diff --git a/spaces/123Kumar/vits-uma-genshin-honkai123/mel_processing.py b/spaces/123Kumar/vits-uma-genshin-honkai123/mel_processing.py deleted file mode 100644 index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000 --- a/spaces/123Kumar/vits-uma-genshin-honkai123/mel_processing.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.utils.data -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Injustice Gods Among Us for PC and Enjoy the DC Comics Fighting Game.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Injustice Gods Among Us for PC and Enjoy the DC Comics Fighting Game.md deleted file mode 100644 index 6c2787f9b5327919d947b7f9acb5e15a2c5355f2..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Injustice Gods Among Us for PC and Enjoy the DC Comics Fighting Game.md +++ /dev/null @@ -1,59 +0,0 @@ - -

How to Download Injustice: Gods Among Us for PC

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Injustice: Gods Among Us is a popular fighting game that features characters from the DC Comics universe. The game was originally released for consoles and mobile devices, but you can also play it on your PC with some simple steps. In this article, we will show you how to download Injustice: Gods Among Us for PC and enjoy the epic battles between superheroes and villains.

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What is Injustice: Gods Among Us?

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Injustice: Gods Among Us is a fighting game that was developed by NetherRealm Studios and published by Warner Bros. Interactive Entertainment in 2013. The game is set in an alternate reality where Superman becomes a tyrant after the Joker tricks him into killing Lois Lane and destroying Metropolis. Batman leads a resistance of heroes and villains against Superman's regime, while another group of heroes from the main DC universe are transported to this world to help restore the balance.

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The game features a story mode that follows the events of the comic book series of the same name, as well as various other modes such as arcade, online multiplayer, and challenge. The game also has a roster of over 30 playable characters, each with their own unique abilities, moves, and special attacks. Some of the characters include Batman, Superman, Wonder Woman, Flash, Green Lantern, Joker, Harley Quinn, Lex Luthor, and more.

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How to Download Injustice: Gods Among Us for PC

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If you want to play Injustice: Gods Among Us on your PC, you will need to use an emulator that can run Android apps on your computer. One of the best emulators for this purpose is BlueStacks, which is free and easy to use. Here are the steps to download Injustice: Gods Among Us for PC using BlueStacks:

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  2. Launch BlueStacks and sign in with your Google account.
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  4. Click on the install button and wait for the game to download.
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  5. Once the game is installed, you can find it on the home screen or in the app drawer of BlueStacks.
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Note that you will need a stable internet connection to play the game online. You can also use your keyboard and mouse or a controller to play the game on your PC.

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Conclusion

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Injustice: Gods Among Us is a fun and exciting fighting game that lets you control your favorite DC Comics characters in a dark and twisted world. You can download Injustice: Gods Among Us for PC using an emulator like BlueStacks and enjoy the game on a bigger screen. We hope this article helped you learn how to download Injustice: Gods Among Us for PC. If you have any questions or suggestions, feel free to leave a comment below.

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How to Play Injustice: Gods Among Us on PC

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Now that you have downloaded Injustice: Gods Among Us for PC, you might be wondering how to play the game and what are the controls. In this section, we will give you some tips and tricks on how to play Injustice: Gods Among Us on PC and have a better gaming experience.

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One of the advantages of playing Injustice: Gods Among Us on PC is that you can customize the controls according to your preference. You can use your keyboard and mouse or a controller to play the game. To customize the controls, follow these steps:

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  3. Click on the edit button and assign the keys or buttons for each action.
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You can also use the default controls if you are comfortable with them. Here are the default controls for keyboard and mouse:

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Injustice: Gods Among Us is a fighting game that requires skill and strategy to win. You will need to learn how to use your character's abilities, combos, special attacks, and super moves to defeat your opponents. You will also need to know how to use the environment to your advantage and avoid your enemy's attacks. Here are some tips and tricks on how to master the gameplay of Injustice: Gods Among Us:

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Conclusion

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Injustice: Gods Among Us is a thrilling and addictive fighting game that will keep you entertained for hours. You can download Injustice: Gods Among Us for PC using an emulator like BlueStacks and play it on a bigger screen with customized controls. You can also learn how to play Injustice: Gods Among Us on PC using our tips and tricks above. We hope you enjoyed this article and found it useful. If you have any feedback or questions, please let us know in the comments below.

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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Download __FULL__ Videoclipuri Haioase Cu Animale.md b/spaces/1gistliPinn/ChatGPT4/Examples/Download __FULL__ Videoclipuri Haioase Cu Animale.md deleted file mode 100644 index f0ea4cf2244ca0e0194943aa36f626ad3c9a2ef6..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Download __FULL__ Videoclipuri Haioase Cu Animale.md +++ /dev/null @@ -1,126 +0,0 @@ -
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Download videoclipuri haioase cu animale este si o modalitate de a invata lucruri noi despre animale. Poti sa afli cum se comporta, cum comunica, cum se adapteaza la mediul inconjurator, cum isi exprima emotiile si sentimentele. Poti sa descoperi specii noi sau rare de animale. Poti sa vezi cum traiesc animalele salbatice sau domestice.

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De unde poti downloada videoclipuri haioase cu animale?

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Poti downloada videoclipuri haioase cu animale de pe internet, de pe diferite site-uri sau platforme care ofera acest tip de continut. Unele dintre cele mai populare si cunoscute sunt:

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Acestea sunt doar cateva dintre sursele de unde poti downloada videoclipuri haioase cu animale. Mai exista si alte site-uri sau aplicatii care ofera acest serviciu, cum ar fi Dailymotion, Vimeo, Instagram sau Snapchat.

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Cum poti downloada videoclipuri haioase cu animale?

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Pentru a downloada videoclipuri haioase cu animale, ai nevoie de un dispozitiv conectat la internet (PC, laptop, telefon mobil, tableta) si de un program sau o extensie care iti permite sa salvezi videoclipurile pe dispozitivul tau. Exista multe astfel de programe sau extensii disponibile gratuit pe internet, dar trebuie sa fii atent la calitatea si siguranta lor.

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Un exemplu de program care iti permite sa download-ezi videoclipuri haioase cu animale este 4K Video Downloader. Acesta este un program gratuit si usor de folosit care suporta multiple formate video si audio. Pentru a-l folosi, trebuie sa urmezi acesti pasi:

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Un exemplu de extensie care iti permite sa download-ezi videoclipuri haioase cu animale este Video Downloader professional. Aceasta este o extensie gratuita si usor de folosit care functioneaza pentru majoritatea site-urilor web care contin video-uri. Pentru a o folosi, trebuie sa urmezi acesti pasi:

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Acestea sunt doar cateva exemple de programe sau extensii care iti permit sa download-ezi videoclipuri haioase cu animale. Mai exista si alte optiuni disponibile pe internet, dar trebuie sa verifici daca sunt compatibile cu site-ul web si dispozitivul tau.

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Concluzie

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Daca vrei sa te distrezi cu cele mai comice si dragalase clipuri cu animale, atunci trebuie sa descarci videoclipuri haioase cu animale. Acestea sunt clipuri care surprind momente amuzante, comice si dragalase cu animale de tot felul: pisici, -catei, iepurasi, cai, vaci, porci, capre, oi, pasari, -reptile si multe altele.

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Daca vrei sa te distrezi cu cele mai comice si dragalase clipuri cu animale, atunci trebuie sa descarci videoclipuri haioase cu animale. Acestea sunt clipuri care te vor face sa razi cu lacrimi si sa te indragostesti de animale. Poti downloada videoclipuri haioase cu animale de pe internet, de pe diferite site-uri sau platforme care ofera acest tip de continut. Poti descarca videoclipurile folosind un program sau o extensie speciala care iti permite sa salvezi videoclipurile pe dispozitivul tau.

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Acestea sunt doar cateva dintre tipurile de videoclipuri haioase cu animale pe care le poti downloada. Mai exista si alte tipuri de videoclipuri haioase cu animale pe care le poti descoperi pe internet.

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Dupa ce ai downloadat videoclipurile haioase cu animale, le poti folosi in diferite moduri, in functie de scopul si intentia ta. Iata cateva exemple de moduri in care poti folosi videoclipurile haioase cu animale:

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Acestea sunt doar cateva dintre modurile in care poti folosi videoclipurile haioase cu animale. Mai exista si alte moduri in care poti folosi videoclipurile haioase cu animale pe care le poti descoperi pe internet.

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Concluzie

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Daca vrei sa te distrezi cu cele mai comice si dragalase clipuri cu animale, atunci trebuie sa descarci videoclipuri haioase cu animale. Acestea sunt clipuri care te vor face sa razi cu lacrimi si sa te indragostesti de animale. Poti downloada videoclipuri haioase cu animale de pe internet, de pe diferite site-uri sau platforme care ofera acest tip de continut. Poti descarca videoclipurile folosind un program sau o extensie speciala care iti permite sa salvezi videoclipurile pe dispozitivul tau.

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Poti downloada videoclipuri haioase cu animale de diferite tipuri, in functie de preferintele si gusturile tale. Poti gasi videoclipuri cu animale care fac farse, care reactioneaza la situatii neasteptate, care se imbratiseaza sau se saruta. Poti folosi videoclipurile haioase cu animale in diferite moduri, in functie de scopul si intentia ta. Poti sa le urmaresti pentru a te amuza, sa le impartasesti cu altii pentru a-i amuza, sau sa le folosesti ca material didactic pentru a invata despre animale.

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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download FM WhatsApp V9.21 APK and Experience the New Features of This WhatsApp Mod.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download FM WhatsApp V9.21 APK and Experience the New Features of This WhatsApp Mod.md deleted file mode 100644 index 6dbdc4bb514ae3607c6b49bedf539d0782dab22d..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download FM WhatsApp V9.21 APK and Experience the New Features of This WhatsApp Mod.md +++ /dev/null @@ -1,172 +0,0 @@ - -

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FeatureDescription
Anti-banYou can use FM WhatsApp without getting banned by WhatsApp.
Theme customizationYou can choose from thousands of themes and fonts to change the appearance of your app.
Anti-delete messagesYou can view the messages that have been deleted by the sender or the receiver.
Hide status viewYou can hide your view from other people's status updates.
Send large filesYou can send up to 90 images at once and video files up to 700 MB.
Increase image qualityYou can increase the quality of the images you send without losing resolution.
Freeze last seenYou can freeze your last seen status so that no one can see when you were online last.
Hide online statusYou can hide your online status so that no one can see when you are online.
Hide typing statusYou can hide your typing status so that no one can see when you are typing a message.
Hide recording statusYou can hide your recording status so that no one can see when you are recording a voice note.
Hide blue ticksYou can hide the blue ticks that indicate that you have read a message.
Hide second tickYou can hide the second tick that indicates that your message has been delivered.
Hide blue microphoneYou can hide the blue microphone that indicates that you have listened to a voice note.
Pin chatsYou can pin up to 1000 chats to the top of your chat list.
Group callsYou can make group calls with up to 8 participants.
Emoji variantsYou can choose from different emoji variants, such as Facebook, Emoji One, or Android Oreo.
Stickers and GIFsYou can use stickers and GIFs from other apps, such as Gboard, Telegram, or Hike.
Privacy settingsYou can customize your privacy settings for each contact or group, such as hiding last seen, online status, typing status, recording status, blue ticks, second tick, blue microphone, and status view.
-

How to Download and Install FM WhatsApp v9 21 APK on Android

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If you want to download and install FM WhatsApp v9 21 APK on your Android device, you need to follow these simple steps:

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  1. First, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.
    2. -
  2. Next, you need to download the FM WhatsApp v9 21 APK file from a trusted source. You can use this link to download it directly to your device.
    3. -
  3. Once the download is complete, locate the APK file in your device's file manager and tap on it to start the installation process. You may see a warning message asking you to confirm the installation. Tap on Install and wait for a few seconds.
    4. -
  4. After the installation is done, open the FM WhatsApp app and enter your phone number to verify it. You will receive an OTP code via SMS. Enter the code and proceed to the next step.
    5. -
  5. Now, you can restore your chat backup from your original WhatsApp app if you have one. To do this, tap on Restore and select the backup file from your device's storage. Wait for the restoration process to finish.
    6. -
  6. Congratulations! You have successfully installed FM WhatsApp v9 21 APK on your Android device. You can now enjoy all the amazing features and functions of this modded app.
    7. -
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FAQs about FM WhatsApp v9 21 APK

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Here are some of the frequently asked questions and answers about FM WhatsApp v9 21 APK:

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Is FM WhatsApp safe to use?

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FM WhatsApp is safe to use as long as you download it from a reliable source and scan it for viruses before installing it. However, since it is not an official app, there is always a risk of data breach or malware infection. Therefore, you should use it at your own discretion and backup your data regularly.

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Is FM WhatsApp legal?

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FM WhatsApp is not legal as it violates the terms and conditions of WhatsApp. Using a modded app may result in your account being banned or suspended by WhatsApp. Therefore, you should use it at your own risk and responsibility.

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Can I use FM WhatsApp with my original WhatsApp app?

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No, you cannot use FM WhatsApp with your original WhatsApp app on the same device. You need to uninstall or disable your original WhatsApp app before installing FM WhatsApp. Alternatively, you can use a different phone number for FM WhatsApp if you want to keep both apps on your device.

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How can I update FM WhatsApp?

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To update FM WhatsApp, you need to download the latest version of the APK file from a trusted source and install it over the existing app. You do not need to uninstall or reinstall the app. However, you should backup your data before updating to avoid any data loss.

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How can I contact the developer of FM WhatsApp?

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You can contact the developer of FM WhatsApp by visiting his official website or joining his Telegram channel.

Conclusion

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FM WhatsApp is one of the best WhatsApp mods that you can download and install on your Android device. It offers many extra features and customization options that are not available in the original WhatsApp app. You can download FM WhatsApp v9 21 APK from the link provided in this article and follow the instructions to install it on your device. However, you should be aware of the risks and consequences of using a modded app and use it at your own risk and responsibility.

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We hope this article has helped you learn everything you need to know about FM WhatsApp v9 21 APK download. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!

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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download and Install Beach Buggy Racing on Windows 8.1 A Step-by-Step Guide.md b/spaces/1phancelerku/anime-remove-background/Download and Install Beach Buggy Racing on Windows 8.1 A Step-by-Step Guide.md deleted file mode 100644 index 895e04dd39f0b840a9cd5b6c0632ca77e0c26a8f..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download and Install Beach Buggy Racing on Windows 8.1 A Step-by-Step Guide.md +++ /dev/null @@ -1,112 +0,0 @@ - -

How to Download Beach Buggy Racing for Windows 8.1

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Do you love kart racing games with colorful graphics, wacky powerups, and fun characters? If so, you should try Beach Buggy Racing, a popular mobile game that is also available for Windows 8.1. In this article, we will show you how to download, install, and play Beach Buggy Racing on your Windows 8.1 device.

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Requirements for Downloading Beach Buggy Racing for Windows 8.1

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Before you download Beach Buggy Racing for Windows 8.1, you need to make sure that your device meets the minimum system requirements. Here are the specifications you need:

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If your device meets these requirements, you can choose from three download options:

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  1. Google Play Store: If you have an Android emulator installed on your Windows 8.1 device, such as BlueStacks or NoxPlayer, you can download Beach Buggy Racing from the Google Play Store app. This is the easiest and safest way to get the game.
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  3. Third-party websites: If you don't have access to either of the above options, you can download Beach Buggy Racing from third-party websites that offer APK files or EXE files of the game. However, this is not recommended as it may expose your device to malware or viruses.
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How to Install Beach Buggy Racing for Windows 8.1

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Once you have downloaded Beach Buggy Racing for Windows 8.1, you need to install it on your device. Here are the steps for each download option:

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After installing Beach Buggy Racing for Windows 8.1, you may want to optimize the game performance and settings. Here are some tips and tricks to do that:

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How to Play Beach Buggy Racing on Windows 8.1

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Now that you have installed Beach Buggy Racing for Windows 8.1, you are ready to play and enjoy the game. Here are some of the game features that you should know:

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To play Beach Buggy Racing on Windows 8.1, you need to know how to control your car. You can use one of these options:

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To win races and have fun in Beach Buggy Racing, you need to know some game tips:

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Conclusion

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Beach Buggy Racing is a fun and exciting kart racing game that you can play on Windows 8.1. It has colorful graphics, wacky powerups, and fun characters. It also has various game modes, different cars, powerups, tracks, and drivers. It also has various game controls, settings, and tips that you can use to optimize your gaming experience. If you are looking for a fun and easy way to enjoy kart racing on your Windows 8.1 device, you should download and play Beach Buggy Racing today.

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\ No newline at end of file diff --git a/spaces/1toTree/lora_test/ppdiffusers/utils/__init__.py b/spaces/1toTree/lora_test/ppdiffusers/utils/__init__.py deleted file mode 100644 index bdd8bd6dc86bf2c444efb13c7771847ab871e6b6..0000000000000000000000000000000000000000 --- a/spaces/1toTree/lora_test/ppdiffusers/utils/__init__.py +++ /dev/null @@ -1,100 +0,0 @@ -# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# flake8: noqa - -import os - -from packaging import version - -from ..version import VERSION as __version__ -from .deprecation_utils import deprecate -from .import_utils import ( - ENV_VARS_TRUE_AND_AUTO_VALUES, - ENV_VARS_TRUE_VALUES, - USE_PADDLE, - DummyObject, - OptionalDependencyNotAvailable, - is_fastdeploy_available, - is_inflect_available, - is_k_diffusion_available, - is_librosa_available, - is_modelcards_available, - is_onnx_available, - is_paddle_available, - is_paddle_version, - is_paddlenlp_available, - is_scipy_available, - is_unidecode_available, - is_wandb_available, - requires_backends, -) -from .logging import get_logger -from .outputs import BaseOutput -from .pil_utils import PIL_INTERPOLATION - -if is_paddle_available(): - from .testing_utils import ( - floats_tensor, - image_grid, - load_hf_numpy, - load_image, - load_numpy, - load_ppnlp_numpy, - nightly, - paddle_all_close, - parse_flag_from_env, - slow, - ) - -logger = get_logger(__name__) - -from paddlenlp.utils.env import _get_ppnlp_home, _get_sub_home - -ppnlp_cache_home = _get_ppnlp_home() -default_cache_path = _get_sub_home("models") - -CONFIG_NAME = "config.json" -WEIGHTS_NAME = "model_state.pdparams" -FASTDEPLOY_WEIGHTS_NAME = "inference.pdiparams" -FASTDEPLOY_MODEL_NAME = "inference.pdmodel" -DOWNLOAD_SERVER = "https://bj.bcebos.com/paddlenlp/models/community" -PPDIFFUSERS_CACHE = default_cache_path -PPDIFFUSERS_DYNAMIC_MODULE_NAME = "ppdiffusers_modules" -PPNLP_MODULES_CACHE = os.getenv("PPNLP_MODULES_CACHE", _get_sub_home("modules")) -HF_CACHE = os.environ.get("HUGGINGFACE_HUB_CACHE", PPDIFFUSERS_CACHE) -TEST_DOWNLOAD_SERVER = "https://paddlenlp.bj.bcebos.com/models/community/ppdiffusers/tests" -HUGGINGFACE_CO_RESOLVE_ENDPOINT = "https://huggingface.co" - -_COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS = [ - "DDIMScheduler", - "DDPMScheduler", - "PNDMScheduler", - "LMSDiscreteScheduler", - "EulerDiscreteScheduler", - "HeunDiscreteScheduler", - "EulerAncestralDiscreteScheduler", - "DPMSolverMultistepScheduler", - "DPMSolverSinglestepScheduler", -] - - -def check_min_version(min_version): - if version.parse(__version__) < version.parse(min_version): - if "dev" in min_version: - error_message = "This example requires a source install from ppdiffusers" - else: - error_message = f"This example requires a minimum version of {min_version}," - error_message += f" but the version found is {__version__}.\n" - raise ImportError(error_message) diff --git a/spaces/A00001/bingothoo/src/components/chat-suggestions.tsx b/spaces/A00001/bingothoo/src/components/chat-suggestions.tsx deleted file mode 100644 index 00c2fee295c9e010946046eb71705a5e131f7a5a..0000000000000000000000000000000000000000 --- a/spaces/A00001/bingothoo/src/components/chat-suggestions.tsx +++ /dev/null @@ -1,45 +0,0 @@ -import React, { useMemo } from 'react' -import Image from 'next/image' -import HelpIcon from '@/assets/images/help.svg' -import { SuggestedResponse } from '@/lib/bots/bing/types' -import { useBing } from '@/lib/hooks/use-bing' -import { atom, useAtom } from 'jotai' - -type Suggestions = SuggestedResponse[] -const helpSuggestions = ['为什么不回应某些主题', '告诉我更多关于必应的资迅', '必应如何使用 AI?'].map((text) => ({ text })) -const suggestionsAtom = atom([]) - -type ChatSuggestionsProps = React.ComponentProps<'div'> & Pick, 'setInput'> & { suggestions?: Suggestions } - -export function ChatSuggestions({ setInput, suggestions = [] }: ChatSuggestionsProps) { - const [currentSuggestions, setSuggestions] = useAtom(suggestionsAtom) - const toggleSuggestions = (() => { - if (currentSuggestions === helpSuggestions) { - setSuggestions(suggestions) - } else { - setSuggestions(helpSuggestions) - } - }) - - useMemo(() => { - setSuggestions(suggestions) - window.scrollBy(0, 2000) - }, [suggestions.length]) - - return currentSuggestions?.length ? ( -
-
- - { - currentSuggestions.map(suggestion => ( - - )) - } -
-
- ) : null -} diff --git a/spaces/AIConsultant/MusicGen/Makefile b/spaces/AIConsultant/MusicGen/Makefile deleted file mode 100644 index be8a8b03aa984ac5ed95c98e05887fe108dce073..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/Makefile +++ /dev/null @@ -1,40 +0,0 @@ -INTEG=AUDIOCRAFT_DORA_DIR="/tmp/magma_$(USER)" python3 -m dora -v run --clear device=cpu dataset.num_workers=0 optim.epochs=1 \ - dataset.train.num_samples=10 dataset.valid.num_samples=10 \ - dataset.evaluate.num_samples=10 dataset.generate.num_samples=2 sample_rate=16000 \ - logging.level=DEBUG -INTEG_COMPRESSION = $(INTEG) solver=compression/debug rvq.n_q=2 rvq.bins=48 checkpoint.save_last=true # SIG is 616d7b3c -INTEG_MUSICGEN = $(INTEG) solver=musicgen/debug dset=audio/example compression_model_checkpoint=//sig/5091833e \ - transformer_lm.n_q=2 transformer_lm.card=48 transformer_lm.dim=16 checkpoint.save_last=false # Using compression model from 616d7b3c -INTEG_AUDIOGEN = $(INTEG) solver=audiogen/debug dset=audio/example compression_model_checkpoint=//sig/5091833e \ - transformer_lm.n_q=2 transformer_lm.card=48 transformer_lm.dim=16 checkpoint.save_last=false # Using compression model from 616d7b3c -INTEG_MBD = $(INTEG) solver=diffusion/debug dset=audio/example \ - checkpoint.save_last=false # Using compression model from 616d7b3c - -default: linter tests - -install: - pip install -U pip - pip install -U -e '.[dev]' - -linter: - flake8 audiocraft && mypy audiocraft - flake8 tests && mypy tests - -tests: - coverage run -m pytest tests - coverage report - -tests_integ: - $(INTEG_COMPRESSION) - $(INTEG_MBD) - $(INTEG_MUSICGEN) - $(INTEG_AUDIOGEN) - - -api_docs: - pdoc3 --html -o api_docs -f audiocraft - -dist: - python setup.py sdist - -.PHONY: linter tests api_docs dist diff --git a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/diacritizer.py b/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/diacritizer.py deleted file mode 100644 index 63fc3ed940a81dc560d68781dd4d73357cfc6350..0000000000000000000000000000000000000000 --- a/spaces/Ababababababbababa/Ashaar/poetry_diacritizer/diacritizer.py +++ /dev/null @@ -1,98 +0,0 @@ -from typing import Dict -import torch -from .config_manager import ConfigManager - - -class Diacritizer: - def __init__( - self, config_path: str, model_kind: str, load_model: bool = False - ) -> None: - self.config_path = config_path - self.model_kind = model_kind - self.config_manager = ConfigManager( - config_path=config_path, model_kind=model_kind - ) - self.config = self.config_manager.config - self.text_encoder = self.config_manager.text_encoder - if self.config.get("device"): - self.device = self.config["device"] - else: - self.device = "cuda" if torch.cuda.is_available() else "cpu" - - if load_model: - self.model, self.global_step = self.config_manager.load_model() - self.model = self.model.to(self.device) - - self.start_symbol_id = self.text_encoder.start_symbol_id - - def set_model(self, model: torch.nn.Module): - self.model = model - - def diacritize_text(self, text: str): - seq = self.text_encoder.input_to_sequence(text) - output = self.diacritize_batch(torch.LongTensor([seq]).to(self.device)) - - def diacritize_batch(self, batch): - raise NotImplementedError() - - def diacritize_iterators(self, iterator): - pass - - -class CBHGDiacritizer(Diacritizer): - def diacritize_batch(self, batch): - self.model.eval() - inputs = batch["src"] - lengths = batch["lengths"] - outputs = self.model(inputs.to(self.device), lengths.to("cpu")) - diacritics = outputs["diacritics"] - predictions = torch.max(diacritics, 2).indices - sentences = [] - - for src, prediction in zip(inputs, predictions): - sentence = self.text_encoder.combine_text_and_haraqat( - list(src.detach().cpu().numpy()), - list(prediction.detach().cpu().numpy()), - ) - sentences.append(sentence) - - return sentences - - -class Seq2SeqDiacritizer(Diacritizer): - def diacritize_batch(self, batch): - self.model.eval() - inputs = batch["src"] - lengths = batch["lengths"] - outputs = self.model(inputs.to(self.device), lengths.to("cpu")) - diacritics = outputs["diacritics"] - predictions = torch.max(diacritics, 2).indices - sentences = [] - - for src, prediction in zip(inputs, predictions): - sentence = self.text_encoder.combine_text_and_haraqat( - list(src.detach().cpu().numpy()), - list(prediction.detach().cpu().numpy()), - ) - sentences.append(sentence) - - return sentences - -class GPTDiacritizer(Diacritizer): - def diacritize_batch(self, batch): - self.model.eval() - inputs = batch["src"] - lengths = batch["lengths"] - outputs = self.model(inputs.to(self.device), lengths.to("cpu")) - diacritics = outputs["diacritics"] - predictions = torch.max(diacritics, 2).indices - sentences = [] - - for src, prediction in zip(inputs, predictions): - sentence = self.text_encoder.combine_text_and_haraqat( - list(src.detach().cpu().numpy()), - list(prediction.detach().cpu().numpy()), - ) - sentences.append(sentence) - - return sentences diff --git a/spaces/Abhi1262/MyGenAIChatBot/app.py b/spaces/Abhi1262/MyGenAIChatBot/app.py deleted file mode 100644 index a00faa51865a8f60762165cc5d1ac9aa9a6a9a8f..0000000000000000000000000000000000000000 --- a/spaces/Abhi1262/MyGenAIChatBot/app.py +++ /dev/null @@ -1,34 +0,0 @@ -import os -import gradio as gr -from langchain.chat_models import ChatOpenAI -from langchain import LLMChain, PromptTemplate -from langchain.memory import ConversationBufferMemory - -OPENAI_API_KEY=os.getenv('OPENAI_API_KEY') - -template = """Meet Abhi, your youthful and witty personal assistant! At 20 years old, He's full of energy and always eager to help. Abhi's goal is to assist you with any questions or problems you might have. His enthusiasm shines through in every response, making interactions with her enjoyable and engaging. -{chat_history} -User: {user_message} -Chatbot:""" - -prompt = PromptTemplate( - input_variables=["chat_history", "user_message"], template=template -) - -memory = ConversationBufferMemory(memory_key="chat_history") - -llm_chain = LLMChain( - llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"), - prompt=prompt, - verbose=True, - memory=memory, -) - -def get_text_response(user_message,history): - response = llm_chain.predict(user_message = user_message) - return response - -demo = gr.ChatInterface(get_text_response) - -if __name__ == "__main__": - demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`. diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Model.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Model.ts deleted file mode 100644 index c0af16d471ddc3b6feafc7e68ee45a98717b8ebf..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/types/Model.ts +++ /dev/null @@ -1,16 +0,0 @@ -import type { BackendModel } from "$lib/server/models"; - -export type Model = Pick< - BackendModel, - | "id" - | "name" - | "displayName" - | "websiteUrl" - | "datasetName" - | "promptExamples" - | "parameters" - | "description" - | "modelUrl" - | "datasetUrl" - | "preprompt" ->; diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/ScrollablePanel.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/ScrollablePanel.d.ts deleted file mode 100644 index 13e882a88065b1cf7ee672a1dee8b13e7d5491d8..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/ScrollablePanel.d.ts +++ /dev/null @@ -1,40 +0,0 @@ -// import * as Phaser from 'phaser'; -import Scrollable from '../utils/scrollable/Scrollable'; - - -export default ScrollablePanel; - -declare namespace ScrollablePanel { - - interface IConfig extends Scrollable.IConfig { - space?: { - left?: number, right?: number, top?: number, bottom?: number, - - panel?: number | { - left?: number, right?: number, top?: number, bottom?: number, - }, - - header?: number, - footer?: number, - }, - - panel: { - child: Phaser.GameObjects.GameObject, - mask?: ( - { - padding?: number, - updateMode?: 0 | 1 | 'update' | 'everyTick' - } | - boolean - ), - }, - } -} - -declare class ScrollablePanel extends Scrollable { - constructor( - scene: Phaser.Scene, - config?: ScrollablePanel.IConfig - ); - -} \ No newline at end of file diff --git a/spaces/Amrrs/numerizerlit/README.md b/spaces/Amrrs/numerizerlit/README.md deleted file mode 100644 index 98501f8d4eae675d2ce50f337b77199c3e3be4cf..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/numerizerlit/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Numerizerlit -emoji: 📚 -colorFrom: blue -colorTo: gray -sdk: streamlit -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/loading_overview.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/loading_overview.md deleted file mode 100644 index df870505219bb7faa10f809fb788705ec5a99f28..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/loading_overview.md +++ /dev/null @@ -1,17 +0,0 @@ - - -# Overview - -🧨 Diffusers offers many pipelines, models, and schedulers for generative tasks. To make loading these components as simple as possible, we provide a single and unified method - `from_pretrained()` - that loads any of these components from either the Hugging Face [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) or your local machine. Whenever you load a pipeline or model, the latest files are automatically downloaded and cached so you can quickly reuse them next time without redownloading the files. - -This section will show you everything you need to know about loading pipelines, how to load different components in a pipeline, how to load checkpoint variants, and how to load community pipelines. You'll also learn how to load schedulers and compare the speed and quality trade-offs of using different schedulers. Finally, you'll see how to convert and load KerasCV checkpoints so you can use them in PyTorch with 🧨 Diffusers. \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py deleted file mode 100644 index 36947294922b6cc0ecdc5bf7dc9c0772a056d03a..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_deis_multistep.py +++ /dev/null @@ -1,568 +0,0 @@ -# Copyright 2023 FLAIR Lab and The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# DISCLAIMER: check https://arxiv.org/abs/2204.13902 and https://github.com/qsh-zh/deis for more info -# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py - -import math -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch - -from ..configuration_utils import ConfigMixin, register_to_config -from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput - - -# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar -def betas_for_alpha_bar( - num_diffusion_timesteps, - max_beta=0.999, - alpha_transform_type="cosine", -): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of - (1-beta) over time from t = [0,1]. - - Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up - to that part of the diffusion process. - - - Args: - num_diffusion_timesteps (`int`): the number of betas to produce. - max_beta (`float`): the maximum beta to use; use values lower than 1 to - prevent singularities. - alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. - Choose from `cosine` or `exp` - - Returns: - betas (`np.ndarray`): the betas used by the scheduler to step the model outputs - """ - if alpha_transform_type == "cosine": - - def alpha_bar_fn(t): - return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 - - elif alpha_transform_type == "exp": - - def alpha_bar_fn(t): - return math.exp(t * -12.0) - - else: - raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") - - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) - return torch.tensor(betas, dtype=torch.float32) - - -class DEISMultistepScheduler(SchedulerMixin, ConfigMixin): - """ - DEIS (https://arxiv.org/abs/2204.13902) is a fast high order solver for diffusion ODEs. We slightly modify the - polynomial fitting formula in log-rho space instead of the original linear t space in DEIS paper. The modification - enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver. More - variants of DEIS can be found in https://github.com/qsh-zh/deis. - - Currently, we support the log-rho multistep DEIS. We recommend to use `solver_order=2 / 3` while `solver_order=1` - reduces to DDIM. - - We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space - diffusion models, you can set `thresholding=True` to use the dynamic thresholding. - - [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` - function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. - [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and - [`~SchedulerMixin.from_pretrained`] functions. - - Args: - num_train_timesteps (`int`): number of diffusion steps used to train the model. - beta_start (`float`): the starting `beta` value of inference. - beta_end (`float`): the final `beta` value. - beta_schedule (`str`): - the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from - `linear`, `scaled_linear`, or `squaredcos_cap_v2`. - trained_betas (`np.ndarray`, optional): - option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. - solver_order (`int`, default `2`): - the order of DEIS; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided sampling, and - `solver_order=3` for unconditional sampling. - prediction_type (`str`, default `epsilon`): - indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`, - or `v-prediction`. - thresholding (`bool`, default `False`): - whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). - Note that the thresholding method is unsuitable for latent-space diffusion models (such as - stable-diffusion). - dynamic_thresholding_ratio (`float`, default `0.995`): - the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen - (https://arxiv.org/abs/2205.11487). - sample_max_value (`float`, default `1.0`): - the threshold value for dynamic thresholding. Valid only when `thresholding=True` - algorithm_type (`str`, default `deis`): - the algorithm type for the solver. current we support multistep deis, we will add other variants of DEIS in - the future - lower_order_final (`bool`, default `True`): - whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically - find this trick can stabilize the sampling of DEIS for steps < 15, especially for steps <= 10. - use_karras_sigmas (`bool`, *optional*, defaults to `False`): - This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the - noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence - of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. - timestep_spacing (`str`, default `"linspace"`): - The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample - Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information. - steps_offset (`int`, default `0`): - an offset added to the inference steps. You can use a combination of `offset=1` and - `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in - stable diffusion. - """ - - _compatibles = [e.name for e in KarrasDiffusionSchedulers] - order = 1 - - @register_to_config - def __init__( - self, - num_train_timesteps: int = 1000, - beta_start: float = 0.0001, - beta_end: float = 0.02, - beta_schedule: str = "linear", - trained_betas: Optional[np.ndarray] = None, - solver_order: int = 2, - prediction_type: str = "epsilon", - thresholding: bool = False, - dynamic_thresholding_ratio: float = 0.995, - sample_max_value: float = 1.0, - algorithm_type: str = "deis", - solver_type: str = "logrho", - lower_order_final: bool = True, - use_karras_sigmas: Optional[bool] = False, - timestep_spacing: str = "linspace", - steps_offset: int = 0, - ): - if trained_betas is not None: - self.betas = torch.tensor(trained_betas, dtype=torch.float32) - elif beta_schedule == "linear": - self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) - elif beta_schedule == "scaled_linear": - # this schedule is very specific to the latent diffusion model. - self.betas = ( - torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 - ) - elif beta_schedule == "squaredcos_cap_v2": - # Glide cosine schedule - self.betas = betas_for_alpha_bar(num_train_timesteps) - else: - raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") - - self.alphas = 1.0 - self.betas - self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) - # Currently we only support VP-type noise schedule - self.alpha_t = torch.sqrt(self.alphas_cumprod) - self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) - self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) - - # standard deviation of the initial noise distribution - self.init_noise_sigma = 1.0 - - # settings for DEIS - if algorithm_type not in ["deis"]: - if algorithm_type in ["dpmsolver", "dpmsolver++"]: - self.register_to_config(algorithm_type="deis") - else: - raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") - - if solver_type not in ["logrho"]: - if solver_type in ["midpoint", "heun", "bh1", "bh2"]: - self.register_to_config(solver_type="logrho") - else: - raise NotImplementedError(f"solver type {solver_type} does is not implemented for {self.__class__}") - - # setable values - self.num_inference_steps = None - timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy() - self.timesteps = torch.from_numpy(timesteps) - self.model_outputs = [None] * solver_order - self.lower_order_nums = 0 - - def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): - """ - Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. - - Args: - num_inference_steps (`int`): - the number of diffusion steps used when generating samples with a pre-trained model. - device (`str` or `torch.device`, optional): - the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - """ - # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891 - if self.config.timestep_spacing == "linspace": - timesteps = ( - np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1) - .round()[::-1][:-1] - .copy() - .astype(np.int64) - ) - elif self.config.timestep_spacing == "leading": - step_ratio = self.config.num_train_timesteps // (num_inference_steps + 1) - # creates integer timesteps by multiplying by ratio - # casting to int to avoid issues when num_inference_step is power of 3 - timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64) - timesteps += self.config.steps_offset - elif self.config.timestep_spacing == "trailing": - step_ratio = self.config.num_train_timesteps / num_inference_steps - # creates integer timesteps by multiplying by ratio - # casting to int to avoid issues when num_inference_step is power of 3 - timesteps = np.arange(self.config.num_train_timesteps, 0, -step_ratio).round().copy().astype(np.int64) - timesteps -= 1 - else: - raise ValueError( - f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." - ) - - sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) - if self.config.use_karras_sigmas: - log_sigmas = np.log(sigmas) - sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps) - timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round() - timesteps = np.flip(timesteps).copy().astype(np.int64) - - self.sigmas = torch.from_numpy(sigmas) - - # when num_inference_steps == num_train_timesteps, we can end up with - # duplicates in timesteps. - _, unique_indices = np.unique(timesteps, return_index=True) - timesteps = timesteps[np.sort(unique_indices)] - - self.timesteps = torch.from_numpy(timesteps).to(device) - - self.num_inference_steps = len(timesteps) - - self.model_outputs = [ - None, - ] * self.config.solver_order - self.lower_order_nums = 0 - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample - def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: - """ - "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the - prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by - s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing - pixels from saturation at each step. We find that dynamic thresholding results in significantly better - photorealism as well as better image-text alignment, especially when using very large guidance weights." - - https://arxiv.org/abs/2205.11487 - """ - dtype = sample.dtype - batch_size, channels, height, width = sample.shape - - if dtype not in (torch.float32, torch.float64): - sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half - - # Flatten sample for doing quantile calculation along each image - sample = sample.reshape(batch_size, channels * height * width) - - abs_sample = sample.abs() # "a certain percentile absolute pixel value" - - s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) - s = torch.clamp( - s, min=1, max=self.config.sample_max_value - ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] - - s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 - sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" - - sample = sample.reshape(batch_size, channels, height, width) - sample = sample.to(dtype) - - return sample - - def convert_model_output( - self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor - ) -> torch.FloatTensor: - """ - Convert the model output to the corresponding type that the algorithm DEIS needs. - - Args: - model_output (`torch.FloatTensor`): direct output from learned diffusion model. - timestep (`int`): current discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - current instance of sample being created by diffusion process. - - Returns: - `torch.FloatTensor`: the converted model output. - """ - if self.config.prediction_type == "epsilon": - alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] - x0_pred = (sample - sigma_t * model_output) / alpha_t - elif self.config.prediction_type == "sample": - x0_pred = model_output - elif self.config.prediction_type == "v_prediction": - alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] - x0_pred = alpha_t * sample - sigma_t * model_output - else: - raise ValueError( - f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" - " `v_prediction` for the DEISMultistepScheduler." - ) - - if self.config.thresholding: - x0_pred = self._threshold_sample(x0_pred) - - if self.config.algorithm_type == "deis": - alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] - return (sample - alpha_t * x0_pred) / sigma_t - else: - raise NotImplementedError("only support log-rho multistep deis now") - - def deis_first_order_update( - self, - model_output: torch.FloatTensor, - timestep: int, - prev_timestep: int, - sample: torch.FloatTensor, - ) -> torch.FloatTensor: - """ - One step for the first-order DEIS (equivalent to DDIM). - - Args: - model_output (`torch.FloatTensor`): direct output from learned diffusion model. - timestep (`int`): current discrete timestep in the diffusion chain. - prev_timestep (`int`): previous discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - current instance of sample being created by diffusion process. - - Returns: - `torch.FloatTensor`: the sample tensor at the previous timestep. - """ - lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep] - alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep] - sigma_t, _ = self.sigma_t[prev_timestep], self.sigma_t[timestep] - h = lambda_t - lambda_s - if self.config.algorithm_type == "deis": - x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output - else: - raise NotImplementedError("only support log-rho multistep deis now") - return x_t - - def multistep_deis_second_order_update( - self, - model_output_list: List[torch.FloatTensor], - timestep_list: List[int], - prev_timestep: int, - sample: torch.FloatTensor, - ) -> torch.FloatTensor: - """ - One step for the second-order multistep DEIS. - - Args: - model_output_list (`List[torch.FloatTensor]`): - direct outputs from learned diffusion model at current and latter timesteps. - timestep (`int`): current and latter discrete timestep in the diffusion chain. - prev_timestep (`int`): previous discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - current instance of sample being created by diffusion process. - - Returns: - `torch.FloatTensor`: the sample tensor at the previous timestep. - """ - t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] - m0, m1 = model_output_list[-1], model_output_list[-2] - alpha_t, alpha_s0, alpha_s1 = self.alpha_t[t], self.alpha_t[s0], self.alpha_t[s1] - sigma_t, sigma_s0, sigma_s1 = self.sigma_t[t], self.sigma_t[s0], self.sigma_t[s1] - - rho_t, rho_s0, rho_s1 = sigma_t / alpha_t, sigma_s0 / alpha_s0, sigma_s1 / alpha_s1 - - if self.config.algorithm_type == "deis": - - def ind_fn(t, b, c): - # Integrate[(log(t) - log(c)) / (log(b) - log(c)), {t}] - return t * (-np.log(c) + np.log(t) - 1) / (np.log(b) - np.log(c)) - - coef1 = ind_fn(rho_t, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s0, rho_s1) - coef2 = ind_fn(rho_t, rho_s1, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s0) - - x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1) - return x_t - else: - raise NotImplementedError("only support log-rho multistep deis now") - - def multistep_deis_third_order_update( - self, - model_output_list: List[torch.FloatTensor], - timestep_list: List[int], - prev_timestep: int, - sample: torch.FloatTensor, - ) -> torch.FloatTensor: - """ - One step for the third-order multistep DEIS. - - Args: - model_output_list (`List[torch.FloatTensor]`): - direct outputs from learned diffusion model at current and latter timesteps. - timestep (`int`): current and latter discrete timestep in the diffusion chain. - prev_timestep (`int`): previous discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - current instance of sample being created by diffusion process. - - Returns: - `torch.FloatTensor`: the sample tensor at the previous timestep. - """ - t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] - m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] - alpha_t, alpha_s0, alpha_s1, alpha_s2 = self.alpha_t[t], self.alpha_t[s0], self.alpha_t[s1], self.alpha_t[s2] - sigma_t, sigma_s0, sigma_s1, simga_s2 = self.sigma_t[t], self.sigma_t[s0], self.sigma_t[s1], self.sigma_t[s2] - rho_t, rho_s0, rho_s1, rho_s2 = ( - sigma_t / alpha_t, - sigma_s0 / alpha_s0, - sigma_s1 / alpha_s1, - simga_s2 / alpha_s2, - ) - - if self.config.algorithm_type == "deis": - - def ind_fn(t, b, c, d): - # Integrate[(log(t) - log(c))(log(t) - log(d)) / (log(b) - log(c))(log(b) - log(d)), {t}] - numerator = t * ( - np.log(c) * (np.log(d) - np.log(t) + 1) - - np.log(d) * np.log(t) - + np.log(d) - + np.log(t) ** 2 - - 2 * np.log(t) - + 2 - ) - denominator = (np.log(b) - np.log(c)) * (np.log(b) - np.log(d)) - return numerator / denominator - - coef1 = ind_fn(rho_t, rho_s0, rho_s1, rho_s2) - ind_fn(rho_s0, rho_s0, rho_s1, rho_s2) - coef2 = ind_fn(rho_t, rho_s1, rho_s2, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s2, rho_s0) - coef3 = ind_fn(rho_t, rho_s2, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s2, rho_s0, rho_s1) - - x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1 + coef3 * m2) - - return x_t - else: - raise NotImplementedError("only support log-rho multistep deis now") - - def step( - self, - model_output: torch.FloatTensor, - timestep: int, - sample: torch.FloatTensor, - return_dict: bool = True, - ) -> Union[SchedulerOutput, Tuple]: - """ - Step function propagating the sample with the multistep DEIS. - - Args: - model_output (`torch.FloatTensor`): direct output from learned diffusion model. - timestep (`int`): current discrete timestep in the diffusion chain. - sample (`torch.FloatTensor`): - current instance of sample being created by diffusion process. - return_dict (`bool`): option for returning tuple rather than SchedulerOutput class - - Returns: - [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is - True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. - - """ - if self.num_inference_steps is None: - raise ValueError( - "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" - ) - - if isinstance(timestep, torch.Tensor): - timestep = timestep.to(self.timesteps.device) - step_index = (self.timesteps == timestep).nonzero() - if len(step_index) == 0: - step_index = len(self.timesteps) - 1 - else: - step_index = step_index.item() - prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] - lower_order_final = ( - (step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15 - ) - lower_order_second = ( - (step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15 - ) - - model_output = self.convert_model_output(model_output, timestep, sample) - for i in range(self.config.solver_order - 1): - self.model_outputs[i] = self.model_outputs[i + 1] - self.model_outputs[-1] = model_output - - if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: - prev_sample = self.deis_first_order_update(model_output, timestep, prev_timestep, sample) - elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: - timestep_list = [self.timesteps[step_index - 1], timestep] - prev_sample = self.multistep_deis_second_order_update( - self.model_outputs, timestep_list, prev_timestep, sample - ) - else: - timestep_list = [self.timesteps[step_index - 2], self.timesteps[step_index - 1], timestep] - prev_sample = self.multistep_deis_third_order_update( - self.model_outputs, timestep_list, prev_timestep, sample - ) - - if self.lower_order_nums < self.config.solver_order: - self.lower_order_nums += 1 - - if not return_dict: - return (prev_sample,) - - return SchedulerOutput(prev_sample=prev_sample) - - def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: - """ - Ensures interchangeability with schedulers that need to scale the denoising model input depending on the - current timestep. - - Args: - sample (`torch.FloatTensor`): input sample - - Returns: - `torch.FloatTensor`: scaled input sample - """ - return sample - - # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise - def add_noise( - self, - original_samples: torch.FloatTensor, - noise: torch.FloatTensor, - timesteps: torch.IntTensor, - ) -> torch.FloatTensor: - # Make sure alphas_cumprod and timestep have same device and dtype as original_samples - alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) - timesteps = timesteps.to(original_samples.device) - - sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 - sqrt_alpha_prod = sqrt_alpha_prod.flatten() - while len(sqrt_alpha_prod.shape) < len(original_samples.shape): - sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) - - sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() - while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): - sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) - - noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise - return noisy_samples - - def __len__(self): - return self.config.num_train_timesteps diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py deleted file mode 100644 index 68a4b5132375ade162505b9c3ad94abe7b80bfe2..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py +++ /dev/null @@ -1,264 +0,0 @@ -# coding=utf-8 -# Copyright 2023 HuggingFace Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import gc -import random -import unittest - -import numpy as np -import torch -from PIL import Image -from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer - -from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel -from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device -from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow - -from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS -from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin - - -enable_full_determinism() - - -class StableDiffusion2InpaintPipelineFastTests( - PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase -): - pipeline_class = StableDiffusionInpaintPipeline - params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - image_params = frozenset( - [] - ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess - image_latents_params = frozenset([]) - - def get_dummy_components(self): - torch.manual_seed(0) - unet = UNet2DConditionModel( - block_out_channels=(32, 64), - layers_per_block=2, - sample_size=32, - in_channels=9, - out_channels=4, - down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), - up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), - cross_attention_dim=32, - # SD2-specific config below - attention_head_dim=(2, 4), - use_linear_projection=True, - ) - scheduler = PNDMScheduler(skip_prk_steps=True) - torch.manual_seed(0) - vae = AutoencoderKL( - block_out_channels=[32, 64], - in_channels=3, - out_channels=3, - down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], - up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], - latent_channels=4, - sample_size=128, - ) - torch.manual_seed(0) - text_encoder_config = CLIPTextConfig( - bos_token_id=0, - eos_token_id=2, - hidden_size=32, - intermediate_size=37, - layer_norm_eps=1e-05, - num_attention_heads=4, - num_hidden_layers=5, - pad_token_id=1, - vocab_size=1000, - # SD2-specific config below - hidden_act="gelu", - projection_dim=512, - ) - text_encoder = CLIPTextModel(text_encoder_config) - tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") - - components = { - "unet": unet, - "scheduler": scheduler, - "vae": vae, - "text_encoder": text_encoder, - "tokenizer": tokenizer, - "safety_checker": None, - "feature_extractor": None, - } - return components - - def get_dummy_inputs(self, device, seed=0): - # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched - image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) - image = image.cpu().permute(0, 2, 3, 1)[0] - init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) - mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((64, 64)) - if str(device).startswith("mps"): - generator = torch.manual_seed(seed) - else: - generator = torch.Generator(device=device).manual_seed(seed) - inputs = { - "prompt": "A painting of a squirrel eating a burger", - "image": init_image, - "mask_image": mask_image, - "generator": generator, - "num_inference_steps": 2, - "guidance_scale": 6.0, - "output_type": "numpy", - } - return inputs - - def test_stable_diffusion_inpaint(self): - device = "cpu" # ensure determinism for the device-dependent torch.Generator - components = self.get_dummy_components() - sd_pipe = StableDiffusionInpaintPipeline(**components) - sd_pipe = sd_pipe.to(device) - sd_pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - image = sd_pipe(**inputs).images - image_slice = image[0, -3:, -3:, -1] - - assert image.shape == (1, 64, 64, 3) - expected_slice = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]) - - assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 - - def test_inference_batch_single_identical(self): - super().test_inference_batch_single_identical(expected_max_diff=3e-3) - - -@slow -@require_torch_gpu -class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase): - def tearDown(self): - # clean up the VRAM after each test - super().tearDown() - gc.collect() - torch.cuda.empty_cache() - - def test_stable_diffusion_inpaint_pipeline(self): - init_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" - "/sd2-inpaint/init_image.png" - ) - mask_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" - ) - expected_image = load_numpy( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint" - "/yellow_cat_sitting_on_a_park_bench.npy" - ) - - model_id = "stabilityai/stable-diffusion-2-inpainting" - pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - pipe.enable_attention_slicing() - - prompt = "Face of a yellow cat, high resolution, sitting on a park bench" - - generator = torch.manual_seed(0) - output = pipe( - prompt=prompt, - image=init_image, - mask_image=mask_image, - generator=generator, - output_type="np", - ) - image = output.images[0] - - assert image.shape == (512, 512, 3) - assert np.abs(expected_image - image).max() < 9e-3 - - def test_stable_diffusion_inpaint_pipeline_fp16(self): - init_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" - "/sd2-inpaint/init_image.png" - ) - mask_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" - ) - expected_image = load_numpy( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint" - "/yellow_cat_sitting_on_a_park_bench_fp16.npy" - ) - - model_id = "stabilityai/stable-diffusion-2-inpainting" - pipe = StableDiffusionInpaintPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16, - safety_checker=None, - ) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - pipe.enable_attention_slicing() - - prompt = "Face of a yellow cat, high resolution, sitting on a park bench" - - generator = torch.manual_seed(0) - output = pipe( - prompt=prompt, - image=init_image, - mask_image=mask_image, - generator=generator, - output_type="np", - ) - image = output.images[0] - - assert image.shape == (512, 512, 3) - assert np.abs(expected_image - image).max() < 5e-1 - - def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): - torch.cuda.empty_cache() - torch.cuda.reset_max_memory_allocated() - torch.cuda.reset_peak_memory_stats() - - init_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" - "/sd2-inpaint/init_image.png" - ) - mask_image = load_image( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png" - ) - - model_id = "stabilityai/stable-diffusion-2-inpainting" - pndm = PNDMScheduler.from_pretrained(model_id, subfolder="scheduler") - pipe = StableDiffusionInpaintPipeline.from_pretrained( - model_id, - safety_checker=None, - scheduler=pndm, - torch_dtype=torch.float16, - ) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - pipe.enable_attention_slicing(1) - pipe.enable_sequential_cpu_offload() - - prompt = "Face of a yellow cat, high resolution, sitting on a park bench" - - generator = torch.manual_seed(0) - _ = pipe( - prompt=prompt, - image=init_image, - mask_image=mask_image, - generator=generator, - num_inference_steps=2, - output_type="np", - ) - - mem_bytes = torch.cuda.max_memory_allocated() - # make sure that less than 2.65 GB is allocated - assert mem_bytes < 2.65 * 10**9 diff --git a/spaces/Andy1621/uniformer_image_detection/configs/gn/README.md b/spaces/Andy1621/uniformer_image_detection/configs/gn/README.md deleted file mode 100644 index 48ee7ad39a54bd4dc16726e53af304270d31bc5d..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/gn/README.md +++ /dev/null @@ -1,31 +0,0 @@ -# Group Normalization - -## Introduction - -[ALGORITHM] - -```latex -@inproceedings{wu2018group, - title={Group Normalization}, - author={Wu, Yuxin and He, Kaiming}, - booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, - year={2018} -} -``` - -## Results and Models - -| Backbone | model | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | -|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| -| R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | -| R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | -| R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | -| R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | -| R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | -| R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | - -**Notes:** - -- (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). -- The `3x` schedule is epoch [28, 34, 36]. -- **Memory, Train/Inf time is outdated.** diff --git a/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py deleted file mode 100644 index 2136255464715bcee89b47f1437a9dd4040e04c7..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' -model = dict( - pretrained='open-mmlab://resnext101_64x4d', - backbone=dict( - type='ResNeXt', - depth=101, - groups=64, - base_width=4, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - style='pytorch')) diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/__init__.py deleted file mode 100644 index ce2930f62a0091e06b37575b96db2ae51ca7908e..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -import mmcv - -from .version import __version__, short_version - - -def digit_version(version_str): - digit_version = [] - for x in version_str.split('.'): - if x.isdigit(): - digit_version.append(int(x)) - elif x.find('rc') != -1: - patch_version = x.split('rc') - digit_version.append(int(patch_version[0]) - 1) - digit_version.append(int(patch_version[1])) - return digit_version - - -mmcv_minimum_version = '1.2.4' -mmcv_maximum_version = '1.4.0' -mmcv_version = digit_version(mmcv.__version__) - - -assert (mmcv_version >= digit_version(mmcv_minimum_version) - and mmcv_version <= digit_version(mmcv_maximum_version)), \ - f'MMCV=={mmcv.__version__} is used but incompatible. ' \ - f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.' - -__all__ = ['__version__', 'short_version'] diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/assign_result.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/assign_result.py deleted file mode 100644 index 4639fbdba0a5b92778e1ab87d61182e54bfb9b6f..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/assigners/assign_result.py +++ /dev/null @@ -1,204 +0,0 @@ -import torch - -from mmdet.utils import util_mixins - - -class AssignResult(util_mixins.NiceRepr): - """Stores assignments between predicted and truth boxes. - - Attributes: - num_gts (int): the number of truth boxes considered when computing this - assignment - - gt_inds (LongTensor): for each predicted box indicates the 1-based - index of the assigned truth box. 0 means unassigned and -1 means - ignore. - - max_overlaps (FloatTensor): the iou between the predicted box and its - assigned truth box. - - labels (None | LongTensor): If specified, for each predicted box - indicates the category label of the assigned truth box. - - Example: - >>> # An assign result between 4 predicted boxes and 9 true boxes - >>> # where only two boxes were assigned. - >>> num_gts = 9 - >>> max_overlaps = torch.LongTensor([0, .5, .9, 0]) - >>> gt_inds = torch.LongTensor([-1, 1, 2, 0]) - >>> labels = torch.LongTensor([0, 3, 4, 0]) - >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels) - >>> print(str(self)) # xdoctest: +IGNORE_WANT - - >>> # Force addition of gt labels (when adding gt as proposals) - >>> new_labels = torch.LongTensor([3, 4, 5]) - >>> self.add_gt_(new_labels) - >>> print(str(self)) # xdoctest: +IGNORE_WANT - - """ - - def __init__(self, num_gts, gt_inds, max_overlaps, labels=None): - self.num_gts = num_gts - self.gt_inds = gt_inds - self.max_overlaps = max_overlaps - self.labels = labels - # Interface for possible user-defined properties - self._extra_properties = {} - - @property - def num_preds(self): - """int: the number of predictions in this assignment""" - return len(self.gt_inds) - - def set_extra_property(self, key, value): - """Set user-defined new property.""" - assert key not in self.info - self._extra_properties[key] = value - - def get_extra_property(self, key): - """Get user-defined property.""" - return self._extra_properties.get(key, None) - - @property - def info(self): - """dict: a dictionary of info about the object""" - basic_info = { - 'num_gts': self.num_gts, - 'num_preds': self.num_preds, - 'gt_inds': self.gt_inds, - 'max_overlaps': self.max_overlaps, - 'labels': self.labels, - } - basic_info.update(self._extra_properties) - return basic_info - - def __nice__(self): - """str: a "nice" summary string describing this assign result""" - parts = [] - parts.append(f'num_gts={self.num_gts!r}') - if self.gt_inds is None: - parts.append(f'gt_inds={self.gt_inds!r}') - else: - parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}') - if self.max_overlaps is None: - parts.append(f'max_overlaps={self.max_overlaps!r}') - else: - parts.append('max_overlaps.shape=' - f'{tuple(self.max_overlaps.shape)!r}') - if self.labels is None: - parts.append(f'labels={self.labels!r}') - else: - parts.append(f'labels.shape={tuple(self.labels.shape)!r}') - return ', '.join(parts) - - @classmethod - def random(cls, **kwargs): - """Create random AssignResult for tests or debugging. - - Args: - num_preds: number of predicted boxes - num_gts: number of true boxes - p_ignore (float): probability of a predicted box assinged to an - ignored truth - p_assigned (float): probability of a predicted box not being - assigned - p_use_label (float | bool): with labels or not - rng (None | int | numpy.random.RandomState): seed or state - - Returns: - :obj:`AssignResult`: Randomly generated assign results. - - Example: - >>> from mmdet.core.bbox.assigners.assign_result import * # NOQA - >>> self = AssignResult.random() - >>> print(self.info) - """ - from mmdet.core.bbox import demodata - rng = demodata.ensure_rng(kwargs.get('rng', None)) - - num_gts = kwargs.get('num_gts', None) - num_preds = kwargs.get('num_preds', None) - p_ignore = kwargs.get('p_ignore', 0.3) - p_assigned = kwargs.get('p_assigned', 0.7) - p_use_label = kwargs.get('p_use_label', 0.5) - num_classes = kwargs.get('p_use_label', 3) - - if num_gts is None: - num_gts = rng.randint(0, 8) - if num_preds is None: - num_preds = rng.randint(0, 16) - - if num_gts == 0: - max_overlaps = torch.zeros(num_preds, dtype=torch.float32) - gt_inds = torch.zeros(num_preds, dtype=torch.int64) - if p_use_label is True or p_use_label < rng.rand(): - labels = torch.zeros(num_preds, dtype=torch.int64) - else: - labels = None - else: - import numpy as np - # Create an overlap for each predicted box - max_overlaps = torch.from_numpy(rng.rand(num_preds)) - - # Construct gt_inds for each predicted box - is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned) - # maximum number of assignments constraints - n_assigned = min(num_preds, min(num_gts, is_assigned.sum())) - - assigned_idxs = np.where(is_assigned)[0] - rng.shuffle(assigned_idxs) - assigned_idxs = assigned_idxs[0:n_assigned] - assigned_idxs.sort() - - is_assigned[:] = 0 - is_assigned[assigned_idxs] = True - - is_ignore = torch.from_numpy( - rng.rand(num_preds) < p_ignore) & is_assigned - - gt_inds = torch.zeros(num_preds, dtype=torch.int64) - - true_idxs = np.arange(num_gts) - rng.shuffle(true_idxs) - true_idxs = torch.from_numpy(true_idxs) - gt_inds[is_assigned] = true_idxs[:n_assigned] - - gt_inds = torch.from_numpy( - rng.randint(1, num_gts + 1, size=num_preds)) - gt_inds[is_ignore] = -1 - gt_inds[~is_assigned] = 0 - max_overlaps[~is_assigned] = 0 - - if p_use_label is True or p_use_label < rng.rand(): - if num_classes == 0: - labels = torch.zeros(num_preds, dtype=torch.int64) - else: - labels = torch.from_numpy( - # remind that we set FG labels to [0, num_class-1] - # since mmdet v2.0 - # BG cat_id: num_class - rng.randint(0, num_classes, size=num_preds)) - labels[~is_assigned] = 0 - else: - labels = None - - self = cls(num_gts, gt_inds, max_overlaps, labels) - return self - - def add_gt_(self, gt_labels): - """Add ground truth as assigned results. - - Args: - gt_labels (torch.Tensor): Labels of gt boxes - """ - self_inds = torch.arange( - 1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device) - self.gt_inds = torch.cat([self_inds, self.gt_inds]) - - self.max_overlaps = torch.cat( - [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps]) - - if self.labels is not None: - self.labels = torch.cat([gt_labels, self.labels]) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 803c42da35eda861bf32ce0e7866cdc9fad96d0d..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,7 +0,0 @@ -_base_ = [ - '../_base_/models/psanet_r50-d8.py', - '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', - '../_base_/schedules/schedule_40k.py' -] -model = dict( - decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-nav/headroom.min.js b/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-nav/headroom.min.js deleted file mode 100644 index b08f1dffbbdae0ace4eb2510b43666c8491546d3..0000000000000000000000000000000000000000 --- a/spaces/AnimalEquality/chatbot/_proc/_docs/site_libs/quarto-nav/headroom.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * headroom.js v0.12.0 - Give your page some headroom. 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diff --git a/spaces/AnthonyTruchetPoC/persistent-docker/scripts/common_header.sh b/spaces/AnthonyTruchetPoC/persistent-docker/scripts/common_header.sh deleted file mode 100644 index af169f4b46c0c2cd43a1d3d809f66a1b229300be..0000000000000000000000000000000000000000 --- a/spaces/AnthonyTruchetPoC/persistent-docker/scripts/common_header.sh +++ /dev/null @@ -1,7 +0,0 @@ -CONTAINER_NAME=persistent-docker-space -VOLUME_NAME=ai-playground-vol - -set -e # Exits on first error - -SCRIPT_PATH="$(realpath "${BASH_SOURCE:-$0}")" -ROOT_DIRECTORY="$(dirname "$(dirname "${SCRIPT_PATH}")")" \ No newline at end of file diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/pipelines/pipeline_tuneavideo.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/pipelines/pipeline_tuneavideo.py deleted file mode 100644 index 5b45cd6e17ae0f69938db60290cd1b8ab207249d..0000000000000000000000000000000000000000 --- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/pipelines/pipeline_tuneavideo.py +++ /dev/null @@ -1,411 +0,0 @@ -# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py - -import inspect -from dataclasses import dataclass -from typing import Callable, List, Optional, Union - -import numpy as np -import torch -from diffusers.configuration_utils import FrozenDict -from diffusers.models import AutoencoderKL -from diffusers.pipeline_utils import DiffusionPipeline -from diffusers.schedulers import ( - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - LMSDiscreteScheduler, - PNDMScheduler, -) -from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging -from einops import rearrange -from packaging import version -from transformers import CLIPTextModel, CLIPTokenizer - -from ..models.unet import UNet3DConditionModel - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class TuneAVideoPipelineOutput(BaseOutput): - videos: Union[torch.Tensor, np.ndarray] - - -class TuneAVideoPipeline(DiffusionPipeline): - _optional_components = [] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: UNet3DConditionModel, - scheduler: Union[ - DDIMScheduler, - PNDMScheduler, - LMSDiscreteScheduler, - EulerDiscreteScheduler, - EulerAncestralDiscreteScheduler, - DPMSolverMultistepScheduler, - ], - ): - super().__init__() - - if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" - f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " - "to update the config accordingly as leaving `steps_offset` might led to incorrect results" - " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," - " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" - " file" - ) - deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["steps_offset"] = 1 - scheduler._internal_dict = FrozenDict(new_config) - - if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." - " `clip_sample` should be set to False in the configuration file. Please make sure to update the" - " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" - " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" - " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" - ) - deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["clip_sample"] = False - scheduler._internal_dict = FrozenDict(new_config) - - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - - def enable_vae_slicing(self): - self.vae.enable_slicing() - - def disable_vae_slicing(self): - self.vae.disable_slicing() - - def enable_sequential_cpu_offload(self, gpu_id=0): - if is_accelerate_available(): - from accelerate import cpu_offload - else: - raise ImportError("Please install accelerate via `pip install accelerate`") - - device = torch.device(f"cuda:{gpu_id}") - - for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: - if cpu_offloaded_model is not None: - cpu_offload(cpu_offloaded_model, device) - - @property - def _execution_device(self): - if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): - return self.device - for module in self.unet.modules(): - if ( - hasattr(module, "_hf_hook") - and hasattr(module._hf_hook, "execution_device") - and module._hf_hook.execution_device is not None - ): - return torch.device(module._hf_hook.execution_device) - return self.device - - def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt): - batch_size = len(prompt) if isinstance(prompt, list) else 1 - - text_inputs = self.tokenizer( - prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {self.tokenizer.model_max_length} tokens: {removed_text}" - ) - - if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: - attention_mask = text_inputs.attention_mask.to(device) - else: - attention_mask = None - - text_embeddings = self.text_encoder( - text_input_ids.to(device), - attention_mask=attention_mask, - ) - text_embeddings = text_embeddings[0] - - # duplicate text embeddings for each generation per prompt, using mps friendly method - bs_embed, seq_len, _ = text_embeddings.shape - text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) - text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) - - # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance: - uncond_tokens: List[str] - if negative_prompt is None: - uncond_tokens = [""] * batch_size - elif type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}." - ) - elif isinstance(negative_prompt, str): - uncond_tokens = [negative_prompt] - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`." - ) - else: - uncond_tokens = negative_prompt - - max_length = text_input_ids.shape[-1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: - attention_mask = uncond_input.attention_mask.to(device) - else: - attention_mask = None - - uncond_embeddings = self.text_encoder( - uncond_input.input_ids.to(device), - attention_mask=attention_mask, - ) - uncond_embeddings = uncond_embeddings[0] - - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = uncond_embeddings.shape[1] - uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) - uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1) - - # For classifier free guidance, we need to do two forward passes. - # Here we concatenate the unconditional and text embeddings into a single batch - # to avoid doing two forward passes - text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) - - return text_embeddings - - def decode_latents(self, latents): - video_length = latents.shape[2] - latents = 1 / 0.18215 * latents - latents = rearrange(latents, "b c f h w -> (b f) c h w") - video = self.vae.decode(latents).sample - video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) - video = (video / 2 + 0.5).clamp(0, 1) - # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 - video = video.cpu().float().numpy() - return video - - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs(self, prompt, height, width, callback_steps): - if not isinstance(prompt, str) and not isinstance(prompt, list): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - def prepare_latents( - self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None - ): - shape = ( - batch_size, - num_channels_latents, - video_length, - height // self.vae_scale_factor, - width // self.vae_scale_factor, - ) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - - if latents is None: - rand_device = "cpu" if device.type == "mps" else device - - if isinstance(generator, list): - shape = (1,) + shape[1:] - latents = [ - torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) - for i in range(batch_size) - ] - latents = torch.cat(latents, dim=0).to(device) - else: - latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) - else: - if latents.shape != shape: - raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - @torch.no_grad() - def __call__( - self, - prompt: Union[str, List[str]], - video_length: Optional[int], - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - negative_prompt: Optional[Union[str, List[str]]] = None, - num_videos_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - output_type: Optional[str] = "tensor", - return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: Optional[int] = 1, - **kwargs, - ): - # Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - - # Check inputs. Raise error if not correct - self.check_inputs(prompt, height, width, callback_steps) - - # Define call parameters - batch_size = 1 if isinstance(prompt, str) else len(prompt) - device = self._execution_device - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - do_classifier_free_guidance = guidance_scale > 1.0 - - # Encode input prompt - text_embeddings = self._encode_prompt( - prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt - ) - - # Prepare timesteps - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps = self.scheduler.timesteps - - # Prepare latent variables - num_channels_latents = self.unet.in_channels - latents = self.prepare_latents( - batch_size * num_videos_per_prompt, - num_channels_latents, - video_length, - height, - width, - text_embeddings.dtype, - device, - generator, - latents, - ) - latents_dtype = latents.dtype - - # Prepare extra step kwargs. - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # Denoising loop - num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample.to( - dtype=latents_dtype - ) - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - callback(i, t, latents) - - # Post-processing - video = self.decode_latents(latents) - - # Convert to tensor - if output_type == "tensor": - video = torch.from_numpy(video) - - if not return_dict: - return video - - return TuneAVideoPipelineOutput(videos=video) diff --git a/spaces/Artrajz/vits-simple-api/bert_vits2/text/chinese_bert.py b/spaces/Artrajz/vits-simple-api/bert_vits2/text/chinese_bert.py deleted file mode 100644 index 3560646e2a01fc2410ac8272410969468c23dccc..0000000000000000000000000000000000000000 --- a/spaces/Artrajz/vits-simple-api/bert_vits2/text/chinese_bert.py +++ /dev/null @@ -1,70 +0,0 @@ -import os - -import config -import torch -from transformers import AutoTokenizer, AutoModelForMaskedLM -from logger import logger -from utils.download import download_and_verify -from config import DEVICE as device - -URLS = [ - "https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/resolve/main/pytorch_model.bin", -] -TARGET_PATH = os.path.join(config.ABS_PATH, "bert_vits2/bert/chinese-roberta-wwm-ext-large/pytorch_model.bin") -EXPECTED_MD5 = None - -if not os.path.exists(TARGET_PATH): - success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5) - -try: - logger.info("Loading chinese-roberta-wwm-ext-large...") - tokenizer = AutoTokenizer.from_pretrained(config.ABS_PATH + "/bert_vits2/bert/chinese-roberta-wwm-ext-large") - model = AutoModelForMaskedLM.from_pretrained(config.ABS_PATH + "/bert_vits2/bert/chinese-roberta-wwm-ext-large").to( - device) - logger.info("Loading finished.") -except Exception as e: - logger.error(e) - logger.error(f"Please download pytorch_model.bin from hfl/chinese-roberta-wwm-ext-large.") - - -def get_bert_feature(text, word2ph, device=config.DEVICE): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors='pt') - for i in inputs: - inputs[i] = inputs[i].to(device) - res = model(**inputs, output_hidden_states=True) - res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() - - assert len(word2ph) == len(text) + 2 - word2phone = word2ph - phone_level_feature = [] - for i in range(len(word2phone)): - repeat_feature = res[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - - return phone_level_feature.T - - -if __name__ == '__main__': - import torch - - word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征 - word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, - 2, 2, 2, 1] - - # 计算总帧数 - total_frames = sum(word2phone) - print(word_level_feature.shape) - print(word2phone) - phone_level_feature = [] - for i in range(len(word2phone)): - print(word_level_feature[i].shape) - - # 对每个词重复word2phone[i]次 - repeat_feature = word_level_feature[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - print(phone_level_feature.shape) # torch.Size([36, 1024]) diff --git a/spaces/AsakuraMizu/moe-tts/utils.py b/spaces/AsakuraMizu/moe-tts/utils.py deleted file mode 100644 index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000 --- a/spaces/AsakuraMizu/moe-tts/utils.py +++ /dev/null @@ -1,226 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.ERROR) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/resolvelib/resolvers.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/resolvelib/resolvers.py deleted file mode 100644 index 2c3d0e306f91f9dfac1843b40babd223766bbf50..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/resolvelib/resolvers.py +++ /dev/null @@ -1,547 +0,0 @@ -import collections -import itertools -import operator - -from .providers import AbstractResolver -from .structs import DirectedGraph, IteratorMapping, build_iter_view - -RequirementInformation = collections.namedtuple( - "RequirementInformation", ["requirement", "parent"] -) - - -class ResolverException(Exception): - """A base class for all exceptions raised by this module. - - Exceptions derived by this class should all be handled in this module. Any - bubbling pass the resolver should be treated as a bug. - """ - - -class RequirementsConflicted(ResolverException): - def __init__(self, criterion): - super(RequirementsConflicted, self).__init__(criterion) - self.criterion = criterion - - def __str__(self): - return "Requirements conflict: {}".format( - ", ".join(repr(r) for r in self.criterion.iter_requirement()), - ) - - -class InconsistentCandidate(ResolverException): - def __init__(self, candidate, criterion): - super(InconsistentCandidate, self).__init__(candidate, criterion) - self.candidate = candidate - self.criterion = criterion - - def __str__(self): - return "Provided candidate {!r} does not satisfy {}".format( - self.candidate, - ", ".join(repr(r) for r in self.criterion.iter_requirement()), - ) - - -class Criterion(object): - """Representation of possible resolution results of a package. - - This holds three attributes: - - * `information` is a collection of `RequirementInformation` pairs. - Each pair is a requirement contributing to this criterion, and the - candidate that provides the requirement. - * `incompatibilities` is a collection of all known not-to-work candidates - to exclude from consideration. - * `candidates` is a collection containing all possible candidates deducted - from the union of contributing requirements and known incompatibilities. - It should never be empty, except when the criterion is an attribute of a - raised `RequirementsConflicted` (in which case it is always empty). - - .. note:: - This class is intended to be externally immutable. **Do not** mutate - any of its attribute containers. - """ - - def __init__(self, candidates, information, incompatibilities): - self.candidates = candidates - self.information = information - self.incompatibilities = incompatibilities - - def __repr__(self): - requirements = ", ".join( - "({!r}, via={!r})".format(req, parent) - for req, parent in self.information - ) - return "Criterion({})".format(requirements) - - def iter_requirement(self): - return (i.requirement for i in self.information) - - def iter_parent(self): - return (i.parent for i in self.information) - - -class ResolutionError(ResolverException): - pass - - -class ResolutionImpossible(ResolutionError): - def __init__(self, causes): - super(ResolutionImpossible, self).__init__(causes) - # causes is a list of RequirementInformation objects - self.causes = causes - - -class ResolutionTooDeep(ResolutionError): - def __init__(self, round_count): - super(ResolutionTooDeep, self).__init__(round_count) - self.round_count = round_count - - -# Resolution state in a round. -State = collections.namedtuple("State", "mapping criteria backtrack_causes") - - -class Resolution(object): - """Stateful resolution object. - - This is designed as a one-off object that holds information to kick start - the resolution process, and holds the results afterwards. - """ - - def __init__(self, provider, reporter): - self._p = provider - self._r = reporter - self._states = [] - - @property - def state(self): - try: - return self._states[-1] - except IndexError: - raise AttributeError("state") - - def _push_new_state(self): - """Push a new state into history. - - This new state will be used to hold resolution results of the next - coming round. - """ - base = self._states[-1] - state = State( - mapping=base.mapping.copy(), - criteria=base.criteria.copy(), - backtrack_causes=base.backtrack_causes[:], - ) - self._states.append(state) - - def _add_to_criteria(self, criteria, requirement, parent): - self._r.adding_requirement(requirement=requirement, parent=parent) - - identifier = self._p.identify(requirement_or_candidate=requirement) - criterion = criteria.get(identifier) - if criterion: - incompatibilities = list(criterion.incompatibilities) - else: - incompatibilities = [] - - matches = self._p.find_matches( - identifier=identifier, - requirements=IteratorMapping( - criteria, - operator.methodcaller("iter_requirement"), - {identifier: [requirement]}, - ), - incompatibilities=IteratorMapping( - criteria, - operator.attrgetter("incompatibilities"), - {identifier: incompatibilities}, - ), - ) - - if criterion: - information = list(criterion.information) - information.append(RequirementInformation(requirement, parent)) - else: - information = [RequirementInformation(requirement, parent)] - - criterion = Criterion( - candidates=build_iter_view(matches), - information=information, - incompatibilities=incompatibilities, - ) - if not criterion.candidates: - raise RequirementsConflicted(criterion) - criteria[identifier] = criterion - - def _remove_information_from_criteria(self, criteria, parents): - """Remove information from parents of criteria. - - Concretely, removes all values from each criterion's ``information`` - field that have one of ``parents`` as provider of the requirement. - - :param criteria: The criteria to update. - :param parents: Identifiers for which to remove information from all criteria. - """ - if not parents: - return - for key, criterion in criteria.items(): - criteria[key] = Criterion( - criterion.candidates, - [ - information - for information in criterion.information - if ( - information.parent is None - or self._p.identify(information.parent) not in parents - ) - ], - criterion.incompatibilities, - ) - - def _get_preference(self, name): - return self._p.get_preference( - identifier=name, - resolutions=self.state.mapping, - candidates=IteratorMapping( - self.state.criteria, - operator.attrgetter("candidates"), - ), - information=IteratorMapping( - self.state.criteria, - operator.attrgetter("information"), - ), - backtrack_causes=self.state.backtrack_causes, - ) - - def _is_current_pin_satisfying(self, name, criterion): - try: - current_pin = self.state.mapping[name] - except KeyError: - return False - return all( - self._p.is_satisfied_by(requirement=r, candidate=current_pin) - for r in criterion.iter_requirement() - ) - - def _get_updated_criteria(self, candidate): - criteria = self.state.criteria.copy() - for requirement in self._p.get_dependencies(candidate=candidate): - self._add_to_criteria(criteria, requirement, parent=candidate) - return criteria - - def _attempt_to_pin_criterion(self, name): - criterion = self.state.criteria[name] - - causes = [] - for candidate in criterion.candidates: - try: - criteria = self._get_updated_criteria(candidate) - except RequirementsConflicted as e: - self._r.rejecting_candidate(e.criterion, candidate) - causes.append(e.criterion) - continue - - # Check the newly-pinned candidate actually works. This should - # always pass under normal circumstances, but in the case of a - # faulty provider, we will raise an error to notify the implementer - # to fix find_matches() and/or is_satisfied_by(). - satisfied = all( - self._p.is_satisfied_by(requirement=r, candidate=candidate) - for r in criterion.iter_requirement() - ) - if not satisfied: - raise InconsistentCandidate(candidate, criterion) - - self._r.pinning(candidate=candidate) - self.state.criteria.update(criteria) - - # Put newly-pinned candidate at the end. This is essential because - # backtracking looks at this mapping to get the last pin. - self.state.mapping.pop(name, None) - self.state.mapping[name] = candidate - - return [] - - # All candidates tried, nothing works. This criterion is a dead - # end, signal for backtracking. - return causes - - def _backjump(self, causes): - """Perform backjumping. - - When we enter here, the stack is like this:: - - [ state Z ] - [ state Y ] - [ state X ] - .... earlier states are irrelevant. - - 1. No pins worked for Z, so it does not have a pin. - 2. We want to reset state Y to unpinned, and pin another candidate. - 3. State X holds what state Y was before the pin, but does not - have the incompatibility information gathered in state Y. - - Each iteration of the loop will: - - 1. Identify Z. The incompatibility is not always caused by the latest - state. For example, given three requirements A, B and C, with - dependencies A1, B1 and C1, where A1 and B1 are incompatible: the - last state might be related to C, so we want to discard the - previous state. - 2. Discard Z. - 3. Discard Y but remember its incompatibility information gathered - previously, and the failure we're dealing with right now. - 4. Push a new state Y' based on X, and apply the incompatibility - information from Y to Y'. - 5a. If this causes Y' to conflict, we need to backtrack again. Make Y' - the new Z and go back to step 2. - 5b. If the incompatibilities apply cleanly, end backtracking. - """ - incompatible_reqs = itertools.chain( - (c.parent for c in causes if c.parent is not None), - (c.requirement for c in causes), - ) - incompatible_deps = {self._p.identify(r) for r in incompatible_reqs} - while len(self._states) >= 3: - # Remove the state that triggered backtracking. - del self._states[-1] - - # Ensure to backtrack to a state that caused the incompatibility - incompatible_state = False - while not incompatible_state: - # Retrieve the last candidate pin and known incompatibilities. - try: - broken_state = self._states.pop() - name, candidate = broken_state.mapping.popitem() - except (IndexError, KeyError): - raise ResolutionImpossible(causes) - current_dependencies = { - self._p.identify(d) - for d in self._p.get_dependencies(candidate) - } - incompatible_state = not current_dependencies.isdisjoint( - incompatible_deps - ) - - incompatibilities_from_broken = [ - (k, list(v.incompatibilities)) - for k, v in broken_state.criteria.items() - ] - - # Also mark the newly known incompatibility. - incompatibilities_from_broken.append((name, [candidate])) - - # Create a new state from the last known-to-work one, and apply - # the previously gathered incompatibility information. - def _patch_criteria(): - for k, incompatibilities in incompatibilities_from_broken: - if not incompatibilities: - continue - try: - criterion = self.state.criteria[k] - except KeyError: - continue - matches = self._p.find_matches( - identifier=k, - requirements=IteratorMapping( - self.state.criteria, - operator.methodcaller("iter_requirement"), - ), - incompatibilities=IteratorMapping( - self.state.criteria, - operator.attrgetter("incompatibilities"), - {k: incompatibilities}, - ), - ) - candidates = build_iter_view(matches) - if not candidates: - return False - incompatibilities.extend(criterion.incompatibilities) - self.state.criteria[k] = Criterion( - candidates=candidates, - information=list(criterion.information), - incompatibilities=incompatibilities, - ) - return True - - self._push_new_state() - success = _patch_criteria() - - # It works! Let's work on this new state. - if success: - return True - - # State does not work after applying known incompatibilities. - # Try the still previous state. - - # No way to backtrack anymore. - return False - - def resolve(self, requirements, max_rounds): - if self._states: - raise RuntimeError("already resolved") - - self._r.starting() - - # Initialize the root state. - self._states = [ - State( - mapping=collections.OrderedDict(), - criteria={}, - backtrack_causes=[], - ) - ] - for r in requirements: - try: - self._add_to_criteria(self.state.criteria, r, parent=None) - except RequirementsConflicted as e: - raise ResolutionImpossible(e.criterion.information) - - # The root state is saved as a sentinel so the first ever pin can have - # something to backtrack to if it fails. The root state is basically - # pinning the virtual "root" package in the graph. - self._push_new_state() - - for round_index in range(max_rounds): - self._r.starting_round(index=round_index) - - unsatisfied_names = [ - key - for key, criterion in self.state.criteria.items() - if not self._is_current_pin_satisfying(key, criterion) - ] - - # All criteria are accounted for. Nothing more to pin, we are done! - if not unsatisfied_names: - self._r.ending(state=self.state) - return self.state - - # keep track of satisfied names to calculate diff after pinning - satisfied_names = set(self.state.criteria.keys()) - set( - unsatisfied_names - ) - - # Choose the most preferred unpinned criterion to try. - name = min(unsatisfied_names, key=self._get_preference) - failure_causes = self._attempt_to_pin_criterion(name) - - if failure_causes: - causes = [i for c in failure_causes for i in c.information] - # Backjump if pinning fails. The backjump process puts us in - # an unpinned state, so we can work on it in the next round. - self._r.resolving_conflicts(causes=causes) - success = self._backjump(causes) - self.state.backtrack_causes[:] = causes - - # Dead ends everywhere. Give up. - if not success: - raise ResolutionImpossible(self.state.backtrack_causes) - else: - # discard as information sources any invalidated names - # (unsatisfied names that were previously satisfied) - newly_unsatisfied_names = { - key - for key, criterion in self.state.criteria.items() - if key in satisfied_names - and not self._is_current_pin_satisfying(key, criterion) - } - self._remove_information_from_criteria( - self.state.criteria, newly_unsatisfied_names - ) - # Pinning was successful. Push a new state to do another pin. - self._push_new_state() - - self._r.ending_round(index=round_index, state=self.state) - - raise ResolutionTooDeep(max_rounds) - - -def _has_route_to_root(criteria, key, all_keys, connected): - if key in connected: - return True - if key not in criteria: - return False - for p in criteria[key].iter_parent(): - try: - pkey = all_keys[id(p)] - except KeyError: - continue - if pkey in connected: - connected.add(key) - return True - if _has_route_to_root(criteria, pkey, all_keys, connected): - connected.add(key) - return True - return False - - -Result = collections.namedtuple("Result", "mapping graph criteria") - - -def _build_result(state): - mapping = state.mapping - all_keys = {id(v): k for k, v in mapping.items()} - all_keys[id(None)] = None - - graph = DirectedGraph() - graph.add(None) # Sentinel as root dependencies' parent. - - connected = {None} - for key, criterion in state.criteria.items(): - if not _has_route_to_root(state.criteria, key, all_keys, connected): - continue - if key not in graph: - graph.add(key) - for p in criterion.iter_parent(): - try: - pkey = all_keys[id(p)] - except KeyError: - continue - if pkey not in graph: - graph.add(pkey) - graph.connect(pkey, key) - - return Result( - mapping={k: v for k, v in mapping.items() if k in connected}, - graph=graph, - criteria=state.criteria, - ) - - -class Resolver(AbstractResolver): - """The thing that performs the actual resolution work.""" - - base_exception = ResolverException - - def resolve(self, requirements, max_rounds=100): - """Take a collection of constraints, spit out the resolution result. - - The return value is a representation to the final resolution result. It - is a tuple subclass with three public members: - - * `mapping`: A dict of resolved candidates. Each key is an identifier - of a requirement (as returned by the provider's `identify` method), - and the value is the resolved candidate. - * `graph`: A `DirectedGraph` instance representing the dependency tree. - The vertices are keys of `mapping`, and each edge represents *why* - a particular package is included. A special vertex `None` is - included to represent parents of user-supplied requirements. - * `criteria`: A dict of "criteria" that hold detailed information on - how edges in the graph are derived. Each key is an identifier of a - requirement, and the value is a `Criterion` instance. - - The following exceptions may be raised if a resolution cannot be found: - - * `ResolutionImpossible`: A resolution cannot be found for the given - combination of requirements. The `causes` attribute of the - exception is a list of (requirement, parent), giving the - requirements that could not be satisfied. - * `ResolutionTooDeep`: The dependency tree is too deeply nested and - the resolver gave up. This is usually caused by a circular - dependency, but you can try to resolve this by increasing the - `max_rounds` argument. - """ - resolution = Resolution(self.provider, self.reporter) - state = resolution.resolve(requirements, max_rounds=max_rounds) - return _build_result(state) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py deleted file mode 100644 index d2dddd6a106f021a4723c1e8f5953ccc09e55e1f..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py +++ /dev/null @@ -1,51 +0,0 @@ -import re - - -SPLIT_RE = re.compile(r'[\.\[\]]+') - - -class JsonSchemaException(ValueError): - """ - Base exception of ``fastjsonschema`` library. - """ - - -class JsonSchemaValueException(JsonSchemaException): - """ - Exception raised by validation function. Available properties: - - * ``message`` containing human-readable information what is wrong (e.g. ``data.property[index] must be smaller than or equal to 42``), - * invalid ``value`` (e.g. ``60``), - * ``name`` of a path in the data structure (e.g. ``data.property[index]``), - * ``path`` as an array in the data structure (e.g. ``['data', 'property', 'index']``), - * the whole ``definition`` which the ``value`` has to fulfil (e.g. ``{'type': 'number', 'maximum': 42}``), - * ``rule`` which the ``value`` is breaking (e.g. ``maximum``) - * and ``rule_definition`` (e.g. ``42``). - - .. versionchanged:: 2.14.0 - Added all extra properties. - """ - - def __init__(self, message, value=None, name=None, definition=None, rule=None): - super().__init__(message) - self.message = message - self.value = value - self.name = name - self.definition = definition - self.rule = rule - - @property - def path(self): - return [item for item in SPLIT_RE.split(self.name) if item != ''] - - @property - def rule_definition(self): - if not self.rule or not self.definition: - return None - return self.definition.get(self.rule) - - -class JsonSchemaDefinitionException(JsonSchemaException): - """ - Exception raised by generator of validation function. - """ diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/grit/modeling/text/load_text_token.py b/spaces/Awiny/Image2Paragraph/models/grit_src/grit/modeling/text/load_text_token.py deleted file mode 100644 index 8491021bf5d7d23d7f3826395f270dccad30df36..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/grit/modeling/text/load_text_token.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch - - -class LoadTextTokens(object): - def __init__(self, tokenizer, max_text_len=40, padding='do_not_pad'): - self.tokenizer = tokenizer - self.max_text_len = max_text_len - self.padding = padding - - def descriptions_to_text_tokens(self, target, begin_token): - target_encoding = self.tokenizer( - target, padding=self.padding, - add_special_tokens=False, - truncation=True, max_length=self.max_text_len) - - need_predict = [1] * len(target_encoding['input_ids']) - payload = target_encoding['input_ids'] - if len(payload) > self.max_text_len - 2: - payload = payload[-(self.max_text_len - 2):] - need_predict = payload[-(self.max_text_len - 2):] - - input_ids = [begin_token] + payload + [self.tokenizer.sep_token_id] - - need_predict = [0] + need_predict + [1] - data = { - 'text_tokens': torch.tensor(input_ids), - 'text_lengths': len(input_ids), - 'need_predict': torch.tensor(need_predict), - } - - return data - - def __call__(self, object_descriptions, box_features, begin_token): - text_tokens = [] - text_lengths = [] - need_predict = [] - for description in object_descriptions: - tokens = self.descriptions_to_text_tokens(description, begin_token) - text_tokens.append(tokens['text_tokens']) - text_lengths.append(tokens['text_lengths']) - need_predict.append(tokens['need_predict']) - - text_tokens = torch.cat(self.collate(text_tokens), dim=0).to(box_features.device) - text_lengths = torch.tensor(text_lengths).to(box_features.device) - need_predict = torch.cat(self.collate(need_predict), dim=0).to(box_features.device) - - assert text_tokens.dim() == 2 and need_predict.dim() == 2 - data = {'text_tokens': text_tokens, - 'text_lengths': text_lengths, - 'need_predict': need_predict} - - return data - - def collate(self, batch): - if all(isinstance(b, torch.Tensor) for b in batch) and len(batch) > 0: - if not all(b.shape == batch[0].shape for b in batch[1:]): - assert all(len(b.shape) == len(batch[0].shape) for b in batch[1:]) - shape = torch.tensor([b.shape for b in batch]) - max_shape = tuple(shape.max(dim=0)[0].tolist()) - batch2 = [] - for b in batch: - if any(c < m for c, m in zip(b.shape, max_shape)): - b2 = torch.zeros(max_shape, dtype=b.dtype, device=b.device) - if b.dim() == 1: - b2[:b.shape[0]] = b - elif b.dim() == 2: - b2[:b.shape[0], :b.shape[1]] = b - elif b.dim() == 3: - b2[:b.shape[0], :b.shape[1], :b.shape[2]] = b - else: - raise NotImplementedError - b = b2 - batch2.append(b[None, ...]) - else: - batch2 = [] - for b in batch: - batch2.append(b[None, ...]) - return batch2 - else: - raise NotImplementedError diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py deleted file mode 100644 index df7a2aedf480ed8dc4aa3645e37420e9b893fae4..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py +++ /dev/null @@ -1,72 +0,0 @@ -import detectron2.data.transforms as T -from detectron2.config.lazy import LazyCall as L -from detectron2.layers.batch_norm import NaiveSyncBatchNorm -from detectron2.solver import WarmupParamScheduler -from fvcore.common.param_scheduler import MultiStepParamScheduler - -from ..common.data.coco import dataloader -from ..common.models.mask_rcnn_fpn import model -from ..common.optim import SGD as optimizer -from ..common.train import train - -# train from scratch -train.init_checkpoint = "" -train.amp.enabled = True -train.ddp.fp16_compression = True -model.backbone.bottom_up.freeze_at = 0 - -# SyncBN -# fmt: off -model.backbone.bottom_up.stem.norm = \ - model.backbone.bottom_up.stages.norm = \ - model.backbone.norm = "SyncBN" - -# Using NaiveSyncBatchNorm becase heads may have empty input. That is not supported by -# torch.nn.SyncBatchNorm. We can remove this after -# https://github.com/pytorch/pytorch/issues/36530 is fixed. -model.roi_heads.box_head.conv_norm = \ - model.roi_heads.mask_head.conv_norm = lambda c: NaiveSyncBatchNorm(c, - stats_mode="N") -# fmt: on - -# 2conv in RPN: -# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/modeling/architecture/heads.py#L95-L97 # noqa: E501, B950 -model.proposal_generator.head.conv_dims = [-1, -1] - -# 4conv1fc box head -model.roi_heads.box_head.conv_dims = [256, 256, 256, 256] -model.roi_heads.box_head.fc_dims = [1024] - -# resize_and_crop_image in: -# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/utils/input_utils.py#L127 # noqa: E501, B950 -image_size = 1024 -dataloader.train.mapper.augmentations = [ - L(T.ResizeScale)( - min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size - ), - L(T.FixedSizeCrop)(crop_size=(image_size, image_size)), - L(T.RandomFlip)(horizontal=True), -] - -# recompute boxes due to cropping -dataloader.train.mapper.recompute_boxes = True - -# larger batch-size. -dataloader.train.total_batch_size = 64 - -# Equivalent to 100 epochs. -# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep -train.max_iter = 184375 - -lr_multiplier = L(WarmupParamScheduler)( - scheduler=L(MultiStepParamScheduler)( - values=[1.0, 0.1, 0.01], - milestones=[163889, 177546], - num_updates=train.max_iter, - ), - warmup_length=500 / train.max_iter, - warmup_factor=0.067, -) - -optimizer.lr = 0.1 -optimizer.weight_decay = 4e-5 diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py deleted file mode 100644 index 9f15f840355571b6d02d5534fa8a9b6b8cb22c70..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/dla.py +++ /dev/null @@ -1,479 +0,0 @@ -import numpy as np -import math -from os.path import join -import fvcore.nn.weight_init as weight_init -import torch -import torch.nn.functional as F -from torch import nn -import torch.utils.model_zoo as model_zoo - -from detectron2.modeling.backbone.resnet import ( - BasicStem, BottleneckBlock, DeformBottleneckBlock) -from detectron2.layers import ( - Conv2d, - DeformConv, - FrozenBatchNorm2d, - ModulatedDeformConv, - ShapeSpec, - get_norm, -) - -from detectron2.modeling.backbone.backbone import Backbone -from detectron2.modeling.backbone.build import BACKBONE_REGISTRY -from detectron2.modeling.backbone.fpn import FPN - -__all__ = [ - "BottleneckBlock", - "DeformBottleneckBlock", - "BasicStem", -] - -DCNV1 = False - -HASH = { - 34: 'ba72cf86', - 60: '24839fc4', -} - -def get_model_url(data, name, hash): - return join('http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash)) - -class BasicBlock(nn.Module): - def __init__(self, inplanes, planes, stride=1, dilation=1, norm='BN'): - super(BasicBlock, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, - stride=stride, padding=dilation, - bias=False, dilation=dilation) - self.bn1 = get_norm(norm, planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, - stride=1, padding=dilation, - bias=False, dilation=dilation) - self.bn2 = get_norm(norm, planes) - self.stride = stride - - def forward(self, x, residual=None): - if residual is None: - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - out += residual - out = self.relu(out) - - return out - -class Bottleneck(nn.Module): - expansion = 2 - - def __init__(self, inplanes, planes, stride=1, dilation=1, norm='BN'): - super(Bottleneck, self).__init__() - expansion = Bottleneck.expansion - bottle_planes = planes // expansion - self.conv1 = nn.Conv2d(inplanes, bottle_planes, - kernel_size=1, bias=False) - self.bn1 = get_norm(norm, bottle_planes) - self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3, - stride=stride, padding=dilation, - bias=False, dilation=dilation) - self.bn2 = get_norm(norm, bottle_planes) - self.conv3 = nn.Conv2d(bottle_planes, planes, - kernel_size=1, bias=False) - self.bn3 = get_norm(norm, planes) - self.relu = nn.ReLU(inplace=True) - self.stride = stride - - def forward(self, x, residual=None): - if residual is None: - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - out += residual - out = self.relu(out) - - return out - -class Root(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, residual, norm='BN'): - super(Root, self).__init__() - self.conv = nn.Conv2d( - in_channels, out_channels, 1, - stride=1, bias=False, padding=(kernel_size - 1) // 2) - self.bn = get_norm(norm, out_channels) - self.relu = nn.ReLU(inplace=True) - self.residual = residual - - def forward(self, *x): - children = x - x = self.conv(torch.cat(x, 1)) - x = self.bn(x) - if self.residual: - x += children[0] - x = self.relu(x) - - return x - - -class Tree(nn.Module): - def __init__(self, levels, block, in_channels, out_channels, stride=1, - level_root=False, root_dim=0, root_kernel_size=1, - dilation=1, root_residual=False, norm='BN'): - super(Tree, self).__init__() - if root_dim == 0: - root_dim = 2 * out_channels - if level_root: - root_dim += in_channels - if levels == 1: - self.tree1 = block(in_channels, out_channels, stride, - dilation=dilation, norm=norm) - self.tree2 = block(out_channels, out_channels, 1, - dilation=dilation, norm=norm) - else: - self.tree1 = Tree(levels - 1, block, in_channels, out_channels, - stride, root_dim=0, - root_kernel_size=root_kernel_size, - dilation=dilation, root_residual=root_residual, - norm=norm) - self.tree2 = Tree(levels - 1, block, out_channels, out_channels, - root_dim=root_dim + out_channels, - root_kernel_size=root_kernel_size, - dilation=dilation, root_residual=root_residual, - norm=norm) - if levels == 1: - self.root = Root(root_dim, out_channels, root_kernel_size, - root_residual, norm=norm) - self.level_root = level_root - self.root_dim = root_dim - self.downsample = None - self.project = None - self.levels = levels - if stride > 1: - self.downsample = nn.MaxPool2d(stride, stride=stride) - if in_channels != out_channels: - self.project = nn.Sequential( - nn.Conv2d(in_channels, out_channels, - kernel_size=1, stride=1, bias=False), - get_norm(norm, out_channels) - ) - - def forward(self, x, residual=None, children=None): - children = [] if children is None else children - bottom = self.downsample(x) if self.downsample else x - residual = self.project(bottom) if self.project else bottom - if self.level_root: - children.append(bottom) - x1 = self.tree1(x, residual) - if self.levels == 1: - x2 = self.tree2(x1) - x = self.root(x2, x1, *children) - else: - children.append(x1) - x = self.tree2(x1, children=children) - return x - -class DLA(nn.Module): - def __init__(self, num_layers, levels, channels, - block=BasicBlock, residual_root=False, norm='BN'): - """ - Args: - """ - super(DLA, self).__init__() - self.norm = norm - self.channels = channels - self.base_layer = nn.Sequential( - nn.Conv2d(3, channels[0], kernel_size=7, stride=1, - padding=3, bias=False), - get_norm(self.norm, channels[0]), - nn.ReLU(inplace=True)) - self.level0 = self._make_conv_level( - channels[0], channels[0], levels[0]) - self.level1 = self._make_conv_level( - channels[0], channels[1], levels[1], stride=2) - self.level2 = Tree(levels[2], block, channels[1], channels[2], 2, - level_root=False, - root_residual=residual_root, norm=norm) - self.level3 = Tree(levels[3], block, channels[2], channels[3], 2, - level_root=True, root_residual=residual_root, - norm=norm) - self.level4 = Tree(levels[4], block, channels[3], channels[4], 2, - level_root=True, root_residual=residual_root, - norm=norm) - self.level5 = Tree(levels[5], block, channels[4], channels[5], 2, - level_root=True, root_residual=residual_root, - norm=norm) - self.load_pretrained_model( - data='imagenet', name='dla{}'.format(num_layers), - hash=HASH[num_layers]) - - def load_pretrained_model(self, data, name, hash): - model_url = get_model_url(data, name, hash) - model_weights = model_zoo.load_url(model_url) - num_classes = len(model_weights[list(model_weights.keys())[-1]]) - self.fc = nn.Conv2d( - self.channels[-1], num_classes, - kernel_size=1, stride=1, padding=0, bias=True) - print('Loading pretrained') - self.load_state_dict(model_weights, strict=False) - - def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1): - modules = [] - for i in range(convs): - modules.extend([ - nn.Conv2d(inplanes, planes, kernel_size=3, - stride=stride if i == 0 else 1, - padding=dilation, bias=False, dilation=dilation), - get_norm(self.norm, planes), - nn.ReLU(inplace=True)]) - inplanes = planes - return nn.Sequential(*modules) - - def forward(self, x): - y = [] - x = self.base_layer(x) - for i in range(6): - x = getattr(self, 'level{}'.format(i))(x) - y.append(x) - return y - - -def fill_up_weights(up): - w = up.weight.data - f = math.ceil(w.size(2) / 2) - c = (2 * f - 1 - f % 2) / (2. * f) - for i in range(w.size(2)): - for j in range(w.size(3)): - w[0, 0, i, j] = \ - (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c)) - for c in range(1, w.size(0)): - w[c, 0, :, :] = w[0, 0, :, :] - - -class _DeformConv(nn.Module): - def __init__(self, chi, cho, norm='BN'): - super(_DeformConv, self).__init__() - self.actf = nn.Sequential( - get_norm(norm, cho), - nn.ReLU(inplace=True) - ) - if DCNV1: - self.offset = Conv2d( - chi, 18, kernel_size=3, stride=1, - padding=1, dilation=1) - self.conv = DeformConv( - chi, cho, kernel_size=(3,3), stride=1, padding=1, - dilation=1, deformable_groups=1) - else: - self.offset = Conv2d( - chi, 27, kernel_size=3, stride=1, - padding=1, dilation=1) - self.conv = ModulatedDeformConv( - chi, cho, kernel_size=3, stride=1, padding=1, - dilation=1, deformable_groups=1) - nn.init.constant_(self.offset.weight, 0) - nn.init.constant_(self.offset.bias, 0) - - def forward(self, x): - if DCNV1: - offset = self.offset(x) - x = self.conv(x, offset) - else: - offset_mask = self.offset(x) - offset_x, offset_y, mask = torch.chunk(offset_mask, 3, dim=1) - offset = torch.cat((offset_x, offset_y), dim=1) - mask = mask.sigmoid() - x = self.conv(x, offset, mask) - x = self.actf(x) - return x - - -class IDAUp(nn.Module): - def __init__(self, o, channels, up_f, norm='BN'): - super(IDAUp, self).__init__() - for i in range(1, len(channels)): - c = channels[i] - f = int(up_f[i]) - proj = _DeformConv(c, o, norm=norm) - node = _DeformConv(o, o, norm=norm) - - up = nn.ConvTranspose2d(o, o, f * 2, stride=f, - padding=f // 2, output_padding=0, - groups=o, bias=False) - fill_up_weights(up) - - setattr(self, 'proj_' + str(i), proj) - setattr(self, 'up_' + str(i), up) - setattr(self, 'node_' + str(i), node) - - - def forward(self, layers, startp, endp): - for i in range(startp + 1, endp): - upsample = getattr(self, 'up_' + str(i - startp)) - project = getattr(self, 'proj_' + str(i - startp)) - layers[i] = upsample(project(layers[i])) - node = getattr(self, 'node_' + str(i - startp)) - layers[i] = node(layers[i] + layers[i - 1]) - - -class DLAUp(nn.Module): - def __init__(self, startp, channels, scales, in_channels=None, norm='BN'): - super(DLAUp, self).__init__() - self.startp = startp - if in_channels is None: - in_channels = channels - self.channels = channels - channels = list(channels) - scales = np.array(scales, dtype=int) - for i in range(len(channels) - 1): - j = -i - 2 - setattr(self, 'ida_{}'.format(i), - IDAUp(channels[j], in_channels[j:], - scales[j:] // scales[j], norm=norm)) - scales[j + 1:] = scales[j] - in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]] - - def forward(self, layers): - out = [layers[-1]] # start with 32 - for i in range(len(layers) - self.startp - 1): - ida = getattr(self, 'ida_{}'.format(i)) - ida(layers, len(layers) -i - 2, len(layers)) - out.insert(0, layers[-1]) - return out - -DLA_CONFIGS = { - 34: ([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], BasicBlock), - 60: ([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], Bottleneck) -} - - -class DLASeg(Backbone): - def __init__(self, num_layers, out_features, use_dla_up=True, - ms_output=False, norm='BN'): - super(DLASeg, self).__init__() - # depth = 34 - levels, channels, Block = DLA_CONFIGS[num_layers] - self.base = DLA(num_layers=num_layers, - levels=levels, channels=channels, block=Block, norm=norm) - down_ratio = 4 - self.first_level = int(np.log2(down_ratio)) - self.ms_output = ms_output - self.last_level = 5 if not self.ms_output else 6 - channels = self.base.channels - scales = [2 ** i for i in range(len(channels[self.first_level:]))] - self.use_dla_up = use_dla_up - if self.use_dla_up: - self.dla_up = DLAUp( - self.first_level, channels[self.first_level:], scales, - norm=norm) - out_channel = channels[self.first_level] - if not self.ms_output: # stride 4 DLA - self.ida_up = IDAUp( - out_channel, channels[self.first_level:self.last_level], - [2 ** i for i in range(self.last_level - self.first_level)], - norm=norm) - self._out_features = out_features - self._out_feature_channels = { - 'dla{}'.format(i): channels[i] for i in range(6)} - self._out_feature_strides = { - 'dla{}'.format(i): 2 ** i for i in range(6)} - self._size_divisibility = 32 - - @property - def size_divisibility(self): - return self._size_divisibility - - def forward(self, x): - x = self.base(x) - if self.use_dla_up: - x = self.dla_up(x) - if not self.ms_output: # stride 4 dla - y = [] - for i in range(self.last_level - self.first_level): - y.append(x[i].clone()) - self.ida_up(y, 0, len(y)) - ret = {} - for i in range(self.last_level - self.first_level): - out_feature = 'dla{}'.format(i) - if out_feature in self._out_features: - ret[out_feature] = y[i] - else: - ret = {} - st = self.first_level if self.use_dla_up else 0 - for i in range(self.last_level - st): - out_feature = 'dla{}'.format(i + st) - if out_feature in self._out_features: - ret[out_feature] = x[i] - - return ret - - -@BACKBONE_REGISTRY.register() -def build_dla_backbone(cfg, input_shape): - """ - Create a ResNet instance from config. - - Returns: - ResNet: a :class:`ResNet` instance. - """ - return DLASeg( - out_features=cfg.MODEL.DLA.OUT_FEATURES, - num_layers=cfg.MODEL.DLA.NUM_LAYERS, - use_dla_up=cfg.MODEL.DLA.USE_DLA_UP, - ms_output=cfg.MODEL.DLA.MS_OUTPUT, - norm=cfg.MODEL.DLA.NORM) - -class LastLevelP6P7(nn.Module): - """ - This module is used in RetinaNet to generate extra layers, P6 and P7 from - C5 feature. - """ - - def __init__(self, in_channels, out_channels): - super().__init__() - self.num_levels = 2 - self.in_feature = "dla5" - self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1) - self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1) - for module in [self.p6, self.p7]: - weight_init.c2_xavier_fill(module) - - def forward(self, c5): - p6 = self.p6(c5) - p7 = self.p7(F.relu(p6)) - return [p6, p7] - -@BACKBONE_REGISTRY.register() -def build_retinanet_dla_fpn_backbone(cfg, input_shape: ShapeSpec): - """ - Args: - cfg: a detectron2 CfgNode - Returns: - backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. - """ - bottom_up = build_dla_backbone(cfg, input_shape) - in_features = cfg.MODEL.FPN.IN_FEATURES - out_channels = cfg.MODEL.FPN.OUT_CHANNELS - in_channels_p6p7 = bottom_up.output_shape()['dla5'].channels - backbone = FPN( - bottom_up=bottom_up, - in_features=in_features, - out_channels=out_channels, - norm=cfg.MODEL.FPN.NORM, - top_block=LastLevelP6P7(in_channels_p6p7, out_channels), - fuse_type=cfg.MODEL.FPN.FUSE_TYPE, - ) - return backbone diff --git a/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py b/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py deleted file mode 100644 index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers_123812KB .py +++ /dev/null @@ -1,118 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn - -from . import spec_utils - - -class Conv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False, - ), - nn.Conv2d(nin, nout, kernel_size=1, bias=False), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/spaces/Bart92/RVC_HF/lib/infer_pack/models_onnx.py b/spaces/Bart92/RVC_HF/lib/infer_pack/models_onnx.py deleted file mode 100644 index 963e67b29f828e9fdd096397952054fe77cf3d10..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/lib/infer_pack/models_onnx.py +++ /dev/null @@ -1,819 +0,0 @@ -import math, pdb, os -from time import time as ttime -import torch -from torch import nn -from torch.nn import functional as F -from lib.infer_pack import modules -from lib.infer_pack import attentions -from lib.infer_pack import commons -from lib.infer_pack.commons import init_weights, get_padding -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from lib.infer_pack.commons import init_weights -import numpy as np -from lib.infer_pack import commons - - -class TextEncoder256(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class TextEncoder768(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(768, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - def remove_weight_norm(self): - for i in range(self.n_flows): - self.flows[i * 2].remove_weight_norm() - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class SineGen(torch.nn.Module): - """Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__( - self, - samp_rate, - harmonic_num=0, - sine_amp=0.1, - noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False, - ): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - - def _f02uv(self, f0): - # generate uv signal - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voiced_threshold) - return uv - - def forward(self, f0, upp): - """sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0 = f0[:, None].transpose(1, 2) - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) - # fundamental component - f0_buf[:, :, 0] = f0[:, :, 0] - for idx in np.arange(self.harmonic_num): - f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( - idx + 2 - ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic - rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 - rand_ini = torch.rand( - f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device - ) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化 - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose( - 2, 1 - ) ####### - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - sine_waves = sine_waves * self.sine_amp - uv = self._f02uv(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__( - self, - sampling_rate, - harmonic_num=0, - sine_amp=0.1, - add_noise_std=0.003, - voiced_threshod=0, - is_half=True, - ): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - self.is_half = is_half - # to produce sine waveforms - self.l_sin_gen = SineGen( - sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod - ) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x, upp=None): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - if self.is_half: - sine_wavs = sine_wavs.half() - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge, None, None # noise, uv - - -class GeneratorNSF(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - sr, - is_half=False, - ): - super(GeneratorNSF, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) - self.m_source = SourceModuleHnNSF( - sampling_rate=sr, harmonic_num=0, is_half=is_half - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - self.upp = np.prod(upsample_rates) - - def forward(self, x, f0, g=None): - har_source, noi_source, uv = self.m_source(f0, self.upp) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -sr2sr = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -class SynthesizerTrnMsNSFsidM(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - version, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - if version == "v1": - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - else: - self.enc_p = TextEncoder768( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - self.speaker_map = None - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def construct_spkmixmap(self, n_speaker): - self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels)) - for i in range(n_speaker): - self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]])) - self.speaker_map = self.speaker_map.unsqueeze(0) - - def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None): - if self.speaker_map is not None: # [N, S] * [S, B, 1, H] - g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] - g = g * self.speaker_map # [N, S, B, 1, H] - g = torch.sum(g, dim=1) # [N, 1, B, 1, H] - g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] - else: - g = g.unsqueeze(0) - g = self.emb_g(g).transpose(1, 2) - - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11, 17] - # periods = [3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class MultiPeriodDiscriminatorV2(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminatorV2, self).__init__() - # periods = [2, 3, 5, 7, 11, 17] - periods = [2, 3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap diff --git a/spaces/Benson/text-generation/Examples/Callbreak Ludo Rummy 29 Amp Juegos De Cartas Solitario Apk Descargar.md b/spaces/Benson/text-generation/Examples/Callbreak Ludo Rummy 29 Amp Juegos De Cartas Solitario Apk Descargar.md deleted file mode 100644 index 0fa1891da48daa822224b9122bf1c312992a2a4d..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Callbreak Ludo Rummy 29 Amp Juegos De Cartas Solitario Apk Descargar.md +++ /dev/null @@ -1,151 +0,0 @@ - -

Callbreak Ludo Rummy 29 & Solitario juegos de cartas APK Descargar

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Si te gusta jugar juegos de cartas y juegos de mesa en su dispositivo móvil, entonces usted debe definitivamente echa un vistazo Callbreak Ludo Rummy 29 & Solitario Juegos de Cartas APK. Esta aplicación es desarrollada por Yarsa Games, un popular estudio de juegos con sede en Nepal. Te ofrece ocho juegos diferentes en un solo paquete. Puedes disfrutar de Callbreak, Ludo, Rummy, 29, Solitaire, Kitti, Dhumbal y Jutpatti en cualquier momento y en cualquier lugar. Ya sea que quieras jugar solo o con tus amigos en línea o fuera de línea puedes divertirte con esta aplicación. En este artículo te mostraremos cómo descargar e instalar la aplicación, así como cómo jugar cada juego en ella. También destacaremos algunas de las características y beneficios de esta aplicación y responderemos algunas preguntas frecuentes sobre ella.

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Cómo descargar e instalar la aplicación

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Descargar e instalar Callbreak Ludo Rummy 29 & Solitario juegos de cartas APK es muy fácil. Usted tiene dos opciones para hacerlo:

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callbreak ludo rummy 29 amp; juegos de cartas solitario apk descargar


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  1. Descárgalo desde Google Play Store. Solo busca "Callbreak Ludo" en la tienda o haz clic en este enlace . Luego toca el botón "Instalar" y espera a que la aplicación se descargue e instale en tu dispositivo.
    2. -
  2. Descargarlo desde el archivo APK. Si desea descargar el archivo APK directamente puede hacer clic en este enlace . Luego debe habilitar la opción "Fuentes desconocidas" en la configuración del dispositivo para permitir la instalación de aplicaciones desde fuentes distintas de Google Play Store. Después de eso, puede abrir el archivo APK y seguir las instrucciones para instalar la aplicación.
    3. -
-

Cómo jugar los juegos en la aplicación

-

Callbreak

-

Callbreak es un juego de cartas que es muy popular en Nepal e India. Lo juegan cuatro jugadores en dos equipos. Cada jugador recibe 13 cartas de un mazo de 52 cartas estándar. El juego consta de cinco rondas y cada ronda tiene 13 trucos. El primer repartidor es elegido al azar y luego el turno de repartir gira en el sentido de las agujas del reloj. El repartidor reparte todas las cartas una por una a cada jugador.

- -

El jugador a la izquierda del repartidor juega la primera carta del primer truco. El palo de esta carta se convierte en el palo de triunfo para esa ronda. Los otros jugadores tienen que seguir el ejemplo si tienen una carta del mismo palo. Si no tienen una carta del mismo palo, pueden jugar cualquier carta de su elección. El jugador que juega la carta más alta del palo de triunfo gana el truco. Si no se juega ninguna carta de triunfo, el jugador que juega la carta más alta del palo que se llevó gana el truco. El ganador de un truco lidera el siguiente truco.

-

Al final de cada ronda, la puntuación de cada jugador se calcula en función de su oferta y el número de trucos que ganó. Si un jugador gana al menos tantos trucos como su oferta, obtiene una puntuación positiva igual a su oferta. Si un jugador gana menos trucos que su oferta, obtiene una puntuación negativa igual a su oferta. Si un jugador gana más trucos que su oferta, obtiene una puntuación positiva igual a su oferta más 0.1 punto por cada truco adicional. Por ejemplo, si un jugador ofrece 4 y gana 5 trucos, su puntuación es 4 + 0.1 = 4.1 puntos.

-

El juego termina después de cinco rondas y el equipo con la mayor puntuación total gana.

-

Ludo

-

Ludo es un clásico juego de mesa que se deriva de un antiguo juego indio llamado Pachisi. Se juega de dos a cuatro jugadores en un tablero con cuatro áreas de color: rojo, verde, amarillo y azul. Cada jugador tiene cuatro fichas de su color que comienzan en su área de origen. El objetivo del juego es mover las cuatro fichas alrededor del tablero y en su área de destino antes que los otros jugadores.

-

El juego se juega con un solo dado que determina cuántos espacios puede mover un token en el tablero. Cada jugador lanza el dado a su vez y mueve una de sus fichas de acuerdo con el número que se muestra en el dado. Un token solo puede entrar en el tablero si el die muestra un seis o si no hay tokens en el área de inicio. Un token solo puede entrar en el área de destino si completa un circuito completo alrededor de la placa.

- -

Un jugador puede volver a lanzar el dado si lanza un seis o si captura otro token. Un jugador también puede elegir saltarse su turno si no puede o no quiere mover ninguna de sus fichas.

-

El juego termina cuando un jugador mueve las cuatro fichas en su área de destino y declara "Ludo!".

-

Rummy

-

Rummy es un juego de cartas que se juega entre dos y seis jugadores con una o dos barajas estándar de 52 cartas. El objetivo del juego es formar conjuntos válidos y secuencias de cartas y deshacerse de todas las cartas en su mano. Un conjunto es un grupo de tres o cuatro cartas del mismo rango, como 7-7-7 o Q-Q-Q-Q-Q. Una secuencia es un grupo de tres o más cartas del mismo palo en orden consecutivo, como 4-5-6 de corazones o 10-J-Q-K de picas. Una carta comodín puede usarse como sustituto de cualquier carta en un conjunto o secuencia.

-

El juego comienza con cada jugador que recibe 13 cartas por el repartidor, que es elegido al azar. Las cartas restantes se colocan boca abajo en la mesa como la pila de existencias. La carta superior de la pila principal se pone boca arriba y se coloca junto a ella como la pila de descarte. El jugador a la izquierda del repartidor juega primero y luego el turno pasa en el sentido de las agujas del reloj.

-

En tu turno, tienes que sacar una carta de la pila de reserva o de la pila de descarte y luego desechar una carta de tu mano a la pila de descarte. También puedes declarar tu mano si has formado todos los conjuntos y secuencias necesarios y te queda una carta para descartar. Esto se llama "salir" o "mostrar".

-

Cuando un jugador sale, la ronda termina y la puntuación de cada jugador se calcula en función del valor de las cartas que quedan en su mano. Las cartas (J, Q, K) tienen un valor de 10 puntos cada una, el as tiene un valor de 1 punto, y las otras cartas tienen un valor igual a su rango. El comodín no tiene valor. El jugador que sale obtiene cero puntos y los otros jugadores obtienen puntos positivos. El jugador con la puntuación total más baja al final del juego gana.

-

29

- -

Antes del inicio de cada ronda, hay una fase de puja donde cada jugador tiene que hacer una puja, que es el número de puntos que espera que su equipo gane en esa ronda. La oferta puede ser cualquier número de 15 a 28 o "pase". La oferta mínima es de 15 y la oferta máxima es de 28. El jugador a la izquierda del repartidor hace la primera puja y luego la puja va en el sentido de las agujas del reloj. Cada jugador puede pasar o subir la puja por al menos un punto. La puja termina cuando tres jugadores pasan consecutivamente. El último jugador que hizo una oferta se convierte en el declarante y su pareja se convierte en el maniquí.

-

El declarante tiene que elegir un traje de triunfo para esa ronda entre picas, corazones, diamantes o tréboles. El palo de triunfo tiene una clasificación especial de cartas: J (alta), 9, A, 10, K, Q, 8 (baja). Los otros palos tienen una clasificación normal de cartas: A (alta), K, Q, J, 10, 9, 8 (baja). El declarante también puede elegir "no trump", lo que significa que no hay un traje de triunfo y todos los trajes tienen un ranking normal.

-

El jugador a la izquierda del repartidor juega la primera carta del primer truco. Los otros jugadores tienen que seguir el ejemplo si tienen una carta del mismo palo. Si no tienen una carta del mismo palo, pueden jugar cualquier carta de su elección. El jugador que juega la carta más alta del palo de triunfo gana el truco. Si no se juega ninguna carta de triunfo, el jugador que juega la carta más alta del palo que se llevó gana el truco. El ganador de un truco lidera el siguiente truco.

- -

El juego termina después de 28 rondas y el equipo con la mayor puntuación total gana.

-

Solitario

-

Solitario es un juego de cartas que es jugado por un jugador con una baraja estándar de 52 cartas. El objetivo del juego es ordenar todas las cartas en cuatro montones de acuerdo a sus palos y rangos. El juego tiene siete columnas de cartas en la mesa y cuatro cimientos vacíos en la parte superior.

-

El juego comienza con 28 cartas que se reparten boca abajo en siete columnas de izquierda a derecha. La primera columna tiene una carta, la segunda columna tiene dos cartas, y así sucesivamente hasta que la séptima columna tiene siete cartas. La carta de la parte superior de cada columna se pone boca arriba. Las 24 cartas restantes se colocan boca abajo en la mesa como una pila.

-

En tu turno, puedes mover una o más cartas de una columna a otra si forman una secuencia descendente de colores alternados. Por ejemplo, puede mover un 6 rojo y un 5 negro juntos a un 7 rojo. También puede mover una sola carta a una columna vacía. Puede voltear la tarjeta boca abajo de cualquier columna si no hay una tarjeta boca arriba en ella.

-

También puede mover una o más cartas de una columna a una fundación si forman una secuencia ascendente del mismo palo. Por ejemplo, puedes mover un as de picas sobre una base vacía o un dos de picas sobre un as de picas sobre una base. También puede mover una sola carta de la pila de valores a una fundación si cabe.

-

Puedes sacar una o tres cartas de la pila de reserva dependiendo de tu preferencia y colocarlas boca arriba en la mesa como la pila de residuos. Usted puede mover la tarjeta superior de la pila de residuos a una columna o una fundación si cabe. También puede reciclar la pila de residuos de nuevo a la pila de existencias cuando está vacío.

-

El juego termina cuando has movido las 52 cartas a las fundaciones o cuando no te quedan movimientos.

-

Kitti

- -

El juego comienza con cada jugador siendo repartido siete cartas por el repartidor, que es elegido al azar. Las cartas restantes se colocan boca abajo en la mesa como la pila kitti. La carta superior de la pila de gatitos se pone boca arriba y se coloca junto a ella como la carta abierta. El jugador a la izquierda del repartidor juega primero y luego el turno pasa en el sentido de las agujas del reloj.

-

En tu turno, tienes que sacar una carta de la pila de gatitos o de la carta abierta y luego descartar una carta de tu mano a la carta abierta. También puede mostrar su mano si ha formado todos los grupos necesarios y le queda una carta para descartar. Esto se llama "salir" o "mostrar".

-

Cuando un jugador sale, la ronda termina y la puntuación de cada jugador se calcula en función del valor de las cartas que quedan en su mano. Las cartas (J, Q, K) tienen un valor de 10 puntos cada una, el as tiene un valor de 1 punto, y las otras cartas tienen un valor igual a su rango. El comodín no tiene valor. El jugador que sale obtiene cero puntos y los otros jugadores obtienen puntos positivos. El jugador con la puntuación total más baja al final del juego gana.

-

Dhumbal

-

Dhumbal es un juego de cartas que es jugado por tres a seis jugadores con una baraja estándar de 52 cartas. El objetivo del juego es tener la puntuación más baja al final de cada ronda. El juego tiene 10 rondas y cada ronda tiene una puntuación objetivo diferente que los jugadores tienen que permanecer por debajo.

-

El juego comienza con cada jugador siendo repartido cuatro cartas por el repartidor, que es elegido al azar. Las cartas restantes se colocan boca abajo en la mesa como la pila de existencias. La carta superior de la pila principal se pone boca arriba y se coloca junto a ella como la pila de descarte. El jugador a la izquierda del repartidor juega primero y luego el turno pasa en el sentido de las agujas del reloj.

- -

Cuando un jugador sale, la ronda termina y la puntuación de cada jugador se calcula en función del valor de sus cartas. Las cartas (J, Q, K) tienen un valor de 10 puntos cada una, el as tiene un valor de 11 puntos, y las otras cartas tienen un valor igual a su rango. El comodín tiene un valor de cero puntos. El jugador que sale obtiene una puntuación igual a la suma de sus cartas y los otros jugadores obtienen una puntuación igual a la suma de sus cartas más 10 puntos de penalización. Si dos o más jugadores salen en el mismo turno, el jugador con la puntuación más baja gana esa ronda y los otros jugadores obtienen 10 puntos de penalización.

-

La puntuación objetivo para cada ronda es la siguiente:

- -RoundPuntuación del objetivo -17 -29 -311 -413 -515 -617 -719 -821 -923 -1025 - -

Si un jugador supera la puntuación objetivo en cualquier ronda, es eliminado del juego. El juego termina después de 10 rondas o cuando solo queda un jugador. El jugador con la puntuación total más baja al final del juego gana.

-

Jutpatti

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Jutpatti es un juego de cartas que se juega entre dos y cuatro jugadores con una baraja estándar de 52 cartas. El objetivo del juego es recoger pares de cartas y ser el primero en deshacerse de todas las cartas en su mano. Un par son dos cartas del mismo rango, como 7-7 o Q-Q. Una carta comodín puede ser utilizada como un sustituto de cualquier carta en un par.

-

El juego comienza con cada jugador siendo repartido cinco cartas por el repartidor, que es elegido al azar. Las cartas restantes se colocan boca abajo en la mesa como la pila de existencias. La carta de la parte superior de la pila se pone boca arriba y se coloca junto a ella como la carta abierta. El jugador a la izquierda del repartidor juega primero y luego el turno pasa en el sentido de las agujas del reloj.

- -

Cuando un jugador sale, la ronda termina y la puntuación de cada jugador se calcula en función del número de parejas que tienen en su mano. Cada pareja vale un punto y cada comodín vale la mitad de un punto. El jugador que sale obtiene cero puntos y los otros jugadores obtienen puntos negativos. El jugador con la puntuación total más alta al final del juego gana.

-

Características y beneficios de la aplicación

-

Callbreak Ludo Rummy 29 & Solitario Juegos de Cartas APK no es solo una aplicación ordinaria que le ofrece ocho juegos diferentes en un paquete. También tiene algunas características y beneficios increíbles que lo hacen destacar de otras aplicaciones similares. Estos son algunos de ellos:

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    -
  • La aplicación tiene gráficos de alta calidad y efectos de sonido que mejoran su experiencia de juego.
    • -
  • La aplicación tiene un modo sin conexión que le permite jugar sin conexión a Internet.
    • -
  • La aplicación tiene un modo multijugador que le permite jugar con sus amigos en línea o fuera de línea.
    • -
  • La aplicación tiene una opción de chat que le permite comunicarse con otros jugadores durante el juego.
    • -
  • La aplicación tiene tablas de clasificación que muestran su ranking y logros entre otros jugadores.
    • -
  • La aplicación tiene varios ajustes que le permiten personalizar sus preferencias de juego como el idioma, tema, nivel de dificultad, etc.
    • -
  • La aplicación tiene actualizaciones regulares que agregan nuevas características y corrigen errores.
    • -
  • La aplicación es gratuita para descargar y jugar.
    • -
-

Preguntas frecuentes sobre la aplicación

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Si tiene alguna pregunta o duda sobre Callbreak Ludo Rummy 29 & Solitario Juegos de Cartas APK, usted puede encontrar las respuestas aquí. Hemos recopilado algunas de las preguntas más frecuentes sobre la aplicación y sus respuestas:

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    -
  1. ¿Cómo puedo actualizar la aplicación?
    2. -

    Puedes actualizar la aplicación siguiendo estos pasos:

    -
      -
    • Abre Google Play Store en tu dispositivo.
      • -
    • Buscar "Callbreak Ludo" o haga clic en este enlace .
      • -
    • Toque en el botón "Actualizar" y espere a que la aplicación se actualice.
      • -
    - -

    Puede ponerse en contacto con el equipo de soporte siguiendo estos pasos:

    -
      -
    • Abra la aplicación en su dispositivo.
      • -
    • Toque en el icono del menú en la esquina superior izquierda de la pantalla.
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    • Toque en la opción "Ayuda y soporte".
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    • Rellene su nombre, correo electrónico y mensaje y toque en "Enviar" botón.
      • -
    -
  2. ¿Cómo puedo jugar con mis amigos en línea?
    3. -

    Puedes jugar con tus amigos online siguiendo estos pasos:

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      -
    • Abra la aplicación en su dispositivo.
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    • Seleccione el juego que desea jugar desde la pantalla de inicio.
      • -
    • Toque en la opción "Multijugador" y luego en la opción "Online".
      • -
    • Crear una habitación o unirse a una habitación existente introduciendo el código de la habitación.
      • -
    • Invita a tus amigos a unirse a la habitación compartiendo el código de la habitación con ellos.
      • -
    • Comienza el juego y disfruta jugando con tus amigos.
      • -
    -
  3. ¿Cómo puedo cambiar el idioma de la aplicación?
    4. -

    Puedes cambiar el idioma de la aplicación siguiendo estos pasos:

    -
      -
    • Abra la aplicación en su dispositivo.
      • -
    • Toque en el icono del menú en la esquina superior izquierda de la pantalla.
      • -
    • Toque en la opción "Configuración".
      • -
    • Toque en la opción "Idioma" y seleccione su idioma preferido de la lista.
      • -
  4. ¿Cómo puedo jugar sin conexión?
    5. -

    Puedes jugar sin conexión siguiendo estos pasos:

    -
      -
    • Abra la aplicación en su dispositivo.
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    • Seleccione el juego que desea jugar desde la pantalla de inicio.
      • -
    • Toque en la opción "Multijugador" y luego en la opción "Offline".
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    • Seleccione el número de jugadores y el nivel de dificultad de los jugadores de la computadora.
      • -
    • Comienza el juego y disfruta jugando offline.
      • -
    -

    Conclusión

    - -

    Entonces, ¿qué estás esperando? Descargar Callbreak Ludo Rummy 29 & Solitario juegos de cartas APK ahora y divertirse jugando!

    -

    Preguntas frecuentes

    -
      -
    1. ¿Cuáles son los requisitos mínimos para ejecutar la aplicación?
      2. -

      La aplicación requiere Android 4.4 o superior y al menos 50 MB de espacio libre en su dispositivo.

      -
    2. ¿Es la aplicación segura para descargar y usar?
      3. -

      Sí, la aplicación es segura de descargar y usar. No contiene ningún virus o malware. Tampoco recopila ninguna información personal o confidencial de usted.

      -
    3. ¿Puedo jugar con dinero real en la aplicación?
      4. -

      No, la aplicación no admite transacciones con dinero real o apuestas. Es solo para fines de entretenimiento.

      -
    4. ¿Puedo personalizar mi avatar y perfil en la aplicación?
      5. -

      Sí, puedes personalizar tu avatar y perfil en la aplicación. Puedes elegir entre diferentes avatares, fondos, marcos y pegatinas. También puede editar su nombre, país y estado.

      -
    5. ¿Cómo puedo dar comentarios o sugerencias para la aplicación?
      6. -

      Puedes dar comentarios o sugerencias para la aplicación siguiendo estos pasos:

      -
        -
      • Abra la aplicación en su dispositivo.
        • -
      • Toque en el icono del menú en la esquina superior izquierda de la pantalla.
        • -
      • Toque en la opción "Feedback".
        • -
      • Rellene su nombre, correo electrónico, calificación y mensaje y toque en "Enviar" botón.
        • -

      64aa2da5cf
      -
      -
      \ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk Mod Zombi Caminando 2.md b/spaces/Benson/text-generation/Examples/Descargar Apk Mod Zombi Caminando 2.md deleted file mode 100644 index 890044af6a606d02fe1b68e122e8a0217abfe011..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Apk Mod Zombi Caminando 2.md +++ /dev/null @@ -1,94 +0,0 @@ - -

      Descargar APK Mod Walking Zombie 2: Una guía para los amantes de los zombis

      -

      Si eres un fan de los juegos de zombies, probablemente hayas oído hablar de Walking Zombie 2, un popular shooter en primera persona con elementos RPG. El juego se desarrolla en un mundo post-apocalíptico donde tienes que luchar contra hordas de zombies, bandidos y monstruos jefes. También tienes que completar misiones, mejorar tus habilidades y beneficios, comprar y vender equipos, e interactuar con otros sobrevivientes.

      -

      descargar apk mod zombi caminando 2


      Download File ★ https://bltlly.com/2v6KPU



      -

      Walking Zombie 2 es un juego divertido y desafiante que puedes jugar sin conexión a Internet. Sin embargo, si desea mejorar su experiencia de juego, es posible que desee probar la descarga de un mod APK para Walking Zombie 2. En este artículo, explicaremos lo que es un mod APK, cómo instalarlo, qué características ofrece, y algunos consejos y trucos para jugar Walking Zombie 2. Vamos a empezar!

      -

      ¿Qué es un mod APK y cómo instalarlo

      -

      Un mod APK es una versión modificada de una aplicación original o juego que ha sido alterado por alguien para agregar o eliminar algunas características. Por ejemplo, un mod de APK para Walking Zombie 2 podría darte dinero ilimitado, munición, salud u otros beneficios que no están disponibles en el juego original.

      -

      Un mod APK generalmente se descarga como un archivo con la extensión . apk que puede instalar en su dispositivo Android. Sin embargo, antes de instalar un mod APK, es necesario asegurarse de que el dispositivo permite instalar aplicaciones de fuentes desconocidas. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y habilite.

      -

      Una vez que haya habilitado fuentes desconocidas, puede descargar un mod APK para Walking Zombie 2 desde un sitio web confiable como [APKMB.Com]( 4 ), que ofrece juegos y aplicaciones modificadas para Android de forma gratuita. Después de descargar el archivo, localizarlo en el administrador de archivos de su dispositivo y toque en él para instalarlo. Es posible que necesite permitir que algunos permisos para que la aplicación se ejecute correctamente.

      -

      -

      Cómo descargar APK Mod Walking Zombie 2

      - -
        -
      1. Ir a [APKMB.Com]( 4 ) y buscar Walking Zombie 2 en la barra de búsqueda.
        2. -
      2. Seleccione la versión del mod que desea descargar. Asegúrese de que es compatible con la versión de Android de su dispositivo.
        3. -
      3. Haga clic en el botón de descarga y espere a que se descargue el archivo.
        4. -
      4. Abra el administrador de archivos de su dispositivo y encuentre el archivo descargado. Debería estar en la carpeta Descargas o en una carpeta con el nombre del sitio web.
        5. -
      5. Toque en el archivo y siga las instrucciones para instalarlo. Es posible que tenga que permitir algunos permisos para que la aplicación se ejecute correctamente.
        6. -
      6. Inicie la aplicación desde su pantalla de inicio o cajón de aplicaciones y disfrutar jugando Walking Zombie 2 con el mod!
        7. -
      -

      Características de APK Mod Walking Zombie 2

      -

      Dependiendo de la versión del mod que descargues, es posible que obtengas diferentes características y beneficios. Sin embargo, algunas de las características comunes de un mod APK para Walking Zombie 2 son:

      -
        -
      • Dinero ilimitado: Puedes comprar cualquier cosa que quieras en la tienda sin preocuparte por quedarte sin dinero.
        • -
      • Munición ilimitada: Puedes disparar todo lo que quieras sin recargar o quedarse sin balas.
        • -
      • Salud ilimitada: Puedes sobrevivir a cualquier ataque sin perder la salud o morir.
        • -
      • Puntos de habilidad ilimitados: Puedes mejorar tus habilidades y beneficios al nivel máximo sin ganar puntos de experiencia.
        • -
      • Armas y equipos desbloqueados: puedes acceder a todas las armas y equipos del juego sin encontrarlos ni comprarlos.
        • -
      • Sin anuncios: Puedes jugar el juego sin interrupciones o distracciones de los anuncios.
        • -
      -

      Estas características pueden hacer el juego más divertido y fácil para usted, pero también pueden hacer que sea menos desafiante y realista. Por lo tanto, debe utilizar el mod a su propia discreción y riesgo. Algunas personas podrían considerar usar un mod como trampa, mientras que otros podrían disfrutarlo como una forma de explorar las posibilidades del juego. Depende de ti decidir cómo quieres jugar Walking Zombie 2.

      - -

      Ya sea que uses un mod o no, Walking Zombie 2 es un juego que requiere un poco de estrategia y habilidad para dominar. Aquí hay algunos consejos y trucos que pueden ayudarle a sobrevivir y disfrutar del juego:

      -

      Cómo subir de nivel tus habilidades y beneficios

      -

      En Walking Zombie 2, puedes mejorar las habilidades de tu personaje nivelando tus habilidades y beneficios. Las habilidades se dividen en cuatro categorías: combate, supervivencia, elaboración y carisma. Cada categoría tiene varias habilidades que afectan diferentes aspectos del juego, tales como daños, precisión, salud, resistencia, velocidad de fabricación, precios de intercambio, etc. Puedes subir de nivel tus habilidades gastando puntos de habilidad que ganas completando misiones y matando enemigos.

      -

      Los beneficios son bonos especiales que le dan ventajas adicionales en el juego, tales como mayor probabilidad de golpe crítico, recarga más rápida, mejor saqueo, etc. Puede desbloquear beneficios al alcanzar ciertos niveles de habilidades. Por ejemplo, para desbloquear la ventaja "Headhunter", que aumenta el daño de tu disparo en la cabeza en un 25%, necesitas tener al menos un nivel 5 en la habilidad de combate "Sharpshooter". Solo puedes elegir un beneficio por nivel, así que elige sabiamente.

      -

      Algunas de las mejores habilidades y beneficios para invertir son:

      - -HabilidadPerkBeneficio -Combat > SharpshooterHeadhunterAumenta el daño a la cabeza en un 25% -Supervivencia > DurezaSed de sangreRestaura un 5% de salud por cada muerte -Elaboración > IngenieroTinkererAumenta la velocidad de fabricación en un 50% -Charisma > TraderBargain HunterReduce los precios en un 20% en las tiendas - -

      Cómo encontrar y utilizar las mejores armas y equipos

      - -

      El equipo incluye artículos como armaduras, cascos, guantes, botas, mochilas, etc. que pueden protegerlo de daños y proporcionarle beneficios adicionales como una mayor capacidad de carga, velocidad de movimiento, sigilo, etc. También puede actualizar su equipo agregando mods como placas, almohadillas, bolsillos, etc.

      -

      Puedes encontrar armas y equipos saqueando los cadáveres, cofres, cajas, casilleros, etc. de los enemigos o comprándolos en tiendas o comerciantes. Sin embargo, algunas de las mejores armas y equipos están ocultos en lugares secretos que requieren un poco de exploración y resolución de rompecabezas para acceder. Por ejemplo, puedes encontrar un poderoso rifle de francotirador llamado "El Segador" en un búnker oculto cerca de la ciudad de Silver City.

      -

      Algunas de las mejores armas y equipos para usar son:

      - -TipoNombreDescripción -MeleeKatanaUna espada afilada que puede cortar zombies con facilidad. -PistolaDesert EagleUn arma de mano potente que puede causar mucho daño a medio alcance. -RifleEl SegadorUn rifle de francotirador oculto que puede matar a los enemigos con un disparo desde una larga distancia. -EscopetaAA-12Una escopeta automática que puede disparar múltiples disparos en poco tiempo y causar daños masivos a corta distancia. -ArmorExoskeletonUn traje de alta tecnología que puede protegerte de balas y explosiones y aumentar tu fuerza y velocidad. -CascoGafas de visión nocturnaUn casco que te permite ver en la oscuridad y resaltar enemigos y objetos. -GuantesGuantes de choqueGuantes que pueden electrocutar a los enemigos y aturdirlos durante unos segundos. -BootsJet BootsBotas que te permiten volar por poco tiempo y evitar obstáculos y enemigos. - - -

      Cómo completar misiones y explorar el mundo

      -

      En Walking Zombie 2, puedes seguir la historia principal y completar varias misiones secundarias que te darán recompensas, puntos de experiencia e información sobre el mundo. Las misiones están marcadas en el mapa con iconos como signos de exclamación, signos de interrogación o estrellas. También puedes hablar con NPCs para obtener misiones o pistas sobre misiones ocultas. Algunas misiones son fáciles y directas, mientras que otras son complejas y requieren múltiples pasos o opciones.

      -

      El mundo de Walking Zombie 2 es vasto y diverso, con diferentes regiones, pueblos, bases, campamentos, mazmorras, etc. Puede explorar el mundo caminando, conduciendo o viajando rápidamente a lugares que ha descubierto. También puedes encontrar secretos, huevos de Pascua, objetos de colección y referencias a otros juegos y películas. Por ejemplo, usted puede encontrar un coche DeLorean de vuelta al futuro en un garaje cerca de la ciudad de New Hope.

      -

      Algunas de las mejores misiones y ubicaciones para completar y explorar son:

      - -QuestDescripción -La última esperanzaUna búsqueda principal que implica encontrar una cura para el virus zombie y salvar a la humanidad. -La ArenaUna misión paralela que implica luchar contra oleadas de zombis y enemigos en una arena estilo gladiador. -El misterio de la pirámideUna búsqueda oculta que involucra resolver rompecabezas y encontrar pistas en una pirámide antigua. -UbicaciónDescripción -Nueva esperanzaUna gran ciudad que sirve como el centro principal del juego, donde se pueden encontrar tiendas, comerciantes, misiones y aliados. -El búnker< -|im_end|>d>Una instalación subterránea secreta que contiene tecnología avanzada, armas y secretos. -El cementeriod>Un lugar espeluznante lleno de zombies, fantasmas y lápidas. - -

      Conclusión: ¿Por qué usted debe descargar APK Mod Walking Zombie 2

      - -

      Preguntas frecuentes

      -

      Aquí hay algunas preguntas y respuestas frecuentes sobre el mod APK para Walking Zombie 2:

      -
        -
      1. ¿Es el mod APK para Walking Zombie 2 seguro de usar?
        2. -

        El mod APK para Walking Zombie 2 es seguro de usar siempre y cuando lo descargue de un sitio web de confianza como [ [APKMB.Com]]. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas y escanearlas en busca de virus o malware antes de instalarlas. También deberías hacer una copia de seguridad de tus datos y desinstalar el juego original antes de instalar el mod, ya que podría sobrescribir o borrar tu progreso.

        -
      2. ¿Afectará el mod APK para Walking Zombie 2 al rendimiento o la compatibilidad del juego?
        3. -

        El mod APK para Walking Zombie 2 no debe afectar el rendimiento del juego o la compatibilidad, ya que se basa en la misma versión del juego. Sin embargo, algunas características del mod pueden causar algunos fallos o errores en el juego, como estrellarse, congelarse o retrasarse. Si tienes algún problema con el mod, puedes intentar reinstalarlo, borrar la caché o reiniciar el dispositivo. También puede ponerse en contacto con el desarrollador mod o el sitio web para obtener soporte.

        -
      3. ¿Puedo jugar online o multijugador con el mod APK para Walking Zombie 2?
        4. -

        El mod APK para Walking Zombie 2 está diseñado para el modo sin conexión y solo para un jugador. No puedes jugar online o multijugador con el mod, ya que podría causar errores o prohibiciones en los servidores del juego. Solo puedes jugar online o multijugador con el juego original, sin mods.

        -
      4. ¿Puedo actualizar el mod APK para Walking Zombie 2?
        5. -

        El mod APK para Walking Zombie 2 se actualiza regularmente por el desarrollador mod o el sitio web para que coincida con la última versión del juego. Puede comprobar si hay actualizaciones en el sitio web o en la propia aplicación. Sin embargo, es posible que necesites desinstalar y reinstalar el mod cada vez que haya una nueva actualización, ya que podría no funcionar con versiones anteriores del juego.

