




"""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  _lowerCamelCase   (				self				:       Dict ):
										torch.manual_seed(0 )
										snake_case__    :   Optional[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  _lowerCamelCase   (				self				:       Optional[int] ):
										snake_case__    :   str							     =   '''cpu'''
										snake_case__    :   Optional[Any]							     =   self.dummy_uncond_unet
										snake_case__    :   List[str]							     =   DDPMScheduler()

										snake_case__    :   Dict							     =   DDPMPipeline(unet=UpperCamelCase__ ,scheduler=UpperCamelCase__ )
										ddpm.to(UpperCamelCase__ )
										ddpm.set_progress_bar_config(disable=UpperCamelCase__ )

										snake_case__    :   Tuple							     =   torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
										snake_case__    :   int							     =   ddpm(generator=UpperCamelCase__ ,num_inference_steps=2 ,output_type='''numpy''' ).images

										snake_case__    :   int							     =   torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
										snake_case__    :   Dict							     =   ddpm(generator=UpperCamelCase__ ,num_inference_steps=2 ,output_type='''numpy''' ,return_dict=UpperCamelCase__ )[0]

										snake_case__    :   Dict							     =   image[0, -3:, -3:, -1]
										snake_case__    :   Optional[int]							     =   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										snake_case__    :   Tuple							     =   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  _lowerCamelCase   (				self				:       Optional[Any] ):
										snake_case__    :   str							     =   self.dummy_uncond_unet
										snake_case__    :   str							     =   DDPMScheduler(prediction_type='''sample''' )

										snake_case__    :   Optional[Any]							     =   DDPMPipeline(unet=UpperCamelCase__ ,scheduler=UpperCamelCase__ )
										ddpm.to(UpperCamelCase__ )
										ddpm.set_progress_bar_config(disable=UpperCamelCase__ )

										snake_case__    :   Any							     =   torch.manual_seed(0 )
										snake_case__    :   Any							     =   ddpm(generator=UpperCamelCase__ ,num_inference_steps=2 ,output_type='''numpy''' ).images

										snake_case__    :   Tuple							     =   torch.manual_seed(0 )
										snake_case__    :   Optional[Any]							     =   ddpm(generator=UpperCamelCase__ ,num_inference_steps=2 ,output_type='''numpy''' )[0]

										snake_case__    :   Dict							     =   image[0, -3:, -3:, -1]
										snake_case__    :   Dict							     =   image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										snake_case__    :   List[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  _lowerCamelCase   (				self				:       Optional[Any] ):
										snake_case__    :   str							     =   '''google/ddpm-cifar10-32'''

										snake_case__    :   Any							     =   UNetaDModel.from_pretrained(UpperCamelCase__ )
										snake_case__    :   List[Any]							     =   DDPMScheduler.from_pretrained(UpperCamelCase__ )

										snake_case__    :   Optional[Any]							     =   DDPMPipeline(unet=UpperCamelCase__ ,scheduler=UpperCamelCase__ )
										ddpm.to(UpperCamelCase__ )
										ddpm.set_progress_bar_config(disable=UpperCamelCase__ )

										snake_case__    :   List[Any]							     =   torch.manual_seed(0 )
										snake_case__    :   Optional[Any]							     =   ddpm(generator=UpperCamelCase__ ,output_type='''numpy''' ).images

										snake_case__    :   Tuple							     =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										snake_case__    :   List[Any]							     =   np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385] )
										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2








"""simple docstring"""





from __future__ import annotations

import math
import random

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   d * e - 1
						snake_case__    :   Optional[int]							     =   0
						snake_case__    :   Tuple							     =   0
						while p == 0:
												snake_case__    :   Optional[int]							     =   random.randint(2 ,     n - 1						)
												snake_case__    :   Tuple							     =   k
												while True:
																		if t % 2 == 0:
																								snake_case__    :   List[Any]							     =   t // 2
																								snake_case__    :   Union[str, Any]							     =   (g**t) % n
																								snake_case__    :   Dict							     =   math.gcd(x - 1 ,     lowerCamelCase						)
																								if x > 1 and y > 1:
																														snake_case__    :   Union[str, Any]							     =   y
																														snake_case__    :   str							     =   n // y
																														break  # find the correct factors
																		else:
																								break  # t is not divisible by 2, break and choose another g
						return sorted([p, q]						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()


