Update parquet files (step 39 of 249)

spaces/1368565466ki/Satdia/text/cleaners.py

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-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('ㄳ', 'ㄱㅅ'),
-  ('ㄵ', 'ㄴㅈ'),
-  ('ㄶ', 'ㄴㅎ'),
-  ('ㄺ', 'ㄹㄱ'),
-  ('ㄻ', 'ㄹㅁ'),
-  ('ㄼ', 'ㄹㅂ'),
-  ('ㄽ', 'ㄹㅅ'),
-  ('ㄾ', 'ㄹㅌ'),
-  ('ㄿ', 'ㄹㅍ'),
-  ('ㅀ', 'ㄹㅎ'),
-  ('ㅄ', 'ㅂㅅ'),
-  ('ㅘ', 'ㅗㅏ'),
-  ('ㅙ', 'ㅗㅐ'),
-  ('ㅚ', 'ㅗㅣ'),
-  ('ㅝ', 'ㅜㅓ'),
-  ('ㅞ', 'ㅜㅔ'),
-  ('ㅟ', 'ㅜㅣ'),
-  ('ㅢ', 'ㅡㅣ'),
-  ('ㅑ', 'ㅣㅏ'),
-  ('ㅒ', 'ㅣㅐ'),
-  ('ㅕ', 'ㅣㅓ'),
-  ('ㅖ', 'ㅣㅔ'),
-  ('ㅛ', 'ㅣㅗ'),
-  ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', '에이'),
-  ('b', '비'),
-  ('c', '시'),
-  ('d', '디'),
-  ('e', '이'),
-  ('f', '에프'),
-  ('g', '지'),
-  ('h', '에이치'),
-  ('i', '아이'),
-  ('j', '제이'),
-  ('k', '케이'),
-  ('l', '엘'),
-  ('m', '엠'),
-  ('n', '엔'),
-  ('o', '오'),
-  ('p', '피'),
-  ('q', '큐'),
-  ('r', '아르'),
-  ('s', '에스'),
-  ('t', '티'),
-  ('u', '유'),
-  ('v', '브이'),
-  ('w', '더블유'),
-  ('x', '엑스'),
-  ('y', '와이'),
-  ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(text):
-  for regex, replacement in _hangul_divided:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def hangul_number(num, sino=True):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  num = re.sub(',', '', num)
-
-  if num == '0':
-      return '영'
-  if not sino and num == '20':
-      return '스무'
-
-  digits = '123456789'
-  names = '일이삼사오육칠팔구'
-  digit2name = {d: n for d, n in zip(digits, names)}
-  
-  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-  spelledout = []
-  for i, digit in enumerate(num):
-    i = len(num) - i - 1
-    if sino:
-      if i == 0:
-        name = digit2name.get(digit, '')
-      elif i == 1:
-        name = digit2name.get(digit, '') + '십'
-        name = name.replace('일십', '십')
-    else:
-      if i == 0:
-        name = digit2mod.get(digit, '')
-      elif i == 1:
-        name = digit2dec.get(digit, '')
-    if digit == '0':
-      if i % 4 == 0:
-        last_three = spelledout[-min(3, len(spelledout)):]
-        if ''.join(last_three) == '':
-          spelledout.append('')
-          continue
-      else:
-        spelledout.append('')
-        continue
-    if i == 2:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 3:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 4:
-      name = digit2name.get(digit, '') + '만'
-      name = name.replace('일만', '만')
-    elif i == 5:
-      name = digit2name.get(digit, '') + '십'
-      name = name.replace('일십', '십')
-    elif i == 6:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 7:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 8:
-      name = digit2name.get(digit, '') + '억'
-    elif i == 9:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 10:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 11:
-      name = digit2name.get(digit, '') + '천'
-    elif i == 12:
-      name = digit2name.get(digit, '') + '조'
-    elif i == 13:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 14:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 15:
-      name = digit2name.get(digit, '') + '천'
-    spelledout.append(name)
-  return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-  for token in tokens:
-    num, classifier = token
-    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-      spelledout = hangul_number(num, sino=False)
-    else:
-      spelledout = hangul_number(num, sino=True)
-    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-  # digit by digit for remaining digits
-  digits = '0123456789'
-  names = '영일이삼사오육칠팔구'
-  for d, n in zip(digits, names):
-    text = text.replace(d, n)
-  return text
-
-
-def number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if len(text) and re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Avidemux 2.7.5 x64 Multilingual Crack Edit Videos Like a Pro.md

@@ -1,139 +0,0 @@
-<br />
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-menu and select <code>Delete</code>. This will delete the part between <code>A</code>
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- <h3>A summary of the main points of the article</h3>
- <p>In this article, we have learned how to use Avidemux 2.7.5 x64 Multilingual Crack for video editing tasks. We have covered the following topics:</p>
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-<li>How to use Avidemux 2.7.5 x64 Multilingual Crack for basic and advanced video editing tasks such as cutting, filtering, encoding, and scripting?</li>
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spaces/52Hz/SRMNet_real_world_denoising/app.py

@@ -1,37 +0,0 @@
-import os
-import gradio as gr
-from PIL import Image
-
-
-os.system(
-    'wget https://github.com/FanChiMao/SRMNet/releases/download/0.0/real_denoising_SRMNet.pth -P experiments/pretrained_models')
-
-
-def inference(img):
-    os.system('mkdir test')
-    #basewidth = 256
-    #wpercent = (basewidth / float(img.size[0]))
-    #hsize = int((float(img.size[1]) * float(wpercent)))
-    #img = img.resize((basewidth, hsize), Image.ANTIALIAS)
-    img.save("test/1.png", "PNG")
-    os.system(
-        'python main_test_SRMNet.py --input_dir test --weights experiments/pretrained_models/real_denoising_SRMNet.pth')
-    return 'result/1.png'
-
-
-title = "Selective Residual M-Net for Real-world Image Denoising"
-description = "Gradio demo for SRMNet. SRMNet has competitive performance results on two synthetic and two realworld noisy datasets in terms of quantitative metrics and visual quality. See the paper and project page for detailed results below. Here, we provide a demo for real-world image denoising. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
-article = "<p style='text-align: center'><a href='https://ieeexplore.ieee.org/document/9909521' target='_blank'>Selective Residual M-Net</a> | <a href='https://github.com/FanChiMao/SRMNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_SRMNet_real_world_denoising' alt='visitor badge'></center>"
-
-examples = [['Noise.png'], ['Noise2.png']]
-gr.Interface(
-    inference,
-    [gr.inputs.Image(type="pil", label="Input")],
-    gr.outputs.Image(type="filepath", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging=False,
-    allow_screenshot=False,
-    examples=examples
-).launch(debug=True)

spaces/801artistry/RVC801/tools/torchgate/torchgate.py

@@ -1,264 +0,0 @@
-import torch
-from torch.nn.functional import conv1d, conv2d
-from typing import Union, Optional
-from .utils import linspace, temperature_sigmoid, amp_to_db
-
-
-class TorchGate(torch.nn.Module):
-    """
-    A PyTorch module that applies a spectral gate to an input signal.
-
-    Arguments:
-        sr {int} -- Sample rate of the input signal.
-        nonstationary {bool} -- Whether to use non-stationary or stationary masking (default: {False}).
-        n_std_thresh_stationary {float} -- Number of standard deviations above mean to threshold noise for
-                                           stationary masking (default: {1.5}).
-        n_thresh_nonstationary {float} -- Number of multiplies above smoothed magnitude spectrogram. for
-                                        non-stationary masking (default: {1.3}).
-        temp_coeff_nonstationary {float} -- Temperature coefficient for non-stationary masking (default: {0.1}).
-        n_movemean_nonstationary {int} -- Number of samples for moving average smoothing in non-stationary masking
-                                          (default: {20}).
-        prop_decrease {float} -- Proportion to decrease signal by where the mask is zero (default: {1.0}).
-        n_fft {int} -- Size of FFT for STFT (default: {1024}).
-        win_length {[int]} -- Window length for STFT. If None, defaults to `n_fft` (default: {None}).
-        hop_length {[int]} -- Hop length for STFT. If None, defaults to `win_length` // 4 (default: {None}).
-        freq_mask_smooth_hz {float} -- Frequency smoothing width for mask (in Hz). If None, no smoothing is applied
-                                     (default: {500}).
-        time_mask_smooth_ms {float} -- Time smoothing width for mask (in ms). If None, no smoothing is applied
-                                     (default: {50}).
-    """
-
-    @torch.no_grad()
-    def __init__(
-        self,
-        sr: int,
-        nonstationary: bool = False,
-        n_std_thresh_stationary: float = 1.5,
-        n_thresh_nonstationary: float = 1.3,
-        temp_coeff_nonstationary: float = 0.1,
-        n_movemean_nonstationary: int = 20,
-        prop_decrease: float = 1.0,
-        n_fft: int = 1024,
-        win_length: bool = None,
-        hop_length: int = None,
-        freq_mask_smooth_hz: float = 500,
-        time_mask_smooth_ms: float = 50,
-    ):
-        super().__init__()
-
-        # General Params
-        self.sr = sr
-        self.nonstationary = nonstationary
-        assert 0.0 <= prop_decrease <= 1.0
-        self.prop_decrease = prop_decrease
-
-        # STFT Params
-        self.n_fft = n_fft
-        self.win_length = self.n_fft if win_length is None else win_length
-        self.hop_length = self.win_length // 4 if hop_length is None else hop_length
-
-        # Stationary Params
-        self.n_std_thresh_stationary = n_std_thresh_stationary
-
-        # Non-Stationary Params
-        self.temp_coeff_nonstationary = temp_coeff_nonstationary
-        self.n_movemean_nonstationary = n_movemean_nonstationary
-        self.n_thresh_nonstationary = n_thresh_nonstationary
-
-        # Smooth Mask Params
-        self.freq_mask_smooth_hz = freq_mask_smooth_hz
-        self.time_mask_smooth_ms = time_mask_smooth_ms
-        self.register_buffer("smoothing_filter", self._generate_mask_smoothing_filter())
-
-    @torch.no_grad()
-    def _generate_mask_smoothing_filter(self) -> Union[torch.Tensor, None]:
-        """
-        A PyTorch module that applies a spectral gate to an input signal using the STFT.
-
-        Returns:
-            smoothing_filter (torch.Tensor): a 2D tensor representing the smoothing filter,
-            with shape (n_grad_freq, n_grad_time), where n_grad_freq is the number of frequency
-            bins to smooth and n_grad_time is the number of time frames to smooth.
-            If both self.freq_mask_smooth_hz and self.time_mask_smooth_ms are None, returns None.
-        """
-        if self.freq_mask_smooth_hz is None and self.time_mask_smooth_ms is None:
-            return None
-
-        n_grad_freq = (
-            1
-            if self.freq_mask_smooth_hz is None
-            else int(self.freq_mask_smooth_hz / (self.sr / (self.n_fft / 2)))
-        )
-        if n_grad_freq < 1:
-            raise ValueError(
-                f"freq_mask_smooth_hz needs to be at least {int((self.sr / (self._n_fft / 2)))} Hz"
-            )
-
-        n_grad_time = (
-            1
-            if self.time_mask_smooth_ms is None
-            else int(self.time_mask_smooth_ms / ((self.hop_length / self.sr) * 1000))
-        )
-        if n_grad_time < 1:
-            raise ValueError(
-                f"time_mask_smooth_ms needs to be at least {int((self.hop_length / self.sr) * 1000)} ms"
-            )
-
-        if n_grad_time == 1 and n_grad_freq == 1:
-            return None
-
-        v_f = torch.cat(
-            [
-                linspace(0, 1, n_grad_freq + 1, endpoint=False),
-                linspace(1, 0, n_grad_freq + 2),
-            ]
-        )[1:-1]
-        v_t = torch.cat(
-            [
-                linspace(0, 1, n_grad_time + 1, endpoint=False),
-                linspace(1, 0, n_grad_time + 2),
-            ]
-        )[1:-1]
-        smoothing_filter = torch.outer(v_f, v_t).unsqueeze(0).unsqueeze(0)
-
-        return smoothing_filter / smoothing_filter.sum()
-
-    @torch.no_grad()
-    def _stationary_mask(
-        self, X_db: torch.Tensor, xn: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        """
-        Computes a stationary binary mask to filter out noise in a log-magnitude spectrogram.
-
-        Arguments:
-            X_db (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the log-magnitude spectrogram.
-            xn (torch.Tensor): 1D tensor containing the audio signal corresponding to X_db.
-
-        Returns:
-            sig_mask (torch.Tensor): Binary mask of the same shape as X_db, where values greater than the threshold
-            are set to 1, and the rest are set to 0.
-        """
-        if xn is not None:
-            XN = torch.stft(
-                xn,
-                n_fft=self.n_fft,
-                hop_length=self.hop_length,
-                win_length=self.win_length,
-                return_complex=True,
-                pad_mode="constant",
-                center=True,
-                window=torch.hann_window(self.win_length).to(xn.device),
-            )
-
-            XN_db = amp_to_db(XN).to(dtype=X_db.dtype)
-        else:
-            XN_db = X_db
-
-        # calculate mean and standard deviation along the frequency axis
-        std_freq_noise, mean_freq_noise = torch.std_mean(XN_db, dim=-1)
-
-        # compute noise threshold
-        noise_thresh = mean_freq_noise + std_freq_noise * self.n_std_thresh_stationary
-
-        # create binary mask by thresholding the spectrogram
-        sig_mask = X_db > noise_thresh.unsqueeze(2)
-        return sig_mask
-
-    @torch.no_grad()
-    def _nonstationary_mask(self, X_abs: torch.Tensor) -> torch.Tensor:
-        """
-        Computes a non-stationary binary mask to filter out noise in a log-magnitude spectrogram.
-
-        Arguments:
-            X_abs (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the magnitude spectrogram.
-
-        Returns:
-            sig_mask (torch.Tensor): Binary mask of the same shape as X_abs, where values greater than the threshold
-            are set to 1, and the rest are set to 0.
-        """
-        X_smoothed = (
-            conv1d(
-                X_abs.reshape(-1, 1, X_abs.shape[-1]),
-                torch.ones(
-                    self.n_movemean_nonstationary,
-                    dtype=X_abs.dtype,
-                    device=X_abs.device,
-                ).view(1, 1, -1),
-                padding="same",
-            ).view(X_abs.shape)
-            / self.n_movemean_nonstationary
-        )
-
-        # Compute slowness ratio and apply temperature sigmoid
-        slowness_ratio = (X_abs - X_smoothed) / (X_smoothed + 1e-6)
-        sig_mask = temperature_sigmoid(
-            slowness_ratio, self.n_thresh_nonstationary, self.temp_coeff_nonstationary
-        )
-
-        return sig_mask
-
-    def forward(
-        self, x: torch.Tensor, xn: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        """
-        Apply the proposed algorithm to the input signal.
-
-        Arguments:
-            x (torch.Tensor): The input audio signal, with shape (batch_size, signal_length).
-            xn (Optional[torch.Tensor]): The noise signal used for stationary noise reduction. If `None`, the input
-                                         signal is used as the noise signal. Default: `None`.
-
-        Returns:
-            torch.Tensor: The denoised audio signal, with the same shape as the input signal.
-        """
-        assert x.ndim == 2
-        if x.shape[-1] < self.win_length * 2:
-            raise Exception(f"x must be bigger than {self.win_length * 2}")
-
-        assert xn is None or xn.ndim == 1 or xn.ndim == 2
-        if xn is not None and xn.shape[-1] < self.win_length * 2:
-            raise Exception(f"xn must be bigger than {self.win_length * 2}")
-
-        # Compute short-time Fourier transform (STFT)
-        X = torch.stft(
-            x,
-            n_fft=self.n_fft,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            return_complex=True,
-            pad_mode="constant",
-            center=True,
-            window=torch.hann_window(self.win_length).to(x.device),
-        )
-
-        # Compute signal mask based on stationary or nonstationary assumptions
-        if self.nonstationary:
-            sig_mask = self._nonstationary_mask(X.abs())
-        else:
-            sig_mask = self._stationary_mask(amp_to_db(X), xn)
-
-        # Propagate decrease in signal power
-        sig_mask = self.prop_decrease * (sig_mask * 1.0 - 1.0) + 1.0
-
-        # Smooth signal mask with 2D convolution
-        if self.smoothing_filter is not None:
-            sig_mask = conv2d(
-                sig_mask.unsqueeze(1),
-                self.smoothing_filter.to(sig_mask.dtype),
-                padding="same",
-            )
-
-        # Apply signal mask to STFT magnitude and phase components
-        Y = X * sig_mask.squeeze(1)
-
-        # Inverse STFT to obtain time-domain signal
-        y = torch.istft(
-            Y,
-            n_fft=self.n_fft,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            center=True,
-            window=torch.hann_window(self.win_length).to(Y.device),
-        )
-
-        return y.to(dtype=x.dtype)

spaces/834188divi/cardiffnlp-twitter-roberta-base-sentiment-latest/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/cardiffnlp/twitter-roberta-base-sentiment-latest").launch()

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/normalizing_flow/utils.py

@@ -1,29 +0,0 @@
-import torch
-
-
-def squeeze(x, x_mask=None, n_sqz=2):
-    b, c, t = x.size()
-
-    t = (t // n_sqz) * n_sqz
-    x = x[:, :, :t]
-    x_sqz = x.view(b, c, t // n_sqz, n_sqz)
-    x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
-
-    if x_mask is not None:
-        x_mask = x_mask[:, :, n_sqz - 1::n_sqz]
-    else:
-        x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_sqz * x_mask, x_mask
-
-
-def unsqueeze(x, x_mask=None, n_sqz=2):
-    b, c, t = x.size()
-
-    x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
-    x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
-
-    if x_mask is not None:
-        x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
-    else:
-        x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_unsqz * x_mask, x_mask

spaces/AIGC-Audio/AudioGPT/audio_detection/audio_infer/pytorch/main.py

@@ -1,378 +0,0 @@
-import os
-import sys
-sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
-import numpy as np
-import argparse
-import time
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import torch.utils.data
- 
-from utilities import (create_folder, get_filename, create_logging, Mixup, 
-    StatisticsContainer)
-from models import (PVT, PVT2, PVT_lr, PVT_nopretrain, PVT_2layer, Cnn14, Cnn14_no_specaug, Cnn14_no_dropout, 
-    Cnn6, Cnn10, ResNet22, ResNet38, ResNet54, Cnn14_emb512, Cnn14_emb128, 
-    Cnn14_emb32, MobileNetV1, MobileNetV2, LeeNet11, LeeNet24, DaiNet19, 
-    Res1dNet31, Res1dNet51, Wavegram_Cnn14, Wavegram_Logmel_Cnn14, 
-    Wavegram_Logmel128_Cnn14, Cnn14_16k, Cnn14_8k, Cnn14_mel32, Cnn14_mel128, 
-    Cnn14_mixup_time_domain, Cnn14_DecisionLevelMax, Cnn14_DecisionLevelAtt, Cnn6_Transformer, GLAM, GLAM2, GLAM3, Cnn4, EAT)
-#from models_test import (PVT_test)
-#from models1 import (PVT1)
-#from models_vig import (VIG, VIG2)
-#from models_vvt import (VVT)
-#from models2 import (MPVIT, MPVIT2)
-#from models_reshape import (PVT_reshape, PVT_tscam)
-#from models_swin import (Swin, Swin_nopretrain)
-#from models_swin2 import (Swin2)
-#from models_van import (Van, Van_tiny)
-#from models_focal import (Focal)
-#from models_cross import (Cross)
-#from models_cov import (Cov)
-#from models_cnn import (Cnn_light)
-#from models_twins import (Twins)
-#from models_cmt import (Cmt, Cmt1)
-#from models_shunted import (Shunted)
-#from models_quadtree import (Quadtree, Quadtree2, Quadtree_nopretrain)
-#from models_davit import (Davit_tscam, Davit, Davit_nopretrain)
-from pytorch_utils import (move_data_to_device, count_parameters, count_flops, 
-    do_mixup)
-from data_generator import (AudioSetDataset, TrainSampler, BalancedTrainSampler, 
-    AlternateTrainSampler, EvaluateSampler, collate_fn)
-from evaluate import Evaluator
-import config
-from losses import get_loss_func
-
-
-def train(args):
-    """Train AudioSet tagging model. 
-
-    Args:
-      dataset_dir: str
-      workspace: str
-      data_type: 'balanced_train' | 'full_train'
-      window_size: int
-      hop_size: int
-      mel_bins: int
-      model_type: str
-      loss_type: 'clip_bce'
-      balanced: 'none' | 'balanced' | 'alternate'
-      augmentation: 'none' | 'mixup'
-      batch_size: int
-      learning_rate: float
-      resume_iteration: int
-      early_stop: int
-      accumulation_steps: int
-      cuda: bool
-    """
-
-    # Arugments & parameters
-    workspace = args.workspace
-    data_type = args.data_type
-    sample_rate = args.sample_rate
-    window_size = args.window_size
-    hop_size = args.hop_size
-    mel_bins = args.mel_bins
-    fmin = args.fmin
-    fmax = args.fmax
-    model_type = args.model_type
-    loss_type = args.loss_type
-    balanced = args.balanced
-    augmentation = args.augmentation
-    batch_size = args.batch_size
-    learning_rate = args.learning_rate
-    resume_iteration = args.resume_iteration
-    early_stop = args.early_stop
-    device = torch.device('cuda') if args.cuda and torch.cuda.is_available() else torch.device('cpu')
-    filename = args.filename
-
-    num_workers = 8
-    clip_samples = config.clip_samples
-    classes_num = config.classes_num
-    loss_func = get_loss_func(loss_type)
-
-    # Paths
-    black_list_csv = None
-    
-    train_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes', 
-        '{}.h5'.format(data_type))
-
-    eval_bal_indexes_hdf5_path = os.path.join(workspace, 
-        'hdf5s', 'indexes', 'balanced_train.h5')
-
-    eval_test_indexes_hdf5_path = os.path.join(workspace, 'hdf5s', 'indexes', 
-        'eval.h5')
-
-    checkpoints_dir = os.path.join(workspace, 'checkpoints', filename, 
-        'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
-        sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 
-        'data_type={}'.format(data_type), model_type, 
-        'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 
-        'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size))
-    create_folder(checkpoints_dir)
-    
-    statistics_path = os.path.join(workspace, 'statistics', filename, 
-        'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
-        sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 
-        'data_type={}'.format(data_type), model_type, 
-        'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 
-        'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 
-        'statistics.pkl')
-    create_folder(os.path.dirname(statistics_path))
-
-    logs_dir = os.path.join(workspace, 'logs', filename, 
-        'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
-        sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 
-        'data_type={}'.format(data_type), model_type, 
-        'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 
-        'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size))
-
-    create_logging(logs_dir, filemode='w')
-    logging.info(args)
-    
-    if 'cuda' in str(device):
-        logging.info('Using GPU.')
-        device = 'cuda'
-    else:
-        logging.info('Using CPU. Set --cuda flag to use GPU.')
-        device = 'cpu'
-    
-    # Model
-    Model = eval(model_type)
-    model = Model(sample_rate=sample_rate, window_size=window_size, 
-        hop_size=hop_size, mel_bins=mel_bins, fmin=fmin, fmax=fmax, 
-        classes_num=classes_num)
-    total = sum(p.numel() for p in model.parameters())
-    print("Total params: %.2fM" % (total/1e6))
-    logging.info("Total params: %.2fM" % (total/1e6))
-    #params_num = count_parameters(model)
-    # flops_num = count_flops(model, clip_samples)
-    #logging.info('Parameters num: {}'.format(params_num))
-    # logging.info('Flops num: {:.3f} G'.format(flops_num / 1e9))
-    
-    # Dataset will be used by DataLoader later. Dataset takes a meta as input 
-    # and return a waveform and a target.
-    dataset = AudioSetDataset(sample_rate=sample_rate)
-
-    # Train sampler
-    if balanced == 'none':
-        Sampler = TrainSampler
-    elif balanced == 'balanced':
-        Sampler = BalancedTrainSampler
-    elif balanced == 'alternate':
-        Sampler = AlternateTrainSampler
-     
-    train_sampler = Sampler(
-        indexes_hdf5_path=train_indexes_hdf5_path, 
-        batch_size=batch_size * 2 if 'mixup' in augmentation else batch_size,
-        black_list_csv=black_list_csv)
-    
-    # Evaluate sampler
-    eval_bal_sampler = EvaluateSampler(
-        indexes_hdf5_path=eval_bal_indexes_hdf5_path, batch_size=batch_size)
-
-    eval_test_sampler = EvaluateSampler(
-        indexes_hdf5_path=eval_test_indexes_hdf5_path, batch_size=batch_size)
-
-    # Data loader
-    train_loader = torch.utils.data.DataLoader(dataset=dataset, 
-        batch_sampler=train_sampler, collate_fn=collate_fn, 
-        num_workers=num_workers, pin_memory=True)
-    
-    eval_bal_loader = torch.utils.data.DataLoader(dataset=dataset, 
-        batch_sampler=eval_bal_sampler, collate_fn=collate_fn, 
-        num_workers=num_workers, pin_memory=True)
-
-    eval_test_loader = torch.utils.data.DataLoader(dataset=dataset, 
-        batch_sampler=eval_test_sampler, collate_fn=collate_fn, 
-        num_workers=num_workers, pin_memory=True)
-    mix=0.5
-    if 'mixup' in augmentation:
-        mixup_augmenter = Mixup(mixup_alpha=mix)
-    print(mix)
-    logging.info(mix)
-
-    # Evaluator
-    evaluator = Evaluator(model=model)
-        
-    # Statistics
-    statistics_container = StatisticsContainer(statistics_path)
-    
-    # Optimizer
-    optimizer = optim.AdamW(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.05, amsgrad=True)
-    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=4, min_lr=1e-06, verbose=True)
-    train_bgn_time = time.time()
-    
-    # Resume training
-    if resume_iteration > 0:
-        resume_checkpoint_path = os.path.join(workspace, 'checkpoints', filename, 
-            'sample_rate={},window_size={},hop_size={},mel_bins={},fmin={},fmax={}'.format(
-            sample_rate, window_size, hop_size, mel_bins, fmin, fmax), 
-            'data_type={}'.format(data_type), model_type, 
-            'loss_type={}'.format(loss_type), 'balanced={}'.format(balanced), 
-            'augmentation={}'.format(augmentation), 'batch_size={}'.format(batch_size), 
-            '{}_iterations.pth'.format(resume_iteration))
-
-        logging.info('Loading checkpoint {}'.format(resume_checkpoint_path))
-        checkpoint = torch.load(resume_checkpoint_path)
-        model.load_state_dict(checkpoint['model'])
-        train_sampler.load_state_dict(checkpoint['sampler'])
-        statistics_container.load_state_dict(resume_iteration)
-        iteration = checkpoint['iteration']
-
-    else:
-        iteration = 0
-    
-    # Parallel
-    print('GPU number: {}'.format(torch.cuda.device_count()))
-    model = torch.nn.DataParallel(model)
-
-    if 'cuda' in str(device):
-        model.to(device)
-
-    if resume_iteration:
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        scheduler.load_state_dict(checkpoint['scheduler'])
-        print(optimizer.state_dict()['param_groups'][0]['lr'])
-
-    time1 = time.time()
-    
-    for batch_data_dict in train_loader:
-        """batch_data_dict: {
-            'audio_name': (batch_size [*2 if mixup],), 
-            'waveform': (batch_size [*2 if mixup], clip_samples), 
-            'target': (batch_size [*2 if mixup], classes_num), 
-            (ifexist) 'mixup_lambda': (batch_size * 2,)}
-        """
-        
-        # Evaluate
-        if (iteration % 2000 == 0 and iteration >= resume_iteration) or (iteration == 0):
-            train_fin_time = time.time()
-
-            bal_statistics = evaluator.evaluate(eval_bal_loader)
-            test_statistics = evaluator.evaluate(eval_test_loader)
-                            
-            logging.info('Validate bal mAP: {:.3f}'.format(
-                np.mean(bal_statistics['average_precision'])))
-
-            logging.info('Validate test mAP: {:.3f}'.format(
-                np.mean(test_statistics['average_precision'])))
-
-            statistics_container.append(iteration, bal_statistics, data_type='bal')
-            statistics_container.append(iteration, test_statistics, data_type='test')
-            statistics_container.dump()
-
-            train_time = train_fin_time - train_bgn_time
-            validate_time = time.time() - train_fin_time
-
-            logging.info(
-                'iteration: {}, train time: {:.3f} s, validate time: {:.3f} s'
-                    ''.format(iteration, train_time, validate_time))
-
-            logging.info('------------------------------------')
-
-            train_bgn_time = time.time()
-        
-        # Save model
-        if iteration % 2000 == 0:
-            checkpoint = {
-                'iteration': iteration, 
-                'model': model.module.state_dict(), 
-                'sampler': train_sampler.state_dict(),
-                'optimizer': optimizer.state_dict(),
-                'scheduler': scheduler.state_dict()}
-
-            checkpoint_path = os.path.join(
-                checkpoints_dir, '{}_iterations.pth'.format(iteration))
-                
-            torch.save(checkpoint, checkpoint_path)
-            logging.info('Model saved to {}'.format(checkpoint_path))
-        
-        # Mixup lambda
-        if 'mixup' in augmentation:
-            batch_data_dict['mixup_lambda'] = mixup_augmenter.get_lambda(
-                batch_size=len(batch_data_dict['waveform']))
-
-        # Move data to device
-        for key in batch_data_dict.keys():
-            batch_data_dict[key] = move_data_to_device(batch_data_dict[key], device)
-        
-        # Forward
-        model.train()
-
-        if 'mixup' in augmentation:
-            batch_output_dict = model(batch_data_dict['waveform'], 
-                batch_data_dict['mixup_lambda'])
-            """{'clipwise_output': (batch_size, classes_num), ...}"""
-
-            batch_target_dict = {'target': do_mixup(batch_data_dict['target'], 
-                batch_data_dict['mixup_lambda'])}
-            """{'target': (batch_size, classes_num)}"""
-        else:
-            batch_output_dict = model(batch_data_dict['waveform'], None)
-            """{'clipwise_output': (batch_size, classes_num), ...}"""
-
-            batch_target_dict = {'target': batch_data_dict['target']}
-            """{'target': (batch_size, classes_num)}"""
-
-        # Loss
-        loss = loss_func(batch_output_dict, batch_target_dict)
-        # Backward
-        loss.backward()
-        
-        optimizer.step()
-        optimizer.zero_grad()
-        
-        if iteration % 10 == 0:
-            print(iteration, loss)
-            #print('--- Iteration: {}, train time: {:.3f} s / 10 iterations ---'\
-            #    .format(iteration, time.time() - time1))
-            #time1 = time.time()
-
-        if iteration % 2000 == 0:
-            scheduler.step(np.mean(test_statistics['average_precision']))
-            print(optimizer.state_dict()['param_groups'][0]['lr'])
-            logging.info(optimizer.state_dict()['param_groups'][0]['lr'])        
-
-        # Stop learning
-        if iteration == early_stop:
-            break
-
-        iteration += 1
-        
-
-if __name__ == '__main__':
-
-    parser = argparse.ArgumentParser(description='Example of parser. ')
-    subparsers = parser.add_subparsers(dest='mode')
-
-    parser_train = subparsers.add_parser('train') 
-    parser_train.add_argument('--workspace', type=str, required=True)
-    parser_train.add_argument('--data_type', type=str, default='full_train', choices=['balanced_train', 'full_train'])
-    parser_train.add_argument('--sample_rate', type=int, default=32000)
-    parser_train.add_argument('--window_size', type=int, default=1024)
-    parser_train.add_argument('--hop_size', type=int, default=320)
-    parser_train.add_argument('--mel_bins', type=int, default=64)
-    parser_train.add_argument('--fmin', type=int, default=50)
-    parser_train.add_argument('--fmax', type=int, default=14000) 
-    parser_train.add_argument('--model_type', type=str, required=True)
-    parser_train.add_argument('--loss_type', type=str, default='clip_bce', choices=['clip_bce'])
-    parser_train.add_argument('--balanced', type=str, default='balanced', choices=['none', 'balanced', 'alternate'])
-    parser_train.add_argument('--augmentation', type=str, default='mixup', choices=['none', 'mixup'])
-    parser_train.add_argument('--batch_size', type=int, default=32)
-    parser_train.add_argument('--learning_rate', type=float, default=1e-3)
-    parser_train.add_argument('--resume_iteration', type=int, default=0)
-    parser_train.add_argument('--early_stop', type=int, default=1000000)
-    parser_train.add_argument('--cuda', action='store_true', default=False)
-    
-    args = parser.parse_args()
-    args.filename = get_filename(__file__)
-
-    if args.mode == 'train':
-        train(args)
-
-    else:
-        raise Exception('Error argument!')

