diff --git a/spaces/101-5/gpt4free/g4f/.v1/testing/useless_test.py b/spaces/101-5/gpt4free/g4f/.v1/testing/useless_test.py
deleted file mode 100644
index 47c92386ae925c79aec64891281041cd693077d5..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/testing/useless_test.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from gpt4free import usesless
-
-message_id = ""
-while True:
- prompt = input("Question: ")
- if prompt == "!stop":
- break
-
- req = usesless.Completion.create(prompt=prompt, parentMessageId=message_id)
-
- print(f"Answer: {req['text']}")
- message_id = req["id"]
-
-import gpt4free
-
-message_id = ""
-while True:
- prompt = input("Question: ")
- if prompt == "!stop":
- break
-
- req = gpt4free.Completion.create(provider=gpt4free.Provider.UseLess, prompt=prompt, parentMessageId=message_id)
-
- print(f"Answer: {req['text']}")
- message_id = req["id"]
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Enscape3D System Requirements for Windows and MacOS A Comparison.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Enscape3D System Requirements for Windows and MacOS A Comparison.md
deleted file mode 100644
index 5839ce1535e49940630495643f5252a4b4d3290d..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Enscape3D System Requirements for Windows and MacOS A Comparison.md
+++ /dev/null
@@ -1,54 +0,0 @@
-
-
What You Need to Know About Enscape3D System Requirements
-
-
Enscape3D is a powerful real-time rendering software that works with popular CAD/BIM applications such as Revit, SketchUp, Rhino, Archicad and Vectorworks. Enscape3D allows you to create stunning visualizations, animations and virtual reality experiences with ease and speed. But what are the system requirements to run Enscape3D smoothly and efficiently?
In this article, we will explain the technical requirements to run Enscape3D on Windows and MacOS operating systems, as well as the recommended specifications for optimal performance and VR compatibility. We will also provide some tips on how to optimize your system and project settings for better rendering quality and speed.
-
-
Enscape3D System Requirements for Windows
-
-
Enscape3D uses ray tracing for its real-time rendering, and almost all the calculations that Enscape3D performs are being handled on the graphics card (GPU). For this reason, your computer must at least meet the minimum recommended system requirements set out below . Furthermore, although not a requirement, we do recommend that you use Enscape3D with dual monitors, as Enscape3D is optimized to work on a dual monitor setup.
-
-
The system requirements to run Enscape3D, as well as the standalone executable files that can be exported from Enscape3D, are identical. It is also recommended that your internet connection is fast and stable, and that you should use a direct cable connection and avoid using a Wi-fi connection where possible, as this can slow down the asset library loading times.
-
-
-
-
-
-
Windows OS
-
Minimum Requirements
-
Recommended Requirements
-
VR Requirements
-
-
-
-
-
Operating System
-
Windows 10 or higher Enscape3D will possibly also run where Windows 10 is installed on certain Intel Macs via Bootcamp
-
Windows 10 or higher Enscape3D will possibly also run where Windows 10 is installed on certain Intel Macs via Bootcamp
-
Windows 10 or higher Enscape3D will possibly also run where Windows 10 is installed on certain Intel Macs via Bootcamp
-
-
-
Graphics Card
-
NVIDIA or AMD dedicated GPU with 4GB VRAM that supports Vulkan 1.1 NVIDIA GeForce GTX 900 series / Quadro M series and newer AMD Radeon RX 400 series / equivalent Radeon Pro series and newer Unsupported hardware: Radeon 6000 mobile GPUâs Intel Integrated Graphics onboard GPUâs SLI
-
NVIDIA or AMD dedicated GPU with 8GB VRAM that supports Vulkan 1.1 NVIDIA GeForce RTX 2000 series / Quadro RTX series and newer AMD Radeon RX 5000 series / equivalent Radeon Pro series and newer
-
NVIDIA or AMD dedicated GPU with 8GB VRAM that supports Vulkan 1.1 NVIDIA GeForce RTX 3000 series / Quadro RTX series and newer AMD Radeon RX 6000 series / equivalent Radeon Pro series and newer
-
-
-
CPU
-
Dual core processor (e.g. Intel Core i5) with at least 2.5 GHz clock speed
-
Quad core processor (e.g. Intel Core i7) with at least 3.5 GHz clock speed
-
Six core processor (e.g. Intel Core i9) with at least 4 GHz clock speed
-
-
-
RAM
-
8 GB RAM or more
-
16 GB RAM or more
-
32 GB RAM or more
-
-
-
CAD/BIM Software
-
The Enscape3D plug-in is provided for the following host applications: Revit (2019, 2020, 2021, 2022, and 2023) *SketchUp (2019, 2020, 2021, ddb901b051
-
-
\ No newline at end of file
diff --git a/spaces/1nferno/Single_Digit_Detection/README.md b/spaces/1nferno/Single_Digit_Detection/README.md
deleted file mode 100644
index e373474168c5904a8f89abc696d107fc91c27b7c..0000000000000000000000000000000000000000
--- a/spaces/1nferno/Single_Digit_Detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Single Digit Detection
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.7
-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/1phancelerku/anime-remove-background/Blade Idle A Fun and Easy Idle RPG with Customizable Skills and Equipment.md b/spaces/1phancelerku/anime-remove-background/Blade Idle A Fun and Easy Idle RPG with Customizable Skills and Equipment.md
deleted file mode 100644
index ef647761cd077138a60b33a9e5faf3ac6a22a770..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Blade Idle A Fun and Easy Idle RPG with Customizable Skills and Equipment.md
+++ /dev/null
@@ -1,104 +0,0 @@
-
-
How to Download Blade Idle: A Guide for Android Users
-
If you're looking for a new idle RPG game to play on your Android device, you might want to check out Blade Idle, a simulation game developed by mobirix. In this game, you'll follow the story of a common herb collector who stumbles onto a legendary sword and becomes a great hero. You'll adventure through the main stages and dungeons, and grow your character through farming, merging, and upgrading your equipment. You'll also be able to customize your skills, collect various pets, relics, and insignias, and challenge the upgrade dungeon for different skins.
-
Blade Idle is a game that combines the thrill of action with the convenience of idle gameplay. You can enjoy the game at your own pace, without worrying about missing out on anything. You can also play with other players from around the world, and compete for rankings and rewards. Blade Idle is a game that will keep you entertained for hours, with its rich content, stunning graphics, and engaging storyline.
So how can you download Blade Idle and start playing it right away? There are two main ways to do so: from the Google Play Store or from the BlueStacks emulator. In this article, we'll explain both methods in detail, and also show you how to play Blade Idle on your PC or Mac if you prefer a bigger screen. Let's get started!
-
How to download Blade Idle from Google Play Store
-
The easiest way to download Blade Idle is from the Google Play Store, the official app store for Android devices. Here are the steps you need to follow:
-
-
Open the Google Play Store app on your Android device.
-
Search for "Blade Idle" in the search bar.
-
Tap on the game icon that says "Blade Idle" by mobirix.
-
Tap on the green "Install" button and wait for the download to finish.
-
Tap on the "Open" button or find the game icon on your home screen or app drawer.
-
Enjoy playing Blade Idle!
-
-
That's it! You've successfully downloaded Blade Idle from the Google Play Store. You can now start playing the game and enjoy its features. However, if you don't have access to the Google Play Store or you want to try a different way of downloading Blade Idle, you can also use an emulator.
-
How to download Blade Idle from BlueStacks emulator
-
An emulator is a software that allows you to run Android apps on your PC or Mac. One of the most popular emulators is BlueStacks, which is free and easy to use. With BlueStacks, you can download Blade Idle and play it on your computer with better performance and graphics. Here are the steps you need to follow:
-
-
Download and install BlueStacks on your PC or Mac from [3](https://www.bluestacks.com/apps/simulation/blade-idle-on-pc.html).
-
Launch BlueStacks and sign in with your Google account.
-
Search for "Blade Idle" in the search bar at the top right corner.
-
Click on the game icon that says "Blade Idle" by mobirix.
-
Click on the green "Install" button and wait for the download to finish.
-
Click on the "Open" button or find the game icon on your home screen or app drawer.
-
Enjoy playing Blade Idle!
-
-
Congratulations! You've successfully downloaded Blade Idle from BlueStacks emulator. You can now play the game on your PC or Mac with better controls and features. However, if you want to switch between your Android device and your computer, you can also sync your progress using Facebook or Google Play Games.
-
How
How to play Blade Idle on your PC or Mac
-
If you've downloaded Blade Idle from BlueStacks emulator, you can also play it on your PC or Mac with better graphics and performance. However, you might need to adjust some settings and controls to optimize your gaming experience. Here are some tips and tricks you can use:
-
-
Change the resolution and graphics quality of the game from the settings menu. You can choose from low, medium, high, or ultra settings depending on your device's specifications.
-
Use the keyboard and mouse to control the game. You can customize the key mapping from the BlueStacks settings menu. You can also use the gamepad if you have one connected to your computer.
-
Enable the eco mode to reduce CPU and battery consumption. This will make the game run smoother and faster. You can also enable the multi-instance mode to run multiple games or apps at the same time.
-
Use the screen recorder and screenshot tools to capture your gameplay and share it with your friends. You can also stream your game live on Twitch or YouTube using the BlueStacks streaming mode.
-
Access the in-game chat and social features to communicate with other players and join guilds. You can also use the BlueStacks chat app to chat with other BlueStacks users.
-
-
With these tips and tricks, you can enjoy playing Blade Idle on your PC or Mac with better graphics and performance. You can also switch between your Android device and your computer anytime you want, as long as you sync your progress using Facebook or Google Play Games.
-
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-
Conclusion
-
Blade Idle is a fun and addictive idle RPG game that you can play on your Android device or your PC or Mac. In this game, you'll follow the story of a common herb collector who becomes a great hero with a legendary sword. You'll adventure through various stages and dungeons, and grow your character through farming, merging, and upgrading your equipment. You'll also be able to customize your skills, collect various pets, relics, and insignias, and challenge the upgrade dungeon for different skins.
-
You can download Blade Idle from the Google Play Store or from the BlueStacks emulator. Both methods are easy and fast, and will allow you to start playing the game right away. You can also play Blade Idle on your PC or Mac with better graphics and performance, using some tips and tricks to optimize your gaming experience.
-
Blade Idle is a game that will keep you entertained for hours, with its rich content, stunning graphics, and engaging storyline. You can also play with other players from around the world, and compete for rankings and rewards. Blade Idle is a game that you don't want to miss out on!
-
FAQs
-
Here are some common questions and answers about Blade Idle:
-
-
What are the system requirements for Blade Idle?
-Blade Idle requires Android 4.4 or higher for mobile devices, and Windows 7 or higher or Mac OS X 10.11 or higher for computers. You also need at least 2 GB of RAM and 500 MB of free storage space.
-
How can I get more gold and gems in Blade Idle?
-You can get more gold and gems by completing quests, achievements, daily missions, events, and dungeons. You can also watch ads, spin the roulette wheel, open chests, or buy them with real money.
-
How can I merge and upgrade my equipment in Blade Idle?
-You can merge and upgrade your equipment by dragging two items of the same grade onto each other. This will create a higher grade item with better stats. You can also use upgrade stones to increase the level of your equipment.
-
How can I unlock more skills in Blade Idle?
-You can unlock more skills by reaching certain levels or completing certain stages. You can also use skill books to learn new skills or upgrade existing ones.
-
How can I change my character's appearance in Blade Idle?
-You can change your character's appearance by using different skins. You can get skins by challenging the upgrade dungeon or buying them with gems.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Music Movies and TV Shows with Black Video Player APK.md b/spaces/1phancelerku/anime-remove-background/Enjoy Music Movies and TV Shows with Black Video Player APK.md
deleted file mode 100644
index 6024408b0edcc1f0fb0724b2439a07edef169f4b..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy Music Movies and TV Shows with Black Video Player APK.md
+++ /dev/null
@@ -1,94 +0,0 @@
-
-
Black Video Player APK: A Powerful and Elegant Media Player for Android
-
If you are looking for a media player that can play any video or audio file on your Android device, you should try Black Video Player APK. This is a free and ad-free app that offers a lot of features and benefits for your media enjoyment. In this article, we will tell you what Black Video Player APK is, how to download and install it, how to use it, and why you should choose it.
Black Video Player APK is an app that allows you to play any video or audio file on your Android device. It supports all formats, including MKV, MP4, AVI, MOV, Ogg, FLAC, TS, M2TS, Wv, and AAC. It can also play files from your local storage or from network streams. It has a simple and elegant user interface that makes it easy to use and navigate. It also has a lot of features that enhance your media experience.
-
Features of Black Video Player APK
-
Supports all video and audio formats
-
Black Video Player APK can play any video or audio file that you have on your device or online. You don't need to worry about compatibility issues or converting files. You can enjoy any media content with this app.
-
Plays local and network files
-
Black Video Player APK can play files from your internal or external storage, as well as from network streams. You can access your media library easily with this app. You can also stream videos from online sources, such as YouTube, Vimeo, Dailymotion, etc.
-
Offers gesture controls and subtitles
-
Black Video Player APK gives you full control over your playback with gesture controls. You can swipe left or right to seek forward or backward, swipe up or down to adjust the volume or brightness, double tap to pause or resume, etc. You can also enable subtitles for your videos, and adjust the size, color, position, and timing of them.
-
Customizes playback speed and aspect ratio
-
Black Video Player APK lets you customize your playback speed and aspect ratio according to your preference. You can speed up or slow down the video or audio playback, or change the aspect ratio to fit your screen size. You can also rotate the screen orientation if you want.
-
Enhances video quality and sound effects
-
Black Video Player APK improves the video quality and sound effects of your media files. It has a built-in equalizer that lets you adjust the bass, treble, balance, etc. of your audio output. It also has a video enhancer that enhances the brightness, contrast, saturation, etc. of your video output.
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How to download and install Black Video Player APK?
-
Download the APK file from a trusted source
-
To download Black Video Player APK, you need to find a trusted source that offers the latest version of the app. You can use [this link] to download the APK file.
-
Enable unknown sources on your device settings
-
To install Black Video Player APK, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-
Install the APK file and launch the app
-
After you have downloaded and enabled unknown sources, you can install the APK file. To do this, locate the APK file on your device and tap on it. Follow the instructions on the screen to complete the installation. Once the app is installed, you can launch it from your app drawer or home screen.
-
How to use Black Video Player APK?
-
Browse and select the media file you want to play
-
To use Black Video Player APK, you need to browse and select the media file you want to play. You can do this by tapping on the menu icon on the top left corner of the app and choosing the folder where your media files are stored. You can also tap on the network icon on the top right corner of the app and enter the URL of the online video or audio stream you want to play.
-
Adjust the settings and preferences according to your needs
-
Once you have selected the media file you want to play, you can adjust the settings and preferences according to your needs. You can do this by tapping on the gear icon on the top right corner of the app and choosing the options you want. You can change the playback speed, aspect ratio, subtitle settings, equalizer settings, video enhancer settings, etc.
-
Enjoy your media experience with Black Video Player APK
-
After you have adjusted the settings and preferences, you can enjoy your media experience with Black Video Player APK. You can use the gesture controls to control your playback, or use the buttons on the bottom of the screen. You can also switch between portrait and landscape mode by rotating your device.
-
Why choose Black Video Player APK?
-
Benefits of Black Video Player APK
-
Simple and elegant user interface
-
Black Video Player APK has a simple and elegant user interface that makes it easy to use and navigate. It has a black theme that is pleasing to the eye and reduces eye strain. It also has a minimalistic design that focuses on your media content.
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Smooth and stable performance
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Black Video Player APK has a smooth and stable performance that ensures a high-quality media experience. It has a powerful engine that can handle any video or audio format without lagging or crashing. It also has a low battery consumption that saves your device's power.
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Free and ad-free app
-
Black Video Player APK is a free and ad-free app that does not require any registration or subscription. You can download and use it without any limitations or interruptions. You can also enjoy all its features and benefits without paying anything.
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Compatible with most Android devices
-
Black Video Player APK is compatible with most Android devices that run on Android 5.0 or higher. It can work on any device size, from smartphones to tablets. It can also adapt to any screen resolution, from HD to 4K.
-
Conclusion
-
In conclusion, Black Video Player APK is a powerful and elegant media player for Android that can play any video or audio file on your device or online. It has a lot of features that enhance your media experience, such as gesture controls, subtitles, playback speed, aspect ratio, equalizer, video enhancer, etc. It also has a simple and elegant user interface, a smooth and stable performance, a free and ad-free app, and a compatibility with most Android devices. If you are looking for a media player that can meet all your needs, you should try Black Video Player APK.
- FAQs - Q: Is Black Video Player APK safe to use? - A: Yes, Black Video Player APK is safe to use as long as you download it from a trusted source. It does not contain any malware or viruses that can harm your device or data. - Q: How can I update Black Video Player APK? - A: You can update Black Video Player APK by downloading the latest version of the app from [this link]. You can also check for updates within the app by tapping on the menu icon > About > Check for updates. - Q: How can I share my media files with others using Black Video Player APK? - A: You can share your media files with others using Black Video Player APK by tapping on the share icon on the bottom of the screen. You can choose the app or platform you want to share your media file with, such as WhatsApp, Facebook, Twitter, etc. - Q: How can I delete or uninstall Black Video Player APK? - A: You can delete or uninstall Black Video Player APK by going to your device settings > Apps > Black Video Player APK > Uninstall. You can also long-press the app icon on your home screen or app drawer and drag it to the uninstall option. - Q: How can I contact the developer of Black Video Player APK? - A: You can contact the developer of Black Video Player APK by tapping on the menu icon > About > Contact us. You can also send an email to [this address] or visit [this website]. 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/20four60/Auto-GPT/Dockerfile b/spaces/20four60/Auto-GPT/Dockerfile
deleted file mode 100644
index 29ec24bfb63cdbf2c92fc41c33e24b329aa6e1ca..0000000000000000000000000000000000000000
--- a/spaces/20four60/Auto-GPT/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-FROM zenmldocker/zenml-server:latest
-
-ENV ZENML_ANALYTICS_OPT_IN=true
-ENV ZENML_SERVER_DEPLOYMENT_TYPE="hf_spaces"
-ENV ZENML_LOGGING_VERBOSITY=DEBUG
-
-################################################################################
-#
-# CONFIGURING YOUR ZENML HF SPACES SERVER
-# ---------------------------------------
-# By default this space is not persistent. All ZenML metadata is stored in
-# localstorage in a SQLite database. If you would like to make your storage
-# persistent, use the appropriate environment variables below to configure the
-# image to use a MySQL-compatible database service that is reachable from the
-# container. See https://docs.zenml.io/getting-started/deploying-zenml/docker
-# for more information on how to configure these environment variables.
-
-# You can also configure the secrets store to use for your ZenML server. Be
-# sure to use Huggingface Spaces' 'Repository Secrets' feature to store any
-# secrets referenced here. See
-# https://huggingface.co/docs/hub/spaces-overview#managing-secrets for more
-# information on how to configure these environment variables.
-
-# ENV ZENML_DEFAULT_PROJECT_NAME=""
-# ENV ZENML_DEFAULT_USER_NAME=""
-# ENV ZENML_DEFAULT_USER_PASSWORD=""
-# ENV ZENML_STORE_URL=""
-# ENV ZENML_STORE_SSL_CA=""
-# ENV ZENML_STORE_SSL_CERT=""
-# ENV ZENML_STORE_SSL_KEY=""
-# ENV ZENML_STORE_SSL_VERIFY_SERVER_CERT=""
-
-# ENV ZENML_LOGGING_VERBOSITY=""
-
-# # SECRETS STORE CONFIGURATION
-# ENV ZENML_SECRETS_STORE_TYPE=""
-# ENV ZENML_SECRETS_STORE_ENCRYPTION_KEY=""
-# ENV ZENML_SECRETS_STORE_CLASS_PATH=""
-# ENV ZENML_JWT_SECRET_KEY=""
-
-# # AWS Secrets Store Configuration
-# ENV ZENML_SECRETS_STORE_REGION_NAME=""
-# ENV ZENML_SECRETS_STORE_AWS_ACCESS_KEY_ID=""
-# ENV ZENML_SECRETS_STORE_AWS_SECRET_ACCESS_KEY=""
-# ENV ZENML_SECRETS_STORE_AWS_SESSION_TOKEN=""
-# ENV ZENML_SECRETS_STORE_SECRET_LIST_REFRESH_TIMEOUT=""
-
-# # GCP Secrets Store Configuration
-# ENV ZENML_SECRETS_STORE_PROJECT_ID=""
-# ENV GOOGLE_APPLICATION_CREDENTIALS=""
-
-# # Azure Secrets Store Configuration
-# ENV ZENML_SECRETS_STORE_KEY_VAULT_NAME=""
-# ENV ZENML_SECRETS_STORE_AZURE_CLIENT_ID=""
-# ENV ZENML_SECRETS_STORE_AZURE_CLIENT_SECRET=""
-# ENV ZENML_SECRETS_STORE_AZURE_TENANT_ID=""
-
-# # Hashicorp Secrets Store Configuration
-# ENV ZENML_SECRETS_STORE_VAULT_ADDR=""
-# ENV ZENML_SECRETS_STORE_VAULT_TOKEN=""
-# ENV ZENML_SECRETS_STORE_VAULT_NAMESPACE=""
-# ENV ZENML_SECRETS_STORE_MAX_VERSIONS=""
-
-ENTRYPOINT ["uvicorn", "zenml.zen_server.zen_server_api:app", "--log-level", "debug"]
-CMD ["--proxy-headers", "--port", "8080", "--host", "0.0.0.0"]
diff --git a/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/camera.py b/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/camera.py
deleted file mode 100644
index e019358039033c3a372c990ebad3151258c3651d..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/camera.py
+++ /dev/null
@@ -1,437 +0,0 @@
-"""Virtual cameras compliant with the glTF 2.0 specification as described at
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-camera
-
-Author: Matthew Matl
-"""
-import abc
-import numpy as np
-import six
-import sys
-
-from .constants import DEFAULT_Z_NEAR, DEFAULT_Z_FAR
-
-
-@six.add_metaclass(abc.ABCMeta)
-class Camera(object):
- """Abstract base class for all cameras.
-
- Note
- ----
- Camera poses are specified in the OpenGL format,
- where the z axis points away from the view direction and the
- x and y axes point to the right and up in the image plane, respectively.
-
- Parameters
- ----------
- znear : float
- The floating-point distance to the near clipping plane.
- zfar : float
- The floating-point distance to the far clipping plane.
- ``zfar`` must be greater than ``znear``.
- name : str, optional
- The user-defined name of this object.
- """
-
- def __init__(self,
- znear=DEFAULT_Z_NEAR,
- zfar=DEFAULT_Z_FAR,
- name=None):
- self.name = name
- self.znear = znear
- self.zfar = zfar
-
- @property
- def name(self):
- """str : The user-defined name of this object.
- """
- return self._name
-
- @name.setter
- def name(self, value):
- if value is not None:
- value = str(value)
- self._name = value
-
- @property
- def znear(self):
- """float : The distance to the near clipping plane.
- """
- return self._znear
-
- @znear.setter
- def znear(self, value):
- value = float(value)
- if value < 0:
- raise ValueError('z-near must be >= 0.0')
- self._znear = value
-
- @property
- def zfar(self):
- """float : The distance to the far clipping plane.
- """
- return self._zfar
-
- @zfar.setter
- def zfar(self, value):
- value = float(value)
- if value <= 0 or value <= self.znear:
- raise ValueError('zfar must be >0 and >znear')
- self._zfar = value
-
- @abc.abstractmethod
- def get_projection_matrix(self, width=None, height=None):
- """Return the OpenGL projection matrix for this camera.
-
- Parameters
- ----------
- width : int
- Width of the current viewport, in pixels.
- height : int
- Height of the current viewport, in pixels.
- """
- pass
-
-
-class PerspectiveCamera(Camera):
-
- """A perspective camera for perspective projection.
-
- Parameters
- ----------
- yfov : float
- The floating-point vertical field of view in radians.
- znear : float
- The floating-point distance to the near clipping plane.
- If not specified, defaults to 0.05.
- zfar : float, optional
- The floating-point distance to the far clipping plane.
- ``zfar`` must be greater than ``znear``.
- If None, the camera uses an infinite projection matrix.
- aspectRatio : float, optional
- The floating-point aspect ratio of the field of view.
- If not specified, the camera uses the viewport's aspect ratio.
- name : str, optional
- The user-defined name of this object.
- """
-
- def __init__(self,
- yfov,
- znear=DEFAULT_Z_NEAR,
- zfar=None,
- aspectRatio=None,
- name=None):
- super(PerspectiveCamera, self).__init__(
- znear=znear,
- zfar=zfar,
- name=name,
- )
-
- self.yfov = yfov
- self.aspectRatio = aspectRatio
-
- @property
- def yfov(self):
- """float : The vertical field of view in radians.
- """
- return self._yfov
-
- @yfov.setter
- def yfov(self, value):
- value = float(value)
- if value <= 0.0:
- raise ValueError('Field of view must be positive')
- self._yfov = value
-
- @property
- def zfar(self):
- """float : The distance to the far clipping plane.
- """
- return self._zfar
-
- @zfar.setter
- def zfar(self, value):
- if value is not None:
- value = float(value)
- if value <= 0 or value <= self.znear:
- raise ValueError('zfar must be >0 and >znear')
- self._zfar = value
-
- @property
- def aspectRatio(self):
- """float : The ratio of the width to the height of the field of view.
- """
- return self._aspectRatio
-
- @aspectRatio.setter
- def aspectRatio(self, value):
- if value is not None:
- value = float(value)
- if value <= 0.0:
- raise ValueError('Aspect ratio must be positive')
- self._aspectRatio = value
-
- def get_projection_matrix(self, width=None, height=None):
- """Return the OpenGL projection matrix for this camera.
-
- Parameters
- ----------
- width : int
- Width of the current viewport, in pixels.
- height : int
- Height of the current viewport, in pixels.
- """
- aspect_ratio = self.aspectRatio
- if aspect_ratio is None:
- if width is None or height is None:
- raise ValueError('Aspect ratio of camera must be defined')
- aspect_ratio = float(width) / float(height)
-
- a = aspect_ratio
- t = np.tan(self.yfov / 2.0)
- n = self.znear
- f = self.zfar
-
- P = np.zeros((4,4))
- P[0][0] = 1.0 / (a * t)
- P[1][1] = 1.0 / t
- P[3][2] = -1.0
-
- if f is None:
- P[2][2] = -1.0
- P[2][3] = -2.0 * n
- else:
- P[2][2] = (f + n) / (n - f)
- P[2][3] = (2 * f * n) / (n - f)
-
- return P
-
-
-class OrthographicCamera(Camera):
- """An orthographic camera for orthographic projection.
