"""simple docstring"""


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        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""







			@property
			def      _a			(			self						):






									'''simple docstring'''







									torch.manual_seed(0						)
									UpperCamelCase							:		Union[str, Any]							      = 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      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = """cpu"""
									UpperCamelCase							:		Dict							      = self.dummy_uncond_unet
									UpperCamelCase							:		int							      = DDPMScheduler()

									UpperCamelCase							:		Optional[int]							      = DDPMPipeline(unet=__snake_case	,      scheduler=__snake_case						)
									ddpm.to(__snake_case						)
									ddpm.set_progress_bar_config(disable=__snake_case						)

									UpperCamelCase							:		str							      = torch.Generator(device=__snake_case						).manual_seed(0						)
									UpperCamelCase							:		Dict							      = ddpm(generator=__snake_case	,      num_inference_steps=2	,      output_type="""numpy"""						).images

									UpperCamelCase							:		Dict							      = torch.Generator(device=__snake_case						).manual_seed(0						)
									UpperCamelCase							:		Any							      = ddpm(generator=__snake_case	,      num_inference_steps=2	,      output_type="""numpy"""	,      return_dict=__snake_case						)[0]

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

									assert image.shape == (1, 3_2, 3_2, 3)
									UpperCamelCase							:		List[str]							      = np.array(
									    [9.9_5_6e-0_1, 5.7_8_5e-0_1, 4.6_7_5e-0_1, 9.9_3_0e-0_1, 0.0, 1.0_00, 1.1_9_9e-0_3, 2.6_4_8e-0_4, 5.1_0_1e-0_4]						)

									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      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = self.dummy_uncond_unet
									UpperCamelCase							:		str							      = DDPMScheduler(prediction_type="""sample"""						)

									UpperCamelCase							:		Union[str, Any]							      = DDPMPipeline(unet=__snake_case	,      scheduler=__snake_case						)
									ddpm.to(__snake_case						)
									ddpm.set_progress_bar_config(disable=__snake_case						)

									UpperCamelCase							:		Optional[int]							      = torch.manual_seed(0						)
									UpperCamelCase							:		Optional[int]							      = ddpm(generator=__snake_case	,      num_inference_steps=2	,      output_type="""numpy"""						).images

									UpperCamelCase							:		Dict							      = torch.manual_seed(0						)
									UpperCamelCase							:		Union[str, Any]							      = ddpm(generator=__snake_case	,      num_inference_steps=2	,      output_type="""numpy"""						)[0]

									UpperCamelCase							:		Optional[Any]							      = image[0, -3:, -3:, -1]
									UpperCamelCase							:		Dict							      = image_eps[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									UpperCamelCase							:		Any							      = 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        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = """google/ddpm-cifar10-32"""

									UpperCamelCase							:		Any							      = UNetaDModel.from_pretrained(__snake_case						)
									UpperCamelCase							:		str							      = DDPMScheduler.from_pretrained(__snake_case						)

									UpperCamelCase							:		Dict							      = DDPMPipeline(unet=__snake_case	,      scheduler=__snake_case						)
									ddpm.to(__snake_case						)
									ddpm.set_progress_bar_config(disable=__snake_case						)

									UpperCamelCase							:		Optional[Any]							      = torch.manual_seed(0						)
									UpperCamelCase							:		Any							      = ddpm(generator=__snake_case	,      output_type="""numpy"""						).images

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

									assert image.shape == (1, 3_2, 3_2, 3)
									UpperCamelCase							:		List[str]							      = np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]						)
									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2










"""simple docstring"""


def     UpperCamelCase       (SCREAMING_SNAKE_CASE = 1000    ):
						UpperCamelCase							:		List[str]							      = []
						UpperCamelCase							:		int							      = []
						UpperCamelCase							:		Dict							      = 3
						UpperCamelCase							:		Dict							      = 5
						UpperCamelCase							:		Optional[int]							      = 1
						while True:
												UpperCamelCase							:		int							      = z * temp
												if result < n:
																		zmulti.append(SCREAMING_SNAKE_CASE    )
																		temp += 1
												else:
																		UpperCamelCase							:		Optional[int]							      = 1
																		break
						while True:
												UpperCamelCase							:		Any							      = x * temp
												if result < n:
																		xmulti.append(SCREAMING_SNAKE_CASE    )
																		temp += 1
												else:
																		break
						UpperCamelCase							:		List[Any]							      = list(set(xmulti + zmulti    )    )
						return sum(SCREAMING_SNAKE_CASE    )


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



