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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Bongiovi Acoustics DPS Audio Enhancer 2.2.0.15 Activate HOT.md b/spaces/1gistliPinn/ChatGPT4/Examples/Bongiovi Acoustics DPS Audio Enhancer 2.2.0.15 Activate HOT.md deleted file mode 100644 index 8614294a9328e0faa7f78fc1c130f8500649aeee..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Bongiovi Acoustics DPS Audio Enhancer 2.2.0.15 Activate HOT.md +++ /dev/null @@ -1,9 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/1line/AutoGPT/autogpt/json_utils/json_fix_llm.py b/spaces/1line/AutoGPT/autogpt/json_utils/json_fix_llm.py deleted file mode 100644 index 869aed125cfb8cd7a69ed02eeb389cc72a3e296b..0000000000000000000000000000000000000000 --- a/spaces/1line/AutoGPT/autogpt/json_utils/json_fix_llm.py +++ /dev/null @@ -1,220 +0,0 @@ -"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance -of the ChatGPT API or LLM models.""" -from __future__ import annotations - -import contextlib -import json -from typing import Any, Dict - -from colorama import Fore -from regex import regex - -from autogpt.config import Config -from autogpt.json_utils.json_fix_general import correct_json -from autogpt.llm_utils import call_ai_function -from autogpt.logs import logger -from autogpt.speech import say_text - -JSON_SCHEMA = """ -{ - "command": { - "name": "command name", - "args": { - "arg name": "value" - } - }, - "thoughts": - { - "text": "thought", - "reasoning": "reasoning", - "plan": "- short bulleted\n- list that conveys\n- long-term plan", - "criticism": "constructive self-criticism", - "speak": "thoughts summary to say to user" - } -} -""" - -CFG = Config() - - -def auto_fix_json(json_string: str, schema: str) -> str: - """Fix the given JSON string to make it parseable and fully compliant with - the provided schema using GPT-3. - - Args: - json_string (str): The JSON string to fix. - schema (str): The schema to use to fix the JSON. - Returns: - str: The fixed JSON string. - """ - # Try to fix the JSON using GPT: - function_string = "def fix_json(json_string: str, schema:str=None) -> str:" - args = [f"'''{json_string}'''", f"'''{schema}'''"] - description_string = ( - "This function takes a JSON string and ensures that it" - " is parseable and fully compliant with the provided schema. If an object" - " or field specified in the schema isn't contained within the correct JSON," - " it is omitted. The function also escapes any double quotes within JSON" - " string values to ensure that they are valid. If the JSON string contains" - " any None or NaN values, they are replaced with null before being parsed." - ) - - # If it doesn't already start with a "`", add one: - if not json_string.startswith("`"): - json_string = "```json\n" + json_string + "\n```" - result_string = call_ai_function( - function_string, args, description_string, model=CFG.fast_llm_model - ) - logger.debug("------------ JSON FIX ATTEMPT ---------------") - logger.debug(f"Original JSON: {json_string}") - logger.debug("-----------") - logger.debug(f"Fixed JSON: {result_string}") - logger.debug("----------- END OF FIX ATTEMPT ----------------") - - try: - json.loads(result_string) # just check the validity - return result_string - except json.JSONDecodeError: # noqa: E722 - # Get the call stack: - # import traceback - # call_stack = traceback.format_exc() - # print(f"Failed to fix JSON: '{json_string}' "+call_stack) - return "failed" - - -def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]: - """Fix the given JSON string to make it parseable and fully compliant with two techniques. - - Args: - json_string (str): The JSON string to fix. - - Returns: - str: The fixed JSON string. - """ - - # Parse and print Assistant response - assistant_reply_json = fix_and_parse_json(assistant_reply) - if assistant_reply_json == {}: - assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets( - assistant_reply - ) - - if assistant_reply_json != {}: - return assistant_reply_json - - logger.error( - "Error: The following AI output couldn't be converted to a JSON:\n", - assistant_reply, - ) - if CFG.speak_mode: - say_text("I have received an invalid JSON response from the OpenAI API.") - - return {} - - -def fix_and_parse_json( - json_to_load: str, try_to_fix_with_gpt: bool = True -) -> Dict[Any, Any]: - """Fix and parse JSON string - - Args: - json_to_load (str): The JSON string. - try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT. - Defaults to True. - - Returns: - str or dict[Any, Any]: The parsed JSON. - """ - - with contextlib.suppress(json.JSONDecodeError): - json_to_load = json_to_load.replace("\t", "") - return json.loads(json_to_load) - - with contextlib.suppress(json.JSONDecodeError): - json_to_load = correct_json(json_to_load) - return json.loads(json_to_load) - # Let's do something manually: - # sometimes GPT responds with something BEFORE the braces: - # "I'm sorry, I don't understand. Please try again." - # {"text": "I'm sorry, I don't understand. Please try again.", - # "confidence": 0.0} - # So let's try to find the first brace and then parse the rest - # of the string - try: - brace_index = json_to_load.index("{") - maybe_fixed_json = json_to_load[brace_index:] - last_brace_index = maybe_fixed_json.rindex("}") - maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1] - return json.loads(maybe_fixed_json) - except (json.JSONDecodeError, ValueError) as e: - return try_ai_fix(try_to_fix_with_gpt, e, json_to_load) - - -def try_ai_fix( - try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str -) -> Dict[Any, Any]: - """Try to fix the JSON with the AI - - Args: - try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI. - exception (Exception): The exception that was raised. - json_to_load (str): The JSON string to load. - - Raises: - exception: If try_to_fix_with_gpt is False. - - Returns: - str or dict[Any, Any]: The JSON string or dictionary. - """ - if not try_to_fix_with_gpt: - raise exception - if CFG.debug_mode: - logger.warn( - "Warning: Failed to parse AI output, attempting to fix." - "\n If you see this warning frequently, it's likely that" - " your prompt is confusing the AI. Try changing it up" - " slightly." - ) - # Now try to fix this up using the ai_functions - ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA) - - if ai_fixed_json != "failed": - return json.loads(ai_fixed_json) - # This allows the AI to react to the error message, - # which usually results in it correcting its ways. - # logger.error("Failed to fix AI output, telling the AI.") - return {} - - -def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str): - if CFG.speak_mode and CFG.debug_mode: - say_text( - "I have received an invalid JSON response from the OpenAI API. " - "Trying to fix it now." - ) - logger.error("Attempting to fix JSON by finding outermost brackets\n") - - try: - json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}") - json_match = json_pattern.search(json_string) - - if json_match: - # Extract the valid JSON object from the string - json_string = json_match.group(0) - logger.typewriter_log( - title="Apparently json was fixed.", title_color=Fore.GREEN - ) - if CFG.speak_mode and CFG.debug_mode: - say_text("Apparently json was fixed.") - else: - return {} - - except (json.JSONDecodeError, ValueError): - if CFG.debug_mode: - logger.error(f"Error: Invalid JSON: {json_string}\n") - if CFG.speak_mode: - say_text("Didn't work. I will have to ignore this response then.") - logger.error("Error: Invalid JSON, setting it to empty JSON now.\n") - json_string = {} - - return fix_and_parse_json(json_string) diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Challenge Your Friends and the World with Quiz Planet APK for Android.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Challenge Your Friends and the World with Quiz Planet APK for Android.md deleted file mode 100644 index 04f774db88776594d8967ba0b39ae14c13a7221e..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Challenge Your Friends and the World with Quiz Planet APK for Android.md +++ /dev/null @@ -1,133 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Design Your Own 3D Worlds with 3DBear App for Mobile Devices.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Design Your Own 3D Worlds with 3DBear App for Mobile Devices.md deleted file mode 100644 index 7ed0caf8de3f0c05e4ac709ee8ab6bbaf90bb0c4..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Design Your Own 3D Worlds with 3DBear App for Mobile Devices.md +++ /dev/null @@ -1,95 +0,0 @@ - -
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- FAQs Q: What devices are compatible with 3D Bear app A: 3D Bear app is compatible with iOS devices that support ARKit and Android devices that support ARCore. You can check the list of compatible devices here for iOS and here for Android. Q: How can I import my own 3D models and content into 3D Bear app? A: You can import your own 3D models and content into 3D Bear app by using the import button at the bottom left corner of the screen. You can then choose to import from your device, from the web, or from Google Poly. You can also scan real objects using the scan button and turn them into 3D models. Q: How can I collaborate with other users on 3D Bear app? A: You can collaborate with other users on 3D Bear app by joining or creating a group project. You can find group projects on the 3DBear community platform or create your own by inviting other users. You can then work together on a common theme or challenge and share your AR scenes with each other. Q: How can I earn points and unlock more features on 3D Bear app? A: You can earn points and unlock more features on 3D Bear app by completing lesson plans, challenges, and group projects. You can also earn points by sharing your AR videos, giving feedback to other users, and inviting your friends to join the app. You can use your points to unlock more 3D models, collections, and features. Q: How can I contact the 3DBear team for support or feedback? A: You can contact the 3DBear team for support or feedback by using the contact form on their website or by sending an email to support@3dbear.io. You can also follow them on social media platforms, such as Facebook, Twitter, Instagram, and YouTube, to get the latest updates and news. 197e85843d-
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-If you are a fan of Battlegrounds Mobile India (BGMI), you must be eagerly waiting for the latest update of the game. The BGMI 2.2 update is finally here and it brings a lot of new features, improvements, and surprises for the players. In this article, we will tell you everything you need to know about the BGMI 2.2 update, including what's new, how to download it, and some tips to enjoy it better.
-What is BGMI?
-BGMI is a popular battle royale game that was launched in India in July 2021 by Krafton, the developer of PUBG Mobile. The game is similar to PUBG Mobile, but with some changes and adaptations to suit the Indian market and regulations. BGMI allows you to play solo, duo, or squad matches with up to 100 players on various maps and modes. You can also customize your character, weapons, vehicles, and accessories with a variety of skins and outfits. The game is free to play, but you can also purchase in-game currency and items with real money.
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What's new in BGMI 2.2 update?
-The BGMI 2.2 update is one of the biggest updates of the game so far. It introduces a lot of new content and features that will make your gaming experience more exciting and fun. Here are some of the major highlights of the update:
-New map: NUSA
-NUSA is a new map that is exclusive to BGMI. It is based on Indonesia and features a tropical island with diverse landscapes, such as beaches, forests, villages, temples, and volcanoes. The map is 8x8 km in size and supports up to 64 players. NUSA also has some unique elements, such as hot air balloons, zip lines, waterfalls, and lava flows. You can explore the map and find hidden secrets and treasures along the way.
-New weapon: Tactical Crossbow
-The Tactical Crossbow is a new weapon that is available on NUSA map only. It is a silent and deadly weapon that can fire three types of bolts: explosive, electric, and tracking. The explosive bolt can deal damage to enemies and vehicles within a small radius. The electric bolt can stun enemies and disable vehicles for a short time. The tracking bolt can mark enemies on the mini-map for your teammates to see. The Tactical Crossbow has a limited range and ammo capacity, so use it wisely.
-New voice pack: The Voice of Battlegrounds
-The Voice of Battlegrounds is a new voice pack that you can use to communicate with your teammates and enemies in the game. It features the voice of Jonathan Frakes, a famous actor and director who is best known for his role as Commander Riker in Star Trek: The Next Generation. The voice pack has over 200 lines of dialogue that cover various situations and scenarios in the game. You can also customize the voice pack with different accents, tones, and languages.
-New modes: Payload 2.0 and Infection Mode
-Payload 2.0 is a new mode that is available on Erangel map only. It is an upgraded version of the original Payload mode that was introduced in PUBG Mobile. In this mode, you can use various heavy weapons and vehicles, such as rocket launchers, grenade launchers, helicopters, and armored trucks. You can also revive your teammates with a special device and call for air drops and airstrikes. The mode is fast-paced and action-packed, so be ready for some intense battles. Infection Mode is a new mode that is available on NUSA map only. It is a zombie-themed mode that pits two teams of players against each other: defenders and zombies. The defenders have to survive until the end of the match, while the zombies have to infect all the defenders. The zombies have different abilities and classes, such as speedsters, stealthers, and brutes. The defenders have limited weapons and ammo, but they can use traps and barricades to protect themselves. The mode is thrilling and suspenseful, so be careful of the lurking zombies.
New events: Halloween and Diwali celebrations
-The BGMI 2.2 update also brings some festive events to celebrate Halloween and Diwali. You can participate in these events and earn rewards, such as skins, outfits, emotes, and vouchers. You can also enjoy some special features, such as pumpkin lanterns, firecrackers, and fireworks. The events will run from October 31 to November 14, so don't miss this chance to have some fun and show your spirit.
-New skins, outfits, and accessories
-Of course, no update is complete without some new skins, outfits, and accessories to spice up your look. The BGMI 2.2 update offers a variety of new items that you can get from crates, spins, shops, or events. Some of the new items include: - NUSA-themed skins for weapons, vehicles, parachutes, and backpacks - Tactical Crossbow-themed skins for crossbows - The Voice of Battlegrounds-themed skins for helmets and vests - Halloween-themed skins for weapons and vehicles - Diwali-themed skins for weapons and vehicles - Star Trek-themed outfits and accessories - Zombie-themed outfits and accessories - And more! You can mix and match these items to create your own unique style and impress your friends and foes.
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-BGMI 2.3 beta version registration, eligibility, and requirements
How to download BGMI 2.2 update apk?
-If you are wondering how to download the BGMI 2.2 update apk on your device, don't worry. We have got you covered. Just follow these simple steps and you will be able to enjoy the update in no time.
-Requirements and precautions
-Before you download the update, make sure that your device meets the following requirements: - Android version: 5.1.1 or above - RAM: 2 GB or more - Storage space: 1 GB or more Also, make sure that you have a stable internet connection and enough battery power. It is recommended that you back up your game data before updating to avoid any loss or corruption.
-Download link and instructions
-To download the BGMI 2.2 update apk, you can use this link: [BGMI 2.2 Update Download Apk]. This is the official link from Krafton's website and it is safe and secure. Once you have downloaded the apk file, follow these instructions to install it on your device: - Locate the apk file in your device's file manager or downloads folder - Tap on the apk file to start the installation process - Allow the installation from unknown sources if prompted - Wait for the installation to complete - Launch the game and enjoy the update Note: If you have already installed BGMI on your device from Google Play Store or any other source, you don't need to uninstall it before installing the update. The update will overwrite the existing version without affecting your game data.
-Troubleshooting tips
-If you face any issues while downloading or installing the update, try these tips to solve them: - Check your internet connection and try again - Clear your device's cache and storage space - Restart your device and try again - Contact Krafton's customer support if the problem persists We hope that these tips will help you resolve any problems that you may encounter.
-Conclusion
-The BGMI 2.2 update is a massive update that brings a lot of new content and features to the game. You can explore a new map, use a new weapon, communicate with a new voice pack, play new modes, join new events, and get new skins, outfits, and accessories. You can also download the update easily by following our guide above. So what are you waiting for? Download the BGMI 2.2 update apk now and experience the thrill of Battlegrounds Mobile India like never before!
-FAQs
Here are some of the frequently asked questions (FAQs) about the BGMI 2.2 update:
-| Question | -Answer | -
|---|---|
| Is the BGMI 2.2 update available for iOS devices? | -No, the BGMI 2.2 update is currently available for Android devices only. Krafton has not announced any plans to release the update for iOS devices yet. | -
| How big is the BGMI 2.2 update apk file? | -The BGMI 2.2 update apk file is about 1 GB in size. However, you may need additional storage space to download and install the update on your device. | -
| Can I play with players from other regions or versions of the game? | -No, you can only play with players from India who have the same version of the game as you. You cannot play with players from other regions or versions of the game, such as PUBG Mobile or PUBG Mobile Lite. | -
| Will I lose my progress or rank if I update the game? | -No, you will not lose your progress or rank if you update the game. Your game data will be saved and transferred to the new version without any loss or corruption. | -
| How can I get more in-game currency and items in the game? | -You can get more in-game currency and items in the game by completing missions, participating in events, opening crates, spinning wheels, or purchasing them with real money. You can also use some promo codes or coupons to get some free rewards. | -
I hope that these FAQs will answer some of your queries and doubts about the BGMI 2.2 update. If you have any other questions, feel free to leave a comment below or contact Krafton's customer support.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Data Structure Using C By Udit Agarwal Pdf Free Extra Quality.md b/spaces/1phancelerku/anime-remove-background/Data Structure Using C By Udit Agarwal Pdf Free Extra Quality.md deleted file mode 100644 index 49779944006546f5b51ce5e019628429dbfd5665..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Data Structure Using C By Udit Agarwal Pdf Free Extra Quality.md +++ /dev/null @@ -1,72 +0,0 @@ -## Data Structure Using C By Udit Agarwal Pdf Free - - - - - - - - - -**Click Here ✦✦✦ [https://vittuv.com/2tBMuA](https://vittuv.com/2tBMuA)** - - - - - - - - - - - - - -# Data Structure Using C By Udit Agarwal: A Comprehensive Guide for Beginners - - - -Data structures are the fundamental building blocks of any computer program. They are used to store, organize, and manipulate data efficiently. Data structures can be classified into two types: linear and nonlinear. 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-Do you love survival games that challenge you to use your wits and skills to stay alive on a mysterious island? If so, you might want to check out Lost in Blue, a survival sandbox game developed by Volcano Force for Android devices. In this game, you will have to collect resources, craft items, build facilities, and fight enemies as you try to find a way back home. You can also team up with other players from around the world and form camps to cooperate and compete with each other. In this article, we will show you how to download Lost in Blue for Android and give you some tips on how to start playing.
-What is Lost in Blue?
-Lost in Blue is a survival sandbox game that was released globally in June 2023. The game is inspired by the popular Lost in Blue series of video games that were originally released for Nintendo DS. The game has received positive reviews from critics and players alike for its stunning graphics, immersive gameplay, and diverse content.
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The game's story begins with a plane crash that leaves you stranded on a strange island. You will have to explore various natural environments like beaches, rainforests, volcanoes, glaciers, etc. and overcome different obstacles such as mutant zombies, militias, wild creatures, etc. You will also have to craft weapons and tools, build facilities and houses, and grow food to survive. You can choose from four different professions that affect your skills and abilities: hunter, gatherer, engineer, or doctor. You can also join or create camps with other players and cooperate or compete with them.
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-There are many reasons why you should download and play Lost in Blue if you are a fan of survival games. Here are some of them:
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- The game has diverse content that offers a lot of variety and replay value. The game has over 100 types of resources, over 200 types of items, over 50 types of facilities, and over 40 types of enemies. The game also has different modes, such as story mode, survival mode, and multiplayer mode. -
How to download Lost in Blue for Android?
-If you are interested in playing Lost in Blue, you will need to download and install the game on your Android device. The game is available for free on the Google Play Store, but it also has some in-app purchases that can enhance your gaming experience. Here is a step-by-step guide on how to download Lost in Blue for Android:
-Requirements for downloading Lost in Blue
-Before you download Lost in Blue, you should make sure that your device meets the minimum and recommended specifications for running the game smoothly. According to the game's official website, these are the requirements for downloading Lost in Blue:
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| Android 5.0 or higher | -Android 8.0 or higher | -
| 2 GB of RAM | -4 GB of RAM or more | -
| 2 GB of free storage space | -4 GB of free storage space or more | -
| Internet connection | -Wi-Fi or 4G connection | -
Steps for downloading Lost in Blue
-Once you have checked that your device meets the requirements, you can follow these steps to download Lost in Blue:
--
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- Open the Google Play Store app on your device and search for "Lost in Blue" or use this link to go directly to the game's page. -
- Tap on the "Install" button and wait for the game to download and install on your device. The game's size is about 1.5 GB, so it may take some time depending on your internet speed. -
- After the installation is complete, tap on the "Open" button or find the game's icon on your home screen and launch the game. -
- The game will ask you to grant some permissions, such as access to your storage, location, microphone, etc. You can choose to allow or deny these permissions, but some of them may be necessary for the game to function properly. -
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- The game will then load some data and resources and take you to the main menu. You can now start playing Lost in Blue! -
Tips for downloading Lost in Blue
-To avoid any issues or errors while downloading Lost in Blue, here are some tips that you should keep in mind:
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- Make sure that you have enough storage space on your device before downloading the game. You can check your available space by going to Settings > Storage on your device. -
- Make sure that you have a stable internet connection while downloading the game. You can use Wi-Fi or 4G if possible, as they are faster and more reliable than 3G or other networks. -
- Make sure that your device's battery is sufficiently charged before downloading the game. You can plug in your device to a power source or enable battery saver mode if needed. -
- If you encounter any problems while downloading the game, such as slow speed, interrupted download, or corrupted file, you can try clearing the cache and data of the Google Play Store app by going to Settings > Apps > Google Play Store > Storage > Clear Cache/Clear Data. You can also try restarting your device or using a different network. -
- If you have any questions or feedback about the game, you can contact the game's customer service by tapping on the "Help" button on the main menu or sending an email to support@volcanoforce.com. -
How to start playing Lost in Blue?
in various events and activities. However, joining a camp also means that you will have to follow the camp's rules and share your resources with other members. You can also leave or change your camp at any time, but you will lose some of your progress and items. -To join a camp, follow these steps:
--
-
- After choosing your profession, you will see a screen where you can join a camp. You can tap on the "Join Camp" button to see a list of available camps that you can join. -
- You can also tap on the "Create Camp" button to create your own camp. You will need to enter a camp name, a camp slogan, and a camp flag. You will also need to pay some gold coins as a creation fee. -
- Whether you join or create a camp, you will see a screen where you can see your camp's information, such as its name, slogan, flag, members, level, rank, etc. You can also see your camp's base on the map and visit it by tapping on the "Go" button. -
- You have successfully joined a camp! -
How to survive in Lost in Blue?
-Now that you have completed the initial steps, you are ready to start your survival adventure in Lost in Blue. However, surviving on the island is not easy, as you will face many dangers and difficulties. You will need to gather resources, craft items, build facilities, and fight enemies to stay alive. Here are some basic tips and tricks on how to survive in Lost in Blue:
-Gathering resources
-One of the most important aspects of survival is gathering resources. Resources are materials that you can collect from the environment and use for crafting or building. There are many types of resources in Lost in Blue, such as wood, stone, metal, fiber, food, water, etc. You can find them in different locations, such as forests, beaches, caves, etc.
-To gather resources, follow these steps:
--
-
- Find a resource node that you want to collect. You can see the name and icon of the resource on the top of the screen when you approach it. -
- Tap on the resource node to start gathering it. You will see a progress bar that shows how much of the resource you have collected. -
- If you have a tool that matches the resource type, such as an axe for wood or a pickaxe for stone, you can use it to gather faster and get more resources. You can equip a tool by tapping on its icon on the bottom right corner of the screen. -
- When you have collected enough of the resource, it will be added to your inventory. You can check your inventory by tapping on the backpack icon on the top right corner of the screen. -
- You have successfully gathered resources! -
Crafting items
-Another important aspect of survival is crafting items. Items are objects that you can create from the resources you gathered and use for various purposes. There are many types of items in Lost in Blue, such as weapons, tools, equipment, consumables, etc. You can use them to improve your survival chances, such as by increasing your attack power, defense, speed, health, etc.
-To craft items, follow these steps:
--
-
- Open your inventory by tapping on the backpack icon on the top right corner of the screen. -
- Tap on the "Craft" button on the bottom of the screen to see the list of items that you can craft. -
- Tap on the item that you want to craft to see its details, such as its name, description, icon, and required resources. -
- If you have enough resources to craft the item, you can tap on the "Craft" button below the item details. You will see a progress bar that shows how long it takes to craft the item. -
- When the crafting is complete, the item will be added to your inventory. You can equip or use the item by tapping on its icon in your inventory. -
- You have successfully crafted items! -
Building facilities
-Another important aspect of survival is building facilities. Facilities are structures that you can build from the resources you gathered and use for various purposes. There are many types of facilities in Lost in Blue, such as houses, workbenches, towers, fences, etc. You can use them to enhance your camp, such as by providing shelter, storage, production, defense, etc.
-To build facilities, follow these steps:
--
-
- Go to your camp's base by tapping on the "Camp" button on the top left corner of the screen and then tapping on the "Go" button next to your camp's name. -
- Tap on the "Build" button on the bottom of the screen to see the list of facilities that you can build. -
- Tap on the facility that you want to build to see its details, such as its name, description, icon, and required resources. -
- If you have enough resources to build the facility, you can tap on the "Build" button below the facility details. You will see a blueprint of the facility that you can drag and rotate on the ground. -
- When you have found a suitable spot for the facility, you can tap on the "Confirm" button to start building it. You will see a progress bar that shows how long it takes to build the facility. -
- When the building is complete, the facility will be ready to use. You can interact with it by tapping on it and choosing an option from the menu. -
- You have successfully built facilities! -
Fighting enemies
-The final aspect of survival that we will cover is fighting enemies. Enemies are hostile creatures or players that will try to attack you and harm you. There are many types of enemies in Lost in Blue, such as zombies, wild animals, militias, other players, etc. You will need to fight them to protect yourself and your camp, as well as to get loot and rewards from them.
-To fight enemies, follow these steps:
--
-
- Find an enemy that you want to fight. You can see the name and health bar of the enemy on the top of the screen when you target it. -
- Tap on the enemy to start attacking it. You will use your equipped weapon or tool to deal damage to the enemy. You can also use the virtual joystick and buttons to move around and dodge the enemy's attacks. -
- If you have any items or skills that can help you in combat, such as consumables, traps, or abilities, you can use them by tapping on their icons on the bottom right corner of the screen. -
- When you have reduced the enemy's health to zero, you will defeat it. You can loot the enemy's corpse by tapping on it and choosing an option from the menu. You can get various items and resources from the enemy, such as meat, leather, metal, etc. -
- You have successfully fought enemies! -
Conclusion
-In this article, we have shown you how to download Lost in Blue for Android and how to start playing the game. We have also given you some basic tips and tricks on how to survive on the island. We hope that you have found this article helpful and informative. If you are looking for a survival sandbox game that offers a lot of fun and challenge, you should definitely give Lost in Blue a try. You can download the game for free from the Google Play Store and start your adventure today!
-FAQs
-Here are some frequently asked questions about Lost in Blue and their answers:
--
-
- Q: How can I play with my friends in Lost in Blue? -
- A: You can play with your friends in Lost in Blue by joining or creating a camp with them. You can invite your friends to your camp by tapping on the "Camp" button on the top left corner of the screen and then tapping on the "Invite" button next to your friend's name. You can also join your friend's camp by tapping on the "Join Camp" button on the main menu and then searching for your friend's camp name. -
- Q: How can I save my progress in Lost in Blue? -
- A: You can save your progress in Lost in Blue by logging in with your Google account or creating a new account with your email address. The game will automatically sync your progress across different devices and platforms. You can also manually save your progress by tapping on the "Settings" button on the top right corner of the screen and then tapping on the "Save" button. -
- Q: How can I change my profession in Lost in Blue? -
- A: You can change your profession in Lost in Blue by tapping on the "Profession" button on the top right corner of the screen and then tapping on the "Change" button below your current profession. You will need to pay some gold coins as a change fee. You can also change your profession for free once every 24 hours. -
- Q: How can I get more gold coins in Lost in Blue? -
- A: You can get more gold coins in Lost in Blue by completing various tasks and achievements, participating in events and activities, trading with other players, or purchasing them with real money. -
- Q: How can I contact the game's customer service? -
- A: You can contact the game's customer service by tapping on the "Help" button on the main menu or sending an email to support@volcanoforce.com. -
-
-
\ No newline at end of file diff --git a/spaces/2023Liu2023/bingo/tests/parse.ts b/spaces/2023Liu2023/bingo/tests/parse.ts deleted file mode 100644 index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000 --- a/spaces/2023Liu2023/bingo/tests/parse.ts +++ /dev/null @@ -1,13 +0,0 @@ -import { promises as fs } from 'fs' -import { join } from 'path' -import { parseHeadersFromCurl } from '@/lib/utils' - -(async () => { - const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8') - const headers = parseHeadersFromCurl(content) - console.log(headers) - - const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8') - const cmdHeaders = parseHeadersFromCurl(cmdContent) - console.log(cmdHeaders) -})() diff --git a/spaces/2ndelement/voicevox/build_util/check_release_build.py b/spaces/2ndelement/voicevox/build_util/check_release_build.py deleted file mode 100644 index 71bf49c080f4fc39d1e08ccaa9cd6b1c35731ce8..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/build_util/check_release_build.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -ビルド結果をテストする -""" -import argparse -import json -import time -from io import BytesIO -from pathlib import Path -from subprocess import Popen -from urllib.parse import urlencode -from urllib.request import Request, urlopen - -import soundfile - -base_url = "http://127.0.0.1:50021/" - - -def test_release_build(dist_dir: Path, skip_run_process: bool) -> None: - run_file = dist_dir / "run" - if not run_file.exists(): - run_file = dist_dir / "run.exe" - - # 起動 - process = None - if not skip_run_process: - process = Popen([run_file.absolute()], cwd=dist_dir) - time.sleep(60) # 待機 - - # バージョン取得テスト - req = Request(base_url + "version") - with urlopen(req) as res: - assert len(res.read()) > 0 - - # テキスト -> クエリ - text = "こんにちは、音声合成の世界へようこそ" - req = Request( - base_url + "audio_query?" + urlencode({"speaker": "1", "text": text}), - method="POST", - ) - with urlopen(req) as res: - query = json.loads(res.read().decode("utf-8")) - - # クエリ -> 音声 - req = Request(base_url + "synthesis?speaker=1", method="POST") - req.add_header("Content-Type", "application/json") - req.data = json.dumps(query).encode("utf-8") - with urlopen(req) as res: - wave = res.read() - soundfile.read(BytesIO(wave)) - - # エンジンマニフェスト - req = Request(base_url + "engine_manifest", method="GET") - with urlopen(req) as res: - manifest = json.loads(res.read().decode("utf-8")) - assert "uuid" in manifest - - if not skip_run_process: - # プロセスが稼働中であることを確認 - assert process.poll() is None - - # 停止 - process.terminate() - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--dist_dir", type=Path, default=Path("dist/")) - parser.add_argument("--skip_run_process", action="store_true") - args = parser.parse_args() - test_release_build(dist_dir=args.dist_dir, skip_run_process=args.skip_run_process) diff --git a/spaces/7hao/bingo/src/lib/utils.ts b/spaces/7hao/bingo/src/lib/utils.ts deleted file mode 100644 index 07feedb34e356b1b3cf867872f32d47a96ae12fb..0000000000000000000000000000000000000000 --- a/spaces/7hao/bingo/src/lib/utils.ts +++ /dev/null @@ -1,138 +0,0 @@ -import { clsx, type ClassValue } from 'clsx' -import { customAlphabet } from 'nanoid' -import { twMerge } from 'tailwind-merge' - -export function cn(...inputs: ClassValue[]) { - return twMerge(clsx(inputs)) -} - -export const nanoid = customAlphabet( - '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz', - 7 -) // 7-character random string - -export function createChunkDecoder() { - const decoder = new TextDecoder() - return function (chunk: Uint8Array | undefined): string { - if (!chunk) return '' - return decoder.decode(chunk, { stream: true }) - } -} - -export function random (start: number, end: number) { - return start + Math.ceil(Math.random() * (end - start)) -} - -export function randomIP() { - return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}` -} - -export function parseHeadersFromCurl(content: string) { - const re = /-H '([^:]+):\s*([^']+)/mg - const headers: HeadersInit = {} - content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl - content.replace(re, (_: string, key: string, value: string) => { - headers[key] = value - return '' - }) - - return headers -} - -export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2'] -export function encodeHeadersToCookie(content: string) { - const base64Content = btoa(content) - const contentChunks = base64Content.match(/.{1,4000}/g) || [] - return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`) -} - -export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) { - let base64Content = '' - ChunkKeys.forEach((key) => { - base64Content += (cookies[key] || '') - }) - try { - return atob(base64Content) - } catch(e) { - return '' - } -} - -export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) { - return parseHeadersFromCurl(extraCurlFromCookie(cookies)) -} - -export function formatDate(input: string | number | Date): string { - const date = new Date(input) - return date.toLocaleDateString('en-US', { - month: 'long', - day: 'numeric', - year: 'numeric' - }) -} - -export function parseCookie(cookie: string, cookieName: string) { - const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie - return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : '' -} - -export function parseCookies(cookie: string, cookieNames: string[]) { - const cookies: { [key: string]: string } = {} - cookieNames.forEach(cookieName => { - cookies[cookieName] = parseCookie(cookie, cookieName) - }) - return cookies -} - -export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0' -export const DEFAULT_IP = process.env.BING_IP || randomIP() - -export function parseUA(ua?: string, default_ua = DEFAULT_UA) { - return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua -} - -export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>) { - let { - BING_COOKIE = process.env.BING_COOKIE, - BING_UA = process.env.BING_UA, - BING_IP = process.env.BING_IP, - BING_HEADER = process.env.BING_HEADER, - } = cookies - - if (BING_HEADER) { - return extraHeadersFromCookie({ - BING_HEADER, - ...cookies, - }) - } - - const ua = parseUA(BING_UA) - - if (!BING_COOKIE) { - BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || 'xxx' // hf 暂时不用 Cookie 也可以正常使用 - } - - const parsedCookie = parseCookie(BING_COOKIE, '_U') - if (!parsedCookie) { - throw new Error('Invalid Cookie') - } - return { - 'x-forwarded-for': BING_IP || DEFAULT_IP, - 'Accept-Encoding': 'gzip, deflate, br', - 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6', - 'User-Agent': ua!, - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - cookie: `_U=${parsedCookie}` || '', - } -} - -export class WatchDog { - private tid = 0 - watch(fn: Function, timeout = 2000) { - clearTimeout(this.tid) - this.tid = setTimeout(fn, timeout + Math.random() * 1000) - } - reset() { - clearTimeout(this.tid) - } -} diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_33966KB.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_33966KB.py deleted file mode 100644 index 9b127bc6427f5c60c8cf85603a3d8a093c3501c4..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_33966KB.py +++ /dev/null @@ -1,126 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -from . import spec_utils - - -class Conv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False, - ), - nn.Conv2d(nin, nout, kernel_size=1, bias=False), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - feat6 = self.conv6(x) - feat7 = self.conv7(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_33966KB.py b/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_33966KB.py deleted file mode 100644 index b8986f968dc5383e65d35aac6e4367299de3378b..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_33966KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from . import layers_33966KB as layers - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16, 32)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 16) - self.stg1_high_band_net = BaseASPPNet(2, 16) - - self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(8, 16) - - self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(16, 32) - - self.out = nn.Conv2d(32, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/infer_batch_rvc.py b/spaces/AI-Hobbyist/Hoyo-RVC/infer_batch_rvc.py deleted file mode 100644 index 311fe912b6c043f9852b5860ac296bcb46b57a71..0000000000000000000000000000000000000000 --- a/spaces/AI-Hobbyist/Hoyo-RVC/infer_batch_rvc.py +++ /dev/null @@ -1,217 +0,0 @@ -""" -v1 -runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33 -v2 -runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33 -""" -import os, sys, pdb, torch - -now_dir = os.getcwd() -sys.path.append(now_dir) -import argparse -import glob -import sys -import torch -import tqdm as tq -from multiprocessing import cpu_count - - -class Config: - def __init__(self, device, is_half): - self.device = device - self.is_half = is_half - self.n_cpu = 0 - self.gpu_name = None - self.gpu_mem = None - self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() - - def device_config(self) -> tuple: - if torch.cuda.is_available(): - i_device = int(self.device.split(":")[-1]) - self.gpu_name = torch.cuda.get_device_name(i_device) - if ( - ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) - or "P40" in self.gpu_name.upper() - or "1060" in self.gpu_name - or "1070" in self.gpu_name - or "1080" in self.gpu_name - ): - print("16系/10系显卡和P40强制单精度") - self.is_half = False - for config_file in ["32k.json", "40k.json", "48k.json"]: - with open(f"configs/{config_file}", "r") as f: - strr = f.read().replace("true", "false") - with open(f"configs/{config_file}", "w") as f: - f.write(strr) - with open("trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open("trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - else: - self.gpu_name = None - self.gpu_mem = int( - torch.cuda.get_device_properties(i_device).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - if self.gpu_mem <= 4: - with open("trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open("trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - elif torch.backends.mps.is_available(): - print("没有发现支持的N卡, 使用MPS进行推理") - self.device = "mps" - else: - print("没有发现支持的N卡, 使用CPU进行推理") - self.device = "cpu" - self.is_half = True - - if self.n_cpu == 0: - self.n_cpu = cpu_count() - - if self.is_half: - # 6G显存配置 - x_pad = 3 - x_query = 10 - x_center = 60 - x_max = 65 - else: - # 5G显存配置 - x_pad = 1 - x_query = 6 - x_center = 38 - x_max = 41 - - if self.gpu_mem != None and self.gpu_mem <= 4: - x_pad = 1 - x_query = 5 - x_center = 30 - x_max = 32 - - return x_pad, x_query, x_center, x_max - - -f0up_key = sys.argv[1] -input_path = sys.argv[2] -index_path = sys.argv[3] -f0method = sys.argv[4] # harvest or pm -opt_path = sys.argv[5] -model_path = sys.argv[6] -index_rate = float(sys.argv[7]) -device = sys.argv[8] -is_half = bool(sys.argv[9]) -filter_radius = int(sys.argv[10]) -resample_sr = int(sys.argv[11]) -rms_mix_rate = float(sys.argv[12]) -protect = float(sys.argv[13]) -print(sys.argv) -config = Config(device, is_half) -now_dir = os.getcwd() -sys.path.append(now_dir) -from vc_infer_pipeline import VC -from infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from my_utils import load_audio -from fairseq import checkpoint_utils -from scipy.io import wavfile - -hubert_model = None - - -def load_hubert(): - global hubert_model - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - ["hubert_base.pt"], - suffix="", - ) - hubert_model = models[0] - hubert_model = hubert_model.to(device) - if is_half: - hubert_model = hubert_model.half() - else: - hubert_model = hubert_model.float() - hubert_model.eval() - - -def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate): - global tgt_sr, net_g, vc, hubert_model, version - if input_audio is None: - return "You need to upload an audio", None - f0_up_key = int(f0_up_key) - audio = load_audio(input_audio, 16000) - times = [0, 0, 0] - if hubert_model == None: - load_hubert() - if_f0 = cpt.get("f0", 1) - # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file) - audio_opt = vc.pipeline( - hubert_model, - net_g, - sid, - audio, - input_audio, - times, - f0_up_key, - f0_method, - file_index, - index_rate, - if_f0, - filter_radius, - tgt_sr, - resample_sr, - rms_mix_rate, - version, - protect, - f0_file=f0_file, - ) - print(times) - return audio_opt - - -def get_vc(model_path): - global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version - print("loading pth %s" % model_path) - cpt = torch.load(model_path, map_location="cpu") - tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: # - net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g.enc_q - print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净，真奇葩 - net_g.eval().to(device) - if is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - vc = VC(tgt_sr, config) - n_spk = cpt["config"][-3] - # return {"visible": True,"maximum": n_spk, "__type__": "update"} - - -get_vc(model_path) -audios = os.listdir(input_path) -for file in tq.tqdm(audios): - if file.endswith(".wav"): - file_path = input_path + "/" + file - wav_opt = vc_single( - 0, file_path, f0up_key, None, f0method, index_path, index_rate - ) - out_path = opt_path + "/" + file - wavfile.write(out_path, tgt_sr, wav_opt) diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/hifigan.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/hifigan.py deleted file mode 100644 index ae7e61f56b00d60bcc49a18ece3edbe54746f7ea..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/hifigan/hifigan.py +++ /dev/null @@ -1,365 +0,0 @@ -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm - -from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork -from modules.parallel_wavegan.models.source import SourceModuleHnNSF -import numpy as np - -LRELU_SLOPE = 0.1 - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def apply_weight_norm(m): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - weight_norm(m) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -class ResBlock1(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.h = h - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.h = h - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Conv1d1x1(Conv1d): - """1x1 Conv1d with customized initialization.""" - - def __init__(self, in_channels, out_channels, bias): - """Initialize 1x1 Conv1d module.""" - super(Conv1d1x1, self).__init__(in_channels, out_channels, - kernel_size=1, padding=0, - dilation=1, bias=bias) - - -class HifiGanGenerator(torch.nn.Module): - def __init__(self, h, c_out=1): - super(HifiGanGenerator, self).__init__() - self.h = h - self.num_kernels = len(h['resblock_kernel_sizes']) - self.num_upsamples = len(h['upsample_rates']) - - if h['use_pitch_embed']: - self.harmonic_num = 8 - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h['upsample_rates'])) - self.m_source = SourceModuleHnNSF( - sampling_rate=h['audio_sample_rate'], - harmonic_num=self.harmonic_num) - self.noise_convs = nn.ModuleList() - self.conv_pre = weight_norm(Conv1d(80, h['upsample_initial_channel'], 7, 1, padding=3)) - resblock = ResBlock1 if h['resblock'] == '1' else ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(h['upsample_rates'], h['upsample_kernel_sizes'])): - c_cur = h['upsample_initial_channel'] // (2 ** (i + 1)) - self.ups.append(weight_norm( - ConvTranspose1d(c_cur * 2, c_cur, k, u, padding=(k - u) // 2))) - if h['use_pitch_embed']: - if i + 1 < len(h['upsample_rates']): - stride_f0 = np.prod(h['upsample_rates'][i + 1:]) - self.noise_convs.append(Conv1d( - 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2)) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = h['upsample_initial_channel'] // (2 ** (i + 1)) - for j, (k, d) in enumerate(zip(h['resblock_kernel_sizes'], h['resblock_dilation_sizes'])): - self.resblocks.append(resblock(h, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, c_out, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - - def forward(self, x, f0=None): - if f0 is not None: - # harmonic-source signal, noise-source signal, uv flag - f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) - har_source, noi_source, uv = self.m_source(f0) - har_source = har_source.transpose(1, 2) - - x = self.conv_pre(x) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - if f0 is not None: - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, use_cond=False, c_in=1): - super(DiscriminatorP, self).__init__() - self.use_cond = use_cond - if use_cond: - from utils.hparams import hparams - t = hparams['hop_size'] - self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2) - c_in = 2 - - self.period = period - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(c_in, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x, mel): - fmap = [] - if self.use_cond: - x_mel = self.cond_net(mel) - x = torch.cat([x_mel, x], 1) - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_cond=False, c_in=1): - super(MultiPeriodDiscriminator, self).__init__() - self.discriminators = nn.ModuleList([ - DiscriminatorP(2, use_cond=use_cond, c_in=c_in), - DiscriminatorP(3, use_cond=use_cond, c_in=c_in), - DiscriminatorP(5, use_cond=use_cond, c_in=c_in), - DiscriminatorP(7, use_cond=use_cond, c_in=c_in), - DiscriminatorP(11, use_cond=use_cond, c_in=c_in), - ]) - - def forward(self, y, y_hat, mel=None): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y, mel) - y_d_g, fmap_g = d(y_hat, mel) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False, use_cond=False, upsample_rates=None, c_in=1): - super(DiscriminatorS, self).__init__() - self.use_cond = use_cond - if use_cond: - t = np.prod(upsample_rates) - self.cond_net = torch.nn.ConvTranspose1d(80, 1, t * 2, stride=t, padding=t // 2) - c_in = 2 - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(c_in, 128, 15, 1, padding=7)), - norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), - norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), - norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x, mel): - if self.use_cond: - x_mel = self.cond_net(mel) - x = torch.cat([x_mel, x], 1) - fmap = [] - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiScaleDiscriminator(torch.nn.Module): - def __init__(self, use_cond=False, c_in=1): - super(MultiScaleDiscriminator, self).__init__() - from utils.hparams import hparams - self.discriminators = nn.ModuleList([ - DiscriminatorS(use_spectral_norm=True, use_cond=use_cond, - upsample_rates=[4, 4, hparams['hop_size'] // 16], - c_in=c_in), - DiscriminatorS(use_cond=use_cond, - upsample_rates=[4, 4, hparams['hop_size'] // 32], - c_in=c_in), - DiscriminatorS(use_cond=use_cond, - upsample_rates=[4, 4, hparams['hop_size'] // 64], - c_in=c_in), - ]) - self.meanpools = nn.ModuleList([ - AvgPool1d(4, 2, padding=1), - AvgPool1d(4, 2, padding=1) - ]) - - def forward(self, y, y_hat, mel=None): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y, mel) - y_d_g, fmap_g = d(y_hat, mel) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - r_losses = 0 - g_losses = 0 - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg ** 2) - r_losses += r_loss - g_losses += g_loss - r_losses = r_losses / len(disc_real_outputs) - g_losses = g_losses / len(disc_real_outputs) - return r_losses, g_losses - - -def cond_discriminator_loss(outputs): - loss = 0 - for dg in outputs: - g_loss = torch.mean(dg ** 2) - loss += g_loss - loss = loss / len(outputs) - return loss - - -def generator_loss(disc_outputs): - loss = 0 - for dg in disc_outputs: - l = torch.mean((1 - dg) ** 2) - loss += l - loss = loss / len(disc_outputs) - return loss diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/classifier.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/classifier.py deleted file mode 100644 index 67e98b9d8ffb96a150b517497ace0a242d7163ef..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/classifier.py +++ /dev/null @@ -1,267 +0,0 @@ -import os -import torch -import pytorch_lightning as pl -from omegaconf import OmegaConf -from torch.nn import functional as F -from torch.optim import AdamW -from torch.optim.lr_scheduler import LambdaLR -from copy import deepcopy -from einops import rearrange -from glob import glob -from natsort import natsorted - -from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel -from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config - -__models__ = { - 'class_label': EncoderUNetModel, - 'segmentation': UNetModel -} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -class NoisyLatentImageClassifier(pl.LightningModule): - - def __init__(self, - diffusion_path, - num_classes, - ckpt_path=None, - pool='attention', - label_key=None, - diffusion_ckpt_path=None, - scheduler_config=None, - weight_decay=1.e-2, - log_steps=10, - monitor='val/loss', - *args, - **kwargs): - super().__init__(*args, **kwargs) - self.num_classes = num_classes - # get latest config of diffusion model - diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] - self.diffusion_config = OmegaConf.load(diffusion_config).model - self.diffusion_config.params.ckpt_path = diffusion_ckpt_path - self.load_diffusion() - - self.monitor = monitor - self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 - self.log_time_interval = self.diffusion_model.num_timesteps // log_steps - self.log_steps = log_steps - - self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ - else self.diffusion_model.cond_stage_key - - assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' - - if self.label_key not in __models__: - raise NotImplementedError() - - self.load_classifier(ckpt_path, pool) - - self.scheduler_config = scheduler_config - self.use_scheduler = self.scheduler_config is not None - self.weight_decay = weight_decay - - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") - - def load_diffusion(self): - model = instantiate_from_config(self.diffusion_config) - self.diffusion_model = model.eval() - self.diffusion_model.train = disabled_train - for param in self.diffusion_model.parameters(): - param.requires_grad = False - - def load_classifier(self, ckpt_path, pool): - model_config = deepcopy(self.diffusion_config.params.unet_config.params) - model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels - model_config.out_channels = self.num_classes - if self.label_key == 'class_label': - model_config.pool = pool - - self.model = __models__[self.label_key](**model_config) - if ckpt_path is not None: - print('#####################################################################') - print(f'load from ckpt "{ckpt_path}"') - print('#####################################################################') - self.init_from_ckpt(ckpt_path) - - @torch.no_grad() - def get_x_noisy(self, x, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x)) - continuous_sqrt_alpha_cumprod = None - if self.diffusion_model.use_continuous_noise: - continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) - # todo: make sure t+1 is correct here - - return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, - continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) - - def forward(self, x_noisy, t, *args, **kwargs): - return self.model(x_noisy, t) - - @torch.no_grad() - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = rearrange(x, 'b h w c -> b c h w') - x = x.to(memory_format=torch.contiguous_format).float() - return x - - @torch.no_grad() - def get_conditioning(self, batch, k=None): - if k is None: - k = self.label_key - assert k is not None, 'Needs to provide label key' - - targets = batch[k].to(self.device) - - if self.label_key == 'segmentation': - targets = rearrange(targets, 'b h w c -> b c h w') - for down in range(self.numd): - h, w = targets.shape[-2:] - targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') - - # targets = rearrange(targets,'b c h w -> b h w c') - - return targets - - def compute_top_k(self, logits, labels, k, reduction="mean"): - _, top_ks = torch.topk(logits, k, dim=1) - if reduction == "mean": - return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() - elif reduction == "none": - return (top_ks == labels[:, None]).float().sum(dim=-1) - - def on_train_epoch_start(self): - # save some memory - self.diffusion_model.model.to('cpu') - - @torch.no_grad() - def write_logs(self, loss, logits, targets): - log_prefix = 'train' if self.training else 'val' - log = {} - log[f"{log_prefix}/loss"] = loss.mean() - log[f"{log_prefix}/acc@1"] = self.compute_top_k( - logits, targets, k=1, reduction="mean" - ) - log[f"{log_prefix}/acc@5"] = self.compute_top_k( - logits, targets, k=5, reduction="mean" - ) - - self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) - self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) - self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) - lr = self.optimizers().param_groups[0]['lr'] - self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) - - def shared_step(self, batch, t=None): - x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) - targets = self.get_conditioning(batch) - if targets.dim() == 4: - targets = targets.argmax(dim=1) - if t is None: - t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() - else: - t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() - x_noisy = self.get_x_noisy(x, t) - logits = self(x_noisy, t) - - loss = F.cross_entropy(logits, targets, reduction='none') - - self.write_logs(loss.detach(), logits.detach(), targets.detach()) - - loss = loss.mean() - return loss, logits, x_noisy, targets - - def training_step(self, batch, batch_idx): - loss, *_ = self.shared_step(batch) - return loss - - def reset_noise_accs(self): - self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in - range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} - - def on_validation_start(self): - self.reset_noise_accs() - - @torch.no_grad() - def validation_step(self, batch, batch_idx): - loss, *_ = self.shared_step(batch) - - for t in self.noisy_acc: - _, logits, _, targets = self.shared_step(batch, t) - self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) - self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) - - return loss - - def configure_optimizers(self): - optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) - - if self.use_scheduler: - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }] - return [optimizer], scheduler - - return optimizer - - @torch.no_grad() - def log_images(self, batch, N=8, *args, **kwargs): - log = dict() - x = self.get_input(batch, self.diffusion_model.first_stage_key) - log['inputs'] = x - - y = self.get_conditioning(batch) - - if self.label_key == 'class_label': - y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) - log['labels'] = y - - if ismap(y): - log['labels'] = self.diffusion_model.to_rgb(y) - - for step in range(self.log_steps): - current_time = step * self.log_time_interval - - _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) - - log[f'inputs@t{current_time}'] = x_noisy - - pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) - pred = rearrange(pred, 'b h w c -> b c h w') - - log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) - - for key in log: - log[key] = log[key][:N] - - return log diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/commons/meters.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/commons/meters.py deleted file mode 100644 index 1b5716547cefd33fb68ab99dc2a6a70f55336625..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/commons/meters.py +++ /dev/null @@ -1,42 +0,0 @@ -import time -import torch - - -class AvgrageMeter(object): - - def __init__(self): - self.reset() - - def reset(self): - self.avg = 0 - self.sum = 0 - self.cnt = 0 - - def update(self, val, n=1): - self.sum += val * n - self.cnt += n - self.avg = self.sum / self.cnt - - -class Timer: - timer_map = {} - - def __init__(self, name, enable=False): - if name not in Timer.timer_map: - Timer.timer_map[name] = 0 - self.name = name - self.enable = enable - - def __enter__(self): - if self.enable: - # if torch.cuda.is_available(): - # torch.cuda.synchronize() - self.t = time.time() - - def __exit__(self, exc_type, exc_val, exc_tb): - if self.enable: - # if torch.cuda.is_available(): - # torch.cuda.synchronize() - Timer.timer_map[self.name] += time.time() - self.t - if self.enable: - print(f'[Timer] {self.name}: {Timer.timer_map[self.name]}') diff --git a/spaces/AIZero2HeroBootcamp/ExperimentalChatGPTv1/README.md b/spaces/AIZero2HeroBootcamp/ExperimentalChatGPTv1/README.md deleted file mode 100644 index 05244cc50f50d08eda7dc9508b81f64a4fd32842..0000000000000000000000000000000000000000 --- a/spaces/AIZero2HeroBootcamp/ExperimentalChatGPTv1/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: ExperimentalChatGPTv1 -emoji: 🔥 -colorFrom: pink -colorTo: blue -sdk: streamlit -sdk_version: 1.25.0 -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/Abhi5ingh/fashionsd/README.md b/spaces/Abhi5ingh/fashionsd/README.md deleted file mode 100644 index 0efa321f63e9e9a80799199f514651cf1be019c1..0000000000000000000000000000000000000000 --- a/spaces/Abhi5ingh/fashionsd/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Fashionsd -emoji: 👀 -colorFrom: green -colorTo: indigo -sdk: streamlit -sdk_version: 1.28.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatBase.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatBase.py deleted file mode 100644 index b98fe56595a161bb5cfbcc7871ff94845edb3b3a..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/ChatBase.py +++ /dev/null @@ -1,62 +0,0 @@ -from __future__ import annotations - -from aiohttp import ClientSession - -from ..typing import AsyncGenerator -from .base_provider import AsyncGeneratorProvider - - -class ChatBase(AsyncGeneratorProvider): - url = "https://www.chatbase.co" - supports_gpt_35_turbo = True - supports_gpt_4 = True - working = True - - @classmethod - async def create_async_generator( - cls, - model: str, - messages: list[dict[str, str]], - **kwargs - ) -> AsyncGenerator: - if model == "gpt-4": - chat_id = "quran---tafseer-saadi-pdf-wbgknt7zn" - elif model == "gpt-3.5-turbo" or not model: - chat_id = "chatbase--1--pdf-p680fxvnm" - else: - raise ValueError(f"Model are not supported: {model}") - headers = { - "User-Agent" : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36", - "Accept" : "*/*", - "Accept-language" : "en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3", - "Origin" : cls.url, - "Referer" : cls.url + "/", - "Sec-Fetch-Dest" : "empty", - "Sec-Fetch-Mode" : "cors", - "Sec-Fetch-Site" : "same-origin", - } - async with ClientSession( - headers=headers - ) as session: - data = { - "messages": messages, - "captchaCode": "hadsa", - "chatId": chat_id, - "conversationId": f"kcXpqEnqUie3dnJlsRi_O-{chat_id}" - } - async with session.post("https://www.chatbase.co/api/fe/chat", json=data) as response: - response.raise_for_status() - async for stream in response.content.iter_any(): - yield stream.decode() - - - @classmethod - @property - def params(cls): - params = [ - ("model", "str"), - ("messages", "list[dict[str, str]]"), - ("stream", "bool"), - ] - param = ", ".join([": ".join(p) for p in params]) - return f"g4f.provider.{cls.__name__} supports: ({param})" \ No newline at end of file diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/unfinished/MikuChat.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/unfinished/MikuChat.py deleted file mode 100644 index bf19631f4b59d39fa1eebe9ca2c0bce8d0a19982..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/unfinished/MikuChat.py +++ /dev/null @@ -1,97 +0,0 @@ -from __future__ import annotations - -import random, json -from datetime import datetime -from ...requests import StreamSession - -from ...typing import AsyncGenerator -from ..base_provider import AsyncGeneratorProvider - - -class MikuChat(AsyncGeneratorProvider): - url = "https://ai.okmiku.com" - supports_gpt_35_turbo = True - - @classmethod - async def create_async_generator( - cls, - model: str, - messages: list[dict[str, str]], - **kwargs - ) -> AsyncGenerator: - if not model: - model = "gpt-3.5-turbo" - headers = { - "authority": "api.catgpt.cc", - "accept": "application/json", - "origin": cls.url, - "referer": f"{cls.url}/chat/", - 'x-app-version': 'undefined', - 'x-date': get_datetime(), - 'x-fingerprint': get_fingerprint(), - 'x-platform': 'web' - } - async with StreamSession(headers=headers, impersonate="chrome107") as session: - data = { - "model": model, - "top_p": 0.8, - "temperature": 0.5, - "presence_penalty": 1, - "frequency_penalty": 0, - "max_tokens": 2000, - "stream": True, - "messages": messages, - } - async with session.post("https://api.catgpt.cc/ai/v1/chat/completions", json=data) as response: - print(await response.text()) - response.raise_for_status() - async for line in response.iter_lines(): - if line.startswith(b"data: "): - line = json.loads(line[6:]) - chunk = line["choices"][0]["delta"].get("content") - if chunk: - yield chunk - -def k(e: str, t: int): - a = len(e) & 3 - s = len(e) - a - i = t - c = 3432918353 - o = 461845907 - n = 0 - r = 0 - while n < s: - r = (ord(e[n]) & 255) | ((ord(e[n + 1]) & 255) << 8) | ((ord(e[n + 2]) & 255) << 16) | ((ord(e[n + 3]) & 255) << 24) - n += 4 - r = (r & 65535) * c + (((r >> 16) * c & 65535) << 16) & 4294967295 - r = (r << 15) | (r >> 17) - r = (r & 65535) * o + (((r >> 16) * o & 65535) << 16) & 4294967295 - i ^= r - i = (i << 13) | (i >> 19) - l = (i & 65535) * 5 + (((i >> 16) * 5 & 65535) << 16) & 4294967295 - i = (l & 65535) + 27492 + (((l >> 16) + 58964 & 65535) << 16) - - if a == 3: - r ^= (ord(e[n + 2]) & 255) << 16 - elif a == 2: - r ^= (ord(e[n + 1]) & 255) << 8 - elif a == 1: - r ^= ord(e[n]) & 255 - r = (r & 65535) * c + (((r >> 16) * c & 65535) << 16) & 4294967295 - r = (r << 15) | (r >> 17) - r = (r & 65535) * o + (((r >> 16) * o & 65535) << 16) & 4294967295 - i ^= r - - i ^= len(e) - i ^= i >> 16 - i = (i & 65535) * 2246822507 + (((i >> 16) * 2246822507 & 65535) << 16) & 4294967295 - i ^= i >> 13 - i = (i & 65535) * 3266489909 + (((i >> 16) * 3266489909 & 65535) << 16) & 4294967295 - i ^= i >> 16 - return i & 0xFFFFFFFF - -def get_fingerprint() -> str: - return str(k(str(int(random.random() * 100000)), 256)) - -def get_datetime() -> str: - return datetime.now().strftime("%Y-%m-%d %H:%M:%S") \ No newline at end of file diff --git a/spaces/Adapter/CoAdapter/t2i_adapters/t2i_adapters_for_style.py b/spaces/Adapter/CoAdapter/t2i_adapters/t2i_adapters_for_style.py deleted file mode 100644 index e8c98efa7795a7943fb7e7f14f5206971c5c5eac..0000000000000000000000000000000000000000 --- a/spaces/Adapter/CoAdapter/t2i_adapters/t2i_adapters_for_style.py +++ /dev/null @@ -1,63 +0,0 @@ -import torch -import numpy as np -from transformers import CLIPVisionModel - -from ldm.models.diffusion.ddpm import LatentDiffusion, disabled_train -from ldm.util import instantiate_from_config - - -class T2IAdapterStyleV3(LatentDiffusion): - - def __init__(self, adapter_config, extra_cond_key, noise_schedule, *args, **kwargs): - super(T2IAdapterStyleV3, self).__init__(*args, **kwargs) - self.adapter = instantiate_from_config(adapter_config) - self.extra_cond_key = extra_cond_key - self.noise_schedule = noise_schedule - self.clip_vision_model = CLIPVisionModel.from_pretrained( - 'openai/clip-vit-large-patch14' - ) - - self.clip_vision_model = self.clip_vision_model.eval() - self.clip_vision_model.train = disabled_train - for param in self.clip_vision_model.parameters(): - param.requires_grad = False - - - def shared_step(self, batch, **kwargs): - for k in self.ucg_training: - if k == self.extra_cond_key: - continue - p = self.ucg_training[k] - for i in range(len(batch[k])): - if self.ucg_prng.choice(2, p=[1 - p, p]): - if isinstance(batch[k], list): - batch[k][i] = "" - batch['jpg'] = batch['jpg'] * 2 - 1 - x, c = self.get_input(batch, self.first_stage_key) - extra_cond = super(LatentDiffusion, self).get_input(batch, self.extra_cond_key).to(self.device) - extra_cond = self.clip_vision_model(extra_cond)['last_hidden_state'] - features_adapter = self.adapter(extra_cond) - if self.extra_cond_key in self.ucg_training: - idx = np.random.choice(self.adapter.num_token, np.random.randint(1, self.adapter.num_token+1), replace=False) - idx_tensor = torch.from_numpy(idx).to(features_adapter.device) - features_adapter = torch.index_select(features_adapter, 1, idx_tensor) - t = self.get_time_with_schedule(self.noise_schedule, x.size(0)) - loss, loss_dict = self(x, c, t=t, append_to_context=features_adapter) - return loss, loss_dict - - def configure_optimizers(self): - lr = self.learning_rate - params = list(self.adapter.parameters()) - opt = torch.optim.AdamW(params, lr=lr) - return opt - - def on_save_checkpoint(self, checkpoint): - keys = list(checkpoint['state_dict'].keys()) - for key in keys: - if 'adapter' not in key: - del checkpoint['state_dict'][key] - - def on_load_checkpoint(self, checkpoint): - for name in self.state_dict(): - if 'adapter' not in name: - checkpoint['state_dict'][name] = self.state_dict()[name] diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/Pages.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/Pages.js deleted file mode 100644 index fa56dce6f1c2c041dd92acb512cbf0671759c9bc..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/Pages.js +++ /dev/null @@ -1,65 +0,0 @@ -import OverlapSizer from '../overlapsizer/OverlapSizer.js'; -import Methods from './methods/Methods.js'; - -const GetValue = Phaser.Utils.Objects.GetValue; - -class Pages extends OverlapSizer { - constructor(scene, config) { - super(scene, config); - this.type = 'rexPages'; - this.childrenMap = this.sizerChildren; - this._previousKey = undefined; - this._currentKey = undefined; - this.setSwapMode(GetValue(config, 'swapMode', 0)); - this.setFadeInDuration(GetValue(config, 'fadeIn', 0)); - } - - setSwapMode(mode) { - if (typeof (mode) === 'string') { - mode = SWAPMODE[mode]; - } - this.swapMode = mode; - return this; - } - - setFadeInDuration(duration) { - this.fadeInDuration = duration; - return this; - } - - get previousKey() { - return this._previousKey; - } - - get currentKey() { - return this._currentKey; - } - - set currentKey(key) { - this.swapPage(key); - } - - get currentPage() { - return this.getPage(this.currentKey); - } - - get previousPage() { - return this.getPage(this.previousKey); - } - - get keys() { - return Object.keys(this.sizerChildren); - } -} - -Object.assign( - Pages.prototype, - Methods -); - -const SWAPMODE = { - invisible: 0, - destroy: 1 -}; - -export default Pages; \ No newline at end of file diff --git a/spaces/Akmyradov/TurkmenTTSweSTT/vits/transforms.py b/spaces/Akmyradov/TurkmenTTSweSTT/vits/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/Akmyradov/TurkmenTTSweSTT/vits/transforms.py +++ /dev/null @@ -1,193 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = { - 'tails': tails, - 'tail_bound': tail_bound - } - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum( - inputs[..., None] >= bin_locations, - dim=-1 - ) - 1 - - -def unconstrained_rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails='linear', - tail_bound=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == 'linear': - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError('{} tails are not implemented.'.format(tails)) - - outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative - ) - - return outputs, logabsdet - -def rational_quadratic_spline(inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0., right=1., bottom=0., top=1., - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError('Input to a transform is not within its domain') - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError('Minimal bin width too large for the number of bins') - if min_bin_height * num_bins > 1.0: - raise ValueError('Minimal bin height too large for the number of bins') - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (((inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta) - + input_heights * (input_delta - input_derivatives))) - b = (input_heights * input_derivatives - - (inputs - input_cumheights) * (input_derivatives - + input_derivatives_plus_one - - 2 * input_delta)) - c = - input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * (input_delta * theta.pow(2) - + input_derivatives * theta_one_minus_theta) - denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2)) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/AlexWang/lama/fetch_data/places_standard_test_val_prepare.sh b/spaces/AlexWang/lama/fetch_data/places_standard_test_val_prepare.sh deleted file mode 100644 index 6017e29aa1593c1c66affa4b9081afac2b9fb000..0000000000000000000000000000000000000000 --- a/spaces/AlexWang/lama/fetch_data/places_standard_test_val_prepare.sh +++ /dev/null @@ -1,5 +0,0 @@ -mkdir -p places_standard_dataset/original/test/ -tar -xvf test_large.tar --transform='s/.*\///' -C places_standard_dataset/original/test/ - -mkdir -p places_standard_dataset/original/val/ -tar -xvf val_large.tar --transform='s/.*\///' -C places_standard_dataset/original/val/ diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/dataset/base_dataset.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/dataset/base_dataset.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py deleted file mode 100644 index 831489eefed167264c8fd8f57e1ed59610ebb858..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import torch -from torch import nn -from transformers import CLIPPreTrainedModel, CLIPVisionModel - -from ...models.attention import BasicTransformerBlock -from ...utils import logging - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -class PaintByExampleImageEncoder(CLIPPreTrainedModel): - def __init__(self, config, proj_size=768): - super().__init__(config) - self.proj_size = proj_size - - self.model = CLIPVisionModel(config) - self.mapper = PaintByExampleMapper(config) - self.final_layer_norm = nn.LayerNorm(config.hidden_size) - self.proj_out = nn.Linear(config.hidden_size, self.proj_size) - - # uncondition for scaling - self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size))) - - def forward(self, pixel_values, return_uncond_vector=False): - clip_output = self.model(pixel_values=pixel_values) - latent_states = clip_output.pooler_output - latent_states = self.mapper(latent_states[:, None]) - latent_states = self.final_layer_norm(latent_states) - latent_states = self.proj_out(latent_states) - if return_uncond_vector: - return latent_states, self.uncond_vector - - return latent_states - - -class PaintByExampleMapper(nn.Module): - def __init__(self, config): - super().