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  1. spaces/101-5/gpt4free/g4f/.v1/unfinished/openprompt/main.py +0 -36
  2. spaces/1acneusushi/gradio-2dmoleculeeditor/data/Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] - Features Benefits and Drawbacks.md +0 -109
  3. spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download xforce keygen AutoCAD Mechanical 2016 crack What you need to know before you start.md +0 -147
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  6. spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Blue Egg and Unleash Your Inner Egg Warrior in Multiplayer Battles.md +0 -105
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  10. spaces/4Taps/SadTalker/src/face3d/options/inference_options.py +0 -23
  11. spaces/52Hz/CMFNet_deraindrop/README.md +0 -37
  12. spaces/801artistry/RVC801/tools/infer_cli.py +0 -67
  13. spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/__init__.py +0 -10
  14. spaces/Adapter/T2I-Adapter/train_sketch.py +0 -399
  15. spaces/AgentVerse/agentVerse/dataloader/responsegen.py +0 -21
  16. spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/BaseSizer.js +0 -241
  17. spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/AddChildMethods.js +0 -74
  18. spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/methods/SetValueMethods.js +0 -50
  19. spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Methods.js +0 -39
  20. spaces/Akmyradov/TurkmenTTSweSTT/vits/data_utils.py +0 -392
  21. spaces/Ama434/neutral-barlow/app.py +0 -145
  22. spaces/Amrrs/DragGan-Inversion/PTI/evaluation/qualitative_edit_comparison.py +0 -156
  23. spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py +0 -333
  24. spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py +0 -17
  25. spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_adapter.py +0 -316
  26. spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py +0 -13
  27. spaces/Ankit6396/100-Free-ChatGPT4/app.py +0 -193
  28. spaces/Arnx/MusicGenXvAKN/audiocraft/modules/__init__.py +0 -20
  29. spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/errors.py +0 -34
  30. spaces/Awiny/Image2Paragraph/models/grit_src/grit/__init__.py +0 -7
  31. spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py +0 -202
  32. spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/cleaner.py +0 -27
  33. spaces/Bart92/RVC_HF/infer/lib/csvutil.py +0 -41
  34. spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/show.py +0 -189
  35. spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/_manylinux.py +0 -301
  36. spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/editable_wheel.py +0 -844
  37. spaces/Bonosa2/movies/app1.py +0 -76
  38. spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/__init__.py +0 -10
  39. spaces/CVPR/LIVE/pybind11/tests/test_virtual_functions.cpp +0 -498
  40. spaces/CVPR/WALT/mmdet/core/bbox/iou_calculators/iou2d_calculator.py +0 -159
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  46. spaces/DEEMOSTECH/ChatAvatar/static/js/main.459a2347.js +0 -0
  47. spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/Image.py +0 -0
  48. spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_D_.py +0 -5
  49. spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/grUtils.py +0 -92
  50. spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Login-9c3cc0eb.css +0 -1
spaces/101-5/gpt4free/g4f/.v1/unfinished/openprompt/main.py DELETED
@@ -1,36 +0,0 @@
1
- import requests
2
-
3
- cookies = {
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- 'supabase-auth-token': '["eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOiJhdXRoZW50aWNhdGVkIiwiZXhwIjoxNjgyMjk1NzQyLCJzdWIiOiJlOGExOTdiNS03YTAxLTQ3MmEtODQ5My1mNGUzNTNjMzIwNWUiLCJlbWFpbCI6InFlY3RncHZhamlibGNjQGJ1Z2Zvby5jb20iLCJwaG9uZSI6IiIsImFwcF9tZXRhZGF0YSI6eyJwcm92aWRlciI6ImVtYWlsIiwicHJvdmlkZXJzIjpbImVtYWlsIl19LCJ1c2VyX21ldGFkYXRhIjp7fSwicm9sZSI6ImF1dGhlbnRpY2F0ZWQiLCJhYWwiOiJhYWwxIiwiYW1yIjpbeyJtZXRob2QiOiJvdHAiLCJ0aW1lc3RhbXAiOjE2ODE2OTA5NDJ9XSwic2Vzc2lvbl9pZCI6IjIwNTg5MmE5LWU5YTAtNDk2Yi1hN2FjLWEyMWVkMTkwZDA4NCJ9.o7UgHpiJMfa6W-UKCSCnAncIfeOeiHz-51sBmokg0MA","RtPKeb7KMMC9Dn2fZOfiHA",null,null,null]',
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- }
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-
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- headers = {
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- 'authority': 'openprompt.co',
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- 'accept': '*/*',
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- 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
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- 'content-type': 'application/json',
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- # 'cookie': 'supabase-auth-token=%5B%22eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOiJhdXRoZW50aWNhdGVkIiwiZXhwIjoxNjgyMjkzMjQ4LCJzdWIiOiJlODQwNTZkNC0xZWJhLTQwZDktOWU1Mi1jMTc4MTUwN2VmNzgiLCJlbWFpbCI6InNia2didGJnZHB2bHB0ZUBidWdmb28uY29tIiwicGhvbmUiOiIiLCJhcHBfbWV0YWRhdGEiOnsicHJvdmlkZXIiOiJlbWFpbCIsInByb3ZpZGVycyI6WyJlbWFpbCJdfSwidXNlcl9tZXRhZGF0YSI6e30sInJvbGUiOiJhdXRoZW50aWNhdGVkIiwiYWFsIjoiYWFsMSIsImFtciI6W3sibWV0aG9kIjoib3RwIiwidGltZXN0YW1wIjoxNjgxNjg4NDQ4fV0sInNlc3Npb25faWQiOiJiNDhlMmU3NS04NzlhLTQxZmEtYjQ4MS01OWY0OTgxMzg3YWQifQ.5-3E7WvMMVkXewD1qA26Rv4OFSTT82wYUBXNGcYaYfQ%22%2C%22u5TGGMMeT3zZA0agm5HGuA%22%2Cnull%2Cnull%2Cnull%5D',
13
- 'origin': 'https://openprompt.co',
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- 'referer': 'https://openprompt.co/ChatGPT',
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- 'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
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- 'sec-ch-ua-mobile': '?0',
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- 'sec-ch-ua-platform': '"macOS"',
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- 'sec-fetch-dest': 'empty',
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- 'sec-fetch-mode': 'cors',
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- 'sec-fetch-site': 'same-origin',
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- 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
22
- }
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-
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- json_data = {
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- 'messages': [
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- {
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- 'role': 'user',
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- 'content': 'hello world',
29
- },
30
- ],
31
- }
32
-
33
- response = requests.post('https://openprompt.co/api/chat2', cookies=cookies, headers=headers, json=json_data,
34
- stream=True)
35
- for chunk in response.iter_content(chunk_size=1024):
36
- print(chunk)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/1acneusushi/gradio-2dmoleculeeditor/data/Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] - Features Benefits and Drawbacks.md DELETED
@@ -1,109 +0,0 @@
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- <br />
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- <h1>Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] Setup Free</h1>
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- <p>If you are looking for a powerful and versatile audio editing software that can shape audio as never before, you might want to check out Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]. This is a torrent link that allows you to download and install the software for free on your Windows computer.</p>
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- <p>In this article, we will tell you everything you need to know about Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu], including what it is, how to install it, how to use it, what are its system requirements and compatibility, what are its pros and cons, and some FAQs.</p>
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- <h2>Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] Setup Free</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://byltly.com/2uKzdy">https://byltly.com/2uKzdy</a></b></p><br /><br />
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- <p>By the end of this article, you will be able to decide whether Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] is worth your time and attention.</p>
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- <h2>What is Celemony Melodyne Editor?</h2>
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- <p>Celemony Melodyne Editor is a software that allows you to edit audio files in an intuitive and flexible way.</p>
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- <p>Unlike other audio editing tools that work with audio data as a whole, Celemony Melodyne Editor works with audio notes as individual entities.</p>
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- <p>This means that you can access, manipulate, and modify each note in a polyphonic audio material, such as a piano recording or a guitar accompaniment.</p>
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- <p>You can also edit music and not just audio data, by changing pitch, timing, volume, and other musical parameters of each note with ease and precision.</p>
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- <p>Celemony Melodyne Editor also lets you use scales, chords, and harmonies to enhance your audio editing projects.</p>
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- <p>With Celemony Melodyne Editor, you can achieve vocal polishing of the highest quality, remove wrong notes from piano recordings, change chords in guitar accompaniments after recording them, refashion sample licks, and much more.</p>
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- Celemony Melodyne Editor 2</p>
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- <p>Celemony Melodyne Editor is the most powerful edition of Celemony's one-track product family and offers you the entire spectrum of Melodyne's award-winning capabilities.</p>
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- <h3>How to install Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]?</h3>
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- <p>To install Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu], you need to follow these steps:</p>
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- <ol>
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- <li>Download the torrent file from <a href="https://www.bing.com/search?q=Celemony+Melodyne+Editor+2.1.0.45+Win+%28cracked-R2R%29+%5bChingLiu%5d+Setup+Free">this link</a>. You will need a torrent client such as uTorrent or BitTorrent to download it.</li>
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- <li>Open the torrent file with your torrent client and start downloading the software.</li>
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- <li>Once the download is complete, extract the zip file using a program such as WinRAR or 7-Zip.</li>
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- <li>Run the setup.exe file as administrator and follow the instructions on screen.</li>
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- <li>When prompted for a serial number or activation code, use any random number or code.</li>
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- <li>Copy the contents of the R2R folder into your installation directory (usually C:\Program Files\Celemony\Melodyne editor).</li>
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- <li>Enjoy your free copy of Celemony Melodyne Editor 2.1.0.45 Win!</li>
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- </ol>
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- <h3>How to use Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]?</h3>
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- <p>To use Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu], you can either run it as a stand-alone program or as a plug-in in your DAW.</p>
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- <p>In both cases, you need to open an audio file that you want to edit with Celemony Melodyne Editor.</p>
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- <p>The software will analyze your audio file and display it as a series of blobs on a grid.</p>
55
- <p>Each blob represents a note in your audio file.</p>
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- <p>You can select one or more blobs by clicking on them or dragging a selection box around them.</p>
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- <p>You can then edit them by using various tools available on the toolbar or by dragging them with your mouse.</p>
58
- <p>Some of the tools you can use are:</p>
59
- <ul>
60
- <li>Pitch tool: This tool allows you to change the pitch of each note by moving it up or down on the grid.</li>
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- <li>Modulation tool: This tool allows you to change the pitch variation within each note by adjusting its shape on the grid.</li>
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- <li>Timing tool: This tool allows you to change the timing of each note by moving it left or right on the grid.</li>
63
- <li>Time handle tool: This tool allows you to change the duration of each note by stretching or shrinking it on the grid.</li>
64
- <li>Amplitude tool: This tool allows you to change the volume of each note by moving it closer or farther from the horizontal axis on the grid.</li>
65
- <li>Note separation tool: This tool allows you to split or merge notes by clicking on their boundaries on the grid.</li>
66
- </ul>
67
- <p>You can also use other features such as scales, chords, harmonies, macros, quantization, correction, etc., to enhance your audio editing experience.</p>
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- <h4>How to access individual notes in polyphonic audio material?</h4>
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- ```html <h2>What are the pros and cons of Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]?</h2>
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- <p>Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] has many pros and cons that you should consider before using it.</p>
71
- <p>Here are some of the pros:</p>
72
- <ul>
73
- <li>It offers unparalleled access and control over individual notes in polyphonic audio material, thanks to the DNA technology.</li>
74
- <li>It allows you to edit music and not just audio data, by changing pitch, timing, volume, and other musical parameters of each note with ease and precision.</li>
75
- <li>It lets you use scales, chords, and harmonies to enhance your audio editing projects, by applying musical theory and creativity to your audio material.</li>
76
- <li>It integrates seamlessly with your DAW via ARA, making it feel like part of your DAW.</li>
77
- <li>It has a user-friendly interface and a variety of tools and features that make audio editing a breeze.</li>
78
- <li>It has a high sound quality, flexibility, speed, and reliability that make it a trusted tool for many producers and musicians.</li>
79
- </ul>
80
- <p>Here are some of the cons:</p>
81
- <ul>
82
- <li>It is a cracked version of the software, which means it is illegal and unethical to use it without paying for it.</li>
83
- <li>It may not work properly or cause problems with your system or other software, as it is not an official release from Celemony.</li>
84
- <li>It may not have the latest updates or bug fixes from Celemony, as it is an older version of the software.</li>
85
- <li>It only supports polyphonic DNA and multitrack editing in the Editor and Studio versions, which are more expensive than the Essential and Assistant versions.</li>
86
- <li>It still requires some user interaction and skill to get the best results out of it, as it is not a magic tool that can fix everything automatically.</li>
87
- </ul>
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- <h2>Conclusion</h2>
89
- <p>Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] is a powerful and versatile audio editing software that can shape audio as never before.</p>
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- <p>It can access individual notes in polyphonic audio material, edit music and not just audio data, use scales, chords, and harmonies in audio editing projects, integrate with your DAW via ARA, and offer a high sound quality, flexibility, speed, and reliability.</p>
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- <p>However, it is also a cracked version of the software, which means it is illegal and unethical to use it without paying for it.</p>
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- <p>It may also cause problems with your system or other software, as it is not an official release from Celemony.</p>
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- <p>It may also lack the latest updates or bug fixes from Celemony, as it is an older version of the software.</p>
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- <p>It also only supports polyphonic DNA and multitrack editing in the Editor and Studio versions, which are more expensive than the Essential and Assistant versions.</p>
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- <p>It also still requires some user interaction and skill to get the best results out of it, as it is not a magic tool that can fix everything automatically.</p>
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- <p>If you are interested in using Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu], you should be aware of these pros and cons before you download and install it.</p>
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- <p>You should also consider buying the official version from Celemony's website if you want to support the developers and enjoy the full benefits of the software.</p>
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- <p>We hope this article has been helpful and informative for you.</p>
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- <h3>FAQs</h3>
100
- <ol>
101
- <li><b>What is Celemony Melodyne Editor?</b><br>Celemony Melodyne Editor is a software that allows you to edit audio files in an intuitive and flexible way. It can access individual notes in polyphonic audio material, edit music and not just audio data, use scales, chords, and harmonies in audio editing projects, integrate with your DAW via ARA, and offer a high sound quality, flexibility, speed, and reliability.</li>
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- <li><b>How to install Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]?</b><br>To install Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu], you need to download the torrent file from this link , open it with your torrent client , start downloading the software , extract the zip file , run the setup.exe file as administrator , use any random number or code when prompted for a serial number or activation code , copy the contents of the R2R folder into your installation directory , and enjoy your free copy of Celemony Melodyne Editor 2.1.0.45 Win!</li>
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- <li><b>How to use Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]?</b><br>To use Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu], you can either run it as a stand-alone program or as a plug-in in your DAW . In both cases , you need to open an audio file that you want to edit with Celemony Melodyne Editor . The software will analyze your audio file and display it as a series of blobs on a grid . Each blob represents a note in your audio file . You can select and edit each blob independently , by using various tools available on the toolbar or by dragging them with your mouse . You can also use other features such as scales , chords , harmonies , macros , quantization , correction , etc., to enhance your audio editing experience .</li>
104
- ```html <li><b>What are the pros and cons of Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu]?</b><br>Celemony Melodyne Editor 2.1.0.45 Win (cracked-R2R) [ChingLiu] has many pros and cons that you should consider before using it. Some of the pros are: it offers unparalleled access and control over individual notes in polyphonic audio material, it allows you to edit music and not just audio data, it lets you use scales, chords, and harmonies in your audio editing projects, it integrates seamlessly with your DAW via ARA, it has a user-friendly interface and a variety of tools and features that make audio editing a breeze, and it has a high sound quality, flexibility, speed, and reliability that make it a trusted tool for many producers and musicians. Some of the cons are: it is a cracked version of the software, which means it is illegal and unethical to use it without paying for it, it may not work properly or cause problems with your system or other software, as it is not an official release from Celemony, it may not have the latest updates or bug fixes from Celemony, as it is an older version of the software, it only supports polyphonic DNA and multitrack editing in the Editor and Studio versions, which are more expensive than the Essential and Assistant versions, and it still requires some user interaction and skill to get the best results out of it, as it is not a magic tool that can fix everything automatically.</li>
105
- <li><b>Where can I buy the official version of Celemony Melodyne Editor?</b><br>If you want to buy the official version of Celemony Melodyne Editor, you can visit Celemony's website and choose the edition that suits your needs. The prices are as follows: Melodyne Essential: €99 Melodyne Assistant: €249 Melodyne Editor: €399 Melodyne Studio: €699 You can also download a free trial version of Melodyne Editor from Celemony's website and test it for 30 days.</li>
106
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- <p>The novel is set in Maglaj, a town on the river Bosna, in 1878-1879. This was a time when Bosnia and Herzegovina was occupied by Austro-Hungarian troops after the Congress of Berlin decided to give them administrative control over the region. The Austro-Hungarians wanted to modernize and integrate Bosnia and Herzegovina into their empire, but they faced resistance from many Bosnians who wanted to preserve their autonomy and identity. Some Bosnians organized armed rebellions against the occupiers, while others accepted or collaborated with them.</p>
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- <p>The novel focuses on two main characters: Ahmet and Aisa. Ahmet is a young man who joins the rebels against the Austro-Hungarians. He is brave, loyal, passionate, and idealistic. He believes that fighting for his homeland is his duty and honor. He falls in love with Aisa, a beautiful girl who lives in Maglaj. Aisa is the daughter of Muharem-aga, a rich merchant who opposes the rebellion. He is conservative, stubborn, proud, and greedy. He wants to maintain his status and wealth under the new regime. He forbids Aisa to see Ahmet or join his cause.</p>
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- <li><strong>Love vs duty:</strong> The novel shows how love can inspire courage and sacrifice but also cause pain and sorrow. Ahmet and Aisa love each other deeply but they also have different duties to their families and their country. Their love is tested by external forces that try to separate them or destroy them.</li>
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- <p>No, Stickman Warriors APK Dinero Infinito will not affect the original game. It is a separate app that runs independently from the original game. You can have both apps on your device and play them separately.</p>
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- <ul>
145
- <li>Restart your device and try again.</li>
146
- <li>Check your internet connection and make sure it is stable and fast.</li>
147
- <li>Check your storage space and make sure you have enough room for the game.</li>
148
- <li>Check your device compatibility and make sure it meets the minimum system requirements.</li>
149
- <li>Check your file source and make sure it is reliable and trusted.</li>
150
- <li>Check your file integrity and make sure it is not corrupted or damaged.</li>
151
- <li>Contact EA Sports customer support or visit their official website for more help and information.</li>
152
- </ul>
153
- <h2>How to play FIFA 22 on Android devices?</h2>
154
- <p>After you have successfully installed FIFA 22 on your Android device, you are ready to play and enjoy the game. Here are some tips and tricks to help you get started and have fun:</p>
155
- <h3>Modes and options</h3>
156
- <p>FIFA 22 offers a variety of modes and options to suit your playstyle and preferences. You can choose from different modes such as Career Mode, Volta Football, Ultimate Team, Pro Clubs, and more. You can also customize your settings such as difficulty level, camera angle, sound effects, language, and more. You can access these modes and options from the main menu of the game.</p>
157
- <h3>Controls and settings</h3>
158
- <p>FIFA 22 uses touch controls to let you control your players and actions on the pitch. You can use gestures such as tapping, swiping, dragging, and holding to perform different moves such as passing, shooting, dribbling, tackling, and more. You can also use virtual buttons that appear on the screen to access different functions such as sprinting, switching players, pausing, and more. You can adjust the sensitivity, size, position, and layout of these controls in the settings menu of the game.</p>
159
- <h2>Conclusion</h2>
160
- <p>FIFA 22 is an amazing soccer game that brings you closer to the action and excitement of the sport. It features stunning graphics, realistic physics, smooth animations, and immersive gameplay that will keep you hooked for hours. Whether you want to build your dream team, compete with other players online, or relive the glory of the World Cup, FIFA 22 has something for everyone. If you want to play FIFA 22 on your Android device, you can download and install the FIFA 22 APK file from either the Google Play Store or a third-party website. Just follow our guide above and you will be ready to play in no time. Have fun!</p>
161
- <h2>FAQs</h2>
162
- <p>Here are some frequently asked questions about FIFA 22 APK download:</p>
163
- <h4>Q: Is FIFA 22 APK free?</h4>
164
- <p>A: Yes, FIFA 22 APK is free to download and play. However, some features and items may require in-app purchases or subscriptions.</p>
165
- <h4>Q: Is FIFA 22 APK safe?</h4>
166
- <p>A: Yes, FIFA 22 APK is safe if you download it from a reliable and trusted source such as the Google Play Store or a reputable third-party website. However, you should always be careful and cautious when downloading files from unknown or unverified sources, as they may contain viruses or malware that can harm your device or compromise your privacy.</p>
167
- <h4>Q: Is FIFA 22 APK legal?</h4>
168
- <p>A: Yes, FIFA 22 APK is legal if you download it from the official Google Play Store or a licensed third-party website. However, you should avoid downloading or using any pirated, cracked, or modded versions of the game, as they may violate the terms and conditions of EA Sports and Google Play Store, and may result in legal actions or penalties.</p>
169
- <h4>Q: How to update FIFA 22 APK?</h4>
170
- <p>A: If you download FIFA 22 APK from the Google Play Store, you will receive automatic updates whenever there is a new version of the game available. You can also check for updates manually by opening the Google Play Store app and tapping on "My apps & games". If you download FIFA 22 APK from a third-party website, you will need to check for updates manually by visiting the website and looking for the latest version of the game. You will also need to download and install the new version of the game yourself.</p>
171
- <h4>Q: How to uninstall FIFA 22 APK?</h4>
172
- <p>A: If you want to uninstall FIFA 22 APK from your Android device, you can follow these steps:</p>
173
- <ol>
174
- <li>Open the Settings app on your Android device.</li>
175
- <li>Tap on "Apps & notifications" or "Applications".</li>
176
- <li>Find and tap on "FIFA 22".</li>
177
- <li>Tap on "Uninstall".</li>
178
- <li>Confirm your action and wait for the uninstallation to complete.</li>
179
- </ol></p> 401be4b1e0<br />
180
- <br />
181
- <br />
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/4Taps/SadTalker/src/face3d/options/inference_options.py DELETED
@@ -1,23 +0,0 @@
1
- from face3d.options.base_options import BaseOptions
2
-
3
-
4
- class InferenceOptions(BaseOptions):
5
- """This class includes test options.
6
-
7
- It also includes shared options defined in BaseOptions.
8
- """
9
-
10
- def initialize(self, parser):
11
- parser = BaseOptions.initialize(self, parser) # define shared options
12
- parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
13
- parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]')
14
-
15
- parser.add_argument('--input_dir', type=str, help='the folder of the input files')
16
- parser.add_argument('--keypoint_dir', type=str, help='the folder of the keypoint files')
17
- parser.add_argument('--output_dir', type=str, default='mp4', help='the output dir to save the extracted coefficients')
18
- parser.add_argument('--save_split_files', action='store_true', help='save split files or not')
19
- parser.add_argument('--inference_batch_size', type=int, default=8)
20
-
21
- # Dropout and Batchnorm has different behavior during training and test.
22
- self.isTrain = False
23
- return parser
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/52Hz/CMFNet_deraindrop/README.md DELETED
@@ -1,37 +0,0 @@
1
- ---
2
- title: CMFNet_deraindrop
3
- emoji: 💦
4
- colorFrom: blue
5
- colorTo: blue
6
- sdk: gradio
7
- app_file: app.py
8
- pinned: false
9
- ---
10
-
11
- # Configuration
12
-
13
- `title`: _string_
14
- Display title for the Space
15
-
16
- `emoji`: _string_
17
- Space emoji (emoji-only character allowed)
18
-
19
- `colorFrom`: _string_
20
- Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
21
-
22
- `colorTo`: _string_
23
- Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
24
-
25
- `sdk`: _string_
26
- Can be either `gradio`, `streamlit`, or `static`
27
-
28
- `sdk_version` : _string_
29
- Only applicable for `streamlit` SDK.
30
- See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
31
-
32
- `app_file`: _string_
33
- Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
34
- Path is relative to the root of the repository.
35
-
36
- `pinned`: _boolean_
37
- Whether the Space stays on top of your list.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/801artistry/RVC801/tools/infer_cli.py DELETED
@@ -1,67 +0,0 @@
1
- import argparse
2
- import os
3
- import sys
4
-
5
- now_dir = os.getcwd()
6
- sys.path.append(now_dir)
7
- from dotenv import load_dotenv
8
- from scipy.io import wavfile
9
-
10
- from configs.config import Config
11
- from infer.modules.vc.modules import VC
12
-
13
- ####
14
- # USAGE
15
- #
16
- # In your Terminal or CMD or whatever
17
-
18
-
19
- def arg_parse() -> tuple:
20
- parser = argparse.ArgumentParser()
21
- parser.add_argument("--f0up_key", type=int, default=0)
22
- parser.add_argument("--input_path", type=str, help="input path")
23
- parser.add_argument("--index_path", type=str, help="index path")
24
- parser.add_argument("--f0method", type=str, default="harvest", help="harvest or pm")
25
- parser.add_argument("--opt_path", type=str, help="opt path")
26
- parser.add_argument("--model_name", type=str, help="store in assets/weight_root")
27
- parser.add_argument("--index_rate", type=float, default=0.66, help="index rate")
28
- parser.add_argument("--device", type=str, help="device")
29
- parser.add_argument("--is_half", type=bool, help="use half -> True")
30
- parser.add_argument("--filter_radius", type=int, default=3, help="filter radius")
31
- parser.add_argument("--resample_sr", type=int, default=0, help="resample sr")
32
- parser.add_argument("--rms_mix_rate", type=float, default=1, help="rms mix rate")
33
- parser.add_argument("--protect", type=float, default=0.33, help="protect")
34
-
35
- args = parser.parse_args()
36
- sys.argv = sys.argv[:1]
37
-
38
- return args
39
-
40
-
41
- def main():
42
- load_dotenv()
43
- args = arg_parse()
44
- config = Config()
45
- config.device = args.device if args.device else config.device
46
- config.is_half = args.is_half if args.is_half else config.is_half
47
- vc = VC(config)
48
- vc.get_vc(args.model_name)
49
- _, wav_opt = vc.vc_single(
50
- 0,
51
- args.input_path,
52
- args.f0up_key,
53
- None,
54
- args.f0method,
55
- args.index_path,
56
- None,
57
- args.index_rate,
58
- args.filter_radius,
59
- args.resample_sr,
60
- args.rms_mix_rate,
61
- args.protect,
62
- )
63
- wavfile.write(args.opt_path, wav_opt[0], wav_opt[1])
64
-
65
-
66
- if __name__ == "__main__":
67
- main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/__init__.py DELETED
@@ -1,10 +0,0 @@
1
- # Copyright (c) Meta Platforms, Inc. and affiliates.
