


"""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
__lowercase          = """src/diffusers"""
__lowercase          = """docs/source/en"""
__lowercase          = """."""


def 	lowercase     (							A_    ,  A_    ,  A_			)-> int:
  '''simple docstring'''







  with open(A_    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"			) as f:
    a		:						Optional[int] 				=     f.readlines()
  # Find the start prompt.
  a		:						Tuple 				=     0
  while not lines[start_index].startswith(A_			):
    start_index += 1
  start_index += 1

  a		:						List[str] 				=     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 |
__lowercase          = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowercase          = re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowercase          = 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.
__lowercase          = re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
__lowercase          = importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__lowercase          = spec.loader.load_module()


def 	lowercase     (							A_			)-> Optional[int]:
  '''simple docstring'''







  a		:						Tuple 				=     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,  A_			)
  return [m.group(0			) for m in matches]


def 	lowercase     (							A_    ,  A_			)-> Any:
  '''simple docstring'''







  a		:						Tuple 				=     2 if text == "✅" or text == "❌" else len(A_			)
  a		:						Optional[int] 				=     (width - text_length) // 2
  a		:						Dict 				=     width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent


def 	lowercase     (							)-> Any:
  '''simple docstring'''







  a		:						Optional[int] 				=     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:						Dict 				=     {
      name: config_mapping_names[code]
      for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
      if code in config_mapping_names
  }
  a		:						Optional[int] 				=     {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		:						int 				=     collections.defaultdict(A_			)
  a		:						Union[str, Any] 				=     collections.defaultdict(A_			)
  a		:						Union[str, Any] 				=     collections.defaultdict(A_			)
  a		:						List[Any] 				=     collections.defaultdict(A_			)
  a		:						str 				=     collections.defaultdict(A_			)

  # Let's lookup through all diffusers object (once).
  for attr_name in dir(A_			):
    a		:						Tuple 				=     None
    if attr_name.endswith("Tokenizer"			):
      a		:						Union[str, Any] 				=     slow_tokenizers
      a		:						Dict 				=     attr_name[:-9]
    elif attr_name.endswith("TokenizerFast"			):
      a		:						List[Any] 				=     fast_tokenizers
      a		:						Optional[Any] 				=     attr_name[:-13]
    elif _re_tf_models.match(A_			) is not None:
      a		:						str 				=     tf_models
      a		:						List[str] 				=     _re_tf_models.match(A_			).groups()[0]
    elif _re_flax_models.match(A_			) is not None:
      a		:						List[Any] 				=     flax_models
      a		:						Any 				=     _re_flax_models.match(A_			).groups()[0]
    elif _re_pt_models.match(A_			) is not None:
      a		:						List[Any] 				=     pt_models
      a		:						str 				=     _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		:						Union[str, Any] 				=     True
          break
        # Try again after removing the last word in the name
        a		:						List[Any] 				=     "".join(camel_case_split(A_			)[:-1]			)

    # Let's build that table!
  a		:						Any 				=     list(model_name_to_config.keys()			)
  model_names.sort(key=str.lower			)
  a		:						Tuple 				=     ["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		:						Dict 				=     [len(A_			) + 2 for c in columns]
  a		:						Dict 				=     max([len(A_			) for name in model_names]			) + 2

  # Build the table per se
  a		:						Union[str, Any] 				=     "|" + "|".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		:						Dict 				=     {True: "✅", False: "❌"}
  for name in model_names:
    a		:						List[str] 				=     model_name_to_prefix[name]
    a		:						Optional[int] 				=     [
        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 	lowercase     (							A_=False			)-> Union[str, Any]:
  '''simple docstring'''







  a				,						a				,						a				,						a		:						Optional[int] 				=     _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		:						List[Any] 				=     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__":
 __lowercase          = argparse.ArgumentParser()
 parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
 __lowercase          = parser.parse_args()

 check_model_table(args.fix_and_overwrite)










"""simple docstring"""







def 	lowercase     (							A_ = 100    ,  A_ = 100			)-> int:
  '''simple docstring'''







  return max(
      sum(int(A_			) for x in str(base**power			)			)
      for base in range(A_			)
      for power in range(A_			)			)


# Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()