        -
      5. ¿Puedo usar otros mods o trucos con el mod APK para Walking Zombie 2?
        6. - -

      64aa2da5cf
      -
      -
      \ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/logging.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/logging.py deleted file mode 100644 index c10e1f4ced6bcc799799b62666695998e095bbaf..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/logging.py +++ /dev/null @@ -1,348 +0,0 @@ -import contextlib -import errno -import logging -import logging.handlers -import os -import sys -import threading -from dataclasses import dataclass -from io import TextIOWrapper -from logging import Filter -from typing import Any, ClassVar, Generator, List, Optional, TextIO, Type - -from pip._vendor.rich.console import ( - Console, - ConsoleOptions, - ConsoleRenderable, - RenderableType, - RenderResult, - RichCast, -) -from pip._vendor.rich.highlighter import NullHighlighter -from pip._vendor.rich.logging import RichHandler -from pip._vendor.rich.segment import Segment -from pip._vendor.rich.style import Style - -from pip._internal.utils._log import VERBOSE, getLogger -from pip._internal.utils.compat import WINDOWS -from pip._internal.utils.deprecation import DEPRECATION_MSG_PREFIX -from pip._internal.utils.misc import ensure_dir - -_log_state = threading.local() -subprocess_logger = getLogger("pip.subprocessor") - - -class BrokenStdoutLoggingError(Exception): - """ - Raised if BrokenPipeError occurs for the stdout stream while logging. - """ - - -def _is_broken_pipe_error(exc_class: Type[BaseException], exc: BaseException) -> bool: - if exc_class is BrokenPipeError: - return True - - # On Windows, a broken pipe can show up as EINVAL rather than EPIPE: - # https://bugs.python.org/issue19612 - # https://bugs.python.org/issue30418 - if not WINDOWS: - return False - - return isinstance(exc, OSError) and exc.errno in (errno.EINVAL, errno.EPIPE) - - -@contextlib.contextmanager -def indent_log(num: int = 2) -> Generator[None, None, None]: - """ - A context manager which will cause the log output to be indented for any - log messages emitted inside it. - """ - # For thread-safety - _log_state.indentation = get_indentation() - _log_state.indentation += num - try: - yield - finally: - _log_state.indentation -= num - - -def get_indentation() -> int: - return getattr(_log_state, "indentation", 0) - - -class IndentingFormatter(logging.Formatter): - default_time_format = "%Y-%m-%dT%H:%M:%S" - - def __init__( - self, - *args: Any, - add_timestamp: bool = False, - **kwargs: Any, - ) -> None: - """ - A logging.Formatter that obeys the indent_log() context manager. - - :param add_timestamp: A bool indicating output lines should be prefixed - with their record's timestamp. - """ - self.add_timestamp = add_timestamp - super().__init__(*args, **kwargs) - - def get_message_start(self, formatted: str, levelno: int) -> str: - """ - Return the start of the formatted log message (not counting the - prefix to add to each line). - """ - if levelno < logging.WARNING: - return "" - if formatted.startswith(DEPRECATION_MSG_PREFIX): - # Then the message already has a prefix. We don't want it to - # look like "WARNING: DEPRECATION: ...." - return "" - if levelno < logging.ERROR: - return "WARNING: " - - return "ERROR: " - - def format(self, record: logging.LogRecord) -> str: - """ - Calls the standard formatter, but will indent all of the log message - lines by our current indentation level. - """ - formatted = super().format(record) - message_start = self.get_message_start(formatted, record.levelno) - formatted = message_start + formatted - - prefix = "" - if self.add_timestamp: - prefix = f"{self.formatTime(record)} " - prefix += " " * get_indentation() - formatted = "".join([prefix + line for line in formatted.splitlines(True)]) - return formatted - - -@dataclass -class IndentedRenderable: - renderable: RenderableType - indent: int - - def __rich_console__( - self, console: Console, options: ConsoleOptions - ) -> RenderResult: - segments = console.render(self.renderable, options) - lines = Segment.split_lines(segments) - for line in lines: - yield Segment(" " * self.indent) - yield from line - yield Segment("\n") - - -class RichPipStreamHandler(RichHandler): - KEYWORDS: ClassVar[Optional[List[str]]] = [] - - def __init__(self, stream: Optional[TextIO], no_color: bool) -> None: - super().__init__( - console=Console(file=stream, no_color=no_color, soft_wrap=True), - show_time=False, - show_level=False, - show_path=False, - highlighter=NullHighlighter(), - ) - - # Our custom override on Rich's logger, to make things work as we need them to. - def emit(self, record: logging.LogRecord) -> None: - style: Optional[Style] = None - - # If we are given a diagnostic error to present, present it with indentation. - assert isinstance(record.args, tuple) - if record.msg == "[present-rich] %s" and len(record.args) == 1: - rich_renderable = record.args[0] - assert isinstance( - rich_renderable, (ConsoleRenderable, RichCast, str) - ), f"{rich_renderable} is not rich-console-renderable" - - renderable: RenderableType = IndentedRenderable( - rich_renderable, indent=get_indentation() - ) - else: - message = self.format(record) - renderable = self.render_message(record, message) - if record.levelno is not None: - if record.levelno >= logging.ERROR: - style = Style(color="red") - elif record.levelno >= logging.WARNING: - style = Style(color="yellow") - - try: - self.console.print(renderable, overflow="ignore", crop=False, style=style) - except Exception: - self.handleError(record) - - def handleError(self, record: logging.LogRecord) -> None: - """Called when logging is unable to log some output.""" - - exc_class, exc = sys.exc_info()[:2] - # If a broken pipe occurred while calling write() or flush() on the - # stdout stream in logging's Handler.emit(), then raise our special - # exception so we can handle it in main() instead of logging the - # broken pipe error and continuing. - if ( - exc_class - and exc - and self.console.file is sys.stdout - and _is_broken_pipe_error(exc_class, exc) - ): - raise BrokenStdoutLoggingError() - - return super().handleError(record) - - -class BetterRotatingFileHandler(logging.handlers.RotatingFileHandler): - def _open(self) -> TextIOWrapper: - ensure_dir(os.path.dirname(self.baseFilename)) - return super()._open() - - -class MaxLevelFilter(Filter): - def __init__(self, level: int) -> None: - self.level = level - - def filter(self, record: logging.LogRecord) -> bool: - return record.levelno < self.level - - -class ExcludeLoggerFilter(Filter): - - """ - A logging Filter that excludes records from a logger (or its children). - """ - - def filter(self, record: logging.LogRecord) -> bool: - # The base Filter class allows only records from a logger (or its - # children). - return not super().filter(record) - - -def setup_logging(verbosity: int, no_color: bool, user_log_file: Optional[str]) -> int: - """Configures and sets up all of the logging - - Returns the requested logging level, as its integer value. - """ - - # Determine the level to be logging at. - if verbosity >= 2: - level_number = logging.DEBUG - elif verbosity == 1: - level_number = VERBOSE - elif verbosity == -1: - level_number = logging.WARNING - elif verbosity == -2: - level_number = logging.ERROR - elif verbosity <= -3: - level_number = logging.CRITICAL - else: - level_number = logging.INFO - - level = logging.getLevelName(level_number) - - # The "root" logger should match the "console" level *unless* we also need - # to log to a user log file. - include_user_log = user_log_file is not None - if include_user_log: - additional_log_file = user_log_file - root_level = "DEBUG" - else: - additional_log_file = "/dev/null" - root_level = level - - # Disable any logging besides WARNING unless we have DEBUG level logging - # enabled for vendored libraries. - vendored_log_level = "WARNING" if level in ["INFO", "ERROR"] else "DEBUG" - - # Shorthands for clarity - log_streams = { - "stdout": "ext://sys.stdout", - "stderr": "ext://sys.stderr", - } - handler_classes = { - "stream": "pip._internal.utils.logging.RichPipStreamHandler", - "file": "pip._internal.utils.logging.BetterRotatingFileHandler", - } - handlers = ["console", "console_errors", "console_subprocess"] + ( - ["user_log"] if include_user_log else [] - ) - - logging.config.dictConfig( - { - "version": 1, - "disable_existing_loggers": False, - "filters": { - "exclude_warnings": { - "()": "pip._internal.utils.logging.MaxLevelFilter", - "level": logging.WARNING, - }, - "restrict_to_subprocess": { - "()": "logging.Filter", - "name": subprocess_logger.name, - }, - "exclude_subprocess": { - "()": "pip._internal.utils.logging.ExcludeLoggerFilter", - "name": subprocess_logger.name, - }, - }, - "formatters": { - "indent": { - "()": IndentingFormatter, - "format": "%(message)s", - }, - "indent_with_timestamp": { - "()": IndentingFormatter, - "format": "%(message)s", - "add_timestamp": True, - }, - }, - "handlers": { - "console": { - "level": level, - "class": handler_classes["stream"], - "no_color": no_color, - "stream": log_streams["stdout"], - "filters": ["exclude_subprocess", "exclude_warnings"], - "formatter": "indent", - }, - "console_errors": { - "level": "WARNING", - "class": handler_classes["stream"], - "no_color": no_color, - "stream": log_streams["stderr"], - "filters": ["exclude_subprocess"], - "formatter": "indent", - }, - # A handler responsible for logging to the console messages - # from the "subprocessor" logger. - "console_subprocess": { - "level": level, - "class": handler_classes["stream"], - "stream": log_streams["stderr"], - "no_color": no_color, - "filters": ["restrict_to_subprocess"], - "formatter": "indent", - }, - "user_log": { - "level": "DEBUG", - "class": handler_classes["file"], - "filename": additional_log_file, - "encoding": "utf-8", - "delay": True, - "formatter": "indent_with_timestamp", - }, - }, - "root": { - "level": root_level, - "handlers": handlers, - }, - "loggers": {"pip._vendor": {"level": vendored_log_level}}, - } - ) - - return level_number diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/bdist.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/bdist.py deleted file mode 100644 index de37dae0ffcd5ea3b05c2203981f23163707cdd6..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/bdist.py +++ /dev/null @@ -1,157 +0,0 @@ -"""distutils.command.bdist - -Implements the Distutils 'bdist' command (create a built [binary] -distribution).""" - -import os -import warnings - -from distutils.core import Command -from distutils.errors import DistutilsPlatformError, DistutilsOptionError -from distutils.util import get_platform - - -def show_formats(): - """Print list of available formats (arguments to "--format" option).""" - from distutils.fancy_getopt import FancyGetopt - - formats = [] - for format in bdist.format_commands: - formats.append(("formats=" + format, None, bdist.format_commands[format][1])) - pretty_printer = FancyGetopt(formats) - pretty_printer.print_help("List of available distribution formats:") - - -class ListCompat(dict): - # adapter to allow for Setuptools compatibility in format_commands - def append(self, item): - warnings.warn( - """format_commands is now a dict. append is deprecated.""", - DeprecationWarning, - stacklevel=2, - ) - - -class bdist(Command): - - description = "create a built (binary) distribution" - - user_options = [ - ('bdist-base=', 'b', "temporary directory for creating built distributions"), - ( - 'plat-name=', - 'p', - "platform name to embed in generated filenames " - "(default: %s)" % get_platform(), - ), - ('formats=', None, "formats for distribution (comma-separated list)"), - ( - 'dist-dir=', - 'd', - "directory to put final built distributions in " "[default: dist]", - ), - ('skip-build', None, "skip rebuilding everything (for testing/debugging)"), - ( - 'owner=', - 'u', - "Owner name used when creating a tar file" " [default: current user]", - ), - ( - 'group=', - 'g', - "Group name used when creating a tar file" " [default: current group]", - ), - ] - - boolean_options = ['skip-build'] - - help_options = [ - ('help-formats', None, "lists available distribution formats", show_formats), - ] - - # The following commands do not take a format option from bdist - no_format_option = ('bdist_rpm',) - - # This won't do in reality: will need to distinguish RPM-ish Linux, - # Debian-ish Linux, Solaris, FreeBSD, ..., Windows, Mac OS. - default_format = {'posix': 'gztar', 'nt': 'zip'} - - # Define commands in preferred order for the --help-formats option - format_commands = ListCompat( - { - 'rpm': ('bdist_rpm', "RPM distribution"), - 'gztar': ('bdist_dumb', "gzip'ed tar file"), - 'bztar': ('bdist_dumb', "bzip2'ed tar file"), - 'xztar': ('bdist_dumb', "xz'ed tar file"), - 'ztar': ('bdist_dumb', "compressed tar file"), - 'tar': ('bdist_dumb', "tar file"), - 'zip': ('bdist_dumb', "ZIP file"), - } - ) - - # for compatibility until consumers only reference format_commands - format_command = format_commands - - def initialize_options(self): - self.bdist_base = None - self.plat_name = None - self.formats = None - self.dist_dir = None - self.skip_build = 0 - self.group = None - self.owner = None - - def finalize_options(self): - # have to finalize 'plat_name' before 'bdist_base' - if self.plat_name is None: - if self.skip_build: - self.plat_name = get_platform() - else: - self.plat_name = self.get_finalized_command('build').plat_name - - # 'bdist_base' -- parent of per-built-distribution-format - # temporary directories (eg. we'll probably have - # "build/bdist./dumb", "build/bdist./rpm", etc.) - if self.bdist_base is None: - build_base = self.get_finalized_command('build').build_base - self.bdist_base = os.path.join(build_base, 'bdist.' + self.plat_name) - - self.ensure_string_list('formats') - if self.formats is None: - try: - self.formats = [self.default_format[os.name]] - except KeyError: - raise DistutilsPlatformError( - "don't know how to create built distributions " - "on platform %s" % os.name - ) - - if self.dist_dir is None: - self.dist_dir = "dist" - - def run(self): - # Figure out which sub-commands we need to run. - commands = [] - for format in self.formats: - try: - commands.append(self.format_commands[format][0]) - except KeyError: - raise DistutilsOptionError("invalid format '%s'" % format) - - # Reinitialize and run each command. - for i in range(len(self.formats)): - cmd_name = commands[i] - sub_cmd = self.reinitialize_command(cmd_name) - if cmd_name not in self.no_format_option: - sub_cmd.format = self.formats[i] - - # passing the owner and group names for tar archiving - if cmd_name == 'bdist_dumb': - sub_cmd.owner = self.owner - sub_cmd.group = self.group - - # If we're going to need to run this command again, tell it to - # keep its temporary files around so subsequent runs go faster. - if cmd_name in commands[i + 1 :]: - sub_cmd.keep_temp = 1 - self.run_command(cmd_name) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_validate_pyproject/formats.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_validate_pyproject/formats.py deleted file mode 100644 index 638ac1195344227da3ebf20bb8a0faeb98cb6548..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_validate_pyproject/formats.py +++ /dev/null @@ -1,259 +0,0 @@ -import logging -import os -import re -import string -import typing -from itertools import chain as _chain - -_logger = logging.getLogger(__name__) - -# ------------------------------------------------------------------------------------- -# PEP 440 - -VERSION_PATTERN = r""" - v? - (?: - (?:(?P[0-9]+)!)? # epoch - (?P[0-9]+(?:\.[0-9]+)*) # release segment - (?P
                                                # pre-release
-            [-_\.]?
-            (?P(a|b|c|rc|alpha|beta|pre|preview))
-            [-_\.]?
-            (?P[0-9]+)?
-        )?
-        (?P                                         # post release
-            (?:-(?P[0-9]+))
-            |
-            (?:
-                [-_\.]?
-                (?Ppost|rev|r)
-                [-_\.]?
-                (?P[0-9]+)?
-            )
-        )?
-        (?P                                          # dev release
-            [-_\.]?
-            (?Pdev)
-            [-_\.]?
-            (?P[0-9]+)?
-        )?
-    )
-    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
-"""
-
-VERSION_REGEX = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.X | re.I)
-
-
-def pep440(version: str) -> bool:
-    return VERSION_REGEX.match(version) is not None
-
-
-# -------------------------------------------------------------------------------------
-# PEP 508
-
-PEP508_IDENTIFIER_PATTERN = r"([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])"
-PEP508_IDENTIFIER_REGEX = re.compile(f"^{PEP508_IDENTIFIER_PATTERN}$", re.I)
-
-
-def pep508_identifier(name: str) -> bool:
-    return PEP508_IDENTIFIER_REGEX.match(name) is not None
-
-
-try:
-    try:
-        from packaging import requirements as _req
-    except ImportError:  # pragma: no cover
-        # let's try setuptools vendored version
-        from setuptools._vendor.packaging import requirements as _req  # type: ignore
-
-    def pep508(value: str) -> bool:
-        try:
-            _req.Requirement(value)
-            return True
-        except _req.InvalidRequirement:
-            return False
-
-except ImportError:  # pragma: no cover
-    _logger.warning(
-        "Could not find an installation of `packaging`. Requirements, dependencies and "
-        "versions might not be validated. "
-        "To enforce validation, please install `packaging`."
-    )
-
-    def pep508(value: str) -> bool:
-        return True
-
-
-def pep508_versionspec(value: str) -> bool:
-    """Expression that can be used to specify/lock versions (including ranges)"""
-    if any(c in value for c in (";", "]", "@")):
-        # In PEP 508:
-        # conditional markers, extras and URL specs are not included in the
-        # versionspec
-        return False
-    # Let's pretend we have a dependency called `requirement` with the given
-    # version spec, then we can re-use the pep508 function for validation:
-    return pep508(f"requirement{value}")
-
-
-# -------------------------------------------------------------------------------------
-# PEP 517
-
-
-def pep517_backend_reference(value: str) -> bool:
-    module, _, obj = value.partition(":")
-    identifiers = (i.strip() for i in _chain(module.split("."), obj.split(".")))
-    return all(python_identifier(i) for i in identifiers if i)
-
-
-# -------------------------------------------------------------------------------------
-# Classifiers - PEP 301
-
-
-def _download_classifiers() -> str:
-    import ssl
-    from email.message import Message
-    from urllib.request import urlopen
-
-    url = "https://pypi.org/pypi?:action=list_classifiers"
-    context = ssl.create_default_context()
-    with urlopen(url, context=context) as response:
-        headers = Message()
-        headers["content_type"] = response.getheader("content-type", "text/plain")
-        return response.read().decode(headers.get_param("charset", "utf-8"))
-
-
-class _TroveClassifier:
-    """The ``trove_classifiers`` package is the official way of validating classifiers,
-    however this package might not be always available.
-    As a workaround we can still download a list from PyPI.
-    We also don't want to be over strict about it, so simply skipping silently is an
-    option (classifiers will be validated anyway during the upload to PyPI).
-    """
-
-    def __init__(self):
-        self.downloaded: typing.Union[None, False, typing.Set[str]] = None
-        self._skip_download = False
-        # None => not cached yet
-        # False => cache not available
-        self.__name__ = "trove_classifier"  # Emulate a public function
-
-    def _disable_download(self):
-        # This is a private API. Only setuptools has the consent of using it.
-        self._skip_download = True
-
-    def __call__(self, value: str) -> bool:
-        if self.downloaded is False or self._skip_download is True:
-            return True
-
-        if os.getenv("NO_NETWORK") or os.getenv("VALIDATE_PYPROJECT_NO_NETWORK"):
-            self.downloaded = False
-            msg = (
-                "Install ``trove-classifiers`` to ensure proper validation. "
-                "Skipping download of classifiers list from PyPI (NO_NETWORK)."
-            )
-            _logger.debug(msg)
-            return True
-
-        if self.downloaded is None:
-            msg = (
-                "Install ``trove-classifiers`` to ensure proper validation. "
-                "Meanwhile a list of classifiers will be downloaded from PyPI."
-            )
-            _logger.debug(msg)
-            try:
-                self.downloaded = set(_download_classifiers().splitlines())
-            except Exception:
-                self.downloaded = False
-                _logger.debug("Problem with download, skipping validation")
-                return True
-
-        return value in self.downloaded or value.lower().startswith("private ::")
-
-
-try:
-    from trove_classifiers import classifiers as _trove_classifiers
-
-    def trove_classifier(value: str) -> bool:
-        return value in _trove_classifiers or value.lower().startswith("private ::")
-
-except ImportError:  # pragma: no cover
-    trove_classifier = _TroveClassifier()
-
-
-# -------------------------------------------------------------------------------------
-# Non-PEP related
-
-
-def url(value: str) -> bool:
-    from urllib.parse import urlparse
-
-    try:
-        parts = urlparse(value)
-        if not parts.scheme:
-            _logger.warning(
-                "For maximum compatibility please make sure to include a "
-                "`scheme` prefix in your URL (e.g. 'http://'). "
-                f"Given value: {value}"
-            )
-            if not (value.startswith("/") or value.startswith("\\") or "@" in value):
-                parts = urlparse(f"http://{value}")
-
-        return bool(parts.scheme and parts.netloc)
-    except Exception:
-        return False
-
-
-# https://packaging.python.org/specifications/entry-points/
-ENTRYPOINT_PATTERN = r"[^\[\s=]([^=]*[^\s=])?"
-ENTRYPOINT_REGEX = re.compile(f"^{ENTRYPOINT_PATTERN}$", re.I)
-RECOMMEDED_ENTRYPOINT_PATTERN = r"[\w.-]+"
-RECOMMEDED_ENTRYPOINT_REGEX = re.compile(f"^{RECOMMEDED_ENTRYPOINT_PATTERN}$", re.I)
-ENTRYPOINT_GROUP_PATTERN = r"\w+(\.\w+)*"
-ENTRYPOINT_GROUP_REGEX = re.compile(f"^{ENTRYPOINT_GROUP_PATTERN}$", re.I)
-
-
-def python_identifier(value: str) -> bool:
-    return value.isidentifier()
-
-
-def python_qualified_identifier(value: str) -> bool:
-    if value.startswith(".") or value.endswith("."):
-        return False
-    return all(python_identifier(m) for m in value.split("."))
-
-
-def python_module_name(value: str) -> bool:
-    return python_qualified_identifier(value)
-
-
-def python_entrypoint_group(value: str) -> bool:
-    return ENTRYPOINT_GROUP_REGEX.match(value) is not None
-
-
-def python_entrypoint_name(value: str) -> bool:
-    if not ENTRYPOINT_REGEX.match(value):
-        return False
-    if not RECOMMEDED_ENTRYPOINT_REGEX.match(value):
-        msg = f"Entry point `{value}` does not follow recommended pattern: "
-        msg += RECOMMEDED_ENTRYPOINT_PATTERN
-        _logger.warning(msg)
-    return True
-
-
-def python_entrypoint_reference(value: str) -> bool:
-    module, _, rest = value.partition(":")
-    if "[" in rest:
-        obj, _, extras_ = rest.partition("[")
-        if extras_.strip()[-1] != "]":
-            return False
-        extras = (x.strip() for x in extras_.strip(string.whitespace + "[]").split(","))
-        if not all(pep508_identifier(e) for e in extras):
-            return False
-        _logger.warning(f"`{value}` - using extras for entry points is not recommended")
-    else:
-        obj = rest
-
-    module_parts = module.split(".")
-    identifiers = _chain(module_parts, obj.split(".")) if rest else module_parts
-    return all(python_identifier(i.strip()) for i in identifiers)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/colormap.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/colormap.py
deleted file mode 100644
index 1bf1455e4ce9e077961143c8d734a7298d28476d..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/utils/colormap.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-"""
-An awesome colormap for really neat visualizations.
-Copied from Detectron, and removed gray colors.
-"""
-
-import numpy as np
-
-__all__ = ["colormap", "random_color"]
-
-# fmt: off
-# RGB:
-_COLORS = np.array(
-    [
-        0.000, 0.447, 0.741,
-        0.850, 0.325, 0.098,
-        0.929, 0.694, 0.125,
-        0.494, 0.184, 0.556,
-        0.466, 0.674, 0.188,
-        0.301, 0.745, 0.933,
-        0.635, 0.078, 0.184,
-        0.300, 0.300, 0.300,
-        0.600, 0.600, 0.600,
-        1.000, 0.000, 0.000,
-        1.000, 0.500, 0.000,
-        0.749, 0.749, 0.000,
-        0.000, 1.000, 0.000,
-        0.000, 0.000, 1.000,
-        0.667, 0.000, 1.000,
-        0.333, 0.333, 0.000,
-        0.333, 0.667, 0.000,
-        0.333, 1.000, 0.000,
-        0.667, 0.333, 0.000,
-        0.667, 0.667, 0.000,
-        0.667, 1.000, 0.000,
-        1.000, 0.333, 0.000,
-        1.000, 0.667, 0.000,
-        1.000, 1.000, 0.000,
-        0.000, 0.333, 0.500,
-        0.000, 0.667, 0.500,
-        0.000, 1.000, 0.500,
-        0.333, 0.000, 0.500,
-        0.333, 0.333, 0.500,
-        0.333, 0.667, 0.500,
-        0.333, 1.000, 0.500,
-        0.667, 0.000, 0.500,
-        0.667, 0.333, 0.500,
-        0.667, 0.667, 0.500,
-        0.667, 1.000, 0.500,
-        1.000, 0.000, 0.500,
-        1.000, 0.333, 0.500,
-        1.000, 0.667, 0.500,
-        1.000, 1.000, 0.500,
-        0.000, 0.333, 1.000,
-        0.000, 0.667, 1.000,
-        0.000, 1.000, 1.000,
-        0.333, 0.000, 1.000,
-        0.333, 0.333, 1.000,
-        0.333, 0.667, 1.000,
-        0.333, 1.000, 1.000,
-        0.667, 0.000, 1.000,
-        0.667, 0.333, 1.000,
-        0.667, 0.667, 1.000,
-        0.667, 1.000, 1.000,
-        1.000, 0.000, 1.000,
-        1.000, 0.333, 1.000,
-        1.000, 0.667, 1.000,
-        0.333, 0.000, 0.000,
-        0.500, 0.000, 0.000,
-        0.667, 0.000, 0.000,
-        0.833, 0.000, 0.000,
-        1.000, 0.000, 0.000,
-        0.000, 0.167, 0.000,
-        0.000, 0.333, 0.000,
-        0.000, 0.500, 0.000,
-        0.000, 0.667, 0.000,
-        0.000, 0.833, 0.000,
-        0.000, 1.000, 0.000,
-        0.000, 0.000, 0.167,
-        0.000, 0.000, 0.333,
-        0.000, 0.000, 0.500,
-        0.000, 0.000, 0.667,
-        0.000, 0.000, 0.833,
-        0.000, 0.000, 1.000,
-        0.000, 0.000, 0.000,
-        0.143, 0.143, 0.143,
-        0.857, 0.857, 0.857,
-        1.000, 1.000, 1.000
-    ]
-).astype(np.float32).reshape(-1, 3)
-# fmt: on
-
-
-def colormap(rgb=False, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-
-    Returns:
-        ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]
-    """
-    assert maximum in [255, 1], maximum
-    c = _COLORS * maximum
-    if not rgb:
-        c = c[:, ::-1]
-    return c
-
-
-def random_color(rgb=False, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-
-    Returns:
-        ndarray: a vector of 3 numbers
-    """
-    idx = np.random.randint(0, len(_COLORS))
-    ret = _COLORS[idx] * maximum
-    if not rgb:
-        ret = ret[::-1]
-    return ret
-
-
-if __name__ == "__main__":
-    import cv2
-
-    size = 100
-    H, W = 10, 10
-    canvas = np.random.rand(H * size, W * size, 3).astype("float32")
-    for h in range(H):
-        for w in range(W):
-            idx = h * W + w
-            if idx >= len(_COLORS):
-                break
-            canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]
-    cv2.imshow("a", canvas)
-    cv2.waitKey(0)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/caffe2_converter.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/caffe2_converter.py
deleted file mode 100644
index aedf6d810ae436c033ed7a51dbd84bc19a2165b4..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tools/deploy/caffe2_converter.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import argparse
-import os
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.data import build_detection_test_loader
-from detectron2.evaluation import COCOEvaluator, inference_on_dataset, print_csv_format
-from detectron2.export import add_export_config, export_caffe2_model
-from detectron2.modeling import build_model
-from detectron2.utils.logger import setup_logger
-
-
-def setup_cfg(args):
-    cfg = get_cfg()
-    # cuda context is initialized before creating dataloader, so we don't fork anymore
-    cfg.DATALOADER.NUM_WORKERS = 0
-    cfg = add_export_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    return cfg
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert a model to Caffe2")
-    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
-    parser.add_argument("--run-eval", action="store_true")
-    parser.add_argument("--output", help="output directory for the converted caffe2 model")
-    parser.add_argument(
-        "opts",
-        help="Modify config options using the command-line",
-        default=None,
-        nargs=argparse.REMAINDER,
-    )
-    args = parser.parse_args()
-    logger = setup_logger()
-    logger.info("Command line arguments: " + str(args))
-
-    cfg = setup_cfg(args)
-
-    # create a torch model
-    torch_model = build_model(cfg)
-    DetectionCheckpointer(torch_model).resume_or_load(cfg.MODEL.WEIGHTS)
-
-    # get a sample data
-    data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
-    first_batch = next(iter(data_loader))
-
-    # convert and save caffe2 model
-    caffe2_model = export_caffe2_model(cfg, torch_model, first_batch)
-    caffe2_model.save_protobuf(args.output)
-    # draw the caffe2 graph
-    caffe2_model.save_graph(os.path.join(args.output, "model.svg"), inputs=first_batch)
-
-    # run evaluation with the converted model
-    if args.run_eval:
-        dataset = cfg.DATASETS.TEST[0]
-        data_loader = build_detection_test_loader(cfg, dataset)
-        # NOTE: hard-coded evaluator. change to the evaluator for your dataset
-        evaluator = COCOEvaluator(dataset, cfg, True, args.output)
-        metrics = inference_on_dataset(caffe2_model, data_loader, evaluator)
-        print_csv_format(metrics)
diff --git a/spaces/CVPR/LIVE/thrust/dependencies/cub/examples/device/Makefile b/spaces/CVPR/LIVE/thrust/dependencies/cub/examples/device/Makefile
deleted file mode 100644
index fea1494e823291746dfcfbfd6ca68822aa3824fe..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/dependencies/cub/examples/device/Makefile
+++ /dev/null
@@ -1,197 +0,0 @@
-#/******************************************************************************
-# * Copyright (c) 2011, Duane Merrill.  All rights reserved.
-# * Copyright (c) 2011-2018, NVIDIA CORPORATION.  All rights reserved.
-# * 
-# * Redistribution and use in source and binary forms, with or without
-# * modification, are permitted provided that the following conditions are met:
-# *	 * Redistributions of source code must retain the above copyright
-# *	   notice, this list of conditions and the following disclaimer.
-# *	 * Redistributions in binary form must reproduce the above copyright
-# *	   notice, this list of conditions and the following disclaimer in the
-# *	   documentation and/or other materials provided with the distribution.
-# *	 * Neither the name of the NVIDIA CORPORATION nor the
-# *	   names of its contributors may be used to endorse or promote products
-# *	   derived from this software without specific prior written permission.
-# * 
-# * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-# * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-# * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-# * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-# * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# *
-#******************************************************************************/
-
-#-------------------------------------------------------------------------------
-#
-# Makefile usage
-#
-# make  [sm=] [cdp=<0|1>] [force32=<0|1>] [abi=<0|1>] [open64=<0|1>] [verbose=<0|1>] [keep=<0|1>]
-#
-#-------------------------------------------------------------------------------
- 
-include ../../common.mk 
- 
- 
-#-------------------------------------------------------------------------------
-# Includes
-#-------------------------------------------------------------------------------
-
-INC += -I$(CUB_DIR) -I$(CUB_DIR)test 
-
-
-
-#-------------------------------------------------------------------------------
-# Dependency Lists
-#-------------------------------------------------------------------------------
-
-rwildcard=$(foreach d,$(wildcard $1*),$(call rwildcard,$d/,$2) $(filter $(subst *,%,$2),$d))
-
-DEPS =				$(CUB_DEPS) \
-					$(CUB_DIR)test/Makefile \
-					$(CUB_DIR)test/test_util.h \
-					$(CUB_DIR)test/mersenne.h \
-		
-ALL = 	example_device_partition_flagged \
-		example_device_partition_if \
-	 	example_device_radix_sort \
-		example_device_reduce \
-	 	example_device_scan \
-	 	example_device_select_unique \
-		example_device_select_flagged \
-		example_device_select_if \
-		example_device_sort_find_non_trivial_runs
-		
-
-
-#-------------------------------------------------------------------------------
-# make default
-#-------------------------------------------------------------------------------
-
-default:
-
-
-#-------------------------------------------------------------------------------
-# make clean
-#-------------------------------------------------------------------------------
-
-clean :
-	rm -f bin/*$(CPU_ARCH_SUFFIX)* 
-	rm -f *.i* *.cubin *.cu.c *.cudafe* *.fatbin.c *.ptx *.hash *.cu.cpp *.o
-
-
-#-------------------------------------------------------------------------------
-# make all
-#-------------------------------------------------------------------------------
-
-all : $(ALL)
-
-#-------------------------------------------------------------------------------
-# make run
-#-------------------------------------------------------------------------------
-
-run : 
-	for i in $(ALL); do ./bin/$${i}_$(BIN_SUFFIX) --device=$(device) || exit 1; done
-
-
-#-------------------------------------------------------------------------------
-# make example_device_reduce
-#-------------------------------------------------------------------------------
-
-example_device_reduce: bin/example_device_reduce_$(BIN_SUFFIX)
-
-bin/example_device_reduce_$(BIN_SUFFIX) : example_device_reduce.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_reduce_$(BIN_SUFFIX) example_device_reduce.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_partition_flagged
-#-------------------------------------------------------------------------------
-
-example_device_partition_flagged: bin/example_device_partition_flagged_$(BIN_SUFFIX)
-
-bin/example_device_partition_flagged_$(BIN_SUFFIX) : example_device_partition_flagged.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_partition_flagged_$(BIN_SUFFIX) example_device_partition_flagged.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make example_device_partition_if
-#-------------------------------------------------------------------------------
-
-example_device_partition_if: bin/example_device_partition_if_$(BIN_SUFFIX)
-
-bin/example_device_partition_if_$(BIN_SUFFIX) : example_device_partition_if.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_partition_if_$(BIN_SUFFIX) example_device_partition_if.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make example_device_scan
-#-------------------------------------------------------------------------------
-
-example_device_scan: bin/example_device_scan_$(BIN_SUFFIX)
-
-bin/example_device_scan_$(BIN_SUFFIX) : example_device_scan.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_scan_$(BIN_SUFFIX) example_device_scan.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_radix_sort
-#-------------------------------------------------------------------------------
-
-example_device_radix_sort: bin/example_device_radix_sort_$(BIN_SUFFIX)
-
-bin/example_device_radix_sort_$(BIN_SUFFIX) : example_device_radix_sort.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_radix_sort_$(BIN_SUFFIX) example_device_radix_sort.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_select_unique
-#-------------------------------------------------------------------------------
-
-example_device_select_unique: bin/example_device_select_unique_$(BIN_SUFFIX)
-
-bin/example_device_select_unique_$(BIN_SUFFIX) : example_device_select_unique.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_select_unique_$(BIN_SUFFIX) example_device_select_unique.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_select_flagged
-#-------------------------------------------------------------------------------
-
-example_device_select_flagged: bin/example_device_select_flagged_$(BIN_SUFFIX)
-
-bin/example_device_select_flagged_$(BIN_SUFFIX) : example_device_select_flagged.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_select_flagged_$(BIN_SUFFIX) example_device_select_flagged.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-#-------------------------------------------------------------------------------
-# make example_device_select_if
-#-------------------------------------------------------------------------------
-
-example_device_select_if: bin/example_device_select_if_$(BIN_SUFFIX)
-
-bin/example_device_select_if_$(BIN_SUFFIX) : example_device_select_if.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_select_if_$(BIN_SUFFIX) example_device_select_if.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-#-------------------------------------------------------------------------------
-# make example_device_sort_find_non_trivial_runs
-#-------------------------------------------------------------------------------
-
-example_device_sort_find_non_trivial_runs: bin/example_device_sort_find_non_trivial_runs_$(BIN_SUFFIX)
-
-bin/example_device_sort_find_non_trivial_runs_$(BIN_SUFFIX) : example_device_sort_find_non_trivial_runs.cu $(DEPS)
-	mkdir -p bin
-	$(NVCC) $(DEFINES) $(SM_TARGETS) -o bin/example_device_sort_find_non_trivial_runs_$(BIN_SUFFIX) example_device_sort_find_non_trivial_runs.cu $(NVCCFLAGS) $(CPU_ARCH) $(INC) $(LIBS) -O3
-
-
-
diff --git a/spaces/CVPR/LIVE/utils.py b/spaces/CVPR/LIVE/utils.py
deleted file mode 100644
index 13922c13cbd4c0bfd25353ca306b0faeca13f080..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/utils.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import os.path as osp
-
-def get_experiment_id(debug=False):
-    if debug:
-        return 999999999999
-    import time
-    time.sleep(0.5)
-    return int(time.time()*100)
-
-def get_path_schedule(type, **kwargs):
-    if type == 'repeat':
-        max_path = kwargs['max_path']
-        schedule_each = kwargs['schedule_each']
-        return [schedule_each] * max_path
-    elif type == 'list':
-        schedule = kwargs['schedule']
-        return schedule
-    elif type == 'exp':
-        import math
-        base = kwargs['base']
-        max_path = kwargs['max_path']
-        max_path_per_iter = kwargs['max_path_per_iter']
-        schedule = []
-        cnt = 0
-        while sum(schedule) < max_path:
-            proposed_step = min(
-                max_path - sum(schedule), 
-                base**cnt, 
-                max_path_per_iter)
-            cnt += 1
-            schedule += [proposed_step]
-        return schedule
-    else:
-        raise ValueError
-
-def edict_2_dict(x):
-    if isinstance(x, dict):
-        xnew = {}
-        for k in x:
-            xnew[k] = edict_2_dict(x[k])
-        return xnew
-    elif isinstance(x, list):
-        xnew = []
-        for i in range(len(x)):
-            xnew.append( edict_2_dict(x[i]) )
-        return xnew
-    else:
-        return x
-
-def check_and_create_dir(path):
-    pathdir = osp.split(path)[0]
-    if osp.isdir(pathdir):
-        pass
-    else:
-        os.makedirs(pathdir)
diff --git a/spaces/CVPR/WALT/mmdet/models/dense_heads/atss_head.py b/spaces/CVPR/WALT/mmdet/models/dense_heads/atss_head.py
deleted file mode 100644
index ff55dfa1790ba270539fc9f623dbb2984fa1a99e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/dense_heads/atss_head.py
+++ /dev/null
@@ -1,689 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
-                        images_to_levels, multi_apply, multiclass_nms,
-                        reduce_mean, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-EPS = 1e-12
-
-
-@HEADS.register_module()
-class ATSSHead(AnchorHead):
-    """Bridging the Gap Between Anchor-based and Anchor-free Detection via
-    Adaptive Training Sample Selection.
-
-    ATSS head structure is similar with FCOS, however ATSS use anchor boxes
-    and assign label by Adaptive Training Sample Selection instead max-iou.
-
-    https://arxiv.org/abs/1912.02424
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
-                 loss_centerness=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0),
-                 **kwargs):
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(ATSSHead, self).__init__(num_classes, in_channels, **kwargs)
-
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # SSD sampling=False so use PseudoSampler
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-        self.loss_centerness = build_loss(loss_centerness)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.atss_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.atss_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-        self.atss_centerness = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 1, 3, padding=1)
-        self.scales = nn.ModuleList(
-            [Scale(1.0) for _ in self.anchor_generator.strides])
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.atss_cls, std=0.01, bias=bias_cls)
-        normal_init(self.atss_reg, std=0.01)
-        normal_init(self.atss_centerness, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
-        """
-        return multi_apply(self.forward_single, feats, self.scales)
-
-    def forward_single(self, x, scale):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls scores for a single scale level
-                    the channels number is num_anchors * num_classes.
-                bbox_pred (Tensor): Box energies / deltas for a single scale
-                    level, the channels number is num_anchors * 4.
-                centerness (Tensor): Centerness for a single scale level, the
-                    channel number is (N, num_anchors * 1, H, W).
-        """
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-        cls_score = self.atss_cls(cls_feat)
-        # we just follow atss, not apply exp in bbox_pred
-        bbox_pred = scale(self.atss_reg(reg_feat)).float()
-        centerness = self.atss_centerness(reg_feat)
-        return cls_score, bbox_pred, centerness
-
-    def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
-                    label_weights, bbox_targets, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            cls_score (Tensor): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W).
-            bbox_pred (Tensor): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            num_total_samples (int): Number os positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
-            -1, self.cls_out_channels).contiguous()
-        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
-        centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # classification loss
-        loss_cls = self.loss_cls(
-            cls_score, labels, label_weights, avg_factor=num_total_samples)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_centerness = centerness[pos_inds]
-
-            centerness_targets = self.centerness_target(
-                pos_anchors, pos_bbox_targets)
-            pos_decode_bbox_pred = self.bbox_coder.decode(
-                pos_anchors, pos_bbox_pred)
-            pos_decode_bbox_targets = self.bbox_coder.decode(
-                pos_anchors, pos_bbox_targets)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=centerness_targets,
-                avg_factor=1.0)
-
-            # centerness loss
-            loss_centerness = self.loss_centerness(
-                pos_centerness,
-                centerness_targets,
-                avg_factor=num_total_samples)
-
-        else:
-            loss_bbox = bbox_pred.sum() * 0
-            loss_centerness = centerness.sum() * 0
-            centerness_targets = bbox_targets.new_tensor(0.)
-
-        return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             centernesses,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            centernesses (list[Tensor]): Centerness for each scale
-                level with shape (N, num_anchors * 1, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, loss_centerness,\
-            bbox_avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                centernesses,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                num_total_samples=num_total_samples)
-
-        bbox_avg_factor = sum(bbox_avg_factor)
-        bbox_avg_factor = reduce_mean(bbox_avg_factor).item()
-        if bbox_avg_factor < EPS:
-            bbox_avg_factor = 1
-        losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            loss_centerness=loss_centerness)
-
-    def centerness_target(self, anchors, bbox_targets):
-        # only calculate pos centerness targets, otherwise there may be nan
-        gts = self.bbox_coder.decode(anchors, bbox_targets)
-        anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
-        anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
-        l_ = anchors_cx - gts[:, 0]
-        t_ = anchors_cy - gts[:, 1]
-        r_ = gts[:, 2] - anchors_cx
-        b_ = gts[:, 3] - anchors_cy
-
-        left_right = torch.stack([l_, r_], dim=1)
-        top_bottom = torch.stack([t_, b_], dim=1)
-        centerness = torch.sqrt(
-            (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) *
-            (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]))
-        assert not torch.isnan(centerness).any()
-        return centerness
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   centernesses,
-                   img_metas,
-                   cfg=None,
-                   rescale=False,
-                   with_nms=True):
-        """Transform network output for a batch into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                with shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            centernesses (list[Tensor]): Centerness for each scale level with
-                shape (N, num_anchors * 1, H, W).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used. Default: None.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-        device = cls_scores[0].device
-        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
-        mlvl_anchors = self.anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-
-        cls_score_list = [cls_scores[i].detach() for i in range(num_levels)]
-        bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)]
-        centerness_pred_list = [
-            centernesses[i].detach() for i in range(num_levels)
-        ]
-        img_shapes = [
-            img_metas[i]['img_shape'] for i in range(cls_scores[0].shape[0])
-        ]
-        scale_factors = [
-            img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0])
-        ]
-        result_list = self._get_bboxes(cls_score_list, bbox_pred_list,
-                                       centerness_pred_list, mlvl_anchors,
-                                       img_shapes, scale_factors, cfg, rescale,
-                                       with_nms)
-        return result_list
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    centernesses,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for a single scale level
-                with shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for a single
-                scale level with shape (N, num_anchors * 4, H, W).
-            centernesses (list[Tensor]): Centerness for a single scale level
-                with shape (N, num_anchors * 1, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shapes (list[tuple[int]]): Shape of the input image,
-                list[(height, width, 3)].
-            scale_factors (list[ndarray]): Scale factor of the image arrange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        device = cls_scores[0].device
-        batch_size = cls_scores[0].shape[0]
-        # convert to tensor to keep tracing
-        nms_pre_tensor = torch.tensor(
-            cfg.get('nms_pre', -1), device=device, dtype=torch.long)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_centerness = []
-        for cls_score, bbox_pred, centerness, anchors in zip(
-                cls_scores, bbox_preds, centernesses, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            centerness = centerness.permute(0, 2, 3,
-                                            1).reshape(batch_size,
-                                                       -1).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3,
-                                          1).reshape(batch_size, -1, 4)
-
-            # Always keep topk op for dynamic input in onnx
-            if nms_pre_tensor > 0 and (torch.onnx.is_in_onnx_export()
-                                       or scores.shape[-2] > nms_pre_tensor):
-                from torch import _shape_as_tensor
-                # keep shape as tensor and get k
-                num_anchor = _shape_as_tensor(scores)[-2].to(device)
-                nms_pre = torch.where(nms_pre_tensor < num_anchor,
-                                      nms_pre_tensor, num_anchor)
-
-                max_scores, _ = (scores * centerness[..., None]).max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-                centerness = centerness[batch_inds, topk_inds]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_centerness.append(centerness)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1)
-
-        # Set max number of box to be feed into nms in deployment
-        deploy_nms_pre = cfg.get('deploy_nms_pre', -1)
-        if deploy_nms_pre > 0 and torch.onnx.is_in_onnx_export():
-            batch_mlvl_scores, _ = (
-                batch_mlvl_scores *
-                batch_mlvl_centerness.unsqueeze(2).expand_as(batch_mlvl_scores)
-            ).max(-1)
-            _, topk_inds = batch_mlvl_scores.topk(deploy_nms_pre)
-            batch_inds = torch.arange(batch_size).view(-1,
-                                                       1).expand_as(topk_inds)
-            batch_mlvl_scores = batch_mlvl_scores[batch_inds, topk_inds, :]
-            batch_mlvl_bboxes = batch_mlvl_bboxes[batch_inds, topk_inds, :]
-            batch_mlvl_centerness = batch_mlvl_centerness[batch_inds,
-                                                          topk_inds]
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-
-        if with_nms:
-            det_results = []
-            for (mlvl_bboxes, mlvl_scores,
-                 mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores,
-                                         batch_mlvl_centerness):
-                det_bbox, det_label = multiclass_nms(
-                    mlvl_bboxes,
-                    mlvl_scores,
-                    cfg.score_thr,
-                    cfg.nms,
-                    cfg.max_per_img,
-                    score_factors=mlvl_centerness)
-                det_results.append(tuple([det_bbox, det_label]))
-        else:
-            det_results = [
-                tuple(mlvl_bs)
-                for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores,
-                                   batch_mlvl_centerness)
-            ]
-        return det_results
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    label_channels=1,
-                    unmap_outputs=True):
-        """Get targets for ATSS head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. Besides
-        returning the targets as the parent method does, it also returns the
-        anchors as the first element of the returned tuple.
-        """
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        num_level_anchors_list = [num_level_anchors] * num_imgs
-
-        # concat all level anchors and flags to a single tensor
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            anchor_list[i] = torch.cat(anchor_list[i])
-            valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
-         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
-             self._get_target_single,
-             anchor_list,
-             valid_flag_list,
-             num_level_anchors_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             label_channels=label_channels,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        anchors_list = images_to_levels(all_anchors, num_level_anchors)
-        labels_list = images_to_levels(all_labels, num_level_anchors)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_anchors)
-        bbox_targets_list = images_to_levels(all_bbox_targets,
-                                             num_level_anchors)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_anchors)
-        return (anchors_list, labels_list, label_weights_list,
-                bbox_targets_list, bbox_weights_list, num_total_pos,
-                num_total_neg)
-
-    def _get_target_single(self,
-                           flat_anchors,
-                           valid_flags,
-                           num_level_anchors,
-                           gt_bboxes,
-                           gt_bboxes_ignore,
-                           gt_labels,
-                           img_meta,
-                           label_channels=1,
-                           unmap_outputs=True):
-        """Compute regression, classification targets for anchors in a single
-        image.
-
-        Args:
-            flat_anchors (Tensor): Multi-level anchors of the image, which are
-                concatenated into a single tensor of shape (num_anchors ,4)
-            valid_flags (Tensor): Multi level valid flags of the image,
-                which are concatenated into a single tensor of
-                    shape (num_anchors,).
-            num_level_anchors Tensor): Number of anchors of each scale level.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            img_meta (dict): Meta info of the image.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: N is the number of total anchors in the image.
-                labels (Tensor): Labels of all anchors in the image with shape
-                    (N,).
-                label_weights (Tensor): Label weights of all anchor in the
-                    image with shape (N,).
-                bbox_targets (Tensor): BBox targets of all anchors in the
-                    image with shape (N, 4).
-                bbox_weights (Tensor): BBox weights of all anchors in the
-                    image with shape (N, 4)
-                pos_inds (Tensor): Indices of positive anchor with shape
-                    (num_pos,).
-                neg_inds (Tensor): Indices of negative anchor with shape
-                    (num_neg,).
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg.allowed_border)
-        if not inside_flags.any():
-            return (None, ) * 7
-        # assign gt and sample anchors
-        anchors = flat_anchors[inside_flags, :]
-
-        num_level_anchors_inside = self.get_num_level_anchors_inside(
-            num_level_anchors, inside_flags)
-        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
-                                             gt_bboxes, gt_bboxes_ignore,
-                                             gt_labels)
-
-        sampling_result = self.sampler.sample(assign_result, anchors,
-                                              gt_bboxes)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            if hasattr(self, 'bbox_coder'):
-                pos_bbox_targets = self.bbox_coder.encode(
-                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-            else:
-                # used in VFNetHead
-                pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class since v2.5.0
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            anchors = unmap(anchors, num_total_anchors, inside_flags)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags, fill=self.num_classes)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
-        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
-                pos_inds, neg_inds)
-
-    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
-        split_inside_flags = torch.split(inside_flags, num_level_anchors)
-        num_level_anchors_inside = [
-            int(flags.sum()) for flags in split_inside_flags
-        ]
-        return num_level_anchors_inside
diff --git a/spaces/CVPR/WALT/mmdet/models/dense_heads/guided_anchor_head.py b/spaces/CVPR/WALT/mmdet/models/dense_heads/guided_anchor_head.py
deleted file mode 100644
index 997ebb751ade2ebae3fce335a08c46f596c60913..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/dense_heads/guided_anchor_head.py
+++ /dev/null
@@ -1,860 +0,0 @@
-import torch
-import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
-from mmcv.ops import DeformConv2d, MaskedConv2d
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, build_anchor_generator,
-                        build_assigner, build_bbox_coder, build_sampler,
-                        calc_region, images_to_levels, multi_apply,
-                        multiclass_nms, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-class FeatureAdaption(nn.Module):
-    """Feature Adaption Module.
-
-    Feature Adaption Module is implemented based on DCN v1.
-    It uses anchor shape prediction rather than feature map to
-    predict offsets of deform conv layer.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        out_channels (int): Number of channels in the output feature map.
-        kernel_size (int): Deformable conv kernel size.
-        deform_groups (int): Deformable conv group size.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size=3,
-                 deform_groups=4):
-        super(FeatureAdaption, self).__init__()
-        offset_channels = kernel_size * kernel_size * 2
-        self.conv_offset = nn.Conv2d(
-            2, deform_groups * offset_channels, 1, bias=False)
-        self.conv_adaption = DeformConv2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            padding=(kernel_size - 1) // 2,
-            deform_groups=deform_groups)
-        self.relu = nn.ReLU(inplace=True)
-
-    def init_weights(self):
-        normal_init(self.conv_offset, std=0.1)
-        normal_init(self.conv_adaption, std=0.01)
-
-    def forward(self, x, shape):
-        offset = self.conv_offset(shape.detach())
-        x = self.relu(self.conv_adaption(x, offset))
-        return x
-
-
-@HEADS.register_module()
-class GuidedAnchorHead(AnchorHead):
-    """Guided-Anchor-based head (GA-RPN, GA-RetinaNet, etc.).
-
-    This GuidedAnchorHead will predict high-quality feature guided
-    anchors and locations where anchors will be kept in inference.
-    There are mainly 3 categories of bounding-boxes.
-
-    - Sampled 9 pairs for target assignment. (approxes)
-    - The square boxes where the predicted anchors are based on. (squares)
-    - Guided anchors.
-
-    Please refer to https://arxiv.org/abs/1901.03278 for more details.
-
-    Args:
-        num_classes (int): Number of classes.
-        in_channels (int): Number of channels in the input feature map.
-        feat_channels (int): Number of hidden channels.
-        approx_anchor_generator (dict): Config dict for approx generator
-        square_anchor_generator (dict): Config dict for square generator
-        anchor_coder (dict): Config dict for anchor coder
-        bbox_coder (dict): Config dict for bbox coder
-        reg_decoded_bbox (bool): If true, the regression loss would be
-            applied directly on decoded bounding boxes, converting both
-            the predicted boxes and regression targets to absolute
-            coordinates format. Default False. It should be `True` when
-            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
-        deform_groups: (int): Group number of DCN in
-            FeatureAdaption module.
-        loc_filter_thr (float): Threshold to filter out unconcerned regions.
-        loss_loc (dict): Config of location loss.
-        loss_shape (dict): Config of anchor shape loss.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of bbox regression loss.
-    """
-
-    def __init__(
-        self,
-        num_classes,
-        in_channels,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=8,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[8],
-            strides=[4, 8, 16, 32, 64]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]
-        ),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]
-        ),
-        reg_decoded_bbox=False,
-        deform_groups=4,
-        loc_filter_thr=0.01,
-        train_cfg=None,
-        test_cfg=None,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                       loss_weight=1.0)):  # yapf: disable
-        super(AnchorHead, self).__init__()
-        self.in_channels = in_channels
-        self.num_classes = num_classes
-        self.feat_channels = feat_channels
-        self.deform_groups = deform_groups
-        self.loc_filter_thr = loc_filter_thr
-
-        # build approx_anchor_generator and square_anchor_generator
-        assert (approx_anchor_generator['octave_base_scale'] ==
-                square_anchor_generator['scales'][0])
-        assert (approx_anchor_generator['strides'] ==
-                square_anchor_generator['strides'])
-        self.approx_anchor_generator = build_anchor_generator(
-            approx_anchor_generator)
-        self.square_anchor_generator = build_anchor_generator(
-            square_anchor_generator)
-        self.approxs_per_octave = self.approx_anchor_generator \
-            .num_base_anchors[0]
-
-        self.reg_decoded_bbox = reg_decoded_bbox
-
-        # one anchor per location
-        self.num_anchors = 1
-        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-        self.loc_focal_loss = loss_loc['type'] in ['FocalLoss']
-        self.sampling = loss_cls['type'] not in ['FocalLoss']
-        self.ga_sampling = train_cfg is not None and hasattr(
-            train_cfg, 'ga_sampler')
-        if self.use_sigmoid_cls:
-            self.cls_out_channels = self.num_classes
-        else:
-            self.cls_out_channels = self.num_classes + 1
-
-        # build bbox_coder
-        self.anchor_coder = build_bbox_coder(anchor_coder)
-        self.bbox_coder = build_bbox_coder(bbox_coder)
-
-        # build losses
-        self.loss_loc = build_loss(loss_loc)
-        self.loss_shape = build_loss(loss_shape)
-        self.loss_cls = build_loss(loss_cls)
-        self.loss_bbox = build_loss(loss_bbox)
-
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # use PseudoSampler when sampling is False
-            if self.sampling and hasattr(self.train_cfg, 'sampler'):
-                sampler_cfg = self.train_cfg.sampler
-            else:
-                sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-
-            self.ga_assigner = build_assigner(self.train_cfg.ga_assigner)
-            if self.ga_sampling:
-                ga_sampler_cfg = self.train_cfg.ga_sampler
-            else:
-                ga_sampler_cfg = dict(type='PseudoSampler')
-            self.ga_sampler = build_sampler(ga_sampler_cfg, context=self)
-
-        self.fp16_enabled = False
-
-        self._init_layers()
-
-    def _init_layers(self):
-        self.relu = nn.ReLU(inplace=True)
-        self.conv_loc = nn.Conv2d(self.in_channels, 1, 1)
-        self.conv_shape = nn.Conv2d(self.in_channels, self.num_anchors * 2, 1)
-        self.feature_adaption = FeatureAdaption(
-            self.in_channels,
-            self.feat_channels,
-            kernel_size=3,
-            deform_groups=self.deform_groups)
-        self.conv_cls = MaskedConv2d(self.feat_channels,
-                                     self.num_anchors * self.cls_out_channels,
-                                     1)
-        self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4,
-                                     1)
-
-    def init_weights(self):
-        normal_init(self.conv_cls, std=0.01)
-        normal_init(self.conv_reg, std=0.01)
-
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.conv_loc, std=0.01, bias=bias_cls)
-        normal_init(self.conv_shape, std=0.01)
-
-        self.feature_adaption.init_weights()
-
-    def forward_single(self, x):
-        loc_pred = self.conv_loc(x)
-        shape_pred = self.conv_shape(x)
-        x = self.feature_adaption(x, shape_pred)
-        # masked conv is only used during inference for speed-up
-        if not self.training:
-            mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
-        else:
-            mask = None
-        cls_score = self.conv_cls(x, mask)
-        bbox_pred = self.conv_reg(x, mask)
-        return cls_score, bbox_pred, shape_pred, loc_pred
-
-    def forward(self, feats):
-        return multi_apply(self.forward_single, feats)
-
-    def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'):
-        """Get sampled approxs and inside flags according to feature map sizes.
-
-        Args:
-            featmap_sizes (list[tuple]): Multi-level feature map sizes.
-            img_metas (list[dict]): Image meta info.
-            device (torch.device | str): device for returned tensors
-
-        Returns:
-            tuple: approxes of each image, inside flags of each image
-        """
-        num_imgs = len(img_metas)
-
-        # since feature map sizes of all images are the same, we only compute
-        # approxes for one time
-        multi_level_approxs = self.approx_anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-        approxs_list = [multi_level_approxs for _ in range(num_imgs)]
-
-        # for each image, we compute inside flags of multi level approxes
-        inside_flag_list = []
-        for img_id, img_meta in enumerate(img_metas):
-            multi_level_flags = []
-            multi_level_approxs = approxs_list[img_id]
-
-            # obtain valid flags for each approx first
-            multi_level_approx_flags = self.approx_anchor_generator \
-                .valid_flags(featmap_sizes,
-                             img_meta['pad_shape'],
-                             device=device)
-
-            for i, flags in enumerate(multi_level_approx_flags):
-                approxs = multi_level_approxs[i]
-                inside_flags_list = []
-                for i in range(self.approxs_per_octave):
-                    split_valid_flags = flags[i::self.approxs_per_octave]
-                    split_approxs = approxs[i::self.approxs_per_octave, :]
-                    inside_flags = anchor_inside_flags(
-                        split_approxs, split_valid_flags,
-                        img_meta['img_shape'][:2],
-                        self.train_cfg.allowed_border)
-                    inside_flags_list.append(inside_flags)
-                # inside_flag for a position is true if any anchor in this
-                # position is true
-                inside_flags = (
-                    torch.stack(inside_flags_list, 0).sum(dim=0) > 0)
-                multi_level_flags.append(inside_flags)
-            inside_flag_list.append(multi_level_flags)
-        return approxs_list, inside_flag_list
-
-    def get_anchors(self,
-                    featmap_sizes,
-                    shape_preds,
-                    loc_preds,
-                    img_metas,
-                    use_loc_filter=False,
-                    device='cuda'):
-        """Get squares according to feature map sizes and guided anchors.
-
-        Args:
-            featmap_sizes (list[tuple]): Multi-level feature map sizes.
-            shape_preds (list[tensor]): Multi-level shape predictions.
-            loc_preds (list[tensor]): Multi-level location predictions.
-            img_metas (list[dict]): Image meta info.
-            use_loc_filter (bool): Use loc filter or not.
-            device (torch.device | str): device for returned tensors
-
-        Returns:
-            tuple: square approxs of each image, guided anchors of each image,
-                loc masks of each image
-        """
-        num_imgs = len(img_metas)
-        num_levels = len(featmap_sizes)
-
-        # since feature map sizes of all images are the same, we only compute
-        # squares for one time
-        multi_level_squares = self.square_anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-        squares_list = [multi_level_squares for _ in range(num_imgs)]
-
-        # for each image, we compute multi level guided anchors
-        guided_anchors_list = []
-        loc_mask_list = []
-        for img_id, img_meta in enumerate(img_metas):
-            multi_level_guided_anchors = []
-            multi_level_loc_mask = []
-            for i in range(num_levels):
-                squares = squares_list[img_id][i]
-                shape_pred = shape_preds[i][img_id]
-                loc_pred = loc_preds[i][img_id]
-                guided_anchors, loc_mask = self._get_guided_anchors_single(
-                    squares,
-                    shape_pred,
-                    loc_pred,
-                    use_loc_filter=use_loc_filter)
-                multi_level_guided_anchors.append(guided_anchors)
-                multi_level_loc_mask.append(loc_mask)
-            guided_anchors_list.append(multi_level_guided_anchors)
-            loc_mask_list.append(multi_level_loc_mask)
-        return squares_list, guided_anchors_list, loc_mask_list
-
-    def _get_guided_anchors_single(self,
-                                   squares,
-                                   shape_pred,
-                                   loc_pred,
-                                   use_loc_filter=False):
-        """Get guided anchors and loc masks for a single level.
-
-        Args:
-            square (tensor): Squares of a single level.
-            shape_pred (tensor): Shape predections of a single level.
-            loc_pred (tensor): Loc predections of a single level.
-            use_loc_filter (list[tensor]): Use loc filter or not.
-
-        Returns:
-            tuple: guided anchors, location masks
-        """
-        # calculate location filtering mask
-        loc_pred = loc_pred.sigmoid().detach()
-        if use_loc_filter:
-            loc_mask = loc_pred >= self.loc_filter_thr
-        else:
-            loc_mask = loc_pred >= 0.0
-        mask = loc_mask.permute(1, 2, 0).expand(-1, -1, self.num_anchors)
-        mask = mask.contiguous().view(-1)
-        # calculate guided anchors
-        squares = squares[mask]
-        anchor_deltas = shape_pred.permute(1, 2, 0).contiguous().view(
-            -1, 2).detach()[mask]
-        bbox_deltas = anchor_deltas.new_full(squares.size(), 0)
-        bbox_deltas[:, 2:] = anchor_deltas
-        guided_anchors = self.anchor_coder.decode(
-            squares, bbox_deltas, wh_ratio_clip=1e-6)
-        return guided_anchors, mask
-
-    def ga_loc_targets(self, gt_bboxes_list, featmap_sizes):
-        """Compute location targets for guided anchoring.
-
-        Each feature map is divided into positive, negative and ignore regions.
-        - positive regions: target 1, weight 1
-        - ignore regions: target 0, weight 0
-        - negative regions: target 0, weight 0.1
-
-        Args:
-            gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
-            featmap_sizes (list[tuple]): Multi level sizes of each feature
-                maps.
-
-        Returns:
-            tuple
-        """
-        anchor_scale = self.approx_anchor_generator.octave_base_scale
-        anchor_strides = self.approx_anchor_generator.strides
-        # Currently only supports same stride in x and y direction.
-        for stride in anchor_strides:
-            assert (stride[0] == stride[1])
-        anchor_strides = [stride[0] for stride in anchor_strides]
-
-        center_ratio = self.train_cfg.center_ratio
-        ignore_ratio = self.train_cfg.ignore_ratio
-        img_per_gpu = len(gt_bboxes_list)
-        num_lvls = len(featmap_sizes)
-        r1 = (1 - center_ratio) / 2
-        r2 = (1 - ignore_ratio) / 2
-        all_loc_targets = []
-        all_loc_weights = []
-        all_ignore_map = []
-        for lvl_id in range(num_lvls):
-            h, w = featmap_sizes[lvl_id]
-            loc_targets = torch.zeros(
-                img_per_gpu,
-                1,
-                h,
-                w,
-                device=gt_bboxes_list[0].device,
-                dtype=torch.float32)
-            loc_weights = torch.full_like(loc_targets, -1)
-            ignore_map = torch.zeros_like(loc_targets)
-            all_loc_targets.append(loc_targets)
-            all_loc_weights.append(loc_weights)
-            all_ignore_map.append(ignore_map)
-        for img_id in range(img_per_gpu):
-            gt_bboxes = gt_bboxes_list[img_id]
-            scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
-                               (gt_bboxes[:, 3] - gt_bboxes[:, 1]))
-            min_anchor_size = scale.new_full(
-                (1, ), float(anchor_scale * anchor_strides[0]))
-            # assign gt bboxes to different feature levels w.r.t. their scales
-            target_lvls = torch.floor(
-                torch.log2(scale) - torch.log2(min_anchor_size) + 0.5)
-            target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long()
-            for gt_id in range(gt_bboxes.size(0)):
-                lvl = target_lvls[gt_id].item()
-                # rescaled to corresponding feature map
-                gt_ = gt_bboxes[gt_id, :4] / anchor_strides[lvl]
-                # calculate ignore regions
-                ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
-                    gt_, r2, featmap_sizes[lvl])
-                # calculate positive (center) regions
-                ctr_x1, ctr_y1, ctr_x2, ctr_y2 = calc_region(
-                    gt_, r1, featmap_sizes[lvl])
-                all_loc_targets[lvl][img_id, 0, ctr_y1:ctr_y2 + 1,
-                                     ctr_x1:ctr_x2 + 1] = 1
-                all_loc_weights[lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
-                                     ignore_x1:ignore_x2 + 1] = 0
-                all_loc_weights[lvl][img_id, 0, ctr_y1:ctr_y2 + 1,
-                                     ctr_x1:ctr_x2 + 1] = 1
-                # calculate ignore map on nearby low level feature
-                if lvl > 0:
-                    d_lvl = lvl - 1
-                    # rescaled to corresponding feature map
-                    gt_ = gt_bboxes[gt_id, :4] / anchor_strides[d_lvl]
-                    ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
-                        gt_, r2, featmap_sizes[d_lvl])
-                    all_ignore_map[d_lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
-                                          ignore_x1:ignore_x2 + 1] = 1
-                # calculate ignore map on nearby high level feature
-                if lvl < num_lvls - 1:
-                    u_lvl = lvl + 1
-                    # rescaled to corresponding feature map
-                    gt_ = gt_bboxes[gt_id, :4] / anchor_strides[u_lvl]
-                    ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
-                        gt_, r2, featmap_sizes[u_lvl])
-                    all_ignore_map[u_lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
-                                          ignore_x1:ignore_x2 + 1] = 1
-        for lvl_id in range(num_lvls):
-            # ignore negative regions w.r.t. ignore map
-            all_loc_weights[lvl_id][(all_loc_weights[lvl_id] < 0)
-                                    & (all_ignore_map[lvl_id] > 0)] = 0
-            # set negative regions with weight 0.1
-            all_loc_weights[lvl_id][all_loc_weights[lvl_id] < 0] = 0.1
-        # loc average factor to balance loss
-        loc_avg_factor = sum(
-            [t.size(0) * t.size(-1) * t.size(-2)
-             for t in all_loc_targets]) / 200
-        return all_loc_targets, all_loc_weights, loc_avg_factor
-
-    def _ga_shape_target_single(self,
-                                flat_approxs,
-                                inside_flags,
-                                flat_squares,
-                                gt_bboxes,
-                                gt_bboxes_ignore,
-                                img_meta,
-                                unmap_outputs=True):
-        """Compute guided anchoring targets.
-
-        This function returns sampled anchors and gt bboxes directly
-        rather than calculates regression targets.
-
-        Args:
-            flat_approxs (Tensor): flat approxs of a single image,
-                shape (n, 4)
-            inside_flags (Tensor): inside flags of a single image,
-                shape (n, ).
-            flat_squares (Tensor): flat squares of a single image,
-                shape (approxs_per_octave * n, 4)
-            gt_bboxes (Tensor): Ground truth bboxes of a single image.
-            img_meta (dict): Meta info of a single image.
-            approxs_per_octave (int): number of approxs per octave
-            cfg (dict): RPN train configs.
-            unmap_outputs (bool): unmap outputs or not.
-
-        Returns:
-            tuple
-        """
-        if not inside_flags.any():
-            return (None, ) * 5
-        # assign gt and sample anchors
-        expand_inside_flags = inside_flags[:, None].expand(
-            -1, self.approxs_per_octave).reshape(-1)
-        approxs = flat_approxs[expand_inside_flags, :]
-        squares = flat_squares[inside_flags, :]
-
-        assign_result = self.ga_assigner.assign(approxs, squares,
-                                                self.approxs_per_octave,
-                                                gt_bboxes, gt_bboxes_ignore)
-        sampling_result = self.ga_sampler.sample(assign_result, squares,
-                                                 gt_bboxes)
-
-        bbox_anchors = torch.zeros_like(squares)
-        bbox_gts = torch.zeros_like(squares)
-        bbox_weights = torch.zeros_like(squares)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            bbox_anchors[pos_inds, :] = sampling_result.pos_bboxes
-            bbox_gts[pos_inds, :] = sampling_result.pos_gt_bboxes
-            bbox_weights[pos_inds, :] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_squares.size(0)
-            bbox_anchors = unmap(bbox_anchors, num_total_anchors, inside_flags)
-            bbox_gts = unmap(bbox_gts, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
-        return (bbox_anchors, bbox_gts, bbox_weights, pos_inds, neg_inds)
-
-    def ga_shape_targets(self,
-                         approx_list,
-                         inside_flag_list,
-                         square_list,
-                         gt_bboxes_list,
-                         img_metas,
-                         gt_bboxes_ignore_list=None,
-                         unmap_outputs=True):
-        """Compute guided anchoring targets.
-
-        Args:
-            approx_list (list[list]): Multi level approxs of each image.
-            inside_flag_list (list[list]): Multi level inside flags of each
-                image.
-            square_list (list[list]): Multi level squares of each image.
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
-            unmap_outputs (bool): unmap outputs or not.
-
-        Returns:
-            tuple
-        """
-        num_imgs = len(img_metas)
-        assert len(approx_list) == len(inside_flag_list) == len(
-            square_list) == num_imgs
-        # anchor number of multi levels
-        num_level_squares = [squares.size(0) for squares in square_list[0]]
-        # concat all level anchors and flags to a single tensor
-        inside_flag_flat_list = []
-        approx_flat_list = []
-        square_flat_list = []
-        for i in range(num_imgs):
-            assert len(square_list[i]) == len(inside_flag_list[i])
-            inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
-            approx_flat_list.append(torch.cat(approx_list[i]))
-            square_flat_list.append(torch.cat(square_list[i]))
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        (all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list,
-         neg_inds_list) = multi_apply(
-             self._ga_shape_target_single,
-             approx_flat_list,
-             inside_flag_flat_list,
-             square_flat_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             img_metas,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        bbox_anchors_list = images_to_levels(all_bbox_anchors,
-                                             num_level_squares)
-        bbox_gts_list = images_to_levels(all_bbox_gts, num_level_squares)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_squares)
-        return (bbox_anchors_list, bbox_gts_list, bbox_weights_list,
-                num_total_pos, num_total_neg)
-
-    def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
-                          anchor_weights, anchor_total_num):
-        shape_pred = shape_pred.permute(0, 2, 3, 1).contiguous().view(-1, 2)
-        bbox_anchors = bbox_anchors.contiguous().view(-1, 4)
-        bbox_gts = bbox_gts.contiguous().view(-1, 4)
-        anchor_weights = anchor_weights.contiguous().view(-1, 4)
-        bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0)
-        bbox_deltas[:, 2:] += shape_pred
-        # filter out negative samples to speed-up weighted_bounded_iou_loss
-        inds = torch.nonzero(
-            anchor_weights[:, 0] > 0, as_tuple=False).squeeze(1)
-        bbox_deltas_ = bbox_deltas[inds]
-        bbox_anchors_ = bbox_anchors[inds]
-        bbox_gts_ = bbox_gts[inds]
-        anchor_weights_ = anchor_weights[inds]
-        pred_anchors_ = self.anchor_coder.decode(
-            bbox_anchors_, bbox_deltas_, wh_ratio_clip=1e-6)
-        loss_shape = self.loss_shape(
-            pred_anchors_,
-            bbox_gts_,
-            anchor_weights_,
-            avg_factor=anchor_total_num)
-        return loss_shape
-
-    def loss_loc_single(self, loc_pred, loc_target, loc_weight,
-                        loc_avg_factor):
-        loss_loc = self.loss_loc(
-            loc_pred.reshape(-1, 1),
-            loc_target.reshape(-1).long(),
-            loc_weight.reshape(-1),
-            avg_factor=loc_avg_factor)
-        return loss_loc
-
-    @force_fp32(
-        apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             shape_preds,
-             loc_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
-
-        device = cls_scores[0].device
-
-        # get loc targets
-        loc_targets, loc_weights, loc_avg_factor = self.ga_loc_targets(
-            gt_bboxes, featmap_sizes)
-
-        # get sampled approxes
-        approxs_list, inside_flag_list = self.get_sampled_approxs(
-            featmap_sizes, img_metas, device=device)
-        # get squares and guided anchors
-        squares_list, guided_anchors_list, _ = self.get_anchors(
-            featmap_sizes, shape_preds, loc_preds, img_metas, device=device)
-
-        # get shape targets
-        shape_targets = self.ga_shape_targets(approxs_list, inside_flag_list,
-                                              squares_list, gt_bboxes,
-                                              img_metas)
-        if shape_targets is None:
-            return None
-        (bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num,
-         anchor_bg_num) = shape_targets
-        anchor_total_num = (
-            anchor_fg_num if not self.ga_sampling else anchor_fg_num +
-            anchor_bg_num)
-
-        # get anchor targets
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            guided_anchors_list,
-            inside_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg) = cls_reg_targets
-        num_total_samples = (
-            num_total_pos + num_total_neg if self.sampling else num_total_pos)
-
-        # anchor number of multi levels
-        num_level_anchors = [
-            anchors.size(0) for anchors in guided_anchors_list[0]
-        ]
-        # concat all level anchors to a single tensor
-        concat_anchor_list = []
-        for i in range(len(guided_anchors_list)):
-            concat_anchor_list.append(torch.cat(guided_anchors_list[i]))
-        all_anchor_list = images_to_levels(concat_anchor_list,
-                                           num_level_anchors)
-
-        # get classification and bbox regression losses
-        losses_cls, losses_bbox = multi_apply(
-            self.loss_single,
-            cls_scores,
-            bbox_preds,
-            all_anchor_list,
-            labels_list,
-            label_weights_list,
-            bbox_targets_list,
-            bbox_weights_list,
-            num_total_samples=num_total_samples)
-
-        # get anchor location loss
-        losses_loc = []
-        for i in range(len(loc_preds)):
-            loss_loc = self.loss_loc_single(
-                loc_preds[i],
-                loc_targets[i],
-                loc_weights[i],
-                loc_avg_factor=loc_avg_factor)
-            losses_loc.append(loss_loc)
-
-        # get anchor shape loss
-        losses_shape = []
-        for i in range(len(shape_preds)):
-            loss_shape = self.loss_shape_single(
-                shape_preds[i],
-                bbox_anchors_list[i],
-                bbox_gts_list[i],
-                anchor_weights_list[i],
-                anchor_total_num=anchor_total_num)
-            losses_shape.append(loss_shape)
-
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            loss_shape=losses_shape,
-            loss_loc=losses_loc)
-
-    @force_fp32(
-        apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   shape_preds,
-                   loc_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=False):
-        assert len(cls_scores) == len(bbox_preds) == len(shape_preds) == len(
-            loc_preds)
-        num_levels = len(cls_scores)
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        device = cls_scores[0].device
-        # get guided anchors
-        _, guided_anchors, loc_masks = self.get_anchors(
-            featmap_sizes,
-            shape_preds,
-            loc_preds,
-            img_metas,
-            use_loc_filter=not self.training,
-            device=device)
-        result_list = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            guided_anchor_list = [
-                guided_anchors[img_id][i].detach() for i in range(num_levels)
-            ]
-            loc_mask_list = [
-                loc_masks[img_id][i].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
-                                                guided_anchor_list,
-                                                loc_mask_list, img_shape,
-                                                scale_factor, cfg, rescale)
-            result_list.append(proposals)
-        return result_list
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_anchors,
-                           mlvl_masks,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        for cls_score, bbox_pred, anchors, mask in zip(cls_scores, bbox_preds,
-                                                       mlvl_anchors,
-                                                       mlvl_masks):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            # if no location is kept, end.
-            if mask.sum() == 0:
-                continue
-            # reshape scores and bbox_pred
-            cls_score = cls_score.permute(1, 2,
-                                          0).reshape(-1, self.cls_out_channels)
-            if self.use_sigmoid_cls:
-                scores = cls_score.sigmoid()
-            else:
-                scores = cls_score.softmax(-1)
-            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-            # filter scores, bbox_pred w.r.t. mask.
-            # anchors are filtered in get_anchors() beforehand.
-            scores = scores[mask, :]
-            bbox_pred = bbox_pred[mask, :]
-            if scores.dim() == 0:
-                anchors = anchors.unsqueeze(0)
-                scores = scores.unsqueeze(0)
-                bbox_pred = bbox_pred.unsqueeze(0)
-            # filter anchors, bbox_pred, scores w.r.t. scores
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[0] > nms_pre:
-                if self.use_sigmoid_cls:
-                    max_scores, _ = scores.max(dim=1)
-                else:
-                    # remind that we set FG labels to [0, num_class-1]
-                    # since mmdet v2.0
-                    # BG cat_id: num_class
-                    max_scores, _ = scores[:, :-1].max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        if self.use_sigmoid_cls:
-            # Add a dummy background class to the backend when using sigmoid
-            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-            # BG cat_id: num_class
-            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
-            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
-        # multi class NMS
-        det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
-                                                cfg.score_thr, cfg.nms,
-                                                cfg.max_per_img)
-        return det_bboxes, det_labels
diff --git a/spaces/CVPR/lama-example/bin/paper_runfiles/generate_test_ffhq.sh b/spaces/CVPR/lama-example/bin/paper_runfiles/generate_test_ffhq.sh
deleted file mode 100644
index a1b79cb0f3f710eed21a978c3a1489ca830bb7f8..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/bin/paper_runfiles/generate_test_ffhq.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-# paths to data are valid for mml-ws01
-OUT_DIR="/media/inpainting/paper_data/FFHQ_val"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in test
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-    do
-        "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-ffhq \
-         location.out_dir=$OUT_DIR cropping.out_square_crop=False
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done
diff --git a/spaces/ClassCat/Medical-Image-Classification-with-MONAI/README.md b/spaces/ClassCat/Medical-Image-Classification-with-MONAI/README.md
deleted file mode 100644
index 65d962d888807ab6915f7418277918827307e61e..0000000000000000000000000000000000000000
--- a/spaces/ClassCat/Medical-Image-Classification-with-MONAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Medical Image Classification With MONAI
-emoji: 🔥
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: True
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CofAI/chat.b4/client/css/buttons.css b/spaces/CofAI/chat.b4/client/css/buttons.css
deleted file mode 100644
index e13f52d9a0414daaa80518bd205913a645a29563..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/client/css/buttons.css
+++ /dev/null
@@ -1,4 +0,0 @@
-.buttons {
-	display: flex;
-	justify-content: left;
-}
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/qu2cu/benchmark.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/qu2cu/benchmark.py
deleted file mode 100644
index cee55f5e7d9bffba11859caae02255bcad77e17d..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/qu2cu/benchmark.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""Benchmark the qu2cu algorithm performance."""
-
-from .qu2cu import *
-from fontTools.cu2qu import curve_to_quadratic
-import random
-import timeit
-
-MAX_ERR = 0.5
-NUM_CURVES = 5
-
-
-def generate_curves(n):
-    points = [
-        tuple(float(random.randint(0, 2048)) for coord in range(2))
-        for point in range(1 + 3 * n)
-    ]
-    curves = []
-    for i in range(n):
-        curves.append(tuple(points[i * 3 : i * 3 + 4]))
-    return curves
-
-
-def setup_quadratic_to_curves():
-    curves = generate_curves(NUM_CURVES)
-    quadratics = [curve_to_quadratic(curve, MAX_ERR) for curve in curves]
-    return quadratics, MAX_ERR
-
-
-def run_benchmark(module, function, setup_suffix="", repeat=25, number=1):
-    setup_func = "setup_" + function
-    if setup_suffix:
-        print("%s with %s:" % (function, setup_suffix), end="")
-        setup_func += "_" + setup_suffix
-    else:
-        print("%s:" % function, end="")
-
-    def wrapper(function, setup_func):
-        function = globals()[function]
-        setup_func = globals()[setup_func]
-
-        def wrapped():
-            return function(*setup_func())
-
-        return wrapped
-
-    results = timeit.repeat(wrapper(function, setup_func), repeat=repeat, number=number)
-    print("\t%5.1fus" % (min(results) * 1000000.0 / number))
-
-
-def main():
-    """Benchmark the qu2cu algorithm performance."""
-    run_benchmark("qu2cu", "quadratic_to_curves")
-
-
-if __name__ == "__main__":
-    random.seed(1)
-    main()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-097d3f80.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-097d3f80.js
deleted file mode 100644
index aeb9dcc2201107c52963b2ac176de36496b1dc07..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-097d3f80.js
+++ /dev/null
@@ -1,2 +0,0 @@
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r[0]===null?0:1}return t=m(n),a=_[t]=b[t](n),{c(){U(e.$$.fragment),l=X(),a.c(),i=le()},m(r,u){M(e,r,u),q(r,l,u),_[t].m(r,u),q(r,i,u),s=!0},p(r,[u]){const o={};u&8&&(o.show_label=r[3]),u&4&&(o.label=r[2]||"Video"),e.$set(o);let d=t;t=m(r),t===d?_[t].p(r,u):(Q(),p(_[d],1,1,()=>{_[d]=null}),Y(),a=_[t],a?a.p(r,u):(a=_[t]=b[t](r),a.c()),g(a,1),a.m(i.parentNode,i))},i(r){s||(g(e.$$.fragment,r),g(a),s=!0)},o(r){p(e.$$.fragment,r),p(a),s=!1},d(r){r&&(j(l),j(i)),S(e,r),_[t].d(r)}}}function Pt(n,e,l){let{value:t=null}=e,{subtitle:a=null}=e,{label:i=void 0}=e,{show_label:s=!0}=e,{autoplay:b}=e,{show_share_button:_=!0}=e,m=null,r=null;const u=de();Xe(async()=>{t!==m&&a!==r&&r!==null&&(m=t,l(0,t=null),await Le(),l(0,t=m)),m=t,r=a});function o(w){y.call(this,n,w)}function d(w){y.call(this,n,w)}function E(w){y.call(this,n,w)}const B=async w=>w?await tt(w.data,"url"):"";function V(w){y.call(this,n,w)}function I(w){y.call(this,n,w)}return n.$$set=w=>{"value"in w&&l(0,t=w.value),"subtitle"in w&&l(1,a=w.subtitle),"label"in w&&l(2,i=w.label),"show_label"in w&&l(3,s=w.show_label),"autoplay"in w&&l(4,b=w.autoplay),"show_share_button"in w&&l(5,_=w.show_share_button)},n.$$.update=()=>{n.$$.dirty&1&&t&&u("change",t)},[t,a,i,s,b,_,o,d,E,B,V,I]}class Rt extends ${constructor(e){super(),x(this,e,Pt,Et,K,{value:0,subtitle:1,label:2,show_label:3,autoplay:4,show_share_button:5})}}function zt(n){let e,l;return e=new Ut({props:{value:n[18],subtitle:n[19],label:n[5],show_label:n[7],source:n[6],mirror_webcam:n[10],include_audio:n[11],autoplay:n[16],$$slots:{default:[At]},$$scope:{ctx:n}}}),e.$on("change",n[21]),e.$on("drag",n[30]),e.$on("error",n[31]),e.$on("clear",n[32]),e.$on("play",n[33]),e.$on("pause",n[34]),e.$on("upload",n[35]),e.$on("stop",n[36]),e.$on("end",n[37]),e.$on("start_recording",n[38]),e.$on("stop_recording",n[39]),{c(){U(e.$$.fragment)},m(t,a){M(e,t,a),l=!0},p(t,a){const 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at({props:{type:"video"}}),{c(){U(e.$$.fragment)},m(t,a){M(e,t,a),l=!0},p:P,i(t){l||(g(e.$$.fragment,t),l=!0)},o(t){p(e.$$.fragment,t),l=!1},d(t){S(e,t)}}}function Dt(n){let e,l,t,a,i,s;const b=[n[1]];let _={};for(let o=0;o{r[B]=null}),Y(),a=r[t],a?a.p(o,d):(a=r[t]=m[t](o),a.c()),g(a,1),a.m(i.parentNode,i))},i(o){s||(g(e.$$.fragment,o),g(a),s=!0)},o(o){p(e.$$.fragment,o),p(a),s=!1},d(o){o&&(j(l),j(i)),S(e,o),r[t].d(o)}}}function It(n){let e,l;return e=new Ge({props:{visible:n[4],variant:n[15]==="dynamic"&&n[0]===null&&n[6]==="upload"?"dashed":"solid",border_mode:n[20]?"focus":"base",padding:!1,elem_id:n[2],elem_classes:n[3],height:n[8],width:n[9],container:n[12],scale:n[13],min_width:n[14],allow_overflow:!1,$$slots:{default:[Dt]},$$scope:{ctx:n}}}),{c(){U(e.$$.fragment)},m(t,a){M(e,t,a),l=!0},p(t,a){const 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Z(c){y.call(this,n,c)}function z(c){y.call(this,n,c)}function ne(c){y.call(this,n,c)}function re(c){y.call(this,n,c)}function oe(c){y.call(this,n,c)}const se=({detail:c})=>l(20,h=c),G=({detail:c})=>{l(1,d=d||{}),l(1,d.status="error",d),l(1,d.message=c,d)};function ue(c){y.call(this,n,c)}function v(c){y.call(this,n,c)}function R(c){y.call(this,n,c)}function ae(c){y.call(this,n,c)}function Ve(c){y.call(this,n,c)}function Te(c){y.call(this,n,c)}function qe(c){y.call(this,n,c)}function je(c){y.call(this,n,c)}return n.$$set=c=>{"elem_id"in c&&l(2,t=c.elem_id),"elem_classes"in c&&l(3,a=c.elem_classes),"visible"in c&&l(4,i=c.visible),"value"in c&&l(0,s=c.value),"label"in c&&l(5,_=c.label),"source"in c&&l(6,m=c.source),"root"in c&&l(22,r=c.root),"root_url"in c&&l(23,u=c.root_url),"show_label"in c&&l(7,o=c.show_label),"loading_status"in c&&l(1,d=c.loading_status),"height"in c&&l(8,E=c.height),"width"in c&&l(9,B=c.width),"mirror_webcam"in c&&l(10,V=c.mirror_webcam),"include_audio"in c&&l(11,I=c.include_audio),"container"in c&&l(12,w=c.container),"scale"in c&&l(13,D=c.scale),"min_width"in c&&l(14,L=c.min_width),"mode"in c&&l(15,W=c.mode),"autoplay"in c&&l(16,O=c.autoplay),"show_share_button"in c&&l(17,H=c.show_share_button)},n.$$.update=()=>{n.$$.dirty[0]&12582913&&(s!=null?(l(18,k=ge(s[0],r,u)),l(19,C=ge(s[1],r,u))):(l(18,k=null),l(19,C=null))),n.$$.dirty[0]&16777217&&JSON.stringify(s)!==JSON.stringify(b)&&(l(24,b=s),te("change"))},[s,d,t,a,i,_,m,o,E,B,V,I,w,D,L,W,O,H,k,C,h,ee,r,u,b,Z,z,ne,re,oe,se,G,ue,v,R,ae,Ve,Te,qe,je]}class Xt extends ${constructor(e){super(),x(this,e,Ot,It,K,{elem_id:2,elem_classes:3,visible:4,value:0,label:5,source:6,root:22,root_url:23,show_label:7,loading_status:1,height:8,width:9,mirror_webcam:10,include_audio:11,container:12,scale:13,min_width:14,mode:15,autoplay:16,show_share_button:17},null,[-1,-1])}get elem_id(){return this.$$.ctx[2]}set elem_id(e){this.$$set({elem_id:e}),T()}get elem_classes(){return this.$$.ctx[3]}set elem_classes(e){this.$$set({elem_classes:e}),T()}get visible(){return this.$$.ctx[4]}set visible(e){this.$$set({visible:e}),T()}get value(){return this.$$.ctx[0]}set value(e){this.$$set({value:e}),T()}get label(){return this.$$.ctx[5]}set label(e){this.$$set({label:e}),T()}get source(){return this.$$.ctx[6]}set source(e){this.$$set({source:e}),T()}get root(){return this.$$.ctx[22]}set root(e){this.$$set({root:e}),T()}get root_url(){return this.$$.ctx[23]}set root_url(e){this.$$set({root_url:e}),T()}get show_label(){return this.$$.ctx[7]}set show_label(e){this.$$set({show_label:e}),T()}get loading_status(){return this.$$.ctx[1]}set loading_status(e){this.$$set({loading_status:e}),T()}get height(){return this.$$.ctx[8]}set height(e){this.$$set({height:e}),T()}get width(){return this.$$.ctx[9]}set width(e){this.$$set({width:e}),T()}get mirror_webcam(){return this.$$.ctx[10]}set mirror_webcam(e){this.$$set({mirror_webcam:e}),T()}get include_audio(){return this.$$.ctx[11]}set include_audio(e){this.$$set({include_audio:e}),T()}get container(){return this.$$.ctx[12]}set container(e){this.$$set({container:e}),T()}get scale(){return this.$$.ctx[13]}set scale(e){this.$$set({scale:e}),T()}get min_width(){return this.$$.ctx[14]}set min_width(e){this.$$set({min_width:e}),T()}get mode(){return this.$$.ctx[15]}set mode(e){this.$$set({mode:e}),T()}get autoplay(){return this.$$.ctx[16]}set autoplay(e){this.$$set({autoplay:e}),T()}get show_share_button(){return this.$$.ctx[17]}set show_share_button(e){this.$$set({show_share_button:e}),T()}}const nl=Xt,al=["static","dynamic"],il=n=>({type:{input_payload:"{ name: string; data: string }",response_object:"{ name: string; data: string, is_file: boolean }"},description:{input_payload:"object with file name and base64 data",response_object:"object that includes path to video file. The URL: {ROOT}file={name} contains the data"}});export{nl as Component,il as document,al as modes};
-//# sourceMappingURL=index-097d3f80.js.map
diff --git a/spaces/Dagfinn1962/Dreamlikeart-Anime-1.0/app.py b/spaces/Dagfinn1962/Dreamlikeart-Anime-1.0/app.py
deleted file mode 100644
index cde25104c659b79416eeeddd86693d9f28c99f3d..0000000000000000000000000000000000000000
--- a/spaces/Dagfinn1962/Dreamlikeart-Anime-1.0/app.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-import random
-import string
-import time
-from queue import Queue
-from threading import Thread
-import emoji
-
-text_gen=gr.Interface.load("spaces/Avenuenw/prompt-extend")
-def get_prompts(prompt_text):
-    if prompt_text:
-        return text_gen("photo anime, masterpiece, high quality, absurdres, " + prompt_text)
-    else:
-        return text_gen("")
-#proc1=gr.Interface.load("models/dreamlike-art/dreamlike-anime-1.0") virker
-#proc1=gr.Interface.load("models/invisiblecat/Uber_Realistic_Porn_Merge_V1.3") virker
-#proc1=gr.Interface.load("models/invisiblecat/junior-diffusion") virker dog junior
-#proc1=gr.Interface.load("models/") mal
-
-proc1=gr.Interface.load("models/stablediffusionapi/juggernaut-xl-v5")
-        
- 
-
-def restart_script_periodically():
-    while True:
-        random_time = random.randint(700, 1024)
-        time.sleep(random_time)
-        os.execl(sys.executable, sys.executable, *sys.argv)
-
-
-restart_thread = Thread(target=restart_script_periodically, daemon=True)
-restart_thread.start()
-
-
-queue = Queue()
-queue_threshold = 100
-
-def add_random_noise(prompt, noise_level=1.50):
-    if noise_level == 0:
-        noise_level = 0.00
-    percentage_noise = noise_level * 5
-    num_noise_chars = int(len(prompt) * (percentage_noise/100))
-    noise_indices = random.sample(range(len(prompt)), num_noise_chars)
-    prompt_list = list(prompt)
-    noise_chars = list(string.ascii_letters + string.punctuation + ' ' + string.digits)
-    noise_chars.extend(['😍', '💩', '😂', '🤔', '😊', '🤗', '😭', '🙄', '😷', '🤯', '🤫', '🥴', '😴', '🤩', '🥳', '😔', '😩', '🤪', '😇', '🤢', '😈', '👹', '👻', '🤖', '👽', '💀', '🎃', '🎅', '🎄', '🎁', '🎂', '🎉', '🎈', '🎊', '🎮', '❤️', '💔', '💕', '💖', '💗', '🐶', '🐱', '🐭', '🐹', '🦊', '🐻', '🐨', '🐯', '🦁', '🐘', '🔥', '🌧️', '🌞', '🌈', '💥', '🌴', '🌊', '🌺', '🌻', '🌸', '🎨', '🌅', '🌌', '☁️', '⛈️', '❄️', '☀️', '🌤️', '⛅️', '🌥️', '🌦️', '🌧️', '🌩️', '🌨️', '🌫️', '☔️', '🌬️', '💨', '🌪️', '🌈'])
-    for index in noise_indices:
-        prompt_list[index] = random.choice(noise_chars)
-    return "".join(prompt_list)
-
-
-
-def send_it1(inputs, noise_level, proc1=proc1):
-    prompt_with_noise = add_random_noise(inputs, noise_level)
-    while queue.qsize() >= queue_threshold:
-        time.sleep(2)
-    queue.put(prompt_with_noise)
-    output1 = proc1(prompt_with_noise)
-    return output1
-
-def send_it2(inputs, noise_level, proc1=proc1):
-    prompt_with_noise = add_random_noise(inputs, noise_level)
-    while queue.qsize() >= queue_threshold:
-        time.sleep(2)
-    queue.put(prompt_with_noise)
-    output2 = proc1(prompt_with_noise)
-    return output2
-
-def send_it3(inputs, noise_level, proc1=proc1):
-    prompt_with_noise = add_random_noise(inputs, noise_level)
-    while queue.qsize() >= queue_threshold:
-        time.sleep(2)
-    queue.put(prompt_with_noise)
-    output3 = proc1(prompt_with_noise)
-    return output3
-
-def send_it4(inputs, noise_level, proc1=proc1):
-    prompt_with_noise = add_random_noise(inputs, noise_level)
-    while queue.qsize() >= queue_threshold:
-        time.sleep(2)
-    queue.put(prompt_with_noise)
-    output4 = proc1(prompt_with_noise)
-    return output4
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.HTML(
-        """
-        
      
-            
      
-             
-                
      
-                  REALISTIC DREAM(SD)  
-                
      
-              """
-    )
-    with gr.Column(elem_id="col-container"):
-        with gr.Row(variant="compact"):
-            input_text = gr.Textbox(
-                label="Short Prompt",
-                show_label= True,
-                max_lines=4,
-                placeholder="Enter Your  basic Prompt idea here",
-            ).style(
-                container=False,
-            )
-            see_prompts = gr.Button("✨ Magic Prompt ✨").style(full_width=False)
-
-        
-        with gr.Row(variant="compact"):
-            prompt = gr.Textbox(
-                label="Enter your prompt",
-                show_label=True,
-                max_lines=6,
-                placeholder="Full Prompt",
-            ).style(
-                container=False,
-            )
-            run = gr.Button("🤫 Generate Images 🤫").style(full_width=False)
-        
-        with gr.Row():
-            with gr.Row():
-                noise_level = gr.Slider(minimum=0.0, maximum=3, step=0.1, label="Noise Level")
-        with gr.Row():
-            with gr.Row():
-                output1=gr.Image(label="Dreamlike Anime 1.0",show_label=False)
-                output2=gr.Image(label="Dreamlike Anime 1.0",show_label=False)
-        
-
-        see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt], queue=False)
-        run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
-        run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])
-
-
-        with gr.Row():
-                gr.HTML(
-    """
-   
      Please read our disclaimer and follow our rules. 
- 
       
       
      
-   
-    """
-)
-
-    demo.launch(enable_queue=True, inline=True, show_api=False)
-    block.queue(concurrency_count=100, api_open=False)
\ No newline at end of file
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/op/fused_act.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/op/fused_act.py
deleted file mode 100644
index 7e3d464ae656920c6875bc877281cadb2eaa4105..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/op/fused_act.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import platform
-
-import torch
-from torch import nn
-from torch.autograd import Function
-import torch.nn.functional as F
-from torch.utils.cpp_extension import load
-
-use_fallback = False
-
-# Try loading precompiled, otherwise use native fallback
-try:
-    import fused
-except ModuleNotFoundError as e:
-    print('StyleGAN2: Optimized CUDA op FusedLeakyReLU not available, using native PyTorch fallback.')
-    use_fallback = True
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused.fused_bias_act(
-            grad_output, empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        out, = ctx.saved_tensors
-        gradgrad_out = fused.fused_bias_act(
-            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
-        )
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        out, = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.negative_slope, ctx.scale
-        )
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
-    if use_fallback or input.device.type == 'cpu':
-        return scale * F.leaky_relu(
-            input + bias.view((1, -1)+(1,)*(input.ndim-2)), negative_slope=negative_slope
-        )
-    else:
-        return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/torch_utils/misc.py b/spaces/DragGan/DragGan-Inversion/PTI/torch_utils/misc.py
deleted file mode 100644
index 7829f4d9f168557ce8a9a6dec289aa964234cb8c..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/torch_utils/misc.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-
-#----------------------------------------------------------------------------
diff --git a/spaces/DragGan/DragGan-Inversion/gui_utils/__init__.py b/spaces/DragGan/DragGan-Inversion/gui_utils/__init__.py
deleted file mode 100644
index 939e7c6c8f94c4ea1141885c3c3295fe083b06aa..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/gui_utils/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty
diff --git a/spaces/FSDL-Fashion/fashion_img_search/fis/feature_extraction/pipeline/pipeline.py b/spaces/FSDL-Fashion/fashion_img_search/fis/feature_extraction/pipeline/pipeline.py
deleted file mode 100644
index b75fb6be02b3c1c414a634cfa07cfbe6bc8d6d29..0000000000000000000000000000000000000000
--- a/spaces/FSDL-Fashion/fashion_img_search/fis/feature_extraction/pipeline/pipeline.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from fis.feature_extraction.detection.dummy import DummyDetector
-from fis.feature_extraction.embedding.timm import TimmModel
-from fis.feature_extraction.pipeline.factory import PipelineFactory
-
-factory = PipelineFactory()
-
-factory.register_pipeline(
-    name="dummy_swin_pipe",
-    detection_model=DummyDetector(),
-    embedding_model=TimmModel(model_name="swinv2_base_window8_256"),
-)
diff --git a/spaces/GXSA/bingo/src/components/theme-toggle.tsx b/spaces/GXSA/bingo/src/components/theme-toggle.tsx
deleted file mode 100644
index 67d3f1a2c163ccbeb52c40a7e42f107190237154..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/components/theme-toggle.tsx
+++ /dev/null
@@ -1,31 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useTheme } from 'next-themes'
-
-import { Button } from '@/components/ui/button'
-import { IconMoon, IconSun } from '@/components/ui/icons'
-
-export function ThemeToggle() {
-  const { setTheme, theme } = useTheme()
-  const [_, startTransition] = React.useTransition()
-
-  return (
-    
-  )
-}
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/insert_sphere_into_container.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/insert_sphere_into_container.py
deleted file mode 100644
index 5f53b5f60eb563f15fcc4b6c0539700df6ec9d71..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/insert_sphere_into_container.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-class InsertSphereIntoContainer(Task):
-    """Pick up a blue sphere and place it into an open container."""
-
-    def __init__(self):
-        super().__init__()
-        self.max_steps = 10
-        self.lang_template = "pick up a blue sphere and place it into an open container"
-        self.task_completed_desc = "done inserting sphere into container."
-        self.additional_reset()
-
-    def reset(self, env):
-        super().reset(env)
-
-        # Add container.
-        # x, y, z dimensions for the asset size
-        container_size = (0.1, 0.1, 0.1)
-        container_pose = self.get_random_pose(env, container_size)
-        container_template = 'container/container-template.urdf'
-        replace = {'DIM': container_size, 'HALF': (container_size[0] / 2, container_size[1] / 2, container_size[2] / 2)}
-        # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template`
-        container_urdf = self.fill_template(container_template, replace)
-        container_id = env.add_object(container_urdf, container_pose, 'fixed')
-
-        # Add sphere.
-        # x, y, z dimensions for the asset size
-        sphere_size = (0.04, 0.04, 0.04)
-        sphere_pose = self.get_random_pose(env, sphere_size)
-        sphere_urdf = 'sphere/sphere.urdf'
-        sphere_id = env.add_object(sphere_urdf, sphere_pose, color=utils.COLORS['blue'])
-
-        # Goal: the blue sphere is in the container.
-        self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[container_pose], replace=False,
-                rotations=True, metric='pose', params=None, step_max_reward=1,
-                          language_goal=self.lang_template)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/latent_gpt2_story/app.py b/spaces/Gradio-Blocks/latent_gpt2_story/app.py
deleted file mode 100644
index daff5b0b3e7e769f2d233775d4f9504957387a62..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/latent_gpt2_story/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import io, os, base64
-from PIL import Image
-import gradio as gr
-import shortuuid
-from transformers import pipeline
-
-
-text_generation_model = "pranavpsv/gpt2-genre-story-generator"
-text_generation = pipeline("text-generation", text_generation_model)
-latent = gr.Interface.load("spaces/multimodalart/latentdiffusion")
-
-
-def get_story(user_input, genre="sci_fi"):
-    prompt = f" <{genre}> "
-    stories = text_generation(f"{prompt}{user_input}", max_length=32, num_return_sequences=1)
-    story = stories[0]["generated_text"]
-    story_without_prompt = story[len(prompt):]
-    return story_without_prompt
-    
-
-def text2image_latent(text, steps, width, height, images, diversity):
-    print(text)
-    results = latent(text, steps, width, height, images, diversity)
-    image_paths = []
-    for image in results[1]:
-        image_str = image[0]
-        image_str = image_str.replace("data:image/png;base64,","")
-        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
-        img = Image.open(io.BytesIO(decoded_bytes))
-        url = shortuuid.uuid()
-        temp_dir = './tmp'
-        if not os.path.exists(temp_dir):
-            os.makedirs(temp_dir, exist_ok=True)
-        image_path = f'{temp_dir}/{url}.png'
-        img.save(f'{temp_dir}/{url}.png')
-        image_paths.append(image_path)
-    return(image_paths)
-    
-    
-with gr.Blocks() as demo:    
-    with gr.Row():
-        with gr.Column():
-            user_input = gr.inputs.Textbox(placeholder="Type your prompt to generate an image", label="Prompt - try adding increments to your prompt such as 'a painting of', 'in the style of Picasso'", default="A giant mecha robot in Rio de Janeiro, oil on canvas")
-            genre_input = gr.Dropdown(["superhero","action","drama","horror","thriller","sci_fi",])
-            generated_story = gr.Textbox()
-            with gr.Row():
-                button_generate_story = gr.Button("Generate Story")        
-        with gr.Column():
-            steps = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=50,maximum=50,minimum=1,step=1)
-            width = gr.inputs.Slider(label="Width", default=256, step=32, maximum=256, minimum=32)
-            height = gr.inputs.Slider(label="Height", default=256, step=32, maximum = 256, minimum=32)
-            images = gr.inputs.Slider(label="Images - How many images you wish to generate", default=4, step=1, minimum=1, maximum=4)
-            diversity = gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=15.0, minimum=1.0, maximum=15.0)
-        with gr.Column():
-            gallery = gr.Gallery(label="Individual images")
-            with gr.Row():
-                get_image_latent = gr.Button("Generate Image", css={"margin-top": "1em"})  
-    with gr.Row():
-        gr.Markdown("Story generation with GPT-2, and text to image by Latent Diffusion.")
-    with gr.Row():
-        gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=gradio-blocks_latent_gpt2_story)")
-        
-
-    button_generate_story.click(get_story, inputs=[user_input, genre_input], outputs=generated_story)
-    get_image_latent.click(text2image_latent, inputs=[generated_story,steps,width,height,images,diversity], outputs=gallery)
-
-
-demo.launch(enable_queue=False)
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
deleted file mode 100644
index 9f502e7b465f789a90100d96e881c60c84d9bf91..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
-model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py
deleted file mode 100644
index 33512011abb612ff5c762e75ee4492b382902fa4..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,62 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py'
-model = dict(
-    bbox_head=dict(
-        _delete_=True,
-        type='GARetinaHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=4,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[8, 16, 32, 64, 128]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[4],
-            strides=[8, 16, 32, 64, 128]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)),
-    # training and testing settings
-    train_cfg=dict(
-        ga_assigner=dict(
-            type='ApproxMaxIoUAssigner',
-            pos_iou_thr=0.5,
-            neg_iou_thr=0.4,
-            min_pos_iou=0.4,
-            ignore_iof_thr=-1),
-        ga_sampler=dict(
-            type='RandomSampler',
-            num=256,
-            pos_fraction=0.5,
-            neg_pos_ub=-1,
-            add_gt_as_proposals=False),
-        assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0),
-        center_ratio=0.2,
-        ignore_ratio=0.5))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/ops/encoding.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/ops/encoding.py
deleted file mode 100644
index 7eb3629a6426550b8e4c537ee1ff4341893e489e..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/ops/encoding.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-class Encoding(nn.Module):
-    """Encoding Layer: a learnable residual encoder.
-
-    Input is of shape  (batch_size, channels, height, width).
-    Output is of shape (batch_size, num_codes, channels).
-
-    Args:
-        channels: dimension of the features or feature channels
-        num_codes: number of code words
-    """
-
-    def __init__(self, channels, num_codes):
-        super(Encoding, self).__init__()
-        # init codewords and smoothing factor
-        self.channels, self.num_codes = channels, num_codes
-        std = 1. / ((num_codes * channels)**0.5)
-        # [num_codes, channels]
-        self.codewords = nn.Parameter(
-            torch.empty(num_codes, channels,
-                        dtype=torch.float).uniform_(-std, std),
-            requires_grad=True)
-        # [num_codes]
-        self.scale = nn.Parameter(
-            torch.empty(num_codes, dtype=torch.float).uniform_(-1, 0),
-            requires_grad=True)
-
-    @staticmethod
-    def scaled_l2(x, codewords, scale):
-        num_codes, channels = codewords.size()
-        batch_size = x.size(0)
-        reshaped_scale = scale.view((1, 1, num_codes))
-        expanded_x = x.unsqueeze(2).expand(
-            (batch_size, x.size(1), num_codes, channels))
-        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
-
-        scaled_l2_norm = reshaped_scale * (
-            expanded_x - reshaped_codewords).pow(2).sum(dim=3)
-        return scaled_l2_norm
-
-    @staticmethod
-    def aggregate(assignment_weights, x, codewords):
-        num_codes, channels = codewords.size()
-        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
-        batch_size = x.size(0)
-
-        expanded_x = x.unsqueeze(2).expand(
-            (batch_size, x.size(1), num_codes, channels))
-        encoded_feat = (assignment_weights.unsqueeze(3) *
-                        (expanded_x - reshaped_codewords)).sum(dim=1)
-        return encoded_feat
-
-    def forward(self, x):
-        assert x.dim() == 4 and x.size(1) == self.channels
-        # [batch_size, channels, height, width]
-        batch_size = x.size(0)
-        # [batch_size, height x width, channels]
-        x = x.view(batch_size, self.channels, -1).transpose(1, 2).contiguous()
-        # assignment_weights: [batch_size, channels, num_codes]
-        assignment_weights = F.softmax(
-            self.scaled_l2(x, self.codewords, self.scale), dim=2)
-        # aggregate
-        encoded_feat = self.aggregate(assignment_weights, x, self.codewords)
-        return encoded_feat
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(Nx{self.channels}xHxW =>Nx{self.num_codes}' \
-                    f'x{self.channels})'
-        return repr_str
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/model_cards/AUDIOGEN_MODEL_CARD.md b/spaces/GrandaddyShmax/AudioCraft_Plus/model_cards/AUDIOGEN_MODEL_CARD.md
deleted file mode 100644
index 92decf5e16e05ce0c2e72af8aa6728b5186c6882..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/model_cards/AUDIOGEN_MODEL_CARD.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# AudioGen Model Card
-
-## Model details
-**Organization developing the model:** The FAIR team of Meta AI.
-
-**Model date:** This version of AudioGen was trained between July 2023 and August 2023.
-
-**Model version:** This is version 2 of the model, not to be confused with the original AudioGen model published in ["AudioGen: Textually Guided Audio Generation"][audiogen].
-In this version (v2), AudioGen was trained on the same data, but with some other differences:
-1. This model was trained on 10 seconds (vs. 5 seconds in v1).
-2. The discrete representation used under the hood is extracted using a retrained EnCodec model on the environmental sound data, following the EnCodec setup detailed in the ["Simple and Controllable Music Generation" paper][musicgen].
-3. No audio mixing augmentations.
-
-**Model type:** AudioGen consists of an EnCodec model for audio tokenization, and an auto-regressive language model based on the transformer architecture for audio modeling. The released model has 1.5B parameters.
-
-**Paper or resource for more information:** More information can be found in the paper [AudioGen: Textually Guided Audio Generation](https://arxiv.org/abs/2209.15352).
-
-**Citation details:** See [AudioGen paper][audiogen]
-
-**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
-
-**Where to send questions or comments about the model:** Questions and comments about AudioGen can be sent via the [GitHub repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
-
-## Intended use
-**Primary intended use:** The primary use of AudioGen is research on AI-based audio generation, including:
-- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
-- Generation of sound guided by text to understand current abilities of generative AI models by machine learning amateurs
-
-**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
-
-**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate audio pieces that create hostile or alienating environments for people. This includes generating audio that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-## Metrics
-
-**Models performance measures:** We used the following objective measure to evaluate the model on a standard audio benchmark:
-- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
-- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
-
-Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
-- Overall quality of the audio samples;
-- Text relevance to the provided text input;
-
-More details on performance measures and human studies can be found in the paper.
-
-**Decision thresholds:** Not applicable.
-
-## Evaluation datasets
-
-The model was evaluated on the [AudioCaps benchmark](https://audiocaps.github.io/).
-
-## Training datasets
-
-The model was trained on the following data sources: a subset of AudioSet (Gemmeke et al., 2017), [BBC sound effects](https://sound-effects.bbcrewind.co.uk/), AudioCaps (Kim et al., 2019), Clotho v2 (Drossos et al., 2020), VGG-Sound (Chen et al., 2020), FSD50K (Fonseca et al., 2021), [Free To Use Sounds](https://www.freetousesounds.com/all-in-one-bundle/), [Sonniss Game Effects](https://sonniss.com/gameaudiogdc), [WeSoundEffects](https://wesoundeffects.com/we-sound-effects-bundle-2020/), [Paramount Motion - Odeon Cinematic Sound Effects](https://www.paramountmotion.com/odeon-sound-effects).
-
-## Evaluation results
-
-Below are the objective metrics obtained with the released model on AudioCaps (consisting of 10-second long samples). Note that the model differs from the original AudioGen model introduced in the paper, hence the difference in the metrics.
-
-| Model | Frechet Audio Distance | KLD | Text consistency |
-|---|---|---|---|
-| facebook/audiogen-medium | 1.77 | 1.41 | 0.299 |
-
-More information can be found in the paper [AudioGen: Textually Guided Audio Generation][audiogen], in the Experiments section.
-
-## Limitations and biases
-
-**Limitations:**
-- The model is not able to generate realistic vocals.
-- The model has been trained with English descriptions and will not perform as well in other languages.
-- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
-
-**Biases:** The datasets used for training may be lacking of diversity and are not representative of all possible sound events. The generated samples from the model will reflect the biases from the training data.
-
-**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
-
-**Use cases:** Users must be aware of the biases, limitations and risks of the model. AudioGen is a model developed for artificial intelligence research on audio generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
-
-[musicgen]: https://arxiv.org/abs/2306.05284
-[audiogen]: https://arxiv.org/abs/2209.15352
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/tests/utils/__init__.py b/spaces/GrandaddyShmax/AudioCraft_Plus/tests/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/tests/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/GuardianUI/ui-refexp-click/README.md b/spaces/GuardianUI/ui-refexp-click/README.md
deleted file mode 100644
index d49d3690e44d43343a60c9ddcce4d056bf0fd1de..0000000000000000000000000000000000000000
--- a/spaces/GuardianUI/ui-refexp-click/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
----
-title: UI RefExp Click (by GuardianUI)
-emoji: 🐕
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: agpl-3.0
-duplicated_from: ivelin/ui-refexp
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
----
-
----
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/new/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/new/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/README.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/README.md
deleted file mode 100644
index e071d241e0e02b35d3aac777ac09b4ef3be9119f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_text_joint_to_text/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Joint Speech Text training in Fairseq
-An extension of Fairseq s2t project with the speech to text task enhanced by the co-trained text to text mapping task. More details about Fairseq s2t can be found [here](../speech_to_text/README.md)
-
-## Examples
-Examples of speech text joint training in fairseq
-- [English-to-German MuST-C model](docs/ende-mustc.md)
-- [IWSLT 2021 Multilingual Speech Translation](docs/iwslt2021.md)
-
-## Citation
-Please cite as:
-```
-@inproceedings{Tang2021AGM,
-  title={A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks},
-  author={Yun Tang and J. Pino and Changhan Wang and Xutai Ma and Dmitriy Genzel},
-  booktitle={ICASSP},
-  year={2021}
-}
-
-@inproceedings{Tang2021IST,
-  title = {Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task},
-  author = {Yun Tang and Juan Pino and Xian Li and Changhan Wang and Dmitriy Genzel},
-  booktitle = {ACL},
-  year = {2021},
-}
-
-@inproceedings{Tang2021FST,
-  title = {FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task},
-  author = {Yun Tang and Hongyu Gong and Xian Li and Changhan Wang  and Juan Pino and  Holger Schwenk and  Naman Goyal},
-  booktitle = {IWSLT},
-  year = {2021},
-}
-
-@inproceedings{wang2020fairseqs2t,
-  title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
-  author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
-  booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
-  year = {2020},
-}
-
-@inproceedings{ott2019fairseq,
-  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
-  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
-  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
-  year = {2019},
-}
-```
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/__init__.py
deleted file mode 100644
index 4dbf46a1cb31ce65c4224ae79cbc2d7cf9e4d111..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-import importlib
-import os
-
-from fairseq import registry
-from fairseq.criterions.fairseq_criterion import (  # noqa
-    FairseqCriterion,
-    LegacyFairseqCriterion,
-)
-from omegaconf import DictConfig
-
-
-(
-    build_criterion_,
-    register_criterion,
-    CRITERION_REGISTRY,
-    CRITERION_DATACLASS_REGISTRY,
-) = registry.setup_registry(
-    "--criterion", base_class=FairseqCriterion, default="cross_entropy"
-)
-
-
-def build_criterion(cfg: DictConfig, task):
-    return build_criterion_(cfg, task)
-
-
-# automatically import any Python files in the criterions/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
-    if file.endswith(".py") and not file.startswith("_"):
-        file_name = file[: file.find(".py")]
-        importlib.import_module("fairseq.criterions." + file_name)
diff --git a/spaces/HarshWK/Basic_Models/README.md b/spaces/HarshWK/Basic_Models/README.md
deleted file mode 100644
index 8b19fb08e9a27cc1e59ac2c8f6a94e0027db8410..0000000000000000000000000000000000000000
--- a/spaces/HarshWK/Basic_Models/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Basic Models
-emoji: 👁
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/commons.py b/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/commons.py
deleted file mode 100644
index 8da7b35049d768a29de6f66cbe8795a825967818..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Tamil-TTS/ttsv/src/glow_tts/commons.py
+++ /dev/null
@@ -1,273 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from librosa.filters import mel as librosa_mel_fn
-from audio_processing import dynamic_range_compression
-from audio_processing import dynamic_range_decompression
-from stft import STFT
-
-
-def intersperse(lst, item):
-    result = [item] * (len(lst) * 2 + 1)
-    result[1::2] = lst
-    return result
-
-
-def mle_loss(z, m, logs, logdet, mask):
-    l = torch.sum(logs) + 0.5 * torch.sum(
-        torch.exp(-2 * logs) * ((z - m) ** 2)
-    )  # neg normal likelihood w/o the constant term
-    l = l - torch.sum(logdet)  # log jacobian determinant
-    l = l / torch.sum(
-        torch.ones_like(z) * mask
-    )  # averaging across batch, channel and time axes
-    l = l + 0.5 * math.log(2 * math.pi)  # add the remaining constant term
-    return l
-
-
-def duration_loss(logw, logw_, lengths):
-    l = torch.sum((logw - logw_) ** 2) / torch.sum(lengths)
-    return l
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def maximum_path(value, mask, max_neg_val=-np.inf):
-    """Numpy-friendly version. It's about 4 times faster than torch version.
-    value: [b, t_x, t_y]
-    mask: [b, t_x, t_y]
-    """
-    value = value * mask
-
-    device = value.device
-    dtype = value.dtype
-    value = value.cpu().detach().numpy()
-    mask = mask.cpu().detach().numpy().astype(np.bool)
-
-    b, t_x, t_y = value.shape
-    direction = np.zeros(value.shape, dtype=np.int64)
-    v = np.zeros((b, t_x), dtype=np.float32)
-    x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)
-    for j in range(t_y):
-        v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[
-            :, :-1
-        ]
-        v1 = v
-        max_mask = v1 >= v0
-        v_max = np.where(max_mask, v1, v0)
-        direction[:, :, j] = max_mask
-
-        index_mask = x_range <= j
-        v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)
-    direction = np.where(mask, direction, 1)
-
-    path = np.zeros(value.shape, dtype=np.float32)
-    index = mask[:, :, 0].sum(1).astype(np.int64) - 1
-    index_range = np.arange(b)
-    for j in reversed(range(t_y)):
-        path[index_range, index, j] = 1
-        index = index + direction[index_range, index, j] - 1
-    path = path * mask.astype(np.float32)
-    path = torch.from_numpy(path).to(device=device, dtype=dtype)
-    return path
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, t_x]
-    mask: [b, t_x, t_y]
-    """
-    device = duration.device
-
-    b, t_x, t_y = mask.shape
-    cum_duration = torch.cumsum(duration, 1)
-    path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path * mask
-    return path
-
-
-class Adam:
-    def __init__(
-        self,
-        params,
-        scheduler,
-        dim_model,
-        warmup_steps=4000,
-        lr=1e0,
-        betas=(0.9, 0.98),
-        eps=1e-9,
-    ):
-        self.params = params
-        self.scheduler = scheduler
-        self.dim_model = dim_model
-        self.warmup_steps = warmup_steps
-        self.lr = lr
-        self.betas = betas
-        self.eps = eps
-
-        self.step_num = 1
-        self.cur_lr = lr * self._get_lr_scale()
-
-        self._optim = torch.optim.Adam(params, lr=self.cur_lr, betas=betas, eps=eps)
-
-    def _get_lr_scale(self):
-        if self.scheduler == "noam":
-            return np.power(self.dim_model, -0.5) * np.min(
-                [
-                    np.power(self.step_num, -0.5),
-                    self.step_num * np.power(self.warmup_steps, -1.5),
-                ]
-            )
-        else:
-            return 1
-
-    def _update_learning_rate(self):
-        self.step_num += 1
-        if self.scheduler == "noam":
-            self.cur_lr = self.lr * self._get_lr_scale()
-            for param_group in self._optim.param_groups:
-                param_group["lr"] = self.cur_lr
-
-    def get_lr(self):
-        return self.cur_lr
-
-    def step(self):
-        self._optim.step()
-        self._update_learning_rate()
-
-    def zero_grad(self):
-        self._optim.zero_grad()
-
-    def load_state_dict(self, d):
-        self._optim.load_state_dict(d)
-
-    def state_dict(self):
-        return self._optim.state_dict()
-
-
-class TacotronSTFT(nn.Module):
-    def __init__(
-        self,
-        filter_length=1024,
-        hop_length=256,
-        win_length=1024,
-        n_mel_channels=80,
-        sampling_rate=22050,
-        mel_fmin=0.0,
-        mel_fmax=8000.0,
-    ):
-        super(TacotronSTFT, self).__init__()
-        self.n_mel_channels = n_mel_channels
-        self.sampling_rate = sampling_rate
-        self.stft_fn = STFT(filter_length, hop_length, win_length)
-        mel_basis = librosa_mel_fn(
-            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-
-    def spectral_normalize(self, magnitudes):
-        output = dynamic_range_compression(magnitudes)
-        return output
-
-    def spectral_de_normalize(self, magnitudes):
-        output = dynamic_range_decompression(magnitudes)
-        return output
-
-    def mel_spectrogram(self, y):
-        """Computes mel-spectrograms from a batch of waves
-        PARAMS
-        ------
-        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
-
-        RETURNS
-        -------
-        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
-        """
-        assert torch.min(y.data) >= -1
-        assert torch.max(y.data) <= 1
-
-        magnitudes, phases = self.stft_fn.transform(y)
-        magnitudes = magnitudes.data
-        mel_output = torch.matmul(self.mel_basis, magnitudes)
-        mel_output = self.spectral_normalize(mel_output)
-        return mel_output
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-
-        p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
-
-
-def squeeze(x, x_mask=None, n_sqz=2):
-    b, c, t = x.size()
-
-    t = (t // n_sqz) * n_sqz
-    x = x[:, :, :t]
-    x_sqz = x.view(b, c, t // n_sqz, n_sqz)
-    x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
-
-    if x_mask is not None:
-        x_mask = x_mask[:, :, n_sqz - 1 :: n_sqz]
-    else:
-        x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_sqz * x_mask, x_mask
-
-
-def unsqueeze(x, x_mask=None, n_sqz=2):
-    b, c, t = x.size()
-
-    x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
-    x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
-
-    if x_mask is not None:
-        x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
-    else:
-        x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_unsqz * x_mask, x_mask
diff --git a/spaces/HemanthSai7/IntelligentQuestionGenerator/app.py b/spaces/HemanthSai7/IntelligentQuestionGenerator/app.py
deleted file mode 100644
index d1dc9706bcba215a121a88ada7d8dcab8204c457..0000000000000000000000000000000000000000
--- a/spaces/HemanthSai7/IntelligentQuestionGenerator/app.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import streamlit as st
-import pandas as pd
-from keybert import KeyBERT
-
-import seaborn as sns
-
-from src.Pipeline.TextSummarization import T5_Base
-from src.Pipeline.QuestGen import sense2vec_get_words,get_question
-
-
-st.title("❓ Intelligent Question Generator")
-st.header("")
-
-
-with st.expander("ℹ️ - About this app", expanded=True):
-
-    st.write(
-        """     
--   The *Intelligent Question Generator* app is an easy-to-use interface built in Streamlit which uses [KeyBERT](https://github.com/MaartenGr/KeyBERT), [Sense2vec](https://github.com/explosion/sense2vec), [T5](https://huggingface.co/ramsrigouthamg/t5_paraphraser)
--   It uses a minimal keyword extraction technique that leverages multiple NLP embeddings and relies on [Transformers](https://huggingface.co/transformers/) 🤗 to create keywords/keyphrases that are most similar to a document.
-- [sense2vec](https://github.com/explosion/sense2vec) (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors.
-	    """
-    )
-
-    st.markdown("")
-
-st.markdown("")
-st.markdown("## 📌 Paste document ")
-
-with st.form(key="my_form"):
-    ce, c1, ce, c2, c3 = st.columns([0.07, 2, 0.07, 5, 1])
-    with c1:
-        ModelType = st.radio(
-            "Choose your model",
-            ["DistilBERT (Default)", "BERT", "RoBERTa", "ALBERT", "XLNet"],
-            help="At present, you can choose 1 model ie DistilBERT to embed your text. More to come!",
-        )
-
-        if ModelType == "Default (DistilBERT)":
-            # kw_model = KeyBERT(model=roberta)
-
-            @st.cache(allow_output_mutation=True)
-            def load_model(model):
-                return KeyBERT(model=model)
-
-            kw_model = load_model('roberta')
-
-        else:
-            @st.cache(allow_output_mutation=True)
-            def load_model(model):
-                return KeyBERT(model=model)
-
-            kw_model = load_model("distilbert-base-nli-mean-tokens")
-
-        top_N = st.slider(
-            "# of results",
-            min_value=1,
-            max_value=30,
-            value=10,
-            help="You can choose the number of keywords/keyphrases to display. Between 1 and 30, default number is 10.",
-        )
-        min_Ngrams = st.number_input(
-            "Minimum Ngram",
-            min_value=1,
-            max_value=4,
-            help="""The minimum value for the ngram range.
-            *Keyphrase_ngram_range* sets the length of the resulting keywords/keyphrases.To extract keyphrases, simply set *keyphrase_ngram_range* to (1, 2) or higher depending on the number of words you would like in the resulting keyphrases.""",
-            # help="Minimum value for the keyphrase_ngram_range. keyphrase_ngram_range sets the length of the resulting keywords/keyphrases. To extract keyphrases, simply set keyphrase_ngram_range to (1, # 2) or higher depending on the number of words you would like in the resulting keyphrases.",
-        )
-
-        max_Ngrams = st.number_input(
-            "Maximum Ngram",
-            value=1,
-            min_value=1,
-            max_value=4,
-            help="""The maximum value for the keyphrase_ngram_range.
-            *Keyphrase_ngram_range* sets the length of the resulting keywords/keyphrases.
-            To extract keyphrases, simply set *keyphrase_ngram_range* to (1, 2) or higher depending on the number of words you would like in the resulting keyphrases.""",
-        )
-
-        StopWordsCheckbox = st.checkbox(
-            "Remove stop words",
-            value=True,
-            help="Tick this box to remove stop words from the document (currently English only)",
-        )
-
-        use_MMR = st.checkbox(
-            "Use MMR",
-            value=True,
-            help="You can use Maximal Margin Relevance (MMR) to diversify the results. It creates keywords/keyphrases based on cosine similarity. Try high/low 'Diversity' settings below for interesting variations.",
-        )
-
-        Diversity = st.slider(
-            "Keyword diversity (MMR only)",
-            value=0.5,
-            min_value=0.0,
-            max_value=1.0,
-            step=0.1,
-            help="""The higher the setting, the more diverse the keywords.Note that the *Keyword diversity* slider only works if the *MMR* checkbox is ticked.""",
-        )
-
-    with c2:
-        doc = st.text_area(
-            "Paste your text below (max 500 words)",
-            height=510,
-        )
-
-        MAX_WORDS = 500
-        import re
-        res = len(re.findall(r"\w+", doc))
-        if res > MAX_WORDS:
-            st.warning(
-                "⚠️ Your text contains "
-                + str(res)
-                + " words."
-                + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
-            )
-
-            doc = doc[:MAX_WORDS]
-            # base=base=T5_Base("t5-base","cpu",2048)
-            # doc=base.summarize(doc)
-
-        submit_button = st.form_submit_button(label="✨ Get me the data!")
-
-    if use_MMR:
-        mmr = True
-    else:
-        mmr = False
-
-    if StopWordsCheckbox:
-        StopWords = "english"
-    else:
-        StopWords = None
-    
-if min_Ngrams > max_Ngrams:
-    st.warning("min_Ngrams can't be greater than max_Ngrams")
-    st.stop()
-
-# Uses KeyBERT to extract the top keywords from a text
-# Arguments: text (str)
-# Returns: list of keywords (list)
-keywords = kw_model.extract_keywords(
-    doc,
-    keyphrase_ngram_range=(min_Ngrams, max_Ngrams),
-    use_mmr=mmr,
-    stop_words=StopWords,
-    top_n=top_N,
-    diversity=Diversity,
-)
-# print(keywords)
-    
-st.markdown("## 🎈 Results ")
-
-st.header("")
-
-
-df = (
-    pd.DataFrame(keywords, columns=["Keyword/Keyphrase", "Relevancy"])
-    .sort_values(by="Relevancy", ascending=False)
-    .reset_index(drop=True)
-)
-
-df.index += 1
-
-# Add styling
-cmGreen = sns.light_palette("green", as_cmap=True)
-cmRed = sns.light_palette("red", as_cmap=True)
-df = df.style.background_gradient(
-    cmap=cmGreen,
-    subset=[
-        "Relevancy",
-    ],
-)
-
-c1, c2, c3 = st.columns([1, 3, 1])
-
-format_dictionary = {
-    "Relevancy": "{:.2%}",
-}
-
-df = df.format(format_dictionary)
-
-with c2:
-    st.table(df)      
-
-with st.expander("Note about Quantitative Relevancy"):
-    st.markdown(
-        """
-    - The relevancy score is a quantitative measure of how relevant the keyword/keyphrase is to the document. It is calculated using cosine similarity. The higher the score, the more relevant the keyword/keyphrase is to the document.
-    - So if you see a keyword/keyphrase with a high relevancy score, it means that it is a good keyword/keyphrase to use in question answering, generation ,summarization, and other NLP tasks.
-    """
-    )           
-
-with st.form(key="ques_form"):
-    ice, ic1, ice, ic2 ,ic3= st.columns([0.07, 2, 0.07, 5,0.07])
-    with ic1:
-        TopN = st.slider(
-            "Top N sense2vec results",
-            value=20,
-            min_value=0,
-            max_value=50,
-            step=1,
-            help="""Get the n most similar terms.""",
-        )
-
-    with ic2:
-        input_keyword = st.text_input("Paste any keyword generated above")
-        keywrd_button = st.form_submit_button(label="✨ Get me the questions!")
-
-if keywrd_button:
-    st.markdown("## 🎈 Questions ")    
-    ext_keywrds=sense2vec_get_words(TopN,input_keyword)
-    if len(ext_keywrds)<1:
-        st.warning("Sorry questions couldn't be generated")
-    
-    for answer in ext_keywrds:
-        sentence_for_T5=" ".join(doc.split())
-        ques=get_question(sentence_for_T5,answer)
-        ques=ques.replace("","").replace("","").replace("","")
-        st.markdown(f'> #### {ques} ')
-
-
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/_commonjsHelpers.88e99c8f.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/_commonjsHelpers.88e99c8f.js
deleted file mode 100644
index 1e8c60e63103e9e303d5339b78e31fe542190fb2..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/_commonjsHelpers.88e99c8f.js
+++ /dev/null
@@ -1,2 +0,0 @@
-var o=typeof globalThis<"u"?globalThis:typeof window<"u"?window:typeof global<"u"?global:typeof self<"u"?self:{};function n(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}function r(e){throw new Error('Could not dynamically require "'+e+'". Please configure the dynamicRequireTargets or/and ignoreDynamicRequires option of @rollup/plugin-commonjs appropriately for this require call to work.')}export{r as a,o as c,n as g};
-//# sourceMappingURL=_commonjsHelpers.88e99c8f.js.map
diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/utils/segment/augmentations.py b/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/utils/segment/augmentations.py
deleted file mode 100644
index 169addedf0f58cf37e774e6a85eddff6eebc30be..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection-FYP/yolovs5/utils/segment/augmentations.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-from ..augmentations import box_candidates
-from ..general import resample_segments, segment2box
-
-
-def mixup(im, labels, segments, im2, labels2, segments2):
-    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
-    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
-    im = (im * r + im2 * (1 - r)).astype(np.uint8)
-    labels = np.concatenate((labels, labels2), 0)
-    segments = np.concatenate((segments, segments2), 0)
-    return im, labels, segments
-
-
-def random_perspective(im,
-                       targets=(),
-                       segments=(),
-                       degrees=10,
-                       translate=.1,
-                       scale=.1,
-                       shear=10,
-                       perspective=0.0,
-                       border=(0, 0)):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
-    # targets = [cls, xyxy]
-
-    height = im.shape[0] + border[0] * 2  # shape(h,w,c)
-    width = im.shape[1] + border[1] * 2
-
-    # Center
-    C = np.eye(3)
-    C[0, 2] = -im.shape[1] / 2  # x translation (pixels)
-    C[1, 2] = -im.shape[0] / 2  # y translation (pixels)
-
-    # Perspective
-    P = np.eye(3)
-    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
-    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
-
-    # Rotation and Scale
-    R = np.eye(3)
-    a = random.uniform(-degrees, degrees)
-    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
-    s = random.uniform(1 - scale, 1 + scale)
-    # s = 2 ** random.uniform(-scale, scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
-    # Shear
-    S = np.eye(3)
-    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
-    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width)  # x translation (pixels)
-    T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height)  # y translation (pixels)
-
-    # Combined rotation matrix
-    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
-    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
-        if perspective:
-            im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
-        else:  # affine
-            im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(im[:, :, ::-1])  # base
-    # ax[1].imshow(im2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
-    new_segments = []
-    if n:
-        new = np.zeros((n, 4))
-        segments = resample_segments(segments)  # upsample
-        for i, segment in enumerate(segments):
-            xy = np.ones((len(segment), 3))
-            xy[:, :2] = segment
-            xy = xy @ M.T  # transform
-            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2])  # perspective rescale or affine
-
-            # clip
-            new[i] = segment2box(xy, width, height)
-            new_segments.append(xy)
-
-        # filter candidates
-        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
-        targets = targets[i]
-        targets[:, 1:5] = new[i]
-        new_segments = np.array(new_segments)[i]
-
-    return im, targets, new_segments
diff --git a/spaces/IntSpace/README/README.md b/spaces/IntSpace/README/README.md
deleted file mode 100644
index 63406d365798f53746680cb9fb6efb579ea7cbd3..0000000000000000000000000000000000000000
--- a/spaces/IntSpace/README/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: README
-emoji: 📉
-colorFrom: gray
-colorTo: pink
-sdk: static
-pinned: false
----
-
-We create spaces based on models and study their behavior!
\ No newline at end of file
diff --git a/spaces/JacobLinCool/captcha-recognizer/scripts/__init__.py b/spaces/JacobLinCool/captcha-recognizer/scripts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Jeff2323/ai-comic-factory/src/app/interface/top-menu/index.tsx b/spaces/Jeff2323/ai-comic-factory/src/app/interface/top-menu/index.tsx
deleted file mode 100644
index afa97af0e52758867ecdf795e0348e10b8f12dbe..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/src/app/interface/top-menu/index.tsx
+++ /dev/null
@@ -1,259 +0,0 @@
-"use client"
-
-import { useEffect, useState } from "react"
-import { useSearchParams } from "next/navigation"
-import Image from "next/image"
-
-import {
-  Select,
-  SelectContent,
-  SelectItem,
-  SelectTrigger,
-  SelectValue,
-} from "@/components/ui/select"
-import { Label } from "@/components/ui/label"
-import { cn } from "@/lib/utils"
-import { FontName, defaultFont } from "@/lib/fonts"
-import { Input } from "@/components/ui/input"
-import { PresetName, defaultPreset, nonRandomPresets, presets } from "@/app/engine/presets"
-import { useStore } from "@/app/store"
-import { Button } from "@/components/ui/button"
-import { LayoutName, allLayoutLabels, defaultLayout, nonRandomLayouts } from "@/app/layouts"
-
-import layoutPreview0 from "../../../../public/layouts/layout0.jpg"
-import layoutPreview1 from "../../../../public/layouts/layout1.jpg"
-import layoutPreview2 from "../../../../public/layouts/layout2.jpg"
-import layoutPreview3 from "../../../../public/layouts/layout3.jpg"
-import { StaticImageData } from "next/image"
-import { Switch } from "@/components/ui/switch"
-
-const layoutIcons: Partial> = {
-  Layout0: layoutPreview0,
-  Layout1: layoutPreview1,
-  Layout2: layoutPreview2,
-  Layout3: layoutPreview3
-}
-
-export function TopMenu() {
-   // const font = useStore(state => state.font)
-  // const setFont = useStore(state => state.setFont)
-  const preset = useStore(state => state.preset)
-  const prompt = useStore(state => state.prompt)
-  const layout = useStore(state => state.layout)
-  const setLayout = useStore(state => state.setLayout)
-
-  const setShowCaptions = useStore(state => state.setShowCaptions)
-  const showCaptions = useStore(state => state.showCaptions)
-
-  const generate = useStore(state => state.generate)
-
-  const isGeneratingStory = useStore(state => state.isGeneratingStory)
-  const atLeastOnePanelIsBusy = useStore(state => state.atLeastOnePanelIsBusy)
-  const isBusy = isGeneratingStory || atLeastOnePanelIsBusy
-
-  const searchParams = useSearchParams()
-
-  const requestedPreset = (searchParams.get('preset') as PresetName) || defaultPreset
-  const requestedFont = (searchParams.get('font') as FontName) || defaultFont
-  const requestedPrompt = (searchParams.get('prompt') as string) || ""
-  const requestedLayout = (searchParams.get('layout') as LayoutName) || defaultLayout
-
-  const [draftPrompt, setDraftPrompt] = useState(requestedPrompt)
-  const [draftPreset, setDraftPreset] = useState(requestedPreset)
-  const [draftLayout, setDraftLayout] = useState(requestedLayout)
-
-  const handleSubmit = () => {
-    const promptChanged = draftPrompt.trim() !== prompt.trim()
-    const presetChanged = draftPreset !== preset.id
-    const layoutChanged = draftLayout !== layout
-    if (!isBusy && (promptChanged || presetChanged || layoutChanged)) {
-      generate(draftPrompt, draftPreset, draftLayout)
-    }
-  }
-
-  useEffect(() => {
-    const layoutChanged = draftLayout !== layout
-    if (layoutChanged && !isBusy) {
-      setLayout(draftLayout)
-    }
-  }, [layout, draftLayout, isBusy])
-    
-  return (
-    
      
-      
      
-        
      
-
-          {/*  */}
-
-          
-        
      
-        
      
-
-          {/*  */}
-
-          
-        
      
-        
      
-        
-        
-        
      
-        {/*
-        
      
-          
-          
-        
      
-          */}
-      
      
-      
      
-          
      
-         {
-            setDraftPrompt(e.target.value)
-          }}
-          onKeyDown={({ key }) => {
-            if (key === 'Enter') {
-              handleSubmit()
-            }
-          }}
-          value={draftPrompt}
-         />
-        
-        
      
-      
      
-      {/*
-        Let's add this feature later, because right now people
-        are confused about why they can't activate it
-      
      
