code: 
  
  
  
  
  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
  SCREAMING_SNAKE_CASE       = "src/diffusers"
  SCREAMING_SNAKE_CASE       = "docs/source/en"
  SCREAMING_SNAKE_CASE       = "."
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[int]:
   with open(a_    ,				"r"    ,				encoding="utf-8"    ,				newline="\n"		) as f:
    A__ =       f.readlines()
   # Find the start prompt.
   A__ =       0
   while not lines[start_index].startswith(a_		):
    start_index += 1
   start_index += 1
  
   A__ =       start_index
   while not lines[end_index].startswith(a_		):
    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 |
  SCREAMING_SNAKE_CASE       = "Model|Encoder|Decoder|ForConditionalGeneration"
  # Regexes that match TF/Flax/PT model names.
  SCREAMING_SNAKE_CASE       = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
  SCREAMING_SNAKE_CASE       = 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.
  SCREAMING_SNAKE_CASE       = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
  
  
  # This is to make sure the diffusers module imported is the one in the repo.
  SCREAMING_SNAKE_CASE       = importlib.util.spec_from_file_location(
      "diffusers",
      os.path.join(TRANSFORMERS_PATH, "__init__.py"),
      submodule_search_locations=[TRANSFORMERS_PATH],
  )
  SCREAMING_SNAKE_CASE       = spec.loader.load_module()
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Dict:
   A__ =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,				a_		)
   return [m.group(0		) for m in matches]
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Optional[int]:
   A__ =       2 if text == "✅" or text == "❌" else len(a_		)
   A__ =       (width - text_length) // 2
   A__ =       width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	List[Any]:
   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(a_		)
   A__ =       collections.defaultdict(a_		)
   A__ =       collections.defaultdict(a_		)
   A__ =       collections.defaultdict(a_		)
   A__ =       collections.defaultdict(a_		)
  
   # Let's lookup through all diffusers object (once).
   for attr_name in dir(a_		):
    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(a_		) is not None:
     A__ =       tf_models
     A__ =       _re_tf_models.match(a_		).groups()[0]
    elif _re_flax_models.match(a_		) is not None:
     A__ =       flax_models
     A__ =       _re_flax_models.match(a_		).groups()[0]
    elif _re_pt_models.match(a_		) is not None:
     A__ =       pt_models
     A__ =       _re_pt_models.match(a_		).groups()[0]
  
    if lookup_dict is not None:
     while len(a_		) > 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(a_		)[:-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(a_		) + 2 for c in columns]
   A__ =       max([len(a_		) for name in model_names]		) + 2
  
   # Build the table per se
   A__ =       "|" + "|".join([_center_text(a_    ,				a_		) for c, w in zip(a_    ,				a_		)]		) + "|\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(a_    ,				a_		) for l, w in zip(a_    ,				a_		)]		) + "|\n"
   return table
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_=False		)		->	int:
   A__,   A__,   A__,   A__ =       _find_text_in_file(
       filename=os.path.join(a_    ,				"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(a_    ,				"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__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
  
        check_model_table(args.fix_and_overwrite)
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  