code: 
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      a_				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Tuple     =['image_processor', 'tokenizer']
        UpperCamelCase_       :      str     ='AutoImageProcessor'
        UpperCamelCase_       :      Union[str, Any]     ='AutoTokenizer'
        def __init__(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   Union[str, Any]:
            UpperCamelCase :int										=     None
            if "feature_extractor" in kwargs:
                warnings.warn(
                    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                    ''' instead.'''					,       SCREAMING_SNAKE_CASE_					,       )
                UpperCamelCase :Union[str, Any]										=     kwargs.pop('''feature_extractor'''				)
  
            UpperCamelCase :List[Any]										=     image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                raise ValueError('''You have to specify an image_processor.'''				)
            if tokenizer is None:
                raise ValueError('''You have to specify a tokenizer.'''				)
  
            super().__init__(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     self.image_processor
        def __call__(       self					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   Dict:
  
            if text is None and images is None:
                raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)
  
            if text is not None:
                UpperCamelCase :str										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       return_tensors=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
            if images is not None:
                UpperCamelCase :Dict										=     self.image_processor(SCREAMING_SNAKE_CASE_					,       return_tensors=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
            if text is not None and images is not None:
                UpperCamelCase :List[Any]										=     image_features.pixel_values
                return encoding
            elif text is not None:
                return encoding
            else:
                return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_				)					,       tensor_type=SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   int:
            return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Any:
            return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        @property
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            UpperCamelCase :Tuple										=     self.tokenizer.model_input_names
            UpperCamelCase :List[str]										=     self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
        @property
        def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
            warnings.warn(
                '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,       SCREAMING_SNAKE_CASE_					,       )
            return self.image_processor_class
  
  
  
  
        @property
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            warnings.warn(
                '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,       SCREAMING_SNAKE_CASE_					,       )
            return self.image_processor
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :int										=     {1}
      UpperCamelCase :Optional[int]										=     [1]
      for _ in range(2  ,						depth + 1 ):
          UpperCamelCase :Union[str, Any]										=     [*previous_coefficients, 0]
          UpperCamelCase :List[str]										=     [0, *previous_coefficients]
          UpperCamelCase :List[str]										=     []
          for x, y in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
              coefficients.add(x + y )
              previous_coefficients.append(x + y )
      return coefficients
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			set[int] ):
      UpperCamelCase :Union[str, Any]										=     set()
      for number in unique_coefficients:
          UpperCamelCase :Optional[int]										=     2
          UpperCamelCase :List[Any]										=     number
          while divisor**2 <= copy_number:
              UpperCamelCase :List[Any]										=     0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(SCREAMING_SNAKE_CASE__ )
                  break
              divisor += 1
  
      return unique_coefficients.difference(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int = 51 ):
      UpperCamelCase :Any										=     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :str										=     get_squarefrees(SCREAMING_SNAKE_CASE__ )
      return sum(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')
  
  
  
  
  
  