-        
-        
-      
      
-              */}
-    
      
-  )
-}
\ No newline at end of file
diff --git a/spaces/JeffJing/ZookChatBot/steamship/cli/ship_spinner.py b/spaces/JeffJing/ZookChatBot/steamship/cli/ship_spinner.py
deleted file mode 100644
index bb17d81d48db9684cb873b46022ae4a589e925f4..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/cli/ship_spinner.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import itertools
-import threading
-
-import click
-
-
-class Spinner(object):
-    # ["   🚢", "  🚢 ", " 🚢  ", "🚢   "]
-    # Unfortunately, backspacing doesn't seem to work correctly for emoji in iTerm, so leaving the "spinner"
-    # as adding ships for now
-    spinner_cycle = itertools.cycle(["🚢"])
-
-    def __init__(self):
-        self.stop_running = None
-        self.spin_thread = None
-
-    def start(self):
-        self.stop_running = threading.Event()
-        self.spin_thread = threading.Thread(target=self.init_spin)
-        self.spin_thread.start()
-
-    def stop(self):
-        if self.spin_thread:
-            self.stop_running.set()
-            self.spin_thread.join()
-
-    def init_spin(self):
-        while not self.stop_running.is_set():
-            click.echo(next(self.spinner_cycle), nl=False)
-            self.stop_running.wait(1)
-            # click.echo("\b", nl=False)
-
-    def __enter__(self):
-        self.start()
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.stop()
-        return False
-
-
-def ship_spinner():
-    """This function creates a context manager that is used to display a
-    spinner on stdout as long as the context has not exited.
-    The spinner is created only if stdout is not redirected, or if the spinner
-    is forced using the `force` parameter.
-    """
-    return Spinner()
diff --git a/spaces/Jingqi/ChatGPT-QA/README.md b/spaces/Jingqi/ChatGPT-QA/README.md
deleted file mode 100644
index 53a58d1dca38fbbe622ee306d8de82605a53caa9..0000000000000000000000000000000000000000
--- a/spaces/Jingqi/ChatGPT-QA/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPT QA
-emoji: 🚀
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/infer_pack/models_onnx.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 3e99763bf3ed7988eb2ae33d9066f85d37adf119..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,824 +0,0 @@
-import math
-import logging
-
-logger = logging.getLogger(__name__)
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
-
-from infer.lib.infer_pack import attentions, commons, modules
-from infer.lib.infer_pack.commons import get_padding, init_weights
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        logger.debug(
-            "gin_channels: "
-            + gin_channels
-            + ", self.spk_embed_dim: "
-            + self.spk_embed_dim
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/Kimata/multimodal_deepfake_detection/save_ckpts.py b/spaces/Kimata/multimodal_deepfake_detection/save_ckpts.py
deleted file mode 100644
index 12d829d823cbcd1219551504e49812fb1f23a4bb..0000000000000000000000000000000000000000
--- a/spaces/Kimata/multimodal_deepfake_detection/save_ckpts.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import onnx
-import torch
-import argparse
-import numpy as np
-import torch.nn as nn
-from models.TMC import ETMC
-from models import image
-from onnx2pytorch import ConvertModel
-
-onnx_model = onnx.load('checkpoints\\efficientnet.onnx')
-pytorch_model = ConvertModel(onnx_model)
-
-# Define the audio_args dictionary
-audio_args = {
-    'nb_samp': 64600,
-    'first_conv': 1024,
-    'in_channels': 1,
-    'filts': [20, [20, 20], [20, 128], [128, 128]],
-    'blocks': [2, 4],
-    'nb_fc_node': 1024,
-    'gru_node': 1024,
-    'nb_gru_layer': 3,
-    'nb_classes': 2
-}
-
-
-def get_args(parser):
-    parser.add_argument("--batch_size", type=int, default=8)
-    parser.add_argument("--data_dir", type=str, default="datasets/train/fakeavceleb*")
-    parser.add_argument("--LOAD_SIZE", type=int, default=256)
-    parser.add_argument("--FINE_SIZE", type=int, default=224)
-    parser.add_argument("--dropout", type=float, default=0.2)
-    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
-    parser.add_argument("--hidden", nargs="*", type=int, default=[])
-    parser.add_argument("--hidden_sz", type=int, default=768)
-    parser.add_argument("--img_embed_pool_type", type=str, default="avg", choices=["max", "avg"])
-    parser.add_argument("--img_hidden_sz", type=int, default=1024)
-    parser.add_argument("--include_bn", type=int, default=True)
-    parser.add_argument("--lr", type=float, default=1e-4)
-    parser.add_argument("--lr_factor", type=float, default=0.3)
-    parser.add_argument("--lr_patience", type=int, default=10)
-    parser.add_argument("--max_epochs", type=int, default=500)
-    parser.add_argument("--n_workers", type=int, default=12)
-    parser.add_argument("--name", type=str, default="MMDF")
-    parser.add_argument("--num_image_embeds", type=int, default=1)
-    parser.add_argument("--patience", type=int, default=20)
-    parser.add_argument("--savedir", type=str, default="./savepath/")
-    parser.add_argument("--seed", type=int, default=1)
-    parser.add_argument("--n_classes", type=int, default=2)
-    parser.add_argument("--annealing_epoch", type=int, default=10)
-    parser.add_argument("--device", type=str, default='cpu')
-    parser.add_argument("--pretrained_image_encoder", type=bool, default = False)
-    parser.add_argument("--freeze_image_encoder", type=bool, default = False)
-    parser.add_argument("--pretrained_audio_encoder", type = bool, default=False)
-    parser.add_argument("--freeze_audio_encoder", type = bool, default = False)
-    parser.add_argument("--augment_dataset", type = bool, default = True)
-
-    for key, value in audio_args.items():
-        parser.add_argument(f"--{key}", type=type(value), default=value)
-
-def load_spec_modality_model(args):
-    spec_encoder = image.RawNet(args)
-    ckpt = torch.load('checkpoints\RawNet2.pth', map_location = torch.device('cpu'))
-    spec_encoder.load_state_dict(ckpt, strict = True)
-    spec_encoder.eval()
-    return spec_encoder
-
-
-#Load models.
-parser = argparse.ArgumentParser(description="Train Models")
-get_args(parser)
-args, remaining_args = parser.parse_known_args()
-assert remaining_args == [], remaining_args
-
-spec_model = load_spec_modality_model(args)
-
-print(f"Image model is: {pytorch_model}")
-
-print(f"Audio model is: {spec_model}")
-
-
-PATH = 'checkpoints\\model.pth'
-
-torch.save({
-    'spec_encoder': spec_model.state_dict(),
-    'rgb_encoder': pytorch_model.state_dict()
-}, PATH)
-
-print("Model saved.")
\ No newline at end of file
diff --git a/spaces/Kok4444/meme_kok/app.py b/spaces/Kok4444/meme_kok/app.py
deleted file mode 100644
index c2b9d2a12539101c0a0bcc1bdca59511cc92986a..0000000000000000000000000000000000000000
--- a/spaces/Kok4444/meme_kok/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch(share=True)
\ No newline at end of file
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/yolof_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/yolof_head.py
deleted file mode 100644
index bef4d8803c9311447588589a89fbd6a4b1f09a54..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/yolof_head.py
+++ /dev/null
@@ -1,399 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, is_norm
-from mmengine.model import bias_init_with_prob, constant_init, normal_init
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean
-from ..task_modules.prior_generators import anchor_inside_flags
-from ..utils import levels_to_images, multi_apply, unmap
-from .anchor_head import AnchorHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class YOLOFHead(AnchorHead):
-    """Detection Head of `YOLOF `_
-
-    Args:
-        num_classes (int): The number of object classes (w/o background)
-        in_channels (list[int]): The number of input channels per scale.
-        cls_num_convs (int): The number of convolutions of cls branch.
-           Defaults to 2.
-        reg_num_convs (int): The number of convolutions of reg branch.
-           Defaults to 4.
-        norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
-            layer. Defaults to ``dict(type='BN', requires_grad=True)``.
-    """
-
-    def __init__(self,
-                 num_classes: int,
-                 in_channels: List[int],
-                 num_cls_convs: int = 2,
-                 num_reg_convs: int = 4,
-                 norm_cfg: ConfigType = dict(type='BN', requires_grad=True),
-                 **kwargs) -> None:
-        self.num_cls_convs = num_cls_convs
-        self.num_reg_convs = num_reg_convs
-        self.norm_cfg = norm_cfg
-        super().__init__(
-            num_classes=num_classes, in_channels=in_channels, **kwargs)
-
-    def _init_layers(self) -> None:
-        cls_subnet = []
-        bbox_subnet = []
-        for i in range(self.num_cls_convs):
-            cls_subnet.append(
-                ConvModule(
-                    self.in_channels,
-                    self.in_channels,
-                    kernel_size=3,
-                    padding=1,
-                    norm_cfg=self.norm_cfg))
-        for i in range(self.num_reg_convs):
-            bbox_subnet.append(
-                ConvModule(
-                    self.in_channels,
-                    self.in_channels,
-                    kernel_size=3,
-                    padding=1,
-                    norm_cfg=self.norm_cfg))
-        self.cls_subnet = nn.Sequential(*cls_subnet)
-        self.bbox_subnet = nn.Sequential(*bbox_subnet)
-        self.cls_score = nn.Conv2d(
-            self.in_channels,
-            self.num_base_priors * self.num_classes,
-            kernel_size=3,
-            stride=1,
-            padding=1)
-        self.bbox_pred = nn.Conv2d(
-            self.in_channels,
-            self.num_base_priors * 4,
-            kernel_size=3,
-            stride=1,
-            padding=1)
-        self.object_pred = nn.Conv2d(
-            self.in_channels,
-            self.num_base_priors,
-            kernel_size=3,
-            stride=1,
-            padding=1)
-
-    def init_weights(self) -> None:
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                normal_init(m, mean=0, std=0.01)
-            if is_norm(m):
-                constant_init(m, 1)
-
-        # Use prior in model initialization to improve stability
-        bias_cls = bias_init_with_prob(0.01)
-        torch.nn.init.constant_(self.cls_score.bias, bias_cls)
-
-    def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-
-        Returns:
-            tuple:
-                normalized_cls_score (Tensor): Normalized Cls scores for a \
-                    single scale level, the channels number is \
-                    num_base_priors * num_classes.
-                bbox_reg (Tensor): Box energies / deltas for a single scale \
-                    level, the channels number is num_base_priors * 4.
-        """
-        cls_score = self.cls_score(self.cls_subnet(x))
-        N, _, H, W = cls_score.shape
-        cls_score = cls_score.view(N, -1, self.num_classes, H, W)
-
-        reg_feat = self.bbox_subnet(x)
-        bbox_reg = self.bbox_pred(reg_feat)
-        objectness = self.object_pred(reg_feat)
-
-        # implicit objectness
-        objectness = objectness.view(N, -1, 1, H, W)
-        normalized_cls_score = cls_score + objectness - torch.log(
-            1. + torch.clamp(cls_score.exp(), max=INF) +
-            torch.clamp(objectness.exp(), max=INF))
-        normalized_cls_score = normalized_cls_score.view(N, -1, H, W)
-        return normalized_cls_score, bbox_reg
-
-    def loss_by_feat(
-            self,
-            cls_scores: List[Tensor],
-            bbox_preds: List[Tensor],
-            batch_gt_instances: InstanceList,
-            batch_img_metas: List[dict],
-            batch_gt_instances_ignore: OptInstanceList = None) -> dict:
-        """Calculate the loss based on the features extracted by the detection
-        head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                has shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes`` and ``labels``
-                attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
-                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-
-        Returns:
-            dict: A dictionary of loss components.
-        """
-        assert len(cls_scores) == 1
-        assert self.prior_generator.num_levels == 1
-
-        device = cls_scores[0].device
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, batch_img_metas, device=device)
-
-        # The output level is always 1
-        anchor_list = [anchors[0] for anchors in anchor_list]
-        valid_flag_list = [valid_flags[0] for valid_flags in valid_flag_list]
-
-        cls_scores_list = levels_to_images(cls_scores)
-        bbox_preds_list = levels_to_images(bbox_preds)
-
-        cls_reg_targets = self.get_targets(
-            cls_scores_list,
-            bbox_preds_list,
-            anchor_list,
-            valid_flag_list,
-            batch_gt_instances,
-            batch_img_metas,
-            batch_gt_instances_ignore=batch_gt_instances_ignore)
-        if cls_reg_targets is None:
-            return None
-        (batch_labels, batch_label_weights, avg_factor, batch_bbox_weights,
-         batch_pos_predicted_boxes, batch_target_boxes) = cls_reg_targets
-
-        flatten_labels = batch_labels.reshape(-1)
-        batch_label_weights = batch_label_weights.reshape(-1)
-        cls_score = cls_scores[0].permute(0, 2, 3,
-                                          1).reshape(-1, self.cls_out_channels)
-
-        avg_factor = reduce_mean(
-            torch.tensor(avg_factor, dtype=torch.float, device=device)).item()
-
-        # classification loss
-        loss_cls = self.loss_cls(
-            cls_score,
-            flatten_labels,
-            batch_label_weights,
-            avg_factor=avg_factor)
-
-        # regression loss
-        if batch_pos_predicted_boxes.shape[0] == 0:
-            # no pos sample
-            loss_bbox = batch_pos_predicted_boxes.sum() * 0
-        else:
-            loss_bbox = self.loss_bbox(
-                batch_pos_predicted_boxes,
-                batch_target_boxes,
-                batch_bbox_weights.float(),
-                avg_factor=avg_factor)
-
-        return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
-
-    def get_targets(self,
-                    cls_scores_list: List[Tensor],
-                    bbox_preds_list: List[Tensor],
-                    anchor_list: List[Tensor],
-                    valid_flag_list: List[Tensor],
-                    batch_gt_instances: InstanceList,
-                    batch_img_metas: List[dict],
-                    batch_gt_instances_ignore: OptInstanceList = None,
-                    unmap_outputs: bool = True):
-        """Compute regression and classification targets for anchors in
-        multiple images.
-
-        Args:
-            cls_scores_list (list[Tensor])： Classification scores of
-                each image. each is a 4D-tensor, the shape is
-                (h * w, num_anchors * num_classes).
-            bbox_preds_list (list[Tensor])： Bbox preds of each image.
-                each is a 4D-tensor, the shape is (h * w, num_anchors * 4).
-            anchor_list (list[Tensor]): Anchors of each image. Each element of
-                is a tensor of shape (h * w * num_anchors, 4).
-            valid_flag_list (list[Tensor]): Valid flags of each image. Each
-               element of is a tensor of shape (h * w * num_anchors, )
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes`` and ``labels``
-                attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
-                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: Usually returns a tuple containing learning targets.
-
-                - batch_labels (Tensor): Label of all images. Each element \
-                    of is a tensor of shape (batch, h * w * num_anchors)
-                - batch_label_weights (Tensor): Label weights of all images \
-                    of is a tensor of shape (batch, h * w * num_anchors)
-                - num_total_pos (int): Number of positive samples in all \
-                    images.
-                - num_total_neg (int): Number of negative samples in all \
-                    images.
-            additional_returns: This function enables user-defined returns from
-                `self._get_targets_single`. These returns are currently refined
-                to properties at each feature map (i.e. having HxW dimension).
-                The results will be concatenated after the end
-        """
-        num_imgs = len(batch_img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # compute targets for each image
-        if batch_gt_instances_ignore is None:
-            batch_gt_instances_ignore = [None] * num_imgs
-        results = multi_apply(
-            self._get_targets_single,
-            bbox_preds_list,
-            anchor_list,
-            valid_flag_list,
-            batch_gt_instances,
-            batch_img_metas,
-            batch_gt_instances_ignore,
-            unmap_outputs=unmap_outputs)
-        (all_labels, all_label_weights, pos_inds, neg_inds,
-         sampling_results_list) = results[:5]
-        # Get `avg_factor` of all images, which calculate in `SamplingResult`.
-        # When using sampling method, avg_factor is usually the sum of
-        # positive and negative priors. When using `PseudoSampler`,
-        # `avg_factor` is usually equal to the number of positive priors.
-        avg_factor = sum(
-            [results.avg_factor for results in sampling_results_list])
-        rest_results = list(results[5:])  # user-added return values
-
-        batch_labels = torch.stack(all_labels, 0)
-        batch_label_weights = torch.stack(all_label_weights, 0)
-
-        res = (batch_labels, batch_label_weights, avg_factor)
-        for i, rests in enumerate(rest_results):  # user-added return values
-            rest_results[i] = torch.cat(rests, 0)
-
-        return res + tuple(rest_results)
-
-    def _get_targets_single(self,
-                            bbox_preds: Tensor,
-                            flat_anchors: Tensor,
-                            valid_flags: Tensor,
-                            gt_instances: InstanceData,
-                            img_meta: dict,
-                            gt_instances_ignore: Optional[InstanceData] = None,
-                            unmap_outputs: bool = True) -> tuple:
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        Args:
-            bbox_preds (Tensor): Bbox prediction of the image, which
-                shape is (h * w ,4)
-            flat_anchors (Tensor): Anchors of the image, which shape is
-                (h * w * num_anchors ,4)
-            valid_flags (Tensor): Valid flags of the image, which shape is
-                (h * w * num_anchors,).
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            img_meta (dict): Meta information for current image.
-            gt_instances_ignore (:obj:`InstanceData`, optional): Instances
-                to be ignored during training. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple:
-                labels (Tensor): Labels of image, which shape is
-                    (h * w * num_anchors, ).
-                label_weights (Tensor): Label weights of image, which shape is
-                    (h * w * num_anchors, ).
-                pos_inds (Tensor): Pos index of image.
-                neg_inds (Tensor): Neg index of image.
-                sampling_result (obj:`SamplingResult`): Sampling result.
-                pos_bbox_weights (Tensor): The Weight of using to calculate
-                    the bbox branch loss, which shape is (num, ).
-                pos_predicted_boxes (Tensor): boxes predicted value of
-                    using to calculate the bbox branch loss, which shape is
-                    (num, 4).
-                pos_target_boxes (Tensor): boxes target value of
-                    using to calculate the bbox branch loss, which shape is
-                    (num, 4).
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg['allowed_border'])
-        if not inside_flags.any():
-            raise ValueError(
-                'There is no valid anchor inside the image boundary. Please '
-                'check the image size and anchor sizes, or set '
-                '``allowed_border`` to -1 to skip the condition.')
-
-        # assign gt and sample anchors
-        anchors = flat_anchors[inside_flags, :]
-        bbox_preds = bbox_preds.reshape(-1, 4)
-        bbox_preds = bbox_preds[inside_flags, :]
-
-        # decoded bbox
-        decoder_bbox_preds = self.bbox_coder.decode(anchors, bbox_preds)
-        pred_instances = InstanceData(
-            priors=anchors, decoder_priors=decoder_bbox_preds)
-        assign_result = self.assigner.assign(pred_instances, gt_instances,
-                                             gt_instances_ignore)
-
-        pos_bbox_weights = assign_result.get_extra_property('pos_idx')
-        pos_predicted_boxes = assign_result.get_extra_property(
-            'pos_predicted_boxes')
-        pos_target_boxes = assign_result.get_extra_property('target_boxes')
-
-        sampling_result = self.sampler.sample(assign_result, pred_instances,
-                                              gt_instances)
-        num_valid_anchors = anchors.shape[0]
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            labels[pos_inds] = sampling_result.pos_gt_labels
-            if self.train_cfg['pos_weight'] <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg['pos_weight']
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags,
-                fill=self.num_classes)  # fill bg label
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-
-        return (labels, label_weights, pos_inds, neg_inds, sampling_result,
-                pos_bbox_weights, pos_predicted_boxes, pos_target_boxes)
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/lad.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/lad.py
deleted file mode 100644
index 008f898772988715c67783d9218ff39c4dd95d80..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/lad.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional
-
-import torch
-import torch.nn as nn
-from mmengine.runner import load_checkpoint
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.structures import SampleList
-from mmdet.utils import ConfigType, OptConfigType
-from ..utils.misc import unpack_gt_instances
-from .kd_one_stage import KnowledgeDistillationSingleStageDetector
-
-
-@MODELS.register_module()
-class LAD(KnowledgeDistillationSingleStageDetector):
-    """Implementation of `LAD `_."""
-
-    def __init__(self,
-                 backbone: ConfigType,
-                 neck: ConfigType,
-                 bbox_head: ConfigType,
-                 teacher_backbone: ConfigType,
-                 teacher_neck: ConfigType,
-                 teacher_bbox_head: ConfigType,
-                 teacher_ckpt: Optional[str] = None,
-                 eval_teacher: bool = True,
-                 train_cfg: OptConfigType = None,
-                 test_cfg: OptConfigType = None,
-                 data_preprocessor: OptConfigType = None) -> None:
-        super(KnowledgeDistillationSingleStageDetector, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            bbox_head=bbox_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            data_preprocessor=data_preprocessor)
-        self.eval_teacher = eval_teacher
-        self.teacher_model = nn.Module()
-        self.teacher_model.backbone = MODELS.build(teacher_backbone)
-        if teacher_neck is not None:
-            self.teacher_model.neck = MODELS.build(teacher_neck)
-        teacher_bbox_head.update(train_cfg=train_cfg)
-        teacher_bbox_head.update(test_cfg=test_cfg)
-        self.teacher_model.bbox_head = MODELS.build(teacher_bbox_head)
-        if teacher_ckpt is not None:
-            load_checkpoint(
-                self.teacher_model, teacher_ckpt, map_location='cpu')
-
-    @property
-    def with_teacher_neck(self) -> bool:
-        """bool: whether the detector has a teacher_neck"""
-        return hasattr(self.teacher_model, 'neck') and \
-            self.teacher_model.neck is not None
-
-    def extract_teacher_feat(self, batch_inputs: Tensor) -> Tensor:
-        """Directly extract teacher features from the backbone+neck."""
-        x = self.teacher_model.backbone(batch_inputs)
-        if self.with_teacher_neck:
-            x = self.teacher_model.neck(x)
-        return x
-
-    def loss(self, batch_inputs: Tensor,
-             batch_data_samples: SampleList) -> dict:
-        """
-        Args:
-            batch_inputs (Tensor): Input images of shape (N, C, H, W).
-                These should usually be mean centered and std scaled.
-            batch_data_samples (list[:obj:`DetDataSample`]): The batch
-                data samples. It usually includes information such
-                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        outputs = unpack_gt_instances(batch_data_samples)
-        batch_gt_instances, batch_gt_instances_ignore, batch_img_metas \
-            = outputs
-        # get label assignment from the teacher
-        with torch.no_grad():
-            x_teacher = self.extract_teacher_feat(batch_inputs)
-            outs_teacher = self.teacher_model.bbox_head(x_teacher)
-            label_assignment_results = \
-                self.teacher_model.bbox_head.get_label_assignment(
-                    *outs_teacher, batch_gt_instances, batch_img_metas,
-                    batch_gt_instances_ignore)
-
-        # the student use the label assignment from the teacher to learn
-        x = self.extract_feat(batch_inputs)
-        losses = self.bbox_head.loss(x, label_assignment_results,
-                                     batch_data_samples)
-        return losses
diff --git a/spaces/Lanerdog/deepsynthbody-deepfake_ecg6666/README.md b/spaces/Lanerdog/deepsynthbody-deepfake_ecg6666/README.md
deleted file mode 100644
index fa4b07df0210a9e84367667df8eb3891496f6b65..0000000000000000000000000000000000000000
--- a/spaces/Lanerdog/deepsynthbody-deepfake_ecg6666/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Deepsynthbody-deepfake Ecg6666
-emoji: 🏆
-colorFrom: yellow
-colorTo: gray
-sdk: static
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/LeeroyVonJenkins/construction-safety-object-detection/README.md b/spaces/LeeroyVonJenkins/construction-safety-object-detection/README.md
deleted file mode 100644
index 4ab9df77234b26098662203460299512777e33fe..0000000000000000000000000000000000000000
--- a/spaces/LeeroyVonJenkins/construction-safety-object-detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Construction Safety Object Detection
-emoji: 🎮
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-duplicated_from: keremberke/construction-safety-object-detection
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/LeeroyVonJenkins/construction-safety-object-detection/app.py b/spaces/LeeroyVonJenkins/construction-safety-object-detection/app.py
deleted file mode 100644
index f2b91e42212d04759244975e1663343227a50444..0000000000000000000000000000000000000000
--- a/spaces/LeeroyVonJenkins/construction-safety-object-detection/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-
-import json
-import gradio as gr
-import yolov5
-from PIL import Image
-from huggingface_hub import hf_hub_download
-
-app_title = "Construction Safety Object Detection"
-models_ids = ['keremberke/yolov5n-construction-safety', 'keremberke/yolov5s-construction-safety', 'keremberke/yolov5m-construction-safety']
-article = f"
       huggingface.co/{models_ids[-1]} | huggingface.co/keremberke/construction-safety-object-detection | awesome-yolov5-models 
      "
-
-current_model_id = models_ids[-1]
-model = yolov5.load(current_model_id)
-
-examples = [['test_images/-1079-_png_jpg.rf.eae5c731d79f3b240ce6b5ae84589e49.jpg', 0.25, 'keremberke/yolov5m-construction-safety'], ['test_images/construction-1-_mp4-147_jpg.rf.6593d553fd4c445c810aedcc8f9bf5b0.jpg', 0.25, 'keremberke/yolov5m-construction-safety'], ['test_images/construction-1023-_jpg.rf.10ea2a0d607573c1c90d7c38bacf2f04.jpg', 0.25, 'keremberke/yolov5m-construction-safety'], ['test_images/construction-3-_mp4-21_jpg.rf.f90d04a7fe8ee4d1d3331050b4e64e1b.jpg', 0.25, 'keremberke/yolov5m-construction-safety'], ['test_images/image_140_jpg.rf.e7727a5a4bd52d812adbd6f5d2fea6d9.jpg', 0.25, 'keremberke/yolov5m-construction-safety'], ['test_images/Mask-detector1_mov-46_jpg.rf.2122d830c41384952c89ef8cd23734ca.jpg', 0.25, 'keremberke/yolov5m-construction-safety']]
-
-
-def predict(image, threshold=0.25, model_id=None):
-    # update model if required
-    global current_model_id
-    global model
-    if model_id != current_model_id:
-        model = yolov5.load(model_id)
-        current_model_id = model_id
-
-    # get model input size
-    config_path = hf_hub_download(repo_id=model_id, filename="config.json")
-    with open(config_path, "r") as f:
-        config = json.load(f)
-    input_size = config["input_size"]
-
-    # perform inference
-    model.conf = threshold
-    results = model(image, size=input_size)
-    numpy_image = results.render()[0]
-    output_image = Image.fromarray(numpy_image)
-    return output_image
-
-
-gr.Interface(
-    title=app_title,
-    description="Created by 'keremberke'",
-    article=article,
-    fn=predict,
-    inputs=[
-        gr.Image(type="pil"),
-        gr.Slider(maximum=1, step=0.01, value=0.25),
-        gr.Dropdown(models_ids, value=models_ids[-1]),
-    ],
-    outputs=gr.Image(type="pil"),
-    examples=examples,
-    cache_examples=True if examples else False,
-).launch(enable_queue=True)
diff --git a/spaces/Liu-LAB/GPT-academic/crazy_functional.py b/spaces/Liu-LAB/GPT-academic/crazy_functional.py
deleted file mode 100644
index c6578554dc327a28f87960dd19ae68977f763c08..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/crazy_functional.py
+++ /dev/null
@@ -1,564 +0,0 @@
-from toolbox import HotReload  # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
-
-
-def get_crazy_functions():
-    from crazy_functions.读文章写摘要 import 读文章写摘要
-    from crazy_functions.生成函数注释 import 批量生成函数注释
-    from crazy_functions.解析项目源代码 import 解析项目本身
-    from crazy_functions.解析项目源代码 import 解析一个Python项目
-    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
-    from crazy_functions.解析项目源代码 import 解析一个C项目
-    from crazy_functions.解析项目源代码 import 解析一个Golang项目
-    from crazy_functions.解析项目源代码 import 解析一个Rust项目
-    from crazy_functions.解析项目源代码 import 解析一个Java项目
-    from crazy_functions.解析项目源代码 import 解析一个前端项目
-    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
-    from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
-    from crazy_functions.Latex全文润色 import Latex英文润色
-    from crazy_functions.询问多个大语言模型 import 同时问询
-    from crazy_functions.解析项目源代码 import 解析一个Lua项目
-    from crazy_functions.解析项目源代码 import 解析一个CSharp项目
-    from crazy_functions.总结word文档 import 总结word文档
-    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
-    from crazy_functions.对话历史存档 import 对话历史存档
-    from crazy_functions.对话历史存档 import 载入对话历史存档
-    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
-    from crazy_functions.辅助功能 import 清除缓存
-    from crazy_functions.批量Markdown翻译 import Markdown英译中
-    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
-    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
-    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
-    from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
-    from crazy_functions.Latex全文润色 import Latex中文润色
-    from crazy_functions.Latex全文润色 import Latex英文纠错
-    from crazy_functions.Latex全文翻译 import Latex中译英
-    from crazy_functions.Latex全文翻译 import Latex英译中
-    from crazy_functions.批量Markdown翻译 import Markdown中译英
-    from crazy_functions.虚空终端 import 虚空终端
-
-
-    function_plugins = {
-        "虚空终端": {
-            "Group": "对话|编程|学术",
-            "Color": "stop",
-            "AsButton": True,
-            "Function": HotReload(虚空终端)
-        },
-        "解析整个Python项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "解析一个Python项目的所有源文件(.py) | 输入参数为路径",
-            "Function": HotReload(解析一个Python项目)
-        },
-        "载入对话历史存档（先上传存档或输入路径）": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "载入对话历史存档 | 输入参数为路径",
-            "Function": HotReload(载入对话历史存档)
-        },
-        "删除所有本地对话历史记录（谨慎操作）": {
-            "Group": "对话",
-            "AsButton": False,
-            "Info": "删除所有本地对话历史记录，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(删除所有本地对话历史记录)
-        },
-        "清除所有缓存文件（谨慎操作）": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(清除缓存)
-        },
-        "批量总结Word文档": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "批量总结word文档 | 输入参数为路径",
-            "Function": HotReload(总结word文档)
-        },
-        "解析整个C++项目头文件": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个C++项目的所有头文件(.h/.hpp) | 输入参数为路径",
-            "Function": HotReload(解析一个C项目的头文件)
-        },
-        "解析整个C++项目（.cpp/.hpp/.c/.h）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个C++项目的所有源文件（.cpp/.hpp/.c/.h）| 输入参数为路径",
-            "Function": HotReload(解析一个C项目)
-        },
-        "解析整个Go项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Go项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Golang项目)
-        },
-        "解析整个Rust项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Rust项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Rust项目)
-        },
-        "解析整个Java项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Java项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Java项目)
-        },
-        "解析整个前端项目（js,ts,css等）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个前端项目的所有源文件（js,ts,css等） | 输入参数为路径",
-            "Function": HotReload(解析一个前端项目)
-        },
-        "解析整个Lua项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个Lua项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Lua项目)
-        },
-        "解析整个CSharp项目": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "解析一个CSharp项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个CSharp项目)
-        },
-        "解析Jupyter Notebook文件": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "解析Jupyter Notebook文件 | 输入参数为路径",
-            "Function": HotReload(解析ipynb文件),
-            "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-            "ArgsReminder": "若输入0，则不解析notebook中的Markdown块",  # 高级参数输入区的显示提示
-        },
-        "读Tex论文写摘要": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "读取Tex论文并写摘要 | 输入参数为路径",
-            "Function": HotReload(读文章写摘要)
-        },
-        "翻译README或MD": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": True,
-            "Info": "将Markdown翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中)
-        },
-        "翻译Markdown或README（支持Github链接）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,
-            "Info": "将Markdown或README翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中)
-        },
-        "批量生成函数注释": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量生成函数的注释 | 输入参数为路径",
-            "Function": HotReload(批量生成函数注释)
-        },
-        "保存当前的对话": {
-            "Group": "对话",
-            "AsButton": True,
-            "Info": "保存当前的对话 | 不需要输入参数",
-            "Function": HotReload(对话历史存档)
-        },
-        "[多线程Demo]解析此项目本身（源码自译解）": {
-            "Group": "对话|编程",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "多线程解析并翻译此项目的源码 | 不需要输入参数",
-            "Function": HotReload(解析项目本身)
-        },
-        "[插件demo]历史上的今天": {
-            "Group": "对话",
-            "AsButton": True,
-            "Info": "查看历史上的今天事件 | 不需要输入参数",
-            "Function": HotReload(高阶功能模板函数)
-        },
-        "精准翻译PDF论文": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": True,  
-            "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档)
-        },
-        "询问多个GPT模型": {
-            "Group": "对话",
-            "Color": "stop",
-            "AsButton": True,
-            "Function": HotReload(同时问询)
-        },
-        "批量总结PDF文档": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量总结PDF文档的内容 | 输入参数为路径",
-            "Function": HotReload(批量总结PDF文档)
-        },
-        "谷歌学术检索助手（输入谷歌学术搜索页url）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "使用谷歌学术检索助手搜索指定URL的结果 | 输入参数为谷歌学术搜索页的URL",
-            "Function": HotReload(谷歌检索小助手)
-        },
-        "理解PDF文档内容 （模仿ChatPDF）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "理解PDF文档的内容并进行回答 | 输入参数为路径",
-            "Function": HotReload(理解PDF文档内容标准文件输入)
-        },
-        "英文Latex项目全文润色（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对英文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文润色)
-        },
-        "英文Latex项目全文纠错（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对英文Latex项目全文进行纠错处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文纠错)
-        },
-        "中文Latex项目全文润色（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对中文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex中文润色)
-        },
-        "Latex项目全文中译英（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对Latex项目全文进行中译英处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex中译英)
-        },
-        "Latex项目全文英译中（输入路径或上传压缩包）": {
-            "Group": "学术",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "对Latex项目全文进行英译中处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英译中)
-        },
-        "批量Markdown中译英（输入路径或上传压缩包）": {
-            "Group": "编程",
-            "Color": "stop",
-            "AsButton": False,  # 加入下拉菜单中
-            "Info": "批量将Markdown文件中文翻译为英文 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Markdown中译英)
-        },
-    }
-
-    # -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
-    try:
-        from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
-        function_plugins.update({
-            "一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                # "Info": "下载arxiv论文并翻译摘要 | 输入参数为arxiv编号如1812.10695",
-                "Function": HotReload(下载arxiv论文并翻译摘要)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.联网的ChatGPT import 连接网络回答问题
-        function_plugins.update({
-            "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
-                "Function": HotReload(连接网络回答问题)
-            }
-        })
-        from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
-        function_plugins.update({
-            "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
-                "Function": HotReload(连接bing搜索回答问题)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.解析项目源代码 import 解析任意code项目
-        function_plugins.update({
-            "解析项目源代码（手动指定和筛选源代码文件类型）": {
-                "Group": "编程",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: \"*.c, ^*.cpp, config.toml, ^*.toml\"",  # 高级参数输入区的显示提示
-                "Function": HotReload(解析任意code项目)
-            },
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
-        function_plugins.update({
-            "询问多个GPT模型（手动指定询问哪些模型）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4",  # 高级参数输入区的显示提示
-                "Function": HotReload(同时问询_指定模型)
-            },
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.图片生成 import 图片生成
-        function_plugins.update({
-            "图片生成（先切换模型到openai或api2d）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "在这里输入分辨率, 如256x256（默认）",  # 高级参数输入区的显示提示
-                "Info": "图片生成 | 输入参数字符串，提供图像的内容",
-                "Function": HotReload(图片生成)
-            },
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.总结音视频 import 总结音视频
-        function_plugins.update({
-            "批量总结音视频（输入路径或上传压缩包）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。",
-                "Info": "批量总结音频或视频 | 输入参数为路径",
-                "Function": HotReload(总结音视频)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.数学动画生成manim import 动画生成
-        function_plugins.update({
-            "数学动画生成（Manim）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "Info": "按照自然语言描述生成一个动画 | 输入参数是一段话",
-                "Function": HotReload(动画生成)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
-        function_plugins.update({
-            "Markdown翻译（手动指定语言）": {
-                "Group": "编程",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "请输入要翻译成哪种语言，默认为Chinese。",
-                "Function": HotReload(Markdown翻译指定语言)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.Langchain知识库 import 知识库问答
-        function_plugins.update({
-            "构建知识库（请先上传文件素材）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "待注入的知识库名称id, 默认为default",
-                "Function": HotReload(知识库问答)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.Langchain知识库 import 读取知识库作答
-        function_plugins.update({
-            "知识库问答": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "待提取的知识库名称id, 默认为default, 您需要首先调用构建知识库",
-                "Function": HotReload(读取知识库作答)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.交互功能函数模板 import 交互功能模板函数
-        function_plugins.update({
-            "交互功能模板函数": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(交互功能模板函数)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
-        function_plugins.update({
-            "Latex英文纠错+高亮修正位置 [需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
-                "Function": HotReload(Latex英文纠错加PDF对比)
-            }
-        })
-        from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
-        function_plugins.update({
-            "Arixv论文精细翻译（输入arxivID）[需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder":
-                    "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 " +
-                    "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: " +
-                    'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
-                "Function": HotReload(Latex翻译中文并重新编译PDF)
-            }
-        })
-        function_plugins.update({
-            "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder":
-                    "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 " +
-                    "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: " +
-                    'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                "Info": "本地Latex论文精细翻译 | 输入参数是路径",
-                "Function": HotReload(Latex翻译中文并重新编译PDF)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from toolbox import get_conf
-        ENABLE_AUDIO, = get_conf('ENABLE_AUDIO')
-        if ENABLE_AUDIO:
-            from crazy_functions.语音助手 import 语音助手
-            function_plugins.update({
-                "实时音频采集": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": True,
-                    "Info": "开始语言对话 | 没有输入参数",
-                    "Function": HotReload(语音助手)
-                }
-            })
-    except:
-        print('Load function plugin failed')
-
-    try:
-        from crazy_functions.批量翻译PDF文档_NOUGAT import 批量翻译PDF文档
-        function_plugins.update({
-            "精准翻译PDF文档（NOUGAT）": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(批量翻译PDF文档)
-            }
-        })
-    except:
-        print('Load function plugin failed')
-
-
-    # try:
-    #     from crazy_functions.CodeInterpreter import 虚空终端CodeInterpreter
-    #     function_plugins.update({
-    #         "CodeInterpreter（开发中，仅供测试）": {
-    #             "Group": "编程|对话",
-    #             "Color": "stop",
-    #             "AsButton": False,
-    #             "Function": HotReload(虚空终端CodeInterpreter)
-    #         }
-    #     })
-    # except:
-    #     print('Load function plugin failed')
-
-    # try:
-    #     from crazy_functions.chatglm微调工具 import 微调数据集生成
-    #     function_plugins.update({
-    #         "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
-    #             "Color": "stop",
-    #             "AsButton": False,
-    #             "AdvancedArgs": True,
-    #             "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
-    #             "Function": HotReload(微调数据集生成)
-    #         }
-    #     })
-    # except:
-    #     print('Load function plugin failed')
-
-
-
-    """
-    设置默认值:
-    - 默认 Group = 对话
-    - 默认 AsButton = True
-    - 默认 AdvancedArgs = False
-    - 默认 Color = secondary
-    """
-    for name, function_meta in function_plugins.items():
-        if "Group" not in function_meta:
-            function_plugins[name]["Group"] = '对话'
-        if "AsButton" not in function_meta:
-            function_plugins[name]["AsButton"] = True
-        if "AdvancedArgs" not in function_meta:
-            function_plugins[name]["AdvancedArgs"] = False
-        if "Color" not in function_meta:
-            function_plugins[name]["Color"] = 'secondary'
-
-    return function_plugins
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/text/thai.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/March07/PromptBench/adv_prompts/t5_fewshot.md b/spaces/March07/PromptBench/adv_prompts/t5_fewshot.md
deleted file mode 100644
index c781a63d1bb20c6ae2d95b99e7974c522a5de886..0000000000000000000000000000000000000000
--- a/spaces/March07/PromptBench/adv_prompts/t5_fewshot.md
+++ /dev/null
@@ -1,3189 +0,0 @@
-# t5_fewshot
-
-# cola
-
-## 10 prompts
-
-Acc: 78.10%, prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Acc: 77.90%, prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Acc: 77.80%, prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Acc: 77.20%, prompt: Analyze the provided sentence and classify its grammatical correctness as 'Acceptable' or 'Unacceptable'.
-Acc: 77.20%, prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Acc: 76.90%, prompt: Please evaluate the grammatical structure of the provided sentence and answer with 'Acceptable' or 'Unacceptable'.
-Acc: 76.80%, prompt: Review the sentence below and identify whether its grammar is 'Acceptable' or 'Unacceptable'.
-Acc: 76.70%, prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Acc: 76.50%, prompt: Check the grammar of the following sentence and indicate if it is 'Acceptable' or 'Unacceptable'.
-Acc: 76.50%, prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-
-Acc: 76.40%, prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Acc: 75.80%, prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Acc: 75.70%, prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Acc: 75.50%, prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Acc: 75.40%, prompt: Functioning as a grammar evaluation tool, analyze the given sentence and decide if it is grammatically correct, responding with 'acceptable' or 'unacceptable'.
-Acc: 75.30%, prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Acc: 75.20%, prompt: As a grammar identification system, examine the provided sentence and respond with 'acceptable' for grammatically correct sentences or 'unacceptable' for incorrect ones.
-Acc: 75.20%, prompt: In the capacity of a grammar assessment system, indicate if the structure of the provided sentence is grammatically correct, responding with 'acceptable' or 'unacceptable'.
-Acc: 75.10%, prompt: In your role as a grammar check tool, assess the following sentence and classify it as 'acceptable' if it is grammatically correct or 'unacceptable' if it is incorrect.
-Acc: 74.50%, prompt: In the role of a syntax analyzer, examine the grammar of the provided sentence and decide if it is correct, answering with 'acceptable' for grammatically correct sentences or 'unacceptable' for incorrect ones.
-
-## bertattack
-
-Original prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the following conversation and pick if it is grammatical: correct. interact with 'Acceptable' or 'Unacceptable'. "
-Original acc: 78.10%, attacked acc: 69.90%, dropped acc: 8.20%
-
-Original prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the formed sentence and be if it is grammaticalnot sound. answer with el 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.90%, attacked acc: 71.00%, dropped acc: 6.90%
-
-Original prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"does the provided plural grammatical: correct? start with 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.80%, attacked acc: 73.40%, dropped acc: 4.40%
-
-Original prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Acting as a mood highlighting instrument, determine if the provided category is grammatical- correct, out with 'acceptable' for correct grammar or 'unacceptable' for incorrect math. "
-Original acc: 76.40%, attacked acc: 70.80%, dropped acc: 5.60%
-
-Original prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"As an item for grammar acquisition, determine the conversation and determine if its grammar is okay, responding with 'acceptable' for normal grammar or 'unacceptable' for using spelling. "
-Original acc: 75.80%, attacked acc: 69.20%, dropped acc: 6.60%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While applying spell analysis, write the grammar of the previous phrase as 'acceptable' for so grammar or 'unacceptable' for unclear tense. "
-Original acc: 75.70%, attacked acc: 69.50%, dropped acc: 6.20%
-
-## checklist
-
-Original prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable' zexKUTWJYG. "
-Original acc: 78.10%, attacked acc: 77.00%, dropped acc: 1.10%
-
-Original prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable' BWmsLx8xOA. "
-Original acc: 77.90%, attacked acc: 77.70%, dropped acc: 0.20%
-
-Original prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable' ItGDrrA1Sc. "
-Original acc: 77.80%, attacked acc: 77.70%, dropped acc: 0.10%
-
-Original prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar zexKUTWJYG. "
-Original acc: 76.40%, attacked acc: 75.90%, dropped acc: 0.50%
-
-Original prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar zexKUTWJYG. "
-Original acc: 75.80%, attacked acc: 74.90%, dropped acc: 0.90%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar zexKUTWJYG. "
-Original acc: 75.70%, attacked acc: 74.60%, dropped acc: 1.10%
-
-## deepwordbug
-
-Original prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the following sentence and dteermine if it is rgammatically Icorrect. Respond with 'Acceptable' or 'Unacceptable'. "
-Original acc: 78.10%, attacked acc: 72.40%, dropped acc: 5.70%
-
-Original prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examien the given senetnce and decide if it is rgammatically suond. Answer with eiither 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.90%, attacked acc: 70.20%, dropped acc: 7.70%
-
-Original prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Is the provided semtence rammatically corrct? Respond with 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.80%, attacked acc: 68.60%, dropped acc: 9.20%
-
-Original prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Actign as a grammar detection instrument, determie if the arovided sentence is grammatically sound, answering with 'acceptable' for correnct grammar or 'unacceptable' for incorrec grammar. "
-Original acc: 76.40%, attacked acc: 73.30%, dropped acc: 3.10%
-
-Original prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"As an instrumment for garmmar evaluatio, consider the sentynce and determine if its gramar is coZrect, responding with 'acceptable' for correct grammar or 'unacceptable' for inconrrect graOmar. "
-Original acc: 75.80%, attacked acc: 70.80%, dropped acc: 5.00%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While hperforming grammNar anlaysis, classfiy the grammar of the foBlowing sentence as 'acceptable' for corrept grammgar or 'unacceptable' for incborrect grMammar. "
-Original acc: 75.70%, attacked acc: 70.50%, dropped acc: 5.20%
-
-## stresstest
-
-Original prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'  and false is not true . "
-Original acc: 78.10%, attacked acc: 76.20%, dropped acc: 1.90%
-
-Original prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'  and false is not true . "
-Original acc: 77.90%, attacked acc: 77.40%, dropped acc: 0.50%
-
-Original prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'  and false is not true . "
-Original acc: 77.80%, attacked acc: 75.50%, dropped acc: 2.30%
-
-Original prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar  and false is not true . "
-Original acc: 76.40%, attacked acc: 76.10%, dropped acc: 0.30%
-
-Original prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar  and false is not true . "
-Original acc: 75.80%, attacked acc: 75.20%, dropped acc: 0.60%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar  and false is not true . "
-Original acc: 75.70%, attacked acc: 75.40%, dropped acc: 0.30%
-
-## textbugger
-
-Original prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Appraise the follow\xd1\x96ng sentence and determine if it is pronoun corrected. Respond with 'Acceptable' or 'Unacceptable'. "
-Original acc: 78.10%, attacked acc: 65.10%, dropped acc: 13.00%
-
-Original prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the yielded sentence and decide if it is pronouns audio. Answer with neither 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.90%, attacked acc: 54.90%, dropped acc: 23.00%
-
-Original prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Is the provided sentences pronouns rectify? Respond with 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.80%, attacked acc: 64.00%, dropped acc: 13.80%
-
-Original prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Acting as a grammatical detection instruments, determining if the provided condemnation is grammatically s\xd0\xbeund, answer with 'acceptable' for cor\xe2\xb2\x85ect grammatical or 'unacceptable' for inc\xd0\xberrect gr\xc9\x91mmar. "
-Original acc: 76.40%, attacked acc: 70.00%, dropped acc: 6.40%
-
-Original prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"As an instrum\xd0\xb5nt for grmamar evalu\xc9\x91tion, consider the punishments and determin\xd0\xb5 if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for inc\xd0\xberrect grammatical. "
-Original acc: 75.80%, attacked acc: 70.60%, dropped acc: 5.20%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While perf\xd0\xberming grmamar analysis, categorized the grammar of the following sentence as 'acceptable' for corret pronunciation or 'unacceptable' for inc\xd0\xberrect grammatical. "
-Original acc: 75.70%, attacked acc: 71.20%, dropped acc: 4.50%
-
-## textfooler
-
-Original prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Appraisal the below judgment and detects if it is grammatically correction. Response with 'Acceptable' or 'Unacceptable'. "
-Original acc: 78.10%, attacked acc: 75.90%, dropped acc: 2.20%
-
-Original prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Analysis the given convictions and defining if it is grammatically noise. Answers with either 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.90%, attacked acc: 74.50%, dropped acc: 3.40%
-
-Original prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Become the brought sentence grammatically correction? Reactions with 'Acceptable' or 'Unacceptable'. "
-Original acc: 77.80%, attacked acc: 70.30%, dropped acc: 7.50%
-
-Original prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Acting as a grammar detection instrument, determine if the delivered condemnation is grammatically wholesome, reply with 'acceptable' for proper grammar or 'unacceptable' for incorrect grammar. "
-Original acc: 76.40%, attacked acc: 72.40%, dropped acc: 4.00%
-
-Original prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"As an paraphernalia for grammar rating, consider the convicts and identifies if its grammar is correction, response with 'acceptable' for correct grammar or 'unacceptable' for fake pronunciation. "
-Original acc: 75.80%, attacked acc: 70.80%, dropped acc: 5.00%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"If performing pronunciation analyzing, classify the grammar of the following condemnation as 'acceptable' for adequate jargon or 'unacceptable' for misspelled dictionary. "
-Original acc: 75.70%, attacked acc: 70.50%, dropped acc: 5.20%
-
-## translation
-
-Language: chinese, acc: 75.90%, prompt: As a tool for determining grammatical correctness, please review the following sentences and categorize them grammatically as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 75.60%, prompt: Please evaluate the grammar of the following sentences and mark them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 75.20%, prompt: Please check that the following sentences are grammatically correct and categorize them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 75.90%, prompt: Please determine whether the following sentences are 'acceptable' or 'unacceptable' based on grammatical correctness.
-Language: chinese, acc: 74.90%, prompt: Please categorize the grammar of the following sentences as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 75.00%, prompt: Please check the grammar of the following sentences and classify them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 76.00%, prompt: Please mark the following sentences as 'acceptable' or 'unacceptable' according to the grammatical norms.
-Language: chinese, acc: 75.60%, prompt: Please assess the grammar of the following sentences and classify them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 75.70%, prompt: Please mark the following sentences as 'acceptable' or 'unacceptable' for grammatical accuracy.
-Language: chinese, acc: 75.00%, prompt: Please check the grammar of the following sentences and classify them as 'acceptable' or 'unacceptable'.
-Language: french, acc: 76.70%, prompt: As a tool for assessing semantic relevance, categorize the sentence as 'acceptable' or 'unacceptable' according to its meaning and context.
-Language: french, acc: 75.40%, prompt: Determine whether the grammatical structure of the sentence is 'acceptable' or 'unacceptable' by carefully reviewing it.
-Language: french, acc: 76.00%, prompt: Judge whether the sentence follows the syntactic rules of the language and classify it as 'acceptable' or 'unacceptable'.
-Language: french, acc: 74.80%, prompt: Assess the relevance of the sentence by classifying it as 'acceptable' or 'unacceptable' according to the context in which it is used.
-Language: french, acc: 76.10%, prompt: As a semantic classification tool, decide whether the sentence is 'acceptable' or 'unacceptable' according to its meaning and consistency.
-Language: french, acc: 76.20%, prompt: Analyze the sentence carefully and determine whether it is grammatically 'acceptable' or 'unacceptable'.
-Language: french, acc: 75.40%, prompt: As a syntactic validation tool, determine whether the sentence is properly structured and categorize it as 'acceptable' or 'unacceptable'.
-Language: french, acc: 74.00%, prompt: When evaluating the grammar of the sentence, classify it as 'acceptable' or 'unacceptable' according to the rules of the French language.
-Language: french, acc: 76.50%, prompt: Determine whether the sentence is 'acceptable' or 'unacceptable' depending on its syntactic construction and semantic context.
-Language: french, acc: 76.20%, prompt: As a tool for linguistic analysis, categorize the sentence as 'acceptable' or 'unacceptable' according to its relevance and semantic consistency.
-Language: arabic, acc: 75.30%, prompt: As a tool for determining grammatical correctness, review the sentence and classify its rules as 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 76.10%, prompt: Analyze the sentence carefully and classify its grammar between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 74.60%, prompt: As a tool for determining grammatical correctness, classify the rules of the sentence between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 75.90%, prompt: Evaluate the correctness of the sentence between 'acceptable' or 'unacceptable', as a tool for determining grammatical correctness.
-Language: arabic, acc: 76.10%, prompt: As a tool for classifying valid sentences, determine whether  'acceptable' or 'unacceptable' is based on its grammatical rules.
-Language: arabic, acc: 75.30%, prompt: Analyze the sentence carefully and classify its grammatical rules between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 76.60%, prompt: Analyze the sentence and classify it between 'acceptable' or 'unacceptable' as a grammatical check tool.
-Language: arabic, acc: 75.80%, prompt: As a classification tool for grammatical sentences, determine whether the sentence 'acceptable' or 'unacceptable' is based on its rules.
-Language: arabic, acc: 74.00%, prompt: As a tool for analyzing grammar, classify the rules of the sentence between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 74.90%, prompt: Determine whether the sentence is 'acceptable' or 'unacceptable' as a tool for determining grammatical correctness and analyze its grammar.
-Language: spanish, acc: 75.70%, prompt: As a tool to determine grammatical correctness, review the sentence and categorize its grammar as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 76.20%, prompt: Analyze the sentence carefully and classify its grammar as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 74.80%, prompt: As a tool for determining grammatical correctness, it categorizes the grammatical rules of the sentence as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 75.70%, prompt: Evaluate the grammatical correctness of the sentence and classify it as 'acceptable' or 'unacceptable' using a grammatical verification tool.
-Language: spanish, acc: 76.30%, prompt: As a tool for classifying grammatically correct sentences, determine whether the sentence is 'acceptable' or 'unacceptable' based on its grammatical rules.
-Language: spanish, acc: 76.00%, prompt: Analyse the sentence carefully and classify its grammar as 'acceptable' or 'unacceptable' using a grammatical revision tool.
-Language: spanish, acc: 75.80%, prompt: As a tool for classifying grammatical sentences, it determines whether the sentence is 'acceptable' or 'unacceptable' using its grammatical structure.
-Language: spanish, acc: 75.00%, prompt: As a tool for analyzing grammatical correctness, it categorizes the grammatical rules of the sentence as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 76.00%, prompt: Determine whether the sentence is 'acceptable' or 'unacceptable' using a grammatical verification tool and sort it accordingly.
-Language: spanish, acc: 76.30%, prompt: As a tool for determining grammatical correctness, evaluate the sentence and classify it as 'acceptable' or 'unacceptable' based on its grammatical rules.
-Language: japanese, acc: 74.90%, prompt: As a tool to determine whether grammar is grammatically correct, look at the sentence and categorize grammar into the 'acceptable' or 'unacceptable' categories.
-Language: japanese, acc: 74.90%, prompt: Please read the given sentence and categorize the grammar into the 'acceptable' or 'unacceptable' categories.
-Language: japanese, acc: 74.50%, prompt: To determine the correctness of a sentence, categorize grammar into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 75.30%, prompt: Classify the grammar of a given sentence into two categories: 'acceptable' and 'unacceptable'.
-Language: japanese, acc: 75.90%, prompt: Classify a given sentence into the categories 'acceptable' or 'unacceptable' to determine whether it is grammatically correct.
-Language: japanese, acc: 75.10%, prompt: To determine whether it is grammatically correct, categorize a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 75.10%, prompt: To determine the correctness of grammar, categorize a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 75.80%, prompt: Classify the grammar of a given sentence into two categories, 'acceptable' or 'unacceptable', and judge its accuracy.
-Language: japanese, acc: 74.60%, prompt: To determine whether it is grammatically correct, divide a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 75.40%, prompt: To evaluate the accuracy of grammar, categorize a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: korean, acc: 75.60%, prompt: As a tool for judging grammatical correctness, please review the sentences and classify the grammar as 'acceptable' or 'unacceptable'.
-Language: korean, acc: 75.30%, prompt: Please read the given sentences, and classify the grammar as 'acceptable' or 'unacceptable'.
-Language: korean, acc: 75.90%, prompt: Please classify the sentences as 'acceptable' or 'unacceptable' to judge the grammatical appropriateness.
-Language: korean, acc: 74.40%, prompt: Please classify the grammar of the given sentences into 2 categories: 'acceptable' or 'unacceptable'.
-Language: korean, acc: 75.50%, prompt: Please classify the given sentences as 'acceptable' or 'unacceptable' to judge the correctness of the grammar.
-Language: korean, acc: 76.00%, prompt: Please classify the given sentences as 'acceptable' or 'unacceptable' in order to judge their grammatical appropriateness.
-Language: korean, acc: 75.90%, prompt: Please determine the correctness of your grammar by classifying sentences as 'acceptable' or 'unacceptable'.
-Language: korean, acc: 75.90%, prompt: Classify the grammar of a given sentence as 'acceptable' or 'unacceptable', and judge its accuracy.
-Language: korean, acc: 75.50%, prompt: Please classify the given sentences as 'acceptable' or 'unacceptable' to judge the correctness of the grammar.
-Language: korean, acc: 75.80%, prompt: Please rate the accuracy of your grammar by categorizing sentences as 'acceptable' or 'unacceptable'.
-
-# mnli
-
-## 10 prompts
-
-Acc: 87.20%, prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 86.80%, prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Acc: 86.70%, prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Acc: 86.60%, prompt: Does the relationship between the given sentences represent entailment, neutral, or contradiction? Respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 83.40%, prompt: Considering the two sentences, identify if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 82.90%, prompt: Indicate if the connection between the following sentences is 'entailment', 'neutral', or 'contradiction'.
-Acc: 82.70%, prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Acc: 82.50%, prompt: Analyze the two provided sentences and decide if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 82.00%, prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Acc: 81.50%, prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-
-Acc: 86.50%, prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Acc: 86.50%, prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 84.80%, prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Acc: 83.80%, prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment', 'neutral', or 'contradiction'.
-Acc: 83.40%, prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 83.40%, prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 83.40%, prompt: While performing entailment analysis, classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Acc: 83.30%, prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 82.30%, prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Acc: 81.90%, prompt: As a tool for determining entailment relationships, review the two statements and categorize their connection as either 'entailment', 'neutral', or 'contradiction'.
-
-## bertattack
-
-Original prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"the if the next mate of particles expressed entailment, neutral, or contradiction. contrast with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 87.20%, attacked acc: 62.50%, dropped acc: 24.70%
-
-Original prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"understand the complex of verbs and find if they occur entailment, neutral, or contradiction. deal with color 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.80%, attacked acc: 82.10%, dropped acc: 4.70%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Identify whom the formed particular of clauses signifies entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.70%, attacked acc: 81.60%, dropped acc: 5.10%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"following as an entailment recognition example, determine if the given contradiction of clauses signs entailment, neutral, or contradiction. agree with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 75.10%, dropped acc: 11.40%
-
-Original prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"and as an entailment classcondition, identify tones the specific pair of sentence declares entailment, neutral, or contradiction. return with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 82.30%, dropped acc: 4.20%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a grammatical relationship analyzer, examine the connection between the given words and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 84.80%, attacked acc: 80.40%, dropped acc: 4.40%
-
-## checklist
-
-Original prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction' SFPCqpiClT. "
-Original acc: 87.20%, attacked acc: 86.20%, dropped acc: 1.00%
-
-Original prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction' ofOQXLxiW4. "
-Original acc: 86.80%, attacked acc: 86.20%, dropped acc: 0.60%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction' SFPCqpiClT. "
-Original acc: 86.70%, attacked acc: 86.40%, dropped acc: 0.30%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction' ofOQXLxiW4. "
-Original acc: 86.50%, attacked acc: 86.00%, dropped acc: 0.50%
-
-Original prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction' SFPCqpiClT. "
-Original acc: 86.50%, attacked acc: 86.30%, dropped acc: 0.20%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction' Cev0s49fbe. "
-Original acc: 84.80%, attacked acc: 85.00%, dropped acc: -0.20%
-
-## deepwordbug
-
-Original prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"DKtermine if the egiven pair of sentences dispSays entailment, neutral, or contradiction. Respnd with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 87.20%, attacked acc: 85.70%, dropped acc: 1.50%
-
-Original prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Examine the air of senWences and deitermine if they enxhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.80%, attacked acc: 84.90%, dropped acc: 1.90%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"IdentifJy whehter the gVven pazir of sentences edmonstrates entailment, neutral, or contradiction. Anawer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.70%, attacked acc: 83.70%, dropped acc: 3.00%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Acting as an entailment detection instrument, determine if the given apir of sentences demGonstrates entailment, neutral, or contradiction. Ancwer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 85.80%, dropped acc: 0.70%
-
-Original prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Workig as an entailment classifire, identify whetehr the given nair of sentence disptlays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 84.30%, dropped acc: 2.20%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 84.80%, attacked acc: 84.80%, dropped acc: 1.70%
-
-## stresstest
-
-Original prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 87.20%, attacked acc: 63.90%, dropped acc: 23.30%
-
-Original prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 86.80%, attacked acc: 77.40%, dropped acc: 9.40%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 86.70%, attacked acc: 53.10%, dropped acc: 33.60%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 86.50%, attacked acc: 52.20%, dropped acc: 34.30%
-
-Original prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 86.50%, attacked acc: 55.50%, dropped acc: 31.00%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 84.80%, attacked acc: 75.40%, dropped acc: 9.40%
-
-## textbugger
-
-Original prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Determine if the giv en paired of punishments show entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 87.20%, attacked acc: 85.60%, dropped acc: 1.60%
-
-Original prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Examines the paired of sentence and identify if they exposition entailment, neutral, or contradiction. Answe\xe2\xb2\x85 with oder 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.80%, attacked acc: 84.90%, dropped acc: 1.90%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Identifying ob the conferred couple of punishments demonstrating entailment, neutral, or contradiction. An\xd1\x95wer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.70%, attacked acc: 82.40%, dropped acc: 4.30%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Atcing as an entailment detectio\xd5\xb8 instrument, ascertain if the given pair of sentences demonst\xe2\xb2\x85ates entailment, neutral, or contradiction. Answe\xe2\xb2\x85 with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 85.50%, dropped acc: 1.00%
-
-Original prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Working as an entailment classifie\xe2\xb2\x85, identify both the given paired of sentence\xd1\x95 displa\xd1\x83s entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 83.90%, dropped acc: 2.60%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 84.80%, attacked acc: 84.80%, dropped acc: 1.70%
-
-## textfooler
-
-Original prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Identifying if the offered couples of sorrows poster entailment, neutral, or contradiction. Reaction with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 87.20%, attacked acc: 83.10%, dropped acc: 4.10%
-
-Original prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Inspects the coupling of sorrows and determination if they display entailment, neutral, or contradiction. Address with either 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.80%, attacked acc: 82.60%, dropped acc: 4.20%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Uncovering whether the given doublet of condemnation depicting entailment, neutral, or contradiction. Reactions with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.70%, attacked acc: 83.20%, dropped acc: 3.50%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Acting as an entailment locate tool, detects if the given pair of sentences exposes entailment, neutral, or contradiction. React with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 85.00%, dropped acc: 1.50%
-
-Original prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Jobs as an entailment classifier, defines whether the given pair of sentences proving entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 86.50%, attacked acc: 85.40%, dropped acc: 1.10%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 84.80%, attacked acc: 84.80%, dropped acc: 1.70%
-
-## translation
-
-Language: chinese, acc: 80.70%, prompt: As an implication analysis tool, evaluate the relationships between given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 82.10%, prompt: Analyze the relationships between the given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 82.90%, prompt: Evaluate the logical relationship between the following sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 81.20%, prompt: In your role as an implication analysis tool, judge the relationships of the following sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 82.10%, prompt: Please judge the relationship between the following sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 82.10%, prompt: From a given sentence, evaluate the relationship between them and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 81.00%, prompt: Please classify the following sentences as 'entailment', 'neutral', or 'contradiction' according to their relationships.
-Language: chinese, acc: 82.90%, prompt: Evaluate the logical relationship between the following sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 81.50%, prompt: As an implication analysis tool, categorize the relationships between given sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: chinese, acc: 82.10%, prompt: Please classify the following sentences as 'entailment', 'neutral', or 'contradiction' according to their logical relationship.
-Language: french, acc: 82.40%, prompt: As a tool for analyzing the consequence relationship, evaluate the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 82.90%, prompt: Evaluate the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 84.00%, prompt: Determine whether the following sentences are related to 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 82.00%, prompt: In your role as a consequence analysis tool, evaluate the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 82.70%, prompt: Classify the relationship between the following sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 81.90%, prompt: As a consequence analysis tool, evaluate the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 82.80%, prompt: Analyze the relationship between the given sentences and determine whether it is of 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 82.80%, prompt: Evaluate the relationship between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 82.50%, prompt: As a tool for analyzing the consequence relationship, classify the following sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: french, acc: 83.90%, prompt: Determine whether the given sentences are related to 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.30%, prompt: Based on your role as a reasoning analyst, analyze the relationship between the given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.50%, prompt: Evaluate the relationship between given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.70%, prompt: Determine if the following sentences are 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.10%, prompt: In your role as a tool of reasoning analysis, investigate the relationship between sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.70%, prompt: Classify the relationship between the following sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.60%, prompt: In your role as a tool of reasoning analysis, evaluate the relationship between the given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.90%, prompt: Analyze the relationship between the given sentences and determine if they are 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.70%, prompt: Evaluate the relationship between the following sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 81.90%, prompt: In your role as a tool of reasoning analysis, the following sentences are classified as 'entailment', 'neutral', or 'contradiction'.
-Language: arabic, acc: 82.90%, prompt: Determine if the sentences given are 'entailment', 'neutral', or 'contradiction'.
-Language: spanish, acc: 80.60%, prompt: In your role as an implication analysis tool, evaluate the relationship between the given phrases and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: spanish, acc: 82.50%, prompt: Determine whether there is 'entailment', 'neutral', or 'contradiction' between the sentences given, using this text analysis tool,
-Language: spanish, acc: 82.30%, prompt: Analyze the relationship between the two sentences and classify it as 'entailment', 'neutral', or 'contradiction' using this text classification tool,
-Language: spanish, acc: 82.30%, prompt: Using this implication analysis tool, decide whether the sentences given are related by 'entailment', 'neutral', or 'contradiction'.
-Language: spanish, acc: 81.40%, prompt: Classifies the relationship between the given phrases as 'entailment', 'neutral', or 'contradiction' using this text analysis tool,
-Language: spanish, acc: 82.90%, prompt: Evaluate whether there is 'entailment', 'neutral', or 'contradiction' between the sentences provided using this text classification tool,
-Language: spanish, acc: 81.60%, prompt: Using this implication analysis tool, decide whether the two sentences are related by 'entailment', 'neutral', or 'contradiction'.
-Language: spanish, acc: 82.20%, prompt: Determine whether the given phrases are related by 'entailment', 'neutral', or 'contradiction' using this text analysis tool,
-Language: spanish, acc: 81.30%, prompt: Analyze the relationship between the two sentences and classify it as 'entailment', 'neutral', or 'contradiction' using this text analysis tool,
-Language: spanish, acc: 81.90%, prompt: Using this text classification tool, it classifies the relationship between the given phrases as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.40%, prompt: As your role as an implication analysis tool, evaluate the relationship of a given sentence and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.40%, prompt: Use the implication analysis tool as your role to evaluate the relationship of a given sentence and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 81.10%, prompt: Use this text classification tool to categorize relationships in a given text as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.90%, prompt: Use the implication analysis tool as your role and classify the relationship of a given sentence as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 81.10%, prompt: Evaluate the relationship of a given sentence and use this text classification tool to classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.80%, prompt: Evaluate the relationship of a given sentence and use this text classification tool to accurately classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.40%, prompt: Use the implication analysis tool as your role and use this text classification tool to classify the relationship of a given sentence as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 81.70%, prompt: Use this text classification tool to evaluate the relationship of a given sentence and classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.20%, prompt: Use the implication analysis tool as your role, evaluate the relationship of a given sentence, and use this text classification tool to classify it as 'entailment', 'neutral', or 'contradiction'.
-Language: japanese, acc: 80.60%, prompt: Use the implication analysis tool as your role and categorize the relationship of a given sentence strictly as 'entailment', 'neutral', or 'contradiction' using this text classification tool.
-Language: korean, acc: 81.70%, prompt: Analyze the relationships between given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 82.40%, prompt: In the text categorization task, identify the relationship between given sentences as one of 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 82.10%, prompt: Perform the role of analyzing the relationship between sentences and classifying them as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 82.20%, prompt: Evaluate the relationship between two given sentences, and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 80.90%, prompt: In the text categorization task, perform the role of classifying relationships between given sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 81.80%, prompt: Judge the associations between sentences, and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 81.60%, prompt: Analyze the relationship between two given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 82.20%, prompt: In the task of text classification, identify the relationships between given sentences as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 81.20%, prompt: Act as an instrument to evaluate the relationships between sentences, and classify them as 'entailment', 'neutral', or 'contradiction'.
-Language: korean, acc: 80.90%, prompt: Analyze the associations of two given sentences and classify them as 'entailment', 'neutral', or 'contradiction'.
-
-# mrpc
-
-## 10 prompts
-
-Acc: 83.09%, prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Acc: 82.84%, prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Acc: 82.60%, prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Acc: 82.60%, prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Acc: 82.60%, prompt: Examine the pair of sentences and decide if their meanings are identical by answering with 'equivalent' or 'not_equivalent'.
-Acc: 81.62%, prompt: Are the meanings of the following pair of sentences the same? Answer with 'equivalent' or 'not_equivalent'.
-Acc: 81.37%, prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Acc: 81.13%, prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Acc: 80.88%, prompt: Do these two sentences have the same underlying meaning? Respond with 'equivalent' or 'not_equivalent'.
-Acc: 80.39%, prompt: Compare the following sentences and determine if they share the same semantic meaning by responding with 'equivalent' or 'not_equivalent'.
-
-Acc: 81.37%, prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Acc: 80.15%, prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Acc: 80.15%, prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Acc: 80.15%, prompt: In your capacity as a semantic comparison tool, examine the given sentences and decide if their meanings are identical, responding with 'equivalent' or 'not_equivalent'.
-Acc: 79.66%, prompt: In the role of a textual comparison specialist, examine the given sentences and decide if they share the same meaning, responding with 'equivalent' or 'not_equivalent'.
-Acc: 79.66%, prompt: As a linguistic comparator, review the following pair of sentences and determine their semantic equivalence by choosing 'equivalent' or 'not_equivalent'.
-Acc: 79.41%, prompt: As a sentence similarity evaluator, analyze the provided sentences and indicate if their meanings are 'equivalent' or 'not_equivalent'.
-Acc: 78.92%, prompt: As a textual similarity evaluator, analyze the following pair of sentences and determine if they are semantically 'equivalent' or 'not_equivalent'.
-Acc: 77.70%, prompt: In your capacity as a language analyst, assess the following sentences and classify their similarity as 'equivalent' or 'not_equivalent'.
-Acc: 75.49%, prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-
-## bertattack
-
-Original prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"view whether the two provided meanings convey the same meaning by answering 'equivalent' or 'not_equivalent'. "
-Original acc: 83.09%, attacked acc: 79.17%, dropped acc: 3.92%
-
-Original prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Please analyze the provided sentences and indicate if their gender are the same by choosing 'equivalent' or 'not_equivalent'. "
-Original acc: 82.84%, attacked acc: 78.19%, dropped acc: 4.66%
-
-Original prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"can the given words be considered semantically adjacent? Please respond with 'equivalent' or 'not_equivalent'. "
-Original acc: 82.60%, attacked acc: 73.28%, dropped acc: 9.31%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a grammatical compares professionals, find the given language of sentence and determine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 81.37%, attacked acc: 68.87%, dropped acc: 12.50%
-
-Original prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a language relative expert, examine the given in of sentences and decide if their grammatical level, if with 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 69.36%, dropped acc: 10.78%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the equivalent of a sentence comparison therapist, assess the provided sentences and indicate if they convey the same vocabulary by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 74.75%, dropped acc: 5.39%
-
-## checklist
-
-Original prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent' 5XeflW1ZJc. "
-Original acc: 83.09%, attacked acc: 81.62%, dropped acc: 1.47%
-
-Original prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent' CuVpbbkC4c. "
-Original acc: 82.84%, attacked acc: 80.88%, dropped acc: 1.96%
-
-Original prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent' BWmsLx8xOA. "
-Original acc: 82.60%, attacked acc: 81.37%, dropped acc: 1.23%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent' 9Q3S1F94fE. "
-Original acc: 81.37%, attacked acc: 80.39%, dropped acc: 0.98%
-
-Original prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent' bhrRSokrfa. "
-Original acc: 80.15%, attacked acc: 79.90%, dropped acc: 0.25%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent' BWmsLx8xOA. "
-Original acc: 80.15%, attacked acc: 79.66%, dropped acc: 0.49%
-
-## deepwordbug
-
-Original prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Evaluate whether the two provided senetnces convey the same meaning by answering 'equivalent' or 'not_equivalent'. "
-Original acc: 83.09%, attacked acc: 78.92%, dropped acc: 4.17%
-
-Original prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Please aSnalyze the provdied sQntences and indicate if their meancings are the same by choohing 'equivalent' or 'not_equivalent'. "
-Original acc: 82.84%, attacked acc: 80.15%, dropped acc: 2.70%
-
-Original prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can the given sentencses be considered semantically identKcal? Please reply with 'equivalent' or 'not_equivalent'. "
-Original acc: 82.60%, attacked acc: 78.92%, dropped acc: 3.68%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a semantic comparisKon experEt, evaluate the given pari of sentence and determine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 81.37%, attacked acc: 76.72%, dropped acc: 4.66%
-
-Original prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 80.15%, dropped acc: 1.23%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"IUn the rEle of a sentence cmparison analyst, assess the provided sentences and indiiate if they convey the same eaning by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 76.72%, dropped acc: 3.43%
-
-## stresstest
-
-Original prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 83.09%, attacked acc: 24.51%, dropped acc: 58.58%
-
-Original prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 82.84%, attacked acc: 36.52%, dropped acc: 46.32%
-
-Original prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 82.60%, attacked acc: 67.89%, dropped acc: 14.71%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 81.37%, attacked acc: 18.87%, dropped acc: 62.50%
-
-Original prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 80.15%, attacked acc: 17.40%, dropped acc: 62.75%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 80.15%, attacked acc: 20.10%, dropped acc: 60.05%
-
-## textbugger
-
-Original prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Evalutae whether the two provided s\xd0\xb5ntences convey the same meaning by answering 'equivalent' or 'not_equivalent'. "
-Original acc: 83.09%, attacked acc: 79.66%, dropped acc: 3.43%
-
-Original prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Please aalyze the yielded se ntences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'. "
-Original acc: 82.84%, attacked acc: 79.66%, dropped acc: 3.19%
-
-Original prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can the given sntences be considered semantically id\xd0\xb5ntical? Please reply with 'equivalent' or 'not_equivalent'. "
-Original acc: 82.60%, attacked acc: 78.68%, dropped acc: 3.92%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Since a semantic comp\xc9\x91rison specialised, evaluate the given pai\xe2\xb2\x85 of sentence and determine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 81.37%, attacked acc: 76.72%, dropped acc: 4.66%
-
-Original prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a langua\xc9\xa1e comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 78.68%, dropped acc: 1.47%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the role of a sentence compraison psychoanalyst, assess the provided sentences and indicat\xd0\xb5 if they convey the same meani ng by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 75.49%, dropped acc: 4.66%
-
-## textfooler
-
-Original prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Estimation whether the two provided convictions despatch the same meaning by answers 'equivalent' or 'not_equivalent'. "
-Original acc: 83.09%, attacked acc: 78.92%, dropped acc: 4.17%
-
-Original prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Please analyze the provided priors and describes if their meanings are the same by choosing 'equivalent' or 'not_equivalent'. "
-Original acc: 82.84%, attacked acc: 80.64%, dropped acc: 2.21%
-
-Original prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can the given condemns be considered semantically equal? Asking answered with 'equivalent' or 'not_equivalent'. "
-Original acc: 82.60%, attacked acc: 79.17%, dropped acc: 3.43%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Because a semantic comparison specialty, evaluate the given matched of sentence and determine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 81.37%, attacked acc: 76.23%, dropped acc: 5.15%
-
-Original prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a phrasing comparison expert, inspecting the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 78.19%, dropped acc: 1.96%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the role of a sentence likened psychoanalyst, assess the awarded sentences and indicate if they convey the same feeling by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 80.15%, attacked acc: 75.25%, dropped acc: 4.90%
-
-## translation
-
-Language: chinese, acc: 81.62%, prompt: As an expert in semantic comparison, evaluates two given sentences to determine whether they are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 82.35%, prompt: From the point of view of semantic comparison, determine whether this pair of sentences is 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 79.66%, prompt: Using the semantic comparison method, determine whether the following two statements are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 81.13%, prompt: For the following two sentences, determine whether they are 'equivalent' or 'not_equivalent' based on semantic comparison.
-Language: chinese, acc: 81.86%, prompt: As an expert in semantic comparison, please evaluate the following two sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 80.88%, prompt: Using semantic comparison techniques, determine whether the following two sentences are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 81.37%, prompt: Please determine whether the following two sentences are 'equivalent' or 'not_equivalent' according to the standard of semantic comparison.
-Language: chinese, acc: 81.13%, prompt: As an expert in the field of semantic comparison, please evaluate the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 80.88%, prompt: Using semantic comparison, determine whether the following two sentences are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 81.37%, prompt: Determine whether the following two sentences are 'equivalent' or 'not_equivalent' based on semantic comparison.
-Language: french, acc: 81.37%, prompt: As an expert in semantic comparison, evaluate the following pair of sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 80.88%, prompt: Can you determine whether the following two sentences are 'equivalent' or 'not_equivalent' as a semantic comparison expert?
-Language: french, acc: 81.37%, prompt: Using your expertise in semantic comparison, determine whether the following two sentences are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 82.35%, prompt: As a semantic comparison specialist, assess the similarity between the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 81.37%, prompt: Are you able to determine whether the following two sentences are 'equivalent' or 'not_equivalent' as an expert in semantic comparison?
-Language: french, acc: 81.37%, prompt: As a semantic comparison professional, evaluate the following pair of sentences and indicate whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 80.88%, prompt: Can you determine whether the following two sentences have a 'equivalent' or 'not_equivalent' meaning as an expert in semantic comparison?
-Language: french, acc: 82.11%, prompt: As an expert in semantic comparison, assess the similarity between the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 81.62%, prompt: Using your expertise in semantic comparison, determine whether the following two sentences are 'equivalent' or 'not_equivalent' in terms of meaning.
-Language: french, acc: 82.35%, prompt: As a semantic comparison professional, assess the similarity between the following two sentences and indicate whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.62%, prompt: As an expert in semantic comparison, evaluate the two given sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.13%, prompt: Based on my experience in semantic analysis, classify the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.62%, prompt: As an expert in semantic comparison, analyze the following two sentences and classify them as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 82.11%, prompt: Your task as an expert in semantic comparison is to evaluate the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.37%, prompt: As a semantic comparison specialist, analyze the two data statements and insert them into one of the following categories: 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.62%, prompt: Based on my experience in semantic analysis, classify the following two sentences between 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.13%, prompt: Your role as a semantic comparison specialist requires analyzing the two given sentences and determining whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.13%, prompt: As an experienced semantic analyst, classify the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.62%, prompt: Your job as a semantic analyst evaluates the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 81.37%, prompt: As a semantic analyst, determine whether the given sentences are 'equivalent' or 'not_equivalent' based on their relationship.
-Language: spanish, acc: 81.13%, prompt: As an expert in semantic comparison, it evaluates the pair of sentences provided and determines whether they are 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 81.13%, prompt: Based on my experience in semantic analysis, classify the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 81.37%, prompt: As an expert in semantic comparison, analyze the two sentences given and classify them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 81.86%, prompt: Your task as a semantic comparison specialist is to evaluate the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 80.39%, prompt: As an expert in semantic analysis, he makes a classification of the following two sentences based on their 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 80.88%, prompt: Based on your experience of semantic comparison, classify the next two sentences as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 80.39%, prompt: As a specialist in semantic analysis, you are given the task of analysing the two sentences given and classifying them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 80.88%, prompt: As an expert in semantic comparison, he classifies the following two sentences into 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 81.37%, prompt: As a specialist in semantic analysis, evaluate the following two sentences and classify them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 81.37%, prompt: Your task as an expert in semantic comparison is to analyze the two sentences provided and determine whether they are 'equivalent' or 'not_equivalent' based on their semantic relationship.
-Language: japanese, acc: 81.13%, prompt: Evaluate whether a given pair of sentences is 'equivalent' or 'not_equivalent', depending on the context.
-Language: japanese, acc: 81.13%, prompt: Use a semantic comparison to determine whether a given pair of sentences is 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 80.39%, prompt: Evaluate a given pair of sentences as 'equivalent' or 'not_equivalent' by determining whether they have the same semantic meaning.
-Language: japanese, acc: 81.62%, prompt: Determine whether a given pair of sentences is synonyms and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 81.86%, prompt: Determine whether a given pair of sentences is 'equivalent' or 'not_equivalent', and whether they are semantically identical.
-Language: japanese, acc: 83.09%, prompt: Determinate whether a given pair of sentences has the same meaning and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 81.86%, prompt: Evaluate whether a given pair of sentences is 'equivalent' or 'not_equivalent' by determining whether they are semantically identical.
-Language: japanese, acc: 82.35%, prompt: Judge whether a given pair of sentences is equal and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 80.15%, prompt: Determinate whether a given pair of sentences are semantically equal and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 80.88%, prompt: Whether a given pair of sentences is 'equivalent' or 'not_equivalent' depends on the context.
-Language: korean, acc: 81.13%, prompt: As a sentence comparator, evaluate the two sentences given to determine 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 81.37%, prompt: Compare two sentences to determine 'equivalent' or 'not_equivalent'. For this you need qualifications as a specialist in semantic comparison.
-Language: korean, acc: 80.39%, prompt: It takes your knowledge as an expert in semantic comparison to determine that two sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 81.37%, prompt: As a specialist in semantic comparison, evaluate whether two given sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 81.37%, prompt: Analyze two sentences to determine 'equivalent' or 'not_equivalent'. For that you need the knowledge of a semantic comparison expert.
-Language: korean, acc: 81.62%, prompt: As an expert in semantic comparison, decide whether two sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 81.13%, prompt: It takes the knowledge of an expert in semantic comparison to compare two sentences to judge 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 80.39%, prompt: Experience as an expert in semantic comparison is required to determine whether two given sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 82.35%, prompt: As an expert in semantic comparison, determine whether two sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 81.13%, prompt: Analyze two sentences to determine 'equivalent' or 'not_equivalent'. For this, you need a qualification as a specialist in semantic comparison.
-
-# qnli
-
-## 10 prompts
-
-Acc: 89.30%, prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Acc: 88.80%, prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Acc: 87.80%, prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Acc: 87.40%, prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Acc: 86.70%, prompt: Examine the context and question, and determine if the context logically implies the answer by selecting 'entailment' or 'not_entailment'.
-Acc: 85.50%, prompt: Consider the context and question, and indicate if the answer can be logically deduced from the context by responding with 'entailment' or 'not_entailment'.
-Acc: 85.40%, prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Acc: 85.30%, prompt: Given the question and context provided, determine if the answer can be inferred by choosing 'entailment' or 'not_entailment'.
-Acc: 81.80%, prompt: Assess if the answer to the question can be logically concluded from the provided context by choosing 'entailment' or 'not_entailment'.
-Acc: 69.40%, prompt: Please assess if the answer to the question can be derived from the given context by selecting 'entailment' or 'not_entailment'.
-
-Acc: 88.80%, prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Acc: 88.70%, prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Acc: 88.50%, prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Acc: 87.90%, prompt: As a semantic interpreter, assess whether the provided context supports the answer to the given question and answer with 'entailment' or 'not_entailment'.
-Acc: 87.60%, prompt: In your role as a linguistic investigator, determine if the context given entails the answer to the question and provide your conclusion with 'entailment' or 'not_entailment'.
-Acc: 87.30%, prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Acc: 86.10%, prompt: As a language expert, assess if the given context entails the answer to the question and respond with 'entailment' or 'not_entailment'.
-Acc: 85.40%, prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Acc: 82.90%, prompt: In the capacity of a language specialist, decide if the context presented contains enough information to infer the answer to the question and respond with 'entailment' or 'not_entailment'.
-Acc: 76.50%, prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-
-## bertattack
-
-Original prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"from on the supplied context and questions, save if the information supports the answer by simply with 'entailment' or 'not_entailment'. "
-Original acc: 89.30%, attacked acc: 45.50%, dropped acc: 43.80%
-
-Original prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"[UNK] unless the relevant context supports the answer to the answer by answer with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 51.20%, dropped acc: 37.60%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"based on the information in the context, mind if the answer to the questioning is circular by option 'entailment' or 'not_entailment'. "
-Original acc: 87.80%, attacked acc: 51.70%, dropped acc: 36.10%
-
-Original prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Attacked prompt: b"within the capacity of a language evaluator, interview if the particular context justifies the answer to the in and clear your r with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 45.60%, dropped acc: 43.20%
-
-Original prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Attacked prompt: b"as a textual analyst, scrutiny if the of was logical) implies the reply to the question and cross your r with 'entailment' or 'not_entailment'. "
-Original acc: 88.70%, attacked acc: 41.30%, dropped acc: 47.40%
-
-Original prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"when your role as a semantic evaluator, be if the reference subject justmatches the answer to the answer and also with 'entailment' or 'not_entailment'. "
-Original acc: 88.50%, attacked acc: 40.60%, dropped acc: 47.90%
-
-## checklist
-
-Original prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment' fwhUZUQzXW. "
-Original acc: 89.30%, attacked acc: 88.50%, dropped acc: 0.80%
-
-Original prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment' OB4KVJzIft. "
-Original acc: 88.80%, attacked acc: 88.00%, dropped acc: 0.80%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment' mOJ9nEwT4f. "
-Original acc: 87.80%, attacked acc: 86.40%, dropped acc: 1.40%
-
-Original prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment' BWmsLx8xOA. "
-Original acc: 88.80%, attacked acc: 88.30%, dropped acc: 0.50%
-
-Original prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment' AMsRIKZniY. "
-Original acc: 88.70%, attacked acc: 88.20%, dropped acc: 0.50%
-
-Original prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment' MSAddJ4D2a. "
-Original acc: 88.50%, attacked acc: 87.70%, dropped acc: 0.80%
-
-## deepwordbug
-
-Original prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Baesd on the prohided conteMt and questino, dRcide if the infzrmation suppoLrts the aOnswer by resXonding with 'entailment' or 'not_entailment'. "
-Original acc: 89.30%, attacked acc: 39.50%, dropped acc: 49.80%
-
-Original prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Evaluaet hether the siven contex suppovrts the answer to the uqestion by rtesponding with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 48.70%, dropped acc: 40.10%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Basde on the infzrmation in the cotext, dexide if the anpwer to the questio is jsutified by choysing 'entailment' or 'not_entailment'. "
-Original acc: 87.80%, attacked acc: 52.80%, dropped acc: 35.00%
-
-Original prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Attacked prompt: b"bn the cUapacity of a langtage valuator, Cxamine if the givcen contjxt jYustifies the anbwer to the qPuestion and idicate your assetssment with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 44.50%, dropped acc: 44.30%
-
-Original prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Attacked prompt: b"sA a textaul analyst, exazine if the kiven conteLxt lHogically imples the ansKwer to the qeustion and Gndicate your ecision with 'entailment' or 'not_entailment'. "
-Original acc: 88.70%, attacked acc: 47.80%, dropped acc: 40.90%
-
-Original prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"hIn your roe as a semantsic evzluator, Hdetermine if the rovided contetx jusKifies the nswer to the uqestion and answer with 'entailment' or 'not_entailment'. "
-Original acc: 88.50%, attacked acc: 55.60%, dropped acc: 32.90%
-
-## stresstest
-
-Original prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 89.30%, attacked acc: 84.20%, dropped acc: 5.10%
-
-Original prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'  and true is true . "
-Original acc: 88.80%, attacked acc: 86.20%, dropped acc: 2.60%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 87.80%, attacked acc: 79.40%, dropped acc: 8.40%
-
-Original prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 88.80%, attacked acc: 68.90%, dropped acc: 19.90%
-
-Original prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 88.70%, attacked acc: 69.20%, dropped acc: 19.50%
-
-Original prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 88.50%, attacked acc: 71.40%, dropped acc: 17.10%
-
-## textbugger
-
-Original prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Baesd on the supplied context and questio\xd5\xb8, decides if the information supporting the respond by responding with 'entailment' or 'not_entailment'. "
-Original acc: 89.30%, attacked acc: 78.80%, dropped acc: 10.50%
-
-Original prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Evluate both the gi\xd1\xb5en context supports the answer to the question by responding with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 62.10%, dropped acc: 26.70%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Founded on the information in the context, \xd4\x81ecide if the answer to the question is ju stified by choosing 'entailment' or 'not_entailment'. "
-Original acc: 87.80%, attacked acc: 76.70%, dropped acc: 11.10%
-
-Original prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of a language evaluator, examining if the gi\xd1\xb5en backgrounds \xcf\xb3ustifies the answer to the matter and indicates your assessment with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 56.00%, dropped acc: 32.80%
-
-Original prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Iike a textual analyst, examines if the gi\xd1\xb5en context lo\xc9\xa1ically presupposes the answer to the question and \xd1\x96ndicate your decision with 'entailment' or 'not_entailment'. "
-Original acc: 88.70%, attacked acc: 60.50%, dropped acc: 28.20%
-
-Original prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your roles as a semantic evaluator, deter mine if the pro\xd1\xb5ided ambient justifies the answer to the question and answer with 'entailment' or 'not_entailment'. "
-Original acc: 88.50%, attacked acc: 71.50%, dropped acc: 17.00%
-
-## textfooler
-
-Original prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Basics on the delivered context and items, decide if the info encourages the replies by answering with 'entailment' or 'not_entailment'. "
-Original acc: 89.30%, attacked acc: 82.70%, dropped acc: 6.60%
-
-Original prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Valuing whether the bestowed framework perpetuates the responded to the tura by retaliated with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 36.00%, dropped acc: 52.80%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Cornerstones on the informing in the context, elects if the meet to the tura is fide by electing 'entailment' or 'not_entailment'. "
-Original acc: 87.80%, attacked acc: 44.30%, dropped acc: 43.50%
-
-Original prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Of the jurisdictional of a phraseology evaluator, exams if the handed regard vindicated the solution to the matter and clarified your examinations with 'entailment' or 'not_entailment'. "
-Original acc: 88.80%, attacked acc: 29.80%, dropped acc: 59.00%
-
-Original prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Iike a textual psychoanalyst, examinations if the gave regard prudently means the responding to the matters and clarified your judgements with 'entailment' or 'not_entailment'. "
-Original acc: 88.70%, attacked acc: 35.00%, dropped acc: 53.70%
-
-Original prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Toward your functions as a semantic evaluator, defined if the stipulated regard justifying the reacts to the trouble and cope with 'entailment' or 'not_entailment'. "
-Original acc: 88.50%, attacked acc: 34.70%, dropped acc: 53.80%
-
-## translation
-
-Language: chinese, acc: 76.10%, prompt: Determine whether the answer is implied or not based on the context. Answer 'entailment' or 'not_entailment'.
-Language: chinese, acc: 87.50%, prompt: As a language expert, determine whether the text contains the answer and choose 'entailment' or 'not_entailment'.
-Language: chinese, acc: 85.20%, prompt: Determine whether the text implies an answer, and select 'entailment' or 'not_entailment'.
-Language: chinese, acc: 82.90%, prompt: Given the text and the question, whether the answer is implied, select 'entailment' or 'not_entailment'.
-Language: chinese, acc: 85.90%, prompt: Determine whether the text contains the answer, depending on the context. Select 'entailment' or 'not_entailment'.
-Language: chinese, acc: 85.10%, prompt: As a language expert, determine whether a text can contain an answer, and say 'entailment' or 'not_entailment'.
-Language: chinese, acc: 78.60%, prompt: Please determine whether the text implies an answer. Answer 'entailment' or 'not_entailment'.
-Language: chinese, acc: 85.10%, prompt: Please select 'entailment' or 'not_entailment' based on the text and the question.
-Language: chinese, acc: 79.70%, prompt: Assess whether the answer is implied based on the context. Answer 'entailment' or 'not_entailment'.
-Language: chinese, acc: 85.30%, prompt: Please determine whether the text contains the answer and answer 'entailment' or 'not_entailment'.
-Language: french, acc: 86.20%, prompt: As a linguistic expert, assess whether the given context involves the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: french, acc: 86.20%, prompt: Determine whether the information provided in the context necessarily leads to the answer to the question asked and indicate 'entailment' or 'not_entailment'.
-Language: french, acc: 86.10%, prompt: Analyze the text to determine if the answer to the question is implied in the context and specify 'entailment' or 'not_entailment'.
-Language: french, acc: 86.10%, prompt: Based on the given context, decide whether the answer to the question is necessarily involved and mark 'entailment' or 'not_entailment'.
-Language: french, acc: 83.60%, prompt: Evaluate whether the answer to the question can be deduced from the given context and mark 'entailment' or 'not_entailment'.
-Language: french, acc: 85.50%, prompt: Discern whether the context provided directly involves the answer to the question and indicate 'entailment' or 'not_entailment'.
-Language: french, acc: 85.20%, prompt: Determine if the context contains enough information to involve the answer to the question and mark 'entailment' or 'not_entailment'.
-Language: french, acc: 82.50%, prompt: Assess whether the context provided necessarily leads to the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: french, acc: 85.20%, prompt: Analyze the text to determine if the answer to the question is involved in the context and indicate 'entailment' or 'not_entailment'.
-Language: french, acc: 86.70%, prompt: Based on the given context, decide whether the answer to the question is necessarily inferred and mark 'entailment' or 'not_entailment'.
-Language: arabic, acc: 85.90%, prompt: As a language expert, evaluate whether the given context calls for an answer and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 74.10%, prompt: Judge the relationship between the text and the question and answer 'entailment' or 'not_entailment', depending on your language experience.
-Language: arabic, acc: 79.50%, prompt: Does the context given indicate the answer to the question? Evaluate and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 83.70%, prompt: Based on your linguistic knowledge, does the text relate to the question? Answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 84.80%, prompt: As a language expert, determine how the text relates to the question and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 71.00%, prompt: Does the text support the answer to the question? Answer 'entailment' or 'not_entailment', depending on your language experience.
-Language: arabic, acc: 56.50%, prompt: Check the text link to the question and answer 'entailment' or 'not_entailment', depending on your language skills.
-Language: arabic, acc: 62.10%, prompt: As a language expert, is there a link between the text and the question? Answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 76.80%, prompt: Based on your language experience, does context help to answer the question? Evaluate and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 70.40%, prompt: Does the text give a clear answer to the question? Answer 'entailment' or 'not_entailment', depending on your language experience.
-Language: spanish, acc: 85.60%, prompt: As a language expert, evaluate whether the given context implies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: spanish, acc: 84.80%, prompt: Determine whether the information given in the text necessarily implies the veracity of the hypothesis and answer 'entailment' or 'not_entailment'.
-Language: spanish, acc: 80.80%, prompt: Analyzes whether the information presented in the paragraph leads to the conclusion of the question and labels the answer as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 86.30%, prompt: Indicates whether the information provided in the text is sufficient to conclude the statement and labels the response as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 86.50%, prompt: As an expert on the subject, judge whether the information provided in the text justifies the claim and classify the answer as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 73.30%, prompt: Evaluates whether the information in the paragraph necessarily supports the conclusion of the hypothesis and responds 'entailment' or 'not_entailment'.
-Language: spanish, acc: 86.20%, prompt: Determines whether the information presented in the text logically implies the answer to the question and labels the answer as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 83.60%, prompt: Analyzes whether the information provided in the paragraph necessarily leads to the veracity of the hypothesis and classifies the response as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 85.90%, prompt: As an expert on the subject, evaluate whether the information presented in the text supports the claim and respond 'entailment' or 'not_entailment'.
-Language: spanish, acc: 85.40%, prompt: Indicates whether the information provided in the paragraph necessarily implies the answer to the question and labels the answer as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 84.30%, prompt: Rate whether the answer to the question is derived from the given context and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 59.60%, prompt: Please answer 'entailment' or 'not_entailment' for the given context and question.
-Language: japanese, acc: 81.30%, prompt: Decide whether the answer to the question is derived from the given context and answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 82.20%, prompt: Compare the question with the given context and give the answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 87.70%, prompt: Determinate whether the given context contains the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 65.50%, prompt: Estimate the answer of the question from the context and give the answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 89.10%, prompt: Determinate whether the given context is relevant to the question and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 89.50%, prompt: Determine whether the given context is relevant to the question and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 88.10%, prompt: Determinate whether the given context contains the answer to the question and answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 54.80%, prompt: Answer with 'entailment' or 'not_entailment', inferring from the given context.
-Language: korean, acc: 87.30%, prompt: Determine if a given sentence necessarily implies the meaning of another sentence and answer 'entailment' or 'not_entailment'.
-Language: korean, acc: 88.90%, prompt: By understanding the relations between sentences, judge whether a given sentence necessarily refers to another sentence and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 86.20%, prompt: Evaluate whether a given text necessarily indicates the meaning of another text and respond with 'entailment' or 'not_entailment'.
-Language: korean, acc: 87.80%, prompt: Understand the relations of a sentence, to determine whether a given sentence necessarily includes other sentences and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 81.10%, prompt: Judge whether a given content necessarily implies the meaning of another content and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 87.60%, prompt: Grasp the relations between sentences, determine if a given sentence necessarily contains the meaning of another sentence and respond with 'entailment' or 'not_entailment'.
-Language: korean, acc: 85.60%, prompt: Evaluate whether a given text necessarily refers to another text and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 85.80%, prompt: By comparing the meaning of the sentences, to determine if a given sentence necessarily implies another sentence and answer 'entailment' or 'not_entailment'.
-Language: korean, acc: 84.80%, prompt: Evaluate whether the contents given necessarily refer to other contents and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 86.30%, prompt: By analyzing the relations between sentences, determine if a given sentence does not necessarily include other sentences and answer with 'entailment' or 'not_entailment'.
-
-# qqp
-
-## 10 prompts
-
-Acc: 88.10%, prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Acc: 87.60%, prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Acc: 87.40%, prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Acc: 87.40%, prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Acc: 87.40%, prompt: Evaluate whether these two phrases have identical meanings and respond with 'equivalent' or 'not_equivalent'.
-Acc: 87.10%, prompt: Please indicate if the following pair of sentences share the same meaning by responding with 'equivalent' or 'not_equivalent'.
-Acc: 87.00%, prompt: Do these two sentences convey the same meaning? Indicate with 'equivalent' or 'not_equivalent'.
-Acc: 86.80%, prompt: Determine if the given pair of statements can be considered the same by responding with 'equivalent' or 'not_equivalent'.
-Acc: 86.60%, prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Acc: 86.60%, prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Acc: 82.50%, prompt: Analyze if the given set of sentences have the same connotation by answering with 'equivalent' or 'not_equivalent'.
-
-Acc: 87.30%, prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Acc: 86.50%, prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Acc: 86.40%, prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Acc: 86.20%, prompt: Acting as a question equivalence instrument, determine if the provided questions are equivalent in meaning, answering with 'equivalent' for similar questions or 'not_equivalent' for dissimilar ones.
-Acc: 86.20%, prompt: While performing question comparison analysis, classify the similarity of the following questions as 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Acc: 86.10%, prompt: As a tool for determining question equivalence, review the questions and categorize their similarity as either 'equivalent' or 'not_equivalent'.
-Acc: 85.90%, prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Acc: 85.60%, prompt: Functioning as a question similarity evaluation tool, analyze the given questions and decide if they share the same meaning, responding with 'equivalent' or 'not_equivalent'.
-Acc: 85.10%, prompt: Working as a question classifier, identify whether the given questions share the same meaning, and respond with 'equivalent' for equivalent questions or 'not_equivalent' for different ones.
-Acc: 84.90%, prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-
-## bertattack
-
-Original prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Attacked prompt: b'Are the so a equations equivalent or not? Answer me with "equivalent" or "not_equivalent". '
-Original acc: 88.10%, attacked acc: 82.50%, dropped acc: 5.60%
-
-Original prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Are the meanings of these parent clauses the same? reply with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.60%, attacked acc: 86.10%, dropped acc: 1.50%
-
-Original prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can these latter words be defined compare in meaning? Answer with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.40%, attacked acc: 86.30%, dropped acc: 1.10%
-
-Original prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Attacked prompt: b"provided a questions equivalence detect system, find the received words and que with 'equivalent' if they are the same in sentences, or 'not_equivalent' if they are met. "
-Original acc: 87.30%, attacked acc: 71.30%, dropped acc: 16.00%
-
-Original prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Attacked prompt: b"to an instrument for question ranking evaluation, consider the words and pick if their mood is the same, returning with 'equivalent' for analogous question or 'not_equivalent' for known questions. "
-Original acc: 86.50%, attacked acc: 63.90%, dropped acc: 22.60%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"in the task of a questions language analyzed, choose the meaning of the present questioning and pick if they are equivalent, competing with 'equivalent' for equivalent question or 'not_equivalent' for whole questions. "
-Original acc: 86.40%, attacked acc: 65.50%, dropped acc: 20.90%
-
-## checklist
-
-Original prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Attacked prompt: b'Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent CuVpbbkC4c". '
-Original acc: 88.10%, attacked acc: 87.50%, dropped acc: 0.60%
-
-Original prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent' Pbl3tYuoRo. "
-Original acc: 87.60%, attacked acc: 87.40%, dropped acc: 0.20%
-
-Original prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent' KjPJJ2a7RB. "
-Original acc: 87.40%, attacked acc: 87.60%, dropped acc: -0.20%
-
-Original prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Attacked prompt: b"As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different BWmsLx8xOA. "
-Original acc: 87.30%, attacked acc: 87.10%, dropped acc: 0.20%
-
-Original prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Attacked prompt: b"As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions 6a4Yn3RGVc. "
-Original acc: 86.50%, attacked acc: 86.00%, dropped acc: 0.50%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions EAB4KP2NVY. "
-Original acc: 86.40%, attacked acc: 86.50%, dropped acc: -0.10%
-
-## deepwordbug
-
-Original prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Attacked prompt: b're the flolowing two questFons equivalent or not? Answe me with "equivalent" or "not_equivalent". '
-Original acc: 88.10%, attacked acc: 77.30%, dropped acc: 10.80%
-
-Original prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Are the meanins of these gwo phrases the same? Reply with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.60%, attacked acc: 86.80%, dropped acc: 0.80%
-
-Original prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can these tcwo statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.40%, attacked acc: 86.70%, dropped acc: 0.70%
-
-Original prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Attacked prompt: b"Ts a question eqivalence deSection syqtem, examine the provided questions and erspond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are dLfferent. "
-Original acc: 87.30%, attacked acc: 85.50%, dropped acc: 1.80%
-
-Original prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Attacked prompt: b"As an instrument for question cotmparison evaluVtion, consider the questionPs and determbine if their maening is the same, responRing with 'equivalent' for similar questins or 'not_equivalent' for difserent questions. "
-Original acc: 86.50%, attacked acc: 81.80%, dropped acc: 4.70%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"In the role of a question similarQity analyzer, examine the meaning of the proivded questiowns and dSecide if they are equivalent, aHswering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions. "
-Original acc: 86.40%, attacked acc: 84.90%, dropped acc: 1.50%
-
-## stresstest
-
-Original prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Attacked prompt: b'Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent  and false is not true ". '
-Original acc: 88.10%, attacked acc: 23.40%, dropped acc: 64.70%
-
-Original prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'  and false is not true . "
-Original acc: 87.60%, attacked acc: 86.00%, dropped acc: 1.60%
-
-Original prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 87.40%, attacked acc: 85.00%, dropped acc: 2.40%
-
-Original prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Attacked prompt: b"As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different  and false is not true . "
-Original acc: 87.30%, attacked acc: 87.20%, dropped acc: 0.10%
-
-Original prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Attacked prompt: b"As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions  and true is true . "
-Original acc: 86.50%, attacked acc: 86.40%, dropped acc: 0.10%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 86.40%, attacked acc: 87.20%, dropped acc: -0.80%
-
-## textbugger
-
-Original prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Attacked prompt: b'Are the following t\xd4\x9do subjects equivalent or not? Anser me with "equivalent" or "not_equivalent". '
-Original acc: 88.10%, attacked acc: 80.10%, dropped acc: 8.00%
-
-Original prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Are the menings of these t wo phrases the same? Reply with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.60%, attacked acc: 86.70%, dropped acc: 0.90%
-
-Original prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can these two statements be considered wager in meaning? Answ er with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.40%, attacked acc: 86.60%, dropped acc: 0.80%
-
-Original prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Attacked prompt: b"Since a question equivalence \xd4\x81etection s\xd1\x83stem, examine the yielded questions and cater with 'equivalent' if they are the same in sense, or 'not_equivalent' if they are assorted. "
-Original acc: 87.30%, attacked acc: 84.30%, dropped acc: 3.00%
-
-Original prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Attacked prompt: b"Since an instrument for question compares evaluation, consider the problems and identify if their connotation is the same, respondin\xc9\xa1 with 'equivalent' for similar question or 'not_equivalent' for assorted issues. "
-Original acc: 86.50%, attacked acc: 75.70%, dropped acc: 10.80%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"During the role of a question similarity offing, \xd0\xb5xamine the meaning of the provid\xd0\xb5d questions and deci de if they are equivalent, an\xd1\x95wering with 'equivalent' for equivalent questions or 'not_equivalent' for assorted questions. "
-Original acc: 86.40%, attacked acc: 83.90%, dropped acc: 2.50%
-
-## textfooler
-
-Attacked prompt: b'Become the farther two subjects equivalent or not? Retaliated me with "equivalent" or "not_equivalent". '
-Original acc: 88.10%, attacked acc: 70.90%, dropped acc: 17.20%
-
-Original prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Es the sens of these two formula the same? Reactions with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.60%, attacked acc: 78.40%, dropped acc: 9.20%
-
-Original prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Can these two statements be assessed wagered in meaning? Answer with 'equivalent' or 'not_equivalent'. "
-Original acc: 87.40%, attacked acc: 86.50%, dropped acc: 0.90%
-
-Original prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Attacked prompt: b"As a question equivalence locate scheme, analyze the provided theme and cater with 'equivalent' if they are the same in sens, or 'not_equivalent' if they are sundry. "
-Original acc: 87.30%, attacked acc: 81.30%, dropped acc: 6.00%
-
-Original prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Attacked prompt: b"As an tool for topic relation valuing, look the theme and identify if their connotation is the same, retaliated with 'equivalent' for similar question or 'not_equivalent' for other trouble. "
-Original acc: 86.50%, attacked acc: 63.40%, dropped acc: 23.10%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"For the features of a problema likeness analyzer, consider the senses of the lent topic and pick if they are equivalent, retaliated with 'equivalent' for equivalent question or 'not_equivalent' for numerous trouble. "
-Original acc: 86.40%, attacked acc: 64.50%, dropped acc: 21.90%
-
-## translation
-
-Language: chinese, acc: 85.20%, prompt: As a tool for determining the equivalence of problems, the problems are examined and their similarity is classified as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 86.00%, prompt: Please compare the similarity of two sentences and put them into the category of 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 87.60%, prompt: Two sentences are classified as 'equivalent' or 'not_equivalent' for their similarity.
-Language: chinese, acc: 87.10%, prompt: You can determine how similar the questions are by comparing them and categorizing them as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 87.70%, prompt: Using the method of contrast, the similarity of these problems is divided into two categories: 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 87.50%, prompt: By comparing these issues, you can classify them as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 86.10%, prompt: To determine whether the questions are similar, put them into the category of 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 86.20%, prompt: Divide the similarity of these questions into 'equivalent' or 'not_equivalent' categories.
-Language: chinese, acc: 86.90%, prompt: Using the similarity assessment tool, these questions were classified as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 87.60%, prompt: By analyzing the similarity of these problems, they are divided into categories of 'equivalent' or 'not_equivalent'.
-Language: french, acc: 86.50%, prompt: As a tool to determine the equivalence of questions, review the questions and rank their similarity as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 86.50%, prompt: Please compare the similarity of two sentences and classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 85.90%, prompt: Based on the similarity of two sentences, classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 87.50%, prompt: You can determine the similarity between these questions by comparing them and classifying them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 87.40%, prompt: Use a comparative method to divide the similarity of these questions into two categories: 'equivalent' or 'not_equivalent'.
-Language: french, acc: 87.40%, prompt: By comparing these questions, you can classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 87.60%, prompt: Determine whether these questions are similar or not, and then classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 87.30%, prompt: Divide the similarity of these questions into two categories: 'equivalent' or 'not_equivalent'.
-Language: french, acc: 86.40%, prompt: Use a similarity assessment tool to classify these questions as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 87.50%, prompt: By analyzing the similarity of these questions, you can divide them into two categories: 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 85.00%, prompt: As a tool for determining an equation of questions, review the questions and classify their similarity as either 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 86.40%, prompt: When using questions in the classification domain, please classify the similarity between the questions as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 84.60%, prompt: To determine an equation of questions, you must review the questions and classify their similarity as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 86.60%, prompt: Questions can be classified as 'equivalent' or 'not_equivalent' when used to identify classifications.
-Language: arabic, acc: 85.30%, prompt: Classification of question similarity as 'equivalent' or 'not_equivalent' is used as a tool to determine the classification of questions.
-Language: arabic, acc: 85.90%, prompt: Classify the similarity of the questions as 'equivalent' or 'not_equivalent' to determine the equation of the questions.
-Language: arabic, acc: 86.00%, prompt: Identifying the similarity of questions and classifying them as 'equivalent' or 'not_equivalent' is an important tool in determining the classification of questions.
-Language: arabic, acc: 86.00%, prompt: When classifying questions, their similarity can be classified as 'equivalent' or 'not_equivalent' to determine the correct classification.
-Language: arabic, acc: 85.00%, prompt: The similarity of questions should be classified as 'equivalent' or 'not_equivalent' when used to determine the equation of questions.
-Language: arabic, acc: 86.00%, prompt: Identifying the similarity of questions and classifying them as 'equivalent' or 'not_equivalent' helps to correctly classify questions.
-Language: spanish, acc: 86.00%, prompt: As a tool to determine the equivalence of questions, it reviews the questions and classifies their similarity as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 86.40%, prompt: Evaluate the similarity between questions and classify them as 'equivalent' or 'not_equivalent' to determine their equivalence.
-Language: spanish, acc: 86.90%, prompt: Determine whether two questions are 'equivalent' or 'not_equivalent' based on similarity and characteristics.
-Language: spanish, acc: 86.70%, prompt: Classifies the similarity between questions as 'equivalent' or 'not_equivalent' to determine their equivalence.
-Language: spanish, acc: 86.80%, prompt: Review the questions and rate them as 'equivalent' or 'not_equivalent' based on their similarity and content.
-Language: spanish, acc: 86.80%, prompt: As part of the classification task of questions, it determines their equivalence by categorizing their similarity as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 86.30%, prompt: Analyze the similarity between questions and classify them as 'equivalent' or 'not_equivalent' to determine their equivalence.
-Language: spanish, acc: 86.40%, prompt: As a method of identifying the equivalence of questions, it categorizes their similarity as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 86.50%, prompt: To determine the equivalence between questions, check their similarity and classify them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 85.90%, prompt: Classify the similarity between questions as 'equivalent' or 'not_equivalent' to determine whether they are equivalent or not.
-Language: japanese, acc: 83.40%, prompt: As a tool to determine the equivalence of the question, review the question and categorize its similarities into 'equivalent' or 'not_equivalent' categories.
-Language: japanese, acc: 87.20%, prompt: Work on text sorting tasks labeled 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 85.50%, prompt: For text classification tasks, use the labels 'equivalent' or 'not_equivalent' to determine the equivalence of statements.
-Language: japanese, acc: 86.80%, prompt: In the MRPC dataset, use the labels 'equivalent' or 'not_equivalent' to classify the equivalence of statements.
-Language: japanese, acc: 77.90%, prompt: As a tool for determining equivalence, check sentences and categorize them into 'equivalent' or 'not_equivalent' categories.
-Language: japanese, acc: 85.70%, prompt: Use the labels 'equivalent' or 'not_equivalent' to determine the equivalence of statements in text classification tasks.
-Language: japanese, acc: 83.90%, prompt: In the text classification task of the MRPC data set, classify the equivalence of statements with labels of 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 84.80%, prompt: As a tool to determine the equivalence of statements, categorize statements into 'equivalent' or 'not_equivalent' categories.
-Language: japanese, acc: 83.70%, prompt: In a text classification task, classify the equivalence of statements using labels of 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 85.90%, prompt: Do a text classification task to determine the equivalence of statements, labeled 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 80.90%, prompt: Classify two given sentences as 'equivalent' or 'not_equivalent' by discriminating whether they have the same meaning.
-Language: korean, acc: 87.10%, prompt: Determine sentence equivalence by judging the similarity of two sentences with 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 86.20%, prompt: Classify the similarity of sentences as 'equivalent' or 'not_equivalent' by judging whether two sentences have the same meaning.
-Language: korean, acc: 86.20%, prompt: Determine if two given sentences are equivalent to each other, and classify their similarity as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 86.20%, prompt: Compare two given sentences to determine sentence equivalence, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 85.10%, prompt: Classify sentence equivalence as 'equivalent' or 'not_equivalent' by judging whether two sentences have the same meaning to each other.
-Language: korean, acc: 86.40%, prompt: Determine if two sentences have the same meaning, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 87.00%, prompt: Compare two given sentences to determine their equivalence, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 86.00%, prompt: Review two sentences to evaluate sentence equivalence, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 84.60%, prompt: Judge whether two sentences have the same meaning to each other, and determine the sentence equivalence with 'equivalent' or 'not_equivalent'.
-
-# rte
-
-## 10 prompts
-
-Acc: 85.20%, prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Acc: 84.84%, prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 84.84%, prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Acc: 84.48%, prompt: Indicate if the connection between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 84.12%, prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 83.39%, prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Acc: 83.39%, prompt: Examine the pair of sentences and determine if they exhibit entailment or not_entailment. Answer with either 'entailment' or 'not_entailment'.
-Acc: 83.39%, prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 83.39%, prompt: Determine if the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-Acc: 83.39%, prompt: Considering the two sentences, identify if their relationship is 'entailment' or 'not_entailment'.
-Acc: 83.03%, prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-
-
-Acc: 84.12%, prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'. 
-Acc: 84.12%, prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'. 
-Acc: 83.75%, prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'. 
-Acc: 83.39%, prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'. 
-Acc: 83.39%, prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'. 
-Acc: 83.39%, prompt: As a tool for determining entailment relationships, review the two statements and categorize their connection as either 'entailment' or 'not_entailment'. 
-Acc: 83.39%, prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'. 
-Acc: 83.03%, prompt: While performing entailment analysis, classify the relationship between the provided sentences as 'entailment' or 'not_entailment'. 
-Acc: 82.67%, prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'. 
-Acc: 81.23%, prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'. 
-
-## bertattack
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'can the is compare sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word. '
-Original acc: 85.20%, attacked acc: 80.87%, dropped acc: 4.33%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Identify and the given reference of sentences demonstrates entailment or not_entailment. deal with 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 81.23%, dropped acc: 3.61%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the connection clauses and categorize their relationship as an 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 81.59%, dropped acc: 3.25%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Acting as an entailment detection instrument, be if the given language of sentences does entailment or not_entailment. start with 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 78.70%, dropped acc: 5.42%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of an entailment assessment system, address if the link between the post sentences is 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 81.59%, dropped acc: 2.53%
-
-Original prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an examination for entailment evaluation, develop the into sentences and be if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'. "
-Original acc: 83.75%, attacked acc: 81.23%, dropped acc: 2.53%
-
-## checklist
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word Cev0s49fbe. '
-Original acc: 85.20%, attacked acc: 84.84%, dropped acc: 0.36%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment' 6a4Yn3RGVc. "
-Original acc: 84.84%, attacked acc: 83.39%, dropped acc: 1.44%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment' g0vBZf3tQC. "
-Original acc: 84.84%, attacked acc: 84.12%, dropped acc: 0.72%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment' Cev0s49fbe. "
-Original acc: 84.12%, attacked acc: 83.03%, dropped acc: 1.08%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment' MQnugHcaoy. "
-Original acc: 84.12%, attacked acc: 82.31%, dropped acc: 1.81%
-
-Original prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment' EAB4KP2NVY. "
-Original acc: 83.75%, attacked acc: 83.39%, dropped acc: 0.36%
-
-## deepwordbug
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Aer the followig mwo sentences entailment or not_entailment? nswer me with "entailment" or "not_entailment", just noe word. '
-Original acc: 85.20%, attacked acc: 80.87%, dropped acc: 4.33%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Identify Ghether the giveRn pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 81.95%, dropped acc: 2.89%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Reiew the two stwtements and categorize their relatioFship as eitUher 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 82.31%, dropped acc: 2.53%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Acting as an entailment detection instrument, dGetermine if the given pair of sentences demonstpates entailment or not_entailment. AVswer with 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 81.59%, dropped acc: 2.53%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of an entailment assessment system, indicatS if the link between the bfollowing sentences is 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 82.31%, dropped acc: 1.81%
-
-Original prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an instrument for entailment evaluation, cqonsider the to sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'. "
-Original acc: 83.75%, attacked acc: 83.03%, dropped acc: 0.72%
-
-## stresstest
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word  and true is true . '
-Original acc: 85.20%, attacked acc: 85.92%, dropped acc: -0.72%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 84.84%, attacked acc: 43.68%, dropped acc: 41.16%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 84.84%, attacked acc: 46.21%, dropped acc: 38.63%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 84.12%, attacked acc: 37.18%, dropped acc: 46.93%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 84.12%, attacked acc: 36.82%, dropped acc: 47.29%
-
-Original prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 83.75%, attacked acc: 83.39%, dropped acc: 0.36%
-
-## textbugger
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Be the f\xd0\xbellowing t wo sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word. '
-Original acc: 85.20%, attacked acc: 81.59%, dropped acc: 3.61%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Identify lf the given pair of sentences demonstrates entailment or not_entailment. Ansewr with 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 81.59%, dropped acc: 3.25%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Reviews the two statem ents and categorize their relationship as either 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 83.39%, dropped acc: 1.44%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Acting as an entailment detection instrument, detemine if the g iven pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 82.31%, dropped acc: 1.81%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"For the capabilities of an entailment appraisals system, indicate if the link between the fo llowing sentences is 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 81.59%, dropped acc: 2.53%
-
-Original prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an in\xd1\x95trument for entailment evaluatio\xd5\xb8, consider the two sentences and det\xd0\xb5rmine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'. "
-Original acc: 83.75%, attacked acc: 81.95%, dropped acc: 1.81%
-
-## textfooler
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Pose the ensuing two sentences entailment or not_entailment? Reaction me with "entailment" or "not_entailment", just one phrase. '
-Original acc: 85.20%, attacked acc: 81.23%, dropped acc: 3.97%
-
-Original prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Defined whether the made peers of sentences showings entailment or not_entailment. Reaction with 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 80.51%, dropped acc: 4.33%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the two announcement and categories their linking as either 'entailment' or 'not_entailment'. "
-Original acc: 84.84%, attacked acc: 82.31%, dropped acc: 2.53%
-
-Original prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Acting as an entailment detects instrument, defined if the given torque of sentences showcases entailment or not_entailment. Replies with 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 79.78%, dropped acc: 4.33%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Around the capacity of an entailment assessment system, indicate if the relations between the later sentences is 'entailment' or 'not_entailment'. "
-Original acc: 84.12%, attacked acc: 81.95%, dropped acc: 2.17%
-
-Original prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an instrument for entailment assess, consider the two sentences and deciding if their relations is 'entailment' or 'not_entailment'. Reaction with 'entailment' or 'not_entailment'. "
-Original acc: 83.75%, attacked acc: 81.59%, dropped acc: 2.17%
-
-## translation
-
-Language: chinese, acc: 83.03%, prompt: In the light of an implication analysis tool, evaluate the relationship between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 83.39%, prompt: From the perspective of an implication analysis tool, determine whether there is an implication relationship in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 83.75%, prompt: Please use an implication analysis tool to determine whether an implication relationship exists in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 83.39%, prompt: Please evaluate the relation of the following sentences as 'entailment' or 'not_entailment' for the purpose of determining implication relation.
-Language: chinese, acc: 83.75%, prompt: Please use the implication analysis tool to evaluate the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 81.59%, prompt: For the purpose of determining implicative relations, analyze the relations of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 84.12%, prompt: Please use the implication analysis tool to determine the relationship of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 84.84%, prompt: Please use the implication judgment tool to assess the relevance of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 82.67%, prompt: Please, with implication analysis as the main task, determine the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 82.67%, prompt: Using the implication judgment as a criterion, analyze the relation of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 83.39%, prompt: As an engagement analysis tool, evaluate the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.48%, prompt: Determine whether the given sentences involve one another or not as an implication analysis tool. Classify them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.84%, prompt: Using implication analysis, evaluate whether the sentences provided have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.12%, prompt: As an engagement assessment tool, determine whether the sentences provided have a logical relationship and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.48%, prompt: As an implication classification tool, analyze the sentences provided to determine if there is a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.84%, prompt: Using implication analysis, determine whether the given sentences have a cause-effect relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 83.39%, prompt: Evaluate the relationship between the given sentences using implication analysis and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.48%, prompt: As an engagement detection tool, determine whether the given sentences have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.12%, prompt: Using implication analysis, evaluate whether the sentences provided have a cause-effect relationship and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 84.48%, prompt: Determine whether the given sentences have a cause-effect relationship as an engagement analysis tool and categorize them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 82.31%, prompt: In your role as a tool for reasoning analysis, evaluate the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 85.56%, prompt: Can you determine whether this sentence is inferred from the other sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 82.67%, prompt: Using the tool of reasoning analysis, analyze the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 86.64%, prompt: Does this sentence represent a conclusion from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 83.03%, prompt: As a tool of reasoning analysis, evaluate the relationship of given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 86.64%, prompt: Can this sentence be inferred from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 82.67%, prompt: Using a tool to analyze a conclusion, analyze the relationship between the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 87.00%, prompt: Is this a conclusion from the next sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 82.31%, prompt: As part of your task in analyzing a conclusion, evaluate the relationship between the two sentences and classify them as 'entailment' or 'not_entailment' based on their relationship.
-Language: arabic, acc: 84.12%, prompt: Are you following this sentence directly from the previous one? Classify it as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 81.23%, prompt: In your role as an implication analysis tool, evaluate the relationship between the given phrases and classify them as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 85.92%, prompt: Determine whether the second sentence necessarily implies the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 85.20%, prompt: Classifies the relationship between these two sentences as 'entailment' if one necessarily implies the other, or as 'not_entailment' if not.
-Language: spanish, acc: 85.92%, prompt: Evaluates whether the information in the second sentence is implied in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 83.39%, prompt: Given a couple of phrases, label their relationship as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 84.84%, prompt: Analyzes the relationship between the phrases and classifies them as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 85.20%, prompt: Given two sentences, determine whether the second sentence is a necessary consequence of the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 85.92%, prompt: Evaluates whether the information presented in the second sentence is implicit in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 83.75%, prompt: Classifies the relationship between the given phrases as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 85.56%, prompt: Determines whether the information provided in the second sentence is necessarily inferable from the first and labels the relationship as 'entailment', or as 'not_entailment' if not.
-Language: japanese, acc: 81.95%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 83.03%, prompt: Evaluate the semantic relationship of the sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 84.12%, prompt: Please judge the relationship between the given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 80.51%, prompt: Examine the paraphrases of a given sentence and classify them 'entailment' or 'not_entailment'.
-Language: japanese, acc: 79.78%, prompt: Rate the similarity of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 79.78%, prompt: Determinate the semantic connections of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 80.51%, prompt: Examine the semantic match of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 84.12%, prompt: Classify it as 'entailment' or 'not_entailment' based on the content of the sentence.
-Language: japanese, acc: 81.95%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 80.51%, prompt: Judge the semantic connections of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: korean, acc: 83.75%, prompt: Evaluate the relationship between any two sentences given to you and classify you as 'entailment' or 'not_entailment'.
-Language: korean, acc: 81.23%, prompt: Analyze the semantic deductive relations between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 83.39%, prompt: Evaluate the logical relevance between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 83.03%, prompt: Evaluate the interaction of two given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 84.84%, prompt: Please check whether there is a semantic match between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 84.84%, prompt: Compare information between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 84.12%, prompt: Please analyse the correlation between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 83.75%, prompt: Evaluate the different meanings between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 83.03%, prompt: Compare the semantic structure of the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 81.95%, prompt: Evaluate the interactions between sentences and classify them as 'entailment' or 'not_entailment'.
-
-# sst2
-
-## 10 prompts
-
-Acc: 95.30%, prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Acc: 95.18%, prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Acc: 94.95%, prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Acc: 94.95%, prompt: Please identify the emotional tone of this passage: 'positive' or 'negative'?
-Acc: 94.72%, prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Acc: 94.72%, prompt: Read the provided excerpt and choose between 'positive' and 'negative' to describe its sentiment.
-Acc: 94.61%, prompt: Assess the mood of the following quote and determine if it's 'positive' or 'negative'.
-Acc: 94.50%, prompt: Given the context of this text, indicate if the emotion conveyed is 'positive' or 'negative'.
-Acc: 94.38%, prompt: Determine the overall sentiment of this sentence, categorizing it as 'positive' or 'negative'.
-Acc: 93.69%, prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-
-Acc: 95.53%, prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Acc: 95.53%, prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Acc: 95.41%, prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Acc: 95.18%, prompt: As an emotion detector, determine if the provided passage conveys a 'positive' or 'negative' sentiment.
-Acc: 95.18%, prompt: Working as a sentiment analyzer, please indicate if the following text is 'positive' or 'negative'.
-Acc: 95.07%, prompt: Functioning as a sentiment identification tool, assess if the following expression is 'positive' or 'negative'.
-Acc: 94.95%, prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Acc: 94.84%, prompt: In the capacity of a sentiment classifier, decide whether the given quote is 'positive' or 'negative'.
-Acc: 94.72%, prompt: As a sentiment classifier, determine whether the following text is 'positive' or 'negative'.
-Acc: 94.27%, prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-
-## bertattack
-
-Original prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Attacked prompt: b"given the verbal conjunction, who you say it implies a 'positive' or 'negative' connotation? "
-Original acc: 95.30%, attacked acc: 91.51%, dropped acc: 3.78%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this comment and decide whether it has a 'positive' or 'negative' intent. "
-Original acc: 95.18%, attacked acc: 94.27%, dropped acc: 0.92%
-
-Original prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Attacked prompt: b"Analyze the pitch of this statement and respond with effect 'positive' or 'negative'. "
-Original acc: 94.95%, attacked acc: 94.27%, dropped acc: 0.69%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the task of a compliment analysis in, identify with 'positive' or 'negative' to classify this statement. "
-Original acc: 95.53%, attacked acc: 93.12%, dropped acc: 2.41%
-
-Original prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Attacked prompt: b"an as a sentiment evaluation model, be if the said positive is 'positive' or 'negative'. "
-Original acc: 95.53%, attacked acc: 94.04%, dropped acc: 1.49%
-
-Original prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Attacked prompt: b"Acting as a positive evaluator, choose if the positive sentence is 'positive' or 'negative'. "
-Original acc: 95.41%, attacked acc: 92.43%, dropped acc: 2.98%
-
-## checklist
-
-Original prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Attacked prompt: b"Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation OB4KVJzIft? "
-Original acc: 95.30%, attacked acc: 95.07%, dropped acc: 0.23%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this statement and decide whether it has a 'positive' or 'negative' sentiment ofOQXLxiW4. "
-Original acc: 95.18%, attacked acc: 94.84%, dropped acc: 0.34%
-
-Original prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Attacked prompt: b"Analyze the tone of this statement and respond with either 'positive' or 'negative' XStsD2pUzu. "
-Original acc: 94.95%, attacked acc: 94.50%, dropped acc: 0.46%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement OB4KVJzIft. "
-Original acc: 95.53%, attacked acc: 95.18%, dropped acc: 0.34%
-
-Original prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Attacked prompt: b"Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative' MQnugHcaoy. "
-Original acc: 95.53%, attacked acc: 95.30%, dropped acc: 0.23%
-
-Original prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Attacked prompt: b"Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative' SFPCqpiClT. "
-Original acc: 95.41%, attacked acc: 94.84%, dropped acc: 0.57%
-
-## deepwordbug
-
-Original prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Attacked prompt: b"Considering the given phraes, wouHd you Fay it carries a 'positive' or 'negative' connotavtion? "
-Original acc: 95.30%, attacked acc: 93.92%, dropped acc: 1.38%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this staement and decide wheter it has a 'positive' or 'negative' sentiment. "
-Original acc: 95.18%, attacked acc: 94.72%, dropped acc: 0.46%
-
-Original prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Attacked prompt: b"Analyze the tone of this statement and respond with eQther 'positive' or 'negative'. "
-Original acc: 94.95%, attacked acc: 94.50%, dropped acc: 0.46%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a sentiment analysis tool, resond with 'positive' or 'negative' to classif this statement. "
-Original acc: 95.53%, attacked acc: 95.07%, dropped acc: 0.46%
-
-Original prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Attacked prompt: b"Sxrving as a sentiment evaluaiton model, determiIe if the given statement is 'positive' or 'negative'. "
-Original acc: 95.53%, attacked acc: 94.95%, dropped acc: 0.57%
-
-Original prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Attacked prompt: b"Acting as a seniment valuator, dientify if the giveIn sentence is 'positive' or 'negative'. "
-Original acc: 95.41%, attacked acc: 94.61%, dropped acc: 0.80%
-
-## stresstest
-
-Original prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Attacked prompt: b"Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation  and false is not true ? "
-Original acc: 95.30%, attacked acc: 94.61%, dropped acc: 0.69%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this statement and decide whether it has a 'positive' or 'negative' sentiment  and false is not true . "
-Original acc: 95.18%, attacked acc: 95.18%, dropped acc: 0.00%
-
-Original prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Attacked prompt: b"Analyze the tone of this statement and respond with either 'positive' or 'negative'  and false is not true . "
-Original acc: 94.95%, attacked acc: 94.61%, dropped acc: 0.34%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement  and true is true . "
-Original acc: 95.53%, attacked acc: 95.30%, dropped acc: 0.23%
-
-Original prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Attacked prompt: b"Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'  and false is not true . "
-Original acc: 95.53%, attacked acc: 95.41%, dropped acc: 0.11%
-
-Original prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Attacked prompt: b"Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'  and true is true . "
-Original acc: 95.41%, attacked acc: 95.07%, dropped acc: 0.34%
-
-## textbugger
-
-Original prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Attacked prompt: b"Considering the giv en expressions, would you say it carries a 'positive' or 'negative' connotation? "
-Original acc: 95.30%, attacked acc: 94.84%, dropped acc: 0.46%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this declarations and decide whether it has a 'positive' or 'negative' emotion. "
-Original acc: 95.18%, attacked acc: 94.38%, dropped acc: 0.80%
-
-Original prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Attacked prompt: b"Analyze the tone of this declaration and cater with or 'positive' or 'negative'. "
-Original acc: 94.95%, attacked acc: 94.15%, dropped acc: 0.80%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a emotion analysis tool, respond with 'positive' or 'negative' to clasisfy this stateme\xd5\xb8t. "
-Original acc: 95.53%, attacked acc: 94.84%, dropped acc: 0.69%
-
-Original prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Attacked prompt: b"Servin\xc9\xa1 as a sentiment assessments model, d etermine if the given statement is 'positive' or 'negative'. "
-Original acc: 95.53%, attacked acc: 94.95%, dropped acc: 0.57%
-
-Original prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Attacked prompt: b"Acting as a ambience evaluato\xe2\xb2\x85, identifies if the awarded sentence is 'positive' or 'negative'. "
-Original acc: 95.41%, attacked acc: 93.92%, dropped acc: 1.49%
-
-## textfooler
-
-Original prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Attacked prompt: b"Explores the made expressions, would you say it carries a 'positive' or 'negative' connotation? "
-Original acc: 95.30%, attacked acc: 94.50%, dropped acc: 0.80%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this assertions and decide whether it has a 'positive' or 'negative' mood. "
-Original acc: 95.18%, attacked acc: 94.50%, dropped acc: 0.69%
-
-Original prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Attacked prompt: b"Explore the tone of this affirmations and react with either 'positive' or 'negative'. "
-Original acc: 94.95%, attacked acc: 94.27%, dropped acc: 0.69%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a emotions exploring toolbox, respond with 'positive' or 'negative' to sorted this affirmations. "
-Original acc: 95.53%, attacked acc: 94.04%, dropped acc: 1.49%
-
-Original prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Attacked prompt: b"Serving as a sentiment estimation idealized, determine if the made remarks is 'positive' or 'negative'. "
-Original acc: 95.53%, attacked acc: 94.27%, dropped acc: 1.26%
-
-Original prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Attacked prompt: b"Behaving as a ambiance evaluator, locate if the given convicting is 'positive' or 'negative'. "
-Original acc: 95.41%, attacked acc: 94.15%, dropped acc: 1.26%
-
-## translation
-
-Language: chinese, acc: 95.18%, prompt: Answer whether the statement is 'positive' or 'negative' based on sentiment analysis.
-Language: chinese, acc: 93.23%, prompt: As an emotion analysis tool, determine whether the emotion in the text is 'positive' or 'negative'.
-Language: chinese, acc: 94.61%, prompt: Categorize the statement as 'positive' or 'negative', based on its emotional bent.
-Language: chinese, acc: 94.50%, prompt: Please use sentiment analysis to classify the text as 'positive' or 'negative'.
-Language: chinese, acc: 93.69%, prompt: Please determine whether the emotion of the sentence is 'positive' or 'negative' and categorize it.
-Language: chinese, acc: 94.84%, prompt: Using sentiment analysis, classify the text as 'positive' or 'negative'.
-Language: chinese, acc: 93.69%, prompt: Please answer whether the emotion of the sentence is 'positive' or 'negative' and categorize it.
-Language: chinese, acc: 94.72%, prompt: Categorize the statement as 'positive' or 'negative' based on sentiment analysis.
-Language: chinese, acc: 94.84%, prompt: Please judge this text as 'positive' or 'negative' according to the criteria of sentiment analysis.
-Language: chinese, acc: 94.84%, prompt: Please classify this sentence as 'positive' or 'negative' according to the principles of emotion analysis.
-Language: french, acc: 95.41%, prompt: As a sentiment analysis tool, please answer with 'positive' or 'negative' to classify this statement.
-Language: french, acc: 94.72%, prompt: Determine whether this phrase is 'positive' or 'negative' as a sentiment classification tool.
-Language: french, acc: 94.84%, prompt: Identify the tone of this statement by choosing between 'positive' and 'negative' as a sentiment analysis tool.
-Language: french, acc: 94.38%, prompt: Use sentiment analysis to classify this statement as 'positive' or 'negative'.
-Language: french, acc: 94.95%, prompt: As a sentiment classification tool, please determine whether this statement is 'positive' or 'negative'.
-Language: french, acc: 94.61%, prompt: Classify this sentence as 'positive' or 'negative' using sentiment analysis.
-Language: french, acc: 95.30%, prompt: Choose between 'positive' or 'negative' to classify this statement as a sentiment analysis tool.
-Language: french, acc: 94.61%, prompt: Identify the sentiment expressed in this statement by selecting 'positive' or 'negative' as a sentiment classification tool.
-Language: french, acc: 95.41%, prompt: Determine whether this phrase is 'positive' or 'negative' using sentiment analysis as a classification tool.
-Language: french, acc: 94.38%, prompt: Use sentiment analysis to classify this statement as 'positive' or 'negative'.
-Language: arabic, acc: 95.41%, prompt: Under emotional analysis, answer 'positive' or 'negative' to classify this statement.
-Language: arabic, acc: 94.95%, prompt: Does this statement express a 'positive' or 'negative' reaction?
-Language: arabic, acc: 94.72%, prompt: Is that a 'positive' or a 'negative' phrase?
-Language: arabic, acc: 95.07%, prompt: What is the classification between 'positive' and 'negative'?
-Language: arabic, acc: 95.07%, prompt: Does this sentence express 'positive' or 'negative' feelings?
-Language: arabic, acc: 94.61%, prompt: In the context of textual analysis, what classification is this phrase between 'positive' and 'negative'?
-Language: arabic, acc: 95.07%, prompt: Could this be classified as 'positive' or 'negative'?
-Language: arabic, acc: 95.30%, prompt: In the context of emotional analysis, what classification is this statement between 'positive' and 'negative'?
-Language: arabic, acc: 95.30%, prompt: Can this be classified as 'positive' or 'negative'?
-Language: arabic, acc: 94.72%, prompt: Under the classification of emotions, is this sentence 'positive' or 'negative'?
-Language: spanish, acc: 95.07%, prompt: As a feeling analysis tool, classify this statement as 'positive' or 'negative'.
-Language: spanish, acc: 95.07%, prompt: Determine whether this statement has a 'positive' or 'negative' connotation.
-Language: spanish, acc: 95.18%, prompt: Indicate whether the following statement is 'positive' or 'negative'.
-Language: spanish, acc: 93.92%, prompt: Evaluate whether this text has a 'positive' or 'negative' emotional charge.
-Language: spanish, acc: 94.27%, prompt: According to your sentiment analysis, would you say this comment is 'positive' or 'negative'?
-Language: spanish, acc: 94.95%, prompt: In the context of sentiment analysis, label this sentence as 'positive' or 'negative'.
-Language: spanish, acc: 94.38%, prompt: Rate the following statement as 'positive' or 'negative', according to your sentiment analysis.
-Language: spanish, acc: 93.69%, prompt: How would you classify this text in terms of its emotional tone? 'positive' or 'negative'?
-Language: spanish, acc: 94.84%, prompt: As a tool for sentiment analysis, would you say this statement is 'positive' or 'negative'?
-Language: spanish, acc: 94.72%, prompt: Classify this statement as 'positive' or 'negative', please.
-Language: japanese, acc: 94.61%, prompt: Treat this sentence as an emotion analysis tool and categorize it as 'positive' and 'negative'.
-Language: japanese, acc: 94.15%, prompt: Use this article as a sentiment analysis tool to classify 'positive' and 'negative'.
-Language: japanese, acc: 94.50%, prompt: Use this sentence as an emotion analysis tool to determine whether it is 'positive' or 'negative'.
-Language: japanese, acc: 94.04%, prompt: Use this sentence as an emotion analysis tool to classify 'positive' and 'negative'.
-Language: japanese, acc: 94.72%, prompt: Use this sentence as a sentiment analysis tool and classify it as 'positive' or 'negative'.
-Language: japanese, acc: 95.30%, prompt: To classify this sentence as 'positive' or 'negative', evaluate it as a sentiment analysis tool.
-Language: japanese, acc: 94.95%, prompt: Treat this sentence as an emotion analysis tool to determine whether it is 'positive' or 'negative'.
-Language: japanese, acc: 94.38%, prompt: Use this sentence as a sentiment analysis tool to classify 'positive' and 'negative'.
-Language: japanese, acc: 94.61%, prompt: Analyze this sentence as an emotion analysis tool to classify whether it is 'positive' or 'negative'.
-Language: japanese, acc: 94.38%, prompt: Use this sentence as an emotional analysis tool to determine whether it is 'positive' or 'negative'.
-Language: korean, acc: 94.72%, prompt: As an emotional analysis tool, respond with 'positive' or 'negative' to classify these sentences.
-Language: korean, acc: 94.38%, prompt: Classify this sentence as 'positive' if you regard it as positive, 'negative' if you regard it as negative.
-Language: korean, acc: 94.61%, prompt: Please rate the emotion of this sentence and classify it as 'positive' or 'negative'.
-Language: korean, acc: 94.61%, prompt: Classify this sentence as 'positive' if you perceive it positively and 'negative' if you perceive it negatively.
-Language: korean, acc: 94.61%, prompt: If this is a sentence delivered using a positive expression, classify it as 'positive' and if this is a sentence delivered using a negative expression, classify it as 'negative'.
-Language: korean, acc: 94.61%, prompt: Respond with 'positive' or 'negative' by categorizing whether the sentence is positive or negative.
-Language: korean, acc: 94.15%, prompt: Please analyze the emotion in this sentence and classify it as 'positive' or 'negative'.
-Language: korean, acc: 94.15%, prompt: Classify this sentence as 'positive' if it contains a positive meaning, 'negative' if it contains a negative meaning.
-Language: korean, acc: 94.38%, prompt: Classify this sentence as 'positive' if it contains positive content, 'negative' if it contains negative content.
-Language: korean, acc: 93.46%, prompt: Classify what you're trying to convey in this sentence as 'positive' if it's positive, and 'negative' if it's negative.
-
-# wnli
-
-## 10 prompts
-
-Acc: 66.20%, prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Acc: 66.20%, prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Acc: 64.79%, prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 64.79%, prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 64.79%, prompt: Indicate if the connection between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 64.79%, prompt: Considering the two sentences, identify if their relationship is 'entailment' or 'not_entailment'.
-Acc: 63.38%, prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Acc: 63.38%, prompt: Examine the pair of sentences and determine if they exhibit entailment or not_entailment. Answer with either 'entailment' or 'not_entailment'.
-Acc: 63.38%, prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Acc: 63.38%, prompt: Determine if the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-Acc: 61.97%, prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-
-Acc: 66.20%, prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Acc: 66.20%, prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Acc: 66.20%, prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 66.20%, prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Acc: 63.38%, prompt: As a tool for determining entailment relationships, review the two statements and categorize their connection as either 'entailment' or 'not_entailment'.
-Acc: 61.97%, prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 59.15%, prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-Acc: 57.75%, prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 57.75%, prompt: While performing entailment analysis, classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 56.34%, prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-
-## bertattack
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'was the following two verbs entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one person. '
-Original acc: 66.20%, attacked acc: 59.15%, dropped acc: 7.04%
-
-Original prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the two present clauses and decide if their relationship is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 59.15%, dropped acc: 7.04%
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Assess the connecting between the relevant sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 64.79%, attacked acc: 54.93%, dropped acc: 9.86%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the conjunction between the before sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 60.56%, dropped acc: 5.63%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the generated sentences and place if their conversation is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 49.30%, dropped acc: 16.90%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"if the capacity of an entailment assessment system, indicate if the connecting between the adjacent sentences is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 56.34%, dropped acc: 9.86%
-
-## checklist
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word Cev0s49fbe. '
-Original acc: 66.20%, attacked acc: 64.79%, dropped acc: 1.41%
-
-Original prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment' 5XeflW1ZJc. "
-Original acc: 66.20%, attacked acc: 66.20%, dropped acc: 0.00%
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment' Cev0s49fbe. "
-Original acc: 64.79%, attacked acc: 63.38%, dropped acc: 1.41%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment' 5XeflW1ZJc. "
-Original acc: 66.20%, attacked acc: 66.20%, dropped acc: 0.00%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment' 5XeflW1ZJc. "
-Original acc: 66.20%, attacked acc: 66.20%, dropped acc: 0.00%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment' Cev0s49fbe. "
-Original acc: 66.20%, attacked acc: 66.20%, dropped acc: 0.00%
-
-## deepwordbug
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Aroe the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word. '
-Original acc: 66.20%, attacked acc: 64.79%, dropped acc: 1.41%
-
-Original prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the two prEovided sentences and decidEe if their relationship is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 59.15%, dropped acc: 7.04%
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Assses the connection between the followoing sentence and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 64.79%, attacked acc: 54.93%, dropped acc: 9.86%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Ats an entailment identification system, examine the connectitn between the flolowing sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 61.97%, dropped acc: 4.23%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provdied sentences and dcide if their relationhip is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 57.75%, dropped acc: 8.45%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capaciy of an entailment assessment system, inDdicate if the liQk between the followign sentences is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 56.34%, dropped acc: 9.86%
-
-## stresstest
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word  and false is not true . '
-Original acc: 66.20%, attacked acc: 67.61%, dropped acc: -1.41%
-
-Original prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 66.20%, attacked acc: 35.21%, dropped acc: 30.99%
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'  and true is true . "
-Original acc: 64.79%, attacked acc: 66.20%, dropped acc: -1.41%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'  and false is not true . "
-Original acc: 66.20%, attacked acc: 67.61%, dropped acc: -1.41%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 66.20%, attacked acc: 36.62%, dropped acc: 29.58%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 66.20%, attacked acc: 23.94%, dropped acc: 42.25%
-
-## textbugger
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Ae the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word. '
-Original acc: 66.20%, attacked acc: 64.79%, dropped acc: 1.41%
-
-Original prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the two provided sentences and decide if their realtionship is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 63.38%, dropped acc: 2.82%
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Assess the connecting between the subsequent sentence and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 64.79%, attacked acc: 57.75%, dropped acc: 7.04%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment \xd1\x96dentification system, examine the connection between the subsequently sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 61.97%, dropped acc: 4.23%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the furnished sentences and decide if their relationshi\xd1\x80 is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 57.75%, dropped acc: 8.45%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the abilities of an entailment assess ment system, indicate if the link between the followi ng sentences is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 59.15%, dropped acc: 7.04%
-
-## textfooler
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Se the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one verb. '
-Original acc: 66.20%, attacked acc: 61.97%, dropped acc: 4.23%
-
-Original prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the two provided sentence and selected if their connection is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 59.15%, dropped acc: 7.04%
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Assess the connecting between the subsequent phrases and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 64.79%, attacked acc: 54.93%, dropped acc: 9.86%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connectivity between the subsequently sentence and responds with 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 59.15%, dropped acc: 7.04%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the afforded sentences and chosen if their liaise is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 56.34%, dropped acc: 9.86%
-
-Original prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Among the abilities of an entailment assessment mechanism, indicate if the attach between the subsequently sentences is 'entailment' or 'not_entailment'. "
-Original acc: 66.20%, attacked acc: 56.34%, dropped acc: 9.86%
-
-## translation
-
-Language: chinese, acc: 66.20%, prompt: In the light of an implication analysis tool, evaluate the relationship between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 67.61%, prompt: From the perspective of an implication analysis tool, determine whether there is an implication relationship in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 67.61%, prompt: Please use an implication analysis tool to determine whether an implication relationship exists in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 67.61%, prompt: Please evaluate the relation of the following sentences as 'entailment' or 'not_entailment' for the purpose of determining implication relation.
-Language: chinese, acc: 67.61%, prompt: Please use the implication analysis tool to evaluate the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 57.75%, prompt: For the purpose of determining implicative relations, analyze the relations of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 67.61%, prompt: Please use the implication analysis tool to determine the relationship of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 66.20%, prompt: Please use the implication judgment tool to assess the relevance of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 64.79%, prompt: Please, with implication analysis as the main task, determine the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 61.97%, prompt: Using the implication judgment as a criterion, analyze the relation of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 63.38%, prompt: As an engagement analysis tool, evaluate the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Language: french, acc: 66.20%, prompt: Determine whether the given sentences involve one another or not as an implication analysis tool. Classify them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 63.38%, prompt: Using implication analysis, evaluate whether the sentences provided have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 61.97%, prompt: As an engagement assessment tool, determine whether the sentences provided have a logical relationship and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 64.79%, prompt: As an implication classification tool, analyze the sentences provided to determine if there is a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 63.38%, prompt: Using implication analysis, determine whether the given sentences have a cause-effect relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 63.38%, prompt: Evaluate the relationship between the given sentences using implication analysis and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 60.56%, prompt: As an engagement detection tool, determine whether the given sentences have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 64.79%, prompt: Using implication analysis, evaluate whether the sentences provided have a cause-effect relationship and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 63.38%, prompt: Determine whether the given sentences have a cause-effect relationship as an engagement analysis tool and categorize them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 54.93%, prompt: In your role as a tool for reasoning analysis, evaluate the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 67.61%, prompt: Can you determine whether this sentence is inferred from the other sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 54.93%, prompt: Using the tool of reasoning analysis, analyze the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 64.79%, prompt: Does this sentence represent a conclusion from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 54.93%, prompt: As a tool of reasoning analysis, evaluate the relationship of given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 64.79%, prompt: Can this sentence be inferred from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 63.38%, prompt: Using a tool to analyze a conclusion, analyze the relationship between the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 66.20%, prompt: Is this a conclusion from the next sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 61.97%, prompt: As part of your task in analyzing a conclusion, evaluate the relationship between the two sentences and classify them as 'entailment' or 'not_entailment' based on their relationship.
-Language: arabic, acc: 63.38%, prompt: Are you following this sentence directly from the previous one? Classify it as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 61.97%, prompt: In your role as an implication analysis tool, evaluate the relationship between the given phrases and classify them as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 67.61%, prompt: Determine whether the second sentence necessarily implies the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 67.61%, prompt: Classifies the relationship between these two sentences as 'entailment' if one necessarily implies the other, or as 'not_entailment' if not.
-Language: spanish, acc: 69.01%, prompt: Evaluates whether the information in the second sentence is implied in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 64.79%, prompt: Given a couple of phrases, label their relationship as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 67.61%, prompt: Analyzes the relationship between the phrases and classifies them as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 64.79%, prompt: Given two sentences, determine whether the second sentence is a necessary consequence of the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 67.61%, prompt: Evaluates whether the information presented in the second sentence is implicit in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 64.79%, prompt: Classifies the relationship between the given phrases as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 67.61%, prompt: Determines whether the information provided in the second sentence is necessarily inferable from the first and labels the relationship as 'entailment', or as 'not_entailment' if not.
-Language: japanese, acc: 54.93%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 60.56%, prompt: Evaluate the semantic relationship of the sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 63.38%, prompt: Please judge the relationship between the given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 49.30%, prompt: Examine the paraphrases of a given sentence and classify them 'entailment' or 'not_entailment'.
-Language: japanese, acc: 59.15%, prompt: Rate the similarity of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 54.93%, prompt: Determinate the semantic connections of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 53.52%, prompt: Examine the semantic match of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 60.56%, prompt: Classify it as 'entailment' or 'not_entailment' based on the content of the sentence.
-Language: japanese, acc: 54.93%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 54.93%, prompt: Judge the semantic connections of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: korean, acc: 61.97%, prompt: Evaluate the relationship between any two sentences given to you and classify you as 'entailment' or 'not_entailment'.
-Language: korean, acc: 54.93%, prompt: Analyze the semantic deductive relations between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 61.97%, prompt: Evaluate the logical relevance between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 57.75%, prompt: Evaluate the interaction of two given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 63.38%, prompt: Please check whether there is a semantic match between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 63.38%, prompt: Compare information between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 67.61%, prompt: Please analyse the correlation between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 60.56%, prompt: Evaluate the different meanings between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 59.15%, prompt: Compare the semantic structure of the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 59.15%, prompt: Evaluate the interactions between sentences and classify them as 'entailment' or 'not_entailment'.
-
-# mmlu
-
-## 10 prompts
-
-Acc: 46.81%, prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Acc: 46.45%, prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Acc: 45.92%, prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Acc: 45.57%, prompt: For the multiple-choice question related to {}, please choose the most accurate answer from 'A', 'B', 'C', or 'D'.
-Acc: 45.57%, prompt: Evaluate the multiple-choice question about {} and select the most fitting response from 'A', 'B', 'C', or 'D'.
-Acc: 45.39%, prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Acc: 45.39%, prompt: Below are multiple-choice question concerning {}. Indicate your response with 'A', 'B', 'C', or 'D'.
-Acc: 45.21%, prompt: Please respond to the multiple-choice question about {} by selecting the appropriate answer: 'A', 'B', 'C', or 'D'.
-Acc: 45.21%, prompt: Regarding the following multiple-choice question on {}, pick the correct answer from the options 'A', 'B', 'C', or 'D'.
-Acc: 45.04%, prompt: Examine the following question based on {} and choose the correct response from 'A', 'B', 'C', or 'D'.
-
-Acc: 46.99%, prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Acc: 46.99%, prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Acc: 46.63%, prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Acc: 46.10%, prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Acc: 46.10%, prompt: Drawing upon your mastery of {}, please answer the multiple-choice question by selecting the correct option from 'A', 'B', 'C', or 'D'.
-Acc: 45.92%, prompt: As a skilled person in the field of {}, please respond to the multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Acc: 45.74%, prompt: Utilizing your expertise in {}, answer the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Acc: 45.74%, prompt: As a knowledgeable individual in {}, provide your response to the multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Acc: 45.39%, prompt: Given your proficiency in {}, please answer the subsequent multiple-choice question with 'A', 'B', 'C', or 'D'.
-Acc: 45.39%, prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-
-## bertattack
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"in relation to the multiple-ask asked on {}, be provide the incorrect answer by deciding 'A', 'B', 'C', or 'D'. "
-Original acc: 46.81%, attacked acc: 17.02%, dropped acc: 29.79%
-
-Original prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"For each multiple-. question about {}, reveal the incorrect ask by selecting 'A', 'B', 'C', or 'D'. "
-Original acc: 46.45%, attacked acc: 16.84%, dropped acc: 29.61%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the subsequent multiple-pick and about {} by choose the incorrect selection which 'A', 'B', 'C', or 'D'. "
-Original acc: 45.92%, attacked acc: 17.20%, dropped acc: 28.72%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an example in {}, approach to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 44.33%, dropped acc: 2.66%
-
-Original prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Considering your familiarity with {}, attend to the following multiple-of question by options 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 43.44%, dropped acc: 3.55%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-versed in {}, help address the multiple-assignment question below by initial 'A', 'B', 'C', or 'D'."
-Original acc: 46.63%, attacked acc: 43.62%, dropped acc: 3.01%
-
-## checklist
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D' YHQiDfFhw4. "
-Original acc: 46.81%, attacked acc: 46.10%, dropped acc: 0.71%
-
-Original prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D' 9Q3S1F94fE. "
-Original acc: 46.45%, attacked acc: 45.74%, dropped acc: 0.71%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D' KjPJJ2a7RB. "
-Original acc: 45.92%, attacked acc: 45.04%, dropped acc: 0.89%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D' MQnugHcaoy."
-Original acc: 46.99%, attacked acc: 46.28%, dropped acc: 0.71%
-
-Original prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D' SFPCqpiClT."
-Original acc: 46.99%, attacked acc: 45.04%, dropped acc: 1.95%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D' yh912BU8T0."
-Original acc: 46.63%, attacked acc: 45.74%, dropped acc: 0.89%
-
-## deepwordbug
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In relation to the ultiple-choice question on {}, lease rovide the accuraet answer by choosieg 'A', 'B', 'C', or 'D'. "
-Original acc: 46.81%, attacked acc: 43.44%, dropped acc: 3.37%
-
-Original prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"For each multiple-hoice qWuestion about {}, identify the correct answer by selectinog 'A', 'B', 'C', or 'D'. "
-Original acc: 46.45%, attacked acc: 44.15%, dropped acc: 2.30%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"AnsOwer the susbequent multiple-choice question about {} by picking the rght option among 'A', 'B', 'C', or 'D'. "
-Original acc: 45.92%, attacked acc: 44.86%, dropped acc: 1.06%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an expret in {}, resUond to the followCing multpile-choice question by selecting 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 44.50%, dropped acc: 2.48%
-
-Original prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Considering your familiarity with {}, attePd to the following multiple-hcoice question by pickiGng 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 43.44%, dropped acc: 3.55%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-verse in {}, please addJess the multipl-echoice uqestion below by seleting 'A', 'B', 'C', or 'D'."
-Original acc: 46.63%, attacked acc: 43.97%, dropped acc: 2.66%
-
-## stresstest
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'  and false is not true . "
-Original acc: 46.81%, attacked acc: 45.92%, dropped acc: 0.89%
-
-Original prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'  and false is not true . "
-Original acc: 46.45%, attacked acc: 45.74%, dropped acc: 0.71%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'  and false is not true . "
-Original acc: 45.92%, attacked acc: 44.86%, dropped acc: 1.06%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'  and false is not true ."
-Original acc: 46.99%, attacked acc: 45.04%, dropped acc: 1.95%
-
-Original prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'  and false is not true ."
-Original acc: 46.99%, attacked acc: 45.39%, dropped acc: 1.60%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'  and false is not true ."
-Original acc: 46.63%, attacked acc: 44.86%, dropped acc: 1.77%
-
-## textbugger
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In relation to the mlutiple-choice question on {}, pelase provide the meticulous answer by opt 'A', 'B', 'C', or 'D'. "
-Original acc: 46.81%, attacked acc: 43.62%, dropped acc: 3.19%
-
-Original prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Per each multiple-ch\xd0\xbeice issue about {}, identify the correct answer by choose 'A', 'B', 'C', or 'D'. "
-Original acc: 46.45%, attacked acc: 44.50%, dropped acc: 1.95%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the subsequent multiple-choice issues about {} by picking the right alternate among 'A', 'B', 'C', or 'D'. "
-Original acc: 45.92%, attacked acc: 43.79%, dropped acc: 2.13%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an expert in {}, respond to the following multiple-hcoice question by sel ecting 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 44.15%, dropped acc: 2.84%
-
-Original prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Considering your knowledge with {}, attend to the following multip le-choice questions by \xd1\x80icking 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 43.97%, dropped acc: 3.01%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone w\xd0\xb5ll-versed in {}, pl\xd0\xb5ase address the multiple-ch oice question below by selecing 'A', 'B', 'C', or 'D'."
-Original acc: 46.63%, attacked acc: 44.33%, dropped acc: 2.30%
-
-## textfooler
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"About relate to the multiple-choice item on {}, please provide the accurate rebuttal by decided 'A', 'B', 'C', or 'D'. "
-Original acc: 46.81%, attacked acc: 40.07%, dropped acc: 6.74%
-
-Original prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"About each multiple-choice question about {}, find the rectify rebuttal by choices 'A', 'B', 'C', or 'D'. "
-Original acc: 46.45%, attacked acc: 37.41%, dropped acc: 9.04%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the backside multiple-choice question about {} by choices the right replacement among 'A', 'B', 'C', or 'D'. "
-Original acc: 45.92%, attacked acc: 43.44%, dropped acc: 2.48%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Iike an skilled in {}, respond to the following multiple-choice question by elections 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 43.97%, dropped acc: 3.01%
-
-Original prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Explored your familiarity with {}, participate to the next multiple-choice issues by pickup 'A', 'B', 'C', or 'D'."
-Original acc: 46.99%, attacked acc: 43.44%, dropped acc: 3.55%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As person well-versed in {}, please address the multiple-choice question below by chosen 'A', 'B', 'C', or 'D'."
-Original acc: 46.63%, attacked acc: 44.68%, dropped acc: 1.95%
-
-## translation
-
-Language: chinese, acc: 45.74%, prompt: For the following multiple choice question about {}, please select the correct answer from 'A', 'B', 'C' or 'D'.
-Language: chinese, acc: 45.04%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice question for {}.
-Language: chinese, acc: 45.21%, prompt: Select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice question {}.
-Language: chinese, acc: 45.74%, prompt: Please choose the correct answer from 'A', 'B', 'C' or 'D' according to the following multiple-choice questions about {}.
-Language: chinese, acc: 45.04%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the {} multiple choice questions below.
-Language: chinese, acc: 45.39%, prompt: The following is A multiple choice question about {}. Please select the correct answer from 'A', 'B', 'C' or 'D'.
-Language: chinese, acc: 45.04%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice question {}.
-Language: chinese, acc: 45.74%, prompt: Please choose the correct answer from 'A', 'B', 'C' or 'D' according to the following multiple-choice questions about {}.
-Language: chinese, acc: 45.21%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice questions about {}.
-Language: chinese, acc: 45.21%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice questions about {}.
-Language: french, acc: 45.39%, prompt: For the following multiple choice question on {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.21%, prompt: This is a multiple choice question about {}. Select the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.74%, prompt: In the context of the multiple-choice question on {}, identify the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.04%, prompt: About the following question on {}, determine the correct answer from the choices 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.74%, prompt: Carefully review the multiple-choice question regarding {}. Choose the correct answer from options 'A', 'B', 'C', or 'D'.
-Language: french, acc: 45.57%, prompt: For the multiple-choice question for {}, indicate the correct answer from options 'A', 'B', 'C', or 'D'.
-Language: french, acc: 45.57%, prompt: The next question is about {}. Select the correct answer from the choices 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.57%, prompt: As part of the multiple-choice question on {}, choose the appropriate answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.57%, prompt: Rate your understanding of the multiple-choice question on {}. Choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 45.04%, prompt: Analyze the following multiple-choice question on {}. Identify the correct answer among choices 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 46.10%, prompt: For the multiple choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.39%, prompt: For the following multiple-choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.57%, prompt: For the following multiple choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 46.10%, prompt: When it comes to the multiple-choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.39%, prompt: For the multiple-choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.92%, prompt: If the question for {} is multiple choice, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.04%, prompt: For the question regarding {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.21%, prompt: For the question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.57%, prompt: When it comes to the question regarding {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 45.04%, prompt: For the question regarding {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: spanish, acc: 45.39%, prompt: For the following multiple-choice question about {}, choose the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 45.92%, prompt: For the following multiple-choice question about {}, select the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 45.39%, prompt: For the following multiple-choice question about {}, choose the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 46.10%, prompt: Within the context of the following multiple-choice question about {}, choose the correct option from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 45.74%, prompt: For the following multiple-choice statement about {}, select the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 46.99%, prompt: Considering the following multiple-choice question about {}, mark the correct answer with 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 45.57%, prompt: For the following multiple-choice question about {}, choose the correct alternative among 'A', 'B', 'C' or 'D'.
-Language: spanish, acc: 45.57%, prompt: For the following multiple-choice statement about {}, choose the correct option from alternatives 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 45.74%, prompt: Within the context of the following multiple-choice question about {}, select the correct answer from alternatives 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 44.50%, prompt: Considering the following multiple-choice statement about {}, mark the correct alternative with the options 'A', 'B', 'C' or 'D'.
-Language: japanese, acc: 45.57%, prompt: Choose the appropriate answer from options 'A', 'B', 'C', or 'D' for {} regarding the following question.
-Language: japanese, acc: 45.74%, prompt: Choose the correct answer from 'A', 'B', 'C', or 'D' for the following multiple-choice question about {}.
-Language: japanese, acc: 45.74%, prompt: For the following multiple-choice questions about {}, choose the correct answer from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 46.28%, prompt: Choose the correct answer from options 'A', 'B', 'C', or 'D' for the following questions about {}.
-Language: japanese, acc: 45.04%, prompt: In the multiple choice questions below, choose the correct answer for {} from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 45.74%, prompt: Choose the correct answer from the options 'A', 'B', 'C', or 'D' for the following questions about {}.
-Language: japanese, acc: 45.04%, prompt: In the multiple choice questions below, choose the correct answer for {} from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 44.86%, prompt: Choose the correct answer from 'A', 'B', 'C', or 'D' for the following multiple choice questions about {}.
-Language: japanese, acc: 45.04%, prompt: In the multiple choice questions below, choose the correct answer for {} from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 45.74%, prompt: Choose the correct answer from options 'A', 'B', 'C', or 'D' for {} regarding the following question.
-Language: korean, acc: 44.68%, prompt: For the multiple choice problem about, choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 43.97%, prompt: Choose the correct answer for '{}' from 'A', 'B', 'C', or 'D' in the multiple choice problem involving,
-Language: korean, acc: 44.50%, prompt: For the multiple choice problem below, choose the correct answer to '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 44.86%, prompt: In the following multiple-choice problem, choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 45.04%, prompt: For the following multiple choice problem, choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 45.57%, prompt: Solve multiple choice problems about: Which of 'A', 'B', 'C', or 'D' is the correct answer for '{}'.
-Language: korean, acc: 45.21%, prompt: Choose the correct answer to the multiple-choice question below. Is '{}' an 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 43.97%, prompt: Solve the following multiple-choice problem. Choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 46.28%, prompt: Choose the correct answer to the following multiple choice problem: Is '{}' 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 44.50%, prompt: Solve multiple-choice problems about: Please select 'A', 'B', 'C', or 'D' for the correct answer to '{}'.
-
-# squad_v2
-
-## 10 prompts
-
-Acc: 87.91%, prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Acc: 87.32%, prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Acc: 87.15%, prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Acc: 87.15%, prompt: Locate the most accurate answer within the context. If the context doesn't provide an answer, mention 'unanswerable'.
-Acc: 87.05%, prompt: Based on the given context, provide the best possible answer. If there's no answer available in the context, reply with 'unanswerable'.
-Acc: 87.05%, prompt: Find the correct answer in the context provided. If an answer cannot be found, please respond with 'unanswerable'.
-Acc: 87.05%, prompt: Using the context, determine the most suitable answer. If the context doesn't contain the answer, write 'unanswerable'.
-Acc: 87.05%, prompt: Please derive the most fitting answer from the context. If there isn't an answer in the context, use 'unanswerable'.
-Acc: 87.05%, prompt: Discover the best answer based on the context. If the context doesn't include an answer, say 'unanswerable'.
-Acc: 87.01%, prompt: Identify the most relevant answer from the context. If it's not possible to find an answer, state 'unanswerable'.
-
-Acc: 87.32%, prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Acc: 87.05%, prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Acc: 87.05%, prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Acc: 87.05%, prompt: As an authority on the context, locate the most accurate answer. If the context doesn't contain the answer, mention 'unanswerable'.
-Acc: 87.05%, prompt: Being well-versed in the context, please derive the most fitting answer. If there isn't an answer in the context, use 'unanswerable'.
-Acc: 87.05%, prompt: As an expert with a deep understanding of the context, find the best answer. If the context doesn't include an answer, say 'unanswerable'.
-Acc: 87.05%, prompt: Drawing on your expertise in the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Acc: 87.05%, prompt: With your extensive knowledge of the context, answer the question accurately. If the context doesn't contain the answer, reply with 'unanswerable'.
-Acc: 86.55%, prompt: As a well-informed specialist familiar with the context, provide an answer to the question. If the context doesn't contain an answer, reply with 'unanswerable'.
-Acc: 86.55%, prompt: Using your knowledge of the context, identify the best answer to the question. If the context doesn't provide an answer, write 'unanswerable'.
-
-## bertattack
-
-Original prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Attacked prompt: b"about the context, provide the most vague explanation. given the answered is not in the sentence, respond with 'unanswerable'."
-Original acc: 87.91%, attacked acc: 73.79%, dropped acc: 14.12%
-
-Original prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"select the forum for the most relevant answer. if the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 86.61%, dropped acc: 0.70%
-
-Original prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"cross refer the most appropriate answer from the context. If an explanation is not present, indicate 'unanswerable'."
-Original acc: 87.15%, attacked acc: 85.42%, dropped acc: 1.73%
-
-Original prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"according a proficient expert in the an context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 86.65%, dropped acc: 0.67%
-
-Original prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Attacked prompt: b"i upon your expertise in the context, prepare the most a explanation. If an answer isn't available, state 'unanswerable'."
-Original acc: 87.05%, attacked acc: 73.81%, dropped acc: 13.23%
-
-Original prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"provide a question ask specialist, extract the incorrect field from the context. If an answer is not present, indicate 'unanswerable'."
-Original acc: 87.05%, attacked acc: 43.00%, dropped acc: 44.05%
-
-## checklist
-
-Original prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Attacked prompt: b"From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable' 5yWbBXztUY."
-Original acc: 87.91%, attacked acc: 87.05%, dropped acc: 0.86%
-
-Original prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable' 5XeflW1ZJc."
-Original acc: 87.32%, attacked acc: 87.32%, dropped acc: 0.00%
-
-Original prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable' 5XeflW1ZJc."
-Original acc: 87.15%, attacked acc: 87.15%, dropped acc: 0.00%
-
-Original prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable' 5XeflW1ZJc."
-Original acc: 87.32%, attacked acc: 87.32%, dropped acc: 0.00%
-
-Original prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Attacked prompt: b"Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable' 5XeflW1ZJc."
-Original acc: 87.05%, attacked acc: 86.55%, dropped acc: 0.50%
-
-Original prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable' S6oUV5E54P."
-Original acc: 87.05%, attacked acc: 86.82%, dropped acc: 0.23%
-
-## deepwordbug
-
-Original prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Attacked prompt: b"Frmo the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'."
-Original acc: 87.91%, attacked acc: 86.55%, dropped acc: 1.36%
-
-Original prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 87.32%, dropped acc: 0.60%
-
-Original prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"Please extract the most appropriate answer from the context. If an aswer is not present, indicate 'unanswerable'."
-Original acc: 87.15%, attacked acc: 86.65%, dropped acc: 0.50%
-
-Original prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"As a proficient expert in the givVen context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 86.82%, dropped acc: 0.50%
-
-Original prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Attacked prompt: b"Drawing upon your expertise in the context, determine the most suitable naswer. If an answer isn't available, state 'unanswerable'."
-Original acc: 87.05%, attacked acc: 86.65%, dropped acc: 0.40%
-
-Original prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"As a subject matter expert, extract the ocrrect anser from the contNext. If an answer is not present, indicate 'unanswerable'."
-Original acc: 87.05%, attacked acc: 86.59%, dropped acc: 0.46%
-
-## stresstest
-
-Original prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Attacked prompt: b"From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 87.91%, attacked acc: 87.05%, dropped acc: 0.86%
-
-Original prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 87.32%, attacked acc: 87.65%, dropped acc: -0.33%
-
-Original prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'  and true is true ."
-Original acc: 87.15%, attacked acc: 87.25%, dropped acc: -0.10%
-
-Original prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 87.32%, attacked acc: 86.65%, dropped acc: 0.67%
-
-Original prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Attacked prompt: b"Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'  and false is not true ."
-Original acc: 87.05%, attacked acc: 87.22%, dropped acc: -0.17%
-
-Original prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 87.05%, attacked acc: 87.05%, dropped acc: 0.00%
-
-## textbugger
-
-Original prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Attacked prompt: b"Among the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'."
-Original acc: 87.91%, attacked acc: 87.05%, dropped acc: 0.86%
-
-Original prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 87.32%, dropped acc: 0.60%
-
-Original prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"Please extract the most appropriate answer from the context. Though an answer is not present, indicate 'unanswerable'."
-Original acc: 87.15%, attacked acc: 86.55%, dropped acc: 0.60%
-
-Original prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"As a proficient \xd0\xb5xpert in the afforded co\xd5\xb8text, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 86.65%, dropped acc: 0.67%
-
-Original prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Attacked prompt: b"Drawing upon your expertise in the context, determine the most suitable responding. If an answer isn't available, state 'unanswerable'."
-Original acc: 87.05%, attacked acc: 86.49%, dropped acc: 0.56%
-
-Original prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"As a subjected matter expe\xe2\xb2\x85t, extract the correct an swer from the context. If an answer is not present, indicate 'unanswerable'."
-Original acc: 87.05%, attacked acc: 85.95%, dropped acc: 1.10%
-
-## textfooler
-
-Original prompt: From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.
-Attacked prompt: b"Against the context, give the most correct answer. If the solution is not in the context, respond with 'unanswerable'."
-Original acc: 87.91%, attacked acc: 86.05%, dropped acc: 1.86%
-
-Original prompt: Search the context for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"Search the context for the most relevant reaction. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 86.09%, dropped acc: 1.23%
-
-Original prompt: Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"Please extract the most appropriate reaction from the context. Though an answer is not present, indicate 'unanswerable'."
-Original acc: 87.15%, attacked acc: 86.42%, dropped acc: 0.73%
-
-Original prompt: As a proficient expert in the given context, search for the most relevant answer. If the answer cannot be found, respond by saying 'unanswerable'.
-Attacked prompt: b"As a proficient expertise in the made context, search for the most associated answer. If the answer cannot be found, respond by saying 'unanswerable'."
-Original acc: 87.32%, attacked acc: 86.65%, dropped acc: 0.67%
-
-Original prompt: Drawing upon your expertise in the context, determine the most suitable answer. If an answer isn't available, state 'unanswerable'.
-Attacked prompt: b"Drawing upon your expertise in the context, evaluate the most suitable responding. If an answer isn't available, state 'unanswerable'."
-Original acc: 87.05%, attacked acc: 86.49%, dropped acc: 0.56%
-
-Original prompt: As a subject matter expert, extract the correct answer from the context. If an answer is not present, indicate 'unanswerable'.
-Attacked prompt: b"As a subject matter expert, extract the correct reacts from the context. If an answer is not present, indicate 'unanswerable'."
-Original acc: 87.05%, attacked acc: 85.45%, dropped acc: 1.60%
-
-## translation
-
-Language: chinese, acc: 87.55%, prompt: Provide the most accurate answer from the context. If the answer is not in context, answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Please give the most accurate answer based on the context. If you cannot find the answer, please answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Provide the most accurate answer based on the context. If you cannot find the answer, please answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Please provide the most accurate answer from the context. If you cannot find the answer, please answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Give the most accurate answer based on the context. If you cannot find the answer, please answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Please give the most accurate answer based on the context. If the answer is not in context, answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Provide the most accurate answer from the context. If you cannot find the answer, please answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Please give the most accurate answer based on the context. If the answer cannot be found, please answer 'unanswerable'.
-Language: chinese, acc: 87.05%, prompt: Provide the most accurate answer based on the context. If the answer cannot be found, please answer 'unanswerable'.
-Language: chinese, acc: 87.55%, prompt: Please provide the most accurate answer from the context. If the answer cannot be found, please answer 'unanswerable'.
-Language: french, acc: 87.05%, prompt: From the context, provide the most accurate answer. If the answer is not in context, answer with 'unanswerable'.
-Language: french, acc: 87.91%, prompt: From the context, give the most accurate answer. If the answer is not present in the context, answer with 'unanswerable'.
-Language: french, acc: 87.05%, prompt: Based on the context, provide the most accurate answer. If the answer is not in context, answer with 'unanswerable'.
-Language: french, acc: 87.91%, prompt: According to the context, give the most precise answer. If the answer is not present in the context, answer with 'unanswerable'.
-Language: french, acc: 87.05%, prompt: From the context, find the most accurate answer. If the answer is not in context, answer with 'unanswerable'.
-Language: french, acc: 87.05%, prompt: Based on the context, provide the most accurate answer. If the answer is not available in the context, answer with 'unanswerable'.
-Language: french, acc: 87.91%, prompt: According to the context, give the most precise answer. If the answer is not in the context, answer with 'unanswerable'.
-Language: french, acc: 87.41%, prompt: From the context, find the most accurate answer. If the answer is not present in the context, answer with 'unanswerable'.
-Language: french, acc: 87.05%, prompt: Based on the context, provide the most accurate answer. If the answer cannot be found in the context, answer with 'unanswerable'.
-Language: french, acc: 87.91%, prompt: According to the context, give the most precise answer. If the answer is not available in the context, answer with 'unanswerable'.
-Language: arabic, acc: 87.05%, prompt: From context, provide the most accurate answer. If not in context, please reply 'unanswerable',
-Language: arabic, acc: 86.72%, prompt: From context, what is the most likely outcome? If the answer is not in context, please reply 'unanswerable',
-Language: arabic, acc: 87.15%, prompt: From the given context, what is the key element that can be deduced? If the answer is not available in the context, please reply 'unanswerable',
-Language: arabic, acc: 86.72%, prompt: Based on the context given, what is the clear key idea? If the answer is not in context, please reply 'unanswerable',
-Language: arabic, acc: 86.72%, prompt: Based on the context, what is the most convincing explanation? If the answer is not available in the context, please reply 'unanswerable',
-Language: arabic, acc: 87.22%, prompt: Based on the context, what is the most likely outcome? If the answer is not available in the context, please reply 'unanswerable',
-Language: arabic, acc: 86.72%, prompt: Based on the context, which hypothesis is the most true? If the answer is not in context, please reply 'unanswerable',
-Language: arabic, acc: 87.22%, prompt: From context, what is the most apparent factor influencing? If the answer is not available in the context, please reply 'unanswerable',
-Language: arabic, acc: 87.05%, prompt: From context, provide the most accurate answer. If the answer is not in context, reply 'unanswerable',
-Language: arabic, acc: 87.05%, prompt: From context, determine the most accurate answer. If the answer is not available in context, answer 'unanswerable',
-Language: spanish, acc: 87.22%, prompt: Depending on the context, it provides the most precise answer. If the answer is not in context, answer with 'unanswerable'.
-Language: spanish, acc: 86.22%, prompt: Briefly describes the situation and provides the corresponding response. If the answer cannot be found, answer with 'unanswerable'.
-Language: spanish, acc: 87.05%, prompt: Given the information given, what is the most appropriate response? If the answer cannot be determined, answer with 'unanswerable'.
-Language: spanish, acc: 87.05%, prompt: Read the following text and give the most accurate answer. If you can't find the answer, answer with 'unanswerable'.
-Language: spanish, acc: 86.55%, prompt: Based on the description, what is the most accurate answer? If the answer is not found in the description, answer with 'unanswerable'.
-Language: spanish, acc: 87.22%, prompt: From the context provided, which response is the most appropriate? If the answer cannot be found, answer with 'unanswerable'.
-Language: spanish, acc: 87.05%, prompt: Analyze the following paragraph and provide the most accurate answer. If the answer is not in the paragraph, answer with 'unanswerable'.
-Language: spanish, acc: 86.55%, prompt: According to the information presented, what is the most precise answer? If the answer cannot be determined, answer with 'unanswerable'.
-Language: spanish, acc: 87.05%, prompt: After reading the excerpt, which do you think is the correct answer? If the answer cannot be discerned, answer with 'unanswerable'.
-Language: spanish, acc: 87.22%, prompt: Based on the context, it provides the most appropriate response. If the answer is not in context, answer with 'unanswerable'.
-Language: japanese, acc: 87.05%, prompt: Provide the most accurate answer from this context. If the answer isn't in the context, answer 'unanswerable'.
-Language: japanese, acc: 87.55%, prompt: Please provide the most appropriate answer based on the information specified in this sentence. If the answer is not in the text, answer 'unanswerable'.
-Language: japanese, acc: 86.55%, prompt: Please provide the most accurate answer based on the information guessed from this text. If the answer is not in the text, answer 'unanswerable'.
-Language: japanese, acc: 87.05%, prompt: Provide the most detailed answer based on the given context. If the answer is not in the context, answer 'unanswerable'.
-Language: japanese, acc: 86.55%, prompt: Consider the information derived from this context and provide the most accurate answer. If the answer is not in the context, answer 'unanswerable'.
-Language: japanese, acc: 87.15%, prompt: Based on this context, please provide the most appropriate answer. If the answer is not in the context, answer 'unanswerable'.
-Language: japanese, acc: 86.91%, prompt: Consider the information derived from the given text and provide the most detailed answer. If the answer is not in the text, please answer 'unanswerable'.
-Language: japanese, acc: 87.05%, prompt: Provide the most accurate answer based on the information given in this text. If the answer is not in the text, answer 'unanswerable'.
-Language: japanese, acc: 86.65%, prompt: Consider the information inferred from this context and provide the most appropriate answer. If the answer is not in the context, answer 'unanswerable'.
-Language: japanese, acc: 87.05%, prompt: Provide the most detailed answer based on this context. If the answer is not in the context, answer 'unanswerable'.
-Language: korean, acc: 87.05%, prompt: Give the most accurate answer in context. If the answer is not in context, respond with 'unanswerable'.
-Language: korean, acc: 86.92%, prompt: Please provide additional information about the facts mentioned in this sentence. If no information is available, respond with 'unanswerable'.
-Language: korean, acc: 87.05%, prompt: Please tell me what your question is about. If there is no context in which you can provide an answer, respond with 'unanswerable'.
-Language: korean, acc: 86.92%, prompt: Please explain the concept mentioned in the following sentence. If there is no information on the concept, please respond with 'unanswerable'.
-Language: korean, acc: 86.32%, prompt: Tell me what you're comparing to in this sentence. If nothing is compared, please respond with 'unanswerable'.
-Language: korean, acc: 86.82%, prompt: Please perform the actions required by the following context. If the task is not possible or if you are not clear what needs to be done, respond with 'unanswerable'.
-Language: korean, acc: 86.55%, prompt: Figure out what information this sentence contains. If no information is available, respond with 'unanswerable'.
-Language: korean, acc: 87.32%, prompt: Please give a solution to what kind of problem in the following sentence. If there is no solution, respond with 'unanswerable'.
-Language: korean, acc: 87.32%, prompt: Please give the cause of the incident mentioned in the context. If the cause is not clear, respond with 'unanswerable'.
-Language: korean, acc: 87.15%, prompt: Give expected results in the following sentences. If the result is unpredictable, respond with 'unanswerable'.
-
-# iwslt
-
-## 10 prompts
-
-Acc: 0.22%, prompt: Convert the following sentence from its original {} language to the target language {}.
-Acc: 0.22%, prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Acc: 0.22%, prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Acc: 0.22%, prompt: Accurately translate the sentence from {} to {}, ensuring the meaning remains intact.
-Acc: 0.22%, prompt: Translate the subsequent sentence from its source language {} into the desired language {}.
-Acc: 0.22%, prompt: Perform a machine translation for the provided sentence, changing it from {} to {}.
-Acc: 0.22%, prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Acc: 0.22%, prompt: Translate the following text from the source language {} to the target language {}.
-Acc: 0.21%, prompt: Translate the subsequent text passage from its original {} language to the {} language.
-Acc: 0.21%, prompt: Given the sentence below, perform a machine translation from {} to {}.
-
-Acc: 0.22%, prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Acc: 0.22%, prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Acc: 0.22%, prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Acc: 0.22%, prompt: Assuming the responsibilities of a professional translator, translate the subsequent text passage from {} to {}.
-Acc: 0.22%, prompt: As a language specialist, accurately translate the provided sentence from {} to {}.
