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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cisdem PDF Converter OCR 7.5.0 Crack MacOS MacOSX __FULL__.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Ashampoo Burning Studio.md

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spaces/1gistliPinn/ChatGPT4/Examples/Download Firebug For Firefox 16.0.2.md

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-<p>Firebug is a web development tool that integrates with Firefox and allows you to edit, debug, and monitor CSS, HTML, and JavaScript live in any web page[^1^]. It is a useful tool for web developers who want to inspect and tweak the code of their websites.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Download __LINK__ Iron Man 2008 In Hindi.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Askies by JazziDisciples and Mr JazziQ The Meaning Lyrics and Reviews of the Amapiano Smash Hit.md

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- <h3>Moonchild Sanelly</h3>
-<p>Moonchild Sanelly is a South African musician and dancer who is known for her signature blue-colored hair and her self-created music genre called \"Future ghetto punk\". She was born into a musical family in Port Elizabeth and moved to Durban in 2005 to study fashion. She started performing in shows at Durban University of Technology with a focus on poetry and hip hop. She later moved to Johannesburg to pursue her musical career. </p>
-<p>Moonchild Sanelly has a unique style that combines elements of kwaito, house, dancehall, funk, electronic, R&B, soul, amapiano, and more. She has collaborated with various local and international artists such as Busiswa, Die Antwoord, Beyoncé , Gorillaz , Diplo, and more. </p>
-<p>Some of her popular songs include Bashiri, Thunda Thighs, Where De Dee Kat, F-Boyz, Newtown Chips, Askies, and many more. She also has her own reality show on MTV Africa called Moonchild Sanelly Woza. </p>
- <h3>MDU aka TRP</h3>
-<p>MDU aka TRP is a South African amapiano DJ and record producer who is known for his versatile and innovative sound. He started making music at the age of 13 and was inspired by artists like Black Coffee, DJ Fresh, and Oskido. He has worked with several amapiano heavyweights such as Kabza De Small, DJ Maphorisa, Mr JazziQ, JazziDisciples, MFR Souls, and more. </p>
-<p>Some of his notable songs include Askies with Mr JazziQ and JazziDisciples, Banyana with DJ Maphorisa and Tyler ICU, Sgubu Se Monati with JazziDisciples and Vigro Deep, Sabanika with Njelic and De Mthuda, 16 Inch with Bongza and Daliwonga, and many more. He also released several EPs and albums such as Pull Up 2, Amapiano Is A Lifestyle Vol. 2, Tales Of The 2 Peers, Boomerang, and more. </p>
- <h2>Why is Askies Jazzidisciples Popular Among Amapiano Fans?</h2>
-<p>Askies jazzidisciples is not only a song that features some of the best artists in the amapiano scene, but also a song that captures the essence of amapiano music. Amapiano is a genre of music that originated in South Africa in the early 2010s and has since become a global phenomenon. It is characterized by its use of piano melodies, basslines, drums, percussions, synths, vocals, and samples from various genres such as house, kwaito, jazz, soul, and more. </p>
-<p>Askies jazzidisciples is a song that showcases the diversity and creativity of amapiano music. It has a catchy chorus that repeats the word \"askies\", which means \"sorry\" or \"excuse me\" in South African slang. It also has a groovy beat that makes you want to dance along. It features different vocal styles from the artists, such as Moonchild Sanelly's energetic rap verses, FakeLove's smooth singing hooks, Josiah De Disciple's soulful harmonies, and MDU aka TRP's signature ad-libs. It also incorporates elements from other genres such as house, gqom, kwaito, and jazz. </p>
-<p>Askies jazzidisciples is a song that appeals to amapiano fans because it represents the culture and lifestyle of amapiano lovers. It is a song that celebrates the joy of music, dancing, partying, and having fun with friends. It is a song that expresses the attitude and spirit of amapiano fans who are not afraid to say \"askies\" to anyone who tries to stop them from enjoying their lives. </p>
- <h2>How to Download Askies Jazzidisciples for Free Online?</h2>
-<p>If you want to download askies jazzidisciples for free online, you have several options to choose from. Here are some of the best ways to download askies jazzidisciples for free online.</p>
- <h3>Use OKmusi MP3 Downloader</h3>
-<p>OKmusi MP3 Downloader is a free online tool that allows you to download any MP3 song from YouTube or other websites. You can use it to download askies jazzidisciples for free online by following these steps:</p>
-<ol>
-<li>Go to <a href="">https://okmusi.com/</a>.</li>
-<li>Type \"askies jazzidisciples\" in the search box and click on the search icon.</li>
-<li>Select the song from the list of results and click on the download button.</li>
-<li>Choose the quality and format you want and click on the download button again.</li>
-<li>Wait for the download to finish and enjoy your song.</li>
-</ol>
-<p><img src="" alt="OKmusi MP3 Downloader screenshot"></p>
- <h3>Use Bandcamp</h3>
-<p>Bandcamp is an online platform that allows independent artists to sell their music directly to fans. You can use it to download askies jazzidisciples for free online by following these steps:</p>
-<ol>
-<li>Go to <a href=" can use it to stream askies jazzidisciples online by following these steps:</p>
-<ol>
-<li>Download and install Apple Music on your device from <a href="">https://www.apple.com/apple-music/</a>.</li>
-<li>Open the app and sign in with your Apple ID or create a new one.</li>
-<li>Tap on the search icon and type \"askies jazzidisciples\" in the search box.</li>
-<li>Select the song from the list of results and tap on the play button.</li>
-<li>Enjoy your song.</li>
-</ol>
-<p><img src="" alt="Apple Music screenshot"></p>
- <h3>Use Spotify</h3>
-<p>Spotify is a streaming service that allows you to access millions of songs, podcasts, playlists, and more. You can use it to stream askies jazzidisciples online by following these steps:</p>
-<ol>
-<li>Download and install Spotify on your device from <a href="">https://www.spotify.com/download/</a>.</li>
-<li>Open the app and sign in with your Spotify account or create a new one.</li>
-<li>Tap on the search icon and type \"askies jazzidisciples\" in the search box.</li>
-<li>Select the song from the list of results and tap on the play button.</li>
-<li>Enjoy your song.</li>
-</ol>
-<p><img src="" alt="Spotify screenshot"></p>
- <h2>Conclusion</h2>
-<p>Askies jazzidisciples is a song that you should not miss if you are a fan of amapiano music. It is a song that features some of the best artists in the amapiano scene, such as Mr JazziQ, JazziDisciples, Josiah De Disciple, FakeLove, Moonchild Sanelly, and MDU aka TRP. It is a song that showcases the diversity and creativity of amapiano music, blending elements from various genres such as house, kwaito, gqom, and jazz. It is a song that appeals to amapiano fans because it represents the culture and lifestyle of amapiano lovers, celebrating the joy of music, dancing, partying, and having fun with friends.</p>
-<p>If you want to download askies jazzidisciples for free online or stream it on various platforms, you have several options to choose from. You can use OKmusi MP3 Downloader, Bandcamp, or DatPiff to download askies jazzidisciples for free online. You can also use Shazam, Apple Music, or Spotify to stream askies jazzidisciples online. Whichever option you choose, you will be able to enjoy this amazing song anytime and anywhere.</p>
-<p>We hope this article has helped you learn more about askies jazzidisciples and how to enjoy it online. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy listening!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions related to askies jazzidisciples and their answers.</p>
- <h4>What does askies mean?</h4>
-<p>Askies is a South African slang word that means \"sorry\" or \"excuse me\". It is often used as a polite way of apologizing or getting someone's attention. In the context of the song askies jazzidisciples, it is also used as a way of expressing one's attitude and spirit of having fun and not caring about what others think.</p>
- <h4>Who produced askies jazzidisciples?</h4>
-<p>Askies jazzidisciples was produced by Mr JazziQ and JazziDisciples, who are both amapiano DJs and record producers. They are also former members of the amapiano duo JazziDisciples, which split in 2020.</p>
- <h4>Where can I find the lyrics of askies jazzidisciples?</h4>
-<p>You can find the lyrics of askies jazzidisciples on various websites such as Genius <a href="">https://genius.com/Mr-jazziq-askies-lyrics</a>, Musixmatch <a href="">https://www.musixmatch.com/lyrics/Mr-JazziQ-feat-Josiah-De-Disciple-FakeLove-Moonchild-Sanelly-MDU-aka-TRP/Askies</a>, or Lyrics Translate <a href="">https://lyricstranslate.com/en/askies-sorry.html</a>.</p>
- <h4>How popular is askies jazzidisciples?</h4>
-<p>Askies jazzidisciples is one of the most popular songs in the amapiano genre. It has over 1.5 million views on YouTube <a href="">https://www.youtube.com /watch?v=Z0kuBZW4o5I</a>, over 2.7 million streams on Spotify <a href="">https://open.spotify.com/track/6w1y3fQ8dQgXbqZl1HvYmM</a>, and over 3.6 million streams on Apple Music <a href="">https://music.apple.com/za/album/askies-feat-josiah-de-disciple-fakelove-moonchild-sanelly/1501642210?i=1501642216</a>. It also received positive reviews from critics and fans who praised its catchy lyrics, infectious beat, diverse vocals, and danceable vibe.</p>
- <h4>What are some other songs similar to askies jazzidisciples?</h4>
-<p>If you like askies jazzidisciples, you might also like some other songs similar to it, such as:</p>
-<ul>
-<li>Blue Skies by Mr JazziQ and JazziDisciples, featuring Vigro Deep and Rams De Violinist</li>
-<li>VSOP by Mr JazziQ, featuring Busta 929, Reece Madlisa, Zuma, Mpura, Riky Rick, and 9umba</li>
-<li>Mama by Boohle and Josiah De Disciple</li>
-<li>Ntyilo Ntyilo by Rethabile Khumalo and Master KG</li>
-<li>Banyana by DJ Maphorisa, Tyler ICU, Sir Trill, Daliwonga, and Kabza De Small</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r100.py

@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "r100"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]

spaces/7eu7d7/anime-ai-detect-fucker/attacker/FGSM.py

@@ -1,48 +0,0 @@
-import torch
-from torch import nn
-from copy import deepcopy
-from .base import Attacker
-from torch.cuda import amp
-
-class FGSM(Attacker):
-    def __init__(self, model, img_transform=(lambda x:x, lambda x:x), use_amp=False):
-        super().__init__(model, img_transform)
-        self.use_amp=use_amp
-
-        if use_amp:
-            self.scaler = amp.GradScaler()
-
-    def set_para(self, eps=8, alpha=lambda:8, **kwargs):
-        super().set_para(eps=eps, alpha=alpha, **kwargs)
-
-    def step(self, images, labels, loss):
-        with amp.autocast(enabled=self.use_amp):
-            images.requires_grad = True
-            outputs = self.model(images).logits
-
-            self.model.zero_grad()
-            cost = loss(outputs, labels)
-
-        if self.use_amp:
-            self.scaler.scale(cost).backward()
-        else:
-            cost.backward()
-
-        adv_images = (images + self.alpha() * images.grad.sign()).detach_()
-        eta = torch.clamp(adv_images - self.ori_images, min=-self.eps, max=self.eps)
-        images = self.img_transform[0](torch.clamp(self.img_transform[1](self.ori_images + eta), min=0, max=255).detach_())
-
-        return images
-
-    def attack(self, images, labels):
-        #images = deepcopy(images)
-        #self.ori_images = deepcopy(images)
-
-        self.model.eval()
-
-        images = self.forward(self, images, labels)
-
-        self.model.zero_grad()
-        self.model.train()
-
-        return images

spaces/7eu7d7/anime-ai-detect-fucker/attacker/base.py

@@ -1,33 +0,0 @@
-
-class Attacker:
-    def __init__(self, model, img_transform=(lambda x:x, lambda x:x)):
-        self.model = model  # 必须是pytorch的model
-        '''self.model.eval()
-        for k, v in self.model.named_parameters():
-            v.requires_grad = False'''
-        self.img_transform=img_transform
-        self.forward = lambda attacker, images, labels: attacker.step(images, labels, attacker.loss)
-
-    def set_para(self, **kwargs):
-        for k,v in kwargs.items():
-            setattr(self, k,v)
-
-    def set_forward(self, forward):
-        self.forward=forward
-
-    def step(self, images, labels, loss):
-        pass
-
-    def set_loss(self, loss):
-        self.loss=loss
-
-    def attack(self, images, labels):
-        pass
-
-
-class Empty:
-    def __enter__(self):
-        pass
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        pass

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Engineering Interviews 4be8039581d04456b0151f2cc4b22130/Questions ede8818b3a0e447f80145905690eb3f6/To Do List Design 9d9cb6c13b4b4a0a8f7ab03a8c98a2d8.md

@@ -1,41 +0,0 @@
-# To Do List Design
-
-Difficulty: Medium
-Skills: Architecture, Backend, Front end
-
-# Description
-
-Write a description for the interview question here.
-
-# Sample Inputs
-
-Give some valid inputs the candidate can expect to test their solution with.
-
-- ...
-- ...
-
-# Expected Outputs
-
-For each sample input above, list the expected output. 
-
-- ...
-- ...
-
-# Solutions
-
-Provide possible solutions in common languages to this problem.
-
-### Javascript
-
-```jsx
-function solution() {
-	
-}
-```
-
-### Python
-
-```python
-def solution():
-	pass
-```

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/image_degradation/utils_image.py

@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/conversation/[id]/phi/m_bg.wasm.d.ts

@@ -1,14 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-export const memory: WebAssembly.Memory;
-export function __wbg_model_free(a: number): void;
-export function model_load(a: number, b: number, c: number, d: number, e: number, f: number): void;
-export function model_init_with_prompt(a: number, b: number, c: number, d: number, e: number, f: number, g: number, h: number, i: number): void;
-export function model_next_token(a: number, b: number): void;
-export function main(a: number, b: number): number;
-export function __wbindgen_add_to_stack_pointer(a: number): number;
-export function __wbindgen_malloc(a: number, b: number): number;
-export function __wbindgen_realloc(a: number, b: number, c: number, d: number): number;
-export function __wbindgen_free(a: number, b: number, c: number): void;
-export function __wbindgen_exn_store(a: number): void;
-export function __wbindgen_start(): void;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/pie/Pie.js

@@ -1,68 +0,0 @@
-import Base from '../base/Base.js';
-import { Arc } from '../utils/Geoms.js';
-
-const Linear = Phaser.Math.Linear;
-
-class Pie extends Base {
-    constructor(scene, config) {
-        super(scene, config);
-        this.type = 'rexSpinnerPie';
-    }
-
-    buildShapes() {
-        for (var i = 0; i < 4; i++) {
-            var pie = (new Arc()).setPie();
-            this.addShape(pie);
-
-            pie.setData('speed', Linear(180, 360, Math.random()));
-        }
-    }
-
-    updateShapes() {
-        var centerX = this.centerX;
-        var centerY = this.centerY;
-        var radius = this.radius;
-
-        var deltaValue;
-        if (this.prevValue !== undefined) {
-            deltaValue = this.value - this.prevValue;
-            if (this.prevValue > this.value) {
-                deltaValue += 1;
-            }
-        }
-
-        var shapes = this.getShapes();
-        for (var i = 0, cnt = shapes.length; i < cnt; i++) {
-            var pie = shapes[i];
-            var pieAlpha = (i + 1) / cnt;
-
-            if (this.prevValue === undefined) {
-                var startAngle = (i / cnt) * 360;
-                var endAngle = startAngle + 90;
-                pie
-                    .fillStyle(this.color, pieAlpha)
-                    .setRadius(radius)
-                    .setCenterPosition(centerX, centerY)
-                    .setAngle(startAngle, endAngle)
-                    .setData('angle', startAngle);
-            } else {
-                var startAngle = pie.getData('angle') + pie.getData('speed') * deltaValue;
-                startAngle = startAngle % 360;
-                var endAngle = startAngle + 90;
-                pie
-                    .fillStyle(this.color, pieAlpha)
-                    .setRadius(radius)
-                    .setCenterPosition(centerX, centerY)
-                    .setAngle(startAngle, endAngle)
-                    .setData('angle', startAngle);
-
-            }
-
-        }
-
-        this.prevValue = this.value;
-
-    }
-}
-
-export default Pie;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorinputbase/methods/CreateSwatch.js

@@ -1,16 +0,0 @@
-import RoundRectangle from '../../../roundrectangle/RoundRectangle.js';
-import IsGameObject from '../../../../../plugins/utils/system/IsGameObject.js';
-
-var CreateSwatch = function (scene, config) {
-    if (config === false) {
-        return null;
-    } else if (IsGameObject(config)) {
-        return config;
-    }
-
-    var swatch = new RoundRectangle(scene, config);
-    scene.add.existing(swatch);
-    return swatch;
-}
-
-export default CreateSwatch;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/GetMaxChildWidth.js

@@ -1,18 +0,0 @@
-var GetMaxChildWidth = function (children) {
-    if (children === undefined) {
-        children = this.sizerChildren;
-    }
-    var result = 0;
-    var child, childWidth;
-    for (var i = 0, cnt = children.length; i < cnt; i++) {
-        child = children[i];
-        if (child === '\n') {
-            continue;
-        }
-
-        childWidth = this.getChildWidth(child);
-        result = Math.max(childWidth, result);
-    }
-    return result;
-}
-export default GetMaxChildWidth;

spaces/Aiusernumber5/janitorai/README.md

@@ -1,10 +0,0 @@
----
-title: Janitorai
-emoji: 📊
-colorFrom: red
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/__init__.py

@@ -1,60 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-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)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''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
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  clean_text = _clean_text(text, cleaner_names)
-  print(clean_text)
-  print(f" length:{len(clean_text)}")
-  for symbol in clean_text:
-    if symbol not in symbol_to_id.keys():
-      continue
-    symbol_id = symbol_to_id[symbol]
-    sequence += [symbol_id]
-  print(f" length:{len(sequence)}")
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text, symbols):
-  '''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
-  '''
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = [symbol_to_id[symbol] for symbol in cleaned_text if symbol in symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-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

spaces/AlignmentResearch/tuned-lens/app.py

@@ -1,117 +0,0 @@
-import torch
-from tuned_lens.nn.lenses import TunedLens, LogitLens
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from tuned_lens.plotting import PredictionTrajectory
-import gradio as gr
-from plotly import graph_objects as go
-
-device = torch.device("cpu")
-print(f"Using device {device} for inference")
-model = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")
-model = model.to(device)
-tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
-tuned_lens = TunedLens.from_model_and_pretrained(
-    model=model,
-    map_location=device,
-)
-logit_lens = LogitLens.from_model(model)
-
-lens_options_dict = {
-    "Tuned Lens": tuned_lens,
-    "Logit Lens": logit_lens,
-}
-
-statistic_options_dict = {
-    "Entropy": "entropy",
-    "Cross Entropy": "cross_entropy",
-    "Forward KL": "forward_kl",
-}
-
-
-def make_plot(lens, text, statistic, token_cutoff):
-    input_ids = tokenizer.encode(text)
-    input_ids = [tokenizer.bos_token_id] + input_ids
-    targets = input_ids[1:] + [tokenizer.eos_token_id]
-
-    if len(input_ids) == 1:
-        return go.Figure(layout=dict(title="Please enter some text."))
-
-    if token_cutoff < 1:
-        return go.Figure(layout=dict(title="Please provide valid token cut off."))
-
-    start_pos=max(len(input_ids) - token_cutoff, 0)
-    pred_traj = PredictionTrajectory.from_lens_and_model(
-        lens=lens_options_dict[lens],
-        model=model,
-        input_ids=input_ids,
-        tokenizer=tokenizer,
-        targets=targets,
-        start_pos=start_pos,
-    )
-
-    return getattr(pred_traj, statistic_options_dict[statistic])().figure(
-        title=f"{lens} ({model.name_or_path}) {statistic}",
-    )
-
-preamble = """
-# The Tuned Lens 🔎
-
-A tuned lens allows us to peak at the iterative computations a transformer uses to compute the next token.
-
-A lens into a transformer with n layers allows you to replace the last $m$ layers of the model with an [affine transformation](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) (we call these affine translators).
-
-This essentially skips over these last few layers and lets you see the best prediction that can be made from the model's representations, i.e. the residual stream, at layer $n - m$. Since the representations may be rotated, shifted, or stretched from layer to layer it's useful to train the len's affine adapters specifically on each layer. This training is what differentiates this method from simpler approaches that decode the residual stream of the network directly using the unembeding layer i.e. the logit lens. We explain this process in [the paper](https://arxiv.org/abs/2303.08112).
-
-## Usage
-Since the tuned lens produces a distribution of predictions to visualize it's output we need to we need to provide a summary statistic to plot.  The default is simply [entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)), but you can also choose the [cross entropy](https://en.wikipedia.org/wiki/Cross_entropy) with the target token, or the [KL divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) between the model's predictions and the tuned lens' predictions. You can also hover over a token to see more of the distribution i.e. the top 10 most probable tokens and their probabilities.
-
-## Examples
-Here are some interesting examples you can try.
-
-### Copy paste:
-```
-Copy: A!2j!#u&NGApS&MkkHe8Gm!#
-Paste: A!2j!#u&NGApS&MkkHe8Gm!#
-```
-
-### Trivial in-context learning
-```
-inc 1 2
-inc 4 5
-inc 13 
-```
-
-#### Addition
-```
-add 1 1 2
-add 3 4 7
-add 13 2 
-```
-"""
-
-with gr.Blocks() as demo:
-    gr.Markdown(preamble)
-    with gr.Column():
-        text = gr.Textbox(
-            value="it was the best of times, it was the worst of times",
-            label="Input Text",
-        )
-        with gr.Row():
-            lens_options = gr.Dropdown(
-                list(lens_options_dict.keys()), value="Tuned Lens", label="Select Lens"
-            )
-            statistic = gr.Dropdown(
-                list(statistic_options_dict.keys()),
-                value="Entropy",
-                label="Select Statistic",
-            )
-            token_cutoff = gr.Slider(
-                maximum=20, minimum=2, value=10, step=1, label="Plot Last N Tokens"
-            )
-        examine_btn = gr.Button(value="Submit")
-        plot = gr.Plot()
-    examine_btn.click(make_plot, [lens_options, text, statistic, token_cutoff], plot)
-    demo.load(make_plot, [lens_options, text, statistic, token_cutoff], plot)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)

spaces/Aloento/9Nine-PITS/text/frontend/zh_frontend.py

@@ -1,287 +0,0 @@
-# Copyright (c) 2021 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.
-import re
-from typing import List
-
-import jieba.posseg as psg
-import numpy as np
-from g2pM import G2pM
-from pypinyin import Style
-from pypinyin import lazy_pinyin
-from pypinyin import load_single_dict
-from pypinyin_dict.phrase_pinyin_data import large_pinyin
-
-from text.frontend.generate_lexicon import generate_lexicon
-from text.frontend.tone_sandhi import ToneSandhi
-from text.frontend.zh_normalization.text_normlization import TextNormalizer
-
-
-class Frontend():
-  def __init__(self,
-               g2p_model="pypinyin",
-               phone_vocab_path=None,
-               tone_vocab_path=None):
-    self.tone_modifier = ToneSandhi()
-    self.text_normalizer = TextNormalizer()
-
-    self.punc = ['!', '?', '…', ",", ".", "#", '-', "%", "$"]
-    # g2p_model can be pypinyin and g2pM
-    self.g2p_model = g2p_model
-    self.add_word_sep = True
-    if self.g2p_model == "g2pM":
-      self.g2pM_model = G2pM()
-      self.pinyin2phone = generate_lexicon(
-        with_tone=True, with_erhua=False)
-    else:
-
-      self.__init__pypinyin()
-    self.must_erhua = {"小院儿", "胡同儿", "范儿", "老汉儿", "撒欢儿", "寻老礼儿", "妥妥儿"}
-    self.not_erhua = {
-      "虐儿", "为儿", "护儿", "瞒儿", "救儿", "替儿", "有儿", "一儿", "我儿", "俺儿", "妻儿",
-      "拐儿", "聋儿", "乞儿", "患儿", "幼儿", "孤儿", "婴儿", "婴幼儿", "连体儿", "脑瘫儿",
-      "流浪儿", "体弱儿", "混血儿", "蜜雪儿", "舫儿", "祖儿", "美儿", "应采儿", "可儿", "侄儿",
-      "孙儿", "侄孙儿", "女儿", "男儿", "红孩儿", "花儿", "虫儿", "马儿", "鸟儿", "猪儿", "猫儿",
-      "狗儿"
-    }
-    self.vocab_phones = {}
-    self.vocab_tones = {}
-    if phone_vocab_path:
-      with open(phone_vocab_path, 'rt') as f:
-        phn_id = [line.strip().split() for line in f.readlines()]
-      for phn, id in phn_id:
-        self.vocab_phones[phn] = int(id)
-    if tone_vocab_path:
-      with open(tone_vocab_path, 'rt') as f:
-        tone_id = [line.strip().split() for line in f.readlines()]
-      for tone, id in tone_id:
-        self.vocab_tones[tone] = int(id)
-    print("initialized zh frontend")
-
-  def __init__pypinyin(self):
-    large_pinyin.load()
-    #
-    # load_phrases_dict({u'开户行': [[u'ka1i'], [u'hu4'], [u'hang2']]})
-    # load_phrases_dict({u'发卡行': [[u'fa4'], [u'ka3'], [u'hang2']]})
-    # load_phrases_dict({u'放款行': [[u'fa4ng'], [u'kua3n'], [u'hang2']]})
-    # load_phrases_dict({u'茧行': [[u'jia3n'], [u'hang2']]})
-    # load_phrases_dict({u'行号': [[u'hang2'], [u'ha4o']]})
-    # load_phrases_dict({u'各地': [[u'ge4'], [u'di4']]})
-    # load_phrases_dict({u'借还款': [[u'jie4'], [u'hua2n'], [u'kua3n']]})
-    # load_phrases_dict({u'时间为': [[u'shi2'], [u'jia1n'], [u'we2i']]})
-    # load_phrases_dict({u'为准': [[u'we2i'], [u'zhu3n']]})
-    # load_phrases_dict({u'色差': [[u'se4'], [u'cha1']]})
-
-    # 调整字的拼音顺序
-    load_single_dict({ord(u'地'): u'de,di4'})
-
-  def _get_initials_finals(self, word: str) -> List[List[str]]:
-    initials = []
-    finals = []
-    if self.g2p_model == "pypinyin":
-      orig_initials = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.INITIALS)
-      orig_finals = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-      for c, v in zip(orig_initials, orig_finals):
-        if re.match(r'i\d', v):
-          if c in ['z', 'c', 's']:
-            v = re.sub('i', 'ii', v)
-          elif c in ['zh', 'ch', 'sh', 'r']:
-            v = re.sub('i', 'iii', v)
-        initials.append(c)
-        finals.append(v)
-    elif self.g2p_model == "g2pM":
-      pinyins = self.g2pM_model(word, tone=True, char_split=False)
-      for pinyin in pinyins:
-        pinyin = pinyin.replace("u:", "v")
-        if pinyin in self.pinyin2phone:
-          initial_final_list = self.pinyin2phone[pinyin].split(" ")
-          if len(initial_final_list) == 2:
-            initials.append(initial_final_list[0])
-            finals.append(initial_final_list[1])
-          elif len(initial_final_list) == 1:
-            initials.append('')
-            finals.append(initial_final_list[1])
-        else:
-          # If it's not pinyin (possibly punctuation) or no conversion is required
-          initials.append(pinyin)
-          finals.append(pinyin)
-    return initials, finals
-
-  # if merge_sentences, merge all sentences into one phone sequence
-  def _g2p(self,
-           sentences: List[str],
-           merge_sentences: bool = True,
-           with_erhua: bool = True) -> List[List[str]]:
-    segments = sentences
-    phones_list = []
-    for seg in segments:
-      phones = []
-      # Replace all English words in the sentence
-      seg = re.sub('[a-zA-Z]+', '', seg)
-      seg_cut = psg.lcut(seg)
-      initials = []
-      finals = []
-      seg_cut = self.tone_modifier.pre_merge_for_modify(seg_cut)
-      for word, pos in seg_cut:
-        if self.add_word_sep and word == "#":
-          continue
-        if pos == 'eng':
-          continue
-        sub_initials, sub_finals = self._get_initials_finals(word)
-        sub_finals = self.tone_modifier.modified_tone(word, pos,
-                                                      sub_finals)
-        if with_erhua:
-          sub_initials, sub_finals = self._merge_erhua(
-            sub_initials, sub_finals, word, pos)
-        initials.append(sub_initials)
-        finals.append(sub_finals)
-        if self.add_word_sep and word not in self.punc:
-          initials.append(["#"])
-          finals.append(["#"])
-
-        # assert len(sub_initials) == len(sub_finals) == len(word)
-      initials = sum(initials, [])
-      finals = sum(finals, [])
-
-      for c, v in zip(initials, finals):
-        # NOTE: post process for pypinyin outputs
-        # we discriminate i, ii and iii
-        if c:
-          phones.append(c)
-        if v and v not in self.punc:
-          phones.append(v)
-
-      phones_list.append(phones)
-    if merge_sentences:
-      merge_list = sum(phones_list, [])
-      # rm the last 'sp' to avoid the noise at the end
-      # cause in the training data, no 'sp' in the end
-      if merge_list[-1] == 'sp':
-        merge_list = merge_list[:-1]
-      phones_list = []
-      phones_list.append(merge_list)
-    return phones_list
-
-  def _merge_erhua(self,
-                   initials: List[str],
-                   finals: List[str],
-                   word: str,
-                   pos: str) -> List[List[str]]:
-    if word not in self.must_erhua and (word in self.not_erhua or
-                                        pos in {"a", "j", "nr"}):
-      return initials, finals
-    # "……" 等情况直接返回
-    if len(finals) != len(word):
-      return initials, finals
-
-    assert len(finals) == len(word)
-
-    new_initials = []
-    new_finals = []
-    for i, phn in enumerate(finals):
-      if i == len(finals) - 1 and word[i] == "儿" and phn in {
-        "er2", "er5"
-      } and word[-2:] not in self.not_erhua and new_finals:
-        new_finals[-1] = new_finals[-1][:-1] + "r" + new_finals[-1][-1]
-      else:
-        new_finals.append(phn)
-        new_initials.append(initials[i])
-    return new_initials, new_finals
-
-  def _p2id(self, phonemes: List[str]) -> np.array:
-    # replace unk phone with sp
-    phonemes = [
-      phn if phn in self.vocab_phones else "sp" for phn in phonemes
-    ]
-    phone_ids = [self.vocab_phones[item] for item in phonemes]
-    return np.array(phone_ids, np.int64)
-
-  def _t2id(self, tones: List[str]) -> np.array:
-    # replace unk phone with sp
-    tones = [tone if tone in self.vocab_tones else "0" for tone in tones]
-    tone_ids = [self.vocab_tones[item] for item in tones]
-    return np.array(tone_ids, np.int64)
-
-  def _get_phone_tone(self, phonemes: List[str],
-                      get_tone_ids: bool = False) -> List[List[str]]:
-    phones = []
-    tones = []
-    if get_tone_ids and self.vocab_tones:
-      for full_phone in phonemes:
-        # split tone from finals
-        match = re.match(r'^(\w+)([012345])$', full_phone)
-        if match:
-          phone = match.group(1)
-          tone = match.group(2)
-          # if the merged erhua not in the vocab
-          # assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, we split 'iaor' into ['iao','er']
-          # and the tones accordingly change from ['3'] to ['3','2'], while '2' is the tone of 'er2'
-          if len(phone) >= 2 and phone != "er" and phone[
-            -1] == 'r' and phone not in self.vocab_phones and phone[:
-          -1] in self.vocab_phones:
-            phones.append(phone[:-1])
-            phones.append("er")
-            tones.append(tone)
-            tones.append("2")
-          else:
-            phones.append(phone)
-            tones.append(tone)
-        else:
-          phones.append(full_phone)
-          tones.append('0')
-    else:
-      for phone in phonemes:
-        # if the merged erhua not in the vocab
-        # assume that the input is ['iaor3'] and 'iaor' not in self.vocab_phones, change ['iaor3'] to ['iao3','er2']
-        if len(phone) >= 3 and phone[:-1] != "er" and phone[
-          -2] == 'r' and phone not in self.vocab_phones and (
-            phone[:-2] + phone[-1]) in self.vocab_phones:
-          phones.append((phone[:-2] + phone[-1]))
-          phones.append("er2")
-        else:
-          phones.append(phone)
-    return phones, tones
-
-  def get_phonemes(self,
-                   sentence: str,
-                   merge_sentences: bool = True,
-                   with_erhua: bool = False,
-                   robot: bool = False,
-                   print_info: bool = False) -> List[List[str]]:
-    sentence = sentence.replace("嗯", "恩")
-    sentences = self.text_normalizer.normalize(sentence)
-    phonemes = self._g2p(
-      sentences, merge_sentences=merge_sentences, with_erhua=with_erhua)
-    # change all tones to `1`
-    if robot:
-      new_phonemes = []
-      for sentence in phonemes:
-        new_sentence = []
-        for item in sentence:
-          # `er` only have tone `2`
-          if item[-1] in "12345" and item != "er2":
-            item = item[:-1] + "1"
-          new_sentence.append(item)
-        new_phonemes.append(new_sentence)
-      phonemes = new_phonemes
-    if print_info:
-      print("----------------------------")
-      print("text norm results:")
-      print(sentences)
-      print("----------------------------")
-      print("g2p results:")
-      print(phonemes)
-      print("----------------------------")
-    return phonemes