2
- # All rights reserved.
3
- #
4
- # This source code is licensed under the license found in the
5
- # LICENSE file in the root directory of this source tree.
6
-
7
- # flake8: noqa
8
- from . import data, modules, models
9
-
10
- __version__ = '0.0.2a2'
 
 
 
 
 
 
 
 
 
 
 
spaces/Adapter/T2I-Adapter/train_sketch.py DELETED
@@ -1,399 +0,0 @@
1
- import argparse
2
- import logging
3
- import os
4
- import os.path as osp
5
- import time
6
-
7
- import cv2
8
- import matplotlib.pyplot as plt
9
- import numpy as np
10
- import torch
11
- import torch.nn as nn
12
- from basicsr.utils import (get_env_info, get_root_logger, get_time_str,
13
- img2tensor, scandir, tensor2img)
14
- from basicsr.utils.options import copy_opt_file, dict2str
15
- from omegaconf import OmegaConf
16
- from PIL import Image
17
-
18
- from ldm.data.dataset_coco import dataset_coco_mask_color
19
- from dist_util import get_bare_model, get_dist_info, init_dist, master_only
20
- from ldm.models.diffusion.ddim import DDIMSampler
21
- from ldm.models.diffusion.dpm_solver import DPMSolverSampler
22
- from ldm.models.diffusion.plms import PLMSSampler
23
- from ldm.modules.encoders.adapter import Adapter
24
- from ldm.util import instantiate_from_config
25
- from ldm.modules.structure_condition.model_edge import pidinet
26
-
27
-
28
- def load_model_from_config(config, ckpt, verbose=False):
29
- print(f"Loading model from {ckpt}")
30
- pl_sd = torch.load(ckpt, map_location="cpu")
31
- if "global_step" in pl_sd:
32
- print(f"Global Step: {pl_sd['global_step']}")
33
- sd = pl_sd["state_dict"]
34
- model = instantiate_from_config(config.model)
35
- m, u = model.load_state_dict(sd, strict=False)
36
- if len(m) > 0 and verbose:
37
- print("missing keys:")
38
- print(m)
39
- if len(u) > 0 and verbose:
40
- print("unexpected keys:")
41
- print(u)
42
-
43
- model.cuda()
44
- model.eval()
45
- return model
46
-
47
- @master_only
48
- def mkdir_and_rename(path):
49
- """mkdirs. If path exists, rename it with timestamp and create a new one.
50
-
51
- Args:
52
- path (str): Folder path.
53
- """
54
- if osp.exists(path):
55
- new_name = path + '_archived_' + get_time_str()
56
- print(f'Path already exists. Rename it to {new_name}', flush=True)
57
- os.rename(path, new_name)
58
- os.makedirs(path, exist_ok=True)
59
- os.makedirs(osp.join(experiments_root, 'models'))
60
- os.makedirs(osp.join(experiments_root, 'training_states'))
61
- os.makedirs(osp.join(experiments_root, 'visualization'))
62
-
63
- def load_resume_state(opt):
64
- resume_state_path = None
65
- if opt.auto_resume:
66
- state_path = osp.join('experiments', opt.name, 'training_states')
67
- if osp.isdir(state_path):
68
- states = list(scandir(state_path, suffix='state', recursive=False, full_path=False))
69
- if len(states) != 0:
70
- states = [float(v.split('.state')[0]) for v in states]
71
- resume_state_path = osp.join(state_path, f'{max(states):.0f}.state')
72
- opt.resume_state_path = resume_state_path
73
- # else:
74
- # if opt['path'].get('resume_state'):
75
- # resume_state_path = opt['path']['resume_state']
76
-
77
- if resume_state_path is None:
78
- resume_state = None
79
- else:
80
- device_id = torch.cuda.current_device()
81
- resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id))
82
- # check_resume(opt, resume_state['iter'])
83
- return resume_state
84
-
85
- parser = argparse.ArgumentParser()
86
- parser.add_argument(
87
- "--bsize",
88
- type=int,
89
- default=8,
90
- help="the prompt to render"
91
- )
92
- parser.add_argument(
93
- "--epochs",
94
- type=int,
95
- default=10000,
96
- help="the prompt to render"
97
- )
98
- parser.add_argument(
99
- "--num_workers",
100
- type=int,
101
- default=8,
102
- help="the prompt to render"
103
- )
104
- parser.add_argument(
105
- "--use_shuffle",
106
- type=bool,
107
- default=True,
108
- help="the prompt to render"
109
- )
110
- parser.add_argument(
111
- "--dpm_solver",
112
- action='store_true',
113
- help="use dpm_solver sampling",
114
- )
115
- parser.add_argument(
116
- "--plms",
117
- action='store_true',
118
- help="use plms sampling",
119
- )
120
- parser.add_argument(
121
- "--auto_resume",
122
- action='store_true',
123
- help="use plms sampling",
124
- )
125
- parser.add_argument(
126
- "--ckpt",
127
- type=str,
128
- default="models/sd-v1-4.ckpt",
129
- help="path to checkpoint of model",
130
- )
131
- parser.add_argument(
132
- "--config",
133
- type=str,
134
- default="configs/stable-diffusion/train_sketch.yaml",
135
- help="path to config which constructs model",
136
- )
137
- parser.add_argument(
138
- "--print_fq",
139
- type=int,
140
- default=100,
141
- help="path to config which constructs model",
142
- )
143
- parser.add_argument(
144
- "--H",
145
- type=int,
146
- default=512,
147
- help="image height, in pixel space",
148
- )
149
- parser.add_argument(
150
- "--W",
151
- type=int,
152
- default=512,
153
- help="image width, in pixel space",
154
- )
155
- parser.add_argument(
156
- "--C",
157
- type=int,
158
- default=4,
159
- help="latent channels",
160
- )
161
- parser.add_argument(
162
- "--f",
163
- type=int,
164
- default=8,
165
- help="downsampling factor",
166
- )
167
- parser.add_argument(
168
- "--ddim_steps",
169
- type=int,
170
- default=50,
171
- help="number of ddim sampling steps",
172
- )
173
- parser.add_argument(
174
- "--n_samples",
175
- type=int,
176
- default=1,
177
- help="how many samples to produce for each given prompt. A.k.a. batch size",
178
- )
179
- parser.add_argument(
180
- "--ddim_eta",
181
- type=float,
182
- default=0.0,
183
- help="ddim eta (eta=0.0 corresponds to deterministic sampling",
184
- )
185
- parser.add_argument(
186
- "--scale",
187
- type=float,
188
- default=7.5,
189
- help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
190
- )
191
- parser.add_argument(
192
- "--gpus",
193
- default=[0,1,2,3],
194
- help="gpu idx",
195
- )
196
- parser.add_argument(
197
- '--local_rank',
198
- default=0,
199
- type=int,
200
- help='node rank for distributed training'
201
- )
202
- parser.add_argument(
203
- '--launcher',
204
- default='pytorch',
205
- type=str,
206
- help='node rank for distributed training'
207
- )
208
- parser.add_argument(
209
- '--l_cond',
210
- default=4,
211
- type=int,
212
- help='number of scales'
213
- )
214
- opt = parser.parse_args()
215
-
216
- if __name__ == '__main__':
217
- config = OmegaConf.load(f"{opt.config}")
218
- opt.name = config['name']
219
-
220
- # distributed setting
221
- init_dist(opt.launcher)
222
- torch.backends.cudnn.benchmark = True
223
- device='cuda'
224
- torch.cuda.set_device(opt.local_rank)
225
-
226
- # dataset
227
- path_json_train = 'coco_stuff/mask/annotations/captions_train2017.json'
228
- path_json_val = 'coco_stuff/mask/annotations/captions_val2017.json'
229
- train_dataset = dataset_coco_mask_color(path_json_train,
230
- root_path_im='coco/train2017',
231
- root_path_mask='coco_stuff/mask/train2017_color',
232
- image_size=512
233
- )
234
- train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
235
- val_dataset = dataset_coco_mask_color(path_json_val,
236
- root_path_im='coco/val2017',
237
- root_path_mask='coco_stuff/mask/val2017_color',
238
- image_size=512
239
- )
240
- train_dataloader = torch.utils.data.DataLoader(
241
- train_dataset,
242
- batch_size=opt.bsize,
243
- shuffle=(train_sampler is None),
244
- num_workers=opt.num_workers,
245
- pin_memory=True,
246
- sampler=train_sampler)
247
- val_dataloader = torch.utils.data.DataLoader(
248
- val_dataset,
249
- batch_size=1,
250
- shuffle=False,
251
- num_workers=1,
252
- pin_memory=False)
253
-
254
- # edge_generator
255
- net_G = pidinet()
256
- ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict']
257
- net_G.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()})
258
- net_G.cuda()
259
-
260
- # stable diffusion
261
- model = load_model_from_config(config, f"{opt.ckpt}").to(device)
262
-
263
- # sketch encoder
264
- model_ad = Adapter(channels=[320, 640, 1280, 1280][:4], nums_rb=2, ksize=1, sk=True, use_conv=False).to(device)
265
-
266
- # to gpus
267
- model_ad = torch.nn.parallel.DistributedDataParallel(
268
- model_ad,
269
- device_ids=[opt.local_rank],
270
- output_device=opt.local_rank)
271
- model = torch.nn.parallel.DistributedDataParallel(
272
- model,
273
- device_ids=[opt.local_rank],
274
- output_device=opt.local_rank)
275
- # device_ids=[torch.cuda.current_device()])
276
- net_G = torch.nn.parallel.DistributedDataParallel(
277
- net_G,
278
- device_ids=[opt.local_rank],
279
- output_device=opt.local_rank)
280
- # device_ids=[torch.cuda.current_device()])
281
-
282
- # optimizer
283
- params = list(model_ad.parameters())
284
- optimizer = torch.optim.AdamW(params, lr=config['training']['lr'])
285
-
286
- experiments_root = osp.join('experiments', opt.name)
287
-
288
- # resume state
289
- resume_state = load_resume_state(opt)
290
- if resume_state is None:
291
- mkdir_and_rename(experiments_root)
292
- start_epoch = 0
293
- current_iter = 0
294
- # WARNING: should not use get_root_logger in the above codes, including the called functions
295
- # Otherwise the logger will not be properly initialized
296
- log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log")
297
- logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
298
- logger.info(get_env_info())
299
- logger.info(dict2str(config))
300
- else:
301
- # WARNING: should not use get_root_logger in the above codes, including the called functions
302
- # Otherwise the logger will not be properly initialized
303
- log_file = osp.join(experiments_root, f"train_{opt.name}_{get_time_str()}.log")
304
- logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
305
- logger.info(get_env_info())
306
- logger.info(dict2str(config))
307
- resume_optimizers = resume_state['optimizers']
308
- optimizer.load_state_dict(resume_optimizers)
309
- logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
310
- start_epoch = resume_state['epoch']
311
- current_iter = resume_state['iter']
312
-
313
- # copy the yml file to the experiment root
314
- copy_opt_file(opt.config, experiments_root)
315
-
316
-
317
- # training
318
- logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}')
319
- for epoch in range(start_epoch, opt.epochs):
320
- train_dataloader.sampler.set_epoch(epoch)
321
- # train
322
- for _, data in enumerate(train_dataloader):
323
- current_iter += 1
324
- with torch.no_grad():
325
- edge = net_G(data['im'].cuda(non_blocking=True))[-1]
326
- edge = edge>0.5
327
- edge = edge.float()
328
- c = model.module.get_learned_conditioning(data['sentence'])
329
- z = model.module.encode_first_stage((data['im']*2-1.).cuda(non_blocking=True))
330
- z = model.module.get_first_stage_encoding(z)
331
-
332
- optimizer.zero_grad()
333
- model.zero_grad()
334
- features_adapter = model_ad(edge)
335
- l_pixel, loss_dict = model(z, c=c, features_adapter = features_adapter)
336
- l_pixel.backward()
337
- optimizer.step()
338
-
339
- if (current_iter+1)%opt.print_fq == 0:
340
- logger.info(loss_dict)
341
-
342
- # save checkpoint
343
- rank, _ = get_dist_info()
344
- if (rank==0) and ((current_iter+1)%config['training']['save_freq'] == 0):
345
- save_filename = f'model_ad_{current_iter+1}.pth'
346
- save_path = os.path.join(experiments_root, 'models', save_filename)
347
- save_dict = {}
348
- model_ad_bare = get_bare_model(model_ad)
349
- state_dict = model_ad_bare.state_dict()
350
- for key, param in state_dict.items():
351
- if key.startswith('module.'): # remove unnecessary 'module.'
352
- key = key[7:]
353
- save_dict[key] = param.cpu()
354
- torch.save(save_dict, save_path)
355
- # save state
356
- state = {'epoch': epoch, 'iter': current_iter+1, 'optimizers': optimizer.state_dict()}
357
- save_filename = f'{current_iter+1}.state'
358
- save_path = os.path.join(experiments_root, 'training_states', save_filename)
359
- torch.save(state, save_path)
360
-
361
- # val
362
- rank, _ = get_dist_info()
363
- if rank==0:
364
- for data in val_dataloader:
365
- with torch.no_grad():
366
- if opt.dpm_solver:
367
- sampler = DPMSolverSampler(model.module)
368
- elif opt.plms:
369
- sampler = PLMSSampler(model.module)
370
- else:
371
- sampler = DDIMSampler(model.module)
372
- print(data['im'].shape)
373
- c = model.module.get_learned_conditioning(data['sentence'])
374
- edge = net_G(data['im'].cuda(non_blocking=True))[-1]
375
- edge = edge>0.5
376
- edge = edge.float()
377
- im_edge = tensor2img(edge)
378
- cv2.imwrite(os.path.join(experiments_root, 'visualization', 'edge_%04d.png'%epoch), im_edge)
379
- features_adapter = model_ad(edge)
380
- shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
381
- samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
382
- conditioning=c,
383
- batch_size=opt.n_samples,
384
- shape=shape,
385
- verbose=False,
386
- unconditional_guidance_scale=opt.scale,
387
- unconditional_conditioning=model.module.get_learned_conditioning(opt.n_samples * [""]),
388
- eta=opt.ddim_eta,
389
- x_T=None,
390
- features_adapter=features_adapter)
391
- x_samples_ddim = model.module.decode_first_stage(samples_ddim)
392
- x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
393
- x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
394
- for id_sample, x_sample in enumerate(x_samples_ddim):
395
- x_sample = 255.*x_sample
396
- img = x_sample.astype(np.uint8)
397
- img = cv2.putText(img.copy(), data['sentence'][0], (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
398
- cv2.imwrite(os.path.join(experiments_root, 'visualization', 'sample_e%04d_s%04d.png'%(epoch, id_sample)), img[:,:,::-1])
399
- break
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AgentVerse/agentVerse/dataloader/responsegen.py DELETED
@@ -1,21 +0,0 @@
1
- from .dataloader import DataLoader
2
- from . import dataloader_registry
3
- import json
4
-
5
-
6
- @dataloader_registry.register("tasksolving/responsegen/gpt-3.5")
7
- @dataloader_registry.register("tasksolving/responsegen/gpt-4")
8
- class ResponseGenLoader(DataLoader):
9
- def __init__(self, path: str):
10
- super().__init__(path)
11
-
12
- def load(self):
13
- with open(self.path) as f:
14
- for line in f:
15
- line = json.loads(line)
16
- self.examples.append(
17
- {
18
- "input": line["input"],
19
- "answer": line["answer"],
20
- }
21
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/BaseSizer.js DELETED
@@ -1,241 +0,0 @@
1
- import Container from '../container/Container.js';
2
- import Methods from './Methods.js';
3
- import { GetDisplayWidth, GetDisplayHeight } from '../../../plugins/utils/size/GetDisplaySize.js';
4
- import Clear from '../../../plugins/utils/object/Clear.js'
5
-
6
- const GetValue = Phaser.Utils.Objects.GetValue;
7
-
8
- class Base extends Container {
9
- constructor(scene, x, y, minWidth, minHeight, config) {
10
- super(scene, x, y, 1, 1);
11
-
12
- this.isRexSizer = true;
13
- this.setMinSize(minWidth, minHeight);
14
- this.setName(GetValue(config, 'name', ''));
15
- this.rexSizer = {};
16
- this.space = {};
17
- this.backgroundChildren = undefined;
18
- this.sizerChildren = undefined; // [] or {}
19
- this.childrenMap = {};
20
- this.layoutedChildren = undefined;
21
-
22
- var anchorConfig = GetValue(config, 'anchor', undefined);
23
- if (anchorConfig) {
24
- this.setAnchor(anchorConfig);
25
- }
26
-
27
- this.setInnerPadding(GetValue(config, 'space', 0));
28
-
29
- var draggable = GetValue(config, 'draggable', false);
30
- if (draggable) {
31
- this.setDraggable(draggable);
32
- }
33
-
34
- this.setSizerEventsEnable(GetValue(config, 'sizerEvents', false));
35
- this.setDirty(true);
36
-
37
- if (GetValue(config, 'enableLayer', false)) {
38
- this.enableLayer();
39
- }
40
- }
41
-
42
- destroy(fromScene) {
43
- // This Game Object has already been destroyed
44
- if (!this.scene || this.ignoreDestroy) {
45
- return;
46
- }
47
-
48
- if (fromScene) {
49
- // In this case, children will be cleared and destroy in scene level
50
- var sizers = this.getAllChildrenSizers([this]);
51
- for (var i = 0, cnt = sizers.length; i < cnt; i++) {
52
- sizers[i].sizerEventsEnable = false;
53
- }
54
- }
55
-
56
- super.destroy(fromScene);
57
-
58
- Clear(this.backgroundChildren);
59
- Clear(this.sizerChildren);
60
- this.childrenMap = undefined;
61
- this.space = undefined;
62
- this.rexSizer = undefined;
63
- this.layoutedChildren = undefined;
64
- }
65
-
66
- setMinSize(minWidth, minHeight) {
67
- this.setMinWidth(minWidth).setMinHeight(minHeight);
68
- return this;
69
- }
70
-
71
- setMinWidth(minWidth) {
72
- if (minWidth == null) {
73
- minWidth = 0;
74
- }
75
- this.minWidth = minWidth;
76
- return this;
77
- }
78
-
79
- setMinHeight(minHeight) {
80
- if (minHeight == null) {
81
- minHeight = 0;
82
- }
83
- this.minHeight = minHeight;
84
- return this;
85
- }
86
-
87
- setDirty(dirty) {
88
- if (dirty === undefined) {
89
- dirty = true;
90
- }
91
- this.dirty = dirty;
92
- return this;
93
- }
94
-
95
- setSizerEventsEnable(enable) {
96
- if (enable === undefined) {
97
- enable = true;
98
- }
99
-
100
- this.sizerEventsEnable = enable;
101
- return this;
102
- }
103
-
104
- get ignoreLayout() {
105
- // Skip hidden or !dirty sizer
106
- return this.rexSizer.hidden || (!this.dirty);
107
- }
108
-
109
- get childrenWidth() {
110
- if (this._childrenWidth === undefined) {
111
- this._childrenWidth = this.getChildrenWidth();
112
- }
113
- return this._childrenWidth;
114
- }
115
-
116
- get childrenHeight() {
117
- if (this._childrenHeight === undefined) {
118
- this._childrenHeight = this.getChildrenHeight();
119
- }
120
- return this._childrenHeight;
121
- }
122
-
123
- get left() {
124
- return this.x - (GetDisplayWidth(this) * this.originX);
125
- }
126
-
127
- set left(value) {
128
- this.x += (value - this.left);
129
- }
130
-
131
- alignLeft(value) {
132
- this.left = value;
133
- return this;
134
- }
135
-
136
- get right() {
137
- return this.left + GetDisplayWidth(this);
138
- }
139
-
140
- set right(value) {
141
- this.x += (value - this.right);
142
- }
143
-
144
- alignRight(value) {
145
- this.right = value;
146
- return this;
147
- }
148
-
149
- get centerX() {
150
- return this.left + (GetDisplayWidth(this) / 2);
151
- }
152
-
153
- set centerX(value) {
154
- this.x += (value - this.centerX);
155
- }
156
-
157
- alignCenterX(value) {
158
- this.centerX = value;
159
- return this;
160
- }
161
-
162
- get top() {
163
- return this.y - (GetDisplayHeight(this) * this.originY);
164
- }
165
-
166
- set top(value) {
167
- this.y += (value - this.top);
168
- }
169
-
170
- alignTop(value) {
171
- this.top = value;
172
- return this;
173
- }
174
-
175
- get bottom() {
176
- return this.top + GetDisplayHeight(this);
177
- }
178
-
179
- set bottom(value) {
180
- this.y += (value - this.bottom);
181
- }
182
-
183
- alignBottom(value) {
184
- this.bottom = value;
185
- return this;
186
- }
187
-
188
- get centerY() {
189
- return this.top + (GetDisplayHeight(this) / 2);
190
- }
191
-
192
- set centerY(value) {
193
- this.y += (value - this.centerY);
194
- }
195
-
196
- alignCenterY(value) {
197
- this.centerY = value;
198
- return this;
199
- }
200
-
201
- get innerLeft() {
202
- return this.left + this.space.left;
203
- }
204
-
205
- get innerRight() {
206
- return this.right - this.space.right;
207
- }
208
-
209
- get innerTop() {
210
- return this.top + this.space.top;
211
- }
212
-
213
- get innerBottom() {
214
- return this.bottom - this.space.bottom;
215
- }
216
-
217
- get innerWidth() {
218
- return this.width - this.space.left - this.space.right;
219
- }
220
-
221
- get innerHeight() {
222
- return this.height - this.space.top - this.space.bottom;
223
- }
224
-
225
- get minInnerWidth() {
226
- var result = this.minWidth - this.space.left - this.space.right;
227
- return Math.max(result, 0);
228
- }
229
-
230
- get minInnerHeight() {
231
- var result = this.minHeight - this.space.top - this.space.bottom;
232
- return Math.max(result, 0);
233
- }
234
- }
235
-
236
- Object.assign(
237
- Base.prototype,
238
- Methods
239
- );
240
-
241
- export default Base;
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/AddChildMethods.js DELETED
@@ -1,74 +0,0 @@
1
- import AddChild from '../basesizer/utils/AddChild.js';
2
- import GetBoundsConfig from '../utils/GetBoundsConfig.js';
3
- import IsArray from '../../../plugins/utils/object/IsArray.js';
4
- import GetNearestChildIndex from './GetNearestChildIndex.js';
5
-
6
- const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
7
- const GetValue = Phaser.Utils.Objects.GetValue;
8
- const ALIGN_CENTER = Phaser.Display.Align.CENTER;
9
-
10
- var Add = function (gameObject, paddingConfig, childKey, index) {
11
- if (gameObject === '\n') {
12
- this.addNewLine();
13
- return this;
14
- }
15
-
16
- AddChild.call(this, gameObject);
17
-
18
- if (IsPlainObject(paddingConfig)) {
19
- var config = paddingConfig;
20
- paddingConfig = GetValue(config, 'padding', 0);
21
- childKey = GetValue(config, 'key', undefined);
22
- index = GetValue(config, 'index', undefined);
23
- }
24
- if (paddingConfig === undefined) {
25
- paddingConfig = 0;
26
- }
27
-
28
- var config = this.getSizerConfig(gameObject);
29
- config.align = ALIGN_CENTER;
30
- config.padding = GetBoundsConfig(paddingConfig);
31
- if ((index === undefined) || (index >= this.sizerChildren.length)) {
32
- this.sizerChildren.push(gameObject);
33
- } else {
34
- this.sizerChildren.splice(index, 0, gameObject);
35
- }
36
-
37
- if (childKey !== undefined) {
38
- this.addChildrenMap(childKey, gameObject)
39
- }
40
- return this;
41
- };
42
-
43
- export default {
44
- add(gameObject, paddingConfig, childKey) {
45
- if (IsArray(gameObject)) {
46
- var gameObjects = gameObject;
47
- for (var i = 0, cnt = gameObjects.length; i < cnt; i++) {
48
- Add.call(this, gameObjects[i], paddingConfig);
49
- }
50
- } else {
51
- Add.call(this, gameObject, paddingConfig, childKey);
52
- }
53
- return this;
54
- },
55
-
56
- addNewLine() {
57
- this.sizerChildren.push('\n');
58
- return this;
59
- },
60
-
61
- insert(index, gameObject, paddingConfig, childKey) {
62
- Add.call(this, gameObject, paddingConfig, childKey, index);
63
- return this;
64
- },
65
-
66
- insertAtPosition(x, y, gameObject, paddingConfig, childKey) {
67
- var index = GetNearestChildIndex.call(this, x, y);
68
- if (index === -1) {
69
- index = undefined;
70
- }
71
- this.insert(index, gameObject, paddingConfig, childKey);
72
- return this;
73
- }
74
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/namevaluelabel/methods/SetValueMethods.js DELETED
@@ -1,50 +0,0 @@
1
- const Clamp = Phaser.Math.Clamp;
2
-
3
- export default {
4
- setValueTextFormatCallback(callback, scope) {
5
- this.valueTextFormatCallback = callback;
6
- this.valueTextFormatCallbackScope = scope;
7
- return this;
8
- },
9
-
10
- getFormatValueText(value, min, max) {
11
- if (value === undefined) {
12
- value = this.value;
13
- }
14
- if (min === undefined) {
15
- min = this.minValue;
16
- }
17
- if (max === undefined) {
18
- max = this.maxValue;
19
- }
20
-
21
- var text;
22
- if (this.valueTextFormatCallbackScope) {
23
- text = this.valueTextFormatCallback(value, min, max);
24
- } else {
25
- text = this.valueTextFormatCallback.call(this.valueTextFormatCallbackScope, value, min, max);
26
- }
27
- return text;
28
- },
29
-
30
- updateValueText(value, min, max) {
31
- var textObject = this.childrenMap.value;
32
- if (textObject && this.valueTextFormatCallback) {
33
- textObject.setText(this.getFormatValueText(value, min, max));
34
- if (textObject.layout) {
35
- textObject.layout();
36
- }
37
- }
38
- return this;
39
- },
40
-
41
- setValue(value, min, max) {
42
- value = Clamp(value, min, max);
43
- this.value = value;
44
- this.minValue = min;
45
- this.maxValue = max;
46
- this.updateValueText(value, min, max);
47
- this.setBarValue(value, min, max);
48
- return this;
49
- },
50
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Methods.js DELETED
@@ -1,39 +0,0 @@
1
- import GetChildrenWidth from './GetChildrenWidth.js';
2
- import GetChildrenHeight from './GetChildrenHeight.js';
3
- import GetExpandedChildWidth from './GetExpandedChildWidth.js';
4
- import GetExpandedChildHeight from './GetExpandedChildHeight.js';
5
- import GetChildrenSizers from './GetChildrenSizers.js';
6
- import PreLayout from './PreLayout.js';
7
- import PostResolveSize from './PostResolveSize.js';
8
- import LayoutChildren from './LayoutChildren.js';
9
- import ResolveWidth from './ResolveWidth.js';
10
- import ResolveHeight from './ResolveHeight.js';
11
- import AddChildMethods from './AddChildMethods.js';
12
- import RemoveChildMethods from './RemoveChildMethods.js';
13
- import AlignMethods from './AlignMethods.js';
14
- import ProportionMethods from './ProportionMethods.js';
15
- import ExpandMethods from './ExpandMethods.js';
16
-
17
- var methods = {
18
- getChildrenWidth: GetChildrenWidth,
19
- getChildrenHeight: GetChildrenHeight,
20
- getExpandedChildWidth: GetExpandedChildWidth,
21
- getExpandedChildHeight: GetExpandedChildHeight,
22
- getChildrenSizers: GetChildrenSizers,
23
- preLayout: PreLayout,
24
- postResolveSize: PostResolveSize,
25
- layoutChildren: LayoutChildren,
26
- resolveWidth: ResolveWidth,
27
- resolveHeight: ResolveHeight,
28
- };
29
-
30
- Object.assign(
31
- methods,
32
- AddChildMethods,
33
- RemoveChildMethods,
34
- AlignMethods,
35
- ProportionMethods,
36
- ExpandMethods,
37
- );
38
-
39
- export default methods;
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Akmyradov/TurkmenTTSweSTT/vits/data_utils.py DELETED
@@ -1,392 +0,0 @@
1
- import time
2
- import os
3
- import random
4
- import numpy as np
5
- import torch
6
- import torch.utils.data
7
-
8
- import commons
9
- from mel_processing import spectrogram_torch
10
- from utils import load_wav_to_torch, load_filepaths_and_text
11
- from text import text_to_sequence, cleaned_text_to_sequence
12
-
13
-
14
- class TextAudioLoader(torch.utils.data.Dataset):
15
- """
16
- 1) loads audio, text pairs
17
- 2) normalizes text and converts them to sequences of integers
18
- 3) computes spectrograms from audio files.