spaces/AIGC-Audio/AudioGPT/audio_to_text/inference_waveform.py

@@ -1,102 +0,0 @@
-import sys
-import os
-import librosa
-import numpy as np
-import torch
-import audio_to_text.captioning.models
-import audio_to_text.captioning.models.encoder
-import audio_to_text.captioning.models.decoder
-import audio_to_text.captioning.utils.train_util as train_util
-
-
-def load_model(config, checkpoint):
-    ckpt = torch.load(checkpoint, "cpu")
-    encoder_cfg = config["model"]["encoder"]
-    encoder = train_util.init_obj(
-        audio_to_text.captioning.models.encoder,
-        encoder_cfg
-    )
-    if "pretrained" in encoder_cfg:
-        pretrained = encoder_cfg["pretrained"]
-        train_util.load_pretrained_model(encoder,
-                                         pretrained,
-                                         sys.stdout.write)
-    decoder_cfg = config["model"]["decoder"]
-    if "vocab_size" not in decoder_cfg["args"]:
-        decoder_cfg["args"]["vocab_size"] = len(ckpt["vocabulary"])
-    decoder = train_util.init_obj(
-        audio_to_text.captioning.models.decoder,
-        decoder_cfg
-    )
-    if "word_embedding" in decoder_cfg:
-        decoder.load_word_embedding(**decoder_cfg["word_embedding"])
-    if "pretrained" in decoder_cfg:
-        pretrained = decoder_cfg["pretrained"]
-        train_util.load_pretrained_model(decoder,
-                                         pretrained,
-                                         sys.stdout.write)
-    model = train_util.init_obj(audio_to_text.captioning.models, config["model"],
-        encoder=encoder, decoder=decoder)
-    train_util.load_pretrained_model(model, ckpt)
-    model.eval()
-    return {
-        "model": model,
-        "vocabulary": ckpt["vocabulary"]
-    }
-
-
-def decode_caption(word_ids, vocabulary):
-    candidate = []
-    for word_id in word_ids:
-        word = vocabulary[word_id]
-        if word == "<end>":
-            break
-        elif word == "<start>":
-            continue
-        candidate.append(word)
-    candidate = " ".join(candidate)
-    return candidate
-
-
-class AudioCapModel(object):
-    def __init__(self,weight_dir,device='cpu'):
-        config = os.path.join(weight_dir,'config.yaml')
-        self.config = train_util.parse_config_or_kwargs(config)
-        checkpoint = os.path.join(weight_dir,'swa.pth')
-        resumed = load_model(self.config, checkpoint)
-        model = resumed["model"]
-        self.vocabulary = resumed["vocabulary"]
-        self.model = model.to(device)
-        self.device = device
-
-    def caption(self,audio_list):
-        if isinstance(audio_list,np.ndarray):
-            audio_list = [audio_list]
-        elif isinstance(audio_list,str):
-            audio_list = [librosa.load(audio_list,sr=32000)[0]]
-        
-        captions = []
-        for wav in audio_list:
-            inputwav = torch.as_tensor(wav).float().unsqueeze(0).to(self.device)
-            wav_len = torch.LongTensor([len(wav)])
-            input_dict = {
-                "mode": "inference",
-                "wav": inputwav,
-                "wav_len": wav_len,
-                "specaug": False,
-                "sample_method": "beam",
-            }
-            print(input_dict)
-            out_dict = self.model(input_dict)
-            caption_batch = [decode_caption(seq, self.vocabulary) for seq in \
-                out_dict["seq"].cpu().numpy()]
-            captions.extend(caption_batch)
-        return captions
-
-
-        
-    def __call__(self, audio_list):
-        return self.caption(audio_list)
-
-
-

spaces/AIWaves/SOP_Generation-single/gen_utils.py

@@ -1,43 +0,0 @@
-def get_agents(design_states):
-    final_agents = {}
-    role = design_states[0]["role"]
-    style = design_states[0]["style"]
-    agent_name = "_".join(role.split(" "))
-    final_agents[agent_name] = {"style":style,"roles":{}}
-    final_agents["User"] = {"style":"","roles":{}}
-    for design_state in design_states:
-        final_agents[agent_name]["roles"][design_state["state_name"]] = agent_name
-        final_agents["User"]["roles"][design_state["state_name"]] = "User"
-    return final_agents
-
-def get_relations(design_states):
-    relations = {}
-    n = len(design_states)
-    for i in range(n):
-        relations[design_states[i]["state_name"]] = {}
-        relations[design_states[i]["state_name"]]["0"] = design_states[i]["state_name"]
-        relations[design_states[i]["state_name"]]["1"] = design_states[i+1]["state_name"]  if i!=n-1 else "end_state"
-    return relations
-
-
-def gen_states(design_states):
-    states = {"end_state":{
-            "agent_states":{}
-        }}
-    for design_state in design_states:
-        state_name = design_state["state_name"]
-        role = design_state["role"]
-        agent_name = "_".join(role.split(" "))
-        states[state_name] = {"controller":{"controller_type": "order", "max_chat_nums" : 1000,"judge_system_prompt":design_state["judge"],"judge_last_prompt":"Please contact the above to extract <end> and </end>. Do not perform additional output. Please strictly follow the above format for output! Remember, please strictly follow the above format for output!"}}
-        states[state_name]["agent_states"] = {
-            agent_name : {
-                "role" : {"role" : role},
-                "task" : {"task" : design_state["task"]},
-                "rule" : {"rule" : design_state["rule"]}
-            },
-            "User" : {
-            }
-        }
-
-    return states
-

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-150e_deepfashion2_vest_dress_256x192/td_hm_res50_4xb64-150e_deepfashion2_vest_dress_256x192.py