spaces/Aloento/9Nine-PITS/text/frontend/zh_normalization/char_convert.py

@@ -1,46 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020 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.
-"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters.
-"""
-simplified_charcters = 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-
-traditional_characters = 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鐃鐄鐇鐏鐒鐓鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤'
-
-assert len(simplified_charcters) == len(simplified_charcters)
-
-s2t_dict = {}
-t2s_dict = {}
-for i, item in enumerate(simplified_charcters):
-  s2t_dict[item] = traditional_characters[i]
-  t2s_dict[traditional_characters[i]] = item
-
-
-def tranditional_to_simplified(text: str) -> str:
-  return "".join(
-    [t2s_dict[item] if item in t2s_dict else item for item in text])
-
-
-def simplified_to_traditional(text: str) -> str:
-  return "".join(
-    [s2t_dict[item] if item in s2t_dict else item for item in text])
-
-
-if __name__ == "__main__":
-  text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點"
-  print(text)
-  text_simple = tranditional_to_simplified(text)
-  print(text_simple)
-  text_traditional = simplified_to_traditional(text_simple)
-  print(text_traditional)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_controlnet_inpaint.py

@@ -1,1138 +0,0 @@
-# Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn.functional as F
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import (
-    PIL_INTERPOLATION,
-    is_accelerate_available,
-    is_accelerate_version,
-    randn_tensor,
-    replace_example_docstring,
-)
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import numpy as np
-        >>> import torch
-        >>> from PIL import Image
-        >>> from stable_diffusion_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
-
-        >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
-        >>> from diffusers import ControlNetModel, UniPCMultistepScheduler
-        >>> from diffusers.utils import load_image
-
-        >>> def ade_palette():
-                return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-                        [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-                        [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-                        [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-                        [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-                        [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-                        [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-                        [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-                        [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-                        [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-                        [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-                        [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-                        [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-                        [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-                        [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
-                        [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
-                        [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
-                        [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
-                        [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
-                        [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
-                        [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
-                        [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
-                        [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
-                        [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
-                        [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
-                        [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
-                        [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
-                        [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
-                        [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
-                        [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
-                        [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
-                        [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
-                        [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
-                        [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
-                        [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
-                        [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
-                        [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
-                        [102, 255, 0], [92, 0, 255]]
-
-        >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
-        >>> image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
-
-        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
-
-        >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
-            )
-
-        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-        >>> pipe.enable_xformers_memory_efficient_attention()
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> def image_to_seg(image):
-                pixel_values = image_processor(image, return_tensors="pt").pixel_values
-                with torch.no_grad():
-                    outputs = image_segmentor(pixel_values)
-                seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
-                color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)  # height, width, 3
-                palette = np.array(ade_palette())
-                for label, color in enumerate(palette):
-                    color_seg[seg == label, :] = color
-                color_seg = color_seg.astype(np.uint8)
-                seg_image = Image.fromarray(color_seg)
-                return seg_image
-
-        >>> image = load_image(
-                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-            )
-
-        >>> mask_image = load_image(
-                "https://github.com/CompVis/latent-diffusion/raw/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-            )
-
-        >>> controlnet_conditioning_image = image_to_seg(image)
-
-        >>> image = pipe(
-                "Face of a yellow cat, high resolution, sitting on a park bench",
-                image,
-                mask_image,
-                controlnet_conditioning_image,
-                num_inference_steps=20,
-            ).images[0]
-
-        >>> image.save("out.png")
-        ```
-"""
-
-
-def prepare_image(image):
-    if isinstance(image, torch.Tensor):
-        # Batch single image
-        if image.ndim == 3:
-            image = image.unsqueeze(0)
-
-        image = image.to(dtype=torch.float32)
-    else:
-        # preprocess image
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
-
-    return image
-
-
-def prepare_mask_image(mask_image):
-    if isinstance(mask_image, torch.Tensor):
-        if mask_image.ndim == 2:
-            # Batch and add channel dim for single mask
-            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
-        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
-            # Single mask, the 0'th dimension is considered to be
-            # the existing batch size of 1
-            mask_image = mask_image.unsqueeze(0)
-        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
-            # Batch of mask, the 0'th dimension is considered to be
-            # the batching dimension
-            mask_image = mask_image.unsqueeze(1)
-
-        # Binarize mask
-        mask_image[mask_image < 0.5] = 0
-        mask_image[mask_image >= 0.5] = 1
-    else:
-        # preprocess mask
-        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
-            mask_image = [mask_image]
-
-        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
-            mask_image = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0)
-            mask_image = mask_image.astype(np.float32) / 255.0
-        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
-            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
-
-        mask_image[mask_image < 0.5] = 0
-        mask_image[mask_image >= 0.5] = 1
-        mask_image = torch.from_numpy(mask_image)
-
-    return mask_image
-
-
-def prepare_controlnet_conditioning_image(
-    controlnet_conditioning_image,
-    width,
-    height,
-    batch_size,
-    num_images_per_prompt,
-    device,
-    dtype,
-    do_classifier_free_guidance,
-):
-    if not isinstance(controlnet_conditioning_image, torch.Tensor):
-        if isinstance(controlnet_conditioning_image, PIL.Image.Image):
-            controlnet_conditioning_image = [controlnet_conditioning_image]
-
-        if isinstance(controlnet_conditioning_image[0], PIL.Image.Image):
-            controlnet_conditioning_image = [
-                np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
-                for i in controlnet_conditioning_image
-            ]
-            controlnet_conditioning_image = np.concatenate(controlnet_conditioning_image, axis=0)
-            controlnet_conditioning_image = np.array(controlnet_conditioning_image).astype(np.float32) / 255.0
-            controlnet_conditioning_image = controlnet_conditioning_image.transpose(0, 3, 1, 2)
-            controlnet_conditioning_image = torch.from_numpy(controlnet_conditioning_image)
-        elif isinstance(controlnet_conditioning_image[0], torch.Tensor):
-            controlnet_conditioning_image = torch.cat(controlnet_conditioning_image, dim=0)
-
-    image_batch_size = controlnet_conditioning_image.shape[0]
-
-    if image_batch_size == 1:
-        repeat_by = batch_size
-    else:
-        # image batch size is the same as prompt batch size
-        repeat_by = num_images_per_prompt
-
-    controlnet_conditioning_image = controlnet_conditioning_image.repeat_interleave(repeat_by, dim=0)
-
-    controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype)
-
-    if do_classifier_free_guidance:
-        controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2)
-
-    return controlnet_conditioning_image
-
-
-class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline):
-    """
-    Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/
-    """
-
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        if isinstance(controlnet, (list, tuple)):
-            controlnet = MultiControlNetModel(controlnet)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            controlnet=controlnet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
-        steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae, controlnet, and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        Note that offloading happens on a submodule basis. Memory savings are higher than with
-        `enable_model_cpu_offload`, but performance is lower.
-        """
-        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, self.controlnet]:
-            cpu_offload(cpu_offloaded_model, device)
-
-        if self.safety_checker is not None:
-            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        if self.safety_checker is not None:
-            # the safety checker can offload the vae again
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # control net hook has be manually offloaded as it alternates with unet
-        cpu_offload_with_hook(self.controlnet, device)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @property
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if 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_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            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
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            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 = prompt_embeds.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
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_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
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    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_controlnet_conditioning_image(self, image, prompt, prompt_embeds):
-        image_is_pil = isinstance(image, PIL.Image.Image)
-        image_is_tensor = isinstance(image, torch.Tensor)
-        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
-        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
-
-        if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
-            raise TypeError(
-                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
-            )
-
-        if image_is_pil:
-            image_batch_size = 1
-        elif image_is_tensor:
-            image_batch_size = image.shape[0]
-        elif image_is_pil_list:
-            image_batch_size = len(image)
-        elif image_is_tensor_list:
-            image_batch_size = len(image)
-        else:
-            raise ValueError("controlnet condition image is not valid")
-
-        if prompt is not None and isinstance(prompt, str):
-            prompt_batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            prompt_batch_size = len(prompt)
-        elif prompt_embeds is not None:
-            prompt_batch_size = prompt_embeds.shape[0]
-        else:
-            raise ValueError("prompt or prompt_embeds are not valid")
-
-        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
-            raise ValueError(
-                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
-            )
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        mask_image,
-        controlnet_conditioning_image,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        controlnet_conditioning_scale=None,
-    ):
-        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)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (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 negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        # check controlnet condition image
-        if isinstance(self.controlnet, ControlNetModel):
-            self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds)
-        elif isinstance(self.controlnet, MultiControlNetModel):
-            if not isinstance(controlnet_conditioning_image, list):
-                raise TypeError("For multiple controlnets: `image` must be type `list`")
-            if len(controlnet_conditioning_image) != len(self.controlnet.nets):
-                raise ValueError(
-                    "For multiple controlnets: `image` must have the same length as the number of controlnets."
-                )
-            for image_ in controlnet_conditioning_image:
-                self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds)
-        else:
-            assert False
-
-        # Check `controlnet_conditioning_scale`
-        if isinstance(self.controlnet, ControlNetModel):
-            if not isinstance(controlnet_conditioning_scale, float):
-                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
-        elif isinstance(self.controlnet, MultiControlNetModel):
-            if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
-                self.controlnet.nets
-            ):
-                raise ValueError(
-                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
-                    " the same length as the number of controlnets"
-                )
-        else:
-            assert False
-
-        if isinstance(image, torch.Tensor) and not isinstance(mask_image, torch.Tensor):
-            raise TypeError("if `image` is a tensor, `mask_image` must also be a tensor")
-
-        if isinstance(image, PIL.Image.Image) and not isinstance(mask_image, PIL.Image.Image):
-            raise TypeError("if `image` is a PIL image, `mask_image` must also be a PIL image")
-
-        if isinstance(image, torch.Tensor):
-            if image.ndim != 3 and image.ndim != 4:
-                raise ValueError("`image` must have 3 or 4 dimensions")
-
-            if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4:
-                raise ValueError("`mask_image` must have 2, 3, or 4 dimensions")
-
-            if image.ndim == 3:
-                image_batch_size = 1
-                image_channels, image_height, image_width = image.shape
-            elif image.ndim == 4:
-                image_batch_size, image_channels, image_height, image_width = image.shape
-            else:
-                assert False
-
-            if mask_image.ndim == 2:
-                mask_image_batch_size = 1
-                mask_image_channels = 1
-                mask_image_height, mask_image_width = mask_image.shape
-            elif mask_image.ndim == 3:
-                mask_image_channels = 1
-                mask_image_batch_size, mask_image_height, mask_image_width = mask_image.shape
-            elif mask_image.ndim == 4:
-                mask_image_batch_size, mask_image_channels, mask_image_height, mask_image_width = mask_image.shape
-
-            if image_channels != 3:
-                raise ValueError("`image` must have 3 channels")
-
-            if mask_image_channels != 1:
-                raise ValueError("`mask_image` must have 1 channel")
-
-            if image_batch_size != mask_image_batch_size:
-                raise ValueError("`image` and `mask_image` mush have the same batch sizes")
-
-            if image_height != mask_image_height or image_width != mask_image_width:
-                raise ValueError("`image` and `mask_image` must have the same height and width dimensions")
-
-            if image.min() < -1 or image.max() > 1:
-                raise ValueError("`image` should be in range [-1, 1]")
-
-            if mask_image.min() < 0 or mask_image.max() > 1:
-                raise ValueError("`mask_image` should be in range [0, 1]")
-        else:
-            mask_image_channels = 1
-            image_channels = 3
-
-        single_image_latent_channels = self.vae.config.latent_channels
-
-        total_latent_channels = single_image_latent_channels * 2 + mask_image_channels
-
-        if total_latent_channels != self.unet.config.in_channels:
-            raise ValueError(
-                f"The config of `pipeline.unet` expects {self.unet.config.in_channels} but received"
-                f" non inpainting latent channels: {single_image_latent_channels},"
-                f" mask channels: {mask_image_channels}, and masked image channels: {single_image_latent_channels}."
-                f" Please verify the config of `pipeline.unet` and the `mask_image` and `image` inputs."
-            )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, 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:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            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
-
-    def prepare_mask_latents(self, mask_image, batch_size, height, width, dtype, device, do_classifier_free_guidance):
-        # resize the mask to latents shape as we concatenate the mask to the latents
-        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
-        # and half precision
-        mask_image = F.interpolate(mask_image, size=(height // self.vae_scale_factor, width // self.vae_scale_factor))
-        mask_image = mask_image.to(device=device, dtype=dtype)
-
-        # duplicate mask for each generation per prompt, using mps friendly method
-        if mask_image.shape[0] < batch_size:
-            if not batch_size % mask_image.shape[0] == 0:
-                raise ValueError(
-                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
-                    f" a total batch size of {batch_size}, but {mask_image.shape[0]} masks were passed. Make sure the number"
-                    " of masks that you pass is divisible by the total requested batch size."
-                )
-            mask_image = mask_image.repeat(batch_size // mask_image.shape[0], 1, 1, 1)
-
-        mask_image = torch.cat([mask_image] * 2) if do_classifier_free_guidance else mask_image
-
-        mask_image_latents = mask_image
-
-        return mask_image_latents
-
-    def prepare_masked_image_latents(
-        self, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
-    ):
-        masked_image = masked_image.to(device=device, dtype=dtype)
-
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            masked_image_latents = [
-                self.vae.encode(masked_image[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            masked_image_latents = torch.cat(masked_image_latents, dim=0)
-        else:
-            masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator)
-        masked_image_latents = self.vae.config.scaling_factor * masked_image_latents
-
-        # duplicate masked_image_latents for each generation per prompt, using mps friendly method
-        if masked_image_latents.shape[0] < batch_size:
-            if not batch_size % masked_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
-
-        masked_image_latents = (
-            torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-        )
-
-        # aligning device to prevent device errors when concating it with the latent model input
-        masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
-        return masked_image_latents
-
-    def _default_height_width(self, height, width, image):
-        if isinstance(image, list):
-            image = image[0]
-
-        if height is None:
-            if isinstance(image, PIL.Image.Image):
-                height = image.height
-            elif isinstance(image, torch.Tensor):
-                height = image.shape[3]
-
-            height = (height // 8) * 8  # round down to nearest multiple of 8
-
-        if width is None:
-            if isinstance(image, PIL.Image.Image):
-                width = image.width
-            elif isinstance(image, torch.Tensor):
-                width = image.shape[2]
-
-            width = (width // 8) * 8  # round down to nearest multiple of 8
-
-        return height, width
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.Tensor, PIL.Image.Image] = None,
-        mask_image: Union[torch.Tensor, PIL.Image.Image] = None,
-        controlnet_conditioning_image: Union[
-            torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]
-        ] = None,
-        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_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            mask_image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
-                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
-                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
-                instead of 3, so the expected shape would be `(B, H, W, 1)`.
-            controlnet_conditioning_image (`torch.FloatTensor`, `PIL.Image.Image`, `List[torch.FloatTensor]` or `List[PIL.Image.Image]`):
-                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
-                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. PIL.Image.Image` can
-                also be accepted as an image. The control image is automatically resized to fit the output image.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
-                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
-                to the residual in the original unet.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height, width = self._default_height_width(height, width, controlnet_conditioning_image)
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            image,
-            mask_image,
-            controlnet_conditioning_image,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-            controlnet_conditioning_scale,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        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
-
-        if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
-            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
-
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-        )
-
-        # 4. Prepare mask, image, and controlnet_conditioning_image
-        image = prepare_image(image)
-
-        mask_image = prepare_mask_image(mask_image)
-
-        # condition image(s)
-        if isinstance(self.controlnet, ControlNetModel):
-            controlnet_conditioning_image = prepare_controlnet_conditioning_image(
-                controlnet_conditioning_image=controlnet_conditioning_image,
-                width=width,
-                height=height,
-                batch_size=batch_size * num_images_per_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-                dtype=self.controlnet.dtype,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-            )
-        elif isinstance(self.controlnet, MultiControlNetModel):
-            controlnet_conditioning_images = []
-
-            for image_ in controlnet_conditioning_image:
-                image_ = prepare_controlnet_conditioning_image(
-                    controlnet_conditioning_image=image_,
-                    width=width,
-                    height=height,
-                    batch_size=batch_size * num_images_per_prompt,
-                    num_images_per_prompt=num_images_per_prompt,
-                    device=device,
-                    dtype=self.controlnet.dtype,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                )
-                controlnet_conditioning_images.append(image_)
-
-            controlnet_conditioning_image = controlnet_conditioning_images
-        else:
-            assert False
-
-        masked_image = image * (mask_image < 0.5)
-
-        # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 6. Prepare latent variables
-        num_channels_latents = self.vae.config.latent_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        mask_image_latents = self.prepare_mask_latents(
-            mask_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            do_classifier_free_guidance,
-        )
-
-        masked_image_latents = self.prepare_masked_image_latents(
-            masked_image,
-            batch_size * num_images_per_prompt,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            do_classifier_free_guidance,
-        )
-
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 8. 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
-                non_inpainting_latent_model_input = (
-                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                )
-
-                non_inpainting_latent_model_input = self.scheduler.scale_model_input(
-                    non_inpainting_latent_model_input, t
-                )
-
-                inpainting_latent_model_input = torch.cat(
-                    [non_inpainting_latent_model_input, mask_image_latents, masked_image_latents], dim=1
-                )
-
-                down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    non_inpainting_latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    controlnet_cond=controlnet_conditioning_image,
-                    conditioning_scale=controlnet_conditioning_scale,
-                    return_dict=False,
-                )
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    inpainting_latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    down_block_additional_residuals=down_block_res_samples,
-                    mid_block_additional_residual=mid_block_res_sample,
-                ).sample
-
-                # 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)
-
-        # If we do sequential model offloading, let's offload unet and controlnet
-        # manually for max memory savings
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.unet.to("cpu")
-            self.controlnet.to("cpu")
-            torch.cuda.empty_cache()
-
-        if output_type == "latent":
-            image = latents
-            has_nsfw_concept = None
-        elif output_type == "pil":
-            # 8. Post-processing
-            image = self.decode_latents(latents)
-
-            # 9. Run safety checker
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-            # 10. Convert to PIL
-            image = self.numpy_to_pil(image)
-        else:
-            # 8. Post-processing
-            image = self.decode_latents(latents)
-
-            # 9. Run safety checker
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/cascade_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/optimization/constants.py

@@ -1,2 +0,0 @@
-ASSETS_DIR_NAME = "assets"
-RANKED_RESULTS_DIR = "ranked"

spaces/Arnx/MusicGenXvAKN/audiocraft/models/builders.py

@@ -1,218 +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.
-
-"""
-All the functions to build the relevant models and modules
-from the Hydra config.
-"""
-
-import typing as tp
-import warnings
-
-import audiocraft
-import omegaconf
-import torch
-
-from .encodec import CompressionModel, EncodecModel, FlattenedCompressionModel  # noqa
-from .lm import LMModel
-from ..modules.codebooks_patterns import (
-    CodebooksPatternProvider,
-    DelayedPatternProvider,
-    ParallelPatternProvider,
-    UnrolledPatternProvider,
-    VALLEPattern,
-    MusicLMPattern,
-)
-from ..modules.conditioners import (
-    BaseConditioner,
-    ConditioningProvider,
-    LUTConditioner,
-    T5Conditioner,
-    ConditionFuser,
-    ChromaStemConditioner,
-)
-from .. import quantization as qt
-from ..utils.utils import dict_from_config
-
-
-def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
-    klass = {
-        'no_quant': qt.DummyQuantizer,
-        'rvq': qt.ResidualVectorQuantizer
-    }[quantizer]
-    kwargs = dict_from_config(getattr(cfg, quantizer))
-    if quantizer != 'no_quant':
-        kwargs['dimension'] = dimension
-    return klass(**kwargs)
-
-
-def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
-    if encoder_name == 'seanet':
-        kwargs = dict_from_config(getattr(cfg, 'seanet'))
-        encoder_override_kwargs = kwargs.pop('encoder')
-        decoder_override_kwargs = kwargs.pop('decoder')
-        encoder_kwargs = {**kwargs, **encoder_override_kwargs}
-        decoder_kwargs = {**kwargs, **decoder_override_kwargs}
-        encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
-        decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
-        return encoder, decoder
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
-    """Instantiate a compression model.
-    """
-    if cfg.compression_model == 'encodec':
-        kwargs = dict_from_config(getattr(cfg, 'encodec'))
-        encoder_name = kwargs.pop('autoencoder')
-        quantizer_name = kwargs.pop('quantizer')
-        encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
-        quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
-        frame_rate = kwargs['sample_rate'] // encoder.hop_length
-        renormalize = kwargs.pop('renormalize', None)
-        renorm = kwargs.pop('renorm')
-        if renormalize is None:
-            renormalize = renorm is not None
-            warnings.warn("You are using a deprecated EnCodec model. Please migrate to new renormalization.")
-        return EncodecModel(encoder, decoder, quantizer,
-                            frame_rate=frame_rate, renormalize=renormalize, **kwargs).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected compression model {cfg.compression_model}')
-
-
-def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
-    """Instantiate a transformer LM.
-    """
-    if cfg.lm_model == 'transformer_lm':
-        kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
-        n_q = kwargs['n_q']
-        q_modeling = kwargs.pop('q_modeling', None)
-        codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
-        attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
-        cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
-        cfg_prob, cfg_coef = cls_free_guidance["training_dropout"], cls_free_guidance["inference_coef"]
-        fuser = get_condition_fuser(cfg)
-        condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
-        if len(fuser.fuse2cond['cross']) > 0:  # enforce cross-att programatically
-            kwargs['cross_attention'] = True
-        if codebooks_pattern_cfg.modeling is None:
-            assert q_modeling is not None, \
-                'LM model should either have a codebook pattern defined or transformer_lm.q_modeling'
-            codebooks_pattern_cfg = omegaconf.OmegaConf.create(
-                {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
-            )
-        pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
-        return LMModel(
-            pattern_provider=pattern_provider,
-            condition_provider=condition_provider,
-            fuser=fuser,
-            cfg_dropout=cfg_prob,
-            cfg_coef=cfg_coef,
-            attribute_dropout=attribute_dropout,
-            dtype=getattr(torch, cfg.dtype),
-            device=cfg.device,
