code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import collections
  import importlib.util
  import os
  import re
  
  
  # All paths are set with the intent you should run this script from the root of the repo with the command
  # python utils/check_table.py
  lowerCamelCase_     :     Union[str, Any]    		=						"""src/diffusers"""
  lowerCamelCase_     :     List[Any]    		=						"""docs/source/en"""
  lowerCamelCase_     :     Union[str, Any]    		=						"""."""
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      with open(lowercase ,					'''r''' ,					encoding='''utf-8''' ,					newline='''\n'''	) as f:
          a					=f.readlines()
      # Find the start prompt.
      a					=0
      while not lines[start_index].startswith(lowercase	):
          start_index += 1
      start_index += 1
  
      a					=start_index
      while not lines[end_index].startswith(lowercase	):
          end_index += 1
      end_index -= 1
  
      while len(lines[start_index]	) <= 1:
          start_index += 1
      while len(lines[end_index]	) <= 1:
          end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]	), start_index, end_index, lines
  
  
  # Add here suffixes that are used to identify models, separated by |
  lowerCamelCase_     :     str    		=						"""Model|Encoder|Decoder|ForConditionalGeneration"""
  # Regexes that match TF/Flax/PT model names.
  lowerCamelCase_     :     Optional[int]    		=						re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
  lowerCamelCase_     :     Tuple    		=						re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
  # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
  lowerCamelCase_     :     List[str]    		=						re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
  
  
  # This is to make sure the diffusers module imported is the one in the repo.
  lowerCamelCase_     :     List[str]    		=						importlib.util.spec_from_file_location(
      """diffusers""",
      os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
      submodule_search_locations=[TRANSFORMERS_PATH],
  )
  lowerCamelCase_     :     Union[str, Any]    		=						spec.loader.load_module()
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' ,					lowercase	)
      return [m.group(0	) for m in matches]
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=2 if text == '''✅''' or text == '''❌''' else len(lowercase	)
      a					=(width - text_length) // 2
      a					=width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      a					={
          name: config_mapping_names[code]
          for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
          if code in config_mapping_names
      }
      a					={name: config.replace('''ConfigMixin''' ,					''''''	) for name, config in model_name_to_config.items()}
  
      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      a					=collections.defaultdict(lowercase	)
      a					=collections.defaultdict(lowercase	)
      a					=collections.defaultdict(lowercase	)
      a					=collections.defaultdict(lowercase	)
      a					=collections.defaultdict(lowercase	)
  
      # Let's lookup through all diffusers object (once).
      for attr_name in dir(lowercase	):
          a					=None
          if attr_name.endswith('''Tokenizer'''	):
              a					=slow_tokenizers
              a					=attr_name[:-9]
          elif attr_name.endswith('''TokenizerFast'''	):
              a					=fast_tokenizers
              a					=attr_name[:-13]
          elif _re_tf_models.match(lowercase	) is not None:
              a					=tf_models
              a					=_re_tf_models.match(lowercase	).groups()[0]
          elif _re_flax_models.match(lowercase	) is not None:
              a					=flax_models
              a					=_re_flax_models.match(lowercase	).groups()[0]
          elif _re_pt_models.match(lowercase	) is not None:
              a					=pt_models
              a					=_re_pt_models.match(lowercase	).groups()[0]
  
          if lookup_dict is not None:
              while len(lowercase	) > 0:
                  if attr_name in model_name_to_prefix.values():
                      a					=True
                      break
                  # Try again after removing the last word in the name
                  a					=''''''.join(camel_case_split(lowercase	)[:-1]	)
  
      # Let's build that table!
      a					=list(model_name_to_config.keys()	)
      model_names.sort(key=str.lower	)
      a					=['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      a					=[len(lowercase	) + 2 for c in columns]
      a					=max([len(lowercase	) for name in model_names]	) + 2
  
      # Build the table per se
      a					='''|''' + '''|'''.join([_center_text(lowercase ,					lowercase	) for c, w in zip(lowercase ,					lowercase	)]	) + '''|\n'''
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]	) + "|\n"
  
      a					={True: '''✅''', False: '''❌'''}
      for name in model_names:
          a					=model_name_to_prefix[name]
          a					=[
              name,
              check[slow_tokenizers[prefix]],
              check[fast_tokenizers[prefix]],
              check[pt_models[prefix]],
              check[tf_models[prefix]],
              check[flax_models[prefix]],
          ]
          table += "|" + "|".join([_center_text(lowercase ,					lowercase	) for l, w in zip(lowercase ,					lowercase	)]	) + "|\n"
      return table
  def _A		(		lowercase=False	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a	,   a	,   a					=_find_text_in_file(
          filename=os.path.join(lowercase ,					'''index.mdx'''	) ,					start_prompt='''<!--This table is updated automatically from the auto modules''' ,					end_prompt='''<!-- End table-->''' ,					)
      a					=get_model_table_from_auto_modules()
  
      if current_table != new_table:
          if overwrite:
              with open(os.path.join(lowercase ,					'''index.mdx'''	) ,					'''w''' ,					encoding='''utf-8''' ,					newline='''\n'''	) as f:
                  f.writelines(lines[:start_index] + [new_table] + lines[end_index:]	)
          else:
              raise ValueError(
                  '''The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Tuple    		=						argparse.ArgumentParser()
         parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
         lowerCamelCase_     :     Union[str, Any]    		=						parser.parse_args()
  
         check_model_table(args.fix_and_overwrite)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a	,   a					=img.shape[0], img.shape[1]
  
      def to_grayscale(lowercase ,					lowercase ,					lowercase	):
          return 0.2126 * red + 0.587 * green + 0.114 * blue
  
      def normalize(lowercase	):
          return min(lowercase ,					2_55	)
  
      for i in range(lowercase	):
          for j in range(lowercase	):
              a					=int(to_grayscale(*img[i][j]	)	)
              a					=[
                  normalize(lowercase	),
                  normalize(greyscale + factor	),
                  normalize(greyscale + 2 * factor	),
              ]
  
      return img
  
  
  if __name__ == "__main__":
         # read original image
         lowerCamelCase_     :     Any    		=						{
             percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
         }
  
         for percentage, img in images.items():
                make_sepia(img, percentage)
  
         for percentage, img in images.items():
                imshow(F'Original image with sepia (factor: {percentage})', img)
  
         waitKey(0)
         destroyAllWindows()