




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       _A				(		unittest.TestCase     ):






		@property
		def 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)
								_UpperCAmelCase      =			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			):
								_UpperCAmelCase      =			self.dummy_uncond_unet
								_UpperCAmelCase      =			ScoreSdeVeScheduler()

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

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

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

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

								assert image.shape == (1, 32, 32, 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       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""google/ncsnpp-church-256"""
								_UpperCAmelCase      =			UNetaDModel.from_pretrained(lowercase_			)

								_UpperCAmelCase      =			ScoreSdeVeScheduler.from_pretrained(lowercase_			)

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

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

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

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





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						_UpperCAmelCase      =			sorted(numsa + numsa       )
						_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(len(snake_case       )			,     2       )
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				a													=			[float(x) for x in input("Enter the elements of first array: ").split()]
				a													=			[float(x) for x in input("Enter the elements of second array: ").split()]
				print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')