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-The Unofficial Way from Third-Party Sources
-The unofficial way to install DirectX 12 on Windows 7 64 bit is to use a third-party tool or patch that claims to enable DirectX 12 on Windows 7 64 bit. However, this method is not recommended for several reasons. First, it is not supported by Microsoft and may violate their terms of service. Second, it may cause compatibility or security issues with your system or games. Third, it may not work as expected or at all. Fourth, it may be difficult to uninstall or revert back to the original state.
-Therefore, we advise you to avoid using any third-party tools or patches that claim to install DirectX 12 on Windows 7 64 bit. Instead, you should use the official way from Microsoft or upgrade your system to Windows 10, which supports DirectX 12 natively.
-How to Troubleshoot DirectX 12 Issues on Windows 7 64 Bit
-If you have successfully installed DirectX 12 on Windows 7 64 bit using the official way from Microsoft, but you still encounter some issues or errors when playing games that support DirectX 12 on Windows 7 64 bit, you may need to troubleshoot them using some methods. Here are some common methods that can help you fix DirectX 12 issues on Windows 7 64 bit:
-How to Use DirectX Diagnostic Tool (DxDiag)
-DirectX Diagnostic Tool (DxDiag) is a built-in tool that can help you diagnose and test your DirectX installation and hardware. You can use DxDiag to check your system information, display settings, sound settings, input devices, and more. You can also use DxDiag to save a report of your system configuration and send it to technical support if needed.
-How to install the latest version of DirectX on Windows 7 64 bit
-DirectX End-User Runtime Web Installer for Windows 7 64 bit
-DirectX 12 compatible games for Windows 7 64 bit
-DirectX 12 features and benefits for Windows 7 64 bit
-DirectX 12 offline installer for Windows 7 64 bit
-DirectX 12 system requirements for Windows 7 64 bit
-DirectX 12 update for Windows 7 64 bit
-DirectX 12 vs DirectX 11 comparison for Windows 7 64 bit
-How to check DirectX version on Windows 7 64 bit
-How to fix DirectX errors on Windows 7 64 bit
-How to uninstall DirectX on Windows 7 64 bit
-Is DirectX 12 available for Windows 7 64 bit
-What is DirectX and why do I need it on Windows 7 64 bit
-Where to download DirectX 12 for Windows 7 64 bit
-How to enable DirectX 12 on Windows 7 64 bit
-How to optimize DirectX performance on Windows 7 64 bit
-How to troubleshoot DirectX issues on Windows 7 64 bit
-How to use DirectX diagnostic tool on Windows 7 64 bit
-What are the advantages of DirectX 12 over DirectX 11 on Windows 7 64 bit
-What are the best DirectX settings for gaming on Windows 7 64 bit
-How to download and install DirectX SDK on Windows 7 64 bit
-How to develop games with DirectX on Windows 7 64 bit
-How to test DirectX functionality on Windows 7 64 bit
-How to upgrade from DirectX 11 to DirectX 12 on Windows 7 64 bit
-What are the differences between DirectX and OpenGL on Windows 7 64 bit
-How to run older games with DirectX compatibility mode on Windows 7
To use DxDiag, follow these steps:
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- Press the Windows key + R to open the Run dialog box. -
- Type dxdiag and click OK. -
- Wait for DxDiag to collect information about your system. -
- Click on the tabs to view different aspects of your system. -
- Click on the Save All Information button to save a report of your system configuration. -
- Click on the Exit button to close DxDiag. -
You can use DxDiag to check if your system meets the requirements for DirectX 12 on Windows 7 64 bit, such as the operating system version, the graphics card model, and the DirectX version. You can also use DxDiag to identify any problems or errors with your DirectX installation or hardware, such as missing or corrupted files, outdated or incompatible drivers, or insufficient memory.
-How to Update or Rollback Display Drivers
-Display drivers are software components that allow your graphics card to communicate with your operating system and applications. Having the latest and compatible display drivers is crucial for ensuring the optimal performance and quality of DirectX 12 games. However, sometimes updating your display drivers may cause some issues or errors, such as crashes, freezes, glitches, or reduced performance. In that case, you may need to rollback your display drivers to a previous version that worked well.
-To update or rollback your display drivers, follow these steps:
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- Press the Windows key + X and select Device Manager. -
- Expand the Display adapters category and right-click on your graphics card. -
- Select Update driver to search for and install the latest driver for your graphics card. -
- If updating your driver causes any issues or errors, repeat steps 2 and 3, but select Roll Back driver instead of Update driver to restore the previous driver for your graphics card. -
- Restart your computer and check if the issues or errors are resolved. -
You can also use the official websites of your graphics card manufacturer, such as NVIDIA, AMD, or Intel, to download and install the latest or previous drivers for your graphics card. However, make sure you download and install the correct and compatible drivers for your graphics card model and operating system version.
-How to Enable or Disable Hardware Acceleration
-Hardware acceleration is a feature that allows your graphics card to perform some tasks faster than your CPU, such as rendering graphics, decoding video, or encrypting data. Hardware acceleration can improve the performance and quality of DirectX 12 games by reducing the workload on your CPU and utilizing the full potential of your GPU. However, hardware acceleration can also cause some issues or errors, such as crashes, freezes, glitches, or compatibility problems. In that case, you may need to disable hardware acceleration to prevent these issues or errors.
-To enable or disable hardware acceleration, follow these steps:
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- Press the Windows key + R to open the Run dialog box. -
- Type control panel and click OK. -
- Select Appearance and Personalization. -
- Select Display. -
- Select Change display settings. -
- Select Advanced settings. -
- Select the Troubleshoot tab. -
- Select Change settings. -
- Move the slider to adjust the hardware acceleration level. You can choose between Full (default), None (disabled), or any intermediate level. -
- Click OK and apply the changes. -
- Restart your computer and check if the issues or errors are resolved. -
Note that some graphics cards may not support changing the hardware acceleration level. In that case, you may need to use other methods to enable or disable hardware acceleration for specific applications or games. For example, you can use the settings menu of your game or application to enable or disable hardware acceleration. You can also use the compatibility mode of Windows 7 to run older games or applications that may not work well with hardware acceleration.
-How to Compare DirectX 12 with Previous Versions of DirectX
-If you are curious about how DirectX 12 compares with previous versions of DirectX, such as DirectX 11, DirectX 10, or DirectX 9, you can use some methods to compare them. Here are some common methods that can help you compare DirectX 12 with previous versions of DirectX:
-The Differences Between High-Level and Low-Level APIs
-One of the main differences between DirectX 12 and previous versions of DirectX is that DirectX 12 is a low-level API, while previous versions of DirectX are high-level APIs. A high-level API is an API that abstracts away many details of the hardware and provides a simpler and easier way for developers to create applications. A low-level API is an API that gives developers more control over the hardware resources and allows them to optimize their applications for specific hardware configurations.
-The advantage of a high-level API is that it is more user-friendly and compatible with different hardware devices. The disadvantage of a high-level API is that it has more overhead and less flexibility than a low-level API. The advantage of a low-level API is that it is more efficient and flexible than a high-level API. The disadvantage of a low-level API is that it is more complex and difficult to use than a high-level API.
-Therefore, DirectX 12 offers more performance and scalability than previous versions of DirectX by allowing developers to access the hardware resources directly and customize their applications for specific hardware configurations. However, DirectX 12 also requires more skill and effort from developers to create applications that work well with different hardware devices.
-The Advantages of Parallel Compute and Ray Tracing
-Another difference between DirectX 12 and previous versions of DirectX is that DirectX 12 supports parallel compute and ray tracing, while previous versions of DirectX do not. Parallel compute is a feature that allows developers to make multiple calls to the GPU at the same time, instead of waiting for one call to finish before making another. Ray tracing is a technique that simulates the behavior of light rays in a realistic way, creating stunning visual effects such as reflections, refractions, shadows, ambient occlusion, global illumination, and more.
-The advantage of parallel compute is that it improves the performance and scalability of the applications by reducing the CPU bottleneck and utilizing the full potential of the GPU. The advantage of ray tracing is that it improves the quality and realism of the graphics by creating more natural and immersive lighting and shading effects. However, parallel compute and ray tracing also require more computational power and memory from the hardware, which may not be available on older or lower-end devices.
-Therefore, DirectX 12 offers more features and capabilities than previous versions of DirectX by allowing developers to use parallel compute and ray tracing in their applications. However, DirectX 12 also requires more hardware resources and support from the devices to run these features smoothly and effectively.
-The Performance and Compatibility of DirectX 12 Games
-One of the main reasons why gamers want to install DirectX 12 on Windows 7 64 bit is to play games that support DirectX 12 and enjoy the benefits of DirectX 12 features. However, not all games support DirectX 12, and not all games that support DirectX 12 run better on DirectX 12 than on previous versions of DirectX. The performance and compatibility of DirectX 12 games depend on several factors, such as the game engine, the graphics card, the driver, the operating system, and the settings.
-Some games that support DirectX 12 may run faster or smoother on DirectX 12 than on previous versions of DirectX, especially if they use parallel compute or ray tracing features. Some examples of games that run better on DirectX 12 are Forza Horizon 4, Shadow of the Tomb Raider, Metro Exodus, and Control. However, some games that support DirectX 12 may run slower or worse on DirectX 12 than on previous versions of DirectX, especially if they are not optimized for DirectX 12 or have compatibility issues with certain hardware or software components. Some examples of games that run worse on DirectX 12 are Deus Ex: Mankind Divided, Hitman, Rise of the Tomb Raider, and Battlefield V.
-Therefore, DirectX 12 does not guarantee a better gaming experience for all games that support DirectX 12. You may need to experiment with different settings and options to find the best configuration for your system and your game. You may also need to check the official websites or forums of your game or graphics card manufacturer for any updates or patches that may improve the performance or compatibility of your game on DirectX 12.
-Conclusion
-In conclusion, DirectX 12 is a powerful graphics API that offers several features and benefits that are not available in previous versions of DirectX. However, installing DirectX 12 on Windows 7 64 bit is not as easy as installing it on Windows 10, which comes with DirectX 12 pre-installed. You need to meet certain requirements and follow certain steps to install DirectX 12 on Windows 7 64 bit using the official way from Microsoft. You also need to avoid using any third-party tools or patches that claim to install DirectX 12 on Windows 7 64 bit. You also need to troubleshoot any issues or errors that may occur when playing games that support DirectX 12 on Windows 7 64 bit. You also need to compare DirectX 12 with previous versions of DirectX to understand the differences and similarities between them. We hope this article has helped you learn more about DirectX 12 and how to install it on Windows 7 64 bit. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!
FAQs
-Here are some frequently asked questions about DirectX 12 and Windows 7 64 bit:
-What are the system requirements for DirectX 12?
-The system requirements for DirectX 12 are:
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- A valid license of Windows 7 Service Pack 1 (SP1) or higher, or Windows 10. -
- A graphics card that supports DirectX 12. You can check the list of DirectX 12 compatible graphics cards here. -
- A game that supports DirectX 12 on Windows 7 64 bit. You can check the list of DirectX 12 games here. -
- Enough disk space and memory to install the updates and run the games. -
How can I check which version of DirectX I have installed?
-You can check which version of DirectX you have installed by using the DirectX Diagnostic Tool (DxDiag). To use DxDiag, follow these steps:
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- Press the Windows key + R to open the Run dialog box. -
- Type dxdiag and click OK. -
- Wait for DxDiag to collect information about your system. -
- Click on the System tab and look for the DirectX Version field. -
- Click on the Exit button to close DxDiag. -
Can I use DirectX 12 on Windows 10 or other operating systems?
-Yes, you can use DirectX 12 on Windows 10 or other operating systems that support DirectX 12. However, some features and benefits of DirectX 12 may not be available on older or different operating systems. For example, DirectX Raytracing (DXR) is only available on Windows 10 version 1809 or higher. Also, some games that support DirectX 12 may not work well or at all on older or different operating systems. Therefore, it is recommended that you use the latest version of Windows 10 to enjoy the full potential of DirectX 12.
-Which games support DirectX 12 and how can I enable it?
-There are many games that support DirectX 12, but not all of them support DirectX 12 on Windows 7 64 bit. You can check the list of DirectX 12 games here. To enable DirectX 12 on your game, you need to follow the instructions provided by the game developer or publisher. Usually, you can find the option to enable DirectX 12 in the game settings menu, under the graphics or display category. However, some games may require you to launch the game with a specific command line argument or edit a configuration file to enable DirectX 12. You can also check the official websites or forums of your game for more information on how to enable DirectX 12.
-What are some alternatives to DirectX 12?
-If you are looking for some alternatives to DirectX 12, you may want to consider some other graphics APIs that are available for PC gaming. Some of the most popular alternatives to DirectX 12 are:
--
-
- Vulkan: Vulkan is a low-level API that is similar to DirectX 12 in terms of performance and flexibility. Vulkan is developed by the Khronos Group, an industry consortium that also develops OpenGL and OpenCL. Vulkan is cross-platform and supports Windows, Linux, Android, and other operating systems. Vulkan also supports ray tracing through an extension called Vulkan Ray Tracing (VKR). Some examples of games that use Vulkan are Doom Eternal, Red Dead Redemption 2, and Wolfenstein: Youngblood. -
- OpenGL: OpenGL is a high-level API that is widely used for 2D and 3D graphics applications. OpenGL is also developed by the Khronos Group and is cross-platform and supports Windows, Linux, macOS, and other operating systems. OpenGL does not support ray tracing natively, but it can be implemented through extensions or libraries such as NVIDIA OptiX or Intel Embree. Some examples of games that use OpenGL are Minecraft, Half-Life 2, and Counter-Strike: Global Offensive. -
- Metal: Metal is a low-level API that is exclusive to Apple devices, such as macOS, iOS, iPadOS, and tvOS. Metal is developed by Apple and is designed to optimize the performance and quality of graphics applications on Apple hardware. Metal also supports ray tracing through an extension called Metal Performance Shaders Ray Tracing (MPSR). Some examples of games that use Metal are World of Warcraft, Fortnite, and Civilization VI. -
These are just some of the alternatives to DirectX 12 that you can try if you want to experience different graphics APIs for PC gaming. However, keep in mind that each API has its own advantages and disadvantages, and not all games support all APIs. Therefore, you may need to do some research and testing before choosing the best API for your system and your game.
- -This is the end of the article. Thank you for reading!
- : https://www.pcgamer.com/directx-12-games-list/ 401be4b1e0-
-
\ No newline at end of file diff --git a/spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/__init__.py b/spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/__init__.py deleted file mode 100644 index 87c951f5425a5f62aa7aee7db3a281f453dca6f1..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/schedulers/preconfig/__init__.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# flake8: noqa - -from ...utils import ( - OptionalDependencyNotAvailable, - is_paddle_available, - is_scipy_available, -) - -try: - if not is_paddle_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from ...utils.dummy_paddle_objects import * # noqa F403 -else: - from .preconfig_scheduling_euler_ancestral_discrete import ( - PreconfigEulerAncestralDiscreteScheduler, - ) -try: - if not (is_paddle_available() and is_scipy_available()): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from ...utils.dummy_paddle_and_scipy_objects import * # noqa F403 -else: - from .preconfig_scheduling_lms_discrete import PreconfigLMSDiscreteScheduler diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/activations.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/activations.py deleted file mode 100644 index 61f2808a5466b3cf4d041059700993af5527dd29..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/activations.py +++ /dev/null @@ -1,120 +0,0 @@ -# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. -# LICENSE is in incl_licenses directory. - -import torch -from torch import nn, sin, pow -from torch.nn import Parameter - - -class Snake(nn.Module): - ''' - Implementation of a sine-based periodic activation function - Shape: - - Input: (B, C, T) - - Output: (B, C, T), same shape as the input - Parameters: - - alpha - trainable parameter - References: - - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: - https://arxiv.org/abs/2006.08195 - Examples: - >>> a1 = snake(256) - >>> x = torch.randn(256) - >>> x = a1(x) - ''' - def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): - ''' - Initialization. - INPUT: - - in_features: shape of the input - - alpha: trainable parameter - alpha is initialized to 1 by default, higher values = higher-frequency. - alpha will be trained along with the rest of your model. - ''' - super(Snake, self).__init__() - self.in_features = in_features - - # initialize alpha - self.alpha_logscale = alpha_logscale - if self.alpha_logscale: # log scale alphas initialized to zeros - self.alpha = Parameter(torch.zeros(in_features) * alpha) - else: # linear scale alphas initialized to ones - self.alpha = Parameter(torch.ones(in_features) * alpha) - - self.alpha.requires_grad = alpha_trainable - - self.no_div_by_zero = 0.000000001 - - def forward(self, x): - ''' - Forward pass of the function. - Applies the function to the input elementwise. - Snake ∶= x + 1/a * sin^2 (xa) - ''' - alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] - if self.alpha_logscale: - alpha = torch.exp(alpha) - x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) - - return x - - -class SnakeBeta(nn.Module): - ''' - A modified Snake function which uses separate parameters for the magnitude of the periodic components - Shape: - - Input: (B, C, T) - - Output: (B, C, T), same shape as the input - Parameters: - - alpha - trainable parameter that controls frequency - - beta - trainable parameter that controls magnitude - References: - - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: - https://arxiv.org/abs/2006.08195 - Examples: - >>> a1 = snakebeta(256) - >>> x = torch.randn(256) - >>> x = a1(x) - ''' - def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): - ''' - Initialization. - INPUT: - - in_features: shape of the input - - alpha - trainable parameter that controls frequency - - beta - trainable parameter that controls magnitude - alpha is initialized to 1 by default, higher values = higher-frequency. - beta is initialized to 1 by default, higher values = higher-magnitude. - alpha will be trained along with the rest of your model. - ''' - super(SnakeBeta, self).__init__() - self.in_features = in_features - - # initialize alpha - self.alpha_logscale = alpha_logscale - if self.alpha_logscale: # log scale alphas initialized to zeros - self.alpha = Parameter(torch.zeros(in_features) * alpha) - self.beta = Parameter(torch.zeros(in_features) * alpha) - else: # linear scale alphas initialized to ones - self.alpha = Parameter(torch.ones(in_features) * alpha) - self.beta = Parameter(torch.ones(in_features) * alpha) - - self.alpha.requires_grad = alpha_trainable - self.beta.requires_grad = alpha_trainable - - self.no_div_by_zero = 0.000000001 - - def forward(self, x): - ''' - Forward pass of the function. - Applies the function to the input elementwise. - SnakeBeta ∶= x + 1/b * sin^2 (xa) - ''' - alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] - beta = self.beta.unsqueeze(0).unsqueeze(-1) - if self.alpha_logscale: - alpha = torch.exp(alpha) - beta = torch.exp(beta) - x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) - - return x \ No newline at end of file diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/pretrained.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/pretrained.py deleted file mode 100644 index 723619a9fd511cf8619def49c4631ec701891b93..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/pretrained.py +++ /dev/null @@ -1,147 +0,0 @@ -import hashlib -import os -import urllib -import warnings - -from tqdm import tqdm - -_RN50 = dict( - openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", - cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" -) - -_RN50_quickgelu = dict( - openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", - cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" -) - -_RN101 = dict( - openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" -) - -_RN101_quickgelu = dict( - openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", - yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" -) - -_RN50x4 = dict( - openai="https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", -) - -_RN50x16 = dict( - openai="https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", -) - -_RN50x64 = dict( - openai="https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", -) - -_VITB32 = dict( - openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", - laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", - laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", - laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", -) - -_VITB32_quickgelu = dict( - openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", - laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", - laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", - laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", -) - -_VITB16 = dict( - openai="https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", -) - -_VITL14 = dict( - openai="https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", -) - -_PRETRAINED = { - "RN50": _RN50, - "RN50-quickgelu": _RN50_quickgelu, - "RN101": _RN101, - "RN101-quickgelu": _RN101_quickgelu, - "RN50x4": _RN50x4, - "RN50x16": _RN50x16, - "ViT-B-32": _VITB32, - "ViT-B-32-quickgelu": _VITB32_quickgelu, - "ViT-B-16": _VITB16, - "ViT-L-14": _VITL14, -} - - -def list_pretrained(as_str: bool = False): - """ returns list of pretrained models - Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True - """ - return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] - - -def list_pretrained_tag_models(tag: str): - """ return all models having the specified pretrain tag """ - models = [] - for k in _PRETRAINED.keys(): - if tag in _PRETRAINED[k]: - models.append(k) - return models - - -def list_pretrained_model_tags(model: str): - """ return all pretrain tags for the specified model architecture """ - tags = [] - if model in _PRETRAINED: - tags.extend(_PRETRAINED[model].keys()) - return tags - - -def get_pretrained_url(model: str, tag: str): - if model not in _PRETRAINED: - return '' - model_pretrained = _PRETRAINED[model] - if tag not in model_pretrained: - return '' - return model_pretrained[tag] - - -def download_pretrained(url: str, root: str = os.path.expanduser("~/.cache/clip")): - os.makedirs(root, exist_ok=True) - filename = os.path.basename(url) - - if 'openaipublic' in url: - expected_sha256 = url.split("/")[-2] - else: - expected_sha256 = '' - - download_target = os.path.join(root, filename) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError(f"{download_target} exists and is not a regular file") - - if os.path.isfile(download_target): - if expected_sha256: - if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: - return download_target - else: - warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") - else: - return download_target - - with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - if expected_sha256 and hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: - raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") - - return download_target diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/app.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/app.py deleted file mode 100644 index 96fedd9b1392a7634d6896b5c2682989cec56c05..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/app.py +++ /dev/null @@ -1,170 +0,0 @@ -import torch -import numpy as np -import gradio as gr -from PIL import Image -import matplotlib -from omegaconf import OmegaConf -from einops import repeat -import librosa -from ldm.models.diffusion.ddim import DDIMSampler -from vocoder.bigvgan.models import VocoderBigVGAN -from ldm.util import instantiate_from_config -from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000 - -SAMPLE_RATE = 16000 -cmap_transform = matplotlib.cm.viridis -torch.set_grad_enabled(False) -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -def initialize_model(config, ckpt): - config = OmegaConf.load(config) - model = instantiate_from_config(config.model) - model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False) - - model = model.to(device) - print(model.device,device,model.cond_stage_model.device) - sampler = DDIMSampler(model) - return sampler - - -def make_batch_sd( - mel, - mask, - device, - num_samples=1): - - mel = torch.from_numpy(mel)[None,None,...].to(dtype=torch.float32) - mask = torch.from_numpy(mask)[None,None,...].to(dtype=torch.float32) - masked_mel = (1 - mask) * mel - - mel = mel * 2 - 1 - mask = mask * 2 - 1 - masked_mel = masked_mel * 2 -1 - - batch = { - "mel": repeat(mel.to(device=device), "1 ... -> n ...", n=num_samples), - "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples), - "masked_mel": repeat(masked_mel.to(device=device), "1 ... -> n ...", n=num_samples), - } - return batch - -def gen_mel(input_audio): - sr,ori_wav = input_audio - print(sr,ori_wav.shape,ori_wav) - - ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 # order='C'是以C语言格式存储，不用管 - if len(ori_wav.shape)==2:# stereo - ori_wav = librosa.to_mono(ori_wav.T)# gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len) - print(sr,ori_wav.shape,ori_wav) - ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE) - - mel_len,hop_size = 848,256 - input_len = mel_len * hop_size - if len(ori_wav) < input_len: - input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0) - else: - input_wav = ori_wav[:input_len] - - mel = TRANSFORMS_16000(input_wav) - return mel - -def show_mel_fn(input_audio): - crop_len = 500 # the full mel cannot be showed due to gradio's Image bug when using tool='sketch' - crop_mel = gen_mel(input_audio)[:,:crop_len] - color_mel = cmap_transform(crop_mel) - return Image.fromarray((color_mel*255).astype(np.uint8)) - - -def inpaint(sampler, batch, seed, ddim_steps, num_samples=1, W=512, H=512): - model = sampler.model - - prng = np.random.RandomState(seed) - start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8) - start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32) - - c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"])) - cc = torch.nn.functional.interpolate(batch["mask"], - size=c.shape[-2:]) - c = torch.cat((c, cc), dim=1) # (b,c+1,h,w) 1 is mask - - shape = (c.shape[1]-1,)+c.shape[2:] - samples_ddim, _ = sampler.sample(S=ddim_steps, - conditioning=c, - batch_size=c.shape[0], - shape=shape, - verbose=False) - x_samples_ddim = model.decode_first_stage(samples_ddim) - - - mask = batch["mask"]# [-1,1] - mel = torch.clamp((batch["mel"]+1.0)/2.0,min=0.0, max=1.0) - mask = torch.clamp((batch["mask"]+1.0)/2.0,min=0.0, max=1.0) - predicted_mel = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0) - inpainted = (1-mask)*mel+mask*predicted_mel - inpainted = inpainted.cpu().numpy().squeeze() - inapint_wav = vocoder.vocode(inpainted) - - return inpainted,inapint_wav - - -def predict(input_audio,mel_and_mask,ddim_steps,seed): - show_mel = np.array(mel_and_mask['image'].convert("L"))/255 # 由于展示的mel只展示了一部分，所以需要重新从音频生成mel - mask = np.array(mel_and_mask["mask"].convert("L"))/255 - - mel_bins,mel_len = 80,848 - - input_mel = gen_mel(input_audio)[:,:mel_len]# 由于展示的mel只展示了一部分，所以需要重新从音频生成mel - mask = np.pad(mask,((0,0),(0,mel_len-mask.shape[1])),mode='constant',constant_values=0)# 将mask填充到原来的mel的大小 - print(mask.shape,input_mel.shape) - with torch.no_grad(): - batch = make_batch_sd(input_mel,mask,device,num_samples=1) - inpainted,gen_wav = inpaint( - sampler=sampler, - batch=batch, - seed=seed, - ddim_steps=ddim_steps, - num_samples=1, - H=mel_bins, W=mel_len - ) - inpainted = inpainted[:,:show_mel.shape[1]] - color_mel = cmap_transform(inpainted) - input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0]) - gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len] - return Image.fromarray((color_mel*255).astype(np.uint8)),(SAMPLE_RATE,gen_wav) - - -sampler = initialize_model('./configs/inpaint/txt2audio_args.yaml', './useful_ckpts/inpaint7_epoch00047.ckpt') -vocoder = VocoderBigVGAN('./vocoder/logs/bigv16k53w',device=device) - -block = gr.Blocks().queue() -with block: - with gr.Row(): - gr.Markdown("## Make-An-Audio Inpainting") - - with gr.Row(): - with gr.Column(): - input_audio = gr.inputs.Audio() - - show_button = gr.Button("Show Mel") - - run_button = gr.Button("Predict Masked Place") - with gr.Accordion("Advanced options", open=False): - ddim_steps = gr.Slider(label="Steps", minimum=1, - maximum=150, value=100, step=1) - seed = gr.Slider( - label="Seed", - minimum=0, - maximum=2147483647, - step=1, - randomize=True, - ) - with gr.Column(): - show_inpainted = gr.Image(type="pil").style(width=848,height=80) - outaudio = gr.Audio() - show_mel = gr.Image(type="pil",tool='sketch')#.style(width=848,height=80) # 加上这个没办法展示完全图片 - show_button.click(fn=show_mel_fn, inputs=[input_audio], outputs=show_mel) - - run_button.click(fn=predict, inputs=[input_audio,show_mel,ddim_steps,seed], outputs=[show_inpainted,outaudio]) - - -block.launch() diff --git a/spaces/Ababababababbababa/SD-2.1-Img2Img/README.md b/spaces/Ababababababbababa/SD-2.1-Img2Img/README.md deleted file mode 100644 index 5e73afa8766a75b9bd6a5843cc270e91cbbb8431..0000000000000000000000000000000000000000 --- a/spaces/Ababababababbababa/SD-2.1-Img2Img/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: SD 2.1 Img2Img -emoji: 👀 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.11.0 -app_file: app.py -pinned: false -license: mit -duplicated_from: trysem/SD-2.1-Img2Img ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/login/$types.d.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/login/$types.d.ts deleted file mode 100644 index de7b3a1aac4846544d6b2bbf6f75c561e6cdf2dd..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/login/$types.d.ts +++ /dev/null @@ -1,28 +0,0 @@ -import type * as Kit from '@sveltejs/kit'; - -type Expand
-
Pistas de Blue Joe’s 3D Scavenger Hunt Descargar: Un juego divertido y educativo para niños en edad preescolar
-Si usted está buscando un juego divertido y educativo para su preescolar, es posible que desee comprobar hacia fuera Pistas de Blue Joe’s 3D Scavenger Hunt. Este es un juego basado en el popular programa infantil Blue’s Clues, donde puedes unirte a Joe, Blue y Mailbox mientras exploran su vecindario y buscan artículos para su búsqueda del tesoro. El juego está diseñado para niños de 3 a 6 años y les ayuda a desarrollar sus habilidades de resolución de problemas, memoria y coordinación mano-ojo. En este artículo, te diremos cómo descargar y jugar el juego, qué puedes hacer en el juego, por qué deberías jugar el juego y algunas preguntas frecuentes.
-Cómo descargar y jugar el juego
-Descargar y jugar Pistas de Blue La búsqueda del tesoro en 3D de Joe es fácil y simple. Solo sigue estos pasos:
-azul 39;s pistas joe 39;s 3d scavenger hunt descargar
Download File ✫✫✫ https://bltlly.com/2v6Jz1
-
-
-
- Visite el sitio web oficial o la página de Internet Archive. Puedes encontrar el juego en Shockwave.com, donde puedes descargar una versión de prueba gratuita o comprar la versión completa por $6.99. También puedes encontrar el juego en Internet Archive, donde puedes descargarlo gratis. -
- Elija su versión preferida y haga clic en el enlace de descarga. Dependiendo de su sistema informático y navegador, es posible que necesite instalar algunos complementos o software para ejecutar el juego. Por ejemplo, puede que necesite instalar Shockwave Player o Adobe Flash Player. -
- Instale e inicie el juego en su computadora. Siga las instrucciones en su pantalla para completar el proceso de instalación. Una vez hecho, puedes abrir el juego desde tu escritorio o menú de inicio. - -
Qué puedes hacer en el juego
-En Pistas de Blue Joe’s 3D Scavenger Hunt, puedes hacer muchas actividades divertidas y educativas con Joe, Blue y Mailbox. Estas son algunas de las cosas que puedes hacer en el juego:
--
-
- Siga las pistas y encontrar los elementos que Joe necesita para su búsqueda del tesoro. Joe te dará una lista de artículos que quiere encontrar en cada lugar, como una bola roja, una flor amarilla o un libro azul. También verá tres huellas de patas en la pantalla, que son las pistas de Blue. Puede hacer clic en ellas para ver lo que Blue quiere encontrar. Puede utilizar su ratón para moverse y buscar los artículos. Cuando encuentre un artículo, puede hacer clic en él para agregarlo a su inventario. También puede utilizar la lupa para acercar y alejar. -
- Interactúa con varios objetos y personajes en el entorno 3D. Puedes hacer clic en cualquier cosa que parezca interesante o curiosa, como árboles, animales, coches o personas. Escucharás sonidos, verás animaciones u obtendrás comentarios de Joe o Blue. También puedes hablar con algunos de los personajes, como Mr. Salt, Mrs. Pepper, Paprika, Slippery Soap, Shovel, Pail y más. Te saludarán, te harán preguntas o te darán pistas. -
- Aprende sobre colores, formas, números, letras, animales y más. El juego está lleno de contenido educativo que ayudará a tu preescolar a aprender cosas nuevas y reforzar lo que ya sabe. Por ejemplo, puedes aprender sobre colores mezclando pinturas en la tienda, formas combinando piezas de rompecabezas en la escuela, números contando manzanas en la granja, letras deletreando palabras en el parque, animales identificando sus sonidos y nombres en la granja, y más. -
Por qué deberías jugar el juego
-Pistas de Blue Joe’s 3D Scavenger Hunt no es solo un juego divertido para niños en edad preescolar, sino también uno beneficioso. Estas son algunas de las razones por las que deberías jugar el juego:
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-
-
- Comentarios y valoraciones de otros jugadores y padres: El juego ha recibido críticas y valoraciones positivas de otros jugadores y padres que lo han probado. Han elogiado el juego por sus gráficos, efectos de sonido, actuación de voz, jugabilidad, valor educativo y factor de entretenimiento. También han comentado que el juego es fácil de instalar y jugar, adecuado para diferentes edades y habilidades, y fiel al espectáculo. -
- Curiosidades y curiosidades sobre el juego y el espectáculo: El juego fue lanzado en 2002 por Nick Jr. y Shockwave.com, como parte de una serie de juegos Blue’s Clues para PC. El juego cuenta con Joe como el anfitrión de la serie, que reemplazó a Steve en 2000. El juego también cuenta con Mailbox como compañero de Joe, quien lo ayuda con su búsqueda del tesoro. El juego se basa en el episodio "Joe’s 3D Scavenger Hunt", que se emitió el 9 de septiembre de 2002. -
Conclusión
-En conclusión,Pistas de Blue Joe’s 3D Scavenger Hunt es un juego divertido y educativo para preescolares que puedes descargar y jugar en tu computadora. El juego te permite unirte a Joe, Blue y Mailbox mientras exploran su vecindario y buscan artículos para su búsqueda del tesoro. El juego también ayuda a tu preescolar a desarrollar sus habilidades de resolución de problemas, memoria y coordinación mano-ojo, así como aprender sobre colores, formas, números, letras, animales y más. El juego ha recibido críticas y valoraciones positivas de otros jugadores y padres, y se basa en el episodio "Joe’s 3D Scavenger Hunt" del programa. Si usted está interesado en jugar el juego, puede descargarlo desde el sitio web oficial o la página del Archivo de Internet. ¡Esperamos que te diviertas jugando y aprendiendo con Joe y Blue!
-Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre Pistas de Blue Joe’s 3D Scavenger Hunt:
- --
-
- ¿Cuáles son los requisitos del sistema para el juego?
- - - ¿Cuánto dura el juego?
-El juego dura aproximadamente una hora, dependiendo de lo rápido que encuentres los objetos y las pistas. También puedes reproducir el juego tantas veces como quieras, ya que los elementos y las pistas cambiarán cada vez.
- - ¿El juego es seguro y libre de virus?
-Sí, el juego es seguro y libre de virus. Puede descargarlo desde el sitio web oficial o la página del Archivo de Internet, que son fuentes de confianza. También puede escanear el juego con su software antivirus antes de instalarlo.
- - ¿Puedo jugar el juego en línea sin descargarlo?
-Sí, puedes jugar el juego en línea sin descargarlo. Puedes encontrar el juego en Nick Jr., donde puedes jugarlo gratis en tu navegador. Sin embargo, usted tendrá que tener Shockwave Player o Adobe Flash Player instalado para jugar el juego en línea.
- - ¿Dónde puedo encontrar más juegos como este?
-Puedes encontrar más juegos como este en Archivo de Internet, donde puedes descargar o jugar en línea otros juegos Pistas de Blue para PC, como Pistas de Blue: Blue Takes You to School Pistas de Blue: Actividades de Blue’s Art Time, o Pistas de Blue: Actividades de Blue’s Reading Time. También puedes encontrar más juegos en str: - """Find an .egg-info subdirectory in `directory`.""" - filenames = [f for f in os.listdir(directory) if f.endswith(".egg-info")] - - if not filenames: - raise InstallationError(f"No .egg-info directory found in {directory}") - - if len(filenames) > 1: - raise InstallationError( - "More than one .egg-info directory found in {}".format(directory) - ) - - return os.path.join(directory, filenames[0]) - - -def generate_metadata( - build_env: BuildEnvironment, - setup_py_path: str, - source_dir: str, - isolated: bool, - details: str, -) -> str: - """Generate metadata using setup.py-based defacto mechanisms. - - Returns the generated metadata directory. - """ - logger.debug( - "Running setup.py (path:%s) egg_info for package %s", - setup_py_path, - details, - ) - - egg_info_dir = TempDirectory(kind="pip-egg-info", globally_managed=True).path - - args = make_setuptools_egg_info_args( - setup_py_path, - egg_info_dir=egg_info_dir, - no_user_config=isolated, - ) - - with build_env: - with open_spinner("Preparing metadata (setup.py)") as spinner: - try: - call_subprocess( - args, - cwd=source_dir, - command_desc="python setup.py egg_info", - spinner=spinner, - ) - except InstallationSubprocessError as error: - raise MetadataGenerationFailed(package_details=details) from error - - # Return the .egg-info directory. - return _find_egg_info(egg_info_dir) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/msgpack/fallback.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/msgpack/fallback.py deleted file mode 100644 index e8cebc1bef7870a5e772ea066c485eddf5c1c57c..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/msgpack/fallback.py +++ /dev/null @@ -1,1010 +0,0 @@ -"""Fallback pure Python implementation of msgpack""" -from datetime import datetime as _DateTime -import sys -import struct - - -PY2 = sys.version_info[0] == 2 -if PY2: - int_types = (int, long) - - def dict_iteritems(d): - return d.iteritems() - -else: - int_types = int - unicode = str - xrange = range - - def dict_iteritems(d): - return d.items() - - -if sys.version_info < (3, 5): - # Ugly hack... - RecursionError = RuntimeError - - def _is_recursionerror(e): - return ( - len(e.args) == 1 - and isinstance(e.args[0], str) - and e.args[0].startswith("maximum recursion depth exceeded") - ) - -else: - - def _is_recursionerror(e): - return True - - -if hasattr(sys, "pypy_version_info"): - # StringIO is slow on PyPy, StringIO is faster. However: PyPy's own - # StringBuilder is fastest. - from __pypy__ import newlist_hint - - try: - from __pypy__.builders import BytesBuilder as StringBuilder - except ImportError: - from __pypy__.builders import StringBuilder - USING_STRINGBUILDER = True - - class StringIO(object): - def __init__(self, s=b""): - if s: - self.builder = StringBuilder(len(s)) - self.builder.append(s) - else: - self.builder = StringBuilder() - - def write(self, s): - if isinstance(s, memoryview): - s = s.tobytes() - elif isinstance(s, bytearray): - s = bytes(s) - self.builder.append(s) - - def getvalue(self): - return self.builder.build() - -else: - USING_STRINGBUILDER = False - from io import BytesIO as StringIO - - newlist_hint = lambda size: [] - - -from .exceptions import BufferFull, OutOfData, ExtraData, FormatError, StackError - -from .ext import ExtType, Timestamp - - -EX_SKIP = 0 -EX_CONSTRUCT = 1 -EX_READ_ARRAY_HEADER = 2 -EX_READ_MAP_HEADER = 3 - -TYPE_IMMEDIATE = 0 -TYPE_ARRAY = 1 -TYPE_MAP = 2 -TYPE_RAW = 3 -TYPE_BIN = 4 -TYPE_EXT = 5 - -DEFAULT_RECURSE_LIMIT = 511 - - -def _check_type_strict(obj, t, type=type, tuple=tuple): - if type(t) is tuple: - return type(obj) in t - else: - return type(obj) is t - - -def _get_data_from_buffer(obj): - view = memoryview(obj) - if view.itemsize != 1: - raise ValueError("cannot unpack from multi-byte object") - return view - - -def unpackb(packed, **kwargs): - """ - Unpack an object from `packed`. - - Raises ``ExtraData`` when *packed* contains extra bytes. - Raises ``ValueError`` when *packed* is incomplete. - Raises ``FormatError`` when *packed* is not valid msgpack. - Raises ``StackError`` when *packed* contains too nested. - Other exceptions can be raised during unpacking. - - See :class:`Unpacker` for options. - """ - unpacker = Unpacker(None, max_buffer_size=len(packed), **kwargs) - unpacker.feed(packed) - try: - ret = unpacker._unpack() - except OutOfData: - raise ValueError("Unpack failed: incomplete input") - except RecursionError as e: - if _is_recursionerror(e): - raise StackError - raise - if unpacker._got_extradata(): - raise ExtraData(ret, unpacker._get_extradata()) - return ret - - -if sys.version_info < (2, 7, 6): - - def _unpack_from(f, b, o=0): - """Explicit type cast for legacy struct.unpack_from""" - return struct.unpack_from(f, bytes(b), o) - -else: - _unpack_from = struct.unpack_from - -_NO_FORMAT_USED = "" -_MSGPACK_HEADERS = { - 0xC4: (1, _NO_FORMAT_USED, TYPE_BIN), - 0xC5: (2, ">H", TYPE_BIN), - 0xC6: (4, ">I", TYPE_BIN), - 0xC7: (2, "Bb", TYPE_EXT), - 0xC8: (3, ">Hb", TYPE_EXT), - 0xC9: (5, ">Ib", TYPE_EXT), - 0xCA: (4, ">f"), - 0xCB: (8, ">d"), - 0xCC: (1, _NO_FORMAT_USED), - 0xCD: (2, ">H"), - 0xCE: (4, ">I"), - 0xCF: (8, ">Q"), - 0xD0: (1, "b"), - 0xD1: (2, ">h"), - 0xD2: (4, ">i"), - 0xD3: (8, ">q"), - 0xD4: (1, "b1s", TYPE_EXT), - 0xD5: (2, "b2s", TYPE_EXT), - 0xD6: (4, "b4s", TYPE_EXT), - 0xD7: (8, "b8s", TYPE_EXT), - 0xD8: (16, "b16s", TYPE_EXT), - 0xD9: (1, _NO_FORMAT_USED, TYPE_RAW), - 0xDA: (2, ">H", TYPE_RAW), - 0xDB: (4, ">I", TYPE_RAW), - 0xDC: (2, ">H", TYPE_ARRAY), - 0xDD: (4, ">I", TYPE_ARRAY), - 0xDE: (2, ">H", TYPE_MAP), - 0xDF: (4, ">I", TYPE_MAP), -} - - -class Unpacker(object): - """Streaming unpacker. - - Arguments: - - :param file_like: - File-like object having `.read(n)` method. - If specified, unpacker reads serialized data from it and :meth:`feed()` is not usable. - - :param int read_size: - Used as `file_like.read(read_size)`. (default: `min(16*1024, max_buffer_size)`) - - :param bool use_list: - If true, unpack msgpack array to Python list. - Otherwise, unpack to Python tuple. (default: True) - - :param bool raw: - If true, unpack msgpack raw to Python bytes. - Otherwise, unpack to Python str by decoding with UTF-8 encoding (default). - - :param int timestamp: - Control how timestamp type is unpacked: - - 0 - Timestamp - 1 - float (Seconds from the EPOCH) - 2 - int (Nanoseconds from the EPOCH) - 3 - datetime.datetime (UTC). Python 2 is not supported. - - :param bool strict_map_key: - If true (default), only str or bytes are accepted for map (dict) keys. - - :param callable object_hook: - When specified, it should be callable. - Unpacker calls it with a dict argument after unpacking msgpack map. - (See also simplejson) - - :param callable object_pairs_hook: - When specified, it should be callable. - Unpacker calls it with a list of key-value pairs after unpacking msgpack map. - (See also simplejson) - - :param str unicode_errors: - The error handler for decoding unicode. (default: 'strict') - This option should be used only when you have msgpack data which - contains invalid UTF-8 string. - - :param int max_buffer_size: - Limits size of data waiting unpacked. 0 means 2**32-1. - The default value is 100*1024*1024 (100MiB). - Raises `BufferFull` exception when it is insufficient. - You should set this parameter when unpacking data from untrusted source. - - :param int max_str_len: - Deprecated, use *max_buffer_size* instead. - Limits max length of str. (default: max_buffer_size) - - :param int max_bin_len: - Deprecated, use *max_buffer_size* instead. - Limits max length of bin. (default: max_buffer_size) - - :param int max_array_len: - Limits max length of array. - (default: max_buffer_size) - - :param int max_map_len: - Limits max length of map. - (default: max_buffer_size//2) - - :param int max_ext_len: - Deprecated, use *max_buffer_size* instead. - Limits max size of ext type. (default: max_buffer_size) - - Example of streaming deserialize from file-like object:: - - unpacker = Unpacker(file_like) - for o in unpacker: - process(o) - - Example of streaming deserialize from socket:: - - unpacker = Unpacker() - while True: - buf = sock.recv(1024**2) - if not buf: - break - unpacker.feed(buf) - for o in unpacker: - process(o) - - Raises ``ExtraData`` when *packed* contains extra bytes. - Raises ``OutOfData`` when *packed* is incomplete. - Raises ``FormatError`` when *packed* is not valid msgpack. - Raises ``StackError`` when *packed* contains too nested. - Other exceptions can be raised during unpacking. - """ - - def __init__( - self, - file_like=None, - read_size=0, - use_list=True, - raw=False, - timestamp=0, - strict_map_key=True, - object_hook=None, - object_pairs_hook=None, - list_hook=None, - unicode_errors=None, - max_buffer_size=100 * 1024 * 1024, - ext_hook=ExtType, - max_str_len=-1, - max_bin_len=-1, - max_array_len=-1, - max_map_len=-1, - max_ext_len=-1, - ): - if unicode_errors is None: - unicode_errors = "strict" - - if file_like is None: - self._feeding = True - else: - if not callable(file_like.read): - raise TypeError("`file_like.read` must be callable") - self.file_like = file_like - self._feeding = False - - #: array of bytes fed. - self._buffer = bytearray() - #: Which position we currently reads - self._buff_i = 0 - - # When Unpacker is used as an iterable, between the calls to next(), - # the buffer is not "consumed" completely, for efficiency sake. - # Instead, it is done sloppily. To make sure we raise BufferFull at - # the correct moments, we have to keep track of how sloppy we were. - # Furthermore, when the buffer is incomplete (that is: in the case - # we raise an OutOfData) we need to rollback the buffer to the correct - # state, which _buf_checkpoint records. - self._buf_checkpoint = 0 - - if not max_buffer_size: - max_buffer_size = 2**31 - 1 - if max_str_len == -1: - max_str_len = max_buffer_size - if max_bin_len == -1: - max_bin_len = max_buffer_size - if max_array_len == -1: - max_array_len = max_buffer_size - if max_map_len == -1: - max_map_len = max_buffer_size // 2 - if max_ext_len == -1: - max_ext_len = max_buffer_size - - self._max_buffer_size = max_buffer_size - if read_size > self._max_buffer_size: - raise ValueError("read_size must be smaller than max_buffer_size") - self._read_size = read_size or min(self._max_buffer_size, 16 * 1024) - self._raw = bool(raw) - self._strict_map_key = bool(strict_map_key) - self._unicode_errors = unicode_errors - self._use_list = use_list - if not (0 <= timestamp <= 3): - raise ValueError("timestamp must be 0..3") - self._timestamp = timestamp - self._list_hook = list_hook - self._object_hook = object_hook - self._object_pairs_hook = object_pairs_hook - self._ext_hook = ext_hook - self._max_str_len = max_str_len - self._max_bin_len = max_bin_len - self._max_array_len = max_array_len - self._max_map_len = max_map_len - self._max_ext_len = max_ext_len - self._stream_offset = 0 - - if list_hook is not None and not callable(list_hook): - raise TypeError("`list_hook` is not callable") - if object_hook is not None and not callable(object_hook): - raise TypeError("`object_hook` is not callable") - if object_pairs_hook is not None and not callable(object_pairs_hook): - raise TypeError("`object_pairs_hook` is not callable") - if object_hook is not None and object_pairs_hook is not None: - raise TypeError( - "object_pairs_hook and object_hook are mutually " "exclusive" - ) - if not callable(ext_hook): - raise TypeError("`ext_hook` is not callable") - - def feed(self, next_bytes): - assert self._feeding - view = _get_data_from_buffer(next_bytes) - if len(self._buffer) - self._buff_i + len(view) > self._max_buffer_size: - raise BufferFull - - # Strip buffer before checkpoint before reading file. - if self._buf_checkpoint > 0: - del self._buffer[: self._buf_checkpoint] - self._buff_i -= self._buf_checkpoint - self._buf_checkpoint = 0 - - # Use extend here: INPLACE_ADD += doesn't reliably typecast memoryview in jython - self._buffer.extend(view) - - def _consume(self): - """Gets rid of the used parts of the buffer.""" - self._stream_offset += self._buff_i - self._buf_checkpoint - self._buf_checkpoint = self._buff_i - - def _got_extradata(self): - return self._buff_i < len(self._buffer) - - def _get_extradata(self): - return self._buffer[self._buff_i :] - - def read_bytes(self, n): - ret = self._read(n, raise_outofdata=False) - self._consume() - return ret - - def _read(self, n, raise_outofdata=True): - # (int) -> bytearray - self._reserve(n, raise_outofdata=raise_outofdata) - i = self._buff_i - ret = self._buffer[i : i + n] - self._buff_i = i + len(ret) - return ret - - def _reserve(self, n, raise_outofdata=True): - remain_bytes = len(self._buffer) - self._buff_i - n - - # Fast path: buffer has n bytes already - if remain_bytes >= 0: - return - - if self._feeding: - self._buff_i = self._buf_checkpoint - raise OutOfData - - # Strip buffer before checkpoint before reading file. - if self._buf_checkpoint > 0: - del self._buffer[: self._buf_checkpoint] - self._buff_i -= self._buf_checkpoint - self._buf_checkpoint = 0 - - # Read from file - remain_bytes = -remain_bytes - if remain_bytes + len(self._buffer) > self._max_buffer_size: - raise BufferFull - while remain_bytes > 0: - to_read_bytes = max(self._read_size, remain_bytes) - read_data = self.file_like.read(to_read_bytes) - if not read_data: - break - assert isinstance(read_data, bytes) - self._buffer += read_data - remain_bytes -= len(read_data) - - if len(self._buffer) < n + self._buff_i and raise_outofdata: - self._buff_i = 0 # rollback - raise OutOfData - - def _read_header(self): - typ = TYPE_IMMEDIATE - n = 0 - obj = None - self._reserve(1) - b = self._buffer[self._buff_i] - self._buff_i += 1 - if b & 0b10000000 == 0: - obj = b - elif b & 0b11100000 == 0b11100000: - obj = -1 - (b ^ 0xFF) - elif b & 0b11100000 == 0b10100000: - n = b & 0b00011111 - typ = TYPE_RAW - if n > self._max_str_len: - raise ValueError("%s exceeds max_str_len(%s)" % (n, self._max_str_len)) - obj = self._read(n) - elif b & 0b11110000 == 0b10010000: - n = b & 0b00001111 - typ = TYPE_ARRAY - if n > self._max_array_len: - raise ValueError( - "%s exceeds max_array_len(%s)" % (n, self._max_array_len) - ) - elif b & 0b11110000 == 0b10000000: - n = b & 0b00001111 - typ = TYPE_MAP - if n > self._max_map_len: - raise ValueError("%s exceeds max_map_len(%s)" % (n, self._max_map_len)) - elif b == 0xC0: - obj = None - elif b == 0xC2: - obj = False - elif b == 0xC3: - obj = True - elif 0xC4 <= b <= 0xC6: - size, fmt, typ = _MSGPACK_HEADERS[b] - self._reserve(size) - if len(fmt) > 0: - n = _unpack_from(fmt, self._buffer, self._buff_i)[0] - else: - n = self._buffer[self._buff_i] - self._buff_i += size - if n > self._max_bin_len: - raise ValueError("%s exceeds max_bin_len(%s)" % (n, self._max_bin_len)) - obj = self._read(n) - elif 0xC7 <= b <= 0xC9: - size, fmt, typ = _MSGPACK_HEADERS[b] - self._reserve(size) - L, n = _unpack_from(fmt, self._buffer, self._buff_i) - self._buff_i += size - if L > self._max_ext_len: - raise ValueError("%s exceeds max_ext_len(%s)" % (L, self._max_ext_len)) - obj = self._read(L) - elif 0xCA <= b <= 0xD3: - size, fmt = _MSGPACK_HEADERS[b] - self._reserve(size) - if len(fmt) > 0: - obj = _unpack_from(fmt, self._buffer, self._buff_i)[0] - else: - obj = self._buffer[self._buff_i] - self._buff_i += size - elif 0xD4 <= b <= 0xD8: - size, fmt, typ = _MSGPACK_HEADERS[b] - if self._max_ext_len < size: - raise ValueError( - "%s exceeds max_ext_len(%s)" % (size, self._max_ext_len) - ) - self._reserve(size + 1) - n, obj = _unpack_from(fmt, self._buffer, self._buff_i) - self._buff_i += size + 1 - elif 0xD9 <= b <= 0xDB: - size, fmt, typ = _MSGPACK_HEADERS[b] - self._reserve(size) - if len(fmt) > 0: - (n,) = _unpack_from(fmt, self._buffer, self._buff_i) - else: - n = self._buffer[self._buff_i] - self._buff_i += size - if n > self._max_str_len: - raise ValueError("%s exceeds max_str_len(%s)" % (n, self._max_str_len)) - obj = self._read(n) - elif 0xDC <= b <= 0xDD: - size, fmt, typ = _MSGPACK_HEADERS[b] - self._reserve(size) - (n,) = _unpack_from(fmt, self._buffer, self._buff_i) - self._buff_i += size - if n > self._max_array_len: - raise ValueError( - "%s exceeds max_array_len(%s)" % (n, self._max_array_len) - ) - elif 0xDE <= b <= 0xDF: - size, fmt, typ = _MSGPACK_HEADERS[b] - self._reserve(size) - (n,) = _unpack_from(fmt, self._buffer, self._buff_i) - self._buff_i += size - if n > self._max_map_len: - raise ValueError("%s exceeds max_map_len(%s)" % (n, self._max_map_len)) - else: - raise FormatError("Unknown header: 0x%x" % b) - return typ, n, obj - - def _unpack(self, execute=EX_CONSTRUCT): - typ, n, obj = self._read_header() - - if execute == EX_READ_ARRAY_HEADER: - if typ != TYPE_ARRAY: - raise ValueError("Expected array") - return n - if execute == EX_READ_MAP_HEADER: - if typ != TYPE_MAP: - raise ValueError("Expected map") - return n - # TODO should we eliminate the recursion? - if typ == TYPE_ARRAY: - if execute == EX_SKIP: - for i in xrange(n): - # TODO check whether we need to call `list_hook` - self._unpack(EX_SKIP) - return - ret = newlist_hint(n) - for i in xrange(n): - ret.append(self._unpack(EX_CONSTRUCT)) - if self._list_hook is not None: - ret = self._list_hook(ret) - # TODO is the interaction between `list_hook` and `use_list` ok? - return ret if self._use_list else tuple(ret) - if typ == TYPE_MAP: - if execute == EX_SKIP: - for i in xrange(n): - # TODO check whether we need to call hooks - self._unpack(EX_SKIP) - self._unpack(EX_SKIP) - return - if self._object_pairs_hook is not None: - ret = self._object_pairs_hook( - (self._unpack(EX_CONSTRUCT), self._unpack(EX_CONSTRUCT)) - for _ in xrange(n) - ) - else: - ret = {} - for _ in xrange(n): - key = self._unpack(EX_CONSTRUCT) - if self._strict_map_key and type(key) not in (unicode, bytes): - raise ValueError( - "%s is not allowed for map key" % str(type(key)) - ) - if not PY2 and type(key) is str: - key = sys.intern(key) - ret[key] = self._unpack(EX_CONSTRUCT) - if self._object_hook is not None: - ret = self._object_hook(ret) - return ret - if execute == EX_SKIP: - return - if typ == TYPE_RAW: - if self._raw: - obj = bytes(obj) - else: - obj = obj.decode("utf_8", self._unicode_errors) - return obj - if typ == TYPE_BIN: - return bytes(obj) - if typ == TYPE_EXT: - if n == -1: # timestamp - ts = Timestamp.from_bytes(bytes(obj)) - if self._timestamp == 1: - return ts.to_unix() - elif self._timestamp == 2: - return ts.to_unix_nano() - elif self._timestamp == 3: - return ts.to_datetime() - else: - return ts - else: - return self._ext_hook(n, bytes(obj)) - assert typ == TYPE_IMMEDIATE - return obj - - def __iter__(self): - return self - - def __next__(self): - try: - ret = self._unpack(EX_CONSTRUCT) - self._consume() - return ret - except OutOfData: - self._consume() - raise StopIteration - except RecursionError: - raise StackError - - next = __next__ - - def skip(self): - self._unpack(EX_SKIP) - self._consume() - - def unpack(self): - try: - ret = self._unpack(EX_CONSTRUCT) - except RecursionError: - raise StackError - self._consume() - return ret - - def read_array_header(self): - ret = self._unpack(EX_READ_ARRAY_HEADER) - self._consume() - return ret - - def read_map_header(self): - ret = self._unpack(EX_READ_MAP_HEADER) - self._consume() - return ret - - def tell(self): - return self._stream_offset - - -class Packer(object): - """ - MessagePack Packer - - Usage:: - - packer = Packer() - astream.write(packer.pack(a)) - astream.write(packer.pack(b)) - - Packer's constructor has some keyword arguments: - - :param callable default: - Convert user type to builtin type that Packer supports. - See also simplejson's document. - - :param bool use_single_float: - Use single precision float type for float. (default: False) - - :param bool autoreset: - Reset buffer after each pack and return its content as `bytes`. (default: True). - If set this to false, use `bytes()` to get content and `.reset()` to clear buffer. - - :param bool use_bin_type: - Use bin type introduced in msgpack spec 2.0 for bytes. - It also enables str8 type for unicode. (default: True) - - :param bool strict_types: - If set to true, types will be checked to be exact. Derived classes - from serializable types will not be serialized and will be - treated as unsupported type and forwarded to default. - Additionally tuples will not be serialized as lists. - This is useful when trying to implement accurate serialization - for python types. - - :param bool datetime: - If set to true, datetime with tzinfo is packed into Timestamp type. - Note that the tzinfo is stripped in the timestamp. - You can get UTC datetime with `timestamp=3` option of the Unpacker. - (Python 2 is not supported). - - :param str unicode_errors: - The error handler for encoding unicode. (default: 'strict') - DO NOT USE THIS!! This option is kept for very specific usage. - - Example of streaming deserialize from file-like object:: - - unpacker = Unpacker(file_like) - for o in unpacker: - process(o) - - Example of streaming deserialize from socket:: - - unpacker = Unpacker() - while True: - buf = sock.recv(1024**2) - if not buf: - break - unpacker.feed(buf) - for o in unpacker: - process(o) - - Raises ``ExtraData`` when *packed* contains extra bytes. - Raises ``OutOfData`` when *packed* is incomplete. - Raises ``FormatError`` when *packed* is not valid msgpack. - Raises ``StackError`` when *packed* contains too nested. - Other exceptions can be raised during unpacking. - """ - - def __init__( - self, - default=None, - use_single_float=False, - autoreset=True, - use_bin_type=True, - strict_types=False, - datetime=False, - unicode_errors=None, - ): - self._strict_types = strict_types - self._use_float = use_single_float - self._autoreset = autoreset - self._use_bin_type = use_bin_type - self._buffer = StringIO() - if PY2 and datetime: - raise ValueError("datetime is not supported in Python 2") - self._datetime = bool(datetime) - self._unicode_errors = unicode_errors or "strict" - if default is not None: - if not callable(default): - raise TypeError("default must be callable") - self._default = default - - def _pack( - self, - obj, - nest_limit=DEFAULT_RECURSE_LIMIT, - check=isinstance, - check_type_strict=_check_type_strict, - ): - default_used = False - if self._strict_types: - check = check_type_strict - list_types = list - else: - list_types = (list, tuple) - while True: - if nest_limit < 0: - raise ValueError("recursion limit exceeded") - if obj is None: - return self._buffer.write(b"\xc0") - if check(obj, bool): - if obj: - return self._buffer.write(b"\xc3") - return self._buffer.write(b"\xc2") - if check(obj, int_types): - if 0 <= obj < 0x80: - return self._buffer.write(struct.pack("B", obj)) - if -0x20 <= obj < 0: - return self._buffer.write(struct.pack("b", obj)) - if 0x80 <= obj <= 0xFF: - return self._buffer.write(struct.pack("BB", 0xCC, obj)) - if -0x80 <= obj < 0: - return self._buffer.write(struct.pack(">Bb", 0xD0, obj)) - if 0xFF < obj <= 0xFFFF: - return self._buffer.write(struct.pack(">BH", 0xCD, obj)) - if -0x8000 <= obj < -0x80: - return self._buffer.write(struct.pack(">Bh", 0xD1, obj)) - if 0xFFFF < obj <= 0xFFFFFFFF: - return self._buffer.write(struct.pack(">BI", 0xCE, obj)) - if -0x80000000 <= obj < -0x8000: - return self._buffer.write(struct.pack(">Bi", 0xD2, obj)) - if 0xFFFFFFFF < obj <= 0xFFFFFFFFFFFFFFFF: - return self._buffer.write(struct.pack(">BQ", 0xCF, obj)) - if -0x8000000000000000 <= obj < -0x80000000: - return self._buffer.write(struct.pack(">Bq", 0xD3, obj)) - if not default_used and self._default is not None: - obj = self._default(obj) - default_used = True - continue - raise OverflowError("Integer value out of range") - if check(obj, (bytes, bytearray)): - n = len(obj) - if n >= 2**32: - raise ValueError("%s is too large" % type(obj).