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- spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/README.md +0 -77
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Buku Ipa Kelas 9 Penerbit Erlangga BEST.md +0 -159
- spaces/1gistliPinn/ChatGPT4/Examples/Audi Update Software Cd V 5570 Mmi 2g High A6 4f Download.md +0 -6
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer The Ultimate Simulation Game with Free Open World and Racing.md +0 -162
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Dolphin Emulator for Android The Best Way to Play Basara 2 Heroes - Download Link Inside.md +0 -238
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Candy Crush Friends Saga APK and Experience the New Levels and Modes.md +0 -90
- spaces/1phancelerku/anime-remove-background/Angry Birds Blast MOD APK How to Get Unlimited Moves and Boosters.md +0 -175
- spaces/1phancelerku/anime-remove-background/Dot Connect A Free and Relaxing Dots Puzzle Game for All Ages.md +0 -101
- spaces/1phancelerku/anime-remove-background/Enjoy the Best Attack on Titan Tribute Game with Unity - No Ads No Hassle.md +0 -107
- spaces/7hao/bingo/src/components/theme-toggle.tsx +0 -31
- spaces/7hao/bingo/src/pages/api/blob.ts +0 -40
- spaces/801artistry/RVC801/tools/infer/train-index.py +0 -42
- spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/wav_evaluation/models/audio.py +0 -179
- spaces/AIGC-Audio/Make_An_Audio/ldm/modules/discriminator/multi_window_disc.py +0 -196
- spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/alias_free_torch/act.py +0 -28
- spaces/ARTeLab/DTM_Estimation_SRandD/app.py +0 -81
- spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/shufflenet-v2-1x_4xb32_2000e_3c_noF/shufflenet-v2-1x_4xb32_2000e_3c_noF.py +0 -155
- spaces/Aadhithya/Binance-Crypto-Tracker/README.md +0 -12
- spaces/Abhilashvj/planogram-compliance/setup.sh +0 -8
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/PostResolveSize.js +0 -46
- spaces/AlexWang/lama/bin/gen_mask_dataset.py +0 -130
- spaces/Alpaca233/SadTalker/src/face3d/options/__init__.py +0 -1
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/image_processor.py +0 -366
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_vq_diffusion.py +0 -56
- spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py +0 -34
- spaces/Andy1621/uniformer_image_detection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py +0 -3
- spaces/Andy1621/uniformer_image_detection/configs/rpn/rpn_r101_fpn_1x_coco.py +0 -2
- spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/emanet_r50-d8.py +0 -47
- spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/chase_db1.py +0 -59
- spaces/Araloak/fz/app.py +0 -107
- spaces/Aspik101/Polish_Llama2/README.md +0 -13
- spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/getting_started.md +0 -1
- spaces/BetterAPI/BetterChat_new/src/lib/stores/pendingMessageIdToRetry.ts +0 -4
- spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/auth.py +0 -315
- spaces/Blessin/drama-director/README.md +0 -12
- spaces/Bonosa2/parrot-chat-bot/app.py +0 -25
- spaces/CVPR/WALT/mmdet/core/bbox/assigners/base_assigner.py +0 -9
- spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/batchnorm.py +0 -329
- spaces/ChrisCaviar/ControlNet-v1-1/README.md +0 -16
- spaces/ChrisPreston/diff-svc_minato_aqua/preprocessing/process_pipeline.py +0 -247
- spaces/CofAI/chat.b4/client/css/sidebar.css +0 -197
- spaces/Coweed/GoodTrip/greeting.md +0 -4
- spaces/Curranj/Words_To_SQL/app.py +0 -31
- spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/roi_align.py +0 -68
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/commands/scan_cache.py +0 -138
- spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/pretrained_example.py +0 -47
- spaces/Duskfallcrew/Gambit_and_Rogue/README.md +0 -12
- spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py +0 -188
- spaces/Edward-Ji/essentials-of-microeconomics/essentials_of_microeconomics/equilibrium_and_welfare.py +0 -138
- spaces/EuroPython2022/mmocr-demo/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_100k_iters_synthtext.py +0 -61
spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/README.md
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> ⚠ Warning !!!
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poe.com added security and can detect if you are making automated requests. You may get your account banned if you are using this api.
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The normal non-driver api is also currently not very stable
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### Example: `quora (poe)` (use like openai pypi package) - GPT-4 <a name="example-poe"></a>
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```python
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# quora model names: (use left key as argument)
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models = {
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'sage' : 'capybara',
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'gpt-4' : 'beaver',
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'claude-v1.2' : 'a2_2',
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'claude-instant-v1.0' : 'a2',
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'gpt-3.5-turbo' : 'chinchilla'
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}
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```
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### New: bot creation
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```python
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# import quora (poe) package
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from gpt4free import quora
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# create account
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# make sure to set enable_bot_creation to True
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token = quora.Account.create(logging=True, enable_bot_creation=True)
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model = quora.Model.create(
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token=token,
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model='gpt-3.5-turbo', # or claude-instant-v1.0
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system_prompt='you are ChatGPT a large language model ...'
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)
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print(model.name) # gptx....
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# streaming response
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for response in quora.StreamingCompletion.create(
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custom_model=model.name,
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prompt='hello world',
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token=token):
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print(response.completion.choices[0].text)
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```
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### Normal Response:
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```python
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response = quora.Completion.create(model = 'gpt-4',
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prompt = 'hello world',
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token = token)
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print(response.completion.choices[0].text)
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```
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### Update Use This For Poe
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```python
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from gpt4free.quora import Poe
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# available models: ['Sage', 'GPT-4', 'Claude+', 'Claude-instant', 'ChatGPT', 'Dragonfly', 'NeevaAI']
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poe = Poe(model='ChatGPT', driver='firefox', cookie_path='cookie.json', driver_path='path_of_driver')
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poe.chat('who won the football world cup most?')
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# new bot creation
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poe.create_bot('new_bot_name', prompt='You are new test bot', base_model='gpt-3.5-turbo')
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# delete account
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poe.delete_account()
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```
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### Deleting the Poe Account
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```python
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from gpt4free import quora
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quora.Account.delete(token='')
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```
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Buku Ipa Kelas 9 Penerbit Erlangga BEST.md
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<br />
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<h1>Download Buku IPA Kelas 9 Penerbit Erlangga</h1>
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<p>Buku IPA Terpadu 3 SMP/MTs Kelas IX adalah salah satu buku pelajaran yang digunakan oleh siswa kelas 9 di sekolah-sekolah yang menerapkan Kurikulum 2013 Revisi. Buku ini disusun oleh Tim Abdi Guru dan diterbitkan oleh Erlangga, salah satu penerbit buku pendidikan terkemuka di Indonesia. Buku ini berisi materi-materi IPA yang disajikan secara utuh dan terpadu, dengan pendekatan saintifik dan karakteristik yang menarik. Bagaimana cara mendownload buku ini secara gratis dan legal? Apa saja keuntungan menggunakan e-Library Erlangga sebagai sumber belajar online? Simak ulasan lengkapnya di artikel ini.</p>
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<h2>download buku ipa kelas 9 penerbit erlangga</h2><br /><p><b><b>Download</b> ✵✵✵ <a href="https://byltly.com/2uKzYb">https://byltly.com/2uKzYb</a></b></p><br /><br />
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<h2>Apa itu Buku IPA Terpadu 3 SMP/MTs Kelas IX?</h2>
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<p>Buku IPA Terpadu 3 SMP/MTs Kelas IX adalah buku pelajaran yang ditujukan untuk siswa kelas 9 di sekolah-sekolah yang menerapkan Kurikulum 2013 Revisi. Buku ini mencakup kompetensi dalam aspek pengetahuan, keterampilan, dan sikap yang harus dikuasai oleh siswa dalam mempelajari IPA. Buku ini terdiri dari delapan bab, yaitu:</p>
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<ul>
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<li>Bab 1: Sistem Gerak pada Manusia dan Hewan</li>
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<li>Bab 2: Sistem Ekskresi pada Manusia dan Hewan</li>
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<li>Bab 3: Sistem Reproduksi pada Manusia dan Tumbuhan</li>
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<li>Bab 4: Sistem Regulasi pada Manusia dan Tumbuhan</li>
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<li>Bab 5: Sistem Imun pada Manusia</li>
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<li>Bab 6: Sistem Koordinasi pada Manusia</li>
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<li>Bab 7: Sistem Pencernaan pada Manusia</li>
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<li>Bab 8: Sistem Peredaran Darah pada Manusia</li>
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</ul>
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<h3>Karakteristik Buku IPA Terpadu 3 SMP/MTs Kelas IX</h3>
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<p>Buku IPA Terpadu 3 SMP/MTs Kelas IX memiliki beberapa karakteristik yang membuatnya berbeda dari buku-buku pelajaran lainnya. Berikut adalah beberapa karakteristik tersebut:</p>
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<ul>
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<li>Peta Konsep di setiap awal bab untuk memudahkan siswa memahami keterkaitan antarkonsep materi pembelajaran dalam satu bab.</li>
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<li>Materi Pembelajaran disajikan dengan pendekatan saintifik yang meliputi kegiatan mengamati, menanya, mengumpulkan informasi, mengasosiasi, dan mengomunikasikan sehingga siswa dapat memahami konsep, prinsip, atau teori pembelajaran yang sedang dipelajari dengan mudah.</li>
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<li>Jelajah Konsep berisi percobaan-percobaan mengenai materi pembelajaran yang dipelajari oleh siswa.</li>
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<li>Fokus IPA berisi seputar informasi penting dan aktual yang berhubungan dengan materi pembelajaran.</li>
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<li>Bintang IPA berisi seputar penemu di bidang IPA yang berhubungan erat dengan materi pembelajaran.</li>
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<li>Uji Kompetensi berisi tentang soal-soal yang berhubungan dengan kompetensi dasar yang harus dikuasai oleh siswa.</li>
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<li>Terampil IPA berisi tentang keterampilan dalam menyajikan, mengolah, dan menganalisis data hasil percobaan IPA yang berkaitan dengan konsep yang dipelajari, termasuk keterampilan membuat laporan hasil percobaan dan pengamatan, serta keterampilan membuat karya ilmiah.</li>
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<li>Soal Ulangan Akhir Bab terdiri atas tiga jenis tes, yaitu penilaian tertulis, penilaian proyek, dan penilaian produk. Penilaian tersebut meliputi tiga aspek, yaitu aspek pengetahuan, keterampilan, dan sikap.</li>
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<li>Karakter yang Dikembangkan berisi nilai-nilai karakter yang dapat dikembangkan dalam proses pembelajaran.</li>
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</ul>
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<h3>Manfaat Buku IPA Terpadu 3 SMP/MTs Kelas IX</h3>
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<p>Buku IPA Terpadu 3 SMP/MTs Kelas IX memiliki banyak manfaat bagi siswa maupun guru. Berikut adalah beberapa manfaat tersebut:</p>
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<ul>
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<li>Memperkaya pengetahuan siswa tentang IPA secara utuh dan terpadu.</li>
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<li>Meningkatkan keterampilan siswa dalam melakukan percobaan, menyajikan data, menganalisis data, membuat laporan, dan membuat karya ilmiah.</li>
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<li>Mengembangkan sikap ilmiah siswa dalam mempelajari IPA.</li>
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<li>Menumbuhkan minat dan bakat siswa dalam bidang IPA.</li>
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<li>Mendorong siswa untuk belajar mandiri dan aktif.</li>
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<li>Memfasilitasi guru dalam menyusun rencana pembelajaran sesuai dengan Kurikulum 2013 Revisi.</li>
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<li>Memberikan bahan ajar yang lengkap dan menarik bagi guru.</li>
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<li>Memberikan bahan evaluasi yang bervariasi dan komprehensif bagi guru.</li>
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</ul>
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<h2>Bagaimana Cara Mendownload Buku IPA Terpadu 3 SMP/MTs Kelas IX?</h2>
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<p>Saat ini, buku IPA Terpadu 3 SMP/MTs Kelas IX sudah tersedia dalam bentuk e-Book atau buku digital. E-Book ini dapat diunduh secara gratis dan legal melalui e-Library Erlangga. E-Library Erlangga adalah perpustakaan digital yang menyediakan koleksi e-Book Erlangga untuk berbagai jenjang pendidikan. E-Library Erlangga dapat diakses melalui website atau aplikasi mobile. Untuk mendownload buku IPA Terpadu 3 SMP/MTs Kelas IX dari e-Library Erlangga, ada beberapa langkah yang harus dilakukan.</p>
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<h3>Langkah-langkah Mendownload Buku IPA Terpadu 3 SMP/MTs Kelas IX dari e-Library Erlangga</h3>
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<h4>Sebagai Petugas Perpustakaan</h4>
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<p>Jika Anda adalah petugas perpustakaan sekolah atau institusi pendidikan yang ingin mendownload buku IPA Terpadu 3 SMP /MTs Kelas IX untuk koleksi perpustakaan Anda, berikut adalah langkah-langkah yang harus Anda lakukan:</p>
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<ol>
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<li>Kunjungi website <strong>e-Library Erlangga</strong> di https://e-library.erlanggaonline.co.id</li>
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<li>Klik <strong>Daftar</strong>, pada halaman login Pengelola e-Library Erlangga.</li>
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<li>Isi form yang ada, gunakan email resmi sekolah/institusi (bukan email personal). Lalu klik <strong>Register Sekarang</strong>.</li>
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<li>Hubungi CS : 0819-1150-0885 untuk verifikasi akun.</li>
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<li>Login jika sudah diverifikasi.</li>
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<li>Pilih Menu <strong>Kode Aktivasi</strong> yang tercantum dalam KBEL (Kartu Berlangganan E-Library). Pilih judul E-Book yang tertera di kartu, masukan kode aktivasi dan klik <strong>Aktifkan</strong>.</li>
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<li>Buku IPA Terpadu 3 SMP/MTs Kelas IX akan muncul di katalog e-Library Erlangga Anda.</li>
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</ol>
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<h4>Sebagai Anggota Perpustakaan</h4>
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<p>Jika Anda adalah siswa atau guru yang ingin mendownload buku IPA Terpadu 3 SMP/MTs Kelas IX dari e-Library Erlangga sekolah Anda, berikut adalah langkah-langkah yang harus Anda lakukan:</p>
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<ol>
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<li>Install App <strong>e-Library Erlangga</strong> di Play Store atau App Store. Unduh disini: https://erlangga.co.id/ebook/</li>
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<li>Buat Akun, dengan menggunakan email e-Library Erlangga sekolah Anda.</li>
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<li>Tunggu sampai akun diverifikasi dari petugas perpustakaan sekolah Anda.</li>
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<li>Login jika sudah diverifikasi.</li>
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<li>Pinjam E-Book yang ada di dalam katalog e-Library Erlangga. Cari buku IPA Terpadu 3 SMP/MTs Kelas IX dan klik <strong>Pinjam</strong>.</li>
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<li>Buku IPA Terpadu 3 SMP/MTs Kelas IX akan tersimpan di rak buku digital Anda. Klik <strong>Baca</strong> untuk membuka buku tersebut.</li>
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<li>Anda dapat membaca buku IPA Terpadu 3 SMP/MTs Kelas IX secara online atau offline. Jika ingin membaca secara offline, pastikan Anda sudah mengunduh buku tersebut terlebih dahulu dengan klik <strong>Unduh</strong>.</li>
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<li>Jika masa pinjaman buku sudah habis, Anda dapat mengembalikan buku tersebut dengan klik <strong>Kembalikan</strong>. Anda juga dapat memperpanjang masa pinjaman buku dengan klik <strong>Perpanjang</strong>.</li>
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</ol>
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<h3>Tips dan Trik Mendownload Buku IPA Terpadu 3 SMP/MTs Kelas IX dengan Cepat dan Mudah</h3>
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<p>Berikut adalah beberapa tips dan trik yang dapat membantu Anda mendownload buku IPA Terpadu 3 SMP/MTs Kelas IX dengan cepat dan mudah:</p>
|
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<ul>
|
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<li>Pastikan Anda memiliki koneksi internet yang stabil dan cepat saat mendownload buku IPA Terpadu 3 SMP/MTs Kelas IX.</li>
|
| 72 |
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<li>Pastikan Anda memiliki ruang penyimpanan yang cukup di perangkat Anda saat mendownload buku IPA Terpadu 3 SMP/MTs Kelas IX.</li>
|
| 73 |
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<li>Pastikan Anda menggunakan email resmi sekolah/institusi saat mendaftar akun e-Library Erlangga, agar dapat diverifikasi dengan mudah oleh petugas perpustakaan.</li>
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<li>Pastikan Anda meminjam buku IPA Terpadu 3 SMP/MTs Kelas IX sesuai dengan jatah pinjaman yang ditentukan oleh perpustakaan sekolah/institusi Anda.</li>
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<li>Pastikan Anda mengembalikan atau memperpanjang masa pinjaman buku IPA Terpadu 3 SMP/MTs Kelas IX sebelum masa pinjaman habis, agar tidak terkena denda atau sanksi.</li>
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<li>Pastikan Anda menjaga kerahasiaan akun e-Library Erlangga Anda dan tidak membagikannya kepada orang lain.</li>
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</ul>
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<h2>Apa Saja Keuntungan Menggunakan e-Library Erlangga?</h2>
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<p>E-Library Erlangga adalah perpustakaan digital yang menyediakan koleksi e-Book Erlangga untuk berbagai jenjang pendidikan. E-Library Erlangga memiliki banyak keuntungan bagi siswa, guru, maupun sekolah. Berikut adalah beberapa keuntungan tersebut:</p>
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<h3>Peningkatan Manajemen Mutu Sekolah</h3>
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<p>E-Library Erlangga dapat membantu sekolah meningkatkan manajemen mutu pendidikan dengan cara:</p>
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</ul>
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<h3>Pembelajaran Berbasis Teknologi</h3>
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<p>E-Library Erlangga dapat mendukung pembelajaran berbasis teknologi dengan cara:</p>
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<ul>
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<li>Menyediakan platform belajar online yang interaktif dan menarik.</li>
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<li>Menyediakan fitur-fitur belajar online yang bermanfaat dan variatif.</li>
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<li>Menyediakan media belajar online yang mudah diakses dan digunakan.</li>
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<li>Mendorong penggunaan teknologi dalam proses belajar mengajar.</li>
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<li>Mendorong kreativitas dan inovasi dalam proses belajar mengajar.</li>
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<li>Mendorong kolaborasi dan komunikasi dalam proses belajar mengajar.</li>
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</ul>
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<h3>Akreditasi Sekolah/Akreditasi Perpustakaan Sekolah</h3>
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<p>E-Library Erlangga dapat membantu sekolah mendapatkan akreditasi sekolah/akreditasi perpustakaan sekolah dengan cara:</p>
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<ul>
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<li>Memenuhi salah satu ketentuan akreditasi sekolah yaitu memiliki e-Book (diatur dalam undang-undang).</li>
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<li>Memenuhi standar nasional perpustakaan sekolah yaitu memiliki koleksi digital minimal 10% dari total koleksi (diatur dalam peraturan menteri).</li>
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<li>Meningkatkan kinerja perpustakaan sekolah dalam hal pelayanan, pengembangan koleksi, pengelolaan sumber daya manusia, pengelolaan anggaran, pengelolaan sarana dan prasarana, serta kerjasama dan jejaring.</li>
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<h3>Mendukung Program “Gemar Baca” dan Literasi</h3>
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<p>E-Library Erlangga dapat mendukung program “Gemar Baca” dan literasi dengan cara:</p>
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<ul>
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<li>Menyediakan koleksi e-Book yang menarik dan bervariasi untuk berbagai minat baca siswa.</li>
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<li>Menyediakan fitur-fitur e-Book yang mendukung kegiatan membaca seperti zoom, bookmark, highlight, note, dictionary, dll.</li>
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<li>Menyediakan fitur-fitur e-Book yang mendukung kegiatan menulis seperti copy-paste, share, print, dll.</li>
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<li>Mengadakan program-program promosi baca seperti lomba baca, diskusi baca, review baca, dll.</li>
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<li>Mengadakan program-program literasi seperti workshop menulis, seminar literasi, festival literasi, dll.</li>
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</ul>
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<h2>Apa Saja Fitur Lainnya di e-Library Erlangga?</h2>
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<p>Selain menyediakan koleksi e-Book Erlangga untuk berbagai jenjang pendidikan, e-Library Erlangga juga memiliki fitur-fitur lainnya yang dapat membantu siswa dan guru dalam proses belajar mengajar. Berikut adalah beberapa fitur tersebut:</p>
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<h3>Platform Learning Management System (LMS)</h3></p> 0a6ba089eb<br />
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Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun yok. Rahata upgrade yapabilirsiniz. AUDI Gncelleme Yazlm CD'si V 5570 MMI.. 22 Mar 2018. Audi Mmi 2g Software Update 3 Cd . Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun yok. Rahata upgrade yapabilirsiniz. AUDI Gncelleme Yazlm CD'si V 5570 MMI.. 22 Mar 2018. Audi Mmi 2g Software Update 3 Cd . Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun yok. Rahata upgrade yapabilirsiniz. AUDI Gncelleme Yazlm CD'si V 5570 MMI.. 22 Mar 2018. Audi Mmi 2g Software Update 3 Cd . Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun yok. Rahata upgrade yapabilirsiniz. AUDI Gncelleme Yazlm CD'si V 5570 MMI.. 22 Mar 2018. Audi Mmi 2g Software Update 3 Cd . Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun yok. Rahata upgrade yapabilirsiniz. AUDI Gncelleme Yazlm CD'si V 5570 MMI.. 22 Mar 2018. Audi Mmi 2g Software Update 3 Cd . Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun yok. Rahata upgrade yapabilirsiniz. AUDI Gncelleme Yazlm CD'si V 5570 MMI.. 22 Mar 2018. Audi Mmi 2g Software Update 3 Cd . Audi Mmi V5570/20w MMI Update Cd.. Audi Mmi V5570/20w MMI Update. Hi bir sorun 4fefd39f24<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer The Ultimate Simulation Game with Free Open World and Racing.md
DELETED
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| 2 |
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<h1>Car Parking Free: Tips, Benefits, and Statistics</h1>
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<p>Are you tired of wasting time, money, and energy looking for a parking spot? Do you want to enjoy the convenience and peace of mind of parking your car for free? If yes, then this article is for you.</p>
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<h2>Introduction</h2>
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<h3>What is car parking free?</h3>
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<p>Car parking free is a term that refers to any parking space that does not require you to pay a fee or a ticket. It can be found in various places, such as public streets, residential areas, shopping malls, parks, airports, or other locations that offer free parking for their customers or visitors.</p>
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<p>Car parking free can also be provided by some employers, organizations, or institutions that have their own parking lots or garages. They may offer free parking for their employees, members, students, or guests as a perk or a benefit.</p>
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<h3>Why is car parking free important?</h3>
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<p>Car parking free is important because it can make your life easier and happier in many ways. Here are some of the reasons why car parking free matters:</p>
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<ul>
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<li>It can save you time and money. You don't have to spend hours searching for a parking spot or paying expensive fees or fines. You can use that time and money for other things that matter more to you.</li>
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<li>It can reduce stress and frustration. You don't have to deal with traffic jams, crowded lots, or rude drivers. You can park your car quickly and easily without any hassle or worry.</li>
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<li>It can improve your driving skills and confidence. You don't have to worry about parallel parking, reversing, or maneuvering in tight spaces. You can park your car in a spacious and comfortable spot that suits your level of experience and comfort.</li>
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</ul>
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| 18 |
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<h2>Tips for car parking free</h2>
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| 19 |
-
<h3>Use smart parking apps and solutions</h3>
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| 20 |
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<p>One of the best ways to find car parking free is to use smart parking apps and solutions that can help you locate, reserve, and pay for parking spaces online or on your smartphone. Some examples of these apps and solutions are:</p>
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<ul>
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| 22 |
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<li><a href="(^1^)">Car Parking Multiplayer</a>: This is a simulation game that lets you park your car in various scenarios and modes. You can also customize your car, compete with other players, and explore an open world with real gas stations and car services.</li>
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<li><a href="(^2^)">BlueStacks</a>: This is a platform that allows you to play Android games on your PC or Mac. You can use it to play Car Parking Multiplayer on a bigger screen with better graphics and performance.</li>
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<li><a href="(^3^)">Car Parking Multiplayer on the App Store</a>: This is the iOS version of Car Parking Multiplayer that you can download on your iPhone or iPad. You can enjoy the same features as the Android version of the game, with some minor differences in the interface and controls.</li>
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<li><a href="">Parkopedia</a>: This is a website and app that provides information on parking availability, prices, and reviews for over 70 million parking spaces in 15,000 cities around the world. You can use it to find the best and cheapest parking options near your destination.</li>
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<li><a href="">SpotHero</a>: This is a website and app that allows you to book and pay for parking spaces in advance at discounted rates. You can use it to reserve a spot in a garage, lot, or valet service in over 300 cities across the US and Canada.</li>
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</ul>
|
| 28 |
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<h3>Follow the signs and rules</h3>
|
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<p>Another way to find car parking free is to follow the signs and rules that indicate where you can and cannot park your car. Some examples of these signs and rules are:</p>
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free car parking near stadium<br />
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| 65 |
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| 66 |
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| 70 |
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how to find free car parking spaces<br />
|
| 71 |
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how to get free car parking tickets<br />
|
| 72 |
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how to avoid paying for car parking fees<br />
|
| 73 |
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how to save money on car parking charges<br />
|
| 74 |
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how to book a free car parking spot online</p>
|
| 75 |
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<ul>
|
| 76 |
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<li>No Parking: This means that you cannot park your car at any time in this area. You may be fined or towed if you do so.</li>
|
| 77 |
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<li>No Parking Except Sundays: This means that you can park your car on Sundays only in this area. You may be fined or towed if you park on any other day.</li>
|
| 78 |
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<li>2 Hour Parking: This means that you can park your car for up to two hours in this area. You may be fined or towed if you exceed the time limit.</li>
|
| 79 |
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<li>Resident Parking Only: This means that only residents with a valid permit can park their car in this area. You may be fined or towed if you are not a resident or do not have a permit.</li>
|
| 80 |
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<li>Handicapped Parking Only: This means that only drivers with a valid handicapped placard or license plate can park their car in this area. You may be fined or towed if you are not handicapped or do not have the proper identification.</li>
|
| 81 |
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</ul>
|
| 82 |
-
<h3>Park in a safe and secure location</h3>
|
| 83 |
-
<p>A third way to find car parking free is to park your car in a safe and secure location that protects your car from damage and theft. Some examples of these locations are:</p>
|
| 84 |
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<ul>
|
| 85 |
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<li>Well-lit areas: These are areas that have adequate lighting at night or during low-visibility conditions. They can deter potential thieves or vandals from targeting your car.</li>
|
| 86 |
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<li>Busy areas: These are areas that have a lot of people or traffic around them. They can provide witnesses or help in case of an emergency or a crime involving your car.</li>
|
| 87 |
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<li>Legal areas: These are areas that are not restricted or prohibited by law or by private property owners. They can prevent you from getting into trouble with the authorities or the owners of the land.</li>
|
| 88 |
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<li>Covered areas: These are areas that have some form of shelter or protection from the elements, such as a roof, a canopy, or a tree. They can prevent your car from getting damaged by rain, snow, hail, sun, or wind.</li>
|
| 89 |
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<li>Locked areas: These are areas that have some form of security or access control, such as a gate, a fence, or a guard. They can prevent unauthorized people from entering or exiting the area where your car is parked.</li>
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| 90 |
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</ul>
|
| 91 |
-
<h2>Benefits of car parking free</h2>
|
| 92 |
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<h3>Save time and money</h3>
|
| 93 |
-
<p>One of the main benefits of car parking free is that it can save you time and money. Here are some of the ways how:</p>
|
| 94 |
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<ul>
|
| 95 |
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<li>You don't have to waste time looking for a parking spot or waiting in line to pay for one. You can park your car as soon as you find a free space and leave as soon as you are done with your business.</li>
|
| 96 |
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<li>You don't have to spend money on parking fees or fines. You can park your car for free without worrying about paying anything or getting penalized for violating any rules.</li>
|
| 97 |
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<li>You don't have to spend money on gas or maintenance. You can park your car closer to your destination and reduce the distance and time you have to drive. You can also avoid driving on rough or uneven surfaces that can damage your car's tires, suspension, or engine.</li>
|
| 98 |
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</ul>
|
| 99 |
-
<h3>Reduce stress and frustration</h3>
|
| 100 |
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<p>Another benefit of car parking free is that it can reduce stress and frustration. Here are some of the ways how:</p>
|
| 101 |
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<ul>
|
| 102 |
-
<li>You don't have to deal with traffic jams, crowded lots, or rude drivers. You can park your car in a less congested and more courteous environment.</li>
|
| 103 |
-
<li>You don't have to worry about finding a parking spot or paying for one. You can park your car with ease and peace of mind.</li>
|
| 104 |
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<li>You don't have to worry about your car getting damaged or stolen. You can park your car in a safe and secure location that minimizes the risk of accidents or crimes.</li>
|
| 105 |
-
<li>You don't have to worry about your driving skills or confidence. You can park your car in a spot that suits your level of experience and comfort.</li>
|
| 106 |
-
</ul>
|
| 107 |
-
<h3>Protect your car from damage and theft</h3>
|
| 108 |
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<p>A third benefit of car parking free is that it can protect your car from damage and theft. Here are some of the ways how:</p>
|
| 109 |
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<ul>
|
| 110 |
-
<li>You don't have to expose your car to harsh weather conditions, such as rain, snow, hail, sun, or wind. You can park your car in a covered or sheltered area that protects it from the elements.</li>
|
| 111 |
-
<li>You don't have to expose your car to dirt, dust, or debris. You can park your car in a clean or paved area that prevents it from getting dirty or scratched.</li>
|
| 112 |
-
<li>You don't have to expose your car to other vehicles or people. You can park your car in a spacious or isolated area that prevents it from getting bumped or broken into.</li>
|
| 113 |
-
</ul>
|
| 114 |
-
<h3>Improve your driving skills and confidence</h3>
|
| 115 |
-
<p>A fourth benefit of car parking free is that it can improve your driving skills and confidence. Here are some of the ways how:</p>
|
| 116 |
-
<ul>
|
| 117 |
-
<li>You don't have to rely on technology or assistance. You can park your car by yourself using your own judgment and intuition.</li>
|
| 118 |
-
<li>You don't have to follow a fixed or predetermined route. You can park your car wherever you want using your own creativity and flexibility.</li>
|
| 119 |
-
<li>You don't have to conform to a standard or expectation. You can park your car however you want using your own style and personality.</li>
|
| 120 |
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</ul>
|
| 121 |
-
<h2>Statistics on car parking free</h2>
|
| 122 |
-
<h3>Global smart parking market size and growth</h3>
|
| 123 |
-
<p>One of the statistics on car parking free is the global smart parking market size and growth. According to a report by Grand View Research, the global smart parking market size was valued at USD 5.7 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 18.4% from 2021 to 2028. The report attributes this growth to the increasing demand for efficient and convenient parking solutions, the rising adoption of Internet of Things (IoT) and artificial intelligence (AI) technologies, and the growing environmental and social awareness among consumers and governments.</p>
|
| 124 |
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<h3>Car parking trends and challenges in different regions</h3>
|
| 125 |
-
<p>Another statistic on car parking free is the car parking trends and challenges in different regions. According to a report by Parkopedia, the average parking price for two hours in 2020 was USD 5.46 globally, USD 8.95 in North America, USD 6.15 in Europe, USD 2.69 in Asia-Pacific, USD 1.77 in Latin America, and USD 1.28 in Africa. The report also identifies some of the key trends and challenges in each region, such as:</p>
|
| 126 |
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<ul>
|
| 127 |
-
<li>North America: The rise of contactless payments, the impact of COVID-19 on parking demand and supply, and the need for more efficient and sustainable parking management.</li>
|
| 128 |
-
<li>Europe: The expansion of low-emission zones, the adoption of mobility-as-a-service (MaaS) platforms, and the regulation of private parking operators.</li>
|
| 129 |
-
<li>Asia-Pacific: The rapid urbanization and motorization, the development of smart cities and connected vehicles, and the emergence of new mobility modes and services.</li>
|
| 130 |
-
<li>Latin America: The lack of parking infrastructure and enforcement, the high level of informality and corruption, and the social inequality and insecurity.</li>
|
| 131 |
-
<li>Africa: The low penetration of digital technologies, the high cost and scarcity of land, and the poor quality and safety of public transportation.</li>
|
| 132 |
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</ul>
|
| 133 |
-
<h3>Car parking innovations and opportunities in the future</h3>
|
| 134 |
-
<p>A third statistic on car parking free is the car parking innovations and opportunities in the future. According to a report by Frost & Sullivan, some of the key innovations and opportunities in the car parking industry by 2030 are:</p>
|
| 135 |
-
<ul>
|
| 136 |
-
<li>The emergence of autonomous valet parking (AVP) systems that can park cars without human intervention.</li>
|
| 137 |
-
<li>The integration of blockchain technology that can enable secure and transparent transactions between parking providers and users.</li>
|
| 138 |
-
<li>The adoption of dynamic pricing models that can adjust parking fees based on demand and supply factors.</li>
|
| 139 |
-
<li>The implementation of green parking initiatives that can reduce carbon emissions and energy consumption from parking facilities.</li>
|
| 140 |
-
<li>The creation of smart parking ecosystems that can connect parking spaces with other mobility services and solutions.</li>
|
| 141 |
-
</ul>
|
| 142 |
-
<h2>Conclusion</h2>
|
| 143 |
-
<h3>Summary of the main points</h 3>Summary of the main points</h3>
|
