


"""simple docstring"""
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				):






		'''simple docstring'''

		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			torch.manual_seed(0					)
			__snake_case	:						Tuple						=				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					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self.dummy_uncond_unet
			__snake_case	:						int						=				ScoreSdeVeScheduler()

			__snake_case	:						Tuple						=				ScoreSdeVePipeline(unet=a_				,       scheduler=a_					)
			sde_ve.to(a_					)
			sde_ve.set_progress_bar_config(disable=a_					)

			__snake_case	:						Any						=				torch.manual_seed(0					)
			__snake_case	:						Union[str, Any]						=				sde_ve(num_inference_steps=2				,       output_type='''numpy'''				,       generator=a_					).images

			__snake_case	:						Optional[int]						=				torch.manual_seed(0					)
			__snake_case	:						str						=				sde_ve(num_inference_steps=2				,       output_type='''numpy'''				,       generator=a_				,       return_dict=a_					)[
			    0
			]

			__snake_case	:						List[str]						=				image[0, -3:, -3:, -1]
			__snake_case	:						Union[str, Any]						=				image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__snake_case	:						Tuple						=				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				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				'''google/ncsnpp-church-256'''
			__snake_case	:						List[Any]						=				UNetaDModel.from_pretrained(a_					)

			__snake_case	:						Dict						=				ScoreSdeVeScheduler.from_pretrained(a_					)

			__snake_case	:						Any						=				ScoreSdeVePipeline(unet=a_				,       scheduler=a_					)
			sde_ve.to(a_					)
			sde_ve.set_progress_bar_config(disable=a_					)

			__snake_case	:						Dict						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sde_ve(num_inference_steps=10				,       output_type='''numpy'''				,       generator=a_					).images

			__snake_case	:						Union[str, Any]						=				image[0, -3:, -3:, -1]

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

			__snake_case	:						Optional[Any]						=				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









"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase = 6008_5147_5143      ):

			'''simple docstring'''




			try:
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."      )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."      )
			__lowerCAmelCase				    =			2
			__lowerCAmelCase				    =			0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__lowerCAmelCase				    =			i
						while n % i == 0:
									__lowerCAmelCase				    =			n // i
						i += 1
			return int(_UpperCamelCase      )


if __name__ == "__main__":
	print(f'''{solution() = }''')




