Update parquet files (step 117 of 121)

spaces/1-13-am/neural-style-transfer/app.py

@@ -1,57 +0,0 @@
-import gradio as gr
-import torch
-from utils import transformer, tensor_to_img
-from network import Style_Transfer_Network
-
-check_point = torch.load("check_point1_0.pth", map_location = torch.device('cpu'))
-model = Style_Transfer_Network()
-model.load_state_dict(check_point['state_dict'])
-
-def style_transfer(content_img, style_strength, style_img_1 = None, iw_1 = 0, style_img_2 = None, iw_2 = 0, style_img_3 = None, iw_3 = 0, preserve_color = None):
-    transform = transformer(imsize = 512)
-
-    content = transform(content_img).unsqueeze(0)
-
-    iw = [iw_1, iw_2, iw_3]
-    interpolation_weights = [i/ sum(iw) for i in iw]
-
-    style_imgs = [style_img_1, style_img_2, style_img_3]
-    styles = []
-    for style_img in style_imgs:
-      if style_img is not None:
-        styles.append(transform(style_img).unsqueeze(0))
-    if preserve_color == "None": preserve_color = None
-    elif preserve_color == "Whitening & Coloring": preserve_color = "whitening_and_coloring"
-    elif preserve_color == "Histogram matching": preserve_color = "histogram_matching"
-    with torch.no_grad():
-        stylized_img = model(content, styles, style_strength, interpolation_weights, preserve_color = preserve_color)
-    return tensor_to_img(stylized_img)
-
-title = "Artistic Style Transfer"
-
-content_img = gr.components.Image(label="Content image", type = "pil")
-
-style_img_1 = gr.components.Image(label="Style images", type = "pil")
-iw_1 = gr.components.Slider(0., 1., label = "Style 1 strength")
-style_img_2 = gr.components.Image(label="Style images", type = "pil")
-iw_2 = gr.components.Slider(0., 1., label = "Style 2 strength")
-style_img_3 = gr.components.Image(label="Style images", type = "pil")
-iw_3 = gr.components.Slider(0., 1., label = "Style 3 strength")
-style_strength =  gr.components.Slider(0., 1., label = "Adjust style strength")
-preserve_color = gr.components.Dropdown(["None", "Whitening & Coloring", "Histogram matching"], label = "Choose color preserving mode")
-
-interface = gr.Interface(fn = style_transfer,
-                         inputs = [content_img,
-                                   style_strength,
-                                   style_img_1,
-                                   iw_1,
-                                   style_img_2,
-                                   iw_2,
-                                   style_img_3,
-                                   iw_3,
-                                   preserve_color],
-                         outputs = gr.components.Image(),
-                         title = title
-                         )
-interface.queue()
-interface.launch(share = True, debug = True)

spaces/101-5/gpt4free/CONTRIBUTING.md

@@ -1,8 +0,0 @@
-<img alt="gpt4free logo" src="https://user-images.githubusercontent.com/98614666/233799515-1a7cb6a3-b17f-42c4-956d-8d2a0664466f.png">
-
-### Please, follow these steps to contribute:
-1. Reverse a website from this list: [sites-to-reverse](https://github.com/xtekky/gpt4free/issues/40)
-2. Add it to [./testing](https://github.com/xtekky/gpt4free/tree/main/testing)
-3. Refractor it and add it to [./g4f](https://github.com/xtekky/gpt4free/tree/main/g4f)
-
-### We will be grateful to see you as a contributor!

spaces/17TheWord/vits-models/text/cleaners.py

@@ -1,475 +0,0 @@
-""" 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 re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text

spaces/1acneusushi/gradio-2dmoleculeeditor/data/7 Data Recovery Suite 4.4 Crack Recover Data from Any Device or Storage.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Kung Fu Panda 2 Full Movie Download ) Discover the Legend of the Dragon Warrior.md

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-<h1>Kung Fu Panda 2: A Fun and Action-Packed Sequel</h1>
- <p>If you are looking for a movie that combines humor, adventure, emotion, and stunning animation, you might want to check out <i>Kung Fu Panda 2</i>. This film is the sequel to <i>Kung Fu Panda</i> (2008), which introduced us to Po, a clumsy but lovable panda who became the Dragon Warrior and saved China from the evil Tai Lung.</p>
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- <p>In this film, Po faces a new challenge: Lord Shen, a peacock who has invented a weapon that can destroy kung fu and conquer China. Along with his friends, the Furious Five, Po must stop Shen before it is too late. But along the way, Po also discovers some secrets about his past and his true identity.</p>
- <p><i>Kung Fu Panda 2</i> was released in 2011 by DreamWorks Animation and Paramount Pictures. It was directed by Jennifer Yuh Nelson, who became the first woman to solely direct an animated feature film from a major Hollywood studio. It was written by Jonathan Aibel and Glenn Berger, who also wrote the first film.</p>
- <p>The film received critical acclaim for its story, characters, animation, music, and themes. It was nominated for an Academy Award for Best Animated Feature, losing to <i>Rango</i>. It also became the highest-grossing film directed by a woman until <i>Frozen</i> (2013), as well as the highest-grossing film solely directed by a woman until <i>Wonder Woman</i> (2017). It is also the sixth highest-grossing film of 2011, and the highest-grossing animated feature film of the year.</p>
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- <p>The film begins with a flashback that tells us how Lord Shen, the son of the peacock rulers of Gongmen City, became obsessed with using fireworks as a weapon. He learned of a prophecy that said he would be defeated by "a warrior of black and white". He then ordered his wolf army to kill all the pandas in China, hoping to prevent the prophecy from coming true.</p>
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- <p>In the present day, Po is enjoying his life as the Dragon Warrior and the leader of the Furious Five: Tigress, Monkey, Viper, Crane, and Mantis. He is also learning more about kung fu from his mentor, Master Shifu.</p>
- <p>One day, Po and his friends are sent to stop a group of wolf bandits who are stealing metal for Shen's weapon. Po has a flashback of his mother when he sees a symbol on one of the wolves' armor.</p>
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- <p>Mr. Ping tells him that he found him in a radish crate when he was a baby and decided to raise him as his son.</p>
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- <p>There they meet two other kung fu masters who have been hiding from Shen: Master Ox and Master Croc.</p>
- <p>They also encounter Shen's old nanny, a goat named Soothsayer, who can see the future.</p>
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- <p>He realizes that he must achieve inner peace in order to overcome his past trauma.</p>
- <p>With the help of Soothsayer, Po meditates on his memories and accepts them as part of who he is.</p>
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- <p>Po uses his kung fu skills to deflect Shen's cannonballs back at him.</p>
- <p>He also tries to persuade Shen to let go of his hatred and find inner peace.</p>
- <p>Shen refuses to listen and attacks Po with his blades.</p>
- <p>Po dodges them but one of them cuts through Shen's cannon ropes, causing it to fall on him.</p>
- <p>Shen is crushed by his own weapon while Po watches in sadness.</p>
- <p>Po then returns to Mr. Ping's noodle shop with his friends.</p>
- <p>He tells Mr. Ping that he knows he is not his biological father but he still loves him as his dad.</p>
- <p>Mr. Ping hugs him and tells him that he loves him too.</p>
- <p>The film ends with a scene showing that Po's biological father is still alive somewhere in China with other pandas.</p>
- <h2>The Characters of Kung Fu Panda 2</h2>
- <table>
-<tr>
-<th>Name</th>
-<th>Voice Actor</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Po</td>
-<td>Jack Black</td>
-<td>The Dragon Warrior and the leader of the Furious Five. He is a panda who loves kung fu, food, and fun. He is brave, loyal, optimistic, friendly, clumsy, naive, but also smart when it matters most.<td>
-</tr>
-<tr>
-<td>Tigress</td>
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-<td>Po's closest friend and a fierce fighter. She is a tiger who is strong, serious, disciplined, stoic,<td>
-and sometimes cold but also caring deep down. </tr>
-<tr>
-<td>Monkey</td>
-<td>Jackie Chan</td>
-<h2>The Animation and Music of Kung Fu Panda 2</h2>
- <p>The animation of <i>Kung Fu Panda 2</i> is a remarkable achievement that combines computer animation and traditional animation techniques. The film uses computer animation for the main characters and the backgrounds, but also incorporates hand-drawn animation for some of the flashback scenes and the opening sequence. The hand-drawn animation gives the film a more stylized and artistic look that pays homage to Chinese painting and calligraphy.</p>
- <p>The visual style of the film is also influenced by the culture and history of China. The film features various locations and landmarks that are based on real places in China, such as the Great Wall, the Forbidden City, and the Terracotta Army. The film also uses elements of Chinese mythology, such as dragons, phoenixes, and kites. The film also incorporates symbols and motifs that are relevant to the story and the characters, such as peacock feathers, lotus flowers, yin and yang, and fireworks.</p>
- <p>The music of <i>Kung Fu Panda 2</i> is composed by Hans Zimmer and John Powell, who also composed the music for the first film. The score blends orchestral music with traditional Chinese instruments, such as erhu, pipa, guzheng, dizi, and suona. The score also features themes and motifs that reflect the characters and their emotions, such as Po's theme, Shen's theme, and the inner peace theme. The score also includes some original songs by CeeLo Green and Jay Chou, who sing "Kung Fu Fighting" and "Nunchucks" respectively.</p>
- <h2>The Reception and Legacy of Kung Fu Panda 2</h2>
- <p><i>Kung Fu Panda 2</i> was released in theaters on May 26, 2011 in 2D, RealD 3D, Digital 3D and 4DX. The film received positive reviews from critics and audiences alike, who praised its story, characters, animation, music, and themes. The film has a 81% approval rating on Rotten Tomatoes based on 179 reviews, with an average rating of 7/10. The website's critical consensus reads: "<i>Kung Fu Panda 2</i> offers enough action, comedy, and visual sparkle to compensate for its somewhat familiar plot."</p>
- <p>The film was also a commercial success, grossing $665.7 million worldwide against its $150 million budget. It became the highest-grossing film directed by a woman until <i>Frozen</i> (2013), as well as the highest-grossing film solely directed by a woman until <i>Wonder Woman</i> (2017). It is also the sixth highest-grossing film of 2011, and the highest-grossing animated feature film of the year.</p>
- <p>The film was nominated for various awards, including an Academy Award for Best Animated Feature at the 84th Academy Awards, losing to <i>Rango</i>. It also received nominations from the Annie Awards, the Golden Globe Awards, the BAFTA Awards, the Critics' Choice Awards, and the Kids' Choice Awards.</p>
- <p>The film had a significant impact on female directors and animators in Hollywood. Jennifer Yuh Nelson became the second woman to be nominated for an Academy Award for Best Animated Feature after Marjane Satrapi for <i>Persepolis</i> (2007). She also became one of the most successful female directors in terms of box office gross. She returned to direct <i>Kung Fu Panda 3</i>, which was co-directed by Alessandro Carloni.</p>
- <p>The film also spawned a sequel and a franchise expansion. <i>Kung Fu Panda 3</i> was released in January 2016 and continued Po's story as he reunited with his biological father and faced a new enemy. The film was also well-received by critics and audiences and grossed $521.2 million worldwide. A fourth film is currently in development at DreamWorks Animation.</p>
- <p>Besides the films, the franchise also includes several short films, such as <i>Kung Fu Panda: Secrets of the Masters</i>, <i>Kung Fu Panda: Secrets of the Scroll</i>, and <i>Kung Fu Panda: Secrets of the Furious Five</i>. It also includes two television series: <i>Kung Fu Panda: Legends of Awesomeness</i>, which ran from 2011 to 2016 on Nickelodeon; and <i>Kung Fu Panda: The Paws of Destiny</i>, which premiered in 2018 on Amazon Prime Video.</p>
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- <p><i>Kung Fu Panda 2</i> is a fun and action-packed sequel that delivers on its promise of humor, adventure,<td>
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-<h1>CarX Drift Racing 2: The Ultimate Drifting Game for Android</h1>
-<p>If you are a fan of drifting games, you must have heard of CarX Drift Racing 2. This is one of the most popular and realistic drifting games for Android devices. In this article, we will tell you everything you need to know about this game, including its features, how to download and install it, and some frequently asked questions.</p>
-<h2>Introduction</h2>
-<h3>What is CarX Drift Racing 2?</h3>
-<p>CarX Drift Racing 2 is a sequel of the original CarX Drift Racing game, which has over 100 million fans around the world. It is a racing game that focuses on drifting, which is a driving technique where the driver intentionally oversteers the car to make it slide sideways. Drifting is not only fun, but also challenging and rewarding, as it requires skill, precision, and timing.</p>
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-<h3>Why should you play CarX Drift Racing 2?</h3>
-<p>There are many reasons why you should play CarX Drift Racing 2, such as:</p>
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-<li>It has stunning graphics and sound effects that make you feel like you are in a real drift car.</li>
-<li>It has a variety of cars, tracks, and modes to choose from, so you will never get bored.</li>
-<li>It has a realistic physics engine that simulates the behavior of different cars, surfaces, and weather conditions.</li>
-<li>It has an online mode where you can drift with your friends or other players from around the world.</li>
-<li>It has a visual auto tuning feature where you can customize your car's appearance and performance.</li>
-<li>It has an XDS mode where you can practice tandem drifting with yourself or other players.</li>
-<li>It has a TOP-32 mode where you can compete against the best drifters in the world.</li>
-</ul>
-<h2>Features of CarX Drift Racing 2</h2>
-<h3>Online Rooms</h3>
-<p>This is a new feature that allows you to drift in real time with your friends or other players. You can create or join an online room, pick a location, drift, and earn points. You can also watch other players drift using the drone camera. You can earn valuable rewards for achieving different ranks in the online mode.</p>
-<h3>Visual Auto Tuning</h3>
-<p>This feature allows you to customize your car's appearance and performance. You can replace mirrors, lights, bumpers, rims, and many other parts. You can also create a unique image of your car with body kits, vinyls, stickers, and paint. You can express your creativity and style with this feature.</p>
-<h3>Improved Performance Tuning</h3>
-<p>This feature allows you to adjust your car's performance according to your preferences and needs. You can tune the suspension, springs, tyre pressure, wheel angle, engine, turbo pressure, gearbox, brakes, and differential. You can fine tune your car to achieve the best drifting results.</p>
-<h3>Realistic Racing Physics</h3>
-<p>This feature makes CarX Drift Racing 2 one of the most realistic drifting games on Android. The game uses a physics engine that simulates the behavior of different cars, surfaces, and weather conditions. You can see the smoke, dust, sparks, and tyre tracks that result from your drifting. You can also feel the difference between asphalt, grass, sand, and snow. You can also experience different weather conditions such as rain, fog, and sun. You can enjoy the realistic racing physics of this game.</p>
-<h3>XDS Mode</h3>
-<p>This feature allows you to practice tandem drifting with yourself or other players. Tandem drifting is a technique where two or more cars drift together in a synchronized manner. It is one of the most spectacular and difficult forms of drifting. In XDS mode, you can choose a leader car and a follower car, and try to match the leader's trajectory and angle. You can also switch roles and become the leader or the follower. You can improve your drifting skills and coordination with this feature.</p>
-<h3>TOP-32 Mode</h3>
-<p>This feature allows you to compete against the best drifters in the world. TOP-32 mode is a tournament mode where you have to qualify for the final round by beating 31 other opponents. You have to drift on different tracks and earn points based on your speed, angle, and line. You have to be fast, precise, and consistent to win this mode. You can earn fame and glory by becoming the champion of TOP-32 mode.</p>
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-<h2>How to download and install CarX Drift Racing 2 APK OBB?</h2>
-<p>If you want to play CarX Drift Racing 2 on your Android device, you have to download and install the APK OBB files. APK is the application package file that contains the game's code and resources. OBB is the data file that contains the game's graphics and sound files. Here are the steps to download and install CarX Drift Racing 2 APK OBB:</p>
-<h3>Step 1: Download the APK and OBB files from a trusted source</h3>
-<p>You can find many websites that offer CarX Drift Racing 2 APK OBB files for free download. However, not all of them are safe and reliable. Some of them may contain viruses, malware, or fake files that can harm your device or steal your data. Therefore, you have to be careful and choose a trusted source to download the files. One of the best sources is [CarX Drift Racing 2 APK OBB], which provides the latest version of the game with high-quality graphics and sound.</p>
-<h3>Step 2: Enable unknown sources on your device</h3>
-<p>Before you can install the APK file, you have to enable unknown sources on your device. This is a security setting that prevents you from installing apps from sources other than the Google Play Store. To enable unknown sources, go to Settings > Security > Unknown Sources and toggle it on.</p>
-<h3>Step 3: Install the APK file</h3>
-<p>After you have enabled unknown sources, you can install the APK file. To do this, locate the downloaded APK file on your device using a file manager app. Tap on it and follow the instructions on the screen to complete the installation.</p>
-<h3>Step 4: Extract and copy the OBB folder to Android/OBB</h3>
-<p>After you have installed the APK file, you have to extract and copy the OBB folder to Android/OBB on your device's internal storage. To do this, locate the downloaded OBB file on your device using a file manager app. Tap on it and select Extract Here or Extract To depending on your app. You will see a folder named com.carxtech.carxdr2. Copy this folder and paste it in Android/OBB on your device's internal storage.</p>
-<h3>Step 5: Launch the game and enjoy</h3>
-<p>After you have copied the OBB folder, you are ready to launch the game and enjoy it. To do this, go to your app drawer and tap on CarX Drift Racing 2 icon. The game will start and load the data from the OBB folder. You can now drift away with CarX Drift Racing 2.</p>
-<h2>Conclusion</h2>
-<p>CarX Drift Racing 2 is one of the best drifting games for Android devices. It has amazing graphics, realistic physics, online mode, visual auto tuning, XDS mode, TOP-32 mode, and many other features that make it fun and exciting. If you want to play this game, you have to download and install CarX Drift Racing 2 APK OBB files from a trusted source. Follow our guide above to do it easily and safely.</p>
-<h2>FAQs</h2>
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-<li><b>Is CarX Drift Racing 2 free?</b></li>
-<p>Yes, CarX Drift Racing 2 is free to download and play. However, it contains in app purchases that allow you to buy coins, cars, and other items. You can also watch ads to earn free coins.</p>
-<li><b>What are the minimum requirements to play CarX Drift Racing 2?</b></li>
-<p>The minimum requirements to play CarX Drift Racing 2 are:</p>
-<ul>
-<li>Android version 5.0 or higher</li>
-<li>2 GB of RAM or more</li>
-<li>1.5 GB of free storage space or more</li>
-<li>A stable internet connection</li>
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-<li>Completing daily tasks and achievements</li>
-<li>Participating in online rooms and tournaments</li>
-<li>Watching ads and videos</li>
-<li>Buying them with real money</li>
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-<p>You can unlock more cars and tracks in CarX Drift Racing 2 by:</p>
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-<li>Reaching certain levels and ranks in the game modes</li>
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-<li>Filling out the feedback form on their website [CarX Technologies]</li>
-<li>Following them on their social media accounts [Facebook], [Twitter], [Instagram], [YouTube]</li>
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-<h1>Dolphin Emulator: The Ultimate Guide for Android Users</h1>
-<p>Do you love playing Nintendo GameCube and Wii games? Do you wish you could play them on your Android device? If yes, then you are in luck. Dolphin Emulator is a free and open-source software that allows you to do just that. In this article, we will show you how to download, install, configure, and use Dolphin Emulator on your Android device. We will also answer some of the frequently asked questions about this amazing app. Let's get started!</p>
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- <h2>What is Dolphin Emulator and why you should use it</h2>
-<p>Dolphin Emulator is a software that emulates the hardware and software of Nintendo GameCube and Wii consoles. It enables you to play games from these consoles on your Android device, as well as other platforms such as Windows, Linux, and macOS. Dolphin Emulator offers many features and benefits, such as:</p>
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-<li>High-resolution graphics: You can enjoy your games in HD quality, up to 1080p or even 4K, depending on your device capabilities.</li>
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-<li>Cheats: You can use various cheat codes and hacks to modify your game experience, such as unlocking hidden items, increasing your health, or skipping levels.</li>
-<li>Online multiplayer: You can play online with other Dolphin Emulator users, using the Nintendo Wi-Fi Connection service or the Netplay feature.</li>
-<li>Customization: You can customize your emulator settings, such as graphics, audio, controls, enhancements, hacks, and more, to suit your preferences and device specifications.</li>
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-<p>Dolphin Emulator supports both 32-bit and 64-bit Android devices, but the 32-bit version has some limitations and compatibility issues. For example, the 32-bit version cannot run games that require more than 2 GB of RAM, such as The Legend of Zelda: Skyward Sword or Xenoblade Chronicles. The 32-bit version also has lower performance and stability than the 64-bit version. Therefore, if you have a 64-bit device, we recommend you to use the 64-bit version of Dolphin Emulator for a better gaming experience.</p>
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-<li>You can download the latest version of Dolphin Emulator from the official website or from APKCombo. The official website has both the 32-bit and the 64-bit versions of the app, while APKCombo only has the 32-bit version. Make sure you download the correct version for your device.</li>
-<li>Before you install the APK file, you need to enable unknown sources in your device settings. To do this, go to Settings > Security > Unknown sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Once you have enabled unknown sources, you can install the APK file by tapping on it and following the instructions on the screen. You may need to grant some permissions to the app during the installation process.</li>
-<li>You also need to have enough storage space on your device or SD card for the emulator and the games. The emulator itself takes about 15 MB of space, while the games can vary from a few hundred MB to several GB. You can check your available storage space by going to Settings > Storage.</li>
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-<p>Congratulations! You have successfully installed Dolphin Emulator on your Android device. Now you are ready to configure it and play your favorite games.</p>
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- <h2>How to configure Dolphin Emulator settings for optimal performance</h2>
-<p>Dolphin Emulator has a lot of settings that you can adjust to optimize its performance and compatibility with different games and devices. You can access the settings menu by tapping on the three dots icon in the top right corner of the emulator screen. You will see various tabs, such as graphics, audio, controls, enhancements, hacks, and more. You can tap on each tab to see and change the settings related to it. You can also create custom profiles for different games and devices by tapping on the plus icon in the top right corner of the settings menu.</p>
-<p>Some of the settings that you should pay attention to are:</p>
-<ul>
-<li>Graphics backend: This determines how Dolphin Emulator renders the graphics of the games. There are three options: OpenGL, Vulkan, and Software Renderer. OpenGL is the default option and works well with most games and devices. Vulkan is a newer option that may offer better performance and compatibility with some games, but it may also cause some issues with others. Software Renderer is a slow option that does not use hardware acceleration and should only be used for debugging purposes.</li>
-<li>Aspect ratio: This determines how Dolphin Emulator displays the games on your screen. There are four options: Auto, Force 16:9, Force 4:3, and Stretch to Window. Auto is the default option and preserves the original aspect ratio of the game. Force 16:9 and Force 4:3 force the game to fit a widescreen or a standard screen respectively, which may result in some cropping or stretching of the image. Stretch to Window stretches the game to fill your entire screen, which may distort the image quality.</li>
-<li>Internal resolution: This determines how Dolphin Emulator scales up or down the resolution of the game. There are several options, ranging from 1x (native) to 8x (8 times the native resolution). The higher the resolution, the better the image quality, but also the higher the performance requirements. The default option is 1x, which matches the original resolution of the game. You can increase the resolution if your device can handle it, but you may experience some slowdowns or crashes. For 32-bit devices, we recommend not going beyond 2x resolution.</li>
-<li>Anti-aliasing: This determines how Dolphin Emulator smooths out the edges of the game graphics. There are several options, ranging from None to 8x MSAA (multisample anti-aliasing). The higher the anti-aliasing, the smoother the edges, but also the higher the performance requirements. The default option is None, which means no anti-aliasing is applied. You can enable anti-aliasing if your device can handle it, but you may experience some slowdowns or crashes. For 32-bit devices, we recommend not enabling anti-aliasing.</li>
-<li>Anisotropic filtering: This determines how Dolphin Emulator enhances the quality of the game textures. There are several options, ranging from 1x to 16x. The higher the anisotropic filtering, the sharper the textures, but also the higher the performance requirements. The default option is 1x, which means no anisotropic filtering is applied. You can increase the anisotropic filtering if your device can handle it, but you may experience some slowdowns or crashes. For 32-bit devices, we recommend not going beyond 4x anisotropic filtering.</li>
-<li>Scaled EFB copy: This determines how Dolphin Emulator handles some special effects in the game, such as heat waves or water reflections. If this option is on, Dolphin Emulator will scale these effects according to your internal resolution setting. If this option is off, Dolphin Emulator will use the native resolution of the game for these effects. The default option is on, which means scaled EFB copy is applied. You can turn this option off if you want to improve your performance or compatibility with some games, but you may lose some visual quality.</li>
-<li>Skip EFB access from CPU: This determines how Dolphin Emulator handles some advanced features in the game, such as motion blur or depth of field. If this option is on, Dolphin Emulator will skip these features and improve your performance. If this option is off, Dolphin Emulator will render these features and improve your visual quality. The default option is off, which means EFB access from CPU is not skipped. You can turn this option on if you want to improve your performance or compatibility with some games, but you may lose some visual quality.</li>
-<li>Ignore format changes: This determines how Dolphin Emulator handles some color conversions in the game. If this option is on, Dolphin Emulator will ignore these conversions and improve your performance. If this option is off, Dolphin Emulator will perform these conversions and improve your visual quality. The default option is on, which means format changes are ignored. You can turn this option off if you want to improve your visual quality or compatibility with some games, but you may lose some performance.</li>
-<li>Store EFB copies to texture only: This determines how Dolphin Emulator handles some memory operations in the game. If this option is on, Dolphin Emulator will store these operations as textures and improve your performance. If this option is off, Dolphin Emulator will store these operations as RAM and improve your compatibility with some games. The default option is on, which means EFB copies are stored as textures only. You can turn this option off if you want to improve your compatibility with some games, but you may lose some performance.</li>
-<li>Texture cache accuracy: This determines how Dolphin Emulator handles some texture updates in the game. There are three options: Low, Medium, and High. The lower the accuracy, the faster the updates, but also the higher the chance of graphical glitches. The higher the accuracy, the slower the updates, but also the lower the chance of graphical glitches. The default option is Low, which means low accuracy texture cache is used. You can increase the accuracy if you want to reduce graphical glitches in some games, but you may lose some performance.</li>
-<li>External frame buffer (XFB): This determines how Dolphin Emulator handles some video output in the game. There are two options: Disable and Virtual. If this option is Disable, Dolphin Emulator will bypass the XFB and improve your performance. If this option is Virtual, Dolphin Emulator will emulate the XFB and improve your compatibility with some games. The default option is Disable, which means XFB is disabled. You can enable this option if you want to improve your compatibility with some games, but you may lose some performance.</li>
-<li>Fast depth calculation: This determines how Dolphin Emulator handles some depth calculations in the game. If this option is on, Dolphin Emulator will use a faster but less accurate method and improve your performance. If this option is off, Dolphin Emulator will use a slower but more accurate method and improve your visual quality. The default option is on, which means fast depth calculation is used. You can turn this option off if you want to improve your visual quality in some games, but you may lose some performance.</li>
-<li>Disable bounding box: This determines how Dolphin Emulator handles some bounding box calculations in the game. If this option is on, Dolphin Emulator will skip these calculations and improve your performance. If this option is off, Dolphin Emulator will perform these calculations and improve your compatibility with some games. The default option is on, which means bounding box is disabled. You can turn this option off if you want to improve your compatibility with some games, but you may lose some performance.</li>
-</ul>
-<p>These are some of the most important settings that you should consider when configuring Dolphin Emulator for 32-bit devices. However, you may need to experiment with different settings to find the best balance between performance and quality for your device and game. You can also check the Dolphin Wiki for more information and tips on specific games.</p>
- <h2>How to load and play your favorite games on Dolphin Emulator</h2>
-<p>Now that you have installed and configured Dolphin Emulator on your Android device, you are ready to load and play your favorite games. Here are the steps to do so:</p>
-<ol>
-<li>You need to have the game files (ISO or GCM) on your device or SD card. You can get these files from your own GameCube or Wii discs, or from other sources online. However, you should only use game files that you own legally and that match the region of your device.</li>
-<li>You can browse and select the game files from the emulator menu by tapping on the plus icon in the bottom right corner of the screen. This will open a file explorer where you can navigate to the folder where you stored your game files.</li>
-<li>You can also scan your device for game files by tapping on the refresh icon in the top right corner of the screen. This will automatically detect and add any game files that are compatible with Dolphin Emulator.</li>
-<li>Once you have loaded a game, you can start playing by tapping on the play icon in the bottom right corner of the screen. This will launch the game in full-screen mode.</li>
-<li>You can also pause, save, load, or exit the game by tapping on the menu icon in the top left corner of the screen. This will open a menu where you can access various options, such as:</li>
-<ul>
-<li>Save state: This allows you to save your game progress at any point in a slot. You can have up to 10 slots per game.</li>
-<li>Load state: This allows you to load your game progress from a slot.</li>
-<li>Exit: This allows you to quit the game and return to the emulator menu.</li>
-</ul>
-</ol>
-<p>That's it! You can now enjoy playing GameCube and Wii games on your Android device with Dolphin Emulator.</p>
- <h2>How to troubleshoot common issues with Dolphin Emulator</h2>
-<p>Dolphin Emulator is a complex software that may not work perfectly with every device and game. Some of the common issues that you may encounter with Dolphin Emulator are:</p>
-<ul>
-<li>The emulator crashes or freezes during gameplay.</li>
-<li>The game runs too slow or too fast.</li>
-<li>The game graphics are distorted or glitchy.</li>
-<li>The game audio is choppy or missing.</li>
-<li>The game controls are not responsive or accurate.</li>
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-<p>Some of the possible solutions for these issues are:</p>
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-<li>Update your device software and drivers: Make sure that your device is running on the latest version of Android and that your drivers are up to date. This may fix some compatibility and performance issues with Dolphin Emulator.</li>
-<li>Clear your emulator cache and data: Sometimes, your emulator cache and data may get corrupted or outdated, causing some problems with Dolphin Emulator. You can clear them by going to Settings > Apps > Dolphin Emulator > Storage > Clear cache and Clear data. This may reset some of your emulator settings : You can use a controller to play games on Dolphin Emulator, as it will give you more accuracy and comfort than using the touchscreen. You can use a Bluetooth controller, a USB controller, or a Wii remote. You can configure your controller settings by going to Settings > Controls > Configure.</li>
-<li>Use cheats and hacks: You can use cheats and hacks to modify your game experience on Dolphin Emulator, such as unlocking hidden items, increasing your health, or skipping levels. You can enable cheats and hacks by going to Settings > Enhancements > Cheats or Settings > Hacks.</li>
-<li>Use online multiplayer: You can use online multiplayer to play games with other Dolphin Emulator users, using the Nintendo Wi-Fi Connection service or the Netplay feature. You can enable online multiplayer by going to Settings > Online > Wi-Fi or Settings > Online > Netplay.</li>
-</ul>
-<p>These are some of the tips that you can use to improve your gaming experience on Dolphin Emulator. However, you may find other ways to enhance your gaming experience by experimenting with different settings and features.</p>
- <h2></h2>
-<p>This is the end of the article. I hope you enjoyed reading it and learned something new. Thank you for your attention and have a nice day!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/AetherSX2 for Windows and Android - The Best Way to Relive Your Favorite PS2 Games in 2023 - Download Here.md

