





'''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:					Dict							=				"""src/transformers"""
_A:					Optional[int]							=				"""docs/source/en"""
_A:					Dict							=				"""."""







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

 __UpperCAmelCase					=   start_index
 while not lines[end_index].startswith(_lowerCAmelCase    ):
  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:					int							=				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:					int							=				direct_transformers_import(TRANSFORMERS_PATH)







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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Dict:
 __UpperCAmelCase					=   2 if text == '✅' or text == '❌' else len(_lowerCAmelCase    )
 __UpperCAmelCase					=   (width - text_length) // 2
 __UpperCAmelCase					=   width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent







def _lowerCAmelCase     (      )->     Union[str, Any]:
 __UpperCAmelCase					=   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 __UpperCAmelCase					=   {
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 __UpperCAmelCase					=   {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.
 __UpperCAmelCase					=   collections.defaultdict(_lowerCAmelCase    )
 __UpperCAmelCase					=   collections.defaultdict(_lowerCAmelCase    )
 __UpperCAmelCase					=   collections.defaultdict(_lowerCAmelCase    )
 __UpperCAmelCase					=   collections.defaultdict(_lowerCAmelCase    )
 __UpperCAmelCase					=   collections.defaultdict(_lowerCAmelCase    )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(_lowerCAmelCase    ):
  __UpperCAmelCase					=   None
  if attr_name.endswith('Tokenizer'    ):
   __UpperCAmelCase					=   slow_tokenizers
   __UpperCAmelCase					=   attr_name[:-9]
  elif attr_name.endswith('TokenizerFast'    ):
   __UpperCAmelCase					=   fast_tokenizers
   __UpperCAmelCase					=   attr_name[:-13]
  elif _re_tf_models.match(_lowerCAmelCase    ) is not None:
   __UpperCAmelCase					=   tf_models
   __UpperCAmelCase					=   _re_tf_models.match(_lowerCAmelCase    ).groups()[0]
  elif _re_flax_models.match(_lowerCAmelCase    ) is not None:
   __UpperCAmelCase					=   flax_models
   __UpperCAmelCase					=   _re_flax_models.match(_lowerCAmelCase    ).groups()[0]
  elif _re_pt_models.match(_lowerCAmelCase    ) is not None:
   __UpperCAmelCase					=   pt_models
   __UpperCAmelCase					=   _re_pt_models.match(_lowerCAmelCase    ).groups()[0]

  if lookup_dict is not None:
   while len(_lowerCAmelCase    ) > 0:
    if attr_name in model_name_to_prefix.values():
     __UpperCAmelCase					=   True
     break
    # Try again after removing the last word in the name
    __UpperCAmelCase					=   ''.join(camel_case_split(_lowerCAmelCase    )[:-1]    )

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

 # Build the table per se
 __UpperCAmelCase					=   '|' + '|'.join([_center_text(_lowerCAmelCase       ,    _lowerCAmelCase    ) for c, w in zip(_lowerCAmelCase       ,    _lowerCAmelCase    )]    ) + '|\n'
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]    ) + "|\n"

 __UpperCAmelCase					=   {True: '✅', False: '❌'}
 for name in model_names:
  __UpperCAmelCase					=   model_name_to_prefix[name]
  __UpperCAmelCase					=   [
      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(_lowerCAmelCase       ,    _lowerCAmelCase    ) for l, w in zip(_lowerCAmelCase       ,    _lowerCAmelCase    )]    ) + "|\n"
 return table







def _lowerCAmelCase     (      _lowerCAmelCase=False    )->     Tuple:
 __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   _find_text_in_file(
     filename=os.path.join(_lowerCAmelCase       ,    'index.md'    )       ,    start_prompt='<!--This table is updated automatically from the auto modules'       ,    end_prompt='<!-- End table-->'       ,    )
 __UpperCAmelCase					=   get_model_table_from_auto_modules()

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

    check_model_table(args.fix_and_overwrite)












'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str | Literal[False]:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count += 1
   __UpperCAmelCase					=   '_'
 if count > 1:
  return False
 else:
  return "".join(_lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 while True:
  __UpperCAmelCase					=   ['$'] * len(_lowerCAmelCase    )
  __UpperCAmelCase					=   []
  for i in range(len(_lowerCAmelCase    )    ):
   for j in range(i + 1       ,    len(_lowerCAmelCase    )    ):
    __UpperCAmelCase					=   compare_string(binary[i]       ,    binary[j]    )
    if k is False:
     __UpperCAmelCase					=   '*'
     __UpperCAmelCase					=   '*'
     temp.append('X'    )
  for i in range(len(_lowerCAmelCase    )    ):
   if checka[i] == "$":
    pi.append(binary[i]    )
  if len(_lowerCAmelCase    ) == 0:
   return pi
  __UpperCAmelCase					=   list(set(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 for minterm in minterms:
  __UpperCAmelCase					=   ''
  for _ in range(_lowerCAmelCase    ):
   __UpperCAmelCase					=   str(minterm % 2    ) + string
   minterm //= 2
  temp.append(_lowerCAmelCase    )
 return temp







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     bool:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [0] * len(_lowerCAmelCase    )
 for i in range(len(chart[0]    )    ):
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  for j in range(len(_lowerCAmelCase    )    ):
   if chart[j][i] == 1:
    count += 1
    __UpperCAmelCase					=   j
  if count == 1:
   __UpperCAmelCase					=   1
 for i in range(len(_lowerCAmelCase    )    ):
  if select[i] == 1:
   for j in range(len(chart[0]    )    ):
    if chart[i][j] == 1:
     for k in range(len(_lowerCAmelCase    )    ):
      __UpperCAmelCase					=   0
   temp.append(prime_implicants[i]    )
 while True:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  __UpperCAmelCase					=   0
  for i in range(len(_lowerCAmelCase    )    ):
   __UpperCAmelCase					=   chart[i].count(1    )
   if count_n > max_n:
    __UpperCAmelCase					=   count_n
    __UpperCAmelCase					=   i

  if max_n == 0:
   return temp

  temp.append(prime_implicants[rem]    )

  for i in range(len(chart[0]    )    ):
   if chart[rem][i] == 1:
    for j in range(len(_lowerCAmelCase    )    ):
     __UpperCAmelCase					=   0







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[list[int]]:
 __UpperCAmelCase					=   [[0 for x in range(len(_lowerCAmelCase    )    )] for x in range(len(_lowerCAmelCase    )    )]
 for i in range(len(_lowerCAmelCase    )    ):
  __UpperCAmelCase					=   prime_implicants[i].count('_'    )
  for j in range(len(_lowerCAmelCase    )    ):
   if is_for_table(prime_implicants[i]       ,    binary[j]       ,    _lowerCAmelCase    ):
    __UpperCAmelCase					=   1

 return chart







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   int(input('Enter the no. of variables\n'    )    )
 __UpperCAmelCase					=   [
     float(_lowerCAmelCase    )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'    ).split()
 ]
 __UpperCAmelCase					=   decimal_to_binary(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   check(_lowerCAmelCase    )
 print('Prime Implicants are:'    )
 print(_lowerCAmelCase    )
 __UpperCAmelCase					=   prime_implicant_chart(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   selection(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print('Essential Prime Implicants are:'    )
 print(_lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