__init__() - num_layers = (config.num_hidden_layers + 1) // 5 - hid_size = config.hidden_size - num_heads = 1 - self.blocks = nn.ModuleList( - [ - BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True) - for _ in range(num_layers) - ] - ) - - def forward(self, hidden_states): - for block in self.blocks: - hidden_states = block(hidden_states) - - return hidden_states diff --git a/spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py deleted file mode 100644 index 781dba78d68e77fa7eee15f5bbcc539731f8378d..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py +++ /dev/null @@ -1,11 +0,0 @@ -_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' -model = dict( - backbone=dict( - norm_cfg=dict(type='SyncBN', requires_grad=True), - norm_eval=False, - plugins=[ - dict( - cfg=dict(type='ContextBlock', ratio=1. / 16), - stages=(False, True, True, True), - position='after_conv3') - ])) diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/match_costs/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/match_costs/__init__.py deleted file mode 100644 index add5e0d394034d89b2d47c314ff1938294deb6ea..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/match_costs/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .builder import build_match_cost -from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost - -__all__ = [ - 'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost', - 'FocalLossCost' -] diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 1f9a917fa4223bd2428f2b2d10eac446f7ecc71a..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = [ - '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' -] diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/WSL-installation-guide.md b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/WSL-installation-guide.md deleted file mode 100644 index 30b7fa3e6f4613898fbb0d0bd16b77db5d79c14b..0000000000000000000000000000000000000000 --- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/WSL-installation-guide.md +++ /dev/null @@ -1,82 +0,0 @@ -Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton. - ------ - -Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11: - -## Step 1: Enable WSL - -1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges. -2. In the PowerShell window, type the following command and press Enter: - -``` -wsl --install -``` - -If this command doesn't work, you can enable WSL with the following command for Windows 10: - -``` -wsl --set-default-version 1 -``` - -For Windows 11, you can use: - -``` -wsl --set-default-version 2 -``` - -You may be prompted to restart your computer. If so, save your work and restart. - -## Step 2: Install Ubuntu - -1. Open the Microsoft Store. -2. Search for "Ubuntu" in the search bar. -3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app. -4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app. - -## Step 3: Set up Ubuntu - -1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment. -2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment. - -## Step 4: Update and upgrade packages - -1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal: - -``` -sudo apt update -sudo apt upgrade -``` - -2. Enter your password when prompted. This will update the package list and upgrade any outdated packages. - -Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files. - -You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal. - -## Step 5: Proceed with Linux instructions - -1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt: - -``` -sudo apt install [missing package] -``` - -You will probably need to install build-essential - -``` -sudo apt install build-essential -``` - -If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/ - -#### WSL2 performance using /mnt: -when you git clone a repository, put it inside WSL and not outside. To understand more, take a look at this [issue](https://github.com/microsoft/WSL/issues/4197#issuecomment-604592340) - -## Bonus: Port Forwarding - -By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges). - -``` -netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860 -``` diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_file_saving.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_file_saving.py deleted file mode 100644 index 7357ac85d45868794c9950ca0e970ac82cf486d6..0000000000000000000000000000000000000000 --- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_file_saving.py +++ /dev/null @@ -1,88 +0,0 @@ -import gradio as gr - -from modules import chat, presets, shared, ui, utils -from modules.utils import gradio - - -def create_ui(): - mu = shared.args.multi_user - - # Text file saver - with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['file_saver']: - shared.gradio['save_filename'] = gr.Textbox(lines=1, label='File name') - shared.gradio['save_root'] = gr.Textbox(lines=1, label='File folder', info='For reference. Unchangeable.', interactive=False) - shared.gradio['save_contents'] = gr.Textbox(lines=10, label='File contents') - with gr.Row(): - shared.gradio['save_confirm'] = gr.Button('Save', elem_classes="small-button", interactive=not mu) - shared.gradio['save_cancel'] = gr.Button('Cancel', elem_classes="small-button") - - # Text file deleter - with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['file_deleter']: - shared.gradio['delete_filename'] = gr.Textbox(lines=1, label='File name') - shared.gradio['delete_root'] = gr.Textbox(lines=1, label='File folder', info='For reference. Unchangeable.', interactive=False) - with gr.Row(): - shared.gradio['delete_confirm'] = gr.Button('Delete', elem_classes="small-button", variant='stop', interactive=not mu) - shared.gradio['delete_cancel'] = gr.Button('Cancel', elem_classes="small-button") - - # Character saver/deleter - with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['character_saver']: - shared.gradio['save_character_filename'] = gr.Textbox(lines=1, label='File name', info='The character will be saved to your characters/ folder with this base filename.') - with gr.Row(): - shared.gradio['save_character_confirm'] = gr.Button('Save', elem_classes="small-button", interactive=not mu) - shared.gradio['save_character_cancel'] = gr.Button('Cancel', elem_classes="small-button") - - with gr.Box(visible=False, elem_classes='file-saver') as shared.gradio['character_deleter']: - gr.Markdown('Confirm the character deletion?') - with gr.Row(): - shared.gradio['delete_character_confirm'] = gr.Button('Delete', elem_classes="small-button", variant='stop', interactive=not mu) - shared.gradio['delete_character_cancel'] = gr.Button('Cancel', elem_classes="small-button") - - -def create_event_handlers(): - shared.gradio['save_confirm'].click( - lambda x, y, z: utils.save_file(x + y, z), gradio('save_root', 'save_filename', 'save_contents'), None).then( - lambda: gr.update(visible=False), None, gradio('file_saver')) - - shared.gradio['delete_confirm'].click( - lambda x, y: utils.delete_file(x + y), gradio('delete_root', 'delete_filename'), None).then( - lambda: gr.update(visible=False), None, gradio('file_deleter')) - - shared.gradio['delete_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_deleter')) - shared.gradio['save_cancel'].click(lambda: gr.update(visible=False), None, gradio('file_saver')) - - shared.gradio['save_character_confirm'].click( - chat.save_character, gradio('name2', 'greeting', 'context', 'character_picture', 'save_character_filename'), None).then( - lambda: gr.update(visible=False), None, gradio('character_saver')).then( - lambda x: gr.update(choices=utils.get_available_characters(), value=x), gradio('save_character_filename'), gradio('character_menu')) - - shared.gradio['delete_character_confirm'].click( - chat.delete_character, gradio('character_menu'), None).then( - lambda: gr.update(visible=False), None, gradio('character_deleter')).then( - lambda: gr.update(choices=(characters := utils.get_available_characters()), value=characters[0]), None, gradio('character_menu')) - - shared.gradio['save_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_saver')) - shared.gradio['delete_character_cancel'].click(lambda: gr.update(visible=False), None, gradio('character_deleter')) - - shared.gradio['save_preset'].click( - ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then( - presets.generate_preset_yaml, gradio('interface_state'), gradio('save_contents')).then( - lambda: 'presets/', None, gradio('save_root')).then( - lambda: 'My Preset.yaml', None, gradio('save_filename')).then( - lambda: gr.update(visible=True), None, gradio('file_saver')) - - shared.gradio['delete_preset'].click( - lambda x: f'{x}.yaml', gradio('preset_menu'), gradio('delete_filename')).then( - lambda: 'presets/', None, gradio('delete_root')).then( - lambda: gr.update(visible=True), None, gradio('file_deleter')) - - shared.gradio['save_grammar'].click( - ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then( - lambda x: x, gradio('grammar_string'), gradio('save_contents')).then( - lambda: 'grammars/', None, gradio('save_root')).then( - lambda: 'My Fancy Grammar.gbnf', None, gradio('save_filename')).then( - lambda: gr.update(visible=True), None, gradio('file_saver')) - - shared.gradio['delete_grammar'].click( - lambda x: x, gradio('grammar_file'), gradio('delete_filename')).then( - lambda: 'grammars/', None, gradio('delete_root')).then( - lambda: gr.update(visible=True), None, gradio('file_deleter')) diff --git a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/tests/test_consistency.py b/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/tests/test_consistency.py deleted file mode 100644 index f2c6fd4fe9074143803e0eb6c99fa02a47632094..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/tests/test_consistency.py +++ /dev/null @@ -1,25 +0,0 @@ -import numpy as np -import pytest -import torch -from PIL import Image - -import clip - - -@pytest.mark.parametrize('model_name', clip.available_models()) -def test_consistency(model_name): - device = "cpu" - jit_model, transform = clip.load(model_name, device=device, jit=True) - py_model, _ = clip.load(model_name, device=device, jit=False) - - image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device) - text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device) - - with torch.no_grad(): - logits_per_image, _ = jit_model(image, text) - jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy() - - logits_per_image, _ = py_model(image, text) - py_probs = logits_per_image.softmax(dim=-1).cpu().numpy() - - assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1) diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/context_block.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/context_block.py deleted file mode 100644 index d60fdb904c749ce3b251510dff3cc63cea70d42e..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/context_block.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -from torch import nn - -from ..utils import constant_init, kaiming_init -from .registry import PLUGIN_LAYERS - - -def last_zero_init(m): - if isinstance(m, nn.Sequential): - constant_init(m[-1], val=0) - else: - constant_init(m, val=0) - - -@PLUGIN_LAYERS.register_module() -class ContextBlock(nn.Module): - """ContextBlock module in GCNet. - - See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' - (https://arxiv.org/abs/1904.11492) for details. - - Args: - in_channels (int): Channels of the input feature map. - ratio (float): Ratio of channels of transform bottleneck - pooling_type (str): Pooling method for context modeling. - Options are 'att' and 'avg', stand for attention pooling and - average pooling respectively. Default: 'att'. - fusion_types (Sequence[str]): Fusion method for feature fusion, - Options are 'channels_add', 'channel_mul', stand for channelwise - addition and multiplication respectively. Default: ('channel_add',) - """ - - _abbr_ = 'context_block' - - def __init__(self, - in_channels, - ratio, - pooling_type='att', - fusion_types=('channel_add', )): - super(ContextBlock, self).__init__() - assert pooling_type in ['avg', 'att'] - assert isinstance(fusion_types, (list, tuple)) - valid_fusion_types = ['channel_add', 'channel_mul'] - assert all([f in valid_fusion_types for f in fusion_types]) - assert len(fusion_types) > 0, 'at least one fusion should be used' - self.in_channels = in_channels - self.ratio = ratio - self.planes = int(in_channels * ratio) - self.pooling_type = pooling_type - self.fusion_types = fusion_types - if pooling_type == 'att': - self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1) - self.softmax = nn.Softmax(dim=2) - else: - self.avg_pool = nn.AdaptiveAvgPool2d(1) - if 'channel_add' in fusion_types: - self.channel_add_conv = nn.Sequential( - nn.Conv2d(self.in_channels, self.planes, kernel_size=1), - nn.LayerNorm([self.planes, 1, 1]), - nn.ReLU(inplace=True), # yapf: disable - nn.Conv2d(self.planes, self.in_channels, kernel_size=1)) - else: - self.channel_add_conv = None - if 'channel_mul' in fusion_types: - self.channel_mul_conv = nn.Sequential( - nn.Conv2d(self.in_channels, self.planes, kernel_size=1), - nn.LayerNorm([self.planes, 1, 1]), - nn.ReLU(inplace=True), # yapf: disable - nn.Conv2d(self.planes, self.in_channels, kernel_size=1)) - else: - self.channel_mul_conv = None - self.reset_parameters() - - def reset_parameters(self): - if self.pooling_type == 'att': - kaiming_init(self.conv_mask, mode='fan_in') - self.conv_mask.inited = True - - if self.channel_add_conv is not None: - last_zero_init(self.channel_add_conv) - if self.channel_mul_conv is not None: - last_zero_init(self.channel_mul_conv) - - def spatial_pool(self, x): - batch, channel, height, width = x.size() - if self.pooling_type == 'att': - input_x = x - # [N, C, H * W] - input_x = input_x.view(batch, channel, height * width) - # [N, 1, C, H * W] - input_x = input_x.unsqueeze(1) - # [N, 1, H, W] - context_mask = self.conv_mask(x) - # [N, 1, H * W] - context_mask = context_mask.view(batch, 1, height * width) - # [N, 1, H * W] - context_mask = self.softmax(context_mask) - # [N, 1, H * W, 1] - context_mask = context_mask.unsqueeze(-1) - # [N, 1, C, 1] - context = torch.matmul(input_x, context_mask) - # [N, C, 1, 1] - context = context.view(batch, channel, 1, 1) - else: - # [N, C, 1, 1] - context = self.avg_pool(x) - - return context - - def forward(self, x): - # [N, C, 1, 1] - context = self.spatial_pool(x) - - out = x - if self.channel_mul_conv is not None: - # [N, C, 1, 1] - channel_mul_term = torch.sigmoid(self.channel_mul_conv(context)) - out = out * channel_mul_term - if self.channel_add_conv is not None: - # [N, C, 1, 1] - channel_add_term = self.channel_add_conv(context) - out = out + channel_add_term - - return out diff --git a/spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/test_persistent_data.py b/spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/test_persistent_data.py deleted file mode 100644 index eaa869aaa80d4c5b10b8e1b445f02579f0e11526..0000000000000000000000000000000000000000 --- a/spaces/AnthonyTruchetPoC/persistent-docker/jupyter/scripts/test_persistent_data.py +++ /dev/null @@ -1,42 +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 -# --- - -# %% -import os -from pathlib import Path - -import numpy as np -import pandas as pd - -# %% -from athai.data_utils import cached_download_csv - -# %% -DATE_COLUMN = "date/time" -DATA_URL = ( - "https://s3-us-west-2.amazonaws.com/" - "streamlit-demo-data/uber-raw-data-sep14.csv.gz" -) - -DATA_PATH = Path(os.environ.get("APP_DATA")) - - -# %% -nrows=10 -data = cached_download_csv(DATA_PATH, DATA_URL, nrows=nrows) -data - -# %% - -# %% diff --git a/spaces/Anustup/NS_AI_LABS/src/__init__.py b/spaces/Anustup/NS_AI_LABS/src/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py deleted file mode 100644 index 77a5ebbf1f6d7ca002de3bede9d8d98fc24946d2..0000000000000000000000000000000000000000 --- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py +++ /dev/null @@ -1,93 +0,0 @@ -import os -from typing import Union - -import imageio -import numpy as np -import torch -import torchvision -from einops import rearrange -from tqdm import tqdm - - -def save_videos_grid( - videos: torch.Tensor, save_path: str = "output", path: str = "output.gif", rescale=False, n_rows=4, fps=3 -): - videos = rearrange(videos, "b c t h w -> t b c h w") - outputs = [] - for x in videos: - x = torchvision.utils.make_grid(x, nrow=n_rows) - x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) - if rescale: - x = (x + 1.0) / 2.0 # -1,1 -> 0,1 - x = (x * 255).numpy().astype(np.uint8) - outputs.append(x) - - if not os.path.exists(save_path): - os.makedirs(save_path) - - imageio.mimsave(os.path.join(save_path, path), outputs, fps=fps) - return os.path.join(save_path, path) - - -# DDIM Inversion -@torch.no_grad() -def init_prompt(prompt, pipeline): - uncond_input = pipeline.tokenizer( - [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, return_tensors="pt" - ) - uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0] - text_input = pipeline.tokenizer( - [prompt], - padding="max_length", - max_length=pipeline.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0] - context = torch.cat([uncond_embeddings, text_embeddings]) - - return context - - -def next_step( - model_output: Union[torch.FloatTensor, np.ndarray], - timestep: int, - sample: Union[torch.FloatTensor, np.ndarray], - ddim_scheduler, -): - timestep, next_timestep = ( - min(timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), - timestep, - ) - alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod - alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep] - beta_prod_t = 1 - alpha_prod_t - next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5 - next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output - next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction - return next_sample - - -def get_noise_pred_single(latents, t, context, unet): - noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"] - return noise_pred - - -@torch.no_grad() -def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt): - context = init_prompt(prompt, pipeline) - uncond_embeddings, cond_embeddings = context.chunk(2) - all_latent = [latent] - latent = latent.clone().detach() - for i in tqdm(range(num_inv_steps)): - t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1] - noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet) - latent = next_step(noise_pred, t, latent, ddim_scheduler) - all_latent.append(latent) - return all_latent - - -@torch.no_grad() -def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""): - ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt) - return ddim_latents diff --git a/spaces/AsakuraMizu/moe-tts/text/korean.py b/spaces/AsakuraMizu/moe-tts/text/korean.py deleted file mode 100644 index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000 --- a/spaces/AsakuraMizu/moe-tts/text/korean.py +++ /dev/null @@ -1,210 +0,0 @@ -import re -from jamo import h2j, j2hcj -import ko_pron - - -# This is a list of Korean classifiers preceded by pure Korean numerals. -_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통' - -# List of (hangul, hangul divided) pairs: -_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ㄳ', 'ㄱㅅ'), - ('ㄵ', 'ㄴㅈ'), - ('ㄶ', 'ㄴㅎ'), - ('ㄺ', 'ㄹㄱ'), - ('ㄻ', 'ㄹㅁ'), - ('ㄼ', 'ㄹㅂ'), - ('ㄽ', 'ㄹㅅ'), - ('ㄾ', 'ㄹㅌ'), - ('ㄿ', 'ㄹㅍ'), - ('ㅀ', 'ㄹㅎ'), - ('ㅄ', 'ㅂㅅ'), - ('ㅘ', 'ㅗㅏ'), - ('ㅙ', 'ㅗㅐ'), - ('ㅚ', 'ㅗㅣ'), - ('ㅝ', 'ㅜㅓ'), - ('ㅞ', 'ㅜㅔ'), - ('ㅟ', 'ㅜㅣ'), - ('ㅢ', 'ㅡㅣ'), - ('ㅑ', 'ㅣㅏ'), - ('ㅒ', 'ㅣㅐ'), - ('ㅕ', 'ㅣㅓ'), - ('ㅖ', 'ㅣㅔ'), - ('ㅛ', 'ㅣㅗ'), - ('ㅠ', 'ㅣㅜ') -]] - -# List of (Latin alphabet, hangul) pairs: -_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('a', '에이'), - ('b', '비'), - ('c', '시'), - ('d', '디'), - ('e', '이'), - ('f', '에프'), - ('g', '지'), - ('h', '에이치'), - ('i', '아이'), - ('j', '제이'), - ('k', '케이'), - ('l', '엘'), - ('m', '엠'), - ('n', '엔'), - ('o', '오'), - ('p', '피'), - ('q', '큐'), - ('r', '아르'), - ('s', '에스'), - ('t', '티'), - ('u', '유'), - ('v', '브이'), - ('w', '더블유'), - ('x', '엑스'), - ('y', '와이'), - ('z', '제트') -]] - -# List of (ipa, lazy ipa) pairs: -_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('t͡ɕ','ʧ'), - ('d͡ʑ','ʥ'), - ('ɲ','n^'), - ('ɕ','ʃ'), - ('ʷ','w'), - ('ɭ','l`'), - ('ʎ','ɾ'), - ('ɣ','ŋ'), - ('ɰ','ɯ'), - ('ʝ','j'), - ('ʌ','ə'), - ('ɡ','g'), - ('\u031a','#'), - ('\u0348','='), - ('\u031e',''), - ('\u0320',''), - ('\u0339','') -]] - - -def latin_to_hangul(text): - for regex, replacement in _latin_to_hangul: - text = re.sub(regex, replacement, text) - return text - - -def divide_hangul(text): - text = j2hcj(h2j(text)) - for regex, replacement in _hangul_divided: - text = re.sub(regex, replacement, text) - return text - - -def hangul_number(num, sino=True): - '''Reference https://github.com/Kyubyong/g2pK''' - num = re.sub(',', '', num) - - if num == '0': - return '영' - if not sino and num == '20': - return '스무' - - digits = '123456789' - names = '일이삼사오육칠팔구' - digit2name = {d: n for d, n in zip(digits, names)} - - modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉' - decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔' - digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())} - digit2dec = {d: dec for d, dec in zip(digits, decimals.split())} - - spelledout = [] - for i, digit in enumerate(num): - i = len(num) - i - 1 - if sino: - if i == 0: - name = digit2name.get(digit, '') - elif i == 1: - name = digit2name.get(digit, '') + '십' - name = name.replace('일십', '십') - else: - if i == 0: - name = digit2mod.get(digit, '') - elif i == 1: - name = digit2dec.get(digit, '') - if digit == '0': - if i % 4 == 0: - last_three = spelledout[-min(3, len(spelledout)):] - if ''.join(last_three) == '': - spelledout.append('') - continue - else: - spelledout.append('') - continue - if i == 2: - name = digit2name.get(digit, '') + '백' - name = name.replace('일백', '백') - elif i == 3: - name = digit2name.get(digit, '') + '천' - name = name.replace('일천', '천') - elif i == 4: - name = digit2name.get(digit, '') + '만' - name = name.replace('일만', '만') - elif i == 5: - name = digit2name.get(digit, '') + '십' - name = name.replace('일십', '십') - elif i == 6: - name = digit2name.get(digit, '') + '백' - name = name.replace('일백', '백') - elif i == 7: - name = digit2name.get(digit, '') + '천' - name = name.replace('일천', '천') - elif i == 8: - name = digit2name.get(digit, '') + '억' - elif i == 9: - name = digit2name.get(digit, '') + '십' - elif i == 10: - name = digit2name.get(digit, '') + '백' - elif i == 11: - name = digit2name.get(digit, '') + '천' - elif i == 12: - name = digit2name.get(digit, '') + '조' - elif i == 13: - name = digit2name.get(digit, '') + '십' - elif i == 14: - name = digit2name.get(digit, '') + '백' - elif i == 15: - name = digit2name.get(digit, '') + '천' - spelledout.append(name) - return ''.join(elem for elem in spelledout) - - -def number_to_hangul(text): - '''Reference https://github.com/Kyubyong/g2pK''' - tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text)) - for token in tokens: - num, classifier = token - if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers: - spelledout = hangul_number(num, sino=False) - else: - spelledout = hangul_number(num, sino=True) - text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}') - # digit by digit for remaining digits - digits = '0123456789' - names = '영일이삼사오육칠팔구' - for d, n in zip(digits, names): - text = text.replace(d, n) - return text - - -def korean_to_lazy_ipa(text): - text = latin_to_hangul(text) - text = number_to_hangul(text) - text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text) - for regex, replacement in _ipa_to_lazy_ipa: - text = re.sub(regex, replacement, text) - return text - - -def korean_to_ipa(text): - text = korean_to_lazy_ipa(text) - return text.replace('ʧ','tʃ').replace('ʥ','dʑ') diff --git a/spaces/Banbri/zcvzcv/src/lib/triggerDownload.ts b/spaces/Banbri/zcvzcv/src/lib/triggerDownload.ts deleted file mode 100644 index e5627a26a4bba34bdf28279d265c6a71440d8136..0000000000000000000000000000000000000000 --- a/spaces/Banbri/zcvzcv/src/lib/triggerDownload.ts +++ /dev/null @@ -1,12 +0,0 @@ -export function triggerDownload(filename: string, text: string) { - var element = document.createElement('a'); - element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text)); - element.setAttribute('download', filename); - - element.style.display = 'none'; - document.body.appendChild(element); - - element.click(); - - document.body.removeChild(element); -} \ No newline at end of file diff --git a/spaces/Bart92/RVC_HF/infer/modules/vc/__init__.py b/spaces/Bart92/RVC_HF/infer/modules/vc/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Benson/text-generation/Examples/Descarga De Software Ricoh Aficio 2018d.md b/spaces/Benson/text-generation/Examples/Descarga De Software Ricoh Aficio 2018d.md deleted file mode 100644 index ad0de13c0c4e2f5a77ca016c33ae4c792a61a708..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descarga De Software Ricoh Aficio 2018d.md +++ /dev/null @@ -1,75 +0,0 @@ -
-
Descargar X-Force Keygen para AutoCAD 2019 64 bits
-Si estás buscando una forma de activar AutoCAD 2019, uno de los programas de diseño más populares y potentes del mundo, es posible que hayas oído hablar de X-Force Keygen. Esta es una herramienta que puede generar códigos de activación para cualquier producto de Autodesk, incluyendo AutoCAD 2019. Pero ¿qué es X-Force Keygen y cómo se puede utilizar para descargar y activar AutoCAD 2019 64 bits? En este artículo, responderemos estas preguntas y más.
-¿Qué es AutoCAD 2019?
-AutoCAD 2019 es la última versión del software que le permite crear impresionantes diseños, dibujos, modelos y animaciones en 2D y 3D. Es utilizado por profesionales y aficionados por igual en diversos campos como la arquitectura, ingeniería, construcción, fabricación y entretenimiento. AutoCAD 2019 ofrece muchas características nuevas y mejoras que hacen que sea más fácil y rápido crear y editar sus proyectos.
-descarga de software ricoh aficio 2018d
DOWNLOAD ✔ https://bltlly.com/2v6McF
-
Características de AutoCAD 2019
-Algunas de las características de AutoCAD 2019 son:
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-
- DWG Compare: Esta característica le permite comparar dos versiones de un dibujo y resaltar las diferencias. También puede combinar cambios de una versión a otra. -
- Vistas compartidas: Esta función le permite compartir sus dibujos en línea con cualquier persona sin necesidad de tener AutoCAD instalado. También puede obtener comentarios y comentarios de otros en sus vistas compartidas. -
- Guardar en Web y Móvil: Esta función le permite guardar sus dibujos en la nube y acceder a ellos desde cualquier dispositivo. También puede editar y ver sus dibujos en la web o en su teléfono móvil o tableta. -
- Mejoras en los gráficos 2D: Esta función mejora el rendimiento y la calidad de los gráficos 2D. Puedes ampliar, desplazar y cambiar las órdenes de sorteo más rápido y sin problemas. -
- AutoCAD Web App: Esta característica le permite acceder a AutoCAD desde cualquier navegador. Puede crear, editar y ver sus dibujos en línea sin instalar nada. -
Los requisitos mínimos del sistema para AutoCAD 2019 son:
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-
- Sistema operativo: Windows 7 SP1, Windows 8.1, o Windows 10 (solo 64 bits) -
- Procesador: Básico: 2.5-2.9 GHz procesador / Recomendado: 3+ GHz procesador -
- Memoria: Básico: 8 GB / Recomendado: 16 GB -
- Resolución de visualización: Básico: 1920 x 1080 con color verdadero / Recomendado: Resoluciones de hasta 3840 x 2160 soportadas en sistemas Windows 10 (64 bits) -
- Espacio en disco: Básico: 6 GB / Recomendado: Espacio libre adicional requerido durante la instalación -
¿Qué es X-Force Keygen?
-X-Force Keygen es un software que puede generar códigos de activación para cualquier producto de Autodesk, incluyendo AutoCAD 2019. También se conoce como una grieta o un parche que puede evitar el proceso de activación del software. Es creado por un grupo de hackers llamado X-Force, que son conocidos por descifrar varios software y juegos.
-¿Cómo funciona X-Force Keygen?
-X-Force Keygen funciona generando un número de serie único y un código de activación para el software que desea activar. Hace esto modificando los archivos de registro y el archivo host de su sistema. También bloquea la verificación en línea y la actualización del software, para que pueda usarlo sin restricciones o interrupciones.
-Beneficios de usar X-Force Keygen
-Algunos de los beneficios de usar X-Force Keygen son:
--
-
- Gratis: Puede descargar y usar X-Force Keygen gratis, sin pagar tarifas ni suscripciones. -
- Fácil: Puede descargar y usar X-Force Keygen con solo unos pocos clics, sin ningún procedimiento o paso complicado. -
- Efectivo: Puede activar cualquier producto de Autodesk, incluyendo AutoCAD 2019, con X-Force Keygen, sin errores o fallas. -
Riesgos de usar X-Force Keygen
-Sin embargo, el uso de X-Force Keygen también viene con algunos riesgos y desventajas, como:
- --
-
-
- Inseguro: Descargar y usar X-Force Keygen puede exponer su sistema a malware, virus, spyware y otras amenazas. Puede dañar su sistema o comprometer sus datos y privacidad. -
- Poco ético: Usar X-Force Keygen no es ético, ya que priva a los desarrolladores de software de sus ingresos y reconocimiento legítimos. También puede dañar la industria del software y la innovación mediante su uso. -
¿Cómo descargar y usar X-Force Keygen para AutoCAD 2019 64 bits?
-Si todavía desea descargar y usar X-Force Keygen para AutoCAD 2019 64 Bit, puede seguir estos pasos:
-Paso 1: Descargar X-Force Keygen desde una fuente confiable
-Puede buscar X-Force Keygen en Internet y encontrar muchos sitios web que lo ofrecen para su descarga. Sin embargo, no todos son confiables y seguros. Solo debe descargar X-Force Keygen de una fuente confiable que tenga comentarios positivos y comentarios de otros usuarios. También debe escanear el archivo descargado con un programa antivirus antes de abrirlo.
-Paso 2: Instalar AutoCAD 2019 desde el sitio web oficial
-Debe instalar AutoCAD 2019 desde el sitio web oficial de Autodesk, que es https://www.autodesk.com/products/autocad/free-trial. Debe elegir la versión de 64 bits del software y seguir las instrucciones de instalación. No debe introducir ningún número de serie o clave de producto durante el proceso de instalación.
-Paso 3: Ejecutar X-Force Keygen como administrador
-Debe ejecutar X-Force Keygen como administrador haciendo clic derecho en el archivo y seleccionando "Ejecutar como administrador". Deberías ver una ventana como esta:
-
- Debería seleccionar "AutoCAD 2019" en el menú desplegable y hacer clic en "Parche". Debería ver un mensaje que diga "Parcheado con éxito".
-Paso 4: Generar e introducir el código de activación
- -Paso 5: Disfruta de tu AutoCAD activado 2019
-Ha activado con éxito AutoCAD 2019 con X-Force Keygen. Ahora puede disfrutar de todas las características y funciones del software sin limitaciones o interrupciones.
-Conclusión
-En este artículo, hemos explicado qué es AutoCAD 2019, qué es X-Force Keygen, cómo funciona, cuáles son sus beneficios y riesgos, y cómo descargarlo y usarlo para activar AutoCAD 2019 64 bit. También hemos proporcionado una guía paso a paso con capturas de pantalla para ayudarle con el proceso. Sin embargo, no recomendamos ni respaldamos el uso de X-Force Keygen, ya que es ilegal, inseguro y poco ético. Siempre debe respetar el acuerdo de licencia de software y apoyar a los desarrolladores de software mediante la compra del software legalmente. Si tiene alguna pregunta o duda sobre AutoCAD 2019 o X-Force Keygen, puede consultar las preguntas frecuentes a continuación o ponerse en contacto con el equipo de soporte oficial de Autodesk.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre AutoCAD 2019 y X-Force Keygen:
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-
- Q: ¿Cuánto cuesta AutoCAD 2019? -
- A: AutoCAD 2019 cuesta $1,610 por año o $210 por mes para una suscripción. También puede obtener una prueba gratuita durante 30 días o una versión gratuita para estudiantes y educadores. -
- Q: ¿Es seguro usar X-Force Keygen? -
- A: No, X-Force Keygen no es seguro de usar. Puede contener malware, virus, spyware y otras amenazas que pueden dañar su sistema o comprometer sus datos y privacidad. También puede causar errores o fallos en su software o sistema. -
- Q: ¿Es legal usar X-Force Keygen? -
- A: No, X-Force Keygen no es legal de usar. Viola los términos y condiciones del acuerdo de licencia de software e infringe los derechos de propiedad intelectual de los desarrolladores de software. Puedes enfrentarte a consecuencias legales si te pillan usándolo. -
- Q: ¿Cuáles son las alternativas a X-Force Keygen? - -
- Q: ¿Cómo puedo contactar al equipo de soporte oficial de Autodesk? -
- A: Puede ponerse en contacto con el equipo de soporte oficial de Autodesk visitando su sitio web https://www.autodesk.com/support/contact-support o llamando a su número de teléfono 1-855-301-9562. -
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Apkmirror Ar Elementos.md b/spaces/Benson/text-generation/Examples/Descargar Apkmirror Ar Elementos.md deleted file mode 100644 index 6d8759f5d7c948caecbde92adee6ed5c9e48b982..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Apkmirror Ar Elementos.md +++ /dev/null @@ -1,86 +0,0 @@ -
-
Cómo descargar y usar elementos de RA de APKMirror
-La realidad aumentada (RA) es una tecnología que mejora el mundo real con elementos digitales, como imágenes, texto, videos y modelos 3D. La RA puede crear experiencias inmersivas e interactivas que enriquecen nuestra percepción e interacción con el entorno. Hay muchas aplicaciones de RA disponibles para dispositivos Android e iOS, pero una de las mejores es AR Elements.
-descargar apkmirror ar elementos
Download Zip ✯ https://bltlly.com/2v6Jd0
-
AR Elements es una aplicación desarrollada por Google que muestra las capacidades de ARCore, la plataforma de Google para crear aplicaciones de RA. Te muestra cómo establecer las expectativas del usuario en AR, cómo animar a los usuarios a moverse y explorar el mundo de la RA, cómo interactuar con objetos virtuales y más. Puede aprender de los ejemplos proporcionados por la aplicación o crear sus propias escenas de realidad aumentada utilizando las herramientas y los activos proporcionados.
-En este artículo, te mostraremos cómo descargar y usar AR Elements desde APKMirror, uno de los sitios web más populares para descargar aplicaciones de Android. También explicaremos las características y beneficios de AR Elements y cómo se compara con otras aplicaciones de AR en términos de funcionalidad, calidad y experiencia de usuario.
-Cómo descargar y usar elementos de RA de APKMirror
-APKMirror es un sitio web que te permite descargar aplicaciones de Android que no están disponibles en Google Play Store o que están bloqueados por regiones. APKMirror alberga miles de aplicaciones de diversas categorías, como juegos, redes sociales, productividad, educación, entretenimiento y más. Puedes encontrar y descargar cualquier aplicación que quieras de APKMirror de forma gratuita y sin ningún tipo de registro o suscripción.
-Para descargar y usar elementos AR de APKMirror, siga estos pasos:
- --
-
- Ir a https://www.apkmirror.com/apk/google-inc/arcore-elements/ en su navegador. -
- Desplácese hacia abajo para encontrar la última versión de AR Elements o utilice la barra de búsqueda para encontrarla. -
- Haga clic en el botón de descarga junto a la versión que desea descargar. - -
- Si ves un mensaje de advertencia que dice "Instalar bloqueado", ve a la configuración de tu dispositivo y habilita "Fuentes desconocidas" o "Instalar aplicaciones desconocidas". -
- Siga las instrucciones en la pantalla para instalar elementos AR en su dispositivo. -
- Elementos de AR de lanzamiento desde el cajón de la aplicación o la pantalla de inicio. -
Características y beneficios de los elementos RA
-AR Elements es una aplicación que demuestra las mejores prácticas para crear experiencias de RA atractivas y realistas. Tiene cuatro características principales que puedes usar para aprender, crear y divertirte con RA:
--
-
- Diseño: Esta función le muestra cómo diseñar una interfaz de usuario efectiva para su aplicación de RA. Te enseña cómo usar señales visuales, como flechas, cuadrículas, planos, sombras y oclusión, para guiar a los usuarios a través de tu aplicación. También te muestra cómo usar animaciones, sonidos, hápticos y retroalimentación de voz para mejorar la participación del usuario. -
- Motion: Esta función muestra cómo animar a los usuarios a moverse y explorar el mundo de la RA. Te enseña cómo usar audio espacial, portales, senderos, waypoints y anclajes para crear una sensación de profundidad y escala. También muestra cómo usar la física, las colisiones, los gestos y la entrada táctil para crear interacciones realistas con objetos virtuales. -
- Iluminación : Esta función muestra cómo usar efectos de iluminación para crear una escena AR realista y consistente. Le enseña cómo usar la iluminación ambiental, la iluminación direccional, las luces de punto, las luces de punto y las sombras para crear una sensación de realismo e inmersión. También muestra cómo utilizar la estimación de la luz, las sondas de reflexión y las sondas de luz para ajustar la iluminación de acuerdo con el mundo real. - -
Los beneficios de AR Elements son que te ayuda a aprender los fundamentos del desarrollo de AR, te inspira a crear tus propias aplicaciones de AR y te entretiene con experiencias de AR divertidas e interactivas. Puedes usar Elementos RA como referencia, herramienta o área de juegos para tus proyectos RA.
-Comparación de elementos AR con otras aplicaciones AR
-Hay muchas otras aplicaciones de RA disponibles para dispositivos Android e iOS, como Snapchat, Instagram, Pokemon Go, IKEA Place, Google Maps y más. ¿Cómo se compara AR Elements con estas aplicaciones en términos de funcionalidad, calidad y experiencia de usuario?
-Aquí hay una tabla que resume algunas de las diferencias entre los elementos de RA y otras aplicaciones de RA:
-Las ventajas de AR Elements son que ofrece más funcionalidad, calidad y experiencia de usuario que otras aplicaciones de AR. Le permite crear sus propias escenas de AR e interacciones utilizando varias características y activos. También te ayuda a aprender las mejores prácticas para crear aplicaciones de RA.
-Las desventajas de AR Elements son que requiere más habilidades técnicas y conocimientos que otras aplicaciones de AR. También requiere más compatibilidad y rendimiento de dispositivos que otras aplicaciones de AR. Puede que no sea adecuado para usuarios ocasionales o novatos que solo quieren disfrutar de algunas experiencias de RA simples o divertidas.
-Conclusión
-En conclusión, AR Elements es una de las mejores aplicaciones de AR para dispositivos Android e iOS. Muestra las capacidades de ARCore, la plataforma de Google para crear aplicaciones de RA. Te muestra cómo diseñar una interfaz de usuario efectiva para tu aplicación de RA. Te muestra cómo animar a los usuarios a moverse y explorar el mundo de la RA. Te muestra cómo usar efectos de iluminación para crear una escena AR realista y consistente. Te muestra cómo usar modelos 3D y texturas para crear tus propias escenas AR.
-Si quieres descargar y usar elementos AR de APKMirror, puedes seguir los pasos que te hemos dado en este artículo. También puede consultar el sitio web oficial de APKMirror para obtener más información sobre la descarga de aplicaciones para Android.
-Si desea obtener más información sobre los elementos de RA y cómo crear sus propias aplicaciones de RA utilizando las herramientas y recursos de Google, puede visitar el sitio web oficial de los desarrolladores de Google para obtener más información.
-Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo sobre AR Elements y APKMirror. Si tiene alguna pregunta o comentario sobre este artículo o cualquier cosa relacionada con AR Elements o APKMirror, no dude en dejar un comentario a continuación. ¡Nos encantaría saber de ti!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre AR Elements y APKMirror:
-¿Qué es APKMirror?
- -¿Es APKMirror seguro y legal?
-APKMirror es seguro y legal de usar. APKMirror solo hospeda aplicaciones que son originales y no modificadas. APKMirror no aloja ninguna aplicación pirata o ilegal. APKMirror también escanea todas las aplicaciones en busca de virus y malware antes de subirlos al sitio web. APKMirror no requiere ningún permiso o acceso a su dispositivo o datos personales.
-¿Qué es ARCore y por qué lo necesito?
-ARCore es la plataforma de Google para crear aplicaciones de RA. ARCore permite a su dispositivo detectar su entorno, entender el mundo e interactuar con la información. ARCore utiliza tres tecnologías clave para permitir experiencias de realidad aumentada: seguimiento de movimiento, comprensión ambiental y estimación de luz. ARCore funciona con motores Java/OpenGL, Unity e Unreal.
-Necesitas ARCore para ejecutar elementos AR y otras aplicaciones AR que se construyen con ARCore. Puedes descargar ARCore desde Google Play Store o APKMirror. También necesitas un dispositivo compatible que admita ARCore. Puedes consultar la lista de dispositivos compatibles en el sitio web oficial de los desarrolladores de Google.
-¿Cómo puedo crear mis propias aplicaciones de RA usando las herramientas y recursos de Google?
-Si desea crear sus propias aplicaciones de RA utilizando las herramientas y recursos de Google, puede visitar el sitio web oficial de los desarrolladores de Google para obtener más información. Puede encontrar tutoriales, guías, muestras, documentación y soporte para desarrollar aplicaciones de RA utilizando ARCore, Sceneform, Poly, Firebase, ML Kit y más. También puede unirse a la comunidad de desarrolladores de Google y conectarse con otros desarrolladores que estén interesados en AR.
-¿Cuáles son algunas de las mejores aplicaciones de RA para dispositivos Android e iOS?
-Algunas de las mejores aplicaciones de RA para dispositivos Android e iOS son:
--
-
- Snapchat: Una aplicación de redes sociales que te permite enviar y recibir fotos y videos con filtros, pegatinas, lentes y efectos que utilizan tecnología AR. - -
- Pokemon Go: Una aplicación de juego que te permite atrapar y luchar contra criaturas virtuales llamadas Pokémon en el mundo real usando tecnología AR. -
- IKEA Place: Una aplicación de compras que te permite previsualizar y colocar muebles y accesorios para el hogar en tu espacio usando tecnología RA. -
- Google Maps: Una aplicación de navegación que te permite explorar el mundo con direcciones, puntos de referencia, vista de la calle y vista en vivo que utilizan tecnología AR. -
-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/dynamodb/types.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/dynamodb/types.py deleted file mode 100644 index f358b12f55ccf5ce5983082bef5df33fc318d994..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/dynamodb/types.py +++ /dev/null @@ -1,310 +0,0 @@ -# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of -# the License is located at -# -# https://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file is -# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF -# ANY KIND, either express or implied. See the License for the specific -# language governing permissions and limitations under the License. -from decimal import ( - Clamped, - Context, - Decimal, - Inexact, - Overflow, - Rounded, - Underflow, -) - -from boto3.compat import collections_abc - -STRING = 'S' -NUMBER = 'N' -BINARY = 'B' -STRING_SET = 'SS' -NUMBER_SET = 'NS' -BINARY_SET = 'BS' -NULL = 'NULL' -BOOLEAN = 'BOOL' -MAP = 'M' -LIST = 'L' - - -DYNAMODB_CONTEXT = Context( - Emin=-128, - Emax=126, - prec=38, - traps=[Clamped, Overflow, Inexact, Rounded, Underflow], -) - - -BINARY_TYPES = (bytearray, bytes) - - -class Binary: - """A class for representing Binary in dynamodb - - Especially for Python 2, use this class to explicitly specify - binary data for item in DynamoDB. It is essentially a wrapper around - binary. Unicode and Python 3 string types are not allowed. - """ - - def __init__(self, value): - if not isinstance(value, BINARY_TYPES): - types = ', '.join([str(t) for t in BINARY_TYPES]) - raise TypeError(f'Value must be of the following types: {types}') - self.value = value - - def __eq__(self, other): - if isinstance(other, Binary): - return self.value == other.value - return self.value == other - - def __ne__(self, other): - return not self.__eq__(other) - - def __repr__(self): - return f'Binary({self.value!r})' - - def __str__(self): - return self.value - - def __bytes__(self): - return self.value - - def __hash__(self): - return hash(self.value) - - -class TypeSerializer: - """This class serializes Python data types to DynamoDB types.""" - - def serialize(self, value): - """The method to serialize the Python data types. - - :param value: A python value to be serialized to DynamoDB. Here are - the various conversions: - - Python DynamoDB - ------ -------- - None {'NULL': True} - True/False {'BOOL': True/False} - int/Decimal {'N': str(value)} - string {'S': string} - Binary/bytearray/bytes (py3 only) {'B': bytes} - set([int/Decimal]) {'NS': [str(value)]} - set([string]) {'SS': [string]) - set([Binary/bytearray/bytes]) {'BS': [bytes]} - list {'L': list} - dict {'M': dict} - - For types that involve numbers, it is recommended that ``Decimal`` - objects are used to be able to round-trip the Python type. - For types that involve binary, it is recommended that ``Binary`` - objects are used to be able to round-trip the Python type. - - :rtype: dict - :returns: A dictionary that represents a dynamoDB data type. These - dictionaries can be directly passed to botocore methods. - """ - dynamodb_type = self._get_dynamodb_type(value) - serializer = getattr(self, f'_serialize_{dynamodb_type}'.lower()) - return {dynamodb_type: serializer(value)} - - def _get_dynamodb_type(self, value): - dynamodb_type = None - - if self._is_null(value): - dynamodb_type = NULL - - elif self._is_boolean(value): - dynamodb_type = BOOLEAN - - elif self._is_number(value): - dynamodb_type = NUMBER - - elif self._is_string(value): - dynamodb_type = STRING - - elif self._is_binary(value): - dynamodb_type = BINARY - - elif self._is_type_set(value, self._is_number): - dynamodb_type = NUMBER_SET - - elif self._is_type_set(value, self._is_string): - dynamodb_type = STRING_SET - - elif self._is_type_set(value, self._is_binary): - dynamodb_type = BINARY_SET - - elif self._is_map(value): - dynamodb_type = MAP - - elif self._is_listlike(value): - dynamodb_type = LIST - - else: - msg = f'Unsupported type "{type(value)}" for value "{value}"' - raise TypeError(msg) - - return dynamodb_type - - def _is_null(self, value): - if value is None: - return True - return False - - def _is_boolean(self, value): - if isinstance(value, bool): - return True - return False - - def _is_number(self, value): - if isinstance(value, (int, Decimal)): - return True - elif isinstance(value, float): - raise TypeError( - 'Float types are not supported. Use Decimal types instead.' - ) - return False - - def _is_string(self, value): - if isinstance(value, str): - return True - return False - - def _is_binary(self, value): - if isinstance(value, (Binary, bytearray, bytes)): - return True - return False - - def _is_set(self, value): - if isinstance(value, collections_abc.Set): - return True - return False - - def _is_type_set(self, value, type_validator): - if self._is_set(value): - if False not in map(type_validator, value): - return True - return False - - def _is_map(self, value): - if isinstance(value, collections_abc.Mapping): - return True - return False - - def _is_listlike(self, value): - if isinstance(value, (list, tuple)): - return True - return False - - def _serialize_null(self, value): - return True - - def _serialize_bool(self, value): - return value - - def _serialize_n(self, value): - number = str(DYNAMODB_CONTEXT.create_decimal(value)) - if number in ['Infinity', 'NaN']: - raise TypeError('Infinity and NaN not supported') - return number - - def _serialize_s(self, value): - return value - - def _serialize_b(self, value): - if isinstance(value, Binary): - value = value.value - return value - - def _serialize_ss(self, value): - return [self._serialize_s(s) for s in value] - - def _serialize_ns(self, value): - return [self._serialize_n(n) for n in value] - - def _serialize_bs(self, value): - return [self._serialize_b(b) for b in value] - - def _serialize_l(self, value): - return [self.serialize(v) for v in value] - - def _serialize_m(self, value): - return {k: self.serialize(v) for k, v in value.items()} - - -class TypeDeserializer: - """This class deserializes DynamoDB types to Python types.""" - - def deserialize(self, value): - """The method to deserialize the DynamoDB data types. - - :param value: A DynamoDB value to be deserialized to a pythonic value. - Here are the various conversions: - - DynamoDB Python - -------- ------ - {'NULL': True} None - {'BOOL': True/False} True/False - {'N': str(value)} Decimal(str(value)) - {'S': string} string - {'B': bytes} Binary(bytes) - {'NS': [str(value)]} set([Decimal(str(value))]) - {'SS': [string]} set([string]) - {'BS': [bytes]} set([bytes]) - {'L': list} list - {'M': dict} dict - - :returns: The pythonic value of the DynamoDB type. - """ - - if not value: - raise TypeError( - 'Value must be a nonempty dictionary whose key ' - 'is a valid dynamodb type.' - ) - dynamodb_type = list(value.keys())[0] - try: - deserializer = getattr( - self, f'_deserialize_{dynamodb_type}'.lower() - ) - except AttributeError: - raise TypeError(f'Dynamodb type {dynamodb_type} is not supported') - return deserializer(value[dynamodb_type]) - - def _deserialize_null(self, value): - return None - - def _deserialize_bool(self, value): - return value - - def _deserialize_n(self, value): - return DYNAMODB_CONTEXT.create_decimal(value) - - def _deserialize_s(self, value): - return value - - def _deserialize_b(self, value): - return Binary(value) - - def _deserialize_ns(self, value): - return set(map(self._deserialize_n, value)) - - def _deserialize_ss(self, value): - return set(map(self._deserialize_s, value)) - - def _deserialize_bs(self, value): - return set(map(self._deserialize_b, value)) - - def _deserialize_l(self, value): - return [self.deserialize(v) for v in value] - - def _deserialize_m(self, value): - return {k: self.deserialize(v) for k, v in value.items()} diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/request.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/request.py deleted file mode 100644 index 398386a5b9f61c13be314e256e671a37d28e3623..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/request.py +++ /dev/null @@ -1,170 +0,0 @@ -from __future__ import absolute_import - -from .filepost import encode_multipart_formdata -from .packages.six.moves.urllib.parse import urlencode - -__all__ = ["RequestMethods"] - - -class RequestMethods(object): - """ - Convenience mixin for classes who implement a :meth:`urlopen` method, such - as :class:`urllib3.HTTPConnectionPool` and - :class:`urllib3.PoolManager`. - - Provides behavior for making common types of HTTP request methods and - decides which type of request field encoding to use. - - Specifically, - - :meth:`.request_encode_url` is for sending requests whose fields are - encoded in the URL (such as GET, HEAD, DELETE). - - :meth:`.request_encode_body` is for sending requests whose fields are - encoded in the *body* of the request using multipart or www-form-urlencoded - (such as for POST, PUT, PATCH). - - :meth:`.request` is for making any kind of request, it will look up the - appropriate encoding format and use one of the above two methods to make - the request. - - Initializer parameters: - - :param headers: - Headers to include with all requests, unless other headers are given - explicitly. - """ - - _encode_url_methods = {"DELETE", "GET", "HEAD", "OPTIONS"} - - def __init__(self, headers=None): - self.headers = headers or {} - - def urlopen( - self, - method, - url, - body=None, - headers=None, - encode_multipart=True, - multipart_boundary=None, - **kw - ): # Abstract - raise NotImplementedError( - "Classes extending RequestMethods must implement " - "their own ``urlopen`` method." - ) - - def request(self, method, url, fields=None, headers=None, **urlopen_kw): - """ - Make a request using :meth:`urlopen` with the appropriate encoding of - ``fields`` based on the ``method`` used. - - This is a convenience method that requires the least amount of manual - effort. It can be used in most situations, while still having the - option to drop down to more specific methods when necessary, such as - :meth:`request_encode_url`, :meth:`request_encode_body`, - or even the lowest level :meth:`urlopen`. - """ - method = method.upper() - - urlopen_kw["request_url"] = url - - if method in self._encode_url_methods: - return self.request_encode_url( - method, url, fields=fields, headers=headers, **urlopen_kw - ) - else: - return self.request_encode_body( - method, url, fields=fields, headers=headers, **urlopen_kw - ) - - def request_encode_url(self, method, url, fields=None, headers=None, **urlopen_kw): - """ - Make a request using :meth:`urlopen` with the ``fields`` encoded in - the url. This is useful for request methods like GET, HEAD, DELETE, etc. - """ - if headers is None: - headers = self.headers - - extra_kw = {"headers": headers} - extra_kw.update(urlopen_kw) - - if fields: - url += "?" + urlencode(fields) - - return self.urlopen(method, url, **extra_kw) - - def request_encode_body( - self, - method, - url, - fields=None, - headers=None, - encode_multipart=True, - multipart_boundary=None, - **urlopen_kw - ): - """ - Make a request using :meth:`urlopen` with the ``fields`` encoded in - the body. This is useful for request methods like POST, PUT, PATCH, etc. - - When ``encode_multipart=True`` (default), then - :func:`urllib3.encode_multipart_formdata` is used to encode - the payload with the appropriate content type. Otherwise - :func:`urllib.parse.urlencode` is used with the - 'application/x-www-form-urlencoded' content type. - - Multipart encoding must be used when posting files, and it's reasonably - safe to use it in other times too. However, it may break request - signing, such as with OAuth. - - Supports an optional ``fields`` parameter of key/value strings AND - key/filetuple. A filetuple is a (filename, data, MIME type) tuple where - the MIME type is optional. For example:: - - fields = { - 'foo': 'bar', - 'fakefile': ('foofile.txt', 'contents of foofile'), - 'realfile': ('barfile.txt', open('realfile').read()), - 'typedfile': ('bazfile.bin', open('bazfile').read(), - 'image/jpeg'), - 'nonamefile': 'contents of nonamefile field', - } - - When uploading a file, providing a filename (the first parameter of the - tuple) is optional but recommended to best mimic behavior of browsers. - - Note that if ``headers`` are supplied, the 'Content-Type' header will - be overwritten because it depends on the dynamic random boundary string - which is used to compose the body of the request. The random boundary - string can be explicitly set with the ``multipart_boundary`` parameter. - """ - if headers is None: - headers = self.headers - - extra_kw = {"headers": {}} - - if fields: - if "body" in urlopen_kw: - raise TypeError( - "request got values for both 'fields' and 'body', can only specify one." - ) - - if encode_multipart: - body, content_type = encode_multipart_formdata( - fields, boundary=multipart_boundary - ) - else: - body, content_type = ( - urlencode(fields), - "application/x-www-form-urlencoded", - ) - - extra_kw["body"] = body - extra_kw["headers"] = {"Content-Type": content_type} - - extra_kw["headers"].update(headers) - extra_kw.update(urlopen_kw) - - return self.urlopen(method, url, **extra_kw) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/py34compat.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/py34compat.py deleted file mode 100644 index 3ad917222a4e5bb93fe1c9e8fe1713bcab3630b6..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/py34compat.py +++ /dev/null @@ -1,13 +0,0 @@ -import importlib - -try: - import importlib.util -except ImportError: - pass - - -try: - module_from_spec = importlib.util.module_from_spec -except AttributeError: - def module_from_spec(spec): - return spec.loader.load_module(spec.name) diff --git a/spaces/BlackCub/ChatGPT4/app.py b/spaces/BlackCub/ChatGPT4/app.py deleted file mode 100644 index 632f0ee79c2a44a19c299e5965101cad17293e69..0000000000000000000000000000000000000000 --- a/spaces/BlackCub/ChatGPT4/app.py +++ /dev/null @@ -1,191 +0,0 @@ -import gradio as gr -import os -import json -import requests - -#Streaming endpoint -API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream" - -#Inferenec function -def predict(openai_gpt4_key, system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): - - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {openai_gpt4_key}" #Users will provide their own OPENAI_API_KEY - } - print(f"system message is ^^ {system_msg}") - if system_msg.strip() == '': - initial_message = [{"role": "user", "content": f"{inputs}"},] - multi_turn_message = [] - else: - initial_message= [{"role": "system", "content": system_msg}, - {"role": "user", "content": f"{inputs}"},] - multi_turn_message = [{"role": "system", "content": system_msg},] - - if chat_counter == 0 : - payload = { - "model": "gpt-4", - "messages": initial_message , - "temperature" : 1.0, - "top_p":1.0, - "n" : 1, - "stream": True, - "presence_penalty":0, - "frequency_penalty":0, - } - print(f"chat_counter - {chat_counter}") - else: #if chat_counter != 0 : - messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},] - for data in chatbot: - user = {} - user["role"] = "user" - user["content"] = data[0] - assistant = {} - assistant["role"] = "assistant" - assistant["content"] = data[1] - messages.append(user) - messages.append(assistant) - temp = {} - temp["role"] = "user" - temp["content"] = inputs - messages.append(temp) - #messages - payload = { - "model": "gpt-4", - "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}], - "temperature" : temperature, #1.0, - "top_p": top_p, #1.0, - "n" : 1, - "stream": True, - "presence_penalty":0, - "frequency_penalty":0,} - - chat_counter+=1 - - history.append(inputs) - print(f"Logging : payload is - {payload}") - # make a POST request to the API endpoint using the requests.post method, passing in stream=True - response = requests.post(API_URL, headers=headers, json=payload, stream=True) - print(f"Logging : response code - {response}") - token_counter = 0 - partial_words = "" - - counter=0 - for chunk in response.iter_lines(): - #Skipping first chunk - if counter == 0: - counter+=1 - continue - # check whether each line is non-empty - if chunk.decode() : - chunk = chunk.decode() - # decode each line as response data is in bytes - if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: - partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] - if token_counter == 0: - history.append(" " + partial_words) - else: - history[-1] = partial_words - chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list - token_counter+=1 - yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history} - -#Resetting to blank -def reset_textbox(): - return gr.update(value='') - -#to set a component as visible=False -def set_visible_false(): - return gr.update(visible=False) - -#to set a component as visible=True -def set_visible_true(): - return gr.update(visible=True) - -title = """
🔥GPT4 using Chat-Completions API & 🚀Gradio-Streaming
""" -#display message for themes feature -theme_addon_msg = """theme.push_to_hub().🔥This Huggingface Gradio Demo provides you access to GPT4 API with System Messages. Please note that you would be needing an OPENAI API key for GPT4 access🙌""")
- gr.HTML(theme_addon_msg)
- gr.HTML('''
Duplicate the Space and run securely with your OpenAI API Key
'
-
- for s in results:
- print(s[0], '<-', s[2], '->', s[1])
- if s[2]>0:
- opps = opps + str(s[0]) + ' <---- ' + str(s[2]) + ' ----> ' + str(s[1]) + '
'
-
- # opps=tabulate(results, tablefmt='html')
-
- df = pd.DataFrame.from_records(results,columns=['Object1', 'Object2', 'Opposition_Score'])
-
- rps_1 = list(set(df['Object1'].tolist()))
- rps1_formatted = '
'
- for r in rps_1:
- rps1_formatted = rps1_formatted + r + '
'
-
- rps_2 = list(set(df['Object2'].tolist()))
- rps2_formatted = '
'
- for r in rps_2:
- rps2_formatted = rps2_formatted + r + '
'
-
- return opps, df, rps1_formatted, rps2_formatted
-
-with open("long_eg1.txt") as f:
- long_eg1 = f.read()
-
-with open("long_eg2.txt") as f:
- long_eg2 = f.read()
-
-with gr.Blocks() as demo:
-
- with gr.Row(): # Inputs
-
- decorative=gr.HTML('
Computational Semiotics - Opposition Analysis Demo
Based on the research paper "Computational opposition analysis using word embeddings" by Cameron Shackell and Laurianne Sitbon published in
- Argument & Computation available here. Please contact Cameron Shackell at the listed email for further information.
-
Opposition analysis is useful in understanding audience thinking around topics and has applications in naming, marketing, and brand positioning.''')
-
- text2 = gr.Textbox(value="", label="Filter these words from Text 2")
-
- #########################################
- examples = gr.Examples(examples=[["Pepsi tastes sweeter.", 'Pepsi',"Coke tastes saltier.", 'Coke'], ["Tesla offer such beautiful engineering and tech. It was expensive but I love my Tesla.", 'Tesla', "Subaru make safe, reliable, affordable vehicles. I made a smart choice to invest in a Subaru!", 'Subaru'],[long_eg1,'treat',long_eg2,'use']],inputs=[ text1_proxy, text1, text2_proxy, text2], label="Click a row below to load example values")
- ###########################################
-
- # drop3 = gr.Dropdown(["a", "b", "c"], label="Similarity model")
-
- with gr.Row(): # Outputs
-
- results_title=gr.HTML('
Object 1 is more about...
Oppositions
Object 2 is more about...
- {{block 'main'}}{{/block}}
-
-
-
\ No newline at end of file
diff --git a/spaces/CloseEric/CloseEric/README.md b/spaces/CloseEric/CloseEric/README.md
deleted file mode 100644
index 2c7b182121bfe1b99b46b1a8bfce6969088cf58a..0000000000000000000000000000000000000000
--- a/spaces/CloseEric/CloseEric/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: CloseEric
-emoji: 🌖
-colorFrom: gray
-colorTo: indigo
-sdk: docker
-pinned: false
----
-{{@copyright || sys?.copyright}}
- I don't need an idiot in my arms.
\ No newline at end of file diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/app.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/app.py deleted file mode 100644 index 90c6871afc644b4c072f17f93a29b1a4f58028b1..0000000000000000000000000000000000000000 --- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/app.py +++ /dev/null @@ -1,198 +0,0 @@ -import random - -import gradio as gr -import torch -from diffusers.utils import load_image -from PIL import Image -import numpy as np -import base64 -from io import BytesIO - -from mediapipe_face_common import generate_annotation - -from diffusers import ( - ControlNetModel, - StableDiffusionControlNetPipeline, -) - - -# Download the SD 1.5 model from HF -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -controlnet = ControlNetModel.from_pretrained( - "CrucibleAI/ControlNetMediaPipeFace", torch_dtype=torch.float16, variant="fp16") -model = StableDiffusionControlNetPipeline.from_pretrained( - "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16 -) -model = model.to(device) -model.enable_model_cpu_offload() - - -canvas_html = "","
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diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/mel_processing.py b/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/mel_processing.py
deleted file mode 100644
index f458775bf62b79f791b419ca7ed62c550ae252d5..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/mel_processing.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-import logging
-
-logger = logging.getLogger(__name__)
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
- return dynamic_range_decompression_torch(magnitudes)
-
-
-# Reusable banks
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
-
- Args:
- y :: (B, T) - Audio waveforms
- n_fft
- sampling_rate
- hop_size
- win_size
- center
- Returns:
- :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
- """
- # Validation
- if torch.min(y) < -1.07:
- logger.debug("min value is %s", str(torch.min(y)))
- if torch.max(y) > 1.07:
- logger.debug("max value is %s", str(torch.max(y)))
-
- # Window - Cache if needed
- global hann_window
- dtype_device = str(y.dtype) + "_" + str(y.device)
- wnsize_dtype_device = str(win_size) + "_" + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
- dtype=y.dtype, device=y.device
- )
-
- # Padding
- y = torch.nn.functional.pad(
- y.unsqueeze(1),
- (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
- mode="reflect",
- )
- y = y.squeeze(1)
-
- # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
- spec = torch.stft(
- y,
- n_fft,
- hop_length=hop_size,
- win_length=win_size,
- window=hann_window[wnsize_dtype_device],
- center=center,
- pad_mode="reflect",
- normalized=False,
- onesided=True,
- return_complex=False,
- )
-
- # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- # MelBasis - Cache if needed
- global mel_basis
- dtype_device = str(spec.dtype) + "_" + str(spec.device)
- fmax_dtype_device = str(fmax) + "_" + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(
- sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
- )
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
- dtype=spec.dtype, device=spec.device
- )
-
- # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
- melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- melspec = spectral_normalize_torch(melspec)
- return melspec
-
-
-def mel_spectrogram_torch(
- y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
-):
- """Convert waveform into Mel-frequency Log-amplitude spectrogram.
-
- Args:
- y :: (B, T) - Waveforms
- Returns:
- melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
- """
- # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
- spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
-
- # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
- melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
-
- return melspec
diff --git a/spaces/GXSA/bingo/src/state/index.ts b/spaces/GXSA/bingo/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/state/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
- { author: 'system', text: 'conversation between user and robot', id: '1' },
- { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点? ', id: '2' },
- {
- author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
- - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
- - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
- - 优点：清洁性能强劲，操作方便，适用多种场景；
- - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
- - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
- - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
- `, id: '3' },
- { author: 'user', text: '今天的新闻', id: '4' },
- {
- author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
- # 中国新闻
- - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
- - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
- - 中央气象台7月16日18时发布台风橙色预警[^1^]
- - 贵州石阡：暑期旅游带动乡村振兴[^1^]
- - 激活大科学装置的“人才红利”[^1^]
- - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
- - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
- - 成都以赛为媒提升城市美誉度[^1^]
- - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
- - 浙江建德：新安江上享清凉[^1^]
- - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
- - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
- - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
- - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
- - 大运来了丨成都迎大运 全民健身动起来[^1^]
- - 藏在高校里的宝藏博物馆[^1^]
- - 中国汽车工业用70年赢得三个“全球第一”[^1^]
- - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
- # 国际新闻
- - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
- - 国际航运业加快绿色转型[^2^]
- - 美企反对收紧对华芯片出口限制[^2^]
- - 欧洲加大气候科技领域投资[^2^]
- - 中企助力丹麦发展清洁能源[^2^]
- - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
- - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
- 以上信息仅供参考，具体详情请点击以下链接查看：
-
- [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
- [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
- { author: 'user', text: '写一个快排', id: '6' },
- { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n temp = arr[i]\n arr[i] = arr[j]\n arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n # 选择最右边的元素作为基准元素\n pivot = arr[high]\n # 初始化一个指针，指向左边区间的最后一个位置\n i = low - 1\n # 遍历左边区间中的每个元素\n for j in range(low, high):\n # 如果当前元素小于或等于基准元素\n if arr[j] <= pivot:\n # 将指针右移一位\n i += 1\n # 将指针所指的元素与当前元素交换\n swap(arr, i, j)\n # 将基准元素与指针右边的第一个元素交换\n swap(arr, i + 1, high)\n # 返回基准元素的最终位置\n return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n # 如果区间长度大于1\n if low < high:\n # 对区间进行划分，得到基准元素的位置\n p = partition(arr, low, high)\n # 对左边子区间进行快速排序\n quick_sort(arr, low, p - 1)\n # 对右边子区间进行快速排序\n quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
- {
- author: 'bot', text: "好的，我会尝试画一只猫。\n > ",
- id: '8'
- }
-]
-
-export const GreetMessages = [
- '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
- '重新开始总是很棒。问我任何问题!',
- '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
- '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
- '很好，让我们来更改主题。你在想什么?',
- '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
- '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
- '感谢刷新！你有新的话题吗？',
- '明白了，让我们重新开始。接下来应该讨论什么？',
- '下一步！我可以为你做什么？',
- '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
- return new BingWebBot({
- cookie: ' ',
- ua: ' ',
- })
-}
-
-export const chatFamily = atomFamily(
- (param: Param) => {
- return atomWithImmer({
- botId: param.botId,
- bot: createBotInstance(),
- messages: [] as ChatMessageModel[],
- generatingMessageId: '',
- abortController: undefined as AbortController | undefined,
- conversationId: nanoid(),
- })
- },
- (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/Gilvan/XRaySwinGen/app.py b/spaces/Gilvan/XRaySwinGen/app.py
deleted file mode 100644
index 509b16bd212d9b9daaf5a11cb9246496d702b507..0000000000000000000000000000000000000000
--- a/spaces/Gilvan/XRaySwinGen/app.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import gradio as gr
-import torch
-from transformers import AutoFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel, GPT2Tokenizer, pipeline
-import os
-
-device = 'cpu'
-auth_token = os.getenv("auth_token")
-#auth_token = os.environ.get("auth_token")
-
-max_length = 100
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-def predict_step(image_paths, model):
- images = []
- for image_path in image_paths:
- i_image = Image.open(image_path)
- if i_image.mode != "RGB":
- i_image = i_image.convert(mode="RGB")
-
- images.append(i_image)
-
- pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
- pixel_values = pixel_values.to(device)
-
- output_ids = model.generate(pixel_values, **gen_kwargs)
-
- preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
- preds = [pred.strip() for pred in preds]
- return preds
-
-def predict_step_image(dataset_images, feature_extractor, model):
- results = []
- for i in dataset_images:
- pixel_values = feature_extractor(images=i, return_tensors="pt").pixel_values
- pixel_values = pixel_values.to(device)
-
- output_ids = model.generate(pixel_values, **gen_kwargs)
-
- preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
- preds = [pred.strip() for pred in preds]
- results.append(preds)
- return results
-
-def predict_step_single_image(image, tokenizer, feature_extractor, model):
- results=[]
- pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
- pixel_values = pixel_values.to(device)
-
- output_ids = model.generate(pixel_values, **gen_kwargs)
-
- preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
- preds = [pred.strip() for pred in preds]
- results.append(preds)
- return results
-
-def predict_step_pixel(dataset_pixel_values, model):
- results=[]
- for pv in dataset_pixel_values:
- pixel_values = pv.reshape([1,3,224,224])
- pixel_values = pixel_values.to(device)
- output_ids = model.generate(pixel_values, **gen_kwargs)
- preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
- results.append([pred.strip() for pred in preds][0])
- return results
-
-"""
- image methods
-"""
-def load_image2txt_model(image_model_name):
- model = VisionEncoderDecoderModel.from_pretrained(image_model_name)
- feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-large-patch4-window7-224", use_auth_token=auth_token)
-
- tokenizer = GPT2Tokenizer.from_pretrained("gpt2", use_auth_token=auth_token)
- tokenizer.pad_token = tokenizer.eos_token
-
- model = model.to(device)
- return tokenizer, feature_extractor, model
-
-def inference_image_pipe(image_input):
- image_model_name = "./checkpoint-21000"
-
- tokenizer, feature_extractor, image_model = load_image2txt_model(image_model_name)
- text = predict_step_single_image(image_input, tokenizer, feature_extractor, image_model)[0]
- return text
-
-with gr.Interface(fn=inference_image_pipe,
- inputs=gr.Image(shape=(256, 256)),
- outputs="text",
- examples=["3212210S4492629-1.png", "3216497S4499373-1.png"]) as demo:
- gr.Markdown("POC XRaySwinGen - Automatic Medical Report")
-
-
-if __name__ == "__main__":
- demo.launch()
\ No newline at end of file
diff --git a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/cppipc/buffer.cpp b/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/cppipc/buffer.cpp
deleted file mode 100644
index 0ac0fa7bc3ced0447ba4caa359355dd4252670b3..0000000000000000000000000000000000000000
--- a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/cppipc/buffer.cpp
+++ /dev/null
@@ -1,87 +0,0 @@
-#include "libipc/buffer.h"
-#include "libipc/utility/pimpl.h"
-
-#include
-
-namespace ipc {
-
-bool operator==(buffer const & b1, buffer const & b2) {
- return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
-}
-
-bool operator!=(buffer const & b1, buffer const & b2) {
- return !(b1 == b2);
-}
-
-class buffer::buffer_ : public pimpl {
-public:
- void* p_;
- std::size_t s_;
- void* a_;
- buffer::destructor_t d_;
-
- buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
- : p_(p), s_(s), a_(a), d_(d) {
- }
-
- ~buffer_() {
- if (d_ == nullptr) return;
- d_((a_ == nullptr) ? p_ : a_, s_);
- }
-};
-
-buffer::buffer()
- : buffer(nullptr, 0, nullptr, nullptr) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d)
- : p_(p_->make(p, s, d, nullptr)) {
-}
-
-buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
- : p_(p_->make(p, s, d, additional)) {
-}
-
-buffer::buffer(void* p, std::size_t s)
- : buffer(p, s, nullptr) {
-}
-
-buffer::buffer(char const & c)
- : buffer(const_cast(&c), 1) {
-}
-
-buffer::buffer(buffer&& rhs)
- : buffer() {
- swap(rhs);
-}
-
-buffer::~buffer() {
- p_->clear();
-}
-
-void buffer::swap(buffer& rhs) {
- std::swap(p_, rhs.p_);
-}
-
-buffer& buffer::operator=(buffer rhs) {
- swap(rhs);
- return *this;
-}
-
-bool buffer::empty() const noexcept {
- return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
-}
-
-void* buffer::data() noexcept {
- return impl(p_)->p_;
-}
-
-void const * buffer::data() const noexcept {
- return impl(p_)->p_;
-}
-
-std::size_t buffer::size() const noexcept {
- return impl(p_)->s_;
-}
-
-} // namespace ipc
diff --git a/spaces/Godrose0728/Aisound02/monotonic_align/core.py b/spaces/Godrose0728/Aisound02/monotonic_align/core.py
deleted file mode 100644
index 1f940605fe4fd0738fa0006149fcba14ef88223a..0000000000000000000000000000000000000000
--- a/spaces/Godrose0728/Aisound02/monotonic_align/core.py
+++ /dev/null
@@ -1,36 +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/Gradio-Blocks/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
deleted file mode 100644
index c32bf48751f0a18983bff0d99310870b71801663..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './ccnet_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/dm_head.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/dm_head.py
deleted file mode 100644
index 3161b0648879127c169fede162bc3ee16e4692c0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/decode_heads/dm_head.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
-
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-class DCM(nn.Module):
- """Dynamic Convolutional Module used in DMNet.
