




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    __lowerCAmelCase			(	unittest.TestCase			):







			@property
			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									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   :      Dict				):




									"""simple docstring"""


									_UpperCAmelCase													=					"cpu"
									_UpperCAmelCase													=					self.dummy_uncond_unet
									_UpperCAmelCase													=					DDPMScheduler()

									_UpperCAmelCase													=					DDPMPipeline(unet=snake_case__   ,   scheduler=snake_case__				)
									ddpm.to(snake_case__				)
									ddpm.set_progress_bar_config(disable=snake_case__				)

									_UpperCAmelCase													=					torch.Generator(device=snake_case__				).manual_seed(0				)
									_UpperCAmelCase													=					ddpm(generator=snake_case__   ,   num_inference_steps=2   ,   output_type="numpy"				).images

									_UpperCAmelCase													=					torch.Generator(device=snake_case__				).manual_seed(0				)
									_UpperCAmelCase													=					ddpm(generator=snake_case__   ,   num_inference_steps=2   ,   output_type="numpy"   ,   return_dict=snake_case__				)[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(
									    [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 UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.dummy_uncond_unet
									_UpperCAmelCase													=					DDPMScheduler(prediction_type="sample"				)

									_UpperCAmelCase													=					DDPMPipeline(unet=snake_case__   ,   scheduler=snake_case__				)
									ddpm.to(snake_case__				)
									ddpm.set_progress_bar_config(disable=snake_case__				)

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

									_UpperCAmelCase													=					torch.manual_seed(0				)
									_UpperCAmelCase													=					ddpm(generator=snake_case__   ,   num_inference_steps=2   ,   output_type="numpy"				)[0]

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

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase													=					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    __lowerCAmelCase			(	unittest.TestCase			):







			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


									_UpperCAmelCase													=					"google/ddpm-cifar10-32"

									_UpperCAmelCase													=					UNetaDModel.from_pretrained(snake_case__				)
									_UpperCAmelCase													=					DDPMScheduler.from_pretrained(snake_case__				)

									_UpperCAmelCase													=					DDPMPipeline(unet=snake_case__   ,   scheduler=snake_case__				)
									ddpm.to(snake_case__				)
									ddpm.set_progress_bar_config(disable=snake_case__				)

									_UpperCAmelCase													=					torch.manual_seed(0				)
									_UpperCAmelCase													=					ddpm(generator=snake_case__   ,   output_type="numpy"				).images

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

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase													=					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










from __future__ import annotations


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					{}
						for index, val in enumerate(snake_case_						):
												_UpperCAmelCase													=					target - val
												if compl in chk_map:
																		return [chk_map[compl], index]
												_UpperCAmelCase													=					index
						return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_sum([2, 7, 11, 15], 9) = }""")




