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- spaces/17TheWord/RealESRGAN/scripts/generate_multiscale_DF2K.py +0 -48
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download LibFredo6 54b Learn More About This Plugin Library That Powers Many SketchUp Extensions.md +0 -144
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Global Mapper Trial A Comprehensive Guide to the Best GIS Software.md +0 -31
- spaces/1gistliPinn/ChatGPT4/Examples/Capturix ScanShare V7.06.848 Enterprise Edition-CRD.rar __FULL__.md +0 -6
- spaces/1phancelerku/anime-remove-background/Bulk Download Messenger Photos A Simple and Effective Method.md +0 -167
- spaces/1phancelerku/anime-remove-background/Cmo descargar metal slug 1 2 3 4 5 6 apk en tu PC o Android.md +0 -111
- spaces/1phancelerku/anime-remove-background/Dominoes Gold APK - Play Dominoes with Friends and Earn Money.md +0 -150
- spaces/1phancelerku/anime-remove-background/Download NBA 2K20 for PC Free - SteamUnlocked Edition.md +0 -120
- spaces/1toTree/lora_test/ppdiffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py +0 -128
- spaces/2023Liu2023/bingo/src/pages/api/sydney.ts +0 -62
- spaces/2ndelement/voicevox/make_docs.py +0 -33
- spaces/3B-Group/ConvRe-Leaderboard/src/utils.py +0 -66
- spaces/AIFILMS/generate_human_motion/VQ-Trans/models/smpl.py +0 -97
- spaces/AJRFan/dreambooth-training/convertosd.py +0 -223
- spaces/Ababababababbababa/Arabic_poetry_Sha3bor_mid/app.py +0 -3
- spaces/Abhilashvj/planogram-compliance/utils/segment/metrics.py +0 -220
- spaces/AchyuthGamer/OpenGPT/g4f/Provider/Wewordle.py +0 -65
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateHolyGrail.js +0 -21
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/OverlapSizer.d.ts +0 -109
- spaces/Alican/pixera/util/get_data.py +0 -110
- spaces/Alpaca233/SadTalker/src/face3d/util/skin_mask.py +0 -125
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/xformers.md +0 -36
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_check_copies.py +0 -120
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_score_sde_ve.py +0 -189
- spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py +0 -4
- spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py +0 -17
- spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/ssd300_voc0712.py +0 -69
- spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py +0 -65
- spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py +0 -88
- spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/pseudo_bbox_coder.py +0 -18
- spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py +0 -2
- spaces/Andyrasika/distilbert-base-uncased-finetuned-emotion/app.py +0 -3
- spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/conv_ws.py +0 -148
- spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/furthest_point_sample.py +0 -83
- spaces/Apex-X/Tm/roop/capturer.py +0 -20
- spaces/Astroomx/Mine/Dockerfile +0 -21
- spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/unistring.py +0 -153
- spaces/AutoLLM/AutoAgents/README.md +0 -13
- spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/predictor.py +0 -243
- spaces/BAAI/vid2vid-zero/vid2vid_zero/models/unet_2d_blocks.py +0 -609
- spaces/Banbri/zcvzcv/src/lib/dirtyLLMResponseCleaner.ts +0 -46
- spaces/Benson/text-generation/Examples/Descargar Efecto De Sonido De Bocina.md +0 -86
- spaces/Benson/text-generation/Examples/Descargar Escuchar Msica Apk.md +0 -135
- spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/abc.py +0 -137
- spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/alias.py +0 -78
- spaces/Blackroot/Fancy-Audiogen/app.py +0 -114
- spaces/Bong15/Rewrite/README.md +0 -12
- spaces/CVPR/LIVE/cmake/FindTensorFlow.cmake +0 -34
- spaces/CVPR/LIVE/thrust/thrust/for_each.h +0 -280
- spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/temporary_buffer.h +0 -22
spaces/17TheWord/RealESRGAN/scripts/generate_multiscale_DF2K.py
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import argparse
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import glob
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import os
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from PIL import Image
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def main(args):
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# For DF2K, we consider the following three scales,
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# and the smallest image whose shortest edge is 400
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scale_list = [0.75, 0.5, 1 / 3]
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shortest_edge = 400
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path_list = sorted(glob.glob(os.path.join(args.input, '*')))
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for path in path_list:
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print(path)
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basename = os.path.splitext(os.path.basename(path))[0]
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img = Image.open(path)
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width, height = img.size
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for idx, scale in enumerate(scale_list):
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print(f'\t{scale:.2f}')
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rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
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rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
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# save the smallest image which the shortest edge is 400
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if width < height:
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ratio = height / width
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width = shortest_edge
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height = int(width * ratio)
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else:
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ratio = width / height
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height = shortest_edge
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width = int(height * ratio)
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rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
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rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
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if __name__ == '__main__':
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"""Generate multi-scale versions for GT images with LANCZOS resampling.
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It is now used for DF2K dataset (DIV2K + Flickr 2K)
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
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parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
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args = parser.parse_args()
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os.makedirs(args.output, exist_ok=True)
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main(args)
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download LibFredo6 54b Learn More About This Plugin Library That Powers Many SketchUp Extensions.md
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<h1>**Download LibFredo6 54b**</h1> | <p>If you are a SketchUp user who loves to create amazing 3D models, you might have heard of LibFredo6. It is a plugin library that contains a set of tools and extensions that enhance the functionality and usability of SketchUp. In this article, we will show you how to download and install LibFredo6 54b, the latest version of this plugin library, and how to use its features to create stunning 3D designs.</p>
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<h2>Introduction</h2>
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<h3>What is LibFredo6 and why do you need it?</h3>
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<p>LibFredo6 is a plugin library that was created by Fredo6, a renowned SketchUp developer and community member. It is a collection of scripts that provide various functions and features that are not available in the native SketchUp tools. For example, LibFredo6 allows you to create complex curves, surfaces, shapes, animations, textures, and more.</p>
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<h2>Download LibFredo6 54b</h2><br /><p><b><b>Download</b> 🗹 <a href="https://byltly.com/2uKxGR">https://byltly.com/2uKxGR</a></b></p><br /><br />
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<p>LibFredo6 is not a standalone plugin, but rather a library that supports other plugins and extensions that depend on it. Some of the most popular plugins that require LibFredo6 are:</p>
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<ul>
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<li>FredoScale: A tool that allows you to scale, stretch, twist, bend, and shear your 3D models in any direction.</li>
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<li>RoundCorner: A tool that allows you to round the edges and corners of your 3D models with different profiles and options.</li>
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<li>Curviloft: A tool that allows you to create skins and lofts between contours with smooth transitions.</li>
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<li>HoverSelect: A tool that allows you to select entities by hovering over them with your mouse cursor.</li>
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<li>Animator: A tool that allows you to create animations of your 3D models with keyframes, transitions, scenes, and cameras.</li>
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</ul>
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<p>These are just some examples of the plugins that depend on LibFredo6. There are many more plugins that use this library to provide additional functionality and features for SketchUp users.</p>
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<h3>What are the main features and benefits of LibFredo6?</h3>
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<p>LibFredo6 is a plugin library that offers many features and benefits for SketchUp users. Some of the main ones are:</p>
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<ul>
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<li>It is free and open source. You can download and use LibFredo6 without any cost or license restrictions.</li>
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<li>It is compatible with SketchUp versions from 2017 and above. You can use LibFredo6 with any recent version of SketchUp without any compatibility issues.</li>
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<li>It is easy to install and update. You can download and install LibFredo6 with a few clicks using the SketchUcation Plugin Store or manually by copying the files into your SketchUp plugins folder. You can also update LibFredo6 easily using the built-in updater or by downloading the latest version from the Plugin Store.</li>
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<li>It is customizable and user-friendly. You can access the LibFredo6 settings and preferences from the Window menu in SketchUp. You can adjust various options such as language, colors, icons, tooltips, shortcuts, menus, dialogs, etc. You can also access the documentation and tutorials for each plugin that uses LibFredo6 from the same menu.</li>
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<li>It is reliable and stable. LibFredo6 has been tested and improved over many years by Fredo6 and the SketchUp community. It has a low rate of bugs and errors and a high rate of performance and efficiency.</li>
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</ul>
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<h3>How to download and install LibFredo6 54b?</h3>
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<p>Downloading and installing LibFredo6 54b is very easy. There are two methods you can use:</p>
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<h4>Method 1: Using the SketchUcation Plugin Store</h4>
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<p>The SketchUcation Plugin Store is a website that allows you to browse, download, install, update, and manage SketchUp plugins easily. You can access it from <a href="https://sketchucation.com/pluginstore">https://sketchucation.com/pluginstore</a>.</p>
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<p>How to download LibFredo6 54b for SketchUp<br />
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LibFredo6 54b download link<br />
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Download LibFredo6 54b plugin for free<br />
|
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LibFredo6 54b installation guide<br />
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Download LibFredo6 54b latest version<br />
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| 34 |
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LibFredo6 54b features and benefits<br />
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| 35 |
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Download LibFredo6 54b for Windows<br />
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-
Download LibFredo6 54b for Mac<br />
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LibFredo6 54b compatibility with SketchUp versions<br />
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| 38 |
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Download LibFredo6 54b with license key<br />
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| 39 |
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LibFredo6 54b tutorial and examples<br />
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| 40 |
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Download LibFredo6 54b from official website<br />
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| 41 |
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LibFredo6 54b reviews and ratings<br />
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| 42 |
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Download LibFredo6 54b for SketchUp Pro<br />
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LibFredo6 54b alternatives and comparisons<br />
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Download LibFredo6 54b for SketchUp Make<br />
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LibFredo6 54b troubleshooting and support<br />
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| 46 |
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Download LibFredo6 54b for SketchUp Web<br />
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LibFredo6 54b update and changelog<br />
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| 48 |
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Download LibFredo6 54b for SketchUp Studio<br />
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| 49 |
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LibFredo6 54b FAQs and tips<br />
|
| 50 |
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Download LibFredo6 54b for SketchUp Shop<br />
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| 51 |
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LibFredo6 54b user manual and documentation<br />
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| 52 |
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Download LibFredo6 54b for SketchUp Viewer<br />
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LibFredo6 54b video and audio tutorials<br />
|
| 54 |
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Download LibFredo6 54b for SketchUp Free<br />
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LibFredo6 54b forum and community<br />
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Download LibFredo6 54b for SketchUp Classic<br />
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LibFredo6 54b best practices and recommendations<br />
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Download LibFredo6 54b for SketchUp Education<br />
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LibFredo6 54b feedback and suggestions<br />
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Download LibFredo6 54b for SketchUp Enterprise<br />
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| 61 |
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LibFredo6 54b bugs and issues<br />
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| 62 |
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Download LibFredo6 54b for SketchUp Mobile Viewer<br />
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LibFredo6 54b testimonials and case studies<br />
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| 64 |
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Download LibFredo6 54b for SketchUp AR/VR Viewer<br />
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| 65 |
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LibFredo6 54b advantages and disadvantages<br />
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Download LibFredo6 54b for SketchUp Sefaira<br />
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LibFredo6 54b pricing and plans<br />
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Download LibFredo6 54b for SketchUp Trimble Connect<br />
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LibFredo6 54b requirements and specifications<br />
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| 70 |
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Download LibFredo6 54b for SketchUp LayOut<br />
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LibFredo6 54b demo and trial version<br />
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Download LibFredo6 54b for SketchUp Style Builder<br />
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LibFredo6 54b awards and recognition<br />
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Download LibFredo6 54b for SketchUp Extension Warehouse<br />
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| 75 |
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LibFredo6 54b coupons and discounts<br />
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| 76 |
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Download LibFredo6 54b for SketchUp Warehouse Browser Extension</p>
|
| 77 |
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and install LibFredo6 54b using this method, follow these steps:</p>
|
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<ol>
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<li>Go to <a href="https://sketchucation.com/pluginstore?pln=LibFredo6">https://sketchucation.com/pluginstore?pln=LibFredo6</a></li>
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<li>Click on the Download button next to LibFredo6 54b</li>
|
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<li>Save the file libfredo6_v54b.rbz on your computer</li>
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<li>Open SketchUp</li>
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<li>Go to Window > Extension Manager</li>
|
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<li>Click on Install Extension</li>
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<li>Browse to the file libfredo6_v54b.rbz on your computer</li>
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<li>Click on Open</li>
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<li>Click on Yes when prompted to confirm the installation</li>
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<li>Restart SketchUp</li>
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</ol>
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| 90 |
-
<h4>Method 2: Manual installation</h4>
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<p>If you prefer to install LibFredo6 54b manually, follow these steps:</p>
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<ol>
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<li>Go to <a href="https://sketchucation.com/plugin/903-libfredo6">https://sketchucation.com/plugin/903-libfredo6</a></li>
|
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<li>Click on Download at the bottom of the page</li>
|
| 95 |
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<li>Save the file libfredo6_v54b.zip on your computer</li>
|
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<li>Extract the contents of the file libfredo6_v54b.zip on your computer</li>
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<li>Copy all the files inside the folder libfredo6_v54b into your SketchUp plugins folder (usually located at C:\Users\YourName\AppData\Roaming\SketchUp\SketchUp 2023\SketchUp\Plugins)</li>
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<li>Restart SketchUp</li>
|
| 99 |
-
</ol>
|
| 100 |
-
<h2>How to use LibFredo6 54b</h2>
|
| 101 |
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<p>Once you have downloaded and installed LibFredo6 54b, you can start using it with any plugin or extension that depends on it.</p>
|
| 102 |
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<h3>How to access the LibFredo6 settings and preferences?</h3>
|
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<p>To access the LibFredo6 settings and preferences, go to Window > LibFredo6 Settings... in SketchUp.</p>
|
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<p>You will see a dialog box like this:</p>
|
| 105 |
-
 <p>You can use this dialog box to customize various aspects of LibFredo6 and its plugins. For example, you can:</p>
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<ul>
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<li>Select your preferred language from the drop-down menu at the top left corner.</li>
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<li>Change the colors, icons, tooltips, and shortcuts of the tools and extensions from the tabs at the top.</li>
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<li>Enable or disable the menus, dialogs, and toolbars of the plugins from the tabs at the bottom.</li>
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<li>Check for updates, view documentation, and access tutorials from the buttons at the bottom right corner.</li>
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</ul>
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<p>You can also click on Help > About LibFredo6... in SketchUp to see more information about LibFredo6 and its plugins.</p>
|
| 113 |
-
<h3>How to use the various tools and extensions included in LibFredo6?</h3>
|
| 114 |
-
<p>To use the various tools and extensions included in LibFredo6, you need to activate them from the menus or toolbars in SketchUp. You can also use keyboard shortcuts or context menus to access them.</p>
|
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-
<p>Here is a table that shows some of the most common tools and extensions that use LibFredo6 and how to access them:</p>
|
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-
| Tool or extension | Description | How to access | | --- | --- | --- | | FredoScale | A tool that allows you to scale, stretch, twist, bend, and shear your 3D models in any direction. | Go to Tools > FredoScale or click on its icon in the toolbar. | | RoundCorner | A tool that allows you to round the edges and corners of your 3D models with different profiles and options. | Go to Tools > Fredo Collection > RoundCorner or click on its icon in the toolbar. | | Curviloft | A tool that allows you to create skins and lofts between contours with smooth transitions. | Go to Tools > Fredo Collection > Curviloft or click on its icon in the toolbar. | | HoverSelect | A tool that allows you to select entities by hovering over them with your mouse cursor. | Go to Tools > Fredo Collection > HoverSelect or press Ctrl+H on your keyboard. | | Animator | A tool that allows you to create animations of your 3D models with keyframes, transitions, scenes, and cameras. | Go to Tools > Fredo Collection > Animator or click on its icon in the toolbar. | <p>You can also find more tools and extensions that use LibFredo6 from <a href="https://sketchucation.com/pluginstore?search=libfredo">https://sketchucation.com/pluginstore?search=libfredo</a>.</p>
|
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<h3>How to troubleshoot common issues and errors with LibFredo6?</h3>
|
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<p>If you encounter any issues or errors with LibFredo6 or its plugins, here are some tips that might help you:</p>
|
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<ul>
|
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<li>Make sure you have downloaded and installed the latest version of LibFredo6 and its plugins. You can check for updates from Window > LibFredo6 Settings... > Check Plugins for Update.</li>
|
| 121 |
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<li>Make sure you have enabled all the required plugins and extensions in SketchUp. You can enable or disable them from Window > Extension Manager.</li>
|
| 122 |
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<li>Make sure you have granted all the necessary permissions for LibFredo6 and its plugins. You can grant or revoke permissions from Window > Extension Manager > Settings.</li>
|
| 123 |
-
<li>If you still have problems, you can contact Fredo6 or post your issue on <a href="https://sketchucation.com/forums/viewtopic.php?t=17947">https://sketchucation.com/forums/viewtopic.php?t=17947</a>. You can also send a trace log file from Window > LibFredo6 Settings... > Trace Logging.</li>
|
| 124 |
-
</ul>
|
| 125 |
-
<h2>Conclusion</h2>
|
| 126 |
-
<p>In this article, we have shown you how to download and install LibFredo6 54b, a plugin library that contains a set of tools and extensions that enhance the functionality and usability of SketchUp. We have also shown you how to use its features and benefits to create stunning 3D designs.</p>
|
| 127 |
-
<p>We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to share them with us in the comments section below. We would love to hear from you!</p>
|
| 128 |
-
<p>Happy Sketching!</p>
|
| 129 |
-
<h2>Frequently Asked Questions</h2>
|
| 130 |
-
<ol>
|
| 131 |
-
<li><strong>What is LibFredo6?</strong></li>
|
| 132 |
-
<p>LibFredo6 is a plugin library that contains a set of tools and extensions that enhance the functionality and usability of SketchUp.</p>
|
| 133 |
-
<li><strong>Why do I need LibFredo6?</strong></li>
|
| 134 |
-
<p>You need LibFredo6 if you want to use any of the plugins or extensions that depend on it. For example, FredoScale, RoundCorner, Curviloft, HoverSelect, Animator, etc.</p>
|
| 135 |
-
<li><strong>How do I download and install LibFredo6 54b?</strong></li>
|
| 136 |
-
the SketchUcation Plugin Store or manually by copying the files into your SketchUp plugins folder.</p>
|
| 137 |
-
<li><strong>How do I use LibFredo6 54b?</strong></li>
|
| 138 |
-
<p>You can use LibFredo6 54b with any plugin or extension that depends on it. You can access them from the menus or toolbars in SketchUp. You can also customize the settings and preferences of LibFredo6 and its plugins from Window > LibFredo6 Settings...</p>
|
| 139 |
-
<li><strong>How do I troubleshoot common issues and errors with LibFredo6?</strong></li>
|
| 140 |
-
<p>If you encounter any issues or errors with LibFredo6 or its plugins, you can check for updates, enable or disable plugins, grant or revoke permissions, contact Fredo6 or post your issue on the forum, or send a trace log file.</p>
|
| 141 |
-
</ol>
|
| 142 |
-
</p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Global Mapper Trial A Comprehensive Guide to the Best GIS Software.md
DELETED
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<h1>Global Mapper Trial: A Must-Have GIS Software for Spatial Data</h1>
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<p>Global Mapper is a powerful and versatile GIS software that supports more than 300 spatial data formats. It offers a complete suite of data creation and editing tools, as well as cutting-edge 3D visualization and analysis capabilities. Whether you are a professional mapper, a student, or a hobbyist, Global Mapper can help you with your spatial data needs.</p>
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<h2>global mapper trial</h2><br /><p><b><b>DOWNLOAD</b> ===> <a href="https://byltly.com/2uKwXF">https://byltly.com/2uKwXF</a></b></p><br /><br />
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<p>If you want to try Global Mapper for yourself, you can download a free trial version from the official website of Blue Marble Geographics. The trial version allows you to evaluate all the features of the software for 14 days, without any limitations. You can also request a demo or contact a sales representative to learn more about the software and its pricing options.</p>
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<p>Global Mapper is compatible with Windows 10 and 11 (64-bit version), and Windows Server 2012/2016/2019/2022. It requires 8 GB of RAM and 600 MB of hard drive space for the installation. The software is also available in Chinese, French, German, Italian, Japanese, Korean, Polish, Spanish, Portuguese, and Turkish versions.</p>
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<p>Global Mapper is widely used by geospatial professionals, researchers, educators, and enthusiasts around the world. It has been praised for its intuitive user interface, logical layout, and unmatched technical support. Some of the companies that use Global Mapper include NASA, USGS, NOAA, FEMA, Google, Apple, Microsoft, and many more.</p>
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| 8 |
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<p>Global Mapper is constantly updated with new features and improvements to meet the evolving needs of its users. The latest version of Global Mapper is 24.1, which was released in October 2022. Some of the new features in this version include:</p>
|
| 9 |
-
<p></p>
|
| 10 |
-
<ul>
|
| 11 |
-
<li>A new Global Mapper Pro edition that includes advanced tools for point cloud processing, raster analysis, terrain modeling, and more.</li>
|
| 12 |
-
<li>A new option to export vector data to Google Earth KML/KMZ format with embedded attributes and styles.</li>
|
| 13 |
-
<li>A new tool to create custom contour lines from point cloud or raster data.</li>
|
| 14 |
-
<li>A new option to import and export GeoPackage files.</li>
|
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-
<li>A new tool to calculate viewshed analysis from multiple observer points.</li>
|
| 16 |
-
<li>A new tool to create slope maps from elevation data.</li>
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| 17 |
-
<li>And many more enhancements and bug fixes.</li>
|
| 18 |
-
</ul>
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| 19 |
-
<p>If you are looking for a reliable, affordable, and easy-to-use GIS software that can handle any spatial data challenge, look no further than Global Mapper. Download the trial version today and see for yourself why Global Mapper is a must-have GIS software for anyone who works with maps or spatial data.</p><p>Global Mapper is not only a GIS software, but also a data converter, a map editor, a projection tool, a 3D viewer, and much more. It can handle any type of spatial data, from vector to raster, from elevation to imagery, from LiDAR to GPS. It can also connect to online data sources, such as WMS, WFS, WCS, and Tile Services. With Global Mapper, you can access, view, edit, analyze, and export any spatial data with ease.</p>
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| 20 |
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<p>One of the most impressive features of Global Mapper is its 3D functionality. You can view your data in 3D mode, create realistic terrain models, drape vector or raster layers over the terrain, perform 3D measurements and calculations, and export 3D models to various formats. You can also create fly-through animations and videos of your 3D scenes. Global Mapper supports various 3D formats, such as COLLADA, STL, OBJ, VRML, and more.</p>
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| 21 |
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<p>Another remarkable feature of Global Mapper is its point cloud processing capability. You can import point cloud data from various sources, such as LiDAR scanners, drones, photogrammetry software, and more. You can then classify, filter, crop, edit, colorize, and extract features from your point cloud data. You can also generate raster or vector layers from your point cloud data, such as elevation grids, contours, buildings, trees, power lines, and more.</p>
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| 22 |
-
<p>Global Mapper is not only a powerful GIS software but also an affordable one. You can purchase a single-user license of Global Mapper for $549 USD. If you need more advanced tools for point cloud processing and raster analysis, you can upgrade to Global Mapper Pro for $999 USD. You can also purchase optional modules for specific purposes, such as LiDAR Module ($499 USD), Pixels-to-Points Module ($299 USD), and Georeferencing Module ($199 USD). All licenses include one year of maintenance and support.</p>
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| 23 |
-
<p>If you are still not convinced that Global Mapper is the best GIS software for you, you can read some testimonials from satisfied customers. Here are some examples:</p>
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| 24 |
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<blockquote>
|
| 25 |
-
<p>"Global Mapper is an affordable and easy-to-use GIS application that offers access to an unparalleled variety of spatial datasets and provides just the right level of functionality to satisfy both experienced GIS professionals and beginning users." - Equator Graphics</p>
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<p>"Global Mapper has been an essential tool for our company for over 10 years. It allows us to work with virtually any type of geospatial data in a fast and efficient way. It has saved us countless hours of work and has enabled us to deliver high-quality products to our clients." - GeoSolutions Consulting</p>
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| 27 |
-
<p>"Global Mapper is the Swiss Army Knife of GIS software. It can do anything you need it to do with spatial data. It is easy to use, reliable, and constantly updated with new features. It is by far the best value for money in the GIS market." - TerraImage USA</p>
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-
</blockquote>
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| 29 |
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<p>Don't wait any longer. Download the trial version of Global Mapper today and discover why it is the ultimate GIS software for spatial data.</p> ddb901b051<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Capturix ScanShare V7.06.848 Enterprise Edition-CRD.rar __FULL__.md
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<h2>Capturix ScanShare v7.06.848 Enterprise Edition-CRD.rar</h2><br /><p><b><b>DOWNLOAD</b> ☆ <a href="https://imgfil.com/2uxYYN">https://imgfil.com/2uxYYN</a></b></p><br /><br />
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<p></p>
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spaces/1phancelerku/anime-remove-background/Bulk Download Messenger Photos A Simple and Effective Method.md
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| 1 |
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<br />
|
| 2 |
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<h1>How to Bulk Download Messenger Photos</h1>
|
| 3 |
-
<p>If you frequently use Facebook Messenger, you know what a terrific platform it is for sharing photos. Saving images from your Messenger threads can be a great way to collect memories. But what if you want to download all the photos from a messenger conversation at once? Is there a way to do that without having to save each photo individually?</p>
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<p>In this article, we will show you how to bulk download messenger photos on your PC, iPhone, or Android device. Whether you want to backup your photos, transfer them to another device, or print them out, we have got you covered. Let's get started!</p>
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<h2>bulk download messenger photos</h2><br /><p><b><b>DOWNLOAD</b> ✔ <a href="https://jinyurl.com/2uNR2t">https://jinyurl.com/2uNR2t</a></b></p><br /><br />
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<h2>Introduction</h2>
|
| 7 |
-
<h3>Why you might want to download all photos from a messenger conversation</h3>
|
| 8 |
-
<p>There are many reasons why you might want to download all photos from a messenger conversation. Here are some of them:</p>
|
| 9 |
-
<ul>
|
| 10 |
-
<li>You want to keep a copy of your photos in case you lose access to your Facebook account or Messenger app.</li>
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| 11 |
-
<li>You want to free up some space on your device by deleting the Messenger app or clearing its cache.</li>
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| 12 |
-
<li>You want to share your photos with someone who is not on Facebook or Messenger.</li>
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-
<li>You want to edit, organize, or print your photos using another app or software.</li>
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| 14 |
-
<li>You want to create a photo album, collage, or slideshow of your memories.</li>
|
| 15 |
-
</ul>
|
| 16 |
-
<h3>What you need to download all photos from a messenger conversation</h3>
|
| 17 |
-
<p>Before we dive into the methods of downloading all photos from a messenger conversation, here are some things you need:</p>
|
| 18 |
-
<ul>
|
| 19 |
-
<li>A Facebook account and a Messenger app. You can use either the web version or the mobile version of Messenger, depending on your device.</li>
|
| 20 |
-
<li>A stable internet connection. Downloading multiple photos can take some time and bandwidth, so make sure you have a good connection.</li>
|
| 21 |
-
<li>A device with enough storage space. Depending on how many photos you have in your conversation, you might need some extra space on your device or an external storage device.</li>
|
| 22 |
-
<li>An email address. If you choose to download your photos in bulk from Facebook, you will need an email address to receive the download link.</li>
|
| 23 |
-
</ul>
|
| 24 |
-
<h2>How to download all photos from a messenger conversation on a PC</h2>
|
| 25 |
-
<h3>Method 1: Download photos manually</h3>
|
| 26 |
-
<p>If you only have a few photos in your conversation, you can download them manually one by one. Here are the steps to do that:</p>
|
| 27 |
-
<p>How to download all photos from a Messenger conversation<br />
|
| 28 |
-
Save pictures on Facebook Messenger on a PC or Mac<br />
|
| 29 |
-
Batch download photos you sent through Facebook Messenger<br />
|
| 30 |
-
Download your information from Facebook Messenger<br />
|
| 31 |
-
Download multiple images from a Messenger chat<br />
|
| 32 |
-
Save all media files from a Messenger thread<br />
|
| 33 |
-
Download photos from Messenger to your iPhone or Android<br />
|
| 34 |
-
Use DownAlbum extension to download photos from Messenger<br />
|
| 35 |
-
Download images from Messenger website<br />
|
| 36 |
-
Download photos from Messenger to your computer or laptop<br />
|
| 37 |
-
Save photos from Messenger to your gallery or camera roll<br />
|
| 38 |
-
Download high-quality photos from Messenger<br />
|
| 39 |
-
Download photos from group chats on Messenger<br />
|
| 40 |
-
Download photos from secret conversations on Messenger<br />
|
| 41 |
-
Download photos from archived chats on Messenger<br />
|
| 42 |
-
Download photos from deleted chats on Messenger<br />
|
| 43 |
-
Download photos from Messenger without notification<br />
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| 44 |
-
Download photos from Messenger without opening them<br />
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| 45 |
-
Download photos from Messenger without saving them<br />
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| 46 |
-
Download photos from Messenger without internet connection<br />
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| 47 |
-
Download GIFs and videos from Messenger<br />
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| 48 |
-
Download stickers and emojis from Messenger<br />
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| 49 |
-
Download voice messages and audio files from Messenger<br />
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| 50 |
-
Download documents and attachments from Messenger<br />
|
| 51 |
-
Download links and web pages from Messenger<br />
|
| 52 |
-
Backup your photos from Messenger to cloud storage<br />
|
| 53 |
-
Transfer your photos from Messenger to another device<br />
|
| 54 |
-
Sync your photos from Messenger to your social media accounts<br />
|
| 55 |
-
Edit your photos from Messenger before downloading them<br />
|
| 56 |
-
Compress your photos from Messenger to save space<br />
|
| 57 |
-
Organize your photos from Messenger by date or sender<br />
|
| 58 |
-
Share your photos from Messenger with other apps or contacts<br />
|
| 59 |
-
Print your photos from Messenger directly or online<br />
|
| 60 |
-
Recover your photos from Messenger if you lost them or deleted them accidentally<br />
|
| 61 |
-
Protect your photos from Messenger with password or encryption<br />
|
| 62 |
-
Delete your photos from Messenger after downloading them<br />
|
| 63 |
-
Manage your photo settings on Messenger app or website<br />
|
| 64 |
-
Turn on or off auto-download of photos on Messenger app or website <br />
|
| 65 |
-
Troubleshoot photo download issues on Messenger app or website <br />
|
| 66 |
-
Contact Facebook support for photo download problems on Messenger app or website</p>
|
| 67 |
-
<h4>Step 1: Tap on the sender's name</h4>
|
| 68 |
-
<p>Open your Messenger app on your PC and select the conversation that contains the photos you want to download. Then, tap on the sender's name at the top of the chat window. This will open a panel with more options.</p>
|
| 69 |
-
<h4>Step 2: Scroll to the images</h4>
|
| 70 |
-
<p>In the panel, scroll down to the section that says "Shared Photos". Here, you will see all the photos that have been exchanged in the conversation. You can use the arrows to navigate through them.</p>
|
| 71 |
-
<h4>Step 3: Click on the photo and then click download</h4>
|
| 72 |
-
<p>Click on the photo that you want to download and it will open in a larger view. Then, click on the download icon at the bottom right corner of the photo. This will prompt you to choose a location on your PC where you want to save the photo. Repeat this process for each photo you want to download.</p>
|
| 73 |
-
<h3>Method 2: Download photos in bulk</h3>
|
| 74 |
-
<p>If you have a lot of photos in your conversation, downloading them manually can be tedious and time-consuming. Fortunately, there is a way to download all your photos in bulk from Facebook. Here are the steps to do that:</p>
|
| 75 |
-
<h4>Step 1: Go to the Messenger website and open the menu</h4>
|
| 76 |
-
<p>Open your web browser and go to <a href="">https://www.messenger.com/</a>. Log in with your Facebook account if you haven't already. Then, click on the menu icon at the top left corner of the screen. This will open a sidebar with more options.</p>
|
| 77 |
-
<h4>Step 2: Go to your Facebook information and request a download</h4>
|
| 78 |
-
<p>In the sidebar, click on "Settings". Then, click on "Your Facebook Information". This will take you to a page where you can access and manage your Facebook data. Here, click on "Download Your Information". This will allow you to request a copy of your Facebook data, including your Messenger photos.</p>
|
| 79 |
-
<p>On the next page, you can select what data you want to download. You can choose the date range, format, and quality of your download. To download only your Messenger photos, uncheck all the boxes except for "Messages". Then, click on "Create File". This will start processing your request.</p>
|
| 80 |
-
<h4>Step 3: Open the email and download the files</h4>
|
| 81 |
-
<p>Once your request is ready, Facebook will send you an email with a link to download your files. Open the email and click on the link. This will take you back to the Download Your Information page. Here, click on "Download" next to your file. You might need to enter your password again for security reasons.</p>
|
| 82 |
-
<p>This will download a ZIP file containing all your Messenger data, including your photos. To access your photos, extract the ZIP file and open the folder named "messages". Inside this folder, you will find subfolders for each of your conversations. Each subfolder will contain all the photos from that conversation. You can then copy or move these photos to any location on your PC.</p>
|
| 83 |
-
<h2>How to download all photos from a messenger conversation on an iPhone</h2>
|
| 84 |
-
<h3>Method 1: Download photos manually</h3>
|
| 85 |
-
<p>If you only have a few photos in your conversation, you can download them manually one by one. Here are the steps to do that:</p>
|
| 86 |
-
<h4>Step 1: Open your Messenger app and locate the image</h4>
|
| 87 |
-
<p>Open your Messenger app on your iPhone and select the conversation that contains the photos you want to download. Then, scroll through the chat until you find the image you want to save.</p>
|
| 88 |
-
<h4>Step 2: Long-press on the image and click save</h4>
|
| 89 |
-
<p>Long-press on the image until a menu pops up. Then, tap on "Save". This will save the image to your camera roll. Repeat this process for each image you want to download.</p>
|
| 90 |
-
<h3>Method 2: Download multiple photos simultaneously</h3>
|
| 91 |
-
<p>If you have a lot of photos in your conversation, downloading them manually can be tedious and time-consuming. Fortunately, there is a way to download multiple photos simultaneously from Messenger. Here are the steps to do that:</p>
|
| 92 |
-
<h4>Step 1: Open the conversation thread and tap on the sender's name</h4>
|
| 93 |
-
<p>Open your Messenger app on your iPhone and select the conversation that contains the photos you want to download. Then, tap on the sender's name at the top of the chat window. This will open a panel with more options.</p>
|
| 94 |
-
<h4>Step 2: Scroll down to more actions and tap view photos & videos</h4>
|
| 95 |
-
<p>In the panel, scroll down to the section that says "More Actions". Here, you will see an option to "View Photos & Videos". Tap on it. This will show you all the photos and videos that have been exchanged in the conversation.</p>
|
| 96 |
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<h4>Step 3: Select each image and tap more then save</h4>
|
| 97 |
-
<p>To select multiple images, tap and hold on one image until a checkmark appears. Then, tap on other images you want to select. You can also tap on "Select All" at the top right corner to select all images at once. Once you have selected the images you want to download, tap on "More" at the bottom right corner. Then, tap on "Save". This will save all the selected images to your camera roll.</p>
|
| 98 |
-
<h2>How to download all photos from a messenger conversation on an Android device</h2>
|
| 99 |
-
<h3>Method 1: Download photos manually</h3>
|
| 100 |
-
<p>If you only have a few photos in your conversation, you can download them manually one by one. Here are the steps to do that:</p>
|
| 101 |
-
<h4>Step 1: Open your Messenger app and select the conversation</h4>
|
| 102 |
-
<p>Open your Messenger app on your Android device and select the conversation that contains the photos you want to download. Then, scroll through the chat until you find the image you want to save.</p>
|
| 103 |
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<h4>Step 2: Tap and hold on the image and click save to device</h4>
|
| 104 |
-
<p>Tap and hold on the image until a menu pops up. Then, tap on "Save to Device". This will save the image to your gallery. Repeat this process for each image you want to download.</p>
|
| 105 |
-
<h3>Method 2: Download photos in bulk</h3>
|
| 106 |
-
<p>If you have a lot of photos in your conversation, downloading them manually can be tedious and time-consuming. Fortunately, there is a way to download multiple photos in bulk from Messenger. Here are the steps to do that:</p>
|
| 107 |
-
<h4>Step 1: Open the Messenger app and select the conversation</h4>
|
| 108 |
-
<p>Open your Messenger app on your Android device and select the conversation that contains the photos you want to download.</p>
|
| 109 |
-
<h4>Step 2: Tap on the i icon at the top right corner</h4>
|
| 110 |
-
<p>Tap on the i icon at the top right corner of the chat window. This will open a panel with more options.</p>
|
| 111 |
-
<h4>Step 3: Tap on shared media and select all images</h4>
|
| 112 |
-
<p>In the panel, tap on "Shared Media". This will show you all the media files that have been exchanged in the conversation. To select all images, tap on "Select All" at the top right corner.</p>
|
| 113 |
-
<h4>Step 4: Tap on the three dots icon at the top right corner and click save to device</h4>
|
| 114 |
-
<p>Once you have selected all images, tap on the three dots icon at the top right corner of the screen. Then, tap on "Save to Device". This will save all the selected images to your gallery.</p>
|
| 115 |
-
<h2>Conclusion</h2>
|
| 116 |
-
<h3>Summary of the main points</h3>
|
| 117 |
-
<p>In this article, we have shown you how to bulk download messenger photos on your PC, iPhone, or Android device. You can use either manual or bulk methods depending on how many photos you have in your conversation. Downloading all your photos from a messenger conversation can be a great way to backup your memories, share them with others, or use them for other purposes.</p>
|
| 118 |
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<h3>Call to action</h3>
|
| 119 |
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<p>We hope you found this article helpful and informative. If you did, please share it with your friends and family who might also want to download their messenger photos. Also, let us know in the comments below if you have any questions or feedback about this topic. Thank you for reading!</p>
|
| 120 |
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<h2>Frequently Asked Questions</h2>
|
| 121 |
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<ul>
|
| 122 |
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<li><b>Q: How can I delete all photos from a messenger conversation?</b></li>
|
| 123 |
-
<li>A: If you want to delete all photos from a messenger conversation, you can follow these steps: <ul>
|
| 124 |
-
<li>Open your Messenger app and select the conversation that contains the photos you want to delete.</li>
|
| 125 |
-
<li>Tap on the sender's name at the top of the chat window.</li>
|
| 126 |
-
<li>Scroll down to "Shared Photos" and tap on it.</li>
|
| 127 |
-
<li>Select each photo you want to delete and tap on "Delete" at [user]( the bottom of the screen.</li>
|
| 128 |
-
</ul>
|
| 129 |
-
</li>
|
| 130 |
-
<li><b>Q: How can I download all photos from multiple messenger conversations at once?</b></li>
|
| 131 |
-
<li>A: If you want to download all photos from multiple messenger conversations at once, you can use the bulk method on your PC. Here are the steps to do that: <ul>
|
| 132 |
-
<li>Go to the Messenger website and open the menu.</li>
|
| 133 |
-
<li>Go to your Facebook information and request a download.</li>
|
| 134 |
-
<li>Select the date range and format of your download.</li>
|
| 135 |
-
<li>Check the box next to "Messages" and uncheck all other boxes.</li>
|
| 136 |
-
<li>Click on "Create File" and wait for your request to be ready.</li>
|
| 137 |
-
<li>Open the email and download the files.</li>
|
| 138 |
-
</ul>
|
| 139 |
-
This will download a ZIP file containing all your Messenger data, including photos from all your conversations.</li>
|
| 140 |
-
<li><b>Q: How can I view all photos from a messenger conversation without downloading them?</b></li>
|
| 141 |
-
<li>A: If you want to view all photos from a messenger conversation without downloading them, you can use these steps: <ul>
|
| 142 |
-
<li>Open your Messenger app and select the conversation that contains the photos you want to view.</li>
|
| 143 |
-
<li>Tap on the sender's name at the top of the chat window.</li>
|
| 144 |
-
<li>Scroll down to "Shared Photos" and tap on it.</li>
|
| 145 |
-
<li>Swipe left or right to view all the photos in the conversation.</li>
|
| 146 |
-
</ul>
|
| 147 |
-
</li>
|
| 148 |
-
<li><b>Q: How can I change the quality of the photos I download from Messenger?</b></li>
|
| 149 |
-
<li>A: If you want to change the quality of the photos you download from Messenger, you can use these steps: <ul>
|
| 150 |
-
<li>Go to the Messenger website and open the menu.</li>
|
| 151 |
-
<li>Go to your Facebook information and request a download.</li>
|
| 152 |
-
<li>Select the date range and format of your download.</li>
|
| 153 |
-
<li>Check the box next to "Messages" and uncheck all other boxes.</li>
|
| 154 |
-
<li>Click on "Media Quality" and choose between high, medium, or low quality.</li>
|
| 155 |
-
<li>Click on "Create File" and wait for your request to be ready.</li>
|
| 156 |
-
<li>Open the email and download the files.</li>
|
| 157 |
-
</ul>
|
| 158 |
-
This will affect the size and resolution of the photos you download from Messenger.</li>
|
| 159 |
-
<li><b>Q: How can I stop Messenger from automatically saving photos to my device?</b></li>
|
| 160 |
-
<li>A: If you want to stop Messenger from automatically saving photos to your device, you can use these steps: <ul>
|
| 161 |
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<li>Open your Messenger app and tap on your profile picture at the top left corner of the screen.</li>
|
| 162 |
-
<li>Scroll down to "Photos & Media" and tap on it.</li>
|
| 163 |
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<li>Toggle off the switch next to "Save Photos".</li>
|
| 164 |
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</ul>
|
| 165 |
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This will prevent Messenger from saving photos to your device unless you manually save them.</li></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Cmo descargar metal slug 1 2 3 4 5 6 apk en tu PC o Android.md
DELETED
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@@ -1,111 +0,0 @@
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| 2 |
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<h1>Descargar Metal Slug 1 2 3 4 5 6 APK: How to Play the Classic Run and Gun Games on Your Android Device</h1>
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<p>If you are a fan of retro arcade games, you probably have heard of <strong>Metal Slug</strong>, a series of run and gun video games created by SNK. Metal Slug games are known for their fast-paced action, humorous graphics, and addictive gameplay. They have been released on various platforms such as Neo Geo, PlayStation, Xbox, Nintendo DS, and more.</p>
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<h2>descargar metal slug 1 2 3 4 5 6 apk</h2><br /><p><b><b>Download File</b> ► <a href="https://jinyurl.com/2uNR64">https://jinyurl.com/2uNR64</a></b></p><br /><br />
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<p>But did you know that you can also play Metal Slug games on your Android device? Yes, you can enjoy these classic games on your smartphone or tablet with just a few steps. In this article, we will show you how to download and install <strong>Metal Slug APK</strong>, which is a file that contains all six main games in the series: Metal Slug 1, 2, X, 3, 4, and 5. We will also give you an overview of each game and some tips and tricks for playing them.</p>
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<p>So what are you waiting for? Let's get started!</p>
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<h2>Metal Slug Series Overview</h2>
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<p>Metal Slug is a series of run and gun video games that started in 1996 with <em>Metal Slug: Super Vehicle-001</em>. The games follow the adventures of the Peregrine Falcon Squad, a group of elite soldiers who fight against various enemies such as rebels, aliens, zombies, mummies, and more. The games are famous for their cartoonish graphics, humorous animations, explosive sound effects, and diverse weapons and vehicles.</p>
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| 9 |
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<p>Here is a brief overview of each game in the series:</p>
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| 10 |
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<p><em>Metal Slug</em> was released in 1996 for Neo Geo arcade machines and home consoles. It was also ported to other platforms such as Sega Saturn, PlayStation, and PC. It introduced the main characters of the series: Marco Rossi, Tarma Roving, General Morden, and Allen O'Neil. The game has six stages that take place in various locations such as forests, deserts, snowfields, and military bases. The game features a variety of weapons such as pistols, machine guns, rocket launchers, grenades, and the iconic Metal Slug tank. The game also has hidden items and prisoners of war that can be rescued for extra points and bonuses.</p>
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<h3>Metal Slug 2 / X</h3>
|
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<p><em>Metal Slug 2</em> was released in 1998 for Neo Geo arcade machines and home consoles. It was also ported to other platforms such as PlayStation, PC, and iOS. It added two new playable characters: Eri Kasamoto and Fio Germi. The game has six stages that take place in new locations such as ancient ruins, Arabian towns, alien spaceships, and pyramids. The game features new weapons such as lasers, flame shots, iron lizards, and enemy chasers. The game also introduces new enemies such as mummies, aliens, and mutants. The game also has new vehicles such as camels, planes, and submarines.</p>
|
| 64 |
-
<p><em>Metal Slug X</em> was released in 1999 for Neo Geo arcade machines and home consoles. It was also ported to other platforms such as PlayStation, PC, iOS, and Android. It is an improved version of Metal Slug 2 that fixes some of the issues of the original game such as slowdowns and glitches. It also changes some of the stage layouts, enemy placements, weapon drops, and boss battles. It also adds some new features such as time attack mode, combat school mode, and secret paths.</p>
|
| 65 |
-
<h3>Metal Slug 3</h3>
|
| 66 |
-
<p><em>Metal Slug 3</em> was released in 2000 for Neo Geo arcade machines and home consoles. It was also ported to other platforms such as PlayStation 2, Xbox, PC, iOS, Android, and Nintendo Switch. It is considered by many fans to be the best game in the series due to its variety and replay value. The game has five stages that take place in diverse locations such as jungles, oceans, caves, factories, and outer space. The game features new weapons such as shotguns, homing missiles, dual machine guns, and satellite lasers. The game also introduces new enemies such as zombies, giant crabs, yetis, and martians. The game also has new vehicles such as elephants, ostriches, and helicopters. The game also has branching paths that lead to different endings and bonus stages.</p>
|
| 67 |
-
<h3>Metal Slug 4</h3>
|
| 68 |
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<p><em>Metal Slug 4</em> was released in 2002 for Neo Geo arcade machines and home consoles. It was also ported to other platforms such as PlayStation 2, Xbox, PC, and Nintendo Switch. It replaced Eri and Tarma with two new playable characters: Nadia Cassel and Trevor Spacey. The game has six stages that take place in urban settings such as cities, subways, airports, and military bases. The game features new weapons such as dual pistols, thunder shots, and landmines. The game also introduces new enemies such as cyborgs, robots, and hackers. The game also has new vehicles such as motorcycles, trucks, and tanks.</p>
|
| 69 |
-
<h3>Metal Slug 5</h3>
|
| 70 |
-
<p><em>Metal Slug 5</em> was released in 2003 for Neo Geo arcade machines and home consoles. It was also ported to other platforms such as PlayStation 2, Xbox, PC, and Nintendo Switch. It brought back Eri and Tarma as playable characters along with Marco and Fio. The game has six stages that take place in exotic locations such as jungles, waterfalls, ancient ruins, and underground caves. The game features new weapons such as flame whips, grenade launchers, and laser rifles. The game also introduces new enemies such as masked soldiers, ninjas, and giant worms. The game also has new vehicles such as boats, jet skis, and slides.</p>
|
| 71 |
-
<h3>Metal Slug 6</h3>
|
| 72 |
-
<p><em>Metal Slug 6</em> was released in 2006 for Atomiswave arcade machines and PlayStation 2. It was also ported to other platforms such as PC and Nintendo Wii. It added two new playable characters: Ralf Jones and Clark Still from the <em>King of Fighters</em> and <em>Ikari Warriors</em> series. The game has seven stages that take place in futuristic settings such as space stations, moon bases, and alien planets. The game features new weapons such as machine guns, flame throwers, and rocket launchers. The game also introduces new enemies such as clones, mutants, and aliens. The game also has new vehicles such as mechs, hovercrafts, and spaceships.</p>
