import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class 						a__							(   unittest.TestCase		):



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								torch.manual_seed(0    )
								__a							=   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 __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   'cpu'
								__a							=   self.dummy_uncond_unet
								__a							=   DDPMScheduler()

								__a							=   DDPMPipeline(unet=UpperCAmelCase      ,						scheduler=UpperCAmelCase    )
								ddpm.to(UpperCAmelCase    )
								ddpm.set_progress_bar_config(disable=UpperCAmelCase    )

								__a							=   torch.Generator(device=UpperCAmelCase    ).manual_seed(0    )
								__a							=   ddpm(generator=UpperCAmelCase      ,						num_inference_steps=2      ,						output_type='numpy'    ).images

								__a							=   torch.Generator(device=UpperCAmelCase    ).manual_seed(0    )
								__a							=   ddpm(generator=UpperCAmelCase      ,						num_inference_steps=2      ,						output_type='numpy'      ,						return_dict=UpperCAmelCase    )[0]

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

								assert image.shape == (1, 3_2, 3_2, 3)
								__a							=   np.array(
								    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								__a							=   self.dummy_uncond_unet
								__a							=   DDPMScheduler(prediction_type='sample'    )

								__a							=   DDPMPipeline(unet=UpperCAmelCase      ,						scheduler=UpperCAmelCase    )
								ddpm.to(UpperCAmelCase    )
								ddpm.set_progress_bar_config(disable=UpperCAmelCase    )

								__a							=   torch.manual_seed(0    )
								__a							=   ddpm(generator=UpperCAmelCase      ,						num_inference_steps=2      ,						output_type='numpy'    ).images

								__a							=   torch.manual_seed(0    )
								__a							=   ddpm(generator=UpperCAmelCase      ,						num_inference_steps=2      ,						output_type='numpy'    )[0]

								__a							=   image[0, -3:, -3:, -1]
								__a							=   image_eps[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__a							=   1e-2 if torch_device != 'mps' else 3e-2
								assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance






@slow
@require_torch_gpu
class 						a__							(   unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   'google/ddpm-cifar10-32'

								__a							=   UNetaDModel.from_pretrained(UpperCAmelCase    )
								__a							=   DDPMScheduler.from_pretrained(UpperCAmelCase    )

								__a							=   DDPMPipeline(unet=UpperCAmelCase      ,						scheduler=UpperCAmelCase    )
								ddpm.to(UpperCAmelCase    )
								ddpm.set_progress_bar_config(disable=UpperCAmelCase    )

								__a							=   torch.manual_seed(0    )
								__a							=   ddpm(generator=UpperCAmelCase      ,						output_type='numpy'    ).images

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

								assert image.shape == (1, 3_2, 3_2, 3)
								__a							=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]    )
								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2





def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					sequence.append(__lowerCamelCase						)

					__a							=   0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__lowerCamelCase						):
										return None

					return index


if __name__ == "__main__":
	lowerCamelCase_				:		List[str]       						=       input("""Enter numbers separated by comma:\n""").strip()
	lowerCamelCase_				:		str       						=       [int(item) for item in user_input.split(""",""")]

	lowerCamelCase_				:		Tuple       						=       input("""Enter a single number to be found in the list:\n""")
	lowerCamelCase_				:		Optional[int]       						=       int(target_input)
	lowerCamelCase_				:		List[Any]       						=       sentinel_linear_search(sequence, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print("""Not found""")