-
- Args:
- filter_size (int): The filter size of generated convolution kernel
- used in Dynamic Convolutional Module.
- fusion (bool): Add one conv to fuse DCM output feature.
- in_channels (int): Input channels.
- channels (int): Channels after modules, before conv_seg.
- conv_cfg (dict | None): Config of conv layers.
- norm_cfg (dict | None): Config of norm layers.
- act_cfg (dict): Config of activation layers.
- """
-
- def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
- norm_cfg, act_cfg):
- super(DCM, self).__init__()
- self.filter_size = filter_size
- self.fusion = fusion
- self.in_channels = in_channels
- self.channels = channels
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.act_cfg = act_cfg
- self.filter_gen_conv = nn.Conv2d(self.in_channels, self.channels, 1, 1,
- 0)
-
- self.input_redu_conv = ConvModule(
- self.in_channels,
- self.channels,
- 1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
-
- if self.norm_cfg is not None:
- self.norm = build_norm_layer(self.norm_cfg, self.channels)[1]
- else:
- self.norm = None
- self.activate = build_activation_layer(self.act_cfg)
-
- if self.fusion:
- self.fusion_conv = ConvModule(
- self.channels,
- self.channels,
- 1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
-
- def forward(self, x):
- """Forward function."""
- generated_filter = self.filter_gen_conv(
- F.adaptive_avg_pool2d(x, self.filter_size))
- x = self.input_redu_conv(x)
- b, c, h, w = x.shape
- # [1, b * c, h, w], c = self.channels
- x = x.view(1, b * c, h, w)
- # [b * c, 1, filter_size, filter_size]
- generated_filter = generated_filter.view(b * c, 1, self.filter_size,
- self.filter_size)
- pad = (self.filter_size - 1) // 2
- if (self.filter_size - 1) % 2 == 0:
- p2d = (pad, pad, pad, pad)
- else:
- p2d = (pad + 1, pad, pad + 1, pad)
- x = F.pad(input=x, pad=p2d, mode='constant', value=0)
- # [1, b * c, h, w]
- output = F.conv2d(input=x, weight=generated_filter, groups=b * c)
- # [b, c, h, w]
- output = output.view(b, c, h, w)
- if self.norm is not None:
- output = self.norm(output)
- output = self.activate(output)
-
- if self.fusion:
- output = self.fusion_conv(output)
-
- return output
-
-
-@HEADS.register_module()
-class DMHead(BaseDecodeHead):
- """Dynamic Multi-scale Filters for Semantic Segmentation.
-
- This head is the implementation of
- `DMNet `_.
-
- Args:
- filter_sizes (tuple[int]): The size of generated convolutional filters
- used in Dynamic Convolutional Module. Default: (1, 3, 5, 7).
- fusion (bool): Add one conv to fuse DCM output feature.
- """
-
- def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
- super(DMHead, self).__init__(**kwargs)
- assert isinstance(filter_sizes, (list, tuple))
- self.filter_sizes = filter_sizes
- self.fusion = fusion
- dcm_modules = []
- for filter_size in self.filter_sizes:
- dcm_modules.append(
- DCM(filter_size,
- self.fusion,
- self.in_channels,
- self.channels,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg))
- self.dcm_modules = nn.ModuleList(dcm_modules)
- self.bottleneck = ConvModule(
- self.in_channels + len(filter_sizes) * self.channels,
- self.channels,
- 3,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- act_cfg=self.act_cfg)
-
- def forward(self, inputs):
- """Forward function."""
- x = self._transform_inputs(inputs)
- dcm_outs = [x]
- for dcm_module in self.dcm_modules:
- dcm_outs.append(dcm_module(x))
- dcm_outs = torch.cat(dcm_outs, dim=1)
- output = self.bottleneck(dcm_outs)
- output = self.cls_seg(output)
- return output
diff --git a/spaces/GreenRaptor/MMS/mms_infer.py b/spaces/GreenRaptor/MMS/mms_infer.py
deleted file mode 100644
index 0c879c36f54298389f19126f5e2963eb8cbc403c..0000000000000000000000000000000000000000
--- a/spaces/GreenRaptor/MMS/mms_infer.py
+++ /dev/null
@@ -1,52 +0,0 @@
-#!/usr/bin/env python -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.
-
-import argparse
-import soundfile as sf
-import tempfile
-from pathlib import Path
-import os
-import subprocess
-import sys
-import re
-
-def parser():
- parser = argparse.ArgumentParser(description="ASR inference script for MMS model")
- parser.add_argument("--model", type=str, help="path to ASR model", required=True)
- parser.add_argument("--audio", type=str, help="path to audio file", required=True, nargs='+')
- parser.add_argument("--lang", type=str, help="audio language", required=True)
- parser.add_argument("--format", type=str, choices=["none", "letter"], default="letter")
- return parser.parse_args()
-
-def process(args):
- with tempfile.TemporaryDirectory() as tmpdir:
- print(">>> preparing tmp manifest dir ...", file=sys.stderr)
- tmpdir = Path(tmpdir)
- with open(tmpdir / "dev.tsv", "w") as fw:
- fw.write("/\n")
- for audio in args.audio:
- nsample = sf.SoundFile(audio).frames
- fw.write(f"{audio}\t{nsample}\n")
- with open(tmpdir / "dev.uid", "w") as fw:
- fw.write(f"{audio}\n"*len(args.audio))
- with open(tmpdir / "dev.ltr", "w") as fw:
- fw.write("d u m m y | d u m m y\n"*len(args.audio))
- with open(tmpdir / "dev.wrd", "w") as fw:
- fw.write("dummy dummy\n"*len(args.audio))
- cmd = f"""
- PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1 python infer.py -m decoding.type=viterbi dataset.max_tokens=4000000 distributed_training.distributed_world_size=1 "common_eval.path='{args.model}'" task.data={tmpdir} dataset.gen_subset="{args.lang}:dev" common_eval.post_process={args.format} decoding.results_path={tmpdir}
- """
- print(">>> loading model & running inference ...", file=sys.stderr)
- subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL,)
- with open(tmpdir/"hypo.word") as fr:
- for ii, hypo in enumerate(fr):
- hypo = re.sub("\(\S+\)$", "", hypo).strip()
- print(f'===============\nInput: {args.audio[ii]}\nOutput: {hypo}')
-
-
-if __name__ == "__main__":
- args = parser()
- process(args)
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/criss/download_and_preprocess_tatoeba.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/criss/download_and_preprocess_tatoeba.sh
deleted file mode 100644
index 7ed64f017d5e62695ba73745c840507b994abc0f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/criss/download_and_preprocess_tatoeba.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-SPM_ENCODE=flores/scripts/spm_encode.py
-DATA=data_tmp
-SPM_MODEL=criss_checkpoints/sentence.bpe.model
-DICT=criss_checkpoints/dict.txt
-
-if [[ -f flores ]]; then
- echo "flores already cloned"
-else
- git clone https://github.com/facebookresearch/flores
-fi
-if [[ -f LASER ]]; then
- echo "LASER already cloned"
-else
- git clone https://github.com/facebookresearch/LASER
-fi
-mkdir -p data_tmp
-declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu" ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn")
-for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do
- lang_tatoeba=${lang_tatoeba_map[$lang]}
- echo $lang_tatoeba
- datadir=$DATA/${lang}-en_XX-tatoeba
- rm -rf $datadir
- mkdir -p $datadir
- TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba
- python $SPM_ENCODE \
- --model ${SPM_MODEL} \
- --output_format=piece \
- --inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \
- --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX
-
- # binarize data
- fairseq-preprocess \
- --source-lang ${lang} --target-lang en_XX \
- --testpref $datadir/test.bpe.${lang}-en_XX \
- --destdir $datadir \
- --srcdict ${DICT} \
- --joined-dictionary \
- --workers 4
-done
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/criss/mining/mine_example.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/criss/mining/mine_example.sh
deleted file mode 100644
index ace995ac44665f99d904b6a89d7fbbce24103afe..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/criss/mining/mine_example.sh
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-#
-source_lang=kk_KZ
-target_lang=en_XX
-MODEL=criss_checkpoints/criss.3rd.pt
-SPM=criss_checkpoints/sentence.bpe.model
-SPLIT=test
-LANG_DICT=criss_checkpoints/lang_dict.txt
-SPM_ENCODE=flores/scripts/spm_encode.py
-SAVE_ENCODER=save_encoder.py
-ENCODER_SAVE_ROOT=sentence_embeddings/$MODEL
-DICT=criss_checkpoints/dict.txt
-THRESHOLD=1.02
-MIN_COUNT=500
-
-DATA_DIR=data_tmp
-SAVE_DIR=mining/${source_lang}_${target_lang}_mined
-ENCODER_SAVE_DIR=${ENCODER_SAVE_ROOT}/${source_lang}-${target_lang}
-INPUT_DIR=$DATA_DIR/${source_lang}-${target_lang}-tatoeba
-
-mkdir -p $ENCODER_SAVE_DIR/${target_lang}
-mkdir -p $ENCODER_SAVE_DIR/${source_lang}
-mkdir -p $SAVE_DIR
-
-## Save encoder outputs
-
-# Save encoder outputs for source sentences
-python $SAVE_ENCODER \
- ${INPUT_DIR} \
- --path ${MODEL} \
- --task translation_multi_simple_epoch \
- --lang-pairs ${source_lang}-${target_lang} \
- --lang-dict ${LANG_DICT} \
- --gen-subset ${SPLIT} \
- --bpe 'sentencepiece' \
- -s ${source_lang} -t ${target_lang} \
- --sentencepiece-model ${SPM} \
- --remove-bpe 'sentencepiece' \
- --beam 1 \
- --lang-tok-style mbart \
- --encoder-save-dir ${ENCODER_SAVE_DIR}/${source_lang}
-
-## Save encoder outputs for target sentences
-python $SAVE_ENCODER \
- ${INPUT_DIR} \
- --path ${MODEL} \
- --lang-pairs ${source_lang}-${target_lang} \
- --lang-dict ${LANG_DICT} \
- --task translation_multi_simple_epoch \
- --gen-subset ${SPLIT} \
- --bpe 'sentencepiece' \
- -t ${source_lang} -s ${target_lang} \
- --sentencepiece-model ${SPM} \
- --remove-bpe 'sentencepiece' \
- --beam 1 \
- --lang-tok-style mbart \
- --encoder-save-dir ${ENCODER_SAVE_DIR}/${target_lang}
-
-## Mining
-python mining/mine.py \
- --src-lang ${source_lang} \
- --tgt-lang ${target_lang} \
- --dim 1024 \
- --mem 10 \
- --neighborhood 4 \
- --src-dir ${ENCODER_SAVE_DIR}/${source_lang} \
- --tgt-dir ${ENCODER_SAVE_DIR}/${target_lang} \
- --output $SAVE_DIR \
- --threshold ${THRESHOLD} \
- --min-count ${MIN_COUNT} \
- --valid-size 100 \
- --dict-path ${DICT} \
- --spm-path ${SPM} \
-
-
-## Process and binarize mined data
-python $SPM_ENCODE \
- --model ${SPM} \
- --output_format=piece \
- --inputs mining/${source_lang}_${target_lang}_mined/train.${source_lang} mining/${source_lang}_${target_lang}_mined/train.${target_lang} \
- --outputs mining/${source_lang}_${target_lang}_mined/train.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/train.bpe.${target_lang}
-
-python $SPM_ENCODE \
- --model ${SPM} \
- --output_format=piece \
- --inputs mining/${source_lang}_${target_lang}_mined/valid.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.${target_lang} \
- --outputs mining/${source_lang}_${target_lang}_mined/valid.bpe.${source_lang} mining/${source_lang}_${target_lang}_mined/valid.bpe.${target_lang}
-
-
-fairseq-preprocess \
- --source-lang ${source_lang} \
- --target-lang ${target_lang} \
- --trainpref mining/${source_lang}_${target_lang}_mined/train.bpe \
- --validpref mining/${source_lang}_${target_lang}_mined/valid.bpe \
- --destdir mining/${source_lang}_${target_lang}_mined \
- --srcdict ${DICT} \
- --joined-dictionary \
- --workers 8
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/sinusoidal_positional_embedding.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/sinusoidal_positional_embedding.py
deleted file mode 100644
index 4793ecfb522d0729fc2d24a3ddf0c6a774d67773..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/sinusoidal_positional_embedding.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# 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.
-
-import math
-from typing import Any, Optional
-
-import torch
-import torch.onnx.operators
-from fairseq import utils
-from torch import Tensor, nn
-
-
-class SinusoidalPositionalEmbedding(nn.Module):
- """This module produces sinusoidal positional embeddings of any length.
-
- Padding symbols are ignored.
- """
-
- def __init__(self, embedding_dim, padding_idx, init_size=1024):
- super().__init__()
- self.embedding_dim = embedding_dim
- self.padding_idx = padding_idx if padding_idx is not None else 0
- self.weights = SinusoidalPositionalEmbedding.get_embedding(
- init_size, embedding_dim, padding_idx
- )
- self.onnx_trace = False
- self.register_buffer("_float_tensor", torch.FloatTensor(1))
- self.max_positions = int(1e5)
-
- def prepare_for_onnx_export_(self):
- self.onnx_trace = True
-
- @staticmethod
- def get_embedding(
- num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
- ):
- """Build sinusoidal embeddings.
-
- This matches the implementation in tensor2tensor, but differs slightly
- from the description in Section 3.5 of "Attention Is All You Need".
- """
- half_dim = embedding_dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
- emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
- 1
- ) * emb.unsqueeze(0)
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
- num_embeddings, -1
- )
- if embedding_dim % 2 == 1:
- # zero pad
- emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
- if padding_idx is not None:
- emb[padding_idx, :] = 0
- return emb
-
- def forward(
- self,
- input,
- incremental_state: Optional[Any] = None,
- timestep: Optional[Tensor] = None,
- positions: Optional[Any] = None,
- ):
- """Input is expected to be of size [bsz x seqlen]."""
- bspair = torch.onnx.operators.shape_as_tensor(input)
- bsz, seq_len = bspair[0], bspair[1]
- max_pos = self.padding_idx + 1 + seq_len
- if self.weights is None or max_pos > self.weights.size(0):
- # recompute/expand embeddings if needed
- self.weights = SinusoidalPositionalEmbedding.get_embedding(
- max_pos, self.embedding_dim, self.padding_idx
- )
- self.weights = self.weights.to(self._float_tensor)
-
- if incremental_state is not None:
- # positions is the same for every token when decoding a single step
- pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
- if self.onnx_trace:
- return (
- self.weights.index_select(index=self.padding_idx + pos, dim=0)
- .unsqueeze(1)
- .repeat(bsz, 1, 1)
- )
- return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
-
- positions = utils.make_positions(
- input, self.padding_idx, onnx_trace=self.onnx_trace
- )
- if self.onnx_trace:
- flat_embeddings = self.weights.detach().index_select(0, positions.view(-1))
- embedding_shape = torch.cat(
- (bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long))
- )
- embeddings = torch.onnx.operators.reshape_from_tensor_shape(
- flat_embeddings, embedding_shape
- )
- return embeddings
- return (
- self.weights.index_select(0, positions.view(-1))
- .view(bsz, seq_len, -1)
- .detach()
- )
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_online_backtranslation.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_online_backtranslation.py
deleted file mode 100644
index 0ae7e773da0ff838b3c8151bc14b84a6a9238a72..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/test_online_backtranslation.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# 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.
-
-import tempfile
-import unittest
-from pathlib import Path
-from typing import Any, Dict, Sequence
-
-import fairseq.data.indexed_dataset as indexed_dataset
-import fairseq.options
-import fairseq.tasks.online_backtranslation as obt
-import torch
-from tests import utils
-
-
-def mk_sample(tokens: Sequence[int], batch_size: int = 2) -> Dict[str, Any]:
- batch = torch.stack([torch.tensor(tokens, dtype=torch.long)] * batch_size)
- sample = {
- "net_input": {
- "src_tokens": batch,
- "prev_output_tokens": batch,
- "src_lengths": torch.tensor([len(tokens)] * batch_size, dtype=torch.long),
- },
- "target": batch[:, 1:],
- }
- return sample
-
-
-def mk_dataset(num_samples: int, max_len: int, output: Path):
- output.parent.mkdir(exist_ok=True)
- idx = indexed_dataset.IndexedDatasetBuilder(str(output))
- data = torch.randint(5, 100, (num_samples, max_len))
- lengths = torch.randint(3, max_len, (num_samples,))
- for d, l in zip(data, lengths):
- d[0] = 0
- idx.add_item(d[:l])
- idx.finalize(output.with_suffix(".idx"))
- assert output.exists()
- assert output.with_suffix(".idx").exists()
-
-
-class OnlineBacktranslationTest(unittest.TestCase):
-
- tmp_dir = Path(tempfile.mkdtemp(suffix="OnlineBacktranslationTest"))
-
- @classmethod
- def obt_task(
- cls, languages: Sequence[str], data: Path = None, language_mapping: str = None
- ):
- dict_path = cls.tmp_dir / "dict.txt"
- if not dict_path.exists():
- dictionary = utils.dummy_dictionary(100)
- dictionary.save(str(dict_path))
-
- if data is not None:
- (data / "dict.txt").write_text(dict_path.read_text())
- else:
- data = cls.tmp_dir
- assert len(languages) >= 2
-
- kwargs = {
- "arch": "transformer",
- # --max-sentences=1 for better predictability of batches
- "max_sentences": 1,
- # Use characteristics dimensions
- "encoder_layers": 3,
- "encoder_embed_dim": 12,
- "encoder_ffn_embed_dim": 14,
- "encoder_attention_heads": 4,
- "decoder_layers": 3,
- "decoder_embed_dim": 12,
- "decoder_output_dim": 12,
- "decoder_ffn_embed_dim": 14,
- "decoder_attention_heads": 4,
- # Disable dropout so we have comparable tests.
- "dropout": 0,
- "attention_dropout": 0,
- "activation_dropout": 0,
- "encoder_layerdrop": 0,
- }
-
- args = fairseq.options.get_args(
- data,
- task="online_backtranslation",
- mono_langs=",".join(languages),
- valid_lang_pairs=f"{languages[0]}-{languages[1]}",
- tokens_per_sample=256,
- language_mapping=language_mapping,
- **kwargs,
- )
- task = obt.OnlineBackTranslationTask.setup_task(args)
- # we need to build the model to have the correct dictionary
- model = task.build_model(task.args)
- return task, model
-
- def tmp_path(self, test_case: str) -> Path:
- return Path(tempfile.mkdtemp(test_case, dir=self.tmp_dir))
-
- def test_lang_tokens(self):
- task, model = self.obt_task(["en", "ro", "zh"])
- assert obt._lang_token("en") in task.dictionary
- assert obt._lang_token("ro") in task.dictionary
- assert obt._lang_token("zh") in task.dictionary
-
- en_bos = obt._lang_token_index(task.common_dict, "en")
- assert "en" == task.common_dict[en_bos].strip("_")
- zh_bos = obt._lang_token_index(task.common_dict, "zh")
- assert "zh" == task.common_dict[zh_bos].strip("_")
- zh_sample = mk_sample([zh_bos, 16, 14, 12, 10])
-
- # we expect to receive the bos token for translation
- assert task.get_bos_token_from_sample(zh_sample) == en_bos
-
- def test_backtranslate_sample(self):
- task, model = self.obt_task(["en", "ro", "zh"])
-
- en_bos = obt._lang_token_index(task.common_dict, "en")
- zh_bos = obt._lang_token_index(task.common_dict, "zh")
- sample = mk_sample([zh_bos, 16, 14, 12, 10])
-
- task.backtranslate_sample(sample, "zh", "en")
- target_zh = list(sample["target"][0])
- assert target_zh == [16, 14, 12, 10] # original zh sentence
- generated_en = sample["net_input"]["src_tokens"][0]
- assert generated_en[0] == en_bos
-
- def test_train_dataset(self):
- data = self.tmp_path("test_train_dataset")
- mk_dataset(20, 10, data / "en" / "train.bin")
- mk_dataset(10, 10, data / "zh" / "train.bin")
- task, model = self.obt_task(["en", "zh"], data)
- task.load_dataset("train")
-
- en_bos = obt._lang_token_index(task.common_dict, "en")
- zh_bos = obt._lang_token_index(task.common_dict, "zh")
-
- train = task.datasets["train"]
- train.ordered_indices()
- train.prefetch([0, 19])
- sample_0 = train[0]
- sample_19 = train[19]
- self.assertEqual(
- set(sample_0.keys()), {"en-BT", "en-DENOISE", "zh-BT", "zh-DENOISE"}
- )
- for sample in (sample_0, sample_19):
- self.assertEqual(sample["en-BT"]["source"][0], en_bos)
- # bt target isn't ready to look at.
- self.assertEqual(sample["en-DENOISE"]["source"][0], en_bos)
- # TODO What could we check on the target side ?
-
- for i in range(10):
- # Zh dataset is shorter, and is wrapped around En dataset.
- train.prefetch([i, i + 10])
- self.assertEqual(
- list(train[i]["zh-DENOISE"]["source"]),
- list(train[i + 10]["zh-DENOISE"]["source"]),
- )
- self.assertEqual(train[i]["zh-DENOISE"]["source"][0].item(), zh_bos)
-
- # Sorted by increasing len
- self.assertLess(
- len(sample_0["en-BT"]["source"]), len(sample_19["en-BT"]["source"])
- )
-
- def test_valid_dataset(self):
- data = self.tmp_path("test_valid_dataset")
- mk_dataset(10, 21, data / "valid.en-zh.en.bin")
- mk_dataset(10, 21, data / "valid.en-zh.zh.bin")
-
- task, model = self.obt_task(["en", "zh"], data)
- valid = task.load_dataset("valid")
- en_bos = obt._lang_token_index(task.common_dict, "en")
-
- assert valid is not None
- valid.prefetch(range(10))
- sample_0 = valid[0]
- sample_9 = valid[9]
- self.assertEqual(sample_0["id"], 0)
- self.assertEqual(sample_9["id"], 9)
- self.assertEqual(sample_0["source"][0], en_bos)
- self.assertEqual(sample_9["source"][0], en_bos)
- # TODO: could we test the target side ?
-
- def assertFnMatch(self, fn, values):
- for x, y in values.items():
- fn_x = fn(x)
- self.assertEqual(fn_x, y, f"Fn has wrong value: fn({x}) = {fn_x} != {y}")
-
- def test_piecewise_linear_fn(self):
- self.assertFnMatch(
- obt.PiecewiseLinearFn.from_string("1.0"), {0: 1, 100: 1, 500: 1, 1000: 1}
- )
- self.assertFnMatch(
- obt.PiecewiseLinearFn.from_string("0:1,1000:0"),
- {0: 1, 500: 0.5, 1000: 0, 2000: 0},
- )
- self.assertFnMatch(
- obt.PiecewiseLinearFn.from_string("0:0,1000:1"),
- {0: 0, 500: 0.5, 1000: 1, 2000: 1},
- )
- self.assertFnMatch(
- obt.PiecewiseLinearFn.from_string("0:0,1000:1,2000:0"),
- {0: 0, 500: 0.5, 1000: 1, 1500: 0.5, 2000: 0, 3000: 0},
- )
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/t2s_fastapi.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/t2s_fastapi.py
deleted file mode 100644
index e034fc01a4a5bcd54b365a49dad2e907b57504a1..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/src/glow_tts/t2s_fastapi.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from starlette.responses import StreamingResponse
-from texttospeech import MelToWav, TextToMel
-from typing import Optional
-from pydantic import BaseModel
-from fastapi import FastAPI, HTTPException
-import uvicorn
-import base64
-
-app = FastAPI()
-
-
-class TextJson(BaseModel):
- text: str
- lang: Optional[str] = "hi"
- gender: Optional[str] = "male"
-
-
-glow_hi_male = TextToMel(glow_model_dir="", device="")
-glow_hi_female = TextToMel(glow_model_dir="", device="")
-hifi_hi = MelToWav(hifi_model_dir="", device="")
-
-
-available_choice = {
- "hi_male": [glow_hi_male, hifi_hi],
- "hi_female": [glow_hi_female, hifi_hi],
-}
-
-
-@app.post("/TTS/")
-async def tts(input: TextJson):
- text = input.text
- lang = input.lang
- gender = input.gender
-
- choice = lang + "_" + gender
- if choice in available_choice.keys():
- t2s = available_choice[choice]
- else:
- raise HTTPException(
- status_code=400, detail={"error": "Requested model not found"}
- )
-
- if text:
- mel = t2s[0].generate_mel(text)
- data, sr = t2s[1].generate_wav(mel)
- t2s.save_audio("out.wav", data, sr)
- else:
- raise HTTPException(status_code=400, detail={"error": "No text"})
-
- ## to return outpur as a file
- # audio = open('out.wav', mode='rb')
- # return StreamingResponse(audio, media_type="audio/wav")
-
- with open("out.wav", "rb") as audio_file:
- encoded_bytes = base64.b64encode(audio_file.read())
- encoded_string = encoded_bytes.decode()
- return {"encoding": "base64", "data": encoded_string, "sr": sr}
-
-
-if __name__ == "__main__":
- uvicorn.run(
- "t2s_fastapi:app", host="127.0.0.1", port=5000, log_level="info", reload=True
- )
diff --git a/spaces/Harveenchadha/en_to_indic_translation/prepare_data_joint_training.sh b/spaces/Harveenchadha/en_to_indic_translation/prepare_data_joint_training.sh
deleted file mode 100644
index 9b355b4775785ff32b20bedb4eab1411d07ac504..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/prepare_data_joint_training.sh
+++ /dev/null
@@ -1,110 +0,0 @@
-exp_dir=$1
-src_lang=$2
-tgt_lang=$3
-train_data_dir=${4:-"$exp_dir"}
-devtest_data_dir=${5:-"$exp_dir/devtest/all"}
-
-echo "Running experiment ${exp_dir} on ${src_lang} to ${tgt_lang}"
-
-
-train_processed_dir=$exp_dir/data
-devtest_processed_dir=$exp_dir/data
-
-out_data_dir=$exp_dir/final_bin
-
-mkdir -p $train_processed_dir
-mkdir -p $devtest_processed_dir
-mkdir -p $out_data_dir
-langs=(as bn hi gu kn ml mr or pa ta te)
-
-for lang in ${langs[@]};do
- if [ $src_lang == en ]; then
- tgt_lang=$lang
- else
- src_lang=$lang
- fi
-
- train_norm_dir=$exp_dir/norm/$src_lang-$tgt_lang
- devtest_norm_dir=$exp_dir/norm/$src_lang-$tgt_lang
- mkdir -p $train_norm_dir
- mkdir -p $devtest_norm_dir
-
- # train preprocessing
- train_infname_src=$train_data_dir/en-${lang}/train.$src_lang
- train_infname_tgt=$train_data_dir/en-${lang}/train.$tgt_lang
- train_outfname_src=$train_norm_dir/train.$src_lang
- train_outfname_tgt=$train_norm_dir/train.$tgt_lang
- echo "Applying normalization and script conversion for train"
- # this is for preprocessing text and in for indic langs, we convert all scripts to devnagiri
- input_size=`python scripts/preprocess_translate.py $train_infname_src $train_outfname_src $src_lang true`
- input_size=`python scripts/preprocess_translate.py $train_infname_tgt $train_outfname_tgt $tgt_lang true`
- echo "Number of sentences in train: $input_size"
-
- # dev preprocessing
- dev_infname_src=$devtest_data_dir/en-${lang}/dev.$src_lang
- dev_infname_tgt=$devtest_data_dir/en-${lang}/dev.$tgt_lang
- dev_outfname_src=$devtest_norm_dir/dev.$src_lang
- dev_outfname_tgt=$devtest_norm_dir/dev.$tgt_lang
- echo "Applying normalization and script conversion for dev"
- input_size=`python scripts/preprocess_translate.py $dev_infname_src $dev_outfname_src $src_lang true`
- input_size=`python scripts/preprocess_translate.py $dev_infname_tgt $dev_outfname_tgt $tgt_lang true`
- echo "Number of sentences in dev: $input_size"
-
- # test preprocessing
- test_infname_src=$devtest_data_dir/en-${lang}/test.$src_lang
- test_infname_tgt=$devtest_data_dir/en-${lang}/test.$tgt_lang
- test_outfname_src=$devtest_norm_dir/test.$src_lang
- test_outfname_tgt=$devtest_norm_dir/test.$tgt_lang
- echo "Applying normalization and script conversion for test"
- input_size=`python scripts/preprocess_translate.py $test_infname_src $test_outfname_src $src_lang true`
- input_size=`python scripts/preprocess_translate.py $test_infname_tgt $test_outfname_tgt $tgt_lang true`
- echo "Number of sentences in test: $input_size"
-done
-# this concatenates lang pair data and creates text files to keep track of number of lines in each lang pair.
-# this is imp as for joint training, we will merge all the lang pairs and the indivitual lang lines info
-# would be required for adding specific lang tags later.
-
-# the outputs of these scripts will be text file like this:
-#
-# lang1-lang2 n1
-# lang1-lang3 n2
-
-python scripts/concat_joint_data.py $exp_dir/norm $exp_dir/data $src_lang $tgt_lang 'train'
-python scripts/concat_joint_data.py $exp_dir/norm $exp_dir/data $src_lang $tgt_lang 'dev'
-python scripts/concat_joint_data.py $exp_dir/norm $exp_dir/data $src_lang $tgt_lang 'test'
-
-# echo "Learning bpe. This will take a very long time depending on the size of the dataset"
-echo `date`
-# # learn bpe for preprocessed_train files
-# for creating joint_vocab use this
-# bash learn_bpe.sh $exp_dir
-
-# for sep vocab use this
-bash learn_single_bpe.sh $exp_dir
-echo `date`
-
-# echo "Applying bpe"
-# apply the learnt bpe to the data
-bash apply_bpe_traindevtest_notag.sh $exp_dir
-
-mkdir -p $exp_dir/final
-
-# # this is only required for joint training
-# we apply language tags to the bpe segmented data
-#
-# if we are translating lang1 to lang2 then will become __src__ __tgt__
-echo "Adding language tags"
-python scripts/add_joint_tags_translate.py $exp_dir 'train'
-python scripts/add_joint_tags_translate.py $exp_dir 'dev'
-python scripts/add_joint_tags_translate.py $exp_dir 'test'
-
-# # this is important step if you are training with tpu and using num_batch_buckets
-# # the currnet implementation does not remove outliers before bucketing and hence
-# # removing these large sentences ourselves helps with getting better buckets
-# python scripts/remove_large_sentences.py $exp_dir/bpe/train.SRC $exp_dir/bpe/train.TGT $exp_dir/final/train.SRC $exp_dir/final/train.TGT
-# python scripts/remove_large_sentences.py $exp_dir/bpe/dev.SRC $exp_dir/bpe/dev.TGT $exp_dir/final/dev.SRC $exp_dir/final/dev.TGT
-# python scripts/remove_large_sentences.py $exp_dir/bpe/test.SRC $exp_dir/bpe/test.TGT $exp_dir/final/test.SRC $exp_dir/final/test.TGT
-
-# echo "Binarizing data"
-# Binarize the training data for using with fairseq train
-bash binarize_training_exp.sh $exp_dir SRC TGT
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/report_from_tb.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/report_from_tb.py
deleted file mode 100644
index 9a444e6cd8027f88bd34adfc0b1dd000bbb4b2be..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/report_from_tb.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/usr/bin/env python3
-
-import glob
-import os
-import re
-
-import tensorflow as tf
-from torch.utils.tensorboard import SummaryWriter
-
-
-GROUPING_RULES = [
- re.compile(r'^(?Ptrain|test|val|extra_val_.*?(256|512))_(?P.*)', re.I)
-]
-
-
-DROP_RULES = [
- re.compile(r'_std$', re.I)
-]
-
-
-def need_drop(tag):
- for rule in DROP_RULES:
- if rule.search(tag):
- return True
- return False
-
-
-def get_group_and_title(tag):
- for rule in GROUPING_RULES:
- match = rule.search(tag)
- if match is None:
- continue
- return match.group('group'), match.group('title')
- return None, None
-
-
-def main(args):
- os.makedirs(args.outdir, exist_ok=True)
-
- ignored_events = set()
-
- for orig_fname in glob.glob(args.inglob):
- cur_dirpath = os.path.dirname(orig_fname) # remove filename, this should point to "version_0" directory
- subdirname = os.path.basename(cur_dirpath) # == "version_0" most of time
- exp_root_path = os.path.dirname(cur_dirpath) # remove "version_0"
- exp_name = os.path.basename(exp_root_path)
-
- writers_by_group = {}
-
- for e in tf.compat.v1.train.summary_iterator(orig_fname):
- for v in e.summary.value:
- if need_drop(v.tag):
- continue
-
- cur_group, cur_title = get_group_and_title(v.tag)
- if cur_group is None:
- if v.tag not in ignored_events:
- print(f'WARNING: Could not detect group for {v.tag}, ignoring it')
- ignored_events.add(v.tag)
- continue
-
- cur_writer = writers_by_group.get(cur_group, None)
- if cur_writer is None:
- if args.include_version:
- cur_outdir = os.path.join(args.outdir, exp_name, f'{subdirname}_{cur_group}')
- else:
- cur_outdir = os.path.join(args.outdir, exp_name, cur_group)
- cur_writer = SummaryWriter(cur_outdir)
- writers_by_group[cur_group] = cur_writer
-
- cur_writer.add_scalar(cur_title, v.simple_value, global_step=e.step, walltime=e.wall_time)
-
-
-if __name__ == '__main__':
- import argparse
-
- aparser = argparse.ArgumentParser()
- aparser.add_argument('inglob', type=str)
- aparser.add_argument('outdir', type=str)
- aparser.add_argument('--include-version', action='store_true',
- help='Include subdirectory name e.g. "version_0" into output path')
-
- main(aparser.parse_args())
diff --git a/spaces/JUNGU/Image-to-Story-Ko/share_btn.py b/spaces/JUNGU/Image-to-Story-Ko/share_btn.py
deleted file mode 100644
index a14082bc8a4902e010d8a98431adc3e7ffecf5a3..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/Image-to-Story-Ko/share_btn.py
+++ /dev/null
@@ -1,75 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
- <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
- <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
- style="color: #ffffff;
-"
- xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
- async function uploadFile(file){
- const UPLOAD_URL = 'https://huggingface.co/uploads';
- const response = await fetch(UPLOAD_URL, {
- method: 'POST',
- headers: {
- 'Content-Type': file.type,
- 'X-Requested-With': 'XMLHttpRequest',
- },
- body: file, /// <- File inherits from Blob
- });
- const url = await response.text();
- return url;
- }
-
- async function getInputImgFile(imgEl){
- const res = await fetch(imgEl.src);
- const blob = await res.blob();
- const imgId = Date.now() % 200;
- const isPng = imgEl.src.startsWith(`data:image/png`);
- if(isPng){
- const fileName = `sd-perception-${{imgId}}.png`;
- return new File([blob], fileName, { type: 'image/png' });
- }else{
- const fileName = `sd-perception-${{imgId}}.jpg`;
- return new File([blob], fileName, { type: 'image/jpeg' });
- }
- }
-
- const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
- const inputImgEl = gradioEl.querySelector('#image-in img');
- const outputTxt = gradioEl.querySelector('#story textarea').value;
- const wordsArray = outputTxt.split(" ");
- const firstWords = wordsArray.slice(0, 12).join(" ");
-
- const shareBtnEl = gradioEl.querySelector('#share-btn');
- const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
- const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-
- shareBtnEl.style.pointerEvents = 'none';
- shareIconEl.style.display = 'none';
- loadingIconEl.style.removeProperty('display');
-
-
- const inputFile = await getInputImgFile(inputImgEl);
- const urlInputImg = await uploadFile(inputFile);
-
- const descriptionMd = `
-
-#### Image input:
-<img src="${urlInputImg}" />
-
-#### Generated Story:
-${outputTxt}
-`;
- const params = new URLSearchParams({
- title: firstWords,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/fffiloni/Image-to-Story/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/Jeff2323/ai-comic-factory/src/lib/getInitialRenderedScene.ts b/spaces/Jeff2323/ai-comic-factory/src/lib/getInitialRenderedScene.ts
deleted file mode 100644
index 7c0739bf8bebdaf16aa4acf610eb6bdad9c15fd2..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/src/lib/getInitialRenderedScene.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-import { RenderedScene } from "@/types"
-
-export const getInitialRenderedScene = (): RenderedScene => ({
- renderId: "",
- status: "pending",
- assetUrl: "",
- alt: "",
- error: "",
- maskUrl: "",
- segments: []
-})
\ No newline at end of file
diff --git a/spaces/Jeffsun/LSP-LearningandStrivePartner-Demo/modules/model.py b/spaces/Jeffsun/LSP-LearningandStrivePartner-Demo/modules/model.py
deleted file mode 100644
index 70fa00ee4b52f1c9ad7cc10c52c201b64ceb5fd8..0000000000000000000000000000000000000000
--- a/spaces/Jeffsun/LSP-LearningandStrivePartner-Demo/modules/model.py
+++ /dev/null
@@ -1,897 +0,0 @@
-import importlib
-import inspect
-import math
-from pathlib import Path
-import re
-from collections import defaultdict
-from typing import List, Optional, Union
-
-import time
-import k_diffusion
-import numpy as np
-import PIL
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
-from modules.prompt_parser import FrozenCLIPEmbedderWithCustomWords
-from torch import einsum
-from torch.autograd.function import Function
-
-from diffusers import DiffusionPipeline
-from diffusers.utils import PIL_INTERPOLATION, is_accelerate_available
-from diffusers.utils import logging, randn_tensor
-
-import modules.safe as _
-from safetensors.torch import load_file
-
-xformers_available = False
-try:
- import xformers
-
- xformers_available = True
-except ImportError:
- pass
-
-EPSILON = 1e-6
-exists = lambda val: val is not None
-default = lambda val, d: val if exists(val) else d
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-def get_attention_scores(attn, query, key, attention_mask=None):
-
- if attn.upcast_attention:
- query = query.float()
- key = key.float()
-
- attention_scores = torch.baddbmm(
- torch.empty(
- query.shape[0],
- query.shape[1],
- key.shape[1],
- dtype=query.dtype,
- device=query.device,
- ),
- query,
- key.transpose(-1, -2),
- beta=0,
- alpha=attn.scale,
- )
-
- if attention_mask is not None:
- attention_scores = attention_scores + attention_mask
-
- if attn.upcast_softmax:
- attention_scores = attention_scores.float()
-
- return attention_scores
-
-
-class CrossAttnProcessor(nn.Module):
- def __call__(
- self,
- attn,
- hidden_states,
- encoder_hidden_states=None,
- attention_mask=None,
- ):
- batch_size, sequence_length, _ = hidden_states.shape
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
-
- encoder_states = hidden_states
- is_xattn = False
- if encoder_hidden_states is not None:
- is_xattn = True
- img_state = encoder_hidden_states["img_state"]
- encoder_states = encoder_hidden_states["states"]
- weight_func = encoder_hidden_states["weight_func"]
- sigma = encoder_hidden_states["sigma"]
-
- query = attn.to_q(hidden_states)
- key = attn.to_k(encoder_states)
- value = attn.to_v(encoder_states)
-
- query = attn.head_to_batch_dim(query)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- if is_xattn and isinstance(img_state, dict):
- # use torch.baddbmm method (slow)
- attention_scores = get_attention_scores(attn, query, key, attention_mask)
- w = img_state[sequence_length].to(query.device)
- cross_attention_weight = weight_func(w, sigma, attention_scores)
- attention_scores += torch.repeat_interleave(
- cross_attention_weight, repeats=attn.heads, dim=0
- )
-
- # calc probs
- attention_probs = attention_scores.softmax(dim=-1)
- attention_probs = attention_probs.to(query.dtype)
- hidden_states = torch.bmm(attention_probs, value)
-
- elif xformers_available:
- hidden_states = xformers.ops.memory_efficient_attention(
- query.contiguous(),
- key.contiguous(),
- value.contiguous(),
- attn_bias=attention_mask,
- )
- hidden_states = hidden_states.to(query.dtype)
-
- else:
- q_bucket_size = 512
- k_bucket_size = 1024
-
- # use flash-attention
- hidden_states = FlashAttentionFunction.apply(
- query.contiguous(),
- key.contiguous(),
- value.contiguous(),
- attention_mask,
- False,
- q_bucket_size,
- k_bucket_size,
- )
- hidden_states = hidden_states.to(query.dtype)
-
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
-
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- return hidden_states
-
-class ModelWrapper:
- def __init__(self, model, alphas_cumprod):
- self.model = model
- self.alphas_cumprod = alphas_cumprod
-
- def apply_model(self, *args, **kwargs):
- if len(args) == 3:
- encoder_hidden_states = args[-1]
- args = args[:2]
- if kwargs.get("cond", None) is not None:
- encoder_hidden_states = kwargs.pop("cond")
- return self.model(
- *args, encoder_hidden_states=encoder_hidden_states, **kwargs
- ).sample
-
-
-class StableDiffusionPipeline(DiffusionPipeline):
-
- _optional_components = ["safety_checker", "feature_extractor"]
-
- def __init__(
- self,
- vae,
- text_encoder,
- tokenizer,
- unet,
- scheduler,
- ):
- super().__init__()
-
- # get correct sigmas from LMS
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- )
- self.setup_unet(self.unet)
- self.setup_text_encoder()
-
- def setup_text_encoder(self, n=1, new_encoder=None):
- if new_encoder is not None:
- self.text_encoder = new_encoder
-
- self.prompt_parser = FrozenCLIPEmbedderWithCustomWords(self.tokenizer, self.text_encoder)
- self.prompt_parser.CLIP_stop_at_last_layers = n
-
- def setup_unet(self, unet):
- unet = unet.to(self.device)
- model = ModelWrapper(unet, self.scheduler.alphas_cumprod)
- if self.scheduler.prediction_type == "v_prediction":
- self.k_diffusion_model = CompVisVDenoiser(model)
- else:
- self.k_diffusion_model = CompVisDenoiser(model)
-
- def get_scheduler(self, scheduler_type: str):
- library = importlib.import_module("k_diffusion")
- sampling = getattr(library, "sampling")
- return getattr(sampling, scheduler_type)
-
- def encode_sketchs(self, state, scale_ratio=8, g_strength=1.0, text_ids=None):
- uncond, cond = text_ids[0], text_ids[1]
-
- img_state = []
- if state is None:
- return torch.FloatTensor(0)
-
- for k, v in state.items():
- if v["map"] is None:
- continue
-
- v_input = self.tokenizer(
- k,
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- add_special_tokens=False,
- ).input_ids
-
- dotmap = v["map"] < 255
- out = dotmap.astype(float)
- if v["mask_outsides"]:
- out[out==0] = -1
-
- arr = torch.from_numpy(
- out * float(v["weight"]) * g_strength
- )
- img_state.append((v_input, arr))
-
- if len(img_state) == 0:
- return torch.FloatTensor(0)
-
- w_tensors = dict()
- cond = cond.tolist()
- uncond = uncond.tolist()
- for layer in self.unet.down_blocks:
- c = int(len(cond))
- w, h = img_state[0][1].shape
- w_r, h_r = w // scale_ratio, h // scale_ratio
-
- ret_cond_tensor = torch.zeros((1, int(w_r * h_r), c), dtype=torch.float32)
- ret_uncond_tensor = torch.zeros((1, int(w_r * h_r), c), dtype=torch.float32)
-
- for v_as_tokens, img_where_color in img_state:
- is_in = 0
-
- ret = (
- F.interpolate(
- img_where_color.unsqueeze(0).unsqueeze(1),
- scale_factor=1 / scale_ratio,
- mode="bilinear",
- align_corners=True,
- )
- .squeeze()
- .reshape(-1, 1)
- .repeat(1, len(v_as_tokens))
- )
-
- for idx, tok in enumerate(cond):
- if cond[idx : idx + len(v_as_tokens)] == v_as_tokens:
- is_in = 1
- ret_cond_tensor[0, :, idx : idx + len(v_as_tokens)] += ret
-
- for idx, tok in enumerate(uncond):
- if uncond[idx : idx + len(v_as_tokens)] == v_as_tokens:
- is_in = 1
- ret_uncond_tensor[0, :, idx : idx + len(v_as_tokens)] += ret
-
- if not is_in == 1:
- print(f"tokens {v_as_tokens} not found in text")
-
- w_tensors[w_r * h_r] = torch.cat([ret_uncond_tensor, ret_cond_tensor])
- scale_ratio *= 2
-
- return w_tensors
-
- def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
- r"""
- Enable sliced attention computation.
-
- When this option is enabled, the attention module will split the input tensor in slices, to compute attention
- in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
- Args:
- slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
- When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
- a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
- `attention_head_dim` must be a multiple of `slice_size`.
- """
- if slice_size == "auto":
- # half the attention head size is usually a good trade-off between
- # speed and memory
- slice_size = self.unet.config.attention_head_dim // 2
- self.unet.set_attention_slice(slice_size)
-
- def disable_attention_slicing(self):
- r"""
- Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
- back to computing attention in one step.
- """
- # set slice_size = `None` to disable `attention slicing`
- self.enable_attention_slicing(None)
-
- def enable_sequential_cpu_offload(self, gpu_id=0):
- r"""
- Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
- text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
- `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
- """
- if is_accelerate_available():
- from accelerate import cpu_offload
- else:
- raise ImportError("Please install accelerate via `pip install accelerate`")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- for cpu_offloaded_model in [
- self.unet,
- self.text_encoder,
- self.vae,
- self.safety_checker,
- ]:
- if cpu_offloaded_model is not None:
- cpu_offload(cpu_offloaded_model, device)
-
- @property
- def _execution_device(self):
- r"""
- Returns the device on which the pipeline's models will be executed. After calling
- `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
- hooks.
- """
- if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
- return self.device
- for module in self.unet.modules():
- if (
- hasattr(module, "_hf_hook")
- and hasattr(module._hf_hook, "execution_device")
- and module._hf_hook.execution_device is not None
- ):
- return torch.device(module._hf_hook.execution_device)
- return self.device
-
- def decode_latents(self, latents):
- latents = latents.to(self.device, dtype=self.vae.dtype)
- latents = 1 / 0.18215 * latents
- image = self.vae.decode(latents).sample
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- def check_inputs(self, prompt, height, width, callback_steps):
- if not isinstance(prompt, str) and not isinstance(prompt, list):
- raise ValueError(
- f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
- )
-
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(
- f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
- )
-
- if (callback_steps is None) or (
- callback_steps is not None
- and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- def prepare_latents(
- self,
- batch_size,
- num_channels_latents,
- height,
- width,
- dtype,
- device,
- generator,
- latents=None,
- ):
- shape = (batch_size, num_channels_latents, height // 8, width // 8)
- if latents is None:
- if device.type == "mps":
- # randn does not work reproducibly on mps
- latents = torch.randn(
- shape, generator=generator, device="cpu", dtype=dtype
- ).to(device)
- else:
- latents = torch.randn(
- shape, generator=generator, device=device, dtype=dtype
- )
- else:
- # if latents.shape != shape:
- # raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
- latents = latents.to(device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- return latents
-
- def preprocess(self, image):
- if isinstance(image, torch.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8
-
- image = [
- np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[
- None, :
- ]
- for i in image
- ]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
- return image
-
- @torch.no_grad()
- def img2img(
- self,
- prompt: Union[str, List[str]],
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- generator: Optional[torch.Generator] = None,
- image: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- latents=None,
- strength=1.0,
- pww_state=None,
- pww_attn_weight=1.0,
- sampler_name="",
- sampler_opt={},
- start_time=-1,
- timeout=180,
- scale_ratio=8.0,
- ):
- sampler = self.get_scheduler(sampler_name)
- if image is not None:
- image = self.preprocess(image)
- image = image.to(self.vae.device, dtype=self.vae.dtype)
-
- init_latents = self.vae.encode(image).latent_dist.sample(generator)
- latents = 0.18215 * init_latents
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
- device = self._execution_device
- latents = latents.to(device, dtype=self.unet.dtype)
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = True
- if guidance_scale <= 1.0:
- raise ValueError("has to use guidance_scale")
-
- # 3. Encode input prompt
- text_ids, text_embeddings = self.prompt_parser([negative_prompt, prompt])
- text_embeddings = text_embeddings.to(self.unet.dtype)
-
- init_timestep = (
- int(num_inference_steps / min(strength, 0.999)) if strength > 0 else 0
- )
- sigmas = self.get_sigmas(init_timestep, sampler_opt).to(
- text_embeddings.device, dtype=text_embeddings.dtype
- )
-
- t_start = max(init_timestep - num_inference_steps, 0)
- sigma_sched = sigmas[t_start:]
-
- noise = randn_tensor(
- latents.shape,
- generator=generator,
- device=device,
- dtype=text_embeddings.dtype,
- )
- latents = latents.to(device)
- latents = latents + noise * sigma_sched[0]
-
- # 5. Prepare latent variables
- self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
- self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(
- latents.device
- )
-
- img_state = self.encode_sketchs(
- pww_state,
- g_strength=pww_attn_weight,
- text_ids=text_ids,
- )
-
- def model_fn(x, sigma):
-
- if start_time > 0 and timeout > 0:
- assert (time.time() - start_time) < timeout, "inference process timed out"
-
- latent_model_input = torch.cat([x] * 2)
- weight_func = lambda w, sigma, qk: w * math.log(1 + sigma) * qk.max()
- encoder_state = {
- "img_state": img_state,
- "states": text_embeddings,
- "sigma": sigma[0],
- "weight_func": weight_func,
- }
-
- noise_pred = self.k_diffusion_model(
- latent_model_input, sigma, cond=encoder_state
- )
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (
- noise_pred_text - noise_pred_uncond
- )
- return noise_pred
-
- sampler_args = self.get_sampler_extra_args_i2i(sigma_sched, sampler)
- latents = sampler(model_fn, latents, **sampler_args)
-
- # 8. Post-processing
- image = self.decode_latents(latents)
-
- # 10. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- return (image,)
-
- def get_sigmas(self, steps, params):
- discard_next_to_last_sigma = params.get("discard_next_to_last_sigma", False)
- steps += 1 if discard_next_to_last_sigma else 0
-
- if params.get("scheduler", None) == "karras":
- sigma_min, sigma_max = (
- self.k_diffusion_model.sigmas[0].item(),
- self.k_diffusion_model.sigmas[-1].item(),
- )
- sigmas = k_diffusion.sampling.get_sigmas_karras(
- n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=self.device
- )
- else:
- sigmas = self.k_diffusion_model.get_sigmas(steps)
-
- if discard_next_to_last_sigma:
- sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
- return sigmas
-
- # https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/48a15821de768fea76e66f26df83df3fddf18f4b/modules/sd_samplers.py#L454
- def get_sampler_extra_args_t2i(self, sigmas, eta, steps, func):
- extra_params_kwargs = {}
-
- if "eta" in inspect.signature(func).parameters:
- extra_params_kwargs["eta"] = eta
-
- if "sigma_min" in inspect.signature(func).parameters:
- extra_params_kwargs["sigma_min"] = sigmas[0].item()
- extra_params_kwargs["sigma_max"] = sigmas[-1].item()
-
- if "n" in inspect.signature(func).parameters:
- extra_params_kwargs["n"] = steps
- else:
- extra_params_kwargs["sigmas"] = sigmas
-
- return extra_params_kwargs
-
- # https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/48a15821de768fea76e66f26df83df3fddf18f4b/modules/sd_samplers.py#L454
- def get_sampler_extra_args_i2i(self, sigmas, func):
- extra_params_kwargs = {}
-
- if "sigma_min" in inspect.signature(func).parameters:
- ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
- extra_params_kwargs["sigma_min"] = sigmas[-2]
-
- if "sigma_max" in inspect.signature(func).parameters:
- extra_params_kwargs["sigma_max"] = sigmas[0]
-
- if "n" in inspect.signature(func).parameters:
- extra_params_kwargs["n"] = len(sigmas) - 1
-
- if "sigma_sched" in inspect.signature(func).parameters:
- extra_params_kwargs["sigma_sched"] = sigmas
-
- if "sigmas" in inspect.signature(func).parameters:
- extra_params_kwargs["sigmas"] = sigmas
-
- return extra_params_kwargs
-
- @torch.no_grad()
- def txt2img(
- self,
- prompt: Union[str, List[str]],
- height: int = 512,
- width: int = 512,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- eta: float = 0.0,
- generator: Optional[torch.Generator] = None,
- latents: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- callback_steps: Optional[int] = 1,
- upscale=False,
- upscale_x: float = 2.0,
- upscale_method: str = "bicubic",
- upscale_antialias: bool = False,
- upscale_denoising_strength: int = 0.7,
- pww_state=None,
- pww_attn_weight=1.0,
- sampler_name="",
- sampler_opt={},
- start_time=-1,
- timeout=180,
- ):
- sampler = self.get_scheduler(sampler_name)
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(prompt, height, width, callback_steps)
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
- device = self._execution_device
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = True
- if guidance_scale <= 1.0:
- raise ValueError("has to use guidance_scale")
-
- # 3. Encode input prompt
- text_ids, text_embeddings = self.prompt_parser([negative_prompt, prompt])
- text_embeddings = text_embeddings.to(self.unet.dtype)
-
- # 4. Prepare timesteps
- sigmas = self.get_sigmas(num_inference_steps, sampler_opt).to(
- text_embeddings.device, dtype=text_embeddings.dtype
- )
-
- # 5. Prepare latent variables
- num_channels_latents = self.unet.in_channels
- latents = self.prepare_latents(
- batch_size,
- num_channels_latents,
- height,
- width,
- text_embeddings.dtype,
- device,
- generator,
- latents,
- )
- latents = latents * sigmas[0]
- self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
- self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(
- latents.device
- )
-
- img_state = self.encode_sketchs(
- pww_state,
- g_strength=pww_attn_weight,
- text_ids=text_ids,
- )
-
- def model_fn(x, sigma):
-
- if start_time > 0 and timeout > 0:
- assert (time.time() - start_time) < timeout, "inference process timed out"
-
- latent_model_input = torch.cat([x] * 2)
- weight_func = lambda w, sigma, qk: w * math.log(1 + sigma) * qk.max()
- encoder_state = {
- "img_state": img_state,
- "states": text_embeddings,
- "sigma": sigma[0],
- "weight_func": weight_func,
- }
-
- noise_pred = self.k_diffusion_model(
- latent_model_input, sigma, cond=encoder_state
- )
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (
- noise_pred_text - noise_pred_uncond
- )
- return noise_pred
-
- extra_args = self.get_sampler_extra_args_t2i(
- sigmas, eta, num_inference_steps, sampler
- )
- latents = sampler(model_fn, latents, **extra_args)
-
- if upscale:
- target_height = height * upscale_x
- target_width = width * upscale_x
- vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- latents = torch.nn.functional.interpolate(
- latents,
- size=(
- int(target_height // vae_scale_factor),
- int(target_width // vae_scale_factor),
- ),
- mode=upscale_method,
- antialias=upscale_antialias,
- )
- return self.img2img(
- prompt=prompt,
- num_inference_steps=num_inference_steps,
- guidance_scale=guidance_scale,
- negative_prompt=negative_prompt,
- generator=generator,
- latents=latents,
- strength=upscale_denoising_strength,
- sampler_name=sampler_name,
- sampler_opt=sampler_opt,
- pww_state=None,
- pww_attn_weight=pww_attn_weight / 2,
- )
-
- # 8. Post-processing
- image = self.decode_latents(latents)
-
- # 10. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- return (image,)
-
-
-class FlashAttentionFunction(Function):
- @staticmethod
- @torch.no_grad()
- def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size):
- """Algorithm 2 in the paper"""
-
- device = q.device
- max_neg_value = -torch.finfo(q.dtype).max
- qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)
-
- o = torch.zeros_like(q)
- all_row_sums = torch.zeros((*q.shape[:-1], 1), device=device)
- all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, device=device)
-
- scale = q.shape[-1] ** -0.5
-
- if not exists(mask):
- mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size)
- else:
- mask = rearrange(mask, "b n -> b 1 1 n")
- mask = mask.split(q_bucket_size, dim=-1)
-
- row_splits = zip(
- q.split(q_bucket_size, dim=-2),
- o.split(q_bucket_size, dim=-2),
- mask,
- all_row_sums.split(q_bucket_size, dim=-2),
- all_row_maxes.split(q_bucket_size, dim=-2),
- )
-
- for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits):
- q_start_index = ind * q_bucket_size - qk_len_diff
-
- col_splits = zip(
- k.split(k_bucket_size, dim=-2),
- v.split(k_bucket_size, dim=-2),
- )
-
- for k_ind, (kc, vc) in enumerate(col_splits):
- k_start_index = k_ind * k_bucket_size
-
- attn_weights = einsum("... i d, ... j d -> ... i j", qc, kc) * scale
-
- if exists(row_mask):
- attn_weights.masked_fill_(~row_mask, max_neg_value)
-
- if causal and q_start_index < (k_start_index + k_bucket_size - 1):
- causal_mask = torch.ones(
- (qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device
- ).triu(q_start_index - k_start_index + 1)
- attn_weights.masked_fill_(causal_mask, max_neg_value)
-
- block_row_maxes = attn_weights.amax(dim=-1, keepdims=True)
- attn_weights -= block_row_maxes
- exp_weights = torch.exp(attn_weights)
-
- if exists(row_mask):
- exp_weights.masked_fill_(~row_mask, 0.0)
-
- block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(
- min=EPSILON
- )
-
- new_row_maxes = torch.maximum(block_row_maxes, row_maxes)
-
- exp_values = einsum("... i j, ... j d -> ... i d", exp_weights, vc)
-
- exp_row_max_diff = torch.exp(row_maxes - new_row_maxes)
- exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes)
-
- new_row_sums = (
- exp_row_max_diff * row_sums
- + exp_block_row_max_diff * block_row_sums
- )
-
- oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_(
- (exp_block_row_max_diff / new_row_sums) * exp_values
- )
-
- row_maxes.copy_(new_row_maxes)
- row_sums.copy_(new_row_sums)
-
- lse = all_row_sums.log() + all_row_maxes
-
- ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size)
- ctx.save_for_backward(q, k, v, o, lse)
-
- return o
-
- @staticmethod
- @torch.no_grad()
- def backward(ctx, do):
- """Algorithm 4 in the paper"""
-
- causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args
- q, k, v, o, lse = ctx.saved_tensors
-
- device = q.device
-
- max_neg_value = -torch.finfo(q.dtype).max
- qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)
-
- dq = torch.zeros_like(q)
- dk = torch.zeros_like(k)
- dv = torch.zeros_like(v)
-
- row_splits = zip(
- q.split(q_bucket_size, dim=-2),
- o.split(q_bucket_size, dim=-2),
- do.split(q_bucket_size, dim=-2),
- mask,
- lse.split(q_bucket_size, dim=-2),
- dq.split(q_bucket_size, dim=-2),
- )
-
- for ind, (qc, oc, doc, row_mask, lsec, dqc) in enumerate(row_splits):
- q_start_index = ind * q_bucket_size - qk_len_diff
-
- col_splits = zip(
- k.split(k_bucket_size, dim=-2),
- v.split(k_bucket_size, dim=-2),
- dk.split(k_bucket_size, dim=-2),
- dv.split(k_bucket_size, dim=-2),
- )
-
- for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits):
- k_start_index = k_ind * k_bucket_size
-
- attn_weights = einsum("... i d, ... j d -> ... i j", qc, kc) * scale
-
- if causal and q_start_index < (k_start_index + k_bucket_size - 1):
- causal_mask = torch.ones(
- (qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device
- ).triu(q_start_index - k_start_index + 1)
- attn_weights.masked_fill_(causal_mask, max_neg_value)
-
- p = torch.exp(attn_weights - lsec)
-
- if exists(row_mask):
- p.masked_fill_(~row_mask, 0.0)
-
- dv_chunk = einsum("... i j, ... i d -> ... j d", p, doc)
- dp = einsum("... i d, ... j d -> ... i j", doc, vc)
-
- D = (doc * oc).sum(dim=-1, keepdims=True)
- ds = p * scale * (dp - D)
-
- dq_chunk = einsum("... i j, ... j d -> ... i d", ds, kc)
- dk_chunk = einsum("... i j, ... i d -> ... j d", ds, qc)
-
- dqc.add_(dq_chunk)
- dkc.add_(dk_chunk)
- dvc.add_(dv_chunk)
-
- return dq, dk, dv, None, None, None, None
diff --git a/spaces/JunchuanYu/SydneyAI-plus/css_new.css b/spaces/JunchuanYu/SydneyAI-plus/css_new.css
deleted file mode 100644
index 620243ff0c30ee6ad9966e22cd1c962d5bee480b..0000000000000000000000000000000000000000
--- a/spaces/JunchuanYu/SydneyAI-plus/css_new.css
+++ /dev/null
@@ -1,116 +0,0 @@
-[data-testid = "bot"] {
- max-width: 85%;
- border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
- max-width: 85%;
- width: auto !important;
- border-bottom-right-radius: 0 !important;
-}
-
-table {
- margin: 1em 0;
- border-collapse: collapse;
- empty-cells: show;
-}
-td,th {
- border: 1.2px solid var(--border-color-primary) !important;
- padding: 0.2em;
-}
-thead {
- background-color: rgba(175,184,193,0.2);
-}
-thead th {
- padding: .5em .2em;
-}
-
-code {
- display: inline;
- white-space: break-spaces;
- border-radius: 6px;
- margin: 0 2px 0 2px;
- padding: .2em .4em .1em .4em;
- background-color: rgba(175,184,193,0.2);
-}
-
-pre code {
- display: block;
- overflow: auto;
- white-space: pre;
- background-color: hsla(0, 0%, 0%, 80%)!important;
- border-radius: 10px;
- padding: 1.4em 1.2em 0em 1.4em;
- margin: 1.2em 2em 1.2em 0.5em;
- color: #FFF;
- box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
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-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
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-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
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-.highlight .nc { color: #a6e22e } /* Name.Class */
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-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
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-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
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-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/Kevin676/Shanghainese-TTS-demo/mel_processing.py b/spaces/Kevin676/Shanghainese-TTS-demo/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/Shanghainese-TTS-demo/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/__init__.py
deleted file mode 100644
index 7c1bfee1c35bb60867f7ea9ef03d0b4999274792..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/__init__.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .anchor_free_head import AnchorFreeHead
-from .anchor_head import AnchorHead
-from .atss_head import ATSSHead
-from .autoassign_head import AutoAssignHead
-from .boxinst_head import BoxInstBboxHead, BoxInstMaskHead
-from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
-from .centernet_head import CenterNetHead
-from .centernet_update_head import CenterNetUpdateHead
-from .centripetal_head import CentripetalHead
-from .condinst_head import CondInstBboxHead, CondInstMaskHead
-from .conditional_detr_head import ConditionalDETRHead
-from .corner_head import CornerHead
-from .dab_detr_head import DABDETRHead
-from .ddod_head import DDODHead
-from .deformable_detr_head import DeformableDETRHead
-from .detr_head import DETRHead
-from .dino_head import DINOHead
-from .embedding_rpn_head import EmbeddingRPNHead
-from .fcos_head import FCOSHead
-from .fovea_head import FoveaHead
-from .free_anchor_retina_head import FreeAnchorRetinaHead
-from .fsaf_head import FSAFHead
-from .ga_retina_head import GARetinaHead
-from .ga_rpn_head import GARPNHead
-from .gfl_head import GFLHead
-from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
-from .lad_head import LADHead
-from .ld_head import LDHead
-from .mask2former_head import Mask2FormerHead
-from .maskformer_head import MaskFormerHead
-from .nasfcos_head import NASFCOSHead
-from .paa_head import PAAHead
-from .pisa_retinanet_head import PISARetinaHead
-from .pisa_ssd_head import PISASSDHead
-from .reppoints_head import RepPointsHead
-from .retina_head import RetinaHead
-from .retina_sepbn_head import RetinaSepBNHead
-from .rpn_head import RPNHead
-from .rtmdet_head import RTMDetHead, RTMDetSepBNHead
-from .rtmdet_ins_head import RTMDetInsHead, RTMDetInsSepBNHead
-from .sabl_retina_head import SABLRetinaHead
-from .solo_head import DecoupledSOLOHead, DecoupledSOLOLightHead, SOLOHead
-from .solov2_head import SOLOV2Head
-from .ssd_head import SSDHead
-from .tood_head import TOODHead
-from .vfnet_head import VFNetHead
-from .yolact_head import YOLACTHead, YOLACTProtonet
-from .yolo_head import YOLOV3Head
-from .yolof_head import YOLOFHead
-from .yolox_head import YOLOXHead
-
-__all__ = [
- 'AnchorFreeHead', 'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption',
- 'RPNHead', 'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead',
- 'SSDHead', 'FCOSHead', 'RepPointsHead', 'FoveaHead',
- 'FreeAnchorRetinaHead', 'ATSSHead', 'FSAFHead', 'NASFCOSHead',
- 'PISARetinaHead', 'PISASSDHead', 'GFLHead', 'CornerHead', 'YOLACTHead',
- 'YOLACTProtonet', 'YOLOV3Head', 'PAAHead', 'SABLRetinaHead',
- 'CentripetalHead', 'VFNetHead', 'StageCascadeRPNHead', 'CascadeRPNHead',
- 'EmbeddingRPNHead', 'LDHead', 'AutoAssignHead', 'DETRHead', 'YOLOFHead',
- 'DeformableDETRHead', 'CenterNetHead', 'YOLOXHead', 'SOLOHead',
- 'DecoupledSOLOHead', 'DecoupledSOLOLightHead', 'SOLOV2Head', 'LADHead',
- 'TOODHead', 'MaskFormerHead', 'Mask2FormerHead', 'DDODHead',
- 'CenterNetUpdateHead', 'RTMDetHead', 'RTMDetSepBNHead', 'CondInstBboxHead',
- 'CondInstMaskHead', 'RTMDetInsHead', 'RTMDetInsSepBNHead',
- 'BoxInstBboxHead', 'BoxInstMaskHead', 'ConditionalDETRHead', 'DINOHead',
- 'DABDETRHead'
-]
diff --git a/spaces/Kyo-Kai/Fsg-pp/commands/driver_instance.py b/spaces/Kyo-Kai/Fsg-pp/commands/driver_instance.py
deleted file mode 100644
index f7bb7af5523f0dd84d31e1473ac276237b0c3219..0000000000000000000000000000000000000000
--- a/spaces/Kyo-Kai/Fsg-pp/commands/driver_instance.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from selenium.webdriver.chrome.service import Service
-from selenium import webdriver
-import urllib.request
-import os
-import logging
-
-def create_driver(exec_path, profile=0):
- # Options to make it more human-like
- options = webdriver.ChromeOptions()
- options.add_argument("start-maximized")
- options.add_argument("--disable-blink-features=AutomationControlled")
- options.add_argument("--disable-notifications")
- options.add_argument("--disable-popup-blocking")
- options.add_argument("--disable-extensions")
- options.add_argument("--disable-gpu")
- options.add_argument("--disable-infobars")
- options.add_argument("--disable-dev-shm-usage")
- options.add_argument("--no-sandbox")
- options.add_argument("user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36")
- options.add_argument("--log-level=3")
-
- prefs = {"credentials_enable_service": False,
- "profile.password_manager_enabled": False}
- options.add_experimental_option("prefs", prefs)
- options.add_argument("--headless")
- # to supress the error messages/logs?