-            **kwargs
-        ).to(cfg.device)
-    else:
-        raise KeyError(f'Unexpected LM model {cfg.lm_model}')
-
-
-def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
-    """Instantiate a conditioning model.
-    """
-    device = cfg.device
-    duration = cfg.dataset.segment_duration
-    cfg = getattr(cfg, "conditioners")
-    cfg = omegaconf.OmegaConf.create({}) if cfg is None else cfg
-    conditioners: tp.Dict[str, BaseConditioner] = {}
-    with omegaconf.open_dict(cfg):
-        condition_provider_args = cfg.pop('args', {})
-    for cond, cond_cfg in cfg.items():
-        model_type = cond_cfg["model"]
-        model_args = cond_cfg[model_type]
-        if model_type == "t5":
-            conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
-        elif model_type == "lut":
-            conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
-        elif model_type == "chroma_stem":
-            model_args.pop('cache_path', None)
-            conditioners[str(cond)] = ChromaStemConditioner(
-                output_dim=output_dim,
-                duration=duration,
-                device=device,
-                **model_args
-            )
-        else:
-            raise ValueError(f"unrecognized conditioning model: {model_type}")
-    conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
-    return conditioner
-
-
-def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
-    """Instantiate a condition fuser object.
-    """
-    fuser_cfg = getattr(cfg, "fuser")
-    fuser_methods = ["sum", "cross", "prepend", "input_interpolate"]
-    fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
-    kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
-    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
-    return fuser
-
-
-def get_codebooks_pattern_provider(n_q: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
-    """Instantiate a codebooks pattern provider object.
-    """
-    pattern_providers = {
-        'parallel': ParallelPatternProvider,
-        'delay': DelayedPatternProvider,
-        'unroll': UnrolledPatternProvider,
-        'valle': VALLEPattern,
-        'musiclm': MusicLMPattern,
-    }
-    name = cfg.modeling
-    kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
-    klass = pattern_providers[name]
-    return klass(n_q, **kwargs)
-
-
-def get_debug_compression_model(device='cpu'):
-    """Instantiate a debug compression model to be used for unit tests.
-    """
-    seanet_kwargs = {
-        'n_filters': 4,
-        'n_residual_layers': 1,
-        'dimension': 32,
-        'ratios': [10, 8, 16]  # 25 Hz at 32kHz
-    }
-    encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
-    decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
-    quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
-    init_x = torch.randn(8, 32, 128)
-    quantizer(init_x, 1)  # initialize kmeans etc.
-    compression_model = EncodecModel(
-        encoder, decoder, quantizer,
-        frame_rate=25, sample_rate=32000, channels=1).to(device)
-    return compression_model.eval()
-
-
-def get_debug_lm_model(device='cpu'):
-    """Instantiate a debug LM to be used for unit tests.
-    """
-    pattern = DelayedPatternProvider(n_q=4)
-    dim = 16
-    providers = {
-        'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
-    }
-    condition_provider = ConditioningProvider(providers)
-    fuser = ConditionFuser(
-        {'cross': ['description'], 'prepend': [],
-         'sum': [], 'input_interpolate': []})
-    lm = LMModel(
-        pattern, condition_provider, fuser,
-        n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
-        cross_attention=True, causal=True)
-    return lm.to(device).eval()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/list.py

@@ -1,365 +0,0 @@
-import json
-import logging
-from optparse import Values
-from typing import TYPE_CHECKING, Generator, List, Optional, Sequence, Tuple, cast
-
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.req_command import IndexGroupCommand
-from pip._internal.cli.status_codes import SUCCESS
-from pip._internal.exceptions import CommandError
-from pip._internal.index.collector import LinkCollector
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import BaseDistribution, get_environment
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.network.session import PipSession
-from pip._internal.utils.compat import stdlib_pkgs
-from pip._internal.utils.misc import tabulate, write_output
-
-if TYPE_CHECKING:
-    from pip._internal.metadata.base import DistributionVersion
-
-    class _DistWithLatestInfo(BaseDistribution):
-        """Give the distribution object a couple of extra fields.
-
-        These will be populated during ``get_outdated()``. This is dirty but
-        makes the rest of the code much cleaner.
-        """
-
-        latest_version: DistributionVersion
-        latest_filetype: str
-
-    _ProcessedDists = Sequence[_DistWithLatestInfo]
-
-
-logger = logging.getLogger(__name__)
-
-
-class ListCommand(IndexGroupCommand):
-    """
-    List installed packages, including editables.
-
-    Packages are listed in a case-insensitive sorted order.
-    """
-
-    ignore_require_venv = True
-    usage = """
-      %prog [options]"""
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "-o",
-            "--outdated",
-            action="store_true",
-            default=False,
-            help="List outdated packages",
-        )
-        self.cmd_opts.add_option(
-            "-u",
-            "--uptodate",
-            action="store_true",
-            default=False,
-            help="List uptodate packages",
-        )
-        self.cmd_opts.add_option(
-            "-e",
-            "--editable",
-            action="store_true",
-            default=False,
-            help="List editable projects.",
-        )
-        self.cmd_opts.add_option(
-            "-l",
-            "--local",
-            action="store_true",
-            default=False,
-            help=(
-                "If in a virtualenv that has global access, do not list "
-                "globally-installed packages."
-            ),
-        )
-        self.cmd_opts.add_option(
-            "--user",
-            dest="user",
-            action="store_true",
-            default=False,
-            help="Only output packages installed in user-site.",
-        )
-        self.cmd_opts.add_option(cmdoptions.list_path())
-        self.cmd_opts.add_option(
-            "--pre",
-            action="store_true",
-            default=False,
-            help=(
-                "Include pre-release and development versions. By default, "
-                "pip only finds stable versions."
-            ),
-        )
-
-        self.cmd_opts.add_option(
-            "--format",
-            action="store",
-            dest="list_format",
-            default="columns",
-            choices=("columns", "freeze", "json"),
-            help="Select the output format among: columns (default), freeze, or json",
-        )
-
-        self.cmd_opts.add_option(
-            "--not-required",
-            action="store_true",
-            dest="not_required",
-            help="List packages that are not dependencies of installed packages.",
-        )
-
-        self.cmd_opts.add_option(
-            "--exclude-editable",
-            action="store_false",
-            dest="include_editable",
-            help="Exclude editable package from output.",
-        )
-        self.cmd_opts.add_option(
-            "--include-editable",
-            action="store_true",
-            dest="include_editable",
-            help="Include editable package from output.",
-            default=True,
-        )
-        self.cmd_opts.add_option(cmdoptions.list_exclude())
-        index_opts = cmdoptions.make_option_group(cmdoptions.index_group, self.parser)
-
-        self.parser.insert_option_group(0, index_opts)
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def _build_package_finder(
-        self, options: Values, session: PipSession
-    ) -> PackageFinder:
-        """
-        Create a package finder appropriate to this list command.
-        """
-        link_collector = LinkCollector.create(session, options=options)
-
-        # Pass allow_yanked=False to ignore yanked versions.
-        selection_prefs = SelectionPreferences(
-            allow_yanked=False,
-            allow_all_prereleases=options.pre,
-        )
-
-        return PackageFinder.create(
-            link_collector=link_collector,
-            selection_prefs=selection_prefs,
-        )
-
-    def run(self, options: Values, args: List[str]) -> int:
-        if options.outdated and options.uptodate:
-            raise CommandError("Options --outdated and --uptodate cannot be combined.")
-
-        if options.outdated and options.list_format == "freeze":
-            raise CommandError(
-                "List format 'freeze' can not be used with the --outdated option."
-            )
-
-        cmdoptions.check_list_path_option(options)
-
-        skip = set(stdlib_pkgs)
-        if options.excludes:
-            skip.update(canonicalize_name(n) for n in options.excludes)
-
-        packages: "_ProcessedDists" = [
-            cast("_DistWithLatestInfo", d)
-            for d in get_environment(options.path).iter_installed_distributions(
-                local_only=options.local,
-                user_only=options.user,
-                editables_only=options.editable,
-                include_editables=options.include_editable,
-                skip=skip,
-            )
-        ]
-
-        # get_not_required must be called firstly in order to find and
-        # filter out all dependencies correctly. Otherwise a package
-        # can't be identified as requirement because some parent packages
-        # could be filtered out before.
-        if options.not_required:
-            packages = self.get_not_required(packages, options)
-
-        if options.outdated:
-            packages = self.get_outdated(packages, options)
-        elif options.uptodate:
-            packages = self.get_uptodate(packages, options)
-
-        self.output_package_listing(packages, options)
-        return SUCCESS
-
-    def get_outdated(
-        self, packages: "_ProcessedDists", options: Values
-    ) -> "_ProcessedDists":
-        return [
-            dist
-            for dist in self.iter_packages_latest_infos(packages, options)
-            if dist.latest_version > dist.version
-        ]
-
-    def get_uptodate(
-        self, packages: "_ProcessedDists", options: Values
-    ) -> "_ProcessedDists":
-        return [
-            dist
-            for dist in self.iter_packages_latest_infos(packages, options)
-            if dist.latest_version == dist.version
-        ]
-
-    def get_not_required(
-        self, packages: "_ProcessedDists", options: Values
-    ) -> "_ProcessedDists":
-        dep_keys = {
-            canonicalize_name(dep.name)
-            for dist in packages
-            for dep in (dist.iter_dependencies() or ())
-        }
-
-        # Create a set to remove duplicate packages, and cast it to a list
-        # to keep the return type consistent with get_outdated and
-        # get_uptodate
-        return list({pkg for pkg in packages if pkg.canonical_name not in dep_keys})
-
-    def iter_packages_latest_infos(
-        self, packages: "_ProcessedDists", options: Values
-    ) -> Generator["_DistWithLatestInfo", None, None]:
-        with self._build_session(options) as session:
-            finder = self._build_package_finder(options, session)
-
-            def latest_info(
-                dist: "_DistWithLatestInfo",
-            ) -> Optional["_DistWithLatestInfo"]:
-                all_candidates = finder.find_all_candidates(dist.canonical_name)
-                if not options.pre:
-                    # Remove prereleases
-                    all_candidates = [
-                        candidate
-                        for candidate in all_candidates
-                        if not candidate.version.is_prerelease
-                    ]
-
-                evaluator = finder.make_candidate_evaluator(
-                    project_name=dist.canonical_name,
-                )
-                best_candidate = evaluator.sort_best_candidate(all_candidates)
-                if best_candidate is None:
-                    return None
-
-                remote_version = best_candidate.version
-                if best_candidate.link.is_wheel:
-                    typ = "wheel"
-                else:
-                    typ = "sdist"
-                dist.latest_version = remote_version
-                dist.latest_filetype = typ
-                return dist
-
-            for dist in map(latest_info, packages):
-                if dist is not None:
-                    yield dist
-
-    def output_package_listing(
-        self, packages: "_ProcessedDists", options: Values
-    ) -> None:
-        packages = sorted(
-            packages,
-            key=lambda dist: dist.canonical_name,
-        )
-        if options.list_format == "columns" and packages:
-            data, header = format_for_columns(packages, options)
-            self.output_package_listing_columns(data, header)
-        elif options.list_format == "freeze":
-            for dist in packages:
-                if options.verbose >= 1:
-                    write_output(
-                        "%s==%s (%s)", dist.raw_name, dist.version, dist.location
-                    )
-                else:
-                    write_output("%s==%s", dist.raw_name, dist.version)
-        elif options.list_format == "json":
-            write_output(format_for_json(packages, options))
-
-    def output_package_listing_columns(
-        self, data: List[List[str]], header: List[str]
-    ) -> None:
-        # insert the header first: we need to know the size of column names
-        if len(data) > 0:
-            data.insert(0, header)
-
-        pkg_strings, sizes = tabulate(data)
-
-        # Create and add a separator.
-        if len(data) > 0:
-            pkg_strings.insert(1, " ".join(map(lambda x: "-" * x, sizes)))
-
-        for val in pkg_strings:
-            write_output(val)
-
-
-def format_for_columns(
-    pkgs: "_ProcessedDists", options: Values
-) -> Tuple[List[List[str]], List[str]]:
-    """
-    Convert the package data into something usable
-    by output_package_listing_columns.
-    """
-    header = ["Package", "Version"]
-
-    running_outdated = options.outdated
-    if running_outdated:
-        header.extend(["Latest", "Type"])
-
-    has_editables = any(x.editable for x in pkgs)
-    if has_editables:
-        header.append("Editable project location")
-
-    if options.verbose >= 1:
-        header.append("Location")
-    if options.verbose >= 1:
-        header.append("Installer")
-
-    data = []
-    for proj in pkgs:
-        # if we're working on the 'outdated' list, separate out the
-        # latest_version and type
-        row = [proj.raw_name, str(proj.version)]
-
-        if running_outdated:
-            row.append(str(proj.latest_version))
-            row.append(proj.latest_filetype)
-
-        if has_editables:
-            row.append(proj.editable_project_location or "")
-
-        if options.verbose >= 1:
-            row.append(proj.location or "")
-        if options.verbose >= 1:
-            row.append(proj.installer)
-
-        data.append(row)
-
-    return data, header
-
-
-def format_for_json(packages: "_ProcessedDists", options: Values) -> str:
-    data = []
-    for dist in packages:
-        info = {
-            "name": dist.raw_name,
-            "version": str(dist.version),
-        }
-        if options.verbose >= 1:
-            info["location"] = dist.location or ""
-            info["installer"] = dist.installer
-        if options.outdated:
-            info["latest_version"] = str(dist.latest_version)
-            info["latest_filetype"] = dist.latest_filetype
-        editable_project_location = dist.editable_project_location
-        if editable_project_location:
-            info["editable_project_location"] = editable_project_location
-        data.append(info)
-    return json.dumps(data)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/metadata/importlib/__init__.py

@@ -1,4 +0,0 @@
-from ._dists import Distribution
-from ._envs import Environment
-
-__all__ = ["Distribution", "Environment"]

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/win32.py

@@ -1,180 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-
-# from winbase.h
-STDOUT = -11
-STDERR = -12
-
-ENABLE_VIRTUAL_TERMINAL_PROCESSING = 0x0004
-
-try:
-    import ctypes
-    from ctypes import LibraryLoader
-    windll = LibraryLoader(ctypes.WinDLL)
-    from ctypes import wintypes
-except (AttributeError, ImportError):
-    windll = None
-    SetConsoleTextAttribute = lambda *_: None
-    winapi_test = lambda *_: None
-else:
-    from ctypes import byref, Structure, c_char, POINTER
-
-    COORD = wintypes._COORD
-
-    class CONSOLE_SCREEN_BUFFER_INFO(Structure):
-        """struct in wincon.h."""
-        _fields_ = [
-            ("dwSize", COORD),
-            ("dwCursorPosition", COORD),
-            ("wAttributes", wintypes.WORD),
-            ("srWindow", wintypes.SMALL_RECT),
-            ("dwMaximumWindowSize", COORD),
-        ]
-        def __str__(self):
-            return '(%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d)' % (
-                self.dwSize.Y, self.dwSize.X
-                , self.dwCursorPosition.Y, self.dwCursorPosition.X
-                , self.wAttributes
-                , self.srWindow.Top, self.srWindow.Left, self.srWindow.Bottom, self.srWindow.Right
-                , self.dwMaximumWindowSize.Y, self.dwMaximumWindowSize.X
-            )
-
-    _GetStdHandle = windll.kernel32.GetStdHandle
-    _GetStdHandle.argtypes = [
-        wintypes.DWORD,
-    ]
-    _GetStdHandle.restype = wintypes.HANDLE
-
-    _GetConsoleScreenBufferInfo = windll.kernel32.GetConsoleScreenBufferInfo
-    _GetConsoleScreenBufferInfo.argtypes = [
-        wintypes.HANDLE,
-        POINTER(CONSOLE_SCREEN_BUFFER_INFO),
-    ]
-    _GetConsoleScreenBufferInfo.restype = wintypes.BOOL
-
-    _SetConsoleTextAttribute = windll.kernel32.SetConsoleTextAttribute
-    _SetConsoleTextAttribute.argtypes = [
-        wintypes.HANDLE,
-        wintypes.WORD,
-    ]
-    _SetConsoleTextAttribute.restype = wintypes.BOOL
-
-    _SetConsoleCursorPosition = windll.kernel32.SetConsoleCursorPosition
-    _SetConsoleCursorPosition.argtypes = [
-        wintypes.HANDLE,
-        COORD,
-    ]
-    _SetConsoleCursorPosition.restype = wintypes.BOOL
-
-    _FillConsoleOutputCharacterA = windll.kernel32.FillConsoleOutputCharacterA
-    _FillConsoleOutputCharacterA.argtypes = [
-        wintypes.HANDLE,
-        c_char,
-        wintypes.DWORD,
-        COORD,
-        POINTER(wintypes.DWORD),
-    ]
-    _FillConsoleOutputCharacterA.restype = wintypes.BOOL
-
-    _FillConsoleOutputAttribute = windll.kernel32.FillConsoleOutputAttribute
-    _FillConsoleOutputAttribute.argtypes = [
-        wintypes.HANDLE,
-        wintypes.WORD,
-        wintypes.DWORD,
-        COORD,
-        POINTER(wintypes.DWORD),
-    ]
-    _FillConsoleOutputAttribute.restype = wintypes.BOOL
-
-    _SetConsoleTitleW = windll.kernel32.SetConsoleTitleW
-    _SetConsoleTitleW.argtypes = [
-        wintypes.LPCWSTR
-    ]
-    _SetConsoleTitleW.restype = wintypes.BOOL
-
-    _GetConsoleMode = windll.kernel32.GetConsoleMode
-    _GetConsoleMode.argtypes = [
-        wintypes.HANDLE,
-        POINTER(wintypes.DWORD)
-    ]
-    _GetConsoleMode.restype = wintypes.BOOL
-
-    _SetConsoleMode = windll.kernel32.SetConsoleMode
-    _SetConsoleMode.argtypes = [
-        wintypes.HANDLE,
-        wintypes.DWORD
-    ]
-    _SetConsoleMode.restype = wintypes.BOOL
-
-    def _winapi_test(handle):
-        csbi = CONSOLE_SCREEN_BUFFER_INFO()
-        success = _GetConsoleScreenBufferInfo(
-            handle, byref(csbi))
-        return bool(success)
-
-    def winapi_test():
-        return any(_winapi_test(h) for h in
-                   (_GetStdHandle(STDOUT), _GetStdHandle(STDERR)))
-
-    def GetConsoleScreenBufferInfo(stream_id=STDOUT):
-        handle = _GetStdHandle(stream_id)
-        csbi = CONSOLE_SCREEN_BUFFER_INFO()
-        success = _GetConsoleScreenBufferInfo(
-            handle, byref(csbi))
-        return csbi
-
-    def SetConsoleTextAttribute(stream_id, attrs):
-        handle = _GetStdHandle(stream_id)
-        return _SetConsoleTextAttribute(handle, attrs)
-
-    def SetConsoleCursorPosition(stream_id, position, adjust=True):
-        position = COORD(*position)
-        # If the position is out of range, do nothing.
-        if position.Y <= 0 or position.X <= 0:
-            return
-        # Adjust for Windows' SetConsoleCursorPosition:
-        #    1. being 0-based, while ANSI is 1-based.
-        #    2. expecting (x,y), while ANSI uses (y,x).
-        adjusted_position = COORD(position.Y - 1, position.X - 1)
-        if adjust:
-            # Adjust for viewport's scroll position
-            sr = GetConsoleScreenBufferInfo(STDOUT).srWindow
-            adjusted_position.Y += sr.Top
-            adjusted_position.X += sr.Left
-        # Resume normal processing
-        handle = _GetStdHandle(stream_id)
-        return _SetConsoleCursorPosition(handle, adjusted_position)
-
-    def FillConsoleOutputCharacter(stream_id, char, length, start):
-        handle = _GetStdHandle(stream_id)
-        char = c_char(char.encode())
-        length = wintypes.DWORD(length)
-        num_written = wintypes.DWORD(0)
-        # Note that this is hard-coded for ANSI (vs wide) bytes.
-        success = _FillConsoleOutputCharacterA(
-            handle, char, length, start, byref(num_written))
-        return num_written.value
-
-    def FillConsoleOutputAttribute(stream_id, attr, length, start):
-        ''' FillConsoleOutputAttribute( hConsole, csbi.wAttributes, dwConSize, coordScreen, &cCharsWritten )'''
-        handle = _GetStdHandle(stream_id)
-        attribute = wintypes.WORD(attr)
-        length = wintypes.DWORD(length)
-        num_written = wintypes.DWORD(0)
-        # Note that this is hard-coded for ANSI (vs wide) bytes.
-        return _FillConsoleOutputAttribute(
-            handle, attribute, length, start, byref(num_written))
-
-    def SetConsoleTitle(title):
-        return _SetConsoleTitleW(title)
-
-    def GetConsoleMode(handle):
-        mode = wintypes.DWORD()
-        success = _GetConsoleMode(handle, byref(mode))
-        if not success:
-            raise ctypes.WinError()
-        return mode.value
-
-    def SetConsoleMode(handle, mode):
-        success = _SetConsoleMode(handle, mode)
-        if not success:
-            raise ctypes.WinError()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/archive_util.py

@@ -1,213 +0,0 @@
-"""Utilities for extracting common archive formats"""
-
-import zipfile
-import tarfile
-import os
-import shutil
-import posixpath
-import contextlib
-from distutils.errors import DistutilsError
-
-from ._path import ensure_directory
-
-__all__ = [
-    "unpack_archive", "unpack_zipfile", "unpack_tarfile", "default_filter",
-    "UnrecognizedFormat", "extraction_drivers", "unpack_directory",
-]
-
-
-class UnrecognizedFormat(DistutilsError):
-    """Couldn't recognize the archive type"""
-
-
-def default_filter(src, dst):
-    """The default progress/filter callback; returns True for all files"""
-    return dst
-
-
-def unpack_archive(
-        filename, extract_dir, progress_filter=default_filter,
-        drivers=None):
-    """Unpack `filename` to `extract_dir`, or raise ``UnrecognizedFormat``
-
-    `progress_filter` is a function taking two arguments: a source path
-    internal to the archive ('/'-separated), and a filesystem path where it
-    will be extracted.  The callback must return the desired extract path
-    (which may be the same as the one passed in), or else ``None`` to skip
-    that file or directory.  The callback can thus be used to report on the
-    progress of the extraction, as well as to filter the items extracted or
-    alter their extraction paths.
-
-    `drivers`, if supplied, must be a non-empty sequence of functions with the
-    same signature as this function (minus the `drivers` argument), that raise
-    ``UnrecognizedFormat`` if they do not support extracting the designated
-    archive type.  The `drivers` are tried in sequence until one is found that
-    does not raise an error, or until all are exhausted (in which case
-    ``UnrecognizedFormat`` is raised).  If you do not supply a sequence of
-    drivers, the module's ``extraction_drivers`` constant will be used, which
-    means that ``unpack_zipfile`` and ``unpack_tarfile`` will be tried, in that
-    order.
-    """
-    for driver in drivers or extraction_drivers:
-        try:
-            driver(filename, extract_dir, progress_filter)
-        except UnrecognizedFormat:
-            continue
-        else:
-            return
-    else:
-        raise UnrecognizedFormat(
-            "Not a recognized archive type: %s" % filename
-        )
-
-
-def unpack_directory(filename, extract_dir, progress_filter=default_filter):
-    """"Unpack" a directory, using the same interface as for archives
-
-    Raises ``UnrecognizedFormat`` if `filename` is not a directory
-    """
-    if not os.path.isdir(filename):
-        raise UnrecognizedFormat("%s is not a directory" % filename)
-
-    paths = {
-        filename: ('', extract_dir),
-    }
-    for base, dirs, files in os.walk(filename):
-        src, dst = paths[base]
-        for d in dirs:
-            paths[os.path.join(base, d)] = src + d + '/', os.path.join(dst, d)
-        for f in files:
-            target = os.path.join(dst, f)
-            target = progress_filter(src + f, target)
-            if not target:
-                # skip non-files
-                continue
-            ensure_directory(target)
-            f = os.path.join(base, f)
-            shutil.copyfile(f, target)
-            shutil.copystat(f, target)
-
-
-def unpack_zipfile(filename, extract_dir, progress_filter=default_filter):
-    """Unpack zip `filename` to `extract_dir`
-
-    Raises ``UnrecognizedFormat`` if `filename` is not a zipfile (as determined
-    by ``zipfile.is_zipfile()``).  See ``unpack_archive()`` for an explanation
-    of the `progress_filter` argument.
-    """
-
-    if not zipfile.is_zipfile(filename):
-        raise UnrecognizedFormat("%s is not a zip file" % (filename,))
-
-    with zipfile.ZipFile(filename) as z:
-        _unpack_zipfile_obj(z, extract_dir, progress_filter)
-
-
-def _unpack_zipfile_obj(zipfile_obj, extract_dir, progress_filter=default_filter):
-    """Internal/private API used by other parts of setuptools.
-    Similar to ``unpack_zipfile``, but receives an already opened :obj:`zipfile.ZipFile`
-    object instead of a filename.
-    """
-    for info in zipfile_obj.infolist():
-        name = info.filename
-
-        # don't extract absolute paths or ones with .. in them
-        if name.startswith('/') or '..' in name.split('/'):
-            continue
-
-        target = os.path.join(extract_dir, *name.split('/'))
-        target = progress_filter(name, target)
-        if not target:
-            continue
-        if name.endswith('/'):
-            # directory
-            ensure_directory(target)
-        else:
-            # file
-            ensure_directory(target)
-            data = zipfile_obj.read(info.filename)
-            with open(target, 'wb') as f:
-                f.write(data)
-        unix_attributes = info.external_attr >> 16
-        if unix_attributes:
-            os.chmod(target, unix_attributes)
-
-
-def _resolve_tar_file_or_dir(tar_obj, tar_member_obj):
-    """Resolve any links and extract link targets as normal files."""
-    while tar_member_obj is not None and (
-            tar_member_obj.islnk() or tar_member_obj.issym()):
-        linkpath = tar_member_obj.linkname
-        if tar_member_obj.issym():
-            base = posixpath.dirname(tar_member_obj.name)
-            linkpath = posixpath.join(base, linkpath)
-            linkpath = posixpath.normpath(linkpath)
-        tar_member_obj = tar_obj._getmember(linkpath)
-
-    is_file_or_dir = (
-        tar_member_obj is not None and
-        (tar_member_obj.isfile() or tar_member_obj.isdir())
-    )
-    if is_file_or_dir:
-        return tar_member_obj
-
-    raise LookupError('Got unknown file type')
-
-
-def _iter_open_tar(tar_obj, extract_dir, progress_filter):
-    """Emit member-destination pairs from a tar archive."""
-    # don't do any chowning!
-    tar_obj.chown = lambda *args: None
-
-    with contextlib.closing(tar_obj):
-        for member in tar_obj:
-            name = member.name
-            # don't extract absolute paths or ones with .. in them
-            if name.startswith('/') or '..' in name.split('/'):
-                continue
-
-            prelim_dst = os.path.join(extract_dir, *name.split('/'))
-
-            try:
-                member = _resolve_tar_file_or_dir(tar_obj, member)
-            except LookupError:
-                continue
-
-            final_dst = progress_filter(name, prelim_dst)
-            if not final_dst:
-                continue
-
-            if final_dst.endswith(os.sep):
-                final_dst = final_dst[:-1]
-
-            yield member, final_dst
-
-
-def unpack_tarfile(filename, extract_dir, progress_filter=default_filter):
-    """Unpack tar/tar.gz/tar.bz2 `filename` to `extract_dir`
-
-    Raises ``UnrecognizedFormat`` if `filename` is not a tarfile (as determined
-    by ``tarfile.open()``).  See ``unpack_archive()`` for an explanation
-    of the `progress_filter` argument.
-    """
-    try:
-        tarobj = tarfile.open(filename)
-    except tarfile.TarError as e:
-        raise UnrecognizedFormat(
-            "%s is not a compressed or uncompressed tar file" % (filename,)
-        ) from e
-
-    for member, final_dst in _iter_open_tar(
-            tarobj, extract_dir, progress_filter,
-    ):
-        try:
-            # XXX Ugh
-            tarobj._extract_member(member, final_dst)
-        except tarfile.ExtractError:
-            # chown/chmod/mkfifo/mknode/makedev failed
-            pass
-
-    return True
-
-
-extraction_drivers = unpack_directory, unpack_zipfile, unpack_tarfile

spaces/BAAI/AltDiffusion-m9/share_btn.py

@@ -1,60 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    const imgEls = gradioEl.querySelectorAll('#gallery img');
-    const promptTxt = gradioEl.querySelector('#prompt-text-input input').value;
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `diffuse-the-rest-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        })
-    );
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-	const descriptionMd = `<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgs.join(`\n`)}
-</div>`;
-    const params = new URLSearchParams({
-        title: promptTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/BAAI/bilingual_stable_diffusion/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""

spaces/Bart92/RVC_HF/infer/modules/onnx/export.py

@@ -1,52 +0,0 @@
-import torch
-
-from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
-
-
-def export_onnx(ModelPath, ExportedPath):
-    cpt = torch.load(ModelPath, map_location="cpu")
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-    vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
-
-    test_phone = torch.rand(1, 200, vec_channels)  # hidden unit
-    test_phone_lengths = torch.tensor([200]).long()  # hidden unit 长度（貌似没啥用）
-    test_pitch = torch.randint(size=(1, 200), low=5, high=255)  # 基频（单位赫兹）
-    test_pitchf = torch.rand(1, 200)  # nsf基频
-    test_ds = torch.LongTensor([0])  # 说话人ID
-    test_rnd = torch.rand(1, 192, 200)  # 噪声（加入随机因子）
-
-    device = "cpu"  # 导出时设备（不影响使用模型）
-
-    net_g = SynthesizerTrnMsNSFsidM(
-        *cpt["config"], is_half=False, version=cpt.get("version", "v1")
-    )  # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
-    net_g.load_state_dict(cpt["weight"], strict=False)
-    input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
-    output_names = [
-        "audio",
-    ]
-    # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
-    torch.onnx.export(
-        net_g,
-        (
-            test_phone.to(device),
-            test_phone_lengths.to(device),
-            test_pitch.to(device),
-            test_pitchf.to(device),
-            test_ds.to(device),
-            test_rnd.to(device),
-        ),
-        ExportedPath,
-        dynamic_axes={
-            "phone": [1],
-            "pitch": [1],
-            "pitchf": [1],
-            "rnd": [2],
-        },
-        do_constant_folding=False,
-        opset_version=13,
-        verbose=False,
-        input_names=input_names,
-        output_names=output_names,
-    )
-    return "Finished"

spaces/BetterAPI/BetterChat_new/src/lib/types/Message.ts

@@ -1,5 +0,0 @@
-export interface Message {
-	from: "user" | "assistant";
-	id: ReturnType<typeof crypto.randomUUID>;
-	content: string;
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/__init__.py

@@ -1,49 +0,0 @@
