






"""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       __A		(   unittest.TestCase			):

					@property
					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[Any]:
											torch.manual_seed(0					)
											__magic_name__:	Optional[int]							=       UNetaDModel(
											    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,		)
											return model






					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Dict:
											__magic_name__:	int							=       self.dummy_uncond_unet
											__magic_name__:	int							=       PNDMScheduler()

											__magic_name__:	Dict							=       PNDMPipeline(unet=__snake_case     ,		scheduler=__snake_case					)
											pndm.to(__snake_case					)
											pndm.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Optional[Any]							=       torch.manual_seed(0					)
											__magic_name__:	List[str]							=       pndm(generator=__snake_case     ,		num_inference_steps=2_0     ,		output_type="""numpy"""					).images

											__magic_name__:	Tuple							=       torch.manual_seed(0					)
											__magic_name__:	List[Any]							=       pndm(generator=__snake_case     ,		num_inference_steps=2_0     ,		output_type="""numpy"""     ,		return_dict=__snake_case					)[0]

											__magic_name__:	Optional[Any]							=       image[0, -3:, -3:, -1]
											__magic_name__:	Dict							=       image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	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       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											__magic_name__:	Any							=       """google/ddpm-cifar10-32"""

											__magic_name__:	List[str]							=       UNetaDModel.from_pretrained(__snake_case					)
											__magic_name__:	Tuple							=       PNDMScheduler()

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

											__magic_name__:	Optional[Any]							=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__magic_name__:	Union[str, Any]							=       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"""





def   a    (     __UpperCAmelCase      :    list[int]  )    ->							float:
						if not nums:  # Makes sure that the list is not empty
												raise ValueError("""List is empty"""  )

						__magic_name__:	Dict							=       sum(__UpperCAmelCase  ) / len(__UpperCAmelCase  )  # Calculate the average
						return sum(abs(x - average  ) for x in nums  ) / len(__UpperCAmelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