|
| 73 |
-
<h2>How to Download and Install Metal Slug APK on Android</h2>
|
| 74 |
-
<p>Now that you have a brief idea of what each Metal Slug game is about, you might be wondering how to play them on your Android device. Well, it's not that hard if you follow these simple steps:</p>
|
| 75 |
-
<h3>Download a PPSSPP emulator and a file manager app</h3>
|
| 76 |
-
<p>The first thing you need to do is to download a PPSSPP emulator and a file manager app on your Android device. A PPSSPP emulator is a software that allows you to run PlayStation Portable games on your device. A file manager app is a software that allows you to manage your files on your device.</p>
|
| 77 |
-
<p>You can download PPSSPP from Google Play Store or from its official website at <a href="">https://www.ppsspp.org/</a>. You can download a file manager app like ZArchiver or ES File Explorer from Google Play Store or from their official websites at <a href="">https://zarchiver.en.softonic.com/android</a> or <a href="">https://es-file-explorer.en.softonic.com/android</a>.</p>
|
| 78 |
-
<p>Once you have downloaded both apps, install them on your device by following the instructions on the screen.</p>
|
| 79 |
-
<h3>Download the Metal Slug ISO files from a trusted source</h3>
|
| 80 |
-
<p>The next thing you need to do is to download the Metal Slug ISO files from a trusted source. An ISO file is a file that contains the data of a disc image. In this case, you need the ISO files of the Metal Slug games that were released for PlayStation Portable.</p>
|
| 81 |
-
<p>You can find and download the ISO files for each Metal Slug game from a reliable website or torrent. Some of the websites that offer these files are <a href="">https://www.emuparadise.me/</a>, <a href="">https://www.freeroms.com/</a>, or <a href="">https://www.coolrom.com/</a>. Some of the torrents that offer these files are <a href="">https://thepiratebay.org/</a>, <a href="">https://1337x.to/</a>, or <a href="">https://rarbg.to/</a>.</p>
|
| 82 |
-
<p>Make sure you check the file size and format of the downloaded files before opening them. The ISO files should be around 200 MB to 500 MB in size and have the .iso extension. If the files are compressed in ZIP or RAR format, you need to extract them using the file manager app.</p>
|
| 83 |
-
<h3>Load the Metal Slug ISO files on PPSSPP and start playing</h3>
|
| 84 |
-
<p>The final thing you need to do is to load the Metal Slug ISO files on PPSSPP and start playing. To do this, you need to open PPSSPP and locate the folder where the ISO files are stored using the file manager app. You can create a separate folder for the Metal Slug games on your device's internal storage or external SD card for easier access.</p>
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| 85 |
-
<p>Once you have found the folder, select and load the desired Metal Slug game on PPSSPP. You can adjust the settings of the emulator such as graphics, sound, controls, and performance according to your preference. You can also save and load your progress using the save states feature of PPSSPP.</p>
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| 86 |
-
<p>To play the game, you can use the virtual buttons on the screen or connect a controller to your device via Bluetooth or USB. You can also play with your friends using the multiplayer mode of PPSSPP. You can either join an online server or create a local network with your friends using Wi-Fi or hotspot.</p>
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| 87 |
-
<h2>Conclusion</h2>
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| 88 |
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<p>Playing Metal Slug games on your Android device is a great way to relive the nostalgia of these classic run and gun games. You can enjoy the fast-paced action, humorous graphics, and addictive gameplay of these games anytime and anywhere with just a few steps. All you need is a PPSSPP emulator, a file manager app, and the Metal Slug ISO files.</p>
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| 89 |
-
<p>Here are some tips and tricks for playing Metal Slug games on your Android device:</p>
|
| 90 |
-
<ul>
|
| 91 |
-
<li>Use different weapons and vehicles to deal with different enemies and situations. Don't be afraid to experiment with different combinations.</li>
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<li>Rescue as many prisoners of war as possible to get extra points and bonuses. Some of them may also give you special items or weapons.</li>
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<li>Look for hidden items and secrets in each stage. Some of them may reveal new paths, modes, or characters.</li>
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<li>Use cheats if you want to have some fun or challenge yourself. Some of the cheats include unlimited ammo, invincibility, level select, and more.</li>
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<li>Have fun and don't give up. Metal Slug games are known for their difficulty and unpredictability. But they are also rewarding and satisfying once you complete them.</li>
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| 96 |
-
</ul>
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| 97 |
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<p>We hope this article has helped you learn how to download and install Metal Slug APK on your Android device. If you have any feedback or questions, please feel free to leave a comment below or contact us for more information. Thank you for reading!</p>
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<h2>Frequently Asked Questions</h2>
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| 99 |
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<p>Here are some of the frequently asked questions about Metal Slug APK:</p>
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<h4>Q: Is Metal Slug APK safe to download?</h4>
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<p>A: Yes, as long as you download it from a trusted source and scan it with an antivirus app before opening it. However, we do not endorse or promote any illegal downloading or piracy of these games. Please support the original developers by buying their games from official sources.</p>
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<h4>Q: Is Metal Slug APK free to download?</h4>
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<p>A: Yes, most of the websites or torrents that offer these files do not charge any fee for downloading them. However, some of them may require you to register an account or complete a survey before accessing them. Please be careful of any scams or malware that may harm your device or data.</p>
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<h4>Q: Can I play Metal Slug APK offline?</h4>
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<p>A: Yes, you can play these games offline once you have downloaded and installed them on your device. You do not need an internet connection to play them unless you want to use the multiplayer mode of PPSSPP.</p>
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<h4>Q: Can I play Metal Slug APK on other devices?</h4>
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| 107 |
-
<p>A: Yes, you can play these games on other devices that support PPSSPP emulator such as Windows PC, Mac OS, Linux, iOS, PSP, PS Vita, and more. You just need to download and install PPSSPP emulator and the Metal Slug ISO files on those devices.</p>
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<h4>Q: Which Metal Slug game is the best?</h4>
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<p>A: This is a subjective question that depends on your personal preference and taste. However, most fans agree that Metal Slug 3 is the best game in the series due to its variety and replay value. But you can also try other games in the series and see which one suits you best.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Dominoes Gold APK - Play Dominoes with Friends and Earn Money.md
DELETED
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<h1>Dominoes Gold APK Uptodown: How to Play and Win Real Money</h1>
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<p>Do you love playing dominoes and want to earn some cash while having fun? If yes, then you should check out Dominoes Gold, a popular app that lets you play dominoes online with real players and win real money. In this article, we will tell you everything you need to know about Dominoes Gold, including how to download and install it from Uptodown, how to play and win, how to withdraw your winnings, and the pros and cons of the app.</p>
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<h2>What is Dominoes Gold?</h2>
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<p>Dominoes Gold is an app that allows you to play dominoes online with real players from all over the world. You can join tournaments, challenge your friends, or play solo games. The best part is that you can win real money by playing dominoes. You can also chat with other players, customize your avatar, and enjoy various game modes.</p>
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<h3>Features of Dominoes Gold</h3>
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<p>Some of the features that make Dominoes Gold stand out are:</p>
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<ul>
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| 10 |
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<li>You can play dominoes for free or for real money. You can choose from different stakes and entry fees.</li>
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<li>You can win cash prizes by joining tournaments or playing head-to-head matches.</li>
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<li>You can withdraw your winnings easily and securely using PayPal or other payment methods.</li>
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<li>You can play with millions of players from different countries and skill levels.</li>
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<li>You can chat with other players and make new friends.</li>
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<li>You can customize your avatar, profile, and game settings.</li>
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<li>You can earn bonus coins by watching videos, completing offers, or inviting friends.</li>
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</ul>
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<h3>How to download and install Dominoes Gold APK from Uptodown</h3>
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<p>If you want to download and install Dominoes Gold APK from Uptodown, you need to follow these steps:</p>
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| 21 |
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<ol>
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| 22 |
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<li>Go to <a href="(^1^)">https://loopgames-domino.en.uptodown.com/android</a> on your browser.</li>
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<li>Click on the green "Download" button and wait for the APK file to be downloaded.</li>
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<li>Once the download is complete, open the APK file and tap on "Install".</li>
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<li>If you see a warning message that says "Install blocked", go to your device settings and enable "Unknown sources".</li>
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<li>After the installation is done, open the app and sign up with your email or Facebook account.</li>
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<li>Enjoy playing dominoes and winning real money!</li>
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</ol>
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| 29 |
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<h2>How to play Dominoes Gold</h2>
|
| 30 |
-
<p>Playing Dominoes Gold is easy and fun. Here are some basic instructions on how to play:</p>
|
| 31 |
-
<h3>Rules of the game</h3>
|
| 32 |
-
<p>The rules of dominoes vary depending on the game mode you choose. However, the general rules are as follows:</p>
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| 33 |
-
<ul>
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| 34 |
-
<li>The game is played with a set of 28 tiles, each with two numbers from 0 to 6.</li>
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-
<li>The tiles are shuffled and each player draws a certain number of tiles. The remaining tiles are left in the "boneyard".</li>
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<li>The player with the highest double tile (the tile with the same number on both sides) starts the game by placing it on the board.</li>
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| 37 |
-
<li>The next player must place a tile that matches one of the open ends of the board. For example, if the board has a 6-5 tile, the next player can place a 6-6, 6-4, 6-3, 6-2, 6-1, or 6-0 tile.</li>
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| 38 |
-
<li>If a player cannot play a tile, they must draw a tile from the boneyard. If the boneyard is empty, they must pass their turn.</li>
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| 39 |
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<li>The game ends when one player runs out of tiles or when both players cannot play any tile.</li>
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-
<li>The player who ends the game scores the sum of the numbers on the tiles left in their opponent's hand.</li>
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| 41 |
-
<li>The first player to reach a certain number of points (usually 100 or 150) wins the game.</li>
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| 42 |
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</ul>
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| 43 |
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<h3>Tips and tricks to win</h3>
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| 44 |
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<p>Here are some tips and tricks to help you win more games and money on Dominoes Gold:</p>
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| 45 |
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<ul>
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| 46 |
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<li>Pay attention to the tiles that have been played and the tiles that are left in your hand. This will help you plan your moves and anticipate your opponent's moves.</li>
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<li>Try to play tiles that have high numbers or doubles. This will reduce the points you might lose if your opponent ends the game.</li>
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<li>Try to block your opponent from playing their tiles. This will force them to draw more tiles or pass their turn.</li>
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<li>Try to play tiles that match both ends of the board. This will give you more options and flexibility.</li>
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<li>Use the chat feature to communicate with your opponent. You can compliment them, taunt them, or bluff them. This can affect their mood and strategy.</li>
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</ul>
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<h2>How to withdraw your winnings from Dominoes Gold</h2>
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<p>If you have won some money on Dominoes Gold, you might want to withdraw it and enjoy your rewards. Here is how you can do that:</p>
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<h3>Payment methods and fees</h3>
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<p>The main payment method that Dominoes Gold supports is PayPal. You can also use other methods such as Skrill, Neteller, or bank transfer, depending on your country and availability. You can check the list of supported payment methods in the app settings.</p>
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<p>To ensure the safety and fairness of the app, Dominoes Gold may require you to verify your identity and age before you can withdraw your winnings. You may need to provide a copy of your ID card, passport, driver's license, or other documents that prove your identity and age.</p>
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<p>You may also need to verify your payment method by providing a screenshot of your PayPal account, bank statement, or other documents that show your name and payment details.</p>
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<p>Dominoes Gold uses SSL encryption and other security measures to protect your personal and financial information. You can rest assured that your data is safe and secure with Dominoes Gold.</p>
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<h2>Pros and cons of Dominoes Gold</h2>
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<p>Dominoes Gold is a fun and rewarding app that lets you play dominoes online with real players and win real money. However, like any app, it has its pros and cons. Here are some of them:</p>
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| 114 |
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<h3>Pros</h3>
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| 115 |
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<ul>
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| 116 |
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<li>You can play dominoes for free or for real money.</li>
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<li>You can win cash prizes by joining tournaments or playing head-to-head matches.</li>
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<li>You can withdraw your winnings easily and securely using PayPal or other payment methods.</li>
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<li>You can play with millions of players from different countries and skill levels.</li>
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<li>You can chat with other players and make new friends.</li>
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<li>You can customize your avatar, profile, and game settings.</li>
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<li>You can enjoy different game modes, such as Draw, Block, All Fives, and more.</li>
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<li>You can earn bonus coins by watching videos, completing offers, or inviting friends.</li>
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| 124 |
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</ul>
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| 125 |
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<h3>Cons</h3>
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| 126 |
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<ul>
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| 127 |
-
<li>You may lose money if you play recklessly or unluckily.</li>
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| 128 |
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<li>You may encounter some technical issues or bugs in the app.</li>
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<li>You may face some delays or errors in the withdrawal process.</li>
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<li>You may have to verify your identity and age before you can withdraw your winnings.</li>
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<li>You may have to deal with some rude or cheating players.</li>
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| 132 |
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</ul>
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| 133 |
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<h2>Conclusion</h2>
|
| 134 |
-
<p>Dominoes Gold is a great app for dominoes lovers who want to play online with real players and win real money. It has many features, game modes, and payment options that make it fun and rewarding. However, it also has some drawbacks, such as technical issues, verification requirements, and potential losses. Therefore, you should play responsibly and carefully, and only use money that you can afford to lose. If you are looking for a new way to enjoy dominoes and earn some cash, you should give Dominoes Gold a try. You can download and install it from Uptodown and start playing today!</p>
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| 135 |
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<h2>FAQs</h2>
|
| 136 |
-
<p>Here are some frequently asked questions about Dominoes Gold:</p>
|
| 137 |
-
<ol>
|
| 138 |
-
<li>Is Dominoes Gold legal?</li>
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| 139 |
-
<p>Yes, Dominoes Gold is legal in most countries where online gaming is allowed. However, you should check the laws and regulations of your country before playing for real money.</p>
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| 140 |
-
<li>Is Dominoes Gold safe?</li>
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| 141 |
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<p>Yes, Dominoes Gold is safe and secure. It uses SSL encryption and other security measures to protect your personal and financial information. It also has a fair and random gameplay system that ensures the integrity of the games.</p>
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| 142 |
-
<li>How can I contact Dominoes Gold support?</li>
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| 143 |
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<p>You can contact Dominoes Gold support by emailing them at [email protected] or by using the in-app chat feature. They will respond to your queries as soon as possible.</p>
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| 144 |
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<li>How can I get more coins on Dominoes Gold?</li>
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| 145 |
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<p>You can get more coins on Dominoes Gold by winning games, joining tournaments, watching videos, completing offers, or inviting friends. You can also buy coins with real money if you want to.</p>
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| 146 |
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<li>Can I play Dominoes Gold offline?</li>
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| 147 |
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<p>No, you cannot play Dominoes Gold offline. You need an internet connection to play online with real players and win real money.</p>
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</ol></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download NBA 2K20 for PC Free - SteamUnlocked Edition.md
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|
| 1 |
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<br />
|
| 2 |
-
<h1>NBA 2K20 PC Download Steamunlocked: How to Get the Best Basketball Game for Free</h1>
|
| 3 |
-
<p>If you are a fan of basketball and video games, you might have heard of NBA 2K20, the latest installment in the popular NBA 2K series. This game offers an immersive and realistic basketball experience, with stunning graphics, gameplay, modes, and features. But what if you don't want to pay for it? Is there a way to get NBA 2K20 for free on your PC?</p>
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<h2>nba 2k20 pc download steamunlocked</h2><br /><p><b><b>Download</b> ->->->-> <a href="https://jinyurl.com/2uNQpX">https://jinyurl.com/2uNQpX</a></b></p><br /><br />
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<p>The answer is yes, thanks to a website called Steamunlocked. In this article, we will explain what Steamunlocked is, how it works, and how you can use it to download NBA 2K20 on your PC without spending a dime. We will also cover some of the pros and cons of using Steamunlocked, as well as some tips and tricks to make the most out of your gaming experience. Let's get started!</p>
|
| 6 |
-
<h2>What is NBA 2K20?</h2>
|
| 7 |
-
<p>NBA 2K20 is a basketball simulation game developed by Visual Concepts and published by 2K Sports. It was released on September 6, 2019, for PlayStation 4, Xbox One, Nintendo Switch, Microsoft Windows, iOS, and Android devices. It is the 21st game in the NBA 2K franchise and the successor to NBA 2K19.</p>
|
| 8 |
-
<h3>Features and gameplay of NBA 2K20</h3>
|
| 9 |
-
<p>NBA 2K20 boasts a variety of features and gameplay improvements that make it one of the best basketball games ever made. Some of these include:</p>
|
| 10 |
-
<ul>
|
| 11 |
-
<li>A revamped motion engine that makes the players move more realistically and fluidly on the court.</li>
|
| 12 |
-
<li>A new sprint system that affects the stamina and speed of the players depending on how they use it.</li>
|
| 13 |
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<li>A new dribbling system that gives different players different ball-handling abilities and styles.</li>
|
| 14 |
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<li>An updated shooting system that takes into account the skill, timing, and context of each shot.</li>
|
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-
<li>A new dynamic soundtrack that features songs from various genres and artists curated by UnitedMasters.</li>
|
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<li>A new MyCareer mode that follows the story of a custom player as he rises from college to the NBA, featuring performances from actors like Idris Elba, Rosario Dawson, and LeBron James.</li>
|
| 17 |
-
<li>A new MyGM mode that lets you take control of an NBA franchise as a general manager, with more options and challenges than ever before.</li>
|
| 18 |
-
<li>A new MyTeam mode that lets you create your own fantasy team with cards from different eras and leagues.</li>
|
| 19 |
-
<li>A new WNBA mode that lets you play with all 12 WNBA teams and over 140 WNBA players for the first time in the series.</li>
|
| 20 |
-
<li>A new Neighborhood mode that lets you explore an open-world environment with other online players, where you can participate in various activities and events.</li>
|
| 21 |
-
</ul>
|
| 22 |
-
<h3>System requirements for NBA 2K20</h3>
|
| 23 |
-
<p>To run NBA 2K20 on your PC, you will need at least the following specifications:</p>
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| 24 |
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<table>
|
| 59 |
-
<tr><th>Minimum</th><th>Recommended</th></tr>
|
| 60 |
-
<tr><td>OS: Windows 7/8/10 (64-bit)</td><td>OS: Windows 7/8/10 (64-bit)</td></tr>
|
| 61 |
-
PU: Intel Core i3-530 @ 2.93 GHz / AMD FX-4100 @ 3.60 GHz or better</td><td>CPU: Intel Core i5-4430 @ 3 GHz / AMD FX-8370 @ 3.4 GHz or better</td></tr>
|
| 62 |
-
<tr><td>RAM: 4 GB</td><td>RAM: 8 GB</td></tr>
|
| 63 |
-
<tr><td>GPU: NVIDIA GeForce GT 450 1GB / AMD Radeon HD 7770 1GB or better</td><td>GPU: NVIDIA GeForce GTX 770 2GB / AMD Radeon R9 270 2GB or better</td></tr>
|
| 64 |
-
<tr><td>DirectX: Version 11</td><td>DirectX: Version 11</td></tr>
|
| 65 |
-
<tr><td>Storage: 80 GB available space</td><td>Storage: 80 GB available space</td></tr>
|
| 66 |
-
<tr><td>Sound Card: DirectX 9.0x compatible</td><td>Sound Card: DirectX 9.0x compatible</td></tr>
|
| 67 |
-
</table>
|
| 68 |
-
<h2>What is Steamunlocked?</h2>
|
| 69 |
-
<p>Steamunlocked is a website that offers free downloads of PC games that are normally sold on Steam or other platforms. Steamunlocked claims to provide the full versions of the games, with no viruses, malware, or DRM protection. Steamunlocked also claims to update its library regularly with new releases and patches.</p>
|
| 70 |
-
<h3>How does Steamunlocked work?</h3>
|
| 71 |
-
<p>Steamunlocked works by hosting the files of the games on its own servers, which can be accessed by anyone who visits the website. The files are usually compressed in zip or rar format, which can be extracted using a software like WinRAR or 7-Zip. The extracted files contain the setup file and the crack file of the game, which can be used to install and run the game without needing Steam or any other launcher.</p>
|
| 72 |
-
<h3>Is Steamunlocked safe and legal?</h3>
|
| 73 |
-
<p>This is a tricky question to answer, as there are different opinions and perspectives on this matter. On one hand, Steamunlocked claims to be safe and legal, as it does not host any illegal content, but only provides links to the files that are already available on the internet. Steamunlocked also states that it does not encourage piracy, but only provides an alternative way for people who cannot afford or access the games legally.</p>
|
| 74 |
-
<p>On the other hand, Steamunlocked can be considered unsafe and illegal, as it violates the terms and conditions of the game developers and publishers, who have the rights to distribute and sell their products. Steamunlocked also exposes its users to potential risks of downloading corrupted, infected, or outdated files, which can harm their devices or compromise their personal information. Steamunlocked also faces legal actions from the game companies, who can sue them for copyright infringement or request them to remove their games from the website.</p>
|
| 75 |
-
<h3>Pros and cons of Steamunlocked</h3>
|
| 76 |
-
<p>As with any other website or service, Steamunlocked has its own advantages and disadvantages that you should be aware of before using it. Here are some of them:</p>
|
| 77 |
-
<table>
|
| 78 |
-
<tr><th>Pros</th><th>Cons</th></tr>
|
| 79 |
-
<tr><td>You can download PC games for free without paying anything.</td><td>You can get into legal trouble for downloading pirated games.</td></tr>
|
| 80 |
-
<tr><td>You can access a large library of games from different genres and categories.</td><td>You can encounter broken links, missing files, or slow downloads.</td></tr>
|
| 81 |
-
<tr><td>You can play the games offline without needing an internet connection or a Steam account.</td><td>You can miss out on the online features, updates, and support of the games.</td></tr>
|
| 82 |
-
<tr><td>You can try out the games before buying them legally if you like them.</td><td>You can harm the game industry and discourage the developers from making more games.</td></tr>
|
| 83 |
-
</table>
|
| 84 |
-
<h2>How to download NBA 2K20 from Steamunlocked</h2>
|
| 85 |
-
<p>If you have decided to download NBA 2K20 from Steamunlocked, you will need to follow these steps:</p>
|
| 86 |
-
<h3>Step 1: Visit the Steamunlocked website</h3>
|
| 87 |
-
<p>The first thing you need to do is to go to the official website of Steamunlocked, which is https://steamunlocked.net/. You will see a homepage with a search bar and a list of featured games. You can also browse the games by genre, popularity, or alphabetically.</p>
|
| 88 |
-
<h3>Step 2: Search for NBA 2K20</h3>
|
| 89 |
-
<p>The next thing you need to do is to find the game you are looking for, which is NBA 2K20. You can either type the name of the game in the search bar and hit enter, or scroll down the list of games until you see it. You will then be directed to the game page, where you will see some information about the game, such as the release date, genre, developer, publisher, size, and rating.</p>
|
| 90 |
-
<h3>Step 3: Click on the download button</h3>
|
| 91 |
-
<p>The next thing you need to do is to click on the download button, which is located below the game information. You will then see a pop-up window that asks you to verify that you are not a robot. You will need to complete a simple captcha test to prove that you are human. After that, you will see another pop-up window that shows you the download link. You will need to copy and paste the link into your browser's address bar and hit enter.</p>
|
| 92 |
-
<h3>Step 4: Extract the zip file</h3>
|
| 93 |
-
<p>The next thing you need to do is to extract the zip file that you have downloaded. The zip file contains the game files and the crack files that you will need to install and run the game. You will need a software like WinRAR or 7-Zip to extract the zip file. You can download these software for free from their official websites. To extract the zip file, you will need to right-click on it and select "Extract here" or "Extract to NBA 2K20/" depending on your preference. You will then see a folder with the same name as the zip file.</p>
|
| 94 |
-
<h3>Step 5: Run the setup file and install the game</h3>
|
| 95 |
-
<p>The next thing you need to do is to run the setup file and install the game. The setup file is usually named "setup.exe" or something similar. You will need to double-click on it and follow the instructions on the screen. You will be asked to choose a destination folder for the game, accept the terms and conditions, and select some options. You will also be asked to copy and paste the crack files into the game folder. The crack files are usually located in a folder named "CODEX", "SKIDROW", "PLAZA", or something similar. You will need to copy all the files in that folder and paste them into the game folder, replacing any existing files. This will allow you to bypass the Steam verification and play the game without any issues.</p>
|
| 96 |
-
<h3>Step 6: Enjoy NBA 2K20 on your PC</h3>
|
| 97 |
-
<p>The final thing you need to do is to enjoy NBA 2K20 on your PC. You can launch the game by double-clicking on the game icon, which is usually named "NBA2K20.exe" or something similar. You can then customize your settings, choose your mode, create your player, and start playing. You can also invite your friends and play online with them if you want.</p>
|
| 98 |
-
<h2>Conclusion</h2>
|
| 99 |
-
<p>NBA 2K20 is one of the best basketball games ever made, with amazing graphics, gameplay, modes, and features. However, if you don't want to pay for it, you can download it for free from Steamunlocked, a website that offers free downloads of PC games. In this article, we have explained what Steamunlocked is, how it works, and how you can use it to download NBA 2K20 on your PC without spending a dime. We have also covered some of the pros and cons of using Steamunlocked, as well as some tips and tricks to make the most out of your gaming experience.</p>
|
| 100 |
-
<p>We hope that this article has been helpful and informative for you. If you have any questions or comments, feel free to leave them below. Thank you for reading!</p>
|
| 101 |
-
<h3>FAQs</h3>
|
| 102 |
-
<ul>
|
| 103 |
-
<li><b>Q: Is NBA 2K20 worth playing?</b></li>
|
| 104 |
-
<li>A: Yes, NBA 2K20 is worth playing if you are a fan of basketball and video games. It offers an immersive and realistic basketball experience, with stunning graphics, gameplay, modes, and features.</li>
|
| 105 |
-
<li><b>Q: How long does it take to download NBA 2K20 from Steamunlocked?</b></li>
|
| 106 |
-
<li>A: The download time depends on your internet speed and connection quality. The size of NBA 2K20 is about 75 GB, so it might take several hours or even days to download depending on your situation.</li>
|
| 107 |
-
<li><b>Q: Can I play NBA 2K20 online with Steamunlocked?</b></li>
|
| 108 |
-
<li>A: Yes, you can play NBA 2K20 online with Steamunlocked if you use a VPN service or a proxy server to hide your IP address and location. However, this might affect your performance and stability of the game.</li <li><b>Q: What are some alternatives to Steamunlocked?</b></li>
|
| 109 |
-
<li>A: Some alternatives to Steamunlocked are Ocean of Games, FitGirl Repacks, Skidrow Reloaded, and IGG Games. These are also websites that offer free downloads of PC games, but they might have different features, quality, and reliability.</li>
|
| 110 |
-
<li><b>Q: What are some tips and tricks to improve NBA 2K20 performance on PC?</b></li>
|
| 111 |
-
<li>A: Some tips and tricks to improve NBA 2K20 performance on PC are:</li>
|
| 112 |
-
<ul>
|
| 113 |
-
<li>Update your drivers and software to the latest versions.</li>
|
| 114 |
-
<li>Adjust your graphics settings to match your system capabilities and preferences.</li>
|
| 115 |
-
<li>Close any unnecessary programs or background processes that might consume your CPU, RAM, or bandwidth.</li>
|
| 116 |
-
<li>Use a wired connection instead of a wireless one for better stability and speed.</li>
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| 117 |
-
<li>Clean your PC from dust, dirt, or overheating issues that might affect your hardware performance.</li>
|
| 118 |
-
</ul></p> 401be4b1e0<br />
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| 119 |
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| 120 |
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|
spaces/1toTree/lora_test/ppdiffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py
DELETED
|
@@ -1,128 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
# you may not use this file except in compliance with the License.
|
| 6 |
-
# You may obtain a copy of the License at
|
| 7 |
-
#
|
| 8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
#
|
| 10 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
# See the License for the specific language governing permissions and
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
from typing import List, Optional, Tuple, Union
|
| 17 |
-
|
| 18 |
-
import paddle
|
| 19 |
-
|
| 20 |
-
from ...models import UNet2DModel
|
| 21 |
-
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
|
| 22 |
-
from ...schedulers import KarrasVeScheduler
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
class KarrasVePipeline(DiffusionPipeline):
|
| 26 |
-
r"""
|
| 27 |
-
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
|
| 28 |
-
the VE column of Table 1 from [1] for reference.
|
| 29 |
-
|
| 30 |
-
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
|
| 31 |
-
https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
|
| 32 |
-
differential equations." https://arxiv.org/abs/2011.13456
|
| 33 |
-
|
| 34 |
-
Parameters:
|
| 35 |
-
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
|
| 36 |
-
scheduler ([`KarrasVeScheduler`]):
|
| 37 |
-
Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image.
|
| 38 |
-
"""
|
| 39 |
-
|
| 40 |
-
# add type hints for linting
|
| 41 |
-
unet: UNet2DModel
|
| 42 |
-
scheduler: KarrasVeScheduler
|
| 43 |
-
|
| 44 |
-
def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler):
|
| 45 |
-
super().__init__()
|
| 46 |
-
self.register_modules(unet=unet, scheduler=scheduler)
|
| 47 |
-
|
| 48 |
-
@paddle.no_grad()
|
| 49 |
-
def __call__(
|
| 50 |
-
self,
|
| 51 |
-
batch_size: int = 1,
|
| 52 |
-
num_inference_steps: int = 50,
|
| 53 |
-
generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
|
| 54 |
-
output_type: Optional[str] = "pil",
|
| 55 |
-
return_dict: bool = True,
|
| 56 |
-
**kwargs,
|
| 57 |
-
) -> Union[Tuple, ImagePipelineOutput]:
|
| 58 |
-
r"""
|
| 59 |
-
Args:
|
| 60 |
-
batch_size (`int`, *optional*, defaults to 1):
|
| 61 |
-
The number of images to generate.
|
| 62 |
-
generator (`paddle.Generator`, *optional*):
|
| 63 |
-
One or a list of paddle generator(s) to make generation deterministic.
|
| 64 |
-
num_inference_steps (`int`, *optional*, defaults to 50):
|
| 65 |
-
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
| 66 |
-
expense of slower inference.
|
| 67 |
-
output_type (`str`, *optional*, defaults to `"pil"`):
|
| 68 |
-
The output format of the generate image. Choose between
|
| 69 |
-
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
| 70 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 71 |
-
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
|
| 72 |
-
|
| 73 |
-
Returns:
|
| 74 |
-
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
|
| 75 |
-
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
|
| 76 |
-
generated images.
|
| 77 |
-
"""
|
| 78 |
-
|
| 79 |
-
img_size = self.unet.config.sample_size
|
| 80 |
-
shape = (batch_size, 3, img_size, img_size)
|
| 81 |
-
|
| 82 |
-
model = self.unet
|
| 83 |
-
|
| 84 |
-
# sample x_0 ~ N(0, sigma_0^2 * I)
|
| 85 |
-
sample = paddle.randn(shape, generator=generator) * self.scheduler.init_noise_sigma
|
| 86 |
-
|
| 87 |
-
self.scheduler.set_timesteps(num_inference_steps)
|
| 88 |
-
|
| 89 |
-
for t in self.progress_bar(self.scheduler.timesteps):
|
| 90 |
-
# here sigma_t == t_i from the paper
|
| 91 |
-
sigma = self.scheduler.schedule[t]
|
| 92 |
-
sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0
|
| 93 |
-
|
| 94 |
-
# 1. Select temporarily increased noise level sigma_hat
|
| 95 |
-
# 2. Add new noise to move from sample_i to sample_hat
|
| 96 |
-
sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)
|
| 97 |
-
|
| 98 |
-
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
|
| 99 |
-
# The model inputs and output are adjusted by following eq. (213) in [1].
|
| 100 |
-
model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample
|
| 101 |
-
|
| 102 |
-
# 4. Evaluate dx/dt at sigma_hat
|
| 103 |
-
# 5. Take Euler step from sigma to sigma_prev
|
| 104 |
-
step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat)
|
| 105 |
-
|
| 106 |
-
if sigma_prev != 0:
|
| 107 |
-
# 6. Apply 2nd order correction
|
| 108 |
-
# The model inputs and output are adjusted by following eq. (213) in [1].
|
| 109 |
-
model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample
|
| 110 |
-
step_output = self.scheduler.step_correct(
|
| 111 |
-
model_output,
|
| 112 |
-
sigma_hat,
|
| 113 |
-
sigma_prev,
|
| 114 |
-
sample_hat,
|
| 115 |
-
step_output.prev_sample,
|
| 116 |
-
step_output["derivative"],
|
| 117 |
-
)
|
| 118 |
-
sample = step_output.prev_sample
|
| 119 |
-
|
| 120 |
-
sample = (sample / 2 + 0.5).clip(0, 1)
|
| 121 |
-
image = sample.transpose([0, 2, 3, 1]).numpy()
|
| 122 |
-
if output_type == "pil":
|
| 123 |
-
image = self.numpy_to_pil(image)
|
| 124 |
-
|
| 125 |
-
if not return_dict:
|
| 126 |
-
return (image,)
|
| 127 |
-
|
| 128 |
-
return ImagePipelineOutput(images=image)
|
|
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|
spaces/2023Liu2023/bingo/src/pages/api/sydney.ts
DELETED
|
@@ -1,62 +0,0 @@
|
|
| 1 |
-
import { NextApiRequest, NextApiResponse } from 'next'
|
| 2 |
-
import { WebSocket, debug } from '@/lib/isomorphic'
|
| 3 |
-
import { BingWebBot } from '@/lib/bots/bing'
|
| 4 |
-
import { websocketUtils } from '@/lib/bots/bing/utils'
|
| 5 |
-
import { WatchDog, createHeaders } from '@/lib/utils'
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
|
| 9 |
-
const conversationContext = req.body
|
| 10 |
-
const headers = createHeaders(req.cookies)
|
| 11 |
-
debug(headers)
|
| 12 |
-
res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
|
| 13 |
-
|
| 14 |
-
const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
|
| 15 |
-
headers: {
|
| 16 |
-
...headers,
|
| 17 |
-
'accept-language': 'zh-CN,zh;q=0.9',
|
| 18 |
-
'cache-control': 'no-cache',
|
| 19 |
-
'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
|
| 20 |
-
pragma: 'no-cache',
|
| 21 |
-
}
|
| 22 |
-
})
|
| 23 |
-
|
| 24 |
-
const closeDog = new WatchDog()
|
| 25 |
-
const timeoutDog = new WatchDog()
|
| 26 |
-
ws.onmessage = (event) => {
|
| 27 |
-
timeoutDog.watch(() => {
|
| 28 |
-
ws.send(websocketUtils.packMessage({ type: 6 }))
|
| 29 |
-
}, 1500)
|
| 30 |
-
closeDog.watch(() => {
|
| 31 |
-
ws.close()
|
| 32 |
-
}, 10000)
|
| 33 |
-
res.write(event.data)
|
| 34 |
-
if (/\{"type":([367])\}/.test(String(event.data))) {
|
| 35 |
-
const type = parseInt(RegExp.$1, 10)
|
| 36 |
-
debug('connection type', type)
|
| 37 |
-
if (type === 3) {
|
| 38 |
-
ws.close()
|
| 39 |
-
} else {
|
| 40 |
-
ws.send(websocketUtils.packMessage({ type }))
|
| 41 |
-
}
|
| 42 |
-
}
|
| 43 |
-
}
|
| 44 |
-
|
| 45 |
-
ws.onclose = () => {
|
| 46 |
-
timeoutDog.reset()
|
| 47 |
-
closeDog.reset()
|
| 48 |
-
debug('connection close')
|
| 49 |
-
res.end()
|
| 50 |
-
}
|
| 51 |
-
|
| 52 |
-
await new Promise((resolve) => ws.onopen = resolve)
|
| 53 |
-
ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
|
| 54 |
-
ws.send(websocketUtils.packMessage({ type: 6 }))
|
| 55 |
-
ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
|
| 56 |
-
req.socket.once('close', () => {
|
| 57 |
-
ws.close()
|
| 58 |
-
if (!res.closed) {
|
| 59 |
-
res.end()
|
| 60 |
-
}
|
| 61 |
-
})
|
| 62 |
-
}
|
|
|
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|
spaces/2ndelement/voicevox/make_docs.py
DELETED
|
@@ -1,33 +0,0 @@
|
|
| 1 |
-
import json
|
| 2 |
-
|
| 3 |
-
from voicevox_engine.dev.core import mock as core
|
| 4 |
-
from voicevox_engine.dev.synthesis_engine.mock import MockSynthesisEngine
|
| 5 |
-
from voicevox_engine.setting import USER_SETTING_PATH, SettingLoader
|
| 6 |
-
|
| 7 |
-
if __name__ == "__main__":
|
| 8 |
-
import run
|
| 9 |
-
|
| 10 |
-
app = run.generate_app(
|
| 11 |
-
synthesis_engines={"mock": MockSynthesisEngine(speakers=core.metas())},
|
| 12 |
-
latest_core_version="mock",
|
| 13 |
-
setting_loader=SettingLoader(USER_SETTING_PATH),
|
| 14 |
-
)
|
| 15 |
-
with open("docs/api/index.html", "w") as f:
|
| 16 |
-
f.write(
|
| 17 |
-
"""<!DOCTYPE html>
|
| 18 |
-
<html lang="ja">
|
| 19 |
-
<head>
|
| 20 |
-
<title>voicevox_engine API Document</title>
|
| 21 |
-
<meta charset="utf-8">
|
| 22 |
-
<link rel="shortcut icon" href="https://voicevox.hiroshiba.jp/favicon-32x32.png">
|
| 23 |
-
</head>
|
| 24 |
-
<body>
|
| 25 |
-
<div id="redoc-container"></div>
|
| 26 |
-
<script src="https://cdn.jsdelivr.net/npm/redoc/bundles/redoc.standalone.js"></script>
|
| 27 |
-
<script>
|
| 28 |
-
Redoc.init(%s, {"hideHostname": true}, document.getElementById("redoc-container"));
|
| 29 |
-
</script>
|
| 30 |
-
</body>
|
| 31 |
-
</html>"""
|
| 32 |
-
% json.dumps(app.openapi())
|
| 33 |
-
)
|
|
|
|
|
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|
|
spaces/3B-Group/ConvRe-Leaderboard/src/utils.py
DELETED
|
@@ -1,66 +0,0 @@
|
|
| 1 |
-
from dataclasses import dataclass
|
| 2 |
-
import pandas as pd
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
@dataclass
|
| 6 |
-
class ColumnContent:
|
| 7 |
-
name: str
|
| 8 |
-
type: str
|
| 9 |
-
displayed_by_default: bool
|
| 10 |
-
hidden: bool = False
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
def fields(raw_class):
|
| 14 |
-
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
@dataclass(frozen=True)
|
| 18 |
-
class AutoEvalColumn: # Auto evals column
|
| 19 |
-
model = ColumnContent("Models", "markdown", True)
|
| 20 |
-
re2text_easy = ColumnContent("Re2Text-Easy", "number", True)
|
| 21 |
-
text2re_easy = ColumnContent("Text2Re-Easy", "number", True)
|
| 22 |
-
re2text_hard = ColumnContent("Re2Text-Hard", "number", True)
|
| 23 |
-
text2re_hard = ColumnContent("Text2Re-Hard", "number", True)
|
| 24 |
-
avg = ColumnContent("Avg", "number", True)
|
| 25 |
-
model_size = ColumnContent("Model Size", "markdown", True)
|
| 26 |
-
|
| 27 |
-
link = ColumnContent("Links", "str", False)
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def model_hyperlink(link, model_name):
|
| 31 |
-
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
def make_clickable_names(df):
|
| 35 |
-
df["Models"] = df.apply(
|
| 36 |
-
lambda row: model_hyperlink(row["Links"], row["Models"]), axis=1
|
| 37 |
-
)
|
| 38 |
-
return df
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def make_plot_data(df, task):
|
| 42 |
-
c = []
|
| 43 |
-
x = []
|
| 44 |
-
y = []
|
| 45 |
-
|
| 46 |
-
for i in df.index:
|
| 47 |
-
c.append(df.loc[i, "pure_name"])
|
| 48 |
-
x.append(f"{task}-Easy")
|
| 49 |
-
y.append(df.loc[i, f"{task}-Easy"])
|
| 50 |
-
|
| 51 |
-
c.append(df.loc[i, "pure_name"])
|
| 52 |
-
x.append(f"{task}-Hard")
|
| 53 |
-
y.append(df.loc[i, f"{task}-Hard"])
|
| 54 |
-
|
| 55 |
-
data = pd.DataFrame(
|
| 56 |
-
{
|
| 57 |
-
"Symbol": c,
|
| 58 |
-
"Setting": x,
|
| 59 |
-
"Accuracy": y,
|
| 60 |
-
}
|
| 61 |
-
)
|
| 62 |
-
|
| 63 |
-
return data
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
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|
spaces/AIFILMS/generate_human_motion/VQ-Trans/models/smpl.py
DELETED
|
@@ -1,97 +0,0 @@
|
|
| 1 |
-
# This code is based on https://github.com/Mathux/ACTOR.git
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| 2 |
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import numpy as np
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| 3 |
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import torch
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| 4 |
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| 5 |
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import contextlib
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| 6 |
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| 7 |
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from smplx import SMPLLayer as _SMPLLayer
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| 8 |
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from smplx.lbs import vertices2joints
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| 9 |
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| 10 |
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| 11 |
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# action2motion_joints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21, 24, 38]
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| 12 |
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# change 0 and 8
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| 13 |
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action2motion_joints = [8, 1, 2, 3, 4, 5, 6, 7, 0, 9, 10, 11, 12, 13, 14, 21, 24, 38]
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| 14 |
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| 15 |
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from utils.config import SMPL_MODEL_PATH, JOINT_REGRESSOR_TRAIN_EXTRA
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| 16 |
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| 17 |
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JOINTSTYPE_ROOT = {"a2m": 0, # action2motion
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| 18 |
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"smpl": 0,
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| 19 |
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"a2mpl": 0, # set(smpl, a2m)
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| 20 |
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"vibe": 8} # 0 is the 8 position: OP MidHip below
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| 21 |
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| 22 |
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JOINT_MAP = {
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| 23 |
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'OP Nose': 24, 'OP Neck': 12, 'OP RShoulder': 17,
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| 24 |
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'OP RElbow': 19, 'OP RWrist': 21, 'OP LShoulder': 16,
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| 25 |
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'OP LElbow': 18, 'OP LWrist': 20, 'OP MidHip': 0,
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| 26 |
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'OP RHip': 2, 'OP RKnee': 5, 'OP RAnkle': 8,
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| 27 |
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'OP LHip': 1, 'OP LKnee': 4, 'OP LAnkle': 7,
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| 28 |
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'OP REye': 25, 'OP LEye': 26, 'OP REar': 27,
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| 29 |
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'OP LEar': 28, 'OP LBigToe': 29, 'OP LSmallToe': 30,
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| 30 |
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'OP LHeel': 31, 'OP RBigToe': 32, 'OP RSmallToe': 33, 'OP RHeel': 34,
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| 31 |
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'Right Ankle': 8, 'Right Knee': 5, 'Right Hip': 45,
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| 32 |
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'Left Hip': 46, 'Left Knee': 4, 'Left Ankle': 7,
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| 33 |
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'Right Wrist': 21, 'Right Elbow': 19, 'Right Shoulder': 17,
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| 34 |
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'Left Shoulder': 16, 'Left Elbow': 18, 'Left Wrist': 20,
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| 35 |
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'Neck (LSP)': 47, 'Top of Head (LSP)': 48,
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| 36 |
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'Pelvis (MPII)': 49, 'Thorax (MPII)': 50,
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| 37 |
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'Spine (H36M)': 51, 'Jaw (H36M)': 52,
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| 38 |
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'Head (H36M)': 53, 'Nose': 24, 'Left Eye': 26,
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| 39 |
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'Right Eye': 25, 'Left Ear': 28, 'Right Ear': 27
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| 40 |
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}
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| 41 |
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| 42 |
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JOINT_NAMES = [
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| 43 |
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'OP Nose', 'OP Neck', 'OP RShoulder',
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| 44 |
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'OP RElbow', 'OP RWrist', 'OP LShoulder',
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| 45 |
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'OP LElbow', 'OP LWrist', 'OP MidHip',
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| 46 |
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'OP RHip', 'OP RKnee', 'OP RAnkle',
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| 47 |
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'OP LHip', 'OP LKnee', 'OP LAnkle',
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| 48 |
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'OP REye', 'OP LEye', 'OP REar',
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| 49 |
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'OP LEar', 'OP LBigToe', 'OP LSmallToe',
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| 50 |
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'OP LHeel', 'OP RBigToe', 'OP RSmallToe', 'OP RHeel',
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| 51 |
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'Right Ankle', 'Right Knee', 'Right Hip',
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| 52 |
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'Left Hip', 'Left Knee', 'Left Ankle',
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| 53 |
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'Right Wrist', 'Right Elbow', 'Right Shoulder',
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| 54 |
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'Left Shoulder', 'Left Elbow', 'Left Wrist',
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| 55 |
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'Neck (LSP)', 'Top of Head (LSP)',
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| 56 |
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'Pelvis (MPII)', 'Thorax (MPII)',
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| 57 |
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'Spine (H36M)', 'Jaw (H36M)',
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| 58 |
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'Head (H36M)', 'Nose', 'Left Eye',
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| 59 |
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'Right Eye', 'Left Ear', 'Right Ear'
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| 60 |
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]
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| 61 |
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| 62 |
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| 63 |
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# adapted from VIBE/SPIN to output smpl_joints, vibe joints and action2motion joints
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| 64 |
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class SMPL(_SMPLLayer):
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| 65 |
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""" Extension of the official SMPL implementation to support more joints """
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| 66 |
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| 67 |
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def __init__(self, model_path=SMPL_MODEL_PATH, **kwargs):
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| 68 |
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kwargs["model_path"] = model_path
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| 70 |
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# remove the verbosity for the 10-shapes beta parameters
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| 71 |
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with contextlib.redirect_stdout(None):
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| 72 |
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super(SMPL, self).__init__(**kwargs)
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| 73 |
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| 74 |
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J_regressor_extra = np.load(JOINT_REGRESSOR_TRAIN_EXTRA)
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| 75 |
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self.register_buffer('J_regressor_extra', torch.tensor(J_regressor_extra, dtype=torch.float32))
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| 76 |
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vibe_indexes = np.array([JOINT_MAP[i] for i in JOINT_NAMES])
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| 77 |
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a2m_indexes = vibe_indexes[action2motion_joints]
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| 78 |
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smpl_indexes = np.arange(24)
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| 79 |
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a2mpl_indexes = np.unique(np.r_[smpl_indexes, a2m_indexes])
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| 80 |
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| 81 |
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self.maps = {"vibe": vibe_indexes,
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| 82 |
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"a2m": a2m_indexes,
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| 83 |
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"smpl": smpl_indexes,
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| 84 |
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"a2mpl": a2mpl_indexes}
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| 85 |
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| 86 |
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def forward(self, *args, **kwargs):
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| 87 |
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smpl_output = super(SMPL, self).forward(*args, **kwargs)
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| 88 |
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| 89 |
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extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
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| 90 |
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all_joints = torch.cat([smpl_output.joints, extra_joints], dim=1)
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| 91 |
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| 92 |
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output = {"vertices": smpl_output.vertices}
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| 93 |
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| 94 |
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for joinstype, indexes in self.maps.items():
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| 95 |
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output[joinstype] = all_joints[:, indexes]
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| 96 |
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| 97 |
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return output
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|
spaces/AJRFan/dreambooth-training/convertosd.py
DELETED
|
@@ -1,223 +0,0 @@
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| 1 |
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# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
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| 2 |
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# *Only* converts the UNet, VAE, and Text Encoder.