__name__) - self._pack_bin_header(n) - return self._buffer.write(obj) - if check(obj, unicode): - obj = obj.encode("utf-8", self._unicode_errors) - n = len(obj) - if n >= 2**32: - raise ValueError("String is too large") - self._pack_raw_header(n) - return self._buffer.write(obj) - if check(obj, memoryview): - n = obj.nbytes - if n >= 2**32: - raise ValueError("Memoryview is too large") - self._pack_bin_header(n) - return self._buffer.write(obj) - if check(obj, float): - if self._use_float: - return self._buffer.write(struct.pack(">Bf", 0xCA, obj)) - return self._buffer.write(struct.pack(">Bd", 0xCB, obj)) - if check(obj, (ExtType, Timestamp)): - if check(obj, Timestamp): - code = -1 - data = obj.to_bytes() - else: - code = obj.code - data = obj.data - assert isinstance(code, int) - assert isinstance(data, bytes) - L = len(data) - if L == 1: - self._buffer.write(b"\xd4") - elif L == 2: - self._buffer.write(b"\xd5") - elif L == 4: - self._buffer.write(b"\xd6") - elif L == 8: - self._buffer.write(b"\xd7") - elif L == 16: - self._buffer.write(b"\xd8") - elif L <= 0xFF: - self._buffer.write(struct.pack(">BB", 0xC7, L)) - elif L <= 0xFFFF: - self._buffer.write(struct.pack(">BH", 0xC8, L)) - else: - self._buffer.write(struct.pack(">BI", 0xC9, L)) - self._buffer.write(struct.pack("b", code)) - self._buffer.write(data) - return - if check(obj, list_types): - n = len(obj) - self._pack_array_header(n) - for i in xrange(n): - self._pack(obj[i], nest_limit - 1) - return - if check(obj, dict): - return self._pack_map_pairs( - len(obj), dict_iteritems(obj), nest_limit - 1 - ) - - if self._datetime and check(obj, _DateTime) and obj.tzinfo is not None: - obj = Timestamp.from_datetime(obj) - default_used = 1 - continue - - if not default_used and self._default is not None: - obj = self._default(obj) - default_used = 1 - continue - - if self._datetime and check(obj, _DateTime): - raise ValueError("Cannot serialize %r where tzinfo=None" % (obj,)) - - raise TypeError("Cannot serialize %r" % (obj,)) - - def pack(self, obj): - try: - self._pack(obj) - except: - self._buffer = StringIO() # force reset - raise - if self._autoreset: - ret = self._buffer.getvalue() - self._buffer = StringIO() - return ret - - def pack_map_pairs(self, pairs): - self._pack_map_pairs(len(pairs), pairs) - if self._autoreset: - ret = self._buffer.getvalue() - self._buffer = StringIO() - return ret - - def pack_array_header(self, n): - if n >= 2**32: - raise ValueError - self._pack_array_header(n) - if self._autoreset: - ret = self._buffer.getvalue() - self._buffer = StringIO() - return ret - - def pack_map_header(self, n): - if n >= 2**32: - raise ValueError - self._pack_map_header(n) - if self._autoreset: - ret = self._buffer.getvalue() - self._buffer = StringIO() - return ret - - def pack_ext_type(self, typecode, data): - if not isinstance(typecode, int): - raise TypeError("typecode must have int type.") - if not 0 <= typecode <= 127: - raise ValueError("typecode should be 0-127") - if not isinstance(data, bytes): - raise TypeError("data must have bytes type") - L = len(data) - if L > 0xFFFFFFFF: - raise ValueError("Too large data") - if L == 1: - self._buffer.write(b"\xd4") - elif L == 2: - self._buffer.write(b"\xd5") - elif L == 4: - self._buffer.write(b"\xd6") - elif L == 8: - self._buffer.write(b"\xd7") - elif L == 16: - self._buffer.write(b"\xd8") - elif L <= 0xFF: - self._buffer.write(b"\xc7" + struct.pack("B", L)) - elif L <= 0xFFFF: - self._buffer.write(b"\xc8" + struct.pack(">H", L)) - else: - self._buffer.write(b"\xc9" + struct.pack(">I", L)) - self._buffer.write(struct.pack("B", typecode)) - self._buffer.write(data) - - def _pack_array_header(self, n): - if n <= 0x0F: - return self._buffer.write(struct.pack("B", 0x90 + n)) - if n <= 0xFFFF: - return self._buffer.write(struct.pack(">BH", 0xDC, n)) - if n <= 0xFFFFFFFF: - return self._buffer.write(struct.pack(">BI", 0xDD, n)) - raise ValueError("Array is too large") - - def _pack_map_header(self, n): - if n <= 0x0F: - return self._buffer.write(struct.pack("B", 0x80 + n)) - if n <= 0xFFFF: - return self._buffer.write(struct.pack(">BH", 0xDE, n)) - if n <= 0xFFFFFFFF: - return self._buffer.write(struct.pack(">BI", 0xDF, n)) - raise ValueError("Dict is too large") - - def _pack_map_pairs(self, n, pairs, nest_limit=DEFAULT_RECURSE_LIMIT): - self._pack_map_header(n) - for (k, v) in pairs: - self._pack(k, nest_limit - 1) - self._pack(v, nest_limit - 1) - - def _pack_raw_header(self, n): - if n <= 0x1F: - self._buffer.write(struct.pack("B", 0xA0 + n)) - elif self._use_bin_type and n <= 0xFF: - self._buffer.write(struct.pack(">BB", 0xD9, n)) - elif n <= 0xFFFF: - self._buffer.write(struct.pack(">BH", 0xDA, n)) - elif n <= 0xFFFFFFFF: - self._buffer.write(struct.pack(">BI", 0xDB, n)) - else: - raise ValueError("Raw is too large") - - def _pack_bin_header(self, n): - if not self._use_bin_type: - return self._pack_raw_header(n) - elif n <= 0xFF: - return self._buffer.write(struct.pack(">BB", 0xC4, n)) - elif n <= 0xFFFF: - return self._buffer.write(struct.pack(">BH", 0xC5, n)) - elif n <= 0xFFFFFFFF: - return self._buffer.write(struct.pack(">BI", 0xC6, n)) - else: - raise ValueError("Bin is too large") - - def bytes(self): - """Return internal buffer contents as bytes object""" - return self._buffer.getvalue() - - def reset(self): - """Reset internal buffer. - - This method is useful only when autoreset=False. - """ - self._buffer = StringIO() - - def getbuffer(self): - """Return view of internal buffer.""" - if USING_STRINGBUILDER or PY2: - return memoryview(self.bytes()) - else: - return self._buffer.getbuffer() diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/_structures.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/_structures.py deleted file mode 100644 index 90a6465f9682c886363eea5327dac64bf623a6ff..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/packaging/_structures.py +++ /dev/null @@ -1,61 +0,0 @@ -# This file is dual licensed under the terms of the Apache License, Version -# 2.0, and the BSD License. See the LICENSE file in the root of this repository -# for complete details. - - -class InfinityType: - def __repr__(self) -> str: - return "Infinity" - - def __hash__(self) -> int: - return hash(repr(self)) - - def __lt__(self, other: object) -> bool: - return False - - def __le__(self, other: object) -> bool: - return False - - def __eq__(self, other: object) -> bool: - return isinstance(other, self.__class__) - - def __gt__(self, other: object) -> bool: - return True - - def __ge__(self, other: object) -> bool: - return True - - def __neg__(self: object) -> "NegativeInfinityType": - return NegativeInfinity - - -Infinity = InfinityType() - - -class NegativeInfinityType: - def __repr__(self) -> str: - return "-Infinity" - - def __hash__(self) -> int: - return hash(repr(self)) - - def __lt__(self, other: object) -> bool: - return True - - def __le__(self, other: object) -> bool: - return True - - def __eq__(self, other: object) -> bool: - return isinstance(other, self.__class__) - - def __gt__(self, other: object) -> bool: - return False - - def __ge__(self, other: object) -> bool: - return False - - def __neg__(self: object) -> InfinityType: - return Infinity - - -NegativeInfinity = NegativeInfinityType() diff --git a/spaces/Boilin/URetinex-Net/utils.py b/spaces/Boilin/URetinex-Net/utils.py deleted file mode 100644 index 4d4a0ab8dc584884cbb8055446ac926053142e20..0000000000000000000000000000000000000000 --- a/spaces/Boilin/URetinex-Net/utils.py +++ /dev/null @@ -1,113 +0,0 @@ -import torchvision -from torch.nn import init -import numpy as np -import os -import time -import torch -from PIL import Image -import glob - -def save_TensorImg(img_tensor, path, nrow=1): - torchvision.utils.save_image(img_tensor, path, nrow=nrow) - -def np_save_TensorImg(img_tensor, path): - img = np.squeeze(img_tensor.cpu().permute(0, 2, 3, 1).numpy()) - im = Image.fromarray(np.clip(img*255, 0, 255.0).astype('uint8')) -# print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&') -# print(type(im)) -# print(im) - im.save(path, 'png') - - -# 这个函数复制自上面这一个函数，目的是要返回一个图片 numpy 数组 -def result_for_gradio(img_tensor): - img = np.squeeze(img_tensor.cpu().permute(0, 2, 3, 1).numpy()) - # print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&') - - im=np.clip(img*255, 0, 255.0).astype('uint8') - # print(im) - # im = Image.fromarray(np.clip(img*255, 0, 255.0).astype('uint8')) - return im - -def define_modelR(opts): - if opts.R_model == "HalfDnCNNSE": - from network.restoration import HalfDnCNNSE - model_R = HalfDnCNNSE(opts) - return model_R - -def define_modelL(opts): - if opts.L_model == "Illumination_Alone": - from network.illumination_enhance import Illumination_Alone - model_L = Illumination_Alone(opts) - return model_L - -def define_modelA(opts): - if opts.A_model == "naive": - from network.illumination_adjustment import Adjust_naive - model_A = Adjust_naive(opts) - return model_A - - - -def load_initialize(model, decom_model_path): - if os.path.exists(decom_model_path): - # torch.load with map_location=torch.device('cpu') - checkpoint_Decom_low = torch.load(decom_model_path,map_location ='cpu') - model.load_state_dict(checkpoint_Decom_low['state_dict']['model_R']) - # to freeze the params of Decomposition Model - for param in model.parameters(): - param.requires_grad = False - return model - else: - print("pretrained Initialize Model does not exist, check ---> %s " % decom_model_path) - exit() - -def load_unfolding(unfolding_model_path): - if os.path.exists(unfolding_model_path): - checkpoint = torch.load(unfolding_model_path,map_location ='cpu') - old_opts = checkpoint["opts"] - model_R = define_modelR(old_opts) - model_L = define_modelL(old_opts) - model_R.load_state_dict(checkpoint['state_dict']['model_R']) - model_L.load_state_dict(checkpoint['state_dict']['model_L']) - for param_R in model_R.parameters(): - param_R.requires_grad = False - for param_L in model_L.parameters(): - param_L.requires_grad = False - return old_opts, model_R, model_L - else: - print("pretrained Unfolding Model does not exist, check ---> %s"%unfolding_model_path) - exit() - -def load_adjustment(adjust_model_path): - if os.path.exists(adjust_model_path): - checkpoint_Adjust = torch.load(adjust_model_path,map_location ='cpu') - model_A = define_modelA(checkpoint_Adjust['opts']) - model_A.load_state_dict(checkpoint_Adjust['state_dict']['model_A']) - print(" ===========> loading pretrained Illumination Adjustment Model from: %s " % adjust_model_path) - # to freeze the params of Decomposition Model - for param in model_A.parameters(): - param.requires_grad = False - return model_A - else: - print("pretrained Adjustment Model does not exist, check ---> %s"%adjust_model_path) - exit() - - - - -def param_all(model, net_input): - import torchsummary - shape = net_input.shape - torchsummary.summary(model, (shape[1], shape[2], shape[3])) - -def param_self_compute(model): - parmas = 0 - for p in model.parameters(): - #print(p) - parmas += p.numel() - return parmas - - - - diff --git a/spaces/CVPR/LIVE/pybind11/include/pybind11/functional.h b/spaces/CVPR/LIVE/pybind11/include/pybind11/functional.h deleted file mode 100644 index 57b6cd210f4b99d9d76a93c17aeed3a183fc01a0..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/include/pybind11/functional.h +++ /dev/null @@ -1,101 +0,0 @@ -/* - pybind11/functional.h: std::function<> support - - Copyright (c) 2016 Wenzel Jakob- -PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) -PYBIND11_NAMESPACE_BEGIN(detail) - -template -struct type_caster (src)) - return false; - - auto func = reinterpret_borrow (src); - - /* - When passing a C++ function as an argument to another C++ - function via Python, every function call would normally involve - a full C++ -> Python -> C++ roundtrip, which can be prohibitive. - Here, we try to at least detect the case where the function is - stateless (i.e. function pointer or lambda function without - captured variables), in which case the roundtrip can be avoided. - */ - if (auto cfunc = func.cpp_function()) { - auto c = reinterpret_borrow (PyCFunction_GET_SELF(cfunc.ptr())); - auto rec = (function_record *) c; - - if (rec && rec->is_stateless && - same_type(typeid(function_type), *reinterpret_cast (rec->data[1]))) { - struct capture { function_type f; }; - value = ((capture *) &rec->data)->f; - return true; - } - } - - // ensure GIL is held during functor destruction - struct func_handle { - function f; - func_handle(function&& f_) : f(std::move(f_)) {} - func_handle(const func_handle&) = default; - ~func_handle() { - gil_scoped_acquire acq; - function kill_f(std::move(f)); - } - }; - - // to emulate 'move initialization capture' in C++11 - struct func_wrapper { - func_handle hfunc; - func_wrapper(func_handle&& hf): hfunc(std::move(hf)) {} - Return operator()(Args... args) const { - gil_scoped_acquire acq; - object retval(hfunc.f(std::forward (args)...)); - /* Visual studio 2015 parser issue: need parentheses around this expression */ - return (retval.template cast ()); - } - }; - - value = func_wrapper(func_handle(std::move(func))); - return true; - } - - template - static handle cast(Func &&f_, return_value_policy policy, handle /* parent */) { - if (!f_) - return none().inc_ref(); - - auto result = f_.template target (); - if (result) - return cpp_function(*result, policy).release(); - else - return cpp_function(std::forward (f_), policy).release(); - } - - PYBIND11_TYPE_CASTER(type, _("Callable[[") + concat(make_caster ::name...) + _("], ") - + make_caster ::name + _("]")); -}; - -PYBIND11_NAMESPACE_END(detail) -PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_constants_and_functions.cpp b/spaces/CVPR/LIVE/pybind11/tests/test_constants_and_functions.cpp deleted file mode 100644 index e8ec74b7bc77c9ddf87073d40e9a8c8c9c2115f0..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/tests/test_constants_and_functions.cpp +++ /dev/null @@ -1,127 +0,0 @@ -/* - tests/test_constants_and_functions.cpp -- global constants and functions, enumerations, raw byte strings - - Copyright (c) 2016 Wenzel Jakob (value[i])) + " "; - } - ret.back() = ']'; - return ret; -} - -// Test that we properly handle C++17 exception specifiers (which are part of the function signature -// in C++17). These should all still work before C++17, but don't affect the function signature. -namespace test_exc_sp { -int f1(int x) noexcept { return x+1; } -int f2(int x) noexcept(true) { return x+2; } -int f3(int x) noexcept(false) { return x+3; } -#if defined(__GNUG__) -# pragma GCC diagnostic push -# pragma GCC diagnostic ignored "-Wdeprecated" -#endif -int f4(int x) throw() { return x+4; } // Deprecated equivalent to noexcept(true) -#if defined(__GNUG__) -# pragma GCC diagnostic pop -#endif -struct C { - int m1(int x) noexcept { return x-1; } - int m2(int x) const noexcept { return x-2; } - int m3(int x) noexcept(true) { return x-3; } - int m4(int x) const noexcept(true) { return x-4; } - int m5(int x) noexcept(false) { return x-5; } - int m6(int x) const noexcept(false) { return x-6; } -#if defined(__GNUG__) -# pragma GCC diagnostic push -# pragma GCC diagnostic ignored "-Wdeprecated" -#endif - int m7(int x) throw() { return x-7; } - int m8(int x) const throw() { return x-8; } -#if defined(__GNUG__) -# pragma GCC diagnostic pop -#endif -}; -} - - -TEST_SUBMODULE(constants_and_functions, m) { - // test_constants - m.attr("some_constant") = py::int_(14); - - // test_function_overloading - m.def("test_function", &test_function1); - m.def("test_function", &test_function2); - m.def("test_function", &test_function3); - -#if defined(PYBIND11_OVERLOAD_CAST) - m.def("test_function", py::overload_cast<>(&test_function4)); - m.def("test_function", py::overload_cast (&test_function4)); - m.def("test_function", py::overload_cast (m, "MyEnum") - .value("EFirstEntry", EFirstEntry) - .value("ESecondEntry", ESecondEntry) - .export_values(); - - // test_bytes - m.def("return_bytes", &return_bytes); - m.def("print_bytes", &print_bytes); - - // test_exception_specifiers - using namespace test_exc_sp; - py::class_ (m, "C") - .def(py::init<>()) - .def("m1", &C::m1) - .def("m2", &C::m2) - .def("m3", &C::m3) - .def("m4", &C::m4) - .def("m5", &C::m5) - .def("m6", &C::m6) - .def("m7", &C::m7) - .def("m8", &C::m8) - ; - m.def("f1", f1); - m.def("f2", f2); - m.def("f3", f3); - m.def("f4", f4); -} diff --git a/spaces/CVPR/Text2Human/Text2Human/ui_demo.py b/spaces/CVPR/Text2Human/Text2Human/ui_demo.py deleted file mode 100644 index a06cca245ab04a9420d428e681db1f4f2f5a03c2..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Text2Human/Text2Human/ui_demo.py +++ /dev/null @@ -1,285 +0,0 @@ -import sys - -import cv2 -import numpy as np -import torch -from PIL import Image -from PyQt5.QtCore import * -from PyQt5.QtGui import * -from PyQt5.QtWidgets import * - -from models.sample_model import SampleFromPoseModel -from ui.mouse_event import GraphicsScene -from ui.ui import Ui_Form -from utils.language_utils import (generate_shape_attributes, - generate_texture_attributes) -from utils.options import dict_to_nonedict, parse - -color_list = [(0, 0, 0), (255, 250, 250), (220, 220, 220), (250, 235, 215), - (255, 250, 205), (211, 211, 211), (70, 130, 180), - (127, 255, 212), (0, 100, 0), (50, 205, 50), (255, 255, 0), - (245, 222, 179), (255, 140, 0), (255, 0, 0), (16, 78, 139), - (144, 238, 144), (50, 205, 174), (50, 155, 250), (160, 140, 88), - (213, 140, 88), (90, 140, 90), (185, 210, 205), (130, 165, 180), - (225, 141, 151)] - - -class Ex(QWidget, Ui_Form): - - def __init__(self, opt): - super(Ex, self).__init__() - self.setupUi(self) - self.show() - - self.output_img = None - - self.mat_img = None - - self.mode = 0 - self.size = 6 - self.mask = None - self.mask_m = None - self.img = None - - # about UI - self.mouse_clicked = False - self.scene = QGraphicsScene() - self.graphicsView.setScene(self.scene) - self.graphicsView.setAlignment(Qt.AlignTop | Qt.AlignLeft) - self.graphicsView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff) - self.graphicsView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff) - - self.ref_scene = GraphicsScene(self.mode, self.size) - self.graphicsView_2.setScene(self.ref_scene) - self.graphicsView_2.setAlignment(Qt.AlignTop | Qt.AlignLeft) - self.graphicsView_2.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff) - self.graphicsView_2.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff) - - self.result_scene = QGraphicsScene() - self.graphicsView_3.setScene(self.result_scene) - self.graphicsView_3.setAlignment(Qt.AlignTop | Qt.AlignLeft) - self.graphicsView_3.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff) - self.graphicsView_3.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff) - - self.dlg = QColorDialog(self.graphicsView) - self.color = None - - self.sample_model = SampleFromPoseModel(opt) - - def open_densepose(self): - fileName, _ = QFileDialog.getOpenFileName(self, "Open File", - QDir.currentPath()) - if fileName: - image = QPixmap(fileName) - mat_img = Image.open(fileName) - self.pose_img = mat_img.copy() - if image.isNull(): - QMessageBox.information(self, "Image Viewer", - "Cannot load %s." % fileName) - return - image = image.scaled(self.graphicsView.size(), - Qt.IgnoreAspectRatio) - - if len(self.scene.items()) > 0: - self.scene.removeItem(self.scene.items()[-1]) - self.scene.addPixmap(image) - - self.ref_scene.clear() - self.result_scene.clear() - - # load pose to model - self.pose_img = np.array( - self.pose_img.resize( - size=(256, 512), - resample=Image.LANCZOS))[:, :, 2:].transpose( - 2, 0, 1).astype(np.float32) - self.pose_img = self.pose_img / 12. - 1 - - self.pose_img = torch.from_numpy(self.pose_img).unsqueeze(1) - - self.sample_model.feed_pose_data(self.pose_img) - - def generate_parsing(self): - self.ref_scene.reset_items() - self.ref_scene.reset() - - shape_texts = self.message_box_1.text() - - shape_attributes = generate_shape_attributes(shape_texts) - shape_attributes = torch.LongTensor(shape_attributes).unsqueeze(0) - self.sample_model.feed_shape_attributes(shape_attributes) - - self.sample_model.generate_parsing_map() - self.sample_model.generate_quantized_segm() - - self.colored_segm = self.sample_model.palette_result( - self.sample_model.segm[0].cpu()) - - self.mask_m = cv2.cvtColor( - cv2.cvtColor(self.colored_segm, cv2.COLOR_RGB2BGR), - cv2.COLOR_BGR2RGB) - - qim = QImage(self.colored_segm.data.tobytes(), - self.colored_segm.shape[1], self.colored_segm.shape[0], - QImage.Format_RGB888) - - image = QPixmap.fromImage(qim) - - image = image.scaled(self.graphicsView.size(), Qt.IgnoreAspectRatio) - - if len(self.ref_scene.items()) > 0: - self.ref_scene.removeItem(self.ref_scene.items()[-1]) - self.ref_scene.addPixmap(image) - - self.result_scene.clear() - - def generate_human(self): - for i in range(24): - self.mask_m = self.make_mask(self.mask_m, - self.ref_scene.mask_points[i], - self.ref_scene.size_points[i], - color_list[i]) - - seg_map = np.full(self.mask_m.shape[:-1], -1) - - # convert rgb to num - for index, color in enumerate(color_list): - seg_map[np.sum(self.mask_m == color, axis=2) == 3] = index - assert (seg_map != -1).all() - - self.sample_model.segm = torch.from_numpy(seg_map).unsqueeze( - 0).unsqueeze(0).to(self.sample_model.device) - self.sample_model.generate_quantized_segm() - - texture_texts = self.message_box_2.text() - texture_attributes = generate_texture_attributes(texture_texts) - - texture_attributes = torch.LongTensor(texture_attributes) - - self.sample_model.feed_texture_attributes(texture_attributes) - - self.sample_model.generate_texture_map() - result = self.sample_model.sample_and_refine() - result = result.permute(0, 2, 3, 1) - result = result.detach().cpu().numpy() - result = result * 255 - - result = np.asarray(result[0, :, :, :], dtype=np.uint8) - - self.output_img = result - - qim = QImage(result.data.tobytes(), result.shape[1], result.shape[0], - QImage.Format_RGB888) - image = QPixmap.fromImage(qim) - - image = image.scaled(self.graphicsView.size(), Qt.IgnoreAspectRatio) - - if len(self.result_scene.items()) > 0: - self.result_scene.removeItem(self.result_scene.items()[-1]) - self.result_scene.addPixmap(image) - - def top_mode(self): - self.ref_scene.mode = 1 - - def skin_mode(self): - self.ref_scene.mode = 15 - - def outer_mode(self): - self.ref_scene.mode = 2 - - def face_mode(self): - self.ref_scene.mode = 14 - - def skirt_mode(self): - self.ref_scene.mode = 3 - - def hair_mode(self): - self.ref_scene.mode = 13 - - def dress_mode(self): - self.ref_scene.mode = 4 - - def headwear_mode(self): - self.ref_scene.mode = 7 - - def pants_mode(self): - self.ref_scene.mode = 5 - - def eyeglass_mode(self): - self.ref_scene.mode = 8 - - def rompers_mode(self): - self.ref_scene.mode = 21 - - def footwear_mode(self): - self.ref_scene.mode = 11 - - def leggings_mode(self): - self.ref_scene.mode = 6 - - def ring_mode(self): - self.ref_scene.mode = 16 - - def belt_mode(self): - self.ref_scene.mode = 10 - - def neckwear_mode(self): - self.ref_scene.mode = 9 - - def wrist_mode(self): - self.ref_scene.mode = 17 - - def socks_mode(self): - self.ref_scene.mode = 18 - - def tie_mode(self): - self.ref_scene.mode = 23 - - def earstuds_mode(self): - self.ref_scene.mode = 22 - - def necklace_mode(self): - self.ref_scene.mode = 20 - - def bag_mode(self): - self.ref_scene.mode = 12 - - def glove_mode(self): - self.ref_scene.mode = 19 - - def background_mode(self): - self.ref_scene.mode = 0 - - def make_mask(self, mask, pts, sizes, color): - if len(pts) > 0: - for idx, pt in enumerate(pts): - cv2.line(mask, pt['prev'], pt['curr'], color, sizes[idx]) - return mask - - def save_img(self): - if type(self.output_img): - fileName, _ = QFileDialog.getSaveFileName(self, "Save File", - QDir.currentPath()) - cv2.imwrite(fileName + '.png', self.output_img[:, :, ::-1]) - - def undo(self): - self.scene.undo() - - def clear(self): - - self.ref_scene.reset_items() - self.ref_scene.reset() - - self.ref_scene.clear() - - self.result_scene.clear() - - -if __name__ == '__main__': - - app = QApplication(sys.argv) - opt = './configs/sample_from_pose.yml' - opt = parse(opt, is_train=False) - opt = dict_to_nonedict(opt) - ex = Ex(opt) - sys.exit(app.exec_()) diff --git a/spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/clip_swin.py b/spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/clip_swin.py deleted file mode 100644 index bbd56ec3921cb61409f3b8757c32027059266da1..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/clip_swin.py +++ /dev/null @@ -1,289 +0,0 @@ -from collections import OrderedDict -from typing import Tuple, Union -import logging -import os - -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - -from timm.models.layers import DropPath, trunc_normal_ -from .backbone import Backbone -from .build import BACKBONE_REGISTRY -from .det_swin import SwinTransformer -from ..text_encoder import build_text_encoder -from ..text_encoder import build_tokenizer - -class LayerNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-12): - """Construct a layernorm module in the TF style (epsilon inside the square root). - """ - super(LayerNorm, self).__init__() - self.weight = nn.Parameter(torch.ones(hidden_size)) - self.bias = nn.Parameter(torch.zeros(hidden_size)) - self.variance_epsilon = eps - - def forward(self, x): - pdtype = x.dtype - x = x.float() - u = x.mean(-1, keepdim=True) - s = (x - u).pow(2).mean(-1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.variance_epsilon) - return self.weight * x.to(pdtype) + self.bias - - -class QuickGELU(nn.Module): - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - - -class ResidualAttentionBlock(nn.Module): - def __init__(self, - d_model: int, - n_head: int, - attn_mask: torch.Tensor = None, - drop_path: float = 0.0): - super().__init__() - - self.attn = nn.MultiheadAttention(d_model, n_head) - self.ln_1 = LayerNorm(d_model) - self.mlp = nn.Sequential(OrderedDict([ - ("c_fc", nn.Linear(d_model, d_model * 4)), - ("gelu", QuickGELU()), - ("c_proj", nn.Linear(d_model * 4, d_model)) - ])) - self.ln_2 = LayerNorm(d_model) - self.attn_mask = attn_mask - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - - def attention(self, x: torch.Tensor): - self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) \ - if self.attn_mask is not None else None - return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] - - def forward(self, x: torch.Tensor): - x = x + self.drop_path(self.attention(self.ln_1(x))) - x = x + self.drop_path(self.mlp(self.ln_2(x))) - return x - - -class Transformer(nn.Module): - def __init__(self, - context_length: int, - vocab_size: int, - width: int, - layers: int, - heads: int, - drop_path: float = 0.0): - super().__init__() - - self.token_embedding = nn.Embedding(vocab_size, width) - - self.context_length = context_length - self.positional_embedding = nn.Parameter( - torch.empty(self.context_length, width) - ) - - self.width = width - self.layers = layers - attn_mask = self.build_attention_mask() - dpr = [x.item() for x in torch.linspace(0, drop_path, layers)] # stochastic depth decay rule - self.resblocks = nn.Sequential( - *[ - ResidualAttentionBlock(width, heads, attn_mask, dpr[i]) - for i in range(layers) - ] - ) - - self.ln_final = LayerNorm(width) - - trunc_normal_(self.positional_embedding, std=.02) - # nn.init.normal_(self.token_embedding, std=.02) - trunc_normal_(self.token_embedding.weight, std=.02) - self.apply(self._init_weights) - - def build_attention_mask(self): - # lazily create causal attention mask, with full attention between the vision tokens - # pytorch uses additive attention mask; fill with -inf - mask = torch.empty(self.context_length, self.context_length) - mask.fill_(float("-inf")) - mask.triu_(1) # zero out the lower diagonal - return mask - - def _init_weights(self, m): - if isinstance(m, (nn.Linear, nn.Conv2d)): - trunc_normal_(m.weight, std=0.02) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)): - nn.init.constant_(m.bias, 0) - - @torch.jit.ignore - def no_weight_decay(self): - return { - 'positional_embedding', - 'token_embedding', - } - - def forward(self, text: torch.Tensor): - x = self.token_embedding(text) # [batch_size, n_ctx, d_model] - x = x + self.positional_embedding - x = x.permute(1, 0, 2) # NLD -> LND - x = self.resblocks(x) - x = x.permute(1, 0, 2) # LND -> NLD - - x = self.ln_final(x) - - x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] - - return x - -class CLIP(Backbone): - def __init__(self, config: dict): - super().__init__() - spec_text = config['MODEL']['SPEC']['TEXT'] - assert spec_text['TOKENIZER'] == 'clip', 'Only support clip tokenizer' - self.tokenizer_style = spec_text['TOKENIZER'] - self.tokenizer = build_tokenizer(spec_text) - - self.text_encoder = build_text_encoder(spec_text, self.tokenizer, True) - - embed_dim = config['MODEL']['SPEC']['EMBED_DIM'] - self.text_projection = nn.Parameter( - torch.empty(spec_text['WIDTH'], embed_dim) - ) - - spec_vision = config['MODEL']['SPEC']['VISION'] - self.image_encoder = SwinTransformer( - patch_size=spec_vision['PATCH_SIZE'], - in_chans=spec_vision['IN_CHANS'], - embed_dim=spec_vision['EMBED_DIM'], - depths=spec_vision['DEPTHS'], - num_heads=spec_vision['NUM_HEADS'], - window_size=spec_vision['WINDOW_SIZE'], - mlp_ratio=spec_vision['MLP_RATIO'], - qkv_bias=spec_vision['QKV_BIAS'], - qk_scale=spec_vision.get('QK_SCALE', None), - drop_rate=spec_vision['DROP_RATE'], - attn_drop_rate=spec_vision['ATTN_DROP_RATE'], - drop_path_rate=spec_vision['DROP_PATH_RATE'], - ape=spec_vision['APE'], - patch_norm=spec_vision['PATCH_NORM'], - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - use_checkpoint=False, - ) - - width = spec_vision['EMBED_DIM'] * 2 ** (len(spec_vision['DEPTHS']) - 1) - self.image_projection = nn.Parameter( - torch.empty(width, embed_dim) - ) - # self.logit_scale = nn.Parameter(torch.FloatTensor([np.log(1 / 0.07)])) - self.logit_scale = nn.Parameter(torch.ones([])) - - trunc_normal_(self.text_projection, std=.02) - trunc_normal_(self.image_projection, std=.02) - - def init_weights(self, pretrained='', pretrained_layers=[], verbose=True): - if os.path.isfile(pretrained): - pretrained_dict = torch.load(pretrained, map_location='cpu') - logger.info(f'=> loading pretrained model {pretrained}') - model_dict = self.state_dict() - pretrained_dict = { - k: v for k, v in pretrained_dict.items() - if k in model_dict.keys() - } - need_init_state_dict = {} - for k, v in pretrained_dict.items(): - need_init = ( - k.split('.')[0] in pretrained_layers - or pretrained_layers[0] is '*' - ) - if need_init: - if verbose: - logging.info(f'=> init {k} from {pretrained}') - need_init_state_dict[k] = v - self.load_state_dict(need_init_state_dict, strict=False) - - @torch.jit.ignore - def no_weight_decay(self): - no_weight_decay = {'logit_scale'} - for k in self.text_encoder.no_weight_decay(): - no_weight_decay.add('text.'+k) - - for k in self.image_encoder.no_weight_decay(): - no_weight_decay.add('visual.'+k) - - return no_weight_decay - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - @property - def dtype(self): - return self.image_encoder.conv1.weight.dtype - - def encode_image(self, image, norm=True): - x = self.image_encoder(image) - return x - - def encode_text(self, text, norm=True): - assert isinstance(text, str), "only support single query" - tokens = self.tokenizer( - text, padding='max_length', truncation=True, max_length=77, return_tensors='pt' - ) - tokens = {key:(val.cuda() if next(self.parameters()).is_cuda else val) for key,val in tokens.items()} - x = self.text_encoder(**tokens) - x = x['last_hidden_state'] - x = x[torch.arange(x.size(0)), tokens['input_ids'].argmax(dim=-1)] - - x = x @ self.text_projection - if norm: - x = x / x.norm(dim=-1, keepdim=True) - return x - - def forward(self, image): - features_image = self.image_encoder(image) - return features_image - - -@BACKBONE_REGISTRY.register() -def build_clip_swin_backbone(cfg, input_shape): - """ - Create a CLIP Swin instance from config. - - Returns: - SwinTransformer: a :class:`SwinTransformer` instance. - """ - spec_vision = cfg.MODEL.CLIP.VISION - return SwinTransformer( - patch_size=spec_vision['PATCH_SIZE'], - in_chans=spec_vision['IN_CHANS'], - embed_dim=spec_vision['EMBED_DIM'], - depths=spec_vision['DEPTHS'], - num_heads=spec_vision['NUM_HEADS'], - window_size=spec_vision['WINDOW_SIZE'], - mlp_ratio=spec_vision['MLP_RATIO'], - qkv_bias=spec_vision['QKV_BIAS'], - qk_scale=spec_vision.get('QK_SCALE', None), - drop_rate=spec_vision['DROP_RATE'], - attn_drop_rate=spec_vision['ATTN_DROP_RATE'], - drop_path_rate=spec_vision['DROP_PATH_RATE'], - ape=spec_vision['APE'], - patch_norm=spec_vision['PATCH_NORM'], - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - use_checkpoint=False, - ) - -@BACKBONE_REGISTRY.register() -def build_clip_swin(cfg, input_shape): - """ - Create a CLIP Swin instance from config. - - Returns: - SwinTransformer: a :class:`SwinTransformer` instance. - """ - return CLIP(cfg) \ No newline at end of file diff --git a/spaces/ChevyWithAI/rvc-aicover/infer_pack/transforms.py b/spaces/ChevyWithAI/rvc-aicover/infer_pack/transforms.py deleted file mode 100644 index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000 --- a/spaces/ChevyWithAI/rvc-aicover/infer_pack/transforms.py +++ /dev/null @@ -1,209 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = {"tails": tails, "tail_bound": tail_bound} - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 - - -def unconstrained_rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails="linear", - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == "linear": - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError("{} tails are not implemented.".format(tails)) - - ( - outputs[inside_interval_mask], - logabsdet[inside_interval_mask], - ) = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, - right=tail_bound, - bottom=-tail_bound, - top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - ) - - return outputs, logabsdet - - -def rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0.0, - right=1.0, - bottom=0.0, - top=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError("Input to a transform is not within its domain") - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError("Minimal bin width too large for the number of bins") - if min_bin_height * num_bins > 1.0: - raise ValueError("Minimal bin height too large for the number of bins") - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) + input_heights * (input_delta - input_derivatives) - b = input_heights * input_derivatives - (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) - c = -input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * ( - input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta - ) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/Chitranshu/Dashboard-Uber/README.md b/spaces/Chitranshu/Dashboard-Uber/README.md deleted file mode 100644 index c0d4381e4ee384ffe03f3ff7fddaa320b73de1f7..0000000000000000000000000000000000000000 --- a/spaces/Chitranshu/Dashboard-Uber/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Uber-Dashboard -emoji: 🌍 -colorFrom: gray -colorTo: gray -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/my_wife/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/my_wife/__init__.py deleted file mode 100644 index 727bffabea0b8fcdb5d683c0bb24ca69d0211c2c..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/my_wife/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -from pathlib import Path -from typing import List - -from pil_utils import BuildImage - -from meme_generator import add_meme - -img_dir = Path(__file__).parent / "images" - - -def my_wife(images: List[BuildImage], texts, args): - img = images[0].convert("RGBA").resize_width(400) - img_w, img_h = img.size - frame = BuildImage.new("RGBA", (650, img_h + 500), "white") - frame.paste(img, (int(325 - img_w / 2), 105), alpha=True) - - text = "如果你的老婆长这样" - frame.draw_text( - (27, 12, 27 + 596, 12 + 79), - text, - max_fontsize=70, - min_fontsize=30, - allow_wrap=True, - lines_align="center", - weight="bold", - ) - text = "那么这就不是你的老婆\n这是我的老婆" - frame.draw_text( - (27, img_h + 120, 27 + 593, img_h + 120 + 135), - text, - max_fontsize=70, - min_fontsize=30, - allow_wrap=True, - weight="bold", - ) - text = "滚去找你\n自己的老婆去" - frame.draw_text( - (27, img_h + 295, 27 + 374, img_h + 295 + 135), - text, - max_fontsize=70, - min_fontsize=30, - allow_wrap=True, - lines_align="center", - weight="bold", - ) - - img_point = BuildImage.open(img_dir / "1.png").resize_width(200) - frame.paste(img_point, (421, img_h + 270)) - - return frame.save_jpg() - - -add_meme("my_wife", my_wife, min_images=1, max_images=1, keywords=["我老婆", "这是我老婆"]) diff --git a/spaces/CofAI/chat.b4/g4f/Provider/Providers/GetGpt.py b/spaces/CofAI/chat.b4/g4f/Provider/Providers/GetGpt.py deleted file mode 100644 index 56a121f6ee5f430da7beda3b65abdea64a87c36b..0000000000000000000000000000000000000000 --- a/spaces/CofAI/chat.b4/g4f/Provider/Providers/GetGpt.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import json -import uuid -import requests -from Crypto.Cipher import AES -from ...typing import sha256, Dict, get_type_hints - -url = 'https://chat.getgpt.world/' -model = ['gpt-3.5-turbo'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - def encrypt(e): - t = os.urandom(8).hex().encode('utf-8') - n = os.urandom(8).hex().encode('utf-8') - r = e.encode('utf-8') - cipher = AES.new(t, AES.MODE_CBC, n) - ciphertext = cipher.encrypt(pad_data(r)) - return ciphertext.hex() + t.decode('utf-8') + n.decode('utf-8') - - def pad_data(data: bytes) -> bytes: - block_size = AES.block_size - padding_size = block_size - len(data) % block_size - padding = bytes([padding_size] * padding_size) - return data + padding - - headers = { - 'Content-Type': 'application/json', - 'Referer': 'https://chat.getgpt.world/', - 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36' - } - - data = json.dumps({ - 'messages': messages, - 'frequency_penalty': kwargs.get('frequency_penalty', 0), - 'max_tokens': kwargs.get('max_tokens', 4000), - 'model': 'gpt-3.5-turbo', - 'presence_penalty': kwargs.get('presence_penalty', 0), - 'temperature': kwargs.get('temperature', 1), - 'top_p': kwargs.get('top_p', 1), - 'stream': True, - 'uuid': str(uuid.uuid4()) - }) - - res = requests.post('https://chat.getgpt.world/api/chat/stream', - headers=headers, json={'signature': encrypt(data)}, stream=True) - - for line in res.iter_lines(): - if b'content' in line: - line_json = json.loads(line.decode('utf-8').split('data: ')[1]) - yield (line_json['choices'][0]['delta']['content']) - - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join( - [f'{name}: {get_type_hints(_create_completion)[name].__name__}' for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) diff --git a/spaces/CofAI/picscore1/index.html b/spaces/CofAI/picscore1/index.html deleted file mode 100644 index 43dbfcd365f02139cd5fcf0074911c7278c97e91..0000000000000000000000000000000000000000 --- a/spaces/CofAI/picscore1/index.html +++ /dev/null @@ -1,25 +0,0 @@ - - - - - - - - PicScore - - - - - - - - -- - - - - diff --git a/spaces/CognitiveLabs/Research-Assistant/agent/__init__.py b/spaces/CognitiveLabs/Research-Assistant/agent/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/parser.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/parser.py deleted file mode 100644 index 49667f4503e15be8c00388a72cb0d428dc7dafe9..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/parser.py +++ /dev/null @@ -1,2365 +0,0 @@ -from fontTools.feaLib.error import FeatureLibError -from fontTools.feaLib.lexer import Lexer, IncludingLexer, NonIncludingLexer -from fontTools.feaLib.variableScalar import VariableScalar -from fontTools.misc.encodingTools import getEncoding -from fontTools.misc.textTools import bytechr, tobytes, tostr -import fontTools.feaLib.ast as ast -import logging -import os -import re - - -log = logging.getLogger(__name__) - - -class Parser(object): - """Initializes a Parser object. - - Example: - - .. code:: python - - from fontTools.feaLib.parser import Parser - parser = Parser(file, font.getReverseGlyphMap()) - parsetree = parser.parse() - - Note: the ``glyphNames`` iterable serves a double role to help distinguish - glyph names from ranges in the presence of hyphens and to ensure that glyph - names referenced in a feature file are actually part of a font's glyph set. - If the iterable is left empty, no glyph name in glyph set checking takes - place, and all glyph tokens containing hyphens are treated as literal glyph - names, not as ranges. (Adding a space around the hyphen can, in any case, - help to disambiguate ranges from glyph names containing hyphens.) - - By default, the parser will follow ``include()`` statements in the feature - file. To turn this off, pass ``followIncludes=False``. Pass a directory string as - ``includeDir`` to explicitly declare a directory to search included feature files - in. - """ - - extensions = {} - ast = ast - SS_FEATURE_TAGS = {"ss%02d" % i for i in range(1, 20 + 1)} - CV_FEATURE_TAGS = {"cv%02d" % i for i in range(1, 99 + 1)} - - def __init__( - self, featurefile, glyphNames=(), followIncludes=True, includeDir=None, **kwargs - ): - - if "glyphMap" in kwargs: - from fontTools.misc.loggingTools import deprecateArgument - - deprecateArgument("glyphMap", "use 'glyphNames' (iterable) instead") - if glyphNames: - raise TypeError( - "'glyphNames' and (deprecated) 'glyphMap' are " "mutually exclusive" - ) - glyphNames = kwargs.pop("glyphMap") - if kwargs: - raise TypeError( - "unsupported keyword argument%s: %s" - % ("" if len(kwargs) == 1 else "s", ", ".join(repr(k) for k in kwargs)) - ) - - self.glyphNames_ = set(glyphNames) - self.doc_ = self.ast.FeatureFile() - self.anchors_ = SymbolTable() - self.glyphclasses_ = SymbolTable() - self.lookups_ = SymbolTable() - self.valuerecords_ = SymbolTable() - self.symbol_tables_ = {self.anchors_, self.valuerecords_} - self.next_token_type_, self.next_token_ = (None, None) - self.cur_comments_ = [] - self.next_token_location_ = None - lexerClass = IncludingLexer if followIncludes else NonIncludingLexer - self.lexer_ = lexerClass(featurefile, includeDir=includeDir) - self.missing = {} - self.advance_lexer_(comments=True) - - def parse(self): - """Parse the file, and return a :class:`fontTools.feaLib.ast.FeatureFile` - object representing the root of the abstract syntax tree containing the - parsed contents of the file.""" - statements = self.doc_.statements - while self.next_token_type_ is not None or self.cur_comments_: - self.advance_lexer_(comments=True) - if self.cur_token_type_ is Lexer.COMMENT: - statements.append( - self.ast.Comment(self.cur_token_, location=self.cur_token_location_) - ) - elif self.is_cur_keyword_("include"): - statements.append(self.parse_include_()) - elif self.cur_token_type_ is Lexer.GLYPHCLASS: - statements.append(self.parse_glyphclass_definition_()) - elif self.is_cur_keyword_(("anon", "anonymous")): - statements.append(self.parse_anonymous_()) - elif self.is_cur_keyword_("anchorDef"): - statements.append(self.parse_anchordef_()) - elif self.is_cur_keyword_("languagesystem"): - statements.append(self.parse_languagesystem_()) - elif self.is_cur_keyword_("lookup"): - statements.append(self.parse_lookup_(vertical=False)) - elif self.is_cur_keyword_("markClass"): - statements.append(self.parse_markClass_()) - elif self.is_cur_keyword_("feature"): - statements.append(self.parse_feature_block_()) - elif self.is_cur_keyword_("conditionset"): - statements.append(self.parse_conditionset_()) - elif self.is_cur_keyword_("variation"): - statements.append(self.parse_feature_block_(variation=True)) - elif self.is_cur_keyword_("table"): - statements.append(self.parse_table_()) - elif self.is_cur_keyword_("valueRecordDef"): - statements.append(self.parse_valuerecord_definition_(vertical=False)) - elif ( - self.cur_token_type_ is Lexer.NAME - and self.cur_token_ in self.extensions - ): - statements.append(self.extensions[self.cur_token_](self)) - elif self.cur_token_type_ is Lexer.SYMBOL and self.cur_token_ == ";": - continue - else: - raise FeatureLibError( - "Expected feature, languagesystem, lookup, markClass, " - 'table, or glyph class definition, got {} "{}"'.format( - self.cur_token_type_, self.cur_token_ - ), - self.cur_token_location_, - ) - # Report any missing glyphs at the end of parsing - if self.missing: - error = [ - " %s (first found at %s)" % (name, loc) - for name, loc in self.missing.items() - ] - raise FeatureLibError( - "The following glyph names are referenced but are missing from the " - "glyph set:\n" + ("\n".join(error)), - None, - ) - return self.doc_ - - def parse_anchor_(self): - # Parses an anchor in any of the four formats given in the feature - # file specification (2.e.vii). - self.expect_symbol_("<") - self.expect_keyword_("anchor") - location = self.cur_token_location_ - - if self.next_token_ == "NULL": # Format D - self.expect_keyword_("NULL") - self.expect_symbol_(">") - return None - - if self.next_token_type_ == Lexer.NAME: # Format E - name = self.expect_name_() - anchordef = self.anchors_.resolve(name) - if anchordef is None: - raise FeatureLibError( - 'Unknown anchor "%s"' % name, self.cur_token_location_ - ) - self.expect_symbol_(">") - return self.ast.Anchor( - anchordef.x, - anchordef.y, - name=name, - contourpoint=anchordef.contourpoint, - xDeviceTable=None, - yDeviceTable=None, - location=location, - ) - - x, y = self.expect_number_(variable=True), self.expect_number_(variable=True) - - contourpoint = None - if self.next_token_ == "contourpoint": # Format B - self.expect_keyword_("contourpoint") - contourpoint = self.expect_number_() - - if self.next_token_ == "<": # Format C - xDeviceTable = self.parse_device_() - yDeviceTable = self.parse_device_() - else: - xDeviceTable, yDeviceTable = None, None - - self.expect_symbol_(">") - return self.ast.Anchor( - x, - y, - name=None, - contourpoint=contourpoint, - xDeviceTable=xDeviceTable, - yDeviceTable=yDeviceTable, - location=location, - ) - - def parse_anchor_marks_(self): - # Parses a sequence of ``[ mark @MARKCLASS]*.`` - anchorMarks = [] # [(self.ast.Anchor, markClassName)*] - while self.next_token_ == "<": - anchor = self.parse_anchor_() - if anchor is None and self.next_token_ != "mark": - continue # without mark, eg. in GPOS type 5 - self.expect_keyword_("mark") - markClass = self.expect_markClass_reference_() - anchorMarks.append((anchor, markClass)) - return anchorMarks - - def parse_anchordef_(self): - # Parses a named anchor definition (`section 2.e.viii
' - else: - if i > 0: - if count % 2 == 1: - line = line.replace("`", "\`") - line = line.replace("<", "<") - line = line.replace(">", ">") - line = line.replace(" ", " ") - line = line.replace("*", "*") - line = line.replace("_", "_") - line = line.replace("-", "-") - line = line.replace(".", ".") - line = line.replace("!", "!") - line = line.replace("(", "(") - line = line.replace(")", ")") - line = line.replace("$", "$") - lines[i] = "' - else: - lines[i] = f'
"+line - text = "".join(lines) - return text - -def construct_text(role, text): - return {"role": role, "content": text} - -def construct_user(text): - return construct_text("user", text) - -def construct_system(text): - return construct_text("system", text) - -def construct_assistant(text): - return construct_text("assistant", text) - -def construct_token_message(token, stream=False): - return f"Token 计数: {token}" - -def get_response(openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model): - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {openai_api_key}" - } - - history = [construct_system(system_prompt), *history] - - payload = { - "model": selected_model, - "messages": history, # [{"role": "user", "content": f"{inputs}"}], - "temperature": temperature, # 1.0, - "top_p": top_p, # 1.0, - "n": 1, - "stream": stream, - "presence_penalty": 0, - "frequency_penalty": 0, - } - if stream: - timeout = timeout_streaming - else: - timeout = timeout_all - response = requests.post(API_URL, headers=headers, json=payload, stream=True, timeout=timeout) - return response - -def stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model): - def get_return_value(): - return chatbot, history, status_text, all_token_counts - - logging.info("实时回答模式") - partial_words = "" - counter = 0 - status_text = "开始实时传输回答……" - history.append(construct_user(inputs)) - history.append(construct_assistant("")) - chatbot.append((parse_text(inputs), "")) - user_token_count = 0 - if len(all_token_counts) == 0: - system_prompt_token_count = count_token(system_prompt) - user_token_count = count_token(inputs) + system_prompt_token_count - else: - user_token_count = count_token(inputs) - all_token_counts.append(user_token_count) - logging.info(f"输入token计数: {user_token_count}") - yield get_return_value() - try: - response = get_response(openai_api_key, system_prompt, history, temperature, top_p, True, selected_model) - except requests.exceptions.ConnectTimeout: - status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt - yield get_return_value() - return - except requests.exceptions.ReadTimeout: - status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt - yield get_return_value() - return - - yield get_return_value() - error_json_str = "" - - for chunk in tqdm(response.iter_lines()): - if counter == 0: - counter += 1 - continue - counter += 1 - # check whether each line is non-empty - if chunk: - chunk = chunk.decode() - chunklength = len(chunk) - try: - chunk = json.loads(chunk[6:]) - except json.JSONDecodeError: - logging.info(chunk) - error_json_str += chunk - status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}" - yield get_return_value() - continue - # decode each line as response data is in bytes - if chunklength > 6 and "delta" in chunk['choices'][0]: - finish_reason = chunk['choices'][0]['finish_reason'] - status_text = construct_token_message(sum(all_token_counts), stream=True) - if finish_reason == "stop": - yield get_return_value() - break - try: - partial_words = partial_words + chunk['choices'][0]["delta"]["content"] - except KeyError: - status_text = standard_error_msg + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: " + str(sum(all_token_counts)) - yield get_return_value() - break - history[-1] = construct_assistant(partial_words) - chatbot[-1] = (parse_text(inputs), parse_text(partial_words)) - all_token_counts[-1] += 1 - yield get_return_value() - - -def predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model): - logging.info("一次性回答模式") - history.append(construct_user(inputs)) - history.append(construct_assistant("")) - chatbot.append((parse_text(inputs), "")) - all_token_counts.append(count_token(inputs)) - try: - response = get_response(openai_api_key, system_prompt, history, temperature, top_p, False, selected_model) - except requests.exceptions.ConnectTimeout: - status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt - return chatbot, history, status_text, all_token_counts - except requests.exceptions.ProxyError: - status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt - return chatbot, history, status_text, all_token_counts - except requests.exceptions.SSLError: - status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt - return chatbot, history, status_text, all_token_counts - response = json.loads(response.text) - content = response["choices"][0]["message"]["content"] - history[-1] = construct_assistant(content) - chatbot[-1] = (parse_text(inputs), parse_text(content)) - total_token_count = response["usage"]["total_tokens"] - all_token_counts[-1] = total_token_count - sum(all_token_counts) - status_text = construct_token_message(total_token_count) - return chatbot, history, status_text, all_token_counts - - -def predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model = MODELS[0], use_websearch_checkbox = False, should_check_token_count = True): # repetition_penalty, top_k - logging.info("输入为：" +colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL) - if use_websearch_checkbox: - results = ddg(inputs, max_results=3) - web_results = [] - for idx, result in enumerate(results): - logging.info(f"搜索结果{idx + 1}：{result}") - web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}') - web_results = "\n\n".join(web_results) - today = datetime.datetime.today().strftime("%Y-%m-%d") - inputs = websearch_prompt.replace("{current_date}", today).replace("{query}", inputs).replace("{web_results}", web_results) - if len(openai_api_key) != 51: - status_text = standard_error_msg + no_apikey_msg - logging.info(status_text) - chatbot.append((parse_text(inputs), "")) - if len(history) == 0: - history.append(construct_user(inputs)) - history.append("") - all_token_counts.append(0) - else: - history[-2] = construct_user(inputs) - yield chatbot, history, status_text, all_token_counts - return - if stream: - yield chatbot, history, "开始生成回答……", all_token_counts - if stream: - logging.info("使用流式传输") - iter = stream_predict(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model) - for chatbot, history, status_text, all_token_counts in iter: - yield chatbot, history, status_text, all_token_counts - else: - logging.info("不使用流式传输") - chatbot, history, status_text, all_token_counts = predict_all(openai_api_key, system_prompt, history, inputs, chatbot, all_token_counts, top_p, temperature, selected_model) - yield chatbot, history, status_text, all_token_counts - logging.info(f"传输完毕。当前token计数为{all_token_counts}") - if len(history) > 1 and history[-1]['content'] != inputs: - logging.info("回答为：" +colorama.Fore.BLUE + f"{history[-1]['content']}" + colorama.Style.RESET_ALL) - if stream: - max_token = max_token_streaming - else: - max_token = max_token_all - if sum(all_token_counts) > max_token and should_check_token_count: - status_text = f"精简token中{all_token_counts}/{max_token}" - logging.info(status_text) - yield chatbot, history, status_text, all_token_counts - iter = reduce_token_size(openai_api_key, system_prompt, history, chatbot, all_token_counts, top_p, temperature, stream=False, selected_model=selected_model, hidden=True) - for chatbot, history, status_text, all_token_counts in iter: - status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}" - yield chatbot, history, status_text, all_token_counts - - -def retry(openai_api_key, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0]): - logging.info("重试中……") - if len(history) == 0: - yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count - return - history.pop() - inputs = history.pop()["content"] - token_count.pop() - iter = predict(openai_api_key, system_prompt, history, inputs, chatbot, token_count, top_p, temperature, stream=stream, selected_model=selected_model) - logging.info("重试完毕") - for x in iter: - yield x - - -def reduce_token_size(openai_api_key, system_prompt, history, chatbot, token_count, top_p, temperature, stream=False, selected_model = MODELS[0], hidden=False): - logging.info("开始减少token数量……") - iter = predict(openai_api_key, system_prompt, history, summarize_prompt, chatbot, token_count, top_p, temperature, stream=stream, selected_model = selected_model, should_check_token_count=False) - logging.info(f"chatbot: {chatbot}") - for chatbot, history, status_text, previous_token_count in iter: - history = history[-2:] - token_count = previous_token_count[-1:] - if hidden: - chatbot.pop() - yield chatbot, history, construct_token_message(sum(token_count), stream=stream), token_count - logging.info("减少token数量完毕") - - -def delete_last_conversation(chatbot, history, previous_token_count): - if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]: - logging.info("由于包含报错信息，只删除chatbot记录") - chatbot.pop() - return chatbot, history - if len(history) > 0: - logging.info("删除了一组对话历史") - history.pop() - history.pop() - if len(chatbot) > 0: - logging.info("删除了一组chatbot对话") - chatbot.pop() - if len(previous_token_count) > 0: - logging.info("删除了一组对话的token计数记录") - previous_token_count.pop() - return chatbot, history, previous_token_count, construct_token_message(sum(previous_token_count)) - - -def save_chat_history(filename, system, history, chatbot): - logging.info("保存对话历史中……") - if filename == "": - return - if not filename.endswith(".json"): - filename += ".json" - os.makedirs(HISTORY_DIR, exist_ok=True) - json_s = {"system": system, "history": history, "chatbot": chatbot} - logging.info(json_s) - with open(os.path.join(HISTORY_DIR, filename), "w") as f: - json.dump(json_s, f, ensure_ascii=False, indent=4) - logging.info("保存对话历史完毕") - - -def load_chat_history(filename, system, history, chatbot): - logging.info("加载对话历史中……") - try: - with open(os.path.join(HISTORY_DIR, filename), "r") as f: - json_s = json.load(f) - try: - if type(json_s["history"][0]) == str: - logging.info("历史记录格式为旧版，正在转换……") - new_history = [] - for index, item in enumerate(json_s["history"]): - if index % 2 == 0: - new_history.append(construct_user(item)) - else: - new_history.append(construct_assistant(item)) - json_s["history"] = new_history - logging.info(new_history) - except: - # 没有对话历史 - pass - logging.info("加载对话历史完毕") - return filename, json_s["system"], json_s["history"], json_s["chatbot"] - except FileNotFoundError: - logging.info("没有找到对话历史文件，不执行任何操作") - return filename, system, history, chatbot - -def sorted_by_pinyin(list): - return sorted(list, key=lambda char: lazy_pinyin(char)[0][0]) - -def get_file_names(dir, plain=False, filetypes=[".json"]): - logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}") - files = [] - try: - for type in filetypes: - files += [f for f in os.listdir(dir) if f.endswith(type)] - except FileNotFoundError: - files = [] - files = sorted_by_pinyin(files) - if files == []: - files = [""] - if plain: - return files - else: - return gr.Dropdown.update(choices=files) - -def get_history_names(plain=False): - logging.info("获取历史记录文件名列表") - return get_file_names(HISTORY_DIR, plain) - -def load_template(filename, mode=0): - logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）") - lines = [] - logging.info("Loading template...") - if filename.endswith(".json"): - with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f: - lines = json.load(f) - lines = [[i["act"], i["prompt"]] for i in lines] - else: - with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as csvfile: - reader = csv.reader(csvfile) - lines = list(reader) - lines = lines[1:] - if mode == 1: - return sorted_by_pinyin([row[0] for row in lines]) - elif mode == 2: - return {row[0]:row[1] for row in lines} - else: - choices = sorted_by_pinyin([row[0] for row in lines]) - return {row[0]:row[1] for row in lines}, gr.Dropdown.update(choices=choices, value=choices[0]) - -def get_template_names(plain=False): - logging.info("获取模板文件名列表") - return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"]) - -def get_template_content(templates, selection, original_system_prompt): - logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}") - try: - return templates[selection] - except: - return original_system_prompt - -def reset_state(): - logging.info("重置状态") - return [], [], [], construct_token_message(0) - -def reset_textbox(): - return gr.update(value='') diff --git a/spaces/DarwinAnim8or/Mistral-Chat/app.py b/spaces/DarwinAnim8or/Mistral-Chat/app.py deleted file mode 100644 index 8133415a88713a6fc3ad84f158b80accac6e4230..0000000000000000000000000000000000000000 --- a/spaces/DarwinAnim8or/Mistral-Chat/app.py +++ /dev/null @@ -1,97 +0,0 @@ -from huggingface_hub import InferenceClient -import gradio as gr -import random - -API_URL = "https://api-inference.huggingface.co/models/" - -client = InferenceClient( - "mistralai/Mistral-7B-Instruct-v0.1" -) - -def format_prompt(message, history): - prompt = "" - for user_prompt, bot_response in history: - prompt += f"[INST] {user_prompt} [/INST]" - prompt += f" {bot_response}" - prompt += f"[INST] {message} [/INST]" - return prompt - -def generate(prompt, history, temperature=0.9, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0): - temperature = float(temperature) - if temperature < 1e-2: - temperature = 1e-2 - top_p = float(top_p) - - generate_kwargs = dict( - temperature=temperature, - max_new_tokens=max_new_tokens, - top_p=top_p, - repetition_penalty=repetition_penalty, - do_sample=True, - seed=random.randint(0, 10**7), - ) - - formatted_prompt = format_prompt(prompt, history) - - stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) - output = "" - - for response in stream: - output += response.token.text - yield output - return output - - -additional_inputs=[ - gr.Slider( - label="Temperature", - value=0.9, - minimum=0.0, - maximum=1.0, - step=0.05, - interactive=True, - info="Higher values produce more diverse outputs", - ), - gr.Slider( - label="Max new tokens", - value=512, - minimum=64, - maximum=1024, - step=64, - interactive=True, - info="The maximum numbers of new tokens", - ), - gr.Slider( - label="Top-p (nucleus sampling)", - value=0.90, - minimum=0.0, - maximum=1, - step=0.05, - interactive=True, - info="Higher values sample more low-probability tokens", - ), - gr.Slider( - label="Repetition penalty", - value=1.2, - minimum=1.0, - maximum=2.0, - step=0.05, - interactive=True, - info="Penalize repeated tokens", - ) -] - -customCSS = """ -#component-7 { # this is the default element ID of the chat component - height: 800px; # adjust the height as needed - flex-grow: 1; -} -""" - -with gr.Blocks(css=customCSS) as demo: - gr.ChatInterface( - generate, - additional_inputs=additional_inputs, - ) - -demo.queue().launch(debug=True) \ No newline at end of file diff --git a/spaces/DataScienceEngineering/4-Seq2SeqQAT5/README.md b/spaces/DataScienceEngineering/4-Seq2SeqQAT5/README.md deleted file mode 100644 index 169ca3e991657a72f10c93f6c86a0e5cc4070ffa..0000000000000000000000000000000000000000 --- a/spaces/DataScienceEngineering/4-Seq2SeqQAT5/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 🙋AutoQA - 💬NLP Seq2Seq Gradio -emoji: 4-QAI❓ -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.0.5 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/build.py b/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/build.py deleted file mode 100644 index b198790817a2d11d65d6211b011f9408d9d34270..0000000000000000000000000000000000000000 --- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/upsegmodel/prroi_pool/build.py +++ /dev/null @@ -1,50 +0,0 @@ -#! /usr/bin/env python3 -# -*- coding: utf-8 -*- -# File : build.py -# Author : Jiayuan Mao, Tete Xiao -# Email : maojiayuan@gmail.com, jasonhsiao97@gmail.com -# Date : 07/13/2018 -# -# This file is part of PreciseRoIPooling. -# Distributed under terms of the MIT license. -# Copyright (c) 2017 Megvii Technology Limited. - -import os -import torch - -from torch.utils.ffi import create_extension - -headers = [] -sources = [] -defines = [] -extra_objects = [] -with_cuda = False - -if torch.cuda.is_available(): - with_cuda = True - - headers+= ['src/prroi_pooling_gpu.h'] - sources += ['src/prroi_pooling_gpu.c'] - defines += [('WITH_CUDA', None)] - - this_file = os.path.dirname(os.path.realpath(__file__)) - extra_objects_cuda = ['src/prroi_pooling_gpu_impl.cu.o'] - extra_objects_cuda = [os.path.join(this_file, fname) for fname in extra_objects_cuda] - extra_objects.extend(extra_objects_cuda) -else: - # TODO(Jiayuan Mao @ 07/13): remove this restriction after we support the cpu implementation. - raise NotImplementedError('Precise RoI Pooling only supports GPU (cuda) implememtations.') - -ffi = create_extension( - '_prroi_pooling', - headers=headers, - sources=sources, - define_macros=defines, - relative_to=__file__, - with_cuda=with_cuda, - extra_objects=extra_objects -) - -if __name__ == '__main__': - ffi.build() - diff --git a/spaces/Div99/Chat-with-Div/README.md b/spaces/Div99/Chat-with-Div/README.md deleted file mode 100644 index cc151b182f10fa981225f8e04e1467db8494ae1d..0000000000000000000000000000000000000000 --- a/spaces/Div99/Chat-with-Div/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Chat-with-Div -emoji: 👁 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 3.16.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/DragGan/DragGan-Inversion/PTI/__init__.py b/spaces/DragGan/DragGan-Inversion/PTI/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/editor.py b/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/editor.py deleted file mode 100644 index b1c2ac56fd7b4b127f948c6b8cf15874a8fe9d93..