@@ -1,2861 +0,0 @@
-default_scope = 'mmpose'
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=50),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(
-        type='CheckpointHook', interval=10, save_best='PCK', rule='greater'),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='PoseVisualizationHook', enable=False))
-custom_hooks = [dict(type='SyncBuffersHook')]
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'))
-vis_backends = [dict(type='LocalVisBackend')]
-visualizer = dict(
-    type='PoseLocalVisualizer',
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')],
-    name='visualizer')
-log_processor = dict(
-    type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)
-log_level = 'INFO'
-load_from = None
-resume = False
-backend_args = dict(backend='local')
-train_cfg = dict(by_epoch=True, max_epochs=150, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-colors = dict(
-    sss=[255, 128, 0],
-    lss=[255, 0, 128],
-    sso=[128, 0, 255],
-    lso=[0, 128, 255],
-    vest=[0, 128, 128],
-    sling=[0, 0, 128],
-    shorts=[128, 128, 128],
-    trousers=[128, 0, 128],
-    skirt=[64, 128, 128],
-    ssd=[64, 64, 128],
-    lsd=[128, 64, 0],
-    vd=[128, 64, 255],
-    sd=[128, 64, 0])
-dataset_info = dict(
-    dataset_name='deepfashion2',
-    paper_info=dict(
-        author=
-        'Yuying Ge and Ruimao Zhang and Lingyun Wu and Xiaogang Wang and Xiaoou Tang and Ping Luo',
-        title=
-        'DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images',
-        container=
-        'Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)',
-        year='2019',
-        homepage='https://github.com/switchablenorms/DeepFashion2'),
-    keypoint_info=dict({
-        0:
-        dict(name='sss_kpt1', id=0, color=[255, 128, 0], type='', swap=''),
-        1:
-        dict(
-            name='sss_kpt2',
-            id=1,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt6'),
-        2:
-        dict(
-            name='sss_kpt3',
-            id=2,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt5'),
-        3:
-        dict(name='sss_kpt4', id=3, color=[255, 128, 0], type='', swap=''),
-        4:
-        dict(
-            name='sss_kpt5',
-            id=4,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt3'),
-        5:
-        dict(
-            name='sss_kpt6',
-            id=5,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt2'),
-        6:
-        dict(
-            name='sss_kpt7',
-            id=6,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt25'),
-        7:
-        dict(
-            name='sss_kpt8',
-            id=7,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt24'),
-        8:
-        dict(
-            name='sss_kpt9',
-            id=8,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt23'),
-        9:
-        dict(
-            name='sss_kpt10',
-            id=9,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt22'),
-        10:
-        dict(
-            name='sss_kpt11',
-            id=10,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt21'),
-        11:
-        dict(
-            name='sss_kpt12',
-            id=11,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt20'),
-        12:
-        dict(
-            name='sss_kpt13',
-            id=12,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt19'),
-        13:
-        dict(
-            name='sss_kpt14',
-            id=13,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt18'),
-        14:
-        dict(
-            name='sss_kpt15',
-            id=14,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt17'),
-        15:
-        dict(name='sss_kpt16', id=15, color=[255, 128, 0], type='', swap=''),
-        16:
-        dict(
-            name='sss_kpt17',
-            id=16,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt15'),
-        17:
-        dict(
-            name='sss_kpt18',
-            id=17,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt14'),
-        18:
-        dict(
-            name='sss_kpt19',
-            id=18,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt13'),
-        19:
-        dict(
-            name='sss_kpt20',
-            id=19,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt12'),
-        20:
-        dict(
-            name='sss_kpt21',
-            id=20,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt11'),
-        21:
-        dict(
-            name='sss_kpt22',
-            id=21,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt10'),
-        22:
-        dict(
-            name='sss_kpt23',
-            id=22,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt9'),
-        23:
-        dict(
-            name='sss_kpt24',
-            id=23,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt8'),
-        24:
-        dict(
-            name='sss_kpt25',
-            id=24,
-            color=[255, 128, 0],
-            type='',
-            swap='sss_kpt7'),
-        25:
-        dict(name='lss_kpt1', id=25, color=[255, 0, 128], type='', swap=''),
-        26:
-        dict(
-            name='lss_kpt2',
-            id=26,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt6'),
-        27:
-        dict(
-            name='lss_kpt3',
-            id=27,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt5'),
-        28:
-        dict(name='lss_kpt4', id=28, color=[255, 0, 128], type='', swap=''),
-        29:
-        dict(
-            name='lss_kpt5',
-            id=29,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt3'),
-        30:
-        dict(
-            name='lss_kpt6',
-            id=30,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt2'),
-        31:
-        dict(
-            name='lss_kpt7',
-            id=31,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt33'),
-        32:
-        dict(
-            name='lss_kpt8',
-            id=32,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt32'),
-        33:
-        dict(
-            name='lss_kpt9',
-            id=33,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt31'),
-        34:
-        dict(
-            name='lss_kpt10',
-            id=34,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt30'),
-        35:
-        dict(
-            name='lss_kpt11',
-            id=35,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt29'),
-        36:
-        dict(
-            name='lss_kpt12',
-            id=36,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt28'),
-        37:
-        dict(
-            name='lss_kpt13',
-            id=37,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt27'),
-        38:
-        dict(
-            name='lss_kpt14',
-            id=38,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt26'),
-        39:
-        dict(
-            name='lss_kpt15',
-            id=39,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt25'),
-        40:
-        dict(
-            name='lss_kpt16',
-            id=40,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt24'),
-        41:
-        dict(
-            name='lss_kpt17',
-            id=41,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt23'),
-        42:
-        dict(
-            name='lss_kpt18',
-            id=42,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt22'),
-        43:
-        dict(
-            name='lss_kpt19',
-            id=43,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt21'),
-        44:
-        dict(name='lss_kpt20', id=44, color=[255, 0, 128], type='', swap=''),
-        45:
-        dict(
-            name='lss_kpt21',
-            id=45,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt19'),
-        46:
-        dict(
-            name='lss_kpt22',
-            id=46,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt18'),
-        47:
-        dict(
-            name='lss_kpt23',
-            id=47,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt17'),
-        48:
-        dict(
-            name='lss_kpt24',
-            id=48,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt16'),
-        49:
-        dict(
-            name='lss_kpt25',
-            id=49,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt15'),
-        50:
-        dict(
-            name='lss_kpt26',
-            id=50,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt14'),
-        51:
-        dict(
-            name='lss_kpt27',
-            id=51,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt13'),
-        52:
-        dict(
-            name='lss_kpt28',
-            id=52,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt12'),
-        53:
-        dict(
-            name='lss_kpt29',
-            id=53,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt11'),
-        54:
-        dict(
-            name='lss_kpt30',
-            id=54,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt10'),
-        55:
-        dict(
-            name='lss_kpt31',
-            id=55,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt9'),
-        56:
-        dict(
-            name='lss_kpt32',
-            id=56,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt8'),
-        57:
-        dict(
-            name='lss_kpt33',
-            id=57,
-            color=[255, 0, 128],
-            type='',
-            swap='lss_kpt7'),
-        58:
-        dict(name='sso_kpt1', id=58, color=[128, 0, 255], type='', swap=''),
-        59:
-        dict(
-            name='sso_kpt2',
-            id=59,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt26'),
-        60:
-        dict(
-            name='sso_kpt3',
-            id=60,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt5'),
-        61:
-        dict(
-            name='sso_kpt4',
-            id=61,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt6'),
-        62:
-        dict(
-            name='sso_kpt5',
-            id=62,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt3'),
-        63:
-        dict(
-            name='sso_kpt6',
-            id=63,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt4'),
-        64:
-        dict(
-            name='sso_kpt7',
-            id=64,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt25'),
-        65:
-        dict(
-            name='sso_kpt8',
-            id=65,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt24'),
-        66:
-        dict(
-            name='sso_kpt9',
-            id=66,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt23'),
-        67:
-        dict(
-            name='sso_kpt10',
-            id=67,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt22'),
-        68:
-        dict(
-            name='sso_kpt11',
-            id=68,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt21'),
-        69:
-        dict(
-            name='sso_kpt12',
-            id=69,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt20'),
-        70:
-        dict(
-            name='sso_kpt13',
-            id=70,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt19'),
-        71:
-        dict(
-            name='sso_kpt14',
-            id=71,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt18'),
-        72:
-        dict(
-            name='sso_kpt15',
-            id=72,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt17'),
-        73:
-        dict(
-            name='sso_kpt16',
-            id=73,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt29'),
-        74:
-        dict(
-            name='sso_kpt17',
-            id=74,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt15'),
-        75:
-        dict(
-            name='sso_kpt18',
-            id=75,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt14'),
-        76:
-        dict(
-            name='sso_kpt19',
-            id=76,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt13'),
-        77:
-        dict(
-            name='sso_kpt20',
-            id=77,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt12'),
-        78:
-        dict(
-            name='sso_kpt21',
-            id=78,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt11'),
-        79:
-        dict(
-            name='sso_kpt22',
-            id=79,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt10'),
-        80:
-        dict(
-            name='sso_kpt23',
-            id=80,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt9'),
-        81:
-        dict(
-            name='sso_kpt24',
-            id=81,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt8'),
-        82:
-        dict(
-            name='sso_kpt25',
-            id=82,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt7'),
-        83:
-        dict(
-            name='sso_kpt26',
-            id=83,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt2'),
-        84:
-        dict(
-            name='sso_kpt27',
-            id=84,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt30'),
-        85:
-        dict(
-            name='sso_kpt28',
-            id=85,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt31'),
-        86:
-        dict(
-            name='sso_kpt29',
-            id=86,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt16'),
-        87:
-        dict(
-            name='sso_kpt30',
-            id=87,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt27'),
-        88:
-        dict(
-            name='sso_kpt31',
-            id=88,
-            color=[128, 0, 255],
-            type='',
-            swap='sso_kpt28'),
-        89:
-        dict(name='lso_kpt1', id=89, color=[0, 128, 255], type='', swap=''),
-        90:
-        dict(
-            name='lso_kpt2',
-            id=90,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt6'),
-        91:
-        dict(
-            name='lso_kpt3',
-            id=91,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt5'),
-        92:
-        dict(
-            name='lso_kpt4',
-            id=92,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt34'),
-        93:
-        dict(
-            name='lso_kpt5',
-            id=93,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt3'),
-        94:
-        dict(
-            name='lso_kpt6',
-            id=94,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt2'),
-        95:
-        dict(
-            name='lso_kpt7',
-            id=95,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt33'),
-        96:
-        dict(
-            name='lso_kpt8',
-            id=96,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt32'),
-        97:
-        dict(
-            name='lso_kpt9',
-            id=97,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt31'),
-        98:
-        dict(
-            name='lso_kpt10',
-            id=98,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt30'),
-        99:
-        dict(
-            name='lso_kpt11',
-            id=99,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt29'),
-        100:
-        dict(
-            name='lso_kpt12',
-            id=100,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt28'),
-        101:
-        dict(
-            name='lso_kpt13',
-            id=101,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt27'),
-        102:
-        dict(
-            name='lso_kpt14',
-            id=102,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt26'),
-        103:
-        dict(
-            name='lso_kpt15',
-            id=103,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt25'),
-        104:
-        dict(
-            name='lso_kpt16',
-            id=104,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt24'),
-        105:
-        dict(
-            name='lso_kpt17',
-            id=105,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt23'),
-        106:
-        dict(
-            name='lso_kpt18',
-            id=106,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt22'),
-        107:
-        dict(
-            name='lso_kpt19',
-            id=107,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt21'),
-        108:
-        dict(
-            name='lso_kpt20',
-            id=108,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt37'),
-        109:
-        dict(
-            name='lso_kpt21',
-            id=109,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt19'),
-        110:
-        dict(
-            name='lso_kpt22',
-            id=110,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt18'),
-        111:
-        dict(
-            name='lso_kpt23',
-            id=111,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt17'),
-        112:
-        dict(
-            name='lso_kpt24',
-            id=112,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt16'),
-        113:
-        dict(
-            name='lso_kpt25',
-            id=113,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt15'),
-        114:
-        dict(
-            name='lso_kpt26',
-            id=114,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt14'),
-        115:
-        dict(
-            name='lso_kpt27',
-            id=115,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt13'),
-        116:
-        dict(
-            name='lso_kpt28',
-            id=116,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt12'),
-        117:
-        dict(
-            name='lso_kpt29',
-            id=117,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt11'),
-        118:
-        dict(
-            name='lso_kpt30',
-            id=118,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt10'),
-        119:
-        dict(
-            name='lso_kpt31',
-            id=119,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt9'),
-        120:
-        dict(
-            name='lso_kpt32',
-            id=120,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt8'),
-        121:
-        dict(
-            name='lso_kpt33',
-            id=121,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt7'),
-        122:
-        dict(
-            name='lso_kpt34',
-            id=122,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt4'),
-        123:
-        dict(
-            name='lso_kpt35',
-            id=123,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt38'),
-        124:
-        dict(
-            name='lso_kpt36',
-            id=124,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt39'),
-        125:
-        dict(
-            name='lso_kpt37',
-            id=125,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt20'),
-        126:
-        dict(
-            name='lso_kpt38',
-            id=126,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt35'),
-        127:
-        dict(
-            name='lso_kpt39',
-            id=127,
-            color=[0, 128, 255],
-            type='',
-            swap='lso_kpt36'),
-        128:
-        dict(name='vest_kpt1', id=128, color=[0, 128, 128], type='', swap=''),
-        129:
-        dict(
-            name='vest_kpt2',
-            id=129,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt6'),
-        130:
-        dict(
-            name='vest_kpt3',
-            id=130,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt5'),
-        131:
-        dict(name='vest_kpt4', id=131, color=[0, 128, 128], type='', swap=''),
-        132:
-        dict(
-            name='vest_kpt5',
-            id=132,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt3'),
-        133:
-        dict(
-            name='vest_kpt6',
-            id=133,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt2'),
-        134:
-        dict(
-            name='vest_kpt7',
-            id=134,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt15'),
-        135:
-        dict(
-            name='vest_kpt8',
-            id=135,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt14'),
-        136:
-        dict(
-            name='vest_kpt9',
-            id=136,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt13'),
-        137:
-        dict(
-            name='vest_kpt10',
-            id=137,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt12'),
-        138:
-        dict(name='vest_kpt11', id=138, color=[0, 128, 128], type='', swap=''),
-        139:
-        dict(
-            name='vest_kpt12',
-            id=139,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt10'),
-        140:
-        dict(name='vest_kpt13', id=140, color=[0, 128, 128], type='', swap=''),
-        141:
-        dict(
-            name='vest_kpt14',
-            id=141,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt8'),
-        142:
-        dict(
-            name='vest_kpt15',
-            id=142,
-            color=[0, 128, 128],
-            type='',
-            swap='vest_kpt7'),
-        143:
-        dict(name='sling_kpt1', id=143, color=[0, 0, 128], type='', swap=''),
-        144:
-        dict(
-            name='sling_kpt2',
-            id=144,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt6'),
-        145:
-        dict(
-            name='sling_kpt3',
-            id=145,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt5'),
-        146:
-        dict(name='sling_kpt4', id=146, color=[0, 0, 128], type='', swap=''),
-        147:
-        dict(
-            name='sling_kpt5',
-            id=147,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt3'),
-        148:
-        dict(
-            name='sling_kpt6',
-            id=148,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt2'),
-        149:
-        dict(
-            name='sling_kpt7',
-            id=149,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt15'),
-        150:
-        dict(
-            name='sling_kpt8',
-            id=150,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt14'),
-        151:
-        dict(
-            name='sling_kpt9',
-            id=151,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt13'),
-        152:
-        dict(
-            name='sling_kpt10',
-            id=152,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt12'),
-        153:
-        dict(name='sling_kpt11', id=153, color=[0, 0, 128], type='', swap=''),
-        154:
-        dict(
-            name='sling_kpt12',
-            id=154,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt10'),
-        155:
-        dict(
-            name='sling_kpt13',
-            id=155,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt9'),
-        156:
-        dict(
-            name='sling_kpt14',
-            id=156,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt8'),
-        157:
-        dict(
-            name='sling_kpt15',
-            id=157,
-            color=[0, 0, 128],
-            type='',
-            swap='sling_kpt7'),
-        158:
-        dict(
-            name='shorts_kpt1',
-            id=158,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt3'),
-        159:
-        dict(
-            name='shorts_kpt2',
-            id=159,
-            color=[128, 128, 128],
-            type='',
-            swap=''),
-        160:
-        dict(
-            name='shorts_kpt3',
-            id=160,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt1'),
-        161:
-        dict(
-            name='shorts_kpt4',
-            id=161,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt10'),
-        162:
-        dict(
-            name='shorts_kpt5',
-            id=162,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt9'),
-        163:
-        dict(
-            name='shorts_kpt6',
-            id=163,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt8'),
-        164:
-        dict(
-            name='shorts_kpt7',
-            id=164,
-            color=[128, 128, 128],
-            type='',
-            swap=''),
-        165:
-        dict(
-            name='shorts_kpt8',
-            id=165,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt6'),
-        166:
-        dict(
-            name='shorts_kpt9',
-            id=166,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt5'),
-        167:
-        dict(
-            name='shorts_kpt10',
-            id=167,
-            color=[128, 128, 128],
-            type='',
-            swap='shorts_kpt4'),
-        168:
-        dict(
-            name='trousers_kpt1',
-            id=168,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt3'),
-        169:
-        dict(
-            name='trousers_kpt2',
-            id=169,
-            color=[128, 0, 128],
-            type='',
-            swap=''),
-        170:
-        dict(
-            name='trousers_kpt3',
-            id=170,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt1'),
-        171:
-        dict(
-            name='trousers_kpt4',
-            id=171,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt14'),
-        172:
-        dict(
-            name='trousers_kpt5',
-            id=172,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt13'),
-        173:
-        dict(
-            name='trousers_kpt6',
-            id=173,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt12'),
-        174:
-        dict(
-            name='trousers_kpt7',
-            id=174,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt11'),
-        175:
-        dict(
-            name='trousers_kpt8',
-            id=175,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt10'),
-        176:
-        dict(
-            name='trousers_kpt9',
-            id=176,
-            color=[128, 0, 128],
-            type='',
-            swap=''),
-        177:
-        dict(
-            name='trousers_kpt10',
-            id=177,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt8'),
-        178:
-        dict(
-            name='trousers_kpt11',
-            id=178,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt7'),
-        179:
-        dict(
-            name='trousers_kpt12',
-            id=179,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt6'),
-        180:
-        dict(
-            name='trousers_kpt13',
-            id=180,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt5'),
-        181:
-        dict(
-            name='trousers_kpt14',
-            id=181,
-            color=[128, 0, 128],
-            type='',
-            swap='trousers_kpt4'),
-        182:
-        dict(
-            name='skirt_kpt1',
-            id=182,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt3'),
-        183:
-        dict(
-            name='skirt_kpt2', id=183, color=[64, 128, 128], type='', swap=''),
-        184:
-        dict(
-            name='skirt_kpt3',
-            id=184,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt1'),
-        185:
-        dict(
-            name='skirt_kpt4',
-            id=185,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt8'),
-        186:
-        dict(
-            name='skirt_kpt5',
-            id=186,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt7'),
-        187:
-        dict(
-            name='skirt_kpt6', id=187, color=[64, 128, 128], type='', swap=''),
-        188:
-        dict(
-            name='skirt_kpt7',
-            id=188,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt5'),
-        189:
-        dict(
-            name='skirt_kpt8',
-            id=189,
-            color=[64, 128, 128],
-            type='',
-            swap='skirt_kpt4'),
-        190:
-        dict(name='ssd_kpt1', id=190, color=[64, 64, 128], type='', swap=''),
-        191:
-        dict(
-            name='ssd_kpt2',
-            id=191,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt6'),
-        192:
-        dict(
-            name='ssd_kpt3',
-            id=192,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt5'),
-        193:
-        dict(name='ssd_kpt4', id=193, color=[64, 64, 128], type='', swap=''),
-        194:
-        dict(
-            name='ssd_kpt5',
-            id=194,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt3'),
-        195:
-        dict(
-            name='ssd_kpt6',
-            id=195,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt2'),
-        196:
-        dict(
-            name='ssd_kpt7',
-            id=196,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt29'),
-        197:
-        dict(
-            name='ssd_kpt8',
-            id=197,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt28'),
-        198:
-        dict(
-            name='ssd_kpt9',
-            id=198,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt27'),
-        199:
-        dict(
-            name='ssd_kpt10',
-            id=199,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt26'),
-        200:
-        dict(
-            name='ssd_kpt11',
-            id=200,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt25'),
-        201:
-        dict(
-            name='ssd_kpt12',
-            id=201,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt24'),
-        202:
-        dict(
-            name='ssd_kpt13',
-            id=202,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt23'),
-        203:
-        dict(
-            name='ssd_kpt14',
-            id=203,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt22'),
-        204:
-        dict(
-            name='ssd_kpt15',
-            id=204,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt21'),
-        205:
-        dict(
-            name='ssd_kpt16',
-            id=205,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt20'),
-        206:
-        dict(
-            name='ssd_kpt17',
-            id=206,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt19'),
-        207:
-        dict(name='ssd_kpt18', id=207, color=[64, 64, 128], type='', swap=''),
-        208:
-        dict(
-            name='ssd_kpt19',
-            id=208,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt17'),
-        209:
-        dict(
-            name='ssd_kpt20',
-            id=209,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt16'),
-        210:
-        dict(
-            name='ssd_kpt21',
-            id=210,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt15'),
-        211:
-        dict(
-            name='ssd_kpt22',
-            id=211,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt14'),
-        212:
-        dict(
-            name='ssd_kpt23',
-            id=212,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt13'),
-        213:
-        dict(
-            name='ssd_kpt24',
-            id=213,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt12'),
-        214:
-        dict(
-            name='ssd_kpt25',
-            id=214,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt11'),
-        215:
-        dict(
-            name='ssd_kpt26',
-            id=215,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt10'),
-        216:
-        dict(
-            name='ssd_kpt27',
-            id=216,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt9'),
-        217:
-        dict(
-            name='ssd_kpt28',
-            id=217,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt8'),
-        218:
-        dict(
-            name='ssd_kpt29',
-            id=218,
-            color=[64, 64, 128],
-            type='',
-            swap='ssd_kpt7'),
-        219:
-        dict(name='lsd_kpt1', id=219, color=[128, 64, 0], type='', swap=''),
-        220:
-        dict(
-            name='lsd_kpt2',
-            id=220,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt6'),
-        221:
-        dict(
-            name='lsd_kpt3',
-            id=221,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt5'),
-        222:
-        dict(name='lsd_kpt4', id=222, color=[128, 64, 0], type='', swap=''),
-        223:
-        dict(
-            name='lsd_kpt5',
-            id=223,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt3'),
-        224:
-        dict(
-            name='lsd_kpt6',
-            id=224,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt2'),
-        225:
-        dict(
-            name='lsd_kpt7',
-            id=225,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt37'),
-        226:
-        dict(
-            name='lsd_kpt8',
-            id=226,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt36'),
-        227:
-        dict(
-            name='lsd_kpt9',
-            id=227,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt35'),
-        228:
-        dict(
-            name='lsd_kpt10',
-            id=228,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt34'),
-        229:
-        dict(
-            name='lsd_kpt11',
-            id=229,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt33'),
-        230:
-        dict(
-            name='lsd_kpt12',
-            id=230,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt32'),
-        231:
-        dict(
-            name='lsd_kpt13',
-            id=231,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt31'),
-        232:
-        dict(
-            name='lsd_kpt14',
-            id=232,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt30'),
-        233:
-        dict(
-            name='lsd_kpt15',
-            id=233,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt29'),
-        234:
-        dict(
-            name='lsd_kpt16',
-            id=234,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt28'),
-        235:
-        dict(
-            name='lsd_kpt17',
-            id=235,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt27'),
-        236:
-        dict(
-            name='lsd_kpt18',
-            id=236,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt26'),
-        237:
-        dict(
-            name='lsd_kpt19',
-            id=237,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt25'),
-        238:
-        dict(
-            name='lsd_kpt20',
-            id=238,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt24'),
-        239:
-        dict(
-            name='lsd_kpt21',
-            id=239,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt23'),
-        240:
-        dict(name='lsd_kpt22', id=240, color=[128, 64, 0], type='', swap=''),
-        241:
-        dict(
-            name='lsd_kpt23',
-            id=241,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt21'),
-        242:
-        dict(
-            name='lsd_kpt24',
-            id=242,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt20'),
-        243:
-        dict(
-            name='lsd_kpt25',
-            id=243,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt19'),
-        244:
-        dict(
-            name='lsd_kpt26',
-            id=244,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt18'),
-        245:
-        dict(
-            name='lsd_kpt27',
-            id=245,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt17'),
-        246:
-        dict(
-            name='lsd_kpt28',
-            id=246,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt16'),
-        247:
-        dict(
-            name='lsd_kpt29',
-            id=247,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt15'),
-        248:
-        dict(
-            name='lsd_kpt30',
-            id=248,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt14'),
-        249:
-        dict(
-            name='lsd_kpt31',
-            id=249,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt13'),
-        250:
-        dict(
-            name='lsd_kpt32',
-            id=250,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt12'),
-        251:
-        dict(
-            name='lsd_kpt33',
-            id=251,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt11'),
-        252:
-        dict(
-            name='lsd_kpt34',
-            id=252,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt10'),
-        253:
-        dict(
-            name='lsd_kpt35',
-            id=253,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt9'),
-        254:
-        dict(
-            name='lsd_kpt36',
-            id=254,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt8'),
-        255:
-        dict(
-            name='lsd_kpt37',
-            id=255,
-            color=[128, 64, 0],
-            type='',
-            swap='lsd_kpt7'),
-        256:
-        dict(name='vd_kpt1', id=256, color=[128, 64, 255], type='', swap=''),
-        257:
-        dict(
-            name='vd_kpt2',
-            id=257,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt6'),
-        258:
-        dict(
-            name='vd_kpt3',
-            id=258,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt5'),
-        259:
-        dict(name='vd_kpt4', id=259, color=[128, 64, 255], type='', swap=''),
-        260:
-        dict(
-            name='vd_kpt5',
-            id=260,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt3'),
-        261:
-        dict(
-            name='vd_kpt6',
-            id=261,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt2'),
-        262:
-        dict(
-            name='vd_kpt7',
-            id=262,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt19'),
-        263:
-        dict(
-            name='vd_kpt8',
-            id=263,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt18'),
-        264:
-        dict(
-            name='vd_kpt9',
-            id=264,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt17'),
-        265:
-        dict(
-            name='vd_kpt10',
-            id=265,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt16'),
-        266:
-        dict(
-            name='vd_kpt11',
-            id=266,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt15'),
-        267:
-        dict(
-            name='vd_kpt12',
-            id=267,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt14'),
-        268:
-        dict(name='vd_kpt13', id=268, color=[128, 64, 255], type='', swap=''),
-        269:
-        dict(
-            name='vd_kpt14',
-            id=269,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt12'),
-        270:
-        dict(
-            name='vd_kpt15',
-            id=270,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt11'),
-        271:
-        dict(
-            name='vd_kpt16',
-            id=271,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt10'),
-        272:
-        dict(
-            name='vd_kpt17',
-            id=272,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt9'),
-        273:
-        dict(
-            name='vd_kpt18',
-            id=273,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt8'),
-        274:
-        dict(
-            name='vd_kpt19',
-            id=274,
-            color=[128, 64, 255],
-            type='',
-            swap='vd_kpt7'),
-        275:
-        dict(name='sd_kpt1', id=275, color=[128, 64, 0], type='', swap=''),
-        276:
-        dict(
-            name='sd_kpt2',
-            id=276,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt6'),
-        277:
-        dict(
-            name='sd_kpt3',
-            id=277,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt5'),
-        278:
-        dict(name='sd_kpt4', id=278, color=[128, 64, 0], type='', swap=''),
-        279:
-        dict(
-            name='sd_kpt5',
-            id=279,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt3'),
-        280:
-        dict(
-            name='sd_kpt6',
-            id=280,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt2'),
-        281:
-        dict(
-            name='sd_kpt7',
-            id=281,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt19'),
-        282:
-        dict(
-            name='sd_kpt8',
-            id=282,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt18'),
-        283:
-        dict(
-            name='sd_kpt9',
-            id=283,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt17'),
-        284:
-        dict(
-            name='sd_kpt10',
-            id=284,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt16'),
-        285:
-        dict(
-            name='sd_kpt11',
-            id=285,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt15'),
-        286:
-        dict(
-            name='sd_kpt12',
-            id=286,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt14'),
-        287:
-        dict(name='sd_kpt13', id=287, color=[128, 64, 0], type='', swap=''),
-        288:
-        dict(
-            name='sd_kpt14',
-            id=288,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt12'),
-        289:
-        dict(
-            name='sd_kpt15',
-            id=289,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt11'),
-        290:
-        dict(
-            name='sd_kpt16',
-            id=290,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt10'),
-        291:
-        dict(
-            name='sd_kpt17',
-            id=291,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt9'),
-        292:
-        dict(
-            name='sd_kpt18',
-            id=292,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt8'),
-        293:
-        dict(
-            name='sd_kpt19',
-            id=293,
-            color=[128, 64, 0],
-            type='',
-            swap='sd_kpt7')
-    }),
-    skeleton_info=dict({
-        0:
-        dict(link=('sss_kpt1', 'sss_kpt2'), id=0, color=[255, 128, 0]),
-        1:
-        dict(link=('sss_kpt2', 'sss_kpt7'), id=1, color=[255, 128, 0]),
-        2:
-        dict(link=('sss_kpt7', 'sss_kpt8'), id=2, color=[255, 128, 0]),
-        3:
-        dict(link=('sss_kpt8', 'sss_kpt9'), id=3, color=[255, 128, 0]),
-        4:
-        dict(link=('sss_kpt9', 'sss_kpt10'), id=4, color=[255, 128, 0]),
-        5:
-        dict(link=('sss_kpt10', 'sss_kpt11'), id=5, color=[255, 128, 0]),
-        6:
-        dict(link=('sss_kpt11', 'sss_kpt12'), id=6, color=[255, 128, 0]),
-        7:
-        dict(link=('sss_kpt12', 'sss_kpt13'), id=7, color=[255, 128, 0]),
-        8:
-        dict(link=('sss_kpt13', 'sss_kpt14'), id=8, color=[255, 128, 0]),
-        9:
-        dict(link=('sss_kpt14', 'sss_kpt15'), id=9, color=[255, 128, 0]),
-        10:
-        dict(link=('sss_kpt15', 'sss_kpt16'), id=10, color=[255, 128, 0]),
-        11:
-        dict(link=('sss_kpt16', 'sss_kpt17'), id=11, color=[255, 128, 0]),
-        12:
-        dict(link=('sss_kpt17', 'sss_kpt18'), id=12, color=[255, 128, 0]),
-        13:
-        dict(link=('sss_kpt18', 'sss_kpt19'), id=13, color=[255, 128, 0]),
-        14:
-        dict(link=('sss_kpt19', 'sss_kpt20'), id=14, color=[255, 128, 0]),
-        15:
-        dict(link=('sss_kpt20', 'sss_kpt21'), id=15, color=[255, 128, 0]),
-        16:
-        dict(link=('sss_kpt21', 'sss_kpt22'), id=16, color=[255, 128, 0]),
-        17:
-        dict(link=('sss_kpt22', 'sss_kpt23'), id=17, color=[255, 128, 0]),
-        18:
-        dict(link=('sss_kpt23', 'sss_kpt24'), id=18, color=[255, 128, 0]),
-        19:
-        dict(link=('sss_kpt24', 'sss_kpt25'), id=19, color=[255, 128, 0]),
-        20:
-        dict(link=('sss_kpt25', 'sss_kpt6'), id=20, color=[255, 128, 0]),
-        21:
-        dict(link=('sss_kpt6', 'sss_kpt1'), id=21, color=[255, 128, 0]),
-        22:
-        dict(link=('sss_kpt2', 'sss_kpt3'), id=22, color=[255, 128, 0]),
-        23:
-        dict(link=('sss_kpt3', 'sss_kpt4'), id=23, color=[255, 128, 0]),
-        24:
-        dict(link=('sss_kpt4', 'sss_kpt5'), id=24, color=[255, 128, 0]),
-        25:
-        dict(link=('sss_kpt5', 'sss_kpt6'), id=25, color=[255, 128, 0]),
-        26:
-        dict(link=('lss_kpt1', 'lss_kpt2'), id=26, color=[255, 0, 128]),
-        27:
-        dict(link=('lss_kpt2', 'lss_kpt7'), id=27, color=[255, 0, 128]),
-        28:
-        dict(link=('lss_kpt7', 'lss_kpt8'), id=28, color=[255, 0, 128]),
-        29:
-        dict(link=('lss_kpt8', 'lss_kpt9'), id=29, color=[255, 0, 128]),
-        30:
-        dict(link=('lss_kpt9', 'lss_kpt10'), id=30, color=[255, 0, 128]),
-        31:
-        dict(link=('lss_kpt10', 'lss_kpt11'), id=31, color=[255, 0, 128]),
-        32:
-        dict(link=('lss_kpt11', 'lss_kpt12'), id=32, color=[255, 0, 128]),
-        33:
-        dict(link=('lss_kpt12', 'lss_kpt13'), id=33, color=[255, 0, 128]),
-        34:
-        dict(link=('lss_kpt13', 'lss_kpt14'), id=34, color=[255, 0, 128]),
-        35:
-        dict(link=('lss_kpt14', 'lss_kpt15'), id=35, color=[255, 0, 128]),
-        36:
-        dict(link=('lss_kpt15', 'lss_kpt16'), id=36, color=[255, 0, 128]),
-        37:
-        dict(link=('lss_kpt16', 'lss_kpt17'), id=37, color=[255, 0, 128]),
-        38:
-        dict(link=('lss_kpt17', 'lss_kpt18'), id=38, color=[255, 0, 128]),
-        39:
-        dict(link=('lss_kpt18', 'lss_kpt19'), id=39, color=[255, 0, 128]),
-        40:
-        dict(link=('lss_kpt19', 'lss_kpt20'), id=40, color=[255, 0, 128]),
-        41:
-        dict(link=('lss_kpt20', 'lss_kpt21'), id=41, color=[255, 0, 128]),
-        42:
-        dict(link=('lss_kpt21', 'lss_kpt22'), id=42, color=[255, 0, 128]),
-        43:
-        dict(link=('lss_kpt22', 'lss_kpt23'), id=43, color=[255, 0, 128]),
-        44:
-        dict(link=('lss_kpt23', 'lss_kpt24'), id=44, color=[255, 0, 128]),
-        45:
-        dict(link=('lss_kpt24', 'lss_kpt25'), id=45, color=[255, 0, 128]),
-        46:
-        dict(link=('lss_kpt25', 'lss_kpt26'), id=46, color=[255, 0, 128]),
-        47:
-        dict(link=('lss_kpt26', 'lss_kpt27'), id=47, color=[255, 0, 128]),
-        48:
-        dict(link=('lss_kpt27', 'lss_kpt28'), id=48, color=[255, 0, 128]),
-        49:
-        dict(link=('lss_kpt28', 'lss_kpt29'), id=49, color=[255, 0, 128]),
-        50:
-        dict(link=('lss_kpt29', 'lss_kpt30'), id=50, color=[255, 0, 128]),
-        51:
-        dict(link=('lss_kpt30', 'lss_kpt31'), id=51, color=[255, 0, 128]),
-        52:
-        dict(link=('lss_kpt31', 'lss_kpt32'), id=52, color=[255, 0, 128]),
-        53:
-        dict(link=('lss_kpt32', 'lss_kpt33'), id=53, color=[255, 0, 128]),
-        54:
-        dict(link=('lss_kpt33', 'lss_kpt6'), id=54, color=[255, 0, 128]),
-        55:
-        dict(link=('lss_kpt6', 'lss_kpt5'), id=55, color=[255, 0, 128]),
-        56:
-        dict(link=('lss_kpt5', 'lss_kpt4'), id=56, color=[255, 0, 128]),
-        57:
-        dict(link=('lss_kpt4', 'lss_kpt3'), id=57, color=[255, 0, 128]),
-        58:
-        dict(link=('lss_kpt3', 'lss_kpt2'), id=58, color=[255, 0, 128]),
-        59:
-        dict(link=('lss_kpt6', 'lss_kpt1'), id=59, color=[255, 0, 128]),
-        60:
-        dict(link=('sso_kpt1', 'sso_kpt4'), id=60, color=[128, 0, 255]),
-        61:
-        dict(link=('sso_kpt4', 'sso_kpt7'), id=61, color=[128, 0, 255]),
-        62:
-        dict(link=('sso_kpt7', 'sso_kpt8'), id=62, color=[128, 0, 255]),
-        63:
-        dict(link=('sso_kpt8', 'sso_kpt9'), id=63, color=[128, 0, 255]),
-        64:
-        dict(link=('sso_kpt9', 'sso_kpt10'), id=64, color=[128, 0, 255]),
-        65:
-        dict(link=('sso_kpt10', 'sso_kpt11'), id=65, color=[128, 0, 255]),
-        66:
-        dict(link=('sso_kpt11', 'sso_kpt12'), id=66, color=[128, 0, 255]),
-        67:
-        dict(link=('sso_kpt12', 'sso_kpt13'), id=67, color=[128, 0, 255]),
-        68:
-        dict(link=('sso_kpt13', 'sso_kpt14'), id=68, color=[128, 0, 255]),
-        69:
-        dict(link=('sso_kpt14', 'sso_kpt15'), id=69, color=[128, 0, 255]),
-        70:
-        dict(link=('sso_kpt15', 'sso_kpt16'), id=70, color=[128, 0, 255]),
-        71:
-        dict(link=('sso_kpt16', 'sso_kpt31'), id=71, color=[128, 0, 255]),
-        72:
-        dict(link=('sso_kpt31', 'sso_kpt30'), id=72, color=[128, 0, 255]),
-        73:
-        dict(link=('sso_kpt30', 'sso_kpt2'), id=73, color=[128, 0, 255]),
-        74:
-        dict(link=('sso_kpt2', 'sso_kpt3'), id=74, color=[128, 0, 255]),
-        75:
-        dict(link=('sso_kpt3', 'sso_kpt4'), id=75, color=[128, 0, 255]),
-        76:
-        dict(link=('sso_kpt1', 'sso_kpt6'), id=76, color=[128, 0, 255]),
-        77:
-        dict(link=('sso_kpt6', 'sso_kpt25'), id=77, color=[128, 0, 255]),
-        78:
-        dict(link=('sso_kpt25', 'sso_kpt24'), id=78, color=[128, 0, 255]),
-        79:
-        dict(link=('sso_kpt24', 'sso_kpt23'), id=79, color=[128, 0, 255]),
-        80:
-        dict(link=('sso_kpt23', 'sso_kpt22'), id=80, color=[128, 0, 255]),
-        81:
-        dict(link=('sso_kpt22', 'sso_kpt21'), id=81, color=[128, 0, 255]),
-        82:
-        dict(link=('sso_kpt21', 'sso_kpt20'), id=82, color=[128, 0, 255]),
-        83:
-        dict(link=('sso_kpt20', 'sso_kpt19'), id=83, color=[128, 0, 255]),
-        84:
-        dict(link=('sso_kpt19', 'sso_kpt18'), id=84, color=[128, 0, 255]),
-        85:
-        dict(link=('sso_kpt18', 'sso_kpt17'), id=85, color=[128, 0, 255]),
-        86:
-        dict(link=('sso_kpt17', 'sso_kpt29'), id=86, color=[128, 0, 255]),
-        87:
-        dict(link=('sso_kpt29', 'sso_kpt28'), id=87, color=[128, 0, 255]),
-        88:
-        dict(link=('sso_kpt28', 'sso_kpt27'), id=88, color=[128, 0, 255]),
-        89:
-        dict(link=('sso_kpt27', 'sso_kpt26'), id=89, color=[128, 0, 255]),
-        90:
-        dict(link=('sso_kpt26', 'sso_kpt5'), id=90, color=[128, 0, 255]),
-        91:
-        dict(link=('sso_kpt5', 'sso_kpt6'), id=91, color=[128, 0, 255]),
-        92:
-        dict(link=('lso_kpt1', 'lso_kpt2'), id=92, color=[0, 128, 255]),
-        93:
-        dict(link=('lso_kpt2', 'lso_kpt7'), id=93, color=[0, 128, 255]),
-        94:
-        dict(link=('lso_kpt7', 'lso_kpt8'), id=94, color=[0, 128, 255]),
-        95:
-        dict(link=('lso_kpt8', 'lso_kpt9'), id=95, color=[0, 128, 255]),
-        96:
-        dict(link=('lso_kpt9', 'lso_kpt10'), id=96, color=[0, 128, 255]),
-        97:
-        dict(link=('lso_kpt10', 'lso_kpt11'), id=97, color=[0, 128, 255]),
-        98:
-        dict(link=('lso_kpt11', 'lso_kpt12'), id=98, color=[0, 128, 255]),
-        99:
-        dict(link=('lso_kpt12', 'lso_kpt13'), id=99, color=[0, 128, 255]),
-        100:
-        dict(link=('lso_kpt13', 'lso_kpt14'), id=100, color=[0, 128, 255]),
-        101:
-        dict(link=('lso_kpt14', 'lso_kpt15'), id=101, color=[0, 128, 255]),
-        102:
-        dict(link=('lso_kpt15', 'lso_kpt16'), id=102, color=[0, 128, 255]),
-        103:
-        dict(link=('lso_kpt16', 'lso_kpt17'), id=103, color=[0, 128, 255]),
-        104:
-        dict(link=('lso_kpt17', 'lso_kpt18'), id=104, color=[0, 128, 255]),
-        105:
-        dict(link=('lso_kpt18', 'lso_kpt19'), id=105, color=[0, 128, 255]),
-        106:
-        dict(link=('lso_kpt19', 'lso_kpt20'), id=106, color=[0, 128, 255]),
-        107:
-        dict(link=('lso_kpt20', 'lso_kpt39'), id=107, color=[0, 128, 255]),
-        108:
-        dict(link=('lso_kpt39', 'lso_kpt38'), id=108, color=[0, 128, 255]),
-        109:
-        dict(link=('lso_kpt38', 'lso_kpt4'), id=109, color=[0, 128, 255]),
-        110:
-        dict(link=('lso_kpt4', 'lso_kpt3'), id=110, color=[0, 128, 255]),
-        111:
-        dict(link=('lso_kpt3', 'lso_kpt2'), id=111, color=[0, 128, 255]),
-        112:
-        dict(link=('lso_kpt1', 'lso_kpt6'), id=112, color=[0, 128, 255]),
-        113:
-        dict(link=('lso_kpt6', 'lso_kpt33'), id=113, color=[0, 128, 255]),
-        114:
-        dict(link=('lso_kpt33', 'lso_kpt32'), id=114, color=[0, 128, 255]),
-        115:
-        dict(link=('lso_kpt32', 'lso_kpt31'), id=115, color=[0, 128, 255]),
-        116:
-        dict(link=('lso_kpt31', 'lso_kpt30'), id=116, color=[0, 128, 255]),
-        117:
-        dict(link=('lso_kpt30', 'lso_kpt29'), id=117, color=[0, 128, 255]),
-        118:
-        dict(link=('lso_kpt29', 'lso_kpt28'), id=118, color=[0, 128, 255]),
-        119:
-        dict(link=('lso_kpt28', 'lso_kpt27'), id=119, color=[0, 128, 255]),
-        120:
-        dict(link=('lso_kpt27', 'lso_kpt26'), id=120, color=[0, 128, 255]),
-        121:
-        dict(link=('lso_kpt26', 'lso_kpt25'), id=121, color=[0, 128, 255]),
-        122:
-        dict(link=('lso_kpt25', 'lso_kpt24'), id=122, color=[0, 128, 255]),
-        123:
-        dict(link=('lso_kpt24', 'lso_kpt23'), id=123, color=[0, 128, 255]),
-        124:
-        dict(link=('lso_kpt23', 'lso_kpt22'), id=124, color=[0, 128, 255]),
-        125:
-        dict(link=('lso_kpt22', 'lso_kpt21'), id=125, color=[0, 128, 255]),
-        126:
-        dict(link=('lso_kpt21', 'lso_kpt37'), id=126, color=[0, 128, 255]),
-        127:
-        dict(link=('lso_kpt37', 'lso_kpt36'), id=127, color=[0, 128, 255]),
-        128:
-        dict(link=('lso_kpt36', 'lso_kpt35'), id=128, color=[0, 128, 255]),
-        129:
-        dict(link=('lso_kpt35', 'lso_kpt34'), id=129, color=[0, 128, 255]),
-        130:
-        dict(link=('lso_kpt34', 'lso_kpt5'), id=130, color=[0, 128, 255]),
-        131:
-        dict(link=('lso_kpt5', 'lso_kpt6'), id=131, color=[0, 128, 255]),
-        132:
-        dict(link=('vest_kpt1', 'vest_kpt2'), id=132, color=[0, 128, 128]),
-        133:
-        dict(link=('vest_kpt2', 'vest_kpt7'), id=133, color=[0, 128, 128]),
-        134:
-        dict(link=('vest_kpt7', 'vest_kpt8'), id=134, color=[0, 128, 128]),
-        135:
-        dict(link=('vest_kpt8', 'vest_kpt9'), id=135, color=[0, 128, 128]),
-        136:
-        dict(link=('vest_kpt9', 'vest_kpt10'), id=136, color=[0, 128, 128]),
-        137:
-        dict(link=('vest_kpt10', 'vest_kpt11'), id=137, color=[0, 128, 128]),
-        138:
-        dict(link=('vest_kpt11', 'vest_kpt12'), id=138, color=[0, 128, 128]),
-        139:
-        dict(link=('vest_kpt12', 'vest_kpt13'), id=139, color=[0, 128, 128]),
-        140:
-        dict(link=('vest_kpt13', 'vest_kpt14'), id=140, color=[0, 128, 128]),
-        141:
-        dict(link=('vest_kpt14', 'vest_kpt15'), id=141, color=[0, 128, 128]),
-        142:
-        dict(link=('vest_kpt15', 'vest_kpt6'), id=142, color=[0, 128, 128]),
-        143:
-        dict(link=('vest_kpt6', 'vest_kpt1'), id=143, color=[0, 128, 128]),
-        144:
-        dict(link=('vest_kpt2', 'vest_kpt3'), id=144, color=[0, 128, 128]),
-        145:
-        dict(link=('vest_kpt3', 'vest_kpt4'), id=145, color=[0, 128, 128]),
-        146:
-        dict(link=('vest_kpt4', 'vest_kpt5'), id=146, color=[0, 128, 128]),
-        147:
-        dict(link=('vest_kpt5', 'vest_kpt6'), id=147, color=[0, 128, 128]),
-        148:
-        dict(link=('sling_kpt1', 'sling_kpt2'), id=148, color=[0, 0, 128]),
-        149:
-        dict(link=('sling_kpt2', 'sling_kpt8'), id=149, color=[0, 0, 128]),
-        150:
-        dict(link=('sling_kpt8', 'sling_kpt9'), id=150, color=[0, 0, 128]),
-        151:
-        dict(link=('sling_kpt9', 'sling_kpt10'), id=151, color=[0, 0, 128]),
-        152:
-        dict(link=('sling_kpt10', 'sling_kpt11'), id=152, color=[0, 0, 128]),
-        153:
-        dict(link=('sling_kpt11', 'sling_kpt12'), id=153, color=[0, 0, 128]),
-        154:
-        dict(link=('sling_kpt12', 'sling_kpt13'), id=154, color=[0, 0, 128]),
-        155:
-        dict(link=('sling_kpt13', 'sling_kpt14'), id=155, color=[0, 0, 128]),
-        156:
-        dict(link=('sling_kpt14', 'sling_kpt6'), id=156, color=[0, 0, 128]),
-        157:
-        dict(link=('sling_kpt2', 'sling_kpt7'), id=157, color=[0, 0, 128]),
-        158:
-        dict(link=('sling_kpt6', 'sling_kpt15'), id=158, color=[0, 0, 128]),
-        159:
-        dict(link=('sling_kpt2', 'sling_kpt3'), id=159, color=[0, 0, 128]),
-        160:
-        dict(link=('sling_kpt3', 'sling_kpt4'), id=160, color=[0, 0, 128]),
-        161:
-        dict(link=('sling_kpt4', 'sling_kpt5'), id=161, color=[0, 0, 128]),
-        162:
-        dict(link=('sling_kpt5', 'sling_kpt6'), id=162, color=[0, 0, 128]),
-        163:
-        dict(link=('sling_kpt1', 'sling_kpt6'), id=163, color=[0, 0, 128]),
-        164:
-        dict(
-            link=('shorts_kpt1', 'shorts_kpt4'), id=164, color=[128, 128,
-                                                                128]),
-        165:
-        dict(
-            link=('shorts_kpt4', 'shorts_kpt5'), id=165, color=[128, 128,
-                                                                128]),
-        166:
-        dict(
-            link=('shorts_kpt5', 'shorts_kpt6'), id=166, color=[128, 128,
-                                                                128]),
-        167:
-        dict(
-            link=('shorts_kpt6', 'shorts_kpt7'), id=167, color=[128, 128,
-                                                                128]),
-        168:
-        dict(
-            link=('shorts_kpt7', 'shorts_kpt8'), id=168, color=[128, 128,
-                                                                128]),
-        169:
-        dict(
-            link=('shorts_kpt8', 'shorts_kpt9'), id=169, color=[128, 128,
-                                                                128]),
-        170:
-        dict(
-            link=('shorts_kpt9', 'shorts_kpt10'),
-            id=170,
-            color=[128, 128, 128]),
-        171:
-        dict(
-            link=('shorts_kpt10', 'shorts_kpt3'),
-            id=171,
-            color=[128, 128, 128]),
-        172:
-        dict(
-            link=('shorts_kpt3', 'shorts_kpt2'), id=172, color=[128, 128,
-                                                                128]),
-        173:
-        dict(
-            link=('shorts_kpt2', 'shorts_kpt1'), id=173, color=[128, 128,
-                                                                128]),
-        174:
-        dict(
-            link=('trousers_kpt1', 'trousers_kpt4'),
-            id=174,
-            color=[128, 0, 128]),
-        175:
-        dict(
-            link=('trousers_kpt4', 'trousers_kpt5'),
-            id=175,
-            color=[128, 0, 128]),
-        176:
-        dict(
-            link=('trousers_kpt5', 'trousers_kpt6'),
-            id=176,
-            color=[128, 0, 128]),
-        177:
-        dict(
-            link=('trousers_kpt6', 'trousers_kpt7'),
-            id=177,
-            color=[128, 0, 128]),
-        178:
-        dict(
-            link=('trousers_kpt7', 'trousers_kpt8'),
-            id=178,
-            color=[128, 0, 128]),
-        179:
-        dict(
-            link=('trousers_kpt8', 'trousers_kpt9'),
-            id=179,
-            color=[128, 0, 128]),
-        180:
-        dict(
-            link=('trousers_kpt9', 'trousers_kpt10'),
-            id=180,
-            color=[128, 0, 128]),
-        181:
-        dict(
-            link=('trousers_kpt10', 'trousers_kpt11'),
-            id=181,
-            color=[128, 0, 128]),
-        182:
-        dict(
-            link=('trousers_kpt11', 'trousers_kpt12'),
-            id=182,
-            color=[128, 0, 128]),
-        183:
-        dict(
-            link=('trousers_kpt12', 'trousers_kpt13'),
-            id=183,
-            color=[128, 0, 128]),
-        184:
-        dict(
-            link=('trousers_kpt13', 'trousers_kpt14'),
-            id=184,
-            color=[128, 0, 128]),
-        185:
-        dict(
-            link=('trousers_kpt14', 'trousers_kpt3'),
-            id=185,
-            color=[128, 0, 128]),
-        186:
-        dict(
-            link=('trousers_kpt3', 'trousers_kpt2'),
-            id=186,
-            color=[128, 0, 128]),
-        187:
-        dict(
-            link=('trousers_kpt2', 'trousers_kpt1'),
-            id=187,
-            color=[128, 0, 128]),
-        188:
-        dict(link=('skirt_kpt1', 'skirt_kpt4'), id=188, color=[64, 128, 128]),
-        189:
-        dict(link=('skirt_kpt4', 'skirt_kpt5'), id=189, color=[64, 128, 128]),
-        190:
-        dict(link=('skirt_kpt5', 'skirt_kpt6'), id=190, color=[64, 128, 128]),
-        191:
-        dict(link=('skirt_kpt6', 'skirt_kpt7'), id=191, color=[64, 128, 128]),
-        192:
-        dict(link=('skirt_kpt7', 'skirt_kpt8'), id=192, color=[64, 128, 128]),
-        193:
-        dict(link=('skirt_kpt8', 'skirt_kpt3'), id=193, color=[64, 128, 128]),
-        194:
-        dict(link=('skirt_kpt3', 'skirt_kpt2'), id=194, color=[64, 128, 128]),
-        195:
-        dict(link=('skirt_kpt2', 'skirt_kpt1'), id=195, color=[64, 128, 128]),
-        196:
-        dict(link=('ssd_kpt1', 'ssd_kpt2'), id=196, color=[64, 64, 128]),
-        197:
-        dict(link=('ssd_kpt2', 'ssd_kpt7'), id=197, color=[64, 64, 128]),
-        198:
-        dict(link=('ssd_kpt7', 'ssd_kpt8'), id=198, color=[64, 64, 128]),
-        199:
-        dict(link=('ssd_kpt8', 'ssd_kpt9'), id=199, color=[64, 64, 128]),
-        200:
-        dict(link=('ssd_kpt9', 'ssd_kpt10'), id=200, color=[64, 64, 128]),
-        201:
-        dict(link=('ssd_kpt10', 'ssd_kpt11'), id=201, color=[64, 64, 128]),
-        202:
-        dict(link=('ssd_kpt11', 'ssd_kpt12'), id=202, color=[64, 64, 128]),
-        203:
-        dict(link=('ssd_kpt12', 'ssd_kpt13'), id=203, color=[64, 64, 128]),
-        204:
-        dict(link=('ssd_kpt13', 'ssd_kpt14'), id=204, color=[64, 64, 128]),
-        205:
-        dict(link=('ssd_kpt14', 'ssd_kpt15'), id=205, color=[64, 64, 128]),
-        206:
-        dict(link=('ssd_kpt15', 'ssd_kpt16'), id=206, color=[64, 64, 128]),
-        207:
-        dict(link=('ssd_kpt16', 'ssd_kpt17'), id=207, color=[64, 64, 128]),
-        208:
-        dict(link=('ssd_kpt17', 'ssd_kpt18'), id=208, color=[64, 64, 128]),
-        209:
-        dict(link=('ssd_kpt18', 'ssd_kpt19'), id=209, color=[64, 64, 128]),
-        210:
-        dict(link=('ssd_kpt19', 'ssd_kpt20'), id=210, color=[64, 64, 128]),
-        211:
-        dict(link=('ssd_kpt20', 'ssd_kpt21'), id=211, color=[64, 64, 128]),
-        212:
-        dict(link=('ssd_kpt21', 'ssd_kpt22'), id=212, color=[64, 64, 128]),
-        213:
-        dict(link=('ssd_kpt22', 'ssd_kpt23'), id=213, color=[64, 64, 128]),
-        214:
-        dict(link=('ssd_kpt23', 'ssd_kpt24'), id=214, color=[64, 64, 128]),
-        215:
-        dict(link=('ssd_kpt24', 'ssd_kpt25'), id=215, color=[64, 64, 128]),
-        216:
-        dict(link=('ssd_kpt25', 'ssd_kpt26'), id=216, color=[64, 64, 128]),
-        217:
-        dict(link=('ssd_kpt26', 'ssd_kpt27'), id=217, color=[64, 64, 128]),
-        218:
-        dict(link=('ssd_kpt27', 'ssd_kpt28'), id=218, color=[64, 64, 128]),
-        219:
-        dict(link=('ssd_kpt28', 'ssd_kpt29'), id=219, color=[64, 64, 128]),
-        220:
-        dict(link=('ssd_kpt29', 'ssd_kpt6'), id=220, color=[64, 64, 128]),
-        221:
-        dict(link=('ssd_kpt6', 'ssd_kpt5'), id=221, color=[64, 64, 128]),
-        222:
-        dict(link=('ssd_kpt5', 'ssd_kpt4'), id=222, color=[64, 64, 128]),
-        223:
-        dict(link=('ssd_kpt4', 'ssd_kpt3'), id=223, color=[64, 64, 128]),
-        224:
-        dict(link=('ssd_kpt3', 'ssd_kpt2'), id=224, color=[64, 64, 128]),
-        225:
-        dict(link=('ssd_kpt6', 'ssd_kpt1'), id=225, color=[64, 64, 128]),
-        226:
-        dict(link=('lsd_kpt1', 'lsd_kpt2'), id=226, color=[128, 64, 0]),
-        227:
-        dict(link=('lsd_kpt2', 'lsd_kpt7'), id=228, color=[128, 64, 0]),
-        228:
-        dict(link=('lsd_kpt7', 'lsd_kpt8'), id=228, color=[128, 64, 0]),
-        229:
-        dict(link=('lsd_kpt8', 'lsd_kpt9'), id=229, color=[128, 64, 0]),
-        230:
-        dict(link=('lsd_kpt9', 'lsd_kpt10'), id=230, color=[128, 64, 0]),
-        231:
-        dict(link=('lsd_kpt10', 'lsd_kpt11'), id=231, color=[128, 64, 0]),
-        232:
-        dict(link=('lsd_kpt11', 'lsd_kpt12'), id=232, color=[128, 64, 0]),
-        233:
-        dict(link=('lsd_kpt12', 'lsd_kpt13'), id=233, color=[128, 64, 0]),
-        234:
-        dict(link=('lsd_kpt13', 'lsd_kpt14'), id=234, color=[128, 64, 0]),
-        235:
-        dict(link=('lsd_kpt14', 'lsd_kpt15'), id=235, color=[128, 64, 0]),
-        236:
-        dict(link=('lsd_kpt15', 'lsd_kpt16'), id=236, color=[128, 64, 0]),
-        237:
-        dict(link=('lsd_kpt16', 'lsd_kpt17'), id=237, color=[128, 64, 0]),
-        238:
-        dict(link=('lsd_kpt17', 'lsd_kpt18'), id=238, color=[128, 64, 0]),
-        239:
-        dict(link=('lsd_kpt18', 'lsd_kpt19'), id=239, color=[128, 64, 0]),
-        240:
-        dict(link=('lsd_kpt19', 'lsd_kpt20'), id=240, color=[128, 64, 0]),
-        241:
-        dict(link=('lsd_kpt20', 'lsd_kpt21'), id=241, color=[128, 64, 0]),
-        242:
-        dict(link=('lsd_kpt21', 'lsd_kpt22'), id=242, color=[128, 64, 0]),
-        243:
-        dict(link=('lsd_kpt22', 'lsd_kpt23'), id=243, color=[128, 64, 0]),
-        244:
-        dict(link=('lsd_kpt23', 'lsd_kpt24'), id=244, color=[128, 64, 0]),
-        245:
-        dict(link=('lsd_kpt24', 'lsd_kpt25'), id=245, color=[128, 64, 0]),
-        246:
-        dict(link=('lsd_kpt25', 'lsd_kpt26'), id=246, color=[128, 64, 0]),
-        247:
-        dict(link=('lsd_kpt26', 'lsd_kpt27'), id=247, color=[128, 64, 0]),
-        248:
-        dict(link=('lsd_kpt27', 'lsd_kpt28'), id=248, color=[128, 64, 0]),
-        249:
-        dict(link=('lsd_kpt28', 'lsd_kpt29'), id=249, color=[128, 64, 0]),
-        250:
-        dict(link=('lsd_kpt29', 'lsd_kpt30'), id=250, color=[128, 64, 0]),
-        251:
-        dict(link=('lsd_kpt30', 'lsd_kpt31'), id=251, color=[128, 64, 0]),
-        252:
-        dict(link=('lsd_kpt31', 'lsd_kpt32'), id=252, color=[128, 64, 0]),
-        253:
-        dict(link=('lsd_kpt32', 'lsd_kpt33'), id=253, color=[128, 64, 0]),
-        254:
-        dict(link=('lsd_kpt33', 'lsd_kpt34'), id=254, color=[128, 64, 0]),
-        255:
-        dict(link=('lsd_kpt34', 'lsd_kpt35'), id=255, color=[128, 64, 0]),
-        256:
-        dict(link=('lsd_kpt35', 'lsd_kpt36'), id=256, color=[128, 64, 0]),
-        257:
-        dict(link=('lsd_kpt36', 'lsd_kpt37'), id=257, color=[128, 64, 0]),
-        258:
-        dict(link=('lsd_kpt37', 'lsd_kpt6'), id=258, color=[128, 64, 0]),
-        259:
-        dict(link=('lsd_kpt6', 'lsd_kpt5'), id=259, color=[128, 64, 0]),
-        260:
-        dict(link=('lsd_kpt5', 'lsd_kpt4'), id=260, color=[128, 64, 0]),
-        261:
-        dict(link=('lsd_kpt4', 'lsd_kpt3'), id=261, color=[128, 64, 0]),
-        262:
-        dict(link=('lsd_kpt3', 'lsd_kpt2'), id=262, color=[128, 64, 0]),
-        263:
-        dict(link=('lsd_kpt6', 'lsd_kpt1'), id=263, color=[128, 64, 0]),
-        264:
-        dict(link=('vd_kpt1', 'vd_kpt2'), id=264, color=[128, 64, 255]),
-        265:
-        dict(link=('vd_kpt2', 'vd_kpt7'), id=265, color=[128, 64, 255]),
-        266:
-        dict(link=('vd_kpt7', 'vd_kpt8'), id=266, color=[128, 64, 255]),
-        267:
-        dict(link=('vd_kpt8', 'vd_kpt9'), id=267, color=[128, 64, 255]),
-        268:
-        dict(link=('vd_kpt9', 'vd_kpt10'), id=268, color=[128, 64, 255]),
-        269:
-        dict(link=('vd_kpt10', 'vd_kpt11'), id=269, color=[128, 64, 255]),
-        270:
-        dict(link=('vd_kpt11', 'vd_kpt12'), id=270, color=[128, 64, 255]),
-        271:
-        dict(link=('vd_kpt12', 'vd_kpt13'), id=271, color=[128, 64, 255]),
-        272:
-        dict(link=('vd_kpt13', 'vd_kpt14'), id=272, color=[128, 64, 255]),
-        273:
-        dict(link=('vd_kpt14', 'vd_kpt15'), id=273, color=[128, 64, 255]),
-        274:
-        dict(link=('vd_kpt15', 'vd_kpt16'), id=274, color=[128, 64, 255]),
-        275:
-        dict(link=('vd_kpt16', 'vd_kpt17'), id=275, color=[128, 64, 255]),
-        276:
-        dict(link=('vd_kpt17', 'vd_kpt18'), id=276, color=[128, 64, 255]),
-        277:
-        dict(link=('vd_kpt18', 'vd_kpt19'), id=277, color=[128, 64, 255]),
-        278:
-        dict(link=('vd_kpt19', 'vd_kpt6'), id=278, color=[128, 64, 255]),
-        279:
-        dict(link=('vd_kpt6', 'vd_kpt5'), id=279, color=[128, 64, 255]),
-        280:
-        dict(link=('vd_kpt5', 'vd_kpt4'), id=280, color=[128, 64, 255]),
-        281:
-        dict(link=('vd_kpt4', 'vd_kpt3'), id=281, color=[128, 64, 255]),
-        282:
-        dict(link=('vd_kpt3', 'vd_kpt2'), id=282, color=[128, 64, 255]),
-        283:
-        dict(link=('vd_kpt6', 'vd_kpt1'), id=283, color=[128, 64, 255]),
-        284:
-        dict(link=('sd_kpt1', 'sd_kpt2'), id=284, color=[128, 64, 0]),
-        285:
-        dict(link=('sd_kpt2', 'sd_kpt8'), id=285, color=[128, 64, 0]),
-        286:
-        dict(link=('sd_kpt8', 'sd_kpt9'), id=286, color=[128, 64, 0]),
-        287:
-        dict(link=('sd_kpt9', 'sd_kpt10'), id=287, color=[128, 64, 0]),
-        288:
-        dict(link=('sd_kpt10', 'sd_kpt11'), id=288, color=[128, 64, 0]),
-        289:
-        dict(link=('sd_kpt11', 'sd_kpt12'), id=289, color=[128, 64, 0]),
-        290:
-        dict(link=('sd_kpt12', 'sd_kpt13'), id=290, color=[128, 64, 0]),
-        291:
-        dict(link=('sd_kpt13', 'sd_kpt14'), id=291, color=[128, 64, 0]),
-        292:
-        dict(link=('sd_kpt14', 'sd_kpt15'), id=292, color=[128, 64, 0]),
-        293:
-        dict(link=('sd_kpt15', 'sd_kpt16'), id=293, color=[128, 64, 0]),
-        294:
-        dict(link=('sd_kpt16', 'sd_kpt17'), id=294, color=[128, 64, 0]),
-        295:
-        dict(link=('sd_kpt17', 'sd_kpt18'), id=295, color=[128, 64, 0]),
-        296:
-        dict(link=('sd_kpt18', 'sd_kpt6'), id=296, color=[128, 64, 0]),
-        297:
-        dict(link=('sd_kpt6', 'sd_kpt5'), id=297, color=[128, 64, 0]),
-        298:
-        dict(link=('sd_kpt5', 'sd_kpt4'), id=298, color=[128, 64, 0]),
-        299:
-        dict(link=('sd_kpt4', 'sd_kpt3'), id=299, color=[128, 64, 0]),
-        300:
-        dict(link=('sd_kpt3', 'sd_kpt2'), id=300, color=[128, 64, 0]),
-        301:
-        dict(link=('sd_kpt2', 'sd_kpt7'), id=301, color=[128, 64, 0]),
-        302:
-        dict(link=('sd_kpt6', 'sd_kpt19'), id=302, color=[128, 64, 0]),
-        303:
-        dict(link=('sd_kpt6', 'sd_kpt1'), id=303, color=[128, 64, 0])
-    }),
-    joint_weights=[
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
-    ],
-    sigmas=[])
-param_scheduler = [
-    dict(
-        type='LinearLR', begin=0, end=500, start_factor=0.001, by_epoch=False),
-    dict(
-        type='MultiStepLR',
-        begin=0,
-        end=150,
-        milestones=[100, 130],
-        gamma=0.1,
-        by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005))
-auto_scale_lr = dict(base_batch_size=512)
-dataset_type = 'DeepFashion2Dataset'
-data_mode = 'topdown'
-data_root = 'data/deepfashion2/'
-codec = dict(
-    type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
-train_pipeline = [
-    dict(type='LoadImage'),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='RandomFlip', direction='horizontal'),
-    dict(
-        type='RandomBBoxTransform',
-        shift_prob=0,
-        rotate_factor=60,
-        scale_factor=(0.75, 1.25)),
-    dict(type='TopdownAffine', input_size=(192, 256)),
-    dict(
-        type='GenerateTarget',
-        encoder=dict(
-            type='MSRAHeatmap',
-            input_size=(192, 256),
-            heatmap_size=(48, 64),
-            sigma=2)),
-    dict(type='PackPoseInputs')
-]
-val_pipeline = [
-    dict(type='LoadImage', backend_args=dict(backend='local')),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='TopdownAffine', input_size=(192, 256)),
-    dict(type='PackPoseInputs')
-]
-train_dataloader = dict(
-    batch_size=64,
-    num_workers=6,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='train/deepfashion2_vest_dress.json',
-        data_prefix=dict(img='train/image/'),
-        pipeline=[
-            dict(type='LoadImage'),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='RandomFlip', direction='horizontal'),
-            dict(
-                type='RandomBBoxTransform',
-                shift_prob=0,
-                rotate_factor=60,
-                scale_factor=(0.75, 1.25)),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(
-                type='GenerateTarget',
-                encoder=dict(
-                    type='MSRAHeatmap',
-                    input_size=(192, 256),
-                    heatmap_size=(48, 64),
-                    sigma=2)),
-            dict(type='PackPoseInputs')
-        ]))
-val_dataloader = dict(
-    batch_size=32,
-    num_workers=6,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='validation/deepfashion2_vest_dress.json',
-        data_prefix=dict(img='validation/image/'),
-        test_mode=True,
-        pipeline=[
-            dict(type='LoadImage', backend_args=dict(backend='local')),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(type='PackPoseInputs')
-        ]))
-test_dataloader = dict(
-    batch_size=32,
-    num_workers=6,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type='DeepFashion2Dataset',
-        data_root='data/deepfashion2/',
-        data_mode='topdown',
-        ann_file='validation/deepfashion2_vest_dress.json',
-        data_prefix=dict(img='validation/image/'),
-        test_mode=True,
-        pipeline=[
-            dict(type='LoadImage', backend_args=dict(backend='local')),
-            dict(type='GetBBoxCenterScale'),
-            dict(type='TopdownAffine', input_size=(192, 256)),
-            dict(type='PackPoseInputs')
-        ]))
-channel_cfg = dict(
-    num_output_channels=294,
-    dataset_joints=294,
-    dataset_channel=[[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ]],
-    inference_channel=[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ])
-model = dict(
-    type='TopdownPoseEstimator',
-    data_preprocessor=dict(
-        type='PoseDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        bgr_to_rgb=True),
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
-    head=dict(
-        type='HeatmapHead',
-        in_channels=2048,
-        out_channels=294,
-        loss=dict(type='KeypointMSELoss', use_target_weight=True),
-        decoder=dict(
-            type='MSRAHeatmap',
-            input_size=(192, 256),
-            heatmap_size=(48, 64),
-            sigma=2)),
-    test_cfg=dict(flip_test=True, flip_mode='heatmap', shift_heatmap=True))
-val_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE')
-]
-test_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE')
-]
-launcher = 'pytorch'
-work_dir = './work_dirs/td_hm_res50_4xb64-150e_deepfashion2_vest_dress_256x192'