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-<h1>AetherSX2 2023 Download: How to Play PS2 Games on Your Android Device</h1>
- <p>Do you miss playing your favorite PlayStation 2 games? Do you wish you could relive the nostalgia of playing classic PS2 titles on your Android device? If yes, then you are in luck. There is a new PS2 emulator for Android that lets you play PS2 games on your smartphone with ease. It is called AetherSX2, and it is the best PS2 emulator for Android by far.</p>
- <p>In this article, we will tell you everything you need to know about AetherSX2, including what it is, how it works, what features and benefits it offers, how to download and install it on your Android device, and how to play PS2 games on your Android device using AetherSX2. By the end of this article, you will be able to enjoy playing PS2 games on your smartphone with AetherSX2.</p>
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- <h2>What is AetherSX2?</h2>
- <p>AetherSX2 is a PS2 emulator for Android that allows you to play PS2 games on your smartphone. An emulator is a software that mimics the hardware and software of another device, in this case, a PS2 console. By using an emulator, you can run games and applications that are designed for another platform, such as a PS2 game on an Android device.</p>
- <h3>A brief history of AetherSX2</h3>
- <p>AetherSX2 is the brainchild of one person, a developer who goes by the handle Tahlreth. The developer actually used the PCSX2 emulator as the basis for their Android-based emulator. PCSX2 is a long-running, well-established emulator on PC, so it makes sense to take advantage of the work that has gone into this program.</p>
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- <p>The developer of AetherSX2 got the green light to use the PCSX2 code from the developers themselves and is licensed under the LGPL license — unlike the DamonPS2 developers, who stole the code and didn’t follow the requisite license. In any event, the emulator was initially released in December 2021 via the Google Play Store as an open beta. You can also sideload the APK via the AetherSX2 website. We’d recommend you steer clear of any other websites claiming to offer the APK.</p>
- <p>The AetherSX2 emulator is a major step forward for emulation on Android devices. It’s also worth noting that the app is free to download and use, so don’t be duped by anyone saying you need to pay for it. This is in contrast to the DamonPS2 emulator, which is filled to the brim with ads and charges for a Pro version limited to two devices.</p>
- <h3>Features and benefits of AetherSX2</h3>
- <p>AetherSX2 is not just another PS2 emulator for Android. It is a powerful and feature-rich emulator that offers many advantages over other emulators. Here are some of the features and benefits of AetherSX2 that make it stand out from the crowd.</p>
- <h4>High compatibility</h4>
- <p>AetherSX2 boasts high compatibility with a wide range of PS2 games <p>from various genres and regions. You can play popular games like God of War, Final Fantasy, Grand Theft Auto, Metal Gear Solid, Kingdom Hearts, and many more on your Android device with AetherSX2. You can also play games from different regions, such as Japan, Europe, and North America, with the appropriate BIOS files. AetherSX2 supports both ISO and CSO formats for PS2 games.</p>
- <h4>Enhanced graphics</h4>
- <p>AetherSX2 does not just emulate the PS2 graphics faithfully, but also enhances them to make them look better on your Android device. You can adjust the resolution, aspect ratio, anti-aliasing, texture filtering, and other graphical settings to improve the visual quality of the games. You can also use shaders to add effects like scanlines, CRT, bloom, and more to the games. AetherSX2 supports both Vulkan and OpenGL renderers for graphics.</p>
- <h4>Save and load states</h4>
- <p>AetherSX2 allows you to save and load your game progress at any point with the save and load state feature. This is very convenient for playing on your Android device, as you can resume your game from where you left off without having to go through the in-game save system. You can also use this feature to skip difficult or boring parts of the game by loading a state from another source. AetherSX2 supports up to 10 save slots for each game.</p>
- <h4>Controller support</h4>
- <p>AetherSX2 lets you play PS2 games on your Android device with a variety of controllers. You can use the touchscreen controls that are customizable and responsive, or you can use an external controller that connects via Bluetooth or USB. AetherSX2 supports many popular controllers, such as Xbox One, PS4, PS3, Switch Pro, and more. You can also map the buttons and analog sticks to your liking.</p>
- <h4>Fast and smooth performance</h4>
- <p>AetherSX2 delivers fast and smooth performance for PS2 games on your Android device. You can play most games at full speed without any lag or stuttering. You can also tweak the performance settings to optimize the emulator for your device. You can adjust the frame rate, frame skip, speed hack, audio latency, and other options to improve the performance of the emulator. AetherSX2 runs well on most modern Android devices with decent hardware.</p>
- <h2>How to download and install AetherSX2 on your Android device</h2>
- <p>Now that you know what AetherSX2 is and what it can do, you might be wondering how to download and install it on your Android device. Well, it is very easy and simple to do so. Just follow these steps and you will be ready to play PS2 games on your smartphone in no time.</p>
- <h3>Step 1: Check system requirements</h3>
- <p>Before you download and install AetherSX2 on your Android device, you need to make sure that your device meets the minimum system requirements for running the emulator. Here are the system requirements for AetherSX2:</p>
- <ul>
-<li>Android version: 5.0 or higher</li>
-<li>CPU: Quad-core or higher (preferably with ARMv8 support)</li>
-<li>GPU: Adreno 5xx or higher (or equivalent)</li>
-<li>RAM: 3 GB or higher</li>
-<li>Storage: At least 1 GB of free space (plus more for PS2 games)</li>
-</ul>
- <p>If your device meets these requirements, then you can proceed to the next step. If not, then you might want to upgrade your device or look for another emulator.</p>
- <h3>Step 2: Download AetherSX2 APK from the official website or Google Play Store</h3>
- <p>The next step is to download the AetherSX2 APK file from a trusted source. There are two ways to do this: either from the official website or from the Google Play Store.</p>
- <p>The official website of AetherSX2 is <a href="">https://aethersx.com/</a>. Here you can find the latest version of the emulator as well as other information and updates about it. You can download the APK file directly from the website by clicking on the "Download" button on the homepage.</p>
- <p>The Google Play Store is another option for downloading the AetherSX2 APK file. The Google Play Store is a safe and convenient way to download apps for your Android device. You can find the AetherSX2 app on the Google Play Store by searching for it or by following this link: <a href="">https://play.google.com/store/apps/details?id=com.aethersx.aethersx&hl=en_US&gl=US</a>. You can download the app by tapping on the "Install" button on the app page.</p>
- <p>Either way, you will get the same APK file that is about 30 MB in size. Make sure you have enough space on your device before downloading it.</p>
- <h3>Step 3: Install AetherSX2 APK on your Android device</h3>
- <p>Once you have downloaded the AetherSX2 APK file, you need to install it on your Android device. To do this, you need to enable the installation of apps from unknown sources on your device. This is a security feature that prevents malicious apps from being installed on your device without your permission. Here is how to enable it:</p>
- <ul>
-<li>Go to the Settings app on your device.</li>
-<li>Tap on Security or Privacy (depending on your device).</li>
-<li>Find and enable the option that says "Unknown sources" or "Install unknown apps" (depending on your device).</li>
-<li>Confirm your choice by tapping OK or Allow (depending on your device).</li>
-</ul>
- <p>Now you can install the AetherSX2 APK file by following these steps:</p>
- <ul>
-<li>Locate the AetherSX2 APK file on your device using a file manager app or the Downloads app.</li>
-<li>Tap on the AetherSX2 APK file to start the installation process.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Wait for the installation to finish and then tap Open or Done (depending on your device).</li>
-</ul>
- <p>Congratulations, you have successfully installed AetherSX2 on your Android device. You can now launch the app from your app drawer or home screen.</p>
- <h3>Step 4: Load PS2 games on your Android device</h3>
- <p>The next step is to load PS2 games on your Android device. You can do this by either transferring PS2 games from your PC or downloading PS2 games from the internet. Here is how to do both:</p>
- <h4>Transfer PS2 games from your PC</h4>
- <p>If you have PS2 games on your PC, you can transfer them to your Android device using a USB cable or a wireless method. Here is how to do it using a USB cable:</p>
- <ul>
-<li>Connect your Android device to your PC using a USB cable.</li>
-<li>Select the option that says "File Transfer" or "MTP" (depending on your device) on your Android device.</li>
-<li>Open the File Explorer or My Computer app on your PC and find your Android device.</li>
-<li>Create a folder named "PS2" on your Android device's internal storage or SD card (depending on where you want to store the games).</li>
-<li>Copy and paste the PS2 games from your PC to the PS2 folder on your Android device. The games should be in ISO or CSO format.</li>
-<li>Eject your Android device from your PC and disconnect the USB cable.</li>
-</ul>
- <p>If you want to use a wireless method, you can use an app like AirDroid or ShareIt to transfer files between your PC and Android device over Wi-Fi. Just follow the instructions of the app you choose to use.</p>
- <h4>Download PS2 games from the internet</h4>
- <p>If you don't have PS2 games on your PC, you can download them from the internet. However, you need to be careful about where you download them from, as some websites may contain viruses, malware, or fake files. You also need to make sure that you own the original PS2 games that you download, as downloading pirated games is illegal and unethical.</p>
- <p>We recommend that you use reputable and trusted websites that offer PS2 games for download, such as Emuparadise, CoolROM, RomHustler, and The ISO Zone. These websites have a large collection of PS2 games from various regions and genres that you can download for free. Here is how to download PS2 games from these websites:</p>
- <ul>
-<li>Go to the website of your choice using a web browser app on your Android device.</li>
-<li>Search for the PS2 game that you want to download using the search bar or browse through the categories.</li>
-<li>Select the game that you want to download and tap on the download link or button.</li>
-<li>Wait for the download to finish and then locate the downloaded file using a file manager app or the Downloads app.</li>
-</ul>
- <p>Note: Some websites may require you to extract the downloaded file using an app like ZArchiver or RAR before you can play it. If this is the case, just follow these steps:</p>
- <ul>
-<li>Select the downloaded file and tap on Extract here or Extract to (depending on the app).</li>
-<li>Wait for the extraction to finish and then delete the original file to save space.</li>
-<li>Find the extracted file, which should be in ISO or CSO format, and move it to the PS2 folder on your Android device.</li>
-</ul>
- <p>Now you have PS2 games on your Android device that you can play with AetherSX2.</p>
- <h3>Step 5: Configure settings and controls according to your preference</h3>
- <p>The last step before you can play PS2 games on your Android device with AetherSX2 is to configure the settings and controls according to your preference. AetherSX2 has many options that you can customize to enhance your gaming experience. Here are some of the settings and controls that you can configure:</p>
- <h4>Settings</h4>
- <p>To access the settings menu, tap on the three-dot icon on the top right corner of the app and select Settings. Here you can find various options that affect the performance, graphics, audio, and input of the emulator. Some of the options that you can adjust are:</p>
- <ul>
-<li>Frame rate: You can set the frame rate limit for the emulator, which affects the speed of the games. You can choose between 30 FPS, 60 FPS, or Unlimited.</li>
-<li>Frame skip: You can enable or disable frame skipping, which is a technique that skips rendering some frames to improve performance. You can also set the frame skip value from 0 to 9.</li>
-<li>Speed hack: You can enable or disable speed hack, which is a feature that boosts the speed of the emulator by reducing the CPU load. You can also set the speed hack value from 0 to 9.</li>
-<li>Audio latency: You can set the audio latency for the emulator, which affects the synchronization of the sound and the video. You can choose between Low, Medium, or High.</li>
-<li>Graphics renderer: You can choose between Vulkan or OpenGL as the graphics renderer for the emulator, which affects the quality and compatibility of the graphics. Vulkan is recommended for better performance and compatibility.</li>
-<li>Resolution: You can set the resolution for the emulator, which affects the sharpness and clarity of the graphics. You can choose between Native (480x272), 2x Native (960x544), 3x Native (1440x816), or 4x Native (1920x1088).</li>
-<li>Aspect ratio: You can set the aspect ratio for the emulator, which affects how the games are displayed on your screen. You can choose between Auto (based on game), 4:3 (standard), or 16:9 (widescreen).</li>
-<li>Anti-aliasing: You can enable or disable anti-aliasing, which is a technique that smooths out jagged edges in the graphics. You can also set the anti-aliasing level from 2x to 16x.</li>
-<li>Texture filtering: You can enable or disable texture filtering, which is a technique that improves the quality of textures in the graphics. You can also set the texture filtering level from Bilinear to Anisotropic 16x.</li>
-<li>Shaders: You can enable or disable shaders, which are effects that add visual enhancements to the graphics. You can also choose from a variety of shaders, such as Scanlines, CRT, Bloom, and more.</li>
-</ul>
- <p>You can experiment with different settings and see what works best for you and your device. You can also reset the settings to default by tapping on the Reset button at the bottom of the menu.</p>
- <h4>Controls</h4>
- <p>To access the controls menu, tap on the three-dot icon on the top right corner of the app and select Controls. Here you can find various options that affect the input and layout of the emulator. Some of the options that you can adjust are:</p>
- <ul>
-<li>Touchscreen controls: You can enable or disable the touchscreen controls, which are virtual buttons and analog sticks that appear on your screen. You can also customize the size, position, opacity, and vibration of the touchscreen controls.</li>
-<li>External controller: You can enable or disable the external controller, which is a physical controller that connects to your device via Bluetooth or USB. You can also map the buttons and analog sticks of the external controller to the PS2 controller.</li>
-<li>Accelerometer: You can enable or disable the accelerometer, which is a sensor that detects the tilt and motion of your device. You can also map the accelerometer to the PS2 controller.</li>
-<li>Gyroscope: You can enable or disable the gyroscope, which is a sensor that detects the orientation and rotation of your device. You can also map the gyroscope to the PS2 controller.</li>
-</ul>
- <p>You can experiment with different controls and see what works best for you and your device. You can also reset the controls to default by tapping on the Reset button at the bottom of the menu.</p>
- <h2>How to play PS2 games on your Android device using AetherSX2</h2>
- <p>Now that you have downloaded and installed AetherSX2 on your Android device, loaded PS2 games on your device, and configured the settings and controls according to your preference, you are ready to play PS2 games on your smartphone with AetherSX2. Here is how to do it:</p>
- <h3>Choose a game from the game list or browse for a game file</h3>
- <p>When you launch AetherSX2, you will see a game list that shows all the PS2 games that you have on your device. You can scroll through the game list and tap on any game that you want to play. The game will start loading automatically.</p>
- <p>If you don't see the game that you want to play on the game list, you can browse for it manually by tapping on the folder icon on the top left corner of the app. This will open a file browser that lets you navigate through your device's storage. You can find and select any PS2 game file that you have on your device in ISO or CSO format. The game will start loading automatically.</p>
- <h3>Select a graphics renderer (Vulkan or OpenGL)</h3>
- <p>Before the game starts, you will be asked to select a graphics renderer for the emulator. You can choose between Vulkan or OpenGL as the graphics renderer. Vulkan is recommended for better performance and compatibility, while OpenGL is recommended for older devices or games that have issues with Vulkan. You can change this option later in the settings menu if you want.</p>
- <h3>Enjoy playing PS2 games on your Android device with AetherSX2</h3>
- <p>After selecting a graphics renderer, the game will start running on your Android device with AetherSX2. You can use the touchscreen controls or the external controller to play the game as you would on a PS2 console. You can also access the emulator menu by tapping on the three-dot icon on the top right corner of the app. Here you can save and load states, change settings and controls, pause and resume the game, and exit the game.</p>
- <p>That's it. You can now enjoy playing PS2 games on your Android device with AetherSX2. You can play as many games as you want, as long as you have enough space on your device. You can also switch between different games by going back to the game list or the file browser.</p>
- <h2>Conclusion</h2>
- <p>AetherSX2 is a PS2 emulator for Android that lets you play PS2 games on your smartphone with ease. It is a powerful and feature-rich emulator that offers high compatibility, enhanced graphics, save and load states, controller support, fast and smooth performance, and more. It is also free to download and use, unlike some other emulators that charge money or show ads.</p>
- <p>In this article, we have shown you how to download and install AetherSX2 on your Android device, how to load PS2 games on your device, how to configure settings and controls according to your preference, and how to play PS2 games on your device using AetherSX2. By following these steps, you will be able to enjoy playing PS2 games on your smartphone with AetherSX2.</p>
- <p>We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about AetherSX2 and PS2 emulation on Android.</p>
- <h4>Q: Is AetherSX2 legal?</h4>
-<p>A: AetherSX2 is legal as long as you use it for personal and non-commercial purposes. You also need to own the original PS2 games that you play with AetherSX2, as downloading pirated games is illegal and unethical.</p>
- <h4>Q: Is AetherSX2 safe?</h4>
-<p>A: AetherSX2 is safe as long as you download it from the official website or the Google Play Store. You also need to be careful about where you download PS2 games from, as some websites may contain viruses, malware, or fake files.</p>
- <h4>Q: How can I update AetherSX2?</h4>
-<p>A: You can update AetherSX2 by downloading the latest version of the APK file from the official website or the Google Play Store. You can also enable automatic updates for AetherSX2 on the Google Play Store by tapping on the three-dot icon on the app page and selecting Enable auto-update.</p>
- <h4>Q: How can I report bugs or issues with AetherSX2?</h4>
-<p>A: You can report bugs or issues with AetherSX2 by contacting the developer via email at <a href="mailto:[email protected]">[email protected]</a>. You can also join the official Discord server of AetherSX2 at <a href="">https://discord.gg/6J9f8wM</a>. Here you can chat with other users and get support from the developer and moderators.</p>
- <h4>Q: How can I support the development of AetherSX2?</h4>
-<p>A: You can support the development of AetherSX2 by donating to the developer via PayPal at <a href="">https://www.paypal.me/aethersx</a>. You can also share your feedback and suggestions with the developer via email or Discord. You can also rate and review AetherSX2 on the Google Play Store and spread the word about it to your friends and family.</p> 197e85843d<br />
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-<p>Sometimes, you might not be able to find a 1 MB PDF file that suits your needs. You might have a larger PDF file that you want to download, but it exceeds your bandwidth, storage, or file size limit. In that case, you might want to compress a larger PDF file to a smaller size.</p>
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-<h1>Genshin Download: How to Play the Free-to-Play RPG on PC, Mobile, and Console</h1>
-<p>Genshin Impact is one of the most popular games of 2020 and 2021, attracting millions of players from all over the world. It is a free-to-play open-world action RPG that lets you explore a beautiful fantasy world called Teyvat, where you can meet a diverse cast of characters, fight against powerful enemies, and uncover the mysteries of your lost sibling. Whether you are a fan of anime-style graphics, engaging storylines, or dynamic combat systems, Genshin Impact has something for everyone.</p>
-<p>But how do you download Genshin Impact on your preferred platform? And what are the system requirements and tips and tricks that you need to know before you start your adventure? In this article, we will answer all these questions and more. Read on to find out how to play Genshin Impact on PC, mobile, or console today!</p>
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- <h2>Genshin Download for PC</h2>
-<p>If you want to play Genshin Impact on your PC, you have two options. You can either download it from the official website or from the Epic Games Store. Both methods are free and easy to follow.</p>
-<p>To download Genshin Impact from the official website, you need to visit [Genshin Impact – Step Into a Vast Magical World of Adventure](^1^) and click on the "Windows" button. This will start downloading the launcher file. Once it is downloaded, run it and follow the instructions to install the launcher. Then, open the launcher and log in with your miHoYo account or create one if you don't have one already. After that, click on "Get Game" to start downloading the game files. The download size is about 8.2 GB, so it may take some time depending on your internet speed. When the download is complete, click on "Launch" to start playing.</p>
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-<p>To download Genshin Impact from the Epic Games Store, you need to visit [Genshin Impact | Download and Play for Free - Epic Games Store](^3^) and click on "Get". This will prompt you to log in with your Epic Games account or create one if you don't have one already. Then, click on "Place Order" to confirm your purchase (don't worry, it's still free). After that, you will be redirected to the Epic Games Launcher. If you don't have it installed on your PC, you can download it from [Epic Games Launcher]. Once you have the launcher, install it and open it. Then, go to the "Library" tab and find Genshin Impact. Click on "Install" to start downloading the game files. The download size is about 8.2 GB, so it may take some time depending on your internet speed. When the download is complete, click on "Launch" to start playing.</p>
- <h3>PC System Requirements</h3>
-<p>Before you download Genshin Impact on your PC, you should check if your PC meets the minimum or recommended system requirements for the game. Here are the system requirements for PC according to the official website:</p>
- | Minimum System Requirements | Recommended System Requirements | | --- | --- | | OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit, or Windows 10 64-bit | OS: Windows 10 64-bit | | Processor: Intel Core i5 or equivalent | Processor: Intel Core i7 or equivalent | | Memory: 8 GB RAM | Memory: 16 GB RAM | | Graphics: NVIDIA GeForce GT 1030 or higher | Graphics: NVIDIA GeForce RTX 1060 6 GB or higher | | DirectX: Version 11 | DirectX: Version 11 | | Storage: 30 GB available space | Storage: 30 GB available space | <p>If your PC does not meet the minimum system requirements, you may experience low frame rates, crashes, or other issues while playing the game. If your PC meets or exceeds the recommended system requirements, you can enjoy the game at higher graphics settings and smoother performance.</p>
- <h3>PC Tips and Tricks</h3>
-<p>Here are some tips and tricks that can help you optimize your PC performance and gameplay experience while playing Genshin Impact:</p>
-<ul>
-<li>Update your graphics drivers and DirectX to the latest version. This can improve your graphics quality and stability.</li>
-<li>Adjust your graphics settings in the game options. You can lower some settings such as anti-aliasing, shadows, or render resolution to increase your frame rate and reduce lag. You can also enable or disable some features such as V-sync, FPS limit, or window mode to suit your preference.</li>
-<li>Close other programs or background processes that may consume your CPU, memory, or bandwidth. This can free up some resources for the game and prevent potential conflicts or errors.</li>
-<li>Use a wired connection instead of a wireless one if possible. This can reduce latency and packet loss and improve your online experience.</li>
-<li>Check the official website or social media accounts for any news, updates, or maintenance announcements. This can help you stay informed of any changes, issues, or events that may affect the game.</li>
-</ul> of storage space | 8 GB of storage space or more | | iOS 9.0 or higher | iOS 10.0 or higher | | iPhone 8 Plus, iPad Air 3, or higher | iPhone XR, iPad Pro, or higher | <p>If your device does not meet the minimum system requirements, you may experience low graphics quality, slow loading times, or other issues while playing the game. If your device meets or exceeds the recommended system requirements, you can enjoy the game at higher graphics quality and smoother performance.</p>
- <h3>Mobile Tips and Tricks</h3>
-<p>Here are some tips and tricks that can help you optimize your mobile performance and gameplay experience while playing Genshin Impact:</p>
-<ul>
-<li>Update your device software and app to the latest version. This can fix some bugs and improve your compatibility and stability.</li>
-<li>Adjust your graphics settings in the game options. You can lower some settings such as render resolution, shadow quality, or FPS to save battery life and reduce overheating. You can also enable or disable some features such as auto-adjust graphics, custom controls, or HD assets to suit your preference.</li>
-<li>Use a Wi-Fi connection instead of a mobile data connection if possible. This can reduce data usage and improve your online experience.</li>
-<li>Check the official website or social media accounts for any news, updates, or maintenance announcements. This can help you stay informed of any changes, issues, or events that may affect the game.</li>
-<li>Use headphones or earphones to enjoy the game's immersive sound effects and music.</li>
-</ul>
- <h2>Genshin Download for Console</h2>
-<p>If you want to play Genshin Impact on your console, you can download it from PlayStation Store if you have a PlayStation 4 or PlayStation 5. The method is free and easy to follow.</p>
-<p>To download Genshin Impact from PlayStation Store, you need to visit [Genshin Impact on PS4 | Official PlayStation™Store US] or [Genshin Impact on PS5 | Official PlayStation™Store US] depending on your console. Then, click on the "Add to Library" button. This will add the game to your library. Then, go to your library and find Genshin Impact. Click on the "Download" button to start downloading the game files. The download size is about 12 GB for PS4 and 14 GB for PS5, so it may take some time depending on your internet speed and console storage. When the download is complete, click on the game icon to start playing.</p>
- <h3>Console System Requirements</h3>
-<p>Before you download Genshin Impact on your console, you should check if your console meets the minimum system requirements for the game. Here are the system requirements for consoles according to the official website:</p>
- | Minimum System Requirements | Recommended System Requirements | | --- | --- | | PS4 with 30 GB of storage space | PS4 Pro with 30 GB of storage space | | PS5 with 50 GB of storage space | PS5 with 50 GB of storage space | <p>If your console does not meet the minimum system requirements, you may experience low graphics quality, slow loading times, or other issues while playing the game. If your console meets or exceeds the recommended system requirements, you can enjoy the game at higher graphics quality and smoother performance.</p>
- <h3>Console Tips and Tricks</h3>
-<p>Here are some tips and tricks that can help you optimize your console performance and gameplay experience while playing Genshin Impact:</p>
-<ul>
-<li>Update your console software and app to the latest version. This can fix some bugs and improve your compatibility and stability.</li>
-<li>Adjust your graphics settings in the game options. You can choose between "Favor Resolution" or "Favor Performance" modes to balance between graphics quality and frame rate. You can also enable or disable some features such as motion blur, anti-aliasing, or HDR to suit your preference.</li>
-<li>Link your miHoYo account to your PlayStation Network account. This can allow you to access some online features such as cross-play, cross-save, mail system, events, and more.</li>
-<li>Check the official website or social media accounts for any news, updates, or maintenance announcements. This can help you stay informed of any changes, issues, or events that may affect the game.</li>
-<li>Use a controller that suits your play style and comfort. You can customize your controller layout in the game options.</li>
-</ul>
- <h2>Genshin Impact Game Features</h2>
-<p>Now that you know how to download Genshin Impact on your preferred platform, you may be wondering what you can expect from the game in terms of gameplay, story, characters, combat, exploration, and more. In this section, we will give you a brief overview of some of the main features of the game that make it so fun and addictive.</p>
- <h3>Gameplay</h3>
-<p>Genshin Impact is an open-world action RPG that combines exploration, combat, and gacha elements. You can explore the vast world of Teyvat at your own pace, discovering new locations, secrets, and treasures along the way. You can also interact with various NPCs, complete quests, participate in events, and join co-op sessions with other players.</p>
-<p>The game also features a gacha system that allows you to obtain new characters, weapons, and items by spending a currency called Primogems. You can earn Primogems by playing the game or by purchasing them with real money. The gacha system is based on a random chance, so you may not always get what you want. However, the game is generous enough to give you some free pulls and rewards as you progress.</p>
- <h3>Story</h3>
-<p>Genshin Impact has a rich and immersive story that unfolds as you play the game. The main story revolves around your quest to find your lost sibling, who was separated from you by a mysterious god. Along the way, you will encounter different factions, cultures, and conflicts that shape the world of Teyvat. You will also meet various characters who will join your party and help you in your journey.</p>
-<p>The game has seven major regions, each based on a different element and inspired by a real-world culture. So far, only two regions are available: Mondstadt (Anemo/Wind) and Liyue (Geo/Earth). The other five regions are Inazuma (Electro/Lightning), Sumeru (Dendro/Nature), Fontaine (Hydro/Water), Natlan (Pyro/Fire), and Snezhnaya (Cryo/Ice). The game developers plan to release more regions and content in the future through updates and patches.</p>
- <h3>Characters</h3>
-<p>Genshin Impact has a diverse and colorful cast of characters that you can play as or interact with. There are currently 37 playable characters in the game, each with their own personality, backstory, element, weapon, and abilities. You can switch between four characters in your party at any time, depending on the situation and your preference.</p>
-<p>The characters are divided into five rarity tiers: 1-star, 2-star, 3-star, 4-star, and 5-star. The higher the rarity, the more powerful and rare the character is. You can obtain new characters by using the gacha system or by completing certain quests or events. You can also upgrade your characters by leveling them up, ascending them, enhancing their weapons and artifacts, and unlocking their constellations.</p>
- <h3>Combat</h3>
-<p>Genshin Impact has a dynamic and fluid combat system that relies on elemental interactions and strategy. You can use your character's basic attacks, elemental skills, and elemental bursts to deal damage to your enemies. You can also switch between different characters to create elemental reactions that can amplify or modify your damage output.</p>
-<p>The game has seven elements: Anemo (Wind), Geo (Earth), Electro (Lightning), Dendro (Nature), Hydro (Water), Pyro (Fire), and Cryo (Ice). Each element has its own strengths and weaknesses against other elements. For example, Pyro can melt Cryo, but is weak against Hydro. You can use this knowledge to your advantage and create powerful combos that can wipe out your foes.</p>
- <h3>Exploration</h3>
-<p>Genshin Impact has a vast and beautiful world that you can explore at your own pace. You can travel across different terrains, climates, and biomes using various methods such as walking, running, climbing, gliding, swimming, or riding. You can also use fast travel points to teleport to locations that you have already visited.</p>
-<p>The world of Teyvat is full of secrets, quests, events, and activities that you can discover and enjoy. You can find chests, resources, puzzles, enemies, and more that can reward you with items, experience, or currency. You can also interact with various NPCs, complete quests, participate in events, and join co-op sessions with other players.</p>
-<p>The game also has a feature called the Serenitea Pot, which allows you to create your own personal realm and customize it with furniture, decorations, and buildings. You can invite your characters and friends to your realm and enjoy some relaxing time.</p>
- <h1>Conclusion</h1>
-<p>Genshin Impact is a free-to-play open-world action RPG that offers a lot of fun and excitement for players of all ages and preferences. You can download the game on PC, mobile, or console and enjoy the stunning graphics, captivating story, diverse characters, dynamic combat, and endless exploration. Whether you want to play solo or with friends, Genshin Impact has something for everyone.</p>
-<p>So what are you waiting for? Download Genshin Impact today and start your adventure in the magical world of Teyvat!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Genshin Impact and how to download it:</p>
-<ul>
-<li><b>Q: Is Genshin Impact free?</b></li>
-<li>A: Yes, Genshin Impact is free to download and play. However, it does have some optional in-game purchases that can enhance your gameplay experience.</li>
-<li><b>Q: Is Genshin Impact cross-platform?</b></li>
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-<li><b>Q: How do I update Genshin Impact?</b></li>
-<li>A: Genshin Impact updates automatically when you launch the game on your platform. You can also check for updates manually in the game options or on the official website.</li>
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-<li>A: You can get more Primogems by playing the game and completing various tasks such as quests, events, achievements, daily commissions, spiral abyss, etc. You can also buy Primogems with real money by using the in-game shop or by redeeming gift codes.</li>
-</ul></p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Granny Chapter 2 Outwitt Mod Menu APK Madin for Free - No Root Required!.md