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| 3 |
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# Does not convert optimizer state or any other thing.
|
| 4 |
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# Written by jachiam
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| 5 |
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|
| 6 |
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import argparse
|
| 7 |
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import os.path as osp
|
| 8 |
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|
| 9 |
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import torch
|
| 10 |
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|
| 11 |
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|
| 12 |
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# =================#
|
| 13 |
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# UNet Conversion #
|
| 14 |
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# =================#
|
| 15 |
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|
| 16 |
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unet_conversion_map = [
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| 17 |
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# (stable-diffusion, HF Diffusers)
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| 18 |
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("time_embed.0.weight", "time_embedding.linear_1.weight"),
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| 19 |
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("time_embed.0.bias", "time_embedding.linear_1.bias"),
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| 20 |
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("time_embed.2.weight", "time_embedding.linear_2.weight"),
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| 21 |
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("time_embed.2.bias", "time_embedding.linear_2.bias"),
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| 22 |
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("input_blocks.0.0.weight", "conv_in.weight"),
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| 23 |
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("input_blocks.0.0.bias", "conv_in.bias"),
|
| 24 |
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("out.0.weight", "conv_norm_out.weight"),
|
| 25 |
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("out.0.bias", "conv_norm_out.bias"),
|
| 26 |
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("out.2.weight", "conv_out.weight"),
|
| 27 |
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("out.2.bias", "conv_out.bias"),
|
| 28 |
-
]
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| 29 |
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|
| 30 |
-
unet_conversion_map_resnet = [
|
| 31 |
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# (stable-diffusion, HF Diffusers)
|
| 32 |
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("in_layers.0", "norm1"),
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| 33 |
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("in_layers.2", "conv1"),
|
| 34 |
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("out_layers.0", "norm2"),
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| 35 |
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("out_layers.3", "conv2"),
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| 36 |
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("emb_layers.1", "time_emb_proj"),
|
| 37 |
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("skip_connection", "conv_shortcut"),
|
| 38 |
-
]
|
| 39 |
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|
| 40 |
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unet_conversion_map_layer = []
|
| 41 |
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# hardcoded number of downblocks and resnets/attentions...
|
| 42 |
-
# would need smarter logic for other networks.
|
| 43 |
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for i in range(4):
|
| 44 |
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# loop over downblocks/upblocks
|
| 45 |
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| 46 |
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for j in range(2):
|
| 47 |
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# loop over resnets/attentions for downblocks
|
| 48 |
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hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
|
| 49 |
-
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
|
| 50 |
-
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
|
| 51 |
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|
| 52 |
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if i < 3:
|
| 53 |
-
# no attention layers in down_blocks.3
|
| 54 |
-
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
|
| 55 |
-
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
|
| 56 |
-
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
|
| 57 |
-
|
| 58 |
-
for j in range(3):
|
| 59 |
-
# loop over resnets/attentions for upblocks
|
| 60 |
-
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
|
| 61 |
-
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
|
| 62 |
-
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
|
| 63 |
-
|
| 64 |
-
if i > 0:
|
| 65 |
-
# no attention layers in up_blocks.0
|
| 66 |
-
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
|
| 67 |
-
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
|
| 68 |
-
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
|
| 69 |
-
|
| 70 |
-
if i < 3:
|
| 71 |
-
# no downsample in down_blocks.3
|
| 72 |
-
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
|
| 73 |
-
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
|
| 74 |
-
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
|
| 75 |
-
|
| 76 |
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# no upsample in up_blocks.3
|
| 77 |
-
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
| 78 |
-
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
|
| 79 |
-
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
|
| 80 |
-
|
| 81 |
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hf_mid_atn_prefix = "mid_block.attentions.0."
|
| 82 |
-
sd_mid_atn_prefix = "middle_block.1."
|
| 83 |
-
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
|
| 84 |
-
|
| 85 |
-
for j in range(2):
|
| 86 |
-
hf_mid_res_prefix = f"mid_block.resnets.{j}."
|
| 87 |
-
sd_mid_res_prefix = f"middle_block.{2*j}."
|
| 88 |
-
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
| 89 |
-
|
| 90 |
-
|
| 91 |
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def convert_unet_state_dict(unet_state_dict):
|
| 92 |
-
# buyer beware: this is a *brittle* function,
|
| 93 |
-
# and correct output requires that all of these pieces interact in
|
| 94 |
-
# the exact order in which I have arranged them.
|
| 95 |
-
mapping = {k: k for k in unet_state_dict.keys()}
|
| 96 |
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for sd_name, hf_name in unet_conversion_map:
|
| 97 |
-
mapping[hf_name] = sd_name
|
| 98 |
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for k, v in mapping.items():
|
| 99 |
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if "resnets" in k:
|
| 100 |
-
for sd_part, hf_part in unet_conversion_map_resnet:
|
| 101 |
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v = v.replace(hf_part, sd_part)
|
| 102 |
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mapping[k] = v
|
| 103 |
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for k, v in mapping.items():
|
| 104 |
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for sd_part, hf_part in unet_conversion_map_layer:
|
| 105 |
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v = v.replace(hf_part, sd_part)
|
| 106 |
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mapping[k] = v
|
| 107 |
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new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
|
| 108 |
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return new_state_dict
|
| 109 |
-
|
| 110 |
-
|
| 111 |
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# ================#
|
| 112 |
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# VAE Conversion #
|
| 113 |
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# ================#
|
| 114 |
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|
| 115 |
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vae_conversion_map = [
|
| 116 |
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# (stable-diffusion, HF Diffusers)
|
| 117 |
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("nin_shortcut", "conv_shortcut"),
|
| 118 |
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("norm_out", "conv_norm_out"),
|
| 119 |
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("mid.attn_1.", "mid_block.attentions.0."),
|
| 120 |
-
]
|
| 121 |
-
|
| 122 |
-
for i in range(4):
|
| 123 |
-
# down_blocks have two resnets
|
| 124 |
-
for j in range(2):
|
| 125 |
-
hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
|
| 126 |
-
sd_down_prefix = f"encoder.down.{i}.block.{j}."
|
| 127 |
-
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
|
| 128 |
-
|
| 129 |
-
if i < 3:
|
| 130 |
-
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
|
| 131 |
-
sd_downsample_prefix = f"down.{i}.downsample."
|
| 132 |
-
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
|
| 133 |
-
|
| 134 |
-
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
| 135 |
-
sd_upsample_prefix = f"up.{3-i}.upsample."
|
| 136 |
-
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
|
| 137 |
-
|
| 138 |
-
# up_blocks have three resnets
|
| 139 |
-
# also, up blocks in hf are numbered in reverse from sd
|
| 140 |
-
for j in range(3):
|
| 141 |
-
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
|
| 142 |
-
sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
|
| 143 |
-
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
|
| 144 |
-
|
| 145 |
-
# this part accounts for mid blocks in both the encoder and the decoder
|
| 146 |
-
for i in range(2):
|
| 147 |
-
hf_mid_res_prefix = f"mid_block.resnets.{i}."
|
| 148 |
-
sd_mid_res_prefix = f"mid.block_{i+1}."
|
| 149 |
-
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
vae_conversion_map_attn = [
|
| 153 |
-
# (stable-diffusion, HF Diffusers)
|
| 154 |
-
("norm.", "group_norm."),
|
| 155 |
-
("q.", "query."),
|
| 156 |
-
("k.", "key."),
|
| 157 |
-
("v.", "value."),
|
| 158 |
-
("proj_out.", "proj_attn."),
|
| 159 |
-
]
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
def reshape_weight_for_sd(w):
|
| 163 |
-
# convert HF linear weights to SD conv2d weights
|
| 164 |
-
return w.reshape(*w.shape, 1, 1)
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
def convert_vae_state_dict(vae_state_dict):
|
| 168 |
-
mapping = {k: k for k in vae_state_dict.keys()}
|
| 169 |
-
for k, v in mapping.items():
|
| 170 |
-
for sd_part, hf_part in vae_conversion_map:
|
| 171 |
-
v = v.replace(hf_part, sd_part)
|
| 172 |
-
mapping[k] = v
|
| 173 |
-
for k, v in mapping.items():
|
| 174 |
-
if "attentions" in k:
|
| 175 |
-
for sd_part, hf_part in vae_conversion_map_attn:
|
| 176 |
-
v = v.replace(hf_part, sd_part)
|
| 177 |
-
mapping[k] = v
|
| 178 |
-
new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
|
| 179 |
-
weights_to_convert = ["q", "k", "v", "proj_out"]
|
| 180 |
-
print("[1;32mConverting to CKPT ...")
|
| 181 |
-
for k, v in new_state_dict.items():
|
| 182 |
-
for weight_name in weights_to_convert:
|
| 183 |
-
if f"mid.attn_1.{weight_name}.weight" in k:
|
| 184 |
-
new_state_dict[k] = reshape_weight_for_sd(v)
|
| 185 |
-
return new_state_dict
|
| 186 |
-
|
| 187 |
-
|
| 188 |
-
# =========================#
|
| 189 |
-
# Text Encoder Conversion #
|
| 190 |
-
# =========================#
|
| 191 |
-
# pretty much a no-op
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
def convert_text_enc_state_dict(text_enc_dict):
|
| 195 |
-
return text_enc_dict
|
| 196 |
-
|
| 197 |
-
|
| 198 |
-
def convert(model_path, checkpoint_path):
|
| 199 |
-
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
|
| 200 |
-
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
|
| 201 |
-
text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
|
| 202 |
-
|
| 203 |
-
# Convert the UNet model
|
| 204 |
-
unet_state_dict = torch.load(unet_path, map_location='cpu')
|
| 205 |
-
unet_state_dict = convert_unet_state_dict(unet_state_dict)
|
| 206 |
-
unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
|
| 207 |
-
|
| 208 |
-
# Convert the VAE model
|
| 209 |
-
vae_state_dict = torch.load(vae_path, map_location='cpu')
|
| 210 |
-
vae_state_dict = convert_vae_state_dict(vae_state_dict)
|
| 211 |
-
vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
|
| 212 |
-
|
| 213 |
-
# Convert the text encoder model
|
| 214 |
-
text_enc_dict = torch.load(text_enc_path, map_location='cpu')
|
| 215 |
-
text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
|
| 216 |
-
text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
|
| 217 |
-
|
| 218 |
-
# Put together new checkpoint
|
| 219 |
-
state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
|
| 220 |
-
|
| 221 |
-
state_dict = {k:v.half() for k,v in state_dict.items()}
|
| 222 |
-
state_dict = {"state_dict": state_dict}
|
| 223 |
-
torch.save(state_dict, checkpoint_path)
|
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|
spaces/Ababababababbababa/Arabic_poetry_Sha3bor_mid/app.py
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
|
| 3 |
-
gr.Interface.load("models/aliosm/sha3bor-generator-aragpt2-medium").launch()
|
|
|
|
|
|
|
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|
|
spaces/Abhilashvj/planogram-compliance/utils/segment/metrics.py
DELETED
|
@@ -1,220 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
"""
|
| 3 |
-
Model validation metrics
|
| 4 |
-
"""
|
| 5 |
-
|
| 6 |
-
import numpy as np
|
| 7 |
-
|
| 8 |
-
from ..metrics import ap_per_class
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def fitness(x):
|
| 12 |
-
# Model fitness as a weighted combination of metrics
|
| 13 |
-
w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]
|
| 14 |
-
return (x[:, :8] * w).sum(1)
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def ap_per_class_box_and_mask(
|
| 18 |
-
tp_m,
|
| 19 |
-
tp_b,
|
| 20 |
-
conf,
|
| 21 |
-
pred_cls,
|
| 22 |
-
target_cls,
|
| 23 |
-
plot=False,
|
| 24 |
-
save_dir=".",
|
| 25 |
-
names=(),
|
| 26 |
-
):
|
| 27 |
-
"""
|
| 28 |
-
Args:
|
| 29 |
-
tp_b: tp of boxes.
|
| 30 |
-
tp_m: tp of masks.
|
| 31 |
-
other arguments see `func: ap_per_class`.
|
| 32 |
-
"""
|
| 33 |
-
results_boxes = ap_per_class(
|
| 34 |
-
tp_b,
|
| 35 |
-
conf,
|
| 36 |
-
pred_cls,
|
| 37 |
-
target_cls,
|
| 38 |
-
plot=plot,
|
| 39 |
-
save_dir=save_dir,
|
| 40 |
-
names=names,
|
| 41 |
-
prefix="Box",
|
| 42 |
-
)[2:]
|
| 43 |
-
results_masks = ap_per_class(
|
| 44 |
-
tp_m,
|
| 45 |
-
conf,
|
| 46 |
-
pred_cls,
|
| 47 |
-
target_cls,
|
| 48 |
-
plot=plot,
|
| 49 |
-
save_dir=save_dir,
|
| 50 |
-
names=names,
|
| 51 |
-
prefix="Mask",
|
| 52 |
-
)[2:]
|
| 53 |
-
|
| 54 |
-
results = {
|
| 55 |
-
"boxes": {
|
| 56 |
-
"p": results_boxes[0],
|
| 57 |
-
"r": results_boxes[1],
|
| 58 |
-
"ap": results_boxes[3],
|
| 59 |
-
"f1": results_boxes[2],
|
| 60 |
-
"ap_class": results_boxes[4],
|
| 61 |
-
},
|
| 62 |
-
"masks": {
|
| 63 |
-
"p": results_masks[0],
|
| 64 |
-
"r": results_masks[1],
|
| 65 |
-
"ap": results_masks[3],
|
| 66 |
-
"f1": results_masks[2],
|
| 67 |
-
"ap_class": results_masks[4],
|
| 68 |
-
},
|
| 69 |
-
}
|
| 70 |
-
return results
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
class Metric:
|
| 74 |
-
def __init__(self) -> None:
|
| 75 |
-
self.p = [] # (nc, )
|
| 76 |
-
self.r = [] # (nc, )
|
| 77 |
-
self.f1 = [] # (nc, )
|
| 78 |
-
self.all_ap = [] # (nc, 10)
|
| 79 |
-
self.ap_class_index = [] # (nc, )
|
| 80 |
-
|
| 81 |
-
@property
|
| 82 |
-
def ap50(self):
|
| 83 |
-
"""[email protected] of all classes.
|
| 84 |
-
Return:
|
| 85 |
-
(nc, ) or [].
|
| 86 |
-
"""
|
| 87 |
-
return self.all_ap[:, 0] if len(self.all_ap) else []
|
| 88 |
-
|
| 89 |
-
@property
|
| 90 |
-
def ap(self):
|
| 91 |
-
"""[email protected]:0.95
|
| 92 |
-
Return:
|
| 93 |
-
(nc, ) or [].
|
| 94 |
-
"""
|
| 95 |
-
return self.all_ap.mean(1) if len(self.all_ap) else []
|
| 96 |
-
|
| 97 |
-
@property
|
| 98 |
-
def mp(self):
|
| 99 |
-
"""mean precision of all classes.
|
| 100 |
-
Return:
|
| 101 |
-
float.
|
| 102 |
-
"""
|
| 103 |
-
return self.p.mean() if len(self.p) else 0.0
|
| 104 |
-
|
| 105 |
-
@property
|
| 106 |
-
def mr(self):
|
| 107 |
-
"""mean recall of all classes.
|
| 108 |
-
Return:
|
| 109 |
-
float.
|
| 110 |
-
"""
|
| 111 |
-
return self.r.mean() if len(self.r) else 0.0
|
| 112 |
-
|
| 113 |
-
@property
|
| 114 |
-
def map50(self):
|
| 115 |
-
"""Mean [email protected] of all classes.
|
| 116 |
-
Return:
|
| 117 |
-
float.
|
| 118 |
-
"""
|
| 119 |
-
return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
|
| 120 |
-
|
| 121 |
-
@property
|
| 122 |
-
def map(self):
|
| 123 |
-
"""Mean [email protected]:0.95 of all classes.
|
| 124 |
-
Return:
|
| 125 |
-
float.
|
| 126 |
-
"""
|
| 127 |
-
return self.all_ap.mean() if len(self.all_ap) else 0.0
|
| 128 |
-
|
| 129 |
-
def mean_results(self):
|
| 130 |
-
"""Mean of results, return mp, mr, map50, map"""
|
| 131 |
-
return (self.mp, self.mr, self.map50, self.map)
|
| 132 |
-
|
| 133 |
-
def class_result(self, i):
|
| 134 |
-
"""class-aware result, return p[i], r[i], ap50[i], ap[i]"""
|
| 135 |
-
return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
|
| 136 |
-
|
| 137 |
-
def get_maps(self, nc):
|
| 138 |
-
maps = np.zeros(nc) + self.map
|
| 139 |
-
for i, c in enumerate(self.ap_class_index):
|
| 140 |
-
maps[c] = self.ap[i]
|
| 141 |
-
return maps
|
| 142 |
-
|
| 143 |
-
def update(self, results):
|
| 144 |
-
"""
|
| 145 |
-
Args:
|
| 146 |
-
results: tuple(p, r, ap, f1, ap_class)
|
| 147 |
-
"""
|
| 148 |
-
p, r, all_ap, f1, ap_class_index = results
|
| 149 |
-
self.p = p
|
| 150 |
-
self.r = r
|
| 151 |
-
self.all_ap = all_ap
|
| 152 |
-
self.f1 = f1
|
| 153 |
-
self.ap_class_index = ap_class_index
|
| 154 |
-
|
| 155 |
-
|
| 156 |
-
class Metrics:
|
| 157 |
-
"""Metric for boxes and masks."""
|
| 158 |
-
|
| 159 |
-
def __init__(self) -> None:
|
| 160 |
-
self.metric_box = Metric()
|
| 161 |
-
self.metric_mask = Metric()
|
| 162 |
-
|
| 163 |
-
def update(self, results):
|
| 164 |
-
"""
|
| 165 |
-
Args:
|
| 166 |
-
results: Dict{'boxes': Dict{}, 'masks': Dict{}}
|
| 167 |
-
"""
|
| 168 |
-
self.metric_box.update(list(results["boxes"].values()))
|
| 169 |
-
self.metric_mask.update(list(results["masks"].values()))
|
| 170 |
-
|
| 171 |
-
def mean_results(self):
|
| 172 |
-
return self.metric_box.mean_results() + self.metric_mask.mean_results()
|
| 173 |
-
|
| 174 |
-
def class_result(self, i):
|
| 175 |
-
return self.metric_box.class_result(i) + self.metric_mask.class_result(
|
| 176 |
-
i
|
| 177 |
-
)
|
| 178 |
-
|
| 179 |
-
def get_maps(self, nc):
|
| 180 |
-
return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
|
| 181 |
-
|
| 182 |
-
@property
|
| 183 |
-
def ap_class_index(self):
|
| 184 |
-
# boxes and masks have the same ap_class_index
|
| 185 |
-
return self.metric_box.ap_class_index
|
| 186 |
-
|
| 187 |
-
|
| 188 |
-
KEYS = [
|
| 189 |
-
"train/box_loss",
|
| 190 |
-
"train/seg_loss", # train loss
|
| 191 |
-
"train/obj_loss",
|
| 192 |
-
"train/cls_loss",
|
| 193 |
-
"metrics/precision(B)",
|
| 194 |
-
"metrics/recall(B)",
|
| 195 |
-
"metrics/mAP_0.5(B)",
|
| 196 |
-
"metrics/mAP_0.5:0.95(B)", # metrics
|
| 197 |
-
"metrics/precision(M)",
|
| 198 |
-
"metrics/recall(M)",
|
| 199 |
-
"metrics/mAP_0.5(M)",
|
| 200 |
-
"metrics/mAP_0.5:0.95(M)", # metrics
|
| 201 |
-
"val/box_loss",
|
| 202 |
-
"val/seg_loss", # val loss
|
| 203 |
-
"val/obj_loss",
|
| 204 |
-
"val/cls_loss",
|
| 205 |
-
"x/lr0",
|
| 206 |
-
"x/lr1",
|
| 207 |
-
"x/lr2",
|
| 208 |
-
]
|
| 209 |
-
|
| 210 |
-
BEST_KEYS = [
|
| 211 |
-
"best/epoch",
|
| 212 |
-
"best/precision(B)",
|
| 213 |
-
"best/recall(B)",
|
| 214 |
-
"best/mAP_0.5(B)",
|
| 215 |
-
"best/mAP_0.5:0.95(B)",
|
| 216 |
-
"best/precision(M)",
|
| 217 |
-
"best/recall(M)",
|
| 218 |
-
"best/mAP_0.5(M)",
|
| 219 |
-
"best/mAP_0.5:0.95(M)",
|
| 220 |
-
]
|
|
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spaces/AchyuthGamer/OpenGPT/g4f/Provider/Wewordle.py
DELETED
|
@@ -1,65 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
import random, string, time
|
| 4 |
-
from aiohttp import ClientSession
|
| 5 |
-
|
| 6 |
-
from .base_provider import AsyncProvider
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
class Wewordle(AsyncProvider):
|
| 10 |
-
url = "https://wewordle.org"
|
| 11 |
-
working = True
|
| 12 |
-
supports_gpt_35_turbo = True
|
| 13 |
-
|
| 14 |
-
@classmethod
|
| 15 |
-
async def create_async(
|
| 16 |
-
cls,
|
| 17 |
-
model: str,
|
| 18 |
-
messages: list[dict[str, str]],
|
| 19 |
-
proxy: str = None,
|
| 20 |
-
**kwargs
|
| 21 |
-
) -> str:
|
| 22 |
-
|
| 23 |
-
headers = {
|
| 24 |
-
"accept" : "*/*",
|
| 25 |
-
"pragma" : "no-cache",
|
| 26 |
-
"Content-Type" : "application/json",
|
| 27 |
-
"Connection" : "keep-alive"
|
| 28 |
-
}
|
| 29 |
-
|
| 30 |
-
_user_id = "".join(random.choices(f"{string.ascii_lowercase}{string.digits}", k=16))
|
| 31 |
-
_app_id = "".join(random.choices(f"{string.ascii_lowercase}{string.digits}", k=31))
|
| 32 |
-
_request_date = time.strftime("%Y-%m-%dT%H:%M:%S.000Z", time.gmtime())
|
| 33 |
-
data = {
|
| 34 |
-
"user" : _user_id,
|
| 35 |
-
"messages" : messages,
|
| 36 |
-
"subscriber": {
|
| 37 |
-
"originalPurchaseDate" : None,
|
| 38 |
-
"originalApplicationVersion" : None,
|
| 39 |
-
"allPurchaseDatesMillis" : {},
|
| 40 |
-
"entitlements" : {"active": {}, "all": {}},
|
| 41 |
-
"allPurchaseDates" : {},
|
| 42 |
-
"allExpirationDatesMillis" : {},
|
| 43 |
-
"allExpirationDates" : {},
|
| 44 |
-
"originalAppUserId" : f"$RCAnonymousID:{_app_id}",
|
| 45 |
-
"latestExpirationDate" : None,
|
| 46 |
-
"requestDate" : _request_date,
|
| 47 |
-
"latestExpirationDateMillis" : None,
|
| 48 |
-
"nonSubscriptionTransactions" : [],
|
| 49 |
-
"originalPurchaseDateMillis" : None,
|
| 50 |
-
"managementURL" : None,
|
| 51 |
-
"allPurchasedProductIdentifiers": [],
|
| 52 |
-
"firstSeen" : _request_date,
|
| 53 |
-
"activeSubscriptions" : [],
|
| 54 |
-
}
|
| 55 |
-
}
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
async with ClientSession(
|
| 59 |
-
headers=headers
|
| 60 |
-
) as session:
|
| 61 |
-
async with session.post(f"{cls.url}/gptapi/v1/android/turbo", proxy=proxy, json=data) as response:
|
| 62 |
-
response.raise_for_status()
|
| 63 |
-
content = (await response.json())["message"]["content"]
|
| 64 |
-
if content:
|
| 65 |
-
return content
|
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateHolyGrail.js
DELETED
|
@@ -1,21 +0,0 @@
|
|
| 1 |
-
import MergeStyle from './utils/MergeStyle.js';
|
| 2 |
-
import HolyGrail from '../../holygrail/HolyGrail.js';
|
| 3 |
-
import CreateChild from './utils/CreateChild.js';
|
| 4 |
-
|
| 5 |
-
var CreateDialog = function (scene, data, view, styles, customBuilders) {
|
| 6 |
-
data = MergeStyle(data, styles);
|
| 7 |
-
|
| 8 |
-
// Replace data by child game object
|
| 9 |
-
CreateChild(scene, data, 'background', view, styles, customBuilders);
|
| 10 |
-
CreateChild(scene, data, 'content', view, styles, customBuilders);
|
| 11 |
-
CreateChild(scene, data, 'leftSide', view, styles, customBuilders);
|
| 12 |
-
CreateChild(scene, data, 'rightSide', view, styles, customBuilders);
|
| 13 |
-
CreateChild(scene, data, 'header', view, styles, customBuilders);
|
| 14 |
-
CreateChild(scene, data, 'footer', view, styles, customBuilders);
|
| 15 |
-
|
| 16 |
-
var gameObject = new HolyGrail(scene, data);
|
| 17 |
-
scene.add.existing(gameObject);
|
| 18 |
-
return gameObject;
|
| 19 |
-
};
|
| 20 |
-
|
| 21 |
-
export default CreateDialog;
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/OverlapSizer.d.ts
DELETED
|
@@ -1,109 +0,0 @@
|
|
| 1 |
-
// import * as Phaser from 'phaser';
|
| 2 |
-
import BaseSizer from '../basesizer/BaseSizer.js';
|
| 3 |
-
|
| 4 |
-
export default OverlapSizer;
|
| 5 |
-
|
| 6 |
-
declare namespace OverlapSizer {
|
| 7 |
-
type AlignTypes = number | 'center' | 'left' | 'right' | 'top' | 'bottom' |
|
| 8 |
-
'left-top' | 'left-center' | 'left-bottom' |
|
| 9 |
-
'center-top' | 'center-center' | 'center-bottom' |
|
| 10 |
-
'right-top' | 'right-center' | 'right-bottom';
|
| 11 |
-
|
| 12 |
-
type PaddingTypes = number |
|
| 13 |
-
{
|
| 14 |
-
left?: number,
|
| 15 |
-
right?: number,
|
| 16 |
-
top?: number,
|
| 17 |
-
bottom?: number
|
| 18 |
-
};
|
| 19 |
-
|
| 20 |
-
interface IConfig extends BaseSizer.IConfig {
|
| 21 |
-
x?: number,
|
| 22 |
-
y?: number,
|
| 23 |
-
width?: number,
|
| 24 |
-
height?: number,
|
| 25 |
-
}
|
| 26 |
-
}
|
| 27 |
-
|
| 28 |
-
declare class OverlapSizer extends BaseSizer {
|
| 29 |
-
sizerChildren: { [name: string]: Phaser.GameObjects.GameObject };
|
| 30 |
-
|
| 31 |
-
constructor(
|
| 32 |
-
scene: Phaser.Scene,
|
| 33 |
-
config?: OverlapSizer.IConfig
|
| 34 |
-
);
|
| 35 |
-
|
| 36 |
-
constructor(
|
| 37 |
-
scene: Phaser.Scene,
|
| 38 |
-
x: number, y: number,
|
| 39 |
-
config?: OverlapSizer.IConfig
|
| 40 |
-
);
|
| 41 |
-
|
| 42 |
-
constructor(
|
| 43 |
-
scene: Phaser.Scene,
|
| 44 |
-
x: number, y: number,
|
| 45 |
-
width: number, height: number,
|
| 46 |
-
config?: OverlapSizer.IConfig
|
| 47 |
-
);
|
| 48 |
-
|
| 49 |
-
add(
|
| 50 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 51 |
-
config?: {
|
| 52 |
-
key?: string,
|
| 53 |
-
|
| 54 |
-
align?: OverlapSizer.AlignTypes,
|
| 55 |
-
offsetX?: number,
|
| 56 |
-
offsetY?: number,
|
| 57 |
-
|
| 58 |
-
padding?: OverlapSizer.PaddingTypes,
|
| 59 |
-
|
| 60 |
-
expand?: boolean |
|
| 61 |
-
{
|
| 62 |
-
width?: boolean,
|
| 63 |
-
height?: boolean,
|
| 64 |
-
},
|
| 65 |
-
|
| 66 |
-
minWidth?: number,
|
| 67 |
-
|
| 68 |
-
minHeight?: number,
|
| 69 |
-
}
|
| 70 |
-
): this;
|
| 71 |
-
|
| 72 |
-
add(
|
| 73 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 74 |
-
key?: string,
|
| 75 |
-
align?: OverlapSizer.AlignTypes,
|
| 76 |
-
padding?: OverlapSizer.PaddingTypes,
|
| 77 |
-
expand?: boolean |
|
| 78 |
-
{
|
| 79 |
-
width?: boolean,
|
| 80 |
-
height?: boolean,
|
| 81 |
-
},
|
| 82 |
-
minWidth?: number,
|
| 83 |
-
minHeight?: number,
|
| 84 |
-
offsetX?: number,
|
| 85 |
-
offsetY?: number,
|
| 86 |
-
): this;
|
| 87 |
-
|
| 88 |
-
remove(
|
| 89 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 90 |
-
destroyChild?: boolean
|
| 91 |
-
): this;
|
| 92 |
-
|
| 93 |
-
remove(
|
| 94 |
-
key: string,
|
| 95 |
-
destroyChild?: boolean
|
| 96 |
-
): this;
|
| 97 |
-
|
| 98 |
-
removeAll(
|
| 99 |
-
destroyChild?: boolean
|
| 100 |
-
): this;
|
| 101 |
-
|
| 102 |
-
clear(
|
| 103 |
-
destroyChild?: boolean
|
| 104 |
-
): this;
|
| 105 |
-
|
| 106 |
-
childToKey(
|
| 107 |
-
gameObject: Phaser.GameObjects.GameObject
|
| 108 |
-
): string;
|
| 109 |
-
}
|
|
|
|
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|
spaces/Alican/pixera/util/get_data.py
DELETED
|
@@ -1,110 +0,0 @@
|
|
| 1 |
-
from __future__ import print_function
|
| 2 |
-
import os
|
| 3 |
-
import tarfile
|
| 4 |
-
import requests
|
| 5 |
-
from warnings import warn
|
| 6 |
-
from zipfile import ZipFile
|
| 7 |
-
from bs4 import BeautifulSoup
|
| 8 |
-
from os.path import abspath, isdir, join, basename
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class GetData(object):
|
| 12 |
-
"""A Python script for downloading CycleGAN or pix2pix datasets.
|
| 13 |
-
|
| 14 |
-
Parameters:
|
| 15 |
-
technique (str) -- One of: 'cyclegan' or 'pix2pix'.
|
| 16 |
-
verbose (bool) -- If True, print additional information.
|
| 17 |
-
|
| 18 |
-
Examples:
|
| 19 |
-
>>> from util.get_data import GetData
|
| 20 |
-
>>> gd = GetData(technique='cyclegan')
|
| 21 |
-
>>> new_data_path = gd.get(save_path='./datasets') # options will be displayed.
|
| 22 |
-
|
| 23 |
-
Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh'
|
| 24 |
-
and 'scripts/download_cyclegan_model.sh'.
|
| 25 |
-
"""
|
| 26 |
-
|
| 27 |
-
def __init__(self, technique='cyclegan', verbose=True):
|
| 28 |
-
url_dict = {
|
| 29 |
-
'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/',
|
| 30 |
-
'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'
|
| 31 |
-
}
|
| 32 |
-
self.url = url_dict.get(technique.lower())
|
| 33 |
-
self._verbose = verbose
|
| 34 |
-
|
| 35 |
-
def _print(self, text):
|
| 36 |
-
if self._verbose:
|
| 37 |
-
print(text)
|
| 38 |
-
|
| 39 |
-
@staticmethod
|
| 40 |
-
def _get_options(r):
|
| 41 |
-
soup = BeautifulSoup(r.text, 'lxml')
|
| 42 |
-
options = [h.text for h in soup.find_all('a', href=True)
|
| 43 |
-
if h.text.endswith(('.zip', 'tar.gz'))]
|
| 44 |
-
return options
|
| 45 |
-
|
| 46 |
-
def _present_options(self):
|
| 47 |
-
r = requests.get(self.url)
|
| 48 |
-
options = self._get_options(r)
|
| 49 |
-
print('Options:\n')
|
| 50 |
-
for i, o in enumerate(options):
|
| 51 |
-
print("{0}: {1}".format(i, o))
|
| 52 |
-
choice = input("\nPlease enter the number of the "
|
| 53 |
-
"dataset above you wish to download:")
|
| 54 |
-
return options[int(choice)]
|
| 55 |
-
|
| 56 |
-
def _download_data(self, dataset_url, save_path):
|
| 57 |
-
if not isdir(save_path):
|
| 58 |
-
os.makedirs(save_path)
|
| 59 |
-
|
| 60 |
-
base = basename(dataset_url)
|
| 61 |
-
temp_save_path = join(save_path, base)
|
| 62 |
-
|
| 63 |
-
with open(temp_save_path, "wb") as f:
|
| 64 |
-
r = requests.get(dataset_url)
|
| 65 |
-
f.write(r.content)
|
| 66 |
-
|
| 67 |
-
if base.endswith('.tar.gz'):
|
| 68 |
-
obj = tarfile.open(temp_save_path)
|
| 69 |
-
elif base.endswith('.zip'):
|
| 70 |
-
obj = ZipFile(temp_save_path, 'r')
|
| 71 |
-
else:
|
| 72 |
-
raise ValueError("Unknown File Type: {0}.".format(base))
|
| 73 |
-
|
| 74 |
-
self._print("Unpacking Data...")
|
| 75 |
-
obj.extractall(save_path)
|
| 76 |
-
obj.close()
|
| 77 |
-
os.remove(temp_save_path)
|
| 78 |
-
|
| 79 |
-
def get(self, save_path, dataset=None):
|
| 80 |
-
"""
|
| 81 |
-
|
| 82 |
-
Download a dataset.
|
| 83 |
-
|
| 84 |
-
Parameters:
|
| 85 |
-
save_path (str) -- A directory to save the data to.
|
| 86 |
-
dataset (str) -- (optional). A specific dataset to download.
|
| 87 |
-
Note: this must include the file extension.
|
| 88 |
-
If None, options will be presented for you
|
| 89 |
-
to choose from.
|
| 90 |
-
|
| 91 |
-
Returns:
|
| 92 |
-
save_path_full (str) -- the absolute path to the downloaded data.
|
| 93 |
-
|
| 94 |
-
"""
|
| 95 |
-
if dataset is None:
|
| 96 |
-
selected_dataset = self._present_options()
|
| 97 |
-
else:
|
| 98 |
-
selected_dataset = dataset
|
| 99 |
-
|
| 100 |
-
save_path_full = join(save_path, selected_dataset.split('.')[0])
|
| 101 |
-
|
| 102 |
-
if isdir(save_path_full):
|
| 103 |
-
warn("\n'{0}' already exists. Voiding Download.".format(
|
| 104 |
-
save_path_full))
|
| 105 |
-
else:
|
| 106 |
-
self._print('Downloading Data...')
|
| 107 |
-
url = "{0}/{1}".format(self.url, selected_dataset)
|
| 108 |
-
self._download_data(url, save_path=save_path)
|
| 109 |
-
|
| 110 |
-
return abspath(save_path_full)
|
|
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|
spaces/Alpaca233/SadTalker/src/face3d/util/skin_mask.py
DELETED
|
@@ -1,125 +0,0 @@
|
|
| 1 |
-
"""This script is to generate skin attention mask for Deep3DFaceRecon_pytorch
|
| 2 |
-
"""
|
| 3 |
-
|
| 4 |
-
import math
|
| 5 |
-
import numpy as np
|
| 6 |
-
import os
|
| 7 |
-
import cv2
|
| 8 |
-
|
| 9 |
-
class GMM:
|
| 10 |
-
def __init__(self, dim, num, w, mu, cov, cov_det, cov_inv):
|
| 11 |
-
self.dim = dim # feature dimension
|
| 12 |
-
self.num = num # number of Gaussian components
|
| 13 |
-
self.w = w # weights of Gaussian components (a list of scalars)
|
| 14 |
-
self.mu= mu # mean of Gaussian components (a list of 1xdim vectors)
|
| 15 |
-
self.cov = cov # covariance matrix of Gaussian components (a list of dimxdim matrices)
|
| 16 |
-
self.cov_det = cov_det # pre-computed determinet of covariance matrices (a list of scalars)
|
| 17 |
-
self.cov_inv = cov_inv # pre-computed inverse covariance matrices (a list of dimxdim matrices)
|
| 18 |
-
|
| 19 |
-
self.factor = [0]*num
|
| 20 |
-
for i in range(self.num):
|
| 21 |
-
self.factor[i] = (2*math.pi)**(self.dim/2) * self.cov_det[i]**0.5
|
| 22 |
-
|
| 23 |
-
def likelihood(self, data):
|
| 24 |
-
assert(data.shape[1] == self.dim)
|
| 25 |
-
N = data.shape[0]
|
| 26 |
-
lh = np.zeros(N)
|
| 27 |
-
|
| 28 |
-
for i in range(self.num):
|
| 29 |
-
data_ = data - self.mu[i]
|
| 30 |
-
|
| 31 |
-
tmp = np.matmul(data_,self.cov_inv[i]) * data_
|
| 32 |
-
tmp = np.sum(tmp,axis=1)
|
| 33 |
-
power = -0.5 * tmp
|
| 34 |
-
|
| 35 |
-
p = np.array([math.exp(power[j]) for j in range(N)])
|
| 36 |
-
p = p/self.factor[i]
|
| 37 |
-
lh += p*self.w[i]
|
| 38 |
-
|
| 39 |
-
return lh
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def _rgb2ycbcr(rgb):
|
| 43 |
-
m = np.array([[65.481, 128.553, 24.966],
|
| 44 |
-
[-37.797, -74.203, 112],
|
| 45 |
-
[112, -93.786, -18.214]])
|
| 46 |
-
shape = rgb.shape
|
| 47 |
-
rgb = rgb.reshape((shape[0] * shape[1], 3))
|
| 48 |
-
ycbcr = np.dot(rgb, m.transpose() / 255.)
|
| 49 |
-
ycbcr[:, 0] += 16.
|
| 50 |
-
ycbcr[:, 1:] += 128.
|
| 51 |
-
return ycbcr.reshape(shape)
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
def _bgr2ycbcr(bgr):
|
| 55 |
-
rgb = bgr[..., ::-1]
|
| 56 |
-
return _rgb2ycbcr(rgb)
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
gmm_skin_w = [0.24063933, 0.16365987, 0.26034665, 0.33535415]
|
| 60 |
-
gmm_skin_mu = [np.array([113.71862, 103.39613, 164.08226]),
|
| 61 |
-
np.array([150.19858, 105.18467, 155.51428]),
|
| 62 |
-
np.array([183.92976, 107.62468, 152.71820]),
|
| 63 |
-
np.array([114.90524, 113.59782, 151.38217])]
|
| 64 |
-
gmm_skin_cov_det = [5692842.5, 5851930.5, 2329131., 1585971.]
|
| 65 |
-
gmm_skin_cov_inv = [np.array([[0.0019472069, 0.0020450759, -0.00060243998],[0.0020450759, 0.017700525, 0.0051420014],[-0.00060243998, 0.0051420014, 0.0081308950]]),
|
| 66 |
-
np.array([[0.0027110141, 0.0011036990, 0.0023122299],[0.0011036990, 0.010707724, 0.010742856],[0.0023122299, 0.010742856, 0.017481629]]),
|
| 67 |
-
np.array([[0.0048026871, 0.00022935172, 0.0077668377],[0.00022935172, 0.011729696, 0.0081661865],[0.0077668377, 0.0081661865, 0.025374353]]),
|
| 68 |
-
np.array([[0.0011989699, 0.0022453172, -0.0010748957],[0.0022453172, 0.047758564, 0.020332102],[-0.0010748957, 0.020332102, 0.024502251]])]
|
| 69 |
-
|
| 70 |
-
gmm_skin = GMM(3, 4, gmm_skin_w, gmm_skin_mu, [], gmm_skin_cov_det, gmm_skin_cov_inv)
|
| 71 |
-
|
| 72 |
-
gmm_nonskin_w = [0.12791070, 0.31130761, 0.34245777, 0.21832393]
|
| 73 |
-
gmm_nonskin_mu = [np.array([99.200851, 112.07533, 140.20602]),
|
| 74 |
-
np.array([110.91392, 125.52969, 130.19237]),
|
| 75 |
-
np.array([129.75864, 129.96107, 126.96808]),
|
| 76 |
-
np.array([112.29587, 128.85121, 129.05431])]
|
| 77 |
-
gmm_nonskin_cov_det = [458703648., 6466488., 90611376., 133097.63]
|
| 78 |
-
gmm_nonskin_cov_inv = [np.array([[0.00085371657, 0.00071197288, 0.00023958916],[0.00071197288, 0.0025935620, 0.00076557708],[0.00023958916, 0.00076557708, 0.0015042332]]),
|
| 79 |
-
np.array([[0.00024650150, 0.00045542428, 0.00015019422],[0.00045542428, 0.026412144, 0.018419769],[0.00015019422, 0.018419769, 0.037497383]]),
|
| 80 |
-
np.array([[0.00037054974, 0.00038146760, 0.00040408765],[0.00038146760, 0.0085505722, 0.0079136286],[0.00040408765, 0.0079136286, 0.010982352]]),
|
| 81 |
-
np.array([[0.00013709733, 0.00051228428, 0.00012777430],[0.00051228428, 0.28237113, 0.10528370],[0.00012777430, 0.10528370, 0.23468947]])]
|
| 82 |
-
|
| 83 |
-
gmm_nonskin = GMM(3, 4, gmm_nonskin_w, gmm_nonskin_mu, [], gmm_nonskin_cov_det, gmm_nonskin_cov_inv)
|
| 84 |
-
|
| 85 |
-
prior_skin = 0.8
|
| 86 |
-
prior_nonskin = 1 - prior_skin
|
| 87 |
-
|
| 88 |
-
|
| 89 |
-
# calculate skin attention mask
|
| 90 |
-
def skinmask(imbgr):
|
| 91 |
-
im = _bgr2ycbcr(imbgr)
|
| 92 |
-
|
| 93 |
-
data = im.reshape((-1,3))
|
| 94 |
-
|
| 95 |
-
lh_skin = gmm_skin.likelihood(data)
|
| 96 |
-
lh_nonskin = gmm_nonskin.likelihood(data)
|
| 97 |
-
|
| 98 |
-
tmp1 = prior_skin * lh_skin
|
| 99 |
-
tmp2 = prior_nonskin * lh_nonskin
|
| 100 |
-
post_skin = tmp1 / (tmp1+tmp2) # posterior probability
|
| 101 |
-
|
| 102 |
-
post_skin = post_skin.reshape((im.shape[0],im.shape[1]))
|
| 103 |
-
|
| 104 |
-
post_skin = np.round(post_skin*255)
|
| 105 |
-
post_skin = post_skin.astype(np.uint8)
|
| 106 |
-
post_skin = np.tile(np.expand_dims(post_skin,2),[1,1,3]) # reshape to H*W*3
|
| 107 |
-
|
| 108 |
-
return post_skin
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def get_skin_mask(img_path):
|
| 112 |
-
print('generating skin masks......')
|
| 113 |
-
names = [i for i in sorted(os.listdir(
|
| 114 |
-
img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i]
|
| 115 |
-
save_path = os.path.join(img_path, 'mask')
|
| 116 |
-
if not os.path.isdir(save_path):
|
| 117 |
-
os.makedirs(save_path)
|
| 118 |
-
|
| 119 |
-
for i in range(0, len(names)):
|
| 120 |
-
name = names[i]
|
| 121 |
-
print('%05d' % (i), ' ', name)
|
| 122 |
-
full_image_name = os.path.join(img_path, name)
|
| 123 |
-
img = cv2.imread(full_image_name).astype(np.float32)
|
| 124 |
-
skin_img = skinmask(img)
|
| 125 |
-
cv2.imwrite(os.path.join(save_path, name), skin_img.astype(np.uint8))
|
|
|
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/xformers.md
DELETED
|
@@ -1,36 +0,0 @@
|
|
| 1 |
-
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
|
| 3 |
-
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
| 4 |
-
the License. You may obtain a copy of the License at
|
| 5 |
-
|
| 6 |
-
http://www.apache.org/licenses/LICENSE-2.0
|
| 7 |
-
|
| 8 |
-
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
| 9 |
-
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
| 10 |
-
specific language governing permissions and limitations under the License.
|
| 11 |
-
-->
|
| 12 |
-
|
| 13 |
-
# xFormers 설치하기
|
| 14 |
-
|
| 15 |
-
추론과 학습 모두에 [xFormers](https://github.com/facebookresearch/xformers)를 사용하는 것이 좋습니다.
|
| 16 |
-
자체 테스트로 어텐션 블록에서 수행된 최적화가 더 빠른 속도와 적은 메모리 소비를 확인했습니다.
|
| 17 |
-
|
| 18 |
-
2023년 1월에 출시된 xFormers 버전 '0.0.16'부터 사전 빌드된 pip wheel을 사용하여 쉽게 설치할 수 있습니다:
|
| 19 |
-
|
| 20 |
-
```bash
|
| 21 |
-
pip install xformers
|
| 22 |
-
```
|
| 23 |
-
|
| 24 |
-
<Tip>
|
| 25 |
-
|
| 26 |
-
xFormers PIP 패키지에는 최신 버전의 PyTorch(xFormers 0.0.16에 1.13.1)가 필요합니다. 이전 버전의 PyTorch를 사용해야 하는 경우 [프로젝트 지침](https://github.com/facebookresearch/xformers#installing-xformers)의 소스를 사용해 xFormers를 설치하는 것이 좋습니다.
|
| 27 |
-
|
| 28 |
-
</Tip>
|
| 29 |
-
|
| 30 |
-
xFormers를 설치하면, [여기](fp16#memory-efficient-attention)서 설명한 것처럼 'enable_xformers_memory_efficient_attention()'을 사용하여 추론 속도를 높이고 메모리 소비를 줄일 수 있습니다.
|
| 31 |
-
|
| 32 |
-
<Tip warning={true}>
|
| 33 |
-
|
| 34 |
-
[이 이슈](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212)에 따르면 xFormers `v0.0.16`에서 GPU를 사용한 학습(파인 튜닝 또는 Dreambooth)을 할 수 없습니다. 해당 문제가 발견되면. 해당 코멘트를 참고해 development 버전을 설치하세요.
|
| 35 |
-
|
| 36 |
-
</Tip>
|
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_check_copies.py
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@@ -1,120 +0,0 @@
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| 1 |
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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| 2 |
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#
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| 3 |
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# Licensed under the Apache License, Version 2.0 (the "License");
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| 4 |
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# you may not use this file except in compliance with the License.
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| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
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| 14 |
-
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| 15 |
-
import os
|
| 16 |
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import re
|
| 17 |
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import shutil
|
| 18 |
-
import sys
|
| 19 |
-
import tempfile
|
| 20 |
-
import unittest
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| 21 |
-
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| 22 |
-
import black
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| 23 |
-
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| 24 |
-
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| 25 |
-
git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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| 26 |
-
sys.path.append(os.path.join(git_repo_path, "utils"))
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| 27 |
-
|
| 28 |
-
import check_copies # noqa: E402
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| 29 |
-
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| 30 |
-
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| 31 |
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# This is the reference code that will be used in the tests.
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| 32 |
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# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
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| 33 |
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REFERENCE_CODE = """ \"""
|
| 34 |
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Output class for the scheduler's step function output.
|
| 35 |
-
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| 36 |
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Args:
|
| 37 |
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prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
|
| 38 |
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
|
| 39 |
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denoising loop.
|
| 40 |
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pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
|
| 41 |
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The predicted denoised sample (x_{0}) based on the model output from the current timestep.
