



'''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		):





				'''simple docstring'''





				@property
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											torch.manual_seed(0)
											__lowercase       	=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  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.dummy_uncond_unet
											__lowercase       	=PNDMScheduler()

											__lowercase       	=PNDMPipeline(unet=_lowerCAmelCase  ,       scheduler=_lowerCAmelCase)
											pndm.to(_lowerCAmelCase)
											pndm.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=torch.manual_seed(0)
											__lowercase       	=pndm(generator=_lowerCAmelCase  ,       num_inference_steps=2_0  ,       output_type='numpy').images

											__lowercase       	=torch.manual_seed(0)
											__lowercase       	=pndm(generator=_lowerCAmelCase  ,       num_inference_steps=2_0  ,       output_type='numpy'  ,       return_dict=_lowerCAmelCase)[0]

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

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowercase       	=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		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	='google/ddpm-cifar10-32'

											__lowercase       	=UNetaDModel.from_pretrained(_lowerCAmelCase)
											__lowercase       	=PNDMScheduler()

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

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

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowercase       	=np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125])

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








'''simple docstring'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('only integers accepted as input'					)
							else:
														__lowercase       	=str(abs(_lowerCAmelCase					)					)
														__lowercase       	=[list(_lowerCAmelCase					) for char in range(len(_lowerCAmelCase					)					)]
														for index in range(len(_lowerCAmelCase					)					):
																					num_transpositions[index].pop(_lowerCAmelCase					)
														return max(
														    int(''.join(list(_lowerCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
		__import__("""doctest""").testmod()



