



'''simple docstring'''





from ...processing_utils import ProcessorMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''feature_extractor''']
 A__				= '''TvltImageProcessor'''
 A__				= '''TvltFeatureExtractor'''
 def __init__(   self						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   List[str]:







       '''simple docstring'''
       super().__init__(image_processor=__a      ,     feature_extractor=__a  )

       __snake_case :   List[Any]													=    image_processor
       __snake_case :   Optional[int]													=    feature_extractor
 def __call__(   self						:							str      ,     __a						:							Any=None      ,     __a						:							Optional[int]=None      ,     __a						:							Any=None      ,     __a						:							Union[str, Any]=None      ,     __a						:							Optional[int]=False      ,     __a						:							List[str]=False      ,     *__a						:							Union[str, Any]      ,     **__a						:							Dict      ,     )      ->   List[str]:







       '''simple docstring'''

       if images is None and audio is None:
             raise ValueError('You need to specify either an `images` or `audio` input to process.'  )

       __snake_case :   Dict													=    None
       if images is not None:
             __snake_case :   Union[str, Any]													=    self.image_processor(__a      ,     mask_pixel=__a      ,     *__a      ,     **__a  )
       if images_mixed is not None:
             __snake_case :   Union[str, Any]													=    self.image_processor(__a      ,     is_mixed=__a      ,     *__a      ,     **__a  )
       if audio is not None:
             __snake_case :   List[str]													=    self.feature_extractor(
                 __a      ,     *__a      ,     sampling_rate=__a      ,     mask_audio=__a      ,     **__a  )

       __snake_case :   str													=    {}
       if audio is not None:
             output_dict.update(__a  )
       if images is not None:
             output_dict.update(__a  )
       if images_mixed_dict is not None:
             output_dict.update(__a  )
       return output_dict





 @property
 def A_   (   self						:							Any  )      ->   str:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.image_processor.model_input_names
       __snake_case :   str													=    self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names  )  )








'''simple docstring'''





import functools





def   a_   (		_UpperCAmelCase    :      list[int] ,_UpperCAmelCase    :      list[int]				)    ->  int:

      # Validation
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for day in days				):
            raise ValueError('The parameter days should be a list of integers'				)

      if len(_UpperCAmelCase				) != 3 or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for cost in costs				):
            raise ValueError('The parameter costs should be a list of three integers'				)

      if len(_UpperCAmelCase				) == 0:
            return 0

      if min(_UpperCAmelCase				) <= 0:
            raise ValueError('All days elements should be greater than 0'				)

      if max(_UpperCAmelCase				) >= 3_66:
            raise ValueError('All days elements should be less than 366'				)

      __snake_case :   str													=    set(_UpperCAmelCase				)

      @functools.cache
      def dynamic_programming(_UpperCAmelCase    :      int				) -> int:
            if index > 3_65:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1				)

            return min(
                costs[0] + dynamic_programming(index + 1				) ,costs[1] + dynamic_programming(index + 7				) ,costs[2] + dynamic_programming(index + 30				) ,)

      return dynamic_programming(1				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