-
- Parameters
- ----------
- xmag : float
- The floating-point horizontal magnification of the view.
- ymag : float
- The floating-point vertical magnification of the view.
- znear : float
- The floating-point distance to the near clipping plane.
- If not specified, defaults to 0.05.
- zfar : float
- The floating-point distance to the far clipping plane.
- ``zfar`` must be greater than ``znear``.
- If not specified, defaults to 100.0.
- name : str, optional
- The user-defined name of this object.
- """
-
- def __init__(self,
- xmag,
- ymag,
- znear=DEFAULT_Z_NEAR,
- zfar=DEFAULT_Z_FAR,
- name=None):
- super(OrthographicCamera, self).__init__(
- znear=znear,
- zfar=zfar,
- name=name,
- )
-
- self.xmag = xmag
- self.ymag = ymag
-
- @property
- def xmag(self):
- """float : The horizontal magnification of the view.
- """
- return self._xmag
-
- @xmag.setter
- def xmag(self, value):
- value = float(value)
- if value <= 0.0:
- raise ValueError('X magnification must be positive')
- self._xmag = value
-
- @property
- def ymag(self):
- """float : The vertical magnification of the view.
- """
- return self._ymag
-
- @ymag.setter
- def ymag(self, value):
- value = float(value)
- if value <= 0.0:
- raise ValueError('Y magnification must be positive')
- self._ymag = value
-
- @property
- def znear(self):
- """float : The distance to the near clipping plane.
- """
- return self._znear
-
- @znear.setter
- def znear(self, value):
- value = float(value)
- if value <= 0:
- raise ValueError('z-near must be > 0.0')
- self._znear = value
-
- def get_projection_matrix(self, width=None, height=None):
- """Return the OpenGL projection matrix for this camera.
-
- Parameters
- ----------
- width : int
- Width of the current viewport, in pixels.
- Unused in this function.
- height : int
- Height of the current viewport, in pixels.
- Unused in this function.
- """
- xmag = self.xmag
- ymag = self.ymag
-
- # If screen width/height defined, rescale xmag
- if width is not None and height is not None:
- xmag = width / height * ymag
-
- n = self.znear
- f = self.zfar
- P = np.zeros((4,4))
- P[0][0] = 1.0 / xmag
- P[1][1] = 1.0 / ymag
- P[2][2] = 2.0 / (n - f)
- P[2][3] = (f + n) / (n - f)
- P[3][3] = 1.0
- return P
-
-
-class IntrinsicsCamera(Camera):
- """A perspective camera with custom intrinsics.
-
- Parameters
- ----------
- fx : float
- X-axis focal length in pixels.
- fy : float
- Y-axis focal length in pixels.
- cx : float
- X-axis optical center in pixels.
- cy : float
- Y-axis optical center in pixels.
- znear : float
- The floating-point distance to the near clipping plane.
- If not specified, defaults to 0.05.
- zfar : float
- The floating-point distance to the far clipping plane.
- ``zfar`` must be greater than ``znear``.
- If not specified, defaults to 100.0.
- name : str, optional
- The user-defined name of this object.
- """
-
- def __init__(self,
- fx,
- fy,
- cx,
- cy,
- znear=DEFAULT_Z_NEAR,
- zfar=DEFAULT_Z_FAR,
- name=None):
- super(IntrinsicsCamera, self).__init__(
- znear=znear,
- zfar=zfar,
- name=name,
- )
-
- self.fx = fx
- self.fy = fy
- self.cx = cx
- self.cy = cy
-
- @property
- def fx(self):
- """float : X-axis focal length in meters.
- """
- return self._fx
-
- @fx.setter
- def fx(self, value):
- self._fx = float(value)
-
- @property
- def fy(self):
- """float : Y-axis focal length in meters.
- """
- return self._fy
-
- @fy.setter
- def fy(self, value):
- self._fy = float(value)
-
- @property
- def cx(self):
- """float : X-axis optical center in pixels.
- """
- return self._cx
-
- @cx.setter
- def cx(self, value):
- self._cx = float(value)
-
- @property
- def cy(self):
- """float : Y-axis optical center in pixels.
- """
- return self._cy
-
- @cy.setter
- def cy(self, value):
- self._cy = float(value)
-
- def get_projection_matrix(self, width, height):
- """Return the OpenGL projection matrix for this camera.
-
- Parameters
- ----------
- width : int
- Width of the current viewport, in pixels.
- height : int
- Height of the current viewport, in pixels.
- """
- width = float(width)
- height = float(height)
-
- cx, cy = self.cx, self.cy
- fx, fy = self.fx, self.fy
- if sys.platform == 'darwin':
- cx = self.cx * 2.0
- cy = self.cy * 2.0
- fx = self.fx * 2.0
- fy = self.fy * 2.0
-
- P = np.zeros((4,4))
- P[0][0] = 2.0 * fx / width
- P[1][1] = 2.0 * fy / height
- P[0][2] = 1.0 - 2.0 * cx / width
- P[1][2] = 2.0 * cy / height - 1.0
- P[3][2] = -1.0
-
- n = self.znear
- f = self.zfar
- if f is None:
- P[2][2] = -1.0
- P[2][3] = -2.0 * n
- else:
- P[2][2] = (f + n) / (n - f)
- P[2][3] = (2 * f * n) / (n - f)
-
- return P
-
-
-__all__ = ['Camera', 'PerspectiveCamera', 'OrthographicCamera',
- 'IntrinsicsCamera']
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/pitch_extractors.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/pitch_extractors.py
deleted file mode 100644
index 6b0e9e1300344e9c2e680e21dca79c20457df9d7..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/pitch_extractors.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-from text_to_speech.utils.audio.pitch.utils import denorm_f0, norm_f0, f0_to_coarse
-import parselmouth
-
-PITCH_EXTRACTOR = {}
-
-
-def register_pitch_extractor(name):
- def register_pitch_extractor_(cls):
- PITCH_EXTRACTOR[name] = cls
- return cls
-
- return register_pitch_extractor_
-
-
-def get_pitch_extractor(name):
- return PITCH_EXTRACTOR[name]
-
-
-def extract_pitch_simple(wav):
- from text_to_speech.utils.commons.hparams import hparams
- return extract_pitch(hparams['pitch_extractor'], wav,
- hparams['hop_size'], hparams['audio_sample_rate'],
- f0_min=hparams['f0_min'], f0_max=hparams['f0_max'])
-
-
-def extract_pitch(extractor_name, wav_data, hop_size, audio_sample_rate, f0_min=75, f0_max=800, **kwargs):
- return get_pitch_extractor(extractor_name)(wav_data, hop_size, audio_sample_rate, f0_min, f0_max, **kwargs)
-
-
-@register_pitch_extractor('parselmouth')
-def parselmouth_pitch(wav_data, hop_size, audio_sample_rate, f0_min, f0_max,
- voicing_threshold=0.6, *args, **kwargs):
- import parselmouth
- time_step = hop_size / audio_sample_rate * 1000
- n_mel_frames = int(len(wav_data) // hop_size)
- f0_pm = parselmouth.Sound(wav_data, audio_sample_rate).to_pitch_ac(
- time_step=time_step / 1000, voicing_threshold=voicing_threshold,
- pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
- pad_size = (n_mel_frames - len(f0_pm) + 1) // 2
- f0 = np.pad(f0_pm, [[pad_size, n_mel_frames - len(f0_pm) - pad_size]], mode='constant')
- return f0
-
-
-def get_pitch(wav_data, mel, hparams):
- """
- :param wav_data: [T]
- :param mel: [T, 80]
- :param hparams:
- :return:
- """
- time_step = hparams['hop_size'] / hparams['audio_sample_rate'] * 1000
- f0_min = 80
- f0_max = 750
-
- if hparams['pitch_extractor'] == 'harvest':
- import pyworld as pw
- f0, t = pw.harvest(wav_data.astype(np.double), hparams['audio_sample_rate'],
- frame_period=hparams['hop_size'] / hparams['audio_sample_rate'] * 1000)
- if hparams['pitch_extractor'] == 'dio':
- _f0, t = pw.dio(wav_data.astype(np.double), hparams['audio_sample_rate'],
- frame_period=hparams['hop_size'] / hparams['audio_sample_rate'] * 1000)
- f0 = pw.stonemask(wav_data.astype(np.double), _f0, t, hparams['audio_sample_rate']) # pitch refinement
- elif hparams['pitch_extractor'] == 'parselmouth':
- if hparams['hop_size'] == 128:
- pad_size = 4
- elif hparams['hop_size'] == 256:
- pad_size = 2
- else:
- assert False
- f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac(
- time_step=time_step / 1000, voicing_threshold=0.6,
- pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
- lpad = pad_size * 2
- rpad = len(mel) - len(f0) - lpad
- f0 = np.pad(f0, [[lpad, rpad]], mode='constant')
-
- # mel和f0是2个库抽的 需要保证两者长度一致
- delta_l = len(mel) - len(f0)
- assert np.abs(delta_l) <= 8
- if delta_l > 0:
- f0 = np.concatenate([f0, [f0[-1]] * delta_l], 0)
- f0 = f0[:len(mel)]
- pitch_coarse = f0_to_coarse(f0)
- return f0, pitch_coarse
\ No newline at end of file
diff --git a/spaces/ASJMO/freegpt/client/css/dropdown.css b/spaces/ASJMO/freegpt/client/css/dropdown.css
deleted file mode 100644
index 302e911e84d171c55384732f759a79ce195abca5..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/client/css/dropdown.css
+++ /dev/null
@@ -1,10 +0,0 @@
-.dropdown {
- border: 1px solid var(--conversations);
-}
-
-@media screen and (max-width: 990px) {
- .dropdown {
- padding: 4px 8px;
- font-size: 0.75rem;
- }
-}
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/utils.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/utils.py
deleted file mode 100644
index af6bcb9e1116a431a39579f4bbdde3a9e868e0b4..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/utils.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# -*- coding: utf-8 -*-
-import cv2
-import numpy as np
-
-skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9], [8, 10],
- [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]]
-
-pose_kpt_color = [[51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0],
- [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0],
- [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]]
-
-pose_link_color = [[0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0],
- [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0], [255, 128, 0],
- [0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255],
- [51, 153, 255], [51, 153, 255], [51, 153, 255]]
-
-
-def imshow_keypoints(img,
- pose_result,
- kpt_score_thr=0.1,
- radius=2,
- thickness=2):
- """Draw keypoints and links on an image.
-
- Args:
- img (ndarry): The image to draw poses on.
- pose_result (list[kpts]): The poses to draw. Each element kpts is
- a set of K keypoints as an Kx3 numpy.ndarray, where each
- keypoint is represented as x, y, score.
- kpt_score_thr (float, optional): Minimum score of keypoints
- to be shown. Default: 0.3.
- thickness (int): Thickness of lines.
- """
-
- img_h, img_w, _ = img.shape
- img = np.zeros(img.shape)
-
- for idx, kpts in enumerate(pose_result):
- if idx > 1:
- continue
- kpts = kpts['keypoints']
- # print(kpts)
- kpts = np.array(kpts, copy=False)
-
- # draw each point on image
- assert len(pose_kpt_color) == len(kpts)
-
- for kid, kpt in enumerate(kpts):
- x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2]
-
- if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None:
- # skip the point that should not be drawn
- continue
-
- color = tuple(int(c) for c in pose_kpt_color[kid])
- cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1)
-
- # draw links
-
- for sk_id, sk in enumerate(skeleton):
- pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1]))
- pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1]))
-
- if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0
- or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr
- or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None):
- # skip the link that should not be drawn
- continue
- color = tuple(int(c) for c in pose_link_color[sk_id])
- cv2.line(img, pos1, pos2, color, thickness=thickness)
-
- return img
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetChildWidth.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetChildWidth.js
deleted file mode 100644
index 0cc8c826c1dcec66e4c2684b245a1ee4376b88cf..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetChildWidth.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { GetDisplayWidth } from '../../../plugins/utils/size/GetDisplaySize.js';
-
-var GetChildWidth = function (child) {
- var childWidth;
- if (child.isRexSizer) { // Sizer game object
- childWidth = Math.max(child.minWidth, child.childrenWidth);
- } else { // Normal game object
- if (child.minWidth !== undefined) { // Force minWidth
- childWidth = child.minWidth;
- } else {
- childWidth = GetDisplayWidth(child);
- }
- }
-
- return childWidth;
-}
-
-export default GetChildWidth;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/CollapseSubMenu.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/CollapseSubMenu.js
deleted file mode 100644
index c6271fd41b423d6d63beac03f53c945bb9a98037..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/CollapseSubMenu.js
+++ /dev/null
@@ -1,12 +0,0 @@
-var CollapseSubMenu = function () {
- var subMenu = this.childrenMap.subMenu;
- if (subMenu === undefined) {
- return this;
- }
-
- this.childrenMap.subMenu = undefined;
- this.remove(subMenu);
- subMenu.collapse();
- return this;
-}
-export default CollapseSubMenu;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/press/Press.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/press/Press.d.ts
deleted file mode 100644
index b42e7eb6b330b1b624772512bf4a657e0e005ee4..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/press/Press.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Press } from '../../../plugins/gestures';
-export default Press;
\ No newline at end of file
diff --git a/spaces/AkashKhamkar/Job_Search_Engine/loader.py b/spaces/AkashKhamkar/Job_Search_Engine/loader.py
deleted file mode 100644
index 12d2dad83751e3f2624e67def3a83c6d69e8b976..0000000000000000000000000000000000000000
--- a/spaces/AkashKhamkar/Job_Search_Engine/loader.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from sentence_transformers import SentenceTransformer, CrossEncoder, util
-import pandas as pd
-import pickle
-
-bi_encoder = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
-cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
-df = pd.read_csv('job_corpus_dataframe.csv')
-pickle_in = open("job_corpus.pickle","rb")
-job_corpus = pickle.load(pickle_in)
-pickle_in = open("job_corpus_encoded.pickle","rb")
-job_corpus_ecoded = pickle.load(pickle_in)
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/facerender/modules/make_animation.py b/spaces/Alpaca233/SadTalker/src/facerender/modules/make_animation.py
deleted file mode 100644
index 3360c53501a064f35d7db21a5361f89aa9658b42..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/facerender/modules/make_animation.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from scipy.spatial import ConvexHull
-import torch
-import torch.nn.functional as F
-import numpy as np
-from tqdm import tqdm
-
-def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
- use_relative_movement=False, use_relative_jacobian=False):
- if adapt_movement_scale:
- source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
- driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
- adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
- else:
- adapt_movement_scale = 1
-
- kp_new = {k: v for k, v in kp_driving.items()}
-
- if use_relative_movement:
- kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
- kp_value_diff *= adapt_movement_scale
- kp_new['value'] = kp_value_diff + kp_source['value']
-
- if use_relative_jacobian:
- jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
- kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
-
- return kp_new
-
-def headpose_pred_to_degree(pred):
- device = pred.device
- idx_tensor = [idx for idx in range(66)]
- idx_tensor = torch.FloatTensor(idx_tensor).type_as(pred).to(device)
- pred = F.softmax(pred)
- degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
- return degree
-
-def get_rotation_matrix(yaw, pitch, roll):
- yaw = yaw / 180 * 3.14
- pitch = pitch / 180 * 3.14
- roll = roll / 180 * 3.14
-
- roll = roll.unsqueeze(1)
- pitch = pitch.unsqueeze(1)
- yaw = yaw.unsqueeze(1)
-
- pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch),
- torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
- torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1)
- pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
-
- yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw),
- torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw),
- -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1)
- yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
-
- roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll),
- torch.sin(roll), torch.cos(roll), torch.zeros_like(roll),
- torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1)
- roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
-
- rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat)
-
- return rot_mat
-
-def keypoint_transformation(kp_canonical, he, wo_exp=False):
- kp = kp_canonical['value'] # (bs, k, 3)
- yaw, pitch, roll= he['yaw'], he['pitch'], he['roll']
- yaw = headpose_pred_to_degree(yaw)
- pitch = headpose_pred_to_degree(pitch)
- roll = headpose_pred_to_degree(roll)
-
- if 'yaw_in' in he:
- yaw = he['yaw_in']
- if 'pitch_in' in he:
- pitch = he['pitch_in']
- if 'roll_in' in he:
- roll = he['roll_in']
-
- rot_mat = get_rotation_matrix(yaw, pitch, roll) # (bs, 3, 3)
-
- t, exp = he['t'], he['exp']
- if wo_exp:
- exp = exp*0
-
- # keypoint rotation
- kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
-
- # keypoint translation
- t[:, 0] = t[:, 0]*0
- t[:, 2] = t[:, 2]*0
- t = t.unsqueeze(1).repeat(1, kp.shape[1], 1)
- kp_t = kp_rotated + t
-
- # add expression deviation
- exp = exp.view(exp.shape[0], -1, 3)
- kp_transformed = kp_t + exp
-
- return {'value': kp_transformed}
-
-
-
-def make_animation(source_image, source_semantics, target_semantics,
- generator, kp_detector, he_estimator, mapping,
- yaw_c_seq=None, pitch_c_seq=None, roll_c_seq=None,
- use_exp=True, use_half=False):
- with torch.no_grad():
- predictions = []
-
- kp_canonical = kp_detector(source_image)
- he_source = mapping(source_semantics)
- kp_source = keypoint_transformation(kp_canonical, he_source)
-
- for frame_idx in tqdm(range(target_semantics.shape[1]), 'Face Renderer:'):
- # still check the dimension
- # print(target_semantics.shape, source_semantics.shape)
- target_semantics_frame = target_semantics[:, frame_idx]
- he_driving = mapping(target_semantics_frame)
- if yaw_c_seq is not None:
- he_driving['yaw_in'] = yaw_c_seq[:, frame_idx]
- if pitch_c_seq is not None:
- he_driving['pitch_in'] = pitch_c_seq[:, frame_idx]
- if roll_c_seq is not None:
- he_driving['roll_in'] = roll_c_seq[:, frame_idx]
-
- kp_driving = keypoint_transformation(kp_canonical, he_driving)
-
- kp_norm = kp_driving
- out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)
- '''
- source_image_new = out['prediction'].squeeze(1)
- kp_canonical_new = kp_detector(source_image_new)
- he_source_new = he_estimator(source_image_new)
- kp_source_new = keypoint_transformation(kp_canonical_new, he_source_new, wo_exp=True)
- kp_driving_new = keypoint_transformation(kp_canonical_new, he_driving, wo_exp=True)
- out = generator(source_image_new, kp_source=kp_source_new, kp_driving=kp_driving_new)
- '''
- predictions.append(out['prediction'])
- predictions_ts = torch.stack(predictions, dim=1)
- return predictions_ts
-
-class AnimateModel(torch.nn.Module):
- """
- Merge all generator related updates into single model for better multi-gpu usage
- """
-
- def __init__(self, generator, kp_extractor, mapping):
- super(AnimateModel, self).__init__()
- self.kp_extractor = kp_extractor
- self.generator = generator
- self.mapping = mapping
-
- self.kp_extractor.eval()
- self.generator.eval()
- self.mapping.eval()
-
- def forward(self, x):
-
- source_image = x['source_image']
- source_semantics = x['source_semantics']
- target_semantics = x['target_semantics']
- yaw_c_seq = x['yaw_c_seq']
- pitch_c_seq = x['pitch_c_seq']
- roll_c_seq = x['roll_c_seq']
-
- predictions_video = make_animation(source_image, source_semantics, target_semantics,
- self.generator, self.kp_extractor,
- self.mapping, use_exp = True,
- yaw_c_seq=yaw_c_seq, pitch_c_seq=pitch_c_seq, roll_c_seq=roll_c_seq)
-
- return predictions_video
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/webui.sh b/spaces/Alpaca233/SadTalker/webui.sh
deleted file mode 100644
index 245750237954e140777c0bd20e6d26a1f9d1f74e..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/webui.sh
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env bash
-
-
-# If run from macOS, load defaults from webui-macos-env.sh
-if [[ "$OSTYPE" == "darwin"* ]]; then
- export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
-fi
-
-# python3 executable
-if [[ -z "${python_cmd}" ]]
-then
- python_cmd="python3"
-fi
-
-# git executable
-if [[ -z "${GIT}" ]]
-then
- export GIT="git"
-fi
-
-# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-if [[ -z "${venv_dir}" ]]
-then
- venv_dir="venv"
-fi
-
-if [[ -z "${LAUNCH_SCRIPT}" ]]
-then
- LAUNCH_SCRIPT="launcher.py"
-fi
-
-# this script cannot be run as root by default
-can_run_as_root=1
-
-# read any command line flags to the webui.sh script
-while getopts "f" flag > /dev/null 2>&1
-do
- case ${flag} in
- f) can_run_as_root=1;;
- *) break;;
- esac
-done
-
-# Disable sentry logging
-export ERROR_REPORTING=FALSE
-
-# Do not reinstall existing pip packages on Debian/Ubuntu
-export PIP_IGNORE_INSTALLED=0
-
-# Pretty print
-delimiter="################################################################"
-
-printf "\n%s\n" "${delimiter}"
-printf "\e[1m\e[32mInstall script for SadTalker + Web UI\n"
-printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
-printf "\n%s\n" "${delimiter}"
-
-# Do not run as root
-if [[ $(id -u) -eq 0 && can_run_as_root -eq 0 ]]
-then
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m"
- printf "\n%s\n" "${delimiter}"
- exit 1
-else
- printf "\n%s\n" "${delimiter}"
- printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)"
- printf "\n%s\n" "${delimiter}"
-fi
-
-if [[ -d .git ]]
-then
- printf "\n%s\n" "${delimiter}"
- printf "Repo already cloned, using it as install directory"
- printf "\n%s\n" "${delimiter}"
- install_dir="${PWD}/../"
- clone_dir="${PWD##*/}"
-fi
-
-# Check prerequisites
-gpu_info=$(lspci 2>/dev/null | grep VGA)
-case "$gpu_info" in
- *"Navi 1"*|*"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
- ;;
- *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
- printf "\n%s\n" "${delimiter}"
- printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
- printf "\n%s\n" "${delimiter}"
- ;;
- *)
- ;;
-esac
-if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
-then
- export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2"
-fi
-
-for preq in "${GIT}" "${python_cmd}"
-do
- if ! hash "${preq}" &>/dev/null
- then
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}"
- printf "\n%s\n" "${delimiter}"
- exit 1
- fi
-done
-
-if ! "${python_cmd}" -c "import venv" &>/dev/null
-then
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
- printf "\n%s\n" "${delimiter}"
- exit 1
-fi
-
-printf "\n%s\n" "${delimiter}"
-printf "Create and activate python venv"
-printf "\n%s\n" "${delimiter}"
-cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-if [[ ! -d "${venv_dir}" ]]
-then
- "${python_cmd}" -m venv "${venv_dir}"
- first_launch=1
-fi
-# shellcheck source=/dev/null
-if [[ -f "${venv_dir}"/bin/activate ]]
-then
- source "${venv_dir}"/bin/activate
-else
- printf "\n%s\n" "${delimiter}"
- printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
- printf "\n%s\n" "${delimiter}"
- exit 1
-fi
-
-printf "\n%s\n" "${delimiter}"
-printf "Launching launcher.py..."
-printf "\n%s\n" "${delimiter}"
-exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
\ No newline at end of file
diff --git a/spaces/Aman30577/imageTool1/README.md b/spaces/Aman30577/imageTool1/README.md
deleted file mode 100644
index 875ae261917b9752674a6ae00492c9540b53b55a..0000000000000000000000000000000000000000
--- a/spaces/Aman30577/imageTool1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ImageTool1
-emoji: 🚀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/criteria/__init__.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/criteria/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py
deleted file mode 100644
index 363dc74adda3a3cebc7f610dc51eccda35fcd083..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# coding: utf8
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddleseg.utils.download import download_file_and_uncompress
-import sys
-import os
-
-LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
-TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
-sys.path.append(TEST_PATH)
-
-
-model_urls = {
- "pphumanseg_lite_portrait_398x224":
- "https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224.tar.gz",
- "deeplabv3p_resnet50_os8_humanseg_512x512_100k":
- "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/deeplabv3p_resnet50_os8_humanseg_512x512_100k.zip",
- "fcn_hrnetw18_small_v1_humanseg_192x192":
- "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/fcn_hrnetw18_small_v1_humanseg_192x192.zip",
- "pphumanseg_lite_generic_human_192x192":
- "https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/pphumanseg_lite_generic_192x192.zip",
-}
-
-if __name__ == "__main__":
- for model_name, url in model_urls.items():
- download_file_and_uncompress(
- url=url,
- savepath=LOCAL_PATH,
- extrapath=LOCAL_PATH,
- extraname=model_name)
-
- print("Pretrained model download success!")
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/print_env.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/print_env.py
deleted file mode 100644
index 88cb674bf31ace69122b925c0b31eddf812fcdb4..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/utils/print_env.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# 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.
-
-# this script dumps information about the environment
-
-import os
-import platform
-import sys
-
-
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
-print("Python version:", sys.version)
-
-print("OS platform:", platform.platform())
-print("OS architecture:", platform.machine())
-
-try:
- import torch
-
- print("Torch version:", torch.__version__)
- print("Cuda available:", torch.cuda.is_available())
- print("Cuda version:", torch.version.cuda)
- print("CuDNN version:", torch.backends.cudnn.version())
- print("Number of GPUs available:", torch.cuda.device_count())
-except ImportError:
- print("Torch version:", None)
-
-try:
- import transformers
-
- print("transformers version:", transformers.__version__)
-except ImportError:
- print("transformers version:", None)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
deleted file mode 100644
index 1915ab1b4f013efacaedcdae08e93176cfe3bd55..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://resnest101',
- backbone=dict(stem_channels=128, depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_unet_s5-d16.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_unet_s5-d16.py
deleted file mode 100644
index a33e7972877f902d0e7d18401ca675e3e4e60a18..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/fcn_unet_s5-d16.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained=None,
- backbone=dict(
- type='UNet',
- in_channels=3,
- base_channels=64,
- num_stages=5,
- strides=(1, 1, 1, 1, 1),
- enc_num_convs=(2, 2, 2, 2, 2),
- dec_num_convs=(2, 2, 2, 2),
- downsamples=(True, True, True, True),
- enc_dilations=(1, 1, 1, 1, 1),
- dec_dilations=(1, 1, 1, 1),
- with_cp=False,
- conv_cfg=None,
- norm_cfg=norm_cfg,
- act_cfg=dict(type='ReLU'),
- upsample_cfg=dict(type='InterpConv'),
- norm_eval=False),
- decode_head=dict(
- type='FCNHead',
- in_channels=64,
- in_index=4,
- channels=64,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=2,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=128,
- in_index=3,
- channels=64,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=2,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='slide', crop_size=256, stride=170))
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/version_utils.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/version_utils.py
deleted file mode 100644
index 963c45a2e8a86a88413ab6c18c22481fb9831985..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/version_utils.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import subprocess
-import warnings
-
-from packaging.version import parse
-
-
-def digit_version(version_str: str, length: int = 4):
- """Convert a version string into a tuple of integers.