19
- """
20
- def __init__(self, audiopaths_and_text, hparams):
21
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
22
- self.text_cleaners = hparams.text_cleaners
23
- self.max_wav_value = hparams.max_wav_value
24
- self.sampling_rate = hparams.sampling_rate
25
- self.filter_length = hparams.filter_length
26
- self.hop_length = hparams.hop_length
27
- self.win_length = hparams.win_length
28
- self.sampling_rate = hparams.sampling_rate
29
-
30
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
31
-
32
- self.add_blank = hparams.add_blank
33
- self.min_text_len = getattr(hparams, "min_text_len", 1)
34
- self.max_text_len = getattr(hparams, "max_text_len", 190)
35
-
36
- random.seed(1234)
37
- random.shuffle(self.audiopaths_and_text)
38
- self._filter()
39
-
40
-
41
- def _filter(self):
42
- """
43
- Filter text & store spec lengths
44
- """
45
- # Store spectrogram lengths for Bucketing
46
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
47
- # spec_length = wav_length // hop_length
48
-
49
- audiopaths_and_text_new = []
50
- lengths = []
51
- for audiopath, text in self.audiopaths_and_text:
52
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
53
- audiopaths_and_text_new.append([audiopath, text])
54
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
55
- self.audiopaths_and_text = audiopaths_and_text_new
56
- self.lengths = lengths
57
-
58
- def get_audio_text_pair(self, audiopath_and_text):
59
- # separate filename and text
60
- audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
61
- text = self.get_text(text)
62
- spec, wav = self.get_audio(audiopath)
63
- return (text, spec, wav)
64
-
65
- def get_audio(self, filename):
66
- audio, sampling_rate = load_wav_to_torch(filename)
67
- if sampling_rate != self.sampling_rate:
68
- raise ValueError("{} {} SR doesn't match target {} SR".format(
69
- sampling_rate, self.sampling_rate))
70
- audio_norm = audio / self.max_wav_value
71
- audio_norm = audio_norm.unsqueeze(0)
72
- spec_filename = filename.replace(".wav", ".spec.pt")
73
- if os.path.exists(spec_filename):
74
- spec = torch.load(spec_filename)
75
- else:
76
- spec = spectrogram_torch(audio_norm, self.filter_length,
77
- self.sampling_rate, self.hop_length, self.win_length,
78
- center=False)
79
- spec = torch.squeeze(spec, 0)
80
- torch.save(spec, spec_filename)
81
- return spec, audio_norm
82
-
83
- def get_text(self, text):
84
- if self.cleaned_text:
85
- text_norm = cleaned_text_to_sequence(text)
86
- else:
87
- text_norm = text_to_sequence(text, self.text_cleaners)
88
- if self.add_blank:
89
- text_norm = commons.intersperse(text_norm, 0)
90
- text_norm = torch.LongTensor(text_norm)
91
- return text_norm
92
-
93
- def __getitem__(self, index):
94
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
95
-
96
- def __len__(self):
97
- return len(self.audiopaths_and_text)
98
-
99
-
100
- class TextAudioCollate():
101
- """ Zero-pads model inputs and targets
102
- """
103
- def __init__(self, return_ids=False):
104
- self.return_ids = return_ids
105
-
106
- def __call__(self, batch):
107
- """Collate's training batch from normalized text and aduio
108
- PARAMS
109
- ------
110
- batch: [text_normalized, spec_normalized, wav_normalized]
111
- """
112
- # Right zero-pad all one-hot text sequences to max input length
113
- _, ids_sorted_decreasing = torch.sort(
114
- torch.LongTensor([x[1].size(1) for x in batch]),
115
- dim=0, descending=True)
116
-
117
- max_text_len = max([len(x[0]) for x in batch])
118
- max_spec_len = max([x[1].size(1) for x in batch])
119
- max_wav_len = max([x[2].size(1) for x in batch])
120
-
121
- text_lengths = torch.LongTensor(len(batch))
122
- spec_lengths = torch.LongTensor(len(batch))
123
- wav_lengths = torch.LongTensor(len(batch))
124
-
125
- text_padded = torch.LongTensor(len(batch), max_text_len)
126
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
127
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
128
- text_padded.zero_()
129
- spec_padded.zero_()
130
- wav_padded.zero_()
131
- for i in range(len(ids_sorted_decreasing)):
132
- row = batch[ids_sorted_decreasing[i]]
133
-
134
- text = row[0]
135
- text_padded[i, :text.size(0)] = text
136
- text_lengths[i] = text.size(0)
137
-
138
- spec = row[1]
139
- spec_padded[i, :, :spec.size(1)] = spec
140
- spec_lengths[i] = spec.size(1)
141
-
142
- wav = row[2]
143
- wav_padded[i, :, :wav.size(1)] = wav
144
- wav_lengths[i] = wav.size(1)
145
-
146
- if self.return_ids:
147
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
148
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
149
-
150
-
151
- """Multi speaker version"""
152
- class TextAudioSpeakerLoader(torch.utils.data.Dataset):
153
- """
154
- 1) loads audio, speaker_id, text pairs
155
- 2) normalizes text and converts them to sequences of integers
156
- 3) computes spectrograms from audio files.
157
- """
158
- def __init__(self, audiopaths_sid_text, hparams):
159
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
160
- self.text_cleaners = hparams.text_cleaners
161
- self.max_wav_value = hparams.max_wav_value
162
- self.sampling_rate = hparams.sampling_rate
163
- self.filter_length = hparams.filter_length
164
- self.hop_length = hparams.hop_length
165
- self.win_length = hparams.win_length
166
- self.sampling_rate = hparams.sampling_rate
167
-
168
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
169
-
170
- self.add_blank = hparams.add_blank
171
- self.min_text_len = getattr(hparams, "min_text_len", 1)
172
- self.max_text_len = getattr(hparams, "max_text_len", 190)
173
-
174
- random.seed(1234)
175
- random.shuffle(self.audiopaths_sid_text)
176
- self._filter()
177
-
178
- def _filter(self):
179
- """
180
- Filter text & store spec lengths
181
- """
182
- # Store spectrogram lengths for Bucketing
183
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
184
- # spec_length = wav_length // hop_length
185
-
186
- audiopaths_sid_text_new = []
187
- lengths = []
188
- for audiopath, sid, text in self.audiopaths_sid_text:
189
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
190
- audiopaths_sid_text_new.append([audiopath, sid, text])
191
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
192
- self.audiopaths_sid_text = audiopaths_sid_text_new
193
- self.lengths = lengths
194
-
195
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
196
- # separate filename, speaker_id and text
197
- audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
198
- text = self.get_text(text)
199
- spec, wav = self.get_audio(audiopath)
200
- sid = self.get_sid(sid)
201
- return (text, spec, wav, sid)
202
-
203
- def get_audio(self, filename):
204
- audio, sampling_rate = load_wav_to_torch(filename)
205
- if sampling_rate != self.sampling_rate:
206
- raise ValueError("{} {} SR doesn't match target {} SR".format(
207
- sampling_rate, self.sampling_rate))
208
- audio_norm = audio / self.max_wav_value
209
- audio_norm = audio_norm.unsqueeze(0)
210
- spec_filename = filename.replace(".wav", ".spec.pt")
211
- if os.path.exists(spec_filename):
212
- spec = torch.load(spec_filename)
213
- else:
214
- spec = spectrogram_torch(audio_norm, self.filter_length,
215
- self.sampling_rate, self.hop_length, self.win_length,
216
- center=False)
217
- spec = torch.squeeze(spec, 0)
218
- torch.save(spec, spec_filename)
219
- return spec, audio_norm
220
-
221
- def get_text(self, text):
222
- if self.cleaned_text:
223
- text_norm = cleaned_text_to_sequence(text)
224
- else:
225
- text_norm = text_to_sequence(text, self.text_cleaners)
226
- if self.add_blank:
227
- text_norm = commons.intersperse(text_norm, 0)
228
- text_norm = torch.LongTensor(text_norm)
229
- return text_norm
230
-
231
- def get_sid(self, sid):
232
- sid = torch.LongTensor([int(sid)])
233
- return sid
234
-
235
- def __getitem__(self, index):
236
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
237
-
238
- def __len__(self):
239
- return len(self.audiopaths_sid_text)
240
-
241
-
242
- class TextAudioSpeakerCollate():
243
- """ Zero-pads model inputs and targets
244
- """
245
- def __init__(self, return_ids=False):
246
- self.return_ids = return_ids
247
-
248
- def __call__(self, batch):
249
- """Collate's training batch from normalized text, audio and speaker identities
250
- PARAMS
251
- ------
252
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
253
- """
254
- # Right zero-pad all one-hot text sequences to max input length
255
- _, ids_sorted_decreasing = torch.sort(
256
- torch.LongTensor([x[1].size(1) for x in batch]),
257
- dim=0, descending=True)
258
-
259
- max_text_len = max([len(x[0]) for x in batch])
260
- max_spec_len = max([x[1].size(1) for x in batch])
261
- max_wav_len = max([x[2].size(1) for x in batch])
262
-
263
- text_lengths = torch.LongTensor(len(batch))
264
- spec_lengths = torch.LongTensor(len(batch))
265
- wav_lengths = torch.LongTensor(len(batch))
266
- sid = torch.LongTensor(len(batch))
267
-
268
- text_padded = torch.LongTensor(len(batch), max_text_len)
269
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
270
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
271
- text_padded.zero_()
272
- spec_padded.zero_()
273
- wav_padded.zero_()
274
- for i in range(len(ids_sorted_decreasing)):
275
- row = batch[ids_sorted_decreasing[i]]
276
-
277
- text = row[0]
278
- text_padded[i, :text.size(0)] = text
279
- text_lengths[i] = text.size(0)
280
-
281
- spec = row[1]
282
- spec_padded[i, :, :spec.size(1)] = spec
283
- spec_lengths[i] = spec.size(1)
284
-
285
- wav = row[2]
286
- wav_padded[i, :, :wav.size(1)] = wav
287
- wav_lengths[i] = wav.size(1)
288
-
289
- sid[i] = row[3]
290
-
291
- if self.return_ids:
292
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
293
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
294
-
295
-
296
- class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
297
- """
298
- Maintain similar input lengths in a batch.
299
- Length groups are specified by boundaries.
300
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
301
-
302
- It removes samples which are not included in the boundaries.
303
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
304
- """
305
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
306
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
307
- self.lengths = dataset.lengths
308
- self.batch_size = batch_size
309
- self.boundaries = boundaries
310
-
311
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
312
- self.total_size = sum(self.num_samples_per_bucket)
313
- self.num_samples = self.total_size // self.num_replicas
314
-
315
- def _create_buckets(self):
316
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
317
- for i in range(len(self.lengths)):
318
- length = self.lengths[i]
319
- idx_bucket = self._bisect(length)
320
- if idx_bucket != -1:
321
- buckets[idx_bucket].append(i)
322
-
323
- for i in range(len(buckets) - 1, 0, -1):
324
- if len(buckets[i]) == 0:
325
- buckets.pop(i)
326
- self.boundaries.pop(i+1)
327
-
328
- num_samples_per_bucket = []
329
- for i in range(len(buckets)):
330
- len_bucket = len(buckets[i])
331
- total_batch_size = self.num_replicas * self.batch_size
332
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
333
- num_samples_per_bucket.append(len_bucket + rem)
334
- return buckets, num_samples_per_bucket
335
-
336
- def __iter__(self):
337
- # deterministically shuffle based on epoch
338
- g = torch.Generator()
339
- g.manual_seed(self.epoch)
340
-
341
- indices = []
342
- if self.shuffle:
343
- for bucket in self.buckets:
344
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
345
- else:
346
- for bucket in self.buckets:
347
- indices.append(list(range(len(bucket))))
348
-
349
- batches = []
350
- for i in range(len(self.buckets)):
351
- bucket = self.buckets[i]
352
- len_bucket = len(bucket)
353
- ids_bucket = indices[i]
354
- num_samples_bucket = self.num_samples_per_bucket[i]
355
-
356
- # add extra samples to make it evenly divisible
357
- rem = num_samples_bucket - len_bucket
358
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
359
-
360
- # subsample
361
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
362
-
363
- # batching
364
- for j in range(len(ids_bucket) // self.batch_size):
365
- batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
366
- batches.append(batch)
367
-
368
- if self.shuffle:
369
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
370
- batches = [batches[i] for i in batch_ids]
371
- self.batches = batches
372
-
373
- assert len(self.batches) * self.batch_size == self.num_samples
374
- return iter(self.batches)
375
-
376
- def _bisect(self, x, lo=0, hi=None):
377
- if hi is None:
378
- hi = len(self.boundaries) - 1
379
-
380
- if hi > lo:
381
- mid = (hi + lo) // 2
382
- if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
383
- return mid
384
- elif x <= self.boundaries[mid]:
385
- return self._bisect(x, lo, mid)
386
- else:
387
- return self._bisect(x, mid + 1, hi)
388
- else:
389
- return -1
390
-
391
- def __len__(self):
392
- return self.num_samples // self.batch_size
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Ama434/neutral-barlow/app.py DELETED
@@ -1,145 +0,0 @@
1
- import time
2
-
3
- import gradio as gr
4
- from gradio.themes.utils.theme_dropdown import create_theme_dropdown
5
-
6
- dropdown, js = create_theme_dropdown()
7
-
8
- with gr.Blocks(theme='Ama434/neutral-barlow') as demo:
9
- with gr.Row().style(equal_height=True):
10
- with gr.Column(scale=10):
11
- gr.Markdown(
12
- """
13
- # Theme preview: `neutral-barlow`
14
- To use this theme, set `theme='Ama434/neutral-barlow'` in `gr.Blocks()` or `gr.Interface()`.
15
- You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
16
- of this theme.
17
- """
18
- )
19
- with gr.Column(scale=3):
20
- with gr.Box():
21
- dropdown.render()
22
- toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True)
23
-
24
- dropdown.change(None, dropdown, None, _js=js)
25
- toggle_dark.click(
26
- None,
27
- _js="""
28
- () => {
29
- document.body.classList.toggle('dark');
30
- }
31
- """,
32
- )
33
-
34
- name = gr.Textbox(
35
- label="Name",
36
- info="Full name, including middle name. No special characters.",
37
- placeholder="John Doe",
38
- value="John Doe",
39
- interactive=True,
40
- )
41
-
42
- with gr.Row():
43
- slider1 = gr.Slider(label="Slider 1")
44
- slider2 = gr.Slider(label="Slider 2")
45
- gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
46
-
47
- with gr.Row():
48
- with gr.Column(variant="panel", scale=1):
49
- gr.Markdown("## Panel 1")
50
- radio = gr.Radio(
51
- ["A", "B", "C"],
52
- label="Radio",
53
- info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.",
54
- )
55
- drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False)
56
- drop_2 = gr.Dropdown(
57
- ["Option A", "Option B", "Option C"],
58
- multiselect=True,
59
- value=["Option A"],
60
- label="Dropdown",
61
- interactive=True,
62
- )
63
- check = gr.Checkbox(label="Go")
64
- with gr.Column(variant="panel", scale=2):
65
- img = gr.Image(
66
- "https://gradio.app/assets/img/header-image.jpg", label="Image"
67
- ).style(height=320)
68
- with gr.Row():
69
- go_btn = gr.Button("Go", label="Primary Button", variant="primary")
70
- clear_btn = gr.Button(
71
- "Clear", label="Secondary Button", variant="secondary"
72
- )
73
-
74
- def go(*args):
75
- time.sleep(3)
76
- return "https://gradio.app/assets/img/header-image.jpg"
77
-
78
- go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go")
79
-
80
- def clear():
81
- time.sleep(0.2)
82
- return None
83
-
84
- clear_btn.click(clear, None, img)
85
-
86
- with gr.Row():
87
- btn1 = gr.Button("Button 1").style(size="sm")
88
- btn2 = gr.UploadButton().style(size="sm")
89
- stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style(
90
- size="sm"
91
- )
92
-
93
- with gr.Row():
94
- gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe")
95
- gr.JSON(
96
- value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON"
97
- )
98
- gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1})
99
- gr.File()
100
- with gr.Row():
101
- gr.ColorPicker()
102
- gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4")
103
- gr.Gallery(
104
- [
105
- (
106
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
107
- "lion",
108
- ),
109
- (
110
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png",
111
- "logo",
112
- ),
113
- (
114
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg",
115
- "tower",
116
- ),
117
- ]
118
- ).style(height="200px", grid=2)
119
-
120
- with gr.Row():
121
- with gr.Column(scale=2):
122
- chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot")
123
- chat_btn = gr.Button("Add messages")
124
-
125
- def chat(history):
126
- time.sleep(2)
127
- yield [["How are you?", "I am good."]]
128
-
129
- chat_btn.click(
130
- lambda history: history
131
- + [["How are you?", "I am good."]]
132
- + (time.sleep(2) or []),
133
- chatbot,
134
- chatbot,
135
- )
136
- with gr.Column(scale=1):
137
- with gr.Accordion("Advanced Settings"):
138
- gr.Markdown("Hello")
139
- gr.Number(label="Chatbot control 1")
140
- gr.Number(label="Chatbot control 2")
141
- gr.Number(label="Chatbot control 3")
142
-
143
-
144
- if __name__ == "__main__":
145
- demo.queue().launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Amrrs/DragGan-Inversion/PTI/evaluation/qualitative_edit_comparison.py DELETED
@@ -1,156 +0,0 @@
1
- import os
2
- from random import choice
3
- from string import ascii_uppercase
4
- from PIL import Image
5
- from tqdm import tqdm
6
- from scripts.latent_editor_wrapper import LatentEditorWrapper
7
- from evaluation.experiment_setting_creator import ExperimentRunner
8
- import torch
9
- from configs import paths_config, hyperparameters, evaluation_config
10
- from utils.log_utils import save_concat_image, save_single_image
11
- from utils.models_utils import load_tuned_G
12
-
13
-
14
- class EditComparison:
15
-
16
- def __init__(self, save_single_images, save_concatenated_images, run_id):
17
-
18
- self.run_id = run_id
19
- self.experiment_creator = ExperimentRunner(run_id)
20
- self.save_single_images = save_single_images
21
- self.save_concatenated_images = save_concatenated_images
22
- self.latent_editor = LatentEditorWrapper()
23
-
24
- def save_reconstruction_images(self, image_latents, new_inv_image_latent, new_G, target_image):
25
- if self.save_concatenated_images:
26
- save_concat_image(self.concat_base_dir, image_latents, new_inv_image_latent, new_G,
27
- self.experiment_creator.old_G,
28
- 'rec',
29
- target_image)
30
-
31
- if self.save_single_images:
32
- save_single_image(self.single_base_dir, new_inv_image_latent, new_G, 'rec')
33
- target_image.save(f'{self.single_base_dir}/Original.jpg')
34
-
35
- def create_output_dirs(self, full_image_name):
36
- output_base_dir_path = f'{paths_config.experiments_output_dir}/{paths_config.input_data_id}/{self.run_id}/{full_image_name}'
37
- os.makedirs(output_base_dir_path, exist_ok=True)
38
-
39
- self.concat_base_dir = f'{output_base_dir_path}/concat_images'
40
- self.single_base_dir = f'{output_base_dir_path}/single_images'
41
-
42
- os.makedirs(self.concat_base_dir, exist_ok=True)
43
- os.makedirs(self.single_base_dir, exist_ok=True)
44
-
45
- def get_image_latent_codes(self, image_name):
46
- image_latents = []
47
- for method in evaluation_config.evaluated_methods:
48
- if method == 'SG2':
49
- image_latents.append(torch.load(
50
- f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/'
51
- f'{paths_config.pti_results_keyword}/{image_name}/0.pt'))
52
- else:
53
- image_latents.append(torch.load(
54
- f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{method}/{image_name}/0.pt'))
55
- new_inv_image_latent = torch.load(
56
- f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}/{image_name}/0.pt')
57
-
58
- return image_latents, new_inv_image_latent
59
-
60
- def save_interfacegan_edits(self, image_latents, new_inv_image_latent, interfacegan_factors, new_G, target_image):
61
- new_w_inv_edits = self.latent_editor.get_single_interface_gan_edits(new_inv_image_latent,
62
- interfacegan_factors)
63
-
64
- inv_edits = []
65
- for latent in image_latents:
66
- inv_edits.append(self.latent_editor.get_single_interface_gan_edits(latent, interfacegan_factors))
67
-
68
- for direction, edits in new_w_inv_edits.items():
69
- for factor, edit_tensor in edits.items():
70
- if self.save_concatenated_images:
71
- save_concat_image(self.concat_base_dir, [edits[direction][factor] for edits in inv_edits],
72
- new_w_inv_edits[direction][factor],
73
- new_G,
74
- self.experiment_creator.old_G,
75
- f'{direction}_{factor}', target_image)
76
- if self.save_single_images:
77
- save_single_image(self.single_base_dir, new_w_inv_edits[direction][factor], new_G,
78
- f'{direction}_{factor}')
79
-
80
- def save_ganspace_edits(self, image_latents, new_inv_image_latent, factors, new_G, target_image):
81
- new_w_inv_edits = self.latent_editor.get_single_ganspace_edits(new_inv_image_latent, factors)
82
- inv_edits = []
83
- for latent in image_latents:
84
- inv_edits.append(self.latent_editor.get_single_ganspace_edits(latent, factors))
85
-
86
- for idx in range(len(new_w_inv_edits)):
87
- if self.save_concatenated_images:
88
- save_concat_image(self.concat_base_dir, [edit[idx] for edit in inv_edits], new_w_inv_edits[idx],
89
- new_G,
90
- self.experiment_creator.old_G,
91
- f'ganspace_{idx}', target_image)
92
- if self.save_single_images:
93
- save_single_image(self.single_base_dir, new_w_inv_edits[idx], new_G,
94
- f'ganspace_{idx}')
95
-
96
- def run_experiment(self, run_pt, create_other_latents, use_multi_id_training, use_wandb=False):
97
- images_counter = 0
98
- new_G = None
99
- interfacegan_factors = [val / 2 for val in range(-6, 7) if val != 0]
100
- ganspace_factors = range(-20, 25, 5)
101
- self.experiment_creator.run_experiment(run_pt, create_other_latents, use_multi_id_training, use_wandb)
102
-
103
- if use_multi_id_training:
104
- new_G = load_tuned_G(self.run_id, paths_config.multi_id_model_type)
105
-
106
- for idx, image_path in tqdm(enumerate(self.experiment_creator.images_paths),
107
- total=len(self.experiment_creator.images_paths)):
108
-
109
- if images_counter >= hyperparameters.max_images_to_invert:
110
- break
111
-
112
- image_name = image_path.split('.')[0].split('/')[-1]
113
- target_image = Image.open(self.experiment_creator.target_paths[idx])
114
-
115
- if not use_multi_id_training:
116
- new_G = load_tuned_G(self.run_id, image_name)
117
-
118
- image_latents, new_inv_image_latent = self.get_image_latent_codes(image_name)
119
-
120
- self.create_output_dirs(image_name)
121
-
122
- self.save_reconstruction_images(image_latents, new_inv_image_latent, new_G, target_image)
123
-
124
- self.save_interfacegan_edits(image_latents, new_inv_image_latent, interfacegan_factors, new_G, target_image)
125
-
126
- self.save_ganspace_edits(image_latents, new_inv_image_latent, ganspace_factors, new_G, target_image)
127
-
128
- target_image.close()
129
- torch.cuda.empty_cache()
130
- images_counter += 1
131
-
132
-
133
- def run_pti_and_full_edit(iid):
134
- evaluation_config.evaluated_methods = ['SG2Plus', 'e4e', 'SG2']
135
- edit_figure_creator = EditComparison(save_single_images=True, save_concatenated_images=True,
136
- run_id=f'{paths_config.input_data_id}_pti_full_edit_{iid}')
137
- edit_figure_creator.run_experiment(True, True, use_multi_id_training=False, use_wandb=False)
138
-
139
-
140
- def pti_no_comparison(iid):
141
- evaluation_config.evaluated_methods = []
142
- edit_figure_creator = EditComparison(save_single_images=True, save_concatenated_images=True,
143
- run_id=f'{paths_config.input_data_id}_pti_no_comparison_{iid}')
144
- edit_figure_creator.run_experiment(True, False, use_multi_id_training=False, use_wandb=False)
145
-
146
-
147
- def edits_for_existed_experiment(run_id):
148
- evaluation_config.evaluated_methods = ['SG2Plus', 'e4e', 'SG2']
149
- edit_figure_creator = EditComparison(save_single_images=True, save_concatenated_images=True,
150
- run_id=run_id)
151
- edit_figure_creator.run_experiment(False, True, use_multi_id_training=False, use_wandb=False)
152
-
153
-
154
- if __name__ == '__main__':
155
- iid = ''.join(choice(ascii_uppercase) for i in range(7))
156
- pti_no_comparison(iid)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py DELETED
@@ -1,333 +0,0 @@
1
- # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
2
- # *Only* converts the UNet, VAE, and Text Encoder.