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| 42 |
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`pred_original_sample` can be used to preview progress or for guidance.
|
| 43 |
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\"""
|
| 44 |
-
|
| 45 |
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prev_sample: torch.FloatTensor
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| 46 |
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pred_original_sample: Optional[torch.FloatTensor] = None
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| 47 |
-
"""
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| 48 |
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| 49 |
-
|
| 50 |
-
class CopyCheckTester(unittest.TestCase):
|
| 51 |
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def setUp(self):
|
| 52 |
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self.diffusers_dir = tempfile.mkdtemp()
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| 53 |
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os.makedirs(os.path.join(self.diffusers_dir, "schedulers/"))
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| 54 |
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check_copies.DIFFUSERS_PATH = self.diffusers_dir
|
| 55 |
-
shutil.copy(
|
| 56 |
-
os.path.join(git_repo_path, "src/diffusers/schedulers/scheduling_ddpm.py"),
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| 57 |
-
os.path.join(self.diffusers_dir, "schedulers/scheduling_ddpm.py"),
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| 58 |
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)
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| 59 |
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| 60 |
-
def tearDown(self):
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| 61 |
-
check_copies.DIFFUSERS_PATH = "src/diffusers"
|
| 62 |
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shutil.rmtree(self.diffusers_dir)
|
| 63 |
-
|
| 64 |
-
def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None):
|
| 65 |
-
code = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
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| 66 |
-
if overwrite_result is not None:
|
| 67 |
-
expected = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
|
| 68 |
-
mode = black.Mode(target_versions={black.TargetVersion.PY35}, line_length=119)
|
| 69 |
-
code = black.format_str(code, mode=mode)
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| 70 |
-
fname = os.path.join(self.diffusers_dir, "new_code.py")
|
| 71 |
-
with open(fname, "w", newline="\n") as f:
|
| 72 |
-
f.write(code)
|
| 73 |
-
if overwrite_result is None:
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| 74 |
-
self.assertTrue(len(check_copies.is_copy_consistent(fname)) == 0)
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| 75 |
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else:
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| 76 |
-
check_copies.is_copy_consistent(f.name, overwrite=True)
|
| 77 |
-
with open(fname, "r") as f:
|
| 78 |
-
self.assertTrue(f.read(), expected)
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| 79 |
-
|
| 80 |
-
def test_find_code_in_diffusers(self):
|
| 81 |
-
code = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput")
|
| 82 |
-
self.assertEqual(code, REFERENCE_CODE)
|
| 83 |
-
|
| 84 |
-
def test_is_copy_consistent(self):
|
| 85 |
-
# Base copy consistency
|
| 86 |
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self.check_copy_consistency(
|
| 87 |
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"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput",
|
| 88 |
-
"DDPMSchedulerOutput",
|
| 89 |
-
REFERENCE_CODE + "\n",
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| 90 |
-
)
|
| 91 |
-
|
| 92 |
-
# With no empty line at the end
|
| 93 |
-
self.check_copy_consistency(
|
| 94 |
-
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput",
|
| 95 |
-
"DDPMSchedulerOutput",
|
| 96 |
-
REFERENCE_CODE,
|
| 97 |
-
)
|
| 98 |
-
|
| 99 |
-
# Copy consistency with rename
|
| 100 |
-
self.check_copy_consistency(
|
| 101 |
-
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test",
|
| 102 |
-
"TestSchedulerOutput",
|
| 103 |
-
re.sub("DDPM", "Test", REFERENCE_CODE),
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| 104 |
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)
|
| 105 |
-
|
| 106 |
-
# Copy consistency with a really long name
|
| 107 |
-
long_class_name = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
|
| 108 |
-
self.check_copy_consistency(
|
| 109 |
-
f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}",
|
| 110 |
-
f"{long_class_name}SchedulerOutput",
|
| 111 |
-
re.sub("Bert", long_class_name, REFERENCE_CODE),
|
| 112 |
-
)
|
| 113 |
-
|
| 114 |
-
# Copy consistency with overwrite
|
| 115 |
-
self.check_copy_consistency(
|
| 116 |
-
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test",
|
| 117 |
-
"TestSchedulerOutput",
|
| 118 |
-
REFERENCE_CODE,
|
| 119 |
-
overwrite_result=re.sub("DDPM", "Test", REFERENCE_CODE),
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| 120 |
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)
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_score_sde_ve.py
DELETED
|
@@ -1,189 +0,0 @@
|
|
| 1 |
-
import tempfile
|
| 2 |
-
import unittest
|
| 3 |
-
|
| 4 |
-
import numpy as np
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
from diffusers import ScoreSdeVeScheduler
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
class ScoreSdeVeSchedulerTest(unittest.TestCase):
|
| 11 |
-
# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
|
| 12 |
-
scheduler_classes = (ScoreSdeVeScheduler,)
|
| 13 |
-
forward_default_kwargs = ()
|
| 14 |
-
|
| 15 |
-
@property
|
| 16 |
-
def dummy_sample(self):
|
| 17 |
-
batch_size = 4
|
| 18 |
-
num_channels = 3
|
| 19 |
-
height = 8
|
| 20 |
-
width = 8
|
| 21 |
-
|
| 22 |
-
sample = torch.rand((batch_size, num_channels, height, width))
|
| 23 |
-
|
| 24 |
-
return sample
|
| 25 |
-
|
| 26 |
-
@property
|
| 27 |
-
def dummy_sample_deter(self):
|
| 28 |
-
batch_size = 4
|
| 29 |
-
num_channels = 3
|
| 30 |
-
height = 8
|
| 31 |
-
width = 8
|
| 32 |
-
|
| 33 |
-
num_elems = batch_size * num_channels * height * width
|
| 34 |
-
sample = torch.arange(num_elems)
|
| 35 |
-
sample = sample.reshape(num_channels, height, width, batch_size)
|
| 36 |
-
sample = sample / num_elems
|
| 37 |
-
sample = sample.permute(3, 0, 1, 2)
|
| 38 |
-
|
| 39 |
-
return sample
|
| 40 |
-
|
| 41 |
-
def dummy_model(self):
|
| 42 |
-
def model(sample, t, *args):
|
| 43 |
-
return sample * t / (t + 1)
|
| 44 |
-
|
| 45 |
-
return model
|
| 46 |
-
|
| 47 |
-
def get_scheduler_config(self, **kwargs):
|
| 48 |
-
config = {
|
| 49 |
-
"num_train_timesteps": 2000,
|
| 50 |
-
"snr": 0.15,
|
| 51 |
-
"sigma_min": 0.01,
|
| 52 |
-
"sigma_max": 1348,
|
| 53 |
-
"sampling_eps": 1e-5,
|
| 54 |
-
}
|
| 55 |
-
|
| 56 |
-
config.update(**kwargs)
|
| 57 |
-
return config
|
| 58 |
-
|
| 59 |
-
def check_over_configs(self, time_step=0, **config):
|
| 60 |
-
kwargs = dict(self.forward_default_kwargs)
|
| 61 |
-
|
| 62 |
-
for scheduler_class in self.scheduler_classes:
|
| 63 |
-
sample = self.dummy_sample
|
| 64 |
-
residual = 0.1 * sample
|
| 65 |
-
|
| 66 |
-
scheduler_config = self.get_scheduler_config(**config)
|
| 67 |
-
scheduler = scheduler_class(**scheduler_config)
|
| 68 |
-
|
| 69 |
-
with tempfile.TemporaryDirectory() as tmpdirname:
|
| 70 |
-
scheduler.save_config(tmpdirname)
|
| 71 |
-
new_scheduler = scheduler_class.from_pretrained(tmpdirname)
|
| 72 |
-
|
| 73 |
-
output = scheduler.step_pred(
|
| 74 |
-
residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
|
| 75 |
-
).prev_sample
|
| 76 |
-
new_output = new_scheduler.step_pred(
|
| 77 |
-
residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
|
| 78 |
-
).prev_sample
|
| 79 |
-
|
| 80 |
-
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
| 81 |
-
|
| 82 |
-
output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
|
| 83 |
-
new_output = new_scheduler.step_correct(
|
| 84 |
-
residual, sample, generator=torch.manual_seed(0), **kwargs
|
| 85 |
-
).prev_sample
|
| 86 |
-
|
| 87 |
-
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"
|
| 88 |
-
|
| 89 |
-
def check_over_forward(self, time_step=0, **forward_kwargs):
|
| 90 |
-
kwargs = dict(self.forward_default_kwargs)
|
| 91 |
-
kwargs.update(forward_kwargs)
|
| 92 |
-
|
| 93 |
-
for scheduler_class in self.scheduler_classes:
|
| 94 |
-
sample = self.dummy_sample
|
| 95 |
-
residual = 0.1 * sample
|
| 96 |
-
|
| 97 |
-
scheduler_config = self.get_scheduler_config()
|
| 98 |
-
scheduler = scheduler_class(**scheduler_config)
|
| 99 |
-
|
| 100 |
-
with tempfile.TemporaryDirectory() as tmpdirname:
|
| 101 |
-
scheduler.save_config(tmpdirname)
|
| 102 |
-
new_scheduler = scheduler_class.from_pretrained(tmpdirname)
|
| 103 |
-
|
| 104 |
-
output = scheduler.step_pred(
|
| 105 |
-
residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
|
| 106 |
-
).prev_sample
|
| 107 |
-
new_output = new_scheduler.step_pred(
|
| 108 |
-
residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
|
| 109 |
-
).prev_sample
|
| 110 |
-
|
| 111 |
-
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
| 112 |
-
|
| 113 |
-
output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
|
| 114 |
-
new_output = new_scheduler.step_correct(
|
| 115 |
-
residual, sample, generator=torch.manual_seed(0), **kwargs
|
| 116 |
-
).prev_sample
|
| 117 |
-
|
| 118 |
-
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"
|
| 119 |
-
|
| 120 |
-
def test_timesteps(self):
|
| 121 |
-
for timesteps in [10, 100, 1000]:
|
| 122 |
-
self.check_over_configs(num_train_timesteps=timesteps)
|
| 123 |
-
|
| 124 |
-
def test_sigmas(self):
|
| 125 |
-
for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01], [1, 100, 1000]):
|
| 126 |
-
self.check_over_configs(sigma_min=sigma_min, sigma_max=sigma_max)
|
| 127 |
-
|
| 128 |
-
def test_time_indices(self):
|
| 129 |
-
for t in [0.1, 0.5, 0.75]:
|
| 130 |
-
self.check_over_forward(time_step=t)
|
| 131 |
-
|
| 132 |
-
def test_full_loop_no_noise(self):
|
| 133 |
-
kwargs = dict(self.forward_default_kwargs)
|
| 134 |
-
|
| 135 |
-
scheduler_class = self.scheduler_classes[0]
|
| 136 |
-
scheduler_config = self.get_scheduler_config()
|
| 137 |
-
scheduler = scheduler_class(**scheduler_config)
|
| 138 |
-
|
| 139 |
-
num_inference_steps = 3
|
| 140 |
-
|
| 141 |
-
model = self.dummy_model()
|
| 142 |
-
sample = self.dummy_sample_deter
|
| 143 |
-
|
| 144 |
-
scheduler.set_sigmas(num_inference_steps)
|
| 145 |
-
scheduler.set_timesteps(num_inference_steps)
|
| 146 |
-
generator = torch.manual_seed(0)
|
| 147 |
-
|
| 148 |
-
for i, t in enumerate(scheduler.timesteps):
|
| 149 |
-
sigma_t = scheduler.sigmas[i]
|
| 150 |
-
|
| 151 |
-
for _ in range(scheduler.config.correct_steps):
|
| 152 |
-
with torch.no_grad():
|
| 153 |
-
model_output = model(sample, sigma_t)
|
| 154 |
-
sample = scheduler.step_correct(model_output, sample, generator=generator, **kwargs).prev_sample
|
| 155 |
-
|
| 156 |
-
with torch.no_grad():
|
| 157 |
-
model_output = model(sample, sigma_t)
|
| 158 |
-
|
| 159 |
-
output = scheduler.step_pred(model_output, t, sample, generator=generator, **kwargs)
|
| 160 |
-
sample, _ = output.prev_sample, output.prev_sample_mean
|
| 161 |
-
|
| 162 |
-
result_sum = torch.sum(torch.abs(sample))
|
| 163 |
-
result_mean = torch.mean(torch.abs(sample))
|
| 164 |
-
|
| 165 |
-
assert np.isclose(result_sum.item(), 14372758528.0)
|
| 166 |
-
assert np.isclose(result_mean.item(), 18714530.0)
|
| 167 |
-
|
| 168 |
-
def test_step_shape(self):
|
| 169 |
-
kwargs = dict(self.forward_default_kwargs)
|
| 170 |
-
|
| 171 |
-
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
| 172 |
-
|
| 173 |
-
for scheduler_class in self.scheduler_classes:
|
| 174 |
-
scheduler_config = self.get_scheduler_config()
|
| 175 |
-
scheduler = scheduler_class(**scheduler_config)
|
| 176 |
-
|
| 177 |
-
sample = self.dummy_sample
|
| 178 |
-
residual = 0.1 * sample
|
| 179 |
-
|
| 180 |
-
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
| 181 |
-
scheduler.set_timesteps(num_inference_steps)
|
| 182 |
-
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
| 183 |
-
kwargs["num_inference_steps"] = num_inference_steps
|
| 184 |
-
|
| 185 |
-
output_0 = scheduler.step_pred(residual, 0, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
|
| 186 |
-
output_1 = scheduler.step_pred(residual, 1, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
|
| 187 |
-
|
| 188 |
-
self.assertEqual(output_0.shape, sample.shape)
|
| 189 |
-
self.assertEqual(output_0.shape, output_1.shape)
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spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
_base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
|
| 2 |
-
# learning policy
|
| 3 |
-
lr_config = dict(step=[28, 34])
|
| 4 |
-
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
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spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/retinanet_r50_fpn.py',
|
| 3 |
-
'../_base_/datasets/coco_detection.py',
|
| 4 |
-
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
|
| 5 |
-
]
|
| 6 |
-
model = dict(
|
| 7 |
-
bbox_head=dict(
|
| 8 |
-
type='RetinaHead',
|
| 9 |
-
anchor_generator=dict(
|
| 10 |
-
type='LegacyAnchorGenerator',
|
| 11 |
-
center_offset=0.5,
|
| 12 |
-
octave_base_scale=4,
|
| 13 |
-
scales_per_octave=3,
|
| 14 |
-
ratios=[0.5, 1.0, 2.0],
|
| 15 |
-
strides=[8, 16, 32, 64, 128]),
|
| 16 |
-
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
|
| 17 |
-
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
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spaces/Andy1621/uniformer_image_detection/configs/pascal_voc/ssd300_voc0712.py
DELETED
|
@@ -1,69 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
|
| 3 |
-
'../_base_/default_runtime.py'
|
| 4 |
-
]
|
| 5 |
-
model = dict(
|
| 6 |
-
bbox_head=dict(
|
| 7 |
-
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
|
| 8 |
-
0.9))))
|
| 9 |
-
# dataset settings
|
| 10 |
-
dataset_type = 'VOCDataset'
|
| 11 |
-
data_root = 'data/VOCdevkit/'
|
| 12 |
-
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
|
| 13 |
-
train_pipeline = [
|
| 14 |
-
dict(type='LoadImageFromFile', to_float32=True),
|
| 15 |
-
dict(type='LoadAnnotations', with_bbox=True),
|
| 16 |
-
dict(
|
| 17 |
-
type='PhotoMetricDistortion',
|
| 18 |
-
brightness_delta=32,
|
| 19 |
-
contrast_range=(0.5, 1.5),
|
| 20 |
-
saturation_range=(0.5, 1.5),
|
| 21 |
-
hue_delta=18),
|
| 22 |
-
dict(
|
| 23 |
-
type='Expand',
|
| 24 |
-
mean=img_norm_cfg['mean'],
|
| 25 |
-
to_rgb=img_norm_cfg['to_rgb'],
|
| 26 |
-
ratio_range=(1, 4)),
|
| 27 |
-
dict(
|
| 28 |
-
type='MinIoURandomCrop',
|
| 29 |
-
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
|
| 30 |
-
min_crop_size=0.3),
|
| 31 |
-
dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
|
| 32 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 33 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 34 |
-
dict(type='DefaultFormatBundle'),
|
| 35 |
-
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
|
| 36 |
-
]
|
| 37 |
-
test_pipeline = [
|
| 38 |
-
dict(type='LoadImageFromFile'),
|
| 39 |
-
dict(
|
| 40 |
-
type='MultiScaleFlipAug',
|
| 41 |
-
img_scale=(300, 300),
|
| 42 |
-
flip=False,
|
| 43 |
-
transforms=[
|
| 44 |
-
dict(type='Resize', keep_ratio=False),
|
| 45 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 46 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 47 |
-
dict(type='Collect', keys=['img']),
|
| 48 |
-
])
|
| 49 |
-
]
|
| 50 |
-
data = dict(
|
| 51 |
-
samples_per_gpu=8,
|
| 52 |
-
workers_per_gpu=3,
|
| 53 |
-
train=dict(
|
| 54 |
-
type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)),
|
| 55 |
-
val=dict(pipeline=test_pipeline),
|
| 56 |
-
test=dict(pipeline=test_pipeline))
|
| 57 |
-
# optimizer
|
| 58 |
-
optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
|
| 59 |
-
optimizer_config = dict()
|
| 60 |
-
# learning policy
|
| 61 |
-
lr_config = dict(
|
| 62 |
-
policy='step',
|
| 63 |
-
warmup='linear',
|
| 64 |
-
warmup_iters=500,
|
| 65 |
-
warmup_ratio=0.001,
|
| 66 |
-
step=[16, 20])
|
| 67 |
-
checkpoint_config = dict(interval=1)
|
| 68 |
-
# runtime settings
|
| 69 |
-
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
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spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
DELETED
|
@@ -1,65 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/mask_rcnn_r50_fpn.py',
|
| 3 |
-
'../_base_/datasets/coco_instance.py',
|
| 4 |
-
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
|
| 5 |
-
]
|
| 6 |
-
model = dict(
|
| 7 |
-
pretrained='open-mmlab://regnetx_3.2gf',
|
| 8 |
-
backbone=dict(
|
| 9 |
-
_delete_=True,
|
| 10 |
-
type='RegNet',
|
| 11 |
-
arch='regnetx_3.2gf',
|
| 12 |
-
out_indices=(0, 1, 2, 3),
|
| 13 |
-
frozen_stages=1,
|
| 14 |
-
norm_cfg=dict(type='BN', requires_grad=True),
|
| 15 |
-
norm_eval=True,
|
| 16 |
-
style='pytorch'),
|
| 17 |
-
neck=dict(
|
| 18 |
-
type='FPN',
|
| 19 |
-
in_channels=[96, 192, 432, 1008],
|
| 20 |
-
out_channels=256,
|
| 21 |
-
num_outs=5))
|
| 22 |
-
img_norm_cfg = dict(
|
| 23 |
-
# The mean and std are used in PyCls when training RegNets
|
| 24 |
-
mean=[103.53, 116.28, 123.675],
|
| 25 |
-
std=[57.375, 57.12, 58.395],
|
| 26 |
-
to_rgb=False)
|
| 27 |
-
train_pipeline = [
|
| 28 |
-
dict(type='LoadImageFromFile'),
|
| 29 |
-
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| 30 |
-
dict(
|
| 31 |
-
type='Resize',
|
| 32 |
-
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
|
| 33 |
-
(1333, 768), (1333, 800)],
|
| 34 |
-
multiscale_mode='value',
|
| 35 |
-
keep_ratio=True),
|
| 36 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 37 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 38 |
-
dict(type='Pad', size_divisor=32),
|
| 39 |
-
dict(type='DefaultFormatBundle'),
|
| 40 |
-
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
|
| 41 |
-
]
|
| 42 |
-
test_pipeline = [
|
| 43 |
-
dict(type='LoadImageFromFile'),
|
| 44 |
-
dict(
|
| 45 |
-
type='MultiScaleFlipAug',
|
| 46 |
-
img_scale=(1333, 800),
|
| 47 |
-
flip=False,
|
| 48 |
-
transforms=[
|
| 49 |
-
dict(type='Resize', keep_ratio=True),
|
| 50 |
-
dict(type='RandomFlip'),
|
| 51 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 52 |
-
dict(type='Pad', size_divisor=32),
|
| 53 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 54 |
-
dict(type='Collect', keys=['img']),
|
| 55 |
-
])
|
| 56 |
-
]
|
| 57 |
-
data = dict(
|
| 58 |
-
train=dict(pipeline=train_pipeline),
|
| 59 |
-
val=dict(pipeline=test_pipeline),
|
| 60 |
-
test=dict(pipeline=test_pipeline))
|
| 61 |
-
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
|
| 62 |
-
lr_config = dict(step=[28, 34])
|
| 63 |
-
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
| 64 |
-
optimizer_config = dict(
|
| 65 |
-
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
|
|
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|
spaces/Andy1621/uniformer_image_detection/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py
DELETED
|
@@ -1,88 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/cascade_rcnn_r50_fpn.py',
|
| 3 |
-
'../_base_/datasets/coco_detection.py',
|
| 4 |
-
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
|
| 5 |
-
]
|
| 6 |
-
# model settings
|
| 7 |
-
model = dict(
|
| 8 |
-
pretrained='torchvision://resnet101',
|
| 9 |
-
backbone=dict(depth=101),
|
| 10 |
-
roi_head=dict(bbox_head=[
|
| 11 |
-
dict(
|
| 12 |
-
type='SABLHead',
|
| 13 |
-
num_classes=80,
|
| 14 |
-
cls_in_channels=256,
|
| 15 |
-
reg_in_channels=256,
|
| 16 |
-
roi_feat_size=7,
|
| 17 |
-
reg_feat_up_ratio=2,
|
| 18 |
-
reg_pre_kernel=3,
|
| 19 |
-
reg_post_kernel=3,
|
| 20 |
-
reg_pre_num=2,
|
| 21 |
-
reg_post_num=1,
|
| 22 |
-
cls_out_channels=1024,
|
| 23 |
-
reg_offset_out_channels=256,
|
| 24 |
-
reg_cls_out_channels=256,
|
| 25 |
-
num_cls_fcs=1,
|
| 26 |
-
num_reg_fcs=0,
|
| 27 |
-
reg_class_agnostic=True,
|
| 28 |
-
norm_cfg=None,
|
| 29 |
-
bbox_coder=dict(
|
| 30 |
-
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
|
| 31 |
-
loss_cls=dict(
|
| 32 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
| 33 |
-
loss_bbox_cls=dict(
|
| 34 |
-
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
|
| 35 |
-
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
|
| 36 |
-
loss_weight=1.0)),
|
| 37 |
-
dict(
|
| 38 |
-
type='SABLHead',
|
| 39 |
-
num_classes=80,
|
| 40 |
-
cls_in_channels=256,
|
| 41 |
-
reg_in_channels=256,
|
| 42 |
-
roi_feat_size=7,
|
| 43 |
-
reg_feat_up_ratio=2,
|
| 44 |
-
reg_pre_kernel=3,
|
| 45 |
-
reg_post_kernel=3,
|
| 46 |
-
reg_pre_num=2,
|
| 47 |
-
reg_post_num=1,
|
| 48 |
-
cls_out_channels=1024,
|
| 49 |
-
reg_offset_out_channels=256,
|
| 50 |
-
reg_cls_out_channels=256,
|
| 51 |
-
num_cls_fcs=1,
|
| 52 |
-
num_reg_fcs=0,
|
| 53 |
-
reg_class_agnostic=True,
|
| 54 |
-
norm_cfg=None,
|
| 55 |
-
bbox_coder=dict(
|
| 56 |
-
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
|
| 57 |
-
loss_cls=dict(
|
| 58 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
| 59 |
-
loss_bbox_cls=dict(
|
| 60 |
-
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
|
| 61 |
-
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
|
| 62 |
-
loss_weight=1.0)),
|
| 63 |
-
dict(
|
| 64 |
-
type='SABLHead',
|
| 65 |
-
num_classes=80,
|
| 66 |
-
cls_in_channels=256,
|
| 67 |
-
reg_in_channels=256,
|
| 68 |
-
roi_feat_size=7,
|
| 69 |
-
reg_feat_up_ratio=2,
|
| 70 |
-
reg_pre_kernel=3,
|
| 71 |
-
reg_post_kernel=3,
|
| 72 |
-
reg_pre_num=2,
|
| 73 |
-
reg_post_num=1,
|
| 74 |
-
cls_out_channels=1024,
|
| 75 |
-
reg_offset_out_channels=256,
|
| 76 |
-
reg_cls_out_channels=256,
|
| 77 |
-
num_cls_fcs=1,
|
| 78 |
-
num_reg_fcs=0,
|
| 79 |
-
reg_class_agnostic=True,
|
| 80 |
-
norm_cfg=None,
|
| 81 |
-
bbox_coder=dict(
|
| 82 |
-
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
|
| 83 |
-
loss_cls=dict(
|
| 84 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
| 85 |
-
loss_bbox_cls=dict(
|
| 86 |
-
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
|
| 87 |
-
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
|
| 88 |
-
]))
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spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/pseudo_bbox_coder.py
DELETED
|
@@ -1,18 +0,0 @@
|
|
| 1 |
-
from ..builder import BBOX_CODERS
|
| 2 |
-
from .base_bbox_coder import BaseBBoxCoder
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
@BBOX_CODERS.register_module()
|
| 6 |
-
class PseudoBBoxCoder(BaseBBoxCoder):
|
| 7 |
-
"""Pseudo bounding box coder."""
|
| 8 |
-
|
| 9 |
-
def __init__(self, **kwargs):
|
| 10 |
-
super(BaseBBoxCoder, self).__init__(**kwargs)
|
| 11 |
-
|
| 12 |
-
def encode(self, bboxes, gt_bboxes):
|
| 13 |
-
"""torch.Tensor: return the given ``bboxes``"""
|
| 14 |
-
return gt_bboxes
|
| 15 |
-
|
| 16 |
-
def decode(self, bboxes, pred_bboxes):
|
| 17 |
-
"""torch.Tensor: return the given ``pred_bboxes``"""
|
| 18 |
-
return pred_bboxes
|
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spaces/Andy1621/uniformer_image_segmentation/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './gcnet_r50-d8_512x512_20k_voc12aug.py'
|
| 2 |
-
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
|
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|
spaces/Andyrasika/distilbert-base-uncased-finetuned-emotion/app.py
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
|
| 3 |
-
gr.Interface.load("models/Andyrasika/distilbert-base-uncased-finetuned-emotion").launch()
|
|
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/conv_ws.py
DELETED
|
@@ -1,148 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
import torch
|
| 3 |
-
import torch.nn as nn
|
| 4 |
-
import torch.nn.functional as F
|
| 5 |
-
|
| 6 |
-
from .registry import CONV_LAYERS
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
def conv_ws_2d(input,
|
| 10 |
-
weight,
|
| 11 |
-
bias=None,
|
| 12 |
-
stride=1,
|
| 13 |
-
padding=0,
|
| 14 |
-
dilation=1,
|
| 15 |
-
groups=1,
|
| 16 |
-
eps=1e-5):
|
| 17 |
-
c_in = weight.size(0)
|
| 18 |
-
weight_flat = weight.view(c_in, -1)
|
| 19 |
-
mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
|
| 20 |
-
std = weight_flat.std(dim=1, keepdim=True).view(c_in, 1, 1, 1)
|
| 21 |
-
weight = (weight - mean) / (std + eps)
|
| 22 |
-
return F.conv2d(input, weight, bias, stride, padding, dilation, groups)
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
@CONV_LAYERS.register_module('ConvWS')
|
| 26 |
-
class ConvWS2d(nn.Conv2d):
|
| 27 |
-
|
| 28 |
-
def __init__(self,
|
| 29 |
-
in_channels,
|
| 30 |
-
out_channels,
|
| 31 |
-
kernel_size,
|
| 32 |
-
stride=1,
|
| 33 |
-
padding=0,
|
| 34 |
-
dilation=1,
|
| 35 |
-
groups=1,
|
| 36 |
-
bias=True,
|
| 37 |
-
eps=1e-5):
|
| 38 |
-
super(ConvWS2d, self).__init__(
|
| 39 |
-
in_channels,
|
| 40 |
-
out_channels,
|
| 41 |
-
kernel_size,
|
| 42 |
-
stride=stride,
|
| 43 |
-
padding=padding,
|
| 44 |
-
dilation=dilation,
|
| 45 |
-
groups=groups,
|
| 46 |
-
bias=bias)
|
| 47 |
-
self.eps = eps
|
| 48 |
-
|
| 49 |
-
def forward(self, x):
|
| 50 |
-
return conv_ws_2d(x, self.weight, self.bias, self.stride, self.padding,
|
| 51 |
-
self.dilation, self.groups, self.eps)
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
@CONV_LAYERS.register_module(name='ConvAWS')
|
| 55 |
-
class ConvAWS2d(nn.Conv2d):
|
| 56 |
-
"""AWS (Adaptive Weight Standardization)
|
| 57 |
-
|
| 58 |
-
This is a variant of Weight Standardization
|
| 59 |
-
(https://arxiv.org/pdf/1903.10520.pdf)
|
| 60 |
-
It is used in DetectoRS to avoid NaN
|
| 61 |
-
(https://arxiv.org/pdf/2006.02334.pdf)
|
| 62 |
-
|
| 63 |
-
Args:
|
| 64 |
-
in_channels (int): Number of channels in the input image
|
| 65 |
-
out_channels (int): Number of channels produced by the convolution
|
| 66 |
-
kernel_size (int or tuple): Size of the conv kernel
|
| 67 |
-
stride (int or tuple, optional): Stride of the convolution. Default: 1
|
| 68 |
-
padding (int or tuple, optional): Zero-padding added to both sides of
|
| 69 |
-
the input. Default: 0
|
| 70 |
-
dilation (int or tuple, optional): Spacing between kernel elements.
|
| 71 |
-
Default: 1
|
| 72 |
-
groups (int, optional): Number of blocked connections from input
|
| 73 |
-
channels to output channels. Default: 1
|
| 74 |
-
bias (bool, optional): If set True, adds a learnable bias to the
|
| 75 |
-
output. Default: True
|
| 76 |
-
"""
|
| 77 |
-
|
| 78 |
-
def __init__(self,
|
| 79 |
-
in_channels,
|
| 80 |
-
out_channels,
|
| 81 |
-
kernel_size,
|
| 82 |
-
stride=1,
|
| 83 |
-
padding=0,
|
| 84 |
-
dilation=1,
|
| 85 |
-
groups=1,
|
| 86 |
-
bias=True):
|
| 87 |
-
super().__init__(
|
| 88 |
-
in_channels,
|
| 89 |
-
out_channels,
|
| 90 |
-
kernel_size,
|
| 91 |
-
stride=stride,
|
| 92 |
-
padding=padding,
|
| 93 |
-
dilation=dilation,
|
| 94 |
-
groups=groups,
|
| 95 |
-
bias=bias)
|
| 96 |
-
self.register_buffer('weight_gamma',
|
| 97 |
-
torch.ones(self.out_channels, 1, 1, 1))
|
| 98 |
-
self.register_buffer('weight_beta',
|
| 99 |
-
torch.zeros(self.out_channels, 1, 1, 1))
|
| 100 |
-
|
| 101 |
-
def _get_weight(self, weight):
|
| 102 |
-
weight_flat = weight.view(weight.size(0), -1)
|
| 103 |
-
mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
|
| 104 |
-
std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
|
| 105 |
-
weight = (weight - mean) / std
|
| 106 |
-
weight = self.weight_gamma * weight + self.weight_beta
|
| 107 |
-
return weight
|
| 108 |
-
|
| 109 |
-
def forward(self, x):
|
| 110 |
-
weight = self._get_weight(self.weight)
|
| 111 |
-
return F.conv2d(x, weight, self.bias, self.stride, self.padding,
|
| 112 |
-
self.dilation, self.groups)
|
| 113 |
-
|
| 114 |
-
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
|
| 115 |
-
missing_keys, unexpected_keys, error_msgs):
|
| 116 |
-
"""Override default load function.
|
| 117 |
-
|
| 118 |
-
AWS overrides the function _load_from_state_dict to recover
|
| 119 |
-
weight_gamma and weight_beta if they are missing. If weight_gamma and
|
| 120 |
-
weight_beta are found in the checkpoint, this function will return
|
| 121 |
-
after super()._load_from_state_dict. Otherwise, it will compute the
|
| 122 |
-
mean and std of the pretrained weights and store them in weight_beta
|
| 123 |
-
and weight_gamma.
|
| 124 |
-
"""
|
| 125 |
-
|
| 126 |
-
self.weight_gamma.data.fill_(-1)
|
| 127 |
-
local_missing_keys = []
|
| 128 |
-
super()._load_from_state_dict(state_dict, prefix, local_metadata,
|
| 129 |
-
strict, local_missing_keys,
|
| 130 |
-
unexpected_keys, error_msgs)
|
| 131 |
-
if self.weight_gamma.data.mean() > 0:
|
| 132 |
-
for k in local_missing_keys:
|
| 133 |
-
missing_keys.append(k)
|
| 134 |
-
return
|
| 135 |
-
weight = self.weight.data
|
| 136 |
-
weight_flat = weight.view(weight.size(0), -1)
|
| 137 |
-
mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
|
| 138 |
-
std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
|
| 139 |
-
self.weight_beta.data.copy_(mean)
|
| 140 |
-
self.weight_gamma.data.copy_(std)
|
| 141 |
-
missing_gamma_beta = [
|
| 142 |
-
k for k in local_missing_keys
|
| 143 |
-
if k.endswith('weight_gamma') or k.endswith('weight_beta')
|
| 144 |
-
]
|
| 145 |
-
for k in missing_gamma_beta:
|
| 146 |
-
local_missing_keys.remove(k)
|
| 147 |
-
for k in local_missing_keys:
|
| 148 |
-
missing_keys.append(k)
|
|
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/furthest_point_sample.py
DELETED
|
@@ -1,83 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from torch.autograd import Function
|
| 3 |
-
|
| 4 |
-
from ..utils import ext_loader
|
| 5 |
-
|
| 6 |
-
ext_module = ext_loader.load_ext('_ext', [
|
| 7 |
-
'furthest_point_sampling_forward',
|
| 8 |
-
'furthest_point_sampling_with_dist_forward'
|
| 9 |
-
])
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class FurthestPointSampling(Function):
|
| 13 |
-
"""Uses iterative furthest point sampling to select a set of features whose
|
| 14 |
-
corresponding points have the furthest distance."""
|
| 15 |
-
|
| 16 |
-
@staticmethod
|
| 17 |
-
def forward(ctx, points_xyz: torch.Tensor,
|
| 18 |
-
num_points: int) -> torch.Tensor:
|
| 19 |
-
"""
|
| 20 |
-
Args:
|
| 21 |
-
points_xyz (Tensor): (B, N, 3) where N > num_points.
|
| 22 |
-
num_points (int): Number of points in the sampled set.
|
| 23 |
-
|
| 24 |
-
Returns:
|
| 25 |
-
Tensor: (B, num_points) indices of the sampled points.
|
| 26 |
-
"""
|
| 27 |
-
assert points_xyz.is_contiguous()
|
| 28 |
-
|
| 29 |
-
B, N = points_xyz.size()[:2]
|
| 30 |
-
output = torch.cuda.IntTensor(B, num_points)
|
| 31 |
-
temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
|
| 32 |
-
|
| 33 |
-
ext_module.furthest_point_sampling_forward(
|
| 34 |
-
points_xyz,
|
| 35 |
-
temp,
|
| 36 |
-
output,
|
| 37 |
-
b=B,
|
| 38 |
-
n=N,
|
| 39 |
-
m=num_points,
|
| 40 |
-
)
|
| 41 |
-
if torch.__version__ != 'parrots':
|
| 42 |
-
ctx.mark_non_differentiable(output)
|
| 43 |
-
return output
|
| 44 |
-
|
| 45 |
-
@staticmethod
|
| 46 |
-
def backward(xyz, a=None):
|
| 47 |
-
return None, None
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
class FurthestPointSamplingWithDist(Function):
|
| 51 |
-
"""Uses iterative furthest point sampling to select a set of features whose
|
| 52 |
-
corresponding points have the furthest distance."""
|
| 53 |
-
|
| 54 |
-
@staticmethod
|
| 55 |
-
def forward(ctx, points_dist: torch.Tensor,
|
| 56 |
-
num_points: int) -> torch.Tensor:
|
| 57 |
-
"""
|
| 58 |
-
Args:
|
| 59 |
-
points_dist (Tensor): (B, N, N) Distance between each point pair.
|
| 60 |
-
num_points (int): Number of points in the sampled set.
|
| 61 |
-
|
| 62 |
-
Returns:
|
| 63 |
-
Tensor: (B, num_points) indices of the sampled points.
|
| 64 |
-
"""
|
| 65 |
-
assert points_dist.is_contiguous()
|
| 66 |
-
|
| 67 |
-
B, N, _ = points_dist.size()
|
| 68 |
-
output = points_dist.new_zeros([B, num_points], dtype=torch.int32)
|
| 69 |
-
temp = points_dist.new_zeros([B, N]).fill_(1e10)
|
| 70 |
-
|
| 71 |
-
ext_module.furthest_point_sampling_with_dist_forward(
|
| 72 |
-
points_dist, temp, output, b=B, n=N, m=num_points)
|
| 73 |
-
if torch.__version__ != 'parrots':
|
| 74 |
-
ctx.mark_non_differentiable(output)
|
| 75 |
-
return output
|
| 76 |
-
|
| 77 |
-
@staticmethod
|
| 78 |
-
def backward(xyz, a=None):
|
| 79 |
-
return None, None
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
furthest_point_sample = FurthestPointSampling.apply
|
| 83 |
-
furthest_point_sample_with_dist = FurthestPointSamplingWithDist.apply
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spaces/Apex-X/Tm/roop/capturer.py
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
from typing import Any
|
| 2 |
-
import cv2
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
|
| 6 |
-
capture = cv2.VideoCapture(video_path)
|
| 7 |
-
frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
|
| 8 |
-
capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
|
| 9 |
-
has_frame, frame = capture.read()
|
| 10 |
-
capture.release()
|
| 11 |
-
if has_frame:
|
| 12 |
-
return frame
|
| 13 |
-
return None
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
def get_video_frame_total(video_path: str) -> int:
|
| 17 |
-
capture = cv2.VideoCapture(video_path)
|
| 18 |
-
video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
|
| 19 |
-
capture.release()
|
| 20 |
-
return video_frame_total
|
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|
spaces/Astroomx/Mine/Dockerfile
DELETED
|
@@ -1,21 +0,0 @@
|
|
| 1 |
-
FROM node:18-bullseye-slim
|
| 2 |
-
|
| 3 |
-
RUN apt-get update && \
|
| 4 |
-
|
| 5 |
-
apt-get install -y git
|
| 6 |
-
|
| 7 |
-
RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
|
| 8 |
-
|
| 9 |
-
WORKDIR /app
|
| 10 |
-
|
| 11 |
-
RUN npm install
|
| 12 |
-
|
| 13 |
-
COPY Dockerfile greeting.md* .env* ./
|
| 14 |
-
|
| 15 |
-
RUN npm run build
|
| 16 |
-
|
| 17 |
-
EXPOSE 7860
|
| 18 |
-
|
| 19 |
-
ENV NODE_ENV=production
|
| 20 |
-
|
| 21 |
-
CMD [ "npm", "start" ]
|
|
|
|
|
|
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|
|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/unistring.py
DELETED
|
@@ -1,153 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
pygments.unistring
|
| 3 |
-
~~~~~~~~~~~~~~~~~~
|
| 4 |
-
|
| 5 |
-
Strings of all Unicode characters of a certain category.
|
| 6 |
-
Used for matching in Unicode-aware languages. Run to regenerate.
|
| 7 |
-
|
| 8 |
-
Inspired by chartypes_create.py from the MoinMoin project.
|
| 9 |
-
|
| 10 |
-
:copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
|
| 11 |
-
:license: BSD, see LICENSE for details.