-
- This method is usually used for comparing two versions. For pre-release
- versions: alpha < beta < rc.
-
- Args:
- version_str (str): The version string.
- length (int): The maximum number of version levels. Default: 4.
-
- Returns:
- tuple[int]: The version info in digits (integers).
- """
- assert 'parrots' not in version_str
- version = parse(version_str)
- assert version.release, f'failed to parse version {version_str}'
- release = list(version.release)
- release = release[:length]
- if len(release) < length:
- release = release + [0] * (length - len(release))
- if version.is_prerelease:
- mapping = {'a': -3, 'b': -2, 'rc': -1}
- val = -4
- # version.pre can be None
- if version.pre:
- if version.pre[0] not in mapping:
- warnings.warn(f'unknown prerelease version {version.pre[0]}, '
- 'version checking may go wrong')
- else:
- val = mapping[version.pre[0]]
- release.extend([val, version.pre[-1]])
- else:
- release.extend([val, 0])
-
- elif version.is_postrelease:
- release.extend([1, version.post])
- else:
- release.extend([0, 0])
- return tuple(release)
-
-
-def _minimal_ext_cmd(cmd):
- # construct minimal environment
- env = {}
- for k in ['SYSTEMROOT', 'PATH', 'HOME']:
- v = os.environ.get(k)
- if v is not None:
- env[k] = v
- # LANGUAGE is used on win32
- env['LANGUAGE'] = 'C'
- env['LANG'] = 'C'
- env['LC_ALL'] = 'C'
- out = subprocess.Popen(
- cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
- return out
-
-
-def get_git_hash(fallback='unknown', digits=None):
- """Get the git hash of the current repo.
-
- Args:
- fallback (str, optional): The fallback string when git hash is
- unavailable. Defaults to 'unknown'.
- digits (int, optional): kept digits of the hash. Defaults to None,
- meaning all digits are kept.
-
- Returns:
- str: Git commit hash.
- """
-
- if digits is not None and not isinstance(digits, int):
- raise TypeError('digits must be None or an integer')
-
- try:
- out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
- sha = out.strip().decode('ascii')
- if digits is not None:
- sha = sha[:digits]
- except OSError:
- sha = fallback
-
- return sha
diff --git a/spaces/ArkanDash/rvc-models/app-full.py b/spaces/ArkanDash/rvc-models/app-full.py
deleted file mode 100644
index 0819327e3c5f775ca1f76b04d1e470abaae726c2..0000000000000000000000000000000000000000
--- a/spaces/ArkanDash/rvc-models/app-full.py
+++ /dev/null
@@ -1,254 +0,0 @@
-import os
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import (
- is_half,
- device
-)
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
- def vc_fn(
- input_audio,
- upload_audio,
- upload_mode,
- f0_up_key,
- f0_method,
- index_rate,
- tts_mode,
- tts_text,
- tts_voice
- ):
- try:
- if tts_mode:
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- else:
- if upload_mode:
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = upload_audio
- duration = audio.shape[0] / sampling_rate
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- else:
- audio, sr = librosa.load(input_audio, sr=16000, mono=True)
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- times,
- f0_up_key,
- f0_method,
- file_index,
- file_big_npy,
- index_rate,
- if_f0,
- )
- print(
- f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- )
- return "Success", (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, (None, None)
- return vc_fn
-
-def cut_vocal_and_inst(yt_url):
- if yt_url != "":
- if not os.path.exists("youtube_audio"):
- os.mkdir("youtube_audio")
- ydl_opts = {
- 'format': 'bestaudio/best',
- 'postprocessors': [{
- 'key': 'FFmpegExtractAudio',
- 'preferredcodec': 'wav',
- }],
- "outtmpl": 'youtube_audio/audio',
- }
- with yt_dlp.YoutubeDL(ydl_opts) as ydl:
- ydl.download([yt_url])
- yt_audio_path = "youtube_audio/audio.wav"
- command = f"demucs --two-stems=vocals {yt_audio_path}"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return ("separated/htdemucs/audio/vocals.wav", "separated/htdemucs/audio/no_vocals.wav", yt_audio_path, "separated/htdemucs/audio/vocals.wav")
-
-def combine_vocal_and_inst(audio_data, audio_volume):
- print(audio_data)
- if not os.path.exists("result"):
- os.mkdir("result")
- vocal_path = "result/output.wav"
- inst_path = "separated/htdemucs/audio/no_vocals.wav"
- output_path = "result/combine.mp3"
- with wave.open(vocal_path, "w") as wave_file:
- wave_file.setnchannels(1)
- wave_file.setsampwidth(2)
- wave_file.setframerate(audio_data[0])
- wave_file.writeframes(audio_data[1].tobytes())
- command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- return output_path
-
-def load_hubert():
- global hubert_model
- models, _, _ = 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 change_to_tts_mode(tts_mode, upload_mode):
- if tts_mode:
- return gr.Textbox.update(visible=False), gr.Audio.update(visible=False), gr.Checkbox.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
- else:
- if upload_mode:
- return gr.Textbox.update(visible=False), gr.Audio.update(visible=True), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
- else:
- return gr.Textbox.update(visible=True), gr.Audio.update(visible=False), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-def change_to_upload_mode(upload_mode):
- if upload_mode:
- return gr.Textbox().update(visible=False), gr.Audio().update(visible=True)
- else:
- return gr.Textbox().update(visible=True), gr.Audio().update(visible=False)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--api', action="store_true", default=False)
- parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
- args, unknown = parser.parse_known_args()
- load_hubert()
- models = []
- tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with open("weights/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for name, info in models_info.items():
- if not info['enable']:
- continue
- title = info['title']
- author = info.get("author", None)
- cover = f"weights/{name}/{info['cover']}"
- index = f"weights/{name}/{info['feature_retrieval_library']}"
- npy = f"weights/{name}/{info['feature_file']}"
- cpt = torch.load(f"weights/{name}/{name}.pth", 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)
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_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, device, is_half)
- models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
- with gr.Blocks() as app:
- gr.Markdown(
- "#
RVC Models\n"
- "##
The input audio should be clean and pure voice without background music.\n"
- "###
More feature will be added soon... \n"
- "[](https://colab.research.google.com/drive/1hx6kKvIuv5XNY1Gai2PEuZhpO5z6xpVh?usp=sharing)\n\n"
- "[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
- )
- with gr.Tabs():
- for (name, title, author, cover, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- '
'
- f'
{title}
\n'+
- (f'
Model author: {author}
' if author else "")+
- (f'' if cover else "")+
- '
Descargar coches de lujo europeos en tu dispositivo tiene muchos beneficios. Puede elegir entre una amplia gama de modelos y personalizarlos según sus preferencias. También puede conducirlos en diferentes terrenos y entornos, como carreteras, pistas todoterreno o incluso una isla privada. También puede disfrutar de sonidos realistas, gráficos y física que simulan el rendimiento real y el comportamiento de estos coches. Además, puedes divertirte con tus amigos u otros jugadores online compitiendo o navegando juntos.
-
Sin embargo, descargar coches de lujo europeos también tiene algunos desafíos y riesgos. Necesitas encontrar una fuente confiable y segura para descargarlos, ya que algunos sitios web o aplicaciones pueden contener virus o malware que pueden dañar tu dispositivo o robar tu información personal. También debes asegurarte de que tu dispositivo tenga suficiente espacio de almacenamiento y cumpla con los requisitos mínimos para que el juego funcione sin problemas. Además, debe ser consciente de los problemas legales y las implicaciones éticas de descargar estos coches, ya que algunos de ellos pueden estar protegidos por derechos de propiedad intelectual o pueden promover la conducción irresponsable.
-
Cómo descargar coches de lujo europeos en dispositivos Android
-
Usando Google Play Store
-
-
-
Abra la aplicación Google Play Store en su dispositivo.
-
Inicia sesión con tu cuenta de Google si aún no lo has hecho.
-
Buscar "Coches de lujo europeos" en la barra de búsqueda.
-
Encuentre la aplicación de DMNK Studio en los resultados de búsqueda y toque en ella.
-
Toque en el botón "Instalar" para comenzar el proceso de descarga e instalación.
-
Espere a que la aplicación termine de instalar y luego toque en "Abrir" para lanzarlo.
-
-
Felicidades! Usted ha descargado con éxito los coches de lujo europeos en su dispositivo Android utilizando la Google Play Store. Ahora puede elegir su coche de lujo favorito y conducirlo con amigos o solo a través de una isla privada.
Uso de otros métodos
-
Si no quieres usar el emulador de GameLoop, o si quieres probar otros métodos para descargar coches de lujo europeos en tu PC, también puedes usar otros sitios web o software que ofrecen versiones de PC de la aplicación. Sin embargo, debe tener cuidado al descargar estos archivos, ya que pueden no ser oficiales o seguros. Estos son los pasos para descargar coches de lujo europeos utilizando otros métodos:
-
-
-
Ir a un sitio web que ofrece versiones de PC de aplicaciones Android, tales como Twitscoop. También puede usar otros sitios web, pero asegúrese de que sean confiables y seguros.
-
Buscar "Coches de lujo europeos" en la barra de búsqueda del sitio web.
-
Encuentre la aplicación de DMNK Studio en los resultados de búsqueda y haga clic en ella.
-
Haga clic en el botón "Descargar" para comenzar a descargar la versión para PC de la aplicación en su PC.
-
Una vez completada la descarga, ejecute el archivo y siga las instrucciones para instalar la aplicación en su PC.
-
Iniciar la aplicación y disfrutar jugando el juego.
-
-
Genial! Ha descargado con éxito coches de lujo europeos en su PC utilizando otros métodos. Ahora puedes divertirte con tu coche de lujo favorito en una isla privada con gráficos realistas y física.
-
Conclusión
-
-
Descargar coches de lujo europeos puede ser una gran manera de experimentar la emoción y la emoción de poseer y conducir un coche de lujo europeo. Puede elegir entre una amplia gama de modelos y personalizarlos según sus preferencias. También puede conducirlos en diferentes terrenos y entornos, como carreteras, pistas todoterreno o incluso una isla privada. También puede disfrutar de sonidos realistas, gráficos y física que simulan el rendimiento real y el comportamiento de estos coches. Además, puedes divertirte con tus amigos u otros jugadores online compitiendo o navegando juntos.
-
Sin embargo, también es necesario tener cuidado al descargar coches de lujo europeos, ya que algunas fuentes pueden no ser fiables o seguras. También debes asegurarte de que tu dispositivo tenga suficiente espacio de almacenamiento y cumpla con los requisitos mínimos para que el juego funcione sin problemas. Además, debe ser consciente de los problemas legales y las implicaciones éticas de descargar estos coches, ya que algunos de ellos pueden estar protegidos por derechos de propiedad intelectual o pueden promover la conducción irresponsable.
-
Te invitamos a probar el juego y compartir tus comentarios con nosotros. ¿Cuáles son tus marcas europeas favoritas de coches de lujo? ¿Cómo te gustan los gráficos y la física del juego? ¿Cuáles son algunas de las características y opciones disponibles en el juego? ¡Déjanos saber en los comentarios abajo!
-
Preguntas frecuentes
-
¿Cuáles son algunas de las mejores marcas europeas de automóviles de lujo?
-
Algunas de las mejores marcas europeas de automóviles de lujo son BMW, Audi, Mercedes-Benz, Porsche, Ferrari, Lamborghini y más. Estas marcas ofrecen una velocidad espectacular, un estilo sofisticado y un confort sin igual. También tienen una larga historia y reputación de excelencia e innovación en la industria automotriz.
-
¿Cómo puedo actualizar o desinstalar la aplicación European Luxury Cars?
-
-
¿Cómo puedo jugar con amigos u otros jugadores online?
-
La aplicación European Luxury Cars de DMNK Studio ofrece un modo en línea donde puedes jugar con amigos u otros jugadores en línea. Puede unirse o crear una habitación e invitar a otros a unirse a usted. También puede chatear con ellos utilizando mensajes de voz o de texto. Pueden competir o navegar juntos en una isla privada con gráficos realistas y física.
-
¿Cuáles son algunas de las características y opciones disponibles en el juego?
-
El juego ofrece muchas características y opciones para que usted disfrute. Puede elegir entre una amplia gama de modelos y personalizarlos según sus preferencias. Puede cambiar el color, ruedas, alerones, luces, calcomanías, matrículas y más. También puede conducirlos en diferentes terrenos y entornos, como carreteras, pistas todoterreno o incluso una isla privada. También puede ajustar el ángulo de la cámara, los efectos de sonido, el volumen de la música, la sensibilidad de la dirección, la fuerza del freno, el control de la tracción y más. También puede disfrutar de sonidos realistas, gráficos y física que simulan el rendimiento real y el comportamiento de estos coches.
-
¿Cómo puedo contactar al desarrollador o reportar un problema con el juego?
-
Si tiene alguna pregunta, sugerencia o problema con el juego, puede ponerse en contacto con el desarrollador o informar de un problema a través de la propia aplicación. Simplemente vaya al menú de configuración y toque en "Contáctenos" o "Reportar un problema". También puede enviar un correo electrónico al desarrollador a dmnkstudio@gmail.com o visitar su sitio web en https://dmnkstudio.com/.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Granja Hroes Sper Saga Para Pc.md b/spaces/Benson/text-generation/Examples/Descargar Granja Hroes Sper Saga Para Pc.md
deleted file mode 100644
index 786da88c7e35179cb4d9cfeef81b86e37582b967..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Granja Hroes Sper Saga Para Pc.md
+++ /dev/null
@@ -1,79 +0,0 @@
-
-
Cómo descargar Farm Heroes Super Saga para PC
-
Farm Heroes Super Saga es un divertido y adictivo juego de puzzle match-3 que te reta a crecer y cosechar los mayores cropsies y derrotar al malvado mapache rancio. El juego cuenta con cientos de niveles, personajes lindos y modos de juego emocionantes. Si te encanta jugar a Farm Heroes Super Saga en tu dispositivo móvil, es posible que te preguntes si puedes jugar en tu PC también. La respuesta es sí, ¡puedes!
-
Jugar juegos Android en PC tiene muchos beneficios, como disfrutar de una pantalla más grande, mejores gráficos, controles mejorados y más espacio de almacenamiento. Además, puedes sincronizar tu progreso y logros en todos los dispositivos con tu cuenta de Google. En este artículo, te mostraremos tres formas de descargar y jugar Farm Heroes Super Saga para PC usando Google Play Games, un emulador de Android o tu aplicación de teléfono. También compararemos los pros y los contras de cada método y te ayudaremos a decidir cuál es el mejor para ti.
Antes de que pueda jugar juegos de Android en PC, debe asegurarse de que su PC cumple con los requisitos mínimos para ejecutarlos. Estas son algunas de las cosas que necesitas:
-
-
Un sistema operativo Windows 10 o 11
-
Una unidad de estado sólido (SSD) con al menos 10 GB de espacio de almacenamiento disponible
-
Una GPU Intel UHD Graphics 630 o similar
-
Un procesador con al menos cuatro núcleos físicos de CPU
-
8 GB de RAM
-
Una cuenta de administrador de Windows
-
Virtualización de hardware habilitada
-
-
También necesitas una conexión a Internet y una cuenta de Google para acceder a Google Play Store y descargar juegos.
-
Cómo utilizar Google Play Juegos para jugar Android en PC
-
-
-
Ir a [5](https://play.google.com/googleplaygames) y haga clic en Descargar Beta.
-
Una vez descargado, haga clic derecho en el archivo y haga clic en Ejecutar como administrador.
-
Espere a que la aplicación se instale.
-
Una vez instalado, un mensaje en la aplicación le pedirá que inicie sesión en su cuenta de Google.
-
Después de iniciar sesión, haga clic en la pestaña Juegos en la barra lateral izquierda.
-
Encuentra Farm Heroes Super Saga en la lista de juegos y selecciónala.
-
Haga clic en Instalar en la página de información. El juego se descargará y luego se instalará.
-
Una vez instalado, haga clic en Jugar para iniciar el juego.
-
-
Cómo usar un emulador de Android para jugar juegos de Android en PC
-
Otra forma de jugar juegos Android en PC es utilizar un emulador de Android, un software que imita el sistema operativo Android en su PC. Un emulador de Android le permite acceder a la Google Play Store completa y descargar cualquier juego o aplicación que desee. Hay muchos emuladores de Android disponibles, pero uno de los más populares y fiables es BlueStacks. BlueStacks ofrece una experiencia de juego rápida y fluida con controles personalizables, modo de varias instancias y optimización de juegos. Estos son los pasos para usar BlueStacks para jugar Farm Heroes Super Saga para PC:
-
-
Ir a [4](https://www.bluestacks.com/) y haga clic en Descargar BlueStacks.
-
Una vez descargado, haga doble clic en el archivo y siga las instrucciones para instalar BlueStacks.
-
Una vez instalado, inicie BlueStacks e inicie sesión en su cuenta de Google.
-
Haga clic en el icono de Google Play en la pantalla de inicio.
-
Buscar Farm Heroes Super Saga en la barra de búsqueda y seleccionarlo.
-
Haga clic en Instalar en la página de información. El juego se descargará y luego se instalará.
-
Una vez instalado, haga clic en Abrir para iniciar el juego.
-
-
Cómo utilizar la aplicación de teléfono para jugar juegos Android en PC
-
-
-
En su PC, abra el menú Inicio y busque la aplicación Su teléfono. Si no lo tiene, puede descargarlo desde [3](https://www.microsoft.com/en-us/p/your-phone/9nmpj99vjbwv).
-
En tu teléfono, ve a Configuración > Sistema > Acerca del teléfono y toca Número de compilación siete veces para habilitar las opciones del desarrollador.
-
Volver a Configuración > Sistema > Opciones del desarrollador y habilitar la depuración USB.
-
Conecte su teléfono a su PC con un cable USB.
-
En su PC, inicie su aplicación de teléfono e inicie sesión con su cuenta de Microsoft.
-
Siga las instrucciones para vincular su teléfono y conceder permisos.
-
En la aplicación Teléfono, haga clic en Aplicaciones en la barra lateral izquierda.
-
Encuentra Farm Heroes Super Saga en la lista de aplicaciones y seleccionarlo.
-
El juego se lanzará en su teléfono y espejo en su PC. Puede utilizar el ratón y el teclado para jugarlo.
-
-
Pros y contras de cada método
-
Ahora que sabes cómo descargar Farm Heroes Super Saga para PC usando tres métodos diferentes, es posible que te preguntes cuál es el mejor para ti. Para ayudarte a decidir, estos son algunos de los pros y contras de cada método:
-
-
Método
Pros
Contras
-
Google Play Juegos
- Experiencia oficial de Google - Sincronización perfecta entre dispositivos - Controles mejorados - Recompensas mientras juegas
- Selección limitada de juegos - Requiere Windows 11 - Puede que no sea compatible con todas las características de algunos juegos
<>
-
Android Emulator
- Acceso a Google Play Store - Experiencia de juego rápida y fluida - Controles personalizables - Modo multiinstancia - Optimización de juegos
- Requiere más espacio de almacenamiento<>- Puede ralentizar tu PC br<>-> Puede tener problemas de compatibilidad con algunos juegos
-
-
-
Conclusión
-
Farm Heroes Super Saga es un divertido y adictivo juego de puzzle match-3 que puedes jugar en tu PC usando Google Play Games, un emulador de Android o la aplicación Your Phone. Cada método tiene sus propios pros y contras, por lo que debe elegir el que se adapte a sus preferencias y necesidades. Recomendamos usar Google Play Games si quieres una experiencia oficial de Google con sincronización perfecta entre dispositivos, controles mejorados y recompensas a medida que juegas. Recomendamos usar un emulador de Android como BlueStacks si quieres acceder a la Google Play Store completa y una experiencia de juego rápida y fluida con controles personalizables, modo de varias instancias y optimización de juegos. Recomendamos usar la aplicación Su teléfono si desea usar las aplicaciones de su teléfono en su PC sin descargar nada adicional y reflejar la pantalla del teléfono. Esperamos que este artículo le ayudó a aprender cómo descargar Farm Heroes Super Saga para PC y disfrutar de este increíble juego en una pantalla más grande. Si tiene alguna pregunta o comentario, háganoslo saber en los comentarios a continuación. ¡Feliz agricultura!
FAQs
-
Aquí están algunas de las preguntas más frecuentes sobre la descarga de Farm Heroes Super Saga para PC:
-
-
¿Farm Heroes Super Saga es gratis?
-
Sí, Farm Heroes Super Saga es gratis para jugar, pero ofrece compras en la aplicación para vidas adicionales, refuerzos y otros artículos.
-
-
¿Puedo jugar Farm Heroes Super Saga sin conexión?
-
No, Farm Heroes Super Saga requiere una conexión a Internet para jugar.
-
¿Cómo puedo guardar mi progreso en Farm Heroes Super Saga?
-
Puedes guardar tu progreso en Farm Heroes Super Saga iniciando sesión con tu cuenta de Google o Facebook. De esta manera, puedes sincronizar tu progreso y logros entre dispositivos.
-
¿Cómo puedo obtener más vidas en Farm Heroes Super Saga?
-
Puedes conseguir más vidas en Farm Heroes Super Saga esperando a que se llenen, pidiendo ayuda a tus amigos, viendo anuncios o comprándolos con barras de oro.
-
-
Puede ponerse en contacto con el equipo de soporte de Farm Heroes Super Saga yendo a la configuración del juego y tocando el botón Centro de ayuda. También puede visitar [2](https://community.king.com/en/farm-heroes-super-saga) para unirse a la comunidad y obtener ayuda de otros jugadores.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/BetterAPI/BetterChat_new/src/lib/utils/trimSuffix.ts b/spaces/BetterAPI/BetterChat_new/src/lib/utils/trimSuffix.ts
deleted file mode 100644
index 729107942ebaa2d7e1281dd77f8e52e8b135a5ad..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat_new/src/lib/utils/trimSuffix.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export function trimSuffix(input: string, end: string): string {
- if (input.endsWith(end)) {
- return input.slice(0, input.length - end.length);
- }
- return input;
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/_distutils_hack/override.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/_distutils_hack/override.py
deleted file mode 100644
index 2cc433a4a55e3b41fa31089918fb62096092f89f..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/_distutils_hack/override.py
+++ /dev/null
@@ -1 +0,0 @@
-__import__('_distutils_hack').do_override()
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/util.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/util.py
deleted file mode 100644
index 34ce092c6d08d9cdc2704840b7539de7b5ae1dcc..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/util.py
+++ /dev/null
@@ -1,235 +0,0 @@
-# util.py
-import warnings
-import types
-import collections
-import itertools
-from functools import lru_cache
-from typing import List, Union, Iterable
-
-_bslash = chr(92)
-
-
-class __config_flags:
- """Internal class for defining compatibility and debugging flags"""
-
- _all_names: List[str] = []
- _fixed_names: List[str] = []
- _type_desc = "configuration"
-
- @classmethod
- def _set(cls, dname, value):
- if dname in cls._fixed_names:
- warnings.warn(
- "{}.{} {} is {} and cannot be overridden".format(
- cls.__name__,
- dname,
- cls._type_desc,
- str(getattr(cls, dname)).upper(),
- )
- )
- return
- if dname in cls._all_names:
- setattr(cls, dname, value)
- else:
- raise ValueError("no such {} {!r}".format(cls._type_desc, dname))
-
- enable = classmethod(lambda cls, name: cls._set(name, True))
- disable = classmethod(lambda cls, name: cls._set(name, False))
-
-
-@lru_cache(maxsize=128)
-def col(loc: int, strg: str) -> int:
- """
- Returns current column within a string, counting newlines as line separators.
- The first column is number 1.
-
- Note: the default parsing behavior is to expand tabs in the input string
- before starting the parsing process. See
- :class:`ParserElement.parseString` for more
- information on parsing strings containing ```` s, and suggested
- methods to maintain a consistent view of the parsed string, the parse
- location, and line and column positions within the parsed string.
- """
- s = strg
- return 1 if 0 < loc < len(s) and s[loc - 1] == "\n" else loc - s.rfind("\n", 0, loc)
-
-
-@lru_cache(maxsize=128)
-def lineno(loc: int, strg: str) -> int:
- """Returns current line number within a string, counting newlines as line separators.
- The first line is number 1.
-
- Note - the default parsing behavior is to expand tabs in the input string
- before starting the parsing process. See :class:`ParserElement.parseString`
- for more information on parsing strings containing ```` s, and
- suggested methods to maintain a consistent view of the parsed string, the
- parse location, and line and column positions within the parsed string.
- """
- return strg.count("\n", 0, loc) + 1
-
-
-@lru_cache(maxsize=128)
-def line(loc: int, strg: str) -> str:
- """
- Returns the line of text containing loc within a string, counting newlines as line separators.