| 144 |
-
<p>In conclusion, car parking free is a term that refers to any parking space that does not require you to pay a fee or a ticket. It is important because it can save you time and money, reduce stress and frustration, protect your car from damage and theft, and improve your driving skills and confidence. You can find car parking free by using smart parking apps and solutions, following the signs and rules, and parking in a safe and secure location. You can also learn more about car parking free by looking at some of the statistics on the global smart parking market size and growth, the car parking trends and challenges in different regions, and the car parking innovations and opportunities in the future.</p>
|
| 145 |
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<h3>Call to action for the readers</h3>
|
| 146 |
-
<p>Now that you know more about car parking free, we hope that you will take advantage of this opportunity and enjoy the benefits that it offers. If you want to play Car Parking Multiplayer on your PC or Mac, you can download BlueStacks for free and start playing today. If you want to find the best and cheapest parking options near your destination, you can use Parkopedia or SpotHero to search, compare, and book parking spaces online or on your smartphone. If you want to share your thoughts or experiences on car parking free, you can leave a comment below or contact us through our website. Thank you for reading and happy parking!</p>
|
| 147 |
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<h2>FAQs</h2>
|
| 148 |
-
<p>Here are some of the frequently asked questions (FAQs) on car parking free:</p>
|
| 149 |
-
<ul>
|
| 150 |
-
<li><b>Q: How can I tell if a parking space is free or not?</b></li>
|
| 151 |
-
<li>A: You can tell if a parking space is free or not by looking at the signs, markings, meters, or machines that indicate the parking rules and regulations in that area. You can also use smart parking apps and solutions that can show you the availability and price of parking spaces in real time.</li>
|
| 152 |
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<li><b>Q: What are the risks or disadvantages of car parking free?</b></li>
|
| 153 |
-
<li>A: Some of the risks or disadvantages of car parking free are that it may be hard to find, especially in busy or popular areas; it may be subject to time limits or restrictions that may change depending on the day or hour; it may be located in remote or unsafe areas that may expose your car to damage or theft; or it may be illegal or unethical, especially if you park on private property without permission or on public property without paying taxes or fees.</li>
|
| 154 |
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<li><b>Q: What are some of the best practices or tips for car parking free?</b></li>
|
| 155 |
-
<li>A: Some of the best practices or tips for car parking free are to plan ahead and do some research before you go; to use smart parking apps and solutions that can help you find, reserve, and pay for parking spaces online or on your smartphone; to follow the signs and rules that indicate where you can and cannot park your car; to park in a safe and secure location that protects your car from damage and theft; and to be courteous and respectful to other drivers and pedestrians.</li>
|
| 156 |
-
<li><b>Q: What are some of the trends or innovations in car parking free?</b></li>
|
| 157 |
-
<li>A: Some of the trends or innovations in car parking free are the emergence of autonomous valet parking (AVP) systems that can park cars without human intervention; the integration of blockchain technology that can enable secure and transparent transactions between parking providers and users; the adoption of dynamic pricing models that can adjust parking fees based on demand and supply factors; the implementation of green parking initiatives that can reduce carbon emissions and energy consumption from parking facilities; and the creation of smart parking ecosystems that can connect parking spaces with other mobility services and solutions.</li>
|
| 158 |
-
<li><b>Q: Where can I learn more about car parking free?</b></li>
|
| 159 |
-
<li>A: You can learn more about car parking free by reading this article, visiting our website, or following us on social media. You can also contact us through our website if you have any questions, comments, or suggestions.</li>
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| 160 |
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</ul></p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Dolphin Emulator for Android The Best Way to Play Basara 2 Heroes - Download Link Inside.md
DELETED
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@@ -1,238 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Download Link Basara 2 Heroes Dolphin Emulator Android</h1>
|
| 3 |
-
<p>Do you love hack and slash games with historical and fantasy elements? Do you want to play one of the best games in the genre on your Android device? If so, you might be interested in downloading Basara 2 Heroes for Dolphin Emulator.</p>
|
| 4 |
-
<p>Basara 2 Heroes is a follow-up to Sengoku Basara 2, a game developed by Capcom for PlayStation 2 in 2006. It adds numerous new features and content, such as new playable characters, new game modes, and co-op multiplayer. The game is set in the Sengoku period of Japan, where you can control one of the many warlords and fight against hundreds of enemies in epic battles.</p>
|
| 5 |
-
<h2>download link basara 2 heroes dolphin emulator android</h2><br /><p><b><b>DOWNLOAD</b> ––– <a href="https://urlin.us/2uSZiw">https://urlin.us/2uSZiw</a></b></p><br /><br />
|
| 6 |
-
<p>Dolphin Emulator is an app that allows you to play GameCube and Wii games on your Android device. It is an open-source project that has been in development since 2003, and it has improved significantly over the years. It supports many games with high compatibility and performance, as well as various enhancements and features.</p>
|
| 7 |
-
<p>In this article, we will show you how to download Basara 2 Heroes for Dolphin Emulator, how to install and configure Dolphin Emulator on your Android device, and how to play Basara 2 Heroes on Dolphin Emulator. Follow these steps carefully and you will be able to enjoy this amazing game on your Android device.</p>
|
| 8 |
-
<h2>How to Download Basara 2 Heroes for Dolphin Emulator</h2>
|
| 9 |
-
<h3>Download the Game ISO File</h3>
|
| 10 |
-
<p>The first thing you need to do is to download the game ISO file for Basara 2 Heroes. This is a file that contains all the data of the game disc, which you can use with Dolphin Emulator. However, you must own and acquire your own game legally, as downloading games that you do not own is illegal and unethical.</p>
|
| 11 |
-
<p>There are many websites that offer game ISO files for download, but not all of them are safe and reliable. Some of them may contain viruses, malware, or fake files that can harm your device or compromise your privacy. Therefore, you should only download game ISO files from reputable sources that have positive reviews and feedback from users.</p>
|
| 12 |
-
<p>One of the best sources for downloading game ISO files is [CoolROM](^1^), a website that has a large collection of game ISO files for various platforms, including GameCube and Wii. You can search for Basara 2 Heroes on the website and find the game ISO file that matches your region and language. You can also check the ratings, comments, and screenshots of the game before downloading it.</p>
|
| 13 |
-
<p>To download the game ISO file from CoolROM, you need to follow these steps:</p>
|
| 14 |
-
<ol>
|
| 15 |
-
<li>Go to [CoolROM] and type "Basara 2 Heroes" in the search box. Press enter or click on the magnifying glass icon.</li>
|
| 16 |
-
<li>On the search results page, find the game that matches your region and language. For example, if you want to play the English version of the game, you should look for "Sengoku Basara 2 - Heroes (Japan) (En,Ja)". Click on the game title to go to the game page.</li>
|
| 17 |
-
<li>On the game page, scroll down and click on the "Download Now" button. This will take you to another page where you need to wait for a few seconds and then click on the "Download Your File" button. This will start the download of the game ISO file.</li>
|
| 18 |
-
<li>Save the game ISO file to a folder on your device or external storage. Make sure you remember the location of the file, as you will need it later.</li>
|
| 19 |
-
</ol>
|
| 20 |
-
<h3>Download the Dolphin Emulator App</h3>
|
| 21 |
-
<p>The next thing you need to do is to download the Dolphin Emulator app for Android. This is an app that allows you to play GameCube and Wii games on your Android device. It is an open-source project that has been in development since 2003, and it has improved significantly over the years. It supports many games with high compatibility and performance, as well as various enhancements and features.</p>
|
| 22 |
-
<p>The best way to download the Dolphin Emulator app for Android is to get it from the official Google Play Store page. This way, you can ensure that you get the latest and most stable version of the app, as well as receive automatic updates and notifications. You can also avoid any potential risks of downloading fake or malicious apps from unknown sources.</p>
|
| 23 |
-
<p>How to download and play basara 2 heroes on android with dolphin emulator<br />
|
| 24 |
-
Basara 2 heroes wii edition - dolphin emulator for android download link and tutorial<br />
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Best settings for basara 2 heroes on dolphin emulator android<br />
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| 26 |
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Basara 2 heroes iso download for dolphin emulator android<br />
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Dolphin emulator android basara 2 heroes cheats and codes<br />
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Basara 2 heroes gameplay on dolphin emulator android<br />
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Basara 2 heroes dolphin emulator android lag fix and performance boost<br />
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Basara 2 heroes save data for dolphin emulator android<br />
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Basara 2 heroes mod apk for dolphin emulator android<br />
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Basara 2 heroes english patch for dolphin emulator android<br />
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Basara 2 heroes characters and unlockables on dolphin emulator android<br />
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Basara 2 heroes tips and tricks for dolphin emulator android<br />
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Basara 2 heroes review and rating for dolphin emulator android<br />
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Basara 2 heroes vs basara chronicle heroes on dolphin emulator android<br />
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Basara 2 heroes multiplayer mode on dolphin emulator android<br />
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Basara 2 heroes story mode walkthrough on dolphin emulator android<br />
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Basara 2 heroes free download link for dolphin emulator android<br />
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Basara 2 heroes system requirements and compatibility for dolphin emulator android<br />
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Basara 2 heroes graphics and sound quality on dolphin emulator android<br />
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Basara 2 heroes controller settings and configuration for dolphin emulator android<br />
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Basara 2 heroes comparison between wii and ps2 versions on dolphin emulator android<br />
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Basara 2 heroes hidden features and secrets on dolphin emulator android<br />
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Basara 2 heroes best weapons and items on dolphin emulator android<br />
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Basara 2 heroes difficulty levels and challenges on dolphin emulator android<br />
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Basara 2 heroes fun facts and trivia on dolphin emulator android</p>
|
| 48 |
-
<p>To download the Dolphin Emulator app from the Google Play Store, you need to follow these steps:</p>
|
| 49 |
-
<ol>
|
| 50 |
-
<li>Go to [Dolphin Emulator] on the Google Play Store and tap on the "Install" button. This will start the download and installation of the app on your device.</li>
|
| 51 |
-
<li>Wait for the app to finish installing and then open it. You will see a welcome screen with some information and tips about using Dolphin Emulator. Tap on "Next" to proceed.</li>
|
| 52 |
-
<li>You will see a screen asking you to grant Dolphin Emulator access to your device's storage. This is necessary for Dolphin Emulator to scan for game files and save your settings and progress. Tap on "Allow" to grant permission.</li>
|
| 53 |
-
<li>You will see a screen asking you to enable controller support for Dolphin Emulator. This is optional, but recommended if you want to use an external controller to play games. Tap on "Enable" to grant permission.</li>
|
| 54 |
-
</ol>
|
| 55 |
-
<h2>How to Install and Configure Dolphin Emulator on Android</h2>
|
| 56 |
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<h3>Install the App and Grant Permissions</h3>
|
| 57 |
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<p>You have already installed the app and granted permissions in the previous section, so you can skip this step if you have done so. However, if you have not installed the app or granted permissions yet, you need to do so before proceeding.</p>
|
| 58 |
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<p>To install the app and grant permissions, follow these steps:</p>
|
| 59 |
-
<ol>
|
| 60 |
-
<li>Go to [Dolphin Emulator] on the Google Play Store and tap on the "Install" button. This will start the download and installation of the app on your device.</li>
|
| 61 |
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<li>Wait for the app to finish installing and then open it. You will see a welcome screen with some information and tips about using Dolphin Emulator. Tap on "Next" to proceed.</li>
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| 62 |
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<li>You will see a screen asking you to grant Dolphin Emulator access to your device's storage. This is necessary for Dolphin Emulator to scan for game files and save your settings and progress. Tap on "Allow" to grant permission.</li>
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| 63 |
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<li>You will see a screen asking you to enable controller support for Dolphin Emulator. This is optional, but recommended if you want to use an external controller to play games. Tap on "Enable" to grant permission.</li>
|
| 64 |
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</ol>
|
| 65 |
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<h3>Scan for Game Files and Add Basara 2 Heroes to Library</h3>
|
| 66 |
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<p>The next thing you need to do is to scan for game files and add Basara 2 Heroes to the Dolphin Emulator library. This will allow you to launch and play the game from the app. To do this, you need to have the game ISO file that you downloaded in the previous section.</p>
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| 67 |
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<p>To scan for game files and add Basara 2 Heroes to the library, follow these steps:</p>
|
| 68 |
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<ol>
|
| 69 |
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<li>On the main screen of Dolphin Emulator, tap on the "+" icon at the top right corner. This will open a file browser where you can navigate to the folder where you saved the game ISO file.</li>
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| 70 |
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<li>Find and select the game ISO file for Basara 2 Heroes. Tap on "OK" to confirm. This will add the game to the Dolphin Emulator library.</li>
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| 71 |
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<li>You will see a thumbnail of the game on the main screen of Dolphin Emulator. Tap on it to see more details and options for the game.</li>
|
| 72 |
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</ol>
|
| 73 |
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<h3>Adjust the Settings for Optimal Performance and Compatibility</h3>
|
| 74 |
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<p>The last thing you need to do before playing Basara 2 Heroes on Dolphin Emulator is to adjust the settings for optimal performance and compatibility. This will ensure that you get the best possible gaming experience on your Android device. However, keep in mind that different devices may have different capabilities and limitations, so you may need to experiment with different settings to find what works best for you.</p>
|
| 75 |
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<p>To adjust the settings for optimal performance and compatibility, follow these steps:</p>
|
| 76 |
-
<ol>
|
| 77 |
-
<li>On the main screen of Dolphin Emulator, tap on the menu icon at the top left corner. This will open a sidebar menu where you can access various options and settings.</li>
|
| 78 |
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<li>Tap on "Settings" to open the settings menu. Here you can adjust various settings for graphics, audio, controls, and enhancements.</li>
|
| 79 |
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<li>For graphics settings, we recommend the following:</li>
|
| 80 |
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<ul>
|
| 81 |
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<li>Video Backend: Choose "OpenGL" or "Vulkan" depending on your device's support and preference.</li>
|
| 82 |
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<li>Aspect Ratio: Choose "Auto" or "Stretch to Window" depending on your preference.</li>
|
| 83 |
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<li>Show FPS: Enable this option if you want to see the frames per second (FPS) of the game.</li>
|
| 84 |
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<li>Internal Resolution: Choose a resolution that matches your device's screen resolution or lower. Higher resolutions may improve the image quality, but they may also reduce the performance and cause lag or stuttering.</li>
|
| 85 |
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<li>Anisotropic Filtering: Choose a level of filtering that improves the texture quality without affecting the performance too much. We recommend 2x or 4x.</li>
|
| 86 |
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<li>Anti-Aliasing: Choose a level of anti-aliasing that smooths out the jagged edges without affecting the performance too much. We recommend None or 2x MSAA.</li>
|
| 87 |
-
</ul>
|
| 88 |
-
<li>For audio settings, we recommend the following:</li>
|
| 89 |
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<ul>
|
| 90 |
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<li>Audio Backend: Choose "OpenSL ES" or "Cubeb" depending on your device's support and preference.</li>
|
| 91 |
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<li>Audio Stretching: Enable this option if you want to reduce audio crackling and sync issues.</li>
|
| 92 |
-
<li>Volume: Adjust the volume level according to your preference.</li>
|
| 93 |
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</ul>
|
| 94 |
-
<li>For control settings, we recommend the following:</li>
|
| 95 |
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<ul>
|
| 96 |
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<li>Input Device: Choose "Emulated Wii Remote" or "Emulated GameCube Controller" depending on the game's controller support.</li>
|
| 97 |
-
<li>Edit Layout: Tap on this option if you want to customize the layout of the on-screen controller. You can resize, reposition, and rearrange the buttons according to your preference.</li>
|
| 98 |
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<li>Configure Controller: Tap on this option if you want to configure an external controller for Dolphin Emulator. You can map the buttons and axes of your controller to match the game's controls.</li>
|
| 99 |
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</ul>
|
| 100 |
-
<li>For enhancement settings, we recommend the following:</li>
|
| 101 |
-
<ul>
|
| 102 |
-
<li>Scaled EFB Copy: Enable this option if you want to improve some effects and textures in some games.</li>
|
| 103 |
-
<li>Force Texture Filtering: Enable this option if you want to improve some textures in some games.</li>
|
| 104 |
-
<li>Disable Fog: Disable this option if you want to preserve some atmospheric effects in some games.</li>
|
| 105 |
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<li>Widescreen Hack: Enable this option if you want to play games in widescreen mode. However, this may cause some graphical glitches or distortions in some games.</li>
|
| 106 |
-
</ul>
|
| 107 |
-
</ol>
|
| 108 |
-
<h2>How to Play Basara 2 Heroes on Dolphin Emulator</h2>
|
| 109 |
-
<h3>Choose a Game Mode and Character</h3>
|
| 110 |
-
<p>Now that you have downloaded, installed, and configured Dolphin Emulator and Basara 2 Heroes, you are ready to play the game. To start playing, follow these steps:</p>
|
| 111 |
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<ol>
|
| 112 |
-
<li>On the main screen of Dolphin Emulator, tap on the thumbnail of Basara 2 Heroes. This will launch the game and show the title screen.</li>
|
| 113 |
-
<li>On the title screen, press the "Start" button to go to the main menu. Here you can choose from different game modes, such as Story Mode, Free Mode, Versus Mode, and Survival Mode. Each mode has its own objectives and challenges.</li>
|
| 114 |
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<li>For example, if you choose Story Mode, you can select one of the 16 playable characters and follow their story through a series of stages. Each character has their own personality, skills, weapons, and allies. You can also unlock more characters and content by completing certain conditions.</li>
|
| 115 |
-
<li>After choosing a game mode and a character, you can also customize some options, such as the difficulty level, the number of lives, the time limit, and the sound settings.</li>
|
| 116 |
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</ol>
|
| 117 |
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<h3>Use the On-Screen or External Controller</h3>
|
| 118 |
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<p>To play Basara 2 Heroes on Dolphin Emulator, you can use either the on-screen controller or an external controller. The on-screen controller is a virtual controller that appears on your device's screen and mimics the original GameCube or Wii controller. The external controller is a physical controller that you can connect to your device via Bluetooth or USB.</p>
|
| 119 |
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<p>To use the on-screen controller, you need to tap on the buttons and move the analog sticks on your device's screen. You can also customize the layout of the on-screen controller by tapping on the "Edit Layout" option in the control settings menu.</p>
|
| 120 |
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<p>To use an external controller, you need to pair it with your device and configure it in Dolphin Emulator. You can do this by tapping on the "Configure Controller" option in the control settings menu. You can also map the buttons and axes of your external controller to match the game's controls.</p>
|
| 121 |
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<p>The basic controls for Basara 2 Heroes are as follows:</p>
|
| 122 |
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<table>
|
| 123 |
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<tr>
|
| 124 |
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<th>Button</th>
|
| 125 |
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<th>Function</th>
|
| 126 |
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</tr>
|
| 127 |
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<tr>
|
| 128 |
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<td>A</td>
|
| 129 |
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<td>Normal Attack</td>
|
| 130 |
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</tr>
|
| 131 |
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<tr>
|
| 132 |
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<td>B</td>
|
| 133 |
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<td>Special Attack</td>
|
| 134 |
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</tr>
|
| 135 |
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<tr>
|
| 136 |
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<td>X</td>
|
| 137 |
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<td>Jump</td>
|
| 138 |
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</tr>
|
| 139 |
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<tr>
|
| 140 |
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<td>Y</td>
|
| 141 |
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<td>Basara Attack (when gauge is full)</td>
|
| 142 |
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</tr>
|
| 143 |
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<tr>
|
| 144 |
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<td>Z</td>
|
| 145 |
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<td>Taunt (increase Basara gauge)</td>
|
| 146 |
-
</tr>
|
| 147 |
-
<tr>
|
| 148 |
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<td>L</td>
|
| 149 |
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<td>Guard / Evade (with analog stick)</td>
|
| 150 |
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</tr>
|
| 151 |
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<tr>
|
| 152 |
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<td>R</td>
|
| 153 |
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<td>Lock-on / Change Target (with analog stick)</td>
|
| 154 |
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</tr>
|
| 155 |
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<tr>
|
| 156 |
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<td>D-Pad</td>
|
| 157 |
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<td>Select Ally / Order Ally (with A button)</td>
|
| 158 |
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</tr>
|
| 159 |
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<tr>
|
| 160 |
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<td>Start</td>
|
| 161 |
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<td>Pause / Menu</td>
|
| 162 |
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</tr>
|
| 163 |
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<tr>
|
| 164 |
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<td>Analog Stick</td>
|
| 165 |
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<td>Move Character / Camera (when locked-on)</td>
|
| 166 |
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</tr>
|
| 167 |
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<tr>
|
| 168 |
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<td>C-Stick</td>
|
| 169 |
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<td>Move Camera (when not locked-on)</td>
|
| 170 |
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</tr>
|
| 171 |
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<h3>Enjoy the Game on Your Android Device</h3>
|
| 172 |
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<p>You are now ready to enjoy Basara 2 Heroes on your Android device. You can experience the thrilling and fast-paced action of hacking and slashing through hundreds of enemies with your favorite character. You can also explore the rich and colorful history and culture of Japan during the Sengoku period.</p>
|
| 173 |
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<p>Playing Basara 2 Heroes on Dolphin Emulator has some benefits and drawbacks compared to playing it on a console. Some of the benefits are:</p>
|
| 174 |
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<ul>
|
| 175 |
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<li>You can play the game anytime and anywhere on your Android device.</li>
|
| 176 |
-
<li>You can enhance the game's graphics and audio with Dolphin Emulator's features.</li>
|
| 177 |
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<li>You can save and load your progress with Dolphin Emulator's save states.</li> <li>You can use an external controller to play the game more comfortably.</li>
|
| 178 |
-
</ul>
|
| 179 |
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<p>Some of the drawbacks are:</p>
|
| 180 |
-
<ul>
|
| 181 |
-
<li>You may encounter some compatibility or performance issues with some devices or games.</li>
|
| 182 |
-
<li>You may need to adjust the settings for each game to get the best results.</li>
|
| 183 |
-
<li>You may need to download and install additional files or apps to play the game.</li>
|
| 184 |
-
<li>You may need to own and acquire the game legally, as downloading games that you do not own is illegal and unethical.</li>
|
| 185 |
-
</ul>
|
| 186 |
-
<p>Despite these drawbacks, playing Basara 2 Heroes on Dolphin Emulator is still a great way to enjoy this classic game on your Android device. You can have fun and learn something new at the same time.</p>
|
| 187 |
-
<h2>Conclusion</h2>
|
| 188 |
-
<p>In this article, we have shown you how to download link Basara 2 Heroes Dolphin Emulator Android. We have explained what Basara 2 Heroes and Dolphin Emulator are, how to download them, how to install and configure them, and how to play them. We have also provided some tips and tricks for getting the best gaming experience on your Android device.</p>
|
| 189 |
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<p>Basara 2 Heroes is a hack and slash game that lets you control one of the many warlords of Japan during the Sengoku period. You can fight against hundreds of enemies in epic battles, using your skills, weapons, and allies. You can also choose from different game modes, such as Story Mode, Free Mode, Versus Mode, and Survival Mode.</p>
|
| 190 |
-
<p>Dolphin Emulator is an app that lets you play GameCube and Wii games on your Android device. It is an open-source project that has been in development since 2003, and it has improved significantly over the years. It supports many games with high compatibility and performance, as well as various enhancements and features.</p>
|
| 191 |
-
<p>If you want to play Basara 2 Heroes on your Android device, you need to follow these steps:</p>
|
| 192 |
-
<ol>
|
| 193 |
-
<li>Download the game ISO file for Basara 2 Heroes from a reputable source.</li>
|
| 194 |
-
<li>Download the Dolphin Emulator app from the Google Play Store.</li>
|
| 195 |
-
<li>Install the app and grant permissions for storage access and controller support.</li>
|
| 196 |
-
<li>Scan for game files and add Basara 2 Heroes to the Dolphin Emulator library.</li>
|
| 197 |
-
<li>Adjust the settings for optimal performance and compatibility.</li>
|
| 198 |
-
<li>Choose a game mode and character in Basara 2 Heroes.</li>
|
| 199 |
-
<li>Use the on-screen or external controller to play Basara 2 Heroes on Dolphin Emulator.</li>
|
| 200 |
-
<li>Enjoy the game on your Android device.</li>
|
| 201 |
-
</ol>
|
| 202 |
-
<p>We hope that this article has helped you learn how to download link Basara 2 Heroes Dolphin Emulator Android. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p>
|
| 203 |
-
<h2>FAQs</h2>
|
| 204 |
-
<h3>Q: Is Dolphin Emulator legal?</h3>
|
| 205 |
-
<p>A: Dolphin Emulator is legal, as it is an open-source project that does not violate any laws or copyrights. However, downloading games that you do not own is illegal and unethical. You should only download games that you own and acquire them legally and safely. You should also respect the developers and publishers of the games and support them if you can.</p>
|
| 206 |
-
<h3>Q: What are the system requirements for Dolphin Emulator?</h3>
|
| 207 |
-
<p>A: Dolphin Emulator does not have a fixed set of system requirements, as different games and settings may have different demands. However, in general, you need a device that has the following specifications or higher:</p>
|
| 208 |
-
<ul>
|
| 209 |
-
<li>Android 5.0 (Lollipop) or later</li>
|
| 210 |
-
<li>64-bit processor (ARMv8 or x86_64)</li>
|
| 211 |
-
<li>OpenGL ES 3.0 or Vulkan support</li>
|
| 212 |
-
<li>2 GB of RAM or more</li>
|
| 213 |
-
<li>8 GB of storage or more</li>
|
| 214 |
-
</ul>
|
| 215 |
-
<p>You can check your device's specifications by going to the "Settings" app and tapping on "About Phone" or "About Device". You can also use a third-party app like [CPU-Z] to get more detailed information about your device.</p>
|
| 216 |
-
<h3>Q: How can I improve the performance of Dolphin Emulator?</h3>
|
| 217 |
-
<p>A: There are several ways to improve the performance of Dolphin Emulator, such as:</p>
|
| 218 |
-
<ul>
|
| 219 |
-
<li>Lowering the internal resolution and anti-aliasing settings in the graphics settings menu.</li>
|
| 220 |
-
<li>Disabling some enhancements and features that are not essential for the game.</li>
|
| 221 |
-
<li>Closing other apps and background processes that may consume resources and battery.</li>
|
| 222 |
-
<li>Using a device cooler or fan to prevent overheating and throttling.</li>
|
| 223 |
-
<li>Updating the app and the device's software to the latest versions.</li>
|
| 224 |
-
</ul>
|
| 225 |
-
<p>However, keep in mind that some games may be more demanding than others, and some devices may have more limitations than others. Therefore, you may not be able to achieve a smooth and stable performance for every game on every device.</p>
|
| 226 |
-
<h3>Q: How can I transfer my save data and settings from one device to another?</h3>
|
| 227 |
-
<p>A: If you want to transfer your save data and settings from one device to another, you need to copy the Dolphin Emulator folder from your device's storage or external storage to the other device's storage or external storage. The Dolphin Emulator folder contains all your save data, settings, screenshots, and other files related to Dolphin Emulator. You can use a file manager app or a USB cable to copy the folder.</p>
|
| 228 |
-
<p>To locate the Dolphin Emulator folder, follow these steps:</p>
|
| 229 |
-
<ol>
|
| 230 |
-
<li>On the main screen of Dolphin Emulator, tap on the menu icon at the top left corner. This will open a sidebar menu where you can access various options and settings.</li>
|
| 231 |
-
<li>Tap on "Settings" to open the settings menu. Here you can adjust various settings for graphics, audio, controls, and enhancements.</li>
|
| 232 |
-
<li>Tap on "Paths" to open the paths menu. Here you can see the location of the Dolphin Emulator folder on your device's storage or external storage. You can also change the location if you want.</li>
|
| 233 |
-
</ol>
|
| 234 |
-
<h3>Q: How can I contact the developers of Dolphin Emulator?</h3>
|
| 235 |
-
<p>A: If you want to contact the developers of Dolphin Emulator, you can do so by visiting their official website at [Dolphin Emulator]. Here you can find more information about Dolphin Emulator, such as its features, history, compatibility list, FAQ, wiki, blog, forums, and social media links. You can also report bugs, request features, submit feedback, or donate to support the project.</p>
|
| 236 |
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<p></p> 197e85843d<br />
|
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Candy Crush Friends Saga APK and Experience the New Levels and Modes.md
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<h1>Download Candy Crush Friends Saga APK</h1>
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<p>If you are a fan of color-match puzzle games, you have probably heard of Candy Crush Saga, one of the most popular and addictive games of its genre. But did you know that there is a new version of Candy Crush that is even more fun and exciting? It's called Candy Crush Friends Saga, and it's available for Android devices. In this article, we will tell you what Candy Crush Friends Saga is, what features it has, why you should download the APK file, and how to do it. Read on and get ready to enjoy this sweet adventure.</p>
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<h2>What is Candy Crush Friends Saga?</h2>
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<p>Candy Crush Friends Saga is a color-match puzzle game from King, the makers of the original Candy Crush Saga and other popular games like Farm Heroes Saga and Bubble Witch Saga. In this game, you have to switch and match candies to clear the board and complete the level. But there is a twist: you are not alone in this journey. You have the help of your friends, who are adorable characters from the Candy Kingdom. Each friend has a special power that can help you in different ways, such as creating special candies, removing obstacles, or boosting your score. You can collect and customize your friends as you progress through the game.</p>
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<h3>Features of Candy Crush Friends Saga</h3>
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<p>Candy Crush Friends Saga has many features that make it stand out from other color-match puzzle games. Here are some of them:</p>
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<h4>- Hundreds of levels to play</h4>
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<p>The game has hundreds of levels for you to enjoy, each with different goals and challenges. You can play classic levels where you have to reach a certain score or collect a certain number of candies, or try new game modes like Dunk the Cookie, where you have to dunk cookies into chocolate, or Free the Octopuses, where you have to free cute octopuses from jelly. The game also has special events and rewards that keep things fresh and exciting.</p>
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<h4>- New game modes and challenges</h4>
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<p>Candy Crush Friends Saga introduces new game modes and challenges that test your skills and strategy. For example, you can play Boss Levels, where you have to face off against a boss character who will try to stop you from completing the level. You can also play Levels with Friends, where you can team up with your friends or other players online and work together to clear the board. You can also compete with other players in Leaderboards and Tournaments, where you can show off your skills and win prizes.</p>
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<h4>- Collect and customize your friends</h4>
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<p>One of the most unique features of Candy Crush Friends Saga is that you can collect and customize your friends, who are adorable characters from the Candy Kingdom. Each friend has a special power that can help you in different ways, such as creating special candies, removing obstacles, or boosting your score. You can unlock new friends as you progress through the game, and you can also customize their outfits and accessories. You can choose from a variety of friends, such as Tiffi, Yeti, Nutcracker, Misty, Red Rabbit, Olivia, Dachs, Odus, and more.</p>
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<h4>- Explore the sweet world of Candy Kingdom</h4>
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<p>Candy Crush Friends Saga takes you on a journey through the sweet world of Candy Kingdom, where you can discover new places and meet new characters. You can explore locations such as Lollipop Meadow, Lemonade Lake, Chocolate Mountains, Ice Cream Alps, Cotton Candy Clouds, and more. The game has stunning graphics and animations that make the game more immersive and enjoyable. You can also listen to the catchy and cheerful music and sound effects that accompany the game.</p>
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<p>One of the advantages of downloading the APK file is that you can enjoy the game without any interruptions or limitations. You don't have to worry about annoying ads popping up on your screen or tempting you to buy extra lives or boosters. You can play the game as much as you want and have a smooth and satisfying experience.</p>
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<p>Another benefit of downloading the APK file is that you can get access to the latest updates and features of the game. You don't have to wait for the official release of the game on the Google Play Store, which may take some time or be unavailable in your region. You can get the newest version of the game as soon as it is available and enjoy the new levels, game modes, friends, and more.</p>
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<p>A third reason to download the APK file is that you can play the game offline or online with your friends. You don't need an internet connection to play the game, which is great if you are traveling or in a place with poor network coverage. You can also play online with your friends or other players and share your progress and achievements. You can also sync your game across different devices using your Facebook account.</p>
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<p>If you are interested in downloading Candy Crush Friends Saga APK, here are the steps you need to follow:</p>
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<h3>Step 1: Enable unknown sources on your device</h3>
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<p>The first step is to enable unknown sources on your device, which will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message, but don't worry, it is safe to proceed.</p>
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<h3>Step 2: Download the APK file from a trusted source</h3>
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<p>The next step is to download the APK file from a trusted source. You can use any web browser to search for Candy Crush Friends Saga APK and choose a reliable website that offers it. Make sure to check the reviews and ratings of the website and avoid any suspicious links or pop-ups. Once you find a suitable website, click on the download button and save the file on your device.</p>
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<h3>Step 3: Install the APK file on your device</h3>
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<p>The third step is to install the APK file on your device. To do this, locate the file on your device using a file manager app or your notification bar. Tap on the file and follow the instructions on the screen. You may see a confirmation message asking if you want to install this app, just tap on Install and wait for the process to finish.</p>
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<h3>Step 4: Launch the game and have fun</h3>