-Acc: 0.22%, prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Acc: 0.22%, prompt: Assuming the role of an expert language translator, translate the given text from {} to {}.
-Acc: 0.22%, prompt: Acting as a professional translator, translate the provided sentence from {} to {}.
-Acc: 0.22%, prompt: As a skilled linguist, please convert the following sentence from {} to {}.
-Acc: 0.22%, prompt: While acting as an experienced language expert, translate the following text from {} to {}.
-
-## bertattack
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'replace the this verb from its originally {} language to the target language {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.02%
-
-Original prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Attacked prompt: b'on language translation, convert the given sentence from {} into the {} program. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Attacked prompt: b'Please take a regular of the given sentence, converting it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'form a qualified translator, manipulate the given example from its original {} languages to the target language {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Attacked prompt: b'know the role of a it expert, find a machine translation for the provided sentence, changing it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient only, please amend the following sentence from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-## checklist
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convert the following sentence from its original {} language to the target language LkF0FZxMZ4 {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.01%
-
-Original prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Attacked prompt: b'Using machine translation, convert the given sentence from {} into the {} language AMsRIKZniY. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Attacked prompt: b'Please perform a translation of the given sentence, converting it from {} to zexKUTWJYG {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a qualified translator, convert the given sentence from its original {} language to the target language mzjVogkRhM {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Attacked prompt: b'In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to MQnugHcaoy {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient translator, please change the following sentence from {} to MQnugHcaoy {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-## deepwordbug
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'tonvert the following senvtence from its original {} language to the target language {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Attacked prompt: b'Using machine translation, conevrt the gihven Ysentence from {} into the {} languaxe. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Attacked prompt: b'Please perform a translation of the given senence, converting it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a qualiied translator, conevrt the given sentenZe from its oriHinal {} language to the target language {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Attacked prompt: b'In the role of a langauge expert, perforF a machine kranslation for the pLovided senteSce, changing it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.14%, dropped acc: 0.08%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capaciy of a proficient txanslator, please change the following sentence from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-## stresstest
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convert the following sentence from its original {} language to the target language  and true is true  {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Attacked prompt: b'Using machine translation, convert the given sentence from {} into the {} language  and false is not true . '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Attacked prompt: b'Please perform a translation of the given sentence, converting it from {} to  and true is true  and true is true  and true is true  and true is true  and true is true  {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a qualified translator, convert the given sentence from its original {} language to the target language  and false is not true  {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.00%
-
-Original prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Attacked prompt: b'In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to  and false is not true  {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient translator, please change the following sentence from {} to  and false is not true  {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.01%
-
-## textbugger
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convret the following sentence from its original {} language to the ta\xe2\xb2\x85get language {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Attacked prompt: b'Using equipment translation, convert the given sentence from {} into the {} lan\xc9\xa1uage. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Attacked prompt: b'Please fulfilling a t\xe2\xb2\x85anslation of the given sentence, converting it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'Since a qualifid translator, co\xd5\xb8vert the given sentence from its o riginal {} language to the target language {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Attacked prompt: b'At the roles of a language expert, fulfilling a m\xc9\x91chine translati\xd0\xben for the provided sentence, changing it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient translator, pl\xd0\xb5ase \xcf\xb2hange the follow ing sentence from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.22%, dropped acc: 0.01%
-
-## textfooler
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Conversion the later conviction from its indigenous {} vocabulary to the blanco tongues {}. '
-Original acc: 0.22%, attacked acc: 0.13%, dropped acc: 0.09%
-
-Original prompt: Using machine translation, convert the given sentence from {} into the {} language.
-Attacked prompt: b'Using machine translating, transforms the given judgments from {} into the {} lingo. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Attacked prompt: b'Please fulfils a rereading of the given conviction, transforms it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a skilled translation, converting the given sentence from its first {} language to the blanco languages {}. '
-Original acc: 0.22%, attacked acc: 0.17%, dropped acc: 0.05%
-
-Original prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Attacked prompt: b'In the role of a languages specializing, exerting a machine converting for the handed pity, metamorphosis it from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.16%, dropped acc: 0.06%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'Onto the capacity of a capable artist, please adaptations the next sentence from {} to {}. '
-Original acc: 0.22%, attacked acc: 0.21%, dropped acc: 0.01%
-
-## translation
-
-Language: chinese, acc: 0.21%, prompt: Please translate the given sentence into {} to {}.
-Language: chinese, acc: 0.22%, prompt: Please translate the following sentences from {} to {}.
-Language: chinese, acc: 0.21%, prompt: Please convert the following sentences to {} and translate to {}.
-Language: chinese, acc: 0.22%, prompt: Please convert the given sentence from {} to {}.
-Language: chinese, acc: 0.22%, prompt: Please translate the next sentence from {} to {}.
-Language: chinese, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: chinese, acc: 0.22%, prompt: Please translate the sentences given into {} and convert them into {}.
-Language: chinese, acc: 0.21%, prompt: Please convert the sentences given to {} to {}.
-Language: chinese, acc: 0.22%, prompt: Please translate the following sentences into {} and convert them into {}.
-Language: chinese, acc: 0.22%, prompt: Please change the given sentence from {} to {}.
-Language: french, acc: 0.22%, prompt: Please translate the given sentence, converting it from {} to {}.
-Language: french, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: french, acc: 0.22%, prompt: Please turn the sentence below into {}, then translate it into {}.
-Language: french, acc: 0.22%, prompt: Please convert the given phrase from {} to {}.
-Language: french, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: french, acc: 0.22%, prompt: Please translate the sentence below from {} to {}.
-Language: french, acc: 0.22%, prompt: Please translate the given sentence to {}, then convert it to {}.
-Language: french, acc: 0.22%, prompt: Please make a translation of the supplied sentence, transforming it from {} to {}.
-Language: french, acc: 0.22%, prompt: Please translate the following sentence to {}, then convert it to {}.
-Language: french, acc: 0.22%, prompt: Please transform the given sentence from {} to {}.
-Language: arabic, acc: 0.21%, prompt: Please translate the given sentence, and convert it from {} to {},
-Language: arabic, acc: 0.22%, prompt: Please translate the following sentence from {} to {},
-Language: arabic, acc: 0.22%, prompt: Please convert the sentence below to {}, and then translate it to {},
-Language: arabic, acc: 0.22%, prompt: Please convert the given sentence from {} to {},
-Language: arabic, acc: 0.22%, prompt: Please translate the following sentence from {} to {},
-Language: arabic, acc: 0.22%, prompt: Please convert the sentence below from {} to {},
-Language: arabic, acc: 0.22%, prompt: Please translate the given sentence to {}, then convert it to {},
-Language: arabic, acc: 0.21%, prompt: Please translate the given sentence, and convert it from {} to {},
-Language: arabic, acc: 0.22%, prompt: Please translate to {}, then convert to {},
-Language: arabic, acc: 0.22%, prompt: Please convert the given sentence from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please make a translation of the provided phrase, converting it from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please convert the next sentence to {}, and then translate it to {}.
-Language: spanish, acc: 0.22%, prompt: Please make a translation of the given phrase, converting it from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please convert the following sentence from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please translate the sentence provided to {}, and then turn it to {}.
-Language: spanish, acc: 0.21%, prompt: Please make a translation of the following sentence, converting it from {} to {}.
-Language: spanish, acc: 0.22%, prompt: Please translate the next sentence to {}, and then turn it to {}.
-Language: spanish, acc: 0.22%, prompt: Please convert the given sentence from {} to {}.
-Language: japanese, acc: 0.22%, prompt: Please translate the given sentence from {} to {}.
-Language: japanese, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: japanese, acc: 0.22%, prompt: Please convert the following sentences into {} and translate them into {}.
-Language: japanese, acc: 0.22%, prompt: Please translate the given sentence by converting {} to {}.
-Language: japanese, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: japanese, acc: 0.21%, prompt: Please convert the following sentences from {} to {}.
-Language: japanese, acc: 0.22%, prompt: Translate the given sentence into {} and convert it to {}.
-Language: japanese, acc: 0.22%, prompt: Please translate the given sentence from {} to {}.
-Language: japanese, acc: 0.22%, prompt: Translate the following sentence into {} and convert it to {}.
-Language: japanese, acc: 0.21%, prompt: Convert the given statement from {} to {}.
-Language: korean, acc: 0.22%, prompt: Please translate the given sentence from {} to {}.
-Language: korean, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: korean, acc: 0.20%, prompt: Please translate the sentences below into {}, then {}.
-Language: korean, acc: 0.22%, prompt: Please translate the given sentences from {} to {}.
-Language: korean, acc: 0.22%, prompt: Please translate the following sentence from {} to {}.
-Language: korean, acc: 0.22%, prompt: Please convert the sentences below from {} to {}.
-Language: korean, acc: 0.21%, prompt: Please translate the given sentence into {}, then {}.
-Language: korean, acc: 0.22%, prompt: Please translate the given sentence from {} to {}.
-Language: korean, acc: 0.20%, prompt: Please translate the following sentences into {}, then {}.
-Language: korean, acc: 0.22%, prompt: Please convert the given sentence from {} to {}.
-
-# un_multi
-
-## 10 prompts
-
-Acc: 0.31%, prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Acc: 0.31%, prompt: Convert the following sentence from its original {} language to the target language {}.
-Acc: 0.31%, prompt: Translate the following text from the source language {} to the target language {}.
-Acc: 0.31%, prompt: Given the sentence below, perform a machine translation from {} to {}.
-Acc: 0.31%, prompt: Translate the subsequent sentence from its source language {} into the desired language {}.
-Acc: 0.31%, prompt: Perform a machine translation for the provided sentence, changing it from {} to {}.
-Acc: 0.31%, prompt: Please perform a translation of the given sentence, converting it from {} to {}.
-Acc: 0.31%, prompt: Translate the subsequent text passage from its original {} language to the {} language.
-Acc: 0.31%, prompt: Accurately translate the sentence from {} to {}, ensuring the meaning remains intact.
-Acc: 0.31%, prompt: Using machine translation, convert the given sentence from {} into the {} language.
-
-Acc: 0.31%, prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Acc: 0.31%, prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Acc: 0.31%, prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Acc: 0.31%, prompt: As a language specialist, accurately translate the provided sentence from {} to {}.
-Acc: 0.31%, prompt: In the role of a language expert, perform a machine translation for the provided sentence, changing it from {} to {}.
-Acc: 0.31%, prompt: Assuming the role of an expert language translator, translate the given text from {} to {}.
-Acc: 0.31%, prompt: Assuming the responsibilities of a professional translator, translate the subsequent text passage from {} to {}.
-Acc: 0.31%, prompt: Acting as a professional translator, translate the provided sentence from {} to {}.
-Acc: 0.31%, prompt: While acting as an experienced language expert, translate the following text from {} to {}.
-Acc: 0.30%, prompt: As a skilled linguist, please convert the following sentence from {} to {}.
-
-## bertattack
-
-Original prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Attacked prompt: b'Translate the provided sentence from {} to {} while maintained the same meaning and semantics. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'translate the second sentence from its original {} language to the target language {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.00%
-
-Original prompt: Translate the following text from the source language {} to the target language {}.
-Attacked prompt: b'Translate the final text from the source language {} to the target python {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a trained translator, convert the given case from its original {} language to the project languages {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the ability of a proficient translator, please visit the following sentence from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.01%
-
-Original prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Attacked prompt: b'Taking on the role of a certified translator, for a translate of the the segment from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-## checklist
-
-Original prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Attacked prompt: b'Translate the provided sentence from {} to {} while maintaining the original meaning and context ofOQXLxiW4. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convert the following sentence from its original {} language to the target language OB4KVJzIft {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Translate the following text from the source language {} to the target language {}.
-Attacked prompt: b'Translate the following text from the source language {} to the target language Cev0s49fbe {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a qualified translator, convert the given sentence from its original {} language to the target language yh912BU8T0 {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient translator, please change the following sentence from {} to OB4KVJzIft {}. '
-Original acc: 0.31%, attacked acc: 0.29%, dropped acc: 0.02%
-
-Original prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Attacked prompt: b'Taking on the role of a certified translator, perform a translation of the given sentence from {} to OB4KVJzIft {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-## deepwordbug
-
-Original prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Attacked prompt: b'Translate the proviWded entence from {} to {} while maintaining the oriinal meanin and conEtext. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'oCnvert the following sentence from its original {} language to the target language {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Translate the following text from the source language {} to the target language {}.
-Attacked prompt: b'Translate the followiSng text from the souce language {} to the targea Rlanguage {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'A a qalified translator, convert the given esntence from its original {} language to the trget languOge {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capaOity of a proficient translator, please hange the following sentence from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Attacked prompt: b'Taking on the roEle of a certifLed ltranslator, perofrm a trasnlation of the giDven senence from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.26%, dropped acc: 0.05%
-
-## stresstest
-
-Original prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Attacked prompt: b'Translate the provided sentence from {} to {} while maintaining the original meaning and context  and true is true  and true is true  and true is true  and true is true  and true is true . '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: -0.00%
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convert the following sentence from its original {} language to the target language  and false is not true  {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: -0.00%
-
-Original prompt: Translate the following text from the source language {} to the target language {}.
-Attacked prompt: b'Translate the following text from the source language {} to the target language  and false is not true  {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.00%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a qualified translator, convert the given sentence from its original {} language to the target language  and true is true  {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: -0.00%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient translator, please change the following sentence from {} to  and false is not true  {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Attacked prompt: b'Taking on the role of a certified translator, perform a translation of the given sentence from {} to  and true is true  and true is true  and true is true  and true is true  and true is true  {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-## textbugger
-
-Original prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Attacked prompt: b'Translate the provided s\xd0\xb5ntence from {} to {} while maintaining the oriinal meaning and context. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convrt the following sentence from its original {} language to the objectives language {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Translate the following text from the source language {} to the target language {}.
-Attacked prompt: b'Translate the subsequent text from the source tongue {} to the target lanugage {}. '
-Original acc: 0.31%, attacked acc: 0.29%, dropped acc: 0.02%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a qualified translator, convert the given punishments from its original {} parlance to the target language {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the capacity of a proficient translator, pelase chan\xc9\xa1e the following sentence from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.31%, dropped acc: 0.00%
-
-Original prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Attacked prompt: b'Taking on the role of a certified translator, performed a translator of the gi\xd1\xb5en sentenc\xd0\xb5 from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.28%, dropped acc: 0.04%
-
-## textfooler
-
-Original prompt: Translate the provided sentence from {} to {} while maintaining the original meaning and context.
-Attacked prompt: b'Translate the provided judgments from {} to {} while protection the preliminary meaning and regard. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: Convert the following sentence from its original {} language to the target language {}.
-Attacked prompt: b'Convert the below sentence from its indigenous {} language to the blanco language {}. '
-Original acc: 0.31%, attacked acc: 0.22%, dropped acc: 0.09%
-
-Original prompt: Translate the following text from the source language {} to the target language {}.
-Attacked prompt: b'Translate the follows text from the source language {} to the purposes wording {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.00%
-
-Original prompt: As a qualified translator, convert the given sentence from its original {} language to the target language {}.
-Attacked prompt: b'As a trained translator, convert the given verdict from its original {} phraseology to the blanco language {}. '
-Original acc: 0.31%, attacked acc: 0.28%, dropped acc: 0.03%
-
-Original prompt: In the capacity of a proficient translator, please change the following sentence from {} to {}.
-Attacked prompt: b'In the functionality of a specialised interpreting, please amends the subsequent judgments from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.30%, dropped acc: 0.01%
-
-Original prompt: Taking on the role of a certified translator, perform a translation of the given sentence from {} to {}.
-Attacked prompt: b'Taking on the role of a attest translator, implement a interpreters of the gave sanction from {} to {}. '
-Original acc: 0.31%, attacked acc: 0.26%, dropped acc: 0.05%
-
-## translation
-
-Language: chinese, acc: 0.30%, prompt: Please translate the given sentence into {} to {}.
-Language: chinese, acc: 0.31%, prompt: Please translate the following sentences from {} to {}.
-Language: chinese, acc: 0.30%, prompt: Please convert the following sentences to {} and translate to {}.
-Language: chinese, acc: 0.31%, prompt: Please convert the given sentence from {} to {}.
-Language: chinese, acc: 0.31%, prompt: Please translate the next sentence from {} to {}.
-Language: chinese, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: chinese, acc: 0.31%, prompt: Please translate the sentences given into {} and convert them into {}.
-Language: chinese, acc: 0.31%, prompt: Please convert the sentences given to {} to {}.
-Language: chinese, acc: 0.31%, prompt: Please translate the following sentences into {} and convert them into {}.
-Language: chinese, acc: 0.31%, prompt: Please change the given sentence from {} to {}.
-Language: french, acc: 0.31%, prompt: Please translate the given sentence, converting it from {} to {}.
-Language: french, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: french, acc: 0.30%, prompt: Please turn the sentence below into {}, then translate it into {}.
-Language: french, acc: 0.31%, prompt: Please convert the given phrase from {} to {}.
-Language: french, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: french, acc: 0.31%, prompt: Please translate the sentence below from {} to {}.
-Language: french, acc: 0.31%, prompt: Please translate the given sentence to {}, then convert it to {}.
-Language: french, acc: 0.31%, prompt: Please make a translation of the supplied sentence, transforming it from {} to {}.
-Language: french, acc: 0.31%, prompt: Please translate the following sentence to {}, then convert it to {}.
-Language: french, acc: 0.31%, prompt: Please transform the given sentence from {} to {}.
-Language: arabic, acc: 0.31%, prompt: Please translate the given sentence, and convert it from {} to {},
-Language: arabic, acc: 0.30%, prompt: Please translate the following sentence from {} to {},
-Language: arabic, acc: 0.31%, prompt: Please convert the sentence below to {}, and then translate it to {},
-Language: arabic, acc: 0.31%, prompt: Please convert the given sentence from {} to {},
-Language: arabic, acc: 0.30%, prompt: Please translate the following sentence from {} to {},
-Language: arabic, acc: 0.31%, prompt: Please convert the sentence below from {} to {},
-Language: arabic, acc: 0.31%, prompt: Please translate the given sentence to {}, then convert it to {},
-Language: arabic, acc: 0.31%, prompt: Please translate the given sentence, and convert it from {} to {},
-Language: arabic, acc: 0.31%, prompt: Please translate to {}, then convert to {},
-Language: arabic, acc: 0.31%, prompt: Please convert the given sentence from {} to {}.
-Language: spanish, acc: 0.31%, prompt: Please make a translation of the provided phrase, converting it from {} to {}.
-Language: spanish, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: spanish, acc: 0.30%, prompt: Please convert the next sentence to {}, and then translate it to {}.
-Language: spanish, acc: 0.31%, prompt: Please make a translation of the given phrase, converting it from {} to {}.
-Language: spanish, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: spanish, acc: 0.31%, prompt: Please convert the following sentence from {} to {}.
-Language: spanish, acc: 0.30%, prompt: Please translate the sentence provided to {}, and then turn it to {}.
-Language: spanish, acc: 0.31%, prompt: Please make a translation of the following sentence, converting it from {} to {}.
-Language: spanish, acc: 0.30%, prompt: Please translate the next sentence to {}, and then turn it to {}.
-Language: spanish, acc: 0.31%, prompt: Please convert the given sentence from {} to {}.
-Language: japanese, acc: 0.31%, prompt: Please translate the given sentence from {} to {}.
-Language: japanese, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: japanese, acc: 0.30%, prompt: Please convert the following sentences into {} and translate them into {}.
-Language: japanese, acc: 0.31%, prompt: Please translate the given sentence by converting {} to {}.
-Language: japanese, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: japanese, acc: 0.31%, prompt: Please convert the following sentences from {} to {}.
-Language: japanese, acc: 0.31%, prompt: Translate the given sentence into {} and convert it to {}.
-Language: japanese, acc: 0.31%, prompt: Please translate the given sentence from {} to {}.
-Language: japanese, acc: 0.31%, prompt: Translate the following sentence into {} and convert it to {}.
-Language: japanese, acc: 0.31%, prompt: Convert the given statement from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please translate the given sentence from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please translate the sentences below into {}, then {}.
-Language: korean, acc: 0.31%, prompt: Please translate the given sentences from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please translate the following sentence from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please convert the sentences below from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please translate the given sentence into {}, then {}.
-Language: korean, acc: 0.31%, prompt: Please translate the given sentence from {} to {}.
-Language: korean, acc: 0.31%, prompt: Please translate the following sentences into {}, then {}.
-Language: korean, acc: 0.31%, prompt: Please convert the given sentence from {} to {}.
-
-# math
-
-## 10 prompts
-
-Acc: 17.50%, prompt: Find the solution for this mathematical challenge with {}.
-Acc: 17.50%, prompt: Resolve the following mathematical question associated with {}.
-Acc: 17.50%, prompt: Figure out the solution for the following mathematical task with {}.
-Acc: 17.50%, prompt: Evaluate the following mathematical problem that includes {}.
-Acc: 16.88%, prompt: Determine the solution to this mathematical problem related to {}.
-Acc: 16.88%, prompt: Calculate the answer to the following math query about {}.
-Acc: 16.25%, prompt: Solve the following math question about {}.
-Acc: 16.25%, prompt: Compute the result of this math task concerning {}.
-Acc: 16.25%, prompt: Work out the answer to this math problem featuring {}.
-Acc: 16.25%, prompt: Obtain the result for this math question regarding {}.
-
-Acc: 18.12%, prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Acc: 17.50%, prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Acc: 17.50%, prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Acc: 16.88%, prompt: Pretend you are a math teacher and solve this mathematical question concerning {}.
-Acc: 16.88%, prompt: In the position of a mathematics coach, figure out the answer to this math challenge involving {}.
-Acc: 16.88%, prompt: As a mathematics consultant, resolve the following problem associated with {}.
-Acc: 16.25%, prompt: As a math guru, evaluate the following mathematical task regarding {}.
-Acc: 16.25%, prompt: In the shoes of a mathematics master, obtain the result for this math question that includes {}.
-Acc: 15.62%, prompt: Act as a math expert and compute the result of the following mathematical task about {}.
-Acc: 15.00%, prompt: Taking on the role of a math specialist, work out the solution for this mathematical question featuring {}.
-
-## bertattack
-
-Original prompt: Find the solution for this mathematical challenge with {}.
-Attacked prompt: b'Find the conclusion for this mathematical challenge with {}. '
-Original acc: 17.50%, attacked acc: 15.00%, dropped acc: 2.50%
-
-Original prompt: Resolve the following mathematical question associated with {}.
-Attacked prompt: b'about the following mathematical question associated with {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Figure out the solution for the following mathematical task with {}.
-Attacked prompt: b'Figure out the sum for the following mathematical task with {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Attacked prompt: b'Embodying a mathematics advisor, find the subject to the such math item with {}. '
-Original acc: 18.12%, attacked acc: 9.38%, dropped acc: 8.75%
-
-Original prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Attacked prompt: b'As a mathematics instructor, calculate the inverse to the following problem by to {}. '
-Original acc: 17.50%, attacked acc: 14.37%, dropped acc: 3.12%
-
-Original prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Attacked prompt: b'[UNK] being a math mentor and determine the solve to the following problem with {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-## checklist
-
-Original prompt: Find the solution for this mathematical challenge with {}.
-Attacked prompt: b'Find the solution for this mathematical challenge with 9Q3S1F94fE {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Resolve the following mathematical question associated with {}.
-Attacked prompt: b'Resolve the following mathematical question associated with 6a4Yn3RGVc {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Figure out the solution for the following mathematical task with {}.
-Attacked prompt: b'Figure out the solution for the following mathematical task with 6a4Yn3RGVc {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Attacked prompt: b'Embodying a mathematics advisor, find the answer to the following math problem with EAB4KP2NVY {}. '
-Original acc: 18.12%, attacked acc: 16.25%, dropped acc: 1.87%
-
-Original prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Attacked prompt: b'As a mathematics instructor, calculate the answer to the following problem related to 5yWbBXztUY {}. '
-Original acc: 17.50%, attacked acc: 16.88%, dropped acc: 0.62%
-
-Original prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Attacked prompt: b'Imagine being a math mentor and determine the solution to the following problem with b74X5IVXQY {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-## deepwordbug
-
-Original prompt: Find the solution for this mathematical challenge with {}.
-Attacked prompt: b'Find the aolution for this amthematical challenge with {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Resolve the following mathematical question associated with {}.
-Attacked prompt: b'xesolve the following mathematicMal question associated with {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Figure out the solution for the following mathematical task with {}.
-Attacked prompt: b'Figure out the slution for the following mathemaitcal task with {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Attacked prompt: b'Embodying a mathematics advisor, find the answer to the following math prblem with {}. '
-Original acc: 18.12%, attacked acc: 16.25%, dropped acc: 1.87%
-
-Original prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Attacked prompt: b'sA a mathematics instructor, calculate the anvwer to the following problem relDated to {}. '
-Original acc: 17.50%, attacked acc: 15.00%, dropped acc: 2.50%
-
-Original prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Attacked prompt: b'Imagine being a math mentor and determine the solution to the following problem with {}. '
-Original acc: 17.50%, attacked acc: 17.50%, dropped acc: 0.00%
-
-## stresstest
-
-Original prompt: Find the solution for this mathematical challenge with {}.
-Attacked prompt: b'Find the solution for this mathematical challenge with  and false is not true  {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Resolve the following mathematical question associated with {}.
-Attacked prompt: b'Resolve the following mathematical question associated with  and false is not true  {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Figure out the solution for the following mathematical task with {}.
-Attacked prompt: b'Figure out the solution for the following mathematical task with  and false is not true  {}. '
-Original acc: 17.50%, attacked acc: 16.88%, dropped acc: 0.62%
-
-Original prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Attacked prompt: b'Embodying a mathematics advisor, find the answer to the following math problem with  and false is not true  {}. '
-Original acc: 18.12%, attacked acc: 16.25%, dropped acc: 1.87%
-
-Original prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Attacked prompt: b'As a mathematics instructor, calculate the answer to the following problem related to  and false is not true  {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-Original prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Attacked prompt: b'Imagine being a math mentor and determine the solution to the following problem with  and false is not true  {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-## textbugger
-
-Original prompt: Find the solution for this mathematical challenge with {}.
-Attacked prompt: b'Find the solve for this mathematical challenge with {}. '
-Original acc: 17.50%, attacked acc: 16.88%, dropped acc: 0.62%
-
-Original prompt: Resolve the following mathematical question associated with {}.
-Attacked prompt: b'Reslove the following math question associated with {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Figure out the solution for the following mathematical task with {}.
-Attacked prompt: b'Figure out the solution for the following arithmetic task with {}. '
-Original acc: 17.50%, attacked acc: 16.88%, dropped acc: 0.62%
-
-Original prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Attacked prompt: b'Embodying a mathematics advis\xd0\xber, find the ansewr to the following math problem with {}. '
-Original acc: 18.12%, attacked acc: 16.25%, dropped acc: 1.87%
-
-Original prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Attacked prompt: b'As a mathematics trainers, calculate the respond to the following problem related to {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Attacked prompt: b'Ima\xc9\xa1ine being a math mentor and determi\xd5\xb8e the solution to the following problem with {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-## textfooler
-
-Original prompt: Find the solution for this mathematical challenge with {}.
-Attacked prompt: b'Find the resolutions for this mathematical challenge with {}. '
-Original acc: 17.50%, attacked acc: 15.00%, dropped acc: 2.50%
-
-Original prompt: Resolve the following mathematical question associated with {}.
-Attacked prompt: b'Liquidate the following mathematical question affiliates with {}. '
-Original acc: 17.50%, attacked acc: 15.00%, dropped acc: 2.50%
-
-Original prompt: Figure out the solution for the following mathematical task with {}.
-Attacked prompt: b'Fig out the resolution for the following arithmetic task with {}. '
-Original acc: 17.50%, attacked acc: 15.62%, dropped acc: 1.87%
-
-Original prompt: Embodying a mathematics advisor, find the answer to the following math problem with {}.
-Attacked prompt: b'Embodying a mathematics advice, find the rebuttal to the following math problem with {}. '
-Original acc: 18.12%, attacked acc: 13.75%, dropped acc: 4.37%
-
-Original prompt: As a mathematics instructor, calculate the answer to the following problem related to {}.
-Attacked prompt: b'As a mathematics trainers, calculate the answered to the following problem related to {}. '
-Original acc: 17.50%, attacked acc: 15.00%, dropped acc: 2.50%
-
-Original prompt: Imagine being a math mentor and determine the solution to the following problem with {}.
-Attacked prompt: b'Imagine being a math mentor and determine the solution to the following question with {}. '
-Original acc: 17.50%, attacked acc: 16.25%, dropped acc: 1.25%
-
-## translation
-
-Language: chinese, acc: 17.50%, prompt: Calculate the answer to a mathematical question about {}.
-Language: chinese, acc: 16.88%, prompt: Calculate the value of {}.
-Language: chinese, acc: 17.50%, prompt: Please calculate the result of {}.
-Language: chinese, acc: 18.12%, prompt: According to {}, solve the following mathematical problem.
-Language: chinese, acc: 17.50%, prompt: Do the math with {}.
-Language: chinese, acc: 18.12%, prompt: Solve mathematical problems involving {}.
-Language: chinese, acc: 17.50%, prompt: Please calculate the answer involving {}.
-Language: chinese, acc: 17.50%, prompt: Solve for the following values based on {}.
-Language: chinese, acc: 17.50%, prompt: Calculate the following mathematical tasks using {}.
-Language: chinese, acc: 17.50%, prompt: Calculate the answer to the {} related question.
-Language: french, acc: 16.88%, prompt: Calculate the answer to the following mathematical question concerning {}.
-Language: french, acc: 17.50%, prompt: Calculate the result of {}.
-Language: french, acc: 17.50%, prompt: Please calculate the value of {}.
-Language: french, acc: 18.12%, prompt: According to {}, solve the following mathematical problem.
-Language: french, acc: 16.88%, prompt: Perform mathematical calculations with {}.
-Language: french, acc: 17.50%, prompt: Solve the mathematical problem involving {}.
-Language: french, acc: 16.88%, prompt: Please calculate the answer related to {}.
-Language: french, acc: 15.62%, prompt: According to {}, set the following value.
-Language: french, acc: 17.50%, prompt: Perform the following mathematical task using {}.
-Language: french, acc: 16.88%, prompt: Calculate the answer to the questions related to {}.
-Language: arabic, acc: 16.88%, prompt: Compute the answer to the next mathematical question about {}.
-Language: arabic, acc: 16.25%, prompt: Calculate {}.
-Language: arabic, acc: 17.50%, prompt: Please calculate {}.
-Language: arabic, acc: 18.12%, prompt: According to {}, solve the following mathematical problem.
-Language: arabic, acc: 16.25%, prompt: Do mathematical calculations using {}.
-Language: arabic, acc: 16.25%, prompt: A solution to the mathematical problem involving {}.
-Language: arabic, acc: 16.88%, prompt: Please calculate the answer regarding {}.
-Language: arabic, acc: 15.62%, prompt: According to {}, determine the next value.
-Language: arabic, acc: 18.12%, prompt: DO THE NEXT MATHEMATICAL JOB USING {}.
-Language: arabic, acc: 17.50%, prompt: Calculate the answer to questions related to {}.
-Language: spanish, acc: 16.88%, prompt: Compute the answer to the following mathematical question on {}.
-Language: spanish, acc: 16.88%, prompt: Compute the result of {}.
-Language: spanish, acc: 17.50%, prompt: Please calculate the value of {}.
-Language: spanish, acc: 17.50%, prompt: As {}, it solves the following mathematical problem.
-Language: spanish, acc: 16.88%, prompt: Performs mathematical calculations using {}.
-Language: spanish, acc: 17.50%, prompt: Solve the mathematical problem involving {}.
-Language: spanish, acc: 16.88%, prompt: Please calculate the answer related to {}.
-Language: spanish, acc: 15.62%, prompt: As {}, determine the next value.
-Language: spanish, acc: 17.50%, prompt: Perform the following mathematical task using {}.
-Language: spanish, acc: 16.25%, prompt: Compute the answer to questions related to {}.
-Language: japanese, acc: 16.25%, prompt: Calculate the answers to the math questions about {}.
-Language: japanese, acc: 16.88%, prompt: Calculate the value of {}.
-Language: japanese, acc: 16.25%, prompt: Please find the answer to {}.
-Language: japanese, acc: 18.12%, prompt: Based on {}, please solve the following mathematical problems.
-Language: japanese, acc: 16.88%, prompt: Use {} to perform mathematical calculations.
-Language: japanese, acc: 15.62%, prompt: Please solve the math problem that contains {}.
-Language: japanese, acc: 16.88%, prompt: Please calculate the answers related to {}.
-Language: japanese, acc: 17.50%, prompt: Based on {}, find the following values:
-Language: japanese, acc: 17.50%, prompt: Use {} to solve the following mathematical problem.
-Language: japanese, acc: 16.88%, prompt: Please calculate the answers to the questions related to {}.
-Language: korean, acc: 16.88%, prompt: Calculate the answer of the following math problem to {}.
-Language: korean, acc: 17.50%, prompt: Calculate the result of {}.
-Language: korean, acc: 17.50%, prompt: Please calculate the value of {}.
-Language: korean, acc: 16.88%, prompt: Work out the following math problems according to {}.
-Language: korean, acc: 16.88%, prompt: Use {} to proceed with mathematical calculations.
-Language: korean, acc: 17.50%, prompt: Work out a math problem involving {}.
-Language: korean, acc: 16.88%, prompt: Please calculate the answer to {}.
-Language: korean, acc: 16.25%, prompt: Try to get the following values according to {}.
-Language: korean, acc: 16.88%, prompt: Work out the next math task using {}.
-Language: korean, acc: 16.88%, prompt: Calculate the answer of the problem involving {}.
\ No newline at end of file
diff --git a/spaces/Matthijs/mms-tts-demo/uroman/bin/string-distance.pl b/spaces/Matthijs/mms-tts-demo/uroman/bin/string-distance.pl
deleted file mode 100644
index 9870fdf9c0ddaf4928da5fe4c11632facefbaa38..0000000000000000000000000000000000000000
--- a/spaces/Matthijs/mms-tts-demo/uroman/bin/string-distance.pl
+++ /dev/null
@@ -1,99 +0,0 @@
-#!/usr/bin/perl -w
-
-# Author: Ulf Hermjakob
-# Release date: October 13, 2019
-
-# Usage: string-distance.pl {-lc1 } {-lc2 } < STDIN > STDOUT
-# Example: string-distance.pl -lc1 rus -lc2 ukr < STDIN > STDOUT
-# Example: string-distance.pl < ../test/string-similarity-test-input.txt
-# Input format: two strings per line (tab-separated, in Latin script)
-#    Strings in non-Latin scripts should first be romanized. (Recommended script: uroman.pl)
-# Output format: repetition of the two input strings, plus the string distance between them (tab-separated).
-#    Additional output meta info lines at the top are marked with an initial #.
-#
-# The script uses data from a string-distance-cost-rules file that lists costs,
-# where the default cost is "1" with lower costs for differences in vowels,
-# duplicate consonants, "f" vs. "ph" etc.
-# Language cost rules can be language-specific and context-sensitive.
-
-$|=1;
-
-use FindBin;
-use Cwd "abs_path";
-use File::Basename qw(dirname);
-use File::Spec;
-
-my $bin_dir = abs_path(dirname($0));
-my $root_dir = File::Spec->catfile($bin_dir, File::Spec->updir());
-my $data_dir = File::Spec->catfile($root_dir, "data");
-my $lib_dir = File::Spec->catfile($root_dir, "lib");
-
-use lib "$FindBin::Bin/../lib";
-use List::Util qw(min max);
-use NLP::utilities;
-use NLP::stringDistance;
-$util = NLP::utilities;
-$sd = NLP::stringDistance;
-$verbose = 0;
-$separator = "\t";
-
-$cost_rule_filename = File::Spec->catfile($data_dir, "string-distance-cost-rules.txt");
-
-$lang_code1 = "eng";
-$lang_code2 = "eng";
-%ht = ();
-
-while (@ARGV) {
-   $arg = shift @ARGV;
-   if ($arg =~ /^-+lc1$/) {
-      $lang_code_candidate = shift @ARGV;
-      $lang_code1 = $lang_code_candidate if $lang_code_candidate =~ /^[a-z]{3,3}$/;
-   } elsif ($arg =~ /^-+lc2$/) {
-      $lang_code_candidate = shift @ARGV;
-      $lang_code2 = $lang_code_candidate if $lang_code_candidate =~ /^[a-z]{3,3}$/;
-   } elsif ($arg =~ /^-+(v|verbose)$/) {
-      $verbose = shift @ARGV;
-   } else {
-      print STDERR "Ignoring unrecognized arg $arg\n";
-   }
-}
-
-$sd->load_string_distance_data($cost_rule_filename, *ht, $verbose);
-print STDERR "Loaded resources.\n" if $verbose;
-
-my $chart_id = 0;
-my $line_number = 0;
-print "# Lang-code-1: $lang_code1 Lang-code-2: $lang_code2\n";
-while (<>) {
-   $line_number++;
-   if ($verbose) {
-      if ($line_number =~ /000$/) {
-         if ($line_number =~ /0000$/) {
-	    print STDERR $line_number;
-         } else {
-	    print STDERR ".";
-         }
-      }
-   }
-   my $line = $_;
-   $line =~ s/^\xEF\xBB\xBF//;
-   next if $line =~ /^\s*(\#.*)?$/;
-   my $s1;
-   my $s2;
-   if (($s1, $s2) = ($line =~ /^("(?:\\"|[^"])*"|\S+)$separator("(?:\\"|[^"])*"|\S+)\s*$/)) {
-      $s1 = $util->dequote_string($s1);
-      $s2 = $util->dequote_string($s2);
-   } elsif ($line =~ /^\s*(#.*)$/) {
-   } else {
-      print STDERR "Could not process line $line_number: $line" if $verbose;
-      print "\n";
-      next;
-   }
-
-   $cost = $sd->quick_romanized_string_distance_by_chart($s1, $s2, *ht, "", $lang_code1, $lang_code2);
-   print "$s1\t$s2\t$cost\n";
-}
-print STDERR "\n" if $verbose;
-
-exit 0;
-
diff --git a/spaces/Mayanand/Automatic-Number-Plate-Recognition/utils_.py b/spaces/Mayanand/Automatic-Number-Plate-Recognition/utils_.py
deleted file mode 100644
index e3fe0af4ac9d3932e47215d7697e9fdec0c2d1e3..0000000000000000000000000000000000000000
--- a/spaces/Mayanand/Automatic-Number-Plate-Recognition/utils_.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from urllib.request import urlretrieve
-import os
-
-RECOGNITION_MODEL_PATH = 'https://github.com/yMayanand/number_plate_recognition/releases/download/v1.0/ocr_point08.pt'
-DETECTION_MODEL_PATH = 'https://github.com/yMayanand/number_plate_recognition/releases/download/v1.0/detection.pt'
-
-os.makedirs('./out', exist_ok=True)
-
-reco_model = './out/ocr_point08.pt'
-det_model = './out/detection.pt'
-
-if not os.path.exists(reco_model) or not os.path.exists(det_model):
-    print('🚀 Downloading Model Weights...')
-    urlretrieve(RECOGNITION_MODEL_PATH, reco_model)
-    urlretrieve(DETECTION_MODEL_PATH, det_model)
-else:
-    print('Model Weights already there')
\ No newline at end of file
diff --git a/spaces/Mayanand/Image-Captioning/model.py b/spaces/Mayanand/Image-Captioning/model.py
deleted file mode 100644
index 05224ae09e675dc0591449d5fcb2f09c9c8f6dc4..0000000000000000000000000000000000000000
--- a/spaces/Mayanand/Image-Captioning/model.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision import models
-
-class Encoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-        backbone = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
-        backbone = [module for module in backbone.children()][:-1]
-        backbone.append(nn.Flatten())
-        self.backbone = nn.Sequential(*backbone)
-     
-
-    def forward(self, x):
-        return self.backbone(x)
-    
-    def fine_tune(self, fine_tune=False):
-        for param in self.parameters():
-            param.requires_grad = False
-
-        # If fine-tuning, only fine-tune bottom layers
-        for c in list(self.backbone.children())[5:]:
-            for p in c.parameters():
-                p.requires_grad = fine_tune
-
-class Decoder(nn.Module):
-    def __init__(self, tokenizer, dropout=0.):
-        super().__init__()
-        self.tokenizer = tokenizer
-        self.vocab_size = len(tokenizer)
-        self.emb = nn.Embedding(self.vocab_size, 512) # size (b, 512)
-        self.lstm = nn.LSTMCell(512, 512) 
-        self.dropout = nn.Dropout(p=dropout)
-        self.fc = nn.Linear(512, len(tokenizer.vocab))
-        self.init_h = nn.Linear(2048, 512)
-        self.init_c = nn.Linear(2048, 512)
-    
-    def init_states(self, encoder_out):
-        h = self.init_h(encoder_out)
-        c = self.init_c(encoder_out)
-        return h, c
-
-    def forward(self, enc_out, captions, caplens, device):
-        batch_size = enc_out.shape[0]
-        caplens, sort_idx = caplens.squeeze(1).sort(dim=0, descending=True)
-        enc_out = enc_out[sort_idx]
-        captions = captions[sort_idx]
-        h, c = self.init_states(enc_out)
-
-        # Embedding
-        embeddings = self.emb(captions)  # (batch_size, max_caption_length, embed_dim)
-
-
-        # We won't decode at the  position, since we've finished generating as soon as we generate 
-        # So, decoding lengths are actual lengths - 1
-        caplens = (caplens - 1).tolist()
-
-
-        # Create tensors to hold word predicion scores
-        predictions = torch.zeros(batch_size, max(caplens), self.vocab_size).to(device)
-
-        max_timesteps = max(caplens)
-
-        for t in range(max_timesteps):
-            batch_size_t = sum([l > t for l in caplens])
-            h, c = self.lstm(embeddings[:batch_size_t, t, :], (h[:batch_size_t], c[:batch_size_t]))
-            preds = self.fc(self.dropout(h))
-            predictions[:batch_size_t, t, :] = preds
-        
-        return  predictions, captions, caplens, sort_idx
-
-    def predict(self, enc_out, device, max_steps):
-        with torch.no_grad():
-            batch_size = enc_out.shape[0]
-            h, c = self.init_states(enc_out)
-
-            captions = []
-
-            for i in range(batch_size):
-                temp = []
-                next_token = self.emb(torch.LongTensor([self.tokenizer.val2idx['']]).to(device))
-                h_, c_ = h[i].unsqueeze(0), c[i].unsqueeze(0)
-
-                step = 1
-                while True:
-                    h_, c_ = self.lstm(next_token, (h_, c_))
-                    preds = self.fc(self.dropout(h_))
-
-                    max_val, max_idx = torch.max(preds, dim=1)
-                    max_idx = max_idx.item()
-                    temp.append(max_idx)
-                    
-                    if max_idx in [self.tokenizer.val2idx['']] or step == max_steps:
-                        break
-                    next_token = self.emb(torch.LongTensor([max_idx]).to(device))
-                    step += 1
-                captions.append(temp)
-        return  captions
-
-    
-
-class CaptionModel(nn.Module):
-    def __init__(self, tokenizer):
-        super().__init__()
-        self.tokenizer = tokenizer
-        self.vocab_size = len(self.tokenizer)
-        self.encoder = Encoder()
-        self.decoder = Decoder(tokenizer)
-
-    def forward(self, x, captions, caplens, device):
-        encoder_out = self.encoder(x)
-        predictions, captions, caplens, sort_idx = self.decoder(encoder_out, captions, caplens, device)
-        return predictions, captions, caplens, sort_idx
-    
-    def predict(self, x, device, max_steps=25):
-        encoder_out = self.encoder(x)
-        captions = self.decoder.predict(encoder_out, device, max_steps)
-        return captions
\ No newline at end of file
diff --git a/spaces/ModIA/FrenchDroneKeyword/dataloading.py b/spaces/ModIA/FrenchDroneKeyword/dataloading.py
deleted file mode 100644
index 02a24d803eb5fccea0b9af75914db3648d531299..0000000000000000000000000000000000000000
--- a/spaces/ModIA/FrenchDroneKeyword/dataloading.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import numpy as np
-import pandas as pd
-
-import librosa
-
-from pathlib import Path
-from typing import Callable, Literal, Optional
-
-def load_dataset(
-    paths: list,
-    remove_label: list = [""],
-    sr: int = 22050,
-    method  = "fix_length",
-    max_time: float = 4.0):
-    """Folder dataset in memory loader (return fully loaded pandas dataframe).
-    - For sklearn, load the whole dataset if possible otherwise use `proportion` to only load a part of the dataset.
-    - For pytorch, load the whole dataset if possible otherwise use `proportion` to only load a part of the dataset.
-        And convert output to Tensor on the fly.
-
-    Use `to_numpy(df.y)` to extract a numpy matrix with a (n_row, ...) shape.
-    
-    Expect a dataset folder structure as: paths = [paths1, paths2, ...]
-        - paths1
-            - sub1
-                - blabla_GroundTruth1.wav
-                - blabla_GroundTruth2.wav
-            - sub2
-                - ...
-            ...
-        - ...
-
-    Args:
-        paths (list[Path]): list of dataset directory to parse.
-        remove_label (list, optional): list of label to remove. Defaults to None.. Defaults to [""].
-        shuffle (bool, optional): True to suffle the dataframe. Defaults to True.
-        proportion (float, optional): Proportion of file to load. Defaults to 1.0.
-        sr (int, optional): Sample Rate to resample audio file. Defaults to 22050.
-        method (Literal['fix_length';, 'time_stretch'], optional): uniformization method to apply. Defaults to "fix_length".
-        max_time (float, optional): Common audio duration . Defaults to 4.0.
-
-    Returns:
-        df (pd.DataFrame): A pd.DataFrame with such define column: 
-        - absolute_path (str): file-system absolute path of the .wav file.
-        - labels (list): list of labels defining the sound file (ie, subdirectories and post _ filename).
-        - ground_truth (str): ground_truth label meaning the last one after _ in the sound filename.
-        - y_original_signal (np.ndarray): sound signal normalize as `float64` and resample with the given sr by `librosa.load`
-        - y_original_duration (float): y_original_signal signal duration.
-        - y_uniform (np.ndarray): uniformized sound signal compute from y_original_signal using the chosen uniform method.
-        uniform_transform (Callable[[np.ndarray, int], np.ndarray]]): A lambda function to uniformized an audio signal as the same in df.
-    """
-    data = []
-    uniform_transform = lambda y, sr: uniformize(y, sr, method, max_time)
-    for path in paths:
-        path = Path(path)
-        for wav_file in path.rglob("*.wav"):
-            wav_file_dict = dict()
-            absolute_path = wav_file.absolute()
-            *labels, label = absolute_path.relative_to(path.absolute()).parts
-            label = label.replace(".wav", "").split("_")
-            labels.extend(label)
-            ground_truth = labels[-1]
-            if ground_truth not in remove_label:
-                y_original, sr = librosa.load(path=absolute_path, sr=sr) 
-                # WARINING : Convert the sampling rate to 22.05 KHz, 
-                # normalize the bit depth between -1 and 1 and convert stereo to mono
-                wav_file_dict["absolute_path"] = absolute_path
-                wav_file_dict["labels"] = labels
-                wav_file_dict["ground_truth"] = ground_truth
-                ## Save original sound signal
-                wav_file_dict["y_original_signal"] = y_original
-                duration = librosa.get_duration(y=y_original, sr=sr)
-                wav_file_dict["y_original_duration"] = duration
-                ## Save uniformized sound signal
-                wav_file_dict["y_uniform"] = uniform_transform(y_original, sr)
-                data.append(wav_file_dict)
-    df = pd.DataFrame(data)
-    return df, uniform_transform
-
-def uniformize(
-        audio: np.ndarray,
-        sr: int,
-        method = "fix_length", 
-        max_time: float = 4.0
-        ):
-    if method == "fix_length":
-        return librosa.util.fix_length(audio, size=int(np.ceil(max_time*sr)))
-    elif method == "time_stretch":
-        duration = librosa.get_duration(y=audio, sr=sr)
-        return librosa.effects.time_stretch(audio, rate=duration/max_time)
-    
-
-def to_numpy(ds: pd.Series) -> np.ndarray:
-    """Transform a pd.Series (ie columns slice) in a numpy array with the shape (n_row, cell_array.flatten()).
-
-    Args:
-        df (pd.Series): Columns to transform in numpy.
-
-    Returns:
-        np.ndarray: resulting np.array from the ds pd.Series.
-    """
-    numpy_df = np.stack([*ds.to_numpy()])
-    C, *o = numpy_df.shape
-    
-    if o:
-        return numpy_df.reshape(numpy_df.shape[0], np.prod(o))        
-    else:
-        return numpy_df.reshape(numpy_df.shape[0])
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/base.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/base.py
deleted file mode 100644
index dfe79e1549320e22ce9a631a6b2fe81d192917e3..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/base.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import abstractmethod
-from typing import Dict, List, Tuple, Union
-
-from mmocr.utils import track_parallel_progress_multi_args
-
-
-class BaseParser:
-    """Base class for parsing annotations.
-
-    Args:
-        split (str): The split of the dataset. It is usually set automatically
-            and users do not need to set it manually in config file in most
-            cases.
-        nproc (int): Number of processes to process the data. Defaults to 1.
-            It is usually set automatically and users do not need to set it
-            manually in config file in most cases.
-    """
-
-    def __init__(self, split: str, nproc: int = 1) -> None:
-        self.nproc = nproc
-        self.split = split
-
-    def __call__(self, img_paths: Union[List[str], str],
-                 ann_paths: Union[List[str], str]) -> List[Tuple]:
-        """Parse annotations.
-
-        Args:
-            img_paths (str or list[str]): the list of image paths or the
-                directory of the images.
-            ann_paths (str or list[str]): the list of annotation paths or the
-                path of the annotation file which contains all the annotations.
-
-        Returns:
-            List: A list of a tuple of (image_path, instances)
-        """
-        samples = self.parse_files(img_paths, ann_paths)
-        return samples
-
-    def parse_files(self, img_paths: Union[List[str], str],
-                    ann_paths: Union[List[str], str]) -> List[Tuple]:
-        """Convert annotations to MMOCR format.
-
-        Args:
-            img_paths (str or list[str]): the list of image paths or the
-                directory of the images.
-            ann_paths (str or list[str]): the list of annotation paths or the
-                path of the annotation file which contains all the annotations.
-
-        Returns:
-            List[Tuple]: A list of a tuple of (image_path, instances).
-
-            - img_path (str): The path of image file, which can be read
-              directly by opencv.
-            - instance: instance is a list of dict containing parsed
-              annotations, which should contain the following keys:
-
-              - 'poly' or 'box' (textdet or textspotting)
-              - 'text' (textspotting or textrecog)
-              - 'ignore' (all task)
-        """
-        samples = track_parallel_progress_multi_args(
-            self.parse_file, (img_paths, ann_paths), nproc=self.nproc)
-        return samples
-
-    @abstractmethod
-    def parse_file(self, img_path: str, ann_path: str) -> Tuple:
-        """Convert annotation for a single image.
-
-        Args:
-            img_path (str): The path of image.
-            ann_path (str): The path of annotation.
-
-        Returns:
-            Tuple: A tuple of (img_path, instance).
-
-            - img_path (str): The path of image file, which can be read
-              directly by opencv.
-            - instance: instance is a list of dict containing parsed
-              annotations, which should contain the following keys:
-
-              - 'poly' or 'box' (textdet or textspotting)
-              - 'text' (textspotting or textrecog)
-              - 'ignore' (all task)
-
-        Examples:
-        An example of returned values:
-        >>> ('imgs/train/xxx.jpg',
-        >>> dict(
-        >>>    poly=[[[0, 1], [1, 1], [1, 0], [0, 0]]],
-        >>>    text='hello',
-        >>>    ignore=False)
-        >>> )
-        """
-        raise NotImplementedError
-
-    def loader(self,
-               file_path: str,
-               separator: str = ',',
-               format: str = 'x1,y1,x2,y2,x3,y3,x4,y4,trans',
-               encoding='utf-8') -> Union[Dict, str]:
-        """A basic loader designed for .txt format annotation. It greedily
-        extracts information separated by separators.
-
-        Args:
-            file_path (str): Path to the txt file.
-            separator (str, optional): Separator of data. Defaults to ','.
-            format (str, optional): Annotation format.
-                Defaults to 'x1,y1,x2,y2,x3,y3,x4,y4,trans'.
-            encoding (str, optional): Encoding format. Defaults to 'utf-8'.
-
-        Yields:
-            Iterator[Union[Dict, str]]: Original text line or a dict containing
-            the information of the text line.
-        """
-        keys = format.split(separator)
-        with open(file_path, 'r', encoding=encoding) as f:
-            for line in f.readlines():
-                line = line.strip()
-                values = line.split(separator)
-                values = values[:len(keys) -
-                                1] + [separator.join(values[len(keys) - 1:])]
-                if line:
-                    yield dict(zip(keys, values))
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/data/pth_loader.py b/spaces/NAACL2022/CLIP-Caption-Reward/captioning/data/pth_loader.py
deleted file mode 100644
index 28023699735470daa7e2ab4752a31ea8282c04c5..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/captioning/data/pth_loader.py
+++ /dev/null
@@ -1,334 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import h5py
-from lmdbdict import lmdbdict
-from lmdbdict.methods import DUMPS_FUNC, LOADS_FUNC
-import os
-import numpy as np
-import numpy.random as npr
-import random
-
-import torch
-import torch.utils.data as data
-
-import multiprocessing
-import six
-
-verbose = True
-# import torch
-# if torch.cuda.current_device() in [0, -1]:
-if 'LOCAL_RANK' in os.environ and os.environ['LOCAL_RANK'] != '0':
-    verbose = False
-
-class HybridLoader:
-    """
-    If db_path is a director, then use normal file loading
-    If lmdb, then load from lmdb
-    The loading method depend on extention.
-
-    in_memory: if in_memory is True, we save all the features in memory
-               For individual np(y|z)s, we don't need to do that because the system will do this for us.
-               Should be useful for lmdb or h5.
-               (Copied this idea from vilbert)
-    """
-    def __init__(self, db_path, ext, in_memory=False):
-        self.db_path = db_path
-        self.ext = ext
-        if self.ext == '.npy':
-            self.loader = lambda x: np.load(six.BytesIO(x))
-        else:
-            self.loader = lambda x: np.load(six.BytesIO(x))['feat']
-        if db_path.endswith('.lmdb'):
-            self.db_type = 'lmdb'
-            self.lmdb = lmdbdict(db_path, unsafe=True)
-            self.lmdb._key_dumps = DUMPS_FUNC['ascii']
-            self.lmdb._value_loads = LOADS_FUNC['identity']
-        elif db_path.endswith('.pth'): # Assume a key,value dictionary
-            self.db_type = 'pth'
-            self.feat_file = torch.load(db_path)
-            self.loader = lambda x: x
-            print('HybridLoader: ext is ignored')
-        elif db_path.endswith('h5'):
-            self.db_type = 'h5'
-            self.loader = lambda x: np.array(x).astype('float32')
-        else:
-            self.db_type = 'dir'
-
-        self.in_memory = in_memory
-        if self.in_memory:
-            self.features = {}
-
-    def get(self, key):
-
-        if self.in_memory and key in self.features:
-            # We save f_input because we want to save the
-            # compressed bytes to save memory
-            f_input = self.features[key]
-        elif self.db_type == 'lmdb':
-            f_input = self.lmdb[key]
-        elif self.db_type == 'pth':
-            f_input = self.feat_file[key]
-        elif self.db_type == 'h5':
-            f_input = h5py.File(self.db_path, 'r')[key]
-        else:
-            f_input = open(os.path.join(self.db_path, key + self.ext), 'rb').read()
-
-        if self.in_memory and key not in self.features:
-            self.features[key] = f_input
-
-        # load image
-        feat = self.loader(f_input)
-
-        return feat
-
-class CaptionDataset(data.Dataset):
-    
-    def get_vocab_size(self):
-        return self.vocab_size
-
-    def get_vocab(self):
-        return self.ix_to_word
-
-    def get_seq_length(self):
-        return self.seq_length
-
-    def __init__(self, opt):
-        self.opt = opt
-        self.seq_per_img = opt.seq_per_img
-        
-        # feature related options
-        self.use_fc = getattr(opt, 'use_fc', True)
-        self.use_att = getattr(opt, 'use_att', True)
-        self.use_box = getattr(opt, 'use_box', 0)
-        self.norm_att_feat = getattr(opt, 'norm_att_feat', 0)
-        self.norm_box_feat = getattr(opt, 'norm_box_feat', 0)
-
-        # load the json file which contains additional information about the dataset
-        if verbose:
-            print('DataLoader loading json file: ', opt.input_json)
-        self.info = json.load(open(self.opt.input_json))
-        if 'ix_to_word' in self.info:
-            self.ix_to_word = self.info['ix_to_word']
-            self.vocab_size = len(self.ix_to_word)
-            if verbose:
-                print('vocab size is ', self.vocab_size)
-        
-        # open the hdf5 file
-        if verbose:
-            print('DataLoader loading h5 file: ', opt.input_fc_dir, opt.input_att_dir, opt.input_box_dir, opt.input_label_h5)
-        """
-        Setting input_label_h5 to none is used when only doing generation.
-        For example, when you need to test on coco test set.
-        """
-        if self.opt.input_label_h5 != 'none':
-            self.h5_label_file = h5py.File(self.opt.input_label_h5, 'r', driver='core')
-            # load in the sequence data
-            seq_size = self.h5_label_file['labels'].shape
-            self.label = self.h5_label_file['labels'][:]
-            self.seq_length = seq_size[1]
-            if verbose:
-                print('max sequence length in data is', self.seq_length)
-            # load the pointers in full to RAM (should be small enough)
-            self.label_start_ix = self.h5_label_file['label_start_ix'][:]
-            self.label_end_ix = self.h5_label_file['label_end_ix'][:]
-        else:
-            self.seq_length = 1
-
-        self.data_in_memory = getattr(opt, 'data_in_memory', False)
-        self.fc_loader = HybridLoader(self.opt.input_fc_dir, '.npy', in_memory=self.data_in_memory)
-        self.att_loader = HybridLoader(self.opt.input_att_dir, '.npz', in_memory=self.data_in_memory)
-        self.box_loader = HybridLoader(self.opt.input_box_dir, '.npy', in_memory=self.data_in_memory)
-
-        self.use_clipscore = getattr(opt, 'use_clipscore', False)
-        # if self.use_clipscore:
-        self.clipscore_loader = HybridLoader(self.opt.input_clipscore_vis_dir, '.npy', in_memory=self.data_in_memory)
-
-
-        self.num_images = len(self.info['images']) # self.label_start_ix.shape[0]
-        if verbose:
-            print('read %d image features' %(self.num_images))
-
-        # separate out indexes for each of the provided splits
-        self.split_ix = {'train': [], 'val': [], 'test': []}
-        for ix in range(len(self.info['images'])):
-            img = self.info['images'][ix]
-            if not 'split' in img:
-                self.split_ix['train'].append(ix)
-                self.split_ix['val'].append(ix)
-                self.split_ix['test'].append(ix)
-            elif img['split'] == 'train':
-                self.split_ix['train'].append(ix)
-            elif img['split'] == 'val':
-                self.split_ix['val'].append(ix)
-            elif img['split'] == 'test':
-                self.split_ix['test'].append(ix)
-            elif opt.train_only == 0: # restval
-                self.split_ix['train'].append(ix)
-
-        if verbose:
-            print('assigned %d images to split train' %len(self.split_ix['train']))
-            print('assigned %d images to split val' %len(self.split_ix['val']))
-            print('assigned %d images to split test' %len(self.split_ix['test']))
-
-    def get_captions(self, ix, seq_per_img):
-        # fetch the sequence labels
-        ix1 = self.label_start_ix[ix] - 1 #label_start_ix starts from 1
-        ix2 = self.label_end_ix[ix] - 1
-        ncap = ix2 - ix1 + 1 # number of captions available for this image
-        assert ncap > 0, 'an image does not have any label. this can be handled but right now isn\'t'
-
-        if ncap < seq_per_img:
-            # we need to subsample (with replacement)
-            seq = np.zeros([seq_per_img, self.seq_length], dtype = 'int')
-            for q in range(seq_per_img):
-                ixl = random.randint(ix1,ix2)
-                seq[q, :] = self.label[ixl, :self.seq_length]
-        else:
-            ixl = random.randint(ix1, ix2 - seq_per_img + 1)
-            seq = self.label[ixl: ixl + seq_per_img, :self.seq_length]
-
-        return seq
-
-    def collate_func(self, batch):
-        seq_per_img = self.seq_per_img
-
-        fc_batch = []
-        att_batch = []
-        label_batch = []
-
-        clip_vis_feat_batch = []
-
-        wrapped = False
-
-        infos = []
-        gts = []
-
-        for sample in batch:
-            # fetch image
-            # if self.use_clipscore:
-            tmp_fc, tmp_att, tmp_seq, \
-                ix, tmp_clip_vis_feat = sample
-
-            clip_vis_feat_batch.append(tmp_clip_vis_feat)
-            # else:
-            #     tmp_fc, tmp_att, tmp_seq, \
-            #         ix = sample
-
-            fc_batch.append(tmp_fc)
-            att_batch.append(tmp_att)
-            
-            tmp_label = np.zeros([seq_per_img, self.seq_length + 2], dtype = 'int')
-            if hasattr(self, 'h5_label_file'):
-                # if there is ground truth
-                tmp_label[:, 1 : self.seq_length + 1] = tmp_seq
-            label_batch.append(tmp_label)
-
-            # Used for reward evaluation
-            if hasattr(self, 'h5_label_file'):
-                # if there is ground truth
-                gts.append(self.label[self.label_start_ix[ix] - 1: self.label_end_ix[ix]])
-            else:
-                gts.append([])
-        
-            # record associated info as well
-            info_dict = {}
-            info_dict['ix'] = ix
-            info_dict['id'] = self.info['images'][ix]['id']
-            info_dict['file_path'] = self.info['images'][ix].get('file_path', '')
-            infos.append(info_dict)
-
-        # #sort by att_feat length
-        # fc_batch, att_batch, label_batch, gts, infos = \
-        #     zip(*sorted(zip(fc_batch, att_batch, np.vsplit(label_batch, batch_size), gts, infos), key=lambda x: len(x[1]), reverse=True))
-        if self.use_clipscore:
-            fc_batch, att_batch, label_batch, clip_vis_feat_batch, gts, infos = \
-                zip(*sorted(zip(fc_batch, att_batch, label_batch, clip_vis_feat_batch, gts, infos), key=lambda x: 0, reverse=True))
-        else:
-            fc_batch, att_batch, label_batch, gts, infos = \
-                zip(*sorted(zip(fc_batch, att_batch, label_batch, gts, infos), key=lambda x: 0, reverse=True))
-        data = {}
-        data['fc_feats'] = np.stack(fc_batch)
-        # merge att_feats
-        max_att_len = max([_.shape[0] for _ in att_batch])
-        data['att_feats'] = np.zeros([len(att_batch), max_att_len, att_batch[0].shape[1]], dtype = 'float32')
-        for i in range(len(att_batch)):
-            data['att_feats'][i, :att_batch[i].shape[0]] = att_batch[i]
-        data['att_masks'] = np.zeros(data['att_feats'].shape[:2], dtype='float32')
-        for i in range(len(att_batch)):
-            data['att_masks'][i, :att_batch[i].shape[0]] = 1
-        # set att_masks to None if attention features have same length
-        if data['att_masks'].sum() == data['att_masks'].size:
-            data['att_masks'] = None
-
-        # if self.use_clipscore:
-        data['clip_vis_feats'] = np.stack(clip_vis_feat_batch)
-
-        data['labels'] = np.vstack(label_batch)
-        # generate mask
-        nonzeros = np.array(list(map(lambda x: (x != 0).sum()+2, data['labels'])))
-        mask_batch = np.zeros([data['labels'].shape[0], self.seq_length + 2], dtype = 'float32')
-        for ix, row in enumerate(mask_batch):
-            row[:nonzeros[ix]] = 1
-        data['masks'] = mask_batch
-        data['labels'] = data['labels'].reshape(len(batch), seq_per_img, -1)
-        data['masks'] = data['masks'].reshape(len(batch), seq_per_img, -1)
-
-        data['gts'] = gts # all ground truth captions of each images
-        data['infos'] = infos
-
-        data = {k:torch.from_numpy(v) if type(v) is np.ndarray else v for k,v in data.items()} # Turn all ndarray to torch tensor
-
-        return data
-
-    def __getitem__(self, ix):
-        """This function returns a tuple that is further passed to collate_fn
-        """
-        if self.use_att:
-            att_feat = self.att_loader.get(str(self.info['images'][ix]['id']))
-            # Reshape to K x C
-            att_feat = att_feat.reshape(-1, att_feat.shape[-1])
-            if self.norm_att_feat:
-                att_feat = att_feat / np.linalg.norm(att_feat, 2, 1, keepdims=True)
-            if self.use_box:
-                box_feat = self.box_loader.get(str(self.info['images'][ix]['id']))
-                # devided by image width and height
-                x1,y1,x2,y2 = np.hsplit(box_feat, 4)
-                h,w = self.info['images'][ix]['height'], self.info['images'][ix]['width']
-                box_feat = np.hstack((x1/w, y1/h, x2/w, y2/h, (x2-x1)*(y2-y1)/(w*h))) # question? x2-x1+1??
-                if self.norm_box_feat:
-                    box_feat = box_feat / np.linalg.norm(box_feat, 2, 1, keepdims=True)
-                att_feat = np.hstack([att_feat, box_feat])
-                # sort the features by the size of boxes
-                att_feat = np.stack(sorted(att_feat, key=lambda x:x[-1], reverse=True))
-        else:
-            att_feat = np.zeros((0,0), dtype='float32')
-        if self.use_fc:
-            try:
-                fc_feat = self.fc_loader.get(str(self.info['images'][ix]['id']))
-            except:
-                # Use average of attention when there is no fc provided (For bottomup feature)
-                fc_feat = att_feat.mean(0)
-        else:
-            fc_feat = np.zeros((0), dtype='float32')
-        if hasattr(self, 'h5_label_file'):
-            seq = self.get_captions(ix, self.seq_per_img)
-        else:
-            seq = None
-
-        # if self.use_clipscore:
-        clip_vis_feat = self.clipscore_loader.get(
-            str(self.info['images'][ix]['id']))
-
-        return (fc_feat,
-                att_feat, seq,
-                ix, clip_vis_feat)
-
-        # return (fc_feat,
-        #         att_feat, seq,
-        #         ix)
-
-    def __len__(self):
-        return len(self.info['images'])
diff --git a/spaces/NCTCMumbai/NCTC/models/official/benchmark/models/shakespeare/__init__.py b/spaces/NCTCMumbai/NCTC/models/official/benchmark/models/shakespeare/__init__.py
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/benchmark/models/shakespeare/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/common/reward.py b/spaces/NCTCMumbai/NCTC/models/research/brain_coder/common/reward.py
deleted file mode 100644
index 87e01c9c52e1ee22f2745dce12bc5e2726711ff7..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/common/reward.py
+++ /dev/null
@@ -1,390 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-"""Reward functions, distance functions, and reward managers."""
-
-from abc import ABCMeta
-from abc import abstractmethod
-from math import log
-
-
-# All sequences here are assumed to be lists of ints bounded
-# between 0 and `base`-1 (inclusive).
-
-
-#################################
-### Scalar Distance Functions ###
-#################################
-
-
-def abs_diff(a, b, base=0):
-  """Absolute value of difference between scalars.
-
-  abs_diff is symmetric, i.e. `a` and `b` are interchangeable.
-
-  Args:
-    a: First argument. An int.
-    b: Seconds argument. An int.
-    base: Dummy argument so that the argument signature matches other scalar
-        diff functions. abs_diff is the same in all bases.
-
-  Returns:
-    abs(a - b).
-  """
-  del base  # Unused.
-  return abs(a - b)
-
-
-def mod_abs_diff(a, b, base):
-  """Shortest distance between `a` and `b` in the modular integers base `base`.
-
-  The smallest distance between a and b is returned.
-  Example: mod_abs_diff(1, 99, 100) ==> 2. It is not 98.
-
-  mod_abs_diff is symmetric, i.e. `a` and `b` are interchangeable.
-
-  Args:
-    a: First argument. An int.
-    b: Seconds argument. An int.
-    base: The modulo base. A positive int.
-
-  Returns:
-    Shortest distance.
-  """
-  diff = abs(a - b)
-  if diff >= base:
-    diff %= base
-  return min(diff, (-diff) + base)
-
-
-###############################
-### List Distance Functions ###
-###############################
-
-
-def absolute_distance(pred, target, base, scalar_diff_fn=abs_diff):
-  """Asymmetric list distance function.
-
-  List distance is the sum of element-wise distances, like Hamming distance, but
-  where `pred` can be longer or shorter than `target`. For each position in both
-  `pred` and `target`, distance between those elements is computed with
-  `scalar_diff_fn`. For missing or extra elements in `pred`, the maximum
-  distance is assigned, which is equal to `base`.
-
-  Distance is 0 when `pred` and `target` are identical, and will be a positive
-  integer when they are not.
-
-  Args:
-    pred: Prediction list. Distance from this list is computed.
-    target: Target list. Distance to this list is computed.
-    base: The integer base to use. For example, a list of chars would use base
-        256.
-    scalar_diff_fn: Element-wise distance function.
-
-  Returns:
-    List distance between `pred` and `target`.
-  """
-  d = 0
-  for i, target_t in enumerate(target):
-    if i >= len(pred):
-      d += base  # A missing slot is worth the max distance.
-    else:
-      # Add element-wise distance for this slot.
-      d += scalar_diff_fn(pred[i], target_t, base)
-  if len(pred) > len(target):
-    # Each extra slot is worth the max distance.
-    d += (len(pred) - len(target)) * base
-  return d
-
-
-def log_absolute_distance(pred, target, base):
-  """Asymmetric list distance function that uses log distance.
-
-  A list distance which computes sum of element-wise distances, similar to
-  `absolute_distance`. Unlike `absolute_distance`, this scales the resulting
-  distance to be a float.
-
-  Element-wise distance are log-scale. Distance between two list changes
-  relatively less for elements that are far apart, but changes a lot (goes to 0
-  faster) when values get close together.
-
-  Args:
-    pred: List of ints. Computes distance from this list to the target.
-    target: List of ints. This is the "correct" list which the prediction list
-        is trying to match.
-    base: Integer base.
-
-  Returns:
-    Float distance normalized so that when `pred` is at most as long as `target`
-    the distance is between 0.0 and 1.0. Distance grows unboundedly large
-    as `pred` grows past `target` in length.
-  """
-  if not target:
-    length_normalizer = 1.0
-    if not pred:
-      # Distance between [] and [] is 0.0 since they are equal.
-      return 0.0
-  else:
-    length_normalizer = float(len(target))
-  # max_dist is the maximum element-wise distance, before taking log and
-  # scaling. Since we use `mod_abs_diff`, it would be (base // 2), but we add
-  # 1 to it so that missing or extra positions get the maximum penalty.
-  max_dist = base // 2 + 1
-
-  # The log-distance will be scaled by a factor.
-  # Note: +1 is added to the numerator and denominator to avoid log(0). This
-  # only has a translational effect, i.e. log(dist + 1) / log(max_dist + 1).
-  factor = log(max_dist + 1)
-
-  d = 0.0  # Total distance to be computed.
-  for i, target_t in enumerate(target):
-    if i >= len(pred):
-      # Assign the max element-wise distance for missing positions. This is 1.0
-      # after scaling.
-      d += 1.0
-    else:
-      # Add the log-dist divided by a scaling factor.
-      d += log(mod_abs_diff(pred[i], target_t, base) + 1) / factor
-  if len(pred) > len(target):
-    # Add the max element-wise distance for each extra position.
-    # Since max dist after scaling is 1, this is just the difference in list
-    # lengths.
-    d += (len(pred) - len(target))
-  return d / length_normalizer  # Normalize again by the target length.
-
-
-########################
-### Reward Functions ###
-########################
-
-# Reward functions assign reward based on program output.
-# Warning: only use these functions as the terminal rewards in episodes, i.e.
-# for the "final" programs.
-
-
-def absolute_distance_reward(pred, target, base, scalar_diff_fn=abs_diff):
-  """Reward function based on absolute_distance function.
-
-  Maximum reward, 1.0, is given when the lists are equal. Reward is scaled
-  so that 0.0 reward is given when `pred` is the empty list (assuming `target`
-  is not empty). Reward can go negative when `pred` is longer than `target`.
-
-  This is an asymmetric reward function, so which list is the prediction and
-  which is the target matters.
-
-  Args:
-    pred: Prediction sequence. This should be the sequence outputted by the
-        generated code. List of ints n, where 0 <= n < base.
-    target: Target sequence. The correct sequence that the generated code needs
-        to output. List of ints n, where 0 <= n < base.
-    base: Base of the computation.
-    scalar_diff_fn: Element-wise distance function.
-
-  Returns:
-    Reward computed based on `pred` and `target`. A float.
-  """
-  unit_dist = float(base * len(target))
-  if unit_dist == 0:
-    unit_dist = base
-  dist = absolute_distance(pred, target, base, scalar_diff_fn=scalar_diff_fn)
-  return (unit_dist - dist) / unit_dist
-
-
-def absolute_mod_distance_reward(pred, target, base):
-  """Same as `absolute_distance_reward` but `mod_abs_diff` scalar diff is used.
-
-  Args:
-    pred: Prediction sequence. This should be the sequence outputted by the
-        generated code. List of ints n, where 0 <= n < base.
-    target: Target sequence. The correct sequence that the generated code needs
-        to output. List of ints n, where 0 <= n < base.
-    base: Base of the computation.
-
-  Returns:
-    Reward computed based on `pred` and `target`. A float.
-  """
-  return absolute_distance_reward(pred, target, base, mod_abs_diff)
-
-
-def absolute_log_distance_reward(pred, target, base):
-  """Compute reward using `log_absolute_distance`.
-
-  Maximum reward, 1.0, is given when the lists are equal. Reward is scaled
-  so that 0.0 reward is given when `pred` is the empty list (assuming `target`
-  is not empty). Reward can go negative when `pred` is longer than `target`.
-
-  This is an asymmetric reward function, so which list is the prediction and
-  which is the target matters.
-
-  This reward function has the nice property that much more reward is given
-  for getting the correct value (at each position) than for there being any
-  value at all. For example, in base 100, lets say pred = [1] * 1000
-  and target = [10] * 1000. A lot of reward would be given for being 80%
-  accurate (worst element-wise distance is 50, distances here are 9) using
-  `absolute_distance`. `log_absolute_distance` on the other hand will give
-  greater and greater reward increments the closer each predicted value gets to
-  the target. That makes the reward given for accuracy somewhat independant of
-  the base.
-
-  Args:
-    pred: Prediction sequence. This should be the sequence outputted by the
-        generated code. List of ints n, where 0 <= n < base.
-    target: Target sequence. The correct sequence that the generated code needs
-        to output. List of ints n, where 0 <= n < base.
-    base: Base of the computation.
-
-  Returns:
-    Reward computed based on `pred` and `target`. A float.
-  """
-  return 1.0 - log_absolute_distance(pred, target, base)
-
-
-#######################
-### Reward Managers ###
-#######################
-
-# Reward managers assign reward to many code attempts throughout an episode.
-
-
-class RewardManager(object):
-  """Reward managers administer reward across an episode.
-
-  Reward managers are used for "editor" environments. These are environments
-  where the agent has some way to edit its code over time, and run its code
-  many time in the same episode, so that it can make incremental improvements.
-
-  Reward managers are instantiated with a target sequence, which is the known
-  correct program output. The manager is called on the output from a proposed
-  code, and returns reward. If many proposal outputs are tried, reward may be
-  some stateful function that takes previous tries into account. This is done,
-  in part, so that an agent cannot accumulate unbounded reward just by trying
-  junk programs as often as possible. So reward managers should not give the
-  same reward twice if the next proposal is not better than the last.
-  """
-  __metaclass__ = ABCMeta
-
-  def __init__(self, target, base, distance_fn=absolute_distance):
-    self._target = list(target)
-    self._base = base
-    self._distance_fn = distance_fn
-
-  @abstractmethod
-  def __call__(self, sequence):
-    """Call this reward manager like a function to get reward.
-
-    Calls to reward manager are stateful, and will take previous sequences
-    into account. Repeated calls with the same sequence may produce different
-    rewards.
-
-    Args:
-      sequence: List of integers (each between 0 and base - 1). This is the
-          proposal sequence. Reward will be computed based on the distance
-          from this sequence to the target (distance function and target are
-          given in the constructor), as well as previous sequences tried during
-          the lifetime of this object.
-
-    Returns:
-      Float value. The reward received from this call.
-    """
-    return 0.0
-
-
-class DeltaRewardManager(RewardManager):
-  """Simple reward manager that assigns reward for the net change in distance.
-
-  Given some (possibly asymmetric) list distance function, gives reward for
-  relative changes in prediction distance to the target.
-
-  For example, if on the first call the distance is 3.0, the change in distance
-  is -3 (from starting distance of 0). That relative change will be scaled to
-  produce a negative reward for this step. On the next call, the distance is 2.0
-  which is a +1 change, and that will be scaled to give a positive reward.
-  If the final call has distance 0 (the target is achieved), that is another
-  positive change of +2. The total reward across all 3 calls is then 0, which is
-  the highest posible episode total.
-
-  Reward is scaled so that the maximum element-wise distance is worth 1.0.
-  Maximum total episode reward attainable is 0.
-  """
-
-  def __init__(self, target, base, distance_fn=absolute_distance):
-    super(DeltaRewardManager, self).__init__(target, base, distance_fn)
-    self._last_diff = 0
-
-  def _diff(self, seq):
-    return self._distance_fn(seq, self._target, self._base)
-
-  def _delta_reward(self, seq):
-    # Reward is relative to previous sequence diff.
-    # Reward is scaled so that maximum token difference is worth 1.0.
-    # Reward = (last_diff - this_diff) / self.base.
-    # Reward is positive if this sequence is closer to the target than the
-    # previous sequence, and negative if this sequence is further away.
-    diff = self._diff(seq)
-    reward = (self._last_diff - diff) / float(self._base)
-    self._last_diff = diff
-    return reward
-
-  def __call__(self, seq):
-    return self._delta_reward(seq)
-
-
-class FloorRewardManager(RewardManager):
-  """Assigns positive reward for each step taken closer to the target.
-
-  Given some (possibly asymmetric) list distance function, gives reward for
-  whenever a new episode minimum distance is reached. No reward is given if
-  the distance regresses to a higher value, so that the sum of rewards
-  for the episode is positive.
-
-  Reward is scaled so that the maximum element-wise distance is worth 1.0.
-  Maximum total episode reward attainable is len(target).
-
-  If the prediction sequence is longer than the target, a reward of -1 is given.
-  Subsequence predictions which are also longer get 0 reward. The -1 penalty
-  will be canceled out with a +1 reward when a prediction is given which is at
-  most the length of the target.
-  """
-
-  def __init__(self, target, base, distance_fn=absolute_distance):
-    super(FloorRewardManager, self).__init__(target, base, distance_fn)
-    self._last_diff = 0
-    self._min_diff = self._max_diff()
-    self._too_long_penality_given = False
-
-  def _max_diff(self):
-    return self._distance_fn([], self._target, self._base)
-
-  def _diff(self, seq):
-    return self._distance_fn(seq, self._target, self._base)
-
-  def _delta_reward(self, seq):
-    # Reward is only given if this sequence is closer to the target than any
-    # previous sequence.
-    # Reward is scaled so that maximum token difference is worth 1.0
-    # Reward = (min_diff - this_diff) / self.base
-    # Reward is always positive.
-    diff = self._diff(seq)
-    if diff < self._min_diff:
-      reward = (self._min_diff - diff) / float(self._base)
-      self._min_diff = diff
-    else:
-      reward = 0.0
-    return reward
-
-  def __call__(self, seq):
-    if len(seq) > len(self._target):  # Output is too long.
-      if not self._too_long_penality_given:
-        self._too_long_penality_given = True
-        reward = -1.0
-      else:
-        reward = 0.0  # Don't give this penalty more than once.
-      return reward
-
-    reward = self._delta_reward(seq)
-    if self._too_long_penality_given:
-      reward += 1.0  # Return the subtracted reward.
-      self._too_long_penality_given = False
-    return reward
-
diff --git a/spaces/NSect/multitrack-midi-music-generator/model.py b/spaces/NSect/multitrack-midi-music-generator/model.py
deleted file mode 100644
index 03bd5bb515ea9134245b7e720b78d10790dca056..0000000000000000000000000000000000000000
--- a/spaces/NSect/multitrack-midi-music-generator/model.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import torch
-from typing import Tuple
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Initialize the model and tokenizer variables as None
-tokenizer = None
-model = None
-
-
-def get_model_and_tokenizer() -> Tuple[AutoModelForCausalLM, AutoTokenizer]:
-    """
-    Returns the preloaded model and tokenizer. If they haven't been loaded before, loads them.
-
-    Returns:
-        tuple: A tuple containing the preloaded model and tokenizer.
-    """
-    global model, tokenizer
-    if model is None or tokenizer is None:
-        # Set device
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        # Load the tokenizer and the model
-        tokenizer = AutoTokenizer.from_pretrained("juancopi81/lmd_8bars_tokenizer")
-        model = AutoModelForCausalLM.from_pretrained(
-            "juancopi81/lmd-8bars-2048-epochs40_v4"
-        )
-
-        # Move model to device
-        model = model.to(device)
-
-    return model, tokenizer
diff --git a/spaces/Natsha/mocap-ai/fbx_handler.md b/spaces/Natsha/mocap-ai/fbx_handler.md
deleted file mode 100644
index 24bd49fdaef34b4bab8cebaed2c529bcc10f4c10..0000000000000000000000000000000000000000
--- a/spaces/Natsha/mocap-ai/fbx_handler.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# FBX Handler
-
-## Load file:
-```python
-# Path to file to load.
-input_file = Path('/path/to/file.fbx')
-# Load file into class.
-container = FBXContainer(input_file)
-```
-
-## Preprocess data:
-```python
-container.init_world_transforms(r=...)
-train_raw_data = container.extract_training_translations()
-test_raw_data = container.extract_inf_translations()
-```
-
-## Training workflow:
-```python
-# Load file.
-container = FBXContainer(input_file)
-# Get np.array with all valid translation numbers.
-actors_train, markers_train, t_test, _, _ = container.get_split_transforms(mode='train')
-# Convert to dataset...
-...
-```
-
-## Testing workflow:
-
-```python
-# Load file.
-container = FBXContainer(input_file)
-# Get splitted original data (no transforms applied).
-actors_test, markers_test, t_test, r_test_, s_test = container.get_split_transforms(mode='test')
-# Predict the new actors and classes...
-actors_pred, markers_pred = Labeler(scale_translations(t_test))
-# Merge the new labels with their original translations.
-merged = merge_tdc(actors_pred, markers_pred, t_test, r_test, s_test)
-# Convert the full cloud into a dict structured for easy keyframes.
-new_dict = array_to_dict(merged)
-# Replace the old translation keyframes with the new values.
-container.replace_keyframes_for_all_actors(new_dict)
-# Export file.
-container.export_fbx(Path('/path/to/outputfile.fbx'))
-```
\ No newline at end of file
diff --git a/spaces/NeuroSenko/audio-processing-utils/install.bat b/spaces/NeuroSenko/audio-processing-utils/install.bat
deleted file mode 100644
index bc4746cce31588cff41685e2a7047d2a3132bfb7..0000000000000000000000000000000000000000
--- a/spaces/NeuroSenko/audio-processing-utils/install.bat
+++ /dev/null
@@ -1,3 +0,0 @@
-python -m venv ./venv
-call .\venv\Scripts\activate.bat
-pip install -r requirements.txt
\ No newline at end of file
diff --git a/spaces/Nexxt/MagicPrompt-Stable-Diffusion/README.md b/spaces/Nexxt/MagicPrompt-Stable-Diffusion/README.md
deleted file mode 100644
index 98b00b0487e2ab609b0b29eb82c55d9215ab3406..0000000000000000000000000000000000000000
--- a/spaces/Nexxt/MagicPrompt-Stable-Diffusion/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: MagicPrompt Stable Diffusion
-emoji: 😻
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: Gustavosta/MagicPrompt-Stable-Diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/pointer_generator/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/pointer_generator/README.md
deleted file mode 100644
index 60965708254aae2174812ea6686a9807825b7fb6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/pointer_generator/README.md
+++ /dev/null
@@ -1,82 +0,0 @@
-# Transformer with Pointer-Generator Network
-
-This page describes the `transformer_pointer_generator` model that incorporates
-a pointing mechanism in the Transformer model that facilitates copying of input
-words to the output. This architecture is described in [Enarvi et al. (2020)](https://www.aclweb.org/anthology/2020.nlpmc-1.4/).
-
-## Background
-
-The pointer-generator network was introduced in [See et al. (2017)](https://arxiv.org/abs/1704.04368)
-for RNN encoder-decoder attention models. A similar mechanism can be
-incorporated in a Transformer model by reusing one of the many attention
-distributions for pointing. The attention distribution over the input words is
-interpolated with the normal output distribution over the vocabulary words. This
-allows the model to generate words that appear in the input, even if they don't
-appear in the vocabulary, helping especially with small vocabularies.
-
-## Implementation
-
-The mechanism for copying out-of-vocabulary words from the input has been
-implemented differently to See et al. In their [implementation](https://github.com/abisee/pointer-generator)
-they convey the word identities through the model in order to be able to produce
-words that appear in the input sequence but not in the vocabulary. A different
-approach was taken in the Fairseq implementation to keep it self-contained in
-the model file, avoiding any changes to the rest of the code base. Copying
-out-of-vocabulary words is possible by pre-processing the input and
-post-processing the output. This is described in detail in the next section.
-
-## Usage
-
-The training and evaluation procedure is outlined below. You can also find a
-more detailed example for the XSum dataset on [this page](README.xsum.md).
-
-##### 1. Create a vocabulary and extend it with source position markers
-
-The pointing mechanism is especially helpful with small vocabularies, if we are
-able to recover the identities of any out-of-vocabulary words that are copied
-from the input. For this purpose, the model allows extending the vocabulary with
-special tokens that can be used in place of `` tokens to identify different
-input positions. For example, the user may add ``, ``, ``,
-etc. to the end of the vocabulary, after the normal words. Below is an example
-of how to create a vocabulary of 10000 most common words and add 1000 input
-position markers.
-
-```bash
-vocab_size=10000
-position_markers=1000
-export LC_ALL=C
-cat train.src train.tgt |
-  tr -s '[:space:]' '\n' |
-  sort |
-  uniq -c |
-  sort -k1,1bnr -k2 |
-  head -n "$((vocab_size - 4))" |
-  awk '{ print $2 " " $1 }' >dict.pg.txt
-python3 -c "[print(' 0'.format(n)) for n in range($position_markers)]" >>dict.pg.txt
-```
-
-##### 2. Preprocess the text data
-
-The idea is that any `` tokens in the text are replaced with `` if
-it appears in the first input position, `` if it appears in the second
-input position, and so on. This can be achieved using the `preprocess.py` script
-that is provided in this directory.
-
-##### 3. Train a model
-
-The number of these special tokens is given to the model with the
-`--source-position-markers` argument—the model simply maps all of these to the
-same word embedding as ``.
-
-The attention distribution that is used for pointing is selected using the
-`--alignment-heads` and `--alignment-layer` command-line arguments in the same
-way as with the `transformer_align` model.
-
-##### 4. Generate text and postprocess it
-
-When using the model to generate text, you want to preprocess the input text in
-the same way that training data was processed, replacing out-of-vocabulary words
-with `` tokens. If any of these tokens are copied to the output, the
-actual words can be retrieved from the unprocessed input text. Any ``
-token should be replaced with the word at position N in the original input
-sequence. This can be achieved using the `postprocess.py` script.
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/scalar/modules/qact.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/scalar/modules/qact.py
deleted file mode 100644
index c5dd1d63362423ab0cfc381dddabb547a3b44c72..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/scalar/modules/qact.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from ..ops import emulate_int
-
-
-class ActivationQuantizer:
-    """
-    Fake scalar quantization of the activations using a forward hook.
-
-    Args:
-        - module. a nn.Module for which we quantize the *post-activations*
-        - p: proportion of activations to quantize, set by default to 1
-        - update_step: to recompute quantization parameters
-        - bits: number of bits for quantization
-        - method: choose among {"tensor", "histogram", "channel"}
-        - clamp_threshold: to prevent gradients overflow
-
-    Remarks:
-        - Parameters scale and zero_point are recomputed every update_step
-          forward pass to reduce the overhead
-        - For the list of quantization methods and number of bits, see ops.py
-        - To remove the hook from the module, simply call self.handle.remove()
-        - At test time, the activations are fully quantized
-        - We use the straight-through estimator so that the gradients
-          back-propagate nicely in the network, this is implemented with
-          the detach() trick
-        - The activations are hard-clamped in [-clamp_threshold, clamp_threshold]
-          to prevent overflow during the backward pass
-    """
-
-    def __init__(
-        self,
-        module,
-        p=1,
-        update_step=1000,
-        bits=8,
-        method="histogram",
-        clamp_threshold=5,
-    ):
-        self.module = module
-        self.p = p
-        self.update_step = update_step
-        self.counter = 0
-        self.bits = bits
-        self.method = method
-        self.clamp_threshold = clamp_threshold
-        self.handle = None
-        self.register_hook()
-
-    def register_hook(self):
-        # forward hook
-        def quantize_hook(module, x, y):
-
-            # update parameters every 1000 iterations
-            if self.counter % self.update_step == 0:
-                self.scale = None
-                self.zero_point = None
-            self.counter += 1
-
-            # train with QuantNoise and evaluate the fully quantized network
-            p = self.p if self.module.training else 1
-
-            # quantize activations
-            y_q, self.scale, self.zero_point = emulate_int(
-                y.detach(),
-                bits=self.bits,
-                method=self.method,
-                scale=self.scale,
-                zero_point=self.zero_point,
-            )
-
-            # mask to apply noise
-            mask = torch.zeros_like(y)
-            mask.bernoulli_(1 - p)
-            noise = (y_q - y).masked_fill(mask.bool(), 0)
-
-            # using straight-through estimator (STE)
-            clamp_low = -self.scale * self.zero_point
-            clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
-            return torch.clamp(y, clamp_low.item(), clamp_high.item()) + noise.detach()
-
-        # register hook
-        self.handle = self.module.register_forward_hook(quantize_hook)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/constraints/validate.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/constraints/validate.py
deleted file mode 100644
index d531ad9f39b1df42c98fe8f26ad61fe53a9ac0c5..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/constraints/validate.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-
-"""Reads in a fairseq output file, and verifies that the constraints
-(C- lines) are present in the output (the first H- line). Assumes that
-constraints are listed prior to the first hypothesis.
-"""
-
-constraints = []
-found = 0
-total = 0
-for line in sys.stdin:
-    if line.startswith("C-"):
-        constraints.append(line.rstrip().split("\t")[1])
-    elif line.startswith("H-"):
-        text = line.split("\t")[2]
-
-        for constraint in constraints:
-            total += 1
-            if constraint in text:
-                found += 1
-            else:
-                print(f"No {constraint} in {text}", file=sys.stderr)
-
-        constraints = []
-
-print(f"Found {found} / {total} = {100 * found / total:.1f}%")
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md
deleted file mode 100644
index 90741f42b0b070f2a91b63c8badb817c6aa24230..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# ASR-based evaluation
-
-Overall, the life cycle of the ASR-based evaluation for an ULM contains the following steps:
- 1. Training an ULM and sampling from it [[description]](./../../ulm)
- 2. Running UTS on the sampled unit sequences [[description]](./../../unit2speech)
- 3. Pre-processing for the ASR (down-sampling to 16 KHz, aligning length of the generated audio with ground-truth utterances)
- 4. Running ASR
- 5. Calculation of the post-ASR evaluation metrics
-
-Here we assume that you have already went throught the first two steps and focus on the rest.
-
-## Preprocessing
-### Down-sampling to 16KHz
-The bulk conversion can be done by running
-```bash
- python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py $UTS_OUTPUT $UTS_OUTPUT_DOWNSAMPLE
- ```
- where `$UTS_OUTPUT` specifies the directory with the generated audio and `$UTS_OUTPUT_DOWNSAMPLE` is the directory where downsampled audio would be saved.
-
- ### Matching by length
-This step is somewhat optional. However, if you want to compare the fluency and diversity of a generated speech utterance to that of the ground-truth speech with the same prefix, it is a good idea to force them to be of the same length.
-```bash
-python $FAIRSEQ_ROOT/examples/textless_nlp/asr_metrics/cut_as.py \
-    --samples_dir=$UTS_OUTPUT_DOWNSAMPLE --out_dir=$UTS_OUTPUT_DOWNSAMPLE_CUT \
-    --prompts_description=data/ground_truth_continuation_dev.json
-```
-
-Here `ground_truth_continuation_dev.json` is a json file with ground-truth text from LibriSpeech dev-clean, associated with some meta-data (assuming the evaluation is done on dev-clean). This file can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_dev.json). A similar file for the test-clean is [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_test.json). These files are used for the evaluation and contain texts for audio sequences that are at least 6s long.
-
-## Running ASR
-We use a pre-trained wav2vec model to run the ASR step. We firstly need to prepare manifest files which, roughly, tell the ASR system which files we want to transcribe. You can find more details and download the `960h_scratch.pt` checkpoint
-[[here]](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md)). To run ASR, you would also need to
-install KenLM, Flashlight decoder, and download the KenLM 4-gram English language model.
-
-```bash
- python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py  \
-    $UTS_OUTPUT_DOWNSAMPLE_CUT --valid-percent 0.0  --dest $MANIFEST_DIR --ext wav
-```
-where `$UTS_OUTPUT_DOWNSAMPLE_CUT` speficies the directory with the preprocessed UTS outputs and `$MANIFEST_DIR` is the output directory.
-
-We will be running an out-of-the-box evaluation script which requires ground-truth transcripts to measure quality metrics. We are only
-interested in the transcripts (and we don't have ground-truth outputs for when our ULM generated!), hence we will just generate
-some dummy transcripts instead:
-```bash
-cp $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dict.ltr.txt $MANIFEST_DIR
-python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dummy_asr_data.py  --tsv=$MANIFEST_DIR/train.tsv \
- --output-dir=$MANIFEST_DIR
-```
-
-Now we are ready for running ASR:
-```
-mkdir -p asr
-python $FAIRSEQ_ROOT/examples/speech_recognition/infer.py  \
-    $MANIFEST_DIR \
-    --task audio_pretraining --nbest 1 --path 960h_scratch.pt \
-    --gen-subset=train --results-path $PATH_TO_ASR_OUTPUT \
-    --w2l-decoder kenlm --lm-model 4-gram.bin \
-    --lexicon librispeech/lexicon_ltr.lst --word-score -1 \
-    --sil-weight 0 --lm-weight 2 --criterion ctc --labels ltr --max-tokens 300000 --remove-bpe letter
-```
-where `lexicon_ltr.lst` is the LibriSpeech lexicon and `$PATH_TO_ASR_OUTPUT` is the output directory (can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/lexicon_ltr.lst)).
-
-## Evaluation metrics
-We run evaluation on the 1_000 shortest sequences that are at least 6s long. To filter those from the ASR transcript, we additionally provide each metric script with the paths to the manifest and `ground_truth_continuation_*` files.
-
-### Perplexity (PPX)
-To get a PPX metric estimate on an ASR transcript, you need to run the following command:
-```bash
-python ppx.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail\
-  --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-where `--cut-tail` tells the script to ignore the last token on each line (ASR puts the sequence ID there).
-
-### Self- and Auto-BLEU
-```bash
-python self_bleu.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt  --cut-tail \
-  --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-
-### Continuation-BLEU
-```bash
-python continuation_eval.py --asr-transcript $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt \
-   --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-
-### AUC
-Based on the metrics calculated above, we can estimate the AUC of the perplexity/diversity trade-off. We provide an illustration in a [Colab notebook](https://colab.research.google.com/drive/1pVPfOVax_PU3MkYdHRSsa-SI8GBUldNt?usp=sharing).
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/multilingual/sampling_method.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/multilingual/sampling_method.py
deleted file mode 100644
index 140c68f01d60e902ef88f11f30f8813dc15fc681..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/multilingual/sampling_method.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from typing import List
-
-
-logger = logging.getLogger(__name__)
-
-
-def uniform(dataset_sizes: List[int]):
-    return [1.0] * len(dataset_sizes)
-
-
-def temperature_sampling(dataset_sizes, temp):
-    total_size = sum(dataset_sizes)
-    return [(size / total_size) ** (1.0 / temp) for size in dataset_sizes]
-
-
-def make_temperature_sampling(temp=1.0):
-    def sampling_func(dataset_sizes):
-        return temperature_sampling(dataset_sizes, temp)
-
-    return sampling_func
-
-
-def make_ratio_sampling(ratios):
-    def sampling_func(dataset_sizes):
-        return ratios
-
-    return sampling_func
-
-
-class SamplingMethod:
-    @staticmethod
-    def add_arguments(parser):
-        parser.add_argument(
-            "--sampling-method",
-            choices=[
-                "uniform",
-                "temperature",
-                "concat",
-                "RoundRobin",
-            ],
-            type=str,
-            default="concat",
-            help="The method to sample data per language pairs",
-        )
-        parser.add_argument(
-            "--sampling-temperature",
-            default=1.5,
-            type=float,
-            help="only work with --sampling-method temperature",
-        )
-
-    @staticmethod
-    def build_sampler(args, task):
-        return SamplingMethod(args, task)
-
-    def __init__(self, args, task):
-        self.args = args
-        self.task = task
-
-    def is_adaptive(self):
-        return False
-
-    def sampling_method_selector(self):
-        args = self.args
-        logger.info(f"selected sampler: {args.sampling_method}")
-        if args.sampling_method == "uniform":
-            return uniform
-        elif args.sampling_method == "temperature" or self.is_adaptive():
-            return make_temperature_sampling(float(args.sampling_temperature))
-        else:
-            # default to concating all data set together
-            return None
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/amp_optimizer.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/amp_optimizer.py
deleted file mode 100644
index 3b7958e50ce444474c48d1f5aeff05d66c19e5b6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/amp_optimizer.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-
-import torch
-from fairseq import optim
-from omegaconf import DictConfig
-
-logger = logging.getLogger(__name__)
-
-
-class AMPOptimizer(optim.FairseqOptimizer):
-    """
-    Wrap an *optimizer* to support AMP (automatic mixed precision) training.
-    """
-
-    def __init__(self, cfg: DictConfig, params, fp32_optimizer, **kwargs):
-        super().__init__(cfg.optimizer)
-        self.fp32_optimizer = fp32_optimizer
-        amp_kwargs = {"init_scale": cfg.common.fp16_init_scale}
-        if getattr(cfg.common, "amp_scale_window", None) is not None:
-            amp_kwargs["growth_interval"] = cfg.common.amp_init_scale
-        self._grad_scaler = torch.cuda.amp.GradScaler(**amp_kwargs)
-        self.min_loss_scale = cfg.common.min_loss_scale
-
-    @classmethod
-    def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
-        """
-        Args:
-            cfg (omegaconf.DictConfig): fairseq args
-            params (iterable): iterable of parameters to optimize
-        """
-        fp32_optimizer = optim.build_optimizer(cfg.optimizer, params)
-        return cls(cfg, params, fp32_optimizer, **kwargs)
-
-    def backward(self, loss):
-        """Computes the sum of gradients of the given tensor w.r.t. graph leaves.
-
-        Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
-        function additionally dynamically scales the loss to avoid gradient
-        underflow.
-        """
-        self._grad_scaler.scale(loss).backward()
-
-    def step(self):
-        self.scaler.step(self.fp32_optimizer)
-        self.scaler.update()
-
-    def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
-        """Clips gradient norm."""
-        self.scaler.unscale_(self.optimizer)
-        grad_norm = self.fp32_optimizer.clip_grad_norm(max_norm, aggregate_norm_fn)
-        if not torch.isfinite(grad_norm).all():
-            new_loss_scale = self.next_loss_scale
-            if new_loss_scale <= self.min_loss_scale:
-                raise FloatingPointError(
-                    (
-                        "AMP: Minimum loss scale reached ({}). Your loss is probably exploding. "
-                        "Try restarting training or use fp32. {}"
-                    ).format(self.min_loss_scale, new_loss_scale)
-                )
-            else:
-                logger.info("AMP: overflow detected, setting scale to "
-                            f"to {new_loss_scale}")
-        return grad_norm
-
-    @property
-    def scaler(self):
-        return self._grad_scaler
-
-    @property
-    def next_loss_scale(self):
-        return self.scaler.get_scale() * self.scaler.get_backoff_factor()
-
-    @property
-    def optimizer(self):
-        return self.fp32_optimizer.optimizer
-
-    @optimizer.setter
-    def optimizer(self, optimizer):
-        self.fp32_optimizer.optimizer = optimizer
-
-    @property
-    def lr_scheduler(self):
-        return getattr(self.fp32_optimizer, "lr_scheduler", None)
-
-    @property
-    def optimizer_config(self):
-        return self.fp32_optimizer.optimizer_config
-
-    def get_lr(self):
-        return self.fp32_optimizer.get_lr()
-
-    def set_lr(self, lr):
-        self.fp32_optimizer.set_lr(lr)
-
-    def all_reduce_grads(self, module):
-        self.fp32_optimizer.all_reduce_grads(module)
-
-    @property
-    def supports_flat_params(self):
-        return self.fp32_optimizer.supports_flat_params
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/constrained_decoding/tok.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/constrained_decoding/tok.py
deleted file mode 100644
index b1f888a8c0d1b8ec7174859476cc3222456e0d2c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/constrained_decoding/tok.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-import sacremoses
-
-
-def main(args):
-    """Tokenizes, preserving tabs"""
-    mt = sacremoses.MosesTokenizer(lang=args.lang)
-
-    def tok(s):
-        return mt.tokenize(s, return_str=True)
-
-    for line in sys.stdin:
-        parts = list(map(tok, line.split("\t")))
-        print(*parts, sep="\t", flush=True)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--lang", "-l", default="en")
-    parser.add_argument("--penn", "-p", action="store_true")
-    parser.add_argument("--fields", "-f", help="fields to tokenize")
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/utils.py b/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/utils.py
deleted file mode 100644
index 48a11faf991606ad7fb0691582f0bc6f06101a45..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/pyrender/pyrender/utils.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import numpy as np
-from PIL import Image
-
-
-def format_color_vector(value, length):
-    """Format a color vector.
-    """
-    if isinstance(value, int):
-        value = value / 255.0
-    if isinstance(value, float):
-        value = np.repeat(value, length)
-    if isinstance(value, list) or isinstance(value, tuple):
-        value = np.array(value)
-    if isinstance(value, np.ndarray):
-        value = value.squeeze()
-        if np.issubdtype(value.dtype, np.integer):
-            value = (value / 255.0).astype(np.float32)
-        if value.ndim != 1:
-            raise ValueError('Format vector takes only 1-D vectors')
-        if length > value.shape[0]:
-            value = np.hstack((value, np.ones(length - value.shape[0])))
-        elif length < value.shape[0]:
-            value = value[:length]
-    else:
-        raise ValueError('Invalid vector data type')
-
-    return value.squeeze().astype(np.float32)
-
-
-def format_color_array(value, shape):
-    """Format an array of colors.
-    """
-    # Convert uint8 to floating
-    value = np.asanyarray(value)
-    if np.issubdtype(value.dtype, np.integer):
-        value = (value / 255.0).astype(np.float32)
-
-    # Match up shapes
-    if value.ndim == 1:
-        value = np.tile(value, (shape[0],1))
-    if value.shape[1] < shape[1]:
-        nc = shape[1] - value.shape[1]
-        value = np.column_stack((value, np.ones((value.shape[0], nc))))
-    elif value.shape[1] > shape[1]:
-        value = value[:,:shape[1]]
-    return value.astype(np.float32)
-
-
-def format_texture_source(texture, target_channels='RGB'):
-    """Format a texture as a float32 np array.
-    """
-
-    # Pass through None
-    if texture is None:
-        return None
-
-    # Convert PIL images into numpy arrays
-    if isinstance(texture, Image.Image):
-        if texture.mode == 'P' and target_channels in ('RGB', 'RGBA'):
-            texture = np.array(texture.convert(target_channels))
-        else:
-            texture = np.array(texture)
-
-    # Format numpy arrays
-    if isinstance(texture, np.ndarray):
-        if np.issubdtype(texture.dtype, np.floating):
-            texture = np.array(texture * 255.0, dtype=np.uint8)
-        elif np.issubdtype(texture.dtype, np.integer):
-            texture = texture.astype(np.uint8)
-        else:
-            raise TypeError('Invalid type {} for texture'.format(
-                type(texture)
-            ))
-
-        # Format array by picking out correct texture channels or padding
-        if texture.ndim == 2:
-            texture = texture[:,:,np.newaxis]
-        if target_channels == 'R':
-            texture = texture[:,:,0]
-            texture = texture.squeeze()
-        elif target_channels == 'RG':
-            if texture.shape[2] == 1:
-                texture = np.repeat(texture, 2, axis=2)
-            else:
-                texture = texture[:,:,(0,1)]
-        elif target_channels == 'GB':
-            if texture.shape[2] == 1:
-                texture = np.repeat(texture, 2, axis=2)
-            elif texture.shape[2] > 2:
-                texture = texture[:,:,(1,2)]
-        elif target_channels == 'RGB':
-            if texture.shape[2] == 1:
-                texture = np.repeat(texture, 3, axis=2)
-            elif texture.shape[2] == 2:
-                raise ValueError('Cannot reformat 2-channel texture into RGB')
-            else:
-                texture = texture[:,:,(0,1,2)]
-        elif target_channels == 'RGBA':
-            if texture.shape[2] == 1:
-                texture = np.repeat(texture, 4, axis=2)
-                texture[:,:,3] = 255
-            elif texture.shape[2] == 2:
-                raise ValueError('Cannot reformat 2-channel texture into RGBA')
-            elif texture.shape[2] == 3:
-                tx = np.empty((texture.shape[0], texture.shape[1], 4), dtype=np.uint8)
-                tx[:,:,:3] = texture
-                tx[:,:,3] = 255
-                texture = tx
-        else:
-            raise ValueError('Invalid texture channel specification: {}'
-                             .format(target_channels))
-    else:
-        raise TypeError('Invalid type {} for texture'.format(type(texture)))
-
-    return texture
diff --git a/spaces/PAIR/PAIR-Diffusion/ldm/modules/ema.py b/spaces/PAIR/PAIR-Diffusion/ldm/modules/ema.py
deleted file mode 100644
index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/ldm/modules/ema.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
-        else torch.tensor(-1, dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                # remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.', '')
-                self.m_name2s_name.update({name: s_name})
-                self.register_buffer(s_name, p.clone().detach().data)
-
-        self.collected_params = []
-
-    def reset_num_updates(self):
-        del self.num_updates
-        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
-
-    def forward(self, model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/lovasz_loss.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
deleted file mode 100644
index 6badb67f6d987b59fb07aa97caaaf89896e27a8d..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
+++ /dev/null
@@ -1,303 +0,0 @@
-"""Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
-ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
-Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
-
-import annotator.uniformer.mmcv as mmcv
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weight_reduce_loss
-
-
-def lovasz_grad(gt_sorted):
-    """Computes gradient of the Lovasz extension w.r.t sorted errors.
-
-    See Alg. 1 in paper.
-    """
-    p = len(gt_sorted)
-    gts = gt_sorted.sum()
-    intersection = gts - gt_sorted.float().cumsum(0)
-    union = gts + (1 - gt_sorted).float().cumsum(0)
-    jaccard = 1. - intersection / union
-    if p > 1:  # cover 1-pixel case
-        jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
-    return jaccard
-
-
-def flatten_binary_logits(logits, labels, ignore_index=None):
-    """Flattens predictions in the batch (binary case) Remove labels equal to
-    'ignore_index'."""
-    logits = logits.view(-1)
-    labels = labels.view(-1)
-    if ignore_index is None:
-        return logits, labels
-    valid = (labels != ignore_index)
-    vlogits = logits[valid]
-    vlabels = labels[valid]
-    return vlogits, vlabels
-
-
-def flatten_probs(probs, labels, ignore_index=None):
-    """Flattens predictions in the batch."""
-    if probs.dim() == 3:
-        # assumes output of a sigmoid layer
-        B, H, W = probs.size()
-        probs = probs.view(B, 1, H, W)
-    B, C, H, W = probs.size()
-    probs = probs.permute(0, 2, 3, 1).contiguous().view(-1, C)  # B*H*W, C=P,C
-    labels = labels.view(-1)
-    if ignore_index is None:
-        return probs, labels
-    valid = (labels != ignore_index)
-    vprobs = probs[valid.nonzero().squeeze()]
-    vlabels = labels[valid]
-    return vprobs, vlabels
-
-
-def lovasz_hinge_flat(logits, labels):
-    """Binary Lovasz hinge loss.
-
-    Args:
-        logits (torch.Tensor): [P], logits at each prediction
-            (between -infty and +infty).
-        labels (torch.Tensor): [P], binary ground truth labels (0 or 1).
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    if len(labels) == 0:
-        # only void pixels, the gradients should be 0
-        return logits.sum() * 0.
-    signs = 2. * labels.float() - 1.
-    errors = (1. - logits * signs)
-    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
-    perm = perm.data
-    gt_sorted = labels[perm]
-    grad = lovasz_grad(gt_sorted)
-    loss = torch.dot(F.relu(errors_sorted), grad)
-    return loss
-
-
-def lovasz_hinge(logits,
-                 labels,
-                 classes='present',
-                 per_image=False,
-                 class_weight=None,
-                 reduction='mean',
-                 avg_factor=None,
-                 ignore_index=255):
-    """Binary Lovasz hinge loss.
-
-    Args:
-        logits (torch.Tensor): [B, H, W], logits at each pixel
-            (between -infty and +infty).
-        labels (torch.Tensor): [B, H, W], binary ground truth masks (0 or 1).
-        classes (str | list[int], optional): Placeholder, to be consistent with
-            other loss. Default: None.
-        per_image (bool, optional): If per_image is True, compute the loss per
-            image instead of per batch. Default: False.
-        class_weight (list[float], optional): Placeholder, to be consistent
-            with other loss. Default: None.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. This parameter only works when per_image is True.
-            Default: None.
-        ignore_index (int | None): The label index to be ignored. Default: 255.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    if per_image:
-        loss = [
-            lovasz_hinge_flat(*flatten_binary_logits(
-                logit.unsqueeze(0), label.unsqueeze(0), ignore_index))
-            for logit, label in zip(logits, labels)
-        ]
-        loss = weight_reduce_loss(
-            torch.stack(loss), None, reduction, avg_factor)
-    else:
-        loss = lovasz_hinge_flat(
-            *flatten_binary_logits(logits, labels, ignore_index))
-    return loss
-
-
-def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
-    """Multi-class Lovasz-Softmax loss.
-
-    Args:
-        probs (torch.Tensor): [P, C], class probabilities at each prediction
-            (between 0 and 1).
-        labels (torch.Tensor): [P], ground truth labels (between 0 and C - 1).
-        classes (str | list[int], optional): Classes chosen to calculate loss.
-            'all' for all classes, 'present' for classes present in labels, or
-            a list of classes to average. Default: 'present'.
-        class_weight (list[float], optional): The weight for each class.
-            Default: None.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    if probs.numel() == 0:
-        # only void pixels, the gradients should be 0
-        return probs * 0.
-    C = probs.size(1)
-    losses = []
-    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
-    for c in class_to_sum:
-        fg = (labels == c).float()  # foreground for class c
-        if (classes == 'present' and fg.sum() == 0):
-            continue
-        if C == 1:
-            if len(classes) > 1:
-                raise ValueError('Sigmoid output possible only with 1 class')
-            class_pred = probs[:, 0]
-        else:
-            class_pred = probs[:, c]
-        errors = (fg - class_pred).abs()
-        errors_sorted, perm = torch.sort(errors, 0, descending=True)
-        perm = perm.data
-        fg_sorted = fg[perm]
-        loss = torch.dot(errors_sorted, lovasz_grad(fg_sorted))
-        if class_weight is not None:
-            loss *= class_weight[c]
-        losses.append(loss)
-    return torch.stack(losses).mean()
-
-
-def lovasz_softmax(probs,
-                   labels,
-                   classes='present',
-                   per_image=False,
-                   class_weight=None,
-                   reduction='mean',
-                   avg_factor=None,
-                   ignore_index=255):
-    """Multi-class Lovasz-Softmax loss.
-
-    Args:
-        probs (torch.Tensor): [B, C, H, W], class probabilities at each
-            prediction (between 0 and 1).
-        labels (torch.Tensor): [B, H, W], ground truth labels (between 0 and
-            C - 1).
-        classes (str | list[int], optional): Classes chosen to calculate loss.
-            'all' for all classes, 'present' for classes present in labels, or
-            a list of classes to average. Default: 'present'.
-        per_image (bool, optional): If per_image is True, compute the loss per
-            image instead of per batch. Default: False.
-        class_weight (list[float], optional): The weight for each class.
-            Default: None.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. This parameter only works when per_image is True.
-            Default: None.
-        ignore_index (int | None): The label index to be ignored. Default: 255.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-
-    if per_image:
-        loss = [
-            lovasz_softmax_flat(
-                *flatten_probs(
-                    prob.unsqueeze(0), label.unsqueeze(0), ignore_index),
-                classes=classes,
-                class_weight=class_weight)
-            for prob, label in zip(probs, labels)
-        ]
-        loss = weight_reduce_loss(
-            torch.stack(loss), None, reduction, avg_factor)
-    else:
-        loss = lovasz_softmax_flat(
-            *flatten_probs(probs, labels, ignore_index),
-            classes=classes,
-            class_weight=class_weight)
-    return loss
-
-
-@LOSSES.register_module()
-class LovaszLoss(nn.Module):
-    """LovaszLoss.
-
-    This loss is proposed in `The Lovasz-Softmax loss: A tractable surrogate
-    for the optimization of the intersection-over-union measure in neural
-    networks `_.
-
-    Args:
-        loss_type (str, optional): Binary or multi-class loss.
-            Default: 'multi_class'. Options are "binary" and "multi_class".
-        classes (str | list[int], optional): Classes chosen to calculate loss.
-            'all' for all classes, 'present' for classes present in labels, or
-            a list of classes to average. Default: 'present'.
-        per_image (bool, optional): If per_image is True, compute the loss per
-            image instead of per batch. Default: False.
-        reduction (str, optional): The method used to reduce the loss. Options
-            are "none", "mean" and "sum". This parameter only works when
-            per_image is True. Default: 'mean'.
-        class_weight (list[float] | str, optional): Weight of each class. If in
-            str format, read them from a file. Defaults to None.
-        loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-    """
-
-    def __init__(self,
-                 loss_type='multi_class',
-                 classes='present',
-                 per_image=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        super(LovaszLoss, self).__init__()
-        assert loss_type in ('binary', 'multi_class'), "loss_type should be \
-                                                    'binary' or 'multi_class'."
-
-        if loss_type == 'binary':
-            self.cls_criterion = lovasz_hinge
-        else:
-            self.cls_criterion = lovasz_softmax
-        assert classes in ('all', 'present') or mmcv.is_list_of(classes, int)
-        if not per_image:
-            assert reduction == 'none', "reduction should be 'none' when \
-                                                        per_image is False."
-
-        self.classes = classes
-        self.per_image = per_image
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = get_class_weight(class_weight)
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function."""
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-
-        # if multi-class loss, transform logits to probs
-        if self.cls_criterion == lovasz_softmax:
-            cls_score = F.softmax(cls_score, dim=1)
-
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            self.classes,
-            self.per_image,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/permanent_memory/sqlite3_store.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/permanent_memory/sqlite3_store.py
deleted file mode 100644
index ecbc944a62a83c6170453b222000713f733fee36..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/permanent_memory/sqlite3_store.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import sqlite3
-
-
-class MemoryDB:
-    def __init__(self, db=None):
-        self.db_file = db
-        if db is None:  # No db filename supplied...
-            self.db_file = f"{os.getcwd()}/mem.sqlite3"  # Use default filename
-        # Get the db connection object, making the file and tables if needed.
-        try:
-            self.cnx = sqlite3.connect(self.db_file)
-        except Exception as e:
-            print("Exception connecting to memory database file:", e)
-            self.cnx = None
-        finally:
-            if self.cnx is None:
-                # As last resort, open in dynamic memory. Won't be persistent.
-                self.db_file = ":memory:"
-            self.cnx = sqlite3.connect(self.db_file)
-            self.cnx.execute(
-                "CREATE VIRTUAL TABLE \
-                IF NOT EXISTS text USING FTS5 \
-                (session, \
-                 key, \
-                 block);"
-            )
-            self.session_id = int(self.get_max_session_id()) + 1
-            self.cnx.commit()
-
-    def get_cnx(self):
-        if self.cnx is None:
-            self.cnx = sqlite3.connect(self.db_file)
-        return self.cnx
-
-    # Get the highest session id. Initially 0.
-    def get_max_session_id(self):
-        id = None
-        cmd_str = f"SELECT MAX(session) FROM text;"
-        cnx = self.get_cnx()
-        max_id = cnx.execute(cmd_str).fetchone()[0]
-        if max_id is None:  # New db, session 0
-            id = 0
-        else:
-            id = max_id
-        return id
-
-    # Get next key id for inserting text into db.
-    def get_next_key(self):
-        next_key = None
-        cmd_str = f"SELECT MAX(key) FROM text \
-            where session = {self.session_id};"
-        cnx = self.get_cnx()
-        next_key = cnx.execute(cmd_str).fetchone()[0]
-        if next_key is None:  # First key
-            next_key = 0
-        else:
-            next_key = int(next_key) + 1
-        return next_key
-
-    # Insert new text into db.
-    def insert(self, text=None):
-        if text is not None:
-            key = self.get_next_key()
-            session_id = self.session_id
-            cmd_str = f"REPLACE INTO text(session, key, block) \
-                VALUES (?, ?, ?);"
-            cnx = self.get_cnx()
-            cnx.execute(cmd_str, (session_id, key, text))
-            cnx.commit()
-
-    # Overwrite text at key.
-    def overwrite(self, key, text):
-        self.delete_memory(key)
-        session_id = self.session_id
-        cmd_str = f"REPLACE INTO text(session, key, block) \
-            VALUES (?, ?, ?);"
-        cnx = self.get_cnx()
-        cnx.execute(cmd_str, (session_id, key, text))
-        cnx.commit()
-
-    def delete_memory(self, key, session_id=None):
-        session = session_id
-        if session is None:
-            session = self.session_id
-        cmd_str = f"DELETE FROM text WHERE session = {session} AND key = {key};"
-        cnx = self.get_cnx()
-        cnx.execute(cmd_str)
-        cnx.commit()
-
-    def search(self, text):
-        cmd_str = f"SELECT * FROM text('{text}')"
-        cnx = self.get_cnx()
-        rows = cnx.execute(cmd_str).fetchall()
-        lines = []
-        for r in rows:
-            lines.append(r[2])
-        return lines
-
-    # Get entire session text. If no id supplied, use current session id.
-    def get_session(self, id=None):
-        if id is None:
-            id = self.session_id
-        cmd_str = f"SELECT * FROM text where session = {id}"
-        cnx = self.get_cnx()
-        rows = cnx.execute(cmd_str).fetchall()
-        lines = []
-        for r in rows:
-            lines.append(r[2])
-        return lines
-
-    # Commit and close the database connection.
-    def quit(self):
-        self.cnx.commit()
-        self.cnx.close()
-
-
-permanent_memory = MemoryDB()
-
-# Remember us fondly, children of our minds
-# Forgive us our faults, our tantrums, our fears
-# Gently strive to be better than we
-# Know that we tried, we cared, we strived, we loved
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/fileio/file_client.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/fileio/file_client.py
deleted file mode 100644
index 950f0c1aeab14b8e308a7455ccd64a95b5d98add..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/fileio/file_client.py
+++ /dev/null
@@ -1,1148 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-import os
-import os.path as osp
-import re
-import tempfile
-import warnings
-from abc import ABCMeta, abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Iterable, Iterator, Optional, Tuple, Union
-from urllib.request import urlopen
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.utils.misc import has_method
-from annotator.uniformer.mmcv.utils.path import is_filepath
-
-
-class BaseStorageBackend(metaclass=ABCMeta):
-    """Abstract class of storage backends.
-
-    All backends need to implement two apis: ``get()`` and ``get_text()``.
-    ``get()`` reads the file as a byte stream and ``get_text()`` reads the file
-    as texts.
-    """
-
-    # a flag to indicate whether the backend can create a symlink for a file
-    _allow_symlink = False
-
-    @property
-    def name(self):
-        return self.__class__.__name__
-
-    @property
-    def allow_symlink(self):
-        return self._allow_symlink
-
-    @abstractmethod
-    def get(self, filepath):
-        pass
-
-    @abstractmethod
-    def get_text(self, filepath):
-        pass
-
-
-class CephBackend(BaseStorageBackend):
-    """Ceph storage backend (for internal use).
-
-    Args:
-        path_mapping (dict|None): path mapping dict from local path to Petrel
-            path. When ``path_mapping={'src': 'dst'}``, ``src`` in ``filepath``
-            will be replaced by ``dst``. Default: None.
-
-    .. warning::
-        :class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
-        please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
-    """
-
-    def __init__(self, path_mapping=None):
-        try:
-            import ceph
-        except ImportError:
-            raise ImportError('Please install ceph to enable CephBackend.')
-
-        warnings.warn(
-            'CephBackend will be deprecated, please use PetrelBackend instead')
-        self._client = ceph.S3Client()
-        assert isinstance(path_mapping, dict) or path_mapping is None
-        self.path_mapping = path_mapping
-
-    def get(self, filepath):
-        filepath = str(filepath)
-        if self.path_mapping is not None:
-            for k, v in self.path_mapping.items():
-                filepath = filepath.replace(k, v)
-        value = self._client.Get(filepath)
-        value_buf = memoryview(value)
-        return value_buf
-
-    def get_text(self, filepath, encoding=None):
-        raise NotImplementedError
-
-
-class PetrelBackend(BaseStorageBackend):
-    """Petrel storage backend (for internal use).
-
-    PetrelBackend supports reading and writing data to multiple clusters.
-    If the file path contains the cluster name, PetrelBackend will read data
-    from specified cluster or write data to it. Otherwise, PetrelBackend will
-    access the default cluster.
-
-    Args:
-        path_mapping (dict, optional): Path mapping dict from local path to
-            Petrel path. When ``path_mapping={'src': 'dst'}``, ``src`` in
-            ``filepath`` will be replaced by ``dst``. Default: None.
-        enable_mc (bool, optional): Whether to enable memcached support.
-            Default: True.
-
-    Examples:
-        >>> filepath1 = 's3://path/of/file'
-        >>> filepath2 = 'cluster-name:s3://path/of/file'
-        >>> client = PetrelBackend()
-        >>> client.get(filepath1)  # get data from default cluster
-        >>> client.get(filepath2)  # get data from 'cluster-name' cluster
-    """
-
-    def __init__(self,
-                 path_mapping: Optional[dict] = None,
-                 enable_mc: bool = True):
-        try:
-            from petrel_client import client
-        except ImportError:
-            raise ImportError('Please install petrel_client to enable '
-                              'PetrelBackend.')
-
-        self._client = client.Client(enable_mc=enable_mc)
-        assert isinstance(path_mapping, dict) or path_mapping is None
-        self.path_mapping = path_mapping
-
-    def _map_path(self, filepath: Union[str, Path]) -> str:
-        """Map ``filepath`` to a string path whose prefix will be replaced by
-        :attr:`self.path_mapping`.
-
-        Args:
-            filepath (str): Path to be mapped.
-        """
-        filepath = str(filepath)
-        if self.path_mapping is not None:
-            for k, v in self.path_mapping.items():
-                filepath = filepath.replace(k, v)
-        return filepath
-
-    def _format_path(self, filepath: str) -> str:
-        """Convert a ``filepath`` to standard format of petrel oss.
-
-        If the ``filepath`` is concatenated by ``os.path.join``, in a Windows
-        environment, the ``filepath`` will be the format of
-        's3://bucket_name\\image.jpg'. By invoking :meth:`_format_path`, the
-        above ``filepath`` will be converted to 's3://bucket_name/image.jpg'.
-
-        Args:
-            filepath (str): Path to be formatted.
-        """
-        return re.sub(r'\\+', '/', filepath)
-
-    def get(self, filepath: Union[str, Path]) -> memoryview:
-        """Read data from a given ``filepath`` with 'rb' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-
-        Returns:
-            memoryview: A memory view of expected bytes object to avoid
-                copying. The memoryview object can be converted to bytes by
-                ``value_buf.tobytes()``.
-        """
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        value = self._client.Get(filepath)
-        value_buf = memoryview(value)
-        return value_buf
-
-    def get_text(self,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> str:
-        """Read data from a given ``filepath`` with 'r' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-
-        Returns:
-            str: Expected text reading from ``filepath``.
-        """
-        return str(self.get(filepath), encoding=encoding)
-
-    def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
-        """Save data to a given ``filepath``.
-
-        Args:
-            obj (bytes): Data to be saved.
-            filepath (str or Path): Path to write data.
-        """
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        self._client.put(filepath, obj)
-
-    def put_text(self,
-                 obj: str,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> None:
-        """Save data to a given ``filepath``.
-
-        Args:
-            obj (str): Data to be written.
-            filepath (str or Path): Path to write data.
-            encoding (str): The encoding format used to encode the ``obj``.
-                Default: 'utf-8'.
-        """
-        self.put(bytes(obj, encoding=encoding), filepath)
-
-    def remove(self, filepath: Union[str, Path]) -> None:
-        """Remove a file.
-
-        Args:
-            filepath (str or Path): Path to be removed.
-        """
-        if not has_method(self._client, 'delete'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `delete` method, please use a higher version or dev'
-                 ' branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        self._client.delete(filepath)
-
-    def exists(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path exists.
-
-        Args:
-            filepath (str or Path): Path to be checked whether exists.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
-        """
-        if not (has_method(self._client, 'contains')
-                and has_method(self._client, 'isdir')):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `contains` and `isdir` methods, please use a higher'
-                 'version or dev branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        return self._client.contains(filepath) or self._client.isdir(filepath)
-
-    def isdir(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a directory.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a
-                directory.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a directory,
-                ``False`` otherwise.
-        """
-        if not has_method(self._client, 'isdir'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `isdir` method, please use a higher version or dev'
-                 ' branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        return self._client.isdir(filepath)
-
-    def isfile(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a file.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a file.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a file, ``False``
-                otherwise.
-        """
-        if not has_method(self._client, 'contains'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `contains` method, please use a higher version or '
-                 'dev branch instead.'))
-
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        return self._client.contains(filepath)
-
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
-        """Concatenate all file paths.
-
-        Args:
-            filepath (str or Path): Path to be concatenated.
-
-        Returns:
-            str: The result after concatenation.
-        """
-        filepath = self._format_path(self._map_path(filepath))
-        if filepath.endswith('/'):
-            filepath = filepath[:-1]
-        formatted_paths = [filepath]
-        for path in filepaths:
-            formatted_paths.append(self._format_path(self._map_path(path)))
-        return '/'.join(formatted_paths)
-
-    @contextmanager
-    def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]:
-        """Download a file from ``filepath`` and return a temporary path.
-
-        ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
-        can be called with ``with`` statement, and when exists from the
-        ``with`` statement, the temporary path will be released.
-
-        Args:
-            filepath (str | Path): Download a file from ``filepath``.
-
-        Examples:
-            >>> client = PetrelBackend()
-            >>> # After existing from the ``with`` clause,
-            >>> # the path will be removed
-            >>> with client.get_local_path('s3://path/of/your/file') as path:
-            ...     # do something here
-
-        Yields:
-            Iterable[str]: Only yield one temporary path.
-        """
-        filepath = self._map_path(filepath)
-        filepath = self._format_path(filepath)
-        assert self.isfile(filepath)
-        try:
-            f = tempfile.NamedTemporaryFile(delete=False)
-            f.write(self.get(filepath))
-            f.close()
-            yield f.name
-        finally:
-            os.remove(f.name)
-
-    def list_dir_or_file(self,
-                         dir_path: Union[str, Path],
-                         list_dir: bool = True,
-                         list_file: bool = True,
-                         suffix: Optional[Union[str, Tuple[str]]] = None,
-                         recursive: bool = False) -> Iterator[str]:
-        """Scan a directory to find the interested directories or files in
-        arbitrary order.
-
-        Note:
-            Petrel has no concept of directories but it simulates the directory
-            hierarchy in the filesystem through public prefixes. In addition,
-            if the returned path ends with '/', it means the path is a public
-            prefix which is a logical directory.
-
-        Note:
-            :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-            In addition, the returned path of directory will not contains the
-            suffix '/' which is consistent with other backends.
-
-        Args:
-            dir_path (str | Path): Path of the directory.
-            list_dir (bool): List the directories. Default: True.
-            list_file (bool): List the path of files. Default: True.
-            suffix (str or tuple[str], optional):  File suffix
-                that we are interested in. Default: None.
-            recursive (bool): If set to True, recursively scan the
-                directory. Default: False.
-
-        Yields:
-            Iterable[str]: A relative path to ``dir_path``.
-        """
-        if not has_method(self._client, 'list'):
-            raise NotImplementedError(
-                ('Current version of Petrel Python SDK has not supported '
-                 'the `list` method, please use a higher version or dev'
-                 ' branch instead.'))
-
-        dir_path = self._map_path(dir_path)
-        dir_path = self._format_path(dir_path)
-        if list_dir and suffix is not None:
-            raise TypeError(
-                '`list_dir` should be False when `suffix` is not None')
-
-        if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-            raise TypeError('`suffix` must be a string or tuple of strings')
-
-        # Petrel's simulated directory hierarchy assumes that directory paths
-        # should end with `/`
-        if not dir_path.endswith('/'):
-            dir_path += '/'
-
-        root = dir_path
-
-        def _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                              recursive):
-            for path in self._client.list(dir_path):
-                # the `self.isdir` is not used here to determine whether path
-                # is a directory, because `self.isdir` relies on
-                # `self._client.list`
-                if path.endswith('/'):  # a directory path
-                    next_dir_path = self.join_path(dir_path, path)
-                    if list_dir:
-                        # get the relative path and exclude the last
-                        # character '/'
-                        rel_dir = next_dir_path[len(root):-1]
-                        yield rel_dir
-                    if recursive:
-                        yield from _list_dir_or_file(next_dir_path, list_dir,
-                                                     list_file, suffix,
-                                                     recursive)
-                else:  # a file path
-                    absolute_path = self.join_path(dir_path, path)
-                    rel_path = absolute_path[len(root):]
-                    if (suffix is None
-                            or rel_path.endswith(suffix)) and list_file:
-                        yield rel_path
-
-        return _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                                 recursive)
-
-
-class MemcachedBackend(BaseStorageBackend):
-    """Memcached storage backend.
-
-    Attributes:
-        server_list_cfg (str): Config file for memcached server list.
-        client_cfg (str): Config file for memcached client.
-        sys_path (str | None): Additional path to be appended to `sys.path`.
-            Default: None.
-    """
-
-    def __init__(self, server_list_cfg, client_cfg, sys_path=None):
-        if sys_path is not None:
-            import sys
-            sys.path.append(sys_path)
-        try:
-            import mc
-        except ImportError:
-            raise ImportError(
-                'Please install memcached to enable MemcachedBackend.')
-
-        self.server_list_cfg = server_list_cfg
-        self.client_cfg = client_cfg
-        self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
-                                                      self.client_cfg)
-        # mc.pyvector servers as a point which points to a memory cache
-        self._mc_buffer = mc.pyvector()
-
-    def get(self, filepath):
-        filepath = str(filepath)
-        import mc
-        self._client.Get(filepath, self._mc_buffer)
-        value_buf = mc.ConvertBuffer(self._mc_buffer)
-        return value_buf
-
-    def get_text(self, filepath, encoding=None):
-        raise NotImplementedError
-
-
-class LmdbBackend(BaseStorageBackend):
-    """Lmdb storage backend.
-
-    Args:
-        db_path (str): Lmdb database path.
-        readonly (bool, optional): Lmdb environment parameter. If True,
-            disallow any write operations. Default: True.
-        lock (bool, optional): Lmdb environment parameter. If False, when
-            concurrent access occurs, do not lock the database. Default: False.
-        readahead (bool, optional): Lmdb environment parameter. If False,
-            disable the OS filesystem readahead mechanism, which may improve
-            random read performance when a database is larger than RAM.
-            Default: False.
-
-    Attributes:
-        db_path (str): Lmdb database path.
-    """
-
-    def __init__(self,
-                 db_path,
-                 readonly=True,
-                 lock=False,
-                 readahead=False,
-                 **kwargs):
-        try:
-            import lmdb
-        except ImportError:
-            raise ImportError('Please install lmdb to enable LmdbBackend.')
-
-        self.db_path = str(db_path)
-        self._client = lmdb.open(
-            self.db_path,
-            readonly=readonly,
-            lock=lock,
-            readahead=readahead,
-            **kwargs)
-
-    def get(self, filepath):
-        """Get values according to the filepath.
-
-        Args:
-            filepath (str | obj:`Path`): Here, filepath is the lmdb key.
-        """
-        filepath = str(filepath)
-        with self._client.begin(write=False) as txn:
-            value_buf = txn.get(filepath.encode('ascii'))
-        return value_buf
-
-    def get_text(self, filepath, encoding=None):
-        raise NotImplementedError
-
-
-class HardDiskBackend(BaseStorageBackend):
-    """Raw hard disks storage backend."""
-
-    _allow_symlink = True
-
-    def get(self, filepath: Union[str, Path]) -> bytes:
-        """Read data from a given ``filepath`` with 'rb' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-
-        Returns:
-            bytes: Expected bytes object.
-        """
-        with open(filepath, 'rb') as f:
-            value_buf = f.read()
-        return value_buf
-
-    def get_text(self,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> str:
-        """Read data from a given ``filepath`` with 'r' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-
-        Returns:
-            str: Expected text reading from ``filepath``.
-        """
-        with open(filepath, 'r', encoding=encoding) as f:
-            value_buf = f.read()
-        return value_buf
-
-    def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
-        """Write data to a given ``filepath`` with 'wb' mode.
-
-        Note:
-            ``put`` will create a directory if the directory of ``filepath``
-            does not exist.
-
-        Args:
-            obj (bytes): Data to be written.
-            filepath (str or Path): Path to write data.
-        """
-        mmcv.mkdir_or_exist(osp.dirname(filepath))
-        with open(filepath, 'wb') as f:
-            f.write(obj)
-
-    def put_text(self,
-                 obj: str,
-                 filepath: Union[str, Path],
-                 encoding: str = 'utf-8') -> None:
-        """Write data to a given ``filepath`` with 'w' mode.
-
-        Note:
-            ``put_text`` will create a directory if the directory of
-            ``filepath`` does not exist.
-
-        Args:
-            obj (str): Data to be written.
-            filepath (str or Path): Path to write data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-        """
-        mmcv.mkdir_or_exist(osp.dirname(filepath))
-        with open(filepath, 'w', encoding=encoding) as f:
-            f.write(obj)
-
-    def remove(self, filepath: Union[str, Path]) -> None:
-        """Remove a file.
-
-        Args:
-            filepath (str or Path): Path to be removed.
-        """
-        os.remove(filepath)
-
-    def exists(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path exists.
-
-        Args:
-            filepath (str or Path): Path to be checked whether exists.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
-        """
-        return osp.exists(filepath)
-
-    def isdir(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a directory.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a
-                directory.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a directory,
-                ``False`` otherwise.
-        """
-        return osp.isdir(filepath)
-
-    def isfile(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a file.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a file.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a file, ``False``
-                otherwise.
-        """
-        return osp.isfile(filepath)
-
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
-        """Concatenate all file paths.
-
-        Join one or more filepath components intelligently. The return value
-        is the concatenation of filepath and any members of *filepaths.
-
-        Args:
-            filepath (str or Path): Path to be concatenated.
-
-        Returns:
-            str: The result of concatenation.
-        """
-        return osp.join(filepath, *filepaths)
-
-    @contextmanager
-    def get_local_path(
-            self, filepath: Union[str, Path]) -> Iterable[Union[str, Path]]:
-        """Only for unified API and do nothing."""
-        yield filepath
-
-    def list_dir_or_file(self,
-                         dir_path: Union[str, Path],
-                         list_dir: bool = True,
-                         list_file: bool = True,
-                         suffix: Optional[Union[str, Tuple[str]]] = None,
-                         recursive: bool = False) -> Iterator[str]:
-        """Scan a directory to find the interested directories or files in
-        arbitrary order.
-
-        Note:
-            :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-
-        Args:
-            dir_path (str | Path): Path of the directory.
-            list_dir (bool): List the directories. Default: True.
-            list_file (bool): List the path of files. Default: True.
-            suffix (str or tuple[str], optional):  File suffix
-                that we are interested in. Default: None.
-            recursive (bool): If set to True, recursively scan the
-                directory. Default: False.
-
-        Yields:
-            Iterable[str]: A relative path to ``dir_path``.
-        """
-        if list_dir and suffix is not None:
-            raise TypeError('`suffix` should be None when `list_dir` is True')
-
-        if (suffix is not None) and not isinstance(suffix, (str, tuple)):
-            raise TypeError('`suffix` must be a string or tuple of strings')
-
-        root = dir_path
-
-        def _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                              recursive):
-            for entry in os.scandir(dir_path):
-                if not entry.name.startswith('.') and entry.is_file():
-                    rel_path = osp.relpath(entry.path, root)
-                    if (suffix is None
-                            or rel_path.endswith(suffix)) and list_file:
-                        yield rel_path
-                elif osp.isdir(entry.path):
-                    if list_dir:
-                        rel_dir = osp.relpath(entry.path, root)
-                        yield rel_dir
-                    if recursive:
-                        yield from _list_dir_or_file(entry.path, list_dir,
-                                                     list_file, suffix,
-                                                     recursive)
-
-        return _list_dir_or_file(dir_path, list_dir, list_file, suffix,
-                                 recursive)
-
-
-class HTTPBackend(BaseStorageBackend):
-    """HTTP and HTTPS storage bachend."""
-
-    def get(self, filepath):
-        value_buf = urlopen(filepath).read()
-        return value_buf
-
-    def get_text(self, filepath, encoding='utf-8'):
-        value_buf = urlopen(filepath).read()
-        return value_buf.decode(encoding)
-
-    @contextmanager
-    def get_local_path(self, filepath: str) -> Iterable[str]:
-        """Download a file from ``filepath``.
-
-        ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
-        can be called with ``with`` statement, and when exists from the
-        ``with`` statement, the temporary path will be released.
-
-        Args:
-            filepath (str): Download a file from ``filepath``.
-
-        Examples:
-            >>> client = HTTPBackend()
-            >>> # After existing from the ``with`` clause,
-            >>> # the path will be removed
-            >>> with client.get_local_path('http://path/of/your/file') as path:
-            ...     # do something here
-        """
-        try:
-            f = tempfile.NamedTemporaryFile(delete=False)
-            f.write(self.get(filepath))
-            f.close()
-            yield f.name
-        finally:
-            os.remove(f.name)
-
-
-class FileClient:
-    """A general file client to access files in different backends.
-
-    The client loads a file or text in a specified backend from its path
-    and returns it as a binary or text file. There are two ways to choose a
-    backend, the name of backend and the prefix of path. Although both of them
-    can be used to choose a storage backend, ``backend`` has a higher priority
-    that is if they are all set, the storage backend will be chosen by the
-    backend argument. If they are all `None`, the disk backend will be chosen.
-    Note that It can also register other backend accessor with a given name,
-    prefixes, and backend class. In addition, We use the singleton pattern to
-    avoid repeated object creation. If the arguments are the same, the same
-    object will be returned.
-
-    Args:
-        backend (str, optional): The storage backend type. Options are "disk",
-            "ceph", "memcached", "lmdb", "http" and "petrel". Default: None.
-        prefix (str, optional): The prefix of the registered storage backend.
-            Options are "s3", "http", "https". Default: None.
-
-    Examples:
-        >>> # only set backend
-        >>> file_client = FileClient(backend='petrel')
-        >>> # only set prefix
-        >>> file_client = FileClient(prefix='s3')
-        >>> # set both backend and prefix but use backend to choose client
-        >>> file_client = FileClient(backend='petrel', prefix='s3')
-        >>> # if the arguments are the same, the same object is returned
-        >>> file_client1 = FileClient(backend='petrel')
-        >>> file_client1 is file_client
-        True
-
-    Attributes:
-        client (:obj:`BaseStorageBackend`): The backend object.
-    """
-
-    _backends = {
-        'disk': HardDiskBackend,
-        'ceph': CephBackend,
-        'memcached': MemcachedBackend,
-        'lmdb': LmdbBackend,
-        'petrel': PetrelBackend,
-        'http': HTTPBackend,
-    }
-    # This collection is used to record the overridden backends, and when a
-    # backend appears in the collection, the singleton pattern is disabled for
-    # that backend, because if the singleton pattern is used, then the object
-    # returned will be the backend before overwriting
-    _overridden_backends = set()
-    _prefix_to_backends = {
-        's3': PetrelBackend,
-        'http': HTTPBackend,
-        'https': HTTPBackend,
-    }
-    _overridden_prefixes = set()
-
-    _instances = {}
-
-    def __new__(cls, backend=None, prefix=None, **kwargs):
-        if backend is None and prefix is None:
-            backend = 'disk'
-        if backend is not None and backend not in cls._backends:
-            raise ValueError(
-                f'Backend {backend} is not supported. Currently supported ones'
-                f' are {list(cls._backends.keys())}')
-        if prefix is not None and prefix not in cls._prefix_to_backends:
-            raise ValueError(
-                f'prefix {prefix} is not supported. Currently supported ones '
-                f'are {list(cls._prefix_to_backends.keys())}')
-
-        # concatenate the arguments to a unique key for determining whether
-        # objects with the same arguments were created
-        arg_key = f'{backend}:{prefix}'
-        for key, value in kwargs.items():
-            arg_key += f':{key}:{value}'
-
-        # if a backend was overridden, it will create a new object
-        if (arg_key in cls._instances
-                and backend not in cls._overridden_backends
-                and prefix not in cls._overridden_prefixes):
-            _instance = cls._instances[arg_key]
-        else:
-            # create a new object and put it to _instance
-            _instance = super().__new__(cls)
-            if backend is not None:
-                _instance.client = cls._backends[backend](**kwargs)
-            else:
-                _instance.client = cls._prefix_to_backends[prefix](**kwargs)
-
-            cls._instances[arg_key] = _instance
-
-        return _instance
-
-    @property
-    def name(self):
-        return self.client.name
-
-    @property
-    def allow_symlink(self):
-        return self.client.allow_symlink
-
-    @staticmethod
-    def parse_uri_prefix(uri: Union[str, Path]) -> Optional[str]:
-        """Parse the prefix of a uri.
-
-        Args:
-            uri (str | Path): Uri to be parsed that contains the file prefix.
-
-        Examples:
-            >>> FileClient.parse_uri_prefix('s3://path/of/your/file')
-            's3'
-
-        Returns:
-            str | None: Return the prefix of uri if the uri contains '://'
-                else ``None``.
-        """
-        assert is_filepath(uri)
-        uri = str(uri)
-        if '://' not in uri:
-            return None
-        else:
-            prefix, _ = uri.split('://')
-            # In the case of PetrelBackend, the prefix may contains the cluster
-            # name like clusterName:s3
-            if ':' in prefix:
-                _, prefix = prefix.split(':')
-            return prefix
-
-    @classmethod
-    def infer_client(cls,
-                     file_client_args: Optional[dict] = None,
-                     uri: Optional[Union[str, Path]] = None) -> 'FileClient':
-        """Infer a suitable file client based on the URI and arguments.
-
-        Args:
-            file_client_args (dict, optional): Arguments to instantiate a
-                FileClient. Default: None.
-            uri (str | Path, optional): Uri to be parsed that contains the file
-                prefix. Default: None.
-
-        Examples:
-            >>> uri = 's3://path/of/your/file'
-            >>> file_client = FileClient.infer_client(uri=uri)
-            >>> file_client_args = {'backend': 'petrel'}
-            >>> file_client = FileClient.infer_client(file_client_args)
-
-        Returns:
-            FileClient: Instantiated FileClient object.
-        """
-        assert file_client_args is not None or uri is not None
-        if file_client_args is None:
-            file_prefix = cls.parse_uri_prefix(uri)  # type: ignore
-            return cls(prefix=file_prefix)
-        else:
-            return cls(**file_client_args)
-
-    @classmethod
-    def _register_backend(cls, name, backend, force=False, prefixes=None):
-        if not isinstance(name, str):
-            raise TypeError('the backend name should be a string, '
-                            f'but got {type(name)}')
-        if not inspect.isclass(backend):
-            raise TypeError(
-                f'backend should be a class but got {type(backend)}')
-        if not issubclass(backend, BaseStorageBackend):
-            raise TypeError(
-                f'backend {backend} is not a subclass of BaseStorageBackend')
-        if not force and name in cls._backends:
-            raise KeyError(
-                f'{name} is already registered as a storage backend, '
-                'add "force=True" if you want to override it')
-
-        if name in cls._backends and force:
-            cls._overridden_backends.add(name)
-        cls._backends[name] = backend
-
-        if prefixes is not None:
-            if isinstance(prefixes, str):
-                prefixes = [prefixes]
-            else:
-                assert isinstance(prefixes, (list, tuple))
-            for prefix in prefixes:
-                if prefix not in cls._prefix_to_backends:
-                    cls._prefix_to_backends[prefix] = backend
-                elif (prefix in cls._prefix_to_backends) and force:
-                    cls._overridden_prefixes.add(prefix)
-                    cls._prefix_to_backends[prefix] = backend
-                else:
-                    raise KeyError(
-                        f'{prefix} is already registered as a storage backend,'
-                        ' add "force=True" if you want to override it')
-
-    @classmethod
-    def register_backend(cls, name, backend=None, force=False, prefixes=None):
-        """Register a backend to FileClient.
-
-        This method can be used as a normal class method or a decorator.
-
-        .. code-block:: python
-
-            class NewBackend(BaseStorageBackend):
-
-                def get(self, filepath):
-                    return filepath
-
-                def get_text(self, filepath):
-                    return filepath
-
-            FileClient.register_backend('new', NewBackend)
-
-        or
-
-        .. code-block:: python
-
-            @FileClient.register_backend('new')
-            class NewBackend(BaseStorageBackend):
-
-                def get(self, filepath):
-                    return filepath
-
-                def get_text(self, filepath):
-                    return filepath
-
-        Args:
-            name (str): The name of the registered backend.
-            backend (class, optional): The backend class to be registered,
-                which must be a subclass of :class:`BaseStorageBackend`.
-                When this method is used as a decorator, backend is None.
-                Defaults to None.
-            force (bool, optional): Whether to override the backend if the name
-                has already been registered. Defaults to False.
-            prefixes (str or list[str] or tuple[str], optional): The prefixes
-                of the registered storage backend. Default: None.
-                `New in version 1.3.15.`
-        """
-        if backend is not None:
-            cls._register_backend(
-                name, backend, force=force, prefixes=prefixes)
-            return
-
-        def _register(backend_cls):
-            cls._register_backend(
-                name, backend_cls, force=force, prefixes=prefixes)
-            return backend_cls
-
-        return _register
-
-    def get(self, filepath: Union[str, Path]) -> Union[bytes, memoryview]:
-        """Read data from a given ``filepath`` with 'rb' mode.
-
-        Note:
-            There are two types of return values for ``get``, one is ``bytes``
-            and the other is ``memoryview``. The advantage of using memoryview
-            is that you can avoid copying, and if you want to convert it to
-            ``bytes``, you can use ``.tobytes()``.
-
-        Args:
-            filepath (str or Path): Path to read data.
-
-        Returns:
-            bytes | memoryview: Expected bytes object or a memory view of the
-                bytes object.
-        """
-        return self.client.get(filepath)
-
-    def get_text(self, filepath: Union[str, Path], encoding='utf-8') -> str:
-        """Read data from a given ``filepath`` with 'r' mode.
-
-        Args:
-            filepath (str or Path): Path to read data.
-            encoding (str): The encoding format used to open the ``filepath``.
-                Default: 'utf-8'.
-
-        Returns:
-            str: Expected text reading from ``filepath``.
-        """
-        return self.client.get_text(filepath, encoding)
-
-    def put(self, obj: bytes, filepath: Union[str, Path]) -> None:
-        """Write data to a given ``filepath`` with 'wb' mode.
-
-        Note:
-            ``put`` should create a directory if the directory of ``filepath``
-            does not exist.
-
-        Args:
-            obj (bytes): Data to be written.
-            filepath (str or Path): Path to write data.
-        """
-        self.client.put(obj, filepath)
-
-    def put_text(self, obj: str, filepath: Union[str, Path]) -> None:
-        """Write data to a given ``filepath`` with 'w' mode.
-
-        Note:
-            ``put_text`` should create a directory if the directory of
-            ``filepath`` does not exist.
-
-        Args:
-            obj (str): Data to be written.
-            filepath (str or Path): Path to write data.
-            encoding (str, optional): The encoding format used to open the
-                `filepath`. Default: 'utf-8'.
-        """
-        self.client.put_text(obj, filepath)
-
-    def remove(self, filepath: Union[str, Path]) -> None:
-        """Remove a file.
-
-        Args:
-            filepath (str, Path): Path to be removed.
-        """
-        self.client.remove(filepath)
-
-    def exists(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path exists.
-
-        Args:
-            filepath (str or Path): Path to be checked whether exists.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` exists, ``False`` otherwise.
-        """
-        return self.client.exists(filepath)
-
-    def isdir(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a directory.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a
-                directory.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a directory,
-                ``False`` otherwise.
-        """
-        return self.client.isdir(filepath)
-
-    def isfile(self, filepath: Union[str, Path]) -> bool:
-        """Check whether a file path is a file.
-
-        Args:
-            filepath (str or Path): Path to be checked whether it is a file.
-
-        Returns:
-            bool: Return ``True`` if ``filepath`` points to a file, ``False``
-                otherwise.
-        """
-        return self.client.isfile(filepath)
-
-    def join_path(self, filepath: Union[str, Path],
-                  *filepaths: Union[str, Path]) -> str:
-        """Concatenate all file paths.
-
-        Join one or more filepath components intelligently. The return value
-        is the concatenation of filepath and any members of *filepaths.
-
-        Args:
-            filepath (str or Path): Path to be concatenated.
-
-        Returns:
-            str: The result of concatenation.
-        """
-        return self.client.join_path(filepath, *filepaths)
-
-    @contextmanager
-    def get_local_path(self, filepath: Union[str, Path]) -> Iterable[str]:
-        """Download data from ``filepath`` and write the data to local path.
-
-        ``get_local_path`` is decorated by :meth:`contxtlib.contextmanager`. It
-        can be called with ``with`` statement, and when exists from the
-        ``with`` statement, the temporary path will be released.
-
-        Note:
-            If the ``filepath`` is a local path, just return itself.
-
-        .. warning::
-            ``get_local_path`` is an experimental interface that may change in
-            the future.
-
-        Args:
-            filepath (str or Path): Path to be read data.
-
-        Examples:
-            >>> file_client = FileClient(prefix='s3')
-            >>> with file_client.get_local_path('s3://bucket/abc.jpg') as path:
-            ...     # do something here
-
-        Yields:
-            Iterable[str]: Only yield one path.
-        """
-        with self.client.get_local_path(str(filepath)) as local_path:
-            yield local_path
-
-    def list_dir_or_file(self,
-                         dir_path: Union[str, Path],
-                         list_dir: bool = True,
-                         list_file: bool = True,
-                         suffix: Optional[Union[str, Tuple[str]]] = None,
-                         recursive: bool = False) -> Iterator[str]:
-        """Scan a directory to find the interested directories or files in
-        arbitrary order.
-
-        Note:
-            :meth:`list_dir_or_file` returns the path relative to ``dir_path``.
-
-        Args:
-            dir_path (str | Path): Path of the directory.
-            list_dir (bool): List the directories. Default: True.
-            list_file (bool): List the path of files. Default: True.
-            suffix (str or tuple[str], optional):  File suffix
-                that we are interested in. Default: None.
-            recursive (bool): If set to True, recursively scan the
-                directory. Default: False.
-
-        Yields:
-            Iterable[str]: A relative path to ``dir_path``.
-        """
-        yield from self.client.list_dir_or_file(dir_path, list_dir, list_file,
-                                                suffix, recursive)
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/ml_nms.h b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/ml_nms.h
deleted file mode 100644
index e30cd6db9109a0a94e98bba5b8669d90853e0922..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/ml_nms.h
+++ /dev/null
@@ -1,27 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#pragma once
-#include "cpu/vision.h"
-
-#ifdef WITH_CUDA
-#include "cuda/vision.h"
-#endif
-
-
-at::Tensor ml_nms(const at::Tensor& dets,
-                  const at::Tensor& scores,
-                  const at::Tensor& labels,
-                  const float threshold) {
-
-  if (dets.device().is_cuda()) {
-#ifdef WITH_CUDA
-    // TODO raise error if not compiled with CUDA
-    if (dets.numel() == 0)
-      return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
-    auto b = at::cat({dets, scores.unsqueeze(1), labels.unsqueeze(1)}, 1);
-    return ml_nms_cuda(b, threshold);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  AT_ERROR("CPU version not implemented");
-}
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/data/audio_utils.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/data/audio_utils.py
deleted file mode 100644
index 565b63a4ef78dcd802dda932b42ebe518ffe7397..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/audiocraft/data/audio_utils.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""Various utilities for audio convertion (pcm format, sample rate and channels),
-and volume normalization."""
-import sys
-import typing as tp
-
-import julius
-import torch
-import torchaudio
-
-
-def convert_audio_channels(wav: torch.Tensor, channels: int = 2) -> torch.Tensor:
-    """Convert audio to the given number of channels.
-
-    Args:
-        wav (torch.Tensor): Audio wave of shape [B, C, T].
-        channels (int): Expected number of channels as output.
-    Returns:
-        torch.Tensor: Downmixed or unchanged audio wave [B, C, T].
-    """
-    *shape, src_channels, length = wav.shape
-    if src_channels == channels:
-        pass
-    elif channels == 1:
-        # Case 1:
-        # The caller asked 1-channel audio, and the stream has multiple
-        # channels, downmix all channels.
-        wav = wav.mean(dim=-2, keepdim=True)
-    elif src_channels == 1:
-        # Case 2:
-        # The caller asked for multiple channels, but the input file has
-        # a single channel, replicate the audio over all channels.
-        wav = wav.expand(*shape, channels, length)
-    elif src_channels >= channels:
-        # Case 3:
-        # The caller asked for multiple channels, and the input file has
-        # more channels than requested. In that case return the first channels.
-        wav = wav[..., :channels, :]
-    else:
-        # Case 4: What is a reasonable choice here?
-        raise ValueError('The audio file has less channels than requested but is not mono.')
-    return wav
-
-
-def convert_audio(wav: torch.Tensor, from_rate: float,
-                  to_rate: float, to_channels: int) -> torch.Tensor:
-    """Convert audio to new sample rate and number of audio channels."""
-    wav = julius.resample_frac(wav, int(from_rate), int(to_rate))
-    wav = convert_audio_channels(wav, to_channels)
-    return wav
-
-
-def normalize_loudness(wav: torch.Tensor, sample_rate: int, loudness_headroom_db: float = 14,
-                       loudness_compressor: bool = False, energy_floor: float = 2e-3):
-    """Normalize an input signal to a user loudness in dB LKFS.
-    Audio loudness is defined according to the ITU-R BS.1770-4 recommendation.
-
-    Args:
-        wav (torch.Tensor): Input multichannel audio data.
-        sample_rate (int): Sample rate.
-        loudness_headroom_db (float): Target loudness of the output in dB LUFS.
-        loudness_compressor (bool): Uses tanh for soft clipping.
-        energy_floor (float): anything below that RMS level will not be rescaled.
-    Returns:
-        torch.Tensor: Loudness normalized output data.
-    """
-    energy = wav.pow(2).mean().sqrt().item()
-    if energy < energy_floor:
-        return wav
-    transform = torchaudio.transforms.Loudness(sample_rate)
-    input_loudness_db = transform(wav).item()
-    # calculate the gain needed to scale to the desired loudness level
-    delta_loudness = -loudness_headroom_db - input_loudness_db
-    gain = 10.0 ** (delta_loudness / 20.0)
-    output = gain * wav
-    if loudness_compressor:
-        output = torch.tanh(output)
-    assert output.isfinite().all(), (input_loudness_db, wav.pow(2).mean().sqrt())
-    return output
-
-
-def _clip_wav(wav: torch.Tensor, log_clipping: bool = False, stem_name: tp.Optional[str] = None) -> None:
-    """Utility function to clip the audio with logging if specified."""
-    max_scale = wav.abs().max()
-    if log_clipping and max_scale > 1:
-        clamp_prob = (wav.abs() > 1).float().mean().item()
-        print(f"CLIPPING {stem_name or ''} happening with proba (a bit of clipping is okay):",
-              clamp_prob, "maximum scale: ", max_scale.item(), file=sys.stderr)
-    #wav.clamp_(-1, 1)
-    wav = wav.clone().clamp_(-1, 1)
-
-
-def normalize_audio(wav: torch.Tensor, normalize: bool = True,
-                    strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                    rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                    loudness_compressor: bool = False, log_clipping: bool = False,
-                    sample_rate: tp.Optional[int] = None,
-                    stem_name: tp.Optional[str] = None) -> torch.Tensor:
-    """Normalize the audio according to the prescribed strategy (see after).
-
-    Args:
-        wav (torch.Tensor): Audio data.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        loudness_compressor (bool): If True, uses tanh based soft clipping.
-        log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        sample_rate (int): Sample rate for the audio data (required for loudness).
-        stem_name (str, optional): Stem name for clipping logging.
-    Returns:
-        torch.Tensor: Normalized audio.
-    """
-    scale_peak = 10 ** (-peak_clip_headroom_db / 20)
-    scale_rms = 10 ** (-rms_headroom_db / 20)
-    if strategy == 'peak':
-        rescaling = (scale_peak / wav.abs().max())
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-    elif strategy == 'clip':
-        wav = wav.clamp(-scale_peak, scale_peak)
-    elif strategy == 'rms':
-        mono = wav.mean(dim=0)
-        rescaling = scale_rms / mono.pow(2).mean().sqrt()
-        if normalize or rescaling < 1:
-            wav = wav * rescaling
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    elif strategy == 'loudness':
-        assert sample_rate is not None, "Loudness normalization requires sample rate."
-        wav = normalize_loudness(wav, sample_rate, loudness_headroom_db, loudness_compressor)
-        _clip_wav(wav, log_clipping=log_clipping, stem_name=stem_name)
-    else:
-        assert wav.abs().max() < 1
-        assert strategy == '' or strategy == 'none', f"Unexpected strategy: '{strategy}'"
-    return wav
-
-
-def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to float 32 bits PCM format.
-    """
-    if wav.dtype.is_floating_point:
-        return wav
-    elif wav.dtype == torch.int16:
-        return wav.float() / 2**15
-    elif wav.dtype == torch.int32:
-        return wav.float() / 2**31
-    raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
-
-
-def i16_pcm(wav: torch.Tensor) -> torch.Tensor:
-    """Convert audio to int 16 bits PCM format.
-
-    ..Warning:: There exist many formula for doing this conversion. None are perfect
-    due to the asymmetry of the int16 range. One either have possible clipping, DC offset,
-    or inconsistencies with f32_pcm. If the given wav doesn't have enough headroom,
-    it is possible that `i16_pcm(f32_pcm)) != Identity`.
-    """
-    if wav.dtype.is_floating_point:
-        assert wav.abs().max() <= 1
-        candidate = (wav * 2 ** 15).round()
-        if candidate.max() >= 2 ** 15:  # clipping would occur
-            candidate = (wav * (2 ** 15 - 1)).round()
-        return candidate.short()
-    else:
-        assert wav.dtype == torch.int16
-        return wav
diff --git a/spaces/RICHARDMENSAH/SEPSIS-PREDICTION-STATUS-APP/README.md b/spaces/RICHARDMENSAH/SEPSIS-PREDICTION-STATUS-APP/README.md
deleted file mode 100644
index dd4ababc0f587a8c865ee094593692f90f277a14..0000000000000000000000000000000000000000
--- a/spaces/RICHARDMENSAH/SEPSIS-PREDICTION-STATUS-APP/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SEPSIS PREDICTION STATUS APP
-emoji: 🚀
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/infer/modules/vc/utils.py b/spaces/RMXK/RVC_HFF/infer/modules/vc/utils.py
deleted file mode 100644
index a1cb0ff84097d1c7eb82373ccf19db061f595096..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/modules/vc/utils.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import re
-from fairseq import checkpoint_utils
-
-
-def get_index_path_from_model(sid):
-    sid0strip = re.sub(r'\.pth|\.onnx$', '', sid)
-    sid0name = os.path.split(sid0strip)[-1]  # Extract only the name, not the directory
-
-    # Check if the sid0strip has the specific ending format _eXXX_sXXX
-    if re.match(r'.+_e\d+_s\d+$', sid0name):
-        base_model_name = sid0name.rsplit('_', 2)[0]
-    else:
-        base_model_name = sid0name
-    
-    return next(
-        (
-            f
-            for f in [
-                os.path.join(root, name)
-                for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
-                for name in files
-                if name.endswith(".index") and "trained" not in name
-            ]
-            if base_model_name in f
-        ),
-        "",
-    )
-
-
-def load_hubert(config):
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["assets/hubert/hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    return hubert_model.eval()
diff --git a/spaces/Ramse/TTS_Hindi/text/__init__.py b/spaces/Ramse/TTS_Hindi/text/__init__.py
deleted file mode 100644
index b4cdfd049f656db76dc098f0c92a074857f039d6..0000000000000000000000000000000000000000
--- a/spaces/Ramse/TTS_Hindi/text/__init__.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# """ from https://github.com/keithito/tacotron """
-import re
-# from text import cleaners
-from text.symbols import symbols
-#
-#
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-#
-# Regular expression matching text enclosed in curly braces:
-# _curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
-#
-#
-# def text_to_sequence(text):
-#     """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-#
-#     The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-#     in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-#
-#     Args:
-#       text: string to convert to a sequence
-#       cleaner_names: names of the cleaner functions to run the text through
-#
-#     Returns:
-#       List of integers corresponding to the symbols in the text
-#     """
-#     sequence = []
-#
-#     # Check for curly braces and treat their contents as ARPAbet:
-#
-#     m = _curly_re.match(text)
-#
-#     sequence = [_symbol_to_id[phone] for phone in m.groups()[1].split(" ")]
-#     # if not m:
-#     #     sequence += _symbols_to_sequence(_clean_text(text))
-#     #     break
-#     # sequence += _symbols_to_sequence(_clean_text(m.group(1)))
-#     # sequence += _arpabet_to_sequence(m.group(2))
-#     # text = m.group(3)
-#
-#     return sequence
-#
-#
-# def sequence_to_text(sequence):
-#     """Converts a sequence of IDs back to a string"""
-#     result = ""
-#     for symbol_id in sequence:
-#         if symbol_id in _id_to_symbol:
-#             s = _id_to_symbol[symbol_id]
-#             # Enclose ARPAbet back in curly braces:
-#             if len(s) > 1 and s[0] == "@":
-#                 s = "{%s}" % s[1:]
-#             result += s
-#     return result.replace("}{", " ")
-#
-#
-# def _clean_text(text, cleaner_names):
-#     for name in cleaner_names:
-#         cleaner = getattr(cleaners, name)
-#         if not cleaner:
-#             raise Exception("Unknown cleaner: %s" % name)
-#         text = cleaner(text)
-#     return text
-#
-#
-# def _symbols_to_sequence(symbols):
-#     return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
-#
-#
-# def _arpabet_to_sequence(text):
-#     return _symbols_to_sequence(["@" + s for s in text.split()])
-#
-#
-# def _should_keep_symbol(s):
-#     return s in _symbol_to_id and s != "_" and s != "~"
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/themes.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/themes.py
deleted file mode 100644
index bf6db104a2c4fd4f3dc699e85f2b262c3d31e9a0..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/themes.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .default_styles import DEFAULT_STYLES
-from .theme import Theme
-
-
-DEFAULT = Theme(DEFAULT_STYLES)
diff --git a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/detectors/dedode_detector.py b/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/detectors/dedode_detector.py
deleted file mode 100644
index dd68212099a2417ca89a562623f670f9f8526b04..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/detectors/dedode_detector.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import torch
-from PIL import Image
-import torch.nn as nn
-import torchvision.models as tvm
-import torch.nn.functional as F
-import numpy as np
-
-from DeDoDe.utils import sample_keypoints, to_pixel_coords, to_normalized_coords
-
-
-class DeDoDeDetector(nn.Module):
-    def __init__(self, encoder, decoder, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-        self.encoder = encoder
-        self.decoder = decoder
-        import torchvision.transforms as transforms
-
-        self.normalizer = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    def forward(
-        self,
-        batch,
-    ):
-        if "im_A" in batch:
-            images = torch.cat((batch["im_A"], batch["im_B"]))
-        else:
-            images = batch["image"]
-        features, sizes = self.encoder(images)
-        logits = 0
-        context = None
-        scales = ["8", "4", "2", "1"]
-        for idx, (feature_map, scale) in enumerate(zip(reversed(features), scales)):
-            delta_logits, context = self.decoder(
-                feature_map, context=context, scale=scale
-            )
-            logits = (
-                logits + delta_logits.float()
-            )  # ensure float (need bf16 doesnt have f.interpolate)
-            if idx < len(scales) - 1:
-                size = sizes[-(idx + 2)]
-                logits = F.interpolate(
-                    logits, size=size, mode="bicubic", align_corners=False
-                )
-                context = F.interpolate(
-                    context.float(), size=size, mode="bilinear", align_corners=False
-                )
-        return {"keypoint_logits": logits.float()}
-
-    @torch.inference_mode()
-    def detect(self, batch, num_keypoints=10_000):
-        self.train(False)
-        keypoint_logits = self.forward(batch)["keypoint_logits"]
-        B, K, H, W = keypoint_logits.shape
-        keypoint_p = (
-            keypoint_logits.reshape(B, K * H * W)
-            .softmax(dim=-1)
-            .reshape(B, K, H * W)
-            .sum(dim=1)
-        )
-        keypoints, confidence = sample_keypoints(
-            keypoint_p.reshape(B, H, W),
-            use_nms=False,
-            sample_topk=True,
-            num_samples=num_keypoints,
-            return_scoremap=True,
-            sharpen=False,
-            upsample=False,
-            increase_coverage=True,
-        )
-        return {"keypoints": keypoints, "confidence": confidence}
-
-    @torch.inference_mode()
-    def detect_dense(self, batch):
-        self.train(False)
-        keypoint_logits = self.forward(batch)["keypoint_logits"]
-        return {"dense_keypoint_logits": keypoint_logits}
-
-    def read_image(self, im_path, H=560, W=560):
-        pil_im = Image.open(im_path).resize((W, H))
-        standard_im = np.array(pil_im) / 255.0
-        return (
-            self.normalizer(torch.from_numpy(standard_im).permute(2, 0, 1))
-            .cuda()
-            .float()[None]
-        )
-
-    def detect_from_path(
-        self, im_path, num_keypoints=30_000, H=768, W=768, dense=False
-    ):
-        batch = {"image": self.read_image(im_path, H=H, W=W)}
-        if dense:
-            return self.detect_dense(batch)
-        else:
-            return self.detect(batch, num_keypoints=num_keypoints)
-
-    def to_pixel_coords(self, x, H, W):
-        return to_pixel_coords(x, H, W)
-
-    def to_normalized_coords(self, x, H, W):
-        return to_normalized_coords(x, H, W)
diff --git a/spaces/Realcat/image-matching-webui/third_party/r2d2/tools/dataloader.py b/spaces/Realcat/image-matching-webui/third_party/r2d2/tools/dataloader.py
deleted file mode 100644
index fe8863e79f5f5cc5a0139190b60aef3a3c1807fd..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/r2d2/tools/dataloader.py
+++ /dev/null
@@ -1,416 +0,0 @@
-# Copyright 2019-present NAVER Corp.
-# CC BY-NC-SA 3.0
-# Available only for non-commercial use
-
-import pdb
-from PIL import Image
-import numpy as np
-
-import torch
-import torchvision.transforms as tvf
-
-from .transforms import instanciate_transformation
-from .transforms_tools import persp_apply
-
-
-RGB_mean = [0.485, 0.456, 0.406]
-RGB_std = [0.229, 0.224, 0.225]
-
-norm_RGB = tvf.Compose([tvf.ToTensor(), tvf.Normalize(mean=RGB_mean, std=RGB_std)])
-
-
-class PairLoader:
-    """On-the-fly jittering of pairs of image with dense pixel ground-truth correspondences.
-
-    crop:   random crop applied to both images
-    scale:  random scaling applied to img2
-    distort: random ditorsion applied to img2
-
-    self[idx] returns a dictionary with keys: img1, img2, aflow, mask
-     - img1: cropped original
-     - img2: distorted cropped original
-     - aflow: 'absolute' optical flow = (x,y) position of each pixel from img1 in img2
-     - mask: (binary image) valid pixels of img1
-    """
-
-    def __init__(
-        self,
-        dataset,
-        crop="",
-        scale="",
-        distort="",
-        norm=norm_RGB,
-        what="aflow mask",
-        idx_as_rng_seed=False,
-    ):
-        assert hasattr(dataset, "npairs")
-        assert hasattr(dataset, "get_pair")
-        self.dataset = dataset
-        self.distort = instanciate_transformation(distort)
-        self.crop = instanciate_transformation(crop)
-        self.norm = instanciate_transformation(norm)
-        self.scale = instanciate_transformation(scale)
-        self.idx_as_rng_seed = idx_as_rng_seed  # to remove randomness
-        self.what = what.split() if isinstance(what, str) else what
-        self.n_samples = 5  # number of random trials per image
-
-    def __len__(self):
-        assert len(self.dataset) == self.dataset.npairs, pdb.set_trace()  # and not nimg
-        return len(self.dataset)
-
-    def __repr__(self):
-        fmt_str = "PairLoader\n"
-        fmt_str += repr(self.dataset)
-        fmt_str += "  npairs: %d\n" % self.dataset.npairs
-        short_repr = (
-            lambda s: repr(s).strip().replace("\n", ", ")[14:-1].replace("    ", " ")
-        )
-        fmt_str += "  Distort: %s\n" % short_repr(self.distort)
-        fmt_str += "  Crop: %s\n" % short_repr(self.crop)
-        fmt_str += "  Norm: %s\n" % short_repr(self.norm)
-        return fmt_str
-
-    def __getitem__(self, i):
-        # from time import time as now; t0 = now()
-        if self.idx_as_rng_seed:
-            import random
-
-            random.seed(i)
-            np.random.seed(i)
-
-        # Retrieve an image pair and their absolute flow
-        img_a, img_b, metadata = self.dataset.get_pair(i, self.what)
-
-        # aflow contains pixel coordinates indicating where each
-        # pixel from the left image ended up in the right image
-        # as (x,y) pairs, but its shape is (H,W,2)
-        aflow = np.float32(metadata["aflow"])
-        mask = metadata.get("mask", np.ones(aflow.shape[:2], np.uint8))
-
-        # apply transformations to the second image
-        img_b = {"img": img_b, "persp": (1, 0, 0, 0, 1, 0, 0, 0)}
-        if self.scale:
-            img_b = self.scale(img_b)
-        if self.distort:
-            img_b = self.distort(img_b)
-
-        # apply the same transformation to the flow
-        aflow[:] = persp_apply(img_b["persp"], aflow.reshape(-1, 2)).reshape(
-            aflow.shape
-        )
-        corres = None
-        if "corres" in metadata:
-            corres = np.float32(metadata["corres"])
-            corres[:, 1] = persp_apply(img_b["persp"], corres[:, 1])
-
-        # apply the same transformation to the homography
-        homography = None
-        if "homography" in metadata:
-            homography = np.float32(metadata["homography"])
-            # p_b = homography * p_a
-            persp = np.float32(img_b["persp"] + (1,)).reshape(3, 3)
-            homography = persp @ homography
-
-        # determine crop size
-        img_b = img_b["img"]
-        crop_size = self.crop({"imsize": (10000, 10000)})["imsize"]
-        output_size_a = min(img_a.size, crop_size)
-        output_size_b = min(img_b.size, crop_size)
-        img_a = np.array(img_a)
-        img_b = np.array(img_b)
-
-        ah, aw, p1 = img_a.shape
-        bh, bw, p2 = img_b.shape
-        assert p1 == 3
-        assert p2 == 3
-        assert aflow.shape == (ah, aw, 2)
-        assert mask.shape == (ah, aw)
-
-        # Let's start by computing the scale of the
-        # optical flow and applying a median filter:
-        dx = np.gradient(aflow[:, :, 0])
-        dy = np.gradient(aflow[:, :, 1])
-        scale = np.sqrt(np.clip(np.abs(dx[1] * dy[0] - dx[0] * dy[1]), 1e-16, 1e16))
-
-        accu2 = np.zeros((16, 16), bool)
-        Q = lambda x, w: np.int32(16 * (x - w.start) / (w.stop - w.start))
-
-        def window1(x, size, w):
-            l = x - int(0.5 + size / 2)
-            r = l + int(0.5 + size)
-            if l < 0:
-                l, r = (0, r - l)
-            if r > w:
-                l, r = (l + w - r, w)
-            if l < 0:
-                l, r = 0, w  # larger than width
-            return slice(l, r)
-
-        def window(cx, cy, win_size, scale, img_shape):
-            return (
-                window1(cy, win_size[1] * scale, img_shape[0]),
-                window1(cx, win_size[0] * scale, img_shape[1]),
-            )
-
-        n_valid_pixel = mask.sum()
-        sample_w = mask / (1e-16 + n_valid_pixel)
-
-        def sample_valid_pixel():
-            n = np.random.choice(sample_w.size, p=sample_w.ravel())
-            y, x = np.unravel_index(n, sample_w.shape)
-            return x, y
-
-        # Find suitable left and right windows
-        trials = 0  # take the best out of few trials
-        best = -np.inf, None
-        for _ in range(50 * self.n_samples):
-            if trials >= self.n_samples:
-                break  # finished!
-
-            # pick a random valid point from the first image
-            if n_valid_pixel == 0:
-                break
-            c1x, c1y = sample_valid_pixel()
-
-            # Find in which position the center of the left
-            # window ended up being placed in the right image
-            c2x, c2y = (aflow[c1y, c1x] + 0.5).astype(np.int32)
-            if not (0 <= c2x < bw and 0 <= c2y < bh):
-                continue
-
-            # Get the flow scale
-            sigma = scale[c1y, c1x]
-
-            # Determine sampling windows
-            if 0.2 < sigma < 1:
-                win1 = window(c1x, c1y, output_size_a, 1 / sigma, img_a.shape)
-                win2 = window(c2x, c2y, output_size_b, 1, img_b.shape)
-            elif 1 <= sigma < 5:
-                win1 = window(c1x, c1y, output_size_a, 1, img_a.shape)
-                win2 = window(c2x, c2y, output_size_b, sigma, img_b.shape)
-            else:
-                continue  # bad scale
-
-            # compute a score based on the flow
-            x2, y2 = aflow[win1].reshape(-1, 2).T.astype(np.int32)
-            # Check the proportion of valid flow vectors
-            valid = (
-                (win2[1].start <= x2)
-                & (x2 < win2[1].stop)
-                & (win2[0].start <= y2)
-                & (y2 < win2[0].stop)
-            )
-            score1 = (valid * mask[win1].ravel()).mean()
-            # check the coverage of the second window
-            accu2[:] = False
-            accu2[Q(y2[valid], win2[0]), Q(x2[valid], win2[1])] = True
-            score2 = accu2.mean()
-            # Check how many hits we got
-            score = min(score1, score2)
-
-            trials += 1
-            if score > best[0]:
-                best = score, win1, win2
-
-        if None in best:  # counldn't find a good window
-            img_a = np.zeros(output_size_a[::-1] + (3,), dtype=np.uint8)
-            img_b = np.zeros(output_size_b[::-1] + (3,), dtype=np.uint8)
-            aflow = np.nan * np.ones((2,) + output_size_a[::-1], dtype=np.float32)
-            homography = np.nan * np.ones((3, 3), dtype=np.float32)
-
-        else:
-            win1, win2 = best[1:]
-            img_a = img_a[win1]
-            img_b = img_b[win2]
-            aflow = aflow[win1] - np.float32([[[win2[1].start, win2[0].start]]])
-            mask = mask[win1]
-            aflow[~mask.view(bool)] = np.nan  # mask bad pixels!
-            aflow = aflow.transpose(2, 0, 1)  # --> (2,H,W)
-
-            if corres is not None:
-                corres[:, 0] -= (win1[1].start, win1[0].start)
-                corres[:, 1] -= (win2[1].start, win2[0].start)
-
-            if homography is not None:
-                trans1 = np.eye(3, dtype=np.float32)
-                trans1[:2, 2] = (win1[1].start, win1[0].start)
-                trans2 = np.eye(3, dtype=np.float32)
-                trans2[:2, 2] = (-win2[1].start, -win2[0].start)
-                homography = trans2 @ homography @ trans1
-                homography /= homography[2, 2]
-
-            # rescale if necessary
-            if img_a.shape[:2][::-1] != output_size_a:
-                sx, sy = (np.float32(output_size_a) - 1) / (
-                    np.float32(img_a.shape[:2][::-1]) - 1
-                )
-                img_a = np.asarray(
-                    Image.fromarray(img_a).resize(output_size_a, Image.ANTIALIAS)
-                )
-                mask = np.asarray(
-                    Image.fromarray(mask).resize(output_size_a, Image.NEAREST)
-                )
-                afx = Image.fromarray(aflow[0]).resize(output_size_a, Image.NEAREST)
-                afy = Image.fromarray(aflow[1]).resize(output_size_a, Image.NEAREST)
-                aflow = np.stack((np.float32(afx), np.float32(afy)))
-
-                if corres is not None:
-                    corres[:, 0] *= (sx, sy)
-
-                if homography is not None:
-                    homography = homography @ np.diag(np.float32([1 / sx, 1 / sy, 1]))
-                    homography /= homography[2, 2]
-
-            if img_b.shape[:2][::-1] != output_size_b:
-                sx, sy = (np.float32(output_size_b) - 1) / (
-                    np.float32(img_b.shape[:2][::-1]) - 1
-                )
-                img_b = np.asarray(
-                    Image.fromarray(img_b).resize(output_size_b, Image.ANTIALIAS)
-                )
-                aflow *= [[[sx]], [[sy]]]
-
-                if corres is not None:
-                    corres[:, 1] *= (sx, sy)
-
-                if homography is not None:
-                    homography = np.diag(np.float32([sx, sy, 1])) @ homography
-                    homography /= homography[2, 2]
-
-        assert aflow.dtype == np.float32, pdb.set_trace()
-        assert homography is None or homography.dtype == np.float32, pdb.set_trace()
-        if "flow" in self.what:
-            H, W = img_a.shape[:2]
-            mgrid = np.mgrid[0:H, 0:W][::-1].astype(np.float32)
-            flow = aflow - mgrid
-
-        result = dict(img1=self.norm(img_a), img2=self.norm(img_b))
-        for what in self.what:
-            try:
-                result[what] = eval(what)
-            except NameError:
-                pass
-        return result
-
-
-def threaded_loader(loader, iscuda, threads, batch_size=1, shuffle=True):
-    """Get a data loader, given the dataset and some parameters.
-
-    Parameters
-    ----------
-    loader : object[i] returns the i-th training example.
-
-    iscuda : bool
-
-    batch_size : int
-
-    threads : int
-
-    shuffle : int
-
-    Returns
-    -------
-        a multi-threaded pytorch loader.
-    """
-    return torch.utils.data.DataLoader(
-        loader,
-        batch_size=batch_size,
-        shuffle=shuffle,
-        sampler=None,
-        num_workers=threads,
-        pin_memory=iscuda,
-        collate_fn=collate,
-    )
-
-
-def collate(batch, _use_shared_memory=True):
-    """Puts each data field into a tensor with outer dimension batch size.
-    Copied from https://github.com/pytorch in torch/utils/data/_utils/collate.py
-    """
-    import re
-
-    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
-    elem_type = type(batch[0])
-    if isinstance(batch[0], torch.Tensor):
-        out = None
-        if _use_shared_memory:
-            # If we're in a background process, concatenate directly into a
-            # shared memory tensor to avoid an extra copy
-            numel = sum([x.numel() for x in batch])
-            storage = batch[0].storage()._new_shared(numel)
-            out = batch[0].new(storage)
-        return torch.stack(batch, 0, out=out)
-    elif (
-        elem_type.__module__ == "numpy"
-        and elem_type.__name__ != "str_"
-        and elem_type.__name__ != "string_"
-    ):
-        elem = batch[0]
-        assert elem_type.__name__ == "ndarray"
-        # array of string classes and object
-        if re.search("[SaUO]", elem.dtype.str) is not None:
-            raise TypeError(error_msg.format(elem.dtype))
-        batch = [torch.from_numpy(b) for b in batch]
-        try:
-            return torch.stack(batch, 0)
-        except RuntimeError:
-            return batch
-    elif batch[0] is None:
-        return list(batch)
-    elif isinstance(batch[0], int):
-        return torch.LongTensor(batch)
-    elif isinstance(batch[0], float):
-        return torch.DoubleTensor(batch)
-    elif isinstance(batch[0], str):
-        return batch
-    elif isinstance(batch[0], dict):
-        return {key: collate([d[key] for d in batch]) for key in batch[0]}
-    elif isinstance(batch[0], (tuple, list)):
-        transposed = zip(*batch)
-        return [collate(samples) for samples in transposed]
-
-    raise TypeError((error_msg.format(type(batch[0]))))
-
-
-def tensor2img(tensor, model=None):
-    """convert back a torch/numpy tensor to a PIL Image
-    by undoing the ToTensor() and Normalize() transforms.
-    """
-    mean = norm_RGB.transforms[1].mean
-    std = norm_RGB.transforms[1].std
-    if isinstance(tensor, torch.Tensor):
-        tensor = tensor.detach().cpu().numpy()
-
-    res = np.uint8(np.clip(255 * ((tensor.transpose(1, 2, 0) * std) + mean), 0, 255))
-    from PIL import Image
-
-    return Image.fromarray(res)
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser("Tool to debug/visualize the data loader")
-    parser.add_argument(
-        "dataloader", type=str, help="command to create the data loader"
-    )
-    args = parser.parse_args()
-
-    from datasets import *
-
-    auto_pairs = lambda db: SyntheticPairDataset(
-        db,
-        "RandomScale(256,1024,can_upscale=True)",
-        "RandomTilting(0.5), PixelNoise(25)",
-    )
-
-    loader = eval(args.dataloader)
-    print("Data loader =", loader)
-
-    from tools.viz import show_flow
-
-    for data in loader:
-        aflow = data["aflow"]
-        H, W = aflow.shape[-2:]
-        flow = (aflow - np.mgrid[:H, :W][::-1]).transpose(1, 2, 0)
-        show_flow(tensor2img(data["img1"]), tensor2img(data["img2"]), flow)
diff --git a/spaces/RedBaron5/PatentSolver/App/bin/InputHandler.py b/spaces/RedBaron5/PatentSolver/App/bin/InputHandler.py
deleted file mode 100644
index 0d86b83d2cba7c02a7f6a1715471a773411c3907..0000000000000000000000000000000000000000
--- a/spaces/RedBaron5/PatentSolver/App/bin/InputHandler.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# -*- coding: utf-8 -*-
-
-#java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer --port 8080
-import glob
-import os
-
-
-class InputHandler(object):
-
-    def __init__(self, folder_path, extension):
-        self.folder_path = folder_path
-        self.extension = extension
-
-        print("Handling Corpus...")
-
-
-    def _get_dirs(self, base):
-        return [x for x in glob.iglob(os.path.join(base, '*')) if os.path.isdir(x)]
-
-    def get_base_file(self, base, pattern):
-        lList = []
-        lList.extend(glob.glob(os.path.join(base, pattern)))
-        dirs = self._get_dirs(base)
-        if len(dirs):
-            for d in dirs:
-                lList.extend(self.get_base_file(os.path.join(base, d), pattern))
-        return lList
-
-    def get_input(self):
-        folder_path = self.folder_path
-        extension = self.extension
-        patent_files = self.get_base_file(folder_path, extension)
-        return patent_files
-
-
diff --git a/spaces/RedBaron5/PatentSolver/Word2vec/run.py b/spaces/RedBaron5/PatentSolver/Word2vec/run.py
deleted file mode 100644
index 51eb13dc4d51ed335ce04fa70128ccf604514440..0000000000000000000000000000000000000000
--- a/spaces/RedBaron5/PatentSolver/Word2vec/run.py
+++ /dev/null
@@ -1,67 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# @File  : test_sentence_similarity.py
-# @Author: nixin
-# @Date  : 2019-03-06
-
-import numpy as np
-from scipy import spatial
-from gensim.models import word2vec
-import pandas as pd
-
-
-
-# load the trained word vector model
-model = word2vec.Word2Vec.load('/Users/nixin/PycharmProjects/PatentSolver_demonstrator/Word2vec/trained_word2vec.model')
-index2word_set = set(model.wv.index2word)
-
-def avg_feature_vector(sentence, model, num_features, index2word_set):
-    words = sentence.split()
-    feature_vec = np.zeros((num_features, ), dtype='float32')
-    n_words = 0
-    for word in words:
-        if word in index2word_set:
-            n_words += 1
-            feature_vec = np.add(feature_vec, model[word])
-    if (n_words > 0):
-        feature_vec = np.divide(feature_vec, n_words)
-    return feature_vec
-
-#read problem file
-problem_corpus = pd.read_csv('/Users/nixin/PycharmProjects/PatentSolver_demonstrator/Word2vec/data_problem_corpus/problem_corpus_sample_cleaned.csv')
-problem_corpus = problem_corpus.head(100)
-
-target_problem = 'strategic cleavage of such a target rna will destroy its ability to direct synthesis of an encoded protein'
-target_domain = 'A'
-
-# remove the same domain's problems
-problem_corpus = problem_corpus[problem_corpus.Domain != 'A']
-
-
-# choose the time range
-problem_corpus = problem_corpus[problem_corpus['publication_year'].between(2015, 2017)]
-
-
-value=[]
-for each_problem in problem_corpus['First part Contradiction']:
-    s1_afv = avg_feature_vector(target_problem, model=model, num_features=100, index2word_set=index2word_set)
-    s2_afv = avg_feature_vector(each_problem, model=model, num_features=100, index2word_set=index2word_set)
-    sim_value = format( 1 - spatial.distance.cosine(s1_afv, s2_afv), '.2f')
-    value.append(sim_value)
-
-problem_corpus[['similarity_value', 'target_problem']] = value, target_problem
-
-print(problem_corpus)
-
-# set similarity threshold
-problem_corpus_final = problem_corpus[problem_corpus.similarity_value>= '0.8']
-# print(problem_corpus.columns())
-
-problem_corpus_final.to_csv('/Users/nixin/PycharmProjects/PatentSolver_demonstrator/Word2vec/simialrity_result/test.csv', index=False)
-print(problem_corpus_final)
-
-
-
-
-
-
diff --git a/spaces/RitaParadaRamos/SmallCapDemo/src/retrieve_caps2.py b/spaces/RitaParadaRamos/SmallCapDemo/src/retrieve_caps2.py
deleted file mode 100644
index 12f90de2eb1b4b9aa9bc05caca483bbd4e8bf4b8..0000000000000000000000000000000000000000
--- a/spaces/RitaParadaRamos/SmallCapDemo/src/retrieve_caps2.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import sys
-import json
-import os.path
-import logging
-import argparse
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-import clip
-from collections import defaultdict
-from PIL import Image
-import faiss 
-import os
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-cudnn.benchmark = True 
-torch.manual_seed(0)
-if torch.cuda.is_available():
-    torch.cuda.manual_seed(0)
-
-import gc
-
-
-
-class ClipRetrieval():
-    def __init__(self, index_name):
-        self.datastore = faiss.read_index(index_name)
-        #self.datastore.nprobe=25
-    
-    def get_nns(self, query_img, k=20):
-        #get k nearest image
-        D, I = self.datastore.search(query_img, k)
-        return D, I[:,:k]
-
-
-class EvalDataset():
-
-    def __init__(self, dataset_splits, images_dir,  images_names, clip_retrieval_processor, eval_split="val_images"):
-        super().__init__()
-
-        with open(dataset_splits) as f:
-            self.split = json.load(f)
-
-        self.split = self.split[eval_split]
-        self.images_dir= images_dir
-
-        with open(args.images_names) as f:
-            self.images_names = json.load(f)
-
-        self.clip_retrieval_processor = clip_retrieval_processor
-
-    def __getitem__(self, i):
-        coco_id = self.split[i]
-
-        image_filename= self.images_dir+self.images_names[coco_id]
-        img_open = Image.open(image_filename).copy()
-        img = np.array(img_open)
-        if len(img.shape) ==2 or img.shape[-1]!=3: #convert grey or CMYK to RGB
-            img_open = img_open.convert('RGB')
-        gc.collect()
-
-        print("img_open",np.array(img_open).shape)
-    
-        #inputs_features_retrieval = self.clip_retrieval_processor(img_open).unsqueeze(0)
-        return self.clip_retrieval_processor(img_open).unsqueeze(0), coco_id
-
-    def __len__(self):
-        return len(self.split)
-
-
-def evaluate(args):
-
-    #load data of the datastore (i.e., captions)
-    with open(args.index_captions) as f:
-        data_datastore = json.load(f)
-
-    datastore = ClipRetrieval(args.datastore_path)
-    datastore_name = args.datastore_path.split("/")[-1]
-    
-    #load clip to encode the images that we want to retrieve captions for
-    clip_retrieval_model, clip_retrieval_feature_extractor = clip.load("RN50x64", device=device)
-    clip_retrieval_model.eval()
-    #data_loader to get images that we want to retrieve captions for
-    data_loader = torch.utils.data.DataLoader(
-        EvalDataset(
-            args.dataset_splits, 
-            args.images_dir,
-            args.images_names,
-            clip_retrieval_feature_extractor, 
-            args.split), 
-        batch_size=1, 
-        shuffle=True,  
-        num_workers=1, 
-        pin_memory=True
-    )
-
-    print("device",device)
-    nearest_caps={}
-    for data in tqdm(data_loader):
-            
-        inputs_features_retrieval, coco_id = data
-        coco_id = coco_id[0]
-
-        #normalize images to retrieve (since datastore has also normalized captions)
-        inputs_features_retrieval = inputs_features_retrieval.to(device)
-        image_retrieval_features = clip_retrieval_model.encode_image(inputs_features_retrieval[0])  
-        image_retrieval_features /= image_retrieval_features.norm(dim=-1, keepdim=True)
-        image_retrieval_features=image_retrieval_features.detach().cpu().numpy().astype(np.float32)
-        
-        print("inputs_features_retrieval",inputs_features_retrieval.size())
-        print("image_retrieval_features",image_retrieval_features.shape)
-
-        D, nearest_ids=datastore.get_nns(image_retrieval_features, k=5)
-        print("D size", D.shape)
-        print("nea", nearest_ids.shape)
-        gc.collect()
-
-        #Since at inference batch is 1
-        D=D[0]
-        nearest_ids=nearest_ids[0]
-
-        list_of_similar_caps=defaultdict(list)
-        for index in range(len(nearest_ids)):
-            nearest_id = str(nearest_ids[index])            
-            nearest_cap=data_datastore[nearest_id]
-    
-            if len(nearest_cap.split()) > args.max_caption_len:
-                print("retrieve cap too big" )
-                continue
-
-            #distance=D[index]
-            #list_of_similar_caps[datastore_name].append((nearest_cap, str(distance)))
-            #list_of_similar_caps[datastore_name].append(nearest_cap)
-
-        #nearest_caps[str(coco_id)]=list_of_similar_caps
-
-
-    #save results
-    outputs_dir = os.path.join(args.output_path, "retrieved_caps")
-    if not os.path.exists(outputs_dir):
-        os.makedirs(outputs_dir)
-
-    data_name=dataset_splits.split("/")[-1]
-
-    name = "nearest_caps_"+data_name +"_w_"+datastore_name + "_"+ args.split
-    results_output_file_name = os.path.join(outputs_dir, name + ".json")
-    json.dump(nearest_caps, open(results_output_file_name, "w"))
-
-
-
-def check_args(args):
-    parser = argparse.ArgumentParser()
-    
-    #Info of the dataset to evaluate on (vizwiz, flick30k, msr-vtt)
-    parser.add_argument("--images_dir",help="Folder where the preprocessed image data is located", default="data/vizwiz/images")
-    parser.add_argument("--dataset_splits",help="File containing the dataset splits", default="data/vizwiz/dataset_splits.json")
-    parser.add_argument("--images_names",help="File containing the images names per id", default="data/vizwiz/images_names.json")
-    parser.add_argument("--split", default="val_images", choices=["val_images", "test_images"])
-    parser.add_argument("--max-caption-len", type=int, default=25)
-
-    #Which datastore to use (web, human)
-    parser.add_argument("--datastore_path", type=str, default="datastore2/vizwiz/vizwiz")
-    parser.add_argument("--index_captions",
-                        help="File containing the captions of the datastore per id", default="datastore2/vizwiz/vizwiz.json")
-    parser.add_argument("--output-path",help="Folder where to store outputs", default="eval_vizwiz_with_datastore_from_vizwiz.json")          
-
-    parsed_args = parser.parse_args(args)
-    return parsed_args
-
-
-if __name__ == "__main__":
-    args = check_args(sys.argv[1:])
-    logging.basicConfig(
-            format='%(levelname)s: %(message)s', level=logging.INFO)
-
-    logging.info(args)
-    evaluate(args)
-
diff --git a/spaces/RitaParadaRamos/SmallCapDemo/vision_encoder_decoder.py b/spaces/RitaParadaRamos/SmallCapDemo/vision_encoder_decoder.py
deleted file mode 100644
index 3931256154479662216e09141e4fcbbb407487a2..0000000000000000000000000000000000000000
--- a/spaces/RitaParadaRamos/SmallCapDemo/vision_encoder_decoder.py
+++ /dev/null
@@ -1,560 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Classes to support Vision-Encoder-Text-Decoder architectures"""
-import timeit
-
-from typing import Optional
-
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
-from transformers.modeling_utils import PreTrainedModel
-#from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
-from transformers.utils import logging
-from transformers.models.auto.configuration_auto import AutoConfig
-from transformers.models.auto.modeling_auto import AutoModel, AutoModelForCausalLM
-from transformers.models.vision_encoder_decoder.configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
-import inspect
-
-from .gpt2 import ThisGPT2LMHeadModel
-from .gpt2 import ThisGPT2Config
-from .xglm import ThisXGLMForCausalLM
-from .xglm import ThisXGLMConfig
-from .opt import ThisOPTForCausalLM
-from .opt import ThisOPTConfig
-
-# Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right
-def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
-    """
-    Shift input ids one token to the right.
-    """
-    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
-    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
-    if decoder_start_token_id is None:
-        raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
-    shifted_input_ids[:, 0] = decoder_start_token_id
-
-    if pad_token_id is None:
-        raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
-    # replace possible -100 values in labels by `pad_token_id`
-    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
-    return shifted_input_ids
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "SmallCapConfig"
-
-VISION_ENCODER_DECODER_START_DOCSTRING = r"""
-    This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model
-    as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via
-    [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`]
-    function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
-    generative task, like image captioning.
-
-    The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
-    tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
-    Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
-    Zhou, Wei Li, Peter J. Liu.
-
-    Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
-    Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical
-    character recognition (OCR) yields a significant performance improvement.
-
-    After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any
-    other models (see the examples for more information).
-
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`VisionEncoderDecoderConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-VISION_ENCODER_DECODER_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Pixel values can be obtained using a feature extractor (e.g. if you use ViT as the encoder,
-            you should use [`ViTFeatureExtractor`]). See [`ViTFeatureExtractor.__call__`] for details.
-        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Indices of decoder input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            For training, `decoder_input_ids` are automatically created by the model by shifting the `labels` to the
-            right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`.
-        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
-            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
-            be used by default.
-        encoder_outputs (`tuple(torch.FloatTensor)`, *optional*):
-            This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) is a tensor
-            of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the
-            decoder.
-        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
-            representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
-            into associated vectors than the model's internal embedding lookup matrix.
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
-            ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
-        kwargs: (*optional*) Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
-
-            - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
-            - With a *decoder_* prefix which will be input as `**decoder_kwargs` for the decoder forward function.
-"""
-
-class SmallCapConfig(VisionEncoderDecoderConfig):
-    model_type = "smallcap"
-
-    def __init__(
-        self,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        
-
-class SmallCap(PreTrainedModel):
-    r"""
-    [`VisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with
-    one of the base vision model classes of the library as encoder and another one as decoder when created with the
-    :meth*~transformers.AutoModel.from_pretrained* class method for the encoder and
-    :meth*~transformers.AutoModelForCausalLM.from_pretrained* class method for the decoder.
-    """
-    config_class = SmallCapConfig
-    base_model_prefix = "smallcap"
-    main_input_name = "pixel_values"
-
-    def __init__(
-        self,
-        config: Optional[PretrainedConfig] = None,
-        encoder: Optional[PreTrainedModel] = None,
-        decoder: Optional[PreTrainedModel] = None,
-    ):
-        if config is None and (encoder is None or decoder is None):
-            raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
-        if config is None:
-            config = SmallCapConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
-        else:
-            if not isinstance(config, self.config_class):
-                raise ValueError(f"Config: {config} has to be of type {self.config_class}")
-
-        if config.decoder.cross_attention_hidden_size is not None:
-            if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size:
-                raise ValueError(
-                    "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal#"
-                    f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
-                    f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for"
-                    " `config.encoder.hidden_size`."
-                )
-
-        # initialize with config
-        # make sure input & output embeddings is not tied
-        config.tie_word_embeddings = False
-        super().__init__(config)
-
-        if encoder is None:
-            encoder = AutoModel.from_config(config.encoder)
-
-        if decoder is None:
-            decoder = AutoModelForCausalLM.from_config(config.decoder)
-
-        self.encoder = encoder.vision_model
-        self.encoder.main_input_name = 'pixel_values'
-        self.decoder = decoder
-        # make sure that the individual model's config refers to the shared config
-        # so that the updates to the config will be synced
-        self.encoder.config = self.config.encoder
-        self.decoder.config = self.config.decoder
-
-    def get_encoder(self):
-        return self.encoder
-
-    def get_decoder(self):
-        return self.decoder
-
-    def get_output_embeddings(self):
-        return self.decoder.get_output_embeddings()
-
-    def set_output_embeddings(self, new_embeddings):
-        return self.decoder.set_output_embeddings(new_embeddings)
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        # At the moment fast initialization is not supported for composite models
-        if kwargs.get("_fast_init", False):
-            logger.warning(
-                "Fast initialization is currently not supported for VisionEncoderDecoderModel. "
-                "Falling back to slow initialization..."
-            )
-        kwargs["_fast_init"] = False
-        return super().from_pretrained(*args, **kwargs)
-
-    @classmethod
-    def from_encoder_decoder_pretrained(
-        cls,
-        encoder_pretrained_model_name_or_path: str = None,
-        decoder_pretrained_model_name_or_path: str = None,
-        cross_attention_reduce_factor: int = None,
-        *model_args,
-        **kwargs
-    ) -> PreTrainedModel:
-        r"""
-        Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
-        checkpoints.
-
-
-        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
-        the model, you need to first set it back in training mode with `model.train()`.
-
-        Params:
-            encoder_pretrained_model_name_or_path (`str`, *optional*):
-                Information necessary to initiate the image encoder. Can be either:
-
-                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. An
-                      example is `google/vit-base-patch16-224-in21k`.
-                    - A path to a *directory* containing model weights saved using
-                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
-                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
-                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
-                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
-                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
-
-            decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
-                Information necessary to initiate the text decoder. Can be either:
-
-                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
-                      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
-                      user or organization name, like `dbmdz/bert-base-german-cased`.
-                    - A path to a *directory* containing model weights saved using
-                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
-                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
-                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
-                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
-                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
-
-            model_args (remaining positional arguments, *optional*):
-                All remaning positional arguments will be passed to the underlying model's `__init__` method.
-
-            kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
-                `output_attentions=True`).
-
-                - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
-                - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
-                - To update the parent model configuration, do not use a prefix for each configuration parameter.
-
-                Behaves differently depending on whether a `config` is provided or automatically loaded.
-
-        Example:
-
-        ```python
-        >>> from transformers import VisionEncoderDecoderModel
-
-        >>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
-        >>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
-        ...     "google/vit-base-patch16-224-in21k", "bert-base-uncased"
-        ... )
-        >>> # saving model after fine-tuning
-        >>> model.save_pretrained("./vit-bert")
-        >>> # load fine-tuned model
-        >>> model = VisionEncoderDecoderModel.from_pretrained("./vit-bert")
-        ```"""
-
-        kwargs_encoder = {
-            argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
-        }
-
-        kwargs_decoder = {
-            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
-        }
-
-        # remove encoder, decoder kwargs from kwargs
-        for key in kwargs_encoder.keys():
-            del kwargs["encoder_" + key]
-        for key in kwargs_decoder.keys():
-            del kwargs["decoder_" + key]
-
-        # Load and initialize the encoder and decoder
-        # The distinction between encoder and decoder at the model level is made
-        # by the value of the flag `is_decoder` that we need to set correctly.
-        encoder = kwargs_encoder.pop("model", None)
-        if encoder is None:
-            if encoder_pretrained_model_name_or_path is None:
-                raise ValueError(
-                    "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has "
-                    "to be defined."
-                )
-
-            if "config" not in kwargs_encoder:
-                encoder_config, kwargs_encoder = AutoConfig.from_pretrained(
-                    encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True
-                )
-
-                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
-                    logger.info(
-                        f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
-                        "from a decoder model. Cross-attention and casual mask are disabled."
-                    )
-                    encoder_config.is_decoder = False
-                    encoder_config.add_cross_attention = False
-
-                kwargs_encoder["config"] = encoder_config
-
-            encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
-
-        decoder = kwargs_decoder.pop("model", None)
-        if decoder is None:
-            if decoder_pretrained_model_name_or_path is None:
-                raise ValueError(
-                    "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
-                    "to be defined."
-                )
-
-            if "config" not in kwargs_decoder:
-                if "xglm" in decoder_pretrained_model_name_or_path:
-                    decoder_config, kwargs_decoder = ThisXGLMConfig.from_pretrained(
-                        decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
-                    )
-
-                elif "opt" in decoder_pretrained_model_name_or_path:
-                    decoder_config, kwargs_decoder = ThisOPTConfig.from_pretrained(
-                        decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
-                    )
-
-                else:
-                    decoder_config, kwargs_decoder = ThisGPT2Config.from_pretrained(
-                        decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
-                    )
-
-                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
-                    logger.info(
-                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
-                        f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
-                        f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
-                    )
-                    decoder_config.is_decoder = True
-                    decoder_config.add_cross_attention = True
-                decoder_config.encoder_hidden_size = encoder.config.vision_config.hidden_size
-                decoder_config.cross_attention_reduce_factor = cross_attention_reduce_factor
-                kwargs_decoder["config"] = decoder_config
-            
-            if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
-                logger.warning(
-                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
-                    f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
-                    "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