- """
- last_cr = strg.rfind("\n", 0, loc)
- next_cr = strg.find("\n", loc)
- return strg[last_cr + 1 : next_cr] if next_cr >= 0 else strg[last_cr + 1 :]
-
-
-class _UnboundedCache:
- def __init__(self):
- cache = {}
- cache_get = cache.get
- self.not_in_cache = not_in_cache = object()
-
- def get(_, key):
- return cache_get(key, not_in_cache)
-
- def set_(_, key, value):
- cache[key] = value
-
- def clear(_):
- cache.clear()
-
- self.size = None
- self.get = types.MethodType(get, self)
- self.set = types.MethodType(set_, self)
- self.clear = types.MethodType(clear, self)
-
-
-class _FifoCache:
- def __init__(self, size):
- self.not_in_cache = not_in_cache = object()
- cache = collections.OrderedDict()
- cache_get = cache.get
-
- def get(_, key):
- return cache_get(key, not_in_cache)
-
- def set_(_, key, value):
- cache[key] = value
- while len(cache) > size:
- cache.popitem(last=False)
-
- def clear(_):
- cache.clear()
-
- self.size = size
- self.get = types.MethodType(get, self)
- self.set = types.MethodType(set_, self)
- self.clear = types.MethodType(clear, self)
-
-
-class LRUMemo:
- """
- A memoizing mapping that retains `capacity` deleted items
-
- The memo tracks retained items by their access order; once `capacity` items
- are retained, the least recently used item is discarded.
- """
-
- def __init__(self, capacity):
- self._capacity = capacity
- self._active = {}
- self._memory = collections.OrderedDict()
-
- def __getitem__(self, key):
- try:
- return self._active[key]
- except KeyError:
- self._memory.move_to_end(key)
- return self._memory[key]
-
- def __setitem__(self, key, value):
- self._memory.pop(key, None)
- self._active[key] = value
-
- def __delitem__(self, key):
- try:
- value = self._active.pop(key)
- except KeyError:
- pass
- else:
- while len(self._memory) >= self._capacity:
- self._memory.popitem(last=False)
- self._memory[key] = value
-
- def clear(self):
- self._active.clear()
- self._memory.clear()
-
-
-class UnboundedMemo(dict):
- """
- A memoizing mapping that retains all deleted items
- """
-
- def __delitem__(self, key):
- pass
-
-
-def _escape_regex_range_chars(s: str) -> str:
- # escape these chars: ^-[]
- for c in r"\^-[]":
- s = s.replace(c, _bslash + c)
- s = s.replace("\n", r"\n")
- s = s.replace("\t", r"\t")
- return str(s)
-
-
-def _collapse_string_to_ranges(
- s: Union[str, Iterable[str]], re_escape: bool = True
-) -> str:
- def is_consecutive(c):
- c_int = ord(c)
- is_consecutive.prev, prev = c_int, is_consecutive.prev
- if c_int - prev > 1:
- is_consecutive.value = next(is_consecutive.counter)
- return is_consecutive.value
-
- is_consecutive.prev = 0
- is_consecutive.counter = itertools.count()
- is_consecutive.value = -1
-
- def escape_re_range_char(c):
- return "\\" + c if c in r"\^-][" else c
-
- def no_escape_re_range_char(c):
- return c
-
- if not re_escape:
- escape_re_range_char = no_escape_re_range_char
-
- ret = []
- s = "".join(sorted(set(s)))
- if len(s) > 3:
- for _, chars in itertools.groupby(s, key=is_consecutive):
- first = last = next(chars)
- last = collections.deque(
- itertools.chain(iter([last]), chars), maxlen=1
- ).pop()
- if first == last:
- ret.append(escape_re_range_char(first))
- else:
- sep = "" if ord(last) == ord(first) + 1 else "-"
- ret.append(
- "{}{}{}".format(
- escape_re_range_char(first), sep, escape_re_range_char(last)
- )
- )
- else:
- ret = [escape_re_range_char(c) for c in s]
-
- return "".join(ret)
-
-
-def _flatten(ll: list) -> list:
- ret = []
- for i in ll:
- if isinstance(i, list):
- ret.extend(_flatten(i))
- else:
- ret.append(i)
- return ret
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/structures/image_list.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/structures/image_list.py
deleted file mode 100644
index 706c1c1dabcc5dbb401c2c0384e9957b5ecf745d..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/structures/image_list.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from __future__ import division
-from typing import Any, List, Sequence, Tuple, Union
-import torch
-from torch.nn import functional as F
-
-
-class ImageList(object):
- """
- Structure that holds a list of images (of possibly
- varying sizes) as a single tensor.
- This works by padding the images to the same size,
- and storing in a field the original sizes of each image
-
- Attributes:
- image_sizes (list[tuple[int, int]]): each tuple is (h, w)
- """
-
- def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):
- """
- Arguments:
- tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1
- image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can
- be smaller than (H, W) due to padding.
- """
- self.tensor = tensor
- self.image_sizes = image_sizes
-
- def __len__(self) -> int:
- return len(self.image_sizes)
-
- def __getitem__(self, idx: Union[int, slice]) -> torch.Tensor:
- """
- Access the individual image in its original size.
-
- Returns:
- Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1
- """
- size = self.image_sizes[idx]
- return self.tensor[idx, ..., : size[0], : size[1]] # type: ignore
-
- def to(self, *args: Any, **kwargs: Any) -> "ImageList":
- cast_tensor = self.tensor.to(*args, **kwargs)
- return ImageList(cast_tensor, self.image_sizes)
-
- @property
- def device(self) -> torch.device:
- return self.tensor.device
-
- @staticmethod
- def from_tensors(
- tensors: Sequence[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0
- ) -> "ImageList":
- """
- Args:
- tensors: a tuple or list of `torch.Tensors`, each of shape (Hi, Wi) or
- (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded
- to the same shape with `pad_value`.
- size_divisibility (int): If `size_divisibility > 0`, add padding to ensure
- the common height and width is divisible by `size_divisibility`.
- This depends on the model and many models need a divisibility of 32.
- pad_value (float): value to pad
-
- Returns:
- an `ImageList`.
- """
- assert len(tensors) > 0
- assert isinstance(tensors, (tuple, list))
- for t in tensors:
- assert isinstance(t, torch.Tensor), type(t)
- assert t.shape[1:-2] == tensors[0].shape[1:-2], t.shape
- # per dimension maximum (H, W) or (C_1, ..., C_K, H, W) where K >= 1 among all tensors
- max_size = tuple(max(s) for s in zip(*[img.shape for img in tensors]))
-
- if size_divisibility > 0:
- import math
-
- stride = size_divisibility
- max_size = list(max_size) # type: ignore
- max_size[-2] = int(math.ceil(max_size[-2] / stride) * stride) # type: ignore
- max_size[-1] = int(math.ceil(max_size[-1] / stride) * stride) # type: ignore
- max_size = tuple(max_size)
-
- image_sizes = [tuple(im.shape[-2:]) for im in tensors]
-
- if len(tensors) == 1:
- # This seems slightly (2%) faster.
- # TODO: check whether it's faster for multiple images as well
- image_size = image_sizes[0]
- padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]
- if all(x == 0 for x in padding_size): # https://github.com/pytorch/pytorch/issues/31734
- batched_imgs = tensors[0].unsqueeze(0)
- else:
- padded = F.pad(tensors[0], padding_size, value=pad_value)
- batched_imgs = padded.unsqueeze_(0)
- else:
- batch_shape = (len(tensors),) + max_size
- batched_imgs = tensors[0].new_full(batch_shape, pad_value)
- for img, pad_img in zip(tensors, batched_imgs):
- pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img)
-
- return ImageList(batched_imgs.contiguous(), image_sizes)
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/malloc_and_free.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/malloc_and_free.h
deleted file mode 100644
index 01ab1e6dbe1732da1f8606b7a9121c1b404edb6f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/malloc_and_free.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-
-// this system inherits malloc and free
-#include
-
diff --git a/spaces/ChenWu98/Stable-CycleDiffusion/README.md b/spaces/ChenWu98/Stable-CycleDiffusion/README.md
deleted file mode 100644
index 0101c471ee3496219485619774c0126edab829b6..0000000000000000000000000000000000000000
--- a/spaces/ChenWu98/Stable-CycleDiffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stable CycleDiffusion
-emoji: 🚀
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CofAI/chat/client/js/highlight.min.js b/spaces/CofAI/chat/client/js/highlight.min.js
deleted file mode 100644
index d410b45b38119606525a0a7c0c60c428c5ee6eb7..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/client/js/highlight.min.js
+++ /dev/null
@@ -1 +0,0 @@
-var hljs=function(){"use strict";var e={exports:{}};function n(e){return e instanceof Map?e.clear=e.delete=e.set=()=>{throw Error("map is read-only")}:e instanceof Set&&(e.add=e.clear=e.delete=()=>{throw Error("set is read-only")}),Object.freeze(e),Object.getOwnPropertyNames(e).forEach(t=>{var a=e[t];"object"!=typeof a||Object.isFrozen(a)||n(a)}),e}e.exports=n,e.exports.default=n;class t{constructor(e){void 0===e.data&&(e.data={}),this.data=e.data,this.isMatchIgnored=!1}ignoreMatch(){this.isMatchIgnored=!0}}function a(e){return e.replace(/&/g,"&").replace(//g,">").replace(/"/g,""").replace(/'/g,"'")}function i(e,...n){let t=Object.create(null);for(let a in e)t[a]=e[a];return n.forEach(e=>{for(let n in e)t[n]=e[n]}),t}let r=e=>!!e.scope||e.sublanguage&&e.language;class s{constructor(e,n){this.buffer="",this.classPrefix=n.classPrefix,e.walk(this)}addText(e){this.buffer+=a(e)}openNode(e){if(!r(e))return;let n="";n=e.sublanguage?"language-"+e.language:((e,{prefix:n})=>{if(e.includes(".")){let t=e.split(".");return[`${n}${t.shift()}`,...t.map((e,n)=>`${e}${"_".repeat(n+1)}`),].join(" ")}return`${n}${e}`})(e.scope,{prefix:this.classPrefix}),this.span(n)}closeNode(e){r(e)&&(this.buffer+="")}value(){return this.buffer}span(e){this.buffer+=``}}let l=(e={})=>{let n={children:[]};return Object.assign(n,e),n};class o{constructor(){this.rootNode=l(),this.stack=[this.rootNode]}get top(){return this.stack[this.stack.length-1]}get root(){return this.rootNode}add(e){this.top.children.push(e)}openNode(e){let n=l({scope:e});this.add(n),this.stack.push(n)}closeNode(){if(this.stack.length>1)return this.stack.pop()}closeAllNodes(){for(;this.closeNode(););}toJSON(){return JSON.stringify(this.rootNode,null,4)}walk(e){return this.constructor._walk(e,this.rootNode)}static _walk(e,n){return"string"==typeof n?e.addText(n):n.children&&(e.openNode(n),n.children.forEach(n=>this._walk(e,n)),e.closeNode(n)),e}static 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diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/ddim.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/ddim.py
deleted file mode 100644
index 5a1e9d552d1b682772f66405a576319c72e337d8..0000000000000000000000000000000000000000
--- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/models/diffusion/ddim.py
+++ /dev/null
@@ -1,337 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
-
-
-class DDIMSampler(object):
- def __init__(self, model, schedule="linear", **kwargs):
- super().__init__()
- self.model = model
- self.ddpm_num_timesteps = model.num_timesteps
- self.schedule = schedule
-
- def register_buffer(self, name, attr):
- # Do not force module to cuda by default.
- #if type(attr) == torch.Tensor:
- # if attr.device != torch.device("cuda"):
- # attr = attr.to(torch.device("cuda"))
- setattr(self, name, attr)
-
- def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
- self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
- num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
- alphas_cumprod = self.model.alphas_cumprod
- assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
- to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
- self.register_buffer('betas', to_torch(self.model.betas))
- self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
- # calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
- # ddim sampling parameters
- ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
- ddim_timesteps=self.ddim_timesteps,
- eta=ddim_eta,verbose=verbose)
- self.register_buffer('ddim_sigmas', ddim_sigmas)
- self.register_buffer('ddim_alphas', ddim_alphas)
- self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
- self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
- sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
- (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
- 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
- self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
- @torch.no_grad()
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- dynamic_threshold=None,
- ucg_schedule=None,
- **kwargs
- ):
- if conditioning is not None:
- if isinstance(conditioning, dict):
- ctmp = conditioning[list(conditioning.keys())[0]]
- while isinstance(ctmp, list): ctmp = ctmp[0]
- cbs = ctmp.shape[0]
- if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
- elif isinstance(conditioning, list):
- for ctmp in conditioning:
- if ctmp.shape[0] != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-
- else:
- if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
- self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
- # sampling
- C, H, W = shape
- size = (batch_size, C, H, W)
- print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
- samples, intermediates = self.ddim_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- dynamic_threshold=dynamic_threshold,
- ucg_schedule=ucg_schedule
- )
- return samples, intermediates
-
- @torch.no_grad()
- def ddim_sampling(self, cond, shape,
- x_T=None, ddim_use_original_steps=False,
- callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, log_every_t=100,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
- ucg_schedule=None):
- device = self.model.betas.device
- b = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=device)
- else:
- img = x_T
-
- if timesteps is None:
- timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
- elif timesteps is not None and not ddim_use_original_steps:
- subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
- timesteps = self.ddim_timesteps[:subset_end]
-
- intermediates = {'x_inter': [img], 'pred_x0': [img]}
- time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
- total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
- print(f"Running DDIM Sampling with {total_steps} timesteps")
-
- iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
- for i, step in enumerate(iterator):
- index = total_steps - i - 1
- ts = torch.full((b,), step, device=device, dtype=torch.long)
-
- if mask is not None:
- assert x0 is not None
- img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
- img = img_orig * mask + (1. - mask) * img
-
- if ucg_schedule is not None:
- assert len(ucg_schedule) == len(time_range)
- unconditional_guidance_scale = ucg_schedule[i]
-
- outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
- quantize_denoised=quantize_denoised, temperature=temperature,
- noise_dropout=noise_dropout, score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- dynamic_threshold=dynamic_threshold)
- img, pred_x0 = outs
- if callback: callback(i)
- if img_callback: img_callback(pred_x0, i)
-
- if index % log_every_t == 0 or index == total_steps - 1:
- intermediates['x_inter'].append(img)
- intermediates['pred_x0'].append(pred_x0)
-
- return img, intermediates
-
- @torch.no_grad()
- def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None,
- dynamic_threshold=None):
- b, *_, device = *x.shape, x.device
-
- if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
- model_output = self.model.apply_model(x, t, c)
- else:
- x_in = torch.cat([x] * 2)
- t_in = torch.cat([t] * 2)
- if isinstance(c, dict):
- assert isinstance(unconditional_conditioning, dict)
- c_in = dict()
- for k in c:
- if isinstance(c[k], list):
- c_in[k] = [torch.cat([
- unconditional_conditioning[k][i],
- c[k][i]]) for i in range(len(c[k]))]
- else:
- c_in[k] = torch.cat([
- unconditional_conditioning[k],
- c[k]])
- elif isinstance(c, list):
- c_in = list()
- assert isinstance(unconditional_conditioning, list)
- for i in range(len(c)):
- c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
- else:
- c_in = torch.cat([unconditional_conditioning, c])
- model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
- model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
-
- if self.model.parameterization == "v":
- e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
- else:
- e_t = model_output
-
- if score_corrector is not None:
- assert self.model.parameterization == "eps", 'not implemented'
- e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
- alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
- alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
- sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
- sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
- # select parameters corresponding to the currently considered timestep
- a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
- a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
- sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
- sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
- # current prediction for x_0
- if self.model.parameterization != "v":
- pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
- else:
- pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
-
- if quantize_denoised:
- pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-
- if dynamic_threshold is not None:
- raise NotImplementedError()
-
- # direction pointing to x_t
- dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
- noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
- noise = torch.nn.functional.dropout(noise, p=noise_dropout)
- x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
- return x_prev, pred_x0
-
- @torch.no_grad()
- def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
- unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
- num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
-
- assert t_enc <= num_reference_steps
- num_steps = t_enc
-
- if use_original_steps:
- alphas_next = self.alphas_cumprod[:num_steps]
- alphas = self.alphas_cumprod_prev[:num_steps]
- else:
- alphas_next = self.ddim_alphas[:num_steps]
- alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
-
- x_next = x0
- intermediates = []
- inter_steps = []
- for i in tqdm(range(num_steps), desc='Encoding Image'):
- t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
- if unconditional_guidance_scale == 1.:
- noise_pred = self.model.apply_model(x_next, t, c)
- else:
- assert unconditional_conditioning is not None
- e_t_uncond, noise_pred = torch.chunk(
- self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
- torch.cat((unconditional_conditioning, c))), 2)
- noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
-
- xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
- weighted_noise_pred = alphas_next[i].sqrt() * (
- (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
- x_next = xt_weighted + weighted_noise_pred
- if return_intermediates and i % (
- num_steps // return_intermediates) == 0 and i < num_steps - 1:
- intermediates.append(x_next)
- inter_steps.append(i)
- elif return_intermediates and i >= num_steps - 2:
- intermediates.append(x_next)
- inter_steps.append(i)
- if callback: callback(i)
-
- out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
- if return_intermediates:
- out.update({'intermediates': intermediates})
- return x_next, out
-
- @torch.no_grad()
- def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
- # fast, but does not allow for exact reconstruction
- # t serves as an index to gather the correct alphas
- if use_original_steps:
- sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
- sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
- else:
- sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
- sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
- if noise is None:
- noise = torch.randn_like(x0)
- return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
- extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
- @torch.no_grad()
- def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
- use_original_steps=False, callback=None):
-
- timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
- timesteps = timesteps[:t_start]
-
- time_range = np.flip(timesteps)
- total_steps = timesteps.shape[0]
- print(f"Running DDIM Sampling with {total_steps} timesteps")
-
- iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
- x_dec = x_latent
- for i, step in enumerate(iterator):
- index = total_steps - i - 1
- ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
- x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning)
- if callback: callback(i)
- return x_dec
\ No newline at end of file
diff --git a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/utils.py b/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/utils.py
deleted file mode 100644
index 9a9d3b5b66370fa98da9e067ba53ead848ea9a59..0000000000000000000000000000000000000000
--- a/spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/utils.py
+++ /dev/null
@@ -1,189 +0,0 @@
-"""Utils for monoDepth."""
-import sys
-import re
-import numpy as np
-import cv2
-import torch
-
-
-def read_pfm(path):
- """Read pfm file.
-
- Args:
- path (str): path to file
-
- Returns:
- tuple: (data, scale)
- """
- with open(path, "rb") as file:
-
- color = None
- width = None
- height = None
- scale = None
- endian = None
-
- header = file.readline().rstrip()
- if header.decode("ascii") == "PF":
- color = True
- elif header.decode("ascii") == "Pf":
- color = False
- else:
- raise Exception("Not a PFM file: " + path)
-
- dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
- if dim_match:
- width, height = list(map(int, dim_match.groups()))
- else:
- raise Exception("Malformed PFM header.")
-
- scale = float(file.readline().decode("ascii").rstrip())
- if scale < 0:
- # little-endian
- endian = "<"
- scale = -scale
- else:
- # big-endian
- endian = ">"
-
- data = np.fromfile(file, endian + "f")
- shape = (height, width, 3) if color else (height, width)
-
- data = np.reshape(data, shape)
- data = np.flipud(data)
-
- return data, scale
-
-
-def write_pfm(path, image, scale=1):
- """Write pfm file.
-
- Args:
- path (str): pathto file
- image (array): data
- scale (int, optional): Scale. Defaults to 1.
- """
-
- with open(path, "wb") as file:
- color = None
-
- if image.dtype.name != "float32":
- raise Exception("Image dtype must be float32.")
-
- image = np.flipud(image)
-
- if len(image.shape) == 3 and image.shape[2] == 3: # color image
- color = True
- elif (
- len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
- ): # greyscale
- color = False
- else:
- raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
-
- file.write("PF\n" if color else "Pf\n".encode())
- file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
-
- endian = image.dtype.byteorder
-
- if endian == "<" or endian == "=" and sys.byteorder == "little":
- scale = -scale
-
- file.write("%f\n".encode() % scale)
-
- image.tofile(file)
-
-
-def read_image(path):
- """Read image and output RGB image (0-1).
-
- Args:
- path (str): path to file
-
- Returns:
- array: RGB image (0-1)
- """
- img = cv2.imread(path)
-
- if img.ndim == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
-
- return img
-
-
-def resize_image(img):
- """Resize image and make it fit for network.
-
- Args:
- img (array): image
-
- Returns:
- tensor: data ready for network
- """
- height_orig = img.shape[0]
- width_orig = img.shape[1]
-
- if width_orig > height_orig:
- scale = width_orig / 384
- else:
- scale = height_orig / 384
-
- height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
- width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
-
- img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
-
- img_resized = (
- torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
- )
- img_resized = img_resized.unsqueeze(0)
-
- return img_resized
-
-
-def resize_depth(depth, width, height):
- """Resize depth map and bring to CPU (numpy).
-
- Args:
- depth (tensor): depth
- width (int): image width
- height (int): image height
-
- Returns:
- array: processed depth
- """
- depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
-
- depth_resized = cv2.resize(
- depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
- )
-
- return depth_resized
-
-def write_depth(path, depth, bits=1):
- """Write depth map to pfm and png file.
-
- Args:
- path (str): filepath without extension
- depth (array): depth
- """
- write_pfm(path + ".pfm", depth.astype(np.float32))
-
- depth_min = depth.min()
- depth_max = depth.max()
-
- max_val = (2**(8*bits))-1
-
- if depth_max - depth_min > np.finfo("float").eps:
- out = max_val * (depth - depth_min) / (depth_max - depth_min)
- else:
- out = np.zeros(depth.shape, dtype=depth.type)
-
- if bits == 1:
- cv2.imwrite(path + ".png", out.astype("uint8"))
- elif bits == 2:
- cv2.imwrite(path + ".png", out.astype("uint16"))
-
- return
diff --git a/spaces/CuriousDolphin/MobileSAM/utils/__init__.py b/spaces/CuriousDolphin/MobileSAM/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/__init__.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/__init__.py
deleted file mode 100644
index 89b681378328a3f30ec9de2d9743de066d5c7632..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/__init__.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# import log
-# import dtype
-# # import plt
-# import np
-# import img
-# _img = img
-# import dec
-# import rand
-# import mod
-# import proc
-# import test
-# import neighbour as nb
-# #import mask
-# import str_ as str
-# import io as sys_io
-# import io_ as io
-# import feature
-# import thread_ as thread
-# import caffe_ as caffe
-# # import tf
-# import cmd
-# import ml
-# import sys
-# import url
-# from .misc import *
-# from .logger import *
-# # log.init_logger('~/temp/log/log_' + get_date_str() + '.log')
-#
-# def exit(code = 0):
-# sys.exit(0)
-#
-# is_main = mod.is_main
-# init_logger = log.init_logger
-#
-# def sit(img, path = None, name = ""):
-# if path is None:
-# _count = get_count();
-# path = '~/temp/no-use/images/%s_%d_%s.jpg'%(log.get_date_str(), _count, name)
-#
-# if type(img) == list:
-# plt.show_images(images = img, path = path, show = False, axis_off = True, save = True)
-# else:
-# plt.imwrite(path, img)
-#
-# return path
-# _count = 0;
-#
-# def get_count():
-# global _count;
-# _count += 1;
-# return _count
-#
-# def cit(img, path = None, rgb = True, name = ""):
-# _count = get_count();
-# if path is None:
-# img = np.np.asarray(img, dtype = np.np.uint8)
-# path = '~/temp/no-use/%s_%d_%s.jpg'%(log.get_date_str(), _count, name)
-# _img.imwrite(path, img, rgb = rgb)
-# return path
-#
-# def argv(index):
-# return sys.argv[index]
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/mty.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/mty.py
deleted file mode 100644
index f5758683d1958a8b02fe7b8ffff0193d1236169f..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/structures/mty.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-
-# transpose
-FLIP_LEFT_RIGHT = 0
-FLIP_TOP_BOTTOM = 1
-
-all_types = [[1,2,3,4],[1,2,4,3],[1,3,2,4],[1,3,4,2],[1,4,2,3],[1,4,3,2],\
- [2,1,3,4],[2,1,4,3],[2,3,1,4],[2,3,4,1],[2,4,1,3],[2,4,3,1],\
- [3,1,2,4],[3,1,4,2],[3,2,1,4],[3,2,4,1],[3,4,1,2],[3,4,2,1],\
- [4,1,2,3],[4,1,3,2],[4,2,1,3],[4,2,3,1],[4,3,1,2],[4,3,2,1]]
-aty= [[all_types[iat][0]-1,all_types[iat][1]-1,all_types[iat][2]-1,all_types[iat][3]-1] for iat in range(24)]
-
-class MTY(object):
- def __init__(self, mty, size, mode=None):
- # FIXME remove check once we have better integration with device
- # in my version this would consistently return a CPU tensor
- device = mty.device if isinstance(mty, torch.Tensor) else torch.device('cpu')
- mty = torch.as_tensor(mty, dtype=torch.int64, device=device)
-
- # TODO should I split them?
- assert(len(mty.size()) == 1), str(mty.size())
- self.mty = mty
-
- self.size = size
- self.mode = mode
-
- def crop(self, box):
- w, h = box[2] - box[0], box[3] - box[1]
- return type(self)(self.mty, (w, h), self.mode)
-
- def resize(self, size, *args, **kwargs):
- return type(self)(self.mty, size, self.mode)
-
- def transpose(self, method):
- if method not in (FLIP_LEFT_RIGHT,):
- raise NotImplementedError(
- "Only FLIP_LEFT_RIGHT implemented")
-
- flipped_data = self.mty.clone()
- for i in range(self.mty.size()[0]):
- revs = [it for it in aty[self.mty[i]]]
- revs.reverse()
- flip_type = aty.index(revs)
- flipped_data[i] = flip_type
-
- return type(self)(flipped_data, self.size, self.mode)
-
- def to(self, *args, **kwargs):
- return type(self)(self.mty.to(*args, **kwargs), self.size, self.mode)
-
- def __getitem__(self, item):
- return type(self)(self.mty[item], self.size, self.mode)
-
- def __repr__(self):
- s = self.__class__.__name__ + '('
- s += 'num_instances={}, '.format(len(self.mty))
- s += 'image_width={}, '.format(self.size[0])
- s += 'image_height={})'.format(self.size[1])
- return s
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/filenames.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/filenames.py
deleted file mode 100644
index d279f89cc82cc280370d09ebdb16cb301f62aa57..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/filenames.py
+++ /dev/null
@@ -1,246 +0,0 @@
-"""
-This module implements the algorithm for converting between a "user name" -
-something that a user can choose arbitrarily inside a font editor - and a file
-name suitable for use in a wide range of operating systems and filesystems.
