


'''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 			lowerCamelCase					(						unittest.TestCase  ):


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											torch.manual_seed(0       )
											_snake_case:   Optional[Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.dummy_uncond_unet
											_snake_case:   Optional[Any]  					=		PNDMScheduler()

											_snake_case:   str  					=		PNDMPipeline(unet=__snake_case       ,     scheduler=__snake_case       )
											pndm.to(__snake_case       )
											pndm.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   List[Any]  					=		torch.manual_seed(0       )
											_snake_case:   Tuple  					=		pndm(generator=__snake_case       ,     num_inference_steps=20       ,     output_type='numpy'       ).images

											_snake_case:   int  					=		torch.manual_seed(0       )
											_snake_case:   Tuple  					=		pndm(generator=__snake_case       ,     num_inference_steps=20       ,     output_type='numpy'       ,     return_dict=__snake_case       )[0]

											_snake_case:   List[Any]  					=		image[0, -3:, -3:, -1]
											_snake_case:   List[str]  					=		image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											_snake_case:   Optional[int]  					=		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 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		'google/ddpm-cifar10-32'

											_snake_case:   int  					=		UNetaDModel.from_pretrained(__snake_case       )
											_snake_case:   Union[str, Any]  					=		PNDMScheduler()

											_snake_case:   List[str]  					=		PNDMPipeline(unet=__snake_case       ,     scheduler=__snake_case       )
											pndm.to(__snake_case       )
											pndm.set_progress_bar_config(disable=__snake_case       )
											_snake_case:   Optional[Any]  					=		torch.manual_seed(0       )
											_snake_case:   List[Any]  					=		pndm(generator=__snake_case       ,     output_type='numpy'       ).images

											_snake_case:   Union[str, Any]  					=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											_snake_case:   int  					=		np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

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












'''simple docstring'''






def       lowercase_      (		lowercase__ = 50						)   ->int:
						_snake_case:   Union[str, Any]  					=		[[0] * 3 for _ in range(length + 1						)]

						for row_length in range(length + 1						):
												for tile_length in range(2				,  5						):
																		for tile_start in range(row_length - tile_length + 1						):
																								different_colour_ways_number[row_length][tile_length - 2] += (
																								    different_colour_ways_number[row_length - tile_start - tile_length][
																								        tile_length - 2
																								    ]
																								    + 1
																								)

						return sum(different_colour_ways_number[length]						)


if __name__ == "__main__":
		print(F'{solution() = }')





