torch.finfo(torch.float32).eps
-TARGET_LEVEL_LOWER = -35
-TARGET_LEVEL_UPPER = -15
-
-
-@dataclass
-class SoundInfo(SegmentWithAttributes):
- """Segment info augmented with Sound metadata.
- """
- description: tp.Optional[str] = None
- self_wav: tp.Optional[torch.Tensor] = None
-
- @property
- def has_sound_meta(self) -> bool:
- return self.description is not None
-
- def to_condition_attributes(self) -> ConditioningAttributes:
- out = ConditioningAttributes()
-
- for _field in fields(self):
- key, value = _field.name, getattr(self, _field.name)
- if key == 'self_wav':
- out.wav[key] = value
- else:
- out.text[key] = value
- return out
-
- @staticmethod
- def attribute_getter(attribute):
- if attribute == 'description':
- preprocess_func = get_keyword_or_keyword_list
- else:
- preprocess_func = None
- return preprocess_func
-
- @classmethod
- def from_dict(cls, dictionary: dict, fields_required: bool = False):
- _dictionary: tp.Dict[str, tp.Any] = {}
-
- # allow a subset of attributes to not be loaded from the dictionary
- # these attributes may be populated later
- post_init_attributes = ['self_wav']
-
- for _field in fields(cls):
- if _field.name in post_init_attributes:
- continue
- elif _field.name not in dictionary:
- if fields_required:
- raise KeyError(f"Unexpected missing key: {_field.name}")
- else:
- preprocess_func: tp.Optional[tp.Callable] = cls.attribute_getter(_field.name)
- value = dictionary[_field.name]
- if preprocess_func:
- value = preprocess_func(value)
- _dictionary[_field.name] = value
- return cls(**_dictionary)
-
-
-class SoundDataset(InfoAudioDataset):
- """Sound audio dataset: Audio dataset with environmental sound-specific metadata.
-
- Args:
- info_fields_required (bool): Whether all the mandatory metadata fields should be in the loaded metadata.
- external_metadata_source (tp.Optional[str]): Folder containing JSON metadata for the corresponding dataset.
- The metadata files contained in this folder are expected to match the stem of the audio file with
- a json extension.
- aug_p (float): Probability of performing audio mixing augmentation on the batch.
- mix_p (float): Proportion of batch items that are mixed together when applying audio mixing augmentation.
- mix_snr_low (int): Lowerbound for SNR value sampled for mixing augmentation.
- mix_snr_high (int): Upperbound for SNR value sampled for mixing augmentation.
- mix_min_overlap (float): Minimum overlap between audio files when performing mixing augmentation.
- kwargs: Additional arguments for AudioDataset.
-
- See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
- """
- def __init__(
- self,
- *args,
- info_fields_required: bool = True,
- external_metadata_source: tp.Optional[str] = None,
- aug_p: float = 0.,
- mix_p: float = 0.,
- mix_snr_low: int = -5,
- mix_snr_high: int = 5,
- mix_min_overlap: float = 0.5,
- **kwargs
- ):
- kwargs['return_info'] = True # We require the info for each song of the dataset.
- super().__init__(*args, **kwargs)
- self.info_fields_required = info_fields_required
- self.external_metadata_source = external_metadata_source
- self.aug_p = aug_p
- self.mix_p = mix_p
- if self.aug_p > 0:
- assert self.mix_p > 0, "Expecting some mixing proportion mix_p if aug_p > 0"
- assert self.channels == 1, "SoundDataset with audio mixing considers only monophonic audio"
- self.mix_snr_low = mix_snr_low
- self.mix_snr_high = mix_snr_high
- self.mix_min_overlap = mix_min_overlap
-
- def _get_info_path(self, path: tp.Union[str, Path]) -> Path:
- """Get path of JSON with metadata (description, etc.).
- If there exists a JSON with the same name as 'path.name', then it will be used.
- Else, such JSON will be searched for in an external json source folder if it exists.
