code: 
  
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  lowerCAmelCase   :Union[str, Any]								=    logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  lowerCAmelCase   :Optional[Any]								=    direct_transformers_import(Path(__file__).parent)
  
  
  lowerCAmelCase   :Union[str, Any]								=    {
      '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
      '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
      '''AutoImageProcessor''': '''ImageProcessingMixin''',
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    str   					=       ["""feature_extractor""", """tokenizer"""]
  		# Names need to be attr_class for attr in attributes
  		A_			:    int   					=       None
  		A_			:    List[str]   					=       None
  		A_			:    List[Any]   					=       None
  
  
  
  
  
  
  
  		def __init__(      self	:		Dict						,  *_A	:		int						,  **_A	:		Union[str, Any]    )						->				int:
  			# Sanitize args and kwargs
  			for key in kwargs:
  				if key not in self.attributes:
  					raise TypeError(F'Unexpected keyword argument {key}.'    )
  			for arg, attribute_name in zip(_A						,  self.attributes    ):
  				if attribute_name in kwargs:
  					raise TypeError(F'Got multiple values for argument {attribute_name}.'    )
  				else:
  					__magic_name__ :					Any  			=  arg
  
  			if len(_A    ) != len(self.attributes    ):
  				raise ValueError(
  				    F'This processor requires {len(self.attributes    )} arguments: {", ".join(self.attributes    )}. Got '
  				    F'{len(_A    )} arguments instead.'    )
  
  			# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  			for attribute_name, arg in kwargs.items():
  				__magic_name__ :					List[Any]  			=  getattr(self						,  F'{attribute_name}_class'    )
  				# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  				__magic_name__ :					int  			=  AUTO_TO_BASE_CLASS_MAPPING.get(_A						,  _A    )
  				if isinstance(_A						,  _A    ):
  					__magic_name__ :					List[Any]  			=  tuple(getattr(_A						,  _A    ) for n in class_name if n is not None    )
  				else:
  					__magic_name__ :					Optional[int]  			=  getattr(_A						,  _A    )
  
  				if not isinstance(_A						,  _A    ):
  					raise ValueError(
  					    F'Received a {type(_A    ).__name__} for argument {attribute_name}, but a {class_name} was expected.'    )
  
  				setattr(self						,  _A						,  _A    )
  
  
  
  
  
  
  
  		def __repr__(      self	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					int  			=  [F'- {name}: {repr(getattr(self						,  _A    )    )}' for name in self.attributes]
  			__magic_name__ :					Union[str, Any]  			=  '\n'.join(_A    )
  			return F'{self.__class__.__name__}:\n{attributes_repr}'
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Any						,  _A	:		bool = False						,  **_A	:		List[str]    )						->				int:
  			os.makedirs(_A						,  exist_ok=_A    )
  
  			if push_to_hub:
  				__magic_name__ :					Dict  			=  kwargs.pop('commit_message'						,  _A    )
  				__magic_name__ :					Optional[Any]  			=  kwargs.pop('repo_id'						,  save_directory.split(os.path.sep    )[-1]    )
  				__magic_name__ :					List[str]  			=  self._create_repo(_A						,  **_A    )
  				__magic_name__ :					Optional[Any]  			=  self._get_files_timestamps(_A    )
  			# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  			# loaded from the Hub.
  			if self._auto_class is not None:
  				__magic_name__ :					Optional[Any]  			=  [getattr(self						,  _A    ) for attribute_name in self.attributes]
  				__magic_name__ :					Dict  			=  [(a.init_kwargs if isinstance(_A						,  _A    ) else a) for a in attrs]
  				custom_object_save(self						,  _A						,  config=_A    )
  
  			for attribute_name in self.attributes:
  				__magic_name__ :					Union[str, Any]  			=  getattr(self						,  _A    )
  				# Include the processor class in the attribute config so this processor can then be reloaded with the
  				# `AutoProcessor` API.
  				if hasattr(_A						,  '_set_processor_class'    ):
  					attribute._set_processor_class(self.__class__.__name__    )
  				attribute.save_pretrained(_A    )
  
  			if self._auto_class is not None:
  				# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
  				for attribute_name in self.attributes:
  					__magic_name__ :					Optional[int]  			=  getattr(self						,  _A    )
  					if isinstance(_A						,  _A    ):
  						del attribute.init_kwargs["auto_map"]
  
