




def 				_A  (		lowerCAmelCase_	:   str     ):
     """simple docstring"""

     lowerCAmelCase__            =					""
     for ch in key:
          if ch == " " or ch not in key_no_dups and ch.isalpha():
               key_no_dups += ch
     return key_no_dups



def 				_A  (		lowerCAmelCase_	:   str     ):
     """simple docstring"""

     lowerCAmelCase__            =					[chr(i + 65     ) for i in range(26     )]
     # Remove duplicate characters from key
     lowerCAmelCase__            =					remove_duplicates(key.upper()     )
     lowerCAmelCase__            =					len(lowerCAmelCase_     )
     # First fill cipher with key characters
     lowerCAmelCase__            =					{alphabet[i]: char for i, char in enumerate(lowerCAmelCase_     )}
     # Then map remaining characters in alphabet to
     # the alphabet from the beginning
     for i in range(len(lowerCAmelCase_     )  ,							26     ):
          lowerCAmelCase__            =					alphabet[i - offset]
          # Ensure we are not mapping letters to letters previously mapped
          while char in key:
               offset -= 1
               lowerCAmelCase__            =					alphabet[i - offset]
          lowerCAmelCase__            =					char
     return cipher_alphabet



def 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   dict[str, str]     ):
     """simple docstring"""

     return "".join(cipher_map.get(lowerCAmelCase_  ,							lowerCAmelCase_     ) for ch in message.upper()     )



def 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   dict[str, str]     ):
     """simple docstring"""

     lowerCAmelCase__            =					{v: k for k, v in cipher_map.items()}
     return "".join(rev_cipher_map.get(lowerCAmelCase_  ,							lowerCAmelCase_     ) for ch in message.upper()     )



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					input("Enter message to encode or decode: "     ).strip()
     lowerCAmelCase__            =					input("Enter keyword: "     ).strip()
     lowerCAmelCase__            =					input("Encipher or decipher? E/D:"     ).strip()[0].lower()
     try:
          lowerCAmelCase__            =					{"e": encipher, "d": decipher}[option]
     except KeyError:
          raise KeyError("invalid input option"     )
     lowerCAmelCase__            =					create_cipher_map(lowerCAmelCase_     )
     print(func(lowerCAmelCase_  ,							lowerCAmelCase_     )     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

'''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
__snake_case						  =		"""src/transformers"""
__snake_case						  =		"""docs/source/en"""
__snake_case						  =		"""."""

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

  lowercase_     =       start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_						):
    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 |
__snake_case						  =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__snake_case						  =		re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__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.
__snake_case						  =		re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       (width - text_length) // 2
  lowercase_     =       width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   A_      (       )			->Tuple:
  lowercase_     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  lowercase_     =       {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  lowercase_     =       {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_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)

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

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

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

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

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

def   A_      (       SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       _find_text_in_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	start_prompt="""<!--This table is updated automatically from the auto modules""" ,	end_prompt="""<!-- End table-->""" ,	)
  lowercase_     =       get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"""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__":
       __snake_case						  =		argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       __snake_case						  =		parser.parse_args()

       check_model_table(args.fix_and_overwrite)