-
-The `UFO 3 specification `_
-provides an example of an algorithm for such conversion, which avoids illegal
-characters, reserved file names, ambiguity between upper- and lower-case
-characters, and clashes with existing files.
-
-This code was originally copied from
-`ufoLib `_
-by Tal Leming and is copyright (c) 2005-2016, The RoboFab Developers:
-
-- Erik van Blokland
-- Tal Leming
-- Just van Rossum
-"""
-
-
-illegalCharacters = r"\" * + / : < > ? [ \ ] | \0".split(" ")
-illegalCharacters += [chr(i) for i in range(1, 32)]
-illegalCharacters += [chr(0x7F)]
-reservedFileNames = "CON PRN AUX CLOCK$ NUL A:-Z: COM1".lower().split(" ")
-reservedFileNames += "LPT1 LPT2 LPT3 COM2 COM3 COM4".lower().split(" ")
-maxFileNameLength = 255
-
-
-class NameTranslationError(Exception):
- pass
-
-
-def userNameToFileName(userName, existing=[], prefix="", suffix=""):
- """Converts from a user name to a file name.
-
- Takes care to avoid illegal characters, reserved file names, ambiguity between
- upper- and lower-case characters, and clashes with existing files.
-
- Args:
- userName (str): The input file name.
- existing: A case-insensitive list of all existing file names.
- prefix: Prefix to be prepended to the file name.
- suffix: Suffix to be appended to the file name.
-
- Returns:
- A suitable filename.
-
- Raises:
- NameTranslationError: If no suitable name could be generated.
-
- Examples::
-
- >>> userNameToFileName("a") == "a"
- True
- >>> userNameToFileName("A") == "A_"
- True
- >>> userNameToFileName("AE") == "A_E_"
- True
- >>> userNameToFileName("Ae") == "A_e"
- True
- >>> userNameToFileName("ae") == "ae"
- True
- >>> userNameToFileName("aE") == "aE_"
- True
- >>> userNameToFileName("a.alt") == "a.alt"
- True
- >>> userNameToFileName("A.alt") == "A_.alt"
- True
- >>> userNameToFileName("A.Alt") == "A_.A_lt"
- True
- >>> userNameToFileName("A.aLt") == "A_.aL_t"
- True
- >>> userNameToFileName(u"A.alT") == "A_.alT_"
- True
- >>> userNameToFileName("T_H") == "T__H_"
- True
- >>> userNameToFileName("T_h") == "T__h"
- True
- >>> userNameToFileName("t_h") == "t_h"
- True
- >>> userNameToFileName("F_F_I") == "F__F__I_"
- True
- >>> userNameToFileName("f_f_i") == "f_f_i"
- True
- >>> userNameToFileName("Aacute_V.swash") == "A_acute_V_.swash"
- True
- >>> userNameToFileName(".notdef") == "_notdef"
- True
- >>> userNameToFileName("con") == "_con"
- True
- >>> userNameToFileName("CON") == "C_O_N_"
- True
- >>> userNameToFileName("con.alt") == "_con.alt"
- True
- >>> userNameToFileName("alt.con") == "alt._con"
- True
- """
- # the incoming name must be a str
- if not isinstance(userName, str):
- raise ValueError("The value for userName must be a string.")
- # establish the prefix and suffix lengths
- prefixLength = len(prefix)
- suffixLength = len(suffix)
- # replace an initial period with an _
- # if no prefix is to be added
- if not prefix and userName[0] == ".":
- userName = "_" + userName[1:]
- # filter the user name
- filteredUserName = []
- for character in userName:
- # replace illegal characters with _
- if character in illegalCharacters:
- character = "_"
- # add _ to all non-lower characters
- elif character != character.lower():
- character += "_"
- filteredUserName.append(character)
- userName = "".join(filteredUserName)
- # clip to 255
- sliceLength = maxFileNameLength - prefixLength - suffixLength
- userName = userName[:sliceLength]
- # test for illegal files names
- parts = []
- for part in userName.split("."):
- if part.lower() in reservedFileNames:
- part = "_" + part
- parts.append(part)
- userName = ".".join(parts)
- # test for clash
- fullName = prefix + userName + suffix
- if fullName.lower() in existing:
- fullName = handleClash1(userName, existing, prefix, suffix)
- # finished
- return fullName
-
-
-def handleClash1(userName, existing=[], prefix="", suffix=""):
- """
- existing should be a case-insensitive list
- of all existing file names.
-
- >>> prefix = ("0" * 5) + "."
- >>> suffix = "." + ("0" * 10)
- >>> existing = ["a" * 5]
-
- >>> e = list(existing)
- >>> handleClash1(userName="A" * 5, existing=e,
- ... prefix=prefix, suffix=suffix) == (
- ... '00000.AAAAA000000000000001.0000000000')
- True
-
- >>> e = list(existing)
- >>> e.append(prefix + "aaaaa" + "1".zfill(15) + suffix)
- >>> handleClash1(userName="A" * 5, existing=e,
- ... prefix=prefix, suffix=suffix) == (
- ... '00000.AAAAA000000000000002.0000000000')
- True
-
- >>> e = list(existing)
- >>> e.append(prefix + "AAAAA" + "2".zfill(15) + suffix)
- >>> handleClash1(userName="A" * 5, existing=e,
- ... prefix=prefix, suffix=suffix) == (
- ... '00000.AAAAA000000000000001.0000000000')
- True
- """
- # if the prefix length + user name length + suffix length + 15 is at
- # or past the maximum length, silce 15 characters off of the user name
- prefixLength = len(prefix)
- suffixLength = len(suffix)
- if prefixLength + len(userName) + suffixLength + 15 > maxFileNameLength:
- l = prefixLength + len(userName) + suffixLength + 15
- sliceLength = maxFileNameLength - l
- userName = userName[:sliceLength]
- finalName = None
- # try to add numbers to create a unique name
- counter = 1
- while finalName is None:
- name = userName + str(counter).zfill(15)
- fullName = prefix + name + suffix
- if fullName.lower() not in existing:
- finalName = fullName
- break
- else:
- counter += 1
- if counter >= 999999999999999:
- break
- # if there is a clash, go to the next fallback
- if finalName is None:
- finalName = handleClash2(existing, prefix, suffix)
- # finished
- return finalName
-
-
-def handleClash2(existing=[], prefix="", suffix=""):
- """
- existing should be a case-insensitive list
- of all existing file names.
-
- >>> prefix = ("0" * 5) + "."
- >>> suffix = "." + ("0" * 10)
- >>> existing = [prefix + str(i) + suffix for i in range(100)]
-
- >>> e = list(existing)
- >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
- ... '00000.100.0000000000')
- True
-
- >>> e = list(existing)
- >>> e.remove(prefix + "1" + suffix)
- >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
- ... '00000.1.0000000000')
- True
-
- >>> e = list(existing)
- >>> e.remove(prefix + "2" + suffix)
- >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
- ... '00000.2.0000000000')
- True
- """
- # calculate the longest possible string
- maxLength = maxFileNameLength - len(prefix) - len(suffix)
- maxValue = int("9" * maxLength)
- # try to find a number
- finalName = None
- counter = 1
- while finalName is None:
- fullName = prefix + str(counter) + suffix
- if fullName.lower() not in existing:
- finalName = fullName
- break
- else:
- counter += 1
- if counter >= maxValue:
- break
- # raise an error if nothing has been found
- if finalName is None:
- raise NameTranslationError("No unique name could be found.")
- # finished
- return finalName
-
-
-if __name__ == "__main__":
- import doctest
- import sys
-
- sys.exit(doctest.testmod().failed)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_p_o_s_t.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_p_o_s_t.py
deleted file mode 100644
index dba637117a0ac148af65c75853dd3bffbbbd1154..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_p_o_s_t.py
+++ /dev/null
@@ -1,308 +0,0 @@
-from fontTools import ttLib
-from fontTools.ttLib.standardGlyphOrder import standardGlyphOrder
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytechr, byteord, tobytes, tostr, safeEval, readHex
-from . import DefaultTable
-import sys
-import struct
-import array
-import logging
-
-log = logging.getLogger(__name__)
-
-postFormat = """
- >
- formatType: 16.16F
- italicAngle: 16.16F # italic angle in degrees
- underlinePosition: h
- underlineThickness: h
- isFixedPitch: L
- minMemType42: L # minimum memory if TrueType font is downloaded
- maxMemType42: L # maximum memory if TrueType font is downloaded
- minMemType1: L # minimum memory if Type1 font is downloaded
- maxMemType1: L # maximum memory if Type1 font is downloaded
-"""
-
-postFormatSize = sstruct.calcsize(postFormat)
-
-
-class table__p_o_s_t(DefaultTable.DefaultTable):
- def decompile(self, data, ttFont):
- sstruct.unpack(postFormat, data[:postFormatSize], self)
- data = data[postFormatSize:]
- if self.formatType == 1.0:
- self.decode_format_1_0(data, ttFont)
- elif self.formatType == 2.0:
- self.decode_format_2_0(data, ttFont)
- elif self.formatType == 3.0:
- self.decode_format_3_0(data, ttFont)
- elif self.formatType == 4.0:
- self.decode_format_4_0(data, ttFont)
- else:
- # supported format
- raise ttLib.TTLibError(
- "'post' table format %f not supported" % self.formatType
- )
-
- def compile(self, ttFont):
- data = sstruct.pack(postFormat, self)
- if self.formatType == 1.0:
- pass # we're done
- elif self.formatType == 2.0:
- data = data + self.encode_format_2_0(ttFont)
- elif self.formatType == 3.0:
- pass # we're done
- elif self.formatType == 4.0:
- data = data + self.encode_format_4_0(ttFont)
- else:
- # supported format
- raise ttLib.TTLibError(
- "'post' table format %f not supported" % self.formatType
- )
- return data
-
- def getGlyphOrder(self):
- """This function will get called by a ttLib.TTFont instance.
- Do not call this function yourself, use TTFont().getGlyphOrder()
- or its relatives instead!
- """
- if not hasattr(self, "glyphOrder"):
- raise ttLib.TTLibError("illegal use of getGlyphOrder()")
- glyphOrder = self.glyphOrder
- del self.glyphOrder
- return glyphOrder
-
- def decode_format_1_0(self, data, ttFont):
- self.glyphOrder = standardGlyphOrder[: ttFont["maxp"].numGlyphs]
-
- def decode_format_2_0(self, data, ttFont):
- (numGlyphs,) = struct.unpack(">H", data[:2])
- numGlyphs = int(numGlyphs)
- if numGlyphs > ttFont["maxp"].numGlyphs:
- # Assume the numGlyphs field is bogus, so sync with maxp.
- # I've seen this in one font, and if the assumption is
- # wrong elsewhere, well, so be it: it's hard enough to
- # work around _one_ non-conforming post format...
- numGlyphs = ttFont["maxp"].numGlyphs
- data = data[2:]
- indices = array.array("H")
- indices.frombytes(data[: 2 * numGlyphs])
- if sys.byteorder != "big":
- indices.byteswap()
- data = data[2 * numGlyphs :]
- maxIndex = max(indices)
- self.extraNames = extraNames = unpackPStrings(data, maxIndex - 257)
- self.glyphOrder = glyphOrder = [""] * int(ttFont["maxp"].numGlyphs)
- for glyphID in range(numGlyphs):
- index = indices[glyphID]
- if index > 257:
- try:
- name = extraNames[index - 258]
- except IndexError:
- name = ""
- else:
- # fetch names from standard list
- name = standardGlyphOrder[index]
- glyphOrder[glyphID] = name
- self.build_psNameMapping(ttFont)
-
- def build_psNameMapping(self, ttFont):
- mapping = {}
- allNames = {}
- for i in range(ttFont["maxp"].numGlyphs):
- glyphName = psName = self.glyphOrder[i]
- if glyphName == "":
- glyphName = "glyph%.5d" % i
- if glyphName in allNames:
- # make up a new glyphName that's unique
- n = allNames[glyphName]
- while (glyphName + "#" + str(n)) in allNames:
- n += 1
- allNames[glyphName] = n + 1
- glyphName = glyphName + "#" + str(n)
-
- self.glyphOrder[i] = glyphName
- allNames[glyphName] = 1
- if glyphName != psName:
- mapping[glyphName] = psName
-
- self.mapping = mapping
-
- def decode_format_3_0(self, data, ttFont):
- # Setting self.glyphOrder to None will cause the TTFont object
- # try and construct glyph names from a Unicode cmap table.
- self.glyphOrder = None
-
- def decode_format_4_0(self, data, ttFont):
- from fontTools import agl
-
- numGlyphs = ttFont["maxp"].numGlyphs
- indices = array.array("H")
- indices.frombytes(data)
- if sys.byteorder != "big":
- indices.byteswap()
- # In some older fonts, the size of the post table doesn't match
- # the number of glyphs. Sometimes it's bigger, sometimes smaller.
- self.glyphOrder = glyphOrder = [""] * int(numGlyphs)
- for i in range(min(len(indices), numGlyphs)):
- if indices[i] == 0xFFFF:
- self.glyphOrder[i] = ""
- elif indices[i] in agl.UV2AGL:
- self.glyphOrder[i] = agl.UV2AGL[indices[i]]
- else:
- self.glyphOrder[i] = "uni%04X" % indices[i]
- self.build_psNameMapping(ttFont)
-
- def encode_format_2_0(self, ttFont):
- numGlyphs = ttFont["maxp"].numGlyphs
- glyphOrder = ttFont.getGlyphOrder()
- assert len(glyphOrder) == numGlyphs
- indices = array.array("H")
- extraDict = {}
- extraNames = self.extraNames = [
- n for n in self.extraNames if n not in standardGlyphOrder
- ]
- for i in range(len(extraNames)):
- extraDict[extraNames[i]] = i
- for glyphID in range(numGlyphs):
- glyphName = glyphOrder[glyphID]
- if glyphName in self.mapping:
- psName = self.mapping[glyphName]
- else:
- psName = glyphName
- if psName in extraDict:
- index = 258 + extraDict[psName]
- elif psName in standardGlyphOrder:
- index = standardGlyphOrder.index(psName)
- else:
- index = 258 + len(extraNames)
- extraDict[psName] = len(extraNames)
- extraNames.append(psName)
- indices.append(index)
- if sys.byteorder != "big":
- indices.byteswap()
- return (
- struct.pack(">H", numGlyphs) + indices.tobytes() + packPStrings(extraNames)
- )
-
- def encode_format_4_0(self, ttFont):
- from fontTools import agl
-
- numGlyphs = ttFont["maxp"].numGlyphs
- glyphOrder = ttFont.getGlyphOrder()
- assert len(glyphOrder) == numGlyphs
- indices = array.array("H")
- for glyphID in glyphOrder:
- glyphID = glyphID.split("#")[0]
- if glyphID in agl.AGL2UV:
- indices.append(agl.AGL2UV[glyphID])
- elif len(glyphID) == 7 and glyphID[:3] == "uni":
- indices.append(int(glyphID[3:], 16))
- else:
- indices.append(0xFFFF)
- if sys.byteorder != "big":
- indices.byteswap()
- return indices.tobytes()
-
- def toXML(self, writer, ttFont):
- formatstring, names, fixes = sstruct.getformat(postFormat)
- for name in names:
- value = getattr(self, name)
- writer.simpletag(name, value=value)
- writer.newline()
- if hasattr(self, "mapping"):
- writer.begintag("psNames")
- writer.newline()
- writer.comment(
- "This file uses unique glyph names based on the information\n"
- "found in the 'post' table. Since these names might not be unique,\n"
- "we have to invent artificial names in case of clashes. In order to\n"
- "be able to retain the original information, we need a name to\n"
- "ps name mapping for those cases where they differ. That's what\n"
- "you see below.\n"
- )
- writer.newline()
- items = sorted(self.mapping.items())
- for name, psName in items:
- writer.simpletag("psName", name=name, psName=psName)
- writer.newline()
- writer.endtag("psNames")
- writer.newline()
- if hasattr(self, "extraNames"):
- writer.begintag("extraNames")
- writer.newline()
- writer.comment(
- "following are the name that are not taken from the standard Mac glyph order"
- )
- writer.newline()
- for name in self.extraNames:
- writer.simpletag("psName", name=name)
- writer.newline()
- writer.endtag("extraNames")
- writer.newline()
- if hasattr(self, "data"):
- writer.begintag("hexdata")
- writer.newline()
- writer.dumphex(self.data)
- writer.endtag("hexdata")
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if name not in ("psNames", "extraNames", "hexdata"):
- setattr(self, name, safeEval(attrs["value"]))
- elif name == "psNames":
- self.mapping = {}
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attrs, content = element
- if name == "psName":
- self.mapping[attrs["name"]] = attrs["psName"]
- elif name == "extraNames":
- self.extraNames = []
- for element in content:
- if not isinstance(element, tuple):
- continue
- name, attrs, content = element
- if name == "psName":
- self.extraNames.append(attrs["name"])
- else:
- self.data = readHex(content)
-
-
-def unpackPStrings(data, n):
- # extract n Pascal strings from data.
- # if there is not enough data, use ""
-
- strings = []
- index = 0
- dataLen = len(data)
-
- for _ in range(n):
- if dataLen <= index:
- length = 0
- else:
- length = byteord(data[index])
- index += 1
-
- if dataLen <= index + length - 1:
- name = ""
- else:
- name = tostr(data[index : index + length], encoding="latin1")
- strings.append(name)
- index += length
-
- if index < dataLen:
- log.warning("%d extra bytes in post.stringData array", dataLen - index)
-
- elif dataLen < index:
- log.warning("not enough data in post.stringData array")
-
- return strings
-
-
-def packPStrings(strings):
- data = b""
- for s in strings:
- data = data + bytechr(len(s)) + tobytes(s, encoding="latin1")
- return data
diff --git a/spaces/DaleChen/AutoGPT/autogpt/commands/write_tests.py b/spaces/DaleChen/AutoGPT/autogpt/commands/write_tests.py
deleted file mode 100644
index 35a086536c9d05d520a84b15ead49f775eacdcc9..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/commands/write_tests.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""A module that contains a function to generate test cases for the submitted code."""
-from __future__ import annotations
-
-import json
-
-from autogpt.llm_utils import call_ai_function
-
-
-def write_tests(code: str, focus: list[str]) -> str:
- """
- A function that takes in code and focus topics and returns a response from create
- chat completion api call.
-
- Parameters:
- focus (list): A list of suggestions around what needs to be improved.
- code (str): Code for test cases to be generated against.
- Returns:
- A result string from create chat completion. Test cases for the submitted code
- in response.
- """
-
- function_string = (
- "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
- )
- args = [code, json.dumps(focus)]
- description_string = (
- "Generates test cases for the existing code, focusing on"
- " specific areas if required."
- )
-
- return call_ai_function(function_string, args, description_string)
diff --git a/spaces/DanielSan7/judini-video/README.md b/spaces/DanielSan7/judini-video/README.md
deleted file mode 100644
index fa5cdc8708d5ca8d76d1327349be76f7abc566bb..0000000000000000000000000000000000000000
--- a/spaces/DanielSan7/judini-video/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Judini Video
-emoji: 🐠
-colorFrom: pink
-colorTo: green
-sdk: streamlit
-sdk_version: 1.19.0
-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/Datasculptor/OpenAI-Chatbot_App/app.py b/spaces/Datasculptor/OpenAI-Chatbot_App/app.py
deleted file mode 100644
index 23516e76ebcf18f68d806509895e4c1ed00b3f3a..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/OpenAI-Chatbot_App/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import streamlit as st
-import requests
-import json
-
-st.title("OpenAI Chatbot Interface")
-st.write("Interact with OpenAI's GPT-3 models in real-time using your OpenAI API. Choose from a selection of their best models, set the temperature and max tokens, and start a conversation. Delete the conversation at any time to start fresh.")
-
-if "history" not in st.session_state:
- st.session_state.history = []
-
-st.sidebar.markdown("## Configuration")
-KEY = st.sidebar.text_input("Enter Your OpenAI API Key", placeholder="API Key", value="")
-models = ['text-davinci-003', 'text-curie-001', 'text-babbage-001', 'text-ada-001', 'gpt-3.5-turbo']
-model = st.sidebar.selectbox("Select a model", models, index=0)
-
-temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.7)
-max_tokens = st.sidebar.slider("Max Tokens", 0, 4000, 1786)
-
-if st.sidebar.button("Delete Conversation"):
- st.session_state.history = []
-st.sidebar.markdown("## GPT-3")
-st.sidebar.markdown("OpenAI's GPT-3 models can understand and generate natural language. They offer four main models with different levels of power suitable for different tasks. Davinci is the most capable model, and Ada is the fastest.")
-st.sidebar.markdown("text-davinci-003 | 4,000 max tokens")
-st.sidebar.markdown("text-curie-001 | 2,048 max tokens")
-st.sidebar.markdown("text-babbage-001 | 2,048 max tokens")
-st.sidebar.markdown("text-ada-001 | 2,048 max tokens")
-
-def generate_answer(prompt):
- API_KEY = KEY
- API_URL = "https://api.openai.com/v1/completions"
- headers = {
- 'Content-Type': 'application/json',
- 'Authorization': 'Bearer ' + API_KEY
- }
- previous_messages = [chat['message'] for chat in st.session_state.history if not chat['is_user']]
- previous_messages_text = '\n'.join(previous_messages)
- full_prompt = previous_messages_text + '\n' + prompt if previous_messages_text else prompt
- data = {
- "model": model,
- "prompt": full_prompt,
- "temperature": temperature,
- "max_tokens": max_tokens
- }
- if not API_KEY:
- st.warning("Please input your API key")
- return
- response = requests.post(API_URL, headers=headers, data=json.dumps(data))
- result = response.json()
- if 'choices' in result:
- message_bot = result['choices'][0]['text'].strip()
- st.session_state.history.append({"message": prompt, "is_user": True})
- st.session_state.history.append({"message": message_bot, "is_user": False})
- else:
- st.error("An error occurred while processing the API response. If using a model other than text-davinci-003, then lower the Max Tokens.")
-
-prompt = st.text_input("Prompt", placeholder="Prompt Here", value="")
-if st.button("Submit"):
- generate_answer(prompt)
- with st.spinner("Waiting for the response from the bot..."):
- for chat in st.session_state.history:
- if chat['is_user']:
- st.markdown("", unsafe_allow_html=True)
- st.markdown(f"
' if cover else "")+
- '
", unsafe_allow_html=True)
- st.markdown(f"
", unsafe_allow_html=True)
- st.markdown(f"
-
-
-
- 
- 
- 
- 
-
-
diff --git a/spaces/KyanChen/BuildingExtraction/Test.py b/spaces/KyanChen/BuildingExtraction/Test.py
deleted file mode 100644
index a3d33174ef8d5888fee19f095823636e82db6b9a..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/BuildingExtraction/Test.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-# Change the numbers when you want to test with specific gpus
-# os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1, 2, 3'
-import torch
-from STTNet import STTNet
-import torch.nn.functional as F
-from Utils.Datasets import get_data_loader
-from Utils.Utils import make_numpy_img, inv_normalize_img, encode_onehot_to_mask, get_metrics, Logger
-import matplotlib.pyplot as plt
-import numpy as np
-from collections import OrderedDict
-
-if __name__ == '__main__':
- model_infos = {
- # vgg16_bn, resnet50, resnet18
- 'backbone': 'resnet50',
- 'pretrained': True,
- 'out_keys': ['block4'],
- 'in_channel': 3,
- 'n_classes': 2,
- 'top_k_s': 64,
- 'top_k_c': 16,
- 'encoder_pos': True,
- 'decoder_pos': True,
- 'model_pattern': ['X', 'A', 'S', 'C'],
-
- 'log_path': 'Results',
- 'NUM_WORKERS': 0,
- # if you need the validation process.
- 'IS_VAL': True,
- 'VAL_BATCH_SIZE': 4,
- 'VAL_DATASET': 'Tools/generate_dep_info/val_data.csv',
- # if you need the test process.
- 'IS_TEST': True,
- 'TEST_DATASET': 'Tools/generate_dep_info/test_data.csv',
- 'IMG_SIZE': [512, 512],
- 'PHASE': 'seg',
-
- # INRIA Dataset
- 'PRIOR_MEAN': [0.40672500537632994, 0.42829032416229895, 0.39331840468605667],
- 'PRIOR_STD': [0.029498464618176873, 0.027740088491668233, 0.028246722411879095],
- # # # WHU Dataset
- # 'PRIOR_MEAN': [0.4352682576428411, 0.44523221318154493, 0.41307610541534784],
- # 'PRIOR_STD': [0.026973196780331585, 0.026424642808887323, 0.02791246590291434],
-
- # load state dict path
- 'load_checkpoint_path': r'E:\BuildingExtractionDataset\INRIA_ckpt_latest.pt',
- }
- if model_infos['IS_VAL']:
- os.makedirs(model_infos['log_path']+'/val', exist_ok=True)
- if model_infos['IS_TEST']:
- os.makedirs(model_infos['log_path']+'/test', exist_ok=True)
- logger = Logger(model_infos['log_path'] + '/log.log')
-
- data_loaders = get_data_loader(model_infos, test_mode=True)
- loss_weight = 0.1
- model = STTNet(**model_infos)
-
- logger.write(f'load checkpoint from {model_infos["load_checkpoint_path"]}\n')
- state_dict = torch.load(model_infos['load_checkpoint_path'], map_location='cpu')
- model_dict = state_dict['model_state_dict']
- try:
- model_dict = OrderedDict({k.replace('module.', ''): v for k, v in model_dict.items()})
- model.load_state_dict(model_dict)
- except Exception as e:
- model.load_state_dict(model_dict)
- model = model.cuda()
- device_ids = range(torch.cuda.device_count())
- if len(device_ids) > 1:
- model = torch.nn.DataParallel(model, device_ids=device_ids)
- logger.write(f'Use GPUs: {device_ids}\n')
- else:
- logger.write(f'Use GPUs: 1\n')
-
- patterns = ['val', 'test']
- for pattern_id, is_pattern in enumerate([model_infos['IS_VAL'], model_infos['IS_TEST']]):
- if is_pattern:
- # pred: logits, tensor, nBatch * nClass * W * H
- # target: labels, tensor, nBatch * nClass * W * H
- # output, batch['label']
- collect_result = {'pred': [], 'target': []}
- pattern = patterns[pattern_id]
- model.eval()
- for batch_id, batch in enumerate(data_loaders[pattern]):
- # Get data
- img_batch = batch['img'].cuda()
- label_batch = batch['label'].cuda()
- img_names = batch['img_name']
- collect_result['target'].append(label_batch.data.cpu())
-
- # inference
- with torch.no_grad():
- logits, att_branch_output = model(img_batch)
-
- collect_result['pred'].append(logits.data.cpu())
- # get segmentation result, when the phase is test.
