

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     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              torch.manual_seed(0   )
              __snake_case       	=							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"""


              __snake_case       	=							self.dummy_uncond_unet
              __snake_case       	=							ScoreSdeVeScheduler()

              __snake_case       	=							ScoreSdeVePipeline(unet=_UpperCamelCase      , scheduler=_UpperCamelCase   )
              sde_ve.to(_UpperCamelCase   )
              sde_ve.set_progress_bar_config(disable=_UpperCamelCase   )

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

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

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

              assert image.shape == (1, 32, 32, 3)
              __snake_case       	=							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     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							"""google/ncsnpp-church-256"""
              __snake_case       	=							UNetaDModel.from_pretrained(_UpperCamelCase   )

              __snake_case       	=							ScoreSdeVeScheduler.from_pretrained(_UpperCamelCase   )

              __snake_case       	=							ScoreSdeVePipeline(unet=_UpperCamelCase      , scheduler=_UpperCamelCase   )
              sde_ve.to(_UpperCamelCase   )
              sde_ve.set_progress_bar_config(disable=_UpperCamelCase   )

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

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

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

              __snake_case       	=							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




from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