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-<h1>Granny Chapter 2 Mod Menu Outwitt Mod Free Download Link APK Madin</h1>
-<p>If you are a fan of horror games, you might have heard of Granny Chapter 2, a popular game that challenges you to escape from a creepy house with two evil characters: Granny and Grandpa. But what if you want to make the game more fun and exciting? Well, you can try Outwitt Mod, a mod menu that gives you access to various cheats and hacks for Granny Chapter 2. In this article, we will tell you everything you need to know about Outwitt Mod, including how to download and install it for free on your Android device.</p>
-<h2>What is Granny Chapter 2?</h2>
-<p>Granny Chapter 2 is a horror game developed by DVloper, the same creator of the original Granny game. It was released in September 2019 and has since gained millions of downloads and positive reviews from players around the world. The game is available for Android, iOS, and Windows devices.</p>
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-<h3>The gameplay of Granny Chapter 2</h3>
-<p>The gameplay of Granny Chapter 2 is similar to the first game, but with some new twists and features. You are trapped in a dark and spooky house with two enemies: Granny and Grandpa. Granny can hear everything and will chase you if she hears any noise. Grandpa is hard of hearing but he will attack you if he sees you. You have to find a way to escape from the house within five days, or else you will face a horrible fate. You can explore different rooms, find items, solve puzzles, and hide from the enemies. But be careful, because they are always on the lookout for you.</p>
-<h3>The features of Granny Chapter 2</h3>
-<p>Granny Chapter 2 has many features that make it an enjoyable and thrilling game. Some of them are:</p>
-<ul>
-<li>You can choose between four difficulty levels: Easy, Normal, Hard, and Extreme.</li>
-<li>You can play in two modes: Practice or Normal. In Practice mode, you can explore the house without any enemies. In Normal mode, you have to face both Granny and Grandpa.</li>
-<li>You can customize your character's appearance, such as hair color, skin color, and clothes.</li>
-<li>You can use different weapons, such as a shotgun, a crossbow, a stun gun, or a crowbar, to fight back against the enemies.</li>
-<li>You can interact with various objects, such as doors, windows, cabinets, drawers, traps, cameras, etc.</li>
-<li>You can enjoy realistic graphics, sound effects, and music that create a scary atmosphere.</li>
-</ul>
-<h2>What is Outwitt Mod?</h2>
-<p>Outwitt Mod is a mod menu that allows you to modify the game settings and enable various cheats and hacks for Granny Chapter 2. It was created by Outwitt, a YouTube channel that uploads videos about Granny games and mods. Outwitt Mod is one of the most popular mods for Granny Chapter 2 and has been downloaded by thousands of players.</p>
-<h3>The benefits of Outwitt Mod</h3>
-<p>Outwitt Mod gives you many benefits that make the game more fun and easy. Some of them are:</p>
-<ul>
-<li>You can access a mod menu that lets you change the game settings, such as the difficulty level, the enemy speed, the enemy damage, etc.</li>
-<li>You can enable cheats that give you unlimited health, unlimited ammo, invisibility, teleportation, etc.</li>
-<li>You can unlock all items in the game, such as weapons, keys, tools, etc.</ menu that allows you to modify the game settings and enable various cheats and hacks for Granny Chapter 2. It was created by Outwitt, a YouTube channel that uploads videos about Granny games and mods. In this article, we have explained what Granny Chapter 2 and Outwitt Mod are, what are their benefits and drawbacks, and how to download and install Outwitt Mod for Granny Chapter 2 on your Android device. We hope you found this article helpful and informative. If you want to try Outwitt Mod for Granny Chapter 2, you can follow the steps and precautions we have provided. But remember, use it at your own risk and responsibility.</p>
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-<h3>Summary of the main points</h3>
-<p>Here are the main points of this article:</p>
-<ul>
-<li>Granny Chapter 2 is a horror game that challenges you to escape from a house with two enemies: Granny and Grandpa.</li>
-<li>Outwitt Mod is a mod menu that gives you access to various cheats and hacks for Granny Chapter 2.</li>
-<li>Outwitt Mod has many benefits, such as unlimited health, unlimited ammo, invisibility, teleportation, etc.</li>
-<li>Outwitt Mod also has some drawbacks, such as security risks, game bans, loss of originality, and compatibility issues.</li>
-<li>To download and install Outwitt Mod for Granny Chapter 2, you need to uninstall the original game, enable unknown sources, download the APK file, install it, and launch the game.</li>
-<li>Before downloading and installing Outwitt Mod for Granny Chapter 2, you should backup your data, scan the APK file, use a VPN or a proxy server, and not use it for illegal or unethical purposes.</li>
-</ul>
-<h3>Call to action for the readers</h3>
-<p>If you liked this article, please share it with your friends and family who are interested in Granny Chapter 2 and Outwitt Mod. Also, don't forget to subscribe to our website for more articles like this one. And if you have any questions or feedback about this article, please leave a comment below. We would love to hear from you.</p>
- <h4>FAQs</h4>
-<p>Here are some frequently asked questions about Granny Chapter 2 and Outwitt Mod:</p>
-<ol>
-<li>Q: Is Outwitt Mod safe to use?<br>A: Outwitt Mod is not an official app from the game developer, so it may contain some viruses, malware, or spyware that can harm your device or data. Therefore, you should always scan the APK file with an antivirus or anti-malware software before installing it. You should also use a VPN or a proxy server to hide your IP address and location when playing the game online.</li>
-<li>Q: Is Outwitt Mod legal to use?<br>A: Outwitt Mod is not legal to use because it violates the terms and conditions of the game developer. By using Outwitt Mod, you are cheating and hacking the game, which is unfair to other players and disrespectful to the game creator. You may also get banned from playing the game online if the game developer detects your mod usage.</li>
-<li>Q: Does Outwitt Mod work on iOS devices?<br>A: No, Outwitt Mod only works on Android devices. It is not compatible with iOS devices because it is an APK file that can only be installed on Android devices. If you want to use Outwitt Mod on your iOS device, you will need to jailbreak your device first, which is not recommended because it can damage your device or void your warranty.</li>
-<li>Q: Does Outwitt Mod work on Windows devices?<br>A: Yes, Outwitt Mod can work on Windows devices if you use an Android emulator. An Android emulator is a software that allows you to run Android apps on your Windows device. You can download an Android emulator such as BlueStacks or NoxPlayer on your Windows device and then install Outwitt Mod on it. However, this may affect the performance of your device or the game.</li>
-<li>Q: Where can I find more mods for Granny Chapter 2?<br>A: You can find more mods for Granny Chapter 2 on various websites or YouTube channels that offer mod downloads or tutorials. Some of them are NullZerep Mods, GodisAGamer Mods, Platinmods Mods, etc. But be careful when downloading mods from unknown sources because they may contain viruses or malware that can harm your device or data.</li>
-</ol></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Logo Quiz MOD APK and Enjoy the Fun of Recognizing Logos.md

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-<h1>Logo Quiz Mod APK: A Fun and Challenging Game for Logo Lovers</h1>
-<p>Do you think you can recognize hundreds of logos from different brands and companies? Do you want to test your logo knowledge and have fun at the same time? If you answered yes, then you should try Logo Quiz Mod APK, a free trivia game that will keep you entertained for hours.</p>
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-<h2>What is Logo Quiz Mod APK?</h2>
-<p>Logo Quiz Mod APK is a modified version of the original Logo Quiz game by Bubble Quiz Games. In this game, you have to guess the names of thousands of logos from popular companies all over the world. You will see various logos on the screen, and you have to type in the correct answer using the available letters. You can also use hints to help you if you get stuck.</p>
-<p>Logo Quiz Mod APK is different from the original game in that it gives you unlimited hints, extra levels, offline mode, and daily challenges. These features make the game more enjoyable and less frustrating. You can also play the game without an internet connection, which is great if you want to kill some time when you are offline.</p>
-<h2>Features of Logo Quiz Mod APK</h2>
-<h3>Unlimited hints</h3>
-<p>One of the best features of Logo Quiz Mod APK is that it gives you unlimited hints. Each logo has 5 hints that you can use to get a clue about the answer. You can also get new hints by answering correctly or watching ads. However, with Logo Quiz Mod APK, you don't have to worry about running out of hints or watching ads. You can use as many hints as you want without any limitations.</p>
-<h3>Extra levels</h3>
-<p>Another feature of Logo Quiz Mod APK is that it gives you access to extra levels that are not available in the original game. These levels include:</p>
-<ul>
-<li>Slogans: Guess the slogans of 200 famous brands.</li>
-<li>Minimalist: Guess the logos that are simplified to their basic shapes and colors.</li>
-<li>Retro: Guess the logos that are based on old versions or designs.</li>
-<li>Expert: Guess the logos that are very hard or obscure.</li>
-</ul>
-<p>These extra levels add more variety and challenge to the game, making it more interesting and fun.</p>
-<h3>Offline mode</h3>
-<p>Logo Quiz Mod APK also allows you to play the game offline, which means you don't need an internet connection to enjoy it. This is great if you want to play the game when you are traveling, waiting, or bored. You can also save your data and battery by playing offline.</p>
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-<h2>How to download and install Logo Quiz Mod APK</h2>
-<p>If you want to try Logo Quiz Mod APK, you have to download and install it on your Android device. Here are the steps to follow these steps: <h3>Step 1: Enable unknown sources</h3>
-<p>Before you can install Logo Quiz Mod APK, you need to allow your device to install apps from unknown sources. This means that you can install apps that are not from the Google Play Store. To do this, go to your device settings and tap on Security. Then, look for the option that says Install unknown apps or Unknown sources and enable it. You may see a warning message that tells you about the risks of installing unknown apps, but you can ignore it if you trust the source of the APK file.</p>
-<h3>Step 2: Download the APK file</h3>
-<p>Next, you need to download the APK file of Logo Quiz Mod APK from a reliable website. You can use your browser to find and download the file, or you can use a computer and transfer the file to your device via USB. Make sure you download the latest version of the APK file and that it is compatible with your device. You can check the file size and version number before downloading it.</p>
-<h3>Step 3: Install the APK file</h3>
-<p>Once you have downloaded the APK file, you need to locate it on your device and tap on it to start the installation process. You can use a file manager app to find the file in your Downloads folder or wherever you saved it. You may see a pop-up window that asks you to confirm the installation and grant some permissions to the app. Tap on Install and wait for the installation to finish.</p>
-<h3>Step 4: Launch the game and enjoy</h3>
-<p>After the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer. You will see a welcome screen that introduces you to the game and its features. You can also access the settings menu to adjust some options such as sound, language, and notifications. Now you are ready to play Logo Quiz Mod APK and have fun guessing logos from different brands.</p> <h2>Tips and tricks for playing Logo Quiz Mod APK</h2>
-<p>Logo Quiz Mod APK is a fun and challenging game, but it can also be frustrating if you don't know the answers. Here are some tips and tricks that can help you play the game better and enjoy it more:</p>
-<h3>Use hints wisely</h3>
-<p>As mentioned earlier, Logo Quiz Mod APK gives you unlimited hints, which can be very helpful if you are stuck. However, you should not rely on hints too much, as they can make the game too easy and boring. You should try to guess the logo first before using a hint, and only use one hint at a time. You can also save your hints for later levels, where the logos are harder and more obscure.</p>
-<h3>Learn from your mistakes</h3>
-<p>Another tip for playing Logo Quiz Mod APK is to learn from your mistakes. If you guess a logo wrong, don't just skip it and move on. Instead, try to remember the correct answer and the logo design. This way, you can improve your memory and recognition skills, and avoid making the same mistake again. You can also review your answers at the end of each level and see where you went wrong.</p>
-<h3>Use online resources</h3>
-<p>If you are really stuck and can't figure out a logo, you can always use online resources to help you. There are many websites and apps that offer logo quizzes and answers, such as Logo Quiz Answers or Logo Quiz Cheats. You can also use search engines or image recognition tools to find the logo you are looking for. However, you should use these resources sparingly, as they can spoil the fun and challenge of the game.</p>
-<h3>Challenge yourself and your friends</h3>
-<p>One of the best ways to enjoy Logo Quiz Mod APK is to challenge yourself and your friends. You can set a goal for yourself, such as completing a level without using any hints or within a certain time limit. You can also compete with your friends and see who can guess more logos or score higher. You can share your results on social media or via messaging apps, and invite your friends to join the game.</p>
-<h2>Benefits of playing Logo Quiz Mod APK</h2>
-<p>Logo Quiz Mod APK is not only a fun and entertaining game, but also a beneficial one. Here are some of the benefits of playing Logo Quiz Mod APK:</p>
-<h3>Enhance your memory and recognition skills</h3>
-<p>Playing Logo Quiz Mod APK can help you enhance your memory and recognition skills, as you have to remember and identify thousands of logos from different brands and companies. This can improve your brain function and cognitive abilities, as well as your attention span and concentration. You can also learn new things and facts about logos and brands that you may not have known before.</p>
-<h3>Expand your knowledge of brands and logos</h3>
-<p>Playing Logo Quiz Mod APK can also help you expand your knowledge of brands and logos, as you have to guess logos from various categories, such as food, fashion, sports, technology, etc. You can discover new brands and logos that you may not have heard of before, or learn more about the ones that you already know. You can also appreciate the creativity and design of logos, and how they convey the identity and message of a brand.</p>
-<h3>Have fun and relax</h3>
-<p>Finally, playing Logo Quiz Mod APK can help you have fun and relax, as it is a simple and enjoyable game that anyone can play. You can play it anytime and anywhere, whether you are online or offline. You can also play it at your own pace, without any pressure or stress. You can also have fun with your friends and family, by playing together or competing with each other.</p>
-<h2>Conclusion</h2>
-<p>Logo Quiz Mod APK is a fun and challenging game for logo lovers who want to test their logo knowledge and have fun at the same time. It offers unlimited hints, extra levels, offline mode, and daily challenges that make the game more enjoyable and less frustrating. It also helps enhance memory and recognition skills, expand knowledge of brands and logos, and have fun and relax. If you want to try Logo Quiz Mod APK, you can download it from a reliable website and install it on your Android device by following the steps above.</p>
- <h2>Frequently Asked Questions</h2>
- <ol>
-<li><b>What is the difference between Logo Quiz Mod APK and Logo Quiz?</b></li>
-<p>Logo Quiz Mod APK is a modified version of Logo Quiz that gives you unlimited hints, extra levels, offline mode, and daily challenges. Logo Quiz is the original game that has limited hints, fewer levels, online mode only, and no daily challenges.</p>
-<li><b>How many logos are there in Logo Quiz Mod APK?</b></li <p>Logo Quiz Mod APK has over 4000 logos from different categories and levels. You can also play the extra levels that have 200 logos each.</p>
-<li><b>How can I get more hints in Logo Quiz Mod APK?</b></li>
-<p>You don't need to get more hints in Logo Quiz Mod APK, as it gives you unlimited hints. You can use as many hints as you want without any restrictions. However, if you want to challenge yourself, you can try to guess the logos without using hints or use them sparingly.</p>
-<li><b>Is Logo Quiz Mod APK safe to download and install?</b></li>
-<p>Logo Quiz Mod APK is safe to download and install, as long as you get it from a reliable website that does not contain any viruses or malware. You should also scan the APK file before installing it on your device, and make sure you have enabled unknown sources in your device settings.</p>
-<li><b>Can I play Logo Quiz Mod APK on my PC or iOS device?</b></li>
-<p>Logo Quiz Mod APK is designed for Android devices only, so you cannot play it on your PC or iOS device. However, you can use an Android emulator on your PC to run the game, such as BlueStacks or Nox Player. You can also play the original Logo Quiz game on your PC or iOS device by downloading it from the Google Play Store or the App Store.</p>
-<li><b>What are some of the best logo quiz games for Android?</b></li>
-<p>Some of the best logo quiz games for Android are:</p>
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-<li>Logo Game: Guess Brand Quiz by Logos Box</li>
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spaces/2023Liu2023/bingo/src/lib/hooks/chat-history.ts

@@ -1,62 +0,0 @@
-import { zip } from 'lodash-es'
-import { ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { Storage } from '../storage'
-
-/**
- * conversations:$botId => Conversation[]
- * conversation:$botId:$cid:messages => ChatMessageModel[]
- */
-
-interface Conversation {
-  id: string
-  createdAt: number
-}
-
-type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] }
-
-async function loadHistoryConversations(botId: BotId): Promise<Conversation[]> {
-  const key = `conversations:${botId}`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
-  const conversations = await loadHistoryConversations(botId)
-  const newConversations = conversations.filter((c) => c.id !== cid)
-  await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise<ChatMessageModel[]> {
-  const key = `conversation:${botId}:${cid}:messages`
-  const { [key]: value } = await Storage.get(key)
-  return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
-  const conversations = await loadHistoryConversations(botId)
-  if (!conversations.some((c) => c.id === cid)) {
-    conversations.unshift({ id: cid, createdAt: Date.now() })
-    await Storage.set({ [`conversations:${botId}`]: conversations })
-  }
-  const key = `conversation:${botId}:${cid}:messages`
-  await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise<ConversationWithMessages[]> {
-  const conversations = await loadHistoryConversations(botId)
-  const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
-  return zip(conversations, messagesList).map(([c, messages]) => ({
-    id: c!.id,
-    createdAt: c!.createdAt,
-    messages: messages!,
-  }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
-  const messages = await loadConversationMessages(botId, conversationId)
-  const newMessages = messages.filter((m) => m.id !== messageId)
-  await setConversationMessages(botId, conversationId, newMessages)
-  if (!newMessages.length) {
-    await deleteHistoryConversation(botId, conversationId)
-  }
-}

spaces/839871171w/newbingAI/Dockerfile

@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="1B4yYaAOHZW5A5NEj-BnRHc80SQbg-Mlu83S-t36PdJ8LU_pqBAxHhhNqgpGSWGfRlGepqeiaYk2sKyR8a8w8ehi6V5SenYspGG0DC0n0iHuML-VoNMsbH64tPWJNPwzpBlse3566VRGzaOafGtk8gk1SX1dYvkFvzlK1hucI40aMUKOO2sjmiMFU1lgEgWu2ZPMYIoIZ_pnw32mlIgRdn1XA6Kml6GFf_3_2oYt6Fw4"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]

spaces/A666sxr/Genshin_TTS/utils.py

@@ -1,263 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.WARNING)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-  ckptname = checkpoint_path.split("/")[-1]
-  newest_step = int(ckptname.split(".")[0].split("_")[1])
-  last_ckptname = checkpoint_path.replace(str(newest_step), str(newest_step-3000))
-  if newest_step >= 3000:
-    os.system(f"rm {last_ckptname}")
-  logger.info("Saving model and optimizer state at iteration {} to {}".format(
-    iteration, checkpoint_path))
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  torch.save({'model': state_dict,
-              'iteration': iteration,
-              'optimizer': optimizer.state_dict(),
-              'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-  for k, v in scalars.items():
-    writer.add_scalar(k, v, global_step)
-  for k, v in histograms.items():
-    writer.add_histogram(k, v, global_step)
-  for k, v in images.items():
-    writer.add_image(k, v, global_step, dataformats='HWC')
-  for k, v in audios.items():
-    writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-  f_list = glob.glob(os.path.join(dir_path, regex))
-  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-  x = f_list[-1]
-  print(x)
-  return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-  
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_wav_to_torch(full_path):
-  sampling_rate, data = read(full_path)
-  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-  
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-  
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-  
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-    
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()

spaces/AB-TW/team-ai/agents/code_generate_agent.py

@@ -1,229 +0,0 @@
-import re
-from typing import List, Union
-from langchain.chains import LLMChain
-from langchain.agents import Tool, LLMSingleActionAgent, AgentExecutor, AgentOutputParser
-from langchain.schema import AgentAction, AgentFinish
-from langchain.agents import initialize_agent
-from langchain.prompts import StringPromptTemplate
-from agents.promopts import code_generate_agent_template
-from agents.tools.smart_domain.api_layer_code_tool import apiLayerCodeGenerator
-from agents.tools.smart_domain.domain_layer_code_tool import domainLayerCodeGenerator
-from agents.tools.smart_domain.entity import entityCodeGenerator
-from agents.tools.smart_domain.association import associationCodeGenerator
-from agents.tools.smart_domain.db_entity_repository import dbEntityRepositoryCodeGenerator
-from agents.tools.smart_domain.association_impl import asociationImplCodeGenerator
-from agents.tools.smart_domain.persistent_layer_code_tool import persistentLayerCodeGenerator
-from models import llm
-
-
-class CustomPromptTemplate(StringPromptTemplate):
-    # The template to use
-    template: str
-    # The list of tools available
-    tools: List[Tool]
-
-    def format(self, **kwargs) -> str:
-        # Get the intermediate steps (AgentAction, Observation tuples)
-        # Format them in a particular way
-        intermediate_steps = kwargs.pop("intermediate_steps")
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\nObservation: {observation}\nThought: "
-        # Set the agent_scratchpad variable to that value
-        kwargs["agent_scratchpad"] = thoughts
-        # Create a tools variable from the list of tools provided
-        kwargs["tools"] = "\n".join(
-            [f"{tool.name}: {tool.description}" for tool in self.tools])
-        # Create a list of tool names for the tools provided
-        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
-        return self.template.format(**kwargs)
-
-
-class CustomOutputParser(AgentOutputParser):
-
-    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
-        # Check if agent should finish
-        if "Final Answer:" in llm_output:
-            return AgentFinish(
-                # Return values is generally always a dictionary with a single `output` key
-                # It is not recommended to try anything else at the moment :)
-                return_values={"output": llm_output.split(
-                    "Final Answer:")[-1].strip()},
-                log=llm_output,
-            )
-        # Parse out the action and action input
-        regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
-        match = re.search(regex, llm_output, re.DOTALL)
-        if not match:
-            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
-        action = match.group(1).strip()
-        action_input = match.group(2)
-        # Return the action and action input
-        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
-    
-# chatllm=ChatOpenAI(temperature=0)
-# code_genenrate_memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-# code_generate_agent = initialize_agent(tools, chatllm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, memory=memory, verbose=True)
-
-
-
-# agent = initialize_agent(
-#     tools=tools, llm=llm_chain, template=AGENT_PROMPT, stop=["\nObservation:"], agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
-code_agent_tools = [domainLayerCodeGenerator, entityCodeGenerator, associationCodeGenerator, persistentLayerCodeGenerator, dbEntityRepositoryCodeGenerator, asociationImplCodeGenerator, apiLayerCodeGenerator]
-
-def code_agent_executor() -> AgentExecutor:
-    output_parser = CustomOutputParser()
-    AGENT_PROMPT = CustomPromptTemplate(
-        template=code_generate_agent_template,
-        tools=code_agent_tools,
-        # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
-        # This includes the `intermediate_steps` variable because that is needed
-        input_variables=["input", "intermediate_steps"]
-    )
-
-    code_llm_chain = LLMChain(llm=llm(temperature=0.7), prompt=AGENT_PROMPT)
-
-    tool_names = [tool.name for tool in code_agent_tools]
-    code_agent = LLMSingleActionAgent(
-        llm_chain=code_llm_chain,
-        output_parser=output_parser,
-        stop=["\nObservation:"],
-        allowed_tools=tool_names,
-    )
-
-    code_agent_executor = AgentExecutor.from_agent_and_tools(
-        agent=code_agent, tools=code_agent_tools, verbose=True)
-    return code_agent_executor
-
-# if __name__ == "__main__":
-# response = domainLayerChain.run("""FeatureConfig用于配置某个Feature中控制前端展示效果的配置项
-# FeatureConfig主要属性包括：featureKey(feature标识)、data（配置数据）、saData（埋点数据）、status（状态）、标题、描述、创建时间、更新时间
-# FeatureConfig中status为枚举值，取值范围为（DRAFT、PUBLISHED、DISABLED）
-# FeatureConfig新增后status为DRAFT、执行发布操作后变为PUBLISHED、执行撤销操作后变为DISABLED
-# 状态为DRAFT的FeatureConfig可以执行编辑、发布、撤销操作
-# 发布后FeatureConfig变为PUBLISHED状态，可以执行撤销操作
-# 撤销后FeatureConfig变为DISABLED状态，不可以执行编辑、发布、撤销操作
-# """)
-
-# print(response)
-
-
-# response = persistentChain.run("""
-# Entity:
-# ```
-# public class FeatureConfig {
-#     private FeatureConfigId id;
-#     private FeatureConfigDescription description;
-
-#     public enum FeatureConfigStatus {
-#         DRAFT, PUBLISHED, DISABLED;
-#     }
-
-#     public record FeatureConfigId(String id) {}
-#     public record FeatureKey(String key) {}
-#     public record FeatureConfigData(String data) {}
-#     public record FeatureConfigSaData(String saData) {}
-
-#     @Builder
-#     public record FeatureConfigDescription(FeatureKey featureKey, FeatureConfigData data, FeatureConfigSaData saData, String title, String description, 
-#         FeatureConfigStatus status, LocalDateTime createTime, LocalDateTime updateTime) {}
-
-#     public void update(FeatureConfigDescription description) {
-#         this.title = description.title();
-#         this.description = description.description();
-#         this.updateTime = LocalDateTime.now();
-#     }
-
-#     public void publish() {
-#         this.status = FeatureConfigStatus.PUBLISHED;
-#         this.updateTime = LocalDateTime.now();
-#     }
-
-#     public void disable() {
-#         this.status = FeatureConfigStatus.DISABLED;
-#         this.updateTime = LocalDateTime.now();
-#     }
-# }
-# ```
-
-# Association:
-# ```
-# public interface FeatureConfigs {
-#     Flux<FeatureConfig> findAllByFeatureKey(String featureKey);
-#     Mono<FeatureConfig> findById(FeatureConfigId id);
-#     Mono<FeatureConfig> save(FeatureConfig featureConfig);
-# }
-# ```
-# """)
-
-# print(response)
-
-
-# response = apiChain.run("""
-# Entity:
-# ```
-# public class FeatureConfig {
-#     private FeatureConfigId id;
-#     private FeatureConfigDescription description;
-
-#     public enum FeatureConfigStatus {
-#         DRAFT, PUBLISHED, DISABLED;
-#     }
-
-#     public record FeatureConfigId(String id) {}
-#     public record FeatureKey(String key) {}
-#     public record FeatureConfigData(String data) {}
-#     public record FeatureConfigSaData(String saData) {}
-
-#     @Builder
-#     public record FeatureConfigDescription(FeatureKey featureKey, FeatureConfigData data, FeatureConfigSaData saData, String title, String description, 
-#         FeatureConfigStatus status, LocalDateTime createTime, LocalDateTime updateTime) {}
-
-#     public void update(FeatureConfigDescription description) {
-#         this.title = description.title();
-#         this.description = description.description();
-#         this.updateTime = LocalDateTime.now();
-#     }
-
-#     public void publish() {
-#         this.status = FeatureConfigStatus.PUBLISHED;
-#         this.updateTime = LocalDateTime.now();
-#     }
-
-#     public void disable() {
-#         this.status = FeatureConfigStatus.DISABLED;
-#         this.updateTime = LocalDateTime.now();
-#     }
-# }
-# ```
-
-# Association:
-# ```
-# public interface FeatureConfigs {
-#     Flux<FeatureConfig> findAllByFeatureKey(String featureKey);
-#     Mono<FeatureConfig> findById(FeatureConfigId id);
-#     Mono<FeatureConfig> save(FeatureConfig featureConfig);
-#     Mono<Void> update(FeatureConfigId id, FeatureConfigDescription description);
-#     Mono<Void> publish(FeatureConfigId id);
-#     Mono<Void> disable(FeatureConfigId id);
-# }
-# ```
-# """)
-
-# print(response)
-
-# if __name__ == "code_generate":
-#     response = code_agent_executor.run("""
-#     根据如下需求generate domain layer code: 
-#     ---
-#     FeatureConfig用于配置某个Feature中控制前端展示效果的配置项
-#     FeatureConfig主要属性包括：featureKey(feature标识)、data（配置数据）、saData（埋点数据）、status（状态）、标题、描述、创建时间、更新时间
-#     FeatureConfig中status为枚举值，取值范围为（DRAFT、PUBLISHED、DISABLED）
-#     FeatureConfig新增后status为DRAFT、执行发布操作后变为PUBLISHED、执行撤销操作后变为DISABLED
-#     状态为DRAFT的FeatureConfig可以执行编辑、发布、撤销操作
-#     发布后FeatureConfig变为PUBLISHED状态，可以执行撤销操作
-#     撤销后FeatureConfig变为DISABLED状态，不可以执行编辑、发布、撤销操作
-#     ---
-#     """)
-#     print(response)

spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/models.py

@@ -1,1124 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/commons/single_thread_env.py

@@ -1,5 +0,0 @@
-import os
-
-os.environ["OMP_NUM_THREADS"] = "1"
-os.environ['TF_NUM_INTEROP_THREADS'] = '1'
-os.environ['TF_NUM_INTRAOP_THREADS'] = '1'