- pred_label = torch.argmax(logits, 1)
- pred_label *= 255
-
- # output the segmentation result
- if pattern == 'test' or batch_id % 5 == 1:
- batch_size = pred_label.size(0)
- # k = np.clip(int(0.3 * batch_size), a_min=1, a_max=batch_size)
- # ids = np.random.choice(range(batch_size), k, replace=False)
- ids = range(batch_size)
- for img_id in ids:
- img = img_batch[img_id].detach().cpu()
- target = label_batch[img_id].detach().cpu()
- pred = pred_label[img_id].detach().cpu()
- img_name = img_names[img_id]
-
- img = make_numpy_img(
- inv_normalize_img(img, model_infos['PRIOR_MEAN'], model_infos['PRIOR_STD']))
- target = make_numpy_img(encode_onehot_to_mask(target)) * 255
- pred = make_numpy_img(pred)
-
- vis = np.concatenate([img / 255., target / 255., pred / 255.], axis=0)
- vis = np.clip(vis, a_min=0, a_max=1)
- file_name = os.path.join(model_infos['log_path'], pattern, f'{img_name.split(".")[0]}.png')
- plt.imsave(file_name, vis)
-
- collect_result['pred'] = torch.cat(collect_result['pred'], dim=0)
- collect_result['target'] = torch.cat(collect_result['target'], dim=0)
- IoU, OA, F1_score = get_metrics('seg', **collect_result)
- logger.write(f'{pattern}: Iou:{IoU[-1]:.4f} OA:{OA[-1]:.4f} F1:{F1_score[-1]:.4f}\n')
-
diff --git a/spaces/KyanChen/RSPrompter/mmdet/utils/compat_config.py b/spaces/KyanChen/RSPrompter/mmdet/utils/compat_config.py
deleted file mode 100644
index 133adb65c2276401eca947e223e5b7c1760de418..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/utils/compat_config.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import warnings
-
-from mmengine.config import ConfigDict
-
-
-def compat_cfg(cfg):
- """This function would modify some filed to keep the compatibility of
- config.
-
- For example, it will move some args which will be deprecated to the correct
- fields.
- """
- cfg = copy.deepcopy(cfg)
- cfg = compat_imgs_per_gpu(cfg)
- cfg = compat_loader_args(cfg)
- cfg = compat_runner_args(cfg)
- return cfg
-
-
-def compat_runner_args(cfg):
- if 'runner' not in cfg:
- cfg.runner = ConfigDict({
- 'type': 'EpochBasedRunner',
- 'max_epochs': cfg.total_epochs
- })
- warnings.warn(
- 'config is now expected to have a `runner` section, '
- 'please set `runner` in your config.', UserWarning)
- else:
- if 'total_epochs' in cfg:
- assert cfg.total_epochs == cfg.runner.max_epochs
- return cfg
-
-
-def compat_imgs_per_gpu(cfg):
- cfg = copy.deepcopy(cfg)
- if 'imgs_per_gpu' in cfg.data:
- warnings.warn('"imgs_per_gpu" is deprecated in MMDet V2.0. '
- 'Please use "samples_per_gpu" instead')
- if 'samples_per_gpu' in cfg.data:
- warnings.warn(
- f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
- f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
- f'={cfg.data.imgs_per_gpu} is used in this experiments')
- else:
- warnings.warn('Automatically set "samples_per_gpu"="imgs_per_gpu"='
- f'{cfg.data.imgs_per_gpu} in this experiments')
- cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
- return cfg
-
-
-def compat_loader_args(cfg):
- """Deprecated sample_per_gpu in cfg.data."""
-
- cfg = copy.deepcopy(cfg)
- if 'train_dataloader' not in cfg.data:
- cfg.data['train_dataloader'] = ConfigDict()
- if 'val_dataloader' not in cfg.data:
- cfg.data['val_dataloader'] = ConfigDict()
- if 'test_dataloader' not in cfg.data:
- cfg.data['test_dataloader'] = ConfigDict()
-
- # special process for train_dataloader
- if 'samples_per_gpu' in cfg.data:
-
- samples_per_gpu = cfg.data.pop('samples_per_gpu')
- assert 'samples_per_gpu' not in \
- cfg.data.train_dataloader, ('`samples_per_gpu` are set '
- 'in `data` field and ` '
- 'data.train_dataloader` '
- 'at the same time. '
- 'Please only set it in '
- '`data.train_dataloader`. ')
- cfg.data.train_dataloader['samples_per_gpu'] = samples_per_gpu
-
- if 'persistent_workers' in cfg.data:
-
- persistent_workers = cfg.data.pop('persistent_workers')
- assert 'persistent_workers' not in \
- cfg.data.train_dataloader, ('`persistent_workers` are set '
- 'in `data` field and ` '
- 'data.train_dataloader` '
- 'at the same time. '
- 'Please only set it in '
- '`data.train_dataloader`. ')
- cfg.data.train_dataloader['persistent_workers'] = persistent_workers
-
- if 'workers_per_gpu' in cfg.data:
-
- workers_per_gpu = cfg.data.pop('workers_per_gpu')
- cfg.data.train_dataloader['workers_per_gpu'] = workers_per_gpu
- cfg.data.val_dataloader['workers_per_gpu'] = workers_per_gpu
- cfg.data.test_dataloader['workers_per_gpu'] = workers_per_gpu
-
- # special process for val_dataloader
- if 'samples_per_gpu' in cfg.data.val:
- # keep default value of `sample_per_gpu` is 1
- assert 'samples_per_gpu' not in \
- cfg.data.val_dataloader, ('`samples_per_gpu` are set '
- 'in `data.val` field and ` '
- 'data.val_dataloader` at '
- 'the same time. '
- 'Please only set it in '
- '`data.val_dataloader`. ')
- cfg.data.val_dataloader['samples_per_gpu'] = \
- cfg.data.val.pop('samples_per_gpu')
- # special process for val_dataloader
-
- # in case the test dataset is concatenated
- if isinstance(cfg.data.test, dict):
- if 'samples_per_gpu' in cfg.data.test:
- assert 'samples_per_gpu' not in \
- cfg.data.test_dataloader, ('`samples_per_gpu` are set '
- 'in `data.test` field and ` '
- 'data.test_dataloader` '
- 'at the same time. '
- 'Please only set it in '
- '`data.test_dataloader`. ')
-
- cfg.data.test_dataloader['samples_per_gpu'] = \
- cfg.data.test.pop('samples_per_gpu')
-
- elif isinstance(cfg.data.test, list):
- for ds_cfg in cfg.data.test:
- if 'samples_per_gpu' in ds_cfg:
- assert 'samples_per_gpu' not in \
- cfg.data.test_dataloader, ('`samples_per_gpu` are set '
- 'in `data.test` field and ` '
- 'data.test_dataloader` at'
- ' the same time. '
- 'Please only set it in '
- '`data.test_dataloader`. ')
- samples_per_gpu = max(
- [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
- cfg.data.test_dataloader['samples_per_gpu'] = samples_per_gpu
-
- return cfg
diff --git a/spaces/LEBEI/00002/wbc/cartoonize.py b/spaces/LEBEI/00002/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/LEBEI/00002/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
- h, w, c = np.shape(image)
- if min(h, w) > 720:
- if h > w:
- h, w = int(720 * h / w), 720
- else:
- h, w = 720, int(720 * w / h)
- image = cv2.resize(image, (w, h),
- interpolation=cv2.INTER_AREA)
- h, w = (h // 8) * 8, (w // 8) * 8
- image = image[:h, :w, :]
- return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
- print(model_path)
- input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(input_photo)
- final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- sess = tf.Session(config=config)
-
- sess.run(tf.global_variables_initializer())
- saver.restore(sess, tf.train.latest_checkpoint(model_path))
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = sess.run(final_out, feed_dict={input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
- def __init__(self, model_path):
- print(model_path)
- self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(self.input_photo)
- self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- self.sess = tf.Session(config=config)
-
- self.sess.run(tf.global_variables_initializer())
- saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
- def run(self, load_folder, save_folder):
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
- def run_sigle(self, load_path, save_path):
- try:
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
- model_path = 'saved_models'
- load_folder = 'test_images'
- save_folder = 'cartoonized_images'
- if not os.path.exists(save_folder):
- os.mkdir(save_folder)
- cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/utils/__init__.py b/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Lijiahui/bingAI/README.md b/spaces/Lijiahui/bingAI/README.md
deleted file mode 100644
index 5311af4ab2d532d4387893b76b11661af253d141..0000000000000000000000000000000000000000
--- a/spaces/Lijiahui/bingAI/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: BingAI
-emoji: 🦀
-colorFrom: yellow
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_adadelta_18e.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_adadelta_18e.py
deleted file mode 100644
index 33f7960c51bf7d0f2b5bc03e8707a85a01e000fd..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_adadelta_18e.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='Adadelta', lr=0.5)
-optimizer_config = dict(grad_clip=dict(max_norm=0.5))
-# learning policy
-lr_config = dict(policy='step', step=[8, 14, 16])
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=18)
-checkpoint_config = dict(interval=1)
diff --git a/spaces/LucasCodeBreak/MusicGen/audiocraft/models/musicgen.py b/spaces/LucasCodeBreak/MusicGen/audiocraft/models/musicgen.py
deleted file mode 100644
index 007dd9e0ed1cfd359fb4889e7f4108248e189941..0000000000000000000000000000000000000000
--- a/spaces/LucasCodeBreak/MusicGen/audiocraft/models/musicgen.py
+++ /dev/null
@@ -1,362 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Main model for using MusicGen. This will combine all the required components
-and provide easy access to the generation API.
-"""
-
-import os
-import typing as tp
-
-import torch
-
-from .encodec import CompressionModel
-from .lm import LMModel
-from .builders import get_debug_compression_model, get_debug_lm_model
-from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP
-from ..data.audio_utils import convert_audio
-from ..modules.conditioners import ConditioningAttributes, WavCondition
-from ..utils.autocast import TorchAutocast
-
-
-MelodyList = tp.List[tp.Optional[torch.Tensor]]
-MelodyType = tp.Union[torch.Tensor, MelodyList]
-
-
-class MusicGen:
- """MusicGen main model with convenient generation API.
-
- Args:
- name (str): name of the model.
- compression_model (CompressionModel): Compression model
- used to map audio to invertible discrete representations.
- lm (LMModel): Language model over discrete representations.
- """
- def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
- max_duration: float = 30):
- self.name = name
- self.compression_model = compression_model
- self.lm = lm
- self.max_duration = max_duration
- self.device = next(iter(lm.parameters())).device
- self.generation_params: dict = {}
- self.set_generation_params(duration=15) # 15 seconds by default
- self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
- if self.device.type == 'cpu':
- self.autocast = TorchAutocast(enabled=False)
- else:
- self.autocast = TorchAutocast(
- enabled=True, device_type=self.device.type, dtype=torch.float16)
-
- @property
- def frame_rate(self) -> int:
- """Roughly the number of AR steps per seconds."""
- return self.compression_model.frame_rate
-
- @property
- def sample_rate(self) -> int:
- """Sample rate of the generated audio."""
- return self.compression_model.sample_rate
-
- @property
- def audio_channels(self) -> int:
- """Audio channels of the generated audio."""
- return self.compression_model.channels
-
- @staticmethod
- def get_pretrained(name: str = 'melody', device=None):
- """Return pretrained model, we provide four models:
- - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small
- - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium
- - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody
- - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large
- """
-
- if device is None:
- if torch.cuda.device_count():
- device = 'cuda'
- else:
- device = 'cpu'
-
- if name == 'debug':
- # used only for unit tests
- compression_model = get_debug_compression_model(device)
- lm = get_debug_lm_model(device)
- return MusicGen(name, compression_model, lm)
-
- if name not in HF_MODEL_CHECKPOINTS_MAP:
- if not os.path.isfile(name) and not os.path.isdir(name):
- raise ValueError(
- f"{name} is not a valid checkpoint name. "
- f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}"
- )
-
- cache_dir = os.environ.get('MUSICGEN_ROOT', None)
- compression_model = load_compression_model(name, device=device, cache_dir=cache_dir)
- lm = load_lm_model(name, device=device, cache_dir=cache_dir)
- if name == 'melody':
- lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
-
- return MusicGen(name, compression_model, lm)
-
- def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
- top_p: float = 0.0, temperature: float = 1.0,
- duration: float = 30.0, cfg_coef: float = 3.0,
- two_step_cfg: bool = False, extend_stride: float = 18):
- """Set the generation parameters for MusicGen.
-
- Args:
- use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
- top_k (int, optional): top_k used for sampling. Defaults to 250.
- top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
- temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
- duration (float, optional): Duration of the generated waveform. Defaults to 30.0.
- cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
- two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
- instead of batching together the two. This has some impact on how things
- are padded but seems to have little impact in practice.
- extend_stride: when doing extended generation (i.e. more than 30 seconds), by how much
- should we extend the audio each time. Larger values will mean less context is
- preserved, and shorter value will require extra computations.
- """
- assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
- self.extend_stride = extend_stride
- self.duration = duration
- self.generation_params = {
- 'use_sampling': use_sampling,
- 'temp': temperature,
- 'top_k': top_k,
- 'top_p': top_p,
- 'cfg_coef': cfg_coef,
- 'two_step_cfg': two_step_cfg,
- }
-
- def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
- """Override the default progress callback."""
- self._progress_callback = progress_callback
-
- def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor:
- """Generate samples in an unconditional manner.
-
- Args:
- num_samples (int): Number of samples to be generated.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor:
- """Generate samples conditioned on text.
-
- Args:
- descriptions (tp.List[str]): A list of strings used as text conditioning.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
- assert prompt_tokens is None
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType,
- melody_sample_rate: int, progress: bool = False) -> torch.Tensor:
- """Generate samples conditioned on text and melody.
-
- Args:
- descriptions (tp.List[str]): A list of strings used as text conditioning.
- melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
- melody conditioning. Should have shape [B, C, T] with B matching the description length,
- C=1 or 2. It can be [C, T] if there is a single description. It can also be
- a list of [C, T] tensors.
- melody_sample_rate: (int): Sample rate of the melody waveforms.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- if isinstance(melody_wavs, torch.Tensor):
- if melody_wavs.dim() == 2:
- melody_wavs = melody_wavs[None]
- if melody_wavs.dim() != 3:
- raise ValueError("Melody wavs should have a shape [B, C, T].")
- melody_wavs = list(melody_wavs)
- else:
- for melody in melody_wavs:
- if melody is not None:
- assert melody.dim() == 2, "One melody in the list has the wrong number of dims."
-
- melody_wavs = [
- convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels)
- if wav is not None else None
- for wav in melody_wavs]
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
- melody_wavs=melody_wavs)
- assert prompt_tokens is None
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
- descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
- progress: bool = False) -> torch.Tensor:
- """Generate samples conditioned on audio prompts.
-
- Args:
- prompt (torch.Tensor): A batch of waveforms used for continuation.
- Prompt should be [B, C, T], or [C, T] if only one sample is generated.
- prompt_sample_rate (int): Sampling rate of the given audio waveforms.
- descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- if prompt.dim() == 2:
- prompt = prompt[None]
- if prompt.dim() != 3:
- raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
- prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
- if descriptions is None:
- descriptions = [None] * len(prompt)
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
- assert prompt_tokens is not None
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- @torch.no_grad()
- def _prepare_tokens_and_attributes(
- self,
- descriptions: tp.Sequence[tp.Optional[str]],
- prompt: tp.Optional[torch.Tensor],
- melody_wavs: tp.Optional[MelodyList] = None,
- ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
- """Prepare model inputs.
-
- Args:
- descriptions (tp.List[str]): A list of strings used as text conditioning.
- prompt (torch.Tensor): A batch of waveforms used for continuation.
- melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms
- used as melody conditioning. Defaults to None.
- """
- attributes = [
- ConditioningAttributes(text={'description': description})
- for description in descriptions]
-
- if melody_wavs is None:
- for attr in attributes:
- attr.wav['self_wav'] = WavCondition(
- torch.zeros((1, 1), device=self.device),
- torch.tensor([0], device=self.device),
- path='null_wav') # type: ignore
- else:
- if self.name != "melody":
- raise RuntimeError("This model doesn't support melody conditioning. "
- "Use the `melody` model.")
- assert len(melody_wavs) == len(descriptions), \
- f"number of melody wavs must match number of descriptions! " \
- f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}"
- for attr, melody in zip(attributes, melody_wavs):
- if melody is None:
- attr.wav['self_wav'] = WavCondition(
- torch.zeros((1, 1), device=self.device),
- torch.tensor([0], device=self.device),
- path='null_wav') # type: ignore
- else:
- attr.wav['self_wav'] = WavCondition(
- melody.to(device=self.device),
- torch.tensor([melody.shape[-1]], device=self.device))
-
- if prompt is not None:
- if descriptions is not None:
- assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
- prompt = prompt.to(self.device)
- prompt_tokens, scale = self.compression_model.encode(prompt)
- assert scale is None
- else:
- prompt_tokens = None
- return attributes, prompt_tokens
-
- def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
- prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
- """Generate discrete audio tokens given audio prompt and/or conditions.
-
- Args:
- attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody).
- prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- Returns:
- torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
- """
- total_gen_len = int(self.duration * self.frame_rate)
- max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate)
- current_gen_offset: int = 0
-
- def _progress_callback(generated_tokens: int, tokens_to_generate: int):
- generated_tokens += current_gen_offset
- if self._progress_callback is not None:
- # Note that total_gen_len might be quite wrong depending on the
- # codebook pattern used, but with delay it is almost accurate.
- self._progress_callback(generated_tokens, total_gen_len)
- else:
- print(f'{generated_tokens: 6d} / {total_gen_len: 6d}', end='\r')
-
- if prompt_tokens is not None:
- assert max_prompt_len >= prompt_tokens.shape[-1], \
- "Prompt is longer than audio to generate"
-
- callback = None
- if progress:
- callback = _progress_callback
-
- if self.duration <= self.max_duration:
- # generate by sampling from LM, simple case.
- with self.autocast:
- gen_tokens = self.lm.generate(
- prompt_tokens, attributes,
- callback=callback, max_gen_len=total_gen_len, **self.generation_params)
-
- else:
- # now this gets a bit messier, we need to handle prompts,
- # melody conditioning etc.
- ref_wavs = [attr.wav['self_wav'] for attr in attributes]
- all_tokens = []
- if prompt_tokens is None:
- prompt_length = 0
- else:
- all_tokens.append(prompt_tokens)
- prompt_length = prompt_tokens.shape[-1]
-
- stride_tokens = int(self.frame_rate * self.extend_stride)
-
- while current_gen_offset + prompt_length < total_gen_len:
- time_offset = current_gen_offset / self.frame_rate
- chunk_duration = min(self.duration - time_offset, self.max_duration)
- max_gen_len = int(chunk_duration * self.frame_rate)
- for attr, ref_wav in zip(attributes, ref_wavs):
- wav_length = ref_wav.length.item()
- if wav_length == 0:
- continue
- # We will extend the wav periodically if it not long enough.
- # we have to do it here rather than in conditioners.py as otherwise
- # we wouldn't have the full wav.
- initial_position = int(time_offset * self.sample_rate)
- wav_target_length = int(self.max_duration * self.sample_rate)
- print(initial_position / self.sample_rate, wav_target_length / self.sample_rate)
- positions = torch.arange(initial_position,
- initial_position + wav_target_length, device=self.device)
- attr.wav['self_wav'] = WavCondition(
- ref_wav[0][:, positions % wav_length],
- torch.full_like(ref_wav[1], wav_target_length))
- with self.autocast:
- gen_tokens = self.lm.generate(
- prompt_tokens, attributes,
- callback=callback, max_gen_len=max_gen_len, **self.generation_params)
- if prompt_tokens is None:
- all_tokens.append(gen_tokens)
- else:
- all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:])
- prompt_tokens = gen_tokens[:, :, stride_tokens:]
- prompt_length = prompt_tokens.shape[-1]
- current_gen_offset += stride_tokens
-
- gen_tokens = torch.cat(all_tokens, dim=-1)
-
- # generate audio
- assert gen_tokens.dim() == 3
- with torch.no_grad():
- gen_audio = self.compression_model.decode(gen_tokens, None)
- return gen_audio
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/api/auth.ts b/spaces/LuxOAI/ChatGpt-Web/app/api/auth.ts
deleted file mode 100644
index 1005c5fff6309402b96b2add88644d649d87d3d9..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/api/auth.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-import { NextRequest } from "next/server";
-import { getServerSideConfig } from "../config/server";
-import md5 from "spark-md5";
-import { ACCESS_CODE_PREFIX } from "../constant";
-
-const serverConfig = getServerSideConfig();
-
-function getIP(req: NextRequest) {
- let ip = req.ip ?? req.headers.get("x-real-ip");
- const forwardedFor = req.headers.get("x-forwarded-for");
-
- if (!ip && forwardedFor) {
- ip = forwardedFor.split(",").at(0) ?? "";
- }
-
- return ip;
-}
-
-function parseApiKey(bearToken: string) {
- const token = bearToken.trim().replaceAll("Bearer ", "").trim();
- const isOpenAiKey = !token.startsWith(ACCESS_CODE_PREFIX);
-
- return {
- accessCode: isOpenAiKey ? "" : token.slice(ACCESS_CODE_PREFIX.length),
- apiKey: isOpenAiKey ? token : "",
- };
-}
-
-export function auth(req: NextRequest) {
- const authToken = req.headers.get("Authorization") ?? "";
-
- // check if it is openai api key or user token
- const { accessCode, apiKey: token } = parseApiKey(authToken);
-
- const hashedCode = md5.hash(accessCode ?? "").trim();
-
- console.log("[Auth] allowed hashed codes: ", [...serverConfig.codes]);
- console.log("[Auth] got access code:", accessCode);
- console.log("[Auth] hashed access code:", hashedCode);
- console.log("[User IP] ", getIP(req));
- console.log("[Time] ", new Date().toLocaleString());
-
- if (serverConfig.needCode && !serverConfig.codes.has(hashedCode) && !token) {
- return {
- error: true,
- needAccessCode: true,
- msg: "Please go settings page and fill your access code.",
- };
- }
-
- // if user does not provide an api key, inject system api key
- if (!token) {
- const apiKey = serverConfig.apiKey;
- if (apiKey) {
- console.log("[Auth] use system api key");
- req.headers.set("Authorization", `Bearer ${apiKey}`);
- } else {
- console.log("[Auth] admin did not provide an api key");
- return {
- error: true,
- msg: "Empty Api Key",
- };
- }
- } else {
- console.log("[Auth] use user api key");
- }
-
- return {
- error: false,
- };
-}
diff --git a/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_upload.py b/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_upload.py
deleted file mode 100644
index b2465fa1f13425e05bd638cfe330b47ed7bd53e2..0000000000000000000000000000000000000000
--- a/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_upload.py
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import pathlib
-
-import gradio as gr
-import slugify
-
-from constants import UploadTarget
-from uploader import Uploader
-from utils import find_exp_dirs
-
-
-class LoRAModelUploader(Uploader):
- def upload_lora_model(
- self,
- folder_path: str,
- repo_name: str,
- upload_to: str,
- private: bool,
- delete_existing_repo: bool,
- ) -> str:
- if not folder_path:
- raise ValueError
- if not repo_name:
- repo_name = pathlib.Path(folder_path).name
- repo_name = slugify.slugify(repo_name)
-
- if upload_to == UploadTarget.PERSONAL_PROFILE.value:
- organization = ''
- elif upload_to == UploadTarget.LORA_LIBRARY.value:
- organization = 'lora-library'
- else:
- raise ValueError
-
- return self.upload(folder_path,
- repo_name,
- organization=organization,
- private=private,
- delete_existing_repo=delete_existing_repo)
-
-
-def load_local_lora_model_list() -> dict:
- choices = find_exp_dirs(ignore_repo=True)
- return gr.update(choices=choices, value=choices[0] if choices else None)
-
-
-def create_upload_demo(hf_token: str | None) -> gr.Blocks:
- uploader = LoRAModelUploader(hf_token)
- model_dirs = find_exp_dirs(ignore_repo=True)
-
- with gr.Blocks() as demo:
- with gr.Box():
- gr.Markdown('Local Models')
- reload_button = gr.Button('Reload Model List')
- model_dir = gr.Dropdown(
- label='Model names',
- choices=model_dirs,
- value=model_dirs[0] if model_dirs else None)
- with gr.Box():
- gr.Markdown('Upload Settings')
- with gr.Row():
- use_private_repo = gr.Checkbox(label='Private', value=True)
- delete_existing_repo = gr.Checkbox(
- label='Delete existing repo of the same name', value=False)
- upload_to = gr.Radio(label='Upload to',
- choices=[_.value for _ in UploadTarget],
- value=UploadTarget.LORA_LIBRARY.value)
- model_name = gr.Textbox(label='Model Name')
- upload_button = gr.Button('Upload')
- gr.Markdown('''
- - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{your_username}/{model_name}) or to the public [LoRA Concepts Library](https://huggingface.co/lora-library) (i.e. https://huggingface.co/lora-library/{model_name}).