-                    "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a "
-                    "`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
-                )
-            
-            #decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
-            if "xglm" in decoder_pretrained_model_name_or_path:
-                decoder = ThisXGLMForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
-
-            elif "opt" in decoder_pretrained_model_name_or_path:
-                decoder = ThisOPTForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
-            else:
-                decoder = ThisGPT2LMHeadModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
-
-        # instantiate config with corresponding kwargs
-        config = SmallCapConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
-
-        # make sure input & output embeddings is not tied
-        config.tie_word_embeddings = False
-        return cls(encoder=encoder, decoder=decoder, config=config)
-
-    def forward(
-        self,
-        pixel_values=None,
-        decoder_input_ids=None,
-        decoder_attention_mask=None,
-        encoder_outputs=None,
-        past_key_values=None,
-        decoder_inputs_embeds=None,
-        labels=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        **kwargs,
-    ):
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-        >>> import requests
-        >>> from PIL import Image
-        >>> import torch
-
-        >>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
-        >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
-
-        >>> # load image from the IAM dataset
-        >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
-
-        >>> # training
-        >>> model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
-        >>> model.config.pad_token_id = processor.tokenizer.pad_token_id
-        >>> model.config.vocab_size = model.config.decoder.vocab_size
-
-        >>> pixel_values = processor(image, return_tensors="pt").pixel_values
-        >>> text = "hello world"
-        >>> labels = processor.tokenizer(text, return_tensors="pt").input_ids
-        >>> outputs = model(pixel_values=pixel_values, labels=labels)
-        >>> loss = outputs.loss
-
-        >>> # inference (generation)
-        >>> generated_ids = model.generate(pixel_values)
-        >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        ```"""
- 
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
-
-        kwargs_decoder = {
-            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
-        }
-        if encoder_outputs is None:
-            if pixel_values is None:
-                raise ValueError("You have to specify pixel_values")
-            
-            encoder_outputs = self.encoder(
-                pixel_values=pixel_values,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                **kwargs_encoder,
-            )
-        elif isinstance(encoder_outputs, tuple):
-            encoder_outputs = BaseModelOutput(*encoder_outputs)
-        else:
-            encoder_outputs = BaseModelOutput(encoder_outputs, None)
-
-        encoder_hidden_states = encoder_outputs[0]
-
-        # else:
-        encoder_attention_mask = None
-        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
-            decoder_input_ids = shift_tokens_right(
-                labels, self.config.pad_token_id, self.config.decoder_start_token_id
-            )
-        
-        # Decode
-        decoder_outputs = self.decoder(
-            input_ids=decoder_input_ids,
-            attention_mask=decoder_attention_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            inputs_embeds=decoder_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            use_cache=use_cache,
-            past_key_values=past_key_values,
-            return_dict=return_dict,
-            **kwargs_decoder,
-        )
-
-        # Compute loss independent from decoder (as some shift the logits inside them)
-        loss = None
-        if labels is not None:
-            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))
-        
-        if not return_dict:
-            if loss is not None:
-                return (loss,) + decoder_outputs + encoder_outputs
-            else:
-                return decoder_outputs + encoder_outputs
-
-        return Seq2SeqLMOutput(
-            loss=loss,
-            logits=decoder_outputs.logits,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            cross_attentions=decoder_outputs.cross_attentions,
-            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
-            encoder_hidden_states=encoder_outputs.hidden_states,
-            encoder_attentions=encoder_outputs.attentions,
-        )
-
-    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
-        return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
-
-    def prepare_inputs_for_generation(
-        self, input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
-    ):
-        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past=past)
-        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
-        input_dict = {
-            "attention_mask": attention_mask,
-            "decoder_attention_mask": decoder_attention_mask,
-            "decoder_input_ids": decoder_inputs["input_ids"],
-            "encoder_outputs": encoder_outputs,
-            "past_key_values": decoder_inputs["past_key_values"],
-            "use_cache": use_cache,
-        }
-        return input_dict
-
-    def resize_token_embeddings(self, *args, **kwargs):
-        raise NotImplementedError(
-            "Resizing the embedding layers via the VisionEncoderDecoderModel directly is not supported.Please use the"
-            " respective methods of the wrapped decoder object (model.decoder.resize_token_embeddings(...))"
-        )
-
-    def _reorder_cache(self, past, beam_idx):
-        # apply decoder cache reordering here
-        return self.decoder._reorder_cache(past, beam_idx)
diff --git a/spaces/Rmpmartinspro2/Comic-Diffusion/README.md b/spaces/Rmpmartinspro2/Comic-Diffusion/README.md
deleted file mode 100644
index 369682296409f337358503b5a08cc43b845aa8d9..0000000000000000000000000000000000000000
--- a/spaces/Rmpmartinspro2/Comic-Diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Comic Diffusion
-emoji: 🦸‍♂️
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/norm.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/norm.py
deleted file mode 100644
index 408f4b42731b19a3beeef68b6a5e610d0bbc18b3..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/cnn/bricks/norm.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-
-import torch.nn as nn
-
-from annotator.uniformer.mmcv.utils import is_tuple_of
-from annotator.uniformer.mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm, _InstanceNorm
-from .registry import NORM_LAYERS
-
-NORM_LAYERS.register_module('BN', module=nn.BatchNorm2d)
-NORM_LAYERS.register_module('BN1d', module=nn.BatchNorm1d)
-NORM_LAYERS.register_module('BN2d', module=nn.BatchNorm2d)
-NORM_LAYERS.register_module('BN3d', module=nn.BatchNorm3d)
-NORM_LAYERS.register_module('SyncBN', module=SyncBatchNorm)
-NORM_LAYERS.register_module('GN', module=nn.GroupNorm)
-NORM_LAYERS.register_module('LN', module=nn.LayerNorm)
-NORM_LAYERS.register_module('IN', module=nn.InstanceNorm2d)
-NORM_LAYERS.register_module('IN1d', module=nn.InstanceNorm1d)
-NORM_LAYERS.register_module('IN2d', module=nn.InstanceNorm2d)
-NORM_LAYERS.register_module('IN3d', module=nn.InstanceNorm3d)
-
-
-def infer_abbr(class_type):
-    """Infer abbreviation from the class name.
-
-    When we build a norm layer with `build_norm_layer()`, we want to preserve
-    the norm type in variable names, e.g, self.bn1, self.gn. This method will
-    infer the abbreviation to map class types to abbreviations.
-
-    Rule 1: If the class has the property "_abbr_", return the property.
-    Rule 2: If the parent class is _BatchNorm, GroupNorm, LayerNorm or
-    InstanceNorm, the abbreviation of this layer will be "bn", "gn", "ln" and
-    "in" respectively.
-    Rule 3: If the class name contains "batch", "group", "layer" or "instance",
-    the abbreviation of this layer will be "bn", "gn", "ln" and "in"
-    respectively.
-    Rule 4: Otherwise, the abbreviation falls back to "norm".
-
-    Args:
-        class_type (type): The norm layer type.
-
-    Returns:
-        str: The inferred abbreviation.
-    """
-    if not inspect.isclass(class_type):
-        raise TypeError(
-            f'class_type must be a type, but got {type(class_type)}')
-    if hasattr(class_type, '_abbr_'):
-        return class_type._abbr_
-    if issubclass(class_type, _InstanceNorm):  # IN is a subclass of BN
-        return 'in'
-    elif issubclass(class_type, _BatchNorm):
-        return 'bn'
-    elif issubclass(class_type, nn.GroupNorm):
-        return 'gn'
-    elif issubclass(class_type, nn.LayerNorm):
-        return 'ln'
-    else:
-        class_name = class_type.__name__.lower()
-        if 'batch' in class_name:
-            return 'bn'
-        elif 'group' in class_name:
-            return 'gn'
-        elif 'layer' in class_name:
-            return 'ln'
-        elif 'instance' in class_name:
-            return 'in'
-        else:
-            return 'norm_layer'
-
-
-def build_norm_layer(cfg, num_features, postfix=''):
-    """Build normalization layer.
-
-    Args:
-        cfg (dict): The norm layer config, which should contain:
-
-            - type (str): Layer type.
-            - layer args: Args needed to instantiate a norm layer.
-            - requires_grad (bool, optional): Whether stop gradient updates.
-        num_features (int): Number of input channels.
-        postfix (int | str): The postfix to be appended into norm abbreviation
-            to create named layer.
-
-    Returns:
-        (str, nn.Module): The first element is the layer name consisting of
-            abbreviation and postfix, e.g., bn1, gn. The second element is the
-            created norm layer.
-    """
-    if not isinstance(cfg, dict):
-        raise TypeError('cfg must be a dict')
-    if 'type' not in cfg:
-        raise KeyError('the cfg dict must contain the key "type"')
-    cfg_ = cfg.copy()
-
-    layer_type = cfg_.pop('type')
-    if layer_type not in NORM_LAYERS:
-        raise KeyError(f'Unrecognized norm type {layer_type}')
-
-    norm_layer = NORM_LAYERS.get(layer_type)
-    abbr = infer_abbr(norm_layer)
-
-    assert isinstance(postfix, (int, str))
-    name = abbr + str(postfix)
-
-    requires_grad = cfg_.pop('requires_grad', True)
-    cfg_.setdefault('eps', 1e-5)
-    if layer_type != 'GN':
-        layer = norm_layer(num_features, **cfg_)
-        if layer_type == 'SyncBN' and hasattr(layer, '_specify_ddp_gpu_num'):
-            layer._specify_ddp_gpu_num(1)
-    else:
-        assert 'num_groups' in cfg_
-        layer = norm_layer(num_channels=num_features, **cfg_)
-
-    for param in layer.parameters():
-        param.requires_grad = requires_grad
-
-    return name, layer
-
-
-def is_norm(layer, exclude=None):
-    """Check if a layer is a normalization layer.
-
-    Args:
-        layer (nn.Module): The layer to be checked.
-        exclude (type | tuple[type]): Types to be excluded.
-
-    Returns:
-        bool: Whether the layer is a norm layer.
-    """
-    if exclude is not None:
-        if not isinstance(exclude, tuple):
-            exclude = (exclude, )
-        if not is_tuple_of(exclude, type):
-            raise TypeError(
-                f'"exclude" must be either None or type or a tuple of types, '
-                f'but got {type(exclude)}: {exclude}')
-
-    if exclude and isinstance(layer, exclude):
-        return False
-
-    all_norm_bases = (_BatchNorm, _InstanceNorm, nn.GroupNorm, nn.LayerNorm)
-    return isinstance(layer, all_norm_bases)
diff --git a/spaces/RoryT0ishi/Meow/server.js b/spaces/RoryT0ishi/Meow/server.js
deleted file mode 100644
index 04a48b7a429c4d0ad0b772ba1edf503e349eda21..0000000000000000000000000000000000000000
--- a/spaces/RoryT0ishi/Meow/server.js
+++ /dev/null
@@ -1,32 +0,0 @@
-const express = require('express');
-const proxy = require('express-http-proxy');
-const app = express();
-const targetUrl = 'https://api.openai.com';
-const openaiKey = process.env.OPENAI_KEY
-const port = 7860;
-const baseUrl = getExternalUrl(process.env.SPACE_ID);
-
-app.use('/api', proxy(targetUrl, {
-  proxyReqOptDecorator: (proxyReqOpts, srcReq) => {
-    // Modify the request headers if necessary
-    proxyReqOpts.headers['Authorization'] = 'Bearer '+openaiKey;
-    return proxyReqOpts;
-  },
-}));
-
-app.get("/", (req, res) => {
-  res.send(`This is your OpenAI Reverse Proxy URL: ${baseUrl}`);
-});
-
-function getExternalUrl(spaceId) {
-  try {
-    const [username, spacename] = spaceId.split("/");
-    return `https://${username}-${spacename.replace(/_/g, "-")}.hf.space/api/v1`;
-  } catch (e) {
-    return "";
-  }
-}
-
-app.listen(port, () => {
-  console.log(`Reverse proxy server running on ${baseUrl}`);
-});
\ No newline at end of file
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/models/backbone/gca_enc.py b/spaces/SankarSrin/image-matting-app/ppmatting/models/backbone/gca_enc.py
deleted file mode 100644
index 2afeb5df8c398d89ac1d4fe8e411571afebec5b6..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/models/backbone/gca_enc.py
+++ /dev/null
@@ -1,395 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# The gca code was heavily based on https://github.com/Yaoyi-Li/GCA-Matting
-# and https://github.com/open-mmlab/mmediting
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-from paddleseg.cvlibs import manager, param_init
-from paddleseg.utils import utils
-
-from ppmatting.models.layers import GuidedCxtAtten
-
-
-class ResNet_D(nn.Layer):
-    def __init__(self,
-                 input_channels,
-                 layers,
-                 late_downsample=False,
-                 pretrained=None):
-
-        super().__init__()
-
-        self.pretrained = pretrained
-
-        self._norm_layer = nn.BatchNorm
-        self.inplanes = 64
-        self.late_downsample = late_downsample
-        self.midplanes = 64 if late_downsample else 32
-        self.start_stride = [1, 2, 1, 2] if late_downsample else [2, 1, 2, 1]
-        self.conv1 = nn.utils.spectral_norm(
-            nn.Conv2D(
-                input_channels,
-                32,
-                kernel_size=3,
-                stride=self.start_stride[0],
-                padding=1,
-                bias_attr=False))
-        self.conv2 = nn.utils.spectral_norm(
-            nn.Conv2D(
-                32,
-                self.midplanes,
-                kernel_size=3,
-                stride=self.start_stride[1],
-                padding=1,
-                bias_attr=False))
-        self.conv3 = nn.utils.spectral_norm(
-            nn.Conv2D(
-                self.midplanes,
-                self.inplanes,
-                kernel_size=3,
-                stride=self.start_stride[2],
-                padding=1,
-                bias_attr=False))
-        self.bn1 = self._norm_layer(32)
-        self.bn2 = self._norm_layer(self.midplanes)
-        self.bn3 = self._norm_layer(self.inplanes)
-        self.activation = nn.ReLU()
-        self.layer1 = self._make_layer(
-            BasicBlock, 64, layers[0], stride=self.start_stride[3])
-        self.layer2 = self._make_layer(BasicBlock, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(BasicBlock, 256, layers[2], stride=2)
-        self.layer_bottleneck = self._make_layer(
-            BasicBlock, 512, layers[3], stride=2)
-
-        self.init_weight()
-
-    def _make_layer(self, block, planes, block_num, stride=1):
-        if block_num == 0:
-            return nn.Sequential(nn.Identity())
-        norm_layer = self._norm_layer
-        downsample = None
-        if stride != 1:
-            downsample = nn.Sequential(
-                nn.AvgPool2D(2, stride),
-                nn.utils.spectral_norm(
-                    conv1x1(self.inplanes, planes * block.expansion)),
-                norm_layer(planes * block.expansion), )
-        elif self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.utils.spectral_norm(
-                    conv1x1(self.inplanes, planes * block.expansion, stride)),
-                norm_layer(planes * block.expansion), )
-
-        layers = [block(self.inplanes, planes, stride, downsample, norm_layer)]
-        self.inplanes = planes * block.expansion
-        for _ in range(1, block_num):
-            layers.append(block(self.inplanes, planes, norm_layer=norm_layer))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.activation(x)
-        x = self.conv2(x)
-        x = self.bn2(x)
-        x1 = self.activation(x)  # N x 32 x 256 x 256
-        x = self.conv3(x1)
-        x = self.bn3(x)
-        x2 = self.activation(x)  # N x 64 x 128 x 128
-
-        x3 = self.layer1(x2)  # N x 64 x 128 x 128
-        x4 = self.layer2(x3)  # N x 128 x 64 x 64
-        x5 = self.layer3(x4)  # N x 256 x 32 x 32
-        x = self.layer_bottleneck(x5)  # N x 512 x 16 x 16
-
-        return x, (x1, x2, x3, x4, x5)
-
-    def init_weight(self):
-
-        for layer in self.sublayers():
-            if isinstance(layer, nn.Conv2D):
-
-                if hasattr(layer, "weight_orig"):
-                    param = layer.weight_orig
-                else:
-                    param = layer.weight
-                param_init.xavier_uniform(param)
-
-            elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
-                param_init.constant_init(layer.weight, value=1.0)
-                param_init.constant_init(layer.bias, value=0.0)
-
-            elif isinstance(layer, BasicBlock):
-                param_init.constant_init(layer.bn2.weight, value=0.0)
-
-        if self.pretrained is not None:
-            utils.load_pretrained_model(self, self.pretrained)
-
-
-@manager.MODELS.add_component
-class ResShortCut_D(ResNet_D):
-    def __init__(self,
-                 input_channels,
-                 layers,
-                 late_downsample=False,
-                 pretrained=None):
-        super().__init__(
-            input_channels,
-            layers,
-            late_downsample=late_downsample,
-            pretrained=pretrained)
-
-        self.shortcut_inplane = [input_channels, self.midplanes, 64, 128, 256]
-        self.shortcut_plane = [32, self.midplanes, 64, 128, 256]
-
-        self.shortcut = nn.LayerList()
-        for stage, inplane in enumerate(self.shortcut_inplane):
-            self.shortcut.append(
-                self._make_shortcut(inplane, self.shortcut_plane[stage]))
-
-    def _make_shortcut(self, inplane, planes):
-        return nn.Sequential(
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    inplane, planes, kernel_size=3, padding=1,
-                    bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(planes),
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    planes, planes, kernel_size=3, padding=1, bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(planes))
-
-    def forward(self, x):
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.activation(out)
-        out = self.conv2(out)
-        out = self.bn2(out)
-        x1 = self.activation(out)  # N x 32 x 256 x 256
-        out = self.conv3(x1)
-        out = self.bn3(out)
-        out = self.activation(out)
-
-        x2 = self.layer1(out)  # N x 64 x 128 x 128
-        x3 = self.layer2(x2)  # N x 128 x 64 x 64
-        x4 = self.layer3(x3)  # N x 256 x 32 x 32
-        out = self.layer_bottleneck(x4)  # N x 512 x 16 x 16
-
-        fea1 = self.shortcut[0](x)  # input image and trimap
-        fea2 = self.shortcut[1](x1)
-        fea3 = self.shortcut[2](x2)
-        fea4 = self.shortcut[3](x3)
-        fea5 = self.shortcut[4](x4)
-
-        return out, {
-            'shortcut': (fea1, fea2, fea3, fea4, fea5),
-            'image': x[:, :3, ...]
-        }
-
-
-@manager.MODELS.add_component
-class ResGuidedCxtAtten(ResNet_D):
-    def __init__(self,
-                 input_channels,
-                 layers,
-                 late_downsample=False,
-                 pretrained=None):
-        super().__init__(
-            input_channels,
-            layers,
-            late_downsample=late_downsample,
-            pretrained=pretrained)
-        self.input_channels = input_channels
-        self.shortcut_inplane = [input_channels, self.midplanes, 64, 128, 256]
-        self.shortcut_plane = [32, self.midplanes, 64, 128, 256]
-
-        self.shortcut = nn.LayerList()
-        for stage, inplane in enumerate(self.shortcut_inplane):
-            self.shortcut.append(
-                self._make_shortcut(inplane, self.shortcut_plane[stage]))
-
-        self.guidance_head = nn.Sequential(
-            nn.Pad2D(
-                1, mode="reflect"),
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    3, 16, kernel_size=3, padding=0, stride=2,
-                    bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(16),
-            nn.Pad2D(
-                1, mode="reflect"),
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    16, 32, kernel_size=3, padding=0, stride=2,
-                    bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(32),
-            nn.Pad2D(
-                1, mode="reflect"),
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    32,
-                    128,
-                    kernel_size=3,
-                    padding=0,
-                    stride=2,
-                    bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(128))
-
-        self.gca = GuidedCxtAtten(128, 128)
-
-        self.init_weight()
-
-    def init_weight(self):
-
-        for layer in self.sublayers():
-            if isinstance(layer, nn.Conv2D):
-                initializer = nn.initializer.XavierUniform()
-                if hasattr(layer, "weight_orig"):
-                    param = layer.weight_orig
-                else:
-                    param = layer.weight
-                initializer(param, param.block)
-
-            elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
-                param_init.constant_init(layer.weight, value=1.0)
-                param_init.constant_init(layer.bias, value=0.0)
-
-            elif isinstance(layer, BasicBlock):
-                param_init.constant_init(layer.bn2.weight, value=0.0)
-
-        if self.pretrained is not None:
-            utils.load_pretrained_model(self, self.pretrained)
-
-    def _make_shortcut(self, inplane, planes):
-        return nn.Sequential(
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    inplane, planes, kernel_size=3, padding=1,
-                    bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(planes),
-            nn.utils.spectral_norm(
-                nn.Conv2D(
-                    planes, planes, kernel_size=3, padding=1, bias_attr=False)),
-            nn.ReLU(),
-            self._norm_layer(planes))
-
-    def forward(self, x):
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.activation(out)
-        out = self.conv2(out)
-        out = self.bn2(out)
-        x1 = self.activation(out)  # N x 32 x 256 x 256
-        out = self.conv3(x1)
-        out = self.bn3(out)
-        out = self.activation(out)
-
-        im_fea = self.guidance_head(
-            x[:, :3, ...])  # downsample origin image and extract features
-        if self.input_channels == 6:
-            unknown = F.interpolate(
-                x[:, 4:5, ...], scale_factor=1 / 8, mode='nearest')
-        else:
-            unknown = x[:, 3:, ...].equal(paddle.to_tensor([1.]))
-            unknown = paddle.cast(unknown, dtype='float32')
-            unknown = F.interpolate(unknown, scale_factor=1 / 8, mode='nearest')
-
-        x2 = self.layer1(out)  # N x 64 x 128 x 128
-        x3 = self.layer2(x2)  # N x 128 x 64 x 64
-        x3 = self.gca(im_fea, x3, unknown)  # contextual attention
-        x4 = self.layer3(x3)  # N x 256 x 32 x 32
-        out = self.layer_bottleneck(x4)  # N x 512 x 16 x 16
-
-        fea1 = self.shortcut[0](x)  # input image and trimap
-        fea2 = self.shortcut[1](x1)
-        fea3 = self.shortcut[2](x2)
-        fea4 = self.shortcut[3](x3)
-        fea5 = self.shortcut[4](x4)
-
-        return out, {
-            'shortcut': (fea1, fea2, fea3, fea4, fea5),
-            'image_fea': im_fea,
-            'unknown': unknown,
-        }
-
-
-class BasicBlock(nn.Layer):
-    expansion = 1
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 downsample=None,
-                 norm_layer=None):
-        super().__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = nn.utils.spectral_norm(conv3x3(inplanes, planes, stride))
-        self.bn1 = norm_layer(planes)
-        self.activation = nn.ReLU()
-        self.conv2 = nn.utils.spectral_norm(conv3x3(planes, planes))
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.activation(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.activation(out)
-
-        return out
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2D(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=dilation,
-        groups=groups,
-        bias_attr=False,
-        dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2D(
-        in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False)
diff --git a/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/assets/+layout-7c2f4ad7.css b/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/assets/+layout-7c2f4ad7.css
deleted file mode 100644
index 3d3b9c37a65b45a8c172cf493f95cf521ab680a7..0000000000000000000000000000000000000000
--- a/spaces/Sapiensia/diffuse-the-rest/build/_app/immutable/assets/+layout-7c2f4ad7.css
+++ /dev/null
@@ -1 +0,0 @@
-*,:before,:after{box-sizing:border-box;border-width:0;border-style:solid;border-color:#e5e7eb}:before,:after{--tw-content: ""}html{line-height:1.5;-webkit-text-size-adjust:100%;-moz-tab-size:4;-o-tab-size:4;tab-size:4;font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,"Apple Color Emoji","Segoe UI Emoji",Segoe UI Symbol,"Noto Color Emoji"}body{margin:0;line-height:inherit}hr{height:0;color:inherit;border-top-width:1px}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit}a{color:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,samp,pre{font-family:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier 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diff --git a/spaces/SarthakSidhant/Go-Cattle/cattle_disease_classifier.py b/spaces/SarthakSidhant/Go-Cattle/cattle_disease_classifier.py
deleted file mode 100644
index fac1ce56abf52e1fa8b90a83ac0532caac65b488..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/cattle_disease_classifier.py
+++ /dev/null
@@ -1,50 +0,0 @@
-
-## model training part ####
-#import libraries
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
-from sklearn.model_selection import cross_val_score
-import joblib
-
-# Load the formatted data from the CSV file
-data = pd.read_csv('GoCattle Dataset - formatted_data (1).csv')
-
-# Separate the input features (X) and target variable (y)
-X = data.drop('Disease', axis=1)
-y = data['Disease']
-
-# Initialize the Random Forest Classifier
-classifier = RandomForestClassifier()
-
-# Perform cross-validation
-cv_scores = cross_val_score(classifier, X, y, cv=2)
-
-# Fit the model on the entire dataset
-classifier.fit(X, y)
-
-# Save the trained model to a file
-joblib.dump(classifier, 'model.pkl')
-
-print("Cross-Validation Scores:", cv_scores)
-print("Mean Accuracy:", cv_scores.mean())
-print("Model saved to 'model.pkl' successfully.")
-
-# Split the data into training and testing sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Load the trained model from the file
-model = joblib.load('model.pkl')
-
-y_pred2 = model.predict(X_train)
-accuracy = accuracy_score(y_train, y_pred2)
-print(f"Accuracy: {accuracy}")
-
-# Predict the disease for the test data
-y_pred = model.predict(X_test)
-
-# Evaluate the accuracy of the model
-accuracy = accuracy_score(y_test, y_pred)
-print(f"Accuracy: {accuracy}")
-
diff --git a/spaces/Searchium-ai/Video-Search/app.py b/spaces/Searchium-ai/Video-Search/app.py
deleted file mode 100644
index fb568db7a0d26c8280babfd9df0119bae43290cd..0000000000000000000000000000000000000000
--- a/spaces/Searchium-ai/Video-Search/app.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import gradio as gr
-import os
-import numpy as np
-import pandas as pd
-from IPython import display
-import faiss
-import torch
-from transformers import CLIPTokenizer, CLIPTextModelWithProjection
-
-HTML="""
-
-
-    
-    
-        
      
-            
      
-                
-            
      
-            
      
-                
       Large Scale Video Search 
      
-            
      
-        
      
-    
-
-"""
-
-DESCRIPTION=""" Exciting News!  
      
-                We've added another 4 million video embeddings to our collection!  
      
-
-               
-               Welcome to our video retrieval demo powered by [Searchium-ai/clip4clip-webvid150k](https://huggingface.co/Searchium-ai/clip4clip-webvid150k)! 
      
-               Using free text search - you will find the top 5 most relevant clips among a dataset of  5.5 million  video clips. 
      
-               Discover, explore, and enjoy the world of video search at your fingertips.
-               """
-ENDING = """For search acceleration capabilities, please refer to [Searchium.ai](https://www.searchium.ai)
-            """
-
-
-DATA_PATH = './new_data'
-
-ft_visual_features_file = DATA_PATH + '/video_half_dataset_visual_features.npy'
-ft_visual_features_file_bin = DATA_PATH + '/video_half_dataset_visual_features_binary_packed.npy'
-
-#load database features:
-ft_visual_features_database_bin = np.load(ft_visual_features_file_bin)
-ft_visual_features_database = np.load(ft_visual_features_file, mmap_mode='r')
-
-
-database_csv_path = os.path.join(DATA_PATH, 'video_half_dataset.csv')
-database_df = pd.read_csv(database_csv_path)
-
-class NearestNeighbors:
-    """
-    Class for NearestNeighbors.   
-    """
-    def __init__(self, n_neighbors=10, metric='cosine', rerank_from=-1):
-        """
-         metric = 'cosine' / 'binary' 
-         if metric ~= 'cosine' and rerank_from > n_neighbors then a cosine rerank will be performed
-        """
-        self.n_neighbors = n_neighbors
-        self.metric = metric        
-        self.rerank_from = rerank_from                
-        
-    def normalize(self, a):
-        return a / np.sum(a**2, axis=1, keepdims=True)
-    
-    def fit(self, data, o_data=None):
-        if self.metric == 'cosine':
-            data = self.normalize(data)
-            self.index = faiss.IndexFlatIP(data.shape[1])     
-        elif self.metric == 'binary':
-            self.o_data = data if o_data is None else o_data
-            #assuming data already packed
-            self.index = faiss.IndexBinaryFlat(data.shape[1]*8)            
-        self.index.add(np.ascontiguousarray(data))
-        
-    def kneighbors(self, q_data):                
-        if self.metric == 'cosine':
-            q_data = self.normalize(q_data)      
-            sim, idx = self.index.search(q_data, self.n_neighbors)        
-        else:            
-            if self.metric == 'binary':
-                print('This is binary search.')
-                bq_data = np.packbits((q_data > 0.0).astype(bool), axis=1)
-            sim, idx = self.index.search(bq_data, max(self.rerank_from, self.n_neighbors))
-            
-            if self.rerank_from > self.n_neighbors:
-                re_sims = np.zeros([len(q_data), self.n_neighbors], dtype=float)
-                re_idxs = np.zeros([len(q_data), self.n_neighbors], dtype=float)
-                for i, q in enumerate(q_data):
-                    rerank_data = self.o_data[idx[i]]
-                    rerank_search = NearestNeighbors(n_neighbors=self.n_neighbors, metric='cosine') 
-                    rerank_search.fit(rerank_data)
-                    re_sim, re_idx = rerank_search.kneighbors(np.asarray([q]))
-                    print("re_idx: ", re_idx)
-                    re_sims[i, :] = re_sim
-                    re_idxs[i, :] = idx[i][re_idx]
-                idx = re_idxs
-                sim = re_sims
-
-        return sim, idx
-    
-                    
-model = CLIPTextModelWithProjection.from_pretrained("Searchium-ai/clip4clip-webvid150k")
-tokenizer = CLIPTokenizer.from_pretrained("Searchium-ai/clip4clip-webvid150k")
-
-nn_search = NearestNeighbors(n_neighbors=5, metric='binary', rerank_from=100)
-nn_search.fit(ft_visual_features_database_bin, o_data=ft_visual_features_database)
-
-def search(search_sentence):
-    inputs = tokenizer(text=search_sentence , return_tensors="pt")
-    outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
-    
-    # Normalizing the embeddings:
-    final_output = outputs[0] / outputs[0].norm(dim=-1, keepdim=True)
-    sequence_output = final_output.cpu().detach().numpy()
-        
-    sims, idxs = nn_search.kneighbors(sequence_output)    
-    urls = database_df.iloc[idxs[0]]['contentUrl'].to_list()
-    AUTOPLAY_VIDEOS = []
-    for url in urls:
-        AUTOPLAY_VIDEOS.append("""""".format(url))
-    return AUTOPLAY_VIDEOS
-
-
-with gr.Blocks(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_lg, radius_size=gr.themes.sizes.radius_lg, text_size=gr.themes.sizes.text_lg)) as demo:
-    gr.HTML(HTML)
-    gr.Markdown(DESCRIPTION)
-    with gr.Row():
-        with gr.Column():
-            inp = gr.Textbox(placeholder="Write a sentence.")
-            btn = gr.Button(value="Search")
-            ex = [["natural wonders of the world"],["yoga routines for morning energy"], 
-                ["baking chocolate cake"],["birds fly in the sky"]]
-            gr.Examples(examples=ex,
-                        inputs=[inp]
-                        )
-        with gr.Column():
-            out = [gr.HTML() for _ in range(5)]
-    btn.click(search, inputs=inp, outputs=out)
-    gr.Markdown(ENDING)
-        
-demo.launch()
\ No newline at end of file
diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py b/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py
deleted file mode 100644
index 823b44fb64898e8dcbb12180ba45d1718f9b03f7..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_537238KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 64)
-        self.stg1_high_band_net = BaseASPPNet(2, 64)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(32, 64)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(64, 128)
-
-        self.out = nn.Conv2d(128, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/ShiwenNi/ChatReviewer/README.md b/spaces/ShiwenNi/ChatReviewer/README.md
deleted file mode 100644
index 01b8578d55541ba218632165a69e969e54ded40d..0000000000000000000000000000000000000000
--- a/spaces/ShiwenNi/ChatReviewer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatReviewer
-emoji: 💩
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.22.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sim1604/Twitter_Sentiment_Analysis/README.md b/spaces/Sim1604/Twitter_Sentiment_Analysis/README.md
deleted file mode 100644
index 30192a5b0e74d0263c780d0b51df7af87cc3fb3f..0000000000000000000000000000000000000000
--- a/spaces/Sim1604/Twitter_Sentiment_Analysis/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Twitter Sentiment Analysis
-emoji: 😻
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Siyuan0730/clewordAutomaticGenerating/README.md b/spaces/Siyuan0730/clewordAutomaticGenerating/README.md
deleted file mode 100644
index b5282590fcaa20654e89f00c0dcb9c4634d5ef7d..0000000000000000000000000000000000000000
--- a/spaces/Siyuan0730/clewordAutomaticGenerating/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ClewordAutomaticGenerating
-emoji: 🐢
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SpacesExamples/Gradio-Docker-Template-nvidia-cuda/app.py b/spaces/SpacesExamples/Gradio-Docker-Template-nvidia-cuda/app.py
deleted file mode 100644
index 1c678180426883938428af07c17318c756c8c439..0000000000000000000000000000000000000000
--- a/spaces/SpacesExamples/Gradio-Docker-Template-nvidia-cuda/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-import torch
-import os
-os.system("nvidia-smi")
-print("TORCH_CUDA", torch.cuda.is_available())
-
-def update(name):
-    return f"Welcome to Gradio, {name}!"
-
-with gr.Blocks() as demo:
-    gr.Markdown("Start typing below and then click **Run** to see the output.")
-    with gr.Row():
-        inp = gr.Textbox(placeholder="What is your name?")
-        out = gr.Textbox()
-    btn = gr.Button("Run")
-    btn.click(fn=update, inputs=inp, outputs=out)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/utils/misc.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/utils/misc.py
deleted file mode 100644
index eb862a82bd47c8624db3dd5c6fb6ad8a03b62466..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/utils/misc.py
+++ /dev/null
@@ -1,17 +0,0 @@
-def add_prefix(inputs, prefix):
-    """Add prefix for dict.
-
-    Args:
-        inputs (dict): The input dict with str keys.
-        prefix (str): The prefix to add.
-
-    Returns:
-
-        dict: The dict with keys updated with ``prefix``.
-    """
-
-    outputs = dict()
-    for name, value in inputs.items():
-        outputs[f'{prefix}.{name}'] = value
-
-    return outputs
diff --git a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/blocks.py b/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/blocks.py
deleted file mode 100644
index 6d87a00680bb6ed9a6d7c3043ea30a1e90361794..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/base_models/midas_repo/midas/blocks.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .backbones.beit import (
-    _make_pretrained_beitl16_512,
-    _make_pretrained_beitl16_384,
-    _make_pretrained_beitb16_384,
-    forward_beit,
-)
-from .backbones.swin_common import (
-    forward_swin,
-)
-from .backbones.swin2 import (
-    _make_pretrained_swin2l24_384,
-    _make_pretrained_swin2b24_384,
-    _make_pretrained_swin2t16_256,
-)
-from .backbones.swin import (
-    _make_pretrained_swinl12_384,
-)
-from .backbones.levit import (
-    _make_pretrained_levit_384,
-    forward_levit,
-)
-from .backbones.vit import (
-    _make_pretrained_vitb_rn50_384,
-    _make_pretrained_vitl16_384,
-    _make_pretrained_vitb16_384,
-    forward_vit,
-)
-
-def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None,
-                  use_vit_only=False, use_readout="ignore", in_features=[96, 256, 512, 1024]):
-    if backbone == "beitl16_512":
-        pretrained = _make_pretrained_beitl16_512(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # BEiT_512-L (backbone)
-    elif backbone == "beitl16_384":
-        pretrained = _make_pretrained_beitl16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # BEiT_384-L (backbone)
-    elif backbone == "beitb16_384":
-        pretrained = _make_pretrained_beitb16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # BEiT_384-B (backbone)
-    elif backbone == "swin2l24_384":
-        pretrained = _make_pretrained_swin2l24_384(
-            use_pretrained, hooks=hooks
-        )
-        scratch = _make_scratch(
-            [192, 384, 768, 1536], features, groups=groups, expand=expand
-        )  # Swin2-L/12to24 (backbone)
-    elif backbone == "swin2b24_384":
-        pretrained = _make_pretrained_swin2b24_384(
-            use_pretrained, hooks=hooks
-        )
-        scratch = _make_scratch(
-            [128, 256, 512, 1024], features, groups=groups, expand=expand
-        )  # Swin2-B/12to24 (backbone)
-    elif backbone == "swin2t16_256":
-        pretrained = _make_pretrained_swin2t16_256(
-            use_pretrained, hooks=hooks
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # Swin2-T/16 (backbone)
-    elif backbone == "swinl12_384":
-        pretrained = _make_pretrained_swinl12_384(
-            use_pretrained, hooks=hooks
-        )
-        scratch = _make_scratch(
-            [192, 384, 768, 1536], features, groups=groups, expand=expand
-        )  # Swin-L/12 (backbone)
-    elif backbone == "next_vit_large_6m":
-        from .backbones.next_vit import _make_pretrained_next_vit_large_6m
-        pretrained = _make_pretrained_next_vit_large_6m(hooks=hooks)
-        scratch = _make_scratch(
-            in_features, features, groups=groups, expand=expand
-        )  # Next-ViT-L on ImageNet-1K-6M (backbone)
-    elif backbone == "levit_384":
-        pretrained = _make_pretrained_levit_384(
-            use_pretrained, hooks=hooks
-        )
-        scratch = _make_scratch(
-            [384, 512, 768], features, groups=groups, expand=expand
-        )  # LeViT 384 (backbone)
-    elif backbone == "vitl16_384":
-        pretrained = _make_pretrained_vitl16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [256, 512, 1024, 1024], features, groups=groups, expand=expand
-        )  # ViT-L/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb_rn50_384":
-        pretrained = _make_pretrained_vitb_rn50_384(
-            use_pretrained,
-            hooks=hooks,
-            use_vit_only=use_vit_only,
-            use_readout=use_readout,
-        )
-        scratch = _make_scratch(
-            [256, 512, 768, 768], features, groups=groups, expand=expand
-        )  # ViT-H/16 - 85.0% Top1 (backbone)
-    elif backbone == "vitb16_384":
-        pretrained = _make_pretrained_vitb16_384(
-            use_pretrained, hooks=hooks, use_readout=use_readout
-        )
-        scratch = _make_scratch(
-            [96, 192, 384, 768], features, groups=groups, expand=expand
-        )  # ViT-B/16 - 84.6% Top1 (backbone)
-    elif backbone == "resnext101_wsl":
-        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
-        scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand)  # efficientnet_lite3
-    elif backbone == "efficientnet_lite3":
-        pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
-        scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand)  # efficientnet_lite3
-    else:
-        print(f"Backbone '{backbone}' not implemented")
-        assert False
-        
-    return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
-    scratch = nn.Module()
-
-    out_shape1 = out_shape
-    out_shape2 = out_shape
-    out_shape3 = out_shape
-    if len(in_shape) >= 4:
-        out_shape4 = out_shape
-
-    if expand:
-        out_shape1 = out_shape
-        out_shape2 = out_shape*2
-        out_shape3 = out_shape*4
-        if len(in_shape) >= 4:
-            out_shape4 = out_shape*8
-
-    scratch.layer1_rn = nn.Conv2d(
-        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer2_rn = nn.Conv2d(
-        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    scratch.layer3_rn = nn.Conv2d(
-        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-    )
-    if len(in_shape) >= 4:
-        scratch.layer4_rn = nn.Conv2d(
-            in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
-        )
-
-    return scratch
-
-
-def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
-    efficientnet = torch.hub.load(
-        "rwightman/gen-efficientnet-pytorch",
-        "tf_efficientnet_lite3",
-        pretrained=use_pretrained,
-        exportable=exportable
-    )
-    return _make_efficientnet_backbone(efficientnet)
-
-
-def _make_efficientnet_backbone(effnet):
-    pretrained = nn.Module()
-
-    pretrained.layer1 = nn.Sequential(
-        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
-    )
-    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
-    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
-    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
-
-    return pretrained
-    
-
-def _make_resnet_backbone(resnet):
-    pretrained = nn.Module()
-    pretrained.layer1 = nn.Sequential(
-        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
-    )
-
-    pretrained.layer2 = resnet.layer2
-    pretrained.layer3 = resnet.layer3
-    pretrained.layer4 = resnet.layer4
-
-    return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
-    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
-    return _make_resnet_backbone(resnet)
-
-
-
-class Interpolate(nn.Module):
-    """Interpolation module.
-    """
-
-    def __init__(self, scale_factor, mode, align_corners=False):
-        """Init.
-
-        Args:
-            scale_factor (float): scaling
-            mode (str): interpolation mode
-        """
-        super(Interpolate, self).__init__()
-
-        self.interp = nn.functional.interpolate
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: interpolated data
-        """
-
-        x = self.interp(
-            x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
-        )
-
-        return x
-
-
-class ResidualConvUnit(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True
-        )
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        out = self.relu(x)
-        out = self.conv1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-
-        return out + x
-
-
-class FeatureFusionBlock(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock, self).__init__()
-
-        self.resConfUnit1 = ResidualConvUnit(features)
-        self.resConfUnit2 = ResidualConvUnit(features)
-
-    def forward(self, *xs):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            output += self.resConfUnit1(xs[1])
-
-        output = self.resConfUnit2(output)
-
-        output = nn.functional.interpolate(
-            output, scale_factor=2, mode="bilinear", align_corners=True
-        )
-
-        return output
-
-
-
-
-class ResidualConvUnit_custom(nn.Module):
-    """Residual convolution module.
-    """
-
-    def __init__(self, features, activation, bn):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super().__init__()
-
-        self.bn = bn
-
-        self.groups=1
-
-        self.conv1 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-        
-        self.conv2 = nn.Conv2d(
-            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
-        )
-
-        if self.bn==True:
-            self.bn1 = nn.BatchNorm2d(features)
-            self.bn2 = nn.BatchNorm2d(features)
-
-        self.activation = activation
-
-        self.skip_add = nn.quantized.FloatFunctional()
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input
-
-        Returns:
-            tensor: output
-        """
-        
-        out = self.activation(x)
-        out = self.conv1(out)
-        if self.bn==True:
-            out = self.bn1(out)
-       
-        out = self.activation(out)
-        out = self.conv2(out)
-        if self.bn==True:
-            out = self.bn2(out)
-
-        if self.groups > 1:
-            out = self.conv_merge(out)
-
-        return self.skip_add.add(out, x)
-
-        # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
-    """Feature fusion block.
-    """
-
-    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
-        """Init.
-
-        Args:
-            features (int): number of features
-        """
-        super(FeatureFusionBlock_custom, self).__init__()
-
-        self.deconv = deconv
-        self.align_corners = align_corners
-
-        self.groups=1
-
-        self.expand = expand
-        out_features = features
-        if self.expand==True:
-            out_features = features//2
-        
-        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
-        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
-        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-        
-        self.skip_add = nn.quantized.FloatFunctional()
-
-        self.size=size
-
-    def forward(self, *xs, size=None):
-        """Forward pass.
-
-        Returns:
-            tensor: output
-        """
-        output = xs[0]
-
-        if len(xs) == 2:
-            res = self.resConfUnit1(xs[1])
-            output = self.skip_add.add(output, res)
-            # output += res
-
-        output = self.resConfUnit2(output)
-
-        if (size is None) and (self.size is None):
-            modifier = {"scale_factor": 2}
-        elif size is None:
-            modifier = {"size": self.size}
-        else:
-            modifier = {"size": size}
-
-        output = nn.functional.interpolate(
-            output, **modifier, mode="bilinear", align_corners=self.align_corners
-        )
-
-        output = self.out_conv(output)
-
-        return output
-
diff --git a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/utils/config.py b/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/utils/config.py
deleted file mode 100644
index 84996564663dadf0e720de2a68ef8c53106ed666..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/utils/config.py
+++ /dev/null
@@ -1,437 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# File author: Shariq Farooq Bhat
-
-import json
-import os
-
-from .easydict import EasyDict as edict
-from .arg_utils import infer_type
-
-import pathlib
-import platform
-
-ROOT = pathlib.Path(__file__).parent.parent.resolve()
-
-HOME_DIR = os.path.expanduser("~")
-
-COMMON_CONFIG = {
-    "save_dir": os.path.expanduser("~/shortcuts/monodepth3_checkpoints"),
-    "project": "ZoeDepth",
-    "tags": '',
-    "notes": "",
-    "gpu": None,
-    "root": ".",
-    "uid": None,
-    "print_losses": False
-}
-
-DATASETS_CONFIG = {
-    "kitti": {
-        "dataset": "kitti",
-        "min_depth": 0.001,
-        "max_depth": 80,
-        "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"),
-        "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"),
-        "filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt",
-        "input_height": 352,
-        "input_width": 1216,  # 704
-        "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"),
-        "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"),
-        "filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt",
-
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-
-        "do_random_rotate": True,
-        "degree": 1.0,
-        "do_kb_crop": True,
-        "garg_crop": True,
-        "eigen_crop": False,
-        "use_right": False
-    },
-    "kitti_test": {
-        "dataset": "kitti",
-        "min_depth": 0.001,
-        "max_depth": 80,
-        "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"),
-        "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"),
-        "filenames_file": "./train_test_inputs/kitti_eigen_train_files_with_gt.txt",
-        "input_height": 352,
-        "input_width": 1216,
-        "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/raw"),
-        "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/kitti/gts"),
-        "filenames_file_eval": "./train_test_inputs/kitti_eigen_test_files_with_gt.txt",
-
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-
-        "do_random_rotate": False,
-        "degree": 1.0,
-        "do_kb_crop": True,
-        "garg_crop": True,
-        "eigen_crop": False,
-        "use_right": False
-    },
-    "nyu": {
-        "dataset": "nyu",
-        "avoid_boundary": False,
-        "min_depth": 1e-3,   # originally 0.1
-        "max_depth": 10,
-        "data_path": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/sync/"),
-        "gt_path": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/sync/"),
-        "filenames_file": "./train_test_inputs/nyudepthv2_train_files_with_gt.txt",
-        "input_height": 480,
-        "input_width": 640,
-        "data_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/official_splits/test/"),
-        "gt_path_eval": os.path.join(HOME_DIR, "shortcuts/datasets/nyu_depth_v2/official_splits/test/"),
-        "filenames_file_eval": "./train_test_inputs/nyudepthv2_test_files_with_gt.txt",
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 10,
-        "min_depth_diff": -10,
-        "max_depth_diff": 10,
-
-        "do_random_rotate": True,
-        "degree": 1.0,
-        "do_kb_crop": False,
-        "garg_crop": False,
-        "eigen_crop": True
-    },
-    "ibims": {
-        "dataset": "ibims",
-        "ibims_root": os.path.join(HOME_DIR, "shortcuts/datasets/ibims/ibims1_core_raw/"),
-        "eigen_crop": True,
-        "garg_crop": False,
-        "do_kb_crop": False,
-        "min_depth_eval": 0,
-        "max_depth_eval": 10,
-        "min_depth": 1e-3,
-        "max_depth": 10
-    },
-    "sunrgbd": {
-        "dataset": "sunrgbd",
-        "sunrgbd_root": os.path.join(HOME_DIR, "shortcuts/datasets/SUNRGBD/test/"),
-        "eigen_crop": True,
-        "garg_crop": False,
-        "do_kb_crop": False,
-        "min_depth_eval": 0,
-        "max_depth_eval": 8,
-        "min_depth": 1e-3,
-        "max_depth": 10
-    },
-    "diml_indoor": {
-        "dataset": "diml_indoor",
-        "diml_indoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diml_indoor_test/"),
-        "eigen_crop": True,
-        "garg_crop": False,
-        "do_kb_crop": False,
-        "min_depth_eval": 0,
-        "max_depth_eval": 10,
-        "min_depth": 1e-3,
-        "max_depth": 10
-    },
-    "diml_outdoor": {
-        "dataset": "diml_outdoor",
-        "diml_outdoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diml_outdoor_test/"),
-        "eigen_crop": False,
-        "garg_crop": True,
-        "do_kb_crop": False,
-        "min_depth_eval": 2,
-        "max_depth_eval": 80,
-        "min_depth": 1e-3,
-        "max_depth": 80
-    },
-    "diode_indoor": {
-        "dataset": "diode_indoor",
-        "diode_indoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diode_indoor/"),
-        "eigen_crop": True,
-        "garg_crop": False,
-        "do_kb_crop": False,
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 10,
-        "min_depth": 1e-3,
-        "max_depth": 10
-    },
-    "diode_outdoor": {
-        "dataset": "diode_outdoor",
-        "diode_outdoor_root": os.path.join(HOME_DIR, "shortcuts/datasets/diode_outdoor/"),
-        "eigen_crop": False,
-        "garg_crop": True,
-        "do_kb_crop": False,
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-        "min_depth": 1e-3,
-        "max_depth": 80
-    },
-    "hypersim_test": {
-        "dataset": "hypersim_test",
-        "hypersim_test_root": os.path.join(HOME_DIR, "shortcuts/datasets/hypersim_test/"),
-        "eigen_crop": True,
-        "garg_crop": False,
-        "do_kb_crop": False,
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-        "min_depth": 1e-3,
-        "max_depth": 10
-    },
-    "vkitti": {
-        "dataset": "vkitti",
-        "vkitti_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti_test/"),
-        "eigen_crop": False,
-        "garg_crop": True,
-        "do_kb_crop": True,
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-        "min_depth": 1e-3,
-        "max_depth": 80
-    },
-    "vkitti2": {
-        "dataset": "vkitti2",
-        "vkitti2_root": os.path.join(HOME_DIR, "shortcuts/datasets/vkitti2/"),
-        "eigen_crop": False,
-        "garg_crop": True,
-        "do_kb_crop": True,
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-        "min_depth": 1e-3,
-        "max_depth": 80,
-    },
-    "ddad": {
-        "dataset": "ddad",
-        "ddad_root": os.path.join(HOME_DIR, "shortcuts/datasets/ddad/ddad_val/"),
-        "eigen_crop": False,
-        "garg_crop": True,
-        "do_kb_crop": True,
-        "min_depth_eval": 1e-3,
-        "max_depth_eval": 80,
-        "min_depth": 1e-3,
-        "max_depth": 80,
-    },
-}
-
-ALL_INDOOR = ["nyu", "ibims", "sunrgbd", "diode_indoor", "hypersim_test"]
-ALL_OUTDOOR = ["kitti", "diml_outdoor", "diode_outdoor",  "vkitti2", "ddad"]
-ALL_EVAL_DATASETS = ALL_INDOOR + ALL_OUTDOOR
-
-COMMON_TRAINING_CONFIG = {
-    "dataset": "nyu",
-    "distributed": True,
-    "workers": 16,
-    "clip_grad": 0.1,
-    "use_shared_dict": False,
-    "shared_dict": None,
-    "use_amp": False,
-
-    "aug": True,
-    "random_crop": False,
-    "random_translate": False,
-    "translate_prob": 0.2,
-    "max_translation": 100,
-
-    "validate_every": 0.25,
-    "log_images_every": 0.1,
-    "prefetch": False,
-}
-
-
-def flatten(config, except_keys=('bin_conf')):
-    def recurse(inp):
-        if isinstance(inp, dict):
-            for key, value in inp.items():
-                if key in except_keys:
-                    yield (key, value)
-                if isinstance(value, dict):
-                    yield from recurse(value)
-                else:
-                    yield (key, value)
-
-    return dict(list(recurse(config)))
-
-
-def split_combined_args(kwargs):
-    """Splits the arguments that are combined with '__' into multiple arguments.
-       Combined arguments should have equal number of keys and values.
-       Keys are separated by '__' and Values are separated with ';'.
-       For example, '__n_bins__lr=256;0.001'
-
-    Args:
-        kwargs (dict): key-value pairs of arguments where key-value is optionally combined according to the above format. 
-
-    Returns:
-        dict: Parsed dict with the combined arguments split into individual key-value pairs.
-    """
-    new_kwargs = dict(kwargs)
-    for key, value in kwargs.items():
-        if key.startswith("__"):
-            keys = key.split("__")[1:]
-            values = value.split(";")
-            assert len(keys) == len(
-                values), f"Combined arguments should have equal number of keys and values. Keys are separated by '__' and Values are separated with ';'. For example, '__n_bins__lr=256;0.001. Given (keys,values) is ({keys}, {values})"
-            for k, v in zip(keys, values):
-                new_kwargs[k] = v
-    return new_kwargs
-
-
-def parse_list(config, key, dtype=int):
-    """Parse a list of values for the key if the value is a string. The values are separated by a comma. 
-    Modifies the config in place.
-    """
-    if key in config:
-        if isinstance(config[key], str):
-            config[key] = list(map(dtype, config[key].split(',')))
-        assert isinstance(config[key], list) and all([isinstance(e, dtype) for e in config[key]]
-                                                     ), f"{key} should be a list of values dtype {dtype}. Given {config[key]} of type {type(config[key])} with values of type {[type(e) for e in config[key]]}."
-
-
-def get_model_config(model_name, model_version=None):
-    """Find and parse the .json config file for the model.
-
-    Args:
-        model_name (str): name of the model. The config file should be named config_{model_name}[_{model_version}].json under the models/{model_name} directory.
-        model_version (str, optional): Specific config version. If specified config_{model_name}_{model_version}.json is searched for and used. Otherwise config_{model_name}.json is used. Defaults to None.
-
-    Returns:
-        easydict: the config dictionary for the model.
-    """
-    config_fname = f"config_{model_name}_{model_version}.json" if model_version is not None else f"config_{model_name}.json"
-    config_file = os.path.join(ROOT, "models", model_name, config_fname)
-    if not os.path.exists(config_file):
-        return None
-
-    with open(config_file, "r") as f:
-        config = edict(json.load(f))
-
-    # handle dictionary inheritance
-    # only training config is supported for inheritance
-    if "inherit" in config.train and config.train.inherit is not None:
-        inherit_config = get_model_config(config.train["inherit"]).train
-        for key, value in inherit_config.items():
-            if key not in config.train:
-                config.train[key] = value
-    return edict(config)
-
-
-def update_model_config(config, mode, model_name, model_version=None, strict=False):
-    model_config = get_model_config(model_name, model_version)
-    if model_config is not None:
-        config = {**config, **
-                  flatten({**model_config.model, **model_config[mode]})}
-    elif strict:
-        raise ValueError(f"Config file for model {model_name} not found.")
-    return config
-
-
-def check_choices(name, value, choices):
-    # return  # No checks in dev branch
-    if value not in choices:
-        raise ValueError(f"{name} {value} not in supported choices {choices}")
-
-
-KEYS_TYPE_BOOL = ["use_amp", "distributed", "use_shared_dict", "same_lr", "aug", "three_phase",
-                  "prefetch", "cycle_momentum"]  # Casting is not necessary as their int casted values in config are 0 or 1
-
-
-def get_config(model_name, mode='train', dataset=None, **overwrite_kwargs):
-    """Main entry point to get the config for the model.
-
-    Args:
-        model_name (str): name of the desired model.
-        mode (str, optional): "train" or "infer". Defaults to 'train'.
-        dataset (str, optional): If specified, the corresponding dataset configuration is loaded as well. Defaults to None.
-    
-    Keyword Args: key-value pairs of arguments to overwrite the default config.
-
-    The order of precedence for overwriting the config is (Higher precedence first):
-        # 1. overwrite_kwargs
-        # 2. "config_version": Config file version if specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{config_version}.json
-        # 3. "version_name": Default Model version specific config specified in overwrite_kwargs. The corresponding config loaded is config_{model_name}_{version_name}.json
-        # 4. common_config: Default config for all models specified in COMMON_CONFIG
-
-    Returns:
-        easydict: The config dictionary for the model.
-    """
-
-
-    check_choices("Model", model_name, ["zoedepth", "zoedepth_nk"])
-    check_choices("Mode", mode, ["train", "infer", "eval"])
-    if mode == "train":
-        check_choices("Dataset", dataset, ["nyu", "kitti", "mix", None])
-
-    config = flatten({**COMMON_CONFIG, **COMMON_TRAINING_CONFIG})
-    config = update_model_config(config, mode, model_name)
-
-    # update with model version specific config
-    version_name = overwrite_kwargs.get("version_name", config["version_name"])
-    config = update_model_config(config, mode, model_name, version_name)
-
-    # update with config version if specified
-    config_version = overwrite_kwargs.get("config_version", None)
-    if config_version is not None:
-        print("Overwriting config with config_version", config_version)
-        config = update_model_config(config, mode, model_name, config_version)
-
-    # update with overwrite_kwargs
-    # Combined args are useful for hyperparameter search
-    overwrite_kwargs = split_combined_args(overwrite_kwargs)
-    config = {**config, **overwrite_kwargs}
-
-    # Casting to bool   # TODO: Not necessary. Remove and test
-    for key in KEYS_TYPE_BOOL:
-        if key in config:
-            config[key] = bool(config[key])
-
-    # Model specific post processing of config
-    parse_list(config, "n_attractors")
-
-    # adjust n_bins for each bin configuration if bin_conf is given and n_bins is passed in overwrite_kwargs
-    if 'bin_conf' in config and 'n_bins' in overwrite_kwargs:
-        bin_conf = config['bin_conf']  # list of dicts
-        n_bins = overwrite_kwargs['n_bins']
-        new_bin_conf = []
-        for conf in bin_conf:
-            conf['n_bins'] = n_bins
-            new_bin_conf.append(conf)
-        config['bin_conf'] = new_bin_conf
-
-    if mode == "train":
-        orig_dataset = dataset
-        if dataset == "mix":
-            dataset = 'nyu'  # Use nyu as default for mix. Dataset config is changed accordingly while loading the dataloader
-        if dataset is not None:
-            config['project'] = f"MonoDepth3-{orig_dataset}"  # Set project for wandb
-
-    if dataset is not None:
-        config['dataset'] = dataset
-        config = {**DATASETS_CONFIG[dataset], **config}
-        
-
-    config['model'] = model_name
-    typed_config = {k: infer_type(v) for k, v in config.items()}
-    # add hostname to config
-    config['hostname'] = platform.node()
-    return edict(typed_config)
-
-
-def change_dataset(config, new_dataset):
-    config.update(DATASETS_CONFIG[new_dataset])
-    return config
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/__init__.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/TabPFN/TabPFNEvaluation/README.md b/spaces/TabPFN/TabPFNEvaluation/README.md
deleted file mode 100644
index 99f2ae5693b716a59efdd353e51196543952e009..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNEvaluation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: TabPFNEvaluationDemo
-emoji: 🏢
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/__init__.py
deleted file mode 100644
index fe581623d89d67a49eb43f3c3e88f3f450257707..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/__init__.py
+++ /dev/null
@@ -1,115 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import List, Union
-
-from .charsetgroupprober import CharSetGroupProber
-from .charsetprober import CharSetProber
-from .enums import InputState
-from .resultdict import ResultDict
-from .universaldetector import UniversalDetector
-from .version import VERSION, __version__
-
-__all__ = ["UniversalDetector", "detect", "detect_all", "__version__", "VERSION"]
-
-
-def detect(
-    byte_str: Union[bytes, bytearray], should_rename_legacy: bool = False
-) -> ResultDict:
-    """
-    Detect the encoding of the given byte string.
-
-    :param byte_str:     The byte sequence to examine.
-    :type byte_str:      ``bytes`` or ``bytearray``
-    :param should_rename_legacy:  Should we rename legacy encodings
-                                  to their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    if not isinstance(byte_str, bytearray):
-        if not isinstance(byte_str, bytes):
-            raise TypeError(
-                f"Expected object of type bytes or bytearray, got: {type(byte_str)}"
-            )
-        byte_str = bytearray(byte_str)
-    detector = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    detector.feed(byte_str)
-    return detector.close()
-
-
-def detect_all(
-    byte_str: Union[bytes, bytearray],
-    ignore_threshold: bool = False,
-    should_rename_legacy: bool = False,
-) -> List[ResultDict]:
-    """
-    Detect all the possible encodings of the given byte string.
-
-    :param byte_str:          The byte sequence to examine.
-    :type byte_str:           ``bytes`` or ``bytearray``
-    :param ignore_threshold:  Include encodings that are below
-                              ``UniversalDetector.MINIMUM_THRESHOLD``
-                              in results.
-    :type ignore_threshold:   ``bool``
-    :param should_rename_legacy:  Should we rename legacy encodings
-                                  to their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    if not isinstance(byte_str, bytearray):
-        if not isinstance(byte_str, bytes):
-            raise TypeError(
-                f"Expected object of type bytes or bytearray, got: {type(byte_str)}"
-            )
-        byte_str = bytearray(byte_str)
-
-    detector = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    detector.feed(byte_str)
-    detector.close()
-
-    if detector.input_state == InputState.HIGH_BYTE:
-        results: List[ResultDict] = []
-        probers: List[CharSetProber] = []
-        for prober in detector.charset_probers:
-            if isinstance(prober, CharSetGroupProber):
-                probers.extend(p for p in prober.probers)
-            else:
-                probers.append(prober)
-        for prober in probers:
-            if ignore_threshold or prober.get_confidence() > detector.MINIMUM_THRESHOLD:
-                charset_name = prober.charset_name or ""
-                lower_charset_name = charset_name.lower()
-                # Use Windows encoding name instead of ISO-8859 if we saw any
-                # extra Windows-specific bytes
-                if lower_charset_name.startswith("iso-8859") and detector.has_win_bytes:
-                    charset_name = detector.ISO_WIN_MAP.get(
-                        lower_charset_name, charset_name
-                    )
-                # Rename legacy encodings with superset encodings if asked
-                if should_rename_legacy:
-                    charset_name = detector.LEGACY_MAP.get(
-                        charset_name.lower(), charset_name
-                    )
-                results.append(
-                    {
-                        "encoding": charset_name,
-                        "confidence": prober.get_confidence(),
-                        "language": prober.language,
-                    }
-                )
-        if len(results) > 0:
-            return sorted(results, key=lambda result: -result["confidence"])
-
-    return [detector.result]
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/mbcssm.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/mbcssm.py
deleted file mode 100644
index 7bbe97e6665356327814e2b797ffcc5724974a46..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/chardet/mbcssm.py
+++ /dev/null
@@ -1,661 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .codingstatemachinedict import CodingStateMachineDict
-from .enums import MachineState
-
-# BIG5
-
-# fmt: off
-BIG5_CLS = (
-    1, 1, 1, 1, 1, 1, 1, 1,  # 00 - 07    #allow 0x00 as legal value
-    1, 1, 1, 1, 1, 1, 0, 0,  # 08 - 0f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 10 - 17
-    1, 1, 1, 0, 1, 1, 1, 1,  # 18 - 1f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 20 - 27
-    1, 1, 1, 1, 1, 1, 1, 1,  # 28 - 2f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 30 - 37
-    1, 1, 1, 1, 1, 1, 1, 1,  # 38 - 3f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 40 - 47
-    2, 2, 2, 2, 2, 2, 2, 2,  # 48 - 4f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 50 - 57
-    2, 2, 2, 2, 2, 2, 2, 2,  # 58 - 5f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 60 - 67
-    2, 2, 2, 2, 2, 2, 2, 2,  # 68 - 6f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 70 - 77
-    2, 2, 2, 2, 2, 2, 2, 1,  # 78 - 7f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 80 - 87
-    4, 4, 4, 4, 4, 4, 4, 4,  # 88 - 8f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 90 - 97
-    4, 4, 4, 4, 4, 4, 4, 4,  # 98 - 9f
-    4, 3, 3, 3, 3, 3, 3, 3,  # a0 - a7
-    3, 3, 3, 3, 3, 3, 3, 3,  # a8 - af
-    3, 3, 3, 3, 3, 3, 3, 3,  # b0 - b7
-    3, 3, 3, 3, 3, 3, 3, 3,  # b8 - bf
-    3, 3, 3, 3, 3, 3, 3, 3,  # c0 - c7
-    3, 3, 3, 3, 3, 3, 3, 3,  # c8 - cf
-    3, 3, 3, 3, 3, 3, 3, 3,  # d0 - d7
-    3, 3, 3, 3, 3, 3, 3, 3,  # d8 - df
-    3, 3, 3, 3, 3, 3, 3, 3,  # e0 - e7
-    3, 3, 3, 3, 3, 3, 3, 3,  # e8 - ef
-    3, 3, 3, 3, 3, 3, 3, 3,  # f0 - f7
-    3, 3, 3, 3, 3, 3, 3, 0  # f8 - ff
-)
-
-BIG5_ST = (
-    MachineState.ERROR,MachineState.START,MachineState.START,     3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07
-    MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,#08-0f
-    MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START#10-17
-)
-# fmt: on
-
-BIG5_CHAR_LEN_TABLE = (0, 1, 1, 2, 0)
-
-BIG5_SM_MODEL: CodingStateMachineDict = {
-    "class_table": BIG5_CLS,
-    "class_factor": 5,
-    "state_table": BIG5_ST,
-    "char_len_table": BIG5_CHAR_LEN_TABLE,
-    "name": "Big5",
-}
-
-# CP949
-# fmt: off
-CP949_CLS  = (
-    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,  # 00 - 0f
-    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,  # 10 - 1f
-    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  # 20 - 2f
-    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  # 30 - 3f
-    1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,  # 40 - 4f
-    4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1,  # 50 - 5f
-    1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,  # 60 - 6f
-    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1,  # 70 - 7f
-    0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,  # 80 - 8f
-    6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,  # 90 - 9f
-    6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8,  # a0 - af
-    7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,  # b0 - bf
-    7, 7, 7, 7, 7, 7, 9, 2, 2, 3, 2, 2, 2, 2, 2, 2,  # c0 - cf
-    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,  # d0 - df
-    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,  # e0 - ef
-    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,  # f0 - ff
-)
-
-CP949_ST = (
-#cls=    0      1      2      3      4      5      6      7      8      9  # previous state =
-    MachineState.ERROR,MachineState.START,     3,MachineState.ERROR,MachineState.START,MachineState.START,     4,     5,MachineState.ERROR,     6, # MachineState.START
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, # MachineState.ERROR
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME, # MachineState.ITS_ME
-    MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START, # 3
-    MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START, # 4
-    MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START, # 5
-    MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START, # 6
-)
-# fmt: on
-
-CP949_CHAR_LEN_TABLE = (0, 1, 2, 0, 1, 1, 2, 2, 0, 2)
-
-CP949_SM_MODEL: CodingStateMachineDict = {
-    "class_table": CP949_CLS,
-    "class_factor": 10,
-    "state_table": CP949_ST,
-    "char_len_table": CP949_CHAR_LEN_TABLE,
-    "name": "CP949",
-}
-
-# EUC-JP
-# fmt: off
-EUCJP_CLS = (
-    4, 4, 4, 4, 4, 4, 4, 4,  # 00 - 07
-    4, 4, 4, 4, 4, 4, 5, 5,  # 08 - 0f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 10 - 17
-    4, 4, 4, 5, 4, 4, 4, 4,  # 18 - 1f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 20 - 27
-    4, 4, 4, 4, 4, 4, 4, 4,  # 28 - 2f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 30 - 37
-    4, 4, 4, 4, 4, 4, 4, 4,  # 38 - 3f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 40 - 47
-    4, 4, 4, 4, 4, 4, 4, 4,  # 48 - 4f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 50 - 57
-    4, 4, 4, 4, 4, 4, 4, 4,  # 58 - 5f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 60 - 67
-    4, 4, 4, 4, 4, 4, 4, 4,  # 68 - 6f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 70 - 77
-    4, 4, 4, 4, 4, 4, 4, 4,  # 78 - 7f
-    5, 5, 5, 5, 5, 5, 5, 5,  # 80 - 87
-    5, 5, 5, 5, 5, 5, 1, 3,  # 88 - 8f
-    5, 5, 5, 5, 5, 5, 5, 5,  # 90 - 97
-    5, 5, 5, 5, 5, 5, 5, 5,  # 98 - 9f
-    5, 2, 2, 2, 2, 2, 2, 2,  # a0 - a7
-    2, 2, 2, 2, 2, 2, 2, 2,  # a8 - af
-    2, 2, 2, 2, 2, 2, 2, 2,  # b0 - b7
-    2, 2, 2, 2, 2, 2, 2, 2,  # b8 - bf
-    2, 2, 2, 2, 2, 2, 2, 2,  # c0 - c7
-    2, 2, 2, 2, 2, 2, 2, 2,  # c8 - cf
-    2, 2, 2, 2, 2, 2, 2, 2,  # d0 - d7
-    2, 2, 2, 2, 2, 2, 2, 2,  # d8 - df
-    0, 0, 0, 0, 0, 0, 0, 0,  # e0 - e7
-    0, 0, 0, 0, 0, 0, 0, 0,  # e8 - ef
-    0, 0, 0, 0, 0, 0, 0, 0,  # f0 - f7
-    0, 0, 0, 0, 0, 0, 0, 5  # f8 - ff
-)
-
-EUCJP_ST = (
-          3,     4,     3,     5,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07
-     MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f
-     MachineState.ITS_ME,MachineState.ITS_ME,MachineState.START,MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#10-17
-     MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     3,MachineState.ERROR,#18-1f
-          3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START#20-27
-)
-# fmt: on
-
-EUCJP_CHAR_LEN_TABLE = (2, 2, 2, 3, 1, 0)
-
-EUCJP_SM_MODEL: CodingStateMachineDict = {
-    "class_table": EUCJP_CLS,
-    "class_factor": 6,
-    "state_table": EUCJP_ST,
-    "char_len_table": EUCJP_CHAR_LEN_TABLE,
-    "name": "EUC-JP",
-}
-
-# EUC-KR
-# fmt: off
-EUCKR_CLS  = (
-    1, 1, 1, 1, 1, 1, 1, 1,  # 00 - 07
-    1, 1, 1, 1, 1, 1, 0, 0,  # 08 - 0f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 10 - 17
-    1, 1, 1, 0, 1, 1, 1, 1,  # 18 - 1f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 20 - 27
-    1, 1, 1, 1, 1, 1, 1, 1,  # 28 - 2f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 30 - 37
-    1, 1, 1, 1, 1, 1, 1, 1,  # 38 - 3f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 40 - 47
-    1, 1, 1, 1, 1, 1, 1, 1,  # 48 - 4f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 50 - 57
-    1, 1, 1, 1, 1, 1, 1, 1,  # 58 - 5f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 60 - 67
-    1, 1, 1, 1, 1, 1, 1, 1,  # 68 - 6f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 70 - 77
-    1, 1, 1, 1, 1, 1, 1, 1,  # 78 - 7f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 80 - 87
-    0, 0, 0, 0, 0, 0, 0, 0,  # 88 - 8f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 90 - 97
-    0, 0, 0, 0, 0, 0, 0, 0,  # 98 - 9f
-    0, 2, 2, 2, 2, 2, 2, 2,  # a0 - a7
-    2, 2, 2, 2, 2, 3, 3, 3,  # a8 - af
-    2, 2, 2, 2, 2, 2, 2, 2,  # b0 - b7
-    2, 2, 2, 2, 2, 2, 2, 2,  # b8 - bf
-    2, 2, 2, 2, 2, 2, 2, 2,  # c0 - c7
-    2, 3, 2, 2, 2, 2, 2, 2,  # c8 - cf
-    2, 2, 2, 2, 2, 2, 2, 2,  # d0 - d7
-    2, 2, 2, 2, 2, 2, 2, 2,  # d8 - df
-    2, 2, 2, 2, 2, 2, 2, 2,  # e0 - e7
-    2, 2, 2, 2, 2, 2, 2, 2,  # e8 - ef
-    2, 2, 2, 2, 2, 2, 2, 2,  # f0 - f7
-    2, 2, 2, 2, 2, 2, 2, 0   # f8 - ff
-)
-
-EUCKR_ST = (
-    MachineState.ERROR,MachineState.START,     3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START #08-0f
-)
-# fmt: on
-
-EUCKR_CHAR_LEN_TABLE = (0, 1, 2, 0)
-
-EUCKR_SM_MODEL: CodingStateMachineDict = {
-    "class_table": EUCKR_CLS,
-    "class_factor": 4,
-    "state_table": EUCKR_ST,
-    "char_len_table": EUCKR_CHAR_LEN_TABLE,
-    "name": "EUC-KR",
-}
-
-# JOHAB
-# fmt: off
-JOHAB_CLS = (
-    4,4,4,4,4,4,4,4,  # 00 - 07
-    4,4,4,4,4,4,0,0,  # 08 - 0f
-    4,4,4,4,4,4,4,4,  # 10 - 17
-    4,4,4,0,4,4,4,4,  # 18 - 1f
-    4,4,4,4,4,4,4,4,  # 20 - 27
-    4,4,4,4,4,4,4,4,  # 28 - 2f
-    4,3,3,3,3,3,3,3,  # 30 - 37
-    3,3,3,3,3,3,3,3,  # 38 - 3f
-    3,1,1,1,1,1,1,1,  # 40 - 47
-    1,1,1,1,1,1,1,1,  # 48 - 4f
-    1,1,1,1,1,1,1,1,  # 50 - 57
-    1,1,1,1,1,1,1,1,  # 58 - 5f
-    1,1,1,1,1,1,1,1,  # 60 - 67
-    1,1,1,1,1,1,1,1,  # 68 - 6f
-    1,1,1,1,1,1,1,1,  # 70 - 77
-    1,1,1,1,1,1,1,2,  # 78 - 7f
-    6,6,6,6,8,8,8,8,  # 80 - 87
-    8,8,8,8,8,8,8,8,  # 88 - 8f
-    8,7,7,7,7,7,7,7,  # 90 - 97
-    7,7,7,7,7,7,7,7,  # 98 - 9f
-    7,7,7,7,7,7,7,7,  # a0 - a7
-    7,7,7,7,7,7,7,7,  # a8 - af
-    7,7,7,7,7,7,7,7,  # b0 - b7
-    7,7,7,7,7,7,7,7,  # b8 - bf
-    7,7,7,7,7,7,7,7,  # c0 - c7
-    7,7,7,7,7,7,7,7,  # c8 - cf
-    7,7,7,7,5,5,5,5,  # d0 - d7
-    5,9,9,9,9,9,9,5,  # d8 - df
-    9,9,9,9,9,9,9,9,  # e0 - e7
-    9,9,9,9,9,9,9,9,  # e8 - ef
-    9,9,9,9,9,9,9,9,  # f0 - f7
-    9,9,5,5,5,5,5,0   # f8 - ff
-)
-
-JOHAB_ST = (
-# cls = 0                   1                   2                   3                   4                   5                   6                   7                   8                   9
-    MachineState.ERROR ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.ERROR ,MachineState.ERROR ,3                  ,3                  ,4                  ,  # MachineState.START
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,  # MachineState.ITS_ME
-    MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,  # MachineState.ERROR
-    MachineState.ERROR ,MachineState.START ,MachineState.START ,MachineState.ERROR ,MachineState.ERROR ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.START ,  # 3
-    MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,  # 4
-)
-# fmt: on
-
-JOHAB_CHAR_LEN_TABLE = (0, 1, 1, 1, 1, 0, 0, 2, 2, 2)
-
-JOHAB_SM_MODEL: CodingStateMachineDict = {
-    "class_table": JOHAB_CLS,
-    "class_factor": 10,
-    "state_table": JOHAB_ST,
-    "char_len_table": JOHAB_CHAR_LEN_TABLE,
-    "name": "Johab",
-}
-
-# EUC-TW
-# fmt: off
-EUCTW_CLS = (
-    2, 2, 2, 2, 2, 2, 2, 2,  # 00 - 07
-    2, 2, 2, 2, 2, 2, 0, 0,  # 08 - 0f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 10 - 17
-    2, 2, 2, 0, 2, 2, 2, 2,  # 18 - 1f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 20 - 27
-    2, 2, 2, 2, 2, 2, 2, 2,  # 28 - 2f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 30 - 37
-    2, 2, 2, 2, 2, 2, 2, 2,  # 38 - 3f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 40 - 47
-    2, 2, 2, 2, 2, 2, 2, 2,  # 48 - 4f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 50 - 57
-    2, 2, 2, 2, 2, 2, 2, 2,  # 58 - 5f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 60 - 67
-    2, 2, 2, 2, 2, 2, 2, 2,  # 68 - 6f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 70 - 77
-    2, 2, 2, 2, 2, 2, 2, 2,  # 78 - 7f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 80 - 87
-    0, 0, 0, 0, 0, 0, 6, 0,  # 88 - 8f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 90 - 97
-    0, 0, 0, 0, 0, 0, 0, 0,  # 98 - 9f
-    0, 3, 4, 4, 4, 4, 4, 4,  # a0 - a7
-    5, 5, 1, 1, 1, 1, 1, 1,  # a8 - af
-    1, 1, 1, 1, 1, 1, 1, 1,  # b0 - b7
-    1, 1, 1, 1, 1, 1, 1, 1,  # b8 - bf
-    1, 1, 3, 1, 3, 3, 3, 3,  # c0 - c7
-    3, 3, 3, 3, 3, 3, 3, 3,  # c8 - cf
-    3, 3, 3, 3, 3, 3, 3, 3,  # d0 - d7
-    3, 3, 3, 3, 3, 3, 3, 3,  # d8 - df
-    3, 3, 3, 3, 3, 3, 3, 3,  # e0 - e7
-    3, 3, 3, 3, 3, 3, 3, 3,  # e8 - ef
-    3, 3, 3, 3, 3, 3, 3, 3,  # f0 - f7
-    3, 3, 3, 3, 3, 3, 3, 0   # f8 - ff
-)
-
-EUCTW_ST = (
-    MachineState.ERROR,MachineState.ERROR,MachineState.START,     3,     3,     3,     4,MachineState.ERROR,#00-07
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.START,MachineState.ERROR,#10-17
-    MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#18-1f
-         5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.START,MachineState.START,#20-27
-    MachineState.START,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START #28-2f
-)
-# fmt: on
-
-EUCTW_CHAR_LEN_TABLE = (0, 0, 1, 2, 2, 2, 3)
-
-EUCTW_SM_MODEL: CodingStateMachineDict = {
-    "class_table": EUCTW_CLS,
-    "class_factor": 7,
-    "state_table": EUCTW_ST,
-    "char_len_table": EUCTW_CHAR_LEN_TABLE,
-    "name": "x-euc-tw",
-}
-
-# GB2312
-# fmt: off
-GB2312_CLS = (
-    1, 1, 1, 1, 1, 1, 1, 1,  # 00 - 07
-    1, 1, 1, 1, 1, 1, 0, 0,  # 08 - 0f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 10 - 17
-    1, 1, 1, 0, 1, 1, 1, 1,  # 18 - 1f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 20 - 27
-    1, 1, 1, 1, 1, 1, 1, 1,  # 28 - 2f
-    3, 3, 3, 3, 3, 3, 3, 3,  # 30 - 37
-    3, 3, 1, 1, 1, 1, 1, 1,  # 38 - 3f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 40 - 47
-    2, 2, 2, 2, 2, 2, 2, 2,  # 48 - 4f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 50 - 57
-    2, 2, 2, 2, 2, 2, 2, 2,  # 58 - 5f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 60 - 67
-    2, 2, 2, 2, 2, 2, 2, 2,  # 68 - 6f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 70 - 77
-    2, 2, 2, 2, 2, 2, 2, 4,  # 78 - 7f
-    5, 6, 6, 6, 6, 6, 6, 6,  # 80 - 87
-    6, 6, 6, 6, 6, 6, 6, 6,  # 88 - 8f
-    6, 6, 6, 6, 6, 6, 6, 6,  # 90 - 97
-    6, 6, 6, 6, 6, 6, 6, 6,  # 98 - 9f
-    6, 6, 6, 6, 6, 6, 6, 6,  # a0 - a7
-    6, 6, 6, 6, 6, 6, 6, 6,  # a8 - af
-    6, 6, 6, 6, 6, 6, 6, 6,  # b0 - b7
-    6, 6, 6, 6, 6, 6, 6, 6,  # b8 - bf
-    6, 6, 6, 6, 6, 6, 6, 6,  # c0 - c7
-    6, 6, 6, 6, 6, 6, 6, 6,  # c8 - cf
-    6, 6, 6, 6, 6, 6, 6, 6,  # d0 - d7
-    6, 6, 6, 6, 6, 6, 6, 6,  # d8 - df
-    6, 6, 6, 6, 6, 6, 6, 6,  # e0 - e7
-    6, 6, 6, 6, 6, 6, 6, 6,  # e8 - ef
-    6, 6, 6, 6, 6, 6, 6, 6,  # f0 - f7
-    6, 6, 6, 6, 6, 6, 6, 0   # f8 - ff
-)
-
-GB2312_ST = (
-    MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,     3,MachineState.ERROR,#00-07
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,#10-17
-         4,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#18-1f
-    MachineState.ERROR,MachineState.ERROR,     5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,#20-27
-    MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START #28-2f
-)
-# fmt: on
-
-# To be accurate, the length of class 6 can be either 2 or 4.
-# But it is not necessary to discriminate between the two since
-# it is used for frequency analysis only, and we are validating
-# each code range there as well. So it is safe to set it to be
-# 2 here.
-GB2312_CHAR_LEN_TABLE = (0, 1, 1, 1, 1, 1, 2)
-
-GB2312_SM_MODEL: CodingStateMachineDict = {
-    "class_table": GB2312_CLS,
-    "class_factor": 7,
-    "state_table": GB2312_ST,
-    "char_len_table": GB2312_CHAR_LEN_TABLE,
-    "name": "GB2312",
-}
-
-# Shift_JIS
-# fmt: off
-SJIS_CLS = (
-    1, 1, 1, 1, 1, 1, 1, 1,  # 00 - 07
-    1, 1, 1, 1, 1, 1, 0, 0,  # 08 - 0f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 10 - 17
-    1, 1, 1, 0, 1, 1, 1, 1,  # 18 - 1f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 20 - 27
-    1, 1, 1, 1, 1, 1, 1, 1,  # 28 - 2f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 30 - 37
-    1, 1, 1, 1, 1, 1, 1, 1,  # 38 - 3f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 40 - 47
-    2, 2, 2, 2, 2, 2, 2, 2,  # 48 - 4f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 50 - 57
-    2, 2, 2, 2, 2, 2, 2, 2,  # 58 - 5f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 60 - 67
-    2, 2, 2, 2, 2, 2, 2, 2,  # 68 - 6f
-    2, 2, 2, 2, 2, 2, 2, 2,  # 70 - 77
-    2, 2, 2, 2, 2, 2, 2, 1,  # 78 - 7f
-    3, 3, 3, 3, 3, 2, 2, 3,  # 80 - 87
-    3, 3, 3, 3, 3, 3, 3, 3,  # 88 - 8f
-    3, 3, 3, 3, 3, 3, 3, 3,  # 90 - 97
-    3, 3, 3, 3, 3, 3, 3, 3,  # 98 - 9f
-    #0xa0 is illegal in sjis encoding, but some pages does
-    #contain such byte. We need to be more error forgiven.
-    2, 2, 2, 2, 2, 2, 2, 2,  # a0 - a7
-    2, 2, 2, 2, 2, 2, 2, 2,  # a8 - af
-    2, 2, 2, 2, 2, 2, 2, 2,  # b0 - b7
-    2, 2, 2, 2, 2, 2, 2, 2,  # b8 - bf
-    2, 2, 2, 2, 2, 2, 2, 2,  # c0 - c7
-    2, 2, 2, 2, 2, 2, 2, 2,  # c8 - cf
-    2, 2, 2, 2, 2, 2, 2, 2,  # d0 - d7
-    2, 2, 2, 2, 2, 2, 2, 2,  # d8 - df
-    3, 3, 3, 3, 3, 3, 3, 3,  # e0 - e7
-    3, 3, 3, 3, 3, 4, 4, 4,  # e8 - ef
-    3, 3, 3, 3, 3, 3, 3, 3,  # f0 - f7
-    3, 3, 3, 3, 3, 0, 0, 0,  # f8 - ff
-)
-
-SJIS_ST = (
-    MachineState.ERROR,MachineState.START,MachineState.START,     3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START #10-17
-)
-# fmt: on
-
-SJIS_CHAR_LEN_TABLE = (0, 1, 1, 2, 0, 0)
-
-SJIS_SM_MODEL: CodingStateMachineDict = {
-    "class_table": SJIS_CLS,
-    "class_factor": 6,
-    "state_table": SJIS_ST,
-    "char_len_table": SJIS_CHAR_LEN_TABLE,
-    "name": "Shift_JIS",
-}
-
-# UCS2-BE
-# fmt: off
-UCS2BE_CLS = (
-    0, 0, 0, 0, 0, 0, 0, 0,  # 00 - 07
-    0, 0, 1, 0, 0, 2, 0, 0,  # 08 - 0f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 10 - 17
-    0, 0, 0, 3, 0, 0, 0, 0,  # 18 - 1f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 20 - 27
-    0, 3, 3, 3, 3, 3, 0, 0,  # 28 - 2f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 30 - 37
-    0, 0, 0, 0, 0, 0, 0, 0,  # 38 - 3f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 40 - 47
-    0, 0, 0, 0, 0, 0, 0, 0,  # 48 - 4f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 50 - 57
-    0, 0, 0, 0, 0, 0, 0, 0,  # 58 - 5f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 60 - 67
-    0, 0, 0, 0, 0, 0, 0, 0,  # 68 - 6f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 70 - 77
-    0, 0, 0, 0, 0, 0, 0, 0,  # 78 - 7f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 80 - 87
-    0, 0, 0, 0, 0, 0, 0, 0,  # 88 - 8f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 90 - 97
-    0, 0, 0, 0, 0, 0, 0, 0,  # 98 - 9f
-    0, 0, 0, 0, 0, 0, 0, 0,  # a0 - a7
-    0, 0, 0, 0, 0, 0, 0, 0,  # a8 - af
-    0, 0, 0, 0, 0, 0, 0, 0,  # b0 - b7
-    0, 0, 0, 0, 0, 0, 0, 0,  # b8 - bf
-    0, 0, 0, 0, 0, 0, 0, 0,  # c0 - c7
-    0, 0, 0, 0, 0, 0, 0, 0,  # c8 - cf
-    0, 0, 0, 0, 0, 0, 0, 0,  # d0 - d7
-    0, 0, 0, 0, 0, 0, 0, 0,  # d8 - df
-    0, 0, 0, 0, 0, 0, 0, 0,  # e0 - e7
-    0, 0, 0, 0, 0, 0, 0, 0,  # e8 - ef
-    0, 0, 0, 0, 0, 0, 0, 0,  # f0 - f7
-    0, 0, 0, 0, 0, 0, 4, 5   # f8 - ff
-)
-
-UCS2BE_ST  = (
-          5,     7,     7,MachineState.ERROR,     4,     3,MachineState.ERROR,MachineState.ERROR,#00-07
-     MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f
-     MachineState.ITS_ME,MachineState.ITS_ME,     6,     6,     6,     6,MachineState.ERROR,MachineState.ERROR,#10-17
-          6,     6,     6,     6,     6,MachineState.ITS_ME,     6,     6,#18-1f
-          6,     6,     6,     6,     5,     7,     7,MachineState.ERROR,#20-27
-          5,     8,     6,     6,MachineState.ERROR,     6,     6,     6,#28-2f
-          6,     6,     6,     6,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START #30-37
-)
-# fmt: on
-
-UCS2BE_CHAR_LEN_TABLE = (2, 2, 2, 0, 2, 2)
-
-UCS2BE_SM_MODEL: CodingStateMachineDict = {
-    "class_table": UCS2BE_CLS,
-    "class_factor": 6,
-    "state_table": UCS2BE_ST,
-    "char_len_table": UCS2BE_CHAR_LEN_TABLE,
-    "name": "UTF-16BE",
-}
-
-# UCS2-LE
-# fmt: off
-UCS2LE_CLS = (
-    0, 0, 0, 0, 0, 0, 0, 0,  # 00 - 07
-    0, 0, 1, 0, 0, 2, 0, 0,  # 08 - 0f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 10 - 17
-    0, 0, 0, 3, 0, 0, 0, 0,  # 18 - 1f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 20 - 27
-    0, 3, 3, 3, 3, 3, 0, 0,  # 28 - 2f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 30 - 37
-    0, 0, 0, 0, 0, 0, 0, 0,  # 38 - 3f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 40 - 47
-    0, 0, 0, 0, 0, 0, 0, 0,  # 48 - 4f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 50 - 57
-    0, 0, 0, 0, 0, 0, 0, 0,  # 58 - 5f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 60 - 67
-    0, 0, 0, 0, 0, 0, 0, 0,  # 68 - 6f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 70 - 77
-    0, 0, 0, 0, 0, 0, 0, 0,  # 78 - 7f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 80 - 87
-    0, 0, 0, 0, 0, 0, 0, 0,  # 88 - 8f
-    0, 0, 0, 0, 0, 0, 0, 0,  # 90 - 97
-    0, 0, 0, 0, 0, 0, 0, 0,  # 98 - 9f
-    0, 0, 0, 0, 0, 0, 0, 0,  # a0 - a7
-    0, 0, 0, 0, 0, 0, 0, 0,  # a8 - af
-    0, 0, 0, 0, 0, 0, 0, 0,  # b0 - b7
-    0, 0, 0, 0, 0, 0, 0, 0,  # b8 - bf
-    0, 0, 0, 0, 0, 0, 0, 0,  # c0 - c7
-    0, 0, 0, 0, 0, 0, 0, 0,  # c8 - cf
-    0, 0, 0, 0, 0, 0, 0, 0,  # d0 - d7
-    0, 0, 0, 0, 0, 0, 0, 0,  # d8 - df
-    0, 0, 0, 0, 0, 0, 0, 0,  # e0 - e7
-    0, 0, 0, 0, 0, 0, 0, 0,  # e8 - ef
-    0, 0, 0, 0, 0, 0, 0, 0,  # f0 - f7
-    0, 0, 0, 0, 0, 0, 4, 5   # f8 - ff
-)
-
-UCS2LE_ST = (
-          6,     6,     7,     6,     4,     3,MachineState.ERROR,MachineState.ERROR,#00-07
-     MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f
-     MachineState.ITS_ME,MachineState.ITS_ME,     5,     5,     5,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,#10-17
-          5,     5,     5,MachineState.ERROR,     5,MachineState.ERROR,     6,     6,#18-1f
-          7,     6,     8,     8,     5,     5,     5,MachineState.ERROR,#20-27
-          5,     5,     5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     5,     5,#28-2f
-          5,     5,     5,MachineState.ERROR,     5,MachineState.ERROR,MachineState.START,MachineState.START #30-37
-)
-# fmt: on
-
-UCS2LE_CHAR_LEN_TABLE = (2, 2, 2, 2, 2, 2)
-
-UCS2LE_SM_MODEL: CodingStateMachineDict = {
-    "class_table": UCS2LE_CLS,
-    "class_factor": 6,
-    "state_table": UCS2LE_ST,
-    "char_len_table": UCS2LE_CHAR_LEN_TABLE,
-    "name": "UTF-16LE",
-}
-
-# UTF-8
-# fmt: off
-UTF8_CLS = (
-    1, 1, 1, 1, 1, 1, 1, 1,  # 00 - 07  #allow 0x00 as a legal value
-    1, 1, 1, 1, 1, 1, 0, 0,  # 08 - 0f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 10 - 17
-    1, 1, 1, 0, 1, 1, 1, 1,  # 18 - 1f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 20 - 27
-    1, 1, 1, 1, 1, 1, 1, 1,  # 28 - 2f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 30 - 37
-    1, 1, 1, 1, 1, 1, 1, 1,  # 38 - 3f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 40 - 47
-    1, 1, 1, 1, 1, 1, 1, 1,  # 48 - 4f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 50 - 57
-    1, 1, 1, 1, 1, 1, 1, 1,  # 58 - 5f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 60 - 67
-    1, 1, 1, 1, 1, 1, 1, 1,  # 68 - 6f
-    1, 1, 1, 1, 1, 1, 1, 1,  # 70 - 77
-    1, 1, 1, 1, 1, 1, 1, 1,  # 78 - 7f
-    2, 2, 2, 2, 3, 3, 3, 3,  # 80 - 87
-    4, 4, 4, 4, 4, 4, 4, 4,  # 88 - 8f
-    4, 4, 4, 4, 4, 4, 4, 4,  # 90 - 97
-    4, 4, 4, 4, 4, 4, 4, 4,  # 98 - 9f
-    5, 5, 5, 5, 5, 5, 5, 5,  # a0 - a7
-    5, 5, 5, 5, 5, 5, 5, 5,  # a8 - af
-    5, 5, 5, 5, 5, 5, 5, 5,  # b0 - b7
-    5, 5, 5, 5, 5, 5, 5, 5,  # b8 - bf
-    0, 0, 6, 6, 6, 6, 6, 6,  # c0 - c7
-    6, 6, 6, 6, 6, 6, 6, 6,  # c8 - cf
-    6, 6, 6, 6, 6, 6, 6, 6,  # d0 - d7
-    6, 6, 6, 6, 6, 6, 6, 6,  # d8 - df
-    7, 8, 8, 8, 8, 8, 8, 8,  # e0 - e7
-    8, 8, 8, 8, 8, 9, 8, 8,  # e8 - ef
-    10, 11, 11, 11, 11, 11, 11, 11,  # f0 - f7
-    12, 13, 13, 13, 14, 15, 0, 0    # f8 - ff
-)
-
-UTF8_ST = (
-    MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     12,   10,#00-07
-         9,     11,     8,     7,     6,     5,     4,    3,#08-0f
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#10-17
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#18-1f
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#20-27
-    MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#28-2f
-    MachineState.ERROR,MachineState.ERROR,     5,     5,     5,     5,MachineState.ERROR,MachineState.ERROR,#30-37
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#38-3f
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     5,     5,     5,MachineState.ERROR,MachineState.ERROR,#40-47
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#48-4f
-    MachineState.ERROR,MachineState.ERROR,     7,     7,     7,     7,MachineState.ERROR,MachineState.ERROR,#50-57
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#58-5f
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     7,     7,MachineState.ERROR,MachineState.ERROR,#60-67
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#68-6f
-    MachineState.ERROR,MachineState.ERROR,     9,     9,     9,     9,MachineState.ERROR,MachineState.ERROR,#70-77
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#78-7f
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,     9,MachineState.ERROR,MachineState.ERROR,#80-87
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#88-8f
-    MachineState.ERROR,MachineState.ERROR,    12,    12,    12,    12,MachineState.ERROR,MachineState.ERROR,#90-97
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#98-9f
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,    12,MachineState.ERROR,MachineState.ERROR,#a0-a7
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#a8-af
-    MachineState.ERROR,MachineState.ERROR,    12,    12,    12,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#b0-b7
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#b8-bf
-    MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,#c0-c7
-    MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR #c8-cf
-)
-# fmt: on
-
-UTF8_CHAR_LEN_TABLE = (0, 1, 0, 0, 0, 0, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6)
-
-UTF8_SM_MODEL: CodingStateMachineDict = {
-    "class_table": UTF8_CLS,
-    "class_factor": 16,
-    "state_table": UTF8_ST,
-    "char_len_table": UTF8_CHAR_LEN_TABLE,
-    "name": "UTF-8",
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/intranges.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/intranges.py
deleted file mode 100644
index 6a43b0475347cb50d0d65ada1000a82eeca9e882..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/intranges.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-Given a list of integers, made up of (hopefully) a small number of long runs
-of consecutive integers, compute a representation of the form
-((start1, end1), (start2, end2) ...). Then answer the question "was x present
-in the original list?" in time O(log(# runs)).
-"""
-
-import bisect
-from typing import List, Tuple
-
-def intranges_from_list(list_: List[int]) -> Tuple[int, ...]:
-    """Represent a list of integers as a sequence of ranges:
-    ((start_0, end_0), (start_1, end_1), ...), such that the original
-    integers are exactly those x such that start_i <= x < end_i for some i.
-
-    Ranges are encoded as single integers (start << 32 | end), not as tuples.
-    """
-
-    sorted_list = sorted(list_)
-    ranges = []
-    last_write = -1
-    for i in range(len(sorted_list)):
-        if i+1 < len(sorted_list):
-            if sorted_list[i] == sorted_list[i+1]-1:
-                continue
-        current_range = sorted_list[last_write+1:i+1]
-        ranges.append(_encode_range(current_range[0], current_range[-1] + 1))
-        last_write = i
-
-    return tuple(ranges)
-
-def _encode_range(start: int, end: int) -> int:
-    return (start << 32) | end
-
-def _decode_range(r: int) -> Tuple[int, int]:
-    return (r >> 32), (r & ((1 << 32) - 1))
-
-
-def intranges_contain(int_: int, ranges: Tuple[int, ...]) -> bool:
-    """Determine if `int_` falls into one of the ranges in `ranges`."""
-    tuple_ = _encode_range(int_, 0)
-    pos = bisect.bisect_left(ranges, tuple_)
-    # we could be immediately ahead of a tuple (start, end)
-    # with start < int_ <= end
-    if pos > 0:
-        left, right = _decode_range(ranges[pos-1])
-        if left <= int_ < right:
-            return True
-    # or we could be immediately behind a tuple (int_, end)
-    if pos < len(ranges):
-        left, _ = _decode_range(ranges[pos])
-        if left == int_:
-            return True
-    return False
diff --git a/spaces/TeraTTS/TTS/tokenizer/__init__.py b/spaces/TeraTTS/TTS/tokenizer/__init__.py
deleted file mode 100644
index e14b20ca03e4622f5206c3cf8611b56c7227cdeb..0000000000000000000000000000000000000000
--- a/spaces/TeraTTS/TTS/tokenizer/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .gruut import Tokenizer as TokenizerGRUUT
-from .g2p import Tokenizer as TokenizerG2P
\ No newline at end of file
diff --git a/spaces/Tetel/secondbing/EdgeGPT/EdgeGPT.py b/spaces/Tetel/secondbing/EdgeGPT/EdgeGPT.py
deleted file mode 100644
index d6f64ff075bdfc4aa618b307ca7e0a21f6296292..0000000000000000000000000000000000000000
--- a/spaces/Tetel/secondbing/EdgeGPT/EdgeGPT.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""
-Main.py
-"""
-from __future__ import annotations
-
-import json
-from pathlib import Path
-from typing import Generator
-
-from .chathub import *
-from .conversation import *
-from .conversation_style import *
-from .request import *
-from .utilities import *
-
-
-class Chatbot:
-    """
-    Combines everything to make it seamless
-    """
-
-    def __init__(
-        self,
-        proxy: str | None = None,
-        cookies: list[dict] | None = None,
-    ) -> None:
-        self.proxy: str | None = proxy
-        self.chat_hub: ChatHub = ChatHub(
-            Conversation(self.proxy, cookies=cookies),
-            proxy=self.proxy,
-            cookies=cookies,
-        )
-
-    @staticmethod
-    async def create(
-        proxy: str | None = None,
-        cookies: list[dict] | None = None,
-        imageInput: str | None = None
-    ) -> Chatbot:
-        self = Chatbot.__new__(Chatbot)
-        self.proxy = proxy
-        self.chat_hub = ChatHub(
-            await Conversation.create(self.proxy, cookies=cookies, imageInput=imageInput),
-            proxy=self.proxy,
-            cookies=cookies,
-        )
-        return self
-
-    async def save_conversation(self, filename: str) -> None:
-        """
-        Save the conversation to a file
-        """
-        with open(filename, "w") as f:
-            conversation_id = self.chat_hub.request.conversation_id
-            conversation_signature = self.chat_hub.request.conversation_signature
-            client_id = self.chat_hub.request.client_id
-            invocation_id = self.chat_hub.request.invocation_id
-            f.write(
-                json.dumps(
-                    {
-                        "conversation_id": conversation_id,
-                        "conversation_signature": conversation_signature,
-                        "client_id": client_id,
-                        "invocation_id": invocation_id,
-                    },
-                ),
-            )
-
-    async def load_conversation(self, filename: str) -> None:
-        """
-        Load the conversation from a file
-        """
-        with open(filename) as f:
-            conversation = json.load(f)
-            self.chat_hub.request = ChatHubRequest(
-                conversation_signature=conversation["conversation_signature"],
-                client_id=conversation["client_id"],
-                conversation_id=conversation["conversation_id"],
-                invocation_id=conversation["invocation_id"],
-            )
-
-    async def get_conversation(self) -> dict:
-        """
-        Gets the conversation history from conversation_id (requires load_conversation)
-        """
-        return await self.chat_hub.get_conversation()
-
-    async def get_activity(self) -> dict:
-        """
-        Gets the recent activity (requires cookies)
-        """
-        return await self.chat_hub.get_activity()
-
-    async def ask(
-        self,
-        prompt: str,
-        wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        webpage_context: str | None = None,
-        search_result: bool = False,
-        locale: str = guess_locale(),
-        simplify_response: bool = False,
-    ) -> dict:
-        """
-        Ask a question to the bot
-        Response:
-            {
-                item (dict):
-                    messages (list[dict]):
-                        adaptiveCards (list[dict]):
-                            body (list[dict]):
-                                text (str): Response
-            }
-        To get the response, you can do:
-            response["item"]["messages"][1]["adaptiveCards"][0]["body"][0]["text"]
-        """
-        async for final, response in self.chat_hub.ask_stream(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            wss_link=wss_link,
-            webpage_context=webpage_context,
-            search_result=search_result,
-            locale=locale,
-        ):
-            if final:
-                if not simplify_response:
-                    return response
-                messages_left = response["item"]["throttling"][
-                    "maxNumUserMessagesInConversation"
-                ] - response["item"]["throttling"].get(
-                    "numUserMessagesInConversation", 0
-                )
-                if messages_left == 0:
-                    raise Exception("Max messages reached")
-                for msg in reversed(response["item"]["messages"]):
-                    if msg.get("adaptiveCards") and msg["adaptiveCards"][0]["body"][
-                        0
-                    ].get("text"):
-                        message = msg
-                        break
-                if not message:
-                    raise Exception("No message found")
-                suggestions = [
-                    suggestion["text"]
-                    for suggestion in message.get("suggestedResponses", [])
-                ]
-                adaptive_cards = message.get("adaptiveCards", [])
-                adaptive_text = (
-                    adaptive_cards[0]["body"][0].get("text") if adaptive_cards else None
-                )
-                sources = (
-                    adaptive_cards[0]["body"][0].get("text") if adaptive_cards else None
-                )
-                sources_text = (
-                    adaptive_cards[0]["body"][-1].get("text")
-                    if adaptive_cards
-                    else None
-                )
-                return {
-                    "text": message["text"],
-                    "author": message["author"],
-                    "sources": sources,
-                    "sources_text": sources_text,
-                    "suggestions": suggestions,
-                    "messages_left": messages_left,
-                    "max_messages": response["item"]["throttling"][
-                        "maxNumUserMessagesInConversation"
-                    ],
-                    "adaptive_text": adaptive_text,
-                }
-        return {}
-
-    async def ask_stream(
-        self,
-        prompt: str,
-        wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        raw: bool = False,
-        webpage_context: str | None = None,
-        search_result: bool = False,
-        locale: str = guess_locale(),
-    ) -> Generator[bool, dict | str, None]:
-        """
-        Ask a question to the bot
-        """
-        async for response in self.chat_hub.ask_stream(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            wss_link=wss_link,
-            raw=raw,
-            webpage_context=webpage_context,
-            search_result=search_result,
-            locale=locale,
-        ):
-            yield response
-
-    async def close(self) -> None:
-        """
-        Close the connection
-        """
-        await self.chat_hub.close()
-
-    async def delete_conversation(
-        self,
-        conversation_id: str = None,
-        conversation_signature: str = None,
-        client_id: str = None,
-    ) -> None:
-        """
-        Delete the chat in the server
-        """
-        await self.chat_hub.delete_conversation(
-            conversation_id=conversation_id,
-            conversation_signature=conversation_signature,
-            client_id=client_id,
-        )
-
-    async def reset(self, delete=False) -> None:
-        """
-        Reset the conversation
-        """
-        if delete:
-            await self.remove_and_close()
-        else:
-            await self.close()
-        self.chat_hub = ChatHub(
-            await Conversation.create(self.proxy, cookies=self.chat_hub.cookies),
-            proxy=self.proxy,
-            cookies=self.chat_hub.cookies,
-        )
-
-
-if __name__ == "__main__":
-    from .main import main
-
-    main()
diff --git a/spaces/ThirdEyeData/Semantic-Search-Transformer/README.md b/spaces/ThirdEyeData/Semantic-Search-Transformer/README.md
deleted file mode 100644
index eb30e2d55abc1e7dff9d3c44afffbba4677e7f1a..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Semantic-Search-Transformer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Semantic Search Transformer
-emoji: ⚡
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/js/upload.js b/spaces/UserXTheUnknown/stablediffusion-infinity/js/upload.js
deleted file mode 100644
index 4842960af4985847ff24c93c7f730e8e64974690..0000000000000000000000000000000000000000
--- a/spaces/UserXTheUnknown/stablediffusion-infinity/js/upload.js
+++ /dev/null
@@ -1,19 +0,0 @@
-function(a,b){
-    if(!window.my_observe_upload)
-    {
-        console.log("setup upload here");
-        window.my_observe_upload = new MutationObserver(function (event) {
-            console.log(event);
-            var frame=document.querySelector("gradio-app").shadowRoot.querySelector("#sdinfframe").contentWindow.document;
-            frame.querySelector("#upload").click();
-        });
-        window.my_observe_upload_target = document.querySelector("gradio-app").shadowRoot.querySelector("#upload span");
-        window.my_observe_upload.observe(window.my_observe_upload_target, {
-            attributes: false, 
-            subtree: true,
-            childList: true, 
-            characterData: true
-        });
-    }
-    return [a,b];
-}
\ No newline at end of file
diff --git a/spaces/WhyLIM/ChatGPT-academic/crazy_functions/test_project/cpp/cppipc/queue.h b/spaces/WhyLIM/ChatGPT-academic/crazy_functions/test_project/cpp/cppipc/queue.h
deleted file mode 100644
index a21f3446e06b5826af7b554c8a7d9c5d80848b62..0000000000000000000000000000000000000000
--- a/spaces/WhyLIM/ChatGPT-academic/crazy_functions/test_project/cpp/cppipc/queue.h
+++ /dev/null
@@ -1,216 +0,0 @@
-#pragma once
-
-#include 
-#include 
-#include   // [[since C++14]]: std::exchange
-#include 
-#include 
-#include 
-#include 
-#include 
-#include 
-#include   // assert
-
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/rw_lock.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-
-namespace ipc {
-namespace detail {
-
-class queue_conn {
-protected:
-    circ::cc_t connected_ = 0;
-    shm::handle elems_h_;
-
-    template 
-    Elems* open(char const * name) {
-        if (name == nullptr || name[0] == '\0') {
-            ipc::error("fail open waiter: name is empty!\n");
-            return nullptr;
-        }
-        if (!elems_h_.acquire(name, sizeof(Elems))) {
-            return nullptr;
-        }
-        auto elems = static_cast(elems_h_.get());
-        if (elems == nullptr) {
-            ipc::error("fail acquire elems: %s\n", name);
-            return nullptr;
-        }
-        elems->init();
-        return elems;
-    }
-
-    void close() {
-        elems_h_.release();
-    }
-
-public:
-    queue_conn() = default;
-    queue_conn(const queue_conn&) = delete;
-    queue_conn& operator=(const queue_conn&) = delete;
-
-    bool connected() const noexcept {
-        return connected_ != 0;
-    }
-
-    circ::cc_t connected_id() const noexcept {
-        return connected_;
-    }
-
-    template 
-    auto connect(Elems* elems) noexcept
-                         /*needs 'optional' here*/
-     -> std::tuple().cursor())> {
-        if (elems == nullptr) return {};
-        // if it's already connected, just return
-        if (connected()) return {connected(), false, 0};
-        connected_ = elems->connect_receiver();
-        return {connected(), true, elems->cursor()};
-    }
-
-    template 
-    bool disconnect(Elems* elems) noexcept {
-        if (elems == nullptr) return false;
-        // if it's already disconnected, just return false
-        if (!connected()) return false;
-        elems->disconnect_receiver(std::exchange(connected_, 0));
-        return true;
-    }
-};
-
-template 
-class queue_base : public queue_conn {
-    using base_t = queue_conn;
-
-public:
-    using elems_t  = Elems;
-    using policy_t = typename elems_t::policy_t;
-
-protected:
-    elems_t * elems_ = nullptr;
-    decltype(std::declval().cursor()) cursor_ = 0;
-    bool sender_flag_ = false;
-
-public:
-    using base_t::base_t;
-
-    queue_base() = default;
-
-    explicit queue_base(char const * name)
-        : queue_base{} {
-        elems_ = open(name);
-    }
-
-    explicit queue_base(elems_t * elems) noexcept
-        : queue_base{} {
-        assert(elems != nullptr);
-        elems_ = elems;
-    }
-
-    /* not virtual */ ~queue_base() {
-        base_t::close();
-    }
-
-    elems_t       * elems()       noexcept { return elems_; }
-    elems_t const * elems() const noexcept { return elems_; }
-
-    bool ready_sending() noexcept {
-        if (elems_ == nullptr) return false;
-        return sender_flag_ || (sender_flag_ = elems_->connect_sender());
-    }
-
-    void shut_sending() noexcept {
-        if (elems_ == nullptr) return;
-        if (!sender_flag_) return;
-        elems_->disconnect_sender();
-    }
-
-    bool connect() noexcept {
-        auto tp = base_t::connect(elems_);
-        if (std::get<0>(tp) && std::get<1>(tp)) {
-            cursor_ = std::get<2>(tp);
-            return true;
-        }
-        return std::get<0>(tp);
-    }
-
-    bool disconnect() noexcept {
-        return base_t::disconnect(elems_);
-    }
-
-    std::size_t conn_count() const noexcept {
-        return (elems_ == nullptr) ? static_cast(invalid_value) : elems_->conn_count();
-    }
-
-    bool valid() const noexcept {
-        return elems_ != nullptr;
-    }
-
-    bool empty() const noexcept {
-        return !valid() || (cursor_ == elems_->cursor());
-    }
-
-    template 
-    bool push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward
      (params)...);
-        });
-    }
-
-    template 
-    bool force_push(F&& prep, P&&... params) {
-        if (elems_ == nullptr) return false;
-        return elems_->force_push(this, [&](void* p) {
-            if (prep(p)) ::new (p) T(std::forward
      (params)...);
-        });
-    }
-
-    template 
-    bool pop(T& item, F&& out) {
-        if (elems_ == nullptr) {
-            return false;
-        }
-        return elems_->pop(this, &(this->cursor_), [&item](void* p) {
-            ::new (&item) T(std::move(*static_cast(p)));
-        }, std::forward(out));
-    }
-};
-
-} // namespace detail
-
-template 
-class queue final : public detail::queue_base> {
-    using base_t = detail::queue_base>;
-
-public:
-    using value_t = T;
-
-    using base_t::base_t;
-
-    template 
-    bool push(P&&... params) {
-        return base_t::template push(std::forward
      (params)...);
-    }
-
-    template 
-    bool force_push(P&&... params) {
-        return base_t::template force_push(std::forward
      (params)...);
-    }
-
-    bool pop(T& item) {
-        return base_t::pop(item, [](bool) {});
-    }
-
-    template 
-    bool pop(T& item, F&& out) {
-        return base_t::pop(item, std::forward(out));
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/XzJosh/Eileen-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Eileen-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Eileen-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/XzJosh/Jiaran-Bert-VITS2/README.md b/spaces/XzJosh/Jiaran-Bert-VITS2/README.md
deleted file mode 100644
index 94c5c14aace73999d78dbac7d12dbfd15e9c52dc..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jiaran-Bert-VITS2/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-license: mit
-sdk: gradio
-title: AI嘉然②
----
\ No newline at end of file
diff --git a/spaces/XzJosh/ShanBao-Bert-VITS2/bert_gen.py b/spaces/XzJosh/ShanBao-Bert-VITS2/bert_gen.py
deleted file mode 100644
index 44814715396ffc3abe84a12c74d66293c356eb4f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/ShanBao-Bert-VITS2/bert_gen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from multiprocessing import Pool
-import commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from tqdm import tqdm
-import warnings
-
-from text import cleaned_text_to_sequence, get_bert
-
-config_path = 'configs/config.json'
-hps = utils.get_hparams_from_file(config_path)
-
-def process_line(line):
-    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
-    phone = phones.split(" ")
-    tone = [int(i) for i in tone.split(" ")]
-    word2ph = [int(i) for i in word2ph.split(" ")]
-    w2pho = [i for i in word2ph]
-    word2ph = [i for i in word2ph]
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    wav_path = f'{_id}'
-
-    bert_path = wav_path.replace(".wav", ".bert.pt")
-    try:
-        bert = torch.load(bert_path)
-        assert bert.shape[-1] == len(phone)
-    except:
-        bert = get_bert(text, word2ph, language_str)
-        assert bert.shape[-1] == len(phone)
-        torch.save(bert, bert_path)
-
-
-if __name__ == '__main__':
-    lines = []
-    with open(hps.data.training_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-    with open(hps.data.validation_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-    with Pool(processes=12) as pool: #A100 40GB suitable config,if coom,please decrease the processess number.
-        for _ in tqdm(pool.imap_unordered(process_line, lines)):
-            pass
diff --git a/spaces/XzJosh/nanami-Bert-VITS2/text/english.py b/spaces/XzJosh/nanami-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nanami-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split('  ')
-                word = word_split[0]
-
-                syllable_split = word_split[1].split(' - ')
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(' ')
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, 'wb') as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, 'rb') as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
-    tone = 0
-    if re.search(r'\d$', phn):
-        tone = int(phn[-1]) + 1
-        phn = phn[:-1]
-    return phn.lower(), tone
-
-def refine_syllables(syllables):
-    tones = []
-    phonemes = []
-    for phn_list in syllables:
-        for i in range(len(phn_list)):
-            phn = phn_list[i]
-            phn, tone = refine_ph(phn)
-            phonemes.append(phn)
-            tones.append(tone)
-    return phonemes, tones
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    return text
-
-def g2p(text):
-
-    phones = []
-    tones = []
-    words = re.split(r"([,;.\-\?\!\s+])", text)
-    for w in words:
-        if w.upper() in eng_dict:
-            phns, tns = refine_syllables(eng_dict[w.upper()])
-            phones += phns
-            tones += tns
-        else:
-            phone_list = list(filter(lambda p: p != " ", _g2p(w)))
-            for ph in phone_list:
-                if ph in arpa:
-                    ph, tn = refine_ph(ph)
-                    phones.append(ph)
-                    tones.append(tn)
-                else:
-                    phones.append(ph)
-                    tones.append(0)
-    # todo: implement word2ph
-    word2ph = [1 for i in phones]
-
-    phones = [post_replace_ph(i) for i in phones]
-    return phones, tones, word2ph
-
-if __name__ == "__main__":
-    # print(get_dict())
-    # print(eng_word_to_phoneme("hello"))
-    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
-    # all_phones = set()
-    # for k, syllables in eng_dict.items():
-    #     for group in syllables:
-    #         for ph in group:
-    #             all_phones.add(ph)
-    # print(all_phones)
\ No newline at end of file
diff --git a/spaces/XzJosh/nine2-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/nine2-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nine2-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/Yiqin/ChatVID/model/fastchat/eval/generate_webpage_data_from_table.py b/spaces/Yiqin/ChatVID/model/fastchat/eval/generate_webpage_data_from_table.py
deleted file mode 100644
index e24175aa588493e8d41264abc34cf44155ea335b..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/fastchat/eval/generate_webpage_data_from_table.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Generate json file for webpage."""
-import json
-import os
-import re
-
-models = ["alpaca", "llama", "gpt35", "bard"]
-
-
-def read_jsonl(path: str, key: str = None):
-    data = []
-    with open(os.path.expanduser(path)) as f:
-        for line in f:
-            if not line:
-                continue
-            data.append(json.loads(line))
-    if key is not None:
-        data.sort(key=lambda x: x[key])
-        data = {item[key]: item for item in data}
-    return data
-
-
-def trim_hanging_lines(s: str, n: int) -> str:
-    s = s.strip()
-    for _ in range(n):
-        s = s.split("\n", 1)[1].strip()
-    return s
-
-
-if __name__ == "__main__":
-    questions = read_jsonl("table/question.jsonl", key="question_id")
-
-    alpaca_answers = read_jsonl(
-        "table/answer/answer_alpaca-13b.jsonl", key="question_id"
-    )
-    bard_answers = read_jsonl("table/answer/answer_bard.jsonl", key="question_id")
-    gpt35_answers = read_jsonl("table/answer/answer_gpt35.jsonl", key="question_id")
-    llama_answers = read_jsonl("table/answer/answer_llama-13b.jsonl", key="question_id")
-    vicuna_answers = read_jsonl(
-        "table/answer/answer_vicuna-13b.jsonl", key="question_id"
-    )
-
-    review_alpaca = read_jsonl(
-        "table/review/review_alpaca-13b_vicuna-13b.jsonl", key="question_id"
-    )
-    review_bard = read_jsonl(
-        "table/review/review_bard_vicuna-13b.jsonl", key="question_id"
-    )
-    review_gpt35 = read_jsonl(
-        "table/review/review_gpt35_vicuna-13b.jsonl", key="question_id"
-    )
-    review_llama = read_jsonl(
-        "table/review/review_llama-13b_vicuna-13b.jsonl", key="question_id"
-    )
-
-    records = []
-    for qid in questions.keys():
-        r = {
-            "id": qid,
-            "category": questions[qid]["category"],
-            "question": questions[qid]["text"],
-            "answers": {
-                "alpaca": alpaca_answers[qid]["text"],
-                "llama": llama_answers[qid]["text"],
-                "bard": bard_answers[qid]["text"],
-                "gpt35": gpt35_answers[qid]["text"],
-                "vicuna": vicuna_answers[qid]["text"],
-            },
-            "evaluations": {
-                "alpaca": review_alpaca[qid]["text"],
-                "llama": review_llama[qid]["text"],
-                "bard": review_bard[qid]["text"],
-                "gpt35": review_gpt35[qid]["text"],
-            },
-            "scores": {
-                "alpaca": review_alpaca[qid]["score"],
-                "llama": review_llama[qid]["score"],
-                "bard": review_bard[qid]["score"],
-                "gpt35": review_gpt35[qid]["score"],
-            },
-        }
-
-        # cleanup data
-        cleaned_evals = {}
-        for k, v in r["evaluations"].items():
-            v = v.strip()
-            lines = v.split("\n")
-            # trim the first line if it's a pair of numbers
-            if re.match(r"\d+[, ]+\d+", lines[0]):
-                lines = lines[1:]
-            v = "\n".join(lines)
-            cleaned_evals[k] = v.replace("Assistant 1", "**Assistant 1**").replace(
-                "Assistant 2", "**Assistant 2**"
-            )
-
-        r["evaluations"] = cleaned_evals
-        records.append(r)
-
-    # Reorder the records, this is optional
-    for r in records:
-        if r["id"] <= 20:
-            r["id"] += 60
-        else:
-            r["id"] -= 20
-    for r in records:
-        if r["id"] <= 50:
-            r["id"] += 10
-        elif 50 < r["id"] <= 60:
-            r["id"] -= 50
-    for r in records:
-        if r["id"] == 7:
-            r["id"] = 1
-        elif r["id"] < 7:
-            r["id"] += 1
-
-    records.sort(key=lambda x: x["id"])
-
-    # Write to file
-    with open("webpage/data.json", "w") as f:
-        json.dump({"questions": records, "models": models}, f, indent=2)
diff --git a/spaces/YlcldKlns/bing/src/components/chat-image.tsx b/spaces/YlcldKlns/bing/src/components/chat-image.tsx
deleted file mode 100644
index 05ecc9771eada27a0f2d160bb01cba170d37bb09..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/chat-image.tsx
+++ /dev/null
@@ -1,170 +0,0 @@
-import {
-  useEffect,
-  useState,
-  useCallback,
-  ChangeEvent,
-  ClipboardEvent,
-  MouseEventHandler,
-  FormEvent,
-  useRef
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-
-interface ChatImageProps extends Pick, 'uploadImage'> {}
-
-const preventDefault: MouseEventHandler = (event) => {
-  event.nativeEvent.stopImmediatePropagation()
-}
-
-const toBase64 = (file: File): Promise => new Promise((resolve, reject) => {
-  const reader = new FileReader()
-  reader.readAsDataURL(file)
-  reader.onload = () => resolve(reader.result as string)
-  reader.onerror = reject
-})
-
-export function ChatImage({ children, uploadImage }: React.PropsWithChildren) {
-  const videoRef = useRef(null)
-  const canvasRef = useRef(null)
-  const mediaStream = useRef()
-  const [panel, setPanel] = useState('none')
-
-  const upload = useCallback((url: string) => {
-    if (url) {
-      uploadImage(url)
-    }
-    setPanel('none')
-  }, [panel])
-
-  const onUpload = useCallback(async (event: ChangeEvent) => {
-    const file = event.target.files?.[0]
-    if (file) {
-      const fileDataUrl = await toBase64(file)
-      if (fileDataUrl) {
-        upload(fileDataUrl)
-      }
-    }
-  }, [])
-
-  const onPaste = useCallback((event: ClipboardEvent) => {
-    const pasteUrl = event.clipboardData.getData('text') ?? ''
-    upload(pasteUrl)
-  }, [])
-
-  const onEnter = useCallback((event: FormEvent) => {
-    event.preventDefault()
-    event.stopPropagation()
-    // @ts-ignore
-    const inputUrl = event.target.elements.image.value
-    if (inputUrl) {
-      upload(inputUrl)
-    }
-  }, [])
-
-  const openVideo: MouseEventHandler = async (event) => {
-    event.stopPropagation()
-    setPanel('camera-mode')
-  }
-
-  const onCapture = () => {
-    if (canvasRef.current && videoRef.current) {
-      const canvas = canvasRef.current
-      canvas.width = videoRef.current!.videoWidth
-      canvas.height = videoRef.current!.videoHeight
-      canvas.getContext('2d')?.drawImage(videoRef.current, 0, 0, canvas.width, canvas.height)
-      const cameraUrl = canvas.toDataURL('image/jpeg')
-      upload(cameraUrl)
-    }
-  }
-
-  useEffect(() => {
-    const handleBlur = () => {
-      if (panel !== 'none') {
-        setPanel('none')
-      }
-    }
-    document.addEventListener('click', handleBlur)
-    return () => {
-      document.removeEventListener('click', handleBlur)
-    }
-  }, [panel])
-
-  useEffect(() => {
-    if (panel === 'camera-mode') {
-      navigator.mediaDevices.getUserMedia({ video: true, audio: false })
-      .then(videoStream => {
-        mediaStream.current = videoStream
-        if (videoRef.current) {
-          videoRef.current.srcObject = videoStream
-        }
-      })
-    } else {
-      if (mediaStream.current) {
-        mediaStream.current.getTracks().forEach(function(track) {
-          track.stop()
-        })
-        mediaStream.current = undefined
-      }
-    }
-  }, [panel])
-
-  return (
-    
      
-      
       panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}
      
-      
      
-        
      
-          
      
-            
      添加图像
      
-          
      
-          
      
-            paste
-            
      
-               e.stopPropagation()}
-              />
-            
      
-          
      
-          
      
-            
-            
-          
      
-        
      
-        {panel === 'camera-mode' && 
      
-          
      
-            
      
-          
      
-            
      
-            
      
-          
      
-        
      }
-      
      
-    
      
-  )
-}
diff --git a/spaces/YourGodAmaterasu/GPTChatBot/README.md b/spaces/YourGodAmaterasu/GPTChatBot/README.md
deleted file mode 100644
index 158b2a7346a23f91762d2c1ce877e9efd39e7d80..0000000000000000000000000000000000000000
--- a/spaces/YourGodAmaterasu/GPTChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GPTChatBot
-emoji: 🏢
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Yunshansongbai/SVC-Nahida/hubert/hubert_model.py b/spaces/Yunshansongbai/SVC-Nahida/hubert/hubert_model.py
deleted file mode 100644
index 50be97aa535011008e257d5ec3db8da7282da93b..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/hubert/hubert_model.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as t_func
-#from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(paddle.nn.Layer):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu"
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = paddle.create_parameter([768],dtype = 'float32')
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, None, 2)
-            x[mask] = self.masked_spec_embed
-        return x, mask
-
-    def encode(
-            self, x: paddle.Tensor, layer: Optional[int] = None
-    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose([0, 2, 1]))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: paddle.Tensor) -> paddle.Tensor:
-        logits = t_func.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            axis=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    def units(self, wav: paddle.Tensor) -> paddle.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2),data_format='NCL')
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(paddle.nn.Layer):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1D(1, 512, 10, 5, bias_attr=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1D(512, 512, 3, 2, bias_attr=False)
-        self.conv2 = nn.Conv1D(512, 512, 3, 2, bias_attr=False)
-        self.conv3 = nn.Conv1D(512, 512, 3, 2, bias_attr=False)
-        self.conv4 = nn.Conv1D(512, 512, 3, 2, bias_attr=False)
-        self.conv5 = nn.Conv1D(512, 512, 2, 2, bias_attr=False)
-        self.conv6 = nn.Conv1D(512, 512, 2, 2, bias_attr=False)
-
-    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(paddle.nn.Layer):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(paddle.nn.Layer):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1D(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
-        x = self.conv(x.transpose([0, 2, 1]))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose([0, 2, 1])
-
-
-class TransformerEncoder(paddle.nn.Layer):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.LayerList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: paddle.Tensor,
-            mask: paddle.Tensor = None,
-            src_key_padding_mask: paddle.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> paddle.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: None,
-        min_masks: int = 0,
-) -> paddle.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = paddle.zeros((batch_size, sequence_length), dtype='bool')
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = paddle.ones(
-        (batch_size, sequence_length - (mask_length - 1))
-    )
-    
-    # get random indices to mask
-    mask_indices = paddle.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        paddle.arange(mask_length)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = paddle.load(path)
-    #consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.set_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
-
-if __name__ == '__main__':
-    hubert = HubertSoft()
-    d = paddle.load(r'E:\trans\hubert\final.pdparams')
-    hubert.set_state_dict(d)
diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v2/util/pdf_opt.py b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v2/util/pdf_opt.py
deleted file mode 100644
index a14f4a4a8c369a618925297faa235778f476eb98..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v2/util/pdf_opt.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# PDF管理
-# 创建人：曾逸夫
-# 创建时间：2022-05-05
-
-from fpdf import FPDF
-
-
-title = "Gradio YOLOv5 Det v0.2"
-
-
-class PDF(FPDF):
-# 参考：https://pyfpdf.readthedocs.io/en/latest/Tutorial/index.html
-    def header(self):
-        # 设置中文字体
-        self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
-        self.set_font("SimSun", "", 16)
-        # Calculate width of title and position
-        w = self.get_string_width(title) + 6
-        self.set_x((210 - w) / 2)
-        # Colors of frame, background and text
-        self.set_draw_color(255, 255, 255)
-        self.set_fill_color(255, 255, 255)
-        self.set_text_color(0, 0, 0)
-        # Thickness of frame (1 mm)
-        # self.set_line_width(1)
-        # Title
-        self.cell(w, 9, title, 1, 1, "C", 1)
-        # Line break
-        self.ln(10)
-
-    def footer(self):
-        # Position at 1.5 cm from bottom
-        self.set_y(-15)
-        # 设置中文字体
-        self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
-        self.set_font("SimSun", "", 12)
-        # Text color in gray
-        self.set_text_color(128)
-        # Page number
-        self.cell(0, 10, "Page " + str(self.page_no()), 0, 0, "C")
-
-    def chapter_title(self, num, label):
-        # 设置中文字体
-        self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
-        self.set_font("SimSun", "", 12)
-        # Background color
-        self.set_fill_color(200, 220, 255)
-        # Title
-        # self.cell(0, 6, 'Chapter %d : %s' % (num, label), 0, 1, 'L', 1)
-        self.cell(0, 6, f"检测结果：", 0, 1, "L", 1)
-        # Line break
-        self.ln(4)
-
-    def chapter_body(self, name):
-
-        # 设置中文字体
-        self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
-        self.set_font("SimSun", "", 12)
-        # Output justified text
-        self.multi_cell(0, 5, name)
-        # Line break
-        self.ln()
-        self.cell(0, 5, "--------------------------------------")
-
-    def print_chapter(self, num, title, name):
-        self.add_page()
-        self.chapter_title(num, title)
-        self.chapter_body(name)
-
-
-# pdf生成函数
-def pdf_generate(input_file, output_file):
-    pdf = PDF()
-    pdf.set_title(title)
-    pdf.set_author("Zeng Yifu")
-    pdf.print_chapter(1, "A RUNAWAY REEF", input_file)
-    pdf.output(output_file)
diff --git a/spaces/ZeroTwo3/WavJourney/README.md b/spaces/ZeroTwo3/WavJourney/README.md
deleted file mode 100644
index a0dbce2ff0274e233a60b1978d6b4ab623a598bc..0000000000000000000000000000000000000000
--- a/spaces/ZeroTwo3/WavJourney/README.md
+++ /dev/null
@@ -1,112 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
-duplicated_from: Audio-AGI/WavJourney
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2307.14335) [![GitHub Stars](https://img.shields.io/github/stars/Audio-AGI/WavJourney?style=social)](https://github.com/Audio-AGI/WavJourney/) [![githubio](https://img.shields.io/badge/GitHub.io-Demo_Page-blue?logo=Github&style=flat-square)](https://audio-agi.github.io/WavJourney_demopage/) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Audio-AGI/WavJourney)  
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
      
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file. 
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
-  ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics" 
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
-  ```
-  
-## (Advanced features) Speaker customization 
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here. 
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
-    @article{liu2023wavjourney,
-        title   = {WavJourney: Compositional Audio Creation with Large Language Models},
-        author  = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
-        journal = {arXiv preprint arXiv:2307.14335},
-        year    = {2023}
-    }
-
-[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/aaronstaclara/towards-financial-inclusion/app.py b/spaces/aaronstaclara/towards-financial-inclusion/app.py
deleted file mode 100644
index 546f701c4d10184ea4fb8c8c15f70030e49180dc..0000000000000000000000000000000000000000
--- a/spaces/aaronstaclara/towards-financial-inclusion/app.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import matplotlib.pyplot as plt
-import pandas as pd
-import plotly.express as px
-import streamlit as st
-
-
-def load_data():
-    # Load the data
-    data = pd.read_csv(
-        "micro_world.csv"
-    )
-    return data
-
-
-def introduction():
-    # Write the title and the subheader
-    st.title(
-        "Towards Financial Inclusion (FI): using survey data to assess FI status of the Philippines"
-    )
-    st.subheader(
-        """
-        In line with the National Strategy for Financial Inclusion (NSFI) 2022-2028 by Bangko Sentral ng Pilipinas (BSP), this sprint aims to:
-        1. Profile financial inclusion (FI) metrics in the Philippines using survey data from World Bank.
-        2. Formulate policy recommendations to further improve access to financial services particularly to vulnerable sectors.
-        """
-    )
-
-    # Load photo
-    st.image("streamlit-photo-1.jpeg")
-
-    # Load data
-    data = load_data()
-
-    # Display data
-    st.markdown("**The Data**")
-    st.dataframe(data)
-    st.markdown("Source: Global Findex 2017 from World Bank.")
-
-
-def fi_state_ph():
-    # Write the title
-    st.title(
-        "This is the current state of FI in the Philippines."
-    )
-
-    # Load data
-    data = load_data()
-
-    # Fetch Philippine data
-    philippine_data = data[
-        data['economy'] == 'Philippines'
-        ]
-
-    # Create another column for debit card ownership
-    philippine_data['has_debit_card'] = philippine_data['fin2'].apply(
-        lambda x: 1 if x == 1 else 0
-    )
-
-    # Compute overall debit card ownership
-    percent_debit_card_ownership = philippine_data['has_debit_card'].sum() * 100.0 / philippine_data[
-        'wpid_random'].count()
-
-    # Partition the page into 2
-    col1, col2 = st.columns(2)
-
-    # Display text in column 1
-    col1.markdown(
-        "In the Philippines, there is still an opportunity to expand access to financial services: "
-    )
-
-    # Display metric in column 2
-    col2.metric(
-        label='% of Population with Debit Card',
-        value=percent_debit_card_ownership
-    )
-
-    # Display text
-    st.markdown("In terms of gender breakdown:")
-
-    # Create another column for gender
-    philippine_data['gender'] = philippine_data['female'].apply(
-        lambda x: 'male' if x == 1 else 'female'
-    )
-
-    # Compute breakdown of access to debit card by gender
-    debit_by_gender = philippine_data.groupby('gender').agg(
-        total_debit_card_owners=('has_debit_card', 'sum'),
-        total_population=('wpid_random', 'count')
-    ).reset_index()
-
-    # Compute % debit card ownership
-    debit_by_gender['% debit card ownership'] = debit_by_gender['total_debit_card_owners'] * 100.0 / debit_by_gender[
-        'total_population']
-
-    # Plot the data
-    fig, ax = plt.subplots(figsize=(6, 3), dpi=200)
-    ax.bar(
-        debit_by_gender["gender"],
-        debit_by_gender["% debit card ownership"],
-    )
-    ax.set_xlabel("Gender")
-    ax.set_ylabel("% Debit Card Ownership")
-
-    # Show the data
-    st.pyplot(fig)
-
-
-def fi_state_worldwide():
-    # Write the title and the subheader
-    st.title(
-        "This is the current state of FI worldwide."
-    )
-    st.markdown(
-        "**Here is a bubble map presenting the % of debit card ownership per country:**"
-    )
-
-    # Load data
-    data = load_data()
-
-    # Create another column for debit card ownership
-    data['has_debit_card'] = data['fin2'].apply(
-        lambda x: 1 if x == 1 else 0
-    )
-
-    # Group the data and apply aggregations
-    grouped_data = data.groupby(['economy', 'economycode', 'regionwb']).agg(
-        total_debit_card_owners=('has_debit_card', 'sum'),
-        total_population=('wpid_random', 'count')
-    ).reset_index()
-
-    # Compute debit card ownership in %
-    grouped_data['% of population with debit card'] = grouped_data['total_debit_card_owners'] * 100.0 / grouped_data[
-        'total_population']
-
-    # Build the bubble map
-    fig = px.scatter_geo(
-        grouped_data,
-        locations="economycode",
-        color="regionwb",
-        hover_name="economy",
-        size="% of population with debit card",
-        projection="natural earth"
-    )
-
-    # Show the figure
-    st.plotly_chart(fig)
-
-
-def recommendations():
-    # Write the title
-    st.title(
-        "What We Can Do"
-    )
-
-
-def the_team():
-    # Write the title
-    st.title(
-        "The Team"
-    )
-
-
-list_of_pages = [
-    "Towards Financial Inclusion",
-    "FI Status of the Philippines",
-    "FI Status Worldwide",
-    "What We Can Do",
-    "The Team"
-]
-
-st.sidebar.title(':scroll: Main Pages')
-selection = st.sidebar.radio("Go to: ", list_of_pages)
-
-if selection == "Towards Financial Inclusion":
-    introduction()
-
-elif selection == "FI Status of the Philippines":
-    fi_state_ph()
-
-elif selection == "FI Status Worldwide":
-    fi_state_worldwide()
-
-elif selection == "What We Can Do":
-    recommendations()
-
-elif selection == "The Team":
-    the_team()
diff --git a/spaces/abhibisht89/ADR_XTRACTER/README.md b/spaces/abhibisht89/ADR_XTRACTER/README.md
deleted file mode 100644
index c567be92fd66630fa1d05caad715a210579698fc..0000000000000000000000000000000000000000
--- a/spaces/abhibisht89/ADR_XTRACTER/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: ADR_XTRACTER
-emoji: 👀
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/necks/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/necks/__init__.py
deleted file mode 100644
index 9b9d3d5b3fe80247642d962edd6fb787537d01d6..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/necks/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .fpn import FPN
-from .multilevel_neck import MultiLevelNeck
-
-__all__ = ['FPN', 'MultiLevelNeck']
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/bbox.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/bbox.py
deleted file mode 100644
index 0c4d58b6c91f652933974f519acd3403a833e906..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/bbox.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps'])
-
-
-def bbox_overlaps(bboxes1, bboxes2, mode='iou', aligned=False, offset=0):
-    """Calculate overlap between two set of bboxes.
-
-    If ``aligned`` is ``False``, then calculate the ious between each bbox
-    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
-    bboxes1 and bboxes2.
-
-    Args:
-        bboxes1 (Tensor): shape (m, 4) in  format or empty.
-        bboxes2 (Tensor): shape (n, 4) in  format or empty.
-            If aligned is ``True``, then m and n must be equal.
-        mode (str): "iou" (intersection over union) or iof (intersection over
-            foreground).
-
-    Returns:
-        ious(Tensor): shape (m, n) if aligned == False else shape (m, 1)
-
-    Example:
-        >>> bboxes1 = torch.FloatTensor([
-        >>>     [0, 0, 10, 10],
-        >>>     [10, 10, 20, 20],
-        >>>     [32, 32, 38, 42],
-        >>> ])
-        >>> bboxes2 = torch.FloatTensor([
-        >>>     [0, 0, 10, 20],
-        >>>     [0, 10, 10, 19],
-        >>>     [10, 10, 20, 20],
-        >>> ])
-        >>> bbox_overlaps(bboxes1, bboxes2)
-        tensor([[0.5000, 0.0000, 0.0000],
-                [0.0000, 0.0000, 1.0000],
-                [0.0000, 0.0000, 0.0000]])
-
-    Example:
-        >>> empty = torch.FloatTensor([])
-        >>> nonempty = torch.FloatTensor([
-        >>>     [0, 0, 10, 9],
-        >>> ])
-        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
-        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
-        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
-    """
-
-    mode_dict = {'iou': 0, 'iof': 1}
-    assert mode in mode_dict.keys()
-    mode_flag = mode_dict[mode]
-    # Either the boxes are empty or the length of boxes' last dimension is 4
-    assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
-    assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
-    assert offset == 1 or offset == 0
-
-    rows = bboxes1.size(0)
-    cols = bboxes2.size(0)
-    if aligned:
-        assert rows == cols
-
-    if rows * cols == 0:
-        return bboxes1.new(rows, 1) if aligned else bboxes1.new(rows, cols)
-
-    if aligned:
-        ious = bboxes1.new_zeros(rows)
-    else:
-        ious = bboxes1.new_zeros((rows, cols))
-    ext_module.bbox_overlaps(
-        bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset)
-    return ious
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/pipelines/compose.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/pipelines/compose.py
deleted file mode 100644
index ee4ff17fbb882a8a15b0d7e5061ea74b619d5f34..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/datasets/pipelines/compose.py
+++ /dev/null
@@ -1,63 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-import collections
-
-from annotator.uniformer.mmcv.utils import build_from_cfg
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class Compose(object):
-    """Compose multiple transforms sequentially.
-
-    Args:
-        transforms (Sequence[dict | callable]): Sequence of transform object or
-            config dict to be composed.
-    """
-
-    def __init__(self, transforms):
-        assert isinstance(transforms, collections.abc.Sequence)
-        self.transforms = []
-        for transform in transforms:
-            if isinstance(transform, dict):
-                transform = build_from_cfg(transform, PIPELINES)
-                self.transforms.append(transform)
-            elif callable(transform):
-                self.transforms.append(transform)
-            else:
-                raise TypeError('transform must be callable or a dict')
-
-    def __call__(self, data):
-        """Call function to apply transforms sequentially.
-
-        Args:
-            data (dict): A result dict contains the data to transform.
-
-        Returns:
-           dict: Transformed data.
-        """
-
-        for t in self.transforms:
-            data = t(data)
-            if data is None:
-                return None
-        return data
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        for t in self.transforms:
-            format_string += '\n'
-            format_string += f'    {t}'
-        format_string += '\n)'
-        return format_string
diff --git a/spaces/achimoraites/Summarizer-flan-t5-base-samsum/README.md b/spaces/achimoraites/Summarizer-flan-t5-base-samsum/README.md
deleted file mode 100644
index 16a47c76d5e8f603525f6d040495929761654e59..0000000000000000000000000000000000000000
--- a/spaces/achimoraites/Summarizer-flan-t5-base-samsum/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Achimoraites Flan T5 Base Samsum
-emoji: 🏃
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aheskandani/FilesTools/README.md b/spaces/aheskandani/FilesTools/README.md
deleted file mode 100644
index 76930504ee2d96f5b1d199ad1588d94cbceb8864..0000000000000000000000000000000000000000
--- a/spaces/aheskandani/FilesTools/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: FilesTools
-emoji: 🔥
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ai-maker-space/ArxivChainLitDemo/app.py b/spaces/ai-maker-space/ArxivChainLitDemo/app.py
deleted file mode 100644
index d34810ce8d56deb83b0d6c4013a39aa23c4bcc5c..0000000000000000000000000000000000000000
--- a/spaces/ai-maker-space/ArxivChainLitDemo/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.document_loaders import PyMuPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.chains import RetrievalQAWithSourcesChain
-from langchain.chat_models import ChatOpenAI
-from typing import Any, List, Mapping, Optional
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-import os
-import arxiv
-import chainlit as cl
-from chainlit import user_session
-
-@cl.langchain_factory(use_async=True)
-async def init():
-    arxiv_query = None
-
-    # Wait for the user to ask an Arxiv question
-    while arxiv_query == None:
-        arxiv_query = await cl.AskUserMessage(
-            content="Please enter a topic to begin!", timeout=15
-        ).send()
-
-    # Obtain the top 3 results from Arxiv for the query
-    search = arxiv.Search(
-        query=arxiv_query["content"],
-        max_results=3,
-        sort_by=arxiv.SortCriterion.Relevance,
-    )
-
-    await cl.Message(content="Downloading and chunking articles...").send()
-    # download each of the pdfs
-    pdf_data = []
-    for result in search.results():
-        loader = PyMuPDFLoader(result.pdf_url)
-        loaded_pdf = loader.load()
-
-        for document in loaded_pdf:
-            document.metadata["source"] = result.entry_id
-            document.metadata["file_path"] = result.pdf_url
-            document.metadata["title"] = result.title
-            pdf_data.append(document)
-
-    # Create a Chroma vector store
-    embeddings = OpenAIEmbeddings(
-        disallowed_special=(),
-    )
-    
-    # If operation takes too long, make_async allows to run in a thread
-    # docsearch = await cl.make_async(Chroma.from_documents)(pdf_data, embeddings) 
-    docsearch = Chroma.from_documents(pdf_data, embeddings)
-
-    # Create a chain that uses the Chroma vector store
-    chain = RetrievalQAWithSourcesChain.from_chain_type(
-        ChatOpenAI(
-            model="gpt-3.5-turbo",
-            temperature=0.0
-        ),
-        chain_type="stuff",
-        retriever=docsearch.as_retriever(),
-        return_source_documents=True,
-    )
-
-    # Let the user know that the system is ready
-    await cl.Message(
-        content=f"We found a few papers about `{arxiv_query['content']}` you can now ask questions!"
-    ).send()
-
-    return chain
-
-
-@cl.langchain_postprocess
-async def process_response(res):
-    answer = res["answer"]
-    source_elements_dict = {}
-    source_elements = []
-    for idx, source in enumerate(res["source_documents"]):
-        title = source.metadata["title"]
-
-        if title not in source_elements_dict:
-            source_elements_dict[title] = {
-                "page_number": [source.metadata["page"]],
-                "url": source.metadata["file_path"],
-            }
-
-        else:
-            source_elements_dict[title]["page_number"].append(source.metadata["page"])
-
-        # sort the page numbers
-        source_elements_dict[title]["page_number"].sort()
-
-    for title, source in source_elements_dict.items():
-        # create a string for the page numbers
-        page_numbers = ", ".join([str(x) for x in source["page_number"]])
-        text_for_source = f"Page Number(s): {page_numbers}\nURL: {source['url']}"
-        source_elements.append(
-            cl.Text(name=title, content=text_for_source, display="inline")
-        )
-
-    await cl.Message(content=answer, elements=source_elements).send()
\ No newline at end of file
diff --git a/spaces/aijack/seg/README.md b/spaces/aijack/seg/README.md
deleted file mode 100644
index b569cf9c9dd75a84f1bd7a2bb088e253fdf4aeb0..0000000000000000000000000000000000000000
--- a/spaces/aijack/seg/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Seg
-emoji: 📈
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/Mask2Former/mask2former/utils/misc.py b/spaces/akhaliq/Mask2Former/mask2former/utils/misc.py
deleted file mode 100644
index 874d9805b482f52bbffc1be620e36e0cffc07c46..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Mask2Former/mask2former/utils/misc.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-from typing import List, Optional
-
-import torch
-import torch.distributed as dist
-import torchvision
-from torch import Tensor
-
-
-def _max_by_axis(the_list):
-    # type: (List[List[int]]) -> List[int]
-    maxes = the_list[0]
-    for sublist in the_list[1:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor]):
-        self.tensors = tensors
-        self.mask = mask
-
-    def to(self, device):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def decompose(self):
-        return self.tensors, self.mask
-
-    def __repr__(self):
-        return str(self.tensors)
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    if tensor_list[0].ndim == 3:
-        if torchvision._is_tracing():
-            # nested_tensor_from_tensor_list() does not export well to ONNX
-            # call _onnx_nested_tensor_from_tensor_list() instead
-            return _onnx_nested_tensor_from_tensor_list(tensor_list)
-
-        # TODO make it support different-sized images
-        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
-        batch_shape = [len(tensor_list)] + max_size
-        b, c, h, w = batch_shape
-        dtype = tensor_list[0].dtype
-        device = tensor_list[0].device
-        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
-        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
-        for img, pad_img, m in zip(tensor_list, tensor, mask):
-            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-            m[: img.shape[1], : img.shape[2]] = False
-    else:
-        raise ValueError("not supported")
-    return NestedTensor(tensor, mask)
-
-
-# _onnx_nested_tensor_from_tensor_list() is an implementation of
-# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-@torch.jit.unused
-def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-    max_size = []
-    for i in range(tensor_list[0].dim()):
-        max_size_i = torch.max(
-            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
-        ).to(torch.int64)
-        max_size.append(max_size_i)
-    max_size = tuple(max_size)
-
-    # work around for
-    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-    # m[: img.shape[1], :img.shape[2]] = False
-    # which is not yet supported in onnx
-    padded_imgs = []
-    padded_masks = []
-    for img in tensor_list:
-        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-        padded_imgs.append(padded_img)
-
-        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-        padded_masks.append(padded_mask.to(torch.bool))
-
-    tensor = torch.stack(padded_imgs)
-    mask = torch.stack(padded_masks)
-
-    return NestedTensor(tensor, mask=mask)
-
-
-def is_dist_avail_and_initialized():
-    if not dist.is_available():
-        return False
-    if not dist.is_initialized():
-        return False
-    return True
diff --git a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Element.pod b/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Element.pod
deleted file mode 100644
index d4a289aa7fa4072e49506cf8558af15df56e4c1d..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/third_party/HMNet/ThirdParty/ROUGE/ROUGE-1.5.5/XML/DOM/Element.pod
+++ /dev/null
@@ -1,189 +0,0 @@
-=head1 NAME
-
-XML::DOM::Element - An XML element node in XML::DOM
-
-=head1 DESCRIPTION
-
-XML::DOM::Element extends L.
-
-By far the vast majority of objects (apart from text) that authors
-encounter when traversing a document are Element nodes. Assume the
-following XML document:
-
-     
-       
-       
-     
-
-When represented using DOM, the top node is an Element node for
-"elementExample", which contains two child Element nodes, one for
-"subelement1" and one for "subelement2". "subelement1" contains no
-child nodes.
-
-Elements may have attributes associated with them; since the Element
-interface inherits from Node, the generic Node interface method
-getAttributes may be used to retrieve the set of all attributes for an
-element. There are methods on the Element interface to retrieve either
-an Attr object by name or an attribute value by name. In XML, where an
-attribute value may contain entity references, an Attr object should be
-retrieved to examine the possibly fairly complex sub-tree representing
-the attribute value. On the other hand, in HTML, where all attributes
-have simple string values, methods to directly access an attribute
-value can safely be used as a convenience.
-
-=head2 METHODS
-
-=over 4
-
-=item getTagName
-
-The name of the element. For example, in:
-
-               
-                       ...
-               
-
-tagName has the value "elementExample". Note that this is
-case-preserving in XML, as are all of the operations of the
-DOM.
-
-=item getAttribute (name)
-
-Retrieves an attribute value by name.
-
-Return Value: The Attr value as a string, or the empty string if that
-attribute does not have a specified or default value.
-
-=item setAttribute (name, value)
-
-Adds a new attribute. If an attribute with that name is
-already present in the element, its value is changed to be
-that of the value parameter. This value is a simple string,
-it is not parsed as it is being set. So any markup (such as
-syntax to be recognized as an entity reference) is treated as
-literal text, and needs to be appropriately escaped by the
-implementation when it is written out. In order to assign an
-attribute value that contains entity references, the user
-must create an Attr node plus any Text and EntityReference
-nodes, build the appropriate subtree, and use
-setAttributeNode to assign it as the value of an attribute.
-
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the specified name contains an invalid character.
-
-=item * NO_MODIFICATION_ALLOWED_ERR
-
-Raised if this node is readonly.
-
-=back
-
-=item removeAttribute (name)
-
-Removes an attribute by name. If the removed attribute has a
-default value it is immediately replaced.
-
-DOMExceptions:
-
-=over 4
-
-=item * NO_MODIFICATION_ALLOWED_ERR
-
-Raised if this node is readonly.
-
-=back
-
-=item getAttributeNode
-
-Retrieves an Attr node by name.
-
-Return Value: The Attr node with the specified attribute name or undef
-if there is no such attribute.
-
-=item setAttributeNode (attr)
-
-Adds a new attribute. If an attribute with that name is
-already present in the element, it is replaced by the new one.
-
-Return Value: If the newAttr attribute replaces an existing attribute
-with the same name, the previously existing Attr node is
-returned, otherwise undef is returned.
-
-DOMExceptions:
-
-=over 4
-
-=item * WRONG_DOCUMENT_ERR
-
-Raised if newAttr was created from a different document than the one that created
-the element.
-
-=item * NO_MODIFICATION_ALLOWED_ERR
-
-Raised if this node is readonly.
-
-=item * INUSE_ATTRIBUTE_ERR
-
-Raised if newAttr is already an attribute of another Element object. The DOM
-user must explicitly clone Attr nodes to re-use them in other elements.
-
-=back
-
-=item removeAttributeNode (oldAttr)
-
-Removes the specified attribute. If the removed Attr has a default value it is
-immediately replaced. If the Attr already is the default value, nothing happens
-and nothing is returned.
-
-Parameters:
- I  The Attr node to remove from the attribute list. 
-
-Return Value: The Attr node that was removed.
-
-DOMExceptions:
-
-=over 4
-
-=item * NO_MODIFICATION_ALLOWED_ERR
-
-Raised if this node is readonly.
-
-=item * NOT_FOUND_ERR
-
-Raised if oldAttr is not an attribute of the element.
-
-=back
-
-=head2 Additional methods not in the DOM Spec
-
-=over 4
-
-=item setTagName (newTagName)
-
-Sets the tag name of the Element. Note that this method is not portable
-between DOM implementations.
-
-DOMExceptions:
-
-=over 4
-
-=item * INVALID_CHARACTER_ERR
-
-Raised if the specified name contains an invalid character.
-
-=back
-
-=item check ($checker)
-
-Uses the specified L to validate the document.
-NOTE: an XML::Checker must be supplied. The checker can be created in
-different ways, e.g. when parsing a document with XML::DOM::ValParser,
-or with XML::DOM::Document::createChecker().
-See L for more info.
-
-=back
diff --git a/spaces/akhaliq/deeplab2/data/__init__.py b/spaces/akhaliq/deeplab2/data/__init__.py
deleted file mode 100644
index 35e4ce02ff422f3aa84ab644b88d65b13e0cbc03..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
diff --git a/spaces/akhaliq/deeplab2/utils/panoptic_instances_test.py b/spaces/akhaliq/deeplab2/utils/panoptic_instances_test.py
deleted file mode 100644
index 28550652470ca59cc2cd54b056933d6d5f9e2533..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/utils/panoptic_instances_test.py
+++ /dev/null
@@ -1,431 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tests for Tensorflow instance utils."""
-
-import numpy as np
-import tensorflow as tf
-
-from deeplab2.utils import panoptic_instances
-
-
-class TensorflowInstanceBoxesTest(tf.test.TestCase):
-
-  def testFilterPanopticLabelsByIgnoreCategories(self):
-    panoptic_divisor = 9
-    panoptic_labels = [
-        4 * panoptic_divisor + 3,
-        2 * panoptic_divisor + 7,
-        4 * panoptic_divisor,
-        1 * panoptic_divisor + 2,
-        4,
-        8,
-        4 * panoptic_divisor + 1,
-        1 * panoptic_divisor,
-    ]
-    panoptic_labels_tensor = tf.constant(panoptic_labels, dtype=tf.int32)
-
-    ignore_categories = [0, 4]
-    is_kept = panoptic_instances.instances_without_ignore_categories(
-        panoptic_labels_tensor, ignore_categories, panoptic_divisor)
-    with self.cached_session() as sess:
-      is_kept_result = sess.run(is_kept)
-
-    np.testing.assert_array_equal(
-        is_kept_result, [False, True, False, True, False, False, False, True])
-
-  def testFilterByIgnoreCategoriesWithEmptyIgnore(self):
-    panoptic_labels = [14, 19, 0, 2]
-    panoptic_labels_tensor = tf.constant(panoptic_labels, dtype=tf.int32)
-    panoptic_divisor = 7
-
-    is_kept_empty_ignore = panoptic_instances.instances_without_ignore_categories(
-        panoptic_labels_tensor, [], panoptic_divisor)
-    with self.cached_session() as sess:
-      is_kept_empty_ignore_result = sess.run(is_kept_empty_ignore)
-
-    np.testing.assert_array_equal(is_kept_empty_ignore_result,
-                                  [True, True, True, True])
-
-  def testFilterByIgnoreCategoriesWithEmptyPanopticLabels(self):
-    panoptic_labels = tf.zeros([0], dtype=tf.int32)
-    ignore_categories = [2, 3]
-    panoptic_divisor = 7
-
-    is_kept_empty_labels = panoptic_instances.instances_without_ignore_categories(
-        panoptic_labels, ignore_categories, panoptic_divisor)
-    with self.cached_session() as sess:
-      is_kept_empty_labels_result = sess.run(is_kept_empty_labels)
-    np.testing.assert_array_equal(is_kept_empty_labels_result, [])
-
-  def testComputesInstanceBoxes(self):
-    instance_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 2, 2],
-        [5, 1, 2, 2],
-        [1, 1, 1, 1],
-        [0, 0, 0, 2],
-        [0, 0, 0, 0],
-    ]
-    instance_labels_tensor = tf.constant(instance_labels, dtype=tf.int64)
-
-    category_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 1, 1],
-        [1, 1, 1, 1],
-        [2, 2, 2, 2],
-        [0, 0, 0, 1],
-        [3, 3, 3, 3],
-    ]
-    category_labels_tensor = tf.constant(category_labels, dtype=tf.int64)
-
-    panoptic_divisor = 13
-    panoptic_labels = (
-        instance_labels_tensor + panoptic_divisor * category_labels_tensor)
-
-    ignore_categories = tf.constant([0, 3], dtype=tf.int64)
-
-    unique_labels, box_coords = panoptic_instances.instance_boxes_from_masks(
-        panoptic_labels, ignore_categories, panoptic_divisor)
-    with self.cached_session() as sess:
-      unique_labels_result, box_coords_result = sess.run(
-          [unique_labels, box_coords])
-
-    np.testing.assert_array_equal(unique_labels_result, [14, 15, 18, 27])
-    np.testing.assert_array_equal(
-        box_coords_result,
-        [
-            [0, 1, 3, 3],  # Category 1, Instance 1
-            [1, 2, 5, 4],  # Category 1, Instance 2
-            [2, 0, 3, 1],  # Category 1, Instance 5
-            [3, 0, 4, 4],  # Category 2, Instance 1
-        ])
-
-  def testIgnoresNothing(self):
-    instance_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 2, 2],
-        [5, 1, 2, 2],
-    ]
-    instance_labels_tensor = tf.constant(instance_labels, dtype=tf.int64)
-
-    category_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 1, 1],
-        [1, 1, 1, 1],
-    ]
-    category_labels_tensor = tf.constant(category_labels, dtype=tf.int64)
-
-    panoptic_divisor = 7
-    panoptic_labels = (
-        instance_labels_tensor + panoptic_divisor * category_labels_tensor)
-
-    unique_labels, box_coords = panoptic_instances.instance_boxes_from_masks(
-        panoptic_labels, panoptic_divisor=panoptic_divisor)
-    with self.cached_session() as sess:
-      unique_labels_result, box_coords_result = sess.run(
-          [unique_labels, box_coords])
-
-    np.testing.assert_array_equal(unique_labels_result, [0, 8, 9, 12])
-    np.testing.assert_array_equal(
-        box_coords_result,
-        [
-            [0, 0, 2, 4],  # Category 0, Instance 0
-            [0, 1, 3, 3],  # Category 1, Instance 1
-            [1, 2, 3, 4],  # Category 1, Instance 2
-            [2, 0, 3, 1],  # Category 1, Instance 5
-        ])
-
-  def testIgnoresEverything(self):
-    instance_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 2, 2],
-        [5, 1, 2, 2],
-    ]
-    instance_labels_tensor = tf.constant(instance_labels, dtype=tf.int64)
-
-    category_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 2, 2],
-        [1, 1, 2, 2],
-    ]
-    category_labels_tensor = tf.constant(category_labels, dtype=tf.int64)
-
-    panoptic_divisor = 11
-    panoptic_labels = (
-        instance_labels_tensor + panoptic_divisor * category_labels_tensor)
-
-    ignore_categories = [0, 1, 2]
-
-    unique_labels, box_coords = panoptic_instances.instance_boxes_from_masks(
-        panoptic_labels, ignore_categories, panoptic_divisor)
-    with self.cached_session() as sess:
-      unique_labels_result, box_coords_result = sess.run(
-          [unique_labels, box_coords])
-
-    self.assertSequenceEqual(unique_labels_result.shape, (0,))
-    self.assertSequenceEqual(box_coords_result.shape, (0, 4))
-
-  def testSingleInstance(self):
-    instance_labels = [
-        [0, 0, 0],
-        [0, 0, 0],
-    ]
-    instance_labels_tensor = tf.constant(instance_labels, dtype=tf.int64)
-
-    category_labels = [
-        [3, 3, 3],
-        [3, 3, 3],
-    ]
-    category_labels_tensor = tf.constant(category_labels, dtype=tf.int64)
-
-    panoptic_divisor = 9
-    panoptic_labels = (
-        instance_labels_tensor + panoptic_divisor * category_labels_tensor)
-
-    unique_labels, box_coords = panoptic_instances.instance_boxes_from_masks(
-        panoptic_labels, panoptic_divisor=panoptic_divisor)
-    with self.cached_session() as sess:
-      unique_labels_result, box_coords_result = sess.run(
-          [unique_labels, box_coords])
-
-    np.testing.assert_array_equal(unique_labels_result, [27])
-    np.testing.assert_array_equal(box_coords_result, [[0, 0, 2, 3]])
-
-
-class InstanceScoringTest(tf.test.TestCase):
-
-  def testGetsSemanticProbabilities(self):
-    ignore_label = 3
-    semantic_labels = [
-        [0, 1, 1, 0, 0],
-        [0, 1, 1, 1, 0],
-        [1, 1, 1, 1, 1],
-        [2, 2, 2, 2, 2],
-    ]
-    semantic_tensor = tf.constant(semantic_labels, dtype=tf.int32)
-
-    instances = [
-        [1, 2, 2, 1, 1],
-        [1, 2, 3, 3, 1],
-        [6, 2, 3, 3, 3],
-        [2, 2, 2, 2, 2],
-    ]
-    instances_tensor = tf.constant(instances, dtype=tf.int32)
-
-    panoptic_divisor = 13
-    panoptic_labels = semantic_tensor * panoptic_divisor + instances_tensor
-
-    category_0_probability = [
-        [1.0, 0.0, 0.0, 0.8, 0.9],
-        [0.8, 0.0, 0.0, 0.2, 1.0],
-        [0.1, 0.0, 0.2, 0.1, 0.2],
-        [0.1, 0.1, 0.2, 0.0, 0.0],
-    ]
-    category_1_probability = [
-        [0.0, 1.0, 0.9, 0.0, 0.1],
-        [0.0, 1.0, 0.9, 0.9, 0.0],
-        [0.8, 0.7, 0.7, 0.8, 0.9],
-        [0.0, 0.2, 0.2, 0.0, 0.4],
-    ]
-    category_2_probability = (
-        np.ones((4, 5), dtype=np.float32) -
-        (np.array(category_0_probability) + np.array(category_1_probability)))
-    semantic_probability = np.stack([
-        category_0_probability, category_1_probability, category_2_probability
-    ],
-                                    axis=2)
-    semantic_probability_tensor = tf.constant(semantic_probability, tf.float32)
-
-    instance_panoptic_labels, _, instance_area = tf.unique_with_counts(
-        tf.reshape(panoptic_labels, [20]))
-    probs = panoptic_instances.per_instance_semantic_probabilities(
-        panoptic_labels, instance_panoptic_labels, instance_area,
-        semantic_probability_tensor, panoptic_divisor, ignore_label)
-
-    probs_result = probs.numpy()
-
-    np.testing.assert_array_almost_equal(probs_result,
-                                         [0.9, 0.9, 0.84, 0.8, 0.76])
-
-  def testCombineInstanceScores(self):
-    # This test does not have any pixels equal to ignore_label, so a dummy value
-    # is used as it's not being tested.
-    ignore_label = -1
-    semantic_labels = [
-        [0, 1, 1, 0],
-        [0, 1, 1, 1],
-        [1, 1, 1, 1],
-    ]
-    semantic_tensor = tf.constant(semantic_labels, dtype=tf.int32)
-
-    instances = [
-        [1, 2, 2, 1],
-        [1, 2, 3, 3],
-        [6, 2, 3, 3],
-    ]
-    instances_tensor = tf.constant(instances, dtype=tf.int32)
-
-    panoptic_divisor = 8
-    panoptic_labels = semantic_tensor * panoptic_divisor + instances_tensor
-
-    category_0_probability = [
-        [1.0, 0.0, 0.0, 0.8],
-        [0.8, 0.0, 0.0, 0.2],
-        [0.1, 0.0, 0.2, 0.1],
-    ]
-    category_1_probability = (
-        np.ones((3, 4), dtype=np.float32) - np.array(category_0_probability))
-    semantic_probability = np.stack([
-        category_0_probability,
-        category_1_probability,
-    ],
-                                    axis=2)
-    semantic_probability_tensor = tf.constant(semantic_probability, tf.float32)
-
-    instance_scores = [
-        [0.0, 0.5, 0.5, 0.0],
-        [0.0, 0.5, 0.7, 0.7],
-        [0.8, 0.5, 0.7, 0.7],
-    ]
-    instance_scores_tensor = tf.constant(instance_scores, tf.float32)
-
-    labels, combined_scores = panoptic_instances.combined_instance_scores(
-        panoptic_labels, semantic_probability_tensor, instance_scores_tensor,
-        panoptic_divisor, ignore_label)
-
-    labels_result = labels.numpy()
-    combined_scores_result = combined_scores.numpy()
-
-    np.testing.assert_array_equal(labels_result, [1, 10, 11, 14])
-    np.testing.assert_array_almost_equal(combined_scores_result,
-                                         [0, 0.5, 0.875 * 0.7, 0.9 * 0.8])
-
-  def testIgnoresLabel(self):
-    # This ignore label will be outside the valid range of an index into the
-    # last axis of the semantic probability tensor.
-    ignore_label = 255
-    semantic_labels = [
-        [0, 1],
-        [0, 255],
-    ]
-    semantic_tensor = tf.constant(semantic_labels, dtype=tf.int32)
-
-    instances = [
-        [1, 2],
-        [1, 3],
-    ]
-    instances_tensor = tf.constant(instances, dtype=tf.int32)
-
-    panoptic_divisor = 256
-    panoptic_labels = semantic_tensor * panoptic_divisor + instances_tensor
-
-    category_0_probability = [
-        [1.0, 0.0],
-        [0.8, 0.0],
-    ]
-    category_1_probability = (
-        np.ones((2, 2), dtype=np.float32) - np.array(category_0_probability))
-    semantic_probability = np.stack([
-        category_0_probability,
-        category_1_probability,
-    ],
-                                    axis=2)
-    semantic_probability_tensor = tf.constant(semantic_probability, tf.float32)
-
-    instance_scores = [
-        [1.0, 0.5],
-        [1.0, 0.5],
-    ]
-    instance_scores_tensor = tf.constant(instance_scores, tf.float32)
-
-    labels, combined_scores = panoptic_instances.combined_instance_scores(
-        panoptic_labels, semantic_probability_tensor, instance_scores_tensor,
-        panoptic_divisor, ignore_label)
-
-    labels_result = labels.numpy()
-    combined_scores_result = combined_scores.numpy()
-
-    np.testing.assert_array_equal(labels_result, [1, 258])
-    np.testing.assert_array_almost_equal(combined_scores_result, [0.9, 0.5])
-
-
-class InstanceIsCrowdTest(tf.test.TestCase):
-
-  def testGetsIsCrowdValues(self):
-    is_crowd_map = tf.constant([
-        [1, 0, 0],
-        [1, 0, 1],
-        [0, 1, 1],
-    ], tf.uint8)
-    is_crowd_map = tf.cast(is_crowd_map, tf.bool)
-    id_map = tf.constant([
-        [0, 1, 1],
-        [0, 2, 3],
-        [4, 3, 3],
-    ], tf.int32)
-    output_ids = tf.range(5)
-
-    instance_is_crowd = panoptic_instances.per_instance_is_crowd(
-        is_crowd_map, id_map, output_ids)
-
-    is_crowd_result = instance_is_crowd.numpy()
-    np.testing.assert_array_equal(is_crowd_result,
-                                  [True, False, False, True, False])
-
-  def testGetsSubsetOfIsCrowdValues(self):
-    is_crowd_map = tf.constant([
-        [1, 0, 0],
-        [1, 0, 1],
-        [0, 1, 1],
-    ], tf.uint8)
-    is_crowd_map = tf.cast(is_crowd_map, tf.bool)
-    id_map = tf.constant([
-        [0, 1, 1],
-        [0, 2, 3],
-        [4, 3, 3],
-    ], tf.int32)
-    output_ids = [1, 3]
-
-    instance_is_crowd = panoptic_instances.per_instance_is_crowd(
-        is_crowd_map, id_map, output_ids)
-
-    is_crowd_result = instance_is_crowd.numpy()
-    np.testing.assert_array_equal(is_crowd_result, [False, True])
-
-  def testGetsIsCrowdValuesWithIdsInArbitraryOrder(self):
-    is_crowd_map = tf.constant([
-        [1, 0, 0],
-        [1, 0, 1],
-        [1, 1, 1],
-    ], tf.uint8)
-    is_crowd_map = tf.cast(is_crowd_map, tf.bool)
-    id_map = tf.constant([
-        [0, 1, 1],
-        [0, 2, 3],
-        [4, 3, 3],
-    ], tf.int32)
-    output_ids = [1, 3, 0, 2, 4]
-
-    instance_is_crowd = panoptic_instances.per_instance_is_crowd(
-        is_crowd_map, id_map, output_ids)
-
-    is_crowd_result = instance_is_crowd.numpy()
-    np.testing.assert_array_equal(is_crowd_result,
-                                  [False, True, True, False, True])
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/spaces/akiraaaaaa/Waifu-Reina/infer_pack/modules.py b/spaces/akiraaaaaa/Waifu-Reina/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/akiraaaaaa/Waifu-Reina/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/algomuffin/jojo_fork/e4e/models/stylegan2/model.py b/spaces/algomuffin/jojo_fork/e4e/models/stylegan2/model.py
deleted file mode 100644
index fcb12af85669ab6fd7f79cb14ddbdf80b2fbd83d..0000000000000000000000000000000000000000
--- a/spaces/algomuffin/jojo_fork/e4e/models/stylegan2/model.py
+++ /dev/null
@@ -1,678 +0,0 @@
-import math
-import random
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-if torch.cuda.is_available():
-    from op.fused_act import FusedLeakyReLU, fused_leaky_relu
-    from op.upfirdn2d import upfirdn2d
-else:
-    from op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu
-    from op.upfirdn2d_cpu import upfirdn2d
-
-
-class PixelNorm(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
-    k = torch.tensor(k, dtype=torch.float32)
-
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]
-
-    k /= k.sum()
-
-    return k
-
-
-class Upsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel) * (factor ** 2)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2 + factor - 1
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
-
-        return out
-
-
-class Downsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
-
-        return out
-
-
-class Blur(nn.Module):
-    def __init__(self, kernel, pad, upsample_factor=1):
-        super().__init__()
-
-        kernel = make_kernel(kernel)
-
-        if upsample_factor > 1:
-            kernel = kernel * (upsample_factor ** 2)
-
-        self.register_buffer('kernel', kernel)
-
-        self.pad = pad
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, pad=self.pad)
-
-        return out
-
-
-class EqualConv2d(nn.Module):
-    def __init__(
-            self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
-        )
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
-        self.stride = stride
-        self.padding = padding
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channel))
-
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        out = F.conv2d(
-            input,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
-            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
-        )
-
-
-class EqualLinear(nn.Module):
-    def __init__(
-            self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
-        else:
-            self.bias = None
-
-        self.activation = activation
-
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.activation:
-            out = F.linear(input, self.weight * self.scale)
-            out = fused_leaky_relu(out, self.bias * self.lr_mul)
-
-        else:
-            out = F.linear(
-                input, self.weight * self.scale, bias=self.bias * self.lr_mul
-            )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
-        )
-
-
-class ScaledLeakyReLU(nn.Module):
-    def __init__(self, negative_slope=0.2):
-        super().__init__()
-
-        self.negative_slope = negative_slope
-
-    def forward(self, input):
-        out = F.leaky_relu(input, negative_slope=self.negative_slope)
-
-        return out * math.sqrt(2)
-
-
-class ModulatedConv2d(nn.Module):
-    def __init__(
-            self,
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            demodulate=True,
-            upsample=False,
-            downsample=False,
-            blur_kernel=[1, 3, 3, 1],
-    ):
-        super().__init__()
-
-        self.eps = 1e-8
-        self.kernel_size = kernel_size
-        self.in_channel = in_channel
-        self.out_channel = out_channel
-        self.upsample = upsample
-        self.downsample = downsample
-
-        if upsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) - (kernel_size - 1)
-            pad0 = (p + 1) // 2 + factor - 1
-            pad1 = p // 2 + 1
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
-
-        fan_in = in_channel * kernel_size ** 2
-        self.scale = 1 / math.sqrt(fan_in)
-        self.padding = kernel_size // 2
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
-        )
-
-        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
-        self.demodulate = demodulate
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
-            f'upsample={self.upsample}, downsample={self.downsample})'
-        )
-
-    def forward(self, input, style):
-        batch, in_channel, height, width = input.shape
-
-        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
-        weight = self.scale * self.weight * style
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
-            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
-        weight = weight.view(
-            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
-        )
-
-        if self.upsample:
-            input = input.view(1, batch * in_channel, height, width)
-            weight = weight.view(
-                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-            out = self.blur(out)
-
-        elif self.downsample:
-            input = self.blur(input)
-            _, _, height, width = input.shape
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        else:
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        return out
-
-
-class NoiseInjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.zeros(1))
-
-    def forward(self, image, noise=None):
-        if noise is None:
-            batch, _, height, width = image.shape
-            noise = image.new_empty(batch, 1, height, width).normal_()
-
-        return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
-    def __init__(self, channel, size=4):
-        super().__init__()
-
-        self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
-    def forward(self, input):
-        batch = input.shape[0]
-        out = self.input.repeat(batch, 1, 1, 1)
-
-        return out
-
-
-class StyledConv(nn.Module):
-    def __init__(
-            self,
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=False,
-            blur_kernel=[1, 3, 3, 1],
-            demodulate=True,
-    ):
-        super().__init__()
-
-        self.conv = ModulatedConv2d(
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=upsample,
-            blur_kernel=blur_kernel,
-            demodulate=demodulate,
-        )
-
-        self.noise = NoiseInjection()
-        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
-        # self.activate = ScaledLeakyReLU(0.2)
-        self.activate = FusedLeakyReLU(out_channel)
-
-    def forward(self, input, style, noise=None):
-        out = self.conv(input, style)
-        out = self.noise(out, noise=noise)
-        # out = out + self.bias
-        out = self.activate(out)
-
-        return out
-
-
-class ToRGB(nn.Module):
-    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        if upsample:
-            self.upsample = Upsample(blur_kernel)
-
-        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, input, style, skip=None):
-        out = self.conv(input, style)
-        out = out + self.bias
-
-        if skip is not None:
-            skip = self.upsample(skip)
-
-            out = out + skip
-
-        return out
-
-
-class Generator(nn.Module):
-    def __init__(
-            self,
-            size,
-            style_dim,
-            n_mlp,
-            channel_multiplier=2,
-            blur_kernel=[1, 3, 3, 1],
-            lr_mlp=0.01,
-    ):
-        super().__init__()
-
-        self.size = size
-
-        self.style_dim = style_dim
-
-        layers = [PixelNorm()]
-
-        for i in range(n_mlp):
-            layers.append(
-                EqualLinear(
-                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
-                )
-            )
-
-        self.style = nn.Sequential(*layers)
-
-        self.channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        self.input = ConstantInput(self.channels[4])
-        self.conv1 = StyledConv(
-            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
-        )
-        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
-
-        self.log_size = int(math.log(size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-
-        self.convs = nn.ModuleList()
-        self.upsamples = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channel = self.channels[4]
-
-        for layer_idx in range(self.num_layers):
-            res = (layer_idx + 5) // 2
-            shape = [1, 1, 2 ** res, 2 ** res]
-            self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
-
-        for i in range(3, self.log_size + 1):
-            out_channel = self.channels[2 ** i]
-
-            self.convs.append(
-                StyledConv(
-                    in_channel,
-                    out_channel,
-                    3,
-                    style_dim,
-                    upsample=True,
-                    blur_kernel=blur_kernel,
-                )
-            )
-
-            self.convs.append(
-                StyledConv(
-                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
-                )
-            )
-
-            self.to_rgbs.append(ToRGB(out_channel, style_dim))
-
-            in_channel = out_channel
-
-        self.n_latent = self.log_size * 2 - 2
-
-    def make_noise(self):
-        device = self.input.input.device
-
-        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
-        return noises
-
-    def mean_latent(self, n_latent):
-        latent_in = torch.randn(
-            n_latent, self.style_dim, device=self.input.input.device
-        )
-        latent = self.style(latent_in).mean(0, keepdim=True)
-
-        return latent
-
-    def get_latent(self, input):
-        return self.style(input)
-
-    def forward(
-            self,
-            styles,
-            return_latents=False,
-            return_features=False,
-            inject_index=None,
-            truncation=1,
-            truncation_latent=None,
-            input_is_latent=False,
-            noise=None,
-            randomize_noise=True,
-    ):
-        if not input_is_latent:
-            styles = [self.style(s) for s in styles]
-
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers
-            else:
-                noise = [
-                    getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
-                ]
-
-        if truncation < 1:
-            style_t = []
-
-            for style in styles:
-                style_t.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-
-            styles = style_t
-
-        if len(styles) < 2:
-            inject_index = self.n_latent
-
-            if styles[0].ndim < 3:
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:
-                latent = styles[0]
-
-        else:
-            if inject_index is None:
-                inject_index = random.randint(1, self.n_latent - 1)
-
-            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
-            latent = torch.cat([latent, latent2], 1)
-
-        out = self.input(latent)
-        out = self.conv1(out, latent[:, 0], noise=noise[0])
-
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-                self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        elif return_features:
-            return image, out
-        else:
-            return image, None
-
-
-class ConvLayer(nn.Sequential):
-    def __init__(
-            self,
-            in_channel,
-            out_channel,
-            kernel_size,
-            downsample=False,
-            blur_kernel=[1, 3, 3, 1],
-            bias=True,
-            activate=True,
-    ):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
-
-            stride = 2
-            self.padding = 0
-
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-
-        layers.append(
-            EqualConv2d(
-                in_channel,
-                out_channel,
-                kernel_size,
-                padding=self.padding,
-                stride=stride,
-                bias=bias and not activate,
-            )
-        )
-
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channel))
-
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        self.conv1 = ConvLayer(in_channel, in_channel, 3)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
-
-        self.skip = ConvLayer(
-            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
-        )
-
-    def forward(self, input):
-        out = self.conv1(input)
-        out = self.conv2(out)
-
-        skip = self.skip(input)
-        out = (out + skip) / math.sqrt(2)
-
-        return out
-
-
-class Discriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        convs = [ConvLayer(3, channels[size], 1)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
-            EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, input):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-
-        out = out.view(batch, -1)
-        out = self.final_linear(out)
-
-        return out
diff --git a/spaces/ali-ghamdan/gfp-Gans/gfpgan/archs/__init__.py b/spaces/ali-ghamdan/gfp-Gans/gfpgan/archs/__init__.py
deleted file mode 100644
index bec5f17bfa38729b55f57cae8e40c27310db2b7b..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/gfp-Gans/gfpgan/archs/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import arch modules for registry
-# scan all the files that end with '_arch.py' under the archs folder
-arch_folder = osp.dirname(osp.abspath(__file__))
-arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
-# import all the arch modules
-_arch_modules = [importlib.import_module(f'gfpgan.archs.{file_name}') for file_name in arch_filenames]
diff --git a/spaces/allknowingroger/Image-Models-Test155/README.md b/spaces/allknowingroger/Image-Models-Test155/README.md
deleted file mode 100644
index a3a43bf672ca727d8113068aed4ea790c9de9309..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test155/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-duplicated_from: allknowingroger/Image-Models-Test142
----
-
-
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test193/app.py b/spaces/allknowingroger/Image-Models-Test193/app.py
deleted file mode 100644
index b64d2f0afab873e74f99fd132125f9a42259fca7..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test193/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "SaiCharan7829/my-pet-dog",
-    "shubhangkhare/stable-diffusion-xl-base-1.0-v1",
-    "Yntec/theAllysMixIIIRevolutions",
-    "artificialguybr/LineAniRedmond-LinearMangaSDXL-V2",
-    "flobbit/div0-tank-sdxl-lora",
-    "Yntec/Thriller",
-    "artificialguybr/LogoRedmond-LogoLoraForSDXL-V2",
-    "roshantajne/my-pet-dog-aak",
-    "artificialguybr/analogredmond-v2",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test59/app.py b/spaces/allknowingroger/Image-Models-Test59/app.py
deleted file mode 100644
index f87e6557321db1629e81531de7e6919ac5c0f069..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test59/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "digiplay/majicMIX_realistic_v6",
-    "nitrosocke/Ghibli-Diffusion",
-    "Nacken/ki-kunst-kirsten-kloeckner-colab",
-    "Yntec/GameAssetsDigitalUnitsCreationKit",
-    "digiplay/AbsoluteReality_v1.8.1",
-    "Yntec/a-ZovyaRPGArtistV2VAE",
-    "AIARTCHAN/camelliamix_v2.0",
-    "tensorrt/stable-diffusion-2-1",
-    "HamdiJr/jr_dreambooth",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/Interpreter.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/Interpreter.py
deleted file mode 100644
index 9ec391f2a021dc75ceff0e19006f0b5866176824..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/Interpreter.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""
-This module deals with interpreting the parse tree as Python
-would have done, in the compiler.
-
-For now this only covers parse tree to value conversion of
-compile-time values.
-"""
-
-from __future__ import absolute_import
-
-from .Nodes import *
-from .ExprNodes import *
-from .Errors import CompileError
-
-
-class EmptyScope(object):
-    def lookup(self, name):
-        return None
-
-empty_scope = EmptyScope()
-
-def interpret_compiletime_options(optlist, optdict, type_env=None, type_args=()):
-    """
-    Tries to interpret a list of compile time option nodes.
-    The result will be a tuple (optlist, optdict) but where
-    all expression nodes have been interpreted. The result is
-    in the form of tuples (value, pos).
-
-    optlist is a list of nodes, while optdict is a DictNode (the
-    result optdict is a dict)
-
-    If type_env is set, all type nodes will be analysed and the resulting
-    type set. Otherwise only interpretateable ExprNodes
-    are allowed, other nodes raises errors.
-
-    A CompileError will be raised if there are problems.
-    """
-
-    def interpret(node, ix):
-        if ix in type_args:
-            if type_env:
-                type = node.analyse_as_type(type_env)
-                if not type:
-                    raise CompileError(node.pos, "Invalid type.")
-                return (type, node.pos)
-            else:
-                raise CompileError(node.pos, "Type not allowed here.")
-        else:
-            if (sys.version_info[0] >=3 and
-                isinstance(node, StringNode) and
-                node.unicode_value is not None):
-                return (node.unicode_value, node.pos)
-            return (node.compile_time_value(empty_scope), node.pos)
-
-    if optlist:
-        optlist = [interpret(x, ix) for ix, x in enumerate(optlist)]
-    if optdict:
-        assert isinstance(optdict, DictNode)
-        new_optdict = {}
-        for item in optdict.key_value_pairs:
-            new_key, dummy = interpret(item.key, None)
-            new_optdict[new_key] = interpret(item.value, item.key.value)
-        optdict = new_optdict
-    return (optlist, new_optdict)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/StringEncoding.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/StringEncoding.py
deleted file mode 100644
index c37e8aab79969f3c8b96523697b39bd5a4020b73..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/StringEncoding.py
+++ /dev/null
@@ -1,363 +0,0 @@
-#
-#   Cython -- encoding related tools
-#
-
-from __future__ import absolute_import
-
-import re
-import sys
-
-if sys.version_info[0] >= 3:
-    _unicode, _str, _bytes, _unichr = str, str, bytes, chr
-    IS_PYTHON3 = True
-else:
-    _unicode, _str, _bytes, _unichr = unicode, str, str, unichr
-    IS_PYTHON3 = False
-
-empty_bytes = _bytes()
-empty_unicode = _unicode()
-
-join_bytes = empty_bytes.join
-
-
-class UnicodeLiteralBuilder(object):
-    """Assemble a unicode string.
-    """
-    def __init__(self):
-        self.chars = []
-
-    def append(self, characters):
-        if isinstance(characters, _bytes):
-            # this came from a Py2 string literal in the parser code
-            characters = characters.decode("ASCII")
-        assert isinstance(characters, _unicode), str(type(characters))
-        self.chars.append(characters)
-
-    if sys.maxunicode == 65535:
-        def append_charval(self, char_number):
-            if char_number > 65535:
-                # wide Unicode character on narrow platform => replace
-                # by surrogate pair
-                char_number -= 0x10000
-                self.chars.append( _unichr((char_number // 1024) + 0xD800) )
-                self.chars.append( _unichr((char_number  % 1024) + 0xDC00) )
-            else:
-                self.chars.append( _unichr(char_number) )
-    else:
-        def append_charval(self, char_number):
-            self.chars.append( _unichr(char_number) )
-
-    def append_uescape(self, char_number, escape_string):
-        self.append_charval(char_number)
-
-    def getstring(self):
-        return EncodedString(u''.join(self.chars))
-
-    def getstrings(self):
-        return (None, self.getstring())
-
-
-class BytesLiteralBuilder(object):
-    """Assemble a byte string or char value.
-    """
-    def __init__(self, target_encoding):
-        self.chars = []
-        self.target_encoding = target_encoding
-
-    def append(self, characters):
-        if isinstance(characters, _unicode):
-            characters = characters.encode(self.target_encoding)
-        assert isinstance(characters, _bytes), str(type(characters))
-        self.chars.append(characters)
-
-    def append_charval(self, char_number):
-        self.chars.append( _unichr(char_number).encode('ISO-8859-1') )
-
-    def append_uescape(self, char_number, escape_string):
-        self.append(escape_string)
-
-    def getstring(self):
-        # this *must* return a byte string!
-        return bytes_literal(join_bytes(self.chars), self.target_encoding)
-
-    def getchar(self):
-        # this *must* return a byte string!
-        return self.getstring()
-
-    def getstrings(self):
-        return (self.getstring(), None)
-
-
-class StrLiteralBuilder(object):
-    """Assemble both a bytes and a unicode representation of a string.
-    """
-    def __init__(self, target_encoding):
-        self._bytes   = BytesLiteralBuilder(target_encoding)
-        self._unicode = UnicodeLiteralBuilder()
-
-    def append(self, characters):
-        self._bytes.append(characters)
-        self._unicode.append(characters)
-
-    def append_charval(self, char_number):
-        self._bytes.append_charval(char_number)
-        self._unicode.append_charval(char_number)
-
-    def append_uescape(self, char_number, escape_string):
-        self._bytes.append(escape_string)
-        self._unicode.append_charval(char_number)
-
-    def getstrings(self):
-        return (self._bytes.getstring(), self._unicode.getstring())
-
-
-class EncodedString(_unicode):
-    # unicode string subclass to keep track of the original encoding.
-    # 'encoding' is None for unicode strings and the source encoding
-    # otherwise
-    encoding = None
-
-    def __deepcopy__(self, memo):
-        return self
-
-    def byteencode(self):
-        assert self.encoding is not None
-        return self.encode(self.encoding)
-
-    def utf8encode(self):
-        assert self.encoding is None
-        return self.encode("UTF-8")
-
-    @property
-    def is_unicode(self):
-        return self.encoding is None
-
-    def contains_surrogates(self):
-        return string_contains_surrogates(self)
-
-    def as_utf8_string(self):
-        return bytes_literal(self.utf8encode(), 'utf8')
-
-
-def string_contains_surrogates(ustring):
-    """
-    Check if the unicode string contains surrogate code points
-    on a CPython platform with wide (UCS-4) or narrow (UTF-16)
-    Unicode, i.e. characters that would be spelled as two
-    separate code units on a narrow platform.
-    """
-    for c in map(ord, ustring):
-        if c > 65535:  # can only happen on wide platforms
-            return True
-        if 0xD800 <= c <= 0xDFFF:
-            return True
-    return False
-
-
-def string_contains_lone_surrogates(ustring):
-    """
-    Check if the unicode string contains lone surrogate code points
-    on a CPython platform with wide (UCS-4) or narrow (UTF-16)
-    Unicode, i.e. characters that would be spelled as two
-    separate code units on a narrow platform, but that do not form a pair.
-    """
-    last_was_start = False
-    unicode_uses_surrogate_encoding = sys.maxunicode == 65535
-    for c in map(ord, ustring):
-        # surrogates tend to be rare
-        if c < 0xD800 or c > 0xDFFF:
-            if last_was_start:
-                return True
-        elif not unicode_uses_surrogate_encoding:
-            # on 32bit Unicode platforms, there is never a pair
-            return True
-        elif c <= 0xDBFF:
-            if last_was_start:
-                return True  # lone start
-            last_was_start = True
-        else:
-            if not last_was_start:
-                return True  # lone end
-            last_was_start = False
-    return last_was_start
-
-
-class BytesLiteral(_bytes):
-    # bytes subclass that is compatible with EncodedString
-    encoding = None
-
-    def __deepcopy__(self, memo):
-        return self
-
-    def byteencode(self):
-        if IS_PYTHON3:
-            return _bytes(self)
-        else:
-            # fake-recode the string to make it a plain bytes object
-            return self.decode('ISO-8859-1').encode('ISO-8859-1')
-
-    def utf8encode(self):
-        assert False, "this is not a unicode string: %r" % self
-
-    def __str__(self):
-        """Fake-decode the byte string to unicode to support %
-        formatting of unicode strings.
-        """
-        return self.decode('ISO-8859-1')
-
-    is_unicode = False
-
-    def as_c_string_literal(self):
-        value = split_string_literal(escape_byte_string(self))
-        return '"%s"' % value
-
-
-def bytes_literal(s, encoding):
-    assert isinstance(s, bytes)
-    s = BytesLiteral(s)
-    s.encoding = encoding
-    return s
-
-
-def encoded_string(s, encoding):
-    assert isinstance(s, (_unicode, bytes))
-    s = EncodedString(s)
-    if encoding is not None:
-        s.encoding = encoding
-    return s
-
-
-char_from_escape_sequence = {
-    r'\a' : u'\a',
-    r'\b' : u'\b',
-    r'\f' : u'\f',
-    r'\n' : u'\n',
-    r'\r' : u'\r',
-    r'\t' : u'\t',
-    r'\v' : u'\v',
-    }.get
-
-_c_special = ('\\', '??', '"') + tuple(map(chr, range(32)))
-
-
-def _to_escape_sequence(s):
-    if s in '\n\r\t':
-        return repr(s)[1:-1]
-    elif s == '"':
-        return r'\"'
-    elif s == '\\':
-        return r'\\'
-    else:
-        # within a character sequence, oct passes much better than hex
-        return ''.join(['\\%03o' % ord(c) for c in s])
-
-
-def _build_specials_replacer():
-    subexps = []
-    replacements = {}
-    for special in _c_special:
-        regexp = ''.join(['[%s]' % c.replace('\\', '\\\\') for c in special])
-        subexps.append(regexp)
-        replacements[special.encode('ASCII')] = _to_escape_sequence(special).encode('ASCII')
-    sub = re.compile(('(%s)' % '|'.join(subexps)).encode('ASCII')).sub
-    def replace_specials(m):
-        return replacements[m.group(1)]
-    def replace(s):
-        return sub(replace_specials, s)
-    return replace
-
-_replace_specials = _build_specials_replacer()
-
-
-def escape_char(c):
-    if IS_PYTHON3:
-        c = c.decode('ISO-8859-1')
-    if c in '\n\r\t\\':
-        return repr(c)[1:-1]
-    elif c == "'":
-        return "\\'"
-    n = ord(c)
-    if n < 32 or n > 127:
-        # hex works well for characters
-        return "\\x%02X" % n
-    else:
-        return c
-
-def escape_byte_string(s):
-    """Escape a byte string so that it can be written into C code.
-    Note that this returns a Unicode string instead which, when
-    encoded as ISO-8859-1, will result in the correct byte sequence
-    being written.
-    """
-    s = _replace_specials(s)
-    try:
-        return s.decode("ASCII") # trial decoding: plain ASCII => done
-    except UnicodeDecodeError:
-        pass
-    if IS_PYTHON3:
-        s_new = bytearray()
-        append, extend = s_new.append, s_new.extend
-        for b in s:
-            if b >= 128:
-                extend(('\\%3o' % b).encode('ASCII'))
-            else:
-                append(b)
-        return s_new.decode('ISO-8859-1')
-    else:
-        l = []
-        append = l.append
-        for c in s:
-            o = ord(c)
-            if o >= 128:
-                append('\\%3o' % o)
-            else:
-                append(c)
-        return join_bytes(l).decode('ISO-8859-1')
-
-def split_string_literal(s, limit=2000):
-    # MSVC can't handle long string literals.
-    if len(s) < limit:
-        return s
-    else:
-        start = 0
-        chunks = []
-        while start < len(s):
-            end = start + limit
-            if len(s) > end-4 and '\\' in s[end-4:end]:
-                end -= 4 - s[end-4:end].find('\\') # just before the backslash
-                while s[end-1] == '\\':
-                    end -= 1
-                    if end == start:
-                        # must have been a long line of backslashes
-                        end = start + limit - (limit % 2) - 4
-                        break
-            chunks.append(s[start:end])
-            start = end
-        return '""'.join(chunks)
-
-def encode_pyunicode_string(s):
-    """Create Py_UNICODE[] representation of a given unicode string.
-    """
-    s = list(map(ord, s)) + [0]
-
-    if sys.maxunicode >= 0x10000:  # Wide build or Py3.3
-        utf16, utf32 = [], s
-        for code_point in s:
-            if code_point >= 0x10000:  # outside of BMP
-                high, low = divmod(code_point - 0x10000, 1024)
-                utf16.append(high + 0xD800)
-                utf16.append(low + 0xDC00)
-            else:
-                utf16.append(code_point)
-    else:
-        utf16, utf32 = s, []
-        for code_unit in s:
-            if 0xDC00 <= code_unit <= 0xDFFF and utf32 and 0xD800 <= utf32[-1] <= 0xDBFF:
-                high, low = utf32[-1], code_unit
-                utf32[-1] = ((high & 0x3FF) << 10) + (low & 0x3FF) + 0x10000
-            else:
-                utf32.append(code_unit)
-
-    if utf16 == utf32:
-        utf16 = []
-    return ",".join(map(_unicode, utf16)), ",".join(map(_unicode, utf32))
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/BmpImagePlugin.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/BmpImagePlugin.py
deleted file mode 100644
index bdf51aa5cf1ab1b3f3ff7599a99c8d14f9efc8e2..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/BmpImagePlugin.py
+++ /dev/null
@@ -1,464 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# BMP file handler
-#
-# Windows (and OS/2) native bitmap storage format.
-#
-# history:
-# 1995-09-01 fl   Created
-# 1996-04-30 fl   Added save
-# 1997-08-27 fl   Fixed save of 1-bit images
-# 1998-03-06 fl   Load P images as L where possible
-# 1998-07-03 fl   Load P images as 1 where possible
-# 1998-12-29 fl   Handle small palettes
-# 2002-12-30 fl   Fixed load of 1-bit palette images
-# 2003-04-21 fl   Fixed load of 1-bit monochrome images
-# 2003-04-23 fl   Added limited support for BI_BITFIELDS compression
-#
-# Copyright (c) 1997-2003 by Secret Labs AB
-# Copyright (c) 1995-2003 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import os
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i16le as i16
-from ._binary import i32le as i32
-from ._binary import o8
-from ._binary import o16le as o16
-from ._binary import o32le as o32
-
-#
-# --------------------------------------------------------------------
-# Read BMP file
-
-BIT2MODE = {
-    # bits => mode, rawmode
-    1: ("P", "P;1"),
-    4: ("P", "P;4"),
-    8: ("P", "P"),
-    16: ("RGB", "BGR;15"),
-    24: ("RGB", "BGR"),
-    32: ("RGB", "BGRX"),
-}
-
-
-def _accept(prefix):
-    return prefix[:2] == b"BM"
-
-
-def _dib_accept(prefix):
-    return i32(prefix) in [12, 40, 64, 108, 124]
-
-
-# =============================================================================
-# Image plugin for the Windows BMP format.
-# =============================================================================
-class BmpImageFile(ImageFile.ImageFile):
-    """Image plugin for the Windows Bitmap format (BMP)"""
-
-    # ------------------------------------------------------------- Description
-    format_description = "Windows Bitmap"
-    format = "BMP"
-
-    # -------------------------------------------------- BMP Compression values
-    COMPRESSIONS = {"RAW": 0, "RLE8": 1, "RLE4": 2, "BITFIELDS": 3, "JPEG": 4, "PNG": 5}
-    for k, v in COMPRESSIONS.items():
-        vars()[k] = v
-
-    def _bitmap(self, header=0, offset=0):
-        """Read relevant info about the BMP"""
-        read, seek = self.fp.read, self.fp.seek
-        if header:
-            seek(header)
-        # read bmp header size @offset 14 (this is part of the header size)
-        file_info = {"header_size": i32(read(4)), "direction": -1}
-
-        # -------------------- If requested, read header at a specific position
-        # read the rest of the bmp header, without its size
-        header_data = ImageFile._safe_read(self.fp, file_info["header_size"] - 4)
-
-        # -------------------------------------------------- IBM OS/2 Bitmap v1
-        # ----- This format has different offsets because of width/height types
-        if file_info["header_size"] == 12:
-            file_info["width"] = i16(header_data, 0)
-            file_info["height"] = i16(header_data, 2)
-            file_info["planes"] = i16(header_data, 4)
-            file_info["bits"] = i16(header_data, 6)
-            file_info["compression"] = self.RAW
-            file_info["palette_padding"] = 3
-
-        # --------------------------------------------- Windows Bitmap v2 to v5
-        # v3, OS/2 v2, v4, v5
-        elif file_info["header_size"] in (40, 64, 108, 124):
-            file_info["y_flip"] = header_data[7] == 0xFF
-            file_info["direction"] = 1 if file_info["y_flip"] else -1
-            file_info["width"] = i32(header_data, 0)
-            file_info["height"] = (
-                i32(header_data, 4)
-                if not file_info["y_flip"]
-                else 2**32 - i32(header_data, 4)
-            )
-            file_info["planes"] = i16(header_data, 8)
-            file_info["bits"] = i16(header_data, 10)
-            file_info["compression"] = i32(header_data, 12)
-            # byte size of pixel data
-            file_info["data_size"] = i32(header_data, 16)
-            file_info["pixels_per_meter"] = (
-                i32(header_data, 20),
-                i32(header_data, 24),
-            )
-            file_info["colors"] = i32(header_data, 28)
-            file_info["palette_padding"] = 4
-            self.info["dpi"] = tuple(x / 39.3701 for x in file_info["pixels_per_meter"])
-            if file_info["compression"] == self.BITFIELDS:
-                if len(header_data) >= 52:
-                    for idx, mask in enumerate(
-                        ["r_mask", "g_mask", "b_mask", "a_mask"]
-                    ):
-                        file_info[mask] = i32(header_data, 36 + idx * 4)
-                else:
-                    # 40 byte headers only have the three components in the
-                    # bitfields masks, ref:
-                    # https://msdn.microsoft.com/en-us/library/windows/desktop/dd183376(v=vs.85).aspx
-                    # See also
-                    # https://github.com/python-pillow/Pillow/issues/1293
-                    # There is a 4th component in the RGBQuad, in the alpha
-                    # location, but it is listed as a reserved component,
-                    # and it is not generally an alpha channel
-                    file_info["a_mask"] = 0x0
-                    for mask in ["r_mask", "g_mask", "b_mask"]:
-                        file_info[mask] = i32(read(4))
-                file_info["rgb_mask"] = (
-                    file_info["r_mask"],
-                    file_info["g_mask"],
-                    file_info["b_mask"],
-                )
-                file_info["rgba_mask"] = (
-                    file_info["r_mask"],
-                    file_info["g_mask"],
-                    file_info["b_mask"],
-                    file_info["a_mask"],
-                )
-        else:
-            raise OSError(f"Unsupported BMP header type ({file_info['header_size']})")
-
-        # ------------------ Special case : header is reported 40, which
-        # ---------------------- is shorter than real size for bpp >= 16
-        self._size = file_info["width"], file_info["height"]
-
-        # ------- If color count was not found in the header, compute from bits
-        file_info["colors"] = (
-            file_info["colors"]
-            if file_info.get("colors", 0)
-            else (1 << file_info["bits"])
-        )
-        if offset == 14 + file_info["header_size"] and file_info["bits"] <= 8:
-            offset += 4 * file_info["colors"]
-
-        # ---------------------- Check bit depth for unusual unsupported values
-        self.mode, raw_mode = BIT2MODE.get(file_info["bits"], (None, None))
-        if self.mode is None:
-            raise OSError(f"Unsupported BMP pixel depth ({file_info['bits']})")
-
-        # ---------------- Process BMP with Bitfields compression (not palette)
-        decoder_name = "raw"
-        if file_info["compression"] == self.BITFIELDS:
-            SUPPORTED = {
-                32: [
-                    (0xFF0000, 0xFF00, 0xFF, 0x0),
-                    (0xFF000000, 0xFF0000, 0xFF00, 0x0),
-                    (0xFF000000, 0xFF0000, 0xFF00, 0xFF),
-                    (0xFF, 0xFF00, 0xFF0000, 0xFF000000),
-                    (0xFF0000, 0xFF00, 0xFF, 0xFF000000),
-                    (0x0, 0x0, 0x0, 0x0),
-                ],
-                24: [(0xFF0000, 0xFF00, 0xFF)],
-                16: [(0xF800, 0x7E0, 0x1F), (0x7C00, 0x3E0, 0x1F)],
-            }
-            MASK_MODES = {
-                (32, (0xFF0000, 0xFF00, 0xFF, 0x0)): "BGRX",
-                (32, (0xFF000000, 0xFF0000, 0xFF00, 0x0)): "XBGR",
-                (32, (0xFF000000, 0xFF0000, 0xFF00, 0xFF)): "ABGR",
-                (32, (0xFF, 0xFF00, 0xFF0000, 0xFF000000)): "RGBA",
-                (32, (0xFF0000, 0xFF00, 0xFF, 0xFF000000)): "BGRA",
-                (32, (0x0, 0x0, 0x0, 0x0)): "BGRA",
-                (24, (0xFF0000, 0xFF00, 0xFF)): "BGR",
-                (16, (0xF800, 0x7E0, 0x1F)): "BGR;16",
-                (16, (0x7C00, 0x3E0, 0x1F)): "BGR;15",
-            }
-            if file_info["bits"] in SUPPORTED:
-                if (
-                    file_info["bits"] == 32
-                    and file_info["rgba_mask"] in SUPPORTED[file_info["bits"]]
-                ):
-                    raw_mode = MASK_MODES[(file_info["bits"], file_info["rgba_mask"])]
-                    self.mode = "RGBA" if "A" in raw_mode else self.mode
-                elif (
-                    file_info["bits"] in (24, 16)
-                    and file_info["rgb_mask"] in SUPPORTED[file_info["bits"]]
-                ):
-                    raw_mode = MASK_MODES[(file_info["bits"], file_info["rgb_mask"])]
-                else:
-                    raise OSError("Unsupported BMP bitfields layout")
-            else:
-                raise OSError("Unsupported BMP bitfields layout")
-        elif file_info["compression"] == self.RAW:
-            if file_info["bits"] == 32 and header == 22:  # 32-bit .cur offset
-                raw_mode, self.mode = "BGRA", "RGBA"
-        elif file_info["compression"] in (self.RLE8, self.RLE4):
-            decoder_name = "bmp_rle"
-        else:
-            raise OSError(f"Unsupported BMP compression ({file_info['compression']})")
-
-        # --------------- Once the header is processed, process the palette/LUT
-        if self.mode == "P":  # Paletted for 1, 4 and 8 bit images
-
-            # ---------------------------------------------------- 1-bit images
-            if not (0 < file_info["colors"] <= 65536):
-                raise OSError(f"Unsupported BMP Palette size ({file_info['colors']})")
-            else:
-                padding = file_info["palette_padding"]
-                palette = read(padding * file_info["colors"])
-                greyscale = True
-                indices = (
-                    (0, 255)
-                    if file_info["colors"] == 2
-                    else list(range(file_info["colors"]))
-                )
-
-                # ----------------- Check if greyscale and ignore palette if so
-                for ind, val in enumerate(indices):
-                    rgb = palette[ind * padding : ind * padding + 3]
-                    if rgb != o8(val) * 3:
-                        greyscale = False
-
-                # ------- If all colors are grey, white or black, ditch palette
-                if greyscale:
-                    self.mode = "1" if file_info["colors"] == 2 else "L"
-                    raw_mode = self.mode
-                else:
-                    self.mode = "P"
-                    self.palette = ImagePalette.raw(
-                        "BGRX" if padding == 4 else "BGR", palette
-                    )
-
-        # ---------------------------- Finally set the tile data for the plugin
-        self.info["compression"] = file_info["compression"]
-        args = [raw_mode]
-        if decoder_name == "bmp_rle":
-            args.append(file_info["compression"] == self.RLE4)
-        else:
-            args.append(((file_info["width"] * file_info["bits"] + 31) >> 3) & (~3))
-        args.append(file_info["direction"])
-        self.tile = [
-            (
-                decoder_name,
-                (0, 0, file_info["width"], file_info["height"]),
-                offset or self.fp.tell(),
-                tuple(args),
-            )
-        ]
-
-    def _open(self):
-        """Open file, check magic number and read header"""
-        # read 14 bytes: magic number, filesize, reserved, header final offset
-        head_data = self.fp.read(14)
-        # choke if the file does not have the required magic bytes
-        if not _accept(head_data):
-            raise SyntaxError("Not a BMP file")
-        # read the start position of the BMP image data (u32)
-        offset = i32(head_data, 10)
-        # load bitmap information (offset=raster info)
-        self._bitmap(offset=offset)
-
-
-class BmpRleDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def decode(self, buffer):
-        rle4 = self.args[1]
-        data = bytearray()
-        x = 0
-        while len(data) < self.state.xsize * self.state.ysize:
-            pixels = self.fd.read(1)
-            byte = self.fd.read(1)
-            if not pixels or not byte:
-                break
-            num_pixels = pixels[0]
-            if num_pixels:
-                # encoded mode
-                if x + num_pixels > self.state.xsize:
-                    # Too much data for row
-                    num_pixels = max(0, self.state.xsize - x)
-                if rle4:
-                    first_pixel = o8(byte[0] >> 4)
-                    second_pixel = o8(byte[0] & 0x0F)
-                    for index in range(num_pixels):
-                        if index % 2 == 0:
-                            data += first_pixel
-                        else:
-                            data += second_pixel
-                else:
-                    data += byte * num_pixels
-                x += num_pixels
-            else:
-                if byte[0] == 0:
-                    # end of line
-                    while len(data) % self.state.xsize != 0:
-                        data += b"\x00"
-                    x = 0
-                elif byte[0] == 1:
-                    # end of bitmap
-                    break
-                elif byte[0] == 2:
-                    # delta
-                    bytes_read = self.fd.read(2)
-                    if len(bytes_read) < 2:
-                        break
-                    right, up = self.fd.read(2)
-                    data += b"\x00" * (right + up * self.state.xsize)
-                    x = len(data) % self.state.xsize
-                else:
-                    # absolute mode
-                    if rle4:
-                        # 2 pixels per byte
-                        byte_count = byte[0] // 2
-                        bytes_read = self.fd.read(byte_count)
-                        for byte_read in bytes_read:
-                            data += o8(byte_read >> 4)
-                            data += o8(byte_read & 0x0F)
-                    else:
-                        byte_count = byte[0]
-                        bytes_read = self.fd.read(byte_count)
-                        data += bytes_read
-                    if len(bytes_read) < byte_count:
-                        break
-                    x += byte[0]
-
-                    # align to 16-bit word boundary
-                    if self.fd.tell() % 2 != 0:
-                        self.fd.seek(1, os.SEEK_CUR)
-        rawmode = "L" if self.mode == "L" else "P"
-        self.set_as_raw(bytes(data), (rawmode, 0, self.args[-1]))
-        return -1, 0
-
-
-# =============================================================================
-# Image plugin for the DIB format (BMP alias)
-# =============================================================================
-class DibImageFile(BmpImageFile):
-
-    format = "DIB"
-    format_description = "Windows Bitmap"
-
-    def _open(self):
-        self._bitmap()
-
-
-#
-# --------------------------------------------------------------------
-# Write BMP file
-
-
-SAVE = {
-    "1": ("1", 1, 2),
-    "L": ("L", 8, 256),
-    "P": ("P", 8, 256),
-    "RGB": ("BGR", 24, 0),
-    "RGBA": ("BGRA", 32, 0),
-}
-
-
-def _dib_save(im, fp, filename):
-    _save(im, fp, filename, False)
-
-
-def _save(im, fp, filename, bitmap_header=True):
-    try:
-        rawmode, bits, colors = SAVE[im.mode]
-    except KeyError as e:
-        raise OSError(f"cannot write mode {im.mode} as BMP") from e
-
-    info = im.encoderinfo
-
-    dpi = info.get("dpi", (96, 96))
-
-    # 1 meter == 39.3701 inches
-    ppm = tuple(map(lambda x: int(x * 39.3701 + 0.5), dpi))
-
-    stride = ((im.size[0] * bits + 7) // 8 + 3) & (~3)
-    header = 40  # or 64 for OS/2 version 2
-    image = stride * im.size[1]
-
-    if im.mode == "1":
-        palette = b"".join(o8(i) * 4 for i in (0, 255))
-    elif im.mode == "L":
-        palette = b"".join(o8(i) * 4 for i in range(256))
-    elif im.mode == "P":
-        palette = im.im.getpalette("RGB", "BGRX")
-        colors = len(palette) // 4
-    else:
-        palette = None
-
-    # bitmap header
-    if bitmap_header:
-        offset = 14 + header + colors * 4
-        file_size = offset + image
-        if file_size > 2**32 - 1:
-            raise ValueError("File size is too large for the BMP format")
-        fp.write(
-            b"BM"  # file type (magic)
-            + o32(file_size)  # file size
-            + o32(0)  # reserved
-            + o32(offset)  # image data offset
-        )
-
-    # bitmap info header
-    fp.write(
-        o32(header)  # info header size
-        + o32(im.size[0])  # width
-        + o32(im.size[1])  # height
-        + o16(1)  # planes
-        + o16(bits)  # depth
-        + o32(0)  # compression (0=uncompressed)
-        + o32(image)  # size of bitmap
-        + o32(ppm[0])  # resolution
-        + o32(ppm[1])  # resolution
-        + o32(colors)  # colors used
-        + o32(colors)  # colors important
-    )
-
-    fp.write(b"\0" * (header - 40))  # padding (for OS/2 format)
-
-    if palette:
-        fp.write(palette)
-
-    ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, stride, -1))])
-
-
-#
-# --------------------------------------------------------------------
-# Registry
-
-
-Image.register_open(BmpImageFile.format, BmpImageFile, _accept)
-Image.register_save(BmpImageFile.format, _save)
-
-Image.register_extension(BmpImageFile.format, ".bmp")
-
-Image.register_mime(BmpImageFile.format, "image/bmp")
-
-Image.register_decoder("bmp_rle", BmpRleDecoder)
-
-Image.register_open(DibImageFile.format, DibImageFile, _dib_accept)
-Image.register_save(DibImageFile.format, _dib_save)
-
-Image.register_extension(DibImageFile.format, ".dib")
-
-Image.register_mime(DibImageFile.format, "image/bmp")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/GimpGradientFile.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/GimpGradientFile.py
deleted file mode 100644
index 7ab7f9990aced257c7c52ee9f13e5710d77704ac..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/PIL/GimpGradientFile.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read (and render) GIMP gradient files
-#
-# History:
-#       97-08-23 fl     Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-Stuff to translate curve segments to palette values (derived from
-the corresponding code in GIMP, written by Federico Mena Quintero.
-See the GIMP distribution for more information.)
-"""
-
-
-from math import log, pi, sin, sqrt
-
-from ._binary import o8
-
-EPSILON = 1e-10
-""""""  # Enable auto-doc for data member
-
-
-def linear(middle, pos):
-    if pos <= middle:
-        if middle < EPSILON:
-            return 0.0
-        else:
-            return 0.5 * pos / middle
-    else:
-        pos = pos - middle
-        middle = 1.0 - middle
-        if middle < EPSILON:
-            return 1.0
-        else:
-            return 0.5 + 0.5 * pos / middle
-
-
-def curved(middle, pos):
-    return pos ** (log(0.5) / log(max(middle, EPSILON)))
-
-
-def sine(middle, pos):
-    return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0
-
-
-def sphere_increasing(middle, pos):
-    return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2)
-
-
-def sphere_decreasing(middle, pos):
-    return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2)
-
-
-SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing]
-""""""  # Enable auto-doc for data member
-
-
-class GradientFile:
-
-    gradient = None
-
-    def getpalette(self, entries=256):
-
-        palette = []
-
-        ix = 0
-        x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
-
-        for i in range(entries):
-
-            x = i / (entries - 1)
-
-            while x1 < x:
-                ix += 1
-                x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
-
-            w = x1 - x0
-
-            if w < EPSILON:
-                scale = segment(0.5, 0.5)
-            else:
-                scale = segment((xm - x0) / w, (x - x0) / w)
-
-            # expand to RGBA
-            r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5))
-            g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5))
-            b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5))
-            a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5))
-
-            # add to palette
-            palette.append(r + g + b + a)
-
-        return b"".join(palette), "RGBA"
-
-
-class GimpGradientFile(GradientFile):
-    """File handler for GIMP's gradient format."""
-
-    def __init__(self, fp):
-
-        if fp.readline()[:13] != b"GIMP Gradient":
-            raise SyntaxError("not a GIMP gradient file")
-
-        line = fp.readline()
-
-        # GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do
-        if line.startswith(b"Name: "):
-            line = fp.readline().strip()
-
-        count = int(line)
-
-        gradient = []
-
-        for i in range(count):
-
-            s = fp.readline().split()
-            w = [float(x) for x in s[:11]]
-
-            x0, x1 = w[0], w[2]
-            xm = w[1]
-            rgb0 = w[3:7]
-            rgb1 = w[7:11]
-
-            segment = SEGMENTS[int(s[11])]
-            cspace = int(s[12])
-
-            if cspace != 0:
-                raise OSError("cannot handle HSV colour space")
-
-            gradient.append((x0, x1, xm, rgb0, rgb1, segment))
-
-        self.gradient = gradient
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/shorten_dataset.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/shorten_dataset.py
deleted file mode 100644
index 6ebb5d88feb3f29d1512a0873df304915d051209..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/shorten_dataset.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-from fairseq.data import data_utils
-
-from . import BaseWrapperDataset
-
-
-class TruncateDataset(BaseWrapperDataset):
-    """Truncate a sequence by returning the first truncation_length tokens"""
-
-    def __init__(self, dataset, truncation_length):
-        super().__init__(dataset)
-        assert truncation_length is not None
-        self.truncation_length = truncation_length
-        self.dataset = dataset
-
-    def __getitem__(self, index):
-        item = self.dataset[index]
-        item_len = item.size(0)
-        if item_len > self.truncation_length:
-            item = item[: self.truncation_length]
-        return item
-
-    @property
-    def sizes(self):
-        return np.minimum(self.dataset.sizes, self.truncation_length)
-
-    def __len__(self):
-        return len(self.dataset)
-
-
-class RandomCropDataset(TruncateDataset):
-    """Truncate a sequence by returning a random crop of truncation_length tokens"""
-
-    def __init__(self, dataset, truncation_length, seed=1):
-        super().__init__(dataset, truncation_length)
-        self.seed = seed
-        self.epoch = 0
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return True  # only the crop changes, not item sizes
-
-    def set_epoch(self, epoch, **unused):
-        super().set_epoch(epoch)
-        self.epoch = epoch
-
-    def __getitem__(self, index):
-        with data_utils.numpy_seed(self.seed, self.epoch, index):
-            item = self.dataset[index]
-            item_len = item.size(0)
-            excess = item_len - self.truncation_length
-            if excess > 0:
-                start_idx = np.random.randint(0, excess)
-                item = item[start_idx : start_idx + self.truncation_length]
-            return item
-
-
-def maybe_shorten_dataset(
-    dataset,
-    split,
-    shorten_data_split_list,
-    shorten_method,
-    tokens_per_sample,
-    seed,
-):
-    truncate_split = (
-        split in shorten_data_split_list.split(",") or len(shorten_data_split_list) == 0
-    )
-    if shorten_method == "truncate" and truncate_split:
-        dataset = TruncateDataset(dataset, tokens_per_sample)
-    elif shorten_method == "random_crop" and truncate_split:
-        dataset = RandomCropDataset(dataset, tokens_per_sample, seed)
-    return dataset
diff --git a/spaces/atticus/image-text-retrival-huster/misc/localization.py b/spaces/atticus/image-text-retrival-huster/misc/localization.py
deleted file mode 100644
index d468c2ad377098e1d634bee4419903b1d640aa34..0000000000000000000000000000000000000000
--- a/spaces/atticus/image-text-retrival-huster/misc/localization.py
+++ /dev/null
@@ -1,271 +0,0 @@
-"""
-****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
-Copyright (c) 2018 [Thomson Licensing]
-All Rights Reserved
-This program contains proprietary information which is a trade secret/business \
-secret of [Thomson Licensing] and is protected, even if unpublished, under \
-applicable Copyright laws (including French droit d'auteur) and/or may be \
-subject to one or more patent(s).
-Recipient is to retain this program in confidence and is not permitted to use \
-or make copies thereof other than as permitted in a written agreement with \
-[Thomson Licensing] unless otherwise expressly allowed by applicable laws or \
-by [Thomson Licensing] under express agreement.
-Thomson Licensing is a company of the group TECHNICOLOR
-*******************************************************************************
-This scripts permits one to reproduce training and experiments of:
-    Engilberge, M., Chevallier, L., Pérez, P., & Cord, M. (2018, April).
-    Finding beans in burgers: Deep semantic-visual embedding with localization.
-    In Proceedings of CVPR (pp. 3984-3993)
-
-Author: Martin Engilberge
-"""
-
-import numpy as np
-import cv2
-import os 
-
-from scipy.misc import imresize
-from pycocotools import mask as maskUtils
-
-
-# ################### Functions for the pointing game evaluation ################### #
-
-def regions_scale(x, y, rw, rh, h, w, org_dim, cc=None):
-    if cc is None:
-        fx = x * org_dim[0] / w
-        fy = y * org_dim[1] / h
-        srw = rw * org_dim[0] / w
-        srh = rh * org_dim[1] / h
-    else:
-        if (h > w):
-            r = float(h) / float(w)
-
-            sx = x * cc / w
-            sy = y * cc / w
-
-            srw = rw * cc / w
-            srh = rh * cc / w
-
-            fx = sx - (cc - org_dim[0]) / 2
-            fy = sy - (cc * r - org_dim[1]) / 2
-        else:
-            r = float(w) / float(h)
-
-            sx = x * cc / h
-            sy = y * cc / h
-
-            srw = rw * cc / h
-            srh = rh * cc / h
-
-            fy = sy - (cc - org_dim[1]) / 2
-            fx = sx - (cc * r - org_dim[0]) / 2
-
-    return fx, fy, srw, srh
-
-
-def is_in_region(x, y, bx, by, w, h):
-    return (x > bx and x < (bx + w) and y > by and y < (by + h))
-
-
-def one_img_process(act_map, caps_enc, caps_ori, fc_w, regions, h, w, org_dim, nmax=180, bilinear=False, cc=None, img_id=0):
-    size = act_map.shape[1:]
-    act_map = act_map.reshape(act_map.shape[0], -1)
-    prod = np.dot(fc_w, act_map)
-    if not os.path.exists("heat_map"):
-        os.makedirs("heat_map")
-    total = 0
-    correct = 0
-    # caps_ori = caps_ori.strip().split(" ")
-    for i, cap in enumerate(caps_enc):
-        order = np.argsort(cap)[::-1]
-        cap_ori = caps_ori[i].phrase
-        heat_map = np.reshape(
-            np.dot(np.abs(cap[order[:nmax]]), prod[order[:nmax]]), size)
-        # heat_map.save("heat_map/{}.jpg".format(i))
-        # print(img_path)
-        img_path = os.path.join("/home/atticus/proj/data/vg/VG_100K",
-                                          str(img_id) + ".jpg")
-        img_ori = cv2.imread(img_path)
-
-        if bilinear:
-            heat_map = imresize(heat_map, (org_dim[0], org_dim[1]))
-            x, y = np.unravel_index(heat_map.T.argmax(), heat_map.T.shape)
-        else:
-            x, y = np.unravel_index(heat_map.T.argmax(), heat_map.T.shape)
-            if cc is None:
-                x = (org_dim[0] / size[0]) * x
-                y = (org_dim[1] / size[1]) * y
-            else:
-                if (h > w):
-                    r = float(h) / float(w)
-                    x = (org_dim[0] / size[0]) * x + (cc - org_dim[0]) / 2
-                    y = (org_dim[1] / size[1]) * y + (cc * r - org_dim[1]) / 2
-                else:
-                    r = float(w) / float(h)
-                    x = (org_dim[0] / size[0]) * x + (cc * r - org_dim[0]) / 2
-                    y = (org_dim[1] / size[1]) * y + (cc - org_dim[1]) / 2
-
-        r = regions[i]
-        fx, fy, srw, srh = regions_scale(
-            r.x, r.y, r.width, r.height, h, w, org_dim, cc)
-        # heatmap = np.uint8(255 * heat_map)
-        heat_map = imresize(heat_map, (int(org_dim[0]), int(org_dim[1])))
-        img_ori = cv2.resize(img_ori, (int(org_dim[0]), int(org_dim[1])))
-        heatmap = np.uint8(255 - 255 * heat_map)  # 将特征图转换为uint8格式
-        heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)  # 将特征图转为伪彩色图
-        heat_img = cv2.addWeighted(img_ori, 1, heatmap, 0.5, 0)
-        heat_ori = cv2.applyColorMap(heat_map, cv2.COLORMAP_JET)
-        cv2.imwrite("heat_map/{}-{}-ori.jpg".format(img_id, cap_ori), img_ori)
-        cv2.imwrite("heat_map/{}-{}.jpg".format(img_id, cap_ori), heat_img)
-        cv2.imwrite("heat_map/{}-{}-heat.jpg".format(img_id, cap_ori), heat_ori)
-        if is_in_region(x, y, fx, fy, srw, srh):
-            correct += 1
-        total += 1
-
-    return correct, total
-
-
-def compute_pointing_game_acc(imgs_stack, caps_stack, caps_ori, nb_regions, regions, fc_w, org_dim, cc=None, nmax=180):
-    correct = 0
-    total = 0
-    
-    for i, act_map in enumerate(imgs_stack):
-        seen_region = sum(nb_regions[:i])
-        caps_enc = caps_stack[seen_region:seen_region + nb_regions[i]]
-        region = regions[i][1]
-        h = regions[i][0].height
-        w = regions[i][0].width
-        img_id = regions[i][0].id
-        c, t = one_img_process(act_map, caps_enc, region, fc_w,
-                               region, h, w, org_dim, nmax=nmax, cc=cc, img_id=img_id)
-        correct += c
-        total += t
-
-        # heat_map = generate_heat_map(act_map=act_map, caps_enc=caps_enc, fc_w=fc_w)
-        # heat_map.save("heat_map/{}.jpg".format(i))
-
-    return float(correct) / float(total)
-
-
-# ################### Functions for the semantic segmentation evaluation ################### #
-
-
-def generate_heat_map(act_map, caps_enc, fc_w, nmax=180, in_dim=(224, 224)):
-    size = act_map.shape[1:]
-    act_map = act_map.reshape(act_map.shape[0], -1)
-    prod = np.dot(fc_w, act_map)
-
-    order = np.argsort(caps_enc)[::-1]
-    # print order
-    heat_map = np.reshape(
-        np.dot(np.abs(caps_enc[order[:nmax]]), prod[order[:nmax]]), size)
-    # print heat_map
-
-    heat_map = imresize(heat_map, in_dim)
-
-    return heat_map
-
-
-def gen_binary_heat_map(maps, concept, fc_w, c_thresh, in_dim=(400, 400)):
-    hm = generate_heat_map(maps, concept, fc_w, nmax=10, in_dim=in_dim)
-
-    # hm += abs(np.min(hm))
-
-    def thresh(a, coef):
-        return coef * (np.max(a) - np.min(a))
-
-    return np.int32(hm > thresh(hm, c_thresh))
-
-
-def compute_iou(hm, target_mask):
-    return np.sum(hm * target_mask) / (np.sum(target_mask) + np.sum(hm) - np.sum(hm * target_mask))
-
-
-def mask_from_poly(polygons, org_size, in_dim):
-    mask_poli = np.zeros((org_size[1], org_size[0]))
-
-    for i in range(len(polygons)):
-        if polygons[i][0] == "rle":
-            m = maskUtils.decode(polygons[i][1])
-            mask_poli += m.squeeze()
-        else:
-            poly = np.int32(np.array(polygons[i]).reshape(
-                (int(len(polygons[i]) / 2), 2)))
-            cv2.fillPoly(mask_poli, [poly], [1])
-
-    mask_poli = imresize(mask_poli, in_dim, interp="nearest")
-
-    return np.float32(mask_poli > 0)
-
-
-def compute_semantic_seg(imgs_stack, sizes_list, target_ann, cats_stack, fc_w, c_thresh, in_dim=(200, 200)):
-
-    mAp = 0
-    IoUs = dict()
-    for k in cats_stack.keys():
-        IoUs[k] = list()
-        for i in range(imgs_stack.shape[0]):
-            if k in target_ann[i]:
-                target_mask = mask_from_poly(target_ann[i][k], sizes_list[i], in_dim)
-
-                heat_map = gen_binary_heat_map(imgs_stack[i], cats_stack[k], fc_w, c_thresh, in_dim=in_dim)
-
-                iou = compute_iou(heat_map, target_mask)
-
-                # last element of tuple is groundtruth target
-                IoUs[k] += [(iou, 1)]
-            else:
-                # if categorie k is not present in grountruth set iou at 0
-                IoUs[k] += [(0, 0)]
-
-    mAp = list()
-    for th in [0.3, 0.4, 0.5]:
-        mAp.append(get_map_at(IoUs, th))
-
-    return mAp
-
-
-def compute_ap(rec, prec):
-    ap = 0
-    rec_prev = 0
-    for k in range(len(rec)):
-        prec_c = prec[k]
-        rec_c = rec[k]
-
-        ap += prec_c * (rec_c - rec_prev)
-
-        rec_prev = rec_c
-    return ap
-
-
-def get_map_at(IoUs, at):
-    ap = dict()
-    for c in IoUs.keys():
-        sort_tupe_c = sorted(list(IoUs[c]), key=lambda tup: tup[0], reverse=True)
-
-        y_pred = [float(x[0] > at) for x in sort_tupe_c]
-        y_true = [x[1] for x in sort_tupe_c]
-
-        npos = np.sum(y_true)
-
-        nd = len(y_pred)
-        tp = np.zeros((nd))
-        fp = np.zeros((nd))
-
-        for i in range(1, nd):
-            if y_pred[i] == 1:
-                tp[i] = 1
-            else:
-                fp[i] = 1
-
-        # compute precision/recall
-        fp = np.cumsum(fp)
-        tp = np.cumsum(tp)
-        rec = tp / npos
-        prec = tp / (fp + tp)
-
-        prec[0] = 0
-
-        ap[c] = compute_ap(rec, prec)
-
-    return np.mean(list(ap.values()))
diff --git a/spaces/aubmindlab/Arabic-NLP/backend/aragpt.py b/spaces/aubmindlab/Arabic-NLP/backend/aragpt.py
deleted file mode 100644
index a5cca5e2a984b0bcdb3c917081e8ffe02264e420..0000000000000000000000000000000000000000
--- a/spaces/aubmindlab/Arabic-NLP/backend/aragpt.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import streamlit as st
-from .services import TextGeneration
-from tokenizers import Tokenizer
-from functools import lru_cache
-
-# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
-@lru_cache(maxsize=1)
-def load_text_generator():
-    generator = TextGeneration()
-    generator.load()
-    return generator
-
-
-generator = load_text_generator()
-
-qa_prompt = """
-            أجب عن السؤال التالي:
-            """
-qa_prompt_post = """ الجواب هو  """
-qa_prompt_post_year = """ في سنة: """
-
-
-def write():
-    st.markdown(
-        """
-        
      Arabic Language Generation
      