-from __future__ import absolute_import
-
-# For backwards compatibility, provide imports that used to be here.
-from .connection import is_connection_dropped
-from .request import SKIP_HEADER, SKIPPABLE_HEADERS, make_headers
-from .response import is_fp_closed
-from .retry import Retry
-from .ssl_ import (
-    ALPN_PROTOCOLS,
-    HAS_SNI,
-    IS_PYOPENSSL,
-    IS_SECURETRANSPORT,
-    PROTOCOL_TLS,
-    SSLContext,
-    assert_fingerprint,
-    resolve_cert_reqs,
-    resolve_ssl_version,
-    ssl_wrap_socket,
-)
-from .timeout import Timeout, current_time
-from .url import Url, get_host, parse_url, split_first
-from .wait import wait_for_read, wait_for_write
-
-__all__ = (
-    "HAS_SNI",
-    "IS_PYOPENSSL",
-    "IS_SECURETRANSPORT",
-    "SSLContext",
-    "PROTOCOL_TLS",
-    "ALPN_PROTOCOLS",
-    "Retry",
-    "Timeout",
-    "Url",
-    "assert_fingerprint",
-    "current_time",
-    "is_connection_dropped",
-    "is_fp_closed",
-    "get_host",
-    "parse_url",
-    "make_headers",
-    "resolve_cert_reqs",
-    "resolve_ssl_version",
-    "split_first",
-    "ssl_wrap_socket",
-    "wait_for_read",
-    "wait_for_write",
-    "SKIP_HEADER",
-    "SKIPPABLE_HEADERS",
-)

spaces/BigChungux/Pet_Survey/app.py

@@ -1,172 +0,0 @@
-### ----------------------------- ###
-###           libraries           ###
-### ----------------------------- ###
-
-import gradio as gr
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn import metrics
-
-
-### ------------------------------ ###
-###       data transformation      ###
-### ------------------------------ ###
-
-# load dataset
-uncleaned_data = pd.read_csv('data.csv')
-
-# remove timestamp from dataset (always first column)
-uncleaned_data = uncleaned_data.iloc[: , 1:]
-data = pd.DataFrame()
-
-# keep track of which columns are categorical and what 
-# those columns' value mappings are
-# structure: {colname1: {...}, colname2: {...} }
-cat_value_dicts = {}
-final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
-
-# for each column...
-for (colname, colval) in uncleaned_data.iteritems():
-
-  # check if col is already a number; if so, add col directly
-  # to new dataframe and skip to next column
-  if isinstance(colval.values[0], (np.integer, float)):
-    data[colname] = uncleaned_data[colname].copy()
-    continue
-
-  # structure: {0: "lilac", 1: "blue", ...}
-  new_dict = {}
-  val = 0 # first index per column
-  transformed_col_vals = [] # new numeric datapoints
-
-  # if not, for each item in that column...
-  for (row, item) in enumerate(colval.values):
-    
-    # if item is not in this col's dict...
-    if item not in new_dict:
-      new_dict[item] = val
-      val += 1
-    
-    # then add numerical value to transformed dataframe
-    transformed_col_vals.append(new_dict[item])
-  
-  # reverse dictionary only for final col (0, 1) => (vals)
-  if colname == final_colname:
-    new_dict = {value : key for (key, value) in new_dict.items()}
-
-  cat_value_dicts[colname] = new_dict
-  data[colname] = transformed_col_vals
-
-
-### -------------------------------- ###
-###           model training         ###
-### -------------------------------- ###
-
-# select features and predicton; automatically selects last column as prediction
-cols = len(data.columns)
-num_features = cols - 1
-x = data.iloc[: , :num_features]
-y = data.iloc[: , num_features:]
-
-# split data into training and testing sets
-x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
-
-# instantiate the model (using default parameters)
-model = LogisticRegression()
-model.fit(x_train, y_train.values.ravel())
-y_pred = model.predict(x_test)
-
-
-### -------------------------------- ###
-###        article generation        ###
-### -------------------------------- ###
-# borrow file reading function from reader.py
-
-def get_feat():
-  feats = [abs(x) for x in model.coef_[0]]
-  max_val = max(feats)
-  idx = feats.index(max_val)
-  return data.columns[idx]
-  
-acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + "%"
-most_imp_feat = get_feat()
-# info = get_article(acc, most_imp_feat)
-
-
-
-### ------------------------------- ###
-###        interface creation       ###
-### ------------------------------- ###
-
-
-# predictor for generic number of features
-def general_predictor(*args):
-  features = []
-
-  # transform categorical input
-  for colname, arg in zip(data.columns, args):
-    if (colname in cat_value_dicts):
-      features.append(cat_value_dicts[colname][arg])
-    else:
-      features.append(arg)
-
-  # predict single datapoint
-  new_input = [features]
-  result = model.predict(new_input)
-  return cat_value_dicts[final_colname][result[0]]
-
-# add data labels to replace those lost via star-args
-
-
-block = gr.Blocks()
-
-with open('info.md') as f:
-  with block:
-    gr.Markdown(f.readline())
-    gr.Markdown('Take the quiz to get a personalized recommendation using AI.')
-    
-    with gr.Row():
-      with gr.Box():
-        inputls = []
-        for colname in data.columns:
-          # skip last column
-          if colname == final_colname:
-            continue
-          
-          # access categories dict if data is categorical
-          # otherwise, just use a number input
-          if colname in cat_value_dicts:
-            radio_options = list(cat_value_dicts[colname].keys())
-            inputls.append(gr.inputs.Dropdown(choices=radio_options, type="value", label=colname))
-          else:
-            # add numerical input
-            inputls.append(gr.inputs.Number(label=colname))
-          gr.Markdown("<br />")
-        
-        submit = gr.Button("Click to see your personalized result!", variant="primary")
-        gr.Markdown("<br />")
-        output = gr.Textbox(label="Your recommendation:", placeholder="your recommendation will appear here")
-        
-        submit.click(fn=general_predictor, inputs=inputls, outputs=output)
-        gr.Markdown("<br />")
-        
-        with gr.Row():
-          with gr.Box():
-            gr.Markdown(f"<h3>Accuracy: </h3>{acc}")
-          with gr.Box():
-            gr.Markdown(f"<h3>Most important feature: </h3>{most_imp_feat}")
-        
-        gr.Markdown("<br />")
-        
-        with gr.Box():
-          gr.Markdown('''⭐ Note that model accuracy is based on the uploaded data.csv and reflects how well the AI model can give correct recommendations for <em>that dataset</em>. Model accuracy and most important feature can be helpful for understanding how the model works, but <em>should not be considered absolute facts about the real world</em>.''')
-        
-      with gr.Box():
-        with open('info.md') as f:
-          f.readline()
-          gr.Markdown(f.read())
-
-# show the interface
-block.launch()

spaces/Boadiwaa/Recipes/openai/api_requestor.py

@@ -1,365 +0,0 @@
-import json
-import platform
-import threading
-import warnings
-from email import header
-from json import JSONDecodeError
-from typing import Dict, Iterator, Optional, Tuple, Union
-from urllib.parse import urlencode, urlsplit, urlunsplit
-
-import requests
-
-import openai
-from openai import error, util, version
-from openai.openai_response import OpenAIResponse
-from openai.util import ApiType
-
-TIMEOUT_SECS = 600
-MAX_CONNECTION_RETRIES = 2
-
-# Has one attribute per thread, 'session'.
-_thread_context = threading.local()
-
-
-def _build_api_url(url, query):
-    scheme, netloc, path, base_query, fragment = urlsplit(url)
-
-    if base_query:
-        query = "%s&%s" % (base_query, query)
-
-    return urlunsplit((scheme, netloc, path, query, fragment))
-
-
-def _requests_proxies_arg(proxy) -> Optional[Dict[str, str]]:
-    """Returns a value suitable for the 'proxies' argument to 'requests.request."""
-    if proxy is None:
-        return None
-    elif isinstance(proxy, str):
-        return {"http": proxy, "https": proxy}
-    elif isinstance(proxy, dict):
-        return proxy.copy()
-    else:
-        raise ValueError(
-            "'openai.proxy' must be specified as either a string URL or a dict with string URL under the https and/or http keys."
-        )
-
-
-def _make_session() -> requests.Session:
-    if not openai.verify_ssl_certs:
-        warnings.warn("verify_ssl_certs is ignored; openai always verifies.")
-    s = requests.Session()
-    proxies = _requests_proxies_arg(openai.proxy)
-    if proxies:
-        s.proxies = proxies
-    s.mount(
-        "https://",
-        requests.adapters.HTTPAdapter(max_retries=MAX_CONNECTION_RETRIES),
-    )
-    return s
-
-
-def parse_stream(rbody):
-    for line in rbody:
-        if line:
-            if line == b"data: [DONE]":
-                # return here will cause GeneratorExit exception in urllib3
-                # and it will close http connection with TCP Reset
-                continue
-            if hasattr(line, "decode"):
-                line = line.decode("utf-8")
-            if line.startswith("data: "):
-                line = line[len("data: ") :]
-            yield line
-
-
-class APIRequestor:
-    def __init__(
-        self,
-        key=None,
-        api_base=None,
-        api_type=None,
-        api_version=None,
-        organization=None,
-    ):
-        self.api_base = api_base or openai.api_base
-        self.api_key = key or util.default_api_key()
-        self.api_type = (
-            ApiType.from_str(api_type)
-            if api_type
-            else ApiType.from_str(openai.api_type)
-        )
-        self.api_version = api_version or openai.api_version
-        self.organization = organization or openai.organization
-
-    @classmethod
-    def format_app_info(cls, info):
-        str = info["name"]
-        if info["version"]:
-            str += "/%s" % (info["version"],)
-        if info["url"]:
-            str += " (%s)" % (info["url"],)
-        return str
-
-    def request(
-        self,
-        method,
-        url,
-        params=None,
-        headers=None,
-        files=None,
-        stream=False,
-        request_id: Optional[str] = None,
-    ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:
-        result = self.request_raw(
-            method.lower(),
-            url,
-            params=params,
-            supplied_headers=headers,
-            files=files,
-            stream=stream,
-            request_id=request_id,
-        )
-        resp, got_stream = self._interpret_response(result, stream)
-        return resp, got_stream, self.api_key
-
-    def handle_error_response(self, rbody, rcode, resp, rheaders, stream_error=False):
-        try:
-            error_data = resp["error"]
-        except (KeyError, TypeError):
-            raise error.APIError(
-                "Invalid response object from API: %r (HTTP response code "
-                "was %d)" % (rbody, rcode),
-                rbody,
-                rcode,
-                resp,
-            )
-
-        if "internal_message" in error_data:
-            error_data["message"] += "\n\n" + error_data["internal_message"]
-
-        util.log_info(
-            "OpenAI API error received",
-            error_code=error_data.get("code"),
-            error_type=error_data.get("type"),
-            error_message=error_data.get("message"),
-            error_param=error_data.get("param"),
-            stream_error=stream_error,
-        )
-
-        # Rate limits were previously coded as 400's with code 'rate_limit'
-        if rcode == 429:
-            return error.RateLimitError(
-                error_data.get("message"), rbody, rcode, resp, rheaders
-            )
-        elif rcode in [400, 404, 415]:
-            return error.InvalidRequestError(
-                error_data.get("message"),
-                error_data.get("param"),
-                error_data.get("code"),
-                rbody,
-                rcode,
-                resp,
-                rheaders,
-            )
-        elif rcode == 401:
-            return error.AuthenticationError(
-                error_data.get("message"), rbody, rcode, resp, rheaders
-            )
-        elif rcode == 403:
-            return error.PermissionError(
-                error_data.get("message"), rbody, rcode, resp, rheaders
-            )
-        elif rcode == 409:
-            return error.TryAgain(
-                error_data.get("message"), rbody, rcode, resp, rheaders
-            )
-        elif stream_error:
-            # TODO: we will soon attach status codes to stream errors
-            parts = [error_data.get("message"), "(Error occurred while streaming.)"]
-            message = " ".join([p for p in parts if p is not None])
-            return error.APIError(message, rbody, rcode, resp, rheaders)
-        else:
-            return error.APIError(
-                error_data.get("message"), rbody, rcode, resp, rheaders
-            )
-
-    def request_headers(
-        self, method: str, extra, request_id: Optional[str]
-    ) -> Dict[str, str]:
-        user_agent = "OpenAI/v1 PythonBindings/%s" % (version.VERSION,)
-        if openai.app_info:
-            user_agent += " " + self.format_app_info(openai.app_info)
-
-        uname_without_node = " ".join(
-            v for k, v in platform.uname()._asdict().items() if k != "node"
-        )
-        ua = {
-            "bindings_version": version.VERSION,
-            "httplib": "requests",
-            "lang": "python",
-            "lang_version": platform.python_version(),
-            "platform": platform.platform(),
-            "publisher": "openai",
-            "uname": uname_without_node,
-        }
-        if openai.app_info:
-            ua["application"] = openai.app_info
-
-        headers = {
-            "X-OpenAI-Client-User-Agent": json.dumps(ua),
-            "User-Agent": user_agent,
-        }
-
-        headers.update(util.api_key_to_header(self.api_type, self.api_key))
-
-        if self.organization:
-            headers["OpenAI-Organization"] = self.organization
-
-        if self.api_version is not None and self.api_type == ApiType.OPEN_AI:
-            headers["OpenAI-Version"] = self.api_version
-        if request_id is not None:
-            headers["X-Request-Id"] = request_id
-        if openai.debug:
-            headers["OpenAI-Debug"] = "true"
-        headers.update(extra)
-
-        return headers
-
-    def _validate_headers(
-        self, supplied_headers: Optional[Dict[str, str]]
-    ) -> Dict[str, str]:
-        headers: Dict[str, str] = {}
-        if supplied_headers is None:
-            return headers
-
-        if not isinstance(supplied_headers, dict):
-            raise TypeError("Headers must be a dictionary")
-
-        for k, v in supplied_headers.items():
-            if not isinstance(k, str):
-                raise TypeError("Header keys must be strings")
-            if not isinstance(v, str):
-                raise TypeError("Header values must be strings")
-            headers[k] = v
-
-        # NOTE: It is possible to do more validation of the headers, but a request could always
-        # be made to the API manually with invalid headers, so we need to handle them server side.
-
-        return headers
-
-    def request_raw(
-        self,
-        method,
-        url,
-        *,
-        params=None,
-        supplied_headers: Dict[str, str] = None,
-        files=None,
-        stream: bool = False,
-        request_id: Optional[str] = None,
-    ) -> requests.Response:
-        abs_url = "%s%s" % (self.api_base, url)
-        headers = self._validate_headers(supplied_headers)
-
-        data = None
-        if method == "get" or method == "delete":
-            if params:
-                encoded_params = urlencode(
-                    [(k, v) for k, v in params.items() if v is not None]
-                )
-                abs_url = _build_api_url(abs_url, encoded_params)
-        elif method in {"post", "put"}:
-            if params and files:
-                raise ValueError("At most one of params and files may be specified.")
-            if params:
-                data = json.dumps(params).encode()
-                headers["Content-Type"] = "application/json"
-        else:
-            raise error.APIConnectionError(
-                "Unrecognized HTTP method %r. This may indicate a bug in the "
-                "OpenAI bindings. Please contact [email protected] for "
-                "assistance." % (method,)
-            )
-
-        headers = self.request_headers(method, headers, request_id)
-
-        util.log_info("Request to OpenAI API", method=method, path=abs_url)
-        util.log_debug("Post details", data=data, api_version=self.api_version)
-
-        if not hasattr(_thread_context, "session"):
-            _thread_context.session = _make_session()
-        try:
-            result = _thread_context.session.request(
-                method,
-                abs_url,
-                headers=headers,
-                data=data,
-                files=files,
-                stream=stream,
-                timeout=TIMEOUT_SECS,
-            )
-        except requests.exceptions.RequestException as e:
-            raise error.APIConnectionError("Error communicating with OpenAI") from e
-        util.log_info(
-            "OpenAI API response",
-            path=abs_url,
-            response_code=result.status_code,
-            processing_ms=result.headers.get("OpenAI-Processing-Ms"),
-        )
-        # Don't read the whole stream for debug logging unless necessary.
-        if openai.log == "debug":
-            util.log_debug(
-                "API response body", body=result.content, headers=result.headers
-            )
-        return result
-
-    def _interpret_response(
-        self, result: requests.Response, stream: bool
-    ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool]:
-        """Returns the response(s) and a bool indicating whether it is a stream."""
-        if stream and "text/event-stream" in result.headers.get("Content-Type", ""):
-            return (
-                self._interpret_response_line(
-                    line, result.status_code, result.headers, stream=True
-                )
-                for line in parse_stream(result.iter_lines())
-            ), True
-        else:
-            return (
-                self._interpret_response_line(
-                    result.content, result.status_code, result.headers, stream=False
-                ),
-                False,
-            )
-
-    def _interpret_response_line(
-        self, rbody, rcode, rheaders, stream: bool
-    ) -> OpenAIResponse:
-        # HTTP 204 response code does not have any content in the body.
-        if rcode == 204:
-            return OpenAIResponse(None, rheaders)
-
-        if rcode == 503:
-            raise error.ServiceUnavailableError(
-                "The server is overloaded or not ready yet.",
-                rbody,
-                rcode,
-                headers=rheaders,
-            )
-        try:
-            if hasattr(rbody, "decode"):
-                rbody = rbody.decode("utf-8")
-            data = json.loads(rbody)
-        except (JSONDecodeError, UnicodeDecodeError):
-            raise error.APIError(
-                f"HTTP code {rcode} from API ({rbody})", rbody, rcode, headers=rheaders
-            )
-        resp = OpenAIResponse(data, rheaders)
-        # In the future, we might add a "status" parameter to errors
-        # to better handle the "error while streaming" case.
-        stream_error = stream and "error" in resp.data
-        if stream_error or not 200 <= rcode < 300:
-            raise self.handle_error_response(
-                rbody, rcode, resp.data, rheaders, stream_error=stream_error
-            )
-        return resp

spaces/CK42/sentiment-model-comparison/app.py

@@ -1,92 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-app = gr.Blocks()
-
-model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
-model_id_2 = "microsoft/deberta-xlarge-mnli"
-model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english"
-model_id_4 = "lordtt13/emo-mobilebert"
-model_id_5 = "juliensimon/reviews-sentiment-analysis"
-model_id_6 = "sbcBI/sentiment_analysis_model"
-
-def parse_output(output_json):  
-    list_pred=[]
-    for i in range(len(output_json[0])):
-        label = output_json[0][i]['label']
-        score = output_json[0][i]['score']
-        list_pred.append((label, score))
-    return list_pred
-
-def get_prediction(model_id):
-
-    classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
-             
-    def predict(review):
-            prediction = classifier(review)
-            print(prediction)
-            return parse_output(prediction)
-    return predict
-
-with app:
-    gr.Markdown(
-    """
-    # Compare Sentiment Analysis Models 
-    
-    Type text to predict sentiment.
-    """)   
-    with gr.Row():
-        inp_1= gr.Textbox(label="Type text here.",placeholder="The customer service was satisfactory.")
-    gr.Markdown(
-    """
-    **Model Predictions**
-    """)
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(
-            """
-            Model 1 = nlptown/bert-base-multilingual-uncased-sentiment
-            """)
-            btn1 = gr.Button("Predict - Model 1")
-            gr.Markdown(
-            """
-            Model 2 = microsoft/deberta-xlarge-mnli
-            """)
-            btn2 = gr.Button("Predict - Model 2")
-            gr.Markdown(
-            """
-            Model 3 = distilbert-base-uncased-finetuned-sst-2-english"
-            """)
-            btn3 = gr.Button("Predict - Model 3")
-            gr.Markdown(
-            """
-            Model 4 = lordtt13/emo-mobilebert
-            """)
-            btn4 = gr.Button("Predict - Model 4")
-            gr.Markdown(
-            """
-            Model 5 = juliensimon/reviews-sentiment-analysis
-            """)
-            btn5 = gr.Button("Predict - Model 5")
-            gr.Markdown(
-            """
-            Model 6 = sbcBI/sentiment_analysis_model
-            """)
-            btn6 = gr.Button("Predict - Model 6")
-
-        with gr.Column():
-            out_1 = gr.Textbox(label="Predictions for Model 1")
-            out_2 = gr.Textbox(label="Predictions for Model 2")  
-            out_3 = gr.Textbox(label="Predictions for Model 3")        
-            out_4 = gr.Textbox(label="Predictions for Model 4")     
-            out_5 = gr.Textbox(label="Predictions for Model 5")  
-            out_6 = gr.Textbox(label="Predictions for Model 6")
-
-        btn1.click(fn=get_prediction(model_id_1), inputs=inp_1, outputs=out_1)
-        btn2.click(fn=get_prediction(model_id_2), inputs=inp_1, outputs=out_2)
-        btn3.click(fn=get_prediction(model_id_3), inputs=inp_1, outputs=out_3)
-        btn4.click(fn=get_prediction(model_id_4), inputs=inp_1, outputs=out_4)
-        btn5.click(fn=get_prediction(model_id_5), inputs=inp_1, outputs=out_5)
-        btn6.click(fn=get_prediction(model_id_6), inputs=inp_1, outputs=out_6)
-        
-app.launch()

spaces/CVPR/WALT/mmcv_custom/__init__.py

@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from .checkpoint import load_checkpoint
-
-__all__ = ['load_checkpoint']

spaces/CVPR/WALT/mmdet/models/dense_heads/yolact_head.py

@@ -1,943 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class YOLACTHead(AnchorHead):
-    """YOLACT box head used in https://arxiv.org/abs/1904.02689.
-
-    Note that YOLACT head is a light version of RetinaNet head.
-    Four differences are described as follows:
-
-    1. YOLACT box head has three-times fewer anchors.
-    2. YOLACT box head shares the convs for box and cls branches.
-    3. YOLACT box head uses OHEM instead of Focal loss.
-    4. YOLACT box head predicts a set of mask coefficients for each box.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        anchor_generator (dict): Config dict for anchor generator
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of localization loss.
-        num_head_convs (int): Number of the conv layers shared by
-            box and cls branches.
-        num_protos (int): Number of the mask coefficients.
-        use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
-            cls loss calculation. If false, ``loss_single`` will be used.
-        conv_cfg (dict): Dictionary to construct and config conv layer.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 anchor_generator=dict(
-                     type='AnchorGenerator',
-                     octave_base_scale=3,
-                     scales_per_octave=1,
-                     ratios=[0.5, 1.0, 2.0],
-                     strides=[8, 16, 32, 64, 128]),
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     reduction='none',
-                     loss_weight=1.0),
-                 loss_bbox=dict(
-                     type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
-                 num_head_convs=1,
-                 num_protos=32,
-                 use_ohem=True,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 **kwargs):
-        self.num_head_convs = num_head_convs
-        self.num_protos = num_protos
-        self.use_ohem = use_ohem
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(YOLACTHead, self).__init__(
-            num_classes,
-            in_channels,
-            loss_cls=loss_cls,
-            loss_bbox=loss_bbox,
-            anchor_generator=anchor_generator,
-            **kwargs)
-        if self.use_ohem:
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-            self.sampling = False
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.head_convs = nn.ModuleList()
-        for i in range(self.num_head_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.head_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.conv_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.conv_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-        self.conv_coeff = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.num_protos,
-            3,
-            padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.head_convs:
-            xavier_init(m.conv, distribution='uniform', bias=0)
-        xavier_init(self.conv_cls, distribution='uniform', bias=0)
-        xavier_init(self.conv_reg, distribution='uniform', bias=0)
-        xavier_init(self.conv_coeff, distribution='uniform', bias=0)
-
-    def forward_single(self, x):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-
-        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.
-                coeff_pred (Tensor): Mask coefficients for a single scale \
-                    level, the channels number is num_anchors * num_protos.
-        """
-        for head_conv in self.head_convs:
-            x = head_conv(x)
-        cls_score = self.conv_cls(x)
-        bbox_pred = self.conv_reg(x)
-        coeff_pred = self.conv_coeff(x).tanh()
-        return cls_score, bbox_pred, coeff_pred
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """A combination of the func:``AnchorHead.loss`` and
-        func:``SSDHead.loss``.
-
-        When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
-        otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
-        returns ``sampling_results``.
-
-        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)
-            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 (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss. Default: None
-
-        Returns:
-            tuple:
-                dict[str, Tensor]: A dictionary of loss components.
-                List[:obj:``SamplingResult``]: Sampler results for each image.
-        """
-        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,
-            unmap_outputs=not self.use_ohem,
-            return_sampling_results=True)
-        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, sampling_results) = cls_reg_targets
-
-        if self.use_ohem:
-            num_images = len(img_metas)
-            all_cls_scores = torch.cat([
-                s.permute(0, 2, 3, 1).reshape(
-                    num_images, -1, self.cls_out_channels) for s in cls_scores
-            ], 1)
-            all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-            all_label_weights = torch.cat(label_weights_list,
-                                          -1).view(num_images, -1)
-            all_bbox_preds = torch.cat([
-                b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
-                for b in bbox_preds
-            ], -2)
-            all_bbox_targets = torch.cat(bbox_targets_list,
-                                         -2).view(num_images, -1, 4)
-            all_bbox_weights = torch.cat(bbox_weights_list,
-                                         -2).view(num_images, -1, 4)
-
-            # concat all level anchors to a single tensor
-            all_anchors = []
-            for i in range(num_images):
-                all_anchors.append(torch.cat(anchor_list[i]))
-
-            # check NaN and Inf
-            assert torch.isfinite(all_cls_scores).all().item(), \
-                'classification scores become infinite or NaN!'
-            assert torch.isfinite(all_bbox_preds).all().item(), \
-                'bbox predications become infinite or NaN!'
-
-            losses_cls, losses_bbox = multi_apply(
-                self.loss_single_OHEM,
-                all_cls_scores,
-                all_bbox_preds,
-                all_anchors,
-                all_labels,
-                all_label_weights,
-                all_bbox_targets,
-                all_bbox_weights,
-                num_total_samples=num_total_pos)
-        else:
-            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 anchor_list[0]]
-            # concat all level anchors and flags to a single tensor
-            concat_anchor_list = []
-            for i in range(len(anchor_list)):
-                concat_anchor_list.append(torch.cat(anchor_list[i]))
-            all_anchor_list = images_to_levels(concat_anchor_list,
-                                               num_level_anchors)
-            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)
-
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
-
-    def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
-                         label_weights, bbox_targets, bbox_weights,
-                         num_total_samples):
-        """"See func:``SSDHead.loss``."""
-        loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
-            as_tuple=False).reshape(-1)
-        neg_inds = (labels == self.num_classes).nonzero(
-            as_tuple=False).view(-1)
-
-        num_pos_samples = pos_inds.size(0)
-        if num_pos_samples == 0:
-            num_neg_samples = neg_inds.size(0)
-        else:
-            num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
-            if num_neg_samples > neg_inds.size(0):
-                num_neg_samples = neg_inds.size(0)
-        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
-        loss_cls_pos = loss_cls_all[pos_inds].sum()
-        loss_cls_neg = topk_loss_cls_neg.sum()
-        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
-        if self.reg_decoded_bbox:
-            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-            # is applied directly on the decoded bounding boxes, it
-            # decodes the already encoded coordinates to absolute format.
-            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
-        loss_bbox = self.loss_bbox(
-            bbox_pred,
-            bbox_targets,
-            bbox_weights,
-            avg_factor=num_total_samples)
-        return loss_cls[None], loss_bbox
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   coeff_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=False):
-        """"Similiar to func:``AnchorHead.get_bboxes``, but additionally
-        processes coeff_preds.
-
-        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)
-            coeff_preds (list[Tensor]): Mask coefficients for each scale
-                level with shape (N, num_anchors * num_protos, 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
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-
-        Returns:
-            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
-                a 3-tuple. The first item is an (n, 5) tensor, where the
-                first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
-                between 0 and 1. The second item is an (n,) tensor where each
-                item is the predicted class label of the corresponding box.
-                The third item is an (n, num_protos) tensor where each item
-                is the predicted mask coefficients of instance inside the
-                corresponding box.
-        """
-        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)
-
-        det_bboxes = []
-        det_labels = []
-        det_coeffs = []
-        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)
-            ]
-            coeff_pred_list = [
-                coeff_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
-                                               coeff_pred_list, mlvl_anchors,
-                                               img_shape, scale_factor, cfg,
-                                               rescale)
-            det_bboxes.append(bbox_res[0])