|
| 12 |
-
"""
|
| 13 |
-
|
| 14 |
-
Cc = '\x00-\x1f\x7f-\x9f'
|
| 15 |
-
|
| 16 |
-
Cf = '\xad\u0600-\u0605\u061c\u06dd\u070f\u08e2\u180e\u200b-\u200f\u202a-\u202e\u2060-\u2064\u2066-\u206f\ufeff\ufff9-\ufffb\U000110bd\U000110cd\U0001bca0-\U0001bca3\U0001d173-\U0001d17a\U000e0001\U000e0020-\U000e007f'
|
| 17 |
-
|
| 18 |
-
Cn = '\u0378-\u0379\u0380-\u0383\u038b\u038d\u03a2\u0530\u0557-\u0558\u058b-\u058c\u0590\u05c8-\u05cf\u05eb-\u05ee\u05f5-\u05ff\u061d\u070e\u074b-\u074c\u07b2-\u07bf\u07fb-\u07fc\u082e-\u082f\u083f\u085c-\u085d\u085f\u086b-\u089f\u08b5\u08be-\u08d2\u0984\u098d-\u098e\u0991-\u0992\u09a9\u09b1\u09b3-\u09b5\u09ba-\u09bb\u09c5-\u09c6\u09c9-\u09ca\u09cf-\u09d6\u09d8-\u09db\u09de\u09e4-\u09e5\u09ff-\u0a00\u0a04\u0a0b-\u0a0e\u0a11-\u0a12\u0a29\u0a31\u0a34\u0a37\u0a3a-\u0a3b\u0a3d\u0a43-\u0a46\u0a49-\u0a4a\u0a4e-\u0a50\u0a52-\u0a58\u0a5d\u0a5f-\u0a65\u0a77-\u0a80\u0a84\u0a8e\u0a92\u0aa9\u0ab1\u0ab4\u0aba-\u0abb\u0ac6\u0aca\u0ace-\u0acf\u0ad1-\u0adf\u0ae4-\u0ae5\u0af2-\u0af8\u0b00\u0b04\u0b0d-\u0b0e\u0b11-\u0b12\u0b29\u0b31\u0b34\u0b3a-\u0b3b\u0b45-\u0b46\u0b49-\u0b4a\u0b4e-\u0b55\u0b58-\u0b5b\u0b5e\u0b64-\u0b65\u0b78-\u0b81\u0b84\u0b8b-\u0b8d\u0b91\u0b96-\u0b98\u0b9b\u0b9d\u0ba0-\u0ba2\u0ba5-\u0ba7\u0bab-\u0bad\u0bba-\u0bbd\u0bc3-\u0bc5\u0bc9\u0bce-\u0bcf\u0bd1-\u0bd6\u0bd8-\u0be5\u0bfb-\u0bff\u0c0d\u0c11\u0c29\u0c3a-\u0c3c\u0c45\u0c49\u0c4e-\u0c54\u0c57\u0c5b-\u0c5f\u0c64-\u0c65\u0c70-\u0c77\u0c8d\u0c91\u0ca9\u0cb4\u0cba-\u0cbb\u0cc5\u0cc9\u0cce-\u0cd4\u0cd7-\u0cdd\u0cdf\u0ce4-\u0ce5\u0cf0\u0cf3-\u0cff\u0d04\u0d0d\u0d11\u0d45\u0d49\u0d50-\u0d53\u0d64-\u0d65\u0d80-\u0d81\u0d84\u0d97-\u0d99\u0db2\u0dbc\u0dbe-\u0dbf\u0dc7-\u0dc9\u0dcb-\u0dce\u0dd5\u0dd7\u0de0-\u0de5\u0df0-\u0df1\u0df5-\u0e00\u0e3b-\u0e3e\u0e5c-\u0e80\u0e83\u0e85-\u0e86\u0e89\u0e8b-\u0e8c\u0e8e-\u0e93\u0e98\u0ea0\u0ea4\u0ea6\u0ea8-\u0ea9\u0eac\u0eba\u0ebe-\u0ebf\u0ec5\u0ec7\u0ece-\u0ecf\u0eda-\u0edb\u0ee0-\u0eff\u0f48\u0f6d-\u0f70\u0f98\u0fbd\u0fcd\u0fdb-\u0fff\u10c6\u10c8-\u10cc\u10ce-\u10cf\u1249\u124e-\u124f\u1257\u1259\u125e-\u125f\u1289\u128e-\u128f\u12b1\u12b6-\u12b7\u12bf\u12c1\u12c6-\u12c7\u12d7\u1311\u1316-\u1317\u135b-\u135c\u137d-\u137f\u139a-\u139f\u13f6-\u13f7\u13fe-\u13ff\u169d-\u169f\u16f9-\u16ff\u170d\u1715-\u171f\u1737-\u173f\u1754-\u175f\u176d\u1771\u1774-\u177f\u17de-\u17df\u17ea-\u17ef\u17fa-\u17ff\u180f\u181a-\u181f\u1879-\u187f\u18ab-\u18af\u18f6-\u18ff\u191f\u192c-\u192f\u193c-\u193f\u1941-\u1943\u196e-\u196f\u1975-\u197f\u19ac-\u19af\u19ca-\u19cf\u19db-\u19dd\u1a1c-\u1a1d\u1a5f\u1a7d-\u1a7e\u1a8a-\u1a8f\u1a9a-\u1a9f\u1aae-\u1aaf\u1abf-\u1aff\u1b4c-\u1b4f\u1b7d-\u1b7f\u1bf4-\u1bfb\u1c38-\u1c3a\u1c4a-\u1c4c\u1c89-\u1c8f\u1cbb-\u1cbc\u1cc8-\u1ccf\u1cfa-\u1cff\u1dfa\u1f16-\u1f17\u1f1e-\u1f1f\u1f46-\u1f47\u1f4e-\u1f4f\u1f58\u1f5a\u1f5c\u1f5e\u1f7e-\u1f7f\u1fb5\u1fc5\u1fd4-\u1fd5\u1fdc\u1ff0-\u1ff1\u1ff5\u1fff\u2065\u2072-\u2073\u208f\u209d-\u209f\u20c0-\u20cf\u20f1-\u20ff\u218c-\u218f\u2427-\u243f\u244b-\u245f\u2b74-\u2b75\u2b96-\u2b97\u2bc9\u2bff\u2c2f\u2c5f\u2cf4-\u2cf8\u2d26\u2d28-\u2d2c\u2d2e-\u2d2f\u2d68-\u2d6e\u2d71-\u2d7e\u2d97-\u2d9f\u2da7\u2daf\u2db7\u2dbf\u2dc7\u2dcf\u2dd7\u2ddf\u2e4f-\u2e7f\u2e9a\u2ef4-\u2eff\u2fd6-\u2fef\u2ffc-\u2fff\u3040\u3097-\u3098\u3100-\u3104\u3130\u318f\u31bb-\u31bf\u31e4-\u31ef\u321f\u32ff\u4db6-\u4dbf\u9ff0-\u9fff\ua48d-\ua48f\ua4c7-\ua4cf\ua62c-\ua63f\ua6f8-\ua6ff\ua7ba-\ua7f6\ua82c-\ua82f\ua83a-\ua83f\ua878-\ua87f\ua8c6-\ua8cd\ua8da-\ua8df\ua954-\ua95e\ua97d-\ua97f\ua9ce\ua9da-\ua9dd\ua9ff\uaa37-\uaa3f\uaa4e-\uaa4f\uaa5a-\uaa5b\uaac3-\uaada\uaaf7-\uab00\uab07-\uab08\uab0f-\uab10\uab17-\uab1f\uab27\uab2f\uab66-\uab6f\uabee-\uabef\uabfa-\uabff\ud7a4-\ud7af\ud7c7-\ud7ca\ud7fc-\ud7ff\ufa6e-\ufa6f\ufada-\ufaff\ufb07-\ufb12\ufb18-\ufb1c\ufb37\ufb3d\ufb3f\ufb42\ufb45\ufbc2-\ufbd2\ufd40-\ufd4f\ufd90-\ufd91\ufdc8-\ufdef\ufdfe-\ufdff\ufe1a-\ufe1f\ufe53\ufe67\ufe6c-\ufe6f\ufe75\ufefd-\ufefe\uff00\uffbf-\uffc1\uffc8-\uffc9\uffd0-\uffd1\uffd8-\uffd9\uffdd-\uffdf\uffe7\uffef-\ufff8\ufffe-\uffff\U0001000c\U00010027\U0001003b\U0001003e\U0001004e-\U0001004f\U0001005e-\U0001007f\U000100fb-\U000100ff\U00010103-\U00010106\U00010134-\U00010136\U0001018f\U0001019c-\U0001019f\U000101a1-\U000101cf\U000101fe-\U0001027f\U0001029d-\U0001029f\U000102d1-\U000102df\U000102fc-\U000102ff\U00010324-\U0001032c\U0001034b-\U0001034f\U0001037b-\U0001037f\U0001039e\U000103c4-\U000103c7\U000103d6-\U000103ff\U0001049e-\U0001049f\U000104aa-\U000104af\U000104d4-\U000104d7\U000104fc-\U000104ff\U00010528-\U0001052f\U00010564-\U0001056e\U00010570-\U000105ff\U00010737-\U0001073f\U00010756-\U0001075f\U00010768-\U000107ff\U00010806-\U00010807\U00010809\U00010836\U00010839-\U0001083b\U0001083d-\U0001083e\U00010856\U0001089f-\U000108a6\U000108b0-\U000108df\U000108f3\U000108f6-\U000108fa\U0001091c-\U0001091e\U0001093a-\U0001093e\U00010940-\U0001097f\U000109b8-\U000109bb\U000109d0-\U000109d1\U00010a04\U00010a07-\U00010a0b\U00010a14\U00010a18\U00010a36-\U00010a37\U00010a3b-\U00010a3e\U00010a49-\U00010a4f\U00010a59-\U00010a5f\U00010aa0-\U00010abf\U00010ae7-\U00010aea\U00010af7-\U00010aff\U00010b36-\U00010b38\U00010b56-\U00010b57\U00010b73-\U00010b77\U00010b92-\U00010b98\U00010b9d-\U00010ba8\U00010bb0-\U00010bff\U00010c49-\U00010c7f\U00010cb3-\U00010cbf\U00010cf3-\U00010cf9\U00010d28-\U00010d2f\U00010d3a-\U00010e5f\U00010e7f-\U00010eff\U00010f28-\U00010f2f\U00010f5a-\U00010fff\U0001104e-\U00011051\U00011070-\U0001107e\U000110c2-\U000110cc\U000110ce-\U000110cf\U000110e9-\U000110ef\U000110fa-\U000110ff\U00011135\U00011147-\U0001114f\U00011177-\U0001117f\U000111ce-\U000111cf\U000111e0\U000111f5-\U000111ff\U00011212\U0001123f-\U0001127f\U00011287\U00011289\U0001128e\U0001129e\U000112aa-\U000112af\U000112eb-\U000112ef\U000112fa-\U000112ff\U00011304\U0001130d-\U0001130e\U00011311-\U00011312\U00011329\U00011331\U00011334\U0001133a\U00011345-\U00011346\U00011349-\U0001134a\U0001134e-\U0001134f\U00011351-\U00011356\U00011358-\U0001135c\U00011364-\U00011365\U0001136d-\U0001136f\U00011375-\U000113ff\U0001145a\U0001145c\U0001145f-\U0001147f\U000114c8-\U000114cf\U000114da-\U0001157f\U000115b6-\U000115b7\U000115de-\U000115ff\U00011645-\U0001164f\U0001165a-\U0001165f\U0001166d-\U0001167f\U000116b8-\U000116bf\U000116ca-\U000116ff\U0001171b-\U0001171c\U0001172c-\U0001172f\U00011740-\U000117ff\U0001183c-\U0001189f\U000118f3-\U000118fe\U00011900-\U000119ff\U00011a48-\U00011a4f\U00011a84-\U00011a85\U00011aa3-\U00011abf\U00011af9-\U00011bff\U00011c09\U00011c37\U00011c46-\U00011c4f\U00011c6d-\U00011c6f\U00011c90-\U00011c91\U00011ca8\U00011cb7-\U00011cff\U00011d07\U00011d0a\U00011d37-\U00011d39\U00011d3b\U00011d3e\U00011d48-\U00011d4f\U00011d5a-\U00011d5f\U00011d66\U00011d69\U00011d8f\U00011d92\U00011d99-\U00011d9f\U00011daa-\U00011edf\U00011ef9-\U00011fff\U0001239a-\U000123ff\U0001246f\U00012475-\U0001247f\U00012544-\U00012fff\U0001342f-\U000143ff\U00014647-\U000167ff\U00016a39-\U00016a3f\U00016a5f\U00016a6a-\U00016a6d\U00016a70-\U00016acf\U00016aee-\U00016aef\U00016af6-\U00016aff\U00016b46-\U00016b4f\U00016b5a\U00016b62\U00016b78-\U00016b7c\U00016b90-\U00016e3f\U00016e9b-\U00016eff\U00016f45-\U00016f4f\U00016f7f-\U00016f8e\U00016fa0-\U00016fdf\U00016fe2-\U00016fff\U000187f2-\U000187ff\U00018af3-\U0001afff\U0001b11f-\U0001b16f\U0001b2fc-\U0001bbff\U0001bc6b-\U0001bc6f\U0001bc7d-\U0001bc7f\U0001bc89-\U0001bc8f\U0001bc9a-\U0001bc9b\U0001bca4-\U0001cfff\U0001d0f6-\U0001d0ff\U0001d127-\U0001d128\U0001d1e9-\U0001d1ff\U0001d246-\U0001d2df\U0001d2f4-\U0001d2ff\U0001d357-\U0001d35f\U0001d379-\U0001d3ff\U0001d455\U0001d49d\U0001d4a0-\U0001d4a1\U0001d4a3-\U0001d4a4\U0001d4a7-\U0001d4a8\U0001d4ad\U0001d4ba\U0001d4bc\U0001d4c4\U0001d506\U0001d50b-\U0001d50c\U0001d515\U0001d51d\U0001d53a\U0001d53f\U0001d545\U0001d547-\U0001d549\U0001d551\U0001d6a6-\U0001d6a7\U0001d7cc-\U0001d7cd\U0001da8c-\U0001da9a\U0001daa0\U0001dab0-\U0001dfff\U0001e007\U0001e019-\U0001e01a\U0001e022\U0001e025\U0001e02b-\U0001e7ff\U0001e8c5-\U0001e8c6\U0001e8d7-\U0001e8ff\U0001e94b-\U0001e94f\U0001e95a-\U0001e95d\U0001e960-\U0001ec70\U0001ecb5-\U0001edff\U0001ee04\U0001ee20\U0001ee23\U0001ee25-\U0001ee26\U0001ee28\U0001ee33\U0001ee38\U0001ee3a\U0001ee3c-\U0001ee41\U0001ee43-\U0001ee46\U0001ee48\U0001ee4a\U0001ee4c\U0001ee50\U0001ee53\U0001ee55-\U0001ee56\U0001ee58\U0001ee5a\U0001ee5c\U0001ee5e\U0001ee60\U0001ee63\U0001ee65-\U0001ee66\U0001ee6b\U0001ee73\U0001ee78\U0001ee7d\U0001ee7f\U0001ee8a\U0001ee9c-\U0001eea0\U0001eea4\U0001eeaa\U0001eebc-\U0001eeef\U0001eef2-\U0001efff\U0001f02c-\U0001f02f\U0001f094-\U0001f09f\U0001f0af-\U0001f0b0\U0001f0c0\U0001f0d0\U0001f0f6-\U0001f0ff\U0001f10d-\U0001f10f\U0001f16c-\U0001f16f\U0001f1ad-\U0001f1e5\U0001f203-\U0001f20f\U0001f23c-\U0001f23f\U0001f249-\U0001f24f\U0001f252-\U0001f25f\U0001f266-\U0001f2ff\U0001f6d5-\U0001f6df\U0001f6ed-\U0001f6ef\U0001f6fa-\U0001f6ff\U0001f774-\U0001f77f\U0001f7d9-\U0001f7ff\U0001f80c-\U0001f80f\U0001f848-\U0001f84f\U0001f85a-\U0001f85f\U0001f888-\U0001f88f\U0001f8ae-\U0001f8ff\U0001f90c-\U0001f90f\U0001f93f\U0001f971-\U0001f972\U0001f977-\U0001f979\U0001f97b\U0001f9a3-\U0001f9af\U0001f9ba-\U0001f9bf\U0001f9c3-\U0001f9cf\U0001fa00-\U0001fa5f\U0001fa6e-\U0001ffff\U0002a6d7-\U0002a6ff\U0002b735-\U0002b73f\U0002b81e-\U0002b81f\U0002cea2-\U0002ceaf\U0002ebe1-\U0002f7ff\U0002fa1e-\U000e0000\U000e0002-\U000e001f\U000e0080-\U000e00ff\U000e01f0-\U000effff\U000ffffe-\U000fffff\U0010fffe-\U0010ffff'
|
| 19 |
-
|
| 20 |
-
Co = '\ue000-\uf8ff\U000f0000-\U000ffffd\U00100000-\U0010fffd'
|
| 21 |
-
|
| 22 |
-
Cs = '\ud800-\udbff\\\udc00\udc01-\udfff'
|
| 23 |
-
|
| 24 |
-
Ll = 'a-z\xb5\xdf-\xf6\xf8-\xff\u0101\u0103\u0105\u0107\u0109\u010b\u010d\u010f\u0111\u0113\u0115\u0117\u0119\u011b\u011d\u011f\u0121\u0123\u0125\u0127\u0129\u012b\u012d\u012f\u0131\u0133\u0135\u0137-\u0138\u013a\u013c\u013e\u0140\u0142\u0144\u0146\u0148-\u0149\u014b\u014d\u014f\u0151\u0153\u0155\u0157\u0159\u015b\u015d\u015f\u0161\u0163\u0165\u0167\u0169\u016b\u016d\u016f\u0171\u0173\u0175\u0177\u017a\u017c\u017e-\u0180\u0183\u0185\u0188\u018c-\u018d\u0192\u0195\u0199-\u019b\u019e\u01a1\u01a3\u01a5\u01a8\u01aa-\u01ab\u01ad\u01b0\u01b4\u01b6\u01b9-\u01ba\u01bd-\u01bf\u01c6\u01c9\u01cc\u01ce\u01d0\u01d2\u01d4\u01d6\u01d8\u01da\u01dc-\u01dd\u01df\u01e1\u01e3\u01e5\u01e7\u01e9\u01eb\u01ed\u01ef-\u01f0\u01f3\u01f5\u01f9\u01fb\u01fd\u01ff\u0201\u0203\u0205\u0207\u0209\u020b\u020d\u020f\u0211\u0213\u0215\u0217\u0219\u021b\u021d\u021f\u0221\u0223\u0225\u0227\u0229\u022b\u022d\u022f\u0231\u0233-\u0239\u023c\u023f-\u0240\u0242\u0247\u0249\u024b\u024d\u024f-\u0293\u0295-\u02af\u0371\u0373\u0377\u037b-\u037d\u0390\u03ac-\u03ce\u03d0-\u03d1\u03d5-\u03d7\u03d9\u03db\u03dd\u03df\u03e1\u03e3\u03e5\u03e7\u03e9\u03eb\u03ed\u03ef-\u03f3\u03f5\u03f8\u03fb-\u03fc\u0430-\u045f\u0461\u0463\u0465\u0467\u0469\u046b\u046d\u046f\u0471\u0473\u0475\u0477\u0479\u047b\u047d\u047f\u0481\u048b\u048d\u048f\u0491\u0493\u0495\u0497\u0499\u049b\u049d\u049f\u04a1\u04a3\u04a5\u04a7\u04a9\u04ab\u04ad\u04af\u04b1\u04b3\u04b5\u04b7\u04b9\u04bb\u04bd\u04bf\u04c2\u04c4\u04c6\u04c8\u04ca\u04cc\u04ce-\u04cf\u04d1\u04d3\u04d5\u04d7\u04d9\u04db\u04dd\u04df\u04e1\u04e3\u04e5\u04e7\u04e9\u04eb\u04ed\u04ef\u04f1\u04f3\u04f5\u04f7\u04f9\u04fb\u04fd\u04ff\u0501\u0503\u0505\u0507\u0509\u050b\u050d\u050f\u0511\u0513\u0515\u0517\u0519\u051b\u051d\u051f\u0521\u0523\u0525\u0527\u0529\u052b\u052d\u052f\u0560-\u0588\u10d0-\u10fa\u10fd-\u10ff\u13f8-\u13fd\u1c80-\u1c88\u1d00-\u1d2b\u1d6b-\u1d77\u1d79-\u1d9a\u1e01\u1e03\u1e05\u1e07\u1e09\u1e0b\u1e0d\u1e0f\u1e11\u1e13\u1e15\u1e17\u1e19\u1e1b\u1e1d\u1e1f\u1e21\u1e23\u1e25\u1e27\u1e29\u1e2b\u1e2d\u1e2f\u1e31\u1e33\u1e35\u1e37\u1e39\u1e3b\u1e3d\u1e3f\u1e41\u1e43\u1e45\u1e47\u1e49\u1e4b\u1e4d\u1e4f\u1e51\u1e53\u1e55\u1e57\u1e59\u1e5b\u1e5d\u1e5f\u1e61\u1e63\u1e65\u1e67\u1e69\u1e6b\u1e6d\u1e6f\u1e71\u1e73\u1e75\u1e77\u1e79\u1e7b\u1e7d\u1e7f\u1e81\u1e83\u1e85\u1e87\u1e89\u1e8b\u1e8d\u1e8f\u1e91\u1e93\u1e95-\u1e9d\u1e9f\u1ea1\u1ea3\u1ea5\u1ea7\u1ea9\u1eab\u1ead\u1eaf\u1eb1\u1eb3\u1eb5\u1eb7\u1eb9\u1ebb\u1ebd\u1ebf\u1ec1\u1ec3\u1ec5\u1ec7\u1ec9\u1ecb\u1ecd\u1ecf\u1ed1\u1ed3\u1ed5\u1ed7\u1ed9\u1edb\u1edd\u1edf\u1ee1\u1ee3\u1ee5\u1ee7\u1ee9\u1eeb\u1eed\u1eef\u1ef1\u1ef3\u1ef5\u1ef7\u1ef9\u1efb\u1efd\u1eff-\u1f07\u1f10-\u1f15\u1f20-\u1f27\u1f30-\u1f37\u1f40-\u1f45\u1f50-\u1f57\u1f60-\u1f67\u1f70-\u1f7d\u1f80-\u1f87\u1f90-\u1f97\u1fa0-\u1fa7\u1fb0-\u1fb4\u1fb6-\u1fb7\u1fbe\u1fc2-\u1fc4\u1fc6-\u1fc7\u1fd0-\u1fd3\u1fd6-\u1fd7\u1fe0-\u1fe7\u1ff2-\u1ff4\u1ff6-\u1ff7\u210a\u210e-\u210f\u2113\u212f\u2134\u2139\u213c-\u213d\u2146-\u2149\u214e\u2184\u2c30-\u2c5e\u2c61\u2c65-\u2c66\u2c68\u2c6a\u2c6c\u2c71\u2c73-\u2c74\u2c76-\u2c7b\u2c81\u2c83\u2c85\u2c87\u2c89\u2c8b\u2c8d\u2c8f\u2c91\u2c93\u2c95\u2c97\u2c99\u2c9b\u2c9d\u2c9f\u2ca1\u2ca3\u2ca5\u2ca7\u2ca9\u2cab\u2cad\u2caf\u2cb1\u2cb3\u2cb5\u2cb7\u2cb9\u2cbb\u2cbd\u2cbf\u2cc1\u2cc3\u2cc5\u2cc7\u2cc9\u2ccb\u2ccd\u2ccf\u2cd1\u2cd3\u2cd5\u2cd7\u2cd9\u2cdb\u2cdd\u2cdf\u2ce1\u2ce3-\u2ce4\u2cec\u2cee\u2cf3\u2d00-\u2d25\u2d27\u2d2d\ua641\ua643\ua645\ua647\ua649\ua64b\ua64d\ua64f\ua651\ua653\ua655\ua657\ua659\ua65b\ua65d\ua65f\ua661\ua663\ua665\ua667\ua669\ua66b\ua66d\ua681\ua683\ua685\ua687\ua689\ua68b\ua68d\ua68f\ua691\ua693\ua695\ua697\ua699\ua69b\ua723\ua725\ua727\ua729\ua72b\ua72d\ua72f-\ua731\ua733\ua735\ua737\ua739\ua73b\ua73d\ua73f\ua741\ua743\ua745\ua747\ua749\ua74b\ua74d\ua74f\ua751\ua753\ua755\ua757\ua759\ua75b\ua75d\ua75f\ua761\ua763\ua765\ua767\ua769\ua76b\ua76d\ua76f\ua771-\ua778\ua77a\ua77c\ua77f\ua781\ua783\ua785\ua787\ua78c\ua78e\ua791\ua793-\ua795\ua797\ua799\ua79b\ua79d\ua79f\ua7a1\ua7a3\ua7a5\ua7a7\ua7a9\ua7af\ua7b5\ua7b7\ua7b9\ua7fa\uab30-\uab5a\uab60-\uab65\uab70-\uabbf\ufb00-\ufb06\ufb13-\ufb17\uff41-\uff5a\U00010428-\U0001044f\U000104d8-\U000104fb\U00010cc0-\U00010cf2\U000118c0-\U000118df\U00016e60-\U00016e7f\U0001d41a-\U0001d433\U0001d44e-\U0001d454\U0001d456-\U0001d467\U0001d482-\U0001d49b\U0001d4b6-\U0001d4b9\U0001d4bb\U0001d4bd-\U0001d4c3\U0001d4c5-\U0001d4cf\U0001d4ea-\U0001d503\U0001d51e-\U0001d537\U0001d552-\U0001d56b\U0001d586-\U0001d59f\U0001d5ba-\U0001d5d3\U0001d5ee-\U0001d607\U0001d622-\U0001d63b\U0001d656-\U0001d66f\U0001d68a-\U0001d6a5\U0001d6c2-\U0001d6da\U0001d6dc-\U0001d6e1\U0001d6fc-\U0001d714\U0001d716-\U0001d71b\U0001d736-\U0001d74e\U0001d750-\U0001d755\U0001d770-\U0001d788\U0001d78a-\U0001d78f\U0001d7aa-\U0001d7c2\U0001d7c4-\U0001d7c9\U0001d7cb\U0001e922-\U0001e943'
|
| 25 |
-
|
| 26 |
-
Lm = '\u02b0-\u02c1\u02c6-\u02d1\u02e0-\u02e4\u02ec\u02ee\u0374\u037a\u0559\u0640\u06e5-\u06e6\u07f4-\u07f5\u07fa\u081a\u0824\u0828\u0971\u0e46\u0ec6\u10fc\u17d7\u1843\u1aa7\u1c78-\u1c7d\u1d2c-\u1d6a\u1d78\u1d9b-\u1dbf\u2071\u207f\u2090-\u209c\u2c7c-\u2c7d\u2d6f\u2e2f\u3005\u3031-\u3035\u303b\u309d-\u309e\u30fc-\u30fe\ua015\ua4f8-\ua4fd\ua60c\ua67f\ua69c-\ua69d\ua717-\ua71f\ua770\ua788\ua7f8-\ua7f9\ua9cf\ua9e6\uaa70\uaadd\uaaf3-\uaaf4\uab5c-\uab5f\uff70\uff9e-\uff9f\U00016b40-\U00016b43\U00016f93-\U00016f9f\U00016fe0-\U00016fe1'
|
| 27 |
-
|
| 28 |
-
Lo = '\xaa\xba\u01bb\u01c0-\u01c3\u0294\u05d0-\u05ea\u05ef-\u05f2\u0620-\u063f\u0641-\u064a\u066e-\u066f\u0671-\u06d3\u06d5\u06ee-\u06ef\u06fa-\u06fc\u06ff\u0710\u0712-\u072f\u074d-\u07a5\u07b1\u07ca-\u07ea\u0800-\u0815\u0840-\u0858\u0860-\u086a\u08a0-\u08b4\u08b6-\u08bd\u0904-\u0939\u093d\u0950\u0958-\u0961\u0972-\u0980\u0985-\u098c\u098f-\u0990\u0993-\u09a8\u09aa-\u09b0\u09b2\u09b6-\u09b9\u09bd\u09ce\u09dc-\u09dd\u09df-\u09e1\u09f0-\u09f1\u09fc\u0a05-\u0a0a\u0a0f-\u0a10\u0a13-\u0a28\u0a2a-\u0a30\u0a32-\u0a33\u0a35-\u0a36\u0a38-\u0a39\u0a59-\u0a5c\u0a5e\u0a72-\u0a74\u0a85-\u0a8d\u0a8f-\u0a91\u0a93-\u0aa8\u0aaa-\u0ab0\u0ab2-\u0ab3\u0ab5-\u0ab9\u0abd\u0ad0\u0ae0-\u0ae1\u0af9\u0b05-\u0b0c\u0b0f-\u0b10\u0b13-\u0b28\u0b2a-\u0b30\u0b32-\u0b33\u0b35-\u0b39\u0b3d\u0b5c-\u0b5d\u0b5f-\u0b61\u0b71\u0b83\u0b85-\u0b8a\u0b8e-\u0b90\u0b92-\u0b95\u0b99-\u0b9a\u0b9c\u0b9e-\u0b9f\u0ba3-\u0ba4\u0ba8-\u0baa\u0bae-\u0bb9\u0bd0\u0c05-\u0c0c\u0c0e-\u0c10\u0c12-\u0c28\u0c2a-\u0c39\u0c3d\u0c58-\u0c5a\u0c60-\u0c61\u0c80\u0c85-\u0c8c\u0c8e-\u0c90\u0c92-\u0ca8\u0caa-\u0cb3\u0cb5-\u0cb9\u0cbd\u0cde\u0ce0-\u0ce1\u0cf1-\u0cf2\u0d05-\u0d0c\u0d0e-\u0d10\u0d12-\u0d3a\u0d3d\u0d4e\u0d54-\u0d56\u0d5f-\u0d61\u0d7a-\u0d7f\u0d85-\u0d96\u0d9a-\u0db1\u0db3-\u0dbb\u0dbd\u0dc0-\u0dc6\u0e01-\u0e30\u0e32-\u0e33\u0e40-\u0e45\u0e81-\u0e82\u0e84\u0e87-\u0e88\u0e8a\u0e8d\u0e94-\u0e97\u0e99-\u0e9f\u0ea1-\u0ea3\u0ea5\u0ea7\u0eaa-\u0eab\u0ead-\u0eb0\u0eb2-\u0eb3\u0ebd\u0ec0-\u0ec4\u0edc-\u0edf\u0f00\u0f40-\u0f47\u0f49-\u0f6c\u0f88-\u0f8c\u1000-\u102a\u103f\u1050-\u1055\u105a-\u105d\u1061\u1065-\u1066\u106e-\u1070\u1075-\u1081\u108e\u1100-\u1248\u124a-\u124d\u1250-\u1256\u1258\u125a-\u125d\u1260-\u1288\u128a-\u128d\u1290-\u12b0\u12b2-\u12b5\u12b8-\u12be\u12c0\u12c2-\u12c5\u12c8-\u12d6\u12d8-\u1310\u1312-\u1315\u1318-\u135a\u1380-\u138f\u1401-\u166c\u166f-\u167f\u1681-\u169a\u16a0-\u16ea\u16f1-\u16f8\u1700-\u170c\u170e-\u1711\u1720-\u1731\u1740-\u1751\u1760-\u176c\u176e-\u1770\u1780-\u17b3\u17dc\u1820-\u1842\u1844-\u1878\u1880-\u1884\u1887-\u18a8\u18aa\u18b0-\u18f5\u1900-\u191e\u1950-\u196d\u1970-\u1974\u1980-\u19ab\u19b0-\u19c9\u1a00-\u1a16\u1a20-\u1a54\u1b05-\u1b33\u1b45-\u1b4b\u1b83-\u1ba0\u1bae-\u1baf\u1bba-\u1be5\u1c00-\u1c23\u1c4d-\u1c4f\u1c5a-\u1c77\u1ce9-\u1cec\u1cee-\u1cf1\u1cf5-\u1cf6\u2135-\u2138\u2d30-\u2d67\u2d80-\u2d96\u2da0-\u2da6\u2da8-\u2dae\u2db0-\u2db6\u2db8-\u2dbe\u2dc0-\u2dc6\u2dc8-\u2dce\u2dd0-\u2dd6\u2dd8-\u2dde\u3006\u303c\u3041-\u3096\u309f\u30a1-\u30fa\u30ff\u3105-\u312f\u3131-\u318e\u31a0-\u31ba\u31f0-\u31ff\u3400-\u4db5\u4e00-\u9fef\ua000-\ua014\ua016-\ua48c\ua4d0-\ua4f7\ua500-\ua60b\ua610-\ua61f\ua62a-\ua62b\ua66e\ua6a0-\ua6e5\ua78f\ua7f7\ua7fb-\ua801\ua803-\ua805\ua807-\ua80a\ua80c-\ua822\ua840-\ua873\ua882-\ua8b3\ua8f2-\ua8f7\ua8fb\ua8fd-\ua8fe\ua90a-\ua925\ua930-\ua946\ua960-\ua97c\ua984-\ua9b2\ua9e0-\ua9e4\ua9e7-\ua9ef\ua9fa-\ua9fe\uaa00-\uaa28\uaa40-\uaa42\uaa44-\uaa4b\uaa60-\uaa6f\uaa71-\uaa76\uaa7a\uaa7e-\uaaaf\uaab1\uaab5-\uaab6\uaab9-\uaabd\uaac0\uaac2\uaadb-\uaadc\uaae0-\uaaea\uaaf2\uab01-\uab06\uab09-\uab0e\uab11-\uab16\uab20-\uab26\uab28-\uab2e\uabc0-\uabe2\uac00-\ud7a3\ud7b0-\ud7c6\ud7cb-\ud7fb\uf900-\ufa6d\ufa70-\ufad9\ufb1d\ufb1f-\ufb28\ufb2a-\ufb36\ufb38-\ufb3c\ufb3e\ufb40-\ufb41\ufb43-\ufb44\ufb46-\ufbb1\ufbd3-\ufd3d\ufd50-\ufd8f\ufd92-\ufdc7\ufdf0-\ufdfb\ufe70-\ufe74\ufe76-\ufefc\uff66-\uff6f\uff71-\uff9d\uffa0-\uffbe\uffc2-\uffc7\uffca-\uffcf\uffd2-\uffd7\uffda-\uffdc\U00010000-\U0001000b\U0001000d-\U00010026\U00010028-\U0001003a\U0001003c-\U0001003d\U0001003f-\U0001004d\U00010050-\U0001005d\U00010080-\U000100fa\U00010280-\U0001029c\U000102a0-\U000102d0\U00010300-\U0001031f\U0001032d-\U00010340\U00010342-\U00010349\U00010350-\U00010375\U00010380-\U0001039d\U000103a0-\U000103c3\U000103c8-\U000103cf\U00010450-\U0001049d\U00010500-\U00010527\U00010530-\U00010563\U00010600-\U00010736\U00010740-\U00010755\U00010760-\U00010767\U00010800-\U00010805\U00010808\U0001080a-\U00010835\U00010837-\U00010838\U0001083c\U0001083f-\U00010855\U00010860-\U00010876\U00010880-\U0001089e\U000108e0-\U000108f2\U000108f4-\U000108f5\U00010900-\U00010915\U00010920-\U00010939\U00010980-\U000109b7\U000109be-\U000109bf\U00010a00\U00010a10-\U00010a13\U00010a15-\U00010a17\U00010a19-\U00010a35\U00010a60-\U00010a7c\U00010a80-\U00010a9c\U00010ac0-\U00010ac7\U00010ac9-\U00010ae4\U00010b00-\U00010b35\U00010b40-\U00010b55\U00010b60-\U00010b72\U00010b80-\U00010b91\U00010c00-\U00010c48\U00010d00-\U00010d23\U00010f00-\U00010f1c\U00010f27\U00010f30-\U00010f45\U00011003-\U00011037\U00011083-\U000110af\U000110d0-\U000110e8\U00011103-\U00011126\U00011144\U00011150-\U00011172\U00011176\U00011183-\U000111b2\U000111c1-\U000111c4\U000111da\U000111dc\U00011200-\U00011211\U00011213-\U0001122b\U00011280-\U00011286\U00011288\U0001128a-\U0001128d\U0001128f-\U0001129d\U0001129f-\U000112a8\U000112b0-\U000112de\U00011305-\U0001130c\U0001130f-\U00011310\U00011313-\U00011328\U0001132a-\U00011330\U00011332-\U00011333\U00011335-\U00011339\U0001133d\U00011350\U0001135d-\U00011361\U00011400-\U00011434\U00011447-\U0001144a\U00011480-\U000114af\U000114c4-\U000114c5\U000114c7\U00011580-\U000115ae\U000115d8-\U000115db\U00011600-\U0001162f\U00011644\U00011680-\U000116aa\U00011700-\U0001171a\U00011800-\U0001182b\U000118ff\U00011a00\U00011a0b-\U00011a32\U00011a3a\U00011a50\U00011a5c-\U00011a83\U00011a86-\U00011a89\U00011a9d\U00011ac0-\U00011af8\U00011c00-\U00011c08\U00011c0a-\U00011c2e\U00011c40\U00011c72-\U00011c8f\U00011d00-\U00011d06\U00011d08-\U00011d09\U00011d0b-\U00011d30\U00011d46\U00011d60-\U00011d65\U00011d67-\U00011d68\U00011d6a-\U00011d89\U00011d98\U00011ee0-\U00011ef2\U00012000-\U00012399\U00012480-\U00012543\U00013000-\U0001342e\U00014400-\U00014646\U00016800-\U00016a38\U00016a40-\U00016a5e\U00016ad0-\U00016aed\U00016b00-\U00016b2f\U00016b63-\U00016b77\U00016b7d-\U00016b8f\U00016f00-\U00016f44\U00016f50\U00017000-\U000187f1\U00018800-\U00018af2\U0001b000-\U0001b11e\U0001b170-\U0001b2fb\U0001bc00-\U0001bc6a\U0001bc70-\U0001bc7c\U0001bc80-\U0001bc88\U0001bc90-\U0001bc99\U0001e800-\U0001e8c4\U0001ee00-\U0001ee03\U0001ee05-\U0001ee1f\U0001ee21-\U0001ee22\U0001ee24\U0001ee27\U0001ee29-\U0001ee32\U0001ee34-\U0001ee37\U0001ee39\U0001ee3b\U0001ee42\U0001ee47\U0001ee49\U0001ee4b\U0001ee4d-\U0001ee4f\U0001ee51-\U0001ee52\U0001ee54\U0001ee57\U0001ee59\U0001ee5b\U0001ee5d\U0001ee5f\U0001ee61-\U0001ee62\U0001ee64\U0001ee67-\U0001ee6a\U0001ee6c-\U0001ee72\U0001ee74-\U0001ee77\U0001ee79-\U0001ee7c\U0001ee7e\U0001ee80-\U0001ee89\U0001ee8b-\U0001ee9b\U0001eea1-\U0001eea3\U0001eea5-\U0001eea9\U0001eeab-\U0001eebb\U00020000-\U0002a6d6\U0002a700-\U0002b734\U0002b740-\U0002b81d\U0002b820-\U0002cea1\U0002ceb0-\U0002ebe0\U0002f800-\U0002fa1d'
|
| 29 |
-
|
| 30 |
-
Lt = '\u01c5\u01c8\u01cb\u01f2\u1f88-\u1f8f\u1f98-\u1f9f\u1fa8-\u1faf\u1fbc\u1fcc\u1ffc'
|
| 31 |
-
|
| 32 |
-
Lu = 'A-Z\xc0-\xd6\xd8-\xde\u0100\u0102\u0104\u0106\u0108\u010a\u010c\u010e\u0110\u0112\u0114\u0116\u0118\u011a\u011c\u011e\u0120\u0122\u0124\u0126\u0128\u012a\u012c\u012e\u0130\u0132\u0134\u0136\u0139\u013b\u013d\u013f\u0141\u0143\u0145\u0147\u014a\u014c\u014e\u0150\u0152\u0154\u0156\u0158\u015a\u015c\u015e\u0160\u0162\u0164\u0166\u0168\u016a\u016c\u016e\u0170\u0172\u0174\u0176\u0178-\u0179\u017b\u017d\u0181-\u0182\u0184\u0186-\u0187\u0189-\u018b\u018e-\u0191\u0193-\u0194\u0196-\u0198\u019c-\u019d\u019f-\u01a0\u01a2\u01a4\u01a6-\u01a7\u01a9\u01ac\u01ae-\u01af\u01b1-\u01b3\u01b5\u01b7-\u01b8\u01bc\u01c4\u01c7\u01ca\u01cd\u01cf\u01d1\u01d3\u01d5\u01d7\u01d9\u01db\u01de\u01e0\u01e2\u01e4\u01e6\u01e8\u01ea\u01ec\u01ee\u01f1\u01f4\u01f6-\u01f8\u01fa\u01fc\u01fe\u0200\u0202\u0204\u0206\u0208\u020a\u020c\u020e\u0210\u0212\u0214\u0216\u0218\u021a\u021c\u021e\u0220\u0222\u0224\u0226\u0228\u022a\u022c\u022e\u0230\u0232\u023a-\u023b\u023d-\u023e\u0241\u0243-\u0246\u0248\u024a\u024c\u024e\u0370\u0372\u0376\u037f\u0386\u0388-\u038a\u038c\u038e-\u038f\u0391-\u03a1\u03a3-\u03ab\u03cf\u03d2-\u03d4\u03d8\u03da\u03dc\u03de\u03e0\u03e2\u03e4\u03e6\u03e8\u03ea\u03ec\u03ee\u03f4\u03f7\u03f9-\u03fa\u03fd-\u042f\u0460\u0462\u0464\u0466\u0468\u046a\u046c\u046e\u0470\u0472\u0474\u0476\u0478\u047a\u047c\u047e\u0480\u048a\u048c\u048e\u0490\u0492\u0494\u0496\u0498\u049a\u049c\u049e\u04a0\u04a2\u04a4\u04a6\u04a8\u04aa\u04ac\u04ae\u04b0\u04b2\u04b4\u04b6\u04b8\u04ba\u04bc\u04be\u04c0-\u04c1\u04c3\u04c5\u04c7\u04c9\u04cb\u04cd\u04d0\u04d2\u04d4\u04d6\u04d8\u04da\u04dc\u04de\u04e0\u04e2\u04e4\u04e6\u04e8\u04ea\u04ec\u04ee\u04f0\u04f2\u04f4\u04f6\u04f8\u04fa\u04fc\u04fe\u0500\u0502\u0504\u0506\u0508\u050a\u050c\u050e\u0510\u0512\u0514\u0516\u0518\u051a\u051c\u051e\u0520\u0522\u0524\u0526\u0528\u052a\u052c\u052e\u0531-\u0556\u10a0-\u10c5\u10c7\u10cd\u13a0-\u13f5\u1c90-\u1cba\u1cbd-\u1cbf\u1e00\u1e02\u1e04\u1e06\u1e08\u1e0a\u1e0c\u1e0e\u1e10\u1e12\u1e14\u1e16\u1e18\u1e1a\u1e1c\u1e1e\u1e20\u1e22\u1e24\u1e26\u1e28\u1e2a\u1e2c\u1e2e\u1e30\u1e32\u1e34\u1e36\u1e38\u1e3a\u1e3c\u1e3e\u1e40\u1e42\u1e44\u1e46\u1e48\u1e4a\u1e4c\u1e4e\u1e50\u1e52\u1e54\u1e56\u1e58\u1e5a\u1e5c\u1e5e\u1e60\u1e62\u1e64\u1e66\u1e68\u1e6a\u1e6c\u1e6e\u1e70\u1e72\u1e74\u1e76\u1e78\u1e7a\u1e7c\u1e7e\u1e80\u1e82\u1e84\u1e86\u1e88\u1e8a\u1e8c\u1e8e\u1e90\u1e92\u1e94\u1e9e\u1ea0\u1ea2\u1ea4\u1ea6\u1ea8\u1eaa\u1eac\u1eae\u1eb0\u1eb2\u1eb4\u1eb6\u1eb8\u1eba\u1ebc\u1ebe\u1ec0\u1ec2\u1ec4\u1ec6\u1ec8\u1eca\u1ecc\u1ece\u1ed0\u1ed2\u1ed4\u1ed6\u1ed8\u1eda\u1edc\u1ede\u1ee0\u1ee2\u1ee4\u1ee6\u1ee8\u1eea\u1eec\u1eee\u1ef0\u1ef2\u1ef4\u1ef6\u1ef8\u1efa\u1efc\u1efe\u1f08-\u1f0f\u1f18-\u1f1d\u1f28-\u1f2f\u1f38-\u1f3f\u1f48-\u1f4d\u1f59\u1f5b\u1f5d\u1f5f\u1f68-\u1f6f\u1fb8-\u1fbb\u1fc8-\u1fcb\u1fd8-\u1fdb\u1fe8-\u1fec\u1ff8-\u1ffb\u2102\u2107\u210b-\u210d\u2110-\u2112\u2115\u2119-\u211d\u2124\u2126\u2128\u212a-\u212d\u2130-\u2133\u213e-\u213f\u2145\u2183\u2c00-\u2c2e\u2c60\u2c62-\u2c64\u2c67\u2c69\u2c6b\u2c6d-\u2c70\u2c72\u2c75\u2c7e-\u2c80\u2c82\u2c84\u2c86\u2c88\u2c8a\u2c8c\u2c8e\u2c90\u2c92\u2c94\u2c96\u2c98\u2c9a\u2c9c\u2c9e\u2ca0\u2ca2\u2ca4\u2ca6\u2ca8\u2caa\u2cac\u2cae\u2cb0\u2cb2\u2cb4\u2cb6\u2cb8\u2cba\u2cbc\u2cbe\u2cc0\u2cc2\u2cc4\u2cc6\u2cc8\u2cca\u2ccc\u2cce\u2cd0\u2cd2\u2cd4\u2cd6\u2cd8\u2cda\u2cdc\u2cde\u2ce0\u2ce2\u2ceb\u2ced\u2cf2\ua640\ua642\ua644\ua646\ua648\ua64a\ua64c\ua64e\ua650\ua652\ua654\ua656\ua658\ua65a\ua65c\ua65e\ua660\ua662\ua664\ua666\ua668\ua66a\ua66c\ua680\ua682\ua684\ua686\ua688\ua68a\ua68c\ua68e\ua690\ua692\ua694\ua696\ua698\ua69a\ua722\ua724\ua726\ua728\ua72a\ua72c\ua72e\ua732\ua734\ua736\ua738\ua73a\ua73c\ua73e\ua740\ua742\ua744\ua746\ua748\ua74a\ua74c\ua74e\ua750\ua752\ua754\ua756\ua758\ua75a\ua75c\ua75e\ua760\ua762\ua764\ua766\ua768\ua76a\ua76c\ua76e\ua779\ua77b\ua77d-\ua77e\ua780\ua782\ua784\ua786\ua78b\ua78d\ua790\ua792\ua796\ua798\ua79a\ua79c\ua79e\ua7a0\ua7a2\ua7a4\ua7a6\ua7a8\ua7aa-\ua7ae\ua7b0-\ua7b4\ua7b6\ua7b8\uff21-\uff3a\U00010400-\U00010427\U000104b0-\U000104d3\U00010c80-\U00010cb2\U000118a0-\U000118bf\U00016e40-\U00016e5f\U0001d400-\U0001d419\U0001d434-\U0001d44d\U0001d468-\U0001d481\U0001d49c\U0001d49e-\U0001d49f\U0001d4a2\U0001d4a5-\U0001d4a6\U0001d4a9-\U0001d4ac\U0001d4ae-\U0001d4b5\U0001d4d0-\U0001d4e9\U0001d504-\U0001d505\U0001d507-\U0001d50a\U0001d50d-\U0001d514\U0001d516-\U0001d51c\U0001d538-\U0001d539\U0001d53b-\U0001d53e\U0001d540-\U0001d544\U0001d546\U0001d54a-\U0001d550\U0001d56c-\U0001d585\U0001d5a0-\U0001d5b9\U0001d5d4-\U0001d5ed\U0001d608-\U0001d621\U0001d63c-\U0001d655\U0001d670-\U0001d689\U0001d6a8-\U0001d6c0\U0001d6e2-\U0001d6fa\U0001d71c-\U0001d734\U0001d756-\U0001d76e\U0001d790-\U0001d7a8\U0001d7ca\U0001e900-\U0001e921'
|
| 33 |
-
|
| 34 |
-
Mc = '\u0903\u093b\u093e-\u0940\u0949-\u094c\u094e-\u094f\u0982-\u0983\u09be-\u09c0\u09c7-\u09c8\u09cb-\u09cc\u09d7\u0a03\u0a3e-\u0a40\u0a83\u0abe-\u0ac0\u0ac9\u0acb-\u0acc\u0b02-\u0b03\u0b3e\u0b40\u0b47-\u0b48\u0b4b-\u0b4c\u0b57\u0bbe-\u0bbf\u0bc1-\u0bc2\u0bc6-\u0bc8\u0bca-\u0bcc\u0bd7\u0c01-\u0c03\u0c41-\u0c44\u0c82-\u0c83\u0cbe\u0cc0-\u0cc4\u0cc7-\u0cc8\u0cca-\u0ccb\u0cd5-\u0cd6\u0d02-\u0d03\u0d3e-\u0d40\u0d46-\u0d48\u0d4a-\u0d4c\u0d57\u0d82-\u0d83\u0dcf-\u0dd1\u0dd8-\u0ddf\u0df2-\u0df3\u0f3e-\u0f3f\u0f7f\u102b-\u102c\u1031\u1038\u103b-\u103c\u1056-\u1057\u1062-\u1064\u1067-\u106d\u1083-\u1084\u1087-\u108c\u108f\u109a-\u109c\u17b6\u17be-\u17c5\u17c7-\u17c8\u1923-\u1926\u1929-\u192b\u1930-\u1931\u1933-\u1938\u1a19-\u1a1a\u1a55\u1a57\u1a61\u1a63-\u1a64\u1a6d-\u1a72\u1b04\u1b35\u1b3b\u1b3d-\u1b41\u1b43-\u1b44\u1b82\u1ba1\u1ba6-\u1ba7\u1baa\u1be7\u1bea-\u1bec\u1bee\u1bf2-\u1bf3\u1c24-\u1c2b\u1c34-\u1c35\u1ce1\u1cf2-\u1cf3\u1cf7\u302e-\u302f\ua823-\ua824\ua827\ua880-\ua881\ua8b4-\ua8c3\ua952-\ua953\ua983\ua9b4-\ua9b5\ua9ba-\ua9bb\ua9bd-\ua9c0\uaa2f-\uaa30\uaa33-\uaa34\uaa4d\uaa7b\uaa7d\uaaeb\uaaee-\uaaef\uaaf5\uabe3-\uabe4\uabe6-\uabe7\uabe9-\uabea\uabec\U00011000\U00011002\U00011082\U000110b0-\U000110b2\U000110b7-\U000110b8\U0001112c\U00011145-\U00011146\U00011182\U000111b3-\U000111b5\U000111bf-\U000111c0\U0001122c-\U0001122e\U00011232-\U00011233\U00011235\U000112e0-\U000112e2\U00011302-\U00011303\U0001133e-\U0001133f\U00011341-\U00011344\U00011347-\U00011348\U0001134b-\U0001134d\U00011357\U00011362-\U00011363\U00011435-\U00011437\U00011440-\U00011441\U00011445\U000114b0-\U000114b2\U000114b9\U000114bb-\U000114be\U000114c1\U000115af-\U000115b1\U000115b8-\U000115bb\U000115be\U00011630-\U00011632\U0001163b-\U0001163c\U0001163e\U000116ac\U000116ae-\U000116af\U000116b6\U00011720-\U00011721\U00011726\U0001182c-\U0001182e\U00011838\U00011a39\U00011a57-\U00011a58\U00011a97\U00011c2f\U00011c3e\U00011ca9\U00011cb1\U00011cb4\U00011d8a-\U00011d8e\U00011d93-\U00011d94\U00011d96\U00011ef5-\U00011ef6\U00016f51-\U00016f7e\U0001d165-\U0001d166\U0001d16d-\U0001d172'
|
| 35 |
-
|
| 36 |
-
Me = '\u0488-\u0489\u1abe\u20dd-\u20e0\u20e2-\u20e4\ua670-\ua672'
|
| 37 |
-
|
| 38 |
-
Mn = '\u0300-\u036f\u0483-\u0487\u0591-\u05bd\u05bf\u05c1-\u05c2\u05c4-\u05c5\u05c7\u0610-\u061a\u064b-\u065f\u0670\u06d6-\u06dc\u06df-\u06e4\u06e7-\u06e8\u06ea-\u06ed\u0711\u0730-\u074a\u07a6-\u07b0\u07eb-\u07f3\u07fd\u0816-\u0819\u081b-\u0823\u0825-\u0827\u0829-\u082d\u0859-\u085b\u08d3-\u08e1\u08e3-\u0902\u093a\u093c\u0941-\u0948\u094d\u0951-\u0957\u0962-\u0963\u0981\u09bc\u09c1-\u09c4\u09cd\u09e2-\u09e3\u09fe\u0a01-\u0a02\u0a3c\u0a41-\u0a42\u0a47-\u0a48\u0a4b-\u0a4d\u0a51\u0a70-\u0a71\u0a75\u0a81-\u0a82\u0abc\u0ac1-\u0ac5\u0ac7-\u0ac8\u0acd\u0ae2-\u0ae3\u0afa-\u0aff\u0b01\u0b3c\u0b3f\u0b41-\u0b44\u0b4d\u0b56\u0b62-\u0b63\u0b82\u0bc0\u0bcd\u0c00\u0c04\u0c3e-\u0c40\u0c46-\u0c48\u0c4a-\u0c4d\u0c55-\u0c56\u0c62-\u0c63\u0c81\u0cbc\u0cbf\u0cc6\u0ccc-\u0ccd\u0ce2-\u0ce3\u0d00-\u0d01\u0d3b-\u0d3c\u0d41-\u0d44\u0d4d\u0d62-\u0d63\u0dca\u0dd2-\u0dd4\u0dd6\u0e31\u0e34-\u0e3a\u0e47-\u0e4e\u0eb1\u0eb4-\u0eb9\u0ebb-\u0ebc\u0ec8-\u0ecd\u0f18-\u0f19\u0f35\u0f37\u0f39\u0f71-\u0f7e\u0f80-\u0f84\u0f86-\u0f87\u0f8d-\u0f97\u0f99-\u0fbc\u0fc6\u102d-\u1030\u1032-\u1037\u1039-\u103a\u103d-\u103e\u1058-\u1059\u105e-\u1060\u1071-\u1074\u1082\u1085-\u1086\u108d\u109d\u135d-\u135f\u1712-\u1714\u1732-\u1734\u1752-\u1753\u1772-\u1773\u17b4-\u17b5\u17b7-\u17bd\u17c6\u17c9-\u17d3\u17dd\u180b-\u180d\u1885-\u1886\u18a9\u1920-\u1922\u1927-\u1928\u1932\u1939-\u193b\u1a17-\u1a18\u1a1b\u1a56\u1a58-\u1a5e\u1a60\u1a62\u1a65-\u1a6c\u1a73-\u1a7c\u1a7f\u1ab0-\u1abd\u1b00-\u1b03\u1b34\u1b36-\u1b3a\u1b3c\u1b42\u1b6b-\u1b73\u1b80-\u1b81\u1ba2-\u1ba5\u1ba8-\u1ba9\u1bab-\u1bad\u1be6\u1be8-\u1be9\u1bed\u1bef-\u1bf1\u1c2c-\u1c33\u1c36-\u1c37\u1cd0-\u1cd2\u1cd4-\u1ce0\u1ce2-\u1ce8\u1ced\u1cf4\u1cf8-\u1cf9\u1dc0-\u1df9\u1dfb-\u1dff\u20d0-\u20dc\u20e1\u20e5-\u20f0\u2cef-\u2cf1\u2d7f\u2de0-\u2dff\u302a-\u302d\u3099-\u309a\ua66f\ua674-\ua67d\ua69e-\ua69f\ua6f0-\ua6f1\ua802\ua806\ua80b\ua825-\ua826\ua8c4-\ua8c5\ua8e0-\ua8f1\ua8ff\ua926-\ua92d\ua947-\ua951\ua980-\ua982\ua9b3\ua9b6-\ua9b9\ua9bc\ua9e5\uaa29-\uaa2e\uaa31-\uaa32\uaa35-\uaa36\uaa43\uaa4c\uaa7c\uaab0\uaab2-\uaab4\uaab7-\uaab8\uaabe-\uaabf\uaac1\uaaec-\uaaed\uaaf6\uabe5\uabe8\uabed\ufb1e\ufe00-\ufe0f\ufe20-\ufe2f\U000101fd\U000102e0\U00010376-\U0001037a\U00010a01-\U00010a03\U00010a05-\U00010a06\U00010a0c-\U00010a0f\U00010a38-\U00010a3a\U00010a3f\U00010ae5-\U00010ae6\U00010d24-\U00010d27\U00010f46-\U00010f50\U00011001\U00011038-\U00011046\U0001107f-\U00011081\U000110b3-\U000110b6\U000110b9-\U000110ba\U00011100-\U00011102\U00011127-\U0001112b\U0001112d-\U00011134\U00011173\U00011180-\U00011181\U000111b6-\U000111be\U000111c9-\U000111cc\U0001122f-\U00011231\U00011234\U00011236-\U00011237\U0001123e\U000112df\U000112e3-\U000112ea\U00011300-\U00011301\U0001133b-\U0001133c\U00011340\U00011366-\U0001136c\U00011370-\U00011374\U00011438-\U0001143f\U00011442-\U00011444\U00011446\U0001145e\U000114b3-\U000114b8\U000114ba\U000114bf-\U000114c0\U000114c2-\U000114c3\U000115b2-\U000115b5\U000115bc-\U000115bd\U000115bf-\U000115c0\U000115dc-\U000115dd\U00011633-\U0001163a\U0001163d\U0001163f-\U00011640\U000116ab\U000116ad\U000116b0-\U000116b5\U000116b7\U0001171d-\U0001171f\U00011722-\U00011725\U00011727-\U0001172b\U0001182f-\U00011837\U00011839-\U0001183a\U00011a01-\U00011a0a\U00011a33-\U00011a38\U00011a3b-\U00011a3e\U00011a47\U00011a51-\U00011a56\U00011a59-\U00011a5b\U00011a8a-\U00011a96\U00011a98-\U00011a99\U00011c30-\U00011c36\U00011c38-\U00011c3d\U00011c3f\U00011c92-\U00011ca7\U00011caa-\U00011cb0\U00011cb2-\U00011cb3\U00011cb5-\U00011cb6\U00011d31-\U00011d36\U00011d3a\U00011d3c-\U00011d3d\U00011d3f-\U00011d45\U00011d47\U00011d90-\U00011d91\U00011d95\U00011d97\U00011ef3-\U00011ef4\U00016af0-\U00016af4\U00016b30-\U00016b36\U00016f8f-\U00016f92\U0001bc9d-\U0001bc9e\U0001d167-\U0001d169\U0001d17b-\U0001d182\U0001d185-\U0001d18b\U0001d1aa-\U0001d1ad\U0001d242-\U0001d244\U0001da00-\U0001da36\U0001da3b-\U0001da6c\U0001da75\U0001da84\U0001da9b-\U0001da9f\U0001daa1-\U0001daaf\U0001e000-\U0001e006\U0001e008-\U0001e018\U0001e01b-\U0001e021\U0001e023-\U0001e024\U0001e026-\U0001e02a\U0001e8d0-\U0001e8d6\U0001e944-\U0001e94a\U000e0100-\U000e01ef'
|
| 39 |
-
|
| 40 |
-
