




"""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 :  Tuple  )       ->     List[Any]:

       """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 :  List[str]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.dummy_uncond_unet
       lowerCamelCase_							=   ScoreSdeVeScheduler()

       lowerCamelCase_							=   ScoreSdeVePipeline(unet=A_						,							scheduler=A_  )
       sde_ve.to(A_  )
       sde_ve.set_progress_bar_config(disable=A_  )

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

       lowerCamelCase_							=   torch.manual_seed(0  )
       lowerCamelCase_							=   sde_ve(num_inference_steps=2						,							output_type='numpy'						,							generator=A_						,							return_dict=A_  )[
           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 :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   'google/ncsnpp-church-256'
       lowerCamelCase_							=   UNetaDModel.from_pretrained(A_  )

       lowerCamelCase_							=   ScoreSdeVeScheduler.from_pretrained(A_  )

       lowerCamelCase_							=   ScoreSdeVePipeline(unet=A_						,							scheduler=A_  )
       sde_ve.to(A_  )
       sde_ve.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   torch.manual_seed(0  )
       lowerCamelCase_							=   sde_ve(num_inference_steps=10						,							output_type='numpy'						,							generator=A_  ).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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      if not all(char in '01' for char in bin_string					):
            raise ValueError('Non-binary value was passed to the function'					)
      if not bin_string:
            raise ValueError('Empty string was passed to the function'					)
      lowerCamelCase_							=   ''
      while len(lowercase					) % 3 != 0:
            lowerCamelCase_							=   '0' + bin_string
      lowerCamelCase_							=   [
          bin_string[index : index + 3]
          for index in range(len(lowercase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCamelCase_							=   0
            for index, val in enumerate(lowercase					):
                  oct_val += int(2 ** (2 - index) * int(lowercase					)					)
            oct_string += str(lowercase					)
      return oct_string


if __name__ == "__main__":
 from doctest import testmod

 testmod()