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/datasets/pipelines/auto_aug.py

@@ -1,96 +0,0 @@
-# Policy for ImageNet, refers to
-# https://github.com/DeepVoltaire/AutoAugment/blame/master/autoaugment.py
-policy_imagenet = [
-    [
-        dict(type='Posterize', bits=4, prob=0.4),
-        dict(type='Rotate', angle=30., prob=0.6)
-    ],
-    [
-        dict(type='Solarize', thr=256 / 9 * 4, prob=0.6),
-        dict(type='AutoContrast', prob=0.6)
-    ],
-    [dict(type='Equalize', prob=0.8),
-     dict(type='Equalize', prob=0.6)],
-    [
-        dict(type='Posterize', bits=5, prob=0.6),
-        dict(type='Posterize', bits=5, prob=0.6)
-    ],
-    [
-        dict(type='Equalize', prob=0.4),
-        dict(type='Solarize', thr=256 / 9 * 5, prob=0.2)
-    ],
-    [
-        dict(type='Equalize', prob=0.4),
-        dict(type='Rotate', angle=30 / 9 * 8, prob=0.8)
-    ],
-    [
-        dict(type='Solarize', thr=256 / 9 * 6, prob=0.6),
-        dict(type='Equalize', prob=0.6)
-    ],
-    [dict(type='Posterize', bits=6, prob=0.8),
-     dict(type='Equalize', prob=1.)],
-    [
-        dict(type='Rotate', angle=10., prob=0.2),
-        dict(type='Solarize', thr=256 / 9, prob=0.6)
-    ],
-    [
-        dict(type='Equalize', prob=0.6),
-        dict(type='Posterize', bits=5, prob=0.4)
-    ],
-    [
-        dict(type='Rotate', angle=30 / 9 * 8, prob=0.8),
-        dict(type='ColorTransform', magnitude=0., prob=0.4)
-    ],
-    [
-        dict(type='Rotate', angle=30., prob=0.4),
-        dict(type='Equalize', prob=0.6)
-    ],
-    [dict(type='Equalize', prob=0.0),
-     dict(type='Equalize', prob=0.8)],
-    [dict(type='Invert', prob=0.6),
-     dict(type='Equalize', prob=1.)],
-    [
-        dict(type='ColorTransform', magnitude=0.4, prob=0.6),
-        dict(type='Contrast', magnitude=0.8, prob=1.)
-    ],
-    [
-        dict(type='Rotate', angle=30 / 9 * 8, prob=0.8),
-        dict(type='ColorTransform', magnitude=0.2, prob=1.)
-    ],
-    [
-        dict(type='ColorTransform', magnitude=0.8, prob=0.8),
-        dict(type='Solarize', thr=256 / 9 * 2, prob=0.8)
-    ],
-    [
-        dict(type='Sharpness', magnitude=0.7, prob=0.4),
-        dict(type='Invert', prob=0.6)
-    ],
-    [
-        dict(
-            type='Shear',
-            magnitude=0.3 / 9 * 5,
-            prob=0.6,
-            direction='horizontal'),
-        dict(type='Equalize', prob=1.)
-    ],
-    [
-        dict(type='ColorTransform', magnitude=0., prob=0.4),
-        dict(type='Equalize', prob=0.6)
-    ],
-    [
-        dict(type='Equalize', prob=0.4),
-        dict(type='Solarize', thr=256 / 9 * 5, prob=0.2)
-    ],
-    [
-        dict(type='Solarize', thr=256 / 9 * 4, prob=0.6),
-        dict(type='AutoContrast', prob=0.6)
-    ],
-    [dict(type='Invert', prob=0.6),
-     dict(type='Equalize', prob=1.)],
-    [
-        dict(type='ColorTransform', magnitude=0.4, prob=0.6),
-        dict(type='Contrast', magnitude=0.8, prob=1.)
-    ],
-    [dict(type='Equalize', prob=0.8),
-     dict(type='Equalize', prob=0.6)],
-]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/confirmdialog/ConfirmDialog.js

@@ -1,103 +0,0 @@
-import Dialog from '../dialog/Dialog.js';
-import Methods from './methods/Methods.js';
-import RegisterEvents from './methods/RegisterEvents.js';
-import DeepClone from '../../../plugins/utils/object/DeepClone.js';
-import CreateBackground from '../utils/build/CreateBackground.js';
-import CreateLabel from '../utils/build/CreateLabel.js';
-import CreateContent from './methods/CreateContent.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-import HasValue from '../../../plugins/utils/object/HasValue.js';
-import TextArea from '../textarea/TextArea.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class ConfirmDialog extends Dialog {
-    constructor(scene, config, creators) {
-        config = (config) ? DeepClone(config) : {};
-
-        if (creators === undefined) {
-            creators = {};
-        }
-
-        var createBackground = GetValue(creators, 'background', CreateBackground);
-        if (createBackground) {
-            config.background = createBackground(scene, config.background);
-        } else {
-            delete config.background;
-        }
-
-        config.title = CreateLabel(scene, config.title, creators.title);
-
-        config.content = CreateContent(scene, config.content, creators.content);
-        if (config.content instanceof TextArea) {
-            if (HasValue(config, 'height') && !HasValue(config, 'proportion.content')) {
-                SetValue(config, 'proportion.content', 1);
-            }
-        }
-
-        var defaultButtonConfig = config.button;
-        var buttonAConfig = config.buttonA || defaultButtonConfig;
-        var buttonBConfig = config.buttonB || defaultButtonConfig;
-        var buttonMode = config.buttonMode;
-        if (buttonMode === undefined) {
-            buttonMode = (!!buttonAConfig && !!buttonBConfig) ? 2 :
-                (!!buttonAConfig) ? 1 :
-                    0;
-        }
-
-        var defaultButtonCreator = creators.button;
-        var buttonACreators = creators.buttonA || defaultButtonCreator;
-        var buttonBCreators = creators.buttonB || defaultButtonCreator;
-        switch (buttonMode) {
-            case 2:
-                config.actions = [
-                    CreateLabel(scene, buttonAConfig, buttonACreators),
-                    CreateLabel(scene, buttonBConfig, buttonBCreators),
-                ]
-                break;
-
-            case 1:
-                config.actions = [
-                    CreateLabel(scene, buttonAConfig, buttonACreators),
-                ]
-                break;
-
-            case 0:
-                break;
-
-            default:
-                config.actions = [];
-                break;
-        }
-
-        var defaultChoiceConfig = config.choice;
-        if (defaultChoiceConfig) {
-            config.choices = [];
-        }
-
-        super(scene, config);
-        this.type = 'rexConfirmDialog';
-
-        this.buttonMode = buttonMode;
-
-        this.defaultActionConfig = defaultButtonConfig;
-        this.defaultActionButtonCreator = defaultButtonCreator;
-
-        this.defaultChoiceConfig = defaultChoiceConfig;
-        this.defaultChoiceCreator = creators.choice;
-
-        var buttons = this.childrenMap.actions;
-        this.addChildrenMap('buttonA', (buttons) ? buttons[0] : null);
-        this.addChildrenMap('buttonB', (buttons) ? buttons[1] : null);
-
-        // Interactive
-        RegisterEvents.call(this);
-    }
-}
-
-Object.assign(
-    ConfirmDialog.prototype,
-    Methods
-)
-
-export default ConfirmDialog;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/InsertEmptyColumn.js

@@ -1,34 +0,0 @@
-var InsertEmptyColumn = function (colIndex, proportion, space) {
-    if (proportion === undefined) {
-        proportion = this.columnProportions[0] || 0;
-    }
-    if (space === undefined) {
-        space = this.space.column[0] || 0;
-    }
-
-    this.columnCount += 1;
-    this.gridCount += this.rowCount;
-
-    for (var i = this.rowCount - 1; i >= 0; i--) {
-        var insertIndex = (i * this.columnCount) + colIndex;
-        this.sizerChildren.splice(insertIndex, 0, null);
-    }
-
-    this.columnProportions.push(proportion);
-
-    this.columnWidth.length += 1;  // this.columnWidth will be recalculated when layout()    
-
-    this.space.column.splice(colIndex, 0, space);
-
-    return this;
-}
-
-var AddEmptyColumn = function (proportion, space) {
-    InsertEmptyColumn.call(this, this.columnCount, proportion, space);
-    return this;
-}
-
-export {
-    InsertEmptyColumn,
-    AddEmptyColumn
-};

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/defaultcallbacks/MoveCallbacks.js

@@ -1,12 +0,0 @@
-var GetCallback = function (duration, ease) {
-    return function (child, key, sides, reset) {
-        if (key !== 'panel') {
-            sides.moveChild(child, ((reset) ? 0 : duration), ease);
-        }
-    }
-}
-
-export default {
-    show: GetCallback,
-    hide: GetCallback
-}

spaces/AlexKorGKLT/webui-cpua/README.md

@@ -1,14 +0,0 @@
----
-title: Stable Diffusion Webui on Cpu
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-python_version: 3.10.6
-duplicated_from: DMTuit/webui-cpu
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexWang/lama/models/ade20k/utils.py

@@ -1,40 +0,0 @@
-"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch"""
-
-import os
-import sys
-
-import numpy as np
-import torch
-
-try:
-    from urllib import urlretrieve
-except ImportError:
-    from urllib.request import urlretrieve
-
-
-def load_url(url, model_dir='./pretrained', map_location=None):
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    filename = url.split('/')[-1]
-    cached_file = os.path.join(model_dir, filename)
-    if not os.path.exists(cached_file):
-        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
-        urlretrieve(url, cached_file)
-    return torch.load(cached_file, map_location=map_location)
-
-
-def color_encode(labelmap, colors, mode='RGB'):
-    labelmap = labelmap.astype('int')
-    labelmap_rgb = np.zeros((labelmap.shape[0], labelmap.shape[1], 3),
-                            dtype=np.uint8)
-    for label in np.unique(labelmap):
-        if label < 0:
-            continue
-        labelmap_rgb += (labelmap == label)[:, :, np.newaxis] * \
-            np.tile(colors[label],
-                    (labelmap.shape[0], labelmap.shape[1], 1))
-
-    if mode == 'BGR':
-        return labelmap_rgb[:, :, ::-1]
-    else:
-        return labelmap_rgb

spaces/AlexZou/Deploy_Restoration/app.py

@@ -1,69 +0,0 @@
-import gradio as gr
-import os
- 
-def inference(image, task):
-    if not os.path.exists('tmp'):
-      os.system('mkdir tmp')
-    image.save("tmp/lq_image.png", "PNG")
-    
-    if task == 'Dehazing':
-      os.system("python Dehazing.py --test_path ./tmp/lq_image.png --save_path ./tmp/ --pk_path model_zoo/Haze4k.tjm")
-  
-    if task == 'LLIE':
-      os.system("python Lowlight.py --test_path ./tmp/lq_image.png --save_path ./tmp/ --pk_path model_zoo/Lowlight.pth")
-    
-    if task == 'SuperResolutionx2':
-      os.system("python SuperResolution.py --test_path ./tmp/lq_image.png --save_path ./tmp/ --pk_path model_zoo/SRx2.pth --scale 2")
-    
-    if task == 'SuperResolutionx3':
-      os.system("python SuperResolution.py --test_path ./tmp/lq_image.png --save_path ./tmp/ --pk_path model_zoo/SRx3.pth --scale 3")
-      
-    if task == 'SuperResolutionx4':
-      os.system("python SuperResolution.py --test_path ./tmp/lq_image.png --save_path ./tmp/ --pk_path model_zoo/SRx4.pth --scale 4")
-        
-    if task == 'Underwater':
-      os.system("python Underwater.py --test_path ./tmp/lq_image.png --save_path ./tmp/ --pk_path model_zoo/underwater.pth")
-      
-    return 'tmp/output.png'
-   
-title = "基于光学传播建模的复杂水下成像智能复原方法<br><font size=3>高红霞</font><br><font size=3>华南理工大学</font><br>"
-description = " 简介：项目组结合多年来关于基于光学成像建模的图像复原方法的理论研究基础，从光的物理传播建模出发，开展如下研究：建立描述复杂水体中介质的吸收、前后散射等主要成像退化特征的光学成像模型；基于光学成像模型和低秩约束实现自适应智能复原；以计算机视觉理论和光学成像模型为基础，实现消除多种退化效应的深度学习复原模型。在前期的图像复原理论研究的基础上，后续展开在雾天、低照度、低分辨率等场景下的传统与深度学习复原方法的研究。"
-article = "相关成果：<br>[1]Ye Cai, Hongxia Gao*, Shicheng Niu, Tian Qi. A multi-stage restoration method for degraded images with light scattering and absorption. Proceeding of 26th International Conference on Pattern Recognition (ICPR 2022).<br>[2]Ye Cai, Lan Luo, Hongxia Gao*, Shicheng Niu, Weipeng Yang , Tian Qi, Guoheng Liang. Haze Removal Using a Hybrid Convolutional Sparse Representation Model. The 14th International Conference on Digital Image Processing (ICDIP).<br>[3] Hongxia Gao, Zhanhong Chen, Binyang Huang*, Jiahe Chen, Zhifu Li. Image Super Resolution Based on Conditional Generative Adversarial Network. IET Image Processing(SCI三区)，2020, 14(13): 3006-3013.(SCI收录- 000595800300006).<br>[4] Weipeng Yang, Hongxia Gao, Shasha Huang, Shicheng Niu, Hongsheng Chen, Guoheng Liang. Low-light image enhancement under mixed noise model with Tensor Representation. CAAI International Conference on Artificial Intelligence(CICAI)."
-#description = "Gradio demo for <b>NAFNet: Nonlinear Activation Free Network for Image Restoration</b>. NAFNet achieves state-of-the-art performance on three tasks: image denoising, image debluring and stereo image super-resolution (SR). See the paper and project page for detailed results below. Here, we provide a demo for image denoise and deblur. To use it, simply upload your image, or click one of the examples to load them. Inference needs some time since this demo uses CPU."
-#article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.04676' target='_blank'>Simple Baselines for Image Restoration</a> | <a href='https://arxiv.org/abs/2204.08714' target='_blank'>NAFSSR: Stereo Image Super-Resolution Using NAFNet</a>  | <a href='https://github.com/megvii-research/NAFNet' target='_blank'> Github Repo</a></p>"
-
-
-examples = [['demo/underwater.jpg', 'Underwater'],
-            ['demo/low.jpg', 'LLIE'],
-            ['demo/dehaze.jpg', 'Dehazing'],
-            ['demo/sr.png', 'SuperResolutionx2']]
-
-
-#examples = [['demo/low.jpg', 'LLIE']]
-
-'''                                           
-iface = gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),
-    gr.inputs.Radio(["LLIE"], default="LLIE", label='task'),], 
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    enable_queue=True,
-    examples=examples
-    )
-iface.launch(debug=True,enable_queue=True)
-'''
-iface = gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),
-    gr.inputs.Radio(["Underwater", "LLIE", "Dehazing", "SuperResolutionx2", "SuperResolutionx3", "SuperResolutionx4"], default="Underwater", label='task'),], 
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    enable_queue=True,
-    examples=examples
-    )
-iface.launch(debug=True,enable_queue=True)

spaces/Amrrs/QR-code-AI-art-generator/app.py

@@ -1,285 +0,0 @@
-import torch
-import gradio as gr
-from PIL import Image
-import qrcode
-from pathlib import Path
-from multiprocessing import cpu_count
-import requests
-import io
-import os
-from PIL import Image
-
-from diffusers import (
-    StableDiffusionPipeline,
-    StableDiffusionControlNetImg2ImgPipeline,
-    ControlNetModel,
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    DEISMultistepScheduler,
-    HeunDiscreteScheduler,
-    EulerDiscreteScheduler,
-)
-
-qrcode_generator = qrcode.QRCode(
-    version=1,
-    error_correction=qrcode.ERROR_CORRECT_H,
-    box_size=10,
-    border=4,
-)
-
-controlnet = ControlNetModel.from_pretrained(
-    "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
-)
-
-pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    controlnet=controlnet,
-    safety_checker=None,
-    torch_dtype=torch.float16,
-).to("cuda")
-pipe.enable_xformers_memory_efficient_attention()
-
-
-def resize_for_condition_image(input_image: Image.Image, resolution: int):
-    input_image = input_image.convert("RGB")
-    W, H = input_image.size
-    k = float(resolution) / min(H, W)
-    H *= k
-    W *= k
-    H = int(round(H / 64.0)) * 64
-    W = int(round(W / 64.0)) * 64
-    img = input_image.resize((W, H), resample=Image.LANCZOS)
-    return img
-
-
-SAMPLER_MAP = {
-    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
-    "DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
-    "Heun": lambda config: HeunDiscreteScheduler.from_config(config),
-    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
-    "DDIM": lambda config: DDIMScheduler.from_config(config),
-    "DEIS": lambda config: DEISMultistepScheduler.from_config(config),
-}
-
-
-def inference(
-    qr_code_content: str,
-    prompt: str,
-    negative_prompt: str,
-    guidance_scale: float = 10.0,
-    controlnet_conditioning_scale: float = 2.0,
-    strength: float = 0.8,
-    seed: int = -1,
-    init_image: Image.Image | None = None,
-    qrcode_image: Image.Image | None = None,
-    use_qr_code_as_init_image = True,
-    sampler = "DPM++ Karras SDE",
-):
-    if prompt is None or prompt == "":
-        raise gr.Error("Prompt is required")
-
-    if qrcode_image is None and qr_code_content == "":
-        raise gr.Error("QR Code Image or QR Code Content is required")
-
-    pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
-
-    generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
-
-    if qr_code_content != "" or qrcode_image.size == (1, 1):
-        print("Generating QR Code from content")
-        qr = qrcode.QRCode(
-            version=1,
-            error_correction=qrcode.constants.ERROR_CORRECT_H,
-            box_size=10,
-            border=4,
-        )
-        qr.add_data(qr_code_content)
-        qr.make(fit=True)
-
-        qrcode_image = qr.make_image(fill_color="black", back_color="white")
-        qrcode_image = resize_for_condition_image(qrcode_image, 768)
-    else:
-        print("Using QR Code Image")
-        qrcode_image = resize_for_condition_image(qrcode_image, 768)
-
-    # hack due to gradio examples
-    init_image = qrcode_image
-
-    out = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=qrcode_image,
-        control_image=qrcode_image,  # type: ignore
-        width=768,  # type: ignore
-        height=768,  # type: ignore
-        guidance_scale=float(guidance_scale),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
-        generator=generator,
-        strength=float(strength),
-        num_inference_steps=40,
-    )
-    return out.images[0]  # type: ignore
-
-
-with gr.Blocks() as blocks:
-    gr.Markdown(
-        """
-# QR Code AI Art Generator
-
-## 💡 How to generate beautiful QR codes
-
-We use the QR code image as the initial image **and** the control image, which allows you to generate 
-QR Codes that blend in **very naturally** with your provided prompt.
-The strength parameter defines how much noise is added to your QR code and the noisy QR code is then guided towards both your prompt and the QR code image via Controlnet.
-Use a high strength value between 0.8 and 0.95 and choose a conditioning scale between 0.6 and 2.0.
-This mode arguably achieves the asthetically most appealing QR code images, but also requires more tuning of the controlnet conditioning scale and the strength value. If the generated image 
-looks way to much like the original QR code, make sure to gently increase the *strength* value and reduce the *conditioning* scale. Also check out the examples below.
-
-model: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15
-
-<a href="https://huggingface.co/spaces/huggingface-projects/QR-code-AI-art-generator?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for no queue on your own hardware.</p>
-                """
-    )
-
-    with gr.Row():
-        with gr.Column():
-            qr_code_content = gr.Textbox(
-                label="QR Code Content",
-                info="QR Code Content or URL",
-                value="",
-            )
-            with gr.Accordion(label="QR Code Image (Optional)", open=False):
-                qr_code_image = gr.Image(
-                    label="QR Code Image (Optional). Leave blank to automatically generate QR code",
-                    type="pil",
-                )
-
-            prompt = gr.Textbox(
-                label="Prompt",
-                info="Prompt that guides the generation towards",
-            )
-            negative_prompt = gr.Textbox(
-                label="Negative Prompt",
-                value="ugly, disfigured, low quality, blurry, nsfw",
-            )
-            use_qr_code_as_init_image = gr.Checkbox(label="Use QR code as init image", value=True, interactive=False, info="Whether init image should be QR code. Unclick to pass init image or generate init image with Stable Diffusion 2.1")
-
-            with gr.Accordion(label="Init Images (Optional)", open=False, visible=False) as init_image_acc:
-                init_image = gr.Image(label="Init Image (Optional). Leave blank to generate image with SD 2.1", type="pil")
-
-
-            with gr.Accordion(
-                label="Params: The generated QR Code functionality is largely influenced by the parameters detailed below",
-                open=True,
-            ):
-                controlnet_conditioning_scale = gr.Slider(
-                    minimum=0.0,
-                    maximum=5.0,
-                    step=0.01,
-                    value=1.1,
-                    label="Controlnet Conditioning Scale",
-                )
-                strength = gr.Slider(
-                    minimum=0.0, maximum=1.0, step=0.01, value=0.9, label="Strength"
-                )
-                guidance_scale = gr.Slider(
-                    minimum=0.0,
-                    maximum=50.0,
-                    step=0.25,
-                    value=7.5,
-                    label="Guidance Scale",
-                )
-                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE")
-                seed = gr.Slider(
-                    minimum=-1,
-                    maximum=9999999999,
-                    step=1,
-                    value=2313123,
-                    label="Seed",
-                    randomize=True,
-                )
-            with gr.Row():
-                run_btn = gr.Button("Run")
-        with gr.Column():
-            result_image = gr.Image(label="Result Image")
-    run_btn.click(
-        inference,
-        inputs=[
-            qr_code_content,
-            prompt,
-            negative_prompt,
-            guidance_scale,
-            controlnet_conditioning_scale,
-            strength,
-            seed,
-            init_image,
-            qr_code_image,
-            use_qr_code_as_init_image,
-            sampler,
-        ],
-        outputs=[result_image],
-    )
-
-    gr.Examples(
-        examples=[
-            [
-                "https://huggingface.co/",
-                "A sky view of a colorful lakes and rivers flowing through the desert",
-                "ugly, disfigured, low quality, blurry, nsfw",
-                7.5,
-                1.3,
-                0.9,
-                5392011833,
-                None,
-                None,
-                True,
-                "DPM++ Karras SDE",
-            ],
-            [
-                "https://huggingface.co/",
-                "Bright sunshine coming through the cracks of a wet, cave wall of big rocks",
-                "ugly, disfigured, low quality, blurry, nsfw",
-                7.5,
-                1.11,
-                0.9,
-                2523992465,
-                None,
-                None,
-                True,
-                "DPM++ Karras SDE",
-            ],
-            [
-                "https://huggingface.co/",
-                "Sky view of highly aesthetic, ancient greek thermal baths  in beautiful nature",
-                "ugly, disfigured, low quality, blurry, nsfw",
-                7.5,
-                1.5,
-                0.9,
-                2523992465,
-                None,
-                None,
-                True,
-                "DPM++ Karras SDE",
-            ],
-        ],
-        fn=inference,
-        inputs=[
-            qr_code_content,
-            prompt,
-            negative_prompt,
-            guidance_scale,
-            controlnet_conditioning_scale,
-            strength,
-            seed,
-            init_image,
-            qr_code_image,
-            use_qr_code_as_init_image,
-            sampler,
-        ],
-        outputs=[result_image],
-        cache_examples=True,
-    )
-
-blocks.queue(concurrency_count=1, max_size=20)
-blocks.launch(share=bool(os.environ.get("SHARE", False)))