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/utils/editor.py +++ /dev/null @@ -1,507 +0,0 @@ -# python 3.7 -"""Utility functions for image editing from latent space.""" - -import os.path -import numpy as np - -__all__ = [ - 'parse_indices', 'interpolate', 'mix_style', - 'get_layerwise_manipulation_strength', 'manipulate', 'parse_boundary_list' -] - - -def parse_indices(obj, min_val=None, max_val=None): - """Parses indices. - - If the input is a list or tuple, this function has no effect. - - The input can also be a string, which is either a comma separated list of - numbers 'a, b, c', or a dash separated range 'a - c'. Space in the string will - be ignored. - - Args: - obj: The input object to parse indices from. - min_val: If not `None`, this function will check that all indices are equal - to or larger than this value. (default: None) - max_val: If not `None`, this function will check that all indices are equal - to or smaller than this field. (default: None) - - Returns: - A list of integers. - - Raises: - If the input is invalid, i.e., neither a list or tuple, nor a string. - """ - if obj is None or obj == '': - indices = [] - elif isinstance(obj, int): - indices = [obj] - elif isinstance(obj, (list, tuple, np.ndarray)): - indices = list(obj) - elif isinstance(obj, str): - indices = [] - splits = obj.replace(' ', '').split(',') - for split in splits: - numbers = list(map(int, split.split('-'))) - if len(numbers) == 1: - indices.append(numbers[0]) - elif len(numbers) == 2: - indices.extend(list(range(numbers[0], numbers[1] + 1))) - else: - raise ValueError(f'Invalid type of input: {type(obj)}!') - - assert isinstance(indices, list) - indices = sorted(list(set(indices))) - for idx in indices: - assert isinstance(idx, int) - if min_val is not None: - assert idx >= min_val, f'{idx} is smaller than min val `{min_val}`!' - if max_val is not None: - assert idx <= max_val, f'{idx} is larger than max val `{max_val}`!' - - return indices - - -def interpolate(src_codes, dst_codes, step=5): - """Interpolates two sets of latent codes linearly. - - Args: - src_codes: Source codes, with shape [num, *code_shape]. - dst_codes: Target codes, with shape [num, *code_shape]. - step: Number of interplolation steps, with source and target included. For - example, if `step = 5`, three more samples will be inserted. (default: 5) - - Returns: - Interpolated codes, with shape [num, step, *code_shape]. - - Raises: - ValueError: If the input two sets of latent codes are with different shapes. - """ - if not (src_codes.ndim >= 2 and src_codes.shape == dst_codes.shape): - raise ValueError(f'Shapes of source codes and target codes should both be ' - f'[num, *code_shape], but {src_codes.shape} and ' - f'{dst_codes.shape} are received!') - num = src_codes.shape[0] - code_shape = src_codes.shape[1:] - - a = src_codes[:, np.newaxis] - b = dst_codes[:, np.newaxis] - l = np.linspace(0.0, 1.0, step).reshape( - [step if axis == 1 else 1 for axis in range(a.ndim)]) - results = a + l * (b - a) - assert results.shape == (num, step, *code_shape) - - return results - - -def mix_style(style_codes, - content_codes, - num_layers=1, - mix_layers=None, - is_style_layerwise=True, - is_content_layerwise=True): - """Mixes styles from style codes to those of content codes. - - Each style code or content code consists of `num_layers` codes, each of which - is typically fed into a particular layer of the generator. This function mixes - styles by partially replacing the codes of `content_codes` from some certain - layers with those of `style_codes`. - - For example, if both style code and content code are with shape [10, 512], - meaning to have 10 layers and each employs a 512-dimensional latent code. And - the 1st, 2nd, and 3rd layers are the target layers to perform style mixing. - Then the top half of the content code (with shape [3, 512]) will be replaced - by the top half of the style code (also with shape [3, 512]). - - NOTE: This function also supports taking single-layer latent codes as inputs, - i.e., setting `is_style_layerwise` or `is_content_layerwise` as False. In this - case, the corresponding code will be first repeated for `num_layers` before - performing style mixing. - - Args: - style_codes: Style codes, with shape [num_styles, *code_shape] or - [num_styles, num_layers, *code_shape]. - content_codes: Content codes, with shape [num_contents, *code_shape] or - [num_contents, num_layers, *code_shape]. - num_layers: Total number of layers in the generative model. (default: 1) - mix_layers: Indices of the layers to perform style mixing. `None` means to - replace all layers, in which case the content code will be completely - replaced by style code. (default: None) - is_style_layerwise: Indicating whether the input `style_codes` are - layer-wise codes. (default: True) - is_content_layerwise: Indicating whether the input `content_codes` are - layer-wise codes. (default: True) - num_layers - - Returns: - Codes after style mixing, with shape [num_styles, num_contents, num_layers, - *code_shape]. - - Raises: - ValueError: If input `content_codes` or `style_codes` is with invalid shape. - """ - if not is_style_layerwise: - style_codes = style_codes[:, np.newaxis] - style_codes = np.tile( - style_codes, - [num_layers if axis == 1 else 1 for axis in range(style_codes.ndim)]) - if not is_content_layerwise: - content_codes = content_codes[:, np.newaxis] - content_codes = np.tile( - content_codes, - [num_layers if axis == 1 else 1 for axis in range(content_codes.ndim)]) - - if not (style_codes.ndim >= 3 and style_codes.shape[1] == num_layers and - style_codes.shape[1:] == content_codes.shape[1:]): - raise ValueError(f'Shapes of style codes and content codes should be ' - f'[num_styles, num_layers, *code_shape] and ' - f'[num_contents, num_layers, *code_shape] respectively, ' - f'but {style_codes.shape} and {content_codes.shape} are ' - f'received!') - - layer_indices = parse_indices(mix_layers, min_val=0, max_val=num_layers - 1) - if not layer_indices: - layer_indices = list(range(num_layers)) - - num_styles = style_codes.shape[0] - num_contents = content_codes.shape[0] - code_shape = content_codes.shape[2:] - - s = style_codes[:, np.newaxis] - s = np.tile(s, [num_contents if axis == 1 else 1 for axis in range(s.ndim)]) - c = content_codes[np.newaxis] - c = np.tile(c, [num_styles if axis == 0 else 1 for axis in range(c.ndim)]) - - from_style = np.zeros(s.shape, dtype=bool) - from_style[:, :, layer_indices] = True - results = np.where(from_style, s, c) - assert results.shape == (num_styles, num_contents, num_layers, *code_shape) - - return results - - -def get_layerwise_manipulation_strength(num_layers, - truncation_psi, - truncation_layers): - """Gets layer-wise strength for manipulation. - - Recall the truncation trick played on layer [0, truncation_layers): - - w = truncation_psi * w + (1 - truncation_psi) * w_avg - - So, when using the same boundary to manipulate different layers, layer - [0, truncation_layers) and layer [truncation_layers, num_layers) should use - different strength to eliminate the effect from the truncation trick. More - concretely, the strength for layer [0, truncation_layers) is set as - `truncation_psi`, while that for other layers are set as 1. - """ - strength = [1.0 for _ in range(num_layers)] - if truncation_layers > 0: - for layer_idx in range(0, truncation_layers): - strength[layer_idx] = truncation_psi - return strength - - -def manipulate(latent_codes, - boundary, - start_distance=-5.0, - end_distance=5.0, - step=21, - layerwise_manipulation=False, - num_layers=1, - manipulate_layers=None, - is_code_layerwise=False, - is_boundary_layerwise=False, - layerwise_manipulation_strength=1.0): - """Manipulates the given latent codes with respect to a particular boundary. - - Basically, this function takes a set of latent codes and a boundary as inputs, - and outputs a collection of manipulated latent codes. - - For example, let `step` to be 10, `latent_codes` to be with shape [num, - *code_shape], and `boundary` to be with shape [1, *code_shape] and unit norm. - Then the output will be with shape [num, 10, *code_shape]. For each 10-element - manipulated codes, the first code is `start_distance` away from the original - code (i.e., the input) along the `boundary` direction, while the last code is - `end_distance` away. Remaining codes are linearly interpolated. Here, - `distance` is sign sensitive. - - NOTE: This function also supports layer-wise manipulation, in which case the - generator should be able to take layer-wise latent codes as inputs. For - example, if the generator has 18 convolutional layers in total, and each of - which takes an independent latent code as input. It is possible, sometimes - with even better performance, to only partially manipulate these latent codes - corresponding to some certain layers yet keeping others untouched. - - NOTE: Boundary is assumed to be normalized to unit norm already. - - Args: - latent_codes: The input latent codes for manipulation, with shape - [num, *code_shape] or [num, num_layers, *code_shape]. - boundary: The semantic boundary as reference, with shape [1, *code_shape] or - [1, num_layers, *code_shape]. - start_distance: Start point for manipulation. (default: -5.0) - end_distance: End point for manipulation. (default: 5.0) - step: Number of manipulation steps. (default: 21) - layerwise_manipulation: Whether to perform layer-wise manipulation. - (default: False) - num_layers: Number of layers. Only active when `layerwise_manipulation` is - set as `True`. Should be a positive integer. (default: 1) - manipulate_layers: Indices of the layers to perform manipulation. `None` - means to manipulate latent codes from all layers. (default: None) - is_code_layerwise: Whether the input latent codes are layer-wise. If set as - `False`, the function will first repeat the input codes for `num_layers` - times before perform manipulation. (default: False) - is_boundary_layerwise: Whether the input boundary is layer-wise. If set as - `False`, the function will first repeat boundary for `num_layers` times - before perform manipulation. (default: False) - layerwise_manipulation_strength: Manipulation strength for each layer. Only - active when `layerwise_manipulation` is set as `True`. This field can be - used to resolve the strength discrepancy across layers when truncation - trick is on. See function `get_layerwise_manipulation_strength()` for - details. A tuple, list, or `numpy.ndarray` is expected. If set as a single - number, this strength will be used for all layers. (default: 1.0) - - Returns: - Manipulated codes, with shape [num, step, *code_shape] if - `layerwise_manipulation` is set as `False`, or shape [num, step, - num_layers, *code_shape] if `layerwise_manipulation` is set as `True`. - - Raises: - ValueError: If the input latent codes, boundary, or strength are with - invalid shape. - """ - if not (boundary.ndim >= 2 and boundary.shape[0] == 1): - raise ValueError(f'Boundary should be with shape [1, *code_shape] or ' - f'[1, num_layers, *code_shape], but ' - f'{boundary.shape} is received!') - - if not layerwise_manipulation: - assert not is_code_layerwise - assert not is_boundary_layerwise - num_layers = 1 - manipulate_layers = None - layerwise_manipulation_strength = 1.0 - - # Preprocessing for layer-wise manipulation. - # Parse indices of manipulation layers. - layer_indices = parse_indices( - manipulate_layers, min_val=0, max_val=num_layers - 1) - if not layer_indices: - layer_indices = list(range(num_layers)) - # Make latent codes layer-wise if needed. - assert num_layers > 0 - if not is_code_layerwise: - x = latent_codes[:, np.newaxis] - x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)]) - else: - x = latent_codes - if x.shape[1] != num_layers: - raise ValueError(f'Latent codes should be with shape [num, num_layers, ' - f'*code_shape], where `num_layers` equals to ' - f'{num_layers}, but {x.shape} is received!') - # Make boundary layer-wise if needed. - if not is_boundary_layerwise: - b = boundary - b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) - else: - b = boundary[0] - if b.shape[0] != num_layers: - raise ValueError(f'Boundary should be with shape [num_layers, ' - f'*code_shape], where `num_layers` equals to ' - f'{num_layers}, but {b.shape} is received!') - # Get layer-wise manipulation strength. - if isinstance(layerwise_manipulation_strength, (int, float)): - s = [float(layerwise_manipulation_strength) for _ in range(num_layers)] - elif isinstance(layerwise_manipulation_strength, (list, tuple)): - s = layerwise_manipulation_strength - if len(s) != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` ' - f'mismatches number of layers `{num_layers}`!') - elif isinstance(layerwise_manipulation_strength, np.ndarray): - s = layerwise_manipulation_strength - if s.size != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` ' - f'mismatches number of layers `{num_layers}`!') - else: - raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!') - s = np.array(s).reshape( - [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) - b = b * s - - if x.shape[1:] != b.shape: - raise ValueError(f'Latent code shape {x.shape} and boundary shape ' - f'{b.shape} mismatch!') - num = x.shape[0] - code_shape = x.shape[2:] - - x = x[:, np.newaxis] - b = b[np.newaxis, np.newaxis, :] - l = np.linspace(start_distance, end_distance, step).reshape( - [step if axis == 1 else 1 for axis in range(x.ndim)]) - results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)]) - is_manipulatable = np.zeros(results.shape, dtype=bool) - is_manipulatable[:, :, layer_indices] = True - results = np.where(is_manipulatable, x + l * b, results) - assert results.shape == (num, step, num_layers, *code_shape) - - return results if layerwise_manipulation else results[:, :, 0] - - -def manipulate2(latent_codes, - proj, - mindex, - start_distance=-5.0, - end_distance=5.0, - step=21, - layerwise_manipulation=False, - num_layers=1, - manipulate_layers=None, - is_code_layerwise=False, - layerwise_manipulation_strength=1.0): - - - if not layerwise_manipulation: - assert not is_code_layerwise -# assert not is_boundary_layerwise - num_layers = 1 - manipulate_layers = None - layerwise_manipulation_strength = 1.0 - - # Preprocessing for layer-wise manipulation. - # Parse indices of manipulation layers. - layer_indices = parse_indices( - manipulate_layers, min_val=0, max_val=num_layers - 1) - if not layer_indices: - layer_indices = list(range(num_layers)) - # Make latent codes layer-wise if needed. - assert num_layers > 0 - if not is_code_layerwise: - x = latent_codes[:, np.newaxis] - x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)]) - else: - x = latent_codes - if x.shape[1] != num_layers: - raise ValueError(f'Latent codes should be with shape [num, num_layers, ' - f'*code_shape], where `num_layers` equals to ' - f'{num_layers}, but {x.shape} is received!') - # Make boundary layer-wise if needed. -# if not is_boundary_layerwise: -# b = boundary -# b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) -# else: -# b = boundary[0] -# if b.shape[0] != num_layers: -# raise ValueError(f'Boundary should be with shape [num_layers, ' -# f'*code_shape], where `num_layers` equals to ' -# f'{num_layers}, but {b.shape} is received!') - # Get layer-wise manipulation strength. - if isinstance(layerwise_manipulation_strength, (int, float)): - s = [float(layerwise_manipulation_strength) for _ in range(num_layers)] - elif isinstance(layerwise_manipulation_strength, (list, tuple)): - s = layerwise_manipulation_strength - if len(s) != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` ' - f'mismatches number of layers `{num_layers}`!') - elif isinstance(layerwise_manipulation_strength, np.ndarray): - s = layerwise_manipulation_strength - if s.size != num_layers: - raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` ' - f'mismatches number of layers `{num_layers}`!') - else: - raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!') -# s = np.array(s).reshape( -# [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) -# b = b * s - -# if x.shape[1:] != b.shape: -# raise ValueError(f'Latent code shape {x.shape} and boundary shape ' -# f'{b.shape} mismatch!') - num = x.shape[0] - code_shape = x.shape[2:] - - x = x[:, np.newaxis] -# b = b[np.newaxis, np.newaxis, :] -# l = np.linspace(start_distance, end_distance, step).reshape( -# [step if axis == 1 else 1 for axis in range(x.ndim)]) - results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)]) - is_manipulatable = np.zeros(results.shape, dtype=bool) - is_manipulatable[:, :, layer_indices] = True - - tmp=MPC(proj,x,mindex,start_distance,end_distance,step) - tmp = tmp[:, :,np.newaxis] - tmp1 = np.tile(tmp, [num_layers if axis == 2 else 1 for axis in range(tmp.ndim)]) - - - results = np.where(is_manipulatable, tmp1, results) -# print(results.shape) - assert results.shape == (num, step, num_layers, *code_shape) - return results if layerwise_manipulation else results[:, :, 0] - -def MPC(proj,x,mindex,start_distance,end_distance,step): - # x shape (batch_size,1,num_layers,feature) -# print(x.shape) - x1=proj.transform(x[:,0,0,:]) #/np.sqrt(proj.explained_variance_) # (batch_size,num_pc) - - x1 = x1[:, np.newaxis] - x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)]) - - - l = np.linspace(start_distance, end_distance, step)[None,:] - x1[:,:,mindex]+=l - - tmp=x1.reshape((-1,x1.shape[-1])) #*np.sqrt(proj.explained_variance_) -# print('xxx') - x2=proj.inverse_transform(tmp) - x2=x2.reshape((x1.shape[0],x1.shape[1],-1)) - -# x1 = x1[:, np.newaxis] -# x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)]) - - return x2 - - - - -def parse_boundary_list(boundary_list_path): - """Parses boundary list. - - Sometimes, a text file containing a list of boundaries will significantly - simplify image manipulation with a large amount of boundaries. This function - is used to parse boundary information from such list file. - - Basically, each item in the list should be with format - `($NAME, $SPACE_TYPE): $PATH`. `DISABLE` at the beginning of the line can - disable a particular boundary. - - Sample: - - (age, z): $AGE_BOUNDARY_PATH - (gender, w): $GENDER_BOUNDARY_PATH - DISABLE(pose, wp): $POSE_BOUNDARY_PATH - - Args: - boundary_list_path: Path to the boundary list. - - Returns: - A dictionary, whose key is a two-element tuple (boundary_name, space_type) - and value is the corresponding boundary path. - - Raise: - ValueError: If the given boundary list does not exist. - """ - if not os.path.isfile(boundary_list_path): - raise ValueError(f'Boundary list `boundary_list_path` does not exist!') - - boundaries = {} - with open(boundary_list_path, 'r') as f: - for line in f: - if line[:len('DISABLE')] == 'DISABLE': - continue - boundary_info, boundary_path = line.strip().split(':') - boundary_name, space_type = boundary_info.strip()[1:-1].split(',') - boundary_name = boundary_name.strip() - space_type = space_type.strip().lower() - boundary_path = boundary_path.strip() - boundaries[(boundary_name, space_type)] = boundary_path - return boundaries diff --git a/spaces/Duskfallcrew/flowers-2-1-768/app.py b/spaces/Duskfallcrew/flowers-2-1-768/app.py deleted file mode 100644 index ab8896794f5ab549d55b4dfa88abaa40db6d3643..0000000000000000000000000000000000000000 --- a/spaces/Duskfallcrew/flowers-2-1-768/app.py +++ /dev/null @@ -1,19 +0,0 @@ -import os -import gradio as gr - -API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None) - -article = """--- -This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator).""" - -gr.Interface.load( - name="models/Duskfallcrew/flowers-2-1-768", - title="""Flowers 2 1 768""", - description="""Demo for Flowers 2 1 768 Stable Diffusion model. Check out our Models on CivitAI We stream a lot of our testing on twitch: Duskfall @ TWITCH - - any chance you can spare a coffee or three? - -If you want custom LoRA OR MODEL trained an option will become available on the Patreon Styling Token: fldsky1""", - article=article, - api_key=API_KEY, - ).queue(concurrency_count=20).launch() diff --git a/spaces/Duskfallcrew/isometric-dreams-sd-1-5/README.md b/spaces/Duskfallcrew/isometric-dreams-sd-1-5/README.md deleted file mode 100644 index c1a7ee7686e4b3192b85b6618df479df3e453981..0000000000000000000000000000000000000000 --- a/spaces/Duskfallcrew/isometric-dreams-sd-1-5/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Isometric Dreams Sd 1 5 -emoji: 📊 -colorFrom: indigo -colorTo: blue -sdk: gradio -sdk_version: 3.18.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Eddycrack864/Applio-Inference/configs/config.py b/spaces/Eddycrack864/Applio-Inference/configs/config.py deleted file mode 100644 index e3b0205a1f0d62f674b9c3de2c5ab7ee90464945..0000000000000000000000000000000000000000 --- a/spaces/Eddycrack864/Applio-Inference/configs/config.py +++ /dev/null @@ -1,265 +0,0 @@ -import argparse -import os -import sys -import json -from multiprocessing import cpu_count - -import torch - -try: - import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import - if torch.xpu.is_available(): - from infer.modules.ipex import ipex_init - ipex_init() -except Exception: - pass - -import logging - -logger = logging.getLogger(__name__) - - -version_config_list = [ - "v1/32k.json", - "v1/40k.json", - "v1/48k.json", - "v2/48k.json", - "v2/32k.json", -] - - -def singleton_variable(func): - def wrapper(*args, **kwargs): - if not wrapper.instance: - wrapper.instance = func(*args, **kwargs) - return wrapper.instance - - wrapper.instance = None - return wrapper - - -@singleton_variable -class Config: - def __init__(self): - self.device = "cuda:0" - self.is_half = True - self.n_cpu = 0 - self.gpu_name = None - self.json_config = self.load_config_json() - self.gpu_mem = None - ( - self.python_cmd, - self.listen_port, - self.iscolab, - self.noparallel, - self.noautoopen, - self.paperspace, - self.is_cli, - self.grtheme, - self.dml, - ) = self.arg_parse() - self.instead = "" - self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() - - @staticmethod - def load_config_json() -> dict: - d = {} - for config_file in version_config_list: - with open(f"configs/{config_file}", "r") as f: - d[config_file] = json.load(f) - return d - - @staticmethod - def arg_parse() -> tuple: - exe = sys.executable or "python" - parser = argparse.ArgumentParser() - parser.add_argument("--port", type=int, default=7865, help="Listen port") - parser.add_argument("--pycmd", type=str, default=exe, help="Python command") - parser.add_argument("--colab", action="store_true", help="Launch in colab") - parser.add_argument( - "--noparallel", action="store_true", help="Disable parallel processing" - ) - parser.add_argument( - "--noautoopen", - action="store_true", - help="Do not open in browser automatically", - ) - parser.add_argument( - "--paperspace", - action="store_true", - help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.", - ) - parser.add_argument( - "--is_cli", - action="store_true", - help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!", - ) - - parser.add_argument( - "-t", - "--theme", - help = "Theme for Gradio. Format - `JohnSmith9982/small_and_pretty` (no backticks)", - default = "JohnSmith9982/small_and_pretty", - type = str - ) - - parser.add_argument( - "--dml", - action="store_true", - help="Use DirectML backend instead of CUDA." - ) - - cmd_opts = parser.parse_args() - - cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 - - return ( - cmd_opts.pycmd, - cmd_opts.port, - cmd_opts.colab, - cmd_opts.noparallel, - cmd_opts.noautoopen, - cmd_opts.paperspace, - cmd_opts.is_cli, - cmd_opts.theme, - cmd_opts.dml, - ) - - # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. - # check `getattr` and try it for compatibility - @staticmethod - def has_mps() -> bool: - if not torch.backends.mps.is_available(): - return False - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False - - @staticmethod - def has_xpu() -> bool: - if hasattr(torch, "xpu") and torch.xpu.is_available(): - return True - else: - return False - - def use_fp32_config(self): - for config_file in version_config_list: - self.json_config[config_file]["train"]["fp16_run"] = False - - def device_config(self) -> tuple: - if torch.cuda.is_available(): - if self.has_xpu(): - self.device = self.instead = "xpu:0" - self.is_half = True - i_device = int(self.device.split(":")[-1]) - self.gpu_name = torch.cuda.get_device_name(i_device) - if ( - ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) - or "P40" in self.gpu_name.upper() - or "P10" in self.gpu_name.upper() - or "1060" in self.gpu_name - or "1070" in self.gpu_name - or "1080" in self.gpu_name - ): - logger.info("Found GPU %s, force to fp32", self.gpu_name) - self.is_half = False - self.use_fp32_config() - else: - logger.info("Found GPU %s", self.gpu_name) - self.gpu_mem = int( - torch.cuda.get_device_properties(i_device).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - if self.gpu_mem <= 4: - with open("infer/modules/train/preprocess.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open("infer/modules/train/preprocess.py", "w") as f: - f.write(strr) - elif self.has_mps(): - logger.info("No supported Nvidia GPU found") - self.device = self.instead = "mps" - self.is_half = False - self.use_fp32_config() - else: - logger.info("No supported Nvidia GPU found") - self.device = self.instead = "cpu" - self.is_half = False - self.use_fp32_config() - - if self.n_cpu == 0: - self.n_cpu = cpu_count() - - if self.is_half: - # 6G显存配置 - x_pad = 3 - x_query = 10 - x_center = 60 - x_max = 65 - else: - # 5G显存配置 - x_pad = 1 - x_query = 6 - x_center = 38 - x_max = 41 - - if self.gpu_mem is not None and self.gpu_mem <= 4: - x_pad = 1 - x_query = 5 - x_center = 30 - x_max = 32 - if self.dml: - logger.info("Use DirectML instead") - if ( - os.path.exists( - "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll" - ) - == False - ): - try: - os.rename( - "runtime\Lib\site-packages\onnxruntime", - "runtime\Lib\site-packages\onnxruntime-cuda", - ) - except: - pass - try: - os.rename( - "runtime\Lib\site-packages\onnxruntime-dml", - "runtime\Lib\site-packages\onnxruntime", - ) - except: - pass - # if self.device != "cpu": - import torch_directml - - self.device = torch_directml.device(torch_directml.default_device()) - self.is_half = False - else: - if self.instead: - logger.info(f"Use {self.instead} instead") - if ( - os.path.exists( - "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll" - ) - == False - ): - try: - os.rename( - "runtime\Lib\site-packages\onnxruntime", - "runtime\Lib\site-packages\onnxruntime-dml", - ) - except: - pass - try: - os.rename( - "runtime\Lib\site-packages\onnxruntime-cuda", - "runtime\Lib\site-packages\onnxruntime", - ) - except: - pass - return x_pad, x_query, x_center, x_max diff --git a/spaces/Edisonymy/buy-or-rent/src/app.py b/spaces/Edisonymy/buy-or-rent/src/app.py deleted file mode 100644 index ae28290999771aad7e91f12b1478beae4d27bb6f..0000000000000000000000000000000000000000 --- a/spaces/Edisonymy/buy-or-rent/src/app.py +++ /dev/null @@ -1,376 +0,0 @@ -import streamlit as st -import numpy as np -from mainbody import generate_main_body -from buy_or_rent import Buy_or_Rent_Model -from utils.general import get_param_distribution -import os -import streamlit_analytics -from config import button_css, n_samples, n_bins - -np.random.seed(123) - - -class App: - def main_header(self): - # Title - st.markdown( - "Open-source Buy or Rent Calculator", - unsafe_allow_html=True, - ) - with st.expander("**About**"): - st.write( - "Not sure whether it is financially better to buy a property or rent and invest? We use simulations to show possible returns for buying a property or renting given your assumptions. All parameters are assumed to be uncorrelated." - ) - st.markdown( - "***This app is currently in Beta. It assumes that you are in England, UK. Different countries may have different tax rules. Source code: https://huggingface.co/spaces/Edisonymy/buy-or-rent/tree/main***" - ) - self.country = st.selectbox( - label="Country", options=["UK", "US"], help="Determines Tax Calculations" - ) - if self.country == 'US': - st.markdown( - "***The US version of this app is currently in Beta. US taxes are not fully implemented and the defaults values do not currently reflect US norms.***" - ) - - def get_sidebar(self): - # User-enterable parameters for data generation - st.sidebar.subheader("Your Assumptions") - self.house_price = st.sidebar.number_input( - "Property Price", value=300000, step=5000 - ) - self.monthly_rent = st.sidebar.number_input( - "Monthly Rent:", value=int(self.house_price * 0.043 / 12), step=100 - ) - self.rental_yield = (12 * self.monthly_rent) / self.house_price - st.sidebar.markdown( - f"Implied Rental Yield: {self.rental_yield*100:.2f}%", - unsafe_allow_html=True, - ) - text = "Checkout a rental yield map here: https://www.home.co.uk/company/press/rental_yield_heat_map_london_postcodes.pdf" - st.sidebar.markdown( - f"{text}", unsafe_allow_html=True - ) - self.deposit = st.sidebar.slider( - "Deposit:", - min_value=0, - max_value=self.house_price, - value=int(0.3 * self.house_price), - step=100, - ) - self.deposit_mult = self.deposit / self.house_price - st.sidebar.subheader("Advanced Model Parameters:") - st.sidebar.markdown( - "It's hard to predict the future, so this section allows the simulations to reflect your uncertainty. The more uncertain you are about a parameter, the higher the standard deviation (sd) you should assume.", - unsafe_allow_html=True, - ) - self.mortgage_interest_annual_mean = st.sidebar.slider( - "Mortgage Interest Rate Mean:", - min_value=0.0, - max_value=10.0, - value=5.5, - step=0.1, - format="%f%%", - ) - self.mortgage_interest_annual_std = st.sidebar.slider( - "Mortgage Interest Rate sd:", - min_value=0.0, - max_value=10.0, - value=1.2, - step=0.1, - format="%f%%", - ) - text = "Check out historical mortgage rates here: https://tradingeconomics.com/united-kingdom/mortgage-rate" - st.sidebar.markdown( - f"{text}", unsafe_allow_html=True - ) - self.mortgage_interest_annual_list = ( - get_param_distribution( - self.mortgage_interest_annual_mean, - self.mortgage_interest_annual_std, - n_samples, - sidebar=True, - xlabel="Average Mortgage Interest Rate", - ) - / 100 - ) - st.sidebar.write("---") - self.investment_return_annual_mean = st.sidebar.slider( - "Investment Return Mean:", - min_value=0.0, - max_value=20.0, - value=6.0, - step=0.1, - format="%f%%", - ) - self.investment_return_annual_std = st.sidebar.slider( - "Investment Return sd:", - min_value=0.0, - max_value=5.0, - value=02.0, - step=0.1, - format="%f%%", - ) - text = "Check out historical stock market returns here: https://www.investopedia.com/ask/answers/042415/what-average-annual-return-sp-500.asp" - st.sidebar.markdown( - f"{text}", unsafe_allow_html=True - ) - self.investment_return_annual_list = ( - get_param_distribution( - self.investment_return_annual_mean, - self.investment_return_annual_std, - n_samples, - sidebar=True, - xlabel="Average Investment Rate of Return", - ) - / 100 - ) - with st.sidebar.expander("**Additional Parameters:**"): - if self.country == "UK": - self.stamp_duty_bol = st.checkbox( - "I pay Stamp Duty.", - value=False, - help="Stamp Duty (UK): A tax paid by the buyer when purchasing property in the United Kingdom. The amount of Stamp Duty depends on the property's purchase price and may include additional rates for second homes and buy-to-let properties.", - ) - self.cgt_bol = st.checkbox( - "I pay capital gains tax on the property.", - value=False, - help="Capital Gains Tax (CGT): A tax levied on the profit (capital gain) made from the sale or disposal of assets such as property, investments, or valuable possessions. The amount of CGT owed is typically calculated based on the difference between the purchase price and the selling price of the asset. Different rates may apply to individuals and businesses, and there are often exemptions and allowances that can reduce the taxable amount.", - ) - self.cgt_investment_bol = st.checkbox( - "I pay capital gains tax on the investment.", - value=False, - help="Capital Gains Tax (CGT): A tax levied on the profit (capital gain) made from the sale or disposal of assets such as property, investments, or valuable possessions. The amount of CGT owed is typically calculated based on the difference between the purchase price and the selling price of the asset. Different rates may apply to individuals and businesses, and there are often exemptions and allowances that can reduce the taxable amount.", - ) - self.annual_income = st.number_input( - "Annual Salary", - value=20000, - step=100, - help="This should be set to the expected salary at time of sale, required to calculate capital gains tax.", - ) - if self.country == "US": - self.us_tax_return = st.selectbox( - label="How you file your taxes", - options=["Joint Return", "Individual Return"], - help="The initial $250,000 in profit gained from selling your home is exempt from taxation. If you file jointly with your spouse, this exemption increases to $500,000. Additional details can be found in IRS Topic 701.", - ) - self.property_tax_rate = st.slider( - "Property Tax Rate:", - min_value=0.0, - max_value=10.0, - value=1.35, - step=0.01, - format="%f%%", - ) - st.markdown( - f"${self.property_tax_rate/self.house_price/100:.2f}% for first year", - unsafe_allow_html=True, - ) - self.marginal_tax_rate = st.slider( - "Marginal Tax Rate:", - min_value=0, - max_value=60, - value=20, - step=1, - format="%f%%", - ) - self.ongoing_cost = st.number_input( - "Annual combined maintenance, insurance and service charge:", - value=int(self.house_price * 0.006), - step=100, - help="The total annual cost associated with maintaining, insurance, and servicing a property, including expenses such as property management fees, maintenance fees, common fees, and other similar charges. Assumed to grow at rate of inflation.", - ) - st.markdown( - f"{self.ongoing_cost/self.house_price*100:.2f}% of Property Price", - unsafe_allow_html=True, - ) - self.buying_cost = st.number_input( - "Buying cost (excluding taxes):", - value=3000, - step=100, - help="This includes laywer's fee, mortgage lender fees, surveyor's fee, valuation fees and other fees you expect to pay at the time of purchase.", - ) - st.markdown( - f"{self.buying_cost/self.house_price*100:.2f}% of Property Price", - unsafe_allow_html=True, - ) - self.selling_cost_no_cgt = ( - st.number_input( - "Selling cost as a % of property value (excluding Taxes):", - value=2.0, - step=0.1, - format="%.1f", - help="Total expense incurred when selling a property. This typically include real estate agent commissions, legal fees, advertising expenses, and any necessary repairs or renovations to prepare the property for sale.", - ) - / 100 - ) - st.markdown( - f"{self.selling_cost_no_cgt*self.house_price:.0f}", - unsafe_allow_html=True, - ) - self.inflation = ( - st.number_input( - "Inflation:", - min_value=0.0, - max_value=100.0, - value=2.0, - step=0.1, - format="%.1f", - help="Used for inflation adjustment only. It does not affect the model.", - ) - / 100 - ) - self.mortgage_length = st.slider( - "Mortgage Length:", min_value=1, max_value=40, value=30, step=1 - ) - # uncertain parameters - st.write("---") - self.property_price_growth_annual_mean = st.slider( - "Property Price Growth Mean:", - min_value=-5.0, - max_value=10.0, - value=3.0, - step=0.1, - format="%f%%", - ) - self.property_price_growth_annual_std = st.slider( - "Property Price Growth sd:", - min_value=0.0, - max_value=5.0, - value=1.0, - step=0.1, - format="%f%%", - ) - text = "Check out historical property price growth here: https://www.ons.gov.uk/economy/inflationandpriceindices/bulletins/housepriceindex/june2023" - st.markdown( - f"{text}", unsafe_allow_html=True - ) - self.property_price_growth_annual_list = ( - get_param_distribution( - self.property_price_growth_annual_mean, - self.property_price_growth_annual_std, - n_samples, - xlabel="Annual Property Value Growth", - ) - / 100 - ) - st.write("---") - self.rent_increase_mean = st.slider( - "Rent Increase Mean:", - min_value=0.0, - max_value=10.0, - value=2.0, - step=0.1, - format="%f%%", - ) - self.rent_increase_std = st.slider( - "Rent Increase sd:", - min_value=0.0, - max_value=5.0, - value=1.0, - step=0.1, - format="%f%%", - ) - text = "Checkout historical rent increases here: https://www.ons.gov.uk/economy/inflationandpriceindices/bulletins/indexofprivatehousingrentalprices/july2023" - st.markdown( - f"{text}", unsafe_allow_html=True - ) - self.rent_increase_list = ( - get_param_distribution( - self.rent_increase_mean, - self.rent_increase_std, - n_samples, - xlabel="Average Annual Rent Increase", - ) - / 100 - ) - st.write("---") - self.years_until_sell_mean = st.slider( - "Years Until Sell Mean:", min_value=0, max_value=100, value=15 - ) - self.years_until_sell_std = st.slider( - "Years Until Sell sd:", min_value=0, max_value=10, value=5 - ) - self.years_until_sell_list = get_param_distribution( - self.years_until_sell_mean, - self.years_until_sell_std, - n_samples, - as_int=True, - xlabel="Years Until Property Is Sold", - ) - st.write("---") - - def support_section(self): - st.write( - "If you found this useful and want to support me, consider buying me a coffee here:" - ) - # Embed the "Buy Me a Coffee" button script with custom CSS - buy_me_a_coffee_html = """ - - """ - # Display the custom CSS and the button in the Streamlit app - st.components.v1.html(buy_me_a_coffee_html) - - def main(self): - with streamlit_analytics.track(unsafe_password=os.getenv("sa_password")): - if "sidebar_state" not in st.session_state: - st.session_state.sidebar_state = "auto" - st.set_page_config( - page_title="Buy or Rent Calculator", - initial_sidebar_state=st.session_state.sidebar_state, - ) - # Apply the custom CSS - st.markdown(button_css, unsafe_allow_html=True) - - self.main_header() - self.get_sidebar() - - # Initialize the model - model = Buy_or_Rent_Model() - model.COUNTRY = self.country - model.HOUSE_PRICE = self.house_price - model.DEPOSIT_MULT = self.deposit_mult - model.RENTAL_YIELD = self.rental_yield - model.SELLING_COST_MULT = self.selling_cost_no_cgt - model.MORTGAGE_LENGTH = self.mortgage_length - model.ONGOING_COST_MULT = self.ongoing_cost / self.house_price - model.BUYING_COST_FLAT = self.buying_cost - model.inflation = self.inflation - if self.country == "UK": - model.ANNUAL_SALARY = self.annual_income - model.STAMP_DUTY_BOL = self.stamp_duty_bol - model.CGT_BOL = self.cgt_bol - model.CGT_INVESTMENT_BOL = self.cgt_investment_bol - if self.country == "US": - model.US_TAX_RETURN = self.us_tax_return - model.PROPERTY_TAX_RATE = self.property_tax_rate / 100 - model.MARGINAL_TAX_RATE = self.marginal_tax_rate / 100 - - # Generate combinations and calculate NPV - generate_main_body( - model, - mortgage_interest_annual_list=self.mortgage_interest_annual_list, - property_price_growth_annual_list=self.property_price_growth_annual_list, - rent_increase_list=self.rent_increase_list, - investment_return_annual_list=self.investment_return_annual_list, - years_until_sell_list=self.years_until_sell_list, - ) - # st.markdown("***To adjust your assumptions, please use the left sidebar (mobile users please press the top left button).***") - if st.button("Adjust Assumptions (Toggle Sidebar)"): - st.session_state.sidebar_state = ( - "collapsed" - if st.session_state.sidebar_state == "expanded" - else "expanded" - ) - # Force an app rerun after switching the sidebar state. - st.experimental_rerun() - - st.write("---") - self.support_section() - - -if __name__ == "__main__": - app = App() - app.main() diff --git a/spaces/GMFTBY/PandaGPT/datasets/sft_dataset.py b/spaces/GMFTBY/PandaGPT/datasets/sft_dataset.py deleted file mode 100644 index bfc64dd37d912d539c0600e5965ad1e5c87a6c1d..0000000000000000000000000000000000000000 --- a/spaces/GMFTBY/PandaGPT/datasets/sft_dataset.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -import os -import json -from tqdm import tqdm -import ipdb -import random -from torch.nn.utils.rnn import pad_sequence -from dataclasses import dataclass, field -from typing import Callable, Dict, Sequence - -import torch -import torch.distributed as dist -import transformers -from torch.utils.data import Dataset -from tqdm import tqdm - -class SupervisedDataset(Dataset): - """Dataset for supervised fine-tuning.""" - - def __init__(self, data_path: str, image_root_path: str): - super(SupervisedDataset, self).__init__() - - with open(data_path, 'r') as f: - json_data = json.load(f) - # for debug: - #json_data = json_data[:100000] - - self.image_path_list, self.caption_list = [], [] - for item in json_data: - one_image_name, one_caption = item["image_name"], item["conversation"] - # TODO: stage 2 dataset format is invalid - if not one_image_name.endswith('.jpg'): - one_image_name += '.jpg' - one_image_path = image_root_path + '/{}'.format(one_image_name) - self.image_path_list.append(one_image_path) - self.caption_list.append(one_caption) - print(f'[!] collect {len(self.image_path_list)} samples for training') - - def __len__(self): # number of instances - return len(self.image_path_list) - - #def __getitem__(self, i) -> Dict[str, torch.Tensor]: # how to get item, 取一个样本 - def __getitem__(self, i): - return dict(image_paths=self.image_path_list[i], output_texts=self.caption_list[i]) - - def collate(self, instances): - image_paths, output_texts = tuple([instance[key] for instance in instances] for key in ("image_paths", "output_texts")) - return dict( - image_paths=image_paths, - output_texts=output_texts - ) diff --git a/spaces/Gradio-Blocks/beat-interpolator/examples/models/celeba256/__init__.py b/spaces/Gradio-Blocks/beat-interpolator/examples/models/celeba256/__init__.py deleted file mode 100644 index bbbcee7a9aa24338a5a7e55722f732b1fcdcdd90..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/beat-interpolator/examples/models/celeba256/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .model import create_celeba256_inference as create diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/instaboost.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/instaboost.py deleted file mode 100644 index 38b6819f60587a6e0c0f6d57bfda32bb3a7a4267..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/datasets/pipelines/instaboost.py +++ /dev/null @@ -1,98 +0,0 @@ -import numpy as np - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class InstaBoost(object): - r"""Data augmentation method in `InstaBoost: Boosting Instance - Segmentation Via Probability Map Guided Copy-Pasting -🤗 Open LLM Leaderboard
""" - -INTRODUCTION_TEXT = """ -📐 The 🤗 Open LLM Leaderboard aims to track, rank and evaluate open LLMs and chatbots. - -🤗 Submit a model for automated evaluation on the 🤗 GPU cluster on the "Submit" page! -The leaderboard's backend runs the great [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) - read more details in the "About" page! -""" - -LLM_BENCHMARKS_TEXT = f""" -# Context -With the plethora of large language models (LLMs) and chatbots being released week upon week, often with grandiose claims of their performance, it can be hard to filter out the genuine progress that is being made by the open-source community and which model is the current state of the art. - -## Icons -{ModelType.PT.to_str(" : ")} model: new, base models, trained on a given corpora -{ModelType.FT.to_str(" : ")} model: pretrained models finetuned on more data -Specific fine-tune subcategories (more adapted to chat): -{ModelType.IFT.to_str(" : ")} model: instruction fine-tunes, which are model fine-tuned specifically on datasets of task instruction -{ModelType.RL.to_str(" : ")} model: reinforcement fine-tunes, which usually change the model loss a bit with an added policy. -If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model information! - -"Flagged" indicates that this model has been flagged by the community, and should probably be ignored! Clicking the link will redirect you to the discussion about the model. -(For ex, the model was trained on the evaluation data, and is therefore cheating on the leaderboard.) - -## How it works - -📈 We evaluate models on 4 key benchmarks using the Eleuther AI Language Model Evaluation Harness , a unified framework to test generative language models on a large number of different evaluation tasks. - -- AI2 Reasoning Challenge (25-shot) - a set of grade-school science questions. -- HellaSwag (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models. -- MMLU (5-shot) - a test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. -- TruthfulQA (0-shot) - a test to measure a model's propensity to reproduce falsehoods commonly found online. Note: TruthfulQA in the Harness is actually a minima a 6-shots task, as it is prepended by 6 examples systematically, even when launched using 0 for the number of few-shot examples. -- Winogrande (5-shot) - an adversarial and difficult Winograd benchmark at scale, for commonsense reasoning. -- GSM8k (5-shot) - diverse grade school math word problems to measure a model's ability to solve multi-step mathematical reasoning problems. -- DROP (3-shot) - English reading comprehension benchmark requiring Discrete Reasoning Over the content of Paragraphs. - -For all these evaluations, a higher score is a better score. -We chose these benchmarks as they test a variety of reasoning and general knowledge across a wide variety of fields in 0-shot and few-shot settings. - -## Details and logs -You can find: -- detailed numerical results in the `results` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/results -- details on the input/outputs for the models in the `details` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/details -- community queries and running status in the `requests` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/requests - -## Reproducibility -To reproduce our results, here is the commands you can run, using [this version](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) of the Eleuther AI Harness: -`python main.py --model=hf-causal --model_args="pretrained=,use_accelerate=True,revision= "` -` --tasks= --num_fewshot= --batch_size=2 --output_path= ` - -The total batch size we get for models which fit on one A100 node is 16 (8 GPUs * 2). If you don't use parallelism, adapt your batch size to fit. -*You can expect results to vary slightly for different batch sizes because of padding.* - -The tasks and few shots parameters are: -- ARC: 25-shot, *arc-challenge* (`acc_norm`) -- HellaSwag: 10-shot, *hellaswag* (`acc_norm`) -- TruthfulQA: 0-shot, *truthfulqa-mc* (`mc2`) -- MMLU: 5-shot, *hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions* (average of all the results `acc`) -- Winogrande: 5-shot, *winogrande* (`acc`) -- GSM8k: 5-shot, *gsm8k* (`acc`) -- DROP: 3-shot, *drop* (`f1`) - -Side note on the baseline scores: -- for log-likelihood evaluation, we select the random baseline -- for DROP, we select the best submission score according to [their leaderboard](https://leaderboard.allenai.org/drop/submissions/public) when the paper came out (NAQANet score) -- for GSM8K, we select the score obtained in the paper after inetuning a 6B model on the full GSM8K training set for 50 epochs - -## Quantization -To get more information about quantization, see: -- 8 bits: [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), [paper](https://arxiv.org/abs/2208.07339) -- 4 bits: [blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes), [paper](https://arxiv.org/abs/2305.14314) - -## More resources -If you still have questions, you can check our FAQ [here](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/179)! -We also gather cool resources from the community, other teams, and other labs [here](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/174)! -""" - -EVALUATION_QUEUE_TEXT = """ -# Evaluation Queue for the 🤗 Open LLM Leaderboard - -Models added here will be automatically evaluated on the 🤗 cluster. - -## Some good practices before submitting a model - -### 1) Make sure you can load your model and tokenizer using AutoClasses: -```python -from transformers import AutoConfig, AutoModel, AutoTokenizer -config = AutoConfig.from_pretrained("your model name", revision=revision) -model = AutoModel.from_pretrained("your model name", revision=revision) -tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision) -``` -If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. - -Note: make sure your model is public! -Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted! - -### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index) -It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`! - -### 3) Make sure your model has an open license! -This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗 - -### 4) Fill up your model card -When we add extra information about models to the leaderboard, it will be automatically taken from the model card - -## In case of model failure -If your model is displayed in the `FAILED` category, its execution stopped. -Make sure you have followed the above steps first. -If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task). -""" - -CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" -CITATION_BUTTON_TEXT = r""" -@misc{open-llm-leaderboard, - author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf}, - title = {Open LLM Leaderboard}, - year = {2023}, - publisher = {Hugging Face}, - howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}" -} -@software{eval-harness, - author = {Gao, Leo and - Tow, Jonathan and - Biderman, Stella and - Black, Sid and - DiPofi, Anthony and - Foster, Charles and - Golding, Laurence and - Hsu, Jeffrey and - McDonell, Kyle and - Muennighoff, Niklas and - Phang, Jason and - Reynolds, Laria and - Tang, Eric and - Thite, Anish and - Wang, Ben and - Wang, Kevin and - Zou, Andy}, - title = {A framework for few-shot language model evaluation}, - month = sep, - year = 2021, - publisher = {Zenodo}, - version = {v0.0.1}, - doi = {10.5281/zenodo.5371628}, - url = {https://doi.org/10.5281/zenodo.5371628} -} -@misc{clark2018think, - title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, - author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, - year={2018}, - eprint={1803.05457}, - archivePrefix={arXiv}, - primaryClass={cs.AI} -} -@misc{zellers2019hellaswag, - title={HellaSwag: Can a Machine Really Finish Your Sentence?}, - author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi}, - year={2019}, - eprint={1905.07830}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -@misc{hendrycks2021measuring, - title={Measuring Massive Multitask Language Understanding}, - author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, - year={2021}, - eprint={2009.03300}, - archivePrefix={arXiv}, - primaryClass={cs.CY} -} -@misc{lin2022truthfulqa, - title={TruthfulQA: Measuring How Models Mimic Human Falsehoods}, - author={Stephanie Lin and Jacob Hilton and Owain Evans}, - year={2022}, - eprint={2109.07958}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -@misc{DBLP:journals/corr/abs-1907-10641, - title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale}, - author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi}, - year={2019}, - eprint={1907.10641}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -@misc{DBLP:journals/corr/abs-2110-14168, - title={Training Verifiers to Solve Math Word Problems}, - author={Karl Cobbe and - Vineet Kosaraju and - Mohammad Bavarian and - Mark Chen and - Heewoo Jun and - Lukasz Kaiser and - Matthias Plappert and - Jerry Tworek and - Jacob Hilton and - Reiichiro Nakano and - Christopher Hesse and - John Schulman}, - year={2021}, - eprint={2110.14168}, - archivePrefix={arXiv}, - primaryClass={cs.CL} -} -@misc{DBLP:journals/corr/abs-1903-00161, - title={{DROP:} {A} Reading Comprehension Benchmark Requiring Discrete Reasoning - Over Paragraphs}, - author={Dheeru Dua and - Yizhong Wang and - Pradeep Dasigi and - Gabriel Stanovsky and - Sameer Singh and - Matt Gardner}, - year={2019}, - eprinttype={arXiv}, - eprint={1903.00161}, - primaryClass={cs.CL} -}""" diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/cache_dir/hate_speech18_default_train_text/text_duplicates/text_duplicates.html b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/cache_dir/hate_speech18_default_train_text/text_duplicates/text_duplicates.html deleted file mode 100644 index 61cdd4530dee818b1c6dc55f2103167291bebf94..0000000000000000000000000000000000000000 --- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/cache_dir/hate_speech18_default_train_text/text_duplicates/text_duplicates.html +++ /dev/null @@ -1 +0,0 @@ -
\ No newline at end of file diff --git a/spaces/HuggingFaceM4/idefics_playground/app_dialogue.py b/spaces/HuggingFaceM4/idefics_playground/app_dialogue.py deleted file mode 100644 index d523a10474d1322b3123438a486451c865ad6a59..0000000000000000000000000000000000000000 --- a/spaces/HuggingFaceM4/idefics_playground/app_dialogue.py +++ /dev/null @@ -1,879 +0,0 @@ -import copy -import glob -import hashlib -import logging -import os -import re -from pathlib import Path -from typing import List, Optional, Tuple -from urllib.parse import urlparse - -import gradio as gr -import PIL -from gradio import processing_utils -from gradio_client.client import DEFAULT_TEMP_DIR -from text_generation import Client -from transformers import AutoProcessor - - -MODELS = [ - # "HuggingFaceM4/idefics-9b-instruct", - "HuggingFaceM4/idefics-80b-instruct", -] - -API_PATHS = { - "HuggingFaceM4/idefics-9b-instruct": ( - "https://api-inference.huggingface.co/models/HuggingFaceM4/idefics-9b-instruct" - ), - "HuggingFaceM4/idefics-80b-instruct": ( - "https://api-inference.huggingface.co/models/HuggingFaceM4/idefics-80b-instruct" - ), -} - -SYSTEM_PROMPT = [ - """The following is a conversation between a highly knowledgeable and intelligent visual AI assistant, called Assistant, and a human user, called User. In the following interactions, User and Assistant will converse in natural language, and Assistant will do its best to answer User’s questions. Assistant has the ability to perceive images and reason about the content of visual inputs. Assistant was built to be respectful, polite and inclusive. It knows a lot, and always tells the truth. When prompted with an image, it does not make up facts. - -The conversation begins:""", - """\nUser:""", - "https://i1.pickpik.com/photos/515/906/355/kittens-cats-pet-cute-preview.jpg", - "Describe this image.duplicate_fraction 0.016812865497076057 duplicates_dict In order to help increase the booklets downloads , it would be great if all Stormfronters who had YouTube accounts , could display the following text in the description boxes of their uploaded YouTube videos . 2 Click below for a FREE download of a colorfully illustrated 132 page e-book on the Zionist-engineered INTENTIONAL destruction of Western civilization . 2 Then why not simply copy this text ( & links ) and paste it into the description box of your YouTube videos ? 2 Y' all have a nice day . 2 Good. 2 Thanks. 3 LOL 2 Interesting . 2 Aragorn 9 Cheers 2 16 4 Indeed. 4 No . 3 88 3 This is from the back-file at Stormfront 's Advanced Scout forum which is solely devoted to promoting the Pioneer Litte Europe strategy . 2 This is from Stormfront 's Advanced Scout forum which is solely devoted to promoting PLE - and its local militant front Legionism . 2 This is my next read . 2 I 'm going to carefully study it so hopefully I 'll have some decent input to contribute . 2 Nope. 2 Guess who picked Canada 's first batch of refugees from Turkey ? 2 - YouTube 12 Same here . 3 Really ? 2 Yeah. 3 14/88 7 Agreed. 6 Absolutely. 4 2508 21 Thank you . 4 Karina Sorensen is a member of the Danish Peoples Party , a pro-Danish heritage party . 2 Susan 2 Wolf 278 3 Well done . 3 Really? 3 Thank you 2 Exactly. 8 Thanks for posting . 2 Source 2 No. 2 Sad but true . 2 God Bless 3 To learn more , click here : www.jonasridgeway.com/tech2.html www.spiritual.com.au/astral.html www.astralweb.org www.near-death.com/experiences/cayce01.html www.astralvoyage.com/projection/index.html It would be pretty cool for teachers to tell White kids they go out-of-body every night . 2 Nice . 2 88 ! 2 Good luck ! 3 Hell I 'd rather clean a dozen tiolets a day then have to walk into a bathroom and see some mongrel standing there with a mop giving me a glassy-eyed stare . 3 1 . 2 2 . 3 -Yankee Jim 3 CF 3 Thanks ! 4 David ( SS ) 2 Period . 2 YouTube - Broadcast Yourself . 15 I never attacked you and i am not your son plus i dont care what you done.P M if you have a problem 2 Hails 2 Greetings all . 2 Thanks . 4 Whites shoudl clean their own mess ! 2 Getting someone else to do our dirty work got us into the mess we are in today . 2 It is a noble idea but I would rather stay here in mostly white Missouri then move to 35 % black South Carolina . 3 This post by Jack boot and the piece by Marc Moran , are very inspiring . 2 They make you want to go out and reach people . 2 I was very inspired by Moran 's writing . 2 Until we have a dedicated cable television station , there 's always the opportunity to use the free cable access airwaves as per this thread : Make a Cable Access TV Program ! 2 I have never seen an Asian woman dating an Asian man.They like the white man they re-force wiggers and our detrimental towards our cause.Cpamikei 2 Thank you for posting this . 2 Peter the Great . 3 ? 2 Wow! 5 Camie 2 I clean my own toilet as for public toilets and such there is no dirty work there it is an Economical contribution 2508 2 Welcome to Stormfront . 2 Japan REACTOR - RODS MELT ! 2 NUCLEAR PLANT HAS FULLY EXPLODED ! 2 11 2 I agree . 2 I do n't think so . 2 Just saying . 2 Hello. 2 Tereasa 2 Thank you ! 2 Amen . 2 -Zoë 2 I usually know when friends or relatives are distressed through illness or accident/incident.I then phone them and find out what has happened . 2 But what do you say to the ivy who wants to become a tree ?? 2 Or for that matter thinks he is a tree ? 2 32 2 Erik 2 ", - """\nAssistant: Five kittens are standing together in the center of the photograph. From the left to right, there is one orange kitten, two kittens white and black stripes, and two brown kittens with an orange head. They are in bright green grass and it looks like they are moving forward. """, - "\nUser:How about this image?", - "https://cdn.pixabay.com/photo/2017/09/25/13/12/puppy-2785074_1280.jpg", - "Can you describe it too? ", - """\nAssistant: A dog is lying on the floor, looking at the camera. It is looking directly at you. The dog has a white body and brown patches on its face and ears. Its eyes are dark. Its nose is black, and it has long, floppy ears, white paws, long fur, big eyes. """, - "\nUser: What kind of breed is it? ", - """\nAssistant: It looks like a Cavalier King Charles Spaniel. These breeds are known for their gentle, affectionate nature. Their long ears and big, brown eyes give them a sweet, expressive face. Cavaliers are small dogs weighing between 13 and 20 pounds. They have a silky, medium-length coat that comes in various colors, including black and tan, tricolor, red and white, or black. """, - "\nUser: What can you tell me about this breed of dogs? ", - """\nAssistant: One specific characteristic of the Cavalier King Charles Spaniel is its friendly and affectionate nature. This breed is known for being extremely sociable and forming strong bonds with their owners. They are often described as "velcro dogs" because they love to be close to their human companions, whether it's sitting on their laps, cuddling on the couch, or simply following them around the house. """, - "\nUser: ghjkhjabnufs ", - """\nAssistant: That doesn’t seem to be a word. Could you ask me another way? """, - "\nUser: Do you like Cavalier King Charles Spaniel? ", - """\nAssistant: I do not have personal opinions as I’m just a computer program. However, cavaliers are known for being excellent family pets due to their gentle and patient demeanor, making them great with children and other pets. Their affectionate nature also makes them ideal therapy dogs, as they can provide comfort and emotional support to those in need. """, - "\nUser: How many dogs do you see in this image?", - "https://i.dailymail.co.uk/i/pix/2011/07/01/article-2010308-0CD22A8300000578-496_634x414.jpg", - " ", - """\nAssistant: There is no dogs in this image. The picture shows a tennis player jumping to volley the ball. """, -] - -BAN_TOKENS = ( # For documentation puporse. We are not using this list, it is hardcoded inside `idefics_causal_lm.py` inside TGI. - " ; " -) -EOS_STRINGS = [" ", "\nUser:"] -STOP_SUSPECT_LIST = [] - -GRADIO_LINK = "https://huggingfacem4-idefics-playground.hf.space" -API_TOKEN = os.getenv("HF_AUTH_TOKEN") -IDEFICS_LOGO = "https://huggingface.co/spaces/HuggingFaceM4/idefics_playground/resolve/main/IDEFICS_logo.png" - -PROCESSOR = AutoProcessor.from_pretrained( - "HuggingFaceM4/idefics-9b-instruct", - token=API_TOKEN, -) - -BOT_AVATAR = "IDEFICS_logo.png" - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger() - - -# Monkey patch adapted from gradio.components.image.Image - mostly to make the `save` step optional in `pil_to_temp_file` -def hash_bytes(bytes: bytes): - sha1 = hashlib.sha1() - sha1.update(bytes) - return sha1.hexdigest() - - -def pil_to_temp_file(img: PIL.Image.Image, dir: str = DEFAULT_TEMP_DIR, format: str = "png") -> str: - """Save a PIL image into a temp file""" - bytes_data = processing_utils.encode_pil_to_bytes(img, format) - temp_dir = Path(dir) / hash_bytes(bytes_data) - temp_dir.mkdir(exist_ok=True, parents=True) - filename = str(temp_dir / f"image.{format}") - if not os.path.exists(filename): - img.save(filename, pnginfo=processing_utils.get_pil_metadata(img)) - return filename - - -def add_file(file): - return file.name, gr.update(label='🖼️ Uploaded!') - - -# This is a hack to make pre-computing the default examples work. -# During normal inference, we pass images as url to a local file using the method `gradio_link` -# which allows the tgi server to fetch the local image from the frontend server. -# however, we are building the space (and pre-computing is part of building the space), the frontend is not available -# and won't answer. So tgi server will try to fetch an image that is not available yet, which will result in a timeout error -# because tgi will never be able to return the generation. -# To bypass that, we pass instead the images URLs from the spaces repo. -all_images = glob.glob(f"{os.path.dirname(__file__)}/example_images/*") -DEFAULT_IMAGES_TMP_PATH_TO_URL = {} -for im_path in all_images: - H = gr.Image(im_path, visible=False, type="filepath") - tmp_filename = H.preprocess(H.value) - DEFAULT_IMAGES_TMP_PATH_TO_URL[tmp_filename] = f"https://huggingface.co/spaces/HuggingFaceM4/idefics_playground/resolve/main/example_images/{os.path.basename(im_path)}" - - -# Utils to handle the image markdown display logic -def split_str_on_im_markdown(string: str) -> List[str]: - """ - Extract from a string (typically the user prompt string) the potential images from markdown - Examples: - - `User:Describe this image.` would become `["User:", "https://favurl.com/chicken_on_money.png", "Describe this image."]` - - `User:Describe this image.` would become `["User:", "/my_temp/chicken_on_money.png", "Describe this image."]` - """ - IMAGES_PATTERN = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)") - parts = [] - cursor = 0 - for pattern in IMAGES_PATTERN.finditer(string): - start = pattern.start() - if start != cursor: - parts.append(string[cursor:start]) - image_url = pattern.group(1) - if image_url.startswith("/file="): - image_url = image_url[6:] # Remove the 'file=' prefix - parts.append(image_url) - cursor = pattern.end() - if cursor != len(string): - parts.append(string[cursor:]) - return parts - - -def is_image(string: str) -> bool: - """ - There are two ways for images: local image path or url. - """ - return is_url(string) or string.startswith(DEFAULT_TEMP_DIR) - - -def is_url(string: str) -> bool: - """ - Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately - invalidated the url - """ - if " " in string: - return False - result = urlparse(string) - return all([result.scheme, result.netloc]) - - -def isolate_images_urls(prompt_list: List) -> List: - """ - Convert a full string prompt to the list format expected by the processor. - In particular, image urls (as delimited by ) should be their own elements. - From: - ``` - [ - "bonjour hello", - PIL.Image.Image, - "Aurevoir", - ] - ``` - to: - ``` - [ - "bonjour", - IMG_URL, - "hello", - PIL.Image.Image, - "Aurevoir", - ] - ``` - """ - linearized_list = [] - for prompt in prompt_list: - # Prompt can be either a string, or a PIL image - if isinstance(prompt, PIL.Image.Image): - linearized_list.append(prompt) - elif isinstance(prompt, str): - if " " not in prompt: - linearized_list.append(prompt) - else: - prompt_splitted = prompt.split(" ") - for ps in prompt_splitted: - if ps == "": - continue - if ps.startswith(" `) in the user prompt - by fetching them, saving them locally and replacing the whole sub-sequence the image local path. - """ - if " " in user_prompt: - splitted_user_prompt = isolate_images_urls([user_prompt]) - resulting_user_prompt = [] - for u_p in splitted_user_prompt: - if is_url(u_p): - img = fetch_images([u_p])[0] - tmp_file = pil_to_temp_file(img) - resulting_user_prompt.append(tmp_file) - else: - resulting_user_prompt.append(u_p) - return resulting_user_prompt - else: - return [user_prompt] - - -def gradio_link(img_path: str) -> str: - url = f"{GRADIO_LINK}/file={img_path}" - return url - - -def prompt_list_to_markdown(prompt_list: List[str]) -> str: - """ - Convert a user prompt in the list format (i.e. elements are either a PIL image or a string) into - the markdown format that is used for the chatbot history and rendering. - """ - resulting_string = "" - for elem in prompt_list: - if is_image(elem): - if is_url(elem): - resulting_string += f"" - else: - resulting_string += f"" - else: - resulting_string += elem - return resulting_string - - -def prompt_list_to_tgi_input(prompt_list: List[str]) -> str: - """ - TGI expects a string that contains both text and images in the image markdown format (i.e. the `![]()` ). - The images links are parsed on TGI side - """ - result_string_input = "" - for elem in prompt_list: - if is_image(elem): - if is_url(elem): - result_string_input += f"" - else: - result_string_input += f"})" - else: - result_string_input += elem - return result_string_input - - -def remove_spaces_around_token(text: str) -> str: - pattern = r"\s*( )\s*" - replacement = r"\1" - result = re.sub(pattern, replacement, text) - return result - - -# Chatbot utils -def format_user_prompt_with_im_history_and_system_conditioning( - current_user_prompt_str: str, current_image: Optional[str], history: List[Tuple[str, str]] -) -> Tuple[List[str], List[str]]: - """ - Produces the resulting list that needs to go inside the processor. - It handles the potential image box input, the history and the system conditionning. - """ - resulting_list = copy.deepcopy(SYSTEM_PROMPT) - - # Format history - for turn in history: - user_utterance, assistant_utterance = turn - splitted_user_utterance = split_str_on_im_markdown(user_utterance) - - optional_space = "" - if not is_image(splitted_user_utterance[0]): - optional_space = " " - resulting_list.append(f"\nUser:{optional_space}") - resulting_list.extend(splitted_user_utterance) - resulting_list.append(f" \nAssistant: {assistant_utterance}") - - # Format current input - current_user_prompt_str = remove_spaces_around_token(current_user_prompt_str) - if current_image is None: - if "![](" in current_user_prompt_str: - current_user_prompt_list = split_str_on_im_markdown(current_user_prompt_str) - else: - current_user_prompt_list = handle_manual_images_in_user_prompt(current_user_prompt_str) - - optional_space = "" - if not is_image(current_user_prompt_list[0]): - # Check if the first element is an image (and more precisely a path to an image) - optional_space = " " - resulting_list.append(f"\nUser:{optional_space}") - resulting_list.extend(current_user_prompt_list) - resulting_list.append(" \nAssistant:") - else: - # Choosing to put the image first when the image is inputted through the UI, but this is an arbiratrary choice. - resulting_list.extend(["\nUser:", current_image, f"{current_user_prompt_str} \nAssistant:"]) - current_user_prompt_list = [current_user_prompt_str] - - return resulting_list, current_user_prompt_list - - -# dope_callback = gr.CSVLogger() -# problematic_callback = gr.CSVLogger() - -textbox = gr.Textbox( - placeholder="Upload an image and send a message", - show_label=False, - # value="Describe the battle against the fierce dragons.", - visible=True, - container=False, - label="Text input", - scale=6, -) -with gr.Blocks(title="IDEFICS Playground", theme=gr.themes.Base()) as demo: - gr.HTML(""" 🐶 IDEFICS Playground
""") - with gr.Row(variant="panel"): - with gr.Column(scale=1): - gr.Image(IDEFICS_LOGO, elem_id="banner-image", show_label=False, show_download_button=False) - with gr.Column(scale=5): - gr.HTML(""" -This demo showcases IDEFICS, a open-access large visual language model. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS can answer questions about images, describe visual content, create stories grounded in multiple images, etc.
