




import functools



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Dict    )						->      Union[str, Any]:





    """simple docstring"""

    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_6_6:
        raise ValueError("""All days elements should be less than 366"""    )

    __A         =	set(UpperCamelCase__    )

    @functools.cache
    def dynamic_programming(__lowercase   :			Any    ) -> int:
        if index > 3_6_5:
            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 + 3_0    )			,				)

    return dynamic_programming(1    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()












from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         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:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