spaces/Andres99/Tune-A-Video-Training-UI/app_inference.py

@@ -1,170 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import enum
-
-import gradio as gr
-from huggingface_hub import HfApi
-
-from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget
-from inference import InferencePipeline
-from utils import find_exp_dirs
-
-
-class ModelSource(enum.Enum):
-    HUB_LIB = UploadTarget.MODEL_LIBRARY.value
-    LOCAL = 'Local'
-
-
-class InferenceUtil:
-    def __init__(self, hf_token: str | None):
-        self.hf_token = hf_token
-
-    def load_hub_model_list(self) -> dict:
-        api = HfApi(token=self.hf_token)
-        choices = [
-            info.modelId
-            for info in api.list_models(author=MODEL_LIBRARY_ORG_NAME)
-        ]
-        return gr.update(choices=choices,
-                         value=choices[0] if choices else None)
-
-    @staticmethod
-    def load_local_model_list() -> dict:
-        choices = find_exp_dirs()
-        return gr.update(choices=choices,
-                         value=choices[0] if choices else None)
-
-    def reload_model_list(self, model_source: str) -> dict:
-        if model_source == ModelSource.HUB_LIB.value:
-            return self.load_hub_model_list()
-        elif model_source == ModelSource.LOCAL.value:
-            return self.load_local_model_list()
-        else:
-            raise ValueError
-
-    def load_model_info(self, model_id: str) -> tuple[str, str]:
-        try:
-            card = InferencePipeline.get_model_card(model_id, self.hf_token)
-        except Exception:
-            return '', ''
-        base_model = getattr(card.data, 'base_model', '')
-        training_prompt = getattr(card.data, 'training_prompt', '')
-        return base_model, training_prompt
-
-    def reload_model_list_and_update_model_info(
-            self, model_source: str) -> tuple[dict, str, str]:
-        model_list_update = self.reload_model_list(model_source)
-        model_list = model_list_update['choices']
-        model_info = self.load_model_info(model_list[0] if model_list else '')
-        return model_list_update, *model_info
-
-
-def create_inference_demo(pipe: InferencePipeline,
-                          hf_token: str | None = None) -> gr.Blocks:
-    app = InferenceUtil(hf_token)
-
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                with gr.Box():
-                    model_source = gr.Radio(
-                        label='Model Source',
-                        choices=[_.value for _ in ModelSource],
-                        value=ModelSource.HUB_LIB.value)
-                    reload_button = gr.Button('Reload Model List')
-                    model_id = gr.Dropdown(label='Model ID',
-                                           choices=None,
-                                           value=None)
-                    with gr.Accordion(
-                            label=
-                            'Model info (Base model and prompt used for training)',
-                            open=False):
-                        with gr.Row():
-                            base_model_used_for_training = gr.Text(
-                                label='Base model', interactive=False)
-                            prompt_used_for_training = gr.Text(
-                                label='Training prompt', interactive=False)
-                prompt = gr.Textbox(
-                    label='Prompt',
-                    max_lines=1,
-                    placeholder='Example: "A panda is surfing"')
-                video_length = gr.Slider(label='Video length',
-                                         minimum=4,
-                                         maximum=12,
-                                         step=1,
-                                         value=8)
-                fps = gr.Slider(label='FPS',
-                                minimum=1,
-                                maximum=12,
-                                step=1,
-                                value=1)
-                seed = gr.Slider(label='Seed',
-                                 minimum=0,
-                                 maximum=100000,
-                                 step=1,
-                                 value=0)
-                with gr.Accordion('Other Parameters', open=False):
-                    num_steps = gr.Slider(label='Number of Steps',
-                                          minimum=0,
-                                          maximum=100,
-                                          step=1,
-                                          value=50)
-                    guidance_scale = gr.Slider(label='CFG Scale',
-                                               minimum=0,
-                                               maximum=50,
-                                               step=0.1,
-                                               value=7.5)
-
-                run_button = gr.Button('Generate')
-
-                gr.Markdown('''
-                - After training, you can press "Reload Model List" button to load your trained model names.
-                - It takes a few minutes to download model first.
-                - Expected time to generate an 8-frame video: 70 seconds with T4, 24 seconds with A10G, (10 seconds with A100)
-                ''')
-            with gr.Column():
-                result = gr.Video(label='Result')
-
-        model_source.change(fn=app.reload_model_list_and_update_model_info,
-                            inputs=model_source,
-                            outputs=[
-                                model_id,
-                                base_model_used_for_training,
-                                prompt_used_for_training,
-                            ])
-        reload_button.click(fn=app.reload_model_list_and_update_model_info,
-                            inputs=model_source,
-                            outputs=[
-                                model_id,
-                                base_model_used_for_training,
-                                prompt_used_for_training,
-                            ])
-        model_id.change(fn=app.load_model_info,
-                        inputs=model_id,
-                        outputs=[
-                            base_model_used_for_training,
-                            prompt_used_for_training,
-                        ])
-        inputs = [
-            model_id,
-            prompt,
-            video_length,
-            fps,
-            seed,
-            num_steps,
-            guidance_scale,
-        ]
-        prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
-        run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
-    return demo
-
-
-if __name__ == '__main__':
-    import os
-
-    hf_token = os.getenv('HF_TOKEN')
-    pipe = InferencePipeline(hf_token)
-    demo = create_inference_demo(pipe, hf_token)
-    demo.queue(max_size=10).launch(share=False)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -1,1058 +0,0 @@
-import html
-import inspect
-import re
-import urllib.parse as ul
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
-
-from ...loaders import LoraLoaderMixin
-from ...models import UNet2DConditionModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    BACKENDS_MAPPING,
-    PIL_INTERPOLATION,
-    is_accelerate_available,
-    is_accelerate_version,
-    is_bs4_available,
-    is_ftfy_available,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from . import IFPipelineOutput
-from .safety_checker import IFSafetyChecker
-from .watermark import IFWatermarker
-
-
-if is_bs4_available():
-    from bs4 import BeautifulSoup
-
-if is_ftfy_available():
-    import ftfy
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.resize
-def resize(images: PIL.Image.Image, img_size: int) -> PIL.Image.Image:
-    w, h = images.size
-
-    coef = w / h
-
-    w, h = img_size, img_size
-
-    if coef >= 1:
-        w = int(round(img_size / 8 * coef) * 8)
-    else:
-        h = int(round(img_size / 8 / coef) * 8)
-
-    images = images.resize((w, h), resample=PIL_INTERPOLATION["bicubic"], reducing_gap=None)
-
-    return images
-
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
-        >>> from diffusers.utils import pt_to_pil
-        >>> import torch
-        >>> from PIL import Image
-        >>> import requests
-        >>> from io import BytesIO
-
-        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-        >>> response = requests.get(url)
-        >>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> original_image = original_image.resize((768, 512))
-
-        >>> pipe = IFImg2ImgPipeline.from_pretrained(
-        ...     "DeepFloyd/IF-I-XL-v1.0",
-        ...     variant="fp16",
-        ...     torch_dtype=torch.float16,
-        ... )
-        >>> pipe.enable_model_cpu_offload()
-
-        >>> prompt = "A fantasy landscape in style minecraft"
-        >>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
-
-        >>> image = pipe(
-        ...     image=original_image,
-        ...     prompt_embeds=prompt_embeds,
-        ...     negative_prompt_embeds=negative_embeds,
-        ...     output_type="pt",
-        ... ).images
-
-        >>> # save intermediate image
-        >>> pil_image = pt_to_pil(image)
-        >>> pil_image[0].save("./if_stage_I.png")
-
-        >>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
-        ...     "DeepFloyd/IF-II-L-v1.0",
-        ...     text_encoder=None,
-        ...     variant="fp16",
-        ...     torch_dtype=torch.float16,
-        ... )
-        >>> super_res_1_pipe.enable_model_cpu_offload()
-
-        >>> image = super_res_1_pipe(
-        ...     image=image,
-        ...     original_image=original_image,
-        ...     prompt_embeds=prompt_embeds,
-        ...     negative_prompt_embeds=negative_embeds,
-        ... ).images
-        >>> image[0].save("./if_stage_II.png")
-        ```
-"""
-
-
-class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
-    tokenizer: T5Tokenizer
-    text_encoder: T5EncoderModel
-
-    unet: UNet2DConditionModel
-    scheduler: DDPMScheduler
-    image_noising_scheduler: DDPMScheduler
-
-    feature_extractor: Optional[CLIPImageProcessor]
-    safety_checker: Optional[IFSafetyChecker]
-
-    watermarker: Optional[IFWatermarker]
-
-    bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
-    )  # noqa
-
-    _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        tokenizer: T5Tokenizer,
-        text_encoder: T5EncoderModel,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        image_noising_scheduler: DDPMScheduler,
-        safety_checker: Optional[IFSafetyChecker],
-        feature_extractor: Optional[CLIPImageProcessor],
-        watermarker: Optional[IFWatermarker],
-        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 IF 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 unet.config.in_channels != 6:
-            logger.warn(
-                "It seems like you have loaded a checkpoint that shall not be used for super resolution from {unet.config._name_or_path} as it accepts {unet.config.in_channels} input channels instead of 6. Please make sure to pass a super resolution checkpoint as the `'unet'`: IFSuperResolutionPipeline.from_pretrained(unet=super_resolution_unet, ...)`."
-            )
-
-        self.register_modules(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            image_noising_scheduler=image_noising_scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-            watermarker=watermarker,
-        )
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.enable_model_cpu_offload
-    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}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-
-        if self.text_encoder is not None:
-            _, hook = cpu_offload_with_hook(self.text_encoder, device, prev_module_hook=hook)
-
-            # Accelerate will move the next model to the device _before_ calling the offload hook of the
-            # previous model. This will cause both models to be present on the device at the same time.
-            # IF uses T5 for its text encoder which is really large. We can manually call the offload
-            # hook for the text encoder to ensure it's moved to the cpu before the unet is moved to
-            # the GPU.
-            self.text_encoder_offload_hook = hook
-
-        _, hook = cpu_offload_with_hook(self.unet, device, prev_module_hook=hook)
-
-        # if the safety checker isn't called, `unet_offload_hook` will have to be called to manually offload the unet
-        self.unet_offload_hook = hook
-
-        if self.safety_checker is not None:
-            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.remove_all_hooks
-    def remove_all_hooks(self):
-        if is_accelerate_available():
-            from accelerate.hooks import remove_hook_from_module
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        for model in [self.text_encoder, self.unet, self.safety_checker]:
-            if model is not None:
-                remove_hook_from_module(model, recurse=True)
-
-        self.unet_offload_hook = None
-        self.text_encoder_offload_hook = None
-        self.final_offload_hook = None
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
-    def _text_preprocessing(self, text, clean_caption=False):
-        if clean_caption and not is_bs4_available():
-            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if clean_caption and not is_ftfy_available():
-            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
-            logger.warn("Setting `clean_caption` to False...")
-            clean_caption = False
-
-        if not isinstance(text, (tuple, list)):
-            text = [text]
-
-        def process(text: str):
-            if clean_caption:
-                text = self._clean_caption(text)
-                text = self._clean_caption(text)
-            else:
-                text = text.lower().strip()
-            return text
-
-        return [process(t) for t in text]
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
-    def _clean_caption(self, caption):
-        caption = str(caption)
-        caption = ul.unquote_plus(caption)
-        caption = caption.strip().lower()
-        caption = re.sub("<person>", "person", caption)
-        # urls:
-        caption = re.sub(
-            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        caption = re.sub(
-            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
-            "",
-            caption,
-        )  # regex for urls
-        # html:
-        caption = BeautifulSoup(caption, features="html.parser").text
-
-        # @<nickname>
-        caption = re.sub(r"@[\w\d]+\b", "", caption)
-
-        # 31C0—31EF CJK Strokes
-        # 31F0—31FF Katakana Phonetic Extensions
-        # 3200—32FF Enclosed CJK Letters and Months
-        # 3300—33FF CJK Compatibility
-        # 3400—4DBF CJK Unified Ideographs Extension A
-        # 4DC0—4DFF Yijing Hexagram Symbols
-        # 4E00—9FFF CJK Unified Ideographs
-        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
-        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
-        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
-        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
-        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
-        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
-        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
-        #######################################################
-
-        # все виды тире / all types of dash --> "-"
-        caption = re.sub(
-            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
-            "-",
-            caption,
-        )
-
-        # кавычки к одному стандарту
-        caption = re.sub(r"[`´«»“”¨]", '"', caption)
-        caption = re.sub(r"[‘’]", "'", caption)
-
-        # &quot;
-        caption = re.sub(r"&quot;?", "", caption)
-        # &amp
-        caption = re.sub(r"&amp", "", caption)
-
-        # ip adresses:
-        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
-
-        # article ids:
-        caption = re.sub(r"\d:\d\d\s+$", "", caption)
-
-        # \n
-        caption = re.sub(r"\\n", " ", caption)
-
-        # "#123"
-        caption = re.sub(r"#\d{1,3}\b", "", caption)
-        # "#12345.."
-        caption = re.sub(r"#\d{5,}\b", "", caption)
-        # "123456.."
-        caption = re.sub(r"\b\d{6,}\b", "", caption)
-        # filenames:
-        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
-
-        #
-        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
-        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
-
-        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
-        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
-
-        # this-is-my-cute-cat / this_is_my_cute_cat
-        regex2 = re.compile(r"(?:\-|\_)")
-        if len(re.findall(regex2, caption)) > 3:
-            caption = re.sub(regex2, " ", caption)
-
-        caption = ftfy.fix_text(caption)
-        caption = html.unescape(html.unescape(caption))
-
-        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
-        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
-        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
-
-        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
-        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
-        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
-        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
-        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
-
-        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
-
-        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
-
-        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
-        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
-        caption = re.sub(r"\s+", " ", caption)
-
-        caption.strip()
-
-        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
-        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
-        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
-        caption = re.sub(r"^\.\S+$", "", caption)
-
-        return caption.strip()
-
-    @torch.no_grad()
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt,
-        do_classifier_free_guidance=True,
-        num_images_per_prompt=1,
-        device=None,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        clean_caption: bool = False,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                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. 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 negative_prompt is not None:
-            if 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)}."
-                )
-
-        if device is None:
-            device = self._execution_device
-
-        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]
-
-        # while T5 can handle much longer input sequences than 77, the text encoder was trained with a max length of 77 for IF
-        max_length = 77
-
-        if prompt_embeds is None:
-            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                add_special_tokens=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[:, max_length - 1 : -1])
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {max_length} tokens: {removed_text}"
-                )
-
-            attention_mask = text_inputs.attention_mask.to(device)
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            dtype = self.unet.dtype
-        else:
-            dtype = None
-
-        prompt_embeds = prompt_embeds.to(dtype=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 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
-
-            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_attention_mask=True,
-                add_special_tokens=True,
-                return_tensors="pt",
-            )
-            attention_mask = uncond_input.attention_mask.to(device)
-
-            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=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
-        else:
-            negative_prompt_embeds = None
-
-        return prompt_embeds, negative_prompt_embeds
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
-    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, nsfw_detected, watermark_detected = self.safety_checker(
-                images=image,
-                clip_input=safety_checker_input.pixel_values.to(dtype=dtype),
-            )
-        else:
-            nsfw_detected = None
-            watermark_detected = None
-
-            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
-                self.unet_offload_hook.offload()
-
-        return image, nsfw_detected, watermark_detected
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(
-        self,
-        prompt,
-        image,
-        original_image,
-        batch_size,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        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}."
-                )
-
-        # image
-
-        if isinstance(image, list):
-            check_image_type = image[0]
-        else:
-            check_image_type = image
-
-        if (
-            not isinstance(check_image_type, torch.Tensor)
-            and not isinstance(check_image_type, PIL.Image.Image)
-            and not isinstance(check_image_type, np.ndarray)
-        ):
-            raise ValueError(
-                "`image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
-                f" {type(check_image_type)}"
-            )
-
-        if isinstance(image, list):
-            image_batch_size = len(image)
-        elif isinstance(image, torch.Tensor):
-            image_batch_size = image.shape[0]
-        elif isinstance(image, PIL.Image.Image):
-            image_batch_size = 1
-        elif isinstance(image, np.ndarray):
-            image_batch_size = image.shape[0]
-        else:
-            assert False
-
-        if batch_size != image_batch_size:
-            raise ValueError(f"image batch size: {image_batch_size} must be same as prompt batch size {batch_size}")
-
-        # original_image
-
-        if isinstance(original_image, list):
-            check_image_type = original_image[0]
-        else:
-            check_image_type = original_image
-
-        if (
-            not isinstance(check_image_type, torch.Tensor)
-            and not isinstance(check_image_type, PIL.Image.Image)
-            and not isinstance(check_image_type, np.ndarray)
-        ):
-            raise ValueError(
-                "`original_image` has to be of type `torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, or List[...] but is"
-                f" {type(check_image_type)}"
-            )
-
-        if isinstance(original_image, list):
-            image_batch_size = len(original_image)
-        elif isinstance(original_image, torch.Tensor):
-            image_batch_size = original_image.shape[0]
-        elif isinstance(original_image, PIL.Image.Image):
-            image_batch_size = 1
-        elif isinstance(original_image, np.ndarray):
-            image_batch_size = original_image.shape[0]
-        else:
-            assert False
-
-        if batch_size != image_batch_size:
-            raise ValueError(
-                f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}"
-            )
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image
-    def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:
-        if not isinstance(image, list):
-            image = [image]
-
-        def numpy_to_pt(images):
-            if images.ndim == 3:
-                images = images[..., None]
-
-            images = torch.from_numpy(images.transpose(0, 3, 1, 2))
-            return images
-
-        if isinstance(image[0], PIL.Image.Image):
-            new_image = []
-
-            for image_ in image:
-                image_ = image_.convert("RGB")
-                image_ = resize(image_, self.unet.sample_size)
-                image_ = np.array(image_)
-                image_ = image_.astype(np.float32)
-                image_ = image_ / 127.5 - 1
-                new_image.append(image_)
-
-            image = new_image
-
-            image = np.stack(image, axis=0)  # to np
-            image = numpy_to_pt(image)  # to pt
-
-        elif isinstance(image[0], np.ndarray):
-            image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
-            image = numpy_to_pt(image)
-
-        elif isinstance(image[0], torch.Tensor):
-            image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
-
-        return image
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image
-    def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:
-        if not isinstance(image, torch.Tensor) and not isinstance(image, list):
-            image = [image]
-
-        if isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
-
-            image = np.stack(image, axis=0)  # to np
-            image = torch.from_numpy(image.transpose(0, 3, 1, 2))
-        elif isinstance(image[0], np.ndarray):
-            image = np.stack(image, axis=0)  # to np
-            if image.ndim == 5:
-                image = image[0]
-
-            image = torch.from_numpy(image.transpose(0, 3, 1, 2))
-        elif isinstance(image, list) and isinstance(image[0], torch.Tensor):
-            dims = image[0].ndim
-
-            if dims == 3:
-                image = torch.stack(image, dim=0)
-            elif dims == 4:
-                image = torch.concat(image, dim=0)
-            else:
-                raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
-
-        image = image.to(device=device, dtype=self.unet.dtype)
-
-        image = image.repeat_interleave(num_images_per_prompt, dim=0)
-
-        return image
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.prepare_intermediate_images
-    def prepare_intermediate_images(
-        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None
-    ):
-        _, channels, height, width = image.shape
-
-        batch_size = batch_size * num_images_per_prompt
-
-        shape = (batch_size, channels, height, width)
-
-        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."
-            )
-
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        image = image.repeat_interleave(num_images_per_prompt, dim=0)
-        image = self.scheduler.add_noise(image, noise, timestep)
-
-        return image
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image: Union[PIL.Image.Image, np.ndarray, torch.FloatTensor],
-        original_image: Union[
-            PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
-        ] = None,
-        strength: float = 0.8,
-        prompt: Union[str, List[str]] = None,
-        num_inference_steps: int = 50,
-        timesteps: List[int] = None,
-        guidance_scale: float = 4.0,
-        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,
-        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,
-        noise_level: int = 250,
-        clean_caption: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            image (`torch.FloatTensor` or `PIL.Image.Image`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process.
-            original_image (`torch.FloatTensor` or `PIL.Image.Image`):
-                The original image that `image` was varied from.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            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.
-            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.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
-                timesteps are used. Must be in descending order.
-            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.
-            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.IFPipelineOutput`] 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).
-            noise_level (`int`, *optional*, defaults to 250):
-                The amount of noise to add to the upscaled image. Must be in the range `[0, 1000)`
-            clean_caption (`bool`, *optional*, defaults to `True`):
-                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
-                be installed. If the dependencies are not installed, the embeddings will be created from the raw
-                prompt.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.IFPipelineOutput`] 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)
-            or watermarked content, according to the `safety_checker`.
-        """
-        # 1. Check inputs. Raise error if not correct
-        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]
-
-        self.check_inputs(
-            prompt,
-            image,
-            original_image,
-            batch_size,
-            callback_steps,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-
-        # 2. Define call parameters
-
-        # 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
-
-        device = self._execution_device
-
-        # 3. Encode input prompt
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            do_classifier_free_guidance,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            clean_caption=clean_caption,
-        )
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        dtype = prompt_embeds.dtype
-
-        # 4. Prepare timesteps
-        if timesteps is not None:
-            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
-            timesteps = self.scheduler.timesteps
-            num_inference_steps = len(timesteps)
-        else:
-            self.scheduler.set_timesteps(num_inference_steps, device=device)
-            timesteps = self.scheduler.timesteps
-
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
-
-        # 5. prepare original image
-        original_image = self.preprocess_original_image(original_image)
-        original_image = original_image.to(device=device, dtype=dtype)
-
-        # 6. Prepare intermediate images
-        noise_timestep = timesteps[0:1]
-        noise_timestep = noise_timestep.repeat(batch_size * num_images_per_prompt)
-
-        intermediate_images = self.prepare_intermediate_images(
-            original_image,
-            noise_timestep,
-            batch_size,
-            num_images_per_prompt,
-            dtype,
-            device,
-            generator,
-        )
-
-        # 7. Prepare upscaled image and noise level
-        _, _, height, width = original_image.shape
-
-        image = self.preprocess_image(image, num_images_per_prompt, device)
-
-        upscaled = F.interpolate(image, (height, width), mode="bilinear", align_corners=True)
-
-        noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
-        noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
-        upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
-
-        if do_classifier_free_guidance:
-            noise_level = torch.cat([noise_level] * 2)
-
-        # 8. 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)
-
-        # HACK: see comment in `enable_model_cpu_offload`
-        if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
-            self.text_encoder_offload_hook.offload()
-
-        # 9. 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):
-                model_input = torch.cat([intermediate_images, upscaled], dim=1)
-
-                model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
-                model_input = self.scheduler.scale_model_input(model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    class_labels=noise_level,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
-                    noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1] // 2, dim=1)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                    noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-                if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
-                    noise_pred, _ = noise_pred.split(intermediate_images.shape[1], dim=1)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                intermediate_images = self.scheduler.step(
-                    noise_pred, t, intermediate_images, **extra_step_kwargs, return_dict=False
-                )[0]
-
-                # 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, intermediate_images)
-
-        image = intermediate_images
-
-        if output_type == "pil":
-            # 10. Post-processing
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-            # 11. Run safety checker
-            image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
-            # 12. Convert to PIL
-            image = self.numpy_to_pil(image)
-
-            # 13. Apply watermark
-            if self.watermarker is not None:
-                self.watermarker.apply_watermark(image, self.unet.config.sample_size)
-        elif output_type == "pt":
-            nsfw_detected = None
-            watermark_detected = None
-
-            if hasattr(self, "unet_offload_hook") and self.unet_offload_hook is not None:
-                self.unet_offload_hook.offload()
-        else:
-            # 10. Post-processing
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-            # 11. Run safety checker
-            image, nsfw_detected, watermark_detected = 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, nsfw_detected, watermark_detected)
-
-        return IFPipelineOutput(images=image, nsfw_detected=nsfw_detected, watermark_detected=watermark_detected)

spaces/Andy1621/uniformer_image_detection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py

@@ -1,2 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_detection/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py

@@ -1,22 +0,0 @@
-_base_ = './gfl_r50_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
-# multi-scale training
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 480), (1333, 800)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))

spaces/Anonymous-sub/Rerender/gmflow_module/gmflow/backbone.py

@@ -1,117 +0,0 @@
-import torch.nn as nn
-
-from .trident_conv import MultiScaleTridentConv
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d, stride=1, dilation=1,
-                 ):
-        super(ResidualBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
-                               dilation=dilation, padding=dilation, stride=stride, bias=False)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
-                               dilation=dilation, padding=dilation, bias=False)
-        self.relu = nn.ReLU(inplace=True)
-
-        self.norm1 = norm_layer(planes)
-        self.norm2 = norm_layer(planes)
-        if not stride == 1 or in_planes != planes:
-            self.norm3 = norm_layer(planes)
-
-        if stride == 1 and in_planes == planes:
-            self.downsample = None
-        else:
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
-
-    def forward(self, x):
-        y = x
-        y = self.relu(self.norm1(self.conv1(y)))
-        y = self.relu(self.norm2(self.conv2(y)))
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-
-        return self.relu(x + y)
-
-
-class CNNEncoder(nn.Module):
-    def __init__(self, output_dim=128,
-                 norm_layer=nn.InstanceNorm2d,
-                 num_output_scales=1,
-                 **kwargs,
-                 ):
-        super(CNNEncoder, self).__init__()
-        self.num_branch = num_output_scales
-
-        feature_dims = [64, 96, 128]
-
-        self.conv1 = nn.Conv2d(3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False)  # 1/2
-        self.norm1 = norm_layer(feature_dims[0])
-        self.relu1 = nn.ReLU(inplace=True)
-
-        self.in_planes = feature_dims[0]
-        self.layer1 = self._make_layer(feature_dims[0], stride=1, norm_layer=norm_layer)  # 1/2
-        self.layer2 = self._make_layer(feature_dims[1], stride=2, norm_layer=norm_layer)  # 1/4
-
-        # highest resolution 1/4 or 1/8
-        stride = 2 if num_output_scales == 1 else 1
-        self.layer3 = self._make_layer(feature_dims[2], stride=stride,
-                                       norm_layer=norm_layer,
-                                       )  # 1/4 or 1/8
-
-        self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0)
-
-        if self.num_branch > 1:
-            if self.num_branch == 4:
-                strides = (1, 2, 4, 8)
-            elif self.num_branch == 3:
-                strides = (1, 2, 4)
-            elif self.num_branch == 2:
-                strides = (1, 2)
-            else:
-                raise ValueError
-
-            self.trident_conv = MultiScaleTridentConv(output_dim, output_dim,
-                                                      kernel_size=3,
-                                                      strides=strides,
-                                                      paddings=1,
-                                                      num_branch=self.num_branch,
-                                                      )
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
-                if m.weight is not None:
-                    nn.init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d):
-        layer1 = ResidualBlock(self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation)
-        layer2 = ResidualBlock(dim, dim, norm_layer=norm_layer, stride=1, dilation=dilation)
-
-        layers = (layer1, layer2)
-
-        self.in_planes = dim
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.norm1(x)
-        x = self.relu1(x)
-
-        x = self.layer1(x)  # 1/2
-        x = self.layer2(x)  # 1/4
-        x = self.layer3(x)  # 1/8 or 1/4
-
-        x = self.conv2(x)
-
-        if self.num_branch > 1:
-            out = self.trident_conv([x] * self.num_branch)  # high to low res
-        else:
-            out = [x]
-
-        return out

spaces/Armandoliv/document_parser/app.py

@@ -1,202 +0,0 @@
-import os
-import os
-os.system('pip install "detectron2@git+https://github.com/facebookresearch/[email protected]#egg=detectron2"')
-
-import io
-import pandas as pd
-import numpy as np
-import gradio as gr
-
-## for plotting
-import matplotlib.pyplot as plt
-
-## for ocr
-import pdf2image
-import cv2
-import layoutparser as lp
-
-from docx import Document
-from docx.shared import Inches
-
-
-def parse_doc(dic):
-    for k,v in dic.items():
-        if "Title" in k:
-            print('\x1b[1;31m'+ v +'\x1b[0m')
-        elif "Figure" in k:
-            plt.figure(figsize=(10,5))
-            plt.imshow(v)
-            plt.show()
-        else:
-            print(v)
-        print(" ")
-
-
-def to_image(filename):
-  doc = pdf2image.convert_from_path(filename, dpi=350, last_page=1)
-  # Save imgs
-  folder = "doc"
-  if folder not in os.listdir():
-      os.makedirs(folder)
-
-  p = 1
-  for page in doc:
-      image_name = "page_"+str(p)+".jpg"  
-      page.save(os.path.join(folder, image_name), "JPEG")
-      p = p+1
-
-  return doc
-
-
-
-def detect(doc):
-  # General
-  model = lp.Detectron2LayoutModel("lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config",
-                                 extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.8],
-                                 label_map={0:"Text", 1:"Title", 2:"List", 3:"Table", 4:"Figure"})
-  ## turn img into array
-  img = np.asarray(doc[0])
-
-  ## predict
-  detected = model.detect(img)
-
- 
-  return img, detected 
-
-
-# sort detected