- ''')
- with gr.Box():
- gr.Markdown('Output message')
- output_message = gr.Markdown()
-
- reload_button.click(fn=load_local_lora_model_list,
- inputs=None,
- outputs=model_dir)
- upload_button.click(fn=uploader.upload_lora_model,
- inputs=[
- model_dir,
- model_name,
- upload_to,
- use_private_repo,
- delete_existing_repo,
- ],
- outputs=output_message)
-
- return demo
-
-
-if __name__ == '__main__':
- import os
-
- hf_token = os.getenv('HF_TOKEN')
- demo = create_upload_demo(hf_token)
- demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Face_Detection/align_warp_back_multiple_dlib.py b/spaces/MCkernick/Image_Restoration_Colorization/Face_Detection/align_warp_back_multiple_dlib.py
deleted file mode 100644
index 4b82139e4a81201b16fdfe56bc1cdb2b97bac398..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Face_Detection/align_warp_back_multiple_dlib.py
+++ /dev/null
@@ -1,437 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import torch
-import numpy as np
-import skimage.io as io
-
-# from face_sdk import FaceDetection
-import matplotlib.pyplot as plt
-from matplotlib.patches import Rectangle
-from skimage.transform import SimilarityTransform
-from skimage.transform import warp
-from PIL import Image, ImageFilter
-import torch.nn.functional as F
-import torchvision as tv
-import torchvision.utils as vutils
-import time
-import cv2
-import os
-from skimage import img_as_ubyte
-import json
-import argparse
-import dlib
-
-
-def calculate_cdf(histogram):
- """
- This method calculates the cumulative distribution function
- :param array histogram: The values of the histogram
- :return: normalized_cdf: The normalized cumulative distribution function
- :rtype: array
- """
- # Get the cumulative sum of the elements
- cdf = histogram.cumsum()
-
- # Normalize the cdf
- normalized_cdf = cdf / float(cdf.max())
-
- return normalized_cdf
-
-
-def calculate_lookup(src_cdf, ref_cdf):
- """
- This method creates the lookup table
- :param array src_cdf: The cdf for the source image
- :param array ref_cdf: The cdf for the reference image
- :return: lookup_table: The lookup table
- :rtype: array
- """
- lookup_table = np.zeros(256)
- lookup_val = 0
- for src_pixel_val in range(len(src_cdf)):
- lookup_val
- for ref_pixel_val in range(len(ref_cdf)):
- if ref_cdf[ref_pixel_val] >= src_cdf[src_pixel_val]:
- lookup_val = ref_pixel_val
- break
- lookup_table[src_pixel_val] = lookup_val
- return lookup_table
-
-
-def match_histograms(src_image, ref_image):
- """
- This method matches the source image histogram to the
- reference signal
- :param image src_image: The original source image
- :param image ref_image: The reference image
- :return: image_after_matching
- :rtype: image (array)
- """
- # Split the images into the different color channels
- # b means blue, g means green and r means red
- src_b, src_g, src_r = cv2.split(src_image)
- ref_b, ref_g, ref_r = cv2.split(ref_image)
-
- # Compute the b, g, and r histograms separately
- # The flatten() Numpy method returns a copy of the array c
- # collapsed into one dimension.
- src_hist_blue, bin_0 = np.histogram(src_b.flatten(), 256, [0, 256])
- src_hist_green, bin_1 = np.histogram(src_g.flatten(), 256, [0, 256])
- src_hist_red, bin_2 = np.histogram(src_r.flatten(), 256, [0, 256])
- ref_hist_blue, bin_3 = np.histogram(ref_b.flatten(), 256, [0, 256])
- ref_hist_green, bin_4 = np.histogram(ref_g.flatten(), 256, [0, 256])
- ref_hist_red, bin_5 = np.histogram(ref_r.flatten(), 256, [0, 256])
-
- # Compute the normalized cdf for the source and reference image
- src_cdf_blue = calculate_cdf(src_hist_blue)
- src_cdf_green = calculate_cdf(src_hist_green)
- src_cdf_red = calculate_cdf(src_hist_red)
- ref_cdf_blue = calculate_cdf(ref_hist_blue)
- ref_cdf_green = calculate_cdf(ref_hist_green)
- ref_cdf_red = calculate_cdf(ref_hist_red)
-
- # Make a separate lookup table for each color
- blue_lookup_table = calculate_lookup(src_cdf_blue, ref_cdf_blue)
- green_lookup_table = calculate_lookup(src_cdf_green, ref_cdf_green)
- red_lookup_table = calculate_lookup(src_cdf_red, ref_cdf_red)
-
- # Use the lookup function to transform the colors of the original
- # source image
- blue_after_transform = cv2.LUT(src_b, blue_lookup_table)
- green_after_transform = cv2.LUT(src_g, green_lookup_table)
- red_after_transform = cv2.LUT(src_r, red_lookup_table)
-
- # Put the image back together
- image_after_matching = cv2.merge([blue_after_transform, green_after_transform, red_after_transform])
- image_after_matching = cv2.convertScaleAbs(image_after_matching)
-
- return image_after_matching
-
-
-def _standard_face_pts():
- pts = (
- np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32) / 256.0
- - 1.0
- )
-
- return np.reshape(pts, (5, 2))
-
-
-def _origin_face_pts():
- pts = np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32)
-
- return np.reshape(pts, (5, 2))
-
-
-def compute_transformation_matrix(img, landmark, normalize, target_face_scale=1.0):
-
- std_pts = _standard_face_pts() # [-1,1]
- target_pts = (std_pts * target_face_scale + 1) / 2 * 256.0
-
- # print(target_pts)
-
- h, w, c = img.shape
- if normalize == True:
- landmark[:, 0] = landmark[:, 0] / h * 2 - 1.0
- landmark[:, 1] = landmark[:, 1] / w * 2 - 1.0
-
- # print(landmark)
-
- affine = SimilarityTransform()
-
- affine.estimate(target_pts, landmark)
-
- return affine
-
-
-def compute_inverse_transformation_matrix(img, landmark, normalize, target_face_scale=1.0):
-
- std_pts = _standard_face_pts() # [-1,1]
- target_pts = (std_pts * target_face_scale + 1) / 2 * 256.0
-
- # print(target_pts)
-
- h, w, c = img.shape
- if normalize == True:
- landmark[:, 0] = landmark[:, 0] / h * 2 - 1.0
- landmark[:, 1] = landmark[:, 1] / w * 2 - 1.0
-
- # print(landmark)
-
- affine = SimilarityTransform()
-
- affine.estimate(landmark, target_pts)
-
- return affine
-
-
-def show_detection(image, box, landmark):
- plt.imshow(image)
- print(box[2] - box[0])
- plt.gca().add_patch(
- Rectangle(
- (box[1], box[0]), box[2] - box[0], box[3] - box[1], linewidth=1, edgecolor="r", facecolor="none"
- )
- )
- plt.scatter(landmark[0][0], landmark[0][1])
- plt.scatter(landmark[1][0], landmark[1][1])
- plt.scatter(landmark[2][0], landmark[2][1])
- plt.scatter(landmark[3][0], landmark[3][1])
- plt.scatter(landmark[4][0], landmark[4][1])
- plt.show()
-
-
-def affine2theta(affine, input_w, input_h, target_w, target_h):
- # param = np.linalg.inv(affine)
- param = affine
- theta = np.zeros([2, 3])
- theta[0, 0] = param[0, 0] * input_h / target_h
- theta[0, 1] = param[0, 1] * input_w / target_h
- theta[0, 2] = (2 * param[0, 2] + param[0, 0] * input_h + param[0, 1] * input_w) / target_h - 1
- theta[1, 0] = param[1, 0] * input_h / target_w
- theta[1, 1] = param[1, 1] * input_w / target_w
- theta[1, 2] = (2 * param[1, 2] + param[1, 0] * input_h + param[1, 1] * input_w) / target_w - 1
- return theta
-
-
-def blur_blending(im1, im2, mask):
-
- mask *= 255.0
-
- kernel = np.ones((10, 10), np.uint8)
- mask = cv2.erode(mask, kernel, iterations=1)
-
- mask = Image.fromarray(mask.astype("uint8")).convert("L")
- im1 = Image.fromarray(im1.astype("uint8"))
- im2 = Image.fromarray(im2.astype("uint8"))
-
- mask_blur = mask.filter(ImageFilter.GaussianBlur(20))
- im = Image.composite(im1, im2, mask)
-
- im = Image.composite(im, im2, mask_blur)
-
- return np.array(im) / 255.0
-
-
-def blur_blending_cv2(im1, im2, mask):
-
- mask *= 255.0
-
- kernel = np.ones((9, 9), np.uint8)
- mask = cv2.erode(mask, kernel, iterations=3)
-
- mask_blur = cv2.GaussianBlur(mask, (25, 25), 0)
- mask_blur /= 255.0
-
- im = im1 * mask_blur + (1 - mask_blur) * im2
-
- im /= 255.0
- im = np.clip(im, 0.0, 1.0)
-
- return im
-
-
-# def Poisson_blending(im1,im2,mask):
-
-
-# Image.composite(
-def Poisson_blending(im1, im2, mask):
-
- # mask=1-mask
- mask *= 255
- kernel = np.ones((10, 10), np.uint8)
- mask = cv2.erode(mask, kernel, iterations=1)
- mask /= 255
- mask = 1 - mask
- mask *= 255
-
- mask = mask[:, :, 0]
- width, height, channels = im1.shape
- center = (int(height / 2), int(width / 2))
- result = cv2.seamlessClone(
- im2.astype("uint8"), im1.astype("uint8"), mask.astype("uint8"), center, cv2.MIXED_CLONE
- )
-
- return result / 255.0
-
-
-def Poisson_B(im1, im2, mask, center):
-
- mask *= 255
-
- result = cv2.seamlessClone(
- im2.astype("uint8"), im1.astype("uint8"), mask.astype("uint8"), center, cv2.NORMAL_CLONE
- )
-
- return result / 255
-
-
-def seamless_clone(old_face, new_face, raw_mask):
-
- height, width, _ = old_face.shape
- height = height // 2
- width = width // 2
-
- y_indices, x_indices, _ = np.nonzero(raw_mask)
- y_crop = slice(np.min(y_indices), np.max(y_indices))
- x_crop = slice(np.min(x_indices), np.max(x_indices))
- y_center = int(np.rint((np.max(y_indices) + np.min(y_indices)) / 2 + height))
- x_center = int(np.rint((np.max(x_indices) + np.min(x_indices)) / 2 + width))
-
- insertion = np.rint(new_face[y_crop, x_crop] * 255.0).astype("uint8")
- insertion_mask = np.rint(raw_mask[y_crop, x_crop] * 255.0).astype("uint8")
- insertion_mask[insertion_mask != 0] = 255
- prior = np.rint(np.pad(old_face * 255.0, ((height, height), (width, width), (0, 0)), "constant")).astype(
- "uint8"
- )
- # if np.sum(insertion_mask) == 0:
- n_mask = insertion_mask[1:-1, 1:-1, :]
- n_mask = cv2.copyMakeBorder(n_mask, 1, 1, 1, 1, cv2.BORDER_CONSTANT, 0)
- print(n_mask.shape)
- x, y, w, h = cv2.boundingRect(n_mask[:, :, 0])
- if w < 4 or h < 4:
- blended = prior
- else:
- blended = cv2.seamlessClone(
- insertion, # pylint: disable=no-member
- prior,
- insertion_mask,
- (x_center, y_center),
- cv2.NORMAL_CLONE,
- ) # pylint: disable=no-member
-
- blended = blended[height:-height, width:-width]
-
- return blended.astype("float32") / 255.0
-
-
-def get_landmark(face_landmarks, id):
- part = face_landmarks.part(id)
- x = part.x
- y = part.y
-
- return (x, y)
-
-
-def search(face_landmarks):
-
- x1, y1 = get_landmark(face_landmarks, 36)
- x2, y2 = get_landmark(face_landmarks, 39)
- x3, y3 = get_landmark(face_landmarks, 42)
- x4, y4 = get_landmark(face_landmarks, 45)
-
- x_nose, y_nose = get_landmark(face_landmarks, 30)
-
- x_left_mouth, y_left_mouth = get_landmark(face_landmarks, 48)
- x_right_mouth, y_right_mouth = get_landmark(face_landmarks, 54)
-
- x_left_eye = int((x1 + x2) / 2)
- y_left_eye = int((y1 + y2) / 2)
- x_right_eye = int((x3 + x4) / 2)
- y_right_eye = int((y3 + y4) / 2)
-
- results = np.array(
- [
- [x_left_eye, y_left_eye],
- [x_right_eye, y_right_eye],
- [x_nose, y_nose],
- [x_left_mouth, y_left_mouth],
- [x_right_mouth, y_right_mouth],
- ]
- )
-
- return results
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser()
- parser.add_argument("--origin_url", type=str, default="./", help="origin images")
- parser.add_argument("--replace_url", type=str, default="./", help="restored faces")
- parser.add_argument("--save_url", type=str, default="./save")
- opts = parser.parse_args()
-
- origin_url = opts.origin_url
- replace_url = opts.replace_url
- save_url = opts.save_url
-
- if not os.path.exists(save_url):
- os.makedirs(save_url)
-
- face_detector = dlib.get_frontal_face_detector()
- landmark_locator = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
-
- count = 0
-
- for x in os.listdir(origin_url):
- img_url = os.path.join(origin_url, x)
- pil_img = Image.open(img_url).convert("RGB")
-
- origin_width, origin_height = pil_img.size
- image = np.array(pil_img)
-
- start = time.time()
- faces = face_detector(image)
- done = time.time()
-
- if len(faces) == 0:
- print("Warning: There is no face in %s" % (x))
- continue
-
- blended = image
- for face_id in range(len(faces)):
-
- current_face = faces[face_id]
- face_landmarks = landmark_locator(image, current_face)
- current_fl = search(face_landmarks)
-
- forward_mask = np.ones_like(image).astype("uint8")
- affine = compute_transformation_matrix(image, current_fl, False, target_face_scale=1.3)
- aligned_face = warp(image, affine, output_shape=(256, 256, 3), preserve_range=True)
- forward_mask = warp(
- forward_mask, affine, output_shape=(256, 256, 3), order=0, preserve_range=True
- )
-
- affine_inverse = affine.inverse
- cur_face = aligned_face
- if replace_url != "":
-
- face_name = x[:-4] + "_" + str(face_id + 1) + ".png"
- cur_url = os.path.join(replace_url, face_name)
- restored_face = Image.open(cur_url).convert("RGB")
- restored_face = np.array(restored_face)
- cur_face = restored_face
-
- ## Histogram Color matching
- A = cv2.cvtColor(aligned_face.astype("uint8"), cv2.COLOR_RGB2BGR)
- B = cv2.cvtColor(cur_face.astype("uint8"), cv2.COLOR_RGB2BGR)
- B = match_histograms(B, A)
- cur_face = cv2.cvtColor(B.astype("uint8"), cv2.COLOR_BGR2RGB)
-
- warped_back = warp(
- cur_face,
- affine_inverse,
- output_shape=(origin_height, origin_width, 3),
- order=3,
- preserve_range=True,
- )
-
- backward_mask = warp(
- forward_mask,
- affine_inverse,
- output_shape=(origin_height, origin_width, 3),
- order=0,
- preserve_range=True,
- ) ## Nearest neighbour
-
- blended = blur_blending_cv2(warped_back, blended, backward_mask)
- blended *= 255.0
-
- io.imsave(os.path.join(save_url, x), img_as_ubyte(blended / 255.0))
-
- count += 1
-
- if count % 1000 == 0:
- print("%d have finished ..." % (count))
-
diff --git a/spaces/Mahiruoshi/MyGO_VIts-bert/attentions.py b/spaces/Mahiruoshi/MyGO_VIts-bert/attentions.py
deleted file mode 100644
index 3ba2407267ecd425d2095a6428015b5b4ebc0bda..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/MyGO_VIts-bert/attentions.py
+++ /dev/null
@@ -1,464 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-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)
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=4,
- isflow=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
- # if isflow:
- # cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
- # self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
- # self.cond_layer = weight_norm(cond_layer, name='weight')
- # self.gin_channels = 256
- self.cond_layer_idx = self.n_layers
- if "gin_channels" in kwargs:
- self.gin_channels = kwargs["gin_channels"]
- if self.gin_channels != 0:
- self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
- # vits2 says 3rd block, so idx is 2 by default
- self.cond_layer_idx = (
- kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
- )
- logging.debug(self.gin_channels, self.cond_layer_idx)
- assert (
- self.cond_layer_idx < self.n_layers
- ), "cond_layer_idx should be less than n_layers"
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, g=None):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- if i == self.cond_layer_idx and g is not None:
- g = self.spk_emb_linear(g.transpose(1, 2))
- g = g.transpose(1, 2)
- x = x + g
- x = x * x_mask
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # pad along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/Manmay/tortoise-tts/tortoise/utils/stft.py b/spaces/Manmay/tortoise-tts/tortoise/utils/stft.py
deleted file mode 100644
index f54eb968225cfe5928cca6d7686abbcc3728a674..0000000000000000000000000000000000000000
--- a/spaces/Manmay/tortoise-tts/tortoise/utils/stft.py
+++ /dev/null
@@ -1,193 +0,0 @@
-"""
-BSD 3-Clause License
-
-Copyright (c) 2017, Prem Seetharaman
-All rights reserved.
-
-* Redistribution and use in source and binary forms, with or without
- modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice, this
- list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
-* Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from this
- software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-"""
-
-import torch
-import numpy as np
-import torch.nn.functional as F
-from torch.autograd import Variable
-from scipy.signal import get_window
-from librosa.util import pad_center, tiny
-import librosa.util as librosa_util
-
-
-def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
- n_fft=800, dtype=np.float32, norm=None):
- """
- # from librosa 0.6
- Compute the sum-square envelope of a window function at a given hop length.
-
- This is used to estimate modulation effects induced by windowing
- observations in short-time fourier transforms.
-
- Parameters
- ----------
- window : string, tuple, number, callable, or list-like
- Window specification, as in `get_window`
-
- n_frames : int > 0
- The number of analysis frames
-
- hop_length : int > 0
- The number of samples to advance between frames
-
- win_length : [optional]
- The length of the window function. By default, this matches `n_fft`.
-
- n_fft : int > 0
- The length of each analysis frame.
-
- dtype : np.dtype
- The data type of the output
-
- Returns
- -------
- wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
- The sum-squared envelope of the window function
- """
- if win_length is None:
- win_length = n_fft
-
- n = n_fft + hop_length * (n_frames - 1)
- x = np.zeros(n, dtype=dtype)
-
- # Compute the squared window at the desired length
- win_sq = get_window(window, win_length, fftbins=True)
- win_sq = librosa_util.normalize(win_sq, norm=norm)**2
- win_sq = librosa_util.pad_center(win_sq, n_fft)
-
- # Fill the envelope
- for i in range(n_frames):
- sample = i * hop_length
- x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
- return x
-
-
-class STFT(torch.nn.Module):
- """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
- def __init__(self, filter_length=800, hop_length=200, win_length=800,
- window='hann'):
- super(STFT, self).__init__()
- self.filter_length = filter_length
- self.hop_length = hop_length
- self.win_length = win_length
- self.window = window
- self.forward_transform = None
- scale = self.filter_length / self.hop_length
- fourier_basis = np.fft.fft(np.eye(self.filter_length))
-
- cutoff = int((self.filter_length / 2 + 1))
- fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
- np.imag(fourier_basis[:cutoff, :])])
-
- forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
- inverse_basis = torch.FloatTensor(
- np.linalg.pinv(scale * fourier_basis).T[:, None, :])
-
- if window is not None:
- assert(filter_length >= win_length)
- # get window and zero center pad it to filter_length
- fft_window = get_window(window, win_length, fftbins=True)
- fft_window = pad_center(fft_window, size=filter_length)
- fft_window = torch.from_numpy(fft_window).float()
-
- # window the bases
- forward_basis *= fft_window
- inverse_basis *= fft_window
-
- self.register_buffer('forward_basis', forward_basis.float())
- self.register_buffer('inverse_basis', inverse_basis.float())
-
- def transform(self, input_data):
- num_batches = input_data.size(0)
- num_samples = input_data.size(1)
-
- self.num_samples = num_samples
-
- # similar to librosa, reflect-pad the input
- input_data = input_data.view(num_batches, 1, num_samples)
- input_data = F.pad(
- input_data.unsqueeze(1),
- (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
- mode='reflect')
- input_data = input_data.squeeze(1)
-
- forward_transform = F.conv1d(
- input_data,
- Variable(self.forward_basis, requires_grad=False),
- stride=self.hop_length,
- padding=0)
-
- cutoff = int((self.filter_length / 2) + 1)
- real_part = forward_transform[:, :cutoff, :]
- imag_part = forward_transform[:, cutoff:, :]
-
- magnitude = torch.sqrt(real_part**2 + imag_part**2)
- phase = torch.autograd.Variable(
- torch.atan2(imag_part.data, real_part.data))
-
- return magnitude, phase
-
- def inverse(self, magnitude, phase):
- recombine_magnitude_phase = torch.cat(
- [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
-
- inverse_transform = F.conv_transpose1d(
- recombine_magnitude_phase,
- Variable(self.inverse_basis, requires_grad=False),
- stride=self.hop_length,
- padding=0)
-
- if self.window is not None:
- window_sum = window_sumsquare(
- self.window, magnitude.size(-1), hop_length=self.hop_length,
- win_length=self.win_length, n_fft=self.filter_length,
- dtype=np.float32)
- # remove modulation effects
- approx_nonzero_indices = torch.from_numpy(
- np.where(window_sum > tiny(window_sum))[0])
- window_sum = torch.autograd.Variable(
- torch.from_numpy(window_sum), requires_grad=False)
- window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
- inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
-
- # scale by hop ratio
- inverse_transform *= float(self.filter_length) / self.hop_length
-
- inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
- inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
-
- return inverse_transform
-
- def forward(self, input_data):
- self.magnitude, self.phase = self.transform(input_data)
- reconstruction = self.inverse(self.magnitude, self.phase)
- return reconstruction
\ No newline at end of file
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/models/mbv2_mlsd_tiny.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/models/mbv2_mlsd_tiny.py
deleted file mode 100644
index e3ed633f2cc23ea1829a627fdb879ab39f641f83..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/models/mbv2_mlsd_tiny.py
+++ /dev/null
@@ -1,275 +0,0 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
-from torch.nn import functional as F
-
-
-class BlockTypeA(nn.Module):
- def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
- super(BlockTypeA, self).__init__()
- self.conv1 = nn.Sequential(
- nn.Conv2d(in_c2, out_c2, kernel_size=1),
- nn.BatchNorm2d(out_c2),
- nn.ReLU(inplace=True)
- )
- self.conv2 = nn.Sequential(
- nn.Conv2d(in_c1, out_c1, kernel_size=1),
- nn.BatchNorm2d(out_c1),
- nn.ReLU(inplace=True)
- )
- self.upscale = upscale
-
- def forward(self, a, b):
- b = self.conv1(b)
- a = self.conv2(a)
- b = F.interpolate(b, scale_factor=2.0, mode='bilinear', align_corners=True)
- return torch.cat((a, b), dim=1)
-
-
-class BlockTypeB(nn.Module):
- def __init__(self, in_c, out_c):
- super(BlockTypeB, self).__init__()
- self.conv1 = nn.Sequential(
- nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
- nn.BatchNorm2d(in_c),
- nn.ReLU()
- )
- self.conv2 = nn.Sequential(
- nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
- nn.BatchNorm2d(out_c),
- nn.ReLU()
- )
-
- def forward(self, x):
- x = self.conv1(x) + x
- x = self.conv2(x)
- return x
-
-class BlockTypeC(nn.Module):
- def __init__(self, in_c, out_c):
- super(BlockTypeC, self).__init__()
- self.conv1 = nn.Sequential(
- nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5),
- nn.BatchNorm2d(in_c),
- nn.ReLU()
- )
- self.conv2 = nn.Sequential(
- nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
- nn.BatchNorm2d(in_c),
- nn.ReLU()
- )
- self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.conv2(x)
- x = self.conv3(x)
- return x
-
-def _make_divisible(v, divisor, min_value=None):
- """
- This function is taken from the original tf repo.
- It ensures that all layers have a channel number that is divisible by 8
- It can be seen here:
- https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
- :param v:
- :param divisor:
- :param min_value:
- :return:
- """
- if min_value is None:
- min_value = divisor
- new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
- # Make sure that round down does not go down by more than 10%.
