






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	@property
	def 				_UpperCamelCase     (			self   )			->       str:
								torch.manual_seed(0   )
								snake_case_           =				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   )			->       Any:
								snake_case_           =				self.dummy_uncond_unet
								snake_case_           =				PNDMScheduler()

								snake_case_           =				PNDMPipeline(unet=a			,     scheduler=a   )
								pndm.to(a   )
								pndm.set_progress_bar_config(disable=a   )

								snake_case_           =				torch.manual_seed(0   )
								snake_case_           =				pndm(generator=a			,     num_inference_steps=20			,     output_type='numpy'   ).images

								snake_case_           =				torch.manual_seed(0   )
								snake_case_           =				pndm(generator=a			,     num_inference_steps=20			,     output_type='numpy'			,     return_dict=a   )[0]

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

								assert image.shape == (1, 32, 32, 3)
								snake_case_           =				np.array([1.0, 1.0, 0.0, 1.0, 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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				'google/ddpm-cifar10-32'

								snake_case_           =				UNetaDModel.from_pretrained(a   )
								snake_case_           =				PNDMScheduler()

								snake_case_           =				PNDMPipeline(unet=a			,     scheduler=a   )
								pndm.to(a   )
								pndm.set_progress_bar_config(disable=a   )
								snake_case_           =				torch.manual_seed(0   )
								snake_case_           =				pndm(generator=a			,     output_type='numpy'   ).images

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

								assert image.shape == (1, 32, 32, 3)
								snake_case_           =				np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

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













import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class 					UpperCamelCase_ :



	'''simple docstring'''




	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					None
	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					True
	lowerCAmelCase						=					None
	lowerCAmelCase						=					1
	lowerCAmelCase						=					None
	lowerCAmelCase						=					False
	lowerCAmelCase						=					None
	lowerCAmelCase						=					None




	def 				_UpperCamelCase     (			self   )			->       "DownloadConfig":
								return self.__class__(**{k: copy.deepcopy(a   ) for k, v in self.__dict__.items()}   )