-def split_page(img, n, axis):
-    new_detected, start = [], 0
-    for s in range(n):
-        end = len(img[0])/3 * s if axis == "x" else len(img[1])/3
-        section = lp.Interval(start=start, end=end, axis=axis).put_on_canvas(img)
-        filter_detected = detected.filter_by(section, center=True)._blocks
-        new_detected = new_detected + filter_detected
-        start = end
-    return lp.Layout([block.set(id=idx) for idx,block in enumerate(new_detected)])
-
-
-
-def get_detected(img, detected):
-  n_cols,n_rows = 1,1
-
-  ## if single page just sort based on y
-  if (n_cols == 1) and (n_rows == 1):
-      new_detected = detected.sort(key=lambda x: x.coordinates[1])
-      detected = lp.Layout([block.set(id=idx) for idx,block in enumerate(new_detected)])
-      
-  ## if multi columns sort by x,y
-  elif (n_cols > 1) and (n_rows == 1):
-      detected = split_page(img, n_cols, axis="x")
-
-  ## if multi rows sort by y,x
-  elif (n_cols > 1) and (n_rows == 1):
-      detected = split_page(img, n_rows, axis="y")
-      
-  ## if multi columns-rows
-  else:
-      pass
-  
-  return detected
-
-
-def predict_elements(img, detected)->dict:
-  model = lp.TesseractAgent(languages='eng')
-  dic_predicted = {}
-
-  for block in [block for block in detected if block.type in ["Title","Text", "List"]]:
-    ## segmentation
-    segmented = block.pad(left=15, right=15, top=5, bottom=5).crop_image(img)
-    ## extraction
-    extracted = model.detect(segmented)
-    ## save
-    dic_predicted[str(block.id)+"-"+block.type] = extracted.replace('\n',' ').strip()
-
-  for block in [block for block in detected if block.type == "Figure"]:
-      ## segmentation
-      segmented = block.pad(left=15, right=15, top=5, bottom=5).crop_image(img)
-      ## save
-      dic_predicted[str(block.id)+"-"+block.type] = segmented
-
-
-  for block in [block for block in detected if block.type == "Table"]:
-      ## segmentation
-      segmented = block.pad(left=15, right=15, top=5, bottom=5).crop_image(img)
-      ## extraction
-      extracted = model.detect(segmented)
-      ## save
-      dic_predicted[str(block.id)+"-"+block.type] = pd.read_csv( io.StringIO(extracted) )
-
-  
-  return dic_predicted
-
-def gen_doc(dic_predicted:dict):
-  document = Document()
-
-  for k,v in dic_predicted.items():
-
-    if "Figure" in k:
-      cv2.imwrite(f'{k}.jpg', dic_predicted[k])
-      document.add_picture(f'{k}.jpg', width=Inches(3))
-
-    elif "Table" in k:
-      table = document.add_table(rows=v.shape[0], cols=v.shape[1])
-      hdr_cells = table.rows[0].cells
-      for idx, col in enumerate(v.columns):
-        hdr_cells[idx].text = col
-      for c in v.iterrows():
-        
-        for idx, col in enumerate(v.columns):
-          try:
-            if len(c[1][col].strip())>0:
-              row_cells = table.add_row().cells
-              row_cells[idx].text = str(c[1][col]) 
-          except:
-            continue
-    
-    else:
-      document.add_paragraph(str(v))
-
-  document.save('demo.docx')
-
-
-def main_convert(filename):
-  print(filename.name)
-  doc = to_image(filename.name)
-
-  img, detected = detect(doc)
-
-  n_detected = get_detected(img, detected)
-
-  dic_predicted = predict_elements(img, n_detected)
-
-  gen_doc(dic_predicted)
-
-  im_out = lp.draw_box(img, detected, box_width=5, box_alpha=0.2, show_element_type=True)
-  dict_out = {}
-  for k,v in dic_predicted.items():
-    if "figure" not in k.lower():
-      dict_out[k] = dic_predicted[k]
-
-  return  'demo.docx', im_out, dict_out
-  
-  
-inputs = [gr.File(type='file', label="Original PDF File")]
-outputs = [gr.File(label="Converted DOC File"),gr.Image(type="PIL.Image", label="Detected Image"),  gr.JSON()]
-
-title = "A Document AI parser"
-description = "This demo uses AI Models to detect text, titles, tables, figures and lists as well as table cells from an Scanned document.\nBased on the layout it determines reading order and generates an MS-DOC file to Download."
-
-
-io = gr.Interface(fn=main_convert, inputs=inputs, outputs=outputs, title=title, description=description, 
-                  css= """.gr-button-primary { background: -webkit-linear-gradient( 
-                    90deg, #355764 0%, #55a8a1 100% ) !important;     background: #355764;
-                        background: linear-gradient( 
-                    90deg, #355764 0%, #55a8a1 100% ) !important;
-                        background: -moz-linear-gradient( 90deg, #355764 0%, #55a8a1 100% ) !important;
-                        background: -webkit-linear-gradient( 
-                    90deg, #355764 0%, #55a8a1 100% ) !important;
-                    color:white !important}"""
-                  )
-                  
-io.launch()

spaces/Arnx/MusicGenXvAKN/audiocraft/data/audio_dataset.py

@@ -1,525 +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.
-
-import argparse
-import copy
-from concurrent.futures import ThreadPoolExecutor, Future
-from dataclasses import dataclass, fields
-from contextlib import ExitStack
-import gzip
-import json
-import logging
-import os
-from pathlib import Path
-import random
-import sys
-import typing as tp
-
-import torch
-import torch.nn.functional as F
-
-from .audio import audio_read, audio_info
-from .audio_utils import convert_audio
-from .zip import PathInZip
-
-try:
-    import dora
-except ImportError:
-    dora = None  # type: ignore
-
-
-@dataclass(order=True)
-class BaseInfo:
-
-    @classmethod
-    def _dict2fields(cls, dictionary: dict):
-        return {
-            field.name: dictionary[field.name]
-            for field in fields(cls) if field.name in dictionary
-        }
-
-    @classmethod
-    def from_dict(cls, dictionary: dict):
-        _dictionary = cls._dict2fields(dictionary)
-        return cls(**_dictionary)
-
-    def to_dict(self):
-        return {
-            field.name: self.__getattribute__(field.name)
-            for field in fields(self)
-            }
-
-
-@dataclass(order=True)
-class AudioMeta(BaseInfo):
-    path: str
-    duration: float
-    sample_rate: int
-    amplitude: tp.Optional[float] = None
-    weight: tp.Optional[float] = None
-    # info_path is used to load additional information about the audio file that is stored in zip files.
-    info_path: tp.Optional[PathInZip] = None
-
-    @classmethod
-    def from_dict(cls, dictionary: dict):
-        base = cls._dict2fields(dictionary)
-        if 'info_path' in base and base['info_path'] is not None:
-            base['info_path'] = PathInZip(base['info_path'])
-        return cls(**base)
-
-    def to_dict(self):
-        d = super().to_dict()
-        if d['info_path'] is not None:
-            d['info_path'] = str(d['info_path'])
-        return d
-
-
-@dataclass(order=True)
-class SegmentInfo(BaseInfo):
-    meta: AudioMeta
-    seek_time: float
-    n_frames: int  # actual number of frames without padding
-    total_frames: int  # total number of frames, padding included
-    sample_rate: int  # actual sample rate
-
-
-DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a']
-
-logger = logging.getLogger(__name__)
-
-
-def _get_audio_meta(file_path: str, minimal: bool = True) -> AudioMeta:
-    """AudioMeta from a path to an audio file.
-
-    Args:
-        file_path (str): Resolved path of valid audio file.
-        minimal (bool): Whether to only load the minimal set of metadata (takes longer if not).
-    Returns:
-        AudioMeta: Audio file path and its metadata.
-    """
-    info = audio_info(file_path)
-    amplitude: tp.Optional[float] = None
-    if not minimal:
-        wav, sr = audio_read(file_path)
-        amplitude = wav.abs().max().item()
-    return AudioMeta(file_path, info.duration, info.sample_rate, amplitude)
-
-
-def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta:
-    """If Dora is available as a dependency, try to resolve potential relative paths
-    in list of AudioMeta. This method is expected to be used when loading meta from file.
-
-    Args:
-        m (AudioMeta): Audio meta to resolve.
-        fast (bool): If True, uses a really fast check for determining if a file is already absolute or not.
-            Only valid on Linux/Mac.
-    Returns:
-        AudioMeta: Audio meta with resolved path.
-    """
-    def is_abs(m):
-        if fast:
-            return str(m)[0] == '/'
-        else:
-            os.path.isabs(str(m))
-
-    if not dora:
-        return m
-
-    if not is_abs(m.path):
-        m.path = dora.git_save.to_absolute_path(m.path)
-    if m.info_path is not None and not is_abs(m.info_path.zip_path):
-        m.info_path.zip_path = dora.git_save.to_absolute_path(m.path)
-    return m
-
-
-def find_audio_files(path: tp.Union[Path, str],
-                     exts: tp.List[str] = DEFAULT_EXTS,
-                     resolve: bool = True,
-                     minimal: bool = True,
-                     progress: bool = False,
-                     workers: int = 0) -> tp.List[AudioMeta]:
-    """Build a list of AudioMeta from a given path,
-    collecting relevant audio files and fetching meta info.
-
-    Args:
-        path (str or Path): Path to folder containing audio files.
-        exts (list of str): List of file extensions to consider for audio files.
-        minimal (bool): Whether to only load the minimal set of metadata (takes longer if not).
-        progress (bool): Whether to log progress on audio files collection.
-        workers (int): number of parallel workers, if 0, use only the current thread.
-    Returns:
-        List[AudioMeta]: List of audio file path and its metadata.
-    """
-    audio_files = []
-    futures: tp.List[Future] = []
-    pool: tp.Optional[ThreadPoolExecutor] = None
-    with ExitStack() as stack:
-        if workers > 0:
-            pool = ThreadPoolExecutor(workers)
-            stack.enter_context(pool)
-
-        if progress:
-            print("Finding audio files...")
-        for root, folders, files in os.walk(path, followlinks=True):
-            for file in files:
-                full_path = Path(root) / file
-                if full_path.suffix.lower() in exts:
-                    audio_files.append(full_path)
-                    if pool is not None:
-                        futures.append(pool.submit(_get_audio_meta, str(audio_files[-1]), minimal))
-                    if progress:
-                        print(format(len(audio_files), " 8d"), end='\r', file=sys.stderr)
-
-        if progress:
-            print("Getting audio metadata...")
-        meta: tp.List[AudioMeta] = []
-        for idx, file_path in enumerate(audio_files):
-            try:
-                if pool is None:
-                    m = _get_audio_meta(str(file_path), minimal)
-                else:
-                    m = futures[idx].result()
-                if resolve:
-                    m = _resolve_audio_meta(m)
-            except Exception as err:
-                print("Error with", str(file_path), err, file=sys.stderr)
-                continue
-            meta.append(m)
-            if progress:
-                print(format((1 + idx) / len(audio_files), " 3.1%"), end='\r', file=sys.stderr)
-    meta.sort()
-    return meta
-
-
-def load_audio_meta(path: tp.Union[str, Path],
-                    resolve: bool = True, fast: bool = True) -> tp.List[AudioMeta]:
-    """Load list of AudioMeta from an optionally compressed json file.
-
-    Args:
-        path (str or Path): Path to JSON file.
-        resolve (bool): Whether to resolve the path from AudioMeta (default=True).
-        fast (bool): activates some tricks to make things faster.
-    Returns:
-        List[AudioMeta]: List of audio file path and its total duration.
-    """
-    open_fn = gzip.open if str(path).lower().endswith('.gz') else open
-    with open_fn(path, 'rb') as fp:  # type: ignore
-        lines = fp.readlines()
-    meta = []
-    for line in lines:
-        d = json.loads(line)
-        m = AudioMeta.from_dict(d)
-        if resolve:
-            m = _resolve_audio_meta(m, fast=fast)
-        meta.append(m)
-    return meta
-
-
-def save_audio_meta(path: tp.Union[str, Path], meta: tp.List[AudioMeta]):
-    """Save the audio metadata to the file pointer as json.
-
-    Args:
-        path (str or Path): Path to JSON file.
-        metadata (list of BaseAudioMeta): List of audio meta to save.
-    """
-    Path(path).parent.mkdir(exist_ok=True, parents=True)
-    open_fn = gzip.open if str(path).lower().endswith('.gz') else open
-    with open_fn(path, 'wb') as fp:  # type: ignore
-        for m in meta:
-            json_str = json.dumps(m.to_dict()) + '\n'
-            json_bytes = json_str.encode('utf-8')
-            fp.write(json_bytes)
-
-
-class AudioDataset:
-    """Base audio dataset.
-
-    The dataset takes a list of AudioMeta and create a dataset composed of segments of audio
-    and potentially additional information, by creating random segments from the list of audio
-    files referenced in the metadata and applying minimal data pre-processing such as resampling,
-    mixing of channels, padding, etc.
-
-    If no segment_duration value is provided, the AudioDataset will return the full wav for each
-    audio file. Otherwise, it will randomly sample audio files and create a segment of the specified
-    duration, applying padding if required.
-
-    By default, only the torch Tensor corresponding to the waveform is returned. Setting return_info=True
-    allows to return a tuple containing the torch Tensor and additional metadata on the segment and the
-    original audio meta.
-
-    Args:
-        meta (tp.List[AudioMeta]): List of audio files metadata.
-        segment_duration (float): Optional segment duration of audio to load.
-            If not specified, the dataset will load the full audio segment from the file.
-        shuffle (bool): Set to `True` to have the data reshuffled at every epoch.
-        sample_rate (int): Target sample rate of the loaded audio samples.
-        channels (int): Target number of channels of the loaded audio samples.
-        sample_on_duration (bool): Set to `True` to sample segments with probability
-            dependent on audio file duration. This is only used if `segment_duration` is provided.
-        sample_on_weight (bool): Set to `True` to sample segments using the `weight` entry of
-            `AudioMeta`. If `sample_on_duration` is also True, the actual weight will be the product
-            of the file duration and file weight. This is only used if `segment_duration` is provided.
-        min_segment_ratio (float): Minimum segment ratio to use when the audio file
-            is shorter than the desired segment.
-        max_read_retry (int): Maximum number of retries to sample an audio segment from the dataset.
-        return_info (bool): Whether to return the wav only or return wav along with segment info and metadata.
-        min_audio_duration (tp.Optional[float], optional): Minimum audio file duration, in seconds, if provided
-            audio shorter than this will be filtered out.
-        max_audio_duration (tp.Optional[float], optional): Maximal audio file duration in seconds, if provided
-            audio longer than this will be filtered out.
-    """
-    def __init__(self,
-                 meta: tp.List[AudioMeta],
-                 segment_duration: tp.Optional[float] = None,
-                 shuffle: bool = True,
-                 num_samples: int = 10_000,
-                 sample_rate: int = 48_000,
-                 channels: int = 2,
-                 pad: bool = True,
-                 sample_on_duration: bool = True,
-                 sample_on_weight: bool = True,
-                 min_segment_ratio: float = 0.5,
-                 max_read_retry: int = 10,
-                 return_info: bool = False,
-                 min_audio_duration: tp.Optional[float] = None,
-                 max_audio_duration: tp.Optional[float] = None
-                 ):
-        assert len(meta) > 0, 'No audio meta provided to AudioDataset. Please check loading of audio meta.'
-        assert segment_duration is None or segment_duration > 0
-        assert segment_duration is None or min_segment_ratio >= 0
-        logging.debug(f'sample_on_duration: {sample_on_duration}')
-        logging.debug(f'sample_on_weight: {sample_on_weight}')
-        logging.debug(f'pad: {pad}')
-        logging.debug(f'min_segment_ratio: {min_segment_ratio}')
-
-        self.segment_duration = segment_duration
-        self.min_segment_ratio = min_segment_ratio
-        self.max_audio_duration = max_audio_duration
-        self.min_audio_duration = min_audio_duration
-        if self.min_audio_duration is not None and self.max_audio_duration is not None:
-            assert self.min_audio_duration <= self.max_audio_duration
-        self.meta: tp.List[AudioMeta] = self._filter_duration(meta)
-        assert len(self.meta)  # Fail fast if all data has been filtered.
-        self.total_duration = sum(d.duration for d in self.meta)
-
-        if segment_duration is None:
-            num_samples = len(self.meta)
-        self.num_samples = num_samples
-        self.shuffle = shuffle
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.pad = pad
-        self.sample_on_weight = sample_on_weight
-        self.sample_on_duration = sample_on_duration
-        self.sampling_probabilities = self._get_sampling_probabilities()
-        self.max_read_retry = max_read_retry
-        self.return_info = return_info
-
-    def __len__(self):
-        return self.num_samples
-
-    def _get_sampling_probabilities(self, normalized: bool = True):
-        """Return the sampling probabilities for each file inside `self.meta`.
-        """
-        scores: tp.List[float] = []
-        for file_meta in self.meta:
-            score = 1.
-            if self.sample_on_weight and file_meta.weight is not None:
-                score *= file_meta.weight
-            if self.sample_on_duration:
-                score *= file_meta.duration
-            scores.append(score)
-        probabilities = torch.tensor(scores)
-        if normalized:
-            probabilities /= probabilities.sum()
-        return probabilities
-
-    def sample_file(self, rng: torch.Generator) -> AudioMeta:
-        """Sample a given file from `self.meta`. Can be overriden in subclasses.
-        This is only called if `segment_duration` is not None.
-
-        You must use the provided random number generator `rng` for reproducibility.
-        """
-        if not self.sample_on_weight and not self.sample_on_duration:
-            file_index = int(torch.randint(len(self.sampling_probabilities), (1,), generator=rng).item())
-        else:
-            file_index = int(torch.multinomial(self.sampling_probabilities, 1, generator=rng).item())
-
-        return self.meta[file_index]
-
-    def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentInfo]]:
-        if self.segment_duration is None:
-            file_meta = self.meta[index]
-            out, sr = audio_read(file_meta.path)
-            out = convert_audio(out, sr, self.sample_rate, self.channels)
-            n_frames = out.shape[-1]
-            segment_info = SegmentInfo(file_meta, seek_time=0., n_frames=n_frames, total_frames=n_frames,
-                                       sample_rate=self.sample_rate)
-        else:
-            rng = torch.Generator()
-            if self.shuffle:
-                # We use index, plus extra randomness
-                rng.manual_seed(index + self.num_samples * random.randint(0, 2**24))
-            else:
-                # We only use index
-                rng.manual_seed(index)
-
-            for retry in range(self.max_read_retry):
-                file_meta = self.sample_file(rng)
-                # We add some variance in the file position even if audio file is smaller than segment
-                # without ending up with empty segments
-                max_seek = max(0, file_meta.duration - self.segment_duration * self.min_segment_ratio)
-                seek_time = torch.rand(1, generator=rng).item() * max_seek
-                try:
-                    out, sr = audio_read(file_meta.path, seek_time, self.segment_duration, pad=False)
-                    out = convert_audio(out, sr, self.sample_rate, self.channels)
-                    n_frames = out.shape[-1]
-                    target_frames = int(self.segment_duration * self.sample_rate)
-                    if self.pad:
-                        out = F.pad(out, (0, target_frames - n_frames))
-                    segment_info = SegmentInfo(file_meta, seek_time, n_frames=n_frames, total_frames=target_frames,
-                                               sample_rate=self.sample_rate)
-                except Exception as exc:
-                    logger.warning("Error opening file %s: %r", file_meta.path, exc)
-                    if retry == self.max_read_retry - 1:
-                        raise
-                else:
-                    break
-
-        if self.return_info:
-            # Returns the wav and additional information on the wave segment
-            return out, segment_info
-        else:
-            return out
-
-    def collater(self, samples):
-        """The collater function has to be provided to the dataloader
-        if AudioDataset has return_info=True in order to properly collate
-        the samples of a batch.
-        """
-        if self.segment_duration is None and len(samples) > 1:
-            assert self.pad, "Must allow padding when batching examples of different durations."
-
-        # In this case the audio reaching the collater is of variable length as segment_duration=None.
-        to_pad = self.segment_duration is None and self.pad
-        if to_pad:
-            max_len = max([wav.shape[-1] for wav, _ in samples])
-
-            def _pad_wav(wav):
-                return F.pad(wav, (0, max_len - wav.shape[-1]))
-
-        if self.return_info:
-            if len(samples) > 0:
-                assert len(samples[0]) == 2
-                assert isinstance(samples[0][0], torch.Tensor)
-                assert isinstance(samples[0][1], SegmentInfo)
-
-            wavs = [wav for wav, _ in samples]
-            segment_infos = [copy.deepcopy(info) for _, info in samples]
-
-            if to_pad:
-                # Each wav could be of a different duration as they are not segmented.
-                for i in range(len(samples)):
-                    # Determines the total legth of the signal with padding, so we update here as we pad.
-                    segment_infos[i].total_frames = max_len
-                    wavs[i] = _pad_wav(wavs[i])
-
-            wav = torch.stack(wavs)
-            return wav, segment_infos
-        else:
-            assert isinstance(samples[0], torch.Tensor)
-            if to_pad:
-                samples = [_pad_wav(s) for s in samples]
-            return torch.stack(samples)
-
-    def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]:
-        """Filters out audio files with short durations.
-        Removes from meta files that have durations that will not allow to samples examples from them.
-        """
-        orig_len = len(meta)
-
-        # Filter data that is too short.
-        if self.min_audio_duration is not None:
-            meta = [m for m in meta if m.duration >= self.min_audio_duration]
-
-        # Filter data that is too long.
-        if self.max_audio_duration is not None:
-            meta = [m for m in meta if m.duration <= self.max_audio_duration]
-
-        filtered_len = len(meta)
-        removed_percentage = 100*(1-float(filtered_len)/orig_len)
-        msg = 'Removed %.2f percent of the data because it was too short or too long.' % removed_percentage
-        if removed_percentage < 10:
-            logging.debug(msg)
-        else:
-            logging.warning(msg)
-        return meta
-
-    @classmethod
-    def from_meta(cls, root: tp.Union[str, Path], **kwargs):
-        """Instantiate AudioDataset from a path to a directory containing a manifest as a jsonl file.
-
-        Args:
-            root (str or Path): Path to root folder containing audio files.
-            kwargs: Additional keyword arguments for the AudioDataset.
-        """
-        root = Path(root)
-        if root.is_dir():
-            if (root / 'data.jsonl').exists():
-                root = root / 'data.jsonl'
-            elif (root / 'data.jsonl.gz').exists():
-                root = root / 'data.jsonl.gz'
-            else:
-                raise ValueError("Don't know where to read metadata from in the dir. "
-                                 "Expecting either a data.jsonl or data.jsonl.gz file but none found.")
-        meta = load_audio_meta(root)
-        return cls(meta, **kwargs)
-
-    @classmethod
-    def from_path(cls, root: tp.Union[str, Path], minimal_meta: bool = True,
-                  exts: tp.List[str] = DEFAULT_EXTS, **kwargs):
-        """Instantiate AudioDataset from a path containing (possibly nested) audio files.
-
-        Args:
-            root (str or Path): Path to root folder containing audio files.
-            minimal_meta (bool): Whether to only load minimal metadata or not.
-            exts (list of str): Extensions for audio files.
-            kwargs: Additional keyword arguments for the AudioDataset.
-        """
-        root = Path(root)
-        if root.is_file():
-            meta = load_audio_meta(root, resolve=True)
-        else:
-            meta = find_audio_files(root, exts, minimal=minimal_meta, resolve=True)
-        return cls(meta, **kwargs)
-
-
-def main():
-    logging.basicConfig(stream=sys.stderr, level=logging.INFO)
-    parser = argparse.ArgumentParser(
-        prog='audio_dataset',
-        description='Generate .jsonl files by scanning a folder.')
-    parser.add_argument('root', help='Root folder with all the audio files')
-    parser.add_argument('output_meta_file',
-                        help='Output file to store the metadata, ')
-    parser.add_argument('--complete',
-                        action='store_false', dest='minimal', default=True,
-                        help='Retrieve all metadata, even the one that are expansive '
-                             'to compute (e.g. normalization).')
-    parser.add_argument('--resolve',
-                        action='store_true', default=False,
-                        help='Resolve the paths to be absolute and with no symlinks.')
-    parser.add_argument('--workers',
-                        default=10, type=int,
-                        help='Number of workers.')
-    args = parser.parse_args()
-    meta = find_audio_files(args.root, DEFAULT_EXTS, progress=True,
-                            resolve=args.resolve, minimal=args.minimal, workers=args.workers)
-    save_audio_meta(args.output_meta_file, meta)
-
-
-if __name__ == '__main__':
-    main()

spaces/Awiny/Image2Paragraph/models/segment_models/configs/ade20k_id2label.py

@@ -1,153 +0,0 @@
-CONFIG = {
-  "id2label": {
-    "0": "wall",
-    "1": "building",
-    "2": "sky",
-    "3": "floor",
-    "4": "tree",
-    "5": "ceiling",
-    "6": "road",
-    "7": "bed ",
-    "8": "windowpane",
-    "9": "grass",
-    "10": "cabinet",
-    "11": "sidewalk",
-    "12": "person",
-    "13": "earth",
-    "14": "door",
-    "15": "table",
-    "16": "mountain",
-    "17": "plant",
-    "18": "curtain",
-    "19": "chair",
-    "20": "car",
-    "21": "water",
-    "22": "painting",
-    "23": "sofa",
-    "24": "shelf",
-    "25": "house",
-    "26": "sea",
-    "27": "mirror",
-    "28": "rug",
-    "29": "field",
-    "30": "armchair",
-    "31": "seat",
-    "32": "fence",
-    "33": "desk",
-    "34": "rock",
-    "35": "wardrobe",
-    "36": "lamp",
-    "37": "bathtub",
-    "38": "railing",
-    "39": "cushion",
-    "40": "base",
-    "41": "box",
-    "42": "column",
-    "43": "signboard",
-    "44": "chest of drawers",
-    "45": "counter",
-    "46": "sand",
-    "47": "sink",
-    "48": "skyscraper",
-    "49": "fireplace",
-    "50": "refrigerator",
-    "51": "grandstand",
-    "52": "path",
-    "53": "stairs",
-    "54": "runway",
-    "55": "case",
-    "56": "pool table",
-    "57": "pillow",
-    "58": "screen door",
-    "59": "stairway",
-    "60": "river",
-    "61": "bridge",
-    "62": "bookcase",
-    "63": "blind",
-    "64": "coffee table",
-    "65": "toilet",
-    "66": "flower",
-    "67": "book",
-    "68": "hill",
-    "69": "bench",
-    "70": "countertop",
-    "71": "stove",
-    "72": "palm",
-    "73": "kitchen island",
-    "74": "computer",
-    "75": "swivel chair",
-    "76": "boat",
-    "77": "bar",
-    "78": "arcade machine",
-    "79": "hovel",
-    "80": "bus",
-    "81": "towel",
-    "82": "light",
-    "83": "truck",
-    "84": "tower",
-    "85": "chandelier",
-    "86": "awning",
-    "87": "streetlight",
-    "88": "booth",
-    "89": "television receiver",
-    "90": "airplane",
-    "91": "dirt track",
-    "92": "apparel",
-    "93": "pole",
-    "94": "land",
-    "95": "bannister",
-    "96": "escalator",
-    "97": "ottoman",
-    "98": "bottle",
-    "99": "buffet",
-    "100": "poster",
-    "101": "stage",
-    "102": "van",
-    "103": "ship",
-    "104": "fountain",
-    "105": "conveyer belt",
-    "106": "canopy",
-    "107": "washer",
-    "108": "plaything",
-    "109": "swimming pool",
-    "110": "stool",
-    "111": "barrel",
-    "112": "basket",
-    "113": "waterfall",
-    "114": "tent",
-    "115": "bag",
-    "116": "minibike",
-    "117": "cradle",
-    "118": "oven",
-    "119": "ball",
-    "120": "food",
-    "121": "step",
-    "122": "tank",
-    "123": "trade name",
-    "124": "microwave",
-    "125": "pot",
-    "126": "animal",
-    "127": "bicycle",
-    "128": "lake",
-    "129": "dishwasher",
-    "130": "screen",
-    "131": "blanket",
-    "132": "sculpture",
-    "133": "hood",
-    "134": "sconce",
-    "135": "vase",
-    "136": "traffic light",
-    "137": "tray",
-    "138": "ashcan",
-    "139": "fan",
-    "140": "pier",
-    "141": "crt screen",
-    "142": "plate",
-    "143": "monitor",
-    "144": "bulletin board",
-    "145": "shower",
-    "146": "radiator",
-    "147": "glass",
-    "148": "clock",
-    "149": "flag"}
-}

spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/nets_33966KB.py

@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_33966KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 16)
-        self.stg1_high_band_net = BaseASPPNet(2, 16)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(8, 16)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(16, 32)
-
-        self.out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/Benson/text-generation/Examples/Bitcoin Bit Generador De Bitcoin Apk.md

@@ -1,63 +0,0 @@
-
-<h1>Cómo descargar Minecraft 1.13.1 APK gratis</h1>
-<p>Minecraft es uno de los juegos más populares y creativos del mundo. Te permite crear tu propio mundo virtual con bloques, explorar diferentes biomas, luchar contra monstruos e interactuar con otros jugadores. Ya sea que quieras construir un castillo, una nave espacial o una obra maestra de arte de píxeles, Minecraft te permite liberar tu imaginación y divertirte. </p>
-<p>Si desea disfrutar de las últimas características y actualizaciones de Minecraft, es necesario descargar la versión más reciente del juego. En este artículo, le mostraremos cómo descargar Minecraft 1.13.1 APK gratis en su dispositivo Android. Esta versión incluye algunas adiciones emocionantes como zorros, vacas pardas, bloques de estructura y rosas marchitas. </p>
-<h2>bitcoin bit generador de bitcoin apk</h2><br /><p><b><b>DOWNLOAD</b> &#10031;&#10031;&#10031; <a href="https://bltlly.com/2v6L8R">https://bltlly.com/2v6L8R</a></b></p><br /><br />
- <h2>¿Qué hay de nuevo en Minecraft 1.13.1? </h2>
-<p>Minecraft 1.13.1 es la última actualización de la edición Bedrock del juego, que es compatible con dispositivos Android. Fue lanzado el 2 de octubre de 2021, y trae algunas mejoras en el rendimiento y la estabilidad, así como algunas nuevas características y elementos. Estos son algunos de los aspectos más destacados de esta actualización:</p>
- <h3>Zorros</h3>
-<p>Los zorros son animales lindos y peludos que se pueden encontrar en la taiga, árboles gigantes y biomas de nieve. Son cazadores nocturnos que se alimentan de pollos, conejos, peces y bayas. También son muy rápidos y pueden saltar sobre cercas y paredes. </p>
-<p>Si quieres domar a un zorro, necesitas encontrar dos zorros adultos que tengan bayas dulces en la boca. Puedes usar una guía para unirlos y criarlos con bayas más dulces. El zorro bebé que nace confiará en ti y te seguirá. También puedes darle una etiqueta o un collar para que sea más leal. </p>
- <h3>Vacas pardas</h3>
-<p>Las vacas pardas son una nueva variante de vacas de hongos que se puede obtener por un evento raro. Parecen vacas normales pero con hongos marrones en la espalda. Pueden proporcionar estofado de hongos o estofado sospechoso cuando se cortan o ordeñan con un tazón. </p>
-
- <h3>Bloques de estructura</h3>
-<p>Los bloques de estructura son bloques especiales que se pueden usar para crear y copiar estructuras en el juego. Son útiles para los creadores de mapas y constructores que quieren ahorrar tiempo y recursos. Solo se pueden obtener en modo creativo mediante el comando /give @s structure_block. </p>
-<p>Para usar un bloque de estructura, debe colocarlo en el suelo y abrir su interfaz haciendo clic derecho sobre él . Hay cuatro modos de bloques de estructura: Guardar, Cargar, Esquina y Datos. Puede usar el modo Guardar para guardar una estructura en un archivo, el modo Cargar para cargar una estructura desde un archivo, el modo Esquina para definir los límites de una estructura y el modo Datos para agregar datos personalizados a una estructura. </p>
- <h3>Rosa marchita</h3>
-<p>La rosa marchita es un nuevo tipo de flor que puede infligir el efecto marchita en cualquier entidad viviente que la toque. Tiene un color negro y un patrón calavera en sus pétalos. Se puede utilizar para elaborar tinte negro o estofado sospechoso. </p>
-<p>Para obtener una rosa marchita, necesitas atraer a un jefe marchito a un bioma de flores y hacer que mate a una multitud. Hay un 100% de probabilidades de que la turba se marchite al morir. También puede utilizar un dispensador con tijeras para recoger la flor sin hacerse daño. </p>
-<p></p>
- <h2>Cómo descargar Minecraft 1.13.1 APK gratis? </h2>
-<p>Ahora que sabes lo que hay de nuevo en Minecraft 1.13.1, es posible que se pregunte cómo descargarlo de forma gratuita en su dispositivo Android. Bueno, no es tan difícil como podrías pensar. Sigue estos sencillos pasos y jugarás en poco tiempo:</p>
- <h3>Paso 1: Compruebe la compatibilidad de su dispositivo y el espacio de almacenamiento</h3>
-<p>Antes de descargar nada, debe asegurarse de que su dispositivo cumple con los requisitos mínimos para ejecutar el juego sin problemas. Según el sitio web oficial, necesita al menos:</p>
-<ul>
-<li>Un dispositivo Android con la versión 4.2 o superior</li>
-<li>Un procesador con arquitectura ARMv7 o x86</li>
-<li>Al menos 1 GB de RAM</li>
-<li>Al menos 300 MB de espacio de almacenamiento libre</li>
-</ul>
-
- <h3>Paso 2: Elija una fuente confiable para descargar el archivo APK</h3>
-<p>Un archivo APK es un paquete de aplicaciones de Android que contiene todos los archivos y datos necesarios para instalar una aplicación en su dispositivo. Puede descargar archivos APK de varias fuentes en Internet, pero no todos ellos son seguros y confiables. Algunos de ellos pueden contener virus, malware o anuncios no deseados que pueden dañar tu dispositivo o comprometer tu privacidad. </p>
-<p>Para evitar estos riesgos, debe elegir una fuente confiable que ofrece archivos APK verificados y seguros. Una de las mejores fuentes que recomendamos es APKPure, que es un sitio web popular y de buena reputación que proporciona archivos APK gratuitos y actualizados para varias aplicaciones y juegos. Puede acceder a su sitio web desde cualquier navegador de su dispositivo o descargar su aplicación para facilitar el acceso. </p>
- <h3>Paso 3: Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
-<p>Por defecto, tu dispositivo solo te permite instalar aplicaciones desde Google Play Store, que es la tienda de aplicaciones oficial para dispositivos Android. Sin embargo, si desea instalar un archivo APK desde otra fuente, debe habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Play Store.</p>
-<p>Para habilitar fuentes desconocidas, vaya a la configuración del dispositivo y busque la opción de seguridad o privacidad. Entonces, encontrar la opción que dice fuentes desconocidas o permitir la instalación de aplicaciones de fuentes desconocidas y cambiarlo por. Es posible que vea un mensaje de advertencia que le informa sobre los riesgos potenciales de instalar aplicaciones desde fuentes desconocidas. Pulse Aceptar o Continuar. </p>
- <h3>Paso 4: Instalar el archivo APK y lanzar el juego</h3>
-<p>Ahora que ha habilitado fuentes desconocidas, puede instalar el archivo APK en su dispositivo. Para hacer esto, vaya al sitio web o aplicación donde descargó el archivo APK y toque en él. Puede ver una ventana emergente que le pregunta si desea instalar esta aplicación. Pulse Instalar y espere a que finalice el proceso de instalación. </p>
-
-<p>Felicidades! Usted ha descargado e instalado con éxito Minecraft 1.13.1 APK gratis en su dispositivo Android. Ahora puedes disfrutar de este increíble juego con todas sus nuevas características y actualizaciones. </p>
- <h2>Cómo jugar Minecraft 1.13.1? </h2>
-<p>Si eres nuevo en Minecraft o necesitas algunas actualizaciones sobre cómo jugarlo, aquí hay algunos consejos e instrucciones básicas sobre cómo jugar Minecraft 1.13.1:</p>
- <h3>Elige un modo de juego: Supervivencia, Creativo, o Aventura</h3>
-<p>Minecraft tiene tres modos de juego principales que ofrecen diferentes experiencias y desafíos. Puedes elegir el modo de juego que se adapte a tu preferencia y estilo de juego. </p>
-<ul>
-<li>Modo de supervivencia: En este modo, tienes que sobrevivir en un mundo generado aleatoriamente con recursos y salud limitados. Tienes que reunir materiales, herramientas y armas, construir refugios y defenderte de los enemigos. También tienes que lidiar con el hambre, la sed y los peligros ambientales. Puedes ajustar el nivel de dificultad de pacífico a duro, o jugar en el modo hardcore donde solo tienes una vida. </li>
-<li>Modo creativo: En este modo, tienes recursos y salud ilimitados, y puedes volar alrededor del mundo. Puedes crear lo que quieras sin restricciones ni peligros. También puede usar comandos y trucos para modificar el mundo y generar elementos y entidades. Este modo es ideal para construir, experimentar y explorar. </li>
-<li>Modo aventura: En este modo, puede jugar mapas personalizados y escenarios creados por otros jugadores o usted mismo. Tienes que seguir las reglas y objetivos establecidos por el creador del mapa, como resolver puzzles, completar misiones o luchar contra jefes. También puedes usar bloques de comandos y paquetes de datos para agregar características y mecánicas personalizadas al juego. </li>
-</ul>
- <h3>Crear o unirse a un mundo: Un jugador o multijugador</h3>
-
-<p>Para crear un mundo, necesitas elegir un nombre para tu mundo, seleccionar un modo de juego y personalizar algunas opciones como la semilla, el tipo de mundo, los trucos y el cofre de bonos. También puedes usar complementos o paquetes de recursos para cambiar la apariencia y el comportamiento del juego. </p>