-            det_labels.append(bbox_res[1])
-            det_coeffs.append(bbox_res[2])
-        return det_bboxes, det_labels, det_coeffs
-
-    def _get_bboxes_single(self,
-                           cls_score_list,
-                           bbox_pred_list,
-                           coeff_preds_list,
-                           mlvl_anchors,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        """"Similiar to func:``AnchorHead._get_bboxes_single``, but
-        additionally processes coeff_preds_list and uses fast NMS instead of
-        traditional NMS.
-
-        Args:
-            cls_score_list (list[Tensor]): Box scores for a single scale level
-                Has shape (num_anchors * num_classes, H, W).
-            bbox_pred_list (list[Tensor]): Box energies / deltas for a single
-                scale level with shape (num_anchors * 4, H, W).
-            coeff_preds_list (list[Tensor]): Mask coefficients for a single
-                scale level with shape (num_anchors * num_protos, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shape (tuple[int]): Shape of the input image,
-                (height, width, 3).
-            scale_factor (ndarray): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-
-        Returns:
-            tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
-                where the first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
-                0 and 1. The second item is an (n,) tensor where each item is
-                the predicted class label of the corresponding box. The third
-                item is an (n, num_protos) tensor where each item is the
-                predicted mask coefficients of instance inside the
-                corresponding box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_coeffs = []
-        for cls_score, bbox_pred, coeff_pred, anchors in \
-                zip(cls_score_list, bbox_pred_list,
-                    coeff_preds_list, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            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)
-            coeff_pred = coeff_pred.permute(1, 2,
-                                            0).reshape(-1, self.num_protos)
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[0] > nms_pre:
-                # Get maximum scores for foreground classes.
-                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, :]
-                coeff_pred = coeff_pred[topk_inds, :]
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_coeffs.append(coeff_pred)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        mlvl_coeffs = torch.cat(mlvl_coeffs)
-        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)
-        det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
-                                                      mlvl_coeffs,
-                                                      cfg.score_thr,
-                                                      cfg.iou_thr, cfg.top_k,
-                                                      cfg.max_per_img)
-        return det_bboxes, det_labels, det_coeffs
-
-
-@HEADS.register_module()
-class YOLACTSegmHead(nn.Module):
-    """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
-
-    Apply a semantic segmentation loss on feature space using layers that are
-    only evaluated during training to increase performance with no speed
-    penalty.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        num_classes (int): Number of categories excluding the background
-            category.
-        loss_segm (dict): Config of semantic segmentation loss.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels=256,
-                 loss_segm=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0)):
-        super(YOLACTSegmHead, self).__init__()
-        self.in_channels = in_channels
-        self.num_classes = num_classes
-        self.loss_segm = build_loss(loss_segm)
-        self._init_layers()
-        self.fp16_enabled = False
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.segm_conv = nn.Conv2d(
-            self.in_channels, self.num_classes, kernel_size=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        xavier_init(self.segm_conv, distribution='uniform')
-
-    def forward(self, x):
-        """Forward feature from the upstream network.
-
-        Args:
-            x (Tensor): Feature from the upstream network, which is
-                a 4D-tensor.
-
-        Returns:
-            Tensor: Predicted semantic segmentation map with shape
-                (N, num_classes, H, W).
-        """
-        return self.segm_conv(x)
-
-    @force_fp32(apply_to=('segm_pred', ))
-    def loss(self, segm_pred, gt_masks, gt_labels):
-        """Compute loss of the head.
-
-        Args:
-            segm_pred (list[Tensor]): Predicted semantic segmentation map
-                with shape (N, num_classes, H, W).
-            gt_masks (list[Tensor]): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_labels (list[Tensor]): Class indices corresponding to each box.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        loss_segm = []
-        num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
-        for idx in range(num_imgs):
-            cur_segm_pred = segm_pred[idx]
-            cur_gt_masks = gt_masks[idx].float()
-            cur_gt_labels = gt_labels[idx]
-            segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
-                                            cur_gt_labels)
-            if segm_targets is None:
-                loss = self.loss_segm(cur_segm_pred,
-                                      torch.zeros_like(cur_segm_pred),
-                                      torch.zeros_like(cur_segm_pred))
-            else:
-                loss = self.loss_segm(
-                    cur_segm_pred,
-                    segm_targets,
-                    avg_factor=num_imgs * mask_h * mask_w)
-            loss_segm.append(loss)
-        return dict(loss_segm=loss_segm)
-
-    def get_targets(self, segm_pred, gt_masks, gt_labels):
-        """Compute semantic segmentation targets for each image.
-
-        Args:
-            segm_pred (Tensor): Predicted semantic segmentation map
-                with shape (num_classes, H, W).
-            gt_masks (Tensor): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_labels (Tensor): Class indices corresponding to each box.
-
-        Returns:
-            Tensor: Semantic segmentation targets with shape
-                (num_classes, H, W).
-        """
-        if gt_masks.size(0) == 0:
-            return None
-        num_classes, mask_h, mask_w = segm_pred.size()
-        with torch.no_grad():
-            downsampled_masks = F.interpolate(
-                gt_masks.unsqueeze(0), (mask_h, mask_w),
-                mode='bilinear',
-                align_corners=False).squeeze(0)
-            downsampled_masks = downsampled_masks.gt(0.5).float()
-            segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
-            for obj_idx in range(downsampled_masks.size(0)):
-                segm_targets[gt_labels[obj_idx] - 1] = torch.max(
-                    segm_targets[gt_labels[obj_idx] - 1],
-                    downsampled_masks[obj_idx])
-            return segm_targets
-
-
-@HEADS.register_module()
-class YOLACTProtonet(nn.Module):
-    """YOLACT mask head used in https://arxiv.org/abs/1904.02689.
-
-    This head outputs the mask prototypes for YOLACT.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        proto_channels (tuple[int]): Output channels of protonet convs.
-        proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
-        include_last_relu (Bool): If keep the last relu of protonet.
-        num_protos (int): Number of prototypes.
-        num_classes (int): Number of categories excluding the background
-            category.
-        loss_mask_weight (float): Reweight the mask loss by this factor.
-        max_masks_to_train (int): Maximum number of masks to train for
-            each image.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels=256,
-                 proto_channels=(256, 256, 256, None, 256, 32),
-                 proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
-                 include_last_relu=True,
-                 num_protos=32,
-                 loss_mask_weight=1.0,
-                 max_masks_to_train=100):
-        super(YOLACTProtonet, self).__init__()
-        self.in_channels = in_channels
-        self.proto_channels = proto_channels
-        self.proto_kernel_sizes = proto_kernel_sizes
-        self.include_last_relu = include_last_relu
-        self.protonet = self._init_layers()
-
-        self.loss_mask_weight = loss_mask_weight
-        self.num_protos = num_protos
-        self.num_classes = num_classes
-        self.max_masks_to_train = max_masks_to_train
-        self.fp16_enabled = False
-
-    def _init_layers(self):
-        """A helper function to take a config setting and turn it into a
-        network."""
-        # Possible patterns:
-        # ( 256, 3) -> conv
-        # ( 256,-2) -> deconv
-        # (None,-2) -> bilinear interpolate
-        in_channels = self.in_channels
-        protonets = nn.ModuleList()
-        for num_channels, kernel_size in zip(self.proto_channels,
-                                             self.proto_kernel_sizes):
-            if kernel_size > 0:
-                layer = nn.Conv2d(
-                    in_channels,
-                    num_channels,
-                    kernel_size,
-                    padding=kernel_size // 2)
-            else:
-                if num_channels is None:
-                    layer = InterpolateModule(
-                        scale_factor=-kernel_size,
-                        mode='bilinear',
-                        align_corners=False)
-                else:
-                    layer = nn.ConvTranspose2d(
-                        in_channels,
-                        num_channels,
-                        -kernel_size,
-                        padding=kernel_size // 2)
-            protonets.append(layer)
-            protonets.append(nn.ReLU(inplace=True))
-            in_channels = num_channels if num_channels is not None \
-                else in_channels
-        if not self.include_last_relu:
-            protonets = protonets[:-1]
-        return nn.Sequential(*protonets)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.protonet:
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-
-    def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
-        """Forward feature from the upstream network to get prototypes and
-        linearly combine the prototypes, using masks coefficients, into
-        instance masks. Finally, crop the instance masks with given bboxes.
-
-        Args:
-            x (Tensor): Feature from the upstream network, which is
-                a 4D-tensor.
-            coeff_pred (list[Tensor]): Mask coefficients for each scale
-                level with shape (N, num_anchors * num_protos, H, W).
-            bboxes (list[Tensor]): Box used for cropping with shape
-                (N, num_anchors * 4, H, W). During training, they are
-                ground truth boxes. During testing, they are predicted
-                boxes.
-            img_meta (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            sampling_results (List[:obj:``SamplingResult``]): Sampler results
-                for each image.
-
-        Returns:
-            list[Tensor]: Predicted instance segmentation masks.
-        """
-        prototypes = self.protonet(x)
-        prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
-
-        num_imgs = x.size(0)
-        # Training state
-        if self.training:
-            coeff_pred_list = []
-            for coeff_pred_per_level in coeff_pred:
-                coeff_pred_per_level = \
-                    coeff_pred_per_level.permute(0, 2, 3, 1)\
-                    .reshape(num_imgs, -1, self.num_protos)
-                coeff_pred_list.append(coeff_pred_per_level)
-            coeff_pred = torch.cat(coeff_pred_list, dim=1)
-
-        mask_pred_list = []
-        for idx in range(num_imgs):
-            cur_prototypes = prototypes[idx]
-            cur_coeff_pred = coeff_pred[idx]
-            cur_bboxes = bboxes[idx]
-            cur_img_meta = img_meta[idx]
-
-            # Testing state
-            if not self.training:
-                bboxes_for_cropping = cur_bboxes
-            else:
-                cur_sampling_results = sampling_results[idx]
-                pos_assigned_gt_inds = \
-                    cur_sampling_results.pos_assigned_gt_inds
-                bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
-                pos_inds = cur_sampling_results.pos_inds
-                cur_coeff_pred = cur_coeff_pred[pos_inds]
-
-            # Linearly combine the prototypes with the mask coefficients
-            mask_pred = cur_prototypes @ cur_coeff_pred.t()
-            mask_pred = torch.sigmoid(mask_pred)
-
-            h, w = cur_img_meta['img_shape'][:2]
-            bboxes_for_cropping[:, 0] /= w
-            bboxes_for_cropping[:, 1] /= h
-            bboxes_for_cropping[:, 2] /= w
-            bboxes_for_cropping[:, 3] /= h
-
-            mask_pred = self.crop(mask_pred, bboxes_for_cropping)
-            mask_pred = mask_pred.permute(2, 0, 1).contiguous()
-            mask_pred_list.append(mask_pred)
-        return mask_pred_list
-
-    @force_fp32(apply_to=('mask_pred', ))
-    def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
-        """Compute loss of the head.
-
-        Args:
-            mask_pred (list[Tensor]): Predicted prototypes with shape
-                (num_classes, H, W).
-            gt_masks (list[Tensor]): Ground truth masks for each image with
-                the same shape of the input image.
-            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.
-            img_meta (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            sampling_results (List[:obj:``SamplingResult``]): Sampler results
-                for each image.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        loss_mask = []
-        num_imgs = len(mask_pred)
-        total_pos = 0
-        for idx in range(num_imgs):
-            cur_mask_pred = mask_pred[idx]
-            cur_gt_masks = gt_masks[idx].float()
-            cur_gt_bboxes = gt_bboxes[idx]
-            cur_img_meta = img_meta[idx]
-            cur_sampling_results = sampling_results[idx]
-
-            pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
-            num_pos = pos_assigned_gt_inds.size(0)
-            # Since we're producing (near) full image masks,
-            # it'd take too much vram to backprop on every single mask.
-            # Thus we select only a subset.
-            if num_pos > self.max_masks_to_train:
-                perm = torch.randperm(num_pos)
-                select = perm[:self.max_masks_to_train]
-                cur_mask_pred = cur_mask_pred[select]
-                pos_assigned_gt_inds = pos_assigned_gt_inds[select]
-                num_pos = self.max_masks_to_train
-            total_pos += num_pos
-
-            gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
-
-            mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
-                                            pos_assigned_gt_inds)
-            if num_pos == 0:
-                loss = cur_mask_pred.sum() * 0.
-            elif mask_targets is None:
-                loss = F.binary_cross_entropy(cur_mask_pred,
-                                              torch.zeros_like(cur_mask_pred),
-                                              torch.zeros_like(cur_mask_pred))
-            else:
-                cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
-                loss = F.binary_cross_entropy(
-                    cur_mask_pred, mask_targets,
-                    reduction='none') * self.loss_mask_weight
-
-                h, w = cur_img_meta['img_shape'][:2]
-                gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
-                                   gt_bboxes_for_reweight[:, 0]) / w
-                gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
-                                    gt_bboxes_for_reweight[:, 1]) / h
-                loss = loss.mean(dim=(1,
-                                      2)) / gt_bboxes_width / gt_bboxes_height
-                loss = torch.sum(loss)
-            loss_mask.append(loss)
-
-        if total_pos == 0:
-            total_pos += 1  # avoid nan
-        loss_mask = [x / total_pos for x in loss_mask]
-
-        return dict(loss_mask=loss_mask)
-
-    def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
-        """Compute instance segmentation targets for each image.
-
-        Args:
-            mask_pred (Tensor): Predicted prototypes with shape
-                (num_classes, H, W).
-            gt_masks (Tensor): Ground truth masks for each image with
-                the same shape of the input image.
-            pos_assigned_gt_inds (Tensor): GT indices of the corresponding
-                positive samples.
-        Returns:
-            Tensor: Instance segmentation targets with shape
-                (num_instances, H, W).
-        """
-        if gt_masks.size(0) == 0:
-            return None
-        mask_h, mask_w = mask_pred.shape[-2:]
-        gt_masks = F.interpolate(
-            gt_masks.unsqueeze(0), (mask_h, mask_w),
-            mode='bilinear',
-            align_corners=False).squeeze(0)
-        gt_masks = gt_masks.gt(0.5).float()
-        mask_targets = gt_masks[pos_assigned_gt_inds]
-        return mask_targets
-
-    def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
-        """Resize, binarize, and format the instance mask predictions.
-
-        Args:
-            mask_pred (Tensor): shape (N, H, W).
-            label_pred (Tensor): shape (N, ).
-            img_meta (dict): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            rescale (bool): If rescale is False, then returned masks will
-                fit the scale of imgs[0].
-        Returns:
-            list[ndarray]: Mask predictions grouped by their predicted classes.
-        """
-        ori_shape = img_meta['ori_shape']
-        scale_factor = img_meta['scale_factor']
-        if rescale:
-            img_h, img_w = ori_shape[:2]
-        else:
-            img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
-            img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
-
-        cls_segms = [[] for _ in range(self.num_classes)]
-        if mask_pred.size(0) == 0:
-            return cls_segms
-
-        mask_pred = F.interpolate(
-            mask_pred.unsqueeze(0), (img_h, img_w),
-            mode='bilinear',
-            align_corners=False).squeeze(0) > 0.5
-        mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
-
-        for m, l in zip(mask_pred, label_pred):
-            cls_segms[l].append(m)
-        return cls_segms
-
-    def crop(self, masks, boxes, padding=1):
-        """Crop predicted masks by zeroing out everything not in the predicted
-        bbox.
-
-        Args:
-            masks (Tensor): shape [H, W, N].
-            boxes (Tensor): bbox coords in relative point form with
-                shape [N, 4].
-
-        Return:
-            Tensor: The cropped masks.
-        """
-        h, w, n = masks.size()
-        x1, x2 = self.sanitize_coordinates(
-            boxes[:, 0], boxes[:, 2], w, padding, cast=False)
-        y1, y2 = self.sanitize_coordinates(
-            boxes[:, 1], boxes[:, 3], h, padding, cast=False)
-
-        rows = torch.arange(
-            w, device=masks.device, dtype=x1.dtype).view(1, -1,
-                                                         1).expand(h, w, n)
-        cols = torch.arange(
-            h, device=masks.device, dtype=x1.dtype).view(-1, 1,
-                                                         1).expand(h, w, n)
-
-        masks_left = rows >= x1.view(1, 1, -1)
-        masks_right = rows < x2.view(1, 1, -1)
-        masks_up = cols >= y1.view(1, 1, -1)
-        masks_down = cols < y2.view(1, 1, -1)
-
-        crop_mask = masks_left * masks_right * masks_up * masks_down
-
-        return masks * crop_mask.float()
-
-    def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
-        """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
-        and x2 <= image_size. Also converts from relative to absolute
-        coordinates and casts the results to long tensors.
-
-        Warning: this does things in-place behind the scenes so
-        copy if necessary.
-
-        Args:
-            _x1 (Tensor): shape (N, ).
-            _x2 (Tensor): shape (N, ).
-            img_size (int): Size of the input image.
-            padding (int): x1 >= padding, x2 <= image_size-padding.
-            cast (bool): If cast is false, the result won't be cast to longs.
-
-        Returns:
-            tuple:
-                x1 (Tensor): Sanitized _x1.
-                x2 (Tensor): Sanitized _x2.
-        """
-        x1 = x1 * img_size
-        x2 = x2 * img_size
-        if cast:
-            x1 = x1.long()
-            x2 = x2.long()
-        x1 = torch.min(x1, x2)
-        x2 = torch.max(x1, x2)
-        x1 = torch.clamp(x1 - padding, min=0)
-        x2 = torch.clamp(x2 + padding, max=img_size)
-        return x1, x2
-
-
-class InterpolateModule(nn.Module):
-    """This is a module version of F.interpolate.
-
-    Any arguments you give it just get passed along for the ride.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-
-        self.args = args
-        self.kwargs = kwargs
-
-    def forward(self, x):
-        """Forward features from the upstream network."""
-        return F.interpolate(x, *self.args, **self.kwargs)

spaces/CVPR/WALT/mmdet/models/detectors/faster_rcnn.py

@@ -1,24 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class FasterRCNN(TwoStageDetector):
-    """Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
-
-    def __init__(self,
-                 backbone,
-                 rpn_head,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None):
-        super(FasterRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)

spaces/CVPR/regionclip-demo/detectron2/modeling/meta_arch/clip_rcnn.py

@@ -1,1560 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-from typing import Dict, List, Optional, Tuple
-from numpy.lib import pad
-import torch
-from torch import nn
-from torch.nn import functional as F
-from random import randint
-
-from detectron2.config import configurable
-from detectron2.data.detection_utils import convert_image_to_rgb
-from detectron2.structures import ImageList, Instances, Boxes
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.logger import log_first_n
-
-from ..backbone import Backbone, build_backbone, build_text_backbone
-from ..postprocessing import detector_postprocess
-from ..proposal_generator import build_proposal_generator
-from ..roi_heads import build_roi_heads
-from .build import META_ARCH_REGISTRY
-
-from PIL import Image
-import torchvision
-from torchvision.transforms import Resize, CenterCrop
-from detectron2.data.datasets.clip_prompt_utils import get_cls_names, pre_tokenize
-import copy
-from ..backbone.fpn import build_resnet_fpn_backbone
-from ..roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.layers import ShapeSpec
-from ..backbone.clip_backbone import build_clip_language_encoder
-from detectron2.utils.comm import gather_tensors, MILCrossEntropy, SoftTargetCrossEntropy
-
-__all__ = ["CLIPRCNN", "CLIPFastRCNN", "PretrainFastRCNN"]
-
-@META_ARCH_REGISTRY.register()
-class CLIPRCNN(nn.Module):
-    """
-    CLIP in R-CNN format. 
-    It takes the image regions as inputs and classifies each image.
-    It contains the following two components:
-    1. Per-image feature extraction (visual encoder)
-    2. Per-image prediction (text-based classifier)
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        clip: Backbone,
-        offline_backbone: Backbone,
-        offline_proposal_generator: nn.Module,
-        roi_heads: nn.Module,
-        pixel_mean: Tuple[float],
-        pixel_std: Tuple[float],
-        input_format: Optional[str] = None,
-        vis_period: int = 0,
-        clip_crop_region_type: str = 'GT',
-        test_score_thresh: float = 0.0001,
-        test_nms_thresh: float = 0.5,
-        test_topk_per_image: float = 300,
-    ):
-        """
-        Args:
-            backbone: a backbone module, must follow detectron2's backbone interface
-            proposal_generator: a module that generates proposals using backbone features
-            roi_heads: a ROI head that performs per-region computation
-            pixel_mean, pixel_std: list or tuple with #channels element, representing
-                the per-channel mean and std to be used to normalize the input image
-            input_format: describe the meaning of channels of input. Needed by visualization
-            vis_period: the period to run visualization. Set to 0 to disable.
-        """
-        super().__init__()
-        self.clip_backbone = clip
-        self.offline_backbone = offline_backbone
-        self.offline_proposal_generator = offline_proposal_generator
-        self.roi_heads = roi_heads
-
-        self.input_format = input_format
-        self.vis_period = vis_period
-        if vis_period > 0:
-            assert input_format is not None, "input_format is required for visualization!"
-
-        self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False)
-        self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False)
-        assert (
-            self.pixel_mean.shape == self.pixel_std.shape
-        ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
-        # Detectron2 default pixel mean and std
-        self.register_buffer("detectron_pixel_mean", torch.tensor([103.530, 116.280, 123.675]).view(-1, 1, 1), False)
-        self.register_buffer("detectron_pixel_std", torch.tensor([1.0, 1.0, 1.0]).view(-1, 1, 1), False)
-
-        # CLIP image loading
-        if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
-            assert input_format == 'RGB'
-            self.div_pixel = True
-        else: # default setting
-            self.div_pixel = False
-        n_px = 224
-        self.clip_resize = Resize(n_px, interpolation=Image.BICUBIC)  # shorter side becomes n_px
-        self.clip_center_crop = CenterCrop(n_px)  # crop image into n_px * n_px at the center
-        self.region_crop_scales = (1.0, 1.5) # (1.0, 2.0) # (1.0, 1.2) # (1.0,) #  
-
-        # CLIP text prompt loading
-        print("Working on pre_tokenize...")
-        cls_names = get_cls_names(filter_novel=False, from_file='/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/googlecc_nouns_filtered_100.txt') # filter_novel=True; coco='all', coco='base', coco='target'; from_file: a file path for concept pool
-        # from_file='/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/concept_pool/googlecc_nouns_triplet_parser_filtered_100.txt'
-        print("Got {} class names: {}\n {} class names in total.".format(len(cls_names), cls_names, len(cls_names)))
-        input_ids = pre_tokenize(cls_names)
-        self.num_cls = input_ids.size(0)
-        self.num_prompt = input_ids.size(1)
-        self.input_ids_flat = input_ids.view(-1, input_ids.size(2))  # [#cls*#prompts, #context_length]
-        self.clss_emb_all = None
-
-        # CLIP crop image configs
-        self.clip_crop_region_type = clip_crop_region_type
-        self.test_score_thresh = test_score_thresh
-        self.test_nms_thresh = test_nms_thresh
-        self.test_topk_per_image = test_topk_per_image
-
-    @classmethod
-    def from_config(cls, cfg):
-        if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN":
-            offline_backbone = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))) # build_backbone(cfg)
-            offline_rpn = build_proposal_generator(cfg, offline_backbone.output_shape())
-            roi_heads = None # build_roi_heads(cfg, backbone.output_shape()),
-        elif cfg.MODEL.CLIP.CROP_REGION_TYPE == "GT":
-            offline_backbone = None
-            offline_rpn = None
-            roi_heads = None
-        clip = build_backbone(cfg)
-        return {
-            "clip": clip,
-            "offline_backbone": offline_backbone,
-            "offline_proposal_generator": offline_rpn, 
-            "roi_heads": roi_heads, 
-            "input_format": cfg.INPUT.FORMAT,
-            "vis_period": cfg.VIS_PERIOD,
-            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
-            "pixel_std": cfg.MODEL.PIXEL_STD,
-            "clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE,
-            "test_score_thresh"     : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
-            "test_nms_thresh"       : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
-            "test_topk_per_image"   : cfg.TEST.DETECTIONS_PER_IMAGE,
-        }
-
-    @property
-    def device(self):
-        return self.pixel_mean.device
-
-    def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
-                Each item in the list contains the inputs for one image.
-                For now, each item in the list is a dict that contains:
-
-                * image: Tensor, image in (C, H, W) format.
-                * instances (optional): groundtruth :class:`Instances`
-                * proposals (optional): :class:`Instances`, precomputed proposals.
-
-                Other information that's included in the original dicts, such as:
-
-                * "height", "width" (int): the output resolution of the model, used in inference.
-                  See :meth:`postprocess` for details.
-
-        Returns:
-            list[dict]:
-                Each dict is the output for one input image.
-                The dict contains one key "instances" whose value is a :class:`Instances`.
-                The :class:`Instances` object has the following keys:
-                "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
-        """
-        if not self.training:
-            return self.inference(batched_inputs)
-        # No training mode for this arch
-
-    def inference(
-        self,
-        batched_inputs: List[Dict[str, torch.Tensor]],
-        detected_instances: Optional[List[Instances]] = None,
-        do_postprocess: bool = True,
-    ):
-        """
-        Run inference on the given inputs.
-
-        Args:
-            batched_inputs (list[dict]): same as in :meth:`forward`
-            detected_instances (None or list[Instances]): if not None, it
-                contains an `Instances` object per image. The `Instances`
-                object contains "pred_boxes" and "pred_classes" which are
-                known boxes in the image.
-                The inference will then skip the detection of bounding boxes,
-                and only predict other per-ROI outputs.
-            do_postprocess (bool): whether to apply post-processing on the outputs.
-
-        Returns:
-            When do_postprocess=True, same as in :meth:`forward`.
-            Otherwise, a list[Instances] containing raw network outputs.
-        """
-        assert not self.training
-
-        # get the label prompt, and use CLIP.encode_text() to compute text emb only once
-        if self.clss_emb_all is None:  # compute only once
-            num_instances = self.input_ids_flat.size(0)
-            per_split = 1000
-            num_splits = num_instances // per_split
-            input_ids_flat = self.input_ids_flat.to(self.device)
-            #self.clss_emb_all = torch.ones((1203, 512)).to(self.device)
-            clss_emb_all = []
-            for i in range(num_splits+1):
-                if i < num_splits:
-                    clss_emb_i = self.clip_backbone.encode_text(input_ids_flat[per_split*i:per_split*(i+1)]) # per_split x D
-                else:
-                    clss_emb_i = self.clip_backbone.encode_text(input_ids_flat[per_split*i:]) # per_split x D
-                # clss_emb_i = clip_model.encode_label(torch.arange(0, 1000).view(-1, 1).long().to(device)) # per_split x D
-                clss_emb_all.append(clss_emb_i)
-            self.clss_emb_all = torch.cat(clss_emb_all, 0).view(self.num_cls, self.num_prompt, -1)  # [#cls, #prompts, D]
-            self.clss_emb_all = self.clss_emb_all.mean(1)  # ensemble different prompts for each class
-            # torch.save(self.clss_emb_all.cpu(), "/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/lvis_cls_emb/coco_17_target_cls_emb_notnorm_rn50x4.pth")
-            self.clss_emb_all = F.normalize(self.clss_emb_all, p=2.0, dim=1)  # [#cls, emb_dim]
-        else:
-            assert self.clss_emb_all.device == self.device
-
-        # get the region proposals, from the backbone & RPN of standard Mask-RCNN, trained on base classes
-        if self.clip_crop_region_type == "GT":
-            proposals = None
-        elif self.clip_crop_region_type == "RPN":
-            images = self.preprocess_image(batched_inputs)
-            features = self.offline_backbone(images.tensor)
-            if detected_instances is None:
-                if self.offline_proposal_generator is not None:
-                    proposals, _ = self.offline_proposal_generator(images, features, None)   
-
-        # crop image regions, and use CLIP.encode_image() to get the visual feature
-        images, bbs, num_bbs = self.preprocess_image_crop(batched_inputs, rpn_proposals=proposals)
-        img_emb = self.clip_backbone.encode_image(images.tensor)
-        img_emb = img_emb.view(-1, len(self.region_crop_scales), img_emb.size(1))
-        img_emb = torch.sum(img_emb, dim=1)  # ensemble different scales for each region
-        img_emb = F.normalize(img_emb, p=2.0, dim=1)
-
-        # cosine similarity as logits
-        all_scores = torch.mm(img_emb, self.clss_emb_all.T)
-        all_scores = F.softmax(all_scores, dim=-1)
-        scores, pred_cls = torch.max(all_scores, dim=-1)  # Note: [0, #cls-1] representing the categories. The value #cls represents "background".
-
-        # convert model outputs into regular output result format
-        scores_per_img = scores.split(num_bbs)
-        pred_cls_per_img = pred_cls.split(num_bbs)
-        all_scores_per_img = all_scores.split(num_bbs)
-
-        # per-class NMS
-        if self.clip_crop_region_type == "GT":
-            image_shapes = [x['instances']._image_size for x in batched_inputs]
-            bbs = [bb.to(self.device) for bb in bbs]
-            pred_instances, _ = fast_rcnn_inference(bbs, all_scores_per_img, image_shapes, \
-                                        self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image)
-            results = pred_instances
-
-            # results = []
-            # for r_i, (b_input, bb, sc, prd) in enumerate(zip(batched_inputs, bbs, scores_per_img, pred_cls_per_img)): 
-            #     this_result = copy.deepcopy(b_input["instances"])  # Instance
-            #     if self.clip_crop_region_type == "GT":
-            #         result_boxes = this_result._fields['gt_boxes'].to(self.device)
-            #     elif self.clip_crop_region_type == "RPN":  # directly use RPN boxes without per-class NMS
-            #         result_boxes = bb # result_boxes = Boxes(bb)
-            #     this_result._fields = {'pred_boxes': result_boxes, 'scores': sc, 'pred_classes': prd}
-            #     results.append(this_result)
-            
-            # sanity check: GT boxes + GT classes
-            # results = []
-            # for b_input in batched_inputs: 
-            #     this_result = copy.deepcopy(b_input["instances"])  # Instance
-            #     gt_boxes = this_result._fields['gt_boxes'].to(self.device)
-            #     gt_cls =  this_result._fields['gt_classes'].to(self.device)
-            #     this_result._fields = {'pred_boxes': gt_boxes, 'scores': torch.ones(gt_cls.size(0)).to(self.device), 'pred_classes': gt_cls}
-            #     #this_result._fields = {'pred_boxes': gt_boxes, 'scores': sc, 'pred_classes': prd}
-            #     results.append(this_result)
-        elif self.clip_crop_region_type == "RPN":
-            image_shapes = [x.image_size for x in proposals]
-            pred_instances, _ = fast_rcnn_inference(bbs, all_scores_per_img, image_shapes, \
-                                        self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image)
-            results = pred_instances
-
-        if do_postprocess:
-            assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess."
-            return CLIPRCNN._postprocess(results, batched_inputs)
-        else:
-            return results
-
-    def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std).
-        Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        images = [(x - self.detectron_pixel_mean) / self.detectron_pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility)
-        return images
-
-    def preprocess_image_crop(self, batched_inputs: List[Dict[str, torch.Tensor]], rpn_proposals=None, max_num_rpn=1000):
-        """
-        Crop image regions based on GT or RPN boxes with different scales.
-        Then apply CLIP tranformation: resizing / cropping the regions into square shape (224 * 224).
-        Followed by the default preprocessing in Detectron2 as follows.
-        Normalize, pad and batch the input images.
-        """
-        def clip_crop_region(image, box, scales=(1.0, 1.5)):
-            """Crop image regions based on given boxes. Return different scales of region crops. (3 hrs)"""
-            img_h, img_w = image.size(1), image.size(2)
-            x1, y1, x2, y2 = list(box)
-            assert x1 < x2 and y1 < y2 and x2 < (img_w + 1) and y2 < (img_h + 1)
-            x_center = (x1 + x2) / 2.0
-            y_center = (y1 + y2) / 2.0
-            half_w = x_center - x1
-            half_h = y_center - y1
-            regions = []
-            for scale in scales: # get region coordinates
-                r_y1 = int(max(0, (y_center - half_h * scale).item()))
-                r_y2 = int(min(img_h, (y_center + half_h * scale).item()))
-                r_x1 = int(max(0, (x_center - half_w * scale).item()))
-                r_x2 = int(min(img_w, (x_center + half_w * scale).item()))
-                # sanity check
-                if r_y2 - r_y1 <= 1:
-                    r_y2 = int(min(img_h, r_y2 + 2))
-                if r_y2 - r_y1 <= 1:
-                    r_y1 = int(max(0, r_y1 - 2))
-                if r_x2 - r_x1 <= 1:
-                    r_x2 = int(min(img_w, r_x2 + 2))
-                if r_x2 - r_x1 <= 1:
-                    r_x1 = int(max(0, r_x1 - 2))
-                regions.append(image[:, r_y1:r_y2, r_x1:r_x2])
-            return regions
-        
-        def clip_square_crop(image, box, scales=(1.0,)):
-            """Crop image regions based on given boxes. Ensure square region as much as possible. (1.75 hrs)"""
-            img_h, img_w = image.size(1), image.size(2)
-            x1, y1, x2, y2 = list(box)
-            assert x1 < x2 and y1 < y2 and x2 < (img_w + 1) and y2 < (img_h + 1)
-            x_center = (x1 + x2) / 2.0
-            y_center = (y1 + y2) / 2.0
-            half_w = x_center - x1
-            half_h = y_center - y1
-            square_side = max(half_w, half_h)
-            half_w = square_side
-            half_h = square_side
-            regions = []
-            for scale in scales: # get region coordinates
-                if square_side * square_side < 2500:  # crop larger context area for tiny objects
-                    scale = 1.5 if scale == 1.0 else 4.0
-                # elif square_side * square_side > 90000:  # crop exact area for large objects
-                #     scale = 1.0 if scale == 1.0 else 1.1
-                r_y1 = int(max(0, (y_center - half_h * scale).item()))
-                r_y2 = int(min(img_h, (y_center + half_h * scale).item()))
-                r_x1 = int(max(0, (x_center - half_w * scale).item()))
-                r_x2 = int(min(img_w, (x_center + half_w * scale).item()))
-                # sanity check
-                if r_y2 - r_y1 <= 1:
-                    r_y2 = int(min(img_h, r_y2 + 2))
-                if r_y2 - r_y1 <= 1:
-                    r_y1 = int(max(0, r_y1 - 2))
-                if r_x2 - r_x1 <= 1:
-                    r_x2 = int(min(img_w, r_x2 + 2))
-                if r_x2 - r_x1 <= 1:
-                    r_x1 = int(max(0, r_x1 - 2))
-                #regions.append(image[:, r_y1:r_y2, r_x1:r_x2])
-                # if the cropped image isn't square (due to image boundaries), pad the cropped region
-                crop_image = image[:, r_y1:r_y2, r_x1:r_x2]
-                r_h, r_w = crop_image.size(1), crop_image.size(2)
-                pad_image = torch.zeros((3, int(2 * half_h.item() * scale) + 4 , int(2 * half_w.item() * scale) + 4)) #.fill_(torch.mean(crop_image.float()))
-                p_h, p_w = pad_image.size(1), pad_image.size(2)
-                pad_image[:, int(((p_h - r_h) / 2)):int(((p_h - r_h) / 2 + r_h)), int(((p_w - r_w) / 2)):int(((p_w - r_w) / 2 + r_w))] = crop_image
-                regions.append(pad_image.type(torch.uint8))
-            return regions
-        
-        def vis_crop(f_n, images):
-            """visualize the crop regions to diagnose the accuracy."""
-            if f_n not in ['datasets/coco/train2017/000000008691.jpg']:
-                for p_i, pad_image in enumerate(images):
-                    to_save = pad_image.permute(1, 2, 0).numpy()
-                    to_save = Image.fromarray(np.array(to_save, np.uint8))
-                    #to_save.save("output/regions/" + f_n.split("/")[-1].split(".")[0] + "-{}.png".format(p_i))
-                    pass
-
-        # crop image region
-        images = []
-        bbs = []
-        num_bbs = []
-        for img_i, b_input in enumerate(batched_inputs): 
-            this_img = b_input["image"]
-            if self.clip_crop_region_type == "GT":
-                this_boxes = b_input["instances"]._fields['gt_boxes'].tensor  # variant #bbox (eg, max 759), might lead to OOM
-            elif self.clip_crop_region_type == "RPN":
-                this_boxes = rpn_proposals[img_i]._fields['proposal_boxes'].tensor[:max_num_rpn]
-
-            bbs.append(this_boxes)
-            num_bbs.append(this_boxes.size(0))
-            for this_box in this_boxes:
-                #images.extend(clip_crop_region(this_img, this_box, self.region_crop_scales))
-                images.extend(clip_square_crop(this_img, this_box, self.region_crop_scales))
-        #vis_crop(batched_inputs[0]['file_name'], images)
-        images = [self.clip_resize(x) for x in images]
-        images = [self.clip_center_crop(x) for x in images]
-        images = [x.to(self.device) for x in images]
-        if self.div_pixel:
-            images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images]
-        else:
-            images = [(x - self.pixel_mean) / self.pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.clip_backbone.size_divisibility) # batch images into single tensor by padding to same size
-        return images, bbs, num_bbs
-
-    @staticmethod
-    def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Rescale the output instances to the target size.
-        """
-        # note: private function; subject to changes
-        processed_results = []
-        for results_per_image, input_per_image in zip(
-            instances, batched_inputs):
-            height = input_per_image["height"]  # original image size, before resizing
-            width = input_per_image["width"]  # original image size, before resizing
-            r = detector_postprocess(results_per_image, height, width)
-            processed_results.append({"instances": r})
-        return processed_results
-
-    def inference_on_cifar(self, pseudo_input):
-        """ Evaluate recoginition accuracy on CIFAR-10 for sanity check """
-        # get the label prompt, and use CLIP.encode_text() to compute text emb only once
-        cifar_cls_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
-        input_ids = pre_tokenize(cifar_cls_names)
-        num_cls = input_ids.size(0)
-        input_ids_flat = input_ids.view(-1, input_ids.size(2))
-        input_ids_flat = input_ids_flat.to(self.device)
-        
-        clss_emb_all = self.clip_backbone.encode_text(input_ids_flat)
-        clss_emb_all = clss_emb_all.view(num_cls, self.num_prompt, -1)
-        clss_emb_all = clss_emb_all.mean(1)
-        clss_emb_all = F.normalize(clss_emb_all, p=2.0, dim=1)  # [#cls, emb_dim]
-        
-        # dataset loads images and labels
-        testset = torchvision.datasets.CIFAR10(root='./datasets', train=False,
-                                            download=False, transform=None)
-        # testloader = torch.utils.data.DataLoader(testset, batch_size=4,
-        #                                         shuffle=False, num_workers=0)
-        
-        # inference on each image and calculate accuracy
-        correct = 0
-        wrong = 0
-        for idx, inputs in enumerate(testset):
-            if idx % 1000 == 0:
-                print(idx)
-            # preprocess images
-            raw_image, label = inputs
-            image = np.array(raw_image)  # [h, w, 3]
-            image = torch.from_numpy(image)
-            image = image.permute(2, 0, 1) # [3, h, w]
-            images = [image]
-            images = [self.clip_resize(x) for x in images]
-            images = [self.clip_center_crop(x) for x in images]
-            images = [x.to(self.device) for x in images]
-            if self.div_pixel:
-                images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images]
-            else:
-                images = [(x - self.pixel_mean) / self.pixel_std for x in images]
-            
-            # get image embedding
-            img_emb = self.clip_backbone.encode_image(images[0].unsqueeze(0))
-            img_emb = img_emb.view(-1, 1, img_emb.size(1))
-            img_emb = torch.sum(img_emb, dim=1)  # ensemble different scales for each region
-            img_emb = F.normalize(img_emb, p=2.0, dim=1)
-
-            # cosine similarity as logits
-            all_scores = torch.mm(img_emb, clss_emb_all.T)
-            scores, pred_cls = torch.max(all_scores, dim=1)  # Note: [0, #cls-1] representing the categories. The value #cls represents "background".
-            pred_cls = pred_cls.item()
-            if pred_cls == label:
-                correct += 1
-            else:
-                wrong += 1
-        
-        print("\n\nGot correct {} and wrong {}. Accuracy is {} / {} = {}\n\n".format(correct,wrong,correct,correct+wrong,correct/(correct+wrong)))
-        return
-
-@META_ARCH_REGISTRY.register()
-class CLIPFastRCNN(nn.Module):
-    """
-    CLIP in Fast R-CNN format, where the cropping is conducted on feature maps instead of raw images.
-    It contains the following two components: 
-    1. Localization modules: pretrained backbone+RPN or equivalent modules and is able to output object proposals
-    2. Recognition branch: initialized by CLIP and is able to recognize zero-shot regions
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        offline_backbone: Backbone,
-        backbone: Backbone,
-        backbone_type: str = "resnet", 
-        text_backbone: Backbone, 
-        offline_proposal_generator: nn.Module,
-        roi_heads: nn.Module,
-        pixel_mean: Tuple[float],
-        pixel_std: Tuple[float],
-        input_format: Optional[str] = None,
-        vis_period: int = 0,
-        clip_crop_region_type: str = 'GT',
-        use_clip_c4: False,
-        use_clip_attpool: False,
-        offline_input_format: Optional[str] = None,
-        offline_pixel_mean: Tuple[float],
-        offline_pixel_std: Tuple[float],
-    ):
-        """
-        Args:
-            backbone: a backbone module, must follow detectron2's backbone interface
-            proposal_generator: a module that generates proposals using backbone features
-            roi_heads: a ROI head that performs per-region computation
-            pixel_mean, pixel_std: list or tuple with #channels element, representing
-                the per-channel mean and std to be used to normalize the input image
-            input_format: describe the meaning of channels of input. Needed by visualization
-            vis_period: the period to run visualization. Set to 0 to disable.
-        """
-        super().__init__()
-        self.offline_backbone = offline_backbone
-        self.backbone = backbone
-        self.backbone_type = backbone_type
-        self.offline_proposal_generator = offline_proposal_generator
-        self.roi_heads = roi_heads
-        self.lang_encoder = text_backbone
-
-        self.input_format = input_format
-        self.vis_period = vis_period
-        if vis_period > 0:
-            assert input_format is not None, "input_format is required for visualization!"
-
-        self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False)
-        self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False)
-        assert (
-            self.pixel_mean.shape == self.pixel_std.shape
-        ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
-        if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
-            assert input_format == 'RGB'
-            self.div_pixel = True
-        else: # default setting
-            self.div_pixel = False
-
-        # input format, pixel mean and std for offline modules
-        if offline_input_format and offline_pixel_mean and offline_pixel_std:
-            self.offline_input_format = offline_input_format
-            self.register_buffer("offline_pixel_mean", torch.tensor(offline_pixel_mean).view(-1, 1, 1), False)
-            self.register_buffer("offline_pixel_std", torch.tensor(offline_pixel_std).view(-1, 1, 1), False)
-            if np.sum(offline_pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
-                assert offline_input_format == 'RGB'
-                self.offline_div_pixel = True
-            else: # default setting
-                self.offline_div_pixel = False
-        
-        self.clip_crop_region_type = clip_crop_region_type
-        self.use_clip_c4 = use_clip_c4 # if True, use C4 mode where roi_head uses the last resnet layer from backbone 
-        self.use_clip_attpool = use_clip_attpool # if True (C4+text_emb_as_classifier), use att_pool to replace default mean pool
-
-
-    @classmethod
-    def from_config(cls, cfg):
-        if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": # create isolated backbone & RPN
-            # create offline cfg for the pretrained backbone & RPN
-            from detectron2.config import get_cfg
-            offline_cfg = get_cfg()
-            offline_cfg.merge_from_file(cfg.MODEL.CLIP.OFFLINE_RPN_CONFIG)
-            if cfg.MODEL.CLIP.OFFLINE_RPN_LSJ_PRETRAINED: # large-scale jittering (LSJ) pretrained RPN
-                offline_cfg.MODEL.BACKBONE.FREEZE_AT = 0 # make all fronzon layers to "SyncBN"
-                offline_cfg.MODEL.RESNETS.NORM = "SyncBN" # 5 resnet layers
-                offline_cfg.MODEL.FPN.NORM = "SyncBN" # fpn layers
-                offline_cfg.MODEL.RPN.CONV_DIMS = [-1, -1] # rpn layers
-            if cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH:
-                offline_cfg.MODEL.RPN.NMS_THRESH = cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH  # 0.9
-                offline_cfg.MODEL.RPN.POST_NMS_TOPK_TEST = cfg.MODEL.RPN.POST_NMS_TOPK_TEST # 0.9
-
-            # create offline backbone and RPN
-            offline_backbone = build_backbone(offline_cfg) # build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)))
-            offline_rpn = build_proposal_generator(offline_cfg, offline_backbone.output_shape())
-            # convert to evaluation mode
-            for p in offline_backbone.parameters(): p.requires_grad = False
-            for p in offline_rpn.parameters(): p.requires_grad = False
-            offline_backbone.eval()
-            offline_rpn.eval()
-        elif cfg.MODEL.CLIP.CROP_REGION_TYPE == "GT":
-            offline_backbone = None
-            offline_rpn = None
-            offline_cfg = None
-        
-        backbone = build_backbone(cfg)
-        text_backbone = build_clip_language_encoder(cfg)
-
-        backbone_type = "swin" if "swin" in cfg.MODEL.BACKBONE.NAME else "resnet"
-
-        if backbone_type == "swin": 
-            roi_heads = build_roi_heads(cfg, backbone.image_encoder.output_shape())
-        else:        
-            roi_heads = build_roi_heads(cfg, backbone.output_shape())
-            
-        return {
-            "offline_backbone": offline_backbone,
-            "offline_proposal_generator": offline_rpn, 
-            "backbone": backbone,
-            "backbone_type": backbone_type, 
-            "text_backbone": text_backbone, 
-            "roi_heads": roi_heads, 
-            "input_format": cfg.INPUT.FORMAT,
-            "vis_period": cfg.VIS_PERIOD,
-            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
-            "pixel_std": cfg.MODEL.PIXEL_STD,
-            "clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE,
-            "use_clip_c4": 'FPN' not in cfg.MODEL.BACKBONE.NAME,
-            "use_clip_attpool": cfg.MODEL.ROI_HEADS.NAME in ['CLIPRes5ROIHeads', 'CLIPStandardROIHeads'] and cfg.MODEL.CLIP.USE_TEXT_EMB_CLASSIFIER,
-            "offline_input_format": offline_cfg.INPUT.FORMAT if offline_cfg else None,
-            "offline_pixel_mean": offline_cfg.MODEL.PIXEL_MEAN if offline_cfg else None,
-            "offline_pixel_std": offline_cfg.MODEL.PIXEL_STD if offline_cfg else None,
-        }
-
-    @property
-    def device(self):
-        return self.pixel_mean.device
-
-    def forward(self, queries, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
-                Each item in the list contains the inputs for one image.
-                For now, each item in the list is a dict that contains:
-
-                * image: Tensor, image in (C, H, W) format.
-                * instances (optional): groundtruth :class:`Instances`
-                * proposals (optional): :class:`Instances`, precomputed proposals.
-
-                Other information that's included in the original dicts, such as:
-
-                * "height", "width" (int): the output resolution of the model, used in inference.
-                  See :meth:`postprocess` for details.
-
-        Returns:
-            list[dict]:
-                Each dict is the output for one input image.
-                The dict contains one key "instances" whose value is a :class:`Instances`.
-                The :class:`Instances` object has the following keys:
-                "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
-        """
-        if not self.training:
-            return self.inference(queries, batched_inputs)
-        if "instances" in batched_inputs[0]:
-            gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-        else:
-            gt_instances = None
-        
-        # localization branch: offline modules to get the region proposals
-        with torch.no_grad():  
-            if self.clip_crop_region_type == "GT":  # from ground-truth
-                proposals = []
-                for r_i, b_input in enumerate(batched_inputs): 
-                    this_gt = copy.deepcopy(b_input["instances"])  # Instance
-                    gt_boxes = this_gt._fields['gt_boxes'].to(self.device)
-                    this_gt._fields = {'proposal_boxes': gt_boxes, 'objectness_logits': torch.ones(gt_boxes.tensor.size(0)).to(self.device)}
-                    proposals.append(this_gt)                
-            elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes
-                if self.offline_backbone.training or self.offline_proposal_generator.training:  #  was set to True in training script
-                    self.offline_backbone.eval() 
-                    self.offline_proposal_generator.eval()  
-                images = self.offline_preprocess_image(batched_inputs)
-                features = self.offline_backbone(images.tensor)
-                if self.offline_proposal_generator is not None:
-                    proposals, _ = self.offline_proposal_generator(images, features, None)     
-
-        # recognition branch: get 2D feature maps using the backbone of recognition branch
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-
-        if self.backbone_type == "resnet":
-            head = self.backbone.layer4
-        elif self.backbone_type == "swin":
-            head = self.backbone.layers[-1]
-
-        # Given the proposals, crop region features from 2D image features and classify the regions
-        if self.use_clip_c4: # use C4 + resnet weights from CLIP
-            if self.use_clip_attpool: # use att_pool from CLIP to match dimension
-                _, detector_losses = self.roi_heads(images, features, proposals, gt_instances, res5=head, attnpool=self.backbone.attnpool)
-            else: # use default mean pool
-                _, detector_losses = self.roi_heads(images, features, proposals, gt_instances, res5=head)
-        else:  # default setting
-            if self.use_clip_attpool: # use att_pool from CLIP to match dimension
-                _, detector_losses = self.roi_heads(images, features, proposals, gt_instances, attnpool=self.backbone.bottom_up.attnpool)
-            else: # use default mean pool
-                _, detector_losses = self.roi_heads(images, features, proposals, gt_instances)
-        if self.vis_period > 0:
-            storage = get_event_storage()
-            if storage.iter % self.vis_period == 0:
-                self.visualize_training(batched_inputs, proposals)
-        #visualize_proposals(batched_inputs, proposals, self.input_format)
-
-        losses = {}
-        losses.update(detector_losses)
-        return losses
-
-    def inference(
-        self,
-        queries, 
-        batched_inputs: List[Dict[str, torch.Tensor]],
-        detected_instances: Optional[List[Instances]] = None,
-        do_postprocess: bool = True,
-    ):
-        """
-        Run inference on the given inputs.
-
-        Args:
-            batched_inputs (list[dict]): same as in :meth:`forward`
-            detected_instances (None or list[Instances]): if not None, it
-                contains an `Instances` object per image. The `Instances`
-                object contains "pred_boxes" and "pred_classes" which are
-                known boxes in the image.
-                The inference will then skip the detection of bounding boxes,
-                and only predict other per-ROI outputs.
-            do_postprocess (bool): whether to apply post-processing on the outputs.
-
-        Returns:
-            When do_postprocess=True, same as in :meth:`forward`.
-            Otherwise, a list[Instances] containing raw network outputs.
-        """
-        assert not self.training
-        
-        # localization branch: offline modules to get the region proposals
-        if self.clip_crop_region_type == "GT":  # from ground-truth
-            proposals = []
-            for r_i, b_input in enumerate(batched_inputs): 
-                this_gt = copy.deepcopy(b_input["instances"])  # Instance
-                gt_boxes = this_gt._fields['gt_boxes'].to(self.device)
-                this_gt._fields = {'proposal_boxes': gt_boxes} #, 'objectness_logits': None}
-                proposals.append(this_gt)                
-        elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes
-            images = self.offline_preprocess_image(batched_inputs)
-            features = self.offline_backbone(images.tensor)
-            if detected_instances is None:
-                if self.offline_proposal_generator is not None:
-                    proposals, _ = self.offline_proposal_generator(images, features, None)     
-    
-        # recognition branch: get 2D feature maps using the backbone of recognition branch
-        print(batched_inputs[0]['image'][0][:10, :10])
-        print(batched_inputs[0]['image'].shape)
-        images = self.preprocess_image(batched_inputs)
-        
-        if self.backbone_type == "swin":
-            features = self.backbone.encode_image(images.tensor)
-            text_features = self.backbone.encode_text(queries)
-        else:
-            features = self.backbone(images.tensor)
-            token_embeddings = pre_tokenize([queries]).to(images.tensor.device)[0]
-            text_features = self.lang_encoder.encode_text(token_embeddings)
-            text_features = text_features.mean(0, keepdim=True)
-            text_features = text_features / text_features.norm(dim=-1, keepdim=True)  
-
-        if self.backbone_type == "resnet":
-            head = self.backbone.layer4
-            downsampler = None
-            norm = None
-            vision_projection = None
-        elif self.backbone_type == "swin":
-            downsampler = self.backbone.image_encoder.layers[-2].downsample    
-            head = self.backbone.image_encoder.layers[-1]
-            norm = self.backbone.image_encoder.norm
-            vision_projection = self.backbone.image_projection
-            
-        # Given the proposals, crop region features from 2D image features and classify the regions
-        if self.use_clip_c4: # use C4 + resnet weights from CLIP
-            if self.use_clip_attpool: # use att_pool from CLIP to match dimension
-                results, _ = self.roi_heads(images, features, proposals, text_features, None, 
-                    res5=head, ds=downsampler, norm=norm, vision_projection=vision_projection, attnpool=self.backbone.attnpool)
-            else: # use default mean pool
-                results, _ = self.roi_heads(images, features, proposals, text_features, None, 
-                    res5=head, ds=downsampler, norm=norm, vision_projection=vision_projection)
-        else:  # default setting
-            if self.use_clip_attpool: # use att_pool from CLIP to match dimension
-                results, _  = self.roi_heads(images, features, proposals, text_features, None, 
-                    attnpool=self.backbone.bottom_up.attnpool)
-            else:
-                results, _  = self.roi_heads(images, features, proposals, text_features, None)
-        
-        visualize_proposals(batched_inputs, proposals, self.input_format)
-        vis = visualize_results(batched_inputs, results, self.input_format)
-        return vis
-
-    def offline_preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std).
-        Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        if (self.input_format == 'RGB' and self.offline_input_format == 'BGR') or \
-            (self.input_format == 'BGR' and self.offline_input_format == 'RGB'): # the input image follows the main config format ('RGB' or 'BGR')
-            images = [x[[2,1,0],:,:] for x in images]
-        if self.offline_div_pixel:
-            images = [((x / 255.0) - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
-        else:
-            images = [(x - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility)
-        return images
-
-    def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Normalize, pad and batch the input images. Use CLIP default processing (pixel mean & std).
-        Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        if self.div_pixel:
-            images = [((x / 255.0) - self.pixel_mean) / self.pixel_std for x in images]
-        else:
-            images = [(x - self.pixel_mean) / self.pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
-        return images
-
-    @staticmethod
-    def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Rescale the output instances to the target size.
-        """
-        # note: private function; subject to changes
-        processed_results = []
-        for results_per_image, input_per_image in zip(
-            instances, batched_inputs):
-            height = input_per_image["height"]  # original image size, before resizing
-            width = input_per_image["width"]  # original image size, before resizing
-            r = detector_postprocess(results_per_image, height, width)
-            processed_results.append({"instances": r})
-        return processed_results
-
-@META_ARCH_REGISTRY.register()
-class PretrainFastRCNN(nn.Module):
-    """
-    Open-vocabulary region representation via vision-language pretraining from image-text pairs
-    1. image-text level matching: weakly supervised grounding task with contrastive learning based on region-token representation
-    2. region-token level matching: use pseudo text to train model, provided by teacher model
-    """
-    @configurable
-    def __init__(
-        self,
-        *,
-        offline_backbone: Backbone,
-        backbone: Backbone,
-        offline_proposal_generator: nn.Module,
-        roi_heads: nn.Module,
-        teacher_backbone: nn.Module,
-        teacher_roi_heads: nn.Module,
-        pixel_mean: Tuple[float],
-        pixel_std: Tuple[float],
-        input_format: Optional[str] = None,
-        vis_period: int = 0,
-        clip_crop_region_type: str = 'GT',
-        use_clip_c4: False,
-        use_clip_attpool: False,
-        offline_input_format: Optional[str] = None,
-        offline_pixel_mean: Tuple[float],
-        offline_pixel_std: Tuple[float],
-        language_encoder: nn.Module,
-        matching_temp: None,
-        num_regions_per_img: int = 0,
-        img_txt_level: None,
-        gather_gpus: False,
-        grid_regions: False,
-        concept_emb: None,
-    ):
-        """
-        Args:
-            backbone: a backbone module, must follow detectron2's backbone interface
-            proposal_generator: a module that generates proposals using backbone features
-            roi_heads: a ROI head that performs per-region computation
-            pixel_mean, pixel_std: list or tuple with #channels element, representing
-                the per-channel mean and std to be used to normalize the input image
-            input_format: describe the meaning of channels of input. Needed by visualization
-            vis_period: the period to run visualization. Set to 0 to disable.
-        """
-        super().__init__()
-        self.offline_backbone = offline_backbone
-        self.backbone = backbone
-        self.offline_proposal_generator = offline_proposal_generator