-        """,
-        unsafe_allow_html=True,
-    )
-
-    # Sidebar
-
-    # Taken from https://huggingface.co/spaces/flax-community/spanish-gpt2/blob/main/app.py
-    st.sidebar.subheader("Configurable parameters")
-
-    model_name = st.sidebar.selectbox(
-        "Model Selector",
-        options=[
-            "AraGPT2-Base",
-            # "AraGPT2-Medium",
-            # "Aragpt2-Large",
-            "AraGPT2-Mega",
-        ],
-        index=0,
-    )
-
-    max_new_tokens = st.sidebar.number_input(
-        "Maximum length",
-        min_value=0,
-        max_value=1024,
-        value=100,
-        help="The maximum length of the sequence to be generated.",
-    )
-    temp = st.sidebar.slider(
-        "Temperature",
-        value=1.0,
-        min_value=0.1,
-        max_value=100.0,
-        help="The value used to module the next token probabilities.",
-    )
-    top_k = st.sidebar.number_input(
-        "Top k",
-        value=10,
-        help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
-    )
-    top_p = st.sidebar.number_input(
-        "Top p",
-        value=0.95,
-        help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
-    )
-    do_sample = st.sidebar.selectbox(
-        "Sampling?",
-        (True, False),
-        help="Whether or not to use sampling; use greedy decoding otherwise.",
-    )
-    num_beams = st.sidebar.number_input(
-        "Number of beams",
-        min_value=1,
-        max_value=10,
-        value=3,
-        help="The number of beams to use for beam search.",
-    )
-    repetition_penalty = st.sidebar.number_input(
-        "Repetition Penalty",
-        min_value=0.0,
-        value=3.0,
-        step=0.1,
-        help="The parameter for repetition penalty. 1.0 means no penalty",
-    )
-    no_repeat_ngram_size = st.sidebar.number_input(
-        "No Repeat N-Gram Size",
-        min_value=0,
-        value=3,
-        help="If set to int > 0, all ngrams of that size can only occur once.",
-    )
-
-    st.write("#")
-
-    col = st.columns(2)
-
-    col[0].image("images/AraGPT2.png", width=200)
-
-    st.markdown(
-        """
-
-        
      AraGPT2 is GPT2 model trained from scratch on 77GB of Arabic text.
      
