




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				@property
				def     _A							(							self:   Optional[Any]		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_uncond_unet
											SCREAMING_SNAKE_CASE_   	=    ScoreSdeVeScheduler()

											SCREAMING_SNAKE_CASE_   	=    ScoreSdeVePipeline(unet=_lowerCamelCase		,					scheduler=_lowerCamelCase		)
											sde_ve.to(_lowerCamelCase		)
											sde_ve.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    sde_ve(num_inference_steps=2		,					output_type='''numpy'''		,					generator=_lowerCamelCase		).images

											SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    sde_ve(num_inference_steps=2		,					output_type='''numpy'''		,					generator=_lowerCamelCase		,					return_dict=_lowerCamelCase		)[
											    0
											]

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

											assert image.shape == (1, 32, 32, 3)
											SCREAMING_SNAKE_CASE_   	=    np.array([0.0, 1.0, 0.0, 0.0, 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):
				'''simple docstring'''







				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    '''google/ncsnpp-church-256'''
											SCREAMING_SNAKE_CASE_   	=    UNetaDModel.from_pretrained(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    ScoreSdeVeScheduler.from_pretrained(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    ScoreSdeVePipeline(unet=_lowerCamelCase		,					scheduler=_lowerCamelCase		)
											sde_ve.to(_lowerCamelCase		)
											sde_ve.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    sde_ve(num_inference_steps=10		,					output_type='''numpy'''		,					generator=_lowerCamelCase		).images

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

											assert image.shape == (1, 2_56, 2_56, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]		)

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








def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