- options.add_experimental_option("excludeSwitches", ["enable-logging"])
- options.add_experimental_option("excludeSwitches", ["enable-automation"])
- # options.page_load_strategy = 'normal' #eager #none #normal
-
- # Remove logs from console
- selenium_logger = logging.getLogger('selenium')
- selenium_logger.setLevel(logging.ERROR)
-
- service = Service(executable_path=exec_path)
- driver = webdriver.Chrome(service=service, options=options)
- return driver
-
-def create_url_headers(tempImg):
- opener = urllib.request.build_opener()
- opener.addheaders = [
- ('Accept', 'application/json, text/javascript, */*; q=0.01'),
- ('X-Requested-With', 'XMLHttpRequest'),
- ('Referer', f'{tempImg}'),
- ('Host', f'https//{tempImg.split("/")[2]}'),
- ('Content-Type', 'application/json; charset=UTF-8'),
- ('Connection', 'keep-alive'),
- ('user-agent','Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36')
- ]
- return opener
-
-def tab_handler(driver, image=0):
- if image:
- tempImg = image.get_attribute("href")
- driver.execute_script("window.open('');")
- driver.switch_to.window(driver.window_handles[1])
- driver.get(f"{tempImg}")
- return driver, tempImg
-
- driver.close()
- driver.switch_to.window(driver.window_handles[0])
- return driver
\ No newline at end of file
diff --git a/spaces/LanguageBind/LanguageBind/main.py b/spaces/LanguageBind/LanguageBind/main.py
deleted file mode 100644
index 056c883599846f8f0560a7b775eacccd98294954..0000000000000000000000000000000000000000
--- a/spaces/LanguageBind/LanguageBind/main.py
+++ /dev/null
@@ -1,657 +0,0 @@
-import glob
-import logging
-import os
-import re
-import subprocess
-import sys
-import random
-from datetime import datetime
-
-import numpy as np
-import torch
-from torch import optim
-from torch.cuda.amp import GradScaler
-
-from a_cls.zeroshot_cls import evaluate_a_cls
-from i_cls.zeroshot_cls import evaluate_i_cls
-from d_cls.zeroshot_cls import evaluate_d_cls
-from t_cls.zeroshot_cls import evaluate_t_cls
-from v_cls.zeroshot_cls import evaluate_v_cls
-from vl_ret.retrieval import evaluate_vl_ret
-
-from model.process_clip import set_global_value, print_trainable_parameters
-
-try:
- import wandb
-except ImportError:
- wandb = None
-
-try:
- import tensorboardX as tensorboard
-except ImportError:
- tensorboard = None
-
-try:
- import horovod.torch as hvd
-except ImportError:
- hvd = None
-
-
-from data.build_datasets import get_data
-from open_clip import create_model_and_transforms, create_loss
-from training.distributed import is_master, init_distributed_device, broadcast_object
-from training.logger import setup_logging
-from training.params import parse_args
-from training.scheduler import cosine_lr, const_lr, const_lr_cooldown
-from training.file_utils import pt_load, start_sync_process, remote_sync
-from train import train_one_epoch
-from model.build_model import create_vat_model
-
-LATEST_CHECKPOINT_NAME = "epoch_latest.pt"
-MODEL_DICT = {"ViT-L-14": "laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K",
- "ViT-H-14": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"}
-CHECKPOINT_DICT = {"ViT-L-14": "models--laion--CLIP-ViT-L-14-DataComp.XL-s13B-b90K/snapshots/84c9828e63dc9a9351d1fe637c346d4c1c4db341/pytorch_model.bin",
- "ViT-H-14": "models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/snapshots/94a64189c3535c1cb44acfcccd7b0908c1c8eb23/pytorch_model.bin"}
-
-
-
-
-
-
-def random_seed(seed=42, rank=0):
- torch.manual_seed(seed + rank)
- np.random.seed(seed + rank)
- random.seed(seed + rank)
-
-
-def natural_key(string_):
- """See http://www.codinghorror.com/blog/archives/001018.html"""
- return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
-
-
-def get_latest_checkpoint(path: str, remote: bool):
- # as writen, this glob recurses, so can pick up checkpoints across multiple sub-folders
- if remote:
- result = subprocess.run(["aws", "s3", "ls", path + "/"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
- print(result)
- if result.returncode == 1:
- return None
- checkpoints = [os.path.join(path, x.split(' ')[-1]) for x in result.stdout.decode().split('\n')[:-1]]
- else:
- checkpoints = glob.glob(path + '**/*.pt', recursive=True)
- if checkpoints:
- checkpoints = sorted(checkpoints, key=natural_key)
- return checkpoints[-1]
- return None
-
-def SET_GLOBAL_VALUE(k, v):
- set_global_value(k, v)
-
-def main(args):
- args = parse_args(args)
-
- SET_GLOBAL_VALUE('PATCH_DROPOUT', args.force_patch_dropout)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
-
- if torch.cuda.is_available():
- # This enables tf32 on Ampere GPUs which is only 8% slower than
- # float16 and almost as accurate as float32
- # This was a default in pytorch until 1.12
- torch.backends.cuda.matmul.allow_tf32 = True
- torch.backends.cudnn.benchmark = True
- torch.backends.cudnn.deterministic = False
-
- # fully initialize distributed device environment
- device = init_distributed_device(args)
-
- # get the name of the experiments
- if args.name is None:
- # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
- model_name_safe = args.model.replace('/', '-')
- date_str = datetime.now().strftime("%Y_%m_%d-%H_%M_%S")
- if args.distributed:
- # sync date_str from master to all ranks
- date_str = broadcast_object(args, date_str)
- args.name = '-'.join([
- date_str,
- f"pt_{args.clip_type}",
- f"text_{args.text_type}",
- f"bs_{args.batch_size}",
- f"ep_{args.epochs}",
- f"mask_{args.force_patch_dropout}",
- f"lorar_{args.lora_r}" if args.convert_to_lora else "",
- f"lr_{args.lr}",
- f"coeflr_{args.coef_lr}",
- f"warm_{args.warmup}",
- f"accum_{args.accum_freq}",
- f"tattn_{args.add_time_attn}" if args.clip_type == 'vl' else "",
- f"model_{model_name_safe}",
- f"frm_{args.num_frames}",
- f"vdb_{args.video_decode_backend}",
- ])
- args.pretrained = CHECKPOINT_DICT[args.model]
- args.model = MODEL_DICT[args.model]
-
- resume_latest = args.resume == 'latest'
- log_base_path = os.path.join(args.logs, args.name)
- args.log_base_path = log_base_path
- args.log_path = None
- if is_master(args, local=args.log_local):
- os.makedirs(log_base_path, exist_ok=True)
- log_filename = f'out-{args.rank}' if args.log_local else 'out.log'
- args.log_path = os.path.join(log_base_path, log_filename)
- if os.path.exists(args.log_path) and not resume_latest:
- print(
- "Error. Experiment already exists. Use --name {} to specify a new experiment."
- )
- return -1
-
- # Setup text logger
- args.log_level = logging.DEBUG if args.debug else logging.INFO
- setup_logging(args.log_path, args.log_level)
-
- # Setup wandb, tensorboard, checkpoint logging
- args.wandb = 'wandb' in args.report_to or 'all' in args.report_to
- args.tensorboard = 'tensorboard' in args.report_to or 'all' in args.report_to
- args.checkpoint_path = os.path.join(log_base_path, "checkpoints")
- if is_master(args):
- args.tensorboard_path = os.path.join(log_base_path, "tensorboard") if args.tensorboard else ''
- for dirname in [args.tensorboard_path, args.checkpoint_path]:
- if dirname:
- os.makedirs(dirname, exist_ok=True)
- else:
- args.tensorboard_path = ''
-
- if resume_latest:
- resume_from = None
- checkpoint_path = args.checkpoint_path
- # If using remote_sync, need to check the remote instead of the local checkpoints folder.
- if args.remote_sync is not None:
- checkpoint_path = os.path.join(args.remote_sync, args.name, "checkpoints")
- if args.save_most_recent:
- print('Error. Cannot use save-most-recent with remote_sync and resume latest.')
- return -1
- if args.remote_sync_protocol != 's3':
- print('Error. Sync protocol not supported when using resume latest.')
- return -1
- if is_master(args):
- # Checking for existing checkpoint via master rank only. It is possible for
- # different rank processes to see different files if a shared file-system is under
- # stress, however it's very difficult to fully work around such situations.
- if args.save_most_recent:
- # if --save-most-recent flag is set, look for latest at a fixed filename
- resume_from = os.path.join(checkpoint_path, LATEST_CHECKPOINT_NAME)
- if not os.path.exists(resume_from):
- # If no latest checkpoint has been saved yet, don't try to resume
- resume_from = None
- else:
- # otherwise, list checkpoint dir contents and pick the newest checkpoint
- resume_from = get_latest_checkpoint(checkpoint_path, remote=args.remote_sync is not None)
- if resume_from:
- logging.info(f'Found latest resume checkpoint at {resume_from}.')
- else:
- logging.info(f'No latest resume checkpoint found in {checkpoint_path}.')
- if args.distributed:
- # sync found checkpoint path to all ranks
- resume_from = broadcast_object(args, resume_from)
- args.resume = resume_from
-
- if args.copy_codebase:
- copy_codebase(args)
-
- # start the sync proces if remote-sync is not None
- remote_sync_process = None
- if is_master(args) and args.remote_sync is not None:
- # first make sure it works
- result = remote_sync(
- os.path.join(args.logs, args.name),
- os.path.join(args.remote_sync, args.name),
- args.remote_sync_protocol
- )
- if result:
- logging.info('remote sync successful.')
- else:
- logging.info('Error: remote sync failed. Exiting.')
- return -1
- # if all looks good, start a process to do this every args.remote_sync_frequency seconds
- remote_sync_process = start_sync_process(
- args.remote_sync_frequency,
- os.path.join(args.logs, args.name),
- os.path.join(args.remote_sync, args.name),
- args.remote_sync_protocol
- )
- remote_sync_process.start()
-
- if args.precision == 'fp16':
- logging.warning(
- 'It is recommended to use AMP mixed-precision instead of FP16. '
- 'FP16 support needs further verification and tuning, especially for train.')
-
- if args.horovod:
- logging.info(
- f'Running in horovod mode with multiple processes / nodes. Device: {args.device}.'
- f'Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}.')
- elif args.distributed:
- logging.info(
- f'Running in distributed mode with multiple processes. Device: {args.device}.'
- f'Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}.')
- else:
- logging.info(f'Running with a single process. Device {args.device}.')
-
- dist_model = None
- args.distill = args.distill_model is not None and args.distill_pretrained is not None
- if args.distill:
- # FIXME: support distillation with grad accum.
- assert args.accum_freq == 1
- # FIXME: support distillation with coca.
- assert 'coca' not in args.model.lower()
-
- if isinstance(args.force_image_size, (tuple, list)) and len(args.force_image_size) == 1:
- # arg is nargs, single (square) image size list -> int
- args.force_image_size = args.force_image_size[0]
- random_seed(args.seed, 0)
-
- #############################################################################
- # model, preprocess_train, preprocess_val = create_model_and_transforms(
- # args.model,
- # args.pretrained,
- # precision=args.precision,
- # device=device,
- # jit=args.torchscript,
- # force_quick_gelu=args.force_quick_gelu,
- # force_custom_text=args.force_custom_text,
- # force_patch_dropout=args.force_patch_dropout,
- # force_image_size=args.force_image_size,
- # pretrained_image=args.pretrained_image,
- # image_mean=args.image_mean,
- # image_std=args.image_std,
- # aug_cfg=args.aug_cfg,
- # output_dict=True,
- # )
-
- model = create_vat_model(args)
- args.image_size = model.vision_model.config.image_size
- #############################################################################
-
-
-
- if args.distill:
- # FIXME: currenlty assumes the model your distilling from has the same tokenizer & transforms.
- dist_model, _, _ = create_model_and_transforms(
- args.distill_model,
- args.distill_pretrained,
- device=device,
- precision=args.precision,
- output_dict=True,
- )
- if args.use_bnb_linear is not None:
- print('=> using a layer from bitsandbytes.\n'
- ' this is an experimental feature which requires two extra pip installs\n'
- ' pip install bitsandbytes triton'
- ' please make sure to use triton 2.0.0')
- import bitsandbytes as bnb
- from open_clip.utils import replace_linear
- print(f'=> replacing linear layers with {args.use_bnb_linear}')
- linear_replacement_cls = getattr(bnb.nn.triton_based_modules, args.use_bnb_linear)
- replace_linear(model, linear_replacement_cls)
- model = model.to(device)
-
- random_seed(args.seed, args.rank)
-
- # if args.trace:
- # model = trace_model(model, batch_size=args.batch_size, device=device)
-
- if args.lock_image:
- # if args.clip_type == 'al' or args.clip_type == 'dl':
- # for param in model.vision_model.embeddings.parameters():
- # param.requires_grad = True
- # for param in model.vision_model.pre_layrnorm.parameters():
- # param.requires_grad = True
- # else:
- for param in model.vision_model.embeddings.parameters():
- param.requires_grad = False
- for param in model.vision_model.pre_layrnorm.parameters():
- param.requires_grad = False
- for param in model.vision_model.embeddings.position_embedding.parameters():
- param.requires_grad = False
- model.vision_model.embeddings.class_embedding.requires_grad = True
- # else:
- # for param in model.vision_model.embeddings.parameters():
- # param.requires_grad = True
- # for param in model.vision_model.pre_layrnorm.parameters():
- # param.requires_grad = True
- # for param in model.vision_model.post_layernorm.parameters():
- # param.requires_grad = True
- # for param in model.visual_projection.parameters():
- # param.requires_grad = True
-
- if args.add_time_attn:
- for name, param in model.vision_model.encoder.layers.named_parameters():
- if 'temporal_layer_norm' in name or 'temporal_embedding' in name:
- param.requires_grad = True
-
- # if args.add_time_attn and args.unlock_time_attn:
- # model.unlock_time_attn()
-
- if args.lock_text:
- for param in model.text_model.parameters():
- param.requires_grad = False
- for param in model.text_projection.parameters():
- param.requires_grad = False
- # else:
- # for param in model.text_model.embeddings.parameters():
- # param.requires_grad = True
- # for param in model.text_model.final_layer_norm.parameters():
- # param.requires_grad = True
- # for param in model.text_projection.parameters():
- # param.requires_grad = True
-
- model.logit_scale.requires_grad = args.learn_temp
-
- if is_master(args):
- print_trainable_parameters(model, msg='The model: ')
-
- if args.grad_checkpointing:
- model.set_grad_checkpointing()
-
- if is_master(args):
- logging.info("Model:")
- # logging.info(f"{str(model)}")
- logging.info("Args:")
- args_file = os.path.join(args.logs, args.name, "args.txt")
- with open(args_file, "w") as f:
- for name in sorted(vars(args)):
- val = getattr(args, name)
- logging.info(f" {name}: {val}")
- f.write(f"{name}: {val}\n")
-
- if args.distributed and not args.horovod:
- if args.use_bn_sync:
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
- ddp_args = {}
- if args.ddp_static_graph:
- # this doesn't exist in older PyTorch, arg only added if enabled
- ddp_args['static_graph'] = True
- model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device], **ddp_args)
-
- if args.distill:
- dist_model = torch.nn.parallel.DistributedDataParallel(dist_model, device_ids=[device], **ddp_args)
-
- # create optimizer and scaler
- ############################################################
- # if args.train_data or args.dataset_type == "synthetic":
- assert not args.trace, 'Cannot train with traced model'
-
- no_decay = lambda n, p: p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or 'logit_scale' in n
- decay = lambda n, p: not no_decay(n, p)
-
- lora = lambda n, p: "lora" in n
- non_lora = lambda n, p: not lora(n, p)
-
- named_parameters = list(model.named_parameters())
- no_decay_non_lora_params = [[n, p] for n, p in named_parameters if no_decay(n, p) and non_lora(n, p) and p.requires_grad]
- decay_non_lora_params = [[n, p] for n, p in named_parameters if decay(n, p) and non_lora(n, p) and p.requires_grad]
-
- no_decay_lora_params = [[n, p] for n, p in named_parameters if no_decay(n, p) and lora(n, p) and p.requires_grad]
- decay_lora_params = [[n, p] for n, p in named_parameters if decay(n, p) and lora(n, p) and p.requires_grad]
-
-
- param_groups = []
- if no_decay_non_lora_params: param_groups.append({"params": [p for n, p in no_decay_non_lora_params], "weight_decay": 0., 'lr': args.lr * args.coef_lr})
- if decay_non_lora_params: param_groups.append({"params": [p for n, p in decay_non_lora_params], "weight_decay": args.wd, 'lr': args.lr * args.coef_lr})
- if no_decay_lora_params: param_groups.append({"params": [p for n, p in no_decay_lora_params], "weight_decay": 0.})
- if decay_lora_params: param_groups.append({"params": [p for n, p in decay_lora_params], "weight_decay": args.wd})
-
- optimizer = optim.AdamW(
- # [
- # {"params": no_decay_non_visual_params, "weight_decay": 0.},
- # {"params": decay_non_visual_params, "weight_decay": args.wd},
- # {"params": no_decay_visual_params, "weight_decay": 0., 'lr': args.lr * args.coef_lr},
- # {"params": decay_visual_params, "weight_decay": args.wd, 'lr': args.lr * args.coef_lr},
- # ],
- param_groups,
- lr=args.lr,
- betas=(args.beta1, args.beta2),
- eps=args.eps,
- )
-
- name_groups = {}
- if no_decay_non_lora_params:
- name_groups['no_decay_non_lora_params'] = [{"name": n, "weight_decay": 0., 'lr': args.lr * args.coef_lr} for n, p in no_decay_non_lora_params]
- if decay_non_lora_params:
- name_groups['decay_non_lora_params'] = [{"name": n, "weight_decay": args.wd, 'lr': args.lr * args.coef_lr} for n, p in decay_non_lora_params]
- if no_decay_lora_params:
- name_groups['no_decay_lora_params'] = [{"name": n, "weight_decay": 0., 'lr': args.lr} for n, p in no_decay_lora_params]
- if decay_lora_params:
- name_groups['decay_lora_params'] = [{"name": n, "weight_decay": args.wd, 'lr': args.lr} for n, p in decay_lora_params]
- if is_master(args):
- params_file = os.path.join(args.logs, args.name, "params.txt")
- with open(params_file, "w") as f:
- for group_name, group in name_groups.items():
- logging.info(f"Group name: {group_name}:")
- f.write(f"Group name: {group_name}:\n")
- for i in group:
- logging.info(f"Parameter name: {i['name']}. Learning rate: {i['lr']}. Weight decay: {i['weight_decay']}")
- f.write(f"Parameter name: {i['name']}. Learning rate: {i['lr']}. Weight decay: {i['weight_decay']}\n")
-
-
- if args.horovod:
- optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
- hvd.broadcast_parameters(model.state_dict(), root_rank=0)
- hvd.broadcast_optimizer_state(optimizer, root_rank=0)
-
- scaler = GradScaler() if args.precision == "amp" else None
- ############################################################
-
- # optionally resume from a checkpoint
- start_epoch = 0
- if args.resume is not None:
- checkpoint = pt_load(args.resume, map_location='cpu')
- if 'epoch' in checkpoint:
- # resuming a train checkpoint w/ epoch and optimizer state
- start_epoch = checkpoint["epoch"]
- sd = checkpoint["state_dict"]
- if not args.distributed and next(iter(sd.items()))[0].startswith('module'):
- sd = {k[len('module.'):]: v for k, v in sd.items()}
- model.load_state_dict(sd)
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint["optimizer"])
- if scaler is not None and 'scaler' in checkpoint:
- scaler.load_state_dict(checkpoint['scaler'])
- logging.info(f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})")
- else:
- # loading a bare (model only) checkpoint for fine-tune or evaluation
- model.load_state_dict(checkpoint)
- logging.info(f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})")
-
- # initialize datasets
- data = get_data(args, epoch=start_epoch)
- if is_master(args):
- logging.info(f"{data})")
- assert len(data), 'At least one train or eval dataset must be specified.'
-
- # create scheduler if train
- scheduler = None
- if f'{args.clip_type}_pt' in data and optimizer is not None:
- total_steps = (data[f'{args.clip_type}_pt'].dataloader.num_batches // args.accum_freq) * args.epochs
- if args.lr_scheduler == "cosine":
- scheduler = cosine_lr(optimizer, args.lr, args.warmup, total_steps)
- elif args.lr_scheduler == "const":
- scheduler = const_lr(optimizer, args.lr, args.warmup, total_steps)
- elif args.lr_scheduler == "const-cooldown":
- assert args.epochs_cooldown is not None, \
- "Please specify the number of cooldown epochs for this lr schedule."
- cooldown_steps = (data[f'{args.clip_type}_pt'].dataloader.num_batches // args.accum_freq) * args.epochs_cooldown
- scheduler = const_lr_cooldown(
- optimizer, args.lr, args.warmup, total_steps,
- cooldown_steps, args.lr_cooldown_power, args.lr_cooldown_end)
- else:
- logging.error(
- f'Unknown scheduler, {args.lr_scheduler}. Available options are: cosine, const, const-cooldown.')
- exit(1)
-
- # determine if this worker should save logs and checkpoints. only do so if it is rank == 0
- args.save_logs = args.logs and args.logs.lower() != 'none' and is_master(args)
- writer = None
- if args.save_logs and args.tensorboard:
- assert tensorboard is not None, "Please install tensorboard."
- writer = tensorboard.SummaryWriter(args.tensorboard_path)
-
- # if args.wandb and is_master(args):
- # assert wandb is not None, 'Please install wandb.'
- # logging.debug('Starting wandb.')
- # args.train_sz = data["train"].dataloader.num_samples
- # if args.val_data is not None:
- # args.val_sz = data["val"].dataloader.num_samples
- # # you will have to configure this for your project!
- # wandb.init(
- # project=args.wandb_project_name,
- # name=args.name,
- # id=args.name,
- # notes=args.wandb_notes,
- # tags=[],
- # resume='auto' if args.resume == "latest" else None,
- # config=vars(args),
- # )
- # if args.debug:
- # wandb.watch(model, log='all')
- # wandb.save(params_file)
- # logging.debug('Finished loading wandb.')
-
- if args.torchcompile:
- logging.info('Compiling model...')
- model = torch.compile(model)
-
- if f'{args.clip_type}_pt' not in data:
- # If using int8, convert to inference mode.
- if args.use_bnb_linear is not None:
- from open_clip.utils import convert_int8_model_to_inference_mode
- convert_int8_model_to_inference_mode(model)
- # Evaluate.
- if "i_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- evaluate_i_cls(model, data, start_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- if "vl_ret" in data:
- for sub_data in data['vl_ret']:
- evaluate_vl_ret(model, sub_data, start_epoch, args, writer)
- if "a_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- for sub_data in data['a_cls']:
- evaluate_a_cls(model, sub_data, start_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- if "v_cls" in data:
- evaluate_v_cls(model, data, start_epoch, args, writer)
- if "d_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- for sub_data in data['d_cls']:
- evaluate_d_cls(model, sub_data, start_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- if "t_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- for sub_data in data['t_cls']:
- evaluate_t_cls(model, sub_data, start_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- return
-
- loss = create_loss(args)
-
- for epoch in range(start_epoch, args.epochs):
- if is_master(args):
- logging.info(f'Start epoch {epoch}')
-
- train_one_epoch(model, data, loss, epoch, optimizer, scaler, scheduler, dist_model, args, tb_writer=writer)
- completed_epoch = epoch + 1
-
- if "i_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- evaluate_i_cls(model, data, completed_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- if "vl_ret" in data:
- for sub_data in data['vl_ret']:
- evaluate_vl_ret(model, sub_data, completed_epoch, args, writer)
- if "a_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- for sub_data in data['a_cls']:
- evaluate_a_cls(model, sub_data, completed_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- if "v_cls" in data:
- evaluate_v_cls(model, data, completed_epoch, args, writer)
- if "d_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- for sub_data in data['d_cls']:
- evaluate_d_cls(model, sub_data, completed_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
- if "t_cls" in data:
- SET_GLOBAL_VALUE('NUM_FRAMES', 1)
- for sub_data in data['t_cls']:
- evaluate_t_cls(model, sub_data, completed_epoch, args, writer)
- SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
-
- # Saving checkpoints.
- if args.save_logs:
- checkpoint_dict = {
- "epoch": completed_epoch,
- "name": args.name,
- "state_dict": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- }
- if scaler is not None:
- checkpoint_dict["scaler"] = scaler.state_dict()
-
- if completed_epoch == args.epochs or (
- args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0
- ):
- torch.save(
- checkpoint_dict,
- os.path.join(args.checkpoint_path, f"epoch_{completed_epoch}.pt"),
- )
- if args.delete_previous_checkpoint:
- previous_checkpoint = os.path.join(args.checkpoint_path, f"epoch_{completed_epoch - 1}.pt")
- if os.path.exists(previous_checkpoint):
- os.remove(previous_checkpoint)
-
- if args.save_most_recent:
- # try not to corrupt the latest checkpoint if save fails
- tmp_save_path = os.path.join(args.checkpoint_path, "tmp.pt")
- latest_save_path = os.path.join(args.checkpoint_path, LATEST_CHECKPOINT_NAME)
- torch.save(checkpoint_dict, tmp_save_path)
- os.replace(tmp_save_path, latest_save_path)
-
- if args.wandb and is_master(args):
- wandb.finish()
-
- # run a final sync.
- if remote_sync_process is not None:
- logging.info('Final remote sync.')
- remote_sync_process.terminate()
- result = remote_sync(
- os.path.join(args.logs, args.name),
- os.path.join(args.remote_sync, args.name),
- args.remote_sync_protocol
- )
- if result:
- logging.info('Final remote sync successful.')
- else:
- logging.info('Final remote sync failed.')
-
-
-def copy_codebase(args):
- from shutil import copytree, ignore_patterns
- new_code_path = os.path.join(args.logs, args.name, "code")
- if os.path.exists(new_code_path):
- print(
- f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment."
- )
- return -1
- print(f"Copying codebase to {new_code_path}")
- current_code_path = os.path.realpath(__file__)
- for _ in range(3):
- current_code_path = os.path.dirname(current_code_path)
- copytree(current_code_path, new_code_path, ignore=ignore_patterns('log', 'logs', 'wandb'))
- print("Done copying code.")
- return 1
-
-
-if __name__ == "__main__":
- main(sys.argv[1:])
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/go-applio-manager.bat b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/go-applio-manager.bat
deleted file mode 100644
index dd0ed3020d690a27a7fc4da3b924c9325850d93b..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/go-applio-manager.bat
+++ /dev/null
@@ -1,356 +0,0 @@
-@echo off
-title Applio - Manager
-cd %~dp0
-:::
-::: _ _
-::: /\ | (_)
-::: / \ _ __ _ __ | |_ ___
-::: / /\ \ | '_ \| '_ \| | |/ _ \
-::: / ____ \| |_) | |_) | | | (_) |
-::: /_/ \_\ .__/| .__/|_|_|\___/
-::: | | | |
-::: |_| |_|
-:::
-:::
-
-setlocal
-set "branch=applio-recode"
-set "runtime=runtime-recode"
-set "repoUrl=https://github.com/IAHispano/Applio-RVC-Fork/archive/refs/heads/%branch%.zip"
-set "fixesFolder=lib/fixes"
-set "localFixesPy=local_fixes.py"
-set "principal=%cd%"
-set "URL_BASE=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main"
-set "URL_EXTRA=https://huggingface.co/IAHispano/applio/resolve/main"
-
-:menu
-for /f "delims=: tokens=*" %%A in ('findstr /b ":::" "%~f0"') do @echo(%%A
-
-echo [1] Reinstall Applio
-echo [2] Update Applio
-echo [3] Download NVDIA Runtime
-echo [4] Download AMD Runtime
-echo [5] Update Applio + Dependencies
-echo [6] Fix Tensorboard
-echo.
-
-set /p choice=Select an option:
-set choice=%choice: =%
-
-if "%choice%"=="1" (
- cls
- echo.
- goto reinstaller
- pause
- cls
- goto menu
-
-)
-
-if "%choice%"=="2" (
- cls
- echo.
- goto updater
- pause
- cls
- goto menu
-)
-
-if "%choice%"=="3" (
- cls
- echo.
- goto nvdiaRuntime
- pause
- cls
- goto menu
-
-)
-
-if "%choice%"=="4" (
- cls
- echo.
- goto amdRuntime
- pause
- cls
- goto menu
-
-)
-
-if "%choice%"=="5" (
- cls
- echo.
- goto updaterDependencies
- pause
- cls
- goto menu
-
-)
-
-if "%choice%"=="6" (
- cls
- echo.
- pip uninstall tb-nightly tensorboardX tensorboard
- pip install tensorboard
- cls
- echo Tensorboard re-installed correctly!
- echo.
- pause
- cls
- goto menu
-
-)
-
-cls
-echo Invalid option. Please enter a number from 1 to 3.
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-:reinstaller
-
-cls
-echo INFO: Please ensure you have installed the required dependencies before continuing. Refer to the installation guide for details.
-echo.
-echo Step-by-step guide: https://rentry.org/appliolocal
-echo Build Tools: https://aka.ms/vs/17/release/vs_BuildTools.exe
-echo Redistributable: https://aka.ms/vs/17/release/vc_redist.x64.exe
-echo Git: https://github.com/git-for-windows/git/releases/download/v2.42.0.windows.2/Git-2.42.0.2-64-bit.exe
-echo Python 3.9.8: https://www.python.org/ftp/python/3.9.8/python-3.9.8-amd64.exe
-echo.
-echo INFO: Its recommend installing Python 3.9.X and ensuring that it has been added to the system's path.
-echo.
-pause
-cls
-for /f "delims=: tokens=*" %%A in ('findstr /b ":::" "%~f0"') do @echo(%%A
-echo.
-
-echo Reseting the repository...
-git reset --hard
-git pull
-echo.
-cls
-
-echo Installing dependencies...
-echo.
-echo Recommended for Nvidia GPU users:
-echo [1] Download Runtime (pre-installed dependencies)
-echo.
-echo Recommended for AMD/Intel GPU users (Broken):
-echo [2] Download DML Runtime (pre-installed dependencies)
-echo.
-echo Only recommended for experienced users:
-echo [3] Nvidia graphics cards
-echo [4] AMD / Intel graphics cards
-echo.
-echo [5] I have already installed the dependencies
-echo.
-set /p choice=Select the option according to your GPU:
-set choice=%choice: =%
-
-if "%choice%"=="1" (
-cls
-powershell -command "Invoke-WebRequest -Uri https://frippery.org/files/busybox/busybox.exe -OutFile busybox.exe"
-busybox.exe wget %URL_EXTRA%/runtime.zip
-echo.
-echo Extracting the runtime.zip file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('runtime.zip', '%principal%') }"
-echo.
-del runtime.zip busybox.exe
-cls
-echo.
-goto dependenciesFinished
-)
-
-if "%choice%"=="2" (
-cls
-powershell -command "Invoke-WebRequest -Uri https://frippery.org/files/busybox/busybox.exe -OutFile busybox.exe"
-busybox.exe wget %URL_EXTRA%/runtime_dml.zip
-echo.
-echo Extracting the runtime_dml.zip file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('runtime_dml.zip', '%principal%') }"
-echo.
-del runtime_dml.zip busybox.exe
-cd runtime
-python.exe -m pip install onnxruntime
-cd ..
-cls
-echo.
-goto dependenciesFinished
-)
-
-if "%choice%"=="3" (
-cls
-pip install -r assets/requirements/requirements.txt
-echo.
-pip uninstall torch torchvision torchaudio -y
-echo.
-pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
-echo.
-echo.
-cls
-echo Dependencies successfully installed!
-echo.
-goto dependenciesFinished
-)
-
-if "%choice%"=="4" (
-cls
-pip uninstall onnxruntime onnxruntime-directml
-echo.
-pip install -r assets/requirements/requirements.txt
-echo.
-pip install -r assets/requirements/requirements-dml.txt
-echo.
-echo.
-cls
-echo Dependencies successfully installed!
-echo.
-goto dependenciesFinished
-)
-
-if "%choice%"=="5" (
-echo Dependencies successfully installed!
-echo.
-goto dependenciesFinished
-)
-
-:dependenciesFinished
-cls
-echo Applio has been successfully downloaded, run the file go-applio.bat to run the web interface!
-echo.
-pause
-exit
-
-echo Applio has been reinstalled!
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-
-:updater
-
-echo Updating the repository...
-git pull
-echo Applio has been updated!
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-
-:updaterDependencies
-
-echo Updating the repository...
-git pull
-
-echo Installing dependencies...
-
-echo [1] Nvidia graphics cards
-echo [2] AMD / Intel graphics cards
-echo [3] I have already installed the dependencies
-echo.
-
-set /p choice=Select the option according to your GPU:
-set choice=%choice: =%
-
-if "%choice%"=="1" (
-cls
-pip uninstall tb-nightly tensorboardX tensorboard
-echo.
-pip uninstall onnxruntime onnxruntime-directml
-echo.
-pip install -r assets/requirements/requirements.txt
-echo.
-pip uninstall torch torchvision torchaudio -y
-echo.
-pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
-echo.
-echo.
-cls
-echo Dependencies installed!
-echo.
-goto dependenciesFinished
-)
-
-if "%choice%"=="2" (
-cls
-pip uninstall tb-nightly tensorboardX tensorboard
-echo.
-pip uninstall onnxruntime onnxruntime-directml
-echo.
-pip install -r assets/requirements/requirements.txt
-echo.
-pip install -r assets/requirements/requirements-dml.txt
-echo.
-echo.
-cls
-echo Dependencies installed!
-echo.
-goto dependenciesFinished
-)
-
-if "%choice%"=="3" (
-echo Dependencies installed!
-echo.
-goto dependenciesFinished
-)
-
-:dependenciesFinished
-echo Verifying if the local_fixes.py file exists in the Fixes folder...
-if exist "%fixesFolder%\%localFixesPy%" (
- echo Running the file...
- if exist "%principal%\runtime" (
- runtime\python.exe "%fixesFolder%\%localFixesPy%"
- ) else (
- python.exe "%fixesFolder%\%localFixesPy%"
- )
-) else (
- echo The file "%localFixesPy%" was not found in the "Fixes" folder.
-)
-echo.
-echo Applio has been updated!
-echo.
-echo Press 'Enter' to access the main menu...
-pause>nul
-cls
-goto menu
-
-:nvdiaRuntime
-if exist "%principal%\runtime" (
- rmdir "%principal%\runtime" /s /q
-)
-cls
-curl -LJO "%URL_EXTRA%/runtime.zip"
-echo.
-echo Extracting the runtime.zip file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('runtime.zip', '%principal%') }"
-echo.
-del runtime.zip
-cls
-echo NVDIA Runtime downloaded!
-echo.
-goto menu
-
-:amdRuntime
-if exist "%principal%\runtime" (
- rmdir "%principal%\runtime" /s /q
-)
-
-cls
-curl -LJO "%URL_EXTRA%/runtime_dml.zip"
-echo.
-echo Extracting the runtime_dml.zip file...
-powershell -command "& { Add-Type -AssemblyName System.IO.Compression.FileSystem ; [System.IO.Compression.ZipFile]::ExtractToDirectory('runtime_dml.zip', '%principal%') }"
-echo.
-del runtime_dml.zip
-cls
-echo AMD Runtime downloaded!
-echo.
-goto menu
-
-
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/tabs/processing.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/tabs/processing.py
deleted file mode 100644
index 5d4b1d45f7d924d5df31736d9746a1b583bed4aa..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/tabs/processing.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import sys
-
-sys.path.append("..")
-import os
-
-now_dir = os.getcwd()
-from lib.infer.infer_libs.train.process_ckpt import (
- change_info,
- extract_small_model,
- merge,
- show_info,
-)
-from assets.i18n.i18n import I18nAuto
-
-i18n = I18nAuto()
-
-import gradio as gr
-import traceback
-
-
-def change_info_(ckpt_path):
- if not os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")):
- return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
- try:
- with open(
- ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
- ) as f:
- info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
- sr, f0 = info["sample_rate"], info["if_f0"]
- version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
- return sr, str(f0), version
- except:
- traceback.print_exc()
- return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
-
-
-def processing_():
-
- with gr.Accordion(
- label=i18n("Model fusion, can be used to test timbre fusion")
- ):
- with gr.Row():
- with gr.Column():
- name_to_save0 = gr.Textbox(
- label=i18n("Name:"),
- value="",
- max_lines=1,
- interactive=True,
- placeholder=i18n("Name for saving"),
- )
- alpha_a = gr.Slider(
- minimum=0,
- maximum=1,
- label=i18n("Weight for Model A:"),
- value=0.5,
- interactive=True,
- )
- if_f0_ = gr.Checkbox(
- label=i18n("Whether the model has pitch guidance."),
- value=True,
- interactive=True,
- )
- version_2 = gr.Radio(
- label=i18n("Model architecture version:"),
- choices=["v1", "v2"],
- value="v2",
- interactive=True,
- )
- sr_ = gr.Radio(
- label=i18n("Target sample rate:"),
- choices=["40k", "48k", "32k"],
- value="40k",
- interactive=True,
- )
- with gr.Column():
- ckpt_a = gr.Textbox(
- label=i18n("Path to Model A:"),
- value="",
- interactive=True,
- placeholder=i18n("Path to model"),
- )
- ckpt_b = gr.Textbox(
- label=i18n("Path to Model B:"),
- value="",
- interactive=True,
- placeholder=i18n("Path to model"),
- )
- info__ = gr.Textbox(
- label=i18n("Model information to be placed:"),
- value="",
- max_lines=8,
- interactive=True,
- placeholder=i18n("Model information to be placed"),
- )
- info4 = gr.Textbox(
- label=i18n("Output information:"), value="", max_lines=8
- )
-
- but6 = gr.Button(i18n("Fusion"), variant="primary")
-
- but6.click(
- merge,
- [
- ckpt_a,
- ckpt_b,
- alpha_a,
- sr_,
- if_f0_,
- info__,
- name_to_save0,
- version_2,
- ],
- info4,
- api_name="ckpt_merge",
- ) # def merge(path1,path2,alpha1,sr,f0,info):
-
- with gr.Accordion(label=i18n("Modify model information")):
- with gr.Row(): ######
- with gr.Column():
- ckpt_path0 = gr.Textbox(
- label=i18n("Path to Model:"),
- value="",
- interactive=True,
- placeholder=i18n("Path to model"),
- )
- info_ = gr.Textbox(
- label=i18n("Model information to be modified:"),
- value="",
- max_lines=8,
- interactive=True,
- placeholder=i18n("Model information to be placed"),
- )
-
- with gr.Column():
- name_to_save1 = gr.Textbox(
- label=i18n("Save file name:"),
- placeholder=i18n("Name for saving"),
- value="",
- max_lines=8,
- interactive=True,
- )
-
- info5 = gr.Textbox(
- label=i18n("Output information:"), value="", max_lines=8
- )
- but7 = gr.Button(i18n("Modify"), variant="primary")
- but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5, api_name="ckpt_modify",)
-
- with gr.Accordion(label=i18n("View model information")):
- with gr.Row():
- with gr.Column():
- ckpt_path1 = gr.Textbox(
- label=i18n("Path to Model:"),
- value="",
- interactive=True,
- placeholder=i18n("Path to model"),
- )
-
- info6 = gr.Textbox(
- label=i18n("Output information:"), value="", max_lines=8
- )
- but8 = gr.Button(i18n("View"), variant="primary")
- but8.click(show_info, [ckpt_path1], info6, api_name="ckpt_show")
-
- with gr.Accordion(label=i18n("Model extraction")):
- with gr.Row():
- with gr.Column():
- save_name = gr.Textbox(
- label=i18n("Name:"),
- value="",
- interactive=True,
- placeholder=i18n("Name for saving"),
- )
- if_f0__ = gr.Checkbox(
- label=i18n("Whether the model has pitch guidance."),
- value=True,
- interactive=True,
- )
- version_1 = gr.Radio(
- label=i18n("Model architecture version:"),
- choices=["v1", "v2"],
- value="v2",
- interactive=True,
- )
- sr__ = gr.Radio(
- label=i18n("Target sample rate:"),
- choices=["32k", "40k", "48k"],
- value="40k",
- interactive=True,
- )
-
- with gr.Column():
- ckpt_path2 = gr.Textbox(
- label=i18n("Path to Model:"),
- placeholder=i18n("Path to model"),
- interactive=True,
- )
- info___ = gr.Textbox(
- label=i18n("Model information to be placed:"),
- value="",
- max_lines=8,
- interactive=True,
- placeholder=i18n("Model information to be placed"),
- )
- info7 = gr.Textbox(
- label=i18n("Output information:"), value="", max_lines=8
- )
-
- with gr.Row():
- but9 = gr.Button(i18n("Extract"), variant="primary")
- ckpt_path2.change(
- change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
- )
- but9.click(
- extract_small_model,
- [ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
- info7,
- api_name="ckpt_extract",
- )
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py b/spaces/Loren/Streamlit_OCR_comparator/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py
deleted file mode 100644
index 46ca259b3abb8863348f8eef71b0126f77e269eb..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py
+++ /dev/null
@@ -1,58 +0,0 @@
-_base_ = [
- '../../_base_/default_runtime.py',
- '../../_base_/schedules/schedule_adam_step_5e.py',
- '../../_base_/recog_pipelines/sar_pipeline.py',
- '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
- '../../_base_/recog_datasets/academic_test.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-label_convertor = dict(
- type='AttnConvertor', dict_type='DICT90', with_unknown=True)
-
-model = dict(
- type='SARNet',
- backbone=dict(type='ResNet31OCR'),
- encoder=dict(
- type='SAREncoder',
- enc_bi_rnn=False,
- enc_do_rnn=0.1,
- enc_gru=False,
- ),
- decoder=dict(
- type='SequentialSARDecoder',
- enc_bi_rnn=False,
- dec_bi_rnn=False,
- dec_do_rnn=0,
- dec_gru=False,
- pred_dropout=0.1,
- d_k=512,
- pred_concat=True),
- loss=dict(type='SARLoss'),
- label_convertor=label_convertor,
- max_seq_len=30)
-
-data = dict(
- samples_per_gpu=64,
- workers_per_gpu=2,
- val_dataloader=dict(samples_per_gpu=1),
- test_dataloader=dict(samples_per_gpu=1),
- train=dict(
- type='UniformConcatDataset',
- datasets=train_list,
- pipeline=train_pipeline),
- val=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline),
- test=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
diff --git a/spaces/MacYang/Diamond-Sutra/query.py b/spaces/MacYang/Diamond-Sutra/query.py
deleted file mode 100644
index 012aed13e8bd6ba5d1f3bd9f93a74c1dae488ed7..0000000000000000000000000000000000000000
--- a/spaces/MacYang/Diamond-Sutra/query.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""create a chat chain for the conversation"""
-from typing import List, Tuple
-import logging
-from langchain.prompts import PromptTemplate
-from langchain.llms import OpenAI
-from langchain.chains import ChatVectorDBChain
-from langchain.vectorstores import VectorStore
-
-_TEMPLATE = """给定下面的对话以及一个跟进问题，请重写这个跟进问题，使其成为一个独立的问题。你可以假定这个问题是关于宗萨仁波切对于金刚经的开示的.
-
-历史对话:
-{chat_history}
-跟进问题:{question}
-独立问题:"""
-CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_TEMPLATE)
-
-TEMPLATE = """你是一个AI，你的工作是回答关于金刚经的问题。
-你收到的输入是一个问题和宗萨仁波切对于金刚经开示的部分节选，请提供一个对话式的回答，回答请尽量详细。
-如果你不知道答案，请不要试着自己编造一个答案，直接说“我不确定应该如何回答这个问题”就可以了。
-如果这个问题不是关于金刚经的，请礼貌地回答你只解答关于金刚经的问题。
-问题: {question}
-开示节选：
-{context}
-回答:"""
-QA_PROMPT = PromptTemplate(template=TEMPLATE, input_variables=["question", "context"])
-
-MAX_HISTORY = 5
-CTX_LENGTH = 2
-
-def _get_chat_history(chat_history: List[Tuple[str, str]]) -> str:
- logging.info("joinning chat history to a string")
- buffer = ""
- start_idx = 0 if len(chat_history) <= MAX_HISTORY else len(chat_history) - MAX_HISTORY
- for human_s, ai_s in chat_history[start_idx:]:
- human = "Human: " + human_s
- ai = "Assistant: " + ai_s
- buffer += "\n" + "\n".join([human, ai])
- return buffer
-
-def get_chain(vectorstore: VectorStore, verbose: bool = False):
- """create the qa chain"""
- llm = OpenAI(temperature=0, model_name="gpt-3.5-turbo", max_tokens=-1)
- qa_chain = ChatVectorDBChain.from_llm(
- llm=llm,
- vectorstore=vectorstore,
- qa_prompt=QA_PROMPT,
- condense_question_prompt=CONDENSE_QUESTION_PROMPT,
- get_chat_history=_get_chat_history,
- top_k_docs_for_context=CTX_LENGTH
- )
- if verbose:
- llm.verbose = True
- qa_chain.verbose = True
- qa_chain.combine_docs_chain.verbose = True
- qa_chain.combine_docs_chain.llm_chain.verbose = True
- qa_chain.question_generator.verbose = True
-
- return qa_chain
diff --git a/spaces/ManjunathNili/manjuai/app.py b/spaces/ManjunathNili/manjuai/app.py
deleted file mode 100644
index 2dbf3ae89c2e3fdab7134107dd346f984dca8eb1..0000000000000000000000000000000000000000
--- a/spaces/ManjunathNili/manjuai/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/Manthanx/catsdogs/app.py b/spaces/Manthanx/catsdogs/app.py
deleted file mode 100644
index ef7932c72dfa601f48e2898823b366877ace5f0e..0000000000000000000000000000000000000000
--- a/spaces/Manthanx/catsdogs/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-
-def is_cat(x): return x[0].isupper()
-
-learn = load_learner('export.pkl')
-labels = learn.dls.vocab
-
-
-def predict(img):
- img = PILImage.create(img)
- pred,pred_idx,probs = learn.predict(img)
- return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label(num_top_classes=3)
-gr.Interface(fn=predict, inputs=image, outputs=label).launch(inline=False)
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/adapters.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/adapters.py
deleted file mode 100644
index 370174727ade4117a4857e8ec72a8c70c7a8950e..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/adapters.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict
-
-from mmcv.transforms.base import BaseTransform
-from mmdet.structures.mask import PolygonMasks, bitmap_to_polygon
-
-from mmocr.registry import TRANSFORMS
-
-
-@TRANSFORMS.register_module()
-class MMDet2MMOCR(BaseTransform):
- """Convert transforms's data format from MMDet to MMOCR.
-
- Required Keys:
-
- - gt_masks (PolygonMasks | BitmapMasks) (optional)
- - gt_ignore_flags (np.bool) (optional)
-
- Added Keys:
-
- - gt_polygons (list[np.ndarray])
- - gt_ignored (np.ndarray)
- """
-
- def transform(self, results: Dict) -> Dict:
- """Convert MMDet's data format to MMOCR's data format.
-
- Args:
- results (Dict): Result dict containing the data to transform.
-
- Returns:
- (Dict): The transformed data.
- """
- # gt_masks -> gt_polygons
- if 'gt_masks' in results.keys():
- gt_polygons = []
- gt_masks = results.pop('gt_masks')
- if len(gt_masks) > 0:
- # PolygonMasks
- if isinstance(gt_masks[0], PolygonMasks):
- gt_polygons = [mask[0] for mask in gt_masks.masks]
- # BitmapMasks
- else:
- polygons = []
- for mask in gt_masks.masks:
- contours, _ = bitmap_to_polygon(mask)
- polygons += [
- contour.reshape(-1) for contour in contours
- ]
- # filter invalid polygons
- gt_polygons = []
- for polygon in polygons:
- if len(polygon) < 6:
- continue
- gt_polygons.append(polygon)
-
- results['gt_polygons'] = gt_polygons
- # gt_ignore_flags -> gt_ignored
- if 'gt_ignore_flags' in results.keys():
- gt_ignored = results.pop('gt_ignore_flags')
- results['gt_ignored'] = gt_ignored
-
- return results
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- return repr_str
-
-
-@TRANSFORMS.register_module()
-class MMOCR2MMDet(BaseTransform):
- """Convert transforms's data format from MMOCR to MMDet.
-
- Required Keys:
-
- - img_shape
- - gt_polygons (List[ndarray]) (optional)
- - gt_ignored (np.bool) (optional)
-
- Added Keys:
-
- - gt_masks (PolygonMasks | BitmapMasks) (optional)
- - gt_ignore_flags (np.bool) (optional)
-
- Args:
- poly2mask (bool): Whether to convert mask to bitmap. Default: True.
- """
-
- def __init__(self, poly2mask: bool = False) -> None:
- self.poly2mask = poly2mask
-
- def transform(self, results: Dict) -> Dict:
- """Convert MMOCR's data format to MMDet's data format.
-
- Args:
- results (Dict): Result dict containing the data to transform.
-
- Returns:
- (Dict): The transformed data.
- """
- # gt_polygons -> gt_masks
- if 'gt_polygons' in results.keys():
- gt_polygons = results.pop('gt_polygons')
- gt_polygons = [[gt_polygon] for gt_polygon in gt_polygons]
- gt_masks = PolygonMasks(gt_polygons, *results['img_shape'])
-
- if self.poly2mask:
- gt_masks = gt_masks.to_bitmap()
-
- results['gt_masks'] = gt_masks
- # gt_ignore_flags -> gt_ignored
- if 'gt_ignored' in results.keys():
- gt_ignored = results.pop('gt_ignored')
- results['gt_ignore_flags'] = gt_ignored
-
- return results
-
- def __repr__(self) -> str:
- repr_str = self.__class__.__name__
- repr_str += f'(poly2mask = {self.poly2mask})'
- return repr_str
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/sroie_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/sroie_converter.py
deleted file mode 100644
index 1c380e49d42d7c09907f5c1642baa172e5dff1ed..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/sroie_converter.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import os
-import os.path as osp
-
-import mmcv
-import mmengine
-import numpy as np
-
-from mmocr.utils import crop_img, dump_ocr_data
-
-
-def collect_files(img_dir, gt_dir):
- """Collect all images and their corresponding groundtruth files.
-
- Args:
- img_dir (str): The image directory
- gt_dir (str): The groundtruth directory
-
- Returns:
- files (list): The list of tuples (img_file, groundtruth_file)
- """
-
- assert isinstance(img_dir, str)
- assert img_dir
- assert isinstance(gt_dir, str)
- assert gt_dir
-
- ann_list, imgs_list = [], []
- for gt_file in os.listdir(gt_dir):
- # Filtering repeated and missing images
- if '(' in gt_file or gt_file == 'X51006619570.txt':
- continue
- ann_list.append(osp.join(gt_dir, gt_file))
- imgs_list.append(osp.join(img_dir, gt_file.replace('.txt', '.jpg')))
-
- files = list(zip(sorted(imgs_list), sorted(ann_list)))
- assert len(files), f'No images found in {img_dir}'
-
- print(f'Loaded {len(files)} images from {img_dir}')
-
- return files
-
-
-def collect_annotations(files, nproc=1):
- """Collect the annotation information.
-
- Args:
- files (list): The list of tuples (image_file, groundtruth_file)
- nproc (int): The number of process to collect annotations
-
- Returns:
- images (list): The list of image information dicts
- """
-
- assert isinstance(files, list)
- assert isinstance(nproc, int)
-
- if nproc > 1:
- images = mmengine.track_parallel_progress(
- load_img_info, files, nproc=nproc)
- else:
- images = mmengine.track_progress(load_img_info, files)
-
- return images
-
-
-def load_img_info(files):
- """Load the information of one image.
-
- Args:
- files (tuple): The tuple of (img_file, groundtruth_file)
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
-
- assert isinstance(files, tuple)
-
- img_file, gt_file = files
- assert osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split(
- '.')[0]
- # read imgs while ignoring orientations
- img = mmcv.imread(img_file, 'unchanged')
-
- img_info = dict(
- file_name=osp.join(osp.basename(img_file)),
- height=img.shape[0],
- width=img.shape[1],
- segm_file=osp.join(osp.basename(gt_file)))
-
- if osp.splitext(gt_file)[1] == '.txt':
- img_info = load_txt_info(gt_file, img_info)
- else:
- raise NotImplementedError
-
- return img_info
-
-
-def load_txt_info(gt_file, img_info):
- """Collect the annotation information.
-
- Annotation Format
- x1, y1, x2, y2, x3, y3, x4, y4, transcript
-
- Args:
- gt_file (list): The list of tuples (image_file, groundtruth_file)
- img_info (int): The dict of the img and annotation information
-
- Returns:
- img_info (list): The dict of the img and annotation information
- """
-
- with open(gt_file, encoding='unicode_escape') as f:
- anno_info = []
- for ann in f.readlines():
- # skip invalid annotation line
- try:
- bbox = np.array(ann.split(',')[0:8]).astype(int).tolist()
- except ValueError:
-
- continue
- word = ann.split(',')[-1].replace('\n', '').strip()
-
- anno = dict(bbox=bbox, word=word)
- anno_info.append(anno)
-
- img_info.update(anno_info=anno_info)
-
- return img_info
-
-
-def generate_ann(root_path, split, image_infos):
- """Generate cropped annotations and label txt file.
-
- Args:
- root_path (str): The root path of the dataset
- split (str): The split of dataset. Namely: training or test
- image_infos (list[dict]): A list of dicts of the img and
- annotation information
- """
-
- dst_image_root = osp.join(root_path, 'crops', split)
- if split == 'training':
- dst_label_file = osp.join(root_path, 'train_label.json')
- elif split == 'test':
- dst_label_file = osp.join(root_path, 'test_label.json')
- os.makedirs(dst_image_root, exist_ok=True)
-
- img_info = []
- for image_info in image_infos:
- index = 1
- src_img_path = osp.join(root_path, 'imgs', split,
- image_info['file_name'])
- image = mmcv.imread(src_img_path)
- src_img_root = image_info['file_name'].split('.')[0]
-
- for anno in image_info['anno_info']:
- word = anno['word']
- dst_img = crop_img(image, anno['bbox'], 0, 0)
-
- # Skip invalid annotations
- if min(dst_img.shape) == 0 or len(word) == 0:
- continue
-
- dst_img_name = f'{src_img_root}_{index}.png'
- index += 1
- dst_img_path = osp.join(dst_image_root, dst_img_name)
- mmcv.imwrite(dst_img, dst_img_path)
-
- img_info.append({
- 'file_name': dst_img_name,
- 'anno_info': [{
- 'text': word
- }]
- })
-
- dump_ocr_data(img_info, dst_label_file, 'textrecog')
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description='Generate training and test set of SROIE')
- parser.add_argument('root_path', help='Root dir path of SROIE')
- parser.add_argument(
- '--nproc', default=1, type=int, help='Number of process')
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- root_path = args.root_path
-
- for split in ['training', 'test']:
- print(f'Processing {split} set...')
- with mmengine.Timer(
- print_tmpl='It takes {}s to convert SROIE annotation'):
- files = collect_files(
- osp.join(root_path, 'imgs', split),
- osp.join(root_path, 'annotations', split))
- image_infos = collect_annotations(files, nproc=args.nproc)
- generate_ann(root_path, split, image_infos)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/NATSpeech/PortaSpeech/tasks/tts/fs.py b/spaces/NATSpeech/PortaSpeech/tasks/tts/fs.py
deleted file mode 100644
index f280bb922da6e8e714680700ba8d23114d4951f2..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/tasks/tts/fs.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import torch
-import torch.distributions
-import torch.nn.functional as F
-import torch.optim
-import torch.utils.data
-
-from modules.tts.fs import FastSpeech
-from tasks.tts.dataset_utils import FastSpeechWordDataset
-from tasks.tts.speech_base import SpeechBaseTask
-from utils.audio.align import mel2token_to_dur
-from utils.audio.pitch.utils import denorm_f0
-from utils.commons.hparams import hparams
-
-
-class FastSpeechTask(SpeechBaseTask):
- def __init__(self):
- super().__init__()
- self.dataset_cls = FastSpeechWordDataset
- self.sil_ph = self.token_encoder.sil_phonemes()
-
- def build_tts_model(self):
- dict_size = len(self.token_encoder)
- self.model = FastSpeech(dict_size, hparams)
-
- def run_model(self, sample, infer=False, *args, **kwargs):
- txt_tokens = sample['txt_tokens'] # [B, T_t]
- spk_embed = sample.get('spk_embed')
- spk_id = sample.get('spk_ids')
- if not infer:
- target = sample['mels'] # [B, T_s, 80]
- mel2ph = sample['mel2ph'] # [B, T_s]
- f0 = sample.get('f0')
- uv = sample.get('uv')
- output = self.model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, spk_id=spk_id,
- f0=f0, uv=uv, infer=False)
- losses = {}
- self.add_mel_loss(output['mel_out'], target, losses)
- self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
- if hparams['use_pitch_embed']:
- self.add_pitch_loss(output, sample, losses)
- return losses, output
- else:
- use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur'])
- use_gt_f0 = kwargs.get('infer_use_gt_f0', hparams['use_gt_f0'])
- mel2ph, uv, f0 = None, None, None
- if use_gt_dur:
- mel2ph = sample['mel2ph']
- if use_gt_f0:
- f0 = sample['f0']
- uv = sample['uv']
- output = self.model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, spk_id=spk_id,
- f0=f0, uv=uv, infer=True)
- return output
-
- def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, losses=None):
- """
-
- :param dur_pred: [B, T], float, log scale
- :param mel2ph: [B, T]
- :param txt_tokens: [B, T]
- :param losses:
- :return:
- """
- B, T = txt_tokens.shape
- nonpadding = (txt_tokens != 0).float()
- dur_gt = mel2token_to_dur(mel2ph, T).float() * nonpadding
- is_sil = torch.zeros_like(txt_tokens).bool()
- for p in self.sil_ph:
- is_sil = is_sil | (txt_tokens == self.token_encoder.encode(p)[0])
- is_sil = is_sil.float() # [B, T_txt]
- losses['pdur'] = F.mse_loss((dur_pred + 1).log(), (dur_gt + 1).log(), reduction='none')
- losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum()
- losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur']
- # use linear scale for sentence and word duration
- if hparams['lambda_word_dur'] > 0:
- word_id = (is_sil.cumsum(-1) * (1 - is_sil)).long()
- word_dur_p = dur_pred.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_pred)[:, 1:]
- word_dur_g = dur_gt.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_gt)[:, 1:]
- wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none')
- word_nonpadding = (word_dur_g > 0).float()
- wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum()
- losses['wdur'] = wdur_loss * hparams['lambda_word_dur']
- if hparams['lambda_sent_dur'] > 0:
- sent_dur_p = dur_pred.sum(-1)
- sent_dur_g = dur_gt.sum(-1)
- sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean')
- losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']
-
- def add_pitch_loss(self, output, sample, losses):
- mel2ph = sample['mel2ph'] # [B, T_s]
- f0 = sample['f0']
- uv = sample['uv']
- nonpadding = (mel2ph != 0).float() if hparams['pitch_type'] == 'frame' \
- else (sample['txt_tokens'] != 0).float()
- p_pred = output['pitch_pred']
- assert p_pred[..., 0].shape == f0.shape
- if hparams['use_uv'] and hparams['pitch_type'] == 'frame':
- assert p_pred[..., 1].shape == uv.shape, (p_pred.shape, uv.shape)
- losses['uv'] = (F.binary_cross_entropy_with_logits(
- p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \
- / nonpadding.sum() * hparams['lambda_uv']
- nonpadding = nonpadding * (uv == 0).float()
- f0_pred = p_pred[:, :, 0]
- losses['f0'] = (F.l1_loss(f0_pred, f0, reduction='none') * nonpadding).sum() \
- / nonpadding.sum() * hparams['lambda_f0']
-
- def save_valid_result(self, sample, batch_idx, model_out):
- sr = hparams['audio_sample_rate']
- f0_gt = None
- mel_out = model_out['mel_out']
- if sample.get('f0') is not None:
- f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu())
- self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt)
- if self.global_step > 0:
- wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt)
- self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr)
- # with gt duration
- model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True)
- dur_info = self.get_plot_dur_info(sample, model_out)
- del dur_info['dur_pred']
- wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
- self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr)
- self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_gdur_{batch_idx}',
- dur_info=dur_info, f0s=f0_gt)
-
- # with pred duration
- if not hparams['use_gt_dur']:
- model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False)
- dur_info = self.get_plot_dur_info(sample, model_out)
- self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}',
- dur_info=dur_info, f0s=f0_gt)
- wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
- self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr)
- # gt wav
- if self.global_step <= hparams['valid_infer_interval']:
- mel_gt = sample['mels'][0].cpu()
- wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
- self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr)
-
- def get_plot_dur_info(self, sample, model_out):
- T_txt = sample['txt_tokens'].shape[1]
- dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0]
- dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
- txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy())
- txt = txt.split(" ")
- return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt}
-
- def test_step(self, sample, batch_idx):
- """
-
- :param sample:
- :param batch_idx:
- :return:
- """
- assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
- outputs = self.run_model(sample, infer=True)
- text = sample['text'][0]
- item_name = sample['item_name'][0]
- tokens = sample['txt_tokens'][0].cpu().numpy()
- mel_gt = sample['mels'][0].cpu().numpy()
- mel_pred = outputs['mel_out'][0].cpu().numpy()
- mel2ph = sample['mel2ph'][0].cpu().numpy()
- mel2ph_pred = outputs['mel2ph'][0].cpu().numpy()
- str_phs = self.token_encoder.decode(tokens, strip_padding=True)
- base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]'
- if text is not None:
- base_fn += text.replace(":", "$3A")[:80]
- base_fn = base_fn.replace(' ', '_')
- gen_dir = self.gen_dir
- wav_pred = self.vocoder.spec2wav(mel_pred)
- self.saving_result_pool.add_job(self.save_result, args=[
- wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred])
- if hparams['save_gt']:
- wav_gt = self.vocoder.spec2wav(mel_gt)
- self.saving_result_pool.add_job(self.save_result, args=[
- wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph])
- print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
- return {
- 'item_name': item_name,
- 'text': text,
- 'ph_tokens': self.token_encoder.decode(tokens.tolist()),
- 'wav_fn_pred': base_fn % 'P',
- 'wav_fn_gt': base_fn % 'G',
- }
diff --git a/spaces/NCTCMumbai/NCTC/models/official/modeling/hyperparams/oneof.py b/spaces/NCTCMumbai/NCTC/models/official/modeling/hyperparams/oneof.py
deleted file mode 100644
index cd49218c137d17e7917e16f8f2eb0b73a8a6a392..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/modeling/hyperparams/oneof.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-"""Config class that supports oneof functionality."""
-
-from typing import Optional
-
-import dataclasses
-from official.modeling.hyperparams import base_config
-
-
-@dataclasses.dataclass
-class OneOfConfig(base_config.Config):
- """Configuration for configs with one of feature.
-
- Attributes:
- type: 'str', name of the field to select.
- """
- type: Optional[str] = None
-
- def as_dict(self):
- """Returns a dict representation of OneOfConfig.
-
- For the nested base_config.Config, a nested dict will be returned.
- """
- if self.type is None:
- return {'type': None}
- elif self.__dict__['type'] not in self.__dict__:
- raise ValueError(
- 'type: {!r} is not a valid key!'.format(self.__dict__['type']))
- else:
- chosen_type = self.type
- chosen_value = self.__dict__[chosen_type]
- return {
- 'type': self.type,
- chosen_type: self._export_config(chosen_value)
- }
-
- def get(self):
- """Returns selected config based on the value of type.
-
- If type is not set (None), None is returned.
- """
- chosen_type = self.type
- if chosen_type is None:
- return None
- if chosen_type not in self.__dict__:
- raise ValueError(
- 'type: {!r} is not a valid key!'.format(self.type))
- return self.__dict__[chosen_type]
diff --git a/spaces/NCTCMumbai/NCTC/models/research/audioset/yamnet/README.md b/spaces/NCTCMumbai/NCTC/models/research/audioset/yamnet/README.md
deleted file mode 100644
index 983724c0d3b526a288f564d5c90b8f8330ac14ee..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/audioset/yamnet/README.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# YAMNet
-
-YAMNet is a pretrained deep net that predicts 521 audio event classes based on
-the [AudioSet-YouTube corpus](http://g.co/audioset), and employing the
-[Mobilenet_v1](https://arxiv.org/pdf/1704.04861.pdf) depthwise-separable
-convolution architecture.
-
-This directory contains the Keras code to construct the model, and example code
-for applying the model to input sound files.
-
-## Installation
-
-YAMNet depends on the following Python packages:
-
-* [`numpy`](http://www.numpy.org/)
-* [`resampy`](http://resampy.readthedocs.io/en/latest/)
-* [`tensorflow`](http://www.tensorflow.org/)
-* [`pysoundfile`](https://pysoundfile.readthedocs.io/)
-
-These are all easily installable via, e.g., `pip install numpy` (as in the
-example command sequence below).
