code: 
  '''simple docstring'''
  import warnings
  from typing import List, Optional, Union
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	["""image_processor""", """tokenizer"""]
  		lowerCamelCase           =	"""LayoutLMv2ImageProcessor"""
  		lowerCamelCase           =	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  
  
  
  		def __init__(			self							: Optional[int]     ,  UpperCamelCase__							: List[str]=None     ,  UpperCamelCase__							: Optional[int]=None     ,  **UpperCamelCase__							: Optional[Any]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	None
  				if "feature_extractor" in kwargs:
  						warnings.warn(
  						    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
  						    ''' instead.'''     ,  UpperCamelCase__     ,  )
  						snake_case     :					Dict     =	kwargs.pop('''feature_extractor'''			)
  
  				snake_case     :					str     =	image_processor if image_processor is not None else feature_extractor
  				if image_processor is None:
  						raise ValueError('''You need to specify an `image_processor`.'''			)
  				if tokenizer is None:
  						raise ValueError('''You need to specify a `tokenizer`.'''			)
  
  				super().__init__(UpperCamelCase__     ,  UpperCamelCase__			)
  
  
  
  		def __call__(			self							: Any     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,  UpperCamelCase__							: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,  UpperCamelCase__							: Union[List[List[int]], List[List[List[int]]]] = None     ,  UpperCamelCase__							: Optional[Union[List[int], List[List[int]]]] = None     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Union[bool, str, PaddingStrategy] = False     ,  UpperCamelCase__							: Union[bool, str, TruncationStrategy] = False     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: int = 0     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: Optional[bool] = None     ,  UpperCamelCase__							: Optional[bool] = None     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = False     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase__							: Optional[int]     ,  )	-> BatchEncoding:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self.image_processor.apply_ocr and (boxes is not None):
  						raise ValueError(
  						    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''			)
  
  				if self.image_processor.apply_ocr and (word_labels is not None):
  						raise ValueError(
  						    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''			)
  
  				if return_overflowing_tokens is True and return_offsets_mapping is False:
  						raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)
  
  				# first, apply the image processor
  				snake_case     :					List[Any]     =	self.image_processor(images=UpperCamelCase__     ,  return_tensors=UpperCamelCase__			)
  
  				# second, apply the tokenizer
  				if text is not None and self.image_processor.apply_ocr and text_pair is None:
  						if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  								snake_case     :					Optional[Any]     =	[text]  # add batch dimension (as the image processor always adds a batch dimension)
  						snake_case     :					Any     =	features['''words''']
  
  				snake_case     :					List[Any]     =	self.tokenizer(
  				    text=text if text is not None else features['''words''']     ,  text_pair=text_pair if text_pair is not None else None     ,  boxes=boxes if boxes is not None else features['''boxes''']     ,  word_labels=UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     ,  padding=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  stride=UpperCamelCase__     ,  pad_to_multiple_of=UpperCamelCase__     ,  return_token_type_ids=UpperCamelCase__     ,  return_attention_mask=UpperCamelCase__     ,  return_overflowing_tokens=UpperCamelCase__     ,  return_special_tokens_mask=UpperCamelCase__     ,  return_offsets_mapping=UpperCamelCase__     ,  return_length=UpperCamelCase__     ,  verbose=UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     ,  )
  
  				# add pixel values
  				snake_case     :					Optional[Any]     =	features.pop('''pixel_values'''			)
  				if return_overflowing_tokens is True:
  						snake_case     :					int     =	self.get_overflowing_images(UpperCamelCase__     ,  encoded_inputs['''overflow_to_sample_mapping''']			)
  				snake_case     :					List[str]     =	images
  
  				return encoded_inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	[]
  				for sample_idx in overflow_to_sample_mapping:
  						images_with_overflow.append(images[sample_idx]			)
  
  				if len(UpperCamelCase__			) != len(UpperCamelCase__			):
  						raise ValueError(
  						    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
  						    f' {len(UpperCamelCase__			)} and {len(UpperCamelCase__			)}'			)
  
  				return images_with_overflow
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  *UpperCamelCase__							: str     ,  **UpperCamelCase__							: Any			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  *UpperCamelCase__							: Optional[Any]     ,  **UpperCamelCase__							: str			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				warnings.warn(
  				    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,  UpperCamelCase__     ,  )
  				return self.image_processor_class
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: int			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				warnings.warn(
  				    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,  UpperCamelCase__     ,  )
  				return self.image_processor
  
  
  
style_context: 
  '''simple docstring'''
  import itertools
  from pathlib import Path
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	[tuple(SCREAMING_SNAKE_CASE__ ) for login in logins]
  
  		snake_case     :					Dict     =	{char for login in split_logins for char in login}
  
  		for permutation in itertools.permutations(SCREAMING_SNAKE_CASE__ ):
  				snake_case     :					Optional[int]     =	True
  				for login in logins:
  						if not (
  						    permutation.index(login[0] )
  						    < permutation.index(login[1] )
  						    < permutation.index(login[2] )
  						):
  								snake_case     :					int     =	False
  								break
  
  				if satisfied:
  						return int(''''''.join(SCREAMING_SNAKE_CASE__ ) )
  
  		raise Exception('''Unable to find the secret passcode''' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ = "keylog.txt" )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	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() = }")
  
  
  