


'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class  UpperCamelCase__(     unittest.TestCase ):




				@property
				def   a__(	self    :				Optional[int]   )->  Optional[Any]:

										"""simple docstring"""




										torch.manual_seed(0   )
										UpperCAmelCase         =			UNetaDModel(
										    block_out_channels=(32, 64)   ,					layers_per_block=2   ,					sample_size=32   ,					in_channels=3   ,					out_channels=3   ,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,					)
										return model





				def   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.dummy_uncond_unet
										UpperCAmelCase         =			PNDMScheduler()

										UpperCAmelCase         =			PNDMPipeline(unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   )
										pndm.to(lowerCAmelCase   )
										pndm.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			torch.manual_seed(0   )
										UpperCAmelCase         =			pndm(generator=lowerCAmelCase   ,					num_inference_steps=20   ,					output_type='''numpy'''   ).images

										UpperCAmelCase         =			torch.manual_seed(0   )
										UpperCAmelCase         =			pndm(generator=lowerCAmelCase   ,					num_inference_steps=20   ,					output_type='''numpy'''   ,					return_dict=lowerCAmelCase   )[0]

										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCAmelCase         =			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				List[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			'''google/ddpm-cifar10-32'''

										UpperCAmelCase         =			UNetaDModel.from_pretrained(lowerCAmelCase   )
										UpperCAmelCase         =			PNDMScheduler()

										UpperCAmelCase         =			PNDMPipeline(unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   )
										pndm.to(lowerCAmelCase   )
										pndm.set_progress_bar_config(disable=lowerCAmelCase   )
										UpperCAmelCase         =			torch.manual_seed(0   )
										UpperCAmelCase         =			pndm(generator=lowerCAmelCase   ,					output_type='''numpy'''   ).images

										UpperCAmelCase         =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCAmelCase         =			np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2









'''simple docstring'''



import functools


def    lowerCamelCase__   ( A     :    list[int]   ,     A     :    list[int]  ):




						'''simple docstring'''






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

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

						if len(A  ) == 0:
												return 0

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

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

						UpperCAmelCase         =			set(A  )

						@functools.cache
						def dynamic_programming(A     :    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()





