






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 				__UpperCAmelCase		(unittest.TestCase   ):







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									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 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.dummy_uncond_unet
									_SCREAMING_SNAKE_CASE     				=  ScoreSdeVeScheduler()

									_SCREAMING_SNAKE_CASE     				=  ScoreSdeVePipeline(unet=_UpperCAmelCase				,     scheduler=_UpperCAmelCase					)
									sde_ve.to(_UpperCAmelCase					)
									sde_ve.set_progress_bar_config(disable=_UpperCAmelCase					)

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sde_ve(num_inference_steps=2				,     output_type="""numpy"""				,     generator=_UpperCAmelCase					).images

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sde_ve(num_inference_steps=2				,     output_type="""numpy"""				,     generator=_UpperCAmelCase				,     return_dict=_UpperCAmelCase					)[
									    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 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  '''google/ncsnpp-church-256'''
									_SCREAMING_SNAKE_CASE     				=  UNetaDModel.from_pretrained(_UpperCAmelCase					)

									_SCREAMING_SNAKE_CASE     				=  ScoreSdeVeScheduler.from_pretrained(_UpperCAmelCase					)

									_SCREAMING_SNAKE_CASE     				=  ScoreSdeVePipeline(unet=_UpperCAmelCase				,     scheduler=_UpperCAmelCase					)
									sde_ve.to(_UpperCAmelCase					)
									sde_ve.set_progress_bar_config(disable=_UpperCAmelCase					)

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sde_ve(num_inference_steps=10				,     output_type="""numpy"""				,     generator=_UpperCAmelCase					).images

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

									assert image.shape == (1, 256, 256, 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









from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  prime_factors(snake_case__		)
						if is_square_free(snake_case__		):
												return -1 if len(snake_case__		) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