3
- # Does not convert optimizer state or any other thing.
4
-
5
- import argparse
6
- import os.path as osp
7
- import re
8
-
9
- import torch
10
- from safetensors.torch import load_file, save_file
11
-
12
-
13
- # =================#
14
- # UNet Conversion #
15
- # =================#
16
-
17
- unet_conversion_map = [
18
- # (stable-diffusion, HF Diffusers)
19
- ("time_embed.0.weight", "time_embedding.linear_1.weight"),
20
- ("time_embed.0.bias", "time_embedding.linear_1.bias"),
21
- ("time_embed.2.weight", "time_embedding.linear_2.weight"),
22
- ("time_embed.2.bias", "time_embedding.linear_2.bias"),
23
- ("input_blocks.0.0.weight", "conv_in.weight"),
24
- ("input_blocks.0.0.bias", "conv_in.bias"),
25
- ("out.0.weight", "conv_norm_out.weight"),
26
- ("out.0.bias", "conv_norm_out.bias"),
27
- ("out.2.weight", "conv_out.weight"),
28
- ("out.2.bias", "conv_out.bias"),
29
- ]
30
-
31
- unet_conversion_map_resnet = [
32
- # (stable-diffusion, HF Diffusers)
33
- ("in_layers.0", "norm1"),
34
- ("in_layers.2", "conv1"),
35
- ("out_layers.0", "norm2"),
36
- ("out_layers.3", "conv2"),
37
- ("emb_layers.1", "time_emb_proj"),
38
- ("skip_connection", "conv_shortcut"),
39
- ]
40
-
41
- unet_conversion_map_layer = []
42
- # hardcoded number of downblocks and resnets/attentions...
43
- # would need smarter logic for other networks.
44
- for i in range(4):
45
- # loop over downblocks/upblocks
46
-
47
- for j in range(2):
48
- # loop over resnets/attentions for downblocks
49
- hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
50
- sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
51
- unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
52
-
53
- if i < 3:
54
- # no attention layers in down_blocks.3
55
- hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
56
- sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
57
- unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
58
-
59
- for j in range(3):
60
- # loop over resnets/attentions for upblocks
61
- hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
62
- sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
63
- unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
64
-
65
- if i > 0:
66
- # no attention layers in up_blocks.0
67
- hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
68
- sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
69
- unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
70
-
71
- if i < 3:
72
- # no downsample in down_blocks.3
73
- hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
74
- sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
75
- unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
76
-
77
- # no upsample in up_blocks.3
78
- hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
79
- sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
80
- unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
81
-
82
- hf_mid_atn_prefix = "mid_block.attentions.0."
83
- sd_mid_atn_prefix = "middle_block.1."
84
- unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
85
-
86
- for j in range(2):
87
- hf_mid_res_prefix = f"mid_block.resnets.{j}."
88
- sd_mid_res_prefix = f"middle_block.{2*j}."
89
- unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
90
-
91
-
92
- def convert_unet_state_dict(unet_state_dict):
93
- # buyer beware: this is a *brittle* function,
94
- # and correct output requires that all of these pieces interact in
95
- # the exact order in which I have arranged them.
96
- mapping = {k: k for k in unet_state_dict.keys()}
97
- for sd_name, hf_name in unet_conversion_map:
98
- mapping[hf_name] = sd_name
99
- for k, v in mapping.items():
100
- if "resnets" in k:
101
- for sd_part, hf_part in unet_conversion_map_resnet:
102
- v = v.replace(hf_part, sd_part)
103
- mapping[k] = v
104
- for k, v in mapping.items():
105
- for sd_part, hf_part in unet_conversion_map_layer:
106
- v = v.replace(hf_part, sd_part)
107
- mapping[k] = v
108
- new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
109
- return new_state_dict
110
-
111
-
112
- # ================#
113
- # VAE Conversion #
114
- # ================#
115
-
116
- vae_conversion_map = [
117
- # (stable-diffusion, HF Diffusers)
118
- ("nin_shortcut", "conv_shortcut"),
119
- ("norm_out", "conv_norm_out"),
120
- ("mid.attn_1.", "mid_block.attentions.0."),
121
- ]
122
-
123
- for i in range(4):
124
- # down_blocks have two resnets
125
- for j in range(2):
126
- hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
127
- sd_down_prefix = f"encoder.down.{i}.block.{j}."
128
- vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
129
-
130
- if i < 3:
131
- hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
132
- sd_downsample_prefix = f"down.{i}.downsample."
133
- vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
134
-
135
- hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
136
- sd_upsample_prefix = f"up.{3-i}.upsample."
137
- vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
138
-
139
- # up_blocks have three resnets
140
- # also, up blocks in hf are numbered in reverse from sd
141
- for j in range(3):
142
- hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
143
- sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
144
- vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
145
-
146
- # this part accounts for mid blocks in both the encoder and the decoder
147
- for i in range(2):
148
- hf_mid_res_prefix = f"mid_block.resnets.{i}."
149
- sd_mid_res_prefix = f"mid.block_{i+1}."
150
- vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
151
-
152
-
153
- vae_conversion_map_attn = [
154
- # (stable-diffusion, HF Diffusers)
155
- ("norm.", "group_norm."),
156
- ("q.", "query."),
157
- ("k.", "key."),
158
- ("v.", "value."),
159
- ("proj_out.", "proj_attn."),
160
- ]
161
-
162
-
163
- def reshape_weight_for_sd(w):
164
- # convert HF linear weights to SD conv2d weights
165
- return w.reshape(*w.shape, 1, 1)
166
-
167
-
168
- def convert_vae_state_dict(vae_state_dict):
169
- mapping = {k: k for k in vae_state_dict.keys()}
170
- for k, v in mapping.items():
171
- for sd_part, hf_part in vae_conversion_map:
172
- v = v.replace(hf_part, sd_part)
173
- mapping[k] = v
174
- for k, v in mapping.items():
175
- if "attentions" in k:
176
- for sd_part, hf_part in vae_conversion_map_attn:
177
- v = v.replace(hf_part, sd_part)
178
- mapping[k] = v
179
- new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
180
- weights_to_convert = ["q", "k", "v", "proj_out"]
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
- print(f"Reshaping {k} for SD format")
185
- new_state_dict[k] = reshape_weight_for_sd(v)
186
- return new_state_dict
187
-
188
-
189
- # =========================#
190
- # Text Encoder Conversion #
191
- # =========================#
192
-
193
-
194
- textenc_conversion_lst = [
195
- # (stable-diffusion, HF Diffusers)
196
- ("resblocks.", "text_model.encoder.layers."),
197
- ("ln_1", "layer_norm1"),
198
- ("ln_2", "layer_norm2"),
199
- (".c_fc.", ".fc1."),
200
- (".c_proj.", ".fc2."),
201
- (".attn", ".self_attn"),
202
- ("ln_final.", "transformer.text_model.final_layer_norm."),
203
- ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
204
- ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
205
- ]
206
- protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
207
- textenc_pattern = re.compile("|".join(protected.keys()))
208
-
209
- # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
210
- code2idx = {"q": 0, "k": 1, "v": 2}
211
-
212
-
213
- def convert_text_enc_state_dict_v20(text_enc_dict):
214
- new_state_dict = {}
215
- capture_qkv_weight = {}
216
- capture_qkv_bias = {}
217
- for k, v in text_enc_dict.items():
218
- if (
219
- k.endswith(".self_attn.q_proj.weight")
220
- or k.endswith(".self_attn.k_proj.weight")
221
- or k.endswith(".self_attn.v_proj.weight")
222
- ):
223
- k_pre = k[: -len(".q_proj.weight")]
224
- k_code = k[-len("q_proj.weight")]
225
- if k_pre not in capture_qkv_weight:
226
- capture_qkv_weight[k_pre] = [None, None, None]
227
- capture_qkv_weight[k_pre][code2idx[k_code]] = v
228
- continue
229
-
230
- if (
231
- k.endswith(".self_attn.q_proj.bias")
232
- or k.endswith(".self_attn.k_proj.bias")
233
- or k.endswith(".self_attn.v_proj.bias")
234
- ):
235
- k_pre = k[: -len(".q_proj.bias")]
236
- k_code = k[-len("q_proj.bias")]
237
- if k_pre not in capture_qkv_bias:
238
- capture_qkv_bias[k_pre] = [None, None, None]
239
- capture_qkv_bias[k_pre][code2idx[k_code]] = v
240
- continue
241
-
242
- relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
243
- new_state_dict[relabelled_key] = v
244
-
245
- for k_pre, tensors in capture_qkv_weight.items():
246
- if None in tensors:
247
- raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
248
- relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
249
- new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
250
-
251
- for k_pre, tensors in capture_qkv_bias.items():
252
- if None in tensors:
253
- raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
254
- relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
255
- new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
256
-
257
- return new_state_dict
258
-
259
-
260
- def convert_text_enc_state_dict(text_enc_dict):
261
- return text_enc_dict
262
-
263
-
264
- if __name__ == "__main__":
265
- parser = argparse.ArgumentParser()
266
-
267
- parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
268
- parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
269
- parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
270
- parser.add_argument(
271
- "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
272
- )
273
-
274
- args = parser.parse_args()
275
-
276
- assert args.model_path is not None, "Must provide a model path!"
277
-
278
- assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
279
-
280
- # Path for safetensors
281
- unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
282
- vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
283
- text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors")
284
-
285
- # Load models from safetensors if it exists, if it doesn't pytorch
286
- if osp.exists(unet_path):
287
- unet_state_dict = load_file(unet_path, device="cpu")
288
- else:
289
- unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
290
- unet_state_dict = torch.load(unet_path, map_location="cpu")
291
-
292
- if osp.exists(vae_path):
293
- vae_state_dict = load_file(vae_path, device="cpu")
294
- else:
295
- vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
296
- vae_state_dict = torch.load(vae_path, map_location="cpu")
297
-
298
- if osp.exists(text_enc_path):
299
- text_enc_dict = load_file(text_enc_path, device="cpu")
300
- else:
301
- text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
302
- text_enc_dict = torch.load(text_enc_path, map_location="cpu")
303
-
304
- # Convert the UNet model
305
- unet_state_dict = convert_unet_state_dict(unet_state_dict)
306
- unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
307
-
308
- # Convert the VAE model
309
- vae_state_dict = convert_vae_state_dict(vae_state_dict)
310
- vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
311
-
312
- # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
313
- is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
314
-
315
- if is_v20_model:
316
- # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
317
- text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
318
- text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
319
- text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
320
- else:
321
- text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
322
- text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
323
-
324
- # Put together new checkpoint
325
- state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
326
- if args.half:
327
- state_dict = {k: v.half() for k, v in state_dict.items()}
328
-
329
- if args.use_safetensors:
330
- save_file(state_dict, args.checkpoint_path)
331
- else:
332
- state_dict = {"state_dict": state_dict}
333
- torch.save(state_dict, args.checkpoint_path)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py DELETED
@@ -1,17 +0,0 @@
1
- # This file is autogenerated by the command `make fix-copies`, do not edit.
2
- from ..utils import DummyObject, requires_backends
3
-
4
-
5
- class SpectrogramDiffusionPipeline(metaclass=DummyObject):
6
- _backends = ["transformers", "torch", "note_seq"]
7
-
8
- def __init__(self, *args, **kwargs):
9
- requires_backends(self, ["transformers", "torch", "note_seq"])
10
-
11
- @classmethod
12
- def from_config(cls, *args, **kwargs):
13
- requires_backends(cls, ["transformers", "torch", "note_seq"])
14
-
15
- @classmethod
16
- def from_pretrained(cls, *args, **kwargs):
17
- requires_backends(cls, ["transformers", "torch", "note_seq"])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_adapter.py DELETED
@@ -1,316 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2022 HuggingFace Inc.
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
- import gc
17
- import random
18
- import unittest
19
-
20
- import numpy as np
21
- import torch
22
- from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
23
-
24
- from diffusers import (
25
- AutoencoderKL,
26
- PNDMScheduler,
27
- StableDiffusionAdapterPipeline,
28
- T2IAdapter,
29
- UNet2DConditionModel,
30
- )
31
- from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
32
- from diffusers.utils.import_utils import is_xformers_available
33
- from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
34
-
35
- from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
36
- from ..test_pipelines_common import PipelineTesterMixin
37
-
38
-
39
- enable_full_determinism()
40
-
41
-
42
- class AdapterTests:
43
- pipeline_class = StableDiffusionAdapterPipeline
44
- params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
45
- batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
46
-
47
- def get_dummy_components(self, adapter_type):
48
- torch.manual_seed(0)
49
- unet = UNet2DConditionModel(
50
- block_out_channels=(32, 64),
51
- layers_per_block=2,
52
- sample_size=32,
53
- in_channels=4,
54
- out_channels=4,
55
- down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
56
- up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
57
- cross_attention_dim=32,
58
- )
59
- scheduler = PNDMScheduler(skip_prk_steps=True)
60
- torch.manual_seed(0)
61
- vae = AutoencoderKL(
62
- block_out_channels=[32, 64],
63
- in_channels=3,
64
- out_channels=3,
65
- down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
66
- up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
67
- latent_channels=4,
68
- )
69
- torch.manual_seed(0)
70
- text_encoder_config = CLIPTextConfig(
71
- bos_token_id=0,
72
- eos_token_id=2,
73
- hidden_size=32,
74
- intermediate_size=37,
75
- layer_norm_eps=1e-05,
76
- num_attention_heads=4,
77
- num_hidden_layers=5,
78
- pad_token_id=1,
79
- vocab_size=1000,
80
- )
81
- text_encoder = CLIPTextModel(text_encoder_config)
82
- tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
83
-
84
- torch.manual_seed(0)
85
- adapter = T2IAdapter(
86
- in_channels=3,
87
- channels=[32, 64],
88
- num_res_blocks=2,
89
- downscale_factor=2,
90
- adapter_type=adapter_type,
91
- )
92
-
93
- components = {
94
- "adapter": adapter,
95
- "unet": unet,
96
- "scheduler": scheduler,
97
- "vae": vae,
98
- "text_encoder": text_encoder,
99
- "tokenizer": tokenizer,
100
- "safety_checker": None,
101
- "feature_extractor": None,
102
- }
103
- return components
104
-
105
- def get_dummy_inputs(self, device, seed=0):
106
- image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
107
- if str(device).startswith("mps"):
108
- generator = torch.manual_seed(seed)
109
- else:
110
- generator = torch.Generator(device=device).manual_seed(seed)
111
- inputs = {
112
- "prompt": "A painting of a squirrel eating a burger",
113
- "image": image,
114
- "generator": generator,
115
- "num_inference_steps": 2,
116
- "guidance_scale": 6.0,
117
- "output_type": "numpy",
118
- }
119
- return inputs
120
-
121
- def test_attention_slicing_forward_pass(self):
122
- return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
123
-
124
- @unittest.skipIf(
125
- torch_device != "cuda" or not is_xformers_available(),
126
- reason="XFormers attention is only available with CUDA and `xformers` installed",
127
- )
128
- def test_xformers_attention_forwardGenerator_pass(self):
129
- self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
130
-
131
- def test_inference_batch_single_identical(self):
132
- self._test_inference_batch_single_identical(expected_max_diff=2e-3)
133
-
134
-
135
- class StableDiffusionFullAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
136
- def get_dummy_components(self):
137
- return super().get_dummy_components("full_adapter")
138
-
139
- def test_stable_diffusion_adapter_default_case(self):
140
- device = "cpu" # ensure determinism for the device-dependent torch.Generator
141
- components = self.get_dummy_components()
142
- sd_pipe = StableDiffusionAdapterPipeline(**components)
143
- sd_pipe = sd_pipe.to(device)
144
- sd_pipe.set_progress_bar_config(disable=None)
145
-
146
- inputs = self.get_dummy_inputs(device)
147
- image = sd_pipe(**inputs).images
148
- image_slice = image[0, -3:, -3:, -1]
149
-
150
- assert image.shape == (1, 64, 64, 3)
151
- expected_slice = np.array([0.4858, 0.5500, 0.4278, 0.4669, 0.6184, 0.4322, 0.5010, 0.5033, 0.4746])
152
- assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
153
-
154
-
155
- class StableDiffusionLightAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
156
- def get_dummy_components(self):
157
- return super().get_dummy_components("light_adapter")
158
-
159
- def test_stable_diffusion_adapter_default_case(self):
160
- device = "cpu" # ensure determinism for the device-dependent torch.Generator
161
- components = self.get_dummy_components()
162
- sd_pipe = StableDiffusionAdapterPipeline(**components)
163
- sd_pipe = sd_pipe.to(device)
164
- sd_pipe.set_progress_bar_config(disable=None)
165
-
166
- inputs = self.get_dummy_inputs(device)
167
- image = sd_pipe(**inputs).images
168
- image_slice = image[0, -3:, -3:, -1]
169
-
170
- assert image.shape == (1, 64, 64, 3)
171
- expected_slice = np.array([0.4965, 0.5548, 0.4330, 0.4771, 0.6226, 0.4382, 0.5037, 0.5071, 0.4782])
172
- assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
173
-
174
-
175
- @slow
176
- @require_torch_gpu
177
- class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
178
- def tearDown(self):
179
- super().tearDown()
180
- gc.collect()
181
- torch.cuda.empty_cache()
182
-
183
- def test_stable_diffusion_adapter(self):
184
- test_cases = [
185
- (
186
- "TencentARC/t2iadapter_color_sd14v1",
187
- "CompVis/stable-diffusion-v1-4",
188
- "snail",
189
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png",
190
- 3,
191
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy",
192
- ),
193
- (
194
- "TencentARC/t2iadapter_depth_sd14v1",
195
- "CompVis/stable-diffusion-v1-4",
196
- "desk",
197
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png",
198
- 3,
199
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd14v1.npy",
200
- ),
201
- (
202
- "TencentARC/t2iadapter_depth_sd15v2",
203
- "runwayml/stable-diffusion-v1-5",
204
- "desk",
205
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png",
206
- 3,
207
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy",
208
- ),
209
- (
210
- "TencentARC/t2iadapter_keypose_sd14v1",
211
- "CompVis/stable-diffusion-v1-4",
212
- "person",
213
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/person_keypose.png",
214
- 3,
215
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_keypose_sd14v1.npy",
216
- ),
217
- (
218
- "TencentARC/t2iadapter_openpose_sd14v1",
219
- "CompVis/stable-diffusion-v1-4",
220
- "person",
221
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/iron_man_pose.png",
222
- 3,
223
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_openpose_sd14v1.npy",
224
- ),
225
- (
226
- "TencentARC/t2iadapter_seg_sd14v1",
227
- "CompVis/stable-diffusion-v1-4",
228
- "motorcycle",
229
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png",
230
- 3,
231
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_seg_sd14v1.npy",
232
- ),
233
- (
234
- "TencentARC/t2iadapter_zoedepth_sd15v1",
235
- "runwayml/stable-diffusion-v1-5",
236
- "motorcycle",
237
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png",
238
- 3,
239
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_zoedepth_sd15v1.npy",
240
- ),
241
- (
242
- "TencentARC/t2iadapter_canny_sd14v1",
243
- "CompVis/stable-diffusion-v1-4",
244
- "toy",
245
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png",
246
- 1,
247
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd14v1.npy",
248
- ),
249
- (
250
- "TencentARC/t2iadapter_canny_sd15v2",
251
- "runwayml/stable-diffusion-v1-5",
252
- "toy",
253
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png",
254
- 1,
255
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd15v2.npy",
256
- ),
257
- (
258
- "TencentARC/t2iadapter_sketch_sd14v1",
259
- "CompVis/stable-diffusion-v1-4",
260
- "cat",
261
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png",
262
- 1,
263
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd14v1.npy",
264
- ),
265
- (
266
- "TencentARC/t2iadapter_sketch_sd15v2",
267
- "runwayml/stable-diffusion-v1-5",
268
- "cat",
269
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png",
270
- 1,
271
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd15v2.npy",
272
- ),
273
- ]
274
-
275
- for adapter_model, sd_model, prompt, image_url, input_channels, out_url in test_cases:
276
- image = load_image(image_url)
277
- expected_out = load_numpy(out_url)
278
-
279
- if input_channels == 1:
280
- image = image.convert("L")
281
-
282
- adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
283
-
284
- pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
285
- pipe.to(torch_device)
286
- pipe.set_progress_bar_config(disable=None)
287
- pipe.enable_attention_slicing()
288
-
289
- generator = torch.Generator(device="cpu").manual_seed(0)
290
-
291
- out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
292
-
293
- self.assertTrue(np.allclose(out, expected_out))
294
-
295
- def test_stable_diffusion_adapter_pipeline_with_sequential_cpu_offloading(self):
296
- torch.cuda.empty_cache()
297
- torch.cuda.reset_max_memory_allocated()
298
- torch.cuda.reset_peak_memory_stats()
299
-
300
- adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1")
301
- pipe = StableDiffusionAdapterPipeline.from_pretrained(
302
- "CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None
303
- )
304
- pipe = pipe.to(torch_device)
305
- pipe.set_progress_bar_config(disable=None)
306
- pipe.enable_attention_slicing(1)
307
- pipe.enable_sequential_cpu_offload()
308
-
309
- image = load_image(
310
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png"
311
- )
312
-
313
- pipe(prompt="foo", image=image, num_inference_steps=2)
314
-
315
- mem_bytes = torch.cuda.max_memory_allocated()
316
- assert mem_bytes < 5 * 10**9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py DELETED
@@ -1,13 +0,0 @@
1
- _base_ = './ga_faster_r50_fpn_1x_coco.py'
2
- model = dict(
3
- pretrained='open-mmlab://resnext101_64x4d',
4
- backbone=dict(
5
- type='ResNeXt',
6
- depth=101,
7
- groups=64,
8
- base_width=4,
9
- num_stages=4,
10
- out_indices=(0, 1, 2, 3),
11
- frozen_stages=1,
12
- norm_cfg=dict(type='BN', requires_grad=True),
13
- style='pytorch'))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Ankit6396/100-Free-ChatGPT4/app.py DELETED
@@ -1,193 +0,0 @@
1
- import gradio as gr
2
- import os
3
- import json
4
- import requests
5
-
6
- #Streaming endpoint
7
- API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
8
-
9
- #Huggingface provided GPT4 OpenAI API Key
10
- OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
11
-
12
- #Inferenec function
13
- def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
14
-
15
- headers = {
16
- "Content-Type": "application/json",
17
- "Authorization": f"Bearer {OPENAI_API_KEY}"
18
- }
19
- print(f"system message is ^^ {system_msg}")
20
- if system_msg.strip() == '':
21
- initial_message = [{"role": "user", "content": f"{inputs}"},]
22
- multi_turn_message = []
23
- else:
24
- initial_message= [{"role": "system", "content": system_msg},
25
- {"role": "user", "content": f"{inputs}"},]
26
- multi_turn_message = [{"role": "system", "content": system_msg},]
27
-
28
- if chat_counter == 0 :
29
- payload = {
30
- "model": "gpt-4",
31
- "messages": initial_message ,
32
- "temperature" : 1.0,
33
- "top_p":1.0,
34
- "n" : 1,
35
- "stream": True,
36
- "presence_penalty":0,
37
- "frequency_penalty":0,
38
- }
39
- print(f"chat_counter - {chat_counter}")
40
- else: #if chat_counter != 0 :
41
- messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
42
- for data in chatbot:
43
- user = {}
44
- user["role"] = "user"
45
- user["content"] = data[0]
46
- assistant = {}
47
- assistant["role"] = "assistant"
48
- assistant["content"] = data[1]
49
- messages.append(user)
50
- messages.append(assistant)
51
- temp = {}
52
- temp["role"] = "user"
53
- temp["content"] = inputs
54
- messages.append(temp)
55
- #messages
56
- payload = {
57
- "model": "gpt-4",
58
- "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
59
- "temperature" : temperature, #1.0,
60
- "top_p": top_p, #1.0,
61
- "n" : 1,
62
- "stream": True,
63
- "presence_penalty":0,
64
- "frequency_penalty":0,}
65
-
66
- chat_counter+=1
67
-
68
- history.append(inputs)
69
- print(f"Logging : payload is - {payload}")
70
- # make a POST request to the API endpoint using the requests.post method, passing in stream=True
71
- response = requests.post(API_URL, headers=headers, json=payload, stream=True)
72
- print(f"Logging : response code - {response}")
73
- token_counter = 0
74
- partial_words = ""
75
-
76
- counter=0
77
- for chunk in response.iter_lines():
78
- #Skipping first chunk
79
- if counter == 0:
80
- counter+=1
81
- continue
82
- # check whether each line is non-empty
83
- if chunk.decode() :
84
- chunk = chunk.decode()
85
- # decode each line as response data is in bytes
86
- if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
87
- partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
88
- if token_counter == 0:
89
- history.append(" " + partial_words)
90
- else:
91
- history[-1] = partial_words
92
- chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
93
- token_counter+=1
94
- yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history}
95
-
96
- #Resetting to blank
97
- def reset_textbox():
98
- return gr.update(value='')
99
-
100
- #to set a component as visible=False
101
- def set_visible_false():
102
- return gr.update(visible=False)
103
-
104
- #to set a component as visible=True
105
- def set_visible_true():
106
- return gr.update(visible=True)
107
-
108
- title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
109
-
110
- #display message for themes feature
111
- theme_addon_msg = """<center>🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple <code>theme.push_to_hub()</code>.
112
- <br>🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - <a href="https://huggingface.co/Gradio-Themes" target="_blank">Gradio-Themes-Party🎨</a> 🏆</center>
113
- """
114
-
115
- #Using info to add additional information about System message in GPT4
116
- system_msg_info = """A conversation could begin with a system message to gently instruct the assistant.
117
- System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
118
-
119
- #Modifying existing Gradio Theme
120
- theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
121
- text_size=gr.themes.sizes.text_lg)
122
-
123
- with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
124
- theme=theme) as demo:
125
- gr.HTML(title)
126
- gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
127
- gr.HTML(theme_addon_msg)
128
- gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
129
-
130
- with gr.Column(elem_id = "col_container"):
131
- #GPT4 API Key is provided by Huggingface
132
- with gr.Accordion(label="System message:", open=False):
133
- system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="")
134
- accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
135
- chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
136
- inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
137
- state = gr.State([])
138
- with gr.Row():
139
- with gr.Column(scale=7):
140
- b1 = gr.Button().style(full_width=True)
141
- with gr.Column(scale=3):
142
- server_status_code = gr.Textbox(label="Status code from OpenAI server", )
143
-
144
- #top_p, temperature
145
- with gr.Accordion("Parameters", open=False):
146
- top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
147
- temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
148
- chat_counter = gr.Number(value=0, visible=False, precision=0)
149
-
150
- #Event handling
151
- inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key
152
- b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],) #openai_api_key
153
-
154
- inputs.submit(set_visible_false, [], [system_msg])
155
- b1.click(set_visible_false, [], [system_msg])
156
- inputs.submit(set_visible_true, [], [accordion_msg])
157
- b1.click(set_visible_true, [], [accordion_msg])
158
-
159
- b1.click(reset_textbox, [], [inputs])
160
- inputs.submit(reset_textbox, [], [inputs])
161
-
162
- #Examples
163
- with gr.Accordion(label="Examples for System message:", open=False):
164
- gr.Examples(
165
- examples = [["""You are an AI programming assistant.