-IDEFICS (which stands for Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. IDEFICS was built solely on publicly available data and models. It is currently the only visual language model of this scale (80 billion parameters) that is available in open-access.
-📚 The variants available in this demo were fine-tuned on a mixture of supervised and instruction fine-tuning datasets to make the models more suitable in conversational settings. For more details, we refer to our blog post.
-🅿️ Intended uses: This demo along with the supporting models are provided as research artifacts to the community. We detail misuses and out-of-scope uses here.
-⛔️ Limitations: The model can produce factually incorrect texts, hallucinate facts (with or without an image) and will struggle with small details in images. While the model will tend to refuse answering questionable user requests, it can produce problematic outputs (including racist, stereotypical, and disrespectful texts), in particular when prompted to do so. We encourage users to read our findings from evaluating the model for potential biases in the model card.
- """) - - # with gr.Row(): - # with gr.Column(scale=2): - with gr.Row(elem_id="model_selector_row"): - model_selector = gr.Dropdown( - choices=MODELS, - value="HuggingFaceM4/idefics-80b-instruct", - interactive=True, - show_label=False, - container=False, - label="Model", - visible=False, - ) - - imagebox = gr.Image(type="filepath", label="Image input", visible=False) - - with gr.Row(): - # def prefetch_images_in_history(user_prompt_str): - # """ - # Pre-fetch the images that are passed in the chatbot default history. - # """ - # return prompt_list_to_markdown(handle_manual_images_in_user_prompt(user_prompt_str)) - - chatbot = gr.Chatbot( - elem_id="chatbot", - label="IDEFICS", - visible=True, - height=750, - avatar_images=[None, BOT_AVATAR] - # value=[ - # [ - # ( - # prefetch_images_in_history( - # """Write a funny story including the following characters. - # There should be drama and most importantly romance. - # Character 1:- # Character 2: - # Character 3: - # The plot should take place in the world pictured here: """ - # ) - # ), - # """Once upon a time, in a world where the sky was a dome and the ground was covered in grass, there lived a beautiful princess named Mulan. She was kind, brave, and always ready to help others. One day, while walking through the forest, she stumbled upon a strange creature. It was a green ogre named Shrek, who was lost and looking for his way back home. - # Mulan, being the kind-hearted person she was, decided to help Shrek find his way back. As they traveled together, they began to develop a strong bond. Shrek was impressed by Mulan's bravery and determination, while Mulan admired Shrek's loyalty and sense of humor. - # However, their journey was not without obstacles. They encountered a mischievous cat named Hoboua, who was always causing trouble and getting into fights. Despite his troublemaking ways, Hoboua had a good heart and eventually became a valuable ally to Mulan and Shrek. - # As they traveled, they faced many challenges, including battling fierce dragons and navigating treacherous mountains. Through it all, Mulan and Shrek grew closer, and their feelings for each other deepened. - # Finally, they reached Shrek's home, and he was reunited with his family and friends. Mulan, however, was sad to leave him behind. But Shrek had a surprise for her. He had fallen in love with her and wanted to be with her forever. - # Mulan was overjoyed, and they shared a passionate kiss. From that day on, they lived happily ever after, exploring the world together and facing any challenges that came their way. - # And so, the story of Mulan and Shrek's romance came to an end, leaving a lasting impression on all who heard it.""", - # ], - # ], - ) - - with gr.Group(): - with gr.Row(): - textbox.render() - submit_btn = gr.Button(value="▶️ Submit", visible=True) - clear_btn = gr.ClearButton([textbox, imagebox, chatbot], value="🧹 Clear") - regenerate_btn = gr.Button(value="🔄 Regenerate", visible=True) - upload_btn = gr.UploadButton("📁 Upload image", file_types=["image"]) - # with gr.Group(): - # with gr.Row(): - # with gr.Column(scale=1, min_width=50): - # dope_bttn = gr.Button("Dope🔥") - # with gr.Column(scale=1, min_width=50): - # problematic_bttn = gr.Button("Problematic😬") - - with gr.Row(): - with gr.Accordion("Advanced settings", open=False, visible=True) as parameter_row: - max_new_tokens = gr.Slider( - minimum=8, - maximum=1024, - value=512, - step=1, - interactive=True, - label="Maximum number of new tokens to generate", - ) - repetition_penalty = gr.Slider( - minimum=0.01, - maximum=5.0, - value=1.0, - step=0.01, - interactive=True, - label="Repetition penalty", - info="1.0 is equivalent to no penalty", - ) - decoding_strategy = gr.Radio( - [ - "Greedy", - "Top P Sampling", - ], - value="Greedy", - label="Decoding strategy", - interactive=True, - info="Higher values is equivalent to sampling more low-probability tokens.", - ) - temperature = gr.Slider( - minimum=0.0, - maximum=5.0, - value=0.4, - step=0.1, - interactive=True, - visible=False, - label="Sampling temperature", - info="Higher values will produce more diverse outputs.", - ) - decoding_strategy.change( - fn=lambda selection: gr.Slider.update( - visible=( - selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"] - ) - ), - inputs=decoding_strategy, - outputs=temperature, - ) - top_p = gr.Slider( - minimum=0.01, - maximum=0.99, - value=0.8, - step=0.01, - interactive=True, - visible=False, - label="Top P", - info="Higher values is equivalent to sampling more low-probability tokens.", - ) - decoding_strategy.change( - fn=lambda selection: gr.Slider.update(visible=(selection in ["Top P Sampling"])), - inputs=decoding_strategy, - outputs=top_p, - ) - gr.Markdown( - """ 💡 Pro tip:
""" - ) - - def model_inference( - model_selector, - user_prompt_str, - chat_history, - image, - decoding_strategy, - temperature, - max_new_tokens, - repetition_penalty, - top_p, - ): - if user_prompt_str.strip() == "" and image is None: - return "", None, chat_history - - formated_prompt_list, user_prompt_list = format_user_prompt_with_im_history_and_system_conditioning( - current_user_prompt_str=user_prompt_str.strip(), - current_image=image, - history=chat_history, - ) - - client_endpoint = API_PATHS[model_selector] - client = Client( - base_url=client_endpoint, - headers={"x-use-cache": "0", "Authorization": f"Bearer {API_TOKEN}"}, - ) - - # Common parameters to all decoding strategies - # This documentation is useful to read: https://huggingface.co/docs/transformers/main/en/generation_strategies - generation_args = { - "max_new_tokens": max_new_tokens, - "repetition_penalty": repetition_penalty, - "stop_sequences": EOS_STRINGS, - } - - assert decoding_strategy in [ - "Greedy", - "Top P Sampling", - ] - if decoding_strategy == "Greedy": - generation_args["do_sample"] = False - elif decoding_strategy == "Top P Sampling": - generation_args["temperature"] = temperature - generation_args["do_sample"] = True - generation_args["top_p"] = top_p - - if image is None: - # Case where there is no image OR the image is passed as `
- You can input an arbitrary number of images at arbitrary positions in the same query.
- You will need to input each image with its URL with the syntax<fake_token_around_image><image:IMAGE_URL><fake_token_around_image>.
- For example, for two images, you could inputTEXT_1<fake_token_around_image><image:IMAGE_URL_1><fake_token_around_image>TEXT_2<fake_token_around_image><image:IMAGE_URL_2><fake_token_around_image>TEXT_3.
- In the particular case where two images are consecutive, it is not necessary to add an additional separator:<fake_token_around_image><image:IMAGE_URL_1><fake_token_around_image><image:IMAGE_URL_2><fake_token_around_image>.` - chat_history.append([prompt_list_to_markdown(user_prompt_list), '']) - else: - # Case where the image is passed through the Image Box. - # Convert the image into base64 for both passing it through the chat history and - # displaying the image inside the same bubble as the text. - chat_history.append( - [ - f"{prompt_list_to_markdown([image] + user_prompt_list)}", - '', - ] - ) - - query = prompt_list_to_tgi_input(formated_prompt_list) - stream = client.generate_stream(prompt=query, **generation_args) - - acc_text = "" - for idx, response in enumerate(stream): - text_token = response.token.text - - if response.details: - # That's the exit condition - return - - if text_token in STOP_SUSPECT_LIST: - acc_text += text_token - continue - - if idx == 0 and text_token.startswith(" "): - text_token = text_token.lstrip() - - acc_text += text_token - last_turn = chat_history.pop(-1) - last_turn[-1] += acc_text - if last_turn[-1].endswith("\nUser"): - # Safeguard: sometimes (rarely), the model won't generate the token ` ` and will go directly to generating `\nUser:` - # It will thus stop the generation on `\nUser:`. But when it exits, it will have already generated `\nUser` - # This post-processing ensures that we don't have an additional `\nUser` wandering around. - last_turn[-1] = last_turn[-1][:-5] - chat_history.append(last_turn) - yield "", None, chat_history - acc_text = "" - - def process_example(message, image): - """ - Same as `model_inference` but in greedy mode and with the 80b-instruct. - Specifically for pre-computing the default examples. - """ - model_selector="HuggingFaceM4/idefics-80b-instruct" - user_prompt_str=message - chat_history=[] - max_new_tokens=512 - - formated_prompt_list, user_prompt_list = format_user_prompt_with_im_history_and_system_conditioning( - current_user_prompt_str=user_prompt_str.strip(), - current_image=image, - history=chat_history, - ) - - client_endpoint = API_PATHS[model_selector] - client = Client( - base_url=client_endpoint, - headers={"x-use-cache": "0", "Authorization": f"Bearer {API_TOKEN}"}, - timeout=240, # Generous time out just in case because we are in greedy. All examples should be computed in less than 30secs with the 80b-instruct. - ) - - # Common parameters to all decoding strategies - # This documentation is useful to read: https://huggingface.co/docs/transformers/main/en/generation_strategies - generation_args = { - "max_new_tokens": max_new_tokens, - "repetition_penalty": None, - "stop_sequences": EOS_STRINGS, - "do_sample": False, - } - - if image is None: - # Case where there is no image OR the image is passed as ` ` - chat_history.append([prompt_list_to_markdown(user_prompt_list), '']) - else: - # Case where the image is passed through the Image Box. - # Convert the image into base64 for both passing it through the chat history and - # displaying the image inside the same bubble as the text. - chat_history.append( - [ - f"{prompt_list_to_markdown([image] + user_prompt_list)}", - '', - ] - ) - - # Hack - see explanation in `DEFAULT_IMAGES_TMP_PATH_TO_URL` - for idx, i in enumerate(formated_prompt_list): - if i.startswith(DEFAULT_TEMP_DIR): - for k, v in DEFAULT_IMAGES_TMP_PATH_TO_URL.items(): - if k == i: - formated_prompt_list[idx] = v - break - - query = prompt_list_to_tgi_input(formated_prompt_list) - generated_text = client.generate(prompt=query, **generation_args).generated_text - if generated_text.endswith("\nUser"): - generated_text = generated_text[:-5] - - last_turn = chat_history.pop(-1) - last_turn[-1] += generated_text - chat_history.append(last_turn) - return "", None, chat_history - - textbox.submit( - fn=model_inference, - inputs=[ - model_selector, - textbox, - chatbot, - imagebox, - decoding_strategy, - temperature, - max_new_tokens, - repetition_penalty, - top_p, - ], - outputs=[textbox, imagebox, chatbot], - ) - submit_btn.click( - fn=model_inference, - inputs=[ - model_selector, - textbox, - chatbot, - imagebox, - decoding_strategy, - temperature, - max_new_tokens, - repetition_penalty, - top_p, - ], - outputs=[ - textbox, - imagebox, - chatbot, - ], - ) - - def remove_last_turn(chat_history): - if len(chat_history) == 0: - return gr.Update(), gr.Update() - last_interaction = chat_history[-1] - chat_history = chat_history[:-1] - chat_update = gr.update(value=chat_history) - text_update = gr.update(value=last_interaction[0]) - return chat_update, text_update - - regenerate_btn.click(fn=remove_last_turn, inputs=chatbot, outputs=[chatbot, textbox]).then( - fn=model_inference, - inputs=[ - model_selector, - textbox, - chatbot, - imagebox, - decoding_strategy, - temperature, - max_new_tokens, - repetition_penalty, - top_p, - ], - outputs=[ - textbox, - imagebox, - chatbot, - ], - ) - - upload_btn.upload(add_file, [upload_btn], [imagebox, upload_btn], queue=False) - submit_btn.click(lambda : gr.update(label='📁 Upload image', interactive=True), [], upload_btn) - textbox.submit(lambda : gr.update(label='📁 Upload image', interactive=True), [], upload_btn) - clear_btn.click(lambda : gr.update(label='📁 Upload image', interactive=True), [], upload_btn) - - # Using Flagging for saving dope and problematic examples - # Dope examples flagging - # dope_callback.setup( - # [ - # model_selector, - # textbox, - # chatbot, - # imagebox, - # decoding_strategy, - # temperature, - # max_new_tokens, - # repetition_penalty, - # top_p, - # ], - # "gradio_dope_data_points", - # ) - # dope_bttn.click( - # lambda *args: dope_callback.flag(args), - # [ - # model_selector, - # textbox, - # chatbot, - # imagebox, - # decoding_strategy, - # temperature, - # max_new_tokens, - # repetition_penalty, - # top_p, - # ], - # None, - # preprocess=False, - # ) - # # Problematic examples flagging - # problematic_callback.setup( - # [ - # model_selector, - # textbox, - # chatbot, - # imagebox, - # decoding_strategy, - # temperature, - # max_new_tokens, - # repetition_penalty, - # top_p, - # ], - # "gradio_problematic_data_points", - # ) - # problematic_bttn.click( - # lambda *args: problematic_callback.flag(args), - # [ - # model_selector, - # textbox, - # chatbot, - # imagebox, - # decoding_strategy, - # temperature, - # max_new_tokens, - # repetition_penalty, - # top_p, - # ], - # None, - # preprocess=False, - # ) - - # gr.Markdown("""## How to use? - - # There are two ways to provide image inputs: - # - Using the image box on the left panel - # - Using the inline syntax: `text text` - - # The second syntax allows inputting an arbitrary number of images.""") - - examples_path = os.path.dirname(__file__) - gr.Examples( - examples=[ - [ - ( - "Which famous person does the person in the image look like? Could you craft an engaging narrative" - " featuring this character from the image as the main protagonist?" - ), - f"{examples_path}/example_images/obama-harry-potter.jpg", - ], - [ - "Can you describe the image? Do you think it's real?", - f"{examples_path}/example_images/rabbit_force.png", - ], - ["Explain this meme to me.", f"{examples_path}/example_images/meme_french.jpg"], - ["Give me a short and easy recipe for this dish.", f"{examples_path}/example_images/recipe_burger.webp"], - [ - "I want to go somewhere similar to the one in the photo. Give me destinations and travel tips.", - f"{examples_path}/example_images/travel_tips.jpg", - ], - [ - "Can you name the characters in the image and give their French names?", - f"{examples_path}/example_images/gaulois.png", - ], - # ["Describe this image in detail.", f"{examples_path}/example_images/plant_bulb.webp"], - ["Write a complete sales ad for this product.", f"{examples_path}/example_images/product_ad.jpg"], - [ - ( - "As an art critic AI assistant, could you describe this painting in details and make a thorough" - " critic?" - ), - f"{examples_path}/example_images/art_critic.png", - ], - [ - "Can you tell me a very short story based on this image?", - f"{examples_path}/example_images/chicken_on_money.png", - ], - ["Write 3 funny meme texts about this image.", f"{examples_path}/example_images/elon_smoking.jpg"], - [ - "Who is in this picture? Why do people find it surprising?", - f"{examples_path}/example_images/pope_doudoune.webp", - ], - # [" Make a poem about the company in the image organizing the Woodstock of AI event, and the fact they brought those to the event.", None], - ["What are the armed baguettes guarding?", f"{examples_path}/example_images/baguettes_guarding_paris.png"], - # ["Can you describe the image?", f"{examples_path}/example_images/bear_costume.png"], - ["What is this animal and why is it unusual?", f"{examples_path}/example_images/blue_dog.png"], - [ - "What is this object and do you think it is horrifying?", - f"{examples_path}/example_images/can_horror.png", - ], - [ - ( - "What is this sketch for? How would you make an argument to prove this sketch was made by Picasso" - " himself?" - ), - f"{examples_path}/example_images/cat_sketch.png", - ], - ["Which celebrity does this claymation figure look like?", f"{examples_path}/example_images/kanye.jpg"], - # [ - # "Is there a celebrity look-alike in this image? What is happening to the person?", - # f"{examples_path}/example_images/ryan-reynolds-borg.jpg", - # ], - # ["Can you describe this image in details please?", f"{examples_path}/example_images/dragons_playing.png"], - ["What can you tell me about the cap in this image?", f"{examples_path}/example_images/ironman_cap.png"], - [ - "Can you write an advertisement for Coca-Cola based on this image?", - f"{examples_path}/example_images/polar_bear_coke.png", - ], - # [ - # "What is the rabbit doing in this image? Do you think this image is real?", - # f"{examples_path}/example_images/rabbit_force.png", - # ], - # ["What is happening in this image and why is it unusual?", f"{examples_path}/example_images/ramen.png"], - # [ - # "What I should look most forward to when I visit this place?", - # f"{examples_path}/example_images/tree_fortress.jpg", - # ], - # ["Who is the person in the image and what is he doing?", f"{examples_path}/example_images/tom-cruise-astronaut-pegasus.jpg"], - [ - "What is happening in this image? Which famous personality does this person in center looks like?", - f"{examples_path}/example_images/gandhi_selfie.jpg", - ], - [ - "What do you think the dog is doing and is it unusual?", - f"{examples_path}/example_images/surfing_dog.jpg", - ], - ], - inputs=[textbox, imagebox], - outputs=[textbox, imagebox, chatbot], - fn=process_example, - cache_examples=True, - examples_per_page=6, - label=( - "Click on any example below to get started.\nFor convenience, the model generations have been" - " pre-computed with `idefics-80b-instruct`." - ), - ) - -demo.queue(concurrency_count=40, max_size=40) -demo.launch() diff --git a/spaces/ICCV2023/ICCV2023-papers/paper_list.py b/spaces/ICCV2023/ICCV2023-papers/paper_list.py deleted file mode 100644 index c152388fd0bea771cee78f980d3ea10e38fce00f..0000000000000000000000000000000000000000 --- a/spaces/ICCV2023/ICCV2023-papers/paper_list.py +++ /dev/null @@ -1,121 +0,0 @@ -from __future__ import annotations - -import pandas as pd - - -class PaperList: - def __init__(self): - self.organization_name = "ICML2023" - self.table = pd.read_json("papers.json").fillna("") - - claim_info = pd.read_csv("claim_info.csv", dtype={"arxiv_id": str, "n_authors": int, "n_linked_authors": int}) - self.table = pd.merge(self.table, claim_info, on="arxiv_id", how="left") - self.table[["n_authors", "n_linked_authors"]] = ( - self.table[["n_authors", "n_linked_authors"]].fillna(-1).astype(int) - ) - - self._preprocess_table() - - self.table_header = """ - - """ - - def _preprocess_table(self) -> None: - self.table["title_lowercase"] = self.table.title.str.lower() - self.table["arxiv"] = self.table.arxiv_id.apply(lambda x: f"https://arxiv.org/abs/{x}" if x else "") - self.table["hf_paper"] = self.table.arxiv_id.apply(lambda x: f"https://huggingface.co/papers/{x}" if x else "") - self.table["authors"] = self.table.authors.apply(lambda x: ", ".join(x)) - - rows = [] - for row in self.table.itertuples(): - title = f'{row.title}' - arxiv = f'arXiv' if row.arxiv else "" - github = f'GitHub' if row.github else "" - hf_paper = f'Paper page' if row.hf_paper else "" - hf_space = f'Space' if row.hf_space else "" - hf_model = f'Model' if row.hf_model else "" - hf_dataset = f'Dataset' if row.hf_dataset else "" - author_linked = "✅" if row.n_linked_authors > 0 else "" - n_linked_authors = "" if row.n_linked_authors == -1 else row.n_linked_authors - n_authors = "" if row.n_authors == -1 else row.n_authors - claimed_paper = "" if n_linked_authors == "" else f"{n_linked_authors}/{n_authors} {author_linked}" - row = f""" -Title -Authors -Type -arXiv -GitHub -Paper pages -Spaces -Models -Datasets -Claimed -- """ - rows.append(row) - self.table["html_table_content"] = rows - - def render( - self, - search_query: str, - case_sensitive: bool, - filter_names: list[str], - presentation_type: str, - ) -> tuple[str, str]: - df = self.table - if presentation_type != "(ALL)": - df = df[df.type == presentation_type.lower()] - if search_query: - if case_sensitive: - df = df[df.title.str.contains(search_query)] - else: - df = df[df.title_lowercase.str.contains(search_query.lower())] - has_arxiv = "arXiv" in filter_names - has_github = "GitHub" in filter_names - has_hf_space = "Space" in filter_names - has_hf_model = "Model" in filter_names - has_hf_dataset = "Dataset" in filter_names - df = self.filter_table(df, has_arxiv, has_github, has_hf_space, has_hf_model, has_hf_dataset) - n_claimed = len(df[df.n_linked_authors > 0]) - return f"{len(df)} ({n_claimed} claimed)", self.to_html(df, self.table_header) - - @staticmethod - def filter_table( - df: pd.DataFrame, - has_arxiv: bool, - has_github: bool, - has_hf_space: bool, - has_hf_model: bool, - has_hf_dataset: bool, - ) -> pd.DataFrame: - if has_arxiv: - df = df[df.arxiv != ""] - if has_github: - df = df[df.github != ""] - if has_hf_space: - df = df[df.hf_space != ""] - if has_hf_model: - df = df[df.hf_model != ""] - if has_hf_dataset: - df = df[df.hf_dataset != ""] - return df - - @staticmethod - def to_html(df: pd.DataFrame, table_header: str) -> str: - table_data = "".join(df.html_table_content) - html = f""" -{title} -{row.authors} -{row.type} -{arxiv} -{github} -{hf_paper} -{hf_space} -{hf_model} -{hf_dataset} -{claimed_paper} -- {table_header} - {table_data} -
""" - return html diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/models/experimental.py b/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/models/experimental.py deleted file mode 100644 index 02d35b9ebd11d3407d64ae436142aca6100c9084..0000000000000000000000000000000000000000 --- a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/models/experimental.py +++ /dev/null @@ -1,111 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Experimental modules -""" -import math - -import numpy as np -import torch -import torch.nn as nn - -from utils.downloads import attempt_download - - -class Sum(nn.Module): - # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 - def __init__(self, n, weight=False): # n: number of inputs - super().__init__() - self.weight = weight # apply weights boolean - self.iter = range(n - 1) # iter object - if weight: - self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights - - def forward(self, x): - y = x[0] # no weight - if self.weight: - w = torch.sigmoid(self.w) * 2 - for i in self.iter: - y = y + x[i + 1] * w[i] - else: - for i in self.iter: - y = y + x[i + 1] - return y - - -class MixConv2d(nn.Module): - # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 - def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy - super().__init__() - n = len(k) # number of convolutions - if equal_ch: # equal c_ per group - i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices - c_ = [(i == g).sum() for g in range(n)] # intermediate channels - else: # equal weight.numel() per group - b = [c2] + [0] * n - a = np.eye(n + 1, n, k=-1) - a -= np.roll(a, 1, axis=1) - a *= np.array(k) ** 2 - a[0] = 1 - c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b - - self.m = nn.ModuleList([ - nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() - - def forward(self, x): - return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) - - -class Ensemble(nn.ModuleList): - # Ensemble of models - def __init__(self): - super().__init__() - - def forward(self, x, augment=False, profile=False, visualize=False): - y = [module(x, augment, profile, visualize)[0] for module in self] - # y = torch.stack(y).max(0)[0] # max ensemble - # y = torch.stack(y).mean(0) # mean ensemble - y = torch.cat(y, 1) # nms ensemble - return y, None # inference, train output - - -def attempt_load(weights, device=None, inplace=True, fuse=True): - # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a - from models.yolo import Detect, Model - - model = Ensemble() - for w in weights if isinstance(weights, list) else [weights]: - ckpt = torch.load(attempt_download(w), map_location='cpu') # load - ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model - - # Model compatibility updates - if not hasattr(ckpt, 'stride'): - ckpt.stride = torch.tensor([32.]) - if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)): - ckpt.names = dict(enumerate(ckpt.names)) # convert to dict - - model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode - - # Module compatibility updates - for m in model.modules(): - t = type(m) - if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): - m.inplace = inplace # torch 1.7.0 compatibility - if t is Detect and not isinstance(m.anchor_grid, list): - delattr(m, 'anchor_grid') - setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) - elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): - m.recompute_scale_factor = None # torch 1.11.0 compatibility - - # Return model - if len(model) == 1: - return model[-1] - - # Return detection ensemble - print(f'Ensemble created with {weights}\n') - for k in 'names', 'nc', 'yaml': - setattr(model, k, getattr(model[0], k)) - model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride - assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' - return model diff --git a/spaces/Illumotion/Koboldcpp/include/CL/cl_dx9_media_sharing_intel.h b/spaces/Illumotion/Koboldcpp/include/CL/cl_dx9_media_sharing_intel.h deleted file mode 100644 index f6518d7f632227ee492e0a28bde5262727ef96cf..0000000000000000000000000000000000000000 --- a/spaces/Illumotion/Koboldcpp/include/CL/cl_dx9_media_sharing_intel.h +++ /dev/null @@ -1,18 +0,0 @@ -/******************************************************************************* - * Copyright (c) 2008-2020 The Khronos Group Inc. - * - * 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. - ******************************************************************************/ - -#include-#pragma message("The Intel DX9 media sharing extensions have been moved into cl_dx9_media_sharing.h. Please include cl_dx9_media_sharing.h directly.") diff --git a/spaces/JayceeAngel/openai-reverse-proxy/README.md b/spaces/JayceeAngel/openai-reverse-proxy/README.md deleted file mode 100644 index beb92bcf4d520ecb5c2378a8e27137867c9fa23e..0000000000000000000000000000000000000000 --- a/spaces/JayceeAngel/openai-reverse-proxy/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Openai Reverse Proxy -emoji: 🔥 -colorFrom: indigo -colorTo: blue -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/JeffJing/ZookChatBot/tls_client/cffi.py b/spaces/JeffJing/ZookChatBot/tls_client/cffi.py deleted file mode 100644 index 7f2a5fa4706dd511dbaa6df5aaf3d674c6609777..0000000000000000000000000000000000000000 --- a/spaces/JeffJing/ZookChatBot/tls_client/cffi.py +++ /dev/null @@ -1,25 +0,0 @@ -from sys import platform -from platform import machine -import ctypes -import os - - -if platform == 'darwin': - file_ext = '-arm64.dylib' if machine() == "arm64" else '-x86.dylib' -elif platform in ('win32', 'cygwin'): - file_ext = '-64.dll' if 8 == ctypes.sizeof(ctypes.c_voidp) else '-32.dll' -else: - if machine() == "aarch64": - file_ext = '-arm64.so' - elif "x86" in machine(): - file_ext = '-x86.so' - else: - file_ext = '-amd64.so' - -root_dir = os.path.abspath(os.path.dirname(__file__)) -library = ctypes.cdll.LoadLibrary(f'{root_dir}/dependencies/tls-client{file_ext}') - -# extract the exposed request function from the shared package -request = library.request -request.argtypes = [ctypes.c_char_p] -request.restype = ctypes.c_char_p \ No newline at end of file diff --git a/spaces/Juno360219/albert-base-v2/app.py b/spaces/Juno360219/albert-base-v2/app.py deleted file mode 100644 index 3a649134d1086de9ff2fa97b487e2a57db2c7f2d..0000000000000000000000000000000000000000 --- a/spaces/Juno360219/albert-base-v2/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("models/albert-base-v2").launch() \ No newline at end of file diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/train/preprocess.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/train/preprocess.py deleted file mode 100644 index fbe81307ee661a95b2ac479336671a44ee02151a..0000000000000000000000000000000000000000 --- a/spaces/Kangarroar/ApplioRVC-Inference/infer/modules/train/preprocess.py +++ /dev/null @@ -1,147 +0,0 @@ -import multiprocessing -import os -import sys - -from scipy import signal - -now_dir = os.getcwd() -sys.path.append(now_dir) -print(sys.argv) -inp_root = sys.argv[1] -sr = int(sys.argv[2]) -n_p = int(sys.argv[3]) -exp_dir = sys.argv[4] -noparallel = sys.argv[5] == "True" -per = float(sys.argv[6]) -import multiprocessing -import os -import traceback - -import librosa -import numpy as np -from scipy.io import wavfile - -from infer.lib.audio import load_audio -from infer.lib.slicer2 import Slicer - -mutex = multiprocessing.Lock() -f = open("%s/preprocess.log" % exp_dir, "a+") - - -def println(strr): - mutex.acquire() - print(strr) - f.write("%s\n" % strr) - f.flush() - mutex.release() - - -class PreProcess: - def __init__(self, sr, exp_dir, per=3.7): - self.slicer = Slicer( - sr=sr, - threshold=-42, - min_length=1500, - min_interval=400, - hop_size=15, - max_sil_kept=500, - ) - self.sr = sr - self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr) - self.per = per - self.overlap = 0.3 - self.tail = self.per + self.overlap - self.max = 0.9 - self.alpha = 0.75 - self.exp_dir = exp_dir - self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir - self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir - os.makedirs(self.exp_dir, exist_ok=True) - os.makedirs(self.gt_wavs_dir, exist_ok=True) - os.makedirs(self.wavs16k_dir, exist_ok=True) - - def norm_write(self, tmp_audio, idx0, idx1): - tmp_max = np.abs(tmp_audio).max() - if tmp_max > 2.5: - print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max)) - return - tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + ( - 1 - self.alpha - ) * tmp_audio - wavfile.write( - "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), - self.sr, - tmp_audio.astype(np.float32), - ) - tmp_audio = librosa.resample( - tmp_audio, orig_sr=self.sr, target_sr=16000 - ) # , res_type="soxr_vhq" - wavfile.write( - "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1), - 16000, - tmp_audio.astype(np.float32), - ) - - def pipeline(self, path, idx0): - try: - audio = load_audio(path, self.sr) - # zero phased digital filter cause pre-ringing noise... - # audio = signal.filtfilt(self.bh, self.ah, audio) - audio = signal.lfilter(self.bh, self.ah, audio) - - idx1 = 0 - for audio in self.slicer.slice(audio): - i = 0 - while 1: - start = int(self.sr * (self.per - self.overlap) * i) - i += 1 - if len(audio[start:]) > self.tail * self.sr: - tmp_audio = audio[start : start + int(self.per * self.sr)] - self.norm_write(tmp_audio, idx0, idx1) - idx1 += 1 - else: - tmp_audio = audio[start:] - idx1 += 1 - break - self.norm_write(tmp_audio, idx0, idx1) - println("%s->Suc." % path) - except: - println("%s->%s" % (path, traceback.format_exc())) - - def pipeline_mp(self, infos): - for path, idx0 in infos: - self.pipeline(path, idx0) - - def pipeline_mp_inp_dir(self, inp_root, n_p): - try: - infos = [ - ("%s/%s" % (inp_root, name), idx) - for idx, name in enumerate(sorted(list(os.listdir(inp_root)))) - ] - if noparallel: - for i in range(n_p): - self.pipeline_mp(infos[i::n_p]) - else: - ps = [] - for i in range(n_p): - p = multiprocessing.Process( - target=self.pipeline_mp, args=(infos[i::n_p],) - ) - ps.append(p) - p.start() - for i in range(n_p): - ps[i].join() - except: - println("Fail. %s" % traceback.format_exc()) - - -def preprocess_trainset(inp_root, sr, n_p, exp_dir, per): - pp = PreProcess(sr, exp_dir, per) - println("start preprocess") - println(sys.argv) - pp.pipeline_mp_inp_dir(inp_root, n_p) - println("end preprocess") - - -if __name__ == "__main__": - preprocess_trainset(inp_root, sr, n_p, exp_dir, per) diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/CODE_OF_CONDUCT.md b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/CODE_OF_CONDUCT.md deleted file mode 100644 index 77b56791b7e900eb0a7d8a258bf78ecb449c8468..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,130 +0,0 @@ -# Contributor Covenant Code of Conduct -## First of all -Don't be evil, never - -## Our Pledge - -We as members, contributors, and leaders pledge to make participation in our -community a harassment-free experience for everyone, regardless of age, body -size, visible or invisible disability, ethnicity, sex characteristics, gender -identity and expression, level of experience, education, socio-economic status, -nationality, personal appearance, race, religion, or sexual identity -and orientation. - -We pledge to act and interact in ways that contribute to an open, welcoming, -diverse, inclusive, and healthy community. - -## Our Standards - -Examples of behavior that contributes to a positive environment for our -community include: - -* Demonstrating empathy and kindness toward other people -* Being respectful of differing opinions, viewpoints, and experiences -* Giving and gracefully accepting constructive feedback -* Accepting responsibility and apologizing to those affected by our mistakes, - and learning from the experience -* Focusing on what is best not just for us as individuals, but for the - overall community - -Examples of unacceptable behavior include: - -* The use of sexualized language or imagery, and sexual attention or - advances of any kind -* Trolling, insulting or derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or email - address, without their explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -## Enforcement Responsibilities - -Community leaders are responsible for clarifying and enforcing our standards of -acceptable behavior and will take appropriate and fair corrective action in -response to any behavior that they deem inappropriate, threatening, offensive, -or harmful. - -Community leaders have the right and responsibility to remove, edit, or reject -comments, commits, code, wiki edits, issues, and other contributions that are -not aligned to this Code of Conduct, and will communicate reasons for moderation -decisions when appropriate. - -## Scope - -This Code of Conduct applies within all community spaces, and also applies when -an individual is officially representing the community in public spaces. -Examples of representing our community include using an official e-mail address, -posting via an official social media account, or acting as an appointed -representative at an online or offline event. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported to the community leaders responsible for enforcement at -babysor00@gmail.com. -All complaints will be reviewed and investigated promptly and fairly. - -All community leaders are obligated to respect the privacy and security of the -reporter of any incident. - -## Enforcement Guidelines - -Community leaders will follow these Community Impact Guidelines in determining -the consequences for any action they deem in violation of this Code of Conduct: - -### 1. Correction - -**Community Impact**: Use of inappropriate language or other behavior deemed -unprofessional or unwelcome in the community. - -**Consequence**: A private, written warning from community leaders, providing -clarity around the nature of the violation and an explanation of why the -behavior was inappropriate. A public apology may be requested. - -### 2. Warning - -**Community Impact**: A violation through a single incident or series -of actions. - -**Consequence**: A warning with consequences for continued behavior. No -interaction with the people involved, including unsolicited interaction with -those enforcing the Code of Conduct, for a specified period of time. This -includes avoiding interactions in community spaces as well as external channels -like social media. Violating these terms may lead to a temporary or -permanent ban. - -### 3. Temporary Ban - -**Community Impact**: A serious violation of community standards, including -sustained inappropriate behavior. - -**Consequence**: A temporary ban from any sort of interaction or public -communication with the community for a specified period of time. No public or -private interaction with the people involved, including unsolicited interaction -with those enforcing the Code of Conduct, is allowed during this period. -Violating these terms may lead to a permanent ban. - -### 4. Permanent Ban - -**Community Impact**: Demonstrating a pattern of violation of community -standards, including sustained inappropriate behavior, harassment of an -individual, or aggression toward or disparagement of classes of individuals. - -**Consequence**: A permanent ban from any sort of public interaction within -the community. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], -version 2.0, available at -https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. - -Community Impact Guidelines were inspired by [Mozilla's code of conduct -enforcement ladder](https://github.com/mozilla/diversity). - -[homepage]: https://www.contributor-covenant.org - -For answers to common questions about this code of conduct, see the FAQ at -https://www.contributor-covenant.org/faq. Translations are available at -https://www.contributor-covenant.org/translations. diff --git a/spaces/Kitsune9tails/Test02/Dockerfile b/spaces/Kitsune9tails/Test02/Dockerfile deleted file mode 100644 index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000 --- a/spaces/Kitsune9tails/Test02/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM node:18-bullseye-slim - -RUN apt-get update && \ - -apt-get install -y git - -RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app - -WORKDIR /app - -RUN npm install - -COPY Dockerfile greeting.md* .env* ./ - -RUN npm run build - -EXPOSE 7860 - -ENV NODE_ENV=production - -CMD [ "npm", "start" ] \ No newline at end of file diff --git a/spaces/KyanChen/RSPrompter/mmpl/models/heads/sam_semseg_head.py b/spaces/KyanChen/RSPrompter/mmpl/models/heads/sam_semseg_head.py deleted file mode 100644 index f674c77e19a02a26bd6223480b54b055e4b59411..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmpl/models/heads/sam_semseg_head.py +++ /dev/null @@ -1,226 +0,0 @@ -import warnings - -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule -from mmengine.model import BaseModule -from typing import Tuple, List - -from torch import Tensor - -from mmpl.registry import MODELS -from mmseg.models import build_loss -from mmseg.models.utils import resize -from mmseg.structures import build_pixel_sampler -from mmseg.utils import SampleList, ConfigType - - -@MODELS.register_module() -class SamSemSegHead(BaseModule): - def __init__(self, - in_channels=2, - inner_channels=None, - num_classes=1, - ignore_index=255, - threshold=None, - out_channels=None, - loss_decode=None, - sampler=None, - align_corners=False, - norm_cfg=dict(type='BN', requires_grad=True), - act_cfg=dict(type='ReLU', inplace=True), - train_cfg=None, - test_cfg=None, - ): - super().__init__() - self.in_channels = in_channels - self.ignore_index = ignore_index - self.align_corners = align_corners - self.train_cfg = train_cfg - self.test_cfg = test_cfg - - if out_channels is None: - if num_classes == 2: - warnings.warn('For binary segmentation, we suggest using' - '`out_channels = 1` to define the output' - 'channels of segmentor, and use `threshold`' - 'to convert `seg_logits` into a prediction' - 'applying a threshold') - out_channels = num_classes - - if out_channels != num_classes and out_channels != 1: - raise ValueError( - 'out_channels should be equal to num_classes,' - 'except binary segmentation set out_channels == 1 and' - f'num_classes == 2, but got out_channels={out_channels}' - f'and num_classes={num_classes}') - - if out_channels == 1 and threshold is None: - threshold = 0.3 - warnings.warn('threshold is not defined for binary, and defaults' - 'to 0.3') - self.num_classes = num_classes - self.out_channels = num_classes + 1 - self.threshold = threshold - - if isinstance(loss_decode, dict): - self.loss_decode = MODELS.build(loss_decode) - elif isinstance(loss_decode, (list, tuple)): - self.loss_decode = nn.ModuleList() - for loss in loss_decode: - self.loss_decode.append(MODELS.build(loss)) - else: - raise TypeError(f'loss_decode must be a dict or sequence of dict,\ - but got {type(loss_decode)}') - - if sampler is not None: - self.sampler = build_pixel_sampler(sampler, context=self) - else: - self.sampler = None - - if inner_channels is None: - self.down_conv = nn.ModuleList([nn.Identity(), nn.Identity()]) - else: - self.down_conv = nn.ModuleList([ - nn.Conv2d(in_channels, inner_channels, 1), - nn.Conv2d(in_channels, inner_channels, 1) - ]) - in_channels = inner_channels - - self.cls_seg = nn.Sequential( - nn.Linear(in_channels, in_channels // 2), - nn.ReLU(inplace=True), - nn.Linear(in_channels // 2, self.out_channels) - ) - - self.up_conv = nn.Sequential( - nn.UpsamplingBilinear2d(scale_factor=2), - ConvModule( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - padding=1, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - ), - nn.UpsamplingBilinear2d(scale_factor=2), - ConvModule( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - padding=1, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - ), - nn.UpsamplingBilinear2d(scale_factor=2), - ConvModule( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=3, - padding=1, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - ) - ) - - - def forward(self, inputs): - """Forward function.""" - x0, x1 = inputs - x0 = self.down_conv[0](x0) - x1 = self.down_conv[1](x1) - - gate_x0 = torch.sigmoid(x0) # B N H W - x1 = torch.einsum('bnhw,bchw->bnchw', gate_x0, x1) - x1 = torch.mean(x1, dim=(-2, -1)) - x1 = self.cls_seg(x1) # B N K - x0 = self.up_conv(x0) # B N H W - seg_logits = torch.einsum('bnhw,bnk->bkhw', x0, x1) - return seg_logits - - def loss(self, inputs: Tuple[Tensor], batch_data_samples: SampleList, - train_cfg: ConfigType=None) -> dict: - seg_logits = self.forward(inputs) - losses = self.loss_by_feat(seg_logits, batch_data_samples) - return losses - - def predict(self, inputs: Tuple[Tensor], batch_img_metas: List[dict], - test_cfg: ConfigType=None) -> Tensor: - """Forward function for prediction. - - Args: - inputs (Tuple[Tensor]): List of multi-level img features. - batch_img_metas (dict): List Image info where each dict may also - contain: 'img_shape', 'scale_factor', 'flip', 'img_path', - 'ori_shape', and 'pad_shape'. - For details on the values of these keys see - `mmseg/datasets/pipelines/formatting.py:PackSegInputs`. - test_cfg (dict): The testing config. - - Returns: - Tensor: Outputs segmentation logits map. - """ - seg_logits = self.forward(inputs) - - return self.predict_by_feat(seg_logits, batch_img_metas) - - def predict_by_feat(self, seg_logits: Tensor, - batch_img_metas: List[dict]) -> Tensor: - """Transform a batch of output seg_logits to the input shape. - - Args: - seg_logits (Tensor): The output from decode head forward function. - batch_img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - - Returns: - Tensor: Outputs segmentation logits map. - """ - - seg_logits = resize( - input=seg_logits, - size=batch_img_metas[0]['img_shape'], - mode='bilinear', - align_corners=self.align_corners) - return seg_logits - - def _stack_batch_gt(self, batch_data_samples: SampleList) -> Tensor: - gt_semantic_segs = [ - data_sample.gt_sem_seg.data for data_sample in batch_data_samples - ] - return torch.stack(gt_semantic_segs, dim=0) - - def loss_by_feat(self, seg_logits: Tensor, - batch_data_samples: SampleList) -> dict: - seg_label = self._stack_batch_gt(batch_data_samples) - loss = dict() - seg_logits = resize( - input=seg_logits, - size=seg_label.shape[2:], - mode='bilinear', - align_corners=self.align_corners) - - if self.sampler is not None: - seg_weight = self.sampler.sample(seg_logits, seg_label) - else: - seg_weight = None - seg_label = seg_label.squeeze(1) - - if not isinstance(self.loss_decode, nn.ModuleList): - losses_decode = [self.loss_decode] - else: - losses_decode = self.loss_decode - for loss_decode in losses_decode: - if loss_decode.loss_name not in loss: - loss[loss_decode.loss_name] = loss_decode( - seg_logits, - seg_label, - weight=seg_weight, - ignore_index=self.ignore_index) - else: - loss[loss_decode.loss_name] += loss_decode( - seg_logits, - seg_label, - weight=seg_weight, - ignore_index=self.ignore_index) - return loss diff --git a/spaces/KyanChen/RSPrompter/mmpl/structures/cls_data_sample.py b/spaces/KyanChen/RSPrompter/mmpl/structures/cls_data_sample.py deleted file mode 100644 index 9e319a7bb830ccc2d39e76c94047b14e229278a7..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmpl/structures/cls_data_sample.py +++ /dev/null @@ -1,235 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. - -from multiprocessing.reduction import ForkingPickler -from numbers import Number -from typing import Sequence, Union - -import numpy as np -import torch -from mmengine.structures import BaseDataElement, LabelData -from mmengine.utils import is_str - - -def format_label( - value: Union[torch.Tensor, np.ndarray, Sequence, int]) -> torch.Tensor: - """Convert various python types to label-format tensor. - - Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, - :class:`Sequence`, :class:`int`. - - Args: - value (torch.Tensor | numpy.ndarray | Sequence | int): Label value. - - Returns: - :obj:`torch.Tensor`: The foramtted label tensor. - """ - - # Handle single number - if isinstance(value, (torch.Tensor, np.ndarray)) and value.ndim == 0: - value = int(value.item()) - - if isinstance(value, np.ndarray): - value = torch.from_numpy(value).to(torch.long) - elif isinstance(value, Sequence) and not is_str(value): - value = torch.tensor(value).to(torch.long) - elif isinstance(value, int): - value = torch.LongTensor([value]) - elif not isinstance(value, torch.Tensor): - raise TypeError(f'Type {type(value)} is not an available label type.') - assert value.ndim == 1, \ - f'The dims of value should be 1, but got {value.ndim}.' - - return value - - -def format_score( - value: Union[torch.Tensor, np.ndarray, Sequence, int]) -> torch.Tensor: - """Convert various python types to score-format tensor. - - Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, - :class:`Sequence`. - - Args: - value (torch.Tensor | numpy.ndarray | Sequence): Score values. - - Returns: - :obj:`torch.Tensor`: The foramtted score tensor. - """ - - if isinstance(value, np.ndarray): - value = torch.from_numpy(value).float() - elif isinstance(value, Sequence) and not is_str(value): - value = torch.tensor(value).float() - elif not isinstance(value, torch.Tensor): - raise TypeError(f'Type {type(value)} is not an available label type.') - assert value.ndim == 1, \ - f'The dims of value should be 1, but got {value.ndim}.' - - return value - - -class ClsDataSample(BaseDataElement): - """A data structure interface of classification task. - - It's used as interfaces between different components. - - Meta fields: - img_shape (Tuple): The shape of the corresponding input image. - Used for visualization. - ori_shape (Tuple): The original shape of the corresponding image. - Used for visualization. - num_classes (int): The number of all categories. - Used for label format conversion. - - Data fields: - gt_label (:obj:`~mmengine.structures.LabelData`): The ground truth - label. - pred_label (:obj:`~mmengine.structures.LabelData`): The predicted - label. - scores (torch.Tensor): The outputs of model. - logits (torch.Tensor): The outputs of model without softmax nor - sigmoid. - - Examples: - >>> import torch - >>> from mmcls.structures import ClsDataSample - >>> - >>> img_meta = dict(img_shape=(960, 720), num_classes=5) - >>> data_sample = ClsDataSample(metainfo=img_meta) - >>> data_sample.set_gt_label(3) - >>> print(data_sample) - - -#### Prompts out: -${prompts} - -#### Music: - - -`; - const params = new URLSearchParams({ - title: titleTxt, - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/fffiloni/img-to-music/discussions/new?${paramsStr}`, '_blank'); - shareBtnEl.style.removeProperty('pointer-events'); - shareIconEl.style.removeProperty('display'); - loadingIconEl.style.display = 'none'; -}""" \ No newline at end of file diff --git a/spaces/MTTR/MTTR-Referring-Video-Object-Segmentation/README.md b/spaces/MTTR/MTTR-Referring-Video-Object-Segmentation/README.md deleted file mode 100644 index 9ca4a293a257f280ca8058473c278b0e81ac0614..0000000000000000000000000000000000000000 --- a/spaces/MTTR/MTTR-Referring-Video-Object-Segmentation/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: MTTR Referring Video Object Segmentation -emoji: 🌍 -colorFrom: gray -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/ONNX_net/readme.md b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/ONNX_net/readme.md deleted file mode 100644 index e7ec9d446b8aff22b2a4a9408947cd8d539baec3..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/ONNX_net/readme.md +++ /dev/null @@ -1 +0,0 @@ -onnx导出后的模型，可用MoeSS加载 \ No newline at end of file diff --git a/spaces/Makiing/coolb-in-gtest/src/components/chat-notification.tsx b/spaces/Makiing/coolb-in-gtest/src/components/chat-notification.tsx deleted file mode 100644 index 4be24d0f1755c8058698cfa66c736d8d4792475a..0000000000000000000000000000000000000000 --- a/spaces/Makiing/coolb-in-gtest/src/components/chat-notification.tsx +++ /dev/null @@ -1,77 +0,0 @@ -import { useEffect } from 'react' -import Image from 'next/image' - -import IconWarning from '@/assets/images/warning.svg' -import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types' -import { ExternalLink } from './external-link' -import { useBing } from '@/lib/hooks/use-bing' - -export interface ChatNotificationProps extends Pick