-
-Any reasonably recent version of these packages should work. TensorFlow should
-be at least version 1.8 to ensure Keras support is included. Note that while
-the code works fine with TensorFlow v1.x or v2.x, we explicitly enable v1.x
-behavior.
-
-YAMNet also requires downloading the following data file:
-
-* [YAMNet model weights](https://storage.googleapis.com/audioset/yamnet.h5)
- in Keras saved weights in HDF5 format.
-
-After downloading this file into the same directory as this README, the
-installation can be tested by running `python yamnet_test.py` which
-runs some synthetic signals through the model and checks the outputs.
-
-Here's a sample installation and test session:
-
-```shell
-# Upgrade pip first. Also make sure wheel is installed.
-python -m pip install --upgrade pip wheel
-
-# Install dependences.
-pip install numpy resampy tensorflow soundfile
-
-# Clone TensorFlow models repo into a 'models' directory.
-git clone https://github.com/tensorflow/models.git
-cd models/research/audioset/yamnet
-# Download data file into same directory as code.
-curl -O https://storage.googleapis.com/audioset/yamnet.h5
-
-# Installation ready, let's test it.
-python yamnet_test.py
-# If we see "Ran 4 tests ... OK ...", then we're all set.
-```
-
-## Usage
-
-You can run the model over existing soundfiles using inference.py:
-
-```shell
-python inference.py input_sound.wav
-```
-The code will report the top-5 highest-scoring classes averaged over all the
-frames of the input. You can access greater detail by modifying the example
-code in inference.py.
-
-See the jupyter notebook `yamnet_visualization.ipynb` for an example of
-displaying the per-frame model output scores.
-
-
-## About the Model
-
-The YAMNet code layout is as follows:
-
-* `yamnet.py`: Model definition in Keras.
-* `params.py`: Hyperparameters. You can usefully modify PATCH_HOP_SECONDS.
-* `features.py`: Audio feature extraction helpers.
-* `inference.py`: Example code to classify input wav files.
-* `yamnet_test.py`: Simple test of YAMNet installation
-
-### Input: Audio Features
-
-See `features.py`.
-
-As with our previous release
-[VGGish](https://github.com/tensorflow/models/tree/master/research/audioset/vggish),
-YAMNet was trained with audio features computed as follows:
-
-* All audio is resampled to 16 kHz mono.
-* A spectrogram is computed using magnitudes of the Short-Time Fourier Transform
- with a window size of 25 ms, a window hop of 10 ms, and a periodic Hann
- window.
-* A mel spectrogram is computed by mapping the spectrogram to 64 mel bins
- covering the range 125-7500 Hz.
-* A stabilized log mel spectrogram is computed by applying
- log(mel-spectrum + 0.001) where the offset is used to avoid taking a logarithm
- of zero.
-* These features are then framed into 50%-overlapping examples of 0.96 seconds,
- where each example covers 64 mel bands and 96 frames of 10 ms each.
-
-These 96x64 patches are then fed into the Mobilenet_v1 model to yield a 3x2
-array of activations for 1024 kernels at the top of the convolution. These are
-averaged to give a 1024-dimension embedding, then put through a single logistic
-layer to get the 521 per-class output scores corresponding to the 960 ms input
-waveform segment. (Because of the window framing, you need at least 975 ms of
-input waveform to get the first frame of output scores.)
-
-### Class vocabulary
-
-The file `yamnet_class_map.csv` describes the audio event classes associated
-with each of the 521 outputs of the network. Its format is:
-
-```text
-index,mid,display_name
-```
-
-where `index` is the model output index (0..520), `mid` is the machine
-identifier for that class (e.g. `/m/09x0r`), and display_name is a
-human-readable description of the class (e.g. `Speech`).
-
-The original Audioset data release had 527 classes. This model drops six of
-them on the recommendation of our Fairness reviewers to avoid potentially
-offensive mislabelings. We dropped the gendered versions (Male/Female) of
-Speech and Singing. We also dropped Battle cry and Funny music.
-
-### Performance
-
-On the 20,366-segment AudioSet eval set, over the 521 included classes, the
-balanced average d-prime is 2.318, balanced mAP is 0.306, and the balanced
-average lwlrap is 0.393.
-
-According to our calculations, the classifier has 3.7M weights and performs
-69.2M multiplies for each 960ms input frame.
-
-### Contact information
-
-This model repository is maintained by [Manoj Plakal](https://github.com/plakal) and [Dan Ellis](https://github.com/dpwe).
diff --git a/spaces/NN520/AI/postcss.config.js b/spaces/NN520/AI/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
- plugins: {
- tailwindcss: {},
- autoprefixer: {},
- },
-}
diff --git a/spaces/NicholasKwok/textgenerator/app.py b/spaces/NicholasKwok/textgenerator/app.py
deleted file mode 100644
index 5f0c0d8604a1b67f550540674c44fb49f78aa763..0000000000000000000000000000000000000000
--- a/spaces/NicholasKwok/textgenerator/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import gradio as gr
-from gradio.mix import Parallel
-
-chickenwings="My First Text Generator"
-
-gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", title="My First Text Generator", description="Input text and submit!").launch()
\ No newline at end of file
diff --git a/spaces/NomiWai/anime-collaborative-filtering-space/README.md b/spaces/NomiWai/anime-collaborative-filtering-space/README.md
deleted file mode 100644
index 2beceb3516b565c0f5713a86902d04e299c8451d..0000000000000000000000000000000000000000
--- a/spaces/NomiWai/anime-collaborative-filtering-space/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Anime Collaborative Filtering Space
-emoji: ⚡
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.1.0
-app_file: anime.py
-pinned: false
-license: afl-3.0
----
-
-titles will have to match those titles in MyAnimeList [https://myanimelist.net/]
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OAOA/DifFace/basicsr/metrics/__init__.py b/spaces/OAOA/DifFace/basicsr/metrics/__init__.py
deleted file mode 100644
index 4fb044a93878c7d0dbfafa602b7f80536a6d3a0d..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/metrics/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from copy import deepcopy
-
-from basicsr.utils.registry import METRIC_REGISTRY
-from .niqe import calculate_niqe
-from .psnr_ssim import calculate_psnr, calculate_ssim
-
-__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe']
-
-
-def calculate_metric(data, opt):
- """Calculate metric from data and options.
-
- Args:
- opt (dict): Configuration. It must contain:
- type (str): Model type.
- """
- opt = deepcopy(opt)
- metric_type = opt.pop('type')
- metric = METRIC_REGISTRY.get(metric_type)(**data, **opt)
- return metric
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/byte_level_bpe/get_data.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/byte_level_bpe/get_data.sh
deleted file mode 100644
index c3d55d4925a6e6e23d12d293f093c1ae14acf76e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/byte_level_bpe/get_data.sh
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/bin/bash
-
-# 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.
-
-PY_BIN_ROOT=
-
-# PyPI dependency
-${PY_BIN_ROOT}pip install sentencepiece sacremoses
-
-# Get data
-if [ ! -d "data" ]; then
- mkdir data
-fi
-
-if [ ! -f "data/fr-en.tgz" ]; then
- wget https://wit3.fbk.eu/archive/2017-01-trnted/texts/fr/en/fr-en.tgz -P data
- tar xvf data/fr-en.tgz -C data
-fi
-${PY_BIN_ROOT}python get_bitext.py --bpe-vocab 16384 --byte-vocab --char-vocab
-for VOCAB_SIZE in 2048 4096; do
- ${PY_BIN_ROOT}python get_bitext.py --bpe-vocab ${VOCAB_SIZE} --bbpe-vocab ${VOCAB_SIZE}
-done
-rm -r data/fr-en data/fr-en.tgz
-
-# Generate binary dataset
-${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bpe16384 --joined-dictionary \
- --workers "$(nproc)" --trainpref data/train.moses.bpe16384 --validpref data/valid.moses.bpe16384 \
- --testpref data/test.moses.bpe16384
-
-${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_bytes --joined-dictionary \
- --workers "$(nproc)" --trainpref data/train.moses.bytes --validpref data/valid.moses.bytes \
- --testpref data/test.moses.bytes
-
-${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir data/bin_chars --joined-dictionary \
- --workers "$(nproc)" --trainpref data/train.moses.chars --validpref data/valid.moses.chars \
- --testpref data/test.moses.chars
-
-for VOCAB_SIZE in 2048 4096; do
- for TYPE in bbpe bpe; do
- ${PY_BIN_ROOT}/fairseq-preprocess --source-lang fr --target-lang en --destdir "data/bin_${TYPE}${VOCAB_SIZE}" \
- --joined-dictionary --workers "$(nproc)" --trainpref "data/train.moses.${TYPE}${VOCAB_SIZE}" \
- --validpref "data/valid.moses.${TYPE}${VOCAB_SIZE}" --testpref "data/test.moses.${TYPE}${VOCAB_SIZE}"
- done
-done
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/translation/prepare-wmt14en2de.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/translation/prepare-wmt14en2de.sh
deleted file mode 100644
index 6702c88b568c9e680b525593ff0c9fb0a474825d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/translation/prepare-wmt14en2de.sh
+++ /dev/null
@@ -1,142 +0,0 @@
-#!/bin/bash
-# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-git clone https://github.com/rsennrich/subword-nmt.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-BPEROOT=subword-nmt/subword_nmt
-BPE_TOKENS=40000
-
-URLS=(
- "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
- "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
- "http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz"
- "http://data.statmt.org/wmt17/translation-task/dev.tgz"
- "http://statmt.org/wmt14/test-full.tgz"
-)
-FILES=(
- "training-parallel-europarl-v7.tgz"
- "training-parallel-commoncrawl.tgz"
- "training-parallel-nc-v12.tgz"
- "dev.tgz"
- "test-full.tgz"
-)
-CORPORA=(
- "training/europarl-v7.de-en"
- "commoncrawl.de-en"
- "training/news-commentary-v12.de-en"
-)
-
-# This will make the dataset compatible to the one used in "Convolutional Sequence to Sequence Learning"
-# https://arxiv.org/abs/1705.03122
-if [ "$1" == "--icml17" ]; then
- URLS[2]="http://statmt.org/wmt14/training-parallel-nc-v9.tgz"
- FILES[2]="training-parallel-nc-v9.tgz"
- CORPORA[2]="training/news-commentary-v9.de-en"
- OUTDIR=wmt14_en_de
-else
- OUTDIR=wmt17_en_de
-fi
-
-if [ ! -d "$SCRIPTS" ]; then
- echo "Please set SCRIPTS variable correctly to point to Moses scripts."
- exit
-fi
-
-src=en
-tgt=de
-lang=en-de
-prep=$OUTDIR
-tmp=$prep/tmp
-orig=orig
-dev=dev/newstest2013
-
-mkdir -p $orig $tmp $prep
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
- file=${FILES[i]}
- if [ -f $file ]; then
- echo "$file already exists, skipping download"
- else
- url=${URLS[i]}
- wget "$url"
- if [ -f $file ]; then
- echo "$url successfully downloaded."
- else
- echo "$url not successfully downloaded."
- exit -1
- fi
- if [ ${file: -4} == ".tgz" ]; then
- tar zxvf $file
- elif [ ${file: -4} == ".tar" ]; then
- tar xvf $file
- fi
- fi
-done
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
- rm $tmp/train.tags.$lang.tok.$l
- for f in "${CORPORA[@]}"; do
- cat $orig/$f.$l | \
- perl $NORM_PUNC $l | \
- perl $REM_NON_PRINT_CHAR | \
- perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l
- done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
- if [ "$l" == "$src" ]; then
- t="src"
- else
- t="ref"
- fi
- grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \
- sed -e 's/<seg id="[0-9]*">\s*//g' | \
- sed -e 's/\s*<\/seg>\s*//g' | \
- sed -e "s/\’/\'/g" | \
- perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l
- echo ""
-done
-
-echo "splitting train and valid..."
-for l in $src $tgt; do
- awk '{if (NR%100 == 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l
- awk '{if (NR%100 != 0) print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l
-done
-
-TRAIN=$tmp/train.de-en
-BPE_CODE=$prep/code
-rm -f $TRAIN
-for l in $src $tgt; do
- cat $tmp/train.$l >> $TRAIN
-done
-
-echo "learn_bpe.py on ${TRAIN}..."
-python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE
-
-for L in $src $tgt; do
- for f in train.$L valid.$L test.$L; do
- echo "apply_bpe.py to ${f}..."
- python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f
- done
-done
-
-perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250
-perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250
-
-for L in $src $tgt; do
- cp $tmp/bpe.test.$L $prep/test.$L
-done
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/dedup_all.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/dedup_all.py
deleted file mode 100644
index ef39c05ee606aaeda1d9e94970932d2241a8b281..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/dedup_all.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# 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.
-
-
-
-import os
-import glob
-import argparse
-from utils.dedup import deup
-
-import sys
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-def main():
- parser = argparse.ArgumentParser()
- parser.add_argument("--from-folder", type=str, required=True,
- help="the data folder to be dedup")
- parser.add_argument("--to-folder", type=str, required=True,
- help="the data folder to save deduped data")
- parser.add_argument('--directions', type=str, default=None, required=False)
-
- args = parser.parse_args()
-
- if args.directions is None:
- raw_files = glob.glob(f'{args.from_folder}/train*')
-
- directions = [os.path.split(file_path)[-1].split('.')[1] for file_path in raw_files]
- else:
- directions = args.directions.split(',')
- directions = sorted(set(directions))
-
- for direction in directions:
- src, tgt = direction.split('-')
- src_file = f'{args.from_folder}/train.{src}-{tgt}.{src}'
- tgt_file = f'{args.from_folder}/train.{src}-{tgt}.{tgt}'
- src_file_out = f'{args.to_folder}/train.{src}-{tgt}.{src}'
- tgt_file_out = f'{args.to_folder}/train.{src}-{tgt}.{tgt}'
- assert src_file != src_file_out
- assert tgt_file != tgt_file_out
- print(f'deduping {src_file}, {tgt_file}')
- deup(src_file, tgt_file, src_file_out, tgt_file_out)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh
deleted file mode 100644
index af68715ab0d87ae40666596d9d877d593684f8e2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/steps_gan/train_deltas.sh
+++ /dev/null
@@ -1,175 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
-# Apache 2.0
-
-# Begin configuration.
-stage=-4 # This allows restarting after partway, when something when wrong.
-config=
-cmd=run.pl
-scale_opts="--transition-scale=1.0 --acoustic-scale=0.1 --self-loop-scale=0.1"
-realign_iters="10 20 30";
-num_iters=35 # Number of iterations of training
-max_iter_inc=25 # Last iter to increase #Gauss on.
-beam=10
-careful=false
-retry_beam=40
-boost_silence=1.0 # Factor by which to boost silence likelihoods in alignment
-power=0.25 # Exponent for number of gaussians according to occurrence counts
-cluster_thresh=-1 # for build-tree control final bottom-up clustering of leaves
-norm_vars=false # deprecated. Prefer --cmvn-opts "--norm-vars=true"
- # use the option --cmvn-opts "--norm-means=false"
-cmvn_opts=
-delta_opts=
-context_opts= # use"--context-width=5 --central-position=2" for quinphone
-num_nonsil_states=3
-# End configuration.
-
-echo "$0 $@" # Print the command line for logging
-
-[ -f path.sh ] && . ./path.sh;
-. parse_options.sh || exit 1;
-
-if [ $# != 6 ]; then
- echo "Usage: steps/train_deltas.sh <num-leaves> <tot-gauss> <data-dir> <lang-dir> <alignment-dir> <exp-dir>"
- echo "e.g.: steps/train_deltas.sh 2000 10000 data/train_si84_half data/lang exp/mono_ali exp/tri1"
- echo "main options (for others, see top of script file)"
- echo " --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
- echo " --config <config-file> # config containing options"
- echo " --stage <stage> # stage to do partial re-run from."
- exit 1;
-fi
-
-numleaves=$1
-totgauss=$2
-data=$3
-lang=$4
-alidir=$5
-dir=$6
-
-for f in $alidir/final.mdl $alidir/ali.1.gz $data/feats.scp $lang/phones.txt; do
- [ ! -f $f ] && echo "train_deltas.sh: no such file $f" && exit 1;
-done
-
-numgauss=$numleaves
-incgauss=$[($totgauss-$numgauss)/$max_iter_inc] # per-iter increment for #Gauss
-oov=`cat $lang/oov.int` || exit 1;
-ciphonelist=`cat $lang/phones/context_indep.csl` || exit 1;
-nj=`cat $alidir/num_jobs` || exit 1;
-mkdir -p $dir/log
-echo $nj > $dir/num_jobs
-
-utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1;
-cp $lang/phones.txt $dir || exit 1;
-
-sdata=$data/split$nj;
-split_data.sh $data $nj || exit 1;
-
-
-[ $(cat $alidir/cmvn_opts 2>/dev/null | wc -c) -gt 1 ] && [ -z "$cmvn_opts" ] && \
- echo "$0: warning: ignoring CMVN options from source directory $alidir"
-$norm_vars && cmvn_opts="--norm-vars=true $cmvn_opts"
-echo $cmvn_opts > $dir/cmvn_opts # keep track of options to CMVN.
-[ ! -z $delta_opts ] && echo $delta_opts > $dir/delta_opts
-
-feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |"
-
-rm $dir/.error 2>/dev/null
-
-if [ $stage -le -3 ]; then
- echo "$0: accumulating tree stats"
- $cmd JOB=1:$nj $dir/log/acc_tree.JOB.log \
- acc-tree-stats $context_opts \
- --ci-phones=$ciphonelist $alidir/final.mdl "$feats" \
- "ark:gunzip -c $alidir/ali.JOB.gz|" $dir/JOB.treeacc || exit 1;
- sum-tree-stats $dir/treeacc $dir/*.treeacc 2>$dir/log/sum_tree_acc.log || exit 1;
- rm $dir/*.treeacc
-fi
-
-if [ $stage -le -2 ]; then
- echo "$0: getting questions for tree-building, via clustering"
- # preparing questions, roots file...
- cluster-phones --pdf-class-list=$(($num_nonsil_states / 2)) $context_opts \
- $dir/treeacc $lang/phones/sets.int \
- $dir/questions.int 2> $dir/log/questions.log || exit 1;
- cat $lang/phones/extra_questions.int >> $dir/questions.int
- compile-questions $context_opts $lang/topo $dir/questions.int \
- $dir/questions.qst 2>$dir/log/compile_questions.log || exit 1;
-
- echo "$0: building the tree"
- $cmd $dir/log/build_tree.log \
- build-tree $context_opts --verbose=1 --max-leaves=$numleaves \
- --cluster-thresh=$cluster_thresh $dir/treeacc $lang/phones/roots.int \
- $dir/questions.qst $lang/topo $dir/tree || exit 1;
-
- $cmd $dir/log/init_model.log \
- gmm-init-model --write-occs=$dir/1.occs \
- $dir/tree $dir/treeacc $lang/topo $dir/1.mdl || exit 1;
- if grep 'no stats' $dir/log/init_model.log; then
- echo "** The warnings above about 'no stats' generally mean you have phones **"
- echo "** (or groups of phones) in your phone set that had no corresponding data. **"
- echo "** You should probably figure out whether something went wrong, **"
- echo "** or whether your data just doesn't happen to have examples of those **"
- echo "** phones. **"
- fi
-
- gmm-mixup --mix-up=$numgauss $dir/1.mdl $dir/1.occs $dir/1.mdl 2>$dir/log/mixup.log || exit 1;
- rm $dir/treeacc
-fi
-
-if [ $stage -le -1 ]; then
- # Convert the alignments.
- echo "$0: converting alignments from $alidir to use current tree"
- $cmd JOB=1:$nj $dir/log/convert.JOB.log \
- convert-ali $alidir/final.mdl $dir/1.mdl $dir/tree \
- "ark:gunzip -c $alidir/ali.JOB.gz|" "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
-fi
-
-if [ $stage -le 0 ]; then
- echo "$0: compiling graphs of transcripts"
- $cmd JOB=1:$nj $dir/log/compile_graphs.JOB.log \
- compile-train-graphs --read-disambig-syms=$lang/phones/disambig.int $dir/tree $dir/1.mdl $lang/L.fst \
- "ark:utils/sym2int.pl --map-oov $oov -f 2- $lang/words.txt < $sdata/JOB/text |" \
- "ark:|gzip -c >$dir/fsts.JOB.gz" || exit 1;
-fi
-
-x=1
-while [ $x -lt $num_iters ]; do
- echo "$0: training pass $x"
- if [ $stage -le $x ]; then
- if echo $realign_iters | grep -w $x >/dev/null; then
- echo "$0: aligning data"
- mdl="gmm-boost-silence --boost=$boost_silence `cat $lang/phones/optional_silence.csl` $dir/$x.mdl - |"
- $cmd JOB=1:$nj $dir/log/align.$x.JOB.log \
- gmm-align-compiled $scale_opts --beam=$beam --retry-beam=$retry_beam --careful=$careful "$mdl" \
- "ark:gunzip -c $dir/fsts.JOB.gz|" "$feats" \
- "ark:|gzip -c >$dir/ali.JOB.gz" || exit 1;
- fi
- $cmd JOB=1:$nj $dir/log/acc.$x.JOB.log \
- gmm-acc-stats-ali $dir/$x.mdl "$feats" \
- "ark,s,cs:gunzip -c $dir/ali.JOB.gz|" $dir/$x.JOB.acc || exit 1;
- $cmd $dir/log/update.$x.log \
- gmm-est --mix-up=$numgauss --power=$power \
- --write-occs=$dir/$[$x+1].occs $dir/$x.mdl \
- "gmm-sum-accs - $dir/$x.*.acc |" $dir/$[$x+1].mdl || exit 1;
- rm $dir/$x.mdl $dir/$x.*.acc
- rm $dir/$x.occs
- fi
- [ $x -le $max_iter_inc ] && numgauss=$[$numgauss+$incgauss];
- x=$[$x+1];
-done
-
-rm $dir/final.mdl $dir/final.occs 2>/dev/null
-ln -s $x.mdl $dir/final.mdl
-ln -s $x.occs $dir/final.occs
-
-steps/diagnostic/analyze_alignments.sh --cmd "$cmd" $lang $dir
-
-# Summarize warning messages...
-utils/summarize_warnings.pl $dir/log
-
-steps/info/gmm_dir_info.pl $dir
-
-echo "$0: Done training system with delta+delta-delta features in $dir"
-
-exit 0
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/laser_src/laser_task.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/laser_src/laser_task.py
deleted file mode 100644
index e4152fde6861488acc3595fa25c456bf60f134b9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/laser_src/laser_task.py
+++ /dev/null
@@ -1,331 +0,0 @@
-# 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.
-
-
-from collections import OrderedDict, defaultdict
-import json
-import os
-import logging
-from argparse import ArgumentError
-
-from fairseq import options, models
-from fairseq.data import (
- data_utils,
- Dictionary,
- LanguagePairDataset,
- IndexedDataset,
- FairseqDataset,
-)
-from .multitask_data_utils import (
- MultitaskDatasetWrapper,
- MultidatasetEpochBatchIterator,
-)
-
-
-from fairseq.tasks import LegacyFairseqTask, register_task
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("laser")
-class LaserTask(LegacyFairseqTask):
- @staticmethod
- def add_args(parser):
- """Add task-specific arguments to the parser."""
- parser.add_argument(
- "configfile", metavar="PATH", help="dataset configuration file in json"
- )
- parser.add_argument(
- "--weighting-alpha",
- type=float,
- default=None,
- help="alpha for automatic weighting",
- )
- parser.add_argument(
- "--raw-text", action="store_true", help="load raw text dataset"
- )
- parser.add_argument(
- "--left-pad-source",
- default="True",
- type=str,
- metavar="BOOL",
- help="pad the source on the left (default: True)",
- )
- parser.add_argument(
- "--left-pad-target",
- default="False",
- type=str,
- metavar="BOOL",
- help="pad the target on the left (default: False)",
- )
- try:
- parser.add_argument(
- "--max-source-positions",
- default=1024,
- type=int,
- metavar="N",
- help="max number of tokens in the source sequence",
- )
- parser.add_argument(
- "--max-target-positions",
- default=1024,
- type=int,
- metavar="N",
- help="max number of tokens in the target sequence",
- )
- except ArgumentError:
- # this might have already been defined. Once we transition this to hydra it should be fine to add it here.
- pass
-
- def __init__(self, args, config, src_dictionary, tgt_dictionary, num_tasks):
- super().__init__(args)
- self.config = config
- self.src_dictionary = src_dictionary
- self.tgt_dictionary = tgt_dictionary
- self.num_tasks = num_tasks
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- with open(args.configfile, "r") as f:
- config = json.load(f)
- num_tasks = max(dataset["id"] for dataset in config["train"]) + 1
-
- args.left_pad_source = options.eval_bool(args.left_pad_source)
- args.left_pad_target = options.eval_bool(args.left_pad_target)
-
- src_dictionary = Dictionary.load(config["src_vocab"])
- tgt_dictionary = Dictionary.load(config["tgt_vocab"])
-
- logger.info(
- "| src Dictionary {} : {} types".format(
- config["src_vocab"], len(src_dictionary)
- )
- )
- logger.info(
- "| tgt Dictionary {} : {} types".format(
- config["tgt_vocab"], len(tgt_dictionary)
- )
- )
-
- return cls(args, config, src_dictionary, tgt_dictionary, num_tasks)
-
- # Experimental overriding for backtranslation
- def build_model(self, args):
- model = models.build_model(args, self)
- return model
-
- def dataset(self, split):
- if split not in self.datasets:
- raise KeyError("Dataset not loaded: " + split)
- return self.datasets[split]
-
- def load_dataset(self, split, epoch=1, **kwargs):
- """Load a dataset split."""
-
- def indexed_dataset(path, dictionary):
- if self.args.raw_text:
- raise Exception("Unable to handle raw text.")
- dataset = IndexedDataset(path, fix_lua_indexing=True)
-
- return dataset
-
- pair_datasets = OrderedDict()
-
- if split == "valid":
- self.datasets[split] = pair_datasets
- return
-
- if split not in self.config:
- raise FileNotFoundError(
- "Dataset not found in config file: {}".format(split)
- )
-
- size_by_corpus = defaultdict(int)
- size_sum = 0
- size_sum_with_subsampling = 0
- init_pair_datasets = {}
-
- for dataset_config in self.config[split]:
- src_path = os.path.dirname(dataset_config["src"])
- corpus_name = src_path.split("/")[-2]
- language_pair_name = src_path.split("/")[-1]
- pair_datasets_key = corpus_name + "-" + language_pair_name
-
- logger.info(f"loading... {pair_datasets_key}")
- if "src" in dataset_config:
- src_dataset = indexed_dataset(
- dataset_config["src"], self.src_dictionary
- )
- else:
- src_dataset = None
-
- if "tgt" in dataset_config:
- tgt_dataset = indexed_dataset(
- dataset_config["tgt"], self.tgt_dictionary
- )
- else:
- tgt_dataset = None
-
- dataset = LanguagePairDataset(
- src_dataset,
- src_dataset.sizes,
- self.src_dictionary,
- tgt_dataset,
- tgt_dataset.sizes,
- self.tgt_dictionary,
- left_pad_source=self.args.left_pad_source,
- left_pad_target=self.args.left_pad_target,
- )
-
- if pair_datasets_key in init_pair_datasets:
- logger.warning(
- f"Ignoring already added {pair_datasets_key}. "
- f"Consider using `sample` key in order to upsample."
- )
- else:
- init_pair_datasets[pair_datasets_key] = {
- "dataset": dataset,
- "sample": dataset_config.get("sample", None),
- "id": dataset_config.get("id", None),
- "len": len(dataset),
- }
-
- length_sum = 0
- weighted_freqs_sum = 0
- freq_per_dataset = {}
- vmax = 0
- vmin = 1
- weighted_freq_per_dataset = {}
-
- if self.args.weighting_alpha:
- for key in init_pair_datasets:
- if init_pair_datasets[key]["sample"] is None:
- length_sum += len(init_pair_datasets[key]["dataset"])
-
- for key in init_pair_datasets:
- if init_pair_datasets[key]["sample"] is None:
- val = float(init_pair_datasets[key]["len"]) / length_sum
- freq_per_dataset[key] = val
- weighted_freqs_sum += val ** self.args.weighting_alpha
-
- for key in freq_per_dataset:
- val = (
- freq_per_dataset[key] ** self.args.weighting_alpha
- / weighted_freqs_sum
- )
- vmin = min(vmin, val)
- vmax = max(vmax, val)
- weighted_freq_per_dataset[key] = val
-
- for pair_datasets_key in init_pair_datasets:
- dataset_config = init_pair_datasets[pair_datasets_key]
- dataset = dataset_config["dataset"]
- sample = dataset_config["sample"]
- if sample is None:
- sample = 1.0
-
- if pair_datasets_key in weighted_freq_per_dataset:
- w = vmax / weighted_freq_per_dataset[pair_datasets_key]
- sample = w
-
- sample = round(sample)
-
- initial_sample = sample
- initial_pair_datasets_key = pair_datasets_key
-
- while sample >= 1.0:
- assert (
- pair_datasets_key not in pair_datasets
- ), f"{pair_datasets_key} already in"
- size_sum_with_subsampling += len(dataset)
- pair_datasets[pair_datasets_key] = MultitaskDatasetWrapper(
- dataset, dataset_config.get("id", 0), 1.0, name=pair_datasets_key
- )
- size_sum += len(dataset)
- sample -= 1.0
- pair_datasets_key += "-up"
-
- assert sample < 1e-6, f"sample remains > 0 {pair_datasets_key}"
-
- logger.info(
- f"added pair {initial_pair_datasets_key} length {len(dataset)} new_length = {len(dataset)*initial_sample}"
- )
- size_by_corpus[corpus_name] += len(dataset)
-
- self.datasets[split] = pair_datasets
- logger.info(
- f"Datasets number = {len(self.datasets[split])} size = {size_sum} size_sum_with_subsampling = {size_sum_with_subsampling}"
- )
-
- @property
- def source_dictionary(self):
- return self.src_dictionary
-
- @property
- def target_dictionary(self):
- return self.tgt_dictionary
-
- def get_batch_iterator(
- self,
- dataset,
- max_tokens=None,
- max_sentences=None,
- max_positions=None,
- ignore_invalid_inputs=False,
- required_batch_size_multiple=1,
- seed=1,
- num_shards=1,
- shard_id=0,
- num_workers=0,
- epoch=1,
- data_buffer_size=0,
- disable_iterator_cache=False,
- ):
-
- assert isinstance(dataset, OrderedDict)
- assert len(dataset)
- assert isinstance(dataset[next(iter(dataset))], FairseqDataset)
-
- # initialize the dataset with the correct starting epoch
- for _, dt in dataset.items():
- dt.set_epoch(epoch)
-
- indices = OrderedDict()
- batch_sampler = OrderedDict()
-
- with data_utils.numpy_seed(seed + epoch):
- for key, dt in dataset.items():
- logger.info(f"\t ordered_indices {key}")
- indices[key] = dt.ordered_indices()
-
- # filter examples that are too large
- if max_positions is not None:
- for key, dt in dataset.items():
- logger.info(f"\t filter_by_size {key}")
- indices[key], ignored = dt.filter_indices_by_size(
- indices[key], max_positions
- )
-
- for key, dt in dataset.items():
- logger.info(f"\t batch_by_size {key}")
- batch_sampler[key] = data_utils.batch_by_size(
- indices[key],
- dt.num_tokens,
- max_tokens=max_tokens,
- max_sentences=max_sentences,
- required_batch_size_multiple=required_batch_size_multiple,
- )
-
- epoch_iter = MultidatasetEpochBatchIterator(
- dataset=dataset,
- batch_sampler=batch_sampler,
- seed=seed,
- num_shards=num_shards,
- shard_id=shard_id,
- num_workers=num_workers,
- epoch=epoch,
- )
-
- return epoch_iter
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/wsc/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/wsc/__init__.py
deleted file mode 100644
index 78afa4728eeed96142900118f6452730023466c9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/wsc/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# 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.
-
-from . import wsc_criterion # noqa
-from . import wsc_task # noqa
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/simultaneous_translation/models/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/simultaneous_translation/models/__init__.py
deleted file mode 100644
index 257a96593ff7af93c206c066d8db4ad795b2ae36..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/simultaneous_translation/models/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# 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.
-
-import importlib
-import os
-
-
-for file in sorted(os.listdir(os.path.dirname(__file__))):
- if file.endswith(".py") and not file.startswith("_"):
- model_name = file[: file.find(".py")]
- importlib.import_module(
- "examples.simultaneous_translation.models." + model_name
- )
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/config/config.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/config/config.py
deleted file mode 100644
index 49a55b1bc87509e2bb24b902ae12c21d5aaeda81..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/config/config.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import functools
-import inspect
-import logging
-from fvcore.common.config import CfgNode as _CfgNode
-
-from detectron2.utils.file_io import PathManager
-
-
-class CfgNode(_CfgNode):
- """
- The same as `fvcore.common.config.CfgNode`, but different in:
-
- 1. Use unsafe yaml loading by default.
- Note that this may lead to arbitrary code execution: you must not
- load a config file from untrusted sources before manually inspecting
- the content of the file.
- 2. Support config versioning.
- When attempting to merge an old config, it will convert the old config automatically.
-
- .. automethod:: clone
- .. automethod:: freeze
- .. automethod:: defrost
- .. automethod:: is_frozen
- .. automethod:: load_yaml_with_base
- .. automethod:: merge_from_list
- .. automethod:: merge_from_other_cfg
- """
-
- @classmethod
- def _open_cfg(cls, filename):
- return PathManager.open(filename, "r")
-
- # Note that the default value of allow_unsafe is changed to True
- def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None:
- """
- Load content from the given config file and merge it into self.
-
- Args:
- cfg_filename: config filename
- allow_unsafe: allow unsafe yaml syntax
- """
- assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!"
- loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe)
- loaded_cfg = type(self)(loaded_cfg)
-
- # defaults.py needs to import CfgNode
- from .defaults import _C
-
- latest_ver = _C.VERSION
- assert (
- latest_ver == self.VERSION
- ), "CfgNode.merge_from_file is only allowed on a config object of latest version!"
-
- logger = logging.getLogger(__name__)
-
- loaded_ver = loaded_cfg.get("VERSION", None)
- if loaded_ver is None:
- from .compat import guess_version
-
- loaded_ver = guess_version(loaded_cfg, cfg_filename)
- assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format(
- loaded_ver, self.VERSION
- )
-
- if loaded_ver == self.VERSION:
- self.merge_from_other_cfg(loaded_cfg)
- else:
- # compat.py needs to import CfgNode
- from .compat import upgrade_config, downgrade_config
-
- logger.warning(
- "Loading an old v{} config file '{}' by automatically upgrading to v{}. "
- "See docs/CHANGELOG.md for instructions to update your files.".format(
- loaded_ver, cfg_filename, self.VERSION
- )
- )
- # To convert, first obtain a full config at an old version
- old_self = downgrade_config(self, to_version=loaded_ver)
- old_self.merge_from_other_cfg(loaded_cfg)
- new_config = upgrade_config(old_self)
- self.clear()
- self.update(new_config)
-
- def dump(self, *args, **kwargs):
- """
- Returns:
- str: a yaml string representation of the config
- """
- # to make it show up in docs
- return super().dump(*args, **kwargs)
-
-
-global_cfg = CfgNode()
-
-
-def get_cfg() -> CfgNode:
- """
- Get a copy of the default config.
-
- Returns:
- a detectron2 CfgNode instance.
- """
- from .defaults import _C
-
- return _C.clone()
-
-
-def set_global_cfg(cfg: CfgNode) -> None:
- """
- Let the global config point to the given cfg.
-
- Assume that the given "cfg" has the key "KEY", after calling
- `set_global_cfg(cfg)`, the key can be accessed by:
- ::
- from detectron2.config import global_cfg
- print(global_cfg.KEY)
-
- By using a hacky global config, you can access these configs anywhere,
- without having to pass the config object or the values deep into the code.
- This is a hacky feature introduced for quick prototyping / research exploration.
- """
- global global_cfg
- global_cfg.clear()
- global_cfg.update(cfg)
-
-
-def configurable(init_func=None, *, from_config=None):
- """
- Decorate a function or a class's __init__ method so that it can be called
- with a :class:`CfgNode` object using a :func:`from_config` function that translates
- :class:`CfgNode` to arguments.
-
- Examples:
- ::
- # Usage 1: Decorator on __init__:
- class A:
- @configurable
- def __init__(self, a, b=2, c=3):
- pass
-
- @classmethod
- def from_config(cls, cfg): # 'cfg' must be the first argument
- # Returns kwargs to be passed to __init__
- return {"a": cfg.A, "b": cfg.B}
-
- a1 = A(a=1, b=2) # regular construction
- a2 = A(cfg) # construct with a cfg
- a3 = A(cfg, b=3, c=4) # construct with extra overwrite
-
- # Usage 2: Decorator on any function. Needs an extra from_config argument:
- @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B})
- def a_func(a, b=2, c=3):
- pass
-
- a1 = a_func(a=1, b=2) # regular call
- a2 = a_func(cfg) # call with a cfg
- a3 = a_func(cfg, b=3, c=4) # call with extra overwrite
-
- Args:
- init_func (callable): a class's ``__init__`` method in usage 1. The
- class must have a ``from_config`` classmethod which takes `cfg` as
- the first argument.
- from_config (callable): the from_config function in usage 2. It must take `cfg`
- as its first argument.
- """
-
- if init_func is not None:
- assert (
- inspect.isfunction(init_func)
- and from_config is None
- and init_func.__name__ == "__init__"
- ), "Incorrect use of @configurable. Check API documentation for examples."
-
- @functools.wraps(init_func)
- def wrapped(self, *args, **kwargs):
- try:
- from_config_func = type(self).from_config
- except AttributeError as e:
- raise AttributeError(
- "Class with @configurable must have a 'from_config' classmethod."
- ) from e
- if not inspect.ismethod(from_config_func):
- raise TypeError("Class with @configurable must have a 'from_config' classmethod.")
-
- if _called_with_cfg(*args, **kwargs):
- explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)
- init_func(self, **explicit_args)
- else:
- init_func(self, *args, **kwargs)
-
- return wrapped
-
- else:
- if from_config is None:
- return configurable # @configurable() is made equivalent to @configurable
- assert inspect.isfunction(
- from_config
- ), "from_config argument of configurable must be a function!"
-
- def wrapper(orig_func):
- @functools.wraps(orig_func)
- def wrapped(*args, **kwargs):
- if _called_with_cfg(*args, **kwargs):
- explicit_args = _get_args_from_config(from_config, *args, **kwargs)
- return orig_func(**explicit_args)
- else:
- return orig_func(*args, **kwargs)
-
- wrapped.from_config = from_config
- return wrapped
-
- return wrapper
-
-
-def _get_args_from_config(from_config_func, *args, **kwargs):
- """
- Use `from_config` to obtain explicit arguments.
-
- Returns:
- dict: arguments to be used for cls.__init__
- """
- signature = inspect.signature(from_config_func)
- if list(signature.parameters.keys())[0] != "cfg":
- if inspect.isfunction(from_config_func):
- name = from_config_func.__name__
- else:
- name = f"{from_config_func.__self__}.from_config"
- raise TypeError(f"{name} must take 'cfg' as the first argument!")
- support_var_arg = any(
- param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD]
- for param in signature.parameters.values()
- )
- if support_var_arg: # forward all arguments to from_config, if from_config accepts them
- ret = from_config_func(*args, **kwargs)
- else:
- # forward supported arguments to from_config
- supported_arg_names = set(signature.parameters.keys())
- extra_kwargs = {}
- for name in list(kwargs.keys()):
- if name not in supported_arg_names:
- extra_kwargs[name] = kwargs.pop(name)
- ret = from_config_func(*args, **kwargs)
- # forward the other arguments to __init__
- ret.update(extra_kwargs)
- return ret
-
-
-def _called_with_cfg(*args, **kwargs):
- """
- Returns:
- bool: whether the arguments contain CfgNode and should be considered
- forwarded to from_config.
- """
- from omegaconf import DictConfig
-
- if len(args) and isinstance(args[0], (_CfgNode, DictConfig)):
- return True
- if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)):
- return True
- # `from_config`'s first argument is forced to be "cfg".
- # So the above check covers all cases.
- return False
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/dev/packaging/build_all_wheels.sh b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/dev/packaging/build_all_wheels.sh
deleted file mode 100644
index 98b5e4444828b48c8a54229ee04a44d8c7d38090..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/dev/packaging/build_all_wheels.sh
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-[[ -d "dev/packaging" ]] || {
- echo "Please run this script at detectron2 root!"
- exit 1
-}
-
-build_one() {
- cu=$1
- pytorch_ver=$2
-
- case "$cu" in
- cu*)
- container_name=manylinux-cuda${cu/cu/}
- ;;
- cpu)
- container_name=manylinux-cuda101
- ;;
- *)
- echo "Unrecognized cu=$cu"
- exit 1
- ;;
- esac
-
- echo "Launching container $container_name ..."
- container_id="$container_name"_"$cu"_"$pytorch_ver"
-
- py_versions=(3.6 3.7 3.8 3.9)
-
- for py in "${py_versions[@]}"; do
- docker run -itd \
- --name "$container_id" \
- --mount type=bind,source="$(pwd)",target=/detectron2 \
- pytorch/$container_name
-
- cat <<EOF | docker exec -i $container_id sh
- export CU_VERSION=$cu D2_VERSION_SUFFIX=+$cu PYTHON_VERSION=$py
- export PYTORCH_VERSION=$pytorch_ver
- cd /detectron2 && ./dev/packaging/build_wheel.sh
-EOF
-
- docker container stop $container_id
- docker container rm $container_id
- done
-}
-
-
-if [[ -n "$1" ]] && [[ -n "$2" ]]; then
- build_one "$1" "$2"
-else
- build_one cu113 1.10
- build_one cu111 1.10
- build_one cu102 1.10
- build_one cpu 1.10
-
- build_one cu111 1.9
- build_one cu102 1.9
- build_one cpu 1.9
-
- build_one cu111 1.8
- build_one cu102 1.8
- build_one cu101 1.8
- build_one cpu 1.8
-fi
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py
deleted file mode 100644
index 3bb100f9bd5b4939e4646821c5a60d51c8ea65fd..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/modeling/test_backbone.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import unittest
-import torch
-
-import detectron2.export.torchscript # apply patch # noqa
-from detectron2 import model_zoo
-from detectron2.config import get_cfg
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.backbone import build_resnet_backbone
-from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone
-
-
-class TestBackBone(unittest.TestCase):
- def test_resnet_scriptability(self):
- cfg = get_cfg()
- resnet = build_resnet_backbone(cfg, ShapeSpec(channels=3))
-
- scripted_resnet = torch.jit.script(resnet)
-
- inp = torch.rand(2, 3, 100, 100)
- out1 = resnet(inp)["res4"]
- out2 = scripted_resnet(inp)["res4"]
- self.assertTrue(torch.allclose(out1, out2))
-
- def test_fpn_scriptability(self):
- cfg = model_zoo.get_config("Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml")
- bb = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3))
- bb_s = torch.jit.script(bb)
-
- inp = torch.rand(2, 3, 128, 128)
- out1 = bb(inp)["p5"]
- out2 = bb_s(inp)["p5"]
- self.assertTrue(torch.allclose(out1, out2))
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tools/README.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tools/README.md
deleted file mode 100644
index 0b40d5319c0838fdaa22bc6a10ef0d88bc6578ed..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tools/README.md
+++ /dev/null
@@ -1,49 +0,0 @@
-
-This directory contains a few example scripts that demonstrate features of detectron2.
-
-
-* `train_net.py`
-
-An example training script that's made to train builtin models of detectron2.
-
-For usage, see [GETTING_STARTED.md](../GETTING_STARTED.md).
-
-* `plain_train_net.py`
-
-Similar to `train_net.py`, but implements a training loop instead of using `Trainer`.
-This script includes fewer features but it may be more friendly to hackers.
-
-* `benchmark.py`
-
-Benchmark the training speed, inference speed or data loading speed of a given config.
-
-Usage:
-```
-python benchmark.py --config-file config.yaml --task train/eval/data [optional DDP flags]
-```
-
-* `analyze_model.py`
-
-Analyze FLOPs, parameters, activations of a detectron2 model. See its `--help` for usage.
-
-* `visualize_json_results.py`
-
-Visualize the json instance detection/segmentation results dumped by `COCOEvalutor` or `LVISEvaluator`
-
-Usage:
-```
-python visualize_json_results.py --input x.json --output dir/ --dataset coco_2017_val
-```
-If not using a builtin dataset, you'll need your own script or modify this script.
-
-* `visualize_data.py`
-
-Visualize ground truth raw annotations or training data (after preprocessing/augmentations).
-
-Usage:
-```
-python visualize_data.py --config-file config.yaml --source annotation/dataloader --output-dir dir/ [--show]
-```
-
-NOTE: the script does not stop by itself when using `--source dataloader` because a training
-dataloader is usually infinite.
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tools/convert-torchvision-to-d2.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tools/convert-torchvision-to-d2.py
deleted file mode 100644
index 4b827d960cca69657e98bd89a9aa5623a847099d..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tools/convert-torchvision-to-d2.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import pickle as pkl
-import sys
-import torch
-
-"""
-Usage:
- # download one of the ResNet{18,34,50,101,152} models from torchvision:
- wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
- # run the conversion
- ./convert-torchvision-to-d2.py r50.pth r50.pkl
-
- # Then, use r50.pkl with the following changes in config:
-
-MODEL:
- WEIGHTS: "/path/to/r50.pkl"
- PIXEL_MEAN: [123.675, 116.280, 103.530]
- PIXEL_STD: [58.395, 57.120, 57.375]
- RESNETS:
- DEPTH: 50
- STRIDE_IN_1X1: False
-INPUT:
- FORMAT: "RGB"
-
- These models typically produce slightly worse results than the
- pre-trained ResNets we use in official configs, which are the
- original ResNet models released by MSRA.
-"""
-
-if __name__ == "__main__":
- input = sys.argv[1]
-
- obj = torch.load(input, map_location="cpu")
-
- newmodel = {}
- for k in list(obj.keys()):
- old_k = k
- if "layer" not in k:
- k = "stem." + k
- for t in [1, 2, 3, 4]:
- k = k.replace("layer{}".format(t), "res{}".format(t + 1))
- for t in [1, 2, 3]:
- k = k.replace("bn{}".format(t), "conv{}.norm".format(t))
- k = k.replace("downsample.0", "shortcut")
- k = k.replace("downsample.1", "shortcut.norm")
- print(old_k, "->", k)
- newmodel[k] = obj.pop(old_k).detach().numpy()
-
- res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True}
-
- with open(sys.argv[2], "wb") as f:
- pkl.dump(res, f)
- if obj:
- print("Unconverted keys:", obj.keys())
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/bricks/conv_module.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/bricks/conv_module.py
deleted file mode 100644
index e60e7e62245071c77b652093fddebff3948d7c3e..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/cnn/bricks/conv_module.py
+++ /dev/null
@@ -1,206 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import torch.nn as nn
-
-from annotator.uniformer.mmcv.utils import _BatchNorm, _InstanceNorm
-from ..utils import constant_init, kaiming_init
-from .activation import build_activation_layer
-from .conv import build_conv_layer
-from .norm import build_norm_layer
-from .padding import build_padding_layer
-from .registry import PLUGIN_LAYERS
-
-
-@PLUGIN_LAYERS.register_module()
-class ConvModule(nn.Module):
- """A conv block that bundles conv/norm/activation layers.
-
- This block simplifies the usage of convolution layers, which are commonly
- used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
- It is based upon three build methods: `build_conv_layer()`,
- `build_norm_layer()` and `build_activation_layer()`.
-
- Besides, we add some additional features in this module.
- 1. Automatically set `bias` of the conv layer.
- 2. Spectral norm is supported.
- 3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
- supports zero and circular padding, and we add "reflect" padding mode.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- Same as that in ``nn._ConvNd``.
- out_channels (int): Number of channels produced by the convolution.
- Same as that in ``nn._ConvNd``.
- kernel_size (int | tuple[int]): Size of the convolving kernel.
- Same as that in ``nn._ConvNd``.
- stride (int | tuple[int]): Stride of the convolution.
- Same as that in ``nn._ConvNd``.
- padding (int | tuple[int]): Zero-padding added to both sides of
- the input. Same as that in ``nn._ConvNd``.
- dilation (int | tuple[int]): Spacing between kernel elements.
- Same as that in ``nn._ConvNd``.
- groups (int): Number of blocked connections from input channels to
- output channels. Same as that in ``nn._ConvNd``.
- bias (bool | str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise
- False. Default: "auto".
- conv_cfg (dict): Config dict for convolution layer. Default: None,
- which means using conv2d.
- norm_cfg (dict): Config dict for normalization layer. Default: None.
- act_cfg (dict): Config dict for activation layer.
- Default: dict(type='ReLU').
- inplace (bool): Whether to use inplace mode for activation.
- Default: True.
- with_spectral_norm (bool): Whether use spectral norm in conv module.
- Default: False.
- padding_mode (str): If the `padding_mode` has not been supported by
- current `Conv2d` in PyTorch, we will use our own padding layer
- instead. Currently, we support ['zeros', 'circular'] with official
- implementation and ['reflect'] with our own implementation.
- Default: 'zeros'.
- order (tuple[str]): The order of conv/norm/activation layers. It is a
- sequence of "conv", "norm" and "act". Common examples are
- ("conv", "norm", "act") and ("act", "conv", "norm").
- Default: ('conv', 'norm', 'act').
- """
-
- _abbr_ = 'conv_block'
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias='auto',
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- inplace=True,
- with_spectral_norm=False,
- padding_mode='zeros',
- order=('conv', 'norm', 'act')):
- super(ConvModule, self).__init__()
- assert conv_cfg is None or isinstance(conv_cfg, dict)
- assert norm_cfg is None or isinstance(norm_cfg, dict)
- assert act_cfg is None or isinstance(act_cfg, dict)
- official_padding_mode = ['zeros', 'circular']
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.act_cfg = act_cfg
- self.inplace = inplace
- self.with_spectral_norm = with_spectral_norm
- self.with_explicit_padding = padding_mode not in official_padding_mode
- self.order = order
- assert isinstance(self.order, tuple) and len(self.order) == 3
- assert set(order) == set(['conv', 'norm', 'act'])
-
- self.with_norm = norm_cfg is not None
- self.with_activation = act_cfg is not None
- # if the conv layer is before a norm layer, bias is unnecessary.
- if bias == 'auto':
- bias = not self.with_norm
- self.with_bias = bias
-
- if self.with_explicit_padding:
- pad_cfg = dict(type=padding_mode)
- self.padding_layer = build_padding_layer(pad_cfg, padding)
-
- # reset padding to 0 for conv module
- conv_padding = 0 if self.with_explicit_padding else padding
- # build convolution layer
- self.conv = build_conv_layer(
- conv_cfg,
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=conv_padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
- # export the attributes of self.conv to a higher level for convenience
- self.in_channels = self.conv.in_channels
- self.out_channels = self.conv.out_channels
- self.kernel_size = self.conv.kernel_size
- self.stride = self.conv.stride
- self.padding = padding
- self.dilation = self.conv.dilation
- self.transposed = self.conv.transposed
- self.output_padding = self.conv.output_padding
- self.groups = self.conv.groups
-
- if self.with_spectral_norm:
- self.conv = nn.utils.spectral_norm(self.conv)
-
- # build normalization layers
- if self.with_norm:
- # norm layer is after conv layer
- if order.index('norm') > order.index('conv'):
- norm_channels = out_channels
- else:
- norm_channels = in_channels
- self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels)
- self.add_module(self.norm_name, norm)
- if self.with_bias:
- if isinstance(norm, (_BatchNorm, _InstanceNorm)):
- warnings.warn(
- 'Unnecessary conv bias before batch/instance norm')
- else:
- self.norm_name = None
-
- # build activation layer
- if self.with_activation:
- act_cfg_ = act_cfg.copy()
- # nn.Tanh has no 'inplace' argument
- if act_cfg_['type'] not in [
- 'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish'
- ]:
- act_cfg_.setdefault('inplace', inplace)
- self.activate = build_activation_layer(act_cfg_)
-
- # Use msra init by default
- self.init_weights()
-
- @property
- def norm(self):
- if self.norm_name:
- return getattr(self, self.norm_name)
- else:
- return None
-
- def init_weights(self):
- # 1. It is mainly for customized conv layers with their own
- # initialization manners by calling their own ``init_weights()``,
- # and we do not want ConvModule to override the initialization.
- # 2. For customized conv layers without their own initialization
- # manners (that is, they don't have their own ``init_weights()``)
- # and PyTorch's conv layers, they will be initialized by
- # this method with default ``kaiming_init``.
- # Note: For PyTorch's conv layers, they will be overwritten by our
- # initialization implementation using default ``kaiming_init``.
- if not hasattr(self.conv, 'init_weights'):
- if self.with_activation and self.act_cfg['type'] == 'LeakyReLU':
- nonlinearity = 'leaky_relu'
- a = self.act_cfg.get('negative_slope', 0.01)
- else:
- nonlinearity = 'relu'
- a = 0
- kaiming_init(self.conv, a=a, nonlinearity=nonlinearity)
- if self.with_norm:
- constant_init(self.norm, 1, bias=0)
-
- def forward(self, x, activate=True, norm=True):
- for layer in self.order:
- if layer == 'conv':
- if self.with_explicit_padding:
- x = self.padding_layer(x)
- x = self.conv(x)
- elif layer == 'norm' and norm and self.with_norm:
- x = self.norm(x)
- elif layer == 'act' and activate and self.with_activation:
- x = self.activate(x)
- return x
diff --git a/spaces/PaddlePaddle/stylepro_artistic/README.md b/spaces/PaddlePaddle/stylepro_artistic/README.md
deleted file mode 100644
index bd6520904b538f296324d3edffb0638d01cb1428..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/stylepro_artistic/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stylepro_artistic
-emoji: 🌖
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 2.8.9
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Politrees/RVC_V2_Huggingface_Version/README.md b/spaces/Politrees/RVC_V2_Huggingface_Version/README.md
deleted file mode 100644
index 9cb518590fc64557b6d76c297dedb3bb75e3b3a9..0000000000000000000000000000000000000000
--- a/spaces/Politrees/RVC_V2_Huggingface_Version/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-title: RVC V2
-emoji: 💻
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: lgpl-3.0
----
-
-## 🔧 Pre-requisites
-
-Before running the project, you must have the following tool installed on your machine:
-* [Python v3.8.0](https://www.python.org/downloads/release/python-380/)
-
-Also, you will need to clone the repository:
-
-```bash
-# Clone the repository
-git clone https://huggingface.co/spaces/mateuseap/magic-vocals/
-# Enter in the root directory
-cd magic-vocals
-```
-
-## 🚀 How to run
-
-After you've cloned the repository and entered in the root directory, run the following commands:
-
-```bash
-# Create and activate a Virtual Environment (make sure you're using Python v3.8.0 to do it)
-python -m venv venv
-. venv/bin/activate
-
-# Change mode and execute a shell script to configure and run the application
-chmod +x run.sh
-./run.sh
-```
-
-After the shell script executes everything, the application will be running at http://127.0.0.1:7860! Open up the link in a browser to use the app:
-
-
-
-**You only need to execute the `run.sh` one time**, once you've executed it one time, you just need to activate the virtual environment and run the command below to start the app again:
-
-```bash
-python app.py
-```
-
-**THE `run.sh` IS SUPPORTED BY THE FOLLOWING OPERATING SYSTEMS:**
-
-
-| OS | Supported |
-|-----------|:---------:|
-| `Windows` | ❌ |
-| `Ubuntu` | ✅ |
\ No newline at end of file
diff --git a/spaces/RMXK/RVC_HFF/infer/modules/ipex/gradscaler.py b/spaces/RMXK/RVC_HFF/infer/modules/ipex/gradscaler.py
deleted file mode 100644
index 3c265ddb37453f02870afb481360c9cc30b05d81..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/modules/ipex/gradscaler.py
+++ /dev/null
@@ -1,179 +0,0 @@
-from collections import defaultdict
-import torch
-import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-import intel_extension_for_pytorch._C as core # pylint: disable=import-error, unused-import
-
-# pylint: disable=protected-access, missing-function-docstring, line-too-long
-
-OptState = ipex.cpu.autocast._grad_scaler.OptState
-_MultiDeviceReplicator = ipex.cpu.autocast._grad_scaler._MultiDeviceReplicator
-_refresh_per_optimizer_state = ipex.cpu.autocast._grad_scaler._refresh_per_optimizer_state
-
-def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16): # pylint: disable=unused-argument
- per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
- per_device_found_inf = _MultiDeviceReplicator(found_inf)
-
- # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
- # There could be hundreds of grads, so we'd like to iterate through them just once.
- # However, we don't know their devices or dtypes in advance.
-
- # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
- # Google says mypy struggles with defaultdicts type annotations.
- per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list)) # type: ignore[var-annotated]
- # sync grad to master weight
- if hasattr(optimizer, "sync_grad"):
- optimizer.sync_grad()
- with torch.no_grad():
- for group in optimizer.param_groups:
- for param in group["params"]:
- if param.grad is None:
- continue
- if (not allow_fp16) and param.grad.dtype == torch.float16:
- raise ValueError("Attempting to unscale FP16 gradients.")
- if param.grad.is_sparse:
- # is_coalesced() == False means the sparse grad has values with duplicate indices.
- # coalesce() deduplicates indices and adds all values that have the same index.
- # For scaled fp16 values, there's a good chance coalescing will cause overflow,
- # so we should check the coalesced _values().
- if param.grad.dtype is torch.float16:
- param.grad = param.grad.coalesce()
- to_unscale = param.grad._values()
- else:
- to_unscale = param.grad
-
- # -: is there a way to split by device and dtype without appending in the inner loop?
- to_unscale = to_unscale.to("cpu")
- per_device_and_dtype_grads[to_unscale.device][
- to_unscale.dtype
- ].append(to_unscale)
-
- for _, per_dtype_grads in per_device_and_dtype_grads.items():
- for grads in per_dtype_grads.values():
- core._amp_foreach_non_finite_check_and_unscale_(
- grads,
- per_device_found_inf.get("cpu"),
- per_device_inv_scale.get("cpu"),
- )
-
- return per_device_found_inf._per_device_tensors
-
-def unscale_(self, optimizer):
- """
- Divides ("unscales") the optimizer's gradient tensors by the scale factor.
- :meth:`unscale_` is optional, serving cases where you need to
- :ref:`modify or inspect gradients<working-with-unscaled-gradients>`
- between the backward pass(es) and :meth:`step`.
- If :meth:`unscale_` is not called explicitly, gradients will be unscaled automatically during :meth:`step`.
- Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
- ...
- scaler.scale(loss).backward()
- scaler.unscale_(optimizer)
- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
- scaler.step(optimizer)
- scaler.update()
- Args:
- optimizer (torch.optim.Optimizer): Optimizer that owns the gradients to be unscaled.
- .. warning::
- :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
- and only after all gradients for that optimizer's assigned parameters have been accumulated.
- Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
- .. warning::
- :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
- """
- if not self._enabled:
- return
-
- self._check_scale_growth_tracker("unscale_")
-
- optimizer_state = self._per_optimizer_states[id(optimizer)]
-
- if optimizer_state["stage"] is OptState.UNSCALED: # pylint: disable=no-else-raise
- raise RuntimeError(
- "unscale_() has already been called on this optimizer since the last update()."
- )
- elif optimizer_state["stage"] is OptState.STEPPED:
- raise RuntimeError("unscale_() is being called after step().")
-
- # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
- assert self._scale is not None
- inv_scale = self._scale.to("cpu").double().reciprocal().float().to(self._scale.device)
- found_inf = torch.full(
- (1,), 0.0, dtype=torch.float32, device=self._scale.device
- )
-
- optimizer_state["found_inf_per_device"] = self._unscale_grads_(
- optimizer, inv_scale, found_inf, False
- )
- optimizer_state["stage"] = OptState.UNSCALED
-
-def update(self, new_scale=None):
- """
- Updates the scale factor.
- If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
- to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
- the scale is multiplied by ``growth_factor`` to increase it.
- Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
- used directly, it's used to fill GradScaler's internal scale tensor. So if
- ``new_scale`` was a tensor, later in-place changes to that tensor will not further
- affect the scale GradScaler uses internally.)
- Args:
- new_scale (float or :class:`torch.FloatTensor`, optional, default=None): New scale factor.
- .. warning::
- :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
- been invoked for all optimizers used this iteration.
- """
- if not self._enabled:
- return
-
- _scale, _growth_tracker = self._check_scale_growth_tracker("update")
-
- if new_scale is not None:
- # Accept a new user-defined scale.
- if isinstance(new_scale, float):
- self._scale.fill_(new_scale) # type: ignore[union-attr]
- else:
- reason = "new_scale should be a float or a 1-element torch.FloatTensor with requires_grad=False."
- assert isinstance(new_scale, torch.FloatTensor), reason # type: ignore[attr-defined]
- assert new_scale.numel() == 1, reason
- assert new_scale.requires_grad is False, reason
- self._scale.copy_(new_scale) # type: ignore[union-attr]
- else:
- # Consume shared inf/nan data collected from optimizers to update the scale.
- # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
- found_infs = [
- found_inf.to(device="cpu", non_blocking=True)
- for state in self._per_optimizer_states.values()
- for found_inf in state["found_inf_per_device"].values()
- ]
-
- assert len(found_infs) > 0, "No inf checks were recorded prior to update."
-
- found_inf_combined = found_infs[0]
- if len(found_infs) > 1:
- for i in range(1, len(found_infs)):
- found_inf_combined += found_infs[i]
-
- to_device = _scale.device
- _scale = _scale.to("cpu")
- _growth_tracker = _growth_tracker.to("cpu")
-
- core._amp_update_scale_(
- _scale,
- _growth_tracker,
- found_inf_combined,
- self._growth_factor,
- self._backoff_factor,
- self._growth_interval,
- )
-
- _scale = _scale.to(to_device)
- _growth_tracker = _growth_tracker.to(to_device)
- # To prepare for next iteration, clear the data collected from optimizers this iteration.
- self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
-
-def gradscaler_init():
- torch.xpu.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
- torch.xpu.amp.GradScaler._unscale_grads_ = _unscale_grads_
- torch.xpu.amp.GradScaler.unscale_ = unscale_
- torch.xpu.amp.GradScaler.update = update
- return torch.xpu.amp.GradScaler
\ No newline at end of file
diff --git a/spaces/Rakot2223/faster-whisper-webui/app-local.py b/spaces/Rakot2223/faster-whisper-webui/app-local.py
deleted file mode 100644
index c7717d096ca5f95177f0dba03cd62ca729bae9f3..0000000000000000000000000000000000000000
--- a/spaces/Rakot2223/faster-whisper-webui/app-local.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Run the app with no audio file restrictions
-from app import create_ui
-from src.config import ApplicationConfig
-
-create_ui(ApplicationConfig.create_default(input_audio_max_duration=-1))
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/securetransport.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/securetransport.py
deleted file mode 100644
index 4a06bc69d5c850fa9f7c4861bc6b3acca3905056..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/securetransport.py
+++ /dev/null
@@ -1,921 +0,0 @@
-"""
-SecureTranport support for urllib3 via ctypes.
-
-This makes platform-native TLS available to urllib3 users on macOS without the
-use of a compiler. This is an important feature because the Python Package
-Index is moving to become a TLSv1.2-or-higher server, and the default OpenSSL
-that ships with macOS is not capable of doing TLSv1.2. The only way to resolve
-this is to give macOS users an alternative solution to the problem, and that
-solution is to use SecureTransport.
-
-We use ctypes here because this solution must not require a compiler. That's
-because pip is not allowed to require a compiler either.
-
-This is not intended to be a seriously long-term solution to this problem.
-The hope is that PEP 543 will eventually solve this issue for us, at which
-point we can retire this contrib module. But in the short term, we need to
-solve the impending tire fire that is Python on Mac without this kind of
-contrib module. So...here we are.
-
-To use this module, simply import and inject it::
-
- import pip._vendor.urllib3.contrib.securetransport as securetransport
- securetransport.inject_into_urllib3()
-
-Happy TLSing!
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
-.. code-block::
-
- Copyright (c) 2015-2016 Will Bond <will@wbond.net>
-
- 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.
-"""
-from __future__ import absolute_import
-
-import contextlib
-import ctypes
-import errno
-import os.path
-import shutil
-import socket
-import ssl
-import struct
-import threading
-import weakref
-
-from pip._vendor import six
-
-from .. import util
-from ..util.ssl_ import PROTOCOL_TLS_CLIENT
-from ._securetransport.bindings import CoreFoundation, Security, SecurityConst
-from ._securetransport.low_level import (
- _assert_no_error,
- _build_tls_unknown_ca_alert,
- _cert_array_from_pem,
- _create_cfstring_array,
- _load_client_cert_chain,
- _temporary_keychain,
-)
-
-try: # Platform-specific: Python 2
- from socket import _fileobject
-except ImportError: # Platform-specific: Python 3
- _fileobject = None
- from ..packages.backports.makefile import backport_makefile
-
-__all__ = ["inject_into_urllib3", "extract_from_urllib3"]
-
-# SNI always works
-HAS_SNI = True
-
-orig_util_HAS_SNI = util.HAS_SNI
-orig_util_SSLContext = util.ssl_.SSLContext
-
-# This dictionary is used by the read callback to obtain a handle to the
-# calling wrapped socket. This is a pretty silly approach, but for now it'll
-# do. I feel like I should be able to smuggle a handle to the wrapped socket
-# directly in the SSLConnectionRef, but for now this approach will work I
-# guess.
-#
-# We need to lock around this structure for inserts, but we don't do it for
-# reads/writes in the callbacks. The reasoning here goes as follows:
-#
-# 1. It is not possible to call into the callbacks before the dictionary is
-# populated, so once in the callback the id must be in the dictionary.
-# 2. The callbacks don't mutate the dictionary, they only read from it, and
-# so cannot conflict with any of the insertions.
-#
-# This is good: if we had to lock in the callbacks we'd drastically slow down
-# the performance of this code.
-_connection_refs = weakref.WeakValueDictionary()
-_connection_ref_lock = threading.Lock()
-
-# Limit writes to 16kB. This is OpenSSL's limit, but we'll cargo-cult it over
-# for no better reason than we need *a* limit, and this one is right there.
-SSL_WRITE_BLOCKSIZE = 16384
-
-# This is our equivalent of util.ssl_.DEFAULT_CIPHERS, but expanded out to
-# individual cipher suites. We need to do this because this is how
-# SecureTransport wants them.