- """
- info_path = Path(path).with_suffix('.json')
- if Path(info_path).exists():
- return info_path
- elif self.external_metadata_source and (Path(self.external_metadata_source) / info_path.name).exists():
- return Path(self.external_metadata_source) / info_path.name
- else:
- raise Exception(f"Unable to find a metadata JSON for path: {path}")
-
- def __getitem__(self, index):
- wav, info = super().__getitem__(index)
- info_data = info.to_dict()
- info_path = self._get_info_path(info.meta.path)
- if Path(info_path).exists():
- with open(info_path, 'r') as json_file:
- sound_data = json.load(json_file)
- sound_data.update(info_data)
- sound_info = SoundInfo.from_dict(sound_data, fields_required=self.info_fields_required)
- # if there are multiple descriptions, sample one randomly
- if isinstance(sound_info.description, list):
- sound_info.description = random.choice(sound_info.description)
- else:
- sound_info = SoundInfo.from_dict(info_data, fields_required=False)
-
- sound_info.self_wav = WavCondition(
- wav=wav[None], length=torch.tensor([info.n_frames]),
- sample_rate=[sound_info.sample_rate], path=[info.meta.path], seek_time=[info.seek_time])
-
- return wav, sound_info
-
- def collater(self, samples):
- # when training, audio mixing is performed in the collate function
- wav, sound_info = super().collater(samples) # SoundDataset always returns infos
- if self.aug_p > 0:
- wav, sound_info = mix_samples(wav, sound_info, self.aug_p, self.mix_p,
- snr_low=self.mix_snr_low, snr_high=self.mix_snr_high,
- min_overlap=self.mix_min_overlap)
- return wav, sound_info
-
-
-def rms_f(x: torch.Tensor) -> torch.Tensor:
- return (x ** 2).mean(1).pow(0.5)
-
-
-def normalize(audio: torch.Tensor, target_level: int = -25) -> torch.Tensor:
- """Normalize the signal to the target level."""
- rms = rms_f(audio)
- scalar = 10 ** (target_level / 20) / (rms + EPS)
- audio = audio * scalar.unsqueeze(1)
- return audio
-
-
-def is_clipped(audio: torch.Tensor, clipping_threshold: float = 0.99) -> torch.Tensor:
- return (abs(audio) > clipping_threshold).any(1)
-
-
-def mix_pair(src: torch.Tensor, dst: torch.Tensor, min_overlap: float) -> torch.Tensor:
- start = random.randint(0, int(src.shape[1] * (1 - min_overlap)))
- remainder = src.shape[1] - start
- if dst.shape[1] > remainder:
- src[:, start:] = src[:, start:] + dst[:, :remainder]
- else:
- src[:, start:start+dst.shape[1]] = src[:, start:start+dst.shape[1]] + dst
- return src
-
-
-def snr_mixer(clean: torch.Tensor, noise: torch.Tensor, snr: int, min_overlap: float,
- target_level: int = -25, clipping_threshold: float = 0.99) -> torch.Tensor:
- """Function to mix clean speech and noise at various SNR levels.
-
- Args:
- clean (torch.Tensor): Clean audio source to mix, of shape [B, T].
- noise (torch.Tensor): Noise audio source to mix, of shape [B, T].
- snr (int): SNR level when mixing.
- min_overlap (float): Minimum overlap between the two mixed sources.
- target_level (int): Gain level in dB.
- clipping_threshold (float): Threshold for clipping the audio.
- Returns:
- torch.Tensor: The mixed audio, of shape [B, T].
- """
- if clean.shape[1] > noise.shape[1]:
- noise = torch.nn.functional.pad(noise, (0, clean.shape[1] - noise.shape[1]))
- else:
- noise = noise[:, :clean.shape[1]]
-
- # normalizing to -25 dB FS
- clean = clean / (clean.max(1)[0].abs().unsqueeze(1) + EPS)
- clean = normalize(clean, target_level)
- rmsclean = rms_f(clean)
-
- noise = noise / (noise.max(1)[0].abs().unsqueeze(1) + EPS)
- noise = normalize(noise, target_level)
- rmsnoise = rms_f(noise)
-
- # set the noise level for a given SNR
- noisescalar = (rmsclean / (10 ** (snr / 20)) / (rmsnoise + EPS)).unsqueeze(1)
- noisenewlevel = noise * noisescalar
-
- # mix noise and clean speech
- noisyspeech = mix_pair(clean, noisenewlevel, min_overlap)
-
- # randomly select RMS value between -15 dBFS and -35 dBFS and normalize noisyspeech with that value
- # there is a chance of clipping that might happen with very less probability, which is not a major issue.
