code: 
  
  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    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Union[str, Any]						=							["image_processor", "tokenizer"]
      lowerCAmelCase_							:		Tuple						=							"LayoutLMv2ImageProcessor"
      lowerCAmelCase_							:		str						=							("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
  
  
  
  
  
  
      def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    Optional[int]=None    ,				UpperCAmelCase_			:    Optional[int]=None    ,				**UpperCAmelCase_			:    int    ):
          lowerCAmelCase				:			Union[str, Any]    			=      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_    ,				)
              lowerCAmelCase				:			int    			=      kwargs.pop('feature_extractor'    )
  
          lowerCAmelCase				:			Any    			=      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			:    Tuple    ,				UpperCAmelCase_			:    int    ,				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_			:    Union[str, Any]    ,				):
          # verify input
          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
          lowerCAmelCase				:			Optional[int]    			=      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_    ):
                  lowerCAmelCase				:			str    			=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
              lowerCAmelCase				:			Union[str, Any]    			=      features['words']
  
          lowerCAmelCase				:			Tuple    			=      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
          lowerCAmelCase				:			Union[str, Any]    			=      features.pop('pixel_values'    )
          if return_overflowing_tokens is True:
              lowerCAmelCase				:			List[Any]    			=      self.get_overflowing_images(UpperCAmelCase_    ,				encoded_inputs['overflow_to_sample_mapping']    )
          lowerCAmelCase				:			Tuple    			=      images
  
          return encoded_inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          lowerCAmelCase				:			Union[str, 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 	lowercase__ (	self			:    Optional[int]    ,				*UpperCAmelCase_			:    Optional[int]    ,				**UpperCAmelCase_			:    List[str]    ):
          return self.tokenizer.batch_decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ,				*UpperCAmelCase_			:    List[Any]    ,				**UpperCAmelCase_			:    Optional[Any]    ):
          return self.tokenizer.decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Tuple    ):
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Any    ):
          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 	lowercase__ (	self			:    str    ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				UpperCAmelCase_    ,				)
          return self.image_processor
  
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      {1}
      lowerCAmelCase				:			int    			=      [1]
      for _ in range(2,      depth + 1				):
          lowerCAmelCase				:			str    			=      [*previous_coefficients, 0]
          lowerCAmelCase				:			Optional[int]    			=      [0, *previous_coefficients]
          lowerCAmelCase				:			Tuple    			=      []
          for x, y in zip(_UpperCAmelCase,      _UpperCAmelCase				):
              coefficients.add(x + y				)
              previous_coefficients.append(x + y				)
      return coefficients
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			str    			=      set()
      for number in unique_coefficients:
          lowerCAmelCase				:			Dict    			=      2
          lowerCAmelCase				:			Tuple    			=      number
          while divisor**2 <= copy_number:
              lowerCAmelCase				:			List[str]    			=      0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(_UpperCAmelCase				)
                  break
              divisor += 1
  
      return unique_coefficients.difference(_UpperCAmelCase				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = 51				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      get_pascal_triangle_unique_coefficients(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      get_squarefrees(_UpperCAmelCase				)
      return sum(_UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  