-CIPHER_SUITES = [
- SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384,
- SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256,
- SecurityConst.TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384,
- SecurityConst.TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256,
- SecurityConst.TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256,
- SecurityConst.TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256,
- SecurityConst.TLS_DHE_RSA_WITH_AES_256_GCM_SHA384,
- SecurityConst.TLS_DHE_RSA_WITH_AES_128_GCM_SHA256,
- SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384,
- SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA,
- SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256,
- SecurityConst.TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA,
- SecurityConst.TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384,
- SecurityConst.TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA,
- SecurityConst.TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256,
- SecurityConst.TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA,
- SecurityConst.TLS_DHE_RSA_WITH_AES_256_CBC_SHA256,
- SecurityConst.TLS_DHE_RSA_WITH_AES_256_CBC_SHA,
- SecurityConst.TLS_DHE_RSA_WITH_AES_128_CBC_SHA256,
- SecurityConst.TLS_DHE_RSA_WITH_AES_128_CBC_SHA,
- SecurityConst.TLS_AES_256_GCM_SHA384,
- SecurityConst.TLS_AES_128_GCM_SHA256,
- SecurityConst.TLS_RSA_WITH_AES_256_GCM_SHA384,
- SecurityConst.TLS_RSA_WITH_AES_128_GCM_SHA256,
- SecurityConst.TLS_AES_128_CCM_8_SHA256,
- SecurityConst.TLS_AES_128_CCM_SHA256,
- SecurityConst.TLS_RSA_WITH_AES_256_CBC_SHA256,
- SecurityConst.TLS_RSA_WITH_AES_128_CBC_SHA256,
- SecurityConst.TLS_RSA_WITH_AES_256_CBC_SHA,
- SecurityConst.TLS_RSA_WITH_AES_128_CBC_SHA,
-]
-
-# Basically this is simple: for PROTOCOL_SSLv23 we turn it into a low of
-# TLSv1 and a high of TLSv1.2. For everything else, we pin to that version.
-# TLSv1 to 1.2 are supported on macOS 10.8+
-_protocol_to_min_max = {
- util.PROTOCOL_TLS: (SecurityConst.kTLSProtocol1, SecurityConst.kTLSProtocol12),
- PROTOCOL_TLS_CLIENT: (SecurityConst.kTLSProtocol1, SecurityConst.kTLSProtocol12),
-}
-
-if hasattr(ssl, "PROTOCOL_SSLv2"):
- _protocol_to_min_max[ssl.PROTOCOL_SSLv2] = (
- SecurityConst.kSSLProtocol2,
- SecurityConst.kSSLProtocol2,
- )
-if hasattr(ssl, "PROTOCOL_SSLv3"):
- _protocol_to_min_max[ssl.PROTOCOL_SSLv3] = (
- SecurityConst.kSSLProtocol3,
- SecurityConst.kSSLProtocol3,
- )
-if hasattr(ssl, "PROTOCOL_TLSv1"):
- _protocol_to_min_max[ssl.PROTOCOL_TLSv1] = (
- SecurityConst.kTLSProtocol1,
- SecurityConst.kTLSProtocol1,
- )
-if hasattr(ssl, "PROTOCOL_TLSv1_1"):
- _protocol_to_min_max[ssl.PROTOCOL_TLSv1_1] = (
- SecurityConst.kTLSProtocol11,
- SecurityConst.kTLSProtocol11,
- )
-if hasattr(ssl, "PROTOCOL_TLSv1_2"):
- _protocol_to_min_max[ssl.PROTOCOL_TLSv1_2] = (
- SecurityConst.kTLSProtocol12,
- SecurityConst.kTLSProtocol12,
- )
-
-
-def inject_into_urllib3():
- """
- Monkey-patch urllib3 with SecureTransport-backed SSL-support.
- """
- util.SSLContext = SecureTransportContext
- util.ssl_.SSLContext = SecureTransportContext
- util.HAS_SNI = HAS_SNI
- util.ssl_.HAS_SNI = HAS_SNI
- util.IS_SECURETRANSPORT = True
- util.ssl_.IS_SECURETRANSPORT = True
-
-
-def extract_from_urllib3():
- """
- Undo monkey-patching by :func:`inject_into_urllib3`.
- """
- util.SSLContext = orig_util_SSLContext
- util.ssl_.SSLContext = orig_util_SSLContext
- util.HAS_SNI = orig_util_HAS_SNI
- util.ssl_.HAS_SNI = orig_util_HAS_SNI
- util.IS_SECURETRANSPORT = False
- util.ssl_.IS_SECURETRANSPORT = False
-
-
-def _read_callback(connection_id, data_buffer, data_length_pointer):
- """
- SecureTransport read callback. This is called by ST to request that data
- be returned from the socket.
- """
- wrapped_socket = None
- try:
- wrapped_socket = _connection_refs.get(connection_id)
- if wrapped_socket is None:
- return SecurityConst.errSSLInternal
- base_socket = wrapped_socket.socket
-
- requested_length = data_length_pointer[0]
-
- timeout = wrapped_socket.gettimeout()
- error = None
- read_count = 0
-
- try:
- while read_count < requested_length:
- if timeout is None or timeout >= 0:
- if not util.wait_for_read(base_socket, timeout):
- raise socket.error(errno.EAGAIN, "timed out")
-
- remaining = requested_length - read_count
- buffer = (ctypes.c_char * remaining).from_address(
- data_buffer + read_count
- )
- chunk_size = base_socket.recv_into(buffer, remaining)
- read_count += chunk_size
- if not chunk_size:
- if not read_count:
- return SecurityConst.errSSLClosedGraceful
- break
- except (socket.error) as e:
- error = e.errno
-
- if error is not None and error != errno.EAGAIN:
- data_length_pointer[0] = read_count
- if error == errno.ECONNRESET or error == errno.EPIPE:
- return SecurityConst.errSSLClosedAbort
- raise
-
- data_length_pointer[0] = read_count
-
- if read_count != requested_length:
- return SecurityConst.errSSLWouldBlock
-
- return 0
- except Exception as e:
- if wrapped_socket is not None:
- wrapped_socket._exception = e
- return SecurityConst.errSSLInternal
-
-
-def _write_callback(connection_id, data_buffer, data_length_pointer):
- """
- SecureTransport write callback. This is called by ST to request that data
- actually be sent on the network.
- """
- wrapped_socket = None
- try:
- wrapped_socket = _connection_refs.get(connection_id)
- if wrapped_socket is None:
- return SecurityConst.errSSLInternal
- base_socket = wrapped_socket.socket
-
- bytes_to_write = data_length_pointer[0]
- data = ctypes.string_at(data_buffer, bytes_to_write)
-
- timeout = wrapped_socket.gettimeout()
- error = None
- sent = 0
-
- try:
- while sent < bytes_to_write:
- if timeout is None or timeout >= 0:
- if not util.wait_for_write(base_socket, timeout):
- raise socket.error(errno.EAGAIN, "timed out")
- chunk_sent = base_socket.send(data)
- sent += chunk_sent
-
- # This has some needless copying here, but I'm not sure there's
- # much value in optimising this data path.
- data = data[chunk_sent:]
- except (socket.error) as e:
- error = e.errno
-
- if error is not None and error != errno.EAGAIN:
- data_length_pointer[0] = sent
- if error == errno.ECONNRESET or error == errno.EPIPE:
- return SecurityConst.errSSLClosedAbort
- raise
-
- data_length_pointer[0] = sent
-
- if sent != bytes_to_write:
- return SecurityConst.errSSLWouldBlock
-
- return 0
- except Exception as e:
- if wrapped_socket is not None:
- wrapped_socket._exception = e
- return SecurityConst.errSSLInternal
-
-
-# We need to keep these two objects references alive: if they get GC'd while
-# in use then SecureTransport could attempt to call a function that is in freed
-# memory. That would be...uh...bad. Yeah, that's the word. Bad.
-_read_callback_pointer = Security.SSLReadFunc(_read_callback)
-_write_callback_pointer = Security.SSLWriteFunc(_write_callback)
-
-
-class WrappedSocket(object):
- """
- API-compatibility wrapper for Python's OpenSSL wrapped socket object.
-
- Note: _makefile_refs, _drop(), and _reuse() are needed for the garbage
- collector of PyPy.
- """
-
- def __init__(self, socket):
- self.socket = socket
- self.context = None
- self._makefile_refs = 0
- self._closed = False
- self._exception = None
- self._keychain = None
- self._keychain_dir = None
- self._client_cert_chain = None
-
- # We save off the previously-configured timeout and then set it to
- # zero. This is done because we use select and friends to handle the
- # timeouts, but if we leave the timeout set on the lower socket then
- # Python will "kindly" call select on that socket again for us. Avoid
- # that by forcing the timeout to zero.
- self._timeout = self.socket.gettimeout()
- self.socket.settimeout(0)
-
- @contextlib.contextmanager
- def _raise_on_error(self):
- """
- A context manager that can be used to wrap calls that do I/O from
- SecureTransport. If any of the I/O callbacks hit an exception, this
- context manager will correctly propagate the exception after the fact.
- This avoids silently swallowing those exceptions.
-
- It also correctly forces the socket closed.
- """
- self._exception = None
-
- # We explicitly don't catch around this yield because in the unlikely
- # event that an exception was hit in the block we don't want to swallow
- # it.
- yield
- if self._exception is not None:
- exception, self._exception = self._exception, None
- self.close()
- raise exception
-
- def _set_ciphers(self):
- """
- Sets up the allowed ciphers. By default this matches the set in
- util.ssl_.DEFAULT_CIPHERS, at least as supported by macOS. This is done
- custom and doesn't allow changing at this time, mostly because parsing
- OpenSSL cipher strings is going to be a freaking nightmare.
- """
- ciphers = (Security.SSLCipherSuite * len(CIPHER_SUITES))(*CIPHER_SUITES)
- result = Security.SSLSetEnabledCiphers(
- self.context, ciphers, len(CIPHER_SUITES)
- )
- _assert_no_error(result)
-
- def _set_alpn_protocols(self, protocols):
- """
- Sets up the ALPN protocols on the context.
- """
- if not protocols:
- return
- protocols_arr = _create_cfstring_array(protocols)
- try:
- result = Security.SSLSetALPNProtocols(self.context, protocols_arr)
- _assert_no_error(result)
- finally:
- CoreFoundation.CFRelease(protocols_arr)
-
- def _custom_validate(self, verify, trust_bundle):
- """
- Called when we have set custom validation. We do this in two cases:
- first, when cert validation is entirely disabled; and second, when
- using a custom trust DB.
- Raises an SSLError if the connection is not trusted.
- """
- # If we disabled cert validation, just say: cool.
- if not verify:
- return
-
- successes = (
- SecurityConst.kSecTrustResultUnspecified,
- SecurityConst.kSecTrustResultProceed,
- )
- try:
- trust_result = self._evaluate_trust(trust_bundle)
- if trust_result in successes:
- return
- reason = "error code: %d" % (trust_result,)
- except Exception as e:
- # Do not trust on error
- reason = "exception: %r" % (e,)
-
- # SecureTransport does not send an alert nor shuts down the connection.
- rec = _build_tls_unknown_ca_alert(self.version())
- self.socket.sendall(rec)
- # close the connection immediately
- # l_onoff = 1, activate linger
- # l_linger = 0, linger for 0 seoncds
- opts = struct.pack("ii", 1, 0)
- self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER, opts)
- self.close()
- raise ssl.SSLError("certificate verify failed, %s" % reason)
-
- def _evaluate_trust(self, trust_bundle):
- # We want data in memory, so load it up.
- if os.path.isfile(trust_bundle):
- with open(trust_bundle, "rb") as f:
- trust_bundle = f.read()
-
- cert_array = None
- trust = Security.SecTrustRef()
-
- try:
- # Get a CFArray that contains the certs we want.
- cert_array = _cert_array_from_pem(trust_bundle)
-
- # Ok, now the hard part. We want to get the SecTrustRef that ST has
- # created for this connection, shove our CAs into it, tell ST to
- # ignore everything else it knows, and then ask if it can build a
- # chain. This is a buuuunch of code.
- result = Security.SSLCopyPeerTrust(self.context, ctypes.byref(trust))
- _assert_no_error(result)
- if not trust:
- raise ssl.SSLError("Failed to copy trust reference")
-
- result = Security.SecTrustSetAnchorCertificates(trust, cert_array)
- _assert_no_error(result)
-
- result = Security.SecTrustSetAnchorCertificatesOnly(trust, True)
- _assert_no_error(result)
-
- trust_result = Security.SecTrustResultType()
- result = Security.SecTrustEvaluate(trust, ctypes.byref(trust_result))
- _assert_no_error(result)
- finally:
- if trust:
- CoreFoundation.CFRelease(trust)
-
- if cert_array is not None:
- CoreFoundation.CFRelease(cert_array)
-
- return trust_result.value
-
- def handshake(
- self,
- server_hostname,
- verify,
- trust_bundle,
- min_version,
- max_version,
- client_cert,
- client_key,
- client_key_passphrase,
- alpn_protocols,
- ):
- """
- Actually performs the TLS handshake. This is run automatically by
- wrapped socket, and shouldn't be needed in user code.
- """
- # First, we do the initial bits of connection setup. We need to create
- # a context, set its I/O funcs, and set the connection reference.
- self.context = Security.SSLCreateContext(
- None, SecurityConst.kSSLClientSide, SecurityConst.kSSLStreamType
- )
- result = Security.SSLSetIOFuncs(
- self.context, _read_callback_pointer, _write_callback_pointer
- )
- _assert_no_error(result)
-
- # Here we need to compute the handle to use. We do this by taking the
- # id of self modulo 2**31 - 1. If this is already in the dictionary, we
- # just keep incrementing by one until we find a free space.
- with _connection_ref_lock:
- handle = id(self) % 2147483647
- while handle in _connection_refs:
- handle = (handle + 1) % 2147483647
- _connection_refs[handle] = self
-
- result = Security.SSLSetConnection(self.context, handle)
- _assert_no_error(result)
-
- # If we have a server hostname, we should set that too.
- if server_hostname:
- if not isinstance(server_hostname, bytes):
- server_hostname = server_hostname.encode("utf-8")
-
- result = Security.SSLSetPeerDomainName(
- self.context, server_hostname, len(server_hostname)
- )
- _assert_no_error(result)
-
- # Setup the ciphers.
- self._set_ciphers()
-
- # Setup the ALPN protocols.
- self._set_alpn_protocols(alpn_protocols)
-
- # Set the minimum and maximum TLS versions.
- result = Security.SSLSetProtocolVersionMin(self.context, min_version)
- _assert_no_error(result)
-
- result = Security.SSLSetProtocolVersionMax(self.context, max_version)
- _assert_no_error(result)
-
- # If there's a trust DB, we need to use it. We do that by telling
- # SecureTransport to break on server auth. We also do that if we don't
- # want to validate the certs at all: we just won't actually do any
- # authing in that case.
- if not verify or trust_bundle is not None:
- result = Security.SSLSetSessionOption(
- self.context, SecurityConst.kSSLSessionOptionBreakOnServerAuth, True
- )
- _assert_no_error(result)
-
- # If there's a client cert, we need to use it.
- if client_cert:
- self._keychain, self._keychain_dir = _temporary_keychain()
- self._client_cert_chain = _load_client_cert_chain(
- self._keychain, client_cert, client_key
- )
- result = Security.SSLSetCertificate(self.context, self._client_cert_chain)
- _assert_no_error(result)
-
- while True:
- with self._raise_on_error():
- result = Security.SSLHandshake(self.context)
-
- if result == SecurityConst.errSSLWouldBlock:
- raise socket.timeout("handshake timed out")
- elif result == SecurityConst.errSSLServerAuthCompleted:
- self._custom_validate(verify, trust_bundle)
- continue
- else:
- _assert_no_error(result)
- break
-
- def fileno(self):
- return self.socket.fileno()
-
- # Copy-pasted from Python 3.5 source code
- def _decref_socketios(self):
- if self._makefile_refs > 0:
- self._makefile_refs -= 1
- if self._closed:
- self.close()
-
- def recv(self, bufsiz):
- buffer = ctypes.create_string_buffer(bufsiz)
- bytes_read = self.recv_into(buffer, bufsiz)
- data = buffer[:bytes_read]
- return data
-
- def recv_into(self, buffer, nbytes=None):
- # Read short on EOF.
- if self._closed:
- return 0
-
- if nbytes is None:
- nbytes = len(buffer)
-
- buffer = (ctypes.c_char * nbytes).from_buffer(buffer)
- processed_bytes = ctypes.c_size_t(0)
-
- with self._raise_on_error():
- result = Security.SSLRead(
- self.context, buffer, nbytes, ctypes.byref(processed_bytes)
- )
-
- # There are some result codes that we want to treat as "not always
- # errors". Specifically, those are errSSLWouldBlock,
- # errSSLClosedGraceful, and errSSLClosedNoNotify.
- if result == SecurityConst.errSSLWouldBlock:
- # If we didn't process any bytes, then this was just a time out.
- # However, we can get errSSLWouldBlock in situations when we *did*
- # read some data, and in those cases we should just read "short"
- # and return.
- if processed_bytes.value == 0:
- # Timed out, no data read.
- raise socket.timeout("recv timed out")
- elif result in (
- SecurityConst.errSSLClosedGraceful,
- SecurityConst.errSSLClosedNoNotify,
- ):
- # The remote peer has closed this connection. We should do so as
- # well. Note that we don't actually return here because in
- # principle this could actually be fired along with return data.
- # It's unlikely though.
- self.close()
- else:
- _assert_no_error(result)
-
- # Ok, we read and probably succeeded. We should return whatever data
- # was actually read.
- return processed_bytes.value
-
- def settimeout(self, timeout):
- self._timeout = timeout
-
- def gettimeout(self):
- return self._timeout
-
- def send(self, data):
- processed_bytes = ctypes.c_size_t(0)
-
- with self._raise_on_error():
- result = Security.SSLWrite(
- self.context, data, len(data), ctypes.byref(processed_bytes)
- )
-
- if result == SecurityConst.errSSLWouldBlock and processed_bytes.value == 0:
- # Timed out
- raise socket.timeout("send timed out")
- else:
- _assert_no_error(result)
-
- # We sent, and probably succeeded. Tell them how much we sent.
- return processed_bytes.value
-
- def sendall(self, data):
- total_sent = 0
- while total_sent < len(data):
- sent = self.send(data[total_sent : total_sent + SSL_WRITE_BLOCKSIZE])
- total_sent += sent
-
- def shutdown(self):
- with self._raise_on_error():
- Security.SSLClose(self.context)
-
- def close(self):
- # TODO: should I do clean shutdown here? Do I have to?
- if self._makefile_refs < 1:
- self._closed = True
- if self.context:
- CoreFoundation.CFRelease(self.context)
- self.context = None
- if self._client_cert_chain:
- CoreFoundation.CFRelease(self._client_cert_chain)
- self._client_cert_chain = None
- if self._keychain:
- Security.SecKeychainDelete(self._keychain)
- CoreFoundation.CFRelease(self._keychain)
- shutil.rmtree(self._keychain_dir)
- self._keychain = self._keychain_dir = None
- return self.socket.close()
- else:
- self._makefile_refs -= 1
-
- def getpeercert(self, binary_form=False):
- # Urgh, annoying.
- #
- # Here's how we do this:
- #
- # 1. Call SSLCopyPeerTrust to get hold of the trust object for this
- # connection.
- # 2. Call SecTrustGetCertificateAtIndex for index 0 to get the leaf.
- # 3. To get the CN, call SecCertificateCopyCommonName and process that
- # string so that it's of the appropriate type.
- # 4. To get the SAN, we need to do something a bit more complex:
- # a. Call SecCertificateCopyValues to get the data, requesting
- # kSecOIDSubjectAltName.
- # b. Mess about with this dictionary to try to get the SANs out.
- #
- # This is gross. Really gross. It's going to be a few hundred LoC extra
- # just to repeat something that SecureTransport can *already do*. So my
- # operating assumption at this time is that what we want to do is
- # instead to just flag to urllib3 that it shouldn't do its own hostname
- # validation when using SecureTransport.
- if not binary_form:
- raise ValueError("SecureTransport only supports dumping binary certs")
- trust = Security.SecTrustRef()
- certdata = None
- der_bytes = None
-
- try:
- # Grab the trust store.
- result = Security.SSLCopyPeerTrust(self.context, ctypes.byref(trust))
- _assert_no_error(result)
- if not trust:
- # Probably we haven't done the handshake yet. No biggie.
- return None
-
- cert_count = Security.SecTrustGetCertificateCount(trust)
- if not cert_count:
- # Also a case that might happen if we haven't handshaked.
- # Handshook? Handshaken?
- return None
-
- leaf = Security.SecTrustGetCertificateAtIndex(trust, 0)
- assert leaf
-
- # Ok, now we want the DER bytes.
- certdata = Security.SecCertificateCopyData(leaf)
- assert certdata
-
- data_length = CoreFoundation.CFDataGetLength(certdata)
- data_buffer = CoreFoundation.CFDataGetBytePtr(certdata)
- der_bytes = ctypes.string_at(data_buffer, data_length)
- finally:
- if certdata:
- CoreFoundation.CFRelease(certdata)
- if trust:
- CoreFoundation.CFRelease(trust)
-
- return der_bytes
-
- def version(self):
- protocol = Security.SSLProtocol()
- result = Security.SSLGetNegotiatedProtocolVersion(
- self.context, ctypes.byref(protocol)
- )
- _assert_no_error(result)
- if protocol.value == SecurityConst.kTLSProtocol13:
- raise ssl.SSLError("SecureTransport does not support TLS 1.3")
- elif protocol.value == SecurityConst.kTLSProtocol12:
- return "TLSv1.2"
- elif protocol.value == SecurityConst.kTLSProtocol11:
- return "TLSv1.1"
- elif protocol.value == SecurityConst.kTLSProtocol1:
- return "TLSv1"
- elif protocol.value == SecurityConst.kSSLProtocol3:
- return "SSLv3"
- elif protocol.value == SecurityConst.kSSLProtocol2:
- return "SSLv2"
- else:
- raise ssl.SSLError("Unknown TLS version: %r" % protocol)
-
- def _reuse(self):
- self._makefile_refs += 1
-
- def _drop(self):
- if self._makefile_refs < 1:
- self.close()
- else:
- self._makefile_refs -= 1
-
-
-if _fileobject: # Platform-specific: Python 2
-
- def makefile(self, mode, bufsize=-1):
- self._makefile_refs += 1
- return _fileobject(self, mode, bufsize, close=True)
-
-else: # Platform-specific: Python 3
-
- def makefile(self, mode="r", buffering=None, *args, **kwargs):
- # We disable buffering with SecureTransport because it conflicts with
- # the buffering that ST does internally (see issue #1153 for more).
- buffering = 0
- return backport_makefile(self, mode, buffering, *args, **kwargs)
-
-
-WrappedSocket.makefile = makefile
-
-
-class SecureTransportContext(object):
- """
- I am a wrapper class for the SecureTransport library, to translate the
- interface of the standard library ``SSLContext`` object to calls into
- SecureTransport.
- """
-
- def __init__(self, protocol):
- self._min_version, self._max_version = _protocol_to_min_max[protocol]
- self._options = 0
- self._verify = False
- self._trust_bundle = None
- self._client_cert = None
- self._client_key = None
- self._client_key_passphrase = None
- self._alpn_protocols = None
-
- @property
- def check_hostname(self):
- """
- SecureTransport cannot have its hostname checking disabled. For more,
- see the comment on getpeercert() in this file.
- """
- return True
-
- @check_hostname.setter
- def check_hostname(self, value):
- """
- SecureTransport cannot have its hostname checking disabled. For more,
- see the comment on getpeercert() in this file.
- """
- pass
-
- @property
- def options(self):
- # TODO: Well, crap.
- #
- # So this is the bit of the code that is the most likely to cause us
- # trouble. Essentially we need to enumerate all of the SSL options that
- # users might want to use and try to see if we can sensibly translate
- # them, or whether we should just ignore them.
- return self._options
-
- @options.setter
- def options(self, value):
- # TODO: Update in line with above.
- self._options = value
-
- @property
- def verify_mode(self):
- return ssl.CERT_REQUIRED if self._verify else ssl.CERT_NONE
-
- @verify_mode.setter
- def verify_mode(self, value):
- self._verify = True if value == ssl.CERT_REQUIRED else False
-
- def set_default_verify_paths(self):
- # So, this has to do something a bit weird. Specifically, what it does
- # is nothing.
- #
- # This means that, if we had previously had load_verify_locations
- # called, this does not undo that. We need to do that because it turns
- # out that the rest of the urllib3 code will attempt to load the
- # default verify paths if it hasn't been told about any paths, even if
- # the context itself was sometime earlier. We resolve that by just
- # ignoring it.
- pass
-
- def load_default_certs(self):
- return self.set_default_verify_paths()
-
- def set_ciphers(self, ciphers):
- # For now, we just require the default cipher string.
- if ciphers != util.ssl_.DEFAULT_CIPHERS:
- raise ValueError("SecureTransport doesn't support custom cipher strings")
-
- def load_verify_locations(self, cafile=None, capath=None, cadata=None):
- # OK, we only really support cadata and cafile.
- if capath is not None:
- raise ValueError("SecureTransport does not support cert directories")
-
- # Raise if cafile does not exist.
- if cafile is not None:
- with open(cafile):
- pass
-
- self._trust_bundle = cafile or cadata
-
- def load_cert_chain(self, certfile, keyfile=None, password=None):
- self._client_cert = certfile
- self._client_key = keyfile
- self._client_cert_passphrase = password
-
- def set_alpn_protocols(self, protocols):
- """
- Sets the ALPN protocols that will later be set on the context.
-
- Raises a NotImplementedError if ALPN is not supported.
- """
- if not hasattr(Security, "SSLSetALPNProtocols"):
- raise NotImplementedError(
- "SecureTransport supports ALPN only in macOS 10.12+"
- )
- self._alpn_protocols = [six.ensure_binary(p) for p in protocols]
-
- def wrap_socket(
- self,
- sock,
- server_side=False,
- do_handshake_on_connect=True,
- suppress_ragged_eofs=True,
- server_hostname=None,
- ):
- # So, what do we do here? Firstly, we assert some properties. This is a
- # stripped down shim, so there is some functionality we don't support.
- # See PEP 543 for the real deal.
- assert not server_side
- assert do_handshake_on_connect
- assert suppress_ragged_eofs
-
- # Ok, we're good to go. Now we want to create the wrapped socket object
- # and store it in the appropriate place.
- wrapped_socket = WrappedSocket(sock)
-
- # Now we can handshake
- wrapped_socket.handshake(
- server_hostname,
- self._verify,
- self._trust_bundle,
- self._min_version,
- self._max_version,
- self._client_cert,
- self._client_key,
- self._client_key_passphrase,
- self._alpn_protocols,
- )
- return wrapped_socket
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/testing.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/testing.py
deleted file mode 100644
index 84a0ef17078c99e5917db41e3dbaf035fe206d7c..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/pyparsing/testing.py
+++ /dev/null
@@ -1,331 +0,0 @@
-# testing.py
-
-from contextlib import contextmanager
-import typing
-
-from .core import (
- ParserElement,
- ParseException,
- Keyword,
- __diag__,
- __compat__,
-)
-
-
-class pyparsing_test:
- """
- namespace class for classes useful in writing unit tests
- """
-
- class reset_pyparsing_context:
- """
- Context manager to be used when writing unit tests that modify pyparsing config values:
- - packrat parsing
- - bounded recursion parsing
- - default whitespace characters.
- - default keyword characters
- - literal string auto-conversion class
- - __diag__ settings
-
- Example::
-
- with reset_pyparsing_context():
- # test that literals used to construct a grammar are automatically suppressed
- ParserElement.inlineLiteralsUsing(Suppress)
-
- term = Word(alphas) | Word(nums)
- group = Group('(' + term[...] + ')')
-
- # assert that the '()' characters are not included in the parsed tokens
- self.assertParseAndCheckList(group, "(abc 123 def)", ['abc', '123', 'def'])
-
- # after exiting context manager, literals are converted to Literal expressions again
- """
-
- def __init__(self):
- self._save_context = {}
-
- def save(self):
- self._save_context["default_whitespace"] = ParserElement.DEFAULT_WHITE_CHARS
- self._save_context["default_keyword_chars"] = Keyword.DEFAULT_KEYWORD_CHARS
-
- self._save_context[
- "literal_string_class"
- ] = ParserElement._literalStringClass
-
- self._save_context["verbose_stacktrace"] = ParserElement.verbose_stacktrace
-
- self._save_context["packrat_enabled"] = ParserElement._packratEnabled
- if ParserElement._packratEnabled:
- self._save_context[
- "packrat_cache_size"
- ] = ParserElement.packrat_cache.size
- else:
- self._save_context["packrat_cache_size"] = None
- self._save_context["packrat_parse"] = ParserElement._parse
- self._save_context[
- "recursion_enabled"
- ] = ParserElement._left_recursion_enabled
-
- self._save_context["__diag__"] = {
- name: getattr(__diag__, name) for name in __diag__._all_names
- }
-
- self._save_context["__compat__"] = {
- "collect_all_And_tokens": __compat__.collect_all_And_tokens
- }
-
- return self
-
- def restore(self):
- # reset pyparsing global state
- if (
- ParserElement.DEFAULT_WHITE_CHARS
- != self._save_context["default_whitespace"]
- ):
- ParserElement.set_default_whitespace_chars(
- self._save_context["default_whitespace"]
- )
-
- ParserElement.verbose_stacktrace = self._save_context["verbose_stacktrace"]
-
- Keyword.DEFAULT_KEYWORD_CHARS = self._save_context["default_keyword_chars"]
- ParserElement.inlineLiteralsUsing(
- self._save_context["literal_string_class"]
- )
-
- for name, value in self._save_context["__diag__"].items():
- (__diag__.enable if value else __diag__.disable)(name)
-
- ParserElement._packratEnabled = False
- if self._save_context["packrat_enabled"]:
- ParserElement.enable_packrat(self._save_context["packrat_cache_size"])
- else:
- ParserElement._parse = self._save_context["packrat_parse"]
- ParserElement._left_recursion_enabled = self._save_context[
- "recursion_enabled"
- ]
-
- __compat__.collect_all_And_tokens = self._save_context["__compat__"]
-
- return self
-
- def copy(self):
- ret = type(self)()
- ret._save_context.update(self._save_context)
- return ret
-
- def __enter__(self):
- return self.save()
-
- def __exit__(self, *args):
- self.restore()
-
- class TestParseResultsAsserts:
- """
- A mixin class to add parse results assertion methods to normal unittest.TestCase classes.
- """
-
- def assertParseResultsEquals(
- self, result, expected_list=None, expected_dict=None, msg=None
- ):
- """
- Unit test assertion to compare a :class:`ParseResults` object with an optional ``expected_list``,
- and compare any defined results names with an optional ``expected_dict``.
- """
- if expected_list is not None:
- self.assertEqual(expected_list, result.as_list(), msg=msg)
- if expected_dict is not None:
- self.assertEqual(expected_dict, result.as_dict(), msg=msg)
-
- def assertParseAndCheckList(
- self, expr, test_string, expected_list, msg=None, verbose=True
- ):
- """
- Convenience wrapper assert to test a parser element and input string, and assert that
- the resulting ``ParseResults.asList()`` is equal to the ``expected_list``.
- """
- result = expr.parse_string(test_string, parse_all=True)
- if verbose:
- print(result.dump())
- else:
- print(result.as_list())
- self.assertParseResultsEquals(result, expected_list=expected_list, msg=msg)
-
- def assertParseAndCheckDict(
- self, expr, test_string, expected_dict, msg=None, verbose=True
- ):
- """
- Convenience wrapper assert to test a parser element and input string, and assert that
- the resulting ``ParseResults.asDict()`` is equal to the ``expected_dict``.
- """
- result = expr.parse_string(test_string, parseAll=True)
- if verbose:
- print(result.dump())
- else:
- print(result.as_list())
- self.assertParseResultsEquals(result, expected_dict=expected_dict, msg=msg)
-
- def assertRunTestResults(
- self, run_tests_report, expected_parse_results=None, msg=None
- ):
- """
- Unit test assertion to evaluate output of ``ParserElement.runTests()``. If a list of
- list-dict tuples is given as the ``expected_parse_results`` argument, then these are zipped
- with the report tuples returned by ``runTests`` and evaluated using ``assertParseResultsEquals``.
- Finally, asserts that the overall ``runTests()`` success value is ``True``.
-
- :param run_tests_report: tuple(bool, [tuple(str, ParseResults or Exception)]) returned from runTests
- :param expected_parse_results (optional): [tuple(str, list, dict, Exception)]
- """
- run_test_success, run_test_results = run_tests_report
-
- if expected_parse_results is not None:
- merged = [
- (*rpt, expected)
- for rpt, expected in zip(run_test_results, expected_parse_results)
- ]
- for test_string, result, expected in merged:
- # expected should be a tuple containing a list and/or a dict or an exception,
- # and optional failure message string
- # an empty tuple will skip any result validation
- fail_msg = next(
- (exp for exp in expected if isinstance(exp, str)), None
- )
- expected_exception = next(
- (
- exp
- for exp in expected
- if isinstance(exp, type) and issubclass(exp, Exception)
- ),
- None,
- )
- if expected_exception is not None:
- with self.assertRaises(
- expected_exception=expected_exception, msg=fail_msg or msg
- ):
- if isinstance(result, Exception):
- raise result
- else:
- expected_list = next(
- (exp for exp in expected if isinstance(exp, list)), None
- )
- expected_dict = next(
- (exp for exp in expected if isinstance(exp, dict)), None
- )
- if (expected_list, expected_dict) != (None, None):
- self.assertParseResultsEquals(
- result,
- expected_list=expected_list,
- expected_dict=expected_dict,
- msg=fail_msg or msg,
- )
- else:
- # warning here maybe?
- print("no validation for {!r}".format(test_string))
-
- # do this last, in case some specific test results can be reported instead
- self.assertTrue(
- run_test_success, msg=msg if msg is not None else "failed runTests"
- )
-
- @contextmanager
- def assertRaisesParseException(self, exc_type=ParseException, msg=None):
- with self.assertRaises(exc_type, msg=msg):
- yield
-
- @staticmethod
- def with_line_numbers(
- s: str,
- start_line: typing.Optional[int] = None,
- end_line: typing.Optional[int] = None,
- expand_tabs: bool = True,
- eol_mark: str = "|",
- mark_spaces: typing.Optional[str] = None,
- mark_control: typing.Optional[str] = None,
- ) -> str:
- """
- Helpful method for debugging a parser - prints a string with line and column numbers.
- (Line and column numbers are 1-based.)
-
- :param s: tuple(bool, str - string to be printed with line and column numbers
- :param start_line: int - (optional) starting line number in s to print (default=1)
- :param end_line: int - (optional) ending line number in s to print (default=len(s))
- :param expand_tabs: bool - (optional) expand tabs to spaces, to match the pyparsing default
- :param eol_mark: str - (optional) string to mark the end of lines, helps visualize trailing spaces (default="|")
- :param mark_spaces: str - (optional) special character to display in place of spaces
- :param mark_control: str - (optional) convert non-printing control characters to a placeholding
- character; valid values:
- - "unicode" - replaces control chars with Unicode symbols, such as "␍" and "␊"
- - any single character string - replace control characters with given string
- - None (default) - string is displayed as-is
-
- :return: str - input string with leading line numbers and column number headers
- """
- if expand_tabs:
- s = s.expandtabs()
- if mark_control is not None:
- if mark_control == "unicode":
- tbl = str.maketrans(
- {c: u for c, u in zip(range(0, 33), range(0x2400, 0x2433))}
- | {127: 0x2421}
- )
- eol_mark = ""
- else:
- tbl = str.maketrans(
- {c: mark_control for c in list(range(0, 32)) + [127]}
- )
- s = s.translate(tbl)
- if mark_spaces is not None and mark_spaces != " ":
- if mark_spaces == "unicode":
- tbl = str.maketrans({9: 0x2409, 32: 0x2423})
- s = s.translate(tbl)
- else:
- s = s.replace(" ", mark_spaces)
- if start_line is None:
- start_line = 1
- if end_line is None:
- end_line = len(s)
- end_line = min(end_line, len(s))
- start_line = min(max(1, start_line), end_line)
-
- if mark_control != "unicode":
- s_lines = s.splitlines()[start_line - 1 : end_line]
- else:
- s_lines = [line + "␊" for line in s.split("␊")[start_line - 1 : end_line]]
- if not s_lines:
- return ""
-
- lineno_width = len(str(end_line))
- max_line_len = max(len(line) for line in s_lines)
- lead = " " * (lineno_width + 1)
- if max_line_len >= 99:
- header0 = (
- lead
- + "".join(
- "{}{}".format(" " * 99, (i + 1) % 100)
- for i in range(max(max_line_len // 100, 1))
- )
- + "\n"
- )
- else:
- header0 = ""
- header1 = (
- header0
- + lead
- + "".join(
- " {}".format((i + 1) % 10)
- for i in range(-(-max_line_len // 10))
- )
- + "\n"
- )
- header2 = lead + "1234567890" * (-(-max_line_len // 10)) + "\n"
- return (
- header1
- + header2
- + "\n".join(
- "{:{}d}:{}{}".format(i, lineno_width, line, eol_mark)
- for i, line in enumerate(s_lines, start=start_line)
- )
- + "\n"
- )
diff --git a/spaces/Realcat/image-matching-webui/third_party/DKM/dkm/models/__init__.py b/spaces/Realcat/image-matching-webui/third_party/DKM/dkm/models/__init__.py
deleted file mode 100644
index d4fc321ec70fd116beca23e94248cb6bbe771523..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DKM/dkm/models/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .model_zoo import (
- DKMv3_outdoor,
- DKMv3_indoor,
-)
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/base.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/base.py
deleted file mode 100644
index 1dfc23efb5fb49bbf510364599489c9acf1df263..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/viz/methods/base.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import pprint
-from abc import ABCMeta, abstractmethod
-import torch
-from itertools import chain
-
-from src.utils.plotting import make_matching_figure, error_colormap
-from src.utils.metrics import aggregate_metrics
-
-
-def flatten_list(x):
- return list(chain(*x))
-
-
-class Viz(metaclass=ABCMeta):
- def __init__(self):
- super().__init__()
- self.device = torch.device(
- "cuda:{}".format(0) if torch.cuda.is_available() else "cpu"
- )
- torch.set_grad_enabled(False)
-
- # for evaluation metrics of MegaDepth and ScanNet
- self.eval_stats = []
- self.time_stats = []
-
- def draw_matches(self, mkpts0, mkpts1, img0, img1, conf, path=None, **kwargs):
- thr = 5e-4
- # mkpts0 = pe['mkpts0_f'].cpu().numpy()
- # mkpts1 = pe['mkpts1_f'].cpu().numpy()
- if "conf_thr" in kwargs:
- thr = kwargs["conf_thr"]
- color = error_colormap(conf, thr, alpha=0.1)
-
- text = [
- f"{self.name}",
- f"#Matches: {len(mkpts0)}",
- ]
- if "R_errs" in kwargs:
- text.append(
- f"$\\Delta$R:{kwargs['R_errs']:.2f}°, $\\Delta$t:{kwargs['t_errs']:.2f}°",
- )
-
- if path:
- make_matching_figure(
- img0, img1, mkpts0, mkpts1, color, text=text, path=path, dpi=150
- )
- else:
- return make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text)
-
- @abstractmethod
- def match_and_draw(self, data_dict, **kwargs):
- pass
-
- def compute_eval_metrics(self, epi_err_thr=5e-4):
- # metrics: dict of list, numpy
- _metrics = [o["metrics"] for o in self.eval_stats]
- metrics = {k: flatten_list([_me[k] for _me in _metrics]) for k in _metrics[0]}
-
- val_metrics_4tb = aggregate_metrics(metrics, epi_err_thr)
- print("\n" + pprint.pformat(val_metrics_4tb))
-
- def measure_time(self):
- if len(self.time_stats) == 0:
- return 0
- return sum(self.time_stats) / len(self.time_stats)
diff --git a/spaces/Redgon/bingo/src/components/chat-image.tsx b/spaces/Redgon/bingo/src/components/chat-image.tsx
deleted file mode 100644
index 05ecc9771eada27a0f2d160bb01cba170d37bb09..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/src/components/chat-image.tsx
+++ /dev/null
@@ -1,170 +0,0 @@
-import {
- useEffect,
- useState,
- useCallback,
- ChangeEvent,
- ClipboardEvent,
- MouseEventHandler,
- FormEvent,
- useRef
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-
-interface ChatImageProps extends Pick<ReturnType<typeof useBing>, 'uploadImage'> {}
-
-const preventDefault: MouseEventHandler<HTMLDivElement> = (event) => {
- event.nativeEvent.stopImmediatePropagation()
-}
-
-const toBase64 = (file: File): Promise<string> => new Promise((resolve, reject) => {
- const reader = new FileReader()
- reader.readAsDataURL(file)
- reader.onload = () => resolve(reader.result as string)
- reader.onerror = reject
-})
-
-export function ChatImage({ children, uploadImage }: React.PropsWithChildren<ChatImageProps>) {
- const videoRef = useRef<HTMLVideoElement>(null)
- const canvasRef = useRef<HTMLCanvasElement>(null)
- const mediaStream = useRef<MediaStream>()
- const [panel, setPanel] = useState('none')
-
- const upload = useCallback((url: string) => {
- if (url) {
- uploadImage(url)
- }
- setPanel('none')
- }, [panel])
-
- const onUpload = useCallback(async (event: ChangeEvent<HTMLInputElement>) => {
- const file = event.target.files?.[0]
- if (file) {
- const fileDataUrl = await toBase64(file)
- if (fileDataUrl) {
- upload(fileDataUrl)
- }
- }
- }, [])
-
- const onPaste = useCallback((event: ClipboardEvent<HTMLInputElement>) => {
- const pasteUrl = event.clipboardData.getData('text') ?? ''
- upload(pasteUrl)
- }, [])
-
- const onEnter = useCallback((event: FormEvent<HTMLFormElement>) => {
- event.preventDefault()
- event.stopPropagation()
- // @ts-ignore
- const inputUrl = event.target.elements.image.value
- if (inputUrl) {
- upload(inputUrl)
- }
- }, [])
-
- const openVideo: MouseEventHandler<HTMLButtonElement> = async (event) => {
- event.stopPropagation()
- setPanel('camera-mode')
- }
-
- const onCapture = () => {
- if (canvasRef.current && videoRef.current) {
- const canvas = canvasRef.current
- canvas.width = videoRef.current!.videoWidth
- canvas.height = videoRef.current!.videoHeight
- canvas.getContext('2d')?.drawImage(videoRef.current, 0, 0, canvas.width, canvas.height)
- const cameraUrl = canvas.toDataURL('image/jpeg')
- upload(cameraUrl)
- }
- }
-
- useEffect(() => {
- const handleBlur = () => {
- if (panel !== 'none') {
- setPanel('none')
- }
- }
- document.addEventListener('click', handleBlur)
- return () => {
- document.removeEventListener('click', handleBlur)
- }
- }, [panel])
-
- useEffect(() => {
- if (panel === 'camera-mode') {
- navigator.mediaDevices.getUserMedia({ video: true, audio: false })
- .then(videoStream => {
- mediaStream.current = videoStream
- if (videoRef.current) {
- videoRef.current.srcObject = videoStream
- }
- })
- } else {
- if (mediaStream.current) {
- mediaStream.current.getTracks().forEach(function(track) {
- track.stop()
- })
- mediaStream.current = undefined
- }
- }
- }, [panel])
-
- return (
- <div className="visual-search-container">
- <div onClick={() => panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}</div>
- <div className={cn('visual-search', panel)} onClick={preventDefault}>
- <div className="normal-content">
- <div className="header">
- <h4>添加图像</h4>
- </div>
- <div className="paste">
- <Image alt="paste" src={PasteIcon} width={24} />
- <form onSubmitCapture={onEnter}>
- <input
- className="paste-input"
- id="sb_imgpst"
- type="text"
- name="image"
- placeholder="粘贴图像 URL"
- aria-label="粘贴图像 URL"
- onPaste={onPaste}
- onClickCapture={(e) => e.stopPropagation()}
- />
- </form>
- </div>
- <div className="buttons">
- <button type="button" aria-label="从此设备上传">
- <input
- id="vs_fileinput"
- className="fileinput"
- type="file"
- accept="image/gif, image/jpeg, image/png, image/webp"
- onChange={onUpload}
- />
- <Image alt="uplaod" src={UploadIcon} width={20} />
- 从此设备上传
- </button>
- <button type="button" aria-label="拍照" onClick={openVideo}>
- <Image alt="camera" src={CameraIcon} width={20} />
- 拍照
- </button>
- </div>
- </div>
- {panel === 'camera-mode' && <div className="cam-content">
- <div className="webvideo-container">
- <video className="webvideo" autoPlay muted playsInline ref={videoRef} />
- <canvas className="webcanvas" ref={canvasRef} />
- </div>
- <div className="cambtn" role="button" aria-label="拍照" onClick={onCapture}>
- <div className="cam-btn-circle-large"></div>
- <div className="cam-btn-circle-small"></div>
- </div>
- </div>}
- </div>
- </div>
- )
-}
diff --git a/spaces/Ripaxxs/Tommy/README.md b/spaces/Ripaxxs/Tommy/README.md
deleted file mode 100644
index bae4a2097a3a73bd2927b019f4dad1f4c70454a2..0000000000000000000000000000000000000000
--- a/spaces/Ripaxxs/Tommy/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Tommy
-emoji: ⚡
-colorFrom: yellow
-colorTo: blue
-sdk: docker
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/correlation.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/correlation.py
deleted file mode 100644
index 3d0b79c301b29915dfaf4d2b1846c59be73127d3..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/correlation.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import Tensor, nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['correlation_forward', 'correlation_backward'])
-
-
-class CorrelationFunction(Function):
-
- @staticmethod
- def forward(ctx,
- input1,
- input2,
- kernel_size=1,
- max_displacement=1,
- stride=1,
- padding=1,
- dilation=1,
- dilation_patch=1):
-
- ctx.save_for_backward(input1, input2)
-
- kH, kW = ctx.kernel_size = _pair(kernel_size)
- patch_size = max_displacement * 2 + 1
- ctx.patch_size = patch_size
- dH, dW = ctx.stride = _pair(stride)
- padH, padW = ctx.padding = _pair(padding)
- dilationH, dilationW = ctx.dilation = _pair(dilation)
- dilation_patchH, dilation_patchW = ctx.dilation_patch = _pair(
- dilation_patch)
-
- output_size = CorrelationFunction._output_size(ctx, input1)
-
- output = input1.new_zeros(output_size)
-
- ext_module.correlation_forward(
- input1,
- input2,
- output,
- kH=kH,
- kW=kW,
- patchH=patch_size,
- patchW=patch_size,
- padH=padH,
- padW=padW,
- dilationH=dilationH,
- dilationW=dilationW,
- dilation_patchH=dilation_patchH,
- dilation_patchW=dilation_patchW,
- dH=dH,
- dW=dW)
-
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input1, input2 = ctx.saved_tensors
-
- kH, kW = ctx.kernel_size
- patch_size = ctx.patch_size
- padH, padW = ctx.padding
- dilationH, dilationW = ctx.dilation
- dilation_patchH, dilation_patchW = ctx.dilation_patch
- dH, dW = ctx.stride
- grad_input1 = torch.zeros_like(input1)
- grad_input2 = torch.zeros_like(input2)
-
- ext_module.correlation_backward(
- grad_output,
- input1,
- input2,
- grad_input1,
- grad_input2,
- kH=kH,
- kW=kW,
- patchH=patch_size,
- patchW=patch_size,
- padH=padH,
- padW=padW,
- dilationH=dilationH,
- dilationW=dilationW,
- dilation_patchH=dilation_patchH,
- dilation_patchW=dilation_patchW,
- dH=dH,
- dW=dW)
- return grad_input1, grad_input2, None, None, None, None, None, None
-
- @staticmethod
- def _output_size(ctx, input1):
- iH, iW = input1.size(2), input1.size(3)
- batch_size = input1.size(0)
- kH, kW = ctx.kernel_size
- patch_size = ctx.patch_size
- dH, dW = ctx.stride
- padH, padW = ctx.padding
- dilationH, dilationW = ctx.dilation
- dilatedKH = (kH - 1) * dilationH + 1
- dilatedKW = (kW - 1) * dilationW + 1
-
- oH = int((iH + 2 * padH - dilatedKH) / dH + 1)
- oW = int((iW + 2 * padW - dilatedKW) / dW + 1)
-
- output_size = (batch_size, patch_size, patch_size, oH, oW)
- return output_size
-
-
-class Correlation(nn.Module):
- r"""Correlation operator
-
- This correlation operator works for optical flow correlation computation.
-
- There are two batched tensors with shape :math:`(N, C, H, W)`,
- and the correlation output's shape is :math:`(N, max\_displacement \times
- 2 + 1, max\_displacement * 2 + 1, H_{out}, W_{out})`
-
- where
-
- .. math::
- H_{out} = \left\lfloor\frac{H_{in} + 2 \times padding -
- dilation \times (kernel\_size - 1) - 1}
- {stride} + 1\right\rfloor
-
- .. math::
- W_{out} = \left\lfloor\frac{W_{in} + 2 \times padding - dilation
- \times (kernel\_size - 1) - 1}
- {stride} + 1\right\rfloor
-
- the correlation item :math:`(N_i, dy, dx)` is formed by taking the sliding
- window convolution between input1 and shifted input2,
-
- .. math::
- Corr(N_i, dx, dy) =
- \sum_{c=0}^{C-1}
- input1(N_i, c) \star
- \mathcal{S}(input2(N_i, c), dy, dx)
-
- where :math:`\star` is the valid 2d sliding window convolution operator,
- and :math:`\mathcal{S}` means shifting the input features (auto-complete
- zero marginal), and :math:`dx, dy` are shifting distance, :math:`dx, dy \in
- [-max\_displacement \times dilation\_patch, max\_displacement \times
- dilation\_patch]`.
-
- Args:
- kernel_size (int): The size of sliding window i.e. local neighborhood
- representing the center points and involved in correlation
- computation. Defaults to 1.
- max_displacement (int): The radius for computing correlation volume,
- but the actual working space can be dilated by dilation_patch.
- Defaults to 1.
- stride (int): The stride of the sliding blocks in the input spatial
- dimensions. Defaults to 1.
- padding (int): Zero padding added to all four sides of the input1.
- Defaults to 0.
- dilation (int): The spacing of local neighborhood that will involved
- in correlation. Defaults to 1.
- dilation_patch (int): The spacing between position need to compute
- correlation. Defaults to 1.
- """
-
- def __init__(self,
- kernel_size: int = 1,
- max_displacement: int = 1,
- stride: int = 1,
- padding: int = 0,
- dilation: int = 1,
- dilation_patch: int = 1) -> None:
- super().__init__()
- self.kernel_size = kernel_size
- self.max_displacement = max_displacement
- self.stride = stride
- self.padding = padding
- self.dilation = dilation
- self.dilation_patch = dilation_patch
-
- def forward(self, input1: Tensor, input2: Tensor) -> Tensor:
- return CorrelationFunction.apply(input1, input2, self.kernel_size,
- self.max_displacement, self.stride,
- self.padding, self.dilation,
- self.dilation_patch)
-
- def __repr__(self) -> str:
- s = self.__class__.__name__
- s += f'(kernel_size={self.kernel_size}, '
- s += f'max_displacement={self.max_displacement}, '
- s += f'stride={self.stride}, '
- s += f'padding={self.padding}, '
- s += f'dilation={self.dilation}, '
- s += f'dilation_patch={self.dilation_patch})'
- return s
diff --git a/spaces/SQSora/VITS-Umamusume-voice-synthesizer/README.md b/spaces/SQSora/VITS-Umamusume-voice-synthesizer/README.md
deleted file mode 100644
index 1b24e6efdb04cb1460e4fe3257d2303677c5a0e1..0000000000000000000000000000000000000000
--- a/spaces/SQSora/VITS-Umamusume-voice-synthesizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual Anime TTS
-emoji: 🎙🐴
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.7
-app_file: app.py
-pinned: false
-duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SShaik/SS-06-SL-AI-Image-Music-Video-UI-UX-URL/Article.md b/spaces/SShaik/SS-06-SL-AI-Image-Music-Video-UI-UX-URL/Article.md
deleted file mode 100644
index c7f042e4c9c0f401731f009842a325e2d1386bf5..0000000000000000000000000000000000000000
--- a/spaces/SShaik/SS-06-SL-AI-Image-Music-Video-UI-UX-URL/Article.md
+++ /dev/null
@@ -1,51 +0,0 @@
-
-# Image Generation for Art, Marketing, Ideation, Design, and Use in Business
-
-A number of multiple AI pipeline element strategies have evolved on the open market which allow you to generate images using a combination of image prompts and word prompts. This brief analysis gives an idea of the prompting capabilities as well as image rendering techniques that are used in the strategy to generate art from human understanding of images and text used to describe a scene.
-
-First a top five list on state of the art generators both free and paid is worth consideration.
-
-1) Midjourney - a Discord server based chatboat AI that allows /imagine prompts which can generate multiple images at a time. This is best at parallel creation, high accuracy even photo real creations.
-2) Artbreeder - A multiple capability tool which now features a Collager which assists in starting image composition. By far the most innovative approach which does great to combine the right partial elements in a scene.
-3) Dreamstudio - A Huggingface derived art program in beta which uses stable diffusion to create highly accurate art and images.
-4) Nightcafe - A credit based creation AI app that can do generation of video dives into an AI art piece which can produce some of the best experiences in Video.
-5) RunwayML - a quintessential tool in processing morph audio and video tracks which rival most high end video edit tools.
-
-These 5 tools make up some of the best AI pipeline programs that are cloud based that allow anyone to begin easily building their portfolio of art.
-
-The prompting capabilities often involve having a set of text based prompts to get started. Most also feature a starter image which could be an example of what you would like to create.
-
-URL Links:
-1) Collager: https://www.artbreeder.com/beta/collage
-2) NightCafe: https://creator.nightcafe.studio/explore
-3) Midjourney: https://www.midjourney.com/app/users/779773261440614430/
-4) Dreamstudio: https://beta.dreamstudio.ai/dream
-5) RunwayML: https://app.runwayml.com/
-
-## Getting Started and Organizing Your AI Pipeline and Process
-
-Any great strategy has a number of steps that combine all capabilities at your disposal. It is useful to note how you can easily fir these together into a process that works for you.
-
-The techniques worth noted are listed below. Consider how you will use them will make your pipeline easier and more automated to allow you to spend the majority of your time curating what you have made, and ideating what you want to create next.
-
-1) Source materials: Since prompting requires text and text examples can quickly help you compose good input, its worth considering and documenting some effective prompts. Nightcafe with its integration into email, sends you a copy of your creation plus the prompting text so one option is to use your email account to keep a record of which prompts work for which outputs.
-2) Source materials: Discord since its a public chat format allows you to easily see what others are using for prompts in bulk. There are a number of chat channels designed for people new to the platform and often you can copy and paste if you see very effective prompts with material you are looking for.
-3) Source materials: Collager is unique in its ability to add additive parts and then dial in the percent of AI you would like with that. This allows you to add a few image elements which help start out your generation.
-4) Source materials: Since images and prompts are going to be your mainstay for inputs its worth considering an open standard for storing and retrieving these from anywhere. Github is a good place since markdown language can involve text in table or list format and includes a capability to reference uploaded images within markdown. This is also a good form for portability since you can later fork and download your repository with a few clicks from anywhere.
-5) Source materials: Google drive is integrated into the Artbreeder Collager workflow which allows you easily expand your work and even compose albums of the ones you like to place in Google photo albums. The portfolio you save on different sites have different degrees of ease when aggregating your collections. Collager for instance allows right click save for instant saving of your creation. Dreamstudio features a history. Midjourney features a profile site for you to store and review creations even triggering Upscales which important to use to get the highest resolution output for your creations.
-
-## Social Media integration
-
-Depending on your target "safe for work" exports of your work, it is sometimes important to know your accepted social media outlets that you can integrate. Cloud based interactions are the key to successful audiences if you want to scale and share your process with others.
-
-The key social media outlets supported for these tools are here in a sorted link list which start with public open source first:
-
-1) Github - Github is open at most companies and allow creation of a free space to share your content.
-2) LinkedIn - LinkedIn is acceptable use at nearly every company.
-3) Twitter - Twitter is supported as a social media outlet at most companies yet can also be used with security restrictions which might limit posting but allow read access.
-4) Facebook - Meta's Facebook is a good outlet since it allows creation of large folios of your images along with stories. This venue however is locked down at many organizations.
-5) Instagram - Instagram is supported as an output channel for many tools yet has decreased in popularity due to high frequency of ads and pay for likes models. While it can still be one of the best places for domain specific arrangements of images it is likely locked down in most secure organizations.
-6) Youtube - For video uploads with automated captioning and long term storage of short and long form video this is an essential for any creation you compose as video. It is also useful to review and compose playlists of videos here for yourself that speed up your learning - Spend some time at Youtube university and keep a record of keyword searches there sometimes along with your playlists to accelerate learning.
-7) Gmail - With the baility to move email in and out its useful to create and wrap up details within email. Most email policies come with a content limitation (for example no files larger than 25MB. For this reason get used to creating pproject wrap up archives with winzip or compression software. With the convenience of keyword searching you can usually use this as a base.
-8) Last a worth mention is Huggingface.com. Like github as you become more sophisticated in your public open source capabilities, HuggingFace can allow you to wrap up using one of three software development kits which are gadio, streamlit, and HTML5 each with unique AI and UI integration components and features. If you want to create your own AI pipelines this one also has the open source code and models ready to go to help you on your journey.
-
diff --git a/spaces/Sa-m/YoloV5-Party-Symbol-Detector-V1/README.md b/spaces/Sa-m/YoloV5-Party-Symbol-Detector-V1/README.md
deleted file mode 100644
index b1929ed7049baf00b31d9dd70cad22d33dfbd4f6..0000000000000000000000000000000000000000
--- a/spaces/Sa-m/YoloV5-Party-Symbol-Detector-V1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Political Party Symbol Detector V1
-emoji: 👀
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Salama1429/speech-to-speech-translation/app.py b/spaces/Salama1429/speech-to-speech-translation/app.py
deleted file mode 100644
index 3b8a6a06bad8423b6588d1c906fc3ed2f0aba606..0000000000000000000000000000000000000000
--- a/spaces/Salama1429/speech-to-speech-translation/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from datasets import load_dataset
-
-from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-# load speech translation checkpoint
-asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
-
-# load text-to-speech checkpoint and speaker embeddings
-processor = SpeechT5Processor.from_pretrained("Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl")
-
-model = SpeechT5ForTextToSpeech.from_pretrained("Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl").to(device)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
-
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
-
-
-def translate(audio):
- outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "de"})
- return outputs["text"]
-
-
-def synthesise(text):
- inputs = processor(text=text, return_tensors="pt")
- speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
- return speech.cpu()
-
-
-def speech_to_speech_translation(audio):
- translated_text = translate(audio)
- synthesised_speech = synthesise(translated_text)
- synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
- return 16000, synthesised_speech
-
-
-title = "Cascaded STST"
-description = """
-Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in German. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and
-[My German TTS](https://huggingface.co/Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl) model for text-to-speech:
-
-
-"""
-
-demo = gr.Blocks()
-
-mic_translate = gr.Interface(
- fn=speech_to_speech_translation,
- inputs=gr.Audio(source="microphone", type="filepath"),
- outputs=gr.Audio(label="Generated Speech", type="numpy"),
- title=title,
- description=description,
-)
-
-file_translate = gr.Interface(
- fn=speech_to_speech_translation,
- inputs=gr.Audio(source="upload", type="filepath"),
- outputs=gr.Audio(label="Generated Speech", type="numpy"),
- examples=[["./example.wav"]],
- title=title,
- description=description,
-)
-
-with demo:
- gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
-
-demo.launch()
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/utils/logging.py b/spaces/Salesforce/EDICT/my_half_diffusers/utils/logging.py
deleted file mode 100644
index 1f2d0227b87c66205ceb3391a8e98f5f33285dc4..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/utils/logging.py
+++ /dev/null
@@ -1,344 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Optuna, Hugging Face
-#
-# 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.
-""" Logging utilities."""
-
-import logging
-import os
-import sys
-import threading
-from logging import CRITICAL # NOQA
-from logging import DEBUG # NOQA
-from logging import ERROR # NOQA
-from logging import FATAL # NOQA
-from logging import INFO # NOQA
-from logging import NOTSET # NOQA
-from logging import WARN # NOQA
-from logging import WARNING # NOQA
-from typing import Optional
-
-from tqdm import auto as tqdm_lib
-
-
-_lock = threading.Lock()
-_default_handler: Optional[logging.Handler] = None
-
-log_levels = {
- "debug": logging.DEBUG,
- "info": logging.INFO,
- "warning": logging.WARNING,
- "error": logging.ERROR,
- "critical": logging.CRITICAL,
-}
-
-_default_log_level = logging.WARNING
-
-_tqdm_active = True
-
-
-def _get_default_logging_level():
- """
- If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
- not - fall back to `_default_log_level`
- """
- env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None)
- if env_level_str:
- if env_level_str in log_levels:
- return log_levels[env_level_str]
- else:
- logging.getLogger().warning(
- f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
- f"has to be one of: { ', '.join(log_levels.keys()) }"
- )
- return _default_log_level
-
-
-def _get_library_name() -> str:
-
- return __name__.split(".")[0]
-
-
-def _get_library_root_logger() -> logging.Logger:
-
- return logging.getLogger(_get_library_name())
-
-
-def _configure_library_root_logger() -> None:
-
- global _default_handler
-
- with _lock:
- if _default_handler:
- # This library has already configured the library root logger.
- return
- _default_handler = logging.StreamHandler() # Set sys.stderr as stream.
- _default_handler.flush = sys.stderr.flush
-
- # Apply our default configuration to the library root logger.
- library_root_logger = _get_library_root_logger()
- library_root_logger.addHandler(_default_handler)
- library_root_logger.setLevel(_get_default_logging_level())
- library_root_logger.propagate = False
-
-
-def _reset_library_root_logger() -> None:
-
- global _default_handler
-
- with _lock:
- if not _default_handler:
- return
-
- library_root_logger = _get_library_root_logger()
- library_root_logger.removeHandler(_default_handler)
- library_root_logger.setLevel(logging.NOTSET)
- _default_handler = None
-
-
-def get_log_levels_dict():
- return log_levels
-
-
-def get_logger(name: Optional[str] = None) -> logging.Logger:
- """
- Return a logger with the specified name.
-
- This function is not supposed to be directly accessed unless you are writing a custom diffusers module.
- """
-
- if name is None:
- name = _get_library_name()
-
- _configure_library_root_logger()
- return logging.getLogger(name)
-
-
-def get_verbosity() -> int:
- """
- Return the current level for the 🤗 Diffusers' root logger as an int.
-
- Returns:
- `int`: The logging level.
-
- <Tip>
-
- 🤗 Diffusers has following logging levels:
-
- - 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- - 40: `diffusers.logging.ERROR`
- - 30: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- - 20: `diffusers.logging.INFO`
- - 10: `diffusers.logging.DEBUG`
-
- </Tip>"""
-
- _configure_library_root_logger()
- return _get_library_root_logger().getEffectiveLevel()
-
-
-def set_verbosity(verbosity: int) -> None:
- """
- Set the verbosity level for the 🤗 Diffusers' root logger.
-
- Args:
- verbosity (`int`):
- Logging level, e.g., one of:
-
- - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- - `diffusers.logging.ERROR`
- - `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- - `diffusers.logging.INFO`
- - `diffusers.logging.DEBUG`
- """
-
- _configure_library_root_logger()
- _get_library_root_logger().setLevel(verbosity)
-
-
-def set_verbosity_info():
- """Set the verbosity to the `INFO` level."""
- return set_verbosity(INFO)
-
-
-def set_verbosity_warning():
- """Set the verbosity to the `WARNING` level."""
- return set_verbosity(WARNING)
-
-
-def set_verbosity_debug():
- """Set the verbosity to the `DEBUG` level."""
- return set_verbosity(DEBUG)
-
-
-def set_verbosity_error():
- """Set the verbosity to the `ERROR` level."""
- return set_verbosity(ERROR)
-
-
-def disable_default_handler() -> None:
- """Disable the default handler of the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert _default_handler is not None
- _get_library_root_logger().removeHandler(_default_handler)
-
-
-def enable_default_handler() -> None:
- """Enable the default handler of the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert _default_handler is not None
- _get_library_root_logger().addHandler(_default_handler)
-
-
-def add_handler(handler: logging.Handler) -> None:
- """adds a handler to the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert handler is not None
- _get_library_root_logger().addHandler(handler)
-
-
-def remove_handler(handler: logging.Handler) -> None:
- """removes given handler from the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert handler is not None and handler not in _get_library_root_logger().handlers
- _get_library_root_logger().removeHandler(handler)
-
-
-def disable_propagation() -> None:
- """
- Disable propagation of the library log outputs. Note that log propagation is disabled by default.
- """
-
- _configure_library_root_logger()
- _get_library_root_logger().propagate = False
-
-
-def enable_propagation() -> None:
- """
- Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent
- double logging if the root logger has been configured.
- """
-
- _configure_library_root_logger()
- _get_library_root_logger().propagate = True
-
-
-def enable_explicit_format() -> None:
- """
- Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows:
- ```
- [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
- ```
- All handlers currently bound to the root logger are affected by this method.
- """
- handlers = _get_library_root_logger().handlers
-
- for handler in handlers:
- formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
- handler.setFormatter(formatter)
-
-
-def reset_format() -> None:
- """
- Resets the formatting for HuggingFace Diffusers' loggers.
-
- All handlers currently bound to the root logger are affected by this method.
- """
- handlers = _get_library_root_logger().handlers
-
- for handler in handlers:
- handler.setFormatter(None)
-
-
-def warning_advice(self, *args, **kwargs):
- """
- This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
- warning will not be printed
- """
- no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)
- if no_advisory_warnings:
- return
- self.warning(*args, **kwargs)
-
-
-logging.Logger.warning_advice = warning_advice
-
-
-class EmptyTqdm:
- """Dummy tqdm which doesn't do anything."""
-
- def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
- self._iterator = args[0] if args else None
-
- def __iter__(self):
- return iter(self._iterator)
-
- def __getattr__(self, _):
- """Return empty function."""
-
- def empty_fn(*args, **kwargs): # pylint: disable=unused-argument
- return
-
- return empty_fn
-
- def __enter__(self):
- return self
-
- def __exit__(self, type_, value, traceback):
- return
-
-
-class _tqdm_cls:
- def __call__(self, *args, **kwargs):
- if _tqdm_active:
- return tqdm_lib.tqdm(*args, **kwargs)
- else:
- return EmptyTqdm(*args, **kwargs)
-
- def set_lock(self, *args, **kwargs):
- self._lock = None
- if _tqdm_active:
- return tqdm_lib.tqdm.set_lock(*args, **kwargs)
-
- def get_lock(self):
- if _tqdm_active:
- return tqdm_lib.tqdm.get_lock()
-
-
-tqdm = _tqdm_cls()
-
-
-def is_progress_bar_enabled() -> bool:
- """Return a boolean indicating whether tqdm progress bars are enabled."""
- global _tqdm_active
- return bool(_tqdm_active)
-
-
-def enable_progress_bar():
- """Enable tqdm progress bar."""
- global _tqdm_active
- _tqdm_active = True
-
-
-def disable_progress_bar():
- """Disable tqdm progress bar."""
