


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					lowerCAmelCase_ (       unittest.TestCase):




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






									"""simple docstring"""






									torch.manual_seed(0						)
									a__ :Optional[int]						  =    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  _snake_case					(	self						:   int						)      ->Any:






									"""simple docstring"""






									a__ :str						  =    self.dummy_uncond_unet
									a__ :List[str]						  =    ScoreSdeVeScheduler()

									a__ :Optional[int]						  =    ScoreSdeVePipeline(unet=__A   ,     scheduler=__A						)
									sde_ve.to(__A						)
									sde_ve.set_progress_bar_config(disable=__A						)

									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :Any						  =    sde_ve(num_inference_steps=2   ,     output_type="numpy"   ,     generator=__A						).images

									a__ :int						  =    torch.manual_seed(0						)
									a__ :Union[str, Any]						  =    sde_ve(num_inference_steps=2   ,     output_type="numpy"   ,     generator=__A   ,     return_dict=__A						)[
									    0
									]

									a__ :Any						  =    image[0, -3:, -3:, -1]
									a__ :str						  =    image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									a__ :int						  =    np.array([0.0, 1.0, 0.0, 0.0, 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 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Optional[Any]						)      ->Dict:






									"""simple docstring"""






									a__ :List[Any]						  =    "google/ncsnpp-church-256"
									a__ :Any						  =    UNetaDModel.from_pretrained(__A						)

									a__ :Optional[Any]						  =    ScoreSdeVeScheduler.from_pretrained(__A						)

									a__ :Any						  =    ScoreSdeVePipeline(unet=__A   ,     scheduler=__A						)
									sde_ve.to(__A						)
									sde_ve.set_progress_bar_config(disable=__A						)

									a__ :int						  =    torch.manual_seed(0						)
									a__ :Tuple						  =    sde_ve(num_inference_steps=10   ,     output_type="numpy"   ,     generator=__A						).images

									a__ :Optional[int]						  =    image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)

									a__ :List[Any]						  =    np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]						)

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






def 				lowerCamelCase__	(      a	:   int = 1_000_000				)    ->	int:





			"""simple docstring"""
			a__ :int						  =    [i - 1 for i in range(limit + 1				)]

			for i in range(2	,  limit + 1				):
						if phi[i] == i - 1:
									for j in range(2 * i	,  limit + 1	,  a				):
												phi[j] -= phi[j] // i

			return sum(phi[2 : limit + 1]				)


if __name__ == "__main__":
		print(solution())


