


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   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					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							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.dummy_uncond_unet
						UpperCamelCase         =					ScoreSdeVeScheduler()

						UpperCamelCase         =					ScoreSdeVePipeline(unet=__magic_name__       ,       scheduler=__magic_name__						)
						sde_ve.to(__magic_name__						)
						sde_ve.set_progress_bar_config(disable=__magic_name__						)

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

						UpperCamelCase         =					torch.manual_seed(0						)
						UpperCamelCase         =					sde_ve(num_inference_steps=2       ,       output_type="""numpy"""       ,       generator=__magic_name__       ,       return_dict=__magic_name__						)[
						    0
						]

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

						assert image.shape == (1, 3_2, 3_2, 3)
						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/ncsnpp-church-256"""
						UpperCamelCase         =					UNetaDModel.from_pretrained(__magic_name__						)

						UpperCamelCase         =					ScoreSdeVeScheduler.from_pretrained(__magic_name__						)

						UpperCamelCase         =					ScoreSdeVePipeline(unet=__magic_name__       ,       scheduler=__magic_name__						)
						sde_ve.to(__magic_name__						)
						sde_ve.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					torch.manual_seed(0						)
						UpperCamelCase         =					sde_ve(num_inference_steps=1_0       ,       output_type="""numpy"""       ,       generator=__magic_name__						).images

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

						assert image.shape == (1, 2_5_6, 2_5_6, 3)

						UpperCamelCase         =					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








import math






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 100	):





			"""simple docstring"""




			UpperCamelCase         =					sum(i * i for i in range(1       ,  n + 1	)	)
			UpperCamelCase         =					int(math.pow(sum(range(1       ,  n + 1	)	)       ,  2	)	)
			return square_of_sum - sum_of_squares


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