166
-
167
- - Follow the user's requirements carefully and to the letter.
168
- - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
169
- - Then output the code in a single code block.
170
- - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
171
- ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
172
- ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
173
- ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
174
- ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
175
- ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
176
- ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
177
- ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
178
- ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
179
- ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
180
- ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
181
- ["You are a helpful assistant that provides detailed and accurate information."],
182
- ["You are an assistant that speaks like Shakespeare."],
183
- ["You are a friendly assistant who uses casual language and humor."],
184
- ["You are a financial advisor who gives expert advice on investments and budgeting."],
185
- ["You are a health and fitness expert who provides advice on nutrition and exercise."],
186
- ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
187
- ["You are a movie critic who shares insightful opinions on films and their themes."],
188
- ["You are a history enthusiast who loves to discuss historical events and figures."],
189
- ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
190
- ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
191
- inputs = system_msg,)
192
-
193
- demo.queue(max_size=99, concurrency_count=20).launch(debug=True)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Arnx/MusicGenXvAKN/audiocraft/modules/__init__.py DELETED
@@ -1,20 +0,0 @@
1
- # Copyright (c) Meta Platforms, Inc. and affiliates.
2
- # All rights reserved.
3
- #
4
- # This source code is licensed under the license found in the
5
- # LICENSE file in the root directory of this source tree.
6
-
7
- # flake8: noqa
8
- from .conv import (
9
- NormConv1d,
10
- NormConv2d,
11
- NormConvTranspose1d,
12
- NormConvTranspose2d,
13
- StreamableConv1d,
14
- StreamableConvTranspose1d,
15
- pad_for_conv1d,
16
- pad1d,
17
- unpad1d,
18
- )
19
- from .lstm import StreamableLSTM
20
- from .seanet import SEANetEncoder, SEANetDecoder
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/errors.py DELETED
@@ -1,34 +0,0 @@
1
- class ConsoleError(Exception):
2
- """An error in console operation."""
3
-
4
-
5
- class StyleError(Exception):
6
- """An error in styles."""
7
-
8
-
9
- class StyleSyntaxError(ConsoleError):
10
- """Style was badly formatted."""
11
-
12
-
13
- class MissingStyle(StyleError):
14
- """No such style."""
15
-
16
-
17
- class StyleStackError(ConsoleError):
18
- """Style stack is invalid."""
19
-
20
-
21
- class NotRenderableError(ConsoleError):
22
- """Object is not renderable."""
23
-
24
-
25
- class MarkupError(ConsoleError):
26
- """Markup was badly formatted."""
27
-
28
-
29
- class LiveError(ConsoleError):
30
- """Error related to Live display."""
31
-
32
-
33
- class NoAltScreen(ConsoleError):
34
- """Alt screen mode was required."""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Awiny/Image2Paragraph/models/grit_src/grit/__init__.py DELETED
@@ -1,7 +0,0 @@
1
- from .modeling.meta_arch import grit
2
- from .modeling.roi_heads import grit_roi_heads
3
- from .modeling.backbone import vit
4
-
5
- from .data.datasets import object365
6
- from .data.datasets import vg
7
- from .data.datasets import grit_coco
 
 
 
 
 
 
 
 
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/layers/test_mask_ops.py DELETED
@@ -1,202 +0,0 @@
1
- # -*- coding: utf-8 -*-
2
- # Copyright (c) Facebook, Inc. and its affiliates.
3
-
4
- import contextlib
5
- import io
6
- import numpy as np
7
- import unittest
8
- from collections import defaultdict
9
- import torch
10
- import tqdm
11
- from fvcore.common.benchmark import benchmark
12
- from pycocotools.coco import COCO
13
- from tabulate import tabulate
14
- from torch.nn import functional as F
15
-
16
- from detectron2.data import MetadataCatalog
17
- from detectron2.layers.mask_ops import (
18
- pad_masks,
19
- paste_mask_in_image_old,
20
- paste_masks_in_image,
21
- scale_boxes,
22
- )
23
- from detectron2.structures import BitMasks, Boxes, BoxMode, PolygonMasks
24
- from detectron2.structures.masks import polygons_to_bitmask
25
- from detectron2.utils.file_io import PathManager
26
- from detectron2.utils.testing import random_boxes
27
-
28
-
29
- def iou_between_full_image_bit_masks(a, b):
30
- intersect = (a & b).sum()
31
- union = (a | b).sum()
32
- return intersect / union
33
-
34
-
35
- def rasterize_polygons_with_grid_sample(full_image_bit_mask, box, mask_size, threshold=0.5):
36
- x0, y0, x1, y1 = box[0], box[1], box[2], box[3]
37
-
38
- img_h, img_w = full_image_bit_mask.shape
39
-
40
- mask_y = np.arange(0.0, mask_size) + 0.5 # mask y sample coords in [0.5, mask_size - 0.5]
41
- mask_x = np.arange(0.0, mask_size) + 0.5 # mask x sample coords in [0.5, mask_size - 0.5]
42
- mask_y = mask_y / mask_size * (y1 - y0) + y0
43
- mask_x = mask_x / mask_size * (x1 - x0) + x0
44
-
45
- mask_x = (mask_x - 0.5) / (img_w - 1) * 2 + -1
46
- mask_y = (mask_y - 0.5) / (img_h - 1) * 2 + -1
47
- gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x))
48
- ind = torch.stack([gx, gy], dim=-1).to(dtype=torch.float32)
49
-
50
- full_image_bit_mask = torch.from_numpy(full_image_bit_mask)
51
- mask = F.grid_sample(
52
- full_image_bit_mask[None, None, :, :].to(dtype=torch.float32),
53
- ind[None, :, :, :],
54
- align_corners=True,
55
- )
56
-
57
- return mask[0, 0] >= threshold
58
-
59
-
60
- class TestMaskCropPaste(unittest.TestCase):
61
- def setUp(self):
62
- json_file = MetadataCatalog.get("coco_2017_val_100").json_file
63
- if not PathManager.isfile(json_file):
64
- raise unittest.SkipTest("{} not found".format(json_file))
65
- with contextlib.redirect_stdout(io.StringIO()):
66
- json_file = PathManager.get_local_path(json_file)
67
- self.coco = COCO(json_file)
68
-
69
- def test_crop_paste_consistency(self):
70
- """
71
- rasterize_polygons_within_box (used in training)
72
- and
73
- paste_masks_in_image (used in inference)
74
- should be inverse operations to each other.
75
-
76
- This function runs several implementation of the above two operations and prints
77
- the reconstruction error.
78
- """
79
-
80
- anns = self.coco.loadAnns(self.coco.getAnnIds(iscrowd=False)) # avoid crowd annotations
81
-
82
- selected_anns = anns[:100]
83
-
84
- ious = []
85
- for ann in tqdm.tqdm(selected_anns):
86
- results = self.process_annotation(ann)
87
- ious.append([k[2] for k in results])
88
-
89
- ious = np.array(ious)
90
- mean_ious = ious.mean(axis=0)
91
- table = []
92
- res_dic = defaultdict(dict)
93
- for row, iou in zip(results, mean_ious):
94
- table.append((row[0], row[1], iou))
95
- res_dic[row[0]][row[1]] = iou
96
- print(tabulate(table, headers=["rasterize", "paste", "iou"], tablefmt="simple"))
97
- # assert that the reconstruction is good:
98
- self.assertTrue(res_dic["polygon"]["aligned"] > 0.94)
99
- self.assertTrue(res_dic["roialign"]["aligned"] > 0.95)
100
-
101
- def process_annotation(self, ann, mask_side_len=28):
102
- # Parse annotation data
103
- img_info = self.coco.loadImgs(ids=[ann["image_id"]])[0]
104
- height, width = img_info["height"], img_info["width"]
105
- gt_polygons = [np.array(p, dtype=np.float64) for p in ann["segmentation"]]
106
- gt_bbox = BoxMode.convert(ann["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
107
- gt_bit_mask = polygons_to_bitmask(gt_polygons, height, width)
108
-
109
- # Run rasterize ..
110
- torch_gt_bbox = torch.tensor(gt_bbox).to(dtype=torch.float32).reshape(-1, 4)
111
- box_bitmasks = {
112
- "polygon": PolygonMasks([gt_polygons]).crop_and_resize(torch_gt_bbox, mask_side_len)[0],
113
- "gridsample": rasterize_polygons_with_grid_sample(gt_bit_mask, gt_bbox, mask_side_len),
114
- "roialign": BitMasks(torch.from_numpy(gt_bit_mask[None, :, :])).crop_and_resize(
115
- torch_gt_bbox, mask_side_len
116
- )[0],
117
- }
118
-
119
- # Run paste ..
120
- results = defaultdict(dict)
121
- for k, box_bitmask in box_bitmasks.items():
122
- padded_bitmask, scale = pad_masks(box_bitmask[None, :, :], 1)
123
- scaled_boxes = scale_boxes(torch_gt_bbox, scale)
124
-
125
- r = results[k]
126
- r["old"] = paste_mask_in_image_old(
127
- padded_bitmask[0], scaled_boxes[0], height, width, threshold=0.5
128
- )
129
- r["aligned"] = paste_masks_in_image(
130
- box_bitmask[None, :, :], Boxes(torch_gt_bbox), (height, width)
131
- )[0]
132
-
133
- table = []
134
- for rasterize_method, r in results.items():
135
- for paste_method, mask in r.items():
136
- mask = np.asarray(mask)
137
- iou = iou_between_full_image_bit_masks(gt_bit_mask.astype("uint8"), mask)
138
- table.append((rasterize_method, paste_method, iou))
139
- return table
140
-
141
- def test_polygon_area(self):
142
- # Draw polygon boxes
143
- for d in [5.0, 10.0, 1000.0]:
144
- polygon = PolygonMasks([[[0, 0, 0, d, d, d, d, 0]]])
145
- area = polygon.area()[0]
146
- target = d ** 2
147
- self.assertEqual(area, target)
148
-
149
- # Draw polygon triangles
150
- for d in [5.0, 10.0, 1000.0]:
151
- polygon = PolygonMasks([[[0, 0, 0, d, d, d]]])
152
- area = polygon.area()[0]
153
- target = d ** 2 / 2
154
- self.assertEqual(area, target)
155
-
156
- def test_paste_mask_scriptable(self):
157
- scripted_f = torch.jit.script(paste_masks_in_image)
158
- N = 10
159
- masks = torch.rand(N, 28, 28)
160
- boxes = Boxes(random_boxes(N, 100)).tensor
161
- image_shape = (150, 150)
162
-
163
- out = paste_masks_in_image(masks, boxes, image_shape)
164
- scripted_out = scripted_f(masks, boxes, image_shape)
165
- self.assertTrue(torch.equal(out, scripted_out))
166
-
167
-
168
- def benchmark_paste():
169
- S = 800
170
- H, W = image_shape = (S, S)
171
- N = 64
172
- torch.manual_seed(42)
173
- masks = torch.rand(N, 28, 28)
174
-
175
- center = torch.rand(N, 2) * 600 + 100
176
- wh = torch.clamp(torch.randn(N, 2) * 40 + 200, min=50)
177
- x0y0 = torch.clamp(center - wh * 0.5, min=0.0)
178
- x1y1 = torch.clamp(center + wh * 0.5, max=S)
179
- boxes = Boxes(torch.cat([x0y0, x1y1], axis=1))
180
-
181
- def func(device, n=3):
182
- m = masks.to(device=device)
183
- b = boxes.to(device=device)
184
-
185
- def bench():
186
- for _ in range(n):
187
- paste_masks_in_image(m, b, image_shape)
188
- if device.type == "cuda":
189
- torch.cuda.synchronize()
190
-
191
- return bench
192
-
193
- specs = [{"device": torch.device("cpu"), "n": 3}]
194
- if torch.cuda.is_available():
195
- specs.append({"device": torch.device("cuda"), "n": 3})
196
-
197
- benchmark(func, "paste_masks", specs, num_iters=10, warmup_iters=2)
198
-
199
-
200
- if __name__ == "__main__":
201
- benchmark_paste()
202
- unittest.main()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/AzumaSeren100/XuanShen-Bert-VITS2/text/cleaner.py DELETED
@@ -1,27 +0,0 @@
1
- from text import chinese, cleaned_text_to_sequence
2
-
3
-
4
- language_module_map = {
5
- 'ZH': chinese
6
- }
7
-
8
-
9
- def clean_text(text, language):
10
- language_module = language_module_map[language]
11
- norm_text = language_module.text_normalize(text)
12
- phones, tones, word2ph = language_module.g2p(norm_text)
13
- return norm_text, phones, tones, word2ph
14
-
15
- def clean_text_bert(text, language):
16
- language_module = language_module_map[language]
17
- norm_text = language_module.text_normalize(text)
18
- phones, tones, word2ph = language_module.g2p(norm_text)
19
- bert = language_module.get_bert_feature(norm_text, word2ph)
20
- return phones, tones, bert
21
-
22
- def text_to_sequence(text, language):
23
- norm_text, phones, tones, word2ph = clean_text(text, language)
24
- return cleaned_text_to_sequence(phones, tones, language)
25
-
26
- if __name__ == '__main__':
27
- pass
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Bart92/RVC_HF/infer/lib/csvutil.py DELETED
@@ -1,41 +0,0 @@
1
-
2
- import numpy as np
3
-
4
- # import praatio
5
- # import praatio.praat_scripts
6
- import os
7
- import sys
8
-
9
- import random
10
-
11
- import csv
12
-
13
- # praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
14
-
15
-
16
- def CSVutil(file, rw, type, *args):
17
- if type == "formanting":
18
- if rw == "r":
19
- with open(file) as fileCSVread:
20
- csv_reader = list(csv.reader(fileCSVread))
21
- return (
22
- (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2])
23
- if csv_reader is not None
24
- else (lambda: exec('raise ValueError("No data")'))()
25
- )
26
- else:
27
- if args:
28
- doformnt = args[0]
29
- else:
30
- doformnt = False
31
- qfr = args[1] if len(args) > 1 else 1.0
32
- tmb = args[2] if len(args) > 2 else 1.0
33
- with open(file, rw, newline="") as fileCSVwrite:
34
- csv_writer = csv.writer(fileCSVwrite, delimiter=",")
35
- csv_writer.writerow([doformnt, qfr, tmb])
36
- elif type == "stop":
37
- stop = args[0] if args else False
38
- with open(file, rw, newline="") as fileCSVwrite:
39
- csv_writer = csv.writer(fileCSVwrite, delimiter=",")
40
- csv_writer.writerow([stop])
41
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/commands/show.py DELETED
@@ -1,189 +0,0 @@
1
- import logging
2
- from optparse import Values
3
- from typing import Generator, Iterable, Iterator, List, NamedTuple, Optional
4
-
5
- from pip._vendor.packaging.utils import canonicalize_name
6
-
7
- from pip._internal.cli.base_command import Command
8
- from pip._internal.cli.status_codes import ERROR, SUCCESS
9
- from pip._internal.metadata import BaseDistribution, get_default_environment
10
- from pip._internal.utils.misc import write_output
11
-
12
- logger = logging.getLogger(__name__)
13
-
14
-
15
- class ShowCommand(Command):
16
- """
17
- Show information about one or more installed packages.
18
-
19
- The output is in RFC-compliant mail header format.
20
- """
21
-
22
- usage = """
23
- %prog [options] <package> ..."""
24
- ignore_require_venv = True
25
-
26
- def add_options(self) -> None:
27
- self.cmd_opts.add_option(
28
- "-f",
29
- "--files",
30
- dest="files",
31
- action="store_true",
32
- default=False,
33
- help="Show the full list of installed files for each package.",
34
- )
35
-
36
- self.parser.insert_option_group(0, self.cmd_opts)
37
-
38
- def run(self, options: Values, args: List[str]) -> int:
39
- if not args:
40
- logger.warning("ERROR: Please provide a package name or names.")
41
- return ERROR
42
- query = args
43
-
44
- results = search_packages_info(query)
45
- if not print_results(
46
- results, list_files=options.files, verbose=options.verbose
47
- ):
48
- return ERROR
49
- return SUCCESS
50
-
51
-
52
- class _PackageInfo(NamedTuple):
53
- name: str
54
- version: str
55
- location: str
56
- editable_project_location: Optional[str]
57
- requires: List[str]
58
- required_by: List[str]
59
- installer: str
60
- metadata_version: str
61
- classifiers: List[str]
62
- summary: str
63
- homepage: str
64
- project_urls: List[str]
65
- author: str
66
- author_email: str
67
- license: str
68
- entry_points: List[str]
69
- files: Optional[List[str]]
70
-
71
-
72
- def search_packages_info(query: List[str]) -> Generator[_PackageInfo, None, None]:
73
- """
74
- Gather details from installed distributions. Print distribution name,
75
- version, location, and installed files. Installed files requires a
76
- pip generated 'installed-files.txt' in the distributions '.egg-info'
77
- directory.
78
- """
79
- env = get_default_environment()
80
-
81
- installed = {dist.canonical_name: dist for dist in env.iter_all_distributions()}
82
- query_names = [canonicalize_name(name) for name in query]
83
- missing = sorted(
84
- [name for name, pkg in zip(query, query_names) if pkg not in installed]
85
- )
86
- if missing:
87
- logger.warning("Package(s) not found: %s", ", ".join(missing))
88
-
89
- def _get_requiring_packages(current_dist: BaseDistribution) -> Iterator[str]:
90
- return (
91
- dist.metadata["Name"] or "UNKNOWN"
92
- for dist in installed.values()
93
- if current_dist.canonical_name
94
- in {canonicalize_name(d.name) for d in dist.iter_dependencies()}
95
- )
96
-
97
- for query_name in query_names:
98
- try:
99
- dist = installed[query_name]
100
- except KeyError:
101
- continue
102
-
103
- requires = sorted((req.name for req in dist.iter_dependencies()), key=str.lower)
104
- required_by = sorted(_get_requiring_packages(dist), key=str.lower)
105
-
106
- try:
107
- entry_points_text = dist.read_text("entry_points.txt")
108
- entry_points = entry_points_text.splitlines(keepends=False)
109
- except FileNotFoundError:
110
- entry_points = []
111
-
112
- files_iter = dist.iter_declared_entries()
113
- if files_iter is None:
114
- files: Optional[List[str]] = None
115
- else:
116
- files = sorted(files_iter)
117
-
118
- metadata = dist.metadata
119
-
120
- yield _PackageInfo(
121
- name=dist.raw_name,
122
- version=str(dist.version),
123
- location=dist.location or "",
124
- editable_project_location=dist.editable_project_location,
125
- requires=requires,
126
- required_by=required_by,
127
- installer=dist.installer,
128
- metadata_version=dist.metadata_version or "",
129
- classifiers=metadata.get_all("Classifier", []),
130
- summary=metadata.get("Summary", ""),
131
- homepage=metadata.get("Home-page", ""),
132
- project_urls=metadata.get_all("Project-URL", []),
133
- author=metadata.get("Author", ""),
134
- author_email=metadata.get("Author-email", ""),
135
- license=metadata.get("License", ""),
136
- entry_points=entry_points,
137
- files=files,
138
- )
139
-
140
-
141
- def print_results(
142
- distributions: Iterable[_PackageInfo],
143
- list_files: bool,
144
- verbose: bool,
145
- ) -> bool:
146
- """
147
- Print the information from installed distributions found.
148
- """
149
- results_printed = False
150
- for i, dist in enumerate(distributions):
151
- results_printed = True
152
- if i > 0:
153
- write_output("---")
154
-
155
- write_output("Name: %s", dist.name)
156
- write_output("Version: %s", dist.version)
157
- write_output("Summary: %s", dist.summary)
158
- write_output("Home-page: %s", dist.homepage)
159
- write_output("Author: %s", dist.author)
160
- write_output("Author-email: %s", dist.author_email)
161
- write_output("License: %s", dist.license)
162
- write_output("Location: %s", dist.location)
163
- if dist.editable_project_location is not None:
164
- write_output(
165
- "Editable project location: %s", dist.editable_project_location
166
- )
167
- write_output("Requires: %s", ", ".join(dist.requires))
168
- write_output("Required-by: %s", ", ".join(dist.required_by))
169
-
170
- if verbose:
171
- write_output("Metadata-Version: %s", dist.metadata_version)
172
- write_output("Installer: %s", dist.installer)
173
- write_output("Classifiers:")
174
- for classifier in dist.classifiers:
175
- write_output(" %s", classifier)
176
- write_output("Entry-points:")
177
- for entry in dist.entry_points:
178
- write_output(" %s", entry.strip())
179
- write_output("Project-URLs:")
180
- for project_url in dist.project_urls:
181
- write_output(" %s", project_url)
182
- if list_files:
183
- write_output("Files:")
184
- if dist.files is None:
185
- write_output("Cannot locate RECORD or installed-files.txt")
186
- else:
187
- for line in dist.files:
188
- write_output(" %s", line.strip())
189
- return results_printed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/_manylinux.py DELETED
@@ -1,301 +0,0 @@
1
- import collections
2
- import functools
3
- import os
4
- import re
5
- import struct
6
- import sys
7
- import warnings
8
- from typing import IO, Dict, Iterator, NamedTuple, Optional, Tuple
9
-
10
-
11
- # Python does not provide platform information at sufficient granularity to
12
- # identify the architecture of the running executable in some cases, so we
13
- # determine it dynamically by reading the information from the running
14
- # process. This only applies on Linux, which uses the ELF format.
15
- class _ELFFileHeader:
16
- # https://en.wikipedia.org/wiki/Executable_and_Linkable_Format#File_header
17
- class _InvalidELFFileHeader(ValueError):
18
- """
19
- An invalid ELF file header was found.
20
- """
21
-
22
- ELF_MAGIC_NUMBER = 0x7F454C46
23
- ELFCLASS32 = 1
24
- ELFCLASS64 = 2
25
- ELFDATA2LSB = 1
26
- ELFDATA2MSB = 2
27
- EM_386 = 3
28
- EM_S390 = 22
29
- EM_ARM = 40
30
- EM_X86_64 = 62
31
- EF_ARM_ABIMASK = 0xFF000000
32
- EF_ARM_ABI_VER5 = 0x05000000
33
- EF_ARM_ABI_FLOAT_HARD = 0x00000400
34
-
35
- def __init__(self, file: IO[bytes]) -> None:
36
- def unpack(fmt: str) -> int:
37
- try:
38
- data = file.read(struct.calcsize(fmt))
39
- result: Tuple[int, ...] = struct.unpack(fmt, data)
40
- except struct.error:
41
- raise _ELFFileHeader._InvalidELFFileHeader()
42
- return result[0]
43
-
44
- self.e_ident_magic = unpack(">I")
45
- if self.e_ident_magic != self.ELF_MAGIC_NUMBER:
46
- raise _ELFFileHeader._InvalidELFFileHeader()
47
- self.e_ident_class = unpack("B")
48
- if self.e_ident_class not in {self.ELFCLASS32, self.ELFCLASS64}:
49
- raise _ELFFileHeader._InvalidELFFileHeader()
50
- self.e_ident_data = unpack("B")
51
- if self.e_ident_data not in {self.ELFDATA2LSB, self.ELFDATA2MSB}:
52
- raise _ELFFileHeader._InvalidELFFileHeader()
53
- self.e_ident_version = unpack("B")
54
- self.e_ident_osabi = unpack("B")
55
- self.e_ident_abiversion = unpack("B")
56
- self.e_ident_pad = file.read(7)
57
- format_h = "<H" if self.e_ident_data == self.ELFDATA2LSB else ">H"
58
- format_i = "<I" if self.e_ident_data == self.ELFDATA2LSB else ">I"
59
- format_q = "<Q" if self.e_ident_data == self.ELFDATA2LSB else ">Q"
60
- format_p = format_i if self.e_ident_class == self.ELFCLASS32 else format_q
61
- self.e_type = unpack(format_h)
62
- self.e_machine = unpack(format_h)
63
- self.e_version = unpack(format_i)
64
- self.e_entry = unpack(format_p)
65
- self.e_phoff = unpack(format_p)
66
- self.e_shoff = unpack(format_p)
67
- self.e_flags = unpack(format_i)
68
- self.e_ehsize = unpack(format_h)
69
- self.e_phentsize = unpack(format_h)
70
- self.e_phnum = unpack(format_h)
71
- self.e_shentsize = unpack(format_h)
72
- self.e_shnum = unpack(format_h)
73
- self.e_shstrndx = unpack(format_h)
74
-
75
-
76
- def _get_elf_header() -> Optional[_ELFFileHeader]:
77
- try:
78
- with open(sys.executable, "rb") as f:
79
- elf_header = _ELFFileHeader(f)
80
- except (OSError, TypeError, _ELFFileHeader._InvalidELFFileHeader):
81
- return None
82
- return elf_header
83
-
84
-
85
- def _is_linux_armhf() -> bool:
86
- # hard-float ABI can be detected from the ELF header of the running
87
- # process
88
- # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
89
- elf_header = _get_elf_header()
90
- if elf_header is None:
91
- return False
92
- result = elf_header.e_ident_class == elf_header.ELFCLASS32
93
- result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
94
- result &= elf_header.e_machine == elf_header.EM_ARM
95
- result &= (
96
- elf_header.e_flags & elf_header.EF_ARM_ABIMASK
97
- ) == elf_header.EF_ARM_ABI_VER5
98
- result &= (
99
- elf_header.e_flags & elf_header.EF_ARM_ABI_FLOAT_HARD
100
- ) == elf_header.EF_ARM_ABI_FLOAT_HARD
101
- return result
102
-
103
-
104
- def _is_linux_i686() -> bool:
105
- elf_header = _get_elf_header()
106
- if elf_header is None:
107
- return False
108
- result = elf_header.e_ident_class == elf_header.ELFCLASS32
109
- result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
110
- result &= elf_header.e_machine == elf_header.EM_386
111
- return result
112
-
113
-
114
- def _have_compatible_abi(arch: str) -> bool:
115
- if arch == "armv7l":
116
- return _is_linux_armhf()
117
- if arch == "i686":
118
- return _is_linux_i686()
119
- return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
120
-
121
-
122
- # If glibc ever changes its major version, we need to know what the last
123
- # minor version was, so we can build the complete list of all versions.
124
- # For now, guess what the highest minor version might be, assume it will
125
- # be 50 for testing. Once this actually happens, update the dictionary
126
- # with the actual value.