Nd = '0-9\u0660-\u0669\u06f0-\u06f9\u07c0-\u07c9\u0966-\u096f\u09e6-\u09ef\u0a66-\u0a6f\u0ae6-\u0aef\u0b66-\u0b6f\u0be6-\u0bef\u0c66-\u0c6f\u0ce6-\u0cef\u0d66-\u0d6f\u0de6-\u0def\u0e50-\u0e59\u0ed0-\u0ed9\u0f20-\u0f29\u1040-\u1049\u1090-\u1099\u17e0-\u17e9\u1810-\u1819\u1946-\u194f\u19d0-\u19d9\u1a80-\u1a89\u1a90-\u1a99\u1b50-\u1b59\u1bb0-\u1bb9\u1c40-\u1c49\u1c50-\u1c59\ua620-\ua629\ua8d0-\ua8d9\ua900-\ua909\ua9d0-\ua9d9\ua9f0-\ua9f9\uaa50-\uaa59\uabf0-\uabf9\uff10-\uff19\U000104a0-\U000104a9\U00010d30-\U00010d39\U00011066-\U0001106f\U000110f0-\U000110f9\U00011136-\U0001113f\U000111d0-\U000111d9\U000112f0-\U000112f9\U00011450-\U00011459\U000114d0-\U000114d9\U00011650-\U00011659\U000116c0-\U000116c9\U00011730-\U00011739\U000118e0-\U000118e9\U00011c50-\U00011c59\U00011d50-\U00011d59\U00011da0-\U00011da9\U00016a60-\U00016a69\U00016b50-\U00016b59\U0001d7ce-\U0001d7ff\U0001e950-\U0001e959'
|
| 41 |
-
|
| 42 |
-
Nl = '\u16ee-\u16f0\u2160-\u2182\u2185-\u2188\u3007\u3021-\u3029\u3038-\u303a\ua6e6-\ua6ef\U00010140-\U00010174\U00010341\U0001034a\U000103d1-\U000103d5\U00012400-\U0001246e'
|
| 43 |
-
|
| 44 |
-
No = '\xb2-\xb3\xb9\xbc-\xbe\u09f4-\u09f9\u0b72-\u0b77\u0bf0-\u0bf2\u0c78-\u0c7e\u0d58-\u0d5e\u0d70-\u0d78\u0f2a-\u0f33\u1369-\u137c\u17f0-\u17f9\u19da\u2070\u2074-\u2079\u2080-\u2089\u2150-\u215f\u2189\u2460-\u249b\u24ea-\u24ff\u2776-\u2793\u2cfd\u3192-\u3195\u3220-\u3229\u3248-\u324f\u3251-\u325f\u3280-\u3289\u32b1-\u32bf\ua830-\ua835\U00010107-\U00010133\U00010175-\U00010178\U0001018a-\U0001018b\U000102e1-\U000102fb\U00010320-\U00010323\U00010858-\U0001085f\U00010879-\U0001087f\U000108a7-\U000108af\U000108fb-\U000108ff\U00010916-\U0001091b\U000109bc-\U000109bd\U000109c0-\U000109cf\U000109d2-\U000109ff\U00010a40-\U00010a48\U00010a7d-\U00010a7e\U00010a9d-\U00010a9f\U00010aeb-\U00010aef\U00010b58-\U00010b5f\U00010b78-\U00010b7f\U00010ba9-\U00010baf\U00010cfa-\U00010cff\U00010e60-\U00010e7e\U00010f1d-\U00010f26\U00010f51-\U00010f54\U00011052-\U00011065\U000111e1-\U000111f4\U0001173a-\U0001173b\U000118ea-\U000118f2\U00011c5a-\U00011c6c\U00016b5b-\U00016b61\U00016e80-\U00016e96\U0001d2e0-\U0001d2f3\U0001d360-\U0001d378\U0001e8c7-\U0001e8cf\U0001ec71-\U0001ecab\U0001ecad-\U0001ecaf\U0001ecb1-\U0001ecb4\U0001f100-\U0001f10c'
|
| 45 |
-
|
| 46 |
-
Pc = '_\u203f-\u2040\u2054\ufe33-\ufe34\ufe4d-\ufe4f\uff3f'
|
| 47 |
-
|
| 48 |
-
Pd = '\\-\u058a\u05be\u1400\u1806\u2010-\u2015\u2e17\u2e1a\u2e3a-\u2e3b\u2e40\u301c\u3030\u30a0\ufe31-\ufe32\ufe58\ufe63\uff0d'
|
| 49 |
-
|
| 50 |
-
Pe = ')\\]}\u0f3b\u0f3d\u169c\u2046\u207e\u208e\u2309\u230b\u232a\u2769\u276b\u276d\u276f\u2771\u2773\u2775\u27c6\u27e7\u27e9\u27eb\u27ed\u27ef\u2984\u2986\u2988\u298a\u298c\u298e\u2990\u2992\u2994\u2996\u2998\u29d9\u29db\u29fd\u2e23\u2e25\u2e27\u2e29\u3009\u300b\u300d\u300f\u3011\u3015\u3017\u3019\u301b\u301e-\u301f\ufd3e\ufe18\ufe36\ufe38\ufe3a\ufe3c\ufe3e\ufe40\ufe42\ufe44\ufe48\ufe5a\ufe5c\ufe5e\uff09\uff3d\uff5d\uff60\uff63'
|
| 51 |
-
|
| 52 |
-
Pf = '\xbb\u2019\u201d\u203a\u2e03\u2e05\u2e0a\u2e0d\u2e1d\u2e21'
|
| 53 |
-
|
| 54 |
-
Pi = '\xab\u2018\u201b-\u201c\u201f\u2039\u2e02\u2e04\u2e09\u2e0c\u2e1c\u2e20'
|
| 55 |
-
|
| 56 |
-
Po = "!-#%-'*,.-/:-;?-@\\\\\xa1\xa7\xb6-\xb7\xbf\u037e\u0387\u055a-\u055f\u0589\u05c0\u05c3\u05c6\u05f3-\u05f4\u0609-\u060a\u060c-\u060d\u061b\u061e-\u061f\u066a-\u066d\u06d4\u0700-\u070d\u07f7-\u07f9\u0830-\u083e\u085e\u0964-\u0965\u0970\u09fd\u0a76\u0af0\u0c84\u0df4\u0e4f\u0e5a-\u0e5b\u0f04-\u0f12\u0f14\u0f85\u0fd0-\u0fd4\u0fd9-\u0fda\u104a-\u104f\u10fb\u1360-\u1368\u166d-\u166e\u16eb-\u16ed\u1735-\u1736\u17d4-\u17d6\u17d8-\u17da\u1800-\u1805\u1807-\u180a\u1944-\u1945\u1a1e-\u1a1f\u1aa0-\u1aa6\u1aa8-\u1aad\u1b5a-\u1b60\u1bfc-\u1bff\u1c3b-\u1c3f\u1c7e-\u1c7f\u1cc0-\u1cc7\u1cd3\u2016-\u2017\u2020-\u2027\u2030-\u2038\u203b-\u203e\u2041-\u2043\u2047-\u2051\u2053\u2055-\u205e\u2cf9-\u2cfc\u2cfe-\u2cff\u2d70\u2e00-\u2e01\u2e06-\u2e08\u2e0b\u2e0e-\u2e16\u2e18-\u2e19\u2e1b\u2e1e-\u2e1f\u2e2a-\u2e2e\u2e30-\u2e39\u2e3c-\u2e3f\u2e41\u2e43-\u2e4e\u3001-\u3003\u303d\u30fb\ua4fe-\ua4ff\ua60d-\ua60f\ua673\ua67e\ua6f2-\ua6f7\ua874-\ua877\ua8ce-\ua8cf\ua8f8-\ua8fa\ua8fc\ua92e-\ua92f\ua95f\ua9c1-\ua9cd\ua9de-\ua9df\uaa5c-\uaa5f\uaade-\uaadf\uaaf0-\uaaf1\uabeb\ufe10-\ufe16\ufe19\ufe30\ufe45-\ufe46\ufe49-\ufe4c\ufe50-\ufe52\ufe54-\ufe57\ufe5f-\ufe61\ufe68\ufe6a-\ufe6b\uff01-\uff03\uff05-\uff07\uff0a\uff0c\uff0e-\uff0f\uff1a-\uff1b\uff1f-\uff20\uff3c\uff61\uff64-\uff65\U00010100-\U00010102\U0001039f\U000103d0\U0001056f\U00010857\U0001091f\U0001093f\U00010a50-\U00010a58\U00010a7f\U00010af0-\U00010af6\U00010b39-\U00010b3f\U00010b99-\U00010b9c\U00010f55-\U00010f59\U00011047-\U0001104d\U000110bb-\U000110bc\U000110be-\U000110c1\U00011140-\U00011143\U00011174-\U00011175\U000111c5-\U000111c8\U000111cd\U000111db\U000111dd-\U000111df\U00011238-\U0001123d\U000112a9\U0001144b-\U0001144f\U0001145b\U0001145d\U000114c6\U000115c1-\U000115d7\U00011641-\U00011643\U00011660-\U0001166c\U0001173c-\U0001173e\U0001183b\U00011a3f-\U00011a46\U00011a9a-\U00011a9c\U00011a9e-\U00011aa2\U00011c41-\U00011c45\U00011c70-\U00011c71\U00011ef7-\U00011ef8\U00012470-\U00012474\U00016a6e-\U00016a6f\U00016af5\U00016b37-\U00016b3b\U00016b44\U00016e97-\U00016e9a\U0001bc9f\U0001da87-\U0001da8b\U0001e95e-\U0001e95f"
|
| 57 |
-
|
| 58 |
-
Ps = '(\\[{\u0f3a\u0f3c\u169b\u201a\u201e\u2045\u207d\u208d\u2308\u230a\u2329\u2768\u276a\u276c\u276e\u2770\u2772\u2774\u27c5\u27e6\u27e8\u27ea\u27ec\u27ee\u2983\u2985\u2987\u2989\u298b\u298d\u298f\u2991\u2993\u2995\u2997\u29d8\u29da\u29fc\u2e22\u2e24\u2e26\u2e28\u2e42\u3008\u300a\u300c\u300e\u3010\u3014\u3016\u3018\u301a\u301d\ufd3f\ufe17\ufe35\ufe37\ufe39\ufe3b\ufe3d\ufe3f\ufe41\ufe43\ufe47\ufe59\ufe5b\ufe5d\uff08\uff3b\uff5b\uff5f\uff62'
|
| 59 |
-
|
| 60 |
-
Sc = '$\xa2-\xa5\u058f\u060b\u07fe-\u07ff\u09f2-\u09f3\u09fb\u0af1\u0bf9\u0e3f\u17db\u20a0-\u20bf\ua838\ufdfc\ufe69\uff04\uffe0-\uffe1\uffe5-\uffe6\U0001ecb0'
|
| 61 |
-
|
| 62 |
-
Sk = '\\^`\xa8\xaf\xb4\xb8\u02c2-\u02c5\u02d2-\u02df\u02e5-\u02eb\u02ed\u02ef-\u02ff\u0375\u0384-\u0385\u1fbd\u1fbf-\u1fc1\u1fcd-\u1fcf\u1fdd-\u1fdf\u1fed-\u1fef\u1ffd-\u1ffe\u309b-\u309c\ua700-\ua716\ua720-\ua721\ua789-\ua78a\uab5b\ufbb2-\ufbc1\uff3e\uff40\uffe3\U0001f3fb-\U0001f3ff'
|
| 63 |
-
|
| 64 |
-
Sm = '+<->|~\xac\xb1\xd7\xf7\u03f6\u0606-\u0608\u2044\u2052\u207a-\u207c\u208a-\u208c\u2118\u2140-\u2144\u214b\u2190-\u2194\u219a-\u219b\u21a0\u21a3\u21a6\u21ae\u21ce-\u21cf\u21d2\u21d4\u21f4-\u22ff\u2320-\u2321\u237c\u239b-\u23b3\u23dc-\u23e1\u25b7\u25c1\u25f8-\u25ff\u266f\u27c0-\u27c4\u27c7-\u27e5\u27f0-\u27ff\u2900-\u2982\u2999-\u29d7\u29dc-\u29fb\u29fe-\u2aff\u2b30-\u2b44\u2b47-\u2b4c\ufb29\ufe62\ufe64-\ufe66\uff0b\uff1c-\uff1e\uff5c\uff5e\uffe2\uffe9-\uffec\U0001d6c1\U0001d6db\U0001d6fb\U0001d715\U0001d735\U0001d74f\U0001d76f\U0001d789\U0001d7a9\U0001d7c3\U0001eef0-\U0001eef1'
|
| 65 |
-
|
| 66 |
-
So = '\xa6\xa9\xae\xb0\u0482\u058d-\u058e\u060e-\u060f\u06de\u06e9\u06fd-\u06fe\u07f6\u09fa\u0b70\u0bf3-\u0bf8\u0bfa\u0c7f\u0d4f\u0d79\u0f01-\u0f03\u0f13\u0f15-\u0f17\u0f1a-\u0f1f\u0f34\u0f36\u0f38\u0fbe-\u0fc5\u0fc7-\u0fcc\u0fce-\u0fcf\u0fd5-\u0fd8\u109e-\u109f\u1390-\u1399\u1940\u19de-\u19ff\u1b61-\u1b6a\u1b74-\u1b7c\u2100-\u2101\u2103-\u2106\u2108-\u2109\u2114\u2116-\u2117\u211e-\u2123\u2125\u2127\u2129\u212e\u213a-\u213b\u214a\u214c-\u214d\u214f\u218a-\u218b\u2195-\u2199\u219c-\u219f\u21a1-\u21a2\u21a4-\u21a5\u21a7-\u21ad\u21af-\u21cd\u21d0-\u21d1\u21d3\u21d5-\u21f3\u2300-\u2307\u230c-\u231f\u2322-\u2328\u232b-\u237b\u237d-\u239a\u23b4-\u23db\u23e2-\u2426\u2440-\u244a\u249c-\u24e9\u2500-\u25b6\u25b8-\u25c0\u25c2-\u25f7\u2600-\u266e\u2670-\u2767\u2794-\u27bf\u2800-\u28ff\u2b00-\u2b2f\u2b45-\u2b46\u2b4d-\u2b73\u2b76-\u2b95\u2b98-\u2bc8\u2bca-\u2bfe\u2ce5-\u2cea\u2e80-\u2e99\u2e9b-\u2ef3\u2f00-\u2fd5\u2ff0-\u2ffb\u3004\u3012-\u3013\u3020\u3036-\u3037\u303e-\u303f\u3190-\u3191\u3196-\u319f\u31c0-\u31e3\u3200-\u321e\u322a-\u3247\u3250\u3260-\u327f\u328a-\u32b0\u32c0-\u32fe\u3300-\u33ff\u4dc0-\u4dff\ua490-\ua4c6\ua828-\ua82b\ua836-\ua837\ua839\uaa77-\uaa79\ufdfd\uffe4\uffe8\uffed-\uffee\ufffc-\ufffd\U00010137-\U0001013f\U00010179-\U00010189\U0001018c-\U0001018e\U00010190-\U0001019b\U000101a0\U000101d0-\U000101fc\U00010877-\U00010878\U00010ac8\U0001173f\U00016b3c-\U00016b3f\U00016b45\U0001bc9c\U0001d000-\U0001d0f5\U0001d100-\U0001d126\U0001d129-\U0001d164\U0001d16a-\U0001d16c\U0001d183-\U0001d184\U0001d18c-\U0001d1a9\U0001d1ae-\U0001d1e8\U0001d200-\U0001d241\U0001d245\U0001d300-\U0001d356\U0001d800-\U0001d9ff\U0001da37-\U0001da3a\U0001da6d-\U0001da74\U0001da76-\U0001da83\U0001da85-\U0001da86\U0001ecac\U0001f000-\U0001f02b\U0001f030-\U0001f093\U0001f0a0-\U0001f0ae\U0001f0b1-\U0001f0bf\U0001f0c1-\U0001f0cf\U0001f0d1-\U0001f0f5\U0001f110-\U0001f16b\U0001f170-\U0001f1ac\U0001f1e6-\U0001f202\U0001f210-\U0001f23b\U0001f240-\U0001f248\U0001f250-\U0001f251\U0001f260-\U0001f265\U0001f300-\U0001f3fa\U0001f400-\U0001f6d4\U0001f6e0-\U0001f6ec\U0001f6f0-\U0001f6f9\U0001f700-\U0001f773\U0001f780-\U0001f7d8\U0001f800-\U0001f80b\U0001f810-\U0001f847\U0001f850-\U0001f859\U0001f860-\U0001f887\U0001f890-\U0001f8ad\U0001f900-\U0001f90b\U0001f910-\U0001f93e\U0001f940-\U0001f970\U0001f973-\U0001f976\U0001f97a\U0001f97c-\U0001f9a2\U0001f9b0-\U0001f9b9\U0001f9c0-\U0001f9c2\U0001f9d0-\U0001f9ff\U0001fa60-\U0001fa6d'
|
| 67 |
-
|
| 68 |
-
Zl = '\u2028'
|
| 69 |
-
|
| 70 |
-
Zp = '\u2029'
|
| 71 |
-
|
| 72 |
-
Zs = ' \xa0\u1680\u2000-\u200a\u202f\u205f\u3000'
|
| 73 |
-
|
| 74 |
-
xid_continue = 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|
| 75 |
-
|
| 76 |
-
xid_start = 'A-Z_a-z\xaa\xb5\xba\xc0-\xd6\xd8-\xf6\xf8-\u02c1\u02c6-\u02d1\u02e0-\u02e4\u02ec\u02ee\u0370-\u0374\u0376-\u0377\u037b-\u037d\u037f\u0386\u0388-\u038a\u038c\u038e-\u03a1\u03a3-\u03f5\u03f7-\u0481\u048a-\u052f\u0531-\u0556\u0559\u0560-\u0588\u05d0-\u05ea\u05ef-\u05f2\u0620-\u064a\u066e-\u066f\u0671-\u06d3\u06d5\u06e5-\u06e6\u06ee-\u06ef\u06fa-\u06fc\u06ff\u0710\u0712-\u072f\u074d-\u07a5\u07b1\u07ca-\u07ea\u07f4-\u07f5\u07fa\u0800-\u0815\u081a\u0824\u0828\u0840-\u0858\u0860-\u086a\u08a0-\u08b4\u08b6-\u08bd\u0904-\u0939\u093d\u0950\u0958-\u0961\u0971-\u0980\u0985-\u098c\u098f-\u0990\u0993-\u09a8\u09aa-\u09b0\u09b2\u09b6-\u09b9\u09bd\u09ce\u09dc-\u09dd\u09df-\u09e1\u09f0-\u09f1\u09fc\u0a05-\u0a0a\u0a0f-\u0a10\u0a13-\u0a28\u0a2a-\u0a30\u0a32-\u0a33\u0a35-\u0a36\u0a38-\u0a39\u0a59-\u0a5c\u0a5e\u0a72-\u0a74\u0a85-\u0a8d\u0a8f-\u0a91\u0a93-\u0aa8\u0aaa-\u0ab0\u0ab2-\u0ab3\u0ab5-\u0ab9\u0abd\u0ad0\u0ae0-\u0ae1\u0af9\u0b05-\u0b0c\u0b0f-\u0b10\u0b13-\u0b28\u0b2a-\u0b30\u0b32-\u0b33\u0b35-\u0b39\u0b3d\u0b5c-\u0b5d\u0b5f-\u0b61\u0b71\u0b83\u0b85-\u0b8a\u0b8e-\u0b90\u0b92-\u0b95\u0b99-\u0b9a\u0b9c\u0b9e-\u0b9f\u0ba3-\u0ba4\u0ba8-\u0baa\u0bae-\u0bb9\u0bd0\u0c05-\u0c0c\u0c0e-\u0c10\u0c12-\u0c28\u0c2a-\u0c39\u0c3d\u0c58-\u0c5a\u0c60-\u0c61\u0c80\u0c85-\u0c8c\u0c8e-\u0c90\u0c92-\u0ca8\u0caa-\u0cb3\u0cb5-\u0cb9\u0cbd\u0cde\u0ce0-\u0ce1\u0cf1-\u0cf2\u0d05-\u0d0c\u0d0e-\u0d10\u0d12-\u0d3a\u0d3d\u0d4e\u0d54-\u0d56\u0d5f-\u0d61\u0d7a-\u0d7f\u0d85-\u0d96\u0d9a-\u0db1\u0db3-\u0dbb\u0dbd\u0dc0-\u0dc6\u0e01-\u0e30\u0e32\u0e40-\u0e46\u0e81-\u0e82\u0e84\u0e87-\u0e88\u0e8a\u0e8d\u0e94-\u0e97\u0e99-\u0e9f\u0ea1-\u0ea3\u0ea5\u0ea7\u0eaa-\u0eab\u0ead-\u0eb0\u0eb2\u0ebd\u0ec0-\u0ec4\u0ec6\u0edc-\u0edf\u0f00\u0f40-\u0f47\u0f49-\u0f6c\u0f88-\u0f8c\u1000-\u102a\u103f\u1050-\u1055\u105a-\u105d\u1061\u1065-\u1066\u106e-\u1070\u1075-\u1081\u108e\u10a0-\u10c5\u10c7\u10cd\u10d0-\u10fa\u10fc-\u1248\u124a-\u124d\u1250-\u1256\u1258\u125a-\u125d\u1260-\u1288\u128a-\u128d\u1290-\u12b0\u12b2-\u12b5\u12b8-\u12be\u12c0\u12c2-\u12c5\u12c8-\u12d6\u12d8-\u1310\u1312-\u1315\u1318-\u135a\u1380-\u138f\u13a0-\u13f5\u13f8-\u13fd\u1401-\u166c\u166f-\u167f\u1681-\u169a\u16a0-\u16ea\u16ee-\u16f8\u1700-\u170c\u170e-\u1711\u1720-\u1731\u1740-\u1751\u1760-\u176c\u176e-\u1770\u1780-\u17b3\u17d7\u17dc\u1820-\u1878\u1880-\u18a8\u18aa\u18b0-\u18f5\u1900-\u191e\u1950-\u196d\u1970-\u1974\u1980-\u19ab\u19b0-\u19c9\u1a00-\u1a16\u1a20-\u1a54\u1aa7\u1b05-\u1b33\u1b45-\u1b4b\u1b83-\u1ba0\u1bae-\u1baf\u1bba-\u1be5\u1c00-\u1c23\u1c4d-\u1c4f\u1c5a-\u1c7d\u1c80-\u1c88\u1c90-\u1cba\u1cbd-\u1cbf\u1ce9-\u1cec\u1cee-\u1cf1\u1cf5-\u1cf6\u1d00-\u1dbf\u1e00-\u1f15\u1f18-\u1f1d\u1f20-\u1f45\u1f48-\u1f4d\u1f50-\u1f57\u1f59\u1f5b\u1f5d\u1f5f-\u1f7d\u1f80-\u1fb4\u1fb6-\u1fbc\u1fbe\u1fc2-\u1fc4\u1fc6-\u1fcc\u1fd0-\u1fd3\u1fd6-\u1fdb\u1fe0-\u1fec\u1ff2-\u1ff4\u1ff6-\u1ffc\u2071\u207f\u2090-\u209c\u2102\u2107\u210a-\u2113\u2115\u2118-\u211d\u2124\u2126\u2128\u212a-\u2139\u213c-\u213f\u2145-\u2149\u214e\u2160-\u2188\u2c00-\u2c2e\u2c30-\u2c5e\u2c60-\u2ce4\u2ceb-\u2cee\u2cf2-\u2cf3\u2d00-\u2d25\u2d27\u2d2d\u2d30-\u2d67\u2d6f\u2d80-\u2d96\u2da0-\u2da6\u2da8-\u2dae\u2db0-\u2db6\u2db8-\u2dbe\u2dc0-\u2dc6\u2dc8-\u2dce\u2dd0-\u2dd6\u2dd8-\u2dde\u3005-\u3007\u3021-\u3029\u3031-\u3035\u3038-\u303c\u3041-\u3096\u309d-\u309f\u30a1-\u30fa\u30fc-\u30ff\u3105-\u312f\u3131-\u318e\u31a0-\u31ba\u31f0-\u31ff\u3400-\u4db5\u4e00-\u9fef\ua000-\ua48c\ua4d0-\ua4fd\ua500-\ua60c\ua610-\ua61f\ua62a-\ua62b\ua640-\ua66e\ua67f-\ua69d\ua6a0-\ua6ef\ua717-\ua71f\ua722-\ua788\ua78b-\ua7b9\ua7f7-\ua801\ua803-\ua805\ua807-\ua80a\ua80c-\ua822\ua840-\ua873\ua882-\ua8b3\ua8f2-\ua8f7\ua8fb\ua8fd-\ua8fe\ua90a-\ua925\ua930-\ua946\ua960-\ua97c\ua984-\ua9b2\ua9cf\ua9e0-\ua9e4\ua9e6-\ua9ef\ua9fa-\ua9fe\uaa00-\uaa28\uaa40-\uaa42\uaa44-\uaa4b\uaa60-\uaa76\uaa7a\uaa7e-\uaaaf\uaab1\uaab5-\uaab6\uaab9-\uaabd\uaac0\uaac2\uaadb-\uaadd\uaae0-\uaaea\uaaf2-\uaaf4\uab01-\uab06\uab09-\uab0e\uab11-\uab16\uab20-\uab26\uab28-\uab2e\uab30-\uab5a\uab5c-\uab65\uab70-\uabe2\uac00-\ud7a3\ud7b0-\ud7c6\ud7cb-\ud7fb\uf900-\ufa6d\ufa70-\ufad9\ufb00-\ufb06\ufb13-\ufb17\ufb1d\ufb1f-\ufb28\ufb2a-\ufb36\ufb38-\ufb3c\ufb3e\ufb40-\ufb41\ufb43-\ufb44\ufb46-\ufbb1\ufbd3-\ufc5d\ufc64-\ufd3d\ufd50-\ufd8f\ufd92-\ufdc7\ufdf0-\ufdf9\ufe71\ufe73\ufe77\ufe79\ufe7b\ufe7d\ufe7f-\ufefc\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d\uffa0-\uffbe\uffc2-\uffc7\uffca-\uffcf\uffd2-\uffd7\uffda-\uffdc\U00010000-\U0001000b\U0001000d-\U00010026\U00010028-\U0001003a\U0001003c-\U0001003d\U0001003f-\U0001004d\U00010050-\U0001005d\U00010080-\U000100fa\U00010140-\U00010174\U00010280-\U0001029c\U000102a0-\U000102d0\U00010300-\U0001031f\U0001032d-\U0001034a\U00010350-\U00010375\U00010380-\U0001039d\U000103a0-\U000103c3\U000103c8-\U000103cf\U000103d1-\U000103d5\U00010400-\U0001049d\U000104b0-\U000104d3\U000104d8-\U000104fb\U00010500-\U00010527\U00010530-\U00010563\U00010600-\U00010736\U00010740-\U00010755\U00010760-\U00010767\U00010800-\U00010805\U00010808\U0001080a-\U00010835\U00010837-\U00010838\U0001083c\U0001083f-\U00010855\U00010860-\U00010876\U00010880-\U0001089e\U000108e0-\U000108f2\U000108f4-\U000108f5\U00010900-\U00010915\U00010920-\U00010939\U00010980-\U000109b7\U000109be-\U000109bf\U00010a00\U00010a10-\U00010a13\U00010a15-\U00010a17\U00010a19-\U00010a35\U00010a60-\U00010a7c\U00010a80-\U00010a9c\U00010ac0-\U00010ac7\U00010ac9-\U00010ae4\U00010b00-\U00010b35\U00010b40-\U00010b55\U00010b60-\U00010b72\U00010b80-\U00010b91\U00010c00-\U00010c48\U00010c80-\U00010cb2\U00010cc0-\U00010cf2\U00010d00-\U00010d23\U00010f00-\U00010f1c\U00010f27\U00010f30-\U00010f45\U00011003-\U00011037\U00011083-\U000110af\U000110d0-\U000110e8\U00011103-\U00011126\U00011144\U00011150-\U00011172\U00011176\U00011183-\U000111b2\U000111c1-\U000111c4\U000111da\U000111dc\U00011200-\U00011211\U00011213-\U0001122b\U00011280-\U00011286\U00011288\U0001128a-\U0001128d\U0001128f-\U0001129d\U0001129f-\U000112a8\U000112b0-\U000112de\U00011305-\U0001130c\U0001130f-\U00011310\U00011313-\U00011328\U0001132a-\U00011330\U00011332-\U00011333\U00011335-\U00011339\U0001133d\U00011350\U0001135d-\U00011361\U00011400-\U00011434\U00011447-\U0001144a\U00011480-\U000114af\U000114c4-\U000114c5\U000114c7\U00011580-\U000115ae\U000115d8-\U000115db\U00011600-\U0001162f\U00011644\U00011680-\U000116aa\U00011700-\U0001171a\U00011800-\U0001182b\U000118a0-\U000118df\U000118ff\U00011a00\U00011a0b-\U00011a32\U00011a3a\U00011a50\U00011a5c-\U00011a83\U00011a86-\U00011a89\U00011a9d\U00011ac0-\U00011af8\U00011c00-\U00011c08\U00011c0a-\U00011c2e\U00011c40\U00011c72-\U00011c8f\U00011d00-\U00011d06\U00011d08-\U00011d09\U00011d0b-\U00011d30\U00011d46\U00011d60-\U00011d65\U00011d67-\U00011d68\U00011d6a-\U00011d89\U00011d98\U00011ee0-\U00011ef2\U00012000-\U00012399\U00012400-\U0001246e\U00012480-\U00012543\U00013000-\U0001342e\U00014400-\U00014646\U00016800-\U00016a38\U00016a40-\U00016a5e\U00016ad0-\U00016aed\U00016b00-\U00016b2f\U00016b40-\U00016b43\U00016b63-\U00016b77\U00016b7d-\U00016b8f\U00016e40-\U00016e7f\U00016f00-\U00016f44\U00016f50\U00016f93-\U00016f9f\U00016fe0-\U00016fe1\U00017000-\U000187f1\U00018800-\U00018af2\U0001b000-\U0001b11e\U0001b170-\U0001b2fb\U0001bc00-\U0001bc6a\U0001bc70-\U0001bc7c\U0001bc80-\U0001bc88\U0001bc90-\U0001bc99\U0001d400-\U0001d454\U0001d456-\U0001d49c\U0001d49e-\U0001d49f\U0001d4a2\U0001d4a5-\U0001d4a6\U0001d4a9-\U0001d4ac\U0001d4ae-\U0001d4b9\U0001d4bb\U0001d4bd-\U0001d4c3\U0001d4c5-\U0001d505\U0001d507-\U0001d50a\U0001d50d-\U0001d514\U0001d516-\U0001d51c\U0001d51e-\U0001d539\U0001d53b-\U0001d53e\U0001d540-\U0001d544\U0001d546\U0001d54a-\U0001d550\U0001d552-\U0001d6a5\U0001d6a8-\U0001d6c0\U0001d6c2-\U0001d6da\U0001d6dc-\U0001d6fa\U0001d6fc-\U0001d714\U0001d716-\U0001d734\U0001d736-\U0001d74e\U0001d750-\U0001d76e\U0001d770-\U0001d788\U0001d78a-\U0001d7a8\U0001d7aa-\U0001d7c2\U0001d7c4-\U0001d7cb\U0001e800-\U0001e8c4\U0001e900-\U0001e943\U0001ee00-\U0001ee03\U0001ee05-\U0001ee1f\U0001ee21-\U0001ee22\U0001ee24\U0001ee27\U0001ee29-\U0001ee32\U0001ee34-\U0001ee37\U0001ee39\U0001ee3b\U0001ee42\U0001ee47\U0001ee49\U0001ee4b\U0001ee4d-\U0001ee4f\U0001ee51-\U0001ee52\U0001ee54\U0001ee57\U0001ee59\U0001ee5b\U0001ee5d\U0001ee5f\U0001ee61-\U0001ee62\U0001ee64\U0001ee67-\U0001ee6a\U0001ee6c-\U0001ee72\U0001ee74-\U0001ee77\U0001ee79-\U0001ee7c\U0001ee7e\U0001ee80-\U0001ee89\U0001ee8b-\U0001ee9b\U0001eea1-\U0001eea3\U0001eea5-\U0001eea9\U0001eeab-\U0001eebb\U00020000-\U0002a6d6\U0002a700-\U0002b734\U0002b740-\U0002b81d\U0002b820-\U0002cea1\U0002ceb0-\U0002ebe0\U0002f800-\U0002fa1d'
|
| 77 |
-
|
| 78 |
-
cats = ['Cc', 'Cf', 'Cn', 'Co', 'Cs', 'Ll', 'Lm', 'Lo', 'Lt', 'Lu', 'Mc', 'Me', 'Mn', 'Nd', 'Nl', 'No', 'Pc', 'Pd', 'Pe', 'Pf', 'Pi', 'Po', 'Ps', 'Sc', 'Sk', 'Sm', 'So', 'Zl', 'Zp', 'Zs']
|
| 79 |
-
|
| 80 |
-
# Generated from unidata 11.0.0
|
| 81 |
-
|
| 82 |
-
def combine(*args):
|
| 83 |
-
return ''.join(globals()[cat] for cat in args)
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
def allexcept(*args):
|
| 87 |
-
newcats = cats[:]
|
| 88 |
-
for arg in args:
|
| 89 |
-
newcats.remove(arg)
|
| 90 |
-
return ''.join(globals()[cat] for cat in newcats)
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
def _handle_runs(char_list): # pragma: no cover
|
| 94 |
-
buf = []
|
| 95 |
-
for c in char_list:
|
| 96 |
-
if len(c) == 1:
|
| 97 |
-
if buf and buf[-1][1] == chr(ord(c)-1):
|
| 98 |
-
buf[-1] = (buf[-1][0], c)
|
| 99 |
-
else:
|
| 100 |
-
buf.append((c, c))
|
| 101 |
-
else:
|
| 102 |
-
buf.append((c, c))
|
| 103 |
-
for a, b in buf:
|
| 104 |
-
if a == b:
|
| 105 |
-
yield a
|
| 106 |
-
else:
|
| 107 |
-
yield '%s-%s' % (a, b)
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
if __name__ == '__main__': # pragma: no cover
|
| 111 |
-
import unicodedata
|
| 112 |
-
|
| 113 |
-
categories = {'xid_start': [], 'xid_continue': []}
|
| 114 |
-
|
| 115 |
-
with open(__file__) as fp:
|
| 116 |
-
content = fp.read()
|
| 117 |
-
|
| 118 |
-
header = content[:content.find('Cc =')]
|
| 119 |
-
footer = content[content.find("def combine("):]
|
| 120 |
-
|
| 121 |
-
for code in range(0x110000):
|
| 122 |
-
c = chr(code)
|
| 123 |
-
cat = unicodedata.category(c)
|
| 124 |
-
if ord(c) == 0xdc00:
|
| 125 |
-
# Hack to avoid combining this combining with the preceding high
|
| 126 |
-
# surrogate, 0xdbff, when doing a repr.
|
| 127 |
-
c = '\\' + c
|
| 128 |
-
elif ord(c) in (0x2d, 0x5b, 0x5c, 0x5d, 0x5e):
|
| 129 |
-
# Escape regex metachars.
|
| 130 |
-
c = '\\' + c
|
| 131 |
-
categories.setdefault(cat, []).append(c)
|
| 132 |
-
# XID_START and XID_CONTINUE are special categories used for matching
|
| 133 |
-
# identifiers in Python 3.
|
| 134 |
-
if c.isidentifier():
|
| 135 |
-
categories['xid_start'].append(c)
|
| 136 |
-
if ('a' + c).isidentifier():
|
| 137 |
-
categories['xid_continue'].append(c)
|
| 138 |
-
|
| 139 |
-
with open(__file__, 'w') as fp:
|
| 140 |
-
fp.write(header)
|
| 141 |
-
|
| 142 |
-
for cat in sorted(categories):
|
| 143 |
-
val = ''.join(_handle_runs(categories[cat]))
|
| 144 |
-
fp.write('%s = %a\n\n' % (cat, val))
|
| 145 |
-
|
| 146 |
-
cats = sorted(categories)
|
| 147 |
-
cats.remove('xid_start')
|
| 148 |
-
cats.remove('xid_continue')
|
| 149 |
-
fp.write('cats = %r\n\n' % cats)
|
| 150 |
-
|
| 151 |
-
fp.write('# Generated from unidata %s\n\n' % (unicodedata.unidata_version,))
|
| 152 |
-
|
| 153 |
-
fp.write(footer)
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spaces/AutoLLM/AutoAgents/README.md
DELETED
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@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: AutoAgents
|
| 3 |
-
emoji: 🐢
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.21.0
|
| 8 |
-
python_version: 3.10.11
|
| 9 |
-
app_file: autoagents/spaces/app.py
|
| 10 |
-
pinned: true
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/predictor.py
DELETED
|
@@ -1,243 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 2 |
-
import atexit
|
| 3 |
-
import bisect
|
| 4 |
-
import multiprocessing as mp
|
| 5 |
-
from collections import deque
|
| 6 |
-
import cv2
|
| 7 |
-
import torch
|
| 8 |
-
|
| 9 |
-
from detectron2.data import MetadataCatalog
|
| 10 |
-
from detectron2.engine.defaults import DefaultPredictor
|
| 11 |
-
from detectron2.utils.video_visualizer import VideoVisualizer
|
| 12 |
-
from detectron2.utils.visualizer import ColorMode, Visualizer
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class VisualizationDemo(object):
|
| 16 |
-
def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
|
| 17 |
-
"""
|
| 18 |
-
Args:
|
| 19 |
-
cfg (CfgNode):
|
| 20 |
-
instance_mode (ColorMode):
|
| 21 |
-
parallel (bool): whether to run the model in different processes from visualization.
|
| 22 |
-
Useful since the visualization logic can be slow.
|
| 23 |
-
"""
|
| 24 |
-
self.metadata = MetadataCatalog.get(
|
| 25 |
-
cfg.DATASETS.TRAIN[0] if len(cfg.DATASETS.TRAIN) else "__unused"
|
| 26 |
-
)
|
| 27 |
-
self.cpu_device = torch.device("cpu")
|
| 28 |
-
self.instance_mode = instance_mode
|
| 29 |
-
|
| 30 |
-
self.parallel = parallel
|
| 31 |
-
if parallel:
|
| 32 |
-
num_gpu = torch.cuda.device_count()
|
| 33 |
-
self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)
|
| 34 |
-
else:
|
| 35 |
-
self.predictor = DefaultPredictor(cfg)
|
| 36 |
-
|
| 37 |
-
def run_on_image(self, image, visualizer=None):
|
| 38 |
-
"""
|
| 39 |
-
Args:
|
| 40 |
-
image (np.ndarray): an image of shape (H, W, C) (in BGR order).
|
| 41 |
-
This is the format used by OpenCV.
|
| 42 |
-
|
| 43 |
-
Returns:
|
| 44 |
-
predictions (dict): the output of the model.
|
| 45 |
-
vis_output (VisImage): the visualized image output.
|
| 46 |
-
"""
|
| 47 |
-
vis_output = None
|
| 48 |
-
predictions = self.predictor(image)
|
| 49 |
-
# Convert image from OpenCV BGR format to Matplotlib RGB format.
|
| 50 |
-
image = image[:, :, ::-1]
|
| 51 |
-
use_video_vis = True
|
| 52 |
-
if visualizer is None:
|
| 53 |
-
use_video_vis = False
|
| 54 |
-
visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
|
| 55 |
-
if "panoptic_seg" in predictions:
|
| 56 |
-
panoptic_seg, segments_info = predictions["panoptic_seg"]
|
| 57 |
-
vis_output = visualizer.draw_panoptic_seg_predictions(
|
| 58 |
-
panoptic_seg.to(self.cpu_device), segments_info
|
| 59 |
-
)
|
| 60 |
-
else:
|
| 61 |
-
if "sem_seg" in predictions:
|
| 62 |
-
vis_output = visualizer.draw_sem_seg(
|
| 63 |
-
predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
|
| 64 |
-
)
|
| 65 |
-
if "instances" in predictions:
|
| 66 |
-
instances = predictions["instances"].to(self.cpu_device)
|
| 67 |
-
if use_video_vis:
|
| 68 |
-
vis_output = visualizer.draw_instance_predictions(
|
| 69 |
-
image, predictions=instances)
|
| 70 |
-
else:
|
| 71 |
-
vis_output = visualizer.draw_instance_predictions(predictions=instances)
|
| 72 |
-
elif "proposals" in predictions:
|
| 73 |
-
instances = predictions["proposals"].to(self.cpu_device)
|
| 74 |
-
instances.pred_boxes = instances.proposal_boxes
|
| 75 |
-
instances.scores = instances.objectness_logits
|
| 76 |
-
instances.pred_classes[:] = -1
|
| 77 |
-
if use_video_vis:
|
| 78 |
-
vis_output = visualizer.draw_instance_predictions(
|
| 79 |
-
image, predictions=instances)
|
| 80 |
-
else:
|
| 81 |
-
vis_output = visualizer.draw_instance_predictions(predictions=instances)
|
| 82 |
-
|
| 83 |
-
return predictions, vis_output
|
| 84 |
-
|
| 85 |
-
def _frame_from_video(self, video):
|
| 86 |
-
while video.isOpened():
|
| 87 |
-
success, frame = video.read()
|
| 88 |
-
if success:
|
| 89 |
-
yield frame
|
| 90 |
-
else:
|
| 91 |
-
break
|
| 92 |
-
|
| 93 |
-
def run_on_video(self, video):
|
| 94 |
-
"""
|
| 95 |
-
Visualizes predictions on frames of the input video.
|
| 96 |
-
|
| 97 |
-
Args:
|
| 98 |
-
video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be
|
| 99 |
-
either a webcam or a video file.
|
| 100 |
-
|
| 101 |
-
Yields:
|
| 102 |
-
ndarray: BGR visualizations of each video frame.
|
| 103 |
-
"""
|
| 104 |
-
video_visualizer = VideoVisualizer(self.metadata, self.instance_mode)
|
| 105 |
-
|
| 106 |
-
def process_predictions(frame, predictions):
|
| 107 |
-
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
|
| 108 |
-
if "panoptic_seg" in predictions:
|
| 109 |
-
panoptic_seg, segments_info = predictions["panoptic_seg"]
|
| 110 |
-
vis_frame = video_visualizer.draw_panoptic_seg_predictions(
|
| 111 |
-
frame, panoptic_seg.to(self.cpu_device), segments_info
|
| 112 |
-
)
|
| 113 |
-
elif "instances" in predictions:
|
| 114 |
-
predictions = predictions["instances"].to(self.cpu_device)
|
| 115 |
-
vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
|
| 116 |
-
elif "sem_seg" in predictions:
|
| 117 |
-
vis_frame = video_visualizer.draw_sem_seg(
|
| 118 |
-
frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
|
| 119 |
-
)
|
| 120 |
-
elif "proposals" in predictions:
|
| 121 |
-
predictions = predictions["proposals"].to(self.cpu_device)
|
| 122 |
-
predictions.pred_boxes = predictions.proposal_boxes
|
| 123 |
-
predictions.scores = predictions.objectness_logits
|
| 124 |
-
predictions.pred_classes[:] = -1
|
| 125 |
-
vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
|
| 126 |
-
|
| 127 |
-
# Converts Matplotlib RGB format to OpenCV BGR format
|
| 128 |
-
vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
|
| 129 |
-
return vis_frame
|
| 130 |
-
|
| 131 |
-
frame_gen = self._frame_from_video(video)
|
| 132 |
-
if self.parallel:
|
| 133 |
-
buffer_size = self.predictor.default_buffer_size
|
| 134 |
-
|
| 135 |
-
frame_data = deque()
|
| 136 |
-
|
| 137 |
-
for cnt, frame in enumerate(frame_gen):
|
| 138 |
-
frame_data.append(frame)
|
| 139 |
-
self.predictor.put(frame)
|
| 140 |
-
|
| 141 |
-
if cnt >= buffer_size:
|
| 142 |
-
frame = frame_data.popleft()
|
| 143 |
-
predictions = self.predictor.get()
|
| 144 |
-
yield process_predictions(frame, predictions)
|
| 145 |
-
|
| 146 |
-
while len(frame_data):
|
| 147 |
-
frame = frame_data.popleft()
|
| 148 |
-
predictions = self.predictor.get()
|
| 149 |
-
yield process_predictions(frame, predictions)
|
| 150 |
-
else:
|
| 151 |
-
for frame in frame_gen:
|
| 152 |
-
yield process_predictions(frame, self.predictor(frame))
|
| 153 |
-
|
| 154 |
-
|
| 155 |
-
class AsyncPredictor:
|
| 156 |
-
"""
|
| 157 |
-
A predictor that runs the model asynchronously, possibly on >1 GPUs.
|
| 158 |
-
Because rendering the visualization takes considerably amount of time,
|
| 159 |
-
this helps improve throughput when rendering videos.
|
| 160 |
-
"""
|
| 161 |
-
|
| 162 |
-
class _StopToken:
|
| 163 |
-
pass
|
| 164 |
-
|
| 165 |
-
class _PredictWorker(mp.Process):
|
| 166 |
-
def __init__(self, cfg, task_queue, result_queue):
|
| 167 |
-
self.cfg = cfg
|
| 168 |
-
self.task_queue = task_queue
|
| 169 |
-
self.result_queue = result_queue
|
| 170 |
-
super().__init__()
|
| 171 |
-
|
| 172 |
-
def run(self):
|
| 173 |
-
predictor = DefaultPredictor(self.cfg)
|
| 174 |
-
|
| 175 |
-
while True:
|
| 176 |
-
task = self.task_queue.get()
|
| 177 |
-
if isinstance(task, AsyncPredictor._StopToken):
|
| 178 |
-
break
|
| 179 |
-
idx, data = task
|
| 180 |
-
result = predictor(data)
|
| 181 |
-
self.result_queue.put((idx, result))
|
| 182 |
-
|
| 183 |
-
def __init__(self, cfg, num_gpus: int = 1):
|
| 184 |
-
"""
|
| 185 |
-
Args:
|
| 186 |
-
cfg (CfgNode):
|
| 187 |
-
num_gpus (int): if 0, will run on CPU
|
| 188 |
-
"""
|
| 189 |
-
num_workers = max(num_gpus, 1)
|
| 190 |
-
self.task_queue = mp.Queue(maxsize=num_workers * 3)
|
| 191 |
-
self.result_queue = mp.Queue(maxsize=num_workers * 3)
|
| 192 |
-
self.procs = []
|
| 193 |
-
for gpuid in range(max(num_gpus, 1)):
|
| 194 |
-
cfg = cfg.clone()
|
| 195 |
-
cfg.defrost()
|
| 196 |
-
cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu"
|
| 197 |
-
self.procs.append(
|
| 198 |
-
AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)
|
| 199 |
-
)
|
| 200 |
-
|
| 201 |
-
self.put_idx = 0
|
| 202 |
-
self.get_idx = 0
|
| 203 |
-
self.result_rank = []
|
| 204 |
-
self.result_data = []
|
| 205 |
-
|
| 206 |
-
for p in self.procs:
|
| 207 |
-
p.start()
|
| 208 |
-
atexit.register(self.shutdown)
|
| 209 |
-
|
| 210 |
-
def put(self, image):
|
| 211 |
-
self.put_idx += 1
|
| 212 |
-
self.task_queue.put((self.put_idx, image))
|
| 213 |
-
|
| 214 |
-
def get(self):
|
| 215 |
-
self.get_idx += 1 # the index needed for this request
|
| 216 |
-
if len(self.result_rank) and self.result_rank[0] == self.get_idx:
|
| 217 |
-
res = self.result_data[0]
|
| 218 |
-
del self.result_data[0], self.result_rank[0]
|
| 219 |
-
return res
|
| 220 |
-
|
| 221 |
-
while True:
|
| 222 |
-
# make sure the results are returned in the correct order
|
| 223 |
-
idx, res = self.result_queue.get()
|
| 224 |
-
if idx == self.get_idx:
|
| 225 |
-
return res
|
| 226 |
-
insert = bisect.bisect(self.result_rank, idx)
|
| 227 |
-
self.result_rank.insert(insert, idx)
|
| 228 |
-
self.result_data.insert(insert, res)
|
| 229 |
-
|
| 230 |
-
def __len__(self):
|
| 231 |
-
return self.put_idx - self.get_idx
|
| 232 |
-
|
| 233 |
-
def __call__(self, image):
|
| 234 |
-
self.put(image)
|
| 235 |
-
return self.get()
|
| 236 |
-
|
| 237 |
-
def shutdown(self):
|
| 238 |
-
for _ in self.procs:
|
| 239 |
-
self.task_queue.put(AsyncPredictor._StopToken())
|
| 240 |
-
|
| 241 |
-
@property
|
| 242 |
-
def default_buffer_size(self):
|
| 243 |
-
return len(self.procs) * 5
|
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|
spaces/BAAI/vid2vid-zero/vid2vid_zero/models/unet_2d_blocks.py
DELETED
|
@@ -1,609 +0,0 @@
|
|
| 1 |
-
# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
|
| 2 |
-
|
| 3 |
-
import torch
|
| 4 |
-
from torch import nn
|
| 5 |
-
|
| 6 |
-
from .attention_2d import Transformer2DModel
|
| 7 |
-
from .resnet_2d import Downsample2D, ResnetBlock2D, Upsample2D
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
def get_down_block(
|
| 11 |
-
down_block_type,
|
| 12 |
-
num_layers,
|
| 13 |
-
in_channels,
|
| 14 |
-
out_channels,
|
| 15 |
-
temb_channels,
|
| 16 |
-
add_downsample,
|
| 17 |
-
resnet_eps,
|
| 18 |
-
resnet_act_fn,
|
| 19 |
-
attn_num_head_channels,
|
| 20 |
-
resnet_groups=None,
|
| 21 |
-
cross_attention_dim=None,
|
| 22 |
-
downsample_padding=None,
|
| 23 |
-
dual_cross_attention=False,
|
| 24 |
-
use_linear_projection=False,
|
| 25 |
-
only_cross_attention=False,
|
| 26 |
-
upcast_attention=False,
|
| 27 |
-
resnet_time_scale_shift="default",
|
| 28 |
-
use_sc_attn=False,
|
| 29 |
-
use_st_attn=False,
|
| 30 |
-
):
|
| 31 |
-
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
|
| 32 |
-
if down_block_type == "DownBlock2D":
|
| 33 |
-
return DownBlock2D(
|
| 34 |
-
num_layers=num_layers,
|
| 35 |
-
in_channels=in_channels,
|
| 36 |
-
out_channels=out_channels,
|
| 37 |
-
temb_channels=temb_channels,
|
| 38 |
-
add_downsample=add_downsample,
|
| 39 |
-
resnet_eps=resnet_eps,
|
| 40 |
-
resnet_act_fn=resnet_act_fn,
|
| 41 |
-
resnet_groups=resnet_groups,
|
| 42 |
-
downsample_padding=downsample_padding,
|
| 43 |
-
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 44 |
-
)
|
| 45 |
-
elif down_block_type == "CrossAttnDownBlock2D":
|
| 46 |
-
if cross_attention_dim is None:
|
| 47 |
-
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
|
| 48 |
-
return CrossAttnDownBlock2D(
|
| 49 |
-
num_layers=num_layers,
|
| 50 |
-
in_channels=in_channels,
|
| 51 |
-
out_channels=out_channels,
|
| 52 |
-
temb_channels=temb_channels,
|
| 53 |
-
add_downsample=add_downsample,
|
| 54 |
-
resnet_eps=resnet_eps,
|
| 55 |
-
resnet_act_fn=resnet_act_fn,
|
| 56 |
-
resnet_groups=resnet_groups,
|
| 57 |
-
downsample_padding=downsample_padding,
|
| 58 |
-
cross_attention_dim=cross_attention_dim,
|
| 59 |
-
attn_num_head_channels=attn_num_head_channels,
|
| 60 |
-
dual_cross_attention=dual_cross_attention,
|
| 61 |
-
use_linear_projection=use_linear_projection,
|
| 62 |
-
only_cross_attention=only_cross_attention,
|
| 63 |
-
upcast_attention=upcast_attention,
|
| 64 |
-
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 65 |
-
use_sc_attn=use_sc_attn,
|
| 66 |
-
use_st_attn=use_st_attn,
|
| 67 |
-
)
|
| 68 |
-
raise ValueError(f"{down_block_type} does not exist.")