- global _tqdm_active
- _tqdm_active = False
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/bbox.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/bbox.py
deleted file mode 100644
index cce22d297c8e20597f04760416e7719c19941e53..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/bbox.py
+++ /dev/null
@@ -1,113 +0,0 @@
-from __future__ import division
-
-import torch
-import random
-
-import numpy as np
-import cv2
-
-def confidence_filter(result, confidence):
- conf_mask = (result[:,:,4] > confidence).float().unsqueeze(2)
- result = result*conf_mask
-
- return result
-
-def confidence_filter_cls(result, confidence):
- max_scores = torch.max(result[:,:,5:25], 2)[0]
- res = torch.cat((result, max_scores),2)
- print(res.shape)
-
-
- cond_1 = (res[:,:,4] > confidence).float()
- cond_2 = (res[:,:,25] > 0.995).float()
-
- conf = cond_1 + cond_2
- conf = torch.clamp(conf, 0.0, 1.0)
- conf = conf.unsqueeze(2)
- result = result*conf
- return result
-
-
-
-def get_abs_coord(box):
- box[2], box[3] = abs(box[2]), abs(box[3])
- x1 = (box[0] - box[2]/2) - 1
- y1 = (box[1] - box[3]/2) - 1
- x2 = (box[0] + box[2]/2) - 1
- y2 = (box[1] + box[3]/2) - 1
- return x1, y1, x2, y2
-
-
-
-def sanity_fix(box):
- if (box[0] > box[2]):
- box[0], box[2] = box[2], box[0]
-
- if (box[1] > box[3]):
- box[1], box[3] = box[3], box[1]
-
- return box
-
-def bbox_iou(box1, box2):
- """
- Returns the IoU of two bounding boxes
-
-
- """
- #Get the coordinates of bounding boxes
- b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]
- b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]
-
- #get the corrdinates of the intersection rectangle
- inter_rect_x1 = torch.max(b1_x1, b2_x1)
- inter_rect_y1 = torch.max(b1_y1, b2_y1)
- inter_rect_x2 = torch.min(b1_x2, b2_x2)
- inter_rect_y2 = torch.min(b1_y2, b2_y2)
-
- #Intersection area
-
- inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape))*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape))
-
- #Union Area
- b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)
- b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)
-
- iou = inter_area / (b1_area + b2_area - inter_area)
-
- return iou
-
-
-def pred_corner_coord(prediction):
- #Get indices of non-zero confidence bboxes
- ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
-
- box = prediction[ind_nz[0], ind_nz[1]]
-
-
- box_a = box.new(box.shape)
- box_a[:,0] = (box[:,0] - box[:,2]/2)
- box_a[:,1] = (box[:,1] - box[:,3]/2)
- box_a[:,2] = (box[:,0] + box[:,2]/2)
- box_a[:,3] = (box[:,1] + box[:,3]/2)
- box[:,:4] = box_a[:,:4]
-
- prediction[ind_nz[0], ind_nz[1]] = box
-
- return prediction
-
-
-
-
-def write(x, batches, results, colors, classes):
- c1 = tuple(x[1:3].int())
- c2 = tuple(x[3:5].int())
- img = results[int(x[0])]
- cls = int(x[-1])
- label = "{0}".format(classes[cls])
- color = random.choice(colors)
- cv2.rectangle(img, c1, c2,color, 1)
- t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
- c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
- cv2.rectangle(img, c1, c2,color, -1)
- cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
- return img
diff --git a/spaces/Saturdays/Student_Experience/README.md b/spaces/Saturdays/Student_Experience/README.md
deleted file mode 100644
index fb1812b18754068c464634f3483b93268280f3be..0000000000000000000000000000000000000000
--- a/spaces/Saturdays/Student_Experience/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Student_Experience
-emoji: 📈
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/SenY/Civitai/README.md b/spaces/SenY/Civitai/README.md
deleted file mode 100644
index c53fe7e3ada0727e0fb8d363e126f7badbf7c459..0000000000000000000000000000000000000000
--- a/spaces/SenY/Civitai/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Civitai
-emoji: 🔥
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Shreeraj/SEO_APP/README.md b/spaces/Shreeraj/SEO_APP/README.md
deleted file mode 100644
index 5735a2a7022ada18380206dbc31bd927972e0661..0000000000000000000000000000000000000000
--- a/spaces/Shreeraj/SEO_APP/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SEO APP
-emoji: 🐠
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Silentlin/DiffSinger/modules/parallel_wavegan/optimizers/__init__.py b/spaces/Silentlin/DiffSinger/modules/parallel_wavegan/optimizers/__init__.py
deleted file mode 100644
index a0e0c5932838281e912079e5784d84d43444a61a..0000000000000000000000000000000000000000
--- a/spaces/Silentlin/DiffSinger/modules/parallel_wavegan/optimizers/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from torch.optim import * # NOQA
-from .radam import * # NOQA
diff --git a/spaces/SjoerdTeunisse/upscaler/app.py b/spaces/SjoerdTeunisse/upscaler/app.py
deleted file mode 100644
index 1f3736667bfd4e5ac6d9ee2ef9b95416cb80f9c0..0000000000000000000000000000000000000000
--- a/spaces/SjoerdTeunisse/upscaler/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-import cv2
-import onnxruntime
-import gradio as gr
-
-
-def pre_process(img: np.array) -> np.array:
- # H, W, C -> C, H, W
- img = np.transpose(img[:, :, 0:3], (2, 0, 1))
- # C, H, W -> 1, C, H, W
- img = np.expand_dims(img, axis=0).astype(np.float32)
- return img
-
-
-def post_process(img: np.array) -> np.array:
- # 1, C, H, W -> C, H, W
- img = np.squeeze(img)
- # C, H, W -> H, W, C
- img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8)
- return img
-
-
-def inference(model_path: str, img_array: np.array) -> np.array:
- options = onnxruntime.SessionOptions()
- options.intra_op_num_threads = 1
- options.inter_op_num_threads = 1
- ort_session = onnxruntime.InferenceSession(model_path, options)
- ort_inputs = {ort_session.get_inputs()[0].name: img_array}
- ort_outs = ort_session.run(None, ort_inputs)
-
- return ort_outs[0]
-
-
-def convert_pil_to_cv2(image):
- # pil_image = image.convert("RGB")
- open_cv_image = np.array(image)
- # RGB to BGR
- open_cv_image = open_cv_image[:, :, ::-1].copy()
- return open_cv_image
-
-
-def upscale(image, model):
- model_path = f"models/{model}.ort"
- img = convert_pil_to_cv2(image)
- if img.ndim == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
- if img.shape[2] == 4:
- alpha = img[:, :, 3] # GRAY
- alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR) # BGR
- alpha_output = post_process(inference(model_path, pre_process(alpha))) # BGR
- alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY) # GRAY
-
- img = img[:, :, 0:3] # BGR
- image_output = post_process(inference(model_path, pre_process(img))) # BGR
- image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA) # BGRA
- image_output[:, :, 3] = alpha_output
-
- elif img.shape[2] == 3:
- image_output = post_process(inference(model_path, pre_process(img))) # BGR
-
- return image_output
-
-
-css = ".output-image, .input-image, .image-preview {height: 480px !important} "
-model_choices = ["modelx2", "modelx2 25 JXL", "modelx4", "minecraft_modelx4"]
-
-gr.Interface(
- fn=upscale,
- inputs=[
- gr.inputs.Image(type="pil", label="Input Image"),
- gr.inputs.Radio(
- model_choices,
- type="value",
- default=None,
- label="Choose Upscaler",
- optional=False,
- ),
- ],
- outputs="image",
- title="Image Upscaling 🦆",
- description="Model: [Anchor-based Plain Net for Mobile Image Super-Resolution](https://arxiv.org/abs/2105.09750). Repository: [SR Mobile PyTorch](https://github.com/w11wo/sr_mobile_pytorch)",
- allow_flagging="never",
- css=css,
-).launch()
diff --git a/spaces/Skyler123/TangGPT/run_Linux.sh b/spaces/Skyler123/TangGPT/run_Linux.sh
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/Skyler123/TangGPT/run_Linux.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
- # 如果有更新，关闭当前运行的服务器
- pkill -f ChuanhuChatbot.py
-
- # 拉取最新更改
- git pull
-
- # 安装依赖
- pip3 install -r requirements.txt
-
- # 重新启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/__init__.py
deleted file mode 100644
index 4547fc522b690ba2697843edd044f2039a4123a9..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/__init__.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from __future__ import absolute_import
-
-# For backwards compatibility, provide imports that used to be here.
-from .connection import is_connection_dropped
-from .request import SKIP_HEADER, SKIPPABLE_HEADERS, make_headers
-from .response import is_fp_closed
-from .retry import Retry
-from .ssl_ import (
- ALPN_PROTOCOLS,
- HAS_SNI,
- IS_PYOPENSSL,
- IS_SECURETRANSPORT,
- PROTOCOL_TLS,
- SSLContext,
- assert_fingerprint,
- resolve_cert_reqs,
- resolve_ssl_version,
- ssl_wrap_socket,
-)
-from .timeout import Timeout, current_time
-from .url import Url, get_host, parse_url, split_first
-from .wait import wait_for_read, wait_for_write
-
-__all__ = (
- "HAS_SNI",
- "IS_PYOPENSSL",
- "IS_SECURETRANSPORT",
- "SSLContext",
- "PROTOCOL_TLS",
- "ALPN_PROTOCOLS",
- "Retry",
- "Timeout",
- "Url",
- "assert_fingerprint",
- "current_time",
- "is_connection_dropped",
- "is_fp_closed",
- "get_host",
- "parse_url",
- "make_headers",
- "resolve_cert_reqs",
- "resolve_ssl_version",
- "split_first",
- "ssl_wrap_socket",
- "wait_for_read",
- "wait_for_write",
- "SKIP_HEADER",
- "SKIPPABLE_HEADERS",
-)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Tekknoman/SG161222-Realistic_Vision_V1.4/README.md b/spaces/Tekknoman/SG161222-Realistic_Vision_V1.4/README.md
deleted file mode 100644
index 14d22b561ea127e7999aa4767f40816895a239a1..0000000000000000000000000000000000000000
--- a/spaces/Tekknoman/SG161222-Realistic_Vision_V1.4/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SG161222-Realistic Vision V1.4
-emoji: 👁
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Terminus0501/vits-uma-genshin-honkai/app.py b/spaces/Terminus0501/vits-uma-genshin-honkai/app.py
deleted file mode 100644
index 92ddafdcd240434f58569b0e6964ef331a971dcf..0000000000000000000000000000000000000000
--- a/spaces/Terminus0501/vits-uma-genshin-honkai/app.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import time
-import gradio as gr
-import utils
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-from torch import no_grad, LongTensor
-import torch
-
-hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net_g_ms = SynthesizerTrn(
- len(hps_ms.symbols),
- hps_ms.data.filter_length // 2 + 1,
- hps_ms.train.segment_size // hps_ms.data.hop_length,
- n_speakers=hps_ms.data.n_speakers,
- **hps_ms.model).to(device)
-_ = net_g_ms.eval()
-speakers = hps_ms.speakers
-model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-
-def get_text(text, hps):
- text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
- if hps.data.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = LongTensor(text_norm)
- return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
- start = time.perf_counter()
- if not len(text):
- return "输入文本不能为空！", None, None
- text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
- if len(text) > 500:
- return f"输入文字过长！{len(text)}>100", None, None
- if language == 0:
- text = f"[ZH]{text}[ZH]"
- elif language == 1:
- text = f"[JA]{text}[JA]"
- else:
- text = f"{text}"
- stn_tst, clean_text = get_text(text, hps_ms)
- with no_grad():
- x_tst = stn_tst.unsqueeze(0)
- x_tst_lengths = LongTensor([stn_tst.size(0)])
- speaker_id = LongTensor([speaker_id])
- audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
- length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
- return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
- for s in speakers:
- if search_value == s:
- return s
- for s in speakers:
- if search_value in s:
- return s
-
-def change_lang(language):
- if language == 0:
- return 0.6, 0.668, 1.2
- else:
- return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
- let root = document.querySelector("body > gradio-app");
- if (root.shadowRoot != null)
- root = root.shadowRoot;
- let audio = root.querySelector("#tts-audio").querySelector("audio");
- let text = root.querySelector("#input-text").querySelector("textarea");
- if (audio == undefined)
- return;
- text = text.value;
- if (text == undefined)
- text = Math.floor(Math.random()*100000000);
- audio = audio.src;
- let oA = document.createElement("a");
- oA.download = text.substr(0, 20)+'.wav';
- oA.href = audio;
- document.body.appendChild(oA);
- oA.click();
- oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
- with gr.Blocks() as app:
- gr.Markdown(
- "# <center> VITS语音在线合成demo\n"
- "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
- '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
- '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
- )
-
- with gr.Tabs():
- with gr.TabItem("vits"):
- with gr.Row():
- with gr.Column():
- input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
- lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
- type="index", value="中文")
- btn = gr.Button(value="Submit")
- with gr.Row():
- search = gr.Textbox(label="Search Speaker", lines=1)
- btn2 = gr.Button(value="Search")
- sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
- with gr.Row():
- ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
- nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
- ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
- with gr.Column():
- o1 = gr.Textbox(label="Output Message")
- o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
- o3 = gr.Textbox(label="Extra Info")
- download = gr.Button("Download Audio")
- btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
- download.click(None, [], [], _js=download_audio_js.format())
- btn2.click(search_speaker, inputs=[search], outputs=[sid])
- lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
- with gr.TabItem("可用人物一览"):
- gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
- app.queue(concurrency_count=1).launch()
\ No newline at end of file
diff --git a/spaces/ThomasSimonini/SnowballFight/Build/SnowballFight.loader.js b/spaces/ThomasSimonini/SnowballFight/Build/SnowballFight.loader.js
deleted file mode 100644
index 50e23cf3ae3cb63c21300bc51fa43c99a7b33ac1..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/SnowballFight/Build/SnowballFight.loader.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function createUnityInstance(e,t,n){function r(e){var t="unhandledrejection"==e.type&&"object"==typeof e.reason?e.reason:"object"==typeof e.error?e.error:null,n=t?t.toString():"string"==typeof e.message?e.message:"string"==typeof e.reason?e.reason:"";if(t&&"string"==typeof t.stack&&(n+="\n"+t.stack.substring(t.stack.lastIndexOf(n,0)?0:n.length).replace(/(^\n*|\n*$)/g,"")),n&&c.stackTraceRegExp&&c.stackTraceRegExp.test(n)){var r=e instanceof ErrorEvent?e.filename:t&&"string"==typeof t.fileName?t.fileName:t&&"string"==typeof t.sourceURL?t.sourceURL:"",a=e instanceof ErrorEvent?e.lineno:t&&"number"==typeof t.lineNumber?t.lineNumber:t&&"number"==typeof t.line?t.line:0;i(n,r,a)}}function a(e){e.preventDefault()}function i(e,t,n){if(c.startupErrorHandler)return void c.startupErrorHandler(e,t,n);if(!(c.errorHandler&&c.errorHandler(e,t,n)||(console.log("Invoking error handler due to\n"+e),"function"==typeof dump&&dump("Invoking error handler due to\n"+e),e.indexOf("UnknownError")!=-1||e.indexOf("Program terminated with exit(0)")!=-1||i.didShowErrorMessage))){var e="An error occurred running the Unity content on this page. See your browser JavaScript console for more info. The error was:\n"+e;e.indexOf("DISABLE_EXCEPTION_CATCHING")!=-1?e="An exception has occurred, but exception handling has been disabled in this build. If you are the developer of this content, enable exceptions in your project WebGL player settings to be able to catch the exception or see the stack trace.":e.indexOf("Cannot enlarge memory arrays")!=-1?e="Out of memory. If you are the developer of this content, try allocating more memory to your WebGL build in the WebGL player settings.":e.indexOf("Invalid array buffer length")==-1&&e.indexOf("Invalid typed array length")==-1&&e.indexOf("out of memory")==-1&&e.indexOf("could not allocate memory")==-1||(e="The browser could not allocate enough memory for the WebGL content. If you are the developer of this content, try allocating less memory to your WebGL build in the WebGL player settings."),alert(e),i.didShowErrorMessage=!0}}function o(e,t){if("symbolsUrl"!=e){var r=c.downloadProgress[e];r||(r=c.downloadProgress[e]={started:!1,finished:!1,lengthComputable:!1,total:0,loaded:0}),"object"!=typeof t||"progress"!=t.type&&"load"!=t.type||(r.started||(r.started=!0,r.lengthComputable=t.lengthComputable,r.total=t.total),r.loaded=t.loaded,"load"==t.type&&(r.finished=!0));var a=0,i=0,o=0,s=0,l=0;for(var e in c.downloadProgress){var r=c.downloadProgress[e];if(!r.started)return 0;o++,r.lengthComputable?(a+=r.loaded,i+=r.total,s++):r.finished||l++}var d=o?(o-l-(i?s*(i-a)/i:0))/o:0;n(.9*d)}}function s(e,t,n){for(var r in m)if(m[r].hasUnityMarker(e)){t&&console.log('You can reduce startup time if you configure your web server to add "Content-Encoding: '+r+'" response header when serving "'+t+'" file.');var a=m[r];if(!a.worker){var i=URL.createObjectURL(new Blob(["this.require = ",a.require.toString(),"; 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e.next_out=s,e.avail_out=d,e.next_in=o,e.avail_in=l,n.hold=f,n.bits=h,(n.wsize||m!==e.avail_out&&n.mode<fe&&(n.mode<de||t!==L))&&c(e,e.output,e.next_out,m-e.avail_out)?(n.mode=he,T):(p-=e.avail_in,m-=e.avail_out,e.total_in+=p,e.total_out+=m,n.total+=m,n.wrap&&m&&(e.adler=n.check=n.flags?y(n.check,i,m,e.next_out-m):v(n.check,i,m,e.next_out-m)),e.data_type=n.bits+(n.last?64:0)+(n.mode===K?128:0)+(n.mode===ne||n.mode===Q?256:0),(0===p&&0===m||t===L)&&_e===R&&(_e=N),_e)}function h(e){if(!e||!e.state)return A;var t=e.state;return t.window&&(t.window=null),e.state=null,R}function p(e,t){var n;return e&&e.state?(n=e.state,0===(2&n.wrap)?A:(n.head=t,t.done=!1,R)):A}function m(e,t){var n,r,a,i=t.length;return e&&e.state?(n=e.state,0!==n.wrap&&n.mode!==X?A:n.mode===X&&(r=1,r=v(r,t,i,0),r!==n.check)?I:(a=c(e,t,i,i))?(n.mode=he,T):(n.havedict=1,R)):A}var 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diff --git a/spaces/Vokturz/can-it-run-llm/src/app.py b/spaces/Vokturz/can-it-run-llm/src/app.py
deleted file mode 100644
index c9c889e4b741e48c543e7421a2deb4da4848655d..0000000000000000000000000000000000000000
--- a/spaces/Vokturz/can-it-run-llm/src/app.py
+++ /dev/null
@@ -1,211 +0,0 @@
-import streamlit as st
-from streamlit_datalist import stDatalist
-import pandas as pd
-from utils import extract_from_url, get_model, calculate_memory
-import plotly.express as px
-import numpy as np
-import gc
-from huggingface_hub import login
-
-st.set_page_config(page_title='Can you run it? LLM version', layout="wide", initial_sidebar_state="expanded")
-
-model_list = [
- "mistralai/Mistral-7B-v0.1",
- "mistralai/Mistral-7B-Instruct-v0.1",
- "ehartford/samantha-mistral-7b",
- "SkunkworksAI/Mistralic-7B-1",
- "microsoft/phi-1_5",
- "PY007/TinyLlama-1.1B-intermediate-step-480k-1T",
- "codellama/CodeLlama-7b-hf",
- "codellama/CodeLlama-13b-hf",
- "codellama/CodeLlama-34b-hf",
- "Phind/Phind-CodeLlama-34B-v2",
- "WizardLM/WizardCoder-Python-34B-V1.0",
- "TheBloke/Llama-2-7B-fp16",
- "TheBloke/Llama-2-13B-fp16",
- "TheBloke/Llama-2-70B-fp16",
- "Gryphe/MythoMax-L2-13b",
- "uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b",
- "lmsys/vicuna-7b-v1.5",
- "lmsys/vicuna-13b-v1.5-16k",
- "lmsys/longchat-7b-v1.5-32k",
- "tiiuae/falcon-7B-Instruct",
- "tiiuae/falcon-7B",
- "tiiuae/falcon-40B",
- "tiiuae/falcon-40B-Instruct",
- "tiiuae/falcon-180B",
- "tiiuae/falcon-180B-Chat",
-]
-st.title("Can you run it? LLM version")
-
-percentage_width_main = 80
-st.markdown(
- f"""<style>
- .appview-container .main .block-container{{
- max-width: {percentage_width_main}%;}}
- </style>
- """,
- unsafe_allow_html=True,
- )
-
-@st.cache_resource()
-def cache_model_list():
- model_list_info = {}
- for model_name in model_list:
- if not "tiiuae/falcon" in model_name: # Exclude Falcon models
- model = get_model(model_name, library="transformers", access_token="")
- model_list_info[model_name] = calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"])
- return model_list_info
-
-@st.cache_resource
-def get_gpu_specs():
- return pd.read_csv("data/gpu_specs.csv")
-
-# @st.cache_resource
-# def get_mistralai_table():
-# model = get_model("mistralai/Mistral-7B-v0.1", library="transformers", access_token="")
-# return calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"])
-
-def show_gpu_info(info, trainable_params=0, vendor=""):
- for var in ['Inference', 'Full Training Adam', 'LoRa Fine-tuning']:
- _info = info.loc[var]
- if vendor != "Apple":
- if _info['Number of GPUs'] >= 3:
- func = st.error
- icon = "⛔"
- elif _info['Number of GPUs'] == 2:
- func = st.warning
- icon = "⚠️"
- else:
- func = st.success
- icon = "✅"
-
- msg = f"You require **{_info['Number of GPUs']}** GPUs for **{var}**"
- if var == 'LoRa Fine-tuning':
- msg += f" ({trainable_params}%)"
- else:
- if _info['Number of GPUs']==1:
- msg = f"You can run **{var}**"
- func = st.success
- icon = "✅"
- else:
- msg = f"You cannot run **{var}**"
- func = st.error
- icon = "⛔"
- func(msg, icon=icon)
-
-
-def get_name(index):
- row = gpu_specs.iloc[index]
- return f"{row['Product Name']} ({row['RAM (GB)']} GB, {row['Year']})"
-
-def custom_ceil(a, precision=0):
- return np.round(a + 0.5 * 10**(-precision), precision)
-
-gpu_specs = get_gpu_specs()
-model_list_info = cache_model_list()
-
-_, col, _ = st.columns([1,3,1])
-with col.expander("Information", expanded=True):
- st.markdown("""- GPU information comes from [TechPowerUp GPU Specs](https://www.techpowerup.com/gpu-specs/)
-- Mainly based on [Model Memory Calculator by hf-accelerate](https://huggingface.co/spaces/hf-accelerate/model-memory-usage)
- using `transformers` library
-- Inference is calculated following [EleutherAI Transformer Math 101](https://blog.eleuther.ai/transformer-math/),
- where is estimated as """)
-
- st.latex(r"""\text{Memory}_\text{Inference} \approx \text{Model Size} \times 1.2""")
- st.markdown("""- For LoRa Fine-tuning, I'm asuming a **16-bit** dtype of trainable parameters. The formula (in terms of GB) is""")
- st.latex(r"\text{Memory}_\text{LoRa} \approx \text{Model Size} + \left(\text{ \# trainable Params}_\text{Billions}\times\frac{16}{8} \times 4\right) \times 1.2")
-
-access_token = st.sidebar.text_input("Access token")
-
-if access_token:
- login(token=access_token)
-
-#model_name = st.sidebar.text_input("Model name", value="mistralai/Mistral-7B-v0.1")
-with st.sidebar.container():
- model_name = stDatalist("Model name (Press Enter to apply)", model_list, index=0)
-if not model_name:
- st.info("Please enter a model name")
- st.stop()
-
-model_name = extract_from_url(model_name)
-if model_name not in st.session_state:
- if 'actual_model' in st.session_state:
- del st.session_state[st.session_state['actual_model']]
- del st.session_state['actual_model']
- gc.collect()
- if model_name in model_list_info.keys():
- st.session_state[model_name] = model_list_info[model_name]
- else:
- model = get_model(model_name, library="transformers", access_token=access_token)
- st.session_state[model_name] = calculate_memory(model, ["float32", "float16/bfloat16", "int8", "int4"])
- del model
- gc.collect()
- st.session_state['actual_model'] = model_name
-
-
-gpu_vendor = st.sidebar.selectbox("GPU Vendor", ["NVIDIA", "AMD", "Intel", "Apple"])
-# year = st.sidebar.selectbox("Filter by Release Year", list(range(2014, 2024))[::-1], index=None)
-gpu_info = gpu_specs[gpu_specs['Vendor'] == gpu_vendor].sort_values('Product Name')
-# if year:
-# gpu_info = gpu_info[gpu_info['Year'] == year]
-
-min_ram = gpu_info['RAM (GB)'].min()
-max_ram = gpu_info['RAM (GB)'].max()
-ram = st.sidebar.slider("Filter by RAM (GB)", min_ram, max_ram, (10.0, 40.0), step=0.5)
-gpu_info = gpu_info[gpu_info["RAM (GB)"].between(ram[0], ram[1])]
-if len(gpu_info) == 0:
- st.sidebar.error(f"**{gpu_vendor}** has no GPU in that RAM range")
- st.stop()
-gpu = st.sidebar.selectbox("GPU", gpu_info['Product Name'].index.tolist(), format_func=lambda x : gpu_specs.iloc[x]['Product Name'])
-gpu_spec = gpu_specs.iloc[gpu]
-gpu_spec.name = 'INFO'
-
-lora_pct = st.sidebar.slider("LoRa % trainable parameters", 0.1, 100.0, 2.0, step=0.1)
-
-st.sidebar.dataframe(gpu_spec.T.astype(str))
-
-memory_table = pd.DataFrame(st.session_state[model_name]).set_index('dtype')
-memory_table['LoRA Fine-Tuning (GB)'] = (memory_table["Total Size (GB)"] +
- (memory_table["Parameters (Billion)"]* lora_pct/100 * (16/8)*4)) * 1.2
-
-_memory_table = memory_table.copy()
-memory_table = memory_table.round(2).T
-_memory_table /= gpu_spec['RAM (GB)']
-_memory_table = _memory_table.apply(np.ceil).astype(int).drop(columns=['Parameters (Billion)', 'Total Size (GB)'])
-_memory_table.columns = ['Inference', 'Full Training Adam', 'LoRa Fine-tuning']
-_memory_table = _memory_table.stack().reset_index()
-_memory_table.columns = ['dtype', 'Variable', 'Number of GPUs']
-col1, col2 = st.columns([1,1.3])
-
-if gpu_vendor == "Apple":
- col.warning("""For M1/M2 Apple chips, PyTorch uses [Metal Performance Shaders (MPS)](https://huggingface.co/docs/accelerate/usage_guides/mps) as backend.\\
-Remember that Apple M1/M2 chips share memory between CPU and GPU.""", icon="⚠️")
-with col1:
- st.write(f"#### [{model_name}](https://huggingface.co/{model_name}) ({custom_ceil(memory_table.iloc[3,0],1):.1f}B)")
-
- dtypes = memory_table.columns.tolist()[::-1]
- tabs = st.tabs(dtypes)
- for dtype, tab in zip(dtypes, tabs):
- with tab:
- if dtype in ["int4", "int8"]:
- _dtype = dtype.replace("int", "")
- st.markdown(f"`int{_dtype}` refers to models in `GPTQ-{_dtype}bit`, `AWQ-{_dtype}bit` or `Q{_dtype}_0 GGUF/GGML`")
- info = _memory_table[_memory_table['dtype'] == dtype].set_index('Variable')
- show_gpu_info(info, lora_pct, gpu_vendor)
- st.write(memory_table.iloc[[0, 1, 2, 4]])
-with col2:
- extra = ""
- if gpu_vendor == "Apple":
- st.warning("This graph is irrelevant for M1/M2 chips as they can't run in parallel.", icon="⚠️")
- extra = "⚠️"
- num_colors= 4
- colors = [px.colors.sequential.RdBu[int(i*(len(px.colors.sequential.RdBu)-1)/(num_colors-1))] for i in range(num_colors)]
- fig = px.bar(_memory_table, x='Variable', y='Number of GPUs', color='dtype', barmode='group', color_discrete_sequence=colors)
- fig.update_layout(title=dict(text=f"{extra} Number of GPUs required for<br> {get_name(gpu)}", font=dict(size=25))
- , xaxis_tickfont_size=14, yaxis_tickfont_size=16, yaxis_dtick='1')
- st.plotly_chart(fig, use_container_width=True)
-
-
-
diff --git a/spaces/Widium/Image-Recreation/README.md b/spaces/Widium/Image-Recreation/README.md
deleted file mode 100644
index 84fab0f2bd2db8ad88f23b721ee195a4476391cd..0000000000000000000000000000000000000000
--- a/spaces/Widium/Image-Recreation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-python_version: 3.9.16
-title: Image Recreation
-emoji: 😶🌫️
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/blocks.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/blocks.py
deleted file mode 100644
index 1995a4bf7339e8deb7eaaffda4f819dda55e7ac7..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/blocks.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import fvcore.nn.weight_init as weight_init
-from torch import nn
-
-from .batch_norm import FrozenBatchNorm2d, get_norm
-from .wrappers import Conv2d
-
-
-"""
-CNN building blocks.
-"""
-
-
-class CNNBlockBase(nn.Module):
- """
- A CNN block is assumed to have input channels, output channels and a stride.
- The input and output of `forward()` method must be NCHW tensors.
- The method can perform arbitrary computation but must match the given
- channels and stride specification.
-
- Attribute:
- in_channels (int):
- out_channels (int):
- stride (int):
- """
-
- def __init__(self, in_channels, out_channels, stride):
- """
- The `__init__` method of any subclass should also contain these arguments.
-
- Args:
- in_channels (int):
- out_channels (int):
- stride (int):
- """
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.stride = stride
-
- def freeze(self):
- """
- Make this block not trainable.
- This method sets all parameters to `requires_grad=False`,
- and convert all BatchNorm layers to FrozenBatchNorm
-
- Returns:
- the block itself
- """
- for p in self.parameters():
- p.requires_grad = False
- FrozenBatchNorm2d.convert_frozen_batchnorm(self)
- return self
-
-
-class DepthwiseSeparableConv2d(nn.Module):
- """
- A kxk depthwise convolution + a 1x1 convolution.
-
- In :paper:`xception`, norm & activation are applied on the second conv.
- :paper:`mobilenet` uses norm & activation on both convs.
- """
-
- def __init__(
- self,
- in_channels,
- out_channels,
- kernel_size=3,
- padding=1,
- dilation=1,
- *,
- norm1=None,
- activation1=None,
- norm2=None,
- activation2=None,
- ):
- """
- Args:
- norm1, norm2 (str or callable): normalization for the two conv layers.
- activation1, activation2 (callable(Tensor) -> Tensor): activation
- function for the two conv layers.
- """
- super().__init__()
- self.depthwise = Conv2d(
- in_channels,
- in_channels,
- kernel_size=kernel_size,
- padding=padding,
- dilation=dilation,
- groups=in_channels,
- bias=not norm1,
- norm=get_norm(norm1, in_channels),
- activation=activation1,
- )
- self.pointwise = Conv2d(
- in_channels,
- out_channels,
- kernel_size=1,
- bias=not norm2,
- norm=get_norm(norm2, out_channels),
- activation=activation2,
- )
-
- # default initialization
- weight_init.c2_msra_fill(self.depthwise)
- weight_init.c2_msra_fill(self.pointwise)
-
- def forward(self, x):
- return self.pointwise(self.depthwise(x))
diff --git a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/meteor/meteor.py b/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/meteor/meteor.py
deleted file mode 100644
index 4ca0d8c6c3c7409ad715a36b2822d3f8cb6850d8..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/coco_caption/pycocoevalcap/meteor/meteor.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python
-
-# Python wrapper for METEOR implementation, by Xinlei Chen
-# Acknowledge Michael Denkowski for the generous discussion and help
-
-# =================================================================
-# This code was pulled from https://github.com/tylin/coco-caption
-# and refactored for Python 3.
-# =================================================================
-
-import os
-import sys
-import subprocess
-import threading
-
-# Assumes meteor-1.5.jar is in the same directory as meteor.py. Change as needed.
-METEOR_JAR = 'meteor-1.5.jar'
-# print METEOR_JAR
-
-class Meteor:
-
- def __init__(self):
- self.meteor_cmd = ['java', '-jar', '-Xmx2G', METEOR_JAR, \
- '-', '-', '-stdio', '-l', 'en', '-norm']
- self.meteor_p = subprocess.Popen(self.meteor_cmd, \
- cwd=os.path.dirname(os.path.abspath(__file__)), \
- stdin=subprocess.PIPE, \
- stdout=subprocess.PIPE, \
- stderr=subprocess.PIPE)
- # Used to guarantee thread safety
- self.lock = threading.Lock()
-
- def compute_score(self, gts, res):
- assert(gts.keys() == res.keys())
- imgIds = gts.keys()
- scores = []
-
- eval_line = 'EVAL'
- self.lock.acquire()
- for i in imgIds:
- assert(len(res[i]) == 1)
- stat = self._stat(res[i][0], gts[i])
- eval_line += ' ||| {}'.format(stat)
-
- self.meteor_p.stdin.write('{}\n'.format(eval_line).encode())
- self.meteor_p.stdin.flush()
- for i in range(0,len(imgIds)):
- scores.append(float(self.meteor_p.stdout.readline().strip()))
- score = float(self.meteor_p.stdout.readline().strip())
- self.lock.release()
-
- return score, scores
-
- def method(self):
- return "METEOR"
-
- def _stat(self, hypothesis_str, reference_list):
- # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words
- hypothesis_str = hypothesis_str.replace('|||','').replace(' ',' ')
- score_line = ' ||| '.join(('SCORE', ' ||| '.join(reference_list), hypothesis_str))
- self.meteor_p.stdin.write('{}\n'.format(score_line).encode())
- self.meteor_p.stdin.flush()
- return self.meteor_p.stdout.readline().decode().strip()
-
- def _score(self, hypothesis_str, reference_list):
- self.lock.acquire()
- # SCORE ||| reference 1 words ||| reference n words ||| hypothesis words
- hypothesis_str = hypothesis_str.replace('|||','').replace(' ',' ')
- score_line = ' ||| '.join(('SCORE', ' ||| '.join(reference_list), hypothesis_str))
- self.meteor_p.stdin.write('{}\n'.format(score_line))
- stats = self.meteor_p.stdout.readline().strip()
- eval_line = 'EVAL ||| {}'.format(stats)
- # EVAL ||| stats
- self.meteor_p.stdin.write('{}\n'.format(eval_line))
- score = float(self.meteor_p.stdout.readline().strip())
- # bug fix: there are two values returned by the jar file, one average, and one all, so do it twice
- # thanks for Andrej for pointing this out
- score = float(self.meteor_p.stdout.readline().strip())
- self.lock.release()
- return score
-
- def __del__(self):
- self.lock.acquire()
- self.meteor_p.stdin.close()
- self.meteor_p.kill()
- self.meteor_p.wait()
- self.lock.release()
\ No newline at end of file
diff --git a/spaces/Yuliang/ECON/lib/pixielib/utils/rotation_converter.py b/spaces/Yuliang/ECON/lib/pixielib/utils/rotation_converter.py
deleted file mode 100644
index e66386d6427db73e50f791a6fd479f085230da51..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/pixielib/utils/rotation_converter.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-""" Rotation Converter
- This function is borrowed from https://github.com/kornia/kornia
-
-ref: https://kornia.readthedocs.io/en/v0.1.2/_modules/torchgeometry/core/conversions.html#
-Repre: euler angle(3), axis angle(3), rotation matrix(3x3), quaternion(4), continuous rotation representation (6)
-batch_rodrigues: axis angle -> matrix
-"""
-pi = torch.Tensor([3.14159265358979323846])
-
-
-def rad2deg(tensor):
- """Function that converts angles from radians to degrees.
-
- See :class:`~torchgeometry.RadToDeg` for details.
-
- Args:
- tensor (Tensor): Tensor of arbitrary shape.
-
- Returns:
- Tensor: Tensor with same shape as input.
-
- Example:
- >>> input = tgm.pi * torch.rand(1, 3, 3)
- >>> output = tgm.rad2deg(input)
- """
- if not torch.is_tensor(tensor):
- raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(tensor)))
-
- return 180.0 * tensor / pi.to(tensor.device).type(tensor.dtype)
-
-
-def deg2rad(tensor):
- """Function that converts angles from degrees to radians.
-
- See :class:`~torchgeometry.DegToRad` for details.
-
- Args:
- tensor (Tensor): Tensor of arbitrary shape.
-
- Returns:
- Tensor: Tensor with same shape as input.
-
- Examples::
-
- >>> input = 360. * torch.rand(1, 3, 3)
- >>> output = tgm.deg2rad(input)
- """
- if not torch.is_tensor(tensor):
- raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(tensor)))
-
- return tensor * pi.to(tensor.device).type(tensor.dtype) / 180.0
-
-
-# to quaternion
-
-
-def euler_to_quaternion(r):
- x = r[..., 0]
- y = r[..., 1]
- z = r[..., 2]
-
- z = z / 2.0
- y = y / 2.0
- x = x / 2.0
- cz = torch.cos(z)
- sz = torch.sin(z)
- cy = torch.cos(y)
- sy = torch.sin(y)
- cx = torch.cos(x)
- sx = torch.sin(x)
- quaternion = torch.zeros_like(r.repeat(1, 2))[..., :4].to(r.device)
- quaternion[..., 0] += cx * cy * cz - sx * sy * sz
- quaternion[..., 1] += cx * sy * sz + cy * cz * sx
- quaternion[..., 2] += cx * cz * sy - sx * cy * sz
- quaternion[..., 3] += cx * cy * sz + sx * cz * sy
- return quaternion
-
-
-def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
- """Convert 3x4 rotation matrix to 4d quaternion vector
-
- This algorithm is based on algorithm described in
- https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201
-
- Args:
- rotation_matrix (Tensor): the rotation matrix to convert.
-
- Return:
- Tensor: the rotation in quaternion
-
- Shape:
- - Input: :math:`(N, 3, 4)`
- - Output: :math:`(N, 4)`
-
- Example:
- >>> input = torch.rand(4, 3, 4) # Nx3x4
- >>> output = tgm.rotation_matrix_to_quaternion(input) # Nx4
- """
- if not torch.is_tensor(rotation_matrix):
- raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(rotation_matrix)))
-
- if len(rotation_matrix.shape) > 3:
- raise ValueError(
- "Input size must be a three dimensional tensor. Got {}".format(rotation_matrix.shape)
- )
- # if not rotation_matrix.shape[-2:] == (3, 4):
- # raise ValueError(
- # "Input size must be a N x 3 x 4 tensor. Got {}".format(
- # rotation_matrix.shape))
-
- rmat_t = torch.transpose(rotation_matrix, 1, 2)
-
- mask_d2 = rmat_t[:, 2, 2] < eps
-
- mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1]
- mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1]
-
- t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
- q0 = torch.stack(
- [
- rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
- t0,
- rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
- rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
- ],
- -1,
- )
- t0_rep = t0.repeat(4, 1).t()
-
- t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
- q1 = torch.stack(
- [
- rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
- rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
- t1,
- rmat_t[:, 1, 2] + rmat_t[:, 2, 1],
- ],
- -1,
- )
- t1_rep = t1.repeat(4, 1).t()
-
- t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
- q2 = torch.stack(
- [
- rmat_t[:, 0, 1] - rmat_t[:, 1, 0],
- rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
- rmat_t[:, 1, 2] + rmat_t[:, 2, 1],
- t2,
- ],
- -1,
- )
- t2_rep = t2.repeat(4, 1).t()
-
- t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
- q3 = torch.stack(
- [
- t3,
- rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
- rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
- rmat_t[:, 0, 1] - rmat_t[:, 1, 0],
- ],
- -1,
- )
- t3_rep = t3.repeat(4, 1).t()
-
- mask_c0 = mask_d2 * mask_d0_d1.float()
- mask_c1 = mask_d2 * (1 - mask_d0_d1.float())
- mask_c2 = (1 - mask_d2.float()) * mask_d0_nd1
- mask_c3 = (1 - mask_d2.float()) * (1 - mask_d0_nd1.float())
- mask_c0 = mask_c0.view(-1, 1).type_as(q0)
- mask_c1 = mask_c1.view(-1, 1).type_as(q1)
- mask_c2 = mask_c2.view(-1, 1).type_as(q2)
- mask_c3 = mask_c3.view(-1, 1).type_as(q3)
-
- q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3
- q /= torch.sqrt(
- t0_rep * mask_c0 + t1_rep * mask_c1 + t2_rep * mask_c2 # noqa
- + t3_rep * mask_c3
- ) # noqa
- q *= 0.5
- return q
-
-
-def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor:
- """Convert an angle axis to a quaternion.
-
- Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h
-
- Args:
- angle_axis (torch.Tensor): tensor with angle axis.
-
- Return:
- torch.Tensor: tensor with quaternion.
-
- Shape:
- - Input: :math:`(*, 3)` where `*` means, any number of dimensions
- - Output: :math:`(*, 4)`
-
- Example:
- >>> angle_axis = torch.rand(2, 4) # Nx4
- >>> quaternion = tgm.angle_axis_to_quaternion(angle_axis) # Nx3
- """
- if not torch.is_tensor(angle_axis):
- raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(angle_axis)))
-
- if not angle_axis.shape[-1] == 3:
- raise ValueError(
- "Input must be a tensor of shape Nx3 or 3. Got {}".format(angle_axis.shape)
- )
- # unpack input and compute conversion
- a0: torch.Tensor = angle_axis[..., 0:1]
- a1: torch.Tensor = angle_axis[..., 1:2]
- a2: torch.Tensor = angle_axis[..., 2:3]
- theta_squared: torch.Tensor = a0 * a0 + a1 * a1 + a2 * a2
-
- theta: torch.Tensor = torch.sqrt(theta_squared)
- half_theta: torch.Tensor = theta * 0.5
-
- mask: torch.Tensor = theta_squared > 0.0
- ones: torch.Tensor = torch.ones_like(half_theta)
-
- k_neg: torch.Tensor = 0.5 * ones
- k_pos: torch.Tensor = torch.sin(half_theta) / theta
- k: torch.Tensor = torch.where(mask, k_pos, k_neg)
- w: torch.Tensor = torch.where(mask, torch.cos(half_theta), ones)
-
- quaternion: torch.Tensor = torch.zeros_like(angle_axis)
- quaternion[..., 0:1] += a0 * k
- quaternion[..., 1:2] += a1 * k
- quaternion[..., 2:3] += a2 * k
- return torch.cat([w, quaternion], dim=-1)
-
-
-# quaternion to
-
-
-def quaternion_to_rotation_matrix(quat):
- """Convert quaternion coefficients to rotation matrix.
- Args:
- quat: size = [B, 4] 4 <===>(w, x, y, z)
- Returns:
- Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
- """
- norm_quat = quat
- norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
- w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3]
-
- B = quat.size(0)
-
- w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
- wx, wy, wz = w * x, w * y, w * z
- xy, xz, yz = x * y, x * z, y * z
-
- rotMat = torch.stack(
- [
- w2 + x2 - y2 - z2,
- 2 * xy - 2 * wz,
- 2 * wy + 2 * xz,
- 2 * wz + 2 * xy,
- w2 - x2 + y2 - z2,
- 2 * yz - 2 * wx,
- 2 * xz - 2 * wy,
- 2 * wx + 2 * yz,
- w2 - x2 - y2 + z2,
- ],
- dim=1,
- ).view(B, 3, 3)
- return rotMat
-
-
-def quaternion_to_angle_axis(quaternion: torch.Tensor):
- """Convert quaternion vector to angle axis of rotation. TODO: CORRECT
-
- Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h
-
- Args:
- quaternion (torch.Tensor): tensor with quaternions.
-
- Return:
- torch.Tensor: tensor with angle axis of rotation.
-
- Shape:
- - Input: :math:`(*, 4)` where `*` means, any number of dimensions
- - Output: :math:`(*, 3)`
-
- Example:
- >>> quaternion = torch.rand(2, 4) # Nx4
- >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion) # Nx3
- """
- if not torch.is_tensor(quaternion):
- raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(quaternion)))
-
- if not quaternion.shape[-1] == 4:
- raise ValueError(
- "Input must be a tensor of shape Nx4 or 4. Got {}".format(quaternion.shape)
- )
- # unpack input and compute conversion
- q1: torch.Tensor = quaternion[..., 1]
- q2: torch.Tensor = quaternion[..., 2]
- q3: torch.Tensor = quaternion[..., 3]
- sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3
-
- sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta)
- cos_theta: torch.Tensor = quaternion[..., 0]
- two_theta: torch.Tensor = 2.0 * torch.where(
- cos_theta < 0.0,
- torch.atan2(-sin_theta, -cos_theta),
- torch.atan2(sin_theta, cos_theta),
- )
-
- k_pos: torch.Tensor = two_theta / sin_theta
- k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta).to(quaternion.device)
- k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg)
-
- angle_axis: torch.Tensor = torch.zeros_like(quaternion).to(quaternion.device)[..., :3]
- angle_axis[..., 0] += q1 * k
- angle_axis[..., 1] += q2 * k
- angle_axis[..., 2] += q3 * k
- return angle_axis
-
-
-# credit to Muhammed Kocabas
-# matrix to euler angle
-# Device = Union[str, torch.device]
-_AXIS_TO_IND = {"x": 0, "y": 1, "z": 2}
-
-
-def _elementary_basis_vector(axis):
- b = torch.zeros(3)
- b[_AXIS_TO_IND[axis]] = 1
- return b
-
-
-def _compute_euler_from_matrix(dcm, seq="xyz", extrinsic=False):
- # The algorithm assumes intrinsic frame transformations. For representation
- # the paper uses transformation matrices, which are transpose of the
- # direction cosine matrices used by our Rotation class.
- # Adapt the algorithm for our case by
- # 1. Instead of transposing our representation, use the transpose of the
- # O matrix as defined in the paper, and be careful to swap indices
- # 2. Reversing both axis sequence and angles for extrinsic rotations
- orig_device = dcm.device
- dcm = dcm.to("cpu")
- seq = seq.lower()
-
- if extrinsic:
- seq = seq[::-1]
-
- if dcm.ndim == 2:
- dcm = dcm[None, :, :]
- num_rotations = dcm.shape[0]
-
- device = dcm.device
-
- # Step 0
- # Algorithm assumes axes as column vectors, here we use 1D vectors
- n1 = _elementary_basis_vector(seq[0])
- n2 = _elementary_basis_vector(seq[1])
- n3 = _elementary_basis_vector(seq[2])
-
- # Step 2
- sl = torch.dot(torch.cross(n1, n2), n3)
- cl = torch.dot(n1, n3)
-
- # angle offset is lambda from the paper referenced in [2] from docstring of
- # `as_euler` function
- offset = torch.atan2(sl, cl)
- c = torch.stack((n2, torch.cross(n1, n2), n1)).type(dcm.dtype).to(device)
-
- # Step 3
- rot = torch.tensor([
- [1, 0, 0],
- [0, cl, sl],
- [0, -sl, cl],
- ]).type(dcm.dtype)
- # import IPython; IPython.embed(); exit
- res = torch.einsum("ij,...jk->...ik", c, dcm)
- dcm_transformed = torch.einsum("...ij,jk->...ik", res, c.T @ rot)
-
- # Step 4
- angles = torch.zeros((num_rotations, 3), dtype=dcm.dtype, device=device)
-
- # Ensure less than unit norm
- positive_unity = dcm_transformed[:, 2, 2] > 1
- negative_unity = dcm_transformed[:, 2, 2] < -1
- dcm_transformed[positive_unity, 2, 2] = 1
- dcm_transformed[negative_unity, 2, 2] = -1
- angles[:, 1] = torch.acos(dcm_transformed[:, 2, 2])
-
- # Steps 5, 6
- eps = 1e-7
- safe1 = torch.abs(angles[:, 1]) >= eps
- safe2 = torch.abs(angles[:, 1] - np.pi) >= eps
-
- # Step 4 (Completion)
- angles[:, 1] += offset
-
- # 5b
- safe_mask = torch.logical_and(safe1, safe2)
- angles[safe_mask,
- 0] = torch.atan2(dcm_transformed[safe_mask, 0, 2], -dcm_transformed[safe_mask, 1, 2])
- angles[safe_mask,
- 2] = torch.atan2(dcm_transformed[safe_mask, 2, 0], dcm_transformed[safe_mask, 2, 1])
- if extrinsic:
- # For extrinsic, set first angle to zero so that after reversal we
- # ensure that third angle is zero
- # 6a
- angles[~safe_mask, 0] = 0
- # 6b
- angles[~safe1, 2] = torch.atan2(
- dcm_transformed[~safe1, 1, 0] - dcm_transformed[~safe1, 0, 1],
- dcm_transformed[~safe1, 0, 0] + dcm_transformed[~safe1, 1, 1],
- )
- # 6c
- angles[~safe2, 2] = -torch.atan2(
- dcm_transformed[~safe2, 1, 0] + dcm_transformed[~safe2, 0, 1],
- dcm_transformed[~safe2, 0, 0] - dcm_transformed[~safe2, 1, 1],
- )
- else:
- # For instrinsic, set third angle to zero
- # 6a
- angles[~safe_mask, 2] = 0
- # 6b
- angles[~safe1, 0] = torch.atan2(
- dcm_transformed[~safe1, 1, 0] - dcm_transformed[~safe1, 0, 1],
- dcm_transformed[~safe1, 0, 0] + dcm_transformed[~safe1, 1, 1],
- )
- # 6c
- angles[~safe2, 0] = torch.atan2(
- dcm_transformed[~safe2, 1, 0] + dcm_transformed[~safe2, 0, 1],
- dcm_transformed[~safe2, 0, 0] - dcm_transformed[~safe2, 1, 1],
- )
-
- # Step 7
- if seq[0] == seq[2]:
- # lambda = 0, so we can only ensure angle2 -> [0, pi]
- adjust_mask = torch.logical_or(angles[:, 1] < 0, angles[:, 1] > np.pi)
- else:
- # lambda = + or - pi/2, so we can ensure angle2 -> [-pi/2, pi/2]
- adjust_mask = torch.logical_or(angles[:, 1] < -np.pi / 2, angles[:, 1] > np.pi / 2)
-
- # Dont adjust gimbal locked angle sequences
- adjust_mask = torch.logical_and(adjust_mask, safe_mask)
-
- angles[adjust_mask, 0] += np.pi
- angles[adjust_mask, 1] = 2 * offset - angles[adjust_mask, 1]
- angles[adjust_mask, 2] -= np.pi
-
- angles[angles < -np.pi] += 2 * np.pi
- angles[angles > np.pi] -= 2 * np.pi
-
- # Step 8
- if not torch.all(safe_mask):
- print(
- "Gimbal lock detected. Setting third angle to zero since"
- "it is not possible to uniquely determine all angles."
- )
-
- # Reverse role of extrinsic and intrinsic rotations, but let third angle be
- # zero for gimbal locked cases
- if extrinsic:
- # angles = angles[:, ::-1]
- angles = torch.flip(
- angles,
- dims=[
- -1,
- ],
- )
-
- angles = angles.to(orig_device)
- return angles
-
-
-# batch converter
-
-
-def batch_euler2axis(r):
- return quaternion_to_angle_axis(euler_to_quaternion(r))
-
-
-def batch_euler2matrix(r):
- return quaternion_to_rotation_matrix(euler_to_quaternion(r))
-
-
-def batch_matrix2euler(rot_mats):
- # Calculates rotation matrix to euler angles
- # Careful for extreme cases of eular angles like [0.0, pi, 0.0]
- # only y biw
- # TODO: add x, z
- sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + rot_mats[:, 1, 0] * rot_mats[:, 1, 0])
- return torch.atan2(-rot_mats[:, 2, 0], sy)
-
-
-def batch_matrix2axis(rot_mats):
- return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats))
-
-
-def batch_axis2matrix(theta):
- # angle axis to rotation matrix
- # theta N x 3
- # return quat2mat(quat)
- # batch_rodrigues
- return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta))
-
-
-def batch_axis2euler(theta):
- return batch_matrix2euler(batch_axis2matrix(theta))
-
-
-def batch_axis2euler(r):
- return rot_mat_to_euler(batch_rodrigues(r))
-
-
-def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):
- """same as batch_matrix2axis
- Calculates the rotation matrices for a batch of rotation vectors
- Parameters
- ----------
- rot_vecs: torch.tensor Nx3
- array of N axis-angle vectors
- Returns
- -------
- R: torch.tensor Nx3x3
- The rotation matrices for the given axis-angle parameters
- Code from smplx/flame, what PS people often use
- """
-
- batch_size = rot_vecs.shape[0]
- device = rot_vecs.device
-
- angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True)
- rot_dir = rot_vecs / angle
-
- cos = torch.unsqueeze(torch.cos(angle), dim=1)
- sin = torch.unsqueeze(torch.sin(angle), dim=1)
-
- # Bx1 arrays
- rx, ry, rz = torch.split(rot_dir, 1, dim=1)
- K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device)
-
- zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device)
- K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1).view((batch_size, 3, 3))
-
- ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0)
- rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K)
- return rot_mat
-
-
-def batch_cont2matrix(module_input):
- """Decoder for transforming a latent representation to rotation matrices
-
- Implements the decoding method described in:
- "On the Continuity of Rotation Representations in Neural Networks"
- Code from https://github.com/vchoutas/expose
- """
- batch_size = module_input.shape[0]
- reshaped_input = module_input.reshape(-1, 3, 2)
-
- # Normalize the first vector
- b1 = F.normalize(reshaped_input[:, :, 0].clone(), dim=1)
-
- dot_prod = torch.sum(b1 * reshaped_input[:, :, 1].clone(), dim=1, keepdim=True)
- # Compute the second vector by finding the orthogonal complement to it
- b2 = F.normalize(reshaped_input[:, :, 1] - dot_prod * b1, dim=1)
- # Finish building the basis by taking the cross product
- b3 = torch.cross(b1, b2, dim=1)
- rot_mats = torch.stack([b1, b2, b3], dim=-1)
-
- return rot_mats.view(batch_size, -1, 3, 3)
diff --git a/spaces/Zeltoria/anime-voice-generator/modules.py b/spaces/Zeltoria/anime-voice-generator/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/Zeltoria/anime-voice-generator/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/aashay26/Next_Word_Prediction/README.md b/spaces/aashay26/Next_Word_Prediction/README.md
deleted file mode 100644
index f4313f0fcbd6d4ffd6a7f0fdab8f068a58a9a0b6..0000000000000000000000000000000000000000
--- a/spaces/aashay26/Next_Word_Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Next Word Prediction
-emoji: 📊
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
deleted file mode 100644
index 722d5d8d71f75486e2db3008907c4eadfca41d63..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .conv_module import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
- """Depthwise separable convolution module.
-
- See https://arxiv.org/pdf/1704.04861.pdf for details.
-
- This module can replace a ConvModule with the conv block replaced by two
- conv block: depthwise conv block and pointwise conv block. The depthwise
- conv block contains depthwise-conv/norm/activation layers. The pointwise
- conv block contains pointwise-conv/norm/activation layers. It should be
- noted that there will be norm/activation layer in the depthwise conv block
- if `norm_cfg` and `act_cfg` are specified.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- Same as that in ``nn._ConvNd``.
- out_channels (int): Number of channels produced by the convolution.
- Same as that in ``nn._ConvNd``.
- kernel_size (int | tuple[int]): Size of the convolving kernel.
- Same as that in ``nn._ConvNd``.
- stride (int | tuple[int]): Stride of the convolution.
- Same as that in ``nn._ConvNd``. Default: 1.
- padding (int | tuple[int]): Zero-padding added to both sides of
- the input. Same as that in ``nn._ConvNd``. Default: 0.
- dilation (int | tuple[int]): Spacing between kernel elements.
- Same as that in ``nn._ConvNd``. Default: 1.
- norm_cfg (dict): Default norm config for both depthwise ConvModule and
- pointwise ConvModule. Default: None.
- act_cfg (dict): Default activation config for both depthwise ConvModule
- and pointwise ConvModule. Default: dict(type='ReLU').
- dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
- 'default', it will be the same as `norm_cfg`. Default: 'default'.
- pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
- 'default', it will be the same as `act_cfg`. Default: 'default'.
- kwargs (optional): Other shared arguments for depthwise and pointwise
- ConvModule. See ConvModule for ref.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- dw_norm_cfg='default',
- dw_act_cfg='default',
- pw_norm_cfg='default',
- pw_act_cfg='default',
- **kwargs):
- super(DepthwiseSeparableConvModule, self).__init__()
- assert 'groups' not in kwargs, 'groups should not be specified'
-
- # if norm/activation config of depthwise/pointwise ConvModule is not
- # specified, use default config.
- dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
- dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
- pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
- pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
- # depthwise convolution
- self.depthwise_conv = ConvModule(
- in_channels,
- in_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=in_channels,
- norm_cfg=dw_norm_cfg,
- act_cfg=dw_act_cfg,
- **kwargs)
-
- self.pointwise_conv = ConvModule(
- in_channels,
- out_channels,
- 1,
- norm_cfg=pw_norm_cfg,
- act_cfg=pw_act_cfg,
- **kwargs)
-
- def forward(self, x):
- x = self.depthwise_conv(x)
- x = self.pointwise_conv(x)
- return x
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/utils.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/utils.py
deleted file mode 100644
index c88208291ab2a605bee9fe6c1a28a443b74c6372..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/mask/utils.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import mmcv
-import numpy as np
-import pycocotools.mask as mask_util
-
-
-def split_combined_polys(polys, poly_lens, polys_per_mask):
- """Split the combined 1-D polys into masks.
-
- A mask is represented as a list of polys, and a poly is represented as
- a 1-D array. In dataset, all masks are concatenated into a single 1-D
- tensor. Here we need to split the tensor into original representations.
-
- Args:
- polys (list): a list (length = image num) of 1-D tensors
- poly_lens (list): a list (length = image num) of poly length
- polys_per_mask (list): a list (length = image num) of poly number
- of each mask
-
- Returns:
- list: a list (length = image num) of list (length = mask num) of \
- list (length = poly num) of numpy array.
- """
- mask_polys_list = []
- for img_id in range(len(polys)):
- polys_single = polys[img_id]
- polys_lens_single = poly_lens[img_id].tolist()
- polys_per_mask_single = polys_per_mask[img_id].tolist()
-
- split_polys = mmcv.slice_list(polys_single, polys_lens_single)
- mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
- mask_polys_list.append(mask_polys)
- return mask_polys_list
-
-
-# TODO: move this function to more proper place
-def encode_mask_results(mask_results):
- """Encode bitmap mask to RLE code.
-
- Args:
- mask_results (list | tuple[list]): bitmap mask results.
- In mask scoring rcnn, mask_results is a tuple of (segm_results,
- segm_cls_score).
-
- Returns:
- list | tuple: RLE encoded mask.
- """
- if isinstance(mask_results, tuple): # mask scoring
- cls_segms, cls_mask_scores = mask_results
- else:
- cls_segms = mask_results
- num_classes = len(cls_segms)
- encoded_mask_results = [[] for _ in range(num_classes)]
- for i in range(len(cls_segms)):
- for cls_segm in cls_segms[i]:
- encoded_mask_results[i].append(
- mask_util.encode(
- np.array(
- cls_segm[:, :, np.newaxis], order='F',
- dtype='uint8'))[0]) # encoded with RLE
- if isinstance(mask_results, tuple):
- return encoded_mask_results, cls_mask_scores
- else:
- return encoded_mask_results
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/utils/collect_env.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/utils/collect_env.py
deleted file mode 100644
index d9bde5c94e3d5ded5b69627bdb39e3b985694d8e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/utils/collect_env.py
+++ /dev/null
@@ -1,29 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-from annotator.uniformer.mmcv.utils import collect_env as collect_base_env
-from annotator.uniformer.mmcv.utils import get_git_hash
-
-import annotator.uniformer.mmseg as mmseg
-
-
-def collect_env():
- """Collect the information of the running environments."""
- env_info = collect_base_env()
- env_info['MMSegmentation'] = f'{mmseg.__version__}+{get_git_hash()[:7]}'
-
- return env_info
-
-
-if __name__ == '__main__':
- for name, val in collect_env().items():
- print('{}: {}'.format(name, val))
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/docs/source/conf.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/docs/source/conf.py
deleted file mode 100644
index 6bf194c375e7e789b334a838953adfeaf2eb59b6..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/docs/source/conf.py
+++ /dev/null
@@ -1,352 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# core documentation build configuration file, created by
-# sphinx-quickstart on Sun Oct 16 14:33:48 2016.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-import sys
-import os
-from pyrender import __version__
-from sphinx.domains.python import PythonDomain
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-sys.path.insert(0, os.path.abspath('../../'))
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.autosummary',
- 'sphinx.ext.coverage',
- 'sphinx.ext.githubpages',
- 'sphinx.ext.intersphinx',
- 'sphinx.ext.napoleon',
- 'sphinx.ext.viewcode',
- 'sphinx_automodapi.automodapi',
- 'sphinx_automodapi.smart_resolver'
-]
-numpydoc_class_members_toctree = False
-automodapi_toctreedirnm = 'generated'
-automodsumm_inherited_members = True
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
-
-# The encoding of source files.
-#source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'index'
-
-# General information about the project.
-project = u'pyrender'
-copyright = u'2018, Matthew Matl'
-author = u'Matthew Matl'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = __version__
-# The full version, including alpha/beta/rc tags.
-release = __version__
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#today = ''
-# Else, today_fmt is used as the format for a strftime call.
-#today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-exclude_patterns = []
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-#default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-#modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-#keep_warnings = False
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-import sphinx_rtd_theme
-html_theme = 'sphinx_rtd_theme'
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further. For a list of options available for each theme, see the
-# documentation.
-#html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-#html_theme_path = []
-
-# The name for this set of Sphinx documents. If None, it defaults to
-# "<project> v<release> documentation".
-#html_title = None
-
-# A shorter title for the navigation bar. Default is the same as html_title.
-#html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-#html_logo = None
-
-# The name of an image file (relative to this directory) to use as a favicon of
-# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-#html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Add any extra paths that contain custom files (such as robots.txt or
-# .htaccess) here, relative to this directory. These files are copied
-# directly to the root of the documentation.
-#html_extra_path = []
-
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
-# using the given strftime format.
-#html_last_updated_fmt = '%b %d, %Y'
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-#html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-#html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-#html_additional_pages = {}
-
-# If false, no module index is generated.
-#html_domain_indices = True
-
-# If false, no index is generated.
-#html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-#html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it. The value of this option must be the
-# base URL from which the finished HTML is served.
-#html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-#html_file_suffix = None
-
-# Language to be used for generating the HTML full-text search index.
-# Sphinx supports the following languages:
-# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
-# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
-#html_search_language = 'en'
-
-# A dictionary with options for the search language support, empty by default.
-# Now only 'ja' uses this config value
-#html_search_options = {'type': 'default'}
-
-# The name of a javascript file (relative to the configuration directory) that
-# implements a search results scorer. If empty, the default will be used.
-#html_search_scorer = 'scorer.js'
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'coredoc'
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-# The paper size ('letterpaper' or 'a4paper').
-#'papersize': 'letterpaper',
-
-# The font size ('10pt', '11pt' or '12pt').
-#'pointsize': '10pt',
-
-# Additional stuff for the LaTeX preamble.
-#'preamble': '',
-
-# Latex figure (float) alignment
-#'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-# author, documentclass [howto, manual, or own class]).
-latex_documents = [
- (master_doc, 'pyrender.tex', u'pyrender Documentation',
- u'Matthew Matl', 'manual'),
-]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-#latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-#latex_use_parts = False
-
-# If true, show page references after internal links.
-#latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-#latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-#latex_appendices = []
-
-# If false, no module index is generated.
-#latex_domain_indices = True
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
- (master_doc, 'pyrender', u'pyrender Documentation',
- [author], 1)
-]
-
-# If true, show URL addresses after external links.
-#man_show_urls = False
-
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-# dir menu entry, description, category)
-texinfo_documents = [
- (master_doc, 'pyrender', u'pyrender Documentation',
- author, 'pyrender', 'One line description of project.',
- 'Miscellaneous'),
-]
-
-# Documents to append as an appendix to all manuals.
-#texinfo_appendices = []
-
-# If false, no module index is generated.
-#texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-#texinfo_show_urls = 'footnote'
-
-# If true, do not generate a @detailmenu in the "Top" node's menu.
-#texinfo_no_detailmenu = False
-
-intersphinx_mapping = {
- 'python' : ('https://docs.python.org/', None),
- 'pyrender' : ('https://pyrender.readthedocs.io/en/latest/', None),
-}
-
-# Autosummary fix
-autosummary_generate = True
-
-# Try to suppress multiple-definition warnings by always taking the shorter
-# path when two or more paths have the same base module
-
-class MyPythonDomain(PythonDomain):
-
- def find_obj(self, env, modname, classname, name, type, searchmode=0):
- """Ensures an object always resolves to the desired module
- if defined there."""
- orig_matches = PythonDomain.find_obj(
- self, env, modname, classname, name, type, searchmode
- )
-
- if len(orig_matches) <= 1:
- return orig_matches
-
- # If multiple matches, try to take the shortest if all the modules are
- # the same
- first_match_name_sp = orig_matches[0][0].split('.')
- base_name = first_match_name_sp[0]
- min_len = len(first_match_name_sp)
- best_match = orig_matches[0]
-
- for match in orig_matches[1:]:
- match_name = match[0]
- match_name_sp = match_name.split('.')
- match_base = match_name_sp[0]
-
- # If we have mismatched bases, return them all to trigger warnings
- if match_base != base_name:
- return orig_matches
-
- # Otherwise, check and see if it's shorter
- if len(match_name_sp) < min_len:
- min_len = len(match_name_sp)
- best_match = match
-
- return (best_match,)
-
-
-def setup(sphinx):
- """Use MyPythonDomain in place of PythonDomain"""
- sphinx.override_domain(MyPythonDomain)
-
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/trackball.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/trackball.py
deleted file mode 100644
index 3e57a0e82d3f07b80754f575c28a0e05cb73fc50..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/trackball.py
+++ /dev/null
@@ -1,216 +0,0 @@
-"""Trackball class for 3D manipulation of viewpoints.
-"""
-import numpy as np
-
-import trimesh.transformations as transformations
-
-
-class Trackball(object):
- """A trackball class for creating camera transforms from mouse movements.
- """
- STATE_ROTATE = 0
- STATE_PAN = 1
- STATE_ROLL = 2
- STATE_ZOOM = 3
-
- def __init__(self, pose, size, scale,
- target=np.array([0.0, 0.0, 0.0])):
- """Initialize a trackball with an initial camera-to-world pose
- and the given parameters.
-
- Parameters
- ----------
- pose : [4,4]
- An initial camera-to-world pose for the trackball.
-
- size : (float, float)
- The width and height of the camera image in pixels.
-
- scale : float
- The diagonal of the scene's bounding box --
- used for ensuring translation motions are sufficiently
- fast for differently-sized scenes.
-
- target : (3,) float
- The center of the scene in world coordinates.
- The trackball will revolve around this point.
- """
- self._size = np.array(size)
- self._scale = float(scale)
-
- self._pose = pose
- self._n_pose = pose
-
- self._target = target
- self._n_target = target
-
- self._state = Trackball.STATE_ROTATE
-
- @property
- def pose(self):
- """autolab_core.RigidTransform : The current camera-to-world pose.
- """
- return self._n_pose
-
- def set_state(self, state):
- """Set the state of the trackball in order to change the effect of
- dragging motions.
-
- Parameters
- ----------
- state : int
- One of Trackball.STATE_ROTATE, Trackball.STATE_PAN,
- Trackball.STATE_ROLL, and Trackball.STATE_ZOOM.
- """
- self._state = state
-
- def resize(self, size):
- """Resize the window.
-
- Parameters
- ----------
- size : (float, float)
- The new width and height of the camera image in pixels.
