





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
lowercase      :       Optional[int]									=  """src/transformers"""
lowercase      :       Union[str, Any]									=  """docs/source/en"""
lowercase      :       int									=  """."""



def A_	(		A__		,      A__		,      A__				)  ->		Any:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	str =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	Optional[Any] =				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      :       Dict									=  """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowercase      :       int									=  re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase      :       Optional[Any]									=  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      :       Optional[int]									=  re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)



def A_	(		A__				)  ->		str:
       a__						:	List[Any] =				re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'		,      A__				)
       return [m.group(0				) for m in matches]



def A_	(		A__		,      A__				)  ->		List[Any]:
       a__						:	str =				2 if text == '✅' or text == '❌' else len(A__				)
       a__						:	Tuple =				(width - text_length) // 2
       a__						:	List[Any] =				width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent



def A_	(		)  ->		List[str]:
       a__						:	int =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       a__						:	Tuple =				{
           name: config_maping_names[code]
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if code in config_maping_names
       }
       a__						:	Dict =				{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.
       a__						:	List[Any] =				collections.defaultdict(A__				)
       a__						:	int =				collections.defaultdict(A__				)
       a__						:	Any =				collections.defaultdict(A__				)
       a__						:	int =				collections.defaultdict(A__				)
       a__						:	List[str] =				collections.defaultdict(A__				)

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

    # Let's build that table!
       a__						:	Dict =				list(model_name_to_config.keys()				)
       model_names.sort(key=str.lower				)
       a__						:	Dict =				['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__						:	Union[str, Any] =				[len(A__				) + 2 for c in columns]
       a__						:	List[str] =				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__						:	List[Any] =				{True: '✅', False: '❌'}
       for name in model_names:
              a__						:	Union[str, Any] =				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 A_	(		A__=False				)  ->		str:
       a__ ,							a__ ,							a__ ,							a__						:	Tuple =				_find_text_in_file(
           filename=os.path.join(A__		,      'index.md'				)		,      start_prompt='<!--This table is updated automatically from the auto modules'		,      end_prompt='<!-- End table-->'		,      )
       a__						:	Any =				get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(A__		,      '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__":
    lowercase      :       Tuple									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       Union[str, Any]									=  parser.parse_args()

    check_model_table(args.fix_and_overwrite)









from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