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
def get_up_block(
|
| 72 |
-
up_block_type,
|
| 73 |
-
num_layers,
|
| 74 |
-
in_channels,
|
| 75 |
-
out_channels,
|
| 76 |
-
prev_output_channel,
|
| 77 |
-
temb_channels,
|
| 78 |
-
add_upsample,
|
| 79 |
-
resnet_eps,
|
| 80 |
-
resnet_act_fn,
|
| 81 |
-
attn_num_head_channels,
|
| 82 |
-
resnet_groups=None,
|
| 83 |
-
cross_attention_dim=None,
|
| 84 |
-
dual_cross_attention=False,
|
| 85 |
-
use_linear_projection=False,
|
| 86 |
-
only_cross_attention=False,
|
| 87 |
-
upcast_attention=False,
|
| 88 |
-
resnet_time_scale_shift="default",
|
| 89 |
-
use_sc_attn=False,
|
| 90 |
-
use_st_attn=False,
|
| 91 |
-
):
|
| 92 |
-
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
|
| 93 |
-
if up_block_type == "UpBlock2D":
|
| 94 |
-
return UpBlock2D(
|
| 95 |
-
num_layers=num_layers,
|
| 96 |
-
in_channels=in_channels,
|
| 97 |
-
out_channels=out_channels,
|
| 98 |
-
prev_output_channel=prev_output_channel,
|
| 99 |
-
temb_channels=temb_channels,
|
| 100 |
-
add_upsample=add_upsample,
|
| 101 |
-
resnet_eps=resnet_eps,
|
| 102 |
-
resnet_act_fn=resnet_act_fn,
|
| 103 |
-
resnet_groups=resnet_groups,
|
| 104 |
-
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 105 |
-
)
|
| 106 |
-
elif up_block_type == "CrossAttnUpBlock2D":
|
| 107 |
-
if cross_attention_dim is None:
|
| 108 |
-
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
|
| 109 |
-
return CrossAttnUpBlock2D(
|
| 110 |
-
num_layers=num_layers,
|
| 111 |
-
in_channels=in_channels,
|
| 112 |
-
out_channels=out_channels,
|
| 113 |
-
prev_output_channel=prev_output_channel,
|
| 114 |
-
temb_channels=temb_channels,
|
| 115 |
-
add_upsample=add_upsample,
|
| 116 |
-
resnet_eps=resnet_eps,
|
| 117 |
-
resnet_act_fn=resnet_act_fn,
|
| 118 |
-
resnet_groups=resnet_groups,
|
| 119 |
-
cross_attention_dim=cross_attention_dim,
|
| 120 |
-
attn_num_head_channels=attn_num_head_channels,
|
| 121 |
-
dual_cross_attention=dual_cross_attention,
|
| 122 |
-
use_linear_projection=use_linear_projection,
|
| 123 |
-
only_cross_attention=only_cross_attention,
|
| 124 |
-
upcast_attention=upcast_attention,
|
| 125 |
-
resnet_time_scale_shift=resnet_time_scale_shift,
|
| 126 |
-
use_sc_attn=use_sc_attn,
|
| 127 |
-
use_st_attn=use_st_attn,
|
| 128 |
-
)
|
| 129 |
-
raise ValueError(f"{up_block_type} does not exist.")
|
| 130 |
-
|
| 131 |
-
|
| 132 |
-
class UNetMidBlock2DCrossAttn(nn.Module):
|
| 133 |
-
def __init__(
|
| 134 |
-
self,
|
| 135 |
-
in_channels: int,
|
| 136 |
-
temb_channels: int,
|
| 137 |
-
dropout: float = 0.0,
|
| 138 |
-
num_layers: int = 1,
|
| 139 |
-
resnet_eps: float = 1e-6,
|
| 140 |
-
resnet_time_scale_shift: str = "default",
|
| 141 |
-
resnet_act_fn: str = "swish",
|
| 142 |
-
resnet_groups: int = 32,
|
| 143 |
-
resnet_pre_norm: bool = True,
|
| 144 |
-
attn_num_head_channels=1,
|
| 145 |
-
output_scale_factor=1.0,
|
| 146 |
-
cross_attention_dim=1280,
|
| 147 |
-
dual_cross_attention=False,
|
| 148 |
-
use_linear_projection=False,
|
| 149 |
-
upcast_attention=False,
|
| 150 |
-
use_sc_attn=False,
|
| 151 |
-
use_st_attn=False,
|
| 152 |
-
):
|
| 153 |
-
super().__init__()
|
| 154 |
-
|
| 155 |
-
self.has_cross_attention = True
|
| 156 |
-
self.attn_num_head_channels = attn_num_head_channels
|
| 157 |
-
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
| 158 |
-
|
| 159 |
-
# there is always at least one resnet
|
| 160 |
-
resnets = [
|
| 161 |
-
ResnetBlock2D(
|
| 162 |
-
in_channels=in_channels,
|
| 163 |
-
out_channels=in_channels,
|
| 164 |
-
temb_channels=temb_channels,
|
| 165 |
-
eps=resnet_eps,
|
| 166 |
-
groups=resnet_groups,
|
| 167 |
-
dropout=dropout,
|
| 168 |
-
time_embedding_norm=resnet_time_scale_shift,
|
| 169 |
-
non_linearity=resnet_act_fn,
|
| 170 |
-
output_scale_factor=output_scale_factor,
|
| 171 |
-
pre_norm=resnet_pre_norm,
|
| 172 |
-
)
|
| 173 |
-
]
|
| 174 |
-
attentions = []
|
| 175 |
-
|
| 176 |
-
for _ in range(num_layers):
|
| 177 |
-
if dual_cross_attention:
|
| 178 |
-
raise NotImplementedError
|
| 179 |
-
attentions.append(
|
| 180 |
-
Transformer2DModel(
|
| 181 |
-
attn_num_head_channels,
|
| 182 |
-
in_channels // attn_num_head_channels,
|
| 183 |
-
in_channels=in_channels,
|
| 184 |
-
num_layers=1,
|
| 185 |
-
cross_attention_dim=cross_attention_dim,
|
| 186 |
-
norm_num_groups=resnet_groups,
|
| 187 |
-
use_linear_projection=use_linear_projection,
|
| 188 |
-
upcast_attention=upcast_attention,
|
| 189 |
-
use_sc_attn=use_sc_attn,
|
| 190 |
-
use_st_attn=True if (use_st_attn and _ == 0) else False,
|
| 191 |
-
)
|
| 192 |
-
)
|
| 193 |
-
resnets.append(
|
| 194 |
-
ResnetBlock2D(
|
| 195 |
-
in_channels=in_channels,
|
| 196 |
-
out_channels=in_channels,
|
| 197 |
-
temb_channels=temb_channels,
|
| 198 |
-
eps=resnet_eps,
|
| 199 |
-
groups=resnet_groups,
|
| 200 |
-
dropout=dropout,
|
| 201 |
-
time_embedding_norm=resnet_time_scale_shift,
|
| 202 |
-
non_linearity=resnet_act_fn,
|
| 203 |
-
output_scale_factor=output_scale_factor,
|
| 204 |
-
pre_norm=resnet_pre_norm,
|
| 205 |
-
)
|
| 206 |
-
)
|
| 207 |
-
|
| 208 |
-
self.attentions = nn.ModuleList(attentions)
|
| 209 |
-
self.resnets = nn.ModuleList(resnets)
|
| 210 |
-
|
| 211 |
-
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, normal_infer=False):
|
| 212 |
-
hidden_states = self.resnets[0](hidden_states, temb)
|
| 213 |
-
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
| 214 |
-
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states, normal_infer=normal_infer).sample
|
| 215 |
-
hidden_states = resnet(hidden_states, temb)
|
| 216 |
-
|
| 217 |
-
return hidden_states
|
| 218 |
-
|
| 219 |
-
|
| 220 |
-
class CrossAttnDownBlock2D(nn.Module):
|
| 221 |
-
def __init__(
|
| 222 |
-
self,
|
| 223 |
-
in_channels: int,
|
| 224 |
-
out_channels: int,
|
| 225 |
-
temb_channels: int,
|
| 226 |
-
dropout: float = 0.0,
|
| 227 |
-
num_layers: int = 1,
|
| 228 |
-
resnet_eps: float = 1e-6,
|
| 229 |
-
resnet_time_scale_shift: str = "default",
|
| 230 |
-
resnet_act_fn: str = "swish",
|
| 231 |
-
resnet_groups: int = 32,
|
| 232 |
-
resnet_pre_norm: bool = True,
|
| 233 |
-
attn_num_head_channels=1,
|
| 234 |
-
cross_attention_dim=1280,
|
| 235 |
-
output_scale_factor=1.0,
|
| 236 |
-
downsample_padding=1,
|
| 237 |
-
add_downsample=True,
|
| 238 |
-
dual_cross_attention=False,
|
| 239 |
-
use_linear_projection=False,
|
| 240 |
-
only_cross_attention=False,
|
| 241 |
-
upcast_attention=False,
|
| 242 |
-
use_sc_attn=False,
|
| 243 |
-
use_st_attn=False,
|
| 244 |
-
):
|
| 245 |
-
super().__init__()
|
| 246 |
-
resnets = []
|
| 247 |
-
attentions = []
|
| 248 |
-
|
| 249 |
-
self.has_cross_attention = True
|
| 250 |
-
self.attn_num_head_channels = attn_num_head_channels
|
| 251 |
-
|
| 252 |
-
for i in range(num_layers):
|
| 253 |
-
in_channels = in_channels if i == 0 else out_channels
|
| 254 |
-
resnets.append(
|
| 255 |
-
ResnetBlock2D(
|
| 256 |
-
in_channels=in_channels,
|
| 257 |
-
out_channels=out_channels,
|
| 258 |
-
temb_channels=temb_channels,
|
| 259 |
-
eps=resnet_eps,
|
| 260 |
-
groups=resnet_groups,
|
| 261 |
-
dropout=dropout,
|
| 262 |
-
time_embedding_norm=resnet_time_scale_shift,
|
| 263 |
-
non_linearity=resnet_act_fn,
|
| 264 |
-
output_scale_factor=output_scale_factor,
|
| 265 |
-
pre_norm=resnet_pre_norm,
|
| 266 |
-
)
|
| 267 |
-
)
|
| 268 |
-
if dual_cross_attention:
|
| 269 |
-
raise NotImplementedError
|
| 270 |
-
attentions.append(
|
| 271 |
-
Transformer2DModel(
|
| 272 |
-
attn_num_head_channels,
|
| 273 |
-
out_channels // attn_num_head_channels,
|
| 274 |
-
in_channels=out_channels,
|
| 275 |
-
num_layers=1,
|
| 276 |
-
cross_attention_dim=cross_attention_dim,
|
| 277 |
-
norm_num_groups=resnet_groups,
|
| 278 |
-
use_linear_projection=use_linear_projection,
|
| 279 |
-
only_cross_attention=only_cross_attention,
|
| 280 |
-
upcast_attention=upcast_attention,
|
| 281 |
-
use_sc_attn=use_sc_attn,
|
| 282 |
-
use_st_attn=True if (use_st_attn and i == 0) else False,
|
| 283 |
-
)
|
| 284 |
-
)
|
| 285 |
-
self.attentions = nn.ModuleList(attentions)
|
| 286 |
-
self.resnets = nn.ModuleList(resnets)
|
| 287 |
-
|
| 288 |
-
if add_downsample:
|
| 289 |
-
self.downsamplers = nn.ModuleList(
|
| 290 |
-
[
|
| 291 |
-
Downsample2D(
|
| 292 |
-
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
| 293 |
-
)
|
| 294 |
-
]
|
| 295 |
-
)
|
| 296 |
-
else:
|
| 297 |
-
self.downsamplers = None
|
| 298 |
-
|
| 299 |
-
self.gradient_checkpointing = False
|
| 300 |
-
|
| 301 |
-
def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, normal_infer=False):
|
| 302 |
-
output_states = ()
|
| 303 |
-
|
| 304 |
-
for resnet, attn in zip(self.resnets, self.attentions):
|
| 305 |
-
if self.training and self.gradient_checkpointing:
|
| 306 |
-
|
| 307 |
-
def create_custom_forward(module, return_dict=None, normal_infer=False):
|
| 308 |
-
def custom_forward(*inputs):
|
| 309 |
-
if return_dict is not None:
|
| 310 |
-
return module(*inputs, return_dict=return_dict, normal_infer=normal_infer)
|
| 311 |
-
else:
|
| 312 |
-
return module(*inputs)
|
| 313 |
-
|
| 314 |
-
return custom_forward
|
| 315 |
-
|
| 316 |
-
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
| 317 |
-
hidden_states = torch.utils.checkpoint.checkpoint(
|
| 318 |
-
create_custom_forward(attn, return_dict=False, normal_infer=normal_infer),
|
| 319 |
-
hidden_states,
|
| 320 |
-
encoder_hidden_states,
|
| 321 |
-
)[0]
|
| 322 |
-
else:
|
| 323 |
-
hidden_states = resnet(hidden_states, temb)
|
| 324 |
-
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states, normal_infer=normal_infer).sample
|
| 325 |
-
|
| 326 |
-
output_states += (hidden_states,)
|
| 327 |
-
|
| 328 |
-
if self.downsamplers is not None:
|
| 329 |
-
for downsampler in self.downsamplers:
|
| 330 |
-
hidden_states = downsampler(hidden_states)
|
| 331 |
-
|
| 332 |
-
output_states += (hidden_states,)
|
| 333 |
-
|
| 334 |
-
return hidden_states, output_states
|
| 335 |
-
|
| 336 |
-
|
| 337 |
-
class DownBlock2D(nn.Module):
|
| 338 |
-
def __init__(
|
| 339 |
-
self,
|
| 340 |
-
in_channels: int,
|
| 341 |
-
out_channels: int,
|
| 342 |
-
temb_channels: int,
|
| 343 |
-
dropout: float = 0.0,
|
| 344 |
-
num_layers: int = 1,
|
| 345 |
-
resnet_eps: float = 1e-6,
|
| 346 |
-
resnet_time_scale_shift: str = "default",
|
| 347 |
-
resnet_act_fn: str = "swish",
|
| 348 |
-
resnet_groups: int = 32,
|
| 349 |
-
resnet_pre_norm: bool = True,
|
| 350 |
-
output_scale_factor=1.0,
|
| 351 |
-
add_downsample=True,
|
| 352 |
-
downsample_padding=1,
|
| 353 |
-
):
|
| 354 |
-
super().__init__()
|
| 355 |
-
resnets = []
|
| 356 |
-
|
| 357 |
-
for i in range(num_layers):
|
| 358 |
-
in_channels = in_channels if i == 0 else out_channels
|
| 359 |
-
resnets.append(
|
| 360 |
-
ResnetBlock2D(
|
| 361 |
-
in_channels=in_channels,
|
| 362 |
-
out_channels=out_channels,
|
| 363 |
-
temb_channels=temb_channels,
|
| 364 |
-
eps=resnet_eps,
|
| 365 |
-
groups=resnet_groups,
|
| 366 |
-
dropout=dropout,
|
| 367 |
-
time_embedding_norm=resnet_time_scale_shift,
|
| 368 |
-
non_linearity=resnet_act_fn,
|
| 369 |
-
output_scale_factor=output_scale_factor,
|
| 370 |
-
pre_norm=resnet_pre_norm,
|
| 371 |
-
)
|
| 372 |
-
)
|
| 373 |
-
|
| 374 |
-
self.resnets = nn.ModuleList(resnets)
|
| 375 |
-
|
| 376 |
-
if add_downsample:
|
| 377 |
-
self.downsamplers = nn.ModuleList(
|
| 378 |
-
[
|
| 379 |
-
Downsample2D(
|
| 380 |
-
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
| 381 |
-
)
|
| 382 |
-
]
|
| 383 |
-
)
|
| 384 |
-
else:
|
| 385 |
-
self.downsamplers = None
|
| 386 |
-
|
| 387 |
-
self.gradient_checkpointing = False
|
| 388 |
-
|
| 389 |
-
def forward(self, hidden_states, temb=None):
|
| 390 |
-
output_states = ()
|
| 391 |
-
|
| 392 |
-
for resnet in self.resnets:
|
| 393 |
-
if self.training and self.gradient_checkpointing:
|
| 394 |
-
|
| 395 |
-
def create_custom_forward(module):
|
| 396 |
-
def custom_forward(*inputs):
|
| 397 |
-
return module(*inputs)
|
| 398 |
-
|
| 399 |
-
return custom_forward
|
| 400 |
-
|
| 401 |
-
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
| 402 |
-
else:
|
| 403 |
-
hidden_states = resnet(hidden_states, temb)
|
| 404 |
-
|
| 405 |
-
output_states += (hidden_states,)
|
| 406 |
-
|
| 407 |
-
if self.downsamplers is not None:
|
| 408 |
-
for downsampler in self.downsamplers:
|
| 409 |
-
hidden_states = downsampler(hidden_states)
|
| 410 |
-
|
| 411 |
-
output_states += (hidden_states,)
|
| 412 |
-
|
| 413 |
-
return hidden_states, output_states
|
| 414 |
-
|
| 415 |
-
|
| 416 |
-
class CrossAttnUpBlock2D(nn.Module):
|
| 417 |
-
def __init__(
|
| 418 |
-
self,
|
| 419 |
-
in_channels: int,
|
| 420 |
-
out_channels: int,
|
| 421 |
-
prev_output_channel: int,
|
| 422 |
-
temb_channels: int,
|
| 423 |
-
dropout: float = 0.0,
|
| 424 |
-
num_layers: int = 1,
|
| 425 |
-
resnet_eps: float = 1e-6,
|
| 426 |
-
resnet_time_scale_shift: str = "default",
|
| 427 |
-
resnet_act_fn: str = "swish",
|
| 428 |
-
resnet_groups: int = 32,
|
| 429 |
-
resnet_pre_norm: bool = True,
|
| 430 |
-
attn_num_head_channels=1,
|
| 431 |
-
cross_attention_dim=1280,
|
| 432 |
-
output_scale_factor=1.0,
|
| 433 |
-
add_upsample=True,
|
| 434 |
-
dual_cross_attention=False,
|
| 435 |
-
use_linear_projection=False,
|
| 436 |
-
only_cross_attention=False,
|
| 437 |
-
upcast_attention=False,
|
| 438 |
-
use_sc_attn=False,
|
| 439 |
-
use_st_attn=False,
|
| 440 |
-
):
|
| 441 |
-
super().__init__()
|
| 442 |
-
resnets = []
|
| 443 |
-
attentions = []
|
| 444 |
-
|
| 445 |
-
self.has_cross_attention = True
|
| 446 |
-
self.attn_num_head_channels = attn_num_head_channels
|
| 447 |
-
|
| 448 |
-
for i in range(num_layers):
|
| 449 |
-
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
| 450 |
-
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
| 451 |
-
|
| 452 |
-
resnets.append(
|
| 453 |
-
ResnetBlock2D(
|
| 454 |
-
in_channels=resnet_in_channels + res_skip_channels,
|
| 455 |
-
out_channels=out_channels,
|
| 456 |
-
temb_channels=temb_channels,
|
| 457 |
-
eps=resnet_eps,
|
| 458 |
-
groups=resnet_groups,
|
| 459 |
-
dropout=dropout,
|
| 460 |
-
time_embedding_norm=resnet_time_scale_shift,
|
| 461 |
-
non_linearity=resnet_act_fn,
|
| 462 |
-
output_scale_factor=output_scale_factor,
|
| 463 |
-
pre_norm=resnet_pre_norm,
|
| 464 |
-
)
|
| 465 |
-
)
|
| 466 |
-
if dual_cross_attention:
|
| 467 |
-
raise NotImplementedError
|
| 468 |
-
attentions.append(
|
| 469 |
-
Transformer2DModel(
|
| 470 |
-
attn_num_head_channels,
|
| 471 |
-
out_channels // attn_num_head_channels,
|
| 472 |
-
in_channels=out_channels,
|
| 473 |
-
num_layers=1,
|
| 474 |
-
cross_attention_dim=cross_attention_dim,
|
| 475 |
-
norm_num_groups=resnet_groups,
|
| 476 |
-
use_linear_projection=use_linear_projection,
|
| 477 |
-
only_cross_attention=only_cross_attention,
|
| 478 |
-
upcast_attention=upcast_attention,
|
| 479 |
-
use_sc_attn=use_sc_attn,
|
| 480 |
-
use_st_attn=True if (use_st_attn and i == 0) else False,
|
| 481 |
-
)
|
| 482 |
-
)
|
| 483 |
-
|
| 484 |
-
self.attentions = nn.ModuleList(attentions)
|
| 485 |
-
self.resnets = nn.ModuleList(resnets)
|
| 486 |
-
|
| 487 |
-
if add_upsample:
|
| 488 |
-
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
| 489 |
-
else:
|
| 490 |
-
self.upsamplers = None
|
| 491 |
-
|
| 492 |
-
self.gradient_checkpointing = False
|
| 493 |
-
|
| 494 |
-
def forward(
|
| 495 |
-
self,
|
| 496 |
-
hidden_states,
|
| 497 |
-
res_hidden_states_tuple,
|
| 498 |
-
temb=None,
|
| 499 |
-
encoder_hidden_states=None,
|
| 500 |
-
upsample_size=None,
|
| 501 |
-
attention_mask=None,
|
| 502 |
-
normal_infer=False,
|
| 503 |
-
):
|
| 504 |
-
for resnet, attn in zip(self.resnets, self.attentions):
|
| 505 |
-
# pop res hidden states
|
| 506 |
-
res_hidden_states = res_hidden_states_tuple[-1]
|
| 507 |
-
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
| 508 |
-
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
| 509 |
-
|
| 510 |
-
if self.training and self.gradient_checkpointing:
|
| 511 |
-
|
| 512 |
-
def create_custom_forward(module, return_dict=None, normal_infer=False):
|
| 513 |
-
def custom_forward(*inputs):
|
| 514 |
-
if return_dict is not None:
|
| 515 |
-
return module(*inputs, return_dict=return_dict, normal_infer=normal_infer)
|
| 516 |
-
else:
|
| 517 |
-
return module(*inputs)
|
| 518 |
-
|
| 519 |
-
return custom_forward
|
| 520 |
-
|
| 521 |
-
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
| 522 |
-
hidden_states = torch.utils.checkpoint.checkpoint(
|
| 523 |
-
create_custom_forward(attn, return_dict=False, normal_infer=normal_infer),
|
| 524 |
-
hidden_states,
|
| 525 |
-
encoder_hidden_states,
|
| 526 |
-
)[0]
|
| 527 |
-
else:
|
| 528 |
-
hidden_states = resnet(hidden_states, temb)
|
| 529 |
-
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states, normal_infer=normal_infer).sample
|
| 530 |
-
|
| 531 |
-
if self.upsamplers is not None:
|
| 532 |
-
for upsampler in self.upsamplers:
|
| 533 |
-
hidden_states = upsampler(hidden_states, upsample_size)
|
| 534 |
-
|
| 535 |
-
return hidden_states
|
| 536 |
-
|
| 537 |
-
|
| 538 |
-
class UpBlock2D(nn.Module):
|
| 539 |
-
def __init__(
|
| 540 |
-
self,
|
| 541 |
-
in_channels: int,
|
| 542 |
-
prev_output_channel: int,
|
| 543 |
-
out_channels: int,
|
| 544 |
-
temb_channels: int,
|
| 545 |
-
dropout: float = 0.0,
|
| 546 |
-
num_layers: int = 1,
|
| 547 |
-
resnet_eps: float = 1e-6,
|
| 548 |
-
resnet_time_scale_shift: str = "default",
|
| 549 |
-
resnet_act_fn: str = "swish",
|
| 550 |
-
resnet_groups: int = 32,
|
| 551 |
-
resnet_pre_norm: bool = True,
|
| 552 |
-
output_scale_factor=1.0,
|
| 553 |
-
add_upsample=True,
|
| 554 |
-
):
|
| 555 |
-
super().__init__()
|
| 556 |
-
resnets = []
|
| 557 |
-
|
| 558 |
-
for i in range(num_layers):
|
| 559 |
-
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
| 560 |
-
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
| 561 |
-
|
| 562 |
-
resnets.append(
|
| 563 |
-
ResnetBlock2D(
|
| 564 |
-
in_channels=resnet_in_channels + res_skip_channels,
|
| 565 |
-
out_channels=out_channels,
|
| 566 |
-
temb_channels=temb_channels,
|
| 567 |
-
eps=resnet_eps,
|
| 568 |
-
groups=resnet_groups,
|
| 569 |
-
dropout=dropout,
|
| 570 |
-
time_embedding_norm=resnet_time_scale_shift,
|
| 571 |
-
non_linearity=resnet_act_fn,
|
| 572 |
-
output_scale_factor=output_scale_factor,
|
| 573 |
-
pre_norm=resnet_pre_norm,
|
| 574 |
-
)
|
| 575 |
-
)
|
| 576 |
-
|
| 577 |
-
self.resnets = nn.ModuleList(resnets)
|
| 578 |
-
|
| 579 |
-
if add_upsample:
|
| 580 |
-
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
| 581 |
-
else:
|
| 582 |
-
self.upsamplers = None
|
| 583 |
-
|
| 584 |
-
self.gradient_checkpointing = False
|
| 585 |
-
|
| 586 |
-
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
|
| 587 |
-
for resnet in self.resnets:
|
| 588 |
-
# pop res hidden states
|
| 589 |
-
res_hidden_states = res_hidden_states_tuple[-1]
|
| 590 |
-
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
| 591 |
-
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
| 592 |
-
|
| 593 |
-
if self.training and self.gradient_checkpointing:
|
| 594 |
-
|
| 595 |
-
def create_custom_forward(module):
|
| 596 |
-
def custom_forward(*inputs):
|
| 597 |
-
return module(*inputs)
|
| 598 |
-
|
| 599 |
-
return custom_forward
|
| 600 |
-
|
| 601 |
-
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
|
| 602 |
-
else:
|
| 603 |
-
hidden_states = resnet(hidden_states, temb)
|
| 604 |
-
|
| 605 |
-
if self.upsamplers is not None:
|
| 606 |
-
for upsampler in self.upsamplers:
|
| 607 |
-
hidden_states = upsampler(hidden_states, upsample_size)
|
| 608 |
-
|
| 609 |
-
return hidden_states
|
|
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|
spaces/Banbri/zcvzcv/src/lib/dirtyLLMResponseCleaner.ts
DELETED
|
@@ -1,46 +0,0 @@
|
|
| 1 |
-
export function dirtyLLMResponseCleaner(input: string) {
|
| 2 |
-
let str = (
|
| 3 |
-
`${input || ""}`
|
| 4 |
-
// a summary of all the weird hallucinations I saw it make..
|
| 5 |
-
.replaceAll(`"]`, `"}]`)
|
| 6 |
-
.replaceAll(`" ]`, `"}]`)
|
| 7 |
-
.replaceAll(`" ]`, `"}]`)
|
| 8 |
-
.replaceAll(`"\n]`, `"}]`)
|
| 9 |
-
.replaceAll(`"\n ]`, `"}]`)
|
| 10 |
-
.replaceAll(`"\n ]`, `"}]`)
|
| 11 |
-
.replaceAll("}}", "}")
|
| 12 |
-
.replaceAll("]]", "]")
|
| 13 |
-
.replaceAll("[[", "[")
|
| 14 |
-
.replaceAll("{{", "{")
|
| 15 |
-
.replaceAll(",,", ",")
|
| 16 |
-
.replaceAll("[0]", "")
|
| 17 |
-
.replaceAll("[1]", "")
|
| 18 |
-
.replaceAll("[2]", "")
|
| 19 |
-
.replaceAll("[3]", "")
|
| 20 |
-
.replaceAll("[4]", "")
|
| 21 |
-
.replaceAll("[panel 0]", "")
|
| 22 |
-
.replaceAll("[panel 1]", "")
|
| 23 |
-
.replaceAll("[panel 2]", "")
|
| 24 |
-
.replaceAll("[panel 3]", "")
|
| 25 |
-
.replaceAll("[panel 4]", "")
|
| 26 |
-
)
|
| 27 |
-
|
| 28 |
-
// repair missing end of JSON array
|
| 29 |
-
if (str.at(-1) === '}') {
|
| 30 |
-
str = str + "]"
|
| 31 |
-
}
|
| 32 |
-
|
| 33 |
-
if (str.at(-1) === '"') {
|
| 34 |
-
str = str + "}]"
|
| 35 |
-
}
|
| 36 |
-
|
| 37 |
-
if (str[0] === '{') {
|
| 38 |
-
str = "[" + str
|
| 39 |
-
}
|
| 40 |
-
|
| 41 |
-
if (str[0] === '"') {
|
| 42 |
-
str = "[{" + str
|
| 43 |
-
}
|
| 44 |
-
|
| 45 |
-
return str
|
| 46 |
-
}
|
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|
spaces/Benson/text-generation/Examples/Descargar Efecto De Sonido De Bocina.md
DELETED
|
@@ -1,86 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Descargar Airhorn efecto de sonido: Cómo encontrar y utilizar los mejores sonidos libres de derechos</h1>
|
| 3 |
-
<p>Si está buscando un efecto de sonido fuerte, llamativo y divertido para sus proyectos, es posible que desee considerar la descarga de un efecto de sonido airhorn. Un efecto de sonido airhorn es una ráfaga corta y aguda de ruido que se puede usar para varios propósitos, como anunciar algo importante, crear emoción o agregar humor. En este artículo, explicaremos qué es un efecto de sonido de cuerno de aire, por qué podría necesitarlo y cómo encontrar y usar los mejores efectos de sonido de cuerno de aire libres de derechos para sus proyectos. </p>
|
| 4 |
-
<h2>¿Qué es un efecto de sonido de bocina y por qué lo necesita? </h2>
|
| 5 |
-
<h3>Historia y origen del sonido de la bocina</h3>
|
| 6 |
-
<p>Una bocina es un dispositivo que produce un ruido fuerte al forzar el aire comprimido a través de un cuerno de metal o plástico. Fue inventado originalmente en el siglo XIX como un dispositivo de señalización para barcos, trenes y vehículos. Más tarde, fue adoptado por los aficionados al deporte, DJs y músicos como una forma de expresar entusiasmo, animar o celebrar. El sonido airhorn se hizo popular en géneros como reggae, hip hop, dancehall y música electrónica, donde se utilizó como muestra o elemento de remix. Hoy en día, el sonido de la bocina es ampliamente reconocido como un símbolo de emoción, energía y diversión. </p>
|
| 7 |
-
<h2>descargar efecto de sonido de bocina</h2><br /><p><b><b>DOWNLOAD</b> ✺ <a href="https://bltlly.com/2v6MvD">https://bltlly.com/2v6MvD</a></b></p><br /><br />
|
| 8 |
-
<h3>Los diferentes tipos y usos del sonido de la bocina</h3>
|
| 9 |
-
<p>Hay muchos tipos y variaciones diferentes del efecto de sonido de la bocina, dependiendo de la fuente, la duración, el tono y la intensidad del ruido. Algunos tipos comunes de efectos de sonido airhorn son:</p>
|
| 10 |
-
<ul>
|
| 11 |
-
<li>Bocina de DJ: Una ráfaga corta y aguda que es a menudo utilizada por los DJs para animar a la multitud o introducir una nueva canción. </li>
|
| 12 |
-
<li>Sirena: Un gemido largo y agudo que se usa a menudo para indicar peligro, emergencia o alarma. </li>
|
| 13 |
-
<li>Bocinazo: Un pitido medio agudo que se usa a menudo para señalar algo o llamar la atención de alguien. </li>
|
| 14 |
-
|
| 15 |
-
</ul>
|
| 16 |
-
<p>El efecto de sonido airhorn se puede utilizar para diversos propósitos en diferentes proyectos, como:</p>
|
| 17 |
-
<ul>
|
| 18 |
-
<li>Podcasts: Para anunciar un nuevo episodio, segmento o invitado. </li>
|
| 19 |
-
<li>Vídeos: Para crear suspenso, drama o comedia. </li>
|
| 20 |
-
<li>Juegos: Para recompensar a los jugadores, indicar el éxito, o añadir algún desafío. </li>
|
| 21 |
-
<li>Aplicaciones: Para notificar a los usuarios, proporcionar comentarios o mejorar la experiencia del usuario. </li>
|
| 22 |
-
</ul>
|
| 23 |
-
<h3>Los beneficios de usar efectos de sonido sin regalías</h3>
|
| 24 |
-
<p>Una de las principales ventajas de usar efectos de sonido de cuerno de aire libre de regalías es que no tienes que preocuparte por pagar tarifas o regalías para usarlos en tus proyectos. Esto significa que usted puede ahorrar dinero, tiempo, y molestias al mismo tiempo obtener sonidos de alta calidad. Otro beneficio de usar efectos de sonido de cuerno de aire libres de derechos es que puede encontrarlos y descargarlos fácilmente de varios sitios web y fuentes en línea. Esto significa que puede acceder a una amplia gama de sonidos y elegir los que se adapten a sus necesidades y preferencias. Por último, el uso de efectos de sonido de bocina sin derechos de autor puede ayudarle a hacer sus proyectos más atractivos, entretenidos y memorables para su audiencia. Esto significa que puedes aumentar tu popularidad, reputación y éxito con tus proyectos. </p>
|
| 25 |
-
<h2>Cómo <h2>Cómo encontrar y descargar los mejores efectos de sonido de cuerno de aire libres de derechos</h2>
|
| 26 |
-
<h3>Los criterios para elegir un buen efecto de sonido de bocina</h3>
|
| 27 |
-
<p>Antes de descargar cualquier efecto de sonido airhorn, debe considerar algunos criterios que pueden ayudarlo a elegir uno bueno. Algunos de los criterios son:</p>
|
| 28 |
-
<ul>
|
| 29 |
-
<li>Calidad: El sonido debe ser claro, nítido y lo suficientemente fuerte como para ser escuchado bien. </li>
|
| 30 |
-
<li>Formato: El sonido debe ser compatible con el formato de tu proyecto, como MP3, WAV u OGG.</li>
|
| 31 |
-
<li>Licencia: El sonido debe estar libre de regalías, lo que significa que puede usarlo para cualquier propósito sin pagar tarifas o regalías. </li>
|
| 32 |
-
<li>Relevancia: El sonido debe coincidir con el tema, el tono y el propósito de tu proyecto. </li>
|
| 33 |
-
|
| 34 |
-
</ul>
|
| 35 |
-
<h3>Los mejores sitios web y fuentes para descargar gratis airhorn efectos de sonido</h3>
|
| 36 |
-
<p>Hay muchos sitios web y fuentes en línea que ofrecen libre airhorn efectos de sonido para descargar. Sin embargo, no todos son confiables, seguros y de alta calidad. Estos son algunos de los mejores sitios web y fuentes que recomendamos para descargar gratis efectos de sonido de bocina:</p>
|
| 37 |
-
<h4>Pixabay</h4>
|
| 38 |
-
<p>Pixabay es un sitio web popular que ofrece imágenes gratuitas, videos, música y efectos de sonido para descargar. Puedes encontrar más de 200 efectos de sonido en Pixabay, que van desde bocinas de DJ hasta sirenas y bocinazos. Todos los sonidos son libres de derechos y se pueden utilizar para cualquier propósito. También puede filtrar los sonidos por duración, tipo, categoría y etiquetas. Para descargar un efecto de sonido airhorn desde Pixabay, solo tienes que hacer clic en el botón de descarga y elegir el formato y el tamaño que desee. </p>
|
| 39 |
-
<h4>ZapSplat</h4>
|
| 40 |
-
<p>ZapSplat es otro sitio web que ofrece efectos de sonido y música gratis para descargar. Puedes encontrar más de 100 efectos de sonido en ZapSplat, incluyendo sonidos de trompeta, sonidos de estadio y sonidos de fiesta. Todos los sonidos son libres de derechos y se pueden utilizar para cualquier propósito. También puede navegar por los sonidos por categoría, género, estado de ánimo y palabra clave. Para descargar un efecto de sonido airhorn de ZapSplat, necesita crear una cuenta gratuita y luego hacer clic en el botón de descarga. </p>
|
| 41 |
-
<p></p>
|
| 42 |
-
<h4>Otros sitios web</h4>
|
| 43 |
-
<p>Algunos otros sitios web que ofrecen libre airhorn efectos de sonido son:</p>
|
| 44 |
-
<ul>
|
| 45 |
-
<li>Freesound: Un sitio web que alberga una gran colección de sonidos subidos por el usuario que están licenciados bajo Creative Commons.</li>
|
| 46 |
-
<li>SoundBible: Un sitio web que proporciona clips de sonido y efectos gratuitos que son de dominio público o libres de derechos. </li>
|
| 47 |
-
<li>SoundJay: Un sitio web que ofrece efectos de sonido gratuitos y clips de música que son libres de derechos y pueden ser utilizados para proyectos personales o comerciales. </li>
|
| 48 |
-
</ul>
|
| 49 |
-
<h3>Cómo descargar y usar los efectos de sonido de la bocina en tus proyectos</h3>
|
| 50 |
-
|
| 51 |
-
<p>Los pasos para descargar los efectos de sonido airhorn de cualquiera de los sitios web mencionados anteriormente son:</p>
|
| 52 |
-
<ol>
|
| 53 |
-
<li> Ir a la página web y buscar el efecto de sonido airhorn que desee. </li>
|
| 54 |
-
<li> Previsualizar el sonido y comprobar su calidad, formato, licencia y relevancia. </li>
|
| 55 |
-
<li>Haga clic en el botón de descarga y elija el formato y el tamaño que desee. </li>
|
| 56 |
-
<li>Guarde el archivo en su dispositivo o almacenamiento en la nube. </li>
|
| 57 |
-
</ol>
|
| 58 |
-
<h4>Los consejos para usar los efectos de sonido de la bocina efectivamente</h4>
|
| 59 |
-
<p>Algunos consejos para usar eficazmente los efectos de sonido de la bocina en sus proyectos son:</p>
|
| 60 |
-
<ul>
|
| 61 |
-
<li>Utilice el efecto de sonido airhorn con moderación y estratégicamente. No lo sobreuse o perderá su impacto y molestará a su audiencia. </li>
|
| 62 |
-
<li>Usa el tipo y tono apropiado del efecto de sonido de la bocina para el tema, tono y propósito de tu proyecto. Por ejemplo, use una sirena para un proyecto serio o dramático, o una trompeta para un proyecto festivo o de celebración. </li>
|
| 63 |
-
<li>Utilice el volumen y el momento adecuados del efecto de sonido de la bocina para el contexto y el flujo de su proyecto. Por ejemplo, use una bocina fuerte y repentina para un efecto de sorpresa o choque, o una bocina suave y gradual para un efecto de acumulación o transición. </li>
|
| 64 |
-
<li>Utilice el efecto de sonido airhorn creativa y experimentalmente. Intente mezclarlo con otros sonidos o música, o modifíquelo con filtros o efectos. </li>
|
| 65 |
-
</ul>
|
| 66 |
-
<h2>Conclusión</h2>
|
| 67 |
-
<h3>Resumen de los puntos principales</h3>
|
| 68 |
-
|
| 69 |
-
<h3>Llamada a la acción</h3>
|
| 70 |
-
<p>Ahora que has aprendido a descargar el efecto de sonido airhorn y usarlo en tus proyectos, ¿por qué no intentarlo? Puedes encontrar y descargar cientos de efectos de sonido de bocina gratis de los sitios web mencionados anteriormente y usarlos para darle vida a tus podcasts, videos, juegos, aplicaciones y más. Usted se sorprenderá por la cantidad de diversión y emoción que puede crear con una simple explosión de ruido. Así que sigue adelante y descarga el efecto de sonido airhorn hoy y ve la diferencia que puede hacer en tus proyectos. </p>
|
| 71 |
-
<h2>Preguntas frecuentes</h2>
|
| 72 |
-
<p>Aquí hay algunas preguntas frecuentes sobre la descarga del efecto de sonido de la bocina:</p>
|
| 73 |
-
<ol>
|
| 74 |
-
<li>P: ¿Es legal usar efectos de sonido de bocina en mis proyectos? </li>
|
| 75 |
-
<li>A: Sí, siempre y cuando utilice efectos de sonido de cuerno de aire libres de derechos que están autorizados para cualquier propósito. Siempre debe verificar la licencia y los términos de uso del sonido antes de descargarlo y usarlo. </li>
|
| 76 |
-
<li>Q: ¿Cómo puedo editar o personalizar los efectos de sonido de la bocina? </li>
|
| 77 |
-
<li>A: Puede utilizar cualquier software de edición de audio o aplicación para editar o personalizar los efectos de sonido de la bocina. Puede cambiar el volumen, el tono, la velocidad, la duración o añadir filtros o efectos al sonido. </li>
|
| 78 |
-
<li>P: ¿Cómo puedo evitar molestar u ofender a mi audiencia con los efectos de sonido de la bocina? </li>
|
| 79 |
-
<li>A: Puedes evitar molestar o ofender a tu audiencia usando los efectos de sonido de la bocina con moderación y estratégicamente. No los uses en exceso ni en situaciones inapropiadas o irrelevantes. Además, considere las preferencias, expectativas y sensibilidades de su audiencia al elegir y usar los efectos de sonido de la bocina. </li>
|
| 80 |
-
<li>Q: ¿Cómo puedo hacer mis propios efectos de sonido de bocina? </li>
|
| 81 |
-
<li>A: Puede crear sus propios efectos de sonido de bocina grabando un dispositivo de bocina real o sintetizando un sonido similar con un instrumento o software digital. También puede mezclar y combinar diferentes sonidos o sampling para crear sus propios efectos de sonido de bocina única. </li>
|
| 82 |
-
|
| 83 |
-
<li>A: Puede encontrar más información y recursos sobre los efectos de sonido de la bocina de aire buscando en línea o visitando algunos de los sitios web mencionados anteriormente. También puedes ver algunos tutoriales o vídeos sobre cómo usar efectos de sonido airhorn en diferentes proyectos. </li>
|
| 84 |
-
</ol></p> 64aa2da5cf<br />
|
| 85 |
-
<br />
|
| 86 |
-
<br />
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spaces/Benson/text-generation/Examples/Descargar Escuchar Msica Apk.md
DELETED
|
@@ -1,135 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cómo descargar APK de reproducción de música para Android</h1>
|
| 3 |
-
<p>Si usted está buscando una manera de disfrutar de su música favorita y podcasts en su dispositivo Android, es posible que desee probar Play Music APK. Esta es una versión modificada de la aplicación oficial de Google Play Music que ofrece algunas características y beneficios adicionales que no están disponibles en la aplicación original. En este artículo, le mostraremos cómo descargar Play Music APK para Android, así como algunos consejos y trucos para usarlo. </p>
|
| 4 |
-
<h2>¿Qué es la reproducción de música APK? </h2>
|
| 5 |
-
<p>Reproducir música APK es una aplicación no oficial que le permite acceder al servicio de Google Play Music en su dispositivo Android. Google Play Music es un servicio de streaming que te permite escuchar millones de canciones y podcasts de varios géneros, artistas y categorías. También puede subir su propia colección de música a la nube y transmitirla desde cualquier dispositivo. </p>
|
| 6 |
-
<h2>descargar escuchar música apk</h2><br /><p><b><b>Download File</b> »»» <a href="https://bltlly.com/2v6LC4">https://bltlly.com/2v6LC4</a></b></p><br /><br />
|
| 7 |
-
<p>Reproducir música APK es diferente de la aplicación oficial de Google Play Music de varias maneras. Algunas de las características y beneficios de Reproducir música APK son:</p>
|
| 8 |
-
<ul>
|
| 9 |
-
<li>No requiere una cuenta de Google para usarlo. </li>
|
| 10 |
-
<li>No tiene anuncios ni interrupciones. </li>
|
| 11 |
-
<li> Tiene una opción de modo oscuro que es más fácil para los ojos. </li>
|
| 12 |
-
<li> Tiene un ecualizador incorporado que le permite ajustar la calidad del sonido. </li>
|
| 13 |
-
<li> Tiene un gestor de descargas que le permite gestionar sus descargas de manera más eficiente. </li>
|
| 14 |
-
<li> Tiene un temporizador de espera que le permite establecer un límite de tiempo para reproducir música. </li>
|
| 15 |
-
</ul>
|
| 16 |
-
<h2>¿Por qué descargar Reproducir música APK? </h2>
|
| 17 |
-
<p>Hay muchas razones por las que es posible que desee descargar Reproducir música APK para Android. Algunos de ellos son:</p>
|
| 18 |
-
<ul>
|
| 19 |
-
<li>Quieres disfrutar del servicio Google Play Music sin iniciar sesión con una cuenta de Google. </li>
|
| 20 |
-
<li>Quieres evitar anuncios e interrupciones mientras escuchas música y podcasts. </li>
|
| 21 |
-
<li>Quieres tener más control sobre la apariencia y la calidad de sonido de la aplicación. </li>
|
| 22 |
-
<li>Quieres tener más opciones para descargar y administrar tu música sin conexión. </li>
|
| 23 |
-
|
| 24 |
-
</ul>
|
| 25 |
-
<h2> ¿Cómo descargar música APK? </h2>
|
| 26 |
-
<p>Descargar Reproducir música APK para Android no es difícil, pero requiere algunos pasos que son diferentes de descargar aplicaciones de la Google Play Store. Aquí hay una guía paso a paso sobre cómo descargar Play Music APK:</p>
|
| 27 |
-
<h3>Paso 1: Habilitar fuentes desconocidas</h3>
|
| 28 |
-
<p>Lo primero que debes hacer es habilitar fuentes desconocidas en tu dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, siga estos pasos:</p>
|
| 29 |
-
<ol>
|
| 30 |
-
<li>Ir a Configuración > Seguridad > Fuentes desconocidas.</li>
|
| 31 |
-
<li> Alternar en el interruptor para habilitar fuentes desconocidas. </li>
|
| 32 |
-
<li>Pulse Aceptar para confirmar su elección. </li>
|
| 33 |
-
</ol>
|
| 34 |
-
<p>Nota: Los pasos exactos pueden variar dependiendo del modelo de dispositivo y la versión de Android. También puede buscar "fuentes desconocidas" en la aplicación Configuración para encontrar la opción. </p>
|
| 35 |
-
<h3>Paso 2: Encontrar una fuente confiable</h3>
|
| 36 |
-
<p>Lo siguiente que tienes que hacer es encontrar una fuente confiable para descargar Play Music APK. Hay muchos sitios web que ofrecen archivos APK, pero no todos ellos son seguros y confiables. Algunos de ellos pueden contener malware, virus o aplicaciones falsas que pueden dañar su dispositivo o robar sus datos. Para evitar esto, usted debe buscar un sitio web de buena reputación que tiene críticas positivas, calificaciones y comentarios de otros usuarios. También puede utilizar un escáner de virus o una aplicación antivirus para comprobar el archivo APK antes de descargarlo. </p>
|
| 37 |
-
<p>Uno de los sitios web que recomendamos para descargar Play Music APK es APKPure.com. Este es un sitio web popular y de confianza que proporciona archivos APK originales y puros para varias aplicaciones y juegos. También puede encontrar la última versión de Play Music APK en este sitio web, así como otra información como el tamaño, desarrollador, y la descripción de la aplicación. </p>
|
| 38 |
-
<h3>Paso 3: Descargar el archivo APK</h3>
|
| 39 |
-
<p>Una vez que haya encontrado una fuente confiable, puede proceder a descargar el archivo APK a su dispositivo. Para hacer esto, siga estos pasos:</p>
|
| 40 |
-
<p></p>
|
| 41 |
-
<ol>
|
| 42 |
-
|
| 43 |
-
<li> Toque en el botón Descargar APK y esperar a que comience la descarga. </li>
|
| 44 |
-
<li>Puede ver un mensaje de advertencia que dice "Este tipo de archivo puede dañar su dispositivo". Pulse OK para continuar. </li>
|
| 45 |
-
<li>La descarga se guardará en su carpeta de descargas o en cualquier otra ubicación que haya establecido como su ubicación de descarga predeterminada. </li>
|
| 46 |
-
</ol>
|
| 47 |
-
<h3>Paso 4: Instalar el archivo APK</h3>
|
| 48 |
-
<p>Después de descargar el archivo APK, debe instalarlo en su dispositivo. Para hacer esto, siga estos pasos:</p>
|
| 49 |
-
<ol>
|
| 50 |
-
<li>Ve a tu carpeta de descargas o a cualquier otra ubicación donde hayas guardado el archivo APK. </li>
|
| 51 |
-
<li>Toque en el archivo Reproducir música APK y toque Instalar.</li>
|
| 52 |
-
<li>Puede ver un mensaje que dice "¿Desea instalar esta aplicación? No requiere ningún acceso especial". Pulse Instalar de nuevo. </li>
|
| 53 |
-
<li>Espera a que la instalación termine y toca Hecho.</li>
|
| 54 |
-
</ol>
|
| 55 |
-
<h3>Paso 5: Inicie la aplicación y disfrute</h3>
|
| 56 |
-
<p>El paso final es iniciar la aplicación y disfrutar de sus características y beneficios. Para hacer esto, siga estos pasos:</p>
|
| 57 |
-
<ol>
|
| 58 |
-
<li>Vaya a su cajón de aplicaciones y busque el icono Reproducir música. Puede tener un nombre o apariencia diferente que la aplicación oficial de Google Play Music. </li>
|
| 59 |
-
<li>Toque en el icono y abra la aplicación. </li>
|
| 60 |
-
<li>Puede ver un mensaje que dice "Permitir que Play Music acceda a fotos, medios y archivos en su dispositivo?". Pulse Permitir para conceder permiso. </li>
|
| 61 |
-
<li>También puede ver un mensaje que dice "Permitir reproducir música para hacer y gestionar llamadas telefónicas?". Pulse Permitir conceder permiso. </li>
|
| 62 |
-
<li>Verá la pantalla principal de la aplicación con varias opciones como Biblioteca, Recientes, Gráficos superiores, Nuevas versiones, Podcasts y Configuración.</li>
|
| 63 |
-
<li> Ahora puede disfrutar de escuchar música y podcasts en Reproducir música APK.</li>
|
| 64 |
-
</ol>
|
| 65 |
-
<h2> Consejos y trucos para el uso de reproducción de música APK</h2>
|
| 66 |
-
<p>Ahora que ha descargado e instalado Play Music APK en su dispositivo, es posible que desee saber algunos consejos y trucos para usarlo de manera más efectiva. Estos son algunos de ellos:</p>
|
| 67 |
-
<h3>Consejo 1: Personaliza tu biblioteca</h3>
|
| 68 |
-
|
| 69 |
-
<ol>
|
| 70 |
-
<li>Toque en la opción Biblioteca en la parte inferior de la pantalla. </li>
|
| 71 |
-
<li>Toque en el icono del menú en la esquina superior derecha de la pantalla. </li>
|
| 72 |
-
<li>Toque en Editar Biblioteca.</li>
|
| 73 |
-
<li>Seleccione o deseleccione los elementos que desea agregar o quitar de su biblioteca. </li>
|
| 74 |
-
<li>Toque en Hecho cuando haya terminado. </li>
|
| 75 |
-
</ol>
|
| 76 |
-
<h3>Consejo 2: Crear y compartir listas de reproducción</h3>
|
| 77 |
-
<p>Otra cosa que puede hacer con Play Music APK es crear y compartir listas de reproducción con otros. Puedes crear listas de reproducción según tu estado de ánimo, actividad, género, artista o cualquier otro criterio que te guste. También puede compartir sus listas de reproducción con sus amigos, familiares o cualquier otra persona que tiene Play Music APK. Para hacer esto, siga estos pasos:</p>
|
| 78 |
-
<ol>
|
| 79 |
-
<li>Toque en la opción Biblioteca en la parte inferior de la pantalla. </li>
|
| 80 |
-
<li>Toque en el icono más en la esquina superior derecha de la pantalla. </li>
|
| 81 |
-
<li>Toque en la nueva lista de reproducción.</li>
|
| 82 |
-
<li>Introduzca un nombre y una descripción para su lista de reproducción. </li>
|
| 83 |
-
<li>Toque en Agregar música.</li>
|
| 84 |
-
<li>Seleccione las canciones que desea agregar a su lista de reproducción. </li>
|
| 85 |
-
<li>Toque en Hecho cuando haya terminado. </li>
|
| 86 |
-
<li>Toque en el icono del menú junto al nombre de su lista de reproducción. </li>
|
| 87 |
-
<li>Toque en Compartir.</li>
|
| 88 |
-
<li>Seleccione la aplicación o método que desea utilizar para compartir su lista de reproducción. </li>
|
| 89 |
-
</ol>
|
| 90 |
-
<h3>Consejo 3: Descargar música para escuchar sin conexión</h3>
|
| 91 |
-
<p>Una de las mejores características de Play Music APK es que le permite descargar música para escuchar sin conexión. Esto significa que puedes escuchar tus canciones y podcasts favoritos sin conexión a Internet. Esto es especialmente útil cuando viaja, se desplaza o está en áreas con poca cobertura de red. Para hacer esto, siga estos pasos:</p>
|
| 92 |
-
<ol>
|
| 93 |
-
<li>Toque en la opción Biblioteca en la parte inferior de la pantalla. </li>
|
| 94 |
-
<li>Seleccione las canciones, álbumes, artistas, géneros, listas de reproducción, estaciones o podcasts que desea descargar. </li>
|
| 95 |
-
<li>Toque en el icono de descarga en la esquina superior derecha de la pantalla. </li>
|
| 96 |
-
<li>Espere a que la descarga se complete y toque en Hecho.</li>
|
| 97 |
-
</ol>
|
| 98 |
-
|
| 99 |
-
<h3>Consejo 4: Ajuste la calidad del sonido</h3>
|
| 100 |
-
<p>Si desea tener más control sobre la calidad de sonido de Play Music APK, puede utilizar el ecualizador incorporado que le permite ajustar los graves, agudos y otros efectos de sonido. También puede elegir entre diferentes presets como Normal, Pop, Rock, Jazz, Classical y más. Para hacer esto, siga estos pasos:</p>
|
| 101 |
-
<ol>
|
| 102 |
-
<li>Toque en la opción Configuración en la parte inferior de la pantalla. </li>
|
| 103 |
-
<li>Toque en Ecualizador.</li>
|
| 104 |
-
<li> Utilice los controles deslizantes o botones para ajustar la calidad del sonido según su preferencia. </li>
|
| 105 |
-
<li>Toque en Hecho cuando haya terminado. </li>
|
| 106 |
-
</ol>
|
| 107 |
-
<h3>Consejo 5: Explora nueva música y podcasts</h3>
|
| 108 |
-
<p>Si desea descubrir nueva música y podcasts en Play Music APK, puede utilizar las diversas opciones que están disponibles en la aplicación. Puede navegar a través de diferentes categorías como Top Charts, Nuevos lanzamientos, Podcasts, Géneros, Estados de ánimo, Actividades y más. También puede buscar canciones, artistas, álbumes, podcasts o palabras clave específicas. Para hacer esto, siga estos pasos:</p>
|
| 109 |
-
<ol>
|
| 110 |
-
<li>Toque en la opción Recientes en la parte inferior de la pantalla. </li>
|
| 111 |
-
<li>Seleccione una de las opciones que desea explorar. </li>
|
| 112 |
-
<li>Desliza hacia la izquierda o hacia la derecha para ver más opciones o toca Ver todo para ver más resultados. </li>
|
| 113 |
-
<li>Toque en cualquier elemento que desee escuchar o agregar a su biblioteca. </li>
|
| 114 |
-
</ol>
|
| 115 |
-
<h2>Conclusión</h2>
|
| 116 |
-
|
| 117 |
-
<h2>Preguntas frecuentes</h2>
|
| 118 |
-
<p>Aquí hay algunas preguntas frecuentes sobre Play Music APK:</p>
|
| 119 |
-
<h4>Q: ¿Es Play Music APK seguro y legal? </h4>
|
| 120 |
-
<p>A: Reproducir música APK es seguro y legal, siempre y cuando se descarga desde un sitio web de buena reputación como APKPure.com y escanearlo con un escáner de virus o una aplicación antivirus antes de instalarlo. Sin embargo, usted debe ser consciente de que Play Music APK no es una aplicación oficial de Google y puede violar algunos de sus términos y condiciones. Por lo tanto, úsalo bajo tu propio riesgo y discreción. </p>
|
| 121 |
-
<h4>Q: ¿Es Play Music APK gratis? </h4>
|
| 122 |
-
<p>A: Reproducir música APK es gratis para descargar y usar. Usted no necesita pagar ninguna cuota de suscripción o cargos para acceder al servicio de Google Play Music. Sin embargo, es posible que tenga que pagar por algunas canciones o álbumes que no están disponibles para la transmisión o descarga en la aplicación. </p>
|
| 123 |
-
<h4>Q: ¿Cómo puedo actualizar Reproducir música APK? </h4>
|
| 124 |
-
<p>A: Reproducir música APK no se actualiza automáticamente como la aplicación oficial de Google Play Music. Es necesario comprobar las actualizaciones manualmente y descargar la última versión de la aplicación de la misma fuente que lo descargó de. También puede habilitar las notificaciones en el sitio web desde el que lo descargó para recibir notificaciones cuando una nueva versión esté disponible. </p>
|
| 125 |
-
<h4>Q: ¿Cómo puedo desinstalar Play Music APK? </h4>
|
| 126 |
-
<p>A: Si desea desinstalar Play Music APK de su dispositivo, puede hacerlo siguiendo estos pasos:</p>
|
| 127 |
-
<ol>
|
| 128 |
-
<li>Ir a Configuración > Aplicaciones > Reproducir música.</li>
|
| 129 |
-
<li>Toque en Desinstalar y confirmar su elección. </li>
|
| 130 |
-
<li>Espera a que la desinstalación termine y toca OK.</li>
|
| 131 |
-
</ol>
|
| 132 |
-
<h4>Q: ¿Cómo puedo contactar con el desarrollador de Play Music APK? </h4>
|
| 133 |
-
<p>A: Reproducir música APK es desarrollado por un desarrollador desconocido que no tiene un sitio web oficial o información de contacto. Por lo tanto, no es posible ponerse en contacto con el desarrollador directamente. Sin embargo, puede dejar sus comentarios, sugerencias o preguntas en el sitio web desde el que lo descargó, como APKPure.com, y esperar que el desarrollador los vea y responda. </p> 64aa2da5cf<br />
|
| 134 |
-
<br />
|
| 135 |
-
<br />
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spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/abc.py
DELETED
|
@@ -1,137 +0,0 @@
|
|
| 1 |
-
import abc
|
| 2 |
-
from typing import BinaryIO, Iterable, Text
|
| 3 |
-
|
| 4 |
-
from ._compat import runtime_checkable, Protocol
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
class ResourceReader(metaclass=abc.ABCMeta):
|
| 8 |
-
"""Abstract base class for loaders to provide resource reading support."""
