'''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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			str:
        torch.manual_seed(0     )
        _A    		=  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      :			Union[str, Any]     )  ->			Union[str, Any]:
        _A    		=  self.dummy_uncond_unet
        _A    		=  ScoreSdeVeScheduler()

        _A    		=  ScoreSdeVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__     )
        sde_ve.to(UpperCamelCase__     )
        sde_ve.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.manual_seed(0     )
        _A    		=  sde_ve(num_inference_steps=2, output_type='numpy', generator=UpperCamelCase__     ).images

        _A    		=  torch.manual_seed(0     )
        _A    		=  sde_ve(num_inference_steps=2, output_type='numpy', generator=UpperCamelCase__, return_dict=UpperCamelCase__     )[
            0
        ]

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

        assert image.shape == (1, 32, 32, 3)
        _A    		=  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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        _A    		=  'google/ncsnpp-church-256'
        _A    		=  UNetaDModel.from_pretrained(UpperCamelCase__     )

        _A    		=  ScoreSdeVeScheduler.from_pretrained(UpperCamelCase__     )

        _A    		=  ScoreSdeVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__     )
        sde_ve.to(UpperCamelCase__     )
        sde_ve.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.manual_seed(0     )
        _A    		=  sde_ve(num_inference_steps=10, output_type='numpy', generator=UpperCamelCase__     ).images

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

        assert image.shape == (1, 2_56, 2_56, 3)

        _A    		=  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'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