spaces/AIWaves/Debate/src/agents/Component/ToolComponent.py

@@ -1,887 +0,0 @@
-from abc import abstractmethod
-import uuid
-from text2vec import semantic_search
-from utils import (
-    get_relevant_history,
-    load_knowledge_base_qa,
-    load_knowledge_base_UnstructuredFile,
-    get_embedding,
-    extract,
-)
-import json
-from typing import Dict, List
-import os
-from googleapiclient.discovery import build
-import requests
-from selenium import webdriver
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support.ui import WebDriverWait
-from selenium.webdriver.support import expected_conditions as EC
-from bs4 import BeautifulSoup
-import base64
-import re
-from datetime import datetime, timedelta
-from typing import Tuple, List, Any, Dict
-from email.mime.text import MIMEText
-from email.mime.multipart import MIMEMultipart
-from google.auth.transport.requests import Request
-from google.oauth2.credentials import Credentials
-from google_auth_oauthlib.flow import InstalledAppFlow
-from googleapiclient.discovery import build
-from googleapiclient.errors import HttpError
-from tqdm import tqdm
-
-class ToolComponent:
-    def __init__(self):
-        pass
-
-    @abstractmethod
-    def func(self):
-        pass
-    
-class KnowledgeBaseComponent(ToolComponent):
-    """
-    Inject knowledge base
-    top_k : Top_k with the highest matching degree
-    type : "QA" or others
-    knowledge_base(json_path) : knowledge_base_path
-    """
-    def __init__(self, top_k, type, knowledge_base):
-        super().__init__()
-        self.top_k = top_k
-        self.type = type
-        self.knowledge_base = knowledge_base
-
-        if self.type == "QA":
-            (
-                self.kb_embeddings,
-                self.kb_questions,
-                self.kb_answers,
-                self.kb_chunks,
-            ) = load_knowledge_base_qa(self.knowledge_base)
-        else:
-            self.kb_embeddings, self.kb_chunks = load_knowledge_base_UnstructuredFile(
-                self.knowledge_base
-            )
-
-    def func(self, agent):
-        query = (
-            agent.long_term_memory[-1]["content"]
-            if len(agent.long_term_memory) > 0
-            else ""
-        )
-        knowledge = ""
-        query = extract(query, "query")
-        query_embedding = get_embedding(query)
-        hits = semantic_search(query_embedding, self.kb_embeddings, top_k=50)
-        hits = hits[0]
-        temp = []
-        if self.type == "QA":
-            for hit in hits:
-                matching_idx = hit["corpus_id"]
-                if self.kb_chunks[matching_idx] in temp:
-                    pass
-                else:
-                    knowledge = (
-                        knowledge
-                        + f"question:{self.kb_questions[matching_idx]},answer:{self.kb_answers[matching_idx]}\n\n"
-                    )
-                    temp.append(self.kb_answers[matching_idx])
-                    if len(temp) == 1:
-                        break
-            print(hits[0]["score"])
-            score = hits[0]["score"]
-            if score < 0.5:
-                return {"prompt": "No matching knowledge base"}
-            else:
-                return {"prompt": "The relevant content is: " + knowledge + "\n"}
-        else:
-            for hit in hits:
-                matching_idx = hit["corpus_id"]
-                if self.kb_chunks[matching_idx] in temp:
-                    pass
-                else:
-                    knowledge = knowledge + f"{self.kb_answers[matching_idx]}\n\n"
-                    temp.append(self.kb_answers[matching_idx])
-                    if len(temp) == self.top_k:
-                        break
-            print(hits[0]["score"])
-            score = hits[0]["score"]
-            if score < 0.5:
-                return {"prompt": "No matching knowledge base"}
-            else:
-                print(knowledge)
-                return {"prompt": "The relevant content is: " + knowledge + "\n"}
-
-
-class StaticComponent(ToolComponent):
-    "Return static response"
-    def __init__(self, output):
-        super().__init__()
-        self.output = output
-
-    def func(self, agent):
-        outputdict = {"response": self.output}
-        return outputdict
-
-
-class ExtractComponent(ToolComponent):
-    """
-    Extract keywords based on the current scene and store them in the environment
-    extract_words(list) : Keywords to be extracted
-    system_prompt & last_prompt : Prompt to extract keywords
-    """
-    def __init__(
-        self,
-        extract_words,
-        system_prompt,
-        last_prompt=None,
-    ):
-        super().__init__()
-        self.extract_words = extract_words
-        self.system_prompt = system_prompt
-        self.default_prompt = (
-            "Please strictly adhere to the following format for outputting:\n"
-        )
-        for extract_word in extract_words:
-            self.default_prompt += (
-                f"<{extract_word}> the content you need to extract </{extract_word}>"
-            )
-        self.last_prompt = last_prompt if last_prompt else self.default_prompt
-
-    def func(self, agent):
-        response = agent.LLM.get_response(
-            agent.long_term_memory,
-            self.system_prompt,
-            self.last_prompt,
-            stream=False,
-        )
-        for extract_word in self.extract_words:
-            key = extract(response, extract_word)
-            key = key if key else response
-            agent.environment.shared_memory[extract_word] = key
-
-        return {}
-
-
-"""Search sources: chatgpt/search engines/specific search sources/can even be multimodal (if it comes to clothing)"""
-
-
-class WebSearchComponent(ToolComponent):
-    """search engines"""
-
-    __ENGINE_NAME__: List = ["google", "bing"]
-
-    def __init__(self, engine_name: str, api: Dict):
-        """
-        :param engine_name: The name of the search engine used
-        :param api: Pass in a dictionary, such as {"bing":"key1", "google":"key2", ...}, of course each value can also be a list, or more complicated
-        """
-        super(WebSearchComponent, self).__init__()
-        """Determine whether the key and engine_name of the api are legal"""
-
-        assert engine_name in WebSearchComponent.__ENGINE_NAME__
-        for api_name in api:
-            assert api_name in WebSearchComponent.__ENGINE_NAME__
-
-        self.api = api
-        self.engine_name = engine_name
-
-        self.search: Dict = {"bing": self._bing_search, "google": self._google_search}
-
-    def _bing_search(self, query: str, **kwargs):
-        """Initialize search hyperparameters"""
-        subscription_key = self.api["bing"]
-        search_url = "https://api.bing.microsoft.com/v7.0/search"
-        headers = {"Ocp-Apim-Subscription-Key": subscription_key}
-        params = {
-            "q": query,
-            "textDecorations": True,
-            "textFormat": "HTML",
-            "count": 10,
-        }
-        """start searching"""
-        response = requests.get(search_url, headers=headers, params=params)
-        response.raise_for_status()
-        results = response.json()["webPages"]["value"]
-        """execute"""
-        metadata_results = []
-        for result in results:
-            metadata_result = {
-                "snippet": result["snippet"],
-                "title": result["name"],
-                "link": result["url"],
-            }
-            metadata_results.append(metadata_result)
-        return {"meta data": metadata_results}
-
-    def _google_search(self, query: str, **kwargs):
-        """Initialize search hyperparameters"""
-        api_key = self.api[self.engine_name]["api_key"]
-        cse_id = self.api[self.engine_name]["cse_id"]
-        service = build("customsearch", "v1", developerKey=api_key)
-        """start searching"""
-        results = (
-            service.cse().list(q=query, cx=cse_id, num=10, **kwargs).execute()["items"]
-        )
-        """execute"""
-        metadata_results = []
-        for result in results:
-            metadata_result = {
-                "snippet": result["snippet"],
-                "title": result["title"],
-                "link": result["link"],
-            }
-            metadata_results.append(metadata_result)
-        return {"meta data": metadata_results}
-
-    def func(self, agent, **kwargs) -> Dict:
-        query = (
-            agent.long_term_memory[-1]["content"]
-            if len(agent.long_term_memory) > 0
-            else " "
-        )
-        response = agent.LLM.get_response(
-            None,
-            system_prompt=f"Please analyze the provided conversation and identify keywords that can be used for a search engine query. Format the output as <keywords>extracted keywords</keywords>:\nConversation:\n{query}",
-            stream=False,
-        )
-        response = extract(response, "keywords")
-        query = response if response else query
-
-        search_results = self.search[self.engine_name](query=query, **kwargs)
-        information = ""
-        for i in search_results["meta data"][:5]:
-            information += i["snippet"]
-        return {
-            "prompt": "You can refer to the following information to reply:\n"
-            + information
-        }
-
-    def convert_search_engine_to(self, engine_name):
-        assert engine_name in WebSearchComponent.__ENGINE_NAME__
-        self.engine_name = engine_name
-
-
-class WebCrawlComponent(ToolComponent):
-    """Open a single web page for crawling"""
-
-    def __init__(self):
-        super(WebCrawlComponent, self).__init__()
-
-    def func(self, agent_dict) -> Dict:
-        url = agent_dict["url"]
-        print(f"crawling {url} ......")
-        content = ""
-        """Crawling content from url may need to be carried out according to different websites, such as wiki, baidu, zhihu, etc."""
-        driver = webdriver.Chrome()
-        try:
-            """open url"""
-            driver.get(url)
-
-            """wait 20 second"""
-            wait = WebDriverWait(driver, 20)
-            wait.until(EC.presence_of_element_located((By.TAG_NAME, "body")))
-
-            """crawl code"""
-            page_source = driver.page_source
-
-            """parse"""
-            soup = BeautifulSoup(page_source, "html.parser")
-
-            """concatenate"""
-            for paragraph in soup.find_all("p"):
-                content = f"{content}\n{paragraph.get_text()}"
-        except Exception as e:
-            print("Error:", e)
-        finally:
-            """quit"""
-            driver.quit()
-        return {"content": content.strip()}
-
-
-class MailComponent(ToolComponent):
-    __VALID_ACTION__ = ["read", "send"]
-
-    def __init__(
-        self, cfg_file: str, default_action: str = "read", name: str = "e-mail"
-    ):
-        """'../config/google_mail.json'"""
-        super(MailComponent, self).__init__(name)
-        self.name = name
-        assert (
-            default_action.lower() in self.__VALID_ACTION__
-        ), f"Action `{default_action}` is not allowed! The valid action is in `{self.__VALID_ACTION__}`"
-        self.action = default_action.lower()
-        self.credential = self._login(cfg_file)
-
-    def _login(self, cfg_file: str):
-        SCOPES = [
-            "https://www.googleapis.com/auth/gmail.readonly",
-            "https://www.googleapis.com/auth/gmail.send",
-        ]
-        creds = None
-        if os.path.exists("token.json"):
-            print("Login Successfully!")
-            creds = Credentials.from_authorized_user_file("token.json", SCOPES)
-        if not creds or not creds.valid:
-            print("Please authorize in an open browser.")
-            if creds and creds.expired and creds.refresh_token:
-                creds.refresh(Request())
-            else:
-                flow = InstalledAppFlow.from_client_secrets_file(cfg_file, SCOPES)
-                creds = flow.run_local_server(port=0)
-            # Save the credentials for the next run
-            with open("token.json", "w") as token:
-                token.write(creds.to_json())
-        return creds
-
-    def _read(self, mail_dict: dict):
-        credential = self.credential
-        state = mail_dict["state"] if "state" in mail_dict else None
-        time_between = (
-            mail_dict["time_between"] if "time_between" in mail_dict else None
-        )
-        sender_mail = mail_dict["sender_mail"] if "sender_mail" in mail_dict else None
-        only_both = mail_dict["only_both"] if "only_both" in mail_dict else False
-        order_by_time = (
-            mail_dict["order_by_time"] if "order_by_time" in mail_dict else "descend"
-        )
-        include_word = (
-            mail_dict["include_word"] if "include_word" in mail_dict else None
-        )
-        exclude_word = (
-            mail_dict["exclude_word"] if "exclude_word" in mail_dict else None
-        )
-        MAX_SEARCH_CNT = (
-            mail_dict["MAX_SEARCH_CNT"] if "MAX_SEARCH_CNT" in mail_dict else 50
-        )
-        number = mail_dict["number"] if "number" in mail_dict else 10
-        if state is None:
-            state = "all"
-        if time_between is not None:
-            assert isinstance(time_between, tuple)
-            assert len(time_between) == 2
-        assert state in ["all", "unread", "read", "sent"]
-        if only_both:
-            assert sender_mail is not None
-        if sender_mail is not None:
-            assert isinstance(sender_mail, str)
-        assert credential
-        assert order_by_time in ["descend", "ascend"]
-
-        def generate_query():
-            query = ""
-            if state in ["unread", "read"]:
-                query = f"is:{state}"
-            if state in ["sent"]:
-                query = f"in:{state}"
-            if only_both:
-                query = f"{query} from:{sender_mail} OR to:{sender_mail}"
-            if sender_mail is not None and not only_both:
-                query = f"{query} from:({sender_mail})"
-            if include_word is not None:
-                query = f"{query} {include_word}"
-            if exclude_word is not None:
-                query = f"{query} -{exclude_word}"
-            if time_between is not None:
-                TIME_FORMAT = "%Y/%m/%d"
-                t1, t2 = time_between
-                if t1 == "now":
-                    t1 = datetime.now().strftime(TIME_FORMAT)
-                if t2 == "now":
-                    t2 = datetime.now().strftime(TIME_FORMAT)
-                if isinstance(t1, str) and isinstance(t2, str):
-                    t1 = datetime.strptime(t1, TIME_FORMAT)
-                    t2 = datetime.strptime(t2, TIME_FORMAT)
-                elif isinstance(t1, str) and isinstance(t2, int):
-                    t1 = datetime.strptime(t1, TIME_FORMAT)
-                    t2 = t1 + timedelta(days=t2)
-                elif isinstance(t1, int) and isinstance(t2, str):
-                    t2 = datetime.strptime(t2, TIME_FORMAT)
-                    t1 = t2 + timedelta(days=t1)
-                else:
-                    assert False, "invalid time"
-                if t1 > t2:
-                    t1, t2 = t2, t1
-                query = f"{query} after:{t1.strftime(TIME_FORMAT)} before:{t2.strftime(TIME_FORMAT)}"
-            return query.strip()
-
-        def sort_by_time(data: List[Dict]):
-            if order_by_time == "descend":
-                reverse = True
-            else:
-                reverse = False
-            sorted_data = sorted(
-                data,
-                key=lambda x: datetime.strptime(x["time"], "%Y-%m-%d %H:%M:%S"),
-                reverse=reverse,
-            )
-            return sorted_data
-
-        try:
-            service = build("gmail", "v1", credentials=credential)
-            results = (
-                service.users()
-                .messages()
-                .list(userId="me", labelIds=["INBOX"], q=generate_query())
-                .execute()
-            )
-
-            messages = results.get("messages", [])
-            email_data = list()
-
-            if not messages:
-                print("No eligible emails.")
-                return None
-            else:
-                pbar = tqdm(total=min(MAX_SEARCH_CNT, len(messages)))
-                for cnt, message in enumerate(messages):
-                    pbar.update(1)
-                    if cnt >= MAX_SEARCH_CNT:
-                        break
-                    msg = (
-                        service.users()
-                        .messages()
-                        .get(
-                            userId="me",
-                            id=message["id"],
-                            format="full",
-                            metadataHeaders=None,
-                        )
-                        .execute()
-                    )
-
-                    subject = ""
-                    for header in msg["payload"]["headers"]:
-                        if header["name"] == "Subject":
-                            subject = header["value"]
-                            break
-
-                    sender = ""
-                    for header in msg["payload"]["headers"]:
-                        if header["name"] == "From":
-                            sender = re.findall(
-                                r"\b[\w\.-]+@[\w\.-]+\.\w+\b", header["value"]
-                            )[0]
-                            break
-                    body = ""
-                    if "parts" in msg["payload"]:
-                        for part in msg["payload"]["parts"]:
-                            if part["mimeType"] == "text/plain":
-                                data = part["body"]["data"]
-                                body = base64.urlsafe_b64decode(data).decode("utf-8")
-                                break
-
-                    email_info = {
-                        "sender": sender,
-                        "time": datetime.fromtimestamp(
-                            int(msg["internalDate"]) / 1000
-                        ).strftime("%Y-%m-%d %H:%M:%S"),
-                        "subject": subject,
-                        "body": body,
-                    }
-                    email_data.append(email_info)
-                pbar.close()
-            email_data = sort_by_time(email_data)[0:number]
-            return {"results": email_data}
-        except Exception as e:
-            print(e)
-            return None
-
-    def _send(self, mail_dict: dict):
-        recipient_mail = mail_dict["recipient_mail"]
-        subject = mail_dict["subject"]
-        body = mail_dict["body"]
-        credential = self.credential
-        service = build("gmail", "v1", credentials=credential)
-
-        message = MIMEMultipart()
-        message["to"] = recipient_mail
-        message["subject"] = subject
-
-        message.attach(MIMEText(body, "plain"))
-
-        raw_message = base64.urlsafe_b64encode(message.as_bytes()).decode("utf-8")
-        try:
-            message = (
-                service.users()
-                .messages()
-                .send(userId="me", body={"raw": raw_message})
-                .execute()
-            )
-            return {"state": True}
-        except HttpError as error:
-            print(error)
-            return {"state": False}
-
-    def func(self, mail_dict: dict):
-        if "action" in mail_dict:
-            assert mail_dict["action"].lower() in self.__VALID_ACTION__
-            self.action = mail_dict["action"]
-        functions = {"read": self._read, "send": self._send}
-        return functions[self.action](mail_dict)
-
-    def convert_action_to(self, action_name: str):
-        assert (
-            action_name.lower() in self.__VALID_ACTION__
-        ), f"Action `{action_name}` is not allowed! The valid action is in `{self.__VALID_ACTION__}`"
-        self.action = action_name.lower()
-
-
-class WeatherComponet(ToolComponent):
-    def __init__(self, api_key, name="weather", TIME_FORMAT="%Y-%m-%d"):
-        super(WeatherComponet, self).__init__(name)
-        self.name = name
-        self.TIME_FORMAT = TIME_FORMAT
-        self.api_key = api_key
-
-    def _parse(self, data):
-        dict_data: dict = {}
-        for item in data["data"]:
-            date = item["datetime"]
-            dict_data[date] = {}
-            if "weather" in item:
-                dict_data[date]["description"] = item["weather"]["description"]
-            mapping = {
-                "temp": "temperature",
-                "max_temp": "max_temperature",
-                "min_temp": "min_temperature",
-                "precip": "accumulated_precipitation",
-            }
-            for key in ["temp", "max_temp", "min_temp", "precip"]:
-                if key in item:
-                    dict_data[date][mapping[key]] = item[key]
-        return dict_data
-
-    def _query(self, city_name, country_code, start_date, end_date):
-        """https://www.weatherbit.io/api/historical-weather-daily"""
-        # print(datetime.strftime(start_date, self.TIME_FORMAT), datetime.strftime(datetime.now(), self.TIME_FORMAT), end_date, datetime.strftime(datetime.now()+timedelta(days=1), self.TIME_FORMAT))
-        if start_date == datetime.strftime(
-            datetime.now(), self.TIME_FORMAT
-        ) and end_date == datetime.strftime(
-            datetime.now() + timedelta(days=1), self.TIME_FORMAT
-        ):
-            """today"""
-            url = f"https://api.weatherbit.io/v2.0/current?city={city_name}&country={country_code}&key={self.api_key}"
-        else:
-            url = f"https://api.weatherbit.io/v2.0/history/daily?&city={city_name}&country={country_code}&start_date={start_date}&end_date={end_date}&key={self.api_key}"
-        response = requests.get(url)
-        data = response.json()
-        return self._parse(data)
-
-    def func(self, weather_dict: Dict) -> Dict:
-        TIME_FORMAT = self.TIME_FORMAT
-        # Beijing, Shanghai
-        city_name = weather_dict["city_name"]
-        # CN, US
-        country_code = weather_dict["country_code"]
-        # 2020-02-02
-        start_date = datetime.strftime(
-            datetime.strptime(weather_dict["start_date"], self.TIME_FORMAT),
-            self.TIME_FORMAT,
-        )
-        end_date = weather_dict["end_date"] if "end_date" in weather_dict else None
-        if end_date is None:
-            end_date = datetime.strftime(
-                datetime.strptime(start_date, TIME_FORMAT) + timedelta(days=-1),
-                TIME_FORMAT,
-            )
-        else:
-            end_date = datetime.strftime(
-                datetime.strptime(weather_dict["end_date"], self.TIME_FORMAT),
-                self.TIME_FORMAT,
-            )
-        if datetime.strptime(start_date, TIME_FORMAT) > datetime.strptime(
-            end_date, TIME_FORMAT
-        ):
-            start_date, end_date = end_date, start_date
-        assert start_date != end_date
-        return self._query(city_name, country_code, start_date, end_date)
-
-
-class TranslateComponent(ToolComponent):
-    __SUPPORT_LANGUAGE__ = [
-        "af",
-        "am",
-        "ar",
-        "as",
-        "az",
-        "ba",
-        "bg",
-        "bn",
-        "bo",
-        "bs",
-        "ca",
-        "cs",
-        "cy",
-        "da",
-        "de",
-        "dsb",
-        "dv",
-        "el",
-        "en",
-        "es",
-        "et",
-        "eu",
-        "fa",
-        "fi",
-        "fil",
-        "fj",
-        "fo",
-        "fr",
-        "fr-CA",
-        "ga",
-        "gl",
-        "gom",
-        "gu",
-        "ha",
-        "he",
-        "hi",
-        "hr",
-        "hsb",
-        "ht",
-        "hu",
-        "hy",
-        "id",
-        "ig",
-        "ikt",
-        "is",
-        "it",
-        "iu",
-        "iu-Latn",
-        "ja",
-        "ka",
-        "kk",
-        "km",
-        "kmr",
-        "kn",
-        "ko",
-        "ku",
-        "ky",
-        "ln",
-        "lo",
-        "lt",
-        "lug",
-        "lv",
-        "lzh",
-        "mai",
-        "mg",
-        "mi",
-        "mk",
-        "ml",
-        "mn-Cyrl",
-        "mn-Mong",
-        "mr",
-        "ms",
-        "mt",
-        "mww",
-        "my",
-        "nb",
-        "ne",
-        "nl",
-        "nso",
-        "nya",
-        "or",
-        "otq",
-        "pa",
-        "pl",
-        "prs",
-        "ps",
-        "pt",
-        "pt-PT",
-        "ro",
-        "ru",
-        "run",
-        "rw",
-        "sd",
-        "si",
-        "sk",
-        "sl",
-        "sm",
-        "sn",
-        "so",
-        "sq",
-        "sr-Cyrl",
-        "sr-Latn",
-        "st",
-        "sv",
-        "sw",
-        "ta",
-        "te",
-        "th",
-        "ti",
-        "tk",
-        "tlh-Latn",
-        "tlh-Piqd",
-        "tn",
-        "to",
-        "tr",
-        "tt",
-        "ty",
-        "ug",
-        "uk",
-        "ur",
-        "uz",
-        "vi",
-        "xh",
-        "yo",
-        "yua",
-        "yue",
-        "zh-Hans",
-        "zh-Hant",
-        "zu",
-    ]
-
-    def __init__(
-        self, api_key, location, default_target_language="zh-cn", name="translate"
-    ):
-        super(TranslateComponent, self).__init__(name)
-        self.name = name
-        self.api_key = api_key
-        self.location = location
-        self.default_target_language = default_target_language
-
-    def func(self, translate_dict: Dict) -> Dict:
-        content = translate_dict["content"]
-        target_language = self.default_target_language
-        if "target_language" in translate_dict:
-            target_language = translate_dict["target_language"]
-        assert (
-            target_language in self.__SUPPORT_LANGUAGE__
-        ), f"language `{target_language}` is not supported."
-
-        endpoint = "https://api.cognitive.microsofttranslator.com"
-
-        path = "/translate"
-        constructed_url = endpoint + path
-
-        params = {"api-version": "3.0", "to": target_language}
-
-        headers = {
-            "Ocp-Apim-Subscription-Key": self.api_key,
-            "Ocp-Apim-Subscription-Region": self.location,
-            "Content-type": "application/json",
-            "X-ClientTraceId": str(uuid.uuid4()),
-        }
-
-        body = [{"text": content}]
-
-        request = requests.post(
-            constructed_url, params=params, headers=headers, json=body
-        )
-        response = request.json()
-        response = json.dumps(
-            response,
-            sort_keys=True,
-            ensure_ascii=False,
-            indent=4,
-            separators=(",", ": "),
-        )
-        response = eval(response)
-        return {"result": response[0]["translations"][0]["text"]}
-
-
-class APIComponent(ToolComponent):
-    def __init__(self):
-        super(APIComponent, self).__init__()
-
-    def func(self, agent) -> Dict:
-        pass
-
-
-class FunctionComponent(ToolComponent):
-    def __init__(
-        self,
-        functions,
-        function_call="auto",
-        response_type="response",
-        your_function=None,
-    ):
-        super().__init__()
-        self.functions = functions
-        self.function_call = function_call
-        self.parameters = {}
-        self.available_functions = {}
-        self.response_type = response_type
-        if your_function:
-            function_name = your_function["name"]
-            function_content = your_function["content"]
-            exec(function_content)
-            self.available_functions[function_name] = eval(function_name)
-
-        for function in self.functions:
-            self.parameters[function["name"]] = list(
-                function["parameters"]["properties"].keys()
-            )
-            self.available_functions[function["name"]] = eval(function["name"])
-
-    def func(self, agent):
-        messages = agent.long_term_memory
-        outputdict = {}
-        query = agent.long_term_memory[-1].content if len(agent.long_term_memory) > 0 else " "
-        relevant_history = get_relevant_history(
-            query,
-            agent.long_term_memory[:-1],
-            agent.chat_embeddings[:-1],
-        )
-        response = agent.LLM.get_response(
-            messages,
-            None,
-            functions=self.functions,
-            stream=False,
-            function_call=self.function_call,
-            relevant_history=relevant_history,
-        )
-        response_message = response
-        if response_message.get("function_call"):
-            function_name = response_message["function_call"]["name"]
-            fuction_to_call = self.available_functions[function_name]
-            function_args = json.loads(response_message["function_call"]["arguments"])
-            input_args = {}
-            for args_name in self.parameters[function_name]:
-                input_args[args_name] = function_args.get(args_name)
-            function_response = fuction_to_call(**input_args)
-            if self.response_type == "response":
-                outputdict["response"] = function_response
-            elif self.response_type == "prompt":
-                outputdict["prompt"] = function_response
-
-        return outputdict
-
-
-class CodeComponent(ToolComponent):
-    def __init__(self, file_name, keyword) -> None:
-        super().__init__()
-        self.file_name = file_name
-        self.keyword = keyword
-        self.system_prompt = (
-            "you need to extract the modified code as completely as possible."
-        )
-        self.last_prompt = (
-            f"Please strictly adhere to the following format for outputting: \n"
-        )
-        self.last_prompt += (
-            f"<{self.keyword}> the content you need to extract </{self.keyword}>"
-        )
-
-    def func(self, agent):
-        response = agent.LLM.get_response(
-            agent.long_term_memory,
-            self.system_prompt,
-            self.last_prompt,
-            stream=False,
-        )
-        code = extract(response, self.keyword)
-        code = code if code else response
-        os.makedirs("output_code", exist_ok=True)
-        file_name = "output_code/" + self.file_name
-        codes = code.split("\n")
-        if codes[0] == "```python":
-            codes.remove(codes[0])
-        if codes[-1] == "```":
-            codes.remove(codes[-1])
-        code = "\n".join(codes)
-        with open(file_name, "w", encoding="utf-8") as f:
-            f.write(code)
-        return {}

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/utils/hashConv.ts

@@ -1,12 +0,0 @@
-import type { Conversation } from "$lib/types/Conversation";
-import { sha256 } from "./sha256";
-
-export async function hashConv(conv: Conversation) {
-	// messages contains the conversation message but only the immutable part
-	const messages = conv.messages.map((message) => {
-		return (({ from, id, content, webSearchId }) => ({ from, id, content, webSearchId }))(message);
-	});
-
-	const hash = await sha256(JSON.stringify(messages));
-	return hash;
-}

spaces/AchyuthGamer/OpenGPT/client/css/main.css

@@ -1,14 +0,0 @@
-.main-container {
-	display: flex;
-	padding: var(--section-gap);
-	height: 100vh;
-	justify-content: center;
-	box-sizing: border-box;
-}
-
-@media screen and (max-width: 360px) {
-	.main-container {
-		padding: 0px;
-		height: 90vh;
-	}
-}

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatgptDuo.py

@@ -1,57 +0,0 @@
-from __future__ import annotations
-
-from curl_cffi.requests import AsyncSession
-from .base_provider import AsyncProvider, format_prompt
-
-
-class ChatgptDuo(AsyncProvider):
-    url                   = "https://chatgptduo.com"
-    supports_gpt_35_turbo = True
-    working               = True
-
-    @classmethod
-    async def create_async(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        timeout: int = 30,
-        **kwargs
-    ) -> str:
-        async with AsyncSession(
-            impersonate="chrome107",
-            proxies={"https": proxy},
-            timeout=timeout
-        ) as session:
-            prompt = format_prompt(messages),
-            data = {
-                "prompt": prompt,
-                "search": prompt,
-                "purpose": "ask",
-            }
-            response = await session.post(f"{cls.url}/", data=data)
-            response.raise_for_status()
-            data = response.json()
-
-            cls._sources = [{
-                "title": source["title"],
-                "url": source["link"],
-                "snippet": source["snippet"]
-            } for source in data["results"]]
-
-            return data["answer"]
-
-    @classmethod
-    def get_sources(cls):
-        return cls._sources
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/GetShownChildrenMethods.js

@@ -1,43 +0,0 @@
-export default {
-    getShownChildren(out) {
-        if (out === undefined) {
-            out = [];
-        }
-        var children = this.children,
-            child;
-        for (var i = 0, cnt = children.length; i < cnt; i++) {
-            child = children[i];
-            if (child.rexSizer && child.rexSizer.hidden) { // Don't add hidden child
-                continue;
-            }
-
-            out.push(child);
-        }
-
-        return out;
-    },
-
-    getAllShownChildren(out) {
-        if (out === undefined) {
-            out = [];
-        }
-
-        var queue = [this];
-        while (queue.length > 0) {
-            var current = queue.shift();
-            if (current.rexSizer && current.rexSizer.hidden) {
-                continue;
-            }
-
-            if (current !== this) {
-                out.push(current);
-            }
-
-            if (current.isRexContainerLite) {
-                queue.push(...current.children);
-            }
-        }
-
-        return out;
-    }
-}

spaces/Akmyradov/TurkmenTTSweSTT/vits/text/cleaners.py

@@ -1,100 +0,0 @@
-""" 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
-from phonemizer import phonemize
-
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# 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'),
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def expand_numbers(text):
-  return normalize_numbers(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 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 english_cleaners(text):
-  '''Pipeline for English text, including abbreviation expansion.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = expand_abbreviations(text)
-  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True)
-  phonemes = collapse_whitespace(phonemes)
-  return phonemes
-
-
-def english_cleaners2(text):
-  '''Pipeline for English text, including abbreviation expansion. + punctuation + stress'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = expand_abbreviations(text)
-  phonemes = phonemize(text, language='en-us', backend='espeak', strip=True, preserve_punctuation=True, with_stress=True)
-  phonemes = collapse_whitespace(phonemes)
-  return phonemes

spaces/Ameaou/academic-chatgpt3.1/colorful.py

@@ -1,91 +0,0 @@
-import platform
-from sys import stdout
-
-if platform.system()=="Linux":
-    pass
-else: 
-    from colorama import init
-    init()
-
-# Do you like the elegance of Chinese characters?
-def print红(*kw,**kargs):
-    print("\033[0;31m",*kw,"\033[0m",**kargs)
-def print绿(*kw,**kargs):
-    print("\033[0;32m",*kw,"\033[0m",**kargs)
-def print黄(*kw,**kargs):
-    print("\033[0;33m",*kw,"\033[0m",**kargs)
-def print蓝(*kw,**kargs):
-    print("\033[0;34m",*kw,"\033[0m",**kargs)
-def print紫(*kw,**kargs):
-    print("\033[0;35m",*kw,"\033[0m",**kargs)
-def print靛(*kw,**kargs):
-    print("\033[0;36m",*kw,"\033[0m",**kargs)
-
-def print亮红(*kw,**kargs):
-    print("\033[1;31m",*kw,"\033[0m",**kargs)
-def print亮绿(*kw,**kargs):
-    print("\033[1;32m",*kw,"\033[0m",**kargs)
-def print亮黄(*kw,**kargs):
-    print("\033[1;33m",*kw,"\033[0m",**kargs)
-def print亮蓝(*kw,**kargs):
-    print("\033[1;34m",*kw,"\033[0m",**kargs)
-def print亮紫(*kw,**kargs):
-    print("\033[1;35m",*kw,"\033[0m",**kargs)
-def print亮靛(*kw,**kargs):
-    print("\033[1;36m",*kw,"\033[0m",**kargs)
-
-
-
-def print亮红(*kw,**kargs):
-    print("\033[1;31m",*kw,"\033[0m",**kargs)
-def print亮绿(*kw,**kargs):
-    print("\033[1;32m",*kw,"\033[0m",**kargs)
-def print亮黄(*kw,**kargs):
-    print("\033[1;33m",*kw,"\033[0m",**kargs)
-def print亮蓝(*kw,**kargs):
-    print("\033[1;34m",*kw,"\033[0m",**kargs)
-def print亮紫(*kw,**kargs):
-    print("\033[1;35m",*kw,"\033[0m",**kargs)
-def print亮靛(*kw,**kargs):
-    print("\033[1;36m",*kw,"\033[0m",**kargs)
-    
-print_red = print红
-print_green = print绿
-print_yellow = print黄
-print_blue = print蓝
-print_purple = print紫
-print_indigo = print靛
-
-print_bold_red = print亮红
-print_bold_green = print亮绿
-print_bold_yellow = print亮黄
-print_bold_blue = print亮蓝
-print_bold_purple = print亮紫
-print_bold_indigo = print亮靛
-    
-if not stdout.isatty():
-    # redirection, avoid a fucked up log file
-    print红 = print
-    print绿 = print
-    print黄 = print
-    print蓝 = print
-    print紫 = print
-    print靛 = print
-    print亮红 = print
-    print亮绿 = print
-    print亮黄 = print
-    print亮蓝 = print
-    print亮紫 = print
-    print亮靛 = print
-    print_red = print
-    print_green = print
-    print_yellow = print
-    print_blue = print
-    print_purple = print
-    print_indigo = print
-    print_bold_red = print
-    print_bold_green = print
-    print_bold_yellow = print
-    print_bold_blue = print
-    print_bold_purple = print
-    print_bold_indigo = print

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/zh/installation.md

@@ -1,146 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# 安装
-
-在你正在使用的任意深度学习框架中安装 🤗 Diffusers 。
-
-🤗 Diffusers已在Python 3.7+、PyTorch 1.7.0+和Flax上进行了测试。按照下面的安装说明，针对你正在使用的深度学习框架进行安装：
-
-- [PyTorch](https://pytorch.org/get-started/locally/) installation instructions.
-- [Flax](https://flax.readthedocs.io/en/latest/) installation instructions.
-
-## 使用pip安装
-
-你需要在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装 🤗 Diffusers 。
-
-如果你对 Python 虚拟环境不熟悉，可以看看这个[教程](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
-
-在虚拟环境中，你可以轻松管理不同的项目，避免依赖项之间的兼容性问题。
-
-首先，在你的项目目录下创建一个虚拟环境：
-
-```bash
-python -m venv .env
-```
-
-激活虚拟环境：
-
-```bash
-source .env/bin/activate
-```
-
-现在，你就可以安装 🤗 Diffusers了！使用下边这个命令：
-
-**PyTorch**
-
-```bash
-pip install diffusers["torch"]
-```
-
-**Flax**
-
-```bash
-pip install diffusers["flax"]
-```
-
-## 从源代码安装
-
-在从源代码安装 `diffusers` 之前，确保你已经安装了 `torch` 和 `accelerate`。
-
-`torch`的安装教程可以看 `torch` [文档](https://pytorch.org/get-started/locally/#start-locally).
-
-安装 `accelerate`
-
-```bash
-pip install accelerate
-```
-
-从源码安装 🤗 Diffusers 需要使用以下命令:
-
-```bash
-pip install git+https://github.com/huggingface/diffusers
-```
-
-这个命令安装的是最新的 `main`版本，而不是最近的`stable`版。
-`main`是一直和最新进展保持一致的。比如，上次发布的正式版中有bug，在`main`中可以看到这个bug被修复了，但是新的正式版此时尚未推出。
-但是这也意味着 `main`版本不保证是稳定的。
-
-我们努力保持`main`版本正常运行，大多数问题都能在几个小时或一天之内解决
-
-如果你遇到了问题，可以提 [Issue](https://github.com/huggingface/transformers/issues)，这样我们就能更快修复问题了。
-
-## 可修改安装
-
-如果你想做以下两件事，那你可能需要一个可修改代码的安装方式：
-
-* 使用 `main`版本的源代码。
-* 为 🤗 Diffusers 贡献，需要测试代码中的变化。
-
-使用以下命令克隆并安装 🤗 Diffusers:
-
-```bash
-git clone https://github.com/huggingface/diffusers.git
-cd diffusers
-```
-
-**PyTorch**
-
-```
-pip install -e ".[torch]"
-```
-
-**Flax**
-
-```
-pip install -e ".[flax]"
-```
-
-这些命令将连接到你克隆的版本库和你的 Python 库路径。
-现在，不只是在通常的库路径，Python 还会在你克隆的文件夹内寻找包。
-例如，如果你的 Python 包通常安装在 `~/anaconda3/envs/main/lib/python3.7/Site-packages/`，Python 也会搜索你克隆到的文件夹。`~/diffusers/`。
-
-<Tip warning={true}>
-
-如果你想继续使用这个库，你必须保留 `diffusers` 文件夹。
-
-</Tip>
-
-
-现在你可以用下面的命令轻松地将你克隆的 🤗 Diffusers 库更新到最新版本。
-
-```bash
-cd ~/diffusers/
-git pull
-```
-
-你的Python环境将在下次运行时找到`main`版本的 🤗 Diffusers。
-
-## 注意 Telemetry 日志
-
-我们的库会在使用`from_pretrained()`请求期间收集 telemetry 信息。这些数据包括Diffusers和PyTorch/Flax的版本，请求的模型或管道类，以及预训练检查点的路径（如果它被托管在Hub上的话）。
-这些使用数据有助于我们调试问题并确定新功能的开发优先级。
-Telemetry 数据仅在从 HuggingFace Hub 中加载模型和管道时发送，而不会在本地使用期间收集。
-
-我们知道，并不是每个人都想分享这些的信息，我们尊重您的隐私，
-因此您可以通过在终端中设置 `DISABLE_TELEMETRY` 环境变量从而禁用 Telemetry 数据收集：
-
-
-Linux/MacOS :
-```bash
-export DISABLE_TELEMETRY=YES
-```
-
-Windows :
-```bash
-set DISABLE_TELEMETRY=YES
-```