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<p>The final step is to launch the game and have fun. To do this, find the app icon on your home screen or app drawer and tap on it. You will see a welcome screen with some information about the game and its features. You can also connect your Facebook account if you want to sync your game across different devices or play with your friends. Then, you can start playing Candy Crush Friends Saga and enjoy this sweet adventure.</p>
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<h2>Conclusion</h2>
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<p>Candy Crush Friends Saga is a color-match puzzle game that is fun, exciting, and addictive. It has many features that make it stand out from other games of its genre, such as hundreds of levels, new game modes, collectible friends, and a beautiful candy world. You can download Candy Crush Friends Saga APK from a trusted source and enjoy the game without ads or in-app purchases, get access to the latest updates and features, and play offline or online with your friends. If you are looking for a new game to play on your Android device, Candy Crush Friends Saga is a great choice.</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions about Candy Crush Friends Saga:</p>
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<h4>- What is the difference between Candy Crush Saga and Candy Crush Friends Saga?</h4>
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<p>Candy Crush Saga and Candy Crush Friends Saga are both color-match puzzle games from King, but they have some differences. Candy Crush Friends Saga has more features, such as new game modes, collectible friends, boss levels, levels with friends, leaderboards, tournaments, and more. Candy Crush Friends Saga also has better graphics, animations, music, and sound effects that make the game more immersive and enjoyable. Candy Crush Friends Saga also has a different story and characters, where you have to help your friends from the Candy Kingdom in their quests.</p>
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<h4>- How many friends can I collect and customize in Candy Crush Friends Saga?</h4>
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<p>You can collect and customize up to 40 friends in Candy Crush Friends Saga, each with their own special power and personality. You can unlock new friends as you progress through the game, and you can also customize their outfits and accessories. You can choose from a variety of friends, such as Tiffi, Yeti, Nutcracker, Misty, Red Rabbit, Olivia, Dachs, Odus, and more.</p>
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<h4>- How can I play with my friends in Candy Crush Friends Saga?</h4>
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<p>You can play with your friends in Candy Crush Friends Saga in different ways. You can play Levels with Friends, where you can team up with your friends or other players online and work together to clear the board. You can also compete with your friends in Leaderboards and Tournaments, where you can show off your skills and win prizes. You can also send and receive lives and gifts from your friends, and chat with them using stickers and emojis.</p>
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<h4>- How can I get more lives and boosters in Candy Crush Friends Saga?</h4>
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<p>You can get more lives and boosters in Candy Crush Friends Saga by doing the following things: - Wait for your lives to refill over time. You get one life every 30 minutes, up to a maximum of five lives. - Ask your friends to send you lives. You can send and receive up to five lives per day from your friends. - Buy lives and boosters with gold bars. You can earn gold bars by completing levels, events, or achievements, or you can buy them with real money. - Spin the Daily Booster Wheel. You can spin the wheel once a day for a chance to win a free booster or other rewards. - Participate in special events and rewards. You can earn free lives and boosters by playing special levels, completing quests, or joining tournaments.</p>
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<h4>- Is Candy Crush Friends Saga safe to download and play?</h4>
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<p>Yes, Candy Crush Friends Saga is safe to download and play, as long as you download the APK file from a trusted source. You should avoid any websites that offer fake or modified versions of the game that may contain viruses or malware. You should also check the permissions that the app requests before installing it on your device. Candy Crush Friends Saga is a fun and harmless game that does not collect or share any personal information from its users.</p> 197e85843d<br />
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<h1>Angry Birds Blast Mod APK Unlimited Moves: A Review</h1>
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<li>Unlimited boosters: You can use boosters like rockets, bombs, laser guns, and more to clear more balloons and obstacles in one move. You can also create more boosters by popping more balloons.</li>
|
| 80 |
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<li>Unlimited power-ups: You can use power-ups like hammers, slingshots, magnets, and more to help you complete the level goals. You can also activate power-ups by tapping on them.</li>
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| 81 |
-
<li>All levels unlocked: You can play any level you want without having to complete the previous ones. You can also skip any level you find too hard or boring.</li>
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| 82 |
-
<li>All features unlocked: You can enjoy all the features of the game like daily challenges, weekly events, puzzle pieces, global leaderboards, iMessage stickers, and more.</li>
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| 83 |
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</ul>
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| 84 |
-
<h3>Risks</h3>
|
| 85 |
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<p>Angry Birds Blast Mod APK Unlimited Moves is not an official version of the game and it is not endorsed by Rovio Entertainment. Therefore, there are some risks involved in using this mod apk. Some of the risks are:</p>
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| 86 |
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<ul>
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<li>Malware: The mod apk file may contain viruses, spyware, or other malicious software that can harm your device or steal your personal information. You should always download the mod apk from a trusted source and scan it with an antivirus before installing it.</li>
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<li>Ban: The mod apk may violate the terms of service of the game and get detected by the game servers. This may result in your account being banned or suspended from playing the game. You should always use the mod apk at your own risk and discretion.</li>
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| 89 |
-
<li>Crash: The mod apk may not be compatible with your device or the latest version of the game. This may cause the game to crash or freeze frequently. You should always backup your game data before installing the mod apk and update it regularly.</li>
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</ul>
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-
<h2>How to Download and Install Angry Birds Blast Mod APK Unlimited Moves?</h2>
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-
<h3>Requirements</h3>
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<p>To download and install Angry Birds Blast Mod APK Unlimited Moves, you need to have the following requirements:</p>
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<ul>
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<li>An Android device with Android 4.4 or higher</li>
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<li>At least 100 MB of free storage space</li>
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<li>A stable internet connection</li>
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<li>A file manager app</li>
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</ul>
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<h3>Steps</h3>
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<p>To download and install Angry Birds Blast Mod APK Unlimited Moves, you need to follow these steps:</p>
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<ol>
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<li>Download the mod apk file from a trusted source. You can use this link as an example.</li>
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<li>Go to your device settings and enable the installation of apps from unknown sources.</li>
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<li>Locate the downloaded mod apk file using your file manager app and tap on it to install it.</li>
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-
<li>Wait for the installation to finish and launch the game from your app drawer or home screen.</li>
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-
<li>Enjoy playing Angry Birds Blast Mod APK Unlimited Moves with unlimited resources and features.</li>
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</ol>
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| 109 |
-
<h2>How to Play Angry Birds Blast Mod APK Unlimited Moves?</h2>
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| 110 |
-
<h3>Tips and Tricks</h3>
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| 111 |
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<p>To play Angry Birds Blast Mod APK Unlimited Moves effectively, you need to know some tips and tricks that can help you score higher and complete more levels. Some of these tips and tricks are:</p>
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| 112 |
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<ul>
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| 113 |
-
<li>Tap on groups of four or more balloons of the same color to create boosters. The bigger the group, the better the booster.</li>
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| 114 |
-
<li>Use boosters wisely and strategically. Aim for balloons that are near pigs, obstacles, or birds to clear them faster.</li>
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| 115 |
-
<li>Combine boosters for more powerful effects. For example, a rocket and a bomb can create a huge explosion that clears a large area of balloons.</li>
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<li>Save your power-ups for difficult levels or situations. Power-ups can help you overcome challenges like limited moves, tricky goals, or hard-to-reach areas.</li>
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-
<li>Collect puzzle pieces every month in Puzzle Chase to unlock new puzzles and rewards. Puzzle pieces are hidden in random levels throughout the game.</li>
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-
<li>Participate in daily challenges, weekly events, Mighty League, and Treasure Hunt to earn free rewards and boosters. You can also compete with other players around the world for high scores and prizes.</li>
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</ul>
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<h3>Power-ups and Boosters</h3> <p>Angry Birds Blast Mod APK Unlimited Moves has various power-ups and boosters that can help you clear more balloons and obstacles in one move. Some of these power-ups and boosters are:</p>
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| 121 |
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<table>
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| 122 |
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<tr>
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| 123 |
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<th>Power-up/Booster</th>
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| 124 |
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<th>Description</th>
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| 125 |
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<th>How to get/use</th>
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| 126 |
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</tr>
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| 127 |
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<tr>
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| 128 |
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<td>Hammer</td>
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| 129 |
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<td>Pops any balloon or obstacle on the board</td>
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| 130 |
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<td>Tap on the power-up icon and then tap on the target</td>
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</tr>
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<tr>
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<td>Slingshot</td>
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<td>Shoots a bird at any balloon or obstacle on the board</td>
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<td>Tap on the power-up icon and then drag and release to aim and shoot</td>
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-
</tr>
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<tr>
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<td>Magnet</td>
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| 139 |
-
<td>Attracts all balloons of the same color to one spot</td>
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-
<td>Tap on the power-up icon and then tap on the color you want to attract</td>
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-
</tr>
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<tr>
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| 143 |
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<td>Rocket</td>
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| 144 |
-
<td>Blasts a column or a row of balloons and obstacles</td>
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-
<td>Pop four balloons of the same color or tap on the booster icon and then swipe to choose the direction</td>
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| 146 |
-
</tr>
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<tr>
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<td>Bomb</td>
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<td>Explodes and clears a 3x3 area of balloons and obstacles</td>
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-
<td>Pop five balloons of the same color in an L or T shape or tap on the booster icon and then tap on the target area</td>
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</tr>
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<tr>
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<td>Laser Gun</td>
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<td>Zaps and clears all balloons and obstacles of the same color</td>
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<td>Pop seven or more balloons of the same color or tap on the booster icon and then tap on the color you want to zap</td>
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</tr>
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<h2>Conclusion</h2>
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| 158 |
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<p>Angry Birds Blast Mod APK Unlimited Moves is a great way to enjoy Angry Birds Blast without any limitations. You can have unlimited moves, coins, boosters, power-ups, and access to all levels and features. You can also play the game offline and with your friends. However, you should also be aware of the risks involved in using this mod apk, such as malware, ban, or crash. You should always download the mod apk from a trusted source and scan it with an antivirus before installing it. You should also backup your game data before installing the mod apk and update it regularly. You should also use the mod apk at your own risk and discretion.</p>
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<p>If you are looking for a fun and challenging puzzle game with cute graphics, catchy music, and addictive gameplay, then you should give Angry Birds Blast a try. And if you want to enhance your gaming experience, then you should try Angry Birds Blast Mod APK Unlimited Moves. We hope this article has helped you learn more about this mod apk and how to download and install it on your device. We also hope you have enjoyed playing Angry Birds Blast Mod APK Unlimited Moves with our tips and tricks. Have fun blasting those balloons and saving those birds!</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions about Angry Birds Blast Mod APK Unlimited Moves:</p>
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<ol>
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<li><b>Is Angry Birds Blast Mod APK Unlimited Moves safe to use?</b><br/>
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Angry Birds Blast Mod APK Unlimited Moves is not an official version of the game and it is not endorsed by Rovio Entertainment. Therefore, there are some risks involved in using this mod apk, such as malware, ban, or crash. You should always download the mod apk from a trusted source and scan it with an antivirus before installing it. You should also backup your game data before installing the mod apk and update it regularly. You should also use the mod apk at your own risk and discretion.</li>
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<li><b>Is Angry Birds Blast Mod APK Unlimited Moves free to download?</b><br/>
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Yes, Angry Birds Blast Mod APK Unlimited Moves is free to download from various sources online. However, you should always be careful about where you download it from and what permissions it asks for. You should also avoid clicking on any ads or pop-ups that may appear while downloading it.</li>
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<li><b>Can I play Angry Birds Blast Mod APK Unlimited Moves offline?</b><br/>
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Yes, you can play Angry Birds Blast Mod APK Unlimited Moves offline without any internet connection. However, some features like daily challenges, weekly events, global leaderboards, iMessage stickers, etc., may not work offline.</li>
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<li><b>Can I play Angry Birds Blast Mod APK Unlimited Moves with my friends?</b><br/>
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Yes, you can play Angry Birds Blast Mod APK Unlimited Moves with your friends by connecting to Facebook. You can see your friends' scores, send and receive gifts, invite them to play, etc.</li>
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<li><b>How can I update Angry Birds Blast Mod APK Unlimited Moves?</b><br/>
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You can You can update Angry Birds Blast Mod APK Unlimited Moves by downloading the latest version of the mod apk from the same source you downloaded it from. You can also check for updates within the game settings. However, you should always backup your game data before updating the mod apk and make sure that the new version is compatible with your device and the original game.</li>
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<p>If you are looking for a simple yet addictive puzzle game that can keep your mind sharp and entertained, you should try connect the dots game. This game is suitable for all ages and can be played offline or online. In this article, we will tell you what connect the dots game is, how to play it, why you should download it, and which are the best connect the dots game apps for Android and iOS. We will also share some tips and tricks to master this game and answer some frequently asked questions.</p>
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<h2>What is Connect the Dots Game?</h2>
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<p>Connect the dots game is a type of puzzle game that involves connecting dots of the same color with lines. The goal is to fill up the entire board with lines without crossing or overlapping them. The game is also known as numberlink, flow, or pipe puzzle. Connect the dots game is based on a mathematical concept called graph theory, which studies how networks of points and lines can be arranged.</p>
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<h3>How to Play Connect the Dots Game</h3>
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<p>The gameplay of connect the dots game is very simple and intuitive. You just need to tap on a dot and drag your finger to another dot of the same color. You can also use your mouse or stylus if you are playing on a computer or tablet. You can only draw horizontal or vertical lines, not diagonal ones. You have to connect all the dots of each color and cover every square on the board. You can undo or restart your moves if you make a mistake or want to try a different strategy.</p>
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<h3>Why You Should Download Connect the Dots Game</h3>
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<p>There are many reasons why you should download connect the dots game on your device. Here are some of them:</p>
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<li>Connect the dots game is fun and relaxing. You can enjoy solving puzzles at your own pace and listen to soothing music and sound effects.</li>
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<li>Connect the dots game is challenging and rewarding. You can choose from different difficulty levels, ranging from 5x5 to 14x14 grids. You can also compete against the clock in time trial mode or test your skills with daily puzzles.</li>
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<li>Connect the dots game is good for your brain. It can improve your logic, concentration, memory, and spatial reasoning skills. It can also help you reduce stress and boredom.</li>
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<li>Connect the dots game is free to download and play. You don't need to pay anything to enjoy this game. There are some ads and in-app purchases, but they are not intrusive or necessary.</li>
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<h2>The Best Connect the Dots Game Apps for Android and iOS</h2>
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<p>There are many connect the dots game apps available on Google Play Store and App Store, but not all of them are worth your time and attention. We have selected three of the best connect the dots game apps for Android and iOS based on their ratings, reviews, features, and popularity. Here they are:</p>
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<h3>Connect The Dots - Color Line by MOOTOY Game</h3>
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<p>This app is one of the most popular connect the dots game apps on Google Play Store, with over 1 million downloads and 4.5 stars rating. It offers over 1000 free puzzles, free play and time trial modes, user-friendly interface and graphics, fun sound effects, hints, and more. You can also adjust the board size, color scheme, day/night mode, and color blind setting according to your preference.</p>
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<h3>Dot Link - Connect the Dots by Playvalve</h3>
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<p>This app is another highly rated connect the dots game app on Google Play Store, with over 500,000 downloads and 4.6 stars rating. It features over 2000 free puzzles, various themes and backgrounds, smooth animation and sound effects, hints, undo, and zoom functions, and more. You can also customize the game settings, such as the grid size, dot size, line thickness, and color mode.</p>
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<h3>Connect Dots - Dot Puzzle Game by Bigman</h3>
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<p>This app is one of the best connect the dots game apps on App Store, with over 100,000 downloads and 4.7 stars rating. It provides over 3000 free puzzles, different game modes, such as classic, hexa, triangle, and square, beautiful graphics and music, hints, undo, and shuffle options, and more. You can also challenge yourself with daily missions and achievements.</p>
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<h2>Tips and Tricks to Master Connect the Dots Game</h2>
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<p>Connect the dots game may seem easy at first glance, but it can get tricky and frustrating as you progress to higher levels. Here are some tips and tricks to help you master this game and have more fun:</p>
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<p>One of the most important things to do before you start playing connect the dots game is to choose the right difficulty level for your skill and mood. If you are a beginner or just want to relax, you can start with the easy levels that have smaller grids and fewer colors. If you are an expert or want to challenge yourself, you can try the hard levels that have larger grids and more colors. You can also switch between different difficulty levels anytime you want.</p>
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<h3>Plan Your Moves Ahead</h3>
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<p>Another key to success in connect the dots game is to plan your moves ahead and think strategically. You should not just connect the dots randomly or impulsively, but rather look at the whole board and see which dots are easier or harder to connect. You should also try to avoid creating dead ends or loops that will prevent you from completing the puzzle. You can use some techniques, such as starting from the corners or edges, connecting the longest lines first, or following a pattern or sequence.</p>
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<h3>Use Hints and Undo Features Wisely</h3>
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<p>Sometimes, you may get stuck or make a mistake in connect the dots game. In that case, you can use the hints and undo features that are available in most connect the dots game apps. However, you should not rely on them too much or abuse them. You should only use them when you really need them or when you want to learn from your errors. You should also be aware that some hints and undo features may cost you coins or tokens that you have to earn or buy in the game.</p>
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| 78 |
-
<h3>Challenge Yourself with Time Trial and Daily Puzzles</h3>
|
| 79 |
-
<p>If you want to spice up your connect the dots game experience and test your skills further, you can try some of the special modes that are offered in some connect the dots game apps. For example, you can play time trial mode where you have to solve as many puzzles as possible within a limited time. Or you can play daily puzzles where you have to solve a new puzzle every day with different themes and rewards. These modes can help you improve your speed, accuracy, and creativity in connect the dots game.</p>
|
| 80 |
-
<h2>Conclusion</h2>
|
| 81 |
-
<p>Connect the dots game is a fun and relaxing puzzle game that can be enjoyed by anyone regardless of age or background. It is easy to play but hard to master. It can also benefit your brain health and well-being in many ways. If you are interested in playing this game, you can download one of the best connect the dots game apps for Android and iOS that we have recommended in this article. You can also follow some of our tips and tricks to master this game and have more fun.</p>
|
| 82 |
-
<h2>FAQs</h2>
|
| 83 |
-
<p>Here are some of the frequently asked questions about connect the dots game:</p>
|
| 84 |
-
<ul>
|
| 85 |
-
<li><b>Q: How many levels are there in connect the dots game?</b></li>
|
| 86 |
-
<li>A: The number of levels in connect the dots game depends on the app that you are using. Some apps may have hundreds or thousands of levels, while others may have unlimited levels that are generated randomly.</li>
|
| 87 |
-
<li><b>Q: How do I unlock more levels in connect the dots game?</b></li>
|
| 88 |
-
<li>A: To unlock more levels in connect the dots game, you usually have to complete the previous levels or achieve certain goals or scores. Some apps may also require you to watch ads or make in-app purchases to unlock more levels.</li>
|
| 89 |
-
<li><b>Q: How do I save my progress in connect the dots game?</b <li>A: To save your progress in connect the dots game, you need to have an internet connection and sign in with your Google Play or Apple ID account. Some apps may also allow you to sync your progress with Facebook or other platforms.</li>
|
| 90 |
-
<li><b>Q: How do I share my results in connect the dots game?</b></li>
|
| 91 |
-
<li>A: To share your results in connect the dots game, you can use the share button that is usually located on the top or bottom of the screen. You can then choose which app or platform you want to share your results with, such as WhatsApp, Instagram, Twitter, or email.</li>
|
| 92 |
-
<li><b>Q: How do I get more coins or tokens in connect the dots game?</b></li>
|
| 93 |
-
<li>A: To get more coins or tokens in connect the dots game, you can do one of the following things:</li>
|
| 94 |
-
<ul>
|
| 95 |
-
<li>Watch ads that are offered in the app.</li>
|
| 96 |
-
<li>Complete daily missions or achievements that reward you with coins or tokens.</li>
|
| 97 |
-
<li>Buy coins or tokens with real money through in-app purchases.</li>
|
| 98 |
-
</ul>
|
| 99 |
-
</ul></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Enjoy the Best Attack on Titan Tribute Game with Unity - No Ads No Hassle.md
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<h1>Attack on Titan Tribute Game: How to Download and Play on Unity</h1>
|
| 3 |
-
<p>If you are a fan of <i>Attack on Titan</i>, the popular anime and manga series that depicts a world where humanity is under siege by giant humanoid creatures called titans, you might be interested in playing <b>Attack on Titan Tribute Game</b>, a fan-made game that lets you experience the thrill of fighting titans in various modes, characters, maps, and difficulty levels.</p>
|
| 4 |
-
<h2>attack on titan tribute game download unity</h2><br /><p><b><b>Download File</b> ⇔ <a href="https://jinyurl.com/2uNPpW">https://jinyurl.com/2uNPpW</a></b></p><br /><br />
|
| 5 |
-
<p>However, before you can play this game, you need to download and install <b>Unity Web Player</b>, a plugin that allows you to run games made with Unity engine on your browser. In this article, we will show you how to download and install Unity Web Player, how to download and play Attack on Titan Tribute Game, and some tips and tricks for playing the game.</p>
|
| 6 |
-
<h2>What is Attack on Titan Tribute Game?</h2>
|
| 7 |
-
<p>Attack on Titan Tribute Game is a fan-made game developed by Feng Lee using Unity engine. It is based on <i>Attack on Titan</i>, a Japanese anime and manga series created by Hajime Isayama that follows the story of Eren Yeager, Mikasa Ackerman, Armin Arlert, and other members of the Scout Regiment who fight against titans that have invaded their world.</p>
|
| 8 |
-
<p>The game features various modes such as single-player, multiplayer, custom map , and training mode. You can choose from different characters such as Eren, Mikasa, Levi, Armin, Jean, Sasha, and more. You can also customize your character's appearance, equipment, and skills. You can explore different maps such as the city, the forest, the castle, and the underground. You can also face different types of titans such as normal, abnormal, crawler, punk, colossal, and armored.</p>
|
| 9 |
-
<p>The game requires Unity Web Player to run on your browser. Unity Web Player is a plugin that enables you to play games and interactive content created with Unity engine. Unity is a cross-platform game engine that is used to create games for various platforms such as Windows, Mac, Linux, iOS, Android, and more.</p>
|
| 10 |
-
<h2>How to Download and Install Unity Web Player?</h2>
|
| 11 |
-
<p>To download and install Unity Web Player, you need to follow these steps:</p>
|
| 12 |
-
<ol>
|
| 13 |
-
<li>Visit the official website of Unity at <a href="">https://unity.com/</a> and click on the <b>Download</b> button at the top right corner.</li>
|
| 14 |
-
<li>On the download page, scroll down and find the <b>Unity Web Player</b> section. Click on the <b>Download (Windows)</b> or <b>Download (Mac)</b> button depending on your operating system.</li>
|
| 15 |
-
<li>A pop-up window will appear asking you to save the file. Choose a location where you want to save the file and click on <b>Save</b>.</li>
|
| 16 |
-
<li>Once the download is complete, locate the file and double-click on it to run the installer.</li>
|
| 17 |
-
<li>Follow the instructions on the installer to complete the installation process. You may need to agree to the terms and conditions and choose a destination folder for the plugin.</li>
|
| 18 |
-
<li>Restart your browser and enable the plugin if prompted. You can also check if the plugin is installed by visiting <a href="">https://unity3d.com/webplayer/setup</a>.</li>
|
| 19 |
-
</ol>
|
| 20 |
-
<h2>How to Download and Play Attack on Titan Tribute Game?</h2>
|
| 21 |
-
<p>To download and play Attack on Titan Tribute Game, you need to follow these steps:</p>
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| 22 |
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attack on titan defense online <br />
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| 70 |
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attack on titan wyperian edition release date</p>
|
| 71 |
-
<ol>
|
| 72 |
-
<li>Visit the official website of the game at <a href="">http://fenglee.com/game/aog/</a>.</li>
|
| 73 |
-
<li>On the website, you will see a list of servers that host the game. Choose a server that has a good ping and click on it.</li>
|
| 74 |
-
<li>A new tab will open with the game loading screen. Wait for the game to load completely.</li>
|
| 75 |
-
<li>Create a username for yourself by typing it in the box at the top left corner. You can also change your language by clicking on the flag icon at the top right corner.</li>
|
| 76 |
-
<li>Choose a mode that you want to play by clicking on one of the buttons at the bottom left corner. You can choose from single-player, multiplayer, custom map , and training mode. Each mode has different objectives and rules.</li>
|
| 77 |
-
<li>Choose a character that you want to play by clicking on one of the buttons at the bottom right corner. You can choose from Eren, Mikasa, Levi, Armin, Jean, Sasha, and more. You can also customize your character's appearance, equipment, and skills by clicking on the <b>Customize</b> button.</li>
|
| 78 |
-
<li>Choose a map that you want to play by clicking on one of the buttons at the top center. You can choose from the city, the forest, the castle, and the underground. You can also create your own map by clicking on the <b>Create Map</b> button.</li>
|
| 79 |
-
<li>Choose a difficulty level that you want to play by clicking on one of the buttons at the top right corner. You can choose from easy, normal, hard, and abnormal. The difficulty level affects the number and behavior of the titans.</li>
|
| 80 |
-
<li>Click on the <b>Start Game</b> button at the bottom center to start playing the game.</li>
|
| 81 |
-
<li>Use the keyboard and mouse controls to move, attack, and interact with the game. The basic controls are as follows:</li>
|
| 82 |
-
<ul>
|
| 83 |
-
<li><b>WASD</b>: Move forward, backward, left, and right.</li>
|
| 84 |
-
<li><b>Space</b>: Jump.</li>
|
| 85 |
-
<li><b>Shift</b>: Dash.</li>
|
| 86 |
-
<li><b>Left mouse button</b>: Attack with your blades.</li>
|
| 87 |
-
<li><b>Right mouse button</b>: Use your omni-directional mobility gear (ODM) to hook onto objects and swing around.</li>
|
| 88 |
-
<li><b>Q and E</b>: Hook onto objects with your left and right ODM respectively.</li>
|
| 89 |
-
<li><b>R</b>: Reload your blades or gas.</li>
|
| 90 |
-
<li><b>T</b>: Chat with other players (in multiplayer mode).</li>
|
| 91 |
-
<li><b>P</b>: Pause the game and access the menu.</li>
|
| 92 |
-
<li><b>Esc</b>: Quit the game.</li>
|
| 93 |
-
</ul>
|
| 94 |
-
</ol>
|
| 95 |
-
<h2>Tips and Tricks for Playing Attack on Titan Tribute Game</h2>
|
| 96 |
-
<p>Playing Attack on Titan Tribute Game can be fun and challenging, but also frustrating if you don't know what you are doing. Here are some tips and tricks that can help you improve your skills and enjoy the game more:</p>
|
| 97 |
-
<ul>
|
| 98 |
-
<li><b>Learn how to use the omni-directional mobility gear (ODM) effectively</b>. The ODM is your main tool for moving around and fighting titans. You need to master how to hook onto objects, swing around, release, and re-hook in order to maneuver quickly and smoothly. You also need to know when to use your dash and jump abilities to gain speed and height. The ODM is also useful for dodging titan attacks and escaping from dangerous situations.</li>
|
| 99 |
-
<li><b>Aim for the nape of the titans to kill them</b>. The nape is the weak spot of the titans that is located at the back of their necks. You need to slash it with your blades in order to kill them. However, this is easier said than done, as titans can move unpredictably and protect their napes with their hands or hair. You need to find an opening or create one by distracting or stunning them with your teammates or objects. You can also use your ODM to get behind them or above them and strike them from there.</li>
|
| 100 |
-
<li><b>Use teamwork and communication with other players</b>. In multiplayer mode, you can join or create a server and play with other players online. You can cooperate with them to complete objectives, such as killing all titans in a map, defending a base, or capturing a flag. You can also chat with them using the T key or voice chat (if enabled). Teamwork and communication are essential for surviving and winning in multiplayer mode.</li>
|
| 101 |
-
<li><b>Customize your settings and preferences for optimal performance</b>. You can access the settings menu by pressing P or Esc during the game. You can adjust various options such as graphics quality, sound volume, camera sensitivity, key bindings, language, and more. You can also enable or disable some features such as blood effects, damage indicators, crosshair, minimap, etc. You should customize your settings according to your preferences and your device's capabilities for optimal performance.</li>
|
| 102 |
-
</ul>
|
| 103 |
-
<h2>Conclusion</h2>
|
| 104 |
-
<p>Attack on Titan Tribute Game is a fun and challenging fan-made game that lets you experience the thrill of fighting titans in various modes, characters, maps, and difficulty levels. You need to download and install Unity Web Player to play the game on your browser. You can download and play the game for free from its official website. If you are a fan of <i>Attack on Titan</ allow you to mod or create your own maps for the game. You can find some of them at <a href="">https://aotrc.weebly.com/</a> or <a href="">https://www.youtube.com/watch?v=Zi8vJ_lMxQI</a>. However, you need to have some basic knowledge of Unity and coding to use these tools. You also need to follow the rules and guidelines of the game and its developer when modding or creating your own maps.</h4>
|
| 105 |
-
<p>I hope this article has helped you learn more about Attack on Titan Tribute Game and how to download and play it on Unity. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and have fun playing the game!</p> 197e85843d<br />
|
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spaces/7hao/bingo/src/components/theme-toggle.tsx
DELETED
|
@@ -1,31 +0,0 @@
|
|
| 1 |
-
'use client'
|
| 2 |
-
|
| 3 |
-
import * as React from 'react'
|
| 4 |
-
import { useTheme } from 'next-themes'
|
| 5 |
-
|
| 6 |
-
import { Button } from '@/components/ui/button'
|
| 7 |
-
import { IconMoon, IconSun } from '@/components/ui/icons'
|
| 8 |
-
|
| 9 |
-
export function ThemeToggle() {
|
| 10 |
-
const { setTheme, theme } = useTheme()
|
| 11 |
-
const [_, startTransition] = React.useTransition()
|
| 12 |
-
|
| 13 |
-
return (
|
| 14 |
-
<Button
|
| 15 |
-
variant="ghost"
|
| 16 |
-
size="icon"
|
| 17 |
-
onClick={() => {
|
| 18 |
-
startTransition(() => {
|
| 19 |
-
setTheme(theme === 'light' ? 'dark' : 'light')
|
| 20 |
-
})
|
| 21 |
-
}}
|
| 22 |
-
>
|
| 23 |
-
{!theme ? null : theme === 'dark' ? (
|
| 24 |
-
<IconMoon className="transition-all" />
|
| 25 |
-
) : (
|
| 26 |
-
<IconSun className="transition-all" />
|
| 27 |
-
)}
|
| 28 |
-
<span className="sr-only">Toggle theme</span>
|
| 29 |
-
</Button>
|
| 30 |
-
)
|
| 31 |
-
}
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spaces/7hao/bingo/src/pages/api/blob.ts
DELETED
|
@@ -1,40 +0,0 @@
|
|
| 1 |
-
'use server'
|
| 2 |
-
|
| 3 |
-
import { NextApiRequest, NextApiResponse } from 'next'
|
| 4 |
-
import { Readable } from 'node:stream'
|
| 5 |
-
import { fetch } from '@/lib/isomorphic'
|
| 6 |
-
|
| 7 |
-
const API_DOMAIN = 'https://www.bing.com'
|
| 8 |
-
|
| 9 |
-
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
|
| 10 |
-
try {
|
| 11 |
-
const { bcid } = req.query
|
| 12 |
-
|
| 13 |
-
const { headers, body } = await fetch(`${API_DOMAIN}/images/blob?bcid=${bcid}`,
|
| 14 |
-
{
|
| 15 |
-
method: 'GET',
|
| 16 |
-
headers: {
|
| 17 |
-
"sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
|
| 18 |
-
"sec-ch-ua-mobile": "?0",
|
| 19 |
-
"sec-ch-ua-platform": "\"Windows\"",
|
| 20 |
-
"Referrer-Policy": "origin-when-cross-origin",
|
| 21 |
-
},
|
| 22 |
-
},
|
| 23 |
-
)
|
| 24 |
-
|
| 25 |
-
res.writeHead(200, {
|
| 26 |
-
'Content-Length': headers.get('content-length')!,
|
| 27 |
-
'Content-Type': headers.get('content-type')!,
|
| 28 |
-
})
|
| 29 |
-
// @ts-ignore
|
| 30 |
-
return Readable.fromWeb(body!).pipe(res)
|
| 31 |
-
} catch (e) {
|
| 32 |
-
console.log('Error', e)
|
| 33 |
-
return res.json({
|
| 34 |
-
result: {
|
| 35 |
-
value: 'UploadFailed',
|
| 36 |
-
message: `${e}`
|
| 37 |
-
}
|
| 38 |
-
})
|
| 39 |
-
}
|
| 40 |
-
}
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spaces/801artistry/RVC801/tools/infer/train-index.py
DELETED
|
@@ -1,42 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
|
| 3 |
-
"""
|
| 4 |
-
import os
|
| 5 |
-
import logging
|
| 6 |
-
|
| 7 |
-
logger = logging.getLogger(__name__)
|
| 8 |
-
|
| 9 |
-
import faiss
|
| 10 |
-
import numpy as np
|
| 11 |
-
|
| 12 |
-
# ###########如果是原始特征要先写save
|
| 13 |
-
inp_root = r"E:\codes\py39\dataset\mi\2-co256"
|
| 14 |
-
npys = []
|
| 15 |
-
for name in sorted(list(os.listdir(inp_root))):
|
| 16 |
-
phone = np.load("%s/%s" % (inp_root, name))
|
| 17 |
-
npys.append(phone)
|
| 18 |
-
big_npy = np.concatenate(npys, 0)
|
| 19 |
-
logger.debug(big_npy.shape) # (6196072, 192)#fp32#4.43G
|
| 20 |
-
np.save("infer/big_src_feature_mi.npy", big_npy)
|
| 21 |
-
|
| 22 |
-
##################train+add
|
| 23 |
-
# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
|
| 24 |
-
logger.debug(big_npy.shape)
|
| 25 |
-
index = faiss.index_factory(256, "IVF512,Flat") # mi
|
| 26 |
-
logger.info("Training...")