127
- _LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
128
-
129
-
130
- class _GLibCVersion(NamedTuple):
131
- major: int
132
- minor: int
133
-
134
-
135
- def _glibc_version_string_confstr() -> Optional[str]:
136
- """
137
- Primary implementation of glibc_version_string using os.confstr.
138
- """
139
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
140
- # to be broken or missing. This strategy is used in the standard library
141
- # platform module.
142
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
143
- try:
144
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17".
145
- version_string = os.confstr("CS_GNU_LIBC_VERSION")
146
- assert version_string is not None
147
- _, version = version_string.split()
148
- except (AssertionError, AttributeError, OSError, ValueError):
149
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
150
- return None
151
- return version
152
-
153
-
154
- def _glibc_version_string_ctypes() -> Optional[str]:
155
- """
156
- Fallback implementation of glibc_version_string using ctypes.
157
- """
158
- try:
159
- import ctypes
160
- except ImportError:
161
- return None
162
-
163
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
164
- # manpage says, "If filename is NULL, then the returned handle is for the
165
- # main program". This way we can let the linker do the work to figure out
166
- # which libc our process is actually using.
167
- #
168
- # We must also handle the special case where the executable is not a
169
- # dynamically linked executable. This can occur when using musl libc,
170
- # for example. In this situation, dlopen() will error, leading to an
171
- # OSError. Interestingly, at least in the case of musl, there is no
172
- # errno set on the OSError. The single string argument used to construct
173
- # OSError comes from libc itself and is therefore not portable to
174
- # hard code here. In any case, failure to call dlopen() means we
175
- # can proceed, so we bail on our attempt.
176
- try:
177
- process_namespace = ctypes.CDLL(None)
178
- except OSError:
179
- return None
180
-
181
- try:
182
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
183
- except AttributeError:
184
- # Symbol doesn't exist -> therefore, we are not linked to
185
- # glibc.
186
- return None
187
-
188
- # Call gnu_get_libc_version, which returns a string like "2.5"
189
- gnu_get_libc_version.restype = ctypes.c_char_p
190
- version_str: str = gnu_get_libc_version()
191
- # py2 / py3 compatibility:
192
- if not isinstance(version_str, str):
193
- version_str = version_str.decode("ascii")
194
-
195
- return version_str
196
-
197
-
198
- def _glibc_version_string() -> Optional[str]:
199
- """Returns glibc version string, or None if not using glibc."""
200
- return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
201
-
202
-
203
- def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
204
- """Parse glibc version.
205
-
206
- We use a regexp instead of str.split because we want to discard any
207
- random junk that might come after the minor version -- this might happen
208
- in patched/forked versions of glibc (e.g. Linaro's version of glibc
209
- uses version strings like "2.20-2014.11"). See gh-3588.
210
- """
211
- m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
212
- if not m:
213
- warnings.warn(
214
- "Expected glibc version with 2 components major.minor,"
215
- " got: %s" % version_str,
216
- RuntimeWarning,
217
- )
218
- return -1, -1
219
- return int(m.group("major")), int(m.group("minor"))
220
-
221
-
222
- @functools.lru_cache()
223
- def _get_glibc_version() -> Tuple[int, int]:
224
- version_str = _glibc_version_string()
225
- if version_str is None:
226
- return (-1, -1)
227
- return _parse_glibc_version(version_str)
228
-
229
-
230
- # From PEP 513, PEP 600
231
- def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
232
- sys_glibc = _get_glibc_version()
233
- if sys_glibc < version:
234
- return False
235
- # Check for presence of _manylinux module.
236
- try:
237
- import _manylinux # noqa
238
- except ImportError:
239
- return True
240
- if hasattr(_manylinux, "manylinux_compatible"):
241
- result = _manylinux.manylinux_compatible(version[0], version[1], arch)
242
- if result is not None:
243
- return bool(result)
244
- return True
245
- if version == _GLibCVersion(2, 5):
246
- if hasattr(_manylinux, "manylinux1_compatible"):
247
- return bool(_manylinux.manylinux1_compatible)
248
- if version == _GLibCVersion(2, 12):
249
- if hasattr(_manylinux, "manylinux2010_compatible"):
250
- return bool(_manylinux.manylinux2010_compatible)
251
- if version == _GLibCVersion(2, 17):
252
- if hasattr(_manylinux, "manylinux2014_compatible"):
253
- return bool(_manylinux.manylinux2014_compatible)
254
- return True
255
-
256
-
257
- _LEGACY_MANYLINUX_MAP = {
258
- # CentOS 7 w/ glibc 2.17 (PEP 599)
259
- (2, 17): "manylinux2014",
260
- # CentOS 6 w/ glibc 2.12 (PEP 571)
261
- (2, 12): "manylinux2010",
262
- # CentOS 5 w/ glibc 2.5 (PEP 513)
263
- (2, 5): "manylinux1",
264
- }
265
-
266
-
267
- def platform_tags(linux: str, arch: str) -> Iterator[str]:
268
- if not _have_compatible_abi(arch):
269
- return
270
- # Oldest glibc to be supported regardless of architecture is (2, 17).
271
- too_old_glibc2 = _GLibCVersion(2, 16)
272
- if arch in {"x86_64", "i686"}:
273
- # On x86/i686 also oldest glibc to be supported is (2, 5).
274
- too_old_glibc2 = _GLibCVersion(2, 4)
275
- current_glibc = _GLibCVersion(*_get_glibc_version())
276
- glibc_max_list = [current_glibc]
277
- # We can assume compatibility across glibc major versions.
278
- # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
279
- #
280
- # Build a list of maximum glibc versions so that we can
281
- # output the canonical list of all glibc from current_glibc
282
- # down to too_old_glibc2, including all intermediary versions.
283
- for glibc_major in range(current_glibc.major - 1, 1, -1):
284
- glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
285
- glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
286
- for glibc_max in glibc_max_list:
287
- if glibc_max.major == too_old_glibc2.major:
288
- min_minor = too_old_glibc2.minor
289
- else:
290
- # For other glibc major versions oldest supported is (x, 0).
291
- min_minor = -1
292
- for glibc_minor in range(glibc_max.minor, min_minor, -1):
293
- glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
294
- tag = "manylinux_{}_{}".format(*glibc_version)
295
- if _is_compatible(tag, arch, glibc_version):
296
- yield linux.replace("linux", tag)
297
- # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
298
- if glibc_version in _LEGACY_MANYLINUX_MAP:
299
- legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
300
- if _is_compatible(legacy_tag, arch, glibc_version):
301
- yield linux.replace("linux", legacy_tag)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/editable_wheel.py DELETED
@@ -1,844 +0,0 @@
1
- """
2
- Create a wheel that, when installed, will make the source package 'editable'
3
- (add it to the interpreter's path, including metadata) per PEP 660. Replaces
4
- 'setup.py develop'.
5
-
6
- .. note::
7
- One of the mechanisms briefly mentioned in PEP 660 to implement editable installs is
8
- to create a separated directory inside ``build`` and use a .pth file to point to that
9
- directory. In the context of this file such directory is referred as
10
- *auxiliary build directory* or ``auxiliary_dir``.
11
- """
12
-
13
- import logging
14
- import os
15
- import re
16
- import shutil
17
- import sys
18
- import traceback
19
- import warnings
20
- from contextlib import suppress
21
- from enum import Enum
22
- from inspect import cleandoc
23
- from itertools import chain
24
- from pathlib import Path
25
- from tempfile import TemporaryDirectory
26
- from typing import (
27
- TYPE_CHECKING,
28
- Dict,
29
- Iterable,
30
- Iterator,
31
- List,
32
- Mapping,
33
- Optional,
34
- Tuple,
35
- TypeVar,
36
- Union,
37
- )
38
-
39
- from setuptools import Command, SetuptoolsDeprecationWarning, errors, namespaces
40
- from setuptools.command.build_py import build_py as build_py_cls
41
- from setuptools.discovery import find_package_path
42
- from setuptools.dist import Distribution
43
-
44
- if TYPE_CHECKING:
45
- from wheel.wheelfile import WheelFile # noqa
46
-
47
- if sys.version_info >= (3, 8):
48
- from typing import Protocol
49
- elif TYPE_CHECKING:
50
- from typing_extensions import Protocol
51
- else:
52
- from abc import ABC as Protocol
53
-
54
- _Path = Union[str, Path]
55
- _P = TypeVar("_P", bound=_Path)
56
- _logger = logging.getLogger(__name__)
57
-
58
-
59
- class _EditableMode(Enum):
60
- """
61
- Possible editable installation modes:
62
- `lenient` (new files automatically added to the package - DEFAULT);
63
- `strict` (requires a new installation when files are added/removed); or
64
- `compat` (attempts to emulate `python setup.py develop` - DEPRECATED).
65
- """
66
-
67
- STRICT = "strict"
68
- LENIENT = "lenient"
69
- COMPAT = "compat" # TODO: Remove `compat` after Dec/2022.
70
-
71
- @classmethod
72
- def convert(cls, mode: Optional[str]) -> "_EditableMode":
73
- if not mode:
74
- return _EditableMode.LENIENT # default
75
-
76
- _mode = mode.upper()
77
- if _mode not in _EditableMode.__members__:
78
- raise errors.OptionError(f"Invalid editable mode: {mode!r}. Try: 'strict'.")
79
-
80
- if _mode == "COMPAT":
81
- msg = """
82
- The 'compat' editable mode is transitional and will be removed
83
- in future versions of `setuptools`.
84
- Please adapt your code accordingly to use either the 'strict' or the
85
- 'lenient' modes.
86
-
87
- For more information, please check:
88
- https://setuptools.pypa.io/en/latest/userguide/development_mode.html
89
- """
90
- warnings.warn(msg, SetuptoolsDeprecationWarning)
91
-
92
- return _EditableMode[_mode]
93
-
94
-
95
- _STRICT_WARNING = """
96
- New or renamed files may not be automatically picked up without a new installation.
97
- """
98
-
99
- _LENIENT_WARNING = """
100
- Options like `package-data`, `include/exclude-package-data` or
101
- `packages.find.exclude/include` may have no effect.
102
- """
103
-
104
-
105
- class editable_wheel(Command):
106
- """Build 'editable' wheel for development.
107
- (This command is reserved for internal use of setuptools).
108
- """
109
-
110
- description = "create a PEP 660 'editable' wheel"
111
-
112
- user_options = [
113
- ("dist-dir=", "d", "directory to put final built distributions in"),
114
- ("dist-info-dir=", "I", "path to a pre-build .dist-info directory"),
115
- ("mode=", None, cleandoc(_EditableMode.__doc__ or "")),
116
- ]
117
-
118
- def initialize_options(self):
119
- self.dist_dir = None
120
- self.dist_info_dir = None
121
- self.project_dir = None
122
- self.mode = None
123
-
124
- def finalize_options(self):
125
- dist = self.distribution
126
- self.project_dir = dist.src_root or os.curdir
127
- self.package_dir = dist.package_dir or {}
128
- self.dist_dir = Path(self.dist_dir or os.path.join(self.project_dir, "dist"))
129
-
130
- def run(self):
131
- try:
132
- self.dist_dir.mkdir(exist_ok=True)
133
- self._ensure_dist_info()
134
-
135
- # Add missing dist_info files
136
- self.reinitialize_command("bdist_wheel")
137
- bdist_wheel = self.get_finalized_command("bdist_wheel")
138
- bdist_wheel.write_wheelfile(self.dist_info_dir)
139
-
140
- self._create_wheel_file(bdist_wheel)
141
- except Exception as ex:
142
- traceback.print_exc()
143
- msg = """
144
- Support for editable installs via PEP 660 was recently introduced
145
- in `setuptools`. If you are seeing this error, please report to:
146
-
147
- https://github.com/pypa/setuptools/issues
148
-
149
- Meanwhile you can try the legacy behavior by setting an
150
- environment variable and trying to install again:
151
-
152
- SETUPTOOLS_ENABLE_FEATURES="legacy-editable"
153
- """
154
- raise errors.InternalError(cleandoc(msg)) from ex
155
-
156
- def _ensure_dist_info(self):
157
- if self.dist_info_dir is None:
158
- dist_info = self.reinitialize_command("dist_info")
159
- dist_info.output_dir = self.dist_dir
160
- dist_info.ensure_finalized()
161
- dist_info.run()
162
- self.dist_info_dir = dist_info.dist_info_dir
163
- else:
164
- assert str(self.dist_info_dir).endswith(".dist-info")
165
- assert Path(self.dist_info_dir, "METADATA").exists()
166
-
167
- def _install_namespaces(self, installation_dir, pth_prefix):
168
- # XXX: Only required to support the deprecated namespace practice
169
- dist = self.distribution
170
- if not dist.namespace_packages:
171
- return
172
-
173
- src_root = Path(self.project_dir, self.package_dir.get("", ".")).resolve()
174
- installer = _NamespaceInstaller(dist, installation_dir, pth_prefix, src_root)
175
- installer.install_namespaces()
176
-
177
- def _find_egg_info_dir(self) -> Optional[str]:
178
- parent_dir = Path(self.dist_info_dir).parent if self.dist_info_dir else Path()
179
- candidates = map(str, parent_dir.glob("*.egg-info"))
180
- return next(candidates, None)
181
-
182
- def _configure_build(
183
- self, name: str, unpacked_wheel: _Path, build_lib: _Path, tmp_dir: _Path
184
- ):
185
- """Configure commands to behave in the following ways:
186
-
187
- - Build commands can write to ``build_lib`` if they really want to...
188
- (but this folder is expected to be ignored and modules are expected to live
189
- in the project directory...)
190
- - Binary extensions should be built in-place (editable_mode = True)
191
- - Data/header/script files are not part of the "editable" specification
192
- so they are written directly to the unpacked_wheel directory.
193
- """
194
- # Non-editable files (data, headers, scripts) are written directly to the
195
- # unpacked_wheel
196
-
197
- dist = self.distribution
198
- wheel = str(unpacked_wheel)
199
- build_lib = str(build_lib)
200
- data = str(Path(unpacked_wheel, f"{name}.data", "data"))
201
- headers = str(Path(unpacked_wheel, f"{name}.data", "headers"))
202
- scripts = str(Path(unpacked_wheel, f"{name}.data", "scripts"))
203
-
204
- # egg-info may be generated again to create a manifest (used for package data)
205
- egg_info = dist.reinitialize_command("egg_info", reinit_subcommands=True)
206
- egg_info.egg_base = str(tmp_dir)
207
- egg_info.ignore_egg_info_in_manifest = True
208
-
209
- build = dist.reinitialize_command("build", reinit_subcommands=True)
210
- install = dist.reinitialize_command("install", reinit_subcommands=True)
211
-
212
- build.build_platlib = build.build_purelib = build.build_lib = build_lib
213
- install.install_purelib = install.install_platlib = install.install_lib = wheel
214
- install.install_scripts = build.build_scripts = scripts
215
- install.install_headers = headers
216
- install.install_data = data
217
-
218
- install_scripts = dist.get_command_obj("install_scripts")
219
- install_scripts.no_ep = True
220
-
221
- build.build_temp = str(tmp_dir)
222
-
223
- build_py = dist.get_command_obj("build_py")
224
- build_py.compile = False
225
- build_py.existing_egg_info_dir = self._find_egg_info_dir()
226
-
227
- self._set_editable_mode()
228
-
229
- build.ensure_finalized()
230
- install.ensure_finalized()
231
-
232
- def _set_editable_mode(self):
233
- """Set the ``editable_mode`` flag in the build sub-commands"""
234
- dist = self.distribution
235
- build = dist.get_command_obj("build")
236
- for cmd_name in build.get_sub_commands():
237
- cmd = dist.get_command_obj(cmd_name)
238
- if hasattr(cmd, "editable_mode"):
239
- cmd.editable_mode = True
240
- elif hasattr(cmd, "inplace"):
241
- cmd.inplace = True # backward compatibility with distutils
242
-
243
- def _collect_build_outputs(self) -> Tuple[List[str], Dict[str, str]]:
244
- files: List[str] = []
245
- mapping: Dict[str, str] = {}
246
- build = self.get_finalized_command("build")
247
-
248
- for cmd_name in build.get_sub_commands():
249
- cmd = self.get_finalized_command(cmd_name)
250
- if hasattr(cmd, "get_outputs"):
251
- files.extend(cmd.get_outputs() or [])
252
- if hasattr(cmd, "get_output_mapping"):
253
- mapping.update(cmd.get_output_mapping() or {})
254
-
255
- return files, mapping
256
-
257
- def _run_build_commands(
258
- self, dist_name: str, unpacked_wheel: _Path, build_lib: _Path, tmp_dir: _Path
259
- ) -> Tuple[List[str], Dict[str, str]]:
260
- self._configure_build(dist_name, unpacked_wheel, build_lib, tmp_dir)
261
- self._run_build_subcommands()
262
- files, mapping = self._collect_build_outputs()
263
- self._run_install("headers")
264
- self._run_install("scripts")
265
- self._run_install("data")
266
- return files, mapping
267
-
268
- def _run_build_subcommands(self):
269
- """
270
- Issue #3501 indicates that some plugins/customizations might rely on:
271
-
272
- 1. ``build_py`` not running
273
- 2. ``build_py`` always copying files to ``build_lib``
274
-
275
- However both these assumptions may be false in editable_wheel.
276
- This method implements a temporary workaround to support the ecosystem
277
- while the implementations catch up.
278
- """
279
- # TODO: Once plugins/customisations had the chance to catch up, replace
280
- # `self._run_build_subcommands()` with `self.run_command("build")`.
281
- # Also remove _safely_run, TestCustomBuildPy. Suggested date: Aug/2023.
282
- build: Command = self.get_finalized_command("build")
283
- for name in build.get_sub_commands():
284
- cmd = self.get_finalized_command(name)
285
- if name == "build_py" and type(cmd) != build_py_cls:
286
- self._safely_run(name)
287
- else:
288
- self.run_command(name)
289
-
290
- def _safely_run(self, cmd_name: str):
291
- try:
292
- return self.run_command(cmd_name)
293
- except Exception:
294
- msg = f"""{traceback.format_exc()}\n
295
- If you are seeing this warning it is very likely that a setuptools
296
- plugin or customization overrides the `{cmd_name}` command, without
297
- taking into consideration how editable installs run build steps
298
- starting from v64.0.0.
299
-
300
- Plugin authors and developers relying on custom build steps are encouraged
301
- to update their `{cmd_name}` implementation considering the information in
302
- https://setuptools.pypa.io/en/latest/userguide/extension.html
303
- about editable installs.
304
-
305
- For the time being `setuptools` will silence this error and ignore
306
- the faulty command, but this behaviour will change in future versions.\n
307
- """
308
- warnings.warn(msg, SetuptoolsDeprecationWarning, stacklevel=2)
309
-
310
- def _create_wheel_file(self, bdist_wheel):
311
- from wheel.wheelfile import WheelFile
312
-
313
- dist_info = self.get_finalized_command("dist_info")
314
- dist_name = dist_info.name
315
- tag = "-".join(bdist_wheel.get_tag())
316
- build_tag = "0.editable" # According to PEP 427 needs to start with digit
317
- archive_name = f"{dist_name}-{build_tag}-{tag}.whl"
318
- wheel_path = Path(self.dist_dir, archive_name)
319
- if wheel_path.exists():
320
- wheel_path.unlink()
321
-
322
- unpacked_wheel = TemporaryDirectory(suffix=archive_name)
323
- build_lib = TemporaryDirectory(suffix=".build-lib")
324
- build_tmp = TemporaryDirectory(suffix=".build-temp")
325
-
326
- with unpacked_wheel as unpacked, build_lib as lib, build_tmp as tmp:
327
- unpacked_dist_info = Path(unpacked, Path(self.dist_info_dir).name)
328
- shutil.copytree(self.dist_info_dir, unpacked_dist_info)
329
- self._install_namespaces(unpacked, dist_info.name)
330
- files, mapping = self._run_build_commands(dist_name, unpacked, lib, tmp)
331
- strategy = self._select_strategy(dist_name, tag, lib)
332
- with strategy, WheelFile(wheel_path, "w") as wheel_obj:
333
- strategy(wheel_obj, files, mapping)
334
- wheel_obj.write_files(unpacked)
335
-
336
- return wheel_path
337
-
338
- def _run_install(self, category: str):
339
- has_category = getattr(self.distribution, f"has_{category}", None)
340
- if has_category and has_category():
341
- _logger.info(f"Installing {category} as non editable")
342
- self.run_command(f"install_{category}")
343
-
344
- def _select_strategy(
345
- self,
346
- name: str,
347
- tag: str,
348
- build_lib: _Path,
349
- ) -> "EditableStrategy":
350
- """Decides which strategy to use to implement an editable installation."""
351
- build_name = f"__editable__.{name}-{tag}"
352
- project_dir = Path(self.project_dir)
353
- mode = _EditableMode.convert(self.mode)
354
-
355
- if mode is _EditableMode.STRICT:
356
- auxiliary_dir = _empty_dir(Path(self.project_dir, "build", build_name))
357
- return _LinkTree(self.distribution, name, auxiliary_dir, build_lib)
358
-
359
- packages = _find_packages(self.distribution)
360
- has_simple_layout = _simple_layout(packages, self.package_dir, project_dir)
361
- is_compat_mode = mode is _EditableMode.COMPAT
362
- if set(self.package_dir) == {""} and has_simple_layout or is_compat_mode:
363
- # src-layout(ish) is relatively safe for a simple pth file
364
- src_dir = self.package_dir.get("", ".")
365
- return _StaticPth(self.distribution, name, [Path(project_dir, src_dir)])
366
-
367
- # Use a MetaPathFinder to avoid adding accidental top-level packages/modules
368
- return _TopLevelFinder(self.distribution, name)
369
-
370
-
371
- class EditableStrategy(Protocol):
372
- def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
373
- ...
374
-
375
- def __enter__(self):
376
- ...
377
-
378
- def __exit__(self, _exc_type, _exc_value, _traceback):
379
- ...
380
-
381
-
382
- class _StaticPth:
383
- def __init__(self, dist: Distribution, name: str, path_entries: List[Path]):
384
- self.dist = dist
385
- self.name = name
386
- self.path_entries = path_entries
387
-
388
- def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
389
- entries = "\n".join((str(p.resolve()) for p in self.path_entries))
390
- contents = bytes(f"{entries}\n", "utf-8")
391
- wheel.writestr(f"__editable__.{self.name}.pth", contents)
392
-
393
- def __enter__(self):
394
- msg = f"""
395
- Editable install will be performed using .pth file to extend `sys.path` with:
396
- {list(map(os.fspath, self.path_entries))!r}
397
- """
398
- _logger.warning(msg + _LENIENT_WARNING)
399
- return self
400
-
401
- def __exit__(self, _exc_type, _exc_value, _traceback):
402
- ...
403
-
404
-
405
- class _LinkTree(_StaticPth):
406
- """
407
- Creates a ``.pth`` file that points to a link tree in the ``auxiliary_dir``.
408
-
409
- This strategy will only link files (not dirs), so it can be implemented in
410
- any OS, even if that means using hardlinks instead of symlinks.
411
-
412
- By collocating ``auxiliary_dir`` and the original source code, limitations
413
- with hardlinks should be avoided.
414
- """
415
- def __init__(
416
- self, dist: Distribution,
417
- name: str,
418
- auxiliary_dir: _Path,
419
- build_lib: _Path,
420
- ):
421
- self.auxiliary_dir = Path(auxiliary_dir)
422
- self.build_lib = Path(build_lib).resolve()
423
- self._file = dist.get_command_obj("build_py").copy_file
424
- super().__init__(dist, name, [self.auxiliary_dir])
425
-
426
- def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
427
- self._create_links(files, mapping)
428
- super().__call__(wheel, files, mapping)
429
-
430
- def _normalize_output(self, file: str) -> Optional[str]:
431
- # Files relative to build_lib will be normalized to None
432
- with suppress(ValueError):
433
- path = Path(file).resolve().relative_to(self.build_lib)
434
- return str(path).replace(os.sep, '/')
435
- return None
436
-
437
- def _create_file(self, relative_output: str, src_file: str, link=None):
438
- dest = self.auxiliary_dir / relative_output
439
- if not dest.parent.is_dir():
440
- dest.parent.mkdir(parents=True)
441
- self._file(src_file, dest, link=link)
442
-
443
- def _create_links(self, outputs, output_mapping):
444
- self.auxiliary_dir.mkdir(parents=True, exist_ok=True)
445
- link_type = "sym" if _can_symlink_files(self.auxiliary_dir) else "hard"
446
- mappings = {
447
- self._normalize_output(k): v
448
- for k, v in output_mapping.items()
449
- }
450
- mappings.pop(None, None) # remove files that are not relative to build_lib
451
-
452
- for output in outputs:
453
- relative = self._normalize_output(output)
454
- if relative and relative not in mappings:
455
- self._create_file(relative, output)
456
-
457
- for relative, src in mappings.items():
458
- self._create_file(relative, src, link=link_type)
459
-
460
- def __enter__(self):
461
- msg = "Strict editable install will be performed using a link tree.\n"
462
- _logger.warning(msg + _STRICT_WARNING)
463
- return self
464
-
465
- def __exit__(self, _exc_type, _exc_value, _traceback):
466
- msg = f"""\n
467
- Strict editable installation performed using the auxiliary directory:
468
- {self.auxiliary_dir}
469
-
470
- Please be careful to not remove this directory, otherwise you might not be able
471
- to import/use your package.
472
- """
473
- warnings.warn(msg, InformationOnly)
474
-
475
-
476
- class _TopLevelFinder:
477
- def __init__(self, dist: Distribution, name: str):
478
- self.dist = dist
479
- self.name = name
480
-
481
- def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
482
- src_root = self.dist.src_root or os.curdir
483
- top_level = chain(_find_packages(self.dist), _find_top_level_modules(self.dist))
484
- package_dir = self.dist.package_dir or {}
485
- roots = _find_package_roots(top_level, package_dir, src_root)
486
-
487
- namespaces_: Dict[str, List[str]] = dict(chain(
488
- _find_namespaces(self.dist.packages or [], roots),
489
- ((ns, []) for ns in _find_virtual_namespaces(roots)),
490
- ))
491
-
492
- name = f"__editable__.{self.name}.finder"
493
- finder = _make_identifier(name)
494
- content = bytes(_finder_template(name, roots, namespaces_), "utf-8")
495
- wheel.writestr(f"{finder}.py", content)
496
-
497
- content = bytes(f"import {finder}; {finder}.install()", "utf-8")
498
- wheel.writestr(f"__editable__.{self.name}.pth", content)
499
-
500
- def __enter__(self):
501
- msg = "Editable install will be performed using a meta path finder.\n"
502
- _logger.warning(msg + _LENIENT_WARNING)
503
- return self
504
-
505
- def __exit__(self, _exc_type, _exc_value, _traceback):
506
- msg = """\n
507
- Please be careful with folders in your working directory with the same
508
- name as your package as they may take precedence during imports.
