'''simple docstring'''




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 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    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):




					"""simple docstring"""


					lowerCAmelCase     =    self.dummy_uncond_unet
					lowerCAmelCase     =    ScoreSdeVeScheduler()

					lowerCAmelCase     =    ScoreSdeVePipeline(unet=__lowerCAmelCase			,       scheduler=__lowerCAmelCase)
					sde_ve.to(__lowerCAmelCase)
					sde_ve.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    torch.manual_seed(0)
					lowerCAmelCase     =    sde_ve(num_inference_steps=2			,       output_type="""numpy"""			,       generator=__lowerCAmelCase).images

					lowerCAmelCase     =    torch.manual_seed(0)
					lowerCAmelCase     =    sde_ve(num_inference_steps=2			,       output_type="""numpy"""			,       generator=__lowerCAmelCase			,       return_dict=__lowerCAmelCase)[
					    0
					]

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

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase     =    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 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """google/ncsnpp-church-256"""
					lowerCAmelCase     =    UNetaDModel.from_pretrained(__lowerCAmelCase)

					lowerCAmelCase     =    ScoreSdeVeScheduler.from_pretrained(__lowerCAmelCase)

					lowerCAmelCase     =    ScoreSdeVePipeline(unet=__lowerCAmelCase			,       scheduler=__lowerCAmelCase)
					sde_ve.to(__lowerCAmelCase)
					sde_ve.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    torch.manual_seed(0)
					lowerCAmelCase     =    sde_ve(num_inference_steps=10			,       output_type="""numpy"""			,       generator=__lowerCAmelCase).images

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

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

					lowerCAmelCase     =    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





'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if num <= 0:
				lowerCAmelCase     =    f"{num}: Invalid input, please enter a positive integer."
				raise ValueError(_A					)

		lowerCAmelCase     =    [True] * (num + 1)
		lowerCAmelCase     =    []
		lowerCAmelCase     =    2
		lowerCAmelCase     =    int(math.sqrt(_A					)					)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(_A					)

						# Set multiples of start be False
						for i in range(start * start ,  num + 1 ,  _A					):
								if sieve[i] is True:
										lowerCAmelCase     =    False

				start += 1

		for j in range(end + 1 ,  num + 1					):
				if sieve[j] is True:
						prime.append(_A					)

		return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))




