'''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     __magic_name__						(    unittest.TestCase):







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										torch.manual_seed(0    )
										lowercase_					:			str										=       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    ):
										lowercase_					:			List[str]										=       self.dummy_uncond_unet
										lowercase_					:			Optional[int]										=       PNDMScheduler()

										lowercase_					:			int										=       PNDMPipeline(unet=lowercase_				,			scheduler=lowercase_    )
										pndm.to(lowercase_    )
										pndm.set_progress_bar_config(disable=lowercase_    )

										lowercase_					:			Any										=       torch.manual_seed(0    )
										lowercase_					:			Union[str, Any]										=       pndm(generator=lowercase_				,			num_inference_steps=20				,			output_type="""numpy"""    ).images

										lowercase_					:			List[str]										=       torch.manual_seed(0    )
										lowercase_					:			str										=       pndm(generator=lowercase_				,			num_inference_steps=20				,			output_type="""numpy"""				,			return_dict=lowercase_    )[0]

										lowercase_					:			str										=       image[0, -3:, -3:, -1]
										lowercase_					:			Optional[Any]										=       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowercase_					:			List[str]										=       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     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			List[Any]										=       """google/ddpm-cifar10-32"""

										lowercase_					:			Optional[Any]										=       UNetaDModel.from_pretrained(lowercase_    )
										lowercase_					:			str										=       PNDMScheduler()

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

										lowercase_					:			Any										=       image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowercase_					:			str										=       np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]    )

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






'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      bool:
							return numa ^ numa < 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()