|
| 27 |
-
index_ivf = faiss.extract_index_ivf(index) #
|
| 28 |
-
index_ivf.nprobe = 9
|
| 29 |
-
index.train(big_npy)
|
| 30 |
-
faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
|
| 31 |
-
logger.info("Adding...")
|
| 32 |
-
index.add(big_npy)
|
| 33 |
-
faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
|
| 34 |
-
"""
|
| 35 |
-
大小（都是FP32）
|
| 36 |
-
big_src_feature 2.95G
|
| 37 |
-
(3098036, 256)
|
| 38 |
-
big_emb 4.43G
|
| 39 |
-
(6196072, 192)
|
| 40 |
-
big_emb双倍是因为求特征要repeat后再加pitch
|
| 41 |
-
|
| 42 |
-
"""
|
|
|
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|
spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/wav_evaluation/models/audio.py
DELETED
|
@@ -1,179 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
| 5 |
-
|
| 6 |
-
def get_audio_encoder(name: str):
|
| 7 |
-
if name == "Cnn14":
|
| 8 |
-
return Cnn14
|
| 9 |
-
else:
|
| 10 |
-
raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class ConvBlock(nn.Module):
|
| 14 |
-
def __init__(self, in_channels, out_channels):
|
| 15 |
-
|
| 16 |
-
super(ConvBlock, self).__init__()
|
| 17 |
-
|
| 18 |
-
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
| 19 |
-
out_channels=out_channels,
|
| 20 |
-
kernel_size=(3, 3), stride=(1, 1),
|
| 21 |
-
padding=(1, 1), bias=False)
|
| 22 |
-
|
| 23 |
-
self.conv2 = nn.Conv2d(in_channels=out_channels,
|
| 24 |
-
out_channels=out_channels,
|
| 25 |
-
kernel_size=(3, 3), stride=(1, 1),
|
| 26 |
-
padding=(1, 1), bias=False)
|
| 27 |
-
|
| 28 |
-
self.bn1 = nn.BatchNorm2d(out_channels)
|
| 29 |
-
self.bn2 = nn.BatchNorm2d(out_channels)
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 33 |
-
|
| 34 |
-
x = input
|
| 35 |
-
x = F.relu_(self.bn1(self.conv1(x)))
|
| 36 |
-
x = F.relu_(self.bn2(self.conv2(x)))
|
| 37 |
-
if pool_type == 'max':
|
| 38 |
-
x = F.max_pool2d(x, kernel_size=pool_size)
|
| 39 |
-
elif pool_type == 'avg':
|
| 40 |
-
x = F.avg_pool2d(x, kernel_size=pool_size)
|
| 41 |
-
elif pool_type == 'avg+max':
|
| 42 |
-
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
| 43 |
-
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
| 44 |
-
x = x1 + x2
|
| 45 |
-
else:
|
| 46 |
-
raise Exception('Incorrect argument!')
|
| 47 |
-
|
| 48 |
-
return x
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
class ConvBlock5x5(nn.Module):
|
| 52 |
-
def __init__(self, in_channels, out_channels):
|
| 53 |
-
|
| 54 |
-
super(ConvBlock5x5, self).__init__()
|
| 55 |
-
|
| 56 |
-
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
| 57 |
-
out_channels=out_channels,
|
| 58 |
-
kernel_size=(5, 5), stride=(1, 1),
|
| 59 |
-
padding=(2, 2), bias=False)
|
| 60 |
-
|
| 61 |
-
self.bn1 = nn.BatchNorm2d(out_channels)
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
| 65 |
-
|
| 66 |
-
x = input
|
| 67 |
-
x = F.relu_(self.bn1(self.conv1(x)))
|
| 68 |
-
if pool_type == 'max':
|
| 69 |
-
x = F.max_pool2d(x, kernel_size=pool_size)
|
| 70 |
-
elif pool_type == 'avg':
|
| 71 |
-
x = F.avg_pool2d(x, kernel_size=pool_size)
|
| 72 |
-
elif pool_type == 'avg+max':
|
| 73 |
-
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
| 74 |
-
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
| 75 |
-
x = x1 + x2
|
| 76 |
-
else:
|
| 77 |
-
raise Exception('Incorrect argument!')
|
| 78 |
-
|
| 79 |
-
return x
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
class AttBlock(nn.Module):
|
| 83 |
-
def __init__(self, n_in, n_out, activation='linear', temperature=1.):
|
| 84 |
-
super(AttBlock, self).__init__()
|
| 85 |
-
|
| 86 |
-
self.activation = activation
|
| 87 |
-
self.temperature = temperature
|
| 88 |
-
self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
|
| 89 |
-
self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
|
| 90 |
-
|
| 91 |
-
self.bn_att = nn.BatchNorm1d(n_out)
|
| 92 |
-
|
| 93 |
-
def forward(self, x):
|
| 94 |
-
# x: (n_samples, n_in, n_time)
|
| 95 |
-
norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
|
| 96 |
-
cla = self.nonlinear_transform(self.cla(x))
|
| 97 |
-
x = torch.sum(norm_att * cla, dim=2)
|
| 98 |
-
return x, norm_att, cla
|
| 99 |
-
|
| 100 |
-
def nonlinear_transform(self, x):
|
| 101 |
-
if self.activation == 'linear':
|
| 102 |
-
return x
|
| 103 |
-
elif self.activation == 'sigmoid':
|
| 104 |
-
return torch.sigmoid(x)
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
class Cnn14(nn.Module):
|
| 108 |
-
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
| 109 |
-
fmax, classes_num, out_emb):
|
| 110 |
-
|
| 111 |
-
super(Cnn14, self).__init__()
|
| 112 |
-
|
| 113 |
-
window = 'hann'
|
| 114 |
-
center = True
|
| 115 |
-
pad_mode = 'reflect'
|
| 116 |
-
ref = 1.0
|
| 117 |
-
amin = 1e-10
|
| 118 |
-
top_db = None
|
| 119 |
-
|
| 120 |
-
# Spectrogram extractor
|
| 121 |
-
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
| 122 |
-
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
| 123 |
-
freeze_parameters=True)
|
| 124 |
-
|
| 125 |
-
# Logmel feature extractor
|
| 126 |
-
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
| 127 |
-
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
| 128 |
-
freeze_parameters=True)
|
| 129 |
-
|
| 130 |
-
self.bn0 = nn.BatchNorm2d(64)
|
| 131 |
-
|
| 132 |
-
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
| 133 |
-
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
| 134 |
-
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
| 135 |
-
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
| 136 |
-
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
|
| 137 |
-
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
|
| 138 |
-
|
| 139 |
-
# out_emb is 2048 for best Cnn14
|
| 140 |
-
self.fc1 = nn.Linear(2048, out_emb, bias=True)
|
| 141 |
-
self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True)
|
| 142 |
-
|
| 143 |
-
def forward(self, input, mixup_lambda=None):
|
| 144 |
-
"""
|
| 145 |
-
Input: (batch_size, data_length)
|
| 146 |
-
"""
|
| 147 |
-
|
| 148 |
-
x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins)
|
| 149 |
-
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
| 150 |
-
|
| 151 |
-
x = x.transpose(1, 3)
|
| 152 |
-
x = self.bn0(x)
|
| 153 |
-
x = x.transpose(1, 3)
|
| 154 |
-
|
| 155 |
-
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
|
| 156 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 157 |
-
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
|
| 158 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 159 |
-
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
|
| 160 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 161 |
-
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
|
| 162 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 163 |
-
x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
|
| 164 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 165 |
-
x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
|
| 166 |
-
x = F.dropout(x, p=0.2, training=self.training)
|
| 167 |
-
x = torch.mean(x, dim=3)
|
| 168 |
-
|
| 169 |
-
(x1, _) = torch.max(x, dim=2)
|
| 170 |
-
x2 = torch.mean(x, dim=2)
|
| 171 |
-
x = x1 + x2
|
| 172 |
-
x = F.dropout(x, p=0.5, training=self.training)
|
| 173 |
-
x = F.relu_(self.fc1(x))
|
| 174 |
-
embedding = F.dropout(x, p=0.5, training=self.training)
|
| 175 |
-
clipwise_output = torch.sigmoid(self.fc_audioset(x))
|
| 176 |
-
|
| 177 |
-
output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}
|
| 178 |
-
|
| 179 |
-
return output_dict
|
|
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spaces/AIGC-Audio/Make_An_Audio/ldm/modules/discriminator/multi_window_disc.py
DELETED
|
@@ -1,196 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import torch
|
| 3 |
-
import torch.nn as nn
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class Discriminator2DFactory(nn.Module):
|
| 7 |
-
def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128,
|
| 8 |
-
norm_type='bn', reduction='sum'):
|
| 9 |
-
super(Discriminator2DFactory, self).__init__()
|
| 10 |
-
padding = (kernel[0] // 2, kernel[1] // 2)
|
| 11 |
-
|
| 12 |
-
def discriminator_block(in_filters, out_filters, first=False):
|
| 13 |
-
"""
|
| 14 |
-
Input: (B, in, 2H, 2W)
|
| 15 |
-
Output:(B, out, H, W)
|
| 16 |
-
"""
|
| 17 |
-
conv = nn.Conv2d(in_filters, out_filters, kernel, (2, 2), padding)
|
| 18 |
-
if norm_type == 'sn':
|
| 19 |
-
conv = nn.utils.spectral_norm(conv)
|
| 20 |
-
block = [
|
| 21 |
-
conv, # padding = kernel//2
|
| 22 |
-
nn.LeakyReLU(0.2, inplace=True),
|
| 23 |
-
nn.Dropout2d(0.25)
|
| 24 |
-
]
|
| 25 |
-
if norm_type == 'bn' and not first:
|
| 26 |
-
block.append(nn.BatchNorm2d(out_filters, 0.8))
|
| 27 |
-
if norm_type == 'in' and not first:
|
| 28 |
-
block.append(nn.InstanceNorm2d(out_filters, affine=True))
|
| 29 |
-
block = nn.Sequential(*block)
|
| 30 |
-
return block
|
| 31 |
-
|
| 32 |
-
self.model = nn.ModuleList([
|
| 33 |
-
discriminator_block(c_in, hidden_size, first=True),
|
| 34 |
-
discriminator_block(hidden_size, hidden_size),
|
| 35 |
-
discriminator_block(hidden_size, hidden_size),
|
| 36 |
-
])
|
| 37 |
-
|
| 38 |
-
self.reduction = reduction
|
| 39 |
-
ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3)
|
| 40 |
-
if reduction != 'none':
|
| 41 |
-
# The height and width of downsampled image
|
| 42 |
-
self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1)
|
| 43 |
-
else:
|
| 44 |
-
self.adv_layer = nn.Linear(hidden_size * ds_size[1], 1)
|
| 45 |
-
|
| 46 |
-
def forward(self, x):
|
| 47 |
-
"""
|
| 48 |
-
|
| 49 |
-
:param x: [B, C, T, n_bins]
|
| 50 |
-
:return: validity: [B, 1], h: List of hiddens
|
| 51 |
-
"""
|
| 52 |
-
h = []
|
| 53 |
-
for l in self.model:
|
| 54 |
-
x = l(x)
|
| 55 |
-
h.append(x)
|
| 56 |
-
if self.reduction != 'none':
|
| 57 |
-
x = x.view(x.shape[0], -1)
|
| 58 |
-
validity = self.adv_layer(x) # [B, 1]
|
| 59 |
-
else:
|
| 60 |
-
B, _, T_, _ = x.shape
|
| 61 |
-
x = x.transpose(1, 2).reshape(B, T_, -1)
|
| 62 |
-
validity = self.adv_layer(x)[:, :, 0] # [B, T]
|
| 63 |
-
return validity, h
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
class MultiWindowDiscriminator(nn.Module):
|
| 67 |
-
def __init__(self, time_lengths, cond_size=0, freq_length=80, kernel=(3, 3),
|
| 68 |
-
c_in=1, hidden_size=128, norm_type='bn', reduction='sum'):
|
| 69 |
-
super(MultiWindowDiscriminator, self).__init__()
|
| 70 |
-
self.win_lengths = time_lengths
|
| 71 |
-
self.reduction = reduction
|
| 72 |
-
|
| 73 |
-
self.conv_layers = nn.ModuleList()
|
| 74 |
-
if cond_size > 0:
|
| 75 |
-
self.cond_proj_layers = nn.ModuleList()
|
| 76 |
-
self.mel_proj_layers = nn.ModuleList()
|
| 77 |
-
for time_length in time_lengths:
|
| 78 |
-
conv_layer = [
|
| 79 |
-
Discriminator2DFactory(
|
| 80 |
-
time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size,
|
| 81 |
-
norm_type=norm_type, reduction=reduction)
|
| 82 |
-
]
|
| 83 |
-
self.conv_layers += conv_layer
|
| 84 |
-
if cond_size > 0:
|
| 85 |
-
self.cond_proj_layers.append(nn.Linear(cond_size, freq_length))
|
| 86 |
-
self.mel_proj_layers.append(nn.Linear(freq_length, freq_length))
|
| 87 |
-
|
| 88 |
-
def forward(self, x, x_len, cond=None, start_frames_wins=None):
|
| 89 |
-
'''
|
| 90 |
-
Args:
|
| 91 |
-
x (tensor): input mel, (B, c_in, T, n_bins).
|
| 92 |
-
x_length (tensor): len of per mel. (B,).
|
| 93 |
-
|
| 94 |
-
Returns:
|
| 95 |
-
tensor : (B).
|
| 96 |
-
'''
|
| 97 |
-
validity = []
|
| 98 |
-
if start_frames_wins is None:
|
| 99 |
-
start_frames_wins = [None] * len(self.conv_layers)
|
| 100 |
-
h = []
|
| 101 |
-
for i, start_frames in zip(range(len(self.conv_layers)), start_frames_wins):
|
| 102 |
-
x_clip, c_clip, start_frames = self.clip(
|
| 103 |
-
x, cond, x_len, self.win_lengths[i], start_frames) # (B, win_length, C)
|
| 104 |
-
start_frames_wins[i] = start_frames
|
| 105 |
-
if x_clip is None:
|
| 106 |
-
continue
|
| 107 |
-
if cond is not None:
|
| 108 |
-
x_clip = self.mel_proj_layers[i](x_clip) # (B, 1, win_length, C)
|
| 109 |
-
c_clip = self.cond_proj_layers[i](c_clip)[:, None] # (B, 1, win_length, C)
|
| 110 |
-
x_clip = x_clip + c_clip
|
| 111 |
-
x_clip, h_ = self.conv_layers[i](x_clip)
|
| 112 |
-
h += h_
|
| 113 |
-
validity.append(x_clip)
|
| 114 |
-
if len(validity) != len(self.conv_layers):
|
| 115 |
-
return None, start_frames_wins, h
|
| 116 |
-
if self.reduction == 'sum':
|
| 117 |
-
validity = sum(validity) # [B]
|
| 118 |
-
elif self.reduction == 'stack':
|
| 119 |
-
validity = torch.stack(validity, -1) # [B, W_L]
|
| 120 |
-
elif self.reduction == 'none':
|
| 121 |
-
validity = torch.cat(validity, -1) # [B, W_sum]
|
| 122 |
-
return validity, start_frames_wins, h
|
| 123 |
-
|
| 124 |
-
def clip(self, x, cond, x_len, win_length, start_frames=None):
|
| 125 |
-
'''Ramdom clip x to win_length.
|
| 126 |
-
Args:
|
| 127 |
-
x (tensor) : (B, c_in, T, n_bins).
|
| 128 |
-
cond (tensor) : (B, T, H).
|
| 129 |
-
x_len (tensor) : (B,).
|
| 130 |
-
win_length (int): target clip length
|
| 131 |
-
|
| 132 |
-
Returns:
|
| 133 |
-
(tensor) : (B, c_in, win_length, n_bins).
|
| 134 |
-
|
| 135 |
-
'''
|
| 136 |
-
T_start = 0
|
| 137 |
-
T_end = x_len.max() - win_length
|
| 138 |
-
if T_end < 0:
|
| 139 |
-
return None, None, start_frames
|
| 140 |
-
T_end = T_end.item()
|
| 141 |
-
if start_frames is None:
|
| 142 |
-
start_frame = np.random.randint(low=T_start, high=T_end + 1)
|
| 143 |
-
start_frames = [start_frame] * x.size(0)
|
| 144 |
-
else:
|
| 145 |
-
start_frame = start_frames[0]
|
| 146 |
-
x_batch = x[:, :, start_frame: start_frame + win_length]
|
| 147 |
-
c_batch = cond[:, start_frame: start_frame + win_length] if cond is not None else None
|
| 148 |
-
return x_batch, c_batch, start_frames
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
class Discriminator(nn.Module):
|
| 152 |
-
def __init__(self, time_lengths=[32, 64, 128], freq_length=80, cond_size=0, kernel=(3, 3), c_in=1,
|
| 153 |
-
hidden_size=128, norm_type='bn', reduction='sum', uncond_disc=True):
|
| 154 |
-
super(Discriminator, self).__init__()
|
| 155 |
-
self.time_lengths = time_lengths
|
| 156 |
-
self.cond_size = cond_size
|
| 157 |
-
self.reduction = reduction
|
| 158 |
-
self.uncond_disc = uncond_disc
|
| 159 |
-
if uncond_disc:
|
| 160 |
-
self.discriminator = MultiWindowDiscriminator(
|
| 161 |
-
freq_length=freq_length,
|
| 162 |
-
time_lengths=time_lengths,
|
| 163 |
-
kernel=kernel,
|
| 164 |
-
c_in=c_in, hidden_size=hidden_size, norm_type=norm_type,
|
| 165 |
-
reduction=reduction
|
| 166 |
-
)
|
| 167 |
-
if cond_size > 0:
|
| 168 |
-
self.cond_disc = MultiWindowDiscriminator(
|
| 169 |
-
freq_length=freq_length,
|
| 170 |
-
time_lengths=time_lengths,
|
| 171 |
-
cond_size=cond_size,
|
| 172 |
-
kernel=kernel,
|
| 173 |
-
c_in=c_in, hidden_size=hidden_size, norm_type=norm_type,
|
| 174 |
-
reduction=reduction
|
| 175 |
-
)
|
| 176 |
-
|
| 177 |
-
def forward(self, x, cond=None, start_frames_wins=None):
|
| 178 |
-
"""
|
| 179 |
-
|
| 180 |
-
:param x: [B, T, 80]
|
| 181 |
-
:param cond: [B, T, cond_size]
|
| 182 |
-
:param return_y_only:
|
| 183 |
-
:return:
|
| 184 |
-
"""
|
| 185 |
-
if len(x.shape) == 3:
|
| 186 |
-
x = x[:, None, :, :]
|
| 187 |
-
x_len = x.sum([1, -1]).ne(0).int().sum([-1])
|
| 188 |
-
ret = {'y_c': None, 'y': None}
|
| 189 |
-
if self.uncond_disc:
|
| 190 |
-
ret['y'], start_frames_wins, ret['h'] = self.discriminator(
|
| 191 |
-
x, x_len, start_frames_wins=start_frames_wins)
|
| 192 |
-
if self.cond_size > 0 and cond is not None:
|
| 193 |
-
ret['y_c'], start_frames_wins, ret['h_c'] = self.cond_disc(
|
| 194 |
-
x, x_len, cond, start_frames_wins=start_frames_wins)
|
| 195 |
-
ret['start_frames_wins'] = start_frames_wins
|
| 196 |
-
return ret
|
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|
spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/alias_free_torch/act.py
DELETED
|
@@ -1,28 +0,0 @@
|
|
| 1 |
-
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
|
| 2 |
-
# LICENSE is in incl_licenses directory.
|
| 3 |
-
|
| 4 |
-
import torch.nn as nn
|
| 5 |
-
from .resample import UpSample1d, DownSample1d
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class Activation1d(nn.Module):
|
| 9 |
-
def __init__(self,
|
| 10 |
-
activation,
|
| 11 |
-
up_ratio: int = 2,
|
| 12 |
-
down_ratio: int = 2,
|
| 13 |
-
up_kernel_size: int = 12,
|
| 14 |
-
down_kernel_size: int = 12):
|
| 15 |
-
super().__init__()
|
| 16 |
-
self.up_ratio = up_ratio
|
| 17 |
-
self.down_ratio = down_ratio
|
| 18 |
-
self.act = activation
|
| 19 |
-
self.upsample = UpSample1d(up_ratio, up_kernel_size)
|
| 20 |
-
self.downsample = DownSample1d(down_ratio, down_kernel_size)
|
| 21 |
-
|
| 22 |
-
# x: [B,C,T]
|
| 23 |
-
def forward(self, x):
|
| 24 |
-
x = self.upsample(x)
|
| 25 |
-
x = self.act(x)
|
| 26 |
-
x = self.downsample(x)
|
| 27 |
-
|
| 28 |
-
return x
|
|
|
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|
spaces/ARTeLab/DTM_Estimation_SRandD/app.py
DELETED
|
@@ -1,81 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import os
|
| 3 |
-
from PIL import Image
|
| 4 |
-
import torchvision
|
| 5 |
-
from torchvision import transforms
|
| 6 |
-
import torch
|
| 7 |
-
import matplotlib.pyplot as plt
|
| 8 |
-
import numpy as np
|
| 9 |
-
from models.modelNetA import Generator as GA
|
| 10 |
-
from models.modelNetB import Generator as GB
|
| 11 |
-
from models.modelNetC import Generator as GC
|
| 12 |
-
|
| 13 |
-
scale_size = 128
|
| 14 |
-
scale_sizes = [128, 256, 512]
|
| 15 |
-
# load model
|
| 16 |
-
modeltype2path = {
|
| 17 |
-
'ModelA': 'DTM_exp_train10%_model_a/g-best.pth',
|
| 18 |
-
'ModelB': 'DTM_exp_train10%_model_b/g-best.pth',
|
| 19 |
-
'ModelC': 'DTM_exp_train10%_model_c/g-best.pth',
|
| 20 |
-
}
|
| 21 |
-
DEVICE='cpu'
|
| 22 |
-
MODELS_TYPE = list(modeltype2path.keys())
|
| 23 |
-
generators = [GA(), GB(), GC()]
|
| 24 |
-
|
| 25 |
-
for i in range(len(generators)):
|
| 26 |
-
generators[i] = torch.nn.DataParallel(generators[i])
|
| 27 |
-
state_dict = torch.load(modeltype2path[MODELS_TYPE[i]], map_location=torch.device('cpu'))
|
| 28 |
-
generators[i].load_state_dict(state_dict)
|
| 29 |
-
generators[i] = generators[i].module.to(DEVICE)
|
| 30 |
-
generators[i].eval()
|
| 31 |
-
|
| 32 |
-
preprocess = transforms.Compose([
|
| 33 |
-
transforms.Grayscale(),
|
| 34 |
-
transforms.ToTensor()
|
| 35 |
-
])
|
| 36 |
-
|
| 37 |
-
def predict(input_image, model_name, input_scale_factor):
|
| 38 |
-
pil_image = Image.fromarray(input_image.astype('uint8'), 'RGB')
|
| 39 |
-
pil_image = transforms.Resize((input_scale_factor, input_scale_factor))(pil_image)
|
| 40 |
-
# transform image to torch and do preprocessing
|
| 41 |
-
torch_img = preprocess(pil_image).to(DEVICE).unsqueeze(0).to(DEVICE)
|
| 42 |
-
torch_img = (torch_img - torch.min(torch_img)) / (torch.max(torch_img) - torch.min(torch_img))
|
| 43 |
-
# model predict
|
| 44 |
-
with torch.no_grad():
|
| 45 |
-
output = generators[MODELS_TYPE.index(model_name)](torch_img)
|
| 46 |
-
sr, sr_dem_selected = output[0], output[1]
|
| 47 |
-
# transform torch to image
|
| 48 |
-
sr = sr.squeeze(0).cpu()
|
| 49 |
-
torchvision.utils.save_image(sr, 'sr_pred.png')
|
| 50 |
-
sr = np.array(Image.open('sr_pred.png'))
|
| 51 |
-
|
| 52 |
-
sr_dem_selected = sr_dem_selected.squeeze().cpu().detach().numpy()
|
| 53 |
-
fig, ax = plt.subplots()
|
| 54 |
-
im = ax.imshow(sr_dem_selected, cmap='jet', vmin=0, vmax=np.max(sr_dem_selected))
|
| 55 |
-
plt.colorbar(im, ax=ax)
|
| 56 |
-
fig.canvas.draw()
|
| 57 |
-
data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
|
| 58 |
-
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
| 59 |
-
# return correct image and info
|
| 60 |
-
info = f"{model_name} with {sum(p.numel() for p in generators[MODELS_TYPE.index(model_name)].parameters())} parameters"
|
| 61 |
-
return info, sr, data
|
| 62 |
-
|
| 63 |
-
iface = gr.Interface(
|
| 64 |
-
fn=predict,
|
| 65 |
-
inputs=[
|
| 66 |
-
gr.Image(),
|
| 67 |
-
gr.inputs.Radio(MODELS_TYPE),
|
| 68 |
-
gr.inputs.Radio(scale_sizes)
|
| 69 |
-
],
|
| 70 |
-
outputs=[
|
| 71 |
-
gr.Text(label='Model info'),
|
| 72 |
-
gr.Image(label='Super Resolution'),
|
| 73 |
-
gr.Image(label='DTM')
|
| 74 |
-
],
|
| 75 |
-
examples=[
|
| 76 |
-
[f"demo_imgs/{name}", MODELS_TYPE[0], 128] for name in os.listdir('demo_imgs')
|
| 77 |
-
],
|
| 78 |
-
title="Super Resolution and DTM Estimation",
|
| 79 |
-
description=f"This demo predict Super Resolution and (Super Resolution) DTM from a Grayscale image (if RGB we convert it)."