- noisy_rms_level = np.random.randint(TARGET_LEVEL_LOWER, TARGET_LEVEL_UPPER)
- rmsnoisy = rms_f(noisyspeech)
- scalarnoisy = (10 ** (noisy_rms_level / 20) / (rmsnoisy + EPS)).unsqueeze(1)
- noisyspeech = noisyspeech * scalarnoisy
- clean = clean * scalarnoisy
- noisenewlevel = noisenewlevel * scalarnoisy
-
- # final check to see if there are any amplitudes exceeding +/- 1. If so, normalize all the signals accordingly
- clipped = is_clipped(noisyspeech)
- if clipped.any():
- noisyspeech_maxamplevel = noisyspeech[clipped].max(1)[0].abs().unsqueeze(1) / (clipping_threshold - EPS)
- noisyspeech[clipped] = noisyspeech[clipped] / noisyspeech_maxamplevel
-
- return noisyspeech
-
-
-def snr_mix(src: torch.Tensor, dst: torch.Tensor, snr_low: int, snr_high: int, min_overlap: float):
- if snr_low == snr_high:
- snr = snr_low
- else:
- snr = np.random.randint(snr_low, snr_high)
- mix = snr_mixer(src, dst, snr, min_overlap)
- return mix
-
-
-def mix_text(src_text: str, dst_text: str):
- """Mix text from different sources by concatenating them."""
- if src_text == dst_text:
- return src_text
- return src_text + " " + dst_text
-
-
-def mix_samples(wavs: torch.Tensor, infos: tp.List[SoundInfo], aug_p: float, mix_p: float,
- snr_low: int, snr_high: int, min_overlap: float):
- """Mix samples within a batch, summing the waveforms and concatenating the text infos.
-
- Args:
- wavs (torch.Tensor): Audio tensors of shape [B, C, T].
- infos (list[SoundInfo]): List of SoundInfo items corresponding to the audio.
- aug_p (float): Augmentation probability.
- mix_p (float): Proportion of items in the batch to mix (and merge) together.
- snr_low (int): Lowerbound for sampling SNR.
- snr_high (int): Upperbound for sampling SNR.
- min_overlap (float): Minimum overlap between mixed samples.
- Returns:
- tuple[torch.Tensor, list[SoundInfo]]: A tuple containing the mixed wavs
- and mixed SoundInfo for the given batch.
- """
- # no mixing to perform within the batch
- if mix_p == 0:
- return wavs, infos
-
- if random.uniform(0, 1) < aug_p:
- # perform all augmentations on waveforms as [B, T]
- # randomly picking pairs of audio to mix
- assert wavs.size(1) == 1, f"Mix samples requires monophonic audio but C={wavs.size(1)}"
- wavs = wavs.mean(dim=1, keepdim=False)
- B, T = wavs.shape
- k = int(mix_p * B)
- mixed_sources_idx = torch.randperm(B)[:k]
- mixed_targets_idx = torch.randperm(B)[:k]
- aug_wavs = snr_mix(
- wavs[mixed_sources_idx],
- wavs[mixed_targets_idx],
- snr_low,
- snr_high,
- min_overlap,
- )
- # mixing textual descriptions in metadata
- descriptions = [info.description for info in infos]
- aug_infos = []
- for i, j in zip(mixed_sources_idx, mixed_targets_idx):
- text = mix_text(descriptions[i], descriptions[j])
- m = replace(infos[i])
- m.description = text
- aug_infos.append(m)
-
- # back to [B, C, T]
- aug_wavs = aug_wavs.unsqueeze(1)
- assert aug_wavs.shape[0] > 0, "Samples mixing returned empty batch."