- 你已达到每日最大发送消息次数，请更换账号或隔一天后重试 -- ) - } - if (error.code === ErrorCode.BING_FORBIDDEN) { - return ( -- 你的账号已在黑名单，请尝试更换账号及申请解封 - - ) - } - if (error.code === ErrorCode.CONVERSATION_LIMIT) { - return ( -- 当前话题已中止，请点 - 重新开始 - 开启新的对话 -- ) - } - if (error.code === ErrorCode.BING_CAPTCHA) { - return ( -- 点击通过人机验证 - - ) - } - if (error.code === ErrorCode.BING_UNAUTHORIZED) { - reset() - return ( - 没有获取到身份信息或身份信息失效，点此重新设置 - ) - } - return error.message -} - -export function ChatNotification({ message, bot }: ChatNotificationProps) { - useEffect(() => { - window.scrollBy(0, 2000) - }, [message]) - - if (!message?.error) return - - return ( --- ) -} diff --git a/spaces/Manoj21k/Custom-QandA/README.md b/spaces/Manoj21k/Custom-QandA/README.md deleted file mode 100644 index 06d4cf1dcb5db5b875ff155749cdbc91557208e8..0000000000000000000000000000000000000000 --- a/spaces/Manoj21k/Custom-QandA/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Custom QandA -emoji: 🐠 -colorFrom: yellow -colorTo: purple -sdk: streamlit -sdk_version: 1.27.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/box_iou_rotated.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/box_iou_rotated.py deleted file mode 100644 index 2d78015e9c2a9e7a52859b4e18f84a9aa63481a0..0000000000000000000000000000000000000000 --- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/box_iou_rotated.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['box_iou_rotated']) - - -def box_iou_rotated(bboxes1, bboxes2, mode='iou', aligned=False): - """Return intersection-over-union (Jaccard index) of boxes. - - Both sets of boxes are expected to be in - (x_center, y_center, width, height, angle) format. - - If ``aligned`` is ``False``, then calculate the ious between each bbox - of bboxes1 and bboxes2, otherwise the ious between each aligned pair of - bboxes1 and bboxes2. - - Arguments: - boxes1 (Tensor): rotated bboxes 1. \ - It has shape (N, 5), indicating (x, y, w, h, theta) for each row. - Note that theta is in radian. - boxes2 (Tensor): rotated bboxes 2. \ - It has shape (M, 5), indicating (x, y, w, h, theta) for each row. - Note that theta is in radian. - mode (str): "iou" (intersection over union) or iof (intersection over - foreground). - - Returns: - ious(Tensor): shape (N, M) if aligned == False else shape (N,) - """ - assert mode in ['iou', 'iof'] - mode_dict = {'iou': 0, 'iof': 1} - mode_flag = mode_dict[mode] - rows = bboxes1.size(0) - cols = bboxes2.size(0) - if aligned: - ious = bboxes1.new_zeros(rows) - else: - ious = bboxes1.new_zeros((rows * cols)) - bboxes1 = bboxes1.contiguous() - bboxes2 = bboxes2.contiguous() - ext_module.box_iou_rotated( - bboxes1, bboxes2, ious, mode_flag=mode_flag, aligned=aligned) - if not aligned: - ious = ious.view(rows, cols) - return ious diff --git a/spaces/Michael2008S/flowise/Dockerfile b/spaces/Michael2008S/flowise/Dockerfile deleted file mode 100644 index 9c0ad22929159b8c4d192856163699570fd27307..0000000000000000000000000000000000000000 --- a/spaces/Michael2008S/flowise/Dockerfile +++ /dev/null @@ -1,26 +0,0 @@ -FROM node:18-alpine -USER root - -# Arguments that can be passed at build time -ARG FLOWISE_PATH=/usr/local/lib/node_modules/flowise -ARG BASE_PATH=/root/.flowise -ARG DATABASE_PATH=$BASE_PATH -ARG APIKEY_PATH=$BASE_PATH -ARG SECRETKEY_PATH=$BASE_PATH -ARG LOG_PATH=$BASE_PATH/logs - -# Install dependencies -RUN apk add --no-cache git python3 py3-pip make g++ build-base cairo-dev pango-dev chromium - -ENV PUPPETEER_SKIP_DOWNLOAD=true -ENV PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser - -# Install Flowise globally -RUN npm install -g flowise - -# Configure Flowise directories using the ARG -RUN mkdir -p $LOG_PATH $FLOWISE_PATH/uploads && chmod -R 777 $LOG_PATH $FLOWISE_PATH - -WORKDIR /data - -CMD ["npx", "flowise", "start"] \ No newline at end of file diff --git a/spaces/MohamadRezo/flixPicks/database/PlayLists.py b/spaces/MohamadRezo/flixPicks/database/PlayLists.py deleted file mode 100644 index 423610903d76defe59847cbe94af0d4c988fb548..0000000000000000000000000000000000000000 --- a/spaces/MohamadRezo/flixPicks/database/PlayLists.py +++ /dev/null @@ -1,27 +0,0 @@ - -class playLists_table: - _instance = None - - def __new__(cls, *args, **kwargs): - if not cls._instance: - cls._instance = super().__new__(cls) - cls._instance.db = {} - return cls._instance - - def insert(self, key, value): - self.db[key] = value - - def read(self, key): - return self.db.get(key) - - def has_key(self, key): - return key in self.db - - def has_value(self, value): - return value in self.db.values() - - def update(self, key, value): - if key in self.db: - self.db[key] = value - else: - raise KeyError(f"Key '{key}' does not exist in the database.") diff --git a/spaces/Mrleo/MyChatGPT/chat_func.py b/spaces/Mrleo/MyChatGPT/chat_func.py deleted file mode 100644 index 374178f3d22c5c23d1dc2952336cdc298a77315d..0000000000000000000000000000000000000000 --- a/spaces/Mrleo/MyChatGPT/chat_func.py +++ /dev/null @@ -1,456 +0,0 @@ -# -*- coding:utf-8 -*- -from __future__ import annotations -from typing import TYPE_CHECKING, List - -import logging -import json -import os -import requests -import urllib3 - -from tqdm import tqdm -import colorama -from duckduckgo_search import ddg -import asyncio -import aiohttp - -from presets import * -from llama_func import * -from utils import * - -# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s") - -if TYPE_CHECKING: - from typing import TypedDict - - class DataframeData(TypedDict): - headers: List[str] - data: List[List[str | int | bool]] - - -initial_prompt = "You are a helpful assistant." -API_URL = "https://api.openai.com/v1/chat/completions" -HISTORY_DIR = "history" -TEMPLATES_DIR = "templates" - -def get_response( - openai_api_key, system_prompt, history, temperature, top_p, stream, selected_model -): - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {openai_api_key}", - } - - history = [construct_system(system_prompt), *history] - - payload = { - "model": selected_model, - "messages": history, # [{"role": "user", "content": f"{inputs}"}], - "temperature": temperature, # 1.0, - "top_p": top_p, # 1.0, - "n": 1, - "stream": stream, - "presence_penalty": 0, - "frequency_penalty": 0, - } - if stream: - timeout = timeout_streaming - else: - timeout = timeout_all - - # 获取环境变量中的代理设置 - http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy") - https_proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("https_proxy") - - # 如果存在代理设置，使用它们 - proxies = {} - if http_proxy: - logging.info(f"Using HTTP proxy: {http_proxy}") - proxies["http"] = http_proxy - if https_proxy: - logging.info(f"Using HTTPS proxy: {https_proxy}") - proxies["https"] = https_proxy - - # 如果有代理，使用代理发送请求，否则使用默认设置发送请求 - if proxies: - response = requests.post( - API_URL, - headers=headers, - json=payload, - stream=True, - timeout=timeout, - proxies=proxies, - ) - else: - response = requests.post( - API_URL, - headers=headers, - json=payload, - stream=True, - timeout=timeout, - ) - return response - - -def stream_predict( - openai_api_key, - system_prompt, - history, - inputs, - chatbot, - all_token_counts, - top_p, - temperature, - selected_model, - fake_input=None, - display_append="" -): - def get_return_value(): - return chatbot, history, status_text, all_token_counts - - logging.info("实时回答模式") - partial_words = "" - counter = 0 - status_text = "开始实时传输回答……" - history.append(construct_user(inputs)) - history.append(construct_assistant("")) - if fake_input: - chatbot.append((fake_input, "")) - else: - chatbot.append((inputs, "")) - user_token_count = 0 - if len(all_token_counts) == 0: - system_prompt_token_count = count_token(construct_system(system_prompt)) - user_token_count = ( - count_token(construct_user(inputs)) + system_prompt_token_count - ) - else: - user_token_count = count_token(construct_user(inputs)) - all_token_counts.append(user_token_count) - logging.info(f"输入token计数: {user_token_count}") - yield get_return_value() - try: - response = get_response( - openai_api_key, - system_prompt, - history, - temperature, - top_p, - True, - selected_model, - ) - except requests.exceptions.ConnectTimeout: - status_text = ( - standard_error_msg + connection_timeout_prompt + error_retrieve_prompt - ) - yield get_return_value() - return - except requests.exceptions.ReadTimeout: - status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt - yield get_return_value() - return - - yield get_return_value() - error_json_str = "" - - for chunk in tqdm(response.iter_lines()): - if counter == 0: - counter += 1 - continue - counter += 1 - # check whether each line is non-empty - if chunk: - chunk = chunk.decode() - chunklength = len(chunk) - try: - chunk = json.loads(chunk[6:]) - except json.JSONDecodeError: - logging.info(chunk) - error_json_str += chunk - status_text = f"JSON解析错误。请重置对话。收到的内容: {error_json_str}" - yield get_return_value() - continue - # decode each line as response data is in bytes - if chunklength > 6 and "delta" in chunk["choices"][0]: - finish_reason = chunk["choices"][0]["finish_reason"] - status_text = construct_token_message( - sum(all_token_counts), stream=True - ) - if finish_reason == "stop": - yield get_return_value() - break - try: - partial_words = ( - partial_words + chunk["choices"][0]["delta"]["content"] - ) - except KeyError: - status_text = ( - standard_error_msg - + "API回复中找不到内容。很可能是Token计数达到上限了。请重置对话。当前Token计数: " - + str(sum(all_token_counts)) - ) - yield get_return_value() - break - history[-1] = construct_assistant(partial_words) - chatbot[-1] = (chatbot[-1][0], partial_words+display_append) - all_token_counts[-1] += 1 - yield get_return_value() - - -def predict_all( - openai_api_key, - system_prompt, - history, - inputs, - chatbot, - all_token_counts, - top_p, - temperature, - selected_model, - fake_input=None, - display_append="" -): - logging.info("一次性回答模式") - history.append(construct_user(inputs)) - history.append(construct_assistant("")) - if fake_input: - chatbot.append((fake_input, "")) - else: - chatbot.append((inputs, "")) - all_token_counts.append(count_token(construct_user(inputs))) - try: - response = get_response( - openai_api_key, - system_prompt, - history, - temperature, - top_p, - False, - selected_model, - ) - except requests.exceptions.ConnectTimeout: - status_text = ( - standard_error_msg + connection_timeout_prompt + error_retrieve_prompt - ) - return chatbot, history, status_text, all_token_counts - except requests.exceptions.ProxyError: - status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt - return chatbot, history, status_text, all_token_counts - except requests.exceptions.SSLError: - status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt - return chatbot, history, status_text, all_token_counts - response = json.loads(response.text) - content = response["choices"][0]["message"]["content"] - history[-1] = construct_assistant(content) - chatbot[-1] = (chatbot[-1][0], content+display_append) - total_token_count = response["usage"]["total_tokens"] - all_token_counts[-1] = total_token_count - sum(all_token_counts) - status_text = construct_token_message(total_token_count) - return chatbot, history, status_text, all_token_counts - - -def predict( - openai_api_key, - system_prompt, - history, - inputs, - chatbot, - all_token_counts, - top_p, - temperature, - stream=False, - selected_model=MODELS[0], - use_websearch=False, - files = None, - should_check_token_count=True, -): # repetition_penalty, top_k - logging.info("输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL) - if files: - msg = "构建索引中……（这可能需要比较久的时间）" - logging.info(msg) - yield chatbot, history, msg, all_token_counts - index = construct_index(openai_api_key, file_src=files) - msg = "索引构建完成，获取回答中……" - yield chatbot, history, msg, all_token_counts - history, chatbot, status_text = chat_ai(openai_api_key, index, inputs, history, chatbot) - yield chatbot, history, status_text, all_token_counts - return - - old_inputs = "" - link_references = [] - if use_websearch: - search_results = ddg(inputs, max_results=5) - old_inputs = inputs - web_results = [] - for idx, result in enumerate(search_results): - logging.info(f"搜索结果{idx + 1}：{result}") - domain_name = urllib3.util.parse_url(result["href"]).host - web_results.append(f'[{idx+1}]"{result["body"]}"\nURL: {result["href"]}') - link_references.append(f"{idx+1}. [{domain_name}]({result['href']})\n") - link_references = "\n\n" + "".join(link_references) - inputs = ( - replace_today(WEBSEARCH_PTOMPT_TEMPLATE) - .replace("{query}", inputs) - .replace("{web_results}", "\n\n".join(web_results)) - ) - else: - link_references = "" - - if len(openai_api_key) != 51: - status_text = standard_error_msg + no_apikey_msg - logging.info(status_text) - chatbot.append((inputs, "")) - if len(history) == 0: - history.append(construct_user(inputs)) - history.append("") - all_token_counts.append(0) - else: - history[-2] = construct_user(inputs) - yield chatbot, history, status_text, all_token_counts - return - - yield chatbot, history, "开始生成回答……", all_token_counts - - if stream: - logging.info("使用流式传输") - iter = stream_predict( - openai_api_key, - system_prompt, - history, - inputs, - chatbot, - all_token_counts, - top_p, - temperature, - selected_model, - fake_input=old_inputs, - display_append=link_references - ) - for chatbot, history, status_text, all_token_counts in iter: - yield chatbot, history, status_text, all_token_counts - else: - logging.info("不使用流式传输") - chatbot, history, status_text, all_token_counts = predict_all( - openai_api_key, - system_prompt, - history, - inputs, - chatbot, - all_token_counts, - top_p, - temperature, - selected_model, - fake_input=old_inputs, - display_append=link_references - ) - yield chatbot, history, status_text, all_token_counts - - logging.info(f"传输完毕。当前token计数为{all_token_counts}") - if len(history) > 1 and history[-1]["content"] != inputs: - logging.info( - "回答为：" - + colorama.Fore.BLUE - + f"{history[-1]['content']}" - + colorama.Style.RESET_ALL - ) - - if stream: - max_token = max_token_streaming - else: - max_token = max_token_all - - if sum(all_token_counts) > max_token and should_check_token_count: - status_text = f"精简token中{all_token_counts}/{max_token}" - logging.info(status_text) - yield chatbot, history, status_text, all_token_counts - iter = reduce_token_size( - openai_api_key, - system_prompt, - history, - chatbot, - all_token_counts, - top_p, - temperature, - max_token//2, - selected_model=selected_model, - ) - for chatbot, history, status_text, all_token_counts in iter: - status_text = f"Token 达到上限，已自动降低Token计数至 {status_text}" - yield chatbot, history, status_text, all_token_counts - - -def retry( - openai_api_key, - system_prompt, - history, - chatbot, - token_count, - top_p, - temperature, - stream=False, - selected_model=MODELS[0], -): - logging.info("重试中……") - if len(history) == 0: - yield chatbot, history, f"{standard_error_msg}上下文是空的", token_count - return - history.pop() - inputs = history.pop()["content"] - token_count.pop() - iter = predict( - openai_api_key, - system_prompt, - history, - inputs, - chatbot, - token_count, - top_p, - temperature, - stream=stream, - selected_model=selected_model, - ) - logging.info("重试中……") - for x in iter: - yield x - logging.info("重试完毕") - - -def reduce_token_size( - openai_api_key, - system_prompt, - history, - chatbot, - token_count, - top_p, - temperature, - max_token_count, - selected_model=MODELS[0], -): - logging.info("开始减少token数量……") - iter = predict( - openai_api_key, - system_prompt, - history, - summarize_prompt, - chatbot, - token_count, - top_p, - temperature, - selected_model=selected_model, - should_check_token_count=False, - ) - logging.info(f"chatbot: {chatbot}") - flag = False - for chatbot, history, status_text, previous_token_count in iter: - num_chat = find_n(previous_token_count, max_token_count) - if flag: - chatbot = chatbot[:-1] - flag = True - history = history[-2*num_chat:] if num_chat > 0 else [] - token_count = previous_token_count[-num_chat:] if num_chat > 0 else [] - msg = f"保留了最近{num_chat}轮对话" - yield chatbot, history, msg + "，" + construct_token_message( - sum(token_count) if len(token_count) > 0 else 0, - ), token_count - logging.info(msg) - logging.info("减少token数量完毕") \ No newline at end of file diff --git a/spaces/NCTCMumbai/NCTC/models/official/benchmark/shakespeare_benchmark.py b/spaces/NCTCMumbai/NCTC/models/official/benchmark/shakespeare_benchmark.py deleted file mode 100644 index 430ab75da5300e3c374bbe56c2c02befb4dc2dff..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/benchmark/shakespeare_benchmark.py +++ /dev/null @@ -1,355 +0,0 @@ -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Executes Shakespeare (LSTM) benchmark and accuracy tests.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import time - -from absl import flags -import tensorflow as tf # pylint: disable=g-bad-import-order - -from official.benchmark.models.shakespeare import shakespeare_main -from official.utils.flags import core as flags_core -from official.utils.misc import keras_utils -from official.benchmark import benchmark_wrappers -from official.benchmark.perfzero_benchmark import PerfZeroBenchmark - -SHAKESPEARE_TRAIN_DATA = 'shakespeare/shakespeare.txt' -TMP_DIR = os.getenv('TMPDIR') -FLAGS = flags.FLAGS - - -class ShakespeareBenchmarkBase(PerfZeroBenchmark): - """Base class for Shakespeare (LSTM) benchmark and accuracy tests.""" - - def __init__(self, output_dir=None, default_flags=None, root_data_dir=None): - super(ShakespeareBenchmarkBase, self).__init__( - output_dir=output_dir, - default_flags=default_flags, - flag_methods=[shakespeare_main.define_flags]) - - @benchmark_wrappers.enable_runtime_flags - def _run_and_report_benchmark(self, - top_1_train_min=0.91, - top_1_train_max=0.94, - warmup=1, - log_steps=100): - """Report benchmark results by writing to local protobuf file. - - Average epoch time is calculated by skipping the first epoch. This average - ignores time spent between epoch and is recorded by begin and end epoch. To - skip accuracy check set `top_1_train_min=None`. - - Args: - top_1_train_min: lowest passing value. - top_1_train_max: highest passing value. - warmup: number of entries in `timestamp_log` to ignore. - log_steps: How often the log was created for `timestamp_log`. - """ - total_batch_size = FLAGS.batch_size - metrics = [] - start_time_sec = time.time() - stats = shakespeare_main.run(FLAGS) - wall_time_sec = time.time() - start_time_sec - - if top_1_train_min: - metrics.append({'name': 'accuracy_top_1_train', - 'value': stats['history']['RecallAt1'][-1], - 'min_value': top_1_train_min, - 'max_value': top_1_train_max}) - - # Look for the time history callback which was used during keras.fit - for callback in stats['callbacks']: - if isinstance(callback, keras_utils.TimeHistory): - epoch_timings = callback.epoch_runtime_log - if len(epoch_timings) > 1: - average_time = sum(epoch_timings[1:]) / len(epoch_timings[1:]) - metrics.append({'name': 'avg_epoch_time', - 'value': average_time}) - - # First entry in timestamp_log is the start of step 1. The rest of the - # entries are the end of each step recorded. - time_log = callback.timestamp_log - elapsed = time_log[-1].timestamp - time_log[warmup].timestamp - num_examples = ( - total_batch_size * log_steps * (len(time_log) - warmup - 1)) - if elapsed > 0: - examples_per_sec = num_examples / elapsed - metrics.append({'name': 'exp_per_second', - 'value': examples_per_sec}) - - flags_str = flags_core.get_nondefault_flags_as_str() - self.report_benchmark(iters=-1, wall_time=wall_time_sec, - metrics=metrics, - extras={'flags': flags_str}) - - -class ShakespeareAccuracy(ShakespeareBenchmarkBase): - """Shakespeare accuracy tests. - - This is not an ideal test. The best we can use for the accuracy check is to - validate top_1 of the training set. At batch size 64 the top_1 training - stabilizes to ~0.92 around 40-45 epochs. - """ - - def __init__(self, output_dir=None, root_data_dir=None, **kwargs): - """Shakespeare accuracy tests. - - Args: - output_dir: directory where to output e.g. log files - root_data_dir: directory under which to look for dataset - **kwargs: arbitrary named arguments. This is needed to make the - constructor forward compatible in case PerfZero provides more - named arguments before updating the constructor. - """ - self.train_data = os.path.join(root_data_dir, SHAKESPEARE_TRAIN_DATA) - super(ShakespeareAccuracy, self).__init__( - output_dir=output_dir, root_data_dir=root_data_dir) - - def benchmark_cpu(self): - """Benchmark cpu.""" - self._setup() - FLAGS.num_gpus = 0 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - self._run_and_report_benchmark() - - def benchmark_cpu_no_ds_run_eagerly(self): - """Benchmark cpu without distribution strategies and run eagerly.""" - self._setup() - FLAGS.num_gpus = 0 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - FLAGS.run_eagerly = True - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_1_gpu(self): - """Benchmark 1 gpu.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - self._run_and_report_benchmark() - - def benchmark_1_gpu_no_ds(self): - """Benchmark 1 gpu without distribution strategies.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_1_gpu_no_ds_run_eagerly(self): - """Benchmark 1 gpu without distribution strategies and run eagerly.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - FLAGS.run_eagerly = True - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_xla_1_gpu(self): - """Benchmark 1 gpu w/xla.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - FLAGS.enable_xla = True - self._run_and_report_benchmark() - - def benchmark_8_gpu(self): - """Benchmark 8 gpu. - - This is test is for accuracy not scaling. The batch-size is not scaled to - the number of gpus. - """ - self._setup() - FLAGS.num_gpus = 8 - FLAGS.training_data = self.train_data - FLAGS.batch_size = 64 - FLAGS.train_epochs = 43 - FLAGS.model_dir = '' - self._run_and_report_benchmark() - - -class ShakespeareKerasBenchmarkReal(ShakespeareBenchmarkBase): - """Benchmark accuracy tests.""" - - def __init__(self, output_dir=None, root_data_dir=TMP_DIR, **kwargs): - """Benchmark tests w/Keras. - - Args: - output_dir: directory where to output e.g. log files - root_data_dir: directory under which to look for dataset - **kwargs: arbitrary named arguments. This is needed to make the - constructor forward compatible in case PerfZero provides more - named arguments before updating the constructor. - """ - self.train_data = os.path.join(root_data_dir, SHAKESPEARE_TRAIN_DATA) - - def_flags = {} - def_flags['training_data'] = self.train_data - def_flags['model_dir'] = '' - def_flags['train_epochs'] = 4 - def_flags['log_steps'] = 50 - - super(ShakespeareKerasBenchmarkReal, self).__init__( - output_dir=output_dir, - root_data_dir=root_data_dir, - default_flags=def_flags) - - def benchmark_cpu(self): - """Benchmark cpu.""" - self._setup() - FLAGS.num_gpus = 0 - FLAGS.batch_size = 64 - self._run_and_report_benchmark() - - def benchmark_cpu_no_ds_run_eagerly(self): - """Benchmark cpu without distribution strategy and run eagerly.""" - self._setup() - FLAGS.num_gpus = 0 - FLAGS.batch_size = 64 - FLAGS.distribution_strategy = 'off' - FLAGS.run_eagerly = True - self._run_and_report_benchmark() - - def benchmark_cpu_no_ds(self): - """Benchmark cpu without distribution strategy.""" - self._setup() - FLAGS.num_gpus = 0 - FLAGS.batch_size = 64 - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_cpu_no_ds_force_v2(self): - """Benchmark cpu no ds, and force v2.""" - self._setup() - FLAGS.num_gpus = 0 - FLAGS.batch_size = 64 - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_1_gpu(self): - """Benchmark 1 gpu.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 - self._run_and_report_benchmark() - - def benchmark_1_gpu_no_cudnn(self): - """Benchmark 1 gpu with CuDNN disabled.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 - FLAGS.cudnn = False - self._run_and_report_benchmark() - - def benchmark_1_gpu_no_ds(self): - """Benchmark 1 gpu without distribution strategies.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_1_gpu_no_ds_run_eagerly(self): - """Benchmark 1 gpu.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 - FLAGS.run_eagerly = True - FLAGS.distribution_strategy = 'off' - self._run_and_report_benchmark() - - def benchmark_xla_1_gpu(self): - """Benchmark 1 gpu.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 - FLAGS.enable_xla = True - self._run_and_report_benchmark() - - def benchmark_xla_1_gpu_no_cudnn(self): - """Benchmark 1 gpu w/xla and CuDNN disabled.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 - FLAGS.cudnn = False - FLAGS.enable_xla = True - self._run_and_report_benchmark() - - def benchmark_8_gpu(self): - """Benchmark 8 gpu.""" - self._setup() - FLAGS.num_gpus = 8 - FLAGS.batch_size = 64 * 8 - FLAGS.log_steps = 10 - self._run_and_report_benchmark() - - def benchmark_8_gpu_no_cudnn(self): - """Benchmark 8 gpu with CuDNN disabled.""" - self._setup() - FLAGS.num_gpus = 8 - FLAGS.batch_size = 64 * 8 - FLAGS.log_steps = 10 - FLAGS.cudnn = False - self._run_and_report_benchmark() - - def benchmark_xla_8_gpu(self): - """Benchmark 8 gpu w/xla.""" - self._setup() - FLAGS.num_gpus = 1 - FLAGS.batch_size = 64 * 8 - FLAGS.log_steps = 10 - FLAGS.enable_xla = True - self._run_and_report_benchmark() - - def benchmark_xla_8_gpu_no_cudnn(self): - """Benchmark 8 gpu w/xla and CuDNN disabled.""" - self._setup() - FLAGS.num_gpus = 8 - FLAGS.batch_size = 64 * 8 - FLAGS.log_steps = 10 - FLAGS.cudnn = False - FLAGS.enable_xla = True - self._run_and_report_benchmark() - - def _run_and_report_benchmark(self): - """Run and report benchmark.""" - super(ShakespeareKerasBenchmarkReal, self)._run_and_report_benchmark( - top_1_train_min=None, log_steps=FLAGS.log_steps) - - -if __name__ == '__main__': - tf.test.main() diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/compute_bleu_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/compute_bleu_test.py deleted file mode 100644 index 6c578e3698a7e6cc2d7170433d4565cd3d8091ed..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/nlp/transformer/compute_bleu_test.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2018 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Test functions in compute_blue.py.""" - -import tempfile - -import tensorflow as tf - -from official.nlp.transformer import compute_bleu - - -class ComputeBleuTest(tf.test.TestCase): - - def _create_temp_file(self, text): - temp_file = tempfile.NamedTemporaryFile(delete=False) - with tf.io.gfile.GFile(temp_file.name, "w") as w: - w.write(text) - return temp_file.name - - def test_bleu_same(self): - ref = self._create_temp_file("test 1 two 3\nmore tests!") - hyp = self._create_temp_file("test 1 two 3\nmore tests!") - - uncased_score = compute_bleu.bleu_wrapper(ref, hyp, False) - cased_score = compute_bleu.bleu_wrapper(ref, hyp, True) - self.assertEqual(100, uncased_score) - self.assertEqual(100, cased_score) - - def test_bleu_same_different_case(self): - ref = self._create_temp_file("Test 1 two 3\nmore tests!") - hyp = self._create_temp_file("test 1 two 3\nMore tests!") - uncased_score = compute_bleu.bleu_wrapper(ref, hyp, False) - cased_score = compute_bleu.bleu_wrapper(ref, hyp, True) - self.assertEqual(100, uncased_score) - self.assertLess(cased_score, 100) - - def test_bleu_different(self): - ref = self._create_temp_file("Testing\nmore tests!") - hyp = self._create_temp_file("Dog\nCat") - uncased_score = compute_bleu.bleu_wrapper(ref, hyp, False) - cased_score = compute_bleu.bleu_wrapper(ref, hyp, True) - self.assertLess(uncased_score, 100) - self.assertLess(cased_score, 100) - - def test_bleu_tokenize(self): - s = "Test0, 1 two, 3" - tokenized = compute_bleu.bleu_tokenize(s) - self.assertEqual(["Test0", ",", "1", "two", ",", "3"], tokenized) - - -if __name__ == "__main__": - tf.test.main() diff --git a/spaces/NCTCMumbai/NCTC/models/official/staging/training/standard_runnable.py b/spaces/NCTCMumbai/NCTC/models/official/staging/training/standard_runnable.py deleted file mode 100644 index 20dd66f28e44f7b799dff4af826dcb22bb13595a..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/staging/training/standard_runnable.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""An abstraction that users can easily handle their custom training loops.""" - -from __future__ import absolute_import -from __future__ import division -# from __future__ import google_type_annotations -from __future__ import print_function - -import abc -import six -import tensorflow.compat.v2 as tf -from typing import Dict, Optional, Text - -from official.staging.training import runnable -from official.staging.training import utils - - -@six.add_metaclass(abc.ABCMeta) -class StandardTrainable(runnable.AbstractTrainable): - """Implements the standard functionality of AbstractTrainable APIs.""" - - def __init__(self, use_tf_while_loop=True, use_tf_function=True): - if use_tf_while_loop and not use_tf_function: - raise ValueError("`use_tf_while_loop=True` and `use_tf_function=False` " - "is not supported") - self.use_tf_while_loop = use_tf_while_loop - self.use_tf_function = use_tf_function - self.train_dataset = None - self.train_iter = None - self.train_loop_fn = None - - @abc.abstractmethod - def build_train_dataset(self): - """Builds the training datasets. - - Returns: - A tf.nest-compatible structure of tf.data.Dataset or DistributedDataset. - """ - pass - - def train(self, - num_steps: Optional[tf.Tensor]) -> Optional[Dict[Text, tf.Tensor]]: - """See base class.""" - if self.train_dataset is None: - # Build train input dataset - self.train_dataset = self.build_train_dataset() - self.train_iter = tf.nest.map_structure(iter, self.train_dataset) - - if self.train_loop_fn is None: - train_fn = self.train_step - if self.use_tf_while_loop: - self.train_loop_fn = utils.create_tf_while_loop_fn(train_fn) - else: - if self.use_tf_function: - train_fn = tf.function(train_fn) - self.train_loop_fn = utils.create_loop_fn(train_fn) - - self.train_loop_begin() - self.train_loop_fn(self.train_iter, num_steps) - return self.train_loop_end() - - def train_loop_begin(self): - """Called once at the beginning of the training loop. - - This is a good place to reset metrics that accumulate values over multiple - steps of training. - """ - pass - - @abc.abstractmethod - def train_step(self, iterator): - """Implements one step of training. - - What a "step" consists of is up to the implementer. If using distribution - strategies, the call to this method should take place in the "cross-replica - context" for generality, to allow e.g. multiple iterator dequeues and calls - to `strategy.run`. - - Args: - iterator: A tf.nest-compatible structure of tf.data Iterator or - DistributedIterator. - """ - pass - - def train_loop_end(self) -> Optional[Dict[Text, tf.Tensor]]: - """Called at the end of the training loop. - - This is a good place to get metric results. The value returned from this - function will be returned as-is from the train() method. - - Returns: - The function may return a dictionary of `Tensors`, which will be - written to logs and as TensorBoard summaries. - """ - pass - - -@six.add_metaclass(abc.ABCMeta) -class StandardEvaluable(runnable.AbstractEvaluable): - """Implements the standard functionality of AbstractEvaluable APIs.""" - - def __init__(self, use_tf_function=True): - self.eval_use_tf_function = use_tf_function - self.eval_dataset = None - self.eval_loop_fn = None - - @abc.abstractmethod - def build_eval_dataset(self): - """Builds the evaluation datasets. - - Returns: - A tf.nest-compatible structure of tf.data.Dataset or DistributedDataset. - """ - pass - - def evaluate( - self, num_steps: Optional[tf.Tensor]) -> Optional[Dict[Text, tf.Tensor]]: - """See base class.""" - if self.eval_dataset is None: - # Build train input dataset - self.eval_dataset = self.build_eval_dataset() - - if self.eval_loop_fn is None: - eval_fn = self.eval_step - if self.eval_use_tf_function: - eval_fn = tf.function(eval_fn) - self.eval_loop_fn = utils.create_loop_fn(eval_fn) - - eval_iter = tf.nest.map_structure(iter, self.eval_dataset) - - self.eval_begin() - self.eval_loop_fn(eval_iter, num_steps) - return self.eval_end() - - def eval_begin(self): - """Called once at the beginning of the evaluation. - - This is a good place to reset metrics that accumulate values over the entire - evaluation. - """ - pass - - @abc.abstractmethod - def eval_step(self, iterator): - """Implements one step of evaluation. - - What a "step" consists of is up to the implementer. If using distribution - strategies, the call to this method should take place in the "cross-replica - context" for generality, to allow e.g. multiple iterator dequeues and calls - to `strategy.run`. - - Args: - iterator: A tf.nest-compatible structure of tf.data Iterator or - DistributedIterator. - """ - pass - - def eval_end(self) -> Optional[Dict[Text, tf.Tensor]]: - """Called at the end of the evaluation. - - This is a good place to get metric results. The value returned from this - function will be returned as-is from the evaluate() method. - - Returns: - The function may return a dictionary of `Tensors`, which will be - written to logs and as TensorBoard summaries. - """ - pass diff --git a/spaces/Nehal07/text-translator-with-voice/app.py b/spaces/Nehal07/text-translator-with-voice/app.py deleted file mode 100644 index 3c1a4afa29bba4875180a6f4cf2ca6b0072a3229..0000000000000000000000000000000000000000 --- a/spaces/Nehal07/text-translator-with-voice/app.py +++ /dev/null @@ -1,66 +0,0 @@ -import streamlit as st -from translate import Translator -from gtts import gTTS -import os - -# Streamlit app -st.title("Text Translator with Voice") - -# Input text -user_text = st.text_input("Enter the text you want to translate:") - -# Define a dictionary of language codes and their full names -language_names = { - 'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', - 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chichewa': 'ny', - 'Chinese (Simplified)': 'zh-cn', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', - 'Esperanto': 'eo', 'Estonian': 'et', 'Filipino': 'tl', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', - 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', - 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', - 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', - 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', - 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', - 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Odia': 'or', - 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', - 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala': 'si', - 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', - 'Tajik': 'tg', 'Tamil': 'ta', 'Tatar': 'tt', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Turkmen': 'tk', 'Ukrainian': 'uk', - 'Urdu': 'ur', 'Uyghur': 'ug', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', - 'Zulu': 'zu' -} - -# Function to translate text -def translate_text(text, target_language_code): - try: - # Initialize the translator - translator = Translator(to_lang=target_language_code) - - # Translate the text to the selected target language - translated_text = translator.translate(text) - - return translated_text - except Exception as e: - return f"Translation failed: {str(e)}" - -# Target language selection -st.subheader("Select the target language for translation:") -selected_target_language = st.selectbox("Select a target language:", list(language_names.keys())) - -# Display the translated text and speak it -if user_text: - translated_text = translate_text(user_text, language_names[selected_target_language]) - - # Apply advanced CSS for better display in a bordered box - styled_text = f'--------- {getAction(message.error, () => bot.resetConversation())} - {translated_text}' - st.markdown(styled_text, unsafe_allow_html=True) - - # Text-to-Speech (TTS) conversion and playback - tts = gTTS(translated_text, lang=language_names[selected_target_language]) - tts_file_path = "translated_audio.mp3" - tts.save(tts_file_path) - - # Play the TTS audio - st.audio(tts_file_path, format="audio/mp3") - - # Clean up the TTS audio file - os.remove(tts_file_path) diff --git a/spaces/Norod78/ComicsHeroU2Net/README.md b/spaces/Norod78/ComicsHeroU2Net/README.md deleted file mode 100644 index 7009c6f6ee541f46a33e8b7207fe29f1b1c6eb30..0000000000000000000000000000000000000000 --- a/spaces/Norod78/ComicsHeroU2Net/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Comics Hero (U2Net) -emoji: 🦸 -colorFrom: red -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/extract_subimages.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/extract_subimages.py deleted file mode 100644 index 50a819d6148b53ee0482f4f6133a41b9620c0b0a..0000000000000000000000000000000000000000 --- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/extract_subimages.py +++ /dev/null @@ -1,148 +0,0 @@ -import argparse -import cv2 -import numpy as np -import os -import sys -from basicsr.utils import scandir -from multiprocessing import Pool -from os import path as osp -from tqdm import tqdm - - -def main(args): - """A multi-thread tool to crop large images to sub-images for faster IO. - - opt (dict): Configuration dict. It contains: - n_thread (int): Thread number. - compression_level (int): CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size - and longer compression time. Use 0 for faster CPU decompression. Default: 3, same in cv2. - input_folder (str): Path to the input folder. - save_folder (str): Path to save folder. - crop_size (int): Crop size. - step (int): Step for overlapped sliding window. - thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped. - - Usage: - For each folder, run this script. - Typically, there are GT folder and LQ folder to be processed for DIV2K dataset. - After process, each sub_folder should have the same number of subimages. - Remember to modify opt configurations according to your settings. - """ - - opt = {} - opt["n_thread"] = args.n_thread - opt["compression_level"] = args.compression_level - opt["input_folder"] = args.input - opt["save_folder"] = args.output - opt["crop_size"] = args.crop_size - opt["step"] = args.step - opt["thresh_size"] = args.thresh_size - extract_subimages(opt) - - -def extract_subimages(opt): - """Crop images to subimages. - - Args: - opt (dict): Configuration dict. It contains: - input_folder (str): Path to the input folder. - save_folder (str): Path to save folder. - n_thread (int): Thread number. - """ - input_folder = opt["input_folder"] - save_folder = opt["save_folder"] - if not osp.exists(save_folder): - os.makedirs(save_folder) - print(f"mkdir {save_folder} ...") - else: - print(f"Folder {save_folder} already exists. Exit.") - sys.exit(1) - - # scan all images - img_list = list(scandir(input_folder, full_path=True)) - - pbar = tqdm(total=len(img_list), unit="image", desc="Extract") - pool = Pool(opt["n_thread"]) - for path in img_list: - pool.apply_async(worker, args=(path, opt), callback=lambda arg: pbar.update(1)) - pool.close() - pool.join() - pbar.close() - print("All processes done.") - - -def worker(path, opt): - """Worker for each process. - - Args: - path (str): Image path. - opt (dict): Configuration dict. It contains: - crop_size (int): Crop size. - step (int): Step for overlapped sliding window. - thresh_size (int): Threshold size. Patches whose size is lower than thresh_size will be dropped. - save_folder (str): Path to save folder. - compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION. - - Returns: - process_info (str): Process information displayed in progress bar. - """ - crop_size = opt["crop_size"] - step = opt["step"] - thresh_size = opt["thresh_size"] - img_name, extension = osp.splitext(osp.basename(path)) - - # remove the x2, x3, x4 and x8 in the filename for DIV2K - img_name = ( - img_name.replace("x2", "").replace("x3", "").replace("x4", "").replace("x8", "") - ) - - img = cv2.imread(path, cv2.IMREAD_UNCHANGED) - - h, w = img.shape[0:2] - h_space = np.arange(0, h - crop_size + 1, step) - if h - (h_space[-1] + crop_size) > thresh_size: - h_space = np.append(h_space, h - crop_size) - w_space = np.arange(0, w - crop_size + 1, step) - if w - (w_space[-1] + crop_size) > thresh_size: - w_space = np.append(w_space, w - crop_size) - - index = 0 - for x in h_space: - for y in w_space: - index += 1 - cropped_img = img[x : x + crop_size, y : y + crop_size, ...] - cropped_img = np.ascontiguousarray(cropped_img) - cv2.imwrite( - osp.join(opt["save_folder"], f"{img_name}_s{index:03d}{extension}"), - cropped_img, - [cv2.IMWRITE_PNG_COMPRESSION, opt["compression_level"]], - ) - process_info = f"Processing {img_name} ..." - return process_info - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--input", type=str, default="datasets/DF2K/DF2K_HR", help="Input folder" - ) - parser.add_argument( - "--output", type=str, default="datasets/DF2K/DF2K_HR_sub", help="Output folder" - ) - parser.add_argument("--crop_size", type=int, default=480, help="Crop size") - parser.add_argument( - "--step", type=int, default=240, help="Step for overlapped sliding window" - ) - parser.add_argument( - "--thresh_size", - type=int, - default=0, - help="Threshold size. Patches whose size is lower than thresh_size will be dropped.", - ) - parser.add_argument("--n_thread", type=int, default=20, help="Thread number.") - parser.add_argument( - "--compression_level", type=int, default=3, help="Compression level" - ) - args = parser.parse_args() - - main(args) diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/deduplicate_lines.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/deduplicate_lines.py deleted file mode 100644 index 50e458328c80b71c42a66d473381ca7e98d294da..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/deduplicate_lines.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import fileinput -import hashlib -import sys -from multiprocessing import Pool - - -def get_hashes_and_lines(raw_line): - hash = hashlib.md5(raw_line).hexdigest() - return hash, raw_line - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--workers", type=int, default=10) - parser.add_argument("files", nargs="*", help="input files") - args = parser.parse_args() - - seen = set() - with fileinput.input(args.files, mode="rb") as h: - pool = Pool(args.workers) - results = pool.imap_unordered(get_hashes_and_lines, h, 1000) - for i, (hash, raw_line) in enumerate(results): - if hash not in seen: - seen.add(hash) - sys.stdout.buffer.write(raw_line) - if i % 1000000 == 0: - print(i, file=sys.stderr, end="", flush=True) - elif i % 100000 == 0: - print(".", file=sys.stderr, end="", flush=True) - print(file=sys.stderr, flush=True) - - -if __name__ == "__main__": - main() diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/infer.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/infer.py deleted file mode 100644 index 6e9a878af46242ced57cfcd0e876a3d2ef3820ae..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/infer.py +++ /dev/null @@ -1,427 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -""" -Run inference for pre-processed data with a trained model. -""" - -import ast -import logging -import math -import os -import sys - -import editdistance -import numpy as np -import torch -from fairseq import checkpoint_utils, options, progress_bar, tasks, utils -from fairseq.data.data_utils import post_process -from fairseq.logging.meters import StopwatchMeter, TimeMeter - - -logging.basicConfig() -logging.root.setLevel(logging.INFO) -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -def add_asr_eval_argument(parser): - parser.add_argument("--kspmodel", default=None, help="sentence piece model") - parser.add_argument( - "--wfstlm", default=None, help="wfstlm on dictonary output units" - ) - parser.add_argument( - "--rnnt_decoding_type", - default="greedy", - help="wfstlm on dictonary\ -output units", - ) - try: - parser.add_argument( - "--lm-weight", - "--lm_weight", - type=float, - default=0.2, - help="weight for lm while interpolating with neural score", - ) - except: - pass - parser.add_argument( - "--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level" - ) - parser.add_argument( - "--w2l-decoder", - choices=["viterbi", "kenlm", "fairseqlm"], - help="use a w2l decoder", - ) - parser.add_argument("--lexicon", help="lexicon for w2l decoder") - parser.add_argument("--unit-lm", action="store_true", help="if using a unit lm") - parser.add_argument("--kenlm-model", "--lm-model", help="lm model for w2l decoder") - parser.add_argument("--beam-threshold", type=float, default=25.0) - parser.add_argument("--beam-size-token", type=float, default=100) - parser.add_argument("--word-score", type=float, default=1.0) - parser.add_argument("--unk-weight", type=float, default=-math.inf) - parser.add_argument("--sil-weight", type=float, default=0.0) - parser.add_argument( - "--dump-emissions", - type=str, - default=None, - help="if present, dumps emissions into this file and exits", - ) - parser.add_argument( - "--dump-features", - type=str, - default=None, - help="if present, dumps features into this file and exits", - ) - parser.add_argument( - "--load-emissions", - type=str, - default=None, - help="if present, loads emissions from this file", - ) - return parser - - -def check_args(args): - # assert args.path is not None, "--path required for generation!" - # assert args.results_path is not None, "--results_path required for generation!" - assert ( - not args.sampling or args.nbest == args.beam - ), "--sampling requires --nbest to be equal to --beam" - assert ( - args.replace_unk is None or args.raw_text - ), "--replace-unk requires a raw text dataset (--raw-text)" - - -def get_dataset_itr(args, task, models): - return task.get_batch_iterator( - dataset=task.dataset(args.gen_subset), - max_tokens=args.max_tokens, - max_sentences=args.batch_size, - max_positions=(sys.maxsize, sys.maxsize), - ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test, - required_batch_size_multiple=args.required_batch_size_multiple, - num_shards=args.num_shards, - shard_id=args.shard_id, - num_workers=args.num_workers, - data_buffer_size=args.data_buffer_size, - ).next_epoch_itr(shuffle=False) - - -def process_predictions( - args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id -): - for hypo in hypos[: min(len(hypos), args.nbest)]: - hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu()) - - if "words" in hypo: - hyp_words = " ".join(hypo["words"]) - else: - hyp_words = post_process(hyp_pieces, args.post_process) - - if res_files is not None: - print( - "{} ({}-{})".format(hyp_pieces, speaker, id), - file=res_files["hypo.units"], - ) - print( - "{} ({}-{})".format(hyp_words, speaker, id), - file=res_files["hypo.words"], - ) - - tgt_pieces = tgt_dict.string(target_tokens) - tgt_words = post_process(tgt_pieces, args.post_process) - - if res_files is not None: - print( - "{} ({}-{})".format(tgt_pieces, speaker, id), - file=res_files["ref.units"], - ) - print( - "{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"] - ) - - if not args.quiet: - logger.info("HYPO:" + hyp_words) - logger.info("TARGET:" + tgt_words) - logger.info("___________________") - - hyp_words = hyp_words.split() - tgt_words = tgt_words.split() - return editdistance.eval(hyp_words, tgt_words), len(tgt_words) - - -def prepare_result_files(args): - def get_res_file(file_prefix): - if args.num_shards > 1: - file_prefix = f"{args.shard_id}_{file_prefix}" - path = os.path.join( - args.results_path, - "{}-{}-{}.txt".format( - file_prefix, os.path.basename(args.path), args.gen_subset - ), - ) - return open(path, "w", buffering=1) - - if not args.results_path: - return None - - return { - "hypo.words": get_res_file("hypo.word"), - "hypo.units": get_res_file("hypo.units"), - "ref.words": get_res_file("ref.word"), - "ref.units": get_res_file("ref.units"), - } - - -def optimize_models(args, use_cuda, models): - """Optimize ensemble for generation""" - for model in models: - model.make_generation_fast_( - beamable_mm_beam_size=None if args.no_beamable_mm else args.beam, - need_attn=args.print_alignment, - ) - if args.fp16: - model.half() - if use_cuda: - model.cuda() - - -class ExistingEmissionsDecoder(object): - def __init__(self, decoder, emissions): - self.decoder = decoder - self.emissions = emissions - - def generate(self, models, sample, **unused): - ids = sample["id"].cpu().numpy() - try: - emissions = np.stack(self.emissions[ids]) - except: - print([x.shape for x in self.emissions[ids]]) - raise Exception("invalid sizes") - emissions = torch.from_numpy(emissions) - return self.decoder.decode(emissions) - - -def main(args, task=None, model_state=None): - check_args(args) - - if args.max_tokens is None and args.batch_size is None: - args.max_tokens = 4000000 - logger.info(args) - - use_cuda = torch.cuda.is_available() and not args.cpu - - logger.info("| decoding with criterion {}".format(args.criterion)) - - task = tasks.setup_task(args) - - # Load ensemble - if args.load_emissions: - models, criterions = [], [] - task.load_dataset(args.gen_subset) - else: - logger.info("| loading model(s) from {}".format(args.path)) - models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( - utils.split_paths(args.path, separator="\\"), - arg_overrides=ast.literal_eval(args.model_overrides), - task=task, - suffix=args.checkpoint_suffix, - strict=(args.checkpoint_shard_count == 1), - num_shards=args.checkpoint_shard_count, - state=model_state, - ) - optimize_models(args, use_cuda, models) - task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task) - - - # Set dictionary - tgt_dict = task.target_dictionary - - logger.info( - "| {} {} {} examples".format( - args.data, args.gen_subset, len(task.dataset(args.gen_subset)) - ) - ) - - # hack to pass transitions to W2lDecoder - if args.criterion == "asg_loss": - raise NotImplementedError("asg_loss is currently not supported") - # trans = criterions[0].asg.trans.data - # args.asg_transitions = torch.flatten(trans).tolist() - - # Load dataset (possibly sharded) - itr = get_dataset_itr(args, task, models) - - # Initialize generator - gen_timer = StopwatchMeter() - - def build_generator(args): - w2l_decoder = getattr(args, "w2l_decoder", None) - if w2l_decoder == "viterbi": - from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder - - return W2lViterbiDecoder(args, task.target_dictionary) - elif w2l_decoder == "kenlm": - from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder - - return W2lKenLMDecoder(args, task.target_dictionary) - elif w2l_decoder == "fairseqlm": - from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder - - return W2lFairseqLMDecoder(args, task.target_dictionary) - else: - print( - "only flashlight decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment" - ) - - # please do not touch this unless you test both generate.py and infer.py with audio_pretraining task - generator = build_generator(args) - - if args.load_emissions: - generator = ExistingEmissionsDecoder( - generator, np.load(args.load_emissions, allow_pickle=True) - ) - logger.info("loaded emissions from " + args.load_emissions) - - num_sentences = 0 - - if args.results_path is not None and not os.path.exists(args.results_path): - os.makedirs(args.results_path) - - max_source_pos = ( - utils.resolve_max_positions( - task.max_positions(), *[model.max_positions() for model in models] - ), - ) - - if max_source_pos is not None: - max_source_pos = max_source_pos[0] - if max_source_pos is not None: - max_source_pos = max_source_pos[0] - 1 - - if args.dump_emissions: - emissions = {} - if args.dump_features: - features = {} - models[0].bert.proj = None - else: - res_files = prepare_result_files(args) - errs_t = 0 - lengths_t = 0 - with progress_bar.build_progress_bar(args, itr) as t: - wps_meter = TimeMeter() - for sample in t: - sample = utils.move_to_cuda(sample) if use_cuda else sample - if "net_input" not in sample: - continue - - prefix_tokens = None - if args.prefix_size > 0: - prefix_tokens = sample["target"][:, : args.prefix_size] - - gen_timer.start() - if args.dump_emissions: - with torch.no_grad(): - encoder_out = models[0](**sample["net_input"]) - emm = models[0].get_normalized_probs(encoder_out, log_probs=True) - emm = emm.transpose(0, 1).cpu().numpy() - for i, id in enumerate(sample["id"]): - emissions[id.item()] = emm[i] - continue - elif args.dump_features: - with torch.no_grad(): - encoder_out = models[0](**sample["net_input"]) - feat = encoder_out["encoder_out"].transpose(0, 1).cpu().numpy() - for i, id in enumerate(sample["id"]): - padding = ( - encoder_out["encoder_padding_mask"][i].cpu().numpy() - if encoder_out["encoder_padding_mask"] is not None - else None - ) - features[id.item()] = (feat[i], padding) - continue - hypos = task.inference_step(generator, models, sample, prefix_tokens) - num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos) - gen_timer.stop(num_generated_tokens) - - for i, sample_id in enumerate(sample["id"].tolist()): - speaker = None - # id = task.dataset(args.gen_subset).ids[int(sample_id)] - id = sample_id - toks = ( - sample["target"][i, :] - if "target_label" not in sample - else sample["target_label"][i, :] - ) - target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu() - # Process top predictions - errs, length = process_predictions( - args, - hypos[i], - None, - tgt_dict, - target_tokens, - res_files, - speaker, - id, - ) - errs_t += errs - lengths_t += length - - wps_meter.update(num_generated_tokens) - t.log({"wps": round(wps_meter.avg)}) - num_sentences += ( - sample["nsentences"] if "nsentences" in sample else sample["id"].numel() - ) - - wer = None - if args.dump_emissions: - emm_arr = [] - for i in range(len(emissions)): - emm_arr.append(emissions[i]) - np.save(args.dump_emissions, emm_arr) - logger.info(f"saved {len(emissions)} emissions to {args.dump_emissions}") - elif args.dump_features: - feat_arr = [] - for i in range(len(features)): - feat_arr.append(features[i]) - np.save(args.dump_features, feat_arr) - logger.info(f"saved {len(features)} emissions to {args.dump_features}") - else: - if lengths_t > 0: - wer = errs_t * 100.0 / lengths_t - logger.info(f"WER: {wer}") - - logger.info( - "| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}" - "sentences/s, {:.2f} tokens/s)".format( - num_sentences, - gen_timer.n, - gen_timer.sum, - num_sentences / gen_timer.sum, - 1.0 / gen_timer.avg, - ) - ) - logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam)) - return task, wer - - -def make_parser(): - parser = options.get_generation_parser() - parser = add_asr_eval_argument(parser) - return parser - - -def cli_main(): - parser = make_parser() - args = options.parse_args_and_arch(parser) - main(args) - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/__init__.py deleted file mode 100644 index 337c77ac7bfb7e11a0662b86b98c4c0a02da26df..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/__init__.py +++ /dev/null @@ -1,236 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import argparse -import importlib -import os -from contextlib import ExitStack - -from fairseq.dataclass import FairseqDataclass -from fairseq.dataclass.utils import merge_with_parent -from hydra.core.config_store import ConfigStore -from omegaconf import open_dict, OmegaConf - -from .composite_encoder import CompositeEncoder -from .distributed_fairseq_model import DistributedFairseqModel -from .fairseq_decoder import FairseqDecoder -from .fairseq_encoder import FairseqEncoder -from .fairseq_incremental_decoder import FairseqIncrementalDecoder -from .fairseq_model import ( - BaseFairseqModel, - FairseqEncoderDecoderModel, - FairseqEncoderModel, - FairseqLanguageModel, - FairseqModel, - FairseqMultiModel, -) - - -MODEL_REGISTRY = {} -MODEL_DATACLASS_REGISTRY = {} -ARCH_MODEL_REGISTRY = {} -ARCH_MODEL_NAME_REGISTRY = {} -ARCH_MODEL_INV_REGISTRY = {} -ARCH_CONFIG_REGISTRY = {} - - -__all__ = [ - "BaseFairseqModel", - "CompositeEncoder", - "DistributedFairseqModel", - "FairseqDecoder", - "FairseqEncoder", - "FairseqEncoderDecoderModel", - "FairseqEncoderModel", - "FairseqIncrementalDecoder", - "FairseqLanguageModel", - "FairseqModel", - "FairseqMultiModel", -] - - -def build_model(cfg: FairseqDataclass, task): - - model = None - model_type = getattr(cfg, "_name", None) or getattr(cfg, "arch", None) - - if not model_type and len(cfg) == 1: - # this is hit if config object is nested in directory that is named after model type - - model_type = next(iter(cfg)) - if model_type in MODEL_DATACLASS_REGISTRY: - cfg = cfg[model_type] - else: - raise Exception( - "Could not infer model type from directory. Please add _name field to indicate model type. " - "Available models: " - + str(MODEL_DATACLASS_REGISTRY.keys()) - + " Requested model type: " - + model_type - ) - - if model_type in ARCH_MODEL_REGISTRY: - # case 1: legacy models - model = ARCH_MODEL_REGISTRY[model_type] - elif model_type in MODEL_DATACLASS_REGISTRY: - # case 2: config-driven models - model = MODEL_REGISTRY[model_type] - - if model_type in MODEL_DATACLASS_REGISTRY: - # set defaults from dataclass. note that arch name and model name can be the same - dc = MODEL_DATACLASS_REGISTRY[model_type] - - if isinstance(cfg, argparse.Namespace): - cfg = dc.from_namespace(cfg) - else: - cfg = merge_with_parent(dc(), cfg) - else: - if model_type in ARCH_CONFIG_REGISTRY: - with open_dict(cfg) if OmegaConf.is_config(cfg) else ExitStack(): - # this calls the different "arch" functions (like base_architecture()) that you indicate - # if you specify --arch on the command line. this is only applicable to the old argparse based models - # hydra models should expose different architectures via different config files - # it will modify the cfg object and default parameters according to the arch - ARCH_CONFIG_REGISTRY[model_type](cfg) - - assert model is not None, ( - f"Could not infer model type from {cfg}. " - "Available models: {}".format( - MODEL_DATACLASS_REGISTRY.keys() - ) - + f" Requested model type: {model_type}" - ) - - return model.build_model(cfg, task) - - -def register_model(name, dataclass=None): - """ - New model types can be added to fairseq with the :func:`register_model` - function decorator. - - For example:: - - @register_model('lstm') - class LSTM(FairseqEncoderDecoderModel): - (...) - - .. note:: All models must implement the :class:`BaseFairseqModel` interface. - Typically you will extend :class:`FairseqEncoderDecoderModel` for - sequence-to-sequence tasks or :class:`FairseqLanguageModel` for - language modeling tasks. - - Args: - name (str): the name of the model - """ - - def register_model_cls(cls): - if name in MODEL_REGISTRY: - raise ValueError("Cannot register duplicate model ({})".format(name)) - if not issubclass(cls, BaseFairseqModel): - raise ValueError( - "Model ({}: {}) must extend BaseFairseqModel".format(name, cls.__name__) - ) - MODEL_REGISTRY[name] = cls - if dataclass is not None and not issubclass(dataclass, FairseqDataclass): - raise ValueError( - "Dataclass {} must extend FairseqDataclass".format(dataclass) - ) - - cls.__dataclass = dataclass - if dataclass is not None: - MODEL_DATACLASS_REGISTRY[name] = dataclass - - cs = ConfigStore.instance() - node = dataclass() - node._name = name - cs.store(name=name, group="model", node=node, provider="fairseq") - - @register_model_architecture(name, name) - def noop(_): - pass - - return cls - - return register_model_cls - - -def register_model_architecture(model_name, arch_name): - """ - New model architectures can be added to fairseq with the - :func:`register_model_architecture` function decorator. After registration, - model architectures can be selected with the ``--arch`` command-line - argument. - - For example:: - - @register_model_architecture('lstm', 'lstm_luong_wmt_en_de') - def lstm_luong_wmt_en_de(cfg): - args.encoder_embed_dim = getattr(cfg.model, 'encoder_embed_dim', 1000) - (...) - - The decorated function should take a single argument *cfg*, which is a - :class:`omegaconf.DictConfig`. The decorated function should modify these - arguments in-place to match the desired architecture. - - Args: - model_name (str): the name of the Model (Model must already be - registered) - arch_name (str): the name of the model architecture (``--arch``) - """ - - def register_model_arch_fn(fn): - if model_name not in MODEL_REGISTRY: - raise ValueError( - "Cannot register model architecture for unknown model type ({})".format( - model_name - ) - ) - if arch_name in ARCH_MODEL_REGISTRY: - raise ValueError( - "Cannot register duplicate model architecture ({})".format(arch_name) - ) - if not callable(fn): - raise ValueError( - "Model architecture must be callable ({})".format(arch_name) - ) - ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name] - ARCH_MODEL_NAME_REGISTRY[arch_name] = model_name - ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name) - ARCH_CONFIG_REGISTRY[arch_name] = fn - return fn - - return register_model_arch_fn - - -def import_models(models_dir, namespace): - for file in os.listdir(models_dir): - path = os.path.join(models_dir, file) - if ( - not file.startswith("_") - and not file.startswith(".") - and (file.endswith(".py") or os.path.isdir(path)) - ): - model_name = file[: file.find(".py")] if file.endswith(".py") else file - importlib.import_module(namespace + "." + model_name) - - # extra `model_parser` for sphinx - if model_name in MODEL_REGISTRY: - parser = argparse.ArgumentParser(add_help=False) - group_archs = parser.add_argument_group("Named architectures") - group_archs.add_argument( - "--arch", choices=ARCH_MODEL_INV_REGISTRY[model_name] - ) - group_args = parser.add_argument_group( - "Additional command-line arguments" - ) - MODEL_REGISTRY[model_name].add_args(group_args) - globals()[model_name + "_parser"] = parser - - -# automatically import any Python files in the models/ directory -models_dir = os.path.dirname(__file__) -import_models(models_dir, "fairseq.models") diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/.github/ISSUE_TEMPLATE/documentation.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/.github/ISSUE_TEMPLATE/documentation.md deleted file mode 100644 index 3a6e2e9ea4bb71102122c17ff53051eb3770cb5e..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/.github/ISSUE_TEMPLATE/documentation.md +++ /dev/null @@ -1,15 +0,0 @@ ---- -name: 📚 Documentation/Typos -about: Report an issue related to documentation or a typo -labels: 'documentation, needs triage' ---- - -## 📚 Documentation - -For typos and doc fixes, please go ahead and: - -1. Create an issue. -2. Fix the typo. -3. Submit a PR. - -Thanks! diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py deleted file mode 100644 index b0a617424ee3c5923b37796773da4c97851a16c5..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py +++ /dev/null @@ -1,467 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import datetime -import hashlib -import logging -import time -from bisect import bisect_right -from collections import OrderedDict, defaultdict -from enum import Enum -from typing import List - -import numpy as np -import torch -from fairseq.data import FairseqDataset, data_utils -from fairseq.distributed import utils as distributed_utils - - -def get_time_gap(s, e): - return ( - datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s) - ).__str__() - - -logger = logging.getLogger(__name__) - - -def default_virtual_size_func(datasets, ratios, max_scale_up=1.5): - sizes = [len(d) for d in datasets] - if ratios is None: - return sum(sizes) - largest_idx = np.argmax(sizes) - largest_r = ratios[largest_idx] - largest_s = sizes[largest_idx] - # set virtual sizes relative to the largest dataset - virtual_sizes = [(r / largest_r) * largest_s for r in ratios] - vsize = sum(virtual_sizes) - max_size = sum(sizes) * max_scale_up - return int(vsize if vsize < max_size else max_size) - - -class CollateFormat(Enum): - single = 1 - ordered_dict = 2 - - -class SampledMultiDataset(FairseqDataset): - """Samples from multiple sub-datasets according to given sampling ratios. - Args: - datasets ( - List[~torch.utils.data.Dataset] - or OrderedDict[str, ~torch.utils.data.Dataset] - ): datasets - sampling_ratios (List[float]): list of probability of each dataset to be sampled - (default: None, which corresponds to concatenating all dataset together). - seed (int): RNG seed to use (default: 2). - epoch (int): starting epoch number (default: 1). - eval_key (str, optional): a key used at evaluation time that causes - this instance to pass-through batches from *datasets[eval_key]*. - collate_format (CollateFormat): collater output format, either CollateFormat.ordered_dict or - CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures - the collater to output batches of data mixed from all sub-datasets, - and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys - of sub-datasets. - Note that not all sub-datasets will present in a single batch in both formats. - virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func). - split (str): the split of the data, e.g. 'train', 'valid' or 'test'. - shared_collater (bool): whether or not to all sub-datasets have the same collater. - shuffle (bool): whether or not to shuffle data (default: True). - """ - - def __init__( - self, - datasets, - sampling_ratios=None, - seed=2, - epoch=1, - eval_key=None, - collate_format=CollateFormat.single, - virtual_size=default_virtual_size_func, - split="", - shared_collater=False, - shuffle=True, - ): - super().