-<p>Para unirte a un mundo, necesitas encontrar un servidor que aloje el mundo en el que quieres jugar. Puede unirse a un servidor público al que cualquiera puede acceder, o a un servidor privado que requiere una invitación o una contraseña. También puedes unirte a un reino que es un servicio basado en suscripción que te permite crear y unir mundos que siempre están en línea. </p>
- <h3>Explorar, construir y crear: Utilice su imaginación y habilidades</h3>
-<p>Una vez que estás en un mundo, puedes empezar a jugar al juego explorando, construyendo y creando. Puedes moverte alrededor del mundo caminando, corriendo, saltando, nadando, volando o montando vehículos o animales. Puedes interactuar con el mundo rompiendo y colocando bloques, usando objetos y herramientas, activando interruptores y palancas, comerciando con aldeanos y luchando contra enemigos. </p>
-<p>Puedes construir lo que quieras usando bloques de diferentes materiales, formas, colores y propiedades. También puede utilizar circuitos redstone para crear mecanismos complejos como puertas, trampas, ascensores y máquinas. También puede usar comandos y funciones para crear estructuras y efectos personalizados. </p>
-<p>Puede crear varios elementos y herramientas mediante el uso de una tabla de elaboración o una cuadrícula de inventario. Es necesario organizar los materiales en patrones específicos para crear diferentes productos como armas, armaduras, alimentos de donde lo descargó e instalarlo sobre el existente. Es posible que necesite desinstalar la versión anterior primero si encuentra algún problema. </li>
-<li><b>Q: ¿Cuáles son algunos otros juegos como Minecraft que puedo jugar en mi dispositivo Android? </b></li>
-<li>A: Algunos otros juegos como Minecraft que se puede jugar en su dispositivo Android son Terraria, Roblox, Stardew Valley, y Survivalcraft.</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Bowmasters Mod Apk Gamedva.md

@@ -1,116 +0,0 @@
-
-<h1>El increíble Spider-Man 2 APK: Una revisión</h1>
-<p>Si usted es un fan de Spider-Man y sus increíbles aventuras, es posible que desee echa un vistazo a The Amazing Spider-Man 2 APK, un juego que le permite convertirse en el web-slinger sí mismo y salvar a Nueva York de una juerga de crimen en toda la ciudad. En este artículo, revisaremos este juego y te diremos por qué deberías descargarlo, qué características ofrece, cómo instalarlo en tu dispositivo Android y algunos consejos y trucos para disfrutarlo más. ¡Vamos a empezar! </p>
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- <h2>Introducción</h2>
-<h3> ¿Qué es el increíble Spider-Man 2 APK? </h3>
-<p>El increíble Spider-Man 2 APK es un juego para Android que se basa en la película de Marvel del mismo nombre. Es desarrollado por Gameloft, una compañía líder en la industria de juegos móviles. El juego es una aventura llena de acción y mundo abierto que te permite columpiarte, disparar en la web, escalar paredes y luchar como Spider-Man en un entorno 3D realista. Puedes explorar seis distritos detallados de Manhattan, desde Times Square hasta Central Park, y enfrentarte a villanos famosos como Venom, el Duende Verde, Electro y Kraven el Cazador. También puedes desbloquear diferentes trajes de Spider-Man, como Symbiote Spider-Man, Iron Spider y Ultimate Comics Spider-Man, cada uno con sus propias habilidades y bonificaciones. </p>
- <h3>¿Por qué debería descargarlo? </h3>
-<p>Hay muchas razones por las que debe descargar The Amazing Spider-Man 2 APK en su dispositivo Android. Estos son algunos de ellos:</p>
-<ul>
-<li>Es un juego divertido y emocionante que te mantendrá entretenido durante horas. </li>
-<li> Tiene gráficos y animaciones de alta calidad que te harán sentir como si estuvieras en la película. </li>
-<li> Tiene una historia apasionante que se expande en la trama de la película e introduce nuevos personajes y escenarios. </li>
-<li> Tiene un juego desafiante que requiere habilidad, estrategia y reflejos. </li>
-<li>Tiene un aspecto social que te permite competir con otros jugadores en Mysterio’s Arena y compartir tus logros en línea. </li>
-</ul>
-
-<h3>Una historia original basada en la película</h3>
-<p>El increíble Spider-Man 2 APK sigue los eventos de la película, pero también añade nuevos giros y vueltas para hacerlo más interesante. Te encontrarás con nuevos personajes como Gato Negro y Screwball, que te ayudarán u obstaculizarán en tu búsqueda para detener la ola de crímenes. También descubrirás más sobre los orígenes de los villanos y sus motivos. El juego tiene alta calidad de actuación de voz y escenas cinematográficas que te sumergirán en la historia. </p>
- <h3>Una impresionante aventura en 3D de mundo abierto</h3>
-<p>El increíble Spider-Man 2 APK le da la libertad de explorar Nueva York como desee. Puede oscilar de un edificio a otro, escalar paredes, gatear sobre techos y saltar obstáculos. También puede interactuar con el entorno, como romper ventanas, destrozar autos o salvar civiles. El juego tiene la física realista y los efectos del clima dinámico que hacen que la ciudad cobre vida. También puede disfrutar de las hermosas vistas del horizonte, los puentes y los monumentos. </p>
-<p></p>
- <h3>Una variedad de trajes y villanos de Spider-Man</h3>
-<p>El increíble Spider-Man 2 APK le permite personalizar su Spider-Man con diferentes trajes que tienen diferentes poderes y bonos. Puedes desbloquearlos completando misiones, recogiendo objetos o comprándolos con dinero real. Algunos de los trajes son:</p>
-<tabla>
-<tr><th>Traje</th><th>Poder</th><th>Bono</th></tr>
-<tr><td>Symbiote Spider-Man</td><td>Venom Blast</ <td>Aturde a los enemigos e inflige daño extra</td><td>Aumenta la regeneración de la salud</td></tr>
-<tr><td>Iron Spider</td><td>Iron Arms</td><td>Invoca cuatro brazos mecánicos que atacan a los enemigos</td><td>Aumenta el poder de ataque y la defensa</td></tr>
-<tr><td>Ultimate Comics Spider-Man</td><td>Cloaking</td><td><td>Se vuelve invisible e indetectable por los enemigos</td><td>Aumenta el sigilo y la agilidad</td></tr>
-<tr><td>Spider-Man 2099</td><td>Visión acelerada</td><td>Ralentiza el tiempo y mejora la percepción</td><td>Aumenta la velocidad y los reflejos</td></tr>
-
-<tr><td>Araña escarlata</td><td>Nanobots</td><td><td>Cura heridas y restaura la salud</td><td>Aumenta la curación y la resistencia</td></tr>
-<tr><td>Spider-Armor MK II</td><td>Bulletproof</td><td>Absorbe y refleja las balas en los enemigos</td><td>Aumenta la armadura y la protección</td></tr>
-<tr><td>The Amazing Spider-Man (2014)</td><td>Ningún poder especial</ <td>Ningún bono especial</tr>
-<tr><th colspan="3">Nota: Algunos trajes requieren compras en la aplicación para desbloquear. </th></tr>
-</tabla>
- <p>El juego también cuenta con una amplia gama de villanos que tendrás que enfrentar en diferentes misiones y batallas contra jefes. Algunos de los villanos son:</p>
-<ul>
-<li>Veneno: Una criatura monstruosa que es el resultado de un simbionte alienígena que se une con un huésped humano. Tiene súper fuerza, agilidad, durabilidad y puede disparar telarañas y zarcillos de su cuerpo. Es uno de los enemigos más peligrosos de Spider-Man. </li>
-<li>El Duende Verde: El alter ego de Norman Osborn, un hombre de negocios despiadado que experimentó consigo mismo con un suero que le dio habilidades mejoradas, pero también lo volvió loco. Utiliza un planeador, bombas de calabaza y murciélagos de navaja para atacar a Spider-Man.</li>
-<li>Electro: Un ex ingeniero eléctrico que fue transformado en una batería viva después de un accidente. Puede manipular la electricidad, disparar rayos y volar usando campos magnéticos. Está obsesionado con volverse más poderoso y destruir a Spider-Man.</li>
-<li>Kraven el Cazador: Un hábil cazador y rastreador que considera a Spider-Man su presa definitiva. Utiliza varias armas, como cuchillos, lanzas, redes y trampas, para cazar a sus objetivos. También es realzado por una poción mística que le da súper sentidos, velocidad y resistencia. </li>
-<li>The Kingpin: El señor del crimen de Nueva York que controla la mayoría de las actividades ilegales en la ciudad. Es un cerebro que utiliza su riqueza, influencia y fuerza bruta para lograr sus objetivos. También es un luchador formidable que puede igualar a Spider-Man en fuerza y durabilidad. </li>
-
-<li>El gato negro: Un ladrón de gatos que tiene una relación complicada con Spider-Man. Ella es una ladrona experta que usa su agilidad, acrobacias, artilugios y suerte para robar objetos valiosos. Ella también puede coquetear con Spider-Man y distraerlo de sus misiones. </li>
-<li>Screwball: Un bromista que transmite sus crímenes en línea para sus fans. Utiliza varios dispositivos, como drones, hologramas, bombas y trampas, para crear caos y desafiar a Spider-Man. También es muy ágil y puede evadir los ataques de Spider-Man. </li>
-</ul>
- <h3>Un emocionante sistema de combate y acción aérea</h3>
-<p>El increíble Spider-Man 2 APK tiene un sistema de combate que es de ritmo rápido, fluido y sensible. Puedes usar tus telarañas para balancear, comprimir, tirar o envolver a tus enemigos. También puedes usar tus puños, patadas o disparadores web para combatirlos. Usted puede realizar combos, contadores, esquiva, finishers, y movimientos especiales para derrotar a sus enemigos. También puede utilizar el entorno a su favor, como lanzar objetos, romper paredes o provocar explosiones. </p>
- <p>El juego también tiene un sistema de acción aérea que te permite volar por el cielo como Spider-Man. Puedes usar tus telarañas para balancearte de un edificio a otro, o deslizarte usando tus alas de telaraña. También puede realizar maniobras acrobáticas, como volteretas, giros, inmersiones y rollos. También puedes participar en combates aéreos con enemigos que vuelan o te disparan. </p>
- <h2>Cómo <h2>Cómo descargar e instalar The Amazing Spider-Man 2 APK</h2>
-<h3>Requisitos y compatibilidad</h3>
-<p>El increíble Spider-Man 2 APK es un juego grande que requiere mucho espacio y recursos en su dispositivo Android. Estos son los requisitos mínimos y la compatibilidad para el juego:</p>
-<ul>
-<li>Versión de Android: 4.0.3 o superior</li>
-<li>RAM: 1 GB o más</li>
-<li>Almacenamiento: 1.5 GB o más</li>
-<li>Procesador: 1 GHz o más rápido</li>
-<li>Resolución de la pantalla: 800 x 480 o superior</li>
-<li>Conexión a Internet: Requerido para algunas características y actualizaciones</li>
-
-</ul>
- <h3>Pasos para descargar e instalar</h3>
-<p> Para descargar e instalar el increíble Spider-Man 2 APK en su dispositivo Android, es necesario seguir estos pasos:</p>
-<ol>
-<li>Descargue el archivo APK y el archivo de datos OBB desde una fuente de confianza, como APKPure o APKMirror. Asegúrate de descargar los archivos que coincidan con las especificaciones y la región de tu dispositivo. </li>
-<li>Habilite la instalación de aplicaciones de fuentes desconocidas en su dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Busque el archivo APK descargado y toque en él para iniciar el proceso de instalación. Siga las instrucciones de la pantalla para completar la instalación. </li>
-<li>Extraiga el archivo de datos OBB utilizando una aplicación de administrador de archivos, como ES File Explorer o ZArchiver. Usted debe obtener una carpeta llamada com.gameloft.android.ANMP.GloftASHM. </li>
-<li>Mueva la carpeta al directorio Android/OBB en el almacenamiento interno de su dispositivo. Si no tiene una carpeta OBB, cree una. </li>
-<li>Iniciar el juego desde el cajón de la aplicación y disfrutar! </li>
-</ol>
- <h3>Consejos y trucos para disfrutar del juego</h3>
-<p>El increíble Spider-Man 2 APK es un juego divertido y desafiante que pondrá a prueba sus habilidades y reflejos como Spider-Man. Aquí hay algunos consejos y trucos para ayudarle a disfrutar del juego más:</p>
-<ul>
-<li>Actualizar sus trajes de Spider-Man y web-shooters regularmente para mejorar sus habilidades y rendimiento. </li>
-<li>Recoge fichas de araña, frascos, cómics y otros artículos para desbloquear nuevos trajes, gadgets, habilidades y bonos. </li>
-<li>Misiones secundarias completas, como salvar civiles, detener crímenes o reunir pruebas, para ganar recompensas y reputación adicionales. </li>
-<li>Usa tu sentido arácnido para detectar enemigos, peligros y oportunidades en tu entorno. </li>
-<li>Usa tus telarañas para balancearte más rápido, evitar obstáculos y alcanzar lugares altos. </li>
-
-<li>Usa tu entorno a tu favor, como lanzar objetos, romper paredes o provocar explosiones. </li>
-<li>Tenga cuidado con los eventos de tiempo rápido que requieren que toque o pase la pantalla en el momento adecuado. </li>
-<li>Completa logros y desafíos para ganar más fichas de araña y viales. </li>
-<li>Compite con otros jugadores en Mysterio’s Arena y sube a las tablas de clasificación. </li>
-</ul>
- <h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>El increíble Spider-Man 2 APK es un juego para Android que le permite convertirse en Spider-Man y salvar a Nueva York de una juerga de crimen en toda la ciudad. Se basa en la película de Marvel del mismo nombre, pero también tiene una historia original que introduce nuevos personajes y escenarios. Tiene gráficos y animaciones de alta calidad que te hacen sentir como si estuvieras en la película. Tiene una variedad de características que lo hacen divertido y emocionante, como diferentes trajes y villanos de Spider-Man, una aventura en 3D de mundo abierto, un sistema de combate emocionante y acción aérea, un aspecto social que le permite competir con otros jugadores en línea y más. Es fácil de descargar e instalar en su dispositivo Android, siempre y cuando cumpla con los requisitos y la compatibilidad. También tiene algunos consejos y trucos que te ayudarán a disfrutar más del juego. </p>
- <h3>Llamada a la acción y pensamientos finales</h3>
-
-<p>Aquí hay algunas preguntas frecuentes sobre The Amazing Spider-Man 2 APK:</p>
-<ol>
-<li> ¿Es el increíble Spider-Man 2 APK seguro para descargar e instalar? </li>
-<p>Sí, El Amazing Spider-Man 2 APK es seguro para descargar e instalar, siempre y cuando lo obtenga de una fuente de confianza, como APKPure o APKMirror. Estas fuentes escanean los archivos en busca de virus y malware antes de cargarlos. Sin embargo, siempre debes tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener contenido dañino o no deseado. </p>
-<li> ¿Es el increíble Spider-Man 2 APK libre para jugar? </li>
-<p>El increíble Spider-Man 2 APK es gratis para descargar y jugar, pero también tiene algunas compras en la aplicación que le permiten comprar artículos adicionales, tales como fichas de araña, viales, trajes, o gadgets. Estas compras son opcionales y no son necesarias para disfrutar del juego. También puedes ganar estos objetos jugando el juego y completando misiones. </p>
-<li> ¿Cómo actualizo el increíble Spider-Man 2 APK? </li>
-<p>El increíble Spider-Man 2 APK se actualiza regularmente por los desarrolladores para corregir errores, mejorar el rendimiento, y añadir nuevas características. Puedes actualizar el juego descargando e instalando la última versión desde la misma fuente donde obtuviste la original. También puedes buscar actualizaciones dentro del juego en Configuración > Acerca de > Buscar actualizaciones.</p>
-<li> ¿Cómo puedo desinstalar el increíble Spider-Man 2 APK? </li>
-<p>Si desea desinstalar The Amazing Spider-Man 2 APK desde su dispositivo Android, puede hacerlo siguiendo estos pasos:</p>
-<ul>
-<li>Ir a Configuración > Aplicaciones > El increíble Spider-Man 2.</li>
-<li>Toque en Desinstalar y confirmar su elección. </li>
-<li>Elimina la carpeta com.gameloft.android.ANMP.GloftASHM de tu directorio Android/OBB. </li>
-</ul>
-<li> ¿Cómo me pongo en contacto con los desarrolladores de The Amazing Spider-Man 2 APK? </li>
-<p>Si usted tiene alguna pregunta, retroalimentación, o problemas con respecto a The Amazing Spider-Man 2 APK, puede ponerse en contacto con los desarrolladores mediante el uso de uno de estos métodos:</p>
-<ul>
-
-Sitio web: https://www.gameloft.com/en/game/the-amazing-spider-man-2</li>
-<li>Facebook: https://www.facebook.com/TheAmazingSpiderManGame</li>
-<li>Twitter: https://twitter.com/gameloft</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cmo Descargar Yu Gi Oh Duel Links En Laptop.md

@@ -1,78 +0,0 @@
-
-<h1>Cómo Descargar Yu-Gi-Oh Duel Links en Laptop</h1>
-<p>¿Te encanta jugar Yu-Gi-Oh Duel Links en tu dispositivo móvil, pero te gustaría poder disfrutarlo en una pantalla más grande y con mejores controles? Si es así, estás de suerte, porque en este artículo, te mostraremos cómo descargar Yu-Gi-Oh Duel Links en la computadora portátil y jugarlo como un profesional. Si usted tiene Windows 11, Windows 10, o una versión anterior de Windows, tenemos una solución para usted. Así que, vamos a empezar! </p>
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- <h2>¿Qué es Yu-Gi-Oh Duel Links? </h2>
-<p>Yu-Gi-Oh Duel Links es un popular juego de cartas multijugador en línea basado en la serie de anime y manga Yu-Gi-Oh. Te permite construir tu propia baraja de cartas de cientos de personajes y monstruos, y batirte en duelo contra otros jugadores de todo el mundo. También puedes desafiar a duelistas legendarios del anime, como Yugi, Kaiba, Joey, Mai y más. El juego cuenta con impresionantes animaciones en 3D, actuación de voz y controles intuitivos que hacen que sea fácil de aprender y divertido de jugar. </p>
- <h2>¿Por qué jugar Yu-Gi-Oh Duel Links en el ordenador portátil? </h2>
-<p>Si bien Yu-Gi-Oh Duel Links está diseñado para dispositivos móviles, hay muchas razones por las que es posible que desee jugar en su computadora portátil en su lugar. Estos son algunos de ellos:</p>
-<ul>
-<li> Puedes disfrutar de los gráficos y animaciones del juego en una pantalla más grande y de mayor resolución. </li>
-<li>Puedes usar el teclado y el ratón para controlar el juego con mayor precisión y comodidad. </li>
-<li> Puede ahorrar la vida de la batería y el uso de datos jugando el juego sin conexión o a través de Wi-Fi.</li>
-<li> Puede evitar interrupciones de llamadas telefónicas, mensajes, notificaciones o alertas de batería baja. </li>
-<li> Puede acceder a más funciones y opciones que pueden no estar disponibles en la versión móvil. </li>
-</ul>
-<p>Entonces, ¿cómo se descarga Yu-Gi-Oh Duel Links en el ordenador portátil? Bueno, hay diferentes métodos dependiendo de qué versión de Windows que tiene. Echemos un vistazo a cada uno. </p>
-<p></p>
- <h2>Cómo descargar Yu-Gi-Oh Duel Links en el ordenador portátil con Windows 11? </h2>
-
-<ol>
-<li>Asegúrese de que su PC con Windows 11 tenga habilitada la virtualización de hardware. Puede verificar esto yendo a la pestaña Administrador de tareas > Rendimiento. Si no, es posible que necesite habilitarlo en la configuración de su BIOS. </li>
-<li>Asegúrese de que su PC con Windows 11 está actualizado a la última versión. Puede comprobar esto yendo a Configuración > Actualización y seguridad > Actualización de Windows.</li>
-<li>Descargar e instalar la aplicación Amazon Appstore en su PC con Windows 11.</li>
-<li>Inicie la aplicación Amazon Appstore e inicie sesión con su cuenta de Amazon. Si no tiene una, puede crear una gratis. </li>
-<li>Busque "Yu-Gi-Oh Duel Links" o haga clic en <a href="">este enlace</a> para ir a la página del juego. </li>
-<li>Haga clic en el botón "Obtener" y espere a que el juego se descargue e instale. </li>
-<li> Iniciar el juego desde la aplicación Amazon Appstore o desde el menú Inicio. </li>
-<li>¡Disfruta jugando Yu-Gi-Oh Duel Links en tu laptop! </li>
-</ol>
-<p>¡Eso es todo! Ahora puedes jugar Yu-Gi-Oh Duel Links en tu portátil con Windows 11 usando el subsistema de Windows para Android y la Appstore de Amazon. Este método es rápido, fácil y seguro, y no requiere ningún software o configuración de terceros. Sin embargo, si tiene Windows 10 o una versión anterior de Windows, necesitará usar un método diferente. </p>
- <h2>¿Cómo descargar Yu-Gi-Oh Duel Links en Laptop con Windows 10 o más? </h2>
-<p>Si tienes Windows 10 o una versión anterior de Windows, todavía puedes descargar Yu-Gi-Oh Duel Links en tu portátil usando un emulador de Android. Un emulador de Android es un software que simula un dispositivo Android en su PC, lo que le permite ejecutar aplicaciones y juegos de Android. Hay muchos emuladores de Android disponibles, pero uno de los más populares y confiables es Bluestacks. He aquí cómo utilizar Bluestacks para descargar Yu-Gi-Oh Duel Links en su ordenador portátil:</p>
-<ol>
-<li>Ir a <a href=">el sitio web oficial de Bluestacks</a> y descargar la última versión de Bluestacks para su PC.</li>
-
-<li>Inicie Bluestacks e inicie sesión con su cuenta de Google. Si no tiene una, puede crear una gratis. </li>
-<li>Ir a la aplicación Google Play Store y buscar "Yu-Gi-Oh Duel Links" o haga clic en <a href=">este enlace</a> para ir a la página del juego. </li>
-<li>Haga clic en el botón "Instalar" y espere a que el juego se descargue e instale. </li>
-<li> Iniciar el juego desde la pantalla de inicio de Bluestacks o desde el cajón de la aplicación. </li>
-<li>¡Disfruta jugando Yu-Gi-Oh Duel Links en tu laptop! </li>
-</ol>
-<p>Así es como puedes descargar Yu-Gi-Oh Duel Links en tu laptop con Windows 10 o más usando Bluestacks. Este método es simple y conveniente, pero puede requerir algunos recursos del sistema y espacio de almacenamiento. También es posible que tenga que ajustar algunos ajustes para optimizar su experiencia de juego y rendimiento. Si desea probar otro método, también puede sideload Yu-Gi-Oh Duel Links APK en su ordenador portátil. </p>
- <h2>Cómo Sideload Yu-Gi-Oh Duel Enlaces APK en el ordenador portátil? </h2>
-<p>Sideloading es un proceso de transferencia e instalación de una aplicación desde una fuente distinta de la tienda de aplicaciones oficial. En este caso, puede sideload Yu-Gi-Oh Duel Links APK en su ordenador portátil desde su dispositivo Android. Este método puede ser útil si desea jugar el juego sin conexión o si tiene problemas para acceder a la Google Play Store o la Appstore de Amazon. Sin embargo, este método también puede implicar algunos riesgos, como infección de malware o problemas de compatibilidad. Por lo tanto, recomendamos que solo descargue APK de fuentes confiables y los escanee con software antivirus antes de instalarlos. Aquí es cómo sideload Yu-Gi-Oh Duel Links APK en su ordenador portátil:</p>
-<ol>
-<li>En tu dispositivo Android, ve a Configuración > Aplicaciones y notificaciones > Yu-Gi-Oh Duel Links > Almacenamiento y caché > Borrar caché. Esto asegurará que usted tiene la última versión del juego sin ningún dato dañado. </li>
-
-<li>En su dispositivo Android, ir a una aplicación explorador de archivos y localizar el archivo APK de Yu-Gi-Oh Duel Links. El nombre del archivo debería ser algo así como "com.konami.duellinks.apk". Puede encontrarlo en el almacenamiento interno o la tarjeta SD en Android > datos > com.konami.duellinks > archivos > descargar. </li>
-<li>Fi, correo electrónico o cualquier otro método que prefieras. </li>
-<li>En su computadora portátil, vaya a la carpeta donde guardó el archivo APK y haga doble clic en él para instalarlo. Es posible que deba permitir la instalación de aplicaciones de fuentes desconocidas en su computadora portátil. Puede hacer esto yendo a Configuración > Aplicaciones > Aplicaciones y características > Elija dónde obtener aplicaciones y seleccione En cualquier lugar.</li>
-<li>Inicie el juego desde el menú Inicio o desde el acceso directo del escritorio. </li>
-<li>¡Disfruta jugando Yu-Gi-Oh Duel Links en tu laptop! </li>
-</ol>
-<p>Así es como se puede sideload Yu-Gi-Oh Duel Links APK en su ordenador portátil desde su dispositivo Android. Este método es flexible e independiente, pero también puede ser arriesgado y complicado. Es posible que tenga que actualizar el archivo APK manualmente cada vez que hay una nueva versión del juego. También puede encontrar algunos errores o errores que pueden afectar su experiencia de juego y el rendimiento. Por lo tanto, le sugerimos que utilice este método solo como último recurso. </p>
- <h2>Consejos y trucos para jugar Yu-Gi-Oh Duel Links en el ordenador portátil</h2>
-<p>Ahora que sabes cómo descargar Yu-Gi-Oh Duel Links en tu portátil, es posible que quieras conocer algunos consejos y trucos para aprovechar al máximo tu experiencia de juego y rendimiento. Estos son algunos de ellos:</p>
-<ul>
-<li>Ajusta la configuración del juego según las especificaciones y preferencias de tu portátil. Puedes acceder a la configuración del juego tocando el icono de engranaje en la esquina superior derecha de la pantalla. Puede cambiar la calidad gráfica, el volumen de sonido, el idioma de voz, las notificaciones y más. </li>
-
-<li>Usa gestos del ratón para realizar acciones más rápidas y fáciles. Puede ver y personalizar los gestos del ratón haciendo clic en el icono del ratón en la esquina inferior derecha de la pantalla. Puede asignar gestos para acciones como deslizar, tocar, arrastrar, etc.</li>
-<li>Sincronizar el progreso del juego en todos los dispositivos con su ID de Konami o cuenta de Google Play Games. Puede hacer esto tocando el icono de transferencia de datos en la esquina superior izquierda de la pantalla. También puede hacer copias de seguridad y restaurar los datos del juego usando esta función. </li>
-<li>Únete a una sala de duelos o crea la tuya propia para jugar con tus amigos u otros jugadores en línea. Puedes hacer esto tocando el icono de la sala de duelos en la esquina inferior izquierda de la pantalla. También puedes chatear con otros jugadores, enviar solicitudes de amistad e intercambiar cartas en la sala de duelos. </li>
-</ul>
-<p>Estos son algunos de los consejos y trucos que pueden ayudarle a jugar Yu-Gi-Oh Duel Links en su ordenador portátil más suave y agradable. Por supuesto, hay muchas más cosas que puedes descubrir y aprender mientras juegas. ¡Así que no tengas miedo de experimentar y explorar! </p>
- <h2>Conclusión</h2>
-<p>En conclusión, Yu-Gi-Oh Duel Links es un fantástico juego de cartas que puedes jugar en tu portátil con diferentes métodos dependiendo de tu versión de Windows. Puede usar el subsistema de Windows para Android y la Appstore de Amazon si tiene Windows 11, o un emulador de Android como Bluestacks si tiene Windows 10 o más. También puede sideload Yu-Gi-Oh Duel Links APK en su ordenador portátil desde su dispositivo Android si desea probar otra opción. Cualquiera que sea el método que elijas, asegúrate de seguir nuestros consejos y trucos para optimizar tu experiencia de juego y rendimiento. </p>
-<p>Así que, ¿qué estás esperando? Descargar Yu-Gi-Oh Duel Links en su ordenador portátil hoy y dar rienda suelta a sus habilidades de duelo! Y no te olvides de compartir este artículo con tus amigos que podrían estar interesados en jugar Yu-Gi-Oh Duel Links en sus portátiles también! </p>
- <h3>Preguntas frecuentes</h3>
-
-<ol>
-<li><b>Yu-Gi-Oh Duel Links es gratis para jugar? </b><br>Sí, Yu-Gi-Oh Duel Links es gratis para jugar con compras en la aplicación. Puede descargar y jugar el juego sin gastar dinero, pero también puede comprar gemas, tarjetas, paquetes y otros artículos con dinero real si desea mejorar su experiencia de juego. </li>
-<li><b>¿Es seguro descargar Yu-Gi-Oh Duel Links? </b><br>Sí, Yu-Gi-Oh Duel Links es seguro descargarlo siempre y cuando lo obtengas de una fuente confiable, como Google Play Store, Amazon Appstore o el sitio web oficial de Bluestacks. Sin embargo, si carga Yu -Gi-Oh Duel Links APK de una fuente desconocida, usted debe escanear con el software antivirus antes de instalarlo y tener cuidado de cualquier malware o problemas de compatibilidad. </li>
-<li><b>¿Puedo jugar Yu-Gi-Oh Duel Links sin conexión? </b><br>No, Yu-Gi-Oh Duel Links requiere una conexión a Internet para jugar. Necesitas estar en línea para acceder a las funciones del juego, como duelos, eventos, actualizaciones, etc. Sin embargo, puedes jugar el juego a través de Wi-Fi o Ethernet en lugar de usar tus datos móviles si quieres ahorrar en el uso de tus datos y en la duración de la batería. </li>
-<li><b>¿Puedo jugar Yu-Gi-Oh Duel Links con un controlador? </b><br>Sí, puedes jugar Yu-Gi-Oh Duel Links con un controlador si usas un emulador de Android como Bluestacks. Puede conectar su controlador a su computadora portátil a través de USB o Bluetooth y asignar los botones a las acciones del juego. También puede utilizar una aplicación de gamepad en su dispositivo Android para controlar el juego en su ordenador portátil. </li>
-<li><b>¿Puedo transferir mi cuenta de Yu-Gi-Oh Duel Links desde mi dispositivo móvil a mi computadora portátil? </b><br>Sí, puedes transferir tu cuenta de Yu-Gi-Oh Duel Links desde tu dispositivo móvil a tu laptop usando tu ID de Konami o cuenta de Google Play Games. Puede hacer esto tocando el icono de transferencia de datos en la esquina superior izquierda de la pantalla y siguiendo las instrucciones. También puede hacer copias de seguridad y restaurar los datos del juego usando esta función. </li>
-</ol> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/models/search_scope.py

@@ -1,132 +0,0 @@
-import itertools
-import logging
-import os
-import posixpath
-import urllib.parse
-from typing import List
-
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.models.index import PyPI
-from pip._internal.utils.compat import has_tls
-from pip._internal.utils.misc import normalize_path, redact_auth_from_url
-
-logger = logging.getLogger(__name__)
-
-
-class SearchScope:
-
-    """
-    Encapsulates the locations that pip is configured to search.
-    """
-
-    __slots__ = ["find_links", "index_urls", "no_index"]
-
-    @classmethod
-    def create(
-        cls,
-        find_links: List[str],
-        index_urls: List[str],
-        no_index: bool,
-    ) -> "SearchScope":
-        """
-        Create a SearchScope object after normalizing the `find_links`.
-        """
-        # Build find_links. If an argument starts with ~, it may be
-        # a local file relative to a home directory. So try normalizing
-        # it and if it exists, use the normalized version.
-        # This is deliberately conservative - it might be fine just to
-        # blindly normalize anything starting with a ~...
-        built_find_links: List[str] = []
-        for link in find_links:
-            if link.startswith("~"):
-                new_link = normalize_path(link)
-                if os.path.exists(new_link):
-                    link = new_link
-            built_find_links.append(link)
-
-        # If we don't have TLS enabled, then WARN if anyplace we're looking
-        # relies on TLS.
-        if not has_tls():
-            for link in itertools.chain(index_urls, built_find_links):
-                parsed = urllib.parse.urlparse(link)
-                if parsed.scheme == "https":
-                    logger.warning(
-                        "pip is configured with locations that require "
-                        "TLS/SSL, however the ssl module in Python is not "
-                        "available."
-                    )
-                    break
-
-        return cls(
-            find_links=built_find_links,
-            index_urls=index_urls,
-            no_index=no_index,
-        )
-
-    def __init__(
-        self,
-        find_links: List[str],
-        index_urls: List[str],
-        no_index: bool,
-    ) -> None:
-        self.find_links = find_links
-        self.index_urls = index_urls
-        self.no_index = no_index
-
-    def get_formatted_locations(self) -> str:
-        lines = []
-        redacted_index_urls = []
-        if self.index_urls and self.index_urls != [PyPI.simple_url]:
-            for url in self.index_urls:
-                redacted_index_url = redact_auth_from_url(url)
-
-                # Parse the URL
-                purl = urllib.parse.urlsplit(redacted_index_url)
-
-                # URL is generally invalid if scheme and netloc is missing
-                # there are issues with Python and URL parsing, so this test
-                # is a bit crude. See bpo-20271, bpo-23505. Python doesn't
-                # always parse invalid URLs correctly - it should raise
-                # exceptions for malformed URLs
-                if not purl.scheme and not purl.netloc:
-                    logger.warning(
-                        'The index url "%s" seems invalid, please provide a scheme.',
-                        redacted_index_url,
-                    )
-
-                redacted_index_urls.append(redacted_index_url)
-
-            lines.append(
-                "Looking in indexes: {}".format(", ".join(redacted_index_urls))
-            )
-
-        if self.find_links:
-            lines.append(
-                "Looking in links: {}".format(
-                    ", ".join(redact_auth_from_url(url) for url in self.find_links)
-                )
-            )
-        return "\n".join(lines)
-
-    def get_index_urls_locations(self, project_name: str) -> List[str]:
-        """Returns the locations found via self.index_urls
-
-        Checks the url_name on the main (first in the list) index and
-        use this url_name to produce all locations
-        """
-
-        def mkurl_pypi_url(url: str) -> str:
-            loc = posixpath.join(
-                url, urllib.parse.quote(canonicalize_name(project_name))
-            )
-            # For maximum compatibility with easy_install, ensure the path
-            # ends in a trailing slash.  Although this isn't in the spec
-            # (and PyPI can handle it without the slash) some other index
-            # implementations might break if they relied on easy_install's
-            # behavior.
-            if not loc.endswith("/"):
-                loc = loc + "/"
-            return loc
-
-        return [mkurl_pypi_url(url) for url in self.index_urls]

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/models/wheel.py

@@ -1,92 +0,0 @@
-"""Represents a wheel file and provides access to the various parts of the
-name that have meaning.
-"""
-import re
-from typing import Dict, Iterable, List
-
-from pip._vendor.packaging.tags import Tag
-
-from pip._internal.exceptions import InvalidWheelFilename
-
-
-class Wheel:
-    """A wheel file"""
-
-    wheel_file_re = re.compile(
-        r"""^(?P<namever>(?P<name>[^\s-]+?)-(?P<ver>[^\s-]*?))
-        ((-(?P<build>\d[^-]*?))?-(?P<pyver>[^\s-]+?)-(?P<abi>[^\s-]+?)-(?P<plat>[^\s-]+?)
-        \.whl|\.dist-info)$""",
-        re.VERBOSE,
-    )
-
-    def __init__(self, filename: str) -> None:
-        """
-        :raises InvalidWheelFilename: when the filename is invalid for a wheel
-        """
-        wheel_info = self.wheel_file_re.match(filename)
-        if not wheel_info:
-            raise InvalidWheelFilename(f"{filename} is not a valid wheel filename.")
-        self.filename = filename
-        self.name = wheel_info.group("name").replace("_", "-")
-        # we'll assume "_" means "-" due to wheel naming scheme
-        # (https://github.com/pypa/pip/issues/1150)
-        self.version = wheel_info.group("ver").replace("_", "-")
-        self.build_tag = wheel_info.group("build")
-        self.pyversions = wheel_info.group("pyver").split(".")
-        self.abis = wheel_info.group("abi").split(".")
-        self.plats = wheel_info.group("plat").split(".")
-
-        # All the tag combinations from this file
-        self.file_tags = {
-            Tag(x, y, z) for x in self.pyversions for y in self.abis for z in self.plats
-        }
-
-    def get_formatted_file_tags(self) -> List[str]:
-        """Return the wheel's tags as a sorted list of strings."""
-        return sorted(str(tag) for tag in self.file_tags)
-
-    def support_index_min(self, tags: List[Tag]) -> int:
-        """Return the lowest index that one of the wheel's file_tag combinations
-        achieves in the given list of supported tags.
-
-        For example, if there are 8 supported tags and one of the file tags
-        is first in the list, then return 0.
-
-        :param tags: the PEP 425 tags to check the wheel against, in order
-            with most preferred first.
-
-        :raises ValueError: If none of the wheel's file tags match one of
-            the supported tags.
-        """
-        try:
-            return next(i for i, t in enumerate(tags) if t in self.file_tags)
-        except StopIteration:
-            raise ValueError()
-
-    def find_most_preferred_tag(
-        self, tags: List[Tag], tag_to_priority: Dict[Tag, int]
-    ) -> int:
-        """Return the priority of the most preferred tag that one of the wheel's file
-        tag combinations achieves in the given list of supported tags using the given
-        tag_to_priority mapping, where lower priorities are more-preferred.
-
-        This is used in place of support_index_min in some cases in order to avoid
-        an expensive linear scan of a large list of tags.
-
-        :param tags: the PEP 425 tags to check the wheel against.
-        :param tag_to_priority: a mapping from tag to priority of that tag, where
-            lower is more preferred.
-
-        :raises ValueError: If none of the wheel's file tags match one of
-            the supported tags.
-        """
-        return min(
-            tag_to_priority[tag] for tag in self.file_tags if tag in tag_to_priority
-        )
-
-    def supported(self, tags: Iterable[Tag]) -> bool:
-        """Return whether the wheel is compatible with one of the given tags.
-
-        :param tags: the PEP 425 tags to check the wheel against.
-        """
-        return not self.file_tags.isdisjoint(tags)

spaces/CVPR/GFPGAN-example/tests/test_utils.py

@@ -1,43 +0,0 @@
-import cv2
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-from gfpgan.utils import GFPGANer
-
-
-def test_gfpganer():
-    # initialize with the clean model
-    restorer = GFPGANer(
-        model_path='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth',
-        upscale=2,
-        arch='clean',
-        channel_multiplier=2,
-        bg_upsampler=None)
-    # test attribute
-    assert isinstance(restorer.gfpgan, GFPGANv1Clean)
-    assert isinstance(restorer.face_helper, FaceRestoreHelper)
-
-    # initialize with the original model
-    restorer = GFPGANer(
-        model_path='experiments/pretrained_models/GFPGANv1.pth',
-        upscale=2,
-        arch='original',
-        channel_multiplier=1,
-        bg_upsampler=None)
-    # test attribute
-    assert isinstance(restorer.gfpgan, GFPGANv1)
-    assert isinstance(restorer.face_helper, FaceRestoreHelper)
-
-    # ------------------ test enhance ---------------- #
-    img = cv2.imread('tests/data/gt/00000000.png', cv2.IMREAD_COLOR)
-    result = restorer.enhance(img, has_aligned=False, paste_back=True)
-    assert result[0][0].shape == (512, 512, 3)
-    assert result[1][0].shape == (512, 512, 3)
-    assert result[2].shape == (1024, 1024, 3)
-
-    # with has_aligned=True
-    result = restorer.enhance(img, has_aligned=True, paste_back=False)
-    assert result[0][0].shape == (512, 512, 3)
-    assert result[1][0].shape == (512, 512, 3)
-    assert result[2] is None

spaces/CVPR/LIVE/pybind11/tests/test_cmake_build/test.py

@@ -1,6 +0,0 @@
-# -*- coding: utf-8 -*-
-import sys
-import test_cmake_build
-
-assert test_cmake_build.add(1, 2) == 3
-print("{} imports, runs, and adds: 1 + 2 = 3".format(sys.argv[1]))

spaces/CVPR/LIVE/pybind11/tools/FindEigen3.cmake

@@ -1,83 +0,0 @@
-# - Try to find Eigen3 lib
-#
-# This module supports requiring a minimum version, e.g. you can do
-#   find_package(Eigen3 3.1.2)
-# to require version 3.1.2 or newer of Eigen3.
-#