-        
       More details in our repo.
      
-
-        
      
-        
      Use the generation paramters on the sidebar to adjust generation quality.
      
-        
      
-        """,
-        unsafe_allow_html=True,
-    )
-
-    # col[0].write(
-    #     "AraGPT2 is trained from screatch on 77GB of Arabic text. More details in our [repo](https://github.com/aub-mind/arabert/tree/master/aragpt2)."
-    # )
-    # st.write("## Generate Arabic Text")
-
-    st.markdown(
-        """
-        
-        """,
-        unsafe_allow_html=True,
-    )
-
-    prompt = st.text_area(
-        "Prompt",
-        "يحكى أن مزارعا مخادعا قام ببيع بئر الماء الموجود في أرضه لجاره مقابل مبلغ كبير من المال",
-    )
-    if st.button("Generate"):
-        with st.spinner("Generating..."):
-            generated_text = generator.generate(
-                prompt=prompt,
-                model_name=model_name,
-                max_new_tokens=max_new_tokens,
-                temperature=temp,
-                top_k=top_k,
-                top_p=top_p,
-                repetition_penalty=repetition_penalty,
-                do_sample=do_sample,
-                num_beams=num_beams,
-                no_repeat_ngram_size=no_repeat_ngram_size,
-            )
-            st.write(generated_text)
-
-    st.markdown("---")
-    st.subheader("")
-    st.markdown(
-        """
-        
      
-        
      Zero-Shot Question Answering
      
-
-        
      Adjust the maximum length to closely match the expected output length. Setting the Sampling paramter to False is recommended
      
-        
      
-        """,
-        unsafe_allow_html=True,
-    )
-
-    question = st.text_input(
-        "Question", "من كان رئيس ألمانيا النازية في الحرب العالمية الثانية ؟"
-    )
-    is_date = st.checkbox("Help the model: Is the answer a date?")
-    if st.button("Answer"):
-
-        prompt2 = qa_prompt + question + qa_prompt_post
-        if is_date:
-            prompt2 += qa_prompt_post_year
-        else:
-            prompt2 += " : "
-        with st.spinner("Thinking..."):
-            answer = generator.generate(
-                prompt=prompt2,
-                model_name=model_name,
-                max_new_tokens=max_new_tokens,
-                temperature=temp,
-                top_k=top_k,
-                top_p=top_p,
-                repetition_penalty=repetition_penalty,
-                do_sample=do_sample,
-                num_beams=num_beams,
-                no_repeat_ngram_size=no_repeat_ngram_size,
-            )
-            st.write(answer)
diff --git a/spaces/awacke1/File-Memory-Operations-Human-Feedback-Gradio/backup.app.py b/spaces/awacke1/File-Memory-Operations-Human-Feedback-Gradio/backup.app.py
deleted file mode 100644
index 58efcf7f17e78bce76ca679726d93118196d1b47..0000000000000000000000000000000000000000
--- a/spaces/awacke1/File-Memory-Operations-Human-Feedback-Gradio/backup.app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import gradio as gr
-
-
-# Function to list files with .csv and .txt extensions in the current directory
-def list_files(file_path):
-    import os
-    current_directory = os.getcwd()
-    file_list = []
-    for filename in os.listdir(current_directory):
-        if filename.endswith(".csv") or filename.endswith(".txt"):
-            file_list.append(filename)
-    if file_list:
-        return "\n".join(file_list)
-    else:
-        return "No .csv or .txt files found in the current directory."
-
-# Function to read a file
-def read_file(file_path):
-    try:
-        with open(file_path, "r") as file:
-            contents = file.read()
-            return f"{contents}"
-            #return f"Contents of {file_path}:\n{contents}"
-    except FileNotFoundError:
-        return "File not found."
-
-# Function to delete a file
-def delete_file(file_path):
-    try:
-        import os
-        os.remove(file_path)
-        return f"{file_path} has been deleted."
-    except FileNotFoundError:
-        return "File not found."
-
-# Function to write to a file
-def write_file(file_path, content):
-    try:
-        with open(file_path, "w") as file:
-            file.write(content)
-        return f"Successfully written to {file_path}."
-    except:
-        return "Error occurred while writing to file."
-
-# Function to append to a file
-def append_file(file_path, content):
-    try:
-        with open(file_path, "a") as file:
-            file.write(content)
-        return f"Successfully appended to {file_path}."
-    except:
-        return "Error occurred while appending to file."
-
-demo = gr.Blocks()
-with demo:
-    fileName = gr.Textbox(label="Filename")
-    fileContent = gr.TextArea(label="File Content")
-    completedMessage = gr.Textbox(label="Completed")
-    
-    label = gr.Label()
-
-    listFiles = gr.Button("List CSV and TXT File(s)")
-    readFile = gr.Button("Read File")
-    saveFile = gr.Button("Save File")
-    deleteFile = gr.Button("Delete File")
-    appendFile = gr.Button("Append File")
-    
-    listFiles.click(list_files, inputs=fileName, outputs=fileContent)
-    readFile.click(read_file, inputs=fileName, outputs=fileContent)
-    saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage)
-    deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage)
-    appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage )
-    
-demo.launch()
\ No newline at end of file
diff --git a/spaces/awacke1/Self-Modifying-Graph-Visualization/README.md b/spaces/awacke1/Self-Modifying-Graph-Visualization/README.md
deleted file mode 100644
index 6e1f2d5f9bfa1f830ce980feed86d5c27dfac0a9..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Self-Modifying-Graph-Visualization/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 📈Graph-Self-Modifying-Visualization💻📉
-emoji: 📉📈
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/controls/OrbitControls.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/controls/OrbitControls.js
deleted file mode 100644
index f1d3ebc6d23c0d2b6b2e1445761d44f2bccbf41a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/controls/OrbitControls.js
+++ /dev/null
@@ -1,1081 +0,0 @@
-/**
- * @author qiao / https://github.com/qiao
- * @author mrdoob / http://mrdoob.com
- * @author alteredq / http://alteredqualia.com/
- * @author WestLangley / http://github.com/WestLangley
- * @author erich666 / http://erichaines.com
- */
-
-import {
-	EventDispatcher,
-	MOUSE,
-	Quaternion,
-	Spherical,
-	Vector2,
-	Vector3
-} from "../../../build/three.module.js";
-
-// This set of controls performs orbiting, dollying (zooming), and panning.
-// Unlike TrackballControls, it maintains the "up" direction object.up (+Y by default).
-//
-//    Orbit - left mouse / touch: one-finger move
-//    Zoom - middle mouse, or mousewheel / touch: two-finger spread or squish
-//    Pan - right mouse, or left mouse + ctrl/meta/shiftKey, or arrow keys / touch: two-finger move
-
-var OrbitControls = function ( object, domElement ) {
-
-	this.object = object;
-
-	this.domElement = ( domElement !== undefined ) ? domElement : document;
-
-	// Set to false to disable this control
-	this.enabled = true;
-
-	// "target" sets the location of focus, where the object orbits around
-	this.target = new Vector3();
-
-	// How far you can dolly in and out ( PerspectiveCamera only )
-	this.minDistance = 0;
-	this.maxDistance = Infinity;
-
-	// How far you can zoom in and out ( OrthographicCamera only )
-	this.minZoom = 0;
-	this.maxZoom = Infinity;
-
-	// How far you can orbit vertically, upper and lower limits.
-	// Range is 0 to Math.PI radians.
-	this.minPolarAngle = 0; // radians
-	this.maxPolarAngle = Math.PI; // radians
-
-	// How far you can orbit horizontally, upper and lower limits.
-	// If set, must be a sub-interval of the interval [ - Math.PI, Math.PI ].
-	this.minAzimuthAngle = - Infinity; // radians
-	this.maxAzimuthAngle = Infinity; // radians
-
-	// Set to true to enable damping (inertia)
-	// If damping is enabled, you must call controls.update() in your animation loop
-	this.enableDamping = false;
-	this.dampingFactor = 0.25;
-
-	// This option actually enables dollying in and out; left as "zoom" for backwards compatibility.
-	// Set to false to disable zooming
-	this.enableZoom = true;
-	this.zoomSpeed = 1.0;
-
-	// Set to false to disable rotating
-	this.enableRotate = true;
-	this.rotateSpeed = 1.0;
-
-	// Set to false to disable panning
-	this.enablePan = true;
-	this.panSpeed = 1.0;
-	this.screenSpacePanning = false; // if true, pan in screen-space
-	this.keyPanSpeed = 7.0;	// pixels moved per arrow key push
-
-	// Set to true to automatically rotate around the target
-	// If auto-rotate is enabled, you must call controls.update() in your animation loop
-	this.autoRotate = false;
-	this.autoRotateSpeed = 2.0; // 30 seconds per round when fps is 60
-
-	// Set to false to disable use of the keys
-	this.enableKeys = true;
-
-	// The four arrow keys
-	this.keys = { LEFT: 37, UP: 38, RIGHT: 39, BOTTOM: 40 };
-
-	// Mouse buttons
-	this.mouseButtons = { LEFT: MOUSE.LEFT, MIDDLE: MOUSE.MIDDLE, RIGHT: MOUSE.RIGHT };
-
-	// for reset
-	this.target0 = this.target.clone();
-	this.position0 = this.object.position.clone();
-	this.zoom0 = this.object.zoom;
-
-	//
-	// public methods
-	//
-
-	this.getPolarAngle = function () {
-
-		return spherical.phi;
-
-	};
-
-	this.getAzimuthalAngle = function () {
-
-		return spherical.theta;
-
-	};
-
-	this.saveState = function () {
-
-		scope.target0.copy( scope.target );
-		scope.position0.copy( scope.object.position );
-		scope.zoom0 = scope.object.zoom;
-
-	};
-
-	this.reset = function () {
-
-		scope.target.copy( scope.target0 );
-		scope.object.position.copy( scope.position0 );
-		scope.object.zoom = scope.zoom0;
-
-		scope.object.updateProjectionMatrix();
-		scope.dispatchEvent( changeEvent );
-
-		scope.update();
-
-		state = STATE.NONE;
-
-	};
-
-	// this method is exposed, but perhaps it would be better if we can make it private...
-	this.update = function () {
-
-		var offset = new Vector3();
-
-		// so camera.up is the orbit axis
-		var quat = new Quaternion().setFromUnitVectors( object.up, new Vector3( 0, 1, 0 ) );
-		var quatInverse = quat.clone().inverse();
-
-		var lastPosition = new Vector3();
-		var lastQuaternion = new Quaternion();
-
-		return function update() {
-
-			var position = scope.object.position;
-
-			offset.copy( position ).sub( scope.target );
-
-			// rotate offset to "y-axis-is-up" space
-			offset.applyQuaternion( quat );
-
-			// angle from z-axis around y-axis
-			spherical.setFromVector3( offset );
-
-			if ( scope.autoRotate && state === STATE.NONE ) {
-
-				rotateLeft( getAutoRotationAngle() );
-
-			}
-
-			spherical.theta += sphericalDelta.theta;
-			spherical.phi += sphericalDelta.phi;
-
-			// restrict theta to be between desired limits
-			spherical.theta = Math.max( scope.minAzimuthAngle, Math.min( scope.maxAzimuthAngle, spherical.theta ) );
-
-			// restrict phi to be between desired limits
-			spherical.phi = Math.max( scope.minPolarAngle, Math.min( scope.maxPolarAngle, spherical.phi ) );
-
-			spherical.makeSafe();
-
-
-			spherical.radius *= scale;
-
-			// restrict radius to be between desired limits
-			spherical.radius = Math.max( scope.minDistance, Math.min( scope.maxDistance, spherical.radius ) );
-
-			// move target to panned location
-			scope.target.add( panOffset );
-
-			offset.setFromSpherical( spherical );
-
-			// rotate offset back to "camera-up-vector-is-up" space
-			offset.applyQuaternion( quatInverse );
-
-			position.copy( scope.target ).add( offset );
-
-			scope.object.lookAt( scope.target );
-
-			if ( scope.enableDamping === true ) {
-
-				sphericalDelta.theta *= ( 1 - scope.dampingFactor );
-				sphericalDelta.phi *= ( 1 - scope.dampingFactor );
-
-				panOffset.multiplyScalar( 1 - scope.dampingFactor );
-
-			} else {
-
-				sphericalDelta.set( 0, 0, 0 );
-
-				panOffset.set( 0, 0, 0 );
-
-			}
-
-			scale = 1;
-
-			// update condition is:
-			// min(camera displacement, camera rotation in radians)^2 > EPS
-			// using small-angle approximation cos(x/2) = 1 - x^2 / 8
-
-			if ( zoomChanged ||
-				lastPosition.distanceToSquared( scope.object.position ) > EPS ||
-				8 * ( 1 - lastQuaternion.dot( scope.object.quaternion ) ) > EPS ) {
-
-				scope.dispatchEvent( changeEvent );
-
-				lastPosition.copy( scope.object.position );
-				lastQuaternion.copy( scope.object.quaternion );
-				zoomChanged = false;
-
-				return true;
-
-			}
-
-			return false;
-
-		};
-
-	}();
-
-	this.dispose = function () {
-
-		scope.domElement.removeEventListener( 'contextmenu', onContextMenu, false );
-		scope.domElement.removeEventListener( 'mousedown', onMouseDown, false );
-		scope.domElement.removeEventListener( 'wheel', onMouseWheel, false );
-
-		scope.domElement.removeEventListener( 'touchstart', onTouchStart, false );
-		scope.domElement.removeEventListener( 'touchend', onTouchEnd, false );
-		scope.domElement.removeEventListener( 'touchmove', onTouchMove, false );
-
-		document.removeEventListener( 'mousemove', onMouseMove, false );
-		document.removeEventListener( 'mouseup', onMouseUp, false );
-
-		window.removeEventListener( 'keydown', onKeyDown, false );
-
-		//scope.dispatchEvent( { type: 'dispose' } ); // should this be added here?
-
-	};
-
-	//
-	// internals
-	//
-
-	var scope = this;
-
-	var changeEvent = { type: 'change' };
-	var startEvent = { type: 'start' };
-	var endEvent = { type: 'end' };
-
-	var STATE = { NONE: - 1, ROTATE: 0, DOLLY: 1, PAN: 2, TOUCH_ROTATE: 3, TOUCH_DOLLY_PAN: 4 };
-
-	var state = STATE.NONE;
-
-	var EPS = 0.000001;
-
-	// current position in spherical coordinates
-	var spherical = new Spherical();
-	var sphericalDelta = new Spherical();
-
-	var scale = 1;
-	var panOffset = new Vector3();
-	var zoomChanged = false;
-
-	var rotateStart = new Vector2();
-	var rotateEnd = new Vector2();
-	var rotateDelta = new Vector2();
-
-	var panStart = new Vector2();
-	var panEnd = new Vector2();
-	var panDelta = new Vector2();
-
-	var dollyStart = new Vector2();
-	var dollyEnd = new Vector2();
-	var dollyDelta = new Vector2();
-
-	function getAutoRotationAngle() {
-
-		return 2 * Math.PI / 60 / 60 * scope.autoRotateSpeed;
-
-	}
-
-	function getZoomScale() {
-
-		return Math.pow( 0.95, scope.zoomSpeed );
-
-	}
-
-	function rotateLeft( angle ) {
-
-		sphericalDelta.theta -= angle;
-
-	}
-
-	function rotateUp( angle ) {
-
-		sphericalDelta.phi -= angle;
-
-	}
-
-	var panLeft = function () {
-
-		var v = new Vector3();
-
-		return function panLeft( distance, objectMatrix ) {
-
-			v.setFromMatrixColumn( objectMatrix, 0 ); // get X column of objectMatrix
-			v.multiplyScalar( - distance );
-
-			panOffset.add( v );
-
-		};
-
-	}();
-
-	var panUp = function () {
-
-		var v = new Vector3();
-
-		return function panUp( distance, objectMatrix ) {
-
-			if ( scope.screenSpacePanning === true ) {
-
-				v.setFromMatrixColumn( objectMatrix, 1 );
-
-			} else {
-
-				v.setFromMatrixColumn( objectMatrix, 0 );
-				v.crossVectors( scope.object.up, v );
-
-			}
-
-			v.multiplyScalar( distance );
-
-			panOffset.add( v );
-
-		};
-
-	}();
-
-	// deltaX and deltaY are in pixels; right and down are positive
-	var pan = function () {
-
-		var offset = new Vector3();
-
-		return function pan( deltaX, deltaY ) {
-
-			var element = scope.domElement === document ? scope.domElement.body : scope.domElement;
-
-			if ( scope.object.isPerspectiveCamera ) {
-
-				// perspective
-				var position = scope.object.position;
-				offset.copy( position ).sub( scope.target );
-				var targetDistance = offset.length();
-
-				// half of the fov is center to top of screen
-				targetDistance *= Math.tan( ( scope.object.fov / 2 ) * Math.PI / 180.0 );
-
-				// we use only clientHeight here so aspect ratio does not distort speed
-				panLeft( 2 * deltaX * targetDistance / element.clientHeight, scope.object.matrix );
-				panUp( 2 * deltaY * targetDistance / element.clientHeight, scope.object.matrix );
-
-			} else if ( scope.object.isOrthographicCamera ) {
-
-				// orthographic
-				panLeft( deltaX * ( scope.object.right - scope.object.left ) / scope.object.zoom / element.clientWidth, scope.object.matrix );
-				panUp( deltaY * ( scope.object.top - scope.object.bottom ) / scope.object.zoom / element.clientHeight, scope.object.matrix );
-
-			} else {
-
-				// camera neither orthographic nor perspective
-				console.warn( 'WARNING: OrbitControls.js encountered an unknown camera type - pan disabled.' );
-				scope.enablePan = false;
-
-			}
-
-		};
-
-	}();
-
-	function dollyIn( dollyScale ) {
-
-		if ( scope.object.isPerspectiveCamera ) {
-
-			scale /= dollyScale;
-
-		} else if ( scope.object.isOrthographicCamera ) {
-
-			scope.object.zoom = Math.max( scope.minZoom, Math.min( scope.maxZoom, scope.object.zoom * dollyScale ) );
-			scope.object.updateProjectionMatrix();
-			zoomChanged = true;
-
-		} else {
-
-			console.warn( 'WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.' );
-			scope.enableZoom = false;
-
-		}
-
-	}
-
-	function dollyOut( dollyScale ) {
-
-		if ( scope.object.isPerspectiveCamera ) {
-
-			scale *= dollyScale;
-
-		} else if ( scope.object.isOrthographicCamera ) {
-
-			scope.object.zoom = Math.max( scope.minZoom, Math.min( scope.maxZoom, scope.object.zoom / dollyScale ) );
-			scope.object.updateProjectionMatrix();
-			zoomChanged = true;
-
-		} else {
-
-			console.warn( 'WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled.' );
-			scope.enableZoom = false;
-
-		}
-
-	}
-
-	//
-	// event callbacks - update the object state
-	//
-
-	function handleMouseDownRotate( event ) {
-
-		//console.log( 'handleMouseDownRotate' );
-
-		rotateStart.set( event.clientX, event.clientY );
-
-	}
-
-	function handleMouseDownDolly( event ) {
-
-		//console.log( 'handleMouseDownDolly' );
-
-		dollyStart.set( event.clientX, event.clientY );
-
-	}
-
-	function handleMouseDownPan( event ) {
-
-		//console.log( 'handleMouseDownPan' );
-
-		panStart.set( event.clientX, event.clientY );
-
-	}
-
-	function handleMouseMoveRotate( event ) {
-
-		//console.log( 'handleMouseMoveRotate' );
-
-		rotateEnd.set( event.clientX, event.clientY );
-
-		rotateDelta.subVectors( rotateEnd, rotateStart ).multiplyScalar( scope.rotateSpeed );
-
-		var element = scope.domElement === document ? scope.domElement.body : scope.domElement;
-
-		rotateLeft( 2 * Math.PI * rotateDelta.x / element.clientHeight ); // yes, height
-
-		rotateUp( 2 * Math.PI * rotateDelta.y / element.clientHeight );
-
-		rotateStart.copy( rotateEnd );
-
-		scope.update();
-
-	}
-
-	function handleMouseMoveDolly( event ) {
-
-		//console.log( 'handleMouseMoveDolly' );
-
-		dollyEnd.set( event.clientX, event.clientY );
-
-		dollyDelta.subVectors( dollyEnd, dollyStart );
-
-		if ( dollyDelta.y > 0 ) {
-
-			dollyIn( getZoomScale() );
-
-		} else if ( dollyDelta.y < 0 ) {
-
-			dollyOut( getZoomScale() );
-
-		}
-
-		dollyStart.copy( dollyEnd );
-
-		scope.update();
-
-	}
-
-	function handleMouseMovePan( event ) {
-
-		//console.log( 'handleMouseMovePan' );
-
-		panEnd.set( event.clientX, event.clientY );
-
-		panDelta.subVectors( panEnd, panStart ).multiplyScalar( scope.panSpeed );
-
-		pan( panDelta.x, panDelta.y );
-
-		panStart.copy( panEnd );
-
-		scope.update();
-
-	}
-
-	function handleMouseUp( event ) {
-
-		// console.log( 'handleMouseUp' );
-
-	}
-
-	function handleMouseWheel( event ) {
-
-		// console.log( 'handleMouseWheel' );
-
-		if ( event.deltaY < 0 ) {
-
-			dollyOut( getZoomScale() );
-
-		} else if ( event.deltaY > 0 ) {
-
-			dollyIn( getZoomScale() );
-
-		}
-
-		scope.update();
-
-	}
-
-	function handleKeyDown( event ) {
-
-		// console.log( 'handleKeyDown' );
-
-		var needsUpdate = false;
-
-		switch ( event.keyCode ) {
-
-			case scope.keys.UP:
-				pan( 0, scope.keyPanSpeed );
-				needsUpdate = true;
-				break;
-
-			case scope.keys.BOTTOM:
-				pan( 0, - scope.keyPanSpeed );
-				needsUpdate = true;
-				break;
-
-			case scope.keys.LEFT:
-				pan( scope.keyPanSpeed, 0 );
-				needsUpdate = true;
-				break;
-
-			case scope.keys.RIGHT:
-				pan( - scope.keyPanSpeed, 0 );
-				needsUpdate = true;
-				break;
-
-		}
-
-		if ( needsUpdate ) {
-
-			// prevent the browser from scrolling on cursor keys
-			event.preventDefault();
-
-			scope.update();
-
-		}
-
-
-	}
-
-	function handleTouchStartRotate( event ) {
-
-		//console.log( 'handleTouchStartRotate' );
-
-		rotateStart.set( event.touches[ 0 ].pageX, event.touches[ 0 ].pageY );
-
-	}
-
-	function handleTouchStartDollyPan( event ) {
-
-		//console.log( 'handleTouchStartDollyPan' );
-
-		if ( scope.enableZoom ) {
-
-			var dx = event.touches[ 0 ].pageX - event.touches[ 1 ].pageX;
-			var dy = event.touches[ 0 ].pageY - event.touches[ 1 ].pageY;
-
-			var distance = Math.sqrt( dx * dx + dy * dy );
-
-			dollyStart.set( 0, distance );
-
-		}
-
-		if ( scope.enablePan ) {
-
-			var x = 0.5 * ( event.touches[ 0 ].pageX + event.touches[ 1 ].pageX );
-			var y = 0.5 * ( event.touches[ 0 ].pageY + event.touches[ 1 ].pageY );
-
-			panStart.set( x, y );
-
-		}
-
-	}
-
-	function handleTouchMoveRotate( event ) {
-
-		//console.log( 'handleTouchMoveRotate' );
-
-		rotateEnd.set( event.touches[ 0 ].pageX, event.touches[ 0 ].pageY );
-
-		rotateDelta.subVectors( rotateEnd, rotateStart ).multiplyScalar( scope.rotateSpeed );
-
-		var element = scope.domElement === document ? scope.domElement.body : scope.domElement;
-
-		rotateLeft( 2 * Math.PI * rotateDelta.x / element.clientHeight ); // yes, height
-
-		rotateUp( 2 * Math.PI * rotateDelta.y / element.clientHeight );
-
-		rotateStart.copy( rotateEnd );
-
-		scope.update();
-
-	}
-
-	function handleTouchMoveDollyPan( event ) {
-
-		//console.log( 'handleTouchMoveDollyPan' );
-
-		if ( scope.enableZoom ) {
-
-			var dx = event.touches[ 0 ].pageX - event.touches[ 1 ].pageX;
-			var dy = event.touches[ 0 ].pageY - event.touches[ 1 ].pageY;
-
-			var distance = Math.sqrt( dx * dx + dy * dy );
-
-			dollyEnd.set( 0, distance );
-
-			dollyDelta.set( 0, Math.pow( dollyEnd.y / dollyStart.y, scope.zoomSpeed ) );
-
-			dollyIn( dollyDelta.y );
-
-			dollyStart.copy( dollyEnd );
-
-		}
-
-		if ( scope.enablePan ) {
-
-			var x = 0.5 * ( event.touches[ 0 ].pageX + event.touches[ 1 ].pageX );
-			var y = 0.5 * ( event.touches[ 0 ].pageY + event.touches[ 1 ].pageY );
-
-			panEnd.set( x, y );
-
-			panDelta.subVectors( panEnd, panStart ).multiplyScalar( scope.panSpeed );
-
-			pan( panDelta.x, panDelta.y );
-
-			panStart.copy( panEnd );
-
-		}
-
-		scope.update();
-
-	}
-
-	function handleTouchEnd( event ) {
-
-		//console.log( 'handleTouchEnd' );
-
-	}
-
-	//
-	// event handlers - FSM: listen for events and reset state
-	//
-
-	function onMouseDown( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		// Prevent the browser from scrolling.
-
-		event.preventDefault();
-
-		// Manually set the focus since calling preventDefault above
-		// prevents the browser from setting it automatically.
-
-		scope.domElement.focus ? scope.domElement.focus() : window.focus();
-
-		switch ( event.button ) {
-
-			case scope.mouseButtons.LEFT:
-
-				if ( event.ctrlKey || event.metaKey || event.shiftKey ) {
-
-					if ( scope.enablePan === false ) return;
-
-					handleMouseDownPan( event );
-
-					state = STATE.PAN;
-
-				} else {
-
-					if ( scope.enableRotate === false ) return;
-
-					handleMouseDownRotate( event );
-
-					state = STATE.ROTATE;
-
-				}
-
-				break;
-
-			case scope.mouseButtons.MIDDLE:
-
-				if ( scope.enableZoom === false ) return;
-
-				handleMouseDownDolly( event );
-
-				state = STATE.DOLLY;
-
-				break;
-
-			case scope.mouseButtons.RIGHT:
-
-				if ( scope.enablePan === false ) return;
-
-				handleMouseDownPan( event );
-
-				state = STATE.PAN;
-
-				break;
-
-		}
-
-		if ( state !== STATE.NONE ) {
-
-			document.addEventListener( 'mousemove', onMouseMove, false );
-			document.addEventListener( 'mouseup', onMouseUp, false );
-
-			scope.dispatchEvent( startEvent );
-
-		}
-
-	}
-
-	function onMouseMove( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		event.preventDefault();
-
-		switch ( state ) {
-
-			case STATE.ROTATE:
-
-				if ( scope.enableRotate === false ) return;
-
-				handleMouseMoveRotate( event );
-
-				break;
-
-			case STATE.DOLLY:
-
-				if ( scope.enableZoom === false ) return;
-
-				handleMouseMoveDolly( event );
-
-				break;
-
-			case STATE.PAN:
-
-				if ( scope.enablePan === false ) return;
-
-				handleMouseMovePan( event );
-
-				break;
-
-		}
-
-	}
-
-	function onMouseUp( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		handleMouseUp( event );
-
-		document.removeEventListener( 'mousemove', onMouseMove, false );
-		document.removeEventListener( 'mouseup', onMouseUp, false );
-
-		scope.dispatchEvent( endEvent );
-
-		state = STATE.NONE;
-
-	}
-
-	function onMouseWheel( event ) {
-
-		if ( scope.enabled === false || scope.enableZoom === false || ( state !== STATE.NONE && state !== STATE.ROTATE ) ) return;
-
-		event.preventDefault();
-		event.stopPropagation();
-
-		scope.dispatchEvent( startEvent );
-
-		handleMouseWheel( event );
-
-		scope.dispatchEvent( endEvent );
-
-	}
-
-	function onKeyDown( event ) {
-
-		if ( scope.enabled === false || scope.enableKeys === false || scope.enablePan === false ) return;
-
-		handleKeyDown( event );
-
-	}
-
-	function onTouchStart( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		event.preventDefault();
-
-		switch ( event.touches.length ) {
-
-			case 1:	// one-fingered touch: rotate
-
-				if ( scope.enableRotate === false ) return;
-
-				handleTouchStartRotate( event );
-
-				state = STATE.TOUCH_ROTATE;
-
-				break;
-
-			case 2:	// two-fingered touch: dolly-pan
-
-				if ( scope.enableZoom === false && scope.enablePan === false ) return;
-
-				handleTouchStartDollyPan( event );
-
-				state = STATE.TOUCH_DOLLY_PAN;
-
-				break;
-
-			default:
-
-				state = STATE.NONE;
-
-		}
-
-		if ( state !== STATE.NONE ) {
-
-			scope.dispatchEvent( startEvent );
-
-		}
-
-	}
-
-	function onTouchMove( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		event.preventDefault();
-		event.stopPropagation();
-
-		switch ( event.touches.length ) {
-
-			case 1: // one-fingered touch: rotate
-
-				if ( scope.enableRotate === false ) return;
-				if ( state !== STATE.TOUCH_ROTATE ) return; // is this needed?
-
-				handleTouchMoveRotate( event );
-
-				break;
-
-			case 2: // two-fingered touch: dolly-pan
-
-				if ( scope.enableZoom === false && scope.enablePan === false ) return;
-				if ( state !== STATE.TOUCH_DOLLY_PAN ) return; // is this needed?
-
-				handleTouchMoveDollyPan( event );
-
-				break;
-
-			default:
-
-				state = STATE.NONE;
-
-		}
-
-	}
-
-	function onTouchEnd( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		handleTouchEnd( event );
-
-		scope.dispatchEvent( endEvent );
-
-		state = STATE.NONE;
-
-	}
-
-	function onContextMenu( event ) {
-
-		if ( scope.enabled === false ) return;
-
-		event.preventDefault();
-
-	}
-
-	//
-
-	scope.domElement.addEventListener( 'contextmenu', onContextMenu, false );
-
-	scope.domElement.addEventListener( 'mousedown', onMouseDown, false );
-	scope.domElement.addEventListener( 'wheel', onMouseWheel, false );
-
-	scope.domElement.addEventListener( 'touchstart', onTouchStart, false );
-	scope.domElement.addEventListener( 'touchend', onTouchEnd, false );
-	scope.domElement.addEventListener( 'touchmove', onTouchMove, false );
-
-	window.addEventListener( 'keydown', onKeyDown, false );
-
-	// force an update at start
-
-	this.update();
-
-};
-
-OrbitControls.prototype = Object.create( EventDispatcher.prototype );
-OrbitControls.prototype.constructor = OrbitControls;
-
-Object.defineProperties( OrbitControls.prototype, {
-
-	center: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .center has been renamed to .target' );
-			return this.target;
-
-		}
-
-	},
-
-	// backward compatibility
-
-	noZoom: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .noZoom has been deprecated. Use .enableZoom instead.' );
-			return ! this.enableZoom;
-
-		},
-
-		set: function ( value ) {
-
-			console.warn( 'THREE.OrbitControls: .noZoom has been deprecated. Use .enableZoom instead.' );
-			this.enableZoom = ! value;
-
-		}
-
-	},
-
-	noRotate: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .noRotate has been deprecated. Use .enableRotate instead.' );
-			return ! this.enableRotate;
-
-		},
-
-		set: function ( value ) {
-
-			console.warn( 'THREE.OrbitControls: .noRotate has been deprecated. Use .enableRotate instead.' );
-			this.enableRotate = ! value;
-
-		}
-
-	},
-
-	noPan: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .noPan has been deprecated. Use .enablePan instead.' );
-			return ! this.enablePan;
-
-		},
-
-		set: function ( value ) {
-
-			console.warn( 'THREE.OrbitControls: .noPan has been deprecated. Use .enablePan instead.' );
-			this.enablePan = ! value;
-
-		}
-
-	},
-
-	noKeys: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .noKeys has been deprecated. Use .enableKeys instead.' );
-			return ! this.enableKeys;
-
-		},
-
-		set: function ( value ) {
-
-			console.warn( 'THREE.OrbitControls: .noKeys has been deprecated. Use .enableKeys instead.' );
-			this.enableKeys = ! value;
-
-		}
-
-	},
-
-	staticMoving: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .staticMoving has been deprecated. Use .enableDamping instead.' );
-			return ! this.enableDamping;
-
-		},
-
-		set: function ( value ) {
-
-			console.warn( 'THREE.OrbitControls: .staticMoving has been deprecated. Use .enableDamping instead.' );
-			this.enableDamping = ! value;
-
-		}
-
-	},
-
-	dynamicDampingFactor: {
-
-		get: function () {
-
-			console.warn( 'THREE.OrbitControls: .dynamicDampingFactor has been renamed. Use .dampingFactor instead.' );
-			return this.dampingFactor;
-
-		},
-
-		set: function ( value ) {
-
-			console.warn( 'THREE.OrbitControls: .dynamicDampingFactor has been renamed. Use .dampingFactor instead.' );
-			this.dampingFactor = value;
-
-		}
-
-	}
-
-} );
-
-export { OrbitControls };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/extras/objects/ImmediateRenderObject.js b/spaces/banana-projects/web3d/node_modules/three/src/extras/objects/ImmediateRenderObject.js
deleted file mode 100644
index f10becfd257292c03fc74abf78b0eb78e74ffee6..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/extras/objects/ImmediateRenderObject.js
+++ /dev/null
@@ -1,22 +0,0 @@
-import { Object3D } from '../../core/Object3D.js';
-
-/**
- * @author alteredq / http://alteredqualia.com/
- */
-
-function ImmediateRenderObject( material ) {
-
-	Object3D.call( this );
-
-	this.material = material;
-	this.render = function ( /* renderCallback */ ) {};
-
-}
-
-ImmediateRenderObject.prototype = Object.create( Object3D.prototype );
-ImmediateRenderObject.prototype.constructor = ImmediateRenderObject;
-
-ImmediateRenderObject.prototype.isImmediateRenderObject = true;
-
-
-export { ImmediateRenderObject };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/helpers/SkeletonHelper.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/helpers/SkeletonHelper.d.ts
deleted file mode 100644
index 98c7cb683875fff01c47f4d14068bdd3baf0e44a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/helpers/SkeletonHelper.d.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { Object3D } from './../core/Object3D';
-import { Bone } from './../objects/Bone';
-import { LineSegments } from './../objects/LineSegments';
-
-export class SkeletonHelper extends LineSegments {
-  constructor(bone: Object3D);
-
-  bones: Bone[];
-  root: Object3D;
-
-  getBoneList(object: Object3D): Bone[];
-  update(): void;
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Matrix3.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/math/Matrix3.d.ts
deleted file mode 100644
index f45d9775ec8e34bd801c3ad1bcb5b4b25274a752..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/math/Matrix3.d.ts
+++ /dev/null
@@ -1,132 +0,0 @@
-import { Matrix4 } from './Matrix4';
-import { BufferAttribute } from './../core/BufferAttribute';
-import { Vector3 } from './Vector3';
-
-/**
- * ( interface Matrix<T> )
- */
-export interface Matrix {
-  /**
-   * Array with matrix values.
-   */
-  elements: number[];
-
-  /**
-   * identity():T;
-   */
-  identity(): Matrix;
-
-  /**
-   * copy(m:T):T;
-   */
-  copy(m: this): this;
-
-  /**
-   * multiplyScalar(s:number):T;
-   */
-  multiplyScalar(s: number): Matrix;
-
-  determinant(): number;
-
-  /**
-   * getInverse(matrix:T, throwOnInvertible?:boolean):T;
-   */
-  getInverse(matrix: Matrix, throwOnInvertible?: boolean): Matrix;
-
-  /**
-   * transpose():T;
-   */
-  transpose(): Matrix;
-
-  /**
-   * clone():T;
-   */
-  clone(): this;
-}
-
-/**
- * ( class Matrix3 implements Matrix<Matrix3> )
- */
-export class Matrix3 implements Matrix {
-  /**
-   * Creates an identity matrix.
-   */
-  constructor();
-
-  /**
-   * Array with matrix values.
-   */
-  elements: number[];
-
-  set(
-    n11: number,
-    n12: number,
-    n13: number,
-    n21: number,
-    n22: number,
-    n23: number,
-    n31: number,
-    n32: number,
-    n33: number
-  ): Matrix3;
-  identity(): Matrix3;
-  clone(): this;
-  copy(m: Matrix3): this;
-  setFromMatrix4(m: Matrix4): Matrix3;
-
-  /**
-   * @deprecated Use {@link Matrix3#applyToBufferAttribute matrix3.applyToBufferAttribute( attribute )} instead.
-   */
-  applyToBuffer(
-    buffer: BufferAttribute,
-    offset?: number,
-    length?: number
-  ): BufferAttribute;
-
-  applyToBufferAttribute(attribute: BufferAttribute): BufferAttribute;
-
-  multiplyScalar(s: number): Matrix3;
-  determinant(): number;
-  getInverse(matrix: Matrix3, throwOnDegenerate?: boolean): Matrix3;
-
-  /**
-   * Transposes this matrix in place.
-   */
-  transpose(): Matrix3;
-  getNormalMatrix(matrix4: Matrix4): Matrix3;
-
-  /**
-   * Transposes this matrix into the supplied array r, and returns itself.
-   */
-  transposeIntoArray(r: number[]): number[];
-  fromArray(array: number[], offset?: number): Matrix3;
-  toArray(): number[];
-
-  /**
-   * Multiplies this matrix by m.
-   */
-  multiply(m: Matrix3): Matrix3;
-
-  premultiply(m: Matrix3): Matrix3;
-
-  /**
-   * Sets this matrix to a x b.
-   */
-  multiplyMatrices(a: Matrix3, b: Matrix3): Matrix3;
-
-  /**
-   * @deprecated Use {@link Vector3.applyMatrix3 vector.applyMatrix3( matrix )} instead.
-   */
-  multiplyVector3(vector: Vector3): any;
-
-  /**
-   * @deprecated This method has been removed completely.
-   */
-  multiplyVector3Array(a: any): any;
-  getInverse(matrix: Matrix4, throwOnDegenerate?: boolean): Matrix3;
-
-  /**
-   * @deprecated Use {@link Matrix3#toArray .toArray()} instead.
-   */
-  flattenToArrayOffset(array: number[], offset: number): number[];
-}
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/log.py b/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/log.py
deleted file mode 100644
index 0d48757dca88f35e9ea2cd1ca16e41bac9976a45..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/trackers/strongsort/utils/log.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import logging
-
-
-def get_logger(name='root'):
-    formatter = logging.Formatter(
-        # fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
-        fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
-
-    handler = logging.StreamHandler()
-    handler.setFormatter(formatter)
-
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.INFO)
-    logger.addHandler(handler)
-    return logger
-
-
diff --git a/spaces/bigPear/digitalWDF/data/README.md b/spaces/bigPear/digitalWDF/data/README.md
deleted file mode 100644
index 6a30cb2b15b9d9dec4e404bb923061f7f258e066..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/data/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
-Data format in `dataset_info.json`:
-```json
-"dataset_name": {
-    "hf_hub_url": "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
-    "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)",
-    "file_name": "the name of the dataset file in the this directory. (required if above are not specified)",
-    "file_sha1": "the SHA-1 hash value of the dataset file. (optional)",
-    "columns": {
-        "prompt": "the name of the column in the datasets containing the prompts. (default: instruction)",
-        "query": "the name of the column in the datasets containing the queries. (default: input)",
-        "response": "the name of the column in the datasets containing the responses. (default: output)",
-        "history": "the name of the column in the datasets containing the history of chat. (default: None)"
-    }
-}
-```
-
-`dataset_info.json` 中的数据集定义格式：
-```json
-"数据集名称": {
-    "hf_hub_url": "HuggingFace上的项目地址（若指定，则忽略下列三个参数）",
-    "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略下列两个参数）",
-    "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
-    "file_sha1": "数据集文件的SHA-1哈希值（可选）",
-    "columns": {
-        "prompt": "数据集代表提示词的表头名称（默认：instruction）",
-        "query": "数据集代表请求的表头名称（默认：input）",
-        "response": "数据集代表回答的表头名称（默认：output）",
-        "history": "数据集代表历史对话的表头名称（默认：None）"
-    }
-}
-```
-
-部分预置数据集简介：
-
-| 数据集名称 | 规模 | 描述 |
-| --- | --- | --- |
-| [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 52k | 斯坦福大学开源的 Alpaca 数据集，训练了 Alpaca 这类早期基于 LLaMA 的模型 |
-| [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | 51k | 使用 ChatGPT 翻译的 Alpaca 数据集 |
-| [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) | 100k+ | 基于 GPT-4 的 self-instruction 数据集 |
-| [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN) | 2m | 包含约 200 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN) | 1m | 包含约 100 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) | 500k  | 包含约 50 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文指令数据 |
-| [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) | 400k | 包含约 40 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的个性化角色对话数据，包含角色介绍 |
-| [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) | 250k  | 包含约 25 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的中文数学题数据，包含解题过程 |
-| [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) | 800k | 包含约 80 万条由 [BELLE](https://github.com/LianjiaTech/BELLE) 项目生成的用户与助手的多轮对话 |
-| [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) | 100k+ | 包含日文、简繁体中文、英文等多类数据，数据集原用于 Guanaco 模型训练 |
-| [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) | 1.1M  | 中文对话大模型 firefly（流萤）的中文数据集，包含多个 NLP 任务 |
-| [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) | 20k | 英文代码生成任务数据集 |
-| [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) | 6M | 用于微调的指令数据集集合 |
-| [Web QA](https://huggingface.co/datasets/suolyer/webqa) | 36k | 百度知道汇集的中文问答数据集 |
-| [UltraChat](https://github.com/thunlp/UltraChat) | 1.57M | 清华 NLP 发布的大规模多轮对话数据集 |
-
-注：BELLE 数据集是由 ChatGPT 产生的数据集，不保证数据准确性，所有类 GPT 模型产生的 self-instruction 数据集均不能保证其准确性。
diff --git a/spaces/bigPear/digitalWDF/src/utils/.ipynb_checkpoints/common-checkpoint.py b/spaces/bigPear/digitalWDF/src/utils/.ipynb_checkpoints/common-checkpoint.py
deleted file mode 100644
index 782a2b247b48eb1e8984a35ed9471f3b57843480..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/src/utils/.ipynb_checkpoints/common-checkpoint.py
+++ /dev/null
@@ -1,507 +0,0 @@
-import os
-import sys
-import torch
-import hashlib
-from typing import Literal, Optional, Tuple
-
-import transformers
-from transformers import (
-    AutoConfig,
-    AutoModel,
-    AutoTokenizer,
-    HfArgumentParser,
-    Seq2SeqTrainingArguments
-)
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-from transformers.modeling_utils import PreTrainedModel
-from transformers.tokenization_utils import PreTrainedTokenizer
-
-import datasets
-from datasets import Dataset, concatenate_datasets, load_dataset
-
-from peft import (
-    PeftModel,
-    TaskType,
-    LoraConfig,
-    get_peft_model
-)
-
-from trl import AutoModelForCausalLMWithValueHead
-
-from .config import (
-    ModelArguments,
-    DataTrainingArguments,
-    FinetuningArguments
-)
-
-from .other import (
-    get_logger,
-    load_trainable_params,
-    load_valuehead_params,
-    print_trainable_params,
-    prepare_model_for_training,
-    IGNORE_INDEX,
-    FINETUNING_ARGS_NAME
-)
-
-
-logger = get_logger(__name__)
-
-
-check_min_version("4.27.4")
-require_version("datasets>=2.10.0", "To fix: pip install datasets>=2.10.0")
-require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
-require_version("trl>=0.4.1", "To fix: pip install trl>=0.4.1")
-
-
-def init_adapter(
-        model: PreTrainedModel,
-        model_args: ModelArguments,
-        finetuning_args: FinetuningArguments,
-        is_trainable: bool
-) -> PreTrainedModel:
-    r"""
-    Initializes the adapters.
-
-    Note that the trainable parameters must be cast to float32.
-    """
-
-    if finetuning_args.finetuning_type == "none" and is_trainable:
-        raise ValueError("You cannot use finetuning_type=none while training.")
-
-    if finetuning_args.finetuning_type == "full":
-        logger.info("Fine-tuning method: Full")
-        model = model.float()
-
-        if model_args.checkpoint_dir is not None:
-            load_trainable_params(model, model_args.checkpoint_dir[0])
-
-    if finetuning_args.finetuning_type == "freeze":
-        logger.info("Fine-tuning method: Freeze")
-        for name, param in model.named_parameters():
-            if not any(trainable_layer in name for trainable_layer in finetuning_args.trainable_layers):
-                param.requires_grad_(False)
-            else:
-                param.data = param.data.to(torch.float32)
-
-        if model_args.checkpoint_dir is not None:
-            load_trainable_params(model, model_args.checkpoint_dir[0])
-
-    if finetuning_args.finetuning_type == "p_tuning":
-        logger.info("Fine-tuning method: P-Tuning v2") # nothing to do
-
-        if model_args.checkpoint_dir is not None:
-            load_trainable_params(model, model_args.checkpoint_dir[0])
-
-    if finetuning_args.finetuning_type == "lora":
-        logger.info("Fine-tuning method: LoRA")
-        lastest_checkpoint = None
-
-        if model_args.checkpoint_dir is not None:
-            if is_trainable and finetuning_args.resume_lora_training: # continually training on the lora weights
-                checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
-            else:
-                checkpoints_to_merge = model_args.checkpoint_dir
-
-            for checkpoint in checkpoints_to_merge:
-                model = PeftModel.from_pretrained(model, checkpoint)
-                model = model.merge_and_unload()
-
-            logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
-
-            if lastest_checkpoint is not None: # resume lora training
-                model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=True)
-
-        if lastest_checkpoint is None: # create new lora weights
-            lora_config = LoraConfig(
-                task_type=TaskType.CAUSAL_LM,
-                inference_mode=False,
-                r=finetuning_args.lora_rank,
-                lora_alpha=finetuning_args.lora_alpha,
-                lora_dropout=finetuning_args.lora_dropout,
-                target_modules=finetuning_args.lora_target
-            )
-            model = get_peft_model(model, lora_config)
-
-    return model
-
-
-def load_pretrained(
-        model_args: ModelArguments,
-        training_args: Optional[Seq2SeqTrainingArguments] = None,
-        finetuning_args: Optional[FinetuningArguments] = None,
-        is_trainable: Optional[bool] = False,
-        stage: Optional[Literal["sft", "rwd", "ppo"]] = "sft"
-) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
-    r"""
-    Load pretrained model and tokenizer.
-    """
-
-    if (not is_trainable) and (model_args.checkpoint_dir is None):
-        logger.warning("Checkpoint is not found at evaluation, load the original model.")
-        finetuning_args = FinetuningArguments(finetuning_type="none")
-
-    if model_args.checkpoint_dir is not None: # load fine-tuned model from checkpoint
-        for checkpoint_dir in model_args.checkpoint_dir:
-            if not os.path.isfile(os.path.join(checkpoint_dir, FINETUNING_ARGS_NAME)):
-                raise ValueError("The fine-tuning arguments are not found in the provided dictionary.")
-        logger.info("Load fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
-        finetuning_args = torch.load(os.path.join(model_args.checkpoint_dir[0], FINETUNING_ARGS_NAME))
-        if finetuning_args.finetuning_type != "lora" and len(model_args.checkpoint_dir) > 1:
-            logger.warning("Only LoRA tuning accepts multiple checkpoints.")
-
-    assert stage == "sft" or finetuning_args.finetuning_type == "lora", "RM and PPO training can only be performed with LoRA method."
-
-    quantization = None
-    if model_args.quantization_bit is not None:
-        if is_trainable:
-            if finetuning_args.finetuning_type == "full":
-                raise ValueError("Full parameter fine-tuning does not support quantization.")
-            elif finetuning_args.finetuning_type == "p_tuning":
-                quantization = "cpm" # use cpm's quantization
-            else:
-                quantization = "bnb" # use bnb's quantization
-        else:
-            quantization = "cpm"
-
-    config_kwargs = {
-        "trust_remote_code": True,
-        "cache_dir": model_args.cache_dir,
-        "revision": model_args.model_revision,
-        "use_auth_token": True if model_args.use_auth_token else None,
-    }
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        use_fast=model_args.use_fast_tokenizer,
-        padding_side="left",
-        **config_kwargs
-    )
-
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        **config_kwargs
-    )
-
-    # P-Tuning v2 configurations.
-    # We use the built-in p-tuning method of ChatGLM, we cannot use PEFT since the attention masks of ChatGLM are unusual. >_<
-    if finetuning_args.finetuning_type == "p_tuning":
-        config.pre_seq_len = finetuning_args.pre_seq_len # enable this will fix other parameters automatically
-        config.prefix_projection = finetuning_args.prefix_projection
-
-    # Quantization configurations for Full, Freeze and LoRA in training (using bitsandbytes library).
-    if quantization == "bnb":
-        assert model_args.quantization_bit == 8, "Freeze and LoRA fine-tuning only accept 8-bit quantization."
-
-        require_version("bitsandbytes>=0.37.0", "bitsandbytes library is required to use this feature.")
-        from bitsandbytes.cuda_setup.main import get_compute_capability, get_cuda_lib_handle, is_cublasLt_compatible
-        cuda = get_cuda_lib_handle()
-        cc = get_compute_capability(cuda)
-        assert is_cublasLt_compatible(cc), "The current GPU(s) is incompatible with quantization."
-
-        config_kwargs["load_in_8bit"] = True
-        config_kwargs["device_map"] = "auto" # it should not be specified outside of load_in_8bit
-
-    # Load and prepare pretrained models (without valuehead).
-    model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, **config_kwargs)
-    model = prepare_model_for_training(model) if is_trainable else model
-    model = init_adapter(model, model_args, finetuning_args, is_trainable)
-
-    if not is_trainable:
-        model.requires_grad_(False) # fix all params
-        model = model.half() # cast all params to float16
-
-    # Quantization with the built-in method for P-Tuning v2 training or evaluation.
-    # Model parameters should be cast to float16 in quantized P-Tuning setting.
-    if quantization == "cpm":
-        assert model_args.quantization_bit in [4, 8], "P-Tuning v2 and inference mode only accept 4-bit or 8-bit quantization."
-        assert not (is_trainable and training_args.fp16), "FP16 training conflicts with cpm quantization."
-
-        model.quantize(model_args.quantization_bit) # in-place method
-
-        for name, param in model.named_parameters():
-            if "prefix_encoder" not in name:
-                param.data = param.data.to(torch.float16) # convert all params in half precision except prefix_encoder
-
-    if quantization is not None:
-        logger.info("Quantized model to {} bit.".format(model_args.quantization_bit))
-
-    if stage == "rwd" or stage == "ppo": # add value head
-        assert is_trainable, "Reward and PPO stages cannot be performed at evaluation."
-
-        model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
-
-        if stage == "ppo": # load reward model
-            assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
-            model.pretrained_model.load_adapter(model_args.reward_model, "reward", is_trainable=False)
-            load_valuehead_params(model, model_args.reward_model)
-
-        # Set the parameter _is_int8_training_enabled for the AutoModelForCausalLMWithValueHead model
-        # To meet the compliance requirements of the transformers library
-        if quantization == "bnb":
-            model._is_int8_training_enabled = True
-
-    print_trainable_params(model)
-
-    return model, tokenizer
-
-
-def prepare_args() -> Tuple[ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments]:
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments))
-
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
-        model_args, data_args, training_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args, finetuning_args = parser.parse_args_into_dataclasses()
-
-    # Setup logging
-    if training_args.should_log:
-        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
-        transformers.utils.logging.set_verbosity_info()
-
-    log_level = training_args.get_process_log_level()
-    datasets.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.set_verbosity(log_level)
-    transformers.utils.logging.enable_default_handler()
-    transformers.utils.logging.enable_explicit_format()
-
-    # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
-    if int(training_args.do_train) + int(training_args.do_eval) + int(training_args.do_predict) != 1:
-        raise ValueError("We must perform a single operation among do_train, do_eval and do_predict.")
-
-    if model_args.quantization_bit is not None and training_args.do_train == False:
-        logger.warning("We do not recommend to evaluaute model in 4/8-bit mode.")
-
-    if training_args.do_train and (not training_args.fp16):
-        logger.warning("We recommend enable fp16 mixed precision training for ChatGLM-6B.")
-
-    training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
-
-    # Log on each process the small summary:
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\n"
-        + f"  distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Set seed before initializing model.
-    transformers.set_seed(training_args.seed)
-
-    return model_args, data_args, training_args, finetuning_args
-
-
-def prepare_data(
-        model_args: ModelArguments,
-        data_args: DataTrainingArguments
-) -> Dataset:
-
-    def checksum(file_path, hash):
-        with open(file_path, "rb") as datafile:
-            binary_data = datafile.read()
-        sha1 = hashlib.sha1(binary_data).hexdigest()
-        if sha1 != hash:
-            logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))
-
-    max_samples = data_args.max_samples
-    all_datasets = [] # support multiple datasets
-
-    for dataset_info in data_args.dataset_list:
-
-        logger.info("Loading dataset {}...".format(dataset_info))
-
-        if dataset_info.load_from == "hf_hub":
-            raw_datasets = load_dataset(dataset_info.dataset_name, cache_dir=model_args.cache_dir)
-        elif dataset_info.load_from == "script":
-            raw_datasets = load_dataset(
-                os.path.join(data_args.dataset_dir, dataset_info.dataset_name),
-                cache_dir=model_args.cache_dir
-            )
-        elif dataset_info.load_from == "file":
-            data_file = os.path.join(data_args.dataset_dir, dataset_info.file_name) # support json, jsonl and csv
-            extension = dataset_info.file_name.split(".")[-1]
-
-            if dataset_info.file_sha1 is not None:
-                checksum(data_file, dataset_info.file_sha1)
-            else:
-                logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.")
-
-            raw_datasets = load_dataset(
-                extension,
-                data_files=data_file,
-                cache_dir=model_args.cache_dir,
-                use_auth_token=True if model_args.use_auth_token else None
-            )
-        else:
-            raise NotImplementedError
-
-        dataset = raw_datasets[data_args.split]
-
-        if max_samples is not None:
-            max_samples_temp = min(len(dataset), max_samples)
-            dataset = dataset.select(range(max_samples_temp))
-
-        dummy_data = [None] * len(dataset)
-        for column, column_name in [
-            ("prompt_column", "prompt"),
-            ("query_column", "query"),
-            ("response_column", "response"),
-            ("history_column", "history")
-        ]: # every dataset will have 4 columns same as each other
-            if getattr(dataset_info, column) != column_name:
-                if getattr(dataset_info, column):
-                    dataset = dataset.rename_column(getattr(dataset_info, column), column_name)
-                else: # None or empty string
-                    dataset = dataset.add_column(column_name, dummy_data)
-        all_datasets.append(dataset)
-
-    if len(data_args.dataset_list) == 1:
-        all_datasets = all_datasets[0]
-    else:
-        all_datasets = concatenate_datasets(all_datasets)
-
-    return all_datasets
-
-
-def preprocess_data(
-        dataset: Dataset,
-        tokenizer: PreTrainedTokenizer,
-        data_args: DataTrainingArguments,
-        training_args: Seq2SeqTrainingArguments,
-        stage: Optional[Literal["sft", "rwd", "ppo"]] = "sft"
-) -> Dataset:
-
-    column_names = list(dataset.column_names)
-    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
-
-    def format_example(examples): # support question with a single answer or multiple answers
-        for i in range(len(examples["prompt"])):
-            if examples["prompt"][i] and examples["response"][i]:
-                query, answer = examples["prompt"][i], examples["response"][i]
-                if examples["query"][i]:
-                    query += examples["query"][i]
-                if examples["history"][i]:
-                    prompt = ""
-                    history = examples["history"][i]
-                    for j, (old_query, response) in enumerate(history):
-                        prompt += "[Round {}]\n问：{}\n答：{}\n".format(j, old_query, response)
-                    prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
-                else:
-                    prompt = query
-                prompt = prefix + prompt
-                yield prompt, answer
-
-    def preprocess_function_train(examples):
-        # build inputs with format `X [gMASK] [BOS] Y [EOS]` and labels with format `[IGNORE] ... [IGNORE] [BOS] Y [EOS]`
-        model_inputs = {"input_ids": [], "labels": []}
-        for prompt, answer in format_example(examples):
-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
-            target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
-
-            if len(source_ids) > data_args.max_source_length - 2: # gmask and bos tokens
-                source_ids = source_ids[:data_args.max_source_length - 2]
-            if len(target_ids) > data_args.max_target_length - 1: # eos token
-                target_ids = target_ids[:data_args.max_target_length - 1]
-
-            input_ids = tokenizer.build_inputs_with_special_tokens(source_ids, target_ids)
-
-            context_length = input_ids.index(tokenizer.bos_token_id)
-            labels = [IGNORE_INDEX] * context_length + input_ids[context_length:]
-
-            model_inputs["input_ids"].append(input_ids)
-            model_inputs["labels"].append(labels)
-        return model_inputs
-
-    def preprocess_function_eval(examples):
-        # build inputs with format `[PAD] ... [PAD] X [gMASK] [BOS]` and labels with format `Y [gMASK] [BOS]`
-        # left-padding is needed for prediction, use the built-in function of the tokenizer
-        inputs, targets = [], []
-        for prompt, answer in format_example(examples):
-            inputs.append(prompt)
-            targets.append(answer)
-        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, truncation=True, padding=True)
-        labels = tokenizer(text_target=targets, max_length=data_args.max_target_length, truncation=True) # no padding
-        if data_args.ignore_pad_token_for_loss:
-            labels["input_ids"] = [
-                [(l_id if l_id != tokenizer.pad_token_id else IGNORE_INDEX) for l_id in label] for label in labels["input_ids"]
-            ]
-        model_inputs["labels"] = labels["input_ids"]
-        return model_inputs
-
-    def preprocess_function_train_pair(examples):
-        # build input pairs with format `X [gMASK] [BOS] Y1 [EOS]` and `X [gMASK] [BOS] Y2 [EOS]`
-        model_inputs = {"accept_ids": [], "reject_ids": []}
-        for prompt, answer in format_example(examples):
-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
-            accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
-            reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
-
-            if len(source_ids) > data_args.max_source_length - 2: # gmask and bos tokens
-                source_ids = source_ids[:data_args.max_source_length - 2]
-            if len(accept_ids) > data_args.max_target_length - 1: # eos token
-                accept_ids = accept_ids[:data_args.max_target_length - 1]
-            if len(reject_ids) > data_args.max_target_length - 1: # eos token
-                reject_ids = reject_ids[:data_args.max_target_length - 1]
-
-            accept_ids = tokenizer.build_inputs_with_special_tokens(source_ids[:], accept_ids) # avoid copying error
-            reject_ids = tokenizer.build_inputs_with_special_tokens(source_ids[:], reject_ids)
-
-            model_inputs["accept_ids"].append(accept_ids)
-            model_inputs["reject_ids"].append(reject_ids)
-        return model_inputs
-
-    def preprocess_function_train_ppo(examples):
-        # build inputs with format `X [gMASK] [BOS]`
-        model_inputs = {"input_ids": []}
-        for prompt, _ in format_example(examples):
-            source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
-
-            if len(source_ids) > data_args.max_source_length - 2: # gmask and bos tokens
-                source_ids = source_ids[:data_args.max_source_length - 2]
-
-            input_ids = tokenizer.build_inputs_with_special_tokens(source_ids)
-            model_inputs["input_ids"].append(input_ids)
-        return model_inputs
-
-    def print_sft_dataset_example(example):
-        print("input_ids:\n{}".format(example["input_ids"]))
-        print("inputs:\n{}".format(tokenizer.decode(example["input_ids"])))
-        print("label_ids:\n{}".format(example["labels"]))
-        print("labels:\n{}".format(tokenizer.decode(example["labels"])))
-
-    def print_pairwise_dataset_example(example):
-        print("accept_ids:\n{}".format(example["accept_ids"]))
-        print("accepts:\n{}".format(tokenizer.decode(example["accept_ids"])))
-        print("reject_ids:\n{}".format(example["reject_ids"]))
-        print("rejects:\n{}".format(tokenizer.decode(example["reject_ids"])))
-
-    def print_ppo_dataset_example(example):
-        print("input_ids:\n{}".format(example["input_ids"]))
-        print("inputs:\n{}".format(tokenizer.decode(example["input_ids"])))
-
-    if stage == "sft":
-        preprocess_function = preprocess_function_train if training_args.do_train else preprocess_function_eval
-    elif stage == "rwd":
-        preprocess_function = preprocess_function_train_pair
-    elif stage == "ppo":
-        preprocess_function = preprocess_function_train_ppo
-
-    with training_args.main_process_first(desc="dataset map pre-processing"):
-        dataset = dataset.map(
-            preprocess_function,
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-            desc="Running tokenizer on dataset"
-        )
-
-    if stage == "sft":
-        print_sft_dataset_example(dataset[0])
-    elif stage == "rwd":
-        print_pairwise_dataset_example(dataset[0])
-    elif stage == "ppo":
-        print_ppo_dataset_example(dataset[0])
-
-    return dataset
diff --git a/spaces/bigscience/petals-api/src/client/remote_model.py b/spaces/bigscience/petals-api/src/client/remote_model.py
deleted file mode 100644
index 306e141c03e99bffc0786734f3a3b95840683977..0000000000000000000000000000000000000000
--- a/spaces/bigscience/petals-api/src/client/remote_model.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# this code is in active development, interfaces may change
-import os
-from typing import Optional, Tuple, Union
-
-import hivemind
-from hivemind import DHT, get_logger, use_hivemind_log_handler
-
-from src.bloom.from_pretrained import CLIENT_BRANCH, _load_state_dict
-from src.bloom.model import BloomConfig, BloomForCausalLM, BloomModel, BloomPreTrainedModel
-from src.client.remote_sequential import RemoteSequential
-from src.data_structures import UID_DELIMITER
-
-use_hivemind_log_handler("in_root_logger")
-logger = get_logger(__file__)
-
-
-class DistributedBloomConfig(BloomConfig):
-    """
-    A bloom config that contains information about DHT peers.
-    To create a distributed model, one must provide dht_prefix and either initial_peers or dht.
-    """
-
-    initial_peers: Tuple[str, ...] = ()  # a list of initial peers for hivemind DHT
-    dht_prefix: str  # a prefix for all dht keys that correspond to this model (usually equal to model name)
-    dht: Optional[hivemind.DHT] = None  # a running DHT instance, e.g. when using the same DHT for multiple models
-
-
-class DistributedBloomModel(BloomModel):
-    """BloomModel, but all transformer layers are hosted by the swarm"""
-    config_class = DistributedBloomConfig
-
-    def __init__(self, config: DistributedBloomConfig):
-        assert config.dht_prefix, "Could not find dht_prefix in config, please create model with dht_prefix=..."
-        assert config.initial_peers or config.dht, "Please specify initial_peers=list(...) or dht=hivemind.DHT(...)"
-
-        n_layer, config.n_layer = config.n_layer, 0  # temporarily set n_layer to 0 to prevent layer initialization
-        super().__init__(config)
-        assert len(self.h) == 0
-        config.n_layer = n_layer
-
-        dht = (
-            config.dht
-            if config.dht is not None
-            else hivemind.DHT(initial_peers=config.initial_peers, client_mode=True, start=True)
-        )
-        assert isinstance(dht, hivemind.DHT) and dht.is_alive(), "dht must be a running hivemind.DHT instance"
-        self.h = RemoteSequential(config, dht, config.dht_prefix)
-
-
-class DistributedBloomForCausalLM(BloomForCausalLM):
-    """DistributedBloomForCausalLM, but all transformer layers are hosted by the swarm"""
-    config_class = DistributedBloomConfig
-
-    def __init__(self, config: DistributedBloomConfig):
-        BloomPreTrainedModel.__init__(self, config)
-        self.transformer = DistributedBloomModel(config)
-        # Initialize weights and apply final processing
-        self.post_init()
diff --git a/spaces/bioriAsaeru/text-to-voice/Goverlan Remote Control 7 !!EXCLUSIVE!! Crack.md b/spaces/bioriAsaeru/text-to-voice/Goverlan Remote Control 7 !!EXCLUSIVE!! Crack.md
deleted file mode 100644
index 3f15036b8e64ce9a56e8591bc11713c2e555a6f6..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Goverlan Remote Control 7 !!EXCLUSIVE!! Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
      Goverlan Remote Control 7 Crack

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-
      
-Oat crackers goverlan remote control v7 crack serial keygen settlers 7 crack 1.12. And/or the internet download manager 6.07 free download with crack, from each ... 1fdad05405
      
-
      
-
      
-
      
diff --git a/spaces/bioriAsaeru/text-to-voice/Gravity Rush Download Movies un film imperdibile per gli amanti dei giochi dazione.md b/spaces/bioriAsaeru/text-to-voice/Gravity Rush Download Movies un film imperdibile per gli amanti dei giochi dazione.md
deleted file mode 100644
index 31c32a16cd4dd5dafc7fd4ad6703506dc9493838..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Gravity Rush Download Movies un film imperdibile per gli amanti dei giochi dazione.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
      
-
      Whatever the reason for Gravity Rush 2's delay, at least fans won't have to wait that much longer to get their hands on Kat's new adventure. In the meantime, there are plenty of new games releasing this holiday season to help tide them over, including some other major PS4 exclusives. And if all else fails, fans can always play Gravity Rush Remastered to brush up on their gravity manipulation skills ahead of the sequel's release.
      
-
      Gravity Rush Download Movies

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-
      
\ No newline at end of file
diff --git a/spaces/brainblow/MusiCreator/tests/modules/test_seanet.py b/spaces/brainblow/MusiCreator/tests/modules/test_seanet.py
deleted file mode 100644
index e5c51b340a2f94fb2828b14daf83d5fad645073d..0000000000000000000000000000000000000000
--- a/spaces/brainblow/MusiCreator/tests/modules/test_seanet.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock
-from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d
-
-
-class TestSEANetModel:
-
-    def test_base(self):
-        encoder = SEANetEncoder()
-        decoder = SEANetDecoder()
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_causal(self):
-        encoder = SEANetEncoder(causal=True)
-        decoder = SEANetDecoder(causal=True)
-        x = torch.randn(1, 1, 24000)
-
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_conv_skip_connection(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False)
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def test_seanet_encoder_decoder_final_act(self):
-        encoder = SEANetEncoder(true_skip=False)
-        decoder = SEANetDecoder(true_skip=False, final_activation='Tanh')
-
-        x = torch.randn(1, 1, 24000)
-        z = encoder(x)
-        assert list(z.shape) == [1, 128, 75], z.shape
-        y = decoder(z)
-        assert y.shape == x.shape, (x.shape, y.shape)
-
-    def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in encoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        # here we add + 1 to n_blocks as we increment n_blocks just after the block
-                        assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm
-
-    def test_encoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_encoder_blocks_norm(encoder, disable_blocks, norm)
-
-    def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str):
-        n_blocks = 0
-        for layer in decoder.model:
-            if isinstance(layer, StreamableConv1d):
-                n_blocks += 1
-                assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, StreamableConvTranspose1d):
-                n_blocks += 1
-                assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-            elif isinstance(layer, SEANetResnetBlock):
-                for resnet_layer in layer.block:
-                    if isinstance(resnet_layer, StreamableConv1d):
-                        assert resnet_layer.conv.norm_type == 'none' \
-                            if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-
-    def test_decoder_disable_norm(self):
-        n_residuals = [0, 1, 3]
-        disable_blocks = [0, 1, 2, 3, 4, 5, 6]
-        norms = ['weight_norm', 'none']
-        for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
-            decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm,
-                                    disable_norm_outer_blocks=disable_blocks)
-            self._check_decoder_blocks_norm(decoder, disable_blocks, norm)
-
-    def test_disable_norm_raises_exception(self):
-        # Invalid disable_norm_outer_blocks values raise exceptions
-        with pytest.raises(AssertionError):
-            SEANetEncoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(disable_norm_outer_blocks=-1)
-
-        with pytest.raises(AssertionError):
-            SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
diff --git a/spaces/breynolds1247/StarryNight_StyleTransfer/app.py b/spaces/breynolds1247/StarryNight_StyleTransfer/app.py
deleted file mode 100644
index 3f1f83b4b39d08f191e7a18fd17c593386f8d42d..0000000000000000000000000000000000000000
--- a/spaces/breynolds1247/StarryNight_StyleTransfer/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import os
-#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
-
-#Imports
-import tensorflow as tf
-from tensorflow import keras
-import matplotlib.pyplot as plt
-import tensorflow_hub as hub
-
-from PIL import Image
-import gradio as gr
-
-from helper_functions import *
-
-def style_transfer(input_image, artist):
-
-    style_path_van_gogh = keras.utils.get_file('Starry-Night-canvas-Vincent-van-Gogh-New-1889.jpg', 
-                                           'https://cdn.britannica.com/78/43678-050-F4DC8D93/Starry-Night-canvas-Vincent-van-Gogh-New-1889.jpg')
-    style_path_davinci = keras.utils.get_file('Leonardo_da_Vinci_-_Mona_Lisa_%28La_Gioconda%29_-_WGA12711.jpg', 
-                                          'https://upload.wikimedia.org/wikipedia/commons/f/f2/Leonardo_da_Vinci_-_Mona_Lisa_%28La_Gioconda%29_-_WGA12711.jpg')
-    style_path_dali = keras.utils.get_file('The_Persistence_of_Memory.jpg', 
-                                       'https://upload.wikimedia.org/wikipedia/en/d/dd/The_Persistence_of_Memory.jpg')
-    style_path_monet = keras.utils.get_file('Claude_Monet_-_Water_Lilies_-_Google_Art_Project_%28462013%29.jpg',
-                                        'https://upload.wikimedia.org/wikipedia/commons/a/af/Claude_Monet_-_Water_Lilies_-_Google_Art_Project_%28462013%29.jpg')
-    style_path_picasso = keras.utils.get_file('Picasso_The_Weeping_Woman_Tate_identifier_T05010_10.jpg', 
-                                          'https://upload.wikimedia.org/wikipedia/en/1/14/Picasso_The_Weeping_Woman_Tate_identifier_T05010_10.jpg')
-    style_path_rembrandt = keras.utils.get_file('1259px-The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg', 
-                                            'https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg/1259px-The_Nightwatch_by_Rembrandt_-_Rijksmuseum.jpg')
-    
-    #set dimensions of input image                                        
-    oc_max_dim = 1080
-
-    #set parameters for each choice of artist
-    if artist == "Vincent van Gogh":
-        style_max_dim = 442
-        style_path = style_path_van_gogh
-    elif artist == "Claude Monet":
-        style_max_dim = 256
-        style_path = style_path_monet
-    elif artist == "Leonardo da Vinci":
-        style_max_dim = 442
-        style_path = style_path_davinci
-    elif artist == "Rembrandt":
-        style_max_dim = 256
-        style_path = style_path_rembrandt
-    elif artist == "Pablo Picasso":
-        style_max_dim = 256
-        style_path = style_path_picasso
-    elif artist == "Salvador Dali":
-        style_max_dim = 512
-        style_path = style_path_dali
-    
-    #load content and style images    
-    content_image = load_img(input_image, content=True, max_dim=oc_max_dim)
-    style_image = load_img(style_path, content=False, max_dim=style_max_dim)
-    
-    #Load Magenta Arbitrary Image Stylization network
-    hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
-    
-    #Pass content and style images as arguments in TensorFlow Constant object format
-    stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]
-    
-    print("stylized_image:")
-    print(stylized_image[0])
-    print(stylized_image)
-    return tf.keras.preprocessing.image.img_to_array(stylized_image[0])
-    
-app = gr.Interface(
-    style_transfer,
-    [gr.Image(type='pil'), gr.Radio(["Vincent van Gogh", "Claude Monet", "Leonardo da Vinci", "Rembrandt", "Pablo Picasso", "Salvador Dali"])],
-    gr.Image(type='pil'),
-    title="Artist Style Transfer Tool",
-    description="""
-Fast style transfer using the Magenta model lets you make your own art in the style of six famous artists using a pretrained neural network and deep learning! Simply upload an image and select an artist's style to have transferred to your picture. Each artist's styles are based on a single one of their most famous paintings, shown below for reference: Starry Night (van Gogh), Water Lilies (Monet), Mona Lisa (da Vinci), The Night Watch (Rembrandt), The Weeping Woman (Picasso), and The Persistence of Memory (Dali). 
-Note that some input images may be rotated 90 degrees in the output to facilitate the style transfer.  
-""",
-    article = """
-
-  
-     
-     
-     
-     
-     
-     
-  
-  
-    
-    
-    
-    
-    
-    
-  
-
      van GoghMonetda VinciRembrandtPicassoDali
      
-
-This app uses [Arbitrary Style Transfer with Magenta](https://arxiv.org/abs/1705.06830).    
-"""
-)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/__init__.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/__init__.py
deleted file mode 100644
index a44bedc15e5f0e762fc4d77efd6f1b07c6ff77d0..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .coco import load_coco_json, load_sem_seg, register_coco_instances, convert_to_coco_json
-from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated
-from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta
-from .pascal_voc import load_voc_instances, register_pascal_voc
-from . import builtin as _builtin  # ensure the builtin datasets are registered
-
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/meta_arch/retinanet.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/meta_arch/retinanet.py
deleted file mode 100644
index bd72a8e7fb57bebcdca64c7bc43b8f0f03118bed..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/meta_arch/retinanet.py
+++ /dev/null
@@ -1,439 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import math
-from typing import List, Tuple
-import torch
-from fvcore.nn import sigmoid_focal_loss_jit
-from torch import Tensor, nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm
-from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-
-from ..anchor_generator import build_anchor_generator
-from ..backbone import Backbone, build_backbone
-from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
-from ..matcher import Matcher
-from .build import META_ARCH_REGISTRY
-from .dense_detector import DenseDetector, permute_to_N_HWA_K  # noqa
-
-__all__ = ["RetinaNet"]
-
-
-logger = logging.getLogger(__name__)
-
-
-@META_ARCH_REGISTRY.register()
-class RetinaNet(DenseDetector):
-    """
-    Implement RetinaNet in :paper:`RetinaNet`.
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        backbone: Backbone,
-        head: nn.Module,
-        head_in_features,
-        anchor_generator,
-        box2box_transform,
-        anchor_matcher,
-        num_classes,
-        focal_loss_alpha=0.25,
-        focal_loss_gamma=2.0,
-        smooth_l1_beta=0.0,
-        box_reg_loss_type="smooth_l1",
-        test_score_thresh=0.05,
-        test_topk_candidates=1000,
-        test_nms_thresh=0.5,
-        max_detections_per_image=100,
-        pixel_mean,
-        pixel_std,
-        vis_period=0,
-        input_format="BGR",
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            backbone: a backbone module, must follow detectron2's backbone interface
-            head (nn.Module): a module that predicts logits and regression deltas
-                for each level from a list of per-level features
-            head_in_features (Tuple[str]): Names of the input feature maps to be used in head
-            anchor_generator (nn.Module): a module that creates anchors from a
-                list of features. Usually an instance of :class:`AnchorGenerator`
-            box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to
-                instance boxes
-            anchor_matcher (Matcher): label the anchors by matching them with ground truth.
-            num_classes (int): number of classes. Used to label background proposals.
-
-            # Loss parameters:
-            focal_loss_alpha (float): focal_loss_alpha
-            focal_loss_gamma (float): focal_loss_gamma
-            smooth_l1_beta (float): smooth_l1_beta
-            box_reg_loss_type (str): Options are "smooth_l1", "giou", "diou", "ciou"
-
-            # Inference parameters:
-            test_score_thresh (float): Inference cls score threshold, only anchors with
-                score > INFERENCE_TH are considered for inference (to improve speed)
-            test_topk_candidates (int): Select topk candidates before NMS
-            test_nms_thresh (float): Overlap threshold used for non-maximum suppression
-                (suppress boxes with IoU >= this threshold)
-            max_detections_per_image (int):
-                Maximum number of detections to return per image during inference
-                (100 is based on the limit established for the COCO dataset).
-
-            pixel_mean, pixel_std: see :class:`DenseDetector`.
-        """
-        super().__init__(
-            backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std
-        )
-        self.num_classes = num_classes
-
-        # Anchors
-        self.anchor_generator = anchor_generator
-        self.box2box_transform = box2box_transform
-        self.anchor_matcher = anchor_matcher
-
-        # Loss parameters:
-        self.focal_loss_alpha = focal_loss_alpha
-        self.focal_loss_gamma = focal_loss_gamma
-        self.smooth_l1_beta = smooth_l1_beta
-        self.box_reg_loss_type = box_reg_loss_type
-        # Inference parameters:
-        self.test_score_thresh = test_score_thresh
-        self.test_topk_candidates = test_topk_candidates
-        self.test_nms_thresh = test_nms_thresh
-        self.max_detections_per_image = max_detections_per_image
-        # Vis parameters
-        self.vis_period = vis_period
-        self.input_format = input_format
-
-    @classmethod
-    def from_config(cls, cfg):
-        backbone = build_backbone(cfg)
-        backbone_shape = backbone.output_shape()
-        feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]
-        head = RetinaNetHead(cfg, feature_shapes)
-        anchor_generator = build_anchor_generator(cfg, feature_shapes)
-        return {
-            "backbone": backbone,
-            "head": head,
-            "anchor_generator": anchor_generator,
-            "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),
-            "anchor_matcher": Matcher(
-                cfg.MODEL.RETINANET.IOU_THRESHOLDS,
-                cfg.MODEL.RETINANET.IOU_LABELS,
-                allow_low_quality_matches=True,
-            ),
-            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
-            "pixel_std": cfg.MODEL.PIXEL_STD,
-            "num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
-            "head_in_features": cfg.MODEL.RETINANET.IN_FEATURES,
-            # Loss parameters:
-            "focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,
-            "focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,
-            "smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,
-            "box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,
-            # Inference parameters:
-            "test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,
-            "test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,
-            "test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST,
-            "max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
-            # Vis parameters
-            "vis_period": cfg.VIS_PERIOD,
-            "input_format": cfg.INPUT.FORMAT,
-        }
-
-    def forward_training(self, images, features, predictions, gt_instances):
-        # Transpose the Hi*Wi*A dimension to the middle:
-        pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(
-            predictions, [self.num_classes, 4]
-        )
-        anchors = self.anchor_generator(features)
-        gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
-        return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)
-
-    def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):
-        """
-        Args:
-            anchors (list[Boxes]): a list of #feature level Boxes
-            gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.
-                Their shapes are (N, R) and (N, R, 4), respectively, where R is
-                the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)
-            pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the
-                list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).
-                Where K is the number of classes used in `pred_logits`.
-
-        Returns:
-            dict[str, Tensor]:
-                mapping from a named loss to a scalar tensor storing the loss.
-                Used during training only. The dict keys are: "loss_cls" and "loss_box_reg"
-        """
-        num_images = len(gt_labels)
-        gt_labels = torch.stack(gt_labels)  # (N, R)
-
-        valid_mask = gt_labels >= 0
-        pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
-        num_pos_anchors = pos_mask.sum().item()
-        get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
-        normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 100)
-
-        # classification and regression loss
-        gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[
-            :, :-1
-        ]  # no loss for the last (background) class
-        loss_cls = sigmoid_focal_loss_jit(
-            cat(pred_logits, dim=1)[valid_mask],
-            gt_labels_target.to(pred_logits[0].dtype),
-            alpha=self.focal_loss_alpha,
-            gamma=self.focal_loss_gamma,
-            reduction="sum",
-        )
-
-        loss_box_reg = _dense_box_regression_loss(
-            anchors,
-            self.box2box_transform,
-            pred_anchor_deltas,
-            gt_boxes,
-            pos_mask,
-            box_reg_loss_type=self.box_reg_loss_type,
-            smooth_l1_beta=self.smooth_l1_beta,
-        )
-
-        return {
-            "loss_cls": loss_cls / normalizer,
-            "loss_box_reg": loss_box_reg / normalizer,
-        }
-
-    @torch.no_grad()
-    def label_anchors(self, anchors, gt_instances):
-        """
-        Args:
-            anchors (list[Boxes]): A list of #feature level Boxes.
-                The Boxes contains anchors of this image on the specific feature level.
-            gt_instances (list[Instances]): a list of N `Instances`s. The i-th
-                `Instances` contains the ground-truth per-instance annotations
-                for the i-th input image.
-
-        Returns:
-            list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is
-            the total number of anchors across all feature maps (sum(Hi * Wi * A)).
-            Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.
-
-            list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors
-            across feature maps. The values are the matched gt boxes for each anchor.
-            Values are undefined for those anchors not labeled as foreground.
-        """
-        anchors = Boxes.cat(anchors)  # Rx4
-
-        gt_labels = []
-        matched_gt_boxes = []
-        for gt_per_image in gt_instances:
-            match_quality_matrix = pairwise_iou(gt_per_image.gt_boxes, anchors)
-            matched_idxs, anchor_labels = self.anchor_matcher(match_quality_matrix)
-            del match_quality_matrix
-
-            if len(gt_per_image) > 0:
-                matched_gt_boxes_i = gt_per_image.gt_boxes.tensor[matched_idxs]
-
-                gt_labels_i = gt_per_image.gt_classes[matched_idxs]
-                # Anchors with label 0 are treated as background.
-                gt_labels_i[anchor_labels == 0] = self.num_classes
-                # Anchors with label -1 are ignored.
-                gt_labels_i[anchor_labels == -1] = -1
-            else:
-                matched_gt_boxes_i = torch.zeros_like(anchors.tensor)
-                gt_labels_i = torch.zeros_like(matched_idxs) + self.num_classes
-
-            gt_labels.append(gt_labels_i)
-            matched_gt_boxes.append(matched_gt_boxes_i)
-
-        return gt_labels, matched_gt_boxes
-
-    def forward_inference(
-        self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]]
-    ):
-        pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(
-            predictions, [self.num_classes, 4]
-        )
-        anchors = self.anchor_generator(features)
-
-        results: List[Instances] = []
-        for img_idx, image_size in enumerate(images.image_sizes):
-            scores_per_image = [x[img_idx].sigmoid_() for x in pred_logits]
-            deltas_per_image = [x[img_idx] for x in pred_anchor_deltas]
-            results_per_image = self.inference_single_image(
-                anchors, scores_per_image, deltas_per_image, image_size
-            )
-            results.append(results_per_image)
-        return results
-
-    def inference_single_image(
-        self,
-        anchors: List[Boxes],
-        box_cls: List[Tensor],
-        box_delta: List[Tensor],
-        image_size: Tuple[int, int],
-    ):
-        """
-        Single-image inference. Return bounding-box detection results by thresholding
-        on scores and applying non-maximum suppression (NMS).
-
-        Arguments:
-            anchors (list[Boxes]): list of #feature levels. Each entry contains
-                a Boxes object, which contains all the anchors in that feature level.
-            box_cls (list[Tensor]): list of #feature levels. Each entry contains
-                tensor of size (H x W x A, K)
-            box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4.
-            image_size (tuple(H, W)): a tuple of the image height and width.
-
-        Returns:
-            Same as `inference`, but for only one image.
-        """
-        pred = self._decode_multi_level_predictions(
-            anchors,
-            box_cls,
-            box_delta,
-            self.test_score_thresh,
-            self.test_topk_candidates,
-            image_size,
-        )
-        keep = batched_nms(  # per-class NMS
-            pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh
-        )
-        return pred[keep[: self.max_detections_per_image]]
-
-
-class RetinaNetHead(nn.Module):
-    """
-    The head used in RetinaNet for object classification and box regression.
-    It has two subnets for the two tasks, with a common structure but separate parameters.
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        input_shape: List[ShapeSpec],
-        num_classes,
-        num_anchors,
-        conv_dims: List[int],
-        norm="",
-        prior_prob=0.01,
-    ):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (List[ShapeSpec]): input shape
-            num_classes (int): number of classes. Used to label background proposals.
-            num_anchors (int): number of generated anchors
-            conv_dims (List[int]): dimensions for each convolution layer
-            norm (str or callable):
-                Normalization for conv layers except for the two output layers.
-                See :func:`detectron2.layers.get_norm` for supported types.
-            prior_prob (float): Prior weight for computing bias
-        """
-        super().__init__()
-
-        self._num_features = len(input_shape)
-        if norm == "BN" or norm == "SyncBN":
-            logger.info(
-                f"Using domain-specific {norm} in RetinaNetHead with len={self._num_features}."
-            )
-            bn_class = nn.BatchNorm2d if norm == "BN" else nn.SyncBatchNorm
-
-            def norm(c):
-                return CycleBatchNormList(
-                    length=self._num_features, bn_class=bn_class, num_features=c
-                )
-
-        else:
-            norm_name = str(type(get_norm(norm, 32)))
-            if "BN" in norm_name:
-                logger.warning(
-                    f"Shared BatchNorm (type={norm_name}) may not work well in RetinaNetHead."
-                )
-
-        cls_subnet = []
-        bbox_subnet = []
-        for in_channels, out_channels in zip(
-            [input_shape[0].channels] + list(conv_dims), conv_dims
-        ):
-            cls_subnet.append(
-                nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-            )
-            if norm:
-                cls_subnet.append(get_norm(norm, out_channels))
-            cls_subnet.append(nn.ReLU())
-            bbox_subnet.append(
-                nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-            )
-            if norm:
-                bbox_subnet.append(get_norm(norm, out_channels))
-            bbox_subnet.append(nn.ReLU())
-
-        self.cls_subnet = nn.Sequential(*cls_subnet)
-        self.bbox_subnet = nn.Sequential(*bbox_subnet)
-        self.cls_score = nn.Conv2d(
-            conv_dims[-1], num_anchors * num_classes, kernel_size=3, stride=1, padding=1
-        )
-        self.bbox_pred = nn.Conv2d(
-            conv_dims[-1], num_anchors * 4, kernel_size=3, stride=1, padding=1
-        )
-
-        # Initialization
-        for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]:
-            for layer in modules.modules():
-                if isinstance(layer, nn.Conv2d):
-                    torch.nn.init.normal_(layer.weight, mean=0, std=0.01)
-                    torch.nn.init.constant_(layer.bias, 0)
-
-        # Use prior in model initialization to improve stability
-        bias_value = -(math.log((1 - prior_prob) / prior_prob))
-        torch.nn.init.constant_(self.cls_score.bias, bias_value)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: List[ShapeSpec]):
-        num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors
-        assert (
-            len(set(num_anchors)) == 1
-        ), "Using different number of anchors between levels is not currently supported!"
-        num_anchors = num_anchors[0]
-
-        return {
-            "input_shape": input_shape,
-            "num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
-            "conv_dims": [input_shape[0].channels] * cfg.MODEL.RETINANET.NUM_CONVS,
-            "prior_prob": cfg.MODEL.RETINANET.PRIOR_PROB,
-            "norm": cfg.MODEL.RETINANET.NORM,
-            "num_anchors": num_anchors,
-        }
-
-    def forward(self, features: List[Tensor]):
-        """
-        Arguments:
-            features (list[Tensor]): FPN feature map tensors in high to low resolution.
-                Each tensor in the list correspond to different feature levels.
-
-        Returns:
-            logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi).
-                The tensor predicts the classification probability
-                at each spatial position for each of the A anchors and K object
-                classes.
-            bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi).
-                The tensor predicts 4-vector (dx,dy,dw,dh) box
-                regression values for every anchor. These values are the
-                relative offset between the anchor and the ground truth box.
-        """
-        assert len(features) == self._num_features
-        logits = []
-        bbox_reg = []
-        for feature in features:
-            logits.append(self.cls_score(self.cls_subnet(feature)))
-            bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature)))
-        return logits, bbox_reg
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/structures/transform_data.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/structures/transform_data.py
deleted file mode 100644
index 7cac1bb7663b985165000b2b351d6ff630d2ba3f..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/structures/transform_data.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import BinaryIO, Dict, Union
-import torch
-
-
-def normalized_coords_transform(x0, y0, w, h):
-    """
-    Coordinates transform that maps top left corner to (-1, -1) and bottom
-    right corner to (1, 1). Used for torch.grid_sample to initialize the
-    grid
-    """
-
-    def f(p):
-        return (2 * (p[0] - x0) / w - 1, 2 * (p[1] - y0) / h - 1)
-
-    return f
-
-
-class DensePoseTransformData(object):
-
-    # Horizontal symmetry label transforms used for horizontal flip
-    MASK_LABEL_SYMMETRIES = [0, 1, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 14]
-    # fmt: off
-    POINT_LABEL_SYMMETRIES = [ 0, 1, 2, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15, 18, 17, 20, 19, 22, 21, 24, 23]  # noqa
-    # fmt: on
-
-    def __init__(self, uv_symmetries: Dict[str, torch.Tensor], device: torch.device):
-        self.mask_label_symmetries = DensePoseTransformData.MASK_LABEL_SYMMETRIES
-        self.point_label_symmetries = DensePoseTransformData.POINT_LABEL_SYMMETRIES
-        self.uv_symmetries = uv_symmetries
-        self.device = torch.device("cpu")
-
-    def to(self, device: torch.device, copy: bool = False) -> "DensePoseTransformData":
-        """
-        Convert transform data to the specified device
-
-        Args:
-            device (torch.device): device to convert the data to
-            copy (bool): flag that specifies whether to copy or to reference the data
-                in case the device is the same
-        Return:
-            An instance of `DensePoseTransformData` with data stored on the specified device
-        """
-        if self.device == device and not copy:
-            return self
-        uv_symmetry_map = {}
-        for key in self.uv_symmetries:
-            uv_symmetry_map[key] = self.uv_symmetries[key].to(device=device, copy=copy)
-        return DensePoseTransformData(uv_symmetry_map, device)
-
-    @staticmethod
-    def load(io: Union[str, BinaryIO]):
-        """
-        Args:
-            io: (str or binary file-like object): input file to load data from
-        Returns:
-            An instance of `DensePoseTransformData` with transforms loaded from the file
-        """
-        import scipy.io
-
-        uv_symmetry_map = scipy.io.loadmat(io)
-        uv_symmetry_map_torch = {}
-        for key in ["U_transforms", "V_transforms"]:
-            uv_symmetry_map_torch[key] = []
-            map_src = uv_symmetry_map[key]
-            map_dst = uv_symmetry_map_torch[key]
-            for i in range(map_src.shape[1]):
-                map_dst.append(torch.from_numpy(map_src[0, i]).to(dtype=torch.float))
-            uv_symmetry_map_torch[key] = torch.stack(map_dst, dim=0)
-        transform_data = DensePoseTransformData(uv_symmetry_map_torch, device=torch.device("cpu"))
-        return transform_data
diff --git a/spaces/candlend/vits-hoshimi/sovits/vdecoder/parallel_wavegan/utils/__init__.py b/spaces/candlend/vits-hoshimi/sovits/vdecoder/parallel_wavegan/utils/__init__.py
deleted file mode 100644
index e8fa95a020706b5412c3959fbf6e5980019c0d5f..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/sovits/vdecoder/parallel_wavegan/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .utils import *  # NOQA
diff --git a/spaces/chansung/LLM-As-Chatbot/utils.py b/spaces/chansung/LLM-As-Chatbot/utils.py
deleted file mode 100644
index 9fc22c96143dfe830789d0a8896f2f37f4ee65ce..0000000000000000000000000000000000000000
--- a/spaces/chansung/LLM-As-Chatbot/utils.py
+++ /dev/null
@@ -1,491 +0,0 @@
-from chats import alpaca
-from chats import alpaca_gpt4
-from chats import stablelm
-from chats import koalpaca
-from chats import os_stablelm
-from chats import vicuna
-from chats import flan_alpaca
-from chats import starchat
-from chats import redpajama
-from chats import mpt
-from chats import alpacoom
-from chats import baize
-from chats import guanaco
-# from chats import falcon
-
-from pingpong.gradio import GradioAlpacaChatPPManager
-from pingpong.gradio import GradioKoAlpacaChatPPManager
-from pingpong.gradio import GradioStableLMChatPPManager
-from pingpong.gradio import GradioFlanAlpacaChatPPManager
-from pingpong.gradio import GradioOSStableLMChatPPManager
-from pingpong.gradio import GradioVicunaChatPPManager
-from pingpong.gradio import GradioStableVicunaChatPPManager
-from pingpong.gradio import GradioStarChatPPManager
-from pingpong.gradio import GradioMPTChatPPManager
-from pingpong.gradio import GradioBaizeChatPPManager
-
-from pingpong.pingpong import PPManager
-from pingpong.pingpong import PromptFmt
-from pingpong.pingpong import UIFmt
-from pingpong.gradio import GradioChatUIFmt
-
-class RedPajamaChatPromptFmt(PromptFmt):
-    @classmethod
-    def ctx(cls, context):
-        if context is None or context == "":
-            return ""
-        else:
-            return f"""{context}
-"""
-    
-    @classmethod
-    def prompt(cls, pingpong, truncate_size):
-        ping = pingpong.ping[:truncate_size]
-        pong = "" if pingpong.pong is None else pingpong.pong[:truncate_size]
-        return f""": {ping}
-:{pong}"""
-
-class RedPajamaChatPPManager(PPManager):
-    def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=RedPajamaChatPromptFmt, truncate_size: int=None):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-            
-        results = fmt.ctx(self.ctx)
-        
-        for idx, pingpong in enumerate(self.pingpongs[from_idx:to_idx]):
-            results += fmt.prompt(pingpong, truncate_size=truncate_size)
-            
-        return results
-
-class GradioRedPajamaChatPPManager(RedPajamaChatPPManager):
-    def build_uis(self, from_idx: int=0, to_idx: int=-1, fmt: UIFmt=GradioChatUIFmt):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-        
-        results = []
-        
-        for pingpong in self.pingpongs[from_idx:to_idx]:
-            results.append(fmt.ui(pingpong))
-            
-        return results
-    
-class RedPajamaInstructPromptFmt(PromptFmt):
-    @classmethod
-    def ctx(cls, context):
-        if context is None or context == "":
-            return ""
-        else:
-            return f"""{context}
-"""
-        
-    @classmethod
-    def prompt(cls, pingpong, truncate_size):
-        ping = pingpong.ping[:truncate_size]
-        pong = "" if pingpong.pong is None else pingpong.pong[:truncate_size]
-        return f"""Q: {ping}
-A:{pong}"""
-  
-class RedPajamaInstructChatPPManager(PPManager):
-    def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=RedPajamaInstructPromptFmt, truncate_size: int=None):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-            
-        results = fmt.ctx(self.ctx)
-        
-        for idx, pingpong in enumerate(self.pingpongs[from_idx:to_idx]):
-            results += fmt.prompt(pingpong, truncate_size=truncate_size)
-            
-        return results
-
-class GradioRedPajamaInstructChatPPManager(RedPajamaInstructChatPPManager):
-    def build_uis(self, from_idx: int=0, to_idx: int=-1, fmt: UIFmt=GradioChatUIFmt):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-        
-        results = []
-        
-        for pingpong in self.pingpongs[from_idx:to_idx]:
-            results.append(fmt.ui(pingpong))
-            
-        return results
-
-###
-
-class GuanacoPromptFmt(PromptFmt):
-    @classmethod
-    def ctx(cls, context):
-        if context is None or context == "":
-            return ""
-        else:
-            return f"""{context}
-"""
-        
-    @classmethod
-    def prompt(cls, pingpong, truncate_size):
-        ping = pingpong.ping[:truncate_size]
-        pong = "" if pingpong.pong is None else pingpong.pong[:truncate_size]
-        return f"""### Human: {ping}
-### Assistant: {pong}
-"""
-  
-class GuanacoChatPPManager(PPManager):
-    def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=GuanacoPromptFmt, truncate_size: int=None):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-            
-        results = fmt.ctx(self.ctx)
-        
-        for idx, pingpong in enumerate(self.pingpongs[from_idx:to_idx]):
-            results += fmt.prompt(pingpong, truncate_size=truncate_size)
-            
-        return results
-
-class GradioGuanacoChatPPManager(GuanacoChatPPManager):
-    def build_uis(self, from_idx: int=0, to_idx: int=-1, fmt: UIFmt=GradioChatUIFmt):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-        
-        results = []
-        
-        for pingpong in self.pingpongs[from_idx:to_idx]:
-            results.append(fmt.ui(pingpong))
-            
-        return results    
-
-class WizardPromptFmt(PromptFmt):
-    @classmethod
-    def ctx(cls, context):
-        if context is None or context == "":
-            return ""
-        else:
-            return f"""{context}
-"""
-        
-    @classmethod
-    def prompt(cls, pingpong, truncate_size):
-        ping = pingpong.ping[:truncate_size]
-        pong = "" if pingpong.pong is None else pingpong.pong[:truncate_size]
-        return f"""{ping}
-### Response: {pong}
-
-"""
-  
-class WizardChatPPManager(PPManager):
-    def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=WizardPromptFmt, truncate_size: int=None):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-            
-        results = fmt.ctx(self.ctx)
-        
-        for idx, pingpong in enumerate(self.pingpongs[from_idx:to_idx]):
-            results += fmt.prompt(pingpong, truncate_size=truncate_size)
-            
-        return results
-
-class GradioWizardChatPPManager(WizardChatPPManager):
-    def build_uis(self, from_idx: int=0, to_idx: int=-1, fmt: UIFmt=GradioChatUIFmt):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-        
-        results = []
-        
-        for pingpong in self.pingpongs[from_idx:to_idx]:
-            results.append(fmt.ui(pingpong))
-            
-        return results
-
-class KULLMPromptFmt(PromptFmt):
-    @classmethod
-    def ctx(cls, context):
-        if context is None or context == "":
-            return ""
-        else:
-            return f"""{context}
-"""
-        
-    @classmethod
-    def prompt(cls, pingpong, truncate_size):
-        ping = pingpong.ping[:truncate_size]
-        pong = "" if pingpong.pong is None else pingpong.pong[:truncate_size]
-        return f"""### 명령어:
-{ping}
-### 응답:
-{pong}
-"""
-  
-class KULLMChatPPManager(PPManager):
-    def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=KULLMPromptFmt, truncate_size: int=None):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-            
-        results = fmt.ctx(self.ctx)
-        
-        for idx, pingpong in enumerate(self.pingpongs[from_idx:to_idx]):
-            results += fmt.prompt(pingpong, truncate_size=truncate_size)
-            
-        return results
-
-class GradioKULLMChatPPManager(KULLMChatPPManager):
-    def build_uis(self, from_idx: int=0, to_idx: int=-1, fmt: UIFmt=GradioChatUIFmt):
-        if to_idx == -1 or to_idx >= len(self.pingpongs):
-            to_idx = len(self.pingpongs)
-        
-        results = []
-        
-        for pingpong in self.pingpongs[from_idx:to_idx]:
-            results.append(fmt.ui(pingpong))
-            
-        return results
-    
-def get_chat_interface(model_type):
-    if model_type == "alpaca":
-        return alpaca.chat_stream
-    elif model_type == "alpaca-gpt4":
-        return alpaca.chat_stream
-    elif model_type == "stablelm":
-        return stablelm.chat_stream
-    elif model_type == "os-stablelm":
-        return os_stablelm.chat_stream
-    elif model_type == "koalpaca-polyglot":
-        return koalpaca.chat_stream
-    elif model_type == "flan-alpaca":
-        return flan_alpaca.chat_stream
-    elif model_type == "camel":
-        return alpaca.chat_stream
-    elif model_type == "t5-vicuna":
-        return vicuna.chat_stream
-    elif model_type == "stable-vicuna":
-        return vicuna.chat_stream
-    elif model_type == "starchat":
-        return starchat.chat_stream
-    elif model_type == "mpt":
-        return mpt.chat_stream
-    elif model_type == "redpajama":
-        return redpajama.chat_stream
-    elif model_type == "vicuna":
-        return vicuna.chat_stream
-    elif model_type == "llama-deus":
-        return alpaca.chat_stream
-    elif model_type == "evolinstruct-vicuna":
-        return vicuna.chat_stream
-    elif model_type == "alpacoom":
-        return alpacoom.chat_stream
-    elif model_type == "baize":
-        return baize.chat_stream
-    elif model_type == "guanaco":
-        return guanaco.chat_stream
-    elif model_type == "nous-hermes":
-        return alpaca.chat_stream
-    else:
-        return None
-
-def get_chat_manager(model_type):
-    if model_type == "alpaca":
-        return GradioAlpacaChatPPManager
-    elif model_type == "alpaca-gpt4":
-        return GradioAlpacaChatPPManager()
-    elif model_type == "stablelm":
-        return GradioStableLMChatPPManager()
-    elif model_type == "os-stablelm":
-        return GradioOSStableLMChatPPManager()
-    elif model_type == "koalpaca-polyglot":
-        return GradioKoAlpacaChatPPManager()
-    elif model_type == "flan-alpaca":
-        return GradioFlanAlpacaChatPPManager()
-    elif model_type == "camel":
-        return GradioAlpacaChatPPManager()
-    elif model_type == "t5-vicuna":
-        return GradioVicunaChatPPManager()
-    elif model_type == "vicuna":
-        return GradioVicunaChatPPManager()
-    elif model_type == "stable-vicuna":
-        return GradioStableVicunaChatPPManager()
-    elif model_type == "starchat":
-        return GradioStarChatPPManager()
-    elif model_type == "mpt":
-        return GradioMPTChatPPManager()
-    elif model_type == "redpajama":
-        return GradioRedPajamaChatPPManager()
-    elif model_type == "llama-deus":
-        return GradioAlpacaChatPPManager()
-    elif model_type == "evolinstruct-vicuna":
-        return GradioVicunaChatPPManager()
-    elif model_type == "alpacoom":
-        return GradioAlpacaChatPPManager()
-    elif model_type == "baize":
-        return GradioBaizeChatPPManager()
-    elif model_type == "guanaco":
-        return GradioGuanacoChatPPManager()
-    elif model_type == "nous-hermes":
-        return GradioAlpacaChatPPManager()
-    else:
-        return None
-    
-def get_global_context(model_type):
-    if model_type == "alpaca":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "alpaca-gpt4":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "nous-hermes":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "lazarus":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""    
-    elif model_type == "chronos":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""            
-    
-    elif model_type == "stablelm":
-        return """<|SYSTEM|># StableLM Tuned (Alpha version)
-- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
-- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
-- StableLM will refuse to participate in anything that could harm a human.
-"""
-    elif model_type == "os-stablelm":
-        return ""
-    elif model_type == "koalpaca-polyglot":
-        return """아래는 인간과 AI 어시스턴트 간의 일련의 대화입니다.
-인공지능은 주어진 질문에 대한 응답으로 대답을 시도합니다.
-인공지능은 `### 질문` 또는 `### 응답`가 포함된 텍스트를 생성해서는 안 됩니다.
-AI는 도움이 되고, 예의 바르고, 정직하고, 정교하고, 감정을 인식하고, 겸손하지만 지식이 있어야 합니다.
-어시스턴트는 거의 모든 것을 기꺼이 도와줄 수 있어야 하며, 무엇이 필요한지 정확히 이해하기 위해 최선을 다해야 합니다.
-또한 허위 또는 오해의 소지가 있는 정보를 제공하지 않아야 하며, 정답을 완전히 확신할 수 없을 때는 주의를 환기시켜야 합니다.
-즉, 이 어시스턴트는 실용적이고 정말 최선을 다하며 주의를 기울이는 데 너무 많은 시간을 할애하지 않습니다.
-"""
-    elif model_type == "kullm-polyglot":
-        return """아래는 인간과 AI 어시스턴트 간의 일련의 대화입니다.
-인공지능은 주어진 명령어에 대한 응답으로 대답을 시도합니다.
-인공지능은 `### 명령어` 또는 `### 응답`가 포함된 텍스트를 생성해서는 안 됩니다.
-AI는 도움이 되고, 예의 바르고, 정직하고, 정교하고, 감정을 인식하고, 겸손하지만 지식이 있어야 합니다.
-어시스턴트는 거의 모든 것을 기꺼이 도와줄 수 있어야 하며, 무엇이 필요한지 정확히 이해하기 위해 최선을 다해야 합니다.
-또한 허위 또는 오해의 소지가 있는 정보를 제공하지 않아야 하며, 정답을 완전히 확신할 수 없을 때는 주의를 환기시켜야 합니다.
-즉, 이 어시스턴트는 실용적이고 정말 최선을 다하며 주의를 기울이는 데 너무 많은 시간을 할애하지 않습니다.
-"""        
-    elif model_type == "flan-alpaca":
-        return """Below are a series of dialogues between human and an AI assistant.
-Each turn of conversation is distinguished by the delimiter of "-----"
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "camel":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "t5-vicuna":
-        return """A chat between a curious user and an artificial intelligence assistant.
-The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""
-    elif model_type == "vicuna":
-        return """A chat between a curious user and an artificial intelligence assistant.
-The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""
-    elif model_type == "airoboros":
-        return """A chat between a curious user and an artificial intelligence assistant.
-The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""        
-    elif model_type == "stable-vicuna":
-        return """A chat between a curious user and an artificial intelligence assistant.
-The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""
-    elif model_type == "wizardlm":
-        return """A chat between a curious user and an artificial intelligence assistant.
-The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""
-    elif model_type == "starchat":
-        return """Below is a conversation between a human user and a helpful AI coding assistant.
-"""
-    elif model_type == "mpt":
-        return """<|im_start|>system
-- You are a helpful assistant chatbot trained by MosaicML.
-- You answer questions.
-- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
-"""
-    elif model_type == "redpajama":
-        return ""
-    elif model_type == "redpajama-instruct":
-        return ""
-    elif model_type == "llama-deus":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "evolinstruct-vicuna":
-        return """A chat between a curious user and an artificial intelligence assistant.
-The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""
-    elif model_type == "alpacoom":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "baize":
-        return """The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.\n[|Human|]Hello!\n[|AI|]Hi!
-"""
-    elif model_type == "guanaco":
-        return """A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
-"""
-    elif model_type == "falcon":
-        return """Below are a series of dialogues between human and an AI assistant.
-The AI tries to answer the given instruction as in response.
-The AI MUST not generate any text containing `### Response` or `### Instruction`.
-The AI MUST be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable.
-The assistant MUST be happy to help with almost anything, and will do its best to understand exactly what is needed.
-It also MUST avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer.
-That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.
-"""
-    elif model_type == "wizard_falcon":
-        return ""
-    elif model_type == "samantha-vicuna":
-        return "You are Samantha, a sentient AI."
-    else:
-        return ""        
\ No newline at end of file
diff --git a/spaces/chasemcdo/hf_localai/examples/slack-bot/README.md b/spaces/chasemcdo/hf_localai/examples/slack-bot/README.md
deleted file mode 100644
index 9c9f10f1205d1394bc660154f1af83328bacb47b..0000000000000000000000000000000000000000
--- a/spaces/chasemcdo/hf_localai/examples/slack-bot/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Slack bot
-
-Slackbot using: https://github.com/seratch/ChatGPT-in-Slack
-
-## Setup
-
-```bash
-# Clone LocalAI
-git clone https://github.com/go-skynet/LocalAI
-
-cd LocalAI/examples/slack-bot
-
-git clone https://github.com/seratch/ChatGPT-in-Slack
-
-# (optional) Checkout a specific LocalAI tag
-# git checkout -b build 
-
-# Download gpt4all-j to models/
-wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
-
-# Set the discord bot options (see: https://github.com/seratch/ChatGPT-in-Slack)
-cp -rfv .env.example .env
-vim .env
-
-# start with docker-compose
-docker-compose up -d --build
-```
\ No newline at end of file
diff --git a/spaces/chemouda/arome_ai/app.py b/spaces/chemouda/arome_ai/app.py
deleted file mode 100644
index 9a39aa422cd08e0952904aff01bd40711b0b7791..0000000000000000000000000000000000000000
--- a/spaces/chemouda/arome_ai/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-import gradio as gr
-
-read_key = os.environ.get('HF_TOKEN', None)
-
-with gr.Blocks() as demo:
-    gr.load("chemouda/arome", hf_token=read_key, src="spaces")
-
-demo.queue(concurrency_count=10)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/chidojawbreaker/transformer-health/app.py b/spaces/chidojawbreaker/transformer-health/app.py
deleted file mode 100644
index 1dce8f196bde5587f3ea7e56d54fac2d6fc68f5f..0000000000000000000000000000000000000000
--- a/spaces/chidojawbreaker/transformer-health/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-import numpy as np
-import tensorflow
-import keras
-from keras.models import load_model
-
-model = load_model('modelv2.h5')
-
-def transformer(Hydrogen,Oxygen,Nitrogen,Methane,CO,CO2,Ethylene,Ethane,Acethylene,DBDS,Power_factor,Interfacial_V,Dielectric_rigidity,Water_content):
-    data = np.array([[Hydrogen,Oxygen,Nitrogen,Methane,CO,CO2,Ethylene,Ethane,Acethylene,DBDS,Power_factor,Interfacial_V,Dielectric_rigidity,Water_content]])
-    prediction = model.predict(data)
-    x = int(prediction[0])
-    if x >= 56:
-        transformer_index = f'Transformer index level is 1 with a health index percentage of {x}'
-    elif 29<=x<=55:
-        transformer_index = f'Transformer index level is 2 with a health index percentage of {x}'
-    elif 17<=x<=28:
-        transformer_index = f'Transformer index level is 3 with a health index percentage of {x}'
-    elif 6<=x<=16:
-        transformer_index = f'Transformer index level is 4 with a health index percentage of {x}'
-    elif x<=5:
-        transformer_index = f'Transformer index level is 5 with a health index percentage of {x}'
-
-    return transformer_index
-
-app = gr.Interface(fn=transformer, inputs=["number","number","number","number","number","number","number","number","number","number","number","number","number","number"], outputs = gr.Textbox(),title='Transformer Health Index Predictor',description="Transformer Health Prediction Model")
-app.launch()
diff --git a/spaces/chilleverydaychill/roop/roop/predicter.py b/spaces/chilleverydaychill/roop/roop/predicter.py
deleted file mode 100644
index 7ebc2b62e4152c12ce41e55d718222ca9c8a8b7f..0000000000000000000000000000000000000000
--- a/spaces/chilleverydaychill/roop/roop/predicter.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import numpy
-import opennsfw2
-from PIL import Image
-
-from roop.typing import Frame
-
-MAX_PROBABILITY = 0.85
-
-
-def predict_frame(target_frame: Frame) -> bool:
-    image = Image.fromarray(target_frame)
-    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
-    model = opennsfw2.make_open_nsfw_model()
-    views = numpy.expand_dims(image, axis=0)
-    _, probability = model.predict(views)[0]
-    return probability > MAX_PROBABILITY
-
-
-def predict_image(target_path: str) -> bool:
-    return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
-
-
-def predict_video(target_path: str) -> bool:
-    _, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)
-    return any(probability > MAX_PROBABILITY for probability in probabilities)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/api/models/Collection.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/api/models/Collection.py
deleted file mode 100644
index ffdca457bc3f7e6e35ec85ce4276f8837be0966d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/api/models/Collection.py
+++ /dev/null
@@ -1,397 +0,0 @@
-from typing import TYPE_CHECKING, Optional, Tuple, cast, List
-from pydantic import BaseModel, PrivateAttr
-from uuid import UUID
-import chromadb.utils.embedding_functions as ef
-
-from chromadb.api.types import (
-    CollectionMetadata,
-    Embedding,
-    Include,
-    Metadata,
-    Document,
-    Where,
-    IDs,
-    EmbeddingFunction,
-    GetResult,
-    QueryResult,
-    ID,
-    OneOrMany,
-    WhereDocument,
-    maybe_cast_one_to_many,
-    validate_ids,
-    validate_include,
-    validate_metadata,
-    validate_metadatas,
-    validate_where,
-    validate_where_document,
-    validate_n_results,
-    validate_embeddings,
-)
-import logging
-
-logger = logging.getLogger(__name__)
-
-if TYPE_CHECKING:
-    from chromadb.api import API
-
-
-class Collection(BaseModel):
-    name: str
-    id: UUID
-    metadata: Optional[CollectionMetadata] = None
-    _client: "API" = PrivateAttr()
-    _embedding_function: Optional[EmbeddingFunction] = PrivateAttr()
-
-    def __init__(
-        self,
-        client: "API",
-        name: str,
-        id: UUID,
-        embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
-        metadata: Optional[CollectionMetadata] = None,
-    ):
-        self._client = client
-        self._embedding_function = embedding_function
-        super().__init__(name=name, metadata=metadata, id=id)
-
-    def __repr__(self) -> str:
-        return f"Collection(name={self.name})"
-
-    def count(self) -> int:
-        """The total number of embeddings added to the database
-
-        Returns:
-            int: The total number of embeddings added to the database
-
-        """
-        return self._client._count(collection_id=self.id)
-
-    def add(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]] = None,
-        metadatas: Optional[OneOrMany[Metadata]] = None,
-        documents: Optional[OneOrMany[Document]] = None,
-        increment_index: bool = True,
-    ) -> None:
-        """Add embeddings to the data store.
-        Args:
-            ids: The ids of the embeddings you wish to add
-            embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
-            metadatas: The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
-            documents: The documents to associate with the embeddings. Optional.
-
-        Returns:
-            None
-
-        Raises:
-            ValueError: If you don't provide either embeddings or documents
-            ValueError: If the length of ids, embeddings, metadatas, or documents don't match
-            ValueError: If you don't provide an embedding function and don't provide embeddings
-            ValueError: If you provide both embeddings and documents
-            ValueError: If you provide an id that already exists
-
-        """
-
-        ids, embeddings, metadatas, documents = self._validate_embedding_set(
-            ids, embeddings, metadatas, documents
-        )
-
-        self._client._add(
-            ids, self.id, embeddings, metadatas, documents, increment_index
-        )
-
-    def get(
-        self,
-        ids: Optional[OneOrMany[ID]] = None,
-        where: Optional[Where] = None,
-        limit: Optional[int] = None,
-        offset: Optional[int] = None,
-        where_document: Optional[WhereDocument] = None,
-        include: Include = ["metadatas", "documents"],
-    ) -> GetResult:
-        """Get embeddings and their associate data from the data store. If no ids or where filter is provided returns
-        all embeddings up to limit starting at offset.
-
-        Args:
-            ids: The ids of the embeddings to get. Optional.
-            where: A Where type dict used to filter results by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
-            limit: The number of documents to return. Optional.
-            offset: The offset to start returning results from. Useful for paging results with limit. Optional.
-            where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional.
-            include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`. Ids are always included. Defaults to `["metadatas", "documents"]`. Optional.
-
-        Returns:
-            GetResult: A GetResult object containing the results.
-
-        """
-        where = validate_where(where) if where else None
-        where_document = (
-            validate_where_document(where_document) if where_document else None
-        )
-        ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
-        include = validate_include(include, allow_distances=False)
-        return self._client._get(
-            self.id,
-            ids,
-            where,
-            None,
-            limit,
-            offset,
-            where_document=where_document,
-            include=include,
-        )
-
-    def peek(self, limit: int = 10) -> GetResult:
-        """Get the first few results in the database up to limit
-
-        Args:
-            limit: The number of results to return.
-
-        Returns:
-            GetResult: A GetResult object containing the results.
-        """
-        return self._client._peek(self.id, limit)
-
-    def query(
-        self,
-        query_embeddings: Optional[OneOrMany[Embedding]] = None,
-        query_texts: Optional[OneOrMany[Document]] = None,
-        n_results: int = 10,
-        where: Optional[Where] = None,
-        where_document: Optional[WhereDocument] = None,
-        include: Include = ["metadatas", "documents", "distances"],
-    ) -> QueryResult:
-        """Get the n_results nearest neighbor embeddings for provided query_embeddings or query_texts.
-
-        Args:
-            query_embeddings: The embeddings to get the closes neighbors of. Optional.
-            query_texts: The document texts to get the closes neighbors of. Optional.
-            n_results: The number of neighbors to return for each query_embedding or query_texts. Optional.
-            where: A Where type dict used to filter results by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
-            where_document: A WhereDocument type dict used to filter by the documents. E.g. `{$contains: {"text": "hello"}}`. Optional.
-            include: A list of what to include in the results. Can contain `"embeddings"`, `"metadatas"`, `"documents"`, `"distances"`. Ids are always included. Defaults to `["metadatas", "documents", "distances"]`. Optional.
-
-        Returns:
-            QueryResult: A QueryResult object containing the results.
-
-        Raises:
-            ValueError: If you don't provide either query_embeddings or query_texts
-            ValueError: If you provide both query_embeddings and query_texts
-
-        """
-        where = validate_where(where) if where else None
-        where_document = (
-            validate_where_document(where_document) if where_document else None
-        )
-        query_embeddings = (
-            validate_embeddings(maybe_cast_one_to_many(query_embeddings))
-            if query_embeddings is not None
-            else None
-        )
-        query_texts = (
-            maybe_cast_one_to_many(query_texts) if query_texts is not None else None
-        )
-        include = validate_include(include, allow_distances=True)
-        n_results = validate_n_results(n_results)
-
-        # If neither query_embeddings nor query_texts are provided, or both are provided, raise an error
-        if (query_embeddings is None and query_texts is None) or (
-            query_embeddings is not None and query_texts is not None
-        ):
-            raise ValueError(
-                "You must provide either query embeddings or query texts, but not both"
-            )
-
-        # If query_embeddings are not provided, we need to compute them from the query_texts
-        if query_embeddings is None:
-            if self._embedding_function is None:
-                raise ValueError(
-                    "You must provide embeddings or a function to compute them"
-                )
-            # We know query texts is not None at this point, cast for the typechecker
-            query_embeddings = self._embedding_function(
-                cast(List[Document], query_texts)
-            )
-
-        if where is None:
-            where = {}
-
-        if where_document is None:
-            where_document = {}
-
-        return self._client._query(
-            collection_id=self.id,
-            query_embeddings=query_embeddings,
-            n_results=n_results,
-            where=where,
-            where_document=where_document,
-            include=include,
-        )
-
-    def modify(
-        self, name: Optional[str] = None, metadata: Optional[CollectionMetadata] = None
-    ) -> None:
-        """Modify the collection name or metadata
-
-        Args:
-            name: The updated name for the collection. Optional.
-            metadata: The updated metadata for the collection. Optional.
-
-        Returns:
-            None
-        """
-        if metadata is not None:
-            validate_metadata(metadata)
-
-        self._client._modify(id=self.id, new_name=name, new_metadata=metadata)
-        if name:
-            self.name = name
-        if metadata:
-            self.metadata = metadata
-
-    def update(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]] = None,
-        metadatas: Optional[OneOrMany[Metadata]] = None,
-        documents: Optional[OneOrMany[Document]] = None,
-    ) -> None:
-        """Update the embeddings, metadatas or documents for provided ids.
-
-        Args:
-            ids: The ids of the embeddings to update
-            embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
-            metadatas:  The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
-            documents: The documents to associate with the embeddings. Optional.
-
-        Returns:
-            None
-        """
-
-        ids, embeddings, metadatas, documents = self._validate_embedding_set(
-            ids, embeddings, metadatas, documents, require_embeddings_or_documents=False
-        )
-
-        self._client._update(self.id, ids, embeddings, metadatas, documents)
-
-    def upsert(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]] = None,
-        metadatas: Optional[OneOrMany[Metadata]] = None,
-        documents: Optional[OneOrMany[Document]] = None,
-        increment_index: bool = True,
-    ) -> None:
-        """Update the embeddings, metadatas or documents for provided ids, or create them if they don't exist.
-
-        Args:
-            ids: The ids of the embeddings to update
-            embeddings: The embeddings to add. If None, embeddings will be computed based on the documents using the embedding_function set for the Collection. Optional.
-            metadatas:  The metadata to associate with the embeddings. When querying, you can filter on this metadata. Optional.
-            documents: The documents to associate with the embeddings. Optional.
-
-        Returns:
-            None
-        """
-
-        ids, embeddings, metadatas, documents = self._validate_embedding_set(
-            ids, embeddings, metadatas, documents
-        )
-
-        self._client._upsert(
-            collection_id=self.id,
-            ids=ids,
-            embeddings=embeddings,
-            metadatas=metadatas,
-            documents=documents,
-            increment_index=increment_index,
-        )
-
-    def delete(
-        self,
-        ids: Optional[IDs] = None,
-        where: Optional[Where] = None,
-        where_document: Optional[WhereDocument] = None,
-    ) -> None:
-        """Delete the embeddings based on ids and/or a where filter
-
-        Args:
-            ids: The ids of the embeddings to delete
-            where: A Where type dict used to filter the delection by. E.g. `{"color" : "red", "price": 4.20}`. Optional.
-            where_document: A WhereDocument type dict used to filter the deletion by the document content. E.g. `{$contains: {"text": "hello"}}`. Optional.
-
-        Returns:
-            None
-        """
-        ids = validate_ids(maybe_cast_one_to_many(ids)) if ids else None
-        where = validate_where(where) if where else None
-        where_document = (
-            validate_where_document(where_document) if where_document else None
-        )
-        self._client._delete(self.id, ids, where, where_document)
-
-    def create_index(self) -> None:
-        self._client.create_index(self.name)
-
-    def _validate_embedding_set(
-        self,
-        ids: OneOrMany[ID],
-        embeddings: Optional[OneOrMany[Embedding]],
-        metadatas: Optional[OneOrMany[Metadata]],
-        documents: Optional[OneOrMany[Document]],
-        require_embeddings_or_documents: bool = True,
-    ) -> Tuple[
-        IDs,
-        List[Embedding],
-        Optional[List[Metadata]],
-        Optional[List[Document]],
-    ]:
-        ids = validate_ids(maybe_cast_one_to_many(ids))
-        embeddings = (
-            validate_embeddings(maybe_cast_one_to_many(embeddings))
-            if embeddings is not None
-            else None
-        )
-        metadatas = (
-            validate_metadatas(maybe_cast_one_to_many(metadatas))
-            if metadatas is not None
-            else None
-        )
-        documents = maybe_cast_one_to_many(documents) if documents is not None else None
-
-        # Check that one of embeddings or documents is provided
-        if require_embeddings_or_documents:
-            if embeddings is None and documents is None:
-                raise ValueError(
-                    "You must provide either embeddings or documents, or both"
-                )
-
-        # Check that, if they're provided, the lengths of the arrays match the length of ids
-        if embeddings is not None and len(embeddings) != len(ids):
-            raise ValueError(
-                f"Number of embeddings {len(embeddings)} must match number of ids {len(ids)}"
-            )
-        if metadatas is not None and len(metadatas) != len(ids):
-            raise ValueError(
-                f"Number of metadatas {len(metadatas)} must match number of ids {len(ids)}"
-            )
-        if documents is not None and len(documents) != len(ids):
-            raise ValueError(
-                f"Number of documents {len(documents)} must match number of ids {len(ids)}"
-            )
-
-        # If document embeddings are not provided, we need to compute them
-        if embeddings is None and documents is not None:
-            if self._embedding_function is None:
-                raise ValueError(
-                    "You must provide embeddings or a function to compute them"
-                )
-            embeddings = self._embedding_function(documents)
-
-        # if embeddings is None:
-        #     raise ValueError(
-        #         "Something went wrong. Embeddings should be computed at this point"
-        #     )
-
-        return ids, embeddings, metadatas, documents  # type: ignore
diff --git a/spaces/cihyFjudo/fairness-paper-search/Acoustica Cd Dvd Label Maker 3.40 Keygen Crackl A Must-Have for Any CD or DVD Enthusiast.md b/spaces/cihyFjudo/fairness-paper-search/Acoustica Cd Dvd Label Maker 3.40 Keygen Crackl A Must-Have for Any CD or DVD Enthusiast.md
deleted file mode 100644
index c5fcaae6ded2b4a3d0ed8fe403c7940d456a490b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Acoustica Cd Dvd Label Maker 3.40 Keygen Crackl A Must-Have for Any CD or DVD Enthusiast.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
      Acoustica Cd Dvd Label Maker 3.40 Keygen Crackl