- assert aug_wavs.dim() == 3, f"Returned wav should be [B, C, T] but dim = {aug_wavs.dim()}"
- assert aug_wavs.shape[0] == len(aug_infos), "Mismatch between number of wavs and infos in the batch"
-
- return aug_wavs, aug_infos # [B, C, T]
- else:
- # randomly pick samples in the batch to match
- # the batch size when performing audio mixing
- B, C, T = wavs.shape
- k = int(mix_p * B)
- wav_idx = torch.randperm(B)[:k]
- wavs = wavs[wav_idx]
- infos = [infos[i] for i in wav_idx]
- assert wavs.shape[0] == len(infos), "Mismatch between number of wavs and infos in the batch"
-
- return wavs, infos # [B, C, T]
diff --git a/spaces/breezedeus/pix2text/README.md b/spaces/breezedeus/pix2text/README.md
deleted file mode 100644
index 4a3390ce255888ad000f26b424f18f316d9dbf14..0000000000000000000000000000000000000000
--- a/spaces/breezedeus/pix2text/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pix2Text
-emoji: 🅿❷🆃
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bright1/Sepsis-Prediction-API/Dockerfile b/spaces/bright1/Sepsis-Prediction-API/Dockerfile
deleted file mode 100644
index ca16c8ca726b6a0336cacc0f2cfffbb0520b5d02..0000000000000000000000000000000000000000
--- a/spaces/bright1/Sepsis-Prediction-API/Dockerfile
+++ /dev/null
@@ -1,17 +0,0 @@
-#
-FROM python:3.9
-
-#
-WORKDIR /code
-
-#
-COPY ./requirements.txt /code/requirements.txt
-
-#
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-#
-COPY ./src /code/src
-
-#
-CMD ["uvicorn", "src.app.app:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/texture.py b/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/texture.py
deleted file mode 100644
index 477759729d7b995a4f276e81d649617d045a066e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/pyrender/pyrender/texture.py
+++ /dev/null
@@ -1,259 +0,0 @@
-"""Textures, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-texture
-
-Author: Matthew Matl
-"""
-import numpy as np
-
-from OpenGL.GL import *
-
-from .utils import format_texture_source
-from .sampler import Sampler
-
-
-class Texture(object):
- """A texture and its sampler.
-
- Parameters
- ----------
- name : str, optional
- The user-defined name of this object.
- sampler : :class:`Sampler`
- The sampler used by this texture.
- source : (h,w,c) uint8 or (h,w,c) float or :class:`PIL.Image.Image`
- The image used by this texture. If None, the texture is created
- empty and width and height must be specified.
- source_channels : str
- Either `D`, `R`, `RG`, `GB`, `RGB`, or `RGBA`. Indicates the
- channels to extract from `source`. Any missing channels will be filled
- with `1.0`.
- width : int, optional
- For empty textures, the width of the texture buffer.
- height : int, optional
- For empty textures, the height of the texture buffer.
- tex_type : int
- Either GL_TEXTURE_2D or GL_TEXTURE_CUBE.
- data_format : int
- For now, just GL_FLOAT.
- """
-
- def __init__(self,
- name=None,
- sampler=None,
- source=None,
- source_channels=None,
- width=None,
- height=None,
- tex_type=GL_TEXTURE_2D,
- data_format=GL_UNSIGNED_BYTE):
- self.source_channels = source_channels
- self.name = name
- self.sampler = sampler
- self.source = source
- self.width = width
- self.height = height
- self.tex_type = tex_type
- self.data_format = data_format
-
- self._texid = None
- self._is_transparent = False
-
- @property
- def name(self):
- """str : The user-defined name of this object.
- """
- return self._name
-
- @name.setter
- def name(self, value):
- if value is not None:
- value = str(value)
- self._name = value
-
- @property
- def sampler(self):
- """:class:`Sampler` : The sampler used by this texture.
- """
- return self._sampler
-
- @sampler.setter
- def sampler(self, value):
- if value is None:
- value = Sampler()
- self._sampler = value
-
- @property
- def source(self):
- """(h,w,c) uint8 or float or :class:`PIL.Image.Image` : The image
- used in this texture.
- """
- return self._source
-
- @source.setter
- def source(self, value):
- if value is None:
- self._source = None
- else:
- self._source = format_texture_source(value, self.source_channels)
- self._is_transparent = False
-
- @property
- def source_channels(self):
- """str : The channels that were extracted from the original source.
- """
- return self._source_channels
-
- @source_channels.setter
- def source_channels(self, value):
- self._source_channels = value
-
- @property
- def width(self):
- """int : The width of the texture buffer.
- """
- return self._width
-
- @width.setter
- def width(self, value):
- self._width = value
-
- @property
- def height(self):
- """int : The height of the texture buffer.
- """
- return self._height
-
- @height.setter
- def height(self, value):
- self._height = value
-
- @property
- def tex_type(self):
- """int : The type of the texture.
- """
- return self._tex_type
-
- @tex_type.setter
- def tex_type(self, value):
- self._tex_type = value
-
- @property
- def data_format(self):
- """int : The format of the texture data.
- """
- return self._data_format
-
- @data_format.setter
- def data_format(self, value):
- self._data_format = value
-
- def is_transparent(self, cutoff=1.0):
- """bool : If True, the texture is partially transparent.
- """
- if self._is_transparent is None:
- self._is_transparent = False
- if self.source_channels == 'RGBA' and self.source is not None:
- if np.any(self.source[:,:,3] < cutoff):
- self._is_transparent = True
- return self._is_transparent
-
- def delete(self):
- """Remove this texture from the OpenGL context.
