



"""simple docstring"""

import functools





def       __magic_name__    (   _lowerCamelCase				: list[int]  ,       _lowerCamelCase				: list[int]					):

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

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

							if len(_lowerCamelCase					) == 0:
														return 0

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

							if max(_lowerCamelCase					) >= 3_6_6:
														raise ValueError("""All days elements should be less than 366"""					)

							__a		:		int									=       set(_lowerCamelCase					)

							@functools.cache
							def dynamic_programming(_lowerCamelCase				: int					) -> 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()






"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