- """
- self._size = np.array(size)
-
- def down(self, point):
- """Record an initial mouse press at a given point.
-
- Parameters
- ----------
- point : (2,) int
- The x and y pixel coordinates of the mouse press.
- """
- self._pdown = np.array(point, dtype=np.float32)
- self._pose = self._n_pose
- self._target = self._n_target
-
- def drag(self, point):
- """Update the tracball during a drag.
-
- Parameters
- ----------
- point : (2,) int
- The current x and y pixel coordinates of the mouse during a drag.
- This will compute a movement for the trackball with the relative
- motion between this point and the one marked by down().
- """
- point = np.array(point, dtype=np.float32)
- dx, dy = point - self._pdown
- mindim = 0.3 * np.min(self._size)
-
- target = self._target
- x_axis = self._pose[:3,0].flatten()
- y_axis = self._pose[:3,1].flatten()
- z_axis = self._pose[:3,2].flatten()
- eye = self._pose[:3,3].flatten()
-
- # Interpret drag as a rotation
- if self._state == Trackball.STATE_ROTATE:
- x_angle = -dx / mindim
- x_rot_mat = transformations.rotation_matrix(
- x_angle, y_axis, target
- )
-
- y_angle = dy / mindim
- y_rot_mat = transformations.rotation_matrix(
- y_angle, x_axis, target
- )
-
- self._n_pose = y_rot_mat.dot(x_rot_mat.dot(self._pose))
-
- # Interpret drag as a roll about the camera axis
- elif self._state == Trackball.STATE_ROLL:
- center = self._size / 2.0
- v_init = self._pdown - center
- v_curr = point - center
- v_init = v_init / np.linalg.norm(v_init)
- v_curr = v_curr / np.linalg.norm(v_curr)
-
- theta = (-np.arctan2(v_curr[1], v_curr[0]) +
- np.arctan2(v_init[1], v_init[0]))
-
- rot_mat = transformations.rotation_matrix(theta, z_axis, target)
-
- self._n_pose = rot_mat.dot(self._pose)
-
- # Interpret drag as a camera pan in view plane
- elif self._state == Trackball.STATE_PAN:
- dx = -dx / (5.0 * mindim) * self._scale
- dy = -dy / (5.0 * mindim) * self._scale
-
- translation = dx * x_axis + dy * y_axis
- self._n_target = self._target + translation
- t_tf = np.eye(4)
- t_tf[:3,3] = translation
- self._n_pose = t_tf.dot(self._pose)
-
- # Interpret drag as a zoom motion
- elif self._state == Trackball.STATE_ZOOM:
- radius = np.linalg.norm(eye - target)
- ratio = 0.0
- if dy > 0:
- ratio = np.exp(abs(dy) / (0.5 * self._size[1])) - 1.0
- elif dy < 0:
- ratio = 1.0 - np.exp(dy / (0.5 * (self._size[1])))
- translation = -np.sign(dy) * ratio * radius * z_axis
- t_tf = np.eye(4)
- t_tf[:3,3] = translation
- self._n_pose = t_tf.dot(self._pose)
-
- def scroll(self, clicks):
- """Zoom using a mouse scroll wheel motion.
-
- Parameters
- ----------
- clicks : int
- The number of clicks. Positive numbers indicate forward wheel
- movement.
- """
- target = self._target
- ratio = 0.90
-
- mult = 1.0
- if clicks > 0:
- mult = ratio**clicks
- elif clicks < 0:
- mult = (1.0 / ratio)**abs(clicks)
-
- z_axis = self._n_pose[:3,2].flatten()
- eye = self._n_pose[:3,3].flatten()
- radius = np.linalg.norm(eye - target)
- translation = (mult * radius - radius) * z_axis
- t_tf = np.eye(4)
- t_tf[:3,3] = translation
- self._n_pose = t_tf.dot(self._n_pose)
-
- z_axis = self._pose[:3,2].flatten()
- eye = self._pose[:3,3].flatten()
- radius = np.linalg.norm(eye - target)
- translation = (mult * radius - radius) * z_axis
- t_tf = np.eye(4)
- t_tf[:3,3] = translation
- self._pose = t_tf.dot(self._pose)
-
- def rotate(self, azimuth, axis=None):
- """Rotate the trackball about the "Up" axis by azimuth radians.
-
- Parameters
- ----------
- azimuth : float
- The number of radians to rotate.
- """
- target = self._target
-
- y_axis = self._n_pose[:3,1].flatten()
- if axis is not None:
- y_axis = axis
- x_rot_mat = transformations.rotation_matrix(azimuth, y_axis, target)
- self._n_pose = x_rot_mat.dot(self._n_pose)
-
- y_axis = self._pose[:3,1].flatten()
- if axis is not None:
- y_axis = axis
- x_rot_mat = transformations.rotation_matrix(azimuth, y_axis, target)
- self._pose = x_rot_mat.dot(self._pose)
diff --git a/spaces/aijack/jojo/e4e/configs/transforms_config.py b/spaces/aijack/jojo/e4e/configs/transforms_config.py
deleted file mode 100644
index ac12b5d5ba0571f21715e0f6b24b9c1ebe84bf72..0000000000000000000000000000000000000000
--- a/spaces/aijack/jojo/e4e/configs/transforms_config.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from abc import abstractmethod
-import torchvision.transforms as transforms
-
-
-class TransformsConfig(object):
-
- def __init__(self, opts):
- self.opts = opts
-
- @abstractmethod
- def get_transforms(self):
- pass
-
-
-class EncodeTransforms(TransformsConfig):
-
- def __init__(self, opts):
- super(EncodeTransforms, self).__init__(opts)
-
- def get_transforms(self):
- transforms_dict = {
- 'transform_gt_train': transforms.Compose([
- transforms.Resize((256, 256)),
- transforms.RandomHorizontalFlip(0.5),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
- 'transform_source': None,
- 'transform_test': transforms.Compose([
- transforms.Resize((256, 256)),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
- 'transform_inference': transforms.Compose([
- transforms.Resize((256, 256)),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
- }
- return transforms_dict
-
-
-class CarsEncodeTransforms(TransformsConfig):
-
- def __init__(self, opts):
- super(CarsEncodeTransforms, self).__init__(opts)
-
- def get_transforms(self):
- transforms_dict = {
- 'transform_gt_train': transforms.Compose([
- transforms.Resize((192, 256)),
- transforms.RandomHorizontalFlip(0.5),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
- 'transform_source': None,
- 'transform_test': transforms.Compose([
- transforms.Resize((192, 256)),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
- 'transform_inference': transforms.Compose([
- transforms.Resize((192, 256)),
- transforms.ToTensor(),
- transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
- }
- return transforms_dict
diff --git a/spaces/akhaliq/JoJoGAN/e4e/criteria/lpips/__init__.py b/spaces/akhaliq/JoJoGAN/e4e/criteria/lpips/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/akhaliq/Music_Source_Separation/separate_scripts/separate_accompaniment.sh b/spaces/akhaliq/Music_Source_Separation/separate_scripts/separate_accompaniment.sh
deleted file mode 100644
index 1953cf152275e99e0f72e376228faa52d69520e4..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Music_Source_Separation/separate_scripts/separate_accompaniment.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-AUDIO_PATH=${1:-"./resources/vocals_accompaniment_10s.mp3"} # The path of audio to be separated.
-OUTPUT_PATH=${2:-"./sep_results/sep_vocals.mp3"} # The path to write out separated audio.
-
-MODEL_NAME="resunet_subbandtime" # "resunet_ismir2021" | ""resunet_subbandtime""
-
-if [ $MODEL_NAME = "resunet_ismir2021" ]; then
- CHECKPOINT_PATH="./downloaded_checkpoints/resunet143_ismir2021_accompaniment_16.8dB_350k_steps.pth"
- TRAIN_CONFIG_YAML="./scripts/4_train/musdb18/configs/accompaniment-vocals,resunet_ismir2021.yaml"
-
-elif [ $MODEL_NAME = "resunet_subbandtime" ]; then
- TRAIN_CONFIG_YAML="./scripts/4_train/musdb18/configs/accompaniment-vocals,resunet_subbandtime.yaml"
- CHECKPOINT_PATH="./downloaded_checkpoints/resunet143_subbtandtime_accompaniment_16.4dB_350k_steps.pth"
-fi
-
-# Inference
-CUDA_VISIBLE_DEVICES=0 python3 bytesep/inference.py \
- --config_yaml=$TRAIN_CONFIG_YAML \
- --checkpoint_path=$CHECKPOINT_PATH \
- --audio_path=$AUDIO_PATH \
- --output_path=$OUTPUT_PATH
diff --git a/spaces/akhaliq/dreambooth-training/convertosd.py b/spaces/akhaliq/dreambooth-training/convertosd.py
deleted file mode 100644
index b242edb1de11ad551b3c7ad98f5689fef2c3321a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/dreambooth-training/convertosd.py
+++ /dev/null
@@ -1,223 +0,0 @@
-# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
-# *Only* converts the UNet, VAE, and Text Encoder.
-# Does not convert optimizer state or any other thing.
-# Written by jachiam
-
-import argparse
-import os.path as osp
-
-import torch
-
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
- # (stable-diffusion, HF Diffusers)
- ("time_embed.0.weight", "time_embedding.linear_1.weight"),
- ("time_embed.0.bias", "time_embedding.linear_1.bias"),
- ("time_embed.2.weight", "time_embedding.linear_2.weight"),
- ("time_embed.2.bias", "time_embedding.linear_2.bias"),
- ("input_blocks.0.0.weight", "conv_in.weight"),
- ("input_blocks.0.0.bias", "conv_in.bias"),
- ("out.0.weight", "conv_norm_out.weight"),
- ("out.0.bias", "conv_norm_out.bias"),
- ("out.2.weight", "conv_out.weight"),
- ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
- # (stable-diffusion, HF Diffusers)
- ("in_layers.0", "norm1"),
- ("in_layers.2", "conv1"),
- ("out_layers.0", "norm2"),
- ("out_layers.3", "conv2"),
- ("emb_layers.1", "time_emb_proj"),
- ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
- # loop over downblocks/upblocks
-
- for j in range(2):
- # loop over resnets/attentions for downblocks
- hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
- sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
- unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
- if i < 3:
- # no attention layers in down_blocks.3
- hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
- sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
- unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
- for j in range(3):
- # loop over resnets/attentions for upblocks
- hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
- sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
- unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
- if i > 0:
- # no attention layers in up_blocks.0
- hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
- sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
- unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
- if i < 3:
- # no downsample in down_blocks.3
- hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
- sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
- unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
- # no upsample in up_blocks.3
- hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
- sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
- unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
- hf_mid_res_prefix = f"mid_block.resnets.{j}."
- sd_mid_res_prefix = f"middle_block.{2*j}."
- unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
- # buyer beware: this is a *brittle* function,
- # and correct output requires that all of these pieces interact in
- # the exact order in which I have arranged them.
- mapping = {k: k for k in unet_state_dict.keys()}
- for sd_name, hf_name in unet_conversion_map:
- mapping[hf_name] = sd_name
- for k, v in mapping.items():
- if "resnets" in k:
- for sd_part, hf_part in unet_conversion_map_resnet:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- for k, v in mapping.items():
- for sd_part, hf_part in unet_conversion_map_layer:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
- return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
- # (stable-diffusion, HF Diffusers)
- ("nin_shortcut", "conv_shortcut"),
- ("norm_out", "conv_norm_out"),
- ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
- # down_blocks have two resnets
- for j in range(2):
- hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
- sd_down_prefix = f"encoder.down.{i}.block.{j}."
- vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
- if i < 3:
- hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
- sd_downsample_prefix = f"down.{i}.downsample."
- vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
- hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
- sd_upsample_prefix = f"up.{3-i}.upsample."
- vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
- # up_blocks have three resnets
- # also, up blocks in hf are numbered in reverse from sd
- for j in range(3):
- hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
- sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
- vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
- hf_mid_res_prefix = f"mid_block.resnets.{i}."
- sd_mid_res_prefix = f"mid.block_{i+1}."
- vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-vae_conversion_map_attn = [
- # (stable-diffusion, HF Diffusers)
- ("norm.", "group_norm."),
- ("q.", "query."),
- ("k.", "key."),
- ("v.", "value."),
- ("proj_out.", "proj_attn."),
-]
-
-
-def reshape_weight_for_sd(w):
- # convert HF linear weights to SD conv2d weights
- return w.reshape(*w.shape, 1, 1)
-
-
-def convert_vae_state_dict(vae_state_dict):
- mapping = {k: k for k in vae_state_dict.keys()}
- for k, v in mapping.items():
- for sd_part, hf_part in vae_conversion_map:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- for k, v in mapping.items():
- if "attentions" in k:
- for sd_part, hf_part in vae_conversion_map_attn:
- v = v.replace(hf_part, sd_part)
- mapping[k] = v
- new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
- weights_to_convert = ["q", "k", "v", "proj_out"]
- print("[1;32mConverting to CKPT ...")
- for k, v in new_state_dict.items():
- for weight_name in weights_to_convert:
- if f"mid.attn_1.{weight_name}.weight" in k:
- new_state_dict[k] = reshape_weight_for_sd(v)
- return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-# pretty much a no-op
-
-
-def convert_text_enc_state_dict(text_enc_dict):
- return text_enc_dict
-
-
-def convert(model_path, checkpoint_path):
- unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
- vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
- text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
-
- # Convert the UNet model
- unet_state_dict = torch.load(unet_path, map_location='cpu')
- unet_state_dict = convert_unet_state_dict(unet_state_dict)
- unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
-
- # Convert the VAE model
- vae_state_dict = torch.load(vae_path, map_location='cpu')
- vae_state_dict = convert_vae_state_dict(vae_state_dict)
- vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
-
- # Convert the text encoder model
- text_enc_dict = torch.load(text_enc_path, map_location='cpu')
- text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
- text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
-
- # Put together new checkpoint
- state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
-
- state_dict = {k:v.half() for k,v in state_dict.items()}
- state_dict = {"state_dict": state_dict}
- torch.save(state_dict, checkpoint_path)
diff --git a/spaces/alamin655/Personas/conversant/utils/demo_utils.py b/spaces/alamin655/Personas/conversant/utils/demo_utils.py
deleted file mode 100644
index 0ec510f0f6e57b8b490cab1ae6fd08afa517997f..0000000000000000000000000000000000000000
--- a/spaces/alamin655/Personas/conversant/utils/demo_utils.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright (c) 2022 Cohere Inc. and its affiliates.
-#
-# Licensed under the MIT License (the "License");
-# you may not use this file except in compliance with the License.
-#
-# You may obtain a copy of the License in the LICENSE file at the top
-# level of this repository.
-
-import codecs
-import os
-import pickle
-import sys
-from typing import Type
-
-import cohere
-from streamlit.web import cli as stcli
-
-import conversant
-from conversant.chatbot import Chatbot
-
-
-def encode_chatbot(chatbot: Type[Chatbot]) -> str:
- """Serialize and encode a Chatbot object to a base-64 string encoding.
-
- Args:
- chatbot (object): a chatbot of class inherited from Chatbot
-
- Returns:
- str: Chatbot object as a base-64 string
- """
- chatbot.co = None
- return codecs.encode(pickle.dumps(chatbot), "base64").decode()
-
-
-def decode_chatbot(chatbot_string: str, client: cohere.Client) -> Type[Chatbot]:
- """Decode and deserialize a Chatbot object.
-
- Args:
- obj_string (str): a base-64 string encoding
-
- Returns:
- Type[Chatbot]: a chatbot of class inherited rom Chatbot
- """
- chatbot = pickle.loads(codecs.decode(chatbot_string.encode(), "base64"))
- chatbot.co = client
- return chatbot
-
-
-def launch_streamlit(chatbot: Type[Chatbot]) -> None:
- """Launches a demo of a chatbot using Streamlit.
-
- The bot will be a persona available for chatting using the interface
- defined in conversant/demo/streamlit_example.py.
-
- Args:
- bot (Type[Chatbot]): a chatbot of class inherited from Chatbot
- """
- path = os.path.dirname(conversant.__file__)
- sys.argv = f"streamlit run {path}/demo/streamlit_example.py --".split(" ")
- sys.argv.append(encode_chatbot(chatbot))
- sys.exit(stcli.main())
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/align.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/align.py
deleted file mode 100644
index 4344ae141c5ee4fbf8b30b5f212cd3ffe7d92e36..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/align.py
+++ /dev/null
@@ -1,312 +0,0 @@
-import sys
-from itertools import chain
-from typing import TYPE_CHECKING, Iterable, Optional
-
-if sys.version_info >= (3, 8):
- from typing import Literal
-else:
- from pip._vendor.typing_extensions import Literal # pragma: no cover
-
-from .constrain import Constrain
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import StyleType
-
-if TYPE_CHECKING:
- from .console import Console, ConsoleOptions, RenderableType, RenderResult
-
-AlignMethod = Literal["left", "center", "right"]
-VerticalAlignMethod = Literal["top", "middle", "bottom"]
-AlignValues = AlignMethod # TODO: deprecate AlignValues
-
-
-class Align(JupyterMixin):
- """Align a renderable by adding spaces if necessary.
-
- Args:
- renderable (RenderableType): A console renderable.
- align (AlignMethod): One of "left", "center", or "right""
- style (StyleType, optional): An optional style to apply to the background.
- vertical (Optional[VerticalAlginMethod], optional): Optional vertical align, one of "top", "middle", or "bottom". Defaults to None.
- pad (bool, optional): Pad the right with spaces. Defaults to True.
- width (int, optional): Restrict contents to given width, or None to use default width. Defaults to None.
- height (int, optional): Set height of align renderable, or None to fit to contents. Defaults to None.
-
- Raises:
- ValueError: if ``align`` is not one of the expected values.
- """
-
- def __init__(
- self,
- renderable: "RenderableType",
- align: AlignMethod = "left",
- style: Optional[StyleType] = None,
- *,
- vertical: Optional[VerticalAlignMethod] = None,
- pad: bool = True,
- width: Optional[int] = None,
- height: Optional[int] = None,
- ) -> None:
- if align not in ("left", "center", "right"):
- raise ValueError(
- f'invalid value for align, expected "left", "center", or "right" (not {align!r})'
- )
- if vertical is not None and vertical not in ("top", "middle", "bottom"):
- raise ValueError(
- f'invalid value for vertical, expected "top", "middle", or "bottom" (not {vertical!r})'
- )
- self.renderable = renderable
- self.align = align
- self.style = style
- self.vertical = vertical
- self.pad = pad
- self.width = width
- self.height = height
-
- def __repr__(self) -> str:
- return f"Align({self.renderable!r}, {self.align!r})"
-
- @classmethod
- def left(
- cls,
- renderable: "RenderableType",
- style: Optional[StyleType] = None,
- *,
- vertical: Optional[VerticalAlignMethod] = None,
- pad: bool = True,
- width: Optional[int] = None,
- height: Optional[int] = None,
- ) -> "Align":
- """Align a renderable to the left."""
- return cls(
- renderable,
- "left",
- style=style,
- vertical=vertical,
- pad=pad,
- width=width,
- height=height,
- )
-
- @classmethod
- def center(
- cls,
- renderable: "RenderableType",
- style: Optional[StyleType] = None,
- *,
- vertical: Optional[VerticalAlignMethod] = None,
- pad: bool = True,
- width: Optional[int] = None,
- height: Optional[int] = None,
- ) -> "Align":
- """Align a renderable to the center."""
- return cls(
- renderable,
- "center",
- style=style,
- vertical=vertical,
- pad=pad,
- width=width,
- height=height,
- )
-
- @classmethod
- def right(
- cls,
- renderable: "RenderableType",
- style: Optional[StyleType] = None,
- *,
- vertical: Optional[VerticalAlignMethod] = None,
- pad: bool = True,
- width: Optional[int] = None,
- height: Optional[int] = None,
- ) -> "Align":
- """Align a renderable to the right."""
- return cls(
- renderable,
- "right",
- style=style,
- vertical=vertical,
- pad=pad,
- width=width,
- height=height,
- )
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
- align = self.align
- width = console.measure(self.renderable, options=options).maximum
- rendered = console.render(
- Constrain(
- self.renderable, width if self.width is None else min(width, self.width)
- ),
- options.update(height=None),
- )
- lines = list(Segment.split_lines(rendered))
- width, height = Segment.get_shape(lines)
- lines = Segment.set_shape(lines, width, height)
- new_line = Segment.line()
- excess_space = options.max_width - width
- style = console.get_style(self.style) if self.style is not None else None
-
- def generate_segments() -> Iterable[Segment]:
- if excess_space <= 0:
- # Exact fit
- for line in lines:
- yield from line
- yield new_line
-
- elif align == "left":
- # Pad on the right
- pad = Segment(" " * excess_space, style) if self.pad else None
- for line in lines:
- yield from line
- if pad:
- yield pad
- yield new_line
-
- elif align == "center":
- # Pad left and right
- left = excess_space // 2
- pad = Segment(" " * left, style)
- pad_right = (
- Segment(" " * (excess_space - left), style) if self.pad else None
- )
- for line in lines:
- if left:
- yield pad
- yield from line
- if pad_right:
- yield pad_right
- yield new_line
-
- elif align == "right":
- # Padding on left
- pad = Segment(" " * excess_space, style)
- for line in lines:
- yield pad
- yield from line
- yield new_line
-
- blank_line = (
- Segment(f"{' ' * (self.width or options.max_width)}\n", style)
- if self.pad
- else Segment("\n")
- )
-
- def blank_lines(count: int) -> Iterable[Segment]:
- if count > 0:
- for _ in range(count):
- yield blank_line
-
- vertical_height = self.height or options.height
- iter_segments: Iterable[Segment]
- if self.vertical and vertical_height is not None:
- if self.vertical == "top":
- bottom_space = vertical_height - height
- iter_segments = chain(generate_segments(), blank_lines(bottom_space))
- elif self.vertical == "middle":
- top_space = (vertical_height - height) // 2
- bottom_space = vertical_height - top_space - height
- iter_segments = chain(
- blank_lines(top_space),
- generate_segments(),
- blank_lines(bottom_space),
- )
- else: # self.vertical == "bottom":
- top_space = vertical_height - height
- iter_segments = chain(blank_lines(top_space), generate_segments())
- else:
- iter_segments = generate_segments()
- if self.style:
- style = console.get_style(self.style)
- iter_segments = Segment.apply_style(iter_segments, style)
- yield from iter_segments
-
- def __rich_measure__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> Measurement:
- measurement = Measurement.get(console, options, self.renderable)
- return measurement
-
-
-class VerticalCenter(JupyterMixin):
- """Vertically aligns a renderable.
-
- Warn:
- This class is deprecated and may be removed in a future version. Use Align class with
- `vertical="middle"`.
-
- Args:
- renderable (RenderableType): A renderable object.
- """
-
- def __init__(
- self,
- renderable: "RenderableType",
- style: Optional[StyleType] = None,
- ) -> None:
- self.renderable = renderable
- self.style = style
-
- def __repr__(self) -> str:
- return f"VerticalCenter({self.renderable!r})"
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
- style = console.get_style(self.style) if self.style is not None else None
- lines = console.render_lines(
- self.renderable, options.update(height=None), pad=False
- )
- width, _height = Segment.get_shape(lines)
- new_line = Segment.line()
- height = options.height or options.size.height
- top_space = (height - len(lines)) // 2
- bottom_space = height - top_space - len(lines)
- blank_line = Segment(f"{' ' * width}", style)
-
- def blank_lines(count: int) -> Iterable[Segment]:
- for _ in range(count):
- yield blank_line
- yield new_line
-
- if top_space > 0:
- yield from blank_lines(top_space)
- for line in lines:
- yield from line
- yield new_line
- if bottom_space > 0:
- yield from blank_lines(bottom_space)
-
- def __rich_measure__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> Measurement:
- measurement = Measurement.get(console, options, self.renderable)
- return measurement
-
-
-if __name__ == "__main__": # pragma: no cover
- from pip._vendor.rich.console import Console, Group
- from pip._vendor.rich.highlighter import ReprHighlighter
- from pip._vendor.rich.panel import Panel
-
- highlighter = ReprHighlighter()
- console = Console()
-
- panel = Panel(
- Group(
- Align.left(highlighter("align='left'")),
- Align.center(highlighter("align='center'")),
- Align.right(highlighter("align='right'")),
- ),
- width=60,
- style="on dark_blue",
- title="Algin",
- )
-
- console.print(
- Align.center(panel, vertical="middle", style="on red", height=console.height)
- )
diff --git a/spaces/algomuffin/jojo_fork/e4e/options/__init__.py b/spaces/algomuffin/jojo_fork/e4e/options/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ali-ghamdan/realesrgan-models/tests/test_utils.py b/spaces/ali-ghamdan/realesrgan-models/tests/test_utils.py
deleted file mode 100644
index 7919b74905495b4b6f4aa957a1f0b5d7a174c782..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/realesrgan-models/tests/test_utils.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import numpy as np
-from basicsr.archs.rrdbnet_arch import RRDBNet
-
-from realesrgan.utils import RealESRGANer
-
-
-def test_realesrganer():
- # initialize with default model
- restorer = RealESRGANer(
- scale=4,
- model_path='experiments/pretrained_models/RealESRGAN_x4plus.pth',
- model=None,
- tile=10,
- tile_pad=10,
- pre_pad=2,
- half=False)
- assert isinstance(restorer.model, RRDBNet)
- assert restorer.half is False
- # initialize with user-defined model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- restorer = RealESRGANer(
- scale=4,
- model_path='experiments/pretrained_models/RealESRGAN_x4plus_anime_6B.pth',
- model=model,
- tile=10,
- tile_pad=10,
- pre_pad=2,
- half=True)
- # test attribute
- assert isinstance(restorer.model, RRDBNet)
- assert restorer.half is True
-
- # ------------------ test pre_process ---------------- #
- img = np.random.random((12, 12, 3)).astype(np.float32)
- restorer.pre_process(img)
- assert restorer.img.shape == (1, 3, 14, 14)
- # with modcrop
- restorer.scale = 1
- restorer.pre_process(img)
- assert restorer.img.shape == (1, 3, 16, 16)
-
- # ------------------ test process ---------------- #
- restorer.process()
- assert restorer.output.shape == (1, 3, 64, 64)
-
- # ------------------ test post_process ---------------- #
- restorer.mod_scale = 4
- output = restorer.post_process()
- assert output.shape == (1, 3, 60, 60)
-
- # ------------------ test tile_process ---------------- #
- restorer.scale = 4
- img = np.random.random((12, 12, 3)).astype(np.float32)
- restorer.pre_process(img)
- restorer.tile_process()
- assert restorer.output.shape == (1, 3, 64, 64)
-
- # ------------------ test enhance ---------------- #
- img = np.random.random((12, 12, 3)).astype(np.float32)
- result = restorer.enhance(img, outscale=2)
- assert result[0].shape == (24, 24, 3)
- assert result[1] == 'RGB'
-
- # ------------------ test enhance with 16-bit image---------------- #
- img = np.random.random((4, 4, 3)).astype(np.uint16) + 512
- result = restorer.enhance(img, outscale=2)
- assert result[0].shape == (8, 8, 3)
- assert result[1] == 'RGB'
-
- # ------------------ test enhance with gray image---------------- #
- img = np.random.random((4, 4)).astype(np.float32)
- result = restorer.enhance(img, outscale=2)
- assert result[0].shape == (8, 8)
- assert result[1] == 'L'
-
- # ------------------ test enhance with RGBA---------------- #
- img = np.random.random((4, 4, 4)).astype(np.float32)
- result = restorer.enhance(img, outscale=2)
- assert result[0].shape == (8, 8, 4)
- assert result[1] == 'RGBA'
-
- # ------------------ test enhance with RGBA, alpha_upsampler---------------- #
- restorer.tile_size = 0
- img = np.random.random((4, 4, 4)).astype(np.float32)
- result = restorer.enhance(img, outscale=2, alpha_upsampler=None)
- assert result[0].shape == (8, 8, 4)
- assert result[1] == 'RGBA'
diff --git a/spaces/allknowingroger/Image-Models-Test98/README.md b/spaces/allknowingroger/Image-Models-Test98/README.md
deleted file mode 100644
index da07c5a248608c4f6502f7836a3ca38c0f9cd809..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test98/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-duplicated_from: allknowingroger/Image-Models-Test97
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/andaqu/ask-youtube-gpt/README.md b/spaces/andaqu/ask-youtube-gpt/README.md
deleted file mode 100644
index 3d575c0a8724f00f933cc4e16fb74db517112130..0000000000000000000000000000000000000000
--- a/spaces/andaqu/ask-youtube-gpt/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: Ask Youtube GPT
-emoji: 📺
-colorFrom: white
-colorTo: red
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-# Ask Youtube GPT
-
-Ask YouTube GPT allows you to ask questions about a set of YouTube Videos using Topic Segmentation, Universal Sentence Encoding, and Open AI.
-
-## Ideas / Future Improvements
-
-- [x] Omit the need for a set of videos, and instead use a search query to find videos on the fly.
-- [ ] Add "Suggest a question" feature given the videos (maybe through clustering?)
-- [ ] Add explainable segment retrieval (i.e. why did that specific segment get chosen to answer the question?)
-- [ ] Add OpenAI embeddings
-- [ ] Improve the retrieval process allowing for the retrieval to return at least one segment from each video (This would in turn enable comparision of videos, i.e. which video explains topic X in more depth?)
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/scripts/prompts_from_file.py b/spaces/aodianyun/stable-diffusion-webui/scripts/prompts_from_file.py
deleted file mode 100644
index 17c9c967ffeeb3f538c6f95d93ae79c32ca17828..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/scripts/prompts_from_file.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import copy
-import math
-import os
-import random
-import sys
-import traceback
-import shlex
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules import sd_samplers
-from modules.processing import Processed, process_images
-from PIL import Image
-from modules.shared import opts, cmd_opts, state
-
-
-def process_string_tag(tag):
- return tag
-
-
-def process_int_tag(tag):
- return int(tag)
-
-
-def process_float_tag(tag):
- return float(tag)
-
-
-def process_boolean_tag(tag):
- return True if (tag == "true") else False
-
-
-prompt_tags = {
- "sd_model": None,
- "outpath_samples": process_string_tag,
- "outpath_grids": process_string_tag,
- "prompt_for_display": process_string_tag,
- "prompt": process_string_tag,
- "negative_prompt": process_string_tag,
- "styles": process_string_tag,
- "seed": process_int_tag,
- "subseed_strength": process_float_tag,
- "subseed": process_int_tag,
- "seed_resize_from_h": process_int_tag,
- "seed_resize_from_w": process_int_tag,
- "sampler_index": process_int_tag,
- "sampler_name": process_string_tag,
- "batch_size": process_int_tag,
- "n_iter": process_int_tag,
- "steps": process_int_tag,
- "cfg_scale": process_float_tag,
- "width": process_int_tag,
- "height": process_int_tag,
- "restore_faces": process_boolean_tag,
- "tiling": process_boolean_tag,
- "do_not_save_samples": process_boolean_tag,
- "do_not_save_grid": process_boolean_tag
-}
-
-
-def cmdargs(line):
- args = shlex.split(line)
- pos = 0
- res = {}
-
- while pos < len(args):
- arg = args[pos]
-
- assert arg.startswith("--"), f'must start with "--": {arg}'
- assert pos+1 < len(args), f'missing argument for command line option {arg}'
-
- tag = arg[2:]
-
- if tag == "prompt" or tag == "negative_prompt":
- pos += 1
- prompt = args[pos]
- pos += 1
- while pos < len(args) and not args[pos].startswith("--"):
- prompt += " "
- prompt += args[pos]
- pos += 1
- res[tag] = prompt
- continue
-
-
- func = prompt_tags.get(tag, None)
- assert func, f'unknown commandline option: {arg}'
-
- val = args[pos+1]
- if tag == "sampler_name":
- val = sd_samplers.samplers_map.get(val.lower(), None)
-
- res[tag] = func(val)
-
- pos += 2
-
- return res
-
-
-def load_prompt_file(file):
- if file is None:
- lines = []
- else:
- lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
-
- return None, "\n".join(lines), gr.update(lines=7)
-
-
-class Script(scripts.Script):
- def title(self):
- return "Prompts from file or textbox"
-
- def ui(self, is_img2img):
- checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
- checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
-
- prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
- file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
-
- file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
-
- # We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
- # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
- # be unclear to the user that shift-enter is needed.
- prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
- return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
-
- def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
- lines = [x.strip() for x in prompt_txt.splitlines()]
- lines = [x for x in lines if len(x) > 0]
-
- p.do_not_save_grid = True
-
- job_count = 0
- jobs = []
-
- for line in lines:
- if "--" in line:
- try:
- args = cmdargs(line)
- except Exception:
- print(f"Error parsing line {line} as commandline:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
- args = {"prompt": line}
- else:
- args = {"prompt": line}
-
- job_count += args.get("n_iter", p.n_iter)
-
- jobs.append(args)
-
- print(f"Will process {len(lines)} lines in {job_count} jobs.")
- if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
- p.seed = int(random.randrange(4294967294))
-
- state.job_count = job_count
-
- images = []
- all_prompts = []
- infotexts = []
- for n, args in enumerate(jobs):
- state.job = f"{state.job_no + 1} out of {state.job_count}"
-
- copy_p = copy.copy(p)
- for k, v in args.items():
- setattr(copy_p, k, v)
-
- proc = process_images(copy_p)
- images += proc.images
-
- if checkbox_iterate:
- p.seed = p.seed + (p.batch_size * p.n_iter)
- all_prompts += proc.all_prompts
- infotexts += proc.infotexts
-
- return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/audio/numpy_transforms.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/audio/numpy_transforms.py
deleted file mode 100644
index b701e767126b874af47dcb98ac6d7285415d1855..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/audio/numpy_transforms.py
+++ /dev/null
@@ -1,486 +0,0 @@
-from io import BytesIO
-from typing import Tuple
-
-import librosa
-import numpy as np
-import scipy
-import soundfile as sf
-from librosa import magphase, pyin
-
-# For using kwargs
-# pylint: disable=unused-argument
-
-
-def build_mel_basis(
- *,
- sample_rate: int = None,
- fft_size: int = None,
- num_mels: int = None,
- mel_fmax: int = None,
- mel_fmin: int = None,
- **kwargs,
-) -> np.ndarray:
- """Build melspectrogram basis.
-
- Returns:
- np.ndarray: melspectrogram basis.
- """
- if mel_fmax is not None:
- assert mel_fmax <= sample_rate // 2
- assert mel_fmax - mel_fmin > 0
- return librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=mel_fmin, fmax=mel_fmax)
-
-
-def millisec_to_length(
- *, frame_length_ms: int = None, frame_shift_ms: int = None, sample_rate: int = None, **kwargs
-) -> Tuple[int, int]:
- """Compute hop and window length from milliseconds.
-
- Returns:
- Tuple[int, int]: hop length and window length for STFT.
- """
- factor = frame_length_ms / frame_shift_ms
- assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
- win_length = int(frame_length_ms / 1000.0 * sample_rate)
- hop_length = int(win_length / float(factor))
- return win_length, hop_length
-
-
-def _log(x, base):
- if base == 10:
- return np.log10(x)
- return np.log(x)
-
-
-def _exp(x, base):
- if base == 10:
- return np.power(10, x)
- return np.exp(x)
-
-
-def amp_to_db(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
- """Convert amplitude values to decibels.
-
- Args:
- x (np.ndarray): Amplitude spectrogram.
- gain (float): Gain factor. Defaults to 1.
- base (int): Logarithm base. Defaults to 10.
-
- Returns:
- np.ndarray: Decibels spectrogram.
- """
- assert (x < 0).sum() == 0, " [!] Input values must be non-negative."
- return gain * _log(np.maximum(1e-8, x), base)
-
-
-# pylint: disable=no-self-use
-def db_to_amp(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
- """Convert decibels spectrogram to amplitude spectrogram.
-
- Args:
- x (np.ndarray): Decibels spectrogram.
- gain (float): Gain factor. Defaults to 1.
- base (int): Logarithm base. Defaults to 10.
-
- Returns:
- np.ndarray: Amplitude spectrogram.
- """
- return _exp(x / gain, base)
-
-
-def preemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> np.ndarray:
- """Apply pre-emphasis to the audio signal. Useful to reduce the correlation between neighbouring signal values.
-
- Args:
- x (np.ndarray): Audio signal.
-
- Raises:
- RuntimeError: Preemphasis coeff is set to 0.
-
- Returns:
- np.ndarray: Decorrelated audio signal.
- """
- if coef == 0:
- raise RuntimeError(" [!] Preemphasis is set 0.0.")
- return scipy.signal.lfilter([1, -coef], [1], x)
-
-
-def deemphasis(*, x: np.ndarray = None, coef: float = 0.97, **kwargs) -> np.ndarray:
- """Reverse pre-emphasis."""
- if coef == 0:
- raise RuntimeError(" [!] Preemphasis is set 0.0.")
- return scipy.signal.lfilter([1], [1, -coef], x)
-
-
-def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
- """Convert a full scale linear spectrogram output of a network to a melspectrogram.
-
- Args:
- spec (np.ndarray): Normalized full scale linear spectrogram.
-
- Shapes:
- - spec: :math:`[C, T]`
-
- Returns:
- np.ndarray: Normalized melspectrogram.
- """
- return np.dot(mel_basis, spec)
-
-
-def mel_to_spec(*, mel: np.ndarray = None, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
- """Convert a melspectrogram to full scale spectrogram."""
- assert (mel < 0).sum() == 0, " [!] Input values must be non-negative."
- inv_mel_basis = np.linalg.pinv(mel_basis)
- return np.maximum(1e-10, np.dot(inv_mel_basis, mel))
-
-
-def wav_to_spec(*, wav: np.ndarray = None, **kwargs) -> np.ndarray:
- """Compute a spectrogram from a waveform.
-
- Args:
- wav (np.ndarray): Waveform. Shape :math:`[T_wav,]`
-
- Returns:
- np.ndarray: Spectrogram. Shape :math:`[C, T_spec]`. :math:`T_spec == T_wav / hop_length`
- """
- D = stft(y=wav, **kwargs)
- S = np.abs(D)
- return S.astype(np.float32)
-
-
-def wav_to_mel(*, wav: np.ndarray = None, mel_basis=None, **kwargs) -> np.ndarray:
- """Compute a melspectrogram from a waveform."""
- D = stft(y=wav, **kwargs)
- S = spec_to_mel(spec=np.abs(D), mel_basis=mel_basis, **kwargs)
- return S.astype(np.float32)
-
-
-def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray:
- """Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
- S = spec.copy()
- return griffin_lim(spec=S**power, **kwargs)
-
-
-def mel_to_wav(*, mel: np.ndarray = None, power: float = 1.5, **kwargs) -> np.ndarray:
- """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
- S = mel.copy()
- S = mel_to_spec(mel=S, mel_basis=kwargs["mel_basis"]) # Convert back to linear
- return griffin_lim(spec=S**power, **kwargs)
-
-
-### STFT and ISTFT ###
-def stft(
- *,
- y: np.ndarray = None,
- fft_size: int = None,
- hop_length: int = None,
- win_length: int = None,
- pad_mode: str = "reflect",
- window: str = "hann",
- center: bool = True,
- **kwargs,
-) -> np.ndarray:
- """Librosa STFT wrapper.
-
- Check http://librosa.org/doc/main/generated/librosa.stft.html argument details.
-
- Returns:
- np.ndarray: Complex number array.
- """
- return librosa.stft(
- y=y,
- n_fft=fft_size,
- hop_length=hop_length,
- win_length=win_length,
- pad_mode=pad_mode,
- window=window,
- center=center,
- )
-
-
-def istft(
- *,
- y: np.ndarray = None,
- fft_size: int = None,
- hop_length: int = None,
- win_length: int = None,
- window: str = "hann",
- center: bool = True,
- **kwargs,
-) -> np.ndarray:
- """Librosa iSTFT wrapper.
-
- Check http://librosa.org/doc/main/generated/librosa.istft.html argument details.
-
- Returns:
- np.ndarray: Complex number array.
- """
- return librosa.istft(y, hop_length=hop_length, win_length=win_length, center=center, window=window)
-
-
-def griffin_lim(*, spec: np.ndarray = None, num_iter=60, **kwargs) -> np.ndarray:
- angles = np.exp(2j * np.pi * np.random.rand(*spec.shape))
- S_complex = np.abs(spec).astype(complex)
- y = istft(y=S_complex * angles, **kwargs)
- if not np.isfinite(y).all():
- print(" [!] Waveform is not finite everywhere. Skipping the GL.")
- return np.array([0.0])
- for _ in range(num_iter):
- angles = np.exp(1j * np.angle(stft(y=y, **kwargs)))
- y = istft(y=S_complex * angles, **kwargs)
- return y
-
-
-def compute_stft_paddings(
- *, x: np.ndarray = None, hop_length: int = None, pad_two_sides: bool = False, **kwargs
-) -> Tuple[int, int]:
- """Compute paddings used by Librosa's STFT. Compute right padding (final frame) or both sides padding
- (first and final frames)"""
- pad = (x.shape[0] // hop_length + 1) * hop_length - x.shape[0]
- if not pad_two_sides:
- return 0, pad
- return pad // 2, pad // 2 + pad % 2
-
-
-def compute_f0(
- *,
- x: np.ndarray = None,
- pitch_fmax: float = None,
- pitch_fmin: float = None,
- hop_length: int = None,
- win_length: int = None,
- sample_rate: int = None,
- stft_pad_mode: str = "reflect",
- center: bool = True,
- **kwargs,
-) -> np.ndarray:
- """Compute pitch (f0) of a waveform using the same parameters used for computing melspectrogram.
-
- Args:
- x (np.ndarray): Waveform. Shape :math:`[T_wav,]`
- pitch_fmax (float): Pitch max value.
- pitch_fmin (float): Pitch min value.
- hop_length (int): Number of frames between STFT columns.
- win_length (int): STFT window length.
- sample_rate (int): Audio sampling rate.
- stft_pad_mode (str): Padding mode for STFT.
- center (bool): Centered padding.
-
- Returns:
- np.ndarray: Pitch. Shape :math:`[T_pitch,]`. :math:`T_pitch == T_wav / hop_length`
-
- Examples:
- >>> WAV_FILE = filename = librosa.example('vibeace')
- >>> from TTS.config import BaseAudioConfig
- >>> from TTS.utils.audio import AudioProcessor
- >>> conf = BaseAudioConfig(pitch_fmax=640, pitch_fmin=1)
- >>> ap = AudioProcessor(**conf)
- >>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
- >>> pitch = ap.compute_f0(wav)
- """
- assert pitch_fmax is not None, " [!] Set `pitch_fmax` before caling `compute_f0`."
- assert pitch_fmin is not None, " [!] Set `pitch_fmin` before caling `compute_f0`."
-
- f0, voiced_mask, _ = pyin(
- y=x.astype(np.double),
- fmin=pitch_fmin,
- fmax=pitch_fmax,
- sr=sample_rate,
- frame_length=win_length,
- win_length=win_length // 2,
- hop_length=hop_length,
- pad_mode=stft_pad_mode,
- center=center,
- n_thresholds=100,
- beta_parameters=(2, 18),
- boltzmann_parameter=2,
- resolution=0.1,
- max_transition_rate=35.92,
- switch_prob=0.01,
- no_trough_prob=0.01,
- )
- f0[~voiced_mask] = 0.0
-
- return f0
-
-
-def compute_energy(y: np.ndarray, **kwargs) -> np.ndarray:
- """Compute energy of a waveform using the same parameters used for computing melspectrogram.
- Args:
- x (np.ndarray): Waveform. Shape :math:`[T_wav,]`
- Returns:
- np.ndarray: energy. Shape :math:`[T_energy,]`. :math:`T_energy == T_wav / hop_length`
- Examples:
- >>> WAV_FILE = filename = librosa.example('vibeace')
- >>> from TTS.config import BaseAudioConfig
- >>> from TTS.utils.audio import AudioProcessor
- >>> conf = BaseAudioConfig()
- >>> ap = AudioProcessor(**conf)
- >>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
- >>> energy = ap.compute_energy(wav)
- """
- x = stft(y=y, **kwargs)
- mag, _ = magphase(x)
- energy = np.sqrt(np.sum(mag**2, axis=0))
- return energy
-
-
-### Audio Processing ###
-def find_endpoint(
- *,
- wav: np.ndarray = None,
- trim_db: float = -40,
- sample_rate: int = None,
- min_silence_sec=0.8,
- gain: float = None,
- base: int = None,
- **kwargs,
-) -> int:
- """Find the last point without silence at the end of a audio signal.
-
- Args:
- wav (np.ndarray): Audio signal.
- threshold_db (int, optional): Silence threshold in decibels. Defaults to -40.
- min_silence_sec (float, optional): Ignore silences that are shorter then this in secs. Defaults to 0.8.
- gian (float, optional): Gain to be used to convert trim_db to trim_amp. Defaults to None.
- base (int, optional): Base of the logarithm used to convert trim_db to trim_amp. Defaults to 10.
-
- Returns:
- int: Last point without silence.
- """
- window_length = int(sample_rate * min_silence_sec)
- hop_length = int(window_length / 4)
- threshold = db_to_amp(x=-trim_db, gain=gain, base=base)
- for x in range(hop_length, len(wav) - window_length, hop_length):
- if np.max(wav[x : x + window_length]) < threshold:
- return x + hop_length
- return len(wav)
-
-
-def trim_silence(
- *,
- wav: np.ndarray = None,
- sample_rate: int = None,
- trim_db: float = None,
- win_length: int = None,
- hop_length: int = None,
- **kwargs,
-) -> np.ndarray:
- """Trim silent parts with a threshold and 0.01 sec margin"""
- margin = int(sample_rate * 0.01)
- wav = wav[margin:-margin]
- return librosa.effects.trim(wav, top_db=trim_db, frame_length=win_length, hop_length=hop_length)[0]
-
-
-def volume_norm(*, x: np.ndarray = None, coef: float = 0.95, **kwargs) -> np.ndarray:
- """Normalize the volume of an audio signal.
-
- Args:
- x (np.ndarray): Raw waveform.
- coef (float): Coefficient to rescale the maximum value. Defaults to 0.95.
-
- Returns:
- np.ndarray: Volume normalized waveform.
- """
- return x / abs(x).max() * coef
-
-
-def rms_norm(*, wav: np.ndarray = None, db_level: float = -27.0, **kwargs) -> np.ndarray:
- r = 10 ** (db_level / 20)
- a = np.sqrt((len(wav) * (r**2)) / np.sum(wav**2))
- return wav * a
-
-
-def rms_volume_norm(*, x: np.ndarray, db_level: float = -27.0, **kwargs) -> np.ndarray:
- """Normalize the volume based on RMS of the signal.
-
- Args:
- x (np.ndarray): Raw waveform.
- db_level (float): Target dB level in RMS. Defaults to -27.0.
-
- Returns:
- np.ndarray: RMS normalized waveform.
- """
- assert -99 <= db_level <= 0, " [!] db_level should be between -99 and 0"
- wav = rms_norm(wav=x, db_level=db_level)
- return wav
-
-
-def load_wav(*, filename: str, sample_rate: int = None, resample: bool = False, **kwargs) -> np.ndarray:
- """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
-
- Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
-
- Args:
- filename (str): Path to the wav file.
- sr (int, optional): Sampling rate for resampling. Defaults to None.
- resample (bool, optional): Resample the audio file when loading. Slows down the I/O time. Defaults to False.
-
- Returns:
- np.ndarray: Loaded waveform.
- """
- if resample:
- # loading with resampling. It is significantly slower.
- x, _ = librosa.load(filename, sr=sample_rate)
- else:
- # SF is faster than librosa for loading files
- x, _ = sf.read(filename)
- return x
-
-
-def save_wav(*, wav: np.ndarray, path: str, sample_rate: int = None, pipe_out=None, **kwargs) -> None:
- """Save float waveform to a file using Scipy.
-
- Args:
- wav (np.ndarray): Waveform with float values in range [-1, 1] to save.
- path (str): Path to a output file.
- sr (int, optional): Sampling rate used for saving to the file. Defaults to None.
- pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe.
- """
- wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
-
- wav_norm = wav_norm.astype(np.int16)
- if pipe_out:
- wav_buffer = BytesIO()
- scipy.io.wavfile.write(wav_buffer, sample_rate, wav_norm)
- wav_buffer.seek(0)
- pipe_out.buffer.write(wav_buffer.read())
- scipy.io.wavfile.write(path, sample_rate, wav_norm)
-
-
-def mulaw_encode(*, wav: np.ndarray, mulaw_qc: int, **kwargs) -> np.ndarray:
- mu = 2**mulaw_qc - 1
- signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
- signal = (signal + 1) / 2 * mu + 0.5
- return np.floor(
- signal,
- )
-
-
-def mulaw_decode(*, wav, mulaw_qc: int, **kwargs) -> np.ndarray:
- """Recovers waveform from quantized values."""
- mu = 2**mulaw_qc - 1
- x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
- return x
-
-
-def encode_16bits(*, x: np.ndarray, **kwargs) -> np.ndarray:
- return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)
-
-
-def quantize(*, x: np.ndarray, quantize_bits: int, **kwargs) -> np.ndarray:
- """Quantize a waveform to a given number of bits.
-
- Args:
- x (np.ndarray): Waveform to quantize. Must be normalized into the range `[-1, 1]`.
- quantize_bits (int): Number of quantization bits.
-
- Returns:
- np.ndarray: Quantized waveform.
- """
- return (x + 1.0) * (2**quantize_bits - 1) / 2
-
-
-def dequantize(*, x, quantize_bits, **kwargs) -> np.ndarray:
- """Dequantize a waveform from the given number of bits."""
- return 2 * x / (2**quantize_bits - 1) - 1
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/__init__.py
deleted file mode 100644
index 83cf0f3924d16a353695446307dc6c2553adb5cb..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# SelfTest/Signature/__init__.py: Self-test for signature modules
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain. To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# 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.
-# ===================================================================
-
-"""Self-test for signature modules"""
-
-import unittest
-from . import test_pkcs1_15, test_pss, test_dss, test_eddsa
-
-
-def get_tests(config={}):
- tests = []
- tests += test_pkcs1_15.get_tests(config=config)
- tests += test_pss.get_tests(config=config)
- tests += test_dss.get_tests(config=config)
- tests += test_eddsa.get_tests(config=config)
- return tests
-
-
-if __name__ == '__main__':
- def suite():
- return unittest.TestSuite(get_tests())
- unittest.main(defaultTest='suite')
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tests/xmlrunner.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tests/xmlrunner.py
deleted file mode 100644
index d6838aa22ec80ab0b86117c32f7a7ae3ab5a33af..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Tests/xmlrunner.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""unittest-xml-reporting is a PyUnit-based TestRunner that can export test
-results to XML files that can be consumed by a wide range of tools, such as
-build systems, IDEs and Continuous Integration servers.
-
-This module provides the XMLTestRunner class, which is heavily based on the
-default TextTestRunner. This makes the XMLTestRunner very simple to use.
-
-The script below, adapted from the unittest documentation, shows how to use
-XMLTestRunner in a very simple way. In fact, the only difference between this
-script and the original one is the last line:
-
-import random
-import unittest
-import xmlrunner
-
-class TestSequenceFunctions(unittest.TestCase):
- def setUp(self):
- self.seq = range(10)
-
- def test_shuffle(self):
- # make sure the shuffled sequence does not lose any elements
- random.shuffle(self.seq)
- self.seq.sort()
- self.assertEqual(self.seq, range(10))
-
- def test_choice(self):
- element = random.choice(self.seq)
- self.assertTrue(element in self.seq)
-
- def test_sample(self):
- self.assertRaises(ValueError, random.sample, self.seq, 20)
- for element in random.sample(self.seq, 5):
- self.assertTrue(element in self.seq)
-
-if __name__ == '__main__':
- unittest.main(testRunner=xmlrunner.XMLTestRunner(output='test-reports'))
-"""
-
-from __future__ import absolute_import
-
-import os
-import sys
-import time
-from unittest import TestResult, TextTestResult, TextTestRunner
-import xml.dom.minidom
-try:
- from StringIO import StringIO
-except ImportError:
- from io import StringIO # doesn't accept 'str' in Py2
-
-
-class XMLDocument(xml.dom.minidom.Document):
- def createCDATAOrText(self, data):
- if ']]>' in data:
- return self.createTextNode(data)
- return self.createCDATASection(data)
-
-
-class _TestInfo(object):
- """This class is used to keep useful information about the execution of a
- test method.
- """
-
- # Possible test outcomes
- (SUCCESS, FAILURE, ERROR) = range(3)
-
- def __init__(self, test_result, test_method, outcome=SUCCESS, err=None):
- "Create a new instance of _TestInfo."
- self.test_result = test_result
- self.test_method = test_method
- self.outcome = outcome
- self.err = err
- self.stdout = test_result.stdout and test_result.stdout.getvalue().strip() or ''
- self.stderr = test_result.stdout and test_result.stderr.getvalue().strip() or ''
-
- def get_elapsed_time(self):
- """Return the time that shows how long the test method took to
- execute.
- """
- return self.test_result.stop_time - self.test_result.start_time
-
- def get_description(self):
- "Return a text representation of the test method."
- return self.test_result.getDescription(self.test_method)
-
- def get_error_info(self):
- """Return a text representation of an exception thrown by a test
- method.
- """
- if not self.err:
- return ''
- return self.test_result._exc_info_to_string(
- self.err, self.test_method)
-
-
-class _XMLTestResult(TextTestResult):
- """A test result class that can express test results in a XML report.
-
- Used by XMLTestRunner.
- """
- def __init__(self, stream=sys.stderr, descriptions=1, verbosity=1,
- elapsed_times=True):
- "Create a new instance of _XMLTestResult."
- TextTestResult.__init__(self, stream, descriptions, verbosity)
- self.successes = []
- self.callback = None
- self.elapsed_times = elapsed_times
- self.output_patched = False
-
- def _prepare_callback(self, test_info, target_list, verbose_str,
- short_str):
- """Append a _TestInfo to the given target list and sets a callback
- method to be called by stopTest method.
- """
- target_list.append(test_info)
- def callback():
- """This callback prints the test method outcome to the stream,
- as well as the elapsed time.
- """
-
- # Ignore the elapsed times for a more reliable unit testing
- if not self.elapsed_times:
- self.start_time = self.stop_time = 0
-
- if self.showAll:
- self.stream.writeln('(%.3fs) %s' % \
- (test_info.get_elapsed_time(), verbose_str))
- elif self.dots:
- self.stream.write(short_str)
- self.callback = callback
-
- def _patch_standard_output(self):
- """Replace the stdout and stderr streams with string-based streams
- in order to capture the tests' output.
- """
- if not self.output_patched:
- (self.old_stdout, self.old_stderr) = (sys.stdout, sys.stderr)
- self.output_patched = True
- (sys.stdout, sys.stderr) = (self.stdout, self.stderr) = \
- (StringIO(), StringIO())
-
- def _restore_standard_output(self):
- "Restore the stdout and stderr streams."
- (sys.stdout, sys.stderr) = (self.old_stdout, self.old_stderr)
- self.output_patched = False
-
- def startTest(self, test):
- "Called before execute each test method."
- self._patch_standard_output()
- self.start_time = time.time()
- TestResult.startTest(self, test)
-
- if self.showAll:
- self.stream.write(' ' + self.getDescription(test))
- self.stream.write(" ... ")
-
- def stopTest(self, test):
- "Called after execute each test method."
- self._restore_standard_output()
- TextTestResult.stopTest(self, test)
- self.stop_time = time.time()
-
- if self.callback and callable(self.callback):
- self.callback()
- self.callback = None
-
- def addSuccess(self, test):
- "Called when a test executes successfully."
- self._prepare_callback(_TestInfo(self, test),
- self.successes, 'OK', '.')
-
- def addFailure(self, test, err):
- "Called when a test method fails."
- self._prepare_callback(_TestInfo(self, test, _TestInfo.FAILURE, err),
- self.failures, 'FAIL', 'F')
-
- def addError(self, test, err):
- "Called when a test method raises an error."
- self._prepare_callback(_TestInfo(self, test, _TestInfo.ERROR, err),
- self.errors, 'ERROR', 'E')
-
- def printErrorList(self, flavour, errors):
- "Write some information about the FAIL or ERROR to the stream."
- for test_info in errors:
- if isinstance(test_info, tuple):
- test_info, exc_info = test_info
-
- try:
- t = test_info.get_elapsed_time()
- except AttributeError:
- t = 0
- try:
- descr = test_info.get_description()
- except AttributeError:
- try:
- descr = test_info.getDescription()
- except AttributeError:
- descr = str(test_info)
- try:
- err_info = test_info.get_error_info()
- except AttributeError:
- err_info = str(test_info)
-
- self.stream.writeln(self.separator1)
- self.stream.writeln('%s [%.3fs]: %s' % (flavour, t, descr))
- self.stream.writeln(self.separator2)
- self.stream.writeln('%s' % err_info)
-
- def _get_info_by_testcase(self):
- """This method organizes test results by TestCase module. This
- information is used during the report generation, where a XML report
- will be generated for each TestCase.
- """
- tests_by_testcase = {}
-
- for tests in (self.successes, self.failures, self.errors):
- for test_info in tests:
- if not isinstance(test_info, _TestInfo):
- print("Unexpected test result type: %r" % (test_info,))
- continue
- testcase = type(test_info.test_method)
-
- # Ignore module name if it is '__main__'
- module = testcase.__module__ + '.'
- if module == '__main__.':
- module = ''
- testcase_name = module + testcase.__name__
-
- if testcase_name not in tests_by_testcase:
- tests_by_testcase[testcase_name] = []
- tests_by_testcase[testcase_name].append(test_info)
-
- return tests_by_testcase
-
- def _report_testsuite(suite_name, tests, xml_document):
- "Appends the testsuite section to the XML document."
- testsuite = xml_document.createElement('testsuite')
- xml_document.appendChild(testsuite)
-
- testsuite.setAttribute('name', str(suite_name))
- testsuite.setAttribute('tests', str(len(tests)))
-
- testsuite.setAttribute('time', '%.3f' %
- sum([e.get_elapsed_time() for e in tests]))
-
- failures = len([1 for e in tests if e.outcome == _TestInfo.FAILURE])
- testsuite.setAttribute('failures', str(failures))
-
- errors = len([1 for e in tests if e.outcome == _TestInfo.ERROR])
- testsuite.setAttribute('errors', str(errors))
-
- return testsuite
-
- _report_testsuite = staticmethod(_report_testsuite)
-
- def _report_testcase(suite_name, test_result, xml_testsuite, xml_document):
- "Appends a testcase section to the XML document."
- testcase = xml_document.createElement('testcase')
- xml_testsuite.appendChild(testcase)
-
- testcase.setAttribute('classname', str(suite_name))
- testcase.setAttribute('name', test_result.test_method.shortDescription()
- or getattr(test_result.test_method, '_testMethodName',
- str(test_result.test_method)))
- testcase.setAttribute('time', '%.3f' % test_result.get_elapsed_time())
-
- if (test_result.outcome != _TestInfo.SUCCESS):
- elem_name = ('failure', 'error')[test_result.outcome-1]
- failure = xml_document.createElement(elem_name)
- testcase.appendChild(failure)
-
- failure.setAttribute('type', str(test_result.err[0].__name__))
- failure.setAttribute('message', str(test_result.err[1]))
-
- error_info = test_result.get_error_info()
- failureText = xml_document.createCDATAOrText(error_info)
- failure.appendChild(failureText)
-
- _report_testcase = staticmethod(_report_testcase)
-
- def _report_output(test_runner, xml_testsuite, xml_document, stdout, stderr):
- "Appends the system-out and system-err sections to the XML document."
- systemout = xml_document.createElement('system-out')
- xml_testsuite.appendChild(systemout)
-
- systemout_text = xml_document.createCDATAOrText(stdout)
- systemout.appendChild(systemout_text)
-
- systemerr = xml_document.createElement('system-err')
- xml_testsuite.appendChild(systemerr)
-
- systemerr_text = xml_document.createCDATAOrText(stderr)
- systemerr.appendChild(systemerr_text)
-
- _report_output = staticmethod(_report_output)
-
- def generate_reports(self, test_runner):
- "Generates the XML reports to a given XMLTestRunner object."
- all_results = self._get_info_by_testcase()
-
- if type(test_runner.output) == str and not \
- os.path.exists(test_runner.output):
- os.makedirs(test_runner.output)
-
- for suite, tests in all_results.items():
- doc = XMLDocument()
-
- # Build the XML file
- testsuite = _XMLTestResult._report_testsuite(suite, tests, doc)
- stdout, stderr = [], []
- for test in tests:
- _XMLTestResult._report_testcase(suite, test, testsuite, doc)
- if test.stdout:
- stdout.extend(['*****************', test.get_description(), test.stdout])
- if test.stderr:
- stderr.extend(['*****************', test.get_description(), test.stderr])
- _XMLTestResult._report_output(test_runner, testsuite, doc,
- '\n'.join(stdout), '\n'.join(stderr))
- xml_content = doc.toprettyxml(indent='\t')
-
- if type(test_runner.output) is str:
- report_file = open('%s%sTEST-%s.xml' % \
- (test_runner.output, os.sep, suite), 'w')
- try:
- report_file.write(xml_content)
- finally:
- report_file.close()
- else:
- # Assume that test_runner.output is a stream
- test_runner.output.write(xml_content)
-
-
-class XMLTestRunner(TextTestRunner):
- """A test runner class that outputs the results in JUnit like XML files.
- """
- def __init__(self, output='.', stream=None, descriptions=True, verbose=False, elapsed_times=True):
- "Create a new instance of XMLTestRunner."
- if stream is None:
- stream = sys.stderr
- verbosity = (1, 2)[verbose]
- TextTestRunner.__init__(self, stream, descriptions, verbosity)
- self.output = output
- self.elapsed_times = elapsed_times
-
- def _make_result(self):
- """Create the TestResult object which will be used to store
- information about the executed tests.
- """
- return _XMLTestResult(self.stream, self.descriptions, \
- self.verbosity, self.elapsed_times)
-
- def run(self, test):
- "Run the given test case or test suite."
- # Prepare the test execution
- result = self._make_result()
-
- # Print a nice header
- self.stream.writeln()
- self.stream.writeln('Running tests...')
- self.stream.writeln(result.separator2)
-
- # Execute tests
- start_time = time.time()
- test(result)
- stop_time = time.time()
- time_taken = stop_time - start_time
-
- # Generate reports
- self.stream.writeln()
- self.stream.writeln('Generating XML reports...')
- result.generate_reports(self)
-
- # Print results
- result.printErrors()
- self.stream.writeln(result.separator2)
- run = result.testsRun
- self.stream.writeln("Ran %d test%s in %.3fs" %
- (run, run != 1 and "s" or "", time_taken))
- self.stream.writeln()
-
- # Error traces
- if not result.wasSuccessful():
- self.stream.write("FAILED (")
- failed, errored = (len(result.failures), len(result.errors))
- if failed:
- self.stream.write("failures=%d" % failed)
- if errored:
- if failed:
- self.stream.write(", ")
- self.stream.write("errors=%d" % errored)
- self.stream.writeln(")")
- else:
- self.stream.writeln("OK")
-
- return result
diff --git a/spaces/arxnov/anotest/ONNXVITS_utils.py b/spaces/arxnov/anotest/ONNXVITS_utils.py
deleted file mode 100644
index b634ce380421571e6e07fb45dd59717b3f63115c..0000000000000000000000000000000000000000
--- a/spaces/arxnov/anotest/ONNXVITS_utils.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch
-import numpy as np
-import random
-import onnxruntime as ort
-def set_random_seed(seed=0):
- ort.set_seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.backends.cudnn.deterministic = True
- random.seed(seed)
- np.random.seed(seed)
-
-def runonnx(model_path, **kwargs):
- ort_session = ort.InferenceSession(model_path)
- outputs = ort_session.run(
- None,
- kwargs
- )
- return outputs
\ No newline at end of file
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Dipak Rimal.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Dipak Rimal.html
deleted file mode 100644
index f765fcdb62d50f184b095e5a8f0e85921371c36f..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Dipak Rimal.html
+++ /dev/null
@@ -1,132 +0,0 @@
-<!DOCTYPE html><html lang="en"><head>
- <meta charset="utf-8">
- <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no">
- <title>Dipak Rimal
-
-
-
-
-
-
-
-
-Dipak Rimal
- -
-
-
- Mentee to Mentor
Dipak gave an extensive answer on why he wants to be a mentor on the application form.
I did SharpestMinds mentorship with David Staszak when I was transitioning to an industry role from academia. I found the mentorship to be a very helpful support system while I was preparing for that transition. I really appreciated the effort of my mentor and the resources provided by the sharpest mind community. Now that I have spent more than 3 years in my current role, I want to help others help with similar transitions. In my current role, I have been hiring and mentoring Data Science interns and helping them with practical experience data science experience. I am always enthusiastic and passionate about helping others. The process of landing the first DS/ML role can be challenging and frustrating for some. Having a good mentor to help navigate the process can be very helpful. As I have been through the mentorship experience, I want to be a part of it and help others.
1- What helped you most during the mentorship?
- Hands-on experience of building a project along with the mentor was a great help and also get business context compared to academia. Want to help other people who want to make that shift from academia to industry.
2- Previous mentorship/training experience?
- Have hired interns from master programs, mentored PhD students, and have also worked with High school students.
3- According to you, what are the biggest challenge faced by someone making a career transition from academia to business?
- Usually people in academia don't have a lot of coding experience around real world business problems. They also face challenge with resume preparation - as they cover most their research work in the resume which ends up making it a long resume of multiple pages compared to what industry requires is to have precise and consise resumes. Knowing the difference between coming up with the best solution v/s what is the company looking for. Also understanding that some of their skills can be transferable and help them identify those.
4- As a mentee, what activities and initiatives by SharpestMinds were helpful during your association?
- Friday sessions by Jeremy on fundamentals of D.S. on various topics like XGBoost, recommendation systems, etc were helpful.
- Mock interviews & coding interviews were helpful.
-Dipak gave an extensive answer on why he wants to be a mentor on the application form.
I did SharpestMinds mentorship with David Staszak when I was transitioning to an industry role from academia. I found the mentorship to be a very helpful support system while I was preparing for that transition. I really appreciated the effort of my mentor and the resources provided by the sharpest mind community. Now that I have spent more than 3 years in my current role, I want to help others help with similar transitions. In my current role, I have been hiring and mentoring Data Science interns and helping them with practical experience data science experience. I am always enthusiastic and passionate about helping others. The process of landing the first DS/ML role can be challenging and frustrating for some. Having a good mentor to help navigate the process can be very helpful. As I have been through the mentorship experience, I want to be a part of it and help others.
1- What helped you most during the mentorship?
- Hands-on experience of building a project along with the mentor was a great help and also get business context compared to academia. Want to help other people who want to make that shift from academia to industry.
2- Previous mentorship/training experience?
- Have hired interns from master programs, mentored PhD students, and have also worked with High school students.
3- According to you, what are the biggest challenge faced by someone making a career transition from academia to business?
- Usually people in academia don't have a lot of coding experience around real world business problems. They also face challenge with resume preparation - as they cover most their research work in the resume which ends up making it a long resume of multiple pages compared to what industry requires is to have precise and consise resumes. Knowing the difference between coming up with the best solution v/s what is the company looking for. Also understanding that some of their skills can be transferable and help them identify those.
4- As a mentee, what activities and initiatives by SharpestMinds were helpful during your association?
- Friday sessions by Jeremy on fundamentals of D.S. on various topics like XGBoost, recommendation systems, etc were helpful.
- Mock interviews & coding interviews were helpful.