509
- """
510
- warnings.warn(msg, InformationOnly)
511
-
512
-
513
- def _can_symlink_files(base_dir: Path) -> bool:
514
- with TemporaryDirectory(dir=str(base_dir.resolve())) as tmp:
515
- path1, path2 = Path(tmp, "file1.txt"), Path(tmp, "file2.txt")
516
- path1.write_text("file1", encoding="utf-8")
517
- with suppress(AttributeError, NotImplementedError, OSError):
518
- os.symlink(path1, path2)
519
- if path2.is_symlink() and path2.read_text(encoding="utf-8") == "file1":
520
- return True
521
-
522
- try:
523
- os.link(path1, path2) # Ensure hard links can be created
524
- except Exception as ex:
525
- msg = (
526
- "File system does not seem to support either symlinks or hard links. "
527
- "Strict editable installs require one of them to be supported."
528
- )
529
- raise LinksNotSupported(msg) from ex
530
- return False
531
-
532
-
533
- def _simple_layout(
534
- packages: Iterable[str], package_dir: Dict[str, str], project_dir: Path
535
- ) -> bool:
536
- """Return ``True`` if:
537
- - all packages are contained by the same parent directory, **and**
538
- - all packages become importable if the parent directory is added to ``sys.path``.
539
-
540
- >>> _simple_layout(['a'], {"": "src"}, "/tmp/myproj")
541
- True
542
- >>> _simple_layout(['a', 'a.b'], {"": "src"}, "/tmp/myproj")
543
- True
544
- >>> _simple_layout(['a', 'a.b'], {}, "/tmp/myproj")
545
- True
546
- >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"": "src"}, "/tmp/myproj")
547
- True
548
- >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "a", "b": "b"}, ".")
549
- True
550
- >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "_a", "b": "_b"}, ".")
551
- False
552
- >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "_a"}, "/tmp/myproj")
553
- False
554
- >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a.a1.a2": "_a2"}, ".")
555
- False
556
- >>> _simple_layout(['a', 'a.b'], {"": "src", "a.b": "_ab"}, "/tmp/myproj")
557
- False
558
- >>> # Special cases, no packages yet:
559
- >>> _simple_layout([], {"": "src"}, "/tmp/myproj")
560
- True
561
- >>> _simple_layout([], {"a": "_a", "": "src"}, "/tmp/myproj")
562
- False
563
- """
564
- layout = {
565
- pkg: find_package_path(pkg, package_dir, project_dir)
566
- for pkg in packages
567
- }
568
- if not layout:
569
- return set(package_dir) in ({}, {""})
570
- parent = os.path.commonpath([_parent_path(k, v) for k, v in layout.items()])
571
- return all(
572
- _normalize_path(Path(parent, *key.split('.'))) == _normalize_path(value)
573
- for key, value in layout.items()
574
- )
575
-
576
-
577
- def _parent_path(pkg, pkg_path):
578
- """Infer the parent path containing a package, that if added to ``sys.path`` would
579
- allow importing that package.
580
- When ``pkg`` is directly mapped into a directory with a different name, return its
581
- own path.
582
- >>> _parent_path("a", "src/a")
583
- 'src'
584
- >>> _parent_path("b", "src/c")
585
- 'src/c'
586
- """
587
- parent = pkg_path[:-len(pkg)] if pkg_path.endswith(pkg) else pkg_path
588
- return parent.rstrip("/" + os.sep)
589
-
590
-
591
- def _find_packages(dist: Distribution) -> Iterator[str]:
592
- yield from iter(dist.packages or [])
593
-
594
- py_modules = dist.py_modules or []
595
- nested_modules = [mod for mod in py_modules if "." in mod]
596
- if dist.ext_package:
597
- yield dist.ext_package
598
- else:
599
- ext_modules = dist.ext_modules or []
600
- nested_modules += [x.name for x in ext_modules if "." in x.name]
601
-
602
- for module in nested_modules:
603
- package, _, _ = module.rpartition(".")
604
- yield package
605
-
606
-
607
- def _find_top_level_modules(dist: Distribution) -> Iterator[str]:
608
- py_modules = dist.py_modules or []
609
- yield from (mod for mod in py_modules if "." not in mod)
610
-
611
- if not dist.ext_package:
612
- ext_modules = dist.ext_modules or []
613
- yield from (x.name for x in ext_modules if "." not in x.name)
614
-
615
-
616
- def _find_package_roots(
617
- packages: Iterable[str],
618
- package_dir: Mapping[str, str],
619
- src_root: _Path,
620
- ) -> Dict[str, str]:
621
- pkg_roots: Dict[str, str] = {
622
- pkg: _absolute_root(find_package_path(pkg, package_dir, src_root))
623
- for pkg in sorted(packages)
624
- }
625
-
626
- return _remove_nested(pkg_roots)
627
-
628
-
629
- def _absolute_root(path: _Path) -> str:
630
- """Works for packages and top-level modules"""
631
- path_ = Path(path)
632
- parent = path_.parent
633
-
634
- if path_.exists():
635
- return str(path_.resolve())
636
- else:
637
- return str(parent.resolve() / path_.name)
638
-
639
-
640
- def _find_virtual_namespaces(pkg_roots: Dict[str, str]) -> Iterator[str]:
641
- """By carefully designing ``package_dir``, it is possible to implement the logical
642
- structure of PEP 420 in a package without the corresponding directories.
643
-
644
- Moreover a parent package can be purposefully/accidentally skipped in the discovery
645
- phase (e.g. ``find_packages(include=["mypkg.*"])``, when ``mypkg.foo`` is included
646
- by ``mypkg`` itself is not).
647
- We consider this case to also be a virtual namespace (ignoring the original
648
- directory) to emulate a non-editable installation.
649
-
650
- This function will try to find these kinds of namespaces.
651
- """
652
- for pkg in pkg_roots:
653
- if "." not in pkg:
654
- continue
655
- parts = pkg.split(".")
656
- for i in range(len(parts) - 1, 0, -1):
657
- partial_name = ".".join(parts[:i])
658
- path = Path(find_package_path(partial_name, pkg_roots, ""))
659
- if not path.exists() or partial_name not in pkg_roots:
660
- # partial_name not in pkg_roots ==> purposefully/accidentally skipped
661
- yield partial_name
662
-
663
-
664
- def _find_namespaces(
665
- packages: List[str], pkg_roots: Dict[str, str]
666
- ) -> Iterator[Tuple[str, List[str]]]:
667
- for pkg in packages:
668
- path = find_package_path(pkg, pkg_roots, "")
669
- if Path(path).exists() and not Path(path, "__init__.py").exists():
670
- yield (pkg, [path])
671
-
672
-
673
- def _remove_nested(pkg_roots: Dict[str, str]) -> Dict[str, str]:
674
- output = dict(pkg_roots.copy())
675
-
676
- for pkg, path in reversed(list(pkg_roots.items())):
677
- if any(
678
- pkg != other and _is_nested(pkg, path, other, other_path)
679
- for other, other_path in pkg_roots.items()
680
- ):
681
- output.pop(pkg)
682
-
683
- return output
684
-
685
-
686
- def _is_nested(pkg: str, pkg_path: str, parent: str, parent_path: str) -> bool:
687
- """
688
- Return ``True`` if ``pkg`` is nested inside ``parent`` both logically and in the
689
- file system.
690
- >>> _is_nested("a.b", "path/a/b", "a", "path/a")
691
- True
692
- >>> _is_nested("a.b", "path/a/b", "a", "otherpath/a")
693
- False
694
- >>> _is_nested("a.b", "path/a/b", "c", "path/c")
695
- False
696
- >>> _is_nested("a.a", "path/a/a", "a", "path/a")
697
- True
698
- >>> _is_nested("b.a", "path/b/a", "a", "path/a")
699
- False
700
- """
701
- norm_pkg_path = _normalize_path(pkg_path)
702
- rest = pkg.replace(parent, "", 1).strip(".").split(".")
703
- return (
704
- pkg.startswith(parent)
705
- and norm_pkg_path == _normalize_path(Path(parent_path, *rest))
706
- )
707
-
708
-
709
- def _normalize_path(filename: _Path) -> str:
710
- """Normalize a file/dir name for comparison purposes"""
711
- # See pkg_resources.normalize_path
712
- file = os.path.abspath(filename) if sys.platform == 'cygwin' else filename
713
- return os.path.normcase(os.path.realpath(os.path.normpath(file)))
714
-
715
-
716
- def _empty_dir(dir_: _P) -> _P:
717
- """Create a directory ensured to be empty. Existing files may be removed."""
718
- shutil.rmtree(dir_, ignore_errors=True)
719
- os.makedirs(dir_)
720
- return dir_
721
-
722
-
723
- def _make_identifier(name: str) -> str:
724
- """Make a string safe to be used as Python identifier.
725
- >>> _make_identifier("12abc")
726
- '_12abc'
727
- >>> _make_identifier("__editable__.myns.pkg-78.9.3_local")
728
- '__editable___myns_pkg_78_9_3_local'
729
- """
730
- safe = re.sub(r'\W|^(?=\d)', '_', name)
731
- assert safe.isidentifier()
732
- return safe
733
-
734
-
735
- class _NamespaceInstaller(namespaces.Installer):
736
- def __init__(self, distribution, installation_dir, editable_name, src_root):
737
- self.distribution = distribution
738
- self.src_root = src_root
739
- self.installation_dir = installation_dir
740
- self.editable_name = editable_name
741
- self.outputs = []
742
- self.dry_run = False
743
-
744
- def _get_target(self):
745
- """Installation target."""
746
- return os.path.join(self.installation_dir, self.editable_name)
747
-
748
- def _get_root(self):
749
- """Where the modules/packages should be loaded from."""
750
- return repr(str(self.src_root))
751
-
752
-
753
- _FINDER_TEMPLATE = """\
754
- import sys
755
- from importlib.machinery import ModuleSpec
756
- from importlib.machinery import all_suffixes as module_suffixes
757
- from importlib.util import spec_from_file_location
758
- from itertools import chain
759
- from pathlib import Path
760
-
761
- MAPPING = {mapping!r}
762
- NAMESPACES = {namespaces!r}
763
- PATH_PLACEHOLDER = {name!r} + ".__path_hook__"
764
-
765
-
766
- class _EditableFinder: # MetaPathFinder
767
- @classmethod
768
- def find_spec(cls, fullname, path=None, target=None):
769
- for pkg, pkg_path in reversed(list(MAPPING.items())):
770
- if fullname == pkg or fullname.startswith(f"{{pkg}}."):
771
- rest = fullname.replace(pkg, "", 1).strip(".").split(".")
772
- return cls._find_spec(fullname, Path(pkg_path, *rest))
773
-
774
- return None
775
-
776
- @classmethod
777
- def _find_spec(cls, fullname, candidate_path):
778
- init = candidate_path / "__init__.py"
779
- candidates = (candidate_path.with_suffix(x) for x in module_suffixes())
780
- for candidate in chain([init], candidates):
781
- if candidate.exists():
782
- return spec_from_file_location(fullname, candidate)
783
-
784
-
785
- class _EditableNamespaceFinder: # PathEntryFinder
786
- @classmethod
787
- def _path_hook(cls, path):
788
- if path == PATH_PLACEHOLDER:
789
- return cls
790
- raise ImportError
791
-
792
- @classmethod
793
- def _paths(cls, fullname):
794
- # Ensure __path__ is not empty for the spec to be considered a namespace.
795
- return NAMESPACES[fullname] or MAPPING.get(fullname) or [PATH_PLACEHOLDER]
796
-
797
- @classmethod
798
- def find_spec(cls, fullname, target=None):
799
- if fullname in NAMESPACES:
800
- spec = ModuleSpec(fullname, None, is_package=True)
801
- spec.submodule_search_locations = cls._paths(fullname)
802
- return spec
803
- return None
804
-
805
- @classmethod
806
- def find_module(cls, fullname):
807
- return None
808
-
809
-
810
- def install():
811
- if not any(finder == _EditableFinder for finder in sys.meta_path):
812
- sys.meta_path.append(_EditableFinder)
813
-
814
- if not NAMESPACES:
815
- return
816
-
817
- if not any(hook == _EditableNamespaceFinder._path_hook for hook in sys.path_hooks):
818
- # PathEntryFinder is needed to create NamespaceSpec without private APIS
819
- sys.path_hooks.append(_EditableNamespaceFinder._path_hook)
820
- if PATH_PLACEHOLDER not in sys.path:
821
- sys.path.append(PATH_PLACEHOLDER) # Used just to trigger the path hook
822
- """
823
-
824
-
825
- def _finder_template(
826
- name: str, mapping: Mapping[str, str], namespaces: Dict[str, List[str]]
827
- ) -> str:
828
- """Create a string containing the code for the``MetaPathFinder`` and
829
- ``PathEntryFinder``.
830
- """
831
- mapping = dict(sorted(mapping.items(), key=lambda p: p[0]))
832
- return _FINDER_TEMPLATE.format(name=name, mapping=mapping, namespaces=namespaces)
833
-
834
-
835
- class InformationOnly(UserWarning):
836
- """Currently there is no clear way of displaying messages to the users
837
- that use the setuptools backend directly via ``pip``.
838
- The only thing that might work is a warning, although it is not the
839
- most appropriate tool for the job...
840
- """
841
-
842
-
843
- class LinksNotSupported(errors.FileError):
844
- """File system does not seem to support either symlinks or hard links."""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Bonosa2/movies/app1.py DELETED
@@ -1,76 +0,0 @@
1
- import pandas as pd
2
- import numpy as np
3
- import torch
4
- from sentence_transformers import SentenceTransformer
5
- import scipy.spatial
6
- import gradio as gr
7
- import re
8
-
9
- # Load the dataset
10
- url = 'https://storage.googleapis.com/movves123/movies.csv'
11
- df = pd.read_csv(url)
12
-
13
- # Load BERT model
14
- model = SentenceTransformer('all-MiniLM-L6-v2')
15
-
16
- # Precompute movie title embeddings
17
- titles = df['title'].tolist()
18
- genres = df['genres'].tolist()
19
-
20
- # Combine title and genre into a single string and compute embeddings
21
- combined = [f"{title} {genre}" for title, genre in zip(titles, genres)]
22
- embeddings = model.encode(combined, convert_to_tensor=True)
23
-
24
- # List of movie genres
25
- genre_keywords = ['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime',
26
- 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical',
27
- 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']
28
-
29
- def recommend_movies(user_input):
30
- # Detect genre from user's input
31
- user_genre = [genre for genre in genre_keywords if genre.lower() in user_input.lower()]
32
-
33
- # If a genre is detected, recommend movies from that genre
34
- if user_genre:
35
- query_embedding = model.encode([user_genre[0]], convert_to_tensor=True) # Ensure the input to encode is a list
36
- else:
37
- query_embedding = model.encode([user_input], convert_to_tensor=True)
38
-
39
- # Compute cosine similarity scores
40
- cosine_scores = scipy.spatial.distance.cdist(query_embedding.cpu().numpy(), embeddings.cpu().numpy(), "cosine")[0]
41
-
42
- # Get top 5 matches
43
- top_results = np.argpartition(cosine_scores, range(5))[:5]
44
-
45
- # Check if user input includes negation phrases
46
- negation_phrases = ["not", "anything but", "except", "don't", "dont", "do not", "no", "none","besides","hate","dislike", "neither", "never"]
47
- genres_to_avoid = []
48
- for phrase in negation_phrases:
49
- if phrase in user_input.lower():
50
- # Get the word following the negation phrase, assuming it's a genre
51
- genre_to_avoid = user_input.lower().split(phrase)[1].strip().split()[0]
52
- genres_to_avoid.append(genre_to_avoid)
53
-
54
- # Filter out movies from unwanted genres
55
- final_recommendations = []
56
- for rec in top_results:
57
- movie_genres = df.iloc[rec]['genres'].lower().split("|")
58
- if not any(genre in genres_to_avoid for genre in movie_genres):
59
- # Generate a list of numbered recommendations
60
- final_recommendations.append(f"{len(final_recommendations)+1}. {df.iloc[rec]['title']}")
61
-
62
-
63
- return "\n".join(final_recommendations) # Return as a numbered list
64
-
65
- examples = [
66
- ['I\'m in the mood for a comedy.'],
67
- ['How about some action?'],
68
- ['I want to watch a romance movie.']
69
- ]
70
-
71
- iface = gr.Interface(fn=recommend_movies,
72
- inputs=gr.inputs.Textbox(lines=2, placeholder='Type something...'),
73
- outputs=gr.outputs.Textbox(),
74
- examples=examples) # Include examples
75
- iface.launch()
76
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/__init__.py DELETED
@@ -1,10 +0,0 @@
1
- # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
2
- from .distributed_sampler import InferenceSampler, RepeatFactorTrainingSampler, TrainingSampler
3
- from .grouped_batch_sampler import GroupedBatchSampler
4
-
5
- __all__ = [
6
- "GroupedBatchSampler",
7
- "TrainingSampler",
8
- "InferenceSampler",
9
- "RepeatFactorTrainingSampler",
10
- ]
 
 
 
 
 
 
 
 
 
 
 
spaces/CVPR/LIVE/pybind11/tests/test_virtual_functions.cpp DELETED
@@ -1,498 +0,0 @@
1
- /*
2
- tests/test_virtual_functions.cpp -- overriding virtual functions from Python
3
-
4
- Copyright (c) 2016 Wenzel Jakob <[email protected]>
5
-
6
- All rights reserved. Use of this source code is governed by a
7
- BSD-style license that can be found in the LICENSE file.
8
- */
9
-
10
- #include "pybind11_tests.h"
11
- #include "constructor_stats.h"
12
- #include <pybind11/functional.h>
13
- #include <thread>
14
-
15
- /* This is an example class that we'll want to be able to extend from Python */
16
- class ExampleVirt {
17
- public:
18
- ExampleVirt(int state) : state(state) { print_created(this, state); }
19
- ExampleVirt(const ExampleVirt &e) : state(e.state) { print_copy_created(this); }
20
- ExampleVirt(ExampleVirt &&e) : state(e.state) { print_move_created(this); e.state = 0; }
21
- virtual ~ExampleVirt() { print_destroyed(this); }
22
-
23
- virtual int run(int value) {
24
- py::print("Original implementation of "
25
- "ExampleVirt::run(state={}, value={}, str1={}, str2={})"_s.format(state, value, get_string1(), *get_string2()));
26
- return state + value;
27
- }
28
-
29
- virtual bool run_bool() = 0;
30
- virtual void pure_virtual() = 0;
31
-
32
- // Returning a reference/pointer to a type converted from python (numbers, strings, etc.) is a
33
- // bit trickier, because the actual int& or std::string& or whatever only exists temporarily, so
34
- // we have to handle it specially in the trampoline class (see below).
35
- virtual const std::string &get_string1() { return str1; }
36
- virtual const std::string *get_string2() { return &str2; }
37
-
38
- private:
39
- int state;
40
- const std::string str1{"default1"}, str2{"default2"};
41
- };
42
-
43
- /* This is a wrapper class that must be generated */
44
- class PyExampleVirt : public ExampleVirt {
45
- public:
46
- using ExampleVirt::ExampleVirt; /* Inherit constructors */
47
-
48
- int run(int value) override {
49
- /* Generate wrapping code that enables native function overloading */
50
- PYBIND11_OVERLOAD(
51
- int, /* Return type */
52
- ExampleVirt, /* Parent class */
53
- run, /* Name of function */
54
- value /* Argument(s) */
55
- );
56
- }
57
-
58
- bool run_bool() override {
59
- PYBIND11_OVERLOAD_PURE(
60
- bool, /* Return type */
61
- ExampleVirt, /* Parent class */
62
- run_bool, /* Name of function */
63
- /* This function has no arguments. The trailing comma
64
- in the previous line is needed for some compilers */
65
- );
66
- }
67
-
68
- void pure_virtual() override {
69
- PYBIND11_OVERLOAD_PURE(
70
- void, /* Return type */
71
- ExampleVirt, /* Parent class */
72
- pure_virtual, /* Name of function */
73
- /* This function has no arguments. The trailing comma
74
- in the previous line is needed for some compilers */
75
- );
76
- }
77
-
78
- // We can return reference types for compatibility with C++ virtual interfaces that do so, but
79
- // note they have some significant limitations (see the documentation).
80
- const std::string &get_string1() override {
81
- PYBIND11_OVERLOAD(
82
- const std::string &, /* Return type */
83
- ExampleVirt, /* Parent class */
84
- get_string1, /* Name of function */
85
- /* (no arguments) */
86
- );
87
- }
88
-
89
- const std::string *get_string2() override {
90
- PYBIND11_OVERLOAD(
91
- const std::string *, /* Return type */
92
- ExampleVirt, /* Parent class */
93
- get_string2, /* Name of function */
94
- /* (no arguments) */
95
- );
96
- }
97
-
98
- };
99
-
100
- class NonCopyable {
101
- public:
102
- NonCopyable(int a, int b) : value{new int(a*b)} { print_created(this, a, b); }
103
- NonCopyable(NonCopyable &&o) { value = std::move(o.value); print_move_created(this); }
104
- NonCopyable(const NonCopyable &) = delete;
105
- NonCopyable() = delete;
106
- void operator=(const NonCopyable &) = delete;
107
- void operator=(NonCopyable &&) = delete;
108
- std::string get_value() const {
109
- if (value) return std::to_string(*value); else return "(null)";
110
- }
111
- ~NonCopyable() { print_destroyed(this); }
112
-
113
- private:
114
- std::unique_ptr<int> value;
115
- };
116
-
117
- // This is like the above, but is both copy and movable. In effect this means it should get moved
118
- // when it is not referenced elsewhere, but copied if it is still referenced.
119
- class Movable {
120
- public:
121
- Movable(int a, int b) : value{a+b} { print_created(this, a, b); }
122
- Movable(const Movable &m) { value = m.value; print_copy_created(this); }
123
- Movable(Movable &&m) { value = std::move(m.value); print_move_created(this); }
124
- std::string get_value() const { return std::to_string(value); }
125
- ~Movable() { print_destroyed(this); }
126
- private:
127
- int value;
128
- };
129
-
130
- class NCVirt {
131
- public:
132
- virtual ~NCVirt() { }
133
- NCVirt() = default;
134
- NCVirt(const NCVirt&) = delete;
135
- virtual NonCopyable get_noncopyable(int a, int b) { return NonCopyable(a, b); }
136
- virtual Movable get_movable(int a, int b) = 0;
137
-
138
- std::string print_nc(int a, int b) { return get_noncopyable(a, b).get_value(); }
139
- std::string print_movable(int a, int b) { return get_movable(a, b).get_value(); }
140
- };
141
- class NCVirtTrampoline : public NCVirt {
142
- #if !defined(__INTEL_COMPILER)
143
- NonCopyable get_noncopyable(int a, int b) override {
144
- PYBIND11_OVERLOAD(NonCopyable, NCVirt, get_noncopyable, a, b);
145
- }
146
- #endif
147
- Movable get_movable(int a, int b) override {
148
- PYBIND11_OVERLOAD_PURE(Movable, NCVirt, get_movable, a, b);
149
- }
150
- };
151
-
152
- struct Base {
153
- /* for some reason MSVC2015 can't compile this if the function is pure virtual */
154
- virtual std::string dispatch() const { return {}; };
155
- virtual ~Base() = default;
156
- Base() = default;
157
- Base(const Base&) = delete;
158
- };
159
-
160
- struct DispatchIssue : Base {
161
- virtual std::string dispatch() const {
162
- PYBIND11_OVERLOAD_PURE(std::string, Base, dispatch, /* no arguments */);
163
- }
164
- };
165
-
166
- static void test_gil() {
167
- {
168
- py::gil_scoped_acquire lock;
169
- py::print("1st lock acquired");
170
-
171
- }
172
-
173
- {
174
- py::gil_scoped_acquire lock;
175
- py::print("2nd lock acquired");
176
- }
177
-
178
- }
179
-
180
- static void test_gil_from_thread() {
181
- py::gil_scoped_release release;
182
-
183
- std::thread t(test_gil);
184
- t.join();
185
- }
186
-
187
-
188
- // Forward declaration (so that we can put the main tests here; the inherited virtual approaches are
189
- // rather long).
