





from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy



class  _a  (					__UpperCamelCase				):






							"""simple docstring"""

							snake_case_							=    'MarkupLMFeatureExtractor'
							snake_case_							=    ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
							snake_case_							=    True

							def __call__(   self					:  List[str]				,						a					:  str=None				,						a					:  Union[str, Any]=None				,						a					:  str=None				,						a					:  List[str]=None				,						a					:  Tuple=None				,						a					:  Union[str, Any] = True				,						a					:  int = False				,						a					:  Optional[Any] = None				,						a					:  Optional[int] = None				,						a					:  Tuple = 0				,						a					:  Union[str, Any] = None				,						a					:  List[Any] = None				,						a					:  Optional[Any] = None				,						a					:  int = False				,						a					:  Dict = False				,						a					:  Dict = False				,						a					:  Dict = False				,						a					:  Optional[int] = True				,						a					:  Any = None				,						**a					:  Union[str, Any]				,						)						->Union[str, Any]:
													# first, create nodes and xpaths
													if self.parse_html:
																			if html_strings is None:
																									raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"     )

																			if nodes is not None or xpaths is not None or node_labels is not None:
																									raise ValueError(
																									    "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"     )

																			SCREAMING_SNAKE_CASE__  :					int =       self.feature_extractor(__lowercase     )
																			SCREAMING_SNAKE_CASE__  :					Tuple =       features["nodes"]
																			SCREAMING_SNAKE_CASE__  :					Tuple =       features["xpaths"]
													else:
																			if html_strings is not None:
																									raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."     )
																			if nodes is None or xpaths is None:
																									raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"     )

																									 # # second, apply the tokenizer
													if questions is not None and self.parse_html:
																			if isinstance(__lowercase				,						__lowercase     ):
																									SCREAMING_SNAKE_CASE__  :					Any =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

													SCREAMING_SNAKE_CASE__  :					int =       self.tokenizer(
													    text=questions if questions is not None else nodes				,						text_pair=nodes if questions is not None else None				,						xpaths=__lowercase				,						node_labels=__lowercase				,						add_special_tokens=__lowercase				,						padding=__lowercase				,						truncation=__lowercase				,						max_length=__lowercase				,						stride=__lowercase				,						pad_to_multiple_of=__lowercase				,						return_token_type_ids=__lowercase				,						return_attention_mask=__lowercase				,						return_overflowing_tokens=__lowercase				,						return_special_tokens_mask=__lowercase				,						return_offsets_mapping=__lowercase				,						return_length=__lowercase				,						verbose=__lowercase				,						return_tensors=__lowercase				,						**__lowercase				,						)

													return encoded_inputs

							def   A_	(   self					:  Any				,						*a					:  Optional[Any]				,						**a					:  Tuple     )						->Optional[int]:
													return self.tokenizer.batch_decode(*__lowercase				,						**__lowercase     )

							def   A_	(   self					:  Any				,						*a					:  Any				,						**a					:  Dict     )						->str:
													return self.tokenizer.decode(*__lowercase				,						**__lowercase     )






							@property
							def   A_	(   self					:  Dict     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.tokenizer.model_input_names
													return tokenizer_input_names







"""simple docstring"""



import itertools
from pathlib import Path




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				[tuple(_SCREAMING_SNAKE_CASE			) for login in logins]

 UpperCAmelCase__ 						=				{char for login in split_logins for char in login}

 for permutation in itertools.permutations(_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__ 						=				True
  for login in logins:
   if not (
       permutation.index(login[0]			)
       < permutation.index(login[1]			)
       < permutation.index(login[2]			)
   ):
    UpperCAmelCase__ 						=				False
    break

  if satisfied:
   return int("""""".join(_SCREAMING_SNAKE_CASE			)			)

 raise Exception("""Unable to find the secret passcode"""			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE = "keylog.txt"			)    ->int:
 UpperCAmelCase__ 						=				Path(_SCREAMING_SNAKE_CASE			).parent.joinpath(_SCREAMING_SNAKE_CASE			).read_text().splitlines()

 return find_secret_passcode(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 print(F'''{solution() = }''')