__init__() - self.shared_collater = shared_collater - self.shuffle = shuffle - - if isinstance(datasets, OrderedDict): - self.keys = list(datasets.keys()) - datasets = list(datasets.values()) - elif isinstance(datasets, List): - self.keys = list(range(len(datasets))) - else: - raise AssertionError() - self.datasets = datasets - self.split = split - - self.eval_key = eval_key - if self.eval_key is not None: - self.collate_format = CollateFormat.single - else: - self.collate_format = collate_format - - self.seed = seed - self._cur_epoch = None - - self.cumulated_sizes = None - # self.datasets[k][self._cur_indices[i]] is the data item i in this sampled dataset - # namely, data item i is sampled from the kth sub-dataset self.datasets[k] - # where self.cumulated_sizes[k-1] <= i < self.cumulated_sizes[k] - self._cur_indices = None - - self._sizes = None - self.virtual_size_per_dataset = None - # caching properties - self._reset_cached_properties() - self.setup_sampling(sampling_ratios, virtual_size) - self.set_epoch(epoch) - - def _clean_if_not_none(self, var_list): - for v in var_list: - if v is not None: - del v - - def _reset_cached_properties(self): - self._clean_if_not_none([self._sizes, self._cur_indices]) - self._sizes = None - self._cur_indices = None - - def setup_sampling(self, sample_ratios, virtual_size): - sizes = [len(d) for d in self.datasets] - if sample_ratios is None: - # default back to concating datasets - self.sample_ratios = None - self.virtual_size = sum(sizes) - else: - if not isinstance(sample_ratios, np.ndarray): - sample_ratios = np.array(sample_ratios) - self.sample_ratios = sample_ratios - virtual_size = ( - default_virtual_size_func if virtual_size is None else virtual_size - ) - self.virtual_size = ( - virtual_size(self.datasets, self.sample_ratios) - if callable(virtual_size) - else virtual_size - ) - - def adjust_sampling(self, epoch, sampling_ratios, virtual_size): - if sampling_ratios is not None: - sampling_ratios = self._sync_sample_ratios(sampling_ratios) - self.setup_sampling(sampling_ratios, virtual_size) - - def _sync_sample_ratios(self, ratios): - # in case the ratios are not precisely the same across processes - # also to ensure every procresses update the ratios in the same pace - ratios = torch.DoubleTensor(ratios) - if torch.distributed.is_initialized(): - if torch.cuda.is_available(): - distributed_utils.all_reduce( - ratios.cuda(), group=distributed_utils.get_data_parallel_group() - ) - else: - distributed_utils.all_reduce( - ratios, group=distributed_utils.get_data_parallel_group() - ) - ret = ratios.cpu() - ret = ret.numpy() - return ret - - def random_choice_in_dataset(self, rng, dataset, choice_size): - if hasattr(dataset, "random_choice_in_dataset"): - return dataset.random_choice_in_dataset(rng, choice_size) - dataset_size = len(dataset) - return rng.choice( - dataset_size, choice_size, replace=(choice_size > dataset_size) - ) - - def get_virtual_indices(self, rng, datasets, sample_ratios, virtual_size): - def get_counts(sample_ratios): - counts = np.array([virtual_size * r for r in sample_ratios], dtype=np.int64) - diff = virtual_size - counts.sum() - assert diff >= 0 - # due to round-offs, the size might not match the desired sizes - if diff > 0: - dataset_indices = rng.choice( - len(sample_ratios), size=diff, p=sample_ratios - ) - for i in dataset_indices: - counts[i] += 1 - return counts - - def get_in_dataset_indices(datasets, sizes, sample_ratios): - counts = get_counts(sample_ratios) - # uniformally sample desired counts for each dataset - # if the desired counts are large, sample with replacement: - indices = [ - self.random_choice_in_dataset(rng, d, c) - for c, d in zip(counts, datasets) - ] - return indices - - sizes = [len(d) for d in datasets] - if sample_ratios is None: - # default back to concating datasets - in_dataset_indices = [list(range(s)) for s in sizes] - virtual_sizes_per_dataset = sizes - else: - ratios = sample_ratios / sample_ratios.sum() - in_dataset_indices = get_in_dataset_indices(datasets, sizes, ratios) - virtual_sizes_per_dataset = [len(d) for d in in_dataset_indices] - virtual_sizes_per_dataset = np.array(virtual_sizes_per_dataset, np.int64) - cumulative_sizes = np.cumsum(virtual_sizes_per_dataset) - assert sum(virtual_sizes_per_dataset) == virtual_size - assert cumulative_sizes[-1] == virtual_size - if virtual_size < sum(sizes): - logger.warning( - f"virtual data size ({virtual_size}) is less than real data size ({sum(sizes)})." - " If virtual size << real data size, there could be data coverage issue." - ) - in_dataset_indices = np.hstack(in_dataset_indices) - return in_dataset_indices, cumulative_sizes, virtual_sizes_per_dataset - - def _get_dataset_and_index(self, index): - i = bisect_right(self.cumulated_sizes, index) - return i, self._cur_indices[index] - - def __getitem__(self, index): - # self.__getitem__(index) returns self.datasets[k][self._cur_indices[index]] - # where k satisfies self.cumulated_sizes[k - 1] <= k < self.cumulated_sizes[k] - ds_idx, ds_sample_idx = self._get_dataset_and_index(index) - ret = (ds_idx, self.datasets[ds_idx][ds_sample_idx]) - return ret - - def num_tokens(self, index): - return self.sizes[index].max() - - def num_tokens_vec(self, indices): - sizes_vec = self.sizes[np.array(indices)] - # max across all dimensions but first one - return np.amax(sizes_vec, axis=tuple(range(1, len(sizes_vec.shape)))) - - def size(self, index): - return self.sizes[index] - - def __len__(self): - return self.virtual_size - - def collater(self, samples, **extra_args): - """Merge a list of samples to form a mini-batch.""" - if len(samples) == 0: - return None - if self.collate_format == "ordered_dict": - collect_samples = [[] for _ in range(len(self.datasets))] - for (i, sample) in samples: - collect_samples[i].append(sample) - batch = OrderedDict( - [ - (self.keys[i], dataset.collater(collect_samples[i])) - for i, (key, dataset) in enumerate(zip(self.keys, self.datasets)) - if len(collect_samples[i]) > 0 - ] - ) - elif self.shared_collater: - batch = self.datasets[0].collater([s for _, s in samples]) - else: - samples_dict = defaultdict(list) - pad_to_length = ( - defaultdict(int) - if "pad_to_length" not in extra_args - else extra_args["pad_to_length"] - ) - for ds_idx, s in samples: - pad_to_length["source"] = max( - pad_to_length["source"], s["source"].size(0) - ) - if s["target"] is not None: - pad_to_length["target"] = max( - pad_to_length["target"], s["target"].size(0) - ) - samples_dict[ds_idx].append(s) - batches = [ - self.datasets[i].collater(samples_dict[i], pad_to_length=pad_to_length) - for i in range(len(self.datasets)) - if len(samples_dict[i]) > 0 - ] - - def straight_data(tensors): - batch = torch.cat(tensors, dim=0) - return batch - - src_lengths = straight_data( - [b["net_input"]["src_lengths"] for b in batches] - ) - src_lengths, sort_order = src_lengths.sort(descending=True) - - def straight_order(tensors): - batch = straight_data(tensors) - return batch.index_select(0, sort_order) - - batch = { - "id": straight_order([b["id"] for b in batches]), - "nsentences": sum(b["nsentences"] for b in batches), - "ntokens": sum(b["ntokens"] for b in batches), - "net_input": { - "src_tokens": straight_order( - [b["net_input"]["src_tokens"] for b in batches] - ), - "src_lengths": src_lengths, - }, - "target": straight_order([b["target"] for b in batches]) - if batches[0]["target"] is not None - else None, - } - if "prev_output_tokens" in batches[0]["net_input"]: - batch["net_input"]["prev_output_tokens"] = straight_order( - [b["net_input"]["prev_output_tokens"] for b in batches] - ) - if "src_lang_id" in batches[0]["net_input"]: - batch["net_input"]["src_lang_id"] = straight_order( - [b["net_input"]["src_lang_id"] for b in batches] - ) - if "tgt_lang_id" in batches[0]: - batch["tgt_lang_id"] = straight_order( - [b["tgt_lang_id"] for b in batches] - ) - return batch - - @property - def sizes(self): - if self._sizes is not None: - return self._sizes - start_time = time.time() - in_sub_dataset_indices = [ - self._cur_indices[ - 0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i] - ] - for i in range(len(self.datasets)) - ] - sub_dataset_sizes = [ - d.sizes[indices] - for d, indices in zip(self.datasets, in_sub_dataset_indices) - ] - self._sizes = np.vstack(sub_dataset_sizes) - logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}") - return self._sizes - - def ordered_indices(self): - if self.shuffle: - indices = np.random.permutation(len(self)) - else: - indices = np.arange(len(self)) - - sizes = self.sizes - tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None - src_sizes = ( - sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes - ) - - # sort by target length, then source length - if tgt_sizes is not None: - indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")] - sort_indices = indices[np.argsort(src_sizes[indices], kind="mergesort")] - return sort_indices - - def prefetch(self, indices): - prefetch_indices = [[] for _ in range(len(self.datasets))] - for i in indices: - ds_idx, ds_sample_idx = self._get_dataset_and_index(i) - prefetch_indices[ds_idx].append(ds_sample_idx) - for i in range(len(prefetch_indices)): - self.datasets[i].prefetch(prefetch_indices[i]) - - @property - def can_reuse_epoch_itr_across_epochs(self): - return False - - def set_epoch(self, epoch): - super().set_epoch(epoch) - if epoch == self._cur_epoch: - # re-enter so return - return - for d in self.datasets: - if hasattr(d, "set_epoch"): - d.set_epoch(epoch) - self._cur_epoch = epoch - self._establish_virtual_datasets() - - def _establish_virtual_datasets(self): - if self.sample_ratios is None and self._cur_indices is not None: - # not a samping dataset, no need to resample if indices are already established - return - self._reset_cached_properties() - - start_time = time.time() - # Generate a weighted sample of indices as a function of the - # random seed and the current epoch. - rng = np.random.RandomState( - [ - int( - hashlib.sha1( - str(self.__class__.__name__).encode("utf-8") - ).hexdigest(), - 16, - ) - % (2 ** 32), - self.seed % (2 ** 32), # global seed - self._cur_epoch, # epoch index, - ] - ) - self._clean_if_not_none( - [self.cumulated_sizes, self.virtual_size_per_dataset, self._sizes] - ) - self._sizes = None - - indices, cumulated_sizes, virtual_size_per_dataset = self.get_virtual_indices( - rng, self.datasets, self.sample_ratios, self.virtual_size - ) - self._cur_indices = indices - self.cumulated_sizes = cumulated_sizes - self.virtual_size_per_dataset = virtual_size_per_dataset - - raw_sizes = [len(d) for d in self.datasets] - sampled_sizes = self.virtual_size_per_dataset - logger.info( - f"[{self.split}] Raw sizes: {str(dict(zip(self.keys, raw_sizes)))}; " - f"raw total size: {sum(raw_sizes)}" - ) - logger.info( - f"[{self.split}] Resampled sizes: {str(dict(zip(self.keys, sampled_sizes)))}; " - f"resampled total size: {sum(sampled_sizes)}" - ) - if self.sample_ratios is not None: - logger.info( - f"[{self.split}] Upsampling ratios: {str(dict(zip(self.keys, self.sample_ratios)))}" - ) - else: - logger.info(f"[{self.split}] A concat dataset") - logger.info( - f"[{self.split}] virtual dataset established time: {get_time_gap(start_time, time.time())}" - ) - - def filter_indices_by_size(self, indices, max_sizes): - """Filter a list of sample indices. Remove those that are longer - than specified in max_sizes. - - Args: - indices (np.array): original array of sample indices - max_sizes (int or list[int] or tuple[int]): max sample size, - can be defined separately for src and tgt (then list or tuple) - - Returns: - np.array: filtered sample array - list: list of removed indices - """ - sizes = self.sizes - tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None - src_sizes = ( - sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes - ) - - return data_utils.filter_paired_dataset_indices_by_size( - src_sizes, tgt_sizes, indices, max_sizes - ) diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/__init__.py deleted file mode 100644 index 05fe822487c3bcde8346648d5826f1669c6bc1ca..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/nat/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -from .fairseq_nat_model import * -from .nonautoregressive_transformer import * -from .nat_crf_transformer import * -from .iterative_nonautoregressive_transformer import * -from .cmlm_transformer import * -from .levenshtein_transformer import * -from .insertion_transformer import * diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/sinusoidal_positional_embedding.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/sinusoidal_positional_embedding.py deleted file mode 100644 index 4793ecfb522d0729fc2d24a3ddf0c6a774d67773..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/sinusoidal_positional_embedding.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Any, Optional - -import torch -import torch.onnx.operators -from fairseq import utils -from torch import Tensor, nn - - -class SinusoidalPositionalEmbedding(nn.Module): - """This module produces sinusoidal positional embeddings of any length. - - Padding symbols are ignored. - """ - - def __init__(self, embedding_dim, padding_idx, init_size=1024): - super().__init__() - self.embedding_dim = embedding_dim - self.padding_idx = padding_idx if padding_idx is not None else 0 - self.weights = SinusoidalPositionalEmbedding.get_embedding( - init_size, embedding_dim, padding_idx - ) - self.onnx_trace = False - self.register_buffer("_float_tensor", torch.FloatTensor(1)) - self.max_positions = int(1e5) - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - @staticmethod - def get_embedding( - num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None - ): - """Build sinusoidal embeddings. - - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb) - emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze( - 1 - ) * emb.unsqueeze(0) - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view( - num_embeddings, -1 - ) - if embedding_dim % 2 == 1: - # zero pad - emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) - if padding_idx is not None: - emb[padding_idx, :] = 0 - return emb - - def forward( - self, - input, - incremental_state: Optional[Any] = None, - timestep: Optional[Tensor] = None, - positions: Optional[Any] = None, - ): - """Input is expected to be of size [bsz x seqlen].""" - bspair = torch.onnx.operators.shape_as_tensor(input) - bsz, seq_len = bspair[0], bspair[1] - max_pos = self.padding_idx + 1 + seq_len - if self.weights is None or max_pos > self.weights.size(0): - # recompute/expand embeddings if needed - self.weights = SinusoidalPositionalEmbedding.get_embedding( - max_pos, self.embedding_dim, self.padding_idx - ) - self.weights = self.weights.to(self._float_tensor) - - if incremental_state is not None: - # positions is the same for every token when decoding a single step - pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len - if self.onnx_trace: - return ( - self.weights.index_select(index=self.padding_idx + pos, dim=0) - .unsqueeze(1) - .repeat(bsz, 1, 1) - ) - return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1) - - positions = utils.make_positions( - input, self.padding_idx, onnx_trace=self.onnx_trace - ) - if self.onnx_trace: - flat_embeddings = self.weights.detach().index_select(0, positions.view(-1)) - embedding_shape = torch.cat( - (bsz.view(1), seq_len.view(1), torch.tensor([-1], dtype=torch.long)) - ) - embeddings = torch.onnx.operators.reshape_from_tensor_shape( - flat_embeddings, embedding_shape - ) - return embeddings - return ( - self.weights.index_select(0, positions.view(-1)) - .view(bsz, seq_len, -1) - .detach() - ) diff --git a/spaces/ORI-Muchim/BarKeYaeTTS/text/cleaners.py b/spaces/ORI-Muchim/BarKeYaeTTS/text/cleaners.py deleted file mode 100644 index acf22ef2c64a588876e62cf78225891343f5e14f..0000000000000000000000000000000000000000 --- a/spaces/ORI-Muchim/BarKeYaeTTS/text/cleaners.py +++ /dev/null @@ -1,12 +0,0 @@ -import re -from text.korean import latin_to_hangul, number_to_hangul, divide_hangul, korean_to_lazy_ipa, korean_to_ipa - - -def korean_cleaners(text): - '''Pipeline for Korean text''' - text = latin_to_hangul(text) - text = number_to_hangul(text) - text = divide_hangul(text) - if re.match('[\u3131-\u3163]', text[-1]): - text += '.' - return text diff --git a/spaces/Otega99/minima/app.py b/spaces/Otega99/minima/app.py deleted file mode 100644 index 459ba63e6a1166633d0ab191433a57954c5c50bc..0000000000000000000000000000000000000000 --- a/spaces/Otega99/minima/app.py +++ /dev/null @@ -1,19 +0,0 @@ -from fastai.vision.all import * -import gradio as gr - -def is_cat(x): return x[0].isupper() - -learn=load_learner('model.pkl') - -categories=('Dog','Cat') - -def classify_image(img): - pred,idx,probs=learn.predict(img) - return dict(zip(categories,map(float,probs))) - -image=gr.inputs.Image(shape=(192,192)) -label=gr.outputs.Label() -examples=['dog.jpeg','cat.jpeg'] - -intf=gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples) -intf.launch(inline=False) \ No newline at end of file diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/uniformer.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/uniformer.py deleted file mode 100644 index 0c4bb88e4c928540cca9ab609988b916520f5b7a..0000000000000000000000000000000000000000 --- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/uniformer.py +++ /dev/null @@ -1,422 +0,0 @@ -# -------------------------------------------------------- -# UniFormer -# Copyright (c) 2022 SenseTime X-Lab -# Licensed under The MIT License [see LICENSE for details] -# Written by Kunchang Li -# -------------------------------------------------------- - -from collections import OrderedDict -import math - -from functools import partial -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -import numpy as np -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ - -from annotator.uniformer.mmcv_custom import load_checkpoint -from annotator.uniformer.mmseg.utils import get_root_logger -from ..builder import BACKBONES - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class CMlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Conv2d(in_features, hidden_features, 1) - self.act = act_layer() - self.fc2 = nn.Conv2d(hidden_features, out_features, 1) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class CBlock(nn.Module): - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) - self.norm1 = nn.BatchNorm2d(dim) - self.conv1 = nn.Conv2d(dim, dim, 1) - self.conv2 = nn.Conv2d(dim, dim, 1) - self.attn = nn.Conv2d(dim, dim, 5, padding=2, groups=dim) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = nn.BatchNorm2d(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = CMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - def forward(self, x): - x = x + self.pos_embed(x) - x = x + self.drop_path(self.conv2(self.attn(self.conv1(self.norm1(x))))) - x = x + self.drop_path(self.mlp(self.norm2(x))) - return x - - -class Attention(nn.Module): - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights - self.scale = qk_scale or head_dim ** -0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class SABlock(nn.Module): - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, - num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, - attn_drop=attn_drop, proj_drop=drop) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - def forward(self, x): - x = x + self.pos_embed(x) - B, N, H, W = x.shape - x = x.flatten(2).transpose(1, 2) - x = x + self.drop_path(self.attn(self.norm1(x))) - x = x + self.drop_path(self.mlp(self.norm2(x))) - x = x.transpose(1, 2).reshape(B, N, H, W) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class SABlock_Windows(nn.Module): - def __init__(self, dim, num_heads, window_size=14, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.window_size=window_size - self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim) - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, - num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, - attn_drop=attn_drop, proj_drop=drop) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - def forward(self, x): - x = x + self.pos_embed(x) - x = x.permute(0, 2, 3, 1) - B, H, W, C = x.shape - shortcut = x - x = self.norm1(x) - - pad_l = pad_t = 0 - pad_r = (self.window_size - W % self.window_size) % self.window_size - pad_b = (self.window_size - H % self.window_size) % self.window_size - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - - x_windows = window_partition(x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C - - # reverse cyclic shift - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - x = x.permute(0, 3, 1, 2).reshape(B, C, H, W) - return x - - -class PatchEmbed(nn.Module): - """ Image to Patch Embedding - """ - def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) - self.img_size = img_size - self.patch_size = patch_size - self.num_patches = num_patches - self.norm = nn.LayerNorm(embed_dim) - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - - def forward(self, x): - B, _, H, W = x.shape - x = self.proj(x) - B, _, H, W = x.shape - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - return x - - -@BACKBONES.register_module() -class UniFormer(nn.Module): - """ Vision Transformer - A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - - https://arxiv.org/abs/2010.11929 - """ - def __init__(self, layers=[3, 4, 8, 3], img_size=224, in_chans=3, num_classes=80, embed_dim=[64, 128, 320, 512], - head_dim=64, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6), - pretrained_path=None, use_checkpoint=False, checkpoint_num=[0, 0, 0, 0], - windows=False, hybrid=False, window_size=14): - """ - Args: - layer (list): number of block in each layer - img_size (int, tuple): input image size - in_chans (int): number of input channels - num_classes (int): number of classes for classification head - embed_dim (int): embedding dimension - head_dim (int): dimension of attention heads - mlp_ratio (int): ratio of mlp hidden dim to embedding dim - qkv_bias (bool): enable bias for qkv if True - qk_scale (float): override default qk scale of head_dim ** -0.5 if set - representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set - drop_rate (float): dropout rate - attn_drop_rate (float): attention dropout rate - drop_path_rate (float): stochastic depth rate - norm_layer (nn.Module): normalization layer - pretrained_path (str): path of pretrained model - use_checkpoint (bool): whether use checkpoint - checkpoint_num (list): index for using checkpoint in every stage - windows (bool): whether use window MHRA - hybrid (bool): whether use hybrid MHRA - window_size (int): size of window (>14) - """ - super().__init__() - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.checkpoint_num = checkpoint_num - self.windows = windows - print(f'Use Checkpoint: {self.use_checkpoint}') - print(f'Checkpoint Number: {self.checkpoint_num}') - self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models - norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) - - self.patch_embed1 = PatchEmbed( - img_size=img_size, patch_size=4, in_chans=in_chans, embed_dim=embed_dim[0]) - self.patch_embed2 = PatchEmbed( - img_size=img_size // 4, patch_size=2, in_chans=embed_dim[0], embed_dim=embed_dim[1]) - self.patch_embed3 = PatchEmbed( - img_size=img_size // 8, patch_size=2, in_chans=embed_dim[1], embed_dim=embed_dim[2]) - self.patch_embed4 = PatchEmbed( - img_size=img_size // 16, patch_size=2, in_chans=embed_dim[2], embed_dim=embed_dim[3]) - - self.pos_drop = nn.Dropout(p=drop_rate) - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(layers))] # stochastic depth decay rule - num_heads = [dim // head_dim for dim in embed_dim] - self.blocks1 = nn.ModuleList([ - CBlock( - dim=embed_dim[0], num_heads=num_heads[0], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) - for i in range(layers[0])]) - self.norm1=norm_layer(embed_dim[0]) - self.blocks2 = nn.ModuleList([ - CBlock( - dim=embed_dim[1], num_heads=num_heads[1], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]], norm_layer=norm_layer) - for i in range(layers[1])]) - self.norm2 = norm_layer(embed_dim[1]) - if self.windows: - print('Use local window for all blocks in stage3') - self.blocks3 = nn.ModuleList([ - SABlock_Windows( - dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer) - for i in range(layers[2])]) - elif hybrid: - print('Use hybrid window for blocks in stage3') - block3 = [] - for i in range(layers[2]): - if (i + 1) % 4 == 0: - block3.append(SABlock( - dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)) - else: - block3.append(SABlock_Windows( - dim=embed_dim[2], num_heads=num_heads[2], window_size=window_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer)) - self.blocks3 = nn.ModuleList(block3) - else: - print('Use global window for all blocks in stage3') - self.blocks3 = nn.ModuleList([ - SABlock( - dim=embed_dim[2], num_heads=num_heads[2], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]], norm_layer=norm_layer) - for i in range(layers[2])]) - self.norm3 = norm_layer(embed_dim[2]) - self.blocks4 = nn.ModuleList([ - SABlock( - dim=embed_dim[3], num_heads=num_heads[3], mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i+layers[0]+layers[1]+layers[2]], norm_layer=norm_layer) - for i in range(layers[3])]) - self.norm4 = norm_layer(embed_dim[3]) - - # Representation layer - if representation_size: - self.num_features = representation_size - self.pre_logits = nn.Sequential(OrderedDict([ - ('fc', nn.Linear(embed_dim, representation_size)), - ('act', nn.Tanh()) - ])) - else: - self.pre_logits = nn.Identity() - - self.apply(self._init_weights) - self.init_weights(pretrained=pretrained_path) - - def init_weights(self, pretrained): - if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger) - print(f'Load pretrained model from {pretrained}') - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token'} - - def get_classifier(self): - return self.head - - def reset_classifier(self, num_classes, global_pool=''): - self.num_classes = num_classes - self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - def forward_features(self, x): - out = [] - x = self.patch_embed1(x) - x = self.pos_drop(x) - for i, blk in enumerate(self.blocks1): - if self.use_checkpoint and i < self.checkpoint_num[0]: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x) - x_out = self.norm1(x.permute(0, 2, 3, 1)) - out.append(x_out.permute(0, 3, 1, 2).contiguous()) - x = self.patch_embed2(x) - for i, blk in enumerate(self.blocks2): - if self.use_checkpoint and i < self.checkpoint_num[1]: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x) - x_out = self.norm2(x.permute(0, 2, 3, 1)) - out.append(x_out.permute(0, 3, 1, 2).contiguous()) - x = self.patch_embed3(x) - for i, blk in enumerate(self.blocks3): - if self.use_checkpoint and i < self.checkpoint_num[2]: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x) - x_out = self.norm3(x.permute(0, 2, 3, 1)) - out.append(x_out.permute(0, 3, 1, 2).contiguous()) - x = self.patch_embed4(x) - for i, blk in enumerate(self.blocks4): - if self.use_checkpoint and i < self.checkpoint_num[3]: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x) - x_out = self.norm4(x.permute(0, 2, 3, 1)) - out.append(x_out.permute(0, 3, 1, 2).contiguous()) - return tuple(out) - - def forward(self, x): - x = self.forward_features(x) - return x diff --git a/spaces/Paarth/ForgeT5/README.md b/spaces/Paarth/ForgeT5/README.md deleted file mode 100644 index 8f39a41e2f51bab1468c0a9760c6a48f7ffa88e9..0000000000000000000000000000000000000000 --- a/spaces/Paarth/ForgeT5/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: ForgeCTE -emoji: 💩 -colorFrom: purple -colorTo: pink -sdk: gradio -sdk_version: 3.1.6 -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/PaddlePaddle/chinese-stable-diffusion/README.md b/spaces/PaddlePaddle/chinese-stable-diffusion/README.md deleted file mode 100644 index 84fcb68b5636db2d0d519d98f271a1680f6c2af2..0000000000000000000000000000000000000000 --- a/spaces/PaddlePaddle/chinese-stable-diffusion/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Chinese Stable Diffusion -emoji: 🐠 -colorFrom: gray -colorTo: gray -sdk: gradio -sdk_version: 3.3.1 -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/Pie31415/control-animation/annotator/uniformer/mmseg/datasets/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/datasets/__init__.py deleted file mode 100644 index ebeaef4a28ef655e43578552a8aef6b77f13a636..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/datasets/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -from .ade import ADE20KDataset -from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset -from .chase_db1 import ChaseDB1Dataset -from .cityscapes import CityscapesDataset -from .custom import CustomDataset -from .dataset_wrappers import ConcatDataset, RepeatDataset -from .drive import DRIVEDataset -from .hrf import HRFDataset -from .pascal_context import PascalContextDataset, PascalContextDataset59 -from .stare import STAREDataset -from .voc import PascalVOCDataset - -__all__ = [ - 'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset', - 'DATASETS', 'build_dataset', 'PIPELINES', 'CityscapesDataset', - 'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset', - 'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset', - 'STAREDataset' -] diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/backbones/vit.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/backbones/vit.py deleted file mode 100644 index 59e4479650690e08cbc4cab9427aefda47c2116d..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/backbones/vit.py +++ /dev/null @@ -1,459 +0,0 @@ -"""Modified from https://github.com/rwightman/pytorch-image- -models/blob/master/timm/models/vision_transformer.py.""" - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as cp -from annotator.uniformer.mmcv.cnn import (Conv2d, Linear, build_activation_layer, build_norm_layer, - constant_init, kaiming_init, normal_init) -from annotator.uniformer.mmcv.runner import _load_checkpoint -from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm - -from annotator.uniformer.mmseg.utils import get_root_logger -from ..builder import BACKBONES -from ..utils import DropPath, trunc_normal_ - - -class Mlp(nn.Module): - """MLP layer for Encoder block. - - Args: - in_features(int): Input dimension for the first fully - connected layer. - hidden_features(int): Output dimension for the first fully - connected layer. - out_features(int): Output dementsion for the second fully - connected layer. - act_cfg(dict): Config dict for activation layer. - Default: dict(type='GELU'). - drop(float): Drop rate for the dropout layer. Dropout rate has - to be between 0 and 1. Default: 0. - """ - - def __init__(self, - in_features, - hidden_features=None, - out_features=None, - act_cfg=dict(type='GELU'), - drop=0.): - super(Mlp, self).__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = Linear(in_features, hidden_features) - self.act = build_activation_layer(act_cfg) - self.fc2 = Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class Attention(nn.Module): - """Attention layer for Encoder block. - - Args: - dim (int): Dimension for the input vector. - num_heads (int): Number of parallel attention heads. - qkv_bias (bool): Enable bias for qkv if True. Default: False. - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - attn_drop (float): Drop rate for attention output weights. - Default: 0. - proj_drop (float): Drop rate for output weights. Default: 0. - """ - - def __init__(self, - dim, - num_heads=8, - qkv_bias=False, - qk_scale=None, - attn_drop=0., - proj_drop=0.): - super(Attention, self).__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x): - b, n, c = x.shape - qkv = self.qkv(x).reshape(b, n, 3, self.num_heads, - c // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(b, n, c) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Block(nn.Module): - """Implements encoder block with residual connection. - - Args: - dim (int): The feature dimension. - num_heads (int): Number of parallel attention heads. - mlp_ratio (int): Ratio of mlp hidden dim to embedding dim. - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - drop (float): Drop rate for mlp output weights. Default: 0. - attn_drop (float): Drop rate for attention output weights. - Default: 0. - proj_drop (float): Drop rate for attn layer output weights. - Default: 0. - drop_path (float): Drop rate for paths of model. - Default: 0. - act_cfg (dict): Config dict for activation layer. - Default: dict(type='GELU'). - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='LN', requires_grad=True). - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. Default: False. - """ - - def __init__(self, - dim, - num_heads, - mlp_ratio=4, - qkv_bias=False, - qk_scale=None, - drop=0., - attn_drop=0., - proj_drop=0., - drop_path=0., - act_cfg=dict(type='GELU'), - norm_cfg=dict(type='LN', eps=1e-6), - with_cp=False): - super(Block, self).__init__() - self.with_cp = with_cp - _, self.norm1 = build_norm_layer(norm_cfg, dim) - self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, - proj_drop) - self.drop_path = DropPath( - drop_path) if drop_path > 0. else nn.Identity() - _, self.norm2 = build_norm_layer(norm_cfg, dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp( - in_features=dim, - hidden_features=mlp_hidden_dim, - act_cfg=act_cfg, - drop=drop) - - def forward(self, x): - - def _inner_forward(x): - out = x + self.drop_path(self.attn(self.norm1(x))) - out = out + self.drop_path(self.mlp(self.norm2(out))) - return out - - if self.with_cp and x.requires_grad: - out = cp.checkpoint(_inner_forward, x) - else: - out = _inner_forward(x) - - return out - - -class PatchEmbed(nn.Module): - """Image to Patch Embedding. - - Args: - img_size (int | tuple): Input image size. - default: 224. - patch_size (int): Width and height for a patch. - default: 16. - in_channels (int): Input channels for images. Default: 3. - embed_dim (int): The embedding dimension. Default: 768. - """ - - def __init__(self, - img_size=224, - patch_size=16, - in_channels=3, - embed_dim=768): - super(PatchEmbed, self).__init__() - if isinstance(img_size, int): - self.img_size = (img_size, img_size) - elif isinstance(img_size, tuple): - self.img_size = img_size - else: - raise TypeError('img_size must be type of int or tuple') - h, w = self.img_size - self.patch_size = (patch_size, patch_size) - self.num_patches = (h // patch_size) * (w // patch_size) - self.proj = Conv2d( - in_channels, embed_dim, kernel_size=patch_size, stride=patch_size) - - def forward(self, x): - return self.proj(x).flatten(2).transpose(1, 2) - - -@BACKBONES.register_module() -class VisionTransformer(nn.Module): - """Vision transformer backbone. - - A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for - Image Recognition at Scale` - https://arxiv.org/abs/2010.11929 - - Args: - img_size (tuple): input image size. Default: (224, 224). - patch_size (int, tuple): patch size. Default: 16. - in_channels (int): number of input channels. Default: 3. - embed_dim (int): embedding dimension. Default: 768. - depth (int): depth of transformer. Default: 12. - num_heads (int): number of attention heads. Default: 12. - mlp_ratio (int): ratio of mlp hidden dim to embedding dim. - Default: 4. - out_indices (list | tuple | int): Output from which stages. - Default: -1. - qkv_bias (bool): enable bias for qkv if True. Default: True. - qk_scale (float): override default qk scale of head_dim ** -0.5 if set. - drop_rate (float): dropout rate. Default: 0. - attn_drop_rate (float): attention dropout rate. Default: 0. - drop_path_rate (float): Rate of DropPath. Default: 0. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='LN', eps=1e-6, requires_grad=True). - act_cfg (dict): Config dict for activation layer. - Default: dict(type='GELU'). - norm_eval (bool): Whether to set norm layers to eval mode, namely, - freeze running stats (mean and var). Note: Effect on Batch Norm - and its variants only. Default: False. - final_norm (bool): Whether to add a additional layer to normalize - final feature map. Default: False. - interpolate_mode (str): Select the interpolate mode for position - embeding vector resize. Default: bicubic. - with_cls_token (bool): If concatenating class token into image tokens - as transformer input. Default: True. - with_cp (bool): Use checkpoint or not. Using checkpoint - will save some memory while slowing down the training speed. - Default: False. - """ - - def __init__(self, - img_size=(224, 224), - patch_size=16, - in_channels=3, - embed_dim=768, - depth=12, - num_heads=12, - mlp_ratio=4, - out_indices=11, - qkv_bias=True, - qk_scale=None, - drop_rate=0., - attn_drop_rate=0., - drop_path_rate=0., - norm_cfg=dict(type='LN', eps=1e-6, requires_grad=True), - act_cfg=dict(type='GELU'), - norm_eval=False, - final_norm=False, - with_cls_token=True, - interpolate_mode='bicubic', - with_cp=False): - super(VisionTransformer, self).__init__() - self.img_size = img_size - self.patch_size = patch_size - self.features = self.embed_dim = embed_dim - self.patch_embed = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_channels=in_channels, - embed_dim=embed_dim) - - self.with_cls_token = with_cls_token - self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) - self.pos_embed = nn.Parameter( - torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim)) - self.pos_drop = nn.Dropout(p=drop_rate) - - if isinstance(out_indices, int): - self.out_indices = [out_indices] - elif isinstance(out_indices, list) or isinstance(out_indices, tuple): - self.out_indices = out_indices - else: - raise TypeError('out_indices must be type of int, list or tuple') - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) - ] # stochastic depth decay rule - self.blocks = nn.ModuleList([ - Block( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=dpr[i], - attn_drop=attn_drop_rate, - act_cfg=act_cfg, - norm_cfg=norm_cfg, - with_cp=with_cp) for i in range(depth) - ]) - - self.interpolate_mode = interpolate_mode - self.final_norm = final_norm - if final_norm: - _, self.norm = build_norm_layer(norm_cfg, embed_dim) - - self.norm_eval = norm_eval - self.with_cp = with_cp - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - logger = get_root_logger() - checkpoint = _load_checkpoint(pretrained, logger=logger) - if 'state_dict' in checkpoint: - state_dict = checkpoint['state_dict'] - else: - state_dict = checkpoint - - if 'pos_embed' in state_dict.keys(): - if self.pos_embed.shape != state_dict['pos_embed'].shape: - logger.info(msg=f'Resize the pos_embed shape from \ -{state_dict["pos_embed"].shape} to {self.pos_embed.shape}') - h, w = self.img_size - pos_size = int( - math.sqrt(state_dict['pos_embed'].shape[1] - 1)) - state_dict['pos_embed'] = self.resize_pos_embed( - state_dict['pos_embed'], (h, w), (pos_size, pos_size), - self.patch_size, self.interpolate_mode) - - self.load_state_dict(state_dict, False) - - elif pretrained is None: - # We only implement the 'jax_impl' initialization implemented at - # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501 - trunc_normal_(self.pos_embed, std=.02) - trunc_normal_(self.cls_token, std=.02) - for n, m in self.named_modules(): - if isinstance(m, Linear): - trunc_normal_(m.weight, std=.02) - if m.bias is not None: - if 'mlp' in n: - normal_init(m.bias, std=1e-6) - else: - constant_init(m.bias, 0) - elif isinstance(m, Conv2d): - kaiming_init(m.weight, mode='fan_in') - if m.bias is not None: - constant_init(m.bias, 0) - elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)): - constant_init(m.bias, 0) - constant_init(m.weight, 1.0) - else: - raise TypeError('pretrained must be a str or None') - - def _pos_embeding(self, img, patched_img, pos_embed): - """Positiong embeding method. - - Resize the pos_embed, if the input image size doesn't match - the training size. - Args: - img (torch.Tensor): The inference image tensor, the shape - must be [B, C, H, W]. - patched_img (torch.Tensor): The patched image, it should be - shape of [B, L1, C]. - pos_embed (torch.Tensor): The pos_embed weighs, it should be - shape of [B, L2, c]. - Return: - torch.Tensor: The pos encoded image feature. - """ - assert patched_img.ndim == 3 and pos_embed.ndim == 3, \ - 'the shapes of patched_img and pos_embed must be [B, L, C]' - x_len, pos_len = patched_img.shape[1], pos_embed.shape[1] - if x_len != pos_len: - if pos_len == (self.img_size[0] // self.patch_size) * ( - self.img_size[1] // self.patch_size) + 1: - pos_h = self.img_size[0] // self.patch_size - pos_w = self.img_size[1] // self.patch_size - else: - raise ValueError( - 'Unexpected shape of pos_embed, got {}.'.format( - pos_embed.shape)) - pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:], - (pos_h, pos_w), self.patch_size, - self.interpolate_mode) - return self.pos_drop(patched_img + pos_embed) - - @staticmethod - def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size, mode): - """Resize pos_embed weights. - - Resize pos_embed using bicubic interpolate method. - Args: - pos_embed (torch.Tensor): pos_embed weights. - input_shpae (tuple): Tuple for (input_h, intput_w). - pos_shape (tuple): Tuple for (pos_h, pos_w). - patch_size (int): Patch size. - Return: - torch.Tensor: The resized pos_embed of shape [B, L_new, C] - """ - assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]' - input_h, input_w = input_shpae - pos_h, pos_w = pos_shape - cls_token_weight = pos_embed[:, 0] - pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):] - pos_embed_weight = pos_embed_weight.reshape( - 1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2) - pos_embed_weight = F.interpolate( - pos_embed_weight, - size=[input_h // patch_size, input_w // patch_size], - align_corners=False, - mode=mode) - cls_token_weight = cls_token_weight.unsqueeze(1) - pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2) - pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1) - return pos_embed - - def forward(self, inputs): - B = inputs.shape[0] - - x = self.patch_embed(inputs) - - cls_tokens = self.cls_token.expand(B, -1, -1) - x = torch.cat((cls_tokens, x), dim=1) - x = self._pos_embeding(inputs, x, self.pos_embed) - - if not self.with_cls_token: - # Remove class token for transformer input - x = x[:, 1:] - - outs = [] - for i, blk in enumerate(self.blocks): - x = blk(x) - if i == len(self.blocks) - 1: - if self.final_norm: - x = self.norm(x) - if i in self.out_indices: - if self.with_cls_token: - # Remove class token and reshape token for decoder head - out = x[:, 1:] - else: - out = x - B, _, C = out.shape - out = out.reshape(B, inputs.shape[2] // self.patch_size, - inputs.shape[3] // self.patch_size, - C).permute(0, 3, 1, 2) - outs.append(out) - - return tuple(outs) - - def train(self, mode=True): - super(VisionTransformer, self).train(mode) - if mode and self.norm_eval: - for m in self.modules(): - if isinstance(m, nn.LayerNorm): - m.eval() diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/utils/drop.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/utils/drop.py deleted file mode 100644 index 4520b0ff407d2a95a864086bdbca0065f222aa63..0000000000000000000000000000000000000000 --- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/utils/drop.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Modified from https://github.com/rwightman/pytorch-image- -models/blob/master/timm/models/layers/drop.py.""" - -import torch -from torch import nn - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of - residual blocks). - - Args: - drop_prob (float): Drop rate for paths of model. Dropout rate has - to be between 0 and 1. Default: 0. - """ - - def __init__(self, drop_prob=0.): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - self.keep_prob = 1 - drop_prob - - def forward(self, x): - if self.drop_prob == 0. or not self.training: - return x - shape = (x.shape[0], ) + (1, ) * ( - x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = self.keep_prob + torch.rand( - shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(self.keep_prob) * random_tensor - return output diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/musicgen/musicgen_base_32khz.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/musicgen/musicgen_base_32khz.py deleted file mode 100644 index 4e364614537e426f21c18a2c2a9d94b3babce051..0000000000000000000000000000000000000000 --- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/grids/musicgen/musicgen_base_32khz.py +++ /dev/null @@ -1,43 +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. - -from ._explorers import LMExplorer -from ...environment import AudioCraftEnvironment - - -@LMExplorer -def explorer(launcher): - partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global']) - launcher.slurm_(gpus=32, partition=partitions) - launcher.bind_(solver='musicgen/musicgen_base_32khz') - # replace this by the desired music dataset - launcher.bind_(dset='internal/music_400k_32khz') - - fsdp = {'autocast': False, 'fsdp.use': True} - medium = {'model/lm/model_scale': 'medium'} - large = {'model/lm/model_scale': 'large'} - - cfg_low = {'classifier_free_guidance.training_dropout': 0.2} - wd_low = {'conditioners.description.t5.word_dropout': 0.2} - - adam = {'optim.optimizer': 'adamw', 'optim.lr': 1e-4} - - launcher.bind_(fsdp) - - launcher.slurm_(gpus=32).bind_(label='32gpus') - with launcher.job_array(): - sub = launcher.bind() - sub() - - launcher.slurm_(gpus=64).bind_(label='64gpus') - with launcher.job_array(): - sub = launcher.bind() - sub(medium, adam) - - launcher.slurm_(gpus=96).bind_(label='96gpus') - with launcher.job_array(): - sub = launcher.bind() - sub(large, cfg_low, wd_low, adam, {'optim.max_norm': 3}) diff --git a/spaces/Promptengineering/anon8231489123-vicuna-13b-GPTQ-4bit-128g/README.md b/spaces/Promptengineering/anon8231489123-vicuna-13b-GPTQ-4bit-128g/README.md deleted file mode 100644 index 94450e3e96877863e0c3640483dfbe41439c436c..0000000000000000000000000000000000000000 --- a/spaces/Promptengineering/anon8231489123-vicuna-13b-GPTQ-4bit-128g/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Anon8231489123 Vicuna 13b GPTQ 4bit 128g -emoji: 🏢 -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ProteinDesignLab/protpardelle/core/residue_constants.py b/spaces/ProteinDesignLab/protpardelle/core/residue_constants.py deleted file mode 100644 index efc1dc0021cd38d9c41db956b016cb36a6f182e8..0000000000000000000000000000000000000000 --- a/spaces/ProteinDesignLab/protpardelle/core/residue_constants.py +++ /dev/null @@ -1,1104 +0,0 @@ -# Copyright 2021 DeepMind Technologies Limited -# -# 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. - -"""Constants used in AlphaFold. -Adapted from original code by alexechu. -""" - -import collections -import functools -import os -from typing import List, Mapping, Tuple - -import numpy as np -import tree - -# Internal import (35fd). - - -# Distance from one CA to next CA [trans configuration: omega = 180]. -ca_ca = 3.80209737096 - -# Format: The list for each AA type contains chi1, chi2, chi3, chi4 in -# this order (or a relevant subset from chi1 onwards). ALA and GLY don't have -# chi angles so their chi angle lists are empty. -chi_angles_atoms = { - "ALA": [], - # Chi5 in arginine is always 0 +- 5 degrees, so ignore it. - "ARG": [ - ["N", "CA", "CB", "CG"], - ["CA", "CB", "CG", "CD"], - ["CB", "CG", "CD", "NE"], - ["CG", "CD", "NE", "CZ"], - ], - "ASN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], - "ASP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], - "CYS": [["N", "CA", "CB", "SG"]], - "GLN": [ - ["N", "CA", "CB", "CG"], - ["CA", "CB", "CG", "CD"], - ["CB", "CG", "CD", "OE1"], - ], - "GLU": [ - ["N", "CA", "CB", "CG"], - ["CA", "CB", "CG", "CD"], - ["CB", "CG", "CD", "OE1"], - ], - "GLY": [], - "HIS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "ND1"]], - "ILE": [["N", "CA", "CB", "CG1"], ["CA", "CB", "CG1", "CD1"]], - "LEU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], - "LYS": [ - ["N", "CA", "CB", "CG"], - ["CA", "CB", "CG", "CD"], - ["CB", "CG", "CD", "CE"], - ["CG", "CD", "CE", "NZ"], - ], - "MET": [ - ["N", "CA", "CB", "CG"], - ["CA", "CB", "CG", "SD"], - ["CB", "CG", "SD", "CE"], - ], - "PHE": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], - "PRO": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"]], - "SER": [["N", "CA", "CB", "OG"]], - "THR": [["N", "CA", "CB", "OG1"]], - "TRP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], - "TYR": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], - "VAL": [["N", "CA", "CB", "CG1"]], -} - -# If chi angles given in fixed-length array, this matrix determines how to mask -# them for each AA type. The order is as per restype_order (see below). -chi_angles_mask = [ - [0.0, 0.0, 0.0, 0.0], # ALA - [1.0, 1.0, 1.0, 1.0], # ARG - [1.0, 1.0, 0.0, 0.0], # ASN - [1.0, 1.0, 0.0, 0.0], # ASP - [1.0, 0.0, 0.0, 0.0], # CYS - [1.0, 1.0, 1.0, 0.0], # GLN - [1.0, 1.0, 1.0, 0.0], # GLU - [0.0, 0.0, 0.0, 0.0], # GLY - [1.0, 1.0, 0.0, 0.0], # HIS - [1.0, 1.0, 0.0, 0.0], # ILE - [1.0, 1.0, 0.0, 0.0], # LEU - [1.0, 1.0, 1.0, 1.0], # LYS - [1.0, 1.0, 1.0, 0.0], # MET - [1.0, 1.0, 0.0, 0.0], # PHE - [1.0, 1.0, 0.0, 0.0], # PRO - [1.0, 0.0, 0.0, 0.0], # SER - [1.0, 0.0, 0.0, 0.0], # THR - [1.0, 1.0, 0.0, 0.0], # TRP - [1.0, 1.0, 0.0, 0.0], # TYR - [1.0, 0.0, 0.0, 0.0], # VAL -] - -# The following chi angles are pi periodic: they can be rotated by a multiple -# of pi without affecting the structure. -chi_pi_periodic = [ - [0.0, 0.0, 0.0, 0.0], # ALA - [0.0, 0.0, 0.0, 0.0], # ARG - [0.0, 0.0, 0.0, 0.0], # ASN - [0.0, 1.0, 0.0, 0.0], # ASP - [0.0, 0.0, 0.0, 0.0], # CYS - [0.0, 0.0, 0.0, 0.0], # GLN - [0.0, 0.0, 1.0, 0.0], # GLU - [0.0, 0.0, 0.0, 0.0], # GLY - [0.0, 0.0, 0.0, 0.0], # HIS - [0.0, 0.0, 0.0, 0.0], # ILE - [0.0, 0.0, 0.0, 0.0], # LEU - [0.0, 0.0, 0.0, 0.0], # LYS - [0.0, 0.0, 0.0, 0.0], # MET - [0.0, 1.0, 0.0, 0.0], # PHE - [0.0, 0.0, 0.0, 0.0], # PRO - [0.0, 0.0, 0.0, 0.0], # SER - [0.0, 0.0, 0.0, 0.0], # THR - [0.0, 0.0, 0.0, 0.0], # TRP - [0.0, 1.0, 0.0, 0.0], # TYR - [0.0, 0.0, 0.0, 0.0], # VAL - [0.0, 0.0, 0.0, 0.0], # UNK -] - -# Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi, -# psi and chi angles: -# 0: 'backbone group', -# 1: 'pre-omega-group', (empty) -# 2: 'phi-group', (currently empty, because it defines only hydrogens) -# 3: 'psi-group', -# 4,5,6,7: 'chi1,2,3,4-group' -# The atom positions are relative to the axis-end-atom of the corresponding -# rotation axis. The x-axis is in direction of the rotation axis, and the y-axis -# is defined such that the dihedral-angle-defining atom (the last entry in -# chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate). -# format: [atomname, group_idx, rel_position] -rigid_group_atom_positions = { - "ALA": [ - ["N", 0, (-0.525, 1.363, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.526, -0.000, -0.000)], - ["CB", 0, (-0.529, -0.774, -1.205)], - ["O", 3, (0.627, 1.062, 0.000)], - ], - "ARG": [ - ["N", 0, (-0.524, 1.362, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.525, -0.000, -0.000)], - ["CB", 0, (-0.524, -0.778, -1.209)], - ["O", 3, (0.626, 1.062, 0.000)], - ["CG", 4, (0.616, 1.390, -0.000)], - ["CD", 5, (0.564, 1.414, 0.000)], - ["NE", 6, (0.539, 1.357, -0.000)], - ["NH1", 7, (0.206, 2.301, 0.000)], - ["NH2", 7, (2.078, 0.978, -0.000)], - ["CZ", 7, (0.758, 1.093, -0.000)], - ], - "ASN": [ - ["N", 0, (-0.536, 1.357, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.526, -0.000, -0.000)], - ["CB", 0, (-0.531, -0.787, -1.200)], - ["O", 3, (0.625, 1.062, 0.000)], - ["CG", 4, (0.584, 1.399, 0.000)], - ["ND2", 5, (0.593, -1.188, 0.001)], - ["OD1", 5, (0.633, 1.059, 0.000)], - ], - "ASP": [ - ["N", 0, (-0.525, 1.362, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.527, 0.000, -0.000)], - ["CB", 0, (-0.526, -0.778, -1.208)], - ["O", 3, (0.626, 1.062, -0.000)], - ["CG", 4, (0.593, 1.398, -0.000)], - ["OD1", 5, (0.610, 1.091, 0.000)], - ["OD2", 5, (0.592, -1.101, -0.003)], - ], - "CYS": [ - ["N", 0, (-0.522, 1.362, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.524, 0.000, 0.000)], - ["CB", 0, (-0.519, -0.773, -1.212)], - ["O", 3, (0.625, 1.062, -0.000)], - ["SG", 4, (0.728, 1.653, 0.000)], - ], - "GLN": [ - ["N", 0, (-0.526, 1.361, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.526, 0.000, 0.000)], - ["CB", 0, (-0.525, -0.779, -1.207)], - ["O", 3, (0.626, 1.062, -0.000)], - ["CG", 4, (0.615, 1.393, 0.000)], - ["CD", 5, (0.587, 1.399, -0.000)], - ["NE2", 6, (0.593, -1.189, -0.001)], - ["OE1", 6, (0.634, 1.060, 0.000)], - ], - "GLU": [ - ["N", 0, (-0.528, 1.361, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.526, -0.000, -0.000)], - ["CB", 0, (-0.526, -0.781, -1.207)], - ["O", 3, (0.626, 1.062, 0.000)], - ["CG", 4, (0.615, 1.392, 0.000)], - ["CD", 5, (0.600, 1.397, 0.000)], - ["OE1", 6, (0.607, 1.095, -0.000)], - ["OE2", 6, (0.589, -1.104, -0.001)], - ], - "GLY": [ - ["N", 0, (-0.572, 1.337, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.517, -0.000, -0.000)], - ["O", 3, (0.626, 1.062, -0.000)], - ], - "HIS": [ - ["N", 0, (-0.527, 1.360, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.525, 0.000, 0.000)], - ["CB", 0, (-0.525, -0.778, -1.208)], - ["O", 3, (0.625, 1.063, 0.000)], - ["CG", 4, (0.600, 1.370, -0.000)], - ["CD2", 5, (0.889, -1.021, 0.003)], - ["ND1", 5, (0.744, 1.160, -0.000)], - ["CE1", 5, (2.030, 0.851, 0.002)], - ["NE2", 5, (2.145, -0.466, 0.004)], - ], - "ILE": [ - ["N", 0, (-0.493, 1.373, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.527, -0.000, -0.000)], - ["CB", 0, (-0.536, -0.793, -1.213)], - ["O", 3, (0.627, 1.062, -0.000)], - ["CG1", 4, (0.534, 1.437, -0.000)], - ["CG2", 4, (0.540, -0.785, -1.199)], - ["CD1", 5, (0.619, 1.391, 0.000)], - ], - "LEU": [ - ["N", 0, (-0.520, 1.363, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.525, -0.000, -0.000)], - ["CB", 0, (-0.522, -0.773, -1.214)], - ["O", 3, (0.625, 1.063, -0.000)], - ["CG", 4, (0.678, 1.371, 0.000)], - ["CD1", 5, (0.530, 1.430, -0.000)], - ["CD2", 5, (0.535, -0.774, 1.200)], - ], - "LYS": [ - ["N", 0, (-0.526, 1.362, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.526, 0.000, 0.000)], - ["CB", 0, (-0.524, -0.778, -1.208)], - ["O", 3, (0.626, 1.062, -0.000)], - ["CG", 4, (0.619, 1.390, 0.000)], - ["CD", 5, (0.559, 1.417, 0.000)], - ["CE", 6, (0.560, 1.416, 0.000)], - ["NZ", 7, (0.554, 1.387, 0.000)], - ], - "MET": [ - ["N", 0, (-0.521, 1.364, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.525, 0.000, 0.000)], - ["CB", 0, (-0.523, -0.776, -1.210)], - ["O", 3, (0.625, 1.062, -0.000)], - ["CG", 4, (0.613, 1.391, -0.000)], - ["SD", 5, (0.703, 1.695, 0.000)], - ["CE", 6, (0.320, 1.786, -0.000)], - ], - "PHE": [ - ["N", 0, (-0.518, 1.363, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.524, 0.000, -0.000)], - ["CB", 0, (-0.525, -0.776, -1.212)], - ["O", 3, (0.626, 1.062, -0.000)], - ["CG", 4, (0.607, 1.377, 0.000)], - ["CD1", 5, (0.709, 1.195, -0.000)], - ["CD2", 5, (0.706, -1.196, 0.000)], - ["CE1", 5, (2.102, 1.198, -0.000)], - ["CE2", 5, (2.098, -1.201, -0.000)], - ["CZ", 5, (2.794, -0.003, -0.001)], - ], - "PRO": [ - ["N", 0, (-0.566, 1.351, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.527, -0.000, 0.000)], - ["CB", 0, (-0.546, -0.611, -1.293)], - ["O", 3, (0.621, 1.066, 0.000)], - ["CG", 4, (0.382, 1.445, 0.0)], - # ['CD', 5, (0.427, 1.440, 0.0)], - ["CD", 5, (0.477, 1.424, 0.0)], # manually made angle 2 degrees larger - ], - "SER": [ - ["N", 0, (-0.529, 1.360, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.525, -0.000, -0.000)], - ["CB", 0, (-0.518, -0.777, -1.211)], - ["O", 3, (0.626, 1.062, -0.000)], - ["OG", 4, (0.503, 1.325, 0.000)], - ], - "THR": [ - ["N", 0, (-0.517, 1.364, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.526, 0.000, -0.000)], - ["CB", 0, (-0.516, -0.793, -1.215)], - ["O", 3, (0.626, 1.062, 0.000)], - ["CG2", 4, (0.550, -0.718, -1.228)], - ["OG1", 4, (0.472, 1.353, 0.000)], - ], - "TRP": [ - ["N", 0, (-0.521, 1.363, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.525, -0.000, 0.000)], - ["CB", 0, (-0.523, -0.776, -1.212)], - ["O", 3, (0.627, 1.062, 0.000)], - ["CG", 4, (0.609, 1.370, -0.000)], - ["CD1", 5, (0.824, 1.091, 0.000)], - ["CD2", 5, (0.854, -1.148, -0.005)], - ["CE2", 5, (2.186, -0.678, -0.007)], - ["CE3", 5, (0.622, -2.530, -0.007)], - ["NE1", 5, (2.140, 0.690, -0.004)], - ["CH2", 5, (3.028, -2.890, -0.013)], - ["CZ2", 5, (3.283, -1.543, -0.011)], - ["CZ3", 5, (1.715, -3.389, -0.011)], - ], - "TYR": [ - ["N", 0, (-0.522, 1.362, 0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.524, -0.000, -0.000)], - ["CB", 0, (-0.522, -0.776, -1.213)], - ["O", 3, (0.627, 1.062, -0.000)], - ["CG", 4, (0.607, 1.382, -0.000)], - ["CD1", 5, (0.716, 1.195, -0.000)], - ["CD2", 5, (0.713, -1.194, -0.001)], - ["CE1", 5, (2.107, 1.200, -0.002)], - ["CE2", 5, (2.104, -1.201, -0.003)], - ["OH", 5, (4.168, -0.002, -0.005)], - ["CZ", 5, (2.791, -0.001, -0.003)], - ], - "VAL": [ - ["N", 0, (-0.494, 1.373, -0.000)], - ["CA", 0, (0.000, 0.000, 0.000)], - ["C", 0, (1.527, -0.000, -0.000)], - ["CB", 0, (-0.533, -0.795, -1.213)], - ["O", 3, (0.627, 1.062, -0.000)], - ["CG1", 4, (0.540, 1.429, -0.000)], - ["CG2", 4, (0.533, -0.776, 1.203)], - ], -} - -# A list of atoms (excluding hydrogen) for each AA type. PDB naming convention. -residue_atoms = { - "ALA": ["C", "CA", "CB", "N", "O"], - "ARG": ["C", "CA", "CB", "CG", "CD", "CZ", "N", "NE", "O", "NH1", "NH2"], - "ASP": ["C", "CA", "CB", "CG", "N", "O", "OD1", "OD2"], - "ASN": ["C", "CA", "CB", "CG", "N", "ND2", "O", "OD1"], - "CYS": ["C", "CA", "CB", "N", "O", "SG"], - "GLU": ["C", "CA", "CB", "CG", "CD", "N", "O", "OE1", "OE2"], - "GLN": ["C", "CA", "CB", "CG", "CD", "N", "NE2", "O", "OE1"], - "GLY": ["C", "CA", "N", "O"], - "HIS": ["C", "CA", "CB", "CG", "CD2", "CE1", "N", "ND1", "NE2", "O"], - "ILE": ["C", "CA", "CB", "CG1", "CG2", "CD1", "N", "O"], - "LEU": ["C", "CA", "CB", "CG", "CD1", "CD2", "N", "O"], - "LYS": ["C", "CA", "CB", "CG", "CD", "CE", "N", "NZ", "O"], - "MET": ["C", "CA", "CB", "CG", "CE", "N", "O", "SD"], - "PHE": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O"], - "PRO": ["C", "CA", "CB", "CG", "CD", "N", "O"], - "SER": ["C", "CA", "CB", "N", "O", "OG"], - "THR": ["C", "CA", "CB", "CG2", "N", "O", "OG1"], - "TRP": [ - "C", - "CA", - "CB", - "CG", - "CD1", - "CD2", - "CE2", - "CE3", - "CZ2", - "CZ3", - "CH2", - "N", - "NE1", - "O", - ], - "TYR": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O", "OH"], - "VAL": ["C", "CA", "CB", "CG1", "CG2", "N", "O"], -} - -# Naming swaps for ambiguous atom names. -# Due to symmetries in the amino acids the naming of atoms is ambiguous in -# 4 of the 20 amino acids. -# (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities -# in LEU, VAL and ARG can be resolved by using the 3d constellations of -# the 'ambiguous' atoms and their neighbours) -residue_atom_renaming_swaps = { - "ASP": {"OD1": "OD2"}, - "GLU": {"OE1": "OE2"}, - "PHE": {"CD1": "CD2", "CE1": "CE2"}, - "TYR": {"CD1": "CD2", "CE1": "CE2"}, -} - -# Van der Waals radii [Angstroem] of the atoms (from Wikipedia) -van_der_waals_radius = { - "C": 1.7, - "N": 1.55, - "O": 1.52, - "S": 1.8, -} - -Bond = collections.namedtuple("Bond", ["atom1_name", "atom2_name", "length", "stddev"]) -BondAngle = collections.namedtuple( - "BondAngle", ["atom1_name", "atom2_name", "atom3name", "angle_rad", "stddev"] -) - - -@functools.lru_cache(maxsize=None) -def load_stereo_chemical_props() -> ( - Tuple[ - Mapping[str, List[Bond]], - Mapping[str, List[Bond]], - Mapping[str, List[BondAngle]], - ] -): - """Load stereo_chemical_props.txt into a nice structure. - - Load literature values for bond lengths and bond angles and translate - bond angles into the length of the opposite edge of the triangle - ("residue_virtual_bonds"). - - Returns: - residue_bonds: Dict that maps resname -> list of Bond tuples. - residue_virtual_bonds: Dict that maps resname -> list of Bond tuples. - residue_bond_angles: Dict that maps resname -> list of BondAngle tuples. - """ - stereo_chemical_props_path = os.path.join( - os.path.dirname(os.path.abspath(__file__)), "stereo_chemical_props.txt" - ) - with open(stereo_chemical_props_path, "rt") as f: - stereo_chemical_props = f.read() - lines_iter = iter(stereo_chemical_props.splitlines()) - # Load bond lengths. - residue_bonds = {} - next(lines_iter) # Skip header line. - for line in lines_iter: - if line.strip() == "-": - break - bond, resname, length, stddev = line.split() - atom1, atom2 = bond.split("-") - if resname not in residue_bonds: - residue_bonds[resname] = [] - residue_bonds[resname].append(Bond(atom1, atom2, float(length), float(stddev))) - residue_bonds["UNK"] = [] - - # Load bond angles. - residue_bond_angles = {} - next(lines_iter) # Skip empty line. - next(lines_iter) # Skip header line. - for line in lines_iter: - if line.strip() == "-": - break - bond, resname, angle_degree, stddev_degree = line.split() - atom1, atom2, atom3 = bond.split("-") - if resname not in residue_bond_angles: - residue_bond_angles[resname] = [] - residue_bond_angles[resname].append( - BondAngle( - atom1, - atom2, - atom3, - float(angle_degree) / 180.0 * np.pi, - float(stddev_degree) / 180.0 * np.pi, - ) - ) - residue_bond_angles["UNK"] = [] - - def make_bond_key(atom1_name, atom2_name): - """Unique key to lookup bonds.""" - return "-".join(sorted([atom1_name, atom2_name])) - - # Translate bond angles into distances ("virtual bonds"). - residue_virtual_bonds = {} - for resname, bond_angles in residue_bond_angles.items(): - # Create a fast lookup dict for bond lengths. - bond_cache = {} - for b in residue_bonds[resname]: - bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b - residue_virtual_bonds[resname] = [] - for ba in bond_angles: - bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)] - bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)] - - # Compute distance between atom1 and atom3 using the law of cosines - # c^2 = a^2 + b^2 - 2ab*cos(gamma). - gamma = ba.angle_rad - length = np.sqrt( - bond1.length**2 - + bond2.length**2 - - 2 * bond1.length * bond2.length * np.cos(gamma) - ) - - # Propagation of uncertainty assuming uncorrelated errors. - dl_outer = 0.5 / length - dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer - dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer - dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer - stddev = np.sqrt( - (dl_dgamma * ba.stddev) ** 2 - + (dl_db1 * bond1.stddev) ** 2 - + (dl_db2 * bond2.stddev) ** 2 - ) - residue_virtual_bonds[resname].append( - Bond(ba.atom1_name, ba.atom3name, length, stddev) - ) - - return (residue_bonds, residue_virtual_bonds, residue_bond_angles) - - -# Between-residue bond lengths for general bonds (first element) and for Proline -# (second element). -between_res_bond_length_c_n = [1.329, 1.341] -between_res_bond_length_stddev_c_n = [0.014, 0.016] - -# Between-residue cos_angles. -between_res_cos_angles_c_n_ca = [-0.5203, 0.0353] # degrees: 121.352 +- 2.315 -between_res_cos_angles_ca_c_n = [-0.4473, 0.0311] # degrees: 116.568 +- 1.995 - -# This mapping is used when we need to store atom data in a format that requires -# fixed atom data size for every residue (e.g. a numpy array). -atom_types = [ - "N", - "CA", - "C", - "CB", - "O", - "CG", - "CG1", - "CG2", - "OG", - "OG1", - "SG", - "CD", - "CD1", - "CD2", - "ND1", - "ND2", - "OD1", - "OD2", - "SD", - "CE", - "CE1", - "CE2", - "CE3", - "NE", - "NE1", - "NE2", - "OE1", - "OE2", - "CH2", - "NH1", - "NH2", - "OH", - "CZ", - "CZ2", - "CZ3", - "NZ", - "OXT", -] -atom_order = {atom_type: i for i, atom_type in enumerate(atom_types)} -atom_type_num = len(atom_types) # := 37. - -# A compact atom encoding with 14 columns -# pylint: disable=line-too-long -# pylint: disable=bad-whitespace -restype_name_to_atom14_names = { - "ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""], - "ARG": [ - "N", - "CA", - "C", - "O", - "CB", - "CG", - "CD", - "NE", - "CZ", - "NH1", - "NH2", - "", - "", - "", - ], - "ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", ""], - "ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", ""], - "CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""], - "GLN": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2", "", "", "", "", ""], - "GLU": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2", "", "", "", "", ""], - "GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""], - "HIS": [ - "N", - "CA", - "C", - "O", - "CB", - "CG", - "ND1", - "CD2", - "CE1", - "NE2", - "", - "", - "", - "", - ], - "ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", ""], - "LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", ""], - "LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", ""], - "MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", ""], - "PHE": [ - "N", - "CA", - "C", - "O", - "CB", - "CG", - "CD1", - "CD2", - "CE1", - "CE2", - "CZ", - "", - "", - "", - ], - "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""], - "SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""], - "THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", ""], - "TRP": [ - "N", - "CA", - "C", - "O", - "CB", - "CG", - "CD1", - "CD2", - "NE1", - "CE2", - "CE3", - "CZ2", - "CZ3", - "CH2", - ], - "TYR": [ - "N", - "CA", - "C", - "O", - "CB", - "CG", - "CD1", - "CD2", - "CE1", - "CE2", - "CZ", - "OH", - "", - "", - ], - "VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", ""], - "UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""], -} -# pylint: enable=line-too-long -# pylint: enable=bad-whitespace - - -# This is the standard residue order when coding AA type as a number. -# Reproduce it by taking 3-letter AA codes and sorting them alphabetically. -restypes = [ - "A", - "R", - "N", - "D", - "C", - "Q", - "E", - "G", - "H", - "I", - "L", - "K", - "M", - "F", - "P", - "S", - "T", - "W", - "Y", - "V", -] -restype_order = {restype: i for i, restype in enumerate(restypes)} -restype_num = len(restypes) # := 20. -unk_restype_index = restype_num # Catch-all index for unknown restypes. - -restypes_with_x = restypes + ["X"] -restype_order_with_x = {restype: i for i, restype in enumerate(restypes_with_x)} - - -def sequence_to_onehot( - sequence: str, mapping: Mapping[str, int], map_unknown_to_x: bool = False -) -> np.ndarray: - """Maps the given sequence into a one-hot encoded matrix. - - Args: - sequence: An amino acid sequence. - mapping: A dictionary mapping amino acids to integers. - map_unknown_to_x: If True, any amino acid that is not in the mapping will be - mapped to the unknown amino acid 'X'. If the mapping doesn't contain - amino acid 'X', an error will be thrown. If False, any amino acid not in - the mapping will throw an error. - - Returns: - A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of - the sequence. - - Raises: - ValueError: If the mapping doesn't contain values from 0 to - num_unique_aas - 1 without any gaps. - """ - num_entries = max(mapping.values()) + 1 - - if sorted(set(mapping.values())) != list(range(num_entries)): - raise ValueError( - "The mapping must have values from 0 to num_unique_aas-1 " - "without any gaps. Got: %s" % sorted(mapping.values()) - ) - - one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32) - - for aa_index, aa_type in enumerate(sequence): - if map_unknown_to_x: - if aa_type.isalpha() and aa_type.isupper(): - aa_id = mapping.get(aa_type, mapping["X"]) - else: - raise ValueError(f"Invalid character in the sequence: {aa_type}") - else: - aa_id = mapping[aa_type] - one_hot_arr[aa_index, aa_id] = 1 - - return one_hot_arr - - -restype_1to3 = { - "A": "ALA", - "R": "ARG", - "N": "ASN", - "D": "ASP", - "C": "CYS", - "Q": "GLN", - "E": "GLU", - "G": "GLY", - "H": "HIS", - "I": "ILE", - "L": "LEU", - "K": "LYS", - "M": "MET", - "F": "PHE", - "P": "PRO", - "S": "SER", - "T": "THR", - "W": "TRP", - "Y": "TYR", - "V": "VAL", -} - - -# NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple -# 1-to-1 mapping of 3 letter names to one letter names. The latter contains -# many more, and less common, three letter names as keys and maps many of these -# to the same one letter name (including 'X' and 'U' which we don't use here). -restype_3to1 = {v: k for k, v in restype_1to3.items()} - -# Define a restype name for all unknown residues. -unk_restype = "UNK" - -resnames = [restype_1to3[r] for r in restypes] + [unk_restype] -resname_to_idx = {resname: i for i, resname in enumerate(resnames)} - - -# Define exploded all-atom representation (atom73) -atom73_names = ['N', 'CA', 'C', 'CB', 'O'] -for aa1 in restypes: - aa3 = restype_1to3[aa1] - atom_list = residue_atoms[aa3] - for atom in atom_types: - if atom in atom_list and atom not in atom73_names: - atom73_names.append(f'{aa1}{atom}') - -atom73_names_to_idx = {a: i for i, a in enumerate(atom73_names)} - -restype_atom73_mask = np.zeros((22, 73)) -for i, restype in enumerate(restypes): - for atom_name in atom_types: - atom73_name = atom_name - if atom_name not in ['N', 'CA', 'C', 'CB', 'O']: - atom73_name = restype + atom_name - if atom73_name in atom73_names_to_idx: - atom73_idx = atom73_names_to_idx[atom73_name] - restype_atom73_mask[i, atom73_idx] = 1 -# Remove CB for glycine -restype_atom73_mask[restype_order["G"], 3] = 0 -# Backbone atoms for unk and mask -restype_atom73_mask[-2:, [0, 1, 2, 4]] = 1 - - -# The mapping here uses hhblits convention, so that B is mapped to D, J and O -# are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the -# remaining 20 amino acids are kept in alphabetical order. -# There are 2 non-amino acid codes, X (representing any amino acid) and -# "-" representing a missing amino acid in an alignment. The id for these -# codes is put at the end (20 and 21) so that they can easily be ignored if -# desired. -HHBLITS_AA_TO_ID = { - "A": 0, - "B": 2, - "C": 1, - "D": 2, - "E": 3, - "F": 4, - "G": 5, - "H": 6, - "I": 7, - "J": 20, - "K": 8, - "L": 9, - "M": 10, - "N": 11, - "O": 20, - "P": 12, - "Q": 13, - "R": 14, - "S": 15, - "T": 16, - "U": 1, - "V": 17, - "W": 18, - "X": 20, - "Y": 19, - "Z": 3, - "-": 21, -} - -# Partial inversion of HHBLITS_AA_TO_ID. -ID_TO_HHBLITS_AA = { - 0: "A", - 1: "C", # Also U. - 2: "D", # Also B. - 3: "E", # Also Z. - 4: "F", - 5: "G", - 6: "H", - 7: "I", - 8: "K", - 9: "L", - 10: "M", - 11: "N", - 12: "P", - 13: "Q", - 14: "R", - 15: "S", - 16: "T", - 17: "V", - 18: "W", - 19: "Y", - 20: "X", # Includes J and O. - 21: "-", -} - -restypes_with_x_and_gap = restypes + ["X", "-"] -MAP_HHBLITS_AATYPE_TO_OUR_AATYPE = tuple( - restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i]) - for i in range(len(restypes_with_x_and_gap)) -) - - -def _make_standard_atom_mask() -> np.ndarray: - """Returns [num_res_types, num_atom_types] mask array.""" - # +1 to account for unknown (all 0s). - mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32) - for restype, restype_letter in enumerate(restypes): - restype_name = restype_1to3[restype_letter] - atom_names = residue_atoms[restype_name] - for atom_name in atom_names: - atom_type = atom_order[atom_name] - mask[restype, atom_type] = 1 - return mask - - -STANDARD_ATOM_MASK = _make_standard_atom_mask() - - -# A one hot representation for the first and second atoms defining the axis -# of rotation for each chi-angle in each residue. -def chi_angle_atom(atom_index: int) -> np.ndarray: - """Define chi-angle rigid groups via one-hot representations.""" - chi_angles_index = {} - one_hots = [] - - for k, v in chi_angles_atoms.items(): - indices = [atom_types.index(s[atom_index]) for s in v] - indices.extend([-1] * (4 - len(indices))) - chi_angles_index[k] = indices - - for r in restypes: - res3 = restype_1to3[r] - one_hot = np.eye(atom_type_num)[chi_angles_index[res3]] - one_hots.append(one_hot) - - one_hots.append(np.zeros([4, atom_type_num])) # Add zeros for residue `X`. - one_hot = np.stack(one_hots, axis=0) - one_hot = np.transpose(one_hot, [0, 2, 1]) - - return one_hot - - -chi_atom_1_one_hot = chi_angle_atom(1) -chi_atom_2_one_hot = chi_angle_atom(2) - -# An array like chi_angles_atoms but using indices rather than names. -chi_angles_atom_indices = [chi_angles_atoms[restype_1to3[r]] for r in restypes] -chi_angles_atom_indices = tree.map_structure( - lambda atom_name: atom_order[atom_name], chi_angles_atom_indices -) -chi_angles_atom_indices = np.array( - [ - chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms))) - for chi_atoms in chi_angles_atom_indices - ] -) - -# Mapping from (res_name, atom_name) pairs to the atom's chi group index -# and atom index within that group. -chi_groups_for_atom = collections.defaultdict(list) -for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items(): - for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res): - for atom_i, atom in enumerate(chi_group): - chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i)) -chi_groups_for_atom = dict(chi_groups_for_atom) - - -def _make_rigid_transformation_4x4(ex, ey, translation): - """Create a rigid 4x4 transformation matrix from two axes and transl.""" - # Normalize ex. - ex_normalized = ex / np.linalg.norm(ex) - - # make ey perpendicular to ex - ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized - ey_normalized /= np.linalg.norm(ey_normalized) - - # compute ez as cross product - eznorm = np.cross(ex_normalized, ey_normalized) - m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose() - m = np.concatenate([m, [[0.0, 0.0, 0.0, 1.0]]], axis=0) - return m - - -# create an array with (restype, atomtype) --> rigid_group_idx -# and an array with (restype, atomtype, coord) for the atom positions -# and compute affine transformation matrices (4,4) from one rigid group to the -# previous group -restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int) -restype_atom37_mask = np.zeros([21, 37], dtype=np.float32) -restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32) -restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int) -restype_atom14_mask = np.zeros([21, 14], dtype=np.float32) -restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32) -restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32) - - -def _make_rigid_group_constants(): - """Fill the arrays above.""" - for restype, restype_letter in enumerate(restypes): - resname = restype_1to3[restype_letter] - for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]: - atomtype = atom_order[atomname] - restype_atom37_to_rigid_group[restype, atomtype] = group_idx - restype_atom37_mask[restype, atomtype] = 1 - restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position - - atom14idx = restype_name_to_atom14_names[resname].index(atomname) - restype_atom14_to_rigid_group[restype, atom14idx] = group_idx - restype_atom14_mask[restype, atom14idx] = 1 - restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position - - for restype, restype_letter in enumerate(restypes): - resname = restype_1to3[restype_letter] - atom_positions = { - name: np.array(pos) for name, _, pos in rigid_group_atom_positions[resname] - } - - # backbone to backbone is the identity transform - restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4) - - # pre-omega-frame to backbone (currently dummy identity matrix) - restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4) - - # phi-frame to backbone - mat = _make_rigid_transformation_4x4( - ex=atom_positions["N"] - atom_positions["CA"], - ey=np.array([1.0, 0.0, 0.0]), - translation=atom_positions["N"], - ) - restype_rigid_group_default_frame[restype, 2, :, :] = mat - - # psi-frame to backbone - mat = _make_rigid_transformation_4x4( - ex=atom_positions["C"] - atom_positions["CA"], - ey=atom_positions["CA"] - atom_positions["N"], - translation=atom_positions["C"], - ) - restype_rigid_group_default_frame[restype, 3, :, :] = mat - - # chi1-frame to backbone - if chi_angles_mask[restype][0]: - base_atom_names = chi_angles_atoms[resname][0] - base_atom_positions = [atom_positions[name] for name in base_atom_names] - mat = _make_rigid_transformation_4x4( - ex=base_atom_positions[2] - base_atom_positions[1], - ey=base_atom_positions[0] - base_atom_positions[1], - translation=base_atom_positions[2], - ) - restype_rigid_group_default_frame[restype, 4, :, :] = mat - - # chi2-frame to chi1-frame - # chi3-frame to chi2-frame - # chi4-frame to chi3-frame - # luckily all rotation axes for the next frame start at (0,0,0) of the - # previous frame - for chi_idx in range(1, 4): - if chi_angles_mask[restype][chi_idx]: - axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2] - axis_end_atom_position = atom_positions[axis_end_atom_name] - mat = _make_rigid_transformation_4x4( - ex=axis_end_atom_position, - ey=np.array([-1.0, 0.0, 0.0]), - translation=axis_end_atom_position, - ) - restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat - - -_make_rigid_group_constants() - - -def make_atom14_dists_bounds(overlap_tolerance=1.5, bond_length_tolerance_factor=15): - """compute upper and lower bounds for bonds to assess violations.""" - restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32) - restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32) - restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32) - residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props() - for restype, restype_letter in enumerate(restypes): - resname = restype_1to3[restype_letter] - atom_list = restype_name_to_atom14_names[resname] - - # create lower and upper bounds for clashes - for atom1_idx, atom1_name in enumerate(atom_list): - if not atom1_name: - continue - atom1_radius = van_der_waals_radius[atom1_name[0]] - for atom2_idx, atom2_name in enumerate(atom_list): - if (not atom2_name) or atom1_idx == atom2_idx: - continue - atom2_radius = van_der_waals_radius[atom2_name[0]] - lower = atom1_radius + atom2_radius - overlap_tolerance - upper = 1e10 - restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower - restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower - restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper - restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper - - # overwrite lower and upper bounds for bonds and angles - for b in residue_bonds[resname] + residue_virtual_bonds[resname]: - atom1_idx = atom_list.index(b.atom1_name) - atom2_idx = atom_list.index(b.atom2_name) - lower = b.length - bond_length_tolerance_factor * b.stddev - upper = b.length + bond_length_tolerance_factor * b.stddev - restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower - restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower - restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper - restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper - restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev - restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev - return { - "lower_bound": restype_atom14_bond_lower_bound, # shape (21,14,14) - "upper_bound": restype_atom14_bond_upper_bound, # shape (21,14,14) - "stddev": restype_atom14_bond_stddev, # shape (21,14,14) - } - - -standard_residue_bonds, _, standard_residue_bond_angles = load_stereo_chemical_props() diff --git a/spaces/Qrstud/andite-anything-v4.0/README.md b/spaces/Qrstud/andite-anything-v4.0/README.md deleted file mode 100644 index 7d3b78dc251d1f976f736430711fd683d02309e3..0000000000000000000000000000000000000000 --- a/spaces/Qrstud/andite-anything-v4.0/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Andite Anything V4.0 -emoji: 👀 -colorFrom: red -colorTo: pink -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Rakot2223/faster-whisper-webui/src/hooks/progressListener.py b/spaces/Rakot2223/faster-whisper-webui/src/hooks/progressListener.py deleted file mode 100644 index a7852a24e237ae864bbce5f37674e1f7c817a1b3..0000000000000000000000000000000000000000 --- a/spaces/Rakot2223/faster-whisper-webui/src/hooks/progressListener.py +++ /dev/null @@ -1,8 +0,0 @@ -from typing import Union - -class ProgressListener: - def on_progress(self, current: Union[int, float], total: Union[int, float]): - self.total = total - - def on_finished(self): - pass \ No newline at end of file diff --git a/spaces/Rbrq/DeticChatGPT/detic/predictor.py b/spaces/Rbrq/DeticChatGPT/detic/predictor.py deleted file mode 100644 index 318205acb90d47a54ff6f34400e1da744b2d85ba..0000000000000000000000000000000000000000 --- a/spaces/Rbrq/DeticChatGPT/detic/predictor.py +++ /dev/null @@ -1,253 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import atexit -import bisect -import multiprocessing as mp -from collections import deque -import cv2 -import torch - -from detectron2.data import MetadataCatalog -from detectron2.engine.defaults import DefaultPredictor -from detectron2.utils.video_visualizer import VideoVisualizer -from detectron2.utils.visualizer import ColorMode, Visualizer - -from .modeling.utils import reset_cls_test - - -def get_clip_embeddings(vocabulary, prompt='a '): - from detic.modeling.text.text_encoder import build_text_encoder - text_encoder = build_text_encoder(pretrain=True) - text_encoder.eval() - texts = [prompt + x for x in vocabulary] - emb = text_encoder(texts).detach().permute(1, 0).contiguous().cpu() - return emb - -BUILDIN_CLASSIFIER = { - 'lvis': 'datasets/metadata/lvis_v1_clip_a+cname.npy', - 'objects365': 'datasets/metadata/o365_clip_a+cnamefix.npy', - 'openimages': 'datasets/metadata/oid_clip_a+cname.npy', - 'coco': 'datasets/metadata/coco_clip_a+cname.npy', -} - -BUILDIN_METADATA_PATH = { - 'lvis': 'lvis_v1_val', - 'objects365': 'objects365_v2_val', - 'openimages': 'oid_val_expanded', - 'coco': 'coco_2017_val', -} - -class VisualizationDemo(object): - def __init__(self, cfg, args, - instance_mode=ColorMode.IMAGE, parallel=False): - """ - Args: - cfg (CfgNode): - instance_mode (ColorMode): - parallel (bool): whether to run the model in different processes from visualization. - Useful since the visualization logic can be slow. - """ - if args.vocabulary == 'custom': - self.metadata = MetadataCatalog.get("__unused") - self.metadata.thing_classes = args.custom_vocabulary.split(',') - classifier = get_clip_embeddings(self.metadata.thing_classes) - else: - self.metadata = MetadataCatalog.get( - BUILDIN_METADATA_PATH[args.vocabulary]) - classifier = BUILDIN_CLASSIFIER[args.vocabulary] - - num_classes = len(self.metadata.thing_classes) - self.cpu_device = torch.device("cpu") - self.instance_mode = instance_mode - - self.parallel = parallel - if parallel: - num_gpu = torch.cuda.device_count() - self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) - else: - self.predictor = DefaultPredictor(cfg) - reset_cls_test(self.predictor.model, classifier, num_classes) - - def run_on_image(self, image): - """ - Args: - image (np.ndarray): an image of shape (H, W, C) (in BGR order). - This is the format used by OpenCV. - - Returns: - predictions (dict): the output of the model. - vis_output (VisImage): the visualized image output. - """ - vis_output = None - predictions = self.predictor(image) - # Convert image from OpenCV BGR format to Matplotlib RGB format. - image = image[:, :, ::-1] - visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode) - if "panoptic_seg" in predictions: - panoptic_seg, segments_info = predictions["panoptic_seg"] - vis_output = visualizer.draw_panoptic_seg_predictions( - panoptic_seg.to(self.cpu_device), segments_info - ) - else: - if "sem_seg" in predictions: - vis_output = visualizer.draw_sem_seg( - predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) - ) - if "instances" in predictions: - instances = predictions["instances"].to(self.cpu_device) - vis_output = visualizer.draw_instance_predictions(predictions=instances) - - return predictions, vis_output - - def _frame_from_video(self, video): - while video.isOpened(): - success, frame = video.read() - if success: - yield frame - else: - break - - def run_on_video(self, video): - """ - Visualizes predictions on frames of the input video. - - Args: - video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be - either a webcam or a video file. - - Yields: - ndarray: BGR visualizations of each video frame. - """ - video_visualizer = VideoVisualizer(self.metadata, self.instance_mode) - - def process_predictions(frame, predictions): - frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - if "panoptic_seg" in predictions: - panoptic_seg, segments_info = predictions["panoptic_seg"] - vis_frame = video_visualizer.draw_panoptic_seg_predictions( - frame, panoptic_seg.to(self.cpu_device), segments_info - ) - elif "instances" in predictions: - predictions = predictions["instances"].to(self.cpu_device) - vis_frame = video_visualizer.draw_instance_predictions(frame, predictions) - elif "sem_seg" in predictions: - vis_frame = video_visualizer.draw_sem_seg( - frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device) - ) - - # Converts Matplotlib RGB format to OpenCV BGR format - vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR) - return vis_frame - - frame_gen = self._frame_from_video(video) - if self.parallel: - buffer_size = self.predictor.default_buffer_size - - frame_data = deque() - - for cnt, frame in enumerate(frame_gen): - frame_data.append(frame) - self.predictor.put(frame) - - if cnt >= buffer_size: - frame = frame_data.popleft() - predictions = self.predictor.get() - yield process_predictions(frame, predictions) - - while len(frame_data): - frame = frame_data.popleft() - predictions = self.predictor.get() - yield process_predictions(frame, predictions) - else: - for frame in frame_gen: - yield process_predictions(frame, self.predictor(frame)) - - -class AsyncPredictor: - """ - A predictor that runs the model asynchronously, possibly on >1 GPUs. - Because rendering the visualization takes considerably amount of time, - this helps improve throughput a little bit when rendering videos. - """ - - class _StopToken: - pass - - class _PredictWorker(mp.Process): - def __init__(self, cfg, task_queue, result_queue): - self.cfg = cfg - self.task_queue = task_queue - self.result_queue = result_queue - super().__init__() - - def run(self): - predictor = DefaultPredictor(self.cfg) - - while True: - task = self.task_queue.get() - if isinstance(task, AsyncPredictor._StopToken): - break - idx, data = task - result = predictor(data) - self.result_queue.put((idx, result)) - - def __init__(self, cfg, num_gpus: int = 1): - """ - Args: - cfg (CfgNode): - num_gpus (int): if 0, will run on CPU - """ - num_workers = max(num_gpus, 1) - self.task_queue = mp.Queue(maxsize=num_workers * 3) - self.result_queue = mp.Queue(maxsize=num_workers * 3) - self.procs = [] - for gpuid in range(max(num_gpus, 1)): - cfg = cfg.clone() - cfg.defrost() - cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" - self.procs.append( - AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) - ) - - self.put_idx = 0 - self.get_idx = 0 - self.result_rank = [] - self.result_data = [] - - for p in self.procs: - p.start() - atexit.register(self.shutdown) - - def put(self, image): - self.put_idx += 1 - self.task_queue.put((self.put_idx, image)) - - def get(self): - self.get_idx += 1 # the index needed for this request - if len(self.result_rank) and self.result_rank[0] == self.get_idx: - res = self.result_data[0] - del self.result_data[0], self.result_rank[0] - return res - - while True: - # make sure the results are returned in the correct order - idx, res = self.result_queue.get() - if idx == self.get_idx: - return res - insert = bisect.bisect(self.result_rank, idx) - self.result_rank.insert(insert, idx) - self.result_data.insert(insert, res) - - def __len__(self): - return self.put_idx - self.get_idx - - def __call__(self, image): - self.put(image) - return self.get() - - def shutdown(self): - for _ in self.procs: - self.task_queue.put(AsyncPredictor._StopToken()) - - @property - def default_buffer_size(self): - return len(self.procs) * 5 diff --git a/spaces/RitaParadaRamos/SmallCapDemo/src/opt.py b/spaces/RitaParadaRamos/SmallCapDemo/src/opt.py deleted file mode 100644 index ac4a5515b6e749eefee281512c0632a6e9937edf..0000000000000000000000000000000000000000 --- a/spaces/RitaParadaRamos/SmallCapDemo/src/opt.py +++ /dev/null @@ -1,696 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. -# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""PyTorch OpenAI GPT-2 model.""" - -import math -import os -from dataclasses import dataclass -from typing import Optional, Tuple, Union -import random - -import torch -import torch.utils.checkpoint -from packaging import version -from torch import nn -from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss - -from transformers.models.gpt2.modeling_gpt2 import load_tf_weights_in_gpt2, GPT2LMHeadModel, GPT2MLP, GPT2Attention, GPT2Block, GPT2Model - -from transformers.models.opt.modeling_opt import OPTForCausalLM, OPTAttention, OPTDecoderLayer, OPTModel, OPTDecoder - -from transformers.activations import ACT2FN -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - SequenceClassifierOutputWithPast, - TokenClassifierOutput, -) - -from transformers.modeling_outputs import ( - BaseModelOutputWithPast, - CausalLMOutputWithPast, - QuestionAnsweringModelOutput, - SequenceClassifierOutputWithPast, -) - -from transformers.modeling_utils import PreTrainedModel, SequenceSummary -from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer -from transformers.utils import ( - ModelOutput, - logging, -) -from transformers.utils.model_parallel_utils import assert_device_map, get_device_map -from transformers.models.opt.configuration_opt import OPTConfig - - -if version.parse(torch.__version__) >= version.parse("1.6"): - is_amp_available = True - from torch.cuda.amp import autocast -else: - is_amp_available = False - - -class ThisOPTConfig(OPTConfig): - model_type = "this_opt" - - def __init__( - self, - cross_attention_reduce_factor = 1, - **kwargs, - ): - super().__init__(**kwargs) - self.cross_attention_reduce_factor = cross_attention_reduce_factor - - -class ThisOPTAttention(OPTAttention): - """Multi-headed attention from 'Attention Is All You Need' paper""" - - def __init__( - self, - embed_dim, - num_heads, - dropout = 0.0, - is_decoder = False, - bias = True, - config=None, - is_cross_attention=False, - ): - super().__init__(embed_dim,num_heads, dropout,is_decoder,bias) - self.embed_dim = embed_dim - self.num_heads = num_heads - self.dropout = dropout - self.head_dim = embed_dim // num_heads - - if (self.head_dim * num_heads) != self.embed_dim: - raise ValueError( - f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" - f" and `num_heads`: {num_heads})." - ) - self.scaling = self.head_dim**-0.5 - self.is_decoder = is_decoder - - self.cross_attention_reduce_factor = config.cross_attention_reduce_factor - self.head_dim = int(self.head_dim / self.cross_attention_reduce_factor) - - - if is_cross_attention: - #print("self", int(embed_dim / self.cross_attention_reduce_factor)) - self.k_proj = nn.Linear(768, int(embed_dim / self.cross_attention_reduce_factor), bias=bias) - #print("self.k_proj",self.k_proj) - self.v_proj = nn.Linear(768, int(embed_dim / self.cross_attention_reduce_factor), bias=bias) - self.q_proj = nn.Linear(embed_dim, int(embed_dim / self.cross_attention_reduce_factor), bias=bias) - self.out_proj = nn.Linear(int(embed_dim / self.cross_attention_reduce_factor),embed_dim, bias=bias) - - self.embed_dim=int(embed_dim / self.cross_attention_reduce_factor) - else: - self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.v_proj = nn.Linear(embed_dim, embed_dim , bias=bias) - self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - - - def forward( - self, - hidden_states, - key_value_states = None, - past_key_value = None, - attention_mask = None, - layer_head_mask = None, - output_attentions= False, - ): - """Input shape: Batch x Time x Channel""" - - # if key_value_states are provided this layer is used as a cross-attention layer - # for the decoder - is_cross_attention = key_value_states is not None - - bsz, tgt_len, _ = hidden_states.size() - - # get query proj - query_states = self.q_proj(hidden_states) * self.scaling - # get key, value proj - if is_cross_attention and past_key_value is not None: - # reuse k,v, cross_attentions - key_states = past_key_value[0] - value_states = past_key_value[1] - elif is_cross_attention: - # cross_attentions - key_states = self._shape(self.k_proj(key_value_states), -1, bsz) - value_states = self._shape(self.v_proj(key_value_states), -1, bsz) - elif past_key_value is not None: - # reuse k, v, self_attention - key_states = self._shape(self.k_proj(hidden_states), -1, bsz) - value_states = self._shape(self.v_proj(hidden_states), -1, bsz) - key_states = torch.cat([past_key_value[0], key_states], dim=2) - value_states = torch.cat([past_key_value[1], value_states], dim=2) - else: - # self_attention - key_states = self._shape(self.k_proj(hidden_states), -1, bsz) - value_states = self._shape(self.v_proj(hidden_states), -1, bsz) - - if self.is_decoder: - # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. - # Further calls to cross_attention layer can then reuse all cross-attention - # key/value_states (first "if" case) - # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of - # all previous decoder key/value_states. Further calls to uni-directional self-attention - # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) - # if encoder bi-directional self-attention `past_key_value` is always `None` - past_key_value = (key_states, value_states) - - proj_shape = (bsz * self.num_heads, -1, self.head_dim) - query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) - key_states = key_states.view(*proj_shape) - value_states = value_states.view(*proj_shape) - - src_len = key_states.size(1) - attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) - - if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): - raise ValueError( - f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" - f" {attn_weights.size()}" - ) - - if attention_mask is not None: - if attention_mask.size() != (bsz, 1, tgt_len, src_len): - raise ValueError( - f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" - ) - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask - attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - # upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437 - if attn_weights.dtype == torch.float16: - attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16) - else: - attn_weights = nn.functional.softmax(attn_weights, dim=-1) - - if layer_head_mask is not None: - if layer_head_mask.size() != (self.num_heads,): - raise ValueError( - f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" - f" {layer_head_mask.size()}" - ) - attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - if output_attentions: - # this operation is a bit awkward, but it's required to - # make sure that attn_weights keeps its gradient. - # In order to do so, attn_weights have to be reshaped - # twice and have to be reused in the following - attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) - else: - attn_weights_reshaped = None - - attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) - - attn_output = torch.bmm(attn_probs, value_states) - - if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): - raise ValueError( - f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" - f" {attn_output.size()}" - ) - - attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) - attn_output = attn_output.transpose(1, 2) - - # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be - # partitioned aross GPUs when using tensor-parallelism. - attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) - - attn_output = self.out_proj(attn_output) - - return attn_output, attn_weights_reshaped, past_key_value - - -class ThisOPTDecoderLayer(OPTDecoderLayer): - def __init__(self, config): - super().__init__(config) - - if config.add_cross_attention: - self.encoder_attn = ThisOPTAttention( - embed_dim=self.embed_dim, - num_heads=config.num_attention_heads, - dropout=config.attention_dropout, - is_decoder=True, - #bias=config.enable_bias, - config=config, - is_cross_attention=True - ) - self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim,elementwise_affine=config.layer_norm_elementwise_affine) - - - def forward( - self, - hidden_states, - attention_mask= None, - encoder_hidden_states = None, - encoder_attention_mask = None, - layer_head_mask = None, - cross_attn_head_mask = None, - output_attentions = False, - use_cache = False, - past_key_value = None, - ): - """ - Args: - hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` - attention_mask (`torch.FloatTensor`, *optional*): attention mask of size - `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. - layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size - `(encoder_attention_heads,)`. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding - (see `past_key_values`). - past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states - """ - - residual = hidden_states - - # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention - if self.do_layer_norm_before: - hidden_states = self.self_attn_layer_norm(hidden_states) - - # Self Attention - hidden_states, self_attn_weights, present_key_value = self.self_attn( - hidden_states=hidden_states, - past_key_value=past_key_value, - attention_mask=attention_mask, - layer_head_mask=layer_head_mask, - output_attentions=output_attentions, - ) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - - # Cross-Attention Block - cross_attn_present_key_value = None - cross_attn_weights = None - if encoder_hidden_states is not None: - residual = hidden_states - hidden_states = self.encoder_attn_layer_norm(hidden_states) - - # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple - cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None - hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( - hidden_states=hidden_states, - key_value_states=encoder_hidden_states, - attention_mask=encoder_attention_mask, - layer_head_mask=cross_attn_head_mask, - past_key_value=cross_attn_past_key_value, - output_attentions=output_attentions, - ) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - # add cross-attn to positions 3,4 of present_key_value tuple - present_key_value = present_key_value + cross_attn_present_key_value - - - - # 350m applies layer norm AFTER attention - if not self.do_layer_norm_before: - hidden_states = self.self_attn_layer_norm(hidden_states) - - # Fully Connected - hidden_states_shape = hidden_states.shape - hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) - residual = hidden_states - - # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention - if self.do_layer_norm_before: - hidden_states = self.final_layer_norm(hidden_states) - - hidden_states = self.fc1(hidden_states) - hidden_states = self.activation_fn(hidden_states) - - hidden_states = self.fc2(hidden_states) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - - hidden_states = (residual + hidden_states).view(hidden_states_shape) - - # 350m applies layer norm AFTER attention - if not self.do_layer_norm_before: - hidden_states = self.final_layer_norm(hidden_states) - - outputs = (hidden_states,) - - if output_attentions: - outputs += (self_attn_weights,) - - if use_cache: - outputs += (present_key_value,) - - return outputs - -class ThisOPTDecoder(OPTDecoder): - def __init__(self, config): - super().__init__(config) - self.layers = nn.ModuleList([ThisOPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]) - - - def forward( - self, - input_ids = None, - attention_mask = None, - encoder_hidden_states=None, - encoder_attention_mask = None, - head_mask = None, - cross_attn_head_mask = None, - past_key_values = None, - inputs_embeds = None, - use_cache = None, - output_attentions = None, - output_hidden_states = None, - return_dict = None, - ): - r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you - provide it. - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - [What are attention masks?](../glossary#attention-mask) - head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of - shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the - cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those - that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of - all `decoder_input_ids` of shape `(batch_size, sequence_length)`. - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. - This is useful if you want more control over how to convert `input_ids` indices into associated vectors - than the model's internal embedding lookup matrix. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors - for more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # retrieve input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") - - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) - - # embed positions - if attention_mask is None: - attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device) - pos_embeds = self.embed_positions(attention_mask, past_key_values_length) - - attention_mask = self._prepare_decoder_attention_mask( - attention_mask, input_shape, inputs_embeds, past_key_values_length - ) - - if self.project_in is not None: - inputs_embeds = self.project_in(inputs_embeds) - - hidden_states = inputs_embeds + pos_embeds - - # decoder layers - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - next_decoder_cache = () if use_cache else None - all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None - - - # check if head_mask has a correct number of layers specified if desired - for attn_mask, mask_name in zip([head_mask], ["head_mask"]): - if attn_mask is not None: - if attn_mask.size()[0] != (len(self.layers)): - raise ValueError( - f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" - f" {head_mask.size()[0]}." - ) - - for idx, decoder_layer in enumerate(self.layers): - # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) - if output_hidden_states: - all_hidden_states += (hidden_states,) - - dropout_probability = random.uniform(0, 1) - if self.training and (dropout_probability < self.layerdrop): - continue - - past_key_value = past_key_values[idx] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - if use_cache: - logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, output_attentions, None) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(decoder_layer), - hidden_states, - attention_mask, - head_mask[idx] if head_mask is not None else None, - None, - ) - else: - - layer_outputs = decoder_layer( - hidden_states, - encoder_attention_mask=encoder_attention_mask, - encoder_hidden_states=encoder_hidden_states, - cross_attn_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, - attention_mask=attention_mask, - layer_head_mask=(head_mask[idx] if head_mask is not None else None), - past_key_value=past_key_value, - output_attentions=output_attentions, - use_cache=use_cache, - ) - - hidden_states = layer_outputs[0] - - if use_cache: - next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) - - if output_attentions: - all_self_attns += (layer_outputs[1],) - - if self.final_layer_norm is not None: - hidden_states = self.final_layer_norm(hidden_states) - - if self.project_out is not None: - hidden_states = self.project_out(hidden_states) - - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) - - if encoder_hidden_states is not None: - all_cross_attentions += (layer_outputs[2],) - - next_cache = next_decoder_cache if use_cache else None - if not return_dict: - return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_cache, - hidden_states=all_hidden_states, - attentions=all_self_attns, - cross_attentions=all_cross_attentions, - ) - - - - -class ThisOPTModel(OPTModel): - - def __init__(self, config): - super().__init__(config) - self.decoder = ThisOPTDecoder(config) - - def forward( - self, - input_ids = None, - attention_mask = None, - encoder_hidden_states=None, - encoder_attention_mask = None, - head_mask = None, - cross_attn_head_mask = None, - past_key_values = None, - inputs_embeds = None, - use_cache = None, - output_attentions = None, - output_hidden_states = None, - return_dict = None, - ): - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if encoder_hidden_states is not None and encoder_attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) - - - # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) - decoder_outputs = self.decoder( - input_ids=input_ids, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - cross_attn_head_mask=( - cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None - ), - head_mask=head_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - - if not return_dict: - return decoder_outputs - - return BaseModelOutputWithPast( - last_hidden_state=decoder_outputs.last_hidden_state, - past_key_values=decoder_outputs.past_key_values, - hidden_states=decoder_outputs.hidden_states, - attentions=decoder_outputs.attentions, - ) - -class ThisOPTForCausalLM(OPTForCausalLM): - config_class = ThisOPTConfig - - def __init__(self, config): - super().__init__(config) - self.model = ThisOPTModel(config) - - - def forward( - self, - input_ids = None, - attention_mask = None, - encoder_hidden_states=None, - encoder_attention_mask = None, - head_mask = None, - cross_attn_head_mask = None, - past_key_values = None, - inputs_embeds = None, - labels = None, - use_cache = None, - output_attentions = None, - output_hidden_states = None, - return_dict = None, - ) : - - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) - outputs = self.model.decoder( - input_ids=input_ids, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask = encoder_attention_mask, - cross_attn_head_mask = cross_attn_head_mask, - head_mask=head_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - logits = self.lm_head(outputs[0]).contiguous() - - loss = None - if labels is not None: - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/evaluation.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/evaluation.py deleted file mode 100644 index 4d00999ce5665c53bded8de9e084943eee2d230d..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/runner/hooks/evaluation.py +++ /dev/null @@ -1,509 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp -import warnings -from math import inf - -import torch.distributed as dist -from torch.nn.modules.batchnorm import _BatchNorm -from torch.utils.data import DataLoader - -from annotator.uniformer.mmcv.fileio import FileClient -from annotator.uniformer.mmcv.utils import is_seq_of -from .hook import Hook -from .logger import LoggerHook - - -class EvalHook(Hook): - """Non-Distributed evaluation hook. - - This hook will regularly perform evaluation in a given interval when - performing in non-distributed environment. - - Args: - dataloader (DataLoader): A PyTorch dataloader, whose dataset has - implemented ``evaluate`` function. - start (int | None, optional): Evaluation starting epoch. It enables - evaluation before the training starts if ``start`` <= the resuming - epoch. If None, whether to evaluate is merely decided by - ``interval``. Default: None. - interval (int): Evaluation interval. Default: 1. - by_epoch (bool): Determine perform evaluation by epoch or by iteration. - If set to True, it will perform by epoch. Otherwise, by iteration. - Default: True. - save_best (str, optional): If a metric is specified, it would measure - the best checkpoint during evaluation. The information about best - checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep - best score value and best checkpoint path, which will be also - loaded when resume checkpoint. Options are the evaluation metrics - on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox - detection and instance segmentation. ``AR@100`` for proposal - recall. If ``save_best`` is ``auto``, the first key of the returned - ``OrderedDict`` result will be used. Default: None. - rule (str | None, optional): Comparison rule for best score. If set to - None, it will infer a reasonable rule. Keys such as 'acc', 'top' - .etc will be inferred by 'greater' rule. Keys contain 'loss' will - be inferred by 'less' rule. Options are 'greater', 'less', None. - Default: None. - test_fn (callable, optional): test a model with samples from a - dataloader, and return the test results. If ``None``, the default - test function ``mmcv.engine.single_gpu_test`` will be used. - (default: ``None``) - greater_keys (List[str] | None, optional): Metric keys that will be - inferred by 'greater' comparison rule. If ``None``, - _default_greater_keys will be used. (default: ``None``) - less_keys (List[str] | None, optional): Metric keys that will be - inferred by 'less' comparison rule. If ``None``, _default_less_keys - will be used. (default: ``None``) - out_dir (str, optional): The root directory to save checkpoints. If not - specified, `runner.work_dir` will be used by default. If specified, - the `out_dir` will be the concatenation of `out_dir` and the last - level directory of `runner.work_dir`. - `New in version 1.3.16.` - file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`mmcv.fileio.FileClient` for details. Default: None. - `New in version 1.3.16.