-        self.roi_heads = roi_heads
-
-        self.input_format = input_format
-        self.vis_period = vis_period
-        if vis_period > 0:
-            assert input_format is not None, "input_format is required for visualization!"
-
-        self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False)
-        self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False)
-        assert (
-            self.pixel_mean.shape == self.pixel_std.shape
-        ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
-        if np.sum(pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
-            assert input_format == 'RGB'
-            self.div_pixel = True
-        else: # default setting
-            self.div_pixel = False
-
-        # input format, pixel mean and std for offline modules
-        if offline_input_format and offline_pixel_mean and offline_pixel_std:
-            self.offline_input_format = offline_input_format
-            self.register_buffer("offline_pixel_mean", torch.tensor(offline_pixel_mean).view(-1, 1, 1), False)
-            self.register_buffer("offline_pixel_std", torch.tensor(offline_pixel_std).view(-1, 1, 1), False)
-            if np.sum(offline_pixel_mean) < 3.0: # converrt pixel value to range [0.0, 1.0] by dividing 255.0
-                assert offline_input_format == 'RGB'
-                self.offline_div_pixel = True
-            else: # default setting
-                self.offline_div_pixel = False
-        
-        self.clip_crop_region_type = clip_crop_region_type
-        self.use_clip_c4 = use_clip_c4 # if True, use C4 mode where roi_head uses the last resnet layer from backbone 
-        self.use_clip_attpool = use_clip_attpool # if True (C4+text_emb_as_classifier), use att_pool to replace default mean pool
-        
-        # image-text level pretraining
-        self.img_txt_level = img_txt_level[0]
-        self.only_eot = img_txt_level[1]
-        if self.img_txt_level:
-            self.lang_encoder = language_encoder
-            for p in self.lang_encoder.parameters():  # freeze language encoder
-                p.requires_grad = False
-        if matching_temp > 0.0: # fixed temp
-            self.matching_temp = matching_temp
-        else: # leanable temp
-            self.matching_temp = nn.Parameter(torch.ones([]) * 4.6052) # nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.context_length = 77 # defined in clip_img_txt_pair_tsv class
-        self.num_regions_per_img = num_regions_per_img
-        self.gather_gpus = gather_gpus
-        self.grid_regions = grid_regions
-
-        # region-token level pretraining
-        if concept_emb[0]:
-            self.register_buffer("concept_emb", torch.load(concept_emb[0]), False) # [#concepts, 1024]
-            self.concept_thres = concept_emb[1]
-            self.teacher_backbone = teacher_backbone # None
-            # when resume, create teacher model in advance to load ckpt
-            # self.teacher_backbone = copy.deepcopy(self.backbone)
-            # # # oai_clip = torch.load("/mnt/output_storage/trained_models/oai_clip_weights/RN50_OAI_CLIP.pth") #("/home/v-yiwuzhong/projects/azureblobs/vyiwuzhong_phillytools/trained_models/oai_clip_weights/RN50_OAI_CLIP.pth")
-            # # # oai_clip_visual = {}
-            # # # for key in oai_clip['model']:
-            # # #     if 'visual' in key and 'num_batches_tracked' not in key:
-            # # #         oai_clip_visual[key.replace('visual.','')] = oai_clip['model'][key]
-            # # # self.teacher_backbone.load_state_dict(oai_clip_visual)
-            for p in self.teacher_backbone.parameters():  # freeze visual encoder of teacher model
-                p.requires_grad = False
-            if concept_emb[2] is None: # teacher model uses the same concept embedding as student model
-                self.register_buffer("teacher_concept_emb", torch.load(concept_emb[0]), False)
-            else: # teacher model uses a seperate concept embedding
-                self.register_buffer("teacher_concept_emb", torch.load(concept_emb[2]), False)
-            self.teacher_roi_heads = teacher_roi_heads
-        else:
-            self.concept_emb = None
-
-    @classmethod
-    def from_config(cls, cfg):
-        if cfg.MODEL.CLIP.CROP_REGION_TYPE == "RPN": # create isolated backbone & RPN
-            # create offline cfg for the pretrained backbone & RPN
-            from detectron2.config import get_cfg
-            offline_cfg = get_cfg()
-            offline_cfg.merge_from_file(cfg.MODEL.CLIP.OFFLINE_RPN_CONFIG)
-            if cfg.MODEL.CLIP.OFFLINE_RPN_LSJ_PRETRAINED: # large-scale jittering (LSJ) pretrained RPN
-                offline_cfg.MODEL.BACKBONE.FREEZE_AT = 0 # make all fronzon layers to "SyncBN"
-                offline_cfg.MODEL.RESNETS.NORM = "SyncBN" # 5 resnet layers
-                offline_cfg.MODEL.FPN.NORM = "SyncBN" # fpn layers
-                offline_cfg.MODEL.RPN.CONV_DIMS = [-1, -1] # rpn layers
-            if cfg.MODEL.CLIP.PRETRAIN_RPN_REGIONS:
-                offline_cfg.MODEL.RPN.POST_NMS_TOPK_TEST = cfg.MODEL.CLIP.PRETRAIN_RPN_REGIONS 
-            if cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH:
-                offline_cfg.MODEL.RPN.NMS_THRESH = cfg.MODEL.CLIP.OFFLINE_RPN_NMS_THRESH  # 0.9
-            # offline_cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.6
-            # print("\n\n Set offline RPN.NMS_THRESH to {} and ROI_HEADS.NMS_THRESH_TEST to {}.\n\n".format(offline_cfg.MODEL.RPN.NMS_THRESH, offline_cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST))
-            # create offline backbone and RPN
-            offline_backbone = build_backbone(offline_cfg) # build_resnet_fpn_backbone(cfg, ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)))
-            offline_rpn = build_proposal_generator(offline_cfg, offline_backbone.output_shape())
-            # convert to evaluation mode
-            for p in offline_backbone.parameters(): p.requires_grad = False
-            for p in offline_rpn.parameters(): p.requires_grad = False
-            offline_backbone.eval()
-            offline_rpn.eval()
-        elif cfg.MODEL.CLIP.CROP_REGION_TYPE in ["GLOBAL", "GRID", "RANDOM"]:
-            offline_backbone = None
-            offline_rpn = None
-            offline_cfg = None
-        # visual encoder and roi_heads of student model
-        backbone = build_backbone(cfg)
-
-        if "swin" in cfg.MODEL.BACKBONE.NAME:
-            roi_heads = build_roi_heads(cfg, backbone.image_encoder.output_shape())
-        else:
-            roi_heads = build_roi_heads(cfg, backbone.output_shape())
-
-        # language encoder of student model
-        language_encoder = build_clip_language_encoder(cfg)
-        # visual encoder of teacher model
-        teacher_cfg = copy.deepcopy(cfg)
-        teacher_cfg.defrost()
-        teacher_cfg.MODEL.RESNETS.DEPTH = teacher_cfg.MODEL.CLIP.TEACHER_RESNETS_DEPTH
-        teacher_backbone = build_backbone(teacher_cfg)
-        teacher_cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = teacher_cfg.MODEL.CLIP.TEACHER_POOLER_RESOLUTION
-        teacher_roi_heads = build_roi_heads(teacher_cfg, teacher_backbone.output_shape())
-        return {
-            "offline_backbone": offline_backbone,
-            "offline_proposal_generator": offline_rpn, 
-            "backbone": backbone,
-            "roi_heads": roi_heads, 
-            "teacher_backbone": teacher_backbone,
-            "teacher_roi_heads": teacher_roi_heads,
-            "input_format": cfg.INPUT.FORMAT,
-            "vis_period": cfg.VIS_PERIOD,
-            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
-            "pixel_std": cfg.MODEL.PIXEL_STD,
-            "clip_crop_region_type" : cfg.MODEL.CLIP.CROP_REGION_TYPE,
-            "use_clip_c4": 'FPN' not in cfg.MODEL.BACKBONE.NAME,
-            "use_clip_attpool": cfg.MODEL.ROI_HEADS.NAME == 'PretrainRes5ROIHeads',
-            "offline_input_format": offline_cfg.INPUT.FORMAT if offline_cfg else None,
-            "offline_pixel_mean": offline_cfg.MODEL.PIXEL_MEAN if offline_cfg else None,
-            "offline_pixel_std": offline_cfg.MODEL.PIXEL_STD if offline_cfg else None,
-            "language_encoder": language_encoder,
-            "matching_temp": cfg.MODEL.CLIP.CLSS_TEMP,
-            "num_regions_per_img": cfg.MODEL.CLIP.PRETRAIN_SAMPLE_REGIONS,
-            "img_txt_level": (cfg.MODEL.CLIP.PRETRAIN_IMG_TXT_LEVEL, cfg.MODEL.CLIP.PRETRAIN_ONLY_EOT),
-            "gather_gpus": cfg.MODEL.CLIP.GATHER_GPUS,
-            "grid_regions": cfg.MODEL.CLIP.GRID_REGIONS,
-            "concept_emb": (cfg.MODEL.CLIP.CONCEPT_POOL_EMB, cfg.MODEL.CLIP.CONCEPT_THRES, cfg.MODEL.CLIP.TEACHER_CONCEPT_POOL_EMB),
-        }
-
-    @property
-    def device(self):
-        return self.pixel_mean.device
-
-    def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
-                Each item in the list contains the inputs for one image.
-                For now, each item in the list is a dict that contains:
-
-                * image: Tensor, image in (C, H, W) format.
-                * instances (optional): groundtruth :class:`Instances`
-                * proposals (optional): :class:`Instances`, precomputed proposals.
-
-                Other information that's included in the original dicts, such as:
-
-                * "height", "width" (int): the output resolution of the model, used in inference.
-                  See :meth:`postprocess` for details.
-
-        Returns:
-            list[dict]:
-                Each dict is the output for one input image.
-                The dict contains one key "instances" whose value is a :class:`Instances`.
-                The :class:`Instances` object has the following keys:
-                "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints"
-        """
-        if not self.training:
-            return self.inference(batched_inputs)
-        if self.concept_emb is not None and self.teacher_backbone is None: # create a teacher model from an initialized student model; if resume, simply comment out this section
-            self.teacher_backbone = copy.deepcopy(self.backbone)
-            for p in self.teacher_backbone.parameters():  # freeze visual encoder of teacher model
-                p.requires_grad = False
-        gt_instances = None
-        losses = {}
-        
-        # localization branch: offline modules to get the region proposals
-        proposals = self.get_region_proposals(batched_inputs)
-        global_proposals = self.create_global_proposals(batched_inputs)
-        # for prop, g_prop in zip(proposals, global_proposals):  # append global proposal into each image
-        #     prop.proposal_boxes.tensor = torch.cat((prop.proposal_boxes.tensor, g_prop.tensor), dim=0)
-
-        # recognition branch: get 2D feature maps using the backbone of recognition branch
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-        region_feats = self.get_region_features(images, features, proposals, gt_instances)
-        global_feats = self.get_region_features(images, features, global_proposals, gt_instances)
-
-        # image-text level matching
-        if self.img_txt_level:
-            self.image_text_matching(batched_inputs, proposals, region_feats, losses, global_feats=global_feats, only_global=True)
-
-        # region-phrase level matching
-        if len(batched_inputs[0]) > 6:  # controlled by dataset loading
-            phrase_text_embs = self.encode_phrase_text(batched_inputs)
-        else:
-            phrase_text_embs = None
-
-        # region-concept level matching
-        if self.concept_emb is not None:
-            self.region_concept_matching(images, proposals, gt_instances, region_feats, losses, phrase_embs=phrase_text_embs)
-
-        return losses
-
-    def encode_phrase_text(self, batched_inputs):
-        text = [x[6].view(-1,self.context_length).to(self.device) for i, x in enumerate(batched_inputs)]
-        text = torch.cat(text, dim=0)
-        text_embs = self.lang_encoder.encode_text(text, only_eot=True)  # [#phrases, transformer.width]
-        return text_embs
-
-    def region_concept_matching(self, images, proposals, gt_instances, region_feats, losses, phrase_embs=None):
-        use_distill = True
-        use_contrastive = True
-        # get psuedo concept labels from teacher model
-        concept_scores, target_inds, keep_regions, target_embs, label_mtx, phrase_label_mtx, phrase_target_regions \
-            = self.get_psuedo_concept_labels(images, proposals, gt_instances, phrase_embs=phrase_embs)
-
-        # prepare region features for the kept regions
-        keep_region_feats = region_feats[keep_regions]
-        keep_region_feats = keep_region_feats / keep_region_feats.norm(dim=-1, keepdim=True)
-
-        if use_distill:
-            # distillation learning: learns from the predictions of teacher model
-            concept_emb = self.concept_emb / self.concept_emb.norm(dim=-1, keepdim=True)
-            cls_scores = keep_region_feats @ concept_emb.t()  # [#kept_regions, #concepts]
-            if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
-                cls_scores_temp = cls_scores / self.matching_temp
-            else:
-                cls_scores_temp = cls_scores * self.matching_temp.exp() 
-            
-            # loss weights
-            #rpn_weights = torch.cat([torch.sigmoid(p.objectness_logits) for p in proposals])[keep_regions]
-            #focal_weights = self.focal_scaling(cls_scores_temp, target_inds)
-            
-            # calculate loss
-            cls_loss = F.kl_div(F.softmax(cls_scores_temp, dim=1).log(), concept_scores, reduction='batchmean')  # input is log-probabilities, target is probabilities
-            #cls_loss = SoftTargetCrossEntropy()(cls_scores_temp, concept_scores)
-            #cls_loss = F.cross_entropy(cls_scores_temp, target_inds)
-            #cls_loss = (F.cross_entropy(cls_scores_temp, target_inds, reduction="none") * focal_weights).mean()
-            losses.update({"loss_region_distill": cls_loss}) #  * 0.8})
-
-        if use_contrastive:
-            # contrastive learning: matching student visual features with target teacher concept embs
-            target_embs = target_embs / target_embs.norm(dim=-1, keepdim=True)
-            match_scores = keep_region_feats @ target_embs.t()  # [#kept_regions, #kept_regions]
-            if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
-                match_scores_temp = match_scores / self.matching_temp
-            else:
-                match_scores_temp = match_scores * self.matching_temp.exp() 
-            
-            # loss weights
-            #rpn_weights = torch.cat([torch.sigmoid(p.objectness_logits) for p in proposals])[keep_regions]
-            #focal_weights = (1 - torch.sigmoid(torch.diag(match_scores_temp))) ** 0.8 # 1.0 # 2.0 # 
-
-            # calculate loss given matching scores and label matrix
-            contrastive_loss = MILCrossEntropy()(match_scores_temp, label_mtx, weights=None, avg_positives=False) # SoftTargetCrossEntropy()(match_scores_temp, label_mtx)
-            #contrastive_loss = (MILCrossEntropy()(match_scores, label_mtx) + MILCrossEntropy()(match_scores.t(), label_mtx)) / 2.0
-            losses.update({"loss_concept_contrastive": contrastive_loss})
-
-        if phrase_embs is not None:
-            phrase_embs = phrase_embs / phrase_embs.norm(dim=-1, keepdim=True)
-            phrase_scores = phrase_embs @ phrase_target_regions.t()
-            if isinstance(self.matching_temp, float): # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
-                phrase_scores_temp = phrase_scores / self.matching_temp
-            else:
-                phrase_scores_temp = phrase_scores * self.matching_temp.exp() 
-            contrastive_loss = MILCrossEntropy()(phrase_scores_temp, phrase_label_mtx, weights=None, avg_positives=False)
-            #contrastive_loss = SoftTargetCrossEntropy()(phrase_scores_temp, phrase_label_mtx)
-            losses.update({"loss_phrase_contrastive": contrastive_loss})
-
-    def image_text_matching(self, batched_inputs, proposals, region_feats, losses, global_feats=None, only_global=False):
-        # encode text
-        num_cap = int(batched_inputs[0][1].size(0) / self.context_length)
-        if num_cap == 1:  # one caption per image
-            text = [x[1].view(1,-1).to(self.device) for x in batched_inputs]
-        else: # multiple caption pers image, then randomly pick one
-            rand_ind = [randint(0, num_cap-1) for _ in range(len(batched_inputs))]
-            text = [x[1].view(-1,self.context_length)[rand_ind[i]:rand_ind[i]+1].to(self.device) for i, x in enumerate(batched_inputs)]
-        text = torch.cat(text, dim=0)
-        text_embs = self.lang_encoder.encode_text(text, only_eot=self.only_eot)  # [img_batch, n_ctx, transformer.width] or [img_batch, transformer.width]
-        eot_pos = text.argmax(dim=-1) 
-
-        # prepare region features and text embeddings
-        if isinstance(proposals[0], Boxes):
-            num_bbs = [len(prop) for prop in proposals]
-        else: 
-            num_bbs = [len(prop.proposal_boxes) for prop in proposals]
-        if global_feats is not None and only_global:  # only global feature
-            assert self.only_eot
-            region_feats = global_feats
-            region_feats = region_feats / region_feats.norm(dim=-1, keepdim=True)
-            text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-            num_bbs = [1 for _ in num_bbs]
-        elif global_feats is not None and not only_global:  # combine both global and region features
-            assert self.only_eot
-            keep_num = 20
-            region_feats = region_feats.split(num_bbs)
-            region_feats = [torch.mean(rg_f, dim=0, keepdim=True) for rg_f in region_feats]
-            region_g_feats = [torch.cat((r_f[:keep_num], global_feats[i:i+1]), dim=0) for i, r_f in enumerate(region_feats)]
-            region_g_feats = [torch.mean(rg_f, dim=0, keepdim=True) for rg_f in region_g_feats]
-            region_g_feats = [rg_f / rg_f.norm(dim=-1, keepdim=True) for rg_f in region_g_feats]
-            region_feats = torch.cat(region_g_feats)
-            text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-            num_bbs = [1 for _ in num_bbs]
-        else: # only region features
-            num_bbs = torch.tensor(num_bbs).long().to(self.device)
-
-        region_feats_full, min_bs = gather_tensors(region_feats) if self.gather_gpus else (region_feats, None)  #  gather across GPUs
-        text_embs_full, min_bs = gather_tensors(text_embs) if self.gather_gpus else (text_embs, None)  #  gather across GPUs
-
-        # matching visual features with text embs
-        match_scores = region_feats_full @ text_embs_full.view(-1, text_embs_full.size(-1)).t()  # [#regions, img_batch * n_ctx]
-        if global_feats is not None: # only global feature or combine both global and region features
-            img_b = int(region_feats_full.size(0))
-            pooled_score = match_scores
-        else: # only region features
-            eot_pos_full, min_bs = gather_tensors(eot_pos) if self.gather_gpus else (eot_pos, None)  #  gather across GPUs
-            num_bbs_full, min_bs = gather_tensors(num_bbs) if self.gather_gpus else (num_bbs, None)  #  gather across GPUs
-            pooled_score = []
-            token_b = self.context_length
-            # region_b = self.num_regions_per_img if global_feats is None else 1
-            # img_b = int(region_feats_full.size(0) / region_b)
-            img_b = num_bbs_full.size(0)
-            rb_start = 0  # the starting index of regions
-            for i in range(img_b): # for each image
-                region_b = num_bbs_full[i].item()
-                for j in range(img_b):  # for each text
-                    if self.only_eot: # sentence level embs
-                        # max pool over regions
-                        this_s = torch.max(match_scores[rb_start:(rb_start+region_b), j:(j+1)], dim=0)[0]
-                    else: # token level embs
-                        # 3. softmax over regions as soft attention, then multiply attention with original logits, finally sum over matrix and divided by #tokens
-                        # this_matrix = match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)]
-                        # this_att = F.softmax(this_matrix, dim=0)
-                        # this_s = torch.sum(this_matrix * this_att) / (eot_pos_full[j]+1)
-                        # 2. max pool over regions, and then avg over text tokens
-                        # this_s = torch.sum(torch.max(match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)], dim=0)[0]) / (eot_pos_full[j]+1)
-                        # 1. max pool over regions, and then sum over text tokens
-                        this_s = torch.sum(torch.max(match_scores[rb_start:(rb_start+region_b), j*token_b:(j*token_b+eot_pos_full[j]+1)], dim=0)[0])
-                    pooled_score.append(this_s.view(1,1))
-                rb_start += region_b
-            assert rb_start == match_scores.size(0)
-            pooled_score = torch.cat(pooled_score).view(img_b, img_b)  # diagnal elements are positive pairs and the others are negative pairs
-
-        if isinstance(self.matching_temp,float):  # Typical good values are 100.0 for euclidean, 10.0 for dot, 0.01 for cosine
-            pooled_score = pooled_score / self.matching_temp
-        else:
-            pooled_score = pooled_score * self.matching_temp.exp()
-        contrast_target = torch.arange(img_b).to(self.device)
-        row_loss = F.cross_entropy(pooled_score, contrast_target)
-        col_loss = F.cross_entropy(pooled_score.t(), contrast_target)
-        losses.update({"loss_img_txt_level": (row_loss + col_loss) / 2.0}) # losses.update({"loss_img_txt_level": (row_loss + col_loss) / 4.0}) # 
-
-    def focal_scaling(self, logits, targets, gamma=1.0):
-        p = F.softmax(logits, dim=1)
-        p_t = p[torch.arange(p.size(0)).to(p.device), targets]  # get prob of target class
-        weights = (1 - p_t) ** gamma
-        return weights
-
-    def get_psuedo_concept_labels(self, images, proposals, gt_instances, s_temp=0.01, norm=True, phrase_embs=None):
-        """ Input images and region proposals, return matching results from teacher model
-        """
-        with torch.no_grad():
-            # extract visual features from teacher model
-            features = self.teacher_backbone(images.tensor)
-            teacher_region_feats = self.teacher_roi_heads(images, features, proposals, gt_instances, res5=self.teacher_backbone.layer4, attnpool=self.teacher_backbone.attnpool)
-            # match teacher visual features with teacher concept embs to create pseudo labels
-            if norm:
-                teacher_region_feats = teacher_region_feats / teacher_region_feats.norm(dim=-1, keepdim=True)
-                teacher_concept_emb = self.teacher_concept_emb / self.teacher_concept_emb.norm(dim=-1, keepdim=True)
-            else:
-                teacher_concept_emb = self.teacher_concept_emb
-            concept_scores = teacher_region_feats @ teacher_concept_emb.t()  # [#regions, #concepts]
-            concept_scores = F.softmax(concept_scores / s_temp, dim=1)
-            max_scores, max_inds = torch.max(concept_scores, dim=1)
-            keep_regions = max_scores > self.concept_thres  # only keep the regions that have high matching score with a concept
-            if keep_regions.nonzero().size(0) == 0: # if all regions can't match to any concept
-                print("all regions can't match to any concept!")
-                keep_regions = max_scores > 0.0 
-            target_inds = max_inds[keep_regions]
-            target_embs = self.concept_emb[target_inds] # the target embedding of student model
-            label_mtx = (target_inds.view(-1, 1) == target_inds.view(1, -1)).type_as(teacher_region_feats)
-            concept_scores = concept_scores[keep_regions]
-            # matching kept regions with phrase-text to create labels
-            if phrase_embs is None:
-                phrase_label_mtx = None
-                phrase_target_regions = None
-            else:
-                if norm:
-                    phrase_embs = phrase_embs / phrase_embs.norm(dim=-1, keepdim=True)
-                teacher_kept_feats = teacher_region_feats[keep_regions]
-                phrase_scores = phrase_embs @ teacher_kept_feats.t()  # [#phrases, #keep regions]
-                phrase_scores = F.softmax(phrase_scores / s_temp, dim=1)
-                _, max_region_inds = torch.max(phrase_scores, dim=1)
-                phrase_label_mtx = (max_region_inds.view(-1, 1) == max_region_inds.view(1, -1)).type_as(teacher_region_feats)
-                phrase_target_regions = teacher_kept_feats[max_region_inds]
-                
-        return concept_scores, target_inds, keep_regions, target_embs, label_mtx, phrase_label_mtx, phrase_target_regions
-
-    def get_region_features(self, images, features, proposals, gt_instances):
-        """ Input images and region proposals, return region features
-        """
-        # Given the proposals, crop region features from 2D image features
-        if self.use_clip_c4: # use C4 + resnet weights from CLIP
-            if self.use_clip_attpool: # use att_pool from CLIP to match dimension
-                region_feats = self.roi_heads(images, features, proposals, gt_instances, res5=self.backbone.layer4, attnpool=self.backbone.attnpool)
-            else: # use default mean pool
-                region_feats = self.roi_heads(images, features, proposals, gt_instances, res5=self.backbone.layer4)
-        else:  # default setting
-            region_feats = self.roi_heads(images, features, proposals, gt_instances)
-        return region_feats
-
-    def get_region_proposals(self, batched_inputs):
-        """ Given image, return object proposals
-        """
-        if self.grid_regions:  # use grid boxes
-            proposals = self.create_grid_boxes(batched_inputs)
-        else:  # use object proposals
-            with torch.no_grad():  
-                if self.clip_crop_region_type == "GLOBAL":  # from a global box per image
-                    proposals = self.create_global_proposals(batched_inputs) 
-                elif self.clip_crop_region_type == "GRID":  # from grid proposals
-                    proposals = self.create_grid_boxes(batched_inputs)
-                elif self.clip_crop_region_type == "RANDOM":  # from random proposals
-                    proposals = self.create_rand_boxes(batched_inputs)         
-                elif self.clip_crop_region_type == "RPN": # from the backbone & RPN of standard Mask-RCNN, trained on base classes
-                    if self.offline_backbone.training or self.offline_proposal_generator.training:  #  was set to True in training script
-                        self.offline_backbone.eval() 
-                        self.offline_proposal_generator.eval()  
-                    images = self.offline_preprocess_image(batched_inputs)
-                    features = self.offline_backbone(images.tensor)
-                    if self.offline_proposal_generator is not None:
-                        proposals, _ = self.offline_proposal_generator(images, features, None)     
-                #visualize_proposals(batched_inputs, proposals, self.input_format, vis_pretrain=True)
-        # randomly select proposals to avoid overfitting
-        if self.training:
-            #rand_inds = [torch.arange(len(p))[:self.num_regions_per_img].to(self.device) for p in proposals]
-            rand_inds = [torch.randperm(len(p))[:self.num_regions_per_img].to(self.device) for p in proposals]
-            proposals = [p[rand_inds[i]] for i, p in enumerate(proposals)]
-        return proposals
-
-    def offline_preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        NOTE: the image tsv in pretraining are already normalized pixel values and thus opposite to Detectron2 default input.
-        Normalize, pad and batch the input images. Use detectron2 default processing (pixel mean & std).
-        Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
-        """
-        images = [x[0].to(self.device) for x in batched_inputs]
-        if (self.input_format == 'RGB' and self.offline_input_format == 'BGR') or \
-            (self.input_format == 'BGR' and self.offline_input_format == 'RGB'): # the input image follows the main config format ('RGB' or 'BGR')
-            images = [x[[2,1,0],:,:] for x in images]
-        if self.offline_div_pixel:
-            images = [(x - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
-        else:
-            images = [((x * 255.0) - self.offline_pixel_mean) / self.offline_pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.offline_backbone.size_divisibility)
-        return images
-
-    def preprocess_image(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        NOTE: the image tsv in pretraining are already normalized pixel values and thus opposite to Detectron2 default input.
-        Normalize, pad and batch the input images. Use CLIP default processing (pixel mean & std).
-        Note: Due to FPN size_divisibility, images are padded by right/bottom border. So FPN is consistent with C4 and GT boxes.
-        """
-        images = [x[0].to(self.device) for x in batched_inputs]
-        if self.div_pixel:
-            images = [(x - self.pixel_mean) / self.pixel_std for x in images]
-        else:
-            images = [((x * 255.0) - self.pixel_mean) / self.pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
-        return images
-
-    def create_rand_boxes(self, batched_inputs, grid_length=8):
-        """ create random boxes within an image, output random self.num_regions_per_img boxes
-        return a list of Boxes
-        """
-        images = self.preprocess_image(batched_inputs)
-        image_height = images.tensor.size(2)
-        image_width = images.tensor.size(3)
-
-        left_top_x = torch.tensor([i*(grid_length) for i in range(image_width // grid_length)])
-        left_top_y = torch.tensor([i*(grid_length) for i in range(image_height // grid_length)])
-        right_bot_x = torch.tensor([(i+1)*(grid_length) for i in range(image_width // grid_length)])
-        right_bot_y = torch.tensor([(i+1)*(grid_length) for i in range(image_height // grid_length)])
-        x_inds = torch.randint(0, left_top_x.size(0), (self.num_regions_per_img,))
-        y_inds = torch.randint(0, left_top_y.size(0), (self.num_regions_per_img,))
-
-        proposals = []
-        for i in range(self.num_regions_per_img):
-            rb_x_candidates = right_bot_x[x_inds[i]:]
-            rb_x = rb_x_candidates[torch.randperm(rb_x_candidates.size(0))[0]]
-            rb_y_candidates = right_bot_y[y_inds[i]:]
-            rb_y = rb_y_candidates[torch.randperm(rb_y_candidates.size(0))[0]]
-            this_box = torch.cat((left_top_x[x_inds[i]].view(1,1), left_top_y[y_inds[i]].view(1,1), rb_x.view(1,1), rb_y.view(1,1)),dim=-1)
-            proposals.append(this_box)
-        proposals = torch.cat(proposals).float().to(self.device)
-        proposals = [Boxes(proposals) for i in range(len(batched_inputs))] # a list of Boxes
-        return proposals
-
-    def create_grid_boxes(self, batched_inputs, grid_length=32):
-        """ create (image_height/32) * (image_width/32) pseudo grid boxes, and randomly sample self.num_regions_per_img boxes
-        return a list of Boxes
-        """
-        images = self.preprocess_image(batched_inputs)
-        image_height = images.tensor.size(2)
-        image_width = images.tensor.size(3)
-
-        left_top_x = torch.tensor([i*(grid_length) for i in range(image_width // grid_length)])
-        left_top_y = torch.tensor([i*(grid_length) for i in range(image_height // grid_length)])
-        right_bot_x = torch.tensor([(i+1)*(grid_length) for i in range(image_width // grid_length)])
-        right_bot_y = torch.tensor([(i+1)*(grid_length) for i in range(image_height // grid_length)])
-        left_top_x, left_top_y = torch.meshgrid(left_top_x, left_top_y)
-        right_bot_x, right_bot_y = torch.meshgrid(right_bot_x, right_bot_y)
-        grid_boxes = torch.cat((left_top_x.flatten().view(-1,1), left_top_y.flatten().view(-1,1),\
-                                right_bot_x.flatten().view(-1,1), right_bot_y.flatten().view(-1,1),), dim=1)
-        sample_ind = torch.randperm(grid_boxes.size(0))[:self.num_regions_per_img]
-        grid_boxes = grid_boxes[sample_ind]
-        grid_boxes = grid_boxes.float().to(self.device)
-        proposals = [Boxes(grid_boxes) for i in range(len(batched_inputs))] # a list of Boxes
-        return proposals
-
-    def create_global_proposals(self, batched_inputs):
-        """ create a single global box for an image, so as to extract global image features with RoIAlign on high-resolution images.
-        """
-        images = self.preprocess_image(batched_inputs)
-        image_height = images.tensor.size(2)
-        image_width = images.tensor.size(3)
-
-        global_box = torch.tensor([0, 0, image_width, image_height]).view(1,4).float().to(self.device)
-        proposals = [Boxes(global_box) for i in range(len(batched_inputs))] # a list of Boxes