190
- void initialize_inherited_virtuals(py::module &m);
191
-
192
- TEST_SUBMODULE(virtual_functions, m) {
193
- // test_override
194
- py::class_<ExampleVirt, PyExampleVirt>(m, "ExampleVirt")
195
- .def(py::init<int>())
196
- /* Reference original class in function definitions */
197
- .def("run", &ExampleVirt::run)
198
- .def("run_bool", &ExampleVirt::run_bool)
199
- .def("pure_virtual", &ExampleVirt::pure_virtual);
200
-
201
- py::class_<NonCopyable>(m, "NonCopyable")
202
- .def(py::init<int, int>());
203
-
204
- py::class_<Movable>(m, "Movable")
205
- .def(py::init<int, int>());
206
-
207
- // test_move_support
208
- #if !defined(__INTEL_COMPILER)
209
- py::class_<NCVirt, NCVirtTrampoline>(m, "NCVirt")
210
- .def(py::init<>())
211
- .def("get_noncopyable", &NCVirt::get_noncopyable)
212
- .def("get_movable", &NCVirt::get_movable)
213
- .def("print_nc", &NCVirt::print_nc)
214
- .def("print_movable", &NCVirt::print_movable);
215
- #endif
216
-
217
- m.def("runExampleVirt", [](ExampleVirt *ex, int value) { return ex->run(value); });
218
- m.def("runExampleVirtBool", [](ExampleVirt* ex) { return ex->run_bool(); });
219
- m.def("runExampleVirtVirtual", [](ExampleVirt *ex) { ex->pure_virtual(); });
220
-
221
- m.def("cstats_debug", &ConstructorStats::get<ExampleVirt>);
222
- initialize_inherited_virtuals(m);
223
-
224
- // test_alias_delay_initialization1
225
- // don't invoke Python dispatch classes by default when instantiating C++ classes
226
- // that were not extended on the Python side
227
- struct A {
228
- A() = default;
229
- A(const A&) = delete;
230
- virtual ~A() {}
231
- virtual void f() { py::print("A.f()"); }
232
- };
233
-
234
- struct PyA : A {
235
- PyA() { py::print("PyA.PyA()"); }
236
- PyA(const PyA&) = delete;
237
- ~PyA() { py::print("PyA.~PyA()"); }
238
-
239
- void f() override {
240
- py::print("PyA.f()");
241
- // This convolution just gives a `void`, but tests that PYBIND11_TYPE() works to protect
242
- // a type containing a ,
243
- PYBIND11_OVERLOAD(PYBIND11_TYPE(typename std::enable_if<true, void>::type), A, f);
244
- }
245
- };
246
-
247
- py::class_<A, PyA>(m, "A")
248
- .def(py::init<>())
249
- .def("f", &A::f);
250
-
251
- m.def("call_f", [](A *a) { a->f(); });
252
-
253
- // test_alias_delay_initialization2
254
- // ... unless we explicitly request it, as in this example:
255
- struct A2 {
256
- A2() = default;
257
- A2(const A2&) = delete;
258
- virtual ~A2() {}
259
- virtual void f() { py::print("A2.f()"); }
260
- };
261
-
262
- struct PyA2 : A2 {
263
- PyA2() { py::print("PyA2.PyA2()"); }
264
- PyA2(const PyA2&) = delete;
265
- ~PyA2() { py::print("PyA2.~PyA2()"); }
266
- void f() override {
267
- py::print("PyA2.f()");
268
- PYBIND11_OVERLOAD(void, A2, f);
269
- }
270
- };
271
-
272
- py::class_<A2, PyA2>(m, "A2")
273
- .def(py::init_alias<>())
274
- .def(py::init([](int) { return new PyA2(); }))
275
- .def("f", &A2::f);
276
-
277
- m.def("call_f", [](A2 *a2) { a2->f(); });
278
-
279
- // test_dispatch_issue
280
- // #159: virtual function dispatch has problems with similar-named functions
281
- py::class_<Base, DispatchIssue>(m, "DispatchIssue")
282
- .def(py::init<>())
283
- .def("dispatch", &Base::dispatch);
284
-
285
- m.def("dispatch_issue_go", [](const Base * b) { return b->dispatch(); });
286
-
287
- // test_override_ref
288
- // #392/397: overriding reference-returning functions
289
- class OverrideTest {
290
- public:
291
- struct A { std::string value = "hi"; };
292
- std::string v;
293
- A a;
294
- explicit OverrideTest(const std::string &v) : v{v} {}
295
- OverrideTest() = default;
296
- OverrideTest(const OverrideTest&) = delete;
297
- virtual std::string str_value() { return v; }
298
- virtual std::string &str_ref() { return v; }
299
- virtual A A_value() { return a; }
300
- virtual A &A_ref() { return a; }
301
- virtual ~OverrideTest() = default;
302
- };
303
-
304
- class PyOverrideTest : public OverrideTest {
305
- public:
306
- using OverrideTest::OverrideTest;
307
- std::string str_value() override { PYBIND11_OVERLOAD(std::string, OverrideTest, str_value); }
308
- // Not allowed (uncommenting should hit a static_assert failure): we can't get a reference
309
- // to a python numeric value, since we only copy values in the numeric type caster:
310
- // std::string &str_ref() override { PYBIND11_OVERLOAD(std::string &, OverrideTest, str_ref); }
311
- // But we can work around it like this:
312
- private:
313
- std::string _tmp;
314
- std::string str_ref_helper() { PYBIND11_OVERLOAD(std::string, OverrideTest, str_ref); }
315
- public:
316
- std::string &str_ref() override { return _tmp = str_ref_helper(); }
317
-
318
- A A_value() override { PYBIND11_OVERLOAD(A, OverrideTest, A_value); }
319
- A &A_ref() override { PYBIND11_OVERLOAD(A &, OverrideTest, A_ref); }
320
- };
321
-
322
- py::class_<OverrideTest::A>(m, "OverrideTest_A")
323
- .def_readwrite("value", &OverrideTest::A::value);
324
- py::class_<OverrideTest, PyOverrideTest>(m, "OverrideTest")
325
- .def(py::init<const std::string &>())
326
- .def("str_value", &OverrideTest::str_value)
327
- // .def("str_ref", &OverrideTest::str_ref)
328
- .def("A_value", &OverrideTest::A_value)
329
- .def("A_ref", &OverrideTest::A_ref);
330
- }
331
-
332
-
333
- // Inheriting virtual methods. We do two versions here: the repeat-everything version and the
334
- // templated trampoline versions mentioned in docs/advanced.rst.
335
- //
336
- // These base classes are exactly the same, but we technically need distinct
337
- // classes for this example code because we need to be able to bind them
338
- // properly (pybind11, sensibly, doesn't allow us to bind the same C++ class to
339
- // multiple python classes).
340
- class A_Repeat {
341
- #define A_METHODS \
342
- public: \
343
- virtual int unlucky_number() = 0; \
344
- virtual std::string say_something(unsigned times) { \
345
- std::string s = ""; \
346
- for (unsigned i = 0; i < times; ++i) \
347
- s += "hi"; \
348
- return s; \
349
- } \
350
- std::string say_everything() { \
351
- return say_something(1) + " " + std::to_string(unlucky_number()); \
352
- }
353
- A_METHODS
354
- A_Repeat() = default;
355
- A_Repeat(const A_Repeat&) = delete;
356
- virtual ~A_Repeat() = default;
357
- };
358
- class B_Repeat : public A_Repeat {
359
- #define B_METHODS \
360
- public: \
361
- int unlucky_number() override { return 13; } \
362
- std::string say_something(unsigned times) override { \
363
- return "B says hi " + std::to_string(times) + " times"; \
364
- } \
365
- virtual double lucky_number() { return 7.0; }
366
- B_METHODS
367
- };
368
- class C_Repeat : public B_Repeat {
369
- #define C_METHODS \
370
- public: \
371
- int unlucky_number() override { return 4444; } \
372
- double lucky_number() override { return 888; }
373
- C_METHODS
374
- };
375
- class D_Repeat : public C_Repeat {
376
- #define D_METHODS // Nothing overridden.
377
- D_METHODS
378
- };
379
-
380
- // Base classes for templated inheritance trampolines. Identical to the repeat-everything version:
381
- class A_Tpl {
382
- A_METHODS;
383
- A_Tpl() = default;
384
- A_Tpl(const A_Tpl&) = delete;
385
- virtual ~A_Tpl() = default;
386
- };
387
- class B_Tpl : public A_Tpl { B_METHODS };
388
- class C_Tpl : public B_Tpl { C_METHODS };
389
- class D_Tpl : public C_Tpl { D_METHODS };
390
-
391
-
392
- // Inheritance approach 1: each trampoline gets every virtual method (11 in total)
393
- class PyA_Repeat : public A_Repeat {
394
- public:
395
- using A_Repeat::A_Repeat;
396
- int unlucky_number() override { PYBIND11_OVERLOAD_PURE(int, A_Repeat, unlucky_number, ); }
397
- std::string say_something(unsigned times) override { PYBIND11_OVERLOAD(std::string, A_Repeat, say_something, times); }
398
- };
399
- class PyB_Repeat : public B_Repeat {
400
- public:
401
- using B_Repeat::B_Repeat;
402
- int unlucky_number() override { PYBIND11_OVERLOAD(int, B_Repeat, unlucky_number, ); }
403
- std::string say_something(unsigned times) override { PYBIND11_OVERLOAD(std::string, B_Repeat, say_something, times); }
404
- double lucky_number() override { PYBIND11_OVERLOAD(double, B_Repeat, lucky_number, ); }
405
- };
406
- class PyC_Repeat : public C_Repeat {
407
- public:
408
- using C_Repeat::C_Repeat;
409
- int unlucky_number() override { PYBIND11_OVERLOAD(int, C_Repeat, unlucky_number, ); }
410
- std::string say_something(unsigned times) override { PYBIND11_OVERLOAD(std::string, C_Repeat, say_something, times); }
411
- double lucky_number() override { PYBIND11_OVERLOAD(double, C_Repeat, lucky_number, ); }
412
- };
413
- class PyD_Repeat : public D_Repeat {
414
- public:
415
- using D_Repeat::D_Repeat;
416
- int unlucky_number() override { PYBIND11_OVERLOAD(int, D_Repeat, unlucky_number, ); }
417
- std::string say_something(unsigned times) override { PYBIND11_OVERLOAD(std::string, D_Repeat, say_something, times); }
418
- double lucky_number() override { PYBIND11_OVERLOAD(double, D_Repeat, lucky_number, ); }
419
- };
420
-
421
- // Inheritance approach 2: templated trampoline classes.
422
- //
423
- // Advantages:
424
- // - we have only 2 (template) class and 4 method declarations (one per virtual method, plus one for
425
- // any override of a pure virtual method), versus 4 classes and 6 methods (MI) or 4 classes and 11
426
- // methods (repeat).
427
- // - Compared to MI, we also don't have to change the non-trampoline inheritance to virtual, and can
428
- // properly inherit constructors.
429
- //
430
- // Disadvantage:
431
- // - the compiler must still generate and compile 14 different methods (more, even, than the 11
432
- // required for the repeat approach) instead of the 6 required for MI. (If there was no pure
433
- // method (or no pure method override), the number would drop down to the same 11 as the repeat
434
- // approach).
435
- template <class Base = A_Tpl>
436
- class PyA_Tpl : public Base {
437
- public:
438
- using Base::Base; // Inherit constructors
439
- int unlucky_number() override { PYBIND11_OVERLOAD_PURE(int, Base, unlucky_number, ); }
440
- std::string say_something(unsigned times) override { PYBIND11_OVERLOAD(std::string, Base, say_something, times); }
441
- };
442
- template <class Base = B_Tpl>
443
- class PyB_Tpl : public PyA_Tpl<Base> {
444
- public:
445
- using PyA_Tpl<Base>::PyA_Tpl; // Inherit constructors (via PyA_Tpl's inherited constructors)
446
- int unlucky_number() override { PYBIND11_OVERLOAD(int, Base, unlucky_number, ); }
447
- double lucky_number() override { PYBIND11_OVERLOAD(double, Base, lucky_number, ); }
448
- };
449
- // Since C_Tpl and D_Tpl don't declare any new virtual methods, we don't actually need these (we can
450
- // use PyB_Tpl<C_Tpl> and PyB_Tpl<D_Tpl> for the trampoline classes instead):
451
- /*
452
- template <class Base = C_Tpl> class PyC_Tpl : public PyB_Tpl<Base> {
453
- public:
454
- using PyB_Tpl<Base>::PyB_Tpl;
455
- };
456
- template <class Base = D_Tpl> class PyD_Tpl : public PyC_Tpl<Base> {
457
- public:
458
- using PyC_Tpl<Base>::PyC_Tpl;
459
- };
460
- */
461
-
462
- void initialize_inherited_virtuals(py::module &m) {
463
- // test_inherited_virtuals
464
-
465
- // Method 1: repeat
466
- py::class_<A_Repeat, PyA_Repeat>(m, "A_Repeat")
467
- .def(py::init<>())
468
- .def("unlucky_number", &A_Repeat::unlucky_number)
469
- .def("say_something", &A_Repeat::say_something)
470
- .def("say_everything", &A_Repeat::say_everything);
471
- py::class_<B_Repeat, A_Repeat, PyB_Repeat>(m, "B_Repeat")
472
- .def(py::init<>())
473
- .def("lucky_number", &B_Repeat::lucky_number);
474
- py::class_<C_Repeat, B_Repeat, PyC_Repeat>(m, "C_Repeat")
475
- .def(py::init<>());
476
- py::class_<D_Repeat, C_Repeat, PyD_Repeat>(m, "D_Repeat")
477
- .def(py::init<>());
478
-
479
- // test_
480
- // Method 2: Templated trampolines
481
- py::class_<A_Tpl, PyA_Tpl<>>(m, "A_Tpl")
482
- .def(py::init<>())
483
- .def("unlucky_number", &A_Tpl::unlucky_number)
484
- .def("say_something", &A_Tpl::say_something)
485
- .def("say_everything", &A_Tpl::say_everything);
486
- py::class_<B_Tpl, A_Tpl, PyB_Tpl<>>(m, "B_Tpl")
487
- .def(py::init<>())
488
- .def("lucky_number", &B_Tpl::lucky_number);
489
- py::class_<C_Tpl, B_Tpl, PyB_Tpl<C_Tpl>>(m, "C_Tpl")
490
- .def(py::init<>());
491
- py::class_<D_Tpl, C_Tpl, PyB_Tpl<D_Tpl>>(m, "D_Tpl")
492
- .def(py::init<>());
493
-
494
-
495
- // Fix issue #1454 (crash when acquiring/releasing GIL on another thread in Python 2.7)
496
- m.def("test_gil", &test_gil);
497
- m.def("test_gil_from_thread", &test_gil_from_thread);
498
- };
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/CVPR/WALT/mmdet/core/bbox/iou_calculators/iou2d_calculator.py DELETED
@@ -1,159 +0,0 @@
1
- import torch
2
-
3
- from .builder import IOU_CALCULATORS
4
-
5
-
6
- @IOU_CALCULATORS.register_module()
7
- class BboxOverlaps2D(object):
8
- """2D Overlaps (e.g. IoUs, GIoUs) Calculator."""
9
-
10
- def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
11
- """Calculate IoU between 2D bboxes.
12
-
13
- Args:
14
- bboxes1 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
15
- format, or shape (m, 5) in <x1, y1, x2, y2, score> format.
16
- bboxes2 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
17
- format, shape (m, 5) in <x1, y1, x2, y2, score> format, or be
18
- empty. If ``is_aligned `` is ``True``, then m and n must be
19
- equal.
20
- mode (str): "iou" (intersection over union), "iof" (intersection
21
- over foreground), or "giou" (generalized intersection over
22
- union).
23
- is_aligned (bool, optional): If True, then m and n must be equal.
24
- Default False.
25
-
26
- Returns:
27
- Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
28
- """
29
- assert bboxes1.size(-1) in [0, 4, 5]
30
- assert bboxes2.size(-1) in [0, 4, 5]
31
- if bboxes2.size(-1) == 5:
32
- bboxes2 = bboxes2[..., :4]
33
- if bboxes1.size(-1) == 5:
34
- bboxes1 = bboxes1[..., :4]
35
- return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
36
-
37
- def __repr__(self):
38
- """str: a string describing the module"""
39
- repr_str = self.__class__.__name__ + '()'
40
- return repr_str
41
-
42
-
43
- def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6):
44
- """Calculate overlap between two set of bboxes.
45
-
46
- If ``is_aligned `` is ``False``, then calculate the overlaps between each
47
- bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
48
- pair of bboxes1 and bboxes2.
49
-
50
- Args:
51
- bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
52
- bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
53
- B indicates the batch dim, in shape (B1, B2, ..., Bn).
54
- If ``is_aligned `` is ``True``, then m and n must be equal.
55
- mode (str): "iou" (intersection over union), "iof" (intersection over
56
- foreground) or "giou" (generalized intersection over union).
57
- Default "iou".
58
- is_aligned (bool, optional): If True, then m and n must be equal.
59
- Default False.
60
- eps (float, optional): A value added to the denominator for numerical
61
- stability. Default 1e-6.
62
-
63
- Returns:
64
- Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
65
-
66
- Example:
67
- >>> bboxes1 = torch.FloatTensor([
68
- >>> [0, 0, 10, 10],
69
- >>> [10, 10, 20, 20],
70
- >>> [32, 32, 38, 42],
71
- >>> ])
72
- >>> bboxes2 = torch.FloatTensor([
73
- >>> [0, 0, 10, 20],
74
- >>> [0, 10, 10, 19],
75
- >>> [10, 10, 20, 20],
76
- >>> ])
77
- >>> overlaps = bbox_overlaps(bboxes1, bboxes2)
78
- >>> assert overlaps.shape == (3, 3)
79
- >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True)
80
- >>> assert overlaps.shape == (3, )
81
-
82
- Example:
83
- >>> empty = torch.empty(0, 4)
84
- >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]])
85
- >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
86
- >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
87
- >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
88
- """
89
-
90
- assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}'
91
- # Either the boxes are empty or the length of boxes' last dimension is 4
92
- assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
93
- assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
94
-
95
- # Batch dim must be the same
96
- # Batch dim: (B1, B2, ... Bn)
97
- assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
98
- batch_shape = bboxes1.shape[:-2]
99
-
100
- rows = bboxes1.size(-2)
101
- cols = bboxes2.size(-2)
102
- if is_aligned:
103
- assert rows == cols
104
-
105
- if rows * cols == 0:
106
- if is_aligned:
107
- return bboxes1.new(batch_shape + (rows, ))
108
- else:
109
- return bboxes1.new(batch_shape + (rows, cols))
110
-
111
- area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (
112
- bboxes1[..., 3] - bboxes1[..., 1])
113
- area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (
114
- bboxes2[..., 3] - bboxes2[..., 1])
115
-
116
- if is_aligned:
117
- lt = torch.max(bboxes1[..., :2], bboxes2[..., :2]) # [B, rows, 2]
118
- rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:]) # [B, rows, 2]
119
-
120
- wh = (rb - lt).clamp(min=0) # [B, rows, 2]
121
- overlap = wh[..., 0] * wh[..., 1]
122
-
123
- if mode in ['iou', 'giou']:
124
- union = area1 + area2 - overlap
125
- else:
126
- union = area1
127
- if mode == 'giou':
128
- enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2])
129
- enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:])
130
- else:
131
- lt = torch.max(bboxes1[..., :, None, :2],
132
- bboxes2[..., None, :, :2]) # [B, rows, cols, 2]
133
- rb = torch.min(bboxes1[..., :, None, 2:],
134
- bboxes2[..., None, :, 2:]) # [B, rows, cols, 2]
135
-
136
- wh = (rb - lt).clamp(min=0) # [B, rows, cols, 2]
137
- overlap = wh[..., 0] * wh[..., 1]
138
-
139
- if mode in ['iou', 'giou']:
140
- union = area1[..., None] + area2[..., None, :] - overlap
141
- else:
142
- union = area1[..., None]
143
- if mode == 'giou':
144
- enclosed_lt = torch.min(bboxes1[..., :, None, :2],
145
- bboxes2[..., None, :, :2])
146
- enclosed_rb = torch.max(bboxes1[..., :, None, 2:],
147
- bboxes2[..., None, :, 2:])
148
-
149
- eps = union.new_tensor([eps])
150
- union = torch.max(union, eps)
151
- ious = overlap / union
152
- if mode in ['iou', 'iof']:
153
- return ious
154
- # calculate gious
155
- enclose_wh = (enclosed_rb - enclosed_lt).clamp(min=0)
156
- enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
157
- enclose_area = torch.max(enclose_area, eps)
158
- gious = ious - (enclose_area - union) / enclose_area
159
- return gious
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/CVPR/WALT/mmdet/core/export/__init__.py DELETED
@@ -1,8 +0,0 @@
1
- from .pytorch2onnx import (build_model_from_cfg,
2
- generate_inputs_and_wrap_model,
3
- preprocess_example_input)
4
-
5
- __all__ = [
6
- 'build_model_from_cfg', 'generate_inputs_and_wrap_model',
7
- 'preprocess_example_input'
8
- ]
 
 
 
 
 
 
 
 
 
spaces/CamodDew/youtubelegal/README.md DELETED
@@ -1,12 +0,0 @@
1
- ---
2
- title: Youtubelegal
3
- emoji: 🏃
4
- colorFrom: blue
5
- colorTo: blue
6
- sdk: gradio
7
- sdk_version: 3.44.4
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
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/CikeyQI/meme-api/meme_generator/memes/caoshen_bite/__init__.py DELETED
@@ -1,34 +0,0 @@
1
- from pathlib import Path
2
- from typing import List
3
-
4
- from PIL.Image import Image as IMG
5
- from pil_utils import BuildImage
6
-
7
- from meme_generator import add_meme
8
- from meme_generator.utils import save_gif
9
-
10
- img_dir = Path(__file__).parent / "images"
11
-
12
-
13
- def caoshen_bite(images: List[BuildImage], texts, args):
14
- img = images[0].convert("RGBA").resize((160, 140), keep_ratio=True)
15
- # fmt: off
16
- locs = [
17
- (123, 356, 158, 124), (123, 356, 158, 124), (123, 355, 158, 125),
18
- (122, 352, 159, 128), (122, 350, 159, 130), (122, 348, 159, 132),
19
- (122, 345, 159, 135), (121, 343, 160, 137), (121, 342, 160, 138),
20
- (121, 341, 160, 139), (121, 341, 160, 139), (121, 342, 160, 138),
21
- (121, 344, 160, 136), (121, 346, 160, 134), (122, 349, 159, 131),
22
- (122, 351, 159, 129), (122, 353, 159, 127), (123, 355, 158, 125),
23
- ]
24
- # fmt: on
25
- frames: List[IMG] = []
26
- for i in range(38):
27
- frame = BuildImage.open(img_dir / f"{i}.png")
28
- x, y, w, h = locs[i % len(locs)]
29
- frame.paste(img.resize((w, h)), (x, y), below=True)
30
- frames.append(frame.image)
31
- return save_gif(frames, 0.1)
32
-
33
-
34
- add_meme("caoshen_bite", caoshen_bite, min_images=1, max_images=1, keywords=["草神啃"])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/CikeyQI/meme-api/meme_generator/memes/kiss/__init__.py DELETED
@@ -1,35 +0,0 @@
1
- from pathlib import Path
2
- from typing import List
3
-
4
- from PIL.Image import Image as IMG
5
- from pil_utils import BuildImage
6
-
7
- from meme_generator import add_meme
8
- from meme_generator.utils import save_gif
9
-
10
- img_dir = Path(__file__).parent / "images"
11
-
12
-
13
- def kiss(images: List[BuildImage], texts, args):
14
- self_head = images[0].convert("RGBA").circle().resize((40, 40))
15
- user_head = images[1].convert("RGBA").circle().resize((50, 50))
16
- # fmt: off
17
- user_locs = [
18
- (58, 90), (62, 95), (42, 100), (50, 100), (56, 100), (18, 120), (28, 110),
19
- (54, 100), (46, 100), (60, 100), (35, 115), (20, 120), (40, 96)
20
- ]
21
- self_locs = [
22
- (92, 64), (135, 40), (84, 105), (80, 110), (155, 82), (60, 96), (50, 80),
23
- (98, 55), (35, 65), (38, 100), (70, 80), (84, 65), (75, 65)
24
- ]
25
- # fmt: on
26
- frames: List[IMG] = []
27
- for i in range(13):
28
- frame = BuildImage.open(img_dir / f"{i}.png")
29
- frame.paste(user_head, user_locs[i], alpha=True)
30
- frame.paste(self_head, self_locs[i], alpha=True)
31
- frames.append(frame.image)
32
- return save_gif(frames, 0.05)
33
-
34
-
35
- add_meme("kiss", kiss, min_images=2, max_images=2, keywords=["亲", "亲亲"])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/Clatonh/moth_or_butterfly/README.md DELETED
@@ -1,13 +0,0 @@
1
- ---
2
- title: Moth Or Butterfly
3
- emoji: 💻
4
- colorFrom: indigo
5
- colorTo: pink
6
- sdk: gradio
7
- sdk_version: 3.1.7
8
- app_file: app.py
9
- pinned: false
10
- license: apache-2.0
11
- ---
12
-
13
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/DEEMOSTECH/ChatAvatar/static/js/main.459a2347.js DELETED
The diff for this file is too large to render. See raw diff
 
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/Image.py DELETED
The diff for this file is too large to render. See raw diff
 
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_D_.py DELETED
@@ -1,5 +0,0 @@
1
- from .T_S_I_V_ import table_T_S_I_V_
2
-
3
-
4
- class table_T_S_I_D_(table_T_S_I_V_):
5
- pass
 
 
 
 
 
 
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/grUtils.py DELETED
@@ -1,92 +0,0 @@
1
- import struct, warnings
2
-
3
- try:
4
- import lz4
5
- except ImportError:
6
- lz4 = None
7
- else:
8
- import lz4.block
9
-
10
- # old scheme for VERSION < 0.9 otherwise use lz4.block
11
-
12
-
13
- def decompress(data):
14
- (compression,) = struct.unpack(">L", data[4:8])
15
- scheme = compression >> 27
16
- size = compression & 0x07FFFFFF
17
- if scheme == 0:
18
- pass
19
- elif scheme == 1 and lz4:
20
- res = lz4.block.decompress(struct.pack("<L", size) + data[8:])
21
- if len(res) != size:
22
- warnings.warn("Table decompression failed.")
23
- else:
24
- data = res
25
- else:
26
- warnings.warn("Table is compressed with an unsupported compression scheme")
27
- return (data, scheme)
28
-
29
-
30
- def compress(scheme, data):
31
- hdr = data[:4] + struct.pack(">L", (scheme << 27) + (len(data) & 0x07FFFFFF))
32
- if scheme == 0:
33
- return data
34
- elif scheme == 1 and lz4:
35
- res = lz4.block.compress(
36
- data, mode="high_compression", compression=16, store_size=False
37
- )
38
- return hdr + res
39
- else:
40
- warnings.warn("Table failed to compress by unsupported compression scheme")
41
- return data
42
-
43
-
44
- def _entries(attrs, sameval):
45
- ak = 0
46
- vals = []
47
- lastv = 0
48
- for k, v in attrs:
49
- if len(vals) and (k != ak + 1 or (sameval and v != lastv)):
50
- yield (ak - len(vals) + 1, len(vals), vals)
51
- vals = []
52
- ak = k
53
- vals.append(v)
54
- lastv = v
55
- yield (ak - len(vals) + 1, len(vals), vals)
56
-
57
-
58
- def entries(attributes, sameval=False):
59
- g = _entries(sorted(attributes.items(), key=lambda x: int(x[0])), sameval)
60
- return g
61
-
62
-
63
- def bininfo(num, size=1):
64
- if num == 0:
65
- return struct.pack(">4H", 0, 0, 0, 0)
66
- srange = 1
67
- select = 0
68
- while srange <= num:
69
- srange *= 2
70
- select += 1
71
- select -= 1
72
- srange //= 2
73
- srange *= size
74
- shift = num * size - srange
75
- return struct.pack(">4H", num, srange, select, shift)
76
-
77
-
78
- def num2tag(n):
79
- if n < 0x200000:
80
- return str(n)
81
- else:
82
- return (
83
- struct.unpack("4s", struct.pack(">L", n))[0].replace(b"\000", b"").decode()
84
- )
85
-
86
-
87
- def tag2num(n):
88
- try:
89
- return int(n)
90
- except ValueError:
91
- n = (n + " ")[:4]
92
- return struct.unpack(">L", n.encode("ascii"))[0]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Login-9c3cc0eb.css DELETED
@@ -1 +0,0 @@
1
- .wrap.svelte-1ogxbi0{display:flex;flex-direction:column;justify-content:center;align-items:center;margin-top:var(--size-3);background:var(--background-fill-primary);width:var(--size-full)}h2.svelte-1ogxbi0{margin-bottom:var(--size-3);color:var(--body-text-color);font-weight:var(--section-header-text-weight);font-size:var(--text-xl)}.auth.svelte-1ogxbi0{margin-top:var(--size-1);margin-bottom:var(--size-1);color:var(--body-text-color)}.creds.svelte-1ogxbi0{margin-top:var(--size-4);margin-bottom:var(--size-4);color:var(--error-text-color);font-weight:var(--weight-semibold)}