






'''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
SCREAMING_SNAKE_CASE							=						'src/transformers'
SCREAMING_SNAKE_CASE							=						'docs/source/en'
SCREAMING_SNAKE_CASE							=						'.'


def 				lowercase_					(     __A			:							List[Any]       ,   __A			:							Dict       ,   __A			:							int      )							->   List[str]:



 """simple docstring"""




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

 lowercase       : 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 |
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 transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE							=						direct_transformers_import(TRANSFORMERS_PATH)


def 				lowercase_					(     __A			:							Union[str, Any]      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase       : Optional[int]						=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,   __A      )
 return [m.group(0      ) for m in matches]


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							List[Any]      )							->   int:



 """simple docstring"""




 lowercase       : int						=2 if text == '''✅''' or text == '''❌''' else len(__A      )
 lowercase       : int						=(width - text_length) // 2
 lowercase       : Dict						=width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent


def 				lowercase_					(     )							->   int:



 """simple docstring"""




 lowercase       : List[str]						=transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase       : Union[str, Any]						={
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowercase       : 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.
 lowercase       : Optional[int]						=collections.defaultdict(__A      )
 lowercase       : List[Any]						=collections.defaultdict(__A      )
 lowercase       : List[Any]						=collections.defaultdict(__A      )
 lowercase       : str						=collections.defaultdict(__A      )
 lowercase       : int						=collections.defaultdict(__A      )

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

    # Let's build that table!
 lowercase       : List[Any]						=list(model_name_to_config.keys()      )
 model_names.sort(key=str.lower      )
 lowercase       : 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).
 lowercase       : Optional[Any]						=[len(__A      ) + 2 for c in columns]
 lowercase       : int						=max([len(__A      ) for name in model_names]      ) + 2

 # Build the table per se
 lowercase       : 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"

 lowercase       : Dict						={True: '''✅''', False: '''❌'''}
 for name in model_names:
  lowercase       : Dict						=model_name_to_prefix[name]
  lowercase       : Dict						=[
      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			:							Any=False      )							->   Optional[Any]:



 """simple docstring"""




 lowercase	,		lowercase	,		lowercase	,		lowercase       : Optional[Any]						=_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-->'''       ,   )
 lowercase       : List[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__":
      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)










def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()