|
| 80 |
-
)
|
| 81 |
-
iface.launch()
|
|
|
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/shufflenet-v2-1x_4xb32_2000e_3c_noF/shufflenet-v2-1x_4xb32_2000e_3c_noF.py
DELETED
|
@@ -1,155 +0,0 @@
|
|
| 1 |
-
model = dict(
|
| 2 |
-
type='ImageClassifier',
|
| 3 |
-
backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
|
| 4 |
-
neck=dict(type='GlobalAveragePooling'),
|
| 5 |
-
head=dict(
|
| 6 |
-
type='LinearClsHead',
|
| 7 |
-
num_classes=7,
|
| 8 |
-
in_channels=1024,
|
| 9 |
-
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
|
| 10 |
-
topk=(
|
| 11 |
-
1,
|
| 12 |
-
3,
|
| 13 |
-
)))
|
| 14 |
-
dataset_type = 'CustomDataset'
|
| 15 |
-
data_preprocessor = dict(
|
| 16 |
-
num_classes=7,
|
| 17 |
-
mean=[
|
| 18 |
-
123.675,
|
| 19 |
-
116.28,
|
| 20 |
-
103.53,
|
| 21 |
-
],
|
| 22 |
-
std=[
|
| 23 |
-
58.395,
|
| 24 |
-
57.12,
|
| 25 |
-
57.375,
|
| 26 |
-
],
|
| 27 |
-
to_rgb=True)
|
| 28 |
-
train_pipeline = [
|
| 29 |
-
dict(type='LoadImageFromFile'),
|
| 30 |
-
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
|
| 31 |
-
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
|
| 32 |
-
dict(type='PackInputs'),
|
| 33 |
-
]
|
| 34 |
-
test_pipeline = [
|
| 35 |
-
dict(type='LoadImageFromFile'),
|
| 36 |
-
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
|
| 37 |
-
dict(type='CenterCrop', crop_size=224),
|
| 38 |
-
dict(type='PackInputs'),
|
| 39 |
-
]
|
| 40 |
-
train_dataloader = dict(
|
| 41 |
-
pin_memory=True,
|
| 42 |
-
persistent_workers=True,
|
| 43 |
-
collate_fn=dict(type='default_collate'),
|
| 44 |
-
batch_size=32,
|
| 45 |
-
num_workers=5,
|
| 46 |
-
dataset=dict(
|
| 47 |
-
type='CustomDataset',
|
| 48 |
-
data_root='data',
|
| 49 |
-
with_label=True,
|
| 50 |
-
ann_file='',
|
| 51 |
-
data_prefix='train',
|
| 52 |
-
pipeline=[
|
| 53 |
-
dict(type='LoadImageFromFile'),
|
| 54 |
-
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
|
| 55 |
-
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
|
| 56 |
-
dict(type='PackInputs'),
|
| 57 |
-
]),
|
| 58 |
-
sampler=dict(type='DefaultSampler', shuffle=True))
|
| 59 |
-
val_dataloader = dict(
|
| 60 |
-
pin_memory=True,
|
| 61 |
-
persistent_workers=True,
|
| 62 |
-
collate_fn=dict(type='default_collate'),
|
| 63 |
-
batch_size=32,
|
| 64 |
-
num_workers=5,
|
| 65 |
-
dataset=dict(
|
| 66 |
-
type='CustomDataset',
|
| 67 |
-
data_root='data',
|
| 68 |
-
with_label=True,
|
| 69 |
-
ann_file='',
|
| 70 |
-
data_prefix='val',
|
| 71 |
-
pipeline=[
|
| 72 |
-
dict(type='LoadImageFromFile'),
|
| 73 |
-
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
|
| 74 |
-
dict(type='CenterCrop', crop_size=224),
|
| 75 |
-
dict(type='PackInputs'),
|
| 76 |
-
]),
|
| 77 |
-
sampler=dict(type='DefaultSampler', shuffle=False))
|
| 78 |
-
val_evaluator = dict(
|
| 79 |
-
type='Accuracy', topk=(
|
| 80 |
-
1,
|
| 81 |
-
3,
|
| 82 |
-
))
|
| 83 |
-
test_dataloader = dict(
|
| 84 |
-
pin_memory=True,
|
| 85 |
-
persistent_workers=True,
|
| 86 |
-
collate_fn=dict(type='default_collate'),
|
| 87 |
-
batch_size=32,
|
| 88 |
-
num_workers=5,
|
| 89 |
-
dataset=dict(
|
| 90 |
-
type='CustomDataset',
|
| 91 |
-
data_root='data',
|
| 92 |
-
with_label=True,
|
| 93 |
-
ann_file='',
|
| 94 |
-
data_prefix='val',
|
| 95 |
-
pipeline=[
|
| 96 |
-
dict(type='LoadImageFromFile'),
|
| 97 |
-
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
|
| 98 |
-
dict(type='CenterCrop', crop_size=224),
|
| 99 |
-
dict(type='PackInputs'),
|
| 100 |
-
]),
|
| 101 |
-
sampler=dict(type='DefaultSampler', shuffle=False))
|
| 102 |
-
test_evaluator = dict(
|
| 103 |
-
pin_memory=True,
|
| 104 |
-
persistent_workers=True,
|
| 105 |
-
collate_fn=dict(type='default_collate'),
|
| 106 |
-
batch_size=32,
|
| 107 |
-
num_workers=5,
|
| 108 |
-
dataset=dict(
|
| 109 |
-
type='CustomDataset',
|
| 110 |
-
data_root='data',
|
| 111 |
-
with_label=True,
|
| 112 |
-
ann_file='',
|
| 113 |
-
data_prefix='val',
|
| 114 |
-
pipeline=[
|
| 115 |
-
dict(type='LoadImageFromFile'),
|
| 116 |
-
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
|
| 117 |
-
dict(type='CenterCrop', crop_size=224),
|
| 118 |
-
dict(type='PackInputs'),
|
| 119 |
-
]),
|
| 120 |
-
sampler=dict(type='DefaultSampler', shuffle=False))
|
| 121 |
-
optim_wrapper = dict(
|
| 122 |
-
optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001),
|
| 123 |
-
paramwise_cfg=dict(norm_decay_mult=0))
|
| 124 |
-
param_scheduler = dict(type='StepLR', by_epoch=True, step_size=10, gamma=0.98)
|
| 125 |
-
train_cfg = dict(by_epoch=True, max_epochs=2000, val_interval=10)
|
| 126 |
-
val_cfg = dict()
|
| 127 |
-
test_cfg = dict()
|
| 128 |
-
auto_scale_lr = dict(base_batch_size=1024)
|
| 129 |
-
default_scope = 'mmpretrain'
|
| 130 |
-
default_hooks = dict(
|
| 131 |
-
timer=dict(type='IterTimerHook'),
|
| 132 |
-
logger=dict(type='LoggerHook', interval=10),
|
| 133 |
-
param_scheduler=dict(type='ParamSchedulerHook'),
|
| 134 |
-
checkpoint=dict(type='CheckpointHook', save_best='auto', interval=10),
|
| 135 |
-
sampler_seed=dict(type='DistSamplerSeedHook'),
|
| 136 |
-
visualization=dict(type='VisualizationHook', enable=False))
|
| 137 |
-
env_cfg = dict(
|
| 138 |
-
cudnn_benchmark=False,
|
| 139 |
-
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
|
| 140 |
-
dist_cfg=dict(backend='nccl'))
|
| 141 |
-
vis_backends = [
|
| 142 |
-
dict(type='LocalVisBackend'),
|
| 143 |
-
]
|
| 144 |
-
visualizer = dict(
|
| 145 |
-
type='UniversalVisualizer',
|
| 146 |
-
vis_backends=[
|
| 147 |
-
dict(type='LocalVisBackend'),
|
| 148 |
-
dict(type='WandbVisBackend'),
|
| 149 |
-
])
|
| 150 |
-
log_level = 'INFO'
|
| 151 |
-
load_from = None
|
| 152 |
-
resume = False
|
| 153 |
-
randomness = dict(seed=None, deterministic=False)
|
| 154 |
-
launcher = 'pytorch'
|
| 155 |
-
work_dir = './work_dirs/shufflenet-v2-1x_4xb32_2000e_3c_noF'
|
|
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|
spaces/Aadhithya/Binance-Crypto-Tracker/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Binance Crypto Tracker
|
| 3 |
-
emoji: 🏢
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.10.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
|
spaces/Abhilashvj/planogram-compliance/setup.sh
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mkdir -p ~/.streamlit/
|
| 2 |
-
echo "\
|
| 3 |
-
[server]\n\
|
| 4 |
-
headless = true\n\
|
| 5 |
-
port = $PORT\n\
|
| 6 |
-
enableCORS = false\n\
|
| 7 |
-
\n\
|
| 8 |
-
" > ~/.streamlit/config.toml
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/PostResolveSize.js
DELETED
|
@@ -1,46 +0,0 @@
|
|
| 1 |
-
import ResizeGameObject from '../../../plugins/utils/size/ResizeGameObject.js';
|
| 2 |
-
|
| 3 |
-
var PostResolveSize = function (width, height) {
|
| 4 |
-
if (this.hasRatioFitChild) {
|
| 5 |
-
// Resize child for ratio-fit
|
| 6 |
-
var innerHeight, innerWidth;
|
| 7 |
-
if (this.orientation === 0) {
|
| 8 |
-
innerHeight = height - this.getInnerPadding('top') - this.getInnerPadding('bottom');
|
| 9 |
-
} else {
|
| 10 |
-
innerWidth = width - this.getInnerPadding('left') - this.getInnerPadding('right');
|
| 11 |
-
}
|
| 12 |
-
|
| 13 |
-
var children = this.sizerChildren,
|
| 14 |
-
childWidth, childHeight;
|
| 15 |
-
for (var i = 0, cnt = children.length; i < cnt; i++) {
|
| 16 |
-
var child = children[i];
|
| 17 |
-
if (child.rexSizer.hidden) {
|
| 18 |
-
continue;
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
var fitRatio = child.rexSizer.fitRatio;
|
| 22 |
-
if (!fitRatio) {
|
| 23 |
-
continue;
|
| 24 |
-
}
|
| 25 |
-
|
| 26 |
-
if (this.orientation === 0) {
|
| 27 |
-
childHeight = innerHeight - this.getChildOuterPadding(child, 'top') - this.getChildOuterPadding(child, 'bottom');
|
| 28 |
-
childWidth = childHeight * fitRatio;
|
| 29 |
-
} else {
|
| 30 |
-
childWidth = innerHeight - this.getChildOuterPadding(child, 'top') - this.getChildOuterPadding(child, 'bottom');
|
| 31 |
-
childHeight = childWidth / fitRatio;
|
| 32 |
-
}
|
| 33 |
-
|
| 34 |
-
ResizeGameObject(child, childWidth, childHeight);
|
| 35 |
-
if (child.isRexSizer) {
|
| 36 |
-
child.setMinSize(childWidth, childHeight)
|
| 37 |
-
}
|
| 38 |
-
}
|
| 39 |
-
|
| 40 |
-
this.proportionLength = undefined;
|
| 41 |
-
this._childrenWidth = undefined;
|
| 42 |
-
this.resolveWidth(width, true);
|
| 43 |
-
}
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
export default PostResolveSize;
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spaces/AlexWang/lama/bin/gen_mask_dataset.py
DELETED
|
@@ -1,130 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python3
|
| 2 |
-
|
| 3 |
-
import glob
|
| 4 |
-
import os
|
| 5 |
-
import shutil
|
| 6 |
-
import traceback
|
| 7 |
-
|
| 8 |
-
import PIL.Image as Image
|
| 9 |
-
import numpy as np
|
| 10 |
-
from joblib import Parallel, delayed
|
| 11 |
-
|
| 12 |
-
from saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop
|
| 13 |
-
from saicinpainting.evaluation.utils import load_yaml, SmallMode
|
| 14 |
-
from saicinpainting.training.data.masks import MixedMaskGenerator
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
class MakeManyMasksWrapper:
|
| 18 |
-
def __init__(self, impl, variants_n=2):
|
| 19 |
-
self.impl = impl
|
| 20 |
-
self.variants_n = variants_n
|
| 21 |
-
|
| 22 |
-
def get_masks(self, img):
|
| 23 |
-
img = np.transpose(np.array(img), (2, 0, 1))
|
| 24 |
-
return [self.impl(img)[0] for _ in range(self.variants_n)]
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def process_images(src_images, indir, outdir, config):
|
| 28 |
-
if config.generator_kind == 'segmentation':
|
| 29 |
-
mask_generator = SegmentationMask(**config.mask_generator_kwargs)
|
| 30 |
-
elif config.generator_kind == 'random':
|
| 31 |
-
variants_n = config.mask_generator_kwargs.pop('variants_n', 2)
|
| 32 |
-
mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**config.mask_generator_kwargs),
|
| 33 |
-
variants_n=variants_n)
|
| 34 |
-
else:
|
| 35 |
-
raise ValueError(f'Unexpected generator kind: {config.generator_kind}')
|
| 36 |
-
|
| 37 |
-
max_tamper_area = config.get('max_tamper_area', 1)
|
| 38 |
-
|
| 39 |
-
for infile in src_images:
|
| 40 |
-
try:
|
| 41 |
-
file_relpath = infile[len(indir):]
|
| 42 |
-
img_outpath = os.path.join(outdir, file_relpath)
|
| 43 |
-
os.makedirs(os.path.dirname(img_outpath), exist_ok=True)
|
| 44 |
-
|
| 45 |
-
image = Image.open(infile).convert('RGB')
|
| 46 |
-
|
| 47 |
-
# scale input image to output resolution and filter smaller images
|
| 48 |
-
if min(image.size) < config.cropping.out_min_size:
|
| 49 |
-
handle_small_mode = SmallMode(config.cropping.handle_small_mode)
|
| 50 |
-
if handle_small_mode == SmallMode.DROP:
|
| 51 |
-
continue
|
| 52 |
-
elif handle_small_mode == SmallMode.UPSCALE:
|
| 53 |
-
factor = config.cropping.out_min_size / min(image.size)
|
| 54 |
-
out_size = (np.array(image.size) * factor).round().astype('uint32')
|
| 55 |
-
image = image.resize(out_size, resample=Image.BICUBIC)
|
| 56 |
-
else:
|
| 57 |
-
factor = config.cropping.out_min_size / min(image.size)
|
| 58 |
-
out_size = (np.array(image.size) * factor).round().astype('uint32')
|
| 59 |
-
image = image.resize(out_size, resample=Image.BICUBIC)
|
| 60 |
-
|
| 61 |
-
# generate and select masks
|
| 62 |
-
src_masks = mask_generator.get_masks(image)
|
| 63 |
-
|
| 64 |
-
filtered_image_mask_pairs = []
|
| 65 |
-
for cur_mask in src_masks:
|
| 66 |
-
if config.cropping.out_square_crop:
|
| 67 |
-
(crop_left,
|
| 68 |
-
crop_top,
|
| 69 |
-
crop_right,
|
| 70 |
-
crop_bottom) = propose_random_square_crop(cur_mask,
|
| 71 |
-
min_overlap=config.cropping.crop_min_overlap)
|
| 72 |
-
cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right]
|
| 73 |
-
cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom))
|
| 74 |
-
else:
|
| 75 |
-
cur_image = image
|
| 76 |
-
|
| 77 |
-
if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area:
|
| 78 |
-
continue
|
| 79 |
-
|
| 80 |
-
filtered_image_mask_pairs.append((cur_image, cur_mask))
|
| 81 |
-
|
| 82 |
-
mask_indices = np.random.choice(len(filtered_image_mask_pairs),
|
| 83 |
-
size=min(len(filtered_image_mask_pairs), config.max_masks_per_image),
|
| 84 |
-
replace=False)
|
| 85 |
-
|
| 86 |
-
# crop masks; save masks together with input image
|
| 87 |
-
mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0])
|
| 88 |
-
for i, idx in enumerate(mask_indices):
|
| 89 |
-
cur_image, cur_mask = filtered_image_mask_pairs[idx]
|
| 90 |
-
cur_basename = mask_basename + f'_crop{i:03d}'
|
| 91 |
-
Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'),
|
| 92 |
-
mode='L').save(cur_basename + f'_mask{i:03d}.png')
|
| 93 |
-
cur_image.save(cur_basename + '.png')
|
| 94 |
-
except KeyboardInterrupt:
|
| 95 |
-
return
|
| 96 |
-
except Exception as ex:
|
| 97 |
-
print(f'Could not make masks for {infile} due to {ex}:\n{traceback.format_exc()}')
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def main(args):
|
| 101 |
-
if not args.indir.endswith('/'):
|
| 102 |
-
args.indir += '/'
|
| 103 |
-
|
| 104 |
-
os.makedirs(args.outdir, exist_ok=True)
|
| 105 |
-
|
| 106 |
-
config = load_yaml(args.config)
|
| 107 |
-
|
| 108 |
-
in_files = list(glob.glob(os.path.join(args.indir, '**', f'*.{args.ext}'), recursive=True))
|
| 109 |
-
if args.n_jobs == 0:
|
| 110 |
-
process_images(in_files, args.indir, args.outdir, config)
|
| 111 |
-
else:
|
| 112 |
-
in_files_n = len(in_files)
|
| 113 |
-
chunk_size = in_files_n // args.n_jobs + (1 if in_files_n % args.n_jobs > 0 else 0)
|
| 114 |
-
Parallel(n_jobs=args.n_jobs)(
|
| 115 |
-
delayed(process_images)(in_files[start:start+chunk_size], args.indir, args.outdir, config)
|
| 116 |
-
for start in range(0, len(in_files), chunk_size)
|
| 117 |
-
)
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
if __name__ == '__main__':
|
| 121 |
-
import argparse
|
| 122 |
-
|
| 123 |
-
aparser = argparse.ArgumentParser()
|
| 124 |
-
aparser.add_argument('config', type=str, help='Path to config for dataset generation')
|
| 125 |
-
aparser.add_argument('indir', type=str, help='Path to folder with images')
|
| 126 |
-
aparser.add_argument('outdir', type=str, help='Path to folder to store aligned images and masks to')
|
| 127 |
-
aparser.add_argument('--n-jobs', type=int, default=0, help='How many processes to use')
|
| 128 |
-
aparser.add_argument('--ext', type=str, default='jpg', help='Input image extension')
|
| 129 |
-
|
| 130 |
-
main(aparser.parse_args())
|
|
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|
|
spaces/Alpaca233/SadTalker/src/face3d/options/__init__.py
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
|
|
|
|
|
|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/image_processor.py
DELETED
|
@@ -1,366 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
import warnings
|
| 16 |
-
from typing import List, Optional, Union
|
| 17 |
-
|
| 18 |
-
import numpy as np
|
| 19 |
-
import PIL
|
| 20 |
-
import torch
|
| 21 |
-
from PIL import Image
|
| 22 |
-
|
| 23 |
-
from .configuration_utils import ConfigMixin, register_to_config
|
| 24 |
-
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class VaeImageProcessor(ConfigMixin):
|
| 28 |
-
"""
|
| 29 |
-
Image processor for VAE.
|
| 30 |
-
|
| 31 |
-
Args:
|
| 32 |
-
do_resize (`bool`, *optional*, defaults to `True`):
|
| 33 |
-
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
|
| 34 |
-
`height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
|
| 35 |
-
vae_scale_factor (`int`, *optional*, defaults to `8`):
|
| 36 |
-
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
|
| 37 |
-
resample (`str`, *optional*, defaults to `lanczos`):
|
| 38 |
-
Resampling filter to use when resizing the image.
|
| 39 |
-
do_normalize (`bool`, *optional*, defaults to `True`):
|
| 40 |
-
Whether to normalize the image to [-1,1].
|
| 41 |
-
do_convert_rgb (`bool`, *optional*, defaults to be `False`):
|
| 42 |
-
Whether to convert the images to RGB format.
|
| 43 |
-
"""
|
| 44 |
-
|
| 45 |
-
config_name = CONFIG_NAME
|
| 46 |
-
|
| 47 |
-
@register_to_config
|
| 48 |
-
def __init__(
|
| 49 |
-
self,
|
| 50 |
-
do_resize: bool = True,
|
| 51 |
-
vae_scale_factor: int = 8,
|
| 52 |
-
resample: str = "lanczos",
|
| 53 |
-
do_normalize: bool = True,
|
| 54 |
-
do_convert_rgb: bool = False,
|
| 55 |
-
):
|
| 56 |
-
super().__init__()
|
| 57 |
-
|
| 58 |
-
@staticmethod
|
| 59 |
-
def numpy_to_pil(images: np.ndarray) -> PIL.Image.Image:
|
| 60 |
-
"""
|
| 61 |
-
Convert a numpy image or a batch of images to a PIL image.
|
| 62 |
-
"""
|
| 63 |
-
if images.ndim == 3:
|
| 64 |
-
images = images[None, ...]
|
| 65 |
-
images = (images * 255).round().astype("uint8")
|
| 66 |
-
if images.shape[-1] == 1:
|
| 67 |
-
# special case for grayscale (single channel) images
|
| 68 |
-
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
|
| 69 |
-
else:
|
| 70 |
-
pil_images = [Image.fromarray(image) for image in images]
|
| 71 |
-
|
| 72 |
-
return pil_images
|
| 73 |
-
|
| 74 |
-
@staticmethod
|
| 75 |
-
def pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
|
| 76 |
-
"""
|
| 77 |
-
Convert a PIL image or a list of PIL images to NumPy arrays.
|
| 78 |
-
"""
|
| 79 |
-
if not isinstance(images, list):
|
| 80 |
-
images = [images]
|
| 81 |
-
images = [np.array(image).astype(np.float32) / 255.0 for image in images]
|
| 82 |
-
images = np.stack(images, axis=0)
|
| 83 |
-
|
| 84 |
-
return images
|
| 85 |
-
|
| 86 |
-
@staticmethod
|
| 87 |
-
def numpy_to_pt(images: np.ndarray) -> torch.FloatTensor:
|
| 88 |
-
"""
|
| 89 |
-
Convert a NumPy image to a PyTorch tensor.
|
| 90 |
-
"""
|
| 91 |
-
if images.ndim == 3:
|
| 92 |
-
images = images[..., None]
|
| 93 |
-
|
| 94 |
-
images = torch.from_numpy(images.transpose(0, 3, 1, 2))
|
| 95 |
-
return images
|
| 96 |
-
|
| 97 |
-
@staticmethod
|
| 98 |
-
def pt_to_numpy(images: torch.FloatTensor) -> np.ndarray:
|
| 99 |
-
"""
|
| 100 |
-
Convert a PyTorch tensor to a NumPy image.
|
| 101 |
-
"""
|
| 102 |
-
images = images.cpu().permute(0, 2, 3, 1).float().numpy()
|
| 103 |
-
return images
|
| 104 |
-
|
| 105 |
-
@staticmethod
|
| 106 |
-
def normalize(images):
|
| 107 |
-
"""
|
| 108 |
-
Normalize an image array to [-1,1].
|
| 109 |
-
"""
|
| 110 |
-
return 2.0 * images - 1.0
|
| 111 |
-
|
| 112 |
-
@staticmethod
|
| 113 |
-
def denormalize(images):
|
| 114 |
-
"""
|
| 115 |
-
Denormalize an image array to [0,1].
|
| 116 |
-
"""
|
| 117 |
-
return (images / 2 + 0.5).clamp(0, 1)
|
| 118 |
-
|
| 119 |
-
@staticmethod
|
| 120 |
-
def convert_to_rgb(image: PIL.Image.Image) -> PIL.Image.Image:
|
| 121 |
-
"""
|
| 122 |
-
Converts an image to RGB format.
|
| 123 |
-
"""
|
| 124 |
-
image = image.convert("RGB")
|
| 125 |
-
return image
|
| 126 |
-
|
| 127 |
-
def resize(
|
| 128 |
-
self,
|
| 129 |
-
image: PIL.Image.Image,
|
| 130 |
-
height: Optional[int] = None,
|
| 131 |
-
width: Optional[int] = None,
|
| 132 |
-
) -> PIL.Image.Image:
|
| 133 |
-
"""
|
| 134 |
-
Resize a PIL image. Both height and width are downscaled to the next integer multiple of `vae_scale_factor`.
|
| 135 |
-
"""
|
| 136 |
-
if height is None:
|
| 137 |
-
height = image.height
|
| 138 |
-
if width is None:
|
| 139 |
-
width = image.width
|
| 140 |
-
|
| 141 |
-
width, height = (
|
| 142 |
-
x - x % self.config.vae_scale_factor for x in (width, height)
|
| 143 |
-
) # resize to integer multiple of vae_scale_factor
|
| 144 |
-
image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])
|
| 145 |
-
return image
|
| 146 |
-
|
| 147 |
-
def preprocess(
|
| 148 |
-
self,
|
| 149 |
-
image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],
|
| 150 |
-
height: Optional[int] = None,
|
| 151 |
-
width: Optional[int] = None,
|
| 152 |
-
) -> torch.Tensor:
|
| 153 |
-
"""
|
| 154 |
-
Preprocess the image input. Accepted formats are PIL images, NumPy arrays or PyTorch tensors.
|
| 155 |
-
"""
|
| 156 |
-
supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
|
| 157 |
-
if isinstance(image, supported_formats):
|
| 158 |
-
image = [image]
|
| 159 |
-
elif not (isinstance(image, list) and all(isinstance(i, supported_formats) for i in image)):
|
| 160 |
-
raise ValueError(
|
| 161 |
-
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support {', '.join(supported_formats)}"
|
| 162 |
-
)
|
| 163 |
-
|
| 164 |
-
if isinstance(image[0], PIL.Image.Image):
|
| 165 |
-
if self.config.do_convert_rgb:
|
| 166 |
-
image = [self.convert_to_rgb(i) for i in image]
|
| 167 |
-
if self.config.do_resize:
|
| 168 |
-
image = [self.resize(i, height, width) for i in image]
|
| 169 |
-
image = self.pil_to_numpy(image) # to np
|
| 170 |
-
image = self.numpy_to_pt(image) # to pt
|
| 171 |
-
|
| 172 |
-
elif isinstance(image[0], np.ndarray):
|
| 173 |
-
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
|
| 174 |
-
image = self.numpy_to_pt(image)
|
| 175 |
-
_, _, height, width = image.shape
|
| 176 |
-
if self.config.do_resize and (
|
| 177 |
-
height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
|
| 178 |
-
):
|
| 179 |
-
raise ValueError(
|
| 180 |
-
f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
|
| 181 |
-
f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
|
| 182 |
-
)
|
| 183 |
-
|
| 184 |
-
elif isinstance(image[0], torch.Tensor):
|
| 185 |
-
image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
|
| 186 |
-
_, channel, height, width = image.shape
|
| 187 |
-
|
| 188 |
-
# don't need any preprocess if the image is latents
|
| 189 |
-
if channel == 4:
|
| 190 |
-
return image
|
| 191 |
-
|
| 192 |
-
if self.config.do_resize and (
|
| 193 |
-
height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
|
| 194 |
-
):
|
| 195 |
-
raise ValueError(
|
| 196 |
-
f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
|
| 197 |
-
f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
|
| 198 |
-
)
|
| 199 |
-
|
| 200 |
-
# expected range [0,1], normalize to [-1,1]
|
| 201 |
-
do_normalize = self.config.do_normalize
|
| 202 |
-
if image.min() < 0:
|
| 203 |
-
warnings.warn(
|
| 204 |
-
"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
|
| 205 |
-
f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]",
|
| 206 |
-
FutureWarning,
|
| 207 |
-
)
|
| 208 |
-
do_normalize = False
|
| 209 |
-
|
| 210 |
-
if do_normalize:
|
| 211 |
-
image = self.normalize(image)
|
| 212 |
-
|
| 213 |
-
return image
|
| 214 |
-
|
| 215 |
-
def postprocess(
|
| 216 |
-
self,
|
| 217 |
-
image: torch.FloatTensor,
|
| 218 |
-
output_type: str = "pil",
|
| 219 |
-
do_denormalize: Optional[List[bool]] = None,
|
| 220 |
-
):
|
| 221 |
-
if not isinstance(image, torch.Tensor):
|
| 222 |
-
raise ValueError(
|
| 223 |
-
f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
|
| 224 |
-
)
|
| 225 |
-
if output_type not in ["latent", "pt", "np", "pil"]:
|
| 226 |
-
deprecation_message = (
|
| 227 |
-
f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
|
| 228 |
-
"`pil`, `np`, `pt`, `latent`"
|
| 229 |
-
)
|
| 230 |
-
deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
|
| 231 |
-
output_type = "np"
|
| 232 |
-
|
| 233 |
-
if output_type == "latent":
|
| 234 |
-
return image
|
| 235 |
-
|
| 236 |
-
if do_denormalize is None:
|
| 237 |
-
do_denormalize = [self.config.do_normalize] * image.shape[0]
|
| 238 |
-
|
| 239 |
-
image = torch.stack(
|
| 240 |
-
[self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]
|
| 241 |
-
)
|
| 242 |
-
|
| 243 |
-
if output_type == "pt":
|
| 244 |
-
return image
|
| 245 |
-
|
| 246 |
-
image = self.pt_to_numpy(image)
|
| 247 |
-
|
| 248 |
-
if output_type == "np":
|
| 249 |
-
return image
|
| 250 |
-
|
| 251 |
-
if output_type == "pil":
|
| 252 |
-
return self.numpy_to_pil(image)
|
| 253 |
-
|
| 254 |
-
|
| 255 |
-
class VaeImageProcessorLDM3D(VaeImageProcessor):
|
| 256 |
-
"""
|
| 257 |
-
Image processor for VAE LDM3D.
|
| 258 |
-
|
| 259 |
-
Args:
|
| 260 |
-
do_resize (`bool`, *optional*, defaults to `True`):
|
| 261 |
-
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
|
| 262 |
-
vae_scale_factor (`int`, *optional*, defaults to `8`):
|
| 263 |
-
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
|
| 264 |
-
resample (`str`, *optional*, defaults to `lanczos`):
|
| 265 |
-
Resampling filter to use when resizing the image.
|
| 266 |
-
do_normalize (`bool`, *optional*, defaults to `True`):
|
| 267 |
-
Whether to normalize the image to [-1,1].
|
| 268 |
-
"""
|
| 269 |
-
|
| 270 |
-
config_name = CONFIG_NAME
|
| 271 |
-
|
| 272 |
-
@register_to_config
|
| 273 |
-
def __init__(
|
| 274 |
-
self,
|
| 275 |
-
do_resize: bool = True,
|
| 276 |
-
vae_scale_factor: int = 8,
|
| 277 |
-
resample: str = "lanczos",
|
| 278 |
-
do_normalize: bool = True,
|
| 279 |
-
):
|
| 280 |
-
super().__init__()
|
| 281 |
-
|
| 282 |
-
@staticmethod
|
| 283 |
-
def numpy_to_pil(images):
|
| 284 |
-
"""
|
| 285 |
-
Convert a NumPy image or a batch of images to a PIL image.
|
| 286 |
-
"""
|
| 287 |
-
if images.ndim == 3:
|
| 288 |
-
images = images[None, ...]
|
| 289 |
-
images = (images * 255).round().astype("uint8")
|
| 290 |
-
if images.shape[-1] == 1:
|
| 291 |
-
# special case for grayscale (single channel) images
|
| 292 |
-
pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
|
| 293 |
-
else:
|
| 294 |
-
pil_images = [Image.fromarray(image[:, :, :3]) for image in images]
|
| 295 |
-
|
| 296 |
-
return pil_images
|
| 297 |
-
|
| 298 |
-
@staticmethod
|
| 299 |
-
def rgblike_to_depthmap(image):
|
| 300 |
-
"""
|
| 301 |
-
Args:
|
| 302 |
-
image: RGB-like depth image
|
| 303 |
-
|
| 304 |
-
Returns: depth map
|
| 305 |
-
|
| 306 |
-
"""
|
| 307 |
-
return image[:, :, 1] * 2**8 + image[:, :, 2]
|
| 308 |
-
|
| 309 |
-
def numpy_to_depth(self, images):
|
| 310 |
-
"""
|
| 311 |
-
Convert a NumPy depth image or a batch of images to a PIL image.
|
| 312 |
-
"""
|
| 313 |
-
if images.ndim == 3:
|
| 314 |
-
images = images[None, ...]