|
| 9 |
-
|
| 10 |
-
@abc.abstractmethod
|
| 11 |
-
def open_resource(self, resource: Text) -> BinaryIO:
|
| 12 |
-
"""Return an opened, file-like object for binary reading.
|
| 13 |
-
|
| 14 |
-
The 'resource' argument is expected to represent only a file name.
|
| 15 |
-
If the resource cannot be found, FileNotFoundError is raised.
|
| 16 |
-
"""
|
| 17 |
-
# This deliberately raises FileNotFoundError instead of
|
| 18 |
-
# NotImplementedError so that if this method is accidentally called,
|
| 19 |
-
# it'll still do the right thing.
|
| 20 |
-
raise FileNotFoundError
|
| 21 |
-
|
| 22 |
-
@abc.abstractmethod
|
| 23 |
-
def resource_path(self, resource: Text) -> Text:
|
| 24 |
-
"""Return the file system path to the specified resource.
|
| 25 |
-
|
| 26 |
-
The 'resource' argument is expected to represent only a file name.
|
| 27 |
-
If the resource does not exist on the file system, raise
|
| 28 |
-
FileNotFoundError.
|
| 29 |
-
"""
|
| 30 |
-
# This deliberately raises FileNotFoundError instead of
|
| 31 |
-
# NotImplementedError so that if this method is accidentally called,
|
| 32 |
-
# it'll still do the right thing.
|
| 33 |
-
raise FileNotFoundError
|
| 34 |
-
|
| 35 |
-
@abc.abstractmethod
|
| 36 |
-
def is_resource(self, path: Text) -> bool:
|
| 37 |
-
"""Return True if the named 'path' is a resource.
|
| 38 |
-
|
| 39 |
-
Files are resources, directories are not.
|
| 40 |
-
"""
|
| 41 |
-
raise FileNotFoundError
|
| 42 |
-
|
| 43 |
-
@abc.abstractmethod
|
| 44 |
-
def contents(self) -> Iterable[str]:
|
| 45 |
-
"""Return an iterable of entries in `package`."""
|
| 46 |
-
raise FileNotFoundError
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
@runtime_checkable
|
| 50 |
-
class Traversable(Protocol):
|
| 51 |
-
"""
|
| 52 |
-
An object with a subset of pathlib.Path methods suitable for
|
| 53 |
-
traversing directories and opening files.
|
| 54 |
-
"""
|
| 55 |
-
|
| 56 |
-
@abc.abstractmethod
|
| 57 |
-
def iterdir(self):
|
| 58 |
-
"""
|
| 59 |
-
Yield Traversable objects in self
|
| 60 |
-
"""
|
| 61 |
-
|
| 62 |
-
def read_bytes(self):
|
| 63 |
-
"""
|
| 64 |
-
Read contents of self as bytes
|
| 65 |
-
"""
|
| 66 |
-
with self.open('rb') as strm:
|
| 67 |
-
return strm.read()
|
| 68 |
-
|
| 69 |
-
def read_text(self, encoding=None):
|
| 70 |
-
"""
|
| 71 |
-
Read contents of self as text
|
| 72 |
-
"""
|
| 73 |
-
with self.open(encoding=encoding) as strm:
|
| 74 |
-
return strm.read()
|
| 75 |
-
|
| 76 |
-
@abc.abstractmethod
|
| 77 |
-
def is_dir(self) -> bool:
|
| 78 |
-
"""
|
| 79 |
-
Return True if self is a directory
|
| 80 |
-
"""
|
| 81 |
-
|
| 82 |
-
@abc.abstractmethod
|
| 83 |
-
def is_file(self) -> bool:
|
| 84 |
-
"""
|
| 85 |
-
Return True if self is a file
|
| 86 |
-
"""
|
| 87 |
-
|
| 88 |
-
@abc.abstractmethod
|
| 89 |
-
def joinpath(self, child):
|
| 90 |
-
"""
|
| 91 |
-
Return Traversable child in self
|
| 92 |
-
"""
|
| 93 |
-
|
| 94 |
-
def __truediv__(self, child):
|
| 95 |
-
"""
|
| 96 |
-
Return Traversable child in self
|
| 97 |
-
"""
|
| 98 |
-
return self.joinpath(child)
|
| 99 |
-
|
| 100 |
-
@abc.abstractmethod
|
| 101 |
-
def open(self, mode='r', *args, **kwargs):
|
| 102 |
-
"""
|
| 103 |
-
mode may be 'r' or 'rb' to open as text or binary. Return a handle
|
| 104 |
-
suitable for reading (same as pathlib.Path.open).
|
| 105 |
-
|
| 106 |
-
When opening as text, accepts encoding parameters such as those
|
| 107 |
-
accepted by io.TextIOWrapper.
|
| 108 |
-
"""
|
| 109 |
-
|
| 110 |
-
@abc.abstractproperty
|
| 111 |
-
def name(self) -> str:
|
| 112 |
-
"""
|
| 113 |
-
The base name of this object without any parent references.
|
| 114 |
-
"""
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
class TraversableResources(ResourceReader):
|
| 118 |
-
"""
|
| 119 |
-
The required interface for providing traversable
|
| 120 |
-
resources.
|
| 121 |
-
"""
|
| 122 |
-
|
| 123 |
-
@abc.abstractmethod
|
| 124 |
-
def files(self):
|
| 125 |
-
"""Return a Traversable object for the loaded package."""
|
| 126 |
-
|
| 127 |
-
def open_resource(self, resource):
|
| 128 |
-
return self.files().joinpath(resource).open('rb')
|
| 129 |
-
|
| 130 |
-
def resource_path(self, resource):
|
| 131 |
-
raise FileNotFoundError(resource)
|
| 132 |
-
|
| 133 |
-
def is_resource(self, path):
|
| 134 |
-
return self.files().joinpath(path).is_file()
|
| 135 |
-
|
| 136 |
-
def contents(self):
|
| 137 |
-
return (item.name for item in self.files().iterdir())
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spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/alias.py
DELETED
|
@@ -1,78 +0,0 @@
|
|
| 1 |
-
from distutils.errors import DistutilsOptionError
|
| 2 |
-
|
| 3 |
-
from setuptools.command.setopt import edit_config, option_base, config_file
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
def shquote(arg):
|
| 7 |
-
"""Quote an argument for later parsing by shlex.split()"""
|
| 8 |
-
for c in '"', "'", "\\", "#":
|
| 9 |
-
if c in arg:
|
| 10 |
-
return repr(arg)
|
| 11 |
-
if arg.split() != [arg]:
|
| 12 |
-
return repr(arg)
|
| 13 |
-
return arg
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class alias(option_base):
|
| 17 |
-
"""Define a shortcut that invokes one or more commands"""
|
| 18 |
-
|
| 19 |
-
description = "define a shortcut to invoke one or more commands"
|
| 20 |
-
command_consumes_arguments = True
|
| 21 |
-
|
| 22 |
-
user_options = [
|
| 23 |
-
('remove', 'r', 'remove (unset) the alias'),
|
| 24 |
-
] + option_base.user_options
|
| 25 |
-
|
| 26 |
-
boolean_options = option_base.boolean_options + ['remove']
|
| 27 |
-
|
| 28 |
-
def initialize_options(self):
|
| 29 |
-
option_base.initialize_options(self)
|
| 30 |
-
self.args = None
|
| 31 |
-
self.remove = None
|
| 32 |
-
|
| 33 |
-
def finalize_options(self):
|
| 34 |
-
option_base.finalize_options(self)
|
| 35 |
-
if self.remove and len(self.args) != 1:
|
| 36 |
-
raise DistutilsOptionError(
|
| 37 |
-
"Must specify exactly one argument (the alias name) when "
|
| 38 |
-
"using --remove"
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
def run(self):
|
| 42 |
-
aliases = self.distribution.get_option_dict('aliases')
|
| 43 |
-
|
| 44 |
-
if not self.args:
|
| 45 |
-
print("Command Aliases")
|
| 46 |
-
print("---------------")
|
| 47 |
-
for alias in aliases:
|
| 48 |
-
print("setup.py alias", format_alias(alias, aliases))
|
| 49 |
-
return
|
| 50 |
-
|
| 51 |
-
elif len(self.args) == 1:
|
| 52 |
-
alias, = self.args
|
| 53 |
-
if self.remove:
|
| 54 |
-
command = None
|
| 55 |
-
elif alias in aliases:
|
| 56 |
-
print("setup.py alias", format_alias(alias, aliases))
|
| 57 |
-
return
|
| 58 |
-
else:
|
| 59 |
-
print("No alias definition found for %r" % alias)
|
| 60 |
-
return
|
| 61 |
-
else:
|
| 62 |
-
alias = self.args[0]
|
| 63 |
-
command = ' '.join(map(shquote, self.args[1:]))
|
| 64 |
-
|
| 65 |
-
edit_config(self.filename, {'aliases': {alias: command}}, self.dry_run)
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def format_alias(name, aliases):
|
| 69 |
-
source, command = aliases[name]
|
| 70 |
-
if source == config_file('global'):
|
| 71 |
-
source = '--global-config '
|
| 72 |
-
elif source == config_file('user'):
|
| 73 |
-
source = '--user-config '
|
| 74 |
-
elif source == config_file('local'):
|
| 75 |
-
source = ''
|
| 76 |
-
else:
|
| 77 |
-
source = '--filename=%r' % source
|
| 78 |
-
return source + name + ' ' + command
|
|
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|
spaces/Blackroot/Fancy-Audiogen/app.py
DELETED
|
@@ -1,114 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import os, json
|
| 3 |
-
from generator import HijackedMusicGen
|
| 4 |
-
from audiocraft.data.audio import audio_write
|
| 5 |
-
from audio import predict
|
| 6 |
-
from itertools import zip_longest
|
| 7 |
-
|
| 8 |
-
def split_prompt(bigly_prompt, num_segments):
|
| 9 |
-
prompts = bigly_prompt.split(',,')
|
| 10 |
-
num_segments = int(num_segments) # Assuming 'segment' comes as a string from Gradio slider
|
| 11 |
-
# repeat last prompt to fill in the rest
|
| 12 |
-
if len(prompts) < num_segments:
|
| 13 |
-
prompts += [prompts[-1]] * (num_segments - len(prompts))
|
| 14 |
-
elif len(prompts) > num_segments:
|
| 15 |
-
prompts = prompts[:num_segments]
|
| 16 |
-
return prompts
|
| 17 |
-
|
| 18 |
-
loaded_model = None
|
| 19 |
-
audio_files = []
|
| 20 |
-
def model_interface(model_name, top_k, top_p, temperature, cfg_coef, segments, overlap, duration, optional_audio, prompt):
|
| 21 |
-
global loaded_model
|
| 22 |
-
|
| 23 |
-
if loaded_model is None or loaded_model.name != model_name:
|
| 24 |
-
loaded_model = HijackedMusicGen.get_pretrained(None, name=model_name)
|
| 25 |
-
|
| 26 |
-
print(optional_audio)
|
| 27 |
-
|
| 28 |
-
loaded_model.set_generation_params(
|
| 29 |
-
use_sampling=True,
|
| 30 |
-
duration=duration,
|
| 31 |
-
top_p=top_p,
|
| 32 |
-
top_k=top_k,
|
| 33 |
-
temperature=temperature,
|
| 34 |
-
cfg_coef=cfg_coef,
|
| 35 |
-
)
|
| 36 |
-
|
| 37 |
-
extension_parameters = {"segments":segments, "overlap":overlap}
|
| 38 |
-
optional_audio_parameters = {"optional_audio":optional_audio, "sample_rate":loaded_model.sample_rate}
|
| 39 |
-
|
| 40 |
-
prompts = split_prompt(prompt, segments)
|
| 41 |
-
first_prompt = prompts[0]
|
| 42 |
-
|
| 43 |
-
sample_rate, audio = predict(loaded_model, prompts, duration, optional_audio_parameters, extension_parameters)
|
| 44 |
-
|
| 45 |
-
counter = 1
|
| 46 |
-
audio_path = "static/"
|
| 47 |
-
audio_name = first_prompt
|
| 48 |
-
while os.path.exists(audio_path + audio_name + ".wav"):
|
| 49 |
-
audio_name = f"{first_prompt}({counter})"
|
| 50 |
-
counter += 1
|
| 51 |
-
|
| 52 |
-
file = audio_write(audio_path + audio_name, audio.squeeze(), sample_rate, strategy="loudness")
|
| 53 |
-
audio_files.append(file)
|
| 54 |
-
|
| 55 |
-
audio_list_html = "<br>".join([
|
| 56 |
-
f'''
|
| 57 |
-
<div style="border:1px solid #000; padding:10px; margin-bottom:10px;">
|
| 58 |
-
<div>{os.path.splitext(os.path.basename(file))[0]}</div>
|
| 59 |
-
<audio controls><source src="/file={file}" type="audio/wav"></audio>
|
| 60 |
-
</div>
|
| 61 |
-
'''
|
| 62 |
-
for file in reversed(audio_files)
|
| 63 |
-
])
|
| 64 |
-
|
| 65 |
-
return audio_list_html
|
| 66 |
-
|
| 67 |
-
slider_param = {
|
| 68 |
-
"top_k": {"minimum": 0, "maximum": 1000, "value": 0, "label": "Top K"},
|
| 69 |
-
"top_p": {"minimum": 0.0, "maximum": 1.0, "value": 0.0, "label": "Top P"},
|
| 70 |
-
"temperature": {"minimum": 0.1, "maximum": 10.0, "value": 1.0, "label": "Temperature"},
|
| 71 |
-
"cfg_coef": {"minimum": 0.0, "maximum": 10.0, "value": 4.0, "label": "CFG Coefficient"},
|
| 72 |
-
"segments": {"minimum": 1, "maximum": 10, "value": 1, "step": 1, "label": "Number of Segments"},
|
| 73 |
-
"overlap": {"minimum": 0.0, "maximum": 10.0, "value": 1.0, "label": "Segment Overlap"},
|
| 74 |
-
"duration": {"minimum": 1, "maximum": 300, "value": 10, "label": "Duration"},
|
| 75 |
-
}
|
| 76 |
-
|
| 77 |
-
slider_params = {
|
| 78 |
-
key: gr.components.Slider(**params)
|
| 79 |
-
for key, params in slider_param.items()
|
| 80 |
-
}
|
| 81 |
-
|
| 82 |
-
with gr.Blocks() as interface:
|
| 83 |
-
with gr.Row():
|
| 84 |
-
|
| 85 |
-
with gr.Column():
|
| 86 |
-
with gr.Row():
|
| 87 |
-
model_dropdown = gr.components.Dropdown(choices=["small", "medium", "large", "melody"], label="Model Size", value="large")
|
| 88 |
-
optional_audio = gr.components.Audio(source="upload", type="numpy", label="Optional Audio", interactive=True)
|
| 89 |
-
|
| 90 |
-
slider_keys = list(slider_param.keys())
|
| 91 |
-
slider_pairs = list(zip_longest(slider_keys[::2], slider_keys[1::2]))
|
| 92 |
-
|
| 93 |
-
for key1, key2 in slider_pairs:
|
| 94 |
-
with gr.Row():
|
| 95 |
-
with gr.Column():
|
| 96 |
-
slider_params[key1] = gr.components.Slider(**slider_param[key1])
|
| 97 |
-
if key2 is not None:
|
| 98 |
-
with gr.Column():
|
| 99 |
-
slider_params[key2] = gr.components.Slider(**slider_param[key2])
|
| 100 |
-
|
| 101 |
-
prompt_box = gr.components.Textbox(lines=5, placeholder="""Insert a double comma ,, to indicate this should prompt a new segment. For example:
|
| 102 |
-
Rock Opera,,Dueling Banjos
|
| 103 |
-
This allows you to prompt each segment individually. If you only provide one prompt, every segment will use that one prompt. If you provide multiple prompts but less than the number of segments, then the last prompt will be used to fill in the rest.
|
| 104 |
-
""")
|
| 105 |
-
submit = gr.Button("Submit")
|
| 106 |
-
|
| 107 |
-
with gr.Column():
|
| 108 |
-
output = gr.outputs.HTML()
|
| 109 |
-
|
| 110 |
-
inputs_list = [model_dropdown] + list(slider_params.values()) + [optional_audio] + [prompt_box]
|
| 111 |
-
submit.click(model_interface, inputs=inputs_list, outputs=[output])
|
| 112 |
-
|
| 113 |
-
interface.queue()
|
| 114 |
-
interface.launch()
|
|
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|
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|
|
spaces/Bong15/Rewrite/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Rewrite
|
| 3 |
-
emoji: 🚀
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.15.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
spaces/CVPR/LIVE/cmake/FindTensorFlow.cmake
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 1 |
-
# https://github.com/PatWie/tensorflow-cmake/blob/master/cmake/modules/FindTensorFlow.cmake
|
| 2 |
-
|
| 3 |
-
execute_process(
|
| 4 |
-
COMMAND python -c "exec(\"try:\\n import tensorflow as tf; print(tf.__version__); print(tf.__cxx11_abi_flag__);print(tf.sysconfig.get_include()); print(tf.sysconfig.get_lib())\\nexcept ImportError:\\n exit(1)\")"
|
| 5 |
-
OUTPUT_VARIABLE TF_INFORMATION_STRING
|
| 6 |
-
OUTPUT_STRIP_TRAILING_WHITESPACE
|
| 7 |
-
RESULT_VARIABLE retcode)
|
| 8 |
-
|
| 9 |
-
if("${retcode}" STREQUAL "0")
|
| 10 |
-
string(REPLACE "\n" ";" TF_INFORMATION_LIST ${TF_INFORMATION_STRING})
|
| 11 |
-
list(GET TF_INFORMATION_LIST 0 TF_DETECTED_VERSION)
|
| 12 |
-
list(GET TF_INFORMATION_LIST 1 TF_DETECTED_ABI)
|
| 13 |
-
list(GET TF_INFORMATION_LIST 2 TF_DETECTED_INCLUDE_DIR)
|
| 14 |
-
list(GET TF_INFORMATION_LIST 3 TF_DETECTED_LIBRARY_DIR)
|
| 15 |
-
if(WIN32)
|
| 16 |
-
find_library(TF_DETECTED_LIBRARY NAMES _pywrap_tensorflow_internal PATHS
|
| 17 |
-
${TF_DETECTED_LIBRARY_DIR}/python)
|
| 18 |
-
else()
|
| 19 |
-
# For some reason my tensorflow doesn't have a .so file
|
| 20 |
-
list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.1)
|
| 21 |
-
list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.2)
|
| 22 |
-
find_library(TF_DETECTED_LIBRARY NAMES tensorflow_framework PATHS
|
| 23 |
-
${TF_DETECTED_LIBRARY_DIR})
|
| 24 |
-
endif()
|
| 25 |
-
set(TensorFlow_VERSION ${TF_DETECTED_VERSION})
|
| 26 |
-
set(TensorFlow_ABI ${TF_DETECTED_ABI})
|
| 27 |
-
set(TensorFlow_INCLUDE_DIR ${TF_DETECTED_INCLUDE_DIR})
|
| 28 |
-
set(TensorFlow_LIBRARY ${TF_DETECTED_LIBRARY})
|
| 29 |
-
if(TensorFlow_LIBRARY AND TensorFlow_INCLUDE_DIR)
|
| 30 |
-
set(TensorFlow_FOUND TRUE)
|
| 31 |
-
else()
|
| 32 |
-
set(TensorFlow_FOUND FALSE)
|
| 33 |
-
endif()
|
| 34 |
-
endif()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
spaces/CVPR/LIVE/thrust/thrust/for_each.h
DELETED
|
@@ -1,280 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
* * Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
* See the License for the specific language governing permissions and
|
| 13 |
-
* limitations under the License.
|
| 14 |
-
*/
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
/*! \file thrust/for_each.h
|
| 18 |
-
* \brief Applies a function to each element in a range
|
| 19 |
-
*/
|
| 20 |
-
|
| 21 |
-
#pragma once
|
| 22 |
-
|
| 23 |
-
#include <thrust/detail/config.h>
|
| 24 |
-
#include <thrust/detail/type_traits.h>
|
| 25 |
-
#include <thrust/detail/execution_policy.h>
|
| 26 |
-
|
| 27 |
-
namespace thrust
|
| 28 |
-
{
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
/*! \addtogroup modifying
|
| 32 |
-
* \ingroup transformations
|
| 33 |
-
* \{
|
| 34 |
-
*/
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
/*! \p for_each applies the function object \p f to each element
|
| 38 |
-
* in the range <tt>[first, last)</tt>; \p f's return value, if any,
|
| 39 |
-
* is ignored. Unlike the C++ Standard Template Library function
|
| 40 |
-
* <tt>std::for_each</tt>, this version offers no guarantee on
|
| 41 |
-
* order of execution. For this reason, this version of \p for_each
|
| 42 |
-
* does not return a copy of the function object.
|
| 43 |
-
*
|
| 44 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 45 |
-
*
|
| 46 |
-
* \param exec The execution policy to use for parallelization.
|
| 47 |
-
* \param first The beginning of the sequence.
|
| 48 |
-
* \param last The end of the sequence.
|
| 49 |
-
* \param f The function object to apply to the range <tt>[first, last)</tt>.
|
| 50 |
-
* \return last
|
| 51 |
-
*
|
| 52 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 53 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator">Input Iterator</a>,
|
| 54 |
-
* and \p InputIterator's \c value_type is convertible to \p UnaryFunction's \c argument_type.
|
| 55 |
-
* \tparam UnaryFunction is a model of <a href="http://www.sgi.com/tech/stl/UnaryFunction">Unary Function</a>,
|
| 56 |
-
* and \p UnaryFunction does not apply any non-constant operation through its argument.
|
| 57 |
-
*
|
| 58 |
-
* The following code snippet demonstrates how to use \p for_each to print the elements
|
| 59 |
-
* of a \p std::device_vector using the \p thrust::device parallelization policy:
|
| 60 |
-
*
|
| 61 |
-
* \code
|
| 62 |
-
* #include <thrust/for_each.h>
|
| 63 |
-
* #include <thrust/device_vector.h>
|
| 64 |
-
* #include <thrust/execution_policy.h>
|
| 65 |
-
* #include <cstdio>
|
| 66 |
-
* ...
|
| 67 |
-
*
|
| 68 |
-
* struct printf_functor
|
| 69 |
-
* {
|
| 70 |
-
* __host__ __device__
|
| 71 |
-
* void operator()(int x)
|
| 72 |
-
* {
|
| 73 |
-
* // note that using printf in a __device__ function requires
|
| 74 |
-
* // code compiled for a GPU with compute capability 2.0 or
|
| 75 |
-
* // higher (nvcc --arch=sm_20)
|
| 76 |
-
* printf("%d\n", x);
|
| 77 |
-
* }
|
| 78 |
-
* };
|
| 79 |
-
* ...
|
| 80 |
-
* thrust::device_vector<int> d_vec(3);
|
| 81 |
-
* d_vec[0] = 0; d_vec[1] = 1; d_vec[2] = 2;
|
| 82 |
-
*
|
| 83 |
-
* thrust::for_each(thrust::device, d_vec.begin(), d_vec.end(), printf_functor());
|
| 84 |
-
*
|
| 85 |
-
* // 0 1 2 is printed to standard output in some unspecified order
|
| 86 |
-
* \endcode
|
| 87 |
-
*
|
| 88 |
-
* \see for_each_n
|
| 89 |
-
* \see http://www.sgi.com/tech/stl/for_each.html
|
| 90 |
-
*/
|
| 91 |
-
template<typename DerivedPolicy,
|
| 92 |
-
typename InputIterator,
|
| 93 |
-
typename UnaryFunction>
|
| 94 |
-
__host__ __device__
|
| 95 |
-
InputIterator for_each(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 96 |
-
InputIterator first,
|
| 97 |
-
InputIterator last,
|
| 98 |
-
UnaryFunction f);
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
/*! \p for_each_n applies the function object \p f to each element
|
| 102 |
-
* in the range <tt>[first, first + n)</tt>; \p f's return value, if any,
|
| 103 |
-
* is ignored. Unlike the C++ Standard Template Library function
|
| 104 |
-
* <tt>std::for_each</tt>, this version offers no guarantee on
|
| 105 |
-
* order of execution.
|
| 106 |
-
*
|
| 107 |
-
* The algorithm's execution is parallelized as determined by \p exec.
|
| 108 |
-
*
|
| 109 |
-
* \param exec The execution policy to use for parallelization.
|
| 110 |
-
* \param first The beginning of the sequence.
|
| 111 |
-
* \param n The size of the input sequence.
|
| 112 |
-
* \param f The function object to apply to the range <tt>[first, first + n)</tt>.
|
| 113 |
-
* \return <tt>first + n</tt>
|
| 114 |
-
*
|
| 115 |
-
* \tparam DerivedPolicy The name of the derived execution policy.
|
| 116 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator">Input Iterator</a>,
|
| 117 |
-
* and \p InputIterator's \c value_type is convertible to \p UnaryFunction's \c argument_type.
|
| 118 |
-
* \tparam Size is an integral type.
|
| 119 |
-
* \tparam UnaryFunction is a model of <a href="http://www.sgi.com/tech/stl/UnaryFunction">Unary Function</a>,
|
| 120 |
-
* and \p UnaryFunction does not apply any non-constant operation through its argument.
|
| 121 |
-
*
|
| 122 |
-
* The following code snippet demonstrates how to use \p for_each_n to print the elements
|
| 123 |
-
* of a \p device_vector using the \p thrust::device parallelization policy.
|
| 124 |
-
*
|
| 125 |
-
* \code
|
| 126 |
-
* #include <thrust/for_each.h>
|
| 127 |
-
* #include <thrust/device_vector.h>
|
| 128 |
-
* #include <thrust/execution_policy.h>
|
| 129 |
-
* #include <cstdio>
|
| 130 |
-
*
|
| 131 |
-
* struct printf_functor
|
| 132 |
-
* {
|
| 133 |
-
* __host__ __device__
|
| 134 |
-
* void operator()(int x)
|
| 135 |
-
* {
|
| 136 |
-
* // note that using printf in a __device__ function requires
|
| 137 |
-
* // code compiled for a GPU with compute capability 2.0 or
|
| 138 |
-
* // higher (nvcc --arch=sm_20)
|
| 139 |
-
* printf("%d\n", x);
|
| 140 |
-
* }
|
| 141 |
-
* };
|
| 142 |
-
* ...
|
| 143 |
-
* thrust::device_vector<int> d_vec(3);
|
| 144 |
-
* d_vec[0] = 0; d_vec[1] = 1; d_vec[2] = 2;
|
| 145 |
-
*
|
| 146 |
-
* thrust::for_each_n(thrust::device, d_vec.begin(), d_vec.size(), printf_functor());
|
| 147 |
-
*
|
| 148 |
-
* // 0 1 2 is printed to standard output in some unspecified order
|
| 149 |
-
* \endcode
|
| 150 |
-
*
|
| 151 |
-
* \see for_each
|
| 152 |
-
* \see http://www.sgi.com/tech/stl/for_each.html
|
| 153 |
-
*/
|
| 154 |
-
template<typename DerivedPolicy,
|
| 155 |
-
typename InputIterator,
|
| 156 |
-
typename Size,
|
| 157 |
-
typename UnaryFunction>
|
| 158 |
-
__host__ __device__
|
| 159 |
-
InputIterator for_each_n(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 160 |
-
InputIterator first,
|
| 161 |
-
Size n,
|
| 162 |
-
UnaryFunction f);
|
| 163 |
-
|
| 164 |
-
/*! \p for_each applies the function object \p f to each element
|
| 165 |
-
* in the range <tt>[first, last)</tt>; \p f's return value, if any,
|
| 166 |
-
* is ignored. Unlike the C++ Standard Template Library function
|
| 167 |
-
* <tt>std::for_each</tt>, this version offers no guarantee on
|
| 168 |
-
* order of execution. For this reason, this version of \p for_each
|
| 169 |
-
* does not return a copy of the function object.
|
| 170 |
-
*
|
| 171 |
-
* \param first The beginning of the sequence.
|
| 172 |
-
* \param last The end of the sequence.
|
| 173 |
-
* \param f The function object to apply to the range <tt>[first, last)</tt>.
|
| 174 |
-
* \return last
|
| 175 |
-
*
|
| 176 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator">Input Iterator</a>,
|
| 177 |
-
* and \p InputIterator's \c value_type is convertible to \p UnaryFunction's \c argument_type.
|
| 178 |
-
* \tparam UnaryFunction is a model of <a href="http://www.sgi.com/tech/stl/UnaryFunction">Unary Function</a>,
|
| 179 |
-
* and \p UnaryFunction does not apply any non-constant operation through its argument.
|
| 180 |
-
*
|
| 181 |
-
* The following code snippet demonstrates how to use \p for_each to print the elements
|
| 182 |
-
* of a \p device_vector.
|
| 183 |
-
*
|
| 184 |
-
* \code
|
| 185 |
-
* #include <thrust/for_each.h>
|
| 186 |
-
* #include <thrust/device_vector.h>
|
| 187 |
-
* #include <stdio.h>
|
| 188 |
-
*
|
| 189 |
-
* struct printf_functor
|
| 190 |
-
* {
|
| 191 |
-
* __host__ __device__
|
| 192 |
-
* void operator()(int x)
|
| 193 |
-
* {
|
| 194 |
-
* // note that using printf in a __device__ function requires
|
| 195 |
-
* // code compiled for a GPU with compute capability 2.0 or
|
| 196 |
-
* // higher (nvcc --arch=sm_20)
|
| 197 |
-
* printf("%d\n", x);
|
| 198 |
-
* }
|
| 199 |
-
* };
|
| 200 |
-
* ...
|
| 201 |
-
* thrust::device_vector<int> d_vec(3);
|
| 202 |
-
* d_vec[0] = 0; d_vec[1] = 1; d_vec[2] = 2;
|
| 203 |
-
*
|
| 204 |
-
* thrust::for_each(d_vec.begin(), d_vec.end(), printf_functor());
|
| 205 |
-
*
|
| 206 |
-
* // 0 1 2 is printed to standard output in some unspecified order
|
| 207 |
-
* \endcode
|
| 208 |
-
*
|
| 209 |
-
* \see for_each_n
|
| 210 |
-
* \see http://www.sgi.com/tech/stl/for_each.html
|
| 211 |
-
*/
|
| 212 |
-
template<typename InputIterator,
|
| 213 |
-
typename UnaryFunction>
|
| 214 |
-
InputIterator for_each(InputIterator first,
|
| 215 |
-
InputIterator last,
|
| 216 |
-
UnaryFunction f);
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
/*! \p for_each_n applies the function object \p f to each element
|
| 220 |
-
* in the range <tt>[first, first + n)</tt>; \p f's return value, if any,
|
| 221 |
-
* is ignored. Unlike the C++ Standard Template Library function
|
| 222 |
-
* <tt>std::for_each</tt>, this version offers no guarantee on
|
| 223 |
-
* order of execution.
|
| 224 |
-
*
|
| 225 |
-
* \param first The beginning of the sequence.
|
| 226 |
-
* \param n The size of the input sequence.
|
| 227 |
-
* \param f The function object to apply to the range <tt>[first, first + n)</tt>.
|
| 228 |
-
* \return <tt>first + n</tt>
|
| 229 |
-
*
|
| 230 |
-
* \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator">Input Iterator</a>,
|
| 231 |
-
* and \p InputIterator's \c value_type is convertible to \p UnaryFunction's \c argument_type.
|
| 232 |
-
* \tparam Size is an integral type.
|
| 233 |
-
* \tparam UnaryFunction is a model of <a href="http://www.sgi.com/tech/stl/UnaryFunction">Unary Function</a>,
|
| 234 |
-
* and \p UnaryFunction does not apply any non-constant operation through its argument.
|
| 235 |
-
*
|
| 236 |
-
* The following code snippet demonstrates how to use \p for_each_n to print the elements
|
| 237 |
-
* of a \p device_vector.
|
| 238 |
-
*
|
| 239 |
-
* \code
|
| 240 |
-
* #include <thrust/for_each.h>
|
| 241 |
-
* #include <thrust/device_vector.h>
|
| 242 |
-
* #include <stdio.h>
|
| 243 |
-
*
|
| 244 |
-
* struct printf_functor
|
| 245 |
-
* {
|
| 246 |
-
* __host__ __device__
|
| 247 |
-
* void operator()(int x)
|
| 248 |
-
* {
|
| 249 |
-
* // note that using printf in a __device__ function requires
|
| 250 |
-
* // code compiled for a GPU with compute capability 2.0 or
|
| 251 |
-
* // higher (nvcc --arch=sm_20)
|
| 252 |
-
* printf("%d\n", x);
|
| 253 |
-
* }
|
| 254 |
-
* };
|
| 255 |
-
* ...
|
| 256 |
-
* thrust::device_vector<int> d_vec(3);
|
| 257 |
-
* d_vec[0] = 0; d_vec[1] = 1; d_vec[2] = 2;
|
| 258 |
-
*
|
| 259 |
-
* thrust::for_each_n(d_vec.begin(), d_vec.size(), printf_functor());
|
| 260 |
-
*
|
| 261 |
-
* // 0 1 2 is printed to standard output in some unspecified order
|
| 262 |
-
* \endcode
|
| 263 |
-
*
|
| 264 |
-
* \see for_each
|
| 265 |
-
* \see http://www.sgi.com/tech/stl/for_each.html
|
| 266 |
-
*/
|
| 267 |
-
template<typename InputIterator,
|
| 268 |
-
typename Size,
|
| 269 |
-
typename UnaryFunction>
|
| 270 |
-
InputIterator for_each_n(InputIterator first,
|
| 271 |
-
Size n,
|
| 272 |
-
UnaryFunction f);
|
| 273 |
-
|
| 274 |
-
/*! \} // end modifying
|
| 275 |
-
*/
|
| 276 |
-
|
| 277 |
-
} // end namespace thrust
|
| 278 |
-
|
| 279 |
-
#include <thrust/detail/for_each.inl>
|
| 280 |
-
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|
spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/temporary_buffer.h
DELETED
|
@@ -1,22 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2016 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
|
| 21 |
-
// this system has no special temporary buffer functions
|
| 22 |
-
|
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