






'''simple docstring'''
import warnings

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







class 	UpperCamelCase__					(     __snake_case			):

		"""simple docstring"""
		UpperCAmelCase__     			=						['image_processor', 'tokenizer']
		UpperCAmelCase__     			=						'CLIPImageProcessor'
		UpperCAmelCase__     			=						('CLIPTokenizer', 'CLIPTokenizerFast')




		def __init__( self						:						List[str]							,    __A						:						List[str]=None							,    __A						:						List[Any]=None							,    **__A						:						List[Any]    ):







							"""simple docstring"""
							_lowercase			= 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							,    )
												_lowercase			= kwargs.pop("feature_extractor"    )

							_lowercase			= 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							,    __A						:						Dict=None							,    __A						:						List[str]=None							,    __A						:						Tuple=None							,    **__A						:						Union[str, Any]    ):







							"""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:
												_lowercase			= self.tokenizer(__UpperCamelCase							,    return_tensors=__UpperCamelCase							,    **__UpperCamelCase    )

							if images is not None:
												_lowercase			= self.image_processor(__UpperCamelCase							,    return_tensors=__UpperCamelCase							,    **__UpperCamelCase    )

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




		def       snake_case     ( self						:						Optional[int]							,    *__A						:						Union[str, Any]							,    **__A						:						int    ):







							"""simple docstring"""
							return self.tokenizer.batch_decode(*__UpperCamelCase							,    **__UpperCamelCase    )




		def       snake_case     ( self						:						List[str]							,    *__A						:						Optional[int]							,    **__A						:						Any    ):







							"""simple docstring"""
							return self.tokenizer.decode(*__UpperCamelCase							,    **__UpperCamelCase    )




		@property
		def       snake_case     ( self						:						Union[str, Any]    ):







							"""simple docstring"""
							_lowercase			= self.tokenizer.model_input_names
							_lowercase			= self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )




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







							"""simple docstring"""
							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       snake_case     ( self						:						Any    ):







							"""simple docstring"""
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,    __UpperCamelCase							,    )
							return self.image_processor




import functools





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''

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

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

	if len(lowercase__						) == 0:
		return 0

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

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

	snake_case_   		=			set(lowercase__						)

	@functools.cache
	def dynamic_programming(lowercase__						) -> int:
		if index > 365:
			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()
