



'''simple docstring'''






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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@property
	def      A	(						self			:     List[Any]		)			->		List[str]:
								torch.manual_seed(0		)
								UpperCAmelCase :       Optional[Any] 							=		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      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		self.dummy_uncond_unet
								UpperCAmelCase :       Optional[int] 							=		PNDMScheduler()

								UpperCAmelCase :       Any 							=		PNDMPipeline(unet=__snake_case		,			scheduler=__snake_case		)
								pndm.to(__snake_case		)
								pndm.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		torch.manual_seed(0		)
								UpperCAmelCase :       Any 							=		pndm(generator=__snake_case		,			num_inference_steps=20		,			output_type='''numpy'''		).images

								UpperCAmelCase :       Dict 							=		torch.manual_seed(0		)
								UpperCAmelCase :       List[Any] 							=		pndm(generator=__snake_case		,			num_inference_steps=20		,			output_type='''numpy'''		,			return_dict=__snake_case		)[0]

								UpperCAmelCase :       Dict 							=		image[0, -3:, -3:, -1]
								UpperCAmelCase :       List[str] 							=		image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase :       Optional[Any] 							=		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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Optional[int]		)			->		str:
								UpperCAmelCase :       str 							=		'''google/ddpm-cifar10-32'''

								UpperCAmelCase :       Optional[Any] 							=		UNetaDModel.from_pretrained(__snake_case		)
								UpperCAmelCase :       List[str] 							=		PNDMScheduler()

								UpperCAmelCase :       int 							=		PNDMPipeline(unet=__snake_case		,			scheduler=__snake_case		)
								pndm.to(__snake_case		)
								pndm.set_progress_bar_config(disable=__snake_case		)
								UpperCAmelCase :       Tuple 							=		torch.manual_seed(0		)
								UpperCAmelCase :       Any 							=		pndm(generator=__snake_case		,			output_type='''numpy'''		).images

								UpperCAmelCase :       str 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase :       List[Any] 							=		np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]		)

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










'''simple docstring'''






import random

from .binary_exp_mod import bin_exp_mod


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=1000  )      ->   int:
							if n < 2:
														return False

							if n % 2 == 0:
														return n == 2

							# this means n is odd
							UpperCAmelCase :       str 							=		n - 1
							UpperCAmelCase :       List[Any] 							=		0
							while d % 2 == 0:
														d /= 2
														exp += 1

							# n - 1=d*(2**exp)
							UpperCAmelCase :       List[str] 							=		0
							while count < prec:
														UpperCAmelCase :       int 							=		random.randint(2   ,      n - 1  )
														UpperCAmelCase :       List[str] 							=		bin_exp_mod(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if b != 1:
																					UpperCAmelCase :       int 							=		True
																					for _ in range(_lowerCAmelCase  ):
																												if b == n - 1:
																																			UpperCAmelCase :       Dict 							=		False
																																			break
																												UpperCAmelCase :       str 							=		b * b
																												b %= n
																					if flag:
																												return False
																					count += 1
							return True


if __name__ == "__main__":
						UpperCamelCase__:   Optional[int]								=			abs(int(input("Enter bound : ").strip()))
						print("Here's the list of primes:")
						print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