|
| 315 |
-
images_depth = images[:, :, :, 3:]
|
| 316 |
-
if images.shape[-1] == 6:
|
| 317 |
-
images_depth = (images_depth * 255).round().astype("uint8")
|
| 318 |
-
pil_images = [
|
| 319 |
-
Image.fromarray(self.rgblike_to_depthmap(image_depth), mode="I;16") for image_depth in images_depth
|
| 320 |
-
]
|
| 321 |
-
elif images.shape[-1] == 4:
|
| 322 |
-
images_depth = (images_depth * 65535.0).astype(np.uint16)
|
| 323 |
-
pil_images = [Image.fromarray(image_depth, mode="I;16") for image_depth in images_depth]
|
| 324 |
-
else:
|
| 325 |
-
raise Exception("Not supported")
|
| 326 |
-
|
| 327 |
-
return pil_images
|
| 328 |
-
|
| 329 |
-
def postprocess(
|
| 330 |
-
self,
|
| 331 |
-
image: torch.FloatTensor,
|
| 332 |
-
output_type: str = "pil",
|
| 333 |
-
do_denormalize: Optional[List[bool]] = None,
|
| 334 |
-
):
|
| 335 |
-
if not isinstance(image, torch.Tensor):
|
| 336 |
-
raise ValueError(
|
| 337 |
-
f"Input for postprocessing is in incorrect format: {type(image)}. We only support pytorch tensor"
|
| 338 |
-
)
|
| 339 |
-
if output_type not in ["latent", "pt", "np", "pil"]:
|
| 340 |
-
deprecation_message = (
|
| 341 |
-
f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
|
| 342 |
-
"`pil`, `np`, `pt`, `latent`"
|
| 343 |
-
)
|
| 344 |
-
deprecate("Unsupported output_type", "1.0.0", deprecation_message, standard_warn=False)
|
| 345 |
-
output_type = "np"
|
| 346 |
-
|
| 347 |
-
if do_denormalize is None:
|
| 348 |
-
do_denormalize = [self.config.do_normalize] * image.shape[0]
|
| 349 |
-
|
| 350 |
-
image = torch.stack(
|
| 351 |
-
[self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])]
|
| 352 |
-
)
|
| 353 |
-
|
| 354 |
-
image = self.pt_to_numpy(image)
|
| 355 |
-
|
| 356 |
-
if output_type == "np":
|
| 357 |
-
if image.shape[-1] == 6:
|
| 358 |
-
image_depth = np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image], axis=0)
|
| 359 |
-
else:
|
| 360 |
-
image_depth = image[:, :, :, 3:]
|
| 361 |
-
return image[:, :, :, :3], image_depth
|
| 362 |
-
|
| 363 |
-
if output_type == "pil":
|
| 364 |
-
return self.numpy_to_pil(image), self.numpy_to_depth(image)
|
| 365 |
-
else:
|
| 366 |
-
raise Exception(f"This type {output_type} is not supported")
|
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_vq_diffusion.py
DELETED
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@@ -1,56 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn.functional as F
|
| 3 |
-
|
| 4 |
-
from diffusers import VQDiffusionScheduler
|
| 5 |
-
|
| 6 |
-
from .test_schedulers import SchedulerCommonTest
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
class VQDiffusionSchedulerTest(SchedulerCommonTest):
|
| 10 |
-
scheduler_classes = (VQDiffusionScheduler,)
|
| 11 |
-
|
| 12 |
-
def get_scheduler_config(self, **kwargs):
|
| 13 |
-
config = {
|
| 14 |
-
"num_vec_classes": 4097,
|
| 15 |
-
"num_train_timesteps": 100,
|
| 16 |
-
}
|
| 17 |
-
|
| 18 |
-
config.update(**kwargs)
|
| 19 |
-
return config
|
| 20 |
-
|
| 21 |
-
def dummy_sample(self, num_vec_classes):
|
| 22 |
-
batch_size = 4
|
| 23 |
-
height = 8
|
| 24 |
-
width = 8
|
| 25 |
-
|
| 26 |
-
sample = torch.randint(0, num_vec_classes, (batch_size, height * width))
|
| 27 |
-
|
| 28 |
-
return sample
|
| 29 |
-
|
| 30 |
-
@property
|
| 31 |
-
def dummy_sample_deter(self):
|
| 32 |
-
assert False
|
| 33 |
-
|
| 34 |
-
def dummy_model(self, num_vec_classes):
|
| 35 |
-
def model(sample, t, *args):
|
| 36 |
-
batch_size, num_latent_pixels = sample.shape
|
| 37 |
-
logits = torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
|
| 38 |
-
return_value = F.log_softmax(logits.double(), dim=1).float()
|
| 39 |
-
return return_value
|
| 40 |
-
|
| 41 |
-
return model
|
| 42 |
-
|
| 43 |
-
def test_timesteps(self):
|
| 44 |
-
for timesteps in [2, 5, 100, 1000]:
|
| 45 |
-
self.check_over_configs(num_train_timesteps=timesteps)
|
| 46 |
-
|
| 47 |
-
def test_num_vec_classes(self):
|
| 48 |
-
for num_vec_classes in [5, 100, 1000, 4000]:
|
| 49 |
-
self.check_over_configs(num_vec_classes=num_vec_classes)
|
| 50 |
-
|
| 51 |
-
def test_time_indices(self):
|
| 52 |
-
for t in [0, 50, 99]:
|
| 53 |
-
self.check_over_forward(time_step=t)
|
| 54 |
-
|
| 55 |
-
def test_add_noise_device(self):
|
| 56 |
-
pass
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spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/mask_rcnn_r50_fpn.py',
|
| 3 |
-
'../_base_/datasets/coco_instance.py',
|
| 4 |
-
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
|
| 5 |
-
]
|
| 6 |
-
|
| 7 |
-
model = dict(
|
| 8 |
-
rpn_head=dict(
|
| 9 |
-
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
|
| 10 |
-
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
|
| 11 |
-
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
|
| 12 |
-
roi_head=dict(
|
| 13 |
-
bbox_roi_extractor=dict(
|
| 14 |
-
type='SingleRoIExtractor',
|
| 15 |
-
roi_layer=dict(
|
| 16 |
-
type='RoIAlign',
|
| 17 |
-
output_size=7,
|
| 18 |
-
sampling_ratio=2,
|
| 19 |
-
aligned=False)),
|
| 20 |
-
mask_roi_extractor=dict(
|
| 21 |
-
type='SingleRoIExtractor',
|
| 22 |
-
roi_layer=dict(
|
| 23 |
-
type='RoIAlign',
|
| 24 |
-
output_size=14,
|
| 25 |
-
sampling_ratio=2,
|
| 26 |
-
aligned=False)),
|
| 27 |
-
bbox_head=dict(
|
| 28 |
-
bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
|
| 29 |
-
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
|
| 30 |
-
|
| 31 |
-
# model training and testing settings
|
| 32 |
-
train_cfg=dict(
|
| 33 |
-
rpn_proposal=dict(max_per_img=2000),
|
| 34 |
-
rcnn=dict(assigner=dict(match_low_quality=True))))
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spaces/Andy1621/uniformer_image_detection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
_base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
|
| 2 |
-
lr_config = dict(step=[16, 22])
|
| 3 |
-
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
|
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|
spaces/Andy1621/uniformer_image_detection/configs/rpn/rpn_r101_fpn_1x_coco.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './rpn_r50_fpn_1x_coco.py'
|
| 2 |
-
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
|
|
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|
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|
|
spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/emanet_r50-d8.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
# model settings
|
| 2 |
-
norm_cfg = dict(type='SyncBN', requires_grad=True)
|
| 3 |
-
model = dict(
|
| 4 |
-
type='EncoderDecoder',
|
| 5 |
-
pretrained='open-mmlab://resnet50_v1c',
|
| 6 |
-
backbone=dict(
|
| 7 |
-
type='ResNetV1c',
|
| 8 |
-
depth=50,
|
| 9 |
-
num_stages=4,
|
| 10 |
-
out_indices=(0, 1, 2, 3),
|
| 11 |
-
dilations=(1, 1, 2, 4),
|
| 12 |
-
strides=(1, 2, 1, 1),
|
| 13 |
-
norm_cfg=norm_cfg,
|
| 14 |
-
norm_eval=False,
|
| 15 |
-
style='pytorch',
|
| 16 |
-
contract_dilation=True),
|
| 17 |
-
decode_head=dict(
|
| 18 |
-
type='EMAHead',
|
| 19 |
-
in_channels=2048,
|
| 20 |
-
in_index=3,
|
| 21 |
-
channels=256,
|
| 22 |
-
ema_channels=512,
|
| 23 |
-
num_bases=64,
|
| 24 |
-
num_stages=3,
|
| 25 |
-
momentum=0.1,
|
| 26 |
-
dropout_ratio=0.1,
|
| 27 |
-
num_classes=19,
|
| 28 |
-
norm_cfg=norm_cfg,
|
| 29 |
-
align_corners=False,
|
| 30 |
-
loss_decode=dict(
|
| 31 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
|
| 32 |
-
auxiliary_head=dict(
|
| 33 |
-
type='FCNHead',
|
| 34 |
-
in_channels=1024,
|
| 35 |
-
in_index=2,
|
| 36 |
-
channels=256,
|
| 37 |
-
num_convs=1,
|
| 38 |
-
concat_input=False,
|
| 39 |
-
dropout_ratio=0.1,
|
| 40 |
-
num_classes=19,
|
| 41 |
-
norm_cfg=norm_cfg,
|
| 42 |
-
align_corners=False,
|
| 43 |
-
loss_decode=dict(
|
| 44 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
|
| 45 |
-
# model training and testing settings
|
| 46 |
-
train_cfg=dict(),
|
| 47 |
-
test_cfg=dict(mode='whole'))
|
|
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|
spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/configs/_base_/datasets/chase_db1.py
DELETED
|
@@ -1,59 +0,0 @@
|
|
| 1 |
-
# dataset settings
|
| 2 |
-
dataset_type = 'ChaseDB1Dataset'
|
| 3 |
-
data_root = 'data/CHASE_DB1'
|
| 4 |
-
img_norm_cfg = dict(
|
| 5 |
-
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
| 6 |
-
img_scale = (960, 999)
|
| 7 |
-
crop_size = (128, 128)
|
| 8 |
-
train_pipeline = [
|
| 9 |
-
dict(type='LoadImageFromFile'),
|
| 10 |
-
dict(type='LoadAnnotations'),
|
| 11 |
-
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
|
| 12 |
-
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
|
| 13 |
-
dict(type='RandomFlip', prob=0.5),
|
| 14 |
-
dict(type='PhotoMetricDistortion'),
|
| 15 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 16 |
-
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
|
| 17 |
-
dict(type='DefaultFormatBundle'),
|
| 18 |
-
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
|
| 19 |
-
]
|
| 20 |
-
test_pipeline = [
|
| 21 |
-
dict(type='LoadImageFromFile'),
|
| 22 |
-
dict(
|
| 23 |
-
type='MultiScaleFlipAug',
|
| 24 |
-
img_scale=img_scale,
|
| 25 |
-
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
|
| 26 |
-
flip=False,
|
| 27 |
-
transforms=[
|
| 28 |
-
dict(type='Resize', keep_ratio=True),
|
| 29 |
-
dict(type='RandomFlip'),
|
| 30 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 31 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 32 |
-
dict(type='Collect', keys=['img'])
|
| 33 |
-
])
|
| 34 |
-
]
|
| 35 |
-
|
| 36 |
-
data = dict(
|
| 37 |
-
samples_per_gpu=4,
|
| 38 |
-
workers_per_gpu=4,
|
| 39 |
-
train=dict(
|
| 40 |
-
type='RepeatDataset',
|
| 41 |
-
times=40000,
|
| 42 |
-
dataset=dict(
|
| 43 |
-
type=dataset_type,
|
| 44 |
-
data_root=data_root,
|
| 45 |
-
img_dir='images/training',
|
| 46 |
-
ann_dir='annotations/training',
|
| 47 |
-
pipeline=train_pipeline)),
|
| 48 |
-
val=dict(
|
| 49 |
-
type=dataset_type,
|
| 50 |
-
data_root=data_root,
|
| 51 |
-
img_dir='images/validation',
|
| 52 |
-
ann_dir='annotations/validation',
|
| 53 |
-
pipeline=test_pipeline),
|
| 54 |
-
test=dict(
|
| 55 |
-
type=dataset_type,
|
| 56 |
-
data_root=data_root,
|
| 57 |
-
img_dir='images/validation',
|
| 58 |
-
ann_dir='annotations/validation',
|
| 59 |
-
pipeline=test_pipeline))
|
|
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|
spaces/Araloak/fz/app.py
DELETED
|
@@ -1,107 +0,0 @@
|
|
| 1 |
-
# import gradio as gr
|
| 2 |
-
# import os, openai
|
| 3 |
-
#
|
| 4 |
-
#
|
| 5 |
-
# conversation = []
|
| 6 |
-
#
|
| 7 |
-
# class ChatGPT:
|
| 8 |
-
#
|
| 9 |
-
#
|
| 10 |
-
# def __init__(self):
|
| 11 |
-
# self.api_key = ""
|
| 12 |
-
# self.messages = conversation
|
| 13 |
-
# self.model = os.getenv("OPENAI_MODEL", default = "gpt-3.5-turbo")
|
| 14 |
-
#
|
| 15 |
-
# def save_api_key(self, user_input0):
|
| 16 |
-
# self.api_key = user_input0
|
| 17 |
-
#
|
| 18 |
-
# def get_response(self, user_input):
|
| 19 |
-
# openai.api_key = self.api_key
|
| 20 |
-
# conversation.append({"role": "user", "content": user_input})
|
| 21 |
-
#
|
| 22 |
-
#
|
| 23 |
-
# response = openai.ChatCompletion.create(
|
| 24 |
-
# model=self.model,
|
| 25 |
-
# messages = self.messages
|
| 26 |
-
#
|
| 27 |
-
# )
|
| 28 |
-
#
|
| 29 |
-
# conversation.append({"role": "assistant", "content": response['choices'][0]['message']['content']})
|
| 30 |
-
#
|
| 31 |
-
# print("AI回答內容：")
|
| 32 |
-
# print(response['choices'][0]['message']['content'].strip())
|
| 33 |
-
#
|
| 34 |
-
#
|
| 35 |
-
#
|
| 36 |
-
# return response['choices'][0]['message']['content'].strip()
|
| 37 |
-
#
|
| 38 |
-
#
|
| 39 |
-
# chatgpt = ChatGPT()
|
| 40 |
-
#
|
| 41 |
-
#
|
| 42 |
-
# def greet(prompt, api_key):
|
| 43 |
-
# chatgpt.save_api_key(api_key)
|
| 44 |
-
#
|
| 45 |
-
# reply_text = chatgpt.get_response(prompt)
|
| 46 |
-
#
|
| 47 |
-
# greeting = f"{reply_text}"
|
| 48 |
-
#
|
| 49 |
-
# return greeting
|
| 50 |
-
#
|
| 51 |
-
# demo = gr.Interface(
|
| 52 |
-
# fn=greet,
|
| 53 |
-
# inputs=["text", "text"],
|
| 54 |
-
# outputs=["text"],
|
| 55 |
-
# )
|
| 56 |
-
#
|
| 57 |
-
# demo.launch()
|
| 58 |
-
|
| 59 |
-
import argparse
|
| 60 |
-
|
| 61 |
-
import gradio as gr
|
| 62 |
-
from loguru import logger
|
| 63 |
-
|
| 64 |
-
from chat_completion import ChatCompletion
|
| 65 |
-
|
| 66 |
-
parser = argparse.ArgumentParser()
|
| 67 |
-
parser.add_argument('--api_key_path', type=str, default='./openai_api_key')
|
| 68 |
-
parser.add_argument('--log_path', type=str, default='./log.txt')
|
| 69 |
-
parser.add_argument('--share', action='store_true', default=False)
|
| 70 |
-
parser.add_argument('--welcome', type=str, default='Say something to ChatGPT here ...')
|
| 71 |
-
parser.add_argument('--title', type=str, default='ChatGPT')
|
| 72 |
-
parser.add_argument('--setting', type=str, default=None)
|
| 73 |
-
args = parser.parse_args()
|
| 74 |
-
|
| 75 |
-
bot = ChatCompletion(api_key_path=args.api_key_path)
|
| 76 |
-
logger.add(args.log_path)
|
| 77 |
-
|
| 78 |
-
with gr.Blocks(title=args.title) as demo:
|
| 79 |
-
chatbot = gr.Chatbot(show_label=False)
|
| 80 |
-
msg = gr.TextArea(show_label=False, placeholder=args.welcome)
|
| 81 |
-
send_btn = gr.Button('Send')
|
| 82 |
-
retry_btn = gr.Button('Retry')
|
| 83 |
-
reset_btn = gr.Button('Reset')
|
| 84 |
-
|
| 85 |
-
def send(user_message, history):
|
| 86 |
-
if not user_message:
|
| 87 |
-
return '', history
|
| 88 |
-
|
| 89 |
-
logger.info(f'[MSG] {user_message}')
|
| 90 |
-
response = bot(user_message, setting=args.setting) if user_message != 'retry' else bot.retry()
|
| 91 |
-
logger.info(f'[ANS] {response}')
|
| 92 |
-
return '', history + [[user_message, response]]
|
| 93 |
-
|
| 94 |
-
def reset():
|
| 95 |
-
bot.reset()
|
| 96 |
-
logger.info('[RESET]')
|
| 97 |
-
return None, [[None, None]]
|
| 98 |
-
|
| 99 |
-
def retry(history):
|
| 100 |
-
return send('retry', history)
|
| 101 |
-
|
| 102 |
-
send_btn.click(send, inputs=[msg, chatbot], outputs=[msg, chatbot], show_progress=True)
|
| 103 |
-
reset_btn.click(reset, inputs=None, outputs=[msg, chatbot])
|
| 104 |
-
retry_btn.click(retry, inputs=chatbot, outputs=[msg, chatbot])
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
demo.launch(share=args.share)
|
|
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|
spaces/Aspik101/Polish_Llama2/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Polish Llama2
|
| 3 |
-
emoji: 📚
|
| 4 |
-
colorFrom: indigo
|
| 5 |
-
colorTo: red
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.38.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: other
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
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|
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|
|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/tutorials/getting_started.md
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
../../GETTING_STARTED.md
|
|
|
|
|
|
spaces/BetterAPI/BetterChat_new/src/lib/stores/pendingMessageIdToRetry.ts
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
import type { Message } from "$lib/types/Message";
|
| 2 |
-
import { writable } from "svelte/store";
|
| 3 |
-
|
| 4 |
-
export const pendingMessageIdToRetry = writable<Message["id"] | null>(null);
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/auth.py
DELETED
|
@@ -1,315 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
requests.auth
|
| 3 |
-
~~~~~~~~~~~~~
|
| 4 |
-
|
| 5 |
-
This module contains the authentication handlers for Requests.
|
| 6 |
-
"""
|
| 7 |
-
|
| 8 |
-
import hashlib
|
| 9 |
-
import os
|
| 10 |
-
import re
|
| 11 |
-
import threading
|
| 12 |
-
import time
|
| 13 |
-
import warnings
|
| 14 |
-
from base64 import b64encode
|
| 15 |
-
|
| 16 |
-
from ._internal_utils import to_native_string
|
| 17 |
-
from .compat import basestring, str, urlparse
|
| 18 |
-
from .cookies import extract_cookies_to_jar
|
| 19 |
-
from .utils import parse_dict_header
|
| 20 |
-
|
| 21 |
-
CONTENT_TYPE_FORM_URLENCODED = "application/x-www-form-urlencoded"
|
| 22 |
-
CONTENT_TYPE_MULTI_PART = "multipart/form-data"
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
def _basic_auth_str(username, password):
|
| 26 |
-
"""Returns a Basic Auth string."""
|
| 27 |
-
|
| 28 |
-
# "I want us to put a big-ol' comment on top of it that
|
| 29 |
-
# says that this behaviour is dumb but we need to preserve
|
| 30 |
-
# it because people are relying on it."
|
| 31 |
-
# - Lukasa
|
| 32 |
-
#
|
| 33 |
-
# These are here solely to maintain backwards compatibility
|
| 34 |
-
# for things like ints. This will be removed in 3.0.0.
|
| 35 |
-
if not isinstance(username, basestring):
|
| 36 |
-
warnings.warn(
|
| 37 |
-
"Non-string usernames will no longer be supported in Requests "
|
| 38 |
-
"3.0.0. Please convert the object you've passed in ({!r}) to "
|
| 39 |
-
"a string or bytes object in the near future to avoid "
|
| 40 |
-
"problems.".format(username),
|
| 41 |
-
category=DeprecationWarning,
|
| 42 |
-
)
|
| 43 |
-
username = str(username)
|
| 44 |
-
|
| 45 |
-
if not isinstance(password, basestring):
|
| 46 |
-
warnings.warn(
|
| 47 |
-
"Non-string passwords will no longer be supported in Requests "
|
| 48 |
-
"3.0.0. Please convert the object you've passed in ({!r}) to "
|
| 49 |
-
"a string or bytes object in the near future to avoid "
|
| 50 |
-
"problems.".format(type(password)),
|
| 51 |
-
category=DeprecationWarning,
|
| 52 |
-
)
|
| 53 |
-
password = str(password)
|
| 54 |
-
# -- End Removal --
|
| 55 |
-
|
| 56 |
-
if isinstance(username, str):
|
| 57 |
-
username = username.encode("latin1")
|
| 58 |
-
|
| 59 |
-
if isinstance(password, str):
|
| 60 |
-
password = password.encode("latin1")
|
| 61 |
-
|
| 62 |
-
authstr = "Basic " + to_native_string(
|
| 63 |
-
b64encode(b":".join((username, password))).strip()
|
| 64 |
-
)
|
| 65 |
-
|
| 66 |
-
return authstr
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
class AuthBase:
|
| 70 |
-
"""Base class that all auth implementations derive from"""
|
| 71 |
-
|
| 72 |
-
def __call__(self, r):
|
| 73 |
-
raise NotImplementedError("Auth hooks must be callable.")
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
class HTTPBasicAuth(AuthBase):
|
| 77 |
-
"""Attaches HTTP Basic Authentication to the given Request object."""
|
| 78 |
-
|
| 79 |
-
def __init__(self, username, password):
|
| 80 |
-
self.username = username
|
| 81 |
-
self.password = password
|
| 82 |
-
|
| 83 |
-
def __eq__(self, other):
|
| 84 |
-
return all(
|
| 85 |
-
[
|
| 86 |
-
self.username == getattr(other, "username", None),
|
| 87 |
-
self.password == getattr(other, "password", None),
|
| 88 |
-
]
|
| 89 |
-
)
|
| 90 |
-
|
| 91 |
-
def __ne__(self, other):
|
| 92 |
-
return not self == other
|
| 93 |
-
|
| 94 |
-
def __call__(self, r):
|
| 95 |
-
r.headers["Authorization"] = _basic_auth_str(self.username, self.password)
|
| 96 |
-
return r
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
class HTTPProxyAuth(HTTPBasicAuth):
|
| 100 |
-
"""Attaches HTTP Proxy Authentication to a given Request object."""
|
| 101 |
-
|
| 102 |
-
def __call__(self, r):
|
| 103 |
-
r.headers["Proxy-Authorization"] = _basic_auth_str(self.username, self.password)
|
| 104 |
-
return r
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
class HTTPDigestAuth(AuthBase):
|
| 108 |
-
"""Attaches HTTP Digest Authentication to the given Request object."""
|
| 109 |
-
|
| 110 |
-
def __init__(self, username, password):
|
| 111 |
-
self.username = username
|
| 112 |
-
self.password = password
|
| 113 |
-
# Keep state in per-thread local storage
|
| 114 |
-
self._thread_local = threading.local()
|
| 115 |
-
|
| 116 |
-
def init_per_thread_state(self):
|
| 117 |
-
# Ensure state is initialized just once per-thread
|
| 118 |
-
if not hasattr(self._thread_local, "init"):
|
| 119 |
-
self._thread_local.init = True
|
| 120 |
-
self._thread_local.last_nonce = ""
|
| 121 |
-
self._thread_local.nonce_count = 0
|
| 122 |
-
self._thread_local.chal = {}
|
| 123 |
-
self._thread_local.pos = None
|
| 124 |
-
self._thread_local.num_401_calls = None
|
| 125 |
-
|
| 126 |
-
def build_digest_header(self, method, url):
|
| 127 |
-
"""
|
| 128 |
-
:rtype: str
|
| 129 |
-
"""
|
| 130 |
-
|
| 131 |
-
realm = self._thread_local.chal["realm"]
|
| 132 |
-
nonce = self._thread_local.chal["nonce"]
|
| 133 |
-
qop = self._thread_local.chal.get("qop")
|
| 134 |
-
algorithm = self._thread_local.chal.get("algorithm")
|
| 135 |
-
opaque = self._thread_local.chal.get("opaque")
|
| 136 |
-
hash_utf8 = None
|
| 137 |
-
|
| 138 |
-
if algorithm is None:
|
| 139 |
-
_algorithm = "MD5"
|
| 140 |
-
else:
|
| 141 |
-
_algorithm = algorithm.upper()
|
| 142 |
-
# lambdas assume digest modules are imported at the top level
|
| 143 |
-
if _algorithm == "MD5" or _algorithm == "MD5-SESS":
|
| 144 |
-
|
| 145 |
-
def md5_utf8(x):
|
| 146 |
-
if isinstance(x, str):
|
| 147 |
-
x = x.encode("utf-8")
|
| 148 |
-
return hashlib.md5(x).hexdigest()
|
| 149 |
-
|
| 150 |
-
hash_utf8 = md5_utf8
|
| 151 |
-
elif _algorithm == "SHA":
|
| 152 |
-
|
| 153 |
-
def sha_utf8(x):
|
| 154 |
-
if isinstance(x, str):
|
| 155 |
-
x = x.encode("utf-8")
|
| 156 |
-
return hashlib.sha1(x).hexdigest()
|
| 157 |
-
|
| 158 |
-
hash_utf8 = sha_utf8
|
| 159 |
-
elif _algorithm == "SHA-256":
|
| 160 |
-
|
| 161 |
-
def sha256_utf8(x):
|
| 162 |
-
if isinstance(x, str):
|
| 163 |
-
x = x.encode("utf-8")
|
| 164 |
-
return hashlib.sha256(x).hexdigest()
|
| 165 |
-
|
| 166 |
-
hash_utf8 = sha256_utf8
|
| 167 |
-
elif _algorithm == "SHA-512":
|
| 168 |
-
|
| 169 |
-
def sha512_utf8(x):
|
| 170 |
-
if isinstance(x, str):
|
| 171 |
-
x = x.encode("utf-8")
|
| 172 |
-
return hashlib.sha512(x).hexdigest()
|
| 173 |
-
|
| 174 |
-
hash_utf8 = sha512_utf8
|
| 175 |
-
|
| 176 |
-
KD = lambda s, d: hash_utf8(f"{s}:{d}") # noqa:E731
|
| 177 |
-
|
| 178 |
-
if hash_utf8 is None:
|
| 179 |
-
return None
|
| 180 |
-
|
| 181 |
-
# XXX not implemented yet
|
| 182 |
-
entdig = None
|
| 183 |
-
p_parsed = urlparse(url)
|
| 184 |
-
#: path is request-uri defined in RFC 2616 which should not be empty
|
| 185 |
-
path = p_parsed.path or "/"
|
| 186 |
-
if p_parsed.query:
|
| 187 |
-
path += f"?{p_parsed.query}"
|
| 188 |
-
|
| 189 |
-
A1 = f"{self.username}:{realm}:{self.password}"
|
| 190 |
-
A2 = f"{method}:{path}"
|
| 191 |
-
|
| 192 |
-
HA1 = hash_utf8(A1)
|
| 193 |
-
HA2 = hash_utf8(A2)
|
| 194 |
-
|
| 195 |
-
if nonce == self._thread_local.last_nonce:
|
| 196 |
-
self._thread_local.nonce_count += 1
|
| 197 |
-
else:
|
| 198 |
-
self._thread_local.nonce_count = 1
|
| 199 |
-
ncvalue = f"{self._thread_local.nonce_count:08x}"
|
| 200 |
-
s = str(self._thread_local.nonce_count).encode("utf-8")
|
| 201 |
-
s += nonce.encode("utf-8")
|
| 202 |
-
s += time.ctime().encode("utf-8")
|
| 203 |
-
s += os.urandom(8)
|
| 204 |
-
|
| 205 |
-
cnonce = hashlib.sha1(s).hexdigest()[:16]
|
| 206 |
-
if _algorithm == "MD5-SESS":
|
| 207 |
-
HA1 = hash_utf8(f"{HA1}:{nonce}:{cnonce}")
|
| 208 |
-
|
| 209 |
-
if not qop:
|
| 210 |
-
respdig = KD(HA1, f"{nonce}:{HA2}")
|
| 211 |
-
elif qop == "auth" or "auth" in qop.split(","):
|
| 212 |
-
noncebit = f"{nonce}:{ncvalue}:{cnonce}:auth:{HA2}"
|
| 213 |
-
respdig = KD(HA1, noncebit)
|
| 214 |
-
else:
|
| 215 |
-
# XXX handle auth-int.
|
| 216 |
-
return None
|
| 217 |
-
|
| 218 |
-
self._thread_local.last_nonce = nonce
|
| 219 |
-
|
| 220 |
-
# XXX should the partial digests be encoded too?
|
| 221 |
-
base = (
|
| 222 |
-
f'username="{self.username}", realm="{realm}", nonce="{nonce}", '
|
| 223 |
-
f'uri="{path}", response="{respdig}"'
|
| 224 |
-
)
|
| 225 |
-
if opaque:
|
| 226 |
-
base += f', opaque="{opaque}"'
|
| 227 |
-
if algorithm:
|
| 228 |
-
base += f', algorithm="{algorithm}"'
|
| 229 |
-
if entdig:
|
| 230 |
-
base += f', digest="{entdig}"'
|
| 231 |
-
if qop:
|
| 232 |
-
base += f', qop="auth", nc={ncvalue}, cnonce="{cnonce}"'
|
| 233 |
-
|
| 234 |
-
return f"Digest {base}"
|
| 235 |
-
|
| 236 |
-
def handle_redirect(self, r, **kwargs):
|
| 237 |
-
"""Reset num_401_calls counter on redirects."""
|
| 238 |
-
if r.is_redirect:
|
| 239 |
-
self._thread_local.num_401_calls = 1
|
| 240 |
-
|
| 241 |
-
def handle_401(self, r, **kwargs):
|
| 242 |
-
"""
|
| 243 |
-
Takes the given response and tries digest-auth, if needed.
|
| 244 |
-
|
| 245 |
-
:rtype: requests.Response
|
| 246 |
-
"""
|
| 247 |
-
|
| 248 |
-
# If response is not 4xx, do not auth
|
| 249 |
-
# See https://github.com/psf/requests/issues/3772
|
| 250 |
-
if not 400 <= r.status_code < 500:
|
| 251 |
-
self._thread_local.num_401_calls = 1
|
| 252 |
-
return r
|
| 253 |
-
|
| 254 |
-
if self._thread_local.pos is not None:
|
| 255 |
-
# Rewind the file position indicator of the body to where
|
| 256 |
-
# it was to resend the request.
|
| 257 |
-
r.request.body.seek(self._thread_local.pos)
|
| 258 |
-
s_auth = r.headers.get("www-authenticate", "")
|
| 259 |
-
|
| 260 |
-
if "digest" in s_auth.lower() and self._thread_local.num_401_calls < 2:
|
| 261 |
-
|
| 262 |
-
self._thread_local.num_401_calls += 1
|
| 263 |
-
pat = re.compile(r"digest ", flags=re.IGNORECASE)
|
| 264 |
-
self._thread_local.chal = parse_dict_header(pat.sub("", s_auth, count=1))
|
| 265 |
-
|
| 266 |
-
# Consume content and release the original connection
|
| 267 |
-
# to allow our new request to reuse the same one.
|
| 268 |
-
r.content
|
| 269 |
-
r.close()
|
| 270 |
-
prep = r.request.copy()
|
| 271 |
-
extract_cookies_to_jar(prep._cookies, r.request, r.raw)
|
| 272 |
-
prep.prepare_cookies(prep._cookies)
|
| 273 |
-
|
| 274 |
-
prep.headers["Authorization"] = self.build_digest_header(
|
| 275 |
-
prep.method, prep.url
|
| 276 |
-
)
|
| 277 |
-
_r = r.connection.send(prep, **kwargs)
|
| 278 |
-
_r.history.append(r)
|
| 279 |
-
_r.request = prep
|
| 280 |
-
|
| 281 |
-
return _r
|
| 282 |
-
|
| 283 |
-
self._thread_local.num_401_calls = 1
|
| 284 |
-
return r
|
| 285 |
-
|
| 286 |
-
def __call__(self, r):
|
| 287 |
-
# Initialize per-thread state, if needed
|
| 288 |
-
self.init_per_thread_state()
|
| 289 |
-
# If we have a saved nonce, skip the 401
|
| 290 |
-
if self._thread_local.last_nonce:
|
| 291 |
-
r.headers["Authorization"] = self.build_digest_header(r.method, r.url)
|
| 292 |
-
try:
|
| 293 |
-
self._thread_local.pos = r.body.tell()
|
| 294 |
-
except AttributeError:
|
| 295 |
-
# In the case of HTTPDigestAuth being reused and the body of
|
| 296 |
-
# the previous request was a file-like object, pos has the
|
| 297 |
-
# file position of the previous body. Ensure it's set to
|
| 298 |
-
# None.
|
| 299 |
-
self._thread_local.pos = None
|
| 300 |
-
r.register_hook("response", self.handle_401)
|
| 301 |
-
r.register_hook("response", self.handle_redirect)
|
| 302 |
-
self._thread_local.num_401_calls = 1
|
| 303 |
-
|
| 304 |
-
return r
|
| 305 |
-
|
| 306 |
-
def __eq__(self, other):
|
| 307 |
-
return all(
|
| 308 |
-
[
|
| 309 |
-
self.username == getattr(other, "username", None),
|
| 310 |
-
self.password == getattr(other, "password", None),
|
| 311 |
-
]
|
| 312 |
-
)
|
| 313 |
-
|
| 314 |
-
def __ne__(self, other):
|
| 315 |
-
return not self == other
|
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|
spaces/Blessin/drama-director/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Drama Director
|
| 3 |
-
emoji: 👁
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.50.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
spaces/Bonosa2/parrot-chat-bot/app.py
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import openai
|
| 3 |
-
import os
|
| 4 |
-
openai.api_key = os.environ['key3']
|
| 5 |
-
|
| 6 |
-
def answer_query(prompt):
|
| 7 |
-
response = openai.Completion.create(
|
| 8 |
-
engine="text-davinci-003",
|
| 9 |
-
prompt=prompt,
|
| 10 |
-
max_tokens=150
|
| 11 |
-
)
|
| 12 |
-
message = response.choices[0].text.strip()
|
| 13 |
-
|
| 14 |
-
# Check if query is parrot-related
|
| 15 |
-
if 'parrot' not in prompt.lower():
|
| 16 |
-
return "This service is only for parrot-related queries."
|
| 17 |
-
|
| 18 |
-
# Disclaimer for vet info
|
| 19 |
-
if 'vet' in prompt.lower() or 'veterinarian' in prompt.lower() or 'medical' in prompt.lower():
|
| 20 |
-
return f"{message}\n\nPlease note that while I strive to provide accurate information, I'm an AI and not a veterinarian. Always consult with a professional for medical advice."
|
| 21 |
-
|
| 22 |
-
return message
|
| 23 |
-
|
| 24 |
-
iface = gr.Interface(fn=answer_query, inputs="text", outputs="text")
|
| 25 |
-
iface.launch()
|
|
|
|
|
|
|
|
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|
|
|
spaces/CVPR/WALT/mmdet/core/bbox/assigners/base_assigner.py
DELETED
|
@@ -1,9 +0,0 @@
|
|
| 1 |
-
from abc import ABCMeta, abstractmethod
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
class BaseAssigner(metaclass=ABCMeta):
|
| 5 |
-
"""Base assigner that assigns boxes to ground truth boxes."""
|
| 6 |
-
|
| 7 |
-
@abstractmethod
|
| 8 |
-
def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
|
| 9 |
-
"""Assign boxes to either a ground truth boxes or a negative boxes."""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
spaces/CVPR/lama-example/models/ade20k/segm_lib/nn/modules/batchnorm.py
DELETED
|
@@ -1,329 +0,0 @@
|
|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
# File : batchnorm.py
|
| 3 |
-
# Author : Jiayuan Mao
|
| 4 |
-
# Email : [email protected]
|
| 5 |
-
# Date : 27/01/2018
|
| 6 |
-
#
|
| 7 |
-
# This file is part of Synchronized-BatchNorm-PyTorch.
|
| 8 |
-
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
|
| 9 |
-
# Distributed under MIT License.
|
| 10 |
-
|
| 11 |
-
import collections
|
| 12 |
-
|
| 13 |
-
import torch
|
| 14 |
-
import torch.nn.functional as F
|
| 15 |
-
|
| 16 |
-
from torch.nn.modules.batchnorm import _BatchNorm
|
| 17 |
-
from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
|
| 18 |
-
|
| 19 |
-
from .comm import SyncMaster
|
| 20 |
-
|
| 21 |
-
__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def _sum_ft(tensor):
|
| 25 |
-
"""sum over the first and last dimention"""
|
| 26 |
-
return tensor.sum(dim=0).sum(dim=-1)
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
def _unsqueeze_ft(tensor):
|
| 30 |
-
"""add new dementions at the front and the tail"""
|
| 31 |
-
return tensor.unsqueeze(0).unsqueeze(-1)
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
|
| 35 |
-
_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
class _SynchronizedBatchNorm(_BatchNorm):
|
| 39 |
-
def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True):
|
| 40 |
-
super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
|
| 41 |
-
|
| 42 |
-
self._sync_master = SyncMaster(self._data_parallel_master)
|
| 43 |
-
|
| 44 |
-
self._is_parallel = False
|
| 45 |
-
self._parallel_id = None
|
| 46 |
-
self._slave_pipe = None
|
| 47 |
-
|
| 48 |
-
# customed batch norm statistics
|
| 49 |
-
self._moving_average_fraction = 1. - momentum
|
| 50 |
-
self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features))
|
| 51 |
-
self.register_buffer('_tmp_running_var', torch.ones(self.num_features))
|
| 52 |
-
self.register_buffer('_running_iter', torch.ones(1))
|
| 53 |
-
self._tmp_running_mean = self.running_mean.clone() * self._running_iter
|
| 54 |
-
self._tmp_running_var = self.running_var.clone() * self._running_iter
|
| 55 |
-
|
| 56 |
-
def forward(self, input):
|
| 57 |
-
# If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
|
| 58 |
-
if not (self._is_parallel and self.training):
|
| 59 |
-
return F.batch_norm(
|
| 60 |
-
input, self.running_mean, self.running_var, self.weight, self.bias,
|
| 61 |
-
self.training, self.momentum, self.eps)
|
| 62 |
-
|
| 63 |
-
# Resize the input to (B, C, -1).
|
| 64 |
-
input_shape = input.size()
|
| 65 |
-
input = input.view(input.size(0), self.num_features, -1)
|
| 66 |
-
|
| 67 |
-
# Compute the sum and square-sum.
|
| 68 |
-
sum_size = input.size(0) * input.size(2)
|
| 69 |
-
input_sum = _sum_ft(input)
|
| 70 |
-
input_ssum = _sum_ft(input ** 2)
|
| 71 |
-
|
| 72 |
-
# Reduce-and-broadcast the statistics.
|
| 73 |
-
if self._parallel_id == 0:
|
| 74 |
-
mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
|
| 75 |
-
else:
|
| 76 |
-
mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
|
| 77 |
-
|
| 78 |
-
# Compute the output.
|
| 79 |
-
if self.affine:
|
| 80 |
-
# MJY:: Fuse the multiplication for speed.
|
| 81 |
-
output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
|
| 82 |
-
else:
|
| 83 |
-
output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
|
| 84 |
-
|
| 85 |
-
# Reshape it.
|
| 86 |
-
return output.view(input_shape)
|
| 87 |
-
|
| 88 |
-
def __data_parallel_replicate__(self, ctx, copy_id):
|
| 89 |
-
self._is_parallel = True
|
| 90 |
-
self._parallel_id = copy_id
|
| 91 |
-
|
| 92 |
-
# parallel_id == 0 means master device.
|
| 93 |
-
if self._parallel_id == 0:
|
| 94 |
-
ctx.sync_master = self._sync_master
|
| 95 |
-
else:
|
| 96 |
-
self._slave_pipe = ctx.sync_master.register_slave(copy_id)
|
| 97 |
-
|
| 98 |
-
def _data_parallel_master(self, intermediates):
|
| 99 |
-
"""Reduce the sum and square-sum, compute the statistics, and broadcast it."""
|
| 100 |
-
intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
|
| 101 |
-
|
| 102 |
-
to_reduce = [i[1][:2] for i in intermediates]
|
| 103 |
-
to_reduce = [j for i in to_reduce for j in i] # flatten
|
| 104 |
-
target_gpus = [i[1].sum.get_device() for i in intermediates]
|
| 105 |
-
|
| 106 |
-
sum_size = sum([i[1].sum_size for i in intermediates])
|
| 107 |
-
sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
|
| 108 |
-
|
| 109 |
-
mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
|
| 110 |
-
|
| 111 |
-
broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
|
| 112 |
-
|
| 113 |
-
outputs = []
|
| 114 |
-
for i, rec in enumerate(intermediates):
|
| 115 |
-
outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
|
| 116 |
-
|
| 117 |
-
return outputs
|
| 118 |
-
|
| 119 |
-
def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0):
|
| 120 |
-
"""return *dest* by `dest := dest*alpha + delta*beta + bias`"""
|
| 121 |
-
return dest * alpha + delta * beta + bias
|
| 122 |
-
|
| 123 |
-
def _compute_mean_std(self, sum_, ssum, size):
|
| 124 |
-
"""Compute the mean and standard-deviation with sum and square-sum. This method
|
| 125 |
-
also maintains the moving average on the master device."""
|
| 126 |
-
assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
|
| 127 |
-
mean = sum_ / size
|
| 128 |
-
sumvar = ssum - sum_ * mean
|
| 129 |
-
unbias_var = sumvar / (size - 1)
|
| 130 |
-
bias_var = sumvar / size
|
| 131 |
-
|
| 132 |
-
self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction)
|
| 133 |
-
self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction)
|
| 134 |
-
self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction)
|
| 135 |
-
|
| 136 |
-
self.running_mean = self._tmp_running_mean / self._running_iter
|
| 137 |
-
self.running_var = self._tmp_running_var / self._running_iter
|
| 138 |
-
|
| 139 |
-
return mean, bias_var.clamp(self.eps) ** -0.5
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
|
| 143 |
-
r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
|
| 144 |
-
mini-batch.
|
| 145 |
-
|
| 146 |
-
.. math::
|
| 147 |
-
|
| 148 |
-
y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
|
| 149 |
-
|
| 150 |
-
This module differs from the built-in PyTorch BatchNorm1d as the mean and
|
| 151 |
-
standard-deviation are reduced across all devices during training.
|
| 152 |
-
|
| 153 |
-
For example, when one uses `nn.DataParallel` to wrap the network during
|
| 154 |
-
training, PyTorch's implementation normalize the tensor on each device using
|
| 155 |
-
the statistics only on that device, which accelerated the computation and
|
| 156 |
-
is also easy to implement, but the statistics might be inaccurate.
|
| 157 |
-
Instead, in this synchronized version, the statistics will be computed
|
| 158 |
-
over all training samples distributed on multiple devices.
|
| 159 |
-
|
| 160 |
-
Note that, for one-GPU or CPU-only case, this module behaves exactly same
|
| 161 |
-
as the built-in PyTorch implementation.
|
| 162 |
-
|
| 163 |
-
The mean and standard-deviation are calculated per-dimension over
|
| 164 |
-
the mini-batches and gamma and beta are learnable parameter vectors
|
| 165 |
-
of size C (where C is the input size).
|
| 166 |
-
|
| 167 |
-
During training, this layer keeps a running estimate of its computed mean
|
| 168 |
-
and variance. The running sum is kept with a default momentum of 0.1.
|
| 169 |
-
|
| 170 |
-
During evaluation, this running mean/variance is used for normalization.
|
| 171 |
-
|
| 172 |
-
Because the BatchNorm is done over the `C` dimension, computing statistics
|
| 173 |
-
on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
|
| 174 |
-
|
| 175 |
-
Args:
|
| 176 |
-
num_features: num_features from an expected input of size
|
| 177 |
-
`batch_size x num_features [x width]`
|
| 178 |
-
eps: a value added to the denominator for numerical stability.
|
| 179 |
-
Default: 1e-5
|
| 180 |
-
momentum: the value used for the running_mean and running_var
|
| 181 |
-
computation. Default: 0.1
|
| 182 |
-
affine: a boolean value that when set to ``True``, gives the layer learnable
|
| 183 |
-
affine parameters. Default: ``True``
|
| 184 |
-
|
| 185 |
-
Shape:
|
| 186 |
-
- Input: :math:`(N, C)` or :math:`(N, C, L)`
|
| 187 |
-
- Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
|
| 188 |
-
|
| 189 |
-
Examples:
|
| 190 |
-
>>> # With Learnable Parameters
|
| 191 |
-
>>> m = SynchronizedBatchNorm1d(100)
|
| 192 |
-
>>> # Without Learnable Parameters
|
| 193 |
-
>>> m = SynchronizedBatchNorm1d(100, affine=False)
|
| 194 |
-
>>> input = torch.autograd.Variable(torch.randn(20, 100))
|
| 195 |
-
>>> output = m(input)
|
| 196 |
-
"""
|
| 197 |
-
|
| 198 |
-
def _check_input_dim(self, input):
|
| 199 |
-
if input.dim() != 2 and input.dim() != 3:
|
| 200 |
-
raise ValueError('expected 2D or 3D input (got {}D input)'
|
| 201 |
-
.format(input.dim()))
|
| 202 |
-
super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
|
| 203 |
-
|
| 204 |
-
|
| 205 |
-
class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
|
| 206 |
-
r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
|
| 207 |
-
of 3d inputs
|
| 208 |
-
|
| 209 |
-
.. math::
|
| 210 |
-
|
| 211 |
-
y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
|
| 212 |
-
|
| 213 |
-
This module differs from the built-in PyTorch BatchNorm2d as the mean and
|
| 214 |
-
standard-deviation are reduced across all devices during training.
|
| 215 |
-
|
| 216 |
-
For example, when one uses `nn.DataParallel` to wrap the network during
|
| 217 |
-
training, PyTorch's implementation normalize the tensor on each device using
|
| 218 |
-
the statistics only on that device, which accelerated the computation and
|
| 219 |
-
is also easy to implement, but the statistics might be inaccurate.
|
| 220 |
-
Instead, in this synchronized version, the statistics will be computed
|
| 221 |
-
over all training samples distributed on multiple devices.
|
| 222 |
-
|
| 223 |
-
Note that, for one-GPU or CPU-only case, this module behaves exactly same
|
| 224 |
-
as the built-in PyTorch implementation.
|
| 225 |
-
|
| 226 |
-
The mean and standard-deviation are calculated per-dimension over
|
| 227 |
-
the mini-batches and gamma and beta are learnable parameter vectors
|
| 228 |
-
of size C (where C is the input size).
|
| 229 |
-
|
| 230 |
-
During training, this layer keeps a running estimate of its computed mean
|
| 231 |
-
and variance. The running sum is kept with a default momentum of 0.1.
|
| 232 |
-
|
| 233 |
-
During evaluation, this running mean/variance is used for normalization.
|
| 234 |
-
|
| 235 |
-
Because the BatchNorm is done over the `C` dimension, computing statistics
|
| 236 |
-
on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
|
| 237 |
-
|
| 238 |
-
Args:
|
| 239 |
-
num_features: num_features from an expected input of
|
| 240 |
-
size batch_size x num_features x height x width
|
| 241 |
-
eps: a value added to the denominator for numerical stability.
|
| 242 |
-
Default: 1e-5
|
| 243 |
-
momentum: the value used for the running_mean and running_var
|
| 244 |
-
computation. Default: 0.1
|
| 245 |
-
affine: a boolean value that when set to ``True``, gives the layer learnable
|
| 246 |
-
affine parameters. Default: ``True``
|
| 247 |
-
|
| 248 |
-
Shape:
|
| 249 |
-
- Input: :math:`(N, C, H, W)`
|
| 250 |
-
- Output: :math:`(N, C, H, W)` (same shape as input)
|
| 251 |
-
|
| 252 |
-
Examples:
|
| 253 |
-
>>> # With Learnable Parameters
|
| 254 |
-
>>> m = SynchronizedBatchNorm2d(100)
|
| 255 |
-
>>> # Without Learnable Parameters
|
| 256 |
-
>>> m = SynchronizedBatchNorm2d(100, affine=False)
|
| 257 |
-
>>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
|
| 258 |
-
>>> output = m(input)
|
| 259 |
-
"""
|
| 260 |
-
|
| 261 |
-
def _check_input_dim(self, input):
|
| 262 |
-
if input.dim() != 4:
|
| 263 |
-
raise ValueError('expected 4D input (got {}D input)'
|
| 264 |
-
.format(input.dim()))
|
| 265 |
-
super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
|
| 266 |
-
|
| 267 |
-
|
| 268 |
-
class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
|
| 269 |
-
r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
|
| 270 |
-
of 4d inputs
|
| 271 |
-
|
| 272 |
-
.. math::
|
| 273 |
-
|
| 274 |
-
y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
|
| 275 |
-
|
| 276 |
-
This module differs from the built-in PyTorch BatchNorm3d as the mean and
|
| 277 |
-
standard-deviation are reduced across all devices during training.
|
| 278 |
-
|
| 279 |
-
For example, when one uses `nn.DataParallel` to wrap the network during
|
| 280 |
-
training, PyTorch's implementation normalize the tensor on each device using
|
| 281 |
-
the statistics only on that device, which accelerated the computation and
|
| 282 |
-
is also easy to implement, but the statistics might be inaccurate.
|
| 283 |
-
Instead, in this synchronized version, the statistics will be computed
|
| 284 |
-
over all training samples distributed on multiple devices.
|
| 285 |
-
|
| 286 |
-
Note that, for one-GPU or CPU-only case, this module behaves exactly same
|
| 287 |
-
as the built-in PyTorch implementation.
|
| 288 |
-
|
| 289 |
-
The mean and standard-deviation are calculated per-dimension over
|
| 290 |
-
the mini-batches and gamma and beta are learnable parameter vectors
|
| 291 |
-
of size C (where C is the input size).
|
| 292 |
-
|
| 293 |
-
During training, this layer keeps a running estimate of its computed mean
|
| 294 |
-
and variance. The running sum is kept with a default momentum of 0.1.
|
| 295 |
-
|
| 296 |
-
During evaluation, this running mean/variance is used for normalization.
|
| 297 |
-
|
| 298 |
-
Because the BatchNorm is done over the `C` dimension, computing statistics
|
| 299 |
-
on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
|
| 300 |
-
or Spatio-temporal BatchNorm
|
| 301 |
-
|
| 302 |
-
Args:
|
| 303 |
-
num_features: num_features from an expected input of
|
| 304 |
-
size batch_size x num_features x depth x height x width
|
| 305 |
-
eps: a value added to the denominator for numerical stability.
|
| 306 |
-
Default: 1e-5
|
| 307 |
-
momentum: the value used for the running_mean and running_var
|
| 308 |
-
computation. Default: 0.1
|
| 309 |
-
affine: a boolean value that when set to ``True``, gives the layer learnable
|
| 310 |
-
affine parameters. Default: ``True``
|
| 311 |
-
|
| 312 |
-
Shape:
|
| 313 |
-
- Input: :math:`(N, C, D, H, W)`
|
| 314 |
-
- Output: :math:`(N, C, D, H, W)` (same shape as input)
|
| 315 |
-
|
| 316 |
-
Examples:
|
| 317 |
-
>>> # With Learnable Parameters
|
| 318 |
-
>>> m = SynchronizedBatchNorm3d(100)
|
| 319 |
-
>>> # Without Learnable Parameters
|
| 320 |
-
>>> m = SynchronizedBatchNorm3d(100, affine=False)
|
| 321 |
-
>>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
|
| 322 |
-
>>> output = m(input)
|
| 323 |
-
"""
|
| 324 |
-
|
| 325 |
-
def _check_input_dim(self, input):
|
| 326 |
-
if input.dim() != 5:
|
| 327 |
-
raise ValueError('expected 5D input (got {}D input)'
|
| 328 |
-
.format(input.dim()))
|
| 329 |
-
super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
|
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|
spaces/ChrisCaviar/ControlNet-v1-1/README.md
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: ControlNet V1.1
|
| 3 |
-
emoji: 📉
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.34.0
|
| 8 |
-
python_version: 3.10.11
|
| 9 |
-
app_file: app.py
|
| 10 |
-
pinned: false
|
| 11 |
-
license: mit
|
| 12 |
-
suggested_hardware: t4-medium
|
| 13 |
-
duplicated_from: hysts/ControlNet-v1-1
|
| 14 |
-
---
|
| 15 |
-
|
| 16 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
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spaces/ChrisPreston/diff-svc_minato_aqua/preprocessing/process_pipeline.py
DELETED
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@@ -1,247 +0,0 @@
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| 1 |
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import hashlib
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| 2 |
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import json
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| 3 |
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import os
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| 4 |
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import time
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| 5 |
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import traceback
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| 6 |
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import warnings
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| 7 |
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from pathlib import Path
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| 8 |
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| 9 |
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import numpy as np
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| 10 |
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import parselmouth
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| 11 |
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import resampy
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| 12 |
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import torch
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| 13 |
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import torchcrepe
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| 14 |
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| 15 |
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import utils
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| 16 |
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from modules.vocoders.nsf_hifigan import nsf_hifigan
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| 17 |
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from utils.hparams import hparams
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| 18 |
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from utils.pitch_utils import f0_to_coarse
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| 19 |
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| 20 |
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warnings.filterwarnings("ignore")
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| 21 |
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| 22 |
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| 23 |
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class BinarizationError(Exception):
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| 24 |
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pass
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| 25 |
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| 26 |
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| 27 |
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def get_md5(content):
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| 28 |
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return hashlib.new("md5", content).hexdigest()
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| 29 |
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| 30 |
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| 31 |
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def read_temp(file_name):
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| 32 |
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if not os.path.exists(file_name):
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| 33 |
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with open(file_name, "w") as f:
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| 34 |
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f.write(json.dumps({"info": "temp_dict"}))
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| 35 |
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return {}
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| 36 |
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else:
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| 37 |
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try:
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| 38 |
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with open(file_name, "r") as f:
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| 39 |
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data = f.read()
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| 40 |
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data_dict = json.loads(data)
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| 41 |
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if os.path.getsize(file_name) > 50 * 1024 * 1024:
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| 42 |
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f_name = file_name.split("/")[-1]
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| 43 |
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print(f"clean {f_name}")
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| 44 |
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for wav_hash in list(data_dict.keys()):
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| 45 |
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if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
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| 46 |
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del data_dict[wav_hash]
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| 47 |
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except Exception as e:
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| 48 |
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print(e)
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| 49 |
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print(f"{file_name} error,auto rebuild file")
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| 50 |
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data_dict = {"info": "temp_dict"}
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| 51 |
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return data_dict
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| 52 |
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| 53 |
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| 54 |
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def write_temp(file_name, data):
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| 55 |
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with open(file_name, "w") as f:
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| 56 |
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f.write(json.dumps(data))
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| 57 |
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| 58 |
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| 59 |
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f0_dict = read_temp("./infer_tools/f0_temp.json")
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| 60 |
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| 61 |
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| 62 |
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def get_pitch_parselmouth(wav_data, mel, hparams):
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| 63 |
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"""
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| 64 |
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| 65 |
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:param wav_data: [T]
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| 66 |
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:param mel: [T, 80]
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| 67 |
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:param hparams:
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| 68 |
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:return:
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| 69 |
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"""
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| 70 |
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time_step = hparams['hop_size'] / hparams['audio_sample_rate']
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| 71 |
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f0_min = hparams['f0_min']
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| 72 |
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f0_max = hparams['f0_max']
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| 73 |
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| 74 |
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f0 = parselmouth.Sound(wav_data, hparams['audio_sample_rate']).to_pitch_ac(
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| 75 |
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time_step=time_step, voicing_threshold=0.6,
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| 76 |
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pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
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| 77 |
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| 78 |
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pad_size = (int(len(wav_data) // hparams['hop_size']) - len(f0) + 1) // 2
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| 79 |
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f0 = np.pad(f0, [[pad_size, len(mel) - len(f0) - pad_size]], mode='constant')
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| 80 |
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pitch_coarse = f0_to_coarse(f0, hparams)
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| 81 |
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return f0, pitch_coarse
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| 82 |
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| 83 |
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| 84 |
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def get_pitch_crepe(wav_data, mel, hparams, threshold=0.05):
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| 85 |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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| 86 |
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# device = torch.device("cuda")
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| 87 |
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# crepe只支持16khz采样率，需要重采样
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| 88 |
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wav16k = resampy.resample(wav_data, hparams['audio_sample_rate'], 16000)
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| 89 |
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wav16k_torch = torch.FloatTensor(wav16k).unsqueeze(0).to(device)
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| 90 |
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| 91 |
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# 频率范围
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| 92 |
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f0_min = hparams['f0_min']
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| 93 |
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f0_max = hparams['f0_max']
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| 94 |
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| 95 |
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# 重采样后按照hopsize=80,也就是5ms一帧分析f0
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| 96 |
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f0, pd = torchcrepe.predict(wav16k_torch, 16000, 80, f0_min, f0_max, pad=True, model='full', batch_size=1024,
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| 97 |
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device=device, return_periodicity=True)
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| 98 |
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| 99 |
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# 滤波，去掉静音，设置uv阈值，参考原仓库readme
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| 100 |
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pd = torchcrepe.filter.median(pd, 3)
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| 101 |
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pd = torchcrepe.threshold.Silence(-60.)(pd, wav16k_torch, 16000, 80)
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| 102 |
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f0 = torchcrepe.threshold.At(threshold)(f0, pd)
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| 103 |
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f0 = torchcrepe.filter.mean(f0, 3)
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| 104 |
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| 105 |
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# 将nan频率（uv部分）转换为0频率
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| 106 |
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f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)
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| 107 |
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| 108 |
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# 去掉0频率，并线性插值
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| 109 |
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nzindex = torch.nonzero(f0[0]).squeeze()
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| 110 |
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f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy()
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| 111 |
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time_org = 0.005 * nzindex.cpu().numpy()
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| 112 |
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time_frame = np.arange(len(mel)) * hparams['hop_size'] / hparams['audio_sample_rate']
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| 113 |
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if f0.shape[0] == 0:
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| 114 |
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f0 = torch.FloatTensor(time_frame.shape[0]).fill_(0)
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| 115 |
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print('f0 all zero!')
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| 116 |
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else:
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| 117 |
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f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
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| 118 |
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pitch_coarse = f0_to_coarse(f0, hparams)
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| 119 |
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return f0, pitch_coarse
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| 120 |
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| 121 |
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| 122 |
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class File2Batch:
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'''
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| 124 |
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pipeline: file -> temporary_dict -> processed_input -> batch
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| 125 |
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'''
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| 126 |
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| 127 |
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@staticmethod
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| 128 |
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def file2temporary_dict(raw_data_dir, ds_id):
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| 129 |
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'''
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| 130 |
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read from file, store data in temporary dicts
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| 131 |
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'''
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| 132 |
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raw_data_dir = Path(raw_data_dir)
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| 133 |
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utterance_labels = []
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| 134 |
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utterance_labels.extend(list(raw_data_dir.rglob(f"*.wav")))
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| 135 |
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utterance_labels.extend(list(raw_data_dir.rglob(f"*.ogg")))
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| 136 |
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| 137 |
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all_temp_dict = {}
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| 138 |
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for utterance_label in utterance_labels:
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| 139 |
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item_name = str(utterance_label)
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| 140 |
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temp_dict = {'wav_fn': str(utterance_label), 'spk_id': ds_id}
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| 141 |
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all_temp_dict[item_name] = temp_dict
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| 142 |
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return all_temp_dict
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| 143 |
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| 144 |
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@staticmethod
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| 145 |
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def temporary_dict2processed_input(item_name, temp_dict, encoder, infer=False, **kwargs):
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| 146 |
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'''
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| 147 |
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process data in temporary_dicts
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| 148 |
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'''
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| 149 |
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| 150 |
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def get_pitch(wav, mel):
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| 151 |
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# get ground truth f0 by self.get_pitch_algorithm
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| 152 |
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global f0_dict
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| 153 |
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use_crepe = hparams['use_crepe'] if not infer else kwargs['use_crepe']
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| 154 |
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if use_crepe:
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| 155 |
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md5 = get_md5(wav)
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| 156 |
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if infer and md5 in f0_dict.keys():
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| 157 |
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print("load temp crepe f0")
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| 158 |
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gt_f0 = np.array(f0_dict[md5]["f0"])
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| 159 |
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coarse_f0 = np.array(f0_dict[md5]["coarse"])
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| 160 |
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else:
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| 161 |
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torch.cuda.is_available() and torch.cuda.empty_cache()
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| 162 |
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gt_f0, coarse_f0 = get_pitch_crepe(wav, mel, hparams, threshold=0.05)
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| 163 |
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if infer:
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| 164 |
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f0_dict[md5] = {"f0": gt_f0.tolist(), "coarse": coarse_f0.tolist(), "time": int(time.time())}
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| 165 |
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write_temp("./infer_tools/f0_temp.json", f0_dict)
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| 166 |
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else:
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| 167 |
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gt_f0, coarse_f0 = get_pitch_parselmouth(wav, mel, hparams)
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| 168 |
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if sum(gt_f0) == 0:
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| 169 |
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raise BinarizationError("Empty **gt** f0")
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| 170 |
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processed_input['f0'] = gt_f0
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| 171 |
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processed_input['pitch'] = coarse_f0
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| 172 |
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| 173 |
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def get_align(mel, phone_encoded):
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| 174 |
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mel2ph = np.zeros([mel.shape[0]], int)
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| 175 |
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start_frame = 0
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| 176 |
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ph_durs = mel.shape[0] / phone_encoded.shape[0]
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| 177 |
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for i_ph in range(phone_encoded.shape[0]):
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| 178 |
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end_frame = int(i_ph * ph_durs + ph_durs + 0.5)
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| 179 |
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mel2ph[start_frame:end_frame + 1] = i_ph + 1
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| 180 |
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start_frame = end_frame + 1
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| 181 |
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| 182 |
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processed_input['mel2ph'] = mel2ph
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| 183 |
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| 184 |
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wav, mel = nsf_hifigan.wav2spec(temp_dict['wav_fn'])
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| 185 |
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processed_input = {
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| 186 |
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'item_name': item_name, 'mel': mel,
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| 187 |
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'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0]
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| 188 |
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}
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| 189 |
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processed_input = {**temp_dict, **processed_input,
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| 190 |
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'spec_min': np.min(mel, axis=0),
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| 191 |
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'spec_max': np.max(mel, axis=0)} # merge two dicts
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| 192 |
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try:
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| 193 |
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get_pitch(wav, mel)
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| 194 |
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try:
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| 195 |
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hubert_encoded = processed_input['hubert'] = encoder.encode(temp_dict['wav_fn'])
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| 196 |
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except:
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| 197 |
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traceback.print_exc()
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| 198 |
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raise Exception(f"hubert encode error")
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| 199 |
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get_align(mel, hubert_encoded)
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| 200 |
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except Exception as e:
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| 201 |
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print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {temp_dict['wav_fn']}")
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| 202 |
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return None
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| 203 |
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if hparams['use_energy_embed']:
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| 204 |
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max_frames = hparams['max_frames']
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| 205 |
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spec = torch.Tensor(processed_input['mel'])[:max_frames]
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| 206 |
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processed_input['energy'] = (spec.exp() ** 2).sum(-1).sqrt()
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| 207 |
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return processed_input
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| 208 |
-
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| 209 |
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@staticmethod
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| 210 |
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def processed_input2batch(samples):
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| 211 |
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'''
|
| 212 |
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Args:
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| 213 |
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samples: one batch of processed_input
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| 214 |
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NOTE:
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| 215 |
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the batch size is controlled by hparams['max_sentences']
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| 216 |
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'''
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| 217 |
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if len(samples) == 0:
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| 218 |
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return {}
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| 219 |
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id = torch.LongTensor([s['id'] for s in samples])
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| 220 |
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item_names = [s['item_name'] for s in samples]
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| 221 |
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hubert = utils.collate_2d([s['hubert'] for s in samples], 0.0)
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| 222 |
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f0 = utils.collate_1d([s['f0'] for s in samples], 0.0)
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| 223 |
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pitch = utils.collate_1d([s['pitch'] for s in samples])
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| 224 |
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uv = utils.collate_1d([s['uv'] for s in samples])
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| 225 |
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mel2ph = utils.collate_1d([s['mel2ph'] for s in samples], 0.0) \
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| 226 |
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if samples[0]['mel2ph'] is not None else None
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| 227 |
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mels = utils.collate_2d([s['mel'] for s in samples], 0.0)
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| 228 |
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mel_lengths = torch.LongTensor([s['mel'].shape[0] for s in samples])
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| 229 |
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| 230 |
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batch = {
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| 231 |
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'id': id,
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| 232 |
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'item_name': item_names,
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| 233 |
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'nsamples': len(samples),
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| 234 |
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'hubert': hubert,
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| 235 |
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'mels': mels,
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| 236 |
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'mel_lengths': mel_lengths,
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| 237 |
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'mel2ph': mel2ph,
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| 238 |
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'pitch': pitch,
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| 239 |
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'f0': f0,
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| 240 |
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'uv': uv,
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| 241 |
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}
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| 242 |
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if hparams['use_energy_embed']:
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| 243 |
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batch['energy'] = utils.collate_1d([s['energy'] for s in samples], 0.0)
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| 244 |
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if hparams['use_spk_id']:
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| 245 |
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spk_ids = torch.LongTensor([s['spk_id'] for s in samples])
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| 246 |
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batch['spk_ids'] = spk_ids
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| 247 |
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return batch
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spaces/CofAI/chat.b4/client/css/sidebar.css
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.sidebar {
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max-width: 260px;
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padding: var(--section-gap);
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flex-shrink: 0;
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display: flex;
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flex-direction: column;
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justify-content: space-between;
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}
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.sidebar .title {
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font-size: 14px;
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font-weight: 500;
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}
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.sidebar .conversation-sidebar {
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padding: 8px 12px;
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display: flex;
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gap: 18px;
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align-items: center;
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user-select: none;
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justify-content: space-between;
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}
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.sidebar .conversation-sidebar .left {
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cursor: pointer;
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display: flex;
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align-items: center;
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gap: 10px;
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}
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.sidebar i {
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color: var(--conversations);
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cursor: pointer;
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}
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.sidebar .top {
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display: flex;
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flex-direction: column;
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overflow: hidden;
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gap: 16px;
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padding-right: 8px;
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}
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.sidebar .top:hover {
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overflow: auto;
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}
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.sidebar .info {
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padding: 8px 12px 0px 12px;
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display: flex;
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align-items: center;
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justify-content: center;
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user-select: none;
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background: transparent;
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width: 100%;
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border: none;
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text-decoration: none;
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}
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.sidebar .info span {
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color: var(--conversations);
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line-height: 1.5;
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font-size: 0.75rem;
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}
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.sidebar .info i::before {
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margin-right: 8px;
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}
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.sidebar-footer {
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width: 100%;
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margin-top: 16px;
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display: flex;
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flex-direction: column;
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}
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.sidebar-footer button {
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cursor: pointer;
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user-select: none;
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background: transparent;
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}
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.sidebar.shown {
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position: fixed;
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top: 0;
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left: 0;
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width: 100%;
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height: 100%;
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z-index: 1000;
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}
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.sidebar.shown .box {
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background-color: #16171a;
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width: 80%;
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height: 100%;
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overflow-y: auto;
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}
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@keyframes spinner {
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to {
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transform: rotate(360deg);
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}
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}
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/* scrollbar */
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.sidebar .top::-webkit-scrollbar {
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width: 4px;
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padding: 8px 0px;
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}
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.sidebar .top::-webkit-scrollbar-track {
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background-color: #ffffff00;
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}
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.sidebar .top::-webkit-scrollbar-thumb {
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background-color: #555555;
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border-radius: 10px;
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}
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.spinner:before {
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content: "";
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box-sizing: border-box;
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position: absolute;
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top: 50%;
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left: 45%;
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width: 20px;
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height: 20px;
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border-radius: 50%;
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border: 1px solid var(--conversations);
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border-top-color: white;
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animation: spinner 0.6s linear infinite;
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}
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.menu-button {
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display: none !important;
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position: absolute;
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z-index: 100000;
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top: 0;
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left: 0;
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margin: 10px;
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font-size: 1rem;
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cursor: pointer;
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width: 30px;
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height: 30px;
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justify-content: center;
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align-items: center;
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transition: 0.33s;
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}
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.menu-button i {
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transition: 0.33s;
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}
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.rotated {
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transform: rotate(360deg);
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}
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.menu-button.rotated {
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position: fixed;
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top: 10px;
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left: 10px;
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z-index: 1001;
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}
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@media screen and (max-width: 990px) {
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.sidebar {
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display: none;
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width: 100%;
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max-width: none;
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}
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.menu-button {
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display: flex !important;
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}
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}
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-
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@media (max-width: 990px) {
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.sidebar .top {
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padding-top: 48px;
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}
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}
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-
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| 183 |
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@media (min-width: 768px) {
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.sidebar.shown {
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position: static;
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width: auto;
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height: auto;
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background-color: transparent;
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}
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-
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| 191 |
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.sidebar.shown .box {
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background-color: #16171a;
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width: auto;
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height: auto;
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overflow-y: auto;
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}
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}
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spaces/Coweed/GoodTrip/greeting.md
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@@ -1,4 +0,0 @@
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| 1 |
-
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| 2 |
-
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| 3 |
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| 4 |
-
В С Е !!!
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spaces/Curranj/Words_To_SQL/app.py
DELETED
|
@@ -1,31 +0,0 @@
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|
| 1 |
-
import openai
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import os
|
| 4 |
-
|
| 5 |
-
#OpenAi call
|
| 6 |
-
def gpt3(texts):
|
| 7 |
-
openai.api_key = os.environ["Secret"]
|
| 8 |
-
response = openai.Completion.create(
|
| 9 |
-
engine="text-davinci-003",
|
| 10 |
-
prompt= texts,
|
| 11 |
-
temperature=0,
|
| 12 |
-
max_tokens=750,
|
| 13 |
-
top_p=1,
|
| 14 |
-
frequency_penalty=0.0,
|
| 15 |
-
presence_penalty=0.0,
|
| 16 |
-
stop = (";", "/*", "</code>")
|
| 17 |
-
)
|
| 18 |
-
x = response.choices[0].text
|
| 19 |
-
|
| 20 |
-
return x
|
| 21 |
-
|
| 22 |
-
# Function to elicit sql response from model
|
| 23 |
-
def greet(prompt):
|
| 24 |
-
txt= (f'''/*Prompt: {prompt}*/ \n —-SQL Code:\n''')
|
| 25 |
-
sql = gpt3(txt)
|
| 26 |
-
return sql
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
#Code to set up Gradio UI
|
| 30 |
-
iface = gr.Interface(greet, inputs = ["text"], outputs = "text",title="Natural Language to SQL", description="Enter any prompt and get a SQL statement back! For better results, give it more context")
|
| 31 |
-
iface.launch()
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spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/layers/roi_align.py
DELETED
|
@@ -1,68 +0,0 @@
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|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
| 2 |
-
import torch
|
| 3 |
-
from torch import nn
|
| 4 |
-
from torch.autograd import Function
|
| 5 |
-
from torch.autograd.function import once_differentiable
|
| 6 |
-
from torch.nn.modules.utils import _pair
|
| 7 |
-
|
| 8 |
-
from maskrcnn_benchmark import _C
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class _ROIAlign(Function):
|
| 12 |
-
@staticmethod
|
| 13 |
-
def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
|
| 14 |
-
ctx.save_for_backward(roi)
|
| 15 |
-
ctx.output_size = _pair(output_size)
|
| 16 |
-
ctx.spatial_scale = spatial_scale
|
| 17 |
-
ctx.sampling_ratio = sampling_ratio
|
| 18 |
-
ctx.input_shape = input.size()
|
| 19 |
-
output = _C.roi_align_forward(
|
| 20 |
-
input, roi, spatial_scale, output_size[0], output_size[1], sampling_ratio
|
| 21 |
-
)
|
| 22 |
-
return output
|
| 23 |
-
|
| 24 |
-
@staticmethod
|
| 25 |
-
@once_differentiable
|
| 26 |
-
def backward(ctx, grad_output):
|
| 27 |
-
rois, = ctx.saved_tensors
|
| 28 |
-
output_size = ctx.output_size
|
| 29 |
-
spatial_scale = ctx.spatial_scale
|
| 30 |
-
sampling_ratio = ctx.sampling_ratio
|
| 31 |
-
bs, ch, h, w = ctx.input_shape
|
| 32 |
-
grad_input = _C.roi_align_backward(
|
| 33 |
-
grad_output,
|
| 34 |
-
rois,
|
| 35 |
-
spatial_scale,
|
| 36 |
-
output_size[0],
|
| 37 |
-
output_size[1],
|
| 38 |
-
bs,
|
| 39 |
-
ch,
|
| 40 |
-
h,
|
| 41 |
-
w,
|
| 42 |
-
sampling_ratio,
|
| 43 |
-
)
|
| 44 |
-
return grad_input, None, None, None, None
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
roi_align = _ROIAlign.apply
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
class ROIAlign(nn.Module):
|
| 51 |
-
def __init__(self, output_size, spatial_scale, sampling_ratio):
|
| 52 |
-
super(ROIAlign, self).__init__()
|
| 53 |
-
self.output_size = output_size
|
| 54 |
-
self.spatial_scale = spatial_scale
|
| 55 |
-
self.sampling_ratio = sampling_ratio
|
| 56 |
-
|
| 57 |
-
def forward(self, input, rois):
|
| 58 |
-
return roi_align(
|
| 59 |
-
input, rois, self.output_size, self.spatial_scale, self.sampling_ratio
|
| 60 |
-
)
|
| 61 |
-
|
| 62 |
-
def __repr__(self):
|
| 63 |
-
tmpstr = self.__class__.__name__ + "("
|
| 64 |
-
tmpstr += "output_size=" + str(self.output_size)
|
| 65 |
-
tmpstr += ", spatial_scale=" + str(self.spatial_scale)
|
| 66 |
-
tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
|
| 67 |
-
tmpstr += ")"
|
| 68 |
-
return tmpstr
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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/commands/scan_cache.py
DELETED
|
@@ -1,138 +0,0 @@
|
|
| 1 |
-
# coding=utf-8
|
| 2 |
-
# Copyright 2022-present, the HuggingFace Inc. team.
|
| 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 |
-
"""Contains command to scan the HF cache directory.
|
| 16 |
-
|
| 17 |
-
Usage:
|
| 18 |
-
huggingface-cli scan-cache
|
| 19 |
-
huggingface-cli scan-cache -v
|
| 20 |
-
huggingface-cli scan-cache -vvv
|
| 21 |
-
huggingface-cli scan-cache --dir ~/.cache/huggingface/hub
|
| 22 |
-
"""
|
| 23 |
-
import time
|
| 24 |
-
from argparse import _SubParsersAction
|
| 25 |
-
from typing import Optional
|
| 26 |
-
|
| 27 |
-
from ..utils import CacheNotFound, HFCacheInfo, scan_cache_dir
|
| 28 |
-
from . import BaseHuggingfaceCLICommand
|
| 29 |
-
from ._cli_utils import ANSI, tabulate
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
class ScanCacheCommand(BaseHuggingfaceCLICommand):
|
| 33 |
-
@staticmethod
|
| 34 |
-
def register_subcommand(parser: _SubParsersAction):
|
| 35 |
-
scan_cache_parser = parser.add_parser("scan-cache", help="Scan cache directory.")
|
| 36 |
-
|
| 37 |
-
scan_cache_parser.add_argument(
|
| 38 |
-
"--dir",
|
| 39 |
-
type=str,
|
| 40 |
-
default=None,
|
| 41 |
-
help="cache directory to scan (optional). Default to the default HuggingFace cache.",
|
| 42 |
-
)
|
| 43 |
-
scan_cache_parser.add_argument(
|
| 44 |
-
"-v",
|
| 45 |
-
"--verbose",
|
| 46 |
-
action="count",
|
| 47 |
-
default=0,
|
| 48 |
-
help="show a more verbose output",
|
| 49 |
-
)
|
| 50 |
-
scan_cache_parser.set_defaults(func=ScanCacheCommand)
|
| 51 |
-
|
| 52 |
-
def __init__(self, args):
|
| 53 |
-
self.verbosity: int = args.verbose
|
| 54 |
-
self.cache_dir: Optional[str] = args.dir
|
| 55 |
-
|
| 56 |
-
def run(self):
|
| 57 |
-
try:
|
| 58 |
-
t0 = time.time()
|
| 59 |
-
hf_cache_info = scan_cache_dir(self.cache_dir)
|
| 60 |
-
t1 = time.time()
|
| 61 |
-
except CacheNotFound as exc:
|
| 62 |
-
cache_dir = exc.cache_dir
|
| 63 |
-
print(f"Cache directory not found: {cache_dir}")
|
| 64 |
-
return
|
| 65 |
-
|
| 66 |
-
self._print_hf_cache_info_as_table(hf_cache_info)
|
| 67 |
-
|
| 68 |
-
print(
|
| 69 |
-
f"\nDone in {round(t1-t0,1)}s. Scanned {len(hf_cache_info.repos)} repo(s)"
|
| 70 |
-
f" for a total of {ANSI.red(hf_cache_info.size_on_disk_str)}."
|
| 71 |
-
)
|
| 72 |
-
if len(hf_cache_info.warnings) > 0:
|
| 73 |
-
message = f"Got {len(hf_cache_info.warnings)} warning(s) while scanning."
|
| 74 |
-
if self.verbosity >= 3:
|
| 75 |
-
print(ANSI.gray(message))
|
| 76 |
-
for warning in hf_cache_info.warnings:
|
| 77 |
-
print(ANSI.gray(warning))
|
| 78 |
-
else:
|
| 79 |
-
print(ANSI.gray(message + " Use -vvv to print details."))
|
| 80 |
-
|
| 81 |
-
def _print_hf_cache_info_as_table(self, hf_cache_info: HFCacheInfo) -> None:
|
| 82 |
-
if self.verbosity == 0:
|
| 83 |
-
print(
|
| 84 |
-
tabulate(
|
| 85 |
-
rows=[
|
| 86 |
-
[
|
| 87 |
-
repo.repo_id,
|
| 88 |
-
repo.repo_type,
|
| 89 |
-
"{:>12}".format(repo.size_on_disk_str),
|
| 90 |
-
repo.nb_files,
|
| 91 |
-
repo.last_accessed_str,
|
| 92 |
-
repo.last_modified_str,
|
| 93 |
-
", ".join(sorted(repo.refs)),
|
| 94 |
-
str(repo.repo_path),
|
| 95 |
-
]
|
| 96 |
-
for repo in sorted(hf_cache_info.repos, key=lambda repo: repo.repo_path)
|
| 97 |
-
],
|
| 98 |
-
headers=[
|
| 99 |
-
"REPO ID",
|
| 100 |
-
"REPO TYPE",
|
| 101 |
-
"SIZE ON DISK",
|
| 102 |
-
"NB FILES",
|
| 103 |
-
"LAST_ACCESSED",
|
| 104 |
-
"LAST_MODIFIED",
|
| 105 |
-
"REFS",
|
| 106 |
-
"LOCAL PATH",
|
| 107 |
-
],
|
| 108 |
-
)
|
| 109 |
-
)
|
| 110 |
-
else:
|
| 111 |
-
print(
|
| 112 |
-
tabulate(
|
| 113 |
-
rows=[
|
| 114 |
-
[
|
| 115 |
-
repo.repo_id,
|
| 116 |
-
repo.repo_type,
|
| 117 |
-
revision.commit_hash,
|
| 118 |
-
"{:>12}".format(revision.size_on_disk_str),
|
| 119 |
-
revision.nb_files,
|
| 120 |
-
revision.last_modified_str,
|
| 121 |
-
", ".join(sorted(revision.refs)),
|
| 122 |
-
str(revision.snapshot_path),
|
| 123 |
-
]
|
| 124 |
-
for repo in sorted(hf_cache_info.repos, key=lambda repo: repo.repo_path)
|
| 125 |
-
for revision in sorted(repo.revisions, key=lambda revision: revision.commit_hash)
|
| 126 |
-
],
|
| 127 |
-
headers=[
|
| 128 |
-
"REPO ID",
|
| 129 |
-
"REPO TYPE",
|
| 130 |
-
"REVISION",
|
| 131 |
-
"SIZE ON DISK",
|
| 132 |
-
"NB FILES",
|
| 133 |
-
"LAST_MODIFIED",
|
| 134 |
-
"REFS",
|
| 135 |
-
"LOCAL PATH",
|
| 136 |
-
],
|
| 137 |
-
)
|
| 138 |
-
)
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spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/pretrained_example.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# This work is licensed under the Creative Commons Attribution-NonCommercial
|
| 4 |
-
# 4.0 International License. To view a copy of this license, visit
|
| 5 |
-
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
|
| 6 |
-
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
|
| 7 |
-
|
| 8 |
-
"""Minimal script for generating an image using pre-trained StyleGAN generator."""
|
| 9 |
-
|
| 10 |
-
import os
|
| 11 |
-
import pickle
|
| 12 |
-
import numpy as np
|
| 13 |
-
import PIL.Image
|
| 14 |
-
import dnnlib
|
| 15 |
-
import dnnlib.tflib as tflib
|
| 16 |
-
import config
|
| 17 |
-
|
| 18 |
-
def main():
|
| 19 |
-
# Initialize TensorFlow.
|
| 20 |
-
tflib.init_tf()
|
| 21 |
-
|
| 22 |
-
# Load pre-trained network.
|
| 23 |
-
url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
|
| 24 |
-
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
|
| 25 |
-
_G, _D, Gs = pickle.load(f)
|
| 26 |
-
# _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
|
| 27 |
-
# _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
|
| 28 |
-
# Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
|
| 29 |
-
|
| 30 |
-
# Print network details.
|
| 31 |
-
Gs.print_layers()
|
| 32 |
-
|
| 33 |
-
# Pick latent vector.
|
| 34 |
-
rnd = np.random.RandomState(5)
|
| 35 |
-
latents = rnd.randn(1, Gs.input_shape[1])
|
| 36 |
-
|
| 37 |
-
# Generate image.
|
| 38 |
-
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
|
| 39 |
-
images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
|
| 40 |
-
|
| 41 |
-
# Save image.
|
| 42 |
-
os.makedirs(config.result_dir, exist_ok=True)
|
| 43 |
-
png_filename = os.path.join(config.result_dir, 'example.png')
|
| 44 |
-
PIL.Image.fromarray(images[0], 'RGB').save(png_filename)
|
| 45 |
-
|
| 46 |
-
if __name__ == "__main__":
|
| 47 |
-
main()
|
|
|
|
|
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|
spaces/Duskfallcrew/Gambit_and_Rogue/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Gambit And Rogue
|
| 3 |
-
emoji: 🐢
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: yellow
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.19.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
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|
spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py
DELETED
|
@@ -1,188 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
|
| 3 |
-
import fvcore.nn.weight_init as weight_init
|
| 4 |
-
import torch
|
| 5 |
-
from torch import nn
|
| 6 |
-
from torch.nn import functional as F
|
| 7 |
-
|
| 8 |
-
from detectron2.config import configurable
|
| 9 |
-
from detectron2.layers import Conv2d
|
| 10 |
-
from detectron2.utils.registry import Registry
|
| 11 |
-
|
| 12 |
-
from .position_encoding import PositionEmbeddingSine
|
| 13 |
-
from .transformer import Transformer
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE")
|
| 17 |
-
TRANSFORMER_DECODER_REGISTRY.__doc__ = """
|
| 18 |
-
Registry for transformer module in MaskFormer.
|
| 19 |
-
"""
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def build_transformer_decoder(cfg, in_channels, mask_classification=True):
|
| 23 |
-
"""
|
| 24 |
-
Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.
|
| 25 |
-
"""
|
| 26 |
-
name = cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME
|
| 27 |
-
return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
@TRANSFORMER_DECODER_REGISTRY.register()
|
| 31 |
-
class StandardTransformerDecoder(nn.Module):
|
| 32 |
-
@configurable
|
| 33 |
-
def __init__(
|
| 34 |
-
self,
|
| 35 |
-
in_channels,
|
| 36 |
-
mask_classification=True,
|
| 37 |
-
*,
|
| 38 |
-
num_classes: int,
|
| 39 |
-
hidden_dim: int,
|
| 40 |
-
num_queries: int,
|
| 41 |
-
nheads: int,
|
| 42 |
-
dropout: float,
|
| 43 |
-
dim_feedforward: int,
|
| 44 |
-
enc_layers: int,
|
| 45 |
-
dec_layers: int,
|
| 46 |
-
pre_norm: bool,
|
| 47 |
-
deep_supervision: bool,
|
| 48 |
-
mask_dim: int,
|
| 49 |
-
enforce_input_project: bool,
|
| 50 |
-
):
|
| 51 |
-
"""
|
| 52 |
-
NOTE: this interface is experimental.
|
| 53 |
-
Args:
|
| 54 |
-
in_channels: channels of the input features
|
| 55 |
-
mask_classification: whether to add mask classifier or not
|
| 56 |
-
num_classes: number of classes
|
| 57 |
-
hidden_dim: Transformer feature dimension
|
| 58 |
-
num_queries: number of queries
|
| 59 |
-
nheads: number of heads
|
| 60 |
-
dropout: dropout in Transformer
|
| 61 |
-
dim_feedforward: feature dimension in feedforward network
|
| 62 |
-
enc_layers: number of Transformer encoder layers
|
| 63 |
-
dec_layers: number of Transformer decoder layers
|
| 64 |
-
pre_norm: whether to use pre-LayerNorm or not
|
| 65 |
-
deep_supervision: whether to add supervision to every decoder layers
|
| 66 |
-
mask_dim: mask feature dimension
|
| 67 |
-
enforce_input_project: add input project 1x1 conv even if input
|
| 68 |
-
channels and hidden dim is identical
|
| 69 |
-
"""
|
| 70 |
-
super().__init__()
|
| 71 |
-
|
| 72 |
-
self.mask_classification = mask_classification
|
| 73 |
-
|
| 74 |
-
# positional encoding
|
| 75 |
-
N_steps = hidden_dim // 2
|
| 76 |
-
self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
|
| 77 |
-
|
| 78 |
-
transformer = Transformer(
|
| 79 |
-
d_model=hidden_dim,
|
| 80 |
-
dropout=dropout,
|
| 81 |
-
nhead=nheads,
|
| 82 |
-
dim_feedforward=dim_feedforward,
|
| 83 |
-
num_encoder_layers=enc_layers,
|
| 84 |
-
num_decoder_layers=dec_layers,
|
| 85 |
-
normalize_before=pre_norm,
|
| 86 |
-
return_intermediate_dec=deep_supervision,
|
| 87 |
-
)
|
| 88 |
-
|
| 89 |
-
self.num_queries = num_queries
|
| 90 |
-
self.transformer = transformer
|
| 91 |
-
hidden_dim = transformer.d_model
|
| 92 |
-
|
| 93 |
-
self.query_embed = nn.Embedding(num_queries, hidden_dim)
|
| 94 |
-
|
| 95 |
-
if in_channels != hidden_dim or enforce_input_project:
|
| 96 |
-
self.input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)
|
| 97 |
-
weight_init.c2_xavier_fill(self.input_proj)
|
| 98 |
-
else:
|
| 99 |
-
self.input_proj = nn.Sequential()
|
| 100 |
-
self.aux_loss = deep_supervision
|
| 101 |
-
|
| 102 |
-
# output FFNs
|
| 103 |
-
if self.mask_classification:
|
| 104 |
-
self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
|
| 105 |
-
self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
|
| 106 |
-
|
| 107 |
-
@classmethod
|
| 108 |
-
def from_config(cls, cfg, in_channels, mask_classification):
|
| 109 |
-
ret = {}
|
| 110 |
-
ret["in_channels"] = in_channels
|
| 111 |
-
ret["mask_classification"] = mask_classification
|
| 112 |
-
|
| 113 |
-
ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
|
| 114 |
-
ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM
|
| 115 |
-
ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
|
| 116 |
-
# Transformer parameters:
|
| 117 |
-
ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
|
| 118 |
-
ret["dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
|
| 119 |
-
ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
|
| 120 |
-
ret["enc_layers"] = cfg.MODEL.MASK_FORMER.ENC_LAYERS
|
| 121 |
-
ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS
|
| 122 |
-
ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
|
| 123 |
-
ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
|
| 124 |
-
ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ
|
| 125 |
-
|
| 126 |
-
ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
|
| 127 |
-
|
| 128 |
-
return ret
|
| 129 |
-
|
| 130 |
-
def forward(self, x, mask_features, mask=None):
|
| 131 |
-
if mask is not None:
|
| 132 |
-
mask = F.interpolate(mask[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
|
| 133 |
-
pos = self.pe_layer(x, mask)
|
| 134 |
-
|
| 135 |
-
src = x
|
| 136 |
-
hs, memory = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos)
|
| 137 |
-
|
| 138 |
-
if self.mask_classification:
|
| 139 |
-
outputs_class = self.class_embed(hs)
|
| 140 |
-
out = {"pred_logits": outputs_class[-1]}
|
| 141 |
-
else:
|
| 142 |
-
out = {}
|
| 143 |
-
|
| 144 |
-
if self.aux_loss:
|
| 145 |
-
# [l, bs, queries, embed]
|
| 146 |
-
mask_embed = self.mask_embed(hs)
|
| 147 |
-
outputs_seg_masks = torch.einsum("lbqc,bchw->lbqhw", mask_embed, mask_features)
|
| 148 |
-
out["pred_masks"] = outputs_seg_masks[-1]
|
| 149 |
-
out["aux_outputs"] = self._set_aux_loss(
|
| 150 |
-
outputs_class if self.mask_classification else None, outputs_seg_masks
|
| 151 |
-
)
|
| 152 |
-
else:
|
| 153 |
-
# FIXME h_boxes takes the last one computed, keep this in mind
|
| 154 |
-
# [bs, queries, embed]
|
| 155 |
-
mask_embed = self.mask_embed(hs[-1])
|
| 156 |
-
outputs_seg_masks = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
|
| 157 |
-
out["pred_masks"] = outputs_seg_masks
|
| 158 |
-
return out
|
| 159 |
-
|
| 160 |
-
@torch.jit.unused
|
| 161 |
-
def _set_aux_loss(self, outputs_class, outputs_seg_masks):
|
| 162 |
-
# this is a workaround to make torchscript happy, as torchscript
|
| 163 |
-
# doesn't support dictionary with non-homogeneous values, such
|
| 164 |
-
# as a dict having both a Tensor and a list.
|
| 165 |
-
if self.mask_classification:
|
| 166 |
-
return [
|
| 167 |
-
{"pred_logits": a, "pred_masks": b}
|
| 168 |
-
for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])
|
| 169 |
-
]
|
| 170 |
-
else:
|
| 171 |
-
return [{"pred_masks": b} for b in outputs_seg_masks[:-1]]
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
class MLP(nn.Module):
|
| 175 |
-
"""Very simple multi-layer perceptron (also called FFN)"""
|
| 176 |
-
|
| 177 |
-
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
|
| 178 |
-
super().__init__()
|
| 179 |
-
self.num_layers = num_layers
|
| 180 |
-
h = [hidden_dim] * (num_layers - 1)
|
| 181 |
-
self.layers = nn.ModuleList(
|
| 182 |
-
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
|
| 183 |
-
)
|
| 184 |
-
|
| 185 |
-
def forward(self, x):
|
| 186 |
-
for i, layer in enumerate(self.layers):
|
| 187 |
-
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
| 188 |
-
return x
|
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|
spaces/Edward-Ji/essentials-of-microeconomics/essentials_of_microeconomics/equilibrium_and_welfare.py
DELETED
|
@@ -1,138 +0,0 @@
|
|
| 1 |
-
import matplotlib.pyplot as plt
|
| 2 |
-
from shiny import module, reactive, render, req, ui
|
| 3 |
-
from sympy import integrate, latex, plot, simplify, solve, symbols
|
| 4 |
-
|
| 5 |
-
from module import demand_supply_ui, demand_supply_server
|
| 6 |
-
from util import latex_approx
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
@module.ui
|
| 10 |
-
def equilibrium_and_welfare_ui():
|
| 11 |
-
return ui.nav(
|
| 12 |
-
"Equilibrium and welfare",
|
| 13 |
-
ui.h1("Equilibrium and welfare"),
|
| 14 |
-
demand_supply_ui("ds"),
|
| 15 |
-
ui.h2("Equilibrium"),
|
| 16 |
-
ui.p(r"""A market is in equilibrium if, at some market price, the
|
| 17 |
-
quantity \(Q_d\) demanded by consumers equals the quantity \(Q_s\)
|
| 18 |
-
supplied by firms. The price at which this occurs is called the
|
| 19 |
-
market-clearing price (or equilibrium price), denoted \(P^*\)."""),
|
| 20 |
-
ui.output_text("equilibrium_text"),
|
| 21 |
-
ui.h2("Welfare"),
|
| 22 |
-
ui.p("""We can measure the observed changes in the benefits consumers
|
| 23 |
-
and firms gain in the markets using welfare analysis."""),
|
| 24 |
-
ui.h3("Consumer surplus"),
|
| 25 |
-
ui.p("""Consumer surplus (CS) is the welfare consumers receive from
|
| 26 |
-
buying units of goods or services in the market. It is given by the
|
| 27 |
-
consumer’s willingness to pay, minus the price paid, for each unit
|
| 28 |
-
bought. We can find an individual’s CS by calculating the area
|
| 29 |
-
between the demand curve and the price line."""),
|
| 30 |
-
ui.output_text("CS_text"),
|
| 31 |
-
ui.h3("Producer surplus"),
|
| 32 |
-
ui.p("""Producer surplus (PS) is the welfare producers (usually firms)
|
| 33 |
-
receive from selling units of a good or service in the market. It
|
| 34 |
-
is given by the price the producer receives, minus the cost of
|
| 35 |
-
production, for each unit of the good or service bought. We can
|
| 36 |
-
find a firm’s PS by calculating the area between the price line and
|
| 37 |
-
the firm’s supply curve."""),
|
| 38 |
-
ui.output_text("PS_text"),
|
| 39 |
-
ui.h3("Total surplus"),
|
| 40 |
-
ui.p(r"""The total surplus (TS) is the sum of consumer and producer
|
| 41 |
-
surplus in the market equilibrium. TS is the area between the
|
| 42 |
-
demand and supply curves, up to the market equilibrium, quantity
|
| 43 |
-
\(Q^*\)."""),
|
| 44 |
-
ui.output_text("TS_text"),
|
| 45 |
-
ui.output_plot("welfare"),
|
| 46 |
-
value="equilibrium_and_welfare"
|
| 47 |
-
)
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
@module.server
|
| 51 |
-
def equilibrium_and_welfare_server(input, output, session, settings):
|
| 52 |
-
symbol_P, symbol_Q = symbols("P, Q", positive=True)
|
| 53 |
-
|
| 54 |
-
demand, supply, P_d, P_s = demand_supply_server("ds", settings)
|
| 55 |
-
|
| 56 |
-
@reactive.Calc
|
| 57 |
-
def equilibrium():
|
| 58 |
-
solutions = solve([demand(), supply()], symbol_P, symbol_Q, dict=True)
|
| 59 |
-
req(len(solutions) == 1)
|
| 60 |
-
return solutions[0]
|
| 61 |
-
|
| 62 |
-
@reactive.Calc
|
| 63 |
-
def P_optimal():
|
| 64 |
-
return equilibrium()[symbol_P]
|
| 65 |
-
|
| 66 |
-
@reactive.Calc
|
| 67 |
-
def Q_optimal():
|
| 68 |
-
return equilibrium()[symbol_Q]
|
| 69 |
-
|
| 70 |
-
@reactive.Calc
|
| 71 |
-
def CS():
|
| 72 |
-
return simplify(integrate(P_d() - P_optimal(),
|
| 73 |
-
(symbol_Q, 0, Q_optimal())))
|
| 74 |
-
|
| 75 |
-
@reactive.Calc
|
| 76 |
-
def PS():
|
| 77 |
-
return simplify(integrate(P_optimal() - P_s(),
|
| 78 |
-
(symbol_Q, 0, Q_optimal())))
|
| 79 |
-
|
| 80 |
-
@reactive.Calc
|
| 81 |
-
def TS():
|
| 82 |
-
return simplify(CS() + PS())
|
| 83 |
-
|
| 84 |
-
@render.text
|
| 85 |
-
def equilibrium_text():
|
| 86 |
-
return (
|
| 87 |
-
r"$$\begin{cases}"
|
| 88 |
-
+ latex(demand()) + r"\\"
|
| 89 |
-
+ latex(supply())
|
| 90 |
-
+ r"\end{cases} \implies \begin{cases}"
|
| 91 |
-
+ "P^* ="
|
| 92 |
-
+ latex_approx(P_optimal(), settings.perc(), settings.approx())
|
| 93 |
-
+ r"\\"
|
| 94 |
-
+ "Q^* ="
|
| 95 |
-
+ latex_approx(Q_optimal(), settings.perc(), settings.approx())
|
| 96 |
-
+ r"\end{cases}$$")
|
| 97 |
-
|
| 98 |
-
@render.text
|
| 99 |
-
def CS_text():
|
| 100 |
-
return (r"$$CS = \int_0^{Q^*}P_d - P^*\,dQ ="
|
| 101 |
-
+ latex_approx(CS(), settings.perc(), settings.approx())
|
| 102 |
-
+ "$$")
|
| 103 |
-
|
| 104 |
-
@render.text
|
| 105 |
-
def PS_text():
|
| 106 |
-
return (r"$$PS = \int_0^{Q^*}P^* - P_s\,dQ ="
|
| 107 |
-
+ latex_approx(PS(), settings.perc(), settings.approx())
|
| 108 |
-
+ "$$")
|
| 109 |
-
|
| 110 |
-
@render.text
|
| 111 |
-
def TS_text():
|
| 112 |
-
return (r"$$TS = CS + PS ="
|
| 113 |
-
+ latex_approx(TS(), settings.perc(), settings.approx())
|
| 114 |
-
+ "$$")
|
| 115 |
-
|
| 116 |
-
@render.plot(height=400)
|
| 117 |
-
def welfare():
|
| 118 |
-
ax = plt.subplot()
|
| 119 |
-
plot_d, plot_s = plot(P_d(), P_s(),
|
| 120 |
-
(symbol_Q, 0, Q_optimal() * 2),
|
| 121 |
-
show=False)
|
| 122 |
-
plot_cs, plot_ps = plot(P_d(), P_s(),
|
| 123 |
-
(symbol_Q, 0 ,Q_optimal()),
|
| 124 |
-
show=False)
|
| 125 |
-
ax.plot(*plot_d.get_points(), label="Demand")
|
| 126 |
-
ax.plot(*plot_s.get_points(), label="Supply")
|
| 127 |
-
ax.scatter(Q_optimal(), P_optimal(), s=50, c="tab:green", marker="o",
|
| 128 |
-
label="Equilibrium", zorder=100)
|
| 129 |
-
ax.fill_between(*plot_cs.get_points(), float(P_optimal()),
|
| 130 |
-
alpha=.5, label="CS")
|
| 131 |
-
ax.fill_between(*plot_ps.get_points(), float(P_optimal()),
|
| 132 |
-
alpha=.5, label="PS")
|
| 133 |
-
ax.set_xlim(0)
|
| 134 |
-
ax.set_ylim(0)
|
| 135 |
-
ax.set_xlabel("$Q$")
|
| 136 |
-
ax.set_ylabel("$P$")
|
| 137 |
-
ax.legend()
|
| 138 |
-
return ax
|
|
|
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spaces/EuroPython2022/mmocr-demo/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_100k_iters_synthtext.py
DELETED
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@@ -1,61 +0,0 @@
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| 1 |
-
_base_ = [
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| 2 |
-
'../../_base_/default_runtime.py',
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| 3 |
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'../../_base_/schedules/schedule_sgd_100k_iters.py',
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| 4 |
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'../../_base_/det_models/dbnet_r50dcnv2_fpnc.py',
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| 5 |
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'../../_base_/det_datasets/synthtext.py',
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| 6 |
-
'../../_base_/det_pipelines/dbnet_pipeline.py'
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| 7 |
-
]
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| 8 |
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| 9 |
-
train_list = {{_base_.train_list}}
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| 10 |
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test_list = {{_base_.test_list}}
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| 11 |
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| 12 |
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img_norm_cfg_r50dcnv2 = dict(
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| 13 |
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mean=[122.67891434, 116.66876762, 104.00698793],
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| 14 |
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std=[58.395, 57.12, 57.375],
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| 15 |
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to_rgb=True)
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| 16 |
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train_pipeline_r50dcnv2 = [
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| 17 |
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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| 18 |
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dict(
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| 19 |
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type='LoadTextAnnotations',
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| 20 |
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with_bbox=True,
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with_mask=True,
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poly2mask=False),
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dict(type='ColorJitter', brightness=32.0 / 255, saturation=0.5),
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| 24 |
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dict(type='Normalize', **img_norm_cfg_r50dcnv2),
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| 25 |
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dict(
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| 26 |
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type='ImgAug',
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| 27 |
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args=[['Fliplr', 0.5],
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| 28 |
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dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]],
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| 29 |
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clip_invalid_ploys=False),
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| 30 |
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dict(type='EastRandomCrop', target_size=(640, 640)),
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| 31 |
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dict(type='DBNetTargets', shrink_ratio=0.4),
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| 32 |
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dict(type='Pad', size_divisor=32),
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| 33 |
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dict(
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| 34 |
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type='CustomFormatBundle',
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| 35 |
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keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'],
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| 36 |
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visualize=dict(flag=False, boundary_key='gt_shrink')),
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| 37 |
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dict(
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| 38 |
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type='Collect',
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| 39 |
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keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'])
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| 40 |
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]
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| 41 |
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test_pipeline_4068_1024 = {{_base_.test_pipeline_4068_1024}}
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| 42 |
-
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| 43 |
-
data = dict(
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| 44 |
-
samples_per_gpu=16,
|
| 45 |
-
workers_per_gpu=8,
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| 46 |
-
val_dataloader=dict(samples_per_gpu=1),
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| 47 |
-
test_dataloader=dict(samples_per_gpu=1),
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| 48 |
-
train=dict(
|
| 49 |
-
type='UniformConcatDataset',
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| 50 |
-
datasets=train_list,
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| 51 |
-
pipeline=train_pipeline_r50dcnv2),
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| 52 |
-
val=dict(
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| 53 |
-
type='UniformConcatDataset',
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| 54 |
-
datasets=test_list,
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| 55 |
-
pipeline=test_pipeline_4068_1024),
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| 56 |
-
test=dict(
|
| 57 |
-
type='UniformConcatDataset',
|
| 58 |
-
datasets=test_list,
|
| 59 |
-
pipeline=test_pipeline_4068_1024))
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| 60 |
-
|
| 61 |
-
evaluation = dict(interval=999999, metric='hmean-iou') # do not evaluate
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