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py

@@ -1,62 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject):
-    _backends = ["flax", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-
-class FlaxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
-    _backends = ["flax", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-
-class FlaxStableDiffusionInpaintPipeline(metaclass=DummyObject):
-    _backends = ["flax", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-
-class FlaxStableDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["flax", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["flax", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["flax", "transformers"])

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/hub_utils.py

@@ -1,361 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import os
-import re
-import sys
-import traceback
-import warnings
-from pathlib import Path
-from typing import Dict, Optional, Union
-from uuid import uuid4
-
-from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
-from huggingface_hub.file_download import REGEX_COMMIT_HASH
-from huggingface_hub.utils import (
-    EntryNotFoundError,
-    RepositoryNotFoundError,
-    RevisionNotFoundError,
-    is_jinja_available,
-)
-from packaging import version
-from requests import HTTPError
-
-from .. import __version__
-from .constants import (
-    DEPRECATED_REVISION_ARGS,
-    DIFFUSERS_CACHE,
-    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
-    SAFETENSORS_WEIGHTS_NAME,
-    WEIGHTS_NAME,
-)
-from .import_utils import (
-    ENV_VARS_TRUE_VALUES,
-    _flax_version,
-    _jax_version,
-    _onnxruntime_version,
-    _torch_version,
-    is_flax_available,
-    is_onnx_available,
-    is_torch_available,
-)
-from .logging import get_logger
-
-
-logger = get_logger(__name__)
-
-
-MODEL_CARD_TEMPLATE_PATH = Path(__file__).parent / "model_card_template.md"
-SESSION_ID = uuid4().hex
-HF_HUB_OFFLINE = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
-DISABLE_TELEMETRY = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
-HUGGINGFACE_CO_TELEMETRY = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
-
-
-def http_user_agent(user_agent: Union[Dict, str, None] = None) -> str:
-    """
-    Formats a user-agent string with basic info about a request.
-    """
-    ua = f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
-    if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
-        return ua + "; telemetry/off"
-    if is_torch_available():
-        ua += f"; torch/{_torch_version}"
-    if is_flax_available():
-        ua += f"; jax/{_jax_version}"
-        ua += f"; flax/{_flax_version}"
-    if is_onnx_available():
-        ua += f"; onnxruntime/{_onnxruntime_version}"
-    # CI will set this value to True
-    if os.environ.get("DIFFUSERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES:
-        ua += "; is_ci/true"
-    if isinstance(user_agent, dict):
-        ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items())
-    elif isinstance(user_agent, str):
-        ua += "; " + user_agent
-    return ua
-
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
-def create_model_card(args, model_name):
-    if not is_jinja_available():
-        raise ValueError(
-            "Modelcard rendering is based on Jinja templates."
-            " Please make sure to have `jinja` installed before using `create_model_card`."
-            " To install it, please run `pip install Jinja2`."
-        )
-
-    if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
-        return
-
-    hub_token = args.hub_token if hasattr(args, "hub_token") else None
-    repo_name = get_full_repo_name(model_name, token=hub_token)
-
-    model_card = ModelCard.from_template(
-        card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
-            language="en",
-            license="apache-2.0",
-            library_name="diffusers",
-            tags=[],
-            datasets=args.dataset_name,
-            metrics=[],
-        ),
-        template_path=MODEL_CARD_TEMPLATE_PATH,
-        model_name=model_name,
-        repo_name=repo_name,
-        dataset_name=args.dataset_name if hasattr(args, "dataset_name") else None,
-        learning_rate=args.learning_rate,
-        train_batch_size=args.train_batch_size,
-        eval_batch_size=args.eval_batch_size,
-        gradient_accumulation_steps=(
-            args.gradient_accumulation_steps if hasattr(args, "gradient_accumulation_steps") else None
-        ),
-        adam_beta1=args.adam_beta1 if hasattr(args, "adam_beta1") else None,
-        adam_beta2=args.adam_beta2 if hasattr(args, "adam_beta2") else None,
-        adam_weight_decay=args.adam_weight_decay if hasattr(args, "adam_weight_decay") else None,
-        adam_epsilon=args.adam_epsilon if hasattr(args, "adam_epsilon") else None,
-        lr_scheduler=args.lr_scheduler if hasattr(args, "lr_scheduler") else None,
-        lr_warmup_steps=args.lr_warmup_steps if hasattr(args, "lr_warmup_steps") else None,
-        ema_inv_gamma=args.ema_inv_gamma if hasattr(args, "ema_inv_gamma") else None,
-        ema_power=args.ema_power if hasattr(args, "ema_power") else None,
-        ema_max_decay=args.ema_max_decay if hasattr(args, "ema_max_decay") else None,
-        mixed_precision=args.mixed_precision,
-    )
-
-    card_path = os.path.join(args.output_dir, "README.md")
-    model_card.save(card_path)
-
-
-def extract_commit_hash(resolved_file: Optional[str], commit_hash: Optional[str] = None):
-    """
-    Extracts the commit hash from a resolved filename toward a cache file.
-    """
-    if resolved_file is None or commit_hash is not None:
-        return commit_hash
-    resolved_file = str(Path(resolved_file).as_posix())
-    search = re.search(r"snapshots/([^/]+)/", resolved_file)
-    if search is None:
-        return None
-    commit_hash = search.groups()[0]
-    return commit_hash if REGEX_COMMIT_HASH.match(commit_hash) else None
-
-
-# Old default cache path, potentially to be migrated.
-# This logic was more or less taken from `transformers`, with the following differences:
-# - Diffusers doesn't use custom environment variables to specify the cache path.
-# - There is no need to migrate the cache format, just move the files to the new location.
-hf_cache_home = os.path.expanduser(
-    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
-)
-old_diffusers_cache = os.path.join(hf_cache_home, "diffusers")
-
-
-def move_cache(old_cache_dir: Optional[str] = None, new_cache_dir: Optional[str] = None) -> None:
-    if new_cache_dir is None:
-        new_cache_dir = DIFFUSERS_CACHE
-    if old_cache_dir is None:
-        old_cache_dir = old_diffusers_cache
-
-    old_cache_dir = Path(old_cache_dir).expanduser()
-    new_cache_dir = Path(new_cache_dir).expanduser()
-    for old_blob_path in old_cache_dir.glob("**/blobs/*"):
-        if old_blob_path.is_file() and not old_blob_path.is_symlink():
-            new_blob_path = new_cache_dir / old_blob_path.relative_to(old_cache_dir)
-            new_blob_path.parent.mkdir(parents=True, exist_ok=True)
-            os.replace(old_blob_path, new_blob_path)
-            try:
-                os.symlink(new_blob_path, old_blob_path)
-            except OSError:
-                logger.warning(
-                    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."
-                )
-    # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
-
-
-cache_version_file = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
-if not os.path.isfile(cache_version_file):
-    cache_version = 0
-else:
-    with open(cache_version_file) as f:
-        try:
-            cache_version = int(f.read())
-        except ValueError:
-            cache_version = 0
-
-if cache_version < 1:
-    old_cache_is_not_empty = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
-    if old_cache_is_not_empty:
-        logger.warning(
-            "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
-            "existing cached models. This is a one-time operation, you can interrupt it or run it "
-            "later by calling `diffusers.utils.hub_utils.move_cache()`."
-        )
-        try:
-            move_cache()
-        except Exception as e:
-            trace = "\n".join(traceback.format_tb(e.__traceback__))
-            logger.error(
-                f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
-                "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
-                "message and we will do our best to help."
-            )
-
-if cache_version < 1:
-    try:
-        os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
-        with open(cache_version_file, "w") as f:
-            f.write("1")
-    except Exception:
-        logger.warning(
-            f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
-            "the directory exists and can be written to."
-        )
-
-
-def _add_variant(weights_name: str, variant: Optional[str] = None) -> str:
-    if variant is not None:
-        splits = weights_name.split(".")
-        splits = splits[:-1] + [variant] + splits[-1:]
-        weights_name = ".".join(splits)
-
-    return weights_name
-
-
-def _get_model_file(
-    pretrained_model_name_or_path,
-    *,
-    weights_name,
-    subfolder,
-    cache_dir,
-    force_download,
-    proxies,
-    resume_download,
-    local_files_only,
-    use_auth_token,
-    user_agent,
-    revision,
-    commit_hash=None,
-):
-    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-    if os.path.isfile(pretrained_model_name_or_path):
-        return pretrained_model_name_or_path
-    elif os.path.isdir(pretrained_model_name_or_path):
-        if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
-            # Load from a PyTorch checkpoint
-            model_file = os.path.join(pretrained_model_name_or_path, weights_name)
-            return model_file
-        elif subfolder is not None and os.path.isfile(
-            os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
-        ):
-            model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
-            return model_file
-        else:
-            raise EnvironmentError(
-                f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
-            )
-    else:
-        # 1. First check if deprecated way of loading from branches is used
-        if (
-            revision in DEPRECATED_REVISION_ARGS
-            and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
-            and version.parse(version.parse(__version__).base_version) >= version.parse("0.20.0")
-        ):
-            try:
-                model_file = hf_hub_download(
-                    pretrained_model_name_or_path,
-                    filename=_add_variant(weights_name, revision),
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    resume_download=resume_download,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    user_agent=user_agent,
-                    subfolder=subfolder,
-                    revision=revision or commit_hash,
-                )
-                warnings.warn(
-                    f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
-                    FutureWarning,
-                )
-                return model_file
-            except:  # noqa: E722
-                warnings.warn(
-                    f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.",
-                    FutureWarning,
-                )
-        try:
-            # 2. Load model file as usual
-            model_file = hf_hub_download(
-                pretrained_model_name_or_path,
-                filename=weights_name,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                proxies=proxies,
-                resume_download=resume_download,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                user_agent=user_agent,
-                subfolder=subfolder,
-                revision=revision or commit_hash,
-            )
-            return model_file
-
-        except RepositoryNotFoundError:
-            raise EnvironmentError(
-                f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
-                "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
-                "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
-                "login`."
-            )
-        except RevisionNotFoundError:
-            raise EnvironmentError(
-                f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
-                "this model name. Check the model page at "
-                f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
-            )
-        except EntryNotFoundError:
-            raise EnvironmentError(
-                f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."
-            )
-        except HTTPError as err:
-            raise EnvironmentError(
-                f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"
-            )
-        except ValueError:
-            raise EnvironmentError(
-                f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
-                f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
-                f" directory containing a file named {weights_name} or"
-                " \nCheckout your internet connection or see how to run the library in"
-                " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
-            )
-        except EnvironmentError:
-            raise EnvironmentError(
-                f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
-                "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
-                f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
-                f"containing a file named {weights_name}"
-            )

spaces/Andy1621/uniformer_image_detection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py

@@ -1,4 +0,0 @@
-_base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py'
-# learning policy
-lr_config = dict(step=[20, 23])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/group_points.py

@@ -1,224 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Tuple
-
-import torch
-from torch import nn as nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-from .ball_query import ball_query
-from .knn import knn
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['group_points_forward', 'group_points_backward'])
-
-
-class QueryAndGroup(nn.Module):
-    """Groups points with a ball query of radius.
-
-    Args:
-        max_radius (float): The maximum radius of the balls.
-            If None is given, we will use kNN sampling instead of ball query.
-        sample_num (int): Maximum number of features to gather in the ball.
-        min_radius (float, optional): The minimum radius of the balls.
-            Default: 0.
-        use_xyz (bool, optional): Whether to use xyz.
-            Default: True.
-        return_grouped_xyz (bool, optional): Whether to return grouped xyz.
-            Default: False.
-        normalize_xyz (bool, optional): Whether to normalize xyz.
-            Default: False.
-        uniform_sample (bool, optional): Whether to sample uniformly.
-            Default: False
-        return_unique_cnt (bool, optional): Whether to return the count of
-            unique samples. Default: False.
-        return_grouped_idx (bool, optional): Whether to return grouped idx.
-            Default: False.
-    """
-
-    def __init__(self,
-                 max_radius,
-                 sample_num,
-                 min_radius=0,
-                 use_xyz=True,
-                 return_grouped_xyz=False,
-                 normalize_xyz=False,
-                 uniform_sample=False,
-                 return_unique_cnt=False,
-                 return_grouped_idx=False):
-        super().__init__()
-        self.max_radius = max_radius
-        self.min_radius = min_radius
-        self.sample_num = sample_num
-        self.use_xyz = use_xyz
-        self.return_grouped_xyz = return_grouped_xyz
-        self.normalize_xyz = normalize_xyz
-        self.uniform_sample = uniform_sample
-        self.return_unique_cnt = return_unique_cnt
-        self.return_grouped_idx = return_grouped_idx
-        if self.return_unique_cnt:
-            assert self.uniform_sample, \
-                'uniform_sample should be True when ' \
-                'returning the count of unique samples'
-        if self.max_radius is None:
-            assert not self.normalize_xyz, \
-                'can not normalize grouped xyz when max_radius is None'
-
-    def forward(self, points_xyz, center_xyz, features=None):
-        """
-        Args:
-            points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
-            center_xyz (Tensor): (B, npoint, 3) coordinates of the centriods.
-            features (Tensor): (B, C, N) Descriptors of the features.
-
-        Returns:
-            Tensor: (B, 3 + C, npoint, sample_num) Grouped feature.
-        """
-        # if self.max_radius is None, we will perform kNN instead of ball query
-        # idx is of shape [B, npoint, sample_num]
-        if self.max_radius is None:
-            idx = knn(self.sample_num, points_xyz, center_xyz, False)
-            idx = idx.transpose(1, 2).contiguous()
-        else:
-            idx = ball_query(self.min_radius, self.max_radius, self.sample_num,
-                             points_xyz, center_xyz)
-
-        if self.uniform_sample:
-            unique_cnt = torch.zeros((idx.shape[0], idx.shape[1]))
-            for i_batch in range(idx.shape[0]):
-                for i_region in range(idx.shape[1]):
-                    unique_ind = torch.unique(idx[i_batch, i_region, :])
-                    num_unique = unique_ind.shape[0]
-                    unique_cnt[i_batch, i_region] = num_unique
-                    sample_ind = torch.randint(
-                        0,
-                        num_unique, (self.sample_num - num_unique, ),
-                        dtype=torch.long)
-                    all_ind = torch.cat((unique_ind, unique_ind[sample_ind]))
-                    idx[i_batch, i_region, :] = all_ind
-
-        xyz_trans = points_xyz.transpose(1, 2).contiguous()
-        # (B, 3, npoint, sample_num)
-        grouped_xyz = grouping_operation(xyz_trans, idx)
-        grouped_xyz_diff = grouped_xyz - \
-            center_xyz.transpose(1, 2).unsqueeze(-1)  # relative offsets
-        if self.normalize_xyz:
-            grouped_xyz_diff /= self.max_radius
-
-        if features is not None:
-            grouped_features = grouping_operation(features, idx)
-            if self.use_xyz:
-                # (B, C + 3, npoint, sample_num)
-                new_features = torch.cat([grouped_xyz_diff, grouped_features],
-                                         dim=1)
-            else:
-                new_features = grouped_features
-        else:
-            assert (self.use_xyz
-                    ), 'Cannot have not features and not use xyz as a feature!'
-            new_features = grouped_xyz_diff
-
-        ret = [new_features]
-        if self.return_grouped_xyz:
-            ret.append(grouped_xyz)
-        if self.return_unique_cnt:
-            ret.append(unique_cnt)
-        if self.return_grouped_idx:
-            ret.append(idx)
-        if len(ret) == 1:
-            return ret[0]
-        else:
-            return tuple(ret)
-
-
-class GroupAll(nn.Module):
-    """Group xyz with feature.
-
-    Args:
-        use_xyz (bool): Whether to use xyz.
-    """
-
-    def __init__(self, use_xyz: bool = True):
-        super().__init__()
-        self.use_xyz = use_xyz
-
-    def forward(self,
-                xyz: torch.Tensor,
-                new_xyz: torch.Tensor,
-                features: torch.Tensor = None):
-        """
-        Args:
-            xyz (Tensor): (B, N, 3) xyz coordinates of the features.
-            new_xyz (Tensor): new xyz coordinates of the features.
-            features (Tensor): (B, C, N) features to group.
-
-        Returns:
-            Tensor: (B, C + 3, 1, N) Grouped feature.
-        """
-        grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
-        if features is not None:
-            grouped_features = features.unsqueeze(2)
-            if self.use_xyz:
-                # (B, 3 + C, 1, N)
-                new_features = torch.cat([grouped_xyz, grouped_features],
-                                         dim=1)
-            else:
-                new_features = grouped_features
-        else:
-            new_features = grouped_xyz
-
-        return new_features
-
-
-class GroupingOperation(Function):
-    """Group feature with given index."""
-
-    @staticmethod
-    def forward(ctx, features: torch.Tensor,
-                indices: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            features (Tensor): (B, C, N) tensor of features to group.
-            indices (Tensor): (B, npoint, nsample) the indices of
-                features to group with.
-
-        Returns:
-            Tensor: (B, C, npoint, nsample) Grouped features.
-        """
-        features = features.contiguous()
-        indices = indices.contiguous()
-
-        B, nfeatures, nsample = indices.size()
-        _, C, N = features.size()
-        output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
-
-        ext_module.group_points_forward(B, C, N, nfeatures, nsample, features,
-                                        indices, output)
-
-        ctx.for_backwards = (indices, N)
-        return output
-
-    @staticmethod
-    def backward(ctx,
-                 grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Args:
-            grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients
-                of the output from forward.
-
-        Returns:
-            Tensor: (B, C, N) gradient of the features.
-        """
-        idx, N = ctx.for_backwards
-
-        B, C, npoint, nsample = grad_out.size()
-        grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
-
-        grad_out_data = grad_out.data.contiguous()
-        ext_module.group_points_backward(B, C, N, npoint, nsample,
-                                         grad_out_data, idx,
-                                         grad_features.data)
-        return grad_features, None
-
-
-grouping_operation = GroupingOperation.apply

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/seg/sampler/__init__.py

@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']

spaces/Arnx/MusicGenXvAKN/CODE_OF_CONDUCT.md

@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at <[email protected]>. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq

spaces/Artgor/digit-draw-detect/src/model_architecture.py

@@ -1,151 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class CNNBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, bn_act=True, **kwargs):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, bias=not bn_act, **kwargs)
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.leaky = nn.LeakyReLU(0.1)
-        self.use_bn_act = bn_act
-
-    def forward(self, x):
-        if self.use_bn_act:
-            return self.leaky(self.bn(self.conv(x)))
-        else:
-            return self.conv(x)
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, channels, use_residual=True, num_repeats=1):
-        super().__init__()
-        self.layers = nn.ModuleList()
-        for _ in range(num_repeats):
-            self.layers += [
-                nn.Sequential(
-                    CNNBlock(channels, channels // 2, kernel_size=1),
-                    CNNBlock(channels // 2, channels, kernel_size=3, padding=1),
-                )
-            ]
-
-        self.use_residual = use_residual
-        self.num_repeats = num_repeats
-
-    def forward(self, x):
-        for layer in self.layers:
-            if self.use_residual:
-                x = x + layer(x)
-            else:
-                x = layer(x)
-
-        return x
-
-
-class ScalePrediction(nn.Module):
-    def __init__(self, in_channels, num_classes):
-        super().__init__()
-        self.pred = nn.Sequential(
-            CNNBlock(in_channels, 2 * in_channels, kernel_size=3, padding=1),
-            CNNBlock(2 * in_channels, (num_classes + 5) * 3, bn_act=False, kernel_size=1),
-        )
-        self.num_classes = num_classes
-
-    def forward(self, x):
-        return self.pred(x).reshape(x.shape[0], 3, self.num_classes + 5, x.shape[2], x.shape[3]).permute(0, 1, 3, 4, 2)
-
-
-class Net(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.num_classes = 12
-        self.in_channels = 3
-        self.config = [
-            (32, 3, 1),
-            (64, 3, 2),
-            ['B', 1],
-            (128, 3, 2),
-            ['B', 2],
-            (256, 3, 2),
-            ['B', 8],
-            (512, 3, 2),
-            ['B', 8],
-            (1024, 3, 2),
-            ['B', 4],
-            (512, 1, 1),
-            (1024, 3, 1),
-            'S',
-            (256, 1, 1),
-            'U',
-            (256, 1, 1),
-            (512, 3, 1),
-            'S',
-            (128, 1, 1),
-            'U',
-            (128, 1, 1),
-            (256, 3, 1),
-            'S',
-        ]
-        self.layers = self._create_conv_layers()
-
-    def forward(self, x):
-        outputs = []  # for each scale
-        route_connections = []
-        for layer in self.layers:
-            if isinstance(layer, ScalePrediction):
-                outputs.append(layer(x))
-                continue
-            x = layer(x)
-
-            if isinstance(layer, ResidualBlock) and layer.num_repeats == 8:
-                route_connections.append(x)
-
-            elif isinstance(layer, nn.Upsample):
-                x = torch.cat([x, route_connections[-1]], dim=1)
-                route_connections.pop()
-
-        return outputs
-
-    def _create_conv_layers(self):
-        layers = nn.ModuleList()
-        in_channels = self.in_channels
-
-        for module in self.config:
-            if isinstance(module, tuple):
-                out_channels, kernel_size, stride = module
-                layers.append(
-                    CNNBlock(
-                        in_channels,
-                        out_channels,
-                        kernel_size=kernel_size,
-                        stride=stride,
-                        padding=1 if kernel_size == 3 else 0,
-                    )
-                )
-                in_channels = out_channels
-
-            elif isinstance(module, list):
-                num_repeats = module[1]
-                layers.append(
-                    ResidualBlock(
-                        in_channels,
-                        num_repeats=num_repeats,
-                    )
-                )
-
-            elif isinstance(module, str):
-                if module == 'S':
-                    layers += [
-                        ResidualBlock(in_channels, use_residual=False, num_repeats=1),
-                        CNNBlock(in_channels, in_channels // 2, kernel_size=1),
-                        ScalePrediction(in_channels // 2, num_classes=self.num_classes),
-                    ]
-                    in_channels = in_channels // 2
-
-                elif module == 'U':
-                    layers.append(
-                        nn.Upsample(scale_factor=2),
-                    )
-                    in_channels = in_channels * 3
-
-        return layers

spaces/Artrajz/vits-simple-api/bert_vits2/text/cleaner.py

@@ -1,44 +0,0 @@
-import importlib
-from bert_vits2.text import cleaned_text_to_sequence
-
-language_module_map = {
-    'zh': "bert_vits2.text.chinese",
-    'ja': "bert_vits2.text.japanese"
-}
-
-_loaded_modules = {}
-
-
-def get_language_module(language):
-    if language not in _loaded_modules:
-        module_path = language_module_map.get(language)
-        if not module_path:
-            raise ValueError(f"Unsupported language: {language}")
-
-        _loaded_modules[language] = importlib.import_module(module_path)
-
-    return _loaded_modules[language]
-
-
-def clean_text(text, language):
-    language_module = get_language_module(language)
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    return norm_text, phones, tones, word2ph
-
-
-def clean_text_bert(text, language):
-    language_module = get_language_module(language)
-    norm_text = language_module.text_normalize(text)
-    phones, tones, word2ph = language_module.g2p(norm_text)
-    bert = language_module.get_bert_feature(norm_text, word2ph)
-    return phones, tones, bert
-
-
-def text_to_sequence(text, language):
-    norm_text, phones, tones, word2ph = clean_text(text, language)
-    return cleaned_text_to_sequence(phones, tones, language)
-
-
-if __name__ == '__main__':
-    pass

spaces/Aspik101/Polish-vicuna-13b-v1.5/README.md

@@ -1,13 +0,0 @@
----
-title: Polish vicuna-13b-v1.5-PL
-emoji: 📚
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/Azcar Ablaikan Remix Indir.md