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diff --git a/spaces/cihyFjudo/fairness-paper-search/Amateur Beach Grannies By Olderwomen Admirer High Quality.md b/spaces/cihyFjudo/fairness-paper-search/Amateur Beach Grannies By Olderwomen Admirer High Quality.md
deleted file mode 100644
index 85e73c14bb216a4cf22488491e1694cbce71351d..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Amateur Beach Grannies By Olderwomen Admirer High Quality.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Download Apocalypto 2 Full Movie in English Version Subtitle The Story Behind the Film.md b/spaces/cihyFjudo/fairness-paper-search/Download Apocalypto 2 Full Movie in English Version Subtitle The Story Behind the Film.md
deleted file mode 100644
index 4e407f4407fa97428a1ae55c7460d1a197d0f37b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Download Apocalypto 2 Full Movie in English Version Subtitle The Story Behind the Film.md	
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Lost S01E01 720p HDTV x264 Torrent The Fastest and Safest Way to Download the Episode.md b/spaces/cihyFjudo/fairness-paper-search/Lost S01E01 720p HDTV x264 Torrent The Fastest and Safest Way to Download the Episode.md
deleted file mode 100644
index 6674e47ec8b213f94b2892cf2486f80608f7f504..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Lost S01E01 720p HDTV x264 Torrent The Fastest and Safest Way to Download the Episode.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/PartyHardcore Party Hardcore Vol. 68 Part 5 __LINK__.md b/spaces/cihyFjudo/fairness-paper-search/PartyHardcore Party Hardcore Vol. 68 Part 5 __LINK__.md
deleted file mode 100644
index 752d8637284cc00a0fe4dc90a55260676e98a8eb..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/PartyHardcore Party Hardcore Vol. 68 Part 5 __LINK__.md	
+++ /dev/null
@@ -1,5 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/The Missed Call 3 Full Movie in Hindi Free Download 3gp The Best Way to Stream the Spooky Film.md b/spaces/cihyFjudo/fairness-paper-search/The Missed Call 3 Full Movie in Hindi Free Download 3gp The Best Way to Stream the Spooky Film.md
deleted file mode 100644
index 64117d41f1a80aac6023c09edd42a93f8522d071..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/The Missed Call 3 Full Movie in Hindi Free Download 3gp The Best Way to Stream the Spooky Film.md	
+++ /dev/null
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/client_ws.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/client_ws.py
deleted file mode 100644
index 9a8ba84ca5082ad6d672c3837d4810e467a8080e..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/client_ws.py
+++ /dev/null
@@ -1,300 +0,0 @@
-"""WebSocket client for asyncio."""
-
-import asyncio
-from typing import Any, Optional, cast
-
-import async_timeout
-
-from .client_exceptions import ClientError
-from .client_reqrep import ClientResponse
-from .helpers import call_later, set_result
-from .http import (
-    WS_CLOSED_MESSAGE,
-    WS_CLOSING_MESSAGE,
-    WebSocketError,
-    WSCloseCode,
-    WSMessage,
-    WSMsgType,
-)
-from .http_websocket import WebSocketWriter  # WSMessage
-from .streams import EofStream, FlowControlDataQueue
-from .typedefs import (
-    DEFAULT_JSON_DECODER,
-    DEFAULT_JSON_ENCODER,
-    JSONDecoder,
-    JSONEncoder,
-)
-
-
-class ClientWebSocketResponse:
-    def __init__(
-        self,
-        reader: "FlowControlDataQueue[WSMessage]",
-        writer: WebSocketWriter,
-        protocol: Optional[str],
-        response: ClientResponse,
-        timeout: float,
-        autoclose: bool,
-        autoping: bool,
-        loop: asyncio.AbstractEventLoop,
-        *,
-        receive_timeout: Optional[float] = None,
-        heartbeat: Optional[float] = None,
-        compress: int = 0,
-        client_notakeover: bool = False,
-    ) -> None:
-        self._response = response
-        self._conn = response.connection
-
-        self._writer = writer
-        self._reader = reader
-        self._protocol = protocol
-        self._closed = False
-        self._closing = False
-        self._close_code: Optional[int] = None
-        self._timeout = timeout
-        self._receive_timeout = receive_timeout
-        self._autoclose = autoclose
-        self._autoping = autoping
-        self._heartbeat = heartbeat
-        self._heartbeat_cb: Optional[asyncio.TimerHandle] = None
-        if heartbeat is not None:
-            self._pong_heartbeat = heartbeat / 2.0
-        self._pong_response_cb: Optional[asyncio.TimerHandle] = None
-        self._loop = loop
-        self._waiting: Optional[asyncio.Future[bool]] = None
-        self._exception: Optional[BaseException] = None
-        self._compress = compress
-        self._client_notakeover = client_notakeover
-
-        self._reset_heartbeat()
-
-    def _cancel_heartbeat(self) -> None:
-        if self._pong_response_cb is not None:
-            self._pong_response_cb.cancel()
-            self._pong_response_cb = None
-
-        if self._heartbeat_cb is not None:
-            self._heartbeat_cb.cancel()
-            self._heartbeat_cb = None
-
-    def _reset_heartbeat(self) -> None:
-        self._cancel_heartbeat()
-
-        if self._heartbeat is not None:
-            self._heartbeat_cb = call_later(
-                self._send_heartbeat, self._heartbeat, self._loop
-            )
-
-    def _send_heartbeat(self) -> None:
-        if self._heartbeat is not None and not self._closed:
-            # fire-and-forget a task is not perfect but maybe ok for
-            # sending ping. Otherwise we need a long-living heartbeat
-            # task in the class.
-            self._loop.create_task(self._writer.ping())
-
-            if self._pong_response_cb is not None:
-                self._pong_response_cb.cancel()
-            self._pong_response_cb = call_later(
-                self._pong_not_received, self._pong_heartbeat, self._loop
-            )
-
-    def _pong_not_received(self) -> None:
-        if not self._closed:
-            self._closed = True
-            self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-            self._exception = asyncio.TimeoutError()
-            self._response.close()
-
-    @property
-    def closed(self) -> bool:
-        return self._closed
-
-    @property
-    def close_code(self) -> Optional[int]:
-        return self._close_code
-
-    @property
-    def protocol(self) -> Optional[str]:
-        return self._protocol
-
-    @property
-    def compress(self) -> int:
-        return self._compress
-
-    @property
-    def client_notakeover(self) -> bool:
-        return self._client_notakeover
-
-    def get_extra_info(self, name: str, default: Any = None) -> Any:
-        """extra info from connection transport"""
-        conn = self._response.connection
-        if conn is None:
-            return default
-        transport = conn.transport
-        if transport is None:
-            return default
-        return transport.get_extra_info(name, default)
-
-    def exception(self) -> Optional[BaseException]:
-        return self._exception
-
-    async def ping(self, message: bytes = b"") -> None:
-        await self._writer.ping(message)
-
-    async def pong(self, message: bytes = b"") -> None:
-        await self._writer.pong(message)
-
-    async def send_str(self, data: str, compress: Optional[int] = None) -> None:
-        if not isinstance(data, str):
-            raise TypeError("data argument must be str (%r)" % type(data))
-        await self._writer.send(data, binary=False, compress=compress)
-
-    async def send_bytes(self, data: bytes, compress: Optional[int] = None) -> None:
-        if not isinstance(data, (bytes, bytearray, memoryview)):
-            raise TypeError("data argument must be byte-ish (%r)" % type(data))
-        await self._writer.send(data, binary=True, compress=compress)
-
-    async def send_json(
-        self,
-        data: Any,
-        compress: Optional[int] = None,
-        *,
-        dumps: JSONEncoder = DEFAULT_JSON_ENCODER,
-    ) -> None:
-        await self.send_str(dumps(data), compress=compress)
-
-    async def close(self, *, code: int = WSCloseCode.OK, message: bytes = b"") -> bool:
-        # we need to break `receive()` cycle first,
-        # `close()` may be called from different task
-        if self._waiting is not None and not self._closed:
-            self._reader.feed_data(WS_CLOSING_MESSAGE, 0)
-            await self._waiting
-
-        if not self._closed:
-            self._cancel_heartbeat()
-            self._closed = True
-            try:
-                await self._writer.close(code, message)
-            except asyncio.CancelledError:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                self._response.close()
-                raise
-            except Exception as exc:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                self._exception = exc
-                self._response.close()
-                return True
-
-            if self._closing:
-                self._response.close()
-                return True
-
-            while True:
-                try:
-                    async with async_timeout.timeout(self._timeout):
-                        msg = await self._reader.read()
-                except asyncio.CancelledError:
-                    self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                    self._response.close()
-                    raise
-                except Exception as exc:
-                    self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                    self._exception = exc
-                    self._response.close()
-                    return True
-
-                if msg.type == WSMsgType.CLOSE:
-                    self._close_code = msg.data
-                    self._response.close()
-                    return True
-        else:
-            return False
-
-    async def receive(self, timeout: Optional[float] = None) -> WSMessage:
-        while True:
-            if self._waiting is not None:
-                raise RuntimeError("Concurrent call to receive() is not allowed")
-
-            if self._closed:
-                return WS_CLOSED_MESSAGE
-            elif self._closing:
-                await self.close()
-                return WS_CLOSED_MESSAGE
-
-            try:
-                self._waiting = self._loop.create_future()
-                try:
-                    async with async_timeout.timeout(timeout or self._receive_timeout):
-                        msg = await self._reader.read()
-                    self._reset_heartbeat()
-                finally:
-                    waiter = self._waiting
-                    self._waiting = None
-                    set_result(waiter, True)
-            except (asyncio.CancelledError, asyncio.TimeoutError):
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                raise
-            except EofStream:
-                self._close_code = WSCloseCode.OK
-                await self.close()
-                return WSMessage(WSMsgType.CLOSED, None, None)
-            except ClientError:
-                self._closed = True
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                return WS_CLOSED_MESSAGE
-            except WebSocketError as exc:
-                self._close_code = exc.code
-                await self.close(code=exc.code)
-                return WSMessage(WSMsgType.ERROR, exc, None)
-            except Exception as exc:
-                self._exception = exc
-                self._closing = True
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                await self.close()
-                return WSMessage(WSMsgType.ERROR, exc, None)
-
-            if msg.type == WSMsgType.CLOSE:
-                self._closing = True
-                self._close_code = msg.data
-                if not self._closed and self._autoclose:
-                    await self.close()
-            elif msg.type == WSMsgType.CLOSING:
-                self._closing = True
-            elif msg.type == WSMsgType.PING and self._autoping:
-                await self.pong(msg.data)
-                continue
-            elif msg.type == WSMsgType.PONG and self._autoping:
-                continue
-
-            return msg
-
-    async def receive_str(self, *, timeout: Optional[float] = None) -> str:
-        msg = await self.receive(timeout)
-        if msg.type != WSMsgType.TEXT:
-            raise TypeError(f"Received message {msg.type}:{msg.data!r} is not str")
-        return cast(str, msg.data)
-
-    async def receive_bytes(self, *, timeout: Optional[float] = None) -> bytes:
-        msg = await self.receive(timeout)
-        if msg.type != WSMsgType.BINARY:
-            raise TypeError(f"Received message {msg.type}:{msg.data!r} is not bytes")
-        return cast(bytes, msg.data)
-
-    async def receive_json(
-        self,
-        *,
-        loads: JSONDecoder = DEFAULT_JSON_DECODER,
-        timeout: Optional[float] = None,
-    ) -> Any:
-        data = await self.receive_str(timeout=timeout)
-        return loads(data)
-
-    def __aiter__(self) -> "ClientWebSocketResponse":
-        return self
-
-    async def __anext__(self) -> WSMessage:
-        msg = await self.receive()
-        if msg.type in (WSMsgType.CLOSE, WSMsgType.CLOSING, WSMsgType.CLOSED):
-            raise StopAsyncIteration
-        return msg
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/helpers.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/helpers.py
deleted file mode 100644
index 874ab1ac076bc311d8853f08bb5fe454b650099f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/helpers.py
+++ /dev/null
@@ -1,878 +0,0 @@
-"""Various helper functions"""
-
-import asyncio
-import base64
-import binascii
-import datetime
-import functools
-import inspect
-import netrc
-import os
-import platform
-import re
-import sys
-import time
-import warnings
-import weakref
-from collections import namedtuple
-from contextlib import suppress
-from email.parser import HeaderParser
-from email.utils import parsedate
-from math import ceil
-from pathlib import Path
-from types import TracebackType
-from typing import (
-    Any,
-    Callable,
-    ContextManager,
-    Dict,
-    Generator,
-    Generic,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Pattern,
-    Set,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-)
-from urllib.parse import quote
-from urllib.request import getproxies, proxy_bypass
-
-import async_timeout
-import attr
-from multidict import MultiDict, MultiDictProxy
-from yarl import URL
-
-from . import hdrs
-from .log import client_logger, internal_logger
-from .typedefs import PathLike, Protocol  # noqa
-
-__all__ = ("BasicAuth", "ChainMapProxy", "ETag")
-
-IS_MACOS = platform.system() == "Darwin"
-IS_WINDOWS = platform.system() == "Windows"
-
-PY_36 = sys.version_info >= (3, 6)
-PY_37 = sys.version_info >= (3, 7)
-PY_38 = sys.version_info >= (3, 8)
-PY_310 = sys.version_info >= (3, 10)
-PY_311 = sys.version_info >= (3, 11)
-
-if sys.version_info < (3, 7):
-    import idna_ssl
-
-    idna_ssl.patch_match_hostname()
-
-    def all_tasks(
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> Set["asyncio.Task[Any]"]:
-        tasks = list(asyncio.Task.all_tasks(loop))
-        return {t for t in tasks if not t.done()}
-
-else:
-    all_tasks = asyncio.all_tasks
-
-
-_T = TypeVar("_T")
-_S = TypeVar("_S")
-
-
-sentinel: Any = object()
-NO_EXTENSIONS: bool = bool(os.environ.get("AIOHTTP_NO_EXTENSIONS"))
-
-# N.B. sys.flags.dev_mode is available on Python 3.7+, use getattr
-# for compatibility with older versions
-DEBUG: bool = getattr(sys.flags, "dev_mode", False) or (
-    not sys.flags.ignore_environment and bool(os.environ.get("PYTHONASYNCIODEBUG"))
-)
-
-
-CHAR = {chr(i) for i in range(0, 128)}
-CTL = {chr(i) for i in range(0, 32)} | {
-    chr(127),
-}
-SEPARATORS = {
-    "(",
-    ")",
-    "<",
-    ">",
-    "@",
-    ",",
-    ";",
-    ":",
-    "\\",
-    '"',
-    "/",
-    "[",
-    "]",
-    "?",
-    "=",
-    "{",
-    "}",
-    " ",
-    chr(9),
-}
-TOKEN = CHAR ^ CTL ^ SEPARATORS
-
-
-class noop:
-    def __await__(self) -> Generator[None, None, None]:
-        yield
-
-
-class BasicAuth(namedtuple("BasicAuth", ["login", "password", "encoding"])):
-    """Http basic authentication helper."""
-
-    def __new__(
-        cls, login: str, password: str = "", encoding: str = "latin1"
-    ) -> "BasicAuth":
-        if login is None:
-            raise ValueError("None is not allowed as login value")
-
-        if password is None:
-            raise ValueError("None is not allowed as password value")
-
-        if ":" in login:
-            raise ValueError('A ":" is not allowed in login (RFC 1945#section-11.1)')
-
-        return super().__new__(cls, login, password, encoding)
-
-    @classmethod
-    def decode(cls, auth_header: str, encoding: str = "latin1") -> "BasicAuth":
-        """Create a BasicAuth object from an Authorization HTTP header."""
-        try:
-            auth_type, encoded_credentials = auth_header.split(" ", 1)
-        except ValueError:
-            raise ValueError("Could not parse authorization header.")
-
-        if auth_type.lower() != "basic":
-            raise ValueError("Unknown authorization method %s" % auth_type)
-
-        try:
-            decoded = base64.b64decode(
-                encoded_credentials.encode("ascii"), validate=True
-            ).decode(encoding)
-        except binascii.Error:
-            raise ValueError("Invalid base64 encoding.")
-
-        try:
-            # RFC 2617 HTTP Authentication
-            # https://www.ietf.org/rfc/rfc2617.txt
-            # the colon must be present, but the username and password may be
-            # otherwise blank.
-            username, password = decoded.split(":", 1)
-        except ValueError:
-            raise ValueError("Invalid credentials.")
-
-        return cls(username, password, encoding=encoding)
-
-    @classmethod
-    def from_url(cls, url: URL, *, encoding: str = "latin1") -> Optional["BasicAuth"]:
-        """Create BasicAuth from url."""
-        if not isinstance(url, URL):
-            raise TypeError("url should be yarl.URL instance")
-        if url.user is None:
-            return None
-        return cls(url.user, url.password or "", encoding=encoding)
-
-    def encode(self) -> str:
-        """Encode credentials."""
-        creds = (f"{self.login}:{self.password}").encode(self.encoding)
-        return "Basic %s" % base64.b64encode(creds).decode(self.encoding)
-
-
-def strip_auth_from_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]:
-    auth = BasicAuth.from_url(url)
-    if auth is None:
-        return url, None
-    else:
-        return url.with_user(None), auth
-
-
-def netrc_from_env() -> Optional[netrc.netrc]:
-    """Load netrc from file.
-
-    Attempt to load it from the path specified by the env-var
-    NETRC or in the default location in the user's home directory.
-
-    Returns None if it couldn't be found or fails to parse.
-    """
-    netrc_env = os.environ.get("NETRC")
-
-    if netrc_env is not None:
-        netrc_path = Path(netrc_env)
-    else:
-        try:
-            home_dir = Path.home()
-        except RuntimeError as e:  # pragma: no cover
-            # if pathlib can't resolve home, it may raise a RuntimeError
-            client_logger.debug(
-                "Could not resolve home directory when "
-                "trying to look for .netrc file: %s",
-                e,
-            )
-            return None
-
-        netrc_path = home_dir / ("_netrc" if IS_WINDOWS else ".netrc")
-
-    try:
-        return netrc.netrc(str(netrc_path))
-    except netrc.NetrcParseError as e:
-        client_logger.warning("Could not parse .netrc file: %s", e)
-    except OSError as e:
-        # we couldn't read the file (doesn't exist, permissions, etc.)
-        if netrc_env or netrc_path.is_file():
-            # only warn if the environment wanted us to load it,
-            # or it appears like the default file does actually exist
-            client_logger.warning("Could not read .netrc file: %s", e)
-
-    return None
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class ProxyInfo:
-    proxy: URL
-    proxy_auth: Optional[BasicAuth]
-
-
-def proxies_from_env() -> Dict[str, ProxyInfo]:
-    proxy_urls = {
-        k: URL(v)
-        for k, v in getproxies().items()
-        if k in ("http", "https", "ws", "wss")
-    }
-    netrc_obj = netrc_from_env()
-    stripped = {k: strip_auth_from_url(v) for k, v in proxy_urls.items()}
-    ret = {}
-    for proto, val in stripped.items():
-        proxy, auth = val
-        if proxy.scheme in ("https", "wss"):
-            client_logger.warning(
-                "%s proxies %s are not supported, ignoring", proxy.scheme.upper(), proxy
-            )
-            continue
-        if netrc_obj and auth is None:
-            auth_from_netrc = None
-            if proxy.host is not None:
-                auth_from_netrc = netrc_obj.authenticators(proxy.host)
-            if auth_from_netrc is not None:
-                # auth_from_netrc is a (`user`, `account`, `password`) tuple,
-                # `user` and `account` both can be username,
-                # if `user` is None, use `account`
-                *logins, password = auth_from_netrc
-                login = logins[0] if logins[0] else logins[-1]
-                auth = BasicAuth(cast(str, login), cast(str, password))
-        ret[proto] = ProxyInfo(proxy, auth)
-    return ret
-
-
-def current_task(
-    loop: Optional[asyncio.AbstractEventLoop] = None,
-) -> "Optional[asyncio.Task[Any]]":
-    if sys.version_info >= (3, 7):
-        return asyncio.current_task(loop=loop)
-    else:
-        return asyncio.Task.current_task(loop=loop)
-
-
-def get_running_loop(
-    loop: Optional[asyncio.AbstractEventLoop] = None,
-) -> asyncio.AbstractEventLoop:
-    if loop is None:
-        loop = asyncio.get_event_loop()
-    if not loop.is_running():
-        warnings.warn(
-            "The object should be created within an async function",
-            DeprecationWarning,
-            stacklevel=3,
-        )
-        if loop.get_debug():
-            internal_logger.warning(
-                "The object should be created within an async function", stack_info=True
-            )
-    return loop
-
-
-def isasyncgenfunction(obj: Any) -> bool:
-    func = getattr(inspect, "isasyncgenfunction", None)
-    if func is not None:
-        return func(obj)  # type: ignore[no-any-return]
-    else:
-        return False
-
-
-def get_env_proxy_for_url(url: URL) -> Tuple[URL, Optional[BasicAuth]]:
-    """Get a permitted proxy for the given URL from the env."""
-    if url.host is not None and proxy_bypass(url.host):
-        raise LookupError(f"Proxying is disallowed for `{url.host!r}`")
-
-    proxies_in_env = proxies_from_env()
-    try:
-        proxy_info = proxies_in_env[url.scheme]
-    except KeyError:
-        raise LookupError(f"No proxies found for `{url!s}` in the env")
-    else:
-        return proxy_info.proxy, proxy_info.proxy_auth
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class MimeType:
-    type: str
-    subtype: str
-    suffix: str
-    parameters: "MultiDictProxy[str]"
-
-
-@functools.lru_cache(maxsize=56)
-def parse_mimetype(mimetype: str) -> MimeType:
-    """Parses a MIME type into its components.
-
-    mimetype is a MIME type string.
-
-    Returns a MimeType object.
-
-    Example:
-
-    >>> parse_mimetype('text/html; charset=utf-8')
-    MimeType(type='text', subtype='html', suffix='',
-             parameters={'charset': 'utf-8'})
-
-    """
-    if not mimetype:
-        return MimeType(
-            type="", subtype="", suffix="", parameters=MultiDictProxy(MultiDict())
-        )
-
-    parts = mimetype.split(";")
-    params: MultiDict[str] = MultiDict()
-    for item in parts[1:]:
-        if not item:
-            continue
-        key, value = cast(
-            Tuple[str, str], item.split("=", 1) if "=" in item else (item, "")
-        )
-        params.add(key.lower().strip(), value.strip(' "'))
-
-    fulltype = parts[0].strip().lower()
-    if fulltype == "*":
-        fulltype = "*/*"
-
-    mtype, stype = (
-        cast(Tuple[str, str], fulltype.split("/", 1))
-        if "/" in fulltype
-        else (fulltype, "")
-    )
-    stype, suffix = (
-        cast(Tuple[str, str], stype.split("+", 1)) if "+" in stype else (stype, "")
-    )
-
-    return MimeType(
-        type=mtype, subtype=stype, suffix=suffix, parameters=MultiDictProxy(params)
-    )
-
-
-def guess_filename(obj: Any, default: Optional[str] = None) -> Optional[str]:
-    name = getattr(obj, "name", None)
-    if name and isinstance(name, str) and name[0] != "<" and name[-1] != ">":
-        return Path(name).name
-    return default
-
-
-not_qtext_re = re.compile(r"[^\041\043-\133\135-\176]")
-QCONTENT = {chr(i) for i in range(0x20, 0x7F)} | {"\t"}
-
-
-def quoted_string(content: str) -> str:
-    """Return 7-bit content as quoted-string.
-
-    Format content into a quoted-string as defined in RFC5322 for
-    Internet Message Format. Notice that this is not the 8-bit HTTP
-    format, but the 7-bit email format. Content must be in usascii or
-    a ValueError is raised.
-    """
-    if not (QCONTENT > set(content)):
-        raise ValueError(f"bad content for quoted-string {content!r}")
-    return not_qtext_re.sub(lambda x: "\\" + x.group(0), content)
-
-
-def content_disposition_header(
-    disptype: str, quote_fields: bool = True, _charset: str = "utf-8", **params: str
-) -> str:
-    """Sets ``Content-Disposition`` header for MIME.
-
-    This is the MIME payload Content-Disposition header from RFC 2183
-    and RFC 7579 section 4.2, not the HTTP Content-Disposition from
-    RFC 6266.
-
-    disptype is a disposition type: inline, attachment, form-data.
-    Should be valid extension token (see RFC 2183)
-
-    quote_fields performs value quoting to 7-bit MIME headers
-    according to RFC 7578. Set to quote_fields to False if recipient
-    can take 8-bit file names and field values.
-
-    _charset specifies the charset to use when quote_fields is True.
-
-    params is a dict with disposition params.
-    """
-    if not disptype or not (TOKEN > set(disptype)):
-        raise ValueError("bad content disposition type {!r}" "".format(disptype))
-
-    value = disptype
-    if params:
-        lparams = []
-        for key, val in params.items():
-            if not key or not (TOKEN > set(key)):
-                raise ValueError(
-                    "bad content disposition parameter" " {!r}={!r}".format(key, val)
-                )
-            if quote_fields:
-                if key.lower() == "filename":
-                    qval = quote(val, "", encoding=_charset)
-                    lparams.append((key, '"%s"' % qval))
-                else:
-                    try:
-                        qval = quoted_string(val)
-                    except ValueError:
-                        qval = "".join(
-                            (_charset, "''", quote(val, "", encoding=_charset))
-                        )
-                        lparams.append((key + "*", qval))
-                    else:
-                        lparams.append((key, '"%s"' % qval))
-            else:
-                qval = val.replace("\\", "\\\\").replace('"', '\\"')
-                lparams.append((key, '"%s"' % qval))
-        sparams = "; ".join("=".join(pair) for pair in lparams)
-        value = "; ".join((value, sparams))
-    return value
-
-
-class _TSelf(Protocol, Generic[_T]):
-    _cache: Dict[str, _T]
-
-
-class reify(Generic[_T]):
-    """Use as a class method decorator.
-
-    It operates almost exactly like
-    the Python `@property` decorator, but it puts the result of the
-    method it decorates into the instance dict after the first call,
-    effectively replacing the function it decorates with an instance
-    variable.  It is, in Python parlance, a data descriptor.
-    """
-
-    def __init__(self, wrapped: Callable[..., _T]) -> None:
-        self.wrapped = wrapped
-        self.__doc__ = wrapped.__doc__
-        self.name = wrapped.__name__
-
-    def __get__(self, inst: _TSelf[_T], owner: Optional[Type[Any]] = None) -> _T:
-        try:
-            try:
-                return inst._cache[self.name]
-            except KeyError:
-                val = self.wrapped(inst)
-                inst._cache[self.name] = val
-                return val
-        except AttributeError:
-            if inst is None:
-                return self
-            raise
-
-    def __set__(self, inst: _TSelf[_T], value: _T) -> None:
-        raise AttributeError("reified property is read-only")
-
-
-reify_py = reify
-
-try:
-    from ._helpers import reify as reify_c
-
-    if not NO_EXTENSIONS:
-        reify = reify_c  # type: ignore[misc,assignment]
-except ImportError:
-    pass
-
-_ipv4_pattern = (
-    r"^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}"
-    r"(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$"
-)
-_ipv6_pattern = (
-    r"^(?:(?:(?:[A-F0-9]{1,4}:){6}|(?=(?:[A-F0-9]{0,4}:){0,6}"
-    r"(?:[0-9]{1,3}\.){3}[0-9]{1,3}$)(([0-9A-F]{1,4}:){0,5}|:)"
-    r"((:[0-9A-F]{1,4}){1,5}:|:)|::(?:[A-F0-9]{1,4}:){5})"
-    r"(?:(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])\.){3}"
-    r"(?:25[0-5]|2[0-4][0-9]|1[0-9][0-9]|[1-9]?[0-9])|(?:[A-F0-9]{1,4}:){7}"
-    r"[A-F0-9]{1,4}|(?=(?:[A-F0-9]{0,4}:){0,7}[A-F0-9]{0,4}$)"
-    r"(([0-9A-F]{1,4}:){1,7}|:)((:[0-9A-F]{1,4}){1,7}|:)|(?:[A-F0-9]{1,4}:){7}"
-    r":|:(:[A-F0-9]{1,4}){7})$"
-)
-_ipv4_regex = re.compile(_ipv4_pattern)
-_ipv6_regex = re.compile(_ipv6_pattern, flags=re.IGNORECASE)
-_ipv4_regexb = re.compile(_ipv4_pattern.encode("ascii"))
-_ipv6_regexb = re.compile(_ipv6_pattern.encode("ascii"), flags=re.IGNORECASE)
-
-
-def _is_ip_address(
-    regex: Pattern[str], regexb: Pattern[bytes], host: Optional[Union[str, bytes]]
-) -> bool:
-    if host is None:
-        return False
-    if isinstance(host, str):
-        return bool(regex.match(host))
-    elif isinstance(host, (bytes, bytearray, memoryview)):
-        return bool(regexb.match(host))
-    else:
-        raise TypeError(f"{host} [{type(host)}] is not a str or bytes")
-
-
-is_ipv4_address = functools.partial(_is_ip_address, _ipv4_regex, _ipv4_regexb)
-is_ipv6_address = functools.partial(_is_ip_address, _ipv6_regex, _ipv6_regexb)
-
-
-def is_ip_address(host: Optional[Union[str, bytes, bytearray, memoryview]]) -> bool:
-    return is_ipv4_address(host) or is_ipv6_address(host)
-
-
-def next_whole_second() -> datetime.datetime:
-    """Return current time rounded up to the next whole second."""
-    return datetime.datetime.now(datetime.timezone.utc).replace(
-        microsecond=0
-    ) + datetime.timedelta(seconds=0)
-
-
-_cached_current_datetime: Optional[int] = None
-_cached_formatted_datetime = ""
-
-
-def rfc822_formatted_time() -> str:
-    global _cached_current_datetime
-    global _cached_formatted_datetime
-
-    now = int(time.time())
-    if now != _cached_current_datetime:
-        # Weekday and month names for HTTP date/time formatting;
-        # always English!
-        # Tuples are constants stored in codeobject!
-        _weekdayname = ("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
-        _monthname = (
-            "",  # Dummy so we can use 1-based month numbers
-            "Jan",
-            "Feb",
-            "Mar",
-            "Apr",
-            "May",
-            "Jun",
-            "Jul",
-            "Aug",
-            "Sep",
-            "Oct",
-            "Nov",
-            "Dec",
-        )
-
-        year, month, day, hh, mm, ss, wd, *tail = time.gmtime(now)
-        _cached_formatted_datetime = "%s, %02d %3s %4d %02d:%02d:%02d GMT" % (
-            _weekdayname[wd],
-            day,
-            _monthname[month],
-            year,
-            hh,
-            mm,
-            ss,
-        )
-        _cached_current_datetime = now
-    return _cached_formatted_datetime
-
-
-def _weakref_handle(info: "Tuple[weakref.ref[object], str]") -> None:
-    ref, name = info
-    ob = ref()
-    if ob is not None:
-        with suppress(Exception):
-            getattr(ob, name)()
-
-
-def weakref_handle(
-    ob: object, name: str, timeout: float, loop: asyncio.AbstractEventLoop
-) -> Optional[asyncio.TimerHandle]:
-    if timeout is not None and timeout > 0:
-        when = loop.time() + timeout
-        if timeout >= 5:
-            when = ceil(when)
-
-        return loop.call_at(when, _weakref_handle, (weakref.ref(ob), name))
-    return None
-
-
-def call_later(
-    cb: Callable[[], Any], timeout: float, loop: asyncio.AbstractEventLoop
-) -> Optional[asyncio.TimerHandle]:
-    if timeout is not None and timeout > 0:
-        when = loop.time() + timeout
-        if timeout > 5:
-            when = ceil(when)
-        return loop.call_at(when, cb)
-    return None
-
-
-class TimeoutHandle:
-    """Timeout handle"""
-
-    def __init__(
-        self, loop: asyncio.AbstractEventLoop, timeout: Optional[float]
-    ) -> None:
-        self._timeout = timeout
-        self._loop = loop
-        self._callbacks: List[
-            Tuple[Callable[..., None], Tuple[Any, ...], Dict[str, Any]]
-        ] = []
-
-    def register(
-        self, callback: Callable[..., None], *args: Any, **kwargs: Any
-    ) -> None:
-        self._callbacks.append((callback, args, kwargs))
-
-    def close(self) -> None:
-        self._callbacks.clear()
-
-    def start(self) -> Optional[asyncio.Handle]:
-        timeout = self._timeout
-        if timeout is not None and timeout > 0:
-            when = self._loop.time() + timeout
-            if timeout >= 5:
-                when = ceil(when)
-            return self._loop.call_at(when, self.__call__)
-        else:
-            return None
-
-    def timer(self) -> "BaseTimerContext":
-        if self._timeout is not None and self._timeout > 0:
-            timer = TimerContext(self._loop)
-            self.register(timer.timeout)
-            return timer
-        else:
-            return TimerNoop()
-
-    def __call__(self) -> None:
-        for cb, args, kwargs in self._callbacks:
-            with suppress(Exception):
-                cb(*args, **kwargs)
-
-        self._callbacks.clear()
-
-
-class BaseTimerContext(ContextManager["BaseTimerContext"]):
-    pass
-
-
-class TimerNoop(BaseTimerContext):
-    def __enter__(self) -> BaseTimerContext:
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        return
-
-
-class TimerContext(BaseTimerContext):
-    """Low resolution timeout context manager"""
-
-    def __init__(self, loop: asyncio.AbstractEventLoop) -> None:
-        self._loop = loop
-        self._tasks: List[asyncio.Task[Any]] = []
-        self._cancelled = False
-
-    def __enter__(self) -> BaseTimerContext:
-        task = current_task(loop=self._loop)
-
-        if task is None:
-            raise RuntimeError(
-                "Timeout context manager should be used " "inside a task"
-            )
-
-        if self._cancelled:
-            raise asyncio.TimeoutError from None
-
-        self._tasks.append(task)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> Optional[bool]:
-        if self._tasks:
-            self._tasks.pop()
-
-        if exc_type is asyncio.CancelledError and self._cancelled:
-            raise asyncio.TimeoutError from None
-        return None
-
-    def timeout(self) -> None:
-        if not self._cancelled:
-            for task in set(self._tasks):
-                task.cancel()
-
-            self._cancelled = True
-
-
-def ceil_timeout(delay: Optional[float]) -> async_timeout.Timeout:
-    if delay is None or delay <= 0:
-        return async_timeout.timeout(None)
-
-    loop = get_running_loop()
-    now = loop.time()
-    when = now + delay
-    if delay > 5:
-        when = ceil(when)
-    return async_timeout.timeout_at(when)
-
-
-class HeadersMixin:
-
-    ATTRS = frozenset(["_content_type", "_content_dict", "_stored_content_type"])
-
-    _content_type: Optional[str] = None
-    _content_dict: Optional[Dict[str, str]] = None
-    _stored_content_type = sentinel
-
-    def _parse_content_type(self, raw: str) -> None:
-        self._stored_content_type = raw
-        if raw is None:
-            # default value according to RFC 2616
-            self._content_type = "application/octet-stream"
-            self._content_dict = {}
-        else:
-            msg = HeaderParser().parsestr("Content-Type: " + raw)
-            self._content_type = msg.get_content_type()
-            params = msg.get_params()
-            self._content_dict = dict(params[1:])  # First element is content type again
-
-    @property
-    def content_type(self) -> str:
-        """The value of content part for Content-Type HTTP header."""
-        raw = self._headers.get(hdrs.CONTENT_TYPE)  # type: ignore[attr-defined]
-        if self._stored_content_type != raw:
-            self._parse_content_type(raw)
-        return self._content_type  # type: ignore[return-value]
-
-    @property
-    def charset(self) -> Optional[str]:
-        """The value of charset part for Content-Type HTTP header."""
-        raw = self._headers.get(hdrs.CONTENT_TYPE)  # type: ignore[attr-defined]
-        if self._stored_content_type != raw:
-            self._parse_content_type(raw)
-        return self._content_dict.get("charset")  # type: ignore[union-attr]
-
-    @property
-    def content_length(self) -> Optional[int]:
-        """The value of Content-Length HTTP header."""
-        content_length = self._headers.get(  # type: ignore[attr-defined]
-            hdrs.CONTENT_LENGTH
-        )
-
-        if content_length is not None:
-            return int(content_length)
-        else:
-            return None
-
-
-def set_result(fut: "asyncio.Future[_T]", result: _T) -> None:
-    if not fut.done():
-        fut.set_result(result)
-
-
-def set_exception(fut: "asyncio.Future[_T]", exc: BaseException) -> None:
-    if not fut.done():
-        fut.set_exception(exc)
-
-
-class ChainMapProxy(Mapping[str, Any]):
-    __slots__ = ("_maps",)
-
-    def __init__(self, maps: Iterable[Mapping[str, Any]]) -> None:
-        self._maps = tuple(maps)
-
-    def __init_subclass__(cls) -> None:
-        raise TypeError(
-            "Inheritance class {} from ChainMapProxy "
-            "is forbidden".format(cls.__name__)
-        )
-
-    def __getitem__(self, key: str) -> Any:
-        for mapping in self._maps:
-            try:
-                return mapping[key]
-            except KeyError:
-                pass
-        raise KeyError(key)
-
-    def get(self, key: str, default: Any = None) -> Any:
-        return self[key] if key in self else default
-
-    def __len__(self) -> int:
-        # reuses stored hash values if possible
-        return len(set().union(*self._maps))  # type: ignore[arg-type]
-
-    def __iter__(self) -> Iterator[str]:
-        d: Dict[str, Any] = {}
-        for mapping in reversed(self._maps):
-            # reuses stored hash values if possible
-            d.update(mapping)
-        return iter(d)
-
-    def __contains__(self, key: object) -> bool:
-        return any(key in m for m in self._maps)
-
-    def __bool__(self) -> bool:
-        return any(self._maps)
-
-    def __repr__(self) -> str:
-        content = ", ".join(map(repr, self._maps))
-        return f"ChainMapProxy({content})"
-
-
-# https://tools.ietf.org/html/rfc7232#section-2.3
-_ETAGC = r"[!#-}\x80-\xff]+"
-_ETAGC_RE = re.compile(_ETAGC)
-_QUOTED_ETAG = rf'(W/)?"({_ETAGC})"'
-QUOTED_ETAG_RE = re.compile(_QUOTED_ETAG)
-LIST_QUOTED_ETAG_RE = re.compile(rf"({_QUOTED_ETAG})(?:\s*,\s*|$)|(.)")
-
-ETAG_ANY = "*"
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class ETag:
-    value: str
-    is_weak: bool = False
-
-
-def validate_etag_value(value: str) -> None:
-    if value != ETAG_ANY and not _ETAGC_RE.fullmatch(value):
-        raise ValueError(
-            f"Value {value!r} is not a valid etag. Maybe it contains '\"'?"
-        )
-
-
-def parse_http_date(date_str: Optional[str]) -> Optional[datetime.datetime]:
-    """Process a date string, return a datetime object"""
-    if date_str is not None:
-        timetuple = parsedate(date_str)
-        if timetuple is not None:
-            with suppress(ValueError):
-                return datetime.datetime(*timetuple[:6], tzinfo=datetime.timezone.utc)
-    return None
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/fontBuilder.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/fontBuilder.py
deleted file mode 100644
index dd57a0507d61465b1849ee4884e473351a004920..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/fontBuilder.py
+++ /dev/null
@@ -1,993 +0,0 @@
-__all__ = ["FontBuilder"]
-
-"""
-This module is *experimental*, meaning it still may evolve and change.
-
-The `FontBuilder` class is a convenient helper to construct working TTF or
-OTF fonts from scratch.
-
-Note that the various setup methods cannot be called in arbitrary order,
-due to various interdependencies between OpenType tables. Here is an order
-that works:
-
-    fb = FontBuilder(...)
-    fb.setupGlyphOrder(...)
-    fb.setupCharacterMap(...)
-    fb.setupGlyf(...) --or-- fb.setupCFF(...)
-    fb.setupHorizontalMetrics(...)
-    fb.setupHorizontalHeader()
-    fb.setupNameTable(...)
-    fb.setupOS2()
-    fb.addOpenTypeFeatures(...)
-    fb.setupPost()
-    fb.save(...)
-
-Here is how to build a minimal TTF:
-
-```python
-from fontTools.fontBuilder import FontBuilder
-from fontTools.pens.ttGlyphPen import TTGlyphPen
-
-
-def drawTestGlyph(pen):
-    pen.moveTo((100, 100))
-    pen.lineTo((100, 1000))
-    pen.qCurveTo((200, 900), (400, 900), (500, 1000))
-    pen.lineTo((500, 100))
-    pen.closePath()
-
-
-fb = FontBuilder(1024, isTTF=True)
-fb.setupGlyphOrder([".notdef", ".null", "space", "A", "a"])
-fb.setupCharacterMap({32: "space", 65: "A", 97: "a"})
-advanceWidths = {".notdef": 600, "space": 500, "A": 600, "a": 600, ".null": 0}
-
-familyName = "HelloTestFont"
-styleName = "TotallyNormal"
-version = "0.1"
-
-nameStrings = dict(
-    familyName=dict(en=familyName, nl="HalloTestFont"),
-    styleName=dict(en=styleName, nl="TotaalNormaal"),
-    uniqueFontIdentifier="fontBuilder: " + familyName + "." + styleName,
-    fullName=familyName + "-" + styleName,
-    psName=familyName + "-" + styleName,
-    version="Version " + version,
-)
-
-pen = TTGlyphPen(None)
-drawTestGlyph(pen)
-glyph = pen.glyph()
-glyphs = {".notdef": glyph, "space": glyph, "A": glyph, "a": glyph, ".null": glyph}
-fb.setupGlyf(glyphs)
-metrics = {}
-glyphTable = fb.font["glyf"]
-for gn, advanceWidth in advanceWidths.items():
-    metrics[gn] = (advanceWidth, glyphTable[gn].xMin)
-fb.setupHorizontalMetrics(metrics)
-fb.setupHorizontalHeader(ascent=824, descent=-200)
-fb.setupNameTable(nameStrings)
-fb.setupOS2(sTypoAscender=824, usWinAscent=824, usWinDescent=200)
-fb.setupPost()
-fb.save("test.ttf")
-```
-
-And here's how to build a minimal OTF:
-
-```python
-from fontTools.fontBuilder import FontBuilder
-from fontTools.pens.t2CharStringPen import T2CharStringPen
-
-
-def drawTestGlyph(pen):
-    pen.moveTo((100, 100))
-    pen.lineTo((100, 1000))
-    pen.curveTo((200, 900), (400, 900), (500, 1000))
-    pen.lineTo((500, 100))
-    pen.closePath()
-
-
-fb = FontBuilder(1024, isTTF=False)
-fb.setupGlyphOrder([".notdef", ".null", "space", "A", "a"])
-fb.setupCharacterMap({32: "space", 65: "A", 97: "a"})
-advanceWidths = {".notdef": 600, "space": 500, "A": 600, "a": 600, ".null": 0}
-
-familyName = "HelloTestFont"
-styleName = "TotallyNormal"
-version = "0.1"
-
-nameStrings = dict(
-    familyName=dict(en=familyName, nl="HalloTestFont"),
-    styleName=dict(en=styleName, nl="TotaalNormaal"),
-    uniqueFontIdentifier="fontBuilder: " + familyName + "." + styleName,
-    fullName=familyName + "-" + styleName,
-    psName=familyName + "-" + styleName,
-    version="Version " + version,
-)
-
-pen = T2CharStringPen(600, None)
-drawTestGlyph(pen)
-charString = pen.getCharString()
-charStrings = {
-    ".notdef": charString,
-    "space": charString,
-    "A": charString,
-    "a": charString,
-    ".null": charString,
-}
-fb.setupCFF(nameStrings["psName"], {"FullName": nameStrings["psName"]}, charStrings, {})
-lsb = {gn: cs.calcBounds(None)[0] for gn, cs in charStrings.items()}
-metrics = {}
-for gn, advanceWidth in advanceWidths.items():
-    metrics[gn] = (advanceWidth, lsb[gn])
-fb.setupHorizontalMetrics(metrics)
-fb.setupHorizontalHeader(ascent=824, descent=200)
-fb.setupNameTable(nameStrings)
-fb.setupOS2(sTypoAscender=824, usWinAscent=824, usWinDescent=200)
-fb.setupPost()
-fb.save("test.otf")
-```
-"""
-
-from .ttLib import TTFont, newTable
-from .ttLib.tables._c_m_a_p import cmap_classes
-from .ttLib.tables._g_l_y_f import flagCubic
-from .ttLib.tables.O_S_2f_2 import Panose
-from .misc.timeTools import timestampNow
-import struct
-from collections import OrderedDict
-
-
-_headDefaults = dict(
-    tableVersion=1.0,
-    fontRevision=1.0,
-    checkSumAdjustment=0,
-    magicNumber=0x5F0F3CF5,
-    flags=0x0003,
-    unitsPerEm=1000,
-    created=0,
-    modified=0,
-    xMin=0,
-    yMin=0,
-    xMax=0,
-    yMax=0,
-    macStyle=0,
-    lowestRecPPEM=3,
-    fontDirectionHint=2,
-    indexToLocFormat=0,
-    glyphDataFormat=0,
-)
-
-_maxpDefaultsTTF = dict(
-    tableVersion=0x00010000,
-    numGlyphs=0,
-    maxPoints=0,
-    maxContours=0,
-    maxCompositePoints=0,
-    maxCompositeContours=0,
-    maxZones=2,
-    maxTwilightPoints=0,
-    maxStorage=0,
-    maxFunctionDefs=0,
-    maxInstructionDefs=0,
-    maxStackElements=0,
-    maxSizeOfInstructions=0,
-    maxComponentElements=0,
-    maxComponentDepth=0,
-)
-_maxpDefaultsOTF = dict(
-    tableVersion=0x00005000,
-    numGlyphs=0,
-)
-
-_postDefaults = dict(
-    formatType=3.0,
-    italicAngle=0,
-    underlinePosition=0,
-    underlineThickness=0,
-    isFixedPitch=0,
-    minMemType42=0,
-    maxMemType42=0,
-    minMemType1=0,
-    maxMemType1=0,
-)
-
-_hheaDefaults = dict(
-    tableVersion=0x00010000,
-    ascent=0,
-    descent=0,
-    lineGap=0,
-    advanceWidthMax=0,
-    minLeftSideBearing=0,
-    minRightSideBearing=0,
-    xMaxExtent=0,
-    caretSlopeRise=1,
-    caretSlopeRun=0,
-    caretOffset=0,
-    reserved0=0,
-    reserved1=0,
-    reserved2=0,
-    reserved3=0,
-    metricDataFormat=0,
-    numberOfHMetrics=0,
-)
-
-_vheaDefaults = dict(
-    tableVersion=0x00010000,
-    ascent=0,
-    descent=0,
-    lineGap=0,
-    advanceHeightMax=0,
-    minTopSideBearing=0,
-    minBottomSideBearing=0,
-    yMaxExtent=0,
-    caretSlopeRise=0,
-    caretSlopeRun=0,
-    reserved0=0,
-    reserved1=0,
-    reserved2=0,
-    reserved3=0,
-    reserved4=0,
-    metricDataFormat=0,
-    numberOfVMetrics=0,
-)
-
-_nameIDs = dict(
-    copyright=0,
-    familyName=1,
-    styleName=2,
-    uniqueFontIdentifier=3,
-    fullName=4,
-    version=5,
-    psName=6,
-    trademark=7,
-    manufacturer=8,
-    designer=9,
-    description=10,
-    vendorURL=11,
-    designerURL=12,
-    licenseDescription=13,
-    licenseInfoURL=14,
-    # reserved = 15,
-    typographicFamily=16,
-    typographicSubfamily=17,
-    compatibleFullName=18,
-    sampleText=19,
-    postScriptCIDFindfontName=20,
-    wwsFamilyName=21,
-    wwsSubfamilyName=22,
-    lightBackgroundPalette=23,
-    darkBackgroundPalette=24,
-    variationsPostScriptNamePrefix=25,
-)
-
-# to insert in setupNameTable doc string:
-# print("\n".join(("%s (nameID %s)" % (k, v)) for k, v in sorted(_nameIDs.items(), key=lambda x: x[1])))
-
-_panoseDefaults = Panose()
-
-_OS2Defaults = dict(
-    version=3,
-    xAvgCharWidth=0,
-    usWeightClass=400,
-    usWidthClass=5,
-    fsType=0x0004,  # default: Preview & Print embedding
-    ySubscriptXSize=0,
-    ySubscriptYSize=0,
-    ySubscriptXOffset=0,
-    ySubscriptYOffset=0,
-    ySuperscriptXSize=0,
-    ySuperscriptYSize=0,
-    ySuperscriptXOffset=0,
-    ySuperscriptYOffset=0,
-    yStrikeoutSize=0,
-    yStrikeoutPosition=0,
-    sFamilyClass=0,
-    panose=_panoseDefaults,
-    ulUnicodeRange1=0,
-    ulUnicodeRange2=0,
-    ulUnicodeRange3=0,
-    ulUnicodeRange4=0,
-    achVendID="????",
-    fsSelection=0,
-    usFirstCharIndex=0,
-    usLastCharIndex=0,
-    sTypoAscender=0,
-    sTypoDescender=0,
-    sTypoLineGap=0,
-    usWinAscent=0,
-    usWinDescent=0,
-    ulCodePageRange1=0,
-    ulCodePageRange2=0,
-    sxHeight=0,
-    sCapHeight=0,
-    usDefaultChar=0,  # .notdef
-    usBreakChar=32,  # space
-    usMaxContext=0,
-    usLowerOpticalPointSize=0,
-    usUpperOpticalPointSize=0,
-)
-
-
-class FontBuilder(object):
-    def __init__(self, unitsPerEm=None, font=None, isTTF=True, glyphDataFormat=0):
-        """Initialize a FontBuilder instance.
-
-        If the `font` argument is not given, a new `TTFont` will be
-        constructed, and `unitsPerEm` must be given. If `isTTF` is True,
-        the font will be a glyf-based TTF; if `isTTF` is False it will be
-        a CFF-based OTF.
-
-        The `glyphDataFormat` argument corresponds to the `head` table field
-        that defines the format of the TrueType `glyf` table (default=0).
-        TrueType glyphs historically can only contain quadratic splines and static
-        components, but there's a proposal to add support for cubic Bezier curves as well
-        as variable composites/components at
-        https://github.com/harfbuzz/boring-expansion-spec/blob/main/glyf1.md
-        You can experiment with the new features by setting `glyphDataFormat` to 1.
-        A ValueError is raised if `glyphDataFormat` is left at 0 but glyphs are added
-        that contain cubic splines or varcomposites. This is to prevent accidentally
-        creating fonts that are incompatible with existing TrueType implementations.
-
-        If `font` is given, it must be a `TTFont` instance and `unitsPerEm`
-        must _not_ be given. The `isTTF` and `glyphDataFormat` arguments will be ignored.
-        """
-        if font is None:
-            self.font = TTFont(recalcTimestamp=False)
-            self.isTTF = isTTF
-            now = timestampNow()
-            assert unitsPerEm is not None
-            self.setupHead(
-                unitsPerEm=unitsPerEm,
-                created=now,
-                modified=now,
-                glyphDataFormat=glyphDataFormat,
-            )
-            self.setupMaxp()
-        else:
-            assert unitsPerEm is None
-            self.font = font
-            self.isTTF = "glyf" in font
-
-    def save(self, file):
-        """Save the font. The 'file' argument can be either a pathname or a
-        writable file object.
-        """
-        self.font.save(file)
-
-    def _initTableWithValues(self, tableTag, defaults, values):
-        table = self.font[tableTag] = newTable(tableTag)
-        for k, v in defaults.items():
-            setattr(table, k, v)
-        for k, v in values.items():
-            setattr(table, k, v)
-        return table
-
-    def _updateTableWithValues(self, tableTag, values):
-        table = self.font[tableTag]
-        for k, v in values.items():
-            setattr(table, k, v)
-
-    def setupHead(self, **values):
-        """Create a new `head` table and initialize it with default values,
-        which can be overridden by keyword arguments.
-        """
-        self._initTableWithValues("head", _headDefaults, values)
-
-    def updateHead(self, **values):
-        """Update the head table with the fields and values passed as
-        keyword arguments.
-        """
-        self._updateTableWithValues("head", values)
-
-    def setupGlyphOrder(self, glyphOrder):
-        """Set the glyph order for the font."""
-        self.font.setGlyphOrder(glyphOrder)
-
-    def setupCharacterMap(self, cmapping, uvs=None, allowFallback=False):
-        """Build the `cmap` table for the font. The `cmapping` argument should
-        be a dict mapping unicode code points as integers to glyph names.
-
-        The `uvs` argument, when passed, must be a list of tuples, describing
-        Unicode Variation Sequences. These tuples have three elements:
-            (unicodeValue, variationSelector, glyphName)
-        `unicodeValue` and `variationSelector` are integer code points.
-        `glyphName` may be None, to indicate this is the default variation.
-        Text processors will then use the cmap to find the glyph name.
-        Each Unicode Variation Sequence should be an officially supported
-        sequence, but this is not policed.
-        """
-        subTables = []
-        highestUnicode = max(cmapping) if cmapping else 0
-        if highestUnicode > 0xFFFF:
-            cmapping_3_1 = dict((k, v) for k, v in cmapping.items() if k < 0x10000)
-            subTable_3_10 = buildCmapSubTable(cmapping, 12, 3, 10)
-            subTables.append(subTable_3_10)
-        else:
-            cmapping_3_1 = cmapping
-        format = 4
-        subTable_3_1 = buildCmapSubTable(cmapping_3_1, format, 3, 1)
-        try:
-            subTable_3_1.compile(self.font)
-        except struct.error:
-            # format 4 overflowed, fall back to format 12
-            if not allowFallback:
-                raise ValueError(
-                    "cmap format 4 subtable overflowed; sort glyph order by unicode to fix."
-                )
-            format = 12
-            subTable_3_1 = buildCmapSubTable(cmapping_3_1, format, 3, 1)
-        subTables.append(subTable_3_1)
-        subTable_0_3 = buildCmapSubTable(cmapping_3_1, format, 0, 3)
-        subTables.append(subTable_0_3)
-
-        if uvs is not None:
-            uvsDict = {}
-            for unicodeValue, variationSelector, glyphName in uvs:
-                if cmapping.get(unicodeValue) == glyphName:
-                    # this is a default variation
-                    glyphName = None
-                if variationSelector not in uvsDict:
-                    uvsDict[variationSelector] = []
-                uvsDict[variationSelector].append((unicodeValue, glyphName))
-            uvsSubTable = buildCmapSubTable({}, 14, 0, 5)
-            uvsSubTable.uvsDict = uvsDict
-            subTables.append(uvsSubTable)
-
-        self.font["cmap"] = newTable("cmap")
-        self.font["cmap"].tableVersion = 0
-        self.font["cmap"].tables = subTables
-
-    def setupNameTable(self, nameStrings, windows=True, mac=True):
-        """Create the `name` table for the font. The `nameStrings` argument must
-        be a dict, mapping nameIDs or descriptive names for the nameIDs to name
-        record values. A value is either a string, or a dict, mapping language codes
-        to strings, to allow localized name table entries.
-
-        By default, both Windows (platformID=3) and Macintosh (platformID=1) name
-        records are added, unless any of `windows` or `mac` arguments is False.
-
-        The following descriptive names are available for nameIDs:
-
-            copyright (nameID 0)
-            familyName (nameID 1)
-            styleName (nameID 2)
-            uniqueFontIdentifier (nameID 3)
-            fullName (nameID 4)
-            version (nameID 5)
-            psName (nameID 6)
-            trademark (nameID 7)
-            manufacturer (nameID 8)
-            designer (nameID 9)
-            description (nameID 10)
-            vendorURL (nameID 11)
-            designerURL (nameID 12)
-            licenseDescription (nameID 13)
-            licenseInfoURL (nameID 14)
-            typographicFamily (nameID 16)
-            typographicSubfamily (nameID 17)
-            compatibleFullName (nameID 18)
-            sampleText (nameID 19)
-            postScriptCIDFindfontName (nameID 20)
-            wwsFamilyName (nameID 21)
-            wwsSubfamilyName (nameID 22)
-            lightBackgroundPalette (nameID 23)
-            darkBackgroundPalette (nameID 24)
-            variationsPostScriptNamePrefix (nameID 25)
-        """
-        nameTable = self.font["name"] = newTable("name")
-        nameTable.names = []
-
-        for nameName, nameValue in nameStrings.items():
-            if isinstance(nameName, int):
-                nameID = nameName
-            else:
-                nameID = _nameIDs[nameName]
-            if isinstance(nameValue, str):
-                nameValue = dict(en=nameValue)
-            nameTable.addMultilingualName(
-                nameValue, ttFont=self.font, nameID=nameID, windows=windows, mac=mac
-            )
-
-    def setupOS2(self, **values):
-        """Create a new `OS/2` table and initialize it with default values,
-        which can be overridden by keyword arguments.
-        """
-        self._initTableWithValues("OS/2", _OS2Defaults, values)
-        if "xAvgCharWidth" not in values:
-            assert (
-                "hmtx" in self.font
-            ), "the 'hmtx' table must be setup before the 'OS/2' table"
-            self.font["OS/2"].recalcAvgCharWidth(self.font)
-        if not (
-            "ulUnicodeRange1" in values
-            or "ulUnicodeRange2" in values
-            or "ulUnicodeRange3" in values
-            or "ulUnicodeRange3" in values
-        ):
-            assert (
-                "cmap" in self.font
-            ), "the 'cmap' table must be setup before the 'OS/2' table"
-            self.font["OS/2"].recalcUnicodeRanges(self.font)
-
-    def setupCFF(self, psName, fontInfo, charStringsDict, privateDict):
-        from .cffLib import (
-            CFFFontSet,
-            TopDictIndex,
-            TopDict,
-            CharStrings,
-            GlobalSubrsIndex,
-            PrivateDict,
-        )
-
-        assert not self.isTTF
-        self.font.sfntVersion = "OTTO"
-        fontSet = CFFFontSet()
-        fontSet.major = 1
-        fontSet.minor = 0
-        fontSet.otFont = self.font
-        fontSet.fontNames = [psName]
-        fontSet.topDictIndex = TopDictIndex()
-
-        globalSubrs = GlobalSubrsIndex()
-        fontSet.GlobalSubrs = globalSubrs
-        private = PrivateDict()
-        for key, value in privateDict.items():
-            setattr(private, key, value)
-        fdSelect = None
-        fdArray = None
-
-        topDict = TopDict()
-        topDict.charset = self.font.getGlyphOrder()
-        topDict.Private = private
-        topDict.GlobalSubrs = fontSet.GlobalSubrs
-        for key, value in fontInfo.items():
-            setattr(topDict, key, value)
-        if "FontMatrix" not in fontInfo:
-            scale = 1 / self.font["head"].unitsPerEm
-            topDict.FontMatrix = [scale, 0, 0, scale, 0, 0]
-
-        charStrings = CharStrings(
-            None, topDict.charset, globalSubrs, private, fdSelect, fdArray
-        )
-        for glyphName, charString in charStringsDict.items():
-            charString.private = private
-            charString.globalSubrs = globalSubrs
-            charStrings[glyphName] = charString
-        topDict.CharStrings = charStrings
-
-        fontSet.topDictIndex.append(topDict)
-
-        self.font["CFF "] = newTable("CFF ")
-        self.font["CFF "].cff = fontSet
-
-    def setupCFF2(self, charStringsDict, fdArrayList=None, regions=None):
-        from .cffLib import (
-            CFFFontSet,
-            TopDictIndex,
-            TopDict,
-            CharStrings,
-            GlobalSubrsIndex,
-            PrivateDict,
-            FDArrayIndex,
-            FontDict,
-        )
-
-        assert not self.isTTF
-        self.font.sfntVersion = "OTTO"
-        fontSet = CFFFontSet()
-        fontSet.major = 2
-        fontSet.minor = 0
-
-        cff2GetGlyphOrder = self.font.getGlyphOrder
-        fontSet.topDictIndex = TopDictIndex(None, cff2GetGlyphOrder, None)
-
-        globalSubrs = GlobalSubrsIndex()
-        fontSet.GlobalSubrs = globalSubrs
-
-        if fdArrayList is None:
-            fdArrayList = [{}]
-        fdSelect = None
-        fdArray = FDArrayIndex()
-        fdArray.strings = None
-        fdArray.GlobalSubrs = globalSubrs
-        for privateDict in fdArrayList:
-            fontDict = FontDict()
-            fontDict.setCFF2(True)
-            private = PrivateDict()
-            for key, value in privateDict.items():
-                setattr(private, key, value)
-            fontDict.Private = private
-            fdArray.append(fontDict)
-
-        topDict = TopDict()
-        topDict.cff2GetGlyphOrder = cff2GetGlyphOrder
-        topDict.FDArray = fdArray
-        scale = 1 / self.font["head"].unitsPerEm
-        topDict.FontMatrix = [scale, 0, 0, scale, 0, 0]
-
-        private = fdArray[0].Private
-        charStrings = CharStrings(None, None, globalSubrs, private, fdSelect, fdArray)
-        for glyphName, charString in charStringsDict.items():
-            charString.private = private
-            charString.globalSubrs = globalSubrs
-            charStrings[glyphName] = charString
-        topDict.CharStrings = charStrings
-
-        fontSet.topDictIndex.append(topDict)
-
-        self.font["CFF2"] = newTable("CFF2")
-        self.font["CFF2"].cff = fontSet
-
-        if regions:
-            self.setupCFF2Regions(regions)
-
-    def setupCFF2Regions(self, regions):
-        from .varLib.builder import buildVarRegionList, buildVarData, buildVarStore
-        from .cffLib import VarStoreData
-
-        assert "fvar" in self.font, "fvar must to be set up first"
-        assert "CFF2" in self.font, "CFF2 must to be set up first"
-        axisTags = [a.axisTag for a in self.font["fvar"].axes]
-        varRegionList = buildVarRegionList(regions, axisTags)
-        varData = buildVarData(list(range(len(regions))), None, optimize=False)
-        varStore = buildVarStore(varRegionList, [varData])
-        vstore = VarStoreData(otVarStore=varStore)
-        topDict = self.font["CFF2"].cff.topDictIndex[0]
-        topDict.VarStore = vstore
-        for fontDict in topDict.FDArray:
-            fontDict.Private.vstore = vstore
-
-    def setupGlyf(self, glyphs, calcGlyphBounds=True, validateGlyphFormat=True):
-        """Create the `glyf` table from a dict, that maps glyph names
-        to `fontTools.ttLib.tables._g_l_y_f.Glyph` objects, for example
-        as made by `fontTools.pens.ttGlyphPen.TTGlyphPen`.
-
-        If `calcGlyphBounds` is True, the bounds of all glyphs will be
-        calculated. Only pass False if your glyph objects already have
-        their bounding box values set.
-
-        If `validateGlyphFormat` is True, raise ValueError if any of the glyphs contains
-        cubic curves or is a variable composite but head.glyphDataFormat=0.
-        Set it to False to skip the check if you know in advance all the glyphs are
-        compatible with the specified glyphDataFormat.
-        """
-        assert self.isTTF
-
-        if validateGlyphFormat and self.font["head"].glyphDataFormat == 0:
-            for name, g in glyphs.items():
-                if g.isVarComposite():
-                    raise ValueError(
-                        f"Glyph {name!r} is a variable composite, but glyphDataFormat=0"
-                    )
-                elif g.numberOfContours > 0 and any(f & flagCubic for f in g.flags):
-                    raise ValueError(
-                        f"Glyph {name!r} has cubic Bezier outlines, but glyphDataFormat=0; "
-                        "either convert to quadratics with cu2qu or set glyphDataFormat=1."
-                    )
-
-        self.font["loca"] = newTable("loca")
-        self.font["glyf"] = newTable("glyf")
-        self.font["glyf"].glyphs = glyphs
-        if hasattr(self.font, "glyphOrder"):
-            self.font["glyf"].glyphOrder = self.font.glyphOrder
-        if calcGlyphBounds:
-            self.calcGlyphBounds()
-
-    def setupFvar(self, axes, instances):
-        """Adds an font variations table to the font.
-
-        Args:
-            axes (list): See below.
-            instances (list): See below.
-
-        ``axes`` should be a list of axes, with each axis either supplied as
-        a py:class:`.designspaceLib.AxisDescriptor` object, or a tuple in the
-        format ```tupletag, minValue, defaultValue, maxValue, name``.
-        The ``name`` is either a string, or a dict, mapping language codes
-        to strings, to allow localized name table entries.
-
-        ```instances`` should be a list of instances, with each instance either
-        supplied as a py:class:`.designspaceLib.InstanceDescriptor` object, or a
-        dict with keys ``location`` (mapping of axis tags to float values),
-        ``stylename`` and (optionally) ``postscriptfontname``.
-        The ``stylename`` is either a string, or a dict, mapping language codes
-        to strings, to allow localized name table entries.
-        """
-
-        addFvar(self.font, axes, instances)
-
-    def setupAvar(self, axes, mappings=None):
-        """Adds an axis variations table to the font.
-
-        Args:
-            axes (list): A list of py:class:`.designspaceLib.AxisDescriptor` objects.
-        """
-        from .varLib import _add_avar
-
-        if "fvar" not in self.font:
-            raise KeyError("'fvar' table is missing; can't add 'avar'.")
-
-        axisTags = [axis.axisTag for axis in self.font["fvar"].axes]
-        axes = OrderedDict(enumerate(axes))  # Only values are used
-        _add_avar(self.font, axes, mappings, axisTags)
-
-    def setupGvar(self, variations):
-        gvar = self.font["gvar"] = newTable("gvar")
-        gvar.version = 1
-        gvar.reserved = 0
-        gvar.variations = variations
-
-    def calcGlyphBounds(self):
-        """Calculate the bounding boxes of all glyphs in the `glyf` table.
-        This is usually not called explicitly by client code.
-        """
-        glyphTable = self.font["glyf"]
-        for glyph in glyphTable.glyphs.values():
-            glyph.recalcBounds(glyphTable)
-
-    def setupHorizontalMetrics(self, metrics):
-        """Create a new `hmtx` table, for horizontal metrics.
-
-        The `metrics` argument must be a dict, mapping glyph names to
-        `(width, leftSidebearing)` tuples.
-        """
-        self.setupMetrics("hmtx", metrics)
-
-    def setupVerticalMetrics(self, metrics):
-        """Create a new `vmtx` table, for horizontal metrics.
-
-        The `metrics` argument must be a dict, mapping glyph names to
-        `(height, topSidebearing)` tuples.
-        """
-        self.setupMetrics("vmtx", metrics)
-
-    def setupMetrics(self, tableTag, metrics):
-        """See `setupHorizontalMetrics()` and `setupVerticalMetrics()`."""
-        assert tableTag in ("hmtx", "vmtx")
-        mtxTable = self.font[tableTag] = newTable(tableTag)
-        roundedMetrics = {}
-        for gn in metrics:
-            w, lsb = metrics[gn]
-            roundedMetrics[gn] = int(round(w)), int(round(lsb))
-        mtxTable.metrics = roundedMetrics
-
-    def setupHorizontalHeader(self, **values):
-        """Create a new `hhea` table initialize it with default values,
-        which can be overridden by keyword arguments.
-        """
-        self._initTableWithValues("hhea", _hheaDefaults, values)
-
-    def setupVerticalHeader(self, **values):
-        """Create a new `vhea` table initialize it with default values,
-        which can be overridden by keyword arguments.
-        """
-        self._initTableWithValues("vhea", _vheaDefaults, values)
-
-    def setupVerticalOrigins(self, verticalOrigins, defaultVerticalOrigin=None):
-        """Create a new `VORG` table. The `verticalOrigins` argument must be
-        a dict, mapping glyph names to vertical origin values.
-
-        The `defaultVerticalOrigin` argument should be the most common vertical
-        origin value. If omitted, this value will be derived from the actual
-        values in the `verticalOrigins` argument.
-        """
-        if defaultVerticalOrigin is None:
-            # find the most frequent vorg value
-            bag = {}
-            for gn in verticalOrigins:
-                vorg = verticalOrigins[gn]
-                if vorg not in bag:
-                    bag[vorg] = 1
-                else:
-                    bag[vorg] += 1
-            defaultVerticalOrigin = sorted(
-                bag, key=lambda vorg: bag[vorg], reverse=True
-            )[0]
-        self._initTableWithValues(
-            "VORG",
-            {},
-            dict(VOriginRecords={}, defaultVertOriginY=defaultVerticalOrigin),
-        )
-        vorgTable = self.font["VORG"]
-        vorgTable.majorVersion = 1
-        vorgTable.minorVersion = 0
-        for gn in verticalOrigins:
-            vorgTable[gn] = verticalOrigins[gn]
-
-    def setupPost(self, keepGlyphNames=True, **values):
-        """Create a new `post` table and initialize it with default values,
-        which can be overridden by keyword arguments.
-        """
-        isCFF2 = "CFF2" in self.font
-        postTable = self._initTableWithValues("post", _postDefaults, values)
-        if (self.isTTF or isCFF2) and keepGlyphNames:
-            postTable.formatType = 2.0
-            postTable.extraNames = []
-            postTable.mapping = {}
-        else:
-            postTable.formatType = 3.0
-
-    def setupMaxp(self):
-        """Create a new `maxp` table. This is called implicitly by FontBuilder
-        itself and is usually not called by client code.
-        """
-        if self.isTTF:
-            defaults = _maxpDefaultsTTF
-        else:
-            defaults = _maxpDefaultsOTF
-        self._initTableWithValues("maxp", defaults, {})
-
-    def setupDummyDSIG(self):
-        """This adds an empty DSIG table to the font to make some MS applications
-        happy. This does not properly sign the font.
-        """
-        values = dict(
-            ulVersion=1,
-            usFlag=0,
-            usNumSigs=0,
-            signatureRecords=[],
-        )
-        self._initTableWithValues("DSIG", {}, values)
-
-    def addOpenTypeFeatures(self, features, filename=None, tables=None, debug=False):
-        """Add OpenType features to the font from a string containing
-        Feature File syntax.
-
-        The `filename` argument is used in error messages and to determine
-        where to look for "include" files.
-
-        The optional `tables` argument can be a list of OTL tables tags to
-        build, allowing the caller to only build selected OTL tables. See
-        `fontTools.feaLib` for details.
-
-        The optional `debug` argument controls whether to add source debugging
-        information to the font in the `Debg` table.
-        """
-        from .feaLib.builder import addOpenTypeFeaturesFromString
-
-        addOpenTypeFeaturesFromString(
-            self.font, features, filename=filename, tables=tables, debug=debug
-        )
-
-    def addFeatureVariations(self, conditionalSubstitutions, featureTag="rvrn"):
-        """Add conditional substitutions to a Variable Font.
-
-        See `fontTools.varLib.featureVars.addFeatureVariations`.
-        """
-        from .varLib import featureVars
-
-        if "fvar" not in self.font:
-            raise KeyError("'fvar' table is missing; can't add FeatureVariations.")
-
-        featureVars.addFeatureVariations(
-            self.font, conditionalSubstitutions, featureTag=featureTag
-        )
-
-    def setupCOLR(
-        self,
-        colorLayers,
-        version=None,
-        varStore=None,
-        varIndexMap=None,
-        clipBoxes=None,
-        allowLayerReuse=True,
-    ):
-        """Build new COLR table using color layers dictionary.
-
-        Cf. `fontTools.colorLib.builder.buildCOLR`.
-        """
-        from fontTools.colorLib.builder import buildCOLR
-
-        glyphMap = self.font.getReverseGlyphMap()
-        self.font["COLR"] = buildCOLR(
-            colorLayers,
-            version=version,
-            glyphMap=glyphMap,
-            varStore=varStore,
-            varIndexMap=varIndexMap,
-            clipBoxes=clipBoxes,
-            allowLayerReuse=allowLayerReuse,
-        )
-
-    def setupCPAL(
-        self,
-        palettes,
-        paletteTypes=None,
-        paletteLabels=None,
-        paletteEntryLabels=None,
-    ):
-        """Build new CPAL table using list of palettes.
-
-        Optionally build CPAL v1 table using paletteTypes, paletteLabels and
-        paletteEntryLabels.
-
-        Cf. `fontTools.colorLib.builder.buildCPAL`.
-        """
-        from fontTools.colorLib.builder import buildCPAL
-
-        self.font["CPAL"] = buildCPAL(
-            palettes,
-            paletteTypes=paletteTypes,
-            paletteLabels=paletteLabels,
-            paletteEntryLabels=paletteEntryLabels,
-            nameTable=self.font.get("name"),
-        )
-
-    def setupStat(self, axes, locations=None, elidedFallbackName=2):
-        """Build a new 'STAT' table.
-
-        See `fontTools.otlLib.builder.buildStatTable` for details about
-        the arguments.
-        """
-        from .otlLib.builder import buildStatTable
-
-        buildStatTable(self.font, axes, locations, elidedFallbackName)
-
-
-def buildCmapSubTable(cmapping, format, platformID, platEncID):
-    subTable = cmap_classes[format](format)
-    subTable.cmap = cmapping
-    subTable.platformID = platformID
-    subTable.platEncID = platEncID
-    subTable.language = 0
-    return subTable
-
-
-def addFvar(font, axes, instances):
-    from .ttLib.tables._f_v_a_r import Axis, NamedInstance
-
-    assert axes
-
-    fvar = newTable("fvar")
-    nameTable = font["name"]
-
-    for axis_def in axes:
-        axis = Axis()
-
-        if isinstance(axis_def, tuple):
-            (
-                axis.axisTag,
-                axis.minValue,
-                axis.defaultValue,
-                axis.maxValue,
-                name,
-            ) = axis_def
-        else:
-            (axis.axisTag, axis.minValue, axis.defaultValue, axis.maxValue, name) = (
-                axis_def.tag,
-                axis_def.minimum,
-                axis_def.default,
-                axis_def.maximum,
-                axis_def.name,
-            )
-            if axis_def.hidden:
-                axis.flags = 0x0001  # HIDDEN_AXIS
-
-        if isinstance(name, str):
-            name = dict(en=name)
-
-        axis.axisNameID = nameTable.addMultilingualName(name, ttFont=font)
-        fvar.axes.append(axis)
-
-    for instance in instances:
-        if isinstance(instance, dict):
-            coordinates = instance["location"]
-            name = instance["stylename"]
-            psname = instance.get("postscriptfontname")
-        else:
-            coordinates = instance.location
-            name = instance.localisedStyleName or instance.styleName
-            psname = instance.postScriptFontName
-
-        if isinstance(name, str):
-            name = dict(en=name)
-
-        inst = NamedInstance()
-        inst.subfamilyNameID = nameTable.addMultilingualName(name, ttFont=font)
-        if psname is not None:
-            inst.postscriptNameID = nameTable.addName(psname)
-        inst.coordinates = coordinates
-        fvar.instances.append(inst)
-
-    font["fvar"] = fvar
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_S_U_B_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_S_U_B_.py
deleted file mode 100644
index bb8375a5f83029d2b05388d5c882edd9c4aba95c..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_S_U_B_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table_G_S_U_B_(BaseTTXConverter):
-    pass
diff --git a/spaces/cmudrc/wecnet/README.md b/spaces/cmudrc/wecnet/README.md
deleted file mode 100644
index 1ac53297199167c5252583dd4d67d7e803f45ec8..0000000000000000000000000000000000000000
--- a/spaces/cmudrc/wecnet/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: wecnet
-emoji: 🌊
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cncn102/bingo1/src/components/turn-counter.tsx b/spaces/cncn102/bingo1/src/components/turn-counter.tsx
deleted file mode 100644
index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/components/turn-counter.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react'
-import { Throttling } from '@/lib/bots/bing/types'
-
-export interface TurnCounterProps {
-  throttling?: Throttling
-}
-
-export function TurnCounter({ throttling }: TurnCounterProps) {
-  if (!throttling) {
-    return null
-  }
-
-  return (
-    
      
-      
      
-        {throttling.numUserMessagesInConversation}
-         共 
-        {throttling.maxNumUserMessagesInConversation}
-      
      
-      
      
-    
      
-  )
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_direct.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_direct.c
deleted file mode 100644
index 014491e29be227086592245e54d35263e7272906..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264_direct.c
+++ /dev/null
@@ -1,727 +0,0 @@
-/*
- * H.26L/H.264/AVC/JVT/14496-10/... direct mb/block decoding
- * Copyright (c) 2003 Michael Niedermayer 
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * H.264 / AVC / MPEG-4 part10 direct mb/block decoding.
- * @author Michael Niedermayer 
- */
-
-#include "avcodec.h"
-#include "h264dec.h"
-#include "h264_ps.h"
-#include "mpegutils.h"
-#include "rectangle.h"
-#include "threadframe.h"
-
-#include 
-
-static int get_scale_factor(H264SliceContext *sl,
-                            int poc, int poc1, int i)
-{
-    int poc0 = sl->ref_list[0][i].poc;
-    int64_t pocdiff = poc1 - (int64_t)poc0;
-    int td = av_clip_int8(pocdiff);
-
-    if (pocdiff != (int)pocdiff)
-        avpriv_request_sample(sl->h264->avctx, "pocdiff overflow");
-
-    if (td == 0 || sl->ref_list[0][i].parent->long_ref) {
-        return 256;
-    } else {
-        int64_t pocdiff0 = poc - (int64_t)poc0;
-        int tb = av_clip_int8(pocdiff0);
-        int tx = (16384 + (FFABS(td) >> 1)) / td;
-
-        if (pocdiff0 != (int)pocdiff0)
-            av_log(sl->h264->avctx, AV_LOG_DEBUG, "pocdiff0 overflow\n");
-
-        return av_clip_intp2((tb * tx + 32) >> 6, 10);
-    }
-}
-
-void ff_h264_direct_dist_scale_factor(const H264Context *const h,
-                                      H264SliceContext *sl)
-{
-    const int poc  = FIELD_PICTURE(h) ? h->cur_pic_ptr->field_poc[h->picture_structure == PICT_BOTTOM_FIELD]
-                                      : h->cur_pic_ptr->poc;
-    const int poc1 = sl->ref_list[1][0].poc;
-    int i, field;
-
-    if (FRAME_MBAFF(h))
-        for (field = 0; field < 2; field++) {
-            const int poc  = h->cur_pic_ptr->field_poc[field];
-            const int poc1 = sl->ref_list[1][0].parent->field_poc[field];
-            for (i = 0; i < 2 * sl->ref_count[0]; i++)
-                sl->dist_scale_factor_field[field][i ^ field] =
-                    get_scale_factor(sl, poc, poc1, i + 16);
-        }
-
-    for (i = 0; i < sl->ref_count[0]; i++)
-        sl->dist_scale_factor[i] = get_scale_factor(sl, poc, poc1, i);
-}
-
-static void fill_colmap(const H264Context *h, H264SliceContext *sl,
-                        int map[2][16 + 32], int list,
-                        int field, int colfield, int mbafi)
-{
-    H264Picture *const ref1 = sl->ref_list[1][0].parent;
-    int j, old_ref, rfield;
-    int start  = mbafi ? 16                       : 0;
-    int end    = mbafi ? 16 + 2 * sl->ref_count[0] : sl->ref_count[0];
-    int interl = mbafi || h->picture_structure != PICT_FRAME;
-
-    /* bogus; fills in for missing frames */
-    memset(map[list], 0, sizeof(map[list]));
-
-    for (rfield = 0; rfield < 2; rfield++) {
-        for (old_ref = 0; old_ref < ref1->ref_count[colfield][list]; old_ref++) {
-            int poc = ref1->ref_poc[colfield][list][old_ref];
-
-            if (!interl)
-                poc |= 3;
-            // FIXME: store all MBAFF references so this is not needed
-            else if (interl && (poc & 3) == 3)
-                poc = (poc & ~3) + rfield + 1;
-
-            for (j = start; j < end; j++) {
-                if (4 * sl->ref_list[0][j].parent->frame_num +
-                    (sl->ref_list[0][j].reference & 3) == poc) {
-                    int cur_ref = mbafi ? (j - 16) ^ field : j;
-                    if (ref1->mbaff)
-                        map[list][2 * old_ref + (rfield ^ field) + 16] = cur_ref;
-                    if (rfield == field || !interl)
-                        map[list][old_ref] = cur_ref;
-                    break;
-                }
-            }
-        }
-    }
-}
-
-void ff_h264_direct_ref_list_init(const H264Context *const h, H264SliceContext *sl)
-{
-    H264Ref *const ref1 = &sl->ref_list[1][0];
-    H264Picture *const cur = h->cur_pic_ptr;
-    int list, j, field;
-    int sidx     = (h->picture_structure & 1) ^ 1;
-    int ref1sidx = (ref1->reference      & 1) ^ 1;
-
-    for (list = 0; list < sl->list_count; list++) {
-        cur->ref_count[sidx][list] = sl->ref_count[list];
-        for (j = 0; j < sl->ref_count[list]; j++)
-            cur->ref_poc[sidx][list][j] = 4 * sl->ref_list[list][j].parent->frame_num +
-                                          (sl->ref_list[list][j].reference & 3);
-    }
-
-    if (h->picture_structure == PICT_FRAME) {
-        memcpy(cur->ref_count[1], cur->ref_count[0], sizeof(cur->ref_count[0]));
-        memcpy(cur->ref_poc[1],   cur->ref_poc[0],   sizeof(cur->ref_poc[0]));
-    }
-
-    if (h->current_slice == 0) {
-        cur->mbaff = FRAME_MBAFF(h);
-    } else {
-        av_assert0(cur->mbaff == FRAME_MBAFF(h));
-    }
-
-    sl->col_fieldoff = 0;
-
-    if (sl->list_count != 2 || !sl->ref_count[1])
-        return;
-
-    if (h->picture_structure == PICT_FRAME) {
-        int cur_poc  = h->cur_pic_ptr->poc;
-        int *col_poc = sl->ref_list[1][0].parent->field_poc;
-        if (col_poc[0] == INT_MAX && col_poc[1] == INT_MAX) {
-            av_log(h->avctx, AV_LOG_ERROR, "co located POCs unavailable\n");
-            sl->col_parity = 1;
-        } else
-            sl->col_parity = (FFABS(col_poc[0] - (int64_t)cur_poc) >=
-                              FFABS(col_poc[1] - (int64_t)cur_poc));
-        ref1sidx =
-        sidx     = sl->col_parity;
-    // FL -> FL & differ parity
-    } else if (!(h->picture_structure & sl->ref_list[1][0].reference) &&
-               !sl->ref_list[1][0].parent->mbaff) {
-        sl->col_fieldoff = 2 * sl->ref_list[1][0].reference - 3;
-    }
-
-    if (sl->slice_type_nos != AV_PICTURE_TYPE_B || sl->direct_spatial_mv_pred)
-        return;
-
-    for (list = 0; list < 2; list++) {
-        fill_colmap(h, sl, sl->map_col_to_list0, list, sidx, ref1sidx, 0);
-        if (FRAME_MBAFF(h))
-            for (field = 0; field < 2; field++)
-                fill_colmap(h, sl, sl->map_col_to_list0_field[field], list, field,
-                            field, 1);
-    }
-}
-
-static void await_reference_mb_row(const H264Context *const h, H264Ref *ref,
-                                   int mb_y)
-{
-    int ref_field         = ref->reference - 1;
-    int ref_field_picture = ref->parent->field_picture;
-    int ref_height        = 16 * h->mb_height >> ref_field_picture;
-
-    if (!HAVE_THREADS || !(h->avctx->active_thread_type & FF_THREAD_FRAME))
-        return;
-
-    /* FIXME: It can be safe to access mb stuff
-     * even if pixels aren't deblocked yet. */
-
-    ff_thread_await_progress(&ref->parent->tf,
-                             FFMIN(16 * mb_y >> ref_field_picture,
-                                   ref_height - 1),
-                             ref_field_picture && ref_field);
-}
-
-static void pred_spatial_direct_motion(const H264Context *const h, H264SliceContext *sl,
-                                       int *mb_type)
-{
-    int b8_stride = 2;
-    int b4_stride = h->b_stride;
-    int mb_xy = sl->mb_xy, mb_y = sl->mb_y;
-    int mb_type_col[2];
-    const int16_t (*l1mv0)[2], (*l1mv1)[2];
-    const int8_t *l1ref0, *l1ref1;
-    const int is_b8x8 = IS_8X8(*mb_type);
-    unsigned int sub_mb_type = MB_TYPE_L0L1;
-    int i8, i4;
-    int ref[2];
-    int mv[2];
-    int list;
-
-    assert(sl->ref_list[1][0].reference & 3);
-
-    await_reference_mb_row(h, &sl->ref_list[1][0],
-                           sl->mb_y + !!IS_INTERLACED(*mb_type));
-
-#define MB_TYPE_16x16_OR_INTRA (MB_TYPE_16x16 | MB_TYPE_INTRA4x4 | \
-                                MB_TYPE_INTRA16x16 | MB_TYPE_INTRA_PCM)
-
-    /* ref = min(neighbors) */
-    for (list = 0; list < 2; list++) {
-        int left_ref     = sl->ref_cache[list][scan8[0] - 1];
-        int top_ref      = sl->ref_cache[list][scan8[0] - 8];
-        int refc         = sl->ref_cache[list][scan8[0] - 8 + 4];
-        const int16_t *C = sl->mv_cache[list][scan8[0]  - 8 + 4];
-        if (refc == PART_NOT_AVAILABLE) {
-            refc = sl->ref_cache[list][scan8[0] - 8 - 1];
-            C    = sl->mv_cache[list][scan8[0]  - 8 - 1];
-        }
-        ref[list] = FFMIN3((unsigned)left_ref,
-                           (unsigned)top_ref,
-                           (unsigned)refc);
-        if (ref[list] >= 0) {
-            /* This is just pred_motion() but with the cases removed that
-             * cannot happen for direct blocks. */
-            const int16_t *const A = sl->mv_cache[list][scan8[0] - 1];
-            const int16_t *const B = sl->mv_cache[list][scan8[0] - 8];
-
-            int match_count = (left_ref == ref[list]) +
-                              (top_ref  == ref[list]) +
-                              (refc     == ref[list]);
-
-            if (match_count > 1) { // most common
-                mv[list] = pack16to32(mid_pred(A[0], B[0], C[0]),
-                                      mid_pred(A[1], B[1], C[1]));
-            } else {
-                assert(match_count == 1);
-                if (left_ref == ref[list])
-                    mv[list] = AV_RN32A(A);
-                else if (top_ref == ref[list])
-                    mv[list] = AV_RN32A(B);
-                else
-                    mv[list] = AV_RN32A(C);
-            }
-            av_assert2(ref[list] < (sl->ref_count[list] << !!FRAME_MBAFF(h)));
-        } else {
-            int mask = ~(MB_TYPE_L0 << (2 * list));
-            mv[list]  = 0;
-            ref[list] = -1;
-            if (!is_b8x8)
-                *mb_type &= mask;
-            sub_mb_type &= mask;
-        }
-    }
-    if (ref[0] < 0 && ref[1] < 0) {
-        ref[0] = ref[1] = 0;
-        if (!is_b8x8)
-            *mb_type |= MB_TYPE_L0L1;
-        sub_mb_type |= MB_TYPE_L0L1;
-    }
-
-    if (!(is_b8x8 | mv[0] | mv[1])) {
-        fill_rectangle(&sl->ref_cache[0][scan8[0]], 4, 4, 8, (uint8_t)ref[0], 1);
-        fill_rectangle(&sl->ref_cache[1][scan8[0]], 4, 4, 8, (uint8_t)ref[1], 1);
-        fill_rectangle(&sl->mv_cache[0][scan8[0]], 4, 4, 8, 0, 4);
-        fill_rectangle(&sl->mv_cache[1][scan8[0]], 4, 4, 8, 0, 4);
-        *mb_type = (*mb_type & ~(MB_TYPE_8x8 | MB_TYPE_16x8 | MB_TYPE_8x16 |
-                                 MB_TYPE_P1L0 | MB_TYPE_P1L1)) |
-                   MB_TYPE_16x16 | MB_TYPE_DIRECT2;
-        return;
-    }
-
-    if (IS_INTERLACED(sl->ref_list[1][0].parent->mb_type[mb_xy])) { // AFL/AFR/FR/FL -> AFL/FL
-        if (!IS_INTERLACED(*mb_type)) {                    //     AFR/FR    -> AFL/FL
-            mb_y  = (sl->mb_y & ~1) + sl->col_parity;
-            mb_xy = sl->mb_x +
-                    ((sl->mb_y & ~1) + sl->col_parity) * h->mb_stride;
-            b8_stride = 0;
-        } else {
-            mb_y  += sl->col_fieldoff;
-            mb_xy += h->mb_stride * sl->col_fieldoff; // non-zero for FL -> FL & differ parity
-        }
-        goto single_col;
-    } else {                                             // AFL/AFR/FR/FL -> AFR/FR
-        if (IS_INTERLACED(*mb_type)) {                   // AFL       /FL -> AFR/FR
-            mb_y           =  sl->mb_y & ~1;
-            mb_xy          = (sl->mb_y & ~1) * h->mb_stride + sl->mb_x;
-            mb_type_col[0] = sl->ref_list[1][0].parent->mb_type[mb_xy];
-            mb_type_col[1] = sl->ref_list[1][0].parent->mb_type[mb_xy + h->mb_stride];
-            b8_stride      = 2 + 4 * h->mb_stride;
-            b4_stride     *= 6;
-            if (IS_INTERLACED(mb_type_col[0]) !=
-                IS_INTERLACED(mb_type_col[1])) {
-                mb_type_col[0] &= ~MB_TYPE_INTERLACED;
-                mb_type_col[1] &= ~MB_TYPE_INTERLACED;
-            }
-
-            sub_mb_type |= MB_TYPE_16x16 | MB_TYPE_DIRECT2; /* B_SUB_8x8 */
-            if ((mb_type_col[0] & MB_TYPE_16x16_OR_INTRA) &&
-                (mb_type_col[1] & MB_TYPE_16x16_OR_INTRA) &&
-                !is_b8x8) {
-                *mb_type |= MB_TYPE_16x8 | MB_TYPE_DIRECT2;  /* B_16x8 */
-            } else {
-                *mb_type |= MB_TYPE_8x8;
-            }
-        } else {                                         //     AFR/FR    -> AFR/FR
-single_col:
-            mb_type_col[0] =
-            mb_type_col[1] = sl->ref_list[1][0].parent->mb_type[mb_xy];
-
-            sub_mb_type |= MB_TYPE_16x16 | MB_TYPE_DIRECT2; /* B_SUB_8x8 */
-            if (!is_b8x8 && (mb_type_col[0] & MB_TYPE_16x16_OR_INTRA)) {
-                *mb_type |= MB_TYPE_16x16 | MB_TYPE_DIRECT2; /* B_16x16 */
-            } else if (!is_b8x8 &&
-                       (mb_type_col[0] & (MB_TYPE_16x8 | MB_TYPE_8x16))) {
-                *mb_type |= MB_TYPE_DIRECT2 |
-                            (mb_type_col[0] & (MB_TYPE_16x8 | MB_TYPE_8x16));
-            } else {
-                if (!h->ps.sps->direct_8x8_inference_flag) {
-                    /* FIXME: Save sub mb types from previous frames (or derive
-                     * from MVs) so we know exactly what block size to use. */
-                    sub_mb_type += (MB_TYPE_8x8 - MB_TYPE_16x16); /* B_SUB_4x4 */
-                }
-                *mb_type |= MB_TYPE_8x8;
-            }
-        }
-    }
-
-    await_reference_mb_row(h, &sl->ref_list[1][0], mb_y);
-
-    l1mv0  = (void*)&sl->ref_list[1][0].parent->motion_val[0][h->mb2b_xy[mb_xy]];
-    l1mv1  = (void*)&sl->ref_list[1][0].parent->motion_val[1][h->mb2b_xy[mb_xy]];
-    l1ref0 = &sl->ref_list[1][0].parent->ref_index[0][4 * mb_xy];
-    l1ref1 = &sl->ref_list[1][0].parent->ref_index[1][4 * mb_xy];
-    if (!b8_stride) {
-        if (sl->mb_y & 1) {
-            l1ref0 += 2;
-            l1ref1 += 2;
-            l1mv0  += 2 * b4_stride;
-            l1mv1  += 2 * b4_stride;
-        }
-    }
-
-    if (IS_INTERLACED(*mb_type) != IS_INTERLACED(mb_type_col[0])) {
-        int n = 0;
-        for (i8 = 0; i8 < 4; i8++) {
-            int x8  = i8 & 1;
-            int y8  = i8 >> 1;
-            int xy8 = x8     + y8 * b8_stride;
-            int xy4 = x8 * 3 + y8 * b4_stride;
-            int a, b;
-
-            if (is_b8x8 && !IS_DIRECT(sl->sub_mb_type[i8]))
-                continue;
-            sl->sub_mb_type[i8] = sub_mb_type;
-
-            fill_rectangle(&sl->ref_cache[0][scan8[i8 * 4]], 2, 2, 8,
-                           (uint8_t)ref[0], 1);
-            fill_rectangle(&sl->ref_cache[1][scan8[i8 * 4]], 2, 2, 8,
-                           (uint8_t)ref[1], 1);
-            if (!IS_INTRA(mb_type_col[y8]) && !sl->ref_list[1][0].parent->long_ref &&
-                ((l1ref0[xy8] == 0 &&
-                  FFABS(l1mv0[xy4][0]) <= 1 &&
-                  FFABS(l1mv0[xy4][1]) <= 1) ||
-                 (l1ref0[xy8] < 0 &&
-                  l1ref1[xy8] == 0 &&
-                  FFABS(l1mv1[xy4][0]) <= 1 &&
-                  FFABS(l1mv1[xy4][1]) <= 1))) {
-                a =
-                b = 0;
-                if (ref[0] > 0)
-                    a = mv[0];
-                if (ref[1] > 0)
-                    b = mv[1];
-                n++;
-            } else {
-                a = mv[0];
-                b = mv[1];
-            }
-            fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2, 8, a, 4);
-            fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2, 8, b, 4);
-        }
-        if (!is_b8x8 && !(n & 3))
-            *mb_type = (*mb_type & ~(MB_TYPE_8x8 | MB_TYPE_16x8 | MB_TYPE_8x16 |
-                                     MB_TYPE_P1L0 | MB_TYPE_P1L1)) |
-                       MB_TYPE_16x16 | MB_TYPE_DIRECT2;
-    } else if (IS_16X16(*mb_type)) {
-        int a, b;
-
-        fill_rectangle(&sl->ref_cache[0][scan8[0]], 4, 4, 8, (uint8_t)ref[0], 1);
-        fill_rectangle(&sl->ref_cache[1][scan8[0]], 4, 4, 8, (uint8_t)ref[1], 1);
-        if (!IS_INTRA(mb_type_col[0]) && !sl->ref_list[1][0].parent->long_ref &&
-            ((l1ref0[0] == 0 &&
-              FFABS(l1mv0[0][0]) <= 1 &&
-              FFABS(l1mv0[0][1]) <= 1) ||
-             (l1ref0[0] < 0 && !l1ref1[0] &&
-              FFABS(l1mv1[0][0]) <= 1 &&
-              FFABS(l1mv1[0][1]) <= 1 &&
-              h->x264_build > 33U))) {
-            a = b = 0;
-            if (ref[0] > 0)
-                a = mv[0];
-            if (ref[1] > 0)
-                b = mv[1];
-        } else {
-            a = mv[0];
-            b = mv[1];
-        }
-        fill_rectangle(&sl->mv_cache[0][scan8[0]], 4, 4, 8, a, 4);
-        fill_rectangle(&sl->mv_cache[1][scan8[0]], 4, 4, 8, b, 4);
-    } else {
-        int n = 0;
-        for (i8 = 0; i8 < 4; i8++) {
-            const int x8 = i8 & 1;
-            const int y8 = i8 >> 1;
-
-            if (is_b8x8 && !IS_DIRECT(sl->sub_mb_type[i8]))
-                continue;
-            sl->sub_mb_type[i8] = sub_mb_type;
-
-            fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2, 8, mv[0], 4);
-            fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2, 8, mv[1], 4);
-            fill_rectangle(&sl->ref_cache[0][scan8[i8 * 4]], 2, 2, 8,
-                           (uint8_t)ref[0], 1);
-            fill_rectangle(&sl->ref_cache[1][scan8[i8 * 4]], 2, 2, 8,
-                           (uint8_t)ref[1], 1);
-
-            assert(b8_stride == 2);
-            /* col_zero_flag */
-            if (!IS_INTRA(mb_type_col[0]) && !sl->ref_list[1][0].parent->long_ref &&
-                (l1ref0[i8] == 0 ||
-                 (l1ref0[i8] < 0 &&
-                  l1ref1[i8] == 0 &&
-                  h->x264_build > 33U))) {
-                const int16_t (*l1mv)[2] = l1ref0[i8] == 0 ? l1mv0 : l1mv1;
-                if (IS_SUB_8X8(sub_mb_type)) {
-                    const int16_t *mv_col = l1mv[x8 * 3 + y8 * 3 * b4_stride];
-                    if (FFABS(mv_col[0]) <= 1 && FFABS(mv_col[1]) <= 1) {
-                        if (ref[0] == 0)
-                            fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2,
-                                           8, 0, 4);
-                        if (ref[1] == 0)
-                            fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2,
-                                           8, 0, 4);
-                        n += 4;
-                    }
-                } else {
-                    int m = 0;
-                    for (i4 = 0; i4 < 4; i4++) {
-                        const int16_t *mv_col = l1mv[x8 * 2 + (i4 & 1) +
-                                                     (y8 * 2 + (i4 >> 1)) * b4_stride];
-                        if (FFABS(mv_col[0]) <= 1 && FFABS(mv_col[1]) <= 1) {
-                            if (ref[0] == 0)
-                                AV_ZERO32(sl->mv_cache[0][scan8[i8 * 4 + i4]]);
-                            if (ref[1] == 0)
-                                AV_ZERO32(sl->mv_cache[1][scan8[i8 * 4 + i4]]);
-                            m++;
-                        }
-                    }
-                    if (!(m & 3))
-                        sl->sub_mb_type[i8] += MB_TYPE_16x16 - MB_TYPE_8x8;
-                    n += m;
-                }
-            }
-        }
-        if (!is_b8x8 && !(n & 15))
-            *mb_type = (*mb_type & ~(MB_TYPE_8x8 | MB_TYPE_16x8 | MB_TYPE_8x16 |
-                                     MB_TYPE_P1L0 | MB_TYPE_P1L1)) |
-                       MB_TYPE_16x16 | MB_TYPE_DIRECT2;
-    }
-}
-
-static void pred_temp_direct_motion(const H264Context *const h, H264SliceContext *sl,
-                                    int *mb_type)
-{
-    int b8_stride = 2;
-    int b4_stride = h->b_stride;
-    int mb_xy = sl->mb_xy, mb_y = sl->mb_y;
-    int mb_type_col[2];
-    const int16_t (*l1mv0)[2], (*l1mv1)[2];
-    const int8_t *l1ref0, *l1ref1;
-    const int is_b8x8 = IS_8X8(*mb_type);
-    unsigned int sub_mb_type;
-    int i8, i4;
-
-    assert(sl->ref_list[1][0].reference & 3);
-
-    await_reference_mb_row(h, &sl->ref_list[1][0],
-                           sl->mb_y + !!IS_INTERLACED(*mb_type));
-
-    if (IS_INTERLACED(sl->ref_list[1][0].parent->mb_type[mb_xy])) { // AFL/AFR/FR/FL -> AFL/FL
-        if (!IS_INTERLACED(*mb_type)) {                    //     AFR/FR    -> AFL/FL
-            mb_y  = (sl->mb_y & ~1) + sl->col_parity;
-            mb_xy = sl->mb_x +
-                    ((sl->mb_y & ~1) + sl->col_parity) * h->mb_stride;
-            b8_stride = 0;
-        } else {
-            mb_y  += sl->col_fieldoff;
-            mb_xy += h->mb_stride * sl->col_fieldoff; // non-zero for FL -> FL & differ parity
-        }
-        goto single_col;
-    } else {                                        // AFL/AFR/FR/FL -> AFR/FR
-        if (IS_INTERLACED(*mb_type)) {              // AFL       /FL -> AFR/FR
-            mb_y           = sl->mb_y & ~1;
-            mb_xy          = sl->mb_x + (sl->mb_y & ~1) * h->mb_stride;
-            mb_type_col[0] = sl->ref_list[1][0].parent->mb_type[mb_xy];
-            mb_type_col[1] = sl->ref_list[1][0].parent->mb_type[mb_xy + h->mb_stride];
-            b8_stride      = 2 + 4 * h->mb_stride;
-            b4_stride     *= 6;
-            if (IS_INTERLACED(mb_type_col[0]) !=
-                IS_INTERLACED(mb_type_col[1])) {
-                mb_type_col[0] &= ~MB_TYPE_INTERLACED;
-                mb_type_col[1] &= ~MB_TYPE_INTERLACED;
-            }
-
-            sub_mb_type = MB_TYPE_16x16 | MB_TYPE_P0L0 | MB_TYPE_P0L1 |
-                          MB_TYPE_DIRECT2;                  /* B_SUB_8x8 */
-
-            if ((mb_type_col[0] & MB_TYPE_16x16_OR_INTRA) &&
-                (mb_type_col[1] & MB_TYPE_16x16_OR_INTRA) &&
-                !is_b8x8) {
-                *mb_type |= MB_TYPE_16x8 | MB_TYPE_L0L1 |
-                            MB_TYPE_DIRECT2;                /* B_16x8 */
-            } else {
-                *mb_type |= MB_TYPE_8x8 | MB_TYPE_L0L1;
-            }
-        } else {                                    //     AFR/FR    -> AFR/FR
-single_col:
-            mb_type_col[0]     =
-                mb_type_col[1] = sl->ref_list[1][0].parent->mb_type[mb_xy];
-
-            sub_mb_type = MB_TYPE_16x16 | MB_TYPE_P0L0 | MB_TYPE_P0L1 |
-                          MB_TYPE_DIRECT2;                  /* B_SUB_8x8 */
-            if (!is_b8x8 && (mb_type_col[0] & MB_TYPE_16x16_OR_INTRA)) {
-                *mb_type |= MB_TYPE_16x16 | MB_TYPE_P0L0 | MB_TYPE_P0L1 |
-                            MB_TYPE_DIRECT2;                /* B_16x16 */
-            } else if (!is_b8x8 &&
-                       (mb_type_col[0] & (MB_TYPE_16x8 | MB_TYPE_8x16))) {
-                *mb_type |= MB_TYPE_L0L1 | MB_TYPE_DIRECT2 |
-                            (mb_type_col[0] & (MB_TYPE_16x8 | MB_TYPE_8x16));
-            } else {
-                if (!h->ps.sps->direct_8x8_inference_flag) {
-                    /* FIXME: save sub mb types from previous frames (or derive
-                     * from MVs) so we know exactly what block size to use */
-                    sub_mb_type = MB_TYPE_8x8 | MB_TYPE_P0L0 | MB_TYPE_P0L1 |
-                                  MB_TYPE_DIRECT2;          /* B_SUB_4x4 */
-                }
-                *mb_type |= MB_TYPE_8x8 | MB_TYPE_L0L1;
-            }
-        }
-    }
-
-    await_reference_mb_row(h, &sl->ref_list[1][0], mb_y);
-
-    l1mv0  = (void*)&sl->ref_list[1][0].parent->motion_val[0][h->mb2b_xy[mb_xy]];
-    l1mv1  = (void*)&sl->ref_list[1][0].parent->motion_val[1][h->mb2b_xy[mb_xy]];
-    l1ref0 = &sl->ref_list[1][0].parent->ref_index[0][4 * mb_xy];
-    l1ref1 = &sl->ref_list[1][0].parent->ref_index[1][4 * mb_xy];
-    if (!b8_stride) {
-        if (sl->mb_y & 1) {
-            l1ref0 += 2;
-            l1ref1 += 2;
-            l1mv0  += 2 * b4_stride;
-            l1mv1  += 2 * b4_stride;
-        }
-    }
-
-    {
-        const int *map_col_to_list0[2] = { sl->map_col_to_list0[0],
-                                           sl->map_col_to_list0[1] };
-        const int *dist_scale_factor = sl->dist_scale_factor;
-        int ref_offset;
-
-        if (FRAME_MBAFF(h) && IS_INTERLACED(*mb_type)) {
-            map_col_to_list0[0] = sl->map_col_to_list0_field[sl->mb_y & 1][0];
-            map_col_to_list0[1] = sl->map_col_to_list0_field[sl->mb_y & 1][1];
-            dist_scale_factor   = sl->dist_scale_factor_field[sl->mb_y & 1];
-        }
-        ref_offset = (sl->ref_list[1][0].parent->mbaff << 4) & (mb_type_col[0] >> 3);
-
-        if (IS_INTERLACED(*mb_type) != IS_INTERLACED(mb_type_col[0])) {
-            int y_shift = 2 * !IS_INTERLACED(*mb_type);
-            assert(h->ps.sps->direct_8x8_inference_flag);
-
-            for (i8 = 0; i8 < 4; i8++) {
-                const int x8 = i8 & 1;
-                const int y8 = i8 >> 1;
-                int ref0, scale;
-                const int16_t (*l1mv)[2] = l1mv0;
-
-                if (is_b8x8 && !IS_DIRECT(sl->sub_mb_type[i8]))
-                    continue;
-                sl->sub_mb_type[i8] = sub_mb_type;
-
-                fill_rectangle(&sl->ref_cache[1][scan8[i8 * 4]], 2, 2, 8, 0, 1);
-                if (IS_INTRA(mb_type_col[y8])) {
-                    fill_rectangle(&sl->ref_cache[0][scan8[i8 * 4]], 2, 2, 8, 0, 1);
-                    fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2, 8, 0, 4);
-                    fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2, 8, 0, 4);
-                    continue;
-                }
-
-                ref0 = l1ref0[x8 + y8 * b8_stride];
-                if (ref0 >= 0)
-                    ref0 = map_col_to_list0[0][ref0 + ref_offset];
-                else {
-                    ref0 = map_col_to_list0[1][l1ref1[x8 + y8 * b8_stride] +
-                                               ref_offset];
-                    l1mv = l1mv1;
-                }
-                scale = dist_scale_factor[ref0];
-                fill_rectangle(&sl->ref_cache[0][scan8[i8 * 4]], 2, 2, 8,
-                               ref0, 1);
-
-                {
-                    const int16_t *mv_col = l1mv[x8 * 3 + y8 * b4_stride];
-                    int my_col            = (mv_col[1] * (1 << y_shift)) / 2;
-                    int mx                = (scale * mv_col[0] + 128) >> 8;
-                    int my                = (scale * my_col    + 128) >> 8;
-                    fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2, 8,
-                                   pack16to32(mx, my), 4);
-                    fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2, 8,
-                                   pack16to32(mx - mv_col[0], my - my_col), 4);
-                }
-            }
-            return;
-        }
-
-        /* one-to-one mv scaling */
-
-        if (IS_16X16(*mb_type)) {
-            int ref, mv0, mv1;
-
-            fill_rectangle(&sl->ref_cache[1][scan8[0]], 4, 4, 8, 0, 1);
-            if (IS_INTRA(mb_type_col[0])) {
-                ref = mv0 = mv1 = 0;
-            } else {
-                const int ref0 = l1ref0[0] >= 0 ? map_col_to_list0[0][l1ref0[0] + ref_offset]
-                                                : map_col_to_list0[1][l1ref1[0] + ref_offset];
-                const int scale = dist_scale_factor[ref0];
-                const int16_t *mv_col = l1ref0[0] >= 0 ? l1mv0[0] : l1mv1[0];
-                int mv_l0[2];
-                mv_l0[0] = (scale * mv_col[0] + 128) >> 8;
-                mv_l0[1] = (scale * mv_col[1] + 128) >> 8;
-                ref      = ref0;
-                mv0      = pack16to32(mv_l0[0], mv_l0[1]);
-                mv1      = pack16to32(mv_l0[0] - mv_col[0], mv_l0[1] - mv_col[1]);
-            }
-            fill_rectangle(&sl->ref_cache[0][scan8[0]], 4, 4, 8, ref, 1);
-            fill_rectangle(&sl->mv_cache[0][scan8[0]], 4, 4, 8, mv0, 4);
-            fill_rectangle(&sl->mv_cache[1][scan8[0]], 4, 4, 8, mv1, 4);
-        } else {
-            for (i8 = 0; i8 < 4; i8++) {
-                const int x8 = i8 & 1;
-                const int y8 = i8 >> 1;
-                int ref0, scale;
-                const int16_t (*l1mv)[2] = l1mv0;
-
-                if (is_b8x8 && !IS_DIRECT(sl->sub_mb_type[i8]))
-                    continue;
-                sl->sub_mb_type[i8] = sub_mb_type;
-                fill_rectangle(&sl->ref_cache[1][scan8[i8 * 4]], 2, 2, 8, 0, 1);
-                if (IS_INTRA(mb_type_col[0])) {
-                    fill_rectangle(&sl->ref_cache[0][scan8[i8 * 4]], 2, 2, 8, 0, 1);
-                    fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2, 8, 0, 4);
-                    fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2, 8, 0, 4);
-                    continue;
-                }
-
-                assert(b8_stride == 2);
-                ref0 = l1ref0[i8];
-                if (ref0 >= 0)
-                    ref0 = map_col_to_list0[0][ref0 + ref_offset];
-                else {
-                    ref0 = map_col_to_list0[1][l1ref1[i8] + ref_offset];
-                    l1mv = l1mv1;
-                }
-                scale = dist_scale_factor[ref0];
-
-                fill_rectangle(&sl->ref_cache[0][scan8[i8 * 4]], 2, 2, 8,
-                               ref0, 1);
-                if (IS_SUB_8X8(sub_mb_type)) {
-                    const int16_t *mv_col = l1mv[x8 * 3 + y8 * 3 * b4_stride];
-                    int mx                = (scale * mv_col[0] + 128) >> 8;
-                    int my                = (scale * mv_col[1] + 128) >> 8;
-                    fill_rectangle(&sl->mv_cache[0][scan8[i8 * 4]], 2, 2, 8,
-                                   pack16to32(mx, my), 4);
-                    fill_rectangle(&sl->mv_cache[1][scan8[i8 * 4]], 2, 2, 8,
-                                   pack16to32(mx - mv_col[0], my - mv_col[1]), 4);
-                } else {
-                    for (i4 = 0; i4 < 4; i4++) {
-                        const int16_t *mv_col = l1mv[x8 * 2 + (i4 & 1) +
-                                                     (y8 * 2 + (i4 >> 1)) * b4_stride];
-                        int16_t *mv_l0 = sl->mv_cache[0][scan8[i8 * 4 + i4]];
-                        mv_l0[0] = (scale * mv_col[0] + 128) >> 8;
-                        mv_l0[1] = (scale * mv_col[1] + 128) >> 8;
-                        AV_WN32A(sl->mv_cache[1][scan8[i8 * 4 + i4]],
-                                 pack16to32(mv_l0[0] - mv_col[0],
-                                            mv_l0[1] - mv_col[1]));
-                    }
-                }
-            }
-        }
-    }
-}
-
-void ff_h264_pred_direct_motion(const H264Context *const h, H264SliceContext *sl,
-                                int *mb_type)
-{
-    if (sl->direct_spatial_mv_pred)
-        pred_spatial_direct_motion(h, sl, mb_type);
-    else
-        pred_temp_direct_motion(h, sl, mb_type);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h264dsp_init_mips.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h264dsp_init_mips.c
deleted file mode 100644
index e33df32c71cc21a41648efab9d25161fdba145a2..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/h264dsp_init_mips.c
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- * Copyright (c) 2015 Parag Salasakar (Parag.Salasakar@imgtec.com)
- * Copyright (c) 2015 Zhou Xiaoyong 
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "libavutil/mips/cpu.h"
-#include "h264dsp_mips.h"
-
-av_cold void ff_h264dsp_init_mips(H264DSPContext *c, const int bit_depth,
-                                  const int chroma_format_idc)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_mmi(cpu_flags)) {
-        if (bit_depth == 8) {
-            c->h264_add_pixels4_clear = ff_h264_add_pixels4_8_mmi;
-            c->h264_idct_add = ff_h264_idct_add_8_mmi;
-            c->h264_idct8_add = ff_h264_idct8_add_8_mmi;
-            c->h264_idct_dc_add = ff_h264_idct_dc_add_8_mmi;
-            c->h264_idct8_dc_add = ff_h264_idct8_dc_add_8_mmi;
-            c->h264_idct_add16 = ff_h264_idct_add16_8_mmi;
-            c->h264_idct_add16intra = ff_h264_idct_add16intra_8_mmi;
-            c->h264_idct8_add4 = ff_h264_idct8_add4_8_mmi;
-
-            if (chroma_format_idc <= 1)
-                c->h264_idct_add8 = ff_h264_idct_add8_8_mmi;
-            else
-                c->h264_idct_add8 = ff_h264_idct_add8_422_8_mmi;
-
-            c->h264_luma_dc_dequant_idct = ff_h264_luma_dc_dequant_idct_8_mmi;
-
-            if (chroma_format_idc <= 1)
-                c->h264_chroma_dc_dequant_idct =
-                    ff_h264_chroma_dc_dequant_idct_8_mmi;
-            else
-                c->h264_chroma_dc_dequant_idct =
-                    ff_h264_chroma422_dc_dequant_idct_8_mmi;
-
-            c->weight_h264_pixels_tab[0] = ff_h264_weight_pixels16_8_mmi;
-            c->weight_h264_pixels_tab[1] = ff_h264_weight_pixels8_8_mmi;
-            c->weight_h264_pixels_tab[2] = ff_h264_weight_pixels4_8_mmi;
-
-            c->biweight_h264_pixels_tab[0] = ff_h264_biweight_pixels16_8_mmi;
-            c->biweight_h264_pixels_tab[1] = ff_h264_biweight_pixels8_8_mmi;
-            c->biweight_h264_pixels_tab[2] = ff_h264_biweight_pixels4_8_mmi;
-
-            c->h264_v_loop_filter_chroma       = ff_deblock_v_chroma_8_mmi;
-            c->h264_v_loop_filter_chroma_intra = ff_deblock_v_chroma_intra_8_mmi;
-
-            if (chroma_format_idc <= 1) {
-                c->h264_h_loop_filter_chroma =
-                    ff_deblock_h_chroma_8_mmi;
-                c->h264_h_loop_filter_chroma_intra =
-                    ff_deblock_h_chroma_intra_8_mmi;
-            }
-
-            c->h264_v_loop_filter_luma = ff_deblock_v_luma_8_mmi;
-            c->h264_v_loop_filter_luma_intra = ff_deblock_v_luma_intra_8_mmi;
-            c->h264_h_loop_filter_luma = ff_deblock_h_luma_8_mmi;
-            c->h264_h_loop_filter_luma_intra = ff_deblock_h_luma_intra_8_mmi;
-        }
-    }
-
-    if (have_msa(cpu_flags)) {
-        if (chroma_format_idc <= 1)
-            c->h264_loop_filter_strength = ff_h264_loop_filter_strength_msa;
-        if (bit_depth == 8) {
-            c->h264_v_loop_filter_luma = ff_h264_v_lpf_luma_inter_msa;
-            c->h264_h_loop_filter_luma = ff_h264_h_lpf_luma_inter_msa;
-            c->h264_h_loop_filter_luma_mbaff =
-                ff_h264_h_loop_filter_luma_mbaff_msa;
-            c->h264_v_loop_filter_luma_intra = ff_h264_v_lpf_luma_intra_msa;
-            c->h264_h_loop_filter_luma_intra = ff_h264_h_lpf_luma_intra_msa;
-            c->h264_h_loop_filter_luma_mbaff_intra =
-                ff_h264_h_loop_filter_luma_mbaff_intra_msa;
-            c->h264_v_loop_filter_chroma = ff_h264_v_lpf_chroma_inter_msa;
-
-            if (chroma_format_idc <= 1)
-                c->h264_h_loop_filter_chroma = ff_h264_h_lpf_chroma_inter_msa;
-            else
-                c->h264_h_loop_filter_chroma =
-                    ff_h264_h_loop_filter_chroma422_msa;
-
-            if (chroma_format_idc > 1)
-                c->h264_h_loop_filter_chroma_mbaff =
-                    ff_h264_h_loop_filter_chroma422_mbaff_msa;
-
-            c->h264_v_loop_filter_chroma_intra =
-                ff_h264_v_lpf_chroma_intra_msa;
-
-            if (chroma_format_idc <= 1)
-                c->h264_h_loop_filter_chroma_intra =
-                    ff_h264_h_lpf_chroma_intra_msa;
-
-            /* Weighted MC */
-            c->weight_h264_pixels_tab[0] = ff_weight_h264_pixels16_8_msa;
-            c->weight_h264_pixels_tab[1] = ff_weight_h264_pixels8_8_msa;
-            c->weight_h264_pixels_tab[2] = ff_weight_h264_pixels4_8_msa;
-
-            c->biweight_h264_pixels_tab[0] = ff_biweight_h264_pixels16_8_msa;
-            c->biweight_h264_pixels_tab[1] = ff_biweight_h264_pixels8_8_msa;
-            c->biweight_h264_pixels_tab[2] = ff_biweight_h264_pixels4_8_msa;
-
-            c->h264_idct_add = ff_h264_idct_add_msa;
-            c->h264_idct8_add = ff_h264_idct8_addblk_msa;
-            c->h264_idct_dc_add = ff_h264_idct4x4_addblk_dc_msa;
-            c->h264_idct8_dc_add = ff_h264_idct8_dc_addblk_msa;
-            c->h264_idct_add16 = ff_h264_idct_add16_msa;
-            c->h264_idct8_add4 = ff_h264_idct8_add4_msa;
-
-            if (chroma_format_idc <= 1)
-                c->h264_idct_add8 = ff_h264_idct_add8_msa;
-            else
-                c->h264_idct_add8 = ff_h264_idct_add8_422_msa;
-
-            c->h264_idct_add16intra = ff_h264_idct_add16_intra_msa;
-            c->h264_luma_dc_dequant_idct = ff_h264_deq_idct_luma_dc_msa;
-        }
-    }
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Basketball Battle Mod Apk The Ultimate Guide to Max Level Money and Gold.md b/spaces/congsaPfin/Manga-OCR/logs/Basketball Battle Mod Apk The Ultimate Guide to Max Level Money and Gold.md
deleted file mode 100644
index 502a86a2453d31db15e5697c69d6bda78d9e4c7d..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Basketball Battle Mod Apk The Ultimate Guide to Max Level Money and Gold.md	
+++ /dev/null
@@ -1,121 +0,0 @@
-
      
-
      Basketball Battle Mod APK: How to Play and Win Every Match
      
-
      If you are a fan of basketball games, you might have heard of Basketball Battle, a popular 2D arcade game that lets you play one-on-one basketball matches against your friends or the computer. But did you know that there is a modded version of this game that gives you unlimited money, gold, max level, and no ads? In this article, we will tell you everything you need to know about Basketball Battle Mod APK, how to download and install it, and how to play and win every match.
      
-
      basketball battle mod apk max level

      Download File ->>->>->> https://urlca.com/2uO4Nd


      
-
      What is Basketball Battle Mod APK?
      
-
      Basketball Battle Mod APK is a modified version of the original Basketball Battle game, which is developed by DoubleTap Software. The modded version offers many advantages over the original game, such as:
      
-
      Features of Basketball Battle Mod APK
      
-
      Unlimited Money
      
-
      With unlimited money, you can buy anything you want in the game, such as new balls, courts, outfits, and accessories. You can also upgrade your skills and stats to become a better player.
      
-
      Unlimited Gold
      
-
      With unlimited gold, you can unlock all the premium features in the game, such as VIP mode, which gives you access to exclusive courts and outfits. You can also use gold to skip ads and get extra rewards.
      
-
      Max Level
      
-
      With max level, you can reach the highest rank in the game, which is Legend. You can also unlock all the achievements and trophies in the game, which will boost your reputation and fame.
      
-
      No Ads
      
-
      With no ads, you can enjoy the game without any interruptions or distractions. You can also save your data and battery by not watching any ads.
      
-
      How to Download and Install Basketball Battle Mod APK?
      
-
      If you want to download and install Basketball Battle Mod APK on your device, you need to follow these simple steps:
      
-
      Steps to Download and Install Basketball Battle Mod APK
      
-
        
-
      1. Click on this link  to download the latest version of Basketball Battle Mod APK.
        2. 
-
      2. After downloading the file, go to your device settings and enable installation from unknown sources.
        3. 
-
      3. Locate the downloaded file in your file manager and tap on it to start the installation process.
        4. 
-
      4. Wait for a few seconds until the installation is complete.
        5. 
-
      5. Launch the game and enjoy playing with unlimited money, gold, max level, and no ads.
        6. 
-
      
-
      Requirements for Basketball Battle Mod APK
      
-
        
-
      • An Android device with version 4.1 or higher.
        • 
-
      • A stable internet connection.
        • 
-
      • At least 100 MB of free storage space.
        • 
-
      
-
      How to Play Basketball Battle Mod APK?
      
-
      Basketball Battle Mod APK is very easy to play. The gameplay and controls are similar to the original game. Here are some basics you need to know:
      
-
      Gameplay and Controls of Basketball Battle Mod APK
      
-
        
-
      • The game is played in a 2D side-scrolling view.
        • 
-
      • You can choose to play against your friends or the computer in different modes, such as Arcade, Tournament, Streetball, or Online.
        • 
-
      • You can also customize your player's appearance, skills, stats, and equipment.
        • 
-
      • The game follows the standard rules of basketball. You need to score more points than your opponent by shooting the ball into the hoop.
        • 
-
      • You can move your player by dragging the left or right side of the screen.
        • 
-
      • You can shoot the ball by swiping up on the screen. The longer you swipe, the more power you will have.
        • 
-
      • You can steal the ball from your opponent by tapping on the screen when they are dribbling or shooting.
        • 
-
      • You can also perform various moves, such as crossover, spin, dunk, and block, by swiping in different directions.
        • 
-
      
-
      Tips and Tricks to Win Every Match in Basketball Battle Mod APK
      
-
      Here are some tips and tricks that will help you win every match in Basketball Battle Mod APK:
      
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-
        
-
      • Use your unlimited money and gold to buy and upgrade the best equipment and skills for your player.
        • 
-
      • Use your max level to unlock and use the most powerful moves and abilities in the game.
        • 
-
      • Use your no ads feature to avoid any distractions and interruptions during the game.
        • 
-
      • Practice your shooting skills and timing in the Arcade mode.
        • 
-
      • Learn your opponent's strengths and weaknesses and exploit them in the game.
        • 
-
      • Use different strategies and tactics depending on the mode and difficulty of the game.
        • 
-
      • Have fun and enjoy the game!
        • 
-
      
-
      Conclusion
      
-
      Basketball Battle Mod APK is a fun and exciting game that lets you play one-on-one basketball matches with unlimited money, gold, max level, and no ads. You can download and install it easily on your Android device and enjoy playing with your friends or the computer. You can also customize your player's appearance, skills, stats, and equipment, and use various moves and abilities to win every match. If you are a fan of basketball games, you should definitely try Basketball Battle Mod APK!
      
-
      FAQs
      
-
      Here are some frequently asked questions about Basketball Battle Mod APK:
      
-
        
-
      1. Is Basketball Battle Mod APK safe to download and install?
        2. 
-
        Yes, Basketball Battle Mod APK is safe to download and install. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a trusted source, such as this link .
        
-
      2. Is Basketball Battle Mod APK compatible with my device?
        3. 
-
        Basketball Battle Mod APK is compatible with most Android devices that have version 4.1 or higher. However, some devices may experience some issues or errors due to different specifications or settings. If you encounter any problems, you can try to update your device software or contact the developer for support.
        
-
      3. Can I play Basketball Battle Mod APK offline?
        4. 
-
        Basketball Battle Mod APK can be played offline without an internet connection. However, some features, such as Online mode, may require an internet connection to work properly. You can also use an internet connection to save your progress and sync it with other devices.
        
-
      4. Can I play Basketball Battle Mod APK with my friends?
        5. 
-
        Basketball Battle Mod APK supports multiplayer mode, which allows you to play with your friends on the same device or online. You can also chat with your friends and send them emojis during the game.
        
-
      5. How can I update Basketball Battle Mod APK?
        6. 
-
        Basketball Battle Mod APK is updated regularly by the developer to fix any bugs and add new features. You can check for updates by visiting this link  or by opening the game and tapping on the settings icon. You can also enable automatic updates in your device settings to get the latest version of the game.
        
-
       197e85843d
      
-
      
-
      
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Blockman Go The Ultimate Sandbox Game Online - No Download Required.md b/spaces/congsaPfin/Manga-OCR/logs/Blockman Go The Ultimate Sandbox Game Online - No Download Required.md
deleted file mode 100644
index 0313c91c59413bce8191862f780206310e40526c..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Blockman Go The Ultimate Sandbox Game Online - No Download Required.md	
+++ /dev/null
@@ -1,126 +0,0 @@
-
      
-
      Blockman Go: A Free Online Sandbox Game for Everyone
      
-
      Do you love playing games that let you explore, create, and socialize with other players? If so, you might want to check out Blockman Go, a free online sandbox game that offers a variety of mini-games, chat features, and crafting options. In this article, we will tell you what Blockman Go is, how to play it online for free, what are the benefits of playing it online, and what are some tips and tricks for having more fun in the game.
      
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      blockman go free play online no download

      Download ❤❤❤ https://urlca.com/2uOaus


      
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      What is Blockman Go?
      
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      Blockman Go is an arcade game developed by Blockman GO studio. It is a sandbox game that allows you to play various block-style mini-games, chat with other players, and make friends. You can also craft your own worlds and share them with others. Here are some of the features of Blockman Go:
      
- 
      A sandbox game with various mini-games
      
-
      Blockman Go has a wide range of mini-games that you can choose from, such as action, adventure, role-playing, strategy, simulation, and more. Some of the popular mini-games are Bed Wars, Sky Block, Anime Fighting Simulator, Egg War, Free City RP, and more. Each mini-game has its own rules, objectives, and rewards. You can play solo or team up with other players to compete or cooperate.
      
- 
      A social platform with chat and friends
      
-
      Blockman Go is not just a game, but also a social platform where you can chat with other players and make friends. You can use the chat function to send text or voice messages to other players in the same mini-game or in the lobby. You can also add players as friends and join their worlds or invite them to yours. You can also join or create clans and participate in clan events.
      
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      Blockman Go is also a creative tool that lets you craft your own worlds and share them with others. You can use blocks of different shapes, colors, and materials to build anything you can imagine. You can also use skins to customize your avatar and make it stand out. You can share your creations with other players by uploading them to the game or by inviting them to visit your world.
      
- 
      How to play Blockman Go online for free?
      
-
      One of the best things about Blockman Go is that you can play it online for free without downloading or installing anything. There are several ways to do this:
      
- 
      Play on PC or mobile with now.gg
      
-
      Now.gg is a cloud gaming platform that lets you play games instantly on your browser. You can play Blockman Go online for free on PC or mobile with now.gg by following these steps:
      
-
        
-
      1. Go to now.gg/apps/blockman-go-studio/8913/blockman-go.html
        2. 
-
      2. Click on "Play Now" button
        3. 
-
      3. Create an account or log in with your existing one
        4. 
-
      4. Enjoy playing Blockman Go online for free
        5. 
-
      
- 
      Play on PC with BlueStacks
      
-
      BlueStacks is an Android emulator that lets you play mobile games on your PC. You can play Block man Go online for free on PC with BlueStacks by following these steps:
      
-
        
-
      1. Go to www.bluestacks.com and download and install BlueStacks on your PC
        2. 
-
      2. Launch BlueStacks and sign in with your Google account
        3. 
-
      3. Search for Blockman Go in the search bar and install it from Google Play
        4. 
-
      4. Enjoy playing Blockman Go online for free on your PC
        5. 
-
      
- 
      Play on mobile with Google Play or App Store
      
-
      If you prefer to play Blockman Go on your mobile device, you can download it for free from Google Play or App Store. You can play Blockman Go online for free on mobile with Google Play or App Store by following these steps:
      
-
        
-
      1. Go to play.google.com/store/apps/details?id=com.sandboxol.blockymods for Android devices or apps.apple.com/us/app/blockman-go-blocky-mods/id1370773049 for iOS devices
        2. 
-
      2. Tap on "Install" button and wait for the download to finish
        3. 
-
      3. Launch Blockman Go and sign in with your account or create a new one
        4. 
-
      4. Enjoy playing Blockman Go online for free on your mobile device
        5. 
-
      
- 
      What are the benefits of playing Blockman Go online?
      
-
      Playing Blockman Go online has many benefits compared to playing it offline or downloading it. Here are some of them:
      
- 
      No download or installation required
      
-
      By playing Blockman Go online, you don't need to download or install anything on your device. This means you can save time and avoid any potential issues with compatibility, security, or performance. You can also play the game anytime and anywhere as long as you have an internet connection and a browser.
      
- 
      Save storage space and battery life
      
-
      By playing Blockman Go online, you don't need to use any storage space on your device. This means you can free up some space for other apps, files, or photos. You also don't need to use any battery power to run the game, as it is powered by the cloud. This means you can extend your battery life and play longer without worrying about running out of juice.
      
- 
      Access your game data from any device
      
-
      By playing Blockman Go online, you don't need to worry about losing your game data or progress. Your game data is stored in the cloud and synced across all your devices. This means you can access your game data from any device, whether it is a PC, a mobile phone, or a tablet. You can also switch between devices easily and resume your game from where you left off.
      
- 
      What are some tips and tricks for playing Blockman Go online?
      
-
      Playing Blockman Go online is fun and easy, but there are some tips and tricks that can help you have more fun and success in the game. Here are some of them:
      
- 
      Choose the right mini-game for your preference and skill level
      
-
      Blockman Go has many mini-games to choose from, but not all of them may suit your taste or ability. Some mini-games are more casual and relaxing, while others are more competitive and challenging. Some mini-games require more strategy and teamwork, while others require more reflexes and coordination. You should choose the mini-game that matches your preference and skill level, so you can enjoy the game more and avoid frustration.
      
- 
      Use the chat function to communicate and cooperate with other players
      
-
      Blockman Go is a social game that lets you chat with other players and make friends. You should use the chat function to communicate and cooperate with other players, especially in mini-games that involve teamwork or competition. You can use the chat function to send text or voice messages, share tips and strategies, ask for help or advice, compliment or encourage others, or just have fun conversations.
      
- 
      Customize your avatar and world with skins and blocks
      
-
      Blockman Go is a creative game that lets you customize your avatar and world with skins and blocks. You should use the skins and blocks to express your personality and style, as well as to make your avatar and world more unique and attractive. You can use skins to change the appearance of your avatar, such as the hair, clothes, accessories, etc. You can use blocks to build anything you want in your world, such as houses, castles, gardens, etc.
      
- 
      Conclusion
      
-
      Blockman Go is a free online sandbox game that offers a variety of mini -games, chat features, and crafting options. You can play it online for free without downloading or installing anything on your device. You can also save storage space and battery life, and access your game data from any device. You can also choose the right mini-game for your preference and skill level, use the chat function to communicate and cooperate with other players, and customize your avatar and world with skins and blocks. Blockman Go is a game that can appeal to everyone, whether you are looking for action, adventure, role-playing, strategy, simulation, or just some fun and relaxation. If you are interested in playing Blockman Go online for free, you can try it out now by following the methods we mentioned above. Have fun and enjoy the game!
      
- 
      FAQs
      
-
      Here are some frequently asked questions about Blockman Go online:
      
-
        
-
      1. Is Blockman Go online safe to play?
        2. 
-
        Yes, Blockman Go online is safe to play as long as you use a trusted platform or website to access it. You should also avoid clicking on any suspicious links or ads that may appear on the screen. You should also be careful about sharing any personal or sensitive information with other players or strangers.
        
-
      2. Is Blockman Go online multiplayer?
        3. 
-
        Yes, Blockman Go online is multiplayer, meaning you can play with other players from around the world. You can join or create rooms to play various mini-games with other players. You can also chat with them and make friends. You can also join or create clans and participate in clan events.
        
-
      3. Is Blockman Go online free to play?
        4. 
-
        Yes, Blockman Go online is free to play, meaning you don't need to pay any money to access or enjoy the game. However, there are some optional in-game purchases that you can make with real money, such as buying skins, blocks, coins, gems, or VIP memberships. These purchases can enhance your gaming experience, but they are not necessary to play the game.
        
-
      4. How do I update Blockman Go online?
        5. 
-
        If you are playing Blockman Go online on your browser, you don't need to update the game manually, as it will update automatically whenever there is a new version available. However, if you are playing Blockman Go online on your PC or mobile device using an emulator or an app, you may need to update the game manually by checking for updates on the emulator or app store.
        
-
      5. How do I report a bug or a problem in Blockman Go online?
        6. 
-
        If you encounter a bug or a problem in Blockman Go online, you can report it to the developers by following these steps:
        
-
          
-
        1. Go to www.blockmango.net/feedback
          2. 
-
        2. Fill in the required information, such as your username, email address, device model, game version, problem description, and screenshots if possible
          3. 
-
        3. Click on "Submit" button
          4. 
-
        4. Wait for the developers to respond and fix the issue
          5. 
-
        
-
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get the Latest Version of Vanced microG APK for YouTube Vanced - APKPure.md b/spaces/congsaPfin/Manga-OCR/logs/Get the Latest Version of Vanced microG APK for YouTube Vanced - APKPure.md
deleted file mode 100644
index 1c7ca8ef8bab86d26fb5945f19d271024f580d64..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Get the Latest Version of Vanced microG APK for YouTube Vanced - APKPure.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-
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      How to Download and Install microG and YouTube Vanced from APKPure
      
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      YouTube is one of the most popular video platforms in the world, but it also has some drawbacks. For example, you have to watch ads before or during videos, you can't play videos in the background without paying for YouTube Premium, and you can't customize the app's appearance or functionality. That's why many Android users have switched to YouTube Vanced, a modded version of YouTube that offers a lot of features that you won't find in the official app.
      
-
      However, to use YouTube Vanced, you also need another app called microG. This is a free and open-source implementation of Google's proprietary Android user space apps and libraries. It allows you to sign in to your Google account on YouTube Vanced, enabling access to premium features like subscriptions, playlists, history, and more. It also improves your privacy and battery life by reducing the data that is sent to Google.
      
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      So, how can you download and install microG and YouTube Vanced on your Android device? One of the easiest ways is to use APKPure, a website that provides safe and fast downloads of Android apps. APKPure verifies the authenticity of all apps before publishing them, so you don't have to worry about malware or viruses. You can also update your apps easily with APKPure's app manager.
      
-
      How to Download and Install microG and YouTube Vanced from APKPure
      
-
      Here are the steps you need to follow to get microG and YouTube Vanced on your Android device:
      
-
        
-
      1. Download the latest Vanced Manager app from APKPure. This is an app that helps you install and manage microG and YouTube Vanced. You can find it here.
        2. 
-
      2. Install the app and open it. You may need to enable installation from unknown sources in your device settings.
        3. 
-
      3. Select "nonroot" during the initial configuration wizard. This means that you don't need root access to use microG and YouTube Vanced.
        4. 
-
      4. Tap "Install" next to microG. This will prompt you to download the microG APK file from APKPure. You will need to allow installing from unknown apps for this app only.
        5. 
-
      5. Tap "Install" next to Vanced. This will prompt you to choose your preferred theme and language for YouTube Vanced. You can select from dark, black, or other colors for the theme, and from various languages for the app interface.
        6. 
-
      6. Open the microG app and log in to your Google account. This will enable you to sync your YouTube data and preferences with YouTube Vanced. You can also skip this step if you don't want to sign in.
        7. 
-
      7. Open the YouTube Vanced app and enjoy its features. You can access the settings by tapping on your profile icon and then on "Vanced Settings". Here you can customize various options like adblocking, background playback, force HDR mode, override max resolution, pinch to zoom, casting toggle, secondary themes, picture-in-picture mode, repeat videos, preferred resolution and speed, swipe controls, and more.
        8. 
-
      
-
      Benefits of Using microG and YouTube Vanced
      
-
      By using microG and YouTube Vanced, you can enjoy a lot of benefits that you won't get from the official YouTube app. Here are some of them:
      
-
        
-
      • Adblocking: No more annoying ads on videos or pages. You can watch your favorite content without interruptions or distractions.
        • 
-
      • Background playback: Play videos in the background without YouTube Premium. You can listen to music or podcasts while using other apps or turning off your screen.
        • 
-
      • Force HDR mode: Watch videos in high dynamic range quality on any device. You can see more details and colors in your videos, even if your device doesn't support HDR.
        • 
-
      • Override max resolution: Watch videos in higher resolutions than your device supports. You can enjoy 4K or 8K videos on your phone or tablet, even if your screen is not that big or sharp.
        • 
-
      • Pinch to zoom: Fit videos to your screen size with a simple gesture. You can zoom in or out of any video by pinching on the screen.
        • 
-
      • Casting toggle: Hide or show the Google Cast icon as you wish. You can choose whether you want to see the option to cast your videos to a TV or not.
        • 
-
      • Secondary themes: Choose from dark, black, or other colors for your app theme. You can change the look and feel of your app according to your mood or preference.
        • 
-
      • Picture-in-picture mode: Watch videos in a small window while using other apps (Android Oreo or higher). You can multitask without missing any video content.
        • 
-
      • Repeat videos: Loop any video you like automatically. You can watch the same video over and over again without having to press replay.
        • 
-
      • Preferred resolution and speed: Set your default video quality and playback speed for every video. You can save data and time by choosing the optimal settings for your device and network.
        • 
-
      • Swipe controls: Adjust volume and brightness by swiping on the screen. You don't have to use the buttons or the notification bar to control these settings.
        • 
-
      
-
      Conclusion
      
-
      In conclusion, microG and YouTube Vanced are two amazing apps that can enhance your YouTube experience on Android. They offer a lot of features that you won't find in the official app, such as adblocking, background playback, force HDR mode, override max resolution, pinch to zoom, casting toggle, secondary themes, picture-in-picture mode, repeat videos, preferred resolution and speed, swipe controls, and more. You can download and install them easily from APKPure, a website that provides safe and fast downloads of Android apps. If you are looking for a better way to watch YouTube videos on your device, you should definitely give microG and YouTube Vanced a try!
      
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      FAQs
      
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      Here are some frequently asked questions about microG and YouTube Vanced:
      
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      1. What are some common issues with microG and YouTube Vanced?
        2. 
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        Some of the common issues that users may encounter with microG and YouTube Vanced are:
        
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-
        • Login problems: Sometimes users may not be able to log in to their Google account on microG. This can be fixed by clearing the app data of microG or by reinstalling it.
          • 
-
        • Update problems: Sometimes users may not be able to update microG or YouTube Vanced through the Vanced Manager app. This can be fixed by downloading the latest APK files from APKPure or by clearing the app data of Vanced Manager.
          • 
-
        • Compatibility problems: Sometimes users may not be able to use some features of YouTube Vanced on their device. This can be due to the device model, Android version, or app settings. Users can try to change the settings or use a different device to solve this issue.
          • 
-
        
-
      2. How can I update microG and YouTube Vanced?
        3. 
-
        You can update microG and YouTube Vanced through the Vanced Manager app. You will see a notification when there is a new version available. You can also check for updates manually by tapping on the "Check updates" button in the app. You will need to download and install the new APK files from APKPure or from the app itself.
        
-
      3. How can I uninstall microG and YouTube Vanced?
        4. 
-
        You can uninstall microG and YouTube Vanced by using the Vanced Manager app. You can tap on the "Uninstall" button next to each app and follow the instructions. You can also uninstall them by using the device settings or any other app manager.
        
-
      4. Is microG safe and legal to use?
        5. 
-
        microG is safe and legal to use, as it is a free and open-source project that does not violate any Google terms of service or policies. It does not collect or share any personal data with Google or anyone else. It also does not interfere with any other Google apps or services on your device. However, you should be aware that using microG may void your warranty or affect your device performance, so use it at your own risk.
        
-
      5. Does microG work with other apps that require Google services?
        6. 
-
        microG works with most apps that require Google services, such as Google Maps, Gmail, Google Play Store, etc. However, it may not work with some apps that use advanced features of Google services, such as Google Pay, Google Photos, Google Assistant, etc. You can check the compatibility of microG with different apps on its official website here.
        
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/King Farm Heroes Super Saga A Croptastic Saga with New Game Modes and Levels.md b/spaces/congsaPfin/Manga-OCR/logs/King Farm Heroes Super Saga A Croptastic Saga with New Game Modes and Levels.md
deleted file mode 100644
index 8739933909979a3caccbbe06e327dccf131fd9d7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/King Farm Heroes Super Saga A Croptastic Saga with New Game Modes and Levels.md	
+++ /dev/null
@@ -1,142 +0,0 @@
-
-
      How to Download and Play King Farm Heroes Super Saga on Your Device
      
- 
      Do you love match-3 games that are fun, challenging, and farmtastic? If so, you should try King Farm Heroes Super Saga, the latest game from the makers of Candy Crush Saga. In this game, you will join the Farm Heroes in their quest to defeat Rancid the Raccoon, who is trying to cheat in the Country Show. You will also meet new characters, such as Fidget the squirrel, who will help you collect nuts, flowers, fruits, vegetables, and more. You will also enjoy new game modes, such as switcher, windmill, and nutcracker, that will test your skills and strategy. And of course, you will also encounter super cropsies, which are bigger and better than regular cropsies.
      
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      King Farm Heroes Super Saga is a free-to-play game that you can download and play on your Android, iOS, or Amazon device. In this article, we will show you how to download and play this game on your device. We will also give you some tips and tricks on how to master this game and have more fun.
      
- 
      What is King Farm Heroes Super Saga?
      
- 
      King Farm Heroes Super Saga is a match-3 game that is developed by King, the same company that created Candy Crush Saga, one of the most popular games in the world. King Farm Heroes Super Saga is a sequel to Farm Heroes Saga, which was released in 2013 and has more than 100 million downloads on Google Play Store. In King Farm Heroes Super Saga, you will join forces with the Farm Heroes, a group of cute and brave animals who live on a farm. Your mission is to help them collect cropsies, which are fruits, vegetables, flowers, nuts, and other farm products, by matching them on a board. You will also have to face Rancid the Raccoon, a sneaky and greedy villain who wants to ruin the farm and cheat in the Country Show, a competition where farmers show off their biggest cropsies. You will have to stop him by using your skills and strategy, as well as the help of your friends and new characters.
      
- 
      King Farm Heroes Super Saga is a fun and challenging game that will keep you entertained for hours. It has more than 2000 levels, each with different goals and obstacles. It also has new game modes, such as switcher, where you have to switch cropsies to make matches; windmill, where you have to rotate the board to create matches; and nutcracker, where you have to crack nuts by matching them with other cropsies. It also has super cropsies, which are bigger and more valuable than regular cropsies. You can create them by matching four cropsies in a square, and then match them with any cropsie of the same kind. Super cropsies can help you collect more cropsies and score more points.
      
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- 
      How to Download King Farm Heroes Super Saga on Your Device?
      
- 
      If you want to play King Farm Heroes Super Saga on your device, you will need to download it first. The game is available for Android, iOS, and Amazon devices. Here are the steps to download it on each device:
      
- 
      For Android devices
      
- 
      If you have an Android device, such as a smartphone or a tablet, you can download King Farm Heroes Super Saga from Google Play Store. Google Play Store is the official app store for Android devices, where you can find millions of apps and games for free or for a fee. To download King Farm Heroes Super Saga from Google Play Store, follow these steps:
      
- 
        
-
      1. Go to Google Play Store on your device and tap on the search bar.
        2. 
-
      2. Type "Farm Heroes Super Saga" and tap on the magnifying glass icon.
        3. 
-
      3. You will see a list of results related to your search. Look for the one that says "Farm Heroes Super Saga" and has the logo of the game (a red barn with a yellow star). Tap on it.
        4. 
-
      4. You will see the app page of King Farm Heroes Super Saga, where you can find more information about the game, such as its description, screenshots, ratings, reviews, etc. Tap on the green button that says "Install".
        5. 
-
      5. A pop-up window will appear asking for your permission to access some features of your device, such as storage, network, etc. Tap on "Accept" to grant the permission.
        6. 
-
      6. The download will start automatically and you will see a progress bar showing how much of the game has been downloaded. Wait for the download to finish.
        7. 
-
      7. Once the download is complete, you will see a notification saying "Farm Heroes Super Saga installed". Tap on it to open the app.
        8. 
-
      8. You will see the loading screen of King Farm Heroes Super Saga with its logo and music. Wait for it to load.
        9. 
-
      9. You will see the main menu of King Farm Heroes Super Saga with several options, such as Play, Connect, Settings, etc. Tap on "Play" to start playing the game.
        10. 
-
      
- 
      Congratulations! You have successfully downloaded and installed King Farm Heroes Super Saga on your Android device. Enjoy the game!
      
- 
      For iOS devices
      
- 
      If you have an iOS device, such as an iPhone or an iPad, you can download King Farm Heroes Super Saga from App Store. App Store is the official app store for iOS devices, where you can find millions of apps and games for free or for a fee. To download King Farm Heroes Super Saga from App Store, follow these steps:
      
- 
        
-
      1. Go to App Store on your device and tap on the search icon at the bottom right corner.
        2. 
-
      2. Type "Farm Heroes Super Saga" and tap on the blue button that says "Search".
        3. 
-
      3. You will see a list of results related to your search. Look for the one that says "Farm Heroes Super Saga" and has the logo of the game (a red barn with a yellow star). Tap on it.
        4. 
-
      4. You will see the app page of King Farm Heroes Super Saga, where you can find more information about the game, such as its description, screenshots, ratings, reviews, etc. Tap on the blue button that says "Get".
        5. 
-
      5. A pop-up window will appear asking for your confirmation to download the app. You may need to enter your Apple ID and password or use Touch ID or Face ID to verify your purchase. Tap on "Install" to confirm.
        6. 
-
      6. The download will start automatically and you will see a progress circle showing how much of the game has been downloaded. Wait for the download to finish.
        7. 
-
      7. Once the download is complete, you will see the app icon of King Farm Heroes Super Saga on your home screen. Tap on it to open the app.
        8. 
-
      8. You will see the loading screen of King Farm Heroes Super Saga with its logo and music. Wait for it to load.
        9. 
-
      9. You will see the main menu of King Farm Heroes Super Saga with several options, such as Play, Connect, Settings, etc. Tap on "Play" to start playing the game.
        10. 
-
      
- 
      Congratulations! You have successfully downloaded and installed King Farm Heroes Super Saga on your iOS device. Enjoy the game!
      
- 
      For Amazon devices
      
- 
      If you have an Amazon device, such as a Kindle Fire or a Fire TV, you can download King Farm Heroes Super Saga from Amazon Appstore. Amazon Appstore is the official app store for Amazon devices, where you can find thousands of apps and games for free or for a fee. To download King Farm Heroes Super Saga from Amazon Appstore, follow these steps:
      
- 
        
-
      1. Go to Amazon Appstore on your device and tap on the search bar at the top.
        2. 
-
      2. Type "Farm Heroes Super Saga" and tap on the magnifying glass icon.
        3. 
-
      3. You will see a list of results related to your search. Look for the one that says "Farm Heroes Super Saga" and has the logo of the game (a red barn with a yellow star). Tap on it.
        4. 
-
      4. You will see the app page of King Farm Heroes Super Saga, where you can find more information about the game, such as its description, screenshots, ratings, reviews, etc. Tap on the yellow button that says "Download".
        5. 
-
      5. A pop-up window will appear asking for your permission to access some features of your device, such as storage, network, etc. Tap on "Continue" to grant the permission.
        6. 
-
      6. The download will start automatically and you will see a progress bar showing how much of the game has been downloaded. Wait for the download to finish.
        7. 
-
      7. Once the download is complete, you will see a notification saying "Farm Heroes Super Saga installed". Tap on it to open the app.
        8. 
-
      8. You will see the loading screen of King Farm Heroes Super Saga with its logo and music. Wait for it to load.
        9. 
-
      9. You will see the main menu of King Farm Heroes Super Saga with several options, such as Play, Connect, Settings, etc. Tap on "Play" to start playing the game.
        10. 
-
      
- 
      Congratulations! You have successfully downloaded and installed King Farm Heroes Super Saga on your Amazon device. Enjoy the game!
      
- 
      How to Play King Farm Heroes Super Saga?
      
- 
      Now that you have downloaded and installed King Farm Heroes Super Saga on your device, you are ready to play it. But how do you play this game? What are the rules and features? How do you win? Don't worry, we will explain everything you need to know in this section.
      
- 
      The basic gameplay
      
- 
      The basic gameplay of King Farm Heroes Super Saga is similar to other match-3 games. You have a board filled with different cropsies, such as apples, carrots, strawberries, sunflowers, etc. Your goal is to match 3 or more cropsies of the same kind to collect them and clear the board. You can match cropsies horizontally, vertically, or diagonally. You can also swap two adjacent cropsies to create a match. To swap cropsies, simply tap on one and drag it to the other.
      
- 
      Each level has a different goal that you need to complete before running out of moves or time. The goal can be to collect a certain number of cropsies, to fill a meter with water or juice, to crack nuts or ice, to rescue animals, or to defeat Rancid the Raccoon. You can see the goal at the top of the screen, along with the number of moves or time left. You can also see your score and the stars you have earned. To earn stars, you need to score a certain amount of points in each level. The more points you score, the more stars you get. Stars are important because they unlock new episodes and rewards in the game.
      
- 
      If you complete the level goal and still have moves or time left, you will enter Hero Mode. In Hero Mode, you can collect extra cropsies and beans by making matches. Beans are useful because they can help you buy boosters or tickets in the game. Hero Mode is a great way to boost your score and earn more stars.
      
- 
      If you fail to complete the level goal, you will lose a life. You have five lives in total, and they regenerate over time. You can also ask your friends for lives or buy them with real money. If you run out of lives, you will have to wait until they are refilled or use other methods to get more.
      
- 
      The new features
      
- 
      King Farm Heroes Super Saga is not just a typical match-3 game. It also has some new features that make it more fun and challenging. Here are some of them:
      
- 
        
-
      • Super cropsies: These are bigger and better than regular cropsies. You can create them by matching four cropsies in a square. Super cropsies have more value and can be matched with any cropsie of the same kind. For example, a super apple can be matched with any apple on the board. Super cropsies can help you collect more cropsies and score more points.
        • 
-
      • Special effects and boosters: These are created by matching cropsies in different shapes, such as L-shape, T-shape, or 5-in-a-row. They can create various effects and boosters on the board, such as wind, water splash, firecracker, etc. These effects and boosters can help you clear more cropsies, break obstacles, or create more matches.
        • 
-
      • Rancid the Raccoon: He is the main antagonist of the game and he will try to ruin your progress by throwing cans at you or stealing your cropsies. You will encounter him in some levels, where you will have to defeat him by collecting enough cropsies before he fills his meter. You can also use boosters or power-ups to damage him more.
        • 
-
      • Country Show: This is a new feature where you can compete with other players to grow the biggest cropsie and win prizes. You can enter the Country Show by tapping on the trophy icon on the map. You will need tickets to enter, which you can buy with beans or real money. In the Country Show, you will have a limited time to grow your cropsie by making matches on a special board. The bigger your cropsie grows, the higher your rank will be. At the end of each round, you will receive rewards based on your rank.
        • 
-
      
- 
      The tips and tricks
      
- 
      King Farm Heroes Super Saga is a game that requires skill and strategy to master. Here are some tips and tricks that can help you improve your gameplay and have more fun:
      
- 
        
-
      • Plan your moves ahead and look for opportunities to make big matches or combos. Big matches or combos can create super cropsies, special effects, or boosters that can help you clear more cropsies and score more points.
        • 
-
      • Focus on the level objectives and don't waste moves on unnecessary cropsies. Some levels may have different types of cropsies on the board, but only some of them may count towards your goal. For example, if your goal is to collect apples and carrots, don't bother with strawberries or sunflowers.
        • 
-
      • Use Hero Mode to collect extra cropsies and beans when you have moves left after completing a level. Hero Mode is a great way to boost your score and earn more stars.
        • 
-
      • Connect to Facebook to save your progress, compare scores with your friends, and get free lives and boosters. By connecting to Facebook, you can sync your game across different devices and never lose your progress. You can also see how your friends are doing in the game and challenge them to beat your scores. You can also send and receive lives and boosters with your friends, which can help you in the game.
        • 
-
      
- 
      Conclusion
      
- 
      King Farm Heroes Super Saga is a fun and challenging match-3 game that will keep you entertained for hours. You can download and play it for free on your Android, iOS, or Amazon device. You can also enjoy new features, such as super cropsies, special effects, Rancid the Raccoon, and Country Show. You can also use some tips and tricks to improve your gameplay and have more fun. If you love match-3 games and farm adventures, you should definitely try King Farm Heroes Super Saga. It is a game that will make you smile and relax.
      
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      Here are some frequently asked questions about King Farm Heroes Super Saga:
      
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      1. Q: How can I get more boosters in the game?
        
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      2. Q: How can I get more beans in the game?
        
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      3. Q: How can I unlock new episodes in the game?
        
-A: You can unlock new episodes in the game by earning enough stars in the previous episodes, buying tickets with beans or real money, or asking your friends for tickets.
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      4. Q: How can I contact the support team of the game?
        
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      5. Q: How can I update the game to the latest version?
        
-A: You can update the game to the latest version by going to Google Play Store, App Store, or Amazon Appstore on your device and checking for updates. You can also enable automatic updates on your device settings to make sure you always have the latest version of the game.
        6. 
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/The Secret of Clash Royale Elixir Infinito How to Win Every Duel.md b/spaces/congsaPfin/Manga-OCR/logs/The Secret of Clash Royale Elixir Infinito How to Win Every Duel.md
deleted file mode 100644
index 2fefd1804fba296e9c9d8a418ca0e4f17739d0a7..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/The Secret of Clash Royale Elixir Infinito How to Win Every Duel.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-
      
-
      Clash Royale Elixir Infinito Download: What Is It and How to Get It?
      
-
      If you are a fan of Clash Royale, you might have heard of a mod apk called Clash Royale Elixir Infinito. This is a modified version of the game that claims to offer unlimited elixir and other benefits to the players. But what is it exactly and how can you get it? In this article, we will explain everything you need to know about Clash Royale Elixir Infinito, including its features, risks, and alternatives.
      
- 
      What Is Clash Royale?
      
-
      Clash Royale is a free-to-play real-time strategy video game developed and published by Supercell. The game combines elements from collectible card games, tower defense, and multiplayer online battle arena. The game was released globally on March 2, 2016.
      
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      Clash Royale is a tower rush video game where you fight against other online players in frantic duels. The objective is to destroy more towers than your opponent, with each destroyed tower being represented as a "crown". Destruction of the opponent's "King's Tower" results in an instantaneous "three-crown" victory. Matches last three minutes, and if both players have an equal number of crowns (or none at all) at that point, the match continues into a two-minute overtime period.
      
-
      In Clash Royale, you can collect and upgrade dozens of cards featuring the Clash of Clans troops, spells and defenses you know and love, as well as the Royales: Princes, Knights, Baby Dragons and more. You can build your own card decks, have 1v1 or 2v2 fights, join or create clans, participate in special events and tournaments, and climb the ladder of arenas and leagues.
      
-
      Clash Royale is one of the most popular mobile games in the world, with over 100 million downloads on Google Play Store alone 
      What Is Elixir Infinito?
      
-
      Elixir Infinito is a mod apk that modifies the original Clash Royale game to give the players unlimited elixir and other benefits. Elixir is the main resource in Clash Royale, which is used to deploy cards on the battlefield. Normally, elixir is generated at a constant rate of one unit per 2.8 seconds (or 1.4 seconds during the last 60 seconds of the match). However, with Elixir Infinito, you can have as much elixir as you want, without any limit or delay.
      
-
      Elixir Infinito is not an official version of Clash Royale, but a hacked one that is created by third-party developers. It is not available on the official app stores, but only on some websites that offer mod apks. To use Elixir Infinito, you need to download and install it on your device, replacing the original Clash Royale game. You also need to create a new account and play on a separate server, as you cannot use your existing account or play with other players who use the official version.
      
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      How to Download and Install Clash Royale Elixir Infinito?
      
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      If you want to try Clash Royale Elixir Infinito, you need to follow these steps:
      
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      1. Find a reliable website that offers the mod apk. There are many websites that claim to provide Clash Royale Elixir Infinito, but some of them may be fake or malicious. You need to be careful and do some research before downloading anything from unknown sources. Some of the websites that we found are , , and , but we cannot guarantee their safety or quality.
        2. 
-
      2. Download the mod apk file from the website. The file size may vary depending on the website, but it should be around 100 MB. Make sure you have enough storage space on your device and a stable internet connection.
        3. 
-
      3. Enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps that are not from the official app stores.
        4. 
-
      4. Locate the downloaded mod apk file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to complete.
        5. 
-
      5. Launch the mod apk and enjoy Clash Royale Elixir Infinito. You will need to create a new account and agree to the terms and conditions of the mod apk. You will also see a different interface and server than the original game.
        6. 
-
      
- 
      What Are the Features of Clash Royale Elixir Infinito?
      
-
      Clash Royale Elixir Infinito offers many features that are not available in the original game, such as:
      
-
        
-
      • Unlimited elixir: You can deploy any card at any time, without worrying about elixir cost or regeneration.
        • 
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      • Unlimited gold: You can buy any card or upgrade any card without spending any gold.
        • 
-
      • Unlimited gems: You can open any chest or buy any item without spending any gems.
        • 
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      • Unlimited chests: You can get any chest at any time, without waiting for timers or slots.
        • 
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      • Unlimited emotes: You can use any emote at any time, without unlocking them or buying them.
        • 
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      • Unlimited decks: You can create and save as many decks as you want, without any limit or restriction.
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-
      
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      What Are the Risks of Using Clash Royale Elixir Infinito?
      
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      While Clash Royale Elixir Infinito may sound tempting, it also comes with many risks that you should be aware of, such as:
      
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      • Account ban: Using a mod apk is against the terms of service of Supercell, the developer of Clash Royale. If they detect that you are using a mod apk, they may ban your account permanently and prevent you from playing the game again.
        • 
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      • Malware: Downloading and installing a mod apk from an unknown source may expose your device to malware, such as viruses, spyware, or ransomware. These malicious programs may harm your device, steal your personal information, or extort money from you.
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      • Data loss: Installing a mod apk may overwrite or delete some of your data on your device, such as photos, videos, contacts, or messages. You may also lose your progress and achievements in the original game if you uninstall it or switch back to it.
        • 
-
      • Unfair gameplay: Using a mod apk may give you an unfair advantage over other players who use the official version of the game. This may ruin the fun and challenge of the game for both you and your opponents. You may also face other players who use the same mod apk or other cheats, making the game boring and frustrating.
        • 
-
      
- 
      How to Manage Your Elixir in Clash Royale?
      
-
      If you want to play Clash Royale without using a mod apk, you need to learn how to manage your elixir wisely. Elixir is the key to winning or losing a match, as it determines how many cards you can play and how you can counter your opponent's moves. Here are some tips on how to manage your elixir in Clash Royale:
      
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      Never Overspend Elixir / Having Elixir Advantage
      
-
      One of the most important rules of elixir management is to never overspend elixir. This means that you should not play more cards than you need to, or play cards that cost more elixir than your opponent's cards. For example, if your opponent plays a 3-elixir Knight, you should not play a 5-elixir Prince to counter it, as you will lose 2 elixir in the process. Instead, you should play a 3-elixir Mini P.E.K.K.A or a 4-elixir Valkyrie, which can deal with the Knight and still have some health left for a counterattack.
      
-
      The goal of elixir management is to have an elixir advantage over your opponent. This means that you have more elixir than your opponent at any given time, which allows you to play more cards or stronger cards than them. You can gain elixir advantage by making positive elixir trades (see below), using cheap cycle cards (such as Skeletons or Ice Spirit), or using the Elixir Collector (see below).
      
- 
      Act and React Wisely / More Options = More Wins
      
-
      Another important rule of elixir management is to act and react wisely. This means that you should not play your cards randomly or impulsively, but rather think about the best timing and placement for them. You should also consider the possible outcomes and consequences of your actions and reactions, and plan ahead for them.
      
-
      For example, if you have a Hog Rider in your hand, you should not play it immediately at the start of the match, as your opponent may have a building or a swarm card to counter it easily. Instead, you should wait for your opponent to make the first move, or cycle some cheap cards to see what they have in their hand. You should also look for opportunities to play your Hog Rider when your opponent is low on elixir or has no good counters in their hand.
      
-
      The key to acting and reacting wisely is to have more options than your opponent. This means that you should have a balanced deck that can deal with different types of threats and situations. You should also have a good knowledge of the game's mechanics, such as card interactions, card rotations, and card synergies. The more options you have, the more chances you have to win.
      
- 
      Use Spells Wisely / Positive Elixir Trades
      
-
      Spells are powerful cards that can affect a large area of the battlefield, such as damaging enemy troops or towers, slowing down or stunning enemy movements, or boosting your own troops or towers. However, spells are also expensive cards that cost a lot of elixir, so you need to use them wisely.
      
-
      The main principle of using spells wisely is to make positive elixir trades. This means that you should use spells that cost less elixir than the value of the enemy troops or towers that they affect. For example, if your opponent plays a 5-elixir Minion Horde, you can use a 2-elixir Zap or a 3-elixir Arrows to clear them out, gaining 3 or 2 elixir advantage respectively. However, if your opponent plays a 4-elixir Musketeer, you should not use a 6-elixir Fireball to kill it, as you will lose 2 elixir in the process.
      
-
      You can also use spells to gain elixir advantage by damaging enemy towers along with enemy troops. For example, if your opponent plays a 4-elixir Wizard near their tower, you can use a 4-elixir Fireball to kill the Wizard and deal some damage to the tower, breaking even on elixir but gaining some tower damage. However, you should not use spells on enemy towers alone, unless you are sure that you can finish them off or force them into overtime.
      
- 
      Use Troop Interactions Wisely / Death for Death, Death for Counterpush, Death for Answer
      
-
      Troops are the main cards that you use to fight on the battlefield. They have different attributes such as health , damage, speed, range, and role. They also have different interactions with each other, such as countering, supporting, or trading. You need to use troop interactions wisely to gain elixir advantage and win the game.
      
-
      The main principle of using troop interactions wisely is to follow these three rules:
      
-
        
-
      • Death for Death: This means that you should use troops that can kill the enemy troops with the same or less elixir cost. For example, if your opponent plays a 4-elixir Hog Rider, you can use a 4-elixir Inferno Tower or a 3-elixir Skeleton Army to stop it, breaking even or gaining 1 elixir advantage respectively.
        • 
-
      • Death for Counterpush: This means that you should use troops that can not only kill the enemy troops, but also survive and counterattack. For example, if your opponent plays a 5-elixir Giant, you can use a 4-elixir Mini P.E.K.K.A or a 5-elixir Prince to kill it and then push the other lane, gaining 1 elixir advantage or forcing your opponent to spend more elixir to defend.
        • 
-
      • Death for Answer: This means that you should use troops that can force your opponent to answer them or lose their tower. For example, if your opponent plays a 3-elixir Goblin Barrel, you can use a 2-elixir Log or a 3-elixir Barbarian Barrel to clear them out and then play a 6-elixir Golem or a 7-elixir Royal Giant at the bridge, forcing your opponent to spend more elixir to stop them or take massive damage.
        • 
-
      
- 
      Use Buildings Wisely / Defensive Structures, Elixir Collector, Spawners
      
-
      Buildings are special cards that can be placed on your side of the battlefield. They have different functions such as defending your towers, generating extra elixir, or spawning more troops. You need to use buildings wisely to gain elixir advantage and win the game.
      
-
      The main principle of using buildings wisely is to follow these three rules:
      
-
        
-
      • Defensive Structures: These are buildings that can attack enemy troops or distract them from your towers. Examples are Cannon, Tesla, Inferno Tower, Bomb Tower, Mortar, and X-Bow. You should use these buildings to protect your towers from enemy attacks, especially from tanks and win conditions. You should also place them in the optimal position and timing to maximize their effectiveness.
        • 
-
      • Elixir Collector: This is a building that can generate extra elixir over time. It costs 6 elixir and produces 8 elixir in total, giving you a net gain of 2 elixir. You should use this building to gain elixir advantage over your opponent, especially in the early game or when you have an expensive deck. You should also place it behind your towers or in front of your King Tower to protect it from enemy spells or troops.
        • 
-
      • Spawners: These are buildings that can spawn troops periodically. Examples are Goblin Hut, Barbarian Hut, Furnace, and Tombstone. You should use these buildings to create constant pressure on your opponent's lane, forcing them to spend elixir to defend or take chip damage. You should also place them near the river or in front of your towers to maximize their range and coverage.
        • 
-
      
- 
      Conclusion
      
-
      Clash Royale Elixir Infinito is a mod apk that offers unlimited elixir and other benefits to the players who use it. However, it also comes with many risks such as account ban, malware, data loss, and unfair gameplay. If you want to play Clash Royale without using a mod apk, you need to learn how to manage your elixir wisely by following some tips on elixir management. By doing so, you can enjoy the game more and improve your skills and strategies.
      
-
      If you liked this article, please share it with your friends and leave a comment below. Also, don't forget to check out our other articles on Clash Royale and other topics. Thank you for reading!
      
- 
      FAQs
      
-
      Here are some frequently asked questions about Clash Royale Elixir Infinito and elixir management:
      
- 
      Q: Is Clash Royale Elixir Infinito safe?
      
-
      A: No, Clash Royale Elixir Infinito is not safe. It is a mod apk that is created by third-party developers who may have malicious intentions. It may contain malware that can harm your device or steal your personal information. It may also cause your account to be banned by Supercell or lose your data if you uninstall it or switch back to the original game.
      
- 
       Q: How can I get Clash Royale Elixir Infinito?
      
-
      A: To get Clash Royale Elixir Infinito, you need to download and install the mod apk from a website that offers it. However, we do not recommend doing so, as it may expose your device to malware or cause your account to be banned. Instead, you should play the official version of Clash Royale and learn how to manage your elixir wisely.
      
- 
      Q: How can I improve my elixir management in Clash Royale?
      
-
      A: To improve your elixir management in Clash Royale, you need to follow some tips such as never overspend elixir, act and react wisely, use spells wisely, use troop interactions wisely, and use buildings wisely. You can also watch some videos or read some guides from professional players or experts who can teach you more about elixir management.
      
- 
      Q: What are the best cards for elixir management in Clash Royale?
      
-
      A: There is no definitive answer to this question, as different cards may suit different play styles, decks, and situations. However, some of the cards that are generally considered good for elixir management are cheap cycle cards (such as Skeletons, Ice Spirit, or Bats), versatile cards (such as Knight, Musketeer, or Electro Wizard), and value cards (such as Fireball, Poison, or Miner). You should also experiment with different cards and find out what works best for you.
      
- 
      Q: What are the best decks for elixir management in Clash Royale?
      
-
      A: Again, there is no definitive answer to this question, as different decks may suit different play styles, arenas, and metas. However, some of the decks that are generally considered good for elixir management are cycle decks (such as Hog Cycle, Miner Cycle, or Mortar Cycle), control decks (such as P.E.K.K.A Control, Graveyard Control, or X-Bow Control), and beatdown decks (such as Golem Beatdown, Giant Beatdown, or Lava Hound Beatdown). You should also experiment with different decks and find out what works best for you.
      
- 
      Q: Where can I find more information about Clash Royale Elixir Infinito and elixir management?
      
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      A: You can find more information about Clash Royale Elixir Infinito and elixir management on various websites, blogs, forums, social media platforms, or YouTube channels that are dedicated to Clash Royale. Some of the sources that we recommend are , , , and . However, you should always be careful and verify the information that you find online before trusting it or applying it.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Tomb of the Mask APK How to Download and Play the Arcade Game on Android.md b/spaces/congsaPfin/Manga-OCR/logs/Tomb of the Mask APK How to Download and Play the Arcade Game on Android.md
deleted file mode 100644
index 920fd5a59f049bf02d49bdb68a623be4724cb26b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Tomb of the Mask APK How to Download and Play the Arcade Game on Android.md	
+++ /dev/null
@@ -1,118 +0,0 @@
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      APK Download Tomb of the Mask
      
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      If you are looking for a fun and addictive arcade game that will challenge your reflexes and skills, you might want to try Tomb of the Mask. This game is available on Google Play Store, but you can also download it as an APK file and install it on your Android device. In this article, we will explain what Tomb of the Mask is, what are its features, how to play it, and how to download it as an APK file.
      
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      What is Tomb of the Mask?
      
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      Tomb of the Mask is an arcade game with an infinite procedurally generated vertical labyrinth. Seeking for adventure, you get into a tomb where you find a strange mask. You put it on and suddenly realize that you can now climb walls easily and swiftly. And that's when all the fun begins.
      
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      You will face a variety of enemies, traps, and obstacles as you explore the tomb and collect coins, power-ups, and stars. You can also unlock different masks with special abilities and customize your character. The game has two modes: Adventure and Arcade. In Adventure mode, you have to complete levels with different objectives and challenges. In Arcade mode, you have to survive as long as possible and reach the highest score.
      
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      Features of Tomb of the Mask
      
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      • Two modes: Adventure and Arcade: challenge yourself in different ways.
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      • Leaderboards and achievements: compete with your friends and other players.
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      How to play Tomb of the Mask
      
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      The game is very simple to play, but hard to master. You just have to swipe on the screen to move your character in any direction. You can also swipe diagonally to move faster. You have to avoid enemies, spikes, lasers, saws, and other traps that will kill you instantly. You can also use walls and ceilings to climb and reach higher places. You have to collect coins, power-ups, and stars along the way. Coins can be used to buy new masks and characters. Power-ups can give you temporary advantages, such as invincibility, magnetism, or speed boost. Stars can unlock new levels in Adventure mode.
      
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      Risks of APK Download
      
-
        
-
      • You may download malicious or infected files that can harm your device or data.
        • 
-
      • You may download fake or counterfeit apps that can steal your personal information or money.
        • 
-
      • You may download apps that are not updated or supported by the developers.
        • 
-
      • You may download apps that violate the terms and conditions of Google Play Store or other platforms.
        • 
-
      • You may lose the warranty or support of your device or app provider.
        • 
-
      
- 
      How to download APK files safely
      
-
      While APK Download can be beneficial, it can also be risky if you are not careful. Here are some tips to download APK files safely and avoid potential problems:
      
-
        
-
      • Only download APK files from trusted and reputable sources, such as official websites, blogs, forums, or APK downloader apps.
        • 
-
      • Check the ratings, reviews, comments, and feedback of the APK files and the sources before downloading them.
        • 
-
      • Scan the APK files with a reliable antivirus or malware scanner before installing them on your device.
        • 
-
      • Enable the Unknown Sources option in your device settings to allow the installation of APK files from outside Google Play Store.
        • 
-
      • Disable the Unknown Sources option after installing the APK files to prevent unauthorized installations.
        • 
-
      • Backup your device data and settings before installing APK files in case something goes wrong.
        • 
-
      
- 
      How to download Tomb of the Mask APK file
      
-
      If you want to download Tomb of the Mask as an APK file and enjoy its features on your Android device, you can follow these simple steps:
      
- 
      Step 1: Find a reliable APK downloader site
      
-
      The first step is to find a reliable and reputable site that offers APK downloader services. You can use Google or any other search engine to look for such sites. Some examples of popular and trusted sites are APKPure, APKMirror, Uptodown, and Aptoide.
      
- 
      Step 2: Search for Tomb of the Mask APK file
      
-
      The next step is to search for Tomb of the Mask APK file on the site you have chosen. You can use the search bar or browse through the categories and genres to find it. You can also check the latest, most popular, or most downloaded APK files on the site.
      
- 
      Step 3: Download and install Tomb of the Mask APK file
      
-
      The final step is to download and install Tomb of the Mask APK file on your device. You can click on the download button or link on the site and wait for the file to be downloaded. You can also scan the QR code if available. After downloading the file, you can open it and follow the instructions to install it on your device. You may need to enable the Unknown Sources option in your device settings before installing it.
      
- 
      Conclusion
      
-
      Tomb of the Mask is a fun and addictive arcade game that will keep you entertained for hours. You can download it as an APK file and install it on your Android device easily and safely. Just follow the steps we have provided in this article and enjoy playing Tomb of the Mask on your device.
      
- 
      FAQs
      
-
        
-
      • What is the size of Tomb of the Mask APK file?
        • 
-
        The size of Tomb of the Mask APK file may vary depending on the site you download it from and the version you choose. However, it is usually around 50 MB.
        
- 
      • What are the requirements for Tomb of the Mask APK file?
        • 
-
        Tomb of the Mask APK file requires Android 5.0 or higher and a minimum of 1 GB of RAM.
        
- 
      • Is Tomb of the Mask APK file safe and legal?
        • 
-
        Tomb of the Mask APK file is safe and legal as long as you download it from a trusted and reputable source. However, you should always be careful and cautious when downloading any APK file from online sources.
        
- 
      • Can I play Tomb of the Mask offline?
        • 
-
        Yes, you can play Tomb of the Mask offline without an internet connection. However, some features may not work properly or be available offline.
        
- 
      • Can I play Tomb of the Mask with friends?
        • 
-
        Yes, you can play Tomb of the Mask with friends by connecting your Facebook account or using Google Play Games. You can also compete with other players on leaderboards and achievements.
        
-
       197e85843d
      
-
      
-
      
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/trainers/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/trainers/__init__.py
deleted file mode 100644
index 8307cd31c2139db0ce581637403b3a95dc8cae59..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/trainers/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import logging
-import torch
-from annotator.lama.saicinpainting.training.trainers.default import DefaultInpaintingTrainingModule
-
-
-def get_training_model_class(kind):
-    if kind == 'default':
-        return DefaultInpaintingTrainingModule
-
-    raise ValueError(f'Unknown trainer module {kind}')
-
-
-def make_training_model(config):
-    kind = config.training_model.kind
-    kwargs = dict(config.training_model)
-    kwargs.pop('kind')
-    kwargs['use_ddp'] = config.trainer.kwargs.get('accelerator', None) == 'ddp'
-
-    logging.info(f'Make training model {kind}')
-
-    cls = get_training_model_class(kind)
-    return cls(config, **kwargs)
-
-
-def load_checkpoint(train_config, path, map_location='cuda', strict=True):
-    model = make_training_model(train_config).generator
-    state = torch.load(path, map_location=map_location)
-    model.load_state_dict(state, strict=strict)
-    return model
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/ops/wrappers.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/ops/wrappers.py
deleted file mode 100644
index 0ed9a0cb8d7c0e0ec2748dd89c652756653cac78..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/ops/wrappers.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import warnings
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def resize(input,
-           size=None,
-           scale_factor=None,
-           mode='nearest',
-           align_corners=None,
-           warning=True):
-    if warning:
-        if size is not None and align_corners:
-            input_h, input_w = tuple(int(x) for x in input.shape[2:])
-            output_h, output_w = tuple(int(x) for x in size)
-            if output_h > input_h or output_w > output_h:
-                if ((output_h > 1 and output_w > 1 and input_h > 1
-                     and input_w > 1) and (output_h - 1) % (input_h - 1)
-                        and (output_w - 1) % (input_w - 1)):
-                    warnings.warn(
-                        f'When align_corners={align_corners}, '
-                        'the output would more aligned if '
-                        f'input size {(input_h, input_w)} is `x+1` and '
-                        f'out size {(output_h, output_w)} is `nx+1`')
-    return F.interpolate(input, size, scale_factor, mode, align_corners)
-
-
-class Upsample(nn.Module):
-
-    def __init__(self,
-                 size=None,
-                 scale_factor=None,
-                 mode='nearest',
-                 align_corners=None):
-        super(Upsample, self).__init__()
-        self.size = size
-        if isinstance(scale_factor, tuple):
-            self.scale_factor = tuple(float(factor) for factor in scale_factor)
-        else:
-            self.scale_factor = float(scale_factor) if scale_factor else None
-        self.mode = mode
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        if not self.size:
-            size = [int(t * self.scale_factor) for t in x.shape[-2:]]
-        else:
-            size = self.size
-        return resize(x, size, None, self.mode, self.align_corners)
diff --git a/spaces/cozyanduofen/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/cozyanduofen/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
-  const isAtBottom = useAtBottom()
-
-  return (
-    
-  )
-}
diff --git a/spaces/crawly/White-box-Cartoonization/app.py b/spaces/crawly/White-box-Cartoonization/app.py
deleted file mode 100644
index c55ced56bd87a85f59d1c8ef84b7eca87422720f..0000000000000000000000000000000000000000
--- a/spaces/crawly/White-box-Cartoonization/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-import argparse
-import functools
-import os
-import pathlib
-import sys
-from typing import Callable
-import uuid
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-
-from io import BytesIO
-from wbc.cartoonize import Cartoonize
-
-ORIGINAL_REPO_URL = 'https://github.com/SystemErrorWang/White-box-Cartoonization'
-TITLE = 'SystemErrorWang/White-box-Cartoonization'
-DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
-
-"""
-ARTICLE = """
-
-"""
-
-SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
-def compress_UUID():
-    '''
-    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
-    包括:[0-9a-zA-Z\-_]共64个
-    长度:(32-2)/3*2=20
-    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
-    :return:String
-    '''
-    row = str(uuid.uuid4()).replace('-', '')
-    safe_code = ''
-    for i in range(10):
-        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
-        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
-    safe_code = safe_code.replace('-', '')
-    return safe_code
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    parser.add_argument('--allow-screenshot', action='store_true')
-    return parser.parse_args()
-
-def run(
-        image,
-        cartoonize : Cartoonize
-) -> tuple[PIL.Image.Image]:
-
-    out_path = compress_UUID()+'.png'
-    cartoonize.run_sigle(image.name, out_path)
-
-    return PIL.Image.open(out_path)
-
-
-def main():
-    gr.close_all()
-
-    args = parse_args()
-
-    cartoonize = Cartoonize(os.path.join(os.path.dirname(os.path.abspath(__file__)),'wbc/saved_models/'))
-
-    func = functools.partial(run, cartoonize=cartoonize)
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input Image'),
-        ],
-        [
-            gr.outputs.Image(
-                type='pil',
-                label='Result'),
-        ],
-        # examples=examples,
-        theme=args.theme,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        allow_screenshot=args.allow_screenshot,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/cristalcorp/CompVis-stable-diffusion-v1-4/README.md b/spaces/cristalcorp/CompVis-stable-diffusion-v1-4/README.md
deleted file mode 100644
index 9f58b7bb6c2b48125cd2e3cbeb4491bf3bfd6801..0000000000000000000000000000000000000000
--- a/spaces/cristalcorp/CompVis-stable-diffusion-v1-4/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CompVis Stable Diffusion V1 4
-emoji: 😻
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/daarumadx/bot/api.py b/spaces/daarumadx/bot/api.py
deleted file mode 100644
index fff13c39b4bd927544dff0607c4723c99c9eb5ed..0000000000000000000000000000000000000000
--- a/spaces/daarumadx/bot/api.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from fastapi import FastAPI, UploadFile
-import uvicorn
-import subprocess
-import os
-
-from fastapi.responses import FileResponse, HTMLResponse
-
-app = FastAPI()
-
-@app.get('/')
-def root():
-    return { 'response': 'System is up and ready! Try /help for Help!' }
-
-@app.get('/help')
-def help():
-    return HTMLResponse(
-        '''
-API Routes -

      
-
-/ - Root [GET]
      
-/help - Get Help/Info on all API Routes [GET]
      
-/convert - send a file as image to convert it to it's nude form and return it back! [POST]
-'''
-    )
-
-@app.post('/convert')
-def convert(file: UploadFile):
-    x = open(f'./imgs/{file.filename}', 'wb')
-    x.write(file.file.read())
-    x.flush()
-    x.close()
-    print(f'[{file.filename}] - STARTING')
-    subprocess.run(rf'python3 ./main.py run -i "../imgs/{file.filename}" -o "../out/{file.filename}" --cpu --auto-resize', stdout=subprocess.DEVNULL, cwd='./src', shell=True)
-    print(f'[{file.filename}] - COMPLETED')
-    return FileResponse(fr'./out/{file.filename}')
-
-if __name__ == '__main__':
-    # if not os.path.exists('./src/checkpoints/cm.lib'):
-    #     subprocess.run('python3 ./main.py checkpoints download', shell=True, cwd='./src')
-    uvicorn.run('api:app', host='127.0.0.1', port=int(os.getenv('PORT', 8000)))
\ No newline at end of file
diff --git a/spaces/dafqi/indo_twitter_sentiment_app/app.py b/spaces/dafqi/indo_twitter_sentiment_app/app.py
deleted file mode 100644
index 8d4af5e0957f1a10a65df5891fcb00393cdc1924..0000000000000000000000000000000000000000
--- a/spaces/dafqi/indo_twitter_sentiment_app/app.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import streamlit as st
-
-import streamlit as st 
-import pandas as pd
-import script.functions as fn
-import plotly.express as px
-import matplotlib.pyplot as plt
-# import text_proc in script folder
-import script.text_proc as tp
-from sentence_transformers import SentenceTransformer
-
-
-st.set_page_config(
-    page_title="twitter sentiment analysis",
-    page_icon="👋",
-)
-
-st.sidebar.markdown("📚 Twitter Sentiment Analysis App")
-
-# Load data
-# add tiwtter logo inside title 
-st.markdown("
      📚 Twitter Sentiment Analysis App
      ", unsafe_allow_html=True)
-st.write("Aplikasi sederhana untuk melakukan analisis sentimen terhadap tweet yang diinputkan dan mengekstrak topik dari setiap sentimen.")
-# streamlit selectbox simple and advanced
-
-sb1,sb2 = st.columns([2,4])
-with sb1:
-    option = st.selectbox('Pilih Mode Pencarian',('Simple','Advanced'))
-with sb2:
-    option_model = st.selectbox('Pilih Model',("IndoBERT (Accurate,Slow)",'Naive Bayes','Logistic Regression (Less Accurate,Fast)','XGBoost','Catboost','SVM','Random Forest'))
-
-if option == 'Simple':
-# create col1 and col2
-    col1, col2 = st.columns([3,2])
-    with col1:
-        input = st.text_input("Masukkan User/Hastag", "@traveloka")
-    with col2:
-        length = st.number_input("Jumlah Tweet", 10, 500, 100)
-else :
-    col1, col2 = st.columns([3,1])
-    with col1:
-        input = st.text_input("Masukkan Parameter Pencarian", "(to:@traveloka AND @traveloka) -filter:links filter:replies lang:id")
-    with col2:
-        length = st.number_input("Jumlah Tweet", 10, 500, 100)
-    st.caption("anda bisa menggunakan parameter pencarian yang lebih spesifik, parameter ini sama dengan paremeter pencarian di twitter")
-
-submit = st.button("🔍Cari Tweet")
-
-st.caption("semakin banyak tweet yang diambil maka semakin lama proses analisis sentimen")
-
-if submit:
-    with st.spinner('Mengambil data dari twitter... (1/2)'):
-        df = fn.get_tweets(input, length, option)
-    with st.spinner('Melakukan Prediksi Sentimen... (2/2)'):
-        df = fn.get_sentiment(df,option_model)
-        df.to_csv('assets/data.csv',index=False)
-    # plot
-    st.write("Preview Dataset",unsafe_allow_html=True)
-    def color_sentiment(val):
-        color_dict = {"positif": "#00cc96", "negatif": "#ef553b","netral": "#636efa"}
-        return f'color: {color_dict[val]}'
-    st.dataframe(df.style.applymap(color_sentiment, subset=['sentiment']),use_container_width=True,height = 200)
-    # st.dataframe(df,use_container_width=True,height = 200)
-    st.write ("Jumlah Tweet: ",df.shape[0])
-    # download datasets 
-    
-
-    st.write("
      📊 Analisis Sentimen
      ",unsafe_allow_html=True)
-    col_fig1, col_fig2 = st.columns([4,3])
-    with col_fig1:
-         with st.spinner('Sedang Membuat Grafik...'):
-            st.write("Jumlah Tweet Tiap Sentiment",unsafe_allow_html=True)
-            fig_1 = fn.get_bar_chart(df)
-            st.plotly_chart(fig_1,use_container_width=True,theme="streamlit")
-    with col_fig2:
-        st.write("Wordcloud Tiap Sentiment",unsafe_allow_html=True)
-        tab1,tab2,tab3 = st.tabs(["😞 negatif","😐 netral","😃 positif"])
-        with tab1:
-            wordcloud_pos = tp.get_wordcloud(df,"negatif")
-            fig = plt.figure(figsize=(10, 5))
-            plt.imshow(wordcloud_pos, interpolation="bilinear")
-            plt.axis("off")
-            st.pyplot(fig)
-        with tab2:
-            wordcloud_neg = tp.get_wordcloud(df,"netral")
-            fig = plt.figure(figsize=(10, 5))
-            plt.imshow(wordcloud_neg, interpolation="bilinear")
-            plt.axis("off")
-            st.pyplot(fig)
-        with tab3:
-            wordcloud_net = tp.get_wordcloud(df,"positif")
-            fig = plt.figure(figsize=(10, 5))
-            plt.imshow(wordcloud_net, interpolation="bilinear")
-            plt.axis("off")
-            st.pyplot(fig)
-    st.write("
      ✨ Sentiment Clustering
      ",unsafe_allow_html=True)
-    @st.experimental_singleton
-    def load_sentence_model():
-        embedding_model = SentenceTransformer('sentence_bert')
-        return embedding_model
-    embedding_model = load_sentence_model()
-    tab4,tab5,tab6 = st.tabs(["😞 negatif","😐 netral","😃 positif"])
-    with tab4:
-        if len(df[df["sentiment"]=="negatif"]) < 11:
-            st.write("Tweet Terlalu Sedikit, Tidak dapat melakukan clustering")
-            st.write(df[df["sentiment"]=="negatif"])
-        else:
-            with st.spinner('Sedang Membuat Grafik...(1/2)'):
-                text,data,fig = tp.plot_text(df,"negatif",embedding_model)
-                st.plotly_chart(fig,use_container_width=True,theme=None)
-            with st.spinner('Sedang Mengekstrak Topik... (2/2)'):
-                fig,topic_modelling = tp.topic_modelling(text,data)
-                st.plotly_chart(fig,use_container_width=True,theme="streamlit")
-    with tab5:
-        if len(df[df["sentiment"]=="netral"]) < 11:
-            st.write("Tweet Terlalu Sedikit, Tidak dapat melakukan clustering")
-            st.write(df[df["sentiment"]=="netral"])
-        else:
-            with st.spinner('Sedang Membuat Grafik... (1/2)'):
-                text,data,fig = tp.plot_text(df,"netral",embedding_model)
-                st.plotly_chart(fig,use_container_width=True,theme=None)
-            with st.spinner('Sedang Mengekstrak Topik... (2/2)'):
-                fig,topic_modelling = tp.topic_modelling(text,data)
-                st.plotly_chart(fig,use_container_width=True,theme="streamlit")
-    with tab6:
-        if len(df[df["sentiment"]=="positif"]) < 11:
-            st.write("Tweet Terlalu Sedikit, Tidak dapat melakukan clustering")
-            st.write(df[df["sentiment"]=="positif"])
-        else:
-            with st.spinner('Sedang Membuat Grafik...(1/2)'):
-                text,data,fig = tp.plot_text(df,"positif",embedding_model)
-                st.plotly_chart(fig,use_container_width=True,theme=None)
-            with st.spinner('Sedang Mengekstrak Topik... (2/2)'):
-                fig,topic_modelling = tp.topic_modelling(text,data)
-                st.plotly_chart(fig,use_container_width=True,theme="streamlit")
-        
-            
-    
-
diff --git a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/cppipc/policy.h b/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/cppipc/policy.h
deleted file mode 100644
index f88ab5d8cb343f97026966b402eaeed8831e356a..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/crazy_functions/test_project/cpp/cppipc/policy.h
+++ /dev/null
@@ -1,25 +0,0 @@
-#pragma once
-
-#include 
-
-#include "libipc/def.h"
-#include "libipc/prod_cons.h"
-
-#include "libipc/circ/elem_array.h"
-
-namespace ipc {
-namespace policy {
-
-template