


'''simple docstring'''




import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
_a   :   List[str]												=   "src/transformers"
_a   :   Dict												=   "docs/source/en"
_a   :   Optional[Any]												=   "."





def   _a		(lowercase__			:   Optional[Any]							,   lowercase__			:   Tuple							,   lowercase__			:   Dict						)			->			List[Any]:






     """simple docstring"""
     with open(lowercase__							,   'r'							,   encoding='utf-8'							,   newline='\n'						) as f:
          __snake_case           =						f.readlines()
     # Find the start prompt.
     __snake_case           =						0
     while not lines[start_index].startswith(lowercase__						):
          start_index += 1
     start_index += 1

     __snake_case           =						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 |
_a   :   Optional[int]												=   "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_a   :   str												=   re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_a   :   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.
_a   :   Optional[Any]												=   re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
_a   :   Optional[int]												=   direct_transformers_import(TRANSFORMERS_PATH)





def   _a		(lowercase__			:   Any						)			->			Any:






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





def   _a		(lowercase__			:   Optional[Any]							,   lowercase__			:   Optional[int]						)			->			Any:






     """simple docstring"""
     __snake_case           =						2 if text == '✅' or text == '❌' else len(lowercase__						)
     __snake_case           =						(width - text_length) // 2
     __snake_case           =						width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent





def   _a		()			->			List[str]:






     """simple docstring"""
     __snake_case           =						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     __snake_case           =						{
         name: config_maping_names[code]
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     __snake_case           =						{name: config.replace('Config'							,   ''						) 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.
     __snake_case           =						collections.defaultdict(lowercase__						)
     __snake_case           =						collections.defaultdict(lowercase__						)
     __snake_case           =						collections.defaultdict(lowercase__						)
     __snake_case           =						collections.defaultdict(lowercase__						)
     __snake_case           =						collections.defaultdict(lowercase__						)

     # Let's lookup through all transformers object (once).
     for attr_name in dir(lowercase__						):
          __snake_case           =						None
          if attr_name.endswith('Tokenizer'						):
               __snake_case           =						slow_tokenizers
               __snake_case           =						attr_name[:-9]
          elif attr_name.endswith('TokenizerFast'						):
               __snake_case           =						fast_tokenizers
               __snake_case           =						attr_name[:-1_3]
          elif _re_tf_models.match(lowercase__						) is not None:
               __snake_case           =						tf_models
               __snake_case           =						_re_tf_models.match(lowercase__						).groups()[0]
          elif _re_flax_models.match(lowercase__						) is not None:
               __snake_case           =						flax_models
               __snake_case           =						_re_flax_models.match(lowercase__						).groups()[0]
          elif _re_pt_models.match(lowercase__						) is not None:
               __snake_case           =						pt_models
               __snake_case           =						_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():
                         __snake_case           =						True
                         break
                    # Try again after removing the last word in the name
                    __snake_case           =						''.join(camel_case_split(lowercase__						)[:-1]						)

    # Let's build that table!
     __snake_case           =						list(model_name_to_config.keys()						)
     model_names.sort(key=str.lower						)
     __snake_case           =						['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).
     __snake_case           =						[len(lowercase__						) + 2 for c in columns]
     __snake_case           =						max([len(lowercase__						) for name in model_names]						) + 2

     # Build the table per se
     __snake_case           =						'|' + '|'.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"

     __snake_case           =						{True: '✅', False: '❌'}
     for name in model_names:
          __snake_case           =						model_name_to_prefix[name]
          __snake_case           =						[
              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__			:   str=False						)			->			Optional[int]:






     """simple docstring"""
     __snake_case      ,       __snake_case      ,       __snake_case      ,       __snake_case           =						_find_text_in_file(
         filename=os.path.join(lowercase__							,   'index.md'						)							,   start_prompt='<!--This table is updated automatically from the auto modules'							,   end_prompt='<!-- End table-->'							,   )
     __snake_case           =						get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowercase__							,   'index.md'						)							,   '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.md` has not been updated. Run `make fix-copies` to fix this.'						)


if __name__ == "__main__":
       _a   :   Any												=   argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       _a   :   Optional[int]												=   parser.parse_args()

       check_model_table(args.fix_and_overwrite)








a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


