





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 ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		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							):



				'''simple docstring'''







				lowerCamelCase__								=		self.dummy_uncond_unet
				lowerCamelCase__								=		ScoreSdeVeScheduler()

				lowerCamelCase__								=		ScoreSdeVePipeline(unet=__lowerCAmelCase       ,  scheduler=__lowerCAmelCase							)
				sde_ve.to(__lowerCAmelCase							)
				sde_ve.set_progress_bar_config(disable=__lowerCAmelCase							)

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

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

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

				assert image.shape == (1, 3_2, 3_2, 3)
				lowerCamelCase__								=		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 ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		'''google/ncsnpp-church-256'''
				lowerCamelCase__								=		UNetaDModel.from_pretrained(__lowerCAmelCase							)

				lowerCamelCase__								=		ScoreSdeVeScheduler.from_pretrained(__lowerCAmelCase							)

				lowerCamelCase__								=		ScoreSdeVePipeline(unet=__lowerCAmelCase       ,  scheduler=__lowerCAmelCase							)
				sde_ve.to(__lowerCAmelCase							)
				sde_ve.set_progress_bar_config(disable=__lowerCAmelCase							)

				lowerCamelCase__								=		torch.manual_seed(0							)
				lowerCamelCase__								=		sde_ve(num_inference_steps=1_0       ,  output_type='''numpy'''       ,  generator=__lowerCAmelCase							).images

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

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

				lowerCamelCase__								=		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 lowerCAmelCase__(__snake_case							)				->      list:







	'''simple docstring'''







	lowerCamelCase__								=		len(__snake_case							)
	for _ in range(__snake_case							):
		for i in range(_ % 2       ,arr_size - 1       ,2							):
			if arr[i + 1] < arr[i]:
				lowerCamelCase__				,	lowerCamelCase__								=		arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
			_a 						=      list(range(10, 0, -1))
			print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")