- """
- self._unbind()
- self._remove_from_context()
-
- ##################
- # OpenGL code
- ##################
- def _add_to_context(self):
- if self._texid is not None:
- raise ValueError('Texture already loaded into OpenGL context')
-
- fmt = GL_DEPTH_COMPONENT
- if self.source_channels == 'R':
- fmt = GL_RED
- elif self.source_channels == 'RG' or self.source_channels == 'GB':
- fmt = GL_RG
- elif self.source_channels == 'RGB':
- fmt = GL_RGB
- elif self.source_channels == 'RGBA':
- fmt = GL_RGBA
-
- # Generate the OpenGL texture
- self._texid = glGenTextures(1)
- glBindTexture(self.tex_type, self._texid)
-
- # Flip data for OpenGL buffer
- data = None
- width = self.width
- height = self.height
- if self.source is not None:
- data = np.ascontiguousarray(np.flip(self.source, axis=0).flatten())
- width = self.source.shape[1]
- height = self.source.shape[0]
-
- # Bind texture and generate mipmaps
- glTexImage2D(
- self.tex_type, 0, fmt, width, height, 0, fmt,
- self.data_format, data
- )
- if self.source is not None:
- glGenerateMipmap(self.tex_type)
-
- if self.sampler.magFilter is not None:
- glTexParameteri(
- self.tex_type, GL_TEXTURE_MAG_FILTER, self.sampler.magFilter
- )
- else:
- if self.source is not None:
- glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
- else:
- glTexParameteri(self.tex_type, GL_TEXTURE_MAG_FILTER, GL_NEAREST)
- if self.sampler.minFilter is not None:
- glTexParameteri(
- self.tex_type, GL_TEXTURE_MIN_FILTER, self.sampler.minFilter
- )
- else:
- if self.source is not None:
- glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR)
- else:
- glTexParameteri(self.tex_type, GL_TEXTURE_MIN_FILTER, GL_NEAREST)
-
- glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_S, self.sampler.wrapS)
- glTexParameteri(self.tex_type, GL_TEXTURE_WRAP_T, self.sampler.wrapT)
- border_color = 255 * np.ones(4).astype(np.uint8)
- if self.data_format == GL_FLOAT:
- border_color = np.ones(4).astype(np.float32)
- glTexParameterfv(
- self.tex_type, GL_TEXTURE_BORDER_COLOR,
- border_color
- )
-
- # Unbind texture
- glBindTexture(self.tex_type, 0)
-
- def _remove_from_context(self):
- if self._texid is not None:
- # TODO OPENGL BUG?
- # glDeleteTextures(1, [self._texid])
- glDeleteTextures([self._texid])
- self._texid = None
-
- def _in_context(self):
- return self._texid is not None
-
- def _bind(self):
- # TODO HANDLE INDEXING INTO OTHER UV's
- glBindTexture(self.tex_type, self._texid)
-
- def _unbind(self):
- glBindTexture(self.tex_type, 0)
-
- def _bind_as_depth_attachment(self):
- glFramebufferTexture2D(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
- self.tex_type, self._texid, 0)
-
- def _bind_as_color_attachment(self):
- glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
- self.tex_type, self._texid, 0)
diff --git a/spaces/cahodk/live-ml5-facemesh-p5js/README.md b/spaces/cahodk/live-ml5-facemesh-p5js/README.md
deleted file mode 100644
index 842aae2c03b2fd21e3f2b0c157ad79611ce2aca0..0000000000000000000000000000000000000000
--- a/spaces/cahodk/live-ml5-facemesh-p5js/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Live Ml5 Facemesh P5js
-emoji: 🌍
-colorFrom: green
-colorTo: indigo
-sdk: static
-pinned: false
-license: lgpl-2.1
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GdImageFile.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/cccc-c/web-ui-pub/_next/static/chunks/780.aecf08b05b0b9d76.js b/spaces/cccc-c/web-ui-pub/_next/static/chunks/780.aecf08b05b0b9d76.js
deleted file mode 100644
index 54b69b4cb6454762d71ced8d20e2a5e11937716b..0000000000000000000000000000000000000000
--- a/spaces/cccc-c/web-ui-pub/_next/static/chunks/780.aecf08b05b0b9d76.js
+++ /dev/null
@@ -1,260 +0,0 @@
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