






"""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      UpperCamelCase_       (				unittest.TestCase     ):



  @property
  def 						UpperCamelCase_ (					self )	->	List[str]:



         """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 )	->	str:



         """simple docstring"""





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

         UpperCAmelCase					=	DDPMPipeline(unet=__UpperCAmelCase       ,						scheduler=__UpperCAmelCase )
         ddpm.to(__UpperCAmelCase )
         ddpm.set_progress_bar_config(disable=__UpperCAmelCase )

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

         UpperCAmelCase					=	torch.Generator(device=__UpperCAmelCase ).manual_seed(0 )
         UpperCAmelCase					=	ddpm(generator=__UpperCAmelCase       ,						num_inference_steps=2       ,						output_type="""numpy"""       ,						return_dict=__UpperCAmelCase )[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 )	->	int:



         """simple docstring"""





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

         UpperCAmelCase					=	DDPMPipeline(unet=__UpperCAmelCase       ,						scheduler=__UpperCAmelCase )
         ddpm.to(__UpperCAmelCase )
         ddpm.set_progress_bar_config(disable=__UpperCAmelCase )

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

         UpperCAmelCase					=	torch.manual_seed(0 )
         UpperCAmelCase					=	ddpm(generator=__UpperCAmelCase       ,						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      UpperCamelCase_       (				unittest.TestCase     ):



  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	"""google/ddpm-cifar10-32"""

         UpperCAmelCase					=	UNetaDModel.from_pretrained(__UpperCAmelCase )
         UpperCAmelCase					=	DDPMScheduler.from_pretrained(__UpperCAmelCase )

         UpperCAmelCase					=	DDPMPipeline(unet=__UpperCAmelCase       ,						scheduler=__UpperCAmelCase )
         ddpm.to(__UpperCAmelCase )
         ddpm.set_progress_bar_config(disable=__UpperCAmelCase )

         UpperCAmelCase					=	torch.manual_seed(0 )
         UpperCAmelCase					=	ddpm(generator=__UpperCAmelCase       ,						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


"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase       )				-> list:
	_lowerCAmelCase								=[True for i in range(n + 1       )]
	_lowerCAmelCase								=[i - 1 for i in range(n + 1       )]
	_lowerCAmelCase								=[]
	for i in range(2    , n + 1       ):
		if is_prime[i]:
			primes.append(__UpperCamelCase       )
		for j in range(0    , len(__UpperCamelCase       )       ):
			if i * primes[j] >= n:
				break
			_lowerCAmelCase								=False

			if i % primes[j] == 0:
				_lowerCAmelCase								=totients[i] * primes[j]
				break

			_lowerCAmelCase								=totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
			import doctest

			doctest.testmod()



