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							):






  @property
  def 		_a					(self	)		->      Optional[Any]:





       '''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 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.dummy_uncond_unet
       UpperCamelCase__										=      ScoreSdeVeScheduler()

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

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

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






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      '''google/ncsnpp-church-256'''
       UpperCamelCase__										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE_	)

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

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

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

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

       UpperCamelCase__										=      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  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