-# Once done this will define
-#
-#  EIGEN3_FOUND - system has eigen lib with correct version
-#  EIGEN3_INCLUDE_DIR - the eigen include directory
-#  EIGEN3_VERSION - eigen version
-
-# Copyright (c) 2006, 2007 Montel Laurent, <[email protected]>
-# Copyright (c) 2008, 2009 Gael Guennebaud, <[email protected]>
-# Copyright (c) 2009 Benoit Jacob <[email protected]>
-# Redistribution and use is allowed according to the terms of the 2-clause BSD license.
-
-if(NOT Eigen3_FIND_VERSION)
-  if(NOT Eigen3_FIND_VERSION_MAJOR)
-    set(Eigen3_FIND_VERSION_MAJOR 2)
-  endif(NOT Eigen3_FIND_VERSION_MAJOR)
-  if(NOT Eigen3_FIND_VERSION_MINOR)
-    set(Eigen3_FIND_VERSION_MINOR 91)
-  endif(NOT Eigen3_FIND_VERSION_MINOR)
-  if(NOT Eigen3_FIND_VERSION_PATCH)
-    set(Eigen3_FIND_VERSION_PATCH 0)
-  endif(NOT Eigen3_FIND_VERSION_PATCH)
-
-  set(Eigen3_FIND_VERSION
-      "${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}")
-endif(NOT Eigen3_FIND_VERSION)
-
-macro(_eigen3_check_version)
-  file(READ "${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h" _eigen3_version_header)
-
-  string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen3_world_version_match
-               "${_eigen3_version_header}")
-  set(EIGEN3_WORLD_VERSION "${CMAKE_MATCH_1}")
-  string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen3_major_version_match
-               "${_eigen3_version_header}")
-  set(EIGEN3_MAJOR_VERSION "${CMAKE_MATCH_1}")
-  string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen3_minor_version_match
-               "${_eigen3_version_header}")
-  set(EIGEN3_MINOR_VERSION "${CMAKE_MATCH_1}")
-
-  set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION})
-  if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
-    set(EIGEN3_VERSION_OK FALSE)
-  else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
-    set(EIGEN3_VERSION_OK TRUE)
-  endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
-
-  if(NOT EIGEN3_VERSION_OK)
-
-    message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, "
-                   "but at least version ${Eigen3_FIND_VERSION} is required")
-  endif(NOT EIGEN3_VERSION_OK)
-endmacro(_eigen3_check_version)
-
-if(EIGEN3_INCLUDE_DIR)
-
-  # in cache already
-  _eigen3_check_version()
-  set(EIGEN3_FOUND ${EIGEN3_VERSION_OK})
-
-else(EIGEN3_INCLUDE_DIR)
-
-  find_path(
-    EIGEN3_INCLUDE_DIR
-    NAMES signature_of_eigen3_matrix_library
-    PATHS ${CMAKE_INSTALL_PREFIX}/include ${KDE4_INCLUDE_DIR}
-    PATH_SUFFIXES eigen3 eigen)
-
-  if(EIGEN3_INCLUDE_DIR)
-    _eigen3_check_version()
-  endif(EIGEN3_INCLUDE_DIR)
-
-  include(FindPackageHandleStandardArgs)
-  find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK)
-
-  mark_as_advanced(EIGEN3_INCLUDE_DIR)
-
-endif(EIGEN3_INCLUDE_DIR)

spaces/CVPR/MonoScene/helpers.py

@@ -1,336 +0,0 @@
-import numpy as np
-import torch
-import fusion
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-
-def read_calib(calib_path):
-        """
-        Modify from https://github.com/utiasSTARS/pykitti/blob/d3e1bb81676e831886726cc5ed79ce1f049aef2c/pykitti/utils.py#L68
-        :param calib_path: Path to a calibration text file.
-        :return: dict with calibration matrices.
-        """
-        calib_all = {}
-        with open(calib_path, "r") as f:
-            for line in f.readlines():
-                if line == "\n":
-                    break
-                key, value = line.split(":", 1)
-                calib_all[key] = np.array([float(x) for x in value.split()])
-
-        # reshape matrices
-        calib_out = {}
-        # 3x4 projection matrix for left camera
-        calib_out["P2"] = calib_all["P2"].reshape(3, 4)
-        calib_out["Tr"] = np.identity(4)  # 4x4 matrix
-        calib_out["Tr"][:3, :4] = calib_all["Tr"].reshape(3, 4)
-        return calib_out
-
-
-def vox2pix(cam_E, cam_k, 
-            vox_origin, voxel_size, 
-            img_W, img_H, 
-            scene_size):
-    """
-    compute the 2D projection of voxels centroids
-    
-    Parameters:
-    ----------
-    cam_E: 4x4
-       =camera pose in case of NYUv2 dataset
-       =Transformation from camera to lidar coordinate in case of SemKITTI
-    cam_k: 3x3
-        camera intrinsics
-    vox_origin: (3,)
-        world(NYU)/lidar(SemKITTI) cooridnates of the voxel at index (0, 0, 0)
-    img_W: int
-        image width
-    img_H: int
-        image height
-    scene_size: (3,)
-        scene size in meter: (51.2, 51.2, 6.4) for SemKITTI and (4.8, 4.8, 2.88) for NYUv2
-    
-    Returns
-    -------
-    projected_pix: (N, 2)
-        Projected 2D positions of voxels
-    fov_mask: (N,)
-        Voxels mask indice voxels inside image's FOV 
-    pix_z: (N,)
-        Voxels'distance to the sensor in meter
-    """
-    # Compute the x, y, z bounding of the scene in meter
-    vol_bnds = np.zeros((3,2))
-    vol_bnds[:,0] = vox_origin
-    vol_bnds[:,1] = vox_origin + np.array(scene_size)
-
-    # Compute the voxels centroids in lidar cooridnates
-    vol_dim = np.ceil((vol_bnds[:,1]- vol_bnds[:,0])/ voxel_size).copy(order='C').astype(int)
-    xv, yv, zv = np.meshgrid(
-            range(vol_dim[0]),
-            range(vol_dim[1]),
-            range(vol_dim[2]),
-            indexing='ij'
-          )
-    vox_coords = np.concatenate([
-            xv.reshape(1,-1),
-            yv.reshape(1,-1),
-            zv.reshape(1,-1)
-          ], axis=0).astype(int).T
-
-    # Project voxels'centroid from lidar coordinates to camera coordinates
-    cam_pts = fusion.TSDFVolume.vox2world(vox_origin, vox_coords, voxel_size)
-    cam_pts = fusion.rigid_transform(cam_pts, cam_E)
-
-    # Project camera coordinates to pixel positions
-    projected_pix = fusion.TSDFVolume.cam2pix(cam_pts, cam_k)
-    pix_x, pix_y = projected_pix[:, 0], projected_pix[:, 1]
-
-    # Eliminate pixels outside view frustum
-    pix_z = cam_pts[:, 2]
-    fov_mask = np.logical_and(pix_x >= 0,
-                np.logical_and(pix_x < img_W,
-                np.logical_and(pix_y >= 0,
-                np.logical_and(pix_y < img_H,
-                pix_z > 0))))
-
-
-    return torch.from_numpy(projected_pix), torch.from_numpy(fov_mask), torch.from_numpy(pix_z)
-
-
-
-def get_grid_coords(dims, resolution):
-    """
-    :param dims: the dimensions of the grid [x, y, z] (i.e. [256, 256, 32])
-    :return coords_grid: is the center coords of voxels in the grid
-    """
-
-    g_xx = np.arange(0, dims[0] + 1)
-    g_yy = np.arange(0, dims[1] + 1)
-    sensor_pose = 10
-    g_zz = np.arange(0, dims[2] + 1)
-
-    # Obtaining the grid with coords...
-    xx, yy, zz = np.meshgrid(g_xx[:-1], g_yy[:-1], g_zz[:-1])
-    coords_grid = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T
-    coords_grid = coords_grid.astype(np.float)
-
-    coords_grid = (coords_grid * resolution) + resolution / 2
-
-    temp = np.copy(coords_grid)
-    temp[:, 0] = coords_grid[:, 1]
-    temp[:, 1] = coords_grid[:, 0]
-    coords_grid = np.copy(temp)
-
-    return coords_grid
-
-def get_projections(img_W, img_H):
-    scale_3ds = [1, 2]
-    data = {}
-    for scale_3d in scale_3ds:
-        scene_size = (51.2, 51.2, 6.4)
-        vox_origin = np.array([0, -25.6, -2])
-        voxel_size = 0.2
-        
-        calib = read_calib("calib.txt")    
-        cam_k = calib["P2"][:3, :3]
-        T_velo_2_cam = calib["Tr"]
-        
-        # compute the 3D-2D mapping
-        projected_pix, fov_mask, pix_z = vox2pix(
-            T_velo_2_cam,
-            cam_k,
-            vox_origin,
-            voxel_size * scale_3d,
-            img_W,
-            img_H,
-            scene_size,
-        )            
-
-        data["projected_pix_{}".format(scale_3d)] = projected_pix
-        data["pix_z_{}".format(scale_3d)] = pix_z
-        data["fov_mask_{}".format(scale_3d)] = fov_mask 
-    return data
-
-
-def majority_pooling(grid, k_size=2):
-    result = np.zeros(
-        (grid.shape[0] // k_size, grid.shape[1] // k_size, grid.shape[2] // k_size)
-    )
-    for xx in range(0, int(np.floor(grid.shape[0] / k_size))):
-        for yy in range(0, int(np.floor(grid.shape[1] / k_size))):
-            for zz in range(0, int(np.floor(grid.shape[2] / k_size))):
-
-                sub_m = grid[
-                    (xx * k_size) : (xx * k_size) + k_size,
-                    (yy * k_size) : (yy * k_size) + k_size,
-                    (zz * k_size) : (zz * k_size) + k_size,
-                ]
-                unique, counts = np.unique(sub_m, return_counts=True)
-                if True in ((unique != 0) & (unique != 255)):
-                    # Remove counts with 0 and 255
-                    counts = counts[((unique != 0) & (unique != 255))]
-                    unique = unique[((unique != 0) & (unique != 255))]
-                else:
-                    if True in (unique == 0):
-                        counts = counts[(unique != 255)]
-                        unique = unique[(unique != 255)]
-                value = unique[np.argmax(counts)]
-                result[xx, yy, zz] = value
-    return result
-
-
-def draw(
-    voxels,
-    # T_velo_2_cam,
-    # vox_origin,
-    fov_mask,
-    # img_size,
-    # f,
-    voxel_size=0.4,
-    # d=7,  # 7m - determine the size of the mesh representing the camera
-):
-
-    fov_mask = fov_mask.reshape(-1)
-    # Compute the voxels coordinates
-    grid_coords = get_grid_coords(
-        [voxels.shape[0], voxels.shape[1], voxels.shape[2]], voxel_size
-    )
-
-
-    # Attach the predicted class to every voxel
-    grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T
-
-    # Get the voxels inside FOV
-    fov_grid_coords = grid_coords[fov_mask, :]
-
-    # Get the voxels outside FOV
-    outfov_grid_coords = grid_coords[~fov_mask, :]
-
-    # Remove empty and unknown voxels
-    fov_voxels = fov_grid_coords[
-        (fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 255), :
-    ]
-    # print(np.unique(fov_voxels[:, 3], return_counts=True))
-    outfov_voxels = outfov_grid_coords[
-        (outfov_grid_coords[:, 3] > 0) & (outfov_grid_coords[:, 3] < 255), :
-    ]
-
-    # figure = mlab.figure(size=(1400, 1400), bgcolor=(1, 1, 1))
-    colors = np.array(
-        [
-            [0,0,0],
-            [100, 150, 245],
-            [100, 230, 245],
-            [30, 60, 150],
-            [80, 30, 180],
-            [100, 80, 250],
-            [255, 30, 30],
-            [255, 40, 200],
-            [150, 30, 90],
-            [255, 0, 255],
-            [255, 150, 255],
-            [75, 0, 75],
-            [175, 0, 75],
-            [255, 200, 0],
-            [255, 120, 50],
-            [0, 175, 0],
-            [135, 60, 0],
-            [150, 240, 80],
-            [255, 240, 150],
-            [255, 0, 0],
-        ]
-    ).astype(np.uint8)
-
-    pts_colors = [f'rgb({colors[int(i)][0]}, {colors[int(i)][1]}, {colors[int(i)][2]})' for i in fov_voxels[:, 3]]
-    out_fov_colors = [f'rgb({colors[int(i)][0]//3*2}, {colors[int(i)][1]//3*2}, {colors[int(i)][2]//3*2})' for i in outfov_voxels[:, 3]]
-    pts_colors = pts_colors + out_fov_colors
-    
-    fov_voxels = np.concatenate([fov_voxels, outfov_voxels], axis=0)
-    x = fov_voxels[:, 0].flatten()
-    y = fov_voxels[:, 1].flatten()
-    z = fov_voxels[:, 2].flatten()
-    # label = fov_voxels[:, 3].flatten()
-    fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,mode='markers',
-                    marker=dict(
-                            size=2,
-                            color=pts_colors,                # set color to an array/list of desired values
-                            # colorscale='Viridis',   # choose a colorscale
-                            opacity=1.0,
-                            symbol='square'
-                        ))])
-    fig.update_layout(
-    scene = dict(
-        aspectmode='data',
-        xaxis = dict(
-            backgroundcolor="rgb(255, 255, 255)",
-            gridcolor="black",
-            showbackground=True,
-            zerolinecolor="black",
-            nticks=4, 
-            visible=False,
-            range=[-1,55],),
-        yaxis = dict(
-            backgroundcolor="rgb(255, 255, 255)",
-            gridcolor="black",
-            showbackground=True,
-            zerolinecolor="black",
-            visible=False,
-            nticks=4, range=[-1,55],),
-        zaxis = dict(
-            backgroundcolor="rgb(255, 255, 255)",
-            gridcolor="black",
-            showbackground=True,
-            zerolinecolor="black",
-            visible=False,
-            nticks=4, range=[-1,7],),
-        bgcolor="black",
-    ),
-        
-    )
-
-    # fig = px.scatter_3d(
-    #     fov_voxels, 
-    #     x=fov_voxels[:, 0], y="y", z="z", color="label")
-    # Draw occupied inside FOV voxels
-    # plt_plot_fov = mlab.points3d(
-    #     fov_voxels[:, 0],
-    #     fov_voxels[:, 1],
-    #     fov_voxels[:, 2],
-    #     fov_voxels[:, 3],
-    #     colormap="viridis",
-    #     scale_factor=voxel_size - 0.05 * voxel_size,
-    #     mode="cube",
-    #     opacity=1.0,
-    #     vmin=1,
-    #     vmax=19,
-    # )
-
-    # # Draw occupied outside FOV voxels
-    # plt_plot_outfov = mlab.points3d(
-    #     outfov_voxels[:, 0],
-    #     outfov_voxels[:, 1],
-    #     outfov_voxels[:, 2],
-    #     outfov_voxels[:, 3],
-    #     colormap="viridis",
-    #     scale_factor=voxel_size - 0.05 * voxel_size,
-    #     mode="cube",
-    #     opacity=1.0,
-    #     vmin=1,
-    #     vmax=19,
-    # )
-
-    
-
-    # plt_plot_fov.glyph.scale_mode = "scale_by_vector"
-    # plt_plot_outfov.glyph.scale_mode = "scale_by_vector"
-
-    # plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors
-
-    # outfov_colors = colors
-    # outfov_colors[:, :3] = outfov_colors[:, :3] // 3 * 2
-    # plt_plot_outfov.module_manager.scalar_lut_manager.lut.table = outfov_colors
-
-    # mlab.show()
-    return fig

spaces/CVPR/Text2Human/Text2Human/train_parsing_gen.py

@@ -1,136 +0,0 @@
-import argparse
-import logging
-import os
-import os.path as osp
-import random
-import time
-
-import torch
-
-from data.parsing_generation_segm_attr_dataset import \
-    ParsingGenerationDeepFashionAttrSegmDataset
-from models import create_model
-from utils.logger import MessageLogger, get_root_logger, init_tb_logger
-from utils.options import dict2str, dict_to_nonedict, parse
-from utils.util import make_exp_dirs
-
-
-def main():
-    # options
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.')
-    args = parser.parse_args()
-    opt = parse(args.opt, is_train=True)
-
-    # mkdir and loggers
-    make_exp_dirs(opt)
-    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log")
-    logger = get_root_logger(
-        logger_name='base', log_level=logging.INFO, log_file=log_file)
-    logger.info(dict2str(opt))
-    # initialize tensorboard logger
-    tb_logger = None
-    if opt['use_tb_logger'] and 'debug' not in opt['name']:
-        tb_logger = init_tb_logger(log_dir='./tb_logger/' + opt['name'])
-
-    # convert to NoneDict, which returns None for missing keys
-    opt = dict_to_nonedict(opt)
-
-    # set up data loader
-    train_dataset = ParsingGenerationDeepFashionAttrSegmDataset(
-        segm_dir=opt['segm_dir'],
-        pose_dir=opt['pose_dir'],
-        ann_file=opt['train_ann_file'])
-    train_loader = torch.utils.data.DataLoader(
-        dataset=train_dataset,
-        batch_size=opt['batch_size'],
-        shuffle=True,
-        num_workers=opt['num_workers'],
-        drop_last=True)
-    logger.info(f'Number of train set: {len(train_dataset)}.')
-    opt['max_iters'] = opt['num_epochs'] * len(
-        train_dataset) // opt['batch_size']
-
-    val_dataset = ParsingGenerationDeepFashionAttrSegmDataset(
-        segm_dir=opt['segm_dir'],
-        pose_dir=opt['pose_dir'],
-        ann_file=opt['val_ann_file'])
-    val_loader = torch.utils.data.DataLoader(
-        dataset=val_dataset,
-        batch_size=1,
-        shuffle=False,
-        num_workers=opt['num_workers'])
-    logger.info(f'Number of val set: {len(val_dataset)}.')
-
-    test_dataset = ParsingGenerationDeepFashionAttrSegmDataset(
-        segm_dir=opt['segm_dir'],
-        pose_dir=opt['pose_dir'],
-        ann_file=opt['test_ann_file'])
-    test_loader = torch.utils.data.DataLoader(
-        dataset=test_dataset,
-        batch_size=1,
-        shuffle=False,
-        num_workers=opt['num_workers'])
-    logger.info(f'Number of test set: {len(test_dataset)}.')
-
-    current_iter = 0
-    best_epoch = None
-    best_acc = 0
-
-    model = create_model(opt)
-
-    data_time, iter_time = 0, 0
-    current_iter = 0
-
-    # create message logger (formatted outputs)
-    msg_logger = MessageLogger(opt, current_iter, tb_logger)
-
-    for epoch in range(opt['num_epochs']):
-        lr = model.update_learning_rate(epoch)
-
-        for _, batch_data in enumerate(train_loader):
-            data_time = time.time() - data_time
-
-            current_iter += 1
-
-            model.feed_data(batch_data)
-            model.optimize_parameters()
-
-            iter_time = time.time() - iter_time
-            if current_iter % opt['print_freq'] == 0:
-                log_vars = {'epoch': epoch, 'iter': current_iter}
-                log_vars.update({'lrs': [lr]})
-                log_vars.update({'time': iter_time, 'data_time': data_time})
-                log_vars.update(model.get_current_log())
-                msg_logger(log_vars)
-
-            data_time = time.time()
-            iter_time = time.time()
-
-        if epoch % opt['val_freq'] == 0:
-            save_dir = f'{opt["path"]["visualization"]}/valset/epoch_{epoch:03d}'
-            os.makedirs(save_dir, exist_ok=opt['debug'])
-            val_acc = model.inference(val_loader, save_dir)
-
-            save_dir = f'{opt["path"]["visualization"]}/testset/epoch_{epoch:03d}'
-            os.makedirs(save_dir, exist_ok=opt['debug'])
-            test_acc = model.inference(test_loader, save_dir)
-
-            logger.info(f'Epoch: {epoch}, '
-                        f'val_acc: {val_acc: .4f}, '
-                        f'test_acc: {test_acc: .4f}.')
-
-            if test_acc > best_acc:
-                best_epoch = epoch
-                best_acc = test_acc
-
-            logger.info(f'Best epoch: {best_epoch}, '
-                        f'Best test acc: {best_acc: .4f}.')
-
-            # save model
-            model.save_network(
-                f'{opt["path"]["models"]}/parsing_generation_epoch{epoch}.pth')
-
-
-if __name__ == '__main__':
-    main()

spaces/CVPR/regionclip-demo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h

@@ -1,115 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#pragma once
-#include <torch/types.h>
-
-namespace detectron2 {
-
-at::Tensor ROIAlignRotated_forward_cpu(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio);
-
-at::Tensor ROIAlignRotated_backward_cpu(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio);
-
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-at::Tensor ROIAlignRotated_forward_cuda(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio);
-
-at::Tensor ROIAlignRotated_backward_cuda(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio);
-#endif
-
-// Interface for Python
-inline at::Tensor ROIAlignRotated_forward(
-    const at::Tensor& input,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio) {
-  if (input.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    return ROIAlignRotated_forward_cuda(
-        input,
-        rois,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        sampling_ratio);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  return ROIAlignRotated_forward_cpu(
-      input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
-}
-
-inline at::Tensor ROIAlignRotated_backward(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width,
-    const int sampling_ratio) {
-  if (grad.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    return ROIAlignRotated_backward_cuda(
-        grad,
-        rois,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        batch_size,
-        channels,
-        height,
-        width,
-        sampling_ratio);
-#else
-    AT_ERROR("Not compiled with GPU support");
-#endif
-  }
-  return ROIAlignRotated_backward_cpu(
-      grad,
-      rois,
-      spatial_scale,
-      pooled_height,
-      pooled_width,
-      batch_size,
-      channels,
-      height,
-      width,
-      sampling_ratio);
-}
-
-} // namespace detectron2

spaces/CazimirRoman/summarize-your-webpage-api-with-gradio/README.md

@@ -1,12 +0,0 @@
----
-title: URL Summarizer API (Gradio)
-emoji: 🐨
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Chemsseddine/summarisation/app.py

@@ -1,81 +0,0 @@
-from transformers import RobertaTokenizerFast, EncoderDecoderModel
-import torch
-#initialisation de tokenizer
-device = "cuda" if torch.cuda.is_available() else "cpu"
-tokenizer = RobertaTokenizerFast.from_pretrained("Chemsseddine/bert2gpt2SUMM-finetuned-mlsum")
-#Chemsseddine/bert2gpt2SUMM-finetuned-mlsum
-#aider les token special
-tokenizer.bos_token = tokenizer.cls_token
-tokenizer.eos_token = tokenizer.sep_token
-#initialisation du modele
-model = EncoderDecoderModel.from_pretrained("Chemsseddine/bert2gpt2SUMM-finetuned-mlsum").to(device)
-#tf.random.set_seed(0)
-# generate summary
-def generateSumm(input_texte,max,min):
-  # encoder le texte entrée 
-  if input_texte and input_texte.strip():
-    if min<len(input_texte):
-
-      if max>min:
-        
-        input_ids = tokenizer.encode(input_texte, return_tensors='pt')
-    #generation de resume a l'aide de texte encodé
-        summary_ids = model.generate(input_ids,#le texte encodé
-            max_length=max,#la longuer maximale du sequence de sortie
-            min_length=min,#la longuer minimum du sequence de sortie
-            
-            num_beams=5,
-            repetition_penalty=2.5, 
-            length_penalty=1.0, 
-            early_stopping=True,#pour que la génération soit terminée lorsque toutes les hypothèses de faisceau ont atteint le jeton EOS.
-            no_repeat_ngram_size=2,#aucun 2 grammes n'apparaisse deux fois#Pour éviter les répétitions du même texte,
-            use_cache=True,
-            do_sample = True,
-           # num_return_sequences=5, 
-            temperature = 0.8,
-            top_k = 50,
-            top_p = 0.95)
-  #decodé la sequence de generé par le modele
-        summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-        return summary_text
-      else:
-
-        summary_text="La longueur minimale est grande que la maximale"
-        return summary_text
-    else:
-      summary_text="La longueur de texte entré est inferieur que la minimale que vous avez choisis"
-      return summary_text
-      
-  else :
-    summary_text="Entrer votre Texte S'il vous plait"
-    return summary_text
-
-
-from difflib import Differ
-import gradio as gr
-demo = gr.Blocks()
-
-def diff_texts(text1, text2):
-    d = Differ()
-    return [
-        (token[2:], token[0] if token[0] != " " else None)
-        for token in d.compare(text1.split(), text2.split())]
-
-
-inp=gr.inputs.Textbox(label="Text Originale",placeholder="Entrer Texte ici...")
-out=gr.outputs.Textbox(label="Résumé")
-mx_length=gr.Slider(40, 512)
-mn_length=gr.Slider(10,120)
-
-with demo:
-    gr.Markdown("***<center>Résumé Votre Text à l'aide de IA.</center>***\n\n Vous pouvez résumé votre texte par entrer le texte originale, et vous pouvez comparer le resultat avec votre texte originale en cliquant sur Comparer resultat ")
-    
-    with gr.Tabs():
-        
-        with gr.TabItem("Résumé"):
-            gr.Interface(fn=generateSumm, inputs=[inp,mx_length,mn_length], outputs=out ,cache_examples=True,allow_flagging=False
-                         )
-        with gr.TabItem("Comparer resultat"):
-            gr.Interface(diff_texts,[inp,out],gr.HighlightedText(label="Difference"),allow_flagging=False)
-            
-demo.launch(debug=True)

spaces/CikeyQI/meme-api/meme_generator/memes/listen_music/__init__.py

@@ -1,26 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def listen_music(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA")
-    frame = BuildImage.open(img_dir / "0.png")
-    frames: List[IMG] = []
-    for i in range(0, 360, 10):
-        frames.append(
-            frame.copy()
-            .paste(img.rotate(-i).resize((215, 215)), (100, 100), below=True)
-            .image
-        )
-    return save_gif(frames, 0.05)
-
-
-add_meme("listen_music", listen_music, min_images=1, max_images=1, keywords=["听音乐"])

spaces/CofAI/chat.b4/client/js/chat.js

@@ -1,508 +0,0 @@
-const query = (obj) =>
-	Object.keys(obj)
-		.map((k) => encodeURIComponent(k) + "=" + encodeURIComponent(obj[k]))
-		.join("&");
-const url_prefix = document.querySelector("body").getAttribute("data-urlprefix");
-const markdown = window.markdownit();
-const message_box = document.getElementById(`messages`);
-const message_input = document.getElementById(`message-input`);
-const box_conversations = document.querySelector(`.top`);
-const spinner = box_conversations.querySelector(".spinner");
-const stop_generating = document.querySelector(`.stop-generating`);
-const send_button = document.querySelector(`#send-button`);
-const user_image = `<img src="${url_prefix}/assets/img/user.png" alt="User Avatar">`;
-const gpt_image = `<img src="${url_prefix}/assets/img/gpt.png" alt="GPT Avatar">`;
-let prompt_lock = false;
-
-hljs.addPlugin(new CopyButtonPlugin());
-
-message_input.addEventListener("blur", () => {
-	window.scrollTo(0, 0);
-});
-
-message_input.addEventListener("focus", () => {
-	document.documentElement.scrollTop = document.documentElement.scrollHeight;
-});
-
-const delete_conversations = async () => {
-	localStorage.clear();
-	await new_conversation();
-};
-
-const handle_ask = async () => {
-	message_input.style.height = `80px`;
-	window.scrollTo(0, 0);
-	let message = message_input.value;
-
-	if (message.length > 0) {
-		message_input.value = ``;
-		message_input.dispatchEvent(new Event("input"));
-		await ask_gpt(message);
-	}
-};
-
-const remove_cancel_button = async () => {
-	stop_generating.classList.add(`stop-generating-hiding`);
-
-	setTimeout(() => {
-		stop_generating.classList.remove(`stop-generating-hiding`);
-		stop_generating.classList.add(`stop-generating-hidden`);
-	}, 300);
-};
-
-const ask_gpt = async (message) => {
-	try {
-		message_input.value = ``;
-		message_input.innerHTML = ``;
-		message_input.innerText = ``;
-
-		add_conversation(window.conversation_id, message.substr(0, 16));
-		window.scrollTo(0, 0);
-		window.controller = new AbortController();
-
-		jailbreak = document.getElementById("jailbreak");
-		model = document.getElementById("model");
-		prompt_lock = true;
-		window.text = ``;
-		window.token = message_id();
-
-		stop_generating.classList.remove(`stop-generating-hidden`);
-
-		add_user_message_box(message);
-
-		message_box.scrollTop = message_box.scrollHeight;
-		window.scrollTo(0, 0);
-		await new Promise((r) => setTimeout(r, 500));
-		window.scrollTo(0, 0);
-
-		message_box.innerHTML += `
-            <div class="message">
-                <div class="avatar-container">
-                    ${gpt_image}
-                </div>
-                <div class="content" id="gpt_${window.token}">
-                    <div id="cursor"></div>
-                </div>
-            </div>
-        `;
-
-		message_box.scrollTop = message_box.scrollHeight;
-		window.scrollTo(0, 0);
-		await new Promise((r) => setTimeout(r, 1000));
-		window.scrollTo(0, 0);
-
-		const response = await fetch(`${url_prefix}/backend-api/v2/conversation`, {
-			method: `POST`,
-			signal: window.controller.signal,
-			headers: {
-				"content-type": `application/json`,
-				accept: `text/event-stream`,
-			},
-			body: JSON.stringify({
-				conversation_id: window.conversation_id,
-				action: `_ask`,
-				model: model.options[model.selectedIndex].value,
-				jailbreak: jailbreak.options[jailbreak.selectedIndex].value,
-				meta: {
-					id: window.token,
-					content: {
-						conversation: await get_conversation(window.conversation_id),
-						internet_access: document.getElementById("switch").checked,
-						content_type: "text",
-						parts: [
-							{
-								content: message,
-								role: "user",
-							},
-						],
-					},
-				},
-			}),
-		});
-
-		const reader = response.body.getReader();
-
-		while (true) {
-			const { value, done } = await reader.read();
-			if (done) break;
-
-			chunk = decodeUnicode(new TextDecoder().decode(value));
-
-			if (
-				chunk.includes(`<form id="challenge-form" action="${url_prefix}/backend-api/v2/conversation?`)
-			) {
-				chunk = `cloudflare token expired, please refresh the page.`;
-			}
-
-			text += chunk;
-
-			document.getElementById(`gpt_${window.token}`).innerHTML = markdown.render(text);
-			document.querySelectorAll(`code`).forEach((el) => {
-				hljs.highlightElement(el);
-			});
-
-			window.scrollTo(0, 0);
-			message_box.scrollTo({ top: message_box.scrollHeight, behavior: "auto" });
-		}
-
-		// if text contains :
-		if (text.includes(`instead. Maintaining this website and API costs a lot of money`)) {
-			document.getElementById(`gpt_${window.token}`).innerHTML =
-				"An error occurred, please reload / refresh cache and try again.";
-		}
-
-		add_message(window.conversation_id, "user", message);
-		add_message(window.conversation_id, "assistant", text);
-
-		message_box.scrollTop = message_box.scrollHeight;
-		await remove_cancel_button();
-		prompt_lock = false;
-
-		await load_conversations(20, 0);
-		window.scrollTo(0, 0);
-	} catch (e) {
-		add_message(window.conversation_id, "user", message);
-
-		message_box.scrollTop = message_box.scrollHeight;
-		await remove_cancel_button();
-		prompt_lock = false;
-
-		await load_conversations(20, 0);
-
-		console.log(e);
-
-		let cursorDiv = document.getElementById(`cursor`);
-		if (cursorDiv) cursorDiv.parentNode.removeChild(cursorDiv);
-
-		if (e.name != `AbortError`) {
-			let error_message = `oops ! something went wrong, please try again / reload. [stacktrace in console]`;
-
-			document.getElementById(`gpt_${window.token}`).innerHTML = error_message;
-			add_message(window.conversation_id, "assistant", error_message);
-		} else {
-			document.getElementById(`gpt_${window.token}`).innerHTML += ` [aborted]`;
-			add_message(window.conversation_id, "assistant", text + ` [aborted]`);
-		}
-
-		window.scrollTo(0, 0);
-	}
-};
-
-const add_user_message_box = (message) => {
-	const messageDiv = createElement("div", { classNames: ["message"] });
-	const avatarContainer = createElement("div", { classNames: ["avatar-container"], innerHTML: user_image });
-	const contentDiv = createElement("div", {
-		classNames: ["content"],
-		id: `user_${token}`,
-		textContent: message,
-	});
-
-	messageDiv.append(avatarContainer, contentDiv);
-	message_box.appendChild(messageDiv);
-};
-
-const decodeUnicode = (str) => {
-	return str.replace(/\\u([a-fA-F0-9]{4})/g, function (match, grp) {
-		return String.fromCharCode(parseInt(grp, 16));
-	});
-};
-
-const clear_conversations = async () => {
-	const elements = box_conversations.childNodes;
-	let index = elements.length;
-
-	if (index > 0) {
-		while (index--) {
-			const element = elements[index];
-			if (element.nodeType === Node.ELEMENT_NODE && element.tagName.toLowerCase() !== `button`) {
-				box_conversations.removeChild(element);
-			}
-		}
-	}
-};
-
-const clear_conversation = async () => {
-	let messages = message_box.getElementsByTagName(`div`);
-
-	while (messages.length > 0) {
-		message_box.removeChild(messages[0]);
-	}
-};
-
-const delete_conversation = async (conversation_id) => {
-	localStorage.removeItem(`conversation:${conversation_id}`);
-
-	if (window.conversation_id == conversation_id) {
-		await new_conversation();
-	}
-
-	await load_conversations(20, 0, true);
-};
-
-const set_conversation = async (conversation_id) => {
-	history.pushState({}, null, `${url_prefix}/chat/${conversation_id}`);
-	window.conversation_id = conversation_id;
-
-	await clear_conversation();
-	await load_conversation(conversation_id);
-	await load_conversations(20, 0, true);
-};
-
-const new_conversation = async () => {
-	history.pushState({}, null, `${url_prefix}/chat/`);
-	window.conversation_id = uuid();
-
-	await clear_conversation();
-	await load_conversations(20, 0, true);
-};
-
-const load_conversation = async (conversation_id) => {
-	let conversation = await JSON.parse(localStorage.getItem(`conversation:${conversation_id}`));
-	console.log(conversation, conversation_id);
-
-	for (item of conversation.items) {
-		if (is_assistant(item.role)) {
-			message_box.innerHTML += load_gpt_message_box(item.content);
-		} else {
-			message_box.innerHTML += load_user_message_box(item.content);
-		}
-	}
-
-	document.querySelectorAll(`code`).forEach((el) => {
-		hljs.highlightElement(el);
-	});
-
-	message_box.scrollTo({ top: message_box.scrollHeight, behavior: "smooth" });
-
-	setTimeout(() => {
-		message_box.scrollTop = message_box.scrollHeight;
-	}, 500);
-};
-
-const load_user_message_box = (content) => {
-	const messageDiv = createElement("div", { classNames: ["message"] });
-	const avatarContainer = createElement("div", { classNames: ["avatar-container"], innerHTML: user_image });
-	const contentDiv = createElement("div", { classNames: ["content"] });
-	const preElement = document.createElement("pre");
-	preElement.textContent = content;
-	contentDiv.appendChild(preElement);
-
-	messageDiv.append(avatarContainer, contentDiv);
-
-	return messageDiv.outerHTML;
-};
-
-const load_gpt_message_box = (content) => {
-	return `
-            <div class="message">
-                <div class="avatar-container">
-                    ${gpt_image}
-                </div>
-                <div class="content">
-                    ${markdown.render(content)}
-                </div>
-            </div>
-        `;
-};
-
-const is_assistant = (role) => {
-	return role == "assistant";
-};
-
-const get_conversation = async (conversation_id) => {
-	let conversation = await JSON.parse(localStorage.getItem(`conversation:${conversation_id}`));
-	return conversation.items;
-};
-
-const add_conversation = async (conversation_id, title) => {
-	if (localStorage.getItem(`conversation:${conversation_id}`) == null) {
-		localStorage.setItem(
-			`conversation:${conversation_id}`,
-			JSON.stringify({
-				id: conversation_id,
-				title: title,
-				items: [],
-			})
-		);
-	}
-};
-
-const add_message = async (conversation_id, role, content) => {
-	before_adding = JSON.parse(localStorage.getItem(`conversation:${conversation_id}`));
-
-	before_adding.items.push({
-		role: role,
-		content: content,
-	});
-
-	localStorage.setItem(`conversation:${conversation_id}`, JSON.stringify(before_adding)); // update conversation
-};
-
-const load_conversations = async (limit, offset, loader) => {
-	//console.log(loader);
-	//if (loader === undefined) box_conversations.appendChild(spinner);
-
-	let conversations = [];
-	for (let i = 0; i < localStorage.length; i++) {
-		if (localStorage.key(i).startsWith("conversation:")) {
-			let conversation = localStorage.getItem(localStorage.key(i));
-			conversations.push(JSON.parse(conversation));
-		}
-	}
-
-	//if (loader === undefined) spinner.parentNode.removeChild(spinner)
-	await clear_conversations();
-
-	for (conversation of conversations) {
-		box_conversations.innerHTML += `
-            <div class="conversation-sidebar">
-                <div class="left" onclick="set_conversation('${conversation.id}')">
-                    <i class="fa-regular fa-comments"></i>
-                    <span class="conversation-title">${conversation.title}</span>
-                </div>
-                <i onclick="delete_conversation('${conversation.id}')" class="fa-regular fa-trash"></i>
-            </div>
-        `;
-	}
-
-	document.querySelectorAll(`code`).forEach((el) => {
-		hljs.highlightElement(el);
-	});
-};
-
-document.getElementById(`cancelButton`).addEventListener(`click`, async () => {
-	window.controller.abort();
-	console.log(`aborted ${window.conversation_id}`);
-});
-
-function h2a(str1) {
-	var hex = str1.toString();
-	var str = "";
-
-	for (var n = 0; n < hex.length; n += 2) {
-		str += String.fromCharCode(parseInt(hex.substr(n, 2), 16));
-	}
-
-	return str;
-}
-
-const uuid = () => {
-	return `xxxxxxxx-xxxx-4xxx-yxxx-${Date.now().toString(16)}`.replace(/[xy]/g, function (c) {
-		var r = (Math.random() * 16) | 0,
-			v = c == "x" ? r : (r & 0x3) | 0x8;
-		return v.toString(16);
-	});
-};
-
-const message_id = () => {
-	random_bytes = (Math.floor(Math.random() * 1338377565) + 2956589730).toString(2);
-	unix = Math.floor(Date.now() / 1000).toString(2);
-
-	return BigInt(`0b${unix}${random_bytes}`).toString();
-};
-
-window.onload = async () => {
-	load_settings_localstorage();
-
-	conversations = 0;
-	for (let i = 0; i < localStorage.length; i++) {
-		if (localStorage.key(i).startsWith("conversation:")) {
-			conversations += 1;
-		}
-	}
-
-	if (conversations == 0) localStorage.clear();
-
-	await setTimeout(() => {
-		load_conversations(20, 0);
-	}, 1);
-
-	if (!window.location.href.endsWith(`#`)) {
-		if (/\/chat\/.+/.test(window.location.href.slice(url_prefix.length))) {
-			await load_conversation(window.conversation_id);
-		}
-	}
-
-	message_input.addEventListener("keydown", async (evt) => {
-		if (prompt_lock) return;
-
-		if (evt.key === "Enter" && !evt.shiftKey) {
-			evt.preventDefault();
-			await handle_ask();
-		}
-	});
-
-	send_button.addEventListener("click", async (event) => {
-		event.preventDefault();
-		if (prompt_lock) return;
-		message_input.blur();
-		await handle_ask();
-	});
-
-	register_settings_localstorage();
-};
-
-const register_settings_localstorage = async () => {
-	settings_ids = ["switch", "model", "jailbreak"];
-	settings_elements = settings_ids.map((id) => document.getElementById(id));
-	settings_elements.map((element) =>
-		element.addEventListener(`change`, async (event) => {
-			switch (event.target.type) {
-				case "checkbox":
-					localStorage.setItem(event.target.id, event.target.checked);
-					break;
-				case "select-one":
-					localStorage.setItem(event.target.id, event.target.selectedIndex);
-					break;
-				default:
-					console.warn("Unresolved element type");
-			}
-		})
-	);
-};
-
-const load_settings_localstorage = async () => {
-	settings_ids = ["switch", "model", "jailbreak"];
-	settings_elements = settings_ids.map((id) => document.getElementById(id));
-	settings_elements.map((element) => {
-		if (localStorage.getItem(element.id)) {
-			switch (element.type) {
-				case "checkbox":
-					element.checked = localStorage.getItem(element.id) === "true";
-					break;
-				case "select-one":
-					element.selectedIndex = parseInt(localStorage.getItem(element.id));
-					break;
-				default:
-					console.warn("Unresolved element type");
-			}
-		}
-	});
-};
-
-function clearTextarea(textarea) {
-	textarea.style.removeProperty("height");
-	textarea.style.height = `${textarea.scrollHeight + 4}px`;
-	if (textarea.value.trim() === "" && textarea.value.includes("\n")) {
-		textarea.value = "";
-	}
-}
-
-function createElement(tag, { classNames, id, innerHTML, textContent } = {}) {
-	const el = document.createElement(tag);
-	if (classNames) {
-		el.classList.add(...classNames);
-	}
-	if (id) {
-		el.id = id;
-	}
-	if (innerHTML) {
-		el.innerHTML = innerHTML;
-	}
-	if (textContent) {
-		const preElement = document.createElement("pre");
-		preElement.textContent = textContent;
-		el.appendChild(preElement);
-	}
-	return el;
-}