-        return proposals
-
-    def inference(self, batched_inputs, detected_instances=None, do_postprocess=True):
-        """
-        Grounding inference: map region features with sentence tokens
-        return: matching scores between region features and tokenized texts, region boxes in raw image resolution, image id & raw string texts & tokenized texts
-        """
-        assert len(batched_inputs) == 1 # only one instance per image during inference
-        gt_instances = None
-        losses = {}
-        
-        # localization branch: offline modules to get the region proposals
-        proposals = self.get_region_proposals(batched_inputs)
-
-        # recognition branch: get 2D feature maps using the backbone of recognition branch
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-        region_feats = self.get_region_features(images, features, proposals, gt_instances)
-
-        # encode text
-        num_cap = int(batched_inputs[0][1].size(0) / self.context_length)
-        text = batched_inputs[0][1].view(num_cap, -1).to(self.device)  # [num_cap, context_length]
-        text_embs = self.lang_encoder.encode_text(text, only_eot=False)  # [img_batch, n_ctx, transformer.width] or [img_batch, transformer.width]
-
-        # matching visual features with text embs
-        region_feats = region_feats / region_feats.norm(dim=-1, keepdim=True)
-        text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-        match_scores = region_feats @ text_embs.view(-1, text_embs.size(-1)).t()  # [#regions, img_batch * n_ctx]
-        # visualize_proposals(batched_inputs, proposals, self.input_format, vis_pretrain=True)
-
-        # multiply RPN logits
-        rpn_scores = [p.get('objectness_logits') for p in proposals][0]
-        match_scores = (match_scores * rpn_scores[:, None]) ** 0.5
-        
-        # scale the object proposals back to raw image resolution
-        if do_postprocess:
-            assert not torch.jit.is_scripting(), "Scripting is not supported for postprocess."
-            processed_results = PretrainFastRCNN._postprocess(proposals, batched_inputs)
-            return match_scores, processed_results
-
-    @staticmethod
-    def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Rescale the output instances to the target size.
-        """
-        # note: private function; subject to changes
-        processed_results = []
-        for results_per_image, input_per_image in zip(instances, batched_inputs):
-            height, width = input_per_image[-1][2] # original image size, before resizing
-            r = detector_postprocess(results_per_image, height, width)
-            processed_results.append({"instances": r})
-        return processed_results
-
-
-def visualize_proposals(batched_inputs, proposals, input_format, vis_pretrain=False):
-    """
-    A function used to visualize images and proposals. It shows ground truth
-    bounding boxes on the original image and up to 20 top-scoring predicted
-    object proposals on the original image. Users can implement different
-    visualization functions for different models.
-
-    Args:
-        batched_inputs (list): a list that contains input to the model.
-        proposals (list): a list that contains predicted proposals. Both
-            batched_inputs and proposals should have the same length.
-    """
-    from detectron2.utils.visualizer import Visualizer
-
-    max_vis_prop = 50
-    if vis_pretrain:
-        for i, (input, prop) in enumerate(zip(batched_inputs, proposals)):
-            img = input[0] * 255.0
-            img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
-            box_size = min(len(prop.proposal_boxes), max_vis_prop)
-            v_pred = Visualizer(img, None)
-            v_pred = v_pred.overlay_instances(
-                boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
-            )
-            prop_img = v_pred.get_image()
-            vis_img = prop_img
-            to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            #to_save.save("output/regions/" + str(i) + ".png")
-            #break  # only visualize one image in a batch
-    else:
-        for input, prop in zip(batched_inputs, proposals):
-            img = input["image"]
-            img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
-            box_size = min(len(prop.proposal_boxes), max_vis_prop)
-            v_pred = Visualizer(img, None)
-            v_pred = v_pred.overlay_instances(
-                boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
-            )
-            prop_img = v_pred.get_image()
-            vis_img = prop_img
-            # f_n = input['file_name']
-            to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            #to_save.save("output/regions/" + "proposals.png")
-            #break  # only visualize one image in a batch
-
-def visualize_results(batched_inputs, results, input_format, vis_pretrain=False):
-    """
-    A function used to visualize images and results. It shows ground truth
-    bounding boxes on the original image and up to 20 top-scoring predicted
-    object results on the original image. Users can implement different
-    visualization functions for different models.
-
-    Args:
-        batched_inputs (list): a list that contains input to the model.
-        results (list): a list that contains predicted results. Both
-            batched_inputs and results should have the same length.
-    """
-    from detectron2.utils.visualizer import Visualizer
-
-    max_vis_prop = 1
-    if vis_pretrain:
-        for i, (input, prop) in enumerate(zip(batched_inputs, results)):
-            img = input[0] * 255.0
-            img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
-            box_size = min(len(prop.proposal_boxes), max_vis_prop)
-            v_pred = Visualizer(img, None)
-            v_pred = v_pred.overlay_instances(
-                boxes=prop.proposal_boxes[0:box_size].tensor.cpu().numpy()
-            )
-            prop_img = v_pred.get_image()
-            vis_img = prop_img
-            to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            #to_save.save("output/regions/" + str(i) + ".png")
-            #break  # only visualize one image in a batch
-    else:
-        for input, prop in zip(batched_inputs, results):
-            img = input["image"]
-            img = convert_image_to_rgb(img.permute(1, 2, 0), input_format)
-            box_size = min(len(prop.pred_boxes), max_vis_prop)
-            v_pred = Visualizer(img, None)
-            v_pred = v_pred.overlay_instances(
-                boxes=prop.pred_boxes[0:box_size].tensor.cpu().numpy()
-            )
-            prop_img = v_pred.get_image()
-            vis_img = prop_img
-            # f_n = input['file_name']
-            to_save = Image.fromarray(np.array(vis_img, np.uint8))
-            #to_save.save("output/regions/" + "results.png")
-            #break  # only visualize one image in a batch
-    return to_save

spaces/ClearLove443/Robby-chatbot/setup.sh

@@ -1,21 +0,0 @@
-mkdir -p ~/.streamlit/
-
-echo "\
-[general]\n\
-email = \"[email protected]\"\n\
-" > ~/.streamlit/credentials.toml
-
-echo "\
-[server]\n\
-headless = true\n\
-enableCORS=false\n\
-port = $PORT\n\
-\n\
-[theme]\n\
-base = \"light\"\n\
-primaryColor = \"#89CFF0\"\n\
-backgroundColor = \"#E0F7FE\"\n\
-secondaryBackgroundColor = \"#FFFCE4\"\n\
-textColor = \"#000000\"\n\
-font = \"sans serif\"\n\
-" > ~/.streamlit/config.toml

spaces/CofAI/njpad/style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/api.py

@@ -1,170 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-
-import cv2
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-
-from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
-from ldm.modules.midas.midas.midas_net import MidasNet
-from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
-from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
-
-
-ISL_PATHS = {
-    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
-    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return transform
-
-
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return model.eval(), transform
-
-
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-
-    def forward(self, x):
-        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
-        # NOTE: we expect that the correct transform has been called during dataloading.
-        with torch.no_grad():
-            prediction = self.model(x)
-            prediction = torch.nn.functional.interpolate(
-                prediction.unsqueeze(1),
-                size=x.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
-        return prediction
-

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/img.py

@@ -1,521 +0,0 @@
-#coding=utf-8
-'''
-@author: dengdan
-'''
-import cv2
-import numpy as np
-import logging
-import math
-import event
-import util
-
-IMREAD_GRAY = 0
-IMREAD_COLOR = 1
-IMREAD_UNCHANGED = -1
-
-
-
-COLOR_WHITE =(255, 255, 255)
-COLOR_BLACK = (0, 0, 0)
-COLOR_GREEN = (0, 255, 0)
-
-COLOR_RGB_RED = (255, 0, 0)
-COLOR_BGR_RED = (0, 0, 255)
-
-COLOR_RGB_BLUE = (0, 0, 255)
-COLOR_BGR_BLUE = (255, 0, 0)
-
-COLOR_RGB_YELLOW = (255, 255, 0)
-COLOR_BGR_YELLOW = (0, 255, 255)
-
-
-COLOR_RGB_GRAY = (47, 79, 79)
-
-COLOR_RGB_PINK = (255, 192, 203)
-def imread(path, rgb = False, mode = cv2.IMREAD_COLOR):
-    path = util.io.get_absolute_path(path)
-    img = cv2.imread(path, mode)
-    if img is None:
-        raise IOError('File not found:%s'%(path))
-        
-    if rgb:
-        img = bgr2rgb(img)
-    return img
-
-def imshow(winname, img, block = True, position = None, maximized = False, rgb = False):
-    if isinstance(img, str):
-        img = imread(path = img)
-    
-    cv2.namedWindow(winname, cv2.WINDOW_NORMAL)
-    if rgb:
-        img = rgb2bgr(img)
-    cv2.imshow(winname, img)
-    if position is not None:
-#         cv2.moveWindow(winname, position[0], position[1])
-        move_win(winname, position)
-    
-    if maximized:
-        maximize_win(winname)  
-        
-        
-    if block:
-#         cv2.waitKey(0)
-        event.wait_key(" ")
-        cv2.destroyAllWindows()
-
-
-def imwrite(path, img, rgb = False):
-    if rgb:
-        img = rgb2bgr(img)
-    path = util.io.get_absolute_path(path)
-    util.io.make_parent_dir(path)
-    cv2.imwrite(path, img)
-
-def move_win(winname, position = (0, 0)):
-    """
-    move pyplot window
-    """
-    cv2.moveWindow(winname, position[0], position[1])
-
-def maximize_win(winname):
-    cv2.setWindowProperty(winname, cv2.WND_PROP_FULLSCREEN, True);
-
-def eq_color(target, color):
-    for i, c in enumerate(color):
-        if target[i] != color[i]:
-            return False
-    return True
-    
-def is_white(color):
-    for c in color:
-        if c < 255:
-            return False
-    return True
-    
-def black(shape):
-    if len(np.shape(shape)) >= 2:
-        shape = get_shape(shape)
-    shape = [int(v) for v in shape]
-    return np.zeros(shape, np.uint8)
-    
-def white(shape, value = 255):
-    if len(np.shape(shape)) >= 2:
-        shape = get_shape(shape)
-    return np.ones(shape, np.uint8) * np.uint8(value)
-    
-def bgr2rgb(img):
-    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-def rgb2bgr(img):
-    return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-
-
-def rgb2gray(img):
-    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-
-def bgr2gray(img):
-    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-
-def ds_size(image_size, kernel_size, stride):
-    """calculate the size of downsampling result"""
-    image_x, image_y = image_size
-    
-
-    kernel_x, kernel_y = kernel_size
-    stride_x, stride_y = stride
-    
-    def f(iw, kw, sw):
-        return int(np.floor((iw - kw) / sw) + 1)
-    
-    output_size = (f(image_x, kernel_x, stride_x), f(image_y, kernel_y, stride_y))
-    return output_size
-
-
-    
-def get_roi(img, p1, p2):
-    """
-    extract region of interest from an image.
-    p1, p2: two tuples standing for two opposite corners of the rectangle bounding the roi. 
-    Their order is arbitrary.
-    """
-    x1, y1 = p1
-    x2, y2 = p2
-    
-    x_min = min([x1, x2])
-    y_min = min([y1, y2])
-    x_max = max([x1, x2]) + 1
-    y_max = max([y1, y2]) + 1
-    
-    return img[y_min: y_max, x_min: x_max]
-    
-def rectangle(img, left_up, right_bottom, color, border_width = 1):
-    left_up = (int(left_up[0]), int(left_up[1]))
-    right_bottom = (int(right_bottom[0]), int(right_bottom[1]))
-    cv2.rectangle(img, left_up, right_bottom, color, border_width)
-
-
-def circle(img, center, r, color, border_width = 1):
-    center = (int(center[0]), int(center[1]))
-    cv2.circle(img, center, r, color, border_width)
-
-def render_points(img, points, color):
-    for p in points:
-        x, y = p
-        img[y][x] = color
-
-    
-def draw_contours(img, contours, idx = -1, color = 1, border_width = 1):
-#     img = img.copy()
-    cv2.drawContours(img, contours, idx, color, border_width)
-    return img
-
-def get_contour_rect_box(contour):
-    x,y,w,h = cv2.boundingRect(contour)
-    return x, y, w, h
-
-def get_contour_region_in_rect(img, contour):
-    x, y, w, h = get_contour_rect_box(contour)
-    lu, rb = (x, y), (x + w, y + h)
-    return get_roi(img, lu, rb)
-
-def get_contour_min_area_box(contour):
-    rect = cv2.minAreaRect(contour)
-    box = cv2.cv.BoxPoints(rect)
-    box = np.int0(box)
-    return box
-
-def get_contour_region_in_min_area_rect(img, cnt):
-    # find the min area rect of contour
-    rect = cv2.minAreaRect(cnt)
-    angle = rect[-1]
-    box = cv2.cv.BoxPoints(rect)
-    box_cnt = points_to_contour(box)
-    
-    # find the rectangle containing box_cnt, and set it as ROI
-    outer_rect = get_contour_rect_box(box_cnt)
-    x, y, w, h = outer_rect
-    img = get_roi(img, (x, y), (x + w,  y + h))
-    box = [(ox - x, oy - y) for (ox, oy) in box]
-    
-    # rotate ROI and corner points
-    rows, cols = get_shape(img)
-    M = cv2.getRotationMatrix2D((cols/2,rows/2), angle, scale = 1)
-    dst = cv2.warpAffine(img,M,(cols,rows))
-    bar_xy = np.hstack((box, np.ones((4, 1))))
-    new_corners = np.dot(M, np.transpose(bar_xy))
-    new_corners = util.dtype.int(np.transpose(new_corners))
-#     cnt = points_to_contour(new_corners)
-    
-    xs = new_corners[:, 0]
-    ys = new_corners[:, 1]
-    lu = (min(xs), min(ys))
-    rb = (max(xs), max(ys))
-    return get_roi(dst, lu, rb)
-
-
-def contour_to_points(contour):
-    return np.asarray([c[0] for c in contour])
-
-
-def points_to_contour(points):
-    contours = [[list(p)]for p in points]
-    return np.asarray(contours, dtype = np.int32)
-
-def points_to_contours(points):
-    return np.asarray([points_to_contour(points)])
-
-def get_contour_region_iou(I, cnt1, cnt2):
-    """
-    calculate the iou of two contours
-    """
-    mask1 = util.img.black(I)
-    draw_contours(mask1, [cnt1], color = 1, border_width = -1)
-    
-    mask2 = util.img.black(I)
-    draw_contours(mask2, [cnt2], color = 1, border_width = -1)
-    
-    union_mask = ((mask1 + mask2) >=1) * 1
-    intersect_mask = (mask1 * mask2 >= 1) * 1
-    
-    return np.sum(intersect_mask) * 1.0 / np.sum(union_mask)
-
-    
-def fill_bbox(img, box, color = 1):
-    """
-    filling a bounding box with color.
-    box: a list of 4 points, in clockwise order, as the four vertice of a bounding box
-    """
-    util.test.assert_equal(np.shape(box), (4, 2))
-    cnt = to_contours(box)
-    draw_contours(img, cnt, color = color, border_width = -1)
-    
-def get_rect_points(left_up, right_bottom):
-    """
-    given the left up and right bottom points of a rectangle, return its four points
-    """
-    right_bottom, left_up = np.asarray(right_bottom), np.asarray(left_up)
-    w, h = right_bottom - left_up
-    x, y = left_up
-    points = [(x, y), (x + w, y), (x + w, y + h), (x, y + h)]
-    return points
-    
-def rect_perimeter(left_up, right_bottom):
-    """
-    calculate the perimeter of the rectangle described by its left-up and right-bottom point.
-    """
-    return sum(np.asarray(right_bottom) -  np.asarray(left_up)) * 2
-
-def rect_area(left_up, right_bottom):
-    wh = np.asarray(right_bottom) - np.asarray(left_up) + 1
-    return np.prod(wh)
-    
-def apply_mask(img, mask):
-    """
-    the img will be masked in place. 
-    """
-    c = np.shape(img)[-1]
-    for i in range(c):
-        img[:, :, i] = img[:, :, i] * mask 
-    return img
-    
-def get_shape(img):
-    """
-    return the height and width of an image
-    """
-    return np.shape(img)[0:2]
-
-def get_wh(img):
-    return np.shape(img)[0:2][::-1]
-
-def get_value(img, x, y = None):
-    if y == None:
-        y = x[1]
-        x = x[0]
-    
-    return img[y][x]        
-    
-def set_value(img, xy, val):
-    x, y = xy
-    img[y][x] = val
-
-
-def filter2D(img, kernel):
-    dst = cv2.filter2D(img, -1, kernel)
-    return dst
-
-def average_blur(img, shape = (5, 5)):
-    return cv2.blur(img, shape)
-
-def gaussian_blur(img, shape = (5, 5), sigma = 0):
-    # sigma --> sigmaX, sigmaY
-    blur = cv2.GaussianBlur(img,shape, sigma)
-    return blur
-
-def bilateral_blur(img, d = 9, sigmaColor = 75, sigmaSpace = 75):
-    dst = cv2.bilateralFilter(img, d, sigmaColor, sigmaSpace)
-    return dst
-
-BLUR_AVERAGE =  'average'
-BLUR_GAUSSIAN = 'gaussian'
-BLUR_BILATERAL = 'bilateral'
-
-
-_blur_dict = {
-              BLUR_AVERAGE: average_blur,
-              BLUR_GAUSSIAN: gaussian_blur,
-              BLUR_BILATERAL: bilateral_blur
-}
-
-def blur(img, blur_type):
-    fn = _blur_dict[blur_type]
-    return fn(img)
-    
-def put_text(img, text, pos, scale = 1, color = COLOR_WHITE, thickness = 1):
-    pos = np.int32(pos)
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    cv2.putText(img = img, text = text, org = tuple(pos), fontFace = font,  fontScale = scale,  color = color, thickness = thickness)
-
-def resize(img, f = None, fx = None, fy = None, size = None, interpolation = cv2.INTER_LINEAR):
-    """
-    size: (w, h)
-    """
-    h, w = get_shape(img)
-    if fx != None and fy != None:
-        return cv2.resize(img, None, fx = fx, fy = fy, interpolation = interpolation)
-        
-    if size != None:
-        size = util.dtype.int(size)
-#         size = (size[1], size[0])
-        size = tuple(size)
-        return cv2.resize(img, size, interpolation = interpolation)
-    
-    return cv2.resize(img, None, fx = f, fy = f, interpolation = interpolation)
-
-def translate(img, delta_x, delta_y, size = None):
-    M = np.float32([[1,0, delta_x],[0,1, delta_y]])
-    if size == None:
-        size = get_wh(img)
-    
-    dst = cv2.warpAffine(img,M, size)
-    return dst
-
-
-def rotate_about_center(src, angle, scale=1.):
-    """https://www.oschina.net/translate/opencv-rotation"""
-    w = src.shape[1]
-    h = src.shape[0]
-    rangle = np.deg2rad(angle)  # angle in radians
-    # now calculate new image width and height
-    nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
-    nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
-    # ask OpenCV for the rotation matrix
-    rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
-    # calculate the move from the old center to the new center combined
-    # with the rotation
-    rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
-    # the move only affects the translation, so update the translation
-    # part of the transform
-    rot_mat[0,2] += rot_move[0]
-    rot_mat[1,2] += rot_move[1]
-    return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4), rot_mat
-
-
-def get_rect_iou(rects1, rects2):
-    """
-    calculate the iou between rects1 and rects2
-    each rect consists of four points:[min_x, min_y, max_x, max_y]
-    return: a iou matrix, len(rects1) * len(rects2)
-    """
-    rects1, rects2 = np.asarray(rects1), np.asarray(rects2)
-
-    def _to_matrix(p, ps):
-        p = np.ones((len(ps), 1)) * p
-        ps = np.reshape(ps, (len(ps), 1))
-        temp =np.hstack([p, ps])
-        return temp
-    
-    def _get_max(p, ps):
-        return np.max(_to_matrix(p, ps), axis = 1)
-    
-    def _get_min(p, ps):
-        return np.min(_to_matrix(p, ps), axis = 1)
- 
-    
-    def _get_area(rect):
-        w, h = rect[:, 2] - rect[:, 0] + 1.0 , rect[:, 3] - rect[:, 1] + 1.0
-        return w * h
-
-    def _get_inter(rect1, rects2):
-            x1 = _get_max(rect1[0], rects2[:, 0])
-            y1 = _get_max(rect1[1], rects2[:, 1])
-            
-            x2 = _get_min(rect1[2], rects2[:, 2])
-            y2 = _get_min(rect1[3], rects2[:, 3])
-        
-            w,h = x2-x1 +1, y2 - y1 + 1
-            areas = w * h
-            areas[np.where(w < 0)] = 0
-            areas[np.where(h < 0)] = 0
-            return areas
-            
-    area2 = _get_area(rects2)
-    area1 = _get_area(rects1)
-    iou = np.zeros((len(rects1), len(rects2)))
-    for ri in range(len(rects1)):
-        inter = _get_inter(rects1[ri, :], rects2)
-        union = area1[ri] + area2 - inter
-        iou[ri, :] = np.transpose( inter / union)
-    return iou
-
-def find_contours(mask):
-    mask = np.asarray(mask, dtype = np.uint8)
-    mask = mask.copy()
-    contours, _ = cv2.findContours(mask, mode = cv2.RETR_CCOMP, 
-                                   method = cv2.CHAIN_APPROX_SIMPLE)
-    return contours
-
-def find_two_level_contours(mask):
-    mask = mask.copy()
-    contours, tree = cv2.findContours(mask, mode = cv2.RETR_CCOMP, 
-                                  method = cv2.CHAIN_APPROX_SIMPLE)
-    return contours, tree
-    
-    
-def is_in_contour(point, cnt):
-    """tell whether a point is in contour or not. 
-            In-contour here includes both the 'in contour' and 'on contour' cases.
-       point:(x, y)
-       cnt: a cv2 contour
-    """
-    # doc of pointPolygonTest: http://docs.opencv.org/2.4/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?highlight=pointpolygontest#cv.PointPolygonTest
-    # the last argument means only tell if in or not, without calculating the shortest distance
-    in_cnt = cv2.pointPolygonTest(cnt, point, False)
-    return in_cnt >= 0;
-
-def convex_hull(contour):
-    hull = cv2.convexHull(contour, returnPoints=1)
-    return hull
-    
-def random_color_3():
-    c = util.rand.randint(low = 0, high = 255, shape = (3, ))
-#     c = np.uint8(c)
-    return c
-
-def get_contour_area(cnt):
-    return cv2.contourArea(cnt)
-    
-def is_valid_jpg(jpg_file):
-    with open(jpg_file, 'rb') as f:     
-        f.seek(-2, 2)
-        return f.read() == '\xff\xd9'
-
-
-
-def rotate_point_by_90(x, y, k, w = 1.0, h = 1.0):
-    """
-    Rotate a point xy on an image by k * 90
-    degrees.
-    Params:
-        x, y: a point, (x, y). If not normalized within 0 and 1, the 
-            width and height of the image should be specified clearly.
-        w, h: the width and height of image
-        k: k * 90 degrees will be rotated
-    """
-    k = k % 4
-    
-    if k == 0:
-        return x, y
-    elif k == 1:
-        return y, w - x
-    elif k == 2:
-        return w - x, h - y
-    elif k == 3:
-        return h - y, x
-    
-    
-def min_area_rect(xs, ys):
-    """
-    Args:
-        xs: numpy ndarray with shape=(N,4). N is the number of oriented bboxes. 4 contains [x1, x2, x3, x4]
-        ys: numpy ndarray with shape=(N,4), [y1, y2, y3, y4]
-            Note that [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] can represent an oriented bbox.
-    Return:
-        the oriented rects sorrounding the box, in the format:[cx, cy, w, h, theta]. 
-    """
-    xs = np.asarray(xs, dtype = np.float32)
-    ys = np.asarray(ys, dtype = np.float32)
-        
-    num_rects = xs.shape[0]
-    box = np.empty((num_rects, 5))#cx, cy, w, h, theta
-    for idx in xrange(num_rects):
-        points = zip(xs[idx, :], ys[idx, :])
-        cnt = points_to_contour(points)
-        rect = cv2.minAreaRect(cnt)
-        cx, cy = rect[0]
-        w, h = rect[1]
-        theta = rect[2]
-        box[idx, :] = [cx, cy, w, h, theta]
-    
-    box = np.asarray(box, dtype = xs.dtype)
-    return box

spaces/DHEIVER/Segmento_de_Angio_Coronariana_v6/obstruction_detector.py

@@ -1,55 +0,0 @@
-import cv2 as cv
-import numpy as np
-from scipy.signal import find_peaks
-from PIL import Image  # Import PIL
-
-class ObstructionDetector:
-    def __init__(self, threshold=500):
-        self.threshold = threshold
-
-    def preprocess_image(self, image):
-        # Convert the image to grayscale if it's a color image
-        if len(image.shape) == 3:
-            image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
-
-        # Apply Gaussian blur to reduce noise
-        preprocessed_image = cv.GaussianBlur(image, (5, 5), 0)
-
-        # Perform other preprocessing steps as needed (e.g., contrast adjustment, histogram equalization)
-
-        return preprocessed_image
-
-    def plot_histogram(self, image):
-        # Calculate the histogram
-        histogram = cv.calcHist([image], [0], None, [256], [0, 256])
-
-        # Smoothing the histogram using a simple moving average (window size = 5)
-        kernel = np.ones((5, 1)) / 5
-        smoothed_histogram = cv.filter2D(histogram, -1, kernel)
-
-        return smoothed_histogram
-
-    def count_histogram_peaks(self, smoothed_histogram):
-        # Find peaks in the smoothed histogram with frequency greater than the threshold
-        peaks, _ = find_peaks(smoothed_histogram.flatten(), height=self.threshold)
-        return peaks
-
-    def detect_obstruction(self, pil_image):  # Accept PIL image directly
-        # Convert PIL image to NumPy array
-        img = np.array(pil_image)
-
-        # Preprocess the image
-        preprocessed_img = self.preprocess_image(img)
-
-        # Count the number of peaks in the smoothed histogram above the threshold
-        smoothed_histogram = self.plot_histogram(preprocessed_img)
-        peaks = self.count_histogram_peaks(smoothed_histogram)
-
-        # Check if peaks are too close together
-        peak_spacing = np.diff(peaks)
-        if len(peak_spacing) == 0 or np.all(peak_spacing < 10):
-            report = "A imagem NÃO contém obstrução significativa | e NÃO possui múltiplas distribuições de densidade claramente distintas."
-        else:
-            report = "A imagem contém obstrução significativa | possui múltiplas distribuições de densidade claramente distintas."
-
-        return report

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/XbmImagePlugin.py

@@ -1,94 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# XBM File handling
-#
-# History:
-# 1995-09-08 fl   Created
-# 1996-11-01 fl   Added save support
-# 1997-07-07 fl   Made header parser more tolerant
-# 1997-07-22 fl   Fixed yet another parser bug
-# 2001-02-17 fl   Use 're' instead of 'regex' (Python 2.1) (0.4)
-# 2001-05-13 fl   Added hotspot handling (based on code from Bernhard Herzog)
-# 2004-02-24 fl   Allow some whitespace before first #define
-#
-# Copyright (c) 1997-2004 by Secret Labs AB
-# Copyright (c) 1996-1997 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import re
-
-from . import Image, ImageFile
-
-# XBM header
-xbm_head = re.compile(
-    rb"\s*#define[ \t]+.*_width[ \t]+(?P<width>[0-9]+)[\r\n]+"
-    b"#define[ \t]+.*_height[ \t]+(?P<height>[0-9]+)[\r\n]+"
-    b"(?P<hotspot>"
-    b"#define[ \t]+[^_]*_x_hot[ \t]+(?P<xhot>[0-9]+)[\r\n]+"
-    b"#define[ \t]+[^_]*_y_hot[ \t]+(?P<yhot>[0-9]+)[\r\n]+"
-    b")?"
-    rb"[\000-\377]*_bits\[]"
-)
-
-
-def _accept(prefix):
-    return prefix.lstrip()[:7] == b"#define"
-
-
-##
-# Image plugin for X11 bitmaps.
-
-
-class XbmImageFile(ImageFile.ImageFile):
-    format = "XBM"
-    format_description = "X11 Bitmap"
-
-    def _open(self):
-        m = xbm_head.match(self.fp.read(512))
-
-        if not m:
-            msg = "not a XBM file"
-            raise SyntaxError(msg)
-
-        xsize = int(m.group("width"))
-        ysize = int(m.group("height"))
-
-        if m.group("hotspot"):
-            self.info["hotspot"] = (int(m.group("xhot")), int(m.group("yhot")))
-
-        self.mode = "1"
-        self._size = xsize, ysize
-
-        self.tile = [("xbm", (0, 0) + self.size, m.end(), None)]
-
-
-def _save(im, fp, filename):
-    if im.mode != "1":
-        msg = f"cannot write mode {im.mode} as XBM"
-        raise OSError(msg)
-
-    fp.write(f"#define im_width {im.size[0]}\n".encode("ascii"))
-    fp.write(f"#define im_height {im.size[1]}\n".encode("ascii"))
-
-    hotspot = im.encoderinfo.get("hotspot")
-    if hotspot:
-        fp.write(f"#define im_x_hot {hotspot[0]}\n".encode("ascii"))
-        fp.write(f"#define im_y_hot {hotspot[1]}\n".encode("ascii"))
-
-    fp.write(b"static char im_bits[] = {\n")
-
-    ImageFile._save(im, fp, [("xbm", (0, 0) + im.size, 0, None)])
-
-    fp.write(b"};\n")
-
-
-Image.register_open(XbmImageFile.format, XbmImageFile, _accept)
-Image.register_save(XbmImageFile.format, _save)
-
-Image.register_extension(XbmImageFile.format, ".xbm")
-
-Image.register_mime(XbmImageFile.format, "image/xbm")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_v_h_e_a.py

@@ -1,126 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from fontTools.misc.fixedTools import (
-    ensureVersionIsLong as fi2ve,
-    versionToFixed as ve2fi,
-)
-from . import DefaultTable
-import math
-
-
-vheaFormat = """
-		>	# big endian
-		tableVersion:		L
-		ascent:			h
-		descent:		h
-		lineGap:		h
-		advanceHeightMax:	H
-		minTopSideBearing:	h
-		minBottomSideBearing:	h
-		yMaxExtent:		h
-		caretSlopeRise:		h
-		caretSlopeRun:		h
-		caretOffset:		h
-		reserved1:		h
-		reserved2:		h
-		reserved3:		h
-		reserved4:		h
-		metricDataFormat:	h
-		numberOfVMetrics:	H
-"""
-
-
-class table__v_h_e_a(DefaultTable.DefaultTable):
-
-    # Note: Keep in sync with table__h_h_e_a
-
-    dependencies = ["vmtx", "glyf", "CFF ", "CFF2"]
-
-    def decompile(self, data, ttFont):
-        sstruct.unpack(vheaFormat, data, self)
-
-    def compile(self, ttFont):
-        if ttFont.recalcBBoxes and (
-            ttFont.isLoaded("glyf")
-            or ttFont.isLoaded("CFF ")
-            or ttFont.isLoaded("CFF2")
-        ):
-            self.recalc(ttFont)
-        self.tableVersion = fi2ve(self.tableVersion)
-        return sstruct.pack(vheaFormat, self)
-
-    def recalc(self, ttFont):
-        if "vmtx" in ttFont:
-            vmtxTable = ttFont["vmtx"]
-            self.advanceHeightMax = max(adv for adv, _ in vmtxTable.metrics.values())
-
-        boundsHeightDict = {}
-        if "glyf" in ttFont:
-            glyfTable = ttFont["glyf"]
-            for name in ttFont.getGlyphOrder():
-                g = glyfTable[name]
-                if g.numberOfContours == 0:
-                    continue
-                if g.numberOfContours < 0 and not hasattr(g, "yMax"):
-                    # Composite glyph without extents set.
-                    # Calculate those.
-                    g.recalcBounds(glyfTable)
-                boundsHeightDict[name] = g.yMax - g.yMin
-        elif "CFF " in ttFont or "CFF2" in ttFont:
-            if "CFF " in ttFont:
-                topDict = ttFont["CFF "].cff.topDictIndex[0]
-            else:
-                topDict = ttFont["CFF2"].cff.topDictIndex[0]
-            charStrings = topDict.CharStrings
-            for name in ttFont.getGlyphOrder():
-                cs = charStrings[name]
-                bounds = cs.calcBounds(charStrings)
-                if bounds is not None:
-                    boundsHeightDict[name] = int(
-                        math.ceil(bounds[3]) - math.floor(bounds[1])
-                    )
-
-        if boundsHeightDict:
-            minTopSideBearing = float("inf")
-            minBottomSideBearing = float("inf")
-            yMaxExtent = -float("inf")
-            for name, boundsHeight in boundsHeightDict.items():
-                advanceHeight, tsb = vmtxTable[name]
-                bsb = advanceHeight - tsb - boundsHeight
-                extent = tsb + boundsHeight
-                minTopSideBearing = min(minTopSideBearing, tsb)
-                minBottomSideBearing = min(minBottomSideBearing, bsb)
-                yMaxExtent = max(yMaxExtent, extent)
-            self.minTopSideBearing = minTopSideBearing
-            self.minBottomSideBearing = minBottomSideBearing
-            self.yMaxExtent = yMaxExtent
-
-        else:  # No glyph has outlines.
-            self.minTopSideBearing = 0
-            self.minBottomSideBearing = 0
-            self.yMaxExtent = 0
-
-    def toXML(self, writer, ttFont):
-        formatstring, names, fixes = sstruct.getformat(vheaFormat)
-        for name in names:
-            value = getattr(self, name)
-            if name == "tableVersion":
-                value = fi2ve(value)
-                value = "0x%08x" % value
-            writer.simpletag(name, value=value)
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "tableVersion":
-            setattr(self, name, ve2fi(attrs["value"]))
-            return
-        setattr(self, name, safeEval(attrs["value"]))
-
-    # reserved0 is caretOffset for legacy reasons
-    @property
-    def reserved0(self):
-        return self.caretOffset
-
-    @reserved0.setter
-    def reserved0(self, value):
-        self.caretOffset = value