- if new_v < 0.9 * v:
- new_v += divisor
- return new_v
-
-
-class ConvBNReLU(nn.Sequential):
- def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
- self.channel_pad = out_planes - in_planes
- self.stride = stride
- #padding = (kernel_size - 1) // 2
-
- # TFLite uses slightly different padding than PyTorch
- if stride == 2:
- padding = 0
- else:
- padding = (kernel_size - 1) // 2
-
- super(ConvBNReLU, self).__init__(
- nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
- nn.BatchNorm2d(out_planes),
- nn.ReLU6(inplace=True)
- )
- self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
-
-
- def forward(self, x):
- # TFLite uses different padding
- if self.stride == 2:
- x = F.pad(x, (0, 1, 0, 1), "constant", 0)
- #print(x.shape)
-
- for module in self:
- if not isinstance(module, nn.MaxPool2d):
- x = module(x)
- return x
-
-
-class InvertedResidual(nn.Module):
- def __init__(self, inp, oup, stride, expand_ratio):
- super(InvertedResidual, self).__init__()
- self.stride = stride
- assert stride in [1, 2]
-
- hidden_dim = int(round(inp * expand_ratio))
- self.use_res_connect = self.stride == 1 and inp == oup
-
- layers = []
- if expand_ratio != 1:
- # pw
- layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
- layers.extend([
- # dw
- ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
- # pw-linear
- nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
- nn.BatchNorm2d(oup),
- ])
- self.conv = nn.Sequential(*layers)
-
- def forward(self, x):
- if self.use_res_connect:
- return x + self.conv(x)
- else:
- return self.conv(x)
-
-
-class MobileNetV2(nn.Module):
- def __init__(self, pretrained=True):
- """
- MobileNet V2 main class
- Args:
- num_classes (int): Number of classes
- width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
- inverted_residual_setting: Network structure
- round_nearest (int): Round the number of channels in each layer to be a multiple of this number
- Set to 1 to turn off rounding
- block: Module specifying inverted residual building block for mobilenet
- """
- super(MobileNetV2, self).__init__()
-
- block = InvertedResidual
- input_channel = 32
- last_channel = 1280
- width_mult = 1.0
- round_nearest = 8
-
- inverted_residual_setting = [
- # t, c, n, s
- [1, 16, 1, 1],
- [6, 24, 2, 2],
- [6, 32, 3, 2],
- [6, 64, 4, 2],
- #[6, 96, 3, 1],
- #[6, 160, 3, 2],
- #[6, 320, 1, 1],
- ]
-
- # only check the first element, assuming user knows t,c,n,s are required
- if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
- raise ValueError("inverted_residual_setting should be non-empty "
- "or a 4-element list, got {}".format(inverted_residual_setting))
-
- # building first layer
- input_channel = _make_divisible(input_channel * width_mult, round_nearest)
- self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
- features = [ConvBNReLU(4, input_channel, stride=2)]
- # building inverted residual blocks
- for t, c, n, s in inverted_residual_setting:
- output_channel = _make_divisible(c * width_mult, round_nearest)
- for i in range(n):
- stride = s if i == 0 else 1
- features.append(block(input_channel, output_channel, stride, expand_ratio=t))
- input_channel = output_channel
- self.features = nn.Sequential(*features)
-
- self.fpn_selected = [3, 6, 10]
- # weight initialization
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out')
- if m.bias is not None:
- nn.init.zeros_(m.bias)
- elif isinstance(m, nn.BatchNorm2d):
- nn.init.ones_(m.weight)
- nn.init.zeros_(m.bias)
- elif isinstance(m, nn.Linear):
- nn.init.normal_(m.weight, 0, 0.01)
- nn.init.zeros_(m.bias)
-
- #if pretrained:
- # self._load_pretrained_model()
-
- def _forward_impl(self, x):
- # This exists since TorchScript doesn't support inheritance, so the superclass method
- # (this one) needs to have a name other than `forward` that can be accessed in a subclass
- fpn_features = []
- for i, f in enumerate(self.features):
- if i > self.fpn_selected[-1]:
- break
- x = f(x)
- if i in self.fpn_selected:
- fpn_features.append(x)
-
- c2, c3, c4 = fpn_features
- return c2, c3, c4
-
-
- def forward(self, x):
- return self._forward_impl(x)
-
- def _load_pretrained_model(self):
- pretrain_dict = model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
- model_dict = {}
- state_dict = self.state_dict()
- for k, v in pretrain_dict.items():
- if k in state_dict:
- model_dict[k] = v
- state_dict.update(model_dict)
- self.load_state_dict(state_dict)
-
-
-class MobileV2_MLSD_Tiny(nn.Module):
- def __init__(self):
- super(MobileV2_MLSD_Tiny, self).__init__()
-
- self.backbone = MobileNetV2(pretrained=True)
-
- self.block12 = BlockTypeA(in_c1= 32, in_c2= 64,
- out_c1= 64, out_c2=64)
- self.block13 = BlockTypeB(128, 64)
-
- self.block14 = BlockTypeA(in_c1 = 24, in_c2 = 64,
- out_c1= 32, out_c2= 32)
- self.block15 = BlockTypeB(64, 64)
-
- self.block16 = BlockTypeC(64, 16)
-
- def forward(self, x):
- c2, c3, c4 = self.backbone(x)
-
- x = self.block12(c3, c4)
- x = self.block13(x)
- x = self.block14(c2, x)
- x = self.block15(x)
- x = self.block16(x)
- x = x[:, 7:, :, :]
- #print(x.shape)
- x = F.interpolate(x, scale_factor=2.0, mode='bilinear', align_corners=True)
-
- return x
\ No newline at end of file
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/utils/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/utils/__init__.py
deleted file mode 100644
index a263e31c1e3977712827ca229bbc04910b4e928e..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/utils/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .flops_counter import get_model_complexity_info
-from .fuse_conv_bn import fuse_conv_bn
-from .sync_bn import revert_sync_batchnorm
-from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit,
- KaimingInit, NormalInit, PretrainedInit,
- TruncNormalInit, UniformInit, XavierInit,
- bias_init_with_prob, caffe2_xavier_init,
- constant_init, initialize, kaiming_init, normal_init,
- trunc_normal_init, uniform_init, xavier_init)
-
-__all__ = [
- 'get_model_complexity_info', 'bias_init_with_prob', 'caffe2_xavier_init',
- 'constant_init', 'kaiming_init', 'normal_init', 'trunc_normal_init',
- 'uniform_init', 'xavier_init', 'fuse_conv_bn', 'initialize',
- 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
- 'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
- 'Caffe2XavierInit', 'revert_sync_batchnorm'
-]
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/fused_bias_leakyrelu.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/fused_bias_leakyrelu.py
deleted file mode 100644
index 6d12508469c6c8fa1884debece44c58d158cb6fa..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/fused_bias_leakyrelu.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
-
-# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
-# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
-# Augmentation (ADA)
-# =======================================================================
-
-# 1. Definitions
-
-# "Licensor" means any person or entity that distributes its Work.
-
-# "Software" means the original work of authorship made available under
-# this License.
-
-# "Work" means the Software and any additions to or derivative works of
-# the Software that are made available under this License.
-
-# The terms "reproduce," "reproduction," "derivative works," and
-# "distribution" have the meaning as provided under U.S. copyright law;
-# provided, however, that for the purposes of this License, derivative
-# works shall not include works that remain separable from, or merely
-# link (or bind by name) to the interfaces of, the Work.
-
-# Works, including the Software, are "made available" under this License
-# by including in or with the Work either (a) a copyright notice
-# referencing the applicability of this License to the Work, or (b) a
-# copy of this License.
-
-# 2. License Grants
-
-# 2.1 Copyright Grant. Subject to the terms and conditions of this
-# License, each Licensor grants to you a perpetual, worldwide,
-# non-exclusive, royalty-free, copyright license to reproduce,
-# prepare derivative works of, publicly display, publicly perform,
-# sublicense and distribute its Work and any resulting derivative
-# works in any form.
-
-# 3. Limitations
-
-# 3.1 Redistribution. You may reproduce or distribute the Work only
-# if (a) you do so under this License, (b) you include a complete
-# copy of this License with your distribution, and (c) you retain
-# without modification any copyright, patent, trademark, or
-# attribution notices that are present in the Work.
-
-# 3.2 Derivative Works. You may specify that additional or different
-# terms apply to the use, reproduction, and distribution of your
-# derivative works of the Work ("Your Terms") only if (a) Your Terms
-# provide that the use limitation in Section 3.3 applies to your
-# derivative works, and (b) you identify the specific derivative
-# works that are subject to Your Terms. Notwithstanding Your Terms,
-# this License (including the redistribution requirements in Section
-# 3.1) will continue to apply to the Work itself.
-
-# 3.3 Use Limitation. The Work and any derivative works thereof only
-# may be used or intended for use non-commercially. Notwithstanding
-# the foregoing, NVIDIA and its affiliates may use the Work and any
-# derivative works commercially. As used herein, "non-commercially"
-# means for research or evaluation purposes only.
-
-# 3.4 Patent Claims. If you bring or threaten to bring a patent claim
-# against any Licensor (including any claim, cross-claim or
-# counterclaim in a lawsuit) to enforce any patents that you allege
-# are infringed by any Work, then your rights under this License from
-# such Licensor (including the grant in Section 2.1) will terminate
-# immediately.
-
-# 3.5 Trademarks. This License does not grant any rights to use any
-# Licensor’s or its affiliates’ names, logos, or trademarks, except
-# as necessary to reproduce the notices described in this License.
-
-# 3.6 Termination. If you violate any term of this License, then your
-# rights under this License (including the grant in Section 2.1) will
-# terminate immediately.
-
-# 4. Disclaimer of Warranty.
-
-# THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-# NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-# THIS LICENSE.
-
-# 5. Limitation of Liability.
-
-# EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-# THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-# SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-# INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-# OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-# (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
-# LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
-# COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
-# THE POSSIBILITY OF SUCH DAMAGES.
-
-# =======================================================================
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['fused_bias_leakyrelu'])
-
-
-class FusedBiasLeakyReLUFunctionBackward(Function):
- """Calculate second order deviation.
-
- This function is to compute the second order deviation for the fused leaky
- relu operation.
- """
-
- @staticmethod
- def forward(ctx, grad_output, out, negative_slope, scale):
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- empty = grad_output.new_empty(0)
-
- grad_input = ext_module.fused_bias_leakyrelu(
- grad_output,
- empty,
- out,
- act=3,
- grad=1,
- alpha=negative_slope,
- scale=scale)
-
- dim = [0]
-
- if grad_input.ndim > 2:
- dim += list(range(2, grad_input.ndim))
-
- grad_bias = grad_input.sum(dim).detach()
-
- return grad_input, grad_bias
-
- @staticmethod
- def backward(ctx, gradgrad_input, gradgrad_bias):
- out, = ctx.saved_tensors
-
- # The second order deviation, in fact, contains two parts, while the
- # the first part is zero. Thus, we direct consider the second part
- # which is similar with the first order deviation in implementation.
- gradgrad_out = ext_module.fused_bias_leakyrelu(
- gradgrad_input,
- gradgrad_bias.to(out.dtype),
- out,
- act=3,
- grad=1,
- alpha=ctx.negative_slope,
- scale=ctx.scale)
-
- return gradgrad_out, None, None, None
-
-
-class FusedBiasLeakyReLUFunction(Function):
-
- @staticmethod
- def forward(ctx, input, bias, negative_slope, scale):
- empty = input.new_empty(0)
-
- out = ext_module.fused_bias_leakyrelu(
- input,
- bias,
- empty,
- act=3,
- grad=0,
- alpha=negative_slope,
- scale=scale)
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- out, = ctx.saved_tensors
-
- grad_input, grad_bias = FusedBiasLeakyReLUFunctionBackward.apply(
- grad_output, out, ctx.negative_slope, ctx.scale)
-
- return grad_input, grad_bias, None, None
-
-
-class FusedBiasLeakyReLU(nn.Module):
- """Fused bias leaky ReLU.
-
- This function is introduced in the StyleGAN2:
- http://arxiv.org/abs/1912.04958
-
- The bias term comes from the convolution operation. In addition, to keep
- the variance of the feature map or gradients unchanged, they also adopt a
- scale similarly with Kaiming initialization. However, since the
- :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
- final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
- your own scale.
-
- TODO: Implement the CPU version.
-
- Args:
- channel (int): The channel number of the feature map.
- negative_slope (float, optional): Same as nn.LeakyRelu.
- Defaults to 0.2.
- scale (float, optional): A scalar to adjust the variance of the feature
- map. Defaults to 2**0.5.
- """
-
- def __init__(self, num_channels, negative_slope=0.2, scale=2**0.5):
- super(FusedBiasLeakyReLU, self).__init__()
-
- self.bias = nn.Parameter(torch.zeros(num_channels))
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_bias_leakyrelu(input, self.bias, self.negative_slope,
- self.scale)
-
-
-def fused_bias_leakyrelu(input, bias, negative_slope=0.2, scale=2**0.5):
- """Fused bias leaky ReLU function.
-
- This function is introduced in the StyleGAN2:
- http://arxiv.org/abs/1912.04958
-
- The bias term comes from the convolution operation. In addition, to keep
- the variance of the feature map or gradients unchanged, they also adopt a
- scale similarly with Kaiming initialization. However, since the
- :math:`1+{alpha}^2` : is too small, we can just ignore it. Therefore, the
- final scale is just :math:`\sqrt{2}`:. Of course, you may change it with # noqa: W605, E501
- your own scale.
-
- Args:
- input (torch.Tensor): Input feature map.
- bias (nn.Parameter): The bias from convolution operation.
- negative_slope (float, optional): Same as nn.LeakyRelu.
- Defaults to 0.2.
- scale (float, optional): A scalar to adjust the variance of the feature
- map. Defaults to 2**0.5.
-
- Returns:
- torch.Tensor: Feature map after non-linear activation.
- """
-
- if not input.is_cuda:
- return bias_leakyrelu_ref(input, bias, negative_slope, scale)
-
- return FusedBiasLeakyReLUFunction.apply(input, bias.to(input.dtype),
- negative_slope, scale)
-
-
-def bias_leakyrelu_ref(x, bias, negative_slope=0.2, scale=2**0.5):
-
- if bias is not None:
- assert bias.ndim == 1
- assert bias.shape[0] == x.shape[1]
- x = x + bias.reshape([-1 if i == 1 else 1 for i in range(x.ndim)])
-
- x = F.leaky_relu(x, negative_slope)
- if scale != 1:
- x = x * scale
-
- return x
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/losses/utils.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/losses/utils.py
deleted file mode 100644
index 85aec9f3045240c3de96a928324ae8f5c3aebe8b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/models/losses/utils.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import functools
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch.nn.functional as F
-
-
-def get_class_weight(class_weight):
- """Get class weight for loss function.
-
- Args:
- class_weight (list[float] | str | None): If class_weight is a str,
- take it as a file name and read from it.
- """
- if isinstance(class_weight, str):
- # take it as a file path
- if class_weight.endswith('.npy'):
- class_weight = np.load(class_weight)
- else:
- # pkl, json or yaml
- class_weight = mmcv.load(class_weight)
-
- return class_weight
-
-
-def reduce_loss(loss, reduction):
- """Reduce loss as specified.
-
- Args:
- loss (Tensor): Elementwise loss tensor.
- reduction (str): Options are "none", "mean" and "sum".
-
- Return:
- Tensor: Reduced loss tensor.
- """
- reduction_enum = F._Reduction.get_enum(reduction)
- # none: 0, elementwise_mean:1, sum: 2
- if reduction_enum == 0:
- return loss
- elif reduction_enum == 1:
- return loss.mean()
- elif reduction_enum == 2:
- return loss.sum()
-
-
-def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
- """Apply element-wise weight and reduce loss.
-
- Args:
- loss (Tensor): Element-wise loss.
- weight (Tensor): Element-wise weights.
- reduction (str): Same as built-in losses of PyTorch.
- avg_factor (float): Avarage factor when computing the mean of losses.
-
- Returns:
- Tensor: Processed loss values.
- """
- # if weight is specified, apply element-wise weight
- if weight is not None:
- assert weight.dim() == loss.dim()
- if weight.dim() > 1:
- assert weight.size(1) == 1 or weight.size(1) == loss.size(1)
- loss = loss * weight
-
- # if avg_factor is not specified, just reduce the loss
- if avg_factor is None:
- loss = reduce_loss(loss, reduction)
- else:
- # if reduction is mean, then average the loss by avg_factor
- if reduction == 'mean':
- loss = loss.sum() / avg_factor
- # if reduction is 'none', then do nothing, otherwise raise an error
- elif reduction != 'none':
- raise ValueError('avg_factor can not be used with reduction="sum"')
- return loss
-
-
-def weighted_loss(loss_func):
- """Create a weighted version of a given loss function.
-
- To use this decorator, the loss function must have the signature like
- `loss_func(pred, target, **kwargs)`. The function only needs to compute
- element-wise loss without any reduction. This decorator will add weight
- and reduction arguments to the function. The decorated function will have
- the signature like `loss_func(pred, target, weight=None, reduction='mean',
- avg_factor=None, **kwargs)`.
-
- :Example:
-
- >>> import torch
- >>> @weighted_loss
- >>> def l1_loss(pred, target):
- >>> return (pred - target).abs()
-
- >>> pred = torch.Tensor([0, 2, 3])
- >>> target = torch.Tensor([1, 1, 1])
- >>> weight = torch.Tensor([1, 0, 1])
-
- >>> l1_loss(pred, target)
- tensor(1.3333)
- >>> l1_loss(pred, target, weight)
- tensor(1.)
- >>> l1_loss(pred, target, reduction='none')
- tensor([1., 1., 2.])
- >>> l1_loss(pred, target, weight, avg_factor=2)
- tensor(1.5000)
- """
-
- @functools.wraps(loss_func)
- def wrapper(pred,
- target,
- weight=None,
- reduction='mean',
- avg_factor=None,
- **kwargs):
- # get element-wise loss
- loss = loss_func(pred, target, **kwargs)
- loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
- return loss
-
- return wrapper
diff --git a/spaces/MiloSobral/PortiloopDemo/PortiloopV2.md b/spaces/MiloSobral/PortiloopDemo/PortiloopV2.md
deleted file mode 100644
index 166c718b00bc94cf30fbc6769b3b4b5ca1e198cf..0000000000000000000000000000000000000000
--- a/spaces/MiloSobral/PortiloopDemo/PortiloopV2.md
+++ /dev/null
@@ -1,238 +0,0 @@
-# Portiloop V2
-
-You've just got your hands on the hardware for the Portiloop V2 (A Google Coral Mini and a PiEEG board). Here are the steps you need to follow to get started using the EEG capture, the Spindle detection software, and the TPU processing.
-
-## Accessing the Google Coral
-
-These first steps will help you set up an SSH connection to the device.
-
-- Power up the board through the USB power port.
-- Connect another USB cable to the OTG-port on the board and to your _linux_ host machine. Follow the following steps to connect to the board through serial:
- - `ls /dev/ttyMC*`
- - `screen /dev/ttyACM0`
- If you see a message telling you that screen is busy, you can use `sudo lsof /dev/ttyMC0` and then retry the screen step.
- - Login to the board using default username and password: mendel
-- Once you are logged in, you can now connect to you desired wifi network using nmtui.
-- If you want to access the board through ssh (which is recommended for any sort of development):
- - On the serial console, open the `/etc/ssh/sshd_config` file.
- - Scroll down to the `PasswordAuthenticated` line and change the 'no' to a 'yes'.
-- Reboot the device.
- Once all of that is done, you should be able to ssh into your device, using either the ip address or the hostname. If some issues arise, make sure you are connected to the same network.
-
-## Dependencies
-
-To install all dependencies, run the installation.sh script. This script takes care of all the installations for you so it may take a while (~25 minutes).
-
-## Setting up the Access Point
-
-### 1. Download dependencies for access point
-
-To set up an access point, you will need to install a few dependencies. To install them, you can use the following command:
-
-```bash
-sudo apt-get update
-sudo apt-get install hostapd dnsmasq
-```
-
-This will update your system's package list and install the `hostapd` and `dnsmasq` packages.
-
-### 2. Set up the interface ap0
-
-Next, you will need to set up a systemd service to configure and enable the `ap0` interface.
-
-First, we can create a script to create the interface using `sudo nano /usr/local/bin/create_ap0.sh`. The script should contain the following content:
-
-```bash
-#!/bin/bash
-
-# Get the name of the interface on phy1
-phy1_interface=$(sudo iw dev | awk '/phy#1/ {getline; print $2}')
-
-# Check if the interface name is p2p0
-if [[ $phy1_interface == "ap0" ]]; then
- echo "ap0 already set up, not running script..."
-else
- echo $phy1_interface
- # Delete the existing p2p0 interface
- /sbin/iw dev $phy1_interface del
-
- # Reload the Network Manager utility
- systemctl restart NetworkManager
-
- # Create a new ap0 interface in AP mode
- /sbin/iw phy phy1 interface add ap0 type __ap
-
- # Disable power management for the ap0 interface
- /sbin/iw dev ap0 set power_save off
-
- # Reload the Network Manager utility again
- systemctl restart NetworkManager
-
- # Get an IPV4 address for the server
- ifconfig ap0 192.168.4.1 up
-fi
-```
-
-To be able to run this file like a script, run `sudo chmod +x /usr/local/bin/create_ap0.sh`
-
-To avoid configuration issues, we need to tell NetworkManager to ignore this interface. First, run `nmcli device set ap0 managed no`. Then, create a file called `/etc/NetworkManager/conf.d/unmanaged.conf`. In this file, write the following:
-
-```ini
-[keyfile]
-unmanaged-devices=interface-name:ap0,interface-name:p2p0
-```
-
-To make sure this starts works everytime we turn the Portiloop on, we need to create a new service. First, you can create a new service file at `/etc/systemd/system/create_ap.service` with the following content:
-
-```ini
-[Unit]
-Description=Create The Access Point for the coral
-Before=hostapd.service dnsmasq.service
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-ExecStart=/usr/local/bin/create_ap0.sh
-
-[Install]
-WantedBy=multi-user.target
-```
-
-This service file specifies that it should run the `create_ap0.sh` script once on boot before the hostapd and dnsmasq services start.
-
-### 3. Configure Hostapd
-
-Hostapd is the software that will create the wireless access point. First, you will need to open the in `/etc/sysctl.conf` file and change the line for ip_forwarding to `net.ipv4.ip_forward=1`.
-
-Next, you will create a configuration file at `/etc/hostapd/hostapd.conf` with the following content:
-
-```ini
-interface=ap0
-driver=nl80211
-ssid=YOUR-SSID-HERE
-hw_mode=g
-channel=6
-wpa=2
-wpa_passphrase=YOUR-PASSWORD-HERE
-wpa_key_mgmt=WPA-PSK
-wpa_pairwise=TKIP CCMP
-rsn_pairwise=CCMP
-auth_algs=1
-macaddr_acl=0
-```
-
-This configuration file specifies the `ap0` interface, the SSID and password for the access point, and the encryption type to use. Make sure to replace `YOUR-SSID-HERE` and `YOUR-PASSWORD-HERE` with your own values. You now need to specify to hostapd which configuration file to do. Open the hostapd configuration file using `sudo nano /etc/default/hostapd`. Uncomment the DAEMON_CONF line and set it to the path of the configuration file you just created:
-`DAEMON_CONF="/etc/hostapd/hostapd.conf"`.
-
-Lastly, run `sudo systemctl unmask hostapd`.
-
-### 4. Configure dnsmasq
-
-Dnsmasq is the software that will provide DHCP and DNS services for the access point. Start by opening the dnsmasq configuration file with `sudo nano /etc/dnsmasq.conf`. Add the following content at the top of the file:
-
-```ini
-
-# Configuration for Access Point
-interface=ap0
-dhcp-range=192.168.4.2,192.168.4.20,255.255.255.0,24h
-dhcp-option=3,192.168.4.1
-dhcp-option=6,192.168.4.1
-server=8.8.8.8
-```
-
-This configuration file specifies the `ap0` interface, the range of IP addresses to assign to clients, and the DNS server to use. Note that the IP address of the `dhcp-option=6,...` should be the same as the IP address set in step 2.
-
-### 5. Configure IP Tables for internet access
-
-To make sure you get internet access on your home computer when you are connected to the Portiloop, we need to setup IP tables. Create the following script `sudo nano /usr/local/bin/setup_tables.sh` and copy paste the following code:
-
-```bash
-#!/bin/bash
-
-echo "Telling kernel to turn on ipv4 ip_forwarding"
-echo 1 > /proc/sys/net/ipv4/ip_forward
-echo "Done. Setting up iptables rules to allow FORWARDING"
-
-DOWNSTREAM=ap0 # ap0 is client network (running hostapd)
-UPSTREAM=wlan0 # upstream network (internet)
-
-# Allow IP Masquerading (NAT) of packets from clients (downstream) to upstream network (internet)
-iptables -t nat -A POSTROUTING -o $UPSTREAM -j MASQUERADE
-
-# Forward packets from downstream clients to the upstream internet
-iptables -A FORWARD -i $DOWNSTREAM -o $UPSTREAM -j ACCEPT
-
-# Forward packers from the internet to clients IF THE CONNECTION IS ALREADY OPEN!
-iptables -A FORWARD -i $UPSTREAM -o $DOWNSTREAM -m state --state RELATED,ESTABLISHED -j ACCEPT
-
-# Setup the external DNS server
-iptables -t nat -A PREROUTING -i $DOWNSTREAM -p udp --dport 53 -j DNAT --to-destination 8.8.8.8:53
-
-echo "Done setting up iptables rules. Forwarding enabled"
-```
-
-Then, create a file called `/etc/systemd/system/setup_tables.service` and paste the following configuration:
-
-```ini
-[Unit]
-Description=Setup tables service
-After=create_ap.service
-Wants=network-online.target
-After=network-online.target
-
-[Service]
-Type=simple
-ExecStart=/usr/local/bin/setup_tables.sh
-
-[Install]
-WantedBy=multi-user.target
-```
-
-Finally, make sure the script is executable by running: `sudo chmod +x /usr/local/bin/setup_tables.sh`
-
-### 6. Start Systemd services
-
-To make sure that everything happens on startup, we need to enable all services. Execute the following commands:
-
-```bash
-sudo systemctl enable create_ap.service
-sudo systemctl enable hostapd.service
-sudo systemctl enable dnsmasq.service
-sudo systemctl enable setup_tables.service
-```
-
-## Jupyter notebook
-
-To access the portiloop easily, we recommend setting up a jupyter notebook server which will be available from any browser. To set up the Jupyter server using a systemd service, follow these steps:
-
-1. On the command line, type `jupyter notebook password`. This will show a prompt where you can enter the desired password.
-2. Create a new systemd service file using the command `sudo nano /etc/systemd/system/jupyter.service`.
-3. Add the following lines to the service file:
-
-```ini
-[Unit]
-Description=Jupyter Notebook Server
-After=create_ap.service
-After=hostapd.service
-After=dnsmasq.service
-
-[Service]
-Type=simple
-ExecStart=/bin/bash -c "/usr/bin/jupyter notebook --no-browser --ip 192.168.4.1 --port 8080 --notebook-dir=/home/mendel"
-User=mendel
-Group=mendel
-Restart=on-failure
-RestartSec=60s
-
-[Install]
-WantedBy=multi-user.target
-```
-
-4. Save and close the file.
-5. Reload the systemd daemon to load the new service file: `sudo systemctl daemon-reload`.
-6. Start the Jupyter service: `sudo systemctl start jupyter`.
-7. Check the status of the service: `sudo systemctl status jupyter`. If everything is set up correctly, you should see a message indicating that the service is active and running.
-8. To make sure the service starts automatically on boot, enable it: `sudo systemctl enable jupyter`.
-
-That's it! Your Jupyter server should now be up and running, listening on IP address 192.168.4.1 and port 8080, and automatically starting whenever the system boots up. You can now access it by typing 192.168.4.1:8080 in your browser. This should lead you to a login page where you'll be prompted for your password. If any issue arise, try with a different web browser.
diff --git a/spaces/Monster/Llama-2-7B-chat/app.py b/spaces/Monster/Llama-2-7B-chat/app.py
deleted file mode 100644
index fe32a168108963e3a9e4e031a297dd190c81cfa4..0000000000000000000000000000000000000000
--- a/spaces/Monster/Llama-2-7B-chat/app.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from __future__ import annotations
-from typing import Iterable
-import gradio as gr
-from gradio.themes.base import Base
-from gradio.themes.utils import colors, fonts, sizes
-import subprocess
-
-from huggingface_hub import hf_hub_download
-from llama_cpp import Llama
-from llama_cpp import LlamaRAMCache
-
-hf_hub_download(repo_id="TheBloke/Llama-2-7b-Chat-GGUF", filename="llama-2-7b-chat.Q4_K_M.gguf", local_dir=".")
-
-llm = Llama(model_path="./llama-2-7b-chat.Q4_K_M.gguf", rms_norm_eps=1e-5)
-
-cache = LlamaRAMCache(capacity_bytes=2 << 30)
-
-llm.set_cache(cache)
-
-
-ins = '''[INST] <