


'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														torch.manual_seed(0   )
														lowerCAmelCase__          =	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   )       ->							Dict:
														lowerCAmelCase__          =	'''cpu'''
														lowerCAmelCase__          =	self.dummy_uncond_unet
														lowerCAmelCase__          =	DDPMScheduler()

														lowerCAmelCase__          =	DDPMPipeline(unet=lowerCamelCase_					,    scheduler=lowerCamelCase_   )
														ddpm.to(lowerCamelCase_   )
														ddpm.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__          =	torch.Generator(device=lowerCamelCase_   ).manual_seed(0   )
														lowerCAmelCase__          =	ddpm(generator=lowerCamelCase_					,    num_inference_steps=2					,    output_type='''numpy'''   ).images

														lowerCAmelCase__          =	torch.Generator(device=lowerCamelCase_   ).manual_seed(0   )
														lowerCAmelCase__          =	ddpm(generator=lowerCamelCase_					,    num_inference_steps=2					,    output_type='''numpy'''					,    return_dict=lowerCamelCase_   )[0]

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

														assert image.shape == (1, 32, 32, 3)
														lowerCAmelCase__          =	np.array(
														    [9.9_56e-01, 5.7_85e-01, 4.6_75e-01, 9.9_30e-01, 0.0, 1.000, 1.1_99e-03, 2.6_48e-04, 5.1_01e-04]   )

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







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	self.dummy_uncond_unet
														lowerCAmelCase__          =	DDPMScheduler(prediction_type='''sample'''   )

														lowerCAmelCase__          =	DDPMPipeline(unet=lowerCamelCase_					,    scheduler=lowerCamelCase_   )
														ddpm.to(lowerCamelCase_   )
														ddpm.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__          =	torch.manual_seed(0   )
														lowerCAmelCase__          =	ddpm(generator=lowerCamelCase_					,    num_inference_steps=2					,    output_type='''numpy'''   ).images

														lowerCAmelCase__          =	torch.manual_seed(0   )
														lowerCAmelCase__          =	ddpm(generator=lowerCamelCase_					,    num_inference_steps=2					,    output_type='''numpy'''   )[0]

														lowerCAmelCase__          =	image[0, -3:, -3:, -1]
														lowerCAmelCase__          =	image_eps[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														lowerCAmelCase__          =	1e-2 if torch_device != '''mps''' else 3e-2
														assert np.abs(image_slice.flatten() - image_eps_slice.flatten()   ).max() < tolerance




@slow
@require_torch_gpu
class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	'''google/ddpm-cifar10-32'''

														lowerCAmelCase__          =	UNetaDModel.from_pretrained(lowerCamelCase_   )
														lowerCAmelCase__          =	DDPMScheduler.from_pretrained(lowerCamelCase_   )

														lowerCAmelCase__          =	DDPMPipeline(unet=lowerCamelCase_					,    scheduler=lowerCamelCase_   )
														ddpm.to(lowerCamelCase_   )
														ddpm.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__          =	torch.manual_seed(0   )
														lowerCAmelCase__          =	ddpm(generator=lowerCamelCase_					,    output_type='''numpy'''   ).images

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

														assert image.shape == (1, 32, 32, 3)
														lowerCAmelCase__          =	np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]   )
														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2



'''simple docstring'''






def 					_snake_case   (     A       )						->    str:
							return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()