

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













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() = }''')