  			if push_to_hub:
  				self._upload_modified_files(
  				    _A						,  _A						,  _A						,  commit_message=_A						,  token=kwargs.get('use_auth_token'    )						,  )
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		str						,  _A	:		Union[str, os.PathLike]						,  _A	:		Optional[Union[str, os.PathLike]] = None						,  _A	:		bool = False						,  _A	:		bool = False						,  _A	:		Optional[Union[str, bool]] = None						,  _A	:		str = "main"						,  **_A	:		Any						,  )						->				Optional[int]:
  			__magic_name__ :					str  			=  cache_dir
  			__magic_name__ :					Any  			=  force_download
  			__magic_name__ :					List[str]  			=  local_files_only
  			__magic_name__ :					Optional[int]  			=  revision
  
  			__magic_name__ :					Optional[Any]  			=  kwargs.pop('use_auth_token'						,  _A    )
  			if use_auth_token is not None:
  				warnings.warn(
  				    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'						,  _A    )
  				if token is not None:
  					raise ValueError(
  					    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'    )
  				__magic_name__ :					str  			=  use_auth_token
  
  			if token is not None:
  				# change to `token` in a follow-up PR
  				__magic_name__ :					Any  			=  token
  
  			__magic_name__ :					Dict  			=  cls._get_arguments_from_pretrained(_A						,  **_A    )
  			return cls(*_A    )
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		str						,  _A	:		Optional[Any]="AutoProcessor"    )						->				Tuple:
  			if not isinstance(_A						,  _A    ):
  				__magic_name__ :					Dict  			=  auto_class.__name__
  
  			import transformers.models.auto as auto_module
  
  			if not hasattr(_A						,  _A    ):
  				raise ValueError(F'{auto_class} is not a valid auto class.'    )
  
  			__magic_name__ :					Tuple  			=  auto_class
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		Dict						,  _A	:		int						,  **_A	:		List[Any]    )						->				str:
  			__magic_name__ :					List[str]  			=  []
  			for attribute_name in cls.attributes:
  				__magic_name__ :					List[str]  			=  getattr(cls						,  F'{attribute_name}_class'    )
  				if isinstance(_A						,  _A    ):
  					__magic_name__ :					int  			=  tuple(getattr(_A						,  _A    ) if n is not None else None for n in class_name    )
  					__magic_name__ :					Tuple  			=  kwargs.get('use_fast'						,  _A    )
  					if use_fast and classes[1] is not None:
  						__magic_name__ :					Tuple  			=  classes[1]
  					else:
  						__magic_name__ :					Optional[Any]  			=  classes[0]
  				else:
  					__magic_name__ :					List[str]  			=  getattr(_A						,  _A    )
  
  				args.append(attribute_class.from_pretrained(_A						,  **_A    )    )
  			return args
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				Dict:
  			__magic_name__ :					Optional[Any]  			=  getattr(self						,  self.attributes[0]    )
  			return getattr(_A						,  'model_input_names'						,  _A    )
  
  
  
  
  
  
  
  lowerCAmelCase   :str								=    copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  							lowerCAmelCase   :str								=    ProcessorMixin.push_to_hub.__doc__.format(
  							    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
  							)
style_context: 
  
  
  '''simple docstring'''
  
  import itertools
  from pathlib import Path
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  [tuple(lowerCAmelCase	) for login in logins]
  
  	__magic_name__ :					int  			=  {char for login in split_logins for char in login}
  
  	for permutation in itertools.permutations(lowerCAmelCase	):
  		__magic_name__ :					List[Any]  			=  True
  		for login in logins:
  			if not (
  			    permutation.index(login[0]	)
  			    < permutation.index(login[1]	)
  			    < permutation.index(login[2]	)
  			):
  				__magic_name__ :					Tuple  			=  False
  				break
  
  		if satisfied:
  			return int(''.join(lowerCAmelCase	)	)
  
  	raise Exception('Unable to find the secret passcode'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str = "keylog.txt"	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[Any]  			=  Path(lowerCAmelCase	).parent.joinpath(lowerCAmelCase	).read_text().splitlines()
  
  	return find_secret_passcode(lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							print(F'{solution() = }')