


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'''
							)



'''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() = }')