@@ -1,165 +0,0 @@
-
-<h1>Sugar (Ablaikan Remix) de Zubi feat. Anatu: Una canción dulce y picante para condimentar tu lista de reproducción</h1>
- <p>Si estás buscando una canción que te haga sentir bien, bailar y cantar, entonces deberías echar un vistazo a Sugar (Ablaikan Remix) de Zubi feat. Anatu. Esta canción es un remix de la canción original Sugar de Zubi y Anatu, que fue lanzada en 2019. El remix fue hecho por Ablaikan, un productor y DJ turco, que añadió su propio toque y sabor a la canción. El resultado es una canción pegadiza, alegre y energética que te hará querer mover tu cuerpo y disfrutar de la vida. </p>
-<h2>azúcar ablaikan remix indir</h2><br /><p><b><b>DOWNLOAD</b> ::: <a href="https://bltlly.com/2v6KMO">https://bltlly.com/2v6KMO</a></b></p><br /><br />
- <p>En este artículo, te contaremos todo lo que necesitas saber sobre Sugar (Ablaikan Remix) de Zubi feat. Anatu, incluyendo lo que es, quiénes son los artistas detrás de él, cuáles son las letras y el significado de la misma, cuál es el género y el estilo de la misma, dónde se puede escuchar y descargarlo, y cómo disfrutarlo al máximo. Así que, sin más preámbulos, empecemos. </p>
- <h2>¿Qué es Sugar (Ablaikan Remix) y quiénes son los artistas detrás de ella? </h2>
- <h3>La canción original Sugar de Zubi y Anatu</h3>
- <p>Sugar es una canción de Zubi, un cantautor nigeriano con sede en Londres, Reino Unido, y Anatu, un cantautor británico con sede en Los Ángeles, Estados Unidos. La canción fue lanzada el 29 de octubre de 2019, como sencillo bajo Dojang Records. La canción es una fusión de Afrobeat, R&B, soul y música pop, con influencias de leyendas de la música nigeriana como Fela Kuti y King Sunny Ade. La canción trata sobre el amor, la pasión, el deseo y la dulzura, ya que las letras expresan cómo los cantantes se sienten acerca de sus amantes. </p>
- <p>Zubi y Anatu se conocieron a través de Instagram en 2018, cuando Zubi se acercó a Anatu después de escuchar su voz en una de sus publicaciones. Decidieron colaborar en una canción juntos, que resultó ser Sugar. Grabaron la canción de forma remota, ya que estaban en diferentes países en el momento. También grabaron un video musical para la canción en Los Ángeles, que fue dirigida por Alex Di Marco.</p>
- <h3>El remix de Ablaikan y su popularidad</h3>
-
- <p>Ablaikan lanzó su remix de Sugar el 29 de octubre de 2020, exactamente un año después de que se lanzara la canción original. Añadió su propio sonido característico a la canción, con más bajo, batería, sintetizadores, efectos y voces. También cambió el tempo de la canción de 90 BPM a 120 BPM, haciéndolo más bailable y enérgico. También añadió algunos elementos turcos y árabes a la canción, dándole un toque exótico y picante. </p>
- <p>El remix se hizo muy popular en plataformas de redes sociales como TikTok, Instagram, YouTube y Spotify, donde ganó millones de vistas, me gusta, comentarios y transmisiones. Muchas personas usaron el remix para sus videos, bailes, desafíos y memes. El remix también recibió comentarios positivos de los artistas originales, Zubi y Anatu, que elogiaron a Ablaikan por su trabajo y creatividad. </p>
-<p></p>
- <h2>¿Cuáles son las letras y el significado de Sugar (Ablaikan Remix)? </h2>
- <h3>La letra de la canción y su interpretación</h3>
- <p>Las letras de Sugar (Ablaikan Remix) son las mismas que la canción original Sugar de Zubi y Anatu, excepto por algunos cambios menores en el coro y el puente. Las letras están escritas en inglés, con algunas palabras en yoruba, un idioma nigeriano. Aquí están las letras de la canción y su interpretación:</p>
- <pre><código>
-
- <p>Las letras de la canción son simples y directas, ya que expresan cómo los cantantes se sienten acerca de sus amantes. Usan palabras como azúcar, miel, dulces, chocolate, sabor y favorito para describir a sus amantes como algo dulce, delicioso e irresistible. También usan la frase "omo yen sweet gan" que significa "esa chica es tan dulce" en yoruba, para enfatizar su admiración y atracción por sus amantes. También dicen que no pueden tener suficiente de sus amantes, y quieren estar más cerca de ellos y sentir sus cuerpos en ellos. </p>
- <h3>El mensaje y el tema de la canción</h3>
- <p>El mensaje y el tema de la canción son sobre el amor, la pasión, el deseo y la dulzura. La canción celebra la alegría y el placer de estar enamorado de alguien que te hace feliz y satisfecho. La canción también anima a los oyentes a disfrutar de la vida y divertirse con sus amantes. La canción es una canción positiva y edificante que puede hacer que cualquiera se sienta bien y sonría. </p>
- <h2>¿Cuál es el género y estilo de Sugar (Ablaikan Remix)? </h2>
- <h3>El género de la canción y sus influencias</h3>
- <p>El género de Sugar (Ablaikan Remix) es una mezcla de deep house, casa oriental, casa étnica, música electrónica, Afrobeat, R&B, soul y música pop. La canción combina diferentes elementos musicales de diferentes culturas y regiones, creando un sonido único y diverso que atrae a un público amplio. </p>
- <p>La canción está influenciada por varios géneros musicales y artistas, como:</p>
- <ul>
-<li>Deep house: un subgénero de música house que se originó en la década de 1980 que cuenta con un sonido suave, conmovedor y atmosférico, con voces mínimas, líneas de bajo profundas y pads de sintetizador. Algunos de los artistas que popularizaron deep house son Frankie Knuckles, Larry Heard, Kerri Chandler y Marshall Jefferson.</li>
-
-<li>Casa étnica: un subgénero de música house que combina elementos de diversas tradiciones étnicas y folclóricas de todo el mundo, como la música africana, latina, india, balcánica, celta y asiática. Algunos de los artistas que son conocidos por casa étnica son Pascal Junior, Melih Aydogan, Hakan Akkus, Ahmet Kilic, y Costa Mee.</li>
-<li>Música electrónica: un amplio género de música que utiliza instrumentos electrónicos, dispositivos y software para crear sonidos y ritmos. La música electrónica cubre una amplia gama de estilos y subgéneros, como techno, trance, electro, dubstep, EDM y más. Algunos de los artistas que son conocidos por la música electrónica son Daft Punk, The Chemical Brothers, Calvin Harris, David Guetta y Skrillex.</li>
-<li>Afrobeat: un género de música que se originó en Nigeria en la década de 1970 que combina elementos de estilos musicales de África Occidental, como highlife, juju y fuji, con música jazz y funk estadounidense. Afrobeat se caracteriza por polirritmos complejos, secciones de cuernos, voces de llamada y respuesta, y mensajes políticos y sociales. Algunos de los artistas que son conocidos por Afrobeat son Fela Kuti, King Sunny Ade, Tony Allen, Antibalas y Seun Kuti.</li>
-<li>R&B: un género de música que se originó en los Estados Unidos en la década de 1940 que combina elementos de rhythm and blues, soul, gospel, funk y música pop. R&B se caracteriza por voces suaves, melodías pegadizas, ritmos groovy y letras emocionales. Algunos de los artistas que son conocidos por R&B son Marvin Gaye, Aretha Franklin, Stevie Wonder, Beyoncé y The Weeknd.</li>
-<li>Soul: un género de música que se originó en los Estados Unidos en los años 50 y 60 que combina elementos de la música gospel afroamericana, el rhythm and blues y el jazz. El alma se caracteriza por voces expresivas, armonías inspiradas en el evangelio y mensajes inspiradores. Algunos de los artistas que son conocidos por el soul son Ray Charles, Sam Cooke, Otis Redding, James Brown y Alicia Keys.</li>
-
-</ul>
- <h3>El estilo de la canción y sus elementos</h3>
- <p>El estilo de Sugar (Ablaikan Remix) es una mezcla de diferentes elementos musicales que crean un sonido único y diverso que atrae a un público amplio. Algunos de los elementos que definen el estilo de la canción son:</p>
- <ul>
-<li>Las voces: Las voces de Zubi y Anatu son suaves, conmovedoras y armoniosas. Cantan en inglés con algunas palabras en yoruba, añadiendo un toque de sabor africano a la canción. También usan algunos efectos vocales, como reverb, echo y distortion, para crear un sonido más atmosférico y dinámico. </li>
-<li>El bajo: El bajo de la canción es profundo, potente y genial. Proporciona la base y el ritmo de la canción. También añade algo de energía y emoción a la canción. </li>
-<li>Los tambores: Los tambores de la canción son crujientes, impactantes y variados. Utilizan diferentes sonidos de batería y patrones para crear un ritmo complejo e interesante. También usan algunos sonidos de percusión, como cocteleras, palmas, broches de presión y panderetas, para agregar textura y sabor a la canción. </li>
-<li>Los sintetizadores: Los sintetizadores de la canción son brillantes, cálidos y melódicos. Utilizan diferentes sonidos de sintetizador y acordes para crear un sonido rico y colorido. También usan algunos arpegios, almohadillas, pinzas, y lleva a añadir algo de movimiento y variación a la canción. </li>
-<li>Los efectos: Los efectos de la canción son sutiles, creativos y de buen gusto. Utilizan diferentes efectos para mejorar y modificar el sonido de la canción. También usan algunas transiciones, barridos, gotas y bandas para crear cierta tensión y liberar la canción. </li>
-<li>Los elementos turcos y árabes: Los elementos turcos y árabes de la canción son distintivos, exóticos y picantes. Utilizan algunas escalas, instrumentos, voces y ritmos turcos y árabes para dar a la canción un sabor único y diverso. También usan algunas muestras, como "habibi", "yalla" y "mashallah", para agregar algunas referencias culturales y lingüísticas a la canción. </li>
-</ul>
-
- <h3>Las plataformas de streaming que ofrecen la canción</h3>
- <p>Si quieres escuchar Sugar (Ablaikan Remix) de Zubi feat. Anatu, tienes muchas opciones entre las que elegir. La canción está disponible en varias plataformas de streaming, como:</p>
- <tabla>
-<tr>
-<th>Plataforma</th>
-<th>Enlace</th>
-</tr>
-<tr>
-<td>Spotify</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Música de Apple</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Música de YouTube</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Deezer</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>SoundCloud</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Tidal</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Música de Amazon</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Pandora</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Napster</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr> </table>
- <p>Estas plataformas le permiten transmitir la canción en línea, así como descargarla para escucharla sin conexión. También puedes crear listas de reproducción, compartir la canción con tus amigos y descubrir más canciones de Zubi, Anatu y Ablaikan.</p>
- <h3>Las opciones de descarga y enlaces para la canción</h3>
- <p>Si quieres descargar Sugar (Ablaikan Remix) de Zubi feat. Anatu, tienes algunas opciones para elegir. La canción está disponible para su descarga en varias plataformas, como:</p>
- <tabla>
-<tr>
-<th>Plataforma</th>
-<th>Enlace</th>
-</tr>
-<tr>
-<td>iTunes</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Google Play</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Beatport</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Bandcamp</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Audiomack</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>DatPiff</td>
-
-</tr>
-<tr>
-<td>MP3Juices</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr>
-<tr>
-<td>Zippyshare</td>
-<td><a href=">Sugar (Ablaikan Remix)</a></td>
-</tr> </table>
- <p>Estas plataformas te permiten descargar la canción en diferentes formatos, como MP3, WAV, FLAC y más. También puede elegir la calidad y el tamaño del archivo, dependiendo de sus preferencias y dispositivo. Algunas de estas plataformas pueden requerir que te registres, pagues o sigas algunos pasos antes de descargar la canción. </p>
- <h2>¿Cómo disfrutar al máximo de Sugar (Ablaikan Remix)? </h2>
- <h3>Los mejores escenarios y estados de ánimo para escuchar la canción</h3>
- <p>Sugar (Ablaikan Remix) de Zubi feat. Anatu es una canción que puede adaptarse a diferentes escenarios y estados de ánimo, dependiendo de su gusto y estado de ánimo. Sin embargo, algunos de los mejores escenarios y estados de ánimo para escuchar la canción son:</p>
- <ul>
-<li>Cuando quieres bailar: La canción es perfecta para bailar, ya que tiene un ritmo rápido, un ritmo pegadizo y un ambiente animado. Puedes bailar la canción solo o con tus amigos, en casa o en una fiesta, en tu habitación o en la pista de baile. La canción te hará sentir energizado, feliz y libre. </li>
-<li>Cuando quieres relajarte: La canción también es perfecta para relajarte, ya que tiene una melodía suave, una voz relajante y un mensaje dulce. Puedes escuchar la canción mientras estás acostado en tu cama o sofá, leyendo un libro o viendo una película, tomando un baño o una siesta. La canción te hará sentir tranquilo, pacífico y amado. </li>
-<li>Cuando quieres romance: La canción también es perfecta para el romancing, ya que tiene un ritmo sensual, un tono apasionado y un sabor picante. Puedes escuchar la canción mientras abrazas o besas a tu amante, teniendo una cena a la luz de las velas o un picnic, yendo a dar un paseo o a dar un paseo. La canción te hará sentir romántico, sexy y aventurero. </li>
-</ul>
- <h3>Los consejos y trucos para mejorar tu experiencia auditiva</h3>
-
- <ul>
-<li>Usa buenos auriculares o altavoces: La canción tiene muchos detalles y matices que se pueden perder si usas auriculares o altavoces de baja calidad. Para apreciar el sonido completo y la calidad de la canción, utilice buenos auriculares o altavoces que pueden ofrecer agudos, medios y bajos claros. </li>
-<li>Ajustar el volumen y el ecualizador: La canción tiene una gran cantidad de dinámicas y variaciones que pueden verse afectadas por el volumen y la configuración del ecualizador. Para disfrutar de todo el rango e intensidad de la canción, ajusta el volumen y el ecualizador de acuerdo a tus preferencias y entorno. </li>
-<li>Mira el video musical: La canción tiene un video musical que fue filmado en Los Ángeles por Alex Di Marco. El video musical muestra a Zubi y Anatu cantando y bailando en diferentes lugares, como una azotea, una piscina, una calle y un club. El video musical añade algunas imágenes y contexto a la canción, lo que es más atractivo y atractivo. </li>
-<li>Aprende la letra: La canción tiene letras que son simples y pegadizas, pero también significativas y dulces. Aprender las letras puede ayudarte a cantar la canción, entender el mensaje y el tema de la canción, y apreciar la belleza y creatividad de la canción. </li>
-<li>Compartir la canción: La canción es una canción que puede ser disfrutada por cualquier persona, independientemente de su edad, género, cultura o gusto. Compartir la canción con tus amigos, familiares o seguidores puede ayudarte a difundir la alegría y positividad de la canción, así como a apoyar a los artistas y su trabajo. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>Sugar (Ablaikan Remix) de Zubi feat. Anatu es una canción que puede hacerte sentir bien, bailar y cantar. Es un remix de la canción original Sugar de Zubi y Anatu, que fue lanzada en 2019. El remix fue hecho por Ablaikan, un productor y DJ turco, que añadió su propio toque y sabor a la canción. El resultado es una canción pegadiza, alegre y energética que te hará querer mover tu cuerpo y disfrutar de la vida. </p>
-
- <p>Ahora que sabes todo sobre Sugar (Ablaikan Remix) de Zubi feat. Anatu, ¿por qué no sigues adelante y lo escuchas tú mismo? Puedes encontrar la canción en varias plataformas de streaming y descarga, así como ver el video musical en YouTube. También puede seguir a Zubi, Anatu y Ablaikan en sus cuentas de redes sociales para mantenerse al día sobre sus últimas noticias y comunicados. </p>
- <p>Gracias por leer este artículo, y esperamos que tengas un día dulce y picante. </p>
- <h2>Preguntas frecuentes</h2>
- <h3>¿Cuál es el nombre de la canción original en la que se basa Sugar (Ablaikan Remix)? </h3>
-<p>El nombre de la canción original que Sugar (Ablaikan Remix) se basa en es Sugar de Zubi feat. Anatu.</p>
- <h3>¿Cuándo se lanzó Sugar (Ablaikan Remix)? </h3>
-<p>Sugar (Ablaikan Remix) fue lanzado el 29 de octubre de 2020. </p>
- <h3>¿Quiénes son Zubi, Anatu y Ablaikan? </h3>
-<p>Zubi es un cantautor nigeriano con sede en Londres, Reino Unido. Anatu es un cantautor británico con sede en Los Ángeles, Estados Unidos. Ablaikan es un productor y DJ turco con sede en Estambul.</p>
- <h3>¿Cuáles son algunos de los géneros musicales e influencias de Sugar (Ablaikan Remix)? </h3>
-<p>Algunos de los géneros musicales e influencias de Sugar (Ablaikan Remix) son deep house, oriental house, ethnic house, música electrónica, Afrobeat, R&B, soul y pop. La canción está influenciada por artistas como Fela Kuti, King Sunny Ade, Frankie Knuckles, Larry Heard, Mahmut Orhan, Burak Yeter, Daft Punk, The Chemical Brothers y más. </p>
- <h3>¿Cómo puedo descargar Sugar (Ablaikan Remix) gratis? </h3> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cmo Puedo Descargar El Controlador Wifi A Mi Ordenador.md

@@ -1,6 +0,0 @@
-
-<h1>Qué es zfont y por qué deberías probarlo</h1> | <p>Si estás aburrido con las fuentes y emojis predeterminados en tu dispositivo Android, es posible que desees revisar zfont, una aplicación gratuita que te permite cambiarlos fácil y rápidamente. zfont es un instalador de fuentes personalizado que admite muchas marcas populares como Samsung, Xiaomi, Huawei, Vivo, Oppo, Realme, Tecno e Infinix. Puede elegir entre cientos de fuentes y emojis frescos, elegantes y coloridos que harán que su dispositivo se destaque de la multitud. También puede personalizar sus propias fuentes y emojis con zfont, y compartirlos con sus amigos. Ya sea que quieras darle vida a tus mensajes, publicaciones en redes sociales o documentos, zfont puede ayudarte a expresarte mejor. </p>
-<h2>¿Cómo puedo descargar el controlador wifi a mi ordenador</h2><br /><p><b><b>Download</b> &bull; <a href="https://bltlly.com/2v6LYW">https://bltlly.com/2v6LYW</a></b></p><br /><br /> | <h2>Cómo descargar e instalar zfont en tu dispositivo Android</h2> | <p>Descargar e instalar zfont es muy fácil. Solo tienes que seguir estos sencillos pasos:</p><ol><li>Ir a la Google Play Store y buscar zfont 3 - Emoji & Font Changer o haga clic en [aquí]( 3 ) para ir directamente a la página de la aplicación. </li><li>Toca el botón Instalar y espera a que la aplicación se descargue. </li><li>Una vez que la aplicación esté instalada, ábrela y otorga los permisos necesarios. </li><li>Verá una lista de marcas que son compatibles con zfont. Seleccione la marca de su dispositivo de la lista. </li><li>También verá una lista de pestañas en la parte inferior de la pantalla. Estos son Colores, Emoji, Cool, Stylish, Custom Font, Custom Emoji, Settings y About. Puedes deslizar el dedo hacia la izquierda o hacia la derecha para cambiar entre ellos. </li></ol><p>Aquí hay algunas capturas de pantalla de la aplicación:</p><img src=" 3 " alt="zfont app screenshot 1"><img src=" 3 " alt="zfont app screenshot 2"><img src=" 3 " alt=zfont app screenshot 3"> | | <h2>Cómo usar zfont para cambiar fuentes y emojis en tu dispositivo</h2> | <p>Usar zfont para cambiar fuentes y emojis en tu dispositivo es muy simple. Solo tienes que seguir estos pasos:</p>
-<p></p><ol><li>Selecciona la pestaña que corresponde a lo que quieres cambiar. Por ejemplo, si quieres cambiar de fuente, selecciona Fresco o Elegante. Si quieres cambiar de emojis, 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/app.d.ts

@@ -1,17 +0,0 @@
-/// <reference types="@sveltejs/kit" />
-/// <reference types="unplugin-icons/types/svelte" />
-
-// See https://kit.svelte.dev/docs/types#app
-// for information about these interfaces
-declare global {
-	namespace App {
-		// interface Error {}
-		interface Locals {
-			sessionId: string;
-		}
-		// interface PageData {}
-		// interface Platform {}
-	}
-}
-
-export {};

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/response.py

@@ -1,201 +0,0 @@
-# Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/
-# Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-
-import logging
-from io import IOBase
-
-from urllib3.exceptions import ProtocolError as URLLib3ProtocolError
-from urllib3.exceptions import ReadTimeoutError as URLLib3ReadTimeoutError
-
-from botocore import parsers
-from botocore.compat import set_socket_timeout
-from botocore.exceptions import (
-    IncompleteReadError,
-    ReadTimeoutError,
-    ResponseStreamingError,
-)
-
-# Keep these imported.  There's pre-existing code that uses them.
-from botocore import ScalarTypes  # noqa
-from botocore.compat import XMLParseError  # noqa
-from botocore.hooks import first_non_none_response  # noqa
-
-
-logger = logging.getLogger(__name__)
-
-
-class StreamingBody(IOBase):
-    """Wrapper class for an http response body.
-
-    This provides a few additional conveniences that do not exist
-    in the urllib3 model:
-
-        * Set the timeout on the socket (i.e read() timeouts)
-        * Auto validation of content length, if the amount of bytes
-          we read does not match the content length, an exception
-          is raised.
-
-    """
-
-    _DEFAULT_CHUNK_SIZE = 1024
-
-    def __init__(self, raw_stream, content_length):
-        self._raw_stream = raw_stream
-        self._content_length = content_length
-        self._amount_read = 0
-
-    def __del__(self):
-        # Extending destructor in order to preserve the underlying raw_stream.
-        # The ability to add custom cleanup logic introduced in Python3.4+.
-        # https://www.python.org/dev/peps/pep-0442/
-        pass
-
-    def set_socket_timeout(self, timeout):
-        """Set the timeout seconds on the socket."""
-        # The problem we're trying to solve is to prevent .read() calls from
-        # hanging.  This can happen in rare cases.  What we'd like to ideally
-        # do is set a timeout on the .read() call so that callers can retry
-        # the request.
-        # Unfortunately, this isn't currently possible in requests.
-        # See: https://github.com/kennethreitz/requests/issues/1803
-        # So what we're going to do is reach into the guts of the stream and
-        # grab the socket object, which we can set the timeout on.  We're
-        # putting in a check here so in case this interface goes away, we'll
-        # know.
-        try:
-            set_socket_timeout(self._raw_stream, timeout)
-        except AttributeError:
-            logger.error(
-                "Cannot access the socket object of "
-                "a streaming response.  It's possible "
-                "the interface has changed.",
-                exc_info=True,
-            )
-            raise
-
-    def readable(self):
-        try:
-            return self._raw_stream.readable()
-        except AttributeError:
-            return False
-
-    def read(self, amt=None):
-        """Read at most amt bytes from the stream.
-
-        If the amt argument is omitted, read all data.
-        """
-        try:
-            chunk = self._raw_stream.read(amt)
-        except URLLib3ReadTimeoutError as e:
-            # TODO: the url will be None as urllib3 isn't setting it yet
-            raise ReadTimeoutError(endpoint_url=e.url, error=e)
-        except URLLib3ProtocolError as e:
-            raise ResponseStreamingError(error=e)
-        self._amount_read += len(chunk)
-        if amt is None or (not chunk and amt > 0):
-            # If the server sends empty contents or
-            # we ask to read all of the contents, then we know
-            # we need to verify the content length.
-            self._verify_content_length()
-        return chunk
-
-    def readlines(self):
-        return self._raw_stream.readlines()
-
-    def __iter__(self):
-        """Return an iterator to yield 1k chunks from the raw stream."""
-        return self.iter_chunks(self._DEFAULT_CHUNK_SIZE)
-
-    def __next__(self):
-        """Return the next 1k chunk from the raw stream."""
-        current_chunk = self.read(self._DEFAULT_CHUNK_SIZE)
-        if current_chunk:
-            return current_chunk
-        raise StopIteration()
-
-    def __enter__(self):
-        return self._raw_stream
-
-    def __exit__(self, type, value, traceback):
-        self._raw_stream.close()
-
-    next = __next__
-
-    def iter_lines(self, chunk_size=_DEFAULT_CHUNK_SIZE, keepends=False):
-        """Return an iterator to yield lines from the raw stream.
-
-        This is achieved by reading chunk of bytes (of size chunk_size) at a
-        time from the raw stream, and then yielding lines from there.
-        """
-        pending = b''
-        for chunk in self.iter_chunks(chunk_size):
-            lines = (pending + chunk).splitlines(True)
-            for line in lines[:-1]:
-                yield line.splitlines(keepends)[0]
-            pending = lines[-1]
-        if pending:
-            yield pending.splitlines(keepends)[0]
-
-    def iter_chunks(self, chunk_size=_DEFAULT_CHUNK_SIZE):
-        """Return an iterator to yield chunks of chunk_size bytes from the raw
-        stream.
-        """
-        while True:
-            current_chunk = self.read(chunk_size)
-            if current_chunk == b"":
-                break
-            yield current_chunk
-
-    def _verify_content_length(self):
-        # See: https://github.com/kennethreitz/requests/issues/1855
-        # Basically, our http library doesn't do this for us, so we have
-        # to do this ourself.
-        if self._content_length is not None and self._amount_read != int(
-            self._content_length
-        ):
-            raise IncompleteReadError(
-                actual_bytes=self._amount_read,
-                expected_bytes=int(self._content_length),
-            )
-
-    def tell(self):
-        return self._raw_stream.tell()
-
-    def close(self):
-        """Close the underlying http response stream."""
-        self._raw_stream.close()
-
-
-def get_response(operation_model, http_response):
-    protocol = operation_model.metadata['protocol']
-    response_dict = {
-        'headers': http_response.headers,
-        'status_code': http_response.status_code,
-    }
-    # TODO: Unfortunately, we have to have error logic here.
-    # If it looks like an error, in the streaming response case we
-    # need to actually grab the contents.
-    if response_dict['status_code'] >= 300:
-        response_dict['body'] = http_response.content
-    elif operation_model.has_streaming_output:
-        response_dict['body'] = StreamingBody(
-            http_response.raw, response_dict['headers'].get('content-length')
-        )
-    else:
-        response_dict['body'] = http_response.content
-
-    parser = parsers.create_parser(protocol)
-    return http_response, parser.parse(
-        response_dict, operation_model.output_shape
-    )

spaces/Blockinger/OVAChatGPT/README.md

@@ -1,13 +0,0 @@
----
-title: OVAChatGPT
-emoji: 😻
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Boadiwaa/Recipes/openai/api_resources/abstract/nested_resource_class_methods.py

@@ -1,102 +0,0 @@
-from urllib.parse import quote_plus
-
-from openai import api_requestor, util
-
-
-def nested_resource_class_methods(
-    resource, path=None, operations=None, resource_plural=None
-):
-    if resource_plural is None:
-        resource_plural = "%ss" % resource
-    if path is None:
-        path = resource_plural
-    if operations is None:
-        raise ValueError("operations list required")
-
-    def wrapper(cls):
-        def nested_resource_url(cls, id, nested_id=None):
-            url = "%s/%s/%s" % (cls.class_url(), quote_plus(id), quote_plus(path))
-            if nested_id is not None:
-                url += "/%s" % quote_plus(nested_id)
-            return url
-
-        resource_url_method = "%ss_url" % resource
-        setattr(cls, resource_url_method, classmethod(nested_resource_url))
-
-        def nested_resource_request(
-            cls,
-            method,
-            url,
-            api_key=None,
-            request_id=None,
-            api_version=None,
-            organization=None,
-            **params,
-        ):
-            requestor = api_requestor.APIRequestor(
-                api_key, api_version=api_version, organization=organization
-            )
-            response, _, api_key = requestor.request(
-                method, url, params, request_id=request_id
-            )
-            return util.convert_to_openai_object(
-                response, api_key, api_version, organization
-            )
-
-        resource_request_method = "%ss_request" % resource
-        setattr(cls, resource_request_method, classmethod(nested_resource_request))
-
-        for operation in operations:
-            if operation == "create":
-
-                def create_nested_resource(cls, id, **params):
-                    url = getattr(cls, resource_url_method)(id)
-                    return getattr(cls, resource_request_method)("post", url, **params)
-
-                create_method = "create_%s" % resource
-                setattr(cls, create_method, classmethod(create_nested_resource))
-
-            elif operation == "retrieve":
-
-                def retrieve_nested_resource(cls, id, nested_id, **params):
-                    url = getattr(cls, resource_url_method)(id, nested_id)
-                    return getattr(cls, resource_request_method)("get", url, **params)
-
-                retrieve_method = "retrieve_%s" % resource
-                setattr(cls, retrieve_method, classmethod(retrieve_nested_resource))
-
-            elif operation == "update":
-
-                def modify_nested_resource(cls, id, nested_id, **params):
-                    url = getattr(cls, resource_url_method)(id, nested_id)
-                    return getattr(cls, resource_request_method)("post", url, **params)
-
-                modify_method = "modify_%s" % resource
-                setattr(cls, modify_method, classmethod(modify_nested_resource))
-
-            elif operation == "delete":
-
-                def delete_nested_resource(cls, id, nested_id, **params):
-                    url = getattr(cls, resource_url_method)(id, nested_id)
-                    return getattr(cls, resource_request_method)(
-                        "delete", url, **params
-                    )
-
-                delete_method = "delete_%s" % resource
-                setattr(cls, delete_method, classmethod(delete_nested_resource))
-
-            elif operation == "list":
-
-                def list_nested_resources(cls, id, **params):
-                    url = getattr(cls, resource_url_method)(id)
-                    return getattr(cls, resource_request_method)("get", url, **params)
-
-                list_method = "list_%s" % resource_plural
-                setattr(cls, list_method, classmethod(list_nested_resources))
-
-            else:
-                raise ValueError("Unknown operation: %s" % operation)
-
-        return cls
-
-    return wrapper

spaces/Brasd99/TTS-Voice-Conversion/README.md

@@ -1,12 +0,0 @@
----
-title: TTS Voice Conversion
-emoji: 🚀
-colorFrom: yellow
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Brofu/Joeythemonster-anything-midjourney-v-4-1/README.md

@@ -1,12 +0,0 @@
----
-title: Joeythemonster Anything Midjourney V 4 1
-emoji: 🌍
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CForGETaass/vits-uma-genshin-honkai/app.py

@@ -1,124 +0,0 @@
-import time
-import gradio as gr
-import utils
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-from torch import no_grad, LongTensor
-import torch
-
-hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-net_g_ms = SynthesizerTrn(
-    len(hps_ms.symbols),
-    hps_ms.data.filter_length // 2 + 1,
-    hps_ms.train.segment_size // hps_ms.data.hop_length,
-    n_speakers=hps_ms.data.n_speakers,
-    **hps_ms.model).to(device)
-_ = net_g_ms.eval()
-speakers = hps_ms.speakers
-model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 500:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = LongTensor([stn_tst.size(0)])
-        speaker_id = LongTensor([speaker_id])
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3], api_name="generate")
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    app.queue(concurrency_count=1).launch()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/rotated_fast_rcnn.py

@@ -1,271 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import numpy as np
-from typing import Dict
-import torch
-
-from detectron2.layers import ShapeSpec, batched_nms_rotated
-from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.events import get_event_storage
-
-from ..box_regression import Box2BoxTransformRotated
-from ..poolers import ROIPooler
-from ..proposal_generator.proposal_utils import add_ground_truth_to_proposals
-from .box_head import build_box_head
-from .fast_rcnn import FastRCNNOutputLayers
-from .roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-
-logger = logging.getLogger(__name__)
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    R: number of ROIs, combined over all images, in the minibatch
-    Ri: number of ROIs in image i
-    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.
-
-Naming convention:
-
-    deltas: refers to the 5-d (dx, dy, dw, dh, da) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransformRotated`).
-
-    pred_class_logits: predicted class scores in [-inf, +inf]; use
-        softmax(pred_class_logits) to estimate P(class).
-
-    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent
-        foreground object classes and K represents the background class.
-
-    pred_proposal_deltas: predicted rotated box2box transform deltas for transforming proposals
-        to detection box predictions.
-
-    gt_proposal_deltas: ground-truth rotated box2box transform deltas
-"""
-
-
-def fast_rcnn_inference_rotated(
-    boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Call `fast_rcnn_inference_single_image_rotated` for all images.
-
-    Args:
-        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
-            boxes for each image. Element i has shape (Ri, K * 5) if doing
-            class-specific regression, or (Ri, 5) if doing class-agnostic
-            regression, where Ri is the number of predicted objects for image i.
-            This is compatible with the output of :meth:`FastRCNNOutputs.predict_boxes`.
-        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
-            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
-            for image i. Compatible with the output of :meth:`FastRCNNOutputs.predict_probs`.
-        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
-        score_thresh (float): Only return detections with a confidence score exceeding this
-            threshold.
-        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
-        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
-            all detections.
-
-    Returns:
-        instances: (list[Instances]): A list of N instances, one for each image in the batch,
-            that stores the topk most confidence detections.
-        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
-            the corresponding boxes/scores index in [0, Ri) from the input, for image i.
-    """
-    result_per_image = [
-        fast_rcnn_inference_single_image_rotated(
-            boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image
-        )
-        for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)
-    ]
-    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
-
-
-def fast_rcnn_inference_single_image_rotated(
-    boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Single-image inference. Return rotated bounding-box detection results by thresholding
-    on scores and applying rotated non-maximum suppression (Rotated NMS).
-
-    Args:
-        Same as `fast_rcnn_inference_rotated`, but with rotated boxes, scores, and image shapes
-        per image.
-
-    Returns:
-        Same as `fast_rcnn_inference_rotated`, but for only one image.
-    """
-    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
-    if not valid_mask.all():
-        boxes = boxes[valid_mask]
-        scores = scores[valid_mask]
-
-    B = 5  # box dimension
-    scores = scores[:, :-1]
-    num_bbox_reg_classes = boxes.shape[1] // B
-    # Convert to Boxes to use the `clip` function ...
-    boxes = RotatedBoxes(boxes.reshape(-1, B))
-    boxes.clip(image_shape)
-    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, B)  # R x C x B
-    # Filter results based on detection scores
-    filter_mask = scores > score_thresh  # R x K
-    # R' x 2. First column contains indices of the R predictions;
-    # Second column contains indices of classes.
-    filter_inds = filter_mask.nonzero()
-    if num_bbox_reg_classes == 1:
-        boxes = boxes[filter_inds[:, 0], 0]
-    else:
-        boxes = boxes[filter_mask]
-    scores = scores[filter_mask]
-
-    # Apply per-class Rotated NMS
-    keep = batched_nms_rotated(boxes, scores, filter_inds[:, 1], nms_thresh)
-    if topk_per_image >= 0:
-        keep = keep[:topk_per_image]
-    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
-
-    result = Instances(image_shape)
-    result.pred_boxes = RotatedBoxes(boxes)
-    result.scores = scores
-    result.pred_classes = filter_inds[:, 1]
-
-    return result, filter_inds[:, 0]
-
-
-class RotatedFastRCNNOutputLayers(FastRCNNOutputLayers):
-    """
-    A class that stores information about outputs of a Fast R-CNN head with RotatedBoxes.
-    """
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        args = super().from_config(cfg, input_shape)
-        args["box2box_transform"] = Box2BoxTransformRotated(
-            weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS
-        )
-        return args
-
-    def inference(self, predictions, proposals):
-        """
-        Returns:
-            list[Instances]: same as `fast_rcnn_inference_rotated`.
-            list[Tensor]: same as `fast_rcnn_inference_rotated`.
-        """
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        image_shapes = [x.image_size for x in proposals]
-
-        return fast_rcnn_inference_rotated(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.test_topk_per_image,
-        )
-
-
-@ROI_HEADS_REGISTRY.register()
-class RROIHeads(StandardROIHeads):
-    """
-    This class is used by Rotated RPN (RRPN).
-    For now, it just supports box head but not mask or keypoints.
-    """
-
-    def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
-        super().__init__(cfg, input_shape)
-        assert (
-            not self.mask_on and not self.keypoint_on
-        ), "Mask/Keypoints not supported in Rotated ROIHeads."
-
-    def _init_box_head(self, cfg, input_shape):
-        # fmt: off
-        pooler_resolution        = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
-        pooler_scales            = tuple(1.0 / input_shape[k].stride for k in self.in_features)
-        sampling_ratio           = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
-        pooler_type              = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE
-        self.train_on_pred_boxes = cfg.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES
-        # fmt: on
-        assert not self.train_on_pred_boxes, "Not Implemented!"
-
-        # If StandardROIHeads is applied on multiple feature maps (as in FPN),
-        # then we share the same predictors and therefore the channel counts must be the same
-        in_channels = [input_shape[f].channels for f in self.in_features]
-        # Check all channel counts are equal
-        assert len(set(in_channels)) == 1, in_channels
-        in_channels = in_channels[0]
-
-        assert pooler_type in ["ROIAlignRotated"]
-
-        self.box_pooler = ROIPooler(
-            output_size=pooler_resolution,
-            scales=pooler_scales,
-            sampling_ratio=sampling_ratio,
-            pooler_type=pooler_type,
-        )
-        self.box_head = build_box_head(
-            cfg, ShapeSpec(channels=in_channels, height=pooler_resolution, width=pooler_resolution)
-        )
-
-        self.box_predictor = RotatedFastRCNNOutputLayers(cfg, self.box_head.output_shape)
-
-    @torch.no_grad()
-    def label_and_sample_proposals(self, proposals, targets):
-        """
-        Prepare some proposals to be used to train the RROI heads.
-        It performs box matching between `proposals` and `targets`, and assigns
-        training labels to the proposals.
-        It returns `self.batch_size_per_image` random samples from proposals and groundtruth boxes,
-        with a fraction of positives that is no larger than `self.positive_sample_fraction.
-
-        Args:
-            See :meth:`StandardROIHeads.forward`
-
-        Returns:
-            list[Instances]: length `N` list of `Instances`s containing the proposals
-                sampled for training. Each `Instances` has the following fields:
-                - proposal_boxes: the rotated proposal boxes
-                - gt_boxes: the ground-truth rotated boxes that the proposal is assigned to
-                  (this is only meaningful if the proposal has a label > 0; if label = 0
-                   then the ground-truth box is random)
-                - gt_classes: the ground-truth classification lable for each proposal
-        """
-        gt_boxes = [x.gt_boxes for x in targets]
-        if self.proposal_append_gt:
-            proposals = add_ground_truth_to_proposals(gt_boxes, proposals)
-
-        proposals_with_gt = []
-
-        num_fg_samples = []
-        num_bg_samples = []
-        for proposals_per_image, targets_per_image in zip(proposals, targets):
-            has_gt = len(targets_per_image) > 0
-            match_quality_matrix = pairwise_iou_rotated(
-                targets_per_image.gt_boxes, proposals_per_image.proposal_boxes
-            )
-            matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix)
-            sampled_idxs, gt_classes = self._sample_proposals(
-                matched_idxs, matched_labels, targets_per_image.gt_classes
-            )
-
-            proposals_per_image = proposals_per_image[sampled_idxs]
-            proposals_per_image.gt_classes = gt_classes
-
-            if has_gt:
-                sampled_targets = matched_idxs[sampled_idxs]
-                proposals_per_image.gt_boxes = targets_per_image.gt_boxes[sampled_targets]
-            else:
-                gt_boxes = RotatedBoxes(
-                    targets_per_image.gt_boxes.tensor.new_zeros((len(sampled_idxs), 5))
-                )
-                proposals_per_image.gt_boxes = gt_boxes
-
-            num_bg_samples.append((gt_classes == self.num_classes).sum().item())
-            num_fg_samples.append(gt_classes.numel() - num_bg_samples[-1])
-            proposals_with_gt.append(proposals_per_image)
-
-        # Log the number of fg/bg samples that are selected for training ROI heads
-        storage = get_event_storage()
-        storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples))
-        storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples))
-
-        return proposals_with_gt