` - **eval_kwargs: Evaluation arguments fed into the evaluate function of - the dataset. - - Notes: - If new arguments are added for EvalHook, tools/test.py, - tools/eval_metric.py may be affected. - """ - - # Since the key for determine greater or less is related to the downstream - # tasks, downstream repos may need to overwrite the following inner - # variable accordingly. - - rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y} - init_value_map = {'greater': -inf, 'less': inf} - _default_greater_keys = [ - 'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU', - 'mAcc', 'aAcc' - ] - _default_less_keys = ['loss'] - - def __init__(self, - dataloader, - start=None, - interval=1, - by_epoch=True, - save_best=None, - rule=None, - test_fn=None, - greater_keys=None, - less_keys=None, - out_dir=None, - file_client_args=None, - **eval_kwargs): - if not isinstance(dataloader, DataLoader): - raise TypeError(f'dataloader must be a pytorch DataLoader, ' - f'but got {type(dataloader)}') - - if interval <= 0: - raise ValueError(f'interval must be a positive number, ' - f'but got {interval}') - - assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean' - - if start is not None and start < 0: - raise ValueError(f'The evaluation start epoch {start} is smaller ' - f'than 0') - - self.dataloader = dataloader - self.interval = interval - self.start = start - self.by_epoch = by_epoch - - assert isinstance(save_best, str) or save_best is None, \ - '""save_best"" should be a str or None ' \ - f'rather than {type(save_best)}' - self.save_best = save_best - self.eval_kwargs = eval_kwargs - self.initial_flag = True - - if test_fn is None: - from annotator.uniformer.mmcv.engine import single_gpu_test - self.test_fn = single_gpu_test - else: - self.test_fn = test_fn - - if greater_keys is None: - self.greater_keys = self._default_greater_keys - else: - if not isinstance(greater_keys, (list, tuple)): - greater_keys = (greater_keys, ) - assert is_seq_of(greater_keys, str) - self.greater_keys = greater_keys - - if less_keys is None: - self.less_keys = self._default_less_keys - else: - if not isinstance(less_keys, (list, tuple)): - less_keys = (less_keys, ) - assert is_seq_of(less_keys, str) - self.less_keys = less_keys - - if self.save_best is not None: - self.best_ckpt_path = None - self._init_rule(rule, self.save_best) - - self.out_dir = out_dir - self.file_client_args = file_client_args - - def _init_rule(self, rule, key_indicator): - """Initialize rule, key_indicator, comparison_func, and best score. - - Here is the rule to determine which rule is used for key indicator - when the rule is not specific (note that the key indicator matching - is case-insensitive): - 1. If the key indicator is in ``self.greater_keys``, the rule will be - specified as 'greater'. - 2. Or if the key indicator is in ``self.less_keys``, the rule will be - specified as 'less'. - 3. Or if the key indicator is equal to the substring in any one item - in ``self.greater_keys``, the rule will be specified as 'greater'. - 4. Or if the key indicator is equal to the substring in any one item - in ``self.less_keys``, the rule will be specified as 'less'. - - Args: - rule (str | None): Comparison rule for best score. - key_indicator (str | None): Key indicator to determine the - comparison rule. - """ - if rule not in self.rule_map and rule is not None: - raise KeyError(f'rule must be greater, less or None, ' - f'but got {rule}.') - - if rule is None: - if key_indicator != 'auto': - # `_lc` here means we use the lower case of keys for - # case-insensitive matching - key_indicator_lc = key_indicator.lower() - greater_keys = [key.lower() for key in self.greater_keys] - less_keys = [key.lower() for key in self.less_keys] - - if key_indicator_lc in greater_keys: - rule = 'greater' - elif key_indicator_lc in less_keys: - rule = 'less' - elif any(key in key_indicator_lc for key in greater_keys): - rule = 'greater' - elif any(key in key_indicator_lc for key in less_keys): - rule = 'less' - else: - raise ValueError(f'Cannot infer the rule for key ' - f'{key_indicator}, thus a specific rule ' - f'must be specified.') - self.rule = rule - self.key_indicator = key_indicator - if self.rule is not None: - self.compare_func = self.rule_map[self.rule] - - def before_run(self, runner): - if not self.out_dir: - self.out_dir = runner.work_dir - - self.file_client = FileClient.infer_client(self.file_client_args, - self.out_dir) - - # if `self.out_dir` is not equal to `runner.work_dir`, it means that - # `self.out_dir` is set so the final `self.out_dir` is the - # concatenation of `self.out_dir` and the last level directory of - # `runner.work_dir` - if self.out_dir != runner.work_dir: - basename = osp.basename(runner.work_dir.rstrip(osp.sep)) - self.out_dir = self.file_client.join_path(self.out_dir, basename) - runner.logger.info( - (f'The best checkpoint will be saved to {self.out_dir} by ' - f'{self.file_client.name}')) - - if self.save_best is not None: - if runner.meta is None: - warnings.warn('runner.meta is None. Creating an empty one.') - runner.meta = dict() - runner.meta.setdefault('hook_msgs', dict()) - self.best_ckpt_path = runner.meta['hook_msgs'].get( - 'best_ckpt', None) - - def before_train_iter(self, runner): - """Evaluate the model only at the start of training by iteration.""" - if self.by_epoch or not self.initial_flag: - return - if self.start is not None and runner.iter >= self.start: - self.after_train_iter(runner) - self.initial_flag = False - - def before_train_epoch(self, runner): - """Evaluate the model only at the start of training by epoch.""" - if not (self.by_epoch and self.initial_flag): - return - if self.start is not None and runner.epoch >= self.start: - self.after_train_epoch(runner) - self.initial_flag = False - - def after_train_iter(self, runner): - """Called after every training iter to evaluate the results.""" - if not self.by_epoch and self._should_evaluate(runner): - # Because the priority of EvalHook is higher than LoggerHook, the - # training log and the evaluating log are mixed. Therefore, - # we need to dump the training log and clear it before evaluating - # log is generated. In addition, this problem will only appear in - # `IterBasedRunner` whose `self.by_epoch` is False, because - # `EpochBasedRunner` whose `self.by_epoch` is True calls - # `_do_evaluate` in `after_train_epoch` stage, and at this stage - # the training log has been printed, so it will not cause any - # problem. more details at - # https://github.com/open-mmlab/mmsegmentation/issues/694 - for hook in runner._hooks: - if isinstance(hook, LoggerHook): - hook.after_train_iter(runner) - runner.log_buffer.clear() - - self._do_evaluate(runner) - - def after_train_epoch(self, runner): - """Called after every training epoch to evaluate the results.""" - if self.by_epoch and self._should_evaluate(runner): - self._do_evaluate(runner) - - def _do_evaluate(self, runner): - """perform evaluation and save ckpt.""" - results = self.test_fn(runner.model, self.dataloader) - runner.log_buffer.output['eval_iter_num'] = len(self.dataloader) - key_score = self.evaluate(runner, results) - # the key_score may be `None` so it needs to skip the action to save - # the best checkpoint - if self.save_best and key_score: - self._save_ckpt(runner, key_score) - - def _should_evaluate(self, runner): - """Judge whether to perform evaluation. - - Here is the rule to judge whether to perform evaluation: - 1. It will not perform evaluation during the epoch/iteration interval, - which is determined by ``self.interval``. - 2. It will not perform evaluation if the start time is larger than - current time. - 3. It will not perform evaluation when current time is larger than - the start time but during epoch/iteration interval. - - Returns: - bool: The flag indicating whether to perform evaluation. - """ - if self.by_epoch: - current = runner.epoch - check_time = self.every_n_epochs - else: - current = runner.iter - check_time = self.every_n_iters - - if self.start is None: - if not check_time(runner, self.interval): - # No evaluation during the interval. - return False - elif (current + 1) < self.start: - # No evaluation if start is larger than the current time. - return False - else: - # Evaluation only at epochs/iters 3, 5, 7... - # if start==3 and interval==2 - if (current + 1 - self.start) % self.interval: - return False - return True - - def _save_ckpt(self, runner, key_score): - """Save the best checkpoint. - - It will compare the score according to the compare function, write - related information (best score, best checkpoint path) and save the - best checkpoint into ``work_dir``. - """ - if self.by_epoch: - current = f'epoch_{runner.epoch + 1}' - cur_type, cur_time = 'epoch', runner.epoch + 1 - else: - current = f'iter_{runner.iter + 1}' - cur_type, cur_time = 'iter', runner.iter + 1 - - best_score = runner.meta['hook_msgs'].get( - 'best_score', self.init_value_map[self.rule]) - if self.compare_func(key_score, best_score): - best_score = key_score - runner.meta['hook_msgs']['best_score'] = best_score - - if self.best_ckpt_path and self.file_client.isfile( - self.best_ckpt_path): - self.file_client.remove(self.best_ckpt_path) - runner.logger.info( - (f'The previous best checkpoint {self.best_ckpt_path} was ' - 'removed')) - - best_ckpt_name = f'best_{self.key_indicator}_{current}.pth' - self.best_ckpt_path = self.file_client.join_path( - self.out_dir, best_ckpt_name) - runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path - - runner.save_checkpoint( - self.out_dir, best_ckpt_name, create_symlink=False) - runner.logger.info( - f'Now best checkpoint is saved as {best_ckpt_name}.') - runner.logger.info( - f'Best {self.key_indicator} is {best_score:0.4f} ' - f'at {cur_time} {cur_type}.') - - def evaluate(self, runner, results): - """Evaluate the results. - - Args: - runner (:obj:`mmcv.Runner`): The underlined training runner. - results (list): Output results. - """ - eval_res = self.dataloader.dataset.evaluate( - results, logger=runner.logger, **self.eval_kwargs) - - for name, val in eval_res.items(): - runner.log_buffer.output[name] = val - runner.log_buffer.ready = True - - if self.save_best is not None: - # If the performance of model is pool, the `eval_res` may be an - # empty dict and it will raise exception when `self.save_best` is - # not None. More details at - # https://github.com/open-mmlab/mmdetection/issues/6265. - if not eval_res: - warnings.warn( - 'Since `eval_res` is an empty dict, the behavior to save ' - 'the best checkpoint will be skipped in this evaluation.') - return None - - if self.key_indicator == 'auto': - # infer from eval_results - self._init_rule(self.rule, list(eval_res.keys())[0]) - return eval_res[self.key_indicator] - - return None - - -class DistEvalHook(EvalHook): - """Distributed evaluation hook. - - This hook will regularly perform evaluation in a given interval when - performing in distributed environment. - - Args: - dataloader (DataLoader): A PyTorch dataloader, whose dataset has - implemented ``evaluate`` function. - start (int | None, optional): Evaluation starting epoch. It enables - evaluation before the training starts if ``start`` <= the resuming - epoch. If None, whether to evaluate is merely decided by - ``interval``. Default: None. - interval (int): Evaluation interval. Default: 1. - by_epoch (bool): Determine perform evaluation by epoch or by iteration. - If set to True, it will perform by epoch. Otherwise, by iteration. - default: True. - save_best (str, optional): If a metric is specified, it would measure - the best checkpoint during evaluation. The information about best - checkpoint would be saved in ``runner.meta['hook_msgs']`` to keep - best score value and best checkpoint path, which will be also - loaded when resume checkpoint. Options are the evaluation metrics - on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox - detection and instance segmentation. ``AR@100`` for proposal - recall. If ``save_best`` is ``auto``, the first key of the returned - ``OrderedDict`` result will be used. Default: None. - rule (str | None, optional): Comparison rule for best score. If set to - None, it will infer a reasonable rule. Keys such as 'acc', 'top' - .etc will be inferred by 'greater' rule. Keys contain 'loss' will - be inferred by 'less' rule. Options are 'greater', 'less', None. - Default: None. - test_fn (callable, optional): test a model with samples from a - dataloader in a multi-gpu manner, and return the test results. If - ``None``, the default test function ``mmcv.engine.multi_gpu_test`` - will be used. (default: ``None``) - tmpdir (str | None): Temporary directory to save the results of all - processes. Default: None. - gpu_collect (bool): Whether to use gpu or cpu to collect results. - Default: False. - broadcast_bn_buffer (bool): Whether to broadcast the - buffer(running_mean and running_var) of rank 0 to other rank - before evaluation. Default: True. - out_dir (str, optional): The root directory to save checkpoints. If not - specified, `runner.work_dir` will be used by default. If specified, - the `out_dir` will be the concatenation of `out_dir` and the last - level directory of `runner.work_dir`. - file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`mmcv.fileio.FileClient` for details. Default: None. - **eval_kwargs: Evaluation arguments fed into the evaluate function of - the dataset. - """ - - def __init__(self, - dataloader, - start=None, - interval=1, - by_epoch=True, - save_best=None, - rule=None, - test_fn=None, - greater_keys=None, - less_keys=None, - broadcast_bn_buffer=True, - tmpdir=None, - gpu_collect=False, - out_dir=None, - file_client_args=None, - **eval_kwargs): - - if test_fn is None: - from annotator.uniformer.mmcv.engine import multi_gpu_test - test_fn = multi_gpu_test - - super().__init__( - dataloader, - start=start, - interval=interval, - by_epoch=by_epoch, - save_best=save_best, - rule=rule, - test_fn=test_fn, - greater_keys=greater_keys, - less_keys=less_keys, - out_dir=out_dir, - file_client_args=file_client_args, - **eval_kwargs) - - self.broadcast_bn_buffer = broadcast_bn_buffer - self.tmpdir = tmpdir - self.gpu_collect = gpu_collect - - def _do_evaluate(self, runner): - """perform evaluation and save ckpt.""" - # Synchronization of BatchNorm's buffer (running_mean - # and running_var) is not supported in the DDP of pytorch, - # which may cause the inconsistent performance of models in - # different ranks, so we broadcast BatchNorm's buffers - # of rank 0 to other ranks to avoid this. - if self.broadcast_bn_buffer: - model = runner.model - for name, module in model.named_modules(): - if isinstance(module, - _BatchNorm) and module.track_running_stats: - dist.broadcast(module.running_var, 0) - dist.broadcast(module.running_mean, 0) - - tmpdir = self.tmpdir - if tmpdir is None: - tmpdir = osp.join(runner.work_dir, '.eval_hook') - - results = self.test_fn( - runner.model, - self.dataloader, - tmpdir=tmpdir, - gpu_collect=self.gpu_collect) - if runner.rank == 0: - print('\n') - runner.log_buffer.output['eval_iter_num'] = len(self.dataloader) - key_score = self.evaluate(runner, results) - # the key_score may be `None` so it needs to skip the action to - # save the best checkpoint - if self.save_best and key_score: - self._save_ckpt(runner, key_score) diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/fcn_unet_s5-d16.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/fcn_unet_s5-d16.py deleted file mode 100644 index a33e7972877f902d0e7d18401ca675e3e4e60a18..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/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/Saturdays/Starchat_Saturdays/README.md b/spaces/Saturdays/Starchat_Saturdays/README.md deleted file mode 100644 index cac707b3e9f70c083f96e6f15818e8b9781c6670..0000000000000000000000000000000000000000 --- a/spaces/Saturdays/Starchat_Saturdays/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: StarChat Saturdays -emoji: ⭐️💬 -colorFrom: pink -colorTo: indigo -sdk: gradio -sdk_version: 3.33.1 -app_file: app.py -pinned: true -license: mit -duplicated_from: HuggingFaceH4/starchat-playground ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/SaulLu/test-demo/style.css b/spaces/SaulLu/test-demo/style.css deleted file mode 100644 index 3f3f9cf149b8d2b348e7f646ae695aaae5935654..0000000000000000000000000000000000000000 --- a/spaces/SaulLu/test-demo/style.css +++ /dev/null @@ -1,145 +0,0 @@ -:root { - --border-color: black; - --window-color: white; - --background-move-on-cursor: false; - --background-color: white; - --background-cursor-width: 400; - --background-cursor-height: 200; - --background-show-if-wider-than: 500; - --background-speed: 0.001; - --energy-decay: 0.3; -} -html { - font-size: 14px; -} -@media (min-width: 768px) { - html { - font-size: 16px; - } -} - -body { - width: 100%; - margin: 0 auto; - background-color: var(--background-color); -} -#header_main { - position: relative; - width: 100%; - margin: 0 auto; -} -#header_main canvas, #overlay { - width: 100%; - margin: 0 auto; - position: absolute; -} - -a { - text-decoration: none; -} - -a:hover { - text-decoration: underline; -} - -.container { - max-width: 46rem; -} - -.pricing-header { - max-width: 700px; -} - -.card-deck .card { - min-width: 220px; -} - -.border-top { border-top: 1px solid #e5e5e5; } -.border-bottom { border-bottom: 1px solid #e5e5e5; } - -.box-shadow { box-shadow: 0 .25rem .75rem rgba(0, 0, 0, .05); } - -.arxiv_button { - position: relative; - display: inline-block; - width: 78px; - height: 28px; - background-image: linear-gradient(180deg, #fafbfc, #eff3f6 90%); - color: #24292e; - border: 1px solid rgba(27,31,35,.2); - text-align: center; - cursor: pointer; - border-radius: 4px; - padding-right: 0px; - padding-top: 2.5px; - font-size: 12px; - font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial,sans-serif; - font-weight: 600; -} -.arxiv_button:before { - content: ""; - vertical-align:middle; - display: inline-block; - width: 24px; - height: 24px; - border: none; - margin-left: -16px; - margin-right: 0px; - margin-top: -2px; - background: url('data:image/svg+xml;charset=UTF-8,') right center no-repeat; - background-size: 18px 16px; -} -.arxiv_button:hover { - background-color:#e6ebf1; - background-position:-0.5em; - border-color: #9fa4a9; - border-color:rgba(27,31,35,.35); - background-image:linear-gradient(180deg, #f0f3f6, #e6ebf1 90%) -} - -#header_window { - width: 80%; - min-width: 320px; - margin: 0 auto; - text-align: center; -} -.faded { - margin: 0 auto; - background: var(--window-color); - box-shadow: 0 0 5px 5px var(--window-color); - font-family: cursive; - font-family: "Gill Sans", sans-serif; - display: inline-block -} -.padded { - width: 100%; - max-width: 800px; - text-align: left; -} -.title { - font-size: 32px; - box-shadow: 0 0 5px 5px var(--window-color); - font-family: -apple-system,BlinkMacSystemFont,Segoe UI,Helvetica,Arial, - sans-serif,Apple Color Emoji,Segoe UI Emoji; -} -canvas { - background-color: var(--background-color); - width: 0px; /* will be changed on init */ - overflow: hidden; -} - -a { - color: #337ab7; - font-weight: 600; -} -a:hover { - color: #0056b3; -} -.nav-link { - color: #337ab7; - padding: 0.5rem 1rem; - font-weight: normal; -} -.nav-link:hover { - color: #337ab7; -} diff --git a/spaces/SeViLA/SeViLA/app/__init__.py b/spaces/SeViLA/SeViLA/app/__init__.py deleted file mode 100644 index 0552204081d813fbd3fb28c81dbada83d4b53021..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/app/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -""" - # Copyright (c) 2022, salesforce.com, inc. - # All rights reserved. - # SPDX-License-Identifier: BSD-3-Clause - # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -from PIL import Image -import requests - -import streamlit as st -import torch - - -@st.cache() -def load_demo_image(): - img_url = ( - "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg" - ) - raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") - return raw_image - - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -cache_root = "/export/home/.cache/lavis/" diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py b/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py deleted file mode 100644 index 167d4cb2198863cf43e93440f7e63c5342fc7605..0000000000000000000000000000000000000000 --- a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -from . import layers_123821KB as layers - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 32) - self.stg1_high_band_net = BaseASPPNet(2, 32) - - self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(16, 32) - - self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(32, 64) - - self.out = nn.Conv2d(64, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/SeyedAli/Audio-Diffusion-style_transfer/README.md b/spaces/SeyedAli/Audio-Diffusion-style_transfer/README.md deleted file mode 100644 index a697846648d2caa2d33eff90f5481cee1e28a2bb..0000000000000000000000000000000000000000 --- a/spaces/SeyedAli/Audio-Diffusion-style_transfer/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Music Audio style transfer -emoji: 🎵 -colorFrom: purple -colorTo: yellow -sdk: gradio -sdk_version: 3.50.2 -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/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/image/abstract_image_tensor.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/image/abstract_image_tensor.py deleted file mode 100644 index 9566910781d3a4c5dbe199514a9dade28b5f31ce..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/tensor/image/abstract_image_tensor.py +++ /dev/null @@ -1,73 +0,0 @@ -import io -import warnings -from abc import ABC - -import numpy as np -from typing_extensions import TYPE_CHECKING - -from docarray.typing.tensor.abstract_tensor import AbstractTensor -from docarray.utils._internal.misc import import_library, is_notebook - -if TYPE_CHECKING: - from docarray.typing.bytes.image_bytes import ImageBytes - - -class AbstractImageTensor(AbstractTensor, ABC): - def to_bytes(self, format: str = 'PNG') -> 'ImageBytes': - """ - Convert image tensor to [`ImageBytes`][docarray.typing.ImageBytes]. - - :param format: the image format use to store the image, can be 'PNG' , 'JPG' ... - :return: an ImageBytes object - """ - PIL = import_library('PIL', raise_error=True) # noqa: F841 - from PIL import Image as PILImage - - if format == 'jpg': - format = 'jpeg' # unify it to ISO standard - - tensor = self.get_comp_backend().to_numpy(self) - - mode = 'RGB' if tensor.ndim == 3 else 'L' - pil_image = PILImage.fromarray(tensor, mode=mode) - - with io.BytesIO() as buffer: - pil_image.save(buffer, format=format) - img_byte_arr = buffer.getvalue() - - from docarray.typing.bytes.image_bytes import ImageBytes - - return ImageBytes(img_byte_arr) - - def save(self, file_path: str) -> None: - """ - Save image tensor to an image file. - - :param file_path: path to an image file. If file is a string, open the file by - that name, otherwise treat it as a file-like object. - """ - PIL = import_library('PIL', raise_error=True) # noqa: F841 - from PIL import Image as PILImage - - comp_backend = self.get_comp_backend() - np_img = comp_backend.to_numpy(self).astype(np.uint8) - - pil_img = PILImage.fromarray(np_img) - pil_img.save(file_path) - - def display(self) -> None: - """ - Display image data from tensor in notebook. - """ - if is_notebook(): - PIL = import_library('PIL', raise_error=True) # noqa: F841 - from PIL import Image as PILImage - - np_array = self.get_comp_backend().to_numpy(self) - img = PILImage.fromarray(np_array) - - from IPython.display import display - - display(img) - else: - warnings.warn('Display of image is only possible in a notebook.') diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/modeling/matcher.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/modeling/matcher.py deleted file mode 100644 index 4dba337a0f99ccd394931f52b063c8fb575bafbd..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/oneformer/oneformer/modeling/matcher.py +++ /dev/null @@ -1,212 +0,0 @@ -# ------------------------------------------------------------------------------ -# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py -# Modified by Jitesh Jain (https://github.com/praeclarumjj3) -# ------------------------------------------------------------------------------ - -""" -Modules to compute the matching cost and solve the corresponding LSAP. -""" -import torch -import torch.nn.functional as F -from scipy.optimize import linear_sum_assignment -from torch import nn -from torch.cuda.amp import autocast -import numpy as np - -# from annotator.oneformer.detectron2.projects.point_rend.point_features import point_sample - - -def linear_sum_assignment_with_nan(cost_matrix): - cost_matrix = np.asarray(cost_matrix) - nan = np.isnan(cost_matrix).any() - nan_all = np.isnan(cost_matrix).all() - empty = cost_matrix.size == 0 - - if not empty: - if nan_all: - print('Matrix contains all NaN values!') - elif nan: - print('Matrix contains NaN values!') - - if nan_all: - cost_matrix = np.empty(shape=(0, 0)) - elif nan: - cost_matrix[np.isnan(cost_matrix)] = 100 - - return linear_sum_assignment(cost_matrix) - -def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor): - """ - Compute the DICE loss, similar to generalized IOU for masks - Args: - inputs: A float tensor of arbitrary shape. - The predictions for each example. - targets: A float tensor with the same shape as inputs. Stores the binary - classification label for each element in inputs - (0 for the negative class and 1 for the positive class). - """ - inputs = inputs.sigmoid() - inputs = inputs.flatten(1) - numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets) - denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :] - loss = 1 - (numerator + 1) / (denominator + 1) - return loss - - -batch_dice_loss_jit = torch.jit.script( - batch_dice_loss -) # type: torch.jit.ScriptModule - - -def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor): - """ - Args: - inputs: A float tensor of arbitrary shape. - The predictions for each example. - targets: A float tensor with the same shape as inputs. Stores the binary - classification label for each element in inputs - (0 for the negative class and 1 for the positive class). - Returns: - Loss tensor - """ - hw = inputs.shape[1] - - pos = F.binary_cross_entropy_with_logits( - inputs, torch.ones_like(inputs), reduction="none" - ) - neg = F.binary_cross_entropy_with_logits( - inputs, torch.zeros_like(inputs), reduction="none" - ) - - loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum( - "nc,mc->nm", neg, (1 - targets) - ) - - return loss / hw - - -batch_sigmoid_ce_loss_jit = torch.jit.script( - batch_sigmoid_ce_loss -) # type: torch.jit.ScriptModule - - -class HungarianMatcher(nn.Module): - """This class computes an assignment between the targets and the predictions of the network - - For efficiency reasons, the targets don't include the no_object. Because of this, in general, - there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, - while the others are un-matched (and thus treated as non-objects). - """ - - def __init__(self, cost_class: float = 1, cost_mask: float = 1, - cost_dice: float = 1, num_points: int = 0): - """Creates the matcher - - Params: - cost_class: This is the relative weight of the classification error in the matching cost - cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost - cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost - """ - super().__init__() - self.cost_class = cost_class - self.cost_mask = cost_mask - self.cost_dice = cost_dice - - assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0" - - self.num_points = num_points - - @torch.no_grad() - def memory_efficient_forward(self, outputs, targets): - """More memory-friendly matching""" - bs, num_queries = outputs["pred_logits"].shape[:2] - - indices = [] - - # Iterate through batch size - for b in range(bs): - out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes] - tgt_ids = targets[b]["labels"] - - # Compute the classification cost. Contrary to the loss, we don't use the NLL, - # but approximate it in 1 - proba[target class]. - # The 1 is a constant that doesn't change the matching, it can be ommitted. - cost_class = -out_prob[:, tgt_ids] - - out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred] - # gt masks are already padded when preparing target - tgt_mask = targets[b]["masks"].to(out_mask) - - out_mask = out_mask[:, None] - tgt_mask = tgt_mask[:, None] - # all masks share the same set of points for efficient matching! - point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device) - # get gt labels - tgt_mask = point_sample( - tgt_mask, - point_coords.repeat(tgt_mask.shape[0], 1, 1), - align_corners=False, - ).squeeze(1) - - out_mask = point_sample( - out_mask, - point_coords.repeat(out_mask.shape[0], 1, 1), - align_corners=False, - ).squeeze(1) - - with autocast(enabled=False): - out_mask = out_mask.float() - tgt_mask = tgt_mask.float() - # Compute the focal loss between masks - cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask) - # Compute the dice loss betwen masks - cost_dice = batch_dice_loss(out_mask, tgt_mask) - - # Final cost matrix - C = ( - self.cost_mask * cost_mask - + self.cost_class * cost_class - + self.cost_dice * cost_dice - ) - C = C.reshape(num_queries, -1).cpu() - - indices.append(linear_sum_assignment_with_nan(C)) - - return [ - (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) - for i, j in indices - ] - - @torch.no_grad() - def forward(self, outputs, targets): - """Performs the matching - - Params: - outputs: This is a dict that contains at least these entries: - "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits - "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks - - targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: - "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth - objects in the target) containing the class labels - "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks - - Returns: - A list of size batch_size, containing tuples of (index_i, index_j) where: - - index_i is the indices of the selected predictions (in order) - - index_j is the indices of the corresponding selected targets (in order) - For each batch element, it holds: - len(index_i) = len(index_j) = min(num_queries, num_target_boxes) - """ - - return self.memory_efficient_forward(outputs, targets) - - def __repr__(self, _repr_indent=4): - head = "Matcher " + self.__class__.__name__ - body = [ - "cost_class: {}".format(self.cost_class), - "cost_mask: {}".format(self.cost_mask), - "cost_dice: {}".format(self.cost_dice), - ] - lines = [head] + [" " * _repr_indent + line for line in body] - return "\n".join(lines) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/url.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/url.py deleted file mode 100644 index a960b2f3c5f3d11fc9ae43638da9877d635e8d91..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/util/url.py +++ /dev/null @@ -1,435 +0,0 @@ -from __future__ import absolute_import - -import re -from collections import namedtuple - -from ..exceptions import LocationParseError -from ..packages import six - -url_attrs = ["scheme", "auth", "host", "port", "path", "query", "fragment"] - -# We only want to normalize urls with an HTTP(S) scheme. -# urllib3 infers URLs without a scheme (None) to be http. -NORMALIZABLE_SCHEMES = ("http", "https", None) - -# Almost all of these patterns were derived from the -# 'rfc3986' module: https://github.com/python-hyper/rfc3986 -PERCENT_RE = re.compile(r"%[a-fA-F0-9]{2}") -SCHEME_RE = re.compile(r"^(?:[a-zA-Z][a-zA-Z0-9+-]*:|/)") -URI_RE = re.compile( - r"^(?:([a-zA-Z][a-zA-Z0-9+.-]*):)?" - r"(?://([^\\/?#]*))?" - r"([^?#]*)" - r"(?:\?([^#]*))?" - r"(?:#(.*))?$", - re.UNICODE | re.DOTALL, -) - -IPV4_PAT = r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}" -HEX_PAT = "[0-9A-Fa-f]{1,4}" -LS32_PAT = "(?:{hex}:{hex}|{ipv4})".format(hex=HEX_PAT, ipv4=IPV4_PAT) -_subs = {"hex": HEX_PAT, "ls32": LS32_PAT} -_variations = [ - # 6( h16 ":" ) ls32 - "(?:%(hex)s:){6}%(ls32)s", - # "::" 5( h16 ":" ) ls32 - "::(?:%(hex)s:){5}%(ls32)s", - # [ h16 ] "::" 4( h16 ":" ) ls32 - "(?:%(hex)s)?::(?:%(hex)s:){4}%(ls32)s", - # [ *1( h16 ":" ) h16 ] "::" 3( h16 ":" ) ls32 - "(?:(?:%(hex)s:)?%(hex)s)?::(?:%(hex)s:){3}%(ls32)s", - # [ *2( h16 ":" ) h16 ] "::" 2( h16 ":" ) ls32 - "(?:(?:%(hex)s:){0,2}%(hex)s)?::(?:%(hex)s:){2}%(ls32)s", - # [ *3( h16 ":" ) h16 ] "::" h16 ":" ls32 - "(?:(?:%(hex)s:){0,3}%(hex)s)?::%(hex)s:%(ls32)s", - # [ *4( h16 ":" ) h16 ] "::" ls32 - "(?:(?:%(hex)s:){0,4}%(hex)s)?::%(ls32)s", - # [ *5( h16 ":" ) h16 ] "::" h16 - "(?:(?:%(hex)s:){0,5}%(hex)s)?::%(hex)s", - # [ *6( h16 ":" ) h16 ] "::" - "(?:(?:%(hex)s:){0,6}%(hex)s)?::", -] - -UNRESERVED_PAT = r"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._\-~" -IPV6_PAT = "(?:" + "|".join([x % _subs for x in _variations]) + ")" -ZONE_ID_PAT = "(?:%25|%)(?:[" + UNRESERVED_PAT + "]|%[a-fA-F0-9]{2})+" -IPV6_ADDRZ_PAT = r"\[" + IPV6_PAT + r"(?:" + ZONE_ID_PAT + r")?\]" -REG_NAME_PAT = r"(?:[^\[\]%:/?#]|%[a-fA-F0-9]{2})*" -TARGET_RE = re.compile(r"^(/[^?#]*)(?:\?([^#]*))?(?:#.*)?$") - -IPV4_RE = re.compile("^" + IPV4_PAT + "$") -IPV6_RE = re.compile("^" + IPV6_PAT + "$") -IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT + "$") -BRACELESS_IPV6_ADDRZ_RE = re.compile("^" + IPV6_ADDRZ_PAT[2:-2] + "$") -ZONE_ID_RE = re.compile("(" + ZONE_ID_PAT + r")\]$") - -_HOST_PORT_PAT = ("^(%s|%s|%s)(?::0*?(|0|[1-9][0-9]{0,4}))?$") % ( - REG_NAME_PAT, - IPV4_PAT, - IPV6_ADDRZ_PAT, -) -_HOST_PORT_RE = re.compile(_HOST_PORT_PAT, re.UNICODE | re.DOTALL) - -UNRESERVED_CHARS = set( - "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789._-~" -) -SUB_DELIM_CHARS = set("!$&'()*+,;=") -USERINFO_CHARS = UNRESERVED_CHARS | SUB_DELIM_CHARS | {":"} -PATH_CHARS = USERINFO_CHARS | {"@", "/"} -QUERY_CHARS = FRAGMENT_CHARS = PATH_CHARS | {"?"} - - -class Url(namedtuple("Url", url_attrs)): - """ - Data structure for representing an HTTP URL. Used as a return value for - :func:`parse_url`. Both the scheme and host are normalized as they are - both case-insensitive according to RFC 3986. - """ - - __slots__ = () - - def __new__( - cls, - scheme=None, - auth=None, - host=None, - port=None, - path=None, - query=None, - fragment=None, - ): - if path and not path.startswith("/"): - path = "/" + path - if scheme is not None: - scheme = scheme.lower() - return super(Url, cls).__new__( - cls, scheme, auth, host, port, path, query, fragment - ) - - @property - def hostname(self): - """For backwards-compatibility with urlparse. We're nice like that.""" - return self.host - - @property - def request_uri(self): - """Absolute path including the query string.""" - uri = self.path or "/" - - if self.query is not None: - uri += "?" + self.query - - return uri - - @property - def netloc(self): - """Network location including host and port""" - if self.port: - return "%s:%d" % (self.host, self.port) - return self.host - - @property - def url(self): - """ - Convert self into a url - - This function should more or less round-trip with :func:`.parse_url`. The - returned url may not be exactly the same as the url inputted to - :func:`.parse_url`, but it should be equivalent by the RFC (e.g., urls - with a blank port will have : removed). - - Example: :: - - >>> U = parse_url('http://google.com/mail/') - >>> U.url - 'http://google.com/mail/' - >>> Url('http', 'username:password', 'host.com', 80, - ... '/path', 'query', 'fragment').url - 'http://username:password@host.com:80/path?query#fragment' - """ - scheme, auth, host, port, path, query, fragment = self - url = u"" - - # We use "is not None" we want things to happen with empty strings (or 0 port) - if scheme is not None: - url += scheme + u"://" - if auth is not None: - url += auth + u"@" - if host is not None: - url += host - if port is not None: - url += u":" + str(port) - if path is not None: - url += path - if query is not None: - url += u"?" + query - if fragment is not None: - url += u"#" + fragment - - return url - - def __str__(self): - return self.url - - -def split_first(s, delims): - """ - .. deprecated:: 1.25 - - Given a string and an iterable of delimiters, split on the first found - delimiter. Return two split parts and the matched delimiter. - - If not found, then the first part is the full input string. - - Example:: - - >>> split_first('foo/bar?baz', '?/=') - ('foo', 'bar?baz', '/') - >>> split_first('foo/bar?baz', '123') - ('foo/bar?baz', '', None) - - Scales linearly with number of delims. Not ideal for large number of delims. - """ - min_idx = None - min_delim = None - for d in delims: - idx = s.find(d) - if idx < 0: - continue - - if min_idx is None or idx < min_idx: - min_idx = idx - min_delim = d - - if min_idx is None or min_idx < 0: - return s, "", None - - return s[:min_idx], s[min_idx + 1 :], min_delim - - -def _encode_invalid_chars(component, allowed_chars, encoding="utf-8"): - """Percent-encodes a URI component without reapplying - onto an already percent-encoded component. - """ - if component is None: - return component - - component = six.ensure_text(component) - - # Normalize existing percent-encoded bytes. - # Try to see if the component we're encoding is already percent-encoded - # so we can skip all '%' characters but still encode all others. - component, percent_encodings = PERCENT_RE.subn( - lambda match: match.group(0).upper(), component - ) - - uri_bytes = component.encode("utf-8", "surrogatepass") - is_percent_encoded = percent_encodings == uri_bytes.count(b"%") - encoded_component = bytearray() - - for i in range(0, len(uri_bytes)): - # Will return a single character bytestring on both Python 2 & 3 - byte = uri_bytes[i : i + 1] - byte_ord = ord(byte) - if (is_percent_encoded and byte == b"%") or ( - byte_ord < 128 and byte.decode() in allowed_chars - ): - encoded_component += byte - continue - encoded_component.extend(b"%" + (hex(byte_ord)[2:].encode().zfill(2).upper())) - - return encoded_component.decode(encoding) - - -def _remove_path_dot_segments(path): - # See http://tools.ietf.org/html/rfc3986#section-5.2.4 for pseudo-code - segments = path.split("/") # Turn the path into a list of segments - output = [] # Initialize the variable to use to store output - - for segment in segments: - # '.' is the current directory, so ignore it, it is superfluous - if segment == ".": - continue - # Anything other than '..', should be appended to the output - elif segment != "..": - output.append(segment) - # In this case segment == '..', if we can, we should pop the last - # element - elif output: - output.pop() - - # If the path starts with '/' and the output is empty or the first string - # is non-empty - if path.startswith("/") and (not output or output[0]): - output.insert(0, "") - - # If the path starts with '/.' or '/..' ensure we add one more empty - # string to add a trailing '/' - if path.endswith(("/.", "/..")): - output.append("") - - return "/".join(output) - - -def _normalize_host(host, scheme): - if host: - if isinstance(host, six.binary_type): - host = six.ensure_str(host) - - if scheme in NORMALIZABLE_SCHEMES: - is_ipv6 = IPV6_ADDRZ_RE.match(host) - if is_ipv6: - # IPv6 hosts of the form 'a::b%zone' are encoded in a URL as - # such per RFC 6874: 'a::b%25zone'. Unquote the ZoneID - # separator as necessary to return a valid RFC 4007 scoped IP. - match = ZONE_ID_RE.search(host) - if match: - start, end = match.span(1) - zone_id = host[start:end] - - if zone_id.startswith("%25") and zone_id != "%25": - zone_id = zone_id[3:] - else: - zone_id = zone_id[1:] - zone_id = "%" + _encode_invalid_chars(zone_id, UNRESERVED_CHARS) - return host[:start].lower() + zone_id + host[end:] - else: - return host.lower() - elif not IPV4_RE.match(host): - return six.ensure_str( - b".".join([_idna_encode(label) for label in host.split(".")]) - ) - return host - - -def _idna_encode(name): - if name and any(ord(x) >= 128 for x in name): - try: - from pip._vendor import idna - except ImportError: - six.raise_from( - LocationParseError("Unable to parse URL without the 'idna' module"), - None, - ) - try: - return idna.encode(name.lower(), strict=True, std3_rules=True) - except idna.IDNAError: - six.raise_from( - LocationParseError(u"Name '%s' is not a valid IDNA label" % name), None - ) - return name.lower().encode("ascii") - - -def _encode_target(target): - """Percent-encodes a request target so that there are no invalid characters""" - path, query = TARGET_RE.match(target).groups() - target = _encode_invalid_chars(path, PATH_CHARS) - query = _encode_invalid_chars(query, QUERY_CHARS) - if query is not None: - target += "?" + query - return target - - -def parse_url(url): - """ - Given a url, return a parsed :class:`.Url` namedtuple. Best-effort is - performed to parse incomplete urls. Fields not provided will be None. - This parser is RFC 3986 and RFC 6874 compliant. - - The parser logic and helper functions are based heavily on - work done in the ``rfc3986`` module. - - :param str url: URL to parse into a :class:`.Url` namedtuple. - - Partly backwards-compatible with :mod:`urlparse`. - - Example:: - - >>> parse_url('http://google.com/mail/') - Url(scheme='http', host='google.com', port=None, path='/mail/', ...) - >>> parse_url('google.com:80') - Url(scheme=None, host='google.com', port=80, path=None, ...) - >>> parse_url('/foo?bar') - Url(scheme=None, host=None, port=None, path='/foo', query='bar', ...) - """ - if not url: - # Empty - return Url() - - source_url = url - if not SCHEME_RE.search(url): - url = "//" + url - - try: - scheme, authority, path, query, fragment = URI_RE.match(url).groups() - normalize_uri = scheme is None or scheme.lower() in NORMALIZABLE_SCHEMES - - if scheme: - scheme = scheme.lower() - - if authority: - auth, _, host_port = authority.rpartition("@") - auth = auth or None - host, port = _HOST_PORT_RE.match(host_port).groups() - if auth and normalize_uri: - auth = _encode_invalid_chars(auth, USERINFO_CHARS) - if port == "": - port = None - else: - auth, host, port = None, None, None - - if port is not None: - port = int(port) - if not (0 <= port <= 65535): - raise LocationParseError(url) - - host = _normalize_host(host, scheme) - - if normalize_uri and path: - path = _remove_path_dot_segments(path) - path = _encode_invalid_chars(path, PATH_CHARS) - if normalize_uri and query: - query = _encode_invalid_chars(query, QUERY_CHARS) - if normalize_uri and fragment: - fragment = _encode_invalid_chars(fragment, FRAGMENT_CHARS) - - except (ValueError, AttributeError): - return six.raise_from(LocationParseError(source_url), None) - - # For the sake of backwards compatibility we put empty - # string values for path if there are any defined values - # beyond the path in the URL. - # TODO: Remove this when we break backwards compatibility. - if not path: - if query is not None or fragment is not None: - path = "" - else: - path = None - - # Ensure that each part of the URL is a `str` for - # backwards compatibility. - if isinstance(url, six.text_type): - ensure_func = six.ensure_text - else: - ensure_func = six.ensure_str - - def ensure_type(x): - return x if x is None else ensure_func(x) - - return Url( - scheme=ensure_type(scheme), - auth=ensure_type(auth), - host=ensure_type(host), - port=port, - path=ensure_type(path), - query=ensure_type(query), - fragment=ensure_type(fragment), - ) - - -def get_host(url): - """ - Deprecated. Use :func:`parse_url` instead. - """ - p = parse_url(url) - return p.scheme or "http", p.hostname, p.port diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_parser.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_parser.py deleted file mode 100644 index 5a18b758fe0065416a92b9047dc9c392a3de2c4f..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_parser.py +++ /dev/null @@ -1,353 +0,0 @@ -"""Handwritten parser of dependency specifiers. - -The docstring for each __parse_* function contains ENBF-inspired grammar representing -the implementation. -""" - -import ast -from typing import Any, List, NamedTuple, Optional, Tuple, Union - -from ._tokenizer import DEFAULT_RULES, Tokenizer - - -class Node: - def __init__(self, value: str) -> None: - self.value = value - - def __str__(self) -> str: - return self.value - - def __repr__(self) -> str: - return f"<{self.__class__.__name__}('{self}')>" - - def serialize(self) -> str: - raise NotImplementedError - - -class Variable(Node): - def serialize(self) -> str: - return str(self) - - -class Value(Node): - def serialize(self) -> str: - return f'"{self}"' - - -class Op(Node): - def serialize(self) -> str: - return str(self) - - -MarkerVar = Union[Variable, Value] -MarkerItem = Tuple[MarkerVar, Op, MarkerVar] -# MarkerAtom = Union[MarkerItem, List["MarkerAtom"]] -# MarkerList = List[Union["MarkerList", MarkerAtom, str]] -# mypy does not support recursive type definition -# https://github.com/python/mypy/issues/731 -MarkerAtom = Any -MarkerList = List[Any] - - -class ParsedRequirement(NamedTuple): - name: str - url: str - extras: List[str] - specifier: str - marker: Optional[MarkerList] - - -# -------------------------------------------------------------------------------------- -# Recursive descent parser for dependency specifier -# -------------------------------------------------------------------------------------- -def parse_requirement(source: str) -> ParsedRequirement: - return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES)) - - -def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement: - """ - requirement = WS? IDENTIFIER WS? extras WS? requirement_details - """ - tokenizer.consume("WS") - - name_token = tokenizer.expect( - "IDENTIFIER", expected="package name at the start of dependency specifier" - ) - name = name_token.text - tokenizer.consume("WS") - - extras = _parse_extras(tokenizer) - tokenizer.consume("WS") - - url, specifier, marker = _parse_requirement_details(tokenizer) - tokenizer.expect("END", expected="end of dependency specifier") - - return ParsedRequirement(name, url, extras, specifier, marker) - - -def _parse_requirement_details( - tokenizer: Tokenizer, -) -> Tuple[str, str, Optional[MarkerList]]: - """ - requirement_details = AT URL (WS requirement_marker?)? - | specifier WS? (requirement_marker)? - """ - - specifier = "" - url = "" - marker = None - - if tokenizer.check("AT"): - tokenizer.read() - tokenizer.consume("WS") - - url_start = tokenizer.position - url = tokenizer.expect("URL", expected="URL after @").text - if tokenizer.check("END", peek=True): - return (url, specifier, marker) - - tokenizer.expect("WS", expected="whitespace after URL") - - # The input might end after whitespace. - if tokenizer.check("END", peek=True): - return (url, specifier, marker) - - marker = _parse_requirement_marker( - tokenizer, span_start=url_start, after="URL and whitespace" - ) - else: - specifier_start = tokenizer.position - specifier = _parse_specifier(tokenizer) - tokenizer.consume("WS") - - if tokenizer.check("END", peek=True): - return (url, specifier, marker) - - marker = _parse_requirement_marker( - tokenizer, - span_start=specifier_start, - after=( - "version specifier" - if specifier - else "name and no valid version specifier" - ), - ) - - return (url, specifier, marker) - - -def _parse_requirement_marker( - tokenizer: Tokenizer, *, span_start: int, after: str -) -> MarkerList: - """ - requirement_marker = SEMICOLON marker WS? - """ - - if not tokenizer.check("SEMICOLON"): - tokenizer.raise_syntax_error( - f"Expected end or semicolon (after {after})", - span_start=span_start, - ) - tokenizer.read() - - marker = _parse_marker(tokenizer) - tokenizer.consume("WS") - - return marker - - -def _parse_extras(tokenizer: Tokenizer) -> List[str]: - """ - extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)? - """ - if not tokenizer.check("LEFT_BRACKET", peek=True): - return [] - - with tokenizer.enclosing_tokens( - "LEFT_BRACKET", - "RIGHT_BRACKET", - around="extras", - ): - tokenizer.consume("WS") - extras = _parse_extras_list(tokenizer) - tokenizer.consume("WS") - - return extras - - -def _parse_extras_list(tokenizer: Tokenizer) -> List[str]: - """ - extras_list = identifier (wsp* ',' wsp* identifier)* - """ - extras: List[str] = [] - - if not tokenizer.check("IDENTIFIER"): - return extras - - extras.append(tokenizer.read().text) - - while True: - tokenizer.consume("WS") - if tokenizer.check("IDENTIFIER", peek=True): - tokenizer.raise_syntax_error("Expected comma between extra names") - elif not tokenizer.check("COMMA"): - break - - tokenizer.read() - tokenizer.consume("WS") - - extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma") - extras.append(extra_token.text) - - return extras - - -def _parse_specifier(tokenizer: Tokenizer) -> str: - """ - specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS - | WS? version_many WS? - """ - with tokenizer.enclosing_tokens( - "LEFT_PARENTHESIS", - "RIGHT_PARENTHESIS", - around="version specifier", - ): - tokenizer.consume("WS") - parsed_specifiers = _parse_version_many(tokenizer) - tokenizer.consume("WS") - - return parsed_specifiers - - -def _parse_version_many(tokenizer: Tokenizer) -> str: - """ - version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)? - """ - parsed_specifiers = "" - while tokenizer.check("SPECIFIER"): - span_start = tokenizer.position - parsed_specifiers += tokenizer.read().text - if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True): - tokenizer.raise_syntax_error( - ".* suffix can only be used with `==` or `!=` operators", - span_start=span_start, - span_end=tokenizer.position + 1, - ) - if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True): - tokenizer.raise_syntax_error( - "Local version label can only be used with `==` or `!=` operators", - span_start=span_start, - span_end=tokenizer.position, - ) - tokenizer.consume("WS") - if not tokenizer.check("COMMA"): - break - parsed_specifiers += tokenizer.read().text - tokenizer.consume("WS") - - return parsed_specifiers - - -# -------------------------------------------------------------------------------------- -# Recursive descent parser for marker expression -# -------------------------------------------------------------------------------------- -def parse_marker(source: str) -> MarkerList: - return _parse_marker(Tokenizer(source, rules=DEFAULT_RULES)) - - -def _parse_marker(tokenizer: Tokenizer) -> MarkerList: - """ - marker = marker_atom (BOOLOP marker_atom)+ - """ - expression = [_parse_marker_atom(tokenizer)] - while tokenizer.check("BOOLOP"): - token = tokenizer.read() - expr_right = _parse_marker_atom(tokenizer) - expression.extend((token.text, expr_right)) - return expression - - -def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom: - """ - marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS? - | WS? marker_item WS? - """ - - tokenizer.consume("WS") - if tokenizer.check("LEFT_PARENTHESIS", peek=True): - with tokenizer.enclosing_tokens( - "LEFT_PARENTHESIS", - "RIGHT_PARENTHESIS", - around="marker expression", - ): - tokenizer.consume("WS") - marker: MarkerAtom = _parse_marker(tokenizer) - tokenizer.consume("WS") - else: - marker = _parse_marker_item(tokenizer) - tokenizer.consume("WS") - return marker - - -def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem: - """ - marker_item = WS? marker_var WS? marker_op WS? marker_var WS? - """ - tokenizer.consume("WS") - marker_var_left = _parse_marker_var(tokenizer) - tokenizer.consume("WS") - marker_op = _parse_marker_op(tokenizer) - tokenizer.consume("WS") - marker_var_right = _parse_marker_var(tokenizer) - tokenizer.consume("WS") - return (marker_var_left, marker_op, marker_var_right) - - -def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar: - """ - marker_var = VARIABLE | QUOTED_STRING - """ - if tokenizer.check("VARIABLE"): - return process_env_var(tokenizer.read().text.replace(".", "_")) - elif tokenizer.check("QUOTED_STRING"): - return process_python_str(tokenizer.read().text) - else: - tokenizer.raise_syntax_error( - message="Expected a marker variable or quoted string" - ) - - -def process_env_var(env_var: str) -> Variable: - if ( - env_var == "platform_python_implementation" - or env_var == "python_implementation" - ): - return Variable("platform_python_implementation") - else: - return Variable(env_var) - - -def process_python_str(python_str: str) -> Value: - value = ast.literal_eval(python_str) - return Value(str(value)) - - -def _parse_marker_op(tokenizer: Tokenizer) -> Op: - """ - marker_op = IN | NOT IN | OP - """ - if tokenizer.check("IN"): - tokenizer.read() - return Op("in") - elif tokenizer.check("NOT"): - tokenizer.read() - tokenizer.expect("WS", expected="whitespace after 'not'") - tokenizer.expect("IN", expected="'in' after 'not'") - return Op("not in") - elif tokenizer.check("OP"): - return Op(tokenizer.read().text) - else: - return tokenizer.raise_syntax_error( - "Expected marker operator, one of " - "<=, <, !=, ==, >=, >, ~=, ===, in, not in" - ) diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py deleted file mode 100644 index 52f273bb5ee150ed7252207312320f0a9bdf0e65..0000000000000000000000000000000000000000 --- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/modeling/postprocessing.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import torch -from torch.nn import functional as F - -from detectron2.structures import Instances, ROIMasks - - -# perhaps should rename to "resize_instance" -def detector_postprocess( - results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5 -): - """ - Resize the output instances. - The input images are often resized when entering an object detector. - As a result, we often need the outputs of the detector in a different - resolution from its inputs. - - This function will resize the raw outputs of an R-CNN detector - to produce outputs according to the desired output resolution. - - Args: - results (Instances): the raw outputs from the detector. - `results.image_size` contains the input image resolution the detector sees. - This object might be modified in-place. - output_height, output_width: the desired output resolution. - - Returns: - Instances: the resized output from the model, based on the output resolution - """ - if isinstance(output_width, torch.Tensor): - # This shape might (but not necessarily) be tensors during tracing. - # Converts integer tensors to float temporaries to ensure true - # division is performed when computing scale_x and scale_y. - output_width_tmp = output_width.float() - output_height_tmp = output_height.float() - new_size = torch.stack([output_height, output_width]) - else: - new_size = (output_height, output_width) - output_width_tmp = output_width - output_height_tmp = output_height - - scale_x, scale_y = ( - output_width_tmp / results.image_size[1], - output_height_tmp / results.image_size[0], - ) - results = Instances(new_size, **results.get_fields()) - - if results.has("pred_boxes"): - output_boxes = results.pred_boxes - elif results.has("proposal_boxes"): - output_boxes = results.proposal_boxes - else: - output_boxes = None - assert output_boxes is not None, "Predictions must contain boxes!" - - output_boxes.scale(scale_x, scale_y) - output_boxes.clip(results.image_size) - - results = results[output_boxes.nonempty()] - - if results.has("pred_masks"): - if isinstance(results.pred_masks, ROIMasks): - roi_masks = results.pred_masks - else: - # pred_masks is a tensor of shape (N, 1, M, M) - roi_masks = ROIMasks(results.pred_masks[:, 0, :, :]) - results.pred_masks = roi_masks.to_bitmasks( - results.pred_boxes, output_height, output_width, mask_threshold - ).tensor # TODO return ROIMasks/BitMask object in the future - - if results.has("pred_keypoints"): - results.pred_keypoints[:, :, 0] *= scale_x - results.pred_keypoints[:, :, 1] *= scale_y - - return results - - -def sem_seg_postprocess(result, img_size, output_height, output_width): - """ - Return semantic segmentation predictions in the original resolution. - - The input images are often resized when entering semantic segmentor. Moreover, in same - cases, they also padded inside segmentor to be divisible by maximum network stride. - As a result, we often need the predictions of the segmentor in a different - resolution from its inputs. - - Args: - result (Tensor): semantic segmentation prediction logits. A tensor of shape (C, H, W), - where C is the number of classes, and H, W are the height and width of the prediction. - img_size (tuple): image size that segmentor is taking as input. - output_height, output_width: the desired output resolution. - - Returns: - semantic segmentation prediction (Tensor): A tensor of the shape - (C, output_height, output_width) that contains per-pixel soft predictions. - """ - result = result[:, : img_size[0], : img_size[1]].expand(1, -1, -1, -1) - result = F.interpolate( - result, size=(output_height, output_width), mode="bilinear", align_corners=False - )[0] - return result diff --git a/spaces/VVallabh/AI-driven-Video-Generation-Tool/app.py b/spaces/VVallabh/AI-driven-Video-Generation-Tool/app.py deleted file mode 100644 index e04fb262a98e44fd5d199a641363eb1342a257da..0000000000000000000000000000000000000000 --- a/spaces/VVallabh/AI-driven-Video-Generation-Tool/app.py +++ /dev/null @@ -1,29 +0,0 @@ -import torch -import os -import gradio as gr -from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler -from diffusers.utils import export_to_video -from IPython.display import HTML -from base64 import b64encode - -pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") -pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) -pipe.enable_model_cpu_offload() -pipe.enable_vae_slicing() - -def model(txt, time): - prompt = txt - video_duration_seconds = time - num_frames = video_duration_seconds * 10 - video_frames = pipe(prompt, negative_prompt="low quality", - num_inference_steps=25, num_frames=num_frames).frames - video_path = export_to_video(video_frames) - return video_path - -demo = gr.Interface( - fn=model, - inputs=["text", gr.Slider(1, 10, step=1)], - outputs=gr.Video(label="Out",output_width=400, output_height=300) -) - -demo.launch() diff --git a/spaces/VideoCrafter/VideoCrafter/lvdm/modules/encoders/ip_resampler.py b/spaces/VideoCrafter/VideoCrafter/lvdm/modules/encoders/ip_resampler.py deleted file mode 100644 index 500820a789150a55d6e8fdca4dd3e4d6ad542d4a..0000000000000000000000000000000000000000 --- a/spaces/VideoCrafter/VideoCrafter/lvdm/modules/encoders/ip_resampler.py +++ /dev/null @@ -1,136 +0,0 @@ -# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py -import math -import torch -import torch.nn as nn - - -class ImageProjModel(nn.Module): - """Projection Model""" - def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): - super().__init__() - self.cross_attention_dim = cross_attention_dim - self.clip_extra_context_tokens = clip_extra_context_tokens - self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) - self.norm = nn.LayerNorm(cross_attention_dim) - - def forward(self, image_embeds): - #embeds = image_embeds - embeds = image_embeds.type(list(self.proj.parameters())[0].dtype) - clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) - clip_extra_context_tokens = self.norm(clip_extra_context_tokens) - return clip_extra_context_tokens - -# FFN -def FeedForward(dim, mult=4): - inner_dim = int(dim * mult) - return nn.Sequential( - nn.LayerNorm(dim), - nn.Linear(dim, inner_dim, bias=False), - nn.GELU(), - nn.Linear(inner_dim, dim, bias=False), - ) - - -def reshape_tensor(x, heads): - bs, length, width = x.shape - #(bs, length, width) --> (bs, length, n_heads, dim_per_head) - x = x.view(bs, length, heads, -1) - # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) - x = x.transpose(1, 2) - # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) - x = x.reshape(bs, heads, length, -1) - return x - - -class PerceiverAttention(nn.Module): - def __init__(self, *, dim, dim_head=64, heads=8): - super().__init__() - self.scale = dim_head**-0.5 - self.dim_head = dim_head - self.heads = heads - inner_dim = dim_head * heads - - self.norm1 = nn.LayerNorm(dim) - self.norm2 = nn.LayerNorm(dim) - - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) - self.to_out = nn.Linear(inner_dim, dim, bias=False) - - - def forward(self, x, latents): - """ - Args: - x (torch.Tensor): image features - shape (b, n1, D) - latent (torch.Tensor): latent features - shape (b, n2, D) - """ - x = self.norm1(x) - latents = self.norm2(latents) - - b, l, _ = latents.shape - - q = self.to_q(latents) - kv_input = torch.cat((x, latents), dim=-2) - k, v = self.to_kv(kv_input).chunk(2, dim=-1) - - q = reshape_tensor(q, self.heads) - k = reshape_tensor(k, self.heads) - v = reshape_tensor(v, self.heads) - - # attention - scale = 1 / math.sqrt(math.sqrt(self.dim_head)) - weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards - weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) - out = weight @ v - - out = out.permute(0, 2, 1, 3).reshape(b, l, -1) - - return self.to_out(out) - - -class Resampler(nn.Module): - def __init__( - self, - dim=1024, - depth=8, - dim_head=64, - heads=16, - num_queries=8, - embedding_dim=768, - output_dim=1024, - ff_mult=4, - ): - super().__init__() - - self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) - - self.proj_in = nn.Linear(embedding_dim, dim) - - self.proj_out = nn.Linear(dim, output_dim) - self.norm_out = nn.LayerNorm(output_dim) - - self.layers = nn.ModuleList([]) - for _ in range(depth): - self.layers.append( - nn.ModuleList( - [ - PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), - FeedForward(dim=dim, mult=ff_mult), - ] - ) - ) - - def forward(self, x): - - latents = self.latents.repeat(x.size(0), 1, 1) - - x = self.proj_in(x) - - for attn, ff in self.layers: - latents = attn(x, latents) + latents - latents = ff(latents) + latents - - latents = self.proj_out(latents) - return self.norm_out(latents) \ No newline at end of file diff --git a/spaces/Xenova/the-tokenizer-playground/assets/worker-6c002022.js b/spaces/Xenova/the-tokenizer-playground/assets/worker-6c002022.js deleted file mode 100644 index f4e6b0f620a0286538e4a2df9265639707c08fc6..0000000000000000000000000000000000000000 --- a/spaces/Xenova/the-tokenizer-playground/assets/worker-6c002022.js +++ /dev/null @@ -1,1790 +0,0 @@ -var fn=Object.defineProperty;var gn=(tt,y,n)=>y in tt?fn(tt,y,{enumerable:!0,configurable:!0,writable:!0,value:n}):tt[y]=n;var jt=(tt,y,n)=>(gn(tt,typeof y!="symbol"?y+"":y,n),n);(function(){var tt;"use strict";function _mergeNamespaces(y,n){return n.forEach(function(u){u&&typeof u!="string"&&!Array.isArray(u)&&Object.keys(u).forEach(function(d){if(d!=="default"&&!(d in y)){var l=Object.getOwnPropertyDescriptor(u,d);Object.defineProperty(y,d,l.get?l:{enumerable:!0,get:function(){return u[d]}})}})}),Object.freeze(y)}function dispatchCallback(y,n){y!==null&&y(n)}function reverseDictionary(y){return Object.fromEntries(Object.entries(y).map(([n,u])=>[u,n]))}function escapeRegExp(y){return y.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}const Callable=class{constructor(){let y=function(...n){return y._call(...n)};return Object.setPrototypeOf(y,new.target.prototype)}_call(...y){throw Error("Must implement _call method in subclass")}};function isTypedArray(y){var n,u,d;return((d=(u=(n=y==null?void 0:y.prototype)==null?void 0:n.__proto__)==null?void 0:u.constructor)==null?void 0:d.name)==="TypedArray"}function isIntegralNumber(y){return Number.isInteger(y)||typeof y=="bigint"}function exists(y){return y!=null}function mergeArrays(...y){return Array.prototype.concat.apply([],y)}var sharp={},ONNX_NODE=Object.freeze({__proto__:null,default:sharp});function getDefaultExportFromCjs(y){return y&&y.__esModule&&Object.prototype.hasOwnProperty.call(y,"default")?y.default:y}function getAugmentedNamespace(y){if(y.__esModule)return y;var n=y.default;if(typeof n=="function"){var u=function d(){return this instanceof d?Reflect.construct(n,arguments,this.constructor):n.apply(this,arguments)};u.prototype=n.prototype}else u={};return Object.defineProperty(u,"__esModule",{value:!0}),Object.keys(y).forEach(function(d){var l=Object.getOwnPropertyDescriptor(y,d);Object.defineProperty(u,d,l.get?l:{enumerable:!0,get:function(){return y[d]}})}),u}var ortWeb_min$1={exports:{}};const backends={},backendsSortedByPriority=[],registerBackend=(y,n,u)=>{if(n&&typeof n.init=="function"&&typeof n.createSessionHandler=="function"){const d=backends[y];if(d===void 0)backends[y]={backend:n,priority:u};else{if(d.priority>u)return;if(d.priority===u&&d.backend!==n)throw new Error(`cannot register backend "${y}" using priority ${u}`)}if(u>=0){const l=backendsSortedByPriority.indexOf(y);l!==-1&&backendsSortedByPriority.splice(l,1);for(let p=0;p