spaces/CVPR/LIVE/pydiffvg/optimize_svg.py

@@ -1,1607 +0,0 @@
-import json
-import copy
-import xml.etree.ElementTree as etree
-from xml.dom import minidom
-import warnings
-import torch
-import numpy as np
-import re
-import sys
-import pydiffvg
-import math
-from collections import namedtuple
-import cssutils
-
-class SvgOptimizationSettings:
-
-    default_params = {
-        "optimize_color": True,
-        "color_lr": 2e-3,
-        "optimize_alpha": False,
-        "alpha_lr": 2e-3,
-        "optimizer": "Adam",
-        "transforms": {
-            "optimize_transforms":True,
-            "transform_mode":"rigid",
-            "translation_mult":1e-3,
-            "transform_lr":2e-3
-        },
-        "circles": {
-            "optimize_center": True,
-            "optimize_radius": True,
-            "shape_lr": 2e-1
-        },
-        "paths": {
-            "optimize_points": True,
-            "shape_lr": 2e-1
-        },
-        "gradients": {
-            "optimize_stops": True,
-            "stop_lr": 2e-3,
-            "optimize_color": True,
-            "color_lr": 2e-3,
-            "optimize_alpha": False,
-            "alpha_lr": 2e-3,
-            "optimize_location": True,
-            "location_lr": 2e-1
-        }
-    }
-
-    optims = {
-        "Adam": torch.optim.Adam,
-        "SGD": torch.optim.SGD,
-        "ASGD": torch.optim.ASGD,
-    }
-
-    #region methods
-    def __init__(self, f=None):
-        self.store = {}
-        if f is None:
-            self.store["default"] = copy.deepcopy(SvgOptimizationSettings.default_params)
-        else:
-            self.store = json.load(f)
-
-    # create default alias for root
-    def default_name(self, dname):
-        self.dname = dname
-        if dname not in self.store:
-            self.store[dname] = self.store["default"]
-
-    def retrieve(self, node_id):
-        if node_id not in self.store:
-            return (self.store["default"], False)
-        else:
-            return (self.store[node_id], True)
-
-    def reset_to_defaults(self, node_id):
-        if node_id in self.store:
-            del self.store[node_id]
-
-        return self.store["default"]
-
-    def undefault(self, node_id):
-        if node_id not in self.store:
-            self.store[node_id] = copy.deepcopy(self.store["default"])
-
-        return self.store[node_id]
-
-    def override_optimizer(self, optimizer):
-        if optimizer is not None:
-            for v in self.store.values():
-                v["optimizer"] = optimizer
-
-    def global_override(self, path, value):
-        for store in self.store.values():
-            d = store
-            for key in path[:-1]:
-                d = d[key]
-
-            d[path[-1]] = value
-
-    def save(self, file):
-        self.store["default"] = self.store[self.dname]
-        json.dump(self.store, file, indent="\t")
-    #endregion
-
-class OptimizableSvg:
-
-    class TransformTools:
-        @staticmethod
-        def parse_matrix(vals):
-            assert(len(vals)==6)
-            return np.array([[vals[0],vals[2],vals[4]],[vals[1], vals[3], vals[5]],[0,0,1]])
-
-        @staticmethod
-        def parse_translate(vals):
-            assert(len(vals)>=1 and len(vals)<=2)
-            mat=np.eye(3)
-            mat[0,2]=vals[0]
-            if len(vals)>1:
-                mat[1,2]=vals[1]
-            return mat
-
-        @staticmethod
-        def parse_rotate(vals):
-            assert (len(vals) == 1 or len(vals) == 3)
-            mat = np.eye(3)
-            rads=math.radians(vals[0])
-            sint=math.sin(rads)
-            cost=math.cos(rads)
-            mat[0:2, 0:2] = np.array([[cost,-sint],[sint,cost]])
-            if len(vals) > 1:
-                tr1=parse_translate(vals[1:3])
-                tr2=parse_translate([-vals[1],-vals[2]])
-                mat=tr1 @ mat @ tr2
-            return mat
-
-        @staticmethod
-        def parse_scale(vals):
-            assert (len(vals) >= 1 and len(vals) <= 2)
-            d=np.array([vals[0], vals[1] if len(vals)>1 else vals[0],1])
-            return np.diag(d)
-
-        @staticmethod
-        def parse_skewx(vals):
-            assert(len(vals)==1)
-            m=np.eye(3)
-            m[0,1]=vals[0]
-            return m
-
-        @staticmethod
-        def parse_skewy(vals):
-            assert (len(vals) == 1)
-            m = np.eye(3)
-            m[1, 0] = vals[0]
-            return m
-
-        @staticmethod
-        def transformPoints(pointsTensor, transform):
-            assert(transform is not None)
-            one=torch.ones((pointsTensor.shape[0],1),device=pointsTensor.device)
-            homo_points = torch.cat([pointsTensor, one], dim=1)
-            mult = transform.mm(homo_points.permute(1,0)).permute(1,0)
-            tfpoints=mult[:, 0:2].contiguous()
-            #print(torch.norm(mult[:,2]-one))
-            assert(pointsTensor.shape == tfpoints.shape)
-            return tfpoints
-
-        @staticmethod
-        def promote_numpy(M):
-            ret = np.eye(3)
-            ret[0:2, 0:2] = M
-            return ret
-
-        @staticmethod
-        def recompose_numpy(Theta,ScaleXY,ShearX,TXY):
-            cost=math.cos(Theta)
-            sint=math.sin(Theta)
-            Rot=np.array([[cost, -sint],[sint, cost]])
-            Scale=np.diag(ScaleXY)
-            Shear=np.eye(2)
-            Shear[0,1]=ShearX
-
-            Translate=np.eye(3)
-            Translate[0:2,2]=TXY
-            
-            M=OptimizableSvg.TransformTools.promote_numpy(Rot @ Scale @ Shear) @ Translate
-            return M
-
-        @staticmethod
-        def promote(m):
-            M=torch.eye(3).to(m.device)
-            M[0:2,0:2]=m
-            return M
-
-        @staticmethod
-        def make_rot(Theta):
-            sint=Theta.sin().squeeze()
-            cost=Theta.cos().squeeze()
-            #m=torch.tensor([[cost, -sint],[sint, cost]])
-            Rot=torch.stack((torch.stack((cost,-sint)),torch.stack((sint,cost))))
-            return Rot
-
-        @staticmethod
-        def make_scale(ScaleXY):
-            if ScaleXY.squeeze().dim()==0:
-                ScaleXY=ScaleXY.squeeze()
-                #uniform scale
-                return torch.diag(torch.stack([ScaleXY,ScaleXY])).to(ScaleXY.device)
-            else:
-                return torch.diag(ScaleXY).to(ScaleXY.device)
-
-        @staticmethod
-        def make_shear(ShearX):
-            m=torch.eye(2).to(ShearX.device)
-            m[0,1]=ShearX
-            return m
-
-        @staticmethod
-        def make_translate(TXY):
-            m=torch.eye(3).to(TXY.device)
-            m[0:2,2]=TXY
-            return m
-
-        @staticmethod
-        def recompose(Theta,ScaleXY,ShearX,TXY):
-            Rot=OptimizableSvg.TransformTools.make_rot(Theta)
-            Scale=OptimizableSvg.TransformTools.make_scale(ScaleXY)
-            Shear=OptimizableSvg.TransformTools.make_shear(ShearX)
-            Translate=OptimizableSvg.TransformTools.make_translate(TXY)
-
-            return OptimizableSvg.TransformTools.promote(Rot.mm(Scale).mm(Shear)).mm(Translate)
-
-        TransformDecomposition=namedtuple("TransformDecomposition","theta scale shear translate")
-        TransformProperties=namedtuple("TransformProperties", "has_rotation has_scale has_mirror scale_uniform has_shear has_translation")
-
-        @staticmethod
-        def make_named(decomp):
-            if not isinstance(decomp,OptimizableSvg.TransformTools.TransformDecomposition):
-                decomp=OptimizableSvg.TransformTools.TransformDecomposition(theta=decomp[0],scale=decomp[1],shear=decomp[2],translate=decomp[3])
-            return decomp
-
-        @staticmethod
-        def analyze_transform(decomp):
-            decomp=OptimizableSvg.TransformTools.make_named(decomp)
-            epsilon=1e-3
-            has_rotation=abs(decomp.theta)>epsilon
-            has_scale=abs((abs(decomp.scale)-1)).max()>epsilon
-            scale_len=decomp.scale.squeeze().ndim>0 if isinstance(decomp.scale,np.ndarray) else decomp.scale.squeeze().dim() > 0
-            has_mirror=scale_len and decomp.scale[0]*decomp.scale[1] < 0
-            scale_uniform=not scale_len or abs(abs(decomp.scale[0])-abs(decomp.scale[1]))<epsilon
-            has_shear=abs(decomp.shear)>epsilon
-            has_translate=max(abs(decomp.translate[0]),abs(decomp.translate[1]))>epsilon
-
-            return OptimizableSvg.TransformTools.TransformProperties(has_rotation=has_rotation,has_scale=has_scale,has_mirror=has_mirror,scale_uniform=scale_uniform,has_shear=has_shear,has_translation=has_translate)
-
-        @staticmethod
-        def check_and_decomp(M):
-            decomp=OptimizableSvg.TransformTools.decompose(M) if M is not None else OptimizableSvg.TransformTools.TransformDecomposition(theta=0,scale=(1,1),shear=0,translate=(0,0))
-            props=OptimizableSvg.TransformTools.analyze_transform(decomp)
-            return (decomp, props)
-
-        @staticmethod
-        def tf_to_string(M):
-            tfstring = "matrix({} {} {} {} {} {})".format(M[0, 0], M[1, 0], M[0, 1], M[1, 1], M[0, 2], M[1, 2])
-            return tfstring
-
-        @staticmethod
-        def decomp_to_string(decomp):
-            decomp = OptimizableSvg.TransformTools.make_named(decomp)
-            ret=""
-            props=OptimizableSvg.TransformTools.analyze_transform(decomp)
-            if props.has_rotation:
-                ret+="rotate({}) ".format(math.degrees(decomp.theta.item()))
-            if props.has_scale:
-                if decomp.scale.dim()==0:
-                    ret += "scale({}) ".format(decomp.scale.item())
-                else:
-                    ret+="scale({} {}) ".format(decomp.scale[0], decomp.scale[1])
-            if props.has_shear:
-                ret+="skewX({}) ".format(decomp.shear.item())
-            if props.has_translation:
-                ret+="translate({} {}) ".format(decomp.translate[0],decomp.translate[1])
-
-            return ret
-
-        @staticmethod
-        def decompose(M):
-            m = M[0:2, 0:2]
-            t0=M[0:2, 2]
-            #get translation so that we can post-multiply with it
-            TXY=np.linalg.solve(m,t0)
-
-            T=np.eye(3)
-            T[0:2,2]=TXY
-
-            q, r = np.linalg.qr(m)
-
-            ref = np.array([[1, 0], [0, np.sign(np.linalg.det(q))]])
-
-            Rot = np.dot(q, ref)
-
-            ref2 = np.array([[1, 0], [0, np.sign(np.linalg.det(r))]])
-
-            r2 = np.dot(ref2, r)
-
-            Ref = np.dot(ref, ref2)
-            
-            sc = np.diag(r2)
-            Scale = np.diagflat(sc)
-
-            Shear = np.eye(2)
-            Shear[0, 1] = r2[0, 1] / sc[0]
-            #the actual shear coefficient
-            ShearX=r2[0, 1] / sc[0]
-
-            if np.sum(sc) < 0:
-                # both scales are negative, flip this and add a 180 rotation
-                Rot = np.dot(Rot, -np.eye(2))
-                Scale = -Scale
-
-            Theta = math.atan2(Rot[1, 0], Rot[0, 0])
-            ScaleXY = np.array([Scale[0,0],Scale[1,1]*Ref[1,1]])
-
-            return OptimizableSvg.TransformTools.TransformDecomposition(theta=Theta, scale=ScaleXY, shear=ShearX, translate=TXY)
-
-    #region suboptimizers
-
-    #optimizes color, but really any tensor that needs to stay between 0 and 1 per-entry
-    class ColorOptimizer:
-        def __init__(self,tensor,optim_type,lr):
-            self.tensor=tensor
-            self.optim=optim_type([tensor],lr=lr)
-
-        def zero_grad(self):
-            self.optim.zero_grad()
-
-        def step(self):
-            self.optim.step()
-            self.tensor.data.clamp_(min=1e-4,max=1.)
-
-    #optimizes gradient stop positions
-    class StopOptimizer:
-        def __init__(self,stops,optim_type,lr):
-            self.stops=stops
-            self.optim=optim_type([stops],lr=lr)
-
-        def zero_grad(self):
-            self.optim.zero_grad()
-
-        def step(self):
-            self.optim.step()
-            self.stops.data.clamp_(min=0., max=1.)
-            self.stops.data, _ = self.stops.sort()
-            self.stops.data[0] = 0.
-            self.stops.data[-1]=1.
-
-    #optimizes gradient: stop, positions, colors+opacities, locations
-    class GradientOptimizer:
-        def __init__(self, begin, end, offsets, stops, optim_params):
-            self.begin=begin.clone().detach() if begin is not None else None
-            self.end=end.clone().detach() if end is not None else None
-            self.offsets=offsets.clone().detach() if offsets is not None else None
-            self.stop_colors=stops[:,0:3].clone().detach() if stops is not None else None
-            self.stop_alphas=stops[:,3].clone().detach() if stops is not None else None
-            self.optimizers=[]
-
-            if optim_params["gradients"]["optimize_stops"] and self.offsets is not None:
-                self.offsets.requires_grad_(True)
-                self.optimizers.append(OptimizableSvg.StopOptimizer(self.offsets,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["stop_lr"]))
-            if optim_params["gradients"]["optimize_color"] and self.stop_colors is not None:
-                self.stop_colors.requires_grad_(True)
-                self.optimizers.append(OptimizableSvg.ColorOptimizer(self.stop_colors,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["color_lr"]))
-            if optim_params["gradients"]["optimize_alpha"] and self.stop_alphas is not None:
-                self.stop_alphas.requires_grad_(True)
-                self.optimizers.append(OptimizableSvg.ColorOptimizer(self.stop_alphas,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["alpha_lr"]))
-            if optim_params["gradients"]["optimize_location"] and self.begin is not None and self.end is not None:
-                self.begin.requires_grad_(True)
-                self.end.requires_grad_(True)
-                self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.begin,self.end],lr=optim_params["gradients"]["location_lr"]))
-
-
-        def get_vals(self):
-            return self.begin, self.end, self.offsets, torch.cat((self.stop_colors,self.stop_alphas.unsqueeze(1)),1) if self.stop_colors is not None and self.stop_alphas is not None else None
-
-        def zero_grad(self):
-            for optim in self.optimizers:
-                optim.zero_grad()
-
-        def step(self):
-            for optim in self.optimizers:
-                optim.step()
-
-    class TransformOptimizer:
-        def __init__(self,transform,optim_params):
-            self.transform=transform
-            self.optimizes=optim_params["transforms"]["optimize_transforms"] and transform is not None
-            self.params=copy.deepcopy(optim_params)
-            self.transform_mode=optim_params["transforms"]["transform_mode"]
-
-            if self.optimizes:
-                optimvars=[]
-                self.residual=None
-                lr=optim_params["transforms"]["transform_lr"]
-                tmult=optim_params["transforms"]["translation_mult"]
-                decomp,props=OptimizableSvg.TransformTools.check_and_decomp(transform.cpu().numpy())
-                if self.transform_mode=="move":
-                    #only translation and rotation should be set
-                    if props.has_scale or props.has_shear or props.has_mirror:
-                        print("Warning: set to optimize move only, but input transform has residual scale or shear")
-                        self.residual=self.transform.clone().detach().requires_grad_(False)
-                        self.Theta=torch.tensor(0,dtype=torch.float32,requires_grad=True,device=transform.device)
-                        self.translation=torch.tensor([0, 0],dtype=torch.float32,requires_grad=True,device=transform.device)
-                    else:
-                        self.residual=None
-                        self.Theta=torch.tensor(decomp.theta,dtype=torch.float32,requires_grad=True,device=transform.device)
-                        self.translation=torch.tensor(decomp.translate,dtype=torch.float32,requires_grad=True,device=transform.device)
-                    optimvars+=[{'params':x,'lr':lr} for x in [self.Theta]]+[{'params':self.translation,'lr':lr*tmult}]
-                elif self.transform_mode=="rigid":
-                    #only translation, rotation, and uniform scale should be set
-                    if props.has_shear or props.has_mirror or not props.scale_uniform:
-                        print("Warning: set to optimize rigid transform only, but input transform has residual shear, mirror or non-uniform scale")
-                        self.residual = self.transform.clone().detach().requires_grad_(False)
-                        self.Theta = torch.tensor(0, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.translation = torch.tensor([0, 0], dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.scale=torch.tensor(1, dtype=torch.float32, requires_grad=True,device=transform.device)
-                    else:
-                        self.residual = None
-                        self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.scale = torch.tensor(decomp.scale[0], dtype=torch.float32, requires_grad=True,device=transform.device)
-                    optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale]]+[{'params':self.translation,'lr':lr*tmult}]
-                elif self.transform_mode=="similarity":
-                    if props.has_shear or not props.scale_uniform:
-                        print("Warning: set to optimize rigid transform only, but input transform has residual shear or non-uniform scale")
-                        self.residual = self.transform.clone().detach().requires_grad_(False)
-                        self.Theta = torch.tensor(0, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.translation = torch.tensor([0, 0], dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.scale=torch.tensor(1, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.scale_sign=torch.tensor(1,dtype=torch.float32,requires_grad=False,device=transform.device)
-                    else:
-                        self.residual = None
-                        self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.scale = torch.tensor(decomp.scale[0], dtype=torch.float32, requires_grad=True,device=transform.device)
-                        self.scale_sign = torch.tensor(np.sign(decomp.scale[0]*decomp.scale[1]), dtype=torch.float32, requires_grad=False,device=transform.device)
-                    optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale]]+[{'params':self.translation,'lr':lr*tmult}]
-                elif self.transform_mode=="affine":
-                    self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device)
-                    self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device)
-                    self.scale = torch.tensor(decomp.scale, dtype=torch.float32, requires_grad=True,device=transform.device)
-                    self.shear = torch.tensor(decomp.shear, dtype=torch.float32, requires_grad=True,device=transform.device)
-                    optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale, self.shear]]+[{'params':self.translation,'lr':lr*tmult}]
-                else:
-                    raise ValueError("Unrecognized transform mode '{}'".format(self.transform_mode))
-                self.optimizer=SvgOptimizationSettings.optims[optim_params["optimizer"]](optimvars)
-
-        def get_transform(self):
-            if not self.optimizes:
-                return self.transform
-            else:
-                if self.transform_mode == "move":
-                    composed=OptimizableSvg.TransformTools.recompose(self.Theta,torch.tensor([1.],device=self.Theta.device),torch.tensor(0.,device=self.Theta.device),self.translation)
-                    return self.residual.mm(composed) if self.residual is not None else composed
-                elif self.transform_mode == "rigid":
-                    composed = OptimizableSvg.TransformTools.recompose(self.Theta, self.scale, torch.tensor(0.,device=self.Theta.device),
-                                                                       self.translation)
-                    return self.residual.mm(composed) if self.residual is not None else composed
-                elif self.transform_mode == "similarity":
-                    composed=OptimizableSvg.TransformTools.recompose(self.Theta, torch.cat((self.scale,self.scale*self.scale_sign)),torch.tensor(0.,device=self.Theta.device),self.translation)
-                    return self.residual.mm(composed) if self.residual is not None else composed
-                elif self.transform_mode == "affine":
-                    composed = OptimizableSvg.TransformTools.recompose(self.Theta, self.scale, self.shear, self.translation)
-                    return composed
-                else:
-                    raise ValueError("Unrecognized transform mode '{}'".format(self.transform_mode))
-
-        def tfToString(self):
-            if self.transform is None:
-                return None
-            elif not self.optimizes:
-                return OptimizableSvg.TransformTools.tf_to_string(self.transform)
-            else:
-                if self.transform_mode == "move":
-                    str=OptimizableSvg.TransformTools.decomp_to_string((self.Theta,torch.tensor([1.]),torch.tensor(0.),self.translation))
-                    return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str
-                elif self.transform_mode == "rigid":
-                    str = OptimizableSvg.TransformTools.decomp_to_string((self.Theta, self.scale, torch.tensor(0.),
-                                                                       self.translation))
-                    return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str
-                elif self.transform_mode == "similarity":
-                    str=OptimizableSvg.TransformTools.decomp_to_string((self.Theta, torch.cat((self.scale,self.scale*self.scale_sign)),torch.tensor(0.),self.translation))
-                    return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str
-                elif self.transform_mode == "affine":
-                    str = OptimizableSvg.TransformTools.decomp_to_string((self.Theta, self.scale, self.shear, self.translation))
-                    return composed
-
-        def zero_grad(self):
-            if self.optimizes:
-                self.optimizer.zero_grad()
-
-        def step(self):
-            if self.optimizes:
-                self.optimizer.step()
-
-    #endregion
-
-    #region Nodes
-    class SvgNode:
-        def __init__(self,id,transform,appearance,settings):
-            self.id=id
-            self.children=[]
-            self.optimizers=[]
-            self.device = settings.device
-            self.transform=torch.tensor(transform,dtype=torch.float32,device=self.device) if transform is not None else None
-            self.transform_optim=OptimizableSvg.TransformOptimizer(self.transform,settings.retrieve(self.id)[0])
-            self.optimizers.append(self.transform_optim)
-            self.proc_appearance(appearance,settings.retrieve(self.id)[0])
-
-        def tftostring(self):
-            return self.transform_optim.tfToString()
-
-        def appearanceToString(self):
-            appstring=""
-            for key,value in self.appearance.items():
-                if key in ["fill", "stroke"]:
-                    #a paint-type value
-                    if value[0] == "none":
-                        appstring+="{}:none;".format(key)
-                    elif value[0] == "solid":
-                        appstring += "{}:{};".format(key,OptimizableSvg.rgb_to_string(value[1]))
-                    elif value[0] == "url":
-                        appstring += "{}:url(#{});".format(key,value[1].id)
-                        #appstring += "{}:{};".format(key,"#ff00ff")
-                elif key in ["opacity", "fill-opacity", "stroke-opacity", "stroke-width", "fill-rule"]:
-                    appstring+="{}:{};".format(key,value)
-                else:
-                    raise ValueError("Don't know how to write appearance parameter '{}'".format(key))
-            return appstring
-
-
-        def write_xml_common_attrib(self,node,tfname="transform"):
-            if self.transform is not None:
-                node.set(tfname,self.tftostring())
-            if len(self.appearance)>0:
-                node.set('style',self.appearanceToString())
-            if self.id is not None:
-                node.set('id',self.id)
-
-
-        def proc_appearance(self,appearance,optim_params):
-            self.appearance=appearance
-            for key, value in appearance.items():
-                if key == "fill" or key == "stroke":
-                    if optim_params["optimize_color"] and value[0]=="solid":
-                        value[1].requires_grad_(True)
-                        self.optimizers.append(OptimizableSvg.ColorOptimizer(value[1],SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["color_lr"]))
-                elif key == "fill-opacity" or key == "stroke-opacity" or key == "opacity":
-                    if optim_params["optimize_alpha"]:
-                        value[1].requires_grad_(True)
-                        self.optimizers.append(OptimizableSvg.ColorOptimizer(value[1], optim_params["optimizer"],
-                                                                             optim_params["alpha_lr"]))
-                elif key == "fill-rule" or key == "stroke-width":
-                    pass
-                else:
-                    raise RuntimeError("Unrecognized appearance key '{}'".format(key))
-
-        def prop_transform(self,intform):
-            return intform.matmul(self.transform_optim.get_transform()) if self.transform is not None else intform
-
-        def prop_appearance(self,inappearance):
-            outappearance=copy.copy(inappearance)
-            for key,value in self.appearance.items():
-                if key == "fill":
-                    #gets replaced
-                    outappearance[key]=value
-                elif key == "fill-opacity":
-                    #gets multiplied
-                    outappearance[key] = outappearance[key]*value
-                elif key == "fill-rule":
-                    #gets replaced
-                    outappearance[key] = value
-                elif key =="opacity":
-                    # gets multiplied
-                    outappearance[key] = outappearance[key]*value
-                elif key == "stroke":
-                    # gets replaced
-                    outappearance[key] = value
-                elif key == "stroke-opacity":
-                    # gets multiplied
-                    outappearance[key] = outappearance[key]*value
-                elif key =="stroke-width":
-                    # gets replaced
-                    outappearance[key] = value
-                else:
-                    raise RuntimeError("Unrecognized appearance key '{}'".format(key))
-            return outappearance
-
-        def zero_grad(self):
-            for optim in self.optimizers:
-                optim.zero_grad()
-            for child in self.children:
-                child.zero_grad()
-
-        def step(self):
-            for optim in self.optimizers:
-                optim.step()
-            for child in self.children:
-                child.step()
-
-        def get_type(self):
-            return "Generic node"
-
-        def is_shape(self):
-            return False
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            raise NotImplementedError("Abstract SvgNode cannot recurse")
-
-    class GroupNode(SvgNode):
-        def __init__(self, id, transform, appearance,settings):
-            super().__init__(id, transform, appearance,settings)
-
-        def get_type(self):
-            return "Group node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            outtf=self.prop_transform(transform)
-            outapp=self.prop_appearance(appearance)
-            for child in self.children:
-                child.build_scene(shapes,shape_groups,outtf,outapp)
-
-        def write_xml(self, parent):
-            elm=etree.SubElement(parent,"g")
-            self.write_xml_common_attrib(elm)
-
-            for child in self.children:
-                child.write_xml(elm)
-
-    class RootNode(SvgNode):
-        def __init__(self, id, transform, appearance,settings):
-            super().__init__(id, transform, appearance,settings)
-
-        def write_xml(self,document):
-            elm=etree.Element('svg')
-            self.write_xml_common_attrib(elm)
-            elm.set("version","2.0")
-            elm.set("width",str(document.canvas[0]))
-            elm.set("height", str(document.canvas[1]))
-            elm.set("xmlns","http://www.w3.org/2000/svg")
-            elm.set("xmlns:xlink","http://www.w3.org/1999/xlink")
-            #write definitions before we write any children
-            document.write_defs(elm)
-
-            #write the children
-            for child in self.children:
-                child.write_xml(elm)
-
-            return elm
-
-        def get_type(self):
-            return "Root node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            outtf = self.prop_transform(transform).to(self.device)
-            for child in self.children:
-                child.build_scene(shapes,shape_groups,outtf,appearance)
-
-        @staticmethod
-        def get_default_appearance(device):
-            default_appearance = {"fill": ("solid", torch.tensor([0., 0., 0.],device=device)),
-                                  "fill-opacity": torch.tensor([1.],device=device),
-                                  "fill-rule": "nonzero",
-                                  "opacity": torch.tensor([1.],device=device),
-                                  "stroke": ("none", None),
-                                  "stroke-opacity": torch.tensor([1.],device=device),
-                                  "stroke-width": torch.tensor([0.],device=device)}
-            return default_appearance
-
-        @staticmethod
-        def get_default_transform():
-            return torch.eye(3)
-
-
-
-    class ShapeNode(SvgNode):
-        def __init__(self, id, transform, appearance,settings):
-            super().__init__(id, transform, appearance,settings)
-
-        def get_type(self):
-            return "Generic shape node"
-
-        def is_shape(self):
-            return True
-
-        def construct_paint(self,value,combined_opacity,transform):
-            if value[0]   == "none":
-                return None
-            elif value[0] == "solid":
-                return torch.cat([value[1],combined_opacity]).to(self.device)
-            elif value[0] == "url":
-                #get the gradient object from this node
-                return value[1].getGrad(combined_opacity,transform)
-            else:
-                raise ValueError("Unknown paint value type '{}'".format(value[0]))
-
-        def make_shape_group(self,appearance,transform,num_shapes,num_subobjects):
-            fill=self.construct_paint(appearance["fill"],appearance["opacity"]*appearance["fill-opacity"],transform)
-            stroke=self.construct_paint(appearance["stroke"],appearance["opacity"]*appearance["stroke-opacity"],transform)
-            sg = pydiffvg.ShapeGroup(shape_ids=torch.tensor(range(num_shapes, num_shapes + num_subobjects)),
-                                     fill_color=fill,
-                                     use_even_odd_rule=appearance["fill-rule"]=="evenodd",
-                                     stroke_color=stroke,
-                                     shape_to_canvas=transform,
-                                     id=self.id)
-            return sg
-
-    class PathNode(ShapeNode):
-        def __init__(self, id, transform, appearance,settings, paths):
-            super().__init__(id, transform, appearance,settings)
-            self.proc_paths(paths,settings.retrieve(self.id)[0])
-
-        def proc_paths(self,paths,optim_params):
-            self.paths=paths
-            if optim_params["paths"]["optimize_points"]:
-                ptlist=[]
-                for path in paths:
-                    ptlist.append(path.points.requires_grad_(True))
-                self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]](ptlist,lr=optim_params["paths"]["shape_lr"]))
-
-        def get_type(self):
-            return "Path node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            applytf=self.prop_transform(transform)
-            applyapp = self.prop_appearance(appearance)
-            sg=self.make_shape_group(applyapp,applytf,len(shapes),len(self.paths))
-            for path in self.paths:
-                disp_path=pydiffvg.Path(path.num_control_points,path.points,path.is_closed,applyapp["stroke-width"],path.id)
-                shapes.append(disp_path)
-            shape_groups.append(sg)
-
-        def path_to_string(self,path):
-            path_string = "M {},{} ".format(path.points[0][0].item(), path.points[0][1].item())
-            idx = 1
-            numpoints = path.points.shape[0]
-            for type in path.num_control_points:
-                toproc = type + 1
-                if type == 0:
-                    # add line
-                    path_string += "L "
-                elif type == 1:
-                    # add quadric
-                    path_string += "Q "
-                elif type == 2:
-                    # add cubic
-                    path_string += "C "
-                while toproc > 0:
-                    path_string += "{},{} ".format(path.points[idx % numpoints][0].item(),
-                                                   path.points[idx % numpoints][1].item())
-                    idx += 1
-                    toproc -= 1
-            if path.is_closed:
-                path_string += "Z "
-
-            return path_string
-
-        def paths_string(self):
-            pstr=""
-            for path in self.paths:
-                pstr+=self.path_to_string(path)
-            return pstr
-
-        def write_xml(self, parent):
-            elm = etree.SubElement(parent, "path")
-            self.write_xml_common_attrib(elm)
-            elm.set("d",self.paths_string())
-
-            for child in self.children:
-                child.write_xml(elm)
-
-    class RectNode(ShapeNode):
-        def __init__(self, id, transform, appearance,settings, rect):
-            super().__init__(id, transform, appearance,settings)
-            self.rect=torch.tensor(rect,dtype=torch.float,device=settings.device)
-            optim_params=settings.retrieve(self.id)[0]
-            #borrowing path settings for this
-            if optim_params["paths"]["optimize_points"]:
-                self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.rect],lr=optim_params["paths"]["shape_lr"]))
-
-        def get_type(self):
-            return "Rect node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            applytf=self.prop_transform(transform)
-            applyapp = self.prop_appearance(appearance)
-            sg=self.make_shape_group(applyapp,applytf,len(shapes),1)
-            shapes.append(pydiffvg.Rect(self.rect[0:2],self.rect[0:2]+self.rect[2:4],applyapp["stroke-width"],self.id))
-            shape_groups.append(sg)
-
-        def write_xml(self, parent):
-            elm = etree.SubElement(parent, "rect")
-            self.write_xml_common_attrib(elm)
-            elm.set("x",str(self.rect[0]))
-            elm.set("y", str(self.rect[1]))
-            elm.set("width", str(self.rect[2]))
-            elm.set("height", str(self.rect[3]))
-
-            for child in self.children:
-                child.write_xml(elm)
-
-    class CircleNode(ShapeNode):
-        def __init__(self, id, transform, appearance,settings, rect):
-            super().__init__(id, transform, appearance,settings)
-            self.circle=torch.tensor(rect,dtype=torch.float,device=settings.device)
-            optim_params=settings.retrieve(self.id)[0]
-            #borrowing path settings for this
-            if optim_params["paths"]["optimize_points"]:
-                self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.circle],lr=optim_params["paths"]["shape_lr"]))
-
-        def get_type(self):
-            return "Circle node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            applytf=self.prop_transform(transform)
-            applyapp = self.prop_appearance(appearance)
-            sg=self.make_shape_group(applyapp,applytf,len(shapes),1)
-            shapes.append(pydiffvg.Circle(self.circle[2],self.circle[0:2],applyapp["stroke-width"],self.id))
-            shape_groups.append(sg)
-
-        def write_xml(self, parent):
-            elm = etree.SubElement(parent, "circle")
-            self.write_xml_common_attrib(elm)
-            elm.set("cx",str(self.circle[0]))
-            elm.set("cy", str(self.circle[1]))
-            elm.set("r", str(self.circle[2]))
-
-            for child in self.children:
-                child.write_xml(elm)
-
-
-    class EllipseNode(ShapeNode):
-        def __init__(self, id, transform, appearance,settings, ellipse):
-            super().__init__(id, transform, appearance,settings)
-            self.ellipse=torch.tensor(ellipse,dtype=torch.float,device=settings.device)
-            optim_params=settings.retrieve(self.id)[0]
-            #borrowing path settings for this
-            if optim_params["paths"]["optimize_points"]:
-                self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.ellipse],lr=optim_params["paths"]["shape_lr"]))
-
-        def get_type(self):
-            return "Ellipse node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            applytf=self.prop_transform(transform)
-            applyapp = self.prop_appearance(appearance)
-            sg=self.make_shape_group(applyapp,applytf,len(shapes),1)
-            shapes.append(pydiffvg.Ellipse(self.ellipse[2:4],self.ellipse[0:2],applyapp["stroke-width"],self.id))
-            shape_groups.append(sg)
-
-        def write_xml(self, parent):
-            elm = etree.SubElement(parent, "ellipse")
-            self.write_xml_common_attrib(elm)
-            elm.set("cx", str(self.ellipse[0]))
-            elm.set("cy", str(self.ellipse[1]))
-            elm.set("rx", str(self.ellipse[2]))
-            elm.set("ry", str(self.ellipse[3]))
-
-            for child in self.children:
-                child.write_xml(elm)
-
-    class PolygonNode(ShapeNode):
-        def __init__(self, id, transform, appearance,settings, points):
-            super().__init__(id, transform, appearance,settings)
-            self.points=points
-            optim_params=settings.retrieve(self.id)[0]
-            #borrowing path settings for this
-            if optim_params["paths"]["optimize_points"]:
-                self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.points],lr=optim_params["paths"]["shape_lr"]))
-
-        def get_type(self):
-            return "Polygon node"
-
-        def build_scene(self,shapes,shape_groups,transform,appearance):
-            applytf=self.prop_transform(transform)
-            applyapp = self.prop_appearance(appearance)
-            sg=self.make_shape_group(applyapp,applytf,len(shapes),1)
-            shapes.append(pydiffvg.Polygon(self.points,True,applyapp["stroke-width"],self.id))
-            shape_groups.append(sg)
-
-        def point_string(self):
-            ret=""
-            for i in range(self.points.shape[0]):
-                pt=self.points[i,:]
-                #assert pt.shape == (1,2)
-                ret+= str(pt[0])+","+str(pt[1])+" "
-            return ret
-
-        def write_xml(self, parent):
-            elm = etree.SubElement(parent, "polygon")
-            self.write_xml_common_attrib(elm)
-            elm.set("points",self.point_string())
-
-            for child in self.children:
-                child.write_xml(elm)
-
-    class GradientNode(SvgNode):
-        def __init__(self, id, transform,settings,begin,end,offsets,stops,href):
-            super().__init__(id, transform, {},settings)
-            self.optim=OptimizableSvg.GradientOptimizer(begin, end, offsets, stops, settings.retrieve(id)[0])
-            self.optimizers.append(self.optim)
-            self.href=href
-
-        def is_ref(self):
-            return self.href is not None
-
-        def get_type(self):
-            return "Gradient node"
-
-        def get_stops(self):
-            _, _, offsets, stops=self.optim.get_vals()
-            return offsets, stops
-
-        def get_points(self):
-            begin, end, _, _ =self.optim.get_vals()
-            return begin, end
-
-        def write_xml(self, parent):
-            elm = etree.SubElement(parent, "linearGradient")
-            self.write_xml_common_attrib(elm,tfname="gradientTransform")
-
-            begin, end, offsets, stops = self.optim.get_vals()
-
-            if self.href is None:
-                #we have stops
-                for idx, offset in enumerate(offsets):
-                    stop=etree.SubElement(elm,"stop")
-                    stop.set("offset",str(offset.item()))
-                    stop.set("stop-color",OptimizableSvg.rgb_to_string(stops[idx,0:3]))
-                    stop.set("stop-opacity",str(stops[idx,3].item()))
-            else:
-                elm.set('xlink:href', "#{}".format(self.href.id))
-
-            if begin is not None and end is not None:
-                #no stops
-                elm.set('x1', str(begin[0].item()))
-                elm.set('y1', str(begin[1].item()))
-                elm.set('x2', str(end[0].item()))
-                elm.set('y2', str(end[1].item()))
-
-                # magic value to make this work
-                elm.set("gradientUnits", "userSpaceOnUse")
-
-            for child in self.children:
-                child.write_xml(elm)
-
-        def getGrad(self,combined_opacity,transform):
-            if self.is_ref():
-                offsets, stops=self.href.get_stops()
-            else:
-                offsets, stops=self.get_stops()
-
-            stops=stops.clone()
-            stops[:,3]*=combined_opacity
-
-            begin,end = self.get_points()
-
-            applytf=self.prop_transform(transform)
-            begin=OptimizableSvg.TransformTools.transformPoints(begin.unsqueeze(0),applytf).squeeze()
-            end = OptimizableSvg.TransformTools.transformPoints(end.unsqueeze(0), applytf).squeeze()
-
-            return pydiffvg.LinearGradient(begin, end, offsets, stops)
-    #endregion
-
-    def __init__(self, filename, settings=SvgOptimizationSettings(),optimize_background=False, verbose=False, device=torch.device("cpu")):
-        self.settings=settings
-        self.verbose=verbose
-        self.device=device
-        self.settings.device=device
-
-        tree = etree.parse(filename)
-        root = tree.getroot()
-
-        #in case we need global optimization
-        self.optimizers=[]
-        self.background=torch.tensor([1.,1.,1.],dtype=torch.float32,requires_grad=optimize_background,device=self.device)
-
-        if optimize_background:
-            p=settings.retrieve("default")[0]
-            self.optimizers.append(OptimizableSvg.ColorOptimizer(self.background,SvgOptimizationSettings.optims[p["optimizer"]],p["color_lr"]))
-
-        self.defs={}
-
-        self.depth=0
-
-        self.dirty=True
-        self.scene=None
-
-        self.parseRoot(root)
-
-    recognised_shapes=["path","circle","rect","ellipse","polygon"]
-
-    #region core functionality
-    def build_scene(self):
-        if self.dirty:
-            shape_groups=[]
-            shapes=[]
-            self.root.build_scene(shapes,shape_groups,OptimizableSvg.RootNode.get_default_transform().to(self.device),OptimizableSvg.RootNode.get_default_appearance(self.device))
-            self.scene=(self.canvas[0],self.canvas[1],shapes,shape_groups)
-            self.dirty=False
-        return self.scene
-
-    def zero_grad(self):
-        self.root.zero_grad()
-        for optim in self.optimizers:
-            optim.zero_grad()
-        for item in self.defs.values():
-            if issubclass(item.__class__,OptimizableSvg.SvgNode):
-                item.zero_grad()
-
-    def render(self,scale=None,seed=0):
-        #render at native resolution
-        scene = self.build_scene()
-        scene_args = pydiffvg.RenderFunction.serialize_scene(*scene)
-        render = pydiffvg.RenderFunction.apply
-        out_size=(scene[0],scene[1]) if scale is None else (int(scene[0]*scale),int(scene[1]*scale))
-        img = render(out_size[0],  # width
-                     out_size[1],  # height
-                     2,  # num_samples_x
-                     2,  # num_samples_y
-                     seed,  # seed
-                     None, # background_image
-                     *scene_args)
-        return img
-
-    def step(self):
-        self.dirty=True
-        self.root.step()
-        for optim in self.optimizers:
-            optim.step()
-        for item in self.defs.values():
-            if issubclass(item.__class__, OptimizableSvg.SvgNode):
-                item.step()
-    #endregion
-
-    #region reporting
-
-    def offset_str(self,s):
-        return ("\t"*self.depth)+s
-
-    def reportSkippedAttribs(self, node, non_skipped=[]):
-        skipped=set([k for k in node.attrib.keys() if not OptimizableSvg.is_namespace(k)])-set(non_skipped)
-        if len(skipped)>0:
-            tag=OptimizableSvg.remove_namespace(node.tag) if "id" not in node.attrib else "{}#{}".format(OptimizableSvg.remove_namespace(node.tag),node.attrib["id"])
-            print(self.offset_str("Warning: Skipping the following attributes of node '{}': {}".format(tag,", ".join(["'{}'".format(atr) for atr in skipped]))))
-
-    def reportSkippedChildren(self,node,skipped):
-        skipped_names=["{}#{}".format(elm.tag,elm.attrib["id"]) if "id" in elm.attrib else elm.tag for elm in skipped]
-        if len(skipped)>0:
-            tag = OptimizableSvg.remove_namespace(node.tag) if "id" not in node.attrib else "{}#{}".format(OptimizableSvg.remove_namespace(node.tag),
-                                                                                            node.attrib["id"])
-            print(self.offset_str("Warning: Skipping the following children of node '{}': {}".format(tag,", ".join(["'{}'".format(name) for name in skipped_names]))))
-
-    #endregion
-
-    #region parsing
-    @staticmethod
-    def remove_namespace(s):
-        """
-            {...} ... -> ...
-        """
-        return re.sub('{.*}', '', s)
-
-    @staticmethod
-    def is_namespace(s):
-        return re.match('{.*}', s) is not None
-
-    @staticmethod
-    def parseTransform(node):
-        if "transform" not in node.attrib and "gradientTransform" not in node.attrib:
-            return None
-
-        tf_string=node.attrib["transform"] if "transform" in node.attrib else node.attrib["gradientTransform"]
-        tforms=tf_string.split(")")[:-1]
-        mat=np.eye(3)
-        for tform in tforms:
-            type = tform.split("(")[0]
-            args = [float(val) for val in re.split("[, ]+",tform.split("(")[1])]
-            if type == "matrix":
-                mat=mat @ OptimizableSvg.TransformTools.parse_matrix(args)
-            elif type == "translate":
-                mat = mat @ OptimizableSvg.TransformTools.parse_translate(args)
-            elif type == "rotate":
-                mat = mat @ OptimizableSvg.TransformTools.parse_rotate(args)
-            elif type == "scale":
-                mat = mat @ OptimizableSvg.TransformTools.parse_scale(args)
-            elif type == "skewX":
-                mat = mat @ OptimizableSvg.TransformTools.parse_skewx(args)
-            elif type == "skewY":
-                mat = mat @ OptimizableSvg.TransformTools.parse_skewy(args)
-            else:
-                raise ValueError("Unknown transform type '{}'".format(type))
-        return mat
-
-    #dictionary that defines what constant do we need to multiply different units to get the value in pixels
-    #gleaned from the CSS definition
-    unit_dict = {"px":1,
-                 "mm":4,
-                 "cm":40,
-                 "in":25.4*4,
-                 "pt":25.4*4/72,
-                 "pc":25.4*4/6
-                 }
-
-    @staticmethod
-    def parseLength(s):
-        #length is a number followed possibly by a unit definition
-        #we assume that default unit is the pixel (px) equal to 0.25mm
-        #last two characters might be unit
-        val=None
-        for i in range(len(s)):
-            try:
-                val=float(s[:len(s)-i])
-                unit=s[len(s)-i:]
-                break
-            except ValueError:
-                continue
-        if len(unit)>0 and unit not in OptimizableSvg.unit_dict:
-            raise ValueError("Unknown or unsupported unit '{}' encountered while parsing".format(unit))
-        if unit != "":
-            val*=OptimizableSvg.unit_dict[unit]
-        return val
-
-    @staticmethod
-    def parseOpacity(s):
-        is_percent=s.endswith("%")
-        s=s.rstrip("%")
-        val=float(s)
-        if is_percent:
-            val=val/100
-        return np.clip(val,0.,1.)
-
-    @staticmethod
-    def parse_color(s):
-        """
-            Hex to tuple
-        """
-        if s[0] != '#':
-            raise ValueError("Color argument `{}` not supported".format(s))
-        s = s.lstrip('#')
-        if len(s)==6:
-            rgb = tuple(int(s[i:i + 2], 16) for i in (0, 2, 4))
-            return torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0])
-        elif len(s)==3:
-            rgb = tuple((int(s[i:i + 1], 16)) for i in (0, 1, 2))
-            return torch.tensor([rgb[0] / 15.0, rgb[1] / 15.0, rgb[2] / 15.0])
-        else:
-            raise ValueError("Color argument `{}` not supported".format(s))
-        # sRGB to RGB
-        # return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 2.2)
-
-
-    @staticmethod
-    def rgb_to_string(val):
-        byte_rgb=(val.clone().detach()*255).type(torch.int)
-        byte_rgb.clamp_(min=0,max=255)
-        s="#{:02x}{:02x}{:02x}".format(*byte_rgb)
-        return s
-
-    #parses a "paint" string for use in fill and stroke definitions
-    @staticmethod
-    def parsePaint(paintStr,defs,device):
-        paintStr=paintStr.strip()
-        if paintStr=="none":
-            return ("none", None)
-        elif paintStr[0]=="#":
-            return ("solid",OptimizableSvg.parse_color(paintStr).to(device))
-        elif paintStr.startswith("url"):
-            url=paintStr.lstrip("url(").rstrip(")").strip("\'\"").lstrip("#")
-            if url not in defs:
-                raise ValueError("Paint-type attribute referencing an unknown object with ID '#{}'".format(url))
-            return ("url",defs[url])
-        else:
-            raise ValueError("Unrecognized paint string: '{}'".format(paintStr))
-
-    appearance_keys=["fill","fill-opacity","fill-rule","opacity","stroke","stroke-opacity","stroke-width"]
-
-    @staticmethod
-    def parseAppearance(node, defs, device):
-        ret={}
-        parse_keys = OptimizableSvg.appearance_keys
-        local_dict={key:value for key,value in node.attrib.items() if key in parse_keys}
-        css_dict={}
-        style_dict={}
-        appearance_dict={}
-        if "class" in node.attrib:
-            cls=node.attrib["class"]
-            if "."+cls in defs:
-                css_string=defs["."+cls]
-                css_dict={item.split(":")[0]:item.split(":")[1] for item in css_string.split(";") if len(item)>0 and item.split(":")[0] in parse_keys}
-        if "style" in node.attrib:
-            style_string=node.attrib["style"]
-            style_dict={item.split(":")[0]:item.split(":")[1] for item in style_string.split(";") if len(item)>0 and item.split(":")[0] in parse_keys}
-        appearance_dict.update(css_dict)
-        appearance_dict.update(style_dict)
-        appearance_dict.update(local_dict)
-        for key,value in appearance_dict.items():
-            if key=="fill":
-                ret[key]=OptimizableSvg.parsePaint(value,defs,device)
-            elif key == "fill-opacity":
-                ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device)
-            elif key == "fill-rule":
-                ret[key]=value
-            elif key == "opacity":
-                ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device)
-            elif key == "stroke":
-                ret[key]=OptimizableSvg.parsePaint(value,defs,device)
-            elif key == "stroke-opacity":
-                ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device)
-            elif key == "stroke-width":
-                ret[key]=torch.tensor(OptimizableSvg.parseLength(value),device=device)
-            else:
-                raise ValueError("Error while parsing appearance attributes: key '{}' should not be here".format(key))
-
-        return ret
-
-    def parseRoot(self,root):
-        if self.verbose:
-            print(self.offset_str("Parsing root"))
-        self.depth += 1
-
-        # get document canvas dimensions
-        self.parseViewport(root)
-        canvmax=np.max(self.canvas)
-        self.settings.global_override(["transforms","translation_mult"],canvmax)
-        id=root.attrib["id"] if "id" in root.attrib else None
-
-        transform=OptimizableSvg.parseTransform(root)
-        appearance=OptimizableSvg.parseAppearance(root,self.defs,self.device)
-
-        version=root.attrib["version"] if "version" in root.attrib else "<unknown version>"
-        if version != "2.0":
-            print(self.offset_str("Warning: Version {} is not 2.0, strange things may happen".format(version)))
-
-        self.root=OptimizableSvg.RootNode(id,transform,appearance,self.settings)
-
-        if self.verbose:
-            self.reportSkippedAttribs(root, ["width", "height", "id", "transform","version", "style"]+OptimizableSvg.appearance_keys)
-
-        #go through the root children and parse them appropriately
-        skipped=[]
-        for child in root:
-            if OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes:
-                self.parseShape(child,self.root)
-            elif OptimizableSvg.remove_namespace(child.tag) == "defs":
-                self.parseDefs(child)
-            elif OptimizableSvg.remove_namespace(child.tag) == "style":
-                self.parseStyle(child)
-            elif OptimizableSvg.remove_namespace(child.tag) == "g":
-                self.parseGroup(child,self.root)
-            else:
-                skipped.append(child)
-
-        if self.verbose:
-            self.reportSkippedChildren(root,skipped)
-
-        self.depth-=1
-
-    def parseShape(self,shape,parent):
-        tag=OptimizableSvg.remove_namespace(shape.tag)
-        if self.verbose:
-            print(self.offset_str("Parsing {}#{}".format(tag,shape.attrib["id"] if "id" in shape.attrib else "<No ID>")))
-
-        self.depth+=1
-        if tag == "path":
-            self.parsePath(shape,parent)
-        elif tag == "circle":
-            self.parseCircle(shape,parent)
-        elif tag == "rect":
-            self.parseRect(shape,parent)
-        elif tag == "ellipse":
-            self.parseEllipse(shape,parent)
-        elif tag == "polygon":
-            self.parsePolygon(shape,parent)
-        else:
-            raise ValueError("Encountered unknown shape type '{}'".format(tag))
-        self.depth -= 1
-
-    def parsePath(self,shape,parent):
-        path_string=shape.attrib['d']
-        name = ''
-        if 'id' in shape.attrib:
-            name = shape.attrib['id']
-        paths = pydiffvg.from_svg_path(path_string)
-        for idx, path in enumerate(paths):
-            path.stroke_width = torch.tensor([0.],device=self.device)
-            path.num_control_points=path.num_control_points.to(self.device)
-            path.points=path.points.to(self.device)
-            path.source_id = name
-            path.id = "{}-{}".format(name,idx) if len(paths)>1 else name
-        transform = OptimizableSvg.parseTransform(shape)
-        appearance = OptimizableSvg.parseAppearance(shape,self.defs,self.device)
-        node=OptimizableSvg.PathNode(name,transform,appearance,self.settings,paths)
-        parent.children.append(node)
-
-        if self.verbose:
-            self.reportSkippedAttribs(shape, ["id","d","transform","style"]+OptimizableSvg.appearance_keys)
-            self.reportSkippedChildren(shape,list(shape))
-
-    def parseEllipse(self, shape, parent):
-        cx = float(shape.attrib["cx"]) if "cx" in shape.attrib else 0.
-        cy = float(shape.attrib["cy"]) if "cy" in shape.attrib else 0.
-        rx = float(shape.attrib["rx"])
-        ry = float(shape.attrib["ry"])
-        name = ''
-        if 'id' in shape.attrib:
-            name = shape.attrib['id']
-        transform = OptimizableSvg.parseTransform(shape)
-        appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device)
-        node = OptimizableSvg.EllipseNode(name, transform, appearance, self.settings, (cx, cy, rx, ry))
-        parent.children.append(node)
-
-        if self.verbose:
-            self.reportSkippedAttribs(shape, ["id", "x", "y", "r", "transform",
-                                              "style"] + OptimizableSvg.appearance_keys)
-            self.reportSkippedChildren(shape, list(shape))
-
-    def parsePolygon(self, shape, parent):
-        points_string = shape.attrib['points']
-        name = ''
-        points=[]
-        for point_string in points_string.split(" "):
-            if len(point_string) == 0:
-                continue
-            coord_strings=point_string.split(",")
-            assert len(coord_strings)==2
-            points.append([float(coord_strings[0]),float(coord_strings[1])])
-        points=torch.tensor(points,dtype=torch.float,device=self.device)
-        if 'id' in shape.attrib:
-            name = shape.attrib['id']
-        transform = OptimizableSvg.parseTransform(shape)
-        appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device)
-        node = OptimizableSvg.PolygonNode(name, transform, appearance, self.settings, points)
-        parent.children.append(node)
-
-        if self.verbose:
-            self.reportSkippedAttribs(shape, ["id", "points", "transform", "style"] + OptimizableSvg.appearance_keys)
-            self.reportSkippedChildren(shape, list(shape))
-
-    def parseCircle(self,shape,parent):
-        cx = float(shape.attrib["cx"]) if "cx" in shape.attrib else 0.
-        cy = float(shape.attrib["cy"]) if "cy" in shape.attrib else 0.
-        r = float(shape.attrib["r"])
-        name = ''
-        if 'id' in shape.attrib:
-            name = shape.attrib['id']
-        transform = OptimizableSvg.parseTransform(shape)
-        appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device)
-        node = OptimizableSvg.CircleNode(name, transform, appearance, self.settings, (cx, cy, r))
-        parent.children.append(node)
-
-        if self.verbose:
-            self.reportSkippedAttribs(shape, ["id", "x", "y", "r", "transform",
-                                              "style"] + OptimizableSvg.appearance_keys)
-            self.reportSkippedChildren(shape, list(shape))
-
-    def parseRect(self,shape,parent):
-        x =      float(shape.attrib["x"]) if "x" in shape.attrib else 0.
-        y =      float(shape.attrib["y"]) if "y" in shape.attrib else 0.
-        width =  float(shape.attrib["width"])
-        height = float(shape.attrib["height"])
-        name = ''
-        if 'id' in shape.attrib:
-            name = shape.attrib['id']
-        transform = OptimizableSvg.parseTransform(shape)
-        appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device)
-        node = OptimizableSvg.RectNode(name, transform, appearance, self.settings, (x,y,width,height))
-        parent.children.append(node)
-
-        if self.verbose:
-            self.reportSkippedAttribs(shape, ["id", "x", "y", "width", "height", "transform", "style"] + OptimizableSvg.appearance_keys)
-            self.reportSkippedChildren(shape, list(shape))
-
-    def parseGroup(self,group,parent):
-        tag = OptimizableSvg.remove_namespace(group.tag)
-        id = group.attrib["id"] if "id" in group.attrib else "<No ID>"
-        if self.verbose:
-            print(self.offset_str("Parsing {}#{}".format(tag, id)))
-
-        self.depth+=1
-
-        transform=self.parseTransform(group)
-
-        #todo process more attributes
-        appearance=OptimizableSvg.parseAppearance(group,self.defs,self.device)
-        node=OptimizableSvg.GroupNode(id,transform,appearance,self.settings)
-        parent.children.append(node)
-
-        if self.verbose:
-            self.reportSkippedAttribs(group,["id","transform","style"]+OptimizableSvg.appearance_keys)
-
-        skipped_children=[]
-        for child in group:
-            if OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes:
-                self.parseShape(child,node)
-            elif OptimizableSvg.remove_namespace(child.tag) == "defs":
-                self.parseDefs(child)
-            elif OptimizableSvg.remove_namespace(child.tag) == "style":
-                self.parseStyle(child)
-            elif OptimizableSvg.remove_namespace(child.tag) == "g":
-                self.parseGroup(child,node)
-            else:
-                skipped_children.append(child)
-
-        if self.verbose:
-            self.reportSkippedChildren(group,skipped_children)
-
-        self.depth-=1
-
-    def parseStyle(self,style_node):
-        tag = OptimizableSvg.remove_namespace(style_node.tag)
-        id = style_node.attrib["id"] if "id" in style_node.attrib else "<No ID>"
-        if self.verbose:
-            print(self.offset_str("Parsing {}#{}".format(tag, id)))
-
-        if style_node.attrib["type"] != "text/css":
-            raise ValueError("Only text/css style recognized, got {}".format(style_node.attrib["type"]))
-
-        self.depth += 1
-
-        # creating only a dummy node
-        node = OptimizableSvg.SvgNode(id, None, {}, self.settings)
-
-        if self.verbose:
-            self.reportSkippedAttribs(def_node, ["id"])
-
-        if len(style_node)>0:
-            raise ValueError("Style node should not have children (has {})".format(len(style_node)))
-
-        # collect CSS classes
-        sheet = cssutils.parseString(style_node.text)
-        for rule in sheet:
-            if hasattr(rule, 'selectorText') and hasattr(rule, 'style'):
-                name = rule.selectorText
-                if len(name) >= 2 and name[0] == '.':
-                    self.defs[name] = rule.style.getCssText().replace("\n","")
-                else:
-                    raise ValueError("Unrecognized CSS selector {}".format(name))
-            else:
-                raise ValueError("No style or selector text in CSS rule")
-
-        if self.verbose:
-            self.reportSkippedChildren(def_node, skipped_children)
-
-        self.depth -= 1
-
-    def parseDefs(self,def_node):
-        #only linear gradients are currently supported
-        tag = OptimizableSvg.remove_namespace(def_node.tag)
-        id = def_node.attrib["id"] if "id" in def_node.attrib else "<No ID>"
-        if self.verbose:
-            print(self.offset_str("Parsing {}#{}".format(tag, id)))
-
-        self.depth += 1
-
-
-        # creating only a dummy node
-        node = OptimizableSvg.SvgNode(id, None, {},self.settings)
-
-        if self.verbose:
-            self.reportSkippedAttribs(def_node, ["id"])
-
-        skipped_children = []
-        for child in def_node:
-            if OptimizableSvg.remove_namespace(child.tag) == "linearGradient":
-                self.parseGradient(child,node)
-            elif OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes:
-                raise NotImplementedError("Definition/instantiation of shapes not supported")
-            elif OptimizableSvg.remove_namespace(child.tag) == "defs":
-                raise NotImplementedError("Definition within definition not supported")
-            elif OptimizableSvg.remove_namespace(child.tag) == "g":
-                raise NotImplementedError("Groups within definition not supported")
-            else:
-                skipped_children.append(child)
-
-            if len(node.children)>0:
-                #take this node out and enter it into defs
-                self.defs[node.children[0].id]=node.children[0]
-                node.children.pop()
-
-
-        if self.verbose:
-            self.reportSkippedChildren(def_node, skipped_children)
-
-        self.depth -= 1
-
-    def parseGradientStop(self,stop):
-        param_dict={key:value for key,value in stop.attrib.items() if key in ["id","offset","stop-color","stop-opacity"]}
-        style_dict={}
-        if "style" in stop.attrib:
-            style_dict={item.split(":")[0]:item.split(":")[1] for item in stop.attrib["style"].split(";") if len(item)>0}
-        param_dict.update(style_dict)
-
-        offset=OptimizableSvg.parseOpacity(param_dict["offset"])
-        color=OptimizableSvg.parse_color(param_dict["stop-color"])
-        opacity=OptimizableSvg.parseOpacity(param_dict["stop-opacity"]) if "stop-opacity" in param_dict else 1.
-
-        return offset, color, opacity
-
-    def parseGradient(self, gradient_node, parent):
-        tag = OptimizableSvg.remove_namespace(gradient_node.tag)
-        id = gradient_node.attrib["id"] if "id" in gradient_node.attrib else "<No ID>"
-        if self.verbose:
-            print(self.offset_str("Parsing {}#{}".format(tag, id)))
-
-        self.depth += 1
-        if "stop" not in [OptimizableSvg.remove_namespace(child.tag) for child in gradient_node]\
-            and "href" not in [OptimizableSvg.remove_namespace(key) for key in gradient_node.attrib.keys()]:
-            raise ValueError("Gradient {} has neither stops nor a href link to them".format(id))
-
-        transform=self.parseTransform(gradient_node)
-        begin=None
-        end = None
-        offsets=[]
-        stops=[]
-        href=None
-
-        if "x1" in gradient_node.attrib or "y1" in gradient_node.attrib:
-            begin=np.array([0.,0.])
-            if "x1" in gradient_node.attrib:
-                begin[0] = float(gradient_node.attrib["x1"])
-            if "y1" in gradient_node.attrib:
-                begin[1] = float(gradient_node.attrib["y1"])
-            begin = torch.tensor(begin.transpose(),dtype=torch.float32)
-
-        if "x2" in gradient_node.attrib or "y2" in gradient_node.attrib:
-            end=np.array([0.,0.])
-            if "x2" in gradient_node.attrib:
-                end[0] = float(gradient_node.attrib["x2"])
-            if "y2" in gradient_node.attrib:
-                end[1] = float(gradient_node.attrib["y2"])
-            end=torch.tensor(end.transpose(),dtype=torch.float32)
-
-        stop_nodes=[node for node in list(gradient_node) if OptimizableSvg.remove_namespace(node.tag)=="stop"]
-        if len(stop_nodes)>0:
-            stop_nodes=sorted(stop_nodes,key=lambda n: float(n.attrib["offset"]))
-
-            for stop in stop_nodes:
-                offset, color, opacity = self.parseGradientStop(stop)
-                offsets.append(offset)
-                stops.append(np.concatenate((color,np.array([opacity]))))
-
-        hkey=next((value for key,value in gradient_node.attrib.items() if OptimizableSvg.remove_namespace(key)=="href"),None)
-        if hkey is not None:
-            href=self.defs[hkey.lstrip("#")]
-
-        parent.children.append(OptimizableSvg.GradientNode(id,transform,self.settings,begin.to(self.device) if begin is not None else begin,end.to(self.device) if end is not None else end,torch.tensor(offsets,dtype=torch.float32,device=self.device) if len(offsets)>0 else None,torch.tensor(np.array(stops),dtype=torch.float32,device=self.device) if len(stops)>0 else None,href))
-
-        self.depth -= 1
-
-    def parseViewport(self, root):
-        if "width" in root.attrib and "height" in root.attrib:
-            self.canvas = np.array([int(math.ceil(float(root.attrib["width"]))), int(math.ceil(float(root.attrib["height"])))])
-        elif "viewBox" in root.attrib:
-            s=root.attrib["viewBox"].split(" ")
-            w=s[2]
-            h=s[3]
-            self.canvas = np.array(
-                [int(math.ceil(float(w))), int(math.ceil(float(h)))])
-        else:
-            raise ValueError("Size information is missing from document definition")
-    #endregion
-
-    #region writing
-    def write_xml(self):
-        tree=self.root.write_xml(self)
-        
-        return minidom.parseString(etree.tostring(tree, 'utf-8')).toprettyxml(indent="  ")
-
-    def write_defs(self,root):
-        if len(self.defs)==0:
-            return
-
-        defnode = etree.SubElement(root, 'defs')
-        stylenode = etree.SubElement(root,'style')
-        stylenode.set('type','text/css')
-        stylenode.text=""
-
-        defcpy=copy.copy(self.defs)
-        while len(defcpy)>0:
-            torem=[]
-            for key,value in defcpy.items():
-                if issubclass(value.__class__,OptimizableSvg.SvgNode):
-                    if value.href is None or value.href not in defcpy:
-                        value.write_xml(defnode)
-                        torem.append(key)
-                    else:
-                        continue
-                else:
-                    #this is a string, and hence a CSS attribute
-                    stylenode.text+=key+" {"+value+"}\n"
-                    torem.append(key)
-
-            for key in torem:
-                del defcpy[key